pax_global_header00006660000000000000000000000064145047126670014526gustar00rootroot0000000000000052 comment=18ca541eaa7604c8c8c9424239163791752f4132 refnx-0.1.38/000077500000000000000000000000001450471266700127415ustar00rootroot00000000000000refnx-0.1.38/.gitattributes000066400000000000000000000004771450471266700156440ustar00rootroot00000000000000# Auto detect text files and perform LF normalization *.py text=auto eol=lf *.pyx text=auto eol=lf *.cpp text=auto eol=lf *.h text=auto eol=lf *.rst text=auto eol=lf *.txt text=auto eol=lf *.yml text=auto eol=lf *.md text=auto eol=lf # Force git to look at some files as binary *.png binary *.pdf binary *.hdf binary refnx-0.1.38/.github/000077500000000000000000000000001450471266700143015ustar00rootroot00000000000000refnx-0.1.38/.github/workflows/000077500000000000000000000000001450471266700163365ustar00rootroot00000000000000refnx-0.1.38/.github/workflows/artifacts.yml000066400000000000000000000004721450471266700210440ustar00rootroot00000000000000name: 'Delete old artifacts' on: schedule: - cron: '0 0 * * 0' jobs: delete-artifacts: runs-on: ubuntu-latest steps: - uses: kolpav/purge-artifacts-action@v1 with: token: ${{ secrets.GITHUB_TOKEN }} expire-in: 7days # Setting this to 0 will delete all artifacts refnx-0.1.38/.github/workflows/build_wheels.yml000066400000000000000000000012101450471266700215210ustar00rootroot00000000000000name: Build Wheels on: [pull_request] jobs: build_wheels: name: Build wheels on ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-22.04, windows-2019, macos-11] steps: - uses: actions/checkout@v4 - name: build wheels uses: pypa/cibuildwheel@v2.15.0 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.test.test_reflect CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ENVIRONMENT_MACOS: MACOSX_DEPLOYMENT_TARGET="10.13" - uses: actions/upload-artifact@v3 with: name: refnx-wheels path: ./wheelhouse/*.whl refnx-0.1.38/.github/workflows/pythonpackage.yml000066400000000000000000000174371450471266700217320ustar00rootroot00000000000000name: Test on: - pull_request jobs: test_linux: runs-on: ubuntu-22.04 strategy: max-parallel: 3 matrix: python-version: [3.9, '3.10', '3.11'] steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies - run: pip install -r .requirements.txt - name: setup apt dependencies run: | sudo apt-get update sudo apt-get install xvfb qt6-base-dev libhdf5-serial-dev libnetcdf-dev build-essential sudo apt-get install '^libxcb.*-dev' libx11-xcb-dev libglu1-mesa-dev libxrender-dev libxi-dev libxkbcommon-dev libxkbcommon-x11-dev python -m pip install --upgrade pip - name: Test with pytest env: MPLBACKEND: agg run: | python -m pip install --upgrade pip python -m pip install wheel python -m pip install -e .[all] # uses xvfb for GUI part of the test xvfb-run pytest # check that refnx gui starts # python tools/app/check_app_starts.py refnx - name: Make sdist if: ${{ matrix.python-version == '3.9' }} run: | git clean -xdf python setup.py sdist - uses: actions/upload-artifact@v1 if: ${{ matrix.python-version == '3.9' }} with: name: refnx-wheel path: dist/ # linux_wheels: # # runs-on: ubuntu-latest # strategy: # max-parallel: 2 # matrix: # PLAT: ["manylinux2014_x86_64"] # # steps: # - uses: actions/checkout@v4 # - name: Make Linux Wheel # run: | # docker run --rm -e="PLAT=${{ matrix.PLAT }}" -v $(pwd):/io quay.io/pypa/${{ matrix.PLAT }} /bin/bash /io/tools/build_manylinux_wheels.sh # # - uses: actions/upload-artifact@v1 # with: # name: refnx-wheel # path: dist/ test_macos: runs-on: macos-latest strategy: max-parallel: 3 matrix: python-version: [3.9, '3.10', '3.11'] steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies - run: pip install -r .requirements.txt - run: mkdir -p dist - name: Install openmp run: | brew install libomp - name: Make wheel env: CC: /usr/bin/clang CXX: /usr/bin/clang++ CXXFLAGS: "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" CFLAGS: "-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" LDFLAGS: "-L/usr/local/opt/libomp/lib -lomp" DYLD_LIBRARY_PATH: /usr/local/opt/libomp/lib run: | python -m pip install --upgrade pip python -m pip install wheel delocate python -m pip wheel . --no-deps -w dist # so that libomp is distributed with wheel delocate-wheel -v dist/refnx*.whl - name: Install package and test with pytest env: MPLBACKEND: agg run: | pushd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx pytest --pyargs refnx popd - name: Make frozen GUI executable if: ${{ matrix.python-version == '3.10' }} run: | # make app in virtualenv python -m venv app source app/bin/activate python -m pip install --upgrade --upgrade-strategy eager -r tools/app/requirements.txt # pin scipy 1.7.3 because of pyinstaller doesn't like arm64 bing in libgfortran.dylib python -m pip install scipy pushd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx popd python -m pip install pyinstaller psutil # compileall in an effort to speedup pyinstaller GUI start python -m compileall pushd tools/app pyinstaller motofit.spec # check to see that the app starts python check_app_starts.py dist/motofit.app/Contents/MacOS/motofit zip -r -X dist/motofit.app.zip dist/motofit.app/ mv dist/motofit.app.zip ../../dist/ popd - uses: actions/upload-artifact@v1 with: name: refnx-wheel path: dist/ test_win: runs-on: windows-latest strategy: max-parallel: 3 matrix: python-version: [3.9, '3.10', '3.11'] steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies - run: pip install -r .requirements.txt - run: mkdir -p dist - name: Make wheel run: | python -m pip install --upgrade pip python -m pip install wheel python -m pip wheel . --no-deps -w dist - name: Install package and test with pytest if: ${{ matrix.python-version == '3.10' }} env: PYOPENCL_CTX: 0 run: | cd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx # python -m pip install pytools mako cffi # choco install opencl-intel-cpu-runtime # python -m pip install --only-binary=pyopencl --find-links http://www.silx.org/pub/wheelhouse/ --trusted-host www.silx.org pyopencl pytest --pyargs refnx cd .. - name: Check refnx gui starts if: ${{ matrix.python-version == '3.10' }} run: | # check that refnx gui starts pip install psutil python tools/app/check_app_starts.py refnx - name: Make frozen GUI executable if: ${{ matrix.python-version == '3.10' }} run: | # make app in virtualenv pip uninstall -y h5py python -m venv app app\Scripts\activate.bat python -m pip install --upgrade --upgrade-strategy eager -r tools/app/requirements.txt cd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx cd .. # fix for multiprocessing on Py3.9 is not merged in PyInstaller # if you are on Py3.7 you can just pip install pyinstaller # pip install git+https://github.com/andyfaff/pyinstaller.git@gh4865 cd tools\app pyinstaller motofit.spec move dist\motofit.exe ..\..\dist\ cd ..\.. - uses: actions/upload-artifact@v1 with: name: refnx-wheel path: dist/ build_doc: runs-on: ubuntu-latest strategy: max-parallel: 1 matrix: python-version: ["3.10"] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4.3.0 with: python-version: ${{ matrix.python-version }} - name: setup apt dependencies run: | sudo apt-get update sudo apt-get install pandoc - name: Build documentation run: | python -m pip install --upgrade pip python -m pip install . python -m pip install wheel cd doc python -m pip install -r requirements.txt make html lint: runs-on: ubuntu-latest strategy: max-parallel: 1 matrix: python-version: ["3.10"] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4.3.0 with: python-version: ${{ matrix.python-version }} - name: Lint with flake8 and black run: | python -m pip install flake8 black # stop the build if there are Python syntax errors or undefined names # the ignores are taken care of by black flake8 --ignore=F401,E501,W504,W503,E203,E231 --count --show-source --statistics --exclude=refnx/_lib/emcee,refnx/reflect/_app/resources_rc.py,refnx/_lib/ptemcee refnx black --check --exclude="refnx/_lib/emcee/|refnx/_lib/ptemcee/" refnx -l 79 refnx-0.1.38/.github/workflows/release.yml000066400000000000000000000053571450471266700205130ustar00rootroot00000000000000# This action releases refnx on PyPI for every version tagged commit (e.g. v0.0.1) name: PyPI/Github Release on: push: tags: - "v*" jobs: build_wheels: runs-on: ${{ matrix.os }} if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') strategy: matrix: os: [ubuntu-22.04, windows-2019, macos-11] steps: - uses: actions/checkout@v4 - name: build wheels uses: pypa/cibuildwheel@v2.15.0 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.test.test_reflect CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ENVIRONMENT_MACOS: MACOSX_DEPLOYMENT_TARGET="10.13" - uses: actions/upload-artifact@v3 with: path: ./wheelhouse/*.whl make_sdist: name: Make sdist runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Build sdist run: pipx run build --sdist - uses: actions/upload-artifact@v3 with: path: dist/*.tar.gz check-version: runs-on: ubuntu-latest needs: [build_wheels, make_sdist] steps: - uses: actions/checkout@v4 - name: Set up Python uses: actions/setup-python@v4 with: python-version: 3.11 - uses: actions/download-artifact@v3 with: name: artifact path: dist - name: Check version run: | python -m pip install numpy scipy orsopy ls dist python -m pip install --only-binary=refnx --no-index --find-links=dist refnx cd dist RNX_VERSION="$(python -c "import refnx;print(refnx.version.release)")" cd .. if [ $RNX_VERSION == "True" ]; then echo "It's a release version of refnx" else echo "This is not a release version of refnx" exit 1 fi pypi-publish: name: Upload release to PyPI runs-on: ubuntu-latest needs: [check-version] environment: name: pypi url: https://pypi.org/p/refnx permissions: id-token: write # IMPORTANT: this permission is mandatory for trusted publishing steps: - uses: actions/download-artifact@v3 with: name: artifact path: dist - name: Upload to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: # repository-url: https://test.pypi.org/legacy/ skip_existing: true release-github: runs-on: ubuntu-latest needs: [ pypi-publish ] steps: - uses: actions/checkout@v4 - uses: actions/download-artifact@v3 with: name: artifact path: dist - uses: ncipollo/release-action@v1 with: artifacts: "dist/refnx*.tar.gz" token: ${{ secrets.GITHUB_TOKEN }} allowUpdates: true generateReleaseNotes: true refnx-0.1.38/.gitignore000066400000000000000000000007111450471266700147300ustar00rootroot00000000000000.vscode/ *.pyc .idea .DS_Store build/ dist/ *.o *.obj *.pyd *.so .eggs *.icns .tox refnx.egg-info/ __pycache__ .ipynb_checkpoints/ refnx.egg-info/ src/_cevent.c src/_cevent.cpp src/_cevent2.c src/_creflect.c src/_creflect.cpp src/_cyreflect.c src/_cyreflect.cpp src/_cutil.c build.log doc/_build/ t.txt t .cache/ refnx/version.py benchmarks/.asv # testing artifacts PLP*dat PLP*xml c_PLP0000708.dat c_PLP0000708.xml offspec.xml test1.dat test.dat test.xml refnx-0.1.38/.requirements.txt000066400000000000000000000003071450471266700163030ustar00rootroot00000000000000numpy cython scipy orsopy h5py pandas xlrd pytest ipywidgets IPython matplotlib traitlets pyqt6 qtpy uncertainties setuptools<=65.5.1 attrs corner tqdm periodictable pyopencl pytensor pymc pytest-qt refnx-0.1.38/.travis.yml.bak000066400000000000000000000052561450471266700156160ustar00rootroot00000000000000sudo: false dist: xenial language: python matrix: include: # - os: linux # env: PY=3.7.3 - os: osx language: generic env: PY=3.7 addons: apt: packages: - libhdf5-serial-dev services: - xvfb #before_script: # - | # if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then # export DISPLAY=:99.0 # sh -e /etc/init.d/xvfb start # sleep 3 # give xvfb some time to start # fi before_install: - echo $TRAVIS_OS_NAME - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then wget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh; elif [[ "$TRAVIS_OS_NAME" == "linux" ]]; then wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; fi - bash miniconda.sh -b -p $HOME/miniconda - export PATH="$HOME/miniconda/bin:$PATH" - hash -r - conda update --yes conda # Useful for debugging any issues with conda - conda info -a - conda config --add channels conda-forge - conda config --set channel_priority strict - conda create --yes -n conda-refnx python=$PY - source activate conda-refnx - conda install --yes -c conda-forge numpy scipy h5py cython pandas xlrd pytest ipywidgets IPython matplotlib traitlets pyqt - conda install --yes -c conda-forge numpydoc sphinx jupyter pandoc nbconvert pyopencl - pip install uncertainties attrs corner nbsphinx jupyter_sphinx sphinx_rtd_theme tqdm pytest-qt periodictable - pip install git+https://github.com/pymc-devs/pymc3 # enable OpenMP support for Apple-clang - | if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew update brew install libomp export CC=/usr/bin/clang export CXX=/usr/bin/clang++ export CXXFLAGS="$CXXFLAGS -Xpreprocessor -fopenmp" export CFLAGS="$CFLAGS -Xpreprocessor -fopenmp" export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include" export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include" export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp" export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib fi script: - export MPLBACKEND=agg # put cwd in the top of the directory stack - pushd . # install and test from the sdist - python setup.py sdist - cd dist - pip install *.tar.gz - python -c 'import refnx;refnx.test()' # restore the working directory to the root refnx directory - popd - sphinx-build -b html doc doc/html notifications: # Perhaps we should have status emails sent to the mailing list, but # let's wait to see what people think before turning that on. email: false refnx-0.1.38/CHANGELOG.txt000066400000000000000000000566301450471266700150030ustar00rootroot00000000000000Details of changes made to refnx ================================ 0.1.1 ----- - removal of Python 2.7 support - added azure pipelines for faster windows CI - remove `uncertainties` as a package dependency (it's still used for testing) - remove `six` as a package dependency - add `refnx.util.refplot` for quick plotting of reflectometry datasets - fixed various deprecation warnings - added the ability to mask (hide) points in a `refnx.dataset.Data1D` dataset 0.1.2 ----- - added save/load model buttons in the interactive reflectometry modeller. - removed `from __future__ import ...` statements as refnx is now solely Py3 - added `cython: language_level=3` statements to cython code - marked cython extensions as c++ language - smarter (faster) cataloging of NeXUS files - removed pandas as a strict dependency, as it's only required for reduction, not analysis - improved documentation of the ManualBeamFinder - adding a pyqt based GUI (alpha state) - improved __repr__ of many classes - Start the GUI via a 'refnx' console command, via a setup.py entry_point. 0.1.3 ----- - GUI machinery can now use Components other than Slab, such as LipidLeaflet (already added). New Components may require extra shim code to be written for them. Specifically how they're to be displayed, and a default way of initialising the Component (which may require a dialogue). - The 'Link equivalent parameters' action has been added, enabling equivalent parameters on other datasets to be linked. This greatly aids setup of multiple contrast datasets. All the datasets to be linked must have the same type of structure. - The initialisation of a LipidLeaflet is made much easier by using a library of lipid head/volumes and scattering lengths for popular lipids which are presented to the user in an initialisation dialogue. - The refnx paper is accepted and the article and manuscript file are included in the repository. - The pyqt GUI to refnx can be made into standalone executables for Windows, macOS. - Fixed a bug that meant most reflectivity calculations were single-threaded instead of multi-threaded. - Added MixedReflectModel to the pyqt GUI, allowing one to model 'patchy' systems, i.e. incoherent averaging of reflectivities. - BACKWARDS INCOMPATIBLE CHANGE: the slabs properties of `Component` and `Structure` have now been changed to methods, taking the optional `structure` kwd parameter. The reason for this is so each `Component` knows what kind of `Structure` it is in. - The Spline Component can be used within the pyqt GUI. - In the pyqt gui Components can be re-ordered within their host structure by drag/drop. Dragging to other Structures copies the Component to that structure. - Added the Stack Component. A Stack contains a series of Components, and the Stack.repeats attribute can be used to produce a multilayer repeat structure. - Folded in a reduction pyqt gui for Platypus data. The app was already in the slim directory. It's now available from refnx.reduce.gui 0.1.4 ----- - fixed bug in reflectivity calculation with significant non-zero absorption. The wavevector calculation was using the wrong branch of the complex sqrt. 0.1.5 ----- - fixed font size in pyqt GUI. - script export from pyqt GUI can use either multiprocessing or MPI for parallelisation during MCMC sampling. - speeded up reflectivity calculation, following on from changes made in 0.1.4 (if the imaginary part of a complex sqrt argument is very small, then the C++ calculation takes a lot longer). - added a plot method to PlatypusNexus - refactor util.PoolWrapper to util.MapWrapper - allow the number of Stack repeats to be fittable. - GUI option to only display those parameters (and datasets) that are going to be varied in a fit. - update testimonials.bib - "to code" button in Jupyter interactive modeller respects the transform popup. 0.1.6 ----- - When parameters are linked in the refnx GUI only the dataset containing the master parameter was being updated (reflectivity/SLD curves) when the master was changed. Now all datasets that have parameters linked to the master parameter (a constraint) are updated. - When a dataset/component/structure containing a master parameter (i.e. a parameter to which other parameters are constrained to) is removed, the GUI now unlinks those dependent parameters from the master parameter. - display number of iterations in GUI progress box. If fit is aborted put best fit so far into Objective that's being fitted. - fixed crash resulting from the use of a comma when entering a floating point number. Entering '3,1' would crash the gui, using '3.1' would work but be displayed as '3,1'. The use of a dot as a decimal point is now enforced. 0.1.7 ----- - print human readable output when fitting with the Jupyter interactive modeller. - added shgo and dual_annealing minimiser options to the refnx gui. - SLD calculator retains state between viewings. - Added dialogue to adjust optimisation parameters. - Fixed bug in export of MCMC code fragment, Gui would crash. - Autocorrelation plot produced from mcmc.py code fragment, this can be used to judge how much to thin the chains by. - `refnx.analysis` now possesses a standalone function, `autocorrelation_chain` for calculating chain autocorrelation. Previously the calculation had to be done using a CurveFitter instance. - Function for calculating autocorrelation time made visible as: `refnx.analysis.integrated_time`. You should pass the autocorrelation array to this function. - GUI can now export an ASCII file representing the model SLD curve. - BUG: when the GUI saves a model it should pickle a ReflectModel. It wasn't doing this, it was pickling a DataObject. This has now been fixed, but the fix affects back compatibility. - *GUI can now do MCMC sampling* - Add option to change context of Mapwrapper (spawn/fork/forkserver) - BUG: when loading (not refreshing) a dataset that was already loaded, the associated model was lost. (GH331) - Added a progress bar for batch fitting. - Speeded up batch fitting 0.1.8 ----- - When GUI experiment file is loaded the correct fitting algorithm wasn't being set correctly (GH338). - Prevent crash when trying to refresh a stale dataset (i.e. is no longer in its original location. - Produce autocorrelation graph when sampling in GUI. - Added links to ptemcee and emcee in the optimisation parameters window. - GUI produces corner plot after MCMC sampling - BUG fix for rebinning code (doesn't affect analysis) - Different interfacial roughness types can be specified between all Components in a Structure. The available types are: Erf (Error Function, default), Tanh, Sinusoidal, Exponential, Step, Linear. User specifiable interfaces can be created by subclassing Interface. - A fix for the PyQt5 interface using 4K screens on Windows was made. Previously the GUI elements and fonts were being displayed in a much too small fashion. - Made various dialogues in the PyQt5 GUI window modal (to prevent them being lost). - fixed bugs if cancel was pressed during the MCMC folder dialogue phase. - made loading of experiment file back compatible. 0.1.9 ----- - Added DOTAP, h-DOPC, 18:1 Diether PC to lipids database - Updated SLIM reduction software to cope with the new monobloc detector - Improve speed of resolution smearing by using splev/splrep - Added shell scripts to build manylinux and macOS wheels (as well as test them). - Some minor optimisations for calculation of `Interval.logp`. - Some minor optimisations for various calculations in `Objective`. - Fixed potential for crashes in ManualBeamFinder if controls specified regions that went outside the detector region. - cythonized contract_by_area, resulting in huge speedup for microsliced structures. - Enabled parallel calculation of reflectivity using OpenMP. Tests show that it should be ~20% faster than the previous calculation in C. - tqdm progress bar for ptemcee sampling (if tqdm is installed). - Cleaned up the _creflect module. Threaded reflectivity calculation in that extension now uses std::thread instead of pthreads (POSIX) or WinAPI (windows). 0.1.10 ------ - Prevent spurious benchmark package installation. - Fixed bug when loading a MTFT file saved in a previous version of refnx. - event mode data reduction sped up by an order of magnitude - Added neutron transmission calculator (if periodictable is present) - event file reader can now read any ANSTO packedbin file (reduction). - Align SLD plots around a specific interface in a slab representation. Useful if plotting many samples at the same time. - Add MaterialSLD object that is constructed from a chemical formula and mass density. This enables the use of specific materials to describe layers, e.g. MaterialSLD('SiO2', density=2.2). - Components can be multiplied by an integer to make them repeat. - Add _open_mp_helpers to MANIFEST.in (gh381). - update bundled vendored emcee 0.1.11 ------ - Fixed a bug in the GUI that prevented load/save of mixed area models. - Fixed a bug in the resynthesis of data. - Fixed a bug in the loading of event data from the Platypus monobloc detector. - Added a FresnelTransform. - Document inequality constraints. - Made refnx SLD calculator GUI more fault tolerant to incorrect formula. - Add example Jupyter notebook for batch reduction. - Autoscaling for Panalytical XRR reduction. - Fixed bug in drag/drop within a Stack in the GUI. - Added a MixedSlab Component which is constituted from several individual Scatterers. The SLD of the MixedSlab is weighted by their volume fractions. 0.1.12 ------ - allow master and slave chopper parameters to be ignored in PLP reduction. - tunable t_0 offset in PLP/SPZ reduction. - possibly_create_parameter accepts default bounds/vary/constraint. This fixes a bug in MixedSlab. - Don't require numpy be installed before setup.py can run. - Speed up reflectivity calculation if there is solvation. - reflect.choose_dq_type for finding out fastest mode of resolution smearing. - User can now select resolution smearing approach in ReflectModel. Choosing between 'pointwise' or 'constant'. - Add progress bar for Curvefitter.fit() (requires tqdm being installed). - A few micro-optimisations. - Optimized pickling/unpickling of Bounds instances. This can lead to a huge performance increase (~ 40%) when doing parallel sampling. - MCMC sampling initialisation made reproducible 0.1.13 ------ - synthesising of datasets (Data1D.synthesise) can now be repeatable by providing a seed. - use Github Actions to test and build macOS wheels across all the Pythons. - The _creflect.abeles reflectivity calculation can release the GIL for a large part of its calculation now. This will enable parallelisation using either Processes or Threads. Processes still have the edge on speed at the moment. - change reduction code for ReflectNexus.phase_angle to use degrees, not radians. (previously angles were returned in a mix of degrees/radians) - Speed of reflectivity calculation is improved between 10 and 20 %. This is achieved by the use of C99 complex arithmetic instead of C++ std::complex. This improvement does not apply to Windows because it doesn't have a C99 standard conformant compiler. - Document how to save a model. - macOS wheels (and CI testing) have the cyreflect openmp option activated. - An openCL reflectivity calculation backend is added that can use a GPU. - Added a `reflect.use_reflect_backend` function to choose between the backends used to calculate reflectivity. 0.1.14 ------ This will be a bug fix release over 0.1.13. The Linux wheels weren't tested enough and the default reflectivity calculation backend didn't work correctly in 0.1.13. - fix default backend calculation. 0.1.15 ------ - Build standalone GUI as part of continuous integration runs. - Fix couple of GUI warnings when started from a terminal. - pyinstaller plist settings adjusted to make retina compatible and include refnx version number. - use Github Actions to test and build Linux wheels across all the Pythons. - Compensate for dq_type being added to ReflectModel (older .mtft files wouldn't load into GUI). - Refactor test_reflect to challenge all reflectivity calculation backends. - refnx.reflect.available_backends lists all reflectivity calculation backends. - Add pytest fixture to download test data. This dramatically reduces the size of the package. - Compensate for _stderr being added to Parameter (older .mtft files wouldn't load properly into GUI). - Fixed progress bar for parallel tempered sampling. - Make sdist 0.1.16 ------ - build more Linux wheels. - vendored ptemcee (bleeding edge), so it's no longer necessary to install it. - use -funsafe-math-optimizations for reflectivity calculation. - fixed bug when retrieving log-probabilities from PTMCMC sampling. - fixed encoding call for Py3 (.decode doesn't exist for Py3). - improve pymc3 model creation. pymc3 offers different ways to carry out MCMC sampling. This may lead to pymc3 being included as a sampling method in Curvefitter. - implement `invcdf` method for Bounds objects. This will allow creation of prior transforms from [0, 1) to the original range specification. - BREAKING CHANGE: `objective.model.logp` and `Objective.logp_extra` are now included in the log-likelihood (`Objective.logl`) instead of log-prior (`Objective.logp`). This makes it easier to work with pymc3 and dynesty. - Add `Objective.prior_transform` that converts random variates in a unit hypercube to parameter values, according to their prior distributions. - manual_beam_find can be sent a name to be displayed on the window title. - ReductionOptions dict is now used for specifying options for reduction and processing of reflectometry data. This will permit different settings to be used for datasets measured in different configurations. - Add AutoReducer, an object that watches a directory for modified/created files and automatically reduces them. 0.1.17 ------ - Drop Python 3.6 for new development (as per https://numpy.org/neps/nep-0029-deprecation_policy.html) - Pin setuptools version on RTD to allow the docs to build. - Add a tutorial to the docs on model selection. - Update emcee vendored code. - Fix repr of Model - Fix several warnings during test run. - Add error bar plotting to refnx GUI. - Improved the specular ridge finding methodology for data reduction. 0.1.18 ------ - SPZ batch reduction - remove some roadblocks to JAXification (float casting in Parameter.value setter) - volume fraction of solvent can now be used when constructing a Slab from a Scatterer. - Fully remove traces from graph when opening new experiments. - Store reduction_options when processing spectra. - RuntimeError if a log transform creates non-finite numbers. - ReflectModels can now use a q_offset to correct for possible angular misalignments. - `possibly_create_parameter` and `is_parameter` now use `BaseParameter` for their operation, rather than `Parameter`. Thus _BinaryOp and _UnaryOp can be given to these operations and work. This enables construction of various objects (SLD/Slab/etc) from constrained parameters, rather than setting a constraint afterwards. - MixedSlab is now allowed to be the first/last Component in a Structure. 0.1.19 ------ - MCMC sampling in GUI now plots parameter value vs step number, so you can see how many samples to burn - Fixed GUI crash when trying to make a snapshot with a name that has already been used. - Give BaseObjective a default weighted attribute, enabling it to be used in GlobalObjective. - Fallback to numerical estimation of Hessian/Covariance matrix if an Objective cannot calculate a residuals array (e.g. mixed use of BaseObjective/Objective in a GlobalObjective). - Vendor scipy.stats.qmc. - Use Latin Hyper Cube sampling to initialise MCMC walkers when initialising with prior. This ensures a good distribution of walkers and no clustering. 0.1.20 ------ - Make default Spline.dz.bounds.lb > 0, so that crashes aren't experienced when a dz is set to 0. GH549 - Add the shgo keyword parameters 'n' and 'iters'. - Add max_delta_z to sld_profile so that the point density in an SLD plot can be changed. - Don't use tqdm progress bar when fitting in GUI if sys.stderr is None. 0.1.21 ------ - check that refnx/analysis doesn't get in the way of handling multidimensional data. - add a header to datafiles during reduction. - make requirements.txt available in the refnx GUI. - add units attribute to Parameter. This is displayed as a tooltip in the GUI. - GUI and refnx command line entry point now reads sys.argv to see if GUI should open an experiment file. - Associate the mtft file extension on macOS with the GUI app, meaning you can double click on a mtft file and it'll open with the app. The mtft files also display with the GUI icon. - BUG: an error was experienced whilst reprocessing an existing chain in the refnx GUI. - BUG: fix gh336, remember the dq/q choice on reloading an experiment file. Previously the GUI reverted to constant dq/q on a reload, even if it was deselected. 0.1.22 ------ - Added polarisation reduction (Oliver Paull) - Introduce Objective.auxiliary_params. These are extra parameters that are modified during the calculation of Objective statistics, and are varied during a fit, but may not be directly part of Model calculating a signal. Their main purpose is to aid in creating constraints. - Add example to show how to co-refine non-spinflip NR data. - Silenced warning during test from slow reflectivity calculation, which erroneously gave the impression that the C based kernel wasn't available. 0.1.23 ------ - bugfix: remove printing of auxiliary parameters in Objective.setp. A remnant print function was in that method. However, those parameters aren't present in a global objective, which caused an AttributeError. 0.1.24 ------ - use oldest-supported-numpy to ensure that older numpy versions are used to build refnx wheels. This will allow refnx to be used with more recent versions as well as the oldest version supported on a given Python version. Previously refnx wheels were built with the newest numpy available at build time, and were probably not compatible with older numpy versions. - Rejig the build process to make macOS universal wheels. 0.1.25 ------ - Add NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION define to cython extension building to remove warnings. This requires a different mode of access to the array data. - Fixed drag and drop of datasets onto refnx GUI. - Add OrsoDataset and a generalised load_data function. - refnx GUI removes horizontal scrollbar. - Fixed crash when trying to access version numbers when saving expt file. 0.1.26 ------ - change wheel builder to make 3.10 wheels. - check that pyinstaller based macOS standalone app runs in CI. - update install documentation - workaround for difficulty using orsopy on Python 3.10 - added fields to ReflectReduce output dict, including omega, reflected_beam, direct_beam. 0.1.27 ------ - Add a little machinery for energy dispersive calculations. This is implemented by adding a wavelength attribute to Structure. The Structure.slabs method then works out the Slab representation for that particular wavelength. The wavelength attribute should be used by the Component.slabs method when asking for the SLD of a Scatterer. The Slab and MixedSlab Components have been altered to permit this. User provided components should work as they currently do, but probably have to be rewritten to permit energy dispersive operation. For energy dispersive operation classes that inherit Scatterer should override the newly introduced Scatterer.complex() method. 0.1.28 ------ - add name="refnx" to pyproject.toml. 0.1.29 ------ - Don't pickle the manual_beam_finder. - Fixed a bug when inserting a Component into a Stack in the refnx GUI. If it was appended to the end of the Stack Component addition worked. Inserting a Component into the middle of the Stack appeared to put it in the correct place in the GUI, but inserted it in the wrong place in the Structure. - Added Lagrangian multipliers to GlobalObjective, allowing the user to modulate the contribution of individual log-likelihoods to the whole. This may be of use when co-refining Neutron/X-ray/Ellipsometry datasets. 0.1.30 ------ - First appearance of Parameter.set_constraint(). - Previously Parameter constraints were limited to algebraic Python expressions. Now Parameter.set_constraint can be given a callable which is evaluated at runtime, allowing more sophisticated constraints to be developed. (N.B this is separate to the inequality constraints that can be made during curvefitting using the NonLinearConstraint machinery). - Add a Parratt recursion for calculating reflectivity, it seems to be a little faster. - Ported the pymc wrapper from pymc3 --> pymc (v4), which was released in Jun2022. - added sequence_to_parameters function. - added LipidLeaflet.make_constraint. 0.1.31 ------ - Amend setup.py package discovery - Change the manual beam finder box to green - When a Parameters object contains a _UnaryOp or _BinaryOp the dependencies of those objects are queried for varying parameters when asking for Parameters.varying_parameters() 0.1.32 ------- - event mode reduction for SPZ - Display SLD profile for first structure in MixedReflectModel - Github: changed the default branch from master to main. This is done to match other repositories behaviour and to reduce cognitive load. - Added a Parameter.corner method to plot the posterior distribution for a given Parameter. - Fixed a bug in MixedSlab 0.1.33 ------ - upgrade to PyQt6. PyQt6 is currently only available as a wheel from PyPI, to be installed by e.g. pip. There are currently no conda packages for PyQt6. PyQt6 enables the gui to be used on macosx_arm64, there are no PyQt5 wheels available on that platform. - further modification to use qtpy. qtpy is a shim that allows either PyQt6 or Pyside6 to be used to run the refnx GUI. qtpy is a new dependency for running the GUI. - add Objective._generate_generative_mcmc to yield fit curves corresponding to MCMC samples. - add Structure._generate_sld_profile_mcmc to yield sld profiles corresponding to MCMC samples. 0.1.34 ------ - Fix bug in ManualBeamFinder - Fix bug in refnx GUI regarding checkbox selection. 0.1.35 ------ - only extract requested keys from catalogue - enable cython3 operations - change install docs to recommend use of refnx[all] - update SPZ reduction for new detector translation 0.1.36 ------ - rudimentary HDF <-> Data1D interconversion. This is functionality that might be subject to change, so the functions are prefixed by "_". - added TOF simulation to reduction code. - print out the fitting statistic in a tqdm progress bar. 0.1.37 ------ - automated the release to PyPI via Github Actions. - removed setup.cfg, transferring all information to pyproject.toml. - fixed various scipy DeprecationWarnings related to keyword only arguments. - converted various os.path to pathlib.Path - stopped writing xml output (nobody uses it?) - added detailed resolution kernel calculation (writes out an HDF file). Refactor detailed kernel calculator. - removed np.asfarray usage, it's deprecated. - updated emcee vendored code. - silence RuntimeWarning if the foreground width is found to be wider than predicted. - fix pandas warnings in batchreduction. - wholesale conversion of os.path to pathlib.Path. Hopefully not too many bugs introduced as a result of this. 0.1.38 ------ - allow detailed Q resolution kernel to work with q_offset. - added lopx_hipx to ReductionOptions to allow one to specify the foreground region. - fix path concatenation issue in reduce_stitch, str or Path should be allowed. refnx-0.1.38/INSTALL_CONTRIBUTE.md000066400000000000000000000143711450471266700161350ustar00rootroot00000000000000# refnx - Installation and Development Instructions refnx is a python package for analysis of neutron and X-ray reflectometry data. It can also be used as a generalised curvefitting tool. It uses Markov Chain Monte Carlo to obtain posterior distributions for curvefitting problems. -------------- # Installation *refnx* has been tested on Python 3.8, 3.9, 3.10, and 3.11. It requires the *numpy, scipy, cython* packages to work. Additional features require the *pytest, pandas, qtpy, pyqt6, h5py, xlrd, attrs, tqdm, matplotlib, pymc, pytensor* packages. To build the bleeding edge code you will need to have access to a C-compiler to build a couple of Python extensions. C-compilers should be installed on Linux. On OSX you will need to install Xcode and the command line tools. On Windows you will need to install the correct [Visual Studio compiler][Visual-studio-compiler] for your Python version. In the current version of *refnx* the *emcee* and *ptemcee* packages are vendored by *refnx*. That is, *refnx* possesses it's own private copy of the package, and there is no need to those packages separately. ## Installation into a *conda* environment Perhaps the easiest way to create a scientific computing environment is to use the [miniconda][miniconda] package manager. Once *conda* has been installed the first step is to create a *conda* environment. ### Creating a conda environment 1) In a shell window create a conda environment and install the dependencies. The **-n** flag indicates that the environment is called *refnx*. ```conda create -n refnx python=3.7 numpy scipy cython pandas h5py xlrd pytest tqdm attrs``` 2) Activate the environment that we're going to be working in: ``` # on OSX conda activate refnx # on windows conda activate refnx ``` ### Installing into a conda environment from source The latest source code can be obtained from either [PyPi][PyPi] or [Github][github-refnx]. You can also build the package from within the refnx git repository (see later in this document). 1) In a shell window navigate into the source directory and build the package. If you are on Windows you'll need to start a Visual Studio command window. ``` python setup.py build python setup.py install ``` 2) Run the tests, they should all work. ``` python setup.py test ``` ### Installing into a conda environment from a released version 1) There are pre-built versions on *conda-forge*, but they're not necessarily at the bleeding edge: ```conda install -c conda-forge refnx``` 2) Start up a Python interpreter and make sure the tests run: ``` >>> import refnx >>> refnx.test() ``` ----------------------- ## Development Workflow These instructions outline the workflow for contributing to refnx development. The refnx community welcomes all contributions that will improve the package. The following instructions are based on use of a command line *git* client. *Git* is a distributed version control program. An example of [how to contribute to the numpy project][numpy-contrib] is a useful reference. ### Setting up a local git repository 1) Create an account on [github](https://github.com/). 2) On the [refnx github][github-refnx] page fork the *refnx* repository to your own github account. Forking means that now you have your own personal repository of the *refnx* code. 3) Now we will make a local copy of your personal repository on your local machine: ``` # is your github username git clone https://github.com//refnx.git ``` 4) Add the *refnx* remote repository, we're going to refer to the remote with the *upstream* name: ``` git remote add upstream https://github.com/refnx/refnx.git ``` 5) List the remote repositories that your local repository knows about: ``` git remote -v ``` ### Keeping your local and remote repositories up to date The main *refnx* repository may be a lot more advanced than your fork, or your local copy, of the git repository. 1) To update your repositories you need to fetch the changes from the main *refnx* repository: ``` git fetch upstream ``` 2) Now update the local branch you're on by rebasing against the *refnx* master branch: ``` git rebase upstream/master ``` 3) Push your updated local branch to the remote fork on github. You have to specify the remote branch you're pushing to. Here we push to the *master* branch: ``` git push origin master ``` ### Adding a feature The git repository is automatically on the master branch to start with. However, when developing features that you'd like to contribute to the *refnx* project you'll need to do it on a feature branch. 1) Create a feature branch and check it out: ``` git branch my_feature_name git checkout my_feature_name ``` 2) Once you're happy with the changes you've made you should check that the tests still work: ``` python setup.py test ``` 3) If the performance of what you've added/changed may be critical, then consider writing a benchmark. The benchmarks use the *asv* package and are run as: ``` cd benchmarks pip install asv asv run asv publish asv preview ``` For an example benchmark look at one of the files in the *benchmarks* directory. 4) Now commit the changes. You'll have to supply a commit message that outlines the changes you made. The commit message should follow the [numpy guidelines][numpy-contib] ``` git commit -a ``` 5) Now you need to push those changes on the *my_feature_branch* branch to *your* fork of the refnx repository on github: ``` git push origin my_feature_branch ``` 6) On the main [refnx][github-refnx] repository you should be able to create a pull request (PR). The PR says that you'd like the *refnx* project to include the changes you made. 7) Once the automated tests have passed, and the *refnx* maintainers are happy with the changes you've made then the PR is merged. You can then delete the feature branch on github, and delete your local feature branch: ``` git branch -D my_feature_branch ``` [PyPi]: [github-refnx]: [Visual-studio-compiler]: [miniconda]: [numpy-contrib]: refnx-0.1.38/LICENSE000066400000000000000000000030001450471266700137370ustar00rootroot00000000000000Copyright 2015-2022 A. R. J. Nelson, Australian Nuclear Science and Technology Organisation Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. refnx-0.1.38/MANIFEST.in000066400000000000000000000001561450471266700145010ustar00rootroot00000000000000# All source files recursive-include refnx * recursive-include src * exclude src/refcalc.o prune */__pycache__refnx-0.1.38/README.md000066400000000000000000000015511450471266700142220ustar00rootroot00000000000000refnx ===== ![Github Action](https://github.com/refnx/refnx/workflows/Lint%20+%20Test/badge.svg) [![Build Status](https://dev.azure.com/refnx/refnx/_apis/build/status/refnx.refnx?branchName=master)](https://dev.azure.com/refnx/refnx/_build/latest?definitionId=1&branchName=master) [![Build status](https://ci.appveyor.com/api/projects/status/gv6965vuqnuufx9u?svg=true)](https://ci.appveyor.com/project/andyfaff/refnx) [![Documentation Status](https://readthedocs.org/projects/refnx/badge/?version=latest)](https://refnx.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/23189/refnx/refnx.svg)](https://zenodo.org/badge/latestdoi/23189/refnx/refnx) [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/refnx/refnx-binder.git/master) Neutron and X-ray reflectometry analysis in Python. Documentation at https://refnx.readthedocs.io.refnx-0.1.38/appveyor.yml.bak000066400000000000000000000060261450471266700160710ustar00rootroot00000000000000environment: # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the # /E:ON and /V:ON options are not enabled in the batch script interpreter # See: http://stackoverflow.com/a/13751649/163740 CMD_IN_ENV: "cmd /E:ON /V:ON /C obvci_appveyor_python_build_env.cmd" # Workaround for https://github.com/conda/conda-build/issues/636 PYTHONIOENCODING: "UTF-8" matrix: # Note: Because we have to separate the py2 and py3 components due to compiler version, we have a race condition for non-python packages. # Not sure how to resolve this, but maybe we should be tracking the VS version in the build string anyway? - TARGET_ARCH: x64 CONDA_NPY: 118 PYTHON_VERSION: 3.8 CONDA_INSTALL_LOCN: C:\\Miniconda38-x64 # We always use a 64-bit machine, but can build x86 distributions # with the TARGET_ARCH variable (which is used by CMD_IN_ENV). platform: - x64 init: - "ECHO %PYTHON_VERSION% %CONDA_INSTALL_LOCN%" - "ECHO %PYTHON% %PYTHON_VERSION% %PYTHON_ARCH%" - "ECHO \"%APPVEYOR_SCHEDULED_BUILD%\"" # cancel build if newer one is submitted; complicated # details for getting this to work are credited to JuliaLang # developers - ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod ` https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | ` Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { ` raise "There are newer queued builds for this pull request, skipping build." } install: - "set PATH=%CONDA_INSTALL_LOCN%;%CONDA_INSTALL_LOCN%\\Scripts;%PATH%" # Set the CONDA_NPY, although it has no impact on the actual build. We need this because of a test within conda-build. - cmd: set CONDA_NPY=19 # Remove cygwin (and therefore the git that comes with it). - cmd: rmdir C:\cygwin /s /q # Add path, activate `conda` and update conda. - conda config --set always_yes yes --set changeps1 no # - cmd: call %CONDA_INSTALL_LOCN%\Scripts\activate.bat - cmd: set PYTHONUNBUFFERED=1 # Add our channels. - conda config --set show_channel_urls true - conda update --yes --quiet conda # create conda environment - conda create --yes -n test python=%PYTHON_VERSION% - conda info --envs # Configure the VM. - activate test - conda install --yes --quiet -c conda-forge numpy scipy h5py cython traitlets ipywidgets xlrd pandas pytest pyqt - pip install corner uncertainties matplotlib IPython pytest-qt periodictable pyqt6 attrs # - pip install git+https://github.com/pymc-devs/pymc3 build_script: # Build the compiled extension - pip install -e . test_script: # Run the project tests - cmd: pytest after_test: # If tests are successful, create binary packages for the project. - python setup.py bdist_wheel - ps: "ls dist" artifacts: # Archive the generated packages in the ci.appveyor.com build report. - path: dist\*.whlrefnx-0.1.38/azure-pipelines.yml.bak000066400000000000000000000033071450471266700173370ustar00rootroot00000000000000# Python package # Create and test a Python package on multiple Python versions. # Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more: # https://docs.microsoft.com/azure/devops/pipelines/languages/python jobs: - job: 'Test' pool: vmImage: 'windows-2022' strategy: matrix: Python310: python.version: '3.10' maxParallel: 4 steps: - task: UsePythonVersion@0 inputs: versionSpec: '$(python.version)' architecture: 'x64' - script: | python -m pip install --upgrade pip python -m pip install wheel pip install numpy scipy orsopy cython traitlets ipython ipywidgets pandas h5py xlrd pytest tqdm corner uncertainties matplotlib pyqt6 pytest-qt periodictable attrs # - script: pip install git+https://github.com/pymc-devs/pymc3 displayName: 'Install dependencies' - script: | python setup.py bdist_wheel displayName: 'make wheel' - script: | cd dist pip install --only-binary=refnx --no-index --find-links=. refnx pip install pytest pytest --pyargs refnx cd .. displayName: 'pytest' - script: | pip uninstall -y pandas h5py xlrd pytest pytest-qt # more recent versions of setuptools don't work with PyInstaller pip install setuptools==44 pyinstaller pyinstaller tools/app/motofit.spec displayName: 'Frozen refnx GUI' condition: in(variables['python.version'], '3.10') - task: CopyFiles@2 inputs: contents: dist/** targetFolder: $(Build.ArtifactStagingDirectory) - task: PublishBuildArtifacts@1 inputs: pathtoPublish: $(Build.ArtifactStagingDirectory) artifactName: refnx_wheels refnx-0.1.38/benchmarks/000077500000000000000000000000001450471266700150565ustar00rootroot00000000000000refnx-0.1.38/benchmarks/__init__.py000066400000000000000000000001121450471266700171610ustar00rootroot00000000000000 import numpy as np import random np.random.seed(1234) random.seed(1234) refnx-0.1.38/benchmarks/asv.conf.json000066400000000000000000000132321450471266700174670ustar00rootroot00000000000000{ // The version of the config file format. Do not change, unless // you know what you are doing. "version": 1, // The name of the project being benchmarked "project": "refnx", // The project's homepage "project_url": "https://github.com/refnx/refnx/", // The URL or local path of the source code repository for the // project being benchmarked "repo": "..", // List of branches to benchmark. If not provided, defaults to "master" // (for git) or "default" (for mercurial). "branches": ["master"], // for git // The DVCS being used. If not set, it will be automatically // determined from "repo" by looking at the protocol in the URL // (if remote), or by looking for special directories, such as // ".git" (if local). "dvcs": "git", // The tool to use to create environments. May be "conda", // "virtualenv" or other value depending on the plugins in use. // If missing or the empty string, the tool will be automatically // determined by looking for tools on the PATH environment // variable. "environment_type": "virtualenv", // timeout in seconds for installing any dependencies in environment // defaults to 10 min //"install_timeout": 600, // the base URL to show a commit for the project. "show_commit_url": "https://github.com/refnx/refnx/commit/", // The Pythons you'd like to test against. If not provided, defaults // to the current version of Python used to run `asv`. // "pythons": ["2.7", "3.3"], // The matrix of dependencies to test. Each key is the name of a // package (in PyPI) and the values are version numbers. An empty // list or empty string indicates to just test against the default // (latest) version. null indicates that the package is to not be // installed. If the package to be tested is only available from // PyPi, and the 'environment_type' is conda, then you can preface // the package name by 'pip+', and the package will be installed via // pip (with all the conda available packages installed first, // followed by the pip installed packages). // "matrix": { "numpy": [], "Cython": [], }, // Combinations of libraries/python versions can be excluded/included // from the set to test. Each entry is a dictionary containing additional // key-value pairs to include/exclude. // // An exclude entry excludes entries where all values match. The // values are regexps that should match the whole string. // // An include entry adds an environment. Only the packages listed // are installed. The 'python' key is required. The exclude rules // do not apply to includes. // // In addition to package names, the following keys are available: // // - python // Python version, as in the *pythons* variable above. // - environment_type // Environment type, as above. // - sys_platform // Platform, as in sys.platform. Possible values for the common // cases: 'linux2', 'win32', 'cygwin', 'darwin'. // // "exclude": [ // {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows // {"environment_type": "conda", "six": null}, // don't run without six on conda // ], // // "include": [ // // additional env for python2.7 // {"python": "2.7", "numpy": "1.8"}, // // additional env if run on windows+conda // {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""}, // ], // The directory (relative to the current directory) that benchmarks are // stored in. If not provided, defaults to "benchmarks" "benchmark_dir": "benchmarks", // The directory (relative to the current directory) to cache the Python // environments in. If not provided, defaults to "env" "env_dir": ".asv/env", // The directory (relative to the current directory) that raw benchmark // results are stored in. If not provided, defaults to "results". "results_dir": ".asv/results", // The directory (relative to the current directory) that the html tree // should be written to. If not provided, defaults to "html". "html_dir": ".asv/html", // The number of characters to retain in the commit hashes. // "hash_length": 8, // `asv` will cache wheels of the recent builds in each // environment, making them faster to install next time. This is // number of builds to keep, per environment. "build_cache_size": 5 // The commits after which the regression search in `asv publish` // should start looking for regressions. Dictionary whose keys are // regexps matching to benchmark names, and values corresponding to // the commit (exclusive) after which to start looking for // regressions. The default is to start from the first commit // with results. If the commit is `null`, regression detection is // skipped for the matching benchmark. // // "regressions_first_commits": { // "some_benchmark": "352cdf", // Consider regressions only after this commit // "another_benchmark": null, // Skip regression detection altogether // } // The thresholds for relative change in results, after which `asv // publish` starts reporting regressions. Dictionary of the same // form as in ``regressions_first_commits``, with values // indicating the thresholds. If multiple entries match, the // maximum is taken. If no entry matches, the default is 5%. // // "regressions_thresholds": { // "some_benchmark": 0.01, // Threshold of 1% // "another_benchmark": 0.5, // Threshold of 50% // } } refnx-0.1.38/benchmarks/benchmarks/000077500000000000000000000000001450471266700171735ustar00rootroot00000000000000refnx-0.1.38/benchmarks/benchmarks/__init__.py000066400000000000000000000000001450471266700212720ustar00rootroot00000000000000refnx-0.1.38/benchmarks/benchmarks/analysis.py000066400000000000000000000032561450471266700213760ustar00rootroot00000000000000import os.path import numpy as np from .common import Benchmark from refnx.analysis import CurveFitter, Objective, Parameter, Model from refnx.dataset import Data1D def line(x, params, *args, **kwds): p_arr = np.array(params) return p_arr[0] + x * p_arr[1] class curvefitter(Benchmark): repeat = 3 def setup(self): # Reproducible results! np.random.seed(123) m_true = -0.9594 b_true = 4.294 f_true = 0.534 m_ls = -1.1040757010910947 b_ls = 5.4405552502319505 # Generate some synthetic data from the model. N = 50 x = np.sort(10 * np.random.rand(N)) y_err = 0.1 + 0.5 * np.random.rand(N) y = m_true * x + b_true y += np.abs(f_true * y) * np.random.randn(N) y += y_err * np.random.randn(N) data = Data1D(data=(x, y, y_err)) p = Parameter(b_ls, 'b', vary=True, bounds=(-100, 100)) p |= Parameter(m_ls, 'm', vary=True, bounds=(-100, 100)) model = Model(p, fitfunc=line) self.objective = Objective(model, data) self.mcfitter = CurveFitter(self.objective) self.mcfitter_t = CurveFitter(self.objective, ntemps=20) self.mcfitter.initialise('prior') self.mcfitter_t.initialise('prior') def time_sampler(self): # to get an idea of how fast the actual sampling is. # i.e. the overhead of objective.lnprob, objective.lnprior, etc self.mcfitter.sampler.run_mcmc(self.mcfitter._state, 100) def time_sampler_pool(self): # see how the multiprocessing in curvefitter performs # automatically use all the cores available self.mcfitter_t.sample(20, pool=-1) refnx-0.1.38/benchmarks/benchmarks/common.py000066400000000000000000000044201450471266700210350ustar00rootroot00000000000000""" Airspeed Velocity benchmark utilities """ import sys import re import time import textwrap import subprocess class Benchmark(object): """ Base class with sensible options """ pass def run_monitored(code): """ Run code in a new Python process, and monitor peak memory usage. Returns ------- duration : float Duration in seconds (including Python startup time) peak_memusage : float Peak memory usage (rough estimate only) in bytes """ if not sys.platform.startswith('linux'): raise RuntimeError("Peak memory monitoring only works on Linux") code = textwrap.dedent(code) process = subprocess.Popen([sys.executable, '-c', code]) peak_memusage = -1 start = time.time() while True: ret = process.poll() if ret is not None: break with open('/proc/%d/status' % process.pid, 'r') as f: procdata = f.read() m = re.search('VmRSS:\s*(\d+)\s*kB', procdata, re.S | re.I) if m is not None: memusage = float(m.group(1)) * 1e3 peak_memusage = max(memusage, peak_memusage) time.sleep(0.01) process.wait() duration = time.time() - start if process.returncode != 0: raise AssertionError("Running failed:\n%s" % code) return duration, peak_memusage def get_mem_info(): """Get information about available memory""" if not sys.platform.startswith('linux'): raise RuntimeError("Memory information implemented only for Linux") info = {} with open('/proc/meminfo', 'r') as f: for line in f: p = line.split() info[p[0].strip(':').lower()] = float(p[1]) * 1e3 return info def set_mem_rlimit(max_mem=None): """ Set address space rlimit """ import resource if max_mem is None: mem_info = get_mem_info() max_mem = int(mem_info['memtotal'] * 0.7) cur_limit = resource.getrlimit(resource.RLIMIT_AS) if cur_limit[0] > 0: max_mem = min(max_mem, cur_limit[0]) resource.setrlimit(resource.RLIMIT_AS, (max_mem, cur_limit[1])) def with_attributes(**attrs): def decorator(func): for key, value in attrs.items(): setattr(func, key, value) return func return decorator refnx-0.1.38/benchmarks/benchmarks/reflect.py000066400000000000000000000065301450471266700211750ustar00rootroot00000000000000import os.path import numpy as np import pickle from .common import Benchmark from refnx.analysis import CurveFitter, Objective, Parameter import refnx.reflect from refnx.reflect._creflect import abeles as c_abeles from refnx.reflect._reflect import abeles from refnx.reflect import SLD, Slab, Structure, ReflectModel, reflectivity from refnx.dataset import ReflectDataset as RD class Abeles(Benchmark): def setup(self): self.q = np.linspace(0.005, 0.5, 50000) self.layers = np.array([[0, 2.07, 0, 3], [50, 3.47, 0.0001, 4], [200, -0.5, 1e-5, 5], [50, 1, 0, 3], [0, 6.36, 0, 3]]) self.repeat = 20 self.number = 10 def time_cabeles(self): c_abeles(self.q, self.layers) def time_abeles(self): abeles(self.q, self.layers) def time_reflectivity_constant_dq_q(self): reflectivity(self.q, self.layers) def time_reflectivity_pointwise_dq(self): reflectivity(self.q, self.layers, dq=0.05 * self.q) class Reflect(Benchmark): timeout = 120. # repeat = 2 def setup(self): pth = os.path.dirname(os.path.abspath(refnx.reflect.__file__)) e361 = RD(os.path.join(pth, 'test', 'e361r.txt')) sio2 = SLD(3.47, name='SiO2') si = SLD(2.07, name='Si') d2o = SLD(6.36, name='D2O') polymer = SLD(1, name='polymer') # e361 is an older dataset, but well characterised structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3) model361 = ReflectModel(structure361, bkg=2e-5) model361.scale.vary = True model361.bkg.vary = True model361.scale.range(0.1, 2) model361.bkg.range(0, 5e-5) model361.dq = 5. # d2o structure361[-1].sld.real.vary = True structure361[-1].sld.real.range(6, 6.36) self.p = structure361[1].thick structure361[1].thick.vary = True structure361[1].thick.range(5, 20) structure361[2].thick.vary = True structure361[2].thick.range(100, 220) structure361[2].sld.real.vary = True structure361[2].sld.real.range(0.2, 1.5) self.structure361 = structure361 self.model361 = model361 # e361.x_err = None self.objective = Objective(self.model361, e361) self.fitter = CurveFitter(self.objective, nwalkers=200) self.fitter.initialise('jitter') def time_reflect_emcee(self): # test how fast the emcee sampler runs in serial mode self.fitter.sampler.run_mcmc(self.fitter._state, 30) def time_reflect_sampling_parallel(self): # discrepancies in different runs may be because of different numbers # of processors self.model361.threads = 1 self.fitter.sample(30, pool=-1) def time_pickle_objective(self): # time taken to pickle an objective s = pickle.dumps(self.objective) pickle.loads(s) def time_pickle_model(self): # time taken to pickle a model s = pickle.dumps(self.model361) pickle.loads(s) def time_pickle_model(self): # time taken to pickle a parameter s = pickle.dumps(self.p) pickle.loads(s) def time_structure_slabs(self): self.structure361.slabs() refnx-0.1.38/benchmarks/run.py000066400000000000000000000034741450471266700162440ustar00rootroot00000000000000#!/usr/bin/env python """ run.py [options] ASV_COMMAND.. Convenience wrapper around the ``asv`` command; just sets environment variables and chdirs to the correct place etc. """ import os import sys import subprocess import json import shutil import argparse import sysconfig import errno EXTRA_PATH = [''] def main(): class ASVHelpAction(argparse.Action): nargs = 0 def __call__(self, parser, namespace, values, option_string=None): sys.exit(run_asv(['--help'])) p = argparse.ArgumentParser(usage=__doc__.strip()) p.add_argument('--help-asv', nargs=0, action=ASVHelpAction, help="""show ASV help""") p.add_argument('asv_command', nargs=argparse.REMAINDER) args = p.parse_args() sys.exit(run_asv(args.asv_command)) def run_asv(args): cwd = os.path.abspath(os.path.dirname(__file__)) repo_dir = os.path.join(cwd, 'refnx') cmd = ['asv'] + list(args) env = dict(os.environ) # Inject ccache/f90cache paths if sys.platform.startswith('linux'): env['PATH'] = os.pathsep.join(EXTRA_PATH + env.get('PATH', '').split(os.pathsep)) # Check refnx version if in dev mode; otherwise clone and setup results # repository if args and (args[0] == 'dev' or '--python=same' in args): import refnx print("Running benchmarks for refnx version %s at %s" % (refnx.__version__, refnx.__file__)) # Run try: return subprocess.call(cmd, env=env, cwd=cwd) except OSError as err: if err.errno == errno.ENOENT: print("Error when running '%s': %s\n" % (" ".join(cmd), str(err),)) print("You need to install Airspeed Velocity https://spacetelescope.github.io/asv/") print("to run refnx benchmarks") return 1 raise if __name__ == "__main__": sys.exit(main()) refnx-0.1.38/doc/000077500000000000000000000000001450471266700135065ustar00rootroot00000000000000refnx-0.1.38/doc/Makefile000066400000000000000000000163551450471266700151600ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinx.ext.napoleon', 'nbsphinx', 'jupyter_sphinx', 'sphinxcontrib.bibtex', 'sphinxcontrib.jquery', 'sphinx_rtd_theme', ] bibtex_bibfiles = ["../testimonials.bib"] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'refnx' copyright = '2015-2022, Andrew Nelson' author = 'Andrew Nelson' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. import refnx # The short X.Y version. version = re.sub(r'\.dev-.*$', r'.dev', refnx.__version__) # The full version, including alpha/beta/rc tags. release = refnx.__version__ intersphinx_mapping = {'py': ('https://docs.python.org/3', None), 'numpy': ('https://docs.scipy.org/doc/numpy/', None), 'scipy': ('https://docs.scipy.org/doc/scipy/reference/', None), 'matplotlib': ('https://matplotlib.org/', None), } extlinks = { 'scipydoc' : ('https://docs.scipy.org/doc/scipy/reference/generated/%s.html', ''), 'numpydoc' : ('https://docs.scipy.org/doc/numpy/reference/generated/numpy.%s.html', ''), } numpydoc_show_class_members = False # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = 'en' # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build', '**.ipynb_checkpoints'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. add_function_parentheses = False # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = '_images/scattering.png' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'refnxdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'refnx.tex', 'refnx Documentation', 'Andrew Nelson', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'refnx', 'refnx Documentation', [author], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'refnx', 'refnx Documentation', author, 'refnx', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False # Example configuration for intersphinx: refer to the Python standard library. #intersphinx_mapping = {'https://docs.python.org/': None} # -------------------------- nbsphinx options-------------------------------- # time out when evaluating notebooks nbsphinx_timeout = 1800refnx-0.1.38/doc/environment.yml000066400000000000000000000005671450471266700166050ustar00rootroot00000000000000channels: - conda-forge dependencies: - python==3.9 - pip: - nbsphinx - jupyter-sphinx - sphinx_rtd_theme - tqdm - corner - periodictable - pandoc - scipy - numpy - sphinx - pandas - numpydoc - h5py - nbconvert - ipywidgets - setuptools - cython - jupyter - matplotlib - pytest - xlrd refnx-0.1.38/doc/examples.rst000066400000000000000000000006301450471266700160550ustar00rootroot00000000000000Examples ======== .. toctree:: :maxdepth: 2 reflectometry_global.ipynb inequality_constraints.ipynb model_selection.ipynb lipid.ipynb batch fitting analytical SLD profiles nsf.ipynb refnx-0.1.38/doc/faq.rst000066400000000000000000000104051450471266700150070ustar00rootroot00000000000000.. _faq_chapter: ==================================== Frequently Asked Questions ==================================== .. _mailing list: https://groups.google.com/group/refnx .. _github issues: https://github.com/refnx/refnx/issues .. _van Well et al: https://doi.org/10.1016/j.physb.2004.11.058 .. _Nelson et al: https://doi.org/10.1107/S1600576714009595 A list of common questions. What's the best way to ask for help or submit a bug report? ----------------------------------------------------------- If you have questions on the use of refnx please use the `mailing list`_. If you find a bug in the code or documentation, use `GitHub Issues`_. How should I cite refnx? ------------------------ The full reference for the refnx paper is: "Nelson, A.R.J. & Prescott, S.W. (2019). J. Appl. Cryst. 52, https://doi.org/10.1107/S1600576718017296." How is instrumental resolution smearing handled? ------------------------------------------------ There are a variety of ways that you can account for instrumental resolution smearing in refnx. The easiest is if the fractional instrumental resolution, :math:`\frac{dQ}{Q}`, is constant. When setting up :class:`refnx.reflect.ReflectModel` the fractional resolution can be specified, and the reflectivity that it calculates is automatically smeared. For a given :math:`Q` value the :math:`dQ` (found by multiplying the fractional resolution by :math:`Q`) value refers to the Full Width at Half Maximum (FWHM) of a Gaussian approximation to the instrumental resolution. This Gaussian distribution is convolved with the unsmeared model to compare with the data. The second way of using the resolution function is for the :math:`dQ` values for each data point to be read in via from a data file (e.g. the 4th column of a text file). In this way point-by-point resolution smearing is achieved. The last way of specifying instrumental resolution is for a full resolution kernel to be provided for each data point. A resolution kernel is a probability distribution that describes the distribution of possible :math:`Q` vectors for each data point. The first two options are typically used, only more advanced users will ever need to apply the last option. For further details on instrumental resolution functions it's a good idea to read the papers by `van Well et al`_, and `Nelson et al`_. What are the units of scattering length density? ------------------------------------------------ If the scattering length density of a material is :math:`(124.88 + 12.85j)\times 10^{-6} A^{-2}` (the X-ray SLD for Au), then you would use 124.88 as the real part and 12.85 as the imaginary part. What are the 'fronting' and 'backing' media? -------------------------------------------- The 'fronting' and 'backing' media are infinite. The 'fronting' medium carries the incident beam of radiation, whilst the 'backing' medium will carry the transmitted beam away from the interface. How do I open the standalone app on macOS Catalina? ---------------------------------------------------- macOS Catalina expects all apps to be code-signed and notarised for them to be able to run via 'double-clicking' in the finder. The project is working towards fulfilling those conditions, but in the meantime you can still open the standalone motofit.app by right-clicking and selecting 'open'. Can I save models/objectives to file? ----------------------------------------- I'm assuming that you have a :class:`refnx.reflect.ReflectModel` or :class:`refnx.analysis.Objective` that you'd like to save to file. The easiest way to do this is via serialisation to a Python pickle:: import pickle # save with open('my_objective.pkl', 'wb+') as f: pickle.dump(objective, f) # load with open('my_objective.pkl', 'rb') as f: restored_objective = pickle.load(f) The saved pickle files are in a binary format, and are not human readable. It may also be useful to save the representation, :code:`repr(objective)`. How do I install pyqt6? ----------------------- PyQt6 and qtpy is needed for the refnx GUI. The `pyqt6` and `qtpy` packages are currently available from PyPI and can be installed as `pip install pyqt6 qtpy`. However, pyqt6 is not currently available via conda-forge. You can use conda to install most of the refnx dependencies, but you will need to use `pip` to install pyqt6. refnx-0.1.38/doc/getting_started.ipynb000066400000000000000000003354401450471266700177510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Getting started" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fitting a data to a user defined model" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "*refnx* can examine most curve fitting problems. Here we demonstrate a fit to a simple user defined model. This line example is taken from the [emcee documentation](http://emcee.readthedocs.io/en/stable/user/line.html) and the reader is referred to that link for more detailed explanation. The errorbars are underestimated, and the modelling will account for that." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "To use *refnx* we need first need to create a dataset. We create a synthetic dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "np.random.seed(123)\n", "\n", "# Choose the \"true\" parameters.\n", "m_true = -0.9594\n", "b_true = 4.294\n", "f_true = 0.534\n", "\n", "N = 50\n", "x = np.sort(10 * np.random.rand(N))\n", "yerr = 0.1 + 0.5*np.random.rand(N)\n", "y = m_true*x + b_true\n", "y += np.abs(f_true * y) * np.random.randn(N)\n", "y += yerr * np.random.randn(N)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We create a `Data1D` object from this synthetic data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from refnx.dataset import Data1D\n", "data = Data1D(data=(x, y, yerr))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Then we need to set up a generative model. Firstly we write a fit-function that returns our straight line model. The `Parameter` objects describe the parameters we're going to use in the fit. We give the parameters values, names, and specify their limits. The parameters are combined into a `Parameters` set, `p`, using the or operator.\n", "Then we create a `Model` object from our parameter set and the fit-function. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from refnx.analysis import Parameter, Model\n", "\n", "def line(x, params, *args, **kwds):\n", " p_arr = np.array(params)\n", " return p_arr[0] + x * p_arr[1]\n", "\n", "# the model needs parameters\n", "p = Parameter(1, 'b', vary=True, bounds=(0, 10))\n", "p |= Parameter(-2, 'm', vary=True, bounds=(-5, 0.5))\n", "\n", "model = Model(p, fitfunc=line)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We set lower and upper limits on each of the parameters. This means that the log-prior probability from those parameters is described by a uniform distribution. Only solutions which have finite probability (i.e. lie between the limits) will be considered by the fit/sampler." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-2.3025850929940455 -inf\n" ] } ], "source": [ "# ln(1 / 10)\n", "# a value lying outside the limits is not possible\n", "print(p[0].logp(1), p[0].logp(-1))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "It's not required to give each parameter bounds unless the fit method requires it. The bounds do not have to be from a uniform distribution, any of the `scipy.stats.rv_continuous` distributions can be used:\n", "\n", "```\n", "import scipy.stats as stats\n", "# a normal distribution of mean 5 and standard deviation 1.\n", "p[0].bounds = stats.norm(5, 1)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we create an `Objective` from the model and the data. We use an extra parameter, `lnsigma`, to describe the underestimated error bars. Objectives use the model and data to calculate statistics about the curve fitting system." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1185.7110246179552 -6.405228458030841 -738.614995170284 -745.0202236283149\n" ] } ], "source": [ "from refnx.analysis import Objective\n", "\n", "lnf = Parameter(0, 'lnf', vary=True, bounds=(-10, 1))\n", "objective = Objective(model, data, lnsigma=lnf)\n", "print(objective.chisqr(), objective.logp(), objective.logl(), objective.logpost())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Then a `CurveFitter` is created from the `Objective`. This is responsible for doing all the curvefitting/Bayesian sampling. Let's do a quick fit using Differential Evolution." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "22it [00:00, 127.49it/s]\n" ] } ], "source": [ "from refnx.analysis import CurveFitter\n", "\n", "fitter = CurveFitter(objective)\n", "fitter.fit('differential_evolution');" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "In the final output of the sampling each varying parameter is given a set of statistics. `Parameter.value` is the median of the chain samples. `Parameter.stderr` is half the [15, 85] percentile, representing a standard deviation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4829542384\n", "Dataset = , 50 points\n", "datapoints = 50\n", "chi2 = 46.739546242716216\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(objective)\n", "objective.plot();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The trouble with a single fit that minimises $\\chi^2$ is that it doesn't reveal the range of solutions that are consistent with the data. It also assumes that the parameter uncertainties will be normally distributed and uni-modal. To investigate the parameter probability distributions we need to use MCMC to sample the posterior probability distribution of the system. \n", "\n", "Note: `pool=` specifies that no parallelisation is done during sampling. On platforms that use `spawn` for multiprocessing special precautions [must be used](https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods) when using `pool > 1`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1000/1000 [00:20<00:00, 48.70it/s]\n" ] } ], "source": [ "fitter.sample(1000, pool=1);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Once the sampling is done we burn/discard some of the initial steps because the initial locations of the walkers won't be around their 'equilibrium' position. We thin out the chain to reduce auto-correlation between successive steps." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: None \n", "\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n" ] } ], "source": [ "from refnx.analysis import process_chain\n", "\n", "process_chain(objective, fitter.chain, nburn=300, nthin=100, flatchain=True)\n", "print(objective.parameters)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we can see the range of solutions that are consistent with the data:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "objective.plot(samples=300);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fitting a neutron reflectometry dataset" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We start off with all the relevant imports we'll need." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import os.path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy\n", "\n", "import refnx\n", "from refnx.dataset import ReflectDataset, Data1D\n", "from refnx.analysis import Transform, CurveFitter, Objective, Model, Parameter\n", "from refnx.reflect import SLD, Slab, ReflectModel" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "It's important to note down the versions of the software that you're using, in order for the analysis to be reproducible." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "refnx: 0.1.18.dev0+0fbfe9f\n", "scipy: 1.5.3\n", "numpy: 1.19.4\n" ] } ], "source": [ "print(f\"refnx: {refnx.version.version}\\n\"\n", " f\"scipy: {scipy.version.version}\\n\"\n", " f\"numpy: {np.version.version}\")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Loading/Creating a dataset" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "*refnx* reads 2, 3, or 4 column plain-text files using `Data1D` or `ReflectDataset`.\n", "\n", "\n", "| columns | data |\n", "|---------|-------------------------|\n", "| 2 | $x, y$ |\n", "| 3 | $x, y, y_{err}$ |\n", "| 4 |$x, y, y_{err}, x_{err}$ |\n", "\n", "\n", "$y_{err}$ being the standard deviation of the measured $y$ data, $x_{err}$ being the uncertainty in $x$.\n", "\n", "In a reflectometry context $x$ is the momentum transfer $Q$ ($A^{-1}$), $y_{err}$ is the uncertainty in the reflectivity, and $x_{err}$ is the full width at half maximum (FWHM) of the Gaussian approximation to the resolution function, $dQ$.\n", "\n", "The dataset we're going to use as an example is distributed with every install. The following cell determines its location." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "pth = os.path.dirname(refnx.__file__)\n", "DATASET_NAME = 'c_PLP0011859_q.txt'\n", "file_path = os.path.join(pth, 'analysis', 'test', DATASET_NAME)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`ReflectDataset` uses a file path to load the data. However, you can also make a dataset directly from numerical arrays." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "data = ReflectDataset(file_path)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Creating an interfacial Structure" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`Structure` objects describe the interface of interest. They are made by assembling a series of `Components` (the simplest `Component` being a `Slab`. However, the first step is to create `SLD` objects that represent each of the materials:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "si = SLD(2.07, name='Si')\n", "sio2 = SLD(3.47, name='SiO2')\n", "film = SLD(2.0, name='film')\n", "d2o = SLD(6.36, name='d2o')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`Slab`s are created from these `SLD`s to represent each layer in the system." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# first number is thickness, second number is roughness\n", "# a native oxide layer\n", "sio2_layer = sio2(30, 3)\n", "\n", "# the film of interest\n", "film_layer = film(250, 3)\n", "\n", "# layer for the solvent\n", "d2o_layer = d2o(0, 3)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "A `Slab` has the following parameters, which are all accessible as attributes:\n", "\n", " - `Slab.thick`\n", " - `Slab.sld.real`\n", " - `Slab.sld.imag`\n", " - `Slab.rough`\n", " - `Slab.vfsolv`\n", " \n", "We need to specify which parameters are going to vary in a fit, and what the limits are on those parameters. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "sio2_layer.thick.setp(bounds=(15, 50), vary=True)\n", "sio2_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "film_layer.thick.setp(bounds=(200, 300), vary=True)\n", "film_layer.sld.real.setp(bounds=(0.1, 3), vary=True)\n", "film_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "d2o_layer.rough.setp(vary=True, bounds=(1, 15))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we assemble the `Structure` from the `Components`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "structure = si | sio2_layer | film_layer | d2o_layer" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(sio2_layer.parameters)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`Structure` has a `sld_profile` method to return the SLD profile. Let's also plot that." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(*structure.sld_profile())\n", "plt.ylabel('SLD /$10^{-6} \\AA^{-2}$')\n", "plt.xlabel('distance / $\\AA$');" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### `ReflectModel` calculates the generative model." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "A `ReflectModel` is made from the `Structure` and is responsible for calculating the reflectivity of the system. `ReflectModel` performs resolution smearing, applies scaling factor and adds a Q-independent constant background. It can use constant `dq/q`, point-by-point, and full resolution kernel smearing. The resolution parameter, `dq`, can be fitted, but this will only be valid if your dataset didn't supply the instrument resolution." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "model = ReflectModel(structure, bkg=3e-6, dq=5.0)\n", "model.scale.setp(bounds=(0.6, 1.2), vary=True)\n", "model.bkg.setp(bounds=(1e-9, 9e-6), vary=True)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's quickly have a look at the model generated by the structure:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "q = np.linspace(0.005, 0.3, 1001)\n", "plt.plot(q, model(q))\n", "plt.xlabel('Q')\n", "plt.ylabel('Reflectivity')\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### `Objective` combines the model and data, calculating statistics" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "An `Objective` is made from a model and dataset. Here we use a `Transform` to fit as logY vs X." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "objective = Objective(model, data, transform=Transform('logY'))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The `Objective` can calculate statistics for the fitting system. Note how the log-probability is the sum of the log-prior and log-likelihood." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "34376.69092305155 -5.013178251637257 -16157.428065740693 -16162.441243992329\n" ] } ], "source": [ "print(objective.chisqr(), objective.logp(), objective.logl(), objective.logpost())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### `CurveFitter` does the fitting/sampling" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The final setup step is to create a `CurveFitter` from the `Objective`. These objects do the fitting/sampling. Let's do an initial fit with differential evolution." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "51it [00:06, 7.53it/s]\n" ] } ], "source": [ "fitter = CurveFitter(objective)\n", "fitter.fit('differential_evolution');" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "An `Objective` has a plot method, which is a quick visualisation. You need matplotlib installed to create a graph. You can see that the fit looks good." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "objective.plot()\n", "plt.legend()\n", "plt.xlabel('Q')\n", "plt.ylabel('logR')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's see the results of the fit. For the case of DifferentialEvolution uncertainties are estimated by estimating the Hessian/Covariance matrix." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4829712976\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 921.484228321403\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now lets do a MCMC sampling of the curvefitting system. First we do 400 samples which we then discard (burn). These samples are discarded because the initial chain might not be representative of an equilibrated system (i.e. distributed around the mean with the correct covariance)." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 400/400 [01:15<00:00, 5.30it/s]\n" ] } ], "source": [ "fitter.sample(400, pool=1)\n", "fitter.reset()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We then follow up with a production run, only saving 1 in 100 samples. This is to remove autocorrelation. We save 15 steps, giving a total of 15 * 200 samples (200 walkers is the default)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 90%|█████████ | 1356/1500 [04:12<00:26, 5.52it/s]" ] } ], "source": [ "res = fitter.sample(15, nthin=100, pool=1)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "In the final output of the sampling each varying parameter is given a set of statistics. `Parameter.value` is the median of the chain samples. `Parameter.stderr` is half the [15, 85] percentile, representing a standard deviation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "print(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "A corner plot shows the covariance between parameters. You need to install the *matplotlib* and *corner* packages to create these graphs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "objective.corner();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Once we've done the sampling we can look at the variation in the model at describing the data. In this example there isn't much spread." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "objective.plot(samples=300);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "In a similar manner we can look at the spread in SLD profiles consistent with the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "structure.plot(samples=300)\n", "plt.ylim(2.2, 6);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fitting the BornAgain example" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "[BornAgain](https://www.bornagainproject.org/) is another program for fitting reflectometry and GISAS data. The following cells repeat the analysis in their [specular fitting example]( https://github.com/scgmlz/BornAgain/blob/master/Examples/python/fitting/ex03_ExtendedExamples/specular/FitSpecularBasics.py). The simulated thickness of the Titanium layer is 30 Angstrom, but we'll start the fit with a value of 50." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# necessary imports\n", "import numpy as np\n", "from refnx.util import q\n", "from refnx.analysis import Objective, CurveFitter, Transform\n", "from refnx.dataset import Data1D\n", "from refnx.reflect import SLD, Stack, ReflectModel" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# first grab the data from the BornAgain repository.\n", "# The data was originally created in genx. \n", "import requests as req\n", "import io\n", "import gzip\n", "url = (\"https://github.com/scgmlz/BornAgain/raw/master\"\n", " \"/Examples/python/fitting/ex03_ExtendedExamples\"\n", " \"/specular/genx_interchanging_layers.dat.gz\")\n", "f = gzip.open(io.BytesIO(req.get(url).content))\n", "dataset = np.loadtxt(f, usecols=(0, 1), skiprows=3).T\n", "\n", "# data is saved as two_theta, convert to Q.\n", "dataset[0] = q(dataset[0] / 2, 1.54)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# make a dataset\n", "data = Data1D(data=dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# make the structure\n", "air = SLD(0)\n", "si = SLD(2.0704) # silicon substrate\n", "ni = SLD(9.4245) # nickel\n", "ti = SLD(-1.9493) # titanium\n", "\n", "# make the layers\n", "ti_layer = ti(50)\n", "ni_layer = ni(70)\n", "\n", "# Make a multilayer by using a Stack Component\n", "stack = Stack()\n", "stack |= ti_layer\n", "stack |= ni_layer\n", "stack.repeats.value = 10\n", "\n", "structure = air | stack | si(0, 0)\n", "\n", "# put the Structure in a Model\n", "model = ReflectModel(structure, bkg=0, dq=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# we're only going to fit the Titanium thickness\n", "ti_layer.thick.setp(vary=True, bounds=(10, 60))\n", "\n", "# now do the fit\n", "objective = Objective(model, data, transform=Transform('logY'))\n", "fitter = CurveFitter(objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# now fit and plot\n", "fitter.fit('differential_evolution');\n", "fig, ax = objective.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "print(objective.chisqr())\n", "print(ti_layer)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 1 }refnx-0.1.38/doc/gui.rst000066400000000000000000000012551450471266700150270ustar00rootroot00000000000000.. _gui_chapter: === GUI === .. _YouTube: https://www.youtube.com/channel/UCvhOxwZsdFMGqSzasE0ZSOw .. _github: https://github.com/refnx/refnx/releases/latest *refnx* offers a sophisticated *PyQt* graphical user interface to analyse data, with pre-built executables available on `github`_. The gui can also be started from the interpreter (requiring the *qtpy, pyqt6, periodictable, matplotlib* packages to be installed): :: >>> from refnx.reflect import gui >>> gui() There are tutorials on how to use the PyQt interface on `YouTube`_. Suggestions for more tutorials are welcomed. .. image:: _images/gui.png :width: 400 :alt: The *refnx* front end GUI.refnx-0.1.38/doc/index.rst000066400000000000000000000044071450471266700153540ustar00rootroot00000000000000.. refnx documentation master file, created by sphinx-quickstart on Fri Oct 23 10:21:57 2015. refnx - Neutron and X-ray reflectometry analysis in Python ========================================================== .. _refnx github repository: http://github.com/refnx/refnx .. _github: https://github.com/refnx/refnx/releases/latest .. _scipy.optimize: http://docs.scipy.org/doc/scipy/reference/optimize.html .. _emcee: http://emcee.readthedocs.io/en/stable/ .. _refnx YouTube channel: https://www.youtube.com/channel/UCvhOxwZsdFMGqSzasE0ZSOw *refnx* is a flexible, powerful, Python package for generalised curvefitting analysis, specifically neutron and X-ray reflectometry data. It uses several `scipy.optimize`_ algorithms for fitting data, and estimating parameter uncertainties. As well as the scipy algorithms *refnx* uses the `emcee`_ Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler for Bayesian parameter estimation. Reflectometry analysis uses a modular and object oriented approach to model parameterisation. Models are made up by sequences of components, frequently slabs of uniform scattering length density, but other components are available, including splines for freeform modelling of a scattering length density profile. These components allow the parameterisation of a model in terms of physically relevant parameters. The Bayesian nature of the package allows the specification of prior probabilities for the model, so parameter bounds can be described in terms of probability distribution functions. These priors not only applicable to any parameter, but can apply to any other user-definable knowledge about the system (such as adsorbed amount). Co-refinement of multiple contrast datasets is straightforward, with sharing of joint parameters across each model. Various tutorials are available from the `refnx YouTube channel`_, and there are GUI programs available on `github`_ as well. The refnx package is free software, using a BSD licence. If you are interested in participating in this project please use the `refnx github repository`_, all contributions are welcomed. .. toctree:: :maxdepth: 2 installation getting_started.ipynb gui examples faq testimonials modules * :ref:`genindex` * :ref:`modindex` refnx-0.1.38/doc/inequality_constraints.ipynb000066400000000000000000000213201450471266700213620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inequality constraints with *refnx*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simple equality constraints can use the mechanisms outlined in this notebook, but are better expressed using the `Parameter.constraint` mechanism, or by sharing `Parameter` objects. It is sometimes also possible to implement different parameterisation of the model to use physically relevant values.\n", "\n", "The following processes can be used to make inequality constraints with *refnx*. The dataset is reflectivity from a clean silicon wafer with a native oxide layer." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import os.path\n", "import numpy as np\n", "\n", "import refnx\n", "from refnx.dataset import ReflectDataset\n", "from refnx.reflect import SLD, MaterialSLD, ReflectModel\n", "from refnx.analysis import Objective, CurveFitter\n", "\n", "np.random.seed(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pth = os.path.dirname(refnx.__file__)\n", "DATASET_NAME = 'c_PLP0000708.dat'\n", "file_path = os.path.join(pth, 'dataset', 'test', DATASET_NAME)\n", "\n", "data = ReflectDataset(file_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "air = SLD(0)\n", "sio2 = MaterialSLD('SiO2', 2.2)\n", "si = MaterialSLD('Si', 2.33)\n", "s = air | sio2(15, 3) | si(0, 3)\n", "\n", "model = ReflectModel(s, bkg=3e-8)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# model.bkg.setp(vary=True, bounds=(0, 1e-6))\n", "# model.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "\n", "# sio2 layer\n", "s[1].rough.setp(vary=True, bounds=(0, 10))\n", "s[1].thick.setp(vary=True, bounds=(0, 20))\n", "\n", "# si/sio2 interface\n", "s[-1].rough.setp(vary=True, bounds=(0, 10))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "objective = Objective(model, data)\n", "fitter = CurveFitter(objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitter.fit('differential_evolution')\n", "print(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inequality constraints with `differential_evolution`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Simple equality constraints can use the following mechanism, but are better expressed using the `Parameter.constraint` mechanism, or by sharing `Parameter` objects. It is sometimes also possible to implement different parameterisation of the model to use physically relevant values.*\n", "\n", "We see that the thickness of the SiO2 layer is 12.45 and the roughness of the air/SiO2 interface is 4.77. Let's make a constraint that the roughness can't be more than a quarter of the layer thickness. In optimisation such constraints are expressed as inequalities:\n", "\n", "\\begin{equation*}\n", "t > 4\\sigma\n", "\\end{equation*}\n", "\n", "We need to rearrange so that all variables are on one side, we do the rearrangement like this so there is no divide by 0:\n", "\n", "\\begin{equation*}\n", "t - 4\\sigma > 0\n", "\\end{equation*}\n", "\n", "Now we create a callable object (has the `__call__` magic method) that encodes this inequality. We're going to create the object with the parameters we want to constrain (`pars`), so we can refer to them later. We'll also store the objective because we'll need to update it with the fitting parameters." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class DEC(object):\n", " def __init__(self, pars, objective):\n", " # we'll store the parameters and objective in this object\n", " # this will be necessary for pickling in the future\n", " self.pars = pars\n", " self.objective = objective\n", "\n", " def __call__(self, x):\n", " # we need to update the varying parameters in the\n", " # objective first\n", " self.objective.setp(x)\n", " return float(self.pars[0] - 4*self.pars[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now lets create an instance of that object, using the parameters we want to constrain. Following that we set up a `scipy.optimize.NonlinearConstraint` for use with `differential_evolution`. Note that we want the constraint calculation to be greater than 0." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pars = (s[1].thick, s[1].rough)\n", "dec = DEC(pars, objective)\n", "\n", "from scipy.optimize import NonlinearConstraint\n", "constraint = NonlinearConstraint(dec, 0, np.inf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now do the fit with the added constraint. Note that you can have more than one constraint." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.random.seed(1)\n", "fitter.fit('differential_evolution', constraints=(constraint,))\n", "print(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inequality constraints during MCMC sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to implement that inequality constraint during sampling we can add an extra log-probability term to the `Objective`. This log-probability term will return 0 if the inequality is satisfied, but `-np.inf` if not." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class LogpExtra(object):\n", " def __init__(self, pars):\n", " # we'll store the parameters and objective in this object\n", " # this will be necessary for pickling in the future\n", " self.pars = pars\n", "\n", " def __call__(self, model, data):\n", " if float(self.pars[0] - 4*self.pars[1]) > 0:\n", " return 0\n", " return -np.inf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "lpe = LogpExtra(pars)\n", "\n", "# set the log_extra attribute of the Objective with our extra log-probability term.\n", "objective.logp_extra = lpe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets check what happens to the probabilities with the specified inequality." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(s[1].thick)\n", "print(s[1].rough)\n", "print(objective.logpost())\n", "\n", "print(\"Now exceed the inequality\")\n", "s[1].rough.value = 5.\n", "print(objective.logpost())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's MCMC sample the system. There will be a user warning because some walkers have initial starting points which disobey the inequality. Normally one would sample for a far longer time, and thin more appropriately. However, the purpose of the following is to produce a corner plot that demonstrates the inequality - note the sharp dropoff in the probability distribution for the roughness. The roughness doesn't like to go much higher than ~2.5, which is around a quarter of the optimal layer thickness of ~10." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s[1].rough.value = 2.\n", "fitter.initialise('covar')\n", "fitter.sample(20, nthin=10, pool=1);\n", "objective.corner();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }refnx-0.1.38/doc/installation.rst000066400000000000000000000071301450471266700167420ustar00rootroot00000000000000.. _installation_chapter: ==================================== Installation ==================================== .. _Visual Studio compiler: https://wiki.python.org/moin/WindowsCompilers .. _miniconda: https://conda.io/miniconda.html .. _github: https://github.com/refnx/refnx .. _homebrew: https://brew.sh/ *refnx* has been tested on Python 3.8, 3.9, 3.10, and 3.11. It requires the *numpy, scipy, cython* packages to work. Additional features require the *pytest, h5py, xlrd, uncertainties, attrs, matplotlib, Jupyter,* *ipywidgets, traitlets, tqdm, pandas, qtpy, pyqt6, periodictable, pymc, pytensor*. To build the bleeding edge code you will need to have access to a C-compiler to build a couple of Python extensions. C-compilers should be installed on Linux. On OSX you will need to install Xcode and the command line tools. On Windows you will need to install the correct `Visual Studio compiler`_ for your Python version. Installation into a *conda* environment ======================================= Perhaps the easiest way to create a scientific computing environment is to use the `miniconda`_ package manager. Once *conda* has been installed the first step is to create a *conda* environment. Creating a conda environment ============================ 1. In a shell window create a conda environment and install the dependencies. Note that not all of these dependencies are essential, but they are required to run the full refnx test suite. The **-n** flag indicates that the environment is called *refnx*. :: conda create -n refnx python=3.9 2. Activate the environment that we're going to be working in: :: # on OSX conda activate refnx # on windows activate refnx 3. Install the remaining dependencies: :: python -m pip install refnx[all] Installing with pip =================== There are refnx wheels available for macOS/Windows/Linux on PyPI. Using the [all] modifier means that all refnx's optional dependencies will also be installed. :: # install refnx and all optional dependencies python -m pip install refnx[all] # alternatively just install refnx itself python -m pip install refnx Installing into a conda environment from a released version =========================================================== 1. There are pre-built versions on *conda-forge*: :: conda install -c conda-forge refnx 2. Start up a Python interpreter and make sure the tests run: :: >>> import refnx >>> refnx.test() Installing from source ======================= The latest source code can be obtained from `github`_. You can build the package from within the refnx git repository. 1. [macOS only] If you wish to enable the parallelised calculation of reflectivity with OpenMP, then you will need to install *libomp*. This is easily achieved via `homebrew`_, and the setting of environment variables. The refnx kernel that uses OpenMP is not normally any better than the default C version. :: brew install libomp export CC=clang export CXX=clang++ export CXXFLAGS="$CXXFLAGS -Xpreprocessor -fopenmp" export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include" export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include" export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp" export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib 2. In a shell window navigate into the source directory and build the package. If you are on Windows you'll need to start a Visual Studio command window. :: pip install . 3. Run the tests, they should all work. :: python setup.py test refnx-0.1.38/doc/lipid.ipynb000066400000000000000000002416521450471266700156640ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysing lipid membrane data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This Jupyter notebook demonstrates the utility of the *refnx* for:\n", "\n", " - the co-refinement of three contrast variation datasets of a DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) bilayer measured at the solid-liquid interface with a common model\n", " - the use of the `LipidLeaflet` component to parameterise the model in terms of physically relevant parameters\n", " - the use of Bayesian Markov Chain Monte Carlo (MCMC) to investigate the Posterior distribution of the curvefitting system.\n", " - the intrinsic usefulness of Jupyter notebooks to facilitate reproducible research in scientific data analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step in most Python scripts is to import modules and functions that are going to be used" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# use matplotlib for plotting\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os.path\n", "\n", "import refnx, scipy\n", "\n", "# the analysis module contains the curvefitting engine\n", "from refnx.analysis import CurveFitter, Objective, Parameter, GlobalObjective, process_chain\n", "\n", "# the reflect module contains functionality relevant to reflectometry\n", "from refnx.reflect import SLD, ReflectModel, Structure, LipidLeaflet\n", "\n", "# the ReflectDataset object will contain the data\n", "from refnx.dataset import ReflectDataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order for the analysis to be exactly reproducible the same package versions must be used. The *conda* packaging manager, and *pip*, can be used to ensure this is the case." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('0.1.23.dev0+1b59a5a', '1.7.1')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# version numbers used in this analysis\n", "refnx.version.version, scipy.version.version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `ReflectDataset` class is used to represent a dataset. They can be constructed by supplying a filename" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pth = os.path.join(os.path.dirname(refnx.__file__), 'analysis', 'test')\n", "\n", "data_d2o = ReflectDataset(os.path.join(pth, 'c_PLP0016596.dat'))\n", "data_d2o.name = \"d2o\"\n", "\n", "data_hdmix = ReflectDataset(os.path.join(pth, 'c_PLP0016601.dat'))\n", "data_hdmix.name = \"hdmix\"\n", "\n", "data_h2o = ReflectDataset(os.path.join(pth, 'c_PLP0016607.dat'))\n", "data_h2o.name = \"h2o\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `SLD` object is used to represent the Scattering Length Density of a material. It has `real` and `imag` attributes because the SLD is a complex number, with the imaginary part accounting for absorption. The units of SLD are $10^{-6} \\mathring{A}^{-2}$\n", "\n", "The `real` and `imag` attributes are `Parameter` objects. These `Parameter` objects contain the: parameter value, whether it allowed to vary, any interparameter constraints, and bounds applied to the parameter. The bounds applied to a parameter are probability distributions which encode the log-prior probability of the parameter having a certain value." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "si = SLD(2.07 + 0j)\n", "sio2 = SLD(3.47 + 0j)\n", "\n", "# the following represent the solvent contrasts used in the experiment\n", "d2o = SLD(6.36 + 0j)\n", "h2o = SLD(-0.56 + 0j)\n", "hdmix = SLD(2.07 + 0j)\n", "\n", "# We want the `real` attribute parameter to vary in the analysis, and we want to apply\n", "# uniform bounds. The `setp` method of a Parameter is a way of changing many aspects of\n", "# Parameter behaviour at once.\n", "d2o.real.setp(vary=True, bounds=(6.1, 6.36))\n", "d2o.real.name='d2o SLD'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `LipidLeaflet` class is used to describe a single lipid leaflet in our interfacial model. A leaflet consists of a head and tail group region. Since we are studying a bilayer then inner and outer `LipidLeaflet`'s are required." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Parameter for the area per molecule each DMPC molecule occupies at the surface. We\n", "# use the same area per molecule for the inner and outer leaflets.\n", "apm = Parameter(56, 'area per molecule', vary=True, bounds=(52, 65))\n", "\n", "# the sum of scattering lengths for the lipid head and tail in Angstrom.\n", "b_heads = Parameter(6.01e-4, 'b_heads')\n", "b_tails = Parameter(-2.92e-4, 'b_tails')\n", "\n", "# the volume occupied by the head and tail groups in cubic Angstrom.\n", "v_heads = Parameter(319, 'v_heads')\n", "v_tails = Parameter(782, 'v_tails')\n", "\n", "# the head and tail group thicknesses.\n", "inner_head_thickness = Parameter(9, 'inner_head_thickness', vary=True, bounds=(4, 11))\n", "outer_head_thickness = Parameter(9, 'outer_head_thickness', vary=True, bounds=(4, 11))\n", "tail_thickness = Parameter(14, 'tail_thickness', vary=True, bounds=(10, 17))\n", "\n", "# finally construct a `LipidLeaflet` object for the inner and outer leaflets.\n", "# Note that here the inner and outer leaflets use the same area per molecule,\n", "# same tail thickness, etc, but this is not necessary if the inner and outer\n", "# leaflets are different.\n", "inner_leaflet = LipidLeaflet(apm,\n", " b_heads, v_heads, inner_head_thickness,\n", " b_tails, v_tails, tail_thickness,\n", " 3, 3)\n", "\n", "# we reverse the monolayer for the outer leaflet because the tail groups face upwards\n", "outer_leaflet = LipidLeaflet(apm,\n", " b_heads, v_heads, outer_head_thickness,\n", " b_tails, v_tails, tail_thickness,\n", " 3, 0, reverse_monolayer=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Slab` Component represents a layer of uniform scattering length density of a given thickness in our interfacial model. Here we make `Slabs` from `SLD` objects, but other approaches are possible." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Slab constructed from SLD object.\n", "sio2_slab = sio2(15, 3)\n", "sio2_slab.thick.setp(vary=True, bounds=(2, 30))\n", "sio2_slab.thick.name = 'sio2 thickness'\n", "sio2_slab.rough.setp(vary=True, bounds=(0, 7))\n", "sio2_slab.rough.name = name='sio2 roughness'\n", "sio2_slab.vfsolv.setp(0.1, vary=True, bounds=(0., 0.5))\n", "sio2_slab.vfsolv.name = 'sio2 solvation'\n", "\n", "solv_roughness = Parameter(3, 'bilayer/solvent roughness')\n", "solv_roughness.setp(vary=True, bounds=(0, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once all the `Component`s have been constructed we can chain them together to compose a `Structure` object. The `Structure` object represents the interfacial structure of our system. We create different `Structure`s for each contrast. It is important to note that each of the `Structure`s share many components, such as the `LipidLeaflet` objects. This means that parameters used to construct those components are shared between all the `Structure`s, which enables co-refinement of multiple datasets. An alternate way to carry this out would be to apply constraints to underlying parameters, but this way is clearer. Note that the final component for each structure is a `Slab` created from the solvent `SLD`s, we give those slabs a zero thickness." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "s_d2o = si | sio2_slab | inner_leaflet | outer_leaflet | d2o(0, solv_roughness)\n", "s_hdmix = si | sio2_slab | inner_leaflet | outer_leaflet | hdmix(0, solv_roughness)\n", "s_h2o = si | sio2_slab | inner_leaflet | outer_leaflet | h2o(0, solv_roughness)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Structure`s created in the previous step describe the interfacial structure, these structures are used to create `ReflectModel` objects that know how to apply resolution smearing, scaling factors and background." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model_d2o = ReflectModel(s_d2o)\n", "model_hdmix = ReflectModel(s_hdmix)\n", "model_h2o = ReflectModel(s_h2o)\n", "\n", "model_d2o.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "\n", "model_d2o.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))\n", "model_hdmix.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))\n", "model_h2o.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An `Objective` is constructed from a `ReflectDataset` and `ReflectModel`. Amongst other things `Objective`s can calculate chi-squared, log-likelihood probability, log-prior probability, etc. We then combine all the individual `Objective`s into a `GlobalObjective`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "objective_d2o = Objective(model_d2o, data_d2o)\n", "objective_hdmix = Objective(model_hdmix, data_hdmix)\n", "objective_h2o = Objective(model_h2o, data_h2o)\n", "\n", "global_objective = GlobalObjective([objective_d2o, objective_hdmix, objective_h2o])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `CurveFitter` object can perform least squares fitting, or MCMC sampling on the `Objective` used to construct it." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "fitter = CurveFitter(global_objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll just do a normal least squares fit here. MCMC sampling is left as an exercise for the reader." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "53it [00:26, 1.67it/s]/Users/andrew/miniconda3/envs/dev3/lib/python3.8/site-packages/scipy/optimize/_numdiff.py:557: RuntimeWarning: invalid value encountered in subtract\n", " df = fun(x) - f0\n", "53it [00:26, 1.98it/s]\n" ] } ], "source": [ "fitter.fit('differential_evolution');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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GhehktqaVcN7e3kRFRVGY2RvsgbOzM97e3nk+3q4CRWF7PaV7pN4jhF8IZ0PgYl5YGk27XddYUUENvMvcA+pq23upOmUy52Ivgw4UmlaiOTo6UrNmzeIuRqljV01P1jSp3SS82zzAuQqO3BdxDSllllpFeGQsIy9WREjJ9PdnMGnVgWIqraZpmu3oQJGLIQFPsbF5eUL2XceUkJIlT7H1eAy7qtXlqsmVe07s5NuNx3Ww0DTN7thVoLBGr6fM/mzmhkuSJGTfNWbvm82iQ4tuvRZSyxNpNLLVtwntTuwEKZm26bjuJqtpml2xq0BhrV5P6cIuhBFWz404NyMdw1Vt4pejv9x6PcjXg6dDa/Fn7ZZ4X42mUfRxpER3k9U0za7YVaCwtmCvYHB0ZEPTstwbcQ2HFJllNtkxDzSg+hP9SBUGuh7agqNR6G6ymqbZFR0ochFYOZDZXWezp50/7jdSaXHwerazybZt04D/fBrT7dA/UMKmB9Y0TSssHSjuILByIPX7v8gNZwOdt1/FjPnWdOTpth6PYU29NtS5HIXv+ZMs2RFVTKXVNE2zPrsKFLZIZgPsv3mcDYFl6Rh+FYcUyeYzmzO8HlLLk3X12wDQ9dA/etpxTdPsil0FCmsns2+dF8maFu54XE+l1f7rbIjakCFPEeTrQfv7Atnu3ZBe+zeQnJyqaxWaptkNuwoUttKzdk/+beLONRcD3f6Ly3bwXZ/m3vzS5H7qXI4i8OwhFoWd1rUKTdPsgg4UeRBYOZDXQ9/hryB3Ouy4imtK1lXvgnw9uNazDzcdnXhkzx8kp0qW6lqFpml2QAeKPPL38Oe3EHfKxpsJjcg+B1K2cgV+qxdKjwMbcUlKIPpaYhGXUtM0zfpKRaAQQjwkhJguhPhJCNG5OMoQdiGM7Q3LctHdga7/xLDi2Iosxzzc3JulTe+nbFI83Q7/w18Ho5n/36liKK2maZr12DxQCCFmCSGihRB7M+3vKoQ4JIQ4KoQYk9s5pJTLpJTDgWeBfrYsLwDj3NXDQrBXMAYHR35r7U7o7mus2/FThuk8QDU/1erTjeMe1Xh8xypSzZJ3f92rcxWappVqRVGjmAN0tdwhhDACU4FuQENggBCioRAiQAixMtOjssVb3057X5ELrBxIrzq9WHGPB46pks5bL/PB1g+yBIs+QT78ENyTZucO0TzqAKlmvfKdpmmlm80DhZRyI3A50+6WwFEp5XEpZRKwEOglpdwjpeye6REtlI+B36WUO7K7jhDiaSFEmBAizGqLkpzelmGzZ+2eHPNxY7+vMw9tikUimfDfhCxdZcs8O4wrzmUYtv0XJHAtPtk65dE0TSsGxZWjqA6cttiOStuXk1HA/UBfIcSz2R0gpZwGvA/sMJlMBS/Zqf/gRAqcToGZnSBszq2XAisH0t6nPb+29aBhZAJ1T8WTKlOzdJV19nBnfmBXuhzZis+V88zYfEI3P2maVmqVimS2lPJ/UsogKeWzUspvczmu8APuTm6CFfGwMUltr3o1Q81iSOMhrG7jSaKDoM/GWAwYskzpEVLLkx+De5IqDDwV9iupZqkH4GmaVmoVV6A4A/hYbHun7SsUq0zh4VoR6jvC8RRIkGA2q+CRJrByICM7vsu64HL02HIFx6QUJm2blKX5aeRj7Vne6F4G7FpD5WuX+Gn7ad0DStO0Uqm4AsV2wF8IUVMIYQL6A8uLqSwZnd8FDR3ADBxKBgH4hWY4JC4pjkXtPSh300zn7XEkm5OzND8NbFWDk8+/gsFsZtSWn3QPKE3TSq2i6B67APgXqCeEiBJCDJVSpgAjgTXAAeBnKeW+wl7LOnM9SahuBHcBe1NAmlWuwqL5KdgrmN0NPThRxcSj6y9jEFmbnwDu6xbCz4Fd6Ld7LT5XzuseUJqmlUpF0etpgJSyqpTSUUrpLaWcmbZ/lZSyrpSytpRygjWuZZWmp6YD1ZoSjR3hWApcN6v9c3veChaBlQOZ2XUWEb1bEXg0nronbzJ5++QMzU+gmqBiXx5NisGBlzbP0z2gNE0rlUpFMjuvrFKj8Gmp/gx0BAnsTruxpyRmyVVcG/Qw8SbBo+suZdv8BGCoXo25Qd3pve9vGp0/qntAaZpW6thVoLDqehQVjeBthB3JICVgBpeMS5w28W/P6jaePLj1Ch43JGevn81Sqwip5cl3bR4lxtWd8X98izlVT0GuaVrpYleBwurrUQQ7QowZTqQCAuIz5hcCKwfS4L2vcUmS9Pj7AosOL2L42uFZekC93i+ET+4dTNDZg/TZ+5de2EjTtFLFrgKF1TVyBFcB/yWB0ZSl9xNAQkN/tjZwY+CfMTikSJJSk7LtAWV6ajDh1erz1vpZuMfF6FqFpmmlhl0FCqsvheogVK3iSAq0/ep2/sLCimMr+KGLJ1Uup9B5exxCiCxrVQD0Dq7B2w++iFvSTcav/Yaftp3S4yo0TSsV7CpQ2GQp1JYmMAKvPJZl7idQy6RualKWE1VMPLn6EoEVmxJYOTDLcUG+HjTvdg9ftH2Mboe38OC+v3l72R4dLDRNK/HsKlDYhJsBgkywKxk+6QgLB2UIGD1r98RodGRu14o0jEzAeeOWLAntdH2aezM7pA/h1eozYc1UfC6f1cFC07QSL0+BQggxSgjhYevCFJbVm57S3ZNWq/g7EQ6uhFldb00WGFg5kN7+vVnZxoOL7g48sfJ8tosagapVvNe7KS/2Gk2qwcjUXz/GMTlJBwtN00q0vNYovIDtQoif0xYcErYsVEHZpOkJoKwBWplgTzKcSwWZmmGywJ61eyKdnfixsydt9l3n0Np5OdYqBraqwfNPduS1B1+m8YVjfLjma8xmqYOFpmklVp4ChZTybcAfmAkMBo4IIT4SQtS2YdlKlrZOqgfUmgQ1rsKcCrvmA7cXNfqpQwWuuhp4avm5bAffpRvYqgYdXh/GlHsG8MjeP3n2vyWYJXouKE3TSqQ85yiklBI4n/ZIATyAxUKIyTYqW/EZl03TlbOADk4QmQp7UgAJO+dnqFWklnFjfqeKdNhxFZ8TuTd/DWxVg0qfTmRFg3aM2TCHBw9s0tORa5pWIuU1R/GiECIcmAz8AwRIKZ8DgoCHbVi+4jMu7vZj6B9Q/0Fo5gjVDapWcdMM5hRVq9j0GYGJSYxuMZofOlXgurMB8eEHvPjXizk2QQEMDPHl5jfT2e7dkC9XfkqHo//p6cg1TStx8lqjqAD0kVJ2kVIuklImA0gpzUB3m5Uun2yWzPZpCf3nw/A/4Y3+ap2K1QkqVxE2G9aNh7k9iYvey9UyDszv5Enn7XGc3rKKoWuG5hos+rWvy++TZrC/ci2+XjaRe46G6XyFpmklSl4DRS0pZaTlDiHEDwBSygNWL1UB2SyZnc6nJTz0IrRzUs1Pey1mgk1NIjghAQfhwNwunlxzMfD8smiSzEk59oJK92Db+jzV/wOOefowY8kHdNu/SQcLTdNKjLwGikaWG0III6rZ6e5zchO0Nak1K1bGQ2zaNOQGBwLrP8xbrd7iRhknvu9SkfvDr9LwRDzLji7LtVYR5OvBq/1bM2DgRCKq1eX/lk/msbAVOlhomlYi5BoohBBvCiGuAU2EEFfTHteAaODXIilhSeMXCkYBD7uo1e8W3YRkCc0Ggk9LHqn3CHO6zWHHwHbEljEyaskFUmVqrr2gQCW33+jfmif7fcC6Oi0Z/+d3fPj7/zF+8Q4dLDRNK1YOub0opZwITBRCTJRSvllEZSrZfFpC9ymw8mV4yAUWxsPKBHCYBQhoOoBAn5aMaj+WOT238vL8KFruv457qzs3hw1sVQOA54xv8fLGHxmxdRH1L57kxRtvsO9sMH2aexPkW+LHPWqaZmdyDRRCiPpSyoPAIiFE88yvSyl32Kxkt8vQAHgRqAisk1J+Y+tr3lHwYPBqqHo8nf9Wjdj2NICYBTvnweCVBPq05Ngbkzi3eggv/HyWJ+qrRfweqfdIrqdODxZvG4zs9arNJ79P4beZI3nn9HM8+t+9dGxYhWfa19YBQ9O0IpNroABeAZ4GPsvmNQl0yO3NQohZqF5R0VLKxhb7uwJTUBNjzJBSTsrpHGnJ8meFEAbge6D4AwXcnkm23Sy4bIb1iVBOQCAqgPi0JFbE89XDXkyYHsX9/8XwkeEj/D38s5000FJ6sHhXCB6oUofPVn7OlJWf0fnIVt6/9jT9DkXTL9hH1zA0TSsSd2p6ejrtz/sKeP45wFeoGzxwKxE+FegERKGmBlmOChoTM73/KSlltBCiJ/Ac8EMBy2EbJzep9bV7Oqu1tZcngEmA+B6qBBJcI5iv23jy2JpLvLjoAn81d2fFsRV3DBSggkW9KmVZsiOKgeUnMfzfxbz4zwLandjB56GP8X1ydxZuO0XHBl66hqFpmk3ldcDd7rTEdr6m7JBSbgQuZ9rdEjgqpTwupUwCFgK9pJR7pJTdMz2i086zXErZDRiUSxmfFkKECSHCLl68mJ9iFpxfKDi4gNEI/dzU0qlL4mF/Aqx6lcDEJN6sP5DP+1ehekwyj629dMceUJaCfD34qHcA7/duyndtHqXT0K8Jr96Q99ZNZ/Wskdx/aAtr953n0e/+1QlvTdNs5k5NT+l6AP2An4UQZuAn4GcpZUHuTtWB0xbbUUCrnA4WQtwL9AGcgFU5HSelnCaEOAf0MJlMRdN116clPLlc1SxcPIFX4IersCgekoFfR/LIpUMc8Pbgr2ZlGb7yIitDPQm7EJanWkU6y9rF/DZN+Wn5r7z291y+++Ujdlatx+T2T/LuMglA7M0kQmp56hqGpmlWk6dAkTbYbjIwWQjhD7wDfIxqLrIpKeXfwN95PHYFsCI4OHi4LcuUgU/L2/mK2BMgv4CFN+GXeLixG0JM9Lx+g/cf9aLtO8d44edzrGu8h4joiHwFiyBfj1s3//n1vehWpyUP7V7Hy5vnsWDhW+yoVo/vDj3MWv8QnEwOzBsWooOFpmlWkdcaBUIIX1Stoh+QCowu4DXPAD4W295p+wpNCNED6FGnTh1rnC7/nMuBk4BBripQrE2Ey2YCu8LA8vH80NmToasu8fP6lQw9s5mZXWbmK1ikS69hfLuhGh0b38vDu/5g2PZf+O6XjzhWoTozWvRmanU37m/mp2sYmqYVWp4ChRDiP8ARWAQ8IqU8Xohrbgf8hRA1UQGiPzCwEOcrOfxCweAADinQ1wX+TIQtSXBJkvhaM6b1vEj3f6/w1g9nGfiea76boCwF+Xow/YlgwiNr8+Wf1egQ2JWuh7bwzLYlTFzzFTEbv2dxwP0satqFLyp6M6xtTcq6OOqgoWlavgk1e/gdDhKinpTyUL5PLsQC4F7UGIgLwHtSyplCiAeAL1FNV7OklBPye+7cBAcHy7Cw3EdC20zYHLWokTkVhAGifGDObpJcDTz3fA08rqby6denmfhYdeq8/cUdx1XkRXhkLINmbCUp2QxIWkXu4bGdv9H5yFYczals9m3K/MBurKvbCulo4pFgHxpVc9e1DU3TMhBChEspg7Pszy1QCCEek1L+KIR4JbvXpZSfW7GMhWbR9DT8yJEjxVeQ09tUgtsvVP057334+ToyTvJ7V3cqnEyh0Yl4+k6sxz0NO9Cz6bAC1yzShUfGsvV4DB6uJsav3EdSshnP65d5dPcfDNi1Bu+r0VxydWd5g/YsCejIvsq1QAicHQ06n6FpGlDwQPGMlPI7IcR72bwspZTjrVlIaynWGkVmp7fB3J5wIx5+i4c9yRz0caLWuSTWNyvL68/74GQ0Mb3LrEIHi3TpQeNafDIzNp9ApqQQemIH/Xb/QYdj23BKTeFAJT+WNO7A8ob3Ua+ZPy/dX1cHC027yxUoUFi8+R4p5T932lfcSkyNIrPT29Ro7fDvOXkghfIrb1ImPhUHM7wwyoeNQe6M9GjOsGYjbvegshLLmsbes3H8sfkA3fb8TZ+96wg8d5hUYWBTzWYsa3I/7v0epl5NL90kpWl3qcIGih1SyuZ32ldSlKgahaWwOfDby+xNNGBcGU+DPfGkGOClF3xoXz2VR24mwAOfqbmkbMQycET88S9+q5bw0N71VLt2iatObqys35ZljTsQUaMRj7SooacJ0bS7SEGbnloDbYCXgC8sXioH9JZSNrVyOa2ixAYKsEh2pxARLmm68hoCWNuyHNXbG2nkBgQ9CU0HWL12kVl6Ejw5MZmQU3vos+8vuh7agltyApHlq/BLo/tY0aQjIZ1a6uS3pt0FChoo2qN6LT0LfGvx0jVghZSyBLXvlOCmp8zSmqJmHFuGy8prDPrzMokOqguYQ0sTtHUCN0eb1y5ABYslO6JYHB5FSooZ56R4uh3eQu+9f9EmcjcGJNurN2Rp4w6satCWxDLleLd7Ix00NM0OFbbpyTfzUqglWYmuUViI2DufEVsmMu/dwzgnmdlR141u2+IQJqCNE4S4QJMHoExlm9cwMucyFodHUfHyBXrt/5s+e//CP+Y0iUZH/vAPYVlABzb6NQNHRx7Rs9hqmt0obKD4AzXQ7kratgewUErZxdoFtYbSEigAxv87niO/zWHuRyf4JdSDfaFleHHJBdwPJoGLgNYmaGkCV2cYvNLmzVHpMtQ0klNpdP4oD+/7i577N1Ah/ioXXcuzvGF7ljbuwJGqtXU+Q9PsQGEDxU4pZbM77SspSlOgiIiOYPja4Ty/IJIhqy4y4iVftjRx45Od0dy/+gocSQFnoIUJ+nWEVo9CfIwao1EEQcOyphF7MwlPR9jwfz/QM+LPW11tD1b05ZfGHVjRrBMj+92jm6U0rZQqbKAIRyWvT6Vt+wK/lLReT6UmR5FJRHQE07d/xYvP/ojn1RT6fOjP1XKOzD53kcDj12BzIhxIUROuNHWE1s7g5apmri2iGoal9NrGH5sP0HnvBvrs/YugswdJMjjwR73WLGjahfDagbzTI0AHDU0rRQobKLoC04ANgABCgaellGusXVBrKE01inQR0RF8OKM/8947zNZGZXjhJT9G1e7NsLg4OLcbdoXBvwmwK1lNyVjHAR5qAY8Mg8TYIqthWLJsnvKNjmRAxBp671mHR8I1TnpU5afArixu3JG4sh46l6FppUChAkXaCSoCIWmbW6WUl6xYPqsqjYECYNGhRZz44GVGzzvD5Me8SXh+OD1r9yQwMUmN7k5JhBspEJYM25PghlRrdQc7QnM3aPNEkXSrzcyyeWrSsp103LeJARGraXl6H0kGB9bWbc28wK7srB3Iuz0a61qGppVQha1RCNTqcrWklOOFEDWAKlLKbdYvauGV1kABEHFhJ659+uO37QiD3qnNUb8yvNXqLR5x9b29QNKBX+HwetiXBNuS4EyqapZq7AitXOHBwVA1sEhzGeksg8aPc37n4bDfeXjvOsonXOdQxRp8H9yTXxrdi9nFVXez1bQSprCB4hvADHSQUjZI6/W0VkrZwvpFLbjSmqPI7McNX9DpodHEOxnoP642ia5OzO46+/ZcUOnzR6UkAmY4Z4btibA3Wa2sV8UAzU3QxAlcHaHZY8VW01iyI4oVW4/Rbe8GBocvp+GF41xxLsNPTbswL6g7UeUqYXLQExNqWklglSk8LHs6CSF26ZHZthERHcH/ffkI0yYdZW0Ld8Y8V4NRQS8wLGDY7YPSZ6h18YTzEbBzPtxIhD1JsCMJzptVLaOhIzRzhFou0Pzx4m2acnFk5TeLGLTtV7oc2oIAfq/bhhkhD9OwV0eql3fRtQtNK0aFDRT/oaby2J4WMCqhahS6e6yNLDq0iPNvv8ioxef45HEfOn26PPfZZdMnHtw5H1KS4FwK7EhWgSMJ8BAQaIKgstDvk2JploLbQaP6tUtEf/w5/cNXUS7xBv/6NuG7Vg+z1T+YecNb62ChacWgsIFiEGoJ1ObAXKAv8LaUcpG1C2oN9hAoACLO78Cj/1P4/LOX32a+RkKrIOKS4gj2Cs45aGSuafw3D/begIgkOJmqjqlthKYmaFwWhq0oli62oIJG+J6TeC+eR7Olc6h6LYYDlfzYPfAZLj/Yi5Z1q+iAoWlFyBq9nuoDHVHdY9dJKQ9Yt4jWYy+BAmDPkU24h3bCJSGV/uNqE+3hiLPRmemdp+dt/QrLmsalRNiVBBGJECfBCWjjBy+8Db2eAiFs/GmyFx4Zy+DvNtFt1188/d9S6sScJrJ8Fb5r25+AMSO5nCR1k5SmFYGCTgpYIbeTSikvW6FsdySEcEON4RgnpVx5p+PtKVDM2DODlSs+Zt4Hxzle1Ykhb9Yk2eRASLUQnmv6XN4XO7Ksaax6A46l1TIOpCXAa3hBl+Yw9Dmo5nV7hb4inDJk6/EYzl2+QfT8xYz6ZwEBF45xqnwVvmrTj1VNOzL3mbY6WGiaDRU0UJwAJKoWQdpz0rallLLWHS46C+gOREspG1vs7wpMQU2YOkNKOekO5xkPXAf2322BIn2Kj3u2XeTL/4tkVYg7bzzjjUEYMRlNea9ZWDq9Df6eCMf/hoRU2J+iahqRqepvtqZRjQBvXA56flzkU4YMmrGV5ORUOh7bzqhN8wi4cIzI8lXZ/tjzRPfqSyt/Lx0wNM0GChoo2kopNwshnKWUCQW4aDvUDf779EAhhDACh4FOQBSwHRiAChoTM53iKaAp4Ima8ejS3RYoQAWLsAthtJq1joAvFzL1ocp8+1BljMLIyGYjM/aGyqv0LrapSarJSZrhcooa+b0rCa5IMAGNHCHQEWqWgW5FEzQyrP+9Yi+hB7fy4j/zaXz+GCc8qvJlhyE88emrBPnlWuHVNC2fChoowqWUQYVZzU4I4QestAgUrVFNSF3Stt8EkFJmDhLp758AuAENgXjUnFPm3K5pb4HiFim5PKAXFX5awdinfVgb6lWwGkU6y+ao1WNU0DAY1YiZkwmqaWpfkmqaqmCAZk7QxAQVnItsnqn0oHE29ibR8xfz2oa51Lt0ilN1mxA24k18e3XRtQtNs5KcAoXDHd6XLISYBngLIf6X+UUp5QsFKEt14LTFdhTQKqeDpZRjAYQQg1E1imyDhBDiaeBpgBo1ahSgWKWAEFSYu4hrZ9oxfmYYDRreV7jz+bS8fbP3ang7LwG3A8ivo2HPDVXLWBcPf8VD7Rtw/X3o1A7q3mfTgBHk60GQr4dqktrZmr9rB9N331+8uOEH+rw4gD++bs3eqV/QuGOO/4Q0TSukO9UoKgL3Ax8D72Z+XUo5944XyFqj6At0lVIOS9t+HGglpRxZkA+Q6Vp2MTL7TnYf3YRTh854X0zi2TH++HceqOaEKmjNIjeWtY75r0F4WhL8qlmtlxHoAi+8CdVdiqxJ6uyVeH755zCDty/nua2LcEtJImbAE6zo/TRNg+vpGoamFVBhx1E0lVLuKuCF/ShE01M+r3VXBIoZe2awcN3nzPnoKGVumhn8Vk3O1ChfuGaovEgPGpdPweJZsCMBDqaopipvIwS7wSuTges2DRq3Et4pZionXmNO1BpqLfmBeEdnptz7BHXeeY3LSWbdpVbT8qmwgaIu8A3gJaVsLIRoAvSUUn6Yh/f6kTFQOKCS2R2BM6hk9kAp5b58fJ5c2W2OIk16T6iK564x96NjAAwbU5uqzdvnr8tsQVkmwm9K2JUA4UlwyawS4AGO0KosjPldHW+DrrbptYuQWp5sPR7D0gXreO+P72h3cif7vWrxdufn2e/bUM8hpWn5UNhAsQF4HfjOYq6nvZZdXnN43wLgXqAicAF4T0o5UwjxAPAlqqfTLCnlhPx9nByvd1fUKEAFixXHVrBrwwKmfXSYFKNg6Bs1OVu9LG2rt8XTxdN2zVGQNRGekghRqRCWAPuSIQWoVwUa3oQGBnBxgq6TbNJryrJL7QOH/uGtP6dT9XoMPzXpzPX3P2DoQ8Uz8lzTSpvCBortUsoWmSYFjJBSBlq/qIVn7zUKSxHRESz7dQKjXluGWcAzr/lxxMcZAJPBxMwuM4umhmEZNK4nwB4z7HGE05dVx+ZAJ2jhqHpPObhYvdeUZZfaz5Zs59kN8xkc9iuibFnOjH6X31o+QCv/yrp2oWm5KGivp3SXhBC1SRtwl5aQPmfF8lmFRY2iuItSZAIrB0KvsTx7+QBfTTrEnInHGfGyLxH+biSbkwm7EGb7QJFd76kRoSAlvN8N/rsO2xJha6KaZ6qVGY5vUMdbqVkqvXcUQL0qZdnarSmHU16lxjuj8R37CiHVvuOdHi8z/s1HdbDQtHzKa42iFmop1DZALHACGCSljLRt8QrmbqpRpIuIjmDj5u/p9fzXVI5NYswzPmwMrkBv/940qNDgzpMJ2kp6beO6AT55B7bfhOtSTRkScBOaGMHVyWbjMqb+dYSjX3zHu39OwzU5nh1DXsQ05g22norTyW5Ny6TQkwKmncQNMAA3gf5SynnWK2Lh3U05ipzs3fcXHo88QdWDZ/jfw17MfLDircn+LCcTTB/tXaTB4/Q2OLIe9ifBtzNhX6RKfgc5wcuvQkgvqye+0/MX5eJieP/P7+h2YDN7vGrz6oMvc6paLZ3s1jQLBR2ZXQ4YgRok9yvwZ9r2q8BuKWUv2xS3cO7GGkUG8fEc69Oe2qu383tLd94fUo0bLsZbU34EewXz+O+PA+RvJlprOr0NJnSDLdfV6G9hgPoGaO0EvmWsWsOw7CEVPWceLSa/Q9nE63za/knKvzWakDqVbr2ug4Z2NytojuIHVFPTv8BwYCxq2rjeUsoIaxeysO7GHEW2XFy4Nuc7vhzZjVFLLtAwMp7Rz/tytGZZzl4/y/Lry28dWmR5jMx8WsLY31UNwlQXpnwOSzfAvhvglwhO06CfhMjNha5hWOYvwgcPomecJ++vmMLYv2ZyPm4//ds8yyk3T70kq6bl4E41ij1SyoC050ZUArtGQSYILEp3fY0iTUR0BKdXzafjmJk4XbrCtO6eTOtRCYPJmSRzElCMNYrMTm+Dad1h+3X4N1GN/PZygDYmaFoGnrLeAkvhkbFsPXaJB8N+p/p7bxIvBW90HcXaBm15pXM9Rtx3l//Q0O5aBW16yjAZYGEmByxKOlBkEhPDkcHd8V+5lSPeTnz8mDe+PZ+kapmqecpRFFk+Iz3xXS0EZn0F03+Gi2bwMMCwPvDBjxC9y6p5jD0bd2AeMICmZw8zL7g7DX6cRvN6Va3wYTSt9ClooEgFbqRvAi6oRHb6ehTlbFDWQtOBIquI6AimT+zF2B/OUS0mmbiu9+L+xTdEVEjINQhEREcUTz7j9DaY3QMO3IBNiXAmBapWhubxEGgEZ+v1lNpx5AKGsW8RuGgWNxs1YemYz2kQ2lw3QWl3nZwChSG3N0kpjVLKcmmPslJKB4vnJS5ICCF6CCGmxcXFFXdRSpzAyoEMf/NX1vz+JWfHjsL9n3Bkw4ZEP9ieP5ZMYvja4Sw6tIgZe2YQER1x631hF24H3PR8RpHwaQlDVsDz78OWzbBmDVRyg9+uwf/i4N+rcHi9VS7V3N+LwJ9ncnTGfJKOHafX0B7Mev1zwiNjrXJ+TSvt8tU9trTQNYo8uHiRnWMHU/vH1ZSLN7O3pgtLQz1Y3cqd5HJujG4xmrikONxN7kzePplkczKOBsfizWec3gbjusKf1+BUilqydeRgaOUB/oWf7nzq+qMsWLSJr5ZNJPDcEbY/PoIWc/4Hhlx/T2ma3bDKOIrSQgeKvImIjuDZpQPptfkKD2+IpW5UAslGwbYGbqxrXo7NTcoQ61XuVtAolgF7mZ3eBic2QpQzfD4NwvdAeQH3l4VP14BvSIFPnT7mQiQkMGHt1/TZ/Sd0787Oj75iy6Vk3X1Ws3s6UGjZSk9UuzuWY8mCN+my7Sodd16lxgXVK+qspyM3Wgfh32UQNGvG7qqw7cYBgr3Uv6Wc8htFkgDf+ClMex/W3YTzZqhTDf43AxpVKHC32ltjLmpWIOi3BciXXuJ4OS+GPfwu57x8dPdZza7pQKHdkWXQWPTreJrtvULw4Zvce8KA48UYAMxCBY/TVZw5XsWRU15OXKrsRpd7n+FcRRNN67QDKJoEePp058mJcMAM/5WFU2fAzxE6OUMN10InvJd+OZ/2bz2HQUqe6fsO7Z/qrbvPanbrrggUegoP68lQI6jUFM6cYe0vH3N4/c/UOpeI37lEfC8k4ZaQcWXa684G4qt4crhMPNEeDlz0MNGwSSfatuwHPj7g7U1E6mnCosOtU9tI71LrFwpegfBGf5i+DG5Itb73u6Ph4Q8KfPrwyFjenLyU7xa8S7WrFznz5TfUGjm0cGXWtBLqrggU6XSNwjbSF0xKNidjFEaQknJXE6l+MZkqlxKoeimJqrGpNEuujPnUSSpdTsIzLgVjpn9iCY6C8xUcuVDRiTpNOuBZvznUqgW1a0Pt2kTIMwUPJKe3wXfdYcM12JoADk7w3BPQ1Q8a3F+g2kV4ZCw7dx6l/4RRlAn7j6g33uPXzoMIqV1RN0NpdkUHCs0qLGsaoHIU2fWMSn+tvKEM3/wxDq/LyXjHCToY6nLu4HaqXk6iWkwKdeIccb2UsTvzdWcDJ6s6caqaC81CB5BQtybbK8ZTN7gbgVWD7pz/SK9lGGvDxKmw8k8oK6BbOfhkDdRoVbAPn5DA5UcGUmHlL8wO6sHkrs/w4/A2OlhodqOw61FoGqDGY1jenNOf+3v4Z7l5B1YOZMaeGUR7OBLt4ch+YaSsf2uWH7t4K6iMbjGaSRvHUf1SMrViJF1T/YnZ+x81zyXS7MA1qv4zFYCaQLzpPWLq1+KoRwxnfV341N+d14bMJbB6i4yFtFwfY/QxqPwf/H4Dfo6DY4/Dj8uhfv38f3hnZxa8NAnnC2aGbv+VMknx/HdvHR0oNLunA4VmFZkDSLpgr2Ccjc63AkOP2j3oUbvHraASdiGMJJOBE9WcOFXdiKd/a5YH3Q4kfap2JmLTT9SNSqD+6USCL9ygU9hV+m6IBc6S8uE93AhowIl6lXDr0JWa3Z+AypVv1zo8qhJY0xWGG2FnKmw4D02awCuvwJBuEB2Wr95RIXUqMajz01w3ufLiPwu4POVNwv2+Zevpq7r7rGa3SnzTkxDiXuADYB+wUEr5953eo5ueSpbcmoos8x6Zm63Sm7csXx/dYjSTt31MpegbBJ5I4skbDbm55W/qn7yBS5L6t5zgX5MV1a+wvb4bOwM8+aTdaAJjz6mA4FwT3ngDZs8GdwM84AIN8zeteXoX2l5/zsf7o/f4y78Vz/d6A5yddfdZrVQrlhyFEGIW0B2IllI2ttjfFZgCGIEZUspJuZyjPTAGuAB8KKU8eqfr6kBRutwp55D5dcvtsAthTNkxBYcUMwGRSYy81pQqOw5Tfvs+ysWbMQuIaeBLpYcGQZcu0Lo1ODrC16Pg/a8h2gyNHWHCW9BzXL7L/vdL73PvlHFs9GvGM33fYeQDAbr7rFZqFVegaAdcB75PDxRp05UfBjoBUcB2YAAqaEzMdIqngEtSSrMQwgv4XEo56E7X1YHi7pFTjeSZ1cPwP3aVtvtu8NjpypQJ3wOpqVC+PHTvDqGN4eRnsPUabEyAcu4w5f+gfd18DdYLj4xl8agPmLjiC/7yb4X7quUE1als40+tabZRbL2ehBB+wEqLQNEaGCel7JK2/SaAlDJzkMh8HhMwX0rZN4fXnwaeBqhRo0ZQZGSJXM5bs4HsaiRZ9sXFwbp1sHw5rFgBly+rGWiD60DovbBuO2zbBv4m6O4CFZzz3BwVHhnL9S/+R/sp46B/f/jxRzAabfmRNc0mSlKvp+rAaYvtKCDH/opCiD5AF6A88FVOx0kppwkhzgE9TCZTkHWKqpUG2SXSs+xzdyeibS3C/NsS/NFIAg9ehWXLSFryM6bNU0l1dcFY3xuORMHUJOieAu035SlQBPl6wJfvQVVnGDMGypSBadNurVWuaaVdie/1JKVcCizN47ErgBXBwcHDbVsqrbTJbl0NGj/FE83+otkRV3ptvUbPsMs4pAISWHoDYpfCkqfAI4/J6TfegGvXYMIELkhHFg98WQ/K0+xCcQSKM4CPxbZ32r5C02tmaznJaV0NaRDsqOfGrvrluPLJMzy14xpX5s6g3L9HMazfAlWrwuuvwzvvEHFl/50nOvzgAy6cuYjXzG+IO5bAoLaP6J5QWqlXHBPtbwf8hRA10/IO/YHlxVAO7S6SPp7DKIw4GhwJ9grOsq+5T2sienUmdLgTHb6ox8/3V8Kcmgoffoi5nBu7nurCT+s+Z/ja4RkWd8pACBY/9ior64cy5u85tDvwL1uPxxTpZ9U0a7N1r6cFwL1ARVT31veklDOFEA8AX6J6Os2SUk6w5nV1ryctO3lJes/YM4MpO6YAYBRGXvTqxpAXPoHDqUggxQirWnuQ/OpL9H3o3WyvEx4Zy9Bv/ub7uW9QO+Y0kcvW0PCBdkX0KTWt4O6KuZ707LFaYWXpbuvVgcB/vuXEEUnlJddxSFULxptSJNwfCg81gZ5PZEl6h0fGsnv7AQaN6ovJ0UH1qKpSpXg+lKbl0V0RKNLpGoVWGBlqGYlJas2L1CT23zDh9Qt4Hr0ALZrCvt1wU0INR5j8JfR7PuvJdu6Etm254V+fHybNpUWD6jpfoZVYd0Wg0DUKzSYs17yoGAAvvgjTp4OPgbP1nSm3PZEyV1KhUyf46CMIzvj/7Nh3P1D72Sf4tUF73ugzmnnDW+tgoZVIOQUKu1o1Xkq5Qkr5tLu7e3EXRbMnPi0h9FX1p4uLGiPxxXuknpc4bE/iuRE+fDGgOinh26FFC+jbFw4cuPX21XVb80no4/Q6sIHeO1br5LZW6thVoNC0IvPSOH79bjgpRsG0TyI54+nAvJXjYdw4WLsWGjeGYcMgOpqQWp7MatePzX6BvPvHNO41Xyru0mtavthVoBBC9BBCTIuLi7vzwZpWSLUefI4h7zfkcA0XPv0qks6/HoR334Xjx1Xz1PffQ716BP22gB+fasWRT6biUK4MjV55BhISirv4mpZndpWjSKeT2VpRiYiOYOepf+k1aSUVlqyCgQOh5goi3Jw46v0IXaaFU3bzNmjeHN4bAQe3wBszYdQo/Fy7ALDkOb1KnlYy3BXJ7HQ6UGhFTkqYOBHGjuWGjwO9X6nFhfKOmAyOLEroj9+7n8GFi9DMEYwOEBbP0IffYV2dVjg7GvToba1EuCuS2brpSSs2QsBbb8EHT2K6kMr0iSepcimZZJnKn609Ye6L0NoEu5JhXwIJFd35ZNUUvK5dIjnFrBPcWolmV4FC93rSit2Tz3PiqXJ4XE/h+4+O438uRa3U17ATdHaGZ9zAw4jzpTjKJVznk9+n4GgUhNTyBNRAvanrjxIeGVvMH0TTbrOrQKFpxc6nJXXfWsvZL56krHBmwSfnCDyVorrWDv0DBrwPmzbAO+9gRNLuxE7+TNhCkK8H4ZGxPPzNFj5Zc4hBM7bqYFGMAuYGEDA3IOc5ve4yOlBomrX5tKT+07Nw/W8nDuXKw333wd9/3x6PUasNjB+P2LABTCa8P5sAr7zCtoNnb51CN0fdln7TLqpzWQaHXCeAvIvoQKFptlKnDmzeDDVqQNeuamU9S6Ght/d98QVPvtSPWjFRADg6GG41R2mKNW/YuZ0rpynp72Z2FSh0MlsrcapXh40boUkT6N2b0c/5ZPxF27kzDB4MRiOuF87yx0+v85X7Od0LKk1ef91bs6YQ7HW700/6lPR3O7sKFDqZrZVInp5qve62bfloWhQdwq9mvElVXAyOqVC/PsZateg+9mmCfpquutyWYtZoMsrvr3tr1BQsF6Wa3nl6zotU3UXsKlBoWolVtiy7Z01gn58Ln3xzmhmfPXr7puZmgI7O8O+/vB10Uc0V9cYb8NhjEB9frMW2hsI0GeXl170tagp7ntzDnif3WD1IWDPfUpR0oNC0IrLtxgGef9WP41Wd+OTLo5xeNV/NTAvQ3BFzNSOvLb6gJh388EOYPx9ql+H+L+qXupuLtRLCefl1b881hXwFlnHu6mEDxbFmtqbdlYK9gpniZuSZ1/2YO/EkD4z6Gj5PUi8aBId6l6Xe11e48O4reP1vFgQEwCO9WDjuGC+NqkFEdESx3dz8xvwG5H26kZ2RW2h0/CZesSl43LhCUviHYKoNly5BTAxcvQpGI5svbCXZKLiv5v3g4ACOjuDqCpUr33q03H+dy+UcGPrBPmJdTnF8cs8M18pvTaEkKM6/y4Io8YFCCGEAPgDKAWFSyrnFXCRNK5D0G8Plcg7cWLUMY+9nYfRsGJBKhI8Lw4IqM76lmfbT57JnxJME9OzJwefK47rgJjM/PsEbCY8wePSiIr/BWI7nGDRja9ZE+/XrsGsXhIfDjh2wYweD9+9nSGqqxVmiwMlJ5Ws8PcHdHRIScL+eitEsIeUIpKSox7VrKqCkvX/mrXM8RqLRgYQlfjjX84fataFOHQJr16bm2QTOVDQxvVfJrSlkrmWVlloN2DhQCCFmAd2BaCllY4v9XYEpqDWzZ0gpJ+Vyml6ANxADRNmwuJpmcxl+0f75p+oi+8N5Do8qQ2JVwYzulXngv6skf/kZvOXCZt8yfP+OF998FsknXx7jb+9p8NLX+bpmem3g5KQHC1Rmy/Ec6eM7giqa4Jdf4Mcf4Y8/wGxWB1SuDEFBiF69eOnKXM5WNPFe9//RqH57VVMQIsO5B6Y1q2T5pW82Q2wsREdDdDTH9h1nzrJtVL96kZpx5wmNjMJ182YVVIDlQKoQJE96FJo3VbWxxo3Vn7VqgdFYoM8O+a9N5SS7JrK8Bop81UBOb8uyNG9h2TpHMQfoarlDCGEEpgLdgIbAACFEQyFEgBBiZaZHZaAesEVK+QrwnI3Lq2lFp3ZtFSzM8NA3F/GMS+GIjzPrg8rTZMHfsO8PghMSiHcz8uzrfhyo6cL9r02DhQsLdDnLmoHfmN9u3QDvJH08h8GcSvtTEfT/6m3w8oLHHyfq351806IPLF8OZ87A+fOwahV88AHrgt054OdCo6AHwM0tS5CwlCWHYTComkeDBtC+PasbhPJD8+5MuncIzz/0JrO/WgpxcRAdzcElq3mp+6tMDXmUTY4VSdgertYFefhhqFsXypZVC0oFmaC7C4SFQWJivr+zwo6Wz2+323zledJzXaCW7rXctgKb1iiklBuFEH6ZdrcEjkopjwMIIRYCvaSUE1G1jwyEEFFAWkMuqZlftzjuaeBpgBo1ahS+8JpWFBo2hI3bMYWGMuX/TvHUGzWpNnEqDp0HwfozBLok8e35aLa5OMOP/0OM/h4GDVI3uiefvOPp79RsFB4Zm2E7u9pHULUyDNu2lOHbl+F1/bJqNho4EB57jNDf4pDCQMsmbQiqlvHXdurNGqTcrJXlGukiJlaCalWAOzfFWA4+vDUYUQioVIl1HrVZ1igFAKOAVzrXY0TLqrB/P+zdC3v2wO7d8E8y7EiG31qoXEjjxhAUpB7BwWqsi8mU4brZ1qYKWKvIbzI9XzWQk5tuP09NUttWrFUUR46iOnDaYjsKaJXL8UuB/xNChAIbczpISjlNCHEO6GEymYKsUlJNKwpBQfDDDzTt25dxs89Q758B0O1HmLUIhks2ebgQ5uzMc62Gwu8D4KGH1CC9hAQCnL8Cck7SZnejs5Rd8DjpPBDGAePiYP16GDmSt/fvV2uCP/00dO8Ozs6ER8YixZZszxMeGcvNyOdzvAZAmLPz7bKl3Qgf+vyMKkOmZjLL92Y+V7ZBxM1N1SJatLh9kvfKwRUJobNVPiU8HJYuhRkz1OtOTmrdkJAQ9WjVipCaFbKe2wry0oyUrxqIX+jt50ZTxm0rKPHJbCnlTWBoHo9dAawIDg4ebttSaZqVPfwwfPABPd55ByZNgrffhnvugXAnZg606PLo6qqaefr2hWefZdDAKszrXDHH02Z3E73Tr+SpKT0Jvb6LJgMGqGaumjXVNXv0yHDu3M6Tl1/iwRar/N2+EZ7J+AHSu3uOi8sxx5JbEMlACPAQ6rvr21ftkxIiI2H7dvjvP9i6Fb75Br74Qp27ShWmlfVjZ7V69BrZj/peLtmf2wbyVQOxrD08udzqOYriCBRnAB+LbW+y/OsoGCFED6BHnTp1rHE6TStaY8eq5pK33lKJ4vvugy1/s6dFJH4pC24f5+ysfgkHujFm/nkcUyQ8manZKO0GGzTu9nQ22d1ELX8lh0fG4pCawpVtjtT+5whmDmN47z01+M8l6w1y6sEXQQwDacryazvbX/mZBCYm3Xp++0Z45lZZ8tPEU9BEPUKAn596PPKI2pecrJqq0gJHnZXr6HxkK2yYq5qmgoNVEL/nHvjnMXA1qNpXHhS0e26+ekdZOUhA8Qy42w74CyFqCiFMQH9UpwVNu7sJATNnQsuWalT2gAFwXfLOtkFAxnwDJhP0dWFVK3de/fkCzL1zr/H0G29Ov8DD9p1m1uL3Gbt+Nlt9Apg38zeVFM4mSAAYXU/hWmNGlvPkdo3Mhl6Jo2lCIoGVA62aOM7VnRK9jo6qOfD55+H776l1+QxcvAi//qrWQgeYMkU1AX5yHb66DsOGqR5gZ6zym7fEsWmgEEIsAP4F6gkhooQQQ6WUKcBIYA1wAPhZSrnPGtfTcz1ppZ6LCyxbBh4e8MEH3GgSyLNbl+CYmpz15mkQjB1ena0N3WDYMFpH7sr2lCcnPZjxF/c4d5WHwOKGfvUqA8c9wz2Ru3ij2yhG9B1Dw9Dmdyyu0fVUxvNkI9vX0m7WI2PjmH4+Gk5vu2M+pTD6vPnF7Y2C9AqqWBF69oTJk+Gff1SPq02boKMTeBpgyRJ4/HHw9lY9rZ55BhYsgHPnrPYZipNNA4WUcoCUsqqU0lFK6S2lnJm2f5WUsq6UsraUcoK1rqdnj9XsQtWqKicQE0Ni7FWqX7vIQ/vWZ3vzTHEw8MqIGsTX9uW7Xz6izqVTef4lXrbBGDU9xOXLcP/9lN0Zxgs9XqdF82OsM71GkOFIns6TU7NPlgCV4UXVS8cBcJQSTm7KU3NVQYUYDtzeSO8VVBjOztC2LbR1ggGuaoDgjh3w2WdQr57K7QwcCNWqqS6+zz2nBiWWUnY115OuUWh2o1kz+OEHKpw+TpyTG4MiVme5eUY4mTBKyTVXA32fcSHB0cicReN48ctVhJv983SZCldTVC5k1y5YupRzDb3o67CZ6oYYm/THvyWtV04KkCwE+IXmPSldAFvNDW5vZOoVlJ8xJTkyGtXf2SuvqDVGYmJUgnzyZNUZ4Mcf4ZM2+Z6LKX1ywuJW4ns95YdOZmt2pU8fePdd3MePJ/DcYZbeU5aG6TfP09sIc3ZGAgjB6QqCZ57qybxvFvH1wvf59/EgglxzrxFUik1mxuSTcMUAK1dCp06EbH369gE26I9/S9o5HQCHp9ZmuUaWIGE52vjqVTh+/Pbjp7fgihnKBKg8T/rDYLj1fOzpOC4ZjJQVN3Gq3QhOz4Qaf4CvLy1Pn+FMucpq+hAHK90SHRxU0js4GF5/XSXIP7DxQlR5TKgXhF0FCt09VrM7777Lf7OW0DJqHw1//Ba6tgVg8rcz6FwmAZMsRzLgIIzsqtCaUT1rMm3pBGr8Gg39clnP4qqZOd+cwPNqCqxZD+3aATD62WEw8yd1jA3642crp0B06j84mwpHUmBJOzDWhqgL6te6JRcB5QXU91aBwWxW3V7THnE3ErlhSuKgrIEBM00vJ+D2yy8qQQ38nH6eacPUQlM1aoCvr5r6IzgYWrWCKlUK9xkdHcGQ88j0ks6uAoWm2R2jkZG9xrDp26dwXrAAvvwSKlZkq7kBoxN/ujVqu/X9H/PQAXfW1fHlzPuTqPHuaFhjguHZzPsjJSyPp2KcZNhoP+anBQmgQP3xrTkTqlNKkkrKP7sCliyASzdAAB4GqJ2iurDWqnX7UbMmfOmr3jxuRbbn/HH9UT5ZcwgAI6m80qUhI+6ro9b6OHWKx95dRPWr0XzcorwaU3HqFPz7L/z0062JCalRQwWM9Efz5mpMSxq/Mb8VvItuKWBXgUI3PWn26GIZDz4LfYyxf89WXTI3bWKHrAvAJte0UduNBwKqnb3G4PawygRbk+DZ++HbPzPe8Hcmw7FUPn+8Kntqu2a9YLpcgoRVZ0KNjobffoPlyzm0di3cvAllykBoC3D6F/wdoJwbPPlDgZrB0vM6RlJxJOV2nsfFBerVY3PNZgA8OjTTpH83b8LOnWo8Rfpj0SL1mtGopvwwxIO3EZ8q5wv22UsJuwoUuulJs0cnJz0I5m5QYanqmjl9OmomHJhZPpvk6MlN0NkJ4syw6hr8NB1eS7vBnj4NaxPAz8jP91XI+t48KsxMqH5jfgMpOXk2BTYlwvgqqpbj7a2mJunZE+69V02pkZ78LcRo4/Sb/ysOiwgxHCDIt8+t13KdC8vV9fbAunTnz8O2bbcDx+ZkCE9mE8Ng/WR44AHo1k015VlMUVLa2VWvJ02zWwYDvPyyej5qFA0vHM/5WL9Q1Vbf2wW8HODDn1XSV0oYPhzMQE8XZCHazPM7E6qloKj9LFzwJvx4UyWh33tP/XI/dQqmToUuXVSQsGSFhPoIh+VZuvzme+xGlSoqkE2YALM/gjfKwnNuJHVxg2qeavqPLl3UzLc9eqjtkycLXfbipgOFppUWTz2lAoDJxNRfJ0JiDsnq9Juqo4DFP4PBCL17q5vWmjVwvzN4GGiakLeptrNToGVFw8OhWzeWzButRjt3c4aRZVSgCAzMdRpyWynU2I2Tm1SZKxsxtHKE8Q+rMSm//QZDhsC+fWp0d82aapbgNQlwPAWSku587hLGrpqedI5Cs2s+PtC1K2zfjk/MBVhhgHoy9xts296wwFU1h7z4IoQ0gxZHAZh+PprhVSoXulg5BomkJNXUtWGDqins2AFOTkS6e2GUEtbEwuoE+MxF9QpKXwo1/eHgAHHXoYyAqGG3k9fpj8qVCx1cCjV2w6JHWDIOOPiFquaqBx5QDynh8GH4/Xe1RsdfB1Te6NdK8OCDqvtz164qH1PC2VWg0DkKze4NGwa//84vjTvyyN51DPjNzILuavW2HHvddO4MderAkSPgX+HWzdVRygwzuBZaUpKa1mLFCnVjPJLNOA5PT6JFeaLcK+PtcUX1aGo9So0zSElRf6Y/UlIg4iRck2qcx4ULGc/l6qom80u+qabRqLdAzZNVq1aBAki+B/hZNIcNSnqLpZmbx4RQo7Tr1YOXXoK3ysGJFCjzqJqmZcEClcfo3FkFjR49oELB80a2ZFeBQtPsXvfuJFesRLmE66z1b8UbS7YTVbEZ5LaG0ezZ6qYdGAjz1sNjzlDTgXBnJ35zc+Pe/HZvTU8w16xB+Wsp8MMPKjisWaMGw5lM6ubo6Khuln36wGuvqWVJTSYeSRsF3TttvinGTc7lWmkjpsedhxs3VHv/iRMZH/8eVDfggWnnq1BBBYz0h+WaFDYQbvZnh6x75xlvTQLqOcK46aoZ8J9/1CzAv/yipmwxGlUSv3dv1butenWbljs/dKDQtNLEZGJPx150/HkW9w3/Dq9rMXw6cxcMDVODwzI7d05NK9G+vZr9tE0bWHyAfSPKM9yvMlKI/HdvvW6GiGTmzj9O06M3QT6h5qfq109NB7J0KSxerG56P/ygejNZg5sbNGqkHpbGuYNZwsObVI+k9MeHH95ay/uknx+43YTqRjVdSUCA6iBQSOFmfwYlvQXkvEBTthwc1N9J+/ZqbEz6IkpLl8LIkeoREqKCRrduajW+YsjhpLOrZLaeFFC7G5ieHo6DNNPz4EaG9X2T2LKOatW5yMisB7/0EiQkqC617u7q12uqxGvBdUzJKhme3r31jqKi4IUXYMp1WJeIc5KZ73pWUnMaRUWpBG76WhoTJqj1wK0VJO7EIKBpU9Wra/p0FQyuXoWNG+HTT1XNIioV1iaqmpWXFzz6KHz7LTUvn1H5hALYam5AUtrv7QLPeCuECvIffQQHD6o1SSZMUM1vb7yhxmt4e6vvd8ECNQFhEbOrGoXOUWh3g8YdWrKhZnNe3LqAdR1O8fyr1fl18kWVQP3nHyhfXh14NAV+/hnGjwf/tEkC69aF3i5UXBjPO3PP8vaw6jga79C99cQJtere7NnqhtrYEdqa6BdcE4DnmzWDiRNV7yUfH9i8Wf0aLm5ubhAaqh4A41arsSXNpsJff8G6dbBoEeuBc2U84dwD0LEjdOigPkcehBgOYCKFBIzWm/G2QQP1eOstFYDXrlXNer/+CnPmqMASFKRyG126QOvWqpnPhuyqRqFpd4sxXUdhMqTw6dxtnPIyqSaLI0fUkqpJSZAsOb02mRNVTDB6dMY313OE9iZ6/XOFV366wIx7v86+2enwYfUr1t9f3aCGDlXX6OUCniqB7nU5Ge6/Xy3d+uijEBGR9yAxLs6mE9lle70vrsETT6jPc+oUHD7MW11GEO7dUPVOGjxYTdfhaVRrSvz0kxo5noMgwxEMvjMxVVpt9RlvAVWTeOopVY6LF9Ugv/ffV+NMPv5YNV15ekKvXvD113DsmHWvn0YHCk0rhc6VqwQ9XGh8Ip6RS6PVr+AZM9Qv5eHDYUMCPheTGf9ktayD1wDaO/HzvR4MWX2Jpr2eVU0e6aJTYclN9at24ULVXn78uErA+vndOqxD+FUWv3NUNT3NmQPz5qnmrdJCCPD3Z35gN0b2ekP1qtq1C7o4QUWD+uz9+6tmqoAA1Yy3fDlcuZLhNEbXUzhV/Nv6QSIzo1E1ob3zjqq1xcSoHwgDB6qlW0eMUL3bVq+2+qXtqulJ0+4qDR1Z3N6ZIb9fUs0oTzyhegW99x4I+PWe8oQ1yKGPvhB8MLg6/wSUZcr8SDXJ3QsvwKFDsOwGOMKsrhX5vktF/n7hy9vvM5vhcDJsTmLK6avs93Wm/Nodqkkrn/K7LrbNGQwqHxDiBCHA2zFq7Mdff6nHtGlqCVSDQTX9dOgAZ1JwqWYm3qkYfnO7u6tkd+/eqknw6FHVTNWmjdUvVeIDhRAiFBiEKmtDKaX1vwVNK6X6tkmBC/XVMpy7dsHYsWpwW3Q0sWWM1IuMVzf3HHr4/BVUDjpMUbWQjz9WvXFCHCHUiS8aWUytnZysfmF//DHsiwd3wcRBVVl0rwc78hEkcp1bqaRxcLjdxXbMGEhMVE0/6YHj888hOZl/5+3neHUnWD9YJaWDglRi3WJ2WZtLqx3dykVZma3XzJ4lhIgWQuzNtL+rEOKQEOKoEGJMbueQUm6SUj4LrATuvIK8pt1NTELdwGNiVA5hzhzVpu5lYPCaGBa/d0y1YffsqZbpDAtTXUmlJGTfdWZNPK5+kYIa8OXgALtT4JSaXtsl0Qz/+59q0njiibQ5pJxhVBnmd/Ik2TF/txBbrottc05OarK/ceNUb6rYWBjkyrSelThXwVE1+YwapX7RlyunaidDhqjAvXUrpPUys9mqgTZk6xrFHOAr4Pv0HUIIIzAV6AREAduFEMsBIzAx0/ufklKmZ5IGAkNtXF5NK32aNlVLbr70krpZtW0LHXZxv3tVWhy6yURzVzWNxoq09RqcgLIGpl+6xoXyDqof//Dh6hfwgQMwaBD8tJOvDkbS5NhNuP6iOufXX6ueVe+XL3BRbbkudpFzc4M6Dnxd0wuAPU/shjNn1JiI8HAVlH/7TQVvUKPQKxpgXigEd4W6gWpQXfrD21sF9WIcL5ETmwYKKeVGIYRfpt0tgaNSyuMAQoiFQC8p5USge3bnEULUAOKklNdsWV5NK038EubfnrbjhRdU+/Qff8C338KiNlzwNLGyjYmJT05Xx5w9mzau4AmIMTP+wWosa1ueHcNevH3SBg3Ur9/33iPk04/Z0rgM9331e8aptgvBlutiZ1gutTgIoW723t6qFxKo3EFUFPzwLqyeD9FmuJYKf/wNP63IOn7DyQmqVbsdPDw81Eh3Jyf1p+Xz7PZ5edlNjqI6cNpiOwpodYf3DAVm53aAEOJp4GmAGjVqFKZ8mlb6CKEGup05oybMW5TNMdWqqV48B58BYFHNHOYVMplg4kSC660AIdhjpSCRmVWChGUzztyehVq3wiaEUGMyHn8OkpaqfQ4uqpxVmqn1Lc6cyfiIirpdM7l6VXV3TkxUf6avuJeT9u3h77+t/jFKfDIbQEr5Xh6OmSaEOAf0MJlMQUVQLE0rWUwmFSSspQQ2gWRxctPt56lJarskBYp0OS0x6+OT58F9gAoUloHD8nlios0WSyqOQHEGsPxmvNP2aZqm5Y/FVN8YTRm386jI17ouTCAzGtUSri4u1itPHhTHgLvtgL8QoqYQwgT0B5Zb48RSyhVSyqfdS9OgH03TCi6nX+qaVdm6e+wC4F+gnhAiSggxVEqZAowE1gAHgJ+llPusdD09KaCm3a10kLAZW/d6GpDD/lXAKhtcT08KqGmaZmV2NdeTrlFomqZZn10FCp2j0LQSYpz77ZXwtFLPrgKFrlFomqZZn10FCl2j0DRNsz67ChS6RqFpmmZ9QhZwrdiSTAhxEchmAeEsKgJFvwCtdZTWspfWcoMue3HRZS86vlLKSpl32mWgyCshRJiUMpfFgkuu0lr20lpu0GUvLrrsxc+ump40TdM069OBQtM0TcvV3R4ophV3AQqhtJa9tJYbdNmLiy57MburcxSapmnand3tNQpN0zTtDnSg0DRN03Jll4FCCNFVCHFICHFUCDEmm9edhBA/pb3+n+W63kKIN9P2HxJCdCnSglPwsgsh/IQQ8UKIiLTHtyWw7O2EEDuEEClCiL6ZXntSCHEk7fFk0ZX61vULU/ZUi+/dKmur5Eceyv6KEGK/EGK3EGKdEMLX4rWS/r3nVvaS/r0/K4TYk1a+zUKIhhavFet9Jt+klHb1AIzAMaAWYAJ2AQ0zHfM88G3a8/7AT2nPG6Yd7wTUTDuPsZSU3Q/YW8K/dz+gCfA90NdifwXgeNqfHmnPPUpD2dNeu17Cv/f7ANe0589Z/JspDd97tmUvJd97OYvnPYHVac+L9T5TkIc91ihaAkellMellEnAQqBXpmN6AXPTni8GOgohRNr+hVLKRCnlCeBo2vmKSmHKXtzuWHYp5Ukp5W7AnOm9XYA/pJSXpZSxwB9A16IodJrClL245aXs66WUN9M2t6KWH4bS8b3nVPbilpeyX7XYdAPSew4V930m3+wxUFQHTltsR6Xty/YYqVbciwM88/heWypM2QFqCiF2CiE2CCHyv3hw4RTmuysN33tunIUQYUKIrUKIh6xasjvLb9mHAr8X8L3WVpiyQyn43oUQI4QQx4DJwAv5eW9JYtMV7rQidQ6oIaWMEUIEAcuEEI0y/arRbMNXSnlGCFEL+EsIsUdKeay4C5WZEOIxIBhoX9xlya8cyl7iv3cp5VRgqhBiIPA2UOR5IGuwxxrFGcDHYts7bV+2xwghHAB3ICaP77WlApc9rRobAyClDEe1e9a1eYmzKVea/Hx3peF7z5GU8kzan8eBv4Fm1izcHeSp7EKI+4GxQE8pZWJ+3mtDhSl7qfjeLSwEHirge4tfcSdJrP1A1ZKOo5JE6UmmRpmOGUHGhPDPac8bkTHJdJyiTWYXpuyV0suKSrCdASqUpLJbHDuHrMnsE6iEqkfa89JSdg/AKe15ReAImZKaxV121A30GOCfaX+J/95zKXtp+N79LZ73AMLSnhfrfaZAn7e4C2Cjv8QHgMNp/8DGpu0bj/pFAuAMLEIlkbYBtSzeOzbtfYeAbqWl7MDDwD4gAtgB9CiBZW+Bao+9garB7bN471Npn+koMKS0lB1oA+xJ+4+/BxhaAsv+J3Ah7d9GBLC8FH3v2Za9lHzvUyz+T67HIpAU930mvw89hYemaZqWK3vMUWiapmlWpAOFpmmalisdKDRN07Rc6UChaZqm5UoHCk3TNC1XOlBomqZpudKBQtM0TcuVDhSaVkyEEP+XtsZFizwcW0sIMVMIsbgoyqZplnSg0LRiIIRwAyoDzwDd73S8VNNZD7V5wTQtG3r2WE3LByGENzAVtfiMEVgFvCotJqvLdPy3wA9Syn8s90spbwghqqIms6thcXwAMDHTaZ6SUkZb7UNoWj7pGoWm5VHaAlFLgWVSSn/AH3BBrTWQkxDUgjuZz+UJuALXgJT0/VLKPVLK7pkeOkhoxUoHCk3Luw5AgpRyNoCUMhV4GXhCCFEm88FCiAbA4bTjMnsb+BQ1aVyjO11YCOGZVjtpJoR4sxCfQdPyTTc9aVreNQLCLXdIKa8KIU4CdVCzhFrqBqzOfBIhhB9q9tNXgLZp592S24WlWmvk2YIVW9MKR9coNM12upBNoAA+BMZLNXXzAfJQo9C04qRrFJqWd/uBvpY7hBDlgCqodQUs97sC5aWUZzPtDwT6AG2FEFNR64vssWGZNa3QdI1C0/JuHeAqhHgCQAhhBD4DvpJSxmc69j7UYjWZfYxa2MZPSukHNEXXKLQSTgcKTcujtKai3kBfIcQR1Ep3ZinlhGwOz5KfEEJ0AFyllH9anPMCUEYIUcF2Jde0wtEr3GlaAQkh2gALgN5Syh2ZXtsBtJJSJhdL4TTNinSg0DRN03Klm540TdO0XOlAoWmapuVKBwpN0zQtVzpQaJqmabnSgULTNE3LlQ4UmqZpWq50oNA0TdNy9f+qZeBel7qhBwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "global_objective.plot()\n", "plt.yscale('log')\n", "plt.xlabel('Q / $\\AA^{-1}$')\n", "plt.ylabel('Reflectivity')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can display out what the fit parameters are by printing out an objective:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_______________________________________________________________________________\n", "\n", "--Global Objective--\n", "________________________________________________________________________________\n", "Objective - 140312336727488\n", "Dataset = d2o\n", "datapoints = 137\n", "chi2 = 417.78640890990016\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140312336636128\n", "Dataset = hdmix\n", "datapoints = 97\n", "chi2 = 116.09164939809303\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140312336633968\n", "Dataset = h2o\n", "datapoints = 104\n", "chi2 = 251.58933827912017\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(global_objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's example the scattering length density profile for each of the systems:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(*s_d2o.sld_profile(), label='d2o')\n", "ax.plot(*s_hdmix.sld_profile(), label='hdmix')\n", "ax.plot(*s_h2o.sld_profile(), label='h2o')\n", "\n", "ax.set_ylabel(\"$\\\\rho$ / $10^{-6} \\AA^{-2}$\")\n", "ax.set_xlabel(\"z / $\\AA$\")\n", "ax.legend();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 2 } refnx-0.1.38/doc/make.bat000066400000000000000000000155031450471266700151170ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . set I18NSPHINXOPTS=%SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "help" ( :help echo.Please use `make ^` where ^ is one of echo. html to make standalone HTML files echo. dirhtml to make HTML files named index.html in directories echo. singlehtml to make a single large HTML file echo. pickle to make pickle files echo. json to make JSON files echo. htmlhelp to make HTML files and a HTML help project echo. qthelp to make HTML files and a qthelp project echo. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. texinfo to make Texinfo files echo. gettext to make PO message catalogs echo. changes to make an overview over all changed/added/deprecated items echo. xml to make Docutils-native XML files echo. pseudoxml to make pseudoxml-XML files for display purposes echo. linkcheck to check all external links for integrity echo. doctest to run all doctests embedded in the documentation if enabled echo. coverage to run coverage check of the documentation if enabled goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) REM Check if sphinx-build is available and fallback to Python version if any %SPHINXBUILD% 2> nul if errorlevel 9009 goto sphinx_python goto sphinx_ok :sphinx_python set SPHINXBUILD=python -m sphinx.__init__ %SPHINXBUILD% 2> nul if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) :sphinx_ok if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\refnx.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\refnx.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdf" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf cd %~dp0 echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdfja" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf-ja cd %~dp0 echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "texinfo" ( %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo if errorlevel 1 exit /b 1 echo. echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. goto end ) if "%1" == "gettext" ( %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale if errorlevel 1 exit /b 1 echo. echo.Build finished. The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) if "%1" == "coverage" ( %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage if errorlevel 1 exit /b 1 echo. echo.Testing of coverage in the sources finished, look at the ^ results in %BUILDDIR%/coverage/python.txt. goto end ) if "%1" == "xml" ( %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml if errorlevel 1 exit /b 1 echo. echo.Build finished. The XML files are in %BUILDDIR%/xml. goto end ) if "%1" == "pseudoxml" ( %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml if errorlevel 1 exit /b 1 echo. echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. goto end ) :end refnx-0.1.38/doc/model_selection.ipynb000066400000000000000000001134611450471266700177240ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model selection using refnx and dynesty\n", "\n", "refnx + dynesty can be used to obtain the Bayesian evidence, which allows you to perform model selection." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import dynesty\n", "\n", "from refnx.analysis import Objective, Model, Parameter, Parameters\n", "from refnx.dataset import Data1D\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def gauss(x, p):\n", " A, loc, sd = p\n", " y = A * np.exp(-((x - loc) / sd)**2)\n", "\n", " return y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll synthesise some experimental data from two Gaussians with a linear background. We'll also add on some noise." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(3, 7, 250)\n", "rng = np.random.default_rng(0)\n", "\n", "y = 4 + 10 * x + gauss(x, [200, 5, 0.5]) + gauss(x, [60, 5.8, 0.2])\n", "dy = np.sqrt(y)\n", "y += dy * rng.normal(size=np.size(y))\n", "\n", "data = Data1D((x, y, dy))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.plot();" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# this is our model that we want to fit.\n", "# It will have a linear background and a number of Gaussian peaks\n", "# The parameters for the background and each of the Gaussian peaks\n", "# will be held in separate entries in `p`.\n", "def n_gauss(x, p):\n", " y = np.zeros_like(x)\n", " \n", " # background parameters\n", " a, b = np.array(p[0])\n", " y += a + b*x\n", " \n", " for i in range(1, len(p)):\n", " g_pars = p[i]\n", " A, loc, sd = np.array(g_pars)\n", " y += gauss(x, [A, loc, sd])\n", " return y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "5907it [00:05, 1075.69it/s, +500 | bound: 14 | nc: 1 | ncall: 28160 | eff(%): 22.752 | loglstar: -inf < -4233.325 < inf | logz: -4244.268 +/- 0.198 | dlogz: 0.001 > 0.509]\n", "12557it [00:18, 696.48it/s, +500 | bound: 48 | nc: 1 | ncall: 53297 | eff(%): 24.499 | loglstar: -inf < -1048.585 < inf | logz: -1072.989 +/- 0.313 | dlogz: 0.001 > 0.509]\n", "17403it [11:47, 24.60it/s, +500 | bound: 2186 | nc: 1 | ncall: 1701762 | eff(%): 1.052 | loglstar: -inf < -913.031 < inf | logz: -947.230 +/- 0.372 | dlogz: 0.001 > 0.509]\n", "19222it [02:40, 119.67it/s, +500 | bound: 261 | nc: 1 | ncall: 461894 | eff(%): 4.270 | loglstar: -inf < -911.813 < inf | logz: -949.640 +/- 0.392 | dlogz: 0.001 > 0.509]\n" ] } ], "source": [ "# the overall parameter set\n", "pars = Parameters(name=\"overall_parameters\")\n", "\n", "# parameters for the background\n", "bkg_pars = Parameters(name='bkg') \n", "intercept = Parameter(1, name='intercept', bounds=(0, 200), vary=True)\n", "gradient = Parameter(1, name='gradient', bounds=(-20, 250), vary=True)\n", "bkg_pars.extend([intercept, gradient])\n", "\n", "pars.append(bkg_pars)\n", "\n", "# now go through and add in gaussian peaks and calculate the log-evidence\n", "model = Model(pars, n_gauss)\n", "logz = []\n", "for i in range(4):\n", " if i:\n", " A = Parameter(5, name=f\"A{i}\", bounds=(40, 250), vary=True)\n", " loc = Parameter(5, name=f\"loc{i}\", bounds=(3, 7), vary=True)\n", " sd = Parameter(5, name=f\"sd{i}\", bounds=(0.1, 2), vary=True)\n", " g_pars = Parameters(data=[A, loc, sd], name=f\"gauss{i}\")\n", " \n", " pars.append(g_pars)\n", " \n", " objective = Objective(model, data)\n", " nested_sampler = dynesty.NestedSampler(\n", " objective.logl,\n", " objective.prior_transform,\n", " ndim=len(pars.varying_parameters())\n", " )\n", " nested_sampler.run_nested()\n", " logz.append(nested_sampler.results.logz[-1])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-4244.267669942061, -1072.989448001302, -947.2295781758878, -949.6400447012933]\n" ] } ], "source": [ "print(logz)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(logz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The log-evidence points to the use of 2 Gaussians to fit the data. There is a sufficient increase in evidence over 1 Gaussian. However, 3 Gaussians is not justified, the logz term does not increase." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.38/doc/modules.rst000066400000000000000000000001041450471266700157030ustar00rootroot00000000000000API reference ============= .. toctree:: :maxdepth: 4 refnx refnx-0.1.38/doc/nsf.ipynb000066400000000000000000002576401450471266700153550ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "8faef39e", "metadata": {}, "source": [ "# Analysing non-spinflip polarised NR data\n", "\n", "Currently (5/Aug/2021) `refnx` does not have the ability to fully analyse polarised neutron reflectometry data. However, it can be used to analyse the non-spinflip channels of a polarised neutron reflectometry measurement. Here we demonstrate how to do this using auxiliary `Parameter`. The datasets of interest have the structure:\n", "\n", "`Si | SiO2 | Permalloy | Au | 2-mercaptoethanol | D2O`" ] }, { "cell_type": "code", "execution_count": 1, "id": "c7d349ad", "metadata": {}, "outputs": [], "source": [ "# some necessary imports\n", "import os.path\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import refnx\n", "from refnx.analysis import Parameter, Objective, CurveFitter, GlobalObjective\n", "from refnx.reflect import SLD, Slab, Structure, ReflectModel\n", "from refnx.dataset import Data1D\n", "from refnx._lib import flatten" ] }, { "cell_type": "code", "execution_count": 2, "id": "f06c6737", "metadata": {}, "outputs": [], "source": [ "# create datasets from the NSF PNR data\n", "pth = os.path.dirname(refnx.__file__)\n", "dd = 'c_PLP0007882.dat'\n", "uu = 'c_PLP0007885.dat'\n", "\n", "file_path_uu = os.path.join(pth, 'reflect', 'test', uu)\n", "file_path_dd = os.path.join(pth, 'reflect', 'test', dd)\n", "\n", "data_uu = Data1D(file_path_uu)\n", "data_dd = Data1D(file_path_dd)" ] }, { "cell_type": "code", "execution_count": 3, "id": "131aed72", "metadata": {}, "outputs": [], "source": [ "# create SLD (Scattering Length Density) objects for each of the materials\n", "si = SLD(2.07, name=\"Si\")\n", "sio2 = SLD(3.47, name=\"SiO2\")\n", "au = SLD(4.66, name=\"Au\")\n", "mercapto = SLD(3.49, name=\"2-mercaptoethanol\")\n", "d2o = SLD(6.35, name=\"d2o\")\n", "\n", "# to describe the Permalloy layer we're going to create parameters to describe the nuclear\n", "# and magnetic parts of the SLD. Instead of using the magnetic moment and angle we\n", "# could just use a magnetic SLD\n", "\n", "nuclear_py = Parameter(9.0, name=\"Py nuclear part\")\n", "mag_moment_py = Parameter(600, name=\"Py emu/cc\")\n", "angle = Parameter(0, name=\"angle\", bounds=(0, 90.))\n", "\n", "# Now create two SLD objects for the Permalloy layer, one for the UU channel, one for the DD channel.\n", "# don't worry that the SLD is set to zero to start with\n", "\n", "py_dd = SLD(0.0, name=\"Py DD SLD\")\n", "py_uu = SLD(0.0, name=\"Py UU SLD\")\n", "\n", "# Now we make constraints for the SLD objects. Each SLD object has two parameters,\n", "# SLD.real and SLD.imag. The conversion factor of 2.85e-3 converts the magnetic moment\n", "# from emu/cc to 10**-6 Å**-2\n", "py_dd.real.constraint = nuclear_py - mag_moment_py * 2.85e-3 * np.cos(angle*np.pi/180)\n", "py_uu.real.constraint = nuclear_py + mag_moment_py * 2.85e-3 * np.cos(angle*np.pi/180)" ] }, { "cell_type": "code", "execution_count": 4, "id": "5a4b88bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7.29+0j), (10.71+0j))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's check on the SLDs. Observe that the SLDs obey the constraints\n", "\n", "complex(py_dd), complex(py_uu)" ] }, { "cell_type": "code", "execution_count": 5, "id": "93678e27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7.005+0j), (10.995000000000001+0j))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's try altering the magnetic part and see if the values are updated in the SLDs.\n", "# `magnetic_Fe` is a Parameter, and Parameter values are modified like this:\n", "\n", "mag_moment_py.value = 700\n", "\n", "# note how both the SLD objects are updated.\n", "\n", "complex(py_dd), complex(py_uu)" ] }, { "cell_type": "code", "execution_count": 6, "id": "d28c798d", "metadata": {}, "outputs": [], "source": [ "# Now make Slabs that describe each layer. These can either be made from SLD objects,\n", "# or by using the `Slab` constructor directly.\n", "\n", "# sio2 slab has a thickness of 20 and roughness of 4 with the Si fronting medium\n", "sio2_l = sio2(20, 4)\n", "\n", "au_l = au(215, 4)\n", "mercapto_l = mercapto(8, 4)\n", "d2o_l = d2o(0, 4)\n", "\n", "# now make the Fe layers for each of the spin channels. Note that we create\n", "# Parameter for the thickness and roughness which will be shared over both spin channels.\n", "\n", "py_thickness = Parameter(50, name=\"Py thickness\")\n", "py_roughness = Parameter(5, name=\"Py roughness\")\n", "\n", "py_dd_l = Slab(py_thickness, py_dd, py_roughness, name=\"Py dd slab\")\n", "py_uu_l = Slab(py_thickness, py_uu, py_roughness, name=\"Py uu slab\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "f18b1435", "metadata": {}, "outputs": [], "source": [ "# now we make structures for each of the spin channels\n", "# note that we use the same `sio2_l` for each of the structures. This\n", "# will share the same sio2 thickness and roughness in the structures\n", "# and will reduce parameter numbers in a fit\n", "\n", "s_dd = si | sio2_l | py_dd_l | au_l | mercapto_l | d2o_l\n", "s_uu = si | sio2_l | py_uu_l | au_l | mercapto_l | d2o_l" ] }, { "cell_type": "code", "execution_count": 8, "id": "b0454b46", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30 30\n", "36\n" ] } ], "source": [ "# The total number of parameters is reduced by sharing Parameter/SLD/Slab objects\n", "# over the two structures.\n", "\n", "# what are the number of parameters in each of the structures?\n", "print(len(list(flatten(s_dd.parameters))), len(list(flatten(s_uu.parameters))))\n", "\n", "# now what are the number of unique parameters in both parameter sets?\n", "combined_set = set(flatten(s_dd.parameters)).union(set(flatten(s_uu.parameters)))\n", "print(len(combined_set))\n", "\n", "# this shows that the unique number of parameters over both datasets is 36, reduced from 60.\n", "# i.e. there are parameters that are joint over both datasets" ] }, { "cell_type": "code", "execution_count": 9, "id": "9fe3999a", "metadata": {}, "outputs": [], "source": [ "# now place the Structures into a ReflectModel. ReflectModel applies resolution smearing, etc.\n", "\n", "model_dd = ReflectModel(s_dd)\n", "model_uu = ReflectModel(s_uu)" ] }, { "cell_type": "code", "execution_count": 10, "id": "aaec7067", "metadata": {}, "outputs": [], "source": [ "objective_dd = Objective(model_dd, data_dd, \n", " auxiliary_params=(nuclear_py, mag_moment_py, angle))\n", "objective_uu = Objective(model_uu, data_uu, \n", " auxiliary_params=(nuclear_py, mag_moment_py, angle))\n", "\n", "global_objective = GlobalObjective([objective_dd, objective_uu])" ] }, { "cell_type": "code", "execution_count": 11, "id": "9eb96e15", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.scatter(data_dd.x, data_dd.y, label=\"dd\", s=4)\n", "plt.plot(data_dd.x, objective_dd.generative())\n", "\n", "plt.scatter(data_uu.x, data_uu.y, label=\"uu\", s=4)\n", "plt.plot(data_uu.x, objective_uu.generative())\n", "\n", "plt.ylabel(\"R\")\n", "plt.xlabel(\"Q / $\\AA^{-1}$\")\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 12, "id": "40bc9afe", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(*s_dd.sld_profile(), label='dd')\n", "plt.plot(*s_uu.sld_profile(), label=\"uu\")\n", "plt.ylabel(\"SLD\")\n", "plt.xlabel(\"z / $\\AA$\")\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 13, "id": "2142473a", "metadata": {}, "outputs": [], "source": [ "# select the parameters to be fitted and their bounds\n", "model_uu.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_uu.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "model_dd.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_dd.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "\n", "sio2_l.thick.setp(vary=True, bounds=(10, 25))\n", "sio2_l.rough.setp(vary=True, bounds=(1, 8))\n", "\n", "py_thickness.setp(vary=True, bounds=(38, 55))\n", "py_roughness.setp(vary=True, bounds=(1, 8))\n", "nuclear_py.setp(vary=True, bounds=(8.5, 9.5))\n", "mag_moment_py.setp(vary=True, bounds=(500, 800))\n", "\n", "au_l.thick.setp(vary=True, bounds=(200, 240))\n", "au_l.rough.setp(vary=True, bounds=(1, 8))\n", "au.real.setp(vary=True, bounds=(4.5, 4.66))\n", "\n", "mercapto_l.thick.setp(vary=True, bounds=(5, 15))\n", "mercapto_l.rough.setp(vary=True, bounds=(1, 8))\n", "mercapto.real.setp(vary=True, bounds=(3, 4))\n", "\n", "d2o_l.rough.setp(vary=True, bounds=(1, 8))\n", "d2o.real.setp(vary=True, bounds=(6.2, 6.36))" ] }, { "cell_type": "code", "execution_count": 14, "id": "49da0113", "metadata": {}, "outputs": [], "source": [ "fitter = CurveFitter(global_objective)" ] }, { "cell_type": "code", "execution_count": 15, "id": "b7676bec", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "77it [00:51, 1.51it/s]\n" ] } ], "source": [ "fitter.fit('differential_evolution', seed=1);" ] }, { "cell_type": "code", "execution_count": 16, "id": "22e8f804", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.scatter(data_dd.x, data_dd.y, label=\"dd\", s=4)\n", "plt.plot(data_dd.x, objective_dd.generative())\n", "\n", "plt.scatter(data_uu.x, data_uu.y, label=\"uu\", s=4)\n", "plt.plot(data_uu.x, objective_uu.generative())\n", "\n", "plt.ylabel(\"R\")\n", "plt.xlabel(\"Q / $\\AA^{-1}$\")\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 17, "id": "2858d05d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(*s_dd.sld_profile(), label='dd')\n", "plt.plot(*s_uu.sld_profile(), label=\"uu\")\n", "plt.ylabel(\"SLD\")\n", "plt.xlabel(\"z / $\\AA$\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "b4730494", "metadata": {}, "source": [ "By printing out the objectives we can see what the parameters are. Here we see that the Permalloy magnetic moment is 615 emu/cc, with a nuclear SLD of $9.15\\times10^{-6}Å^{-2}$" ] }, { "cell_type": "code", "execution_count": 18, "id": "c206bbbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 140698445453824\n", "Dataset = c_PLP0007882\n", "datapoints = 94\n", "chi2 = 301.3613165434196\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Py dd slab' \n", "\n", ">\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Au' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '2-mercaptoethanol'\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective_dd)" ] }, { "cell_type": "code", "execution_count": null, "id": "67844b6d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.38/doc/reflectometry_global.ipynb000066400000000000000000032216011450471266700207620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Co-refinement of multiple contrast datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A demonstration of how to do co-refinement of several datasets with *refnx*." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import os.path\n", "\n", "from refnx.dataset import ReflectDataset\n", "from refnx.analysis import Transform, CurveFitter, Objective, GlobalObjective, Parameter\n", "from refnx.reflect import SLD, ReflectModel\n", "import refnx\n", "\n", "import scipy\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "refnx: 0.1.25.dev0+3805297\n", "scipy: 1.7.1\n", "numpy: 1.20.3\n" ] } ], "source": [ "print(f'refnx: {refnx.version.version}\\nscipy: {scipy.version.version}\\nnumpy: {np.version.version}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are datasets used in refnx testing, distributed with every refnx install." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pth = os.path.dirname(refnx.__file__)\n", "\n", "e361 = ReflectDataset(os.path.join(pth, 'analysis', 'test', 'e361r.txt'))\n", "e365 = ReflectDataset(os.path.join(pth, 'analysis', 'test', 'e365r.txt'))\n", "e366 = ReflectDataset(os.path.join(pth, 'analysis', 'test', 'e366r.txt'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make some `SLD` objects to represent all the materials." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "si = SLD(2.07, 'Si')\n", "sio2 = SLD(3.47, 'SiO2')\n", "polymer = SLD(2.0, 'polymer')\n", "d2o = SLD(6.36, 'D2O')\n", "h2o = SLD(-0.56, 'H2O')\n", "cm3 = SLD(3.5, 'cm3.5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `SLD`s are used to create `Slab`s." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "sio2_l = sio2(30, 3)\n", "polymer_l = polymer(250, 3)\n", "\n", "# we're going to share the water/polymer roughness across all 3 datasets\n", "water_poly_rough = Parameter(3, 'water_poly_rough')\n", "d2o_l = d2o(0, water_poly_rough)\n", "h2o_l = h2o(0, water_poly_rough)\n", "cm3_l = cm3(0, water_poly_rough)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the limits for the parameters we wish to vary. Each contrast uses the same polymer SLD. We account for contrast change using the volume fraction of solvent." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "sio2_l.thick.setp(vary=True, bounds=(1, 50))\n", "\n", "polymer_l.thick.setp(vary=True, bounds=(200, 300))\n", "polymer_l.sld.real.setp(vary=True, bounds=(0.1, 2))\n", "polymer_l.vfsolv.setp(vary=True, bounds=(0, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create a different `Structure` for each contrast of interest. It's important to note here that the `Structure`s all share the same `si`, `sio2_l`, `polymer_l` objects. This means the `Structure`s all share the same parameters. The only thing that's different is the solvent contrast. By default the `Structure` object solvates with the SLD of the last slab. This behaviour can be modified by changing the `Structure.solvent` attribute." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "structure361 = si | sio2_l | polymer_l | d2o_l\n", "structure365 = si | sio2_l | polymer_l | cm3_l\n", "structure366 = si | sio2_l | polymer_l | h2o_l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a `ReflectModel` from the `Structure`. These are responsible for calculating the generative model, doing resolution smearing, applying a scale factor, and adding a Q-independent constant background." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model361 = ReflectModel(structure361)\n", "model365 = ReflectModel(structure365)\n", "model366 = ReflectModel(structure366)\n", "\n", "model361.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model361.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model365.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model365.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model366.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Objective`s are created from the datasets and the model. Here we also add a `Transform` to fit as logR vs Q." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "objective361 = Objective(model361, e361, transform=Transform('logY'))\n", "objective365 = Objective(model365, e365, transform=Transform('logY'))\n", "objective366 = Objective(model366, e366, transform=Transform('logY'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `GlobalObjective` is formed from the individual `Objective`s. This means that they're all analysed together." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "global_objective = GlobalObjective([objective361, objective365, objective366])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the `CurveFitter` and fit with differential evolution." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "49it [00:16, 2.95it/s]\n" ] } ], "source": [ "# create the fit instance\n", "fitter = CurveFitter(global_objective)\n", "fitter.fit('differential_evolution');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "global_objective.plot()\n", "plt.legend()\n", "plt.xlabel('Q')\n", "plt.ylabel('logR');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_______________________________________________________________________________\n", "\n", "--Global Objective--\n", "________________________________________________________________________________\n", "Objective - 140332980321968\n", "Dataset = e361r\n", "datapoints = 99\n", "chi2 = 564.3594634863424\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'D2O' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140332980320480\n", "Dataset = e365r\n", "datapoints = 99\n", "chi2 = 403.876417319329\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'cm3.5' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140332980322256\n", "Dataset = e366r\n", "datapoints = 99\n", "chi2 = 362.86669265391066\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'H2O' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(global_objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're going to do some MCMC sampling. We discard the first 400 steps, then save 1 in every 100 steps, for a total of 30 saved steps." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|█████████████████████████████████████████| 400/400 [01:26<00:00, 4.63it/s]\n", "100%|███████████████████████████████████████| 3000/3000 [11:26<00:00, 4.37it/s]\n" ] } ], "source": [ "fitter.sample(400, random_state=1)\n", "fitter.sampler.reset()\n", "fitter.sample(30, nthin=100, random_state=1);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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VFevWrcPpdNKzZ89duoFZS/OlLfOjpUzjTNubCq16bEg4HKa+vh673S5iSH799Ve+/PJLfvvtN3777TdWr17NypUrk8Thbt260bdvX0aMGEH//v3p168fffr04T//+Q/Tpk3jiCOO4PLLL+fKK68Uwr/+fE2mxj9D5eXlIsdaz7IOh8NigaSmpkbM7cTr0gXtptneesSJ2+3G7Xa3+jO6sxeCJH9wxhlnAPD88893uDF3RG0SiUQikUgkEolEIpF0VKRo/CfRhbnWCsDBYFBkEBcUFGQ8LlUDsMRtusAGNMs5DgQChMNh/H4/FRUVmEwmPB4PRqMRm83GnDlzePrpp9l999259NJLCYfDhEIhwuFw0j+/38+WLVuor68XcQKtoUuXLtx///0ceeSRqKpKdXU1VquVhoYGIVQmup91mrpjVVXFarXSrVu3Fpvi7Qq0JPS2pxMZkqNBmjp49W2JzyE3NxdoFPe3bt3KtGnTmDdvnjhGF4cPP/xw+vfvT9++fenbt6/IGtaztHXOPfdcxo4dy7XXXss999zDK6+8wsMPP8yYMWPwer0YDAY8Hg8A+fn5VFZWUlNTQ1lZGcXFxWKOu93upOaJic0U9UzjptneesSJ0+ls0wJNWxeCJO3Hpk2bOuyYO6I2iUQikUgkEolEIpFIOipSNG4nUjVg0kkUfxMziHVHaTqCwSB1dXX4/X7RgExVVcLhcNLxmqahaRpGo5FoNEpDQwNWq5Xs7GysVivxeFy4OLdt28aZZ57Jzz//zPnnn89tt92WJE4Gg0HhTG6K3mzM7/fj8/mor68XP6urq0XkQX19PS+++CITJ07klVdeoUePHqiqKhzMmzZtYuPGjRiNRrKzs4nH40LUUxRF3BPddRsMBsnOzhbvx+NxcT8TG/o1fR5/VX5tprnQEtvjXG96nYlRE4FAgJUrV2KxWNhtt91wOp1JzQ0jkQgej4e8vDzi8TgNDQ0A3HHHHdx///1Eo1GuuOIKjjvuOPr06YPL5Uo5R3RhOjFXW8fhcPDII48wbtw4pk6dyrHHHsvxxx/PlVdeSXl5OWvXrmX16tWsWbOG9evXs379ehoaGjjkkEMYN24c48aNw+PxoGkaZrOZYDCI3+9HURQ0TSMejwtBvK6ujlgsliQ0BwKBlFET6eZIWxeC2oKqqjJfWSKRSCQSiUQikUgkEkmHR4rGO4Gmjbp0J6TNZhPCaFM3se4kNhgMxGIxwuEwDodDCLMWiwWDwUA8HhcOOF0YCwaDOJ1OzGYzXq9XiLLPPfccN998Mzk5Obz22muMHj26Wa2KomQUs3ShNzs7u9k1Jgqdp59+OkcccQT/93//xz//+U922203XC4XRqORkpIS4vE42dnZaJpGJBLBaDSmdFNrmobD4cBqtQqxTY9SgF0rPqA1IqGiKM2uqa0itD7fXC6XiPvQx9DjPqLRKNAo9ppMJsLhMEajkU8++YTbbruN3377jWOOOYY777yTnj17Jo3fNFKk6bZ0Qv4hhxzC0qVLefDBB7n99tt54403xDa3281uu+3GgAEDOO6448jKyuI///kP//jHP5g6dSpHH300J5xwAscffzxZWVmEw2EKCwuJx+NYLBaMRiNOp5OysjKqqqrIz8+nuLiYaDQq5ordbv/Ls4rb20kukUgkEolEIpFIJBKJRLIjkKLxdtDWXOKmkQuJTbwCgUBKEUmPmLDZbEQiEeEa1bNo9WZ0TZuOWSwWgsEgWVlZBINB4ey94IILWLRoEcceeywPPvggRUVFf/5GZKBHjx68/fbbHHnkkfzjH//g3XffBRrvXVZWFoWFhSJDNxQK4fV6cTgcSaJeMBjE5/OJzFvdzfpXxQf8Lrx2+M5oevNCVVXJy8ujX79+SU0PY7EYZrMZl8uFpmlEo1G2bt3KPffcw/z589ljjz146623GDVqVLvXZjabmTp1Kqeccgpff/01Xbt2pWfPnmKRI5GbbrqJpUuX8sILL/DKK6/w2muvUVhYyHHHHcexxx6L1WqloKBACOIOh4PCwkKRyaxpmvi86ILxX73YIKMvOh7dunVL+3e8W7dulJaW7tyCJBKJRCKRSCQSiUQi6QBI0Xg7aKtbMFOObVNBOfF9vQGY3gQsFosRCATIz88nFosBjUJmly5dWLp0Kd999x3r1q1jy5YtlJWVUVdXx9atW1m/fj0mk4nZs2czYcKEZkLzjkIXH48++miOP/54nnrqKYYMGYLD4WgWNQEIUU+PDbBarQQCAXGtLpdLCPXb69LUoxu2Jx4gGAwCpLbR7mAy5Vs3RXcC19fXYzAYkhYI9HmWlZUFNM7N119/ndtuuw273c4999zD5MmTd3h0Qrdu3ejWrZt4nWpOKorCvvvuy7777st9993He++9x/PPP8/TTz/N22+/zfvvvy+a9enoTfQAEWGhv04UbBNjInam6/jPzN3/Bfbff/+dPmYmUTjxc7AjapNIJBKJRCKRSCQSiaSjovyZ/NVdkSFDhmjLli1r83GJ96mtTuNMgl+m7F09nsHn8xEIBESecWFhITabjUAgQGVlJffddx+zZ88Wx9lsNoqLiykuLqaoqIguXbpw4YUXipiBcDgs3LtN8fv9aTONQ6FQWodm03iKRJYuXcppp52Gz+fjySef5JhjjhHNy/Sf+rGBQEBkPpvNZnJycqitrRVN/JqS7t41va/6M1BVFb/fj9vtbrN493tu9Leapu2dbp+9995bW7ZsWdqs3Laii5vp6o7H4yJrWs+8DoVCWCwW8T784dK22WziPmiaxn333ccdd9zBYYcdxrx584QIqwv1qciUe51pHsRisbSNDCORiBCyU5H47FesWMGIESMYNGgQH3zwAUajkaysLJG1rS8upGsACIh9M82DHSGct/T31mAwLNc0bUiqbdv7t+t/BT3juqOP+VehKErauSWRSCQSiUQikUgkEklTpNN4O0jnFkwnJjfNNIZkITmTOGUwGIhEIpSXl2MymcTX7XVeeuklZs+ezYUXXsjkyZMpLi5uljf8VzN06FCWLl3K+PHjOf3007n44ouZNm2aEPJ0t6cukMZiMWKxGAaDgcrKSiorK4lGo2RnZ+N0OrdLzNOfgd4gLVHYbO0iwO/b1EznicfjBIPBdnOT6q52ve6moms4HKa2tha/3y+ysuvr68U1KoqCz+dj/fr1WK1W3G43fr8fgLfeeos77riDESNG8Prrr6cVdDsagwcP5tFHH+Xss8/mhhtu4LbbbhMRLgaDAavVisFgENeeiM1mE4sV+mudtri5JRKJRCKRSCQSiUQikUj+zkjRuB1JF1vRNIJCVVUqKiqEm7MlgdHj8eD3+4lGozidThwOB6qqsnTpUm699VaOPPJIHnzwwST3cGvccVu2bOGJJ56gZ8+enH766WkbmLUHJSUlfPTRR0yZMoXHHnuMJUuW8MADD3DggQcKwVbPd3Y6nRQXFxMOh6mrq6OqqopgMEg0Gk3Kg9avszWCb+IzaCoItmdzMqPR2K55tfpY6YRMq9WKyWQiFosJh7HBYMBisTTbz+l0kpeXh81m4/nnn+eqq67a5QRjndNOO41PPvmE+++/n3322Yfhw4eLhYdwOIzT6RT3TtM0ysvLgcb4ClVt1P0dDgeKojRzoevbJDufk046CYBXX321w425I2qTSCQSiUQikUgkEomkoyJF43YkXT4xkCRg6W5ak8mEyWQiHo8Ll62+r8FgIBaLCSE3Ly+PzZs3Y7VaUVWV5cuXc+qpp9KzZ0+eeuopGhoaaGhoEOdr6rBM5KeffuLf//43zz//vMiSvfvuu7nkkksYOnQoJSUlKY+rrq4mJycn5Tafz4fH40l7TpfLBcCdd97JgQceyDXXXMOIESOYMGECl156KdnZ2bjdbgwGAzabjXg8Lq7f4/HgdrvJysoS90u/l6FQiIqKCrxerziHvi1RRG4qNifSns3JdIdrW0kn8iuKItzoqfbRNI3c3FxqamrIysoS4mcoFCIrKwuj0YjFYqGgoEA0SXz55Ze59NJLOeSQQ3j22WebzR1A3ONUbNmyRTh16+rqqKysJBaLEY/HqampwW63E4/HicfjWCwW8vPz8Xg8RCIR8vLyUo5ZV1dHbm5u2nN6vd5m791+++18+eWXXHbZZSxevJhu3boRDodpaGigrq4Ou92O0WgU99BqtWK1WqmrqxNCu8lkauZC1xsJNhXp2+JE3tGZ0H9XqqqqOuyYO6I2iUQikUgkEolEIpFIOipSNG4lLYlAmqalFSYVRRGORj2b2O12Y7FYqK6uJjc3l0gkQkVFBQAFBQXCBamfV3ePhkIhfD4fEyZMQFEU/vOf/6QU4lKJWtFolOeee457772Xuro6jjzySCZNmsSqVauYNWsWl19+OYMHD+b2229n772bx/aazWaqqqpYuHAhH330EdXV1YwePZrjjjsOh8ORNue2KWPHjuXQQw/l9ttv58knn+S9995j8uTJnHXWWXi9XpFzXFlZSW1tLXV1ddTW1lJUVITJZMLj8bRKtGutcJfYLK09aG/BMPFamjZwUxSFSCRCLBajpqZGzAW9iaDNZiMSiWC32wmFQjz99NNceeWVHHLIIbz44otpHcaZXOd6hvD777/P1VdfLRY7WsJut+P1eoWInJubK5zzmqZRVFQkHMLFxcX0798/Yz0ul4vnnnuOQw45hIkTJ/L+++9jMBgoLy8nHA7TtWtXrFarWBgxGAwiysNms4mok1Qu9FQCfaqYmbYixeQ/R/fu3Vm/fn3KbYmNFSUSiUQikUgkEolEIpH8OaRo3E60xoUYDAbF1+QLCwupqakRr3Nzc7Hb7dhstiQhT3fT2mw2XC4XkUiECy+8kLVr1/LOO++w2267tao2Pb92w4YN7L333lxxxRX07dsXgC5dunDIIYfwxhtvMHv2bMaMGcOYMWOYNm0a3bt355tvvuGDDz5g/vz5rFq1ShyTk5PDzJkzueOOO9hnn3045ZRTOOaYYzI6RnXcbjd33XUX48eP5/LLL+fGG2/kiy++4KGHHqJLly7EYjGsVitdu3alrq6OaDRKNBqloaGBUCgkRDu9yVmiSLor59G2FLcRCAQoLy+noKBAOKttNptwr4dCIRRFIRQKEQwGCQaD+P1+7HY7b775JldeeSUjRozgmWee2e5IClVVeeCBB5g1axaDBw9mwoQJwtXs8/nIy8vDaDRiNBoJhUJUVlZSVVXFhg0bCAaDVFVVsXbtWmprawkEAoRCoZTnOffcc5k2bVrapo0Ae+yxB/fffz8XXXQRt9xyC7fddlszQddqteLz+ViwYAE2mw2v14vdbhfO8kwu9MRrVlUVp9O5y0V5/J1Yv37936YxnUQikUgkEolEIpFIJB0ZKRq3Ey25EFVVRdM08vPzMRgM2O12zGYzwWCQnJycJDeknt+rf2Vf35aXl8ekSZNYsGABDz/8MAcffHDGmjRN46OPPuLuu+/m+++/p3///rzwwgsUFxfjdruT9s3KyuLkk09mxIgRzJ8/n8cff5z3338ft9tNTU0NRqORwYMHc9111zFixAh69+6NoiiUlpby5ptv8tprr3H11Vdz3XXXMXz4cE466SSOO+64Fp2V++yzDx988AHPPfcct9xyC4MGDeKiiy7i+OOPp6CgAKfTSffu3amqqiISiRCJRESch6IoIs5Cj0hId/93FVLlK2uaJrJ2dfHS7/eL7QaDAY/HQygUEvs6HA7sdjuaplFVVcVbb70lIkFef/11otFom2vTNI0lS5Zw22238cUXXzBu3DhuvfXWpPzksrKylFESgBC7mxKPx9m0aRN2u12I3G+88QZz585l1apV3HXXXWnHBDj99NNZsmQJ99xzD3379uWggw5i9erVzJ8/n59++okffviBr7/+mlgshsfj4Y033iAQCFBZWYnL5RILD5kIhUL4/X4RoSKRSCQSiUQikUgkEolE8ndGisbtQCoXYqLzWFEUIYa5XC4RhRCNRrFYLEIU1hvcbdq0KUkELSwsxGg0ct555/Gvf/2Lq6++mrPPPjttPZqmsXDhQu69915WrFhBly5dePjhhznppJMwGAysXLky7bF2u52rrrqKs846i1mzZlFXV8eIESM49NBDaWhoaJZp3L17dy677DImTJjA1q1befXVV3n77bf54IMP+Oabb5g+fXqLwrHJZGLKlCmccMIJXHXVVdx///3MnTuXc889l4svvhhFUcjNzaWqqorq6moCgQDdu3fHZrMRDodRVVXk0+7qjuNU+cqJDvWCggLcbjexWIxgMCjyd/WICP21z+fDZrPhcDgoLCzkscceY9CgQaLpXVtE40gkwmuvvcbjjz/O999/j9vt5pZbbuGMM85ol7gFo9GI0+mksLBQvDdo0CD69OkjGj3OmDGD8847L+0zffjhh/ntt98455xzkt53u93069ePKVOmsGbNGl5//XU0TcPhcOD3+wkGgxQXFzdbaNAb6unzqGleeWsbMEraxsiRIzvsmDuiNolEIpFIJBKJRCKRSDoqyq72VV9FUSyapkW29/ghQ4Zoy5Yta8+S8Pv91NfX43a7hfgUCATEe7rjMzGLFhCO0Xg8TlVVFR6Ph0AgwC+//ML69ev55Zdf2LJlC7/88gsrV64kEokwY8YMrrnmGkKhULOv7Wuaxvz585k5cybLly+nS5cuTJkyhZNPPpmsrCyx38qVK5OaxiUSCoXo2bNnym0VFRVpG+HV19eTn58vruvmm2/mySefZOLEiUyfPl00TktFPB5Pcj5/++233Hbbbbz99tvk5ORw9tlnc8UVV+B2uyktLQUaxWpACKe6QA+NDau8Xi/Z2dlpz7m9KIqyXNO0Iem2b+/8yvQ51LOw4Q/3cTAYxGq1CrG4oqJCZGWrqsr69etxOp107dqVl156iYsuuoi33nqLo446CmhsPJcpt1gXQhcvXswFF1zA1q1b6dOnD5MmTeKAAw5I2/Rwe5zG0PjMEkVjnY0bNzJt2jQ+//xzDjjgAGbNmsXuu+/e7P7YbDZisRhPPfUU4XCY/v3707dvXwoLC8XnZMaMGcyYMYPVq1eL+o1GY0qnceLnN5VzvaXt2yskZ5pfO+Jv165GuoaQf5fz7Uha+tslkUgkEolEIpFIJBJJIruU01hRlBFAX0VRntQ0re3fr99B6K5Q3eUZCoWwWCy43W4hZOqRFLpwDH8If3qu6y233MJTTz2VlPHatWtX+vXrx+GHH86IESM48sgjm52/qVjcrVs37rvvvmZi8c7CYDBw6623AvDEE08AcOedd7ZaSNtrr7149dVX+fbbb5kxYwYPPfQQzz77LJdeeimnn346BQUFWK1WQqEQVquVqqoq4bxNdOj+nVAUJWnBwel04vf7hataF5Cj0SgOhwOv14vL5SIUCnHnnXcyZMgQRo8e3aZzzp07l6lTp9KjRw9ef/11hg8fjqIobNy4cUdcYkq6dOnCvHnzmD9/PtOmTWP//ffn2muv5fLLL2+2aGIymbjooouS3tPd+tCYh1tSUoKmaeIbAA0NDSnPqy9yJOYXJ4r3qRzhEolEIpFIJBKJRCKRSCR/F3YZ0VhRlNHA3cDkv0IwzuQ20wU9+COTVncYQ6OT1mAw4Pf7qaysFI7cyspK3G43iqJQWVnJY489xmGHHcaJJ55Iv3796Nq1q9hXRxeU165di8lkQlVV7rjjDl566SU6derE9OnTGTt2LJs2bWLFihUp6y0tLU2b+xsOh9m6dWvKbbW1tWmdxrFYTLh/dc455xzq6up44oknaGho4IYbbkgpHDc0NKRsdtanTx/mzp3L6tWrueOOO5gxYwZff/01zz//PMFgkEAgQENDAz6fD5fLhdfrxWazEYlEsFqtLT6zXYVgMIjP5wP+ECn1SBSbzYamadTW1mI0GonFYmI+RqNR5syZQ2lpKffdd1+SQKrnVKeioaGBK6+8knfeeYd9992XadOm4XA4WLp0KUDSHG7Khg0bmuVl61RUVKTdFggEUjqNdYYNG8bLL7/M3XffzfTp05k/fz533XUXLpcLTdPo0aNHyuNisZhw1ZeWltKlSxfRXBIQOdCpPg+JUTOQHBNSVFS0XdnZfxfX6o5Cd8K/9957HW7MHVGbRCKRSCQSiUQikUgkHZUWRWNFUfI0Tatu8l4PTdPW7biymtUwEHgRuEDTtM8URfEACuBqTR2KokwEJkKjc3cH1QiQlH2qv2cwGMTvmqYJN7IuJjscDl544QUUReGf//wnXbp0ARpFqnQZrpqmoWkat956K6+++ipnnXUWV1xxhXAWZxJFQ6FQWoFu27ZtaQXBqqoq8vLyUm6LRqNJrkydadOmYTKZePrpp8nKykqZcRyJRNIKmFlZWQwZMoRXX32VqVOn8thjj1FdXU2XLl1E1qzdbic3N5eGhgaRj7uTv8L+p+dXYgZ202euu14tFouI4wiHwwQCASFcms1mDAYDJpOJcDgsMnufeeYZhgwZwpgxY5Luu91uTynUV1VVcf7557NkyRJOPvlkJkyYgNFoFMIpNAqx6aI/YrEYZrM55bZMiw4+n4/OnTunvT+KouD1ernnnnt44403uP3225kwYQIPPvggJSUlaT8nJpNJXOemTZsYMGAA+fn5OBwONE3DZDKlzCVWFKXZezabTURvpJrrrSHTc5aQ9C2LjjbmjqhNIpFIJBKJRCKRSCSSjkprnMZvK4pylKZp9QCKovQDXgb23KGVJWP9/ZxFiqIMAW4DyoD9FUWZqWnaM5kO1jTtCeAJaMwF3ZGF6iJwKvTsVN0l6na7sVqtbN26lZdeeolTTjlFCMYtEYvFuPHGG3n33Xe56KKLuOSSSzqke1ZRFK6++mocDoeIqmhNc7xUjBo1ioceeogvvvgCp9NJbm4uZrOZwsJCkTELbJcD9M/QHvMrFAqlrV8XxxObI+qipcViQVEUioqKsFqtlJeXs2XLFtxuNwsWLGD9+vU89NBDrbrfP//8M2eddRbl5eVcffXV7dr4Kx6PU1lZSWVlJZs2bWLz5s1EIhF69epFnz59MmZeN+X444+nc+fOomHjXXfdRe/evTMeo6oqGzduZPTo0UJEjkQi2Gy2Zlnjer3V1dV4PJ6kBQ39WWyv4JvpOUskEolEIpFIJBKJRCKRdBRaIxrPpFE4HgP0AZ4D/m+HVtUETdO+VhQlCziZRsH4OhpFuv2AlxRFWapp2k87s6aWSHQU6uiCsqqqGAwGIfI9/vjjBAIBpkyZ0qqxNU3j+uuv54MPPmDKlClccMEF21WjpmksXLiQLVu2iPeaukF1J6bdbhfCrMPhwG634/F4WiV8KYqSlHHc0NDArbfe2ua85QMPPBCz2cySJUs49NBDqampobCwEIPBgMViET+bsiu4OxMd6jqJ+dh67InT6RTX4XA4hFtdbwYIjQ3nqqqquPfee1udZfzRRx9x/vnn43Q6eeONN/70AkR9fT2LFy/m119/ZcOGDWzevDkpHsPj8WA2m1m+fDnQ2JBu9913Z88992TEiBEtLp4MGTKEefPmcdlllzF58mQMBgPjx49Pu39ZWRkNDQ24XC5UVRXxJsFgULionU4n0HjfN2/eTE1NDYBo3hcKhURMiL6vTmvnWKrnLJFIJBKJRCKRSCQSiUTS0WhRNNY07d3fBdsFgAs4QdO0VTu8suZ1fK4oigp8pmnaq4qiKJqmLVYU5X0gsrPraYlER2FiNqouLDkcDpHJu2jRIvbaay8GDhzYqrG///57PvjgAyZPnvynBOPnn3+ed955R7hV9RoTRa+Ghgbi8XjKMSwWCyeddBLHHntsi+fThWODwcDs2bOx2+3ceOONbarZbDbjcDioq6vDbDYTjUapqKjA4/GIZniRSKRZ7MKu4O5M5VDX69YzeXVRvLKykry8PAwGg3DJhkIh/H4/DoeDgQMHsnTpUtavX8/MmTNbFIA3bNjAxIkT6dGjB88//zzFxcV8++23bb6GcDjMsmXL+O677/jmm2+Ix+Pk5+fTtWtXBg8ejMFgYPDgwZSUlIjPhM/nY9WqVSxfvpyysjJee+01XnnlFQYMGMB1110nrj0VXbp04dlnn+Uf//gHV111FaNHj06bmWwymcjOzubZZ5/l9NNPp2vXrrhcLhH1YbVa8fv9orGioijk5OTg8XhoaGhg8+bNYoHCbDaLBoT6Z6W1c6zpc94VFjQkEolEIpFIJBKJRCKR/O+RVjRWFOURIPGr9tnAGuASRVHQNO0fO6ooRVH2A3oDq4Fv9MZ3mqZ9oSiK+fffNUVRTgH2AnZa2OT2OApVVSUcDhOLxaiqqsLr9QrBKisri3g8nrEJWFM++ugjAE4++eTtugZN03jxxRd55513GD16NBMmTBDC4m+//UavXr2S9o1GowSDQVatWoXX6xUOzc8//5wXXniBjz/+mNNPP51BgwZlPK+iKEyfPp1AIMBjjz3GkUceyb777tvquhctWkRNTQ1HHnkkVqsVVVWJxWJUVFTg9/uFC7cpiYLgrkTTCAqDwYDP52Pbtm0Eg0Fyc3Oprq4mPz9fNMiz2Wy43W5KS0sBOOCAAzKeQ1VVLrvsMjRN4+mnn6a4uLhNNaqqyjfffMP777/PRx99RCAQIC8vj7FjxzJ8+PCk5og//fRTs2aJLpeLffbZh+LiYvr27UtNTQ0LFy5k3rx5PPbYY1xzzTUZz+9yubjiiis466yzePbZZ7n00ktT7uf1ennvvfcYPnw41157La+99hqKohAOh7Hb7aLZYCAQIBaLYbVayc/Px2AwsHHjRtauXUsgEMDj8VBTUyMiZnSX9/Y6iHeFBY2dyTHHHNNhx9wRtUkkEolEIpFIJBKJRNJRyeQ0Xtbk9fIdWYiOoijH0hhB8S1wHDCNRvGY393FUUVRTMDpwFXAaZqmbd4ZtUFzkUdVVQKBANAoGOk5qYmOQl2Q0tGPsdls5OTkUFdX12ImayIff/wxvXr1Ek252soHH3zAwoULGTVqVJJgnApFUbBYLFgsFgoKCpLqPPDAA/n222+ZO3cu9913H9988w1XXXUVnTp1ynj+W265hU8//ZR//OMfLFy4MGVDtlS8+uqrOJ1OBg8eTEVFhbh/+v10Op0phfxwOCyE+11JmEuMM9EbKFosFtxuNyaTScRRpFrIWLZsGcXFxZSUlGQ8x5w5c1iyZAkPPPBAm5v4/fTTT9xyyy1s2LABu93OYYcdRu/evTn44IPTNjZsidzcXMaNG4eqqjz33HPst99+HHrooRmP6du3L4ceeiiPPfYYF154YdpGfEOHDuWee+7hsssu44EHHmDy5Mn4/X4AIbqbzWZqampEw8dAICCE9OLiYiEoRyIRVFVN+luwPXNLxlUkc9VVV3XYMXdEbRKJRCKRSCQSiUQikXRU0qp1mqY9C6AoigMIa5oW//21EWgeHNsOKIriASYDp2ua9qOiKHOBwYqi1AF+TdOCv9cWUxTFB5yoadpvO6KWpmhao+k6UeTRNI1QKERZWRmAyEuFZNegxWJBVVWysrL43aWNz+cjFovh8/mora3F5XIRjUaTzqnHMCQSDodZsmQJo0aNYt26dWnrXbUqdYKILhh36tQJRVF4+umnk7YXFRUJl2pTVFVl9erVzd4/4ogjWLduHZ9//jmLFy/m8MMPZ8SIEUmZxf3790865rLLLuOqq65iypQpXHHFFUK0a0osFsNiseDz+XjllVc4+OCDyc3NxefzoSgKwWCQvLw8ioqKxDOBP54XNH9mraG9mwq2dN5M23VRuLKyEmjMA45EImRnZ2O321FVlYqKCnJzc4VovHTpUgYPHiwWNJqyevVqNm/ezIwZM9hvv/3YZ599kubM2rVr09azadMmnnvuOT766CNcLhfjxo2jT58+mM1mNm/ezJtvvpnyuKysLPFZSXX9uoAL0K1bNzp37swjjzxCLBbjoIMOSluPoiicffbZnHvuucyZM4dx48aJbcFgUGQSQ6M7f+HChdx444306dOHUaNGicxoPRdav9+KoggHuy6om0wmAoFAUkO8tswrTdOS5lamxpkSiUQikUgkEolEIpFIJH8VrbF4LgQOB3RFx0ZjvnHm771vH7Hfx99DUZQNwHDACxwPrANuUBRlLyCiadrrO+D8adGFHkVRkkQem80moiXsdrv4untiRrDebCsQCIivtDudTiFO1dfXk5ub20wg1uMrEvnqq68Ih8Psv//+5Ofnp61Xd6Em8vrrr/P++++LPOAVK1Y026ewsJDBgwenHHPVqlVp3agFBQVce+21vPXWW7z33nssXbqUU089lZ49ewI0cxPvvffejBs3jldeeYVDDz00bUyFLra/8sor+Hw+LrjgAkwmE8XFxUQiEZFj3LQxGfzxzBIFvfYWg7cXTdNEHrGiKM3qSnQPG43GZsJiLBajrq4Oj8eD3+8nEAhgsVgIh8NUV1ezevVqTjnllLTNBjVN484778RisXDDDTc0ayDYNEZCp6qqimeeeYYtW7bQt29fjjrqKKxWK5qmEYlEWL58eVoHfDweZ++99065bdu2bXTr1i3pvTPOOIP777+fd955h+OPPz5tHEw4HGbIkCH069ePuXPncvrpp4t9I5FI0r1VFIX77ruPH3/8kYsvvpglS5aQn5+P3+8X88TpdIp72VQQrqqqora2lkgkQo8ePTJmLqci1bOW/MHw4cOBxiiajjbmjqhNIpFIJBKJRCKRSCSSjkprOi9ZNU0TFsDff09tC/2TaJpWBzxMYyTFAuBpTdPGAnOAroqiDAMOBWp2xPm3B4PBgMvlEpEBTSMSgsEglZWVbNq0Cb/fLwRAPesYGpvN5eTktOp8n3zyCSaTqcX84Ka89dZbvPDCCxx88ME4HI4kUbu1LsmWyM3N5eyzz2bSpEkAPProo7z11ls0NDSk3P/888+nW7du3HXXXdTUpH+kmqbx+OOPM3jwYPr3709lZSUNDQ14vV7htk2H7sKtra1NinMIBALtdt3bQzAYpL6+nmAw2Gxb05r1hQeHw4HL5cLhcGAymYjFYgSDQUKhEFarFYfDgcfj4bvvvgNgr732Snv+l19+mR9++IErr7yy1TEnX375JZMnT6a8vJwxY8Zw/PHHZ8yJDofDhMPhVo2divz8fMaOHcvq1at57bXXMu6rKAoXXXQRq1evZvbs2SI2IhUul4snn3ySuro6LrzwQurq6rDb7fj9furr61EUhUgkQiAQED8DgQCVlZVEo1FMJpO47sTFIIlEIpFIJBKJRCKRSCSSvwutEY0DiqIIe6CiKPuwAxvPaZr2HxqdzZ/RmGuMpmkfAU7Armnag5qmbd1R529v7HY7+fn5dO7cGY/HI7JpQ6EQ1dXVInLA7Xa3arxFixYxdOjQjEJpU959913mzZvHgQceyOTJk4VgHI1GqayspLKyEr/f327C1+67785VV13F/vvvz8cff8z999+fMkrDbDYzbdo0qqurM+aFfv755/z000+cccYZdOrUSYj04XAYq9UqnNypCIVCxGIxTCaTiKnQ3d2pBNudhd1ux+12p3yOTWsOhUL4fD5Rr8FgwOv14na7CQQCQsjUxWQ9WiKdq/eXX37hqaeeYvjw4Rx55JEt1hqLxXjssce49dZbyc/P58QTT2TQoEFpHbN1dXV8++23LFq0iEWLFrFs2TLKysq2S6Tfb7/92GOPPXj88cfTxqboHHPMMQwaNIiZM2ey1157cc455/D++++nPG///v2ZOXMmH374IQ888IDIvTYYDGiahsViweVykZeXh81mo6KiQjyXzp0706lTJxwOh5hLqZz9EolEIpFIJBKJRCKRSCS7Kq0RjacAryiK8pmiKIuBfwOX7MiiNE2rAT4CTlIU5Yjfm+N1pzGiYpdCdyI7HA62bt3K1q1bqa6uxmKxYLfbhcP4xx9/bHGszz//nBUrVjBy5MhWn//HH3/k2WefZdiwYVxyySVJDcr8fj8GgwGz2UwwGKS6urrdhFSLxcLJJ5/MxIkT8fl8PPjggyn369OnD+PHj+ell15i27ZtKfdZsmQJ0BiZEI/HsVqt1NfXU19fz8aNG6mrq0uqO9H9qTfL83q9wgFus9nSCrY7Cz3mJJXwmlgzNDqtnU5nUr0GgwFFUVBVFVVVMZvNRCIRLBYLP/74Iz179hTN3Jry8ssv09DQwNSpU1sVlTB//nwREfHAAw9kdMVHIhG+/vprqqur6dGjBz179sTn8/Htt9+yePFivv766za5jxVFYfz48dhsNqZOnUpFRUXafbOysnjrrbd44403OPPMM/nhhx+46qqr2LhxY8r9TzvtNM444wwefvhhamtryc7Oxul04vf7RUPLSCQihOFAIIDZbKahoQGHwyHyjN1ud7NGdpqm/eVudolEIpFIJBKJRCKRSCSS7aVF0VjTtKXAHsAk4CKgr6Zpy3d0YcAXNDqNr6exOd45mqaV7oTztonWfj29urqacDiMxWIhJyeH6upqVFWluLiYiRMnMnv2bD777LO0x/v9fi655BJ69OjBhRde2KrafD4fjzzyCEVFRUyePDkpV1jTNNFkLjs7m9zcXKCxAdqqVavaTezq27cvAwcOzOgS1Zvkbd2a2kB+8cUXM2DAAM477zzWrl1LOBzG7XZjNBoxmUyYTKYkQTUYDOLz+QiFQqLRWGJkiP5eR82W1cXIUChEIBBI64rW3colJSWYTCYsFgu1tbV89NFHGR3EvXr1Ahodwa1h/vz57LbbblxwwQVpM5KhcU6tX78egAMOOIA+ffrQu3dvDj30UAYPHozVauXjjz/mscceY8GCBVRXV7fq/G63m7vvvpu6ujqmTJmS8TiDwcA+++zDLbfcwvXXXw+QNh5FURTuvPNOTCYT9913H06nUzS3s1gsBAIBysrKsFgslJSU0KtXL7Kzs0W0jN5ksOn8gszxIxKJRCKRSCQSiUQikUgkHZ20orGiKCN+/3kiMBbY/fd/Y39/b4eiaVqtpmkPA8cAJ2ua9t2OPuf20Nqog7y8PEpKSigpKSESiRCPxzEajZjNZm644QZ69OjBBRdcIISoptx1111s2LCBRx99tFlTtFRomsbs2bOpq6vjsssua+aE1EVuXUjOysoiLy8Ph8PBV199xRdffEEsFmvNLWgRr9dLfX19WpFSF6zLy8tTbnc6nbz++utYLBbOOOMMKisr2bZtG9nZ2ZjNZjweT5JoZ7fbcblcza55V0KfV9AohOpO10R08buhoQFVVYlEInz55ZcEg0HGjBmTduwhQ4YA8NNPP7VYR2lpKb/99hujRo1qUWRfsWIFPp+PPfbYI+neGwwGioqKGDp0KGeffTZ9+vTh+++/Z86cOXz55ZetikXp168fd999N2VlZVx++eX4fL4Wj2kNJSUlnHvuuTz77LNs3LhRxFREIhGRJW0wGCgoKMBsNguBOBAIsHXr1mbPRBeTrVbrX+5m3xX54Ycf+OSTT0TDwKb/mjZLbA3jx49n/Pjxf7q29hpHIpFIJBKJRCKRSCSSXYFMTuNDf/85NsW/Y3ZwXQJN03yapqXvavUXY7VaMRgMKRuCqaqK3+8nFosRiUSwWq3CYay7GsPhMIqi8Mgjj7Bu3TpmzJjRbJyysjLmzJnDKaecwn777dequhYuXMhXX33FaaedRs+ePZtt1wXhRPexwWCge/fuDBgwgDVr1jB//vx2EecKCgoA2LRpU8rteoxCWVlZ2jG6devG7Nmz+emnn3jllVeIRCJs2bKFWCwmhD6fzyfqdTgcxGIx1q5dSzQaFeN0hCZ4rUGPPXA4HDidTgDq6+vx+XzNRFabzSZE8nfeeQe73c7w4cPTjt27d2+cTmerROPPP/8cRVE4+OCDM+5XV1fHwoULcblcdO7cOe1+RUVFjBkzhkmTJtGrVy8++eQTXnrpJfx+f9pjdAYNGsSdd97Jhg0buPPOO9vtGU6dOhVN07jllltoaGjAbreLse12ezMXcSZ0sT8cDndoN3tHpbq6Gk3T0v5rKdc6FRdffDEXX3zxn66tvcaRSCQSiUQikUgkEolkV8CUboOmaTf//vOcnVfOrocehaC7WxOpr69n/fr1zZywRqMRg8GAz+cTgt+oUaMYMWIEH330kdi3oqICo9HInXfeSUNDAxMmTBDCam1tbdqogPXr1/PUU09RUFBAOBzm5ZdfTtquqqoQxSwWS1JtidnK1dXVvPHGG+K1x+NJKyI7nc60Gbq6ILh161YGDx7cbLvH4xHbEwVeaGzWp+cwH3nkkRx33HE8+uijHHTQQRQUFNC1a1fsdjvBYJBt27YRDAYpKCigoKCAzZs3s3btWhRFoUePHkCyg7c1ju2/Ct1FDH/EUOgN17xeL+FwWNw3RVGwWCz4/X4++OADRowYgdlsJhaLpRQtjUYje+65JytXrhQu76b8+uuvAHz66af07t2beDwu8oQ3bNiQJPJqmsaiRYvEvPr2229TjlleXs4HH3zQ7P2NGzeyefNmNm3aRLdu3VLWnPje6NGjeeedd3jkkUfYb7/9MJlMFBcXNztGb05ntVpTOn6j0SiRSISioiLOOOMMnnvuOXbbbTfOOussNE0jKyuLgoICIf7qzmO73Y7D4aC4uLiZm11/vSu73P9q9HvcnuMBf3rM9hpHIpFIJBKJRCKRSCSSXYEWLXSKolgURTldUZTrFEW5Sf+3M4rr6OhO4nRfr/f7/ZSXl6OqKi6XC6/XS2FhIVarVQjNieJS9+7dm+X6VlRU8NJLLzF27NhWfTU7Go0yffp0jEYj++67b1qnY0NDAwaDoU0uytraWmpra1sVJ5CI0+nEaDSyYcOGlNttNhtOpzNjkzOde++9l6ysLB599FEhCiqKgt1uF45cv99PKBSiU6dO9OjRA7fbLRy66RqXdWQMBgNer5eioiIKCwsJBoNs3bqVqqoq4ZwOBAIsW7aM0tJSjjrqqBbHHDBgACtXrhT3MBVlZWVs3LhRxFmkY926dWzdupVBgwa1aT4lYjab+eqrr/jyyy+bLRw05dBDD6V37968+eabaSNN2srdd9/Nvvvuyy233MKCBQswGo3EYrGk2Bl9gSgYDKb97BsMhja7kyXJHH300e0+3vaOqS9i6I0r9QUE/V/37t3btVaJRCKRSCQSiUQikUg6Cq1RNt4EjgNiQCDh3/88oVAIVVXJzs5O6Vp1Op0UFBTgdrtxOp2YTCacTic1NTWUl5cTDoeJRCJC5OzcuTPl5eVJotmcOXOIxWJcdNFFrarpiSeeYNWqVQwdOjSjMBqLxZKiKVrLli1bWL16NeXl5WkbjDXFaDTSqVMnNm7cmHafgoKCjPEUOp06deKWW27hww8/ZNGiRcTjceF+9ng8OJ1O8vPzsdlsmM1mioqKqK2tpby8PG1jvJ1Na5snJmIwGHC5XGLxobi4GI/HQzgcxu/3o2macPG2RiAbMGAA8Xicn3/+Oe0+y5YtA2CfffZJu08wGGT58uV4vV523333Vl9PU7p06cKAAQPYuHEj77//fsa5YDAYOO200zCZTPzrX/9ql+xtl8vFm2++yb777stFF13EggULMJlMxGIxAoEAfr8fq9WKy+XCbrdTVVUlhHvJ35fS0lIRjXHooYdy6KGHJsVl6I0fJRKJRCKRSCQSiUQi+bvRGuWss6Zpp2iadremaffp/3Z4ZbsANpuNnJwcvF5vShHS5XLRs2dP7HZ7kivR4/FQVFSEx+PBarXidDqxWCwi3qKyslKM8eqrrzJq1KhWuYy3bdvGSy+9xHHHHUdJSUnGffVGfG0hJyeHbt26YbPZqKysTOscTkVeXl5GIdDtdlNbW9uqsSZOnMiAAQN47bXXqKmpoaKigmAwSE1NDX6/n2g0KhzWNpuN/Px87HY7Foul1fXuSPSIjEwuX0gWlxN/N5lMeL1eIpEIWVlZYu4tW7aMvn37ZswU1unfvz8Av/zyS9p9Vq9ejcvlIicnJ+0+a9asoaGhgaFDh/6p/F5FUejXrx8jR44kGo3y6aefZlyUyM7OZvz48WzatImvv/467ZhAqxc3EoXjSZMmoaoqbrcbv99PXV0d4XBYRKFkZ2fjdrvTxnukYnsWCyQSiUQikUgkEolEIpFI/gpaIxovURRlwA6vZBekJdeqvj0SiSR9rT0cDuP1epOcvuFwmB9//BGXy0VhYaF4v7i4uNXN6LZt2waQsQmajslkarWYpqN/RVuPgcjOzm7VcdFolJ9//pk999wz5fZYLMbKlStb7VQ1Go243W6ys7PJz88nLy8PVVXJzc0VYjw0inS6uxggEomIMf5KAa+1ERmBQICtW7cSDAaF0KxHUQQCAerr66mrq0NVVfFsWktrXOYHHnggPp+Pd955J+0+Xq8XSF7o2F7i8TgrV64kHo/Tp0+ftJndOnvuuSdmszntYsRuu+0GkNFN3RSXy8Wpp55KNBoVor4u1OuCsc/no66uDpvNJj7bqRoUNkV/homRFxKJRCKRSCQSiUQikUgkHZG0orGiKD8oivI9cBDwjaIoKxVF+T7hfUkr0ZvdWa1WKioqqKurw+fzUVZWJr76Xl9fz4IFC9hvv/2SHMD77LMP3377bau+gl9dXQ2QtiFdIlarlVgsRjweb9O1hEIhysrKcDqdQpxtidLSUqLRKCNHjky5fc2aNYRCIfbdd99W17Fy5Uq6deuGwWAgGo0SCASIRqOiSZymaUlN75qKtK11+3YUdKEZSLqm7OxsDAYDFouFwsLCVkV8ACICJZP7eujQoey333689dZb/Pbbbyn3KSwsJC8vj59//vlPCfCqqrJ48WI2btzIoEGDGDhwYIvHKIpCbm5u2oiInj17YjabWbFiRZtrgcaFHGi8z3rzM73hpcfjEc+jrKyMsrKytHNJX6CwWq1JY0kkEolEIpFIJBKJRCKRdFQy2Q2P2WlV/M0xGAw4nU78fr/IEg4Gg2zbtg23201eXh7XXXcd27Zt45VXXkk6dp999uGll15i5cqVIlIgHTU1NQCt+sq8LhZGIpFWi1iqqrJp0yaMRiMlJSWtjiNYs2YNRUVFaev/4YcfAFotGldWVlJZWcmee+4pxPjE6ADdma1fl81ma+YG1wVkm80mHMmp9tsRJIrZmdzBDoeD4uJirFareE+vUf9ZX1+PqqrU1dXhdruprq4mGo1iNpsz1qC7rluK7DjzzDNZs2YN9913H9OmTaNr165J2xVFoX///nz22WcZM6tbYtOmTUQiEYYMGULPnj1bfVxeXp5YLGlKVlYWvXv3brNorC+kKIqCzWYjHA5TV1dHMBjE6/UKp73JZEJVVfHNgHTO8abP+8/EePwvMGHChA45XnvXJZFIJBKJRCKRSCQSSUcmrWisadp6AEVR5mmadmbiNkVR5gFnpjxwF0DTtDYfo0cAtGa/cDiM2WwmGo1itVrRNA2DwSB+14XKSCSCoih89dVXPPnkk5x77rkMHjxYCHq1tbX07t0bgMWLF9OpUydxni1btlBXV5d07jVr1qAoSot5w3qNiqIkZS23hN/vp6GhgYKCgmauSpvNxubNm5sdE41G2bhxIyeccEJace+bb76hpKSE3NzcZl/d1+9lIj/++CMAJSUlzaIZdOHOYrGgKApWq5VQKITVak0ShBVFEaJyMBgUop7+XnsLe4lzLlGwzjQX9Rp1Ubu+vl44VXX3qtFoFHnNBQUFQOPc6Ny5M4FAIK14vGXLFqDx2rdu3dpse0VFhfj9ggsu4NFHH+XOO+9k8uTJBIPBZtfjcDj4/vvvUVUVv9/fhjvTSDgcZvfdd8dsNqcUn9PlKhsMBiorK1m3bl3K7d27d+fTTz+lvr6+2YJAOnFdj22JRCKEQiEsFguRSIRAICCuVR9LURScTqd4Rlartdncafq8pWicGSkaSyQSiUQikUgkEolE8tfTcrApJNlDFUUxAvvsmHI6LoqiZBR7dNFIF80MBoMQY202G4qiYDQahUtRURRycnKoqanhpptuIj8/nxkzZiRFU3Tu3JkePXrQqVMnVq9eneTy3LJlixhLJxaL4Xa76dSpU8Z84M2bN+N2u9m8eTM+n49OnTqJaystLU1ytuqEw2GCwSAHHHAA++zT/PHX1NQwePDgZu9/9dVXaJrGmWeeSd++fVPW89tvv7Hffvs1ux5oFH+b1qMLhF27dqW8vJzi4mIhGuv3WBfnwuFwi67exOiBHSXoJY6bKn84k9tZj5/QfyZel9PpFA0U9QzfqqoqunfvjsPhSJtdrF9zQUGBEJsTOfTQQ5Ne77333kycOJGnn36a8847jz59+iRt79y5M3Pnzs34Oenbty9DhgwRr30+HwsXLiQUCrHnnnuy3377pTwOEIsnTdmyZQs//fRTUiPJRAYMGMC7777Ltm3bmjmYs7KyUjqt9fvvdDqx2WxJ7nVI/bcg0zxra970/zqVlZXk5+e363jAnx6zvcaRSCQSiUQikUgkEolkVyBTpvE0RVF8wEBFUep//+cDyoE3d1qFfzGaphEIBFp0JzfNz9UzT1N9ZT0Wi7FhwwbKysqYO3cuy5Yt484770wbKzFs2DC+/vrrFmuora1tVTSFTqJDMhOxWIxgMIjb7Wbvvfdu9fgAK1asID8/n379+qXcvm3bNrZu3cqwYcNaPeavv/6KzWZj4MCBeL1eYrFY2kZkrWk6p4t6O9sBqruF4/E4FRUV1NbWpn0WkUhEuNPhj+sym80Eg0Hi8bhoTFheXt7iuVuTaZxIt27dePTRR4lGo8yePbuZa3z//fcnNze31S7+2tpa5s+fTyQS4fDDD29VDncq9ON053RTdHG7LREVejyFx+MhFAqJPPH8/Py0US6tbW4oaZlx48a1+3jtMWZ7jSORSCQSiUQikUgkEsmuQFrRWNO0OzRNcwH3aJrm/v2fS9M0j6Zp03ZijX8penRBJmFVVVUhLOlfXzeZTElfY0+kurqa2tpaYrEYjz76KMOHD+fUU09NO/6+++5LRUWFiGVIR11dXZuabOnux/LyciFGNkXTNOGc7tGjR5uEVZ/Px2+//cbgwYPTHqeLeW1pgvftt9+y++6743Q6MZlMVFRUpGxE1tS9q4u0f6ZhW3uiLzRUVlaKrOt0omNTUdJgMOBwOIhGo/h8Pqqrq4VLPV1UQyK6aNyWe9GrVy8efvhhQqEQDz74YJJAbDKZGD16NJA5/kXTNNauXcv8+fMBOOKII/B6va2uoSl6bMW2bdtSbt9tt92wWCx88803rR5Tr7+6uhq/308oFBL3O13mdUvbJRKJRCKRSCQSiUQikUh2JVpUOP6XBOJU2O32Fh2EoVCIyspKgsFgSvFVVVV8Pp9ww+bl5VFSUkIkEqG2tpZzzz03oxg7ZswYcnJyuPnmmzPW2qdPH3799ddWOU2hUegrKioiHA6zZs0atmzZ0kxEjEajqKqK3W5PG3WQjp9//hlN0xg4cGDafdasWQOQ1onclE8//ZTFixczcuRIoNEp63A48Hq9zZ6RLsrqYnLT1381uhCcn59PTk4OXq83oyhps9lEBIougJvNZhwOB1lZWXTq1IkBAwbw8MMPi1zedPTr1w+Hw8FLL73Uppr79evHmDFj2LhxY7PcbD3SQtO0lMJxNBpl4cKFfP7557jdbo488sg2OeNToWcvFxUVpdxuMpkYNmwYCxcubPWYujt55cqVOJ1OHA6HWIzRFx30+x+LxZJ+dpQFCYlEIpFIJBKJRCKRSCSSP8P/nC1OVdU2NcJrKbpAF5Dy8/MpKChIKS6Hw2EqKiqoqKggEAgQiUTIz88XjtCmWatNycvL4/LLL+err77K6DY+/vjjAXjjjTdad3G/j92rVy/y8vKora0V2cX6fQqHwxiNRrKyslo9ps5PP/1Ebm4uxcXFafcpLS2lqKioVV/rV1WVa6+9li5dunD66adTWVlJRUUFqqpiMpmaCa5N3bmtiRDQ40h2pvinz7GWXKqJorf+u+4Y1uMZrrrqKtasWcOcOXMyjpWdnc2ECRP44IMP+Omnn9pU75577omiKCxfvjzpfT1vGZKFY/33tWvXUllZyb777suRRx6ZMoO4rfz222/YbDZ69eqVdp/Ro0ezcuXKVjmwoVH8NplMfPzxx2iahsPhoKCgAIPBIJzHoVCI2tpaNmzYQG1tLVVVVeLZ6ILy9jTclEgkEolEIpFIJBKJRCLpCPzPicbxeJxgMPinx9GFoUAggN/vx2Qy4XK5koS/RDeox+PBarXi8/moqamhsrKS1atXA380MMvEiSeeiNls5uWXX067T2FhIYcccgjvvfeeEBNbg+447tWrF0ajkXA4TF1dHT6fj3g8jtVqbXPebyQSYdWqVfTv3z/jsevXr09q8JeJf//733z77bdce+21FBQUEAwGxb1PJQQ3jQxoTYSAHkfSHnOkJdrqfNZFb4vFgqqqOJ1O0STQbDZjNBoZM2YMBx10ELfffjuBQCDjeBMmTCA7O5uHHnqoTXU7nU523333lJEPiU3idLFY/+d0Ojn22GPp06dPu8U4rFmzht122y2pgWRT9NgMPRKjJdxuN/vuuy+fffYZFRUVhMNhHA6HiDkxm82oqorBYCArKwuTyZSUYa4/150xhyQSiUQikUgkEolEIpFIdgRtyhtQFGWipmlP7KhidgZGo7HF3F9N0wgGg9jt9iTBM9E5qAtDTqcTt9uN1WoV21VVJRwOo6oqfr9fCJV6MzODwUAoFOK7774jLy8Pm82WMtbC7/cLUdBmszFixAhee+01rrzySioqKkTjvUQOOeQQFi1axIoVK+jRo0fK64vFYtTV1aXcZjab0TSNhoYGYrEYiqKgaZqoXY+TSHXcpk2bxOvffvuNWCxGQUEBmzZtEjECTSktLeXggw8mHA6n3B6NRrHZbFRXVzNt2jT23ntvzjrrLHFPzGYzVquVUCiE1WoVYqR+n/VnoW/TXzd9tjr63GhLNvT2ogvdVquVQCCAzWZrNt9CoRAWiwWj0YiiKNjtdiGWu91uUWt2djaxWAyj0ciNN97IkUceyaxZs7j66qtTntvn82E2mznzzDOZNWsWixcvZtCgQWJ7poiTiooKevTowfvvv8/y5cvJz89PqjmV21hRFDp37kxNTQ01NTXNxgyHw6xduzbtOVOJzD6fj6qqKvbaa6+UYwI0NDTQr18/evbsydtvv53UyCwej6dtBDh8+HDuuOMONE0TIr2+SBSLxQiFQni9Xkwmk5hb+pyxWCwZHe3SgZyZSZMmdcjx2rsuiUQikUgkEolEIpFIOjJtC6mFi4BdWjQ2GAxpXY66mKO7TeGPZnFNBcam4mIwGBROxHA4nCQom81mFEXB4/GgqirBYBCfz0dFRQU9e/ZMK1wVFhZiNpvF63PPPZf333+fb7/9lj322EMIyon07duXt99+m9LSUi699NKUDsyGhoYkoS+R7777joKCAgDRRE7HYDCIvNemxGIx9t9/f/F6xYoVOBwOTjrpJOFkbkp9fT21tbXsscce4j43xWazYbFYuPbaa6mqquLtt99GVVVsNhtWqxWj0UggEBDPS3d66iK+3mQuJycHh8Mhng00OmaboihKyvf/LKkEaqPRiNPpxO/3N5tv8Mc8dDqdItNY/6nHohgMBoxGo3Be2+12Ro0axXHHHcesWbOYPHkyHo+n2bm7d+9OVlYWV1xxBf/+9795+umnee2110Sdme7Btm3b6NmzJ++//z61tbWMGDFCbPvHP/5BSUkJ0OgiLy8vZ+DAgVgsFr777ru0rvoVK1bQrVu3tOdMlXn9xRdfAHDggQfSt2/flMfV1tZit9s58sgjeeKJJ4jFYkJsj8ViaWNXRo0axcyZM/n8889FzfoCiv4ZhsbnFYvFKC8vx+PxYDKZMBqNYh7qz0zSek455ZQOOV571yWRSCQSiUQikUgkEklHpq1qRtsyCnZR9OZ3mdymiVnHuriX2KRMb6Clu4z1aATdsVtQUMDatWvTuoFTMXLkSAoLC3nhhRcy7nf66adTU1OTMj6gLbQmZzgVqqryzTffMHjw4IzN80pLS4FGATMTb7/9Nv/617+45ppr6NSpE9u2bROiXeL9TowGCIfDhEIhYrGYiK9I3HdnOIlbS7pmi3ocBZAUY6HPJz1fV39PF5cBZsyYgd/v55577sl4bofDweWXX86SJUt45JFHWl2z1+ulZ8+efPXVV2n36datG0OHDk27KPJnWbVqFU6nU4jUmRg1ahSxWIxPPvmkVWPvvffe5OTksGjRIpFx7XA4xGKLnnmtqiobNmxg8+bNVFVVieM7WtPFXYmNGze2+3jtMWZ7jSORSCQSiUQikUgkEsmuQFtF47E7pIoORkvN75qSKPqFQiH8fn9KR3MoFCIej2MwGIhGo2zYsKFNorHJZGL8+PHMnz8/ZTSFzoEHHkhhYSELFizYqQ3ddL777jvq6+sZOnRoxv30qItMmcZ+v59JkyYxaNAgTj/99CTBGGh2v3Wh1Wq1YrPZyMnJwev1ikiQdM/mryRd1rL+vt1ux+l0CtEbmjf1U1UVn8+Hz+dDVVX23HNPzjjjDB5//PEWG8Cdc845nHDCCdx+++3861//anXdw4YNY/Xq1Uli6c5C0zRWrVpF7969W/U53XPPPbFYLCxcuLBV45tMJkaMGMHHH3+M3+8nEAgIATgcDos5FAqFyMrKIicnRzi6my5k6NEWf8VncVfkzDPPbPfx2mPM9hpHIpFIJBKJRCKRSCSSXYE2KWeapm1qea8dh6IogxVF6asoSurvov9F6CJzomiZyqVrs9lwOp2Ew2HKysrIzs5us3Ntw4YNSdm9qTAYDIwZM4ZNmzaxYMGCNl/Pn2HVqlU89NBDFBUVsddee6XdLxqNMnv2bDp16pTRabxixQq2bdvGlClThFBXUFCA3W4nHo9TX1+P3W4X9ztRgG0qxmZ6Nh2ZRGexnqsLiGtTVZWKigq2bdtGeXm5ENVvuOEGzGYzF154YUbB0mAw8Mgjj3DYYYdxzTXXsHTp0lbVpUdG/BXuyy1btlBbW5s2liKRX3/9lbFjxxKJRNhjjz1afY5evXqxbds2gKQIkYKCAnr06CGa4+Xl5dG9e3fhqtcjUBKfl3QdSyQSiUQikUgkEolEItmV6Dh2yxZQFOUo4G3gYuAVRVHO+YtLSkk616i+DRqzVG02GyNHjuTdd98lGo22auzly5fz5ptvcskll7SYuztkyBD22Wcf3n77bdavX9/2C9kOVq1axcyZM8nOzuamm25KmbmsM2fOHFavXs0tt9ySlNvclF9++QVovB6n00l+fj55eXmEQiGqqqooKysTzs+WyPRsOjrpoiqgMfs4FovhdDqFoB6LxbDb7cyYMYNPP/2URx99NOP4WVlZ/POf/6RTp06ce+65GRvh6eTk5ACkbaq4I/nhhx+ARgdxOjRN48UXX+Too4+mqqqKefPmcdFFF7X6HOFwGJvNht/vJxgMinmjx87oz6DpnLJarZhMJvx+P2VlZQDNFis0TSMQCMimeBKJRCKRSCQSiUQikUg6JB1ePVMacQKXApM1TbsUOB+4XlGUVilAiqJMVBRlmaIoyyoqKnZYra39GrrBYCAvL4+xY8dSV1fHxx9/3Krxb731VjweD5MnT25xX0VROO2008jOzmbu3LmEw+FWnWN72bZtW5JgnKr5ms6GDRt45JFHOPLII5OaqKXil19+we1207lzZ8LhMOFwmOrqasrLy7FYLBQXF2c8185gZ80vSO2W1kVKr9eLy+XCYDBQVVVFRUUFJ5xwAmPGjOGGG24QAnw6cnJyeOaZZ/D7/Vx22WUtLmboonFNTc2fvq628uOPP9K1a1eys7NTbq+oqOC8887j7rvv5sADD+TDDz9sca41JRwOC1d/fn4+drudYDBIKBQSmcW6kzjxM28wGPB6vRQVFVFYWJiUba6j56AnRq1IJBKJRCKRSCQSiUQikXQU0ncpAxRFcQNeTdPWNHl/oKZp3+/Qyn5Ha7Ti+RVFWQa4FUXJ0jTtS0VRTqXRcRzWNO2ZFsZ4AngCYMiQIe1q7VNVlVAolNSEDUhqtKbnGEOj6OdyuYjH44waNQqXy8Wrr76aUtAqKysjKysLgC+++IJFixYxbdo0gsEgFRUVuFyutHVt3boVgLFjxzJv3jweeughTjvtNLKysgiFQlRWVqY8rqGhIW1GbU5OTkoHamVlJR999BE5OTlccsklaJrWbHw9UkHTNG688UaMRiNXXnklgUCAWCyWVqD85Zdf6Nu3L7FYDE3TyM3NFVmyuktT0zTxu6qqQoiz2+3b7SpubZ717+f/0/Mrk+NU0zQxtxIbNGqaRjweJxwOi+s2GAxYrVby8vKIx+NkZ2fz8MMPM2zYMM477zw+/fRTDAYDgUBAzK1Eunbtyt13380ll1zCTTfdxLXXXttsn7KyMurq6tA0DYvFwiuvvMKqVavYa6+9CAQCGI3GlNehz9tUhEIhMWdTobusoXEurVu3joMPPphNmzaRnZ2d5Hb+7LPPuPnmmwkEAlx22WVMmjQJRVHw+/1JY8bjcWKxWMrzNTQ0EAgEhCBvMBiwWCyoqkpDQwNGo1G4/Zt+5lVVFY0JdTRNQ1VV4V7WRf9dLSpFIpFIJBKJRCKRSCQSyf8GaUVjRVHGAw8C5YqiZAETNE3Tw06fAfbe4dUlsw0YCbwFNGiatkxRlDOBBxVF+UTTtMzdvlpBJqEwlainZ8nqwpMuGtnt9qSxjEajEJazsrLwer2EQiFUVeWwww7j3Xff5fHHH28m4lksFkwmE9FolNtvv51OnTpx9tlnYzab6dGjBwUFBWnr1YW7gw8+mOLiYu6++27ee+89ZsyYAUBxcXHK4xYtWkRJSUnKbbFYjIMOOijpPT2SIjc3l3nz5lFYWJjy2E6dOgHwzjvvsHjxYm677Tb22WcfoFGgSyeerVy5kqOPPpotW7YQCARwuVzk5ORQW1tLMBgUAndhYSGqqlJZWSlEPLfbLZrgdXQyzT1FUdLOLUVRUFUVh8MBgM/nA/5YnLBarXTv3p177rmHc845hzfffJNTTz01430544wz+PXXX5k1axYHHnggZ5xxRtL2Hj16CEH05ptvZv78+Xz11Vd8+eWXZGdnM2LECIYPH06vXr2Sal27di277bZbynN+8cUXYo6koqioSPyuN7M7/vjj6dmzZ9L2O++8kwcffJC+ffvy+OOP07lzZ3Jzc1OOGY/HsVgsKbeZTCYikQg2m414PI7NZiMSiWAwGITwXVxcjM1mE1nm+rUaDAbxe+Kikp51DH9EXEiac+WVV3bI8dq7LolEIpFIJBKJRCKRSDoymZzG1wH7aJq2VVGUfYF5iqJM0zTtdaD1Nsx2QtO0xxRF+TfwuKIoFwNBTdMWK4ryPfCXBIOGQiFisRgmk0mIeamEoFAoRE1NDRUVFXTp0gWj0YjNZiMQCHDiiSfy1ltv8emnnzJy5MiU55k9ezarV69m7ty5aUWuTBx++OEA3H333dx4440cddRRbR4jFYkZxldffXVawVgnFApxww030LdvXy644IIWx6+oqKCyspLevXvTqVMnIpEIeXl5VFdXU1NTIzKKdVEwGAwSjUaF2zYWixEKhXYJcS5RXEwl5maaW7qYrgvvekxKIBAQx40YMYIBAwZw8803c9JJJ7VYz9SpU/n555+54YYb6NOnD0OHDk2535577smee+5JJBLh66+/5r333uPtt9/m9ddfp6SkhH79+mGxWMjKyiIQCFBQUEBWVhZms5nCwkKGDRvWZlF/6dKleDyeZgK03+/noYce4phjjmHWrFlYrdZm7uK2oMdTZGdnU1lZiaqquFwuIbjrzyrT/Er89oF0F7eOsWPHdsjx2rsuiUQikUgkEolEIpFIOjKZRGOjpmlbATRN+1pRlMOAdxRF6cIOFmkVRdkP6A2sBr7RNC36ex2nKIryIo0O6C8VRTEBhwKpv2O+g9GdwB6PJ6NT1GazUVFRQW1tLXa7nfz8fCorK4nFYowaNQqHw8Grr76aUjQuLS1l1qxZjBkzhsMOO2y7a00Ujmtra7n44oszxlu0xLfffstDDz0kMoxbEsI0TeOuu+5i48aNvPnmm5hMGZNRAPjpp58A8Hg8mEwmIQ7n5eVRWVlJXV0dBoOBaDSK2WzGbrcTCAQwGAy43W4h7O0KJIqLbRG5E4VIg8GAwWDA5/NhtVqFyA6N7teTTz6Zm266iWeeeYbx48dnHNdoNPLII48wZswYJk6cyH//+9+07nRo/CwcfPDBdOnShcLCQj7//HM+/fRTVqxYQUNDA9FolGg0SjweTzqud+/enHXWWa1qCKdpGmvWrGHFihUceuihzT5zP/74I5qmccopp2Rswtga1q5dy4YNG7Db7axZswaXy0Vubq5wHOtxNOlEfp1MQnFLCwX/q6xcuZI+ffq063jAnx6zvcaRSCQSiUQikUgkEolkVyCTcudTFKWnnmf8u+P4MOB1oP+OKkhRlGOB24BvgeOAacBqRVEMmqapmqadpijKuUAJMAg4VtO0TTuqnkxEIhFUVSUSiaQUQTVNIxgMYrVa6dKlC3a7HY/Hk+RQdrvdnHDCCbz44ovceOONzYS5devWEY1G20Wo0IXje+65h+nTp3PSSSex3377tSm/t7S0lH/96198//33lJSUcMMNN+DxeDI29PL5fJx//vm88cYbnHbaac0iLlKxadMmLrnkEtxuN3vvnZyEYjKZ2H333amsrMRut4voBr0BWTAYbDHPWH82TeMe/ira6kJNnFuJIrN+vM/nw+fzYbPZyM/Px+FwcOqpp/LGG28we/bsFkVjaMywnjNnDsceeyxnnnkmr732WlK2cDpcLhejR49m9OjRSe+vXbuW7t27CxH566+/5rnnnuPGG2+ksLCQY445hr333rtZJnJNTQ1Llizh448/ZuPGjZjN5pQLLLW1tQBp4ygyoaoqS5Ys4d133+Xdd9/l119/BeD//u//8Pv95Obm4vF4CIfD+Hw+/H4/gUCAwsLCjIsviU7kQCCQtDCwvQsFf3cuvPBCFi1a1K7jAX96zPYaRyKRSCQSiUQikUgkkl2BTKLxJCBJddM0rV5RlNFAy4rTdqAoigeYDJyuadqPiqLMBQYrilIH+IHg73XM/X1/i6ZpkR1RS2toSegLBoPU19ejaRoOh0NkECceF4vFuO666/j3v//NjBkzeOyxx5LGGD58OKNHj+aRRx5hxIgR9O//5/T6ww8/nLq6OhYsWMBzzz3H559/zuGHH06/fv0wm81pj/P5fCxevJiVK1ficDg466yzOOKII1I2U0vkhx9+4Nprr2Xbtm3cdNNNXHrppS3WuGnTJkaPHk15eTnPPfcc/fr1S4pe0N2ZhYWFGAwGVFXF7/cLodjpdGZ0rjbNou4Igl1LMQdNaTq3mo5jtVoxGAzk5eURCoUIhUIUFBQwduxYpk+fztq1a+nVq1eL59ljjz146qmnOPPMMzn33HP517/+tV3Xl1ifxWLBYrEwcuRIDjnkED7++GPmzZvHnDlzePPNNxk1ahT77LMPP/74I19++SUrV65E0zT69u3LpEmTOPDAA5OazOnon40ffviBIUOGtKmuSZMm8fTTT5OVlcUhhxzCueeeyxFHHEFxcTFr1qwRzmX9p6qqQjx2OBzNFihisRhVVVXCJQ/N/17IuAqJRCKRSCQSiUQikUgkHZW0orGmad8BKIrSgz+cxT9rmrYW+HPKUXpigA3YQ1GUDcBwwAscD6wDblAUZS9A0TTtGyC6g+poFS0JfboD1mq1JomdTenZsycTJ07k8ccf59JLL6Vv375im6IozJw5k9GjR3PWWWcxbNgw+vXrR+fOnTnggAMoKipqs1O2oKCAK6+8ks8//5z333+f2bNnYzQa6d69OxaLhXg8TnFxsWgGtnTpUr755hsAjjnmGI4//viUol0iqqry3HPPMWvWLLxeL++++27aXNxEEgXjxx57jBEjRoj8Zx39d/3+BoNB0QCupbqgeRb1rohet8ViIRAINIs4MBqN5Ofni8gKaBQnzzjjDKZPn86bb77Z6sZehxxyCA888ACXXnopl112Gaecckq7XUdWVhZHHHEETqeTyspK5s+fz4svvsiLL74IQH5+PkcffTTHHHNMxngMgM6dO+P1evn6668555xzWl3Dyy+/zNNPP82kSZO45ZZbyM3NJR6PYzKZhCgcDocJh8PC2a4oCuFwOG1udlVVFVu3bgUQWd8GgyHJ2d7WhQKJRCKRSCQSiUQikUgkkp1FWtFYURQ38BQwBFjx+9uDFUVZDpynaVp9exejaVqdoigP0xhJcRXwtKZpMxRFGQFMUBRlGLA/8NLv+7drtnKm4TRNa7M4qyiKEDxVVSUYDBKPx4XYpGmayOS98sormTdvHjfffDMvvPAC0ChuZmVlYbPZePTRR3nyySf56aefeO+998Q5cnJy2GOPPRg0aBDnn3++EBN1ETUVgUCAmpoa+vXrR69evSgtLWXdunWsXbuWNWvW8PPPP2M0GvF6vdTW1hIOh+nZsyf77rsvhx12GPF4nLq6uqQxY7EY0Wijhl9VVcX06dP58ssvGTFiBNdffz39+vUT25vS0NCAqqqsW7eOY489loqKCp566imGDRtGNBpF0zQ2bNiAxWIRcQC6EK/fY2gURfVnmOlZ6vt3lGgKaF6vpmlikUFRlGZ16g3uamtr2bhxI507dyYrKwuLxUIkEsFisRAOh4WYbLfbUVUVp9PJvvvuy+uvv87kyZPT1tP0WR1zzDFs3ryZO++8k1AoxIQJE1LeO90BnYpgMJi2MV0oFKJ79+5MnDiRdevWieZ+3bp1Q1EUcnNzCYfDaWstKysDYNiwYXz88cds2bIFo9FIJBJJu5AQj8dZt24dkydPZtiwYUydOpWsrCxUVSUej2MwGNA0jc6dO4v9dZHYaDTi8XiIRCJYrdak56eqqsiSzsvLS9rWzn+yJBKJRCKRSCQSiUQikUh2CJniKR4GfgZO1TRNBVAaVaIbgVnAWTuiIE3T/qMoykLgOhpzjdE07SNFUS4B7JqmPbgjztsSqYS7RJqKQbqzWI9OcDgcuFwuVFUlFosJQaqhoYFQKITVauX000/niSeeYPPmzfTo0YOuXbuKfNfOnTuLjNj6+nq++uorVq9ezQ8//MCPP/7Ik08+ydKlS3nhhRfIz8/PmD2bk5NDdnZ2ym2//vor5eXlLFu2jOXLl7PbbrsxadIk+vbtS11dHd27d095XCwWo1OnTrzzzjtMnjyZQCDAQw89xLnnnouiKM1yapveq1WrVnHUUUcRiUR47LHHGDVqFCaTCavVSigUwmw243Q6cTqdwjmraRqapgkHKDQKk7pwmq7BmKIo2O32DpVp3LSGRPHV4XCkrTEUCuHz+aiqqsLhcIgFCj06wW6343a7cTgcRCIRotEoI0eO5I477qC8vJyePXumHDdVTu8NN9yAz+fj0UcfZdiwYVx00UXN9qmoqMDj8aQcs2/fvuTn56fcVlxcTE5OTsptACUlJWm3bd26VTh2Dz/8cN555x02bNjAoEGDUFU17bixWIxTTz0VVVWZPXs2ubm5OJ1OFEXBZDIRDAYJhULiXtTV1VFTU0MsFkNRFIqLi5s5hVVVFWK97jBuSkeYbxKJRCKRSCQSiUQikUgkmcgkGh+oadqExDd+d/beqijK6h1ZlKZpNYqifASMVxQlCliB7jRGVOwSBAIBysrK8Hg8GAwGrFYr0WhUiJuBQACfzyfEZIvFwiWXXMKTTz7JP//5T+666660Y7vdboYOHcohhxwi3vvvf//LxIkTOeqoo3j55ZcpKirarrodDgcHH3wwBx98cJuO8/v9TJ48mWeffZZBgwbx1FNPJcVsZGLJkiWMGzcOp9PJq6++yt577y3EON1t26lTp5QicGIzMWgU1PWcY/16IDkL2WAwNBNlOxr6PGkpPsPr9Yrs4mg0KgTzWCxGRUUFgUCAUCgkBEyTycRxxx3HHXfcwWuvvcbVV1/d6poUReHOO++ktLSU22+/ncLCQk444YTtv8jt4LfffuNf//oXvXv3ZtSoUXi93qTtBx10EIqi8OmnnzJo0KCMY91777189tlnPP744+I+Jgq6TZ+BLshbrVacTmfKqBn9c99Sg7z/Zbp378769etTbisoKOCGG25o1/O113jtXZdEIpFIJBKJRCKRSCQdmUyicSZ2hlXuC6A3cD0QBs7RNK10J5xXoGlaq92oTUVJHf0r9TU1NULINBgMIs7BbrcL8alLly4cc8wxzJ07l5tuuqlNtR599NG8/vrr/N///R9HHXUUc+fOZa+99mrTGNvLihUrmDJlCps2beKqq67iuuuuy9hUL5H333+f0047jW7duvHKK6+Qm5sr7pPuotWjGFKRqplYotNYJ1FcdjgcrRZl/yoyXXMiJpNJCMJ6U0KTyYSqqvTo0QNVVcV89Pv9OJ1O+vfvz9ChQ3n99dfbJBpD49y9++67qamp4corr8Tj8SQtXrQGv9/Pzz//jMvlavXCQmVlJTNmzODFF18kHo8Djfdon3324YADDuDkk0+mc+fOeDweBgwYwKeffpqx6eLXX3/NjBkzOPXUUxk7diwulytpLujxIFarVfwN8Hq94nd9YSJVnnTitwtSOd3/11m/fv1Ojek4/PDDO9Q4EolEIpFIJBKJRCKR7ApkEo2XKIpyEzAjMTtYUZQbaRR0dyiaptUCDyuK8jSNje/aPUO5JdriRm0qSjocDoqLi0W2rNVqTYpRCAQChMNhqqurMZkaH0N9fT3nnHMOb775JpdddhkPPvhgm+odOnQo7777Lqeccgonnngil1xyCZdeemmrBdztoaqqivPOOw+Hw8H777/PgQce2OpjNU3jwgsvZPfdd+e///0vkUiE9evX43K56NGjR0pBOFGcVxSlWTMx/Xej0YimaULUazpWa0XZv4K2LFakw2AwiKgFQIjHusA5fvx4rr76ap5++uk2NY2DRlH+ySefZNy4cVxwwQVccMEF5Ofnk5eXh8FgoEePHuTl5ZGTk0N9fT0//fQTP/30EytWrGD16tWUlpaKPO958+YxfPjwFs95+eWX88EHH3D++eczZcoUtm3bxvz583n//fd5+OGHefTRRznmmGM46aSTqK2tFQsPqQiHw5x//vmUlJRw5ZVXikWExHut5y/r7mL443Ot38+KigpisZjYpv/Mzs5O2yBPkpkVK1YAMHjw4A43Zqpx9MztVHTr1o3S0tI/dU6JRCKRSCQSiUQikUj+KjKJxpcCc4DfFEVZ8ft7g2nMGT5/x5b1B5qmpe/otoNpixu1qSiZKGbqmbSJDbm8Xi82mw1VVcXPSCTCXnvtxTXXXMNdd91Ft27d2uwE7d27NwsXLuSaa67hwQcfZMGCBdx///3079+/TeO0lrvuugu/38/zzz/fJsEY/shPnjJlisiRLSwsxOPxJDVwS3R1+v1+IeLpzd0CgQBAM2enLuQ3FUw7OjsiOqOpuH7++eezYMECLrvsMrp168aIESPaNJ7b7WbevHlMmDCBhx56qFXHdOrUiUGDBjFu3Dj69evH3XffzaRJk3j77bczPpeVK1fy3//+l2uuuYapU6cCjTEGAwcO5Oqrr+abb77h3//+Ny+++CJvvvkmTqeTuXPnph3vjjvuYNWqVTz33HMUFRXh9/sxGo1J98dqteL3+zGbzRiNxmZ/A0KhELFYDJPJlLSoYTAYyM/PF7nG0HyhQ5KeKVOmALBo0aION2aqcTKJwvJZSyQSiUQikUgkEolkVyataPy7s/dkRVF6Av1+f/tnTdPW7JTKOgBtcaM2FeVas7/dbqeyshK73U4kEiErK4uGhgZuueUW1q9fz6233krPnj058cQT21R3bm4uDz30EGPGjGHatGmMHj2a8ePHC1dve/HVV1/x6quvMmnSJHr37t3m4xcvXgw0OqQtFgsGg4GCggIaGhqa7RsKhSgrK0NVVbKzs4Ugp78PNGtMlijadeT84qbsjOiMhoYGZs6cyYQJE/i///s/Fi5cSL9+/Vo+MIHCwkLee+89YrEYtbW1VFVVsW7dOmKxGDU1NVRVVWG32+nfvz/9+vVD07SkRnj9+vVjzJgxnH322cyaNSttw7rZs2fjcrmYOHFiyu3FxcVcf/31XHrppbz66qsMGzYs7bX88MMP3HfffYwdO5ZBgwYRDAZxu91YrVYCgYCIo9AXKILBIIWFhc0EwMRFoqaCd9O/BYnfQkh8pk0jbX5fDOn4qxoSiUQikUgkEolEIpFI/vakFY0VRdlD07RfgWxg8+9vZyuKsjeApmnf7IT6/pbomadlZWVUVVWhqioFBQX4fD7C4TDBYJCZM2dSWlrKxIkT6dSpE8OGDWvzeY444giGDh3KP//5T+bOncvLL7/M4YcfzpgxYzj44IP/lBMuGo1y44030qVLFyZPnrxdY3z++ecUFRUxaNAgIpEIqqpSW1tLLBYjEAiQl5dHJBIRuc96dq/uKNY0DZvNJrJmLRZL0vi6eKc7jS0WS8oM2o7GzorOcDqdPPvss4wZM4aTTjqJN998c7sWFUwmE/n5+SKiwuPxpNyvtrY26XXnzp156qmnGD9+PDfeeCNz5swRucw6v/32Gx9++CGXX355WlFZx+12Z4zaiMfjXHTRReTm5jJ9+nSys7MxGo14PB6CwSBlZWVCvFUURbjcrVZrs8iQxLnV0pxKFbMCzSNtQqEQgDHjRUokEolEIpFIJBKJRCKR7AQyxVNcAUwE7kuxTQPa9n32DkSmJkw74ivFqqoSDAaBP8Sh8vJy0eQtFovh9/vFV+Krqqowm808/vjjjBs3jtNOO43Fixez2267iTHz8vIwGtPrS/o2p9PJ7bffzhVXXMGcOXN44okn+PDDD9lnn3245JJLGDNmTNI4ubm5uFyulGNWVFTw3XffsXjxYhYuXMiaNWv497//TXFxMaqqNhNtm96DRDRN4/PPP+eggw7CaDRSU1OD0+nE4/FQVVVFLBajurpaCMherzdlXQaDQWRCRyIR8Xvic9QFvkAgIES6xKiQjkC6Roo76lwWi4WioiIMBgMvvfQSJ598Mocccghz587lpJNOIhqNpj3e4/Gkzcm22+1YrdaU2xwOR7Nthx9+OI8++igXXHAB9913HzNnzkx6ds888ww2m41//OMfac9ZUlKS1pWtqqqYE/fffz/Lly9nzpw59OjRg8rKSqLRKKFQSIjCqqri9XpxOp1omsamTZuw2WyYTCZ8Pp+4Dp2mwi80zr3EazAajWK+Jf7taeoo//1nPOWFSCQSiUQikUgkEolEIpHsRDLFU0z8/edhO6+cvwdNBcBwOJwUoaC7Zr1eLz6fj6qqKsLhMAUFBbhcLiKRCNFolE6dOvHGG29wyCGHcNxxx/Hpp5+Sm5u7XTV5PB6mTp3KJZdcwtNPP81TTz3FOeecw2677cbBBx9Mbm6uyBIuKSnB4/GQm5vLli1b+Pzzz1m8eDHLly8nGo1iNBoZPHgwd9xxB4cffnizcy1fvpzrr7+eU045hdNPPz2lmLx+/Xo2bdrEwIEDhRPY6XRiMpmEc9hsNrN582ZxP9O5b9M5OZuyM2IftpdU4mNbaIvoHAqF8Pv9GAwGYrEYu+++O5988gnnnHMOJ598MldeeSXTp08XYuuO5rTTTmPp0qU88cQT7L777sItvGbNGt566y0uvPDCtO7l1qBpGnfeeSc33ngjJ5xwAmeddRaapol5pwvZ+oKFw+HA6XRSUVGB3+8nFApRUFAANJ9jrZ17qWjqKP9daE7fwU8ikUgkEolEIpFIJBKJZCeRKZ5iKLBR07Rtv78+CzgJWA/comla9c4pcddBF+70+An4w12pRyvoop7umtUdjk6nUwhP8XicmpoaQqEQXbp04eWXX+boo49mzJgxTJ8+PaVQ21rsdjtnnnkmEydO5N133+WJJ57gvffeo6qqing8tclRURQGDhzI2WefzciRIxk2bBhutzvlvg0NDZx//vn8/PPPLFy4kFtuuYVLL72U8847j+zsbLGfnmfcp08fDAYDhYWFmM1mysrK8Hg8OJ1O0YgssdlYqqZirc2T3lmxD9vDnxEfoW2is954UXfAq6pKly5d+Pjjj/nHP/7Bfffdx9KlS5k3b56YtzuaK6+8ktLSUqZPn07Pnj055JBDmDVrFhaLJW2WcWuIx+NcccUV/POf/+S0005jzpw5wgnsdDqTBHaXy0U4HBbveb1eDAYDHo8n7Rxra5a5JDMzZ87ssGPuiNokEolEIpFIJBKJRCLpqCjpohoURfkGOFzTtGpFUQ4BXgIuBQYDfTVNG7fTqmxHhgwZoi1dulS8bppVur3xFJqmifgDXYzSBWJN09KOG41GCYfDWCwWqqqqRGRFIBDAZDLRpUsXDAYDTz/9NDNnzqSiooLBgwczbdo0jj322LTj/p6PmhK9xqb119fXU1paSiQSobq6mqqqKnJzc9l///3Jzs7G5/MlCb+JqKpKTk4Os2bN4vLLL2fOnDkUFBTwwAMP8NFHH+F2u7ngggu45JJLyM3NZfTo0axevZpPPvmErl274nQ6KSsrY+vWrRQWFuJyubBarYTDYeEMThTk3W63eGbbw584brmmaUPSbR8yZIi2bNmyNo+b6nOYKJCniyLRj0vlNM407/x+Pz6fD6fTKfJ7g8EgPp+Pd955hylTppCfn897771Hr169ko4NhUJpoyLC4XDaeIpQKJR229atW4nFYpx44omsXbuWeDxOPB7nvPPO46abbsrosG9oaEjpHg+FQpx11lm8/fbbTJo0iWuvvRaTyURubi4NDQ2YzWYURaG6upqcnByRq61jNBqx2+2Ew+GkZnWhUEjkHacj3X3PFI0DYDAY0s6v7Z1bHQ1FUVq8D38HOtp1tvS3SyKRSCQSiUQikUgkkkQyff/cmOAmPgV4QtO0V4FXFUVZscMr20kEg8E/FQuQiO4StVgsRCKRNh2rRwbU1dWRnZ1NYWEhWVlZBINBcnNzOeussxg5ciT/+c9/eOGFFzjllFMYPHgw119/Pcccc8yfzmJWFIXs7Gy6d++eNtO4JQKBAHfccQeHHnooRxxxBG63m9GjR7N06VIeeOABHnjgAR5++GF69uzJypUrufHGG6mtrSUnJwen00lubi7BYBCr1ZqUH6uL6LrY7Xa7sVgsBIPBlHEMicLejsio3lkkuofTZTAnisWtmb/6/npkSKL4qQuv5513HgMHDuTYY49lxIgRvPPOOwwcOLA9LikjDoeD559/nttuu41t27YxYMAArrjiiu0aq6qqipNOOomvvvqKO+64gzPOOIONGzcSj8fJycnB7XZjMpnQNE3EUABCPNeFYqfTKXK18/PzCQQCVFRUiCgZ2Ll51H93lixZAsABBxzQ4cbcEbVJJBKJRCKRSCQSiUTSUckoGiuKYtI0LQaMpLEpXmuO26Voz5zbVA3XWhLywuEwfr8fu91OQUEBHo+HSCRCbm4umzdvpry8HGgUDXNycjjjjDMYP348CxYs4NFHH+Xkk09m8ODBXHPNNYwcOTJtbMTOYNasWZSXl/PEE0/gdDqFsNu3b1/uvfdepkyZwnPPPce///1vZs6cyYgRIzAajWRlZaGqKtFoFKvVislkwuVyJT2TxPgGg8GQ8R7rYqueWburisetiaxoaxayvr/L5cLpdArndqKr22AwMGjQIJ5//nnOO+88Ro0axdSpUzn99NMpLi5un4tLg9fr5aGHHvpTY6xbt47jjjuO9evXM336dI4//njC4TDZ2dnEYjGcTqdogGexWHA4HLjdbsrKysjOzsZut4t9s7Oz2bp1qxCGU/Fn86glf3DdddcBsGjRog435o6oTSKRSCQSiUQikUgkko5KJlvci8AniqK8CYSAzwAURekF1O2E2nYKes5te4qKNpsNt9vdYj6tnitrt9ux2+04nU6MRiPBYJCamhpUVRV5vvqYXbp0oXPnzpx33nl88cUXzJ49G5/Px2mnnUZhYSFDhw7lkksu4aWXXmLt2rU77evRtbW13HvvvYwZM4YuXboQi8Worq4W4q3RaGTgwIHccccdbN68mTPOOIOsrCwMBgN1dXUiIkR31Db9+r8uyOvvZbrH+jZojOIIBoM74Q60P02vuSn6/EnMw24Ji8WCwWDAbDbj8/koKyujrq6Oqqoq6urqKCsrw+fz4fP58Hg8PPPMMwwcOJDrr7+eXr16ceKJJ/LOO+8QjUbb81LbhYaGBr744gsOO+wwKioqmDt3LieddBImkwmTyYTFYqF3797k5eWRl5dHJBJh69ataJomricSiRCPxykvLycajbJ161ZxvP5ZLSwsTBKHW/t5hz9ibDpSbIFEIpFIJBKJRCKRSCQSSVPSOoY1TbtdUZSFQDGwQPtD5TDQmG38P0VLIk/idj0jFhqFvUAgADQXQkOhkGiC1zT/0mq1YrFYKCgowG63o2mayKi1Wq3U19djs9kYN24c48aN4+WXX+bLL79k8+bNvPzyyzz11FMAeDwehg4dyuDBgxk0aBADBgzAbrcnZbcm0tDQQENDQ8pt0Wg07bZHHnmE2tpazjzzTHw+H7W1tVitVoxGo7i2uro6kSGtqiperxer1YqiKNhsNrEtFosJMV93C+uoqipyc9PFAehiq6Zpwmmc6XmlYldwJgeDQZHvnDh/9HtksViaZSHrub01NTX4/X4hOpvNZvx+P7FYjGAwiKZpNDQ0sPvuu7NgwQJWrlzJ008/zcsvv8x///tfPB4Pp5xyCmeccQb5+fls2rSJzZs3U1paSnl5ORs3bmTLli0MHDiQmTNnYrfbiUajaQXwhoYGsrKy0l6rPl/Ly8tZsmQJ69evZ8uWLeK8W7dupaysDE3T6NKlC++++y677747dXV15ObmUldXRzweF272eDxObm6ucBrbbDbC4TDQOM9jsZjI8DabzdhsNnw+H6FQCI/Hk3S/Ez/vLc2r9ozDkUgkEolEIpFIJBKJRCLZUWSMmdA07csU763aceXsHHaEIJip4di6deuw2WyUlJQIIVT/aryei2o0GjEYDLjdbrKysrDZbEljBgIBgsGgEAEbGhqIx+OoqorL5eLcc8/l+OOPFw29SktL+fLLL/nss89YtmwZ8+fPF4KW1+tlr732YvDgwey1114ceuihFBQUAI1CVrqmay6XK6Wwt23bNp566ilOOeUUDjzwQAwGAx6PRwh0ujCu3ye9uZguoutiuM/no66uTjTB0/ONE+9DOBxOEt0yPUtFUdJmAXc0p+f2zEldqEycK6qqUllZSSwWIycnp5kwabPZhEPZarUKEbesrAxVVXG73SJaxOVy4fF4MJlM7LbbbkydOpWbb76Zzz77jOeee44nn3ySxx57LGVdXbp0obCwkDlz5rB8+XJeffVVioqK0orG3bp1S9lcLxgM8tlnn/HRRx/x4Ycf8v3334ttbrebzp07U1BQQN++fSksLKRz586ceuqpFBYWEggEiMfjbNu2jZKSEhoaGrDZbIRCIUKhEG63m5ycHHEfdbE4Pz8fp9NJXl4e0WhUOIhDoRCxWIxIJLLdgm9iHM6usDAhkUgkEolEIpFIJBKJ5H+Tv002cUdGb6hlsVgIBAIiR9btdmMwGIQIajAYkhqaNXUeBwIBrFYr0Cgi5+TkCCFMP08kEsFms7HbbrvRvXt3xo4di8PhoL6+nm+++YY1a9bw9ddf8+uvv3L//fcTi8UwGAwccMABnHDCCYwZM4YePXq06fruvvtuIpEIp512Gl6vVwjLRqNRCJj61/sdDodwCuuRFInimcFgwOl0EolEUjoyW5Pz+7+C7qhORBc2TSaTmCtN0d3cuqisqiqFhYUASXEYhYWFSZnH0Ch2Hn300YwZM4YNGzbw4osvEggE6NGjB927d6dfv354vV7hdH7zzTe54IILGDp0KPPmzWPEiBEZrykWi/HNN9/w8ccf8+GHH7JkyRKi0Shms5kDDzyQa6+9lsMOO4wuXbrg8Xior68nJydHOHh10Rsa50hVVZVwvet543pEh9VqFUK1Hj2h3wPdvW00GkWTO6/XK37fXjItZEgkEolEIpFIJBKJRCKRdBSUjua43NEMGTJEW7ZsWZuP2977pEcu6A7H+vp6nE6nEIg1TSMcDicJyboQGI/HCYVC2O12gsEgdXV1IuM4EAjgcrnE7z6fj8rKSux2O263Wwi0iejHqKpKRUUFwWCQNWvWsHz5ct5++23h4pw0aRIPPvhgs2uJRqPNnMbvvfcep5xyCiNHjuTWW28Vzk9AZOY6nU4hJOpxHQaDQbikdcew7sBOdHbqERfp2F63pqqqKUXrlsZVFGW5pmlD0o27vfNre2k6LxPvoaIoza7D5/OxdetWXC4XhYWFaZ2/On6/H5/P16xxni6wVlVViWaObreb7OxsIc5CowC7Zs0aTjjhBFatWsUdd9zBpZdemrKuJ554gvvuu4/q6moABgwYwF577cXYsWM5+OCDicfjrF+/Xswjh8NBXl6eEGGj0SiqqlJSUkIkEgEgKyuL+vp6LBYLVVVVAOLz5PV6Rfa1fp2Jgq7D4RCf2cTPZaZnkWlO6bSwLe382tlza0fRNIonkRUrVgAwePDgdjtfe43Z1nEyXedfQUt/uyQSiUQikUgkEolEIklEOo13MHocg57bCyRl8WqahsPhEFmyFotFHBsKhfD5fECj0BUIBIhEIvj9fjwejxCiKyoq8Pv9Yj/dSel0OgkGg/h8PhwOBy6XC7vdTjweJz8/n8rKSrp27crAgQO5+OKLWbp0Kc899xyPP/44Bx10EOPGjUt7Xaqqctddd3HrrbcycOBApk6dSl5eHh6PJ2k/XRxv6i5OvDep0F208XicQCCQNr94e/m7Zssmuo/TCVb63GjN/dSfj9Vqxe/309DQwObNm+ncuTNutxuPx0NVVZVorme1WgmFQpSVlREOh+nWrRu9e/fm888/Z8KECUydOpVvvvmGxx57DLvdTkNDA8888wy3334727Zt46ijjmL48OEccMAB9OjRQ1yLvvCix1iEw2HhrE90sJtMJmpra0Ves9vtJj8/v9mCRNPmiImZxOXl5QAUFRW1ydn+d51TO5P2FIvbe8wdUZtEIpFIJBKJRCKRSCQdFSkabwetdRQ2JVWcgI4ufkUiEUymxseiC1WJ2bV+v1+4lbOzs8XX5nNzc4lEIsJR6fV6k0RZ/acuTOsuZUVRyMrKwmKx0KNHD66//no2bdrExRdfzL777kvXrl2Trvunn37iP//5D6+++iqrVq3ihBNO4IEHHsDpdOJ2u4UY53A4RNSBqqrU1dVRUVGByWQiFothNpvJzs4WmcaAcHXqx0PzDGNIdtNmciFnoul9+V/B4XBQXFycNroikUQ3tr74EAwGCQaDhEIhnE4noVCInJwcamtrycvLQ9O0pKgH3Vnucrl4/vnnmTFjBvfeey+//PILp556KnPmzOG3335jv/3246mnnmL//fensrISh8NBQUGBcGvqn7P8/HwikQg5OTlibumuZrPZjMlkIicnR+Q2x2IxQqGQuJbEz1TiPdCFdFVVhVNeX6horQD8vzqnUtG9e3fWr1+fclu3bt3SHvfhhx8CcPjhh7dbLe015o6oTSKRSCQSiUQikUgkko6KjKdoJYn3KRAItPor6/qxqcTlpu7HxK/062KqLlDr4pjeRC43N5esrCwxhqqq+Hw+8dX9wsJC0WQvsW7ddax/DV8fNxgMUltbS1VVFcFgkHHjxjF48GAWLFjAr7/+yn/+8x/+85//sGrVKgwGAwcffDBHH300J598Mjk5OdhsNhoaGigrKwMa83ATRd7KykrRaMxkMmE2m/F6vUmN8BLvh+6CjcfjwlWqv5d4/7c3H7aleb+rxlM03ZbuOjJt00mMbNBF19zcXOrq6sjLyxOCfuKcg8bnk7hgoIvGDQ0N+Hw+Pv74Yy6++GJCoRD9+vXjwgsvZOzYsXg8HrKysti0aRM5OTlkZWWJ6BLddZ8Ya6I77bOyskRzu/z8fBGXoc8/PS6joqJCCOCAmH96Q8a23p/W0nSR6X8hnmJ7oxmGDx8OwKJFi9qtlvYas63jyHgKiUQikUgkEolEIpHsykin8XawPY7CVOJBorNWF67q6+uFkKWLTU3jJdxuN/F4XGzXjzEYDJhMJjRNw2Kx4Pf7hdiqqirxeFw01LNYLGJfvXmaxWKhoKAAu93Ovffey0UXXSSu0WAwcOCBB3LccccxevRoevfujaqqmEwmLBYL8Xgci8VCfn4+8Xicuro6GhoakpqS6f/0Zn26qKKqKgaDIckZrd8vRVGaCfOyGd4fJM4r/Vnq91a/r6lI3KaqqogPgT+iG/TXmqZRWVmJpmkYDAZyc3NF7EgwGCQejxONRgGwWCwoioLFYiESiWC1WlEURfx0uVyMHz+ekSNHsm3bNrxeLx6PR+xbUVHBli1bqK6uFlEn+ngOh0OMazabqa2tZd26dRQWForz6w34gsGgqF+fV4WFhZjN5qT5pMe/NJ1j27uokAoZWyGRSCQSiUQikUgkEolkV0OKxq0kUSRSFGW7Ha6JJIqf8Xic+vp67HZ79M588gABAABJREFUUhMz3WnZNArDaDSK7cFgkFgsJjKMY7GYaCamZymXlZWJRnmhUAgAp9OJ3+8XcRGFhYWUlZURCoU48cQTycvL45VXXqF///6cfPLJIg5Db8YXDoeF2B2Px4UoWFFRQWlpKbFYjEGDBmE0GgkEArjdblF3ouNSF4zT3fem24xG45++/+3lIv2rSbyOpuJkpvuauC0xAkRVVdatW4fNZqOoqAiDwYDVaqWgoEDMlYaGBsxmMwAej0fEUTidThF3UlFRQTQaTXKUG41GIZqWlJSQlZVFNBolEAiQn58vtutZyTk5OQAirxsQud42m43a2lrKy8vJzs6mqKgIaPwsVVVVEYvFiEQiYjElEomIuWoymQiFQpjNZoxGo/i8Zbq3+r1p6oRvDYmLTH+XeSeRSCQSiUQikUgkEonk780uJxoriuLQNC3wV9fRHuiOYkVRqKioYNu2bZSUlJCdnS32SeW0bTqG1+sVERZ6LnKiuBsIBNiwYQOBQIDevXuTlZWF1WqlvLxcNAvTG5pt3ryZLVu2MGDAAI444gjGjh0rspIjkUhSA7VUwq2qqlgsFux2O5WVlfh8PrKzs3E6naKWsrIyvF6vqE8Kae3D9jqwE48LBAKYzWYURSEWi1FVVSWiThwOB6FQSDh4Y7EYGzduxGQyYTKZRNSIHh+iz5OKigqRsa2TuI/BYCAYDOJyuXC5XPTs2RP4w5Wru6YrKiqoqqoSLunOnTtjMBgoKSkRDSQDgYCIQAHEOVRVFWPqDv/WxstA47wuKyvD5/NRXFyM2+1u9f3V85IlEolEIpFIJBKJRCKRSHYVdinRWFGUw4HhiqLcrmla6K+upz3Rv4qv/2wLiU28dHSnqU5eXh4FBQVkZ2cTCoUwmUyi4VeiWzkvLw9AuDz1GALdvak7fxNzYBPzlvVs2YKCAiEk6i7jxHr0CASQjcPai7Y0bdMjKZo2e3M4HGRnZ+P3+5OeUeL4enRDVVUVNTU15ObmNhOqDQaDyBeOxWIEg8Fmten7JM4j3QHt8/mEo9/pdNLQ0IDdbic7O5uGhgby8vIwGo1069YtKUqiqXBuMBjEgkfTba1pBqijZyrrLn2JRCKRSCQSiUQikUgkkr8zu4xorCjKUcBtwBVtFYwVRZkITATo2rXrDqhu+0j8urvJZBKNxNpyvN5gS3ds6g5IPSs4EAigqirFxcVCtNNFukRhWRdu8/PzKSoqQtM0/H6/yMmNRCLCyayLibp7MhgMiiZ++rh6ZnJOTk7SsQ6Hg+Li4mZC3vZ+9b8j0FHnVyYSIykSxVx9ASIQCOB0OkWTw6aoqorVaqW4uFjET+g4HA4KCwux2+24XC4xRxOx2WzCfV5TU0MsFgMQ+ceQvJgQCASorKykS5cu5OTkJDWQDIVCYi43Fc6tVmuzRor6PvF4nEAgkDROuvlns9koLi5udr8k7cvs2bM77Jg7ojaJRCKRSCQSiUQikUg6KruEaKwoSh/gdeA8TdM+URSlALADTk3TfmzpeE3TngCeABgyZMhf1s5eF0Z1wVRVVZHXmihEtVZA1ZvkQeqoiFAoREVFBQCFhYVJolmik1N/z263i+Z5OoFAgHg8Tjwex2g0UllZSX5+PlarFb/fj9VqRdM0HA4HqqpSVVWF1+slGo2iqirRaBRFUfD7/UKs069VjxDQheldtVlYR5lfibQ0h8xms3DhNkUX9jPNv1AoRDAYxO12i+eoo+cgV1RU4PF4xBxLdATrDuTNmzdjMplERnJdXR0+n6/ZfNadvlVVVcK9rpM4t5oSDAYpLy+noKAAl8uVtE0XzvWFEF24TjWOwWBodryk/enTp0+HHXNH1CaRSCQSiUQikUgkEklHZZcQjQEfMAsYpijKOuBWoBQYqyjKLZqmPf5XFtdadGFUz1h1Op243e5mTs5UAqqmacKxqWfDWq1WAoEAVqs1SYDWG5LZbDa8Xi+QnHObyskJjQJbUxFbd2Lm5uZSXV1NXV0dTqcTo9GIz+cTTmY9K1aPyEgVE5ApazcxMkA/567mOO5IJM4hm83WTEDWRf1IJJJS9G1JuM/KyiIcDqeNU6mqqmLr1q0AKR30drtdzB2z2Ux+fj6KoohmfsFgkIKCAuE8ttlsFBYWCnFZdynrtJTjrLvuEx3u+udAVVWRg2yz2XZp1/uuzttvvw3A2LFjO9yYO6I2iUQikUgkEolEIpFIOiq7hGisadoWRVEeAiYBi4ArNU17RFGUIcB8RVG+1TTty7+4xozbdBEXSBKu9IzgRFI1NNPFNPhD0A2HwyKbFhCuYl30S+eOTGyup9etqqpwDFssFvx+v3AxGwwGQqEQeXl5OJ1OPB5PknCt16E31bNarUJ00/fTBepU90nTNFGz3iAt8TolbSdxDqVahNAFU5vNlnHupkJ3lG/btg2r1UpRUZHYps91XUzOzc3F7/c3W6BQFIX8/PykmAn9vcR9Excm9AUQve7EOagoSsrr0OtTVVXcA12Qdjgc2O12Mf/0MVN91iQ7h/vuuw9oX2G2vcZs6zjdunVL2+SzW7dulJaW/ql6JBKJRCKRSCQSiUQi2ZF0WNFYUZTOQLmmaVEATdM2KoryGPD/7J13eBRV28bv2d5TN5tAIKErKFJFrNh7RQFRsYvYENur2AtiF8sHvFixglhREcECdkXaS4dIh5BsNtlke5vz/RHOcXazu9mEBAI8v+vKlWRn5syZmWdOrtznOffzE2NsriRJEmPsb0mSPgQQ26edbQQuDKvVarHsXqvVptyf76cUwbiQxr9z4ZcXpQMQV9guXYYkz1pWZlIGg0F4vV5YLBa43W6xbF+r1SI/Px+RSKRB5iW/FovFIuwruEiZzjIg1f1JvM5UggvxL6nukTLWkt1TtVrdbEGUW5nwL94mz9A1mUzQarUoLCwUkw/KiYpkfeRis1qtFhMdvDCe0pfY5/OhoqICDocDVqtVWKCo1eqkMa/RaKDVakVb0WhUWFvw/RP7RjFItATpRGGKK4IgCIIgCIIgCKKt0ybXXkuSdDqAWQCKd/+uBgDG2HYA3+3+mUmSNBLAcQAq9lFX9xpKkTjZZ5IkwWq1wmq1NipI8MzTQODfeoJGo1EU0YtGo8I6Q6/XIxKJNDh3IjxTk3s282N5lmim8EJsZAvQcrTGPbVarejatWtcJjuPK14oEagXXm02W1z2sLJAY2P9NpvNQuBO1v9ksZyqLT7p4XA40grm/L2iGCQIgiAIgiAIgiAI4mClzakikiSdBuBpAEUAxgIAY0xkEjPGopIk6SRJugTAeAAjGWNb90ln91O4QJzMB9ZgMCA7OxsOhwMOhwM2m01YTzDG0op9XMALhULC47i8vDyuaBmx/8LFXqWdiFJY5XGlFIiTCdbphN7GBGVepI+LvqliOVk7ygz4REGYx3ZTrToIgiAIgiAIgiAIgiAORNqUaCxJ0ikAJgO4DEA3AIdKknR84n67LSu2AziHMbZq7/ayaTQmtLbGuRoTvpIJflzIC4fDYhv/qqurg9PphMfjSSsCpxOjif0fn8+X9vkni6tkKONEKUQD/8ahz+dL+t4knoNbZSSek7fD/b6Bei9xlUoFvV7foE/KAnwEQRAEQRAEQRAEQRAHO23N01gNYBRjbJUkSdkA1gHoBeCn3R7GTJKkQQCsjLHv9mVHM0VZVIv7t7bFc3GhlxdIU37u9XoRjUYbFdQSPYx5VmgqEZl7K5N37MGFMk4SCx/yWFEWrsvEe5n7KXMLimTxHAqFIMsyQqEQNJr4oS/RM5zYN7z77rttts3W6BtBEARBEARBEARBtFXalGjMGPsWACRJUjHG3JIkfQ3gbUmSfmGMrZAkSQugM4AF+7KfTaGlxKhMBNbmnkspuCW2rVKpYLfb47yKM80kbqwQ3t4U1InMSFYkkdPYJEBz4G3x7zxmeOG6VOdKjFmeWcz7ydtRZt0nnktJskJ9xN6nQ4cObbbN1ugbQRAEQRAEQRAEQbRV2pRozGGMybu/z5UkaRqAcyRJWsUYi0iSNIPtR8ajSjGqqd1WCsVKgTWVuJXpuRhjceKwUnBLJaiZTCYwxoQgndh+YpuZoBS50/WXspD3HuniLJkQ2xSSHaeMrWTnSnVsIBCA2+2G1+tFXl5eSkFYeRw/l/Lz5sZWY/cgVbv70dC1T5g5cyYAYPjw4W2uzdboG0EQBEEQBEEQBEG0VdqkaJzAcgDjADwLQG6rgnE68am5XVYKeEqBVZKkPRKfJEmK629i240dm0mbmfbDYrGQkLYPyCRbPd2zbslz8m1NaVdpmxIKhWCxWJJmqzfmr0y0LaZMmQKgZYXZlmqzNfpGEARBEARBEARBEG2VNq+oMMY+AbATQPG+7svexmQywWaziUJfFoul2SIYLziWrCBfqrZTFdZL1xaxf7Oncdba8NgDALvdjuzs7CbbZezL+M20WCVBEARBEARBEARBEMS+pG0qQ7uRdqceMsaGMcY27+Pu7BHNEYt4Jm5LCHjcgiIQCGR8DM90TiyA15y20kFCGpEpytjjFhZNfT94Gz6fLy7u9kYcpnqnCIIgCIIgCIIgCIIg2hJt2p6irVpRNIdMPImbi7IoWCoBLV0RsFSkKqxnNBohy7L42lNRuzXvDdG2yaTAo5LmxHGqNmRZjou7PY3D1ixW2RYpLS3Fli1bkm4rKSnZy70hCIIgCIIgCIIgCKIladOi8YFEa4pFPp8PFRUVcDgcsFqtSfdJLCyWCcrCesC/ohgXp+vq6prVbiIHkpBGNA2lUJvMkziRxHjjEybcwiUViRMrZrNZTHgkxl9z4zCTa0l8p/ZntmzZQqsDCIIgCIIgCIIgCOIAhUTjvcTeFIsyyTxuDnxZP9AyGZ+cA0lII5pGc4RaZXwrYzKd6Kzcj8daogC9p3FIkx97zscff9xm22yNvhEEQRAEQRAEQRBEW4VE4wMAs9mMoqIiIeAmE8iaQ+Jye6VQ3BIZxkTbRZZl8exbsyhec4TaZJMXjQm1LTnJkQqa/Nhz8vPz22ybrdE3giAIgiAIgiAIgmirkGi8F8jEq3VP2lSr1XFZlsqMxz05d6LHq0qlSpnNyRiLExoTz5uuH61xf4g9ozF/3+Y+s6Z6GCc7nzK+08Wk8rjEd2RPae71U6yn5+233wYAXHXVVW2uzdboG0EQBEEQBEEQBEG0VUg0PgBpTEjLlKYut6eCdgcOrWW10BIx0lLxTbQ9SDQmCIIgCIIgCIIgiLYBicZESpq63J48XQ8cWstqgWKEIICSkpKUWeclJSXYvHnz3u0QQRAEQRAEQRAEQSRAojHRYpCnK9EYFCMEgbSiMFmYEARBEARBEARBEG2B1qtwRRAEQezXlJaWQpKkpF8lJSX7unsEQRAEQRAEQRAEQbQSlGlMEARBJGXLli1gjO3rbhAEQRAEQRAEQRAEsZch0ZggCIJoE8yZM6fNttkafSMIgiAIgiAIgiCItgqJxgcojWUHkm8mcTDQ3PeA3p99Q2sUSGypNql4I0EQBEEQBEEQBHEwQaIxEUdzxTAS0Q4sWuN5UowQjTF58mQAwE033dTm2myNvhEEQRAEQRAEQRBEW4UK4REEQRBtgo8++ggfffRRm2yzNfpGEARBEARBEARBEG0VEo0PUGRZhs/ngyzL+7orBLFfwhiDz+ejQnDEXqWkpASSJCX9Ki0t3dfdIwiCIAiCIAiCIA4SyJ7iACUQCKCurg4AYDab93FvCGL/w+/30ztE7HU2b96cchtZvBAEQRAEQRAEQRB7CxKND1CMRmPcd4IgmgYvfHYgF0ArLS3Fli1bUm4vKSnZi70hCIIgCIIgCIIgCKKtQPYUByCyLCMQCMBoNEKlokdMHNw012ZCkiSYzeb9PruztLQ0pd0BUH9/Un2ly3ol9j7prCvIvoIgCIIgCIIgCIJoSaSDza9TkiQngNSpdS1PPoCqvXg+oH4yQA0gBiB3H5w/kX1xD1rr/CWMMXuqjfsgvjj7+h5z2mI/lO/D3jb5bur9SBlf+zC2OG3x2e5L9rd+pB27CIIgCIIgCIIgCELJQSca720kSfqbMTbgYD1/W+jDvj7/3qCtXCP1o232oyVoK9dC/Wib/SAIgiAIgiAIgiAOLMi7gCAIgiAIgiAIgiAIgiAIghCQaEwQBEEQBEEQBEEQBEEQBEEISDRufaYd5OcH9n0f9vX59wZt5RqpH/G0lX60BG3lWqgf8bSVfhAEQRAEQRAEQRAHEORpTBAEQRAEQRAEQRAEQRAEQQgo05ggCIIgCIIgCIIgCIIgCIIQkGhMEARBEARBEARBEARBEARBCDT7ugN7m/z8fFZaWrqvu3HQIMsyYrEY1Go1VKr9f45i8eLFVYwxe6rtbT2+DrTncaCRLr7aemwRDWlL79vBFFv//PMPAKBLly5tsr3WanNf0djfRYIgCIIgCIIg9k8OOtG4tLQUf//9977uxj6FMQZZlhEIBGA0GoWYwT8zmUxNFjhSeWMzxuD3+2EymSBJUsrP+Pe2jiRJW9Jtb0vxleyZ8GdsMBgQDAab9axbg3Te6jxelH1OjJf9JX4aI118taXYIuJjNnFMY4xBkqS4cRZAgzE32VgItE48U2wRrUVjfxcJgiAIgiAIgtg/OehEY6KeQCCAuro6AIDZbG7wmcViaZHzSJIk2uf4/f4G5yb2DiqVCmazGT6fr8WfdWvB48Xr9UKWZQAUN0TbItWYxt83AHHvHP+MxkKCIAiCIAiCIAiirUKi8UEKz3zj35U/m0ymVj03b7+1z0OkZm8965aA91GZaUwQbYlMxrRkYy6Nha3DfffdBwCYOHFim2yvtdokCIIgCIIgCIJoSUg0PkhRZsAlftbaS/2TZR8TexeVStXmM4w5ynihuCHaIpmMacnGXBoLW4fff/+9TbfXWm0SBEEQBEEQBEG0JPvezJQgCIIgCIIgCIIgCIIgCIJoM1Cm8X5KusJhQPpCSowxMMZEUaZMi4o1VqxsTzKU0xWVOlCKnDWXPXnWrdFmYwX21Gp1yuOaE1vJCoVlSmvcO+LAorEYSXccj5/EwqKNtakcf5tSiJLimSAIgiAIgiAIgthbUKbxQYgkSaLoXSAQEMJsawm0sizD5/OJImaNwYtD+f3+Fu/LwUTic23N58zjKRgMNruNZH1UxkJzr6Wp8UcQTUU5ngKNv3vK/Sk+CYIgCIIgCIIgiLYIZRrvxyRm5DaFvVmAiQskQGaetFQcau+zJ7EE/Fvcy2AwtGi/WiIWmhp/BNFUjEYjZFkWX429Q8qieBSfLU9xcXGbbq+12iQIgiAIgiAIgmhJSDTej+FZmEDTxYbmFmBKXIadCUqBpDX7RjSf5saSMh7MZnOzl/qngsfCnmRGNzX+CKKpqFQqqFQq1NXVQaVSxU1yJBszlUXxKD5bnvfee69Nt9dabRIEQRAEQRAEQbQkJBrvx+yLjNzmZMUpBZJEuKDS3AxXomVobiwli4fmTCy0JunijyBaCqX4yzP3M8kkpvgkCIIgCIIgCIIg2iIkGu/H7IuM3GRZcU0RCRP3paXZTWdPrSSS0dxYShYPic9U+cxpYoDYn1G+e4koxV+fzyfegdbIJG6NMeBA4vbbbwcATJo0qU2211ptEgRBEARBEARBtCQkGhNNIllWHBcJZVmGSqUSgnAyMTlRUORCCvkXZ86e2JK0NMniIVEkSzcxkE6EaynaWuYz0fZJJcoq3z2j0SiyiRPjSvkOtEYmcVsaA9oiy5Yta9PttVabBEEQBEEQBEEQLQmJxsQewwUSWZbjBBWn04loNAoAKf07aWl202mKlYQsy0L82luCaeIzTZdpuTfEL8pmJ5pKqrhUvnvpYjdV5n5LTWBQsdCWp7S0FFu2bEm6Ta/X46ijjtrLPSIIgiAIgiAIgti3kGjchtmTomLpjmWMpVzS3NTjuAhiMBjENlmW4fP5EA6HEYlEoNfrxf7JROLm9gfAAbM0uynXKUkSLBaLOC7VsTxbMpmwxY9LJWA1JQ4as5/gzzxZm9z/1WAwNNiWqcCWLn6AeIHtQImXg5E9GQuaOq4ZjUbIsiy+ePwpxWCDwQDGmIjhxDb5+6XX6xEKhTLyN870OpVjANEybNmyJeW9p3GDIAiCIAiCIIiDERKNDzL2xH84GYkiiEqlQl1dHSwWC3Q6HVQqFUKhEDSahqGmFJzVavWeXxzRgHQZiS2VgatsJ9l5eLaz0Whs8JxVKhUsFktSsUbZrtFohM/nE31tSpYmPwex/7O3vHxVKpUYy1KthlB+nqxfgUAAbrcboVBITJy1hr8xQRAEQRAEQRAEQbQGJBrvh3CxtTnCSVOEwkSv4mTibmJGnlIUMZvNQnTe076QL23zSFfgbk+LGqZrR4nf74fH4wFjDFartcH2VBYaynYDgQAqKiogyzKsVissFkuTxWNi/6cl7Ux4rCszgVPFXyb98ng8AP61rjAYDNBoNJBlGRqNBnq9nsawvUT37t3bfJut0UeCIAiCIAiCIIiWhETj/ZDGxFbGGLxer9jeXCGE7xONRlFZWYn8/HxoNJoGooff70c0GhVF8DKF75vMmiAR8qVtedIVNQTi73MqgU0pMgOAz+drIP6aTCYwxoTntcViaRA/Ho8HkiQJgTixgJjRaITD4YDX64XH44HP50NRURHFwkFGS3r58ljnccwnx5Qxnm5iLhqNoqqqCiaTScQhF4x5PNvtdhHPjb1bynGVJsn2jGnTprX5NlujjwRBEARBEARBEC0Jicb7IVygSyWc+P1+VFRUAECcsMaFCJ1Ol1KkUGYTc9GutrYWPp8PBoNB7M/bDAQCiEajQkxWCiMA0gq9ybxuE8USvuybC8u0rLt10ev1QjhTkiiwARCZ5PwZy7KMyspKFBQUwGq1xmUQS5IEl8uFWCyGQCAAu90uYslgMMDn80Gr1SYtngjUx4rVaoXZbBZWExQLBx/pMuczRTnWARAiMZ/UUMY4H2OTibjV1dXYvHkzLBYLSkpKxOf8GL4vHxf1ej1sNltc4VCfzwev1xv3TgE0SUYQBEEQBEEQBEHse0g03g9J5bHJMZlMcDgcAOKFtVTCn3Kb8jOlqGE0GmGxWIQ4zEnMFpZlGRaLJek+meDz+VBRUQGHwwGr1XpQiyd7y79VCRfPEn2o+TNUZmEqP1d6DnP8fr+YcMjLy0N+fj58Ph+i0Sj8fr8Qf4PBIGRZRk1NjZiA0Ov18Pl8DTItuXicin1xz4j9Ay78yrIctxKDMQaz2Zw005jDxyXlZEd2djZKS0uFSMztKcxmc1y2PR/DbDZb3BimtFzJyspKOmbSxEjzuOGGGwC0bDbvDTfc0OLtAZRxTBAEQRAEQRBE24VE4zZOU5YpKwUzLqxx4ddoNAoBQqfTIRwOxwkSWq0WgUAAubm5iEajcLlcMBgMqKqqEkIwz5pLFPHMZjNisZjIErXZbKLPyYTedMuxuZjNOZjFE6V/a3MLufEM8WAwmJGQmup+KzMmlRnnygkMs9kMh8MhRDSDwYCKigpEo1FxTF5ensg4jkajCAaDYsLBYDCIomFOpxMejwdFRUVpReJElPeMWwUk2mUQBydcvLVYLHEZvxxlLPMJE74KgmfNe71eMU5ZrVbxTvFs+UgkAo/HEzfxJssyTCZTnAUG8K/livKzxCxoonmsX7++zbfZGn0kCIIgCIIgCIJoSUg0bsMwxuIybZV2FIyxBkKYUjDjgoiyCJnRaBQiCG+L/15TUwOPxwO32w1ZlrF161YUFxfDbreL9r1eL1QqVVJbDKVNBWMMdXV1YIwl3ZeLy3w7YwzBYFAIOkVFRTAYDCn7qiSVCJq4b1MzUNtCluqe+rfyeFAuf1cut5ckSYhhfBsXhyVJanAPeSzyzMxEuCexJEmIxWJCHA6Hw+Icfr8fgUAAPp8PNTU10Gq1YIzFWQUEAgF4vV6Ew2HIsoxoNNqgAKMSZT+VondjRdMYY2mfc1uIAaJlUMaYctKDe20rx9JEn26VSiXGPS44e71e+P1+uFwumEwmmEwmBAIBVFVVQZIkFBcXw2q1wufzwel0iveOC8zBYFBMqik9kIH6jOVk7yyx79Dr9Vi4cGHSMaGkpASbN2/e+50iCIIgCIIgCIJoZUg0bsNIkhQnHHIhz+/3J828VQpmfF+e5RaLxVBZWSmWQmu12rhj8/LyIMsy9Ho9YrGYOKcyY1kpKCZiMBiEyKH08kzc1+PxCHFYKeAq+y7LMqqqqpCXlwe1Wt0i4l1jAuK+Jtk1SpLUaIZxunuTzFKC78/jw+Vyoa6uDrIsQ6fTobi4GBqNJmm7SiuSZNu52MYFtGg0CqvViqKiorgCd9zKIhwOi4x3ZXt6vR5msxn5+flxx/FsaWVWJn8XEovyKYsykuh2YLAn44AkSVCr1Q3efeWkCS8aGggE4qwmuI8yj2OekezxeOJWRuj1emi1WlRXV8dNyni9Xuh0OmRlZYkseq/XK7LyPR6PEJ45PN5TjbfE3uWoo44CACxYsKDBNno+BEEQBEEQBEEcqJBo3MZJLPyUTvxULq/m4rLP54NKpRKZa8mWZQP1y7FtNptoOz8/Py67U5lhnMxrln/OvTsLCgpSWgLwfir/2Vb23el0ory8HABQUFCQwV1qnD3N2t0fSbbcXgkXdi0WC/x+P9xuN0wmU8p7nqxwoRKeMQnE328eBxaLBZIkwWazwWKxiIzzYDAIvV4vRLJQKCT6HAgE4HQ6ReawxWKBw+GASqWKO5/FYmngf81jlkQdIhU8vhhjcDgcYjKLMSYm3dRqddy7pJwcUavVYtxTZiQr7VtUKhWCwSBycnIQCoXg9XoRDAYBAC6XC5WVlSgsLITNZgPw7zvTFie3CIIgCIIgCIIgiIMHEo33M7gYl8rjlwsaer0esiyLokx8f4vFAsYYfD5fXAYqLwClUqmQk5MjCkNxf87Eok68D4FAAFqtFjt27EAwGITH4xHFypSih9IeQul7m4y8vLy478rrysTbORmJ4vvBDvdXtVqtIvPXZDIhNze32W1yX1edTpfUS1gptqlUKtjtdrFfXV0dKioqoFarxdJ+3i+eJe/3+0WfubgHNPRhbq7/9Z7GGNE2ScxIByB823NycoQFD48nlUoFSZKEZURitr+yKJ6ygB4vrmexWESGsdFoRPv27YUVi0ajgd1uF+8B9zo2GAyIxWIim57YM/r06bPX2ispKUk5MZXOuqKl+0gQBEEQBEEQBNHSkGi8n8HFz1TZnlzU5cv1rVYr1Go1LBYLvF4vPB6PEN80Go1YXm02mxEKhRCNRlFTUwOj0QiXywUgPmNVKczxc/HvVqsV3bt3B/CvKMn7FIvFUFVVBbvdDqvVKjxv8/LyGgiLoVAIdrsdKpVKXGdiFinRPLgwGovF4PP5YLVaEQqFEAgEYLVaoVKphICcTDjlAlwyQdjlciEcDmPHjh3Q6XQA4ovR8UJiAERhRaUgV1NTI8Q9nnVpNpuh1Wrh8/lEHCsLivFJkJYQfCnGDkwSV2fIsoxt27ahpqYGQP1qhkTRL9nkXLJindx/m3/GLXVcLhcYY7Db7aitrUVdXR3cbjfsdrsYd10ulxgPa2trRdFRh8Mh4r+l2D2OHjQzIZMmTdpr7aXzM063yqGl+0gQBEEQBEEQBNHS7LeisSRJEkulnB4kJBPKlD62XBzmWb5Op1OIcmazWRSEUgrBXq8X0WgUAEQGHvcZ5tlxiQJyXl4eampqkJubC41GI4qc8Uw+n8+HYDCIaDQKn88Hi8UCl8uFXbt2xZ0H+DeLz+FwCD9lfj0qlQo6nS6lPQbROD6fD5WVlcjLy4PVahWF5/gz9vl88Pl8YnIisSiY0+lEOByGz+cTwj5QL8xFo1HEYjFoNBpoNBohFCstK3ghMK/XGyc8G41G5OXliSxljslkQnFxMYxGozieW64oBedMBV9lxnsiSs/mdMI5sX+RKAAHAgFoNBrk5OSI8SnxWXObCeUECY8xXqxTo9HA7/cjGAyitrYWtbW1sFgssNvtYrKLx2p2djZyc3MRi8XE+xAOh+F0OpGXlwefz4doNIpwOCxWgjQ2xjWluOfudyp1NUmCIAiCIAiCIAiCSGC/E40lSWrHGNt5sAvGQHKhTOk7q1KphF2EyWSC3W6PWwLt9/ths9ni/Dfz8vKEtyzPVuZCIi/upCxOxoVFbmHBs0DD4TDq6upgMBig0WhE5invbzILinSEQiHIsiyKTCmveU9piviyv5EqA5fHCX+2QL2wxMVkLrDV1dVh0aJF6N+/P/R6PaLRKKLRqMhI5sUauQ8s9yjmGeSJhRxNJpPwMubZwiaTCaFQSHjEarVaqNVq0Qe1Wo2ioiJxPcp2OZlaUyizThPbUN4Tvk9jhQiJtk+iH7Esy8jOzobZbEYgEBCCL7dM4ZnAPE75BEpijG3YsAGLFi3CmjVrsHr1apSXl8Nms6GwsBBZWVkwGo3QarUwmUzo3bs3OnbsiKqqKlRWVqKgoAAajUa8L3a7PS52M5kAaUpxz92xHmvuPdzfuPzyywEA7733Xptsr7XaJAiCIAiCIAiCaEn2K9FYkqSzAFwjSdJ4xtj6fd2fTEmnbzPGUgqVjW0zGAziOxdSudih1+uFlzEX+ngxMZ59zIuORSIRIeqFw2HEYjHx5XK5xPJqh8OB6upqxGIxuN1u5OXlwW63IxQKoa6uTmQoO51OABDLrEOhkOjrtm3bUFVVJURsACLDjmeTFhYWwmg0IhaLicxoWZZhsVhgNBoRDAZFe+lE3kwtC5oivuwLmhI/iZnByokFLu4WFhZCpVIhEomIjOBIJCJsK3Q6HebNm4evv/4ac+bMwaZNm5CXl4dTTjkFxxxzDM4880xYLBZEo1FheZKbmwtZllFTU4NwOIzq6mp06tQJWq0Wer1eTFY4nU6o1WrodDrEYjHU1tbC4/GI/lqtVgSDQeEJyzPMldem1+uFXyy/RpPJJLLrtVot3G43cnNzIUlSXEFHpfCX6r4aDAZxv5o7N5XuvSWaR3PGUW6notfrAQDV1dUivvR6PSRJgtPphCzLYvzKz89HIBBAJBIR56yqqsLSpUvx448/YtmyZVi7dm2cLUFhYSE6duyIXbt2Yd26dairq4PH44mzsrjlllswYcIEWCwW5OTkiEKPfCxTCtt80iTZNfPrbKy4Z5LxT0664wHI9u3b23R7rdUmQRAEQRAEQRBES7LfiMaSJA0EMBnA1YmC8cFoVSHLclxGMMflcmHjxo0Ih8Po1q0bLBaLWO7PBRSDwSA8i51OJ4xGIwoLC2EymRAOhxEIBMAYQyAQEBnDkUgEHo8HXq8XgUAATqdTZP3u3LkT69atw44dO7Bp0yZUVFRApVLh1VdfxfDhw+Hz+aDRaKDX64XALUkSJEkSQiMXbMxmsxBPfD4fvF5vXAE0jUaTcfZnOssCpcCUmA3blkkmCitjIFEkVoqkXEA1m81CEAsGgwDq7/UPP/yAn376CQsWLEB1dTV0Oh2GDBmC66+/HitWrMC8efMwc+ZMqFQqDB48GKeffjqOPPJI2O12kR1ss9lQXl6OyspKGI1GdOzYUcRSXV0damtrYbPZoNVq8fvvv2P9+vXYsmULtm7divLychE/Wq0Wb731Fs455xzxXILBIHw+H/x+f5xI6PF4xH3gVij8HuTn54s4U2bHA6mFRrVavV/EAhFPMr9tbo/C/zzwiY5wOCxWW1RXV8NgMCA/Px/Z2dnCQgUAysrK8PLLL+Pjjz/Gli1bAABdunRBv379MGrUKAwYMABHHHEEHA5HXF/C4TC0Wq2YkHr++efx6quvIhgMYvz48aitrRWTGvw95O8oYyxuwoRPlCiLlgJoUKQv8U8geXQTBEEQBEEQBEEQe8J+IxoD6AJgJmPsR0mS2gMYACCfMfYGY4ylE44lSboBwA0A0LFjx73X41YkGAwKb0xlhpper4fFYkFVVRVcLhcsFgtMJhO8Xi+2b98OWZZRUlKCcDiMiooKABCZprt27YJarY7zNY7FYtBqtVi2bBk2b96M5cuXY82aNXFZdiqVCsXFxejcuTPOPvtsdOrUCd9//z1uvvlmVFVV4YorroDX60VVVRUMBoNYGg7Ui5W1tbVQq9Ww2+3CX5RbGfCMaJ5dnOiFm45MLQsS/XGbyt6ML6UQBDRcxs6zsvkXF4mTEQwGUVVVhUWLFuG6665DIBBAbm4uzjrrLJxxxhk45ZRT4u5LLBbDkiVLMGfOHHz33Xd46KGHAAA2mw2HH344SktLcfjhh2PgwIFwOBwoKioCYwwbNmzA8uXLsXr1aixbtgyrVq1KGT9nnXUWOnXqhA8//BDjxo3D0UcfLUR9vV4Pn8+H7OxsUbSRH6+cdNDr9dDpdNDr9aJIHrca4PeIZ3hKktSsAnotUXgvEw7Esas1kGUZVVVViEajYjKAZ/DyFQoA4lZY5ObmIjs7Gz6fT0yE8WNnz56NWbNmYdGiRVCpVDjppJPw0EMP4ayzzkJWVhaAemGYi8vJ4LYWZrMZzz77LCRJwiuvvIJAIIAJEyaISTfuZ+zxeIQ9Ch+bLRZLXGFT/lkmZDr+EQRBEARBEARBEEQy9ifReBuAEyRJ6gDgcwDzAAyRJOlsxthF6TKNGWPTAEwDgAEDBuzzjGSl4JSYBZlOgFLux8UKpSAQCAQQCARQUlICu90Og8GAJUuW4LnnnoPL5RJ+sGq1GrFYTIiKvHid8hZqtVqoVCpUVVVh06ZN4vNOnTqhf//+uP7663H44YejS5cu6NixIyRJglarFfvdeuutuOiii/Dwww8jEAjg7rvvBvCvv6jyOrlfscvlEp+ZzWYhHPNM6UAgAK/XCyAz4UTpZdqa7M34SiYEKX9WqVRQqVRCaEp3/Ty78o033kBubi6mT5+Oo48+GhqNBuFwuMH+arUaAwcORN++ffHEE0+gvLwc8+bNw+LFi7Fy5Up8+eWXeP/99wHUC2ZdunRBLBbLKH5UKlVc/Jx//vkYPHgwLrjgAowZMwbnnXceTCZTXIGwyspKBINBtG/fHjqdTojBgUAAwWAQfr9fxHs0GoVGoxH7JArv/N3IVATeW1mcbW3saqsEAgHxjIH67HPuw26xWMQkAd/PZrMJO56ioiIsWbIEn376KbZv345vv/0W0WgUvXv3xoQJEzB8+HC0a9duj/onSRKeeeYZAMArr7yCwsJC3H///SLmqqur4Xa7UVVVhfbt26OoqEj4ggOIyzTOlL01/hEEQRAEQRAEQRAHJvuTaOwCUAzgagAfMMaeBwBJkhZIknQXY+y5fdq7JpBoIZCpAKXcT7nMXpZlIZKYzWZYrVZUVlbitttuw6effoqioiJ069ZNtMMYE4XpODx7LnHJfr9+/XDdddehX79+OOyww1BQUJC0b5FIJO53o9GITz/9FFdccQUmTpyIv/76C//973+Rn58v+slFjfz8fJFBzK0BeF+qqqriipLxzOmDlUQhKJmNAheW0mVB8rYsFgt8Ph+Kiopw/PHHN6kvRUVFuPLKK3HllVcCqH9eGzduxOrVq7FixQr873//g0qlyih+eBYw59BDD8X06dNx5513YsyYMbjpppswaNAgDBo0CCNGjED//v3FxIGyMB63qOA+2cr7wQVh5f3h906W5SaJwJTFuW9JnGjjz0E5NvCJAADC5iYcDiMajcJgMGDXrl34/PPP8e2332L+/PkIh8MoLi7GbbfdhpEjR6J3794IBoNCiN5TuHBcVlaGDz/8ELfeeitisRiKi4uRn58vYjAUCsHhcIi+82KjTWVvZcO3RQYPHtym22utNgmCIAiCIAiCIFoSaX+yApYk6QoAjwD4DsC9jLEaSZLuAlDLGHstkzYGDBjA/v7771bsZUMS73FipjH3fE38514p4PJlzMqMSF7gy+fzYdOmTfB4PLBarfjggw8wadIkqNVq3H333Rg3blycmKLMyEsk3TZeOC3VNmWmqPIa3njjDdxzzz3QaDS45557MHToULRr106Ic5FIBDU1NcjLy4srWubz+URhNS6A8gxBblHR3IJjzfWrlSRpMWNsQKrtrRFfTS0AxuNLp9MJz+Jk2d18Wf6UKVPw5JNPYuvWrSgsLASApJnGHF5UMdW2ZHEApI+fVMcxxrB8+XJ8/fXX+PLLL7FkyRIAwDnnnIOnn34axcXF0Ov1ol3GWNx7YjKZUp4z8f1SvoOp/I4ZY6I4Y2KBvkS/2WTEYrG0Qp5KpUoZX/ti7GpLKN8Dn8+Huro62Gw2mM3mBs/L4/GgsrISBQUFwvJBo9Fg/fr1+O6777Bw4ULMmzcP4XAY7du3x8UXX4yhQ4fiyCOPjHsu6UTjdPYU4XC4wcQc5/XXX8ctt9yCzz//HO3bt0dxcTHsdruYJItGo3EWPonXm2qVReI4kXiPKLbSw/8Wt/U22yKN/V0kCIIgCIIgCGL/pM2mH0kKBYD/zBh7F8D9ADoBuEaSpMcBXAPgl33SyWaizBbl1guJoh6Hi8W8KFwwGBTf+XZZltGuXTv89NNPOO200/D000/j4osvxsqVK3H//ffv08xcSZJw3XXXYcmSJejZsyfuv/9+PPjgg9i8ebPoOy+E5/f742wyTCYTdDodDAYDbDabyIaura2F0+nMOPtOKbgfDPCM9OrqapSXl2Pjxo3w+Xxx+4RCIWzfvh3btm3DwIEDAQB//vnnvuhuWiRJQp8+ffDggw/ir7/+wqZNm/DYY49hwYIF6NOnD8aPH4+amhqxfyAQgM/nEzYdmZLMNiUZvLBZIBCIO2fiZ6nw+XwoLy9v8DyIpmE0GmGz2cTkhSzL8Hq9Dd5xXhzPaDRi586dePTRR3H33Xdj2bJluPHGG/Hzzz9jw4YNePbZZ3HUUUftlYzcE044AQCwaNEi6HQ6aLVaIXrn5+cjKyurwaRM4vVmQnOOIQiCIAiCIAiCIAhOo+tuJUl6jDH2kOJ3NYB3GGOXtUaHJEnqBqCOMVbBi9spC90xxmZIkrQJgB3AQAAXMMbWt0ZfmkK6bCJlJiIA8XOi3UTiPjqdDqFQSBRIslqt0Gq1cLvdQmz1+/0oKyvDHXfcgWXLlmHQoEH46KOP0K9fPwD14mAidXV1KTPknE5nSpGBF0pLhtfrTeszXFxcjLlz5+LFF1/EhAkT8Mknn+D888/HqFGjcMQRR0Cv14vl2R6PB7m5uVCpVLDZbIjFYiIzk4tAXGTOJOsu2X1OZuvQGLvbbJaq1Fi2Gc9gTdYvWZaT9jUxw5X/rtfrYbVaodPpxLG8IBxHo9GgXbt2CIVCaN++PTQaDf766y+cc845ABDn+ZuIz+cTAn4iGzZsSJlhGYvFUhZz4xmRqeCTLLm5uRg7dixGjBiBJ554ApMnT8aHH36IBx98ENdff73IVA+FQqirq0N2djZsNpsopJh4H5WZ7UpSPa90ntLKYmtEapqaOc9Rxi9jTHzxSSGv1ysK3en1ehQUFECWZbjdbmzcuBGjR49GWVkZJk6ciJtuukkIxFu2bEmZTVxXV5dyzPP7/Sm3BYPBlO9Iu3btUFRUhFWrVsFoNMLtdgOoz07Ozs6GTqdrkKHKf1ZabiTC91GuXFFa/RxMDB06FADwySeftMn2WqtNgiAIgiAIgiCIliQTs8YOkiTdxxibKEmSHsBHAJa2RmckSTofwCsAvpQk6WnG2NYUwjFPifyqNfrRHNKJfskKbwHxYpNyH1mWUVFRIZZWK/etqqpCOByG1+tFNBrFP//8g+uuuw6xWAxvv/02hg0b1uiSWI1Gk1IsA9JbN6QSV3hBvVSoVCrodDr85z//waWXXor//ve/ePPNN/HZZ5+hf//+uOWWW3DGGWcgGAwKsZxbC/Brj0ajcLlcyMnJQSQSEcJ3Y/YAXDjhRfWaW8Bsd1Z46hu3B6TrlyRJSZ9JMBiEx+MBUH9tfGl7VlYWLBYLZFlGQUEBrFarsELh8AxuoP7+9e7dG0uWLBEWEXq9PmVfw+FwyvgJh8PQaDSIxWKYO3cuIpEIzjjjDBgMBoTD4bSZnE3ZVlRUhP/7v//D6NGj8Z///Afjxo3DtGnT8MADD+C4446DLMuIxWIIBoNQqVRwuVyw2+2wWCxxolq6WE9l+6G0hgHqYz+TwoxAvS83t81orkXKgUCqCQ+DwZB2bOLPjgvFQP37wicE+HfujS7LMr788kvceuut0Gg0mD17NoYMGdKgL+n6mWpcU6lUKbdptdqUkydAfbbxTz/9hNzcXEiShOrqavH+2+32BrERDAbFxEqqc/Jj9mSMO1BQFlVti+21VpsEQRAEQRAEQRAtSSZZk9cAOFySpPsAfAngR8bYIy3dEUmSsgBcj3pReguAsZIkdeRiMQDs/vlESZL67j6mzagu/B91bjehRLlMWPkzF4+4R3GypcTKZfOBQADhcBi1tbWQJAmbNm3CddddB1mWMXfuXAwfPrzFhKhgMIhZs2bhgw8+QG1tbYu0yenYsSMmTJiAsrIyTJo0CbW1tbj66qvRv39/vPTSS/D7/cK6QonL5UJ5eTlqampgNBoRDAaF2JTOHkCSJJjNZuGFbLPZmmXZsfuYWDMuWcA9dxNF/eb0y2g0wmq1CjGcF5PzeDxxheUS7Tl4tjb/XaVSYeDAgfj7779bxMJj2bJluOqqq/DYY49h4sSJuOiii/D+++9nZN/QVHr37o0vv/wSs2bNQjgcxmWXXYYxY8bA6/XCarUiFovB5XI1yDz3eDxN7k9TbCiUKJ+5JElCOD6YSbyX/Hduu5PuOI/HIzJu+cSH0uecE41G8dhjj+HKK69EaWkpfvnllwaCcSKhUCjp6oyW5oQTTsDOnTvxzz//oKamBiqVCllZWcjNzU1qp8P/PihXiCj34z8zxvZojDvQKS0tFZNwiV8lJSX7unsEQRAEQRAEQRBtipTKhSRJ/SRJ6gegL4CXAAwHsAHAT7s/b1EYY7UAbgbwKIAFAIIAbpckqZTFK2xdAbh2H9Nm1tym+0edi5bcZzWZd6ryc7PZjKKiogZtGY1G6HQ6ZGVlYfPmzbjmmmsgyzK++eYb9OzZs0WuQ5ZlTJ48GYMHD8add96Je++9FwMGDMCtt96KX3/9FbHYHmmmcZjNZowePRqLFy/GF198gZ49e+L//u//cNNNN8Hn88UJSLIsw2AwwOFwIC8vL050aop3p1JAbiq7j9kjVTXV5EJj/UomJCljxmQyISsrCyqVChUVFSKLLRAIwOl0wul0wufziQJhiaLpwIEDUVtbiw0bNjT72iorK/Hkk09i9OjRcLvdmDBhAqZMmYLOnTvj5ZdfxrBhw/B///d/iEQizT5HMiRJwvnnn49ly5Zh4sSJWLBgAfr27Yt3330XGo1G2HXwgmmyLMNsNjfZ67W5HrHpJpQOVhLvZTJRNBkGg0GMnXzlQTIf4+XLl2P48OGYMGECRowYge+++y6lNQpQL+x/8cUXGDRoEPr06YNzzjkHd911F9599138+OOPKC8vb1GLB+5rPH/+fNTW1oIxhuLiYoRCoaS+2cn+bijHQP6z3+/fozHuQGfLli1x1ibKr82bN+/r7hEEQRAEQRAEQbQp0tlTPJ/wew2Anrs/ZwBOaokOcMuJ3b9uY4zJAP7anUV8PoDbJEm6C0B/AGsZY6+1xHlbGv6PekvABYJEMUSlUiE/Px+BQAAvvPAC/H4/fvzxxxYTjKPRKO677z588cUXOPHEE3HjjTfCbDZj1qxZ+Oyzz/DFF1+gffv2GDFiBEaOHIlOnTq1yHlVKhXOPPNMnHnmmbj66qsxa9Ys6PX6OAEpEAjA7/eL5dlcPOEZ20299+nsRFoTpVVGU1Dal/Br5RnDPMPSYrEIUS0vLw9A/f3hIrssy0Jks1gsiEajwi978eLFAJrnfcoYwwcffICHHnoIPp8PV199Na688kohCPbr1w8rVqzAa6+9hscffxyfffYZJk2ahMMOO6zJ50qHXq/HnXfeieHDh+OWW27Bvffei6OOOgpHHHGEENF4sUWr1drk596cOAOa/8wPZBLvJf9d6d2baDkDQKwu4NYQ4XAYVVVVyMnJgSzLCAaDmDRpEh555BFotVo8++yzGDt2bNqJCqfTiQkTJmD+/Pno168fjjzySKxZswZ//PEHvvjiC7FfVlYWBg8ejCuvvBLHHHPMHl1/t27doNVqUVVVBZ1OB4fDgerqakQiEWg0GlgslpSTE0rv8sRJDIoxgiAIgiAIgiAIoqVIKRozxk5s7ZNLknQ2gB6SJE1jjHkZY7LSt3i3qHM8gF9QL1j3BuBp7X61ZVQqFZYsWYI5c+bgscceazHBOBgM4uqrr8Y333yDu+++G7fccosQ1Xr37o37778fX331Fb766iu88MILeP7559G7d2907NgR7dq1Q15eHjp16oSioiJR6KmxrMFEGGOYN28eTj75ZOHbGQgEoFarhTCi1+vh8/lEkSe+9F+v1yMUCjUQmVKxr3w/mzO5wLNjE4UkvlSf2x4YDAYEg0HY7XbhE6v0XVWpVLDb7QAgBOQtW7Zg1qxZ+O9//4uxY8fikEMOaVLfNm7ciDvuuAM///wzBg8ejBtvvDFpG4cffjieffZZlJWV4d5778UZZ5yBkSNHolu3bigsLITFYkHXrl3hcDjSesFmQnFxMd544w107doVzz33HKZOnQqgPnY8Ho+wiUmG0ls3GAxmHE/paMkJpYOFVJMkPEuci6OyLIuimIFAAI8//jimTp2KSy65BC+88AIKCwtTnoMxho8++gjjxo1DIBDAf/7zH1x99dVxnspbtmxBTU0N1q5dizVr1mDu3LmYO3cuunbtihEjRuDaa6/N2M9aiSzLiEQikGUZ4XAYFRUV0Ov1Qtz2+/1xMZMYl9zfWLkPxdi/nHzyyW26vdZqkyAIgiAIgiAIoiVptBCeJElPAniGMebe/XsOgDsZYw/syYklSRqIev/icgBeSZI+2C0cswTh+FIARQCOZYxt3ZNztiVSFX5SZtgpf9fpdHC5XNDr9XjggQfgcDhw0003tUhf6urqMHLkSPz666946KGHcN111zXYx2Aw4KyzzsJVV12FHTt2YObMmfjll1+wYcMGLFy4UBRk4+j1elx11VW4/fbb4XA4MurHrl274HQ60b9/f0SjUWzfvl14OdvtdpjNZvh8PiEmmUwmIS5xkRTITDzZn7I/A4EAvF4vbDZbnIBpNBrF0mqPxxNnX6EUspQWAEC9IPX7779jypQp+Prrr6HRaHDllVfiiSeeyKg/wWAQ8+bNw4wZM7BgwQKYzWY8//zzGDVqFFauXJn22LPOOgtHH300HnnkEcycOTOpf2x+fj66dOmC8ePHY9CgQRn1KZG8vDxce+21mDx5MtavX4/i4mKoVCrU1tYiKysLZrMZoVAo5ZJ/r9fbpHgiWhaj0ShE4mg0Cp/PJ7Jys7Ky4HK5EI1GxXOMRCK48847MWPGDIwePRovvfRS2oJ6u3btwu23344vv/wSRxxxBJ5++ml06dKlwX5ZWVno0aMHjjrqKADAo48+iq+++gpvvfUWnnjiCUyaNAnDhw/Htddei27dumV8fbyIn8FgQFZWFtq1a4dIJAK9Xo/q6mpxzdzv3ufzobKyMmlxVKIhDz74YJtur7XaJAiCIAiCIAiCaEkaFY0BnMkYG89/YYzVSJJ0FoA9Eo0BWABcCKAK9ZYXWkmSpiuEYz0AE4ATAVzEGEuvRrUye+pnmXi8MpPOZDIJqwGv14toNCrEkVAoBJ/Ph1AohIqKCvz888/45ZdfMGHCBASDwZSFo9IVc3K5XMjKygIAVFdX47rrrsO6devw3HPPoVOnTvjf//6X9LhAIIB27doBgLCT4FRUVECSJFRUVKCiogJLlizB66+/junTp+OSSy7B2LFjkZ+fn/LeFBYWYu3atQCAXr16IRgMorKyEjqdDiaTCbFYDPn5+VCr1WCMIRwOQ6vVimxmnmlsMBiEkJouQ7StZX+miy/uyWswGBpYluj1elHISafTIRwOQ6fTwev1QpIkqNVqsZS9oqICH330Ed544w2sWrUK+fn5uPvuu3HttdeisLBQ+CYDaODvyRjD8uXL8dVXX2Hu3LlCyB86dCjOPPNM5Obm4tdff0VlZWXKSQKXyyVi/vLLL8dll12Guro6uFwu7NixA7FYDFVVVaiqqsKiRYtwwQUXYOjQobjuuuvSWqHEYjG0b9++weejR4/G5MmT8fbbb+O5556DwWCA3+9HOByG0+kE8G8cKCdxrFZrg3hqCo3tfzB4zaa7B7IsJ70HjDHxDLj/u9vthtPphMfjQSgUgt1uRywWQyQSgVqtRiwWw44dO/Dwww/j66+/xrhx43DPPffA7XbHtb1lyxZxjnnz5uG5555DMBjErbfeigEDBsDn8yUd95xOpxgrOYWFhbjvvvtQVlaG3377De+88w7eeOMNnHjiiXjiiSeg0WggyzL0en3Ke8Dfs5ycHBQVFcXFmnIikb8v/L55vV6YTCYYDIa4e8VR3td9ZcFDEARBEARBEARBHBhkIhqrJUnSM8ZCACBJkhFA6v+GG0GSpK6oF4OXANAwxlySJN0LYGL9Zultxph397YaSZIGMcaSK6P7Ccn+YecZrkajEZIkCZFUlmWo1WqoVCqEw2Gxn16vRzAYxLvvvosuXbrgsssuE7YDyVAWrIvFYqioqMC2bduwbds2rF27Fi6XC9u2bcOGDRsQCAQwbdo0nHzyyVi0aFFKe4CampqU2Xt6vR6dO3cW1gTDhg3DTTfdhClTpuD999/Hxx9/jKuuugpjxoxBbm5ug+O1Wi3KysoAAIMGDYLFYhHCUV1dHdRqtcie9fv98Pv9UKvVcUu0092PRPa2iLIn50vnpassrgjU30de8M5oNCISicDv9+P999/Ho48+CpfLhd69e+PVV1/F8OHDU1qIcMErFovh7bffxieffIKtW7fCaDSiT58+OO2009CrV6+4omRAfeZk586dk7ZZU1MT97skScjKykJWVhYsFgt69Oghtvn9fkyZMgUff/wx/vzzTzz33HMYOHBg0nYlSUr67EtLSzFs2DB8/PHHuPvuu9GxY0fxHplMJqjVaiGo+Xw+OJ1O2O122Gw2MMag1Wrj2lN6SDf2PEmkSw2f5Egk0TLGaDSKjO/8/HxIkoS8vDwxGWIwGLBlyxbce++9+O677/DYY49h9OjRSc/JGENFRQWeeeYZ/PrrrzjssMPwwAMPoLS0FCtXrkz5HrjdbthstqTb2rVrh4kTJ+Luu+/Ge++9hzfffBMzZszAtddeC1mW02YCV1VVAaj/O+B2u+H3+5GXl4dQKIRoNCrOqbSksdlsworD7/cjGo0iOzs75diwryx42gJ8QvObb75pk+21VpsEQRAEQRAEQRAtSSYq2/sAvpck6a3dv18NYHpzTiZJ0jkAngRQDWAHgKcAuHbbUIzfva1KkqR8AEdJknQ1gHBzzrW3SFWwqTF4hiPPyDMajSgoKBA/h0IhaLVaqFQqSJKEUCiE7777DitXrsR7773XQNBSwhjDunXr8NNPP+Hnn3/Gn3/+GZeRLEkSioqK0KFDB5xyyim47LLL0K9fv2begdR07NgREydOxA033IDXX38dU6dOxTvvvINrrrkGt912WwNRZd26dTCbzcjNzYXBYECPHj0QiUQQDAaFiARA+PImWngkPoPmPpt9Qbq+NpYxmHgsX9ofjUZhNBrxwQcfYOzYsRg8eDAeffRRHHfccfD7/WljiPPf//4XkydPxoABA3DDDTfg1FNPxfLly5MK/y2JyWTCnXfeiSFDhuCpp57CiBEjcNVVV+Guu+5q0rL8cePG4YMPPsC7776LRx55BGazGfn5+bBarQDQpLjw+/3ChiXRx1b5DEgwbh6JNircg5vf11gsBkmSRLa9y+XCFVdcgcWLF+PFF1/EyJEjk7YryzI+/vhjTJ48GbIsY+zYsRg2bFha+4qmkJubi1tvvRXbtm3DlClTcPzxxye1ulDCM40tFgv0ej2qqqqg0WjgdruRm5srxPLs7GwRo/xe8Hdbo9GkfRf2JwueliYQCLTp9lqrTYIgCIIgCIIgiJakUdGYMfa0JEn/A8CrtjzOGPu2qSeSJOloAM8CGMkYWypJ0mQA4wBcs9vD+A9Jkq4E8BeAKICzGWOpS963EZIVbGoOKpVKCFlAfdYsz97U6/Vwu92YNGkSjjjiCFxyySUiU43DGMPXX3+NefPmYeHChaioqAAAdOnSBcOHD0ePHj3QoUMHdOjQAXq9XthM7A06deqEl19+GbfeeismTZqEV155BXV1dZgwYULcfmvXrkWPHj3g8/mEqF5ZWYlAIACr1Sp8iyVJihPnUj0Dn8+HiooKOByOuHvbFkkXR7zgXbJtybbz7GOPx4M33ngD9957L0466SR88sknTRJc//rrL0ydOhXnnnsuJk6c2NxL2yP69++P6dOn45133sFbb72FH374AS+++CL69OmT0fGHHHIIzj33XLz++uu48sorEY1GUVxcDEmS4iwUeGY7t4rxer3ic6BeMObZqMlEuES7GbIGaBmUWfR89UQwGERZWRmuuOIKbNy4ETNnzsTgwYOTHl9WVoZx48bhr7/+wpFHHol77723VcY+SZIwfvx4LF68GA888ADeeeedtPtz0bioqAgajQYmkwnhcFj4GGdnZ0On04kJIG4xA9THF58c4oIy30cZb23NgocgCIIgCIIgCILYv8hoPT9j7BsALbGG8mnG2NLdPz8M4LXd3sURAAxABwBGAMcwxla1wPlancTsuFSkE5Eay4gNhUL48MMPsWnTJsyePTvpPuvWrcN1112HnJwcHH/88TjmmGNw3HHHJRVIEgXnvUW3bt3wf//3fzAYDHjvvffw+OOPx12L0+lEdnY2LBYLdDodtmzZAo/HE3e/eFE4xhgkSYpbvr0/F4ZKdw38s1QZg3q9Hj6fL85D1Wg04p9//sEDDzyAIUOGNFkwnj9/PsaPH4+SkpJ9XrDJZDLh0UcfxZlnnom77roLt99+OxYsWJDx8ffccw+OP/54nHLKKbjyyisxZsyYBpYDKpVKCMRerxeVlZXw+/0oLS0VBfR8Ph/sdnvS9y9ZscGD1RqguaSbOFF6TgeDQYwaNQrbtm3DV199hRNOOEF4VCv56KOPcM8998BgMOChhx7CmWee2aoCfk5ODu655x7ce++9WLRoEXr16pVyX54tHYvFYDKZYLfbEY1GoVKpoNFooNPpRKzx4p980kyW5QYx2FKTl0TLUlJSkjbm0vleEwRBEARBEARB7GtSisaSJP3CGDtWkiQP6gVdsQkAY4wlN3pMzZ8AVu5uW416X+QSADbGmFOSpHa7P+vPGPuniW3vNRIF3lR+s4n7pRKRGGNwOp2IRqNCDODH8Dbee+89PPjggxg0aBDOOOOMpP1as2YNAODTTz/FoYcemrYQ3r6GF7RLFD6OOeYYvPHGG5AkCTU1NaioqIBWq0VRURGi0SjKy8thNBphNBrh8/mE/7PZbE76DMxmM4qKivYLMTmdb7EkSTAYDHA6ncjJyYnzuvZ6vfD7/ZBlGaFQSBTh8vl8eOWVV6BWq/Hmm29mfA9kWcYzzzyDCRMmoHfv3njppZdSitU+nw/3338/GGPo3bs3evfundLPOBGeNdoUi4CjjjoKV111FZ588knhP5zpcQsWLMAdd9yBp556CrIs44knnmiwH/cs1ul0UKlUwh/abrfD5/MhGo3C6/WKjOR01hYHszVAc+CWC8C/QppyDOWTRaFQCMOHD8eGDRuEYJxIIBDAfffdhw8//BBHH300pkyZstesAA4//HAAQGVlZdr9evbsCQBYunSpELN5oT+eIczji7+7vDijLMsN/pY0NrFE7BsSC4oqGTJkCBYuXLj3OkMQBEEQBEEQBNFEUorGjLFjd39vkXX9jLEYAF4KXgLgBlC9WzC+DMAgAPcxxnwtcb7WorGMLi50KJe4m83mlCJSIBAQ/pRAfeElr9cr/CsnTJiA5557DieddBJmzZqVMmtp3bp1UKvVGYt2meByuRAOh+FwOFrUE9jn8yUVN0477TS8+uqrmDFjBs477zyUlJSIwmXr1q1DTU0NiouL4XA4IMtyo56e6YTY/Q2Xy4WdO3eisrISOp1OFAB0uVyIxWKw2WziXgQCAfz000+YPXs27rzzzoyX4/t8Ptx44434/PPPce655+KRRx5Jmwn3zjvvYOfOnTjssMPw/fff45tvvoFarUbv3r0xePBgDB48GDk5Odi6dSu2bt2KxYsXo6amBuXl5aioqIDZbMZdd92Fvn37ZnwfjjjiCAD1Yttpp52W8XFHH300Fi5ciOuuuw7PPPMM6urq8OKLL4r3TpZlMXnDJ2x0Oh1kWUYwGITdbhfiPM9+52Im/660pyBrgIbw1RbJfJ8DgYDIFrbZbNBoNMJehmfcGgwG3HLLLVi4cCHeeustnHjiiQ3OUV1djVGjRuHvv//GHXfcgTvvvBMajQabNm3aK9fIvb6rq6vT7peXl4eOHTti+fLlkGUZBoNB3Bs+GclRjmNqtRqMMTG5mGwfAjjnnHPadHu8TRKNCYIgCIIgCIJoy2RkT7E7M9ih3J8xtrW5J2WMRQF4JUnaJknSRACnAbi6rQvGQON2FFw8slgscUJeKhFJ2Z4sy9i1axc8Hg90Oh0eeeQRvPLKKxgxYgTeeOMN6HS6lP1av349OnXq1OzlrowxbNu2DT/++CPKysqwYsUK7Ny5E0B9hltxcTEKCgrQu3dvlJaWolOnTiguLhaiW1PgNh2JDBkyBAaDATfccANuuOEGAIDVaoXNZoPNZoPVasWZZ56Ju+++G7t27UJubm5cNnc0GoXT6YTJZILVat3vvWR5xjBjDDk5OfD7/QgGg3C73UIkysvLS5qZOGXKFGRlZeGuu+7K6Fxbt27FiBEjsGrVKkyYMAHnnXde2vu3bNkyfP/997jgggtw+eWXIxKJYN26dVi8eDE2bdqEKVOmYPLkyXHHGAwGtGvXDqWlpRg8eDAWLVqEhx9+GKNGjcJxxx2XUT979+4Nk8mEhQsXNkk0BurjePr06Wjfvj2ef/557Ny5E6+99hry8vLg9/vF5I1Op0NVVRWKi4uh1WpFVrHFYolbDaAUig8Ei5TWJnG1hTKT2Gg0wuFwAGh4D7lY//TTT2PGjBl47LHHcNlllzVof8uWLRg5ciS2bduG119/vclCH2MMZWVlMJlMaNeuXbPGD6PRCJPJBJfL1ei+ffv2xYoVKxAIBMAYQ11dHWRZFisxUkECceNkOu7tq/Z4m3fffXeLt0sQBEEQBEEQBNFSNKr4SZJ0K+r9hysAyLs/ZgB6N/ekUv1/41oAx+3+fjJjbENz29sbcAFBrVYL71MlvKiWUjxSFilKJUBEIhHEYjHEYjEEAgH4/X5UVlbi6aefxqxZs3DzzTfjqaeeAgCEw+G445TZaOvWrUOPHj1EP7Zv357yWr788kvk5eVBlmUsX74ca9aswcaNG1FbWwugXtDp2rUrjj76aBgMBpSXl2PXrl1YuXIlfv/9d9GO2WzGiSeeiCFDhgjRLRUDBw4UPwcCgTiRMxaLIRqNQqfTYf78+Vi5ciU8Hg9qa2uFl2xdXR3Kysrw6KOPolevXiguLkYsFoMkSbDb7TCZTNi2bRt27doFq9WK0tLSBr61+xuBQAAVFRWQJAkOhwMlJSXCloMLl0oBnvuk/vTTT/jmm2/w0EMPwWAwJF2av2rVKhGrK1aswB133IFwOIwXX3wRxxxzTFrf67///hszZsxAfn4+evXqhZUrV4ptHo8Hhx9+OLp3747KykpEIhFYrVYhuPbr10/s2717d8yePRtvv/02fv31VzzwwAMpRVduRaFWq3HMMcfghx9+QCQSgSRJ8Hq9ohBgIl6vFzk5OXGfjR8/Hnl5ebj//vtxwQUX4Omnn0bfvn1htVqF9YnJZIJGo2nwriu9j5WrBxInhZK979z+Yn8pjqcsFNgS8GfLCwomrtqwWq2IxWJiskSr1cJisSA7Oxsvv/wyXn31VVx22WW45pprGngYf/DBB3jyyScRiUTw5JNPori4GMuWLRPblyxZkrJf8+fPRyAQwNq1a1FTUyP6U1RUhJycHJx44olJJ7n0en3Se5SXlweXy4VIJHUdV71ej759+2L27NkIBoPQ6XQIBoMA6osvqtVqUSxPOVY2Fjf7Q1wRBEEQBEEQBEEQ+weZpImOBdCDMdZ46lSGsPr/tMOSJD0OYFFbF4ybQrossGg0iurqauTm5ooCR16vV4h9er0eOp0Ozz77LH788Uc89thjuOOOOxoVAoLBIDZt2oRzzz03435u3boVn332GbZv3w6r1YrOnTujc+fOMJlMOPbYY1MW2+vQoQMqKipQXl6OJUuW4KuvvsJ3332HgQMHoqSkBNnZ2Y2e2+fzpbxHAwcOjBOYuZjMj+vfvz/uvfdevPHGG9BqtUKoDgaD0Gq1sNvtMBqNB0SBIZPJJLIvebar1WoV4mMgEIDH44Hf70dubi5CoRAMBgPuv/9+tGvXDtdff32j51i4cCHGjx8Pu92O1157DaWlpY0e88svv8Dj8eDaa6+FVqtNuo9er0eHDh3iPvP7/XG/63Q6DB06FO3bt8e8efMwfvx43HXXXWjfvn3a85900kmYP38+1qxZI7xhm8ro0aPhcDhw00034brrrsO0adNw9NFHA/g3WzSVgM0zZE0mU5MyPg/04njpin0C/46NySbYlHD/YqD+ni1YsADjx4/HySefjCeffLJB2wsXLsSDDz4Im82GZ555Bh07dsyov16vF3/88Qd++eUXhMNhZGdnY9CgQWCMoby8HFu2bEFZWRkWLVqEdu3aoUuXLujXr1+DSYhE8vLyGrWnAIA+ffqAMYY5c+bgkksuQV5eHoxGo5joqaioAAAUFRUdkPHSmgwZMgQAmlQwc2+2p2yTIAiCIAiCIAiirZKJaLwNQG0rnX86a+l0tjYEF5f0ej0kScL27dvhdrtFpigXkCwWC4xGIzZu3IiRI0di1apVePPNNzF06NCMMsc2btwIWZbRo0ePRvetqanBnDlz8L///Q82mw0jR45Enz59xHm2bduW1r/YYDCgpKQEJSUlOOqoo7B9+3bMnTsXv/zyC/7880+ceuqpuOiii5Cfn5+yjVSexo1hNpsxbdo0nHLKKXjrrbfwzDPPwOv1QqvVIhQKQa1WIz8/H4FAAKFQaL8UjhOFN6vVCsZYXBz4/X7U1tZCpVJBkiTEYjFUVVXB7/fjzz//xB9//IHJkyc3eo8/++wzTJw4EYcccggmTZok/FjT8ddff2H16tU45phjUFxcvMfXK0kSBg8ejKysLMydOxfjx4/HzTffjCOPPDLlMUOGDIEkSfjhhx+aLRoDwHnnnYfS0lIMHToUI0aMwIwZM9C7d/0CCsYYgsFg0lUFfr9fZDY3Rcw70IvjNVUUV06w8bFSp9MJETkSiWDBggW4/vrr0bdvX/zf//1fAzucWbNmiYmGiRMnIi8vr9HzlpeX49dff8WyZcsQi8WQn5+PI444AoWFheI96969O2RZxvbt26FSqfDPP//gl19+werVq3HzzTenHSNzc3Mz8lDmXt5LlizBcccdB51Oh1AoBIvFAoPBILzwyfKEIAiCIAiCIAiC2BekFI0lSbpj948bASyQJOlrACG+nTH2wp6e/EATjJUenSqVSiy/tlrrawmqVCrodDro9XoEAgEhDphMJng8Htx2220oKyvDZ599htNPPz2prUAyPvnkEwDAoYcemna/SCSCW265BVu3bsXxxx+PU089VSwVby7FxcW47rrrsGbNGqxevRpz587Fb7/9htdeey1lFmo0GhVLsZvK8ccfjzFjxmDKlCm45ZZboNVqxX03GAzi3u+PgjGQWnhTWhvwePF6vcjPz4dGo0E0GoXf78err76Kbt264eqrr26Q2atk+/btmDBhAo4++mg89dRTGQmZwWAQL7zwAnJyclo8S65jx4546qmn8MILL+C5557DoEGDcPnll4tMayV5eXno06cPZs6ciQsuuCCj7PZUHHfccfjxxx9x3nnn4aqrrsK8efOQn58Pi8UCk8kUd9+5UMgFfYPBICZAMpncOdC9aJsjinMrCr7iQukDv3DhQtx6660oKCjAF1980cAKYubMmbjjjjtw7LHH4oYbbmhUMHa73fj222+xbNkyaLVaDBw4EEcffTT+97//obCwsMH+KpUKubm56NOnD4YMGYKlS5fiiy++wK5du9IWlzQajSntUpRUV1dDpVKhuroaWVlZCIVCYmzksabRaOJsjniGO9lQEARBEARBEARBEK1N6nQpwLr7ayuA+QB0is8apt8RQiTmYq/RaITNZhNipsFgQHZ2NlQqFTQajRCnQqEQ1q9fjwULFmDMmDE4/fTTMz7nu+++i8mTJ2PkyJHo3r172n0//fRTbN68GRdeeCHOPffcPRaMleTn5+PWW2/Ff/7zH9TU1GD58uUp9z3vvPOwfPly/PLLL806l7Jomt1uF7Ye3PtTlmWEQqE0LbRdTCYTbDZbA+GNZ7f6/X7hq6tSqaBSqUThP61Wi7/++guXXnppowUKua3H2WefnbHIN3PmTFRUVODEE09MOSGwJ+Tn5+PRRx/FsGHDsGzZMowbNw7vvfdeUvH74YcfRjAYxBVXXCEKNjaXXr16YejQoaiqqkJ+fj7MZjMMBgP8fj98Ph88Hk+csMnF32AwiLq6urTi/MEE93XmgiYXhGVZjvtZSSAQwK5du1BRUSEm1b777jtccsklGDp0KPLz8zFv3rwGou5XX32Fu+++GyeccALefffdtGJ8MBjE/Pnz8cILL2DlypUYMmQI7rvvPpx//vnCKzsTuKd8suxzjizL+Ouvv3DYYYelbWvlypU47bTTUFhYiNGjR4sYslqt4u+FUkAH/v37QvFGEARBEARBEARB7A1SisaMsUcZY48CWM1/Vny2Zu91cf8h8R99Li5xcS8/Px9ZWVkNhE2j0YhNmzYhEong1FNPzfh88+fPx7333ouTTz4ZTz/9dNrsM7fbjbfeeguDBg1C165d9+xC09CvXz+YzWb8+uuvKfe54oorUFRUhKeffrpZxba4x3EkEoHf74fX64XVahUF8bjwsj+SKLxxlNfl9XphNBrhcDiEuAkAf/zxBxhjOOmkkxo9D8/O5YW/GmPXrl344IMPMGTIkEY9h/cErVaLiy++GC+99BKOPfZYfPnll7jtttvw4YcfxhVa7NmzJ95++214vV5cf/312Lp1a4uc32g0ioKUtbW18Hq9QiSsra2F0+kUwmcqgf9AhDEGn8/XpPdVOYmWOKHGUXr4lpWVYdiwYTjrrLOwePFiPPXUU/j777/RuXPnuH689NJLGD16NPr06YPXXntNjAeJyLKM+fPn45prrsEPP/yAQw89FHfccQdOP/30Zlk+7Nq1S7yHqVi1ahWcTidOPPHElPusXr0ap512GrRaLWbOnImSkhJEIhExIcG/lAXw+L06WOKNIAiCIAiCIAiC2PekyzTm3JfhZwc9yf7Rl2UZXq8XtbW18Hg8kGUZRqMRVqtVCBeSJOG3336D0WgUxbgaY/ny5Rg9ejQOO+ww/Pe//200s/SNN96A3+/HLbfc0qpLm7VaLY488kj8+eefiEQiSfcxGAwYN24cFi9ejO+++67J5+AikcvlQkVFBSorK4UtxYEKzy7mBbK4vUkoFBKZsHPmzIHVak3rB8yxWq2QJAlutzuj80+dOhWSJOHGG2/cwyvJjNzcXNx0002YOHEi2rdvj4cffhjnn39+XHZ6r1698Pbbb8Pn8+Giiy5qEeHY5XKJImwajQayLAshX2kDAqQW+A9EuG1KU7JclZNoyTJngfq43rRpEyZNmoQTTjgBP/30Ex599FGUlZXhrrvuihNIA4EAbr75ZjzzzDO46KKLMHPmzJQZxitXrsTYsWPx3HPPIT8/HzfeeCMuvfTSRovYpWPnzp0oKipK+7x//PFHaDQaHHvssUm3r169Gueffz60Wi3efPNNFBQUQK1Wo7i4GFlZWWknu/jfl4Mh3vaUYcOGYdiwYW22Pd4mQRAEQRAEQRBEWyadp/GZAM4C0F6SpJcVm2wAosmPOjjhS6+TeU36/X64XC4hthiNRtjt9rgMUY1Gg++++w7HHXccVCqVEFurq6uTisGbN2/GlVdeiZycHEycOBFVVVWoqqqK20dpD7Fz50589tlnOP744+HxeFBWVoYVK1YkvRaz2SxEs0SCwWBKr06tVisEjw4dOuDHH3/E999/j06dOgFAA9/Z/v37o3379nj88cfRqVOnlNl7yWwm+D1mjMHhcMDn8wH49zm4XC7k5eWltVDY28JLYxma6bZz4ZLvJ0kSGGOQZRlqtRpGoxF1dXWYM2cOTjnlFAD1WdherzflZEJVVRWsViu2b9+e1N6hrKws7ueFCxfi9NNPx86dO7Fjxw7s2LEjabsejydljGRnZ2PZsmVJt5nNZnz77bdJtw0ePBgDBgzA119/jWuuuQaHHHIIhg4dCpvNBgC49dZb8corr+D888/Ha6+9Fuc36/V6U95bxlhcITagfkJCrVZDo9HAZDIhGAxCp9MhFoshJycHfr8f0Wg07pnwtrifufLzRJ/zxKKG+wtN9Svm16ncn/8cCAQgSRIikQjGjRuH6dOnw2Qy4T//+Q+GDRuG3NxcuN3uuAmNXbt24YorrsA///yDa665BsOGDcM///wjtv/666/CJ33ZsmX47bffYDabccopp6Bbt25YuXIlVq1albSvwWAwZcZ9bm4uNm/ejEgkgsrKShQWFmLz5s0A6q1xnE5n3P7z58/H4YcfDlmWEYvF4ratWbMGF154ITQaDV566SUceuihCIVCMBqNCIVCMBgM4t1ONx7sj/Gzt7npppvadHu8zZtvvrnF2yUIgiAIgiAIgmgp0qWn7gTwN4DzACxWfO4BMK41O7W/4fV6UVlZiYKCgjjxU5ZlMMaQk5OD7Oxs+P1+mM1mGI1GuFwuhMNhUQhp3bp1uPbaa+NEPp1O10D0c7lcuPLKKyHLMqZOnYqOHTsm7RMXaRljmDJlCoxGIy699FLYbDb8888/KTP0amtrccQRRyTdVlVVheLi4qTbQqEQSkpKxLnfeecd1NXVic+SLQcfPXo0HnroIfz888/o379/0nYBNLgHvC23242CggI4nU4h9HHrj0AgIERFpWjXVkknBEmSJLZzL2M+QcGzX7/44gu4XC7cfPPNUKvVACCyY5NRXFyM/Px8RKNR8YyUcDEsFoth9uzZyMvLwwUXXACdTodoNIqCgoKk7a5evRr5+flJt9XV1aUsIFZWVobS0tKU96Br164YPXo0Fi1ahIULF2LKlCkYNWoUrFYrbDYbpk+fjquuugqjR4/GO++8gw4dOgCoj/9UHrSyLIv7w2PDbDbD7XZDp9OJ+8wFYV7AMhaLQavVxr1DygKGidmxysKGymfZ1lH2U5KkuPvY2CSIJEkNBHOg3kvb5XJBq9Vi7NixmDVrFm655Rbcc889yM/Px/bt2xu0tXz5ctxwww2oq6vDY489hmOOOabBPuvXr4fVakVZWRkWLVqEDh064KijjoJGo4Hb7ca6detS9lWn06W0XDEYDOjfvz/WrVsHxhiOOeYYUXBUo9HExWxZWRm2b9+Oa665BiUlJcjKyhLbVq5ciYsuugg6nQ5vvvkmDj/8cCGOq1QqeL3eA75Q4t6ET8i2lJVHS7enbJMgCIIgCIIgCKKtks7TeDljbDqAroyx6YqvTxljmRmhHuRwv121Wg2tViu8KrlgHA6H4XK58OWXXwJAo37Gfr9fFP569tlnRRZvOhYtWoQVK1bEZWa2NllZWXA4HFi/fn3a/U499VR07twZ06ZNi/OrbQxuT8HFYy5ScoHQZrMJwSoQCMDtdsd50e4PpPKQ5dmbdXV12LlzJyorK+F2u/Hqq6+iX79+KZfFJyMnJ6dRT+MFCxZg+/btGDZsWErv2L2FRqPB4MGDMXLkSHi9Xrzzzjsiq5lbVXi9Xlx66aVYs6Z5tututxuVlZWora0VnwUCAXi9XlRXVyMWi4nsbiWp7BdSfX4wkMzH2OVyYdu2bRgzZgxmzZqFCRMm4Jlnnkk50TB79mwMHz4cOp0OEydOTCoYc7Zu3YpFixahqKgIgwcPbtSypyls2rQJAJJOsHDmz58PACLbn7Nx40bhYTx16lT07dsXPp8PVVVVIn5TxUhzvKQJ4KyzzsJZZ53VZtvjbRIEQRAEQRAEQbRlGk29ZIwlN6YlBBaLBQ6HI26pu8fjQSwWg8VigcFggCzLYns0GoVGo4HBYIBWq8VPP/2Edu3aiQy2VDz44INYsmQJJk+ejMMPP7zRfsViMbz99tsoLS3FaaedlnSflStXYteuXU284sbp3r17nMVBMtRqNUaPHo0tW7Zg9uzZGbfNxcva2lrU1taiuroaHo8Hfr8fkiTBbrcjKysLRqMRer0eoVAI4XC4QRGufU06QSidh6zf78e2bduwbt061NTUYPHixVi/fj0uu+yyJmWxZiIaf/755zjkkEPSZoIngzGGtWvXYvny5aiurm5R0atjx4649NJL4fV68dFHH4nPe/XqhQ8++AAqlQqjRo0SdgWZwK0EcnNzUVBQgKysLDDG4Pf7odfrYbFYkJ2dDZVKBZ1OB5/PFzcJkczPPN3nBwN6vR4qlQp6vR5A/bio0+kwe/ZsfPHFF7jvvvswblzqRStffPEFxo4di969e+Pzzz9PK9g6nU78/vvvsNvtOPbYY0W2fSKyLMPpdCIcDjfpWjZu3IjCwsKUmaaMMXz77bfo3bs3CgsL47ZNnDgRfr8fH3zwAXr06NHAciddjCSOAyQiEwRBEARBEARBEHuLg0/JaAV4kTL+T7/f70dlZSUqKyvh9/sRCATg8/nEcnduoSDLMqqrq/HDDz/g4osvTiv4VVVV4aOPPsLVV1+Ns88+O6N+7dixA263G2eddVZSESUcDsPtdmPHjh0tLkLk5OSk9EZW0q9fPwBAeXl5xm3zjOmqqiqEw2FoNBrhQQv8m42rUqkQCoWg1+uh0+naXLZnOmHYZDLBZrMlFalMJhM6dOiAHj16oF27djjssMPQoUMHPPvss8JvNRMMBkNSz2gldrsdFRUVTRbZduzYgaqqKgQCAaxevRrLly+H2+1usTjr2LEj+vXrh8rKyrjPu3XrhvPOO0/4XGfK2rVr0alTJwQCAZH1um3bNtTU1KC6uhp6vR5utxterxdbt27FypUrU3o3E/WEQiHIsixizOfzYdu2bfj111/Rt29fPPDAA2mPnzJlCnr27In33nsPeXl5Kff7559/sHjxYlitVhx//PFpM4wjkYgQXjPF6/Vi3bp16NmzZ8p9li5ditWrV+PCCy9ssO3PP//ECSecgMMPPxzBYBBGoxFFRUVwOBwoKipKa0mROA40pyAhQRAEQRAEQRAEQTSHJonGkiQVNr4XYTKZUFBQIGwEZFmG1WqF0WhEMBiE1+tFVVUVNBoNPvvsM8RiMYwZMyZtmzNnzkQ4HMbVV1+dcT82btwIAOjcuXPS7Vw44aJ2S6LVahGNRjMuANeUTMzc3FwA9ZnGBoMh7fJ/o9GI7Oxs2O32NpftmU4YliRJeOAC9ZmwlZWViMVikCQJWVlZaNeuHTQaDcxmM/773/8iGAw2KT5UKlWDYl2JDBs2DDU1Nfj9998zbtfj8WDLli3Iy8vDkUceia5duyISiWDDhg345ptvWmySgvsrJ9qOLFu2DIceeqgozNgYsixj0aJF6Nq1qxC6a2trUVNTI7JSd+zYgUgkAovFArPZjEgkst9ZnuxtkllzqNVq/P333zj++OPTTpL9888/WLduHS6++OK0tihOpxP33Xcf1Go1hgwZkrGFSlPib8mSJZBlGQMHDky5z+uvv46srKwGonEoFML69etxyCGHAKi/J/yd5d/TjUuJ40C6MYMgCIIgCIIgCIIgWpKmqmhzWqUX+zmyLMPr9QoBSaVSiQJd3MeYiwNGoxEWiwUmkwkajQbvvfcezjrrrJTCLuerr75Cnz590L1794z7tXHjRhiNxgbLpTlcKJYkSRQ+aynUajUYY42Kkpym2Crw+1pXV4dYLBZnT5FIJvYAic9vb5EoCKVj27Zt2LlzJ1wuV9znRqMRJpMJAwYMwLhx4/Dbb7/hjz/+yOj8Go2m0efTo0cPOBwOLFq0KKM2Y7EY1q1bB51Oh65du0KlUqGwsBD9+/dHaWkpgsEgfvjhB3zzzTcoKysT11VXVweXywW32w2Px5NRZrNWqwVQnz3KiUajWLFiBfr27ZtRfwFgw4YNcLvd6NevH3JyciDLMmw2G3JycmC32xGLxaDRaKDVamG321FSUoLi4mJRGE+JLMvw+/2IRqMNLCwOFpSZvMp3z2g0YuPGjQiFQhg8eHDaNubOnQsAOOOMM1Lu4/P5MH78ePh8Phx55JEZCanNeR5///03ioqKUhbLW7t2Lb777juMGjWqQR/Wrl2LWCyGDh06QKVSweFw7JHg25QxgyAIgiAIgiAIgiD2hKZWCqL/VJPg9/vFUnXlUmOTyQSHwwGNRgOPxwOtVova2lpRqO2DDz5AZWUlrr/++qQ2AZWVldBqtaiqqsKSJUtw6623CnG3oqIi7RLlFStWYNWqVSgoKMCqVavitnm9XpFFqVarodPpsGvXLmg0GjgcDmzYsCFpm7IsY8uWLUm3GY3GuMJ3brcbQL1ootPphK9pIny/aDSa1HM4lQ9xTk4OnE4n3G43srOzodFoYDQaRQE8g8GQsbDCl3wD/z6/lhZl9iSzllubaLVaaLVaxGIxIcTJsgxJkhAIBHDppZfixRdfxMsvv4xBgwYhEAik9HatqKhAIBBAJBLBjh07GmzfuXOn+PmQQw7BTz/9hPXr18NiscDtdqO6ujrpNe7cuRPBYBAFBQVxbQD1GeJWqxU1NTWoqKhokL2sjFNJktCpUyfxPJTCMIcL6OXl5eK9WLduHQKBADp37gyn04lAIICcnJyk94BbxPz1118AgMMPPxy1tbXCyqR9+/YIBoPQaDSoq6tDdnY2GGOQJAklJSWoqqqCwWCIe7a8+Bu3oAHix4T9yYs2XV/5fUgGf58YY3FZxl6vFz/++CMAoH///knHvO3bt0OtVmP27Nno2bMnIpEItm7dCqBevOXHxGIxfP7559iyZQsuuugi7Ny5UxSrSwYXsflkRCwWE59pNJoGNiccrVaLTZs24aSTTmqwj8Vigdfrxauvvgqz2YyhQ4cKSx7+/FeuXAkAOOyww8AYg1arRSAQEJOJiciyLCws2trKiP2Jq666qk23x9tcuHBhi7dLEARBEARBEATRUjRVNH6tVXqxn8Mzx4xGY5yQolarYbFY4PF44PV6EQwG4fF44PP5UFlZiffffx/du3fHaaedllSAsVqt0Gq1+OqrrwAAZ599NqxWKwDA4XAIm4Zk5Ofno7KyEieccEKDTONAIACj0YhIJAKDwQCLxYKKigrEYjHEYjF07NgxaZt+vz9lQbTKykp06dJF/M4F5A4dOsBisaQsYmWxWADUWw2kshPgGaVKcnNzUVFRgVAohGg0isLCQgSDQZE1zDPyMoELW63peZxOhOZCt1Io4p/p9XowxqDRaBCJRLBt2zZ06tRJxIFOp4NOpxNi2rBhw/D6669jx44dyMvLS3newsJC4Q2dLINSucy/oKAACxcuRCgUwqmnnooffvhBnF/Jtm3b4PP5YDAYEA6HG2QLu91ucVx+fj6i0SiAegEyGAzCbDaDMQbGGGpqarB161a0b98eKpUKHTp0aHA+LvQbDAYR4/PnzwcAnHjiiSgsLEQkEhEexcnuu0ajweLFi2GxWNC1a1dotVqo1Wqo1WpoNBpYrVYxOROJRBCLxYTNDBf4lHHGY8hgMCAYDMJkMh0wWaG8MGBjmbJGo1EIxsprDwQCWL9+Pbp3754ya9dqtaKyshLr16/H7bffjqysLLFt27Zt4l7//PPP2Lx5M0444QTY7Xbs3LkzaYxw1q5dCwBxMafRaCBJErRabcrxqaamBpIkYfTo0bDb7Q3uRygUwvz58zF27Ni44qSMMajVaqxatQparRbt27eHx+NBTU0NdDod1Gp10vEpGAwKSyOVSiXiSDk2HCjx1BqUlpbGTWwm2vWkK6aYjtYSjZtiJ0QQBEEQBEEQBLG3aVIqE2Nscmt1ZH+mMQsEg8EAq9WKdu3aweFwICcnBxs2bMDff/+NG2+8sVER4LvvvkNxcbHwxcyE8vLytAKwLMsIh8PQ6XSwWCyQJKlFC3vxYlRcpEkFz8ZsqhCSk5MDv98Pm80GvV6PmpoaISKm8zhORiYWFq0Jz07lWdWyLIssapfLJTKNU/VPpVIhPz8fGo0Gl156KVQqFV599dVGz6tWqzOyD+ncuTOKi4vTZsV5PB6sXLkSKpUqZVa5Ei7WabVa6HQ6aLVamEwmmM1mWCwWISrX1NSkbIPHmDILeenSpbDb7SlFyWT89ddf6N+/P7KyspCdnQ2DwYDq6mqRiWo0GoXY7fF4RCY7F/WU8FjSaDRxxTE5sizH2VYk/t6WyaQIG78ebs/BJwH8fj+ys7Px22+/NWpN8f333wMATj755KTb169fj1WrVuGII47AoYce2qRrUN7nTPzWN27ciD59+jQQjDmTJk2C0WhM6Um/atUqHHLIITAajYhGo6JoZzoPdj6ZU1dXh6qqqrixgUjPli1bwBiD0+mE0+kU8ce/mlIoVElVVRWqqqpatK8t3R5BEARBEARBEERLs1+tf5UkaYAkSUMkSTp6X/elKXAhSafToaCgADabDXPnzoXNZsPIkSPTHhsMBvHzzz/jlFNOaZKwyrOtUonGPAtUr9eL/nk8nhYTr5IJesngwk1TRePs7GyRYZydnY28vDzYbDaYzeaknp9tWZxTFgzjgnE4HIZGo0FeXh6sVivy8vJQUFCAkpKSpBmK/BkWFxfjggsuwOuvv95occNMRWNJknD88cdj+fLlqK2tTbrP8uXLodFooNPpWiQTkt+Turo6BIPBpPvwDHSlzcHSpUtRXFyc8XMOhUJYvnw5evToAbvdLiYPZFlGbW0tKioqIMsyTCaTyPbU6/VxmcZNIXGCIPH3toyyCBv3LU68z4FAAOXl5Vi/fr2wQPH5fKioqMDff/8Nt9uNo49OPXzLsoy5c+eie/fuSTOHXS4XfvrpJxQVFWHQoEFN6j9jLK6/jcVIXV0dPB4PTj311KTby8vL8fHHH+Oqq65Kms3OGMOKFSvQqVMnyLKMdu3aIScnB/n5+SmtKfiEhNlshs1mQ35+fpMnwQjg4osvxsUXX9xm2+NtEgRBEARBEARBtGX2G9FYkqRzUG+PcRWAsZIk3dKEY2+QJOlvSZL+bumCb0pSCZPKz7kwwP0tlTYAyWCMIRqNJrUDSMeGDRuQlZWV0sKCi108KzQ7OxuyLGP79u1NOk8qeDZiY2IH9zRWLkNvjLq6Ovz111/o3LkzrFaryOxMly3cmuLcnsaXMtM5EAggGo1Cp9PBbrdDo9HAZDIJkZ+L8Ylxxn+22+047rjj4PF4Gs0cb4q/rsFgEPGbjEAggLy8vBbN1uY2CKmEbR7byuXoffv2xdKlSzF8+HCUlZU1eg6dTofOnTvjiy++wM6dO+Hz+WA0GmE2m7Fjxw5s2rRJ+DeHQiHx/sqyDIvF0mQxTzlBkOz3RPbW2JUJyiJsybLjfT4f9Ho9rFYrsrKyYDKZRMzIsoxnn30WZrMZp5xySspzvPvuu1i9ejWGDx/eYFsoFMK3334LnU6HU089tUmxxvvHixo2BmMMZWVlMBgMOP7445Pu88UXX0CSpJRZxn///Te2bduGAQMGCHsZo9Eo4icRfk+VExH8npO/MUEQBEEQBEEQBLG3SfufqCRJakmSftxbnUnTjz4AJgC4ijF2FYAZALpnejxjbBpjbABjbECqZcYtQSphknsZBwIBBAIBeDweDB06FC6XC/PmzUvbptFoRI8ePbBs2bKM+xGJRLB27Vr07NkzZdZnMBiEWq0W2ZpGoxHZ2dlwOp3Ytm1bxudKRUVFBUwmk/AsTgU/l9IPuTFef/11uN1ujB07NiMxhYtWzRH5MqEl44s/B7vdHudvLMsyzGazEJ14PHE8Hg+2bNkCWZZRXl4OjUaDgoKCtOfiy+UzYdGiRejWrVvKonJ2ux1VVVUtWujN7/dDkqSUzyw7OxtFRUVYvny5+Oz555/HCy+8gE2bNuHcc8/F1KlT02a7S5KEmTNnwuv1YujQofjll1/Efc3KyoLD4UB2djb8fj/0ej3MZjN8Pp8odtdUMS/RCqUxa5S9NXY1lUSxm499oVAIDocD7du3h9VqRW1tLVwuF3777Td89dVXeOihh1LG5a+//oopU6bg1FNPxUUXXRS3TZZl/Prrr/B6vTjttNMa9VVWwj3lo9EojEajmKhLlxG/Y8cOeDweHHnkkUnPFQqFMHfuXJx99tlo165d0jZef/11mEwmnH322aIYaFVVFWpra5NOXvF7ajAY9qsMdIIgCIIgCIIgCOLAJK3iwRiLAZAlSco8DbR1kABMYYxxdWgxgKMkSeoktaGqQMp/+pWZoNzT2Gg0Cn/Us88+GwUFBfjwww8bbbdfv35YtmxZxkvuly5dimAwmNbvMxQKQa/XxwkneXl5MBqN+Omnn/ZYrKisrITD4Wh0Py4ad+rUKaN2A4EAXnnlFZx88sno2bOnEFTTWU8EAgF4vd44kW9f2lXIsiwygRPPn0xEDAaDwteYH282m6HX6+H3+0X2q8fjgcvlwrZt20QBuXRkmnXp9XqxatUqDBw4MOU+drsdkUikxe4ntz9QFgBLxiGHHIKKigpR6EySJJx//vn49ttvccopp2DSpEk48cQT44TlRA477DC899572Lp1K84++2xce+21WLduHXJzc1FaWopIJILa2lqRccwL6JFlwL8ky5jmPr6VlZV47LHHcOihh+K2225LenxlZSVGjRqFdu3a4eGHH24g6E6dOhU7duzAMccc06CwZzoqKirw/fffgzEGi8UiCksCqUXjUCiETZs2IScnB6WlpUn3+eGHH+DxeHDNNdck3e7xeDBjxgxceOGFKCwsRG5urijamSp2lO9+YxnoBEEQBEEQBEEQBNHaZJJm6AWwQpKk+QCESSpjLPl//60AY2ypJEmbAECSJA2ASgAVAOoYY0ySpB6MsXVNaC/ltlRCQrJjuFhnNBohSZLIinQ6naIAHM9SSxRTZFnGRRddhDfffBMVFRVJbSTcbje0Wi26d+8Oj8eDpUuXiozcLVu2INVy9c8++0wIbWvWrGmwPRAIIBwOQ6VSNSjG06FDB2zYsAFz5sxB9+7d4+5HTk4Ofvjhh6TntNvt2Lhxo/h9x44dKCoqEp/l5eUlPY7baBgMhqT+sMFgMM7CY/r06aisrMTNN98Mr9cLvV4PWZZRVVWF/Px8kdnMGBP3QGkFwJ8jz+QDkDSTcE/mIlLFCm8zEAigoqICAFBUVNRo1iS3EOFZxj6fD1arFaFQCB6PB4wx5ObmIhgMQqPRYPv27Wjfvj0CgQDUanXSNsvLy4U1yNatWxtsV1o7LF++HLIsIy8vDytWrEBdXR28Xm/SdmOxWEqfX41GI+55sm1KO41IJIJYLAaVSgWPx4N//vkn6XE6nQ4qlQpTp07FqFGj4rZdffXV6NmzJ9566y0MGTIEo0aNwrXXXivup0qlQvfu9QsWTjnlFPzvf//D//3f/+GVV17B559/jpEjR+LBBx9EcXExwuEwYrEYdDodTCYTVCoVfD6f+FmJMvYSYYy1iOdzS9HczHC/3y9iL3GM8/v9qK2thdlshsPhwOTJk7Fp0yZ88803AIDt27fH3Z9YLIYbbrgBLpcLN998M3799de4c61evRrTp09HUVERZFnGypUrG/TH5/PFTQwwxuB2u+F0OkWhxWg0Kr6AenFYkiTYbLY4r+5NmzZBlmUUFRUhHA4nLVj26aefori4GAMGDIjz1ObMmjULPp8PV111lVhhYjAYoNFokJOTk3YihMcIv6+Jz6gtxQ9BEARBEARBEARx4JKJaPzp7q99CmPMvft7FEB09z/OsiRJlwMYIUnSKMZY9d7oi9LXlAto3E7A7/cjGo2KYlqMMajVapE5CQBmsxnhcBgXXnghpk6dii+//BI33HBDg/M4HA5oNBoMGTIEQL1QPHjwYAD1Ql8q64elS5eic+fOQhBLxOVyAagXZBMFPqPRiI4dOwpRWpktvHbt2pRZfhaLBYcccgiAehHI7Xbj+OOPF5+lytirrq5GSUlJyow6nU4ntkUiEbz88ssYPHgwzjjjDFRXVwsBKBaLCdGR+65ycYX7ggLxGcbcrmJviDDK/phMJjgcjjiP4HR90Gg0wkaEX5tSUOLX0KFDBwQCAezYsQP9+/eHxWJJKRo7HA7o9Xro9XoUFRU12F5TUyN+3rRpE0wmE/r16we1Wo1DDz00aRxUVFRg165dKbOXUwnNANC+ffu4bPPNmzejrq4Ohx12GDQaTdqs+bKyMvz666+4+uqrG1xv79698fHHH+OFF17AW2+9hb///htTpkwRMaUU77Kzs3H//fdj9OjRePbZZ/Haa6/ho48+wsiRI3HhhReiW7ducDgcCIVCqKysBFAv+icrTJgKZRzsz/D7ZzAY4Pf7xaSPwWAQ76TBYMC6devw6quvYtiwYTjppJMA1N8D5XOaMmUKfvnlFzzxxBPwer3CvxsAnE4nZsyYgfbt2yM/Px8GgyFpf/73v/+l7Gs4HI4bK7lHNn+nOnXqhBEjRoh2li5digsuuACnn346gPpM9MRz/fPPP7j//vtT2rV8+OGHOPzwwzFw4EA4nU7EYjHs2LFDeBvziYtkkH/xnpPKZ7qttMfbXLhwYYu3SxAEQRAEQRAE0VI0+t8pY2w6gI8A/MEYm86/Wrtj6WwnJElSoz7reRKA2wH8Z28JxkB9pqjb7RaWB9FoVNg5cE9ai8UCr9crxOWqqipUVFTA6XSK7OQjjzwSvXr1wvvvv5/2fF26dEFWVhYWL17caN927dqFzZs3o0ePHs2+vqKiIthsNmzevBl+v7/J2YhVVVWIxWIZ21N07Ngxo3ZnzpyJLVu2YOzYsQiFQnC5XKIIms1mQ11dHdasWROXsZpoQ8GzfJ1OZ7M8aRuDTxSkQ5IkWK1WaDQaeL1eUTQwE5QCOPfZ5YUVeaY7zzRujEw8jRljWLFiBXr16pVSgOZ06tQJjLE99jVmjKGqqgpZWVkZ2Wf07NkT1dXVWLFiRdLtNpsNjzzyCCZOnIhVq1bh3nvvFcJmMvLz8zFhwgSsXr0al19+Od59911ceeWVWLNmDaqrq6HX62G324VFSKbsS0uUlobHIfdrd7lc4rvT6YTT6cT27dtx2WWXwWaz4Zlnnknazu+//46XXnoJ5513XoPid6FQCB9++CHUajVGjBjRYu9qqmzvQCCAGTNmoF27dmmL9c2YMQMmkwnnnXde0u1Lly7F0qVLcf311wuRu7KyEi6XS/gqE63L8OHDkxZTbCvt8TYJgiAIgiAIgiDaMo3+Fy5J0rkAlgGYu/v3PpIkzW6tDkmS1E2SJMdu24mkwvFur2UtgMEARjLGVrVWf5JhNBqh0WhEtmp2dnZc5qLZbIbZbBaelMFgENFoFCaTCSaTCQaDASqVCmq1Gueddx7++OMPbNiwIeX5VCoV+vbtiyVLljTatz///BMARIZvc5AkCV27doUsy1i+fDn++OMP/P7779i0aRN+//138VVWVpZUIOTWC415j4bDYezcuTMj0Xjr1q2499570bt37waCjkqlQn5+PnQ6HWKxGHw+n8j4djqdcLvdcaK+w+GAw+FIKt7sqbAXi8UyFoFNJhNsNltKewrel2T32O/3o66uDtXV1airqxNF6FwuF4LBYMaicWNCMBdKe/fu3Wh7PFN4T0RRxhiqq6sRDAZTWpok0qVLF5jNZixYsCDtfqeddhr+85//4JdffkkpYirp0KEDpk6dij/++AN1dXX44YcfEIlEUFVVJbykk1kTpIJbojRlkqCtwz3a8/LyYLVahfWCLMu45557sGbNGrz11ltJs9krKipw++23o3Pnznj88ccbCLmfffYZXC4Xhg8fjuzs7BbrcyrR+JNPPoHb7cZll12W8r1wuVyYM2cOzjvvvJQrPaZNmwaDwYDLLrsMNTU1qKyshM/nQ15eHjp06ECZxHuBbdu2tUhB19Zqj7fZvn17sfog8SvV6hyCIAiCIAiCIIi9RSb/vT4C4EgAbgBgjC0D0Lk1OiNJ0vkAvgfwkCRJHdMJxwDeB3AhY2x9a/QlHSqVCna7HTabDbIsIxqNNhAalUWNDAYDsrKyYLVaAdQLfj6fDzqdDscddxwAYNWq9Lr3wIEDsWbNmjjbgGTs2rULKpUqY8EtEcYYampqsGnTpkb3ra6uTioQLl68GGq1Gh06dEh7/JtvvgnGGI444ohGz/X000+jtrYWd9xxh7Bn6NChAzp16iTuc0lJiRAQedZjYuEplUoFq9UKs9ksssCV7Kmwp1arG/Uo5vBszVQh7vP5UF5eLmxNlHDBOTc3F2q1GrFYDIFAAFqtFmazGZ988gkikUja8/Ol8unYtWsXAAg7hnTwSYJ0WbypkGUZ5eXlWLp0KdasWQOdTpdxDHs8nrReykp69eoFvV6PZcuWZdy3I444At27d0dZWRnUajV8Ph88Hk+Ti+Hx4maZxse+hNvppMsaV3q6q9VqGI1G1NTUQKfTwe124/PPP8fYsWNx6qmnJj3+22+/hcvlwvPPP9/A4iMUCmHVqlUYNGgQOnfe8z83gUBAjLvcK1vJypUr8euvv+LUU09Neb5AIIBbb70VjDFcdtllDbYzxvD4449j+vTpuOSSS4R/cWlpKbp06YKSkpKMMueJPeeKK67AFVdc0Wbb42127dpVrM5I/OKraAiCIAiCIAiCIPYVmfwHG2GM1SYIWy2+vlqSpCwA16PeCqMSwFhJkl5ijG2VJEliu9ULSZJOAlDNGNunPsvc2oBnkZlMppT+plxA5sKeLMvweDwIh8Oorq531WhMIDv22GPx7LPP4rfffsPZZ5+dcr/i4mLIsoyamppmCceVlZUoLy+HVqtF+/btYbPZROZTdXV1XJsGg6FBRp7P58P333+Po48+GllZWSnPs3r1arzyyis466yzhHCeCo/Hgw8++ACnnXYaBg0aBKBewDGZTMKXFKgXbAsKCiDLMjQaDQwGA0KhkLBwMBqNQixSFsJTPjMuAjZX2FOpVC3iWSvLMrxeb8qsXS44R6NRaLVaeL1e5OXlQZZl3HvvvXjwwQfxwAMP4Lnnnkt5jvz8fJGhnKrPQ4YMwYYNG/Dll19Cp9Ph/PPPT9ke96qNxWKQZblJGZW7du0CYwxmsxldu3aF3W5vNAsaqPe5nj17NrRaLa699tq0+65ZswY33XQTcnNzMWnSpIz7FovFsGnTJhxzzDFiFYEkSU3OGOXjwP7gacwz2QE0GNMYY3GZ/BaLBQUFBaLAZjQaxdy5c6FSqXDrrbemPMe2bdtgMBjQq1evBtv4BEBBQUGLXA+Pcf5+KseNSCSC9957D0VFRTjnnHOSHu/z+TB27FgsX74cL774Irp06RInqMdiMdxxxx147bXXcOGFF+KGG27Azp07UVxcnDTLmiAIgiAIgiAIgiDaOpmIxqskSRoJQC1JUjcAtwH4raU7sluYvhlANYBDAZwP4HZJkl5mjG1W7NoFQFkLnhd+vx8mk6nJYo7RaBQipUqlgtFojMu+SxSVgsGg2FelUiEcDosMzvz8/LTn6tu3L8xmM3766ae0ojG3enA6nc0SjdVqNTp37ozc3NwG/ff7/SJbOhXfffcdgsEgzj333JT7hEIh3H333cjNzcXDDz/caJ8++OADeL1e3HzzzeL8Xq9XFBlMvN8qlQomkwkqlQoajQY+n6+BAMbF4cRM0bYi7PHJhaysrLSF1gKBAHbu3AmfzweLxQK9Xo8zzzwTq1atwuTJkzFo0CBccsklSY/Nz89HKBSC1+tN+VxVKhWuv/56APXL94GG9yxx/1gshmg0Cp1Ol+nlwmAwoFu3brBarU269/Pnz4fT6cRDDz0Eu92ecr8FCxbggQceQHZ2NqZNm4Z27dplfI6NGzciFAqhT58+Iq5CoRDcbjf8fr8ogqkk3TiwP8AnTZJNnvj9fuEb7vf74ff7xXVqNBqEw2G8//77OP3001FcXJzyHNu3b0eHDh2SPm8uGjfFMzod6SaB/vnnH3g8HowZMyZOTOZUV1djzJgxWLVqFR5//HGcdtppcdsZYxgzZgzee+89jBkzBnfffTfKy8ubnImejD35+0QQBEEQBEEQBEEQe0ImovGtAO4HEALwIYBvATzeUh1QZhED2MYYkwH8tduW4nwAt0mSdBeA/gDWMsZea6lzAw0z6lItx5ZlucE/7ZIkCV9LLhLxDFHgX6GCH2swGMAYg16vh8/nE0vdASArKwvhcDiufbfbHSe8HXnkkVi4cCHq6upQU1OT1E+Vi38rVqxALBZr8v3Iz8+HRqMR90RJLBZDbW1t0uNCoRC2b9+OL7/8Et26dYPRaER5ebnYrlzy/fzzz6OsrAyvvvoqtFotfD5fSmuBWCyG//73v+jTpw9KSkpQXV2Ndu3awWKxQKfToaqqStghKIUh5fPixaj4/Qf+FYf3Jk0pEGcwGJCdnS36rjyWMSYESa1Wi3bt2iEUCiErK0t47Y4bNw4bN27ETTfdhEMPPbSBx/WuXbtEJu+qVasaWIlwX2rOeeedh0AggE8++QSHHHIIBgwYkLLv3NM2EolkLJjm5uYKATIZ3CZDSVlZGVasWIF+/fqhU6dOImtfSSAQwOuvv46pU6fi0EMPxdNPPw273Y5IJALGWEp7jkgkIt69336rnyPr0aMHGGOIxWIwGAzQaDSiCGZi7AUCAXg8HpE9zWGM7RciMreAARrGrcFgQCwWE5Nk1dXV8Pl8YrLm66+/Rnl5OZ5++umk40h5eTl0Oh02bdoEh8MRF2s7duyALMtwOp0A6ie/1q5dC6BeSE4WB5mQyjIlFovB6/Xi5JNPRk5OToP+1tXVYeLEiaioqMDzzz+PIUOGxI273JLivffew1133YVHHnkEsixDq9XCbreL8Z1PNnAyFYDTZXwTBEEQBEEQBEEQRGvSqGjMGPOjXjS+v6VPLknS2QB6SJI0jTHmZYzJXERmjP25+x/r4wH8AqAngN4APC1wXvFzuow64F8BiBevS0UwGERdXR0sFosogMfFPb1eL461WCzC/9hqtcLr9UKlUsHhcDRoPy8vLy7z7ZRTTsH9998Pr9eLXr16pSzElJOTg1gsljLL75JLLkGPHj3E79z3FwDWr1+P7t27Jz1u69at6NmzZ9Jt3AvZ7Xbj/vvvR79+/eK286I+ixYtwvvvv49rr70WI0eOBFBfEC9VRt7vv/+OFStWYMqUKbBarairqxMCKV8OH4lEGmR0cksN/nOqz1qDlmhbrVYLkSjRPkIpJBkMBlitVkQiEajVamg0GmEL8sQTT+Dyyy/H5Zdfjt9++w02m0200aFDByEkazSaBsUIk2W+Dx48GE8++STmzp2LXr16JbWqWLNmDYxGI/744w9kZ2fjsMMOA1A/AbJ161ZUV1fDZrNhyJAh6Nevn4i7WbNmIScnJ+X9SMzu3Lp1Kz788EP07NkTt9xyC4466qgGxwSDQTzwwAP4+uuvMWzYMEyePDkuzqLRqBDlE9HpdNBoNKiursaDDz6I3r17Y/DgwWK1gEajQV5enpgoUorBXDwF6seVtpwtmq4/iWKxciyUZRlVVVUwmUwIBoOwWCxQq9UwGAz44osvUFhYiDPPPDOph29OTo6YVDr66KPjMsTz8/NhNpuFLUtJSYkYx7Zu3ZqyuGbnzp3Rv3//lNficrkafBYIBPD+++8jLy8PL730UoPM+DVr1uDaa69FLBbDV199hcGDBzdo491338VTTz2Fa665Bk8//bS4n0ajEVVVVaitrYVKpUppXZSMVH+f2lrsEARBEARBEARBEAc2jYrGkiQNADAeQKlyf8ZY7z05sSRJA1HvX1wOwCtJ0ge7hWOWIBxfCqAIwLGMsa17cs5kqFQqIb4mywRN5X2biNLugItH3BaBF17j6PV6aDQa5Obmwul0Ij8/P6PswxNOOAEA8NNPP+H4449PuV+XLl2alJHXEsWZGGOYMWMGSkpKkgp4QL3odN9996GwsBAPPfRQRu2+/vrrsNlsGDFihBBNlEv/eYZjMBhM+XyUz9BoNIrM0H2V8bmnIiKfhNDr9ZBlGdu3b0dtbS0YYzAYDLBYLMjJyUFeXh6mTZuGoUOH4vrrr8eMGTPizsfFOp7V2RhqtRrjx49HZWWlsKpIJhxrNBq0b98eW7ZsQXl5OSorK1FTUwO9Xo+zzz4bAwYMSGoDkCl+vx8vv/wyTCYTbr755qTPsbKyErfccgtWrFiBRx55BHfddVez7vW4ceNQVVWF2bNnIxqNYuvWrZBlGWazGUVFRVCpVKirq2uQua4cV7xer8g6bqz4YVtD+a7x7Gmfzwen0wmPx4PS0lIRRwaDAcuWLcM333yDsWPHph1Xampq4PP5UhbLbGl7imQsWLAAoVAIF1xwQQPB+M8//8SYMWNgsVgwZ86cpJNl33zzDW666SaceeaZeO6558R7bTQaEQwGEY1GYbVaYbFYmm1ToYwjInPuvPPONt1ea7VJEARBEARBEATRkmSiFr4P4G4AK9CyBfAsAC4EUAXgeQBaSZKmK4RjPQATgBMBXMQYW9mC584Y/s9+qoxETjK7A36sUvjgy7mj0ShCoRCqqqoa9TPmdO/eHQ6HAwsXLkwrGnfu3BmrVq3KqM2WYtOmTVi3bh3uueeelGLszJkzsWTJEpE13Bgulwuffvoprr76aqjVang8HpjNZuEfLUkS7Ha7ELVSoRT0lQLyvhJjmrrkXJZlBINBMSERCoUgy7L4rlarodPpoNVqsX37dhgMBiGKn3nmmXj88ccxfvx4TJo0CePGjRPtcrGvqqoq476r1WoMHz4cRqMxrXBcXFyMLVu2YN26ddBqtejSpQu6dOmSNFuzKTDG8Prrr6OiogLjx49HdnZ2A4uJ1atXY8yYMfB6vXjllVdwzTXXNOtcX375JT744APcfvvt6NatG3w+H3Q6HcxmM7KysmA0GoU9hdI/nAut/BkobR48Ho8QjvcHuFAMxI+FvLik3W5HMBiEx+NBIBDA22+/DVmWccUVV6Rtd9u2bQCQUjTmFhCtJRpv2LABZWVlOOqoo+BwOOK2zZ07F3feeSdKSkowbdq0pILx4sWLceWVV6Jv37545ZVXYLPZ4sYWg8EgMvv5mEXsPdJ56reF9lqrTYIgCIIgCIIgiJYkE9HYyRib3VInlCSpK+rF4CUANIwxlyRJ9wKYWL9Zepsx5t29rUaSpEGMseSGt62MMssuVWZgNBqFy+VCfn6+8IjlcCGZL7UG6kUYbgfBM9ISvYxTIUkSTj75ZHz22We47LLL0Lt38mTvnj174qOPPsLy5ctxxBFHZHi1zaempgYff/wxcnNzccYZZyTdp7KyEg899BAGDBiQsjBbIjwTcNiwYcI/Ffg3m5tbAjQmwCn34cJXusJYrU0yS5TGCigqRWalCB6LxUTmeiQSEZnGQL04rVKpMGbMGPz+++94+OGHcf3114vtJpMJeXl5+OGHH3DFFVc0iN9UJBbHCwQCGDZsWFy/tVotjj32WFRXVyM3NxcajSbj9lMRDocxbdo0/PnnnxgxYgQOPfTQpPs99thjAOoLKHbr1q3Z53v22WdxyCGHYPz48ZBlGT6fD1arFXa7XWRK19TUCI9xLj76/X5UVlaioKAAVqtVZItyn+22blmhJHEFBZ94sVgsyMrKQiwWg8fjgclkgslkwrfffouTTjoJnTp1StvuH3/8ASDe61wJf9ebcm8YY3A6ndi4cSPKy8uh1+thMpnEGGwwGGA0GuF2u/Hdd9/B4XDEWVowxjB16lS8+OKL6NevH6ZOnZp0cqu2thaXXXYZ7HY73njjDZSWlop7w++VJEkps9CJ1mfdunUAEGfD1Jbaa602CYIgCIIgCIIgWpJMROOHJUl6HcD3qC+GBwBgjH3a1JNJknQOgCcBVAPYAeApAK7dNhTjd2+rkiQpH8BRkiRdDSAzRbUFSVfULhGXy4WdO3cCQIOMtWQoxU9ZlnHyySdj3rx5WLVqFXr16tXo8ePHj8f8+fNx77334tNPP02aAX3xxRdj+vTp+Oabb2C1WlMKMy1BTU0Npk2bhkAggFdffTVpZiBjDOPGjYPX68XLL7+ccdYdL16Vl5eHyspKcd8yFWEShdh0wuzeJFmmqc/nQ0VFBRwORwOhij9jLkopxTudTof8/Hzhna1cCs8YE/E2YsQIfPnll5g3bx4uuugi0fadd96J8ePH4+2338a1116b8TVw4dhgMGDOnDmoqqrCDTfcELePRqNBQUFB5jcmDW63Gy+88AI2btyIESNG4Jxzzkm6XzgcxurVqzFq1Cj06NEjbsKmKTDGsGrVKgwfPhw5OTliHEiEZxgnZhpz33IlPG4lSRLWNUDbLnDGY40XdAPq33mv14tu3bqhpqYGlZWVKCwsxOrVq/HPP//g/vvT29/7/X68+eabOO6444TXeSK5ubkA6rPg061KCIVCqKysxI4dO0SRUKB+FUEkEklaLBSoz7I/55xzxDgQiUTw8MMP4+OPP8Z5552HJ554QhT8S+Suu+5CeXk5vvrqKxx22GFxXtZKH3KliAzEj0d7OoFCpGf06NEA6ice22J7rdUmQRAEQRAEQRBES5KJaHw1gEMAaPGvPQUD0CTRWJKkowE8C2AkY2ypJEmTAYwDcM1uD+M/JEm6EsBfAKIAzmaMRdI02WrwZcbKonbJ4NlrhYWFGVlMyLIMj8cDp9MJWZZhs9lw6qmn4r777sPMmTNFhmQ6HA4HXn31VQwfPhwTJ07Eo48+2mAfrVaLCy+8EO+99x4+++wzXH755RkJ2k1FKRhfe+21KTM/58yZg2+++QaPPfaYKL7WFCoqKsQSeYfDkfJaEjPDE/2o24I1RXNIJpIrY9RsNovtGo1G3AedTicmPwYPHoy8vDzMnj07TjQ+44wzsGDBAkydOhVFRUU466yzmtSvUaNGIT8/Hx999BFcLlerCKBerxcPPfQQvF4vbr/9dgwYMCDlvuvWrUMkEsHhhx++R+fcsWMH6urq0L17d3i9XlFwkNtRqNVqEWuJ8ah8Xl6vN6l/dmMFONsSgUBA+F7zCZy6ujqYTCZhL5GXl4fHH38cBoMBF154Ydr2ZsyYgZqaGtx2220p9+GTDU6nU2Qty7Isim263W64XC7U1NQAqB/zunXrJmxQsrOzAUA8r507dyIQCIiVHl26dBETXOFwGDfeeCN+/vln3HzzzbjttttSZjh//vnn+PDDD3HLLbeIOPT5fEknohLf2/11/CEIgiAIgiAIgiAOTjIRjQcyxlpq/eTTjLGlu39+GMBru72LI6gXojsAMAI4hjG2d015FSQrapeqSJ7f74fNZssoc8zn82HdunWora2FTqeD3W5HSUkJTjzxRMyaNQuPPvpoRsuxTzrpJIwaNQrvvPMOjj76aJx++ukN9tHr9Rg2bBimT5+Ojz76CN26dYMsy2CMoba2FmvWrBGZkD179myymFtdXY3XXnsNgUAA1113Hdq3b590v127duGll17C4MGDMWbMmCadg8OF+3A4jGg0CqfTGSeUcpSijMlkapDpl/i9rcBjy+FwZCy68mtILOAF1N8HnhnLGIPJZILNZsPJJ5+MOXPmxHkAS5KEBx54ANXV1bj//vuxadOmJj0nSZJw9tlno7CwEP/P3nmHOVXlb/xz09tkSqaCOKAiNpQVVOxd7AUrgqirYtufKGvXRcWKvSP2iq5rwcXewLKiwO6ia9u1ABZgeknPJLm/P8ZzvMkkmcwwDeZ8nscHSDk5995zT8x73vN+58yZA8C2226bUxTTdZ1ffvmF5uZmNtlkk5zHXF9fz9dff01hYSEzZ87M6kwV/Oc//wFgm222yfsYMvH1118DMGTIEOrq6igvL8fn86UUhcvmFHa5XJSXlxMIBOQ9ln4+BmKucTKZlJEZRgHU6XTK/GtoF3RdLhdDhw7FbDbj8/lYvXo1zz//PIcffjher1eem3T8fj9PPvkke+yxR87oHLfbjdVq5e2332b16tXU1NSkFGw0m80UFxez9dZbU1lZidPpzLiYYLFYKCgoyLqoFwwGeeGFF6itreW6667juOOOy9qntWvXct5557H99ttz5plnykWZfB3jA3X+USgUCoVCoVAoFAqFIhP5iMafaJq2la7rX6/jZ30GfAmgaZoZsAPVgFfX9TpN04b89thYXdd/WMfP6hZCsDWbzRlFnnSMbsFcYq8QYDRNo6ioCLvdLrcoa5rGkUceyf/93//xr3/9ix122EG+z2q1YrFkvkSXXnop//nPf7jyyisZP358h4JSJ5xwAgB77bUXF110EatWrcJsNsss4EQigclkIhwO88ILLzBp0iRmzJjBW2+9JV166RQXFzN+/Hj+85//MHv2bOLxOPfeey9bbrkl0Wi0Q45pMpnkkksuQdd15syZk1HghHZhp7PzZ7PZZIG3QCAg83pdLpcU8oyijBDARFSAKEZl3D6ei57Omc3VXjAYJBAI4PV6847NEMeSSCRkdIBRtPJ4PNjtdmKxGE6nk9raWrbffnuef/55/vGPf7DTTjul9EkU/3r44Yepr69n5syZWcWtkSNHpoiIAJtuuinbbbcdF1xwAV999RUXXHAB22+/fcprvv76a3788UfeeecdWQhN0zS23HJLNt10Uw444ACGDx+Opmnous78+fP54IMP2GyzzXjggQeyRl00NTVJ1+jXX3+Nz+djxIgRaJqG3++XLvV0xE6BTHz77bcA7LDDDpSVlcl7PD0bO9M5MplMMg7FYrH0iZu4u+PZ+L5MBRo1TcNsNsuibsK5W1FRgclkoq2tjV9++YWXXnqJ+vp6TjjhBBKJBJFI5hj6OXPm0NzczNSpUzMWYPR4PDLq4+ijj+a5557jl19+YejQoWy33Xbss88+jBw5kqFDh6bcK999913WhSsgY8byDz/8wCmnnEJzczMvvvhixjz2ZDKJ3W7nu+++46STTiIUCnHdddcRj8dJJBIyL1n8Z5zr0+nP+WdDYvjw4axatSrjc9XV1X3cG4VCoVAoFAqFQqHYcMlHNB4PLNc0bQXtmcYaoOu6nrkKWxZ0XU8Awn6mAc1A42+C8WRgJ+AyXdeDWZoYcIgCV/nidrvZaKONsNvtRKNRkskkra2tHHroocyYMYPnn38+RTTOhc1mY86cOey///6cc845vPTSS7I4l5GRI0cyf/78lMd+/fVXKbC0tbVx55138vTTT/PFF18wYcKErKIxtEdN3HzzzVRUVDB79uyczs/HHnuMjz/+mJtvvrnTwli5MJlMxGIxGREgMk4dDkcHp58QZYLBILW1tVIwrqysHHDOToEQFbvjQAyHwzLv2ev1SlG9oKAAi8Uix4Tdbmf8+PE4nU5eeeUVdtppp5R2bDYbd999N6NGjeKKK67g+++/57777utSJvGoUaO48MILefTRR7nppps45ZRT2Hvvvfnss89YtGgRX331Fbqus+2223LccccxfPhwli1bxqeffsqCBQtYsGABZWVljBs3jkgkwsKFC9l111254IIL8uqHrut8/vnnbLvttussun399deUl5dTXFxMQ0MDdrs95f7qLFc7XVQWEQYDWQzMtOiS7joWLnaRnx2JRDCZTLz//vuUlJRwwAEHZG0/EAjwwAMPsOOOO7LVVlt12p/x48czZswYKezH43F22223dTzKdpYuXcoZZ5yBxWJh3rx5WQt4JhIJ7rzzTq644grsdjv33HMPm266KXV1dYTDYYqKijCZTDQ3NxMKhSgrK+vXvPTBwKpVqzoV3RUKhUKhUCgUCoVCse7kIxpn/jW9Dui6HgcCmqb9rGnajcABwKnrk2DcHYxCk8ie1XUdh8PBhAkT+Nvf/sbs2bPzFh2qq6u55ZZbOOuss7jlllu4/PLLu9wnq9XKRRddxB/+8Aeuvvpq7r//fqZOncq226auCSSTSRYsWMCiRYsYN24c1157rXQfZuL777/n+uuvZ5999mHy5Mld7pcRp9NJVVWVdBQL93WmYlMCh8NBeXm5dP8N5C3hxsJ2uYr0ZSrk53Q6ZRSCKBxYUFAghWjxHpPJxNChQznggAN45ZVXuP766zsImJqm8ac//YlNN92UU089leOOO4777rsvrwKNgqKiImbNmsXdd9/No48+ylNPPUVbWxuVlZUcfPDBnHDCCVRVVcnXb7nllpx00kksWbKEtWvXsnTpUhYuXEgkEuH4449n8uTJed8PL730Et9//z1TpkzJu7/Z+Oabb9hqq61oamrip59+AlJdjNmKKhofF/e63++XRQ47i+0A+k1xNM5PwWCQlpYW/H6/zM0W7mm/34/VapXzV3NzMx988AFTp07NupsA4JFHHqGpqalL1yebE3xdeOONNzj//PMZOnQojz/+OBtttFHG133//fecccYZLF68mIMPPphZs2axySab4Pf7qaqqwufzpTiMhQvb6CYWwvtAXizYELnyyisHdHu91aZCoVAoFAqFQqFQ9CT5iMbnAY/0QDyFRGv/BW0Fdv/tz311Xf+up9ofSAjHXjZxyWazEYlEOPbYY3n11VdZvHgxu+66a97tH3HEEXz44Yfce++97LHHHt124u23336MGjWKadOm8eCDD3LMMcew1157AdDa2soTTzzBf//7XyZOnMj06dOzxmYAfPXVV5x44ok4HA5uvfXWbgkmdXV1zJs3D2gXM4Vg1djYSGlpqfx3NBrNWYQqHA5jt9s7FWT7m3yyUTO9xmQyUVBQgNvt7uAMFWPM7/fjdrupqKjg4IMP5pVXXuHbb7/N6vY86KCDmDdvHmeffTYnnXQSt912G3vvvXfex+J0Ornwwgv5+9//Tn19PbvvvjujRo2ipqYmRTA2UlhYyLbbbssBBxxAW1sbLS0teRWXFKxevZprrrmGHXfccZ0WKZLJJPfeey/Lly/ntNNOw2q1MmzYMIYMGYKu63IcRSIReS2Mgn9XMm7TCYVCAJ2Ho3cBo3DZlbHvdDoJBAK0trYSCoWk4C2iN0QxOoAPPviAcDgsI3Gy8corr7D55pszalRPReR3nccee4xrr72W7bffnoceeoji4mKZO23k22+/Zc8990TTNB599FEOOOAAGQNTXFzcodhmMpkkFothNpulq7wrY0EJzD3LfvvtN6Db6602FQqFQqFQKBQKhaInyUc0/ob2gnUW4DHgWV3XW9blQ/V2S11M07RrgaUbqmAMmXNCIVUATCaT7LnnnpjNZt54440uicYA1157LZ999hn/93//x3vvvUdJSUm3+jps2DDOOOMMXn75ZV544QXi8Tgbb7wxjz/+OOFwmOOOO47p06fnbCORSHDhhReSTCZ57rnnqKys7HI/3n77bc4880waGxu59tprKSgokIJNa2srTqeTgoICIpEILS0tBAKBjNvCxTkWArNwBIp4kIEk0ORTJKuzHF2ji1VkOQunphDMR44cCUBtbW3OiIBRo0bx/PPPc/bZZ/OnP/2J/fffnyOPPDLvRQmz2cxRRx2V12vTsVqtXRKMdV1n5syZJBIJbrnllryKUmbiu+++48wzz+Qf//gHBx10EMcffzxr1qxh4403pq2tjWg02iE/Ol0czHSN3G43VVVVnbrdf3OHJ7rV+SwY55+uROmYTCZKS0ulY93hcBAIBHA6nYwYMSKlmOK3336Ly+XqEHmSzhFHHMFNN93EwoULOfzww7txNN0nmUxyww038MgjjzBhwgTuvPPOrC7mhoYGJk6ciN1u580332SzzTZjzZo1MorD6CQW4vqaNWuko1+0a8y874xs3xOK7rF8+XIAxowZMyDb6602FQqFQqFQKBQKhaIn6VQ01nX9YeBhTdNGAacCX2ia9g/gIV3XF67j5z+hb+DhhNnyau12u3SJxuNxrFYru+22Gy+++CLXXHNNl4Qvl8vFnDlzOOSQQ7jwwgt55JFHut1fq9XKaaedxhNPPCGzkCsqKjj33HPzEvKeeuopPv/8c+67774OERedEQgE+Mtf/sIDDzzA1ltvzezZs9lzzz2xWCzE43Ep/tbV1eF0OnE4HASDwQ7bwoXD1m634/V6O2RIi3Zg4Ag0nWXk5vsaQTAYpK6ujtLSUpkBLYp6AbS0dL7uU1payhNPPME999zDK6+8wltvvUVpaSm77bYbRx99NJtssklefelt3n//fd5//30uv/zyDgUh8+XVV19lypQp2O127rjjDk477TRaWlpIJpMkEgnWrl1LSUmJXHQwIu5l8Wf6YkS+BdB+e09H2+s60BXhMh0xP0H7vSkKCrrdbvx+v1yIWL58Odtvv33O3QcA5557Lu+++y733nsv48eP71Je9roQi8WYPn06r776KlOnTmXmzJlZ59dYLMakSZP45ZdfuOmmm9hkk02wWq0y3iX9/jOZTJSVleFwOAiFQvh8Ptra2qQTOd/7dV2uk6Ij559/PgCLFi0akO31VpsKhUKhUCgUCoVC0ZPktV9Z0zQzsMVv/9UDnwMzNE17bl0+fEMXjKFjTqgQK6PRKPF4nJaWFhwOB83NzRx00EH88MMPvPbaa13+nNGjR3PZZZfxxhtv8Mwzz6xTn81mMyeffDInnXQSxxxzDBdffLEsnJeL2tpabrrpJnbbbTeOPPLILn3mu+++y9ixY3nggQf44x//yN/+9jdGjx6Ny+WitLSUwsJCSktLsdlstLa2Ul9fTyQSwefzUVRUlCLKC+dnOBwGfr8Gbrcbr9eLz+eTRePWB4RrONM2+vTXBQKBDq8T7w8Gg1LYy0c0hnYR65JLLmHRokXcc889bLfddixYsICTTjqJ008/nZdeeolAINC9A+sBWlpauPvuuxk9ejSnnHJKt9pIJBJccskljBgxgnfeeYczzzyTtrY2NE3DarUSiUSIRCJy4SEajaaMMePjAw0hXK5rLIvL5ZJZ2cFgkLVr1/LTTz8RDodZvnw5O+64Y6dtWCwW7r77buLxONdff32n47kn8Pv9XHLJJbz66qtceumlXH311TkX5P785z/z4YcfMm3aNDbeeGN++uknfvrpJ2pqaohGoxnPo8lkorCwkKqqKmw2W7fOt7hOA2Xng0KhUCgUCoVCoVAoFJ06jTVNuwM4DHgPuEHX9SW/PTVb07T/9mbneos83H5deg9kL4wF7aJUOByWUQo+nw+73Y7FYiEajVJfX4/X6+XII4/k/vvv58477+SQQw6RYlQm/H4/iUTqTvZJkybx7rvvMnPmTKqqqth4440zvre+vj7rcRjFRJE9Gg6HCYfDxONx6urqsr737rvvJhKJcO2118qibIL0fwuampq44oor+Otf/8rmm2/Oa6+9xk477UQgEJAilaZpUuDdaKONcLlc2O12/H5/ynPiGont4cJZDHRoZ6A4jAW5xlckEqG5uVnGcBjHqIjcgPYt7sIN6nK58Pl80o0dDAax2+0UFhYC7VvwY7FY1s/M9Nwee+zBHnvswaeffsry5ct56623uO2225gzZw4HH3wwEydOTNlmn05LSws1NTUZn2toaMgptFmt1oyP33bbbbS0tPDII48Qj8c7jDMh6mYikUiQTCZ55ZVX+P7773n00Uele9pqtWI2m7FarSQSCVwuF8XFxbS1tWG322XOtij8Jv6u63rKNUlnXdfJuvv+bO9LJpNZ5zzjcYj3iz9DoRCxWIzvvvuOaDTKH/7wh5TIimxjwOfzMWXKFB5++GGeeuqpDjEVgUAgq2M5mUyyevXqjM81NTVJV7Sgrq6OWbNmsWbNGm655RYOO+ywjONauMk/+OADHnroIaZNm8ZZZ50li0u2trZiNptxOp0p5zGRSMi4kkyZ0UoAVigUCoVCoVAoFArF+kw+mcZfAFfquh7M8Fzn9rJBQq6iR5qm4XQ68fv9tLa2SsG4pKSEhoYGEokEuq6zySabcOaZZ3LFFVfwxRdfsM0222QVn5LJZEYhbe7cuey5557cfPPNvPLKK1LUSkeIh+nss88+WV3FNTU1bLTRRhmfW7x4MfPnz+eyyy5j/PjxHZ7P5O576aWXmD59OvX19Vx22WWcf/75UqDx+Xxyi3f6Vv+KigqSySRms5lkMomu6ynnyWw243a75eOZsosHmqCTqz8ul0sKWMYYDmg/H6KIljFLVWQcC6Fc0zSSyaR0M6a3k85mm22W9bnS0lKOPvporr32WpYvX86cOXN4+eWXmT9/PgcddBBnn312xmJnK1aswOfzZWzTYrHkzL/O9L7Fixfz9ttvc9ppp7H//vtnfF84HM7qKE8mk1gsFm6//XY22WQTxo8fzy+//MKwYcPkGAoGgzQ2NuJwOCgsLJTCZCgUIh6PywUYUQhNRBnkup69MfZytdnZIlm29xqPQxRUhPb5beONNyYSifDJJ58AMH78+BSxt7i4OOtn/ulPf+LHH3/k8ccf55hjjkkZax6PJ+sY+fnnn7OOkdLSUoYPHy7//fXXX3PllVcSCoV4+umnOfTQQ7P2R8wlF198McOHD+fUU0+luLiYIUOGsGrVKhwOBxUVFXIOE3NNOByWiyBVVVUZ532FQqFQKBQKhUKhUCjWV7Ja+zRN217TtO1pj6IYJf5teJx1LYi3IeF0OvF6vVmLXYmIBJPJJAWYcDiMw+HA4/FIh/JZZ51FQUEBd911V7f6UVVVxZ133skXX3zBLbfcsi6HlDexWIxrr72WESNGcNFFF3X6+jVr1nDsscdywgknMGTIED7++GOuv/76DoWpkskkoVAoY9yCiJ4IBALy7+I5EeWwIW35Li8vp6ioKKMAKhzGkUgEj8fTYaEhPZ6jsLAw73iKzhgzZgxz585l2bJlnHbaabz77rscfPDBnHrqqSxevHidnbXZCIfDXHPNNWy88cZMmzat2+189NFHLF26lLPOOgur1YrNZiMcDqeMK+M9KnA6nVgsFvx+P36/H4vF0mmhu4GIrut5RZ8Y4ylEsbdIJMLixYuprKzsUpa0pmncdNNNuFwuZsyYkeJQXleSySTPP/88xxxzDJqm8eKLL7LLLrt0+r4HHniAL7/8kgsvvJCioiKgXRwfMWIEI0eOxOv1EggEpGs/mUzidDqpqKigoqJivbz2CoVCoVAoFAqFQqFQ5CKX0/i2HM/pwD493Jc+QThTe0pIFC5Pp9OJ0+nMGlEBSNFYFGYTTkWXyyVdaoWFhZx66qncf//9nHvuuWy//fZd7tPBBx/MlClTmDNnDj///DMTJ05kr732yuo6XhcSiQQ33HADK1asYP78+Z2KJ99++y177rkn4XCY6dOnM2nSJIYNGyZFK1EcK1vBOlEILx6P4/V6Owj1uRzf6zO5imqJCAqj6C7Ec+O2eTH2iouL+eijj/jxxx97rJjdsGHDuP7665k8eTLz58/niSeeYMqUKYwePZqzzjorqxO4O8TjcWbNmsUvv/zCo48+2mGxIV9aW1u59NJLKSsr46STTiIej8sdAJFIRI6rqqoq+fdgMIjT6cRsNlNWVpYSd7KuucGd0dNzF+R/vwihuK6ujuLiYlauXMnatWv5z3/+w7hx47rcp7KyMq6//nrOOeccJk+ezMknn7xOYyQSifDss8/y6KOP8t///pexY8dy//33U1VVlfN9uq7zt7/9jZkzZ7Lffvtx+umn09TURDweJxKJ4Ha75Y4GMR8ZI2/SIzEUA4MbbrhhQLfXW20qFAqFQqFQKBQKRU+SVTTWdX3vvuxIX5FIJAiFQj0mKKZnuGYTYNIzj00mE7W1tYTDYXRdJxwOY7fbqa+v5+yzz+bll19m4sSJfPDBB1RXV3e5X1dffTV2u5358+ezYMECSktLOeGEE5g0adK6HbCBSCTCxRdfzDvvvMMf//hHJkyYkPP10WiUqVOnYjKZmD9/PmPHjiUWi1FYWCiFYFHUziiup4vCQtxzuVwdYi+cTifJZDKjwCYE/kxxFesriUSCn3/+GYvFIp3G0D4ug8FgitPX7XbT2NjI6aefzk033cTYsWO59NJLOf/887Hb7T3Sn8LCQs4991xZJO+hhx7i3HPPZZNNNuGoo47i+OOPz5pP3BltbW0sX76cJ554gkWLFnHuueey4447dsj2zoe6ujomTpzIf/7zH55++mlKS0vlPRiNRnE4HCSTSRobGykpKQHas8BjsRjBYJCysjJMJlOfioY9PXcB8t7KtNgj5iyx8NDQ0MCaNWtkHyoqKggGg1njJDrjoIMO4qqrruKhhx7iT3/6Ez6fjwMPPJDJkydnzWNPZ/Xq1fztb3/jxRdfxO/3s9VWW3HPPfdw+OGHd3qPr1y5kvPPP593332XcePGcf/99xONRjssGhjnbmgX0JWzeGCTj7u8P9vrrTYVCoVCoVAoFAqFoifJpxCeFTgb2OO3hxYBc3Vd77k9xX2I2WzOmnHaHURb6VvXhdNT/DsSidDS0kIwGJQCVTKZxOfz4Xa7cTqd1NfXs3LlSiwWC8899xyHHHIIhx9+OO+//36XhRmn08msWbOYOXMmCxcu5Nlnn2XOnDnce++9jBs3jhNOOIG99tqr2wJebW0tF1xwAf/+97+55JJLOPnkkzt9z1VXXcXy5ct5+OGHqaioANqFzFgsRiwWIx6PY7fbZZwCtF8vo/hjFLkyiUJCkBdOZaPAZhT4hbi6vtPQ0EBTUxPFxcUp49rlckmRMRAIAO3HbLVaOfTQQ9ljjz244447uPrqq5k3bx533303e+65Z4/1y263M2nSJI477jjefPNN5s6dy2233cZTTz3FlClTOProozu9D3VdZ+XKlSxbtozPP/+cZcuWEQ6HsVgsXHLJJZx00knd6tsvv/zC4Ycfzk8//cQjjzzCwQcfLLOfRdSHWGBYu3Yt0D5ORaE9ETHT1072np674HcHu5ivjLskjDnGxqzh4uJiQqEQVVVVmM3mrEUu8+GUU07hpJNO4sMPP+TZZ5/lueee45lnnmH8+PEce+yxbLPNNkQiEQKBACtWrMBms+H3+wkGgyxfvpxFixahaRq77767XEToTCxua2vjvvvu4/rrr8dsNnPdddfx5z//mVgsRiAQ6LDYlO7GFte9t6JXFOuOyNruKWG2p9vrrTYVCoVCoVAoFAqFoifROvvhq2naw4AVeOK3h04CErqun97LfesVxo0bpy9dujTr89kEh64KBMFgkDVr1gC/Ry6ILMzCwkKcTqf8t8ihjcfj/Pzzz8TjcSorK/n444855phj+MMf/sDrr7+eIkw3NzfnFHyj0WiHx9asWcNzzz3HU089RU1NDT6fjyOOOIJ99tmHLbbYAqvVyurVq7MWwluxYgXffPMNCxYsYPHixZjNZm6++WYOPPBAdF1nxIgRWfuzaNEiDjroIE455RSmT58OtG9Rh3aBU8R1FBUVpYhxubbjZ3vO6JDM5jTubpSApmn/1HV9XLbnx40bpy9btqzL7RrHV7ojOtc5iMfjNDQ04PP5UgqRQbvr3e/3y2MNBAJEIhEcDgeapmE2m3nrrbe45ZZbWLFiBZMmTeLGG2+UhQaz0dzcnNWZ/Ouvv2YU5EUMwIsvvsiyZcvwer1ss802VFdXo+s6zc3NKePb7/fz73//m4aGBgCGDx/OzjvvzPjx49lxxx1THL6JRCJrzEZ6Ibz//e9/HH744bS2tjJv3jzGjh1LSUmJdKwnEgkaGxspLi5G0zT590gkArQvVkSj0ZxjSFzLTK72ztzuJpMp6/jKNXd1pRCesQ/ivcFgkNbWVrxer7z/EolEitMYfo89icfjuFwutt56a3bccUeeeOKJlM/IlZm9Zs0avF5vxuf++c9/smjRIl588UU5f2ajqKiIiRMncuyxx+J2u1MK4aVTUlJCPB7nlVde4ZZbbuHLL7/k0EMP5ayzzmKTTTZh6NChMl5I5Dx7PB55nTLFDnUWFTLQdjPkmru6O2/1B2JOzMVee+0FtH/v9AQ93V4+beZznAOFzr4XFQqFQqFQKBQKxfpJp05jYAdd17cz/Pt9TdM+760OrQ+kb1cWBe2MwkoymaSsrIxkMkl9fT1WqxWv1yuzQaH9R2EoFJIuR13X5VZ4h8PBLrvswuzZs7nggguYOnUqzzzzjBS3wuFwzm35mYpL+Xw+zj33XPbbbz++/fZbXn75ZR5//HEeffRR7HY7W2+9NcOHD2f8+PGMHj2aoqIi4vE4n376KW+//TYffPABsViMoUOHctppp3H44YczfPhw6cDMVtCqoaGB0047jVGjRnHFFVeQSCTweDyUlJQQjUax2+0UFBRIQbOtrU3GAqTHT6RfB5EzKt5rMpnk+UwXbXLlAg8kjI7ozlyNmqZJ8T1d6LXb7WiaJseby+WS50zTNPx+PwceeCD77bcfDz/8MHfccQdvvPEGs2bN4qSTTsoqigoBNVvfs12zLbfckjvuuIOvvvqKZ599lpUrV/LFF1/I4zN+nt1uZ/To0Wy//faMHTuWLbfcMqWtWCyW8ndjMcT0vorj//zzzznqqKMAmDdvHltttRWBQACr1YrZbJbF3YRD2+v1UlZWJt3HIhLFKObnOg+ZomqMj/e0azhfjH1Ij6dwOp0pxyX6KOa0uro66urqcDgcMu83Fot1uPfFLotMtLa2ZhVUHQ4HU6ZMYdKkSXz33Xf897//lZnv4XCYESNG4Ha7paArxkyuYn6tra08/fTTMuN900035YEHHmD33XeXMRRi54KYhzVNk+fCZDLJ85C+uNPdRcaBJigrBh7V1dVZx0l1dTUrV67s2w4pFAqFQqFQKBSKQUc+onFC07RNdV3/AUDTtE2AroeIDiDW9Qe7cbsydMwxFtupvV4vgUCApqYmbDZbiotPbIMWURahUEi2XVBQgNVqxWQyMXHiRFpbW5k5cyaXX345d955J0Cngmq669RIUVERe+yxB9OmTaOmpoZPPvmETz/9lMWLF/P3v/+dl156CYBNN92UpqYmKeCeeOKJnHzyyey8884dzmEsFstYaE/XdaZPn05dXR2PPfYYDocDi8UiM2EdDocUX8S5qa+vly5DEWORCSF+hkIhampqqKioSHGgrk/CjLGvQqDqLHtZZGELsTP99VarNcURXFBQIBc7hKglzv0NN9zAKaecwrnnnst5553H008/zT333MMf/vCHDp9bWlqadXxZrdashemE0Ddq1CgmTpyY8lwkEqGoqCjrseYa66FQKOtnCnHzyy+/5PDDD6egoIA33niDoUOH0tTURF1dHTabTbptnU4nbrc7RZB0uVzSXRuNRvNaeMh2DfO9ttnoiTGdHvEi/ks/LiGMizETiURobm6mvr6eoUOHkkwmsVqt6LreYddDrmupaVrWeBi32y1dyJtvvjmHHHKIfC4ajWZtN5lMdhDhV6xYwT333MNjjz2G3+9nzz33ZNasWRx22GF4vV650GDMRhcLUPkUNhTnLRvGiKK+KJSo2LDIJQqvT99tCoVCoVAoFAqFYv0lH9H4ImChpmk/AhpQDZzaq70a4GQqHpXp706nE4fDQSgUIhaLUV9fL8WJmpoaQqEQHo9Huj9LS0txu920tbWxdu1aCgsLKSws5OKLL2bNmjXcd999HHPMMey22249diwVFRUcddRR0oG5Zs0avvnmG5YsWcKSJUvweDxMnDiRvffeG7PZTGFhYZfaf+SRR/j73//OjBkzGDduHFartcN29/TICGN2ajAY3KAK1+WDyWTKK3NZOGCFy1KMo2zny7jYIbbgC6e2yWRi8803Z/78+bz88stcfPHF7LzzzvzpT3/iqquu6tOCbz3Njz/+yIEHHojD4eBvf/sbm2++uTwX4XAYTdOwWCz4/X5CoRDl5eUp589kMlFWVpayu6Azsl3DgeB2N2Z9d+aINeYaO51ObDabXIgwmUxYLJZ1yjTuDRYvXsydd97Jyy+/jMlkkjEUO++8M4FAALvdjsVikWPaeA7S84uzRd3kQzgcpqamBoCqqqp+v+4KhUKhUCgUCoVCoVB0hU5FY13X39M0bSQw6reH/qvresfA3EFEeoG1dDEg/fmNN96YQCAgHZFCUAoEAgQCAUpLS/F4PNjtdn799VfWrl1LNBpl1KhRVFZWEgqFmDFjBgsWLOD//u//ePPNN3utkJvT6WTnnXdm55137vBcrjiMTHzzzTdceOGF7LPPPkyZMiVFSBNZvA6Hg0AgQDAYlO5ji8VCRUWFzFmFjufYiNvtpqqqKm9Bb0NBbKUXCxPGLfoOh6ND1nGmCAKjS1kUzZs4cSIHHnggl19+Offccw8vvPACf//739l2223750DXgbq6Og444ACi0ShPP/00lZWVUvwdOnQoPp8Pn8+HyWTCbrcTDodT3NnGKJpMY7CzjOL1CXGsYkzYbDYpCIsClRtvvDEmk0lGgpjNZrlLor9ZsWIFU6dOZfHixRQVFXH66aczefJkRo4cKXPlTSZTznkifUEwXUTuCk6nU+6UGGxzk0KhUCgUCoVCoVAo1n86FY01TXMA5wC7ATrwkaZpD+i6nj3YtA/RNE3TB3i1GJPJJB3GYuuzKDbW2NiIy+WioKCA2tpaampqZM5mPB6npqYGr9eLpmncddddTJ48mbFjxzJ37lz23XfffjumeDzOjz/+yLBhwzIKIq2trRx33HF4PB5mz55NQUFByvbshoYGVq9ejdfrxeVyEY/HCYfDKcKMaLez7Nd0kX6wYMxuFo5hkbtbV1cnXY5CuEp3mAqXO/yeWSuiGRwOBw888ACnnnoqxx13HCeddBKffvrpeiV+tbW1cfLJJ7NmzRruvfdeRowYIZ2yYqwZnfPiHhWF7iBVNBSF0owF0bJlF6+PiGMNBALSfR4IBID2c2KxWDCZTPh8Ptra2jCZTOy0007MnTuXjz/+uEd3QHSV5cuXc+yxxxKLxbjppps4/fTT5fW02+3U1dVRXFzcaTvpc0m+c1C2ttZnh/76jIhxGqjt9VabCoVCoVAoFAqFQtGT5BNP8STgB+757d8nAk8Bx/ZWp3KhadoewBAgpuv6SwNdMBYYs0yhXfATxd+EAFVSUkIikZCCTX19PY2NjZSWlhKLxdhss8149913mTZtGkcddRQzZszgyiuvzJlf3BuEw2EOO+wwPvroIzRNY+ONN2bTTTdl1KhRjBw5kpEjR/Lwww/zww8/8PLLL7PFFlsQCARSBEefz0coFMJiseDxeDI6AMVjG4qTc13ozNGanpUrIj58Pp90kFqtVpqamvD5fFJQS4+4EOJ+MplE13U22WQT/vKXv3DOOedwxRVXcPvtt/fdQa8jV155JR9//DGzZ89mzJgxeDweSktLZeSC2+2W9444XrfbnTVqJpPrNP2+7gr94VLOVMRTiODiMaPTON19HQ6HU7Kfr7vuOt566y0OPvhg/v3vf7Ppppv2yXEYee+995g8eTIlJSU8++yzbLHFFjIXORwO09TUxJo1a1KEY3H9jOcjPXN4XaIpFP3LmDFjBnR7vdWmQqFQKBQKhUKhUPQk+aiN2+i6vpXh3ws1Tfu6tzqUC03TDgFuBF4DttQ0rVXX9Xd/e25AO44zZZmmOz0tFgter1cKWuJwKisrqampoaWlBa/XyzvvvMN5553Hbbfdxscff8xjjz3GsGHD+uQ42tramDRpEh9//DGXXHIJbW1t/PLLL3z//fc8/fTTMv8U4KabbmLChAlSkIxEIimxGg6HA5fLJUWZTOLNumwP35DozNEqxpfIKhYF4EwmU0p+r2hDbNcXYqfNZpMFG0XuazgcJpFIcOihh7J06VLuvfdeDj74YHbdddc+Ouru8+yzz3L//fczbdo0jjzySCKRiBTEhThqtVplprYYo16vl2QySUNDA263m4KCAnm+M2WZr0tGcX+4lHMV8RQuWzEniaxn8T4R4QG/F9r0eDw89thjnHTSSey4445yN0RfMW/ePM455xy22GILXn31VWw2G4lEgmAwKMevy+WiqKgIs9mMxWLBbrdL4VucD7FQZ5x/1Nyz/vLuu+8CsN9++w3I9nqrTYVCoVAoFAqFQqHoSfIRjf+ladp4Xdc/BdA0bSdgWe92qyOapnmBGcDZuq7/Q9O0awGHpmmb6br+va7rejbhWNO0acA0aM8X7g3SP1bXdSmC6rou/y3yZ+F3ISIYDErxyeFwoOu6LDrl9XpxOp14vV4aGhqkqHHllVcycuRI7rvvPnbeeWduvPFGCgsLqampoa6uTsYT1NTU0NzczC677MJxxx3HbrvtRjwez+pOjsViHRx3xuf+9Kc/8cYbbzBr1iyOOOIInE4nLpeLQCBAOBxG13V++eUXGhsb2WeffYjH49jtdsxmM7qu09bWRjQaxe/3U1tbS2VlJR6Ph3A4THNzs8x4Fn1wOBxA95ycfUVvjy9d11McrbnWRtKdw5qmyXPo8/mk0ziRSMhz7HK55OubmpqIx+PyfW63G7fbzR133MHixYs5/fTTWbRokRSd04nH4+i6Tl1dHbNnzyYcDjNt2jS22247EolE1qJpsVgsZ0G1XMfc1taG1WqV/16+fDnnnXceu+22G7NmzZJCp8vlko5iXddJJBI0NTXJhY6CggIcDgeNjY38+uuv2Gw2Nttss5TdAeLvna1P5eNMzcelnO/YytUf43OZhG8x5wjEvWixWCguLpbjIx6PY7PZKCkpIRaLoWmadErfdddd3HbbbZx22mm8+eab3HTTTVmLZordFJmIx+NZs9Pj8bh8n67r3HbbbcyaNYu99tqLBx98kMrKSqA9AqitrY26ujpKSkpwOp1yvnO5XEQiESkGi3sjmUymiMcOhyOvuSfbedd1XbmT+5HrrrsO6DlBtqfb6602FQqFQqFQKBQKhaIn0fIQP76hvQjeT789tDHwXyAO6Lqu90l1LE3TCoHngUuBemAR8B/ACqzRdf30fNoZN26cvmxZz2ve6edRFHEToq8Q84w5oRUVFXIruLEgmRDzsgkPQuAIh8P88ssvnHvuuaQfU2lpKZWVldhsNsrKyvj4448JBoMMGTKEY445hkmTJjF27NgO7cdisYyCsq7r/N///R8PPfQQ119/PX/605+IxWI0NzczdOhQYrEY0C6EB4NBVq1ahcPhoKKiApPJhK7rBAIBGX0gxBsRlSCExlgsJvucTbw20tfCjKZp/9R1fVy253tjfHVFoBTOWTGe0p3byWSSYDAoM43Fe0VUgiigJ0Q1r9dLWVkZAO+//z6HHHIIhx12GPPmzct47uPxOI8++ih/+ctfCAQCstDhPvvsw/Tp05kwYULG69rW1pYzZiXXdU4kEvK9dXV17LTTTmiaxqeffkppaamM4hDHKM6ByWQiHo/LAo3CbRuPx6mrq8Plcsk88a7S3XGZa3zlGlvd3WSRaY5JJpMp96LT6cTv98u5QQjHZrOZQCDAzz//jN/vp7S0lLvvvps5c+YwdOhQHn/88Yw5x2JXRSaMixnp7/nmm29YunQpS5YsYfHixXz55ZeceOKJ3HjjjVgsFgoKCqR7uKmpie+++45Ro0ZRWFiI3+/H6/VKJ7Wx4J8xqiOZTBIIBORrIfu17ImFg76ku2NroKFpWqfnfq+99gJg0aJFPfKZPd3euraZzznoSzr7XlQoFAqFQqFQKBTrJ/k4jQ/s9V7kga7rLZqmLaI9W9kLPKPr+kxN0yqAJzRNm6Dr+lv92kkDxnxQUVhKPGa32+VWaE3T8Hg80nkrXHzFxcVEo1EpZAmEKOjxeOTW+VdffZWPPvoIt9vNpptuSklJSUqBPafTSUNDAy+//DJvvfUWc+bM4e6772bkyJEcf/zxnHbaaWy00UY5j2fmzJk89NBDXHTRRVx22WUA1NTU4Pf7aWlpoaysTIokTqeT0tJSebwtLS1SmHM6nUSjUZxOp3Qi6rqOyWSirKyMurq6jEXxFKlky8MV5xkyb6kPh8PU1NTI4nkul0u2I14vcpCTySTxeFw640eNGsU555zDXXfdxbx58zrEEHz66adMnz6d5cuXs++++3LjjTdSUVHBvHnzuPfeezniiCPYYostOP/88znxxBOlk3Nd8Pv9fPXVV/z3v//l66+/5u2336auro633nqL0tLSlHMVj8dpaGjA5/NRUFCA3W6XRe+M95jFYqGysjIv0a8/col7C2O+r/FejMfjxGIxKisraW1tJRaL0djYiM/nk/EyLS0t1NfXc8YZZ3DUUUcxbdo09ttvPy699FKZu67rOmvWrGH58uV8+eWXfP7553z55Zfouk5xcTElJSXyz5KSEoqKiqitrWXJkiUsWbJELmQUFRWx4447ctRRRzF9+nSZDW+326mtrZWLFWVlZTKjWtM0OR+JGA6xsAdIR73RaSwiLMSig6LvGD58OKtWrcr4XHV1dR/3RqFQKBQKhUKhUCgGJ52KxrquZ/7l1oeI2Ald12/UNO0+4CxgIYCu6zWapv0EdG5L7UOMwkRdXZ1019lsNpmtakS4H8WfwvUpRGXhfjRuz9Y0Db/fj8Vi4fDDDycajcos2oqKCik4B4NBgsEg48ePZ+zYsdx+++28++67/PWvf+X666/ntttu4+yzz2bixIlst912HY7llltu4eabb+b000/npptuko8bi61Bu8u0sbERu91OMpkkGo1SUlJCOByWjk6LxZLVZSiEY2OhLkVmjHm4xpzoznA6nVRUVMjIC+GAh99F5kgkAiDHqdieHwwGmT59OsuWLeP8889nhx12YPPNNyccDnPBBRfw2GOPMXToUJ577jmOPfZYKaJecsklTJ8+nSeffJI5c+Zw1llnMXPmTM455xzOPffcrDEGmfjpp5948MEH+eKLL/j666/56aef5HMOh4NRo0bx8MMPU1VVRTAYxOVySfFTuNzF/dHZuctHEO6PXOJ86aqgnZ7h6/P5iEQi+P1+WltbcblclJaWUltbi9/vl7EzQ4YMweFwYLVaaWtrY4sttmDRokWcd9553HDDDbzxxhuUlJTw+eefU19fLz9vxIgRDB8+XEbvrF69mi+//JLGxkY5Jk0mE6NHj+aoo46iurqa7bbbjq222oqioiJKS0tTFqpE7ERzczMjR46kuLhYOu0zXZtMUR2ZBOWu3F+KnmHVqlUDykmrUCgUCoVCoVAoFIORfJzG/c5vecUmXdeTuq63appWBzysadpkYBtge2B2//YyM8K1JzJBhWAqRBEhyAmxoqSkBK/XK53GIod11apVrFixAp/Px5AhQ6R7Tgg6TqcTt9tNfX19yrbycDiM3W6nrKxM5imXlJRwwgkncNBBB/Hjjz8yd+5c7rzzTm6//XbKyso48MADOeigg9hnn3248cYbueuuuzj++OO56667pBAlckKF+BaLxfjuu++IRCJUVlYSi8WkcFxaWpqyDdyI0d1oMpmyCjyKVPLJw82EyWSioKBALj6IHGGj61e4LF0uV4rLsqysjEAgwD333MP+++/PnnvuyWOPPcZdd93FwoULOfHEE5k5cyabb755h891OBz88Y9/5Pjjj+fvf/87zz77LFdffTV33nkn06dP58wzz5SLD5n46aefuPnmm3nsscfQdZ2tt96arbbaiokTJ7L99tszduxYqqqq8Hg8BINBamtrZQyHuO98Pp+MnxBiai7yEYS7ex36gq4K2ukiqlgwEDsjSkpKpGO3vr4+JcKjrKyMhoYGWfCyrKyMe+65h/HjxzN37lw0TWOvvfZi6NChbLXVVkyYMIFEIkFzc7PMNU8kEjQ0NFBaWorNZuOHH36QMTwbb7xxyu6LgoKClGgVMc+ZTCZsNpvcySHmlq66hTMJypnYkJzmCoVCoVAoFAqFQqFQGBmQorGmafsDmwMmXdfv+c1pnDQIx49pmlYN3Et7tvKpuq7/0K+dzoIxL9XooDVugRZbo0VxMbHd2mazUVdXh8PhIB6Py9eIjFlRPE/EBwhHr3BVNjY2SnEwfeu12WymtbWVoqIi7rvvPm6//XZee+013n77bV599VWeeuopeQynnHIKd999N2azGb/fz6pVq6iurk5xiDY1NRGNRmXxNGjf5i/EYLvdTl1dHT6fL8VpnO5uzAcl1Px+7deFUCgkhT5oP/8ej4dIJCLFP2N8hYi0KCsr46233mLKlCkcccQRANx1113svvvu2O12/H5/SqyKiFSxWCwkEgn23ntvDj74YL744gvuvPNOrrnmGu666y6mT5/ewXksxOLHH38cgFNPPZWLL76YkpIS/ve//+H3+yksLKSyslIKg0IkbGtrIxQKUVBQIPOby8rKMgrGoo/icbEwAp0WqhuwixzdFbTFQo7VaiUWi2G32zvE5ESjUblzwOFwEA6HZTFDsUOitLSUM844g9NOO41IJEIsFiMWi+FyuaioqJDjrqioiLa2NqB914I4n0OHDsVsNhOJROS8Jj67oKAAaJ8L6uvrUxbkLBYLJpOJcDgsF+eEW9i4SJVr7sl38WogO80HM3Pnzh3Q7fVWmwqFQqFQKBQKhULRkww40VjTtN2AebQXvDtV07TNgb9qmvaprutxQ1TF1b8Vx4vpuh7u1053ESFI+P1+uV1bOOf8fr8s1hWNRmlubqaiooIhQ4YwZMgQKd4YhT7hqgNkFq14TOQHh8NhGhoaAKSQq+u6zCb1er1MmDCBAw88EI/Hw8KFC/nnP/9JdXU122yzDW1tbdhsNkKhEMFgkEAgkCIKFxUVEQqFqKqqIhaLybxlITQ1NDTw66+/EgqFqK6ulo/n6+gz0t1oBkUqLpdLuuADgQCBQIBwOCwdv5niK1wuF7FYjBEjRvDuu++yYMECLBYLBxxwACaTiVAoRG1tLRUVFfLaGNsIBoNAe4G9sWPHMmfOHGbMmMFtt92WIh4fccQRzJkzR4rFJ510EpdddpnMM9V1nc0220zeP+Fw6hRgtVppbm7GbrenFPwziryi6JnD4aCuro5AIEBlZSUAtbW1lJeXS3FyfSSToJ1rwUWIqGJ+Ea5ssXMBkPnPRUVF6LpOLBajvr5euoNNJlNKAUaXy0VDQwPxeBy73S7niXA4TDgcxmaz0draSmtrK5FIhGHDhqFpmhxHa9eupaioiMLCQoqKimhqaqK1tRW73Y7FYpG52xaLhZKSEjmPGK+xcU40CsXdmXvSGchO88HMqFGjBnR7vdWmQqFQKBQKhUKhUPQkA040BnYE7tV1/RFN054BrgSOAZLAJ79FVRwI/FvX9Zr+7KiRXPmLwrWZHkkhXG/JZBJd11MK5UWjUbxeLwAlJSVYrVb5OSJP1GKxoGlaimChaRrhcBiXyyXdysLl6XK5cDgc0lUpnJdut1tGV8RiMYLBIGPHjmXs2LGEw2F5bMlkktLSUulWFuKLy+WSovKaNWuw2WwUFhamVHj3+XyyH6J/xvOm63rWc5gubvWEUNNZXub64mDubNxlOw6Rie3xeKQTXkSoCIcxpApr4rOEqCciB2prawmFQpSXl0uXbzKZJJFIpCxoGP9ujMPYeeedefbZZ/n888+56aabuOaaa7jmmmuwWq1MnjyZ888/n9LSUinuFhUVYbFY5MJEIBCQLmjRruiDcLBmOk/CiSpE83ThubPzm4tc570/SXfGGvsjzp2YfywWC42NjbS1teFwOOTzLpeL8vJykskkv/zyC36/H5PJRHFxsRSMxaJWKBQikUhgsVgoLi4G2iNR6uvr5cKVKLwo5ot4PI7f70fTNBwOB4WFhZSVlaHruhynYrGgtLSUwsJCbDYbZrO5wyKSyWTC7/djNptlUTz4feEs/Rykk888oRzGA48FCxYAcNhhhw3I9nqrTYVCoVAoFAqFQqHoSQaiaPw5cKGmaZvruv4/TdOuBa4CTgQ++e0144Bv+6uDmcglMprNZunsE2KFeFzkcOq6TiQSweFwyCzjUCiE3W6Xgiy0izCBQABAPmbcWu92u9E0TbooAQKBgMz6jMViFBcXY7PZcDgcFBUVYbPZaGpqoqSkhFgshsPhoKGhQTqGhaioaRqaplFRUSHFRbvdLo9LFPITArVxO7vJZKK6ulpuDRfnKxKJ0NzcjMVioaysrEOBwGznejAJNV0VsI1b8NPfa3SaAvLvXq+XgoKCjC7UdCHO4/HIcSRe5/V65UJBMBiUQqLH45FiXiKRkIsj4jqLMWw2mxk9ejS33347p512Gv/5z3+YOHEiRUVFuN1uzGYzLS0trFixgoKCAqqrqykoKJDFJQOBgPy36GMwGJSieKZxJc6Pw+GQ2eHQLopWVlZmPH/5nve+Jlc/jcKnzWYjHA5TUlJCMBjE4XCkzEfGaBld1/F4PIRCITwej3T2RiIRKbYKwVc4ksVCVzwel4tDZrNZXvNkMonf76epqQm73U5xcTHxeJzy8nLC4bCce2praykrK6OwsFBG2ui6LnPPo9EobW1tcmzpui4zkY0ROGIsiz8zCcvdWSAy3kf5zFmKvuW2224Dek6Q7en2eqtNhUKhUCgUCoVCoehJBoRoLCInfvvnN8DXwG6apvl1XV+jados4ENN0/6o6/qjuq5f13+97R6ZMjLdbjdVVVUd8jWDwSAVFRUZHbXpDlD4ffu/cI+6XC4psMTjcWpra0kmk3i9XpLJJKtXr2ajjTaSIpHYBp5MJikoKJC5tXV1dTIf2WQykUgkpJMwGo3KCA2RIVpWVpZS1C6fc+B0OgkEAsTjcRldIM6NEmO6R66sVqPTFEh5Xb5ivLj2sViMiooKCgoKpHiWSCRIJpMyyiK9Xy0tLQQCgYwLBKJo5F577cVBBx0knb92u52WlhbsdjvxeJy6ujpMJhMjRoyQ7tF0x70QisV9EgwGU8alED/FYwUFBQSDQRnPIETG9EKNnZ335uZmAoEA5eXlA3L8NjY2dohnEHnn6ccpRPTy8nI5LoxRIwKv1yvPv1jAamtro76+Ho/HIzPYjYtKJSUlDB06VC5SCcE4HA5LR3JxcTGxWKxDlrLdbsfpdMrFLEFDQwNr1qwBkAU6xZzT0zsHVESOQqFQKBQKhUKhUCg2dPpdNNY07RBglKZpD+q6HtB1fbWmaR8Bh7Q/rf1D1/VvNU1bQHvRuw0GozglBByjeCqeD4VCOBwOGRtg3FItoi2EcNHU1ERdXR0bbbSRFI/Ly8sJBAL4fD6ZCep0OmXhveLiYvkZYlu42+1OEYEBmTNqFJGM4nW6KJyP4GYymSgtLZXF12pq2hNHqqqqUsSswVz0rqvkymrNtRCRTrbrZywQJ9zEwtmeTCYJBoNy8cHoeHc6nVgsFulCTReoRVwGILOzCwoKaG5upqamhvLycsaMGSOLRxqPLx6PU1NTQ1lZGWazWY5hXdcziuiZHjOeNyEqG/tvzMnNdl7EAohw5w40RF61cVcBZD4f4XBYOrjFcRoXrXRdp6WlhdbWVioqKjCZTAQCASkqRyIR4HdRVQjyBQUFsk2z2SwL2fn9fsLhMGVlZZSVlVFTUyNzpkXBT7E7ItOcIo5N/NmbqCxjhUKhUCgUCoVCoVBs6PSraKxp2g7A88AaIKBp2nO6rrfquj5f0zQd2AWYqmnacuAEYK9+62weZCsyJR7vTDwVIql4TTwep7GxUQrJxuJ3AhFXIdyetbW1rF69mmQyycYbbyzFLbfbneLKFG4+QIpHZrOZgoICKZp11TWcTi7Ha7ZjFw5BIeDl20ZXyVUQbH0nV1arUUztzFmc7dwLodiIcKC63W45FsXjdXV1QLv7M30hIp1EIkFdXR3JZBKPx4PT6cRqtcpIBZvNRkFBAeFwmHg8Lovd/fzzzzKGpaysTF5bXddT2hJkEtaNY1q4jj0eD16vN+V1uc6LOL58xMS+GoPGe9lisch7zGq1yjGS6XwY86cDgUDKIoGu6wSDQRoaGggGg1IMdjgc6LpOW1sbJSUluFwurFYrTU1N0oVsdH8bC9nZ7XaZ8V5XV0drayvRaBT4fXyJBbZMc5Px2Hobce9saHOHQqFQKBQKhUKhUCgUgv52GnuAo4B64DbAqmnaU78Jx69omvYJsAUwCrhH1/Xv+7GvnZJeZErQFeFTREG0trbKwnTxeJyysjJisZgUwoTb0uh4M5lMlJSU8NNPPxGLxaivr6e+vh6bzSazYdva2qSII7aNizgBoxAooimMfRZZr3a7XW73F8cnxCJjpmgux6v4XPFes9kshScjRldrT5LtWm0o9IRDO915KxzwIqdWIFyiwgVvfM5YQE3kd2dyo9tsNkwmE/X19XzxxRfYbDa23HJLmaPrcDhktrcoYBeJRGhtbaW8vJxhw4bJaIO6ujpZ0FFkbYusXWNMRa5zI/qcadEk17juShxCX43BfOYf43UR18Rut6PruhTxITWKweVyUV1dLYVvEZETCoVobm4mFovh8Xhobm5m7dq18jwK97ff75dCts/nk5nD0D6mvF6vjKAQWchirNXV1cloFCPGcaoibhQKhUKhUCgUCoVCoeg+/SIaa5q2GeAC/gVYdF1v0DTtUuDG9qe1J3VdbwVCuq5/BHzUH/3sKtm2LHcmnqYjXmez2WhsbMRkMhGLxaRDVAi6Yju/UcixWCxstNFG0lkpCl2JSAAhookt9ICMAUgmkzQ0NOBwOKT7L5MTU7gwBaKQnTFeQmxX746T1Ug+jubusKFvL18Xh7bRGW903oprW1lZmTLmGhoaqKmpoaqqSoqzgkgkImMKotEoZrNZCpIiGzkQCODxeHC5XDidTtm+0+lMcS+ni9h2ux2Hw4HP58NqtVJYWCjHtsjiBqRTuKuLN709JvtqDHZ1/hHnScRNiDx0EV8Dvy8cFBYWUlhYSDKZxGw2o+u6nDtEPIndbsfr9coFMGMcTzKZTCmuJ86ryEgW1zAUCsnIEqO4LRYBhEgfDoc7RNwoBh9PPfXUgG6vt9pUKBQKhUKhUCgUip6kz0VjTdMOBW4AGoFfgZuABl3XP9M07fLfnqvXNK0U2EnTtFOBhJ5pr/0AI9t2/1wxAMaCXEBKnISu63KrvcPh6LCd2yg2xeNxGhoaKC4upqqqSm71rqiokIXrhLji8/lkPrLA4XDw888/09LSQkVFRdbcUCHyGLf7ixxXj8dDVVUVxcXFHQqPZUJse7fb7fmf5B4i36Jv/YnYzm8Uz/JlXRzamRywTqczJTrE2LdcWbIul4uysjIZExEMBmWMhBhHIhNZ13UKCgoYPnx4SpyC0+lMcS8LN340GqWgoACLxSKFbuEQFhngdrudWCyWcdzmQ1cK4XWVvhqDnYnc6ccozo/VaiWZTOJyuSgoKJCZ5tC+KGRcODBmW2uaJmM6WltbaWxsZNiwYZjNZulYFuNB7HLo7Jo4HA5cLpd0s4uinmKcGrOW0yNuFIOPYcOGDej21rXN6urqrLsZqqurWblyZbfbVigUCoVCoVAoFApBn4rGmqbtAtwCnKjr+r81TbsfuAD4o6Zpmq7rn2qadjKwhPaid4four7eF7/LtVVdbLEXGMU6TdOkmzgYDOL3+/F4PBkF3YaGBtasWSNdgZFIRG7Lt1qtWK3WlP4YBR/RvsViobCwkLKyMiyWjkMjmUzKolVFRUXy8405rkVFRQQCAXkcuYqBiUJj0Wg04+flwpgH2x0xb6BlkWZaEzGKt9nOY7bjMJvN3S7Eli3vV0QBiHMv4ghElq/JZOpwHCaTCa/X2yEjWDiNhfO9vr5efkYsFqOmpgaz2Zw1o9ZYQFLTtJTMW3HcYlwL16y4N8R4EfdXLtLdyeuaQzzQxh10PEbjjgJAuoZdLpcsnmmMqDHef8a862g0ys8//0wwGKS8vJyqqip5PaLRqHQtV1VVpSwKGK+lELtFtrHoC/weyWN0GmeKuFkXBuL1UnTOX//6VwCOP/74AdneuraZSxRWY1ahUCgUCoVCoVD0FP0RTzFb1/V///b3q4CHNE2zA22ADgwDnMCuuq5/1Q/96xbdFZPSBTrhvjMWxIPUreyapklxTrgERWE7h8ORtYBXJoT7M5lMUlRUJLedG7NB7XY7TU1N2Gw2WlpaUqICoGOOa77b7tNf15VzmI+gur7T1xEa2fJgjdnTkUgEh8ORMl6zRT6kjyEh8IkFAvGncQwClJaWpvxp7IMQKY3uWV3Xcblc8r4RRf7gd1e0uJ+66j5NvwYbWha2cPwaz5+47sZcZ7GTQeSVGyNqMmVUi/NcUlJCaWmpbN9YXM/oDjZitVqJRCIpOxaM86SISsmUaZx+bBtqsUtFbubMmQP0nMjb0+31VpsKhUKhUCgUCoVC0ZP0tWj8GfAlgKZpZsAOVANeXdfrNE0b8ttjY3Vd/6GP+7ZOdFdMSt+iLorgCVHMKHxkale4BL1eLxUVFSSTSbmlP5sD1xiJkZ4BCu1xE7quy63oJpNJisX5YHQb5vM6IYB35Rxu6JnE0PcRGsFgkB9//BGn08nQoUPlZ4sxJvJtAZk/K0RGEZ8iihqK94mxJTKzM7nKhZgbCoUoKCjI6DAWfTCKv8bxrWlah3vH+HggEEhxGedL+jXY0MZdOBxOiQsR51YsEohjr6urY82aNUB7NIVRUDYKxeFwmObmZgKBAD6fj2HDhsncaovFIot3apqWdT5pamqiqamJYDAoF8OM78uXwbCwpFAoFAqFQqFQKBQKRW/Rp6KxrusJQGQxaEAz0PibYDwZ2Am4TNf1YF/2qyfoKTEp3XncmZAqHJwiFzifIl3GSIz0DFCxNdzj8VBeXi7dh6LgWFNTkyyq19OCZlfOoRDzlIOwZ3E6nR2c5EZ3qHAaCzIVR8yWgyziKNIRGbW5xpUY50KANH6OoKcKUeYil5C/Pjpb069tpoKB4v6vqKiQudXGecbv90vnr9vtlhnn0Wg0ZWEh3/Pv8/kIhUKYTKYOxThFn6qqqjptb0MT+Dc0hg8fzqpVqzI+V11d3ce9USgUCoVCoVAoFApFOv0RTwHAb1nFAU3TftY07UbgAODU9VEwhp5zhaaLvp0JH9FoVG4dF7mynWF0CaZngBoLqAmHpnAJms1mmV/cG0Wm1ofidBsybreboUOHZsypFddFuN/TMYqPxvcZx5bZbM4opppMJkpLS1MKQkJq1EE0GpXiozF2RbxGCLWZxk8+Cyk9wUB2tmYTtI3nzCj2w++5xaLoodfr7TR7XORbG+eIrp5/i8VCdXV11gKE+ban5pOBzapVqzLOJQqFQqFQKBQKhUKhGBh0vYJYD6G1YwN2ByYDJ+i6/kV/9acv0XWdYDCY1w/mzhy1IiM2FotRV1cnncHGjNh0hOiSSWAWkRHGYlOi/dra2ozFrxQbBrnGxbq8N308xuNxampqiMfjOd8vHK/hcDglE9n4OvGaUCiUd187uz+6i8vlwuv1DkhnqxC08zlP4lpEo1EpgmfKRxfn0el0UlVVJQVao6ib6TyL+S/b+U/PrxaPhUKhHr9mCoVCoVAoFAqFQqFQKDLTn05jHYhpmnYtsFTX9e/6qy99jdGRuK4Ck3D21dXVye39QI8V6zK239raSjAYTBGIjAhhR7iUc2F87fqylX8g05Vz39ekxx00NDSk5ONmw+FwEAgEZHRCpkxkozO+O/3pSUfwQHa2dieqIX1HQjrGPPVceevwe8xFOBzOGjGS6/n0thSKXLzwwgsDur3ealOhUCgUCoVCoVAoepJ+E40NPKEPsj2q6QJOrsPPR1DNtCUc2sUe0bau6/J5XddzFskT2bFGt19paansr7FdgWg/X2Gnu4UDFal0VkQw2/hZl1tO1/UutWuMrtB1Xebi+nw++Xox7owIodiYoyzaEGiaJtvP9NnG9oXL1uFw4PV6U6I0NnQ6y2LORrY5SuQcQ8drInA4HMTjcVpbW7HZbMRiMVpbW/F4PNK5bHyfmD+Mzwt6MpdaseFTWlo6oNvrrTYVCoVCoVAoFAqFoifpd9F4sAnG6XTXZatpWsp7zWZzimsyXSBKdzdn+1yTyYSmaUQiEfx+vxSbzGZzSj5tJtKzUHPFWKSLQIPdbbyux99VJ2ku0TcUCuW8duljz0im94gxJMRmi8XSwWEsxp1xscI4RjrLsc11/sR4rqmpAcjqlM+3vQ2JXMdpMplSFpuMUREifzrXeTSbzfK8i4UtyO5cTs9TT29roOVEKwYujz/+OACnnHLKgGyvt9pUKBQKhUKhUCgUip6k30XjwUh/uGyziYqZ8kOhe84+IewFg0FaW1ule3Rdilkp8qOnohH6emwaRWpN0zq41bvbBzGuHQ4HZrMZp9MphWrlVs1MpmJ5mXYPGB9zOp1ZC9YBKa5ycc9nWycUzw8WwV7ReyjRWKFQKBQKhUKhUCjWHSUa9wPdyRddV4yiYvqW8ObmZgKBgHQCChGvu4KdEOWSyaSKoFjPMEaQ9AXpInV3YwjSFz/SxU6TydTBKS/eMxBzoPuDTAsGma6H8bFsucV2u51oNJoi1isUCoVCoVAoFAqFQqFYf1CicT/QVVdoJgdgT5BMJqUbuCtF9Drrj3AMGp3GivUD49jM5kLvSdJF6q460LMVTzPmKOfznsEUfZDt/s20mNXZ9UgXlYWILHLRxXO9PY4UA5Phw4ezatWqjM9VV1f3cW8GB9XV1Vn/P6G6upqVK1f2bYcUCoVCoVAoFArFeosSjXuZnhB8eysyIBwOEwgE8Hg8HcTdXEJvvv3JJDj1hRCp6Bm6UtgwE/mMfWPecVdIF37Ti6flikJIL7jWl47/gUCuoon5XOdMESLJZJJgMIjNZsNkMlFcXExbW1tGN7Ji8LBq1ap1Krqp6Dq5RGEV/aJQKBQKhUKhUCi6ghKNe5nOBNZ8hLXeirMwFq6LRqNAfsLRuvRHCUjrD06nU7rRhWu8K/TGYkcusTjf/hndsWazuUf6tT6xrvNJpsiKdIexUTC22+0pgn4+xRYVCoVCoVAoFAqFQqFQ9C9KNO5lOhNo8hHWeqrImRFd19E0DZfLldIHUZAs13vWpT/dzaxV9D0mkwmTySTFwK5e855c7BBuxXSXsNFZnOt9RsS413U9qwtSjPVs9JdjL1t/u9Kfrty/2c5ftsKZxixjca28Xm/K56mFI0Vv8/rrrw/o9nqrTYVCoVAoFAqFQqHoSZRo3EPk2n6fKy/VKKz1lRCV/jnpfehOP/J9n9lsHlT5sb1Nd8dMvu/r6vg0viZ97PeECJutP9ner5ysndMT97vxvrZarUD2a9Ufc55icNHTu3J6I8JmsMXiKBQKhUKhUCgUivUPpaj0MyaTSWYK9xfCediZgKPrOsFgUGVUDiIGwvg0ku9YTWewj93+OP5sY2egjSnFhsf999/P/fffP2Db6602O0MUycv23/Dhw/u0PwqFQqFQKBQKhWJgo361K/JGxFiEQqH+7opC0SUG+9gd7Mev6B2GDx+eU4Ssrq7ul349//zzPP/88wO2vd5qszNWrlwpY3ky/bdq1ao+7Y9CoVAoFAqFQqEY2Kh4CkXe9FZBPoWitxnsY3ewH7+id1i1atWgde8rFAqFQqFQKBQKxYaOchoPApLJJMFgkGQyuU7tdDcaQKHoKbobszDYx25PHn9PzSeK9YNcbuL+chIrFAqFQqFQKBQKhaL3UaLxBopR2AmHw7S2thIOh/u7WwrFOtGXMQsDNQe5v/sl5hMVdTE4EG7iTP+tXLmyv7un6EFyZR6rvGOFQqFQKBQKhWLwoeIpNlCEsAPgdDpT/hRCssvlGrTOS8X6SU/HLASDwaz3gRCoAdxud498Xk/Q1/0S84XT6cRkMsl5REVdbDj85z//yfpdoNzEg4dciwDCca5QKBQKhUKhUCgGD9pAc9H1Npqm1QF9We2lFKjvw88zYgJKMny+CTADCaAv9pj35zno6c+v1nW9LNuT/TC+BP19jgXrUz/yuQ9MOZ7rqX4YyTq+0sbWuvarK4jzVAzU9tFn5mJ9GmN9Qb79yHdsDRQGyvlNR/WrIzm/FxUKhUKhUCgUCsX6yaATjfsaTdOW6bo+brB+/kDoQ39/fl8wUI5R9WNg9qMnGCjHovoxMPvR0wzU41L9UigUCoVCoVAoFIMFlWmsUCgUCoVCoVAoFAqFQqFQKBQKiRKNFQqFQqFQKBQKhUKhUCgUCoVCIVGice/z4CD/fOj/PvT35/cFA+UYVT9SGSj96AkGyrGofqQyUPrR0wzU41L9UigUCoVCoVAoFIMClWmsUCgUCoVCoVAoFAqFQqFQKBQKiXIaKxQKhUKhUCgUCoVCoVAoFAqFQmLp7w50B03TzLquJ7rz3tLSUn348OE93KMNj2QySSKRwGw2YzINnLWF/u7XP//5z3pd18uyPT/Qx1dPnL/+vgYbMrnG10AfW+s769u47mp/1dja8Mg0BvpjHA/2sfXDDz8AsOmmmw6K9vqSzv6fS6FQKBQKhULRu6x3orGmabsAPmCBpmkmXdeTXXn/8OHDWbZsWe90bgCRT+yIruuEQiFcLheapsnHNE0jmUwSDodxOp098sNTtN9V0o8jvV/dbbe7aJq2KtfzA2l8ZRoDxmu+LqJx+rjJl2yvN/Y109jrjevc2T3S12Prt8/MOr4G0tjqD9YlSinXtRTtps+H4t+55sCemte620b6fZirP4NhbOU6r+K7Lf2xfObDrrZrpDfnrkxjoLPv7t7oz2AYW4r+obP/51IoFAqFQqFQ9C4D305lQNO0CcBC4DaArgrGgxVd1wkGgx1++IZCIVpbWwmFQh3eYzKZcLvdXRIWs31OT9Kdfil+R9M03G73OgkHPdFGJpLJJMFgkGAwSGtrK+FwWD4eCARIJtXtrsifrs5H6eNazI/GcRgMBgfMOOyt+3Awkes7MBeZxlZffP+lk2kMiMfUd6RCoVAoFAqFQqFYV9abXxWaph0KzAJ2Az7RNG16P3dpvSHbD2OXy4XX68XlcnW7beMP5e7+AFcoAILBIGvWrCGZTOL1enE6nQCEw2E1rhQ5ySTYret8JObH9HEoRGTFwKczIbe734GZxpb6/hu8XHbZZVx22WWDpj2FQqFQKBQKxeBhvYin0DStEjgbuELX9aWapi0GtunC+6cB0wA23njj3unkAEb8IE7/YSwcSel0JZpC/FDO9TkbOoN9fOVDV2IxhJtcIES7wTauQI2tfDHOQ2LsrOt8pGlayrZ/MQ7Fn71BpriB3mIwjK1M309Gsn0HdkamsTXQv//E9/q6RBMpMrN48eJB1Z5CoVAoFAqFYvCwvvxyaAFO0XX93d/+/RpwoKZpp+XzZl3XH9R1fZyu6+PKygZfPY2ubmPuiqPO6NTqze3SA21ruJHBPr7yIR8XntvtpqqqqoOIYzKZ8Hg8fSZ0CHfiQBhramzlRybHaE/PRz0djdMb7ugufv4GP7Z6YjdNJnLFQgyEuJBM35dqx4ZCoVAoFAqFQqHoKgPaaaxp2jZAHEDX9W+19l9jZl3Xf9E07ULgGE3TXtd1fU2/dnQDoyuOuu46tbqK+MEL9MnnKXqWfFx46Q7j/sLoTvR4PP3cG0U+9NU81JP0hjtakYpxXPRl1nB/k+n7cjDv2FAoFAqFQqFQKBTdY8CKxpqmHQzMBhYDozVNu0bX9Tf5TUQG/gcUAMOBQScar8sP4PT4CeO/NU3LKt7lqhLfWQX5demv2CYOpGzb1nU9Z7sDwfHVE6zrucvWZnfb7c44MG71z/S5oj/psSi9uaU62zEYx1q2c7ShjK3+oDfG87p8phizXYnlydamaMPhcGA2m7O+N9uclo/4rcZedvry3KR/b2bCOEbSx9e6fJ92RqbFB/G9rsaPQqFQKBQKhUKhyJcBKRprmjYOuB04GVgCnAQcpGnaW4BJ1/WEruvfaJr2X+AWTdN2B9AHk5VoHUh3IRn/nc2F1FVBpacREQWKvqevrn0md5zxsb66/gPF8azoXdJFv57YzdCVNtSctv5hHDNdHS99uVtmfXTer89stNFGg6o9hUKhUCgUCsXgYUCKxkApcL2u658BaJr2I3AqvwnGmqaZdV1PAFcAZYNdLO5q8aT0+InO4iiSySR1dXXE4+0mb/FjNB6P09DQgM/nS3HVdSYy9oYI2ZcFpAYyPXEeRBvi+uQSOzJdy3yur/E10C6o2O12vF5vyjjszy3Vakz1Hrl2O/TVopQY18lkEpPJhMPhADrOg9n6arfbiUajKX0W7xVtZWtD0X9051qI9ySTSQKBAAB2ux2TyYTdbs/rvXa7HY/HQzKZJJlMdtndrsbQwOXpp58eVO0pFAqFQqFQKAYPA1I01nX9TU3TqgwPLQfCvwnFAGXAWl3X4wzCaIp0MmVj5iLdhWR0VmbS38PhMPF4HIvFkiKoNDQ0sGZN++kvLy9PeX2u/vSG46qr52BDpSfOQ3obuRYVOnMHZ+uD8TUAra2teL3ejEXw+mtLtRpTvUeu3Q59da7FeE4mk12ar8S/hYBsfJ8Yr+nzqMpkHzh051qI93g8HrmwJUTkaDSKxZL9f6XEe71eLyaTSY4dl8vVpb6oMaRQKBQKhUKhUCj6mgEnGmuapuntGMVgC7CRpmlm2qMqjtU07TggNNhdxpB/8SSjg7QrIpzdbsdisVBSUgJAMBjE6XTi8/kA5J+CzpzLXSm0ly+qgFQ7PXEexHudTmen7jaHw0EgEEhxVubjXE8mk3g8noyu4oGCGlO9R1d3O/QGQuAVTuN85yvhMC0uLqatrS3j+5LJJKFQCOh84UXRt3TnWoi5UPzdOF46ayfT67oy7o1O5fSdGEaMOyMUfcv5558PwJ133jko2lMoFAqFQqFQDB4GhGisadoooARYBiSBhKZpJl3Xk7+9JAz8CFwCHA6cput6sF86OwDJN7+wu87JaDRKMpkkFosBpLRRVlbWoRiQUYwRArNRcOyNzNjBkOGYT1xCT5wHYxvBYDDnmIlEIiSTSSKRSAe3ZSaMUSdFRUVyXAzECIjBMKb6i/Qx0p850p1d5/S+BYNBampqcDgcFBYWZnxPJBKhpqYGgKqqKtxutxpLA4TujDWTyZTiEna73Vld5Z19nvh7IpEgEolkXJATc6oxOznTTgwjamdE/7F8+fJB1Z5CoVAoFAqFYvDQ76KxpmkTgRuAX3/7b5mmaY/rut4qhGNd16OaplUAU4CjdV3/pj/7vL7SFQepEWNOZyKRIBwOS3dxri2znW2nzdQHlSObnb4SBYyO9HQnXPo1y+aUyza+gsEgLS0tFBQU5OX0E+24XC6V4zkI6cn5INuYTM/w7oxQKEQgECAUCknROL1th8NBRUUF0DPuYjUv9gziOjkcjqyCbTa64lDOZ1dPJBLJOp8bn8s2B6ePhWw7I9TYGdgMHz6cVatWZX1u5cqVfdshhUKhUCgUCoXCQL+KxpqmWYHjaXcO/0PTtKOB8cAlmqbdrOt6i+HlC4CXdF3/tj/62hm53Ea6rmcteiOey0ZP/sgTjjrRD/Gj1PgjU2zVTn+fy+UimUzS1NSE3++nqamJ8vJybDYbJpMJq9Xa4RwYt3MHg8GUwlGapmUUlYUwKrYCA3g8noznobNzl+uarI8/nvOJS0gXCLKdA+OYFK8XGa3Goocul0te+2AwmFIISnyG6I+u6/LzQqEQfr8fXdfltU0mk+i6jtlsTnlM/GkspijIJ8fTOA5yLYZkEk96Yxx05jxcH8deT9LZXGm8bsaFks7Gva7rOa99MBiktraW8vJyPB5PymeIsSqcnWKOMvZHjK2SkhIZTyGOxThOhSBpXOjobEx0tohnPA/Gvg9G8vmuzYTxey8QCBCPxwkEApSWlqJpWk7xWLRrnOsEwjFsnFeCwSB+v59kMonL5SIYbN8YZcxnt9lseL1ebDYbfr8fQI4Zm81GQUFBh+KMYr42vt5Ipv7lew+Bmpv6g1WrVnUY03vttRcAH3zwQT/0SKFQKBQKhUKh+J1+dxoDXmAk8A/gZaAeOASYBDygadpOQJOu6zf0Xxd7ht4qZJOva9josjK6l4w/FE0mU9YfjkIoCYVCFBcXo2madEQlk0kqKipSPl+I1CLiIL1wlNHBLDD+OK6trZWfq7bb/n4+hQCWyTnWFTdyukDs8XjktRJ5w6J9cZ2NhaCyiXRGcSVdoBXbujsbd/nmeKaT6x7ryrlR4knvkW0BSDgzjXTF3Znr2qd/pnGRIZlMyvkoHA7T0tJCMBiUYqJ4rXjOYrFQVlaW8pyxn+n9yGd+7uy7QeVrrzuapqUsZDY0NBCLxaivr6e0tBRInXfEAmeu70RonxuFiCuunXFXTzgcpq6uDkj9LhOLZ2IxA6CyshK3243FYslaXE/Mqdnm4HThvKuLjWpHh0KhUCgUCoVCoRD0q2is63qbpmm3A/+nadoPuq5/pGnax8BQ4FBN054AdgGe689+9hS9VQwpXzHa+LpsxZs6EzdisRgOh4NoNEosFpPvEY68srKyDu81/lAXP8Th9x/QRpeNeCwej0uXqxJKUsklfnZFXPL7/axZs4aysrKU15tMJjweT8p1TF9k0DQtZ96xaCMdo2hiHG/pokw4HKa5uRmLxYLP5+tylEqm8a2Et4FLtjFtHC+6rmcV9aDz+dXtdlNZWZnyfDgcJhgMUlBQgMlkwm63y3ktGAymLHQIx2gsFqOuro7S0lLpjjf2M70f+czPnfVd5Wv3DMbrVFZWJhfNRCa7uFbpC5yZMI5FSHX5hkIhHA6HXAgpKyuT4zdd1HU6nZSXl8u/p5O+Q6IrmfOQOnayubSVk33d2HzzzXulvZ5yGvd0/xQKhUKhUCgUg4eB4DT+CBgFnKRpmqbr+ofAPE3TpgFDdF2/o3+713OkO2a7kiucC/FD02q1UlNTQ3FxMW1tbR3aFVnG8XicmpqaDj+K00XlTH0z/jD2+/14PB5GjBhBIBCQYkpZWZlsT7xffIbFYslaIM9INBqVr8/l8jKew/TPzMSG4KjKJX52RVwKhUKEw2FZ5Anax4IQMIxRJeniHaQKXeI6WK1W6aorKyuTiwyRSASHw5ESQWF0daaPWafTKbeQNzQ0EI/HpdNZiHiZROdcrnQlvA1cjM5MQaZ7W8SjGEU941yVTwFO4/NC8IvH4ySTSaLRKBaLRf47HA6jaRoOh4OGhgaKi4tpamoiHo8TDodTBDZjf40O+1yCsPE96YspKoO2c7ryHWosLifmPFHINT0GIn2BM9dYLCgowOVyye8VER/h9/sJh8OUlpZSUFCA3++Xc6PH40n5HiooKOgwn4nvKmM0kEAs6uWzEG38zsuGWlBbNx588MFeae+hhx7q0fYUCoVCoVAoFIqu0u+isa7rEU3TngF04DJN07YAokAZEOjXzvUy3dnGnAkhhtTU1LBmzRopiIh24fcfvQD19fUkk0m8Xq/MG04vepbNHSccpMlkUsYQCBdefX29FFOAbhfIE30w/pkNY1u5PlOwITiqekr8FK7w4uJiAoGAvJaapuH3+zt1BRufF463SCTCihUrgN9d6ULw0HU95Zw7nU6CwSDxeJympqaURQyjoCO2kre2thIMBqmqqkpxBYr3DCRU8amuYYxeEWS6t0U8ihD17HZ7SsRKPuMivd1wOEwoFMJiscgxKURFj8eDw+Hg559/prm5GWi/b4xRGuLeSM/7FuRayMjU14E8rgca+ZwrIboGAoEOC6Xpu13SFzgzfQ6kjkWHw0EgEJALpmKOE0VjMyGEZeNcnl5nQLzG7XZLUVmIziLaIp/opnwyjUU/1Fw1sKiurs55Taqrq1WhPIVCoVAoFApFr9LvojGArutNmqY9BHwNnAlEgCm6rtf0b8/ypzsiUbowGgwGqampoaKigoKCgi73wefzAaS4NgWi7bKyspStsMLFCe0/pB0Oh3SMiu3amUiPHzCKfMbPTRd9RYaoyMzN5RTORzDJJC7nEpqVo+p3LBYLFRUVBAIBmckpRDJRuFAg4iKyuX1F9rDP58NmswG/O41tNhtNTU2yPeM1Lykpoba2llgs1iG7OH0rebobtbfiXnqCruQnKzqSPk8IxNwgxL9wOEw8HpeCr3iN8c/0xbj050WerXi+pKSExsZGnE4nZrNZCspFRUX4fL6UqApRmEz0NdecmQm73d7hPaJfao7qHGMufjAY7LCbAdrHSE1NDbqudykf3YjxOhnjUEwmU9bIh3RRV4wPEVshcrSz5bc7HA5ZoFG8p6SkRO7ayBf1ndf7TJs2Deg5R69orzNBON//1+zp/ikUCoVCoVAoBg8DQjQG0HU9BizUNO3D9n/qyc7eM5DojkgkxIeecvcIERDaK7Mbt2EbP9Pj8chM2lgsRjwel6KFKHhmMplkNIDIlBXHlu6CFsKJ8Ydspq3gJpOJcDhMIBDA6/ViMpkIBoNSjBRtic9JJ5Mwny4u51PgbENwVHXXyWosOCbeJ8QE4ZgT2Ztiqz4g4yJaW1sJhUKyYJNwx3m9XunYGzp0qPw8m82G3+8nEAhIEa6+vp5YLCaF6Z9//plQKMSWW25JYWFhhz4bRT8RWwKZM7Ezva8/3L5KqOk+xgKNRUVFKSKxQIw7j8dDUVERDocjY5RKerFH4SQWGOdD8frGxkZisRiNjY34fD5cLleKExXaF+FWrlxJIpHA7XbL+UzcN+J+gcxzpiAajXa411Thz/wR5ypXtq/T6aSioiIlbqczxPeWaE98b7lcLgoKCjJmyXs8nhSHsHhveoHPmpoaQqGQLBzr9/tZu3YtHo9HPqbrOpFIhFAoRCgUIhKJpPRfZDCn9zlTRnw+mcaKdeN///vfoGpPoVAoFAqFQjF4GDCisUDX9UR/96E7dCYSZfuxZnzc7XZTVVUlq6KL4nKZSCQSGYUwkdsocmSFU8npdFJZWYnD4SAWi2E2m7FarZjNZiKRCIFAIMU5XFBQQEtLC21tbdTV1REKhdA0Tf5wNTquwuEwfr9fOu8SiQQmk0k6TOPxuOyDcOQ5HA7poLJYLPJHdkFBATabTbrGNE2Tn7MhuzfTx0dnUSX5nIv0gkvG9+m6Lt8nRIVgMChzqj0eDzabjUTi99uxuLiYZDKJ3W7HZrMRj8ex2Ww4nU7a2tpoa2tD1/WUbd3GfiQSCQKBAG1tbcTjcfmY0+nEZrOlZHIb3YKhUCglx1YUriopKcFiseQs7mQUbzo75+nkEpqN7810rTb0/OR1EZ8yjUtBIpGgoaFBOtTFPJG+dV8sUAmxWDiT0wmFQnKsaZrG2rVr5TxltVqprKwEkO544boX8RQtLS24XC7a2tr44YcfcDgcbLbZZkQiEaB9d4eYN0XxTqvVKnd2AFRVVWX9XjAeh/Gcru+LWp2xLuNHRBzmcqMAAQAASURBVCMZMbpz09vWNA2PxyPnN5FRLb4nTSYTbW1tHeac2tpaAMrLy+V3WFtbG9FoVIq6DoeDRCJBPB4nkUikRD3F43EZoQLthUd//vlnEokEZrMZXddJJBLoui7bFHFBIntZ1AdwOp0p3+vic43HkCneItO4y3XuN/Rxp1AoFAqFQqFQKLrGgBON11fS4xq6gvihZhSaOhMNhZCS/rwQigGZV2y322UGIrQLM8Jt6nK5CAQC8t8tLS1SmCspKaGhoQGLxYLdbsfhcEjnnjG7UfxQF07QZDIphRexddzhcMjM48LCQvkD3Ww2U1paisvlkoK0ELEhVYRP31YuhKANkc6yOjMVDssHIdxnWpQwOiqFOGEcX+LaxmIx4PfzbzKZ5PUSOdfCbWl0TYriYsa8bZfLJT83EolIoUaIJaLPRqHH7/dLQU446zONg/SFnM7c2d3NFM90rTbUcbmuGJ3umc5xNBqVixEicxtS731N01LmM/jdAZx+jcW1N5vNNDY2yozYSCRCS0sLdrsdl8slReNQKISu6xQVFRGLxbBarQQCAWbPns0999xDMpnk5JNP5vzzz8dms2E2mzGZTNTV1aHruiyK5nA45NgUfck0/ox/qjGTH5nOlTETWyxuisgHMRcZi92JhU743Qme/j6xw8Zut6fEU4j86lAoBLQvOLhcLhKJBH6/Xy60NTY2UlxcLBfGNE2T36PiO1GM0Y033rhDvruYW0XWvNVqBZB/prurjfdItkXFXAs2CoVCoVAoFAqFQpGOEo0HKJ2JhkZx2FhEz2azyR+3mbY+i/cat956vV4ZRSEcx0KgCwQCNDY2YrPZKCgo6ODkEs5mm81GNBrFarXKPFC32y1/sBuzR4WQI354O51OCgoK5I9gUWAoXRAdTNu2O8vrzVQ4LB+EuFZbW0t5eXlKdrZwV4oIivSoECFGtLW1SXE1GAyi6zp2u536+npsNpvcSi3eK9ptamqSQnVLSwuxWAyPxyOLPLlcLiKRCEVFRXJsCLdeWVkZFotFjk2TySQzvMXYF2NWiCzpCzmdubO7W4BsIGcrDzQ6uwYOh4OioqIOonJ6lE8mgV/XdWpqaggEApSXl2M2m3G5XLjdbllEccWKFfztb39jzJgxTJgwQRZZFONF7JIIh8Pous6zzz7LrFmzWLNmDVOmTMHj8fDII48wb948Tj75ZM455xzcbjc+n4+Ghgba2tqkmJgefZHp2EVWeCAQSBHJFR3JFr+QCXEvG6NNxHcSpC6oZnqfyMhOJpM0NTVRXFxMOBzmqaee4r333mOLLbZgzz335A9/+INcCI1Go/zyyy9yPIj2mpubKSsrkzuJxKJqY2OjXDgT0RbGeSyZTFJfX58yT4t50eVypQjZ6edGxeMoFAqFQqFQKBSKnkCJxr2MURjtiiDQmRBldE0BKc6pUCgkf1C63W7sdjt+v1/+eBYCnehTcXGxFDxEBqxwfopt3DU1Nfj9foqKiqioqJACYjwep76+Xhb4icVitLa2Ul5eLn94C8EHkIKe6KexAJvxmMVr2traZLZoeuzBhkxvCeTJZFLmFmdCjBvhtBTCiRDCvF4vy5cv56uvvmL58uV8/fXXlJeXs/vuuzNy5Eg23XRTKioqaG5ultv0xXZtn89Ha2srn3zyCfPnz6euro4xY8aw3377MXr0aDwej1zk0DSNuro6GhoapANdODeN2d3w+1Zy4VYPBoMZBbjOhJTuir+DaTEjF/nkbOfrkO/M9Z1J4A8EAtKpLsareN7hcLBy5UqmTp1Kc3Mzjz/+OC6Xi0MPPZTDDjuMPffcE7fbjdlsJplMsnz5cm666Sb+8Y9/MHbsWJ599ll23HFHAM477zyuv/56HnzwQZ599lnOO+88TjnlFKxWK21tbSSTSRoaGoDUsZFp/Ins43g8TjgcVuMoB50t6hhd7GLeEnOH+LfYPZFtQdVutxOLxUgkEjQ1NbFkyRK++OILli9fzkcffUQ8HqeiooIFCxZw88034/F42H333dljjz3Yd999cblclJeXY7Vapfs4HA4TDAZxu9243W65QNvW1kZrays2m42NNtooxT0vduakR66I70zj4ok4FrEQKBYq1FjqO8aMGTOo2lMoFAqFQqFQDB4GjwrXT2Ryl+XjmOpqkTwhwsTjcUKhkCxcV1BQQDQalbnE0C5aFBcX89lnn/Hee+/x3//+l5qaGtra2qivr6exsZGGhga5zdZms1FeXi4jKaqqqigvL6eyspJdd92V4uJiueW2tLRUxli0tLTI484U32EUkIxF94wiUWNjI2vWrAFIEQo3JLq7sNAdhOPbbDbLMZNeaM4o3La1tXHvvffy2WefsXLlSr766itZjM5qtTJy5EiWLVvG888/j9lsZscdd2SvvfZi/PjxbL/99sTjcb788kuWLl3KO++8w5IlS0gkEng8HoYMGcLChQu54447MJlMbLPNNmy11Vbsscce7LnnngwZMoTi4mKi0SjFxcWyEJVwD6ZndgqnciwWkxEqxvPZmZCixN91I5+cbeGC1HU9pUCmwFiIM1dRTDF2xYKYyIcVmek+n49YLCbHx9KlSzn22GMpLy/ngw8+oKWlhUceeYTnn3+e559/nk022YRDDz2UyZMn89xzz3HPPfdQWFjIfffdx8knn5zSx+HDh/PQQw9x3nnnMWvWLK677jqeeuopZsyYwZFHHonJZJJjzzg+M40/8Vpx/w1G8i3q2dmiTvr4E7E5okihcZHS2Jb47ikqKqK5uZmlS5cye/ZsvvjiCxkTsfXWW3P++edz9NFHM2bMGJqamvjggw94//33ee+993jjjTeA9vzqTTbZBJPJhNfrpbi4GKvVitPppKSkhAMOOIAxY8Zgs9mw2WzU19dTV1eHzWaTu2tERFMymZQFRgWZFl3Sizsq+p4777xzULWnUCgUCoVCoRg8aIOtmva4ceP0ZcuW9dnnZRIERQyD1+vN+EPZuN3eWJzH+JwoulNQUCDjHxwOhxSG7XY7zc3NlJSUYDKZCAaDrFy5kldffZXPPvuMjz76iObmZjRNo7KyEp/Ph8/no7i4mLKyMkpKSvD5fOi6Tm1tLfX19dTW1lJXV0ddXR01NTWyGNTQoUPZfffd2WeffTjwwAMxm83y+MrLy2VepBGjSGw2m6mtrWXNmjVUVFSk/Hg2Oo3FtnFjTmX6+eltNE37p67r47I9353xFQgE5PnqqmjZWVGx9FxikUkdj8cpKiqSBfCam5uxWCz4fD6i0SiJRILa2lpmzJjBq6++SkVFBdtuuy2jR49m6623ZsyYMWyxxRbYbDZisRifffYZ77zzDu+++y7Lli1D13UKCwsBaGlpQdM0xo0bx/7778/+++/PjjvuKAslLlmyhMWLF/PZZ5+xZMkSKfxUV1ez//77M2HCBHbaaSei0SilpaVSDEomkxQWFkrXO7QLKA0NDXLLeK6c8WznTtxjRld8pvfmoru5obnGV1/PXbnIdPz5in/idZnGvBizxnscSJkPhZtcUFtbK6+lWFioqqqS7S5evJiDDjqI8vJy3n77bTbaaCP53mAwyEsvvcQzzzzDwoUL5eMnn3wys2fPxuPx5CxG6nA4WLRoERdeeCGff/45RxxxBPfccw9VVVV5z03ieIQrVgie2dgQxpZx/Bi/D/OZ/7Ldt+l52em5xmazmZaWFllAU5z3uro6GUnx2muvMXPmTEaMGMEJJ5zAzjvvzE477ZSzX/F4nNWrV7Nw4UIWLlzI2rVrCQQC8r9gMEgwGCQWi2Eymbjllls48sgj8Xq91NbWsnbtWoYMGSJjmdxuN4lEgmg0mnGRzDiXGxcbjAvR2cZIZw7+DWFsrW+IvPO+fu/6Qmf/z6VQKBQKhUKh6F2UaNwHpJ/jdKdx+g+1TD+ixXuSyaQU1YRLTeQbi8Jz6T8q586dyxtvvMHXX38NwJAhQ9h///054IAD2HfffWX+IiBdqJlIJBJyK6+u66xcuZL33nuP9957j/fff5/GxkYARo8ezdixY7nyyisZMWIE8PuPUfFDfe3atdTW1rLJJptQWVlJIpGgoaFBCt9CODb+wBXipqhIX1RU1O3ig92lN0TjdXEaZxJQjGNFOO2MuddCRDH+KYRWr9cLQENDA1OmTGHx4sXcfffdnH322bL9XGNEOMzff/993n33XQD2228/9t13X+n4y/Y+IQh+8803fPTRR7zzzju8//77+P1+zGYzO++8M+PHj2fnnXdm1KhR+Hw+ysrK0DSNYDBIS0tLSuGozsQ3UQwwXUQx3n/ZxpfYdj5YxZd1+d7IdN5zLZSlz4ctLS18++23FBUVMWTIEAAZ8WCxWHC73XKLfi7BWJBIJLDZbKxcuZL58+ez/fbbs8ceewDtzufORGPx97vuuouZM2fi8Xi46667mDRpUl7jQMyHwnktFtuAjCL8hjC2jOMn02JDLnEz10JZpuf8fn9KfI3YERMOh1mzZo0sXvfII4/w4IMPsu+++/LXv/5VLnoBctdNJkThxkyIeCeA1tZWpk6dyoIFCzj66KO5+eab5fe5y+WiqKhIfhcHAgHC4bDMcg8EAvj9fgoKCuSclE1sz3V+jAuEmSJ8NoSx1R9MmTIFgKeffrrL780k/ObbXr6i8br0r79RorFCoVAoFApF/6LiKfqA9P+pNxaqEQKK+LsQ9QoKCmSBG2j/EVlXV0dJSQmxWIy2tjbMZrPcbisK+sTjcVpaWli7di2vvPIKDz30ELFYjN13353Jkyez7777Ul1dnbK91ejaS9/yb0S4+ASlpaUcf/zxHH/88YRCIX744QcWLVrEwoULefrpp5k/fz4zZ87kxBNPpLCwUDqF6+rqZBEiUY0+kUhQUlIi2xbH0dzcjM/nw+PxYLfbsVgsxONxYrGY/DHdHbG1tyrIixzNTO13FkUC2cW4XCKJUYDTNE1mf7rd7pRxFAqFaGlpwWKxUFhYKHOwRear6P9XX33F6aefzo8//sgjjzzCEUccIaNGAGKxWEoBPSOBQAC73c6ECROYMGFCynPNzc1ZRdhgMCiFl+rqaqqrq5kyZQptbW0sWbKEhQsX8t5773HrrbcC7ZEpW221Fdtttx1bbLEF2267LUOHDsVqtVJbW0tpaSlWqxVN07BarRkFKLFQEY/Hgd+3fhvzwrNdj+4WzRuMpAuA4pwa41FEdEA8Hk9ZKBOuY7GA1NbWJjO5LRYLFouFcDhMLBZD0zS57T8ej/Ppp59y2GGHUV5ezgsvvIDH46G5ublD/yKRCIWFhZSWlnL66acD7eMY2l3y2eIQwuGwXGQBmDZtGnvttRfnnnsuU6ZM4cUXX+T222+Xbngh8ITDYQoKCqRYnkwmKS0tpbS0VObEC+f8hjrG0u+r9PnPeH+lfx8lk8mUOS+ZTNLY2EhRURFtbW0ZdwhomobFYpECsd/vx+Fw4HK5aGlp4cYbb2T+/PlMmjSJ2bNny6gmQa7okEAgIOeMdH799deUYns33XQTpaWlPPbYY9TW1nLPPfdQXl4uawyIe+XXX3+VmcZlZWVYrVbsdjtNTU3EYjG8Xi92u126ko2LxaLYaPr5Fv9vIb5Dg8GgrEUg7stcOyt66ztzQ+CXX34ZVO0pFAqFQqFQKAYPSjTuAzrbqi2eN2YuCmeo8b3ix7EQRoSQIH78JZNJHA4HTz75JPfccw9r167lyCOP5JprrmGTTTaR7YTD4axOUYvFkvU5s9mc9Uel2Wxm++23Z/vtt2fGjBl8++23XHDBBcyYMYN58+Zx2223sfPOO2O1WtF1naqqKhKJBG63m1AoRGNjI6WlpVJMEaKNruvSMQvg8/mk0Nfc3Jwz97Q/SCQShEKhLvdnHRxeHbI8jXmvQkQWxaGCwaAU3YUYI86v2Wymrq6OE088kcbGRp5//nnpuDRiMpmyjpFMUSQCs9mctZhhtrFlt9vZfffd2Wuvvbjmmmuoq6vjgw8+4N///jeff/45r776Kk888YT87M0335wpU6YwceJEWVwxm8AbjUalO9WYL242m3PGxjidzpSiZkpMyU2m859+zux2O8FgEKvVKl33kUhE3vcmk0kKviUlJVRXV8u8659//pn6+nrcbrcsmPn5559Lwfjtt99OcY2mYzKZss5rVqs1q4u0ra2tw/u22GIL3nrrLe666y5mz57NRx99xDXXXMOkSZOw2+2sXr0ak8lENBrF5/NRV1dHIBCgsrKSgoICCgoKOuQbb4hZx519J4ps6my5/+FwWI6NYDDImjVrZC5xeja52HEQi8WwWq0pAqnJZGLGjBl8/PHHXH755fzf//1fxs8TbuFM5PrOFC7kVatW8dhjj+H1epkxYwbDhw/n2muv5cQTT2TevHkprl/hthdCsdiNFI1G+fXXX3G5XGyyySZybhOCsfGY04/BGAcjMrSTyWSHWCI1nykUCoVCoVAoFAojSjQeQBiL86RnGLrdbrxeL5FIhHg8TmVlJZqm0dDQgN1ux2Qy8d5773Httdfy5ZdfssMOO/DUU08xfvz4fjmWLbbYgtdff53nnnuOK664gr333ptzzjmHP/3pT7JIH7T/QBbHJMRWo+gpcpPr6upkYaDi4mKampooLi6mra1tQIkqQnDsS9ILRInxEo/Hqa+vJxaLEQwGpZtR5KeK9wSDQTRNY9myZZx66qmYzWYWLFjAdttt16fHkQ9lZWUcc8wxHHPMMcDvMSnffvstn3/+Oa+88gqzZs1im222oaKigmQyKe8Po+MP2sdeUVFRRqe6cLka80KNixcej6fPo1EGKp3lpGYqYJYelRKPx2ltbZVOdWi//0OhEFarlWg0itfrlU5S4fA1mUyUlJSwevVqVqxYwdq1a/nhhx946aWXUiIpxGJcX2CxWDjvvPM46qijOOOMMzj33HN5//33ueaaa4hEIlKIFtnwRtILoA6UxbC+JBQKpRSwg475veI7UoiyXq9Xirfp3wdiPrRYLHJxNRAI8M0333DqqaeyatUq7rvvPo4++ugeP5YVK1bwxBNP8Nprr2E2m2lra+Pbb7/ltttuY9iwYcyYMYMDDjiAhx56iAMOOEAWIhXjIBwOy4W4wsJChg0bJhcAjYKxcKxnW/wQ50S4isWitHC2NzQ0DLgFWIVCoVAoFAqFQtH/KNF4AGD8QWx0DwcCATRNk06p0tJS6uvricfjRKNRwuEw9fX1BINBZs2axdtvv82IESOYN28ehx56aL87hjRNY9KkSRx44IHccMMN3Hfffbz44ovcfPPNHHjggTQ0NEjntMVikW7EsrIyeR6MAopwyTY0NEg3osfjySlY9TW5nLb5km9BMeNnZvqhL8T4WCwmBVARK9HQ0ICmaTIr+l//+heTJk2ioqKC119/XWaqDnQ0TWOjjTZi5MiRHHbYYZx99tnsuuuunH322bz22muMGDGCRCJBMpkkGo2mOJ2znTdoP3diPEJ7TIAoVDWQFikGAp1FdWQ6z+FwmJaWFhkzE4vF5D0tnN9CqG9paZGCFrQLce+//z7Lli3j22+/5Ztvvkm5VuXl5ey2227ce++9GTOM+4ott9ySRYsWcfvtt3PttdeybNkynnrqKUaOHCl3hYj8ZSXU/Y5YdDPeZ8YxZrPZ5GKiyNEXBTKFKAodFzNMJhNOp5PW1lY++OADZsyYga7rvPnmm2y++eY9egxr167l8ssvZ/78+TgcDk4++WROOeUUPvjgA2bNmsWJJ57I448/zocffshRRx3Fcccdx7XXXsu4ceOIRqM0NjbK4rONjY3SoSzmZXEs0O5mrqurA8gaGyTuQWMsiKiJYFy86esFT4VCoVAoFAqFQjGwUaJxH5FLCBQV3MvKyuSPPrFd25iVaDKZZDyDzWYjmUzS1tbGKaecwtq1a7n55ps5++yz5Q/AgUJxcTF33303U6dOZdq0aZxxxhk88cQTbLvtttI56PF4+P7774nH44TDYTbaaCMp8JlMJgoKCnC73dJdFQqFSCaT1NXVyUzaDUV4SY+b6C5i7LhcLn766SeZX2zE6XTS2NjIGWecQXV1NW+//TaVlZUpItz6hM/n4/zzz2f69Ol8//33DBs2TLr0jRm62YquCYy5xoKBsjAx0MjkJM7nPcLxLgorCtdwQUEBwWAQXddxOp20tbXxr3/9SxZX/OKLL4B2oXCbbbZh8uTJjBo1im222YYtt9yS0tLSXjnO7mCxWLj44ovZfffd2XvvvXnttdc4+eSTicViVFdXM2TIkEE7rrJ9J4qFUiPGMeb3+6mvr6ekpERea7GwmkgkZEFBo9DsdDoJBAIEAgFefPFFrrzySiorK/n73//OyJEjpegK8MUXX3DVVVdx2GGHcfzxx3d5Dv73v//NlClTaG5u5pRTTuH000+Xef3HHnssDQ0N3HXXXfzvf/9jypQpfPzxxxx88MFcfPHFOduNRCLcfPPN6Louj1EUhhSOYfF4Now7KISQLo7PbDb3+0Lz+srOO+88qNpTKBQKhUKhUAwe1jvRWNO0nYDVuq7/3N996QqdCYHJZFL+4BNbVI0/8IRjKhAIsHbtWpqbm6mpqeH000+npqaGN954g5122qmvD6tLjBs3jjfffJN99tmHqVOnMnPmTA4++GAAuf22paWFaDQqXcSZCgx5PB7pkBXbkzck96cxLxe67jw2bv13u934/X5MJhOhUIhwOJzi4m5paeHYY4+lqamJ1157jcrKyt47sD5A13WefPJJRo4cyejRo2lqapKOTkE4HJZZnsYsUUG6sAKp96w4v5kKbg1G0p3EncVViPcYo1JETrlYNFq1ahX//Oc/efbZZ1m2bJks4LjLLrtw/fXXM2HCBEaPHi3vh7a2tqx52enU1tZSVFSUNau4N9hyyy3l3zVNk+cmmyCZzzlc38n1nZjJJZz+GrGYaHxPQ0MDiUQC+D3CQny3NjY28tJLL3HllVey1VZbsWDBAioqKlLa1HWdmTNnsnTpUhYvXszs2bOZPHky5513HsOGDct5PPX19SxYsIDLL7+csrIy3nzzTdxud4p7V9d13nnnHUaMGCG/r30+Hy+88AJvvPGGjF/yer34fD6Ki4spKiriz3/+M3fddRd77bUXe+21lyyUKMRyl8tFNBqVi8XZonOMOyg2lEXWgcCNN96Y8/nhw4ezatWqjM9VV1d3ub2u0tPtKRQKhUKhUCgGD+uVaKxp2kHAo8Dxmqat1nU9oWmapqeXYh+ApAuBAtF1j8dDPB7nl19+wWKxYLFYZEX1cDgsMznD4TANDQ34/X4uuugiampqeP3117slGCeTSRYsWIDVamWPPfboUkZrMBgkkUjIbNF8KS8vZ9GiRUycOJErr7ySeDzOqaeeislkorCwEIfDQSKRwOFwEAgE0HVdCgPG8wDtVes9Hk9G4W99Jt1p11XnsXDYCXe22+2WP0yFCCME44MOOogvvviC559/njFjxvTK8fQln376Kf/85z+57bbbMJlMMrPYiFh8icVi0uEvckFFJILI1800towORpVrnEpX3P9CDAwGg/h8PlwuFzabDV3XaW1t5dJLL8VkMnH00Udz4IEHsvvuu+Pz+brVL13XWbhwIffffz+LFi3CarWyxRZbMHr0aEaNGsWOO+7INttsk7Ng3roQDAaB9vnf5XJRVFSU89ykR34YRdRs7vj1jWzfiZA78sTlclFeXo7dbsfv98voEhHHIxYRxfgS0TKvvvoql156KbvssgsvvfQSRUVFHT73/fff59NPP+XGG29k2223Ze7cufK/I444gnPPPVd+1/7444988sknfPjhhyxdupTvvvsOgPHjx/Pkk09SVlbGjz/+mNL+kiVL+Prrr7nmmmtS5pVhw4Yxbdo0+e/0RZDZs2fzySefcPrpp/P6668zcuTIlOMX94UxtiIdIaCLAoOKvmPVqlWsB/+bqlAoFAqFQqFQdGC9EI21djtZOXAVcJKu6x9qmmYHEoDptz9zvX8aMA1g44037lYf1uV/+HVdz7jlFtoFQeEwbm5ulltsjcVthEgginkVFBRwxhlnUF9fz4svvsg222wjRQmBKNiViZ9//pm1a9cya9Ysli9fDrRvox4zZgzjxo3joIMOYsstt0z5UdvQ0MD777/Pt99+y7/+9S+++eYbLBYLF110ESeccALhcLiDa8uIUYzxeDzMnz+f0047jauuuopffvmF6667Dq/XK13GgkQiIZ1jQogSBbLMZjMejwdN0zJeH+FCzOTU7ex6dmWbbk+Mr1zk2v6v67r8T4hKDocDXdexWq3y3LlcrpRtzatXr2bixIl89dVXPPXUU+yzzz7SvQa5x8+aNWtknMitt95KY2MjhYWFUvSvqKjA6/VSVFTE8OHDGTNmDFarVYqwmaitrcXj8RCLxVi6dCkrVqzg+OOPx2q1Asjt3Zloa2uTrtG7776boqIidtttN4LBYErBJ2PeqYh5EVu6jQWyPB4PFotFul7TRS1jdEV35oWBNLZykevYsj0n4mMsFgsOhyMli1iMUZvNJuc0u92eMkZbWlpwOp38/e9/p76+nvfff58dd9xRti2ybNNZs2aNHCtGotEoCxYs4MEHH+T777+ntLSUM888k2g0yn//+1/efPNN5s2bJ18/dOhQtttuO8aNG8cOO+xAcXFx1nzv1tbWrFEY8Xg8pfBiS0sL0C6AxmIx6uvrcTgcaJomne1ifIpYDvh9jHWWG52JznYo9MX3YraxLt5n3E1hRNzPbW1tstibQCwqhkIhGhoaZGFVu92Oy+WisLCQWCxGNBrFZrPhcrm4++67+ctf/sJ+++3H448/jtVqTfnOXL16NWazmWuvvZahQ4ey7777YrVaueaaazjrrLN44YUXeOGFF3jppZcYOXIkjY2NNDQ0AO05wmPGjGHChAmMGTOG0aNH09raSmtrKzU1NSlz3kMPPURJSQn77LMPTU1NclykI+YhI3PnzmXfffflggsu4LnnnsNkMlFbW0s0GpXzo1gQE+81Fg8U9RCKiorQNI1EItFh3IlrId5nvH7p10iMLQWyiOKLL77Yp+1VV1dnvceqq6tZuXJlr/RPoVAoFAqFQjF4WC9EY13XdU3TAsBXuq6/q2laFXCrpmkh4DtN0+bruv6/HO9/EHgQYNy4cQPK7iF+0AuRz2Kx4PP5iEQimM1mQqEQuq6TTCZpampixYoV/PGPf6S+vp758+dLQSUdo2BhJBaL8dhjj/H444/jcrm47rrrqKys5B//+AeffPIJDzzwAA888AClpaXsvvvu2O12li5dyg8//AC0/5jfbrvtOOOMM/jyyy+59tpr+eijj7jqqqsybrMUpLvjCgoKePbZZ/nzn//MQw89hN/v584770wRBzVNo66ujlAohMlkIhAIYDKZiEajFBQUYDabcbvdnYpw6U5dIab01Nbv9PHVk7mQxh/wmfoqBCexBRuQ7mKx+CAIBoP4/X5Wr17N5MmT+eqrr3jmmWc46KCDOrSby0GbTCa5//77mTt3Lh6Ph2222YbW1lZWr15Nc3Mzfr9fCoHQfq133XVXdtxxRw499NAOxckaGxt5/fXXWbx4MR9++KF0k3/99dfceuutaJqWdTwDWK1WbDYbP/74I/Pnz5fOdbfbTUlJCZFIhJaWFoLBoCyYZTab8fl8MgomvdCdy+WSERXp11OMu/Rz0htxAgN17ko/J+KecjgcFBUVpQj0gtbWVlatWkVpaamMQYlGo2ialrKDIBgM8sQTT3DAAQekZHGK+TGfPrW0tDBv3jwef/xxamtrGTlyJDfccAMHH3xwh1iKr776isbGRr755hu+/vprlixZwuuvvw60Ly7ssssujB8/nvHjx7PZZpvJz4nFYlkdm4lEIuVzYrEY0H4v1NfXE4vFcDgcVFVVZYwMSF9k7E5udGc7FPpzbHU2R0ajUUKhkJz7gZQFCCFWFhUVYbfbpQNbLEgEAgHq6upwuVzccccd3HHHHRx99NHMnTs3YyyJ0+mUi6K33HILxcXF8rmRI0dy0UUXce655zJ//nxef/11tthiC8aOHcv222+P1Wqlqqoq43GUlpYyYsQIAL799lv+8Y9/cOmll7LFFlsQCASyusbFHGVk1KhR3HrrrZxzzjncfffdXHHFFXJRweFwyIWuxsZGWeTUeF+JxRwxp2Ubd5nGjVj4FgSDQRVzYUAsIPR1e0IUzoTxevV0/xQKhUKhUCgUg4f1QjT+jQiwqaZplwFbAx8AzcAWwBRN064HYgM1qiKbqGQymfB4PCSTSTRNkzmpHo+H5ubmlMI2P/30E3/84x+pqanhxRdfzCoYZ+Of//wnM2bM4JtvvuGggw7ikksukdtad9ppJ2bMmMGKFSv4/vvv+fDDD/nggw9IJBLssMMOHHfccWy66abssssuUsDTdZ2nnnqKm266iYkTJ3LvvfdywAEH5N0fs9nMHXfcQWVlJddeey21tbU888wzOBwO7HY7gUCApqYm6ZzyeDwyI1KIKvkItOnboNN/FA/k/NBMP+CN/Y1EIrLAXUFBQU5Ryel08uuvvzJ16tScgnEuli5dytlnn82PP/7IIYccwmWXXZYi9Le2tjJ06FCCwSDNzc188cUXLFq0iEWLFvHmm28ya9YsNttsM/bee29KS0t57733WLZsGclkkrKyMg455BD2228/Pv/8c+655x4qKyu56KKL8urb7bffjsVi4ayzzpIFFmOxGHa7nVgsJs+buOaRSESOCbGtW1x/Xde7JIZ0xwm6IZE+To0FycQ5FdE6gBSvxGtisRiRSIRAIMBf//pX6uvrufzyy7vVl5deeom//OUvhEIhdtttN26++WZ22GGHrHOFz+djm222YY899gDar/3KlStZunQp//jHP1iyZAkLFiwAYJNNNmHatGlMnDixS30SCzper5eCggL8fr8ce/kIwmIRRHy95TNn5Yp/GKgYi1T6fD4ZF9PQ0JCS4xsOh6XT1WQyYbVacbvdUmxOJBLU19dz55138sILLzBt2jRuuOGGrDnW8XicO++8k5EjR3LYYYdlfI3L5eLEE0/kxBNPTHn8p59+yuvY7rjjDpxOJ6eeemoXzkgqJ5xwAh988AG33347O+20E/vtt59c6BIisVhAFA5n47gyLuZkG3ciGipTPQHB+ji2FAqFQqFQKBQKRdcZ0KKxpmnbAG2AVdf1LzVNuxw4Cyj5zSWFpmn7AVN1XY/2Y1c7Jb2Se/r2T5Ezm77lUwhQP/74I6eddprMMN5mm23y/uxoNMp1113Hgw8+SFVVFbfccgsHHnhgxtf6fD7GjBnDMcccIwUK0ce1a9emOD41TWPq1KmMHz+e888/n8mTJ3PKKadw8cUXZ40hSEfTNC688EKqq6s566yzOOCAA5gzZw7V1dWEQiG55bi8vFxmG3elKJz4DKOQly7WDGTBL72vkNpfm80mBU+LxZJSUNFIMplk9erVTJ06lS+++IKnnnqqS4JxNBrl6quv5sEHH6S8vJw5c+aw5557ZnytpmlS5N9oo404+OCD0XWdf/3rX/z73/9m4cKFPPnkk0SjUbbeemvOO+88xo0bx6677ioFjf3224+6ujruvfdeysvLueCCC3L2r6amhscff5wpU6YwevRoGhsbsdls1NbWomkaNpsNm82G3W6XxdeCwaAcG7muu7GwoIhPSRfquuME3ZBIF5HC4XAHd3dpaSmjRo2SCwpi7EL7OE4mk5hMJp588kkOOOCALue0RyIR7r33Xu677z522mknZs6cyVZbbSX7ky+apjFixAhGjBjBhAkTqKqqYtWqVXzyySc888wzXHrppdx2222ceOKJXHTRRRljMdIRcS+VlZVUVVUxbNgwOebE/Su+E4RrG34vdJZOPnNWtkikgYwxj10U73Q6nZSVlXWIOhHOdovFIvN9RRb5Z599xmOPPcZHH33EZZddxlVXXZUSv5POK6+8wooVK5gzZ06vZEY//fTTvPTSS/z5z3/OGbWTD7fccgv//ve/mT59OkuXLsXn81FfXy8zmo2RPGLsGMdYZwU8xc6VSCSyQY0thUKhUCgUCoVC0XUGrGisadrBwGxgMTBa07SrgHeAkcC1mqZdqOv6rUAxUKZpWqGu65kDAvsZES8hCtAYf/ALkSUej/Pzzz9jsVjQdZ1AIEAgEMDn8xGPx7n44otTit6lZxjn4vbbb2fu3Lmccsop/OUvf6Gmpiav9+Urym6++eY888wzPPbYY9x///08/fTTTJgwgVtuuSVv8Xjq1KlUVVVx3HHHcfXVV/PII49QWloqnWaRSISCggJCoZDcctvdH609sfW7rxDuc2MesbG/oVBIbkkuKSmhtraW+vp6qqurcblcUiANh8PMmTOHZcuWMX/+fPbZZ58u9eOhhx5i7ty5/PGPf2Tq1KlZc15zHccmm2zCDjvswLRp02SWtxgftbW1KUKGpmlcf/311NXVMXPmTD7++GNuuOEGRo0albH9JUuWEIlEKC0txWKxUF5ejt/vJxqNdigqKbKOszmz012c4n4VucfQcewJJ+hgQJwf48JN+j2V7u52Op0yViYcDqfkE4simC6Xi9NPP536+nquuOKKvPuzatUqbrvtNl588UWampo49thjue6667K6SruKpmkMHz6c4cOHM2nSJBnjc9ddd1FYWMi5557baRurV68Gsjszjd8JgJyjq6qqMo6rgTxndReR4e9yueTfBeL+MplMtLW1EQ6HKS0tleMqHA7z5ZdfMm/ePF599VV+/vlnqqqquO+++zj99NNzfub999/Pddddxx/+8Af23XffXjm2J598EqvVygknnLDObXk8HubNm8f48eOZPXs2l156KTU1NVJgNy4gGr8rRSFZca9mW3jINLY6y8dWKBQKhUKhUCgUGyYDUjTWNG0ccDtwMrAEmAocquv625qmvQz8DFytadrWwM7AMf0lGGcSUATih5YosOX1elOqmxt/lDU0NNDc3CzdQoFAgEgkQjgc5tZbb+Wzzz7jqaee6rL77rvvvmPOnDlMnDiRm2++GSBv0bgr2Gw2rr76ak488USee+45Hn74YQ455BCef/55hg8fnlcb+++/P7NmzeLPf/4zf//73zniiCNwuVyEw2Gam5tlhAHkty023x+6A0Hw66yvwWCQmpoaKioqKCgokP11OBwEg0EpHNfX11NfXy8ziRsaGqTLcfXq1Xi9Xg499NCcrrtMfXvyySfZcccdufXWW/nxxx/X+XhdLlen19BisTB37lyeeOIJ7rrrLsaPH8+ZZ57JpZdeKu8TwSGHHMLkyZO5+eab+fjjjykvL8fr9eJ2uxk6dChlZWV4PB523XVXSkpKpGvYiLiXxf0qnK92ux2v14vVaqWpqSlnvvKGilH8zcflKoqQAfj9fllU0O12U1ZWJhdDRG50c3MzhxxyCP/85z95+OGHO43eSSaTLFy4kAceeIDXX38dk8nE/vvvL3c+9JawpWkau+66K7vuuitHH300jz32GGeddVan7tR33nmHoUOHMnz4cP73v/9RVFTEkCFD5PdBeuG7srIyQqGQLNg2EKNzMpHvnJspXiMcDsvc+mQyidvtpqKiQoqf4n5sa2ujsbGR0tJSQqEQr776Kn/729947bXXSCaT7Lvvvtx8880cdthhOV3gdXV1nH322bz99tvsvffe3Hzzzb02bm699VaOOuoodtppJ8aMGcPuu+/ODjvswD777JMzBiIbYjfQE088wcyZMyktLSUejxOPx1MKKYrxJUR4t9vd4f8/xO4Lh8OB2WzO+H2Yfs8rETmVnl5sGOjtKRQKhUKhUCgGD1lFY03TcoY26rr+Us93R1IKXK/r+me/9eUH4FRN0yy6rrcC72ua9gGwMRDUdb22F/uSk1wCisj5NBbYglSRUkRAiGxhn8+HyWSivLycwP+zd97hUVRvG75nd7MtvSf0XqRIFRClWFD5YcOCoFQRUMCCIIpiQcSGooANUFBBREUUBEVERUBBUClKJ3TS+/Y23x/xjLvJ7mZpip9zXxcXkGlnZs6cyTznfZ/XYiErK4vZs2dz22230a9fv1NqW1FREbfffjsxMTE8/vjjZ3SekdKkSRMef/xxevfuzYABA+jduzdLliyJWOweNWoU8+bN4+WXX6Zly5bUqFGDmJgYXC4XFotFEUxF5G04X+PqCkGdT5xuW0Xav7BPECKJiPJMSUlBr9dTVFREcXFxlSJ0kbBx40YOHDjA66+/fsrbnilRUVEMHz6coUOHMmXKFF5//XWWLFnC+++/zyWXXKKsp9FomDdvHunp6WzdupX9+/eTn59PYWFhQFG+evXqsXnzZqBCzJRlWRGGCwsL8Xg8xMXFERcXF1AgTxRg9Pl8OJ1OJQI5lJj3/01U8R/nDAYDGo0mrNglxrry8nLy8vIUe5nKNismk4mcnByuvvpq9u/fzwcffMB1110Xcr/FxcUsWrSIuXPnsn//flJTU5kwYQK9e/cOW4jzXDBgwAAeeOABVq9eTe/evUOu53a7Wbt2LX379sXhcGC1WhWLAn/LIoFGo1EK/hUXFweNbj9fLXUiHceCtd9f3BTRsEJAFn8L8dhiseByubjtttvYuXMnaWlpjB8/nsGDB9OoUaNq2/ndd98xcuRIiouLmT59Or169TqnUdutW7dm9erVLF26lHXr1jF79my8Xi8Gg4FOnTrRrVs3+vTpQ5MmTSLe5z333MOHH37I4sWL6dGjB8XFxej1elJSUoiKigo4H6vVSn5+PqmpqQGWFdHR0REVtRP7ClUX4L/O5MmT/1P7U1FRUVFRUVFR+e8QLnTp2j//3Am8Ddz+5595wLBz2ShZlr8CvvH70TbABngBJEmqJcuyV5blQ/+EYCz8UH0+HyaTibi4uKBRk2azWRGLPR4Pubm5eDyeKvuAiqjK9PR0dDqdUjndarVy5513UqNGDWbOnHlKbXS73dx5552cPHmSd999lxo1apz5iZ8CHTp04IsvvkCv13Pdddexbt26iLaLiori5Zdf5tChQ3z66adoNBqSk5OpW7cuaWlp+Hw+RZArLy/HYrFgtVoV8d0fcf3/DcV6qmtrdHQ0mZmZSvGwYKnbOp0OvV6P2WzG4/EoP9dqtZjNZnJzc6lZs+Ypt+3dd98lPj6e66+//rTP71Sw2Wz89NNPAeeYmprKrFmz+OGHH0hKSuLmm29m06ZNAdtFRUXxwgsv8O2337J9+3ZOnjypiCW7d+9m6dKlZGdnc9tttynPYVlZGSUlJYpgLAq0AcqzLdYDlOdZiF6hvHKFqHIqEd3nM+JaCDsAYRkTCtH3kpKSiIuLIykpKcCfV6TK79q1i+7du3PkyBFWrlxJnz59gu5v27Zt3HvvvTRq1IiHHnqIpKQk5s2bx759+3jyySfJzMw8J+cdjp49e1KrVi3mzZsXdr3NmzdTWlpKz549MZlMpKSkkJaWRnR0NHFxcRgMBvLz85XCp8LLPS4ujuTk5IAJR4H//finEO8w/7E30jE3WPs1Gg0ajQaLxaI8V/4Ryf7e47Is88QTT7Br1y4WLFjA4cOHmTZtGvXr1w97XJvNxoQJE7j++uuJi4vj22+/ZcSIEX/LxE6jRo2YOHEiq1at4sCBA7z99tsMGzaMwsJCpk6dSo8ePThw4EDE++vSpQtt27blrbfewmazodfradCgAYmJicq9KS8vDxijKv/eAX/di3CTQJULz/6b3q0qKioqKioqKioqKqdPyEhjWZaHAkiS9DVwgSzL2X/+PxNYcK4aJEmSJFeQXamdtQCNJEkDgVskSboFsMvB1MLTJNJdVY6Sqq5YTHl5OUePHsVqtaLRaEhNTcVqtWKxWPB4PIqVgBBijEajYvdw8OBBVq5cSXR0NG63W9m3EBZCncf48ePZuHEjL7/8Ms2bN1csCgA2bdpETEwMsizz1Vdfodfrad++PQkJCeTm5tKqVaug+83KygoZxXXixAkaN25c5efTp0/n4Ycf5oYbbuC1114LWpne7XYrkXUA3bp147rrruOdd95hwIABpKWlYTabKSgowO12Y7PZSEhIQJIkJepMlmXlOopr8G8q1qPRaALaXxlxLpWjwiRJChA8zGYzGRkZwF/WFSIi+8SJE1xwwQWK6B4upV70tTVr1rBixQoGDhyIJEmKZUoov9j169eTmJiIw+Hg008/xePx8L///Y/k5GTsdjsdO3YMut2XX35JcnIyO3bsULxpmzVrxu23305CQgJdu3ZVznnWrFkMHz6cG2+8kTfffJPmzZuH9Dq22+0YjUZq1apFrVq1mDVrFiNGjOCee+7hiSeeQKPRKOKcw+FQilGWlpai0+kUOxSR5i1Ez+o8ZSsXhhOcxeHqb8U/OyJYccbKiPN0uVzo9XpKSkqULArhafzjjz8yevRoPB4PK1eupGPHjhw9elSJlnc6naxatYpFixbx22+/ERUVRdeuXbn88ssVcXDlypVAhdVAuOKgOTk5IZcdOHAg6NgFFc+BKMBZGZfLxZAhQ5g6dSp//PEHzZs3D1gu+sqvv/4KwIUXXohWqyUtLU2xoTAajdhsNmUMFNG08fHxyvX2Hxv99x1sbDsX4meoPusfbSrGLlEEU2wXattQY7PJZFLei0Ls9PflFRH/y5cv55NPPmHKlCn07dtXsWYoKioKKWR+/vnnPPPMMxw+fJj+/ftz3333YTQaycrK4rvvvlOyfYJx7NixgPMuLS1V6g4cOHBAGWvtdjsXXnihUoDR33qkMg0bNqRHjx6MHz+eY8eOcd111zFq1Cg+/PBDZFkmJSUlZHvEpNfIkSMZNWoUe/fupUuXLorFlcPhqJLhpNVqlewJcT3hr77k9XoVmwphGxPKEuXf9G79OxBFZb/88sv/xP5UVFRUVFRUVFT+O0TiaVy7koCbS4UtxFlDkqSmQBKwFfABXkmSNLIsi3AYO5AFTASuA+6UZfmchfAF81v0J5QgFAqj0UhCQgJ6vR6tVovFYlE+5PR6vVLpvKysjKKiIurUqcOaNWv48MMPuffee+nevfsptX/evHksXryYUaNGccMNN4Rcb82aNSxbtgyAjz76iMaNG9OsWTPq1q2rRFieKampqcyYMYPnnnuOESNGMG3aNIYOHVrtds8++yzt2rVj+vTpLFq0iPz8fMrKypRI7JKSEsVaIDY29j8T8eTvQ1m5IKDos0LkEsKC+HdOTk7E9hTZ2dlMnjyZVatW0bRpU0aNGhVxG/fu3cv8+fMpLCxEq9Xy448/ctlll4UtvldUVMTHH3/Mrl27yMzMpHfv3nzzzTc8++yz9OvXTxGNoaJPvfXWWwwfPpx77rmHt956K6RoXJn+/fuzfft2XnvtNTp16sSwYcMUMUoIxkajkfLycsrKyjCbzQEe0oJwPtjhfM7/PyD8iCM5N1Gs0eFwcOTIEZKTk3G73bzxxhs888wz1K5dm48//riK4Jqfn8+gQYPYt28f9evX57HHHiMhIUGZEDlf6N+/P9OnT+ftt99m+vTpQdfZs2cPKSkp1KpVi8LCQqKiopTClU6nk6ioKHQ6HUlJSUiSpPTF853KlgVnAzGpKqxdBEKUNRqNHDx4kGnTptGrVy/uvffeavfp8Xh45ZVXeOmll0hJSeHNN9+kc+fOEbfJ5/Nx7Ngx9u7dy549e8jOzq6yjk6nUyZiP/30U3w+X9hJjMrUrl2bxx9/nPHjx7NgwQIGDx4c0Xb9+vXjqaee4vXXX6dTp06Ul5djNBpJTEys8vuLEIbFOQnbD4HD4aCsrEzxcxfbqFRPqIyT/6/7U1FRUVFRUVFR+e8QiWi8VpKk1cDiP//fj0DriDPiT+/kacCJP/9slSRpgSzLZUI4lmXZKUlSOnAHcJMsy7vP1vGDUZ1f5KlG2TgcDlwuFy6Xi+zsbIxGoxLdKMsyDoeD/Px8CgoKsNvtaDQa7rvvPlq0aMGTTz55Sm3/9ttvefTRR+nVqxfjx48Pud6ePXtYunQp7dq14/rrr2fr1q38/PPPLF++nJUrV3LhhRdy6aWX0qxZM/R6PVFRUTidTrxeb7VFnyoTFxfHhx9+yKhRo3jkkUfIy8vjoYceCis61atXj/vvv5/nn3+e4cOH06BBA1wuF2lpaeh0OgwGg/IhZDablY9fIcBXFrWqmwj4t+DvQykKGmZmZuLz+QIK5QFK5JjJZOLkyZP4fL5q7Sm8Xi/z58/nmWeewePxMGnSJEaOHBm2oJTA4XCwfPly1q9fT2pqKhMnTiQxMZHPP/+cNWvW8MMPPzBkyBBuu+02JRXa6XTy3nvvsWDBAnQ6HTfeeCPdu3dHq9XSvn173nvvPebPn09ubi4TJ05Uzi0jI0MRju+++24aNWpURXgMxdSpU9m1axcTJ06kY8eOtG7dWhGMhRAfExOjFLEU1xD+EuahwicUCLBdEOv8f/b7FJHqkTxLwl7myJEjHD58mKKiImbOnMmiRYvo1asX8+fPJzExMWCbkydPMnDgQPLy8njjjTe48sorkSSJ1atXn5X2iwknk8l0xkUNk5KS6Nu3L0uXLuXRRx+tci4Au3fvpmnTpjidTsrLy4mKisLn87Fjxw5SU1MxGAyKV7YoEHmqReTONcGOKcaisz0xUnlCRox35eXlFBcXM2rUKGXiqLrzP3jwIKNHj+bXX3+lV69ePPbYYxFNiLpcLnbs2MG2bdvYvn07paWlSJJEnTp16NWrF8nJycTExBAbG8uxY8do2rQpkiThdrtZvHgxn332GVqtlnbt2kV83jfccANffvklL730Et26dYtogs9kMjFp0iTGjh3Lb7/9xiWXXBIQpS4Q90+v12Oz2QLsiwRiTPaPNFZRUVFRUVFRUVFR+W9TrWgsy/KYP4XdS//80RxZlpedjYNLkhRFhQh9pyzLGyVJugnoDEyUJOkFWZZL/VZfAXwqy/Kes3HscFSXeh4J/oWwTCYTqampSvSO3W7H5XJRWFhIQkICRqOR1NRU4uPjOXz4MCNGjKC4uJjPPvvslESN5cuXM3bsWC644AJeeumlkB/UBQUFzJkzh4yMDIYMGYLRaOTaa6+lT58+bN++nZycHNavX8+rr74adHutVoterycuLo6WLVvSqlUrJY0/FGazmXfeeYcJEyYwY8YM6taty2233RZ2mwcffJCFCxcyfvx4vv76axITEykqKqJmzZpKxLGIjIqJiSE6OrqK8CeoLOT5iyBi+b9JUBZ9StiUCC9Uf6/K4uJipfhRYWEhQFjv1+PHj3P33XezadMmevTowbPPPhtxcTFZlnn44YdZv349PXv25Oabb1b67rBhw7jqqqv4+OOPefPNN1mzZg0vv/wysbGx3H///ezYsYOWLVty2223ER8fr+wzLS1NKTS2evVqsrKyePvtt5V7VrNmTd566y3uvPNO+vbty+bNmyMShHQ6HfPnz6dHjx7cfvvtrF27lujoaEXw83q9is2Hfzo3/OVt7PP5OHToEAaDgYSEhIACU+ciAvN8onIRrMqCorCMEZGMZrOZxMRE8vPzmTNnDosWLeKhhx5i8uTJVSagZFlm9OjRFBQUMH/+fDp06BC0DSdOnOCPP/7gkksuifg6l5aW8tFHH/HDDz8oP5MkCb1eT0xMDCaTCbPZTPfu3U8pu2PIkCF88MEHfPnllwwYMKDK8pycHJKSkjCbzcTHx5ORkcH+/fvJysrC4/HQpk0bxSe6cvRnMP6JSYkzPeaZCN2yLOPz+TCbzYwZM4ajR48qdjbhtpk+fTqvvvoq0dHRzJs3j2bNmoW1ABKUlJTwwgsvcPz4ccxmM61bt6Z27do0adIkaF/LyclRhPOoqChuu+02Fi1axKeffhq2jZWRJImpU6dy9dVX89RTT/HNN5HNzQ8ZMoTp06czd+5cLrvsMux2Oz6fr4qHeFlZGRqNpop3u8BfRP7/ONmloqKioqKioqKionLqRBJpjCzLnwKfnqM2xAGNgY3AMqAA+B/QH3hTkqROQLEsy9PO0fGr4P/xFO5j118Yrhxt5S+sGI3GAN9ijUaD1WpVfBtFobLjx48zZMgQsrKyWLRoES1atIiovV6vl2nTpvHqq6/SoUMHFixYEFJIKS4uZu7cuciyzD333BNQ/EaSJDIzM+nVqxcDBw5k3759nDx5EpfLhdvtJicnh7i4ONxuNy6Xi7y8PH7++WfWrl0LQI0aNbjwwgtp06YN7dq1qxJ1p9PpePnll/njjz+YOXMmt9xyS9io5ejoaF577TVuvPFGHn30UR577DGOHDmCJEnUq1cPvV6vRHDbbDbS09NDWodUngjwF0GAf11kqEajUbxO7XZ70MhPcf2TkpKU8w5V7GjZsmWMHz8en8/H7NmzufHGG08pgnDNmjV8++239O7dm759+1ZZXrNmTUaOHInH4+HRRx9l+PDh1KhRg99//52nn34aj8cTIBgLtFotvXv3plevXowbN46pU6cydepUpW116tThpZdeYujQoTz++OO88sorEbU3OTmZV199leuvv57FixczZMgQiouLledWr9dTu3ZtpTiXiGwX4mJ5ebnyLIvnuLJH6Knyp+B/3s9aVPY0FlHvIsrdarVy5MgRfD4fMTExSnZAXFwcn376KTfddFPIDIoff/yRHTt2MG3atJCCMcD8+fPZs2cPn376KX369KFt27Yh1/V6vaxZs4ZPPvkEp9PJNddcQ0ZGhuKtnJ2djcFgwOFwkJuby9y5czl+/Dj9+/eP6HqkpaUBgXYw/jz88MOMGDGCESNGMH36dDweD0lJSURFRSkZHHa7XYn+FNc31PvlbExqnipnckyfz0d+fr5yfU712bDZbFitVvbv38/ixYsZOXJkWHsJWZZ59NFHmTdvHn379uWpp54iPT2dffv2VXusw4cP88orr2CxWBg7dixt27ZFp9MFeBpXh16vp3///sybN48PPviAfv36hfUn9ictLY2xY8cydepUvvvuO3r27FntNlFRUSQkJOB0Ojl+/Dhut1uxchFe7P52WC6X6181QaqioqKioqKioqKi8s8RUjSWJKkckAHpz7+VRYAsy/IZm97KsuyWJOllYKwkSQdlWV4vSdIGoCbQR5Kkd4GLgQ/P9FinS7gIq8oRd/74i5ci+tPr9XL06FE8Hg/p6elotVq8Xi/l5eV4PB4GDhzIyZMn+fLLL7n00ksj8qGTZZlx48axaNEiBg0axLPPPovBYAgQRAVOp5NRo0ZRVFTEuHHjSE9PD7lfSZJo2rRpgFdssEJ4Pp+PI0eOsH79ek6cOMEPP/zAl19+icFgYPbs2VWq2UuSxNixY7nrrrv44osvuP7668Oe3+WXX87DDz/Ms88+S/v27bn66qupUaMG8FeRLSHKC5sK4S3pj/h4FhMAwUSQf1s6rmiviFCMi4sL6IdarVaJABfXI5hI/8033zBixAg6dOjAG2+8Qb169QKKLlZHaWkp06ZN44ILLqBbt25h1+3SpQtvvfUW48aNY8eOHTz++ONceeWV1Rbo6d69O3fffTevv/46zZs354477lCWtWrVijFjxjBr1iz69u1bbRsEPXv25LLLLmPWrFnceeedJCcnYzabycvLo7S0FLPZTHp6ulLMMi8vj7S0NEVIjomJITU1NWjU3unw5/N+at4v/wCReBobjUbFD1pcm82bN1NcXMyQIUNCbjdnzhzS09PDerFnZWWxZ88errjiCvLy8vjwww9ZuXIlgwYNonfv3gFFGnft2sWsWbM4cOAALVq0YODAgVUsWvwL4Xm9XhYuXMiXX35JTk4OI0aMqPZ6OJ1O5ZyDMXToUHJzc5k8eTLJycnMmDEDvV5Pq1atSExMVIrfmUwm5VnWarVh3y9/N6c7EQIognio5yTYxKwsy0rxVvEufeGFF4iNjeWhhx4KeSyfz8f48eN5//33GTlyJFOmTIl48mvLli289dZbxMTE8Nhjj1GvXr1TPNO/MBqN3HLLLcyfP58JEyYwZ86ciCx+AAYMGMDbb7/Nk08+SY8ePSJqf05ODo0bNyY+Pp6SkhLl9w4xZonn8Fxk1Pz5bvlPK9B9+vT5T+1PRUVFRUVFRUXlv0NI0ViW5eAl488+64GmwEBJkiRZln8APpAkaQRQQ5blGX9TO4ISLsIqXEG8YL7HRUVF5ObmotFoiIuLIzU1VfEI/uSTT9i2bRvvvvsul156aZX9BUOWZSZPnsyiRYt48MEHeeSRR0Ku6/P5eOihh9iyZQsDBw5URJIzRaPRUL9+ffR6PY0bN8br9bJv3z4mTpzI/PnzmTJlSpVtevfuTaNGjZg5cybXXXddtcd44okn2LBhAxMnTqRTp07Ur18fj8ej2FLExsYqafH+xeEqf2z7TwD4388zEUT+KYTQYjQaleJZIhq2siggfHkhuGi8Zs0azGYzn3/+eYDgFikvvfQSpaWlvPnmmxw5cqTa9Rs3bsyCBQvIzs4+pWJRw4YNY/fu3bz66qs0b96c9u3bK8smT57MypUrGT16NJs2bYr4fk6dOpWLL76YefPmcf/99xMdHU3dunUpLCwMm1oeHR1NZmbmKQkw1XkB/9knvRHt7DwiOjqa1NRUpcBndHQ0GRkZAefp8/lYuHAh9evXD2n98PPPP7N582YeeeSRsLY8X331FUajkVtvvRWz2czevXv54IMPeO211/joo4+44447uOiii1iwYAGrV68mJSWFMWPGcNFFF1UrwGm1WgYPHkxmZibvv/8+06ZNo3HjxmFtXcTkXrg2T5w4kfz8fGbOnElqaioPPPAAZrMZnU6H1WpVnme73U5aWhrx8fEh3y//Ns9sIYYLKovElSPVoWJCNjc3F4fDQd26ddm6dSurVq3iueeeIykpKehxvF4vEyZMYOnSpdx3331MmjQpIsFVlmVWr17NV199RcOGDbnvvvtISEg44/NOT0/nxhtv5KOPPuKVV15hwoQJEW1nMBgYM2YMjz76KF988QXXXntt2PU9Hg95eXmkpqai1WrRarXExMRUeQf4R3ufTQHZZrPBv2Cy61wSrn7E/8f9qaioqKioqKio/Heo1p5CkqSGwPE/i9H1AFoD78myXHI2GiDLskOSpEVURDM/IklSM8AJpAKWs3GMM0EIipUjVyGygniiMJvJZCIxMZH69esr0WR2ux2v14vdbmfatGm0aNGC66+/XhEhioqKQgoRx44d47333uPNN9+kf//+DBw4kBMnTijLd+7cGbD+xx9/zIoVK7j11lsxmUxs2bIlZJvz8vKC/tztdpOVlRV0mV6vV3xzoSKidO3atXz88cfUrl27igA3cOBAnnjiCT7++GO6dOkSMkpPpNrOnz+fLl26cM899/Ddd99RWlpKUVGRIsALQU60XUSI+uM/AeAvvETqiXomEVXB+k84/IUV8W/4q+ifzWajtLQUrVarRCtarVbF9kREw0ZFRWG32xUxHSrEFXHtADZu3Ejbtm0D1gnVB6CiiKK4tr///jufffYZ1157LW63m71794aMUk5KSqqy7McffwQq/JQLCgpCHlOI2TfccAO///47Dz/8MI8//rgS0RoXF8eUKVO444476N69O0OGDKFPnz64XK6QqeE+n4/27dvTp08fXnrpJa655hoaNmxIdHQ0sbGxSuEoEdGelpaG0WjE6/VWsRGoTDCxqrrI0T/vh6/KgjPkVPue/3ahRDefzxewzGazcfToUaDi2TMajciyjN1uJy8vj4KCAr777juefPJJysvLg05evPDCC8TExNC+fXv27t1bZfnBgwcpLy9n06ZNtG3bNsBu4Nprr6W0tJQ1a9YwY0bFPKNGo6Fbt2706NGD3bt3s2LFiqDnEh0dTXFxccDPEhISuPbaa1m9ejV9+/Zl9OjRVbImhEdxUVGRck3E/XU4HFXEuIcffpgTJ04wZcoUEhISGDp0KB6PB5vNhk6nw+12Y7FYSExMVIovBusnoSYyT/c+V0e4/cqyHFJ09BeKJUmivLxc6TPl5eXIsozZbFbegV6vV9lGo9EgSRI6nY7S0lImTJhA7dq1GTVqFAUFBVX6j8fjYdy4cSxfvpz+/fvTs2dPNm3aFLBOVlZWlfepy+Xi/fff55dffqF58+ZcccUVHDp0qMq5BPuZwOl0smdP8FIL6enpdO/enffff18p7imQZVnpO5Vp2bIlDRs25IknnuDiiy+ucr7+3u0nT55ElmVq1aqF1+tFkiRkWVYycIxGo1L8TqfTYTQag77/wj3v4fhz+3/dZJeKioqKioqKioqKSvVE4mm8FOggSVIjYA7wOfAB0PtsNUKW5WJJkuYCu4CRgAO4Q5bl3LN1jEg4W1Xg/QU/u92OxWJRxOfMzMyA5cXFxSxcuJCDBw+yZMmSgBRWvV4fMvJz6dKlvP7669xwww088cQTVT7c/c/l+++/5/PPP6dHjx5cd911rFq1KqQ1xa5du6qII4J9+/bRoEGDoMtEgTrB5ZdfzubNm1m7di133313lQJEt9xyC2+99RbvvfceV1xxRUhxXIiftWrV4qmnnuKee+5h1apV/O9//wMqvGnFuZrNZsVfVHwI+3uC+ov8/sJLpPf9bEZUhfPDhsBoQlmWA8RwYcUhBOLi4mKl8FFsbCwej4eCggKSkpIwGo0B4lJUVBRarZbo6Gi0Wi3FxcXs27eP6667LuAe+QvIwYiNjeXQoUPMnTuXGjVqMGjQIAwGA/n5+SGj9PLy8mjYsGHQZQaDIWTfgr9S/41GIyNGjGDatGl8+umnDBs2DFmW0el0dO3alVdffZXXXnuNhx9+mBdeeEER/ISliT9C+HzyySfp2LEjr732GtOnT0eSJFwuF3a7XRFgRKFFMQnkX2QqGMHuqX/k6Nkaa/5uxNhlMBgCzl8UkTMYDEpxRqgQ23Jzc/noo4/QaDQMHjwYvV5fRQTbu3cvq1at4vbbb1ee4cpoNBr27t2LLMtcfvnlARGnhw4d4sILL6R169bs2rWLffv20aVLFzIyMgDIzQ39KomKiuKiiy6q8vMWLVpgMplYu3Yt06dP58477wzwWZZlmaSkJGWMTklJUdpUWlpapX9oNBpmzpxJSUkJ48ePp0aNGrRt2xaLxULdunWVSOPi4mJlwkLYy/hHhQprlL+L0+2r/ttVjpqWZVkZe8UEjBCKoWKyMCoqCofDwbJly/jtt9945513MJlMGAwGxc8dKoTf++67j5UrV9KvXz/69eunHMOfoqIiateurfy/uLiY119/naNHj3LRRRdx3XXXhTzXcJ7Gdrs9ZMHQoqIibrjhBo4dO8YHH3xAjRo1lKj1srKykJOlAI888gjDhw9XJnv98f+9QEy2CVshcU2dTicej4eioiJl0tZoNKLVagPef2JyJ9y7MFwf+HPZWZ/s+jfRo0cPoOJ3rf/C/lRUVFRUVFRUVP47RBI16ZNl2QPcCMySZXkCEDpX9zSRZdkly/J3wO3AMFmWfzvbx/i7EIKf+BCLi4tTIkaFTYAQL202G7NmzaJDhw6KEFodixcv5sUXX+SKK65g2rRpYcWrjRs38s4779CqVSuGDh36t4lVBoOByy+/nAMHDgQtQBQVFcXw4cPZunUrW7dujWifd9xxB3Xr1mX69OnY7Xbi4+MpLCxUUm41Gg2xsbGKXYWI7PxT7A1AiPinkpp7NiOqKrdN9A0Raeffb0wmEykpKZjNZkVk0Gg0JCcno9VqSUhIIDY2lujo6IBzEgKn8BQV2/mzdetWZFmmU6dOp9T+b7/9lgkTJuDz+ZgwYUJQ0d/hcOBwOE7twkRAkyZN6N27N+vWrWPbtm0By6677jq++uorFi1aRPv27Xn77bfp0KEDd911F5s3bw4aNdm6dWtuu+02FixYwE033cSyZcvQarVIkkRiYqJS/A5QJoEi8RuvjBD7/m0FqPz7ptVqJTs7G5vNFvDz2NhY6tevj06no7y8HKvVSmFhIdHR0SQnJ7Nq1SquuuoqatWqFfQYM2fOxGQyhfU4d7lcbN68mZYtW4a0KJAkiRYtWnDjjTcqgvGZkJSUxKRJk6hTpw5vvvkm33zzTZV1hKdxOHsKQVRUFPPmzeOiiy5i0KBBfPXVV7jdbsVSQKfT4fP5cLvdSoFF//fJv5HKE2TCExsqiiiazWZFHBfjn9FoRKPRUFRUxIwZM2jTpk3QwoROp5Phw4ezcuVKpkyZEpHdkdvtZuvWrUybNo2cnBzuuece2rRpc87ejTqdjmHDhmE0GnnppZf44osvgr6TKnPttdfSqlUrXnjhBVwuV8j1srOzAahbt64yUSgmCzUajfKeMZlMOBwOpYCneFf82/uXioqKioqKioqKisq5IxL1wi1JUn9gMPDFnz+LrKLLaSDLsleW5X9N1IosywFiHwQKfv5RVOLjTNgIWK1Wli1bxvHjxyP2X/z555+599576dSpE6+88kpAxFVlNm/ezOuvv06TJk249957w657Lrj44otJSEgIKrQA3HzzzSQlJTF//vyI9qfX65k4cSJbtmxh1apVZGdns3//fg4fPhxw/QXCtiBS+4nqOJsRVZXbVvnD3f+jXvwRArBACAB2uz2gD0ZHRysF20pKSsjPz1ciOytHeP7888/o9XratGkTUbtlWeaTTz7hlVdeoWnTpsyYMSNo9LDX62XlypUsXbr0nAjHN954I7Vr1+add96pIqhIksTFF1/MnDlzWLlyJSNHjmTdunVcd911PProo0H398Ybb/D8889z8OBB+vXrx80334zT6aS4uDjA99NkMgWIyP/fEV6oJSUlVUQlYXtitVqVCTIR7Q4VlgEmk4mTJ09y4sQJhg4dGvQYOTk5fPzxxwwcODCsl+zu3btxOBwRe74Ho6SkhJ9//pmsrCxlIqU64uLiGD9+PG3atOHjjz+uYikgRONIx9fo6GiWL19O48aNeeyxx6hdu7bSn2rVqkVGRga1atVCp9NhMBgC3ifnC+K9Fwk2m43y8vIqQqndbqe0tFSxNRL9SOBwOHj66ac5efIk06dPDzrZ8vjjj/P111/z3HPPRVS0sKysjEcffZQ5c+ag1+t5+OGHufDCCyM6jzMhPj6e+++/nwsuuIDVq1fz9NNPU1JSEnYbjUbDo48+ypEjR/j4449DrpeTkwNARkYGpaWlWK1WiouLleKv/u+J0tJS8vPzQ/6+oqKioqKioqKioqKi4k8kovFQoAvwjCzLhyRJqg+8f26b9e9BRIxarVbKy8sDCrFV/sgVH2eAYj3QtGlToKoHcSieeeYZUlNTmT59etiiZWVlZcyfP58GDRrw0EMPnZZw6vF4IhZWgqHT6WjevLkSCVUZk8nEddddx8aNGyOKvAKUaOwDBw4QHR2N2+3G7XYHjZISdhTnoxWAf9tkWcbn852yGGk2m4mOjqaoqIjs7OwqYpbRaMTtdlNeXs6BAwcAqFOnTsA62dnZZGZmhk2TFni9XqZOncrXX39N7969mTJlComJiUHXzcrKwmKx4PF4lGOfTaKiorj99tspLS2t4l3qT61atXjiiSf47bffSEhIUJ67ypjNZh544AH27t3LlClTWLNmDR999JHityrwL5oonvdgExb/XxBR6jqdThGFMzMzMZvNmEwmRSAW4rGYvBBjXXJysjK2devWLegxli1bhsfjYdiwYWHbkpeXh8lkqtKHI6WoqIjff/8dr9fL8ePH2bp1K7m5uRF5AUdFRdGzZ0+8Xm8V721RVPS33yJPjklKSqJbt274fD4SEhJwOp1YLBacTidmsxm3243P58PpdJ5WVgRUzV44m4TL4qiM2WwmNja2yjvIZDKh0+mUd4ywf/H5fIon9A8//MA999wTcqLg999/55JLLmHIkCERtXvLli2UlJQwatQonn766QBLpXNNamoqw4YNY/z48djtdtavX1/tNpdddhmZmZlh1xXjk9PpRKvVEhsbq/hiw18++P7XW2xTuSihioqKioqKioqKioqKP9V+JciyvEuW5XtlWV785/8PybL8/Llv2r8DETEKFb6Zubm5IdM8xYcboHikdu3alWuuuYYZM2ZUG3m0ceNGNmzYwL333lttAb733nsPm83GyJEjw4rLocjOzmbx4sV89NFHIYW2SIiPj8dmsynReJXp1q0bLpeLDRs2RLS/lJQUtFotRUVFeL1eatWqRUJCwilHSZ1LQeVUsdlsiu91qA/3YBGuwgNUr9cTExOD1+vF5XJRUFBAWVkZDoeDxMREUlNTWb58OQ0aNKjiF1taWkp8fHy1bXS5XEyaNInly5fTp08fRo4cGbSYGVREIf7+++8kJSWRnp6u+NCebZo1a0bNmjX55ptvqt2/0+mkpKSECy64IOx6UVFRPPTQQ3Tu3Jnp06djsVgCbAd8Ph/l5eXk5uaSnZ1d5Xk/n/rV2cBkMpGQkEBqaqrSP/0j4EV/FILfkSNHyM3Nxel0KmPUsWPHMBqNIS0lPv30U1q1akWTJk3CtqWkpKRKQc1IycvL448//sBsNtO+fXvatm2LwWBg7969/PLLLyEntvwR51o5cr5evXo0btw4ZEZFMGRZ5quvvqJnz55ERUVhMBhwOp243W4cDsdZif48l7YDp5LF4e8l7z/RYrfbSU5OJj4+XulTItpYr9eTm5uL2+0O+8yWlZWFnLgKxpYtW6hduzbt2rX7x0TSunXr0rlzZ7Zu3Rq2+CdUXLv27dvzyy+/hFzH661wTJJlGb1eT40aNXA4HIqPtygKKyyN/N+XdrtdyUb5/zJmqaioqKioqKioqKicPdTQkjNEfBBHR0eTnp5Oenp6lQ99ITSJaDz/CC2n08mYMWMoKSnh1VdfDXusF154gfT0dAYPHhx2vTVr1vDTTz9x4403hvQQDYUsy/z2228sX74crVaL0+nks88+C1nlvTpEJGKoj+OLLroIo9EYseCi1WpJS0tTCr3VqFGD9PT0UxYAzicfRyHAhBOI/FOMLRZLgPdxQkIC0dHRFBQUcOLECYqLiykqKlIK4i1btoxff/2VJ554osp+S0tLw1oCQMW1GjduHN9++y0PPPBA2IJRAEePHqWsrIyWLVvStGlTLBYLJ0+ejOxinAKSJHH55Zdz+PDhKt7Gldm1axdQUdysOjQaDa+88gqFhYVMmzaN/fv3K5Gf+fn5HD58mNLSUmJjY6s876JfWSyWgPv0TyKsBCIV7v3Xry7KVVijiH/r9XrMZrMSMWq32zl48CC1a9cO2meOHDnCL7/8Qt++fattV0lJSUjhORw//vgje/bsITY2ltatW6PX64mNjaVNmzY0adIEm83GggUL+PLLL8NGzopo/GB2K9dccw3r169nx44dEbVp//79HDp0iM6dO1NQUKAUF4yKilL8fE8nurhyezUaTURZBKeKvxDsT+W+JssyZWVl5OTkUF5eTn5+Pvn5+RQUFJCbm6s8I+Xl5YqdiYg03r9/P/BXJHcwSktLlUnb6igpKSErK4uOHTuGXU+W5ZCTnOGw2Wz8+OOPHD58uNp1r7zySsXqpzrat2/P4cOHlWKolRFjTExMjJK1IhATO3a7PeDnYnJLFBX0jz5WOXVuvfXWKsUK/z/vT0VFRUVFRUVF5b/D32tye55zJtGQkiQFVLT335fFYlFE0+TkZCRJoqysjKioKFwuF82aNeOmm27itdde46677lKiQYuKioiKqrCP3rJlCxs2bOCRRx7BarWyf//+oBHEFouFxx57jJSUFNLT09m8eXOVdfLz84N+gLpcLnbs2EFJSQnx8fHUrFkTt9vNoUOHWLp0Kc2aNQt5jWJjY4N+LIsP0V9++SWkB2arVq1Ys2ZN0OUiTdkfIRq73W5FMBDtEoKD+BgWxeBEiq7Av3r8P41/Yahg19fn82EwGJQIVpvNhizLikhgMpmIiopCo9EQFRXFoUOH8Hq9lJWVUVxczIsvvkjbtm256aabcLvdQIWIr9VqKSwsJCUlpYqo//vvvwMVRaOef/55srKyGDZsGK1bt2bXrl0hhZH8/Hy2b9+OwWDAbrcjyzI6nY6tW7fSrVs3RbytTHZ2NidOnAAq7nl2djZ169ZV7lkosTc1NRWDwcBrr73G+PHjqywX10lYB9StW1fpZ263O2TEfpMmTRg5ciRvvfUW3bp1w2QykZycTHl5OS6Xi5SUFOU5tdvtikBnMBjQaDT4fD4sFgtAtVkB5xphJRCuLf7FysT6omCWODd/PB4PGo0GvV6vRBl7PB70ej1Go5G8vDzMZjOJiYmUlpZSo0YNpe/5e2wvXLgQqMg4EIJ8MG9gr9dLaWkpsiyzZ8+eKstLSkqU/uPPgQMH2LNnD0ajkZiYGI4ePVplnbZt23L06FG2b9/Ozp07adiwoTKRUrduXfbu3Qv8ZSl0+PBh4uPjSU5OVo550003sXDhQsaOHct7772H2+0Om+Hx5ZdfAnD55ZcrExLR0dGYzWZkWa4yDlQe14Ihy3KAMC8mmhwOxzntg/4WB2LSBCrGVqvVyrFjx7BardSpU4eUlBRsNpsyPohJh5KSEmXyy263ExMTw6FDh4CKSG5/33LxXpRlmZKSEgwGA8XFxUBFRHmogoQ//vij8u+NGzcGLMvLy+P333/H6XSSlZVFcXExCQkJ1KpVi7i4uLAiclRUFB9++CHZ2dlK1G96ejq1a9emRo0aAcf1p2nTpqxatYp27dpVyfYwm83Uq1cPgJYtWwKwfPlypVCk/3tL2HsI33Gfz0dmZiYpKSl4vV6OHDmiXC9AyWrx+XzExcWRkpKCw+HAaDSG/R3ofLR5Ol+45557/lP7U1FRUVFRUVFR+e9wSqKxJEkZsiznnKvG/JuRJCmkQCmKGRmNRiUKr6CgQPGwBLjzzjv57LPPmDlzJi+//DJQIcQKceXTTz8lISGBYcOGKd6EwYSAN998k9LSUgYNGhQgYvtjsVhIT08P+FlhYSFbtmxRouhKS0spLS0NWGf37t3Uq1ePzMzMKvv0+XxBi6klJibyxRdfYLfbQ0aEdenShZkzZ5Kbm6t8KAuEMOVPRkYGOTk56PV65boL0cLhcFBWVkZsbCwxMTHYbDaKi4vJz8+ndu3aiuCi1WpDXp9/AlmWw3pLiv5iNBoVf1BJktDpdEiSpAjPPp+PevXqUVpaSmpqKvPmzeP48eO88847ygQEVNwXnU6HxWIhNTW1SgSnSPletWoVBw8eZNy4cYonrc1mIyMjI+h5ZGdnY7VaadSokSJsZGZmcuzYMcrLy0P60W7evJmkpCRsNhu//vordrudoqIimjdvDkCDBg1CXruOHTvyww8/cN9991WJmpZlmcTERLKyskhLSwuwQHC5XCEFOEmSmDJlCp988gnvvPMOt912GzqdDo1GQ1xcHHFxcWi1WiVzACoEWafTqYitInrcv39Chcgs7t/fgZiQqGwlEEwo9l/P5/OFFJslSVLOKSYmJsCH1uVyKYKlVqvl5MmTdOvWTbnWJpNJGde+/PJL2rVrp0STJiYmBh0ncnJykGWZBg0aBC28uGjRogDhTZZljh07RnZ2NsnJyRQWFoaMds/Ly1PGZrvdzr59+4iJiVGKmF5xxRUAShRyXFwcjRo1UpaLazZ58mQeeOABli5dysCBA8PaJnz99dc0adKEDh06UFBQgMvlwm63K5GhNpstoL+ISYi4uDjlOobqV2L88G/bmfS16rb17zv+k3EiGrl27dpYLJaAdmi1WlJTU5UoaKfTid1uJyEhAZvNhk6nIzs7m4SEBDIzMwPaEB8fT1RUFEVFRbhcLurWravc++bNmyvZLZU5ePAgqampQe14vv32W6UdUOHHX1paSklJCRqNhrZt25KWlhbQDp/Px7Fjx/j1118Va4jY2FjFGqKgoICSkhIuuuiioO1p1aoVe/fuZcuWLVU8mcVkF1Rk46Snp/PDDz9w++23AwSM5QLx7jabzWi1WrRaLYcPH6agoIA6deooE4yi7aKviaj2c2Eh9F9BjA1nq+ju+b4/FRUVFRUVFRWV/w6nmvu66py04v8JNpstaHVy/w9R4QVqNBpxOByKuNKkSROuv/56FixYoEQoCnw+H99//z2XXXZZ2MjYrVu3snr1avr161dFFA6FLMvs27ePDRs2hPSoFURFRfHVV18FjdYLhRDxCgsLQ67TqVMnANauXRvRPoVoLPC3mqjsBWo2m/F4PBQXF4dtwz9NOLsMYUkRbJlIM7ZareTn51NYWKhEdGZnZ/P666/Tp08funfvXmVbWZbDehq73W4+/fRTmjdvHrIQVWXy8/PR6/UBorKYZKiu35SXl7NlyxY8Hg8ZGRkcP36c48ePV3vMrl274na7+eKLL0Ku88cff0RkTeFPQkICzz77LD/99BNLlizB6XRiNBoVD1agSn8T/xeWNRqNJuDein9HWvjxbBCqIKR/MTN/j1qxvhCUgkVuOhyOgD6p0WhISUlRit/VqVOH2NhYXC4XJ06cCFpwbPv27ezatYsbbrih2nMQz3sk9hSyLHP48GGys7NJS0sLKjIHQ6vVKqKKiJL3J5w9BUDv3r3p0aMHr7zyStCoZ4HdbmfdunVcddVVynXTaDQcOnSIgwcPKp7k/v0FKsRqf6uJ6vqVRqNRxO9zif8zUNlWQ5Ik4uLiiI2NpaCgAK/Xq4iV0dHRAeKxsG+Cisjy/fv306RJk5CitegToSaw/Dl27BglJSXUr1+/yjKr1Yrb7VaKDprNZmUSQa/XI8syv/zyCxs2bODEiRP4fD5OnjzJDz/8wB9//IEkScTHxyuCfkxMDHFxcfh8Pg4ePBjS0z0uLo5u3brx9ddfK5HSwZAkiSuvvJJ169YF7Xvidw2z2UzDhg2VsclsNpOSkkJKSgpmsxmr1aqco81mC+uhr3Jq9O7dm6SkJGUCt/KfunXrnvL+evfufVbbdzb3p6KioqKioqKi8t/hVL8Y1PzEMJjNZsUf0P/j3WAwIEkSUVFRSmquRqPB7XZjNBpJS0ujVq1ajBgxgvLycpYtWxaw3x07dlBUVMRll10W8thut5tXXnmFOnXqKNFI1eH1etm8eTO7du2iRo0a9OjRI+z69erVIzExkTVr1kQk5kFFtLQkSWEF21q1alG3bt2IReP09HTy8/MVcTScaKHRaKhduzY1a9Y87SJafwfhCl+ZTCbS0tJITk7GYrFQWlqK1WrFYrFgs9koLy8HKqwaUlNTlX08//zz2Gw2pk2bFvSYVqsVj8cT0tN4w4YNFBQUcOutt0YUqSg8SmvWrBkgRogiaMeOHQsqpvl8PgoLC9m6dSuSJNGhQwdatmxJcnIye/bsUc4vFBkZGbRt25bly5cHXe7xeNizZ88pi8YAAwcOpEuXLjzyyCO4XC5FFPXvX0KkEYJXMCsUcW/Fv8+HiK9gQrH/fXY6nXg8HgoLCxVhyt8L1d+7OFjUa0JCAseOHcPr9VK7du2AY8uyzJw5czCZTBH5GR87dgyITDQ+dOgQeXl5ZGZmUq9evVOKshWCpigqWXmZXq8PKRpLksSTTz6JRqPh+edD14rdsGEDDoeDq6++WtmvEPIqF8Hzn4QQmRT+fubnY78Kh8PhUCbxxHmIAm1ikiE/P5/du3eza9cuGjVqFHJfYiyJRDQWkcSVM1kKCwv57LPP8Pl86PX6gEwPUWTUbDbTunVrZFlm+/btrFmzhm3btqHRaOjQoYNiD+SPXq8nMTGR6Ohofvnllyp2GIK+ffvi8XhYuXJl2PZfeeWV2Gw2Nm3aVGWZsMQoLS3F5XIFtD8zM5OGDRuSnp5ObGxsQF85H6yZ/j/hdDoVa5nKfyLxuVZRUVFRUVFRUVE5HzlV0XjuOWnFvxhR+EcIxaIavBBUysvLKSgooLy8nOLiYiwWCyUlJXi9XgoKChTPV61WG9IDU3wUhkqlh4qo0ry8PPr16xfWS9O/3b/++is5OTm0atWKDh06BE159Uen09G7d2+io6MVz9vqED67lQUYf4RgFW4dfzp06IDP51MiS6srGqXT6UhPTw9I6z4fipSFKzgmxDmRRqzVanE4HErqNqCIqbGxsURHRxMbG6v82bt3LwsWLODuu++madOmQY//9ddfA3DBBRcEXS6iSFNSUiI6H9F24bHpj4i0mj17Np999pkisO7bt49Zs2Zx+PBhzGYzHTt2JCYmBkmSiI2NrVLYKRTClzNYRJ+YsDmdQk8+n0+JzsvNzcVgMJCfn09paakilopI71CR4v73Vvz7fPAHDRWBLBA2OP5FskREq7gmZWVlSuHDvLw8ysrK2L9/PydPnmTXrl3cddddxMTEBExIybLMlClTWLFiBSNGjKi2kJnVauXDDz8MaV1RmbKyMmJiYqhTp85pXWfRj4P1pcTExJBFPQFq1KhBhw4dOHLkSMh1RNHGrl27AhV9zGQy0bx5c5o3b05MTEyV/gIVUfzCt1a0s/J6/0TkaOUsCf+xSxATE0NqaqoyoSLel1arVYk+rl27NsnJyVitVoYNG0ZxcTH9+vULekwx6ZCQkECzZs2qbeOhQ4eU6H9/Tpw4gcvlQq/XK3ZHlZEkiVq1anHppZfSrl070tLSaN26NZdeemkVywp/NBoNdevWpVmzZhw9erRKBhFUZGEkJydXW2hWTHgGy1AQUfxOp7OKJYp4xrVarfKs/5N9RUVFRUVFRUVFRUXl38UpeRrLsvz6uWrIvxWR4i0KywCKF6HFYiEvL08RyJKTk3G5XCQkJChimCRJeDwefD4fs2fPJjY2tkrkXevWrTGZTGzatIk+ffoEbYf46ExNTa22zbIss3PnTk6cOEGLFi0iTt+GigiqBg0asHPnTqV4TnV4vd6wgrTNZmP37t1ce+21EbXh6quvpm7durz55pvcdtttighRWFhIcnJyWHHd4/Fw7NgxZZ1/0tc4XIEy/7R0s9mM2WxWCq/5ryvLslIkDyqEvqKiIu69914aNGjAlClTQh5/wYIFNGzYkEsuuSTo8k6dOjFnzhw2bdpUJVI0GKmpqSQkJHDixAlq1KgRYGsQGxtL9+7dsVqtbNq0iR07dpCens6hQ4dISkqifv36NGzYUBFgrFYrR44cITMzM6R9hsDn8/Hzzz/TuXPnoAKOVqulU6dOIQtShWPWrFn8+uuvvPrqqzRo0IDi4mIOHz6MJEnUq1ePmJgYLBaLkpJuMBgUP9rKooyIxv07/YzPBEmSSE1NVSKIRbE7QMmWyMvLIzc3VxH+iouLcTqdWK1WxowZw7Fjx/jiiy8UL2lZlpk6dSrvvPMOw4YNY9y4cdW2Y86cORQVFXHTTTdFJHSJSbjTQZZlHA4HkiQFteWoXbu2UqAtFIcPHw45UQOwf/9+atasqRQMFEXhhO1JMLHabrfj8XjQ6XTK8x7K//zvpnJRUTF2ybIcMFaJc/D3HBbZEmJybPny5YwbN47Y2Fi+/vprunTpEvSYK1euZOPGjUybNi2kh7E/5eXlQSdTxYRsJM+jJElkZGREFNnsv03jxo3Zs2cPx48fryJwu1wuCgsLg9YJ8EcUYwzWr0QG0pYtW2jfvr0ySes/zgj/cjW6+L9D3bp1q/Rr8f+6deuq0c8qKioqKioqKioR8c9/cZ4m0t+guvhHgobCbDYTExOD0WgkJiYmID1Y+CLWrFmTOnXqoNVqkWUZt9tNSkoK8fHx2Gw2Dh8+zPz58/n0008ZPXp0FQExKiqKiy66iJ9++ilkO4QnYiTp2ydPniQrK4uGDRuGTf8NRYMGDZBlOWw0ncDn81UrGu/ZswdZlunQoUNEx9dqtYwcOZINGzawefNmoCLNODs7O8AGI1hEcWFhIcXFxbjd7n88ldvfHqAylVOINRqNEkUsIgslSaK8vJzjx4+Tm5tLfn4+RUVFPPLII2RlZfHWW28FLZYIFdGO27ZtY/DgwSGFp+TkZJo2bRq231UmPT09ZN+Iioriuuuu49577yUzM5PCwkL69OnDuHHjFD9IqHju9u7di0ajUQqkhePo0aOUlpbSuXPnkOt06dKF3bt3VxvR58+hQ4d48skn6dOnD7fffjtOp5P4+HjS0tKIiooiNzcXj8cT4NHqdDopKytToo/9+94/4Wd8pvhHJdrtdgoKCrDZbIqPbnx8vGLbkJKSQq1atTCbzYwbN479+/fz6aefKpMSsiwzYcIERTB+4oknqhXrtm7dyldffcVNN90UsVgnoqNPB4/Hg9frVSyFKlO7dm0KCwuVSZrKOJ1Ojh07FnYi7sCBAzRp0oQTJ06Qk5OD3W5XrANCYTKZSEhIICUlReljpxM5fy6oHLkazP7AZrMpzwr8NREWExNDUlISJpOJGTNmMHLkSFq2bMmPP/4YUjC22+089dRTNG/enDvuuCOiNpaXlwd9B3k8HrRa7TmdxImNjSUuLi6oNU9ubi6yLFfbt/fu3YvBYKhirwEVAmDDhg3ZuHGjkslUXl6OzWZTfocRtkYFBQX/eIaNyt/D4cOHFXuM7t270717d+X/kfzupqKioqKioqKiogKnGGl8PiBJUgtZlv+Q/4ZS3+EiQf3ag0ajwWKxKOn0FosFg8FAUVERPp9PKbAky3JABJvBYMDtdnPw4EEmTpxIhw4dmDBhgiIGlJaWKh+6rVu3Zt26dWRnZxMTE8PJkycDImr3798PQElJCW63mx07dgRt74kTJzh69KhStEdEMEXKrl27lOjWnTt3KpFamZmZ7N69u8r6Qryx2+2cPHky6D63bt0KQMuWLZWigQK73R404m/AgAFMnTqVN998kxYtWiiFhxISEnC5XEq0oEgJFvdPiOopKSlIkhRyQuDviAQVqcNQNRVekiRFTBZReJUxmUyUl5crfTAhIYGtW7eyYMECRo8eTdu2bUMKS7NmzcJsNtOyZUt27dpVZbnwrG7evDmfffYZ27dvV1Kkc3NzycvLC7pfWZZJTEwkJyeH6OjogHun0WgUv25RoCk/P58VK1YogiRU9PvCwkJq1qxJeXk55eXlbN++PeR1/PXXX9FoNDRr1ozS0tKAZV6vl6KiIlq1agXAmjVruPLKK4GKvhkqUt7j8TB69Gg0Gg0PPfQQJ0+exGQyKcKoJEl4vV7F39bpdKLVapX9+Xy+gEhx+KuQ2vkU7SfGpFDL/Pul0WgkNTUVWZYVUQoqJnGEzUBMTAzDhg1j586dzJkzh+bNm5OdnY0syzz99NO8/fbb3HTTTYwcOZLc3Nwqx9y3b5/S1x0OB6+88gqpqam0atWK7du3h/RSdzqdyv48Hk/A/yPB7Xbj8/lwuVzK9RA/E3YS4mcA3333He3bt69iVXHw4EF8Ph/p6elVxjLBgQMHuP7664mLi0OWZZKSkgKsc0QGij9iPJBlOSCy9294DYY9Rri2igwau92OXq8PiJJ2uVyK0HzixAlGjhzJmjVrGDp0KC+99JIy4ReMF154gRMnTjBp0qQq7xRhc1OZoqIiZFkmKysr4OfFxcVIkqTY0IQinMgmy3LIbX0+H/v378dsNpObm8uePXuUKPgGDRooNk86nS5AVDYYDJSUlCj///3336lfv75iSVQ5+6J79+58/PHHwF++3EajURGM/est2O12pRCvXq/H5XJhNBqDjgNCdAb+lqKK/1aGDBnCunXrzur+ziZne38qKioqKioqKir/HcKKxpIkaYFvZFnu+Te1JyySJF0FPC9J0i2yLO8/B/sP+L/4+KwunVysZzKZsNvtShV4kU4cExOjfCiKFFmXy8XJkyfxeDzMmDEDWZZ59913A0Ss5ORkRXS7+OKLmTVrFjk5OXTp0oUaNWpUEZ90Oh3NmzdXKtZXJjs7m507d6LVajEYDEErtotq38GwWCzUqFEDqBCJjxw5QlRUFHq9Hp/PF7RCuBAtMzIyQkY1Hz9+nIYNGwbd3mAwBBX2MjIyGDBgAO+99x5jx44lKSmJzMxMnE4nPp8Pm82miBL+H8RRUVGkp6cHbcc/Sbj+pdFokGVZsQkQH+5arZbk5GQKCwuVwl2jR4+mYcOGTJ06FY1GE9SqIycnh/Xr13P99deHjExv3bo1UCGyf/bZZxQVFdGzZ8Uw8OOPP4b0lq1duzadOnXiww8/xOPxBBRvnDs3tCV6+/btueaaa5Blmfnz5xMfH8+gQYOU5yac0HrkyBHat2/PxRdfXGVZaWkpZrOZTp06YTAY+PXXX7n++uuBCkE5lGi8aNEi1qxZwyuvvELr1q2V6DxJkhSRRafTkZycjNPpxGQyIUlSwASREG/Evf03WFL4Uzm1XUSG5ufnY7ValUktvV6Px+MhJyeHSZMmsWXLFl5//XUuv/xyZXshGA8bNoy77rqriveqQKfTKX1r8eLFlJWVMWHCBFJTU7FYLCHtdwwGg2Lf4na7ycnJoU6dOsryUMUexXnabDb27t2L2WymZ8+eyhhRXFwccEyz2cwXX3yBw+EgOjq6iq3AL7/8AkC7du2CipcFBQUUFxdTr149ioqKlAhup9NJcnKyko3ibyXg/9yLexJqEvPvQojBRqMxwIfdv62iuJ/VasVutyuTX0J8FD7ht956K9u2bWP27NnceeedACFF2GPHjrFo0SJ69epFt27dqiyPj48P2kccDgfx8fE0aNAg4OfZ2dkR+Z2H6q9QMZnStGlTZFkmPz+fqKgoEhISkCSJnTt3Ktvm5OTg8XgUj/jo6GhlUrhnz54BVkkejyfgPLKysrj00kuVn1WOmr7iiit45513+Pnnn2nXrp3yO4cYi6Di9wlRaNFqtZKbmxvQj4L9nmOz2cjOzsZut9OgQYOIPMX/iwwZMoShQ4ee1f2dTVTRWEVFRUVFRUVF5XQJGzYiy7IX8EmSFN5U9G9AkqTrgCeA0bIs7z8VewpJkkZIkrRVkqSt+fn5ER/T/wO3uvWEQClScxMTE9HpdCQmJgZUvBecPHmS7Oxs3njjDX7++WdmzZoVNPVUICIld+7cGXR5cXExiYmJIYWpwsJCNm7cqBTpO1MBKyMjA1mWq43mE5HGoewpZFnm999/p2PHjqfchlGjRuFyuZg3bx4nTpxg//79lJSUKAKX8Jm22+1VrAIisR6JlNPtX/74F48S//Z4PMrflYtg+R2b5ORk4uLieO6558jKymLOnDlhRaX58+fj8Xi44YYbqm1XrVq1yMjI4Lfffov4XIxGI23atOHIkSPk5OREvB1UWEKcPHmSiy++OCJf2rKyMo4fP64I2qEwGAx06NCBtWvXVnvPCwoKeOihh+jUqRMDBw5UxC6NRqNE3QlbEfHMC1FMEKzYVCQFwypzNvrWmWKz2ZTIYmEzEB0dTY0aNahTpw5169aldu3aPPXUU6xfv5533nmHa665Bvir6J0QjCOxpIAKsX/jxo1ccskl1K9f/5Taq9Pp8Hq91abhy7JMUVERO3fuZN++fbRo0YKbbrop7KRSdHQ0cXFxIft1VlYWkiSFbPOBAwcAqFOnjlIczm63k5OTo1jrOBwOpZ9U7jNnkzPpW6JdItLe/2fCmsXr9Sq+xklJSYp9kyjIVlRUxK233sr27dtZvHixIhiH4/HHHwfgwQcfjLitIvsnlKfx6fpfC3w+HwcOHGDlypV88803fPnllyxfvpwtW7ZQXl6Oz+cjPj6eqKgoKl/n48ePk5CQENZbv7i4mJycnLAF/7p164YkSXz33XfodLoAWyMxbonMAH/bJmGRFGpSzmQyVWudokLY4pinu7+zuc+zvT8VFRUVFRUVFZX/DpHYU1iAnZIkrQEUI0dZlu89Z62qxJ8C8RSgXJbljZIkpQMDJUlKAZYAB2RZLg+1vSzLc4A5AB06dDin+bxCLBJi34kTJ5RIXCEwaTQaatSowdq1a1m4cCFDhw7l5ptvDrvfjIwM0tLSQtpOCNE4GFarlfXr12M0GunWrRvff//9mZ4msbGxxMTEkJ2dHbZIWnWicW5uLgUFBaclGjdr1oxu3bqxYsUKbrrpJlJSUhTfTH/Rzt8qQIipkViPRMrZ6F/+he+AgOKKotCfvxggxPCoqCg0Gg35+fm8+eab3HnnnVx66aUhj+N2u5k3bx4dO3aMqLgdVERNrl+/PqRNRjBatmzJH3/8wc8//8y1114bkVAoyzLr168nNjaWCy+8MKLj7Nu3D6Ba0Rigb9++PPjgg/zyyy9h/bMfeeQRSkpKeOWVVygqKsJisaDRaKhduzaxsbHo9XqOHz+uXHsgZF/yj74UxeTEH/97HqoPnouxy79NkdwX/4wLn89HeXk5brcbh8NBTEwM0dHR3HzzzaxZs4a5c+fSv39/RVR9+eWXT1kwBli3bh0+n48rrrjilM9PjDWFhYUkJSUFFQXdbjdHjhzBYrEQHR3NFVdcodivVEdGRgbZ2dlBlx04cICaNWuGFNlEf23cuDFpaWloNBpl8kEcX2RG+O9D9J2zWUjxVPpW5Shi0Tb/SH3/sUmMX5XHLYHD4eD6669n+/btfPDBByELvPqzYcMGli1bxogRI5SMl0hwuVy43e6g7yCv13tG1/LEiRMcO3YMj8dDYmIinTt3xufzceLECbKysvB6vRw7dozk5GTcbnfAGA8VkdM1a9YMe4w9e/YAwYvgCZKSkmjbti2//fYbOp2OvLw8UlNTFesNMb6ILCghZAuPfAhuQ6LRaEhPTw/pva9SQXW/v53u/s7G72rnYn8qKioqKioqKir/HSIRjT/9888/hizLsiRJlwCbJUn6BIgH1gKJwH3AXGDjP9hEIPDD2mAw4HK5AiwChB9hdHQ0er2e77//nsTERF544YWI9i1SmYNhNBrJysrC6XRW8QAuLy9X0p+D+QOfDiKKr7qCUyJCtVatWkGXCxGlbdu2p3T8bdu2MXXqVH744QfS0tKoVauWEiEoLCmEuGG1WomJiQkQLvyFsPMBf59SgcFgwOl0otPpKCkpITk5WRGSc3NzlXT9mJgY9u/fj9vtVoqOhcLr9VJaWkpGRkbEInCbNm1YtWoVhw8frpLeHQqdTkfbtm3ZuHEjmzZtClukTvDzzz9z7NgxrrnmmqC2GpVxOBx89913pKWl0bx582rXFxMTBw8eDCsar169mhtuuEERaYxGIzabTZmIyMvLo6SkhISEhCrCHlQ8GyINvLIwrNFoFEEt2D3/O/BvU3X939+aQpIkHA6HYjkAFef6008/sWLFCqZMmcLgwYMDtv/kk0/o2rXrKQnGUCGmiYmyU0Vkh+zevVux6hERk2azGa/Xy4EDB3C73UokfaSCcVlZGSdOnAiapu/xeNi0aRPt27cPuq3P52P+/PlKfxXPssPhICUlRXkWRT8T2wjCTTL43ycg4J6dDYL14+jo6AChsfJ4KyyQXC4Xx48fVyJ9o6OjKSgoYPv27YotTSQsWLCAlJQUBg4ceEptF9k5wSZm4uPjOXz48CntT5CTk8OhQ4eIjY2lY8eOpKenK9e7UaNGeDweNm/ejNfrVaI8/aOd9+/fz44dO+jfv3/Y43z22WdERUWFnUhzuVzK/cnKygqwx8nPzyc1NTXArklY6kQy/ot7/W+z11FRUVFRUVFRUVFROXOq/WKQZfld4CNgkyzL74o/575pIElSS0mSmkqS1FqWZQvQCWgD/CjL8nOyLI8EsoFBf0d7qsM/ldjpdKLX6zEajSQnJysfaAaDAY/HQ15eHvn5+dSuXTsi4XLr1q3k5ORw9dVXB11+3XXXUVJSwjfffFNlWUZGBm3atOHkyZNs2rTpjC0ZPB4Pe/bswWKxhPQphoriQytXrqRFixYhI4nFB3u4KCp/tm3bxi233ELnzp3ZsGEDo0aNYuXKlUphIIfDEeD9abfblUhR/w9kUYTufPkQ9o+Mrmxt4HK5Ago1iXPyn0Do3r079evX56233gp7f41GIy+88ALbtm3jo48+iqhtF1xwAUDQgnnhaN68OS1btuT3339ny5YtYde1WCysXbuWpk2b0q5du4j2v3z5csrKyhgwYEBE91EUfQyX5g0VUfQWi4UjR46g0WjIyMggMzNTEZ2Sk5OpWbMmNWrUIDc3l/Ly8gABRtgLiIg+/wkLYV8j1q9sYXGuEVHOlSdRQuFvTQEVInNqaio1a9YkJSUFWZaZN28eOp2uimAMFQUPtVrtKT9nycnJlJSUnNZYFRcXR5cuXWjVqhU1a9bE6/WSk5PD/v372blzJ7///jsul4sGDRooBTEjwe12s2TJEtxut+KL7c8PP/xAfn4+N954Y9DtFy5cyObNm5k4caIiOgsP/FCTgf7vFNF3gr0vROaEsBAR/z5b+PfbyvjbrPiPt8JzXRTBExHHPp+P5ORkpk2bxqZNm5gyZUq1x/d6vXz77bdcfvnlIX3IQ7Fs2TISExOr+E9DxWRlpOONPxaLhYMHD5KQkEDjxo3JyMio0o+EP/cFF1xAt27d6NmzpzJZVVBQwIYNG2jXrl1YL9zjx4+zePFiBgwYoHghB2POnDkcOHCAvn37ApCamorBYKhimSMK4lUed0QdgOosXVRUVFRUVFRUVFRU/ltUq1ZIknQtsA346s//t5Ekafk5bheSJPUGFgMPAm9JknTtn8Jxc+BJP0/jA4Dlz6J9/ygmk4mYmBh8Ph8Gg4GEhARSU1PRaDRKgTan00lRURE5OTmUlpaG/RD0Z9GiRRgMhpCicatWrWjWrBlLly7F6/VWWd60aVPatGnDsWPHsNvtpyXGCA/jH3/8kRMnTlCnTp2QhamgIspQlmVuueWWkMLM4cOHSU5ODltoCCqE6lGjRtG5c2fWr1/PuHHj2Lx5M4899hjp6enk5+cr6fIGg0HxmDUYDIpYfz5QnZdyZQGmpKQEi8VSRXjMyMigfv36AVGwo0ePZtOmTXz77bdh2zB06FC6du3KvHnz2L+/+nqStWrVIiYmht27d5/SuUqSROfOnWnevDnbt28Pu25WVhbx8fERW1ls27aN7du307Nnz7Be4P6IIpDVTVAkJCQEpJFXFne1Wi3p6emUlpZy6NAhjhw5EiD6GY1GRRSsPGEhJiv+TqHYn1CTKP4IC4ry8nL0er0SnSiiWWNiYtDpdPh8Pg4dOsSyZcvo169fUFEuIyPjlH2tAcXrV0Q0nyparZbExEQaNGhAu3btaNmyJXXr1iUxMZHU1FTq168f1ke2MidOnOCDDz4gOzubG2+8MWgE9CeffEJSUhI9evSosqy4uJjJkyfTuXNnevTogcViUWw+4C+bByHeCT9zg8FAXFycMqaFshQR3rRmszng32eLYBMcoq1Wq7WKsG0ymZT+YjAYiI+PV6K58/Pzyc/Pp2/fvtx222288MILLFu2LOzxt2/fTlFR0SnblZw4cYKff/6ZPn36BLUpkSQpYiscgdvtZvfu3ej1epo2bRrxpENUVBSSJFFWVsb27dtJSEjgqaeeCmndBDBr1iwAxo4dG3KdgoICpk2bxhVXXEH//v1p1KgRqampikd2enp6tRZM59I7W0VFRUVFRUVFRUXl30sk9hRPAhcB3wPIsrxNkqTIctRPE0mSOgAvA4OBn4GBwBWSJH0JeOU/FTdJkoYDdwOD/izaFxHhBNNQH4DBtqnsDSpJEhqNRinGJjwFXS4XHo8Hk8mkRF35fD7y8/OpVatWQDEhf3JycjAYDBw8eFCJNhJFhnJzc6tEffXq1YuZM2eyYsWKKgV3oML3sFGjRhw4cIDi4uKQAkSwc5Vlmby8PI4ePYrJZKJJkyZER0crH6ZRUVEB4tDevXvZuXMnl112GW63m7y8vKDnuH//fmrUqIHL5Qq63OVyIUkSw4cP5+OPP+bee+9lxIgReDwefD4fkiQpvqXx8fG43W7sdjs2m00RCcR98ret+KcijEN5KYtr7p8GbjKZsFgsik+zJEl4vV4cDofig1pSUkJRUZEyoTBjxgymTJlCjx49cLlcIfv6qFGj2LNnD1OmTOHVV1+tEr1XUlIS8P/69euzfft2jh8/HlZcsNvtHDx4MOBnGRkZlJSUhPSBhYpJgXbt2nHo0KGgyxs2bKj8u7S0lM8//5yaNWvStm1bysvLQxZkLC8vVyYMfv31Vxo2bIjL5VKitxMSEqpsEx8fT0lJCXXq1EGWZTweD5IkIcuy8rzrdDqSkpKUYlJCVBUI/18xYWG1WhWv2nNBuDFNPCcQaIMiy3LQ7ex2O7m5uYpQKJ4fQOmPZWVl+Hw+Fi9ejNVqZezYsQHjmOg/SUlJ/PjjjwH9qaCgIGREo/A4FxNf27ZtU/xrhR9rMNxud9hCTzabDUmSiI2NVa6JaFNiYiJFRUUh2/Prr79y5MgRDAYDV1xxBTVq1KC8vBydTqc8q6WlpXzzzTfceuutOBwOZFkOmLx76qmnKCwsZMGCBRiNRoqKivB4PJSWlpKYmKhYZ1itVgoLC5XnUYi/1Xmwi8kIwZn6tPsTqm+JsSomJoa4uDilf/tbZIh7kpycjM1mUybNRDbOgw8+SFZWFnfddRcXXXQRGRkZQNXx54svvkCSJGWMCCW0HjhwgOPHjyv/X758OZIkUa9ePX788ceQ79rqEFkKUPEMuFwuatWqpbxHT5w4EXQ7If4LHA4HO3bsQKvV0rNnT1wuV8i+d+DAARYvXsytt95KYmJiQOS4/4TH1KlTKS8vZ+zYsWi1WvR6vfL7iEA86/5jgT+iv4lxIRjnS1aOioqKioqKioqKisrfRySisVuW5dJKHwznOocxBXhGluXNAJIkZQFDqbA39kmSpAMaAzcAQ2RZ/uMct0fBXyj2jwRNS0tTPpiFsGK324mOjsblcikFjITnsCRJ5Ofnk56eHjLdVngfz5w5E7PZzP333698kDdu3JikpKSA9Vu2bMnKlStZu3Yt9957b8hIwldffZUDBw6QkJBA586dA9Y7fvx4gG+t1+tl165d7Ny5E7fbzWWXXUb79u2r7Fuj0dC9e3egQkyYM2cO9evXZ/z48eh0OurUqRO0LSdOnOCqq64KeQ00Gg0jRozg448/5plnnmHixImUl5eTl5eHVqtV0u0zMzOxWCxKJGTlVOrKxfD+7g9gcTx/L+VQH+8Wi0URt9PS0rDZbIrAL6wPoOID3+FwKEJAVFQUo0ePZtKkSaxbt45LL700aHQdwOWXX86cOXO4+eab+fzzz5k+fXrA8srC70UXXcT8+fPJyMiga9euISPMP//886DL6taty6pVq8jLy6NTp060aNECqLBd2bFjBy1atKBVq1Yhr1+/fv2AvyLOo6KieOONN6hZsybFxcVVngX/65mUlITVamXr1q3ccccdyrperzeo+JSUlMTRo0eRJAmLxYLD4SA5ORmHw4HValXEx6ioKCW6VkRdCtsJSZLQarVKUcyzVXTxdPAXkCqLi6GiVtPT05FlGbPZrPgzi/VLS0vJzs4mLi6O9957j2uuuYbWrVsH7ENkUNSrV4/y8nIlAhYqrnswsR4qvGBjY2NJTk5m2bJlxMbGKvveu3dvyMwMMZEVCovFEnKZwWDgyiuvVP4vyzK///47S5cu5Y8//iAxMZH777+ffv36BVw7u92uiJwrV67E7XYzbNgwMjIykGVZGdN27tzJvHnzGDRoEJdffjlFRUW43W5kWSYuLo7o6GhlEkgULhM2AmKciNSD/VyMa6H2KdoSyhu3cl+3Wq1ER0crEcdxcXG43W6ee+45LrvsMpYuXcq4ceMC9i1Yv349bdq0oXbt2hQWFoZ8jjQaTYBV0ZYtW+jQoQO1a9emdevWIe2UkpKSqFOnjmKDsX///gDh2n8SVojX9evXV86rcv8X+Hw+5b1YXl7OhAkT0Ov1vPDCC6Snp4fczu12K2PypEmTqvR70bf++OMPpdBkvXr1sNvt6PV6UlJSSE9PV7IG/O2YxP30/11Gq9VWeSdV9jNXCc3dd9/NunXrzur+ziZne38qKioqKioqKir/HSIRjf+QJGkAoJUkqTFwL/DjuWyULMtfSZLkn+u8DbD7RRMnyrK8W5Kk2/60rPjbCBYJ6nK5yMvLU4RjIfT5f1RDxUeaxWJRRCSLxVKtPcXPP//M2rVrGT9+fEhhTKDRaBgyZAiTJ0/mjz/+CCnC1alTh5iYGLZt24bP56Nt27Z4vV4l8k1Ug3e5XOzdu5fS0lLq1KlDo0aNQnoT+/Pee++Rn5/PpEmTwhY0Ky4uprS0VPn4rozH42H48OF89NFHjB8/nnvuuUeJUExLS8NoNCrRtEJoEVFS/kKGiJb0Lz72T6HRaMKmxQsR2OFwKB/5/h/t/pGiwoIjLi6OpKQkioqKuPXWW3n99deZMmUKX3/9ddi2dOvWjdGjRzN79mwuu+wyevfuHXLdFi1a4PP5TtmiQqDRaGjWrBlms5nNmzcrUaw7duygSZMmIQslVmbWrFn89ttvTJkyhZo1a0Z8/HXr1uFwOCIquiUija1WqxKRLMQYh8MR9DkMV1zO/56Jff2TQoy/WBRM7BOiuJiM8I82NplMeDwezGYzn332GYWFhTz44IMhjyWihLOzswOixatDZGmEylA4V+zcuZMlS5awd+9eEhMTGTBgAPfdd1/YccPlcvHGG2/Qtm1bWrZsWWX5m2++icFg4NZbb8Vut5OcnIzL5SIqKori4mKlwCVUjFUZGRlV+kdlsf+fRgiKwfqQ6CtGo5HY2FhkWVaKwJrNZgoLC7FarTgcDhITE2nSpAkXXXQRCxcuVERjf0pKSvjll1+4//77T6mNmzZtwuFwcNlll0W8jVar5corr6RHjx4BkeIHDhxQ/u1f3DZSdu3axezZs8nJyeHpp5+mbt26YaOeT548yfvvv8+gQYNCjo2yLDN+/HhiY2N56qmnMJlMlJeXY7FYiI6OVp5h//de5SKdYtJbPG/+VBfdrvIX/fr147bbbjur+zubnO39qaioqKioqKio/HeI5MtnLPAo4KTCY3g18PS5apAkSZJcgX8uuw6o9adv8UDgFkmSbvm7BWMIFIA0Gg2pqank5+fj8Xiw2WyK0Fc5ms9fuNTpdGzevBkgrGgsyzLPPvssGRkZYYvl+NOrVy/eeOMNvv76a1q2bBlSmBK+rtu2bQuZWgsVH4s9e/akVq1aIdNo/Tl48CDLli2jd+/eSjRpKLKysgACIpsFXq+XYcOG8dFHHzFp0iQGDhyIzWbD7XZz4MABatasqQgrpaWlAXYgIrLKX5wVIuX5TqiIQiHE+HszCxFS9EUhZj700EPce++9fPfddwERlMF45JFH+OGHH3jggQdo0KBByCJxohjeH3/8Qe3atU/r3EQ0utfr5aeffgIq0tY7d+4ckTi4atUqFi5cyK233sr//ve/Uzr2V199RXx8PF26dKl23cTERIqLi5V7oNFokGWZoqIiysrKlEhcf/zHhcr4971/OuoYAgXucG3wFwZFwTaoKBSYlpbGW2+9RefOnbn44otD7kNE4p6qaKzX60lKSgppO3Iu2LBhAzNnziQ5OZnhw4fTs2dPxVYoHB9++CHHjh3jueeeq7LM4XCwZMkSunXrpvQZ0R+Ed7nT6VREyOrGqfMl+tNfUBT9Q4xDon/Jskx0dHSATYWI2hc+7FqtlqSkJG666SYmTpzIjh07qkTfrl+/Hp/PR8+ePSNuX0lJCV988QX169cP+n6pjqioqIAsBL1ef8r7gIpnbebMmXz99dekpKTw+OOPh82oEMydOxcgqIguWLVqFd988w3PPvuskt0hbFfE5KoQjMX9qZyp4p8VVfmdE2l0uwocO3bsnOzvdN+153p/KioqKioqKioq/x2qFY1lWbZRIRo/eq4aIUlSUyAJ2EqF9YVXkiSNLMvCBsMOZAETgeuAO/9s199O5Y96IRwH++jyxz9Sr6CgQBFggnkPC3Jycti+fTsTJkyIOEJWp9MxePBgnn32WTZu3Mgll1wSct2mTZtSq1YtcnJylOr2wstVp9MRFRWl2CREQl5eHk8++SRxcXEMGzas2vU3bdoEVFhtVGbWrFlKhPHgwYMBFPuJkydPAhUFy4T4IkT7ytHd/zZCRRQKIUaj0SiCgEhrF+KRKHLWr18/nn76aRYuXFitaKzX65kzZw6dO3fmtddeUwovVSYhIYGGDRuyZMkSBg8eHLYAYjiEl+e6deuw2+1069Ytoqi9n3/+mSlTptC+ffuwQkowZFnmm2++4fLLLw9bdAoqJjI+++wzpfCgiPwuLS1V7EKEl7G/UFZZnAkl6Il++XcJMZUjQoUfsdlsrvYZ8ReKxbpGoxG73U5hYSFHjx7l+uuvD7uPBg0aoNFomD17Nu3btz+l57Ju3bps2rSJGjVq0KdPn4i3Ox1+++03Zs2aRfPmzZk0aZIyOePxeEJu43a7eeGFF3j11Vdp3749l19+eZV15syZQ2lpKaNGjSI9PV2Z6HE4HEqRO/8o0HAR4HD+RH/6j7OVJyHEOfp8PsXiRIwXYruEhATsdjtJSUloNBrat28PwE8//VRFNBbj/akUVNy2bRtlZWVh33/nmv3797N69Wrsdjt9+/alf//+EfX/I0eOsHTp0rBRxjabjXHjxtG0adOAInliHBLvCZGNIjKbkpKSlD7n/7tLsHadb9Ht5zMDBw48J/v7/vvvz8v9qaioqKioqKio/HeoVg2UJKmDJEmfSpL0qyRJO8Sfs9UASZL6Ap8DU4G3gdGSJMX96V2sAZBl2QmkA3cAQ8+mh7HP51Mivk4XISSHi/yyWq3k5eVhtVpJSkqiZ8+eXH311cyePZvS0tKg26SlpWE2m085RfvGG2/kggsu4JNPPglZWEwQHR1Nw4YNqVu3LjVr1lSq3MfHxys+m5FgsVh46KGHKC8vZ+rUqcTFxYVd3+l08sEHH9CtWzfFF1Zw5MgRpkyZwv/+9z9GjRoFoLSlZs2aXHjhhTRp0kQRtFNTUwPaGxMTE3G7/y2YTCZiYmIwGo2KoBkKg8FAy5Ytq733gszMTDQaTbUWEVOnTsXn8zF37txTEnAqo9PpuPzyy+nTp0+1/QQqolTHjRtH3bp1mT59erXCb2V2795Nfn6+4i0aiq1bt9K9e3cKCgp4//33cbvdWCwWCgoKlEKNMTExaLVaRSgToowoUBmuSCBENlacTYTIKNpUWFhITk4ODoej2mfEZDIRGxsb0NdsNhulpaVERUVxyy238Oabb7Jz586Q+8jMzOTll19m/fr1DB06NOy1qczw4cNp37694mceqgjemZKXl8dLL71E3bp1mThxYkA0fyh2797NkCFDeOmll7jxxhtZtGhRlXual5fHlClTuPLKK2nZsiVerxen06lEezqdTqKjo5X74N+nQmE2m5XieP8k/h65JpMpQPwWkyhWqzWgAGFsbCySJGGz2SgqKsJms+F0OrHb7UrWSbAo3IEDB9KxY0dGjx7N+vXrI2pf165dufDCC1m1ahWff/552EKRZxu73c4XX3zBp59+itlsZsaMGQwbNiziCZMXX3yRqKgoJk6cGHKdJ598kqysLGbPnq0UhQUUgT46OjpAzNfpdIrllH+fE+PR/7f3pYqKioqKioqKiorK2SGSL4VFwALgJuBavz9njCRJUUA/KiKHL6dCPK4NTJQkKd4v0hhgBdBXluXTM1X9ExEBJYjkQ/1so9PpyMzMZMKECZSUlPDaa68FXU+r1dKiRYuwokyo7QYPHkxiYiLz5s0LKUqfLWw2G4sXL6aoqIhnnnkmbEEqwYoVKygsLKxiuyHLMvfffz+SJDFmzBjcbjexsbGkpqaSkpJCVFQUdevWVQppWSyWkIK/x+MhNzcXl8t1xhMD/zRCiBFp3f4f+UK0NJlMSkRfvXr1IhaNd+3ahc/nqzZtun79+rz++uvIssyLL754RsJxpBQWFvL555+TlJTE7NmzIxKZK/PDDz8AFR7O4da58sorMZlMLF68mJ49eypCvdlsJjk5mbi4OPR6Pfn5+URFRSlCmb84ExcXh8FgoLy8nPLy8n+8zwmRUQhWycnJ1KhRQylGFg4hDArLARF1rNFosFgsjBw5ksTEREaMGIHb7Q65n1tvvZUZM2YownE4L1d/YmNjGTNmDKNGjeL48eN8//337N2796wKgKWlpYp1ySOPPFKtGOt2u3nttde4/fbbKS4u5r333mP27NkkJiZWWffRRx/FarVy//33U1paitvtxmg0YjQalX7iPy75i6+yLGO1Wqucq79Ye74QTHgUEw7Brqd4nlJTUzEYDPh8Pnbu3Iler6ddu3ZB11+4cCENGjRg0KBB7Nu3r9o2RUVFMWrUKLp27crKlSt5//33z/mz6PV62b17N2+//Ta7d++ma9euDBw48JRsWX755RdWr17NnXfeqVi7VObXX3/l1Vdf5fbbb6dp06aUlpZSWFhY5fwMBoMSeZyamkpCQkLIgrMqKioqKioqKioqKirBiEQ0zpdlebksy4dkWT4i/pzFNsQBwp9gGfAFEAX0B5AkqZMkSU1kWZ4my/KeMz2Y1+sNEIgrR0mdKkKY9C+aE4zo6GjS0tIwmUyKUNCsWTOuvfZaZs2aFdIvuFWrVvzxxx9h06SDYTabueuuu3A4HLzzzjunvH2k2O12lixZQklJCVOnTq3WxxgqRM758+fTvHlzOnfuHLDss88+48svv+SBBx6gXr16mEymAAsAm82mRHcWFhZSWlpKfn4+ubm5VSIRCwsLyc7O5vjx43/7xMC5IFRfFbYUDocDSZKwWq3UrFmT/Pz8iKIzt2zZAkCbNm2qXbd+/foMHz4cn893zoXj0tJSPv30U7RaLa+//vppW2KsX7+eBg0ahI2knjZtGikpKSxdupR69eopkbipqalkZGQQExNDdHQ0JSUl5OTkUFxcrAhl4r5ER0cTHR2N0+kkNzeX3Nzcf7zPiUKKQtDT6XSkp6dXsQTxFyn9/y2sOUpLSzGZTIr9AFREET///PNs376d6dOnh22Hv3D8wAMPRCwcS5JE165dmTZtGomJiWzcuJFvvvnmrFxXu93ON998A8Bjjz0WVPj1Z9++ffTv35+33nqLq6++msWLF3PVVVcFXffXX39lwYIFDBw4kAYNGpCQkECtWrWUcUz0E/9xyV98FRHiNts/4sJ0xojnwmKxUFZWRnl5OV6vN6BQW0xMDE6nE6vVyi+//EK7du1CipqJiYksWbKEpKQkHn30UY4ePVptG7RaLYMGDeJ///sfGzZsYPny5TidzrN6nrIsU1hYyFdffcXs2bNZvnw50dHRDBo0iEsuuQStVntK+3r22WdJT09nyJAhQddxu92MHTuW9PR0Bg8eTGFhIW63G6fTybFjx7BYLFgsFux2O06nU/HMrizsn40Mq/8i9erVQ5KkKn/WrVsXUYaCioqKioqKioqKyr+NSArhPSFJ0jxgLRXF8ACQZfnTMz24LMtuSZJeBsZKknRQluX1kiRtAGoCfSRJehe4GPjwTI8l0Gq1AT6wWq02oGBaKEJFdhUWFiqei5WLY/lvK2wTrFYrFosFSZJITk5m1KhRfPHFF8yaNYunnw6sL2gwGGjTpg3vvPMOhw8fpnnz5sqyxMTEsCJHmzZtaNOmDQaDgSeffJIlS5bw9NNPYzQaKS4uJi0tLeh2x44dU4qeVSYnJydAFLZarTzyyCMUFxczceJErr766pDtEUXaANasWcPBgwd57bXXSE5OxufzodVqkWWZiRMncuGFFzJx4kS8Xi9ut5ujR49Ss2ZNdDqd4uNrt9vxer3odDqMRqPysSw8GIVfpji2y+X61/ocC8SHv39ksRBn/H1rJUlSCh0eOnQo6P0UdgsAa9eupVGjRqSmpioRo/Xq1QvZjj59+tCpUyfuueceZsyYweOPP0779u0VH+waNWoE3W7Pnj3UrVs36LLCwsKA/l1YWMjIkSPRarW88cYbYYtZGY3GkJYVkiTx448/csstt1QRcERRyuzsbNatW8c999xDgwYNKCwsrLKuuOZRUVGkpaUFFLCs7HNuMpmUsSBYpOXfHSUqhI1giEhWf69cgLKyMiUrY9++fbjdbjIzM5UoV71ej81mo3nz5vTr14/nnnuO22+/nUaNGgEEFVAGDhyITqdj7NixjBw5kvnz51d5Jhs3bkxsbGyVbRs3bszzzz/Pzz//zDvvvMOqVau499576dq1K5IksXbt2pDjL1Qdm202G8888wxut5sxY8ZU8dH1v3b16tUjNzeXUaNGodVqWbJkCddccw1utzuo56vP52Py5MlkZGQwbNgw4uLiSE1NrdKnQhVPFNc9lA1NuHv5d9owVIfNZiM/P1/xmjebzRQUFGA0GklJScFsNiuTmb/88gt33323ImIGKzxXt25dli1bRq9evZgwYQIrVqyoEo1rsViqvM9btmzJBRdcwPTp05k9ezb33XdflYyFWrVqhY0I9p/clGWZAwcO8MMPP7B+/XqKioowGo10796dq6++mi5duijjUXFxccgxz2KxBPT1zz77jO3btzNjxgwyMzODPkOvvPIKv//+O++++y7169cnKSmJ2NhYjhw5Qk5OjuKTLQqkhpoQj7QYpkogR44cCfqM9ejR4+9vjIqKioqKioqKisrfQCSi8VCgGRXRvyIsRQbOWDT+k/VAU2CgJEmSLMs/AB9IkjQCqCHL8oyzdByAKqn9Z4oQjyJJ9xZigIjWs9vttG7dmr59+zJ79mzGjh1bRcy98MILAdi+fXuAqBYpl19+OVarlZdeeokHHniA559//pT3EQyr1crkyZM5ePAgkydPDik0B+ONN94gMzOTG264IeDn+/fv5+jRozz44IPodDolKtxqtVJYWAigCCk+n4/Y2FhFIHA4HEoVeKgQveLi4hSx6FR9cM9n7HY7JSUlSkEzm82mFNcSIqUQKg4fPhz23tjtdjZv3nzKhXyEVcWYMWMUEebSSy+lUaNGJCcnn3bUlc/nY+/evTzzzDMUFhYya9asU0rvrszOnTuxWCxhrSk+/vhjfD4fN9xwAxqNhvT0dPR6PT6fj4KCAtxuNy6XSylEWK9evbARhBqNJqjweb4SSqTU6XTk5+ej1WoxGAxotVoKCwtJSkoiMzOTY8eO4XA4mDhxIp999hnTp0/nzTffDHus/v37Y7FYeOSRRxg6dGhQ4TgUGo2Gvn370q5dO1588UWefvpp2rRpw4gRI07pfN1uN6+88grHjx/nwQcfrNbL2+fzMWLECKxWK+vWraNZs2Zh13/vvff45ZdfeOaZZ2jWrBklJSVYrVZF7I2JiQk7cSAyB+Li4v7VXrMGgwGj0Yher1eeB71ej0ajwWAw4HA4sNls/PLLLzidTrp06VLtPhs1asQ777zDHXfcQf/+/Vm2bJliVRSOG264gdzcXBYvXsy0adN48MEHTylzwWKxsHPnTrZv386vv/6qFI9t164dHTp0YMCAAWc0Kel0OnnmmWe44IILuPXWW4Ous2/fPqZNm0bfvn3p3LkzPp9PKWCbkpKC1WpV3gcej6fKZJY/oSYsVE6PBx988D+1PxUVFRUVFRUVlf8OkYjGHWVZbnquGiDLskOSpEVUCNGPSJLUjIqI5lTAcq6Oe7bQ6XRkZGREFOFltVrJz88nNTVViRD1eDw88cQTLFu2jBdffJEXX3wxYJt69eoRGxvL9u3bue22206rjddddx3x8fE89dRTjB49mptuuilkpHE4HA4H3333HRs3bmTLli243W4eeeQROnfuHBClGI4VK1awYcMGHn/88SpC7k8//QRUWHIUFxcrUWh169bFYDBw4sQJAKUyfGxsrCKqBKsCHyyCT6xzKmnD5xsi7VtYcxgMBvR6veK36/F4FCHlyJHwTjKbN2/G4XCcVqRU/fr1Wbp0KZs2beL7779n3bp1rFy5krfeeouOHTtyySWX0KlTp2oj2TweDzt37mT58uVs2LBBOacXXniBli1bnnK7/Nm0aROSJHHppZeGXOfDDz/kwgsv5KKLLlKitd1utyIYu91uPB4PTqfz/6XIEkykjI6Opry8HLvdjl6vp1atWoqvsXh+RJRjrVq1uOOOO3j33XeZPHkyNWvWDHu8vn37YjQaeeCBB05ZOIaKMfHVV19l5cqVLFy4kNGjR9OiRQtuvPHGagXE8vJy3nnnHXbt2sWoUaNo3bp1SGsgwauvvsp3333HzJkzqxWMS0pKmDx5Mp07d2b8+PHKJJbNZlMmd+x2O8nJyRQWFipjXOVIdf+//22Ic/R6vYoNiVarxWQy4XQ6lWfJbDbjdruZPn06MTExXHLJJRHtv1WrVixYsIABAwYwaNAgPvzww4gKA7Zo0YIJEybw6quvMnXqVMaMGUODBg2Cvgu8Xi/79u1j165d7N69myNHjuDz+TAajbRo0YKbb76Ziy++mJiYGEpKSs74Xr300kscO3aMJUuWoNVqq9hd+Xw+7r77bkwmE+PGjVNsJwSxsbE0bNgQn8+HzWZDp9MpntliTPMnmKAsy7ISFX4+eWb/G7j22rNS5uNfsz8VFRUVFRUVFZX/DpGIxj9KknSBLMu7zlUjZFkuliRpLrALGAk4gDtkWc49V8f8JxAfdCIFV6PRYDQaqV27NgMGDGDOnDlMnTo1IEpTo9HQpk0bvv76a+69914yMzNP69jdu3dn+vTpPPLII7z99ttMmDAhouho0e7169fzwQcfYLFYSEpK4sorr+SKK66oVkTx38e8efN4+umnad++PcOHD6+yzsaNG0lISKBJkybEx8dTWlqKx+MhNjYWk8mETqercu38hWD/j+BgH77+KbmRWJKcrwivXbPZHNCnhNCu0+lo2rQpsbGxfPfdd4waNSqkCLBrV8VjHSwdPBKMRiM9evSgR48euN1uPv/8c/bs2cOPP/7I+vXr0Wq1tGrViosuuoi0tDTq1KkT0JZjx47x0ksvkZWVhclkolOnTnTr1o2uXbsSHx9/Wm0SrFmzhoULF9KmTZsAexR/jh07xubNm3nmmWeU6H+DwUBRURFut5uoqCjS09Ox2+2kp6crhcj+PxFKpNTr9URHR5OSkkJMTIzitSuWiWuj0WgYPXo0CxYs4LXXXmPatGnVHvPWW2/F6/Xy4IMPsmjRoqDjQTh0Oh3XX389l19+OR9++CGffvopWVlZjBkzJqhNhcPhYNWqVaxcuRKn00n//v0jEil//fVXnn76aW688UYGDx5c7fozZsygoKCAOXPm4HQ6letmNBqxWq0UFBTgcrkUwVin01W57qH6mBjr/D2qz0fEOBsdHa1E8wrhMjk5Gbvdjk6nQ6PR8N1337FhwwZee+21AMuX6ujatSuvv/46I0aM4Pnnn+epp56KaLvGjRszadIkXn75ZaZNm4ZWqyUxMRGz2Uzt2rVJTk4mKSmJr7/+mpMnT6LVaqlfvz633norbdq0oXHjxmc1ayU7O5vHHnuMlStXctttt9G9e/eg6z399NNs3LiRJ598ksaNG+NyucjIyFD6ib91kYjkDjUpIfB/dwLKhGOo9VVCs3fvXgDFFur/+/5UVFRUVFRUVFT+O0QiGncGtkmSdIiKCGAJkGVZDm4CeZrIsuwCvpMk6Yc/9/+fqNBis9koKiqiY8eOLFy4kOzs7Cp+sg8//DC33nord9xxB0uWLDmlj2t/2rVrx8yZMxkzZgwvv/wy48aNq1Y4Pnz4MAsWLGD//v3Uq1ePJ554ghYtWpySaJGfn8+YMWP4/vvvueqqq3jllVeqFDySZZlvv/2Wrl27AhUp5ImJiRQXFytV4GNjY7FarZSXlytRxlarldzcXNLT06u1BPi3R/D5I66HLMvExsZis9kwGo04HA7l73HjxvHUU0+xePFiBgwYEHQ/ffv2ZdGiRQwdOpQ5c+ackTdjVFQUrVq14qqrrmLs2LHs3r2bTZs2sXnzZubMmQNARkYGHTt2pEOHDhw9epT3338fo9HIiBEjuP32289KMaGSkhKefPJJli1bRrNmzZg1a1bIdb/77jugwpNSRNJarVbFL1uIXf59q7KntL/4Euy5OJ8j+CoLR/7n5XK5lIjGmJgYzGYz6enpGI1GZaJCRDvGxsZy5ZVXsmTJEp555pmIji1sR053IgwqJn+GDx9OXFwcS5YsYe7cuYwdO1aZcPD5fGzZsoWvv/6akpISLrroIm688UZq165d7b6PHz/O448/TqNGjZg5c2a1987hcPD2229z7bXX0r59e/R6PRZLRbKMRqNBq9ViNBrR6XQkJycrkeuRjqXny6SXf5+pfE2CWZ34Fy0UGTbl5eWYzWbq1KmjbHeq/O9//+P6669nyZIlPPzwwxGP6zVq1ODxxx/nt99+o6CggIKCAo4fP86uXbsoKSlBlmUkSeKWW26he/fumEym0y7CGQqv18vixYt5/fXX8Xg8TJo0idGjRwddd+7cuTz77LPcfPPNjBo1ipKSEqBi4iTUO89ut4eclPBfxz9DSKwfSdS2SiAjR44E4Pvvv/9P7E9FRUVFRUVFReW/QySicejqZucAWZa91a/170B8XOv1esWb12g0BhUJRHRcTk5OFdG4devWzJ8/n0GDBtGvXz+WLFly2m1q2rQpgwcP5v33368iHMuyTHl5Odu2bePw4cMcOnSIX375hdjYWEaMGEHjxo1p1arVKR1v7dq1PPHEEzidTl588UUGDRoUVHzJysriyJEjjBgxApPJhMFgUCJnnU4nOl1FVxV+xuJPKITHr78oE87j8XxEiI3BxBn4K3LdbDYrIlJMTAwWi4Xy8nIGDx7MN998wwMPPMDFF18ctLhdRkYGn3zyCQMHDuSOO+7gqaeeYtiwYWfcdo1GQ4sWLWjRogV33nknubm5LF++nKysLFavXs2KFSuAiomM++67D5/Pd1YE47Vr1zJx4kSKi4u5//77GTZsWFjP2h9++IHExESaNm2Kx+NRBBYxqSEiawFFTPL/uclkqjZCz7/I3N/Z/yIRqysLR/7tNBqNlJeXK5YU/pHt/t60oujk9ddfz1dffcVPP/0UUDAzFN9//z1arTZiW4JwJCcnc9ddd/H6668zd+5cRo8ezdGjR1mxYgXZ2dk0atSIe++9lyZNmkS0v/Lych555BE0Gg1LliyJKOr9o48+oqCgQHl+iouLA0RjIcaJSGExpgk8Hg+FhYUkJydXWQZ/TXadjqj3p33SWQlP9u8zldvib3UCBI12tdlsFBQUoNfryczMpF69enz11Ven7E0NcMcdd7Bs2TJWrFgR0gs4GHFxcQFRvXv37qVx48a43W6KioqIjo4+Z8L8H3/8weTJk9m5cyc9e/bkueeeC1ksb8OGDTzwwANcddVVLFiwAK1Wi06nU2w+7HZ7wJgi7k1MTEyVYn+VCTaJej5ObKmoqKioqKioqKio/HNEIhrfC7x9Lu0p/r9QOeJQFCxzuVy4XC5sNhtxcXEBH9rCVkFEXGVnZwfdd5cuXXj33XcZPHgw/fr1Y/bs2SFT7qujZs2a3Hfffbz66qs8+uijiifxsWPHKC8vV9ZLT0/nqquuom/fvkRHR5OTkxPxMWw2G88//zxLly7lggsuYO7cuTRu3Djk+t988w0AzZs3x26343Q6cblc6PV6xZvRYDDgdDrx+Xzk5+cTExNDamoqmZmZyoeviAK1WCx4PB70er3iIf1vozqxUUTGQmDkoehfOp2OJ598kptuuok777yT1atXBxWjMjMz+fzzzxkzZgyTJ09WCi6dzTTs9PR0Lr30Uu644w6cTic7duygVq1aZGRkIEmSUujwTFi/fj3Dhw+nadOmvPvuu7Ro0SIgyjEY69at4+KLL+bEiRMYjUbi4uLw+XwBAh9AXl4excXFJCYmKv3JaDRGFNFXeV9/F/79J5QA5i8c+ae2w182CVarFavVClRcB5vNRmpqKikpKRQUFGC1WklLS+O2227jgQceYMmSJUyZMqXa9n333Xe0a9fujG1IBLVq1WLIkCHMmzePxx57DICkpCQGDhxIr169IhbDhM98dnY2L7/8MvXr1692G1mWef3117ngggvo2rUrkiQRExOj9CUhxoWbNCgsLFTG/2AWG2LS63REvT8nOU7LyL3ye63yxJ2/J7DoOx6PB6/XG/TZ8Pl8lJaW4nK50Gg0dOrUiRUrVuB0Ok954qhLly40bNiQhQsXnpJoHAphuXIusFqtzJw5kwULFpCUlMSzzz7LkCFDQt7Po0eP0r9/f+rXr8+7776LVqtVROKEhIQqHv4Q+DwLATnYZGmw7AhVMFZRUVFRUVFRUVFRqUwkovFuYK4kSTpgPrBYluXSc9us84/qCt3JslwlAkukIvt8PmRZRqPRYLFYyM3NJSYmRhFk/CMcT5w4oURmuVyugON27NiRt99+mzvvvJMRI0YwZ86ckMLxoUOHQra1qKhIEVNWrlzJ5s2bSU9Pp1GjRsTGxtKyZUtq1KihtElEG5aVlSmpsZWx2+1K0aM9e/YwadIkjh07xuDBgxk1ahQ1a9ZUllfG5/Oxdu1aatWqRatWrdDr9RQXFxMdHU1cXBxOp5PS0lLFY1aj0eB0OpEkKUCE93q9FBQUKPdAq9Uq4v2pCnZn++M5kkKJlTEajVgsFgwGQ9Dthaez+NtfCBBRoc2aNeOpp57igQce4IUXXuDhhx8GKvqWP1FRUbzxxhu8+OKLvPHGG+zbt49XXnmFxMTEKsctKCgIeS+Li4urWI8IROE+QIl6LigoUJaFE47Dib82mw2r1cro0aNp1KgRixYtIjo6GofDgc1mq3KuguPHj5OVlcVdd92FwWBAq9USHx+P2+3G6/VitVqVgm8xMTHIskxMTAySJAVEsgMB4ot/RHIwTqcvnA6iz5tMprDHFOuJCRmn04lWq8Xj8SDLMiaTSbE/EdfVbrdTUFBAYWGhMlGTlJREr169+Pjjj3n44YeDTlBAxT0/duwYO3bs4O677yY39y/r+iNHjoTsPwUFBSGF+bKyMmRZJi4ujv/973+cOHGC2NhY2rZti06nCzluQUX/Ef1LlmVee+01fv31V8aNG0eTJk2U8bgyLpdLETm//vprtm3bxosvvkhRUREJCQlKv9NqtVX6Q7D7ITI+IvWaPxX+vMdhs3iCtUlM0IlrIKxvACwWS4AQ7vP5KCwspLy8nLy8PFJSUoiOjsbj8XD06FFq1KiBTqdTxm2v14tWq+Waa65hyZIl/Pbbb3To0AEIjPCvTElJScA9ufnmm3n++ef55ZdfsFqtITNQhPVEMEpLSzl69GjIY4bzfC8rKws5dpWXl2Oz2Th+/Dhjxozh0KFD3Hzzzdx3331Kwc1gWCwWBgwYgNPp5L333iMuLo7CwkK8Xi+yLJOQkKA8t/7nJEkSRqNR+Rsq7plYR0wMhYsWV1FRUVFRUVFRUVFREVQrGsuyPA+YJ0lSU2AosEOSpI3AXFmWvzvXDfy3IElSgIAkSRJarZbU1FQsFouSfi88FB0OB1qtlujoaJxOJ2lpaeh0OnJzc5UIz5o1a1YRXm666SaSk5O54YYbGDNmTEiPY/+I4WCI6LkBAwYoEblQIdqESpUtLy8PWUjF5XKRmZnJvHnzmDRpEikpKaxYsYJLL70UIGiFeoHb7eb777+nd+/e1K5dW/kAN5lMeL1eTCaTIgYIP1AROelv3eBwOPB4PMTExCgerA6H47yLoKrOA1fgcDgUEc8/Uqxy8T//8/cXckSE49ChQ5Vibw0aNGDQoEHUqFEj6LFfeukl2rdvzz333EP//v1ZuHBhlQjxhg0bhkx9jo2NJSMjI+iyxMRE0tLSgi4rKipSou1PlZKSEm6//Xa0Wi1Lly4NsOFwOp0hRZFt27YBcOWVVxIfH4/H41H6j9frVSK5o6OjSU9PJy4uTnnGhfenRqOpNo3977CnCNa/RbRrOMFYkiRlW//xS5ZlpRClJEnY7XY0Gg1paWmYzWalYKAQpqxWK5Ik0atXL5YvX862bdu4/PLLgx4zKSmJL7/8ElmWufLKKwP6UmJiIgkJCUG3i4uL44ILLgi6zOv1KtsF8+UOFy3scDiUvvfmm2+ycuVKxo4dy6RJk/B6vSHv7+HDh3nzzTf55JNP+OOPP8jIyOCWW26huLgYk8lEfHw8Go0GvV6v2MyEe951Op0S5Xo641W4bf5cdsrGwZUj6SvbHwiB0n9drVarTKLqdDoOHz5MQUEBkiRRr149xYO9vLwcjUajeFvv27ePLl26AOGfk+Tk5IBnesiQIcyYMYPPP/+c+++/P2Tkus/nCztu1axZM+QxW7cOXcIhPz8/pOexw+EgOzubIUOG4Ha7Wb58ufJOdLlcQT2JZVnmwQcfZPv27Ur0upjA0ev1xMfHB/WSFu8En89HQUEBycnJVa6jRqNBkqSA7Af/fZ1P70kVFRUVFRUVFRUVlX+eSCKNkSRJCzT7808BsB0YJ0nSSFmWbzuH7ftXESwNVKPRKAXbYmNjiY6OVlJztVotJSUlWK1WioqKyMjIiMgCokePHkrEsfA4Pt3ieELUOFPKy8t5+OGH+fzzz+nVqxdvvvlmxBFz27dvp7i4mKuuuorCwkLKysqIi4tTIrM1Gg2pqakB6bjCHsBfhAkW9Wk2m8+7ImT+UV7hxJFQhfv8tzcYDErEa2WrAX+/47lz55Kfn8+dd94JEDaVe8CAAWRkZDBkyBB69+7N3Llzz6hA3rnE6/Xy4IMPcvDgQZYvXx7UtzkU69evJzExkVatWinRd/79y2w2k5+fr0Tn+d+rU/E7/afsKfwRUfgpKSkhJ3D8z9FqteJyufB4PAHClhjPxLXKzMxUvHhNJhM333wzkyZN4qOPPgopGkOFLUhSUhItW7Y8uyd6muzfv5/JkyfzzTffcM011yj2FpXZvXs3S5cuZenSpfzxxx9IkkTXrl15/vnnueKKK5QobJvNRnFxMT6fT/kb/l5P61NBZCqIcVOIkEajkYSEhABrCvhrjJVlWRlrxARCeno6VqsVm81GYmIier2elJQUatasqfhsJyYmKvuqUaMGBoOB3bt3n1bbk5OT6dOnDx999BGjRo06a3YnZ4Nvv/2Whx56iNTUVD766KOIPLVnzJjBxx9/zD333EOvXr0C+owYQ8SkcHR0dJXIYYvFEtbmBAiIEFc5c0KNF/9f96eioqKioqKiovLfoVqjV0mSZgB7gd7ANFmW28uy/Lwsy9cCbc91A/8/YDQaFcFYpEofP36c48ePKx96GRkZZGZmsmrVKh555BE2btyI1xs6m1h4HB89epQBAwaETec91xQUFNCvXz+++OILnnrqKZYsWXJKKdYLFy4E4MILL6SkpITS0lIlFV7YLEDVD+TKFglC9PIXkkWUZ6TXR5blsCnOZwMhdodKwxeE8jA1mUxK1KvD4aC0tFSxeRBCQE5ODkeOHFGi3H0+H0uXLuWyyy7jzjvvVDykQ9GxY0e++uoratWqRf/+/bnvvvvYtev8szV/4YUXWLduHS+88IISwRcJubm5rF69mgsvvDDg2mk0GkUEAxRrhjPpD0Kg+ScnLQoKCjh58qRyrtVhMpnQ6/Xo9XrcbrcSVe2PEBHFpE5sbCypqanceOONLF++PGTqPcDWrVtp1KjReTGR88knn9CtWzc2b97MlClTePvtt4MK62PHjuXCCy/k6aefJjExkWeffZasrCy++OILxowZQ2xsLOXl5RgMBmJiYkhMTESj0ZCYmBgQpX4+IiLrBf5jrP+9DzbGWq1WcnNzsdvtxMTEoNPplOvncrlISEigfv36uFwupUin0+lUMkL0ej3NmjVj1apVnDx58rTaP3DgQEpLS5k9e/bfZv8SDqvVynPPPce9995L06ZN+frrryMSjFevXs3kyZO5+eabuf/++6ldu7YyUSMmqvLz88nJySEvL0+5Z/7vhKSkJDIzM8O+g8V77ny4Vv8fuOKKK7jiiiv+M/tTUVFRUVFRUVH57xBJdbAdwIWyLI+UZfnnSssuOgdt+tdRndDo/6FtMBgwm80kJCRgNBpxOp3Y7Xbi4uJ46KGHaN26Na+++io9e/akfv36DB06lE8++YTS0qo20hdffDFz585lz549jB8/PqTP7LnEZrNx1113kZ2draQHn0rRuZkzZzJv3jwGDRpEQkICer1e8dEVH8QWiyVA0BAfyMLLN9y1N5vNVYoPVnc+pyIynw7CdiLS+1W5f/n3J+Gb7fF4lIJl5eXlHDlyhOzsbCXiLy8vD5/Px6JFi2jcuDETJkwIK+oB1K5dmy+++IKhQ4fy+eef07NnT2699VZ++umn80JsmDdvHm+//Ta33347w4cPj3i73NxcrrnmGsrKyhg8eLAiwHi9XsWPtbS0FI1GoxTGq66g3unyd4k3KSkp1KhRI+KMBCF26nQ6kpKSFFsKqHimhCAl+pzP51Ou25VXXklpaSl//PFHyP1fddVV/Pzzz8yePfvMT+40kWWZWbNmMW3aNLp3787mzZu55557QmZefPvtt1x88cUcPHiQFStWMGbMGAwGA2VlZZSWliqRurGxsSQlJeF2u/H5fLjd7ipCK1Dt2PV3otVqA0RtfxHydNopMhxEnxF2FFBhBWEwGAImYyZPnszRo0fp2LEja9asOeX2X3zxxfTv35/58+czevToc/a8Vocsy6xevZpevXoxZ84cbrjhBlasWBHSlsefffv2MWTIEFq1asVjjz1Genp6FXsqm82Gx+NRrIeEp7b/O0HYnITyFBf7Odfvuf8S27ZtU+yO/gv7U1FRUVFRUVFR+e8QUt2TJKmdJEntqLCiaCr+7/dz/osF8fwRgo/FYqGsrKzKh2qwj23hsVqjRg0yMjICCtldf/31fPTRR3z77bfMnj2bHj168PXXX/N/7J13eFRV+se/Z3pNnUkhIQlSBF3roqKiYgMrqKiIXfFnb+uuFV0ruLuKi11ZdlUUFTsWVGzoWlbFsouua0GIEELKpE2v5/dHOMc7N3dKwiSZkPfzPD6SzMydc+9577k53/Oe73vqqaeiqqoKp59+eo+iTFOmTMFVV12FV155BZMmTcKCBQvQ3Nzcvye+hVgshksuuQTffPMNFixY0KtMTwBYsmQJrrvuOhx33HG49NJLYTQaMXLkSJSVlSEUCsksbGWWXiwWQ0tLC8xmc1LWcSqRoLdZnr0VmfuCUpARpBNmMp2j1WqVFgJer1daBxQXF8Nut8vMWb/fj2g0it///vf48ccfsXjx4oxttdvtmD9/Pr766itcd911+N///odLL70U06dPx/PPP5+yyFx/8+KLL+JPf/oTjjjiCFx33XVZ968QjH/55Rc8+uijmDx5MoDuhQmPx4OWlhb4fD4YDAbY7Xa43W65Pb8/RL6BEm/0ej3Ky8vTeourEYXxIpFIkujJOUd7e7tcyHI6nQgGg1i3bh1++OEH6VG7evXqlMe+5pprMHPmTCxcuBAnnngi3nnnnV7FEuc846JHOmKxGG666SY8+OCDmD59Oh5//PG0wl4ikcAvv/yC3XffHSNGjADQfX2E329hYSEcDgdKS0uTiiQ6HA7EYjF4vV4ZNyKO/H5/2vt6IBHZrMqfRZ+LsThVO4Xndzq7A5vNJl+32WwIh8Pwer1y4WzKlCl44oknUFZWhqOOOgq33nprygKEWjDGsHDhQlxxxRV45ZVXcPzxx2dl9bS1cM6xadMmvP7667jzzjsxa9YsXHDBBSgoKMAzzzyD2267LSsbCI/HgxNPPBEmkwmPPfYYqqure/jY+3w+WCwWFBYWysUsUYgyG8RzgHM+IM+54cTll1+Oyy+/fMgcr7a2VvrZq//rjcUTQRAEQRAEse2TztN4QZrXOICDctyWIYcQfERRoFTes8KaQtgEiJ/FxDwUCiVt/62oqMCoUaNw1FFHwWw245NPPsHrr7+Ov/3tb9hll13w+9//Pul7LrroIuy2225YvHgx7r77btx///2YMmUKjj/++Ky2xPYFzjluuOEGrFq1CrfeeisOOqh34bB8+XJcfPHFOPjggzF//nzY7XaUlJTAYDBAp9MhFoshGo3C5XLJbCqge3It/BrLyspS+v72lYGwEtDyvk7nc5zuHAOBgCzEpMxmt9vtiEajsmCZEG3C4TCmTZuGAw44APPmzcNJJ50kM7vTUVxcjMsuuwznn38+/v73v+Opp57Cddddh7lz52L//fdHQUGB3D5dVlYGh8MBu92Ompoa7LLLLjnxzRa89957uOaaa7DPPvvgjjvuyFoIVQrGN9xwA3baaSdYLBZ5bWw2m7zGyi35Sp9f0Ue98TROx2D7HSsFTrWfrbjv1IsbGzduRGdnJwoLC2G1WhEOh1FSUiKzIMvKyuByubB69eqUGeA6nQ633347dtllF9x1110499xzUVhYiD333BOHH3649JkWxONxrF27Fp9++ik+//xzfP755wgGgzj44IMxffr0jFYvSkKhEH7/+9/jvffew3nnnYdzzz1XFh9NRVNTE8LhMGpqamSBRHFtwuEwAoGA9PVVjuU6nU4u4lVUVMButyMYDKKjo0NaDuSzbQUAeW1FO5VF1xhjmsUglYshbrdbvk8UvxMF9MSxg8EgKioq8OKLL+L222/HnXfeiU8++QR///vfUVlZmVU7GWOYM2cOfvOb3+CSSy7BkUceiYULF2Lfffft1e6XdHDO8e677+Kzzz7DN998g2+//VaOCQaDAWPGjMHcuXNxxhlnwGAwpNxN0tjYiA8//BBfffUVPv74Y6xZswY6nQ7PP/88RowY0WPnQSAQQGdnJ/x+P0pLS2VhylgshmAwmJUwrX7GkKfx8GX9+vUpX8sHyyCCIAiCIAgif0gpGnPODxzIhgxFUlUgF4hJttlsltXk/X6/FA4sFgssFosURoX/rMvlgslkQmtrK/R6PaZNm4bJkyejubkZt9xyCw4//HCZ+SiYOHEiJk6ciPXr12PJkiV45pln8NZbb+E3v/kNjjnmGOy0004oKioCYwyRSCRl1piYnKZ6TWSr3nHHHXjmmWfwf//3f5gxYwa8Xm/abEmlKLZq1SqcddZZmDhxIv72t7/BYrGAc45QKAS73Q6LxQKj0QidTodwOJwkOIrM7JKSEnDONQVYoHtyP5QmP0phWMuqQFw/kaUIoEcmmogpIeK0tLRIQdntdsstzR6PB3PnzsW0adMwb948/OlPf+rxfamyOHU6HaZOnYrjjz8e//rXv/DOO+/gyy+/xNq1a2XWvfqzFosFu+22G8aNG4f9998f22+/fQ+hV8RWe3s7Nm/ejKamJrS2tqKlpQWtra1J/25pacH48ePx17/+Vd5Xqfy/4/G49HO+9tpr0dHRgcWLF2PChAkoKiqSQjHnHC0tLTI7FECPfhACl4jXXDDYBamUGf1msxkejwcWiwWBQCBp8UTEVzweRyQSQWFhIaqqquTnDQYDRo4ciXA4DLPZjN133x2rV6/WzB4OhULy+p1wwgmYMWMGPv74Y7z22mt466238NZbb8HlcuGAAw5AYWEh1qxZg2+//VaOL9XV1TjggANgMpmwcuVKvPbaa7Db7Zg8ebKML6VIGIvF8N///hfr16/H+vXr8e677+K7777DVVddhRNPPBHBYBDhcFjz+sTjcUSjUaxduxYAsN1228FgMCAQCCAcDssidw6HA4yxpAJx8XgcFotFZjCLuFFayqgzfPMRxpgcf8T9JmJGXYxUYLVa4fV64fV65U4IcZ9ZrVZp3REIBGC1WlFaWiotUR566CHsueee+P3vf4/Jkyfj4YcfTlqUjEajKbOQfT4fJk2ahCeeeAIXXXQRZs+eDZfLhUMOOQSjR4/Gvvvuq2nb4PV60/p9i9hbvnw55s6dC5PJhHHjxmHatGmoqanB3nvvjXHjxsmFFpEJH4vFwDkH5xyvv/46XnnlFfzrX/9CfX09gO5xfY899sDll1+O4447DuPGjUNjYyNMJhNisRhcLpeMK7/fL4tOipgTArwoSBgKhWA2mzUX0pQLYgRBEARBEARBENmQLtMYAMAYMwK4AMD+W361CsDDnPO+7w3OAYwxxgfZWFUIPsKmQl0sSq/Xw+FwSIFEiMuJRAJer1dOxq1Wq9weD3RXPBcTwE2bNqGqqgo6nQ5/+MMf8OGHH+L888/Hm2++qZm9WVlZib333hu33347lixZgoceegi33XYbAKCwsBCjR49GbW0txo0bhzFjxmD06NHYYYcd5LFGjhyZUsQKBoMIBAI455xz8NVXX+Hiiy/GLbfcAsZYxmwnMWH98ssvcfLJJ2PcuHH4xz/+AaPR2CP7Wq/Xo6ysTAoKQqiwWq0wGo3y+gSDQc0CXdn23UCS6ftSid/qzwcCATQ1NQHo7mu73Q69Xp8UU+I4QmAvLS2VQonP54PP50NhYSFmzJiBRYsW4cILL+yRkZ4uM9jpdMJkMmHMmDE49dRTk14LBoNgjEnB6Ntvv8WqVauwatUqPPbYY3jsscdQUFCAyZMnY+zYsdi8eTMaGhqwYcMGbN68uYd4xxiDy+VCRUUFKioqsOuuu6K6uhrnnXee9OcVHp9arF+/HldccQXeffdd7LbbbnjxxRex++67y0zaUCiEYDAIn8+H9evXw263Y7vttpMF8JQZuML3VcRduj5N9dpgDFnp2qlcrGhpaUFjYyPKy8vl9nefzyfPXyxqhcNhlJeXw2g0Qq/XywzjSCQCh8MBv9+Puro6vPXWW4hGo9I6RVBXV9cjs3fMmDE4/fTT0drainfffRfPPvssli9fjlgshvHjx2P27NmYOHEipkyZgqqqKvm5aDSKd955B0uXLsXKlSvx5ptvYuTIkTjkkEOwefNm/PTTT1i3bl2SyFhVVYV//OMfmDFjBoDuYm3qNgri8TiMRiMaGhoAAKNGjZKFJ71eL1wuF0pKSlBVVYVIJIJEIoG2tja0tbXBYDDA6XT2OLZyfMsURwNJqnYobUlENjXwq/io9TnxO7F4xxiDXq+X1yIQCEjxs6CgAG63OymjeM6cOfjtb3+Ls846CzNnzpR9f9BBB2HPPfdEQUFBynMwGAxwu9346KOPsHLlSrz66qt4+eWXEQgEUFxcjMMOOwxHHXUUpkyZIs+lsLAwrZhaVVWFTZs24c9//jMmTZqE5cuXyzFSK8YFnHP897//xRVXXIEPPvgAZWVl2HXXXXH22Wfj0EMPxc477wyv1yt32Xi9XlgsFuj1eulTr9PpYLPZ4Ha75fUPh8NJYxPw6wJQQUGB5rMkHwpyEgRBEARBEAQxtMgoGgN4EIARwANbfj5ty++yrzyVQxhjlZzzRs45zwfhGPh1Ky6Qftu6EAZFwSTltmSbzYaysjIkEgmZjWUwGODz+fDTTz9hxIgRGDNmDO6++26ceuqpuPfee3vYVCgpKirCpZdeiosuugjvv/8+vvvuO6xduxY//vgjPv/8c7z00ktSwCouLsYxxxyDE044AbW1tSmPuXLlSsydOxcA8MQTT+DII4/s1XX65ZdfcMwxx6C0tBTPPfcc6urq0NbWhng8LrcvK6+VENyV11eIdkLQArK3ClBuq+6Nt2s+YbVaUV5eLv8thACtmBIFkZSIbONAIIC5c+finXfewTXXXIMXXnghZ200mUwoLS1FaWkp6urqZJysXbsWn332Gf75z3/i/fffx5tvvonKykpUVVVhl112wfTp01FdXY2qqiqMGDEC5eXlcLvdUpzsDV6vF/PmzcM999wDh8OBBQsW4KKLLpJipd1uh9frxfr162G1WlFWVoa6urqk66O0IBDZ3QBk3G0L27uVixWiuJ1YZIjH4z1iSmyHB7rtOqxWK1wul7yvEokEYrEYdt99d3DO8dVXX2H//ffv+cUpsNvtOOGEE3DCCSego6NDZlsqv0+J0WjEYYcdhsmTJyMej+O1117Ds88+i2eeeQY1NTWYMGECZsyYgTFjxmDs2LEYM2ZMSoEvHSIz1Ol0Sm9Zp9OJ6upqFBYWgjEGo9EoFxCVwpzIDlUu3mhZOuQ7ynFYxEyqx68Yo8U5ivFJIJ53Pp8PsVgMgUAg6b2tra2orKzEypUrcf/99+ODDz7A3XffjTvvvBNGoxGTJk2SIvKkSZM0xweHw4HjjjsOxx13HILBIFasWIF3330Xr732Gp566im43W4sWLAgq+cY5xyXX345otEo7r///qzsdjZv3owbb7wRjz/+OEpLS3H//fdj1qxZ0i9deZ2E3YTVakVlZSVMJhMikQii0Sjq6+tRU1OTJAaLMV/cE8KWSPyfIAiCIAiCIAgiF2QjGu/BOd9F8fO7jLF/91eD0sEY2w/AQ4yx+znnD+SLcNxbX1KlUCOartPppCAhMkarq6vBOZcZfFarFSeeeCKef/55zJs3DwcffDB23XXXtN+l1+tx0EEHJW3v7ezsBOcc69atw/fff48VK1bg6aefxiOPPIIRI0bghhtuSJpIJxIJ3H777Xj44Yex++674x//+EdacVmL9vZ2TJ8+HX6/H8uXL8d2220HAFJoEttrU211Fv8XW6OFj3Rvttoqt1UPNcFGIOJEoIwftV2FyGRUi/EFBQUIhUIoLS3FlVdeiT/+8Y948cUXceyxx/Zr291uN2bOnImZM2fKtgtxzefz5axPfvjhB0ybNg0NDQ2YPXs2br31Vin8dnR0oKqqSorHwo5CZISKbegiw10IMyK7W2ThWiwWubugr5l7ykWMwbYoEBnEbrcbOp1O0+tYCFVCUBaiutIfVVyrmpoaAMCHH37YK9FYSVFRUa/e73Q6cdJJJ+Gkk07SPL++kkgk8Pnnn8PlcqGqqkoWMw0Gg9Dr9WCMycUtm82G8vJy+W+g24e9oaEBgUAAtbW1g97XfSXb55zw7hfZwF1dXfD5fHIsEjtKxO4Sv9+fNP4HAgHE43Ho9Xq4XC5ceeWVOOOMMwAAa9aswUcffYT3338ft912G2699VbMmDEDzz77bNo2Wa1WTJ06FSeccAIikQj++c9/4rbbbsPpp5+O448/Huecc07K80okEliwYAHeeust3H777fLZlY53330Xs2bNQjgcxkUXXYQbb7wRJSUlSUUQrVYrrFarLO5qMplk8VLOOYxGIzZv3gyfz4dAIICCgoKkOFN7FIvCleFwWNOCg+g/5s+fP6yORxAEQRAEQQwfspm9xhljo8UPjLHtAGgbiPY/XgBtAMYyxq4AgMEWjIHubZ/CXzAbxMRRS8iw2WxwOp2w2WwwGAyoq6uT9hSi2vz9998Pl8uFY445Bj/99FOf2my1WrHDDjvg2GOPxd/+9jd8//33eOihh1BSUoILLrgA99xzj/Rl/N3vfoeHH34Ys2fPxuuvv95rwdjv92P69On48ccfsWjRIlRUVKC5uVkWFtu0aRM6Ojo0PZHVwprVapUZV0J86M05axUsHOpoxZMo1iV8hpWv2e12uFwu2Gw2nHLKKdh9991x0kknYdasWfjss88GrN39tU364YcfRmtrK15//XXce++9KCkpgd1uR1tbG37++WdpNWCz2VBZWYny8nIpVnV2dqKlpQVAt8AusorLy8tRXl4u405sBU/n452ORCKBlpYWdHR0pPQXH0iEACXaov5ZIEQ/MU6pi+QJ4c/hcODAAw/EnXfeif/9738Dei65xOv1YtasWVi+fDlOPvlkRKNROByOHuO9yEwPBALSgzYYDCIWi8FisaCwsBAGgyEv+rqvZGtvIOxexPvFjpn6+no0Nzcn3TPiGornAdB9XxYUFMDlciUVFSwrK8MxxxyD+fPn46OPPsK///1vOJ1O/Pzzz706D5PJhIMPPhhvvvkmrrzySrz00kuYNWsWXn755R6Z001NTTj//PMxb948HH300Tj33HOz+o4lS5bAarVi5cqVmD9/vrQKCgaDaGpqQn19PQKBgPTt7+rqQiwW6/Fscrvd2G677eB2uwH8GmfimehwOOR9l+75JjLg8+DPpW2SffbZB/vss8+wOR5BEARBEAQxfMhGcbsSwHuMsVWMsfcBvAsgtS9CP8G6Z6pBAD4AXwIYzRg7kzE2hjE2IsNnz2WMrWaMrRaC0GASDAbR0dGBlpaWrDLghPhgMBikPcHy5cvBOcdRRx0lRbCtweFw4MQTT8TSpUtx7LHH4s4778T555+PM888Ey+++CKuvPJK3HjjjVlty1USDodx8skn47PPPsOSJUswbdo0RCIRKRBEo1FEo1GEQiE0Nzfjk08+SRIV1OKVMlMtE2oxtTef7Q39FV/ZTvTFNRIZbEJAEBnJyiKFIgPZbDbLLeGLFy/GBRdcgPfeew+TJ0/GgQceiFdffXWrsjMHi0QigRdeeEH6hba3t6OlpQXBYFAKxMI7VYibIh7EQo2wYEi3QCGErb4WlVLf06kYqLFLLThlWmBR3kvxeBzNzc3Sx1pk286bNw92ux2nnHJKn8X1weTHH3/EQQcdhFdffRV/+ctfcOONN0qBzmazSTsPznnSYh/QvVC2efNmtLS0IBAIoKysDCUlJXm1YNVfsaWMHZ1OB7fbjYqKCowcORI2m036CGuhLI4nMt6BX/3Zxc/hcBhtbW3wer044YQT+tROk8mEa665BqtWrUJNTQ3mzp2Lc889Fxs2bAAAvPnmmzjuuOPw9ddf46677sJjjz2W9bPju+++w4477ogJEybA6/UiHA6jubkZRqMRDocj6RqUlJRgxIgRqK6u7nF84X+t1+tlNrbdbpfXRyxsCXuLVKK+sBYZivfhUODjjz/Gxx9/PGyORxAEQRAEQQwfMu5h5Jy/wxgbC2D7Lb/6nnOuXWq+H9mSUfw9Y+wjAJ8CWAfgagA3ADgOwKZUVhWc80UAFgHAxIkTBz3VRhS+i0QiaG1tlVvCASR5qVosFng8HkQiERgMBpjNZrmttaamBi+++CKOPPJIHH300XjzzTdlNtLWYDKZsHDhQowZMwZ33HEHAGDevHk47bTTep0lF4vFcM455+C9997DLbfcgkMPPRQA5ORYTH6Fr/GqVavQ2NgIg8GAPfbYQ14rMVlOZV+RCvX23f6iv+JLy0MUSM6+FlmN4vfK94ttzqLgYiwWw4YNG2AwGKDX69Ha2ioFr2uuuQaXXXYZXn75Zdx33304/vjjMXbsWFx22WU49dRT0wo9+cSnn36KhoYGXH/99SgqKpIiUywWg9frRWlpKSKRiMwAVVt3iGJTQpRR2i4or62y4GBvEH1nNptRVFSU0ZpioMYutb2JKL6VTTZ4S0sL1q9fj5qaGhgMBkQiEVitVlnscvr06bjiiivw0EMP9Vfzc84bb7yBs88+G3q9Hk8++SSOPfZYBAIBWRjQYrFIP1px7bTiwWKxIBKJwGw2Ixod1NqxPeiv2BLXQ8t6KRQKIRQKSasTca+Vl5cneYlzzmG322XxVWEBotPpYDKZYLFYsHTpUphMJpx99tlb1d4JEyZg0aJFeO2117Bw4UIce+yxsiDnb37zG9x+++2YPHly1seLx+P4/vvvccYZZ8Dr9cLn86GtrU0WYywrK4PT6YTRaERnZyeCwSBcLleSpYS4NkD3gi5jDIFAAH6/H06nU44ZykWedPTWQovoHddddx0AYNWqVcPieARBEARBEMTwIaNozBizALgQwGQAHMA/GWMPcc5D/d04xthvAEQB6Djn32359WgAFejOkp4I4CsA+wD492BaVaT76kQikSS+MMbgcrnQ2toqiwAJwUE5ufP7/YjFYnKS7PF4EIvFEI1GodfrMW7cODz99NM47rjjcPTRR+PKK6/EIYccIgvIpSIUSt11oojTnDlzcPjhh0Ov10uPzmAwmFL4ENXcldfj0ksvxfLly3H99dfjpJNOkqKz8GcWgoEodLXvvvuisbERO+64o2y/KJDX1dUlhaxsEMUEgcwT6oGgL6EpzlUI5wKlGC6EY5vNJkUVpdCuFAM9Hg86OztRVFQEu90uCyeJ61NeXo5LL70UF1xwAZ5++mncf//9uPjii3HLLbfgoYcewmGHHQYAiEQiKdscCoVSiqDhcDhlQbtwONznAk6xWEwKMs8++yzMZjMOP/xwhMNhcM6lyGKxWBAKhRCPx9Ha2oqSkpKkTF8hxAtBS3nNhWhusViSvJh7i+g7ZVGrwUQrLjnnUthO1V8itsRijhBNHQ6HXOgCgL333huXXnop7r77bkyePBknn3wyQqFQyiz2UCiU0o81FAqlbE8wGEzr45quv6LRKOLxuDyvO++8E/PmzcPOO++M++67D7/97W/BOU+KARFHRqMxadFGxJDNZkNFRYX0hm5vb5fnnA/9rkWqMSrd2JXpuaccC5TjsbhWSl9s5XuMRiN8Ph/MZrMcq4LBIOLxuPzcU089hZkzZ0qPdqB7HEk1/kQiESkGq4lGo5g5cyb2228//PnPf0ZjYyP2228/nHPOOTAajTI+Uh1X+Vz86aefEA6Hpa+3y+WC0+nEhg0bwBhDZ2cndDodotEoGhoa4Pf75YKVIBgMJtnk2O126aMuxiAAcuwH0vdFXxe5CIIgCIIgCIIY3mRTLWUJur2E793y88kAHgfQtz2hWcIYOwLAnwF8AmAnxtitnPMVABYDOAfAwQB+B8AD4DDGWAnnvK0f25P2dfWETZ0Nqv68Xq9HaWkpPB6PfI8Qa0Qmo8Vigc/ng9FoBOccJSUl8Hg8aG5uhsViwYgRI7DPPvvgjjvuwM0334zTTz8dFosFhx56KGbMmIEjjzxSbqFWUlpamlJgEeIaAFRUVCS9Fo1Gk4qwKbHb7dK6gnOOq6++Gk888QSuuuoq3HDDDQiHwzCZTGhvb4fNZkMoFJKTZVEAra6uDnV1dUnHVU6Ke1N0TIjNQ6XgndZ5Ca9sdWypr4eyOJI6W1R53NLSUimu2u12RKNRxGIxFBQUJInHoVAIxx9/PGbOnIm3334b8+bNwwknnIDFixfj9NNP14xn5XemsjDR6/WyAJ0aq9Wa1vpELRbG43HU19fjf//7H7777jv88MMP+P7777F69WpMnTo1yYJCZDAGg0HYbDa0tLQgGo3C4/HAYrHIcxEFKK1Wa48icHq9PqPoouyHVNenL7Hcn6Tbyp5K2BbFtqxWK8LhMGKxmLzOBoMBbrcbfr8f8XgcHo8Hl19+Ob788ktceuml2GuvvTBq1KiUwp5er0+Z1a7T6VLGj9iJkYpUYjPQvYjV2dmJN998E48//jjefvttHHzwwbj11lux4447JrVHxKjI1lfGSCgUkjFkt9thNBqlcCqE5nzpdy3SxW+6NovX1P7z6vFHr9dLD95gMCjvPfH8E8finKOtrS1p0UvYL/j9fhiNRjz88MPw+Xy46KKLkp5lLpdL9pdYWBMF+dJZE+20006wWCwYP368ZuHGdPFjtVqT2iDqDFitVmzatAljx46VGdRip5DFYoHD4UBlZSU6OjpQXFzcw/6mrKxM/psxllToLtVYpOzDoVpwkSAIgiAIgiCI/CEb0fg3nPMdFD+/xxj7b381CAAYYxMB3AXgDACfATgNwDQAK9DtaVwI4FzO+RuMMTuATznnXf3Zpt6izAZNlR0rMqaEH6bajiAcDiMQCKC5uRk6nQ61tbWoqamRvpBCDJs6dSoOOeQQrFmzBm+++SZWrlyJV155BXq9Hvvttx+OPfZYnHXWWQNiMfDhhx/i1ltvxbvvvouzzz4bt9xyixRvfT4fYrEYPB6PFLOFMJ7KizEbIW64oc4aS2VjocZgMMBgMEgLELfbnSTyAN2Z5s3NzQC6izBNmzYNe+yxB84++2ycddZZaGlpwSWXXNKPZ5eajo4OLFmyBEuXLsW3336blDHvcrmw/fbb45hjjsGcOXPQ1dUls4hDoZDM2hOilclkktdDmYkuCpgpr6daDEuFsh9SLVYMhYUMZYa7FsKT3efzobS0FIlEApFIBO3t7bKooN1uTyrA+Mc//hEnn3wyZs+ejXfffXfQrwHnHN9//z1ee+01vPrqq/jkk0+QSCRQXl6O2267DYcddhicTmdKIVrLvkNrZ4Oyvwf7nDORaRzJdB9kaweULts+GAyisbERwWAQZWVlsFqt8Pv9aGlpQWlpqbS5WLZsGSZNmoTdd9+9x/HXrl2Lu+++G4888ggA4KyzzsJll12Gqqqq7C9GCn766Se88sorqKysxO67744xY8b0eM9333Vvitpjjz0wcuRI6cfMOYfZbAZjTGbbC5uc9vb2JJsqYemhJJudM9mMQQRBEARBEARBENmSjWj8JWNsEuf8XwDAGNsLwOr+bRZcAOZxzj/d8p0/AzibMWbgnP+bMXY257xjy8/+fm5LnxATO4vFkuSRmg6176DVaoXb7ZbHEFlGZWVlMvs0kUjA4XDIYjozZsyAz+fDxx9/jHfeeQdvvPEGLrvsMixfvhz3338/Ro8enfNzjcViePPNN3H//ffj3XffRVlZGebPn4/f/e53SRlaFotFFv8TWXctLS2IxWIpt89mK4gOZ7LxqxSCT3FxMQCgqKhICkAAZAac1WqV8SUySQsKCnDPPffgpptuwlVXXYVffvkF8+bN67OdRG/5+eefceedd+LJJ59EMBjExIkTcc4552CnnXZCXV0dysrKZCZnLBZDV1cXfvzxR9jtdtTU1MDpdMLtdvfY0i1iUNyXQiAW95TStiKbGNxWfEPFvZhqu7vwZI/FYgiHw7LAmRCQhbWF2WxGKBRCU1MTzGYz7rjjDpx11lm45JJL8NBDDw1Y/AgikQg+/PBDrFixAitWrJAZoTvvvDMuuOACnHjiiZg0aRI459i4cSOMRqPcIaFGeZ4i61rtCz3USBW/YuEukUjA5/MB0L4P1KKmGOeVRe3ENROLN1oWFk6nU2Yg+/1+eL1eWZyyrKwMr776KtauXYs//vGPSd//7bff4rbbbsPLL78Mo9GIE044AXq9HosXL8aDDz6I6dOn4+abb8a4ceN6dV18Ph9WrFiBJUuW4J133kl6raCgALvuuit++9vfYvLkydhrr73w8ccfo7a2FmVlZXIXh7DhcDgcMBgMcDqdshhsIBCQ56depAKQJNRniq9tZQwiCIIgCIIgCCI/yEY0/i2Ajxljv2z5uQbdBenWoLs+3c65btSWDOJKxa++BhAAIIwwrQA6OOexXH93rhATPHUBLSV2ux2VlZVygqcWThljcDqdcntuIpFIKpIXDAbh8XiSvDITiQQ6OjowZswYbL/99rjyyivx5JNPYu7cudhhhx0wffp0XHjhhdh33323OnN33bp1eOyxx/D444+joaEB5eXluOaaa3DmmWeisrISOp0OgUBA+lFyzmWGp8iSjsVi8mcttuVJcK6yqLPxqxTCp81mQyKRQGtrK4BfCy4J2wohTgj/TJHhV1NTgyeeeALl5eW477778Oabb+K+++7DAQcckPb8RPv6QktLC26//XYsWrQIBoMBJ5xwAi666CKMHz8ePp9PCrt+vx9+vx+BQAAFBQUoLy+X19bv98tt6YlEQhaS0uv1mlu4g8EgfD6fFHuAX7PhM2Xqb+u+oUoxy+VyJQmC5eXlAH4tGCh2FggrnYqKCuy1115Yv349brrpJnz99de49957exQYi8fj+OGHH7BmzRqsWbNGitLV1dWoqKjAqFGjUFJSknVMbd68GStXrsTrr7+Od955B16vF2azGQceeCDOP/98TJ06FSNGjJBFx1paWqRVitryQil6Ct/maDQq3zPU+z5V/Iqxw+FwoKCgIGWmq1rUDIVCSc8+ZYax0qdebalTXl4Op9OJRCIhFxlNJpO8/qKv/v73v2P//fdHIpHArbfeiiVLlsDpdOLCCy/EWWedhREjRsBqteLSSy/FY489hsceewyvv/46/vCHP+DKK69Mu2gRjUbx9ttv4+mnn8arr76KQCCAmpoaXHvttTj66KPR2NiI//3vf6ivr8eXX36J+++/H3/961/l50866SSUlJTAZDKhtbUVjY2N6OjowPjx42U2vk6nk+OY0h5IuUgFoFeLpqIPaVfOwLJw4cJhdTyCIAiCIAhi+JCNaHxYv7dCAWOM8W4aFb82AKje8vKZAE5gjJ0AIDiYxe+yId2W0mwz08SEUghgotCZsKgQGWCBQAA6nQ4dHR1wOp1wuVywWq04+eSTceCBB+LJJ5/EI488guXLl2OPPfbApZdeiunTp6ctIKUmFovh9ddfx8MPP4y3334bOp0OU6dOxRVXXIHdd99dZg2LNnk8HimKOhwOFBYWSgE4Ho9Dp9OhuLg4rZdmb8SYoWRnMZBbiUX8dXV1Yf369bJQFwAp3CutGZS3lTJO77nnHuy333649tprMW3aNBx22GE4/PDD0dHRgc2bN2PTpk1obm5GY2MjNm/eDKPRiNGjR2PMmDGoq6vDuHHjMGbMGIwePRputxuBQAA+nw/t7e0Ih8Pw+Xzwer344osvsHDhQgSDQZx11lm44oorpN9tV1dXUnEsi8UixcuSkhLo9XrE43Fs3LgRsVgMGzZsgMlkgtPphNPplIK41j2pdb+KreShUGjIC4Nbg9pyR+taiOKKFosF8XgcLpcLLpdLCmXXX389xo8fj6uvvhpTp07F4YcfjsmTJ+PHH3/EN998g//+979SFNTr9dDr9T0KL9psNowcORLl5eVwOByySJjT6ZQ/B4NBrFy5El9++SUAoKqqCtOnT8fUqVMxbtw4jB07VnrdRqNRRCIRNDU1IRgMoqamBoWFhUl+18rz1+l0siCp0WhMKgi4LfrIKu+J3pyfWGRR31PKwp7pnovKYoliZ4DVasWsWbPQ1taGG2+8EaNGjQLQXTjvnHPOwUUXXYSxY8eio6MD0WgUnHOMHj0ad9xxB8477zzccsstmD9/Pp599ln8/ve/RzgchsfjQUtLCzo6OuDxeNDa2op169ahvb0dJSUlOProo3H66adjr732QkNDA0wmE2pqanDggQfCarWitbUVbW1t+Pe//42vvvoKbrcb++67LyKRiByLhDBsNpuTfI3Fsz1VwUD174j8ZNdddx1WxyMIgiAIgiCGDywfNFfG2PYAStBte5HgnMcZYzrOeWLL62YAy9DtbzwdwBzO+bd9+a6JEyfy1atz766RqcK8WsDMxic1Ho/D6/XK9wWDQZl5LKrICz9ggdlsRkdHB4qKiqQ1hBC8hFj28MMPY/Hixfj555/hdrtx8sknY//998ekSZNSFqJqbm7GI488gscffxwbN25EVVUVTjnlFBx22GGorKwE5xycc3R2dqKqqkp+t5ggFxYWJhUi8vl8aGpqAvCrz2yqwlu9QWQ6FhQU9IsQyxj7gnM+MdXrqeJLKz6yLVokPttbQVwr7mKxGFpaWqSPNpC8/Vlk4yrtKYxGIzo6OlBQUICOjg4p5N1555147LHH0NnZCQAoLCxEeXk5RowYgfLycpmNunbtWvzyyy+or69HPB7P2G7Bsccei5tvvhljx45FW1sbmpubEQwGAXQXTRS+uTabTcaQEByB7sJ20WgUBoMBJpMJbrcbjDG55V2IjJmuXbaexkr6umCRLr76Y+zqzbglYgPoFvG0ioMpM40tFgva2tqkkC8IBAJobGzEn/70JyxbtgyBQAAlJSXYaaedUF1djV133RXjx4/HqFGjUFtbi/b2dvz444/44Ycf0NjYiIaGBtTX18vscr/fLxfNRAE+xhj22GMPTJ48Gfvvvz922WUXNDQ0yD6sqqqC0WiUCw9msxkNDQ2wWCwoLy+X7eWcQ6fTJVlRqP8vFsjU41e+LVpliq3PP/9c83Na44jWa+r7JNvPCdTFY7V+r9Pp0N7ejq6uLqxZswZvvfUWWltbcdZZZ8HlcsFgMKCmpgZ2ux1NTU1obW1FTU0NDAYDWlpa0NnZiTVr1uCmm27C+vXr5Xc4nU6UlpaitLQUhYWFGDlyJPbbbz/suOOOqKysRHl5OTwejxw3xE4ZxpgUpzs7O+XY6HA4UF1dDaB7zBVjqLBscTgcchzXGlvi8XiPYpy9YaBjb6DHrf6irq4O9fX1KV+vra1NihvB22+/DQA45JBDctKOwTyeKEaZL2T6m4sgCIIgCILoXwZdNGaMHQdgPoCGLf+tBvAo57xLJRx/gu4CeDM559/19fv6OoHZmuukNUFWipuphFKv14v6+noEAgGMHDlSbsfX6XRSGBEimcVikd6aItNXiIzArxXY/X4/NmzYgHA4jA8++ADvvvsuVq5cKTOEJ0yYgL333huTJk3CpEmT8Msvv+Dvf/87XnnlFUSjURx88ME49thjsddee6GmpgbRaBTRaBSMMUQiESQSCbjdbinUtbe3IxaLoaioKOk8OedSgLJYLIhEIn2eIKuvdTbCan+IekDvRONs2yM+m03MZPud6bIiRQa0yNpsaWlBU1MT9Ho9Ojs7wRhDdXW1LNTY1taG4uJi2O12FBcXJxVwEtmZIk7q6+tRX1+PjRs3or29HQ6HQ4owI0aMgNlsBuccJSUl2H333RGNRhGPx9He3g6r1Qqz2Zy0MBEKhWQ2n9jODkAWlTIajYjFYkmxJUQok8mEaDQqM/7U10eINlp9kyl+8kk01oqDbITwRCLR4zxEbBQUFMhdDmJBSiw6hEIhmM1mKeKKe1HsMOjo6JDjVSAQQENDA8xmM0aMGCELxzU2NqKzs1MuPghh1mq1oq2tDV1dXWhtbZVx43A45He2tbXBbDbDZrOhra1NWgC0tbXJ7OCqqipp9+N0OuUCXVlZGVwuV9I1iEajSeesfE15zuo4ymYhKBX9Ifr1h2isjBGt2FBfA845gsEgjEZjj3tPOe4oLZfE4qhYXOOcywJyQuAS74lEInC73XA4HGhpacHGjRtRXV2N4uJifPfdd2hsbITL5UJVVRW+++47cM5RVlaG4uJiFBYWoqWlBeXl5TAajfD7/XLRw2g0oqurC3q9XsZjW1sbioqK4Pf70dHRIWOptLRUxrfRaJQ1CZTnIBZmI5GIZmypr6VGX2bq67Sv95ZM8arT6bYJ0bivgumUKVMAAKtWrcpJOwbzeCQaEwRBEARBEEqy9yXoBxhjRgCz0J05/BFjbCaASQCuZoz9hXPeqXj7KwBe4Jz/bzDamoneCpTKYnepENuwA4GAFMGEIGowGMAYg16vR0dHB1pbW+XWcLvdDr1eD7/fD4/HA6B7q7dOp5MFhkpKSjBnzhzMmTMHgUAAX331FT7//HN8/PHHWLZsGf7+97/LdhQVFeH888/Heeedh9raWnR0dKC+vh6xWAwVFRVSGGxtbZWTbyGmCHFT7bMo/JqB7gmp0WgE0PusTi07C4fDkVeTHmDrJvHis0p/560VBUQsqFEWghOZ4aWlpQC6M4l/+OEH+Hw+6T3a1dUlC1sJgUOZVWowGKQ3rMfjgU6nQ21tLUwmE6qqqhAOh+U56fV6JBIJNDU1wefzJfl6At0Z6UKcE2KyWlSy2+0yi1oITOrMeb1eD4fDkdZvXO3HqibfskizRdxfmYqaAd3nqD5PZYFPxliP66TX62URPbElX4iz4r6ORqOwWq0ys7OoqEh6H4tYqampgcfjQWlpqbTPMRqNMkuzoaFB2u+IzGDGGEwmE8xmszw/UYysuLgYNpsNa9euRTQahd1uR3t7uyyGWFVVhfb29qTvA7ozPsPhsFywU14PcQ+Jcx7qpIrpdLGuHKPV45PWGCPEUBEXwK/xpyyg19raKnefCA9r0adOpxMlJSVJx7XZbNLmSGlVEwqFUFxcjM7OTpnxPnLkSBgMBmy//faIRCJwuVyw2WzYsGEDurq64HQ6UV5eDpPJJIsgivFJnJvX64Xf75cZ9WazOenYPp8PwWAQBoNBxofaikI51qrHdKVFRV8WrQiir9TW1qaMr1SZ1gRBEARBEMS2y6CKxlsoADAWwEcAXgTQCuBIALMBPMQY2wtAO+d8/uA1MfeIjLpU4qYQd5xOJwoLCwFA+r0C3ZNJkdFnMBiQSCSkL61AZNuJybzI4qqoqEiyRCgsLERlZSX2339/nHPOOfD5fPj222/x7bffwul04qijjkJFRYXMnhKZxKWlpVKkNplMUmAUx1UWFVNOQtJlAyt9U7cFISaXiJjJBaIP1OK86DMhugLdwm95eTlisZgU30pLS6VXZygUQkFBAZxOZ494VvohC3HG5/MhkUggEolIAQaAzEZ1OBwyPoRdSkFBARKJhPRfBn4tECVEYSFCGQyGrBYc1J6rStJ5kQ9l0hU1S3dfKhdzhCgMpL9Owo9cZIKKYotOpzPp/Xa7HWVlZTIuxO9EcT2BUqAuKSmR4p5YHAC0/XDFgltBQYH0MVbaGAgxUYjc6u9UF0bMJUPJgz0T2fjPi2eRsPZQxoGy70pLS+HxeBAKhdDW1iazv5VZump0Ol3SdRSZzJ2dnYhEIuCcY4cddpDPS1EDQK/Xy6zgoqIiuUgmitVpFezTOiflM1V5nsKiSf35VM9HrWvZF4uc/kYZu8S2QzpReKiPUQRBEARBEETvGVTRmHMeZYzdBeASxthazvk/GWMfAqgCcBRj7DEA+wB4ejDbORgEg0F0dnbC7/dL0UNMziwWC1pbW6Xtg9vt1pxQiixOkb0nREKRlScQE2jheWyz2bDvvvvi6KOPlh6yRqMRGzZsgMFgQElJSQ9BRwiLSlIJSsoCcOqJ9LYq1uUbqcT5dNff4/Fg48aNcDgc6OzsRCKRkCKv+v1aIodOp5Oe3GqxgXMOr9eLeDyOQCAAl8sFu92OUCiUJCaJbD1xPJGJp/W6aIewaFF6aovPp8uy3RYXLdIVNUt3XyrjRfiri2OIay/EMfVxGWOy6KHoc3U/iMxTMVb5/f4kYVEpFprNZil4K4+jjjm14CeykkVGusiWF+KX+tyVmaD9NR6lu+bbIsqFBFHcFUi+N3U6HQwGg7QtisVi8Pv98t4W1kaZBFSr1Sr7UCwieDwe+TxVxqSw4EhVlE4tjIoFBvF7YamhtKBwOBxJC72pnnXZiK75uJiqjF2CIAiCIAiCILZN8iHT+J8AtgdwGmOMcc4/APAkY+xcACM4538d3OalJ9tiZr3FbDZLj+BAIJCUYRoMBhGJRBCNRpMsKbSOISq2i4JNwWBQZnQKoaalpUVmAZaWlkov12AwCL/fD6fTiY6ODnR2dqKgoABmszlpIpzqvFO1S5mZJVBnMuaSbSmbL1eoxWGt6y9iT8RKcXExysrKZJZnOBxGLBaTQomWlzEAKSoqjyUKSQnRWXh0e71ebN68GUVFRVL08/v9KC0tTRIQxXGB5KJRWjYTouBiZWVl1rGVKhN7qJNtxqQaZbxoCVjBYBAdHR3w+Xyy4CDnXFpgCLsTINkfVcSd2WxGUVERLBZLUkaxWEBQ97kW6napY1y5iKbuU637QYyLIhaVhQDVdjt9Jd0139YJBoMp703RD+LZFYvF0NTUJAV8j8eDsrIy+W+1rYiIK/E9wsYmFovJ56ly5wbnXDO2Ut0vykUlZfFQEe82mw1+v19z0aEvzzq1AK5VhHKgGc6xSxAEQRAEQRDDhUEXjTnnIcbYUgAcwLWMsfEAwgDcAHyD2rgsUGbb5MI6QOk5ajKZZMaVz+eTk1KHwyHtIMLhMHQ6XcpM40QikbQNWPiLivYKX1fhqwj09JgUBcgASPFOa7KvzvRLtaVWK4uzPzOphls2Xzao+0Dr+gcCgaRYiUQi0k9bxJQQ0QRCHFaLJcFgMOlY6mMLAUeIhkIAMhgMiMVi8Hg8PTxQBWpfXWXcWSwWmQEvhBfxWjrRT+t6KD+bD6JNrkmXXa0Uz7Sy0a1WK3w+H2KxmLxGLS0tMtM3lfjm9/vR3NyMsrIyaW8ijisEPvEfAFRUVABAyix5dWapknRZ9Mr2CcE4EonAZDIlxbBy3NMSy3obI9tqRns2iMxv8W8lyv7Q6XRobm6WMaDE4/GgsbERAOSxhCdyLBaD0+mE0+mUPteJRCKlvUVfEWOZsqhnIBBAc3MzgG5bnpaWFils9+VZp86cz5VN0dYwnGNXzcMPPzysjkcQBEEQBEEMHwZdNAYAznk7Y+xvAP4L4DwAIQCncs6bBrdlmclVto26QJXD4UBhYaGcgKonpTabLSkjOJPVgHIbucg0VhbDEpNQYU8BQGYAiv8rt6VrTfZFO4RNQDbFtgTKrOhs6E2GN2VEZUYtuAk7AL/fL0UWcf3i8bgU+ux2OyoqKmQchEIhKZZUVFRAp9MhFouhq6sryV/bYrFIOwoRk0C3SFxbWyv7VlhZCD9trYw9k8kkt4eL34n7QZ0BrSx+JwRqrWxiLYFRedx8EG0GC7V3rPid0iYnEAggEonIgmSJRCIpI1wLdQam0q5CZI2qhWp1u5TCGgB0dHTIRTG73d5jHFJnD4sFuFgsBpPJlJTlbLVapaWB1jil3rUxHGJka3ZxCLuaTIjnohhvhMe5WMz0+/0oLi5Oer/wPrdarQiHw/D7/di4cSOMRqP03hcxpZU13hsPYeWYFgqFkEgkwDmXsePz+eRiQ3l5eZ8tmMi6KX/Zfvvth9XxCIIgCIIgiOFDXojGAMA5jwB4jzH2QfePvGeqWB4ism22dquyVoEqMSFXinZiAhuPx+VnU00mtbL7hC9kIBAA51xWqBfinM/nk+ciKsQLYUVkO1dWVmpO9pXZzKmKbaVCmRWt3Gacit5keFNGVGa0PGCFABIKhZKygUWGsPic8tpaLBaUlZUB+FWI3rBhA9rb21FVVQWn0wmfz4d4PI6WlhaZkak8hjqTTvxbHRfinhFFykTb04krmWwWBFoxQ6JNepSxILbn63Q6abMjxD/g1z5VLjqoF52U2/dFFrIYm1Ldz+o+6urqQlNTE3w+H0aMGKG5w6GpqQmBQACjRo1KKtKnzkYXXrtA93ilXoRSi5XDgYHYxaFe/OScy4zdRCIBq9WKaDQKk8nU4/3KHQ4WiwWMMcRiMbS0tMjxR8u2RsvDO5UwzhhL8i8Wz0zh3y7arSywpywmmS3prGWIweWVV14BABx99NHD4ngEQRAEQRDE8CFvRGMB5zye+V35R28ngEpE5i+QLAyLSaiYlCpRb8nvzWQyFArJzOWioiI5qRWTYjHpZozJLD+xrVdZ/Ed9DsoiR1rZiFpZfVrnr3VsNcr3p7r2w8G/uK9xl+maAr9m4GUqAqVEnT3o9/thMBhQXFwss/uUIo7SFkWQbZafMjtdmQ2qFFfU10dtsyCyk5WCZKrrkw+iTV9iPVf3QbbWHgCg1+tRVlYmF71CoZD8v7K/1UKz+J50QqSWDY5y14HyM0LMS1XMTpnVrNUm9fVWC8rK66A1hqciH8emdGNJqvamW0hRX5/etEPpJ67X63s8/4RYrV6YZIzJsSkYDEqhWBRPFDEYi8VSjj/q88rGTkKMISLOxK4IxhiMRqO0VVFfn76Srq/UY1kuvjcf4zWfWLBgAYDcibL5fjyCIAiCIAhi+JB3ovFwRcunMJuMyVSWC+mEN5G5rH5NLfQqJ8itra1JW7XTkSqzN13ho95mA+eDgLetoe6DVH3Sm76yWq0oKSlJykIXQo0Qe0T2oCCTn7CIQWV2em8XTgDt7Gol+VpAcTDapbZeyMbqRTmmKf+fTvgWi0DKjHE16vhQ+2Ursdvt0n9Ya+zS6XQoKyuTBT79fn/axYp0406+eM32Ba2Fk2zQuh5b6/2dTfayOvtYjXIXgjiGXq+XY5ewPtEaf9TnlelZK77P5/OhoKAABoMhq90y2dIbqwyCIAiCIAiCIIhcQqJxHxgo0SbThDydSJYuO0rpb5xJJBG+xCKbT9AXwTZd4SNi8OjPeFbGsLA4KSgo6GFJoURrscTv96OpqQnl5eUyk1nLhzkblPdQugz3fC2gOBjtSmW9kGr3wNaQaUFIHR/Cykf4tGfrda7+PqXfdT7190AQj8cRCARyct5b6/0t7kWtZ0S2z7/eeuQLGx6tZ2E2C5T9aV3Tn0ViCYIgCIIgCIIg0kGicR8YTDEp3QRSKf4pJ7Fa27mFX226QnXK7EKRHZrKliAbsi18RAws/RXPIu5EBmc6mwAl2WaRZ8oUVrdDxL/6Hkr1uXwtoDgY7dLylQV+3T2QSCRQWFgIt9u91cJxpsxKdV+Lnxlj8Pl86OzshN/vR2lpKTweD2KxWFbZ8dkIf9tq1qder89ZPG2tgJqur7R8r7X6QbkLQdzzFotFZj6rxwC13VM2KG00lOQ6RshLnSAIgiAIgiCIwYJE4z6gJdoMVPZxOh9WtfinzPDUmiBnKlQnsgtNJhNsNpvMEu3P8+vNdcxX+4ChRn+JkH6/H83NzbDZbGhpaYHf78d2222HwsLCXgsrwmZAHauZso0TiQRaW1ulrYIoTCU+m458LaA4GO1KJcqL3QM+nw+xWAzBYHCr26ZVhCwbH2Xg1wJ8otiZ1kJFPB6XdjtK+4RsFiu21axPnU6XkzG0v0V1pc1NR0cHfD6f5kJFJk9i9RigtGzKFuXzFoAUswOBQNJ401eyzaomCIIgCIIgCILoL0g07gNaos1AZR+ny67UEv+EmKYUTkwmk8zSEt6LYoKqzMZST6x7k2WsFnSzFRN6cx3z1T5gqJFOhEwnzGcr2ttsNtTW1iIQCMDlcgFI7VucyuoglaCXKds4FAohEokgGo3Krerkh507xO4B4S2s3NnQG4sI4Nd4Ugp4yjhJVzBRxJ9Op4Pb7UYgEJCWC6Jwo6C1tRWbNm0CAGmXoz5eqnarFyn64tm7LdOforryGcUYk+Ks3++XzzNlHKgFYhFX4nXlgkRfxgQtG41EIqFp49IXenMtlWOx1u9oUfVX6urqUF9fr/labW1tn475+OOPb02ThtzxCIIgCIIgiOEDicY5oi/ZmspJXW/ElVSFebTEP6UXrPiOSCQit+4K0VhrgqqeSPemUJha0M12Atyb65iv9gGDQSaBoK8CQjphPpNob7fbUVFRIUUZZZZ6Kt/in3/+GVarFVVVVb0qtqc+lsBischFEmW8E7lF7V3dF09brXhSCn7CCkCMY8oxxWKxSKFaYLfbNW0XxMKF+L8gmzFKvUgxVAvf9RcD6e3rdrsRDAaRSCR6tbCgdbxMzxCt8VM8b8WOH1EnIFf05loq7x1xLrSoqk19fX3KYpx9ZeTIkcPqeARBEARBEMTwgRSUHNGXLePKSV1vxIdssqK0JkXid2azuYcthVY2VibSTUrVgq6yMJFW29QT8WzIV/uAwSCTQNBXASGVMK+VwS4Q/csYk5/jnCf1e6rXrFYrnE6nLG6mfH8q0sUBY0yKS+pjZjou0TeUi1q9EWeUmZvqGEonKFutVgQCAXi9Xnks8W+tuNDr9UkZxup2ZxLp1AX4tOjrguBQJ1dZ/FrXVfmMEkKtiDGROay2bEonDGfTjwIRf0ovZfE9yu/M1mc9G3rzea1xmhZVB45ly5YBAGbNmjUsjkcQBEEQBEEMH0g07gdSZSWpyTSpy4WgpfSCFcczGAwwGo1J79Pr9b3OmlO2X91WxljS8ZSFibQmwn05VxL8fr0G6foim9fTHV8dF5xzBINB6XGtFMUyHTtVtrP4nqqqqpSvp2tjKkTb0onKhDZ9veZ6vb5XuxKUx8ymKKH4vHLMEoXwxPvEv9ONv+pz0RoDtT4n3pdOaOzrguBgMpBjcF+/SywgaGX8ah1bGTdq8V7Z35lEY6WXsnrxgjGW8jsH6vpojdNavyP6hwcffBBA7kTZfD8eQRAEQRAEMXwg0bgfyDarcyC2N/enf2tv2k8V4PuXTH2R61jrS39mui8oc3zbZWu3yovYyFZwHuw4oizP/qG3cZSrcU88R5WZxv39nQRBEARBEAQkSiHYAAEAAElEQVRBEIPNkBWNGWOM59qYLkeQYNATKj62bbE1haPovhh+DLe+pyzP/mGw44ieYwRBEARBEARBDCeGnNkiY8wCAIMtGHPO4ff7U/rzpsuK628SiQT8fn9Oi/IQRDaI2Mun+yLdvTqUSCQSQ/YcBqLvt5V+JlKjtB3p7/6m5yhBEARBEARBEMOdISUaM8aOBPAAY+wxxlj1lt8NijIrtskGAoHB+Pq0iLYFg8HBbkpKaEK+baDuR1F4Kp/ui3y+V3tDPB4f8ucg6A/Bb1vpZyI7ct3fqcayfH6OEsRAUltbKz28tf6rq6sb7CYSBEEQBEEQOWbI2FNsEYznAfgdgDMA3APguMHKOB7sbbLpEG3KZ/9gZVV72u47dFH3o4i5fLov8vle7Q16vX7In4Ngaz2OtdhW+pnIjlz3d6qxLJ+fo0R+8Nxzzw2L461fvz7t61RYliAIgiAIYttjSIjGjDEHgDMBXMM5f48xtgnAbYyx3wFYAWAT59yb5vPnAjgXAGpqanLVpn4XOzPp4an+QM/ku6g8Ludcsxp9LtujhXJCnsvjDgb9EV9DBbWw0l/3xdbEiLpNWjGfy+/LJerYyvd7IVssFgt8Ph8sFkvOjpku9vKlP/OJ/h630l1zznnaey/T/Qlo9/fW9LN6LFM+Ryl+iHS4XK5hdTyCIAiCIAhi+DAk7Ck45z4A/8c5f4MxVgLgdQC/ACgHcDWA3YDUVhWc80Wc84mc84lut7tPbUi3JS+b/waabNszUFu61d+v1+vhcDig1+uH/IQ7F/E1lEjXj5n6Mh/un6FkY6COrXwZX7aWUCiERCKBUCiUk+MNxvibb2N+b8nXcUt5f2pdV51Ol/Prnmosy9fnO5FfPProo3j00UeHzfEIgiAIgiCI4UNeZxozxn4DIAYAnPP/bfm1H8ApnPNPtrxnPoBTAXww2MXxhiK93eKbbRYYQQjyLWbIxmDwyYc+yLe4JLrJh9jQguKFSIUQZM8888xhcTyCIAiCIAhi+JC3mcaMsSMAPAXgCgCPMMYOAwDOeZhz/okiq/gHAH7GmH6QmjqkEVt8s50ED6UsTSI/yLeY6W3ME7knH/og3+KS6IYxBofDAZ0uv/48oXghCIIgCIIgCGK4kZeZxoyxiQDuQnfBu88AnAbgcMbYSgDgnCc455wxNgfAhQBO55zHB63Bw4h8zQIj8heKGSIfobgkegPFC0EQBEEQBEEQw438SuX5FReAeZzzT7dYTvwMYGcAjHOeYIyZGWN7AjgOwJmc828Hs7HDiYHKEOScw+/3ZyxAROQ/+ZBV2lcoDgeHgbjuQzkuiezIZRxRvBAEQRAEQRAEMdzIS9GYc/4GgLcVv/oaQFCRTezgnH8GYDbnfM1At28wGS4iFm0FJtIxUPcBxeHW0dd+ous+9MmHZxXF0fCkrq4uZdHC2trawW4eQRAEQRAEQQwZ8s6egjHGeDeNil8bAFRv8S0+HcDxjLETOOddg9PKwUNMggHA4XAMcmuSSScO9DY7i7YCE+lQ3gd2u73fvoficOvoaz9Zrdak/xNDj/66R9M9ZzjnSc+aXN2/mYRvyj7OL+rr6wd0sWLFihXD6nipqK2tTXkv1NbWYv369QPSDoIgCIIgCCJ35IVozBjbHkAJgNUAEgDijDEd5zyx5S1BdFtUXA1gOoA5nPNhmTqUi0lwXye46T63NRM0reOKYkjE0GKgxJNsRcWtjXWKw74hrp9yvMqmL8Q4otPpcio0kqg38KTr+/7qD5FNqvw5F/cvxQ+RjlwvKub78VKRThSme4ggCIIgCGJoMuiiMWPsOADzATRs+W81Y+xRznmXEI4552HGWDmAUwHM5Jx/N5htHkyEryJBDGdyLSoS/YNOpyPRfZhCfU8MFx544AEAwIUXXjgsjkcQBEEQBEEMHwbV05gxZgQwC92ZwwcDWA5gJICrGWOFikxjAHgFwHHDWTAmCIIgCIIg8odnnnkGzzzzzLA5HkEQBEEQBDF8yIdCeAUAxm7594sAXgVgBDAbABhjezHGxnHO53PO/zdIbRw08qGYEEHkC4lEAn6/H4lEIvObiSEH9S+RDfRcJKjYHUEQBEEQBEH0P4MqGnPOowDuAnAcY2y/LZnFHwL4GsD+jDErgH0AeAevlYMLVX8niF8JBoPo6upCMBgc7KYQ/QD1L5EN9FwkRLE7rf+o4Fr+IYrkaf1XV1c32M0jCIIgCIIgUjDonsYA/glgewCnMcYY5/wDAE8yxs4FMIJz/tfBbd7gkqvq7wSxLZBtATxiaEL9S2QDPRcJYmhBRfIIgiAIgiCGJoMuGnPOQ4yxpQA4gGsZY+MBhAG4AfgGtXF5wFAqfMc5RyAQgM1mo0kA0S+IAnjCxsBqtUKnyweXHSIX6HQ6WK1WBINB6lsiJYP9XKRnXfasWbOmX64RWVBsO4gsZIIgCIIgCCL/YPniCcgYMwHYF8B5AEIA7uacf9UP39MCoD7Xx02DC0DrAH7fYH6/DoAeQByA0pR0W7oGtZxzd6oXByG+BIN9jQUD1Y5UsTbQ7chEb9uRMr4GMbYE1LfJDLV25HNsaZEv11eNC0Ab0sfoYDCY16u/Ymswz2mw42+4nrv6u9P+zUUQBEEQBEH0L3kjGgsYY3oAfIu/8ZCHMbaacz5xuH5/PrRhsL9/IMiXc6R25Gc7ckG+nAu1Iz/bkWvy9byoXQPHYJ7TYF/P4Xrug33dCYIgCIIgiGQG3Z5CDec8PthtIAiCIAiCIAiCIAiCIAiCGK6QYSRBEARBEARBEARBEARBEAQhIdG4/1k0zL8fGPw2DPb3DwT5co7UjmTypR25IF/OhdqRTL60I9fk63lRuwaOwTynwb6ew/XcB/u6EwRBEARBEAryztOYIAiCIAiCIAiCIAiCIAiCGDwo05ggCIIgCIIgCIIgCIIgCIKQ5F0hvP7G5XLxurq6wW4GsZUkEgnE43Ho9XrodAO39vHFF1+0cs7dqV6n+Bo6DFYMpSNdfFFs5Z58jIH+gmKrfxlOsaQm32JrOPfFtka+xVZvWbt2LQBg9OjRg36cXLVlWyHT3/MEQRAEQXQz7ETjuro6rF69erCbQWRJKvsUzjkCgQBsNtuATgoZY/XpXqf4GjokEgkZQ4yxHq9r/a6/SRdfFFu5J1MMAIMTB/0BxVb/IJ5RymeSMma2lfhJR77FVj6O7UTfyLfYIrYdMv09TxAEQRBEN8NONCa2DRhjsNvtNPkj+oyIIWL4otPpKAaInEDjSf5AfUEQBEEQBEEQuYH27REEQRAEQRAEQeSQa6+9Ftdee21eHCdXbSEIgiAIYnhBmcYEQRAEQRAEQRA55JNPPsmb4+SqLQRBEARBDC8o05ggCIIgCIIgCIIgCIIgCIKQkGhMEARBEARBEARBEARBEARBSIa8aMyoEhpBEARBEARBEARBEARBEETOGHKexoyxXQGYAHg452s553yQm0T0I7QmQPQXFFsEQHFAbB0UP/kH9QmRL1RXV+fNcXLVFoIgCIIghhdDSjRmjE0HcDOA9QA2McZu5Jy3bnmNkYBMEARBEARBEMRg88QTT+TNcXLVFoIgCIIghhcZ7SkYY6Oy+V1/wxirAXA9gNkATgRQBWAEY8wCAJxznsqqgjF2LmNsNWNsdUtLy4C1mRgeUHwR/QXFFtFfUGwR/QXFFkEQBEEQBEFsG2Tjafy8xu+ey3VDssAGIIbuLGM9gPEA5gNYyBibA3QLx1of5Jwv4pxP5JxPdLvdA9RcYrhA8UX0FxRbRH9BsUX0FxRbBNHN5ZdfjssvvzwvjpOrthAEQRAEMbxIaU/BGBsPYEcAhYyx4xQvFQCw9HfDFO0o5py3c87/xxj7CsDHAAoB/A3AYgCHAjiEMfYm53zjQLWLIAiCIAiCIAhCi6+//jpvjpOrthAEQRAEMbxI52m8PYCjABQBOFrxey+A/+vHNkkYY4cAuJExdifnfDnn/CLG2AQAfwDwKOe8lTH2CoDT0C0kk2hMEARBEARBEMSQp66uDvX19QB6Fnmsra3F+vXrB6FVBEEQBEEMF1KKxpzz5QCWM8b25px/MoBtUmMHsDtjzMg5f45z/h1j7H8ArmWM3QhgHwAlADoGsY1EBjLVKKRq5wML9QdBbB10DxH9BcXWtgX1J7E11NfX44ADDgAArFq1Kuk1ih2CIAiCIPqbdJnGgp8YY9cBqFO+n3N+dn81SkEngDYAUQCTGWMeAD8C+PeW9ryObl/mcznnDQPQHoIgCIIgCIIgCIIgCIIgiG2abETj5QD+CeBtAPH+bU4PvtjyvY8BOAzAJQBGAjh5i1XFOABtnPPWAW4XsZVwzhEIBGCz2ShTIs/gnMPv98Nms0Gny6ZWJkEMb2g8I/obirGhC/Xd8GXcuHF5c5xctYUgCIIgiOFFNqKxjXN+db+3BABjbFcAYQDgnH+35dd7AXgRwGYA+wH4FMAoAD9yzn8YiHYRuScQCKCrqwsAYLfbB7k1hBJl3zgcjkFuDUHkPzSeEf0NjctDFxofhi+LFi3Km+Pkqi0EQRAEQQwvshGNX2WMHcE5X9GfDWGMHQ5gEYCXABzIGFvIOV/MGFsC4Hx0F+P7HYBiAFMYY//inHf1Z5uI/sNmsyX9n8gfqG8IonfQPUP0NxRjQxfqO4IgCIIgCGKoko1ofBmA6xhjEQARAAwA55wX5KIBrHuvnh3d1hMXcc5fZoxNAvAEYywO4DMANwD4Ped8OWOsGICOBOOhDWOMMm7yFMYYZbIRRC+g8YzobyjGhi7Ud8OXc889F8DWZ/nm4ji5agtBEARBEMOLjKIx59zZnw3g3WWlfYyx1QAKGGNGzvm/GGOzATyLbiF5dwBgjOk55+392R6CIAiCIAiCIIit4YcfcuOil4vj5KotBEEQBEEMLzJWuWLdnMoYu2HLzyMZY3v2Q1s2AzgYgBUAOOefAzgNwM2MsTFbfjfQhfiIYQjnPOV/RHrSXbt016+vnyOI/mRrYpLimRgMhsNYOhzOMRN0DQiCIAiCIIiBIKNoDOABAHsDOHnLzz4A9+eqAVvsKcA5fwCADcCDjLHCLRnH/wTwHwCxXH0fQRDd22XT/UcQRGbo/iEIIhP0vCUyUVdXlzI+amtrB7t5BEEQBEEMY7IRjffinF8EIAQAW+whTFvzpYyx7RljezPGjMo2cM5nbfl5IYCzGWMXATgAJBoTBEEQBEEQBLGNUV9fnzJrfP369YPdPIIgCIIghjHZFMKLMsb0ADgAMMbcABJ9/ULG2HEA5gNo2PLfasbYo6KwHed8NmPsbAAjAOwCYDrnfGNfv48gCIIgCIIgCGIg2XXXXfPmOLlqC0EQBEEQw4tsRON7ALwIoIwxNg/A8QCu78uXbcksngVgDuf8I8bYTACTAFzNGPsL57wTADjn/9jyfjPnPNyX7yIIgiAIgiAIghgMFi5cmDfHyVVbCIIgCIIYXmQUjTnnSxljX6C7SB0DcAzn/Lut+M4CAGMBfIRuMboVwJEAZgN4aEuRvRjn/EsAka34HoIgCIIgCIIgCIIgCIIgCKKXZPQ0ZozdA6CEc34/5/y+rRGMOedRAHcBOI4xth/nPAHgQwBfA9ifMWYFsC+ATVveTyWgtxE45/D7/UO6qveWtmfjAz6s6WtfbwsxQmz79CVOKbaJXME5h8/nQyKR3iVsW4+5bf38+oq4LpnigxgYTj31VJx66ql5cZxctYUgCIIgiOFFNvYUXwC4njG2Pbozg5/mnK/eiu/8J4DtAZzGGGOc8w8APMkYOxfACM75X7fi2EQW9HWSxTlPWek73WsAEAgE0NXVBQCw2+05aQ+AtO3JNYFAAAD0OT/wILA11ydTHKTq63Tfme5z2Xy2r3HZ1/ihivfp6Wt/bc339fWYvf1sujjN9jMUX31noGMrE339zr6OW9nGX1/idKDZmvugr+c3GH//9PW4fUF5XRwOR8rvTAeNP7lj48bclGTJxXFy1RaCIAiCIIYX2dhTPAbgMcZYCYCZAP7MGKvhnI/tyxdyzkOMsaXoLqx3LWNsPIAwADcAX1+OSQwciUQCwWAQVqsVOl32SbdWqxUAYLFY4Pf7e/35gUKIlzabrUf7bDYbAMQHpWEDiOhjm83Wp8mj6Gvx//7+nBJlfALoU6wSQ5O+jk2Zjqe+D7aMAynjVOtz4jPi/0R+o3wOaI2BylhTvp7rGNRCxF26WGKMJcXctigCDtQ9JWJB2dda/TwQfa9Gq19prCEIgiAIgiBySW/+sh0DYDyAWgD/25ov5Zy3A/gbgL8AOAjAgQBO5Zw3bc1xif4nGAyiq6sLwWAw5XsSiUSP7ZE6nQ52ux2hUCjj5wcTkaWzJas4iS0TwW1+z6foY61rkA2ir3s7ce7r55Qo4zNVrGrFJzH4iH7payZgNmNTX46nvg8YY7Db7SmFOK3PZfoMsfUkEgkEAoGc3NfpngNA6ljLdQxqIcbJTLGk0+ngcDi22QWzrbmnevMMELGg7FOtfh6Ivs+Gbb3fCYIgCIIgiIElY6YxY+wvAI4FsBbAMgC3cs47tvaLOecRAO8xxj7o/pGTgjPAqDNj1BlyWtlWZrMZOp0OZrM55XHF5AnozopSfodWxnG6Sd/WZr32luGUpZMqM8pqtSKRSCCRSORk66z4HovFAr1er/laujhQfj4UCqXN5FJmKycSCeh0OlgslqT3KOMz1fZdYuBR9kum7eZaY5PFYoHP5+vR3wJ1Frrf7wfQfa9rxWUikYDD4ej1WJDr+4fIjt7ED5A+m1jrORCLxeDxeFBaWppyV4R6/En37FJmsCrHs1Tj8mBksg40A3WOvYkVrZ0Fon9jsRg6Ozvl3zYFBQUwm81Ju6ly+TdMpgx4giAIgiAIgsg12XgarwWwN+e8tT8awDnf5rf75yvqiZP6Zy3PwHA4jEQigXA4DIMhOXzE5MhsNqOgoEAKxspjiCwpv98vf6+cmKsnjb0VAraW4ZQRmOra6nQ66HQ6dHV1yf7KRDrxN10fZtO/4j3K4k+p3qtsbzAYRCwWQ2trK9xutxQhcmGDQeSebLbdC7TGplAohEQigVAopBkfIo5ERqoQeyorK3ssbgWDQfh8PhQUFPR6LOjL/UNsPb29r9N54orngBKPx4NNmzYBAMrLy+Xrysx4ZX8rn3Hp4lH9ujJOhRiZ6Vm4rYiJuRT+09GbWBGxoO5nnU6HxsZGtLW1oaSkBFVVVT3+ttH6u2prGAo+1UQye++9d94cJ1dtIQiCIAhieJFSNGaM7b7ln58DqGGM1Shf55x/2Z8NI/of9cRJLdpoZVulm2yJyZHNZkMoFILZbM4qI0vrGED3pKg3QpKabWUi3V+o+0Ap/PY24zoYDKKjowM+nw8ulyspI1iZXZ6pDenaqcw0zvb8fD4fYrEYgsGgnGSTkDcw9Pb+602/9HZsUv5eZAg6nU44HA6YTCa0tLQgFosBSB53Uh0rVZao+ruGw46FfEGn02nGWqrs1d6OcaWlpUn/10L5XcoY0LoXMj0bE4mE5rNQKya3FTExl8J/OnLxDLBarXA6neCcw263y90Fou0i41gsovdmLMhVzBKDz+23396vx6mtrU35fK2trcX69etz3haCIAiCIIYX6TKNF6R5jaPbi5gYwqgnTuqftbKtlO9RbtfV6/VystTV1YWmpm57amVGltZxRNZfOvuKvk7utpWJdH+h7m+1YN+ba6YUaFtbW5MygsX3iEwtZUZ6OBzOWExK2c7eCgNutzvJkqC30MJD30l3/2Xahp7pumcam7RQjjnKDE6/349YLAaDwSDjJNOxMo0t6s9rnQ/FVt/ojYVBIpHosSAg0IqhdBgMBpSXl6d9j9qaSaAVL6m+X4jfnHMZp5nOeSiLiepz602f9PW8c2GDodPpUF5ejoKCAiQSCfh8Ptl+u90Or9eLpqYmlJeXw+l09qpd4njA1sUsse2jFIXV0HOFIAiCIIhckFI05pwfOJANIYYeHo8HjY2NAICysjI5YRK+x6WlpRmrjPv9/qSJlZZ9hRAceyuwDOWJ9GDQm+xKdR8oBdpMGcFCWNHpdIjFYvD5fCgtLZUC8tZu51VbZWzNJJsWHvpOuvsvUx/353VXC1MWiwVFRUUpPWTTed6qBT3OOVpbW+FyuZJ8krXOh2Krb/RmfBAWNcoFgf5EOYYq25nuXhBF2YBfF9mEzYpyYSPd83Aoi4lbM9739by35jvV3uhA9xiiXPTuK+LvIbfbLS2++hNauOp/Zs6cCQB4/vnnB/04uWoLQRAEQRDDi2wK4RkBXABg/y2/WgXgYc55tB/bRSDZK1GLdH/kZ/psus8xxlIWyVMKK8rtukqBRYgyOp0OgUAAXq8XwK+TM+WETZlZo0RtaZBKYEl3nukmlMNhgpTu2mgV6OpLdqU6LsTk02q1ysmoOpNLuX3X4/HIjHURA+psc4vFkhRbmdASawTZFN5TXp9c+x/39b4ciqS7/9JdV8ZYksimzMztD5QZyD6fT8aZz+eTftjqTEHOuWyP0oO2tbUV7e3tAJCUlZrJTmM4FDnLFVqxkQrlNc7mfleTrl+0Cnwqx1CxiKBevFLutNDr9QgEAmhubgbQHTNWqxWtra2adinC8iBVJqryfFKRT8++VPc6kHyP9ZZMz75U448yDlL1vfL5AnTvrBJWN0rsdjsqKip6vWgthFzlLhmtxSytwrJa55GO3i5ciT5JJTTnU2zlCx6PJ2+Ok6u2EARBEAQxvMimEN6DAIwAHtjy82lbfndOfzWKGFiUkwBBpiJ5QPJ2XZ/PJ8VhAPLfYput8PsT2TixWAxdXV2wWCxJYqDS8kJpaaA1yctlVfLhSm/EKnHtTSaTtA5JlbHl8/nQ1NQEh8OB8vLypGMrRTplxqbINFZnmysL4ImJubA10er7bHy3gewy0EW8kqDXe9KJC9lYSahFmFRkG8OZ3qdc4FJ+dyKRQGdnpxRyDAaDfK9YWFBuTy8uLu7he5vJTiNT0TTiVzLFRiq7g74IkNkU8eScw2az9VgkE2OHyCIWY5yIFafTKQXh0tJSKQRqZUerx0SHw5E2E3UoZZD25l7PBZzzrMaLVH2v9XxR+vWLZ5OWh386lM+4RCIhF1IzFURUZz5nsygq6MtuLNohQRAEQRAEMbzIRjTeg3O+i+Lndxlj/+6vBhEDj3ISoBTxxP+FwGuz2TIWmRLZoeLfOp0OjDF4vd4eGcjr1q1DbW0tKioq5OeVlhfKTD0tkSmbLaZDafI8GPRmm66WdYiwIjGbzZrHFhNet9vd4/oHg0H4fD4UFBRAr9f3+H5lTInFATEh55ynzbRLRbrif6kEhFST9Gwzn4crW+Np3Bv8fj82bdoEg8GA2tpaGAzaj7VMsS7GPovFAp/PB4vFIt9XX18Pv98vfUyV46Tw3U4kErBYLHC73b0+p1xntA9nlEU5e9MXWuKbKGKm1S/qZ15zczPKyspgt9vlMycYDMosYiEGOhwO+W8xPoqYVdr6pNulkem+2VaEvd4+v7N5f7bPvFT3pPpvEbVff1NTE3w+X5IQnmmRIxQKyXoALpcLBoNBev6nKogoRGmtzOdM5yboi70H2X4RBEEQBEEML7IRjeOMsdGc87UAwBjbDkC8f5tF5IJsLCYA7UmAcmLU0tIifYeV2U+ptm42NzdL4VgILMqt/uK7xORZOcHLVKE+VXX6VGwrk+f+Ip1YlSpeTCaTFIrFpDYcDieJdQ6HA3V1dfD7/YhEImhpaenh85pJKFPGoJh0+3w+dHZ2yuw0Zd9rFRESQnOqzEMtAUH9fnU7lZ/Z2gy5bXlRY2s8jbNB2d/t7e3SWqKyslLz/VpFNpXjotIKRwjEQtQbOXIk2traUFxcDODX2BR9lk7My6aP1RntqbadE5lRFuUMBoNZx5eW+FZQUJB2wUPY8IgsUVF4LxKJwO/3o6SkBGVlZbJdWr7tOp1Ojqkmk0nGopYonC5DX9mubUXY6+3zO5v3p9u1pLYaUd6TmWwrRN93dHSgq6sLLpcLRqNRsw+UO6rC4XBS3Cn7WK/XJxVEVL6ebheW1rnlaqfMUPbPJgiCIAiCIHpPNqLxlQDeY4z9DIABqAVwVr+2Kg2Msd8CSHDOvxqsNgwVlF6bQgDJVJFbawuvEHAtFos8ns/nk5Pq8vJyBINBKebZbDb4/X65BdfhcPSYZDidTowePbpHxqfZbIbD4UgpcqvFpkyTl21l8pwr1MXs0okQ6vgR/RCJRKRQnCrTWKfToaCgAA6HAy0tLYjFYvD7/dDr9UnZmeq4S1U4UbnF2+/3IxaLyfarvWXVW7eVMaMWAbQm3OoYU1+jbBYrsmVbXtToq6dxJtSLAw6HAyNHjkRLS4vmIoI63tRWEMFgEF6vV8YVAGkzEQ6HkzJHw+EwIpGIFIDVAqK6oBmQfR/ncjFiOKMsypkqvpQiYSgUgsViySi+CdTistfrhcPhQFlZGXw+HyKRCDo7O2E2m2Gz2WSMafkdJxIJuQtHWBIIG5SCgoIe7e3NjohtYTzp7fNb+X6txZpU1hQiO91gMCRlp2vZGaVa8AoEAojFYjCZTCgqKkIoFEJhYaGmH3FXVxeampoAACUlJXK80cpqVz9/1AJ3psxn8fxVt5cYGA4++OC8OU6u2kIQBEEQxPAio2jMOX+HMTYWwPZbfvU95zzcv83qCev+q78awKsAfmSMXc05/0S8xodRhSnlZChd5ohSkM3GB1GJUsQNh8Nwu93gnMNgMEixpr29XYqDSjGvoKBATni1RBxxTDFxU2Z8ismzQC1a9lZsoqyYZFIJWFqihDp+xGdERqYQYLUyjcXnAoEASktLZfam6NtMQrXyPcot3uXl5XC73T08uJXtVZ6DuuiR3+/H5s2bk7yWU9lipMu+zpU1xXBd1NC67tleW63FAYvFAoPBkHRMpWWAspCdun+VXt0NDQ2IRCJSZC4pKUnyJ+WcJxX2VAuIIk4rKipkW7LtY7KoGDhEvyn90tVCayovc3U/KT2NheWEeMaJ71LbZYjFVRHzxcXFiEQiiMViCAQCKdsr2qn1LN0W46e3z2/l+7V8wlM9/6xWK7q6utDZ2QmbzSbHi3QLCerrLO7v8vJyeDweucPG7XYD+NWuSbkjQmQaJxIJRKPRjOeqFIGLiorSZsEzxjT9sakWxMByww035M1xctUWgiAIgiCGFxlFY8bYCQDe4Jz/hzF2PYDdGWO3cc6/7P/m/coWUXgDY+w5AB4A1zHGFnDOV2USjBlj5wI4FwBqamr6v7H9jHLiky4jTVlwTJkpmg1iohuJRGAymQBAehp7vV7YbDbpvScmKEoxT+t7tIRhZeYVgKRJsCAWi0l7DFE8SJAP2/vzOb7U10ftWS3IVFxHxI/wX0xl3aCckCoLi4k+V05etUg1SRdbvNMJb6myptW/DwaDAKC59Vy8P1U85TobNJMoks+xlWuyvbZaiwNaixciQ125CAX0jAdhdeLz+WAymaRAHIvF0NbWhkQiAc65zEwX95EQCB0OB8xmM4LBIFwuV9J9AaTu41SZ0APFthxbmexPlFYlItM422NoZXYqx1ZhfyIWG/R6fUq7DPEsTyQScowzGAw92qPe0aF8lsZiMSlI58sCaT7EltZiTarnnxgDxE4BoOf9Kf7MTPecEeOW2+2WNiViB4TYFSEWugoLCwGgh8WNWvhVohSBtWLW7/cn/a2kNVaq30MQBEEQBEEQ6cjGnuIGzvmzjLHJAA4GcCeABwHs1a8tU8EYMwDgAPQAWgG8COBixtg4AGHO+WOpMo4554sALAKAiRMn5lVGcl8SpJUTn1SfV/5eOaHlnEuPPC0SiQQYYzJzTwh9FosFsVgM4XAYra2tAIDCwkJ53HA4DL1eD7PZjHg8jng8LgVGi8UCo9EofRuLiooQjUZhsVik8KLX6zUneeJ4jDG5nVcQj8fR1tamufVSnKdyC3KuPP3UDGZ8pYsfIRgrhQ8hYMXjcenrqrZpUHv92mw22SeBQACdnZ1IJBLydyaTCX6/P0ksEUXBrNbuQooWi0V6WAPdfaecGIuFDdEGAEl9rRZahBgtxLtUHrDK44r3jxo1CkB3bKW6fsrYVX5eTNTTide53PSQr7HVH8dWx6AaMTapxzMAMBqNcDqdMJvNMm4YY3A4HGCMyfFIa6dDPB6XY5zITBdWAUajEQaDQRZe5JzLGBaZjOJ1v98Pp9Mpt8ZnOn+tre/ZkKvFsaH6XBQCvhYiRpQiqzhWqmeicgFAeZx4PA673Z52nAC6FxfEGCEWDwTBYBAOhwOlpaUIhUJJ8RmLxeRCB2MMfr8fNpst6bkoCIVCckyy2WxJz9KOjg4pSItzSnV90o136ewv0l3zFN+TFFvprt/WxHO6RWPxXBN2TKIor3jmqNtks9lQUVEBs9ksF5uU3vjxeDzl3w/K18S9XVxcDI/HA6/XC7PZDKfTmfQ8UfpgK5954vnGOYfZbE56tpnNZhQWFsqxSuu6Kq04tMZKYmA5/PDDAQCvv/76oB8nV20hCIIgCGJ4kVUhvC3/PxLA3zjnrzHGbuvHNiXBGCvmnLdzzmNbfn4OQBXn/HHG2M4A/gpgLiCzkbd5xEQg3WSrrxMxIcqIzGGxfVdMaIVPo9gOKwQ/IbApxZhgMCirgZtMppTbMMVkLh0ii0r5vnA4LCfsykr0glRbkIcTqbbShkIhTTFZ63PKay4ENeXvQ6GQFHHFpFiZPenz+WR2lsjYam1tzcpnUW1ZEIvF4PV6k+JPxI+YcItFAjFZFrGr0+mg1+tldlU6IUR9fURbcrH4MNy3BKc6/0zjmhB/RQEppR2KwWCA0WiUP4v73eVyJdmc+Hw+uagh+t9isYAxBr1eLwXhUCgkLVXsdrv0phV+oYFAAGazGQaDQcaxyOwTsagstqd1LqnuMaL3KH1edTpdUoFOtX+/1ueEyCwIhUJyEUCv12fsHyFMhkIhaT/gcDiSYkttByTisLi4WC7MKdugHnfF/xlj0lde+GsrX+vLdUtV72AwSXcu4k+9TH7hWn72JpMJoVBIfkZcc9FHgUAAHR0dMnNY/ZzRQvmaGGfEMzUQCKT0JVZbdInnmvh7RvwtJlDGkdbzy2q1JsWdFna7HZWVlX2yMaFxqvcoF5EG+zi5agtBEARBEMOLbJSPBsbYwwBmAVjBGDNn+bmthjF2CICXGWPTFb82ANiDMXYEgKMBPAbgCMbYPgPRpm0dkTEXj8flpHbDhg1oampCMBhMmowL/H4/WlpasHHjRni93h7+tQIxodfaVikQk1fx2UAggJaWFgDoIb5YLBYUFRXJreXqP4itVisKCgrgcrmSvJzV37EtoxRrlVgslrT+1qk+J/pPCA1A8tZpZcV5kUknFhkEomCQyO70+/3gnCMWi6G5uVmKcMCvE3DRt21tbWhubkY4HE4q7igKmXm9XpndFQgEZIHGlpYWORHPpu9F7CjbLQQImnj1L2pbCeXPHo8HjY2N8Hg8AH61rlHGDPDrmAT8Om6Irf1Go1FasDQ3N2t6yFqtVrjdbpjNZqxfvx6bN2+WiyAiEzAcDsPlcqGwsBB2uz3J41ssOqSLlVT3GJEZdYyIe1OIgakKdKpRf04gxLvOzk65syIVoq9bW1tlIVexwJBKZAsGg/B4PNiwYQPa2tpkbIlzU/oje71eeL3epDaI8QmAXJTrSxwpRdVs6x3kEzabDQUFBSkz9ZXjuDhX8QzZvHlz0nPB6/XC5/PJxSCxCK11XVNZ3wDd40soFJLjTHl5uRR6xTNO9KkYH0QxTlEwUafTwe/3y9dTxYG6PWL8iUQiKa8ZjTuDR11dXVJihvK/2trawW4eQRAEQRCEJtlkGp8I4DAAd3LOOxhjlQCu7N9mJWFHt4+ykXP+POd8JWNsJoBHAfwf53w5Y+xsABsHsE2Djsh2U2eyKTOuRLZlNtkh4nPxeBxerxctLS1yC6ywFjCbzQiFQjLjU2TGeL1edHR0SDHx8ccfx+LFi3HGGWfg9NNPlyJxIBCAz+eD1WqF0+lMytgTW0xTFa9K124hGKkFAmV2j9rTNpe+tEOR3vqnikkp0D3xbW1thd1uR0lJCTZt2gS9Xo+2tjYUFhais7NTisLC8xVA0vZ9ANL3uLOzE3q9HpxzWUisrKxMbhEXIlxnZyfMZjPKyspQVFQkJ+2tra3gnMuYUca+WARRFphSFjLLtC1bybZYaGqgyaYIk9pLVvirGwwGFBcXI5FIwGQyobOzE62trejs7AQA6QkrhOSuri5pkSKE3qamJkSjUWy//fZp+1un08HpdMoYEwscPp9PjmdiEUxZeEuIOsoFlVTXQBlzvYnD4YrymaeOEaVHMfDrYpNYsPJ4PCgpKYHRaEy61kovfTXt7e1obGyE0+nEdtttl3K8FAtfwnJH3X9KgVHEi8VikeNUZ2cn4vG4fL9ouzhOS0uL/HnkyJEwGAxJ9QWAbqEyXWZ7KtQZzEMtkzSTJ7zy/hQLCUVFRdJjWPlcaG5uBmNMFlsVGejiGiuLyymfh2J8MZlMaGtrg8/nk7EnFp6am5tl3Cr/vhH2TWL3jLDGEPEo4tnv96O+vh4mkwlFRUXS7kQ8S0VxPFGEON3CvIDGnIGnvr6ebEIIgiAIghhyZBSNOecBAC8ofm4E0NifjVLQCaANQAzAfoyxVgA/A/gWwKOc80+2vG+JsK8YLqQq0pOtJYN6wiA+Z7fbk7yMi4uLodPpUFpaimAwKLdrKzNKhfAbDodx22234eWXX0ZNTQ1uuukmPP3007j++utx4IEHSq9G4X/b0NAAg8EAk8mUMpNLTK6VQqTVaoXf70dbWxui0ajcgqwsgpUOMVHujY9ovpPK27E3E0P1e5U/B4NBbN68GcFgUE5YAUjRQxxbFA/78MMP8dFHHyGRSGDWrFnYZ5990NzcjOrqapmZKTyqha+syMoqKSmRYrDX60UoFJKxV1JSgoqKCoTDYRmLJSUlUpRxOp1JBYk450kFGkXGllKMTncfKX8/0IXKtkUyFSgDeorzVqsVXV1dsgCnw+HAunXrpE1KcXGxFJNFxp4oQuVwOBCJRKQ41NDQgBUrVmCXXXbB1KlT4Xa75XZwZeFHIQiVlJRAp9OhuLgYLS0t6OrqQkFBAcrLy1O2OxaLYePGjWCMwePxYNy4cXKME4KTGKPdbnfS+Jvuuijv8eEo8iitCJRirxBLxZiiRmSnA0B5eXmPhUmxeCQQY49Op4Pb7Za2TGqUYnBJSYl8FsZiMfj9frhcLgDdMS9iJxKJYNy4cQiFQmhra4PX60VbWxu++uorVFZWYvz48VKojEQi8tkrFiw8Hk+S9YCoL6AsLivG62wWjJVjWl/9jvMRrTYr7bGEYCuup06nk4tO4jN2u13GCgB5zwuP4lAohOLiYpk5LOy4nE4nGGMyE7ytrQ319fWw2+2oqqpCQUEBSkpK5JjQ0NCAzZs3Y9SoUTAYDEkitVgk8fv9MJlM0g7H4/FIix2fzyfHJaUFVLprAWQ3FhMEQRAEQRBENpnGg8kXAN5GtwXFYQB+B6ACwGmc8x8ZY3rOeXxbFYzTFXlJlfWorgqfShhVV9BWWgs4HA4EAgEwxtDU1ASdTie9XNXF6KxWK8rLy9HS0oLLLrsM77//Pq666irccssteO2113DVVVfh1FNPxdFHH42rr74ao0ePRigUwr///W8wxlBaWgqXy4XNmzdLn9Ly8nIp8LW3t6O1tRUej0cKhDqdDi0tLTK7tKKiQrY9G5Si4rZCKm/HVBNDtT9vIpGQ2UrivUpxxW63y2sm/DfFYoHJZMJ//vMfLF++HP/5z3/w2WefgXMOl8uFcDiMZcuWoaqqClOmTMGZZ56Jgw46CJxzKewZDAY4HA55zHfeeQdr1qxBVVUVamtrMX78eACQlgSRSERmqwuBJBQKIRQKJfnSCpT9HQgE5H9iAi18K8V1ERlmudiune4eHmrk4lxytWBjtVrl1nQh+gmfbLvdjlGjRsmFLLHYtGrVKtx0001SQASAiooK7Lnnnthtt90wevRoTJo0CWVlZWhoaEBTUxN23nlnlJeXy+xB4XUqrAeEj7uyuJ7f74fRaERDQwPC4TBsNhu22247eS/abDYpYPn9fmmjkCnelPf4tjZ+ZYPSu1cp6mmNb3a7HRUVFUnZxCUlJT2OKRaRAoEA3G439Hq9tNYxmUxS2NNakPT7/Vi3bp3M6vzoo4/w+uuvy900hYWFKCoqQnFxsTyOEI83btyIlpYW1NfXo62tTR7zoIMOwrnnnovddttNCpoFBQUoKyuDwWCQ46U4JyF0ms1mtLe3y0J8fREDU+1e0jqeMtM2HwVHdZtF9r9yrFcWF3Q4HHL3k9pLWlxjpVjv9XrR1dWFRCKBjo4OeDweFBcXy8KJkUgEP//8M8aNGycXQZX+xc3NzXA4HPB4PPI5GAwGpS+7sh+FZVNBQQHcbresHxEKhRCPx9HU1AQAGDFihGaWu/K5Lp6XIqs5GwsXYus46qij8uY4uWoLQRAEQRDDi7wSjRljuwIIAwDn/Lstv94LwIsANgPYF8C/AIwC8CPnPK5xmG0Gn8+XJOwqSbUtM5UlQzrExEL4+X311VdYsmQJnn/+eRgMBpx44om4+uqrMXLkSCkk2mw2lJeXQ6fTYf369TjuuOPQ0NCARx55BKeccgoA4Oijj8bUqVNx7733Yv78+XjjjTdw6aWXYubMmQiHwygpKcHIkSPh8XjQ1tYGv9+P8ePHw+FwyDZFIhG4XC64XC5YrVaEw2F0dXXJKuI1NTUyU29bRvhcpsoyVFo+KMm2EJ4QSgwGQ1L1eIFOp0N5eTkKCgpgMBjQ3t6O5uZmLFy4ECtXrkRTUxMYY5g4cSKuv/56HHbYYfjtb3+LSCSCV199FUuXLsWyZcuwdOlS7LTTTpgxYwZ23HFH/Pe//8Uvv/yC9evX45tvvpHCsPocxo4di8rKStTV1WGHHXbA7NmzZea6yPQTk2n1dVPatVgsFikCAN33WEFBQQ/hvKioKCdiSKZCTUOJVOeSjZiszHZTZjZqfU6dCer3++Hz+aRXLNAtkCjFLZHhKX4WnrKJRALffPMNbr75Zrz88svYeeedsWzZMhiNRnz22Wf49NNP8dlnn+Hll18G0B3nVVVVqKqqAmMM48aNw4QJE1BZWYni4mLU1dXJ+FEW1RN+qA6HQ2YR1tbWIhgMorq6GkByJnIsFpM/K7ME05HqHh8uiGeeMotSnXFssVig1+vlc1AIvm63W8aYUlAOBoOor6+XthJlZWVJC0idnZ0yU13pn2w0GvHll19ixYoV+Oqrr/D+++8jGo3C5XKhrKxMLjL4fL4e/rJWqxV1dXWora3FXnvthdraWtTV1WHdunW45557cNJJJ2G33XbDBRdcgMmTJ6OjowNms1kK4Eo/WqfTmTRuiYULoPdxkkps1nqGBINBKVZWVlamPa7IiB3IRTN1m4Xlh3Ks55zLxUrxfBWCr/Jz4lqI14FfdyAYDAbU19dDp9PJe95qteKnn35COBxGW1sb3G43Kioq4PV6ccstt+Cee+7p4cGupLa2FjvvvDP2228/jBs3Tma7l5eXJwnGwhYF+LVQsECI4cK+Syw0KPtYvE/E/lDKJB9K/OEPf8ib4+SqLQRBEARBDC9YJn8txtgoAI2c89CWn60Ayjnn63PaEMYOB7AIwEsADgSwkHO+mDF2LID90V307iYAxQAqAdzOOff29nsmTpzIV69enatmbzXprn9nZyfWr1+Puro6FBYWJr0mKmurjyWEmXQTNFHETmk98Omnn+LZZ5/F22+/jQ0bNsDhcGDGjBnwer149dVXwRjDkUceidNOOw1jx46V28Fff/11XHPNNTCZTHj00UcxceJEze/0+/246aab8Pjjj6OsrAznnnsuLrzwQhQUFCAajWLjxo0IhUIwm82oqqqCTqeDz+eDXq9HaWlpjyJBQHfmj8lkgs/nkyKTyMATtgepyDSBTfU6Y+wLzrn2SSJ1fGVxn6V8TfjyaolL6SbjWjEiiMfjcuIpJtHKCanIiBSTYWFHIYpDffLJJ7jiiitQX1+PY489FtOmTcMhhxwCk8mUMnNp06ZNeOutt7Bs2TJ8/vnn8vcOhwPbb789dtxxR+ywww6YMGECqqqqsHHjRqxduzbpv/Xr10tR96yzzsKZZ54pt+2aTKYe2ZqiGJ44R5GdLK6BEAhE4aHOzk4YDAa4XK6tih+BljCa7rPp4mugxy51zKYTedWxqf6sEJxF/4jrrf5cPB6H3+9Ha2srXC4XHA6HzMysqKiQ44VYTBMetcLzlXOOxsZGmM1mlJSU4B//+Aduu+02xGIxXHvttbjgggtkNp9AZKp/8cUX+OKLL7B+/Xps2rQJjY2N2LRpE3w+n3yv0WjEYYcdhlNOOQX77LOPHGuV32uz2aQ9gBArOefy/hLFPcWOCuV9KK5dX8WbfIqtTHYH6Ww2tD6r3AWg1+uTfi+yzAsLC5PEUuGZbjAYUFRUJLOFRX+YTCbpd11cXAy9Xo/HHnsMd955J4xGI5xOp/x/aWkpCgoKkEgk8Omnn+I///kPAGDMmDE48sgjccQRR2CPPfZIapuIS7HwYbFY4HK5pDeueqFA7My45557sHbtWmy33XaYPXs2Zs2ahcLCQjgcDlgsFkSjUQCQ8dPe3o54PC6fnco2qK+hiBH13wvp/n5Qx6Q609hgMKSMrV122YV/9NFHKRdF+iImZ3qeql9XPt9E33u9XumxL/6G0Ov1MBqN0lqiq6sLer1ePvdisRiMRiO8Xi9aW1sxYcIEtLW1IR6Pw+l0yqJ8sVgMnZ2dKCwshF6vx9NPP43rrrsOjY2NmD17NnbeeWe54CnqRHDOEY1G8f333+OLL77Ahg0bAAAGgwE77bQTdtppJxx00EGYMmWK7CeRGa/8G0mMP8K2RSzQ6/X6pB1GAJL8mcW14JzL94r7TSxGpBuXtmZhIJ/Grf5E3Gfb6vcNNTL9PU8QBEEQRDfZiMarAezDOY9s+dkE4CPO+R45aUD3X4t2AM8AeIhz/jJjbBKAJwDMA/AZgMcB3Lyl6F0xAB3nvGdKYhbk2x+Z6a6/KNyklWms/ANd6ZUpsmmUE2el2AMgqWjdU089haVLl+LTTz+FTqfDAQccgJNPPhlHHHGEfP8vv/yCRYsWYcmSJfB6vdh7770xZ84c/O9//8Ndd92F3/zmN1i8eDHq6upSnouYMH7xxRe44oorsHr1ahxyyCH485//jLFjx8JkMuH7779HU1MTSktLUV5eLievwq9YOdERr+l0uh7XKRcejPkmGmudk/A2dLlcmiJBtoKyKGwnLB8AyGI8Yhu98M50uVy45557cNttt6GyshKPPPII9t57b3lcUaxOi1AoJCfjQgTefvvtMXLkyCQ/WS3EOUejUXz99ddYsGABXnvtNRQVFWH27Nk488wzpV9ye3s7LBYL7HY7AoGAzLJSx5DFYknacq7M5u7PolDbwgQ5lWcm51wKvsqYVI9BjDFNUQLoFo7b2tpQUlICg8GAzs5ObNiwAZWVlSgsLMQvv/yC+vp61NbWorq6Gh6PB9FoFEVFRTLr8pdffsH111+Pzz77DIcccgjuuusujBo1SvNc/H5/yqxMsQW8sbERDQ0NeOutt/DUU0+hpaUFZWVlOOaYY3DmmWdi9913h8/nkwXRurq6UFVVJRdRWltb4ff7pdVLOhEmk/iSLrM7n2JLS7hTP6dS2WxojZepFs78fr8slCi8hYFfnzlCUBbPRS2hTCxU/PnPf8aiRYuw5557YuTIkejs7JR+2p2dndLKYq+99sIRRxyBI488EtXV1SkLj3V1daUc1wKBQMpir6FQCO+99x4WLFiAzz//HJWVlVi6dCn23ntv+P1+NDQ0wGKxoLKyElarVZ6j8HRPJcgrYyvV9UxFX2Nr991356tXrx7QLNZUz0zg18xn5Wterxfr1q0DAJSWlkobC4/HA6/XC7vdjlgshg8//BBPPfUUVq5ciXg8jsrKShx99NE49thjsd1222HEiBFJuwi+/vprXHvttXjvvfewyy67YMGCBdhrr72S2ppq/Nm8eTP+9a9/4T//+Q8+//xzfPHFF/D5fDjxxBPxf//3f3C5XLDZbHIhtLm5GaFQCDU1NbBarYhEIklxno1XuhAZxTURu8oAZIyTVOOWVvFT9Xfm07iVa6ZMmQIAWLVq1VaJuMrjZIv6+/pyjG0ZEo0JgiAIIjuysacwCMEYADjnkS3CcU7g3X/R+LaI0wWMMSPn/F+MsdkAngVwEed8dwDY4mHcnqvvzndsNpucFKYTQsWWQ4fDkZRpKYqkiImusJPw+Xyor6/Hrbfeipdeegk77LAD5s2bh5NOOgkOhwMmU3L31tTU4LbbbsMf/vAHvPDCC7jvvvtwzjnnAACmT5+ORx55RGYvZ+K3v/0t3n33Xfztb3/DjTfeiMmTJ+MPf/gDLr/8clRWVsoJVDwel8Ve9Hp90qRcFIoRkxm73S6vk/J6xGIxhEKhlKLqUEKrCJvH48GmTZsAoEdhrt4I58pCPbW1tdDpdDLjWFSBLykpwS+//IILL7wQb7/9No455hjcd999KC4u7tP5jB49GqNHj+7154xGI/bYYw88/fTT+Prrr/GnP/0JDz74IJ566imcd955OPHEExEIBGA0GlFVVQWgewu38J0VNhwejwdut1uK2ACk2JIrD+Jtyc84HcrYbGpq0oxJpZ2OmMSKzwlxQimopvKRFdni33zzDR544AF4vV50dHQgHo/LQmSiSGZJSQkeffRRnHTSSTIzsy84nU44nU6MGzcOBx54IG6++WasXLkSS5YswT/+8Q8sWrQIEydOxKmnnoqDDjpI3jPhcBihUAjhcBjRaFTaFCivhbKYmtIrWQu16AoMLcsT9XMqk32COptV6devRGlFILK4BaKQnNJGxOPxoLCwEKFQSPocr127Fpdffjk++OADXH755bjllluSYk8pvKjFMbHzJZfo9Xocc8wxmDFjBt5//32cfvrpOOecc/DGG2/AaDQiFovBZrPJ8d3lcsmxrbW1VdoVpHsGpLIuUrO1BRh1Ot2g2B5k4+2sfE4KW5FEIoFffvlFPj86Ozvx4osvYtmyZfj3v/+NoqIiXHLJJdhll13w/PPP45FHHsGiRYswYcIETJs2DUcddRSqqqrwwAMP4MEHH4TD4cBdd92Fs88+u1d/h1RUVOCII47ACSecAKA7C/3OO+/EX/7yF6xcuRLXXXcd5syZIxcelGOK8OwXfa/1N6ToV2HzJOx11NhstiQf7d6i1Q/bkm0TQRAEQRDEtk42onELY2w65/xlAGCMzQDQ2g9t2QzgYAAvA4hyzj9njJ0G4K+Mse855z9tix7G6cQ9pS+jukiZEuXkT7nFWUygRbaUyExZv3495syZg/Xr12P+/Pm4/PLL5SRY6XenxuFw4OKLL8YFF1yAV199FZs3b8b//d//QafTZS0aA90T4vPPPx9HHXUUrrjiCtx666149dVX8eSTT8LlcqG0tBThcFhuGxYejqJwi/KclddJfT26urqwefNmAD1F1W2B0tJSAN3Zv2rSTZjFpFLEiyjUo/QyFsURfT4fQqEQlixZgrvuugterxd333035syZM+hC6K677oqnn34aX3zxBRYsWIA///nPePjhh3HqqafitNNOg9/vl+cm/DeBZK/PSCSCDRs2oKSkBIWFhTLmgOyKjaW7f4fjxFjEolZMZiKRSMisTgDSR1aI+UD3Qsn8+fPxwAMPYMSIEaioqIDdbofFYpGex6I454UXXijvkVxiNBpx5JFHYtq0afB6vXjqqaewZMkSXH755aiursb8+fNxwAEHSOsfsdV7xIgRUpwRGaHCG1aIcpWVldJjVR1XvRVd8w31cyrT+KH2zRU+1epMPeVzMpFIyD5X+rKLzEmB8GT3+XyIx+M45phj0NTUhL///e+YPXt22nYN5LjHGMOUKVOwbNkyTJs2Deeddx7mzp2LoqKipPFGuQCjXFTo6uqCz+eD2+1O+fdFJoZqAcZ0ongwGJR+1SaTCYWFhSgrK4PFYpF/M/n9frzxxht48MEHpRXFvffei9mzZ8vrdtJJJ8Hj8eD555/HM888g4ULF2LhwoUoLCxEZ2cnzjzzTMybNy9pcbKvmM1mzJ07FyeccAIuueQSXHXVVXj++edx1VVX4cgjj0RZWZm0QWltbUUikUBFRYX0YBfxLnZkiWed+AyApLFW6f2dyZZC/D2hdW9oeWwPd392giAIgiCIoUQ2ovH5AJYyxu4DwABsAHB6rhrAGGO8mwcYY8sAPMgYuxBAgHP+T8bYfwCkrhoyxMkmG0ZZpExrApRq8mez2VBaWoquri6Ew2G0traitbUVc+bMgcfjwRtvvIHJkyf3us16vR4zZszo9efUVFdXY9myZXj22Wdx1llnYcmSJbJQh9lsRllZGdxud1LmoRCFhL0AgCRxRSm2CJ/QvghYQwGDwYCysrK0EzWteBGFxtxuN5xOJwwGAyoqKuD3+xEMBmEwGGSm8Q8//IBLLrkE3377LSZOnIjFixdj1KhRgy4YK9lll13w3HPP4auvvsKtt96K++67D88//zzOOussnHfeeTKrUNxDbrcbhYWFsFgs2LBhA7777ju43W7suOOOvZ7Mprt/h+PEWK/X93qBxmq1SjFq48aNaG5uht1uh8vlgsFgkItGkUgEL7zwAh544AGcccYZuO++++SuiGg02sOreCAoKyvDZZddhksvvRTvvPMOfve73+H888/Hk08+iR122EF6jRoMBoTDYXg8HjkuiYWw8vJyKY6LjMDm5mZ4vV5UVlbKTEKl+JJP91+2ZCtSCoT4L/6dCeHjKq5XIpFIemZyzmGxWBAOhxGJRLBp0ya88847uOmmm2Cz2fDWW2+l9OQfbPbee28sWLAAl1xyCfbee2/MmjVLPuuU19Rut8PpdMoFZoPBgEgkgpaWlqRigED2u1GG6jgm7ietcxQF78S9qV5wNxgMmD9/Pl577TUccsghuPLKK7H33ntrjjGlpaU4++yzccEFF2D9+vV4+umnsWbNGlxyySWYNGkSAPRqUT0T48aNwxtvvIElS5Zg3rx5mDlzJrbbbjucdtpp2G+//VBYWIhYLCZ3momM866urqSijKI/TSYTEokEbDZbUh+nu35KtBZH1bGlvu9TFXImCIIgCIIg8o+MojHnfC2ASYwxx5affRk+khHG2PYASgCsBpAAEN9y7FmMsacALATwL8aYAcAB2IZF42y2iGplEitRb+MV72GMwWAwwGazwWw2o62tDeeeey42b96M1157rYe33mDAGMPxxx+PF154AQ8++CDOOOMM2f7S0lK5HVsUJNq8ebPMKDOZTLKIDdB9fdQZ2dkIWLnwQM43hMVEMBiE0WhEe3s7SktLe2z3F4hrKrwPOefYvHkzzjrrLHR1deHJJ5/EzJkzZQGnfGS33XbDCy+8gI8//hhz587F/Pnz8dxzz+H666/HkUceKT2whb8x0J3BKMQ5kZXocDiy9h0U96bI0lbG0HCdGGt5WKZ7b0tLiywSVl1djeLiYlit1iQxx2w241//+heuueYaTJ48Gffff/9WicTC09Tv96OlpUUW4AsGg9htt916FB7NBGMMhxxyCN5++21MnToVJ598MhYtWoTDDz9cxkgsFpPZ00JkKSoq6uFr6/f74fV6e9xnvRVdhzo6nS5JABb3lzqmlBYfShsL4NdnZjwel1vxha3DmjVrcNVVV2HChAl45plnUFtbO6Dn11vmzJmDRx99FI888gguu+wyuTAqrgvQLZwXFRWho6MDVqsVdrtdPhPFPQn8et+l2r2kRIxjQ2mhIpOVi1iIdzgc0gdbLNiEw2E88cQTeO2113DHHXfg0ksvBWMsK+G3rq4O11xzTb+dl4AxhlNPPRVnnHEGXnrpJdx77724+eabAXR7D++zzz7Yc889ccABB6C4uFjuUFBaBIl+9Xq98ho5nU5ZeNdqtcoF5rKyspT+2yKmlPddNskQBEEQBEEQxNAgm0xjMMaOBLAjAIuikMUtfflCxthxAOYDaNjy32rG2KOc864tx53NGDsbwAgAuwCYzjnf2JfvGgpkk82RSSzQ2sYrEFue7XY7TjjhBHz//fd44YUX+lUwDoVC6Orqktsis+H666/HXnvthaVLl+KKK64A0F15/r///S+A7gr1wWAQ9fX1sFqtKCgokNdK+DiLwj6iiE06lELxQE9wcu11m0r0Fv2gPL/y8nIpppvNZrmdWUywCwsLodPp8OOPP2LatGno6urCG2+8gd12222r2zlQ7LPPPnj33Xfx6quv4oYbbsDpp5+OPffcE7fffjv2339/WTRKTJzdbjdisZi8RgUFBUlbblMtJAxmDOUzvbkWfr8fjY2NCIfDKCsrQ0FBAQoLC5NiMhgMYuPGjbjgggtQWFiIpUuX9lowTiQS+Pjjj/H000/jtddeg8fjSbkwUFlZifvvvx+HHHJIr74D6L6/Vq5cialTp+Lcc8/F008/jfHjx0t7gGg0Kj1wjUZjUtFIca4mkwmVlZUAKJYEfr9fFjtVWyQon39lZWWyuOKiRYuwatUqxGIxaUMRiUQQDocRDAaxdu1aHHLIIXjmmWcGJUu9t+h0Otx111044IAD8Oc//xl33HEH/H6/tFKwWq3wer0y617szHG73fKaiDEt0+6loU6qOg9CRBZWJaKIoLBt0Ol0ePPNN/Hggw/iggsuwGWXXZbTdv3000+44YYbYDKZMGrUKIwaNQqVlZWYMGECRowY0etFa4PBgOOPPx7HH388Nm3ahA8++ED+98YbbwDoXgQ96KCDcOONN2L06NHw+/3ShkJcC5/PlySuiySEbBDis3I8zdYvezhw4okn5s1xctUWgiAIgiCGFxlFY8bYQwBsAA4EsBjA8QA+68uXMcaMAGYBmMM5/4gxNhPAJABXM8b+wjnvBADO+T+2vN/MOc/dvr48RGS8CZHFZrPJ34ltzED3ZCcUCsnfxeNxKTiaTCYp0JpMJkSj0aRJEeccZ5xxBlavXo2lS5di1113RWdnp2Z7GhoaUv6h39LSkuTNFw6HUV9fj7Vr1+Kbb75BY2Mjfv75Z2zYsAGJRAIHHHAAzj33XIwfPz5twbREIoGddtoJRx55JO69915ceOGFKCwsRCQSQXFxMeLxuMw2rKmpgclkktu71cV5dDqdFBXSZYsqr7lygiM+059ZVVrbOdO1NZFIpGyPOn6UCxBmsxlOpxMlJSWw2+0oKSmRBcP0er2cLIdCIQBAcXExIpEIfvrpJ8yYMQNerxfPP/88Ro8eneR13dramjJG1q5dK197//33sW7dOtTW1qK2thZOpxPV1dWan9u8eTMsFgsaGhqwceNG+Hw+7LvvvigqKpJtS3d9tI576KGHYu+998ZLL72E+fPn4+CDD8Y555yD6667TmZci7aKTERxTYXPI+dcFscTlh1ii7syi01YpVgslpRV5AV9rZ4+0KjbqRTJgdT3iLgGwhNbLI4IId5sNoMxhlAoJO9txhi6urpkwTiLxYJAIIBoNAq9Xo8rr7wS9fX1ePHFF2G1WnuMX83NzZrb53/66Se89NJLePHFF9HY2Air1YopU6agqqoKVqtVFt10u92wWq2IRqO47777cNxxx2HmzJm44IILMG7cOM3zjMVimgtjJSUleOmll3DMMcfgpJNOwt13342pU6fCZrOhuLgYFosFBoNBFmR0uVzS37irqwsFBQVJwqi6H9KNBflEuvZwzjP6pKYiFotJK6JAIIB4PC770u/3Y/Pmzbj66qvx9ttvY8yYMXA6nfKai3+bTCacdNJJuOKKK2AwGLBu3bqUuzCAnsX3lGzcuBHRaBTLli3D888/j3HjxuGaa67BiBEjEAqFMGLECM3PtbW1ybFXTTgc1rwGO+ywA0488UTcd999mD17NrbbbjswxhCJRBCPx8E5RywWg9lslgXOgF+LBIq/CywWC4qKimCxWMAYQzweRygUGnDrk0wx29dYF/1lMpkQiUTAOZfPyubmZgSDwSTv60gkgkQigc8//xx//OMfsf/+++O2225L6p/Ozs6UceD3+9P6PTc0NODLL7/E5ZdfjkQigaKiIixfvhzx+K9lOkwmE6qrq1FRUQGXywWXyyWL07pcLrjdbtTW1sqFpnA4nLSTymaz4bDDDsNhhx0mF0Y++ugjfPjhh9IyY9myZairq5MZ6mIB1Wg0yt0Wyp0RNpsNbrdb7o6w2+1ZFfMbbrsi0nHhhRfmzXFy1RaCIAiCIIYX2WQa78M535kx9h/O+c2MsQUAXt+K7ywAMBbARwBeRHdRvSMBzAbwEGNsTwAxzvmXACIpj7KNwBhLEi0ZY5qiYiAQwObNm+FwOFBeXg7GmJxQ6fV6OBwOOYkWGbdie+95552H9957D4sXL8YxxxyDjo6OlJlVRqOxx8QoHA7j559/xkcffYSmpib8/PPPSeIw0D1JqK2txbhx43DEEUcgkUjgqaeewimnnIKDDz4Yv//97/Gb3/xG8zsTiQT0ej1uuOEGTJo0CZdddhkWLlyIcDiMeDwOvV6Pjo4O2Gw2WfDI4/HA7XbLIlMig6q8vFxOfNNNfpU+jQNhI6Bsi/q7gfSTYPW5KMU7dfyoFyDERFZMLn0+Hzo7O6XHr91ul7YfTqcTn376KS688EJ4vV689NJL2GWXXXq0R6/Xp4wfIVrccccdePnll5Ne0+l0qK6uxqhRo1BXVwen04mGhgb88ssvWLduHdra2pLebzQaMWXKFBxzzDE49NBDU/ZnPB5PKfhYLBaceeaZmDVrFubOnYuHH34YU6ZMwYwZM6DT6eS9E4lEwBiTIoroGyEYe71eeL1emSEOoEcWm7Kvhoqw1xvUsZUqJvV6vcxWEyKV3W6X11GI6l6vF1arFU6nE4lEAg0NDdDr9YhGo7IoVSgUwuLFi/Haa69h/vz52HfffVO2T1nM87nnnsNLL72ENWvWQKfTYdKkSbj44osxZcqUHgseGzZsSFp02HvvvfHAAw9g6dKl+Ne//oUHH3wQe+65Z4/vS3cfVFdXy4xjMZ5NmzZNClV6vR6BQACxWAyhUCjJViFVQSklud6t0B9sTbvUn7Xb7aisrEwq1insRcRCjl6vx5o1a3DOOeegubkZf/3rX3H22WfLYwlRVAuliP3LL7/ghRdewIQJE7DPPvtIayQtWltb8eCDD+Lll1+G3+/HXnvthX//+9+YNWsWLrroIsyYMSPls0UsoGgh/Ji1uOmmm7BixQrccsstWLBggcwkjkaj0kJBaVMlzk/LvkOMVWJXCgA55uVzbCnR2mmj1+ulXZUodicWhsTCvN1uT1qkb2trw5lnnonKyko88cQTPf4OMplMKePAYDCkFVPffPNNzJ07F1VVVXjggQdQU1ODWCyGxsZGfPfddwgEAqivr8eGDRvQ1NSEL7/8Ei0tLT0sMUpLS3HxxRfjpJNOAoCUCy+MMVRUVGDmzJmYOXMmTj75ZMycORPHHnssnn32Weyxxx6w2WxwuVxSNI9EIjI2AoEAAoGAzL4WmfxKyxit7yR6Ihajt9YTPBfHyVVbCIIgCIIYXmQjGotUiwBjbAQAD4DKvnwZ5zzKGLsLwCWMsbVbCt19CKAKwFGMsccA7AvgqS3vH7oKSy9QZmWIbfI2my1p0pJIJNDW1oZEIoGCgoIeExohpLa1taGzsxOVlZVobGzE8ccfjzVr1uCvf/0rTj311KzbFIvFcPfdd2PFihWor6+X4rBer8fIkSMxevRoHHrooRg9ejS222472Gw2jBkzJukYc+bMweOPP47HHnsMRxxxBKZOnYrLL788pXi82267Ye7cuZg3bx6am5sxd+5cmSkEAB0dHRgxYkRSsRWxtVLpRwtkl8E0WJkwymzobFBbJajFO2X8qD12hfAm3ieqw4uMzvLycnR1daG9vR0+nw8XXnghurq6UgrGmaivr8dtt92GdevWYc6cOTj11FPR0NCAdevW4ZtvvoHH48H69evx8ccfIxqNwuVyobq6GrvssgvGjh2L6upqVFdXQ6fTYcWKFXj99dfx1ltvYdSoUZg1axaOPfZYlJSU9LpdVqsVf/rTn/Dhhx/iqquuwqRJk5KytNrb2+V1EIXJxPWz2Wwy1kTBIHXxJLFQA2y7lgLpthxrWVKYTCaEQiEplIqJqhDmge57WIxlojAVY0zuJnj//fexcOFCzJ49G3PmzEnbvqamJvzjH//Ak08+CZ/Ph5122gk33HADjj766LRZpGrMZjN+97vf4YADDsAf//hHzJgxAxdeeCGuuuqqlCKfFkqrit/97nd44YUXMGrUKFn0zu12IxQKyWOK+1hr7FILY1oLi+k+s4Uha9guro3IjhQZ/iIj0+/347777sNf/vIXVFdX46233uqTpc53332Hc845Bx6PB0C3GLjrrrvikEMOwYEHHogJEyaAMYbGxkY8/PDDWLp0KcLhMA499FCcffbZGDt2LBobG3H77bdjwYIFeP3113H//fdjwoQJObsWlZWVuO6663DdddfhjDPOwFFHHQWgu7+NRqMco5QLC2Ix2el0QqfT9RivlEUWs4mtfCKVHY6w4IhGo/K5KQpsisXnQCAg76dTTjkFPp8PL7/8shyzthbOORYsWIB58+Zh4sSJuPvuu6VfusFgwMiRI+FwODR3ynDOsW7dOnDO0dLSgs2bN+Ppp5/GzTffjEceeQQXX3wxzjrrrKwsLfbYYw+sWLEC06dPx/Tp07F8+XLstdde0ue4tbUVkUhEXg+XyyUXTQHA7XYDILGxLxxxxBEAgFWrVg34cWpra9OK+bW1tVi/fv1WtYsgCIIgiG0floW4dgOAewEcDOB+ABzA3zjnf+zTFzJmAXAOgJ0BPME5/2DL71eh27ZibV+Omy0TJ07kq1ev7s+v6BXq69/U1ITGxkZUVlairKxMZsHEYjE0NzfLTGP1FngxCWxtbUVraytWr16NW2+9FTabDYsXL8Zhhx0m35su0/j777/Ht99+i0cffRQfffQRDjjgAOy0004YO3YsrFYrdt55Z5hMph6fa2lpQU1NjeYxvV4vli1bhr/97W/o6urCoYceirvuuktOnhKJhPTwBIClS5fi/PPPR3V1Na666ipMnToV4XAYfr8fZWVlcLvdMgt5w4YNMJvNKCoqkr6h4rrmMtNN8fsvOOcTU32ur/GV7j4Ufav0bgYgixNptVWZbS4K3DkcDnR1daGpqUlmXOl0OnR1deG9997DTTfdJD2MU23JB7qz67TEhOeeew5XXXUVrFYrbr311h6+2Y2NjTJGhL+oECt+/vnnpBgQhEIhvPvuu1ixYgW++OILGI1GTJs2DVdddZV8fzwex8iRIzXbKrIQBV988QWmTJmCmTNnYsGCBdJHUlgFiGKBTU1N2Lx5MyoqKlBeXi4ztA0Gg5xAK0U8tajX10xjnU6XMr4GeuzKZC+QKtNYp9OBc47m5mZ5DcvKyhCPx+HxeFBcXAy9Xi8zRoWve1tbG6LRKBwOB3Q6HX744QccdthhGDFiBP75z38iGo1qir/Nzc24/vrr8fLLLyMWi+Goo47Ceeedhx122EG+p729PeW5qDONlXg8Hjz22GNYunQpJkyYgAcffBDjx48H0B13LpdL83PCVxYAvv76a+y111649tprMXPmTLjdbhQUFCAQCMit6sr7SbmVXlxP5RgghGV1NmiqZ4KwFHI6nf/hnGuuBPVHbPUmfrJFuVAYCARQVlYGs9mM0047Dc8//zwOP/xwPPTQQ5p2NukyjYXF0vnnnw+73Y5Fixahq6sLH3zwAf75z3/iu+++A9C9ELDTTjvh/9k77/AoqveLf2az2Wyym17pvRcBAQUEUQQVxYKCoggWBEQBCyAKKhbAglhQsAEKggqiIIqg0qUICKJIb4EESC/bN5ud3x/x3t9usrvZICB+3fM8eSCZdmfmzp255z3veTds2EBpaSm33norN954I61atapwfitXrmTatGlYLBYefvhh6cktzmPv3r2YTCZOnjxJZmYmRqORu+66C51Oh81mk+OML4SHh9OmTRvCwsLYtGmTDD6cPn2amJgYGVgzmUxER0dLMjgiIgKn00lERAQOh8OrgJ7ns+tLaRzofgV6LwbqW+fCnsKfp7/4u+e5irHearWSnZ0NlKl3hw4dypIlS/jss8+46aabfB7TbDb7DRqZzeYK70Sn08njjz/OggULuPHGG3nhhRd8fjsVFBT4HX+ys7O9xhhVVVm/fj3Tpk1j3759tG7dmmnTpnHppZd6bWe3272sxAQOHTrETTfdhNls5vvvv6dFixayD3gW4BT+8r5woRXFZ9u3LhZ0794dKCN7hU3T393PuWgL8Lfa87+Ayr7nQwghhBBCCCGEMgSUXymKogFWq6paCCxRFOVbQC+8h88GqqraFUVZQBn5/JSiKE0BB5AMmM92v/8rEAoXT789MXGoXr16hYmP+F2v12M2myksLGTKlCmsXbuW7t27M2fOHL+eilBmPbFjxw42bNjA+vXr2blzp/SInDJlikyDhDJiz9ekpzJER0czevRo7rvvPubMmcP06dP5/PPPGTZsmM/17777burXr8+AAQMYPnw4PXr04PHHH5fnIUimnJwcIiIiiImJ8SKM/9dQ3n7CbDbLQoD+PviFP7On0lioZwHpTWgymVi8eDHPPfccUVFRsuhdVQrhWK1WJk6cyOeff84ll1zC1KlTAxIe8P/pw5VBr9fTu3dvBg0axMGDB6Vv6Pbt2/nwww+rrOC79NJLefjhh5kxYwYDBgygdevW1K5dG61W66U8FqRLeZWsINpNJpMszCXUe8Go8v4NtgJnA1/nX34sy8vL49SpU9hsNhISEsjJyQHKiDitVktSUhLp6el88sknfPbZZ2zbto2kpCQWLVqEwWCgsLDQa/+qqrJ48WKeeuopzGYzd9xxBw8++KDf4NXZwGAw8Prrr3Pdddfx+OOP07t3b958802/xJIvtG7dmgYNGjBv3jy6d+8uyRhhQRCMcru8yjuYTAkfyvBSvyv/SyCyNDyLdT355JMsWbKEKVOmMHz48CqpygVWr17NU089RbVq1bzeme3bt+fxxx/HbDazbt061q5dy65du+jfvz8PPfQQtWvX5tChQxX2pygK119/PW3atGHu3Lm8+eabzJ49m1atWpGZmcmpU6coKSmpsN3ChQuZPn06DRo0CNjeiIgIpk6dSr9+/Vi9ejXXXXcdRUVF0utbBMPcbrfMwvG0jBHBB6iYJfFPZuGcDfyNvZ5/9+wTbrcbVVVJTExEo9Fw+vRplixZwuOPP84NN9xwTtqUl5fHoEGD2Lx5M+PGjeOee+45J+O9oih0796dbt26sWTJEmbMmMFNN93EBx98EFTbGzVqxKpVq7j22msZNmwY3377LfD/VlYiY030o6ioKK9MpRBCCCGEEEIIIYQQ/nsI+BWoqqqbMnWx+N3xdwhjj/0UAB8CrwJXU1Zkb6Cqqll/d9//dgjyqvzE13MCZLFYsFqtmM1mbDYbUKZ0mT59Or1792bHjh3MmDGDFStW+CWMXS6XnMTedNNNTJ8+Hbfbzf3338+CBQvYuXOnF2F8LhATE8Ojjz5Kw4YN2bx5c8B1O3XqxJ9//snUqVP5448/uOGGGxgzZgxr1qwhKytLFkASXoX/ixMaUZAN/t+jMjIysoKPriCDBTkA/99ftFqtVG4KZaMgDPLz8xkyZAhjx46lc+fO7Nixo8op3YcOHeKGG27giy++4NFHH+W1116rlDA+WzRu3JhnnnmGL774AoABAwZU2o984emnnyYpKYnXX3+d3NxcmYruCaEoFt7Pguj9u/1MpH4Lb8H/NYi+qKoqYWFhJCcnSy/j+Ph44uLipPdncnKy7CuLFi3i1ltvpWnTpjz22GPYbDZefvlldu3aVcH2BuDUqVMyqNSgQQO+/PJLXnzxxXNKGHuiZ8+e/PDDDzRr1oyhQ4fy/PPP43K5gtpWo9Hw2WefkZ+fz4svvkh+fr4sKupv7Cr/nIvnuSr9z3Obv7ZzV7bNuYQYv86lkk0UnxTj4pdffsmMGTMYNWoU48aNOyti7qOPPmLMmDE0bdqUzz77zOc7MzU1lTvuuIP33nuPX375halTpwbV1+Lj43n33Xf56aefuPrqq3E6nbRt25bhw4fzzDPPMG/ePNasWcOBAweYPXs2eXl59OnTh7feekvaMvlDnz59qFGjBh9//LH0+w8PD0en00lPWkAW9SwuLqaoqIioqCgvb2hf75QLhfPRRyqD3W7HZDLhcDiIiopi06ZNANx1111/e9+nTp3i448/pkePHvz66698+OGHPPXUU+c8QKjRaLjxxhtZs2YNzZs3Z/DgwXz00UdBbVu7dm1effVVfv/9d5mdUVxcLG2+nE4nubm55OTkkJeXJ738QwghhBBCCCGEEEL4byIYSc5qRVFuA746lx7Dqqo6gbWKomwo+1W9oBPaix2eKZdpaWleaaSiirXRaESn0/HLL7/w6KOPsn37dm644Qbeffdd0tLS/O5bWBEcOHCALl26MGzYMK644gpiY2Nlmuz5ROfOnfnyyy+9Urh9wWAwMHr0aEaMGMH777/Pq6++yuHDh2nWrJm0AHA6neTk5MiCLZ5F8USRvPLEK/w7FJ++vCV9qar8eTqWh1AbQxnZMmrUKGw2G2+99RbDhw+v0nVwu93Mnj2bqVOnYjAYWLhwIVdeeaVM4z6faNKkCYsXL+aBBx5gyJAhTJkypUpVwWNiYnj22WcZNWoUmzZtol27dnKZeO70ej02mw2TyeRFyItnQxRwqyrR4qlY/l+BZxp4Xl4eLpdLFpoym80cO3YMgHr16lGrVi3y8vKIjIzEbrczYcIE5s2bR3FxMdWrV2fUqFHcddddtG7d2u/xfvjhB0aMGIHNZmPy5MkMHTpUFmo6n6hWrRpff/01zz77LLNmzWL37t188cUXpKSkVLrtJZdcwnvvvcfgwYN59913eemllwB8ptWDf/Xkvwln441bmdWA2+0mOzsbu92OzWZjzJgxtG/fnpdffrnK7VNVlRdffJFXX32Vrl278tZbb52357Jly5a8//77Xn/LyckhLi5O/t6rVy86dOjApEmTePfdd1m3bh0zZ870+yyEhYUxePBgXn75ZdLT00lMTKRRo0byuhUVFXn5ZO/fv5/S0lIaNGiAVqv1ujf/VF87X/7J/voRlGWvWK1WSkpKyMvLY9WqVdJ2pHzhuWCOs3PnTpYvX87atWv5/fffAahfvz7ffvst7duf38z3pKQkli1bxoMPPsiTTz5JQUEBY8aMqXS7fv368eabbzJ58mQ6depEcXExaWlpxMTEEBMTg6IoJCQkyO+s/6X3VQghhBBCCCGEEEIIVUMwpPEw4HHApSiKHVAoI3l9G55VEaqq/utTZuHs/RvdbrfPZSaTiczMTKpXr050dDSqqlJaWopWq0VRFJna/PPPPzNo0CBsNhtz5szhtttuQ1EUSdR44siRI7zyyits3LiRlJQURo8eTfv27VEURSo2i4qKpO9ieQj/YF9QFCWgGsXTr7ZZs2ZYrVZ++uknWWzN3zEdDgcGg4GHHnqIWrVqMWDAAH755Rf69OkDQHFxMaqqyuItguhTVZWoqChyc3Olt29qaqqcmF7oYj9nE28RhKRer/e7fWlpKREREURHRxMREeGlNnY4HBX6Vm5uLk8//TSLFi2ibdu2vPXWWzRs2LCC2jY/P99vuzZs2MDMmTPZs2cPl112GY8++iiJiYns2bOH33//3W96uMlk8pnKLdpV3iPXc7LvizgZP348r7/+OmPHjiUzM5MhQ4ZUOF+n0+nTK7lv377MnDmTd955hwcffFBeu7y8PCwWC0lJSfK5i4iIkIpjUaTS4XDgdrtxOByyyFJkZKT0CAxU1f5CEzRnG+srPzZ5+ux6nqMIWgjbHK1WK0kpz/soVKJFRUXs37+fYcOGcfjwYe644w4GDBhA165dKSwsJCIigtzc3ArtOXToEB9++CGffvopjRs35pVXXqFu3bpkZGRw8uRJvx6cgCRzoMxD1G63y3Y6HA4OHjwof/c85/Dw8Ao2Avfeey+pqalMnz6djh078t5779GmTRuvdTwLAArccMMNPPTQQ8yaNYtOnToxYMAATCYTLpcLRVG8+o+/ZyhQ3zpfOFvf2UCFE/3t02azUVhYiNlsltfPbrfL4J/BYCAiIoKioiIGDx5MWFgYH3/8MaWlpXJbfxZKGRkZsi0ul4sXX3yRr7/+mltvvZVrrrmGQN6o5QvPeuLMmTN+n+nS0lIyMjJ8LnM4HNSoUaPC3x9++GFatmzJzJkz6dGjB0OGDGHYsGFeAdZ69eoBZVZOr7zyCi+++KKsYRAWFiaDXqJvqapKfHw8Wq1WkoHCuqK0tFRaHQXqW39HM+Bv28qCaP628/fdJMao0tJSqWAWwVLhb+9yuYiNjaWgoACHw8H69evp0aOH9Mv2F8g+c+aM7AeHDx9m9uzZrF+/nry8PDQaDa1bt+aRRx6hS5cu1K9fH0VR5Pvu119/9XttzGaz30C93W736c8NeL3vp0yZgk6n4+WXX+bUqVM88sgjAe9XWFgYU6dOpUePHnzyySfceeedhIWFER0djc1mIywsTAZpjEYjDocDh8OBTqcLeK8q8b32u+x/Gffee+9Fs59z1ZYQQgghhBBCCOG/Bb+ksaIoXVRV3QQkq6pqv4Bt+k/BXyEzT/sJo9EoJ0DCmkKj0fDnn39yxx13EB4ezqpVq2jRooXXfgUKCgqYMWMGX3zxBVFRUfTp04fevXsTHh5eQVlz5swZL/VT+WX+UnJtNptf4hfwmvhcddVVKIrCn3/+Sffu3SXR5AulpaUynb1Pnz40atSIuXPnMmDAAFlESpBVwntPURSvSbOYNBYVFaHX69Fqtf8KxaenSszfhEtM9oXC2lNNLfqWUFWvW7eOUaNGkZ2dzRNPPMFjjz3m97r7Cg643W4++eQTpk2bhk6nY/z48fTq1curbcXFxV7ewJ7IzMz069W5c+dO3G43GRkZZGRkYLFYaN++Pe3bt0er1fqcyMfFxfHss8/y1ltv8eabb3LmzBkmTpwo+4u4Pr7OJSIigpdeeonbb7+dd999lyeeeAKbzSZ9dqFsYi0m80LRDngFH0SBqfIBiEBKt38Lyo9NnufpqdAXRJxnwSlhIWAwGKhdu7b0ETWbzSxatIipU6cSExPD8uXL6datm9cxfV2v9PR0HnroIfbu3cuAAQMYN26c133V6XQBxx/RJzZt2sTChQsDnnOnTp246667CA8Px2az+ex7N910E3Xr1mXy5Mn069ePF1980Su9XVEUr34o8MILL/Dnn3/y2GOP0aRJE9q0aeOlZBf952wIFn/vkn8KgdTS/toaGRlJcXExxcXF8tkxmUwYDAYvZf+oUaM4fPgw33zzjSRQxfb+ApvCpqOwsJAxY8awdetWhg8fzsMPP8z333/v970HZe8hf8jNzfW77enTpysEDwQsFovf90/nzp3p06cPL730Eu+99x7r169n/vz5so+LvtWgQQMeeugh3n33XQYNGkSnTp3k8oSEBPLz8yXRGRsbS3x8vOzPogiquE+CmL+QY5a/IFplmUDl+4+nEt1sNnsFB0pLS8nPz5d2MjqdTgaZt27dSk5ODj179pRke6B3ol6v5/Dhw9xzzz2UlJRw9dVX06NHD2rVqnXW1jjp6ek+LXig7J3pr2/Z7XavgMxrr71GbGws8+bNw2w2M2vWLJ9jEJTd+y5dunDrrbcye/ZsoqKipFVQWFgYSUlJMtjaokUL+X1hNBqD+mb6X3j/nSuESOMQQgghhBBCCOHfjkBK47eBS4HNQLsA6/3nIT6o/47VgcvlIi8vTxZniYqKolatWiQmJmKz2cjNzZUkcmRkJHv37mXw4MHEx8ezfPly6tev73O/GzduZMyYMbJQ1COPPMKWLVsC2kIEgqqqZGRk8Pvvv+NyubjmmmuqrJyMj4+nWbNmbN26lYcffjjo7TQaDaNGjWLkyJHs2LGD9u3bS+sOz+roQqkYHx9PbGwsqqpy7NgxLBaL9Iz+txX78YTnhEyoY8XkvzxRI5Ru06ZN46OPPqJJkyYsWrSIOnXqVKlg1PHjx3nqqafYsWMHl156KePHj/eq6l4VOBwOCgoKOH36NIcPH+bIkSNS6azX66X6btu2bRw6dIhrrrnG777Cw8MZMWIEzZs3Z/bs2eTk5PDaa68FZRvRvXt3evfuzdSpU7nnnnskuRITE0N+fr70CIUyMkpMqoUi1JM49vwXgrcM+TfBn3K0fMEpQRBHRERIwg/g4MGDTJkyheXLl3PVVVfx0UcfBWXtsGzZMsaMGYOqqrz55pv06tXrrNr/yy+/8Nlnn9GsWTM6deqEy+XC5XJJYq+kpIT8/Hw2btzImTNneOihhwKOk40aNWLFihU8/PDDPPnkk2zbto0nn3zSp7JdQKvVMnv2bHr06MG9997LihUriI2N9VsQzxc87UA8x71/K1wuFzk5OURFRREdHY3RaJRBh6SkJEmoi/N8/fXX+eabb3j55Zfp3r17lY61b98+Hn30UbKzs3nhhRfo27fveTijc4O4uDimTZtGr169GDlyJNOmTePFF1+ssJ4oQvrss8/yww8/YLfbZcDDU7Ut/GqFErv882y32y+aMauqmUBivBW+86WlpZw4cYK4uDji4uJkYDoyMpLS0lL5DK1ZswYg4DvGEydPnuTuu+8mLCyMr776SgYsDh8+HPS55efnU1xcjNPpxOl0cuTIEQoKCigpKaGkpASj0UhKSkqVawNoNBqeeeYZjEYjs2bNwu12M3PmzIDFi1966SV++eWXgPYuN9xwA7Nnz8ZoNEp1c2Xjj+h7xcXFsnClPwL7fx0ia+Zsv5fO5X7OVVtCCCGEEEIIIYT/FgKxRiWKonwA1FQU5e3yC1VVHXX+mvXvQlUnOJ6knyCZ8/LyOH36tNyH2WwmLCxMFj9LSkpCp9ORm5vLnj17GDp0KLVq1WL58uU+U1wBPv30U6ZMmULjxo157bXXaNSoUYV1jh49SnFxsZysC7WO5ySgqKiI9PR0NmzYwPHjxykoKJDLf/vtNwYMGFBllc3ll1/O/Pnz/Sr5/OGuu+7ihRde4K233uL999+XyiBPi4C8vDzOnDkDlKlCXS4XiYmJ8gf+OU/jqh7Xl2LHk5BUVRWXyyUnZqLAkSACMjIyGD9+PEuXLuX+++9n2rRpREZG+kz/9wWLxcLnn3/Om2++iU6n49VXX6V+/fp+1XOeOHXqFIcOHSIvL4/8/HwyMjKwWq3SYxrKSOL69etTvXp1WrZsSVJSkrwux48fZ82aNSxevBir1co999zjs69oNBrGjBlDtWrVmDx5MnfeeSdvvvmmlwLRH15++WXatWvHpEmTGDduHDqdjpycHKnmr1u3rrwH/gqR+QpAeBIy/yuqKzEWCc9nfySAzWaTGREnT54kISGBgoICRowYwcGDB3n66acZN25cpSSC1Wpl0qRJzJs3j3bt2jFx4kSaNm16Vm3fsWMH8+fPp3HjxgwdOtSLSDly5AhNmjSRvzdr1oy5c+cydepUhgwZEnC/8fHxzJ8/n9dff51Zs2axfPly7rrrLkaOHOmX9ElKSmLx4sVceeWVPPLII8yfP5/4+HiffaM8QaPX6yW5JywG4J8n+QKhsv6fl5fH8ePHMRgMNGjQAIPBQExMjCzIJdSjRqORzZs389JLL8kAaLBQVZUVK1YwZcoU4uLimDdvHq1atfK5rsPhYO/evYSFhREZGYleryc6Ohq9Xk9kZCRarZbS0lLpq5yTk4OiKNhsNux2OwaDgeTkZBISEs76mnmiV69eDB48mLlz59K3b98KxUrj4uKYNGkSjzzyCLNnz+aKK64gOTkZg8FAYWEhpaWlkpD3tNkoH2AUy8q/Q/6JsauqmUCircJyQ5y32+2muLhYEseiqKnVakVVVX7++WdatmwZMNAjkJ2dzT333IPVamXRokVBvV/KY9WqVSxdujTo9Y1GIzVr1qRatWqkpaXRsmVLv8WNoexd9Pjjj6PX63njjTcwm818+OGHREdH+1y/QYMGHDlyRBYHNJvN0r7HbDazbds2Jk+ezPjx45k+fTrgbUfkb/wR2SZFRUVYLBaqVat23mtlXKy4/fbbAVi3bt0/vp9z1ZYQQgghhBBCCOG/hUCk8Y3ANcC1gH9DthCqPMHxJP3ENoKEi4+Plz6pwqdRpOYC7Nq1i6FDh9K4cWOWLl3ql5g4fvw4L730Et27d+f111/3SSrk5eXxyiuvVEi91Wg0GI1GoqOjsVqtFBQUAGVpi40bN6ZXr160aNGCgoICFi5cyEcffcQ999wjU2ODQadOnZgzZw5bt26la9euQW8XGRnJPffcw/Tp0+WkHryvqbiWnp6YGo2G6OhoNBoNZrNZpsrDhSVcKgsweBIsUFYsSaTVivXLE5Li/xqNBovFgslkAspUjYsWLWLp0qVMnTqV0aNHB01Ub926la+++opVq1ZhtVrp3r07L730EqmpqV7+sL5gs9n47rvvWLdunfRnTUxMRK/X06BBA+Lj40lISCA5OZlq1aqh0WjYvHlzhb5ct25d7r77bjZv3sx3331HYmIiN998s9/j3n333dSqVYvx48czfPhwli1bVinR0bRpU4YOHcr777/PbbfdRo0aNUhOTsZqtcpgjPDJhuD7iichY7FY5D0XpOvFXIAxECwWC1lZWSQnJ0vlXvmCZW63WwYwTCYTGo2GIUOGYLFYWLVqFR06dKiUMP7tt9945JFHOHz4MA8//DDjx48nMzOzyu11uVzMnDmTuXPn0qBBA4YNGxZQeQdw6aWXkpSUJLe78cYbA7Y3LCyMcePGceedd/LOO+/wySefYDAYZLE7X2jXrh0PPfQQb7zxBrt376Zr164+lf++/KITExOJiYnxUvpdzKhMdZ+YmCjHuIiICGkdIPzorVYrNpuNrKwsHnvsMVq2bMk777wT9POTmZnJ2LFj+f7772nfvj2vv/6636DXqVOn+OCDDzh16pTf/YWFhQW0q/BcLzY2ltq1a5OSkkJqaiqXXnpplRWkAPfffz9z587lxx9/rEAaA9x333188MEHzJo1iyZNmqAoCtHR0TRp0gSbzUZSUhJarVZaGfkigsWY5TleAf+I+riyTCARfBXn4GmxodFoZBDG6XRy4sQJ6XUtvOnNZjPh4eFs2rSJhx56qNL2mEwmhg0bRk5ODgsWLKBZs2ZVPqf09HSWLl1Ku3btuPzyy9HpdOh0Ok6cOEGDBg3Q6XTSCz47O5vs7GyOHz+O3W5n27ZtMlPq7rvv5sorrwzY/++77z5q1qzJmDFj6NixI08++SSDBw/2a/URGRlJZGQkKSkpXmr0a6+9lry8PN577z3uvfdemjdvTkREBEaj0SvDyxeioqLQ6/Uy2BhCCCGEEEIIIYQQwr8TfkljVVVzgc8VRdmnquruC9imfx2qWuneV5q3sE0wmUxkZ2fL9DFBwghCcOTIkSQlJbFkyZKAk8+VK1cCMGnSJL9tW758uVSmlJaWYjabOXHiBFFRUZhMJoqLi9Hr9dStW5fatWuTn5/vpVY2GAyMHDmSmTNnsmDBAi699FIvX+VAuPzyy4mJiWHFihVVIo0BL48/MekVZENERARarZaUlBRJDAsFlfCfNZlMGI1GYmJiLrincWUBBk+CBfBKqxXwnCALX0NxHTz9ZU0mE5s2baJBgwY8+uijlbbt6NGjLFq0iMWLF5OZmYnRaOTGG2/k1ltv5dJLL62UpFFVlT///JOffvqJ4uJirrjiCq699lpiY2PRaDT88ccffj2N/SEiIoKrrroKl8vFqlWr6NOnT0AiuFu3brz++uvcf//9zJo1Kyj7E9GH16xZw6RJkygpKaFatWo4nU6vwIROp+PMmTNEREQQHh6O0WiscE3Kkxng/bz/220rfPmeep6HzWbDbDZLtWhqaiqvvPIKR48e5YcffqB79+5eSvPycLlcTJ8+nenTp5OSksLixYurPD4IZGdnM378eHbt2kWXLl24/fbbKyWMBerUqUP//v354IMPWL16dVCWGLVr1+bVV19l/fr1lRLcZrOZzz77jM6dOxMdHY1WqyUnJ4f4+Hgvgtrzec7Ly8PlcsnioIAkmgURKNLzLyZ4js2+oNVqiYuLo7i4GIfDITNHkpKS5DhmtVpZvHgxp06dYubMmUH7qs6ZM4cXXngBl8vFqFGjuP/++/3a8mzZsoUFCxYQERHB8OHDiY+Px263Y7fbcTqdUllst9vR6XRShZyfn0/dunWln7LJZCInJ4ecnByZmbN//37sdjvfffcd06ZN83st/OHrr78GyqwCfCEsLIwHH3yQkSNHYrFYqFmzJgDR0dEVvL49xyAxJnlmPfn6PrnYSD9/46ggKHNycqTPuU6nk99WcXFxZGVlkZGRwZEjR3A4HFx99dUBj1VSUsKgQYM4ePAgc+bMoV27s3Nr27x5M+Hh4QwcONDrelqtVq9vOYPBIJXPGRkZtGjRAlVVyc/PZ+7cuXzyySccOnSIQYMGBTze4MGDadWqFc899xxjxozh9OnTPPXUU1Vud1paGgBZWVnUqVOHkpISYmJiAo4z4j0QExPjFSi92MamEEIIIYQQQgghhBAqR6BCeDMA9a//V1gesqcIHr5UMeXTvIWnsSA1RIEzi8WCRqMhPDycO++8k5ycHDZs2FCpF+jKlStp27at/OAvj6ysLDZv3szVV19N8+bN5d+rV6/uNwV827ZtFf4WGRnJgw8+yIwZM3j66aeDtgXQ6XT07NmTlStXVijGVxk81bWCLBHqIZGGC2WTMZEemZycLL2ioWxi9k8oPStTUPmbsIvJlqdKzJcCVpB6TqeTkydPsm7dOh588MGAbTp8+DCPPfYY27dvR6PRcOWVVzJmzBiuueYar3TmQMjIyOCtt95ix44d1KpVi6FDh1K3bt2gtg0G119/PdOnT2fXrl1ceumlAdft1KkTt956K3PmzKFnz56VWqc0aNCAHj16MG/ePPr160fr1q3lpFf4qhoMBrKzs0lPT0dRFOk97nkvVVUlOzu7gjLccz1xX/+pAozB2KN4jlfl1xHnYjQaKwQz3G63l2IU4MSJE3zwwQfccccdXHXVVQHbduTIEe677z527dpF3759pZXA2WDr1q08/fTT2Gw2XnrpJa9CnMGibdu2VKtWjY8//pgePXoE7cmZkpJSqf3LG2+8wZkzZxg3bhw1a9bkzJkz8llOSEjweq5F30lOTvbKQvCEJ4l2IdLAq2Kz43A4cLvdkhD2pXT1RSxrNBq0Wi12ux2r1cqCBQto27ZtpSQflJHygwcPZs2aNXTv3p033njDb8FVm83GokWL2LFjB40bN2bIkCEV+l0gC6V9+/ZVKGQm3qEnTpygWbNmqKrKnj17ePnll/nuu++q5KVstVqZO3cuV155ZcCgbN++fXn88cdZvnw5l112GU6n0yvAaLPZ0Ol0uN1uqRT1lfVUfhtf1hT/lL2TgD9/dUB+D2i1Wln8z3PdsLAwwsPDWbt2LVqtliuuuMLvcVRVZfTo0axZs4bJkydX2UNbwOl0sn37dtq2bXtWBLx45zz++ON88803fPPNN6Snp1OvXj2/9SygLKPhm2++4dFHH+X1118nJSWFBx54oErHzszMJCkpibS0NBRFwWg0yu8Ms9ksv8cAaeHkL1D6X7WoCCGEEEIIIYQQQvg3I1DYfwdlthT+fkIIEuKj2Waz+f2b8DQWFaojIyPR6XRYrVZKSkp48skn+fnnn/noo48qVbocOnSI/fv3c9111/ldZ/ny5Wi1Wq6//vq/fX4xMTHcd9996HQ6xo0bJ72ZK0Pv3r2xWCxs3LixSsdTVRUoIyNEKq3Vaq2wnlC2FBcXY7FY5ERHbH8xQkzYPVNuPSflnv0mMjKS6OhoSZ5bLBZyc3M5c+YMBQUFbN26FYfDEfAeFxcXM2jQII4cOcIzzzzDzp07+eyzz7jxxhuDIoydTidz587l/vvvZ9++fVx33XWMGzfunBLGAB07diQuLo5Vq1YFtf7YsWOJiYnh+eefDyqVfMqUKaiqSs+ePVmxYoVMqy0uLiY/P5/ff/9dejNWr16d5OTkCsSv1Wr1qQz3hK97eiEh7FF8PS/l1/EcrwQMBgOpqamSGBC+lhaLBYvFQk5ODhaLBYfDgdVqZerUqQDyX19QVZU5c+bQuXNnjhw5wnvvvcfMmTODIowdDgf79+9n7dq1vPPOOzz22GPcfPPNPPTQQyQkJLBgwQK/6szKoNFo6NWrF+np6axduzbo7ZKTk8nJyfG7PCsri9dff52+ffty2223ERUVRfXq1aVNg8lk4tixY5w+fdrrHoigly+1XmRk5AXNnKisH4k+4Xa7ZdvKWwl5npsnsVx+W6PRyJYtWzhw4ABjx46t9NkpLCykb9++rFu3junTp/PVV1/5HY+OHDnCgAED+PXXX7nhhht47LHHzjpQEQiKotCqVSs6duzIt99+Kwt/BoNXXnlFeoIHQmJiItdddx3ff/89JSUlGAwGr2tuMpkoKCjAbDZ7BSHF90Z5+LpPAsGMI+UhguDn4v0rLBVEDQZPREZGEhsbS1JSks9nJSEhgerVq3Po0CE6duwYkMicOnUqCxYsYPz48dx2221n3d7du3djs9mqZOHlCxqNhltuuYUnnniCoqIi+vbty4oVKwJuoygKr7/+Otdddx3jx49n2bJlVTpmRkYGNWrUoHbt2nJ8EdZeMTExANJOQ/QVz++YCz02hRBCCCGEEEIIIYRwbhHInuITz98VRTH+9Xfz+W7UvxGBJrKB0j31ej2qqkqPRVGEJicnh8LCQo4ePcrevXuZMWMGI0aMoG/fvjidThwOh98U16+++gqA1q1bk5GRUWH5zz//zNatW+nQoQOnTp3y8m+cPn2633NJSUnxW3QvLS2NIUOGMGPGDEaPHs3o0aPlhALwWYilefPmxMXFsXz5cgYOHOhzvy6Xy2978vPzvYoU2e129Ho9ZrOZyMhIwsLCMBqNssALlBE28P++zRcKwVg7BAPPviRIJEVRpJdxZGQkaWlpxMfH8/PPP2M0GunQoUMFNfeePXsIDw/nmWeeIT09nddff53WrVuTn59Pfn5+wDasWLGC+Ph4TCYTixcvJj8/n6ZNm9K9e3e+/vprDhw44HM7vV7vN6CQn58fsKJ3Xl4enTp1YuXKlezZs8dr3by8PJ/967777uP111/n3Xff9amucrlcUsXZunVrNmzYQL9+/bj99tt58cUXGTJkCDabjby8PPbv309cXBytW7cmLCwMu91OdHS01331fKYrS8O9kEo9z/172qMoiuLV74SyUAQLfAUNRDCjuLhYqubNZjNZWVlERUVJoiY8PJxff/2V7777jpEjR1KtWjWpQvbMqMjJyWHcuHH89NNPXHHFFYwYMYJGjRpx8uTJCsf+/vvvSUhIQFVVNm7cyMaNG8nJyZHnINR4SUlJXHXVVXTs2JHffvuN3377jX379vm9PkVFRX6fv4SEBGrVqsXs2bO55JJLvO6ryWTymfERFxfHjh07/JIk06ZNw+Fw8NJLLxEbGyuzTDxTusW4FmwhRc+x4EIExTzHIV/HK+/d7qnIj4yMpLS0FIfDQXFxMQkJCZSWlmIwGGQhSofDQXh4OImJiSQkJPDJJ59Qv359brnlFlkk1hdWr17Ns88+S3p6Os8++yxt2rTht99+A2DZsmVe48auXbtYvnw5Op2O5s2bEx4e7jcolZ2d7fdaKIoi3y/lsXnzZq8MHZ1OR0lJCVOnTuWmm27yq1zV6/XEx8czZ84cFi5cyMiRI+nRo4dcLp6l8ujfvz/Lly9n06ZN3HjjjaiqSnFxMaWlpaiqSnR0NCaTifDwcOmXHxcX57NflbdSCjSOVAbxjjqXilPxzlNVtcL+FEWRP54Qlk46nY6dO3cyfvx4r6Ci8D4G+Pzzz3nllVfo378/I0aMYM+ePV7fNJ7wNV55Ys2aNcTGxhIZGcmJEye8lh04cMAv+b57926/79POnTuzf/9+Ro8ezRdffMH1118vlfSKolTI7Hjqqac4ffo0w4cPJykpiW7duvncr8vl8vq2zMzMJCYmRj6v1atXl2SwyBwQ46Cv4INYt7xt038FwXhmX6j9nKu2hBBCCCGEEEII/y0EKoQHgKIoLYH5QELZr0oOMEhV1T/Pd+P8tKcGoFFV9aTH3xT1IpaPeqanexIAosI0lE2ihA+v+DgvLCzk+PHjvPbaa7Rq1SpgYSVPLF26lBYtWvj1PN61axfh4eG0b9/+3JzgX6hevTpDhw5l5syZvPfee4wePTqgd6NWq+Xqq69mxYoVWCyWoD1ehbLIZrNhs9lITU1FURSioqLIysqiuLiYmJgYef7R0dFym9TUVOCfswc4W3j2G1/Xqfwk3u12s2bNGq6++mq/Pq7z589ny5YtPPLII7Ru3bpK7SkqKmLRokXYbDZuu+22s6okX1V06dKFVatWsXHjRm699dZK1+/WrRtr167llVde4brrrvMb8BCoU6cOa9as4b777mPChAn8+eefTJgwgTp16gBlQZNq1aqRn59PUVGRlxUK/P9zXn4o8kX6VVYQ8XwhULCkvE+ovyFVEIWez5DVasVsNpOWlkZqaiolJSW89tprGAwGxowZ43M/2dnZ3HDDDRQUFDBp0iTuu+8+Dh48GLD9TqeTRYsWsX37dho0aCAtePLy8iShfy6hKAp33HEH06ZNY/PmzQFT2QUSExMpLCykpKSkgq3Bvn37mDt3LsOHD6dx48aYzWYOHz6MyWQiISGBuLg4qlevjsvl8ipu6auvXKjAg6/+66+vC/gKlJbfT2ZmJhaLRQb7DAYD+fn5OJ1OSYrm5OSwa9cutm/fzjvvvOPXjxjKyK1x48aRm5vL5MmT6dChg8/1bDYb3377Lb///jt169alX79+bN68+ayuTVURFRVFnTp1OHbsmAxg+sPGjRt5+umn6dmzJxMnTgxq/9dffz0xMTEsWbKE2267DZvNRm5uruwnwvaqoKDAKyvC0xNb9KVAVkpnE3StasHgYPcnAsXi/ETdAqg4tgrF9YoVK3C73X5J+2PHjvHMM89wxRVX8PLLL/+t5ysrK4vjx4/TtWvXc/qcRkZGMnToUL7//ns2bdrEiRMnGDBgAAkJCT7X1+v1TJs2jWHDhjFw4EC+/fbboN77mZmZtGjRAp1OR0FBAQUFBRgMBi//cYPBIFXfNpvNq0inRqM5ay//v77b/tUs8x133HHR7OdctSWEEEIIIYQQQvhvIZiPsQ+Ax1VVraOqam3gCeDD89ss31AU5XbgK2CJoigvKIpyK4CqqqryT+V7VxHi41moibOzs8nJyZFKE1H4LikpidTUVL7++mssFguffPJJUHYBhw4d4o8//vCrIjl8+DDHjx/n0ksvDTh5E1XW3W43qqrKn8pQr1497r//fk6dOsWiRYsq3aZXr17Y7XZ+/PHHSvft2TaA0tJSnE6n9J41m82YzWYKCwsxm83k5eVhNpsxGo3ExcVhMBiIjo4mOjr6X6d2CZQqDEjS3Gq1oqoqW7Zs4fTp034LeP3yyy988skn9OzZMygC1hNFRUV8/vnnOBwO+vfvf0EIY4D4+Hhat27Nli1bcDqdla6vKArDhg1DVVWefvrpoPqv0Wjkiy++4PHHH2fhwoU8/PDD5OXlUbNmTRISEnA6nURERPhMi/YHX/cuKirqokvZLW8j4AlPywBfqtaEhASsViunT58mMzOTlStXsnz5ch577DGfCvLS0lJGjhxJYWEhX331FQ888EClz2RRURFvv/0227dvp3fv3jzyyCP07t2bdu3aVSggdy7RpUsXatasyRdffBHUPU9OTpb+1uUxYcIEDAYDjz32mPxbdHQ0SUlJ1KlTh5o1a1JSUlKhkKKv+3I2FgFng8rGnvLwVKx73lPP/ej1emrVqkWdOnWoXr26DL64XC50Op1UphcXF/POO++QkpLCPffc4/eYR48epXfv3hQUFPDqq6/6JYwPHjzIjBkz2LNnD1dddRX33nuvX/Xo+UK9evXQ6XRs3rzZ75iUkZHB/fffT/369fnggw+C7tt6vZ5bbrmFH374gZycHNxuNwkJCbKwWkxMDDExMSQmJhIXFye9/sW9OZ99SRDN5+rdK/Znt9sxmUyYTCaysrLQ6XR+xzGRgbR161YiIyPp2LFjhXXcbjdPPvkk4eHhTJ8+PaCfdTAQ6vVWrVqd9T5cLhc5OTmSDBcICwvjxhtv5O677yY3N5e3335bKut9ISYmhrfeeovY2Fj69evHsWPHAh7XarWSl5dHrVq1MBqNJCUlUatWrQrXV/SfvLw8r3/FmBHo3RIIf21/fgb2C4STJ09WqkS/UPs5V20JIYQQQgghhBD+Wwjm692gqqo0dFRVdR1w4aRxf0FRlFjKCOuHgFuBM0AvRVGG/NUuv4yQoihDFUXZoSjKjkBekxcC4uPZ7XZTXFxMZGSklz9qXl4e2dnZ2O12li1bxpo1a3j11Vf9FqfzhMPh4JlnnkFRFJ+kscPh4M0330Sn01VaTMzzcgrCONiCdc2aNeO6665jx44dHD16NOC6bdq0IS4urkqksc1mQ6PR4HK5cDqdWCwWioqKgDLCpmbNmiQnJ5OYmEhMTIxMjz5fRPGF6F+VTbpUVSUrK4vTp09jNpv55JNPUBSFnj17+lz/7bffpmHDhjz++ONVUj+ZzWZWrlyJy+Wif//+fgstni9069YNi8XC1q1bg1o/NTWVsWPHsnr1an7++eegttFoNDz//PPMmTOHzZs3M2jQIElSm81mHA5HlfqTuHfCekZVVaniq+zaX8ixy9OHsjwsFgunT5/GZDJ5+ZJarVYsFgtarRaj0YjD4SAjI4P33nuP5ORkHnnkEZ/Hmj9/Pps3b2by5MlBqd2ys7OZM2cO2dnZDBkyhGuvvfaCBX7CwsLo168f6enp7N27t9L1BUl+5swZr7+fOHGC7777joEDB8p1IiIicLvdxMXFodVqyc/Pp6CgQBJ+4P++VBZ48CT6fSHYvlVVwkcQSHa73e9+NBoNsbGxpKWlodPp5L6FD69QMf7www/88MMPPPLIIwHHvkGDBkkPbX/k3OnTp5k/fz6RkZEMGzaMq6+++rwFGgJBq9XSsGFDsrOz2b59u891Zs2ahdPp5NNPP/VpvRMI1157LWazmZ9//lk+m06nU1peGQwGtFqtV586176z53vccrvdsgibyPgQQauCggKffscimBEbG8vvv//O5Zdf7jMTas+ePWzZsoWHH35Yku1ni1OnTrFs2TLq1q1LbGxslbZ1uVwUFBTw66+/8sMPP7B161Y2bNhQgTgGaNmyJaNGjSItLY0vvvgiYMZGSkoKS5YsoaSkhLvuuivgd51QB2dmZkohgdPp9Oo7QlxgNBrlN5f4Vzyz4n1X1TH7r+0rL0pwEeOee+4JGPC6kPs5V20JIYQQQgghhBD+WwjmC+6ooijPKIpS96+fiUBgJvD8QAu4gEJVVTOBL4A1QDtFUW4MtKGqqh+oqtpeVdX2/iwbLhQ8rSpsNhsGgwGj0SgVoomJiZKImzZtGpdffjn3339/pfu12WwMGDCAFStW8Oqrr1awpnC73bzyyiscOHCALl26BKVaBm8fw6oobkQad3n/vvIICwtDr9dXibjctm0bbdq0ITo6mqioKEkei/RUu92ORqOpMDGujEQ5W1yI/uWLOHK73bLfCHsAu93O+vXrmT17Ng8++KC04/CEw+EgPz+frl27BrQP8YUZM2ZgsVi49dZbffq5nm80btyYBg0asGLFigqklD8MHjyYuLg4Pv/88yoda+DAgXzxxRfs3r2biRMnyqJcCQkJGAwGWbSsqorjKhaPumBjl6/no/zfyhfJE4RNcnIy9erVo1mzZuh0OlatWsWwYcN8El4lJSW8//77dOjQgf79+1faLrPZzKOPPordbmfkyJF/S7F3tmjYsCFApX7fgPSnL28/kJycjFarlURlaWkpp06dwm63y+srxrKioiIsFkvA41QWeKhMIRxs3yo/9lQ2jgoCsvw7JtB+bDabLKAozvvQoUNMnDiRSy+9lNGjR/tt3+7du9m3bx8TJ06U98kX/vjjDyIiIhg6dCjVq1f3u96FgCAQCwoKfC6Pj4+ntLSUmjVrVnnfq1evRq/X06JFC0nCC1ujC1Wg83yPW8KCwmq1ysyHqKgoUlNTSUxMDFh8uKSkhNzcXL/XtmHDhiQnJ7Nu3bq/5Q+en5/Pk08+SWlpqd/gbXm4XC6OHTvG2rVrWbhwIUePHpVtbdu2LWFhYfz+++8+2xUfH8+QIUMwGo388ssvAY/TuHFj3n//ffbv38+0adP8rpeWlsbw4cNZsGABr732mtdYJSC82H19cwULf2PKX/s5tx9sIYQQQgghhBBCCCFUCcF82d0PJFNmC/EVkPTX3y4IFEWJB1BVNY8ykvhFRVFS/vp9LXAICCybvYihqio5OTkUFRVhtVoJCwsjJSWF+fPnc/r0aV588cVKJ3LFxcXcfvvtrF69mnfeeYdhw4ZVWGf27Nls2LCBBx980G8leU+UP6aiKFVSZRmNRmJiYryK7PmDxWIJ2h/Rbrezbds2OnfuTEJCAsnJyeh0OrRaLXl5eT6JDDEhEd6gwaZZ/5MoP4nyNamy2WwUFRWRk5ODXq+X12LkyJE0btyYZ5991ue+BfFVVeXTunXr+PHHH2nTpk1QpEtRURG5ublBK9SDgaIo9O3bF5PJFLQ6PSIigltuuYWVK1dSWFhYpePdcMMNTJw4kc8//5zt27djMBikuv3EiRNkZWVV2p88/RwvNksKTwQiWjQaDdWqVSM5OdmngiwsLIzo6GhiY2NZuHAhWq2W++67z+dxvvvuOzIyMhgxYkSlbXK5XIwfP56jR4/Sr18/atWqdW5OtooQgT5/Bc88IVTE5UnjyMhI2rZty2+//YbJZCI/Px+tVktCQgI1a9bEYDAQGxtLYmLiOVFRC3uIYAOEwaIyMjqQYl3A7XaTm5tLUVGR9EDVaDTExcXJYm0PPPAAsbGxLFmyJKDKecmSJYSHh3PTTTf5XcdisXDo0CFatWpV5UDZ+YB4l/q7hm3atMFut/P7779Xab8mk4lFixbRvXt3eU01Gg3R0dHUrVv3rAvQqaoqlb0XA0SwKioqCrfbTU5ODmazmejoaOnTXF4dL65HeHg4JpPJr4I7KiqKUaNGsXXrVjZu3HhW7bNYLDz11FPk5eUxZcqUgEVeBU6cOMGiRYtYu3Ytp0+fpmHDhjRq1IiePXvSunVratasSbNmzcjPz/drMaDVamnbti379u2rNMDVs2dPBgwYwBtvvMHu3bv9rvfGG29w9913M23aND7++OMKfcBXkKiqljZVXf9iRd26db2KMCqKwvr161m/fj2Kosj6CCGEEEIIIYQQQgj/JgQzM62nquooVVXb/fXzqKqqvuUx5xiKolwDfKMoyi1//WkWkAGM+Ys4zqWMyO6pKEpFSeU/BM/USX8Q6hi73S4L0kRFRaGqKpmZmbzyyitce+21dOnSJeCxDh8+TI8ePdi8eTMffvghgwYNqrDOt99+yxdffEGfPn3o169flc7l7yhtqlWrxunTpyvdv9VqDXoyu2PHDhwOB5dddhmqquJ0OiVZ6nK50Gg0pKamehVbKU/aVdVX73ygfB8pTwqXn0R5emGL9SIiIrDb7RQWFmK1WtFqtTz//PNkZGQwd+5cv+RkXl4eUDXSOCcnhzfeeIMmTZrQpk2bSte32Wzk5+djMpk4deoUp06doqioyKtS/dmibt26tG/fntWrV/tV6pXHHXfcgcPhYNmyZVU+3qhRo9DpdCxcuFBed4PBQO3atUlNTa3Qn8rfSzGpFjYpF6v9uufk32KxUFpaKtOORduFkszfOVitVj7++GNuuukmn4EFVVX54IMPaNy4MVdffXXA9qiqyuTJk/nll194+umnA6pIzzfE+FSZ+hf+X2nsy9P4sssuk2NYREQEsbGx1K5dG41GI31ZIyMjqVat2t8ukChUgcEq8oNFVewq/AW/rFarHK+F0tjtdlNSUoJWq+XOO+8kJyeHr776KmCAqrS0lK+++ooePXoQHx/vd721a9ficrlo165d1U/4PECQxv7ujRhjK1OMlscXX3yB2Wxm1KhRMgvH5XJJYt6XUjQYXCj/7Mog3puA9PC3WCxe31DgndElniubzYbL5aKwsFAWy/WHAQMGUKNGDd5+++0qt9HpdPLcc89x9OhRnnvuOZo3bx5w/dLSUjZt2sRPP/1EVFQU1113HXfeeSedO3cmJibGK/hSu3ZtEhIS2Lt3r99g7KWXXorb7WblypWVtnXy5MkkJyczbNiwgEGgDz74gJtvvpk33niD999/H5PJhMvl8ptx4GuMCJShcLaexxcb0tPTvWqAqKrKlVdeyZVXXomqqhw/fvyfbmIIIYQQQgghhBBCleG/FPn/43VFUdKAL4EvVFXdc57bVB4GoM1fRZeWKooyHxgAzFIUZQJwyV/r/W05YyCCVPiQBgMxwYIyhVr5j2ThDRcREYHT6SQsLIzY2FhcLhd5eXm8+uqrFBQU8Pjjj/slxf744w927tzJlClT0Gg0TJ06lTp16shJ5oYNGwD4/fff+eabb2jYsCGtW7dmw4YNHD9+nF27dgU8V3/Xwh8JHBcXV8HvMyIigsOHD7Nnzx6/3oAOh4PS0lL0er3PCamwnRAQio327dsTHh4uC/UlJSVhs9mIjIyUBbpKS0ux2+1ERERIMuyfKoBX/nqW7yMWi4WsrCxSU1MxGo1y8hQZGelF3sH/+wzC/xcDtNlsfP/993z++eeMGTOGNm3acOrUKZ+WIsIyxG63k56e7rO9ngpxt9vNJ598gt1up3fv3vz5558cOnTI53Yul0uSxaJQkdPpxOl0curUKbKysmQxJs+2xcXFsWXLFr/Xr/yz16hRI3bu3Mm8efPo2bMnBw4c8LmdXq8nJSWFmJgYGjZsyGeffcZVV10FlE2GRaX5oqIiTp48ScuWLeW18dxHjx49WL58Oc899xwlJSUoikJ4eLjsm+UV4MJ3UhSME+tVZRzxBX/P5dnu03N/nsUUhbpY9Lvyhc0E+S+eOfH/L774goKCAgYOHCh9xj3x008/ceDAASZNmuSTVN27d688xsqVK/nuu++4/vrrSUlJYfv27X490k+fPi3Hn+LiYrKystBqteh0Oi+FYXh4eIUxIDY21m//SUlJkX1dq9Vy8uRJr74vCOLyMBqNZGRkVBjT2rZti81m47fffqNNmzZye6vVSmZmpky3F9Yv5e+3qqoBxzDP9T3HkGCCf/7WcbvdXv2rfH/2B0EGFxUVYTabSUxMxG63y36hKAoRERGYzWZJaMbGxvLggw+yZcsWPvzwQ+rVq1fhHZiZmSlVjVu3buXMmTP07NmTrKws0tPT0el0Fdry9ddfEx0dzZ49e/jzzz8rLC8uLvbZHwUCKSDT0tL8eqzb7Xaf70zx/GRmZvrs06qqUrt2bTZt2sTAgQMrLPeV9aOqKh999BGtWrXikksuwWq1YrfbcTgcXoSd5/0rD399S6x/IbMkfPUtMTYJayaXy0VMTAyxsbFe737P9cV9TUlJQaPRcOrUKVRVJSoqqgLxKvoiQL9+/XjzzTc5ePAgNWrUICsry2+myqFDhzh69CiqqvL111+zZ88ebrnlFmkX5e97q6ioiF27dmG320lJSaFatWpkZWXJLAWn01mhWF1CQgL5+fns3r3bb2ApLS2NxYsX07ZtW5/vBk9ydtKkSQwfPpwxY8Ywfvx4VFX1+d3wwQcfUFRUxMSJEwG4++67ZRtF8UFP9bY4hvhGs9vt8tulfD8KdkwJIYQQQgghhBBCCOHCo1LSWFXVq/4ijfsD7yuKEkMZefzSeW8dFAH5lHkZX60oSjZwEvgc6AO8AeiBUaqqFl6A9lSAmBgLcggqTrDKf7RbrVZyc3M5c+YM+fn51KxZk6ioKPLz8zlx4gRz5szhzjvvpHXr1n4nhwsXLuTTTz+lTp06TJo0ibS0NC/iSlEUtm/fzvfff0/dunW5/fbb5b7Wrl1bYZ8CMTExUrFVUFCAw+EgNTUVRVHIzs72qwSx2+0VUsdzcnL4/fffiYqK8ksaixTKpKQkn8pXl8vllU68efNmWrZsiV6vJzc3l5iYGMLCwjAYDERFRXkRxzk5ObhcLuLi4v62au9co3wfKT+pF2n/UKZuzM7OJiUlRf7N7XYTGRkpSc+9e/cyZswYWrVqxYQJE1AUBb1e7zMVWxBZTZo0oX79+j7b55naunHjRg4fPsxtt91GzZo1WbZsmV/yoLi42CsoItSEERERkrwoKCigoKDA67mobKJYPvU8KSmJjh07snXrVjp06EDt2rV9bme32+VE9tprr2XmzJk4HA6SkpLkNQJ45plneOutt3jmmWeYOHEiiqKg1f7/8NivXz++//57Nm/eTNu2baVPqKIocgwQBb7E38W/FzMCEQqe90wQNeL8NBqNVPmJa5CVlcX7779PixYt6Natm899v/vuu6SlpdG/f3+fxERJSQnR0dEcOnSIFStWcNlll3HzzTejKArFxcV+Cy/+9NNPQZ9zeHg4NWrUkH3Y4XBQo0YNn+vqdDrq1asHlBHB4eHh8ner1ep3u+TkZHJyciqco8gc2blzJ61atUJRFJl1UqNGDWw2G/Hx8aiqKtWhDodDXvNAENdbZG9ERUWdk3FPjEeVHRfwehbEOCCKltrtdiIjI7FYLJhMJjnGGQwGcnNzKSws5L333mPBggU8+eST3HLLLT6P5xmsWbVqFQaDgd69e8uisuXfI8eOHePYsWPo9Xr279/vc59FRUWEhYXJcah8IVjPZeKchfVDfn6+30yZ5ORkGYjyhNvtZu/evSiKIsdwT5SUlHDZZZexdu1ajEZjhevv6z38yy+/sGfPHiZPniyDEVFRUURGRlJQUEB8fLxXcThf99TffVYU5aytLc4lPIlIoS4OZIUSFRUlAzCetSSgzAO4/PtRFGaEMi/8N998kw0bNvDoo49Su3Ztv1Yvv/zyC/Hx8axdu5Y9e/Zw9dVXe2XklD+OqqocOnSI/fv3oygKycnJRERE+LSUKO+9HB0djc1mIzMzk8LCQp91C5o0acL69espKCigSZMmFZZ7Bla6d+/OPffcw/z58+nRowedOnXyOTaHh4ezcOFCbr/9dp577jlq1KjBNddcI5/xQP0KvINYgcYTz/HrYn9/BoMnnnjiotnPuWpLCCGEEEIIIYTw30JQsktVVc+oqvo2MBz4DfBtlnru8SvwEzAb2AWMBZYCdlVVp1BGZF+vqurOC9SeCvBV3KqyAkV6vV7+iEmz3W4nOzubN954Q6Y3+oLVauWhhx5i3rx5dOnShTfffLMCkaKqKj///DPff/89jRs35q677vKpvvKFkpISqYKKj48nLS2twnmIlOfK0p5Fu8p7e3pCpHsHMyEtKSlh69atXH755ZSUlOB0OnG5XF5Kx8LCQnJycrxSVi/GlMfyfSQYH1CxnkajwWw2SzLJbDYzfPhwAD777LNKPTvFxDQuLq7Sdqanp/Ptt9/SrFkzOnXqVOn6npN5z4CHIJ78qT3PBp07dyYyMpINGzYEpU666qqrUFVVqvA98dtvvxEeHs6LL77IwIEDKygLe/fujU6nY8mSJWi1WpKSkuQ5CGWx2OZsK8VfLBB9UQRjDAaDTOUWlilut1uOEzqdDqvVyrZt29i5cyfDhw/3OfZt27aNLVu2cM899wQsqmmz2Zg7dy5JSUncdddd54Q4qFOnDtWrV5cFQjMzM6tslWIwGILyNIYyBbKvca9mzZpUr16dX3/9Vdp+CKI1OjqatLQ0tFqtHMtOnDhBYWGhV3+srBDdP2kl4GmrY7fbpeI+OjqaiIgIbDYbcXFxREVFyYKSGo0GnU7Hli1bmDZtGv379w+K3FiyZAlLly7l2muvDTjGr1y5Eq1W69XnVFXF5XLhcDiwWq2UlpZSUlIif4Stg8vlktkcnsudTid2ux2bzSaL9zmdzqCtH8Q4Hugd2rFjR3JzcysoTf3hww8/JDo6mgceeEC2Rbyn3W43TqdT2qDk5OT8KxWdYmyKiooiLi6O5OTkgOOs8HOOjo6W24pgoD9PY4G6devSoUMHFi9eHFTbdu/ezfr162nTpg1du3b1u57VamXTpk3s27ePatWq+SwaWRlq1KiBTqdj27ZtPsexhg0bEhERwQ8//BDU/p544gkaNmzIU089FdD332AwsHTpUlq0aMGwYcPYtGkTRqNRZqgEGnOC/ca5WKxQzhX69OlDnz59Lor9nKu2hBBCCCGEEEII/y1UymooitJMUZRJiqL8AcwANgNVL+kdBBRFafPX8Zp5/PkyIAY4A1wBnAIaAKiqalJV9dwaNlYRwostUNqmqqpYLBY5SRMTOaPRSL169ahTpw6JiYno9XpWrVrFkCFDaNCggc99DRo0iK+//pqBAwcyYcIEn5ONlStXsmbNGlq1akW/fv28FJOVwWazkZGR4ZccEZ6AYoIQaOIpiOCcnBy/64jU40D+ggJr167FarXSvn17SWjXqlVLTkIiIyPRarW4XC6AoCaVFwsMBoNfH9OoqCjS0tLkMk//P4fDwZNPPsnhw4dZuHChX+WwJ8Q1r4yodzqdzJ49m9jYWO64446gyDuhlA5ECgoVryCQA60bCHq9ni5dunDy5EmOHDlS6fqNGzcmJSWFn3/+ucKyAwcOcOeddzJmzBgWL15coUhbbGws3bp1Y9u2bURFRXn1qcjISKKjo/92cOKvZ+kf66yBKth7kscRERHk5eXJ4lOZmZm4XC5WrlyJ0Wj0mU4PMH/+fPR6PbfeemvAdnzzzTcUFBTwwAMPnLMibgaDQY4HNWrUoKSkRHp7Bwuj0RiUpzGU9Ql/41779u05fPgwkZGR2Gw2qbz1JIbFWBYeHi7/zcrKorS0tNKiUVFRUf9YwUUxNkVERHgpEDUaDQ6Hg6KiImkPoNVqvZTrTz/9NJdeeimzZ8+udKz59NNPeeKJJ2jTpg1PPfWU3/XcbjerV6+mc+fOXs+sp0e8J3Gr0WgICwtDq9XKn/DwcPR6vcwuiIyMJCIiQga/SktLsVgsFBYWkpubG5SHtFAvByoS2qFDBwB+/fXXSvd35swZvvzyS26++Wa0Wi16vR6tViuLdgqiNSoqSr4jxftbeP7+m0hkTwKysiAKlPUDk8kkawIAQX0X3X777ezdu5d9+/YFXM9kMrFs2TLq1KnDjTfe6Lf/lpSUsG7dOgoLC2nbti3t27c/q++TsLAwatWqhclk4vDhwxWW63Q6rrjiCtatW4fT6ax0f3q9XtqivfXWWwHXjY2N5dtvv6VmzZrcddddbNmyhcjISEkel4coomgymYIKqvyT49f5wIEDB/zaH13o/ZyrtoQQQgghhBBCCP8tBPO1OgcoAK5VVbW7qqqzVFX1b/53llAU5XpgOTACWKwoyhBVVd3APMoUzjOAx4AfgG6KogSWiVwgiMlLoEmu1WrFZDJhNpuxWCzo9XqSk5OpVq0aNWrUICwsjLCwMA4ePIjL5WLw4ME+97Nr1y42bNjAs88+y2233eb3mCtWrCAtLY1bbrnFp71FZecD/idUwhtU+NX6g8vl4osvvkCr1dK4cWO/6y1atIioqCguv/zygO1auXIlgwcPpk6dOvTq1Qu73S6VsllZWZIoFhMOQXL9GwhjCKzCKb/M83edTseBAwe45ppr6NatW1DHElYOlRVlKSgowGKxcO2111aqyvLEhSQfxIQ4mPaZzWYKCgp8pvPGx8ezYMEC5s2bR0REBNdff73X8sLCQnbs2EHTpk2Bssn/mTNn5DUKxj6gMvylrKraA3sOURkZKfqdpw+53W6XQYCDBw/SuXNnv/eidevW2O32Sq0kMjMzqVevnrSBONfw5X0aDLRabVDq5CNHjrB582auvPJKn8vPnDkjszCEElcojgWZB2XWBgkJCSQnJ1NQUMDp06fJy8urtGhUZZku5wKVBRiE+tZgMEi/WUGEiyyEiIgISeRptVrMZjN33nlnUMGXVatW0bBhQxYsWCDV476Ql5dHcXFxheKdnlZS8fHxhIWFSYI+LCxMKoE1Go20oihv0yHsNzz3GRERERQZaTabKSkpqWDp5AnR14IJnEyaNInS0lKuv/566Y0tCsQK0l4cV6/XEx0dLdWhOTk55OTk/OuUnYIIFl7D5cctz2Kzwts4OzubRo0akZKSwocffljpGHDjjTcC8OOPPwZcT6/Xy8KDgb65xLdeXFwctWvX/lte9Pn5+SiK4tdXvVu3bthsNn7//feg9tm8eXM6dOhQoTaFLyQnJ/PBBx/gcrk4ceKELOxpMpk4c+aM1zgp+lhWVlZAb3CBCzF+XUgMGzaMYcOGXRT7OVdtCSGEEEIIIYQQ/luolOVQVbWTqqpvqap6qrJ1zwZKGYzASOBhVVVHAkOA8Yqi3AccBK4EnlBV9VPgU+B1VVVN56M95VFeJXw2iIqKkkSKyWSSPquiwFRpaSlFRUWsXbuW8PBwnx6IAHPmzMFgMDBo0CC/xzp16hQHDx6kZcuWZ/XR7Xa7iY+P9ztRFemecXFx6HQ6n8dQVZWlS5eSnp5O3759/fp+HjhwgPXr19O/f3+/E5/S0lImTZrEzTffTM2aNfn4449xuVzk5OSQn5/PyZMnyczMlBNfUcDrf2XCEQhut1uq2wIRJ+VxxRVXAJUr2ASh5ctr2h/KF0o7n1BVld27d1OrVi2fRHB5rFu3jpKSEq699toKy5o1a4bb7SYlJYWtW7fSv39/r+UzZ86ksLCQJ554QhYtS09P59ChQ9IORZBonuq9YNPVQfpbV3rhzsWY5AuByEhPklCsJxSL4pwPHTpEw4YN/e7/gQce4PLLL+fVV1/lzJkzftezWCzn1T/V6XSiqmqlNi5nA1VVee2114iKimLChAkVlptMJn799VfatWsnx1IRADKZTGRkZFBUVITFYvHyyU5MTKRatWokJiZKda6nP+3fhbiHwUIEGDxVteX7iDg34U0q/Ht1Oh1xcXE4HA5yc3PJzs7G7XYTFRUli3QGgnjuO3ToUClBK4rQlffU1+v1hIWFYbPZqnTewkrE4XDgcrlkVkV8fLz05Q+GNBb9v2PHjn7XEUG9unXrBtzXb7/9xieffMJtt93GJZdcIgvZCq/c6OhoSbCeOHGCo0ePyvGptLSUpKQkkpOT/zXKTkECC69/s9mMVqslIiLCK5AhgvXCHzcpKUkGCR599FHWr1/PmjVrAh6revXqNG3aNGAdCCjz++3RoweZmZn88ccfftfTaDQ0aNCAvLw8v0WOg0FWVhYFBQVccsklJCUl+VyndevWREREsH379qD3m5qaGtBOzBPiG1FkCxUUFHD06FH27dtHVlaW7GN6vZ6kpCQMBoMcc10ul1ewP4QQQgghhBBCCCGEixf/uAxTLYMZ2AHEKIoSrqrqVmAA8BxQV1XVdqqqLlMUJUxV1QJVVauWV/w3IPzVfKlYKkuJFBDKCaPRKBU+Yt8mk4nMzEwOHTrEvn37aNmypU8yIzc3l6VLl3LHHXcEVFUKv9YWLVpU5TQlwsPDq6Qq9deG3377jauvvprWrVv7Xe+jjz4iOjqaO+64w+fynJwcbr/9dqZOncott9zCnDlzqFu3LmFhYURFRUlVV3x8vFT5XKwexucDwpsyLy/P78TRF1JTU6ldu7bfiu6e+4fg/KYFBDlUUlJy3hXHx48fp6ioiFatWlW6rsViYeHChaSmpvrsk59//jlbtmxh8+bNFZ6dwsJCZsyYwY033kirVq2IiooiNTUVg8FA3bp10ev1mEwmioqKsNls2Gw2srKyglZWCfwV6Kh0QDlfno++1O5inLNYLHIcFIXbEhMTZeGo9PR0ioqK/NrqiP2//fbbuFwunnnmGb/9w2w2n9fClYLoPFfWF57YuHEjmzZtYvjw4T4DOVu3bqW0tJRrrrkGg8EgyV+dTidti8T193zvaLVaUlNTpYqxMlV4VVFaWhp0f3K73bjdboxGo9c19GyTRqNBr9fLoKjwDc7NzZV91+12S19jl8tFnTp1OHnyZKXHT09Pp7i4mEsuuaTSdf2RxqKwm0idD2asUlUVh8MhFcWRkZHo9XpJmlUlUHnmzBmio6MDKo2Fl3Egxb2qqowdO5bExEQee+wxSf4KJb24pxaLRf7fbreTm5vLiRMnyM3NJSwsjOjo6Is20Fr+W0v0M4CUlBTS0tJITEwkLy/PS3EsgvXCTkgU7ywsLOT222+ndu3aPPvss5Xe+x49erBly5ZKbUcuueQSqlWrxk8//RTQEqJOnTpotVqfthLBwGw2c/z4cWJjY2nWrJnf9SIiImjdujXbt28P+l2cmppKbm5uUEFfcT0EYSxsy0QGmAjk2+12OW4JO5a8vDxOnTpVZYugEEIIIYQQQgghhBAuPII3uz3/OAP0AL4BSlRV3a4oyj3AG4qiHFBV9bCqqudfvlgOguAV1eAFPCculVV5Fj6vnvtTVRW9Xo+iKISFheF0Ovn555+ZOHGinHCcOnVKEgizZ8/G4XDQu3dvMjMzOXLkiE9F06pVq6hbt25Qii1f0Ov18rx8oaSkxO855uTkcPToUX788UcaN25My5Ytpa9n+eIq+/btY+vWrdKKo7ziZOvWrTzwwAMUFBQwadIkbrrpJrRaLSUlJeTn58trrtPpSEtLQ6PRSC/Hs0k9L4+/M4E+m2MH2kaoAO12O3q9Xvo45uXlUVRUhMPhkMo9TxQXF/slxho3bszGjRvJyMjw2Y/27t3LoUOHgLJ+6FnVXRSP8gWRyi2KR5W3bDhbBfLRo0cr/G3Tpk3odDpSUlJ8Vp2Hsn51/PhxxowZw+HDh3n22WclGa4oihcRIMhiu90uiwwCjBs3jqKiIoYPH47JZJJepELxJkg0z4JGQvlcftwQx/07EGOIpzLwbPu759jkCUE4CYWiIJSFV21JSYkkj7Ozs6VPYr169XC5XPIal0dKSgoPPPAA7777LgsXLqxQlOf48eNSHbh7926vZWazOWgVXHl49g/RNkGkRURESHKxPBISEqRftiCjxO9ardbrPEtKSnj55ZepXbs2N954o8+xct26dYSHh9OtWzfcbjf5+fky2CWKswHyOdfr9V73Vvxf9M3y/ets+5YIxPmC2L+qqlLdbDabpQ+9WC7aGhERgdlslmSx8GuOjIyUBeNEcTaRql9YWEhiYiLp6em4XK6ApNv69euBsmKD5b3Mjxw54lX09ffff0ej0ZCTk0NpaWmF94xOp8PpdKIoSkDVo7g3wotZVVU5lmm1Wr/exKqqVuhbqqpy5swZ6tWrF1Bxf+DAAZKSknC73RXeySKosmXLFjZu3Mj48eNlRoiwAhHX3mAwEB0dLb3ja9asKcdlRVG8gqxCpSzUuf8kkSz6VflvLdFe8YxAWSBNPEeiH4pzs1qtchtB+EdFRTF+/HhGjBjBl19+KX3Wfan3r7jiCt59911+/vlnLrvsMp9tLSwsxG6306FDB7755htWrVpF+/bt5fLy/uapqalkZmZy8uRJ3G43RUVFPvcbExPjtW1paSkZGRmEhYVRo0YNvxZT9erV448//qBGjRps376dNWvWkJKSIpcnJCT43E6o0g8dOlShwDKUvU9FVpjwhk5MTCQ8PFyq9uPj40lJSZHjpRijFEWR90bsIzExUS4LIYQQQgghhBBCCOHiRJVIY0VR0lRV9T/LOQsoiqL8pTaeqSjKF8AsRVFGAFZVVTcqivI7cEFy2Hx9uHoqkgTKK608JyWeKcWe+/Dct0hxFWolgOXLl6MoCoMHD5Yknmcxm0WLFtG5c2epqjQajVLRIXDq1ClOnTrFoEGDvCbO5REXF4der8fpdEoyRXjjifb7QtOmTf36dGo0GurWrcsHH3xAo0aNmDZtmhdh6elrrKoqkydPJikpiSeffFIWURHL3nrrLZ566inq1avH7NmzueSSS9DpdBQWFspiceIaO51OnE6n9DD2xD8xEREEb1WPLQgZX964iqJgs9m8/E/tdjvh4eGyX6amplZQqCckJPhNwe/Ro4dURPlSpe/bt48DBw7IdFrPNoWFhfktXGi1WjEYDGRlZaHVar1sR3JycnwWvRMBlIKCAlJSUmjfvn2F9cqriS0WC4sXL+byyy+nZcuWfv2cDx48yOjRozl16hRz5szhmmuukcsEyeALglyYP38+8+fP57HHHqNHjx7k5eVhNptJTk4mLS2NhIQESTwLla6qqj7V+uK5/7tkjMhcOBcQAZbyfc+TcNJoNFL9qygKVquV8PBwSTQmJydLkq9hw4ZSuegPo0aNYseOHUyfPp2bb77ZSwWq1+tlynx5y5GwsLAK3rQCO3bsAODkyZMkJydXGBs9fz948CCRkZHS2zs5OZkePXr43G9+fr5UT4txUfyuKIpX4cn33nuPkydP8umnn9KwYUOfJOzmzZvp2LGj9CR1Op0ytT4iIkKSVmJME4EJcW9Ev/HXB87HmCfeX0LhbjQapY2J57tN/Guz2TCbzfJ6uVwuSktLJZlpsVgkWSuI3KioKGrUqMGaNWsICwvzS2pBmQI3KiqKli1bVvCPjYiI8FJ4WywWae3RpEmTCv1SVVX27dtHdnZ2wMBLcnIyWVlZ1KxZs0JWR3h4uN9sh6Kiogp99uTJk6xfv5727dv7VeY7HA4yMzNp0KBBhb4szhNgyZIl6PV6Bg8eLJ+XkpISOSYZjUb5DAuST6fTSYW7r/eNuM9AUOPM+SrgWT7Q7mlzUn4M9QyieJ6P8DJOSUmR10Gj0VBUVMSgQYOYNWsWY8aMoUePHqSkpEh/a09cc8016PV60tPT/VqDdenShfj4eKDsPffrr79y8803SyVweYuR4uJiXn31Vfld4w8pKSkyM0ZVVbZt24bL5ZJ+xWIMKw+tVktKSgpXXHEFS5cuJSMjw8v2rGZN37WsRXvz8vJ87lt4f8P/CwiMRiOxsbFoNBoKCwupWbNmhQK3qqp69SWROSEQIo1DCCGEEEIIIYQQLl5U9UN/xbk4qKIoTRRF6aQoSrhnG1RVveOv398E7lcU5WHK/IwvKuMzMSnWaDTY7XaZkusvZVikV7pcLpnqbTKZyM/Pl79/8cUXXHPNNT7TVVeuXEl2djb33ntvwHZt2bIFRVEqLSon4EmYxcXFBeXF6A9Wq5XnnnuOyMhInn/++YCp35s2bWLr1q08/PDDFYiV++67j3HjxtGnTx82b97MZZddRmRkJIWFhRQXF2MymUhLSyM2NlYWvQvWjqIqliJni6qkeXvCV9/x5REqis24XC5iY2PlBNefJ7Q/tG3bFkVR+OWXX/yuI4jCqhZ4E4SWw+Hwq0wXUFUVp9NJQUEBdevW5bLLLvNJLJfHrl27KC0t9VJzlcfhw4cZOnQoeXl5fP75516EcTDYt28fjzzyCN26dWPkyJFSfSxU7cIuIFARQ0+Ie3yxFZzy1fc8PWk9VYsmk4nTp0+Tk5Mj+6HVauXYsWMoihJU8TqNRsMbb7xBSUkJY8eOrZDBAZyVv2pERAQ6nc6vyllAqCjPJXJycnj99de5+uqr/faz4uJidu7cSevWrbHZbPLdER4eTl5eHhqNhpSUFFk4Ds69DYU/BDNuCS9rf0VGRR+BMsWisAOw2+1ERUXJc7Db7RQUFMhxWBDKDRs2DKog2969e2nWrFlQRV6zsrK8FJbloShKwEKtnvtJSEio8jjrC0IdHcj/G8rI8UDPk8vl4ssvv6Rr166kpaVJQl70HbPZDPy/etbTszxQvxLv1WCfkfNdwDOYwmiBxmChjIcyEryoqIisrCyKi4uZN28eRUVFDBkyxG/QIDIykiuuuIItW7YE1d7BgweTlJTECy+8IO3CyiMmJoY2bdpUWlfAE8eOHSMzM5MWLVoE3Q8TExOpUaNGhawNfxDqYn+ZF54Q6nrxrIvnMZCNx4X4BrvYMHHiRCZOnHhR7OdctSWEEEIIIYQQQvhvoaqk8d+WAyiK0hdYBrwEzAYeVhRFyhZVVR0AbASSge7ATaqqZvzd455LeE6+hErW7XYTERFRgcT0tBHIz8/3mlQnJCQQFhbG+vXrycjIkFYN5TF//nzq169P165dA7Zry5YtNG3aNKBKyxOiwFJcXNzfKgrlcrn4+uuvycvL47nnngvor1taWsprr71G9erVueuuu7yW7dixg4ULF/LQQw/x+eefExcXh0ajIS8vD5fLRVxcnNe5BUvWCYiJsmfRsnONQGnegRAREYFGo/G6D57FpsS5OhwO6WOs0WjIzc0FqJKnMZRNWps1axawSM7f8ZYV10CQ/SJ1tXyqvcPhkMrS1q1bB3UvVVVl+/bt1K5d2y8hdPz4ce655x5UVWXJkiV06NChSu0vKSlhwIABGAwGnnzySRwOh0wlF6o1YcmSn59PTk5Opf1JjBsXU8EpVVVlETKRQQFI6wmhzBNq47y8PKxWK3l5ebII5YkTJ9izZw+1atUK2ie4fv36PPXUU/z000+sWPH/sUjPlOaqQlEUoqOjpTewUOxarVZycnLIzMzk6NGjlJSUVHn/QvHrD2+99RY2m43nn3/e7zqrVq2itLSUnj17StsBh8PB6dOnZUBR3IPyRQfPt097MONWZeNtVFSUzBiJioqSKvTU1FQ0Gg3Z2dnYbDaSk5OJjIwkJyeHjIwMsrOzKSgokAVTA1krORwODh06RPPmzYM6r5ycnErfh8EESyMjI6lZs+bfVkS63W72799PtWrVAnrFm81mTp8+7aVkL4/vvvuOnJwc+vfvj8FgkOOQw+HAYDDIbBDxzvNUFgfqV8GQtJ4ItoDn+UIgIlJcB5HlARAfH09qaioJCQm0bNmSl19+me+//57x48f7JTx79OjBsWPHyMio/FM0MTGRyZMn07RpU2bMmMHSpUt9rte1a9dKg6oCxcXF7Nu3j6SkJBo1ahTUNgKtW7fm0KFDlXoyw//bKgVDGgezv/IoH6z4L5DI11xzTZUD1udrP+eqLSGEEEIIIYQQwn8LVSWNP/w7B/tLWXwH8ICqqj0oI49rAU8qihIr1lNVdY6qqi8BA1VV/fPvHPN8wHPy7EmoiMma56TaZrPJNPiEhASio6OJjIyUk5iYmBjmz59PampqBX9Pgfz8fBo3blwpoZabm1tppfXyCA8P/1sFoUpLS/nmm284ceIETzzxRMDCLACffvopu3fvZuzYsRWIalH458orr5QEjfAfTE5OJj4+HovFQlFRESaTqQLBUhkEwW82m70K5pzLiYtnCnlVIDxyPb0xxcRer9fLNor0a0G8CGIgPT29ysds27Yte/bs8TtxjY+Pl4rSqiIsLIzY2Fip8iooKMDlcklFvt1ulwo4nU5HfHx80Nft4MGD5Obm+vWXBPjss8+w2+28//77lfZJX1i8eDF//vknM2bM4JJLLsFqtWKz2SShotFo5LNdUlIizy0QxLhxMaTiqqoqgydmsxmr1UpWVpZMYfdEZGQkCQkJUmGdkpJCYmIiERERxMfHU7NmTfLy8gISXL4gVOKe6dlGoxFFUYIiLXwhJiaGsLAwTp48yZEjR0hPTyczM5Pjx49z6tQpiouLiY6OlqnkwaC0tJR3332XY8eO+bTHyM/PZ8GCBdx6661+CZ2NGzcydOhQmjVrxuWXX45Wq6V69epEREQQHR2NVqvFbDaTm5tLTk6OLKIYbGBM3M+zHcOCHbcqGyutViv5+fmkp6fjdruJjo6WAdKYmBhiYmKw2WzSjzYiIkJ6+m/btg2dThcwAPbHH3/gcrmCLvRao0YNfv31VxlcO1vUrVu3yhkX5eF2u1m2bBmZmZl07Ngx4LoLFiwA8Gu7s3//foYMGULTpk3ld4N4N4hnVSjCPcljMUaJ5cDffvcFW8DzfCEQEanRaKRdTWRkJC6Xi4KCAhmwV1WVwYMHc++99zJ9+nQ6derE6tWrKxxD1FSYO3duUG0yGo1MmDCBLl26sGDBAv74448K66SmpnLppZcG3I/b7eaPP/5g9erVuN1umjdvXqX3h8vlYs+ePRiNxqCU+cIXO1BdCwHPd0VpaSlut1tmpvhD+WDFxZp9cy7x22+/8dtvv10U+zlXbQkhhBBCCCGEEP5bqNIsSFXVmefgmDGAmFl/DXwLhAMDABRF6agoSru/lvuvhnMRIZBqJzIyktjYWJKSktBqtURGRpKdnc3evXs5ffo0W7du5ccff+Thhx/2q/Zt3rw5+/btq7QdWq32rAuNnQ1UVeX777/n0KFD9OrVi6uvvjrg+llZWbz22mt07dqVW265pcJyMbkVZKIg6kShmJycHNLT0zlw4ABZWVmSeCwsLAxK6SkIfrfbLe+FON6FSAEvD0H0qKrqsw95Tuxzc3Nl0TtBxGg0Gtq3b09CQgI//vhjlY/fsmVLHA5HhWJSAp06dcLtdgedllseBoOB1NRUqlWrRlJSEmFhYWi1WumjGxYWJn27q4KNGzcSGxvr10e0pKSEFStW0L17d6lerArcbjfTp0+nZcuWXHvttURFRWEwGGRKsOhnkZGRxMXFUbt2bUlKnE8Iv+xzAU/f0srUzw6HQ6rK9Ho9KSkpJCcnk5KSQlhYGAaDgaNHj1aZNP7000+Jioripptukn8zGo00aNCA3bt3nxWRpdVqqVWrllQSpqamUqNGDVq3bs2ll15KmzZtaNq0adCBMpfLxeuvv87atWvp378/ffv2rbDOxx9/jM1mY8SIET738dtvv3HTTTdRu3Ztvv76a0lYOxwOdDodERERJCYmSgWoCAp5Kr+DIWsvBPniz0bHbDZLCyaTyURhYSEFBQVynZKSEvR6PdnZ2Zw5cwan00mdOnWoV68etWrVwmq18uWXX9KvX7+ApPHOnTuBMn/9YDBkyBDcbjfvvffe33p2/k4mDpSN9StXrmTnzp107949oK1ORkYG8+fPp2/fvj7Xy87Opk+fPoSHh/Pxxx+TkJAgg3HR0dFe5GB58rj8GPVPvfuqAs/3pC/4IyLLE+Qiayk7O5v8/HxUVZVBmuHDh/Pmm2+iKAo333wz99xzD6dOnZLHqFOnDjfffDOLFy8mMzMzqHZrtVoefvhhmjRpwuLFi732JyAK8PlDeno6hw4donbt2jJDoSpYsWIFJ0+eZNCgQUHZPol1AhWFFBDBZHFdCwoKsFgsfotC+sLFmH1zrvHoo4/y6KOPXhT7OVdtCSGEEEIIIYQQ/ls458VLAkFV1RJgOtBXUZSuqqq6gZ+B34BuiqJEAl2AU3+tf24YkvMEMZEHpE+g56ReqIl1Op1UJebm5pKdnY3ZbEar1fLRRx9hNBp58MEH/R6nefPmpKenV6r4DA8PD+pj/1xAVVV+/PFH/vzzT7p161apYgbghRdewOl08uKLL/pUywiVi5j0Ch9CrVYrVV56vZ6YmBhZhFCk2wr1cWWqKUH0JScny31eqBTw8vAkesorCj1JIrvdLtXqwtO4oKBAki9dunThxx9/rDLJJtR6f/7pW8yfkJBAixYt2LFjx1mpjQUURUGn06HRaNDpdPK+CUuOquDYsWMcO3aMzp07+1VObd68mfz8fL/K/crw3XffceDAAYYPH45er8dgMJCWliZV6nl5eVLFZjAY0Gq1XvcuWNVnZWRIeZyNX7a/YwjfUkEoRUdHyyKTnhDEZWJioiRiRao7IBXK2dnZfot6+UJxcTFff/01t956a4XiZG3btqWoqIjjx49X6VwFwsPDiYmJITExURISZ9PXRAHSrVu3ct9993HnnXdWGLdsNhsfffQR11xzjU9Fe2lpKQ899BCxsbGsXLmSevXqebVD9KHo6GhSU1O9MljMZrO8zpWRe1X1oT1b+BorPb2MY2JiqFGjhvSdL+/JXr16ddLS0qT1UGRkJA6Hg6+++gqz2czDDz8c8Pi7du0iJibGq3hiIKSmpnLPPfewf/9+srKyzv7E/ybWr1/Pli1b6NSpU6XB1enTp6PRaJg0aVKFZVarlYEDB3LmzBk+++wzmjRpAvy/B7m/d5i/LId/6t1XFQQKiIhvLM8CeIHOKTExkaSkJCIiIrBYLJSWlmIwGKhfvz69e/dm/fr1PPnkk3z//fe0a9eOhQsXym2HDh2KoijMnBm8diI8PJwxY8YQFRXFvHnzKrxHK1P/hoWFceWVV3LppZdWOSMsMzOTb775hssuu4x27dpVvsFfx1MUJSjbDHEuYnwVAa9AfSkQoR/ChUedOnVkccnyP1XNWgwhhBBCCCGEEP53cfaVz84eG4EmwD2Koiiqqm4AFiqKMhSorqrqG/9AmyqFL2LHYrGQlZUlPRuFck9M3MUHsqIokmQqLS0lJSWF1NRUzGYzS5Ys4aGHHiIyMrKCR1xeXh7h4eGyivW2bdto27YtAKdOnSI7O7tCG/Pz8zl48GBAuwLh9ekP/pYJf2aXy8WWLVs4cOAArVu3plGjRphMJun56gsnTpzgu+++Y/To0VSvXt3rGG63G5fLJa+bKIYUFRWFTqcjOjqavLw8nE4nGo1Gkl2qqqIoCkajEYvF4jWpjIyM9ElMe6p3A/3tQsCzKnz5/iX6jlCXRkdHy2JSubm5krhMSUnhyiuvZPny5ezatUtWWoey++hPxSv6ltFoZMeOHXTp0sVrubAHaNq0KXv37uWtt96icePGtG7dOqAVg6qqAYlNf0p4VVUD+kXWrl0bVVVZtmwZRqOROnXqyP4WFxfn9SwsXryYmJgYmjRpQkFBgd+iQSUlJTJQ4dmO1157jTp16tC7d2/sdjsGg0F6/goC32az+SXobDYb+fn5lJSUULt27QrEgOiXnmrfYPrf2fhlW61WCgsLMZvNXoESYbMhfKYVRZH7Fn1RFLnLz88nISGBqKgorFarTPUXmQGir9SqVUs+14GCDDk5OXz//ffYbDb69OlDXl6eXJaXl0d8fDw6nY4tW7Z4XReTycTevXt97tPlcgX02CwqKvK7LC4uroJ6UBCZmZmZDB48mC5dupCfn++1jk6n49NPPyU/P5+hQ4d6KexEuvb777/Pzp07eeONN6T3vbCh0ev1pKamotfr5fUXBKzRaPRaXxAx/giZqvrQVgWeY1P5fuJ2uwkPD8fhcJCQkIDT6cRut2M0GnE6nV42AcKSIj4+nszMTJl54HK5mDt3LpdddhmXXHIJTqezwrUW2L59O/Xr1/fqM57wLLAn0KRJE1q3bs0ff/whn+WqIlB/jomJ4eTJkz6X6XQ61q1bx+rVq2nevDmdOnWSfdFgMHipsQF+/fVXVq9ezf33309KSopXELi0tJRhw4axa9cuFi1axKWXXorJZMJms5GYmCjPS1xvXxD3UlVVSbZ63s+LwTqnPDzfk+Uh3pPw/2Oo8GMH70BbVFQUiqLIAo0iYOZ5HK1Wy4QJE+jbty+PPfYYI0eOpEOHDjRs2BCj0citt97Kl19+yYABA+R3GQQeXwB69erF119/zezZs7nllluCsoqAsjHVbrf7VDc7nU6/gZDExETee+899Ho9vXr1qvCtGMj+R6vVYrVafVoVlZaWyiBfUVGRV8FLsa2iKJSWlmK32+Uz7zkGQFnwv/y4EsKFR6DAbOiehBBCCCGEEEIIAgFJY0VRwoCfVFW96lwdUFVVu6IoCwAVeEpRlKaAg7LCd+ZzdZxzjco+oDwn9WJd8TetVovT6SQiIkKSbQaDgZdffhlFURg1apTP1MFq1aoRERHBlVdeCcCZM2dkun2NGjUqFNIRCs4GDRoE9KRLSUmhZcuWPpcdPnzYbzV5m81GzZo1mTJlCkePHuWOO+5g8ODBKIpCXl6e35Rhu93OwIEDady4MZMmTaqQ6uvp+Qz/rzAOCwtDp9Oh1WpJTk5Gr9dLMtlisRAWFiYVecnJyRiNRux2u1Qd/VMfvb6O6yvoIIgeX/AkACwWi/Q9FevHx8djt9vR6XTS93L9+vVeXplJSUnodDqf+xcena1bt+bYsWPUqlXLa7lnsZTOnTuzfPlyNm7cyL59+2jcuDF9+/alYcOGFfY7Z84c4uLifB4zNzfXr+drenp6QHuDHj16sHr1anJycnjsscfkMwFlk+eaNWsCZeTO5s2b6devH/Xq1cPpdPotlidS5j2xbt06duzYwbPPPiv9Zj3V3+K++AtKQNlzWFJSQkFBgbToEBC2K6JQGPgmQ3xBKFCrgqioKMxmsyS6y/e3QM+IRqPBYrFgMpmIj4+Xk369Xk9YWBiFhYVotVr27NkDQKNGjWSQorx62BNxcXEsWbKEFi1a0KVLF682XHnllRiNRk6cOMGaNWvo1q2bvD6bNm2ievXqPvd59913B/S59WfBAmUEsWfQpKioiOeff54zZ84watQohg0b5nM7p9PJc889R4cOHbj++uu9zkME8J577jmuueYabrjhBrRaLVqtltzcXFnYs/wYLogtQSAXFxd7WQz8ExDKM1/QaDQUFhZSWFhIZGQkiYmJWK1W+XyEhYXhdrsxmUxyvCsoKCA/P196tP74448cOXKESZMmyf7jK9hlt9s5cOAAAwcO9Dn2ALKoZnm0bNmSfv36UVhYyAMPPFBhXDSbzQEL0912221+l+3Zs8evyv7w4cOsWLGCbt268eqrr3q9500mk9eYV1JSwtChQ6lVqxZPPvlkhQJ+TzzxBN9//z3Tp0/n2muvRa/Xywwmh8MhgwwOh0O+G31BURS/AauLiSgSbRGBYV/LPMdQX2232WySME1NTUVVVYqLiwkLCyMlJQWHw4HJZMJgMMjrZzKZqF69OnPnzqVDhw6MGTOGH374gQYNGjBx4kS++eYbFi5cyKxZs+RxunbtKoNovmAwGKhWrRpvv/02+/btk6plKLvvNWrUwGKxsHv3bgoKCuQ4+Msvv8h3W3kUFRVJEUF5bNiwgYyMDJ5//nmfhc/KB0s9odVqCQsL8/keLy0tlX1YfJeYTCby8/OpWbOmfO7tdrtX/xLfY8LOKIQQQgghhBBCCCGEfw8CMhCqqpYCbs8idecCqqoWUFZU71XgauAqyore/XP5o2cBMREQH8Xl0+w809fF5CY7O5vDhw+zf/9+5syZQ//+/Sv1XRX+oYKc8Qeh2jpf2LNnDyNHjiQnJ4fnn3+ee++9N6hJ5uzZszl+/DhvvfVWQG9IMZERSjuRQipUXoJIEGrjyMhIcnNzOXPmDHa7XU5I/hdSHT0VfUajUf5fo9HIQl7VqlWTqbitW7eu4GtcUlLC9u3beeONN+jXrx+DBg2qoMJr2bJlpZXVa9WqxYgRI5g5cya33norJ06cYOrUqUyePJkdO3ZckMrnNpuNTz/9lMaNG/stDgWwcuVKHA4HN99881kdZ9q0aaSmplYgidxutywK6Ene+vKa1Wg01K5dm5o1a1ZQOXsW/jmf6lDPtngWgqpK0UeLxSJVxUItJhTUmZmZFBQUUFBQwLx586hTp45fj+ny+OOPP9i7dy8DBw70e+49evTA6XTy888/B3+y5wB5eXlMmDCBjIwMnnrqKS6//HK/6/70008cO3aMRx991Od5jB07FrvdzoQJE1BVVQYNRZBMBBXLF+4SfeJCWU78HbjdbqmYTkxMxG63S0W+sJ8ApFLf7XZLT2dFUSguLubnn38mPDy8UjuZ/fv343K5zqqwZWxsLH369CE7O5tvvvmm6id6Fjh69ChLliyhbdu2TJ06tVJP2fnz53Pw4EGeffbZCsGsGTNmMGPGDEaOHMn999+PyWTCbreTnJxMTEwMpaWl0p5JqI/9PeviPhiNxovaliIYaDQajEZjhXe+8NkWHuwpKSlERUXhcrmkrZPD4fCy9RBZPMI6rEGDBkyZMoXVq1dLm4rk5GQefPBBli5dGlSdCU9cdtll3HrrrWzYsIHvv/++wnKDwUDnzp254YYbAgYwKkNOTg4//vgjXbt2pUePHlXePjw8PGh7iujoaFkIVoyBomDvxW57EkIIIYQQQgghhBBCcAjGnsIM/KEoyo+AzFdTVXXU3zmwqqpOYK2iKBvKflX/serbZ4tAtgaefsciRS83N5dDhw5hs9lYs2YNVquV0aNHB3Wsli1bBkUa22y2c1YsS6CkpIRvv/2WrVu30rx5c8aPH09ycnJQ2+7fv5/58+dz9913e6lDfUFM/CwWC4qi4HA4yM3Nxe12ExsbKz1KhZJYVVWpKqtqgZh/A6xWq1TACXVq+cmx0+nEbDbTuHFjvvrqKxYvXsz+/fv5+eef2bZtm0wbTU1NJSsrizvuuIMbbrhBbt+yZUtKS0vZv38/bdq0Cdie+Ph4BgwYQKNGjcjIyODHH39k1qxZNGzYkAceeMCvovdc4Msvv6SgoIAnn3wyIMm6dOlS6tatyyWXXFLlY+zatYuffvqJZ599lsTERFwuFzk5OSQnJ2O1WnE6nbhcLi9Cx9OixmAwYLPZZHE/T4WxgCeJdqHgOU5ZLJYqWWJ4KqKFB2hhYSFFRUXEx8fz22+/8euvv/Lee+8FVWgJYNGiRURFRfkshinQuHFjatWqxYIFC0hMTAzKM/3v4uTJk7z00ksUFxfz7LPP0rJlS79WK263mzlz5tCoUSNuvPHGCsvXrVvHwoULGTFiBG63m4yMDGJiYkhKSsLtdnv1YV8p9uA7E0HYv/hTVl5o2Gw2rFarVOSrquoV5LLZbJjNZq8UdSgj30T2zdatW2nXrl2lBNPu3buB4IvglUf9+vXp3r0769ato0GDBn5VmucCGRkZLFmyhOTkZKZPn16pH212djbTp0+na9eu9OrVy2vZjz/+yJgxY7j55psZMWIEJSUl8hoLBWd2djYWi0WSyIIE9dWvxD2JiYn5nwiw+oLVaqWoqEheE3GeIjtHp9NJVazIxiguLvbyetdoNAwbNoz58+fzxBNPyAKsDz30EHPnzuWFF15g9uzZVRrL+/btS0ZGBgsXLsRoNHLFFVec0/N2uVx89dVXaLVannjiiSqPEaqqotFogi6EJwovGo1GWZCxuLhYXsNA8PSjDtau49+IKVOmXDT7OVdtCSGEEEIIIYQQ/lsIZsbwFfAMsAH41ePnnEBV1dJ/I2FcGURaZHZ2tlRxRkZGkpaWJj0ZY2Nj/abZlkeHDh04ePAgR48e9btO3bp12bNnD2+//XaVC2b5Q25uLu+++y5bt26lS5cuvPLKK0ETxuvWrWPo0KHExcUxefJkv+upqsqXX34pib4mTZoQGRlJZGQkSUlJREdHyxTc8krisLAwUlNT/ycnHWIC63a7ycrKwmQyYTabvZRjer2e5ORkbrjhBsLCwhg4cCCTJ0+msLCQ/v378+abb/Lnn39y5MgRoqOj+eGHH7yO0aZNG7RaLStWrAi6XTqdjquvvprJkyfzwAMPkJmZybPPPsuKFSvOuepYVVV27drFkiVLuPrqqwMSRqtXr2bbtm3079//rAi1559/nri4OIYOHSqJX+HrK/y1dTqd9DXOysrymlyLCXN51bY/JemFhGhDsAow4VcrPGBFACwmJoa4uDicTieqqvLbb7+h1WoZOHBg0G0pKiqipKSEzZs3+11HURTGjh2L0Wjk+eefZ9q0aT49Ns8FMjMzmTJlCiNHjqSwsJCJEyf6te8RePPNN9m3bx9jx46tMPacOHGC+++/n8aNGzN69GgaNmxIzZo1ZfG38kXuRJG+8PDwSlXggYqCnUsEq0gXSk1hp2E2mysoDkXRUqFsjYyMlEFAm81GXl5eUEE/kXHyd9SLN9xwA7Vr12bevHl8+umnFBYWnvW+/OHEiRN8/vnnREdHM3jw4IBWLTabjffff59evXrhcDh47rnnKgQUHnnkEZo0acILL7xAXl4eJ06ckMFU4XkviEuRwSBU3p6+2AL/huJ3fxfCn9jpdJKTkyPPPzk5mTp16vj1uRcKZbG+oig8++yzZGdns2XLFqAseDp27FjWrl3LFVdcwVdffRV0oF6j0TB8+HDq16/P+++/z5NPPsmhQ4f8ev1XBXa7nY8//pjjx4/Tp0+foL/TBEpKSpgwYQKFhYWVfpcePXqUjRs3UrNmTSIiIkhLS5N9LiYmRmaJBRo/bDYbhYWFXvfnfxGdO3emc+fOF8V+zlVbQgghhBBCCCGE/xYqVRqrqvqJoiiRQG1VVQ9cgDZd1PBVrdvX8vDwcKnSEyojg8FAXFwc4eHh3HbbbSxatIhVq1YFlUrfr18/pk+fzrx583xWVQe4//77SU1NZcmSJfzxxx/06tXLr4esPzgcDjIzM8nLyyM7O5v169ej1Wq57777qFOnjt/CauWvwUcffcQHH3xA8+bNee211/yqUPfs2SNVPK1bt2bGjBn07NnTq3hNTEyMvOb/S6hMNSiUhoIs8SSKBEmj1WpJSkrimmuuYc2aNeTk5HDZZZfhdrvRarWUlpZKciYlJaVCganExET69OnDl19+SbNmzbjpppuCJuA1Gg2dO3emWbNmLFy4kCVLlmAwGGjTpo1fX+OqoLS0lC1btnDkyBF69uzJ8OHD/a5bWFjIM888Q9OmTRk8eHCVj7Vy5UpWrlzJY489JlO8jUajJMSEzYO4Xzk5OZw+fZrU1FRpEyJQXlXoT/F3IVEVBRggC+BBWZujo6OlvYIoNGWxWCgpKaG0tDSocUFgypQpDB06lOHDh/Pee+9x7bXX+lyvbt26vPnmmyxevJgvv/ySbdu2ccstt3D55Zf7Jd3z8vL4/fffOXnyJKmpqdSsWbOCXzeUPXuHDh1i8+bNZGRkEB0dLVX4gbxJAZYsWcLs2bPp378/AwYM8FpmNpvp378/TqeT999/n9q1a1NYWIhGo6GkpMRL4Sj6jMPhkEVGLRYLqampfonGqvpgny08+2ygYwl7gPJtEwX9BIFZXtkaFRVFSUkJ+fn5XHLJJaxdu7bSQmzCKuTXX3+lbt26Z3VeWq2WRx55hB9++IG1a9eye/duLr/88qDS8QWcTidOp1MWOzSZTJL4ys/P59tvvyUmJoa77rrLr82Aw+FgyZIlfP755+Tk5NCtWzfGjh1boZ7A9OnTOXbsGEuXLqV+/fqcPn1aqv2joqIwGAzy2op+62llJAr0emYb/FOFXy8kxHidk5MjA39Go1HWBcjJyQGQNjGRkZFotVr5DNrtdurUqYPVapUZI57F5IYOHUrr1q159tlnGTFiBC1btmTChAlBZbjo9XomTZrEL7/8wtKlS1m3bh27d++ma9eutG7dukpjqUBOTg4LFy4kOzubfv36VVlFbzabGT16NBs3bmT48OGMGDHC77qqqvLQQw8RHh7OQw89RGlpKfn5+TLI6qly9yyAV/67IjIyUvrti/vzvwgRHP27ZO252M+5aksIIYQQQgghhPDfQqVfp4qi9AGmATqgnqIobYAXVFW96Ty37aJEZQSQxWLhzJkzaDQaqUqMiopCVVVyc3MpKSkhIiKC7t27k5aWxqeffhoUaZySksINN9zA4sWLGTt2rM91wsLCuOmmm2jbti3Tp09n2bJlNGvWjB49eshq1Xa7nfz8fPbt20dRURFFRUXk5+eTn59PXl6eVL8J1KtXjzvvvJO4uLgKy3zhjz/+4LXXXmPv3r3ccMMNPP300z59jPPz83nxxRf58MMPiY2NZcKECVx55ZWkpKRQUFAgVYXJycnExsb+T05y/RUjKg+DwUBKSoqcgAnCLjs7m7i4OEk6tWjRAoPBQHZ2NgcOHCA8PJzU1FQ5GUtISCAvL6/C/h9++GEOHjzIxIkTmTt3LqNGjaJatWpBq2Hj4+N5+OGH2blzJx988AGbNm2iXr16NG7c+KwmwFBGzKxbt47Tp0/Tpk0bRowYEbA9L730EgUFBXz00Ud+i//5gslk4vnnn2fmzJk0aNCAO+64A4fDQUFBgVc6ffkJrVCqJSYmep2jwWCooDrzLJL5T8GzDZUFvqCMeBLqT6HuFx7HLpeLlJQUmdqtqqq0GggG0dHRLFy4kLvuuovhw4czb948unbt6nPd8PBw7rrrLrp168bUqVP5/PPP2b59O3fccQepqamUlpZy7Ngxtm7dymeffUZmZiZQRsx4Kr7FMySeh927d5OTk0NMTAxXX321VJdXhl9++YUXXniBzp07M378eK8+6Xa7GTJkCPv27WPx4sV069YNi8UibU3E9SlP2ol743K5KlVTByqeeS5Rvs+W7zPid08ySBDILpcLk8lEVFSUV58v3/+LioowmUy0a9eORYsWcfToUb8F5aAsK8JoNLJ9+/aAhekqQ3h4ODfccAOXX345P/30E5s3b5YK09TU1Arjh6qqHD9+nMOHD3P48GFOnToVUFmalJTklzB2Op0sW7aMOXPmkJ2dTadOnZg5cyaXXXZZhXXT09N55ZVXuO2222jevLm0TxCFNhMTE6UlCPz/c+rZtoth7PmnUD7QJyCKkQrbMNGXRcFdUXRWbFevXj3AmzSGsiDGypUr+eKLL3jppZcYMGAAffr04bHHHiMtLa3StnXq1InLLruMefPmsWfPHr755htWr15Nu3btaNq0KdWqVQu4D5fLxa5du9ixYwdHjx5Fp9Nx77330qhRoypZlGVnZzN06FAOHDjASy+9xC233BLwXTt37lzWrl3L888/LwPSycnJuN1uzpw5Q0pKCgaDwUu1HRcX5zVuiWuemJiIw+G4qH3b/y6efvppoCzz7p/ez7lqSwghhBBCCCGE8N9CMIzOJKAjsA5AVdXfFEWpH2iDix2BPqj9fSyLbTwnYf72Y7fbiYiIwOVyodFoJNlqtVopKSlBURQKCgro0aMHn3/+ORkZGT6VuCaTCafTKX/v168fS5culX54/oqXRUZG0rt3b3bv3s1vv/3Gvn37iImJkZ6knhCF1YRVRlhYGHXr1iUuLo7Y2FgiIiIoLS2VZKNQvZbHmTNnWLBgAd9//z1JSUlMmjSJ6667DkVRpCILyhQtn376KVOmTKGoqIjbb7+dJ598klq1akmCXSh/ROpteQ/Q8vfEHwJNfP7OtucK/lSDvkg9UfE+Ojraq22i8FRYWJgMDOh0OnlfRSFGu91ObGwseXl5uFwuzGazJHtiYmKYM2cOa9euZdasWYwePZr69evTr18/n0WniouLJTnnidTUVFq2bElWVhbHjh3j9OnTVKtWjaSkJKKiorz6UXmUlJRQWFiIxWKhoKCAQ4cOSUKpdu3aZGX5r5H5+++/s2zZMoYPH06DBg1wOBxymSDsfOG7775jwoQJZGZmMnDgQMaOHYtWq0Wj0ZCamopGoyE7O1vaSYjAj7BMEc9s+b6kqiqqqsp76FnUsDI15dkiUH8Wz48oRiaIX8AncezLa1JVVfR6PUlJSZjNZvLz82UxLUAWCBTwvAflIXy6P/jgA2699Vbeeust6actlH7lIYi+kydPsnr1al555RXCwsKk97lGo6F+/fr06dOHZs2akZKSgsVi4fTp05w+fZrDhw+Tm5vL9u3bKS0tJSkpieuvv14qO8UzUR4Oh0OOs+np6Tz66KPUrl2bKVOmeI1rAJMmTeLbb7/l6aefpkuXLvJZFIWibDabTyLR895otdqgyOvzgfLjjiB5xN89lcfid1VVK5DYeXl5ZGVlUa1aNXm+YmwqLS1FVVWp5FdVlfbt2wPw559/SgWxr3sBZTZNW7du9TseZGdn+1UNm81mL0JYp9PRu3dvOnXqxEcffUReXh55eXnExcURHx+P3W7HbDZjsVjYvXs3iqJQrVo1OnbsKLMPNBoN+fn50jc3LCyM6tWry3NUFEW+/9etW8eMGTPIysqiVatWPPHEE5L89nzPQ9nzNm7cOBRF4emnnyYqKorY2FgZdBaFx8SPOFb5oFX5scfXOKGqqnzmfBWVuxhR2ftbLBeKfqvVSkREhByXhEpbq9Xidrulejw+Pp6kpCScTqe0izEajSQlJXH69Gmffev222+nSZMmLFmyhPnz5/Pjjz9y3333cf/998uAYlFRkd+2pqam0qxZM9LT0/n999/ZuHEjGzdulPe8sLCQlJQU6RlfUFDA8ePHSU9Px+VyERcXR7du3WjVqhVGo5HCwkLCwsL8vmsBmcmQnp7OsGHDKCgo4O2336Zr167YbLYK/VEgIyODcePGceWVV9K/f39sNhsGg0E+y+J7z2q1yuKMWq0WnU7ndc88awH8W/pcCCGEEEIIIYQQwn8VwZDGJaqqFpUjOv53Dcj8QJx/ZUovg8EgJ74RERHk5eVJckWQTHq9nry8PK677joWLFjAV199xaOPPlphX2lpaV6T3F69enHJJZewaNEiPvroo4DtaNq0KQMGDODQoUNs2bKFEydOkJycTHJyMi6XixYtWpCcnExcXJxX2uCZM2cqpMgK2O32Cp6yDoeDjz76iLfffpuSkhLGjRvH+PHjvdKrXS4XP/30EwsXLmTZsmVYrVauuuoqBg8eTFJSEiUlJYSHh0uyWHjjmUwmsrOzZaEVX1YOZ1sU6kIXlPJnPyFIFc8JVXk1u6eiU/wYDAbS0tKkZQAgyWK9Xk/16tUxGo0kJydjs9mw2+0kJiZy+PBhtFot9evXr5Au2qBBA+69916++OILXn75ZV555RW6devGU0895ZV2azQa/aaSVq9enQYNGvDHH3/w8ccfs3PnTo4dOyZ9D+Pj46lXrx716tXDaDRy9OhRjh49Sn5+Pnv27JFkUXR0NC+99JIsgBYfH+/zeEVFRUyYMIFWrVoxZcqUCipBl8tVgZQ/ffo0jz32GF999RUtW7bk7bffpkOHDiiKgsVikepJERgyGAwYDAYsFoucGAfqM2I//7QlhejjgrgGpB+zSMv2dR6eE3rP5zgsLIzY2FigrGic2+32Usl6FsKrzJ4kIiKClJQU7rrrLl5//XVUVSU1NZVLLrnE7/WKiYmhWrVqPPjgg8yePZvc3FwSExO5/PLLqVu3Lq1atfJ7PLFPl8tFdna2l5K+uLjYrzLQbrdjtVp59913+eSTT4iKiuLrr7+mbt26uN1u2bcWLlzI9OnTGThwII8//jgGg0ESJ6IfCKsGfwgLC5Pt/CeK3HmOO57PtxiPgQrWGnq9vkJbhQo/Pj5eLvMMotjtdux2u1R2CkLtyJEjsg8lJCT4bOM111zD2rVrcbvdVK9evcJyh8Ph18s1NjbWp1WJ2K/L5WLevHl888030us4LS2NHj16cOmll9K2bVuftiE5OTl+FdIul4uGDRsyZ84cJk6cyCWXXMLMmTO56qqrKCkp8WtDsnr1ar7++mtGjx4t+6bb7ZbBX0+SWK/XYzKZJCEfaHzy9Xer1SpJeE+7kX8zygdWTSaT9Ng1Go2kpaVVUM3bbDby8/OltZjIBBKWMUVFRX69tzt27EjXrl157LHHeOaZZ5g1axbHjx/n448/lgS1P9SsWdPLY7mgoIBffvmFzZs3s3nzZrZs2YJWq6Vly5aYTCb5nHTs2JFBgwbRrl27CuNKQUFBQAuXtLQ09u3bx5AhQ3C5XKxcuZJ27doB+FX+qqrKxIkTKSkpYfLkydLzPiUlhejoaNxuN+Hh4dKyRexLBPb8FUq9GAp6hhBCCCGEEEIIIYTgH8GQxn8qinIXEKYoSiNgFOC/gtF/FGLiIYoaCV83q9UqUx/FBNFisRAXF0fnzp3p2LEj8+fPZ/To0ZV+PCuKwpAhQxg5ciQ7d+70m9LtiUaNGlXwNT58+LBMuTxbqKrKTz/9xPPPP8/x48fp2bMnb7/9tiygoqoq27dvZ+HChSxatIicnBzi4+O5+eab6devH40bN5YKPjFBE/A1wfJl5RCsvYMv/J1tzzf8pRRbrVapXhdEpqh2XlhYiNVqJTk5GYPBQI0aNQgPD/cifFJSUgKqj6DM4/Huu++mT58+zJs3j7fffpvrr7+eRx55hPHjxwetCGrVqhWvvfYaGRkZHDhwgIMHD/Lrr7+yYcMGfvzxR691NRoNaWlpdO3alSZNmtCkSRNq164dlLfy9OnTKSwsZPny5ZXaUgiv7QkTJmC32xk3bhx9+/YlMTFRFunSaDSyUFutWrWoVq2aVBUaDAZSU1MDptJ6pu3D+feeDQRffdyzb5VPZfeESC32fNbEdYCyschTuReMdY0v3HLLLbz22mssW7aMoUOHBrVNQkJCBYue3NzcoLbVarU+yUZf2LdvHzNmzGD58uUoikK/fv0YO3ZsBTJm7969DB8+nK5du/Liiy8SHh6O0+mUARDhlxoREYHZbPa6jhcTAvkll7fU8HzHlVera7VaUlNTZaaIyWQiLy9PqqgTEhIwGAxye/H879+/v9I2duvWDSjz5rz99tv/7il7IS0tjXHjxjF48GB+/fVXmjdvTp06dVAUxW9WTzCYNWsWTz/9NL169eKTTz6pVEleUlLC2LFjqV+/Pg8++CBRUVGEhYXJwKEgPkU2Tl5eniRFtVqtvH/B2NBA2f0Wvr3/izYB4pssISEBp9NZwV9X2FgItX9ERARWq5Xw8HBsNhslJSUkJCRIH+RAqFOnDvPmzeOjjz7i8ccf58Ybb2TRokVVam98fDzXXXcd1113Hbt376aoqIjt27ezY8cOdDodw4cPp3v37jidTpo3b17l6wGwe/dubr75ZsLDw1m5cmXAArMCS5YsYcWKFTzzzDO0aNGC9PR0tFotYWFhsn8JWwoo6381a9bEbrdX6PMGg6FCLYB/C+rWrUt6errPZXXq1LnArQkhhBBCCCGEEEI4/wiGNB4JTAAcwGfAKuDF89mofxPExEyQLBqNRqY6JyYmkpKSIlO7LRaLLI4HZRO0e+65h5EjR/Lrr7/KNN1AuOWWW3juuef48ssvgyKNzwcKCwsZP3483377LQ0bNmTBggV069ZNKqK+/vprJk6cyMGDB4mIiODGG2/ktttuo3fv3litVuLi4jh16hSFhYWyOGBSUhIOh8PLQxPKiE5P5ZPnpPbvFIW6UAWlzga+ChXZbDYvhagnPAvKWCwWSei43W4v4iApKamC5Yk/6PV6hg4dyoABA3jhhRd45513OHDgAO+++26VzqN27drUrl2bnj17sn37dtLS0jh9+jTHjh3DYrFQr1496tSpQ2FhIS1atAh63wAbNmxg5cqVPPDAA7Ru3TrguocOHWLYsGH8/PPPXHXVVTz66KN07NiR7OxseT1EUUHheVledReMCu9iKHonIPq2Z3/xVwTLs58YDAZiY2NlgSL4f1saEeCJi4vzUpyeLanWtGlTWrRowVdffRU0aXy+sWPHDt566y1Wr15NVFQUw4YNY8SIET5VqhaLhTvvvBODwcD7779PXFycVKgriiKLa0VEREh1dlpaml+F6T+Jyvp3eRLSX1/3DKDa7XbS09PJzc0lJSUFrVZbYZw3GAw0adIkKNK4WbNmJCQknBfSWCA1NZXevXv/7f2oqsqcOXOYNWsWN910Ex9++GFQfuuzZs1i//79LFy4kPj4eAwGgyQ5LRYLFotFFhW0WCy4XC6io6O9iGRhWyQCF4HGIkVRZH/8N6k+gyXFhee/0+n06Tkv9iOWiSKOUHa9bTYbsbGx7NmzJ+i2DRkyhJSUFO6//3569uzJe++9R+3/Y++8w6Oq0j/+PdN7JmVSCJDQRP3ZBQuWtaDi2tay2HBVVCwr2FEsoGsHO7hiWxHF3nXtdXUtK65rQ7BBKIEkkzq9nt8f4T2euZmZzKSHnM/z8IRk7r1z7r3vPfec97zv9x05Mu9z1Ov12H777bH99ttj+vTpKZ9pi9rmyv/+9z+cddZZcDqdePXVV7PqiBNerxeXXHIJdt11V0ydOhU6nU4EHsiLFOFwWNwPGmtQbQ/tu7S/349dpaamJi/NaIVCoVAoFIrBTqdOY855EO1O46t6vzmDD5o4OxwOuFwuIUkRjUbR0NAAh8MBu92OUCiE1tZWBAIBkYqYTCaFFMTy5ctzchpbrVacfvrpuP322zF//nxcdNFFGdP+epp4PI5HHnkEt99+O3w+H6644gqcffbZMBqNYhC9fv16nHDCCRg/fjzmz5+PY489VkSWhUIhUfl92LBhojBUYWEhDAZDSkGxUCgkqsLTRFY7yehOUai+KijVU2gjRGUoUoocyyQtQJF8VIyGJB5qampS0mGz4XQ6MX/+fGy77baYO3cuTj/9dPz1r3/t8nnodDpUVlaisrKyy8cAgLVr1+L666/HuHHjcPrpp2fcLhwO47bbbsOCBQtgtVpx00034eijjwZjDPF4HCaTCa2trWKRQqfTobS0FD6fD21tbcJRT0WmOoPu00BYjEincZoJrQNQW0CKZFEoxdvpdMJsNov7eP311+Ohhx7q0nlPnToV8+bNw6JFi7D//vvnvX9PsWLFCixcuBAvv/yyiGY+8cQTszpVLrjgAvz4449CLoh0bjnncDgc2Lhxo7AvOTJ7sEALKNSXyDaSKSMiFAqhpaUFBoMBRUVFYiHG7XYLuwkGg8JB53A4sP3222Px4sV45513cNBBB2VsD2MM++yzD1599VWMHTsW55xzzoCM2g6FQrjxxhvxz3/+E3/+85/x97//PaeioKtXr8b111+Pgw8+GCNHjsSmTZtgt9uFU1e7EJTuvUDSOCQjRIuHA/E6dYdcF+jkBVS5PoJ2wR9oXyyla0qLiRUVFRg+fDhef/11fPzxxzkv1h955JF45ZVXcMIJJ+Coo47CFVdcgalTp/arY76lpQXnnXceCgsL8dprr+XsyL7tttvQ1NSEhQsXimuktWe6HzTGpXcFSYBsiTaYC3fdddeAOU5PtUWhUCgUCsXQotNZDGNsAoArAVTL23POs4f29SKMMSPnPH21mz5GlqOgQbTH40FDQwPa2tqEZABFYZhMJqFzHAgEsHTpUphMJhx11FE5f+ell16K9evX46mnnsLKlStx6623ZtRx7Ck+++wzLFy4EGvXrsXee++NuXPnpk2NfOGFF5BMJrF06VIUFBTA7/enFN+jQimxWAwulws+n0/orNJkLRQKCUd4PB7vteJhg4nOInPoc22hQpowB4NBcb/+9a9/4eijj875uxljOP3001FQUIDzzz8f0WgU1157bU7yEb1Ba2srLr74Yuh0Otx6660ZF03ee+89XHTRRfjtt99w/PHH4+qrrxZ2RoV7AAhHnxwh2dLSgtraWhF9TAV76Fpmkhjo7D71tZZ2OuQIPWqD1glF5yFruVPxMpfLhXg8DrPZjIkTJ2LOnDm45ZZb8NNPP+HJJ5/sIIfTGeeccw6+/vprzJs3D5s2bcKZZ57Zg2ebHc45/vOf/+Dpp5/GBx98AJvNhgsvvBDnn38+bDZb1gjqJ598EkuWLMG0adMwfvx4xGKxDg5Um80Gp9OJESNGiPeD1j5yjZjsD2SJk3Q2ks6OyTmeTCYRiURQXl4Og8EgpHUikYjIunE4HDCbzbjkkkvwr3/9C1OnTsW7776b1YYuv/xyxONx3Hnnnfjvf/+LW2+9NedFsL5g/fr1uOSSS/Dzzz9jxowZuPnmm3O6r4lEAmeddRYMBgOuuOIKNDc3d7rgk66/ISkA2dE/mCM7M5Fp0UIL9e90Hcjxrl3wlzMEbDabsFeDwYDzzz8f7733Hk455RR8+umnOUvcTJo0CZ988gnOPPNMXHPNNXj77bdx4403ZtRQ721uuOEGNDc346WXXsrZYdza2opHHnkERx55JPbcc0/odLoOkcNAu915vV4x1i0pKRFOeZ/Ph1AoJApGDiWoyOtAOE5PtUWhUCgUCsXQIpfR2zIASwAcC+AI6V+/wBj7I4BzGGOunjgeRc/lk25GDl+aFNNPQqfTCZ1UKpxCjmKTyYTi4mI4nU7EYjE899xzmDZtGioqKnL+fqPRiJkzZ+Kmm27CypUrcfLJJ+N///tfzvvnw2+//YaLL74Yl112GRKJBP7xj3/gySefzKil99xzz2GHHXbADjvsAI/Hg+HDhwuHNkXzUBqt1WoVEVRtbW2iGE1bWxuam5sRDAbh9XpT9I4V2SGtQNlBEA6HEYvFUFVVhbKyMnz44YddOvYxxxyDG264AV9++SXuvPNOEZ3bV3DO8cMPP2D27NnYtGkT5s+fnzZiedOmTTjttNNw1FFHgTGGf/zjH7j11luFpEIwGERLSwtCoZAoikSR8D6fD01NTTAYDCgvL8fYsWNhs9mEI4ZSlzNp+FLfkKk/ISdcf9o0PWPyOZBTKdOE3mKxQKfTCV1er9eL5uZmGI1GTJ8+Hc8++yw2bNiASZMm4eWXX86rPQaDAffffz/+9Kc/4b777sPSpUu7dX65EI/H8c9//hNHH300zjnnHHz//feYM2cOli9fjtmzZ3caMb1y5Upceuml2HfffTFv3jxUV1dj2LBhwklCOJ1OjB49GgUFBXA6nXA6nR2ucbr7MVCw2WzCodaZjRCRSARms1k4igOBAIqKiuB0OhGPxxGPx2GxWIQ+L53/1VdfjeLiYhx55JFYvXp11jbdeeeduPbaa/Hpp5/ikEMOwdKlSxGPx3v69PMiGAzinXfewcknn4yNGzfi7rvvxllnnZWzk+yee+7Bp59+imuvvRbbbLMNhg8fjurq6hTnXGf9D5D6LFutVnH/tjTk85THZOlIdx3ob1QfIF0Gj9Vqhc1mQ0VFBe644w74fD5MmzYNsVjuMQsjR47EI488gnnz5mH58uU47LDD8PDDD3dLJ7srvPfee3j55Zdx7rnndirnJLNkyRL4fD6cd955cLvd0Ol0SCQSKdrFfr8foVAIJpMJJpMJFoslRe6I+oKB2Mf1Nu+++y7efffdAXGcnmqLQqFQKBSKoUUumsYNnPNXer0lOcAYmwzgXgDncM7bNJ8x3gWhsa4URJPTIinSmKQWCHIiU/EfwuFwgDEGi8WCBQsWIBqN4qKLLsq32QCAKVOmYMyYMbjkkkswffp07LLLLpg8eTIOOOCAbkUxtrW14T//+Q8++eQTfPDBB7BarZg5cyb++Mc/Yvvtt8+437p16/DZZ5/hiiuuAICUyS7nXBSVIf1AmvQlEokOWr1msxkWiwXBYLDTwkFbErlGHcoF17RFfSjCTK/Xo6WlBdFoFIlEAmazGbvvvjs++uijLmvyTZ8+Hd999x2efvppOJ1OzJgxo1cjZhOJBH744QcsX74cH374Ierr62EwGDB37lzsuOOOHbZ/5JFHcNVVVyESieCvf/0rzjrrLIwcOVIUbrPb7fD7/fD7/eJ3us5kfxQpabfbEQwGEY1GRWRVOp1gmc5SpgeClja1nZ6vXCJcSSO1ubkZfr8f4XAYRUVFiEQi0Ol02H777fHggw/i5ptvxtSpU3HRRRfhuuuuyyklH/jdcdza2oq7774bAPCXv/yleyea4TyeffZZ/OMf/8C6deswatQoXHPNNTj99NNz7mcCgQBOPfVU2Gw2LFq0CFarFUVFRWhqakIgEEjRYc9FJ1gubDbQ6EqEKp1HIpFAXV0dwuEwqqqqoNPpRDExeg4BoKSkBMOHD0dhYSGefvpp/PGPf8TUqVPx+uuvZ8ygYYzh5JNPxm677YYbbrgB119/PZ566imcddZZOOSQQzptYzQaRSwW63L0LecctbW1om+qqanBzz//jEQigXHjxuH222/HiBEjcnJkJ5NJvPfee7juuutw1FFH4YwzzkAkEsGoUaPEeIEyFOQo4lzYEiOM09FZvytfBznDJF3UP+nyUkaK1WpFbW0thg0bhhtuuAEXX3wx5syZg9tuuy3n9ul0Opx88snYZ599cN111+HWW2/FI488gvPOOy/rmKqnaGlpwTXXXIOtt94a55xzTs77xWIxLFq0CPvuuy9KSkrQ1NSE5uZmAMCYMWPgdDrFQgZl2xUXFyMcDousJ7vdjpKSEnFdhxo33HADAGDy5Mn9fpyeaotCoVAoFIqhRS4z+nmMsYcAvIf2YngAAM75C73WKg2SQ/gYAH/jnL/FGCsGUAQgwTn/jXPOMzmOGWMzAMwAIBxIhNaJI0c/yc4UWSJBToukyDJyntB+8jbxeFw4B9ra2mC32/Hbb79h6dKlOOqoozBmzJi0Tjy9Xp/R6TJ69Gg4HA6MHTsWkyZNwhNPPIFHH30U8+fPx/z58zFx4kQcccQROPzwwztEY1ZXV8PtdovfOef46aef8M477+Dtt9/G8uXLkUgkUFxcjBkzZuDyyy8XheoyOVaSySTuv/9+AO0V7r1eb0oqIumrklwFOeXoM6PR2OG607mHw+GUyV1PpPj3pKNTa1/dgfRA/X5/1lROmiSTdioRj8exbt06GAwGmEwmxGIxJBIJVFZWIhKJYN9998Urr7yCtWvXZq2Yni2K+JprroHD4cDDDz+MsWPH4oILLkAikYDX60VLSwtqampQV1eHTZs2gXOOsrIylJaWYsSIERgzZgxKS0s7LLLU1dXBaDSKQk/r16/Hm2++iTfffBONjY2wWCw44IADcMQRR2DKlClCnxmAKDT52muvYebMmdh///0xd+5cbLXVViKana4jfW9zczOsVqsoykUpzBT5bjQakUgk4PP5UopXkhOQMhS0Nij3J5lsLB8nTq62lY89k7MkEAjkZGvA7+dDz2lpaam4VqFQCNFoFGPGjMErr7yCOXPm4M4778Ty5cvx+OOPd8iiMJvNaaVN9Ho9Hn74YVx88cW4++67UVRUhPPPP198TtGq6WhsbITLlTn5hHOOpUuX4q677sKmTZuw22674eabb8ahhx6KRCKR8bgkwyEza9YsrFq1Co899hjsdjs2bdoknjmDwSDuRSAQ6OCQp4hIACK6nbTb5XdNNnqq7+rJfkuG7Csej6f0TVarFcXFxaKwmNlsRigUgs1mg16vh8vlQkVFBV566SUccsghmDZtGt5+++0OTveRI0eKd0NZWRneeOMN/POf/8SVV16J2bNn44MPPsA111yDqqqqlP02bdqE5557Dh988AE++eQTAMCVV16JM844A5WVlSgoKMh4Tm63G5xzvPfee1i6dCk+//zzFAf4xIkTcdRRR2G33XbDfvvtJ96TiUQiraOMc45vvvkGL7zwAp5//nls2LABlZWVuO2229Da2opgMIjy8nJhE+SYY4yJ66G1pXTPb39Jn/SWbUnHT/k9l343FyKRCFpaWuD1elFUVATOOZqbmxGLxeBwOHDqqafixx9/xL333os999wTxx9/PID290W261tdXS1+vvfee/j4448xd+5cXHvttRgxYgQuv/xyHHfccR3Ge5WVlRn7Na/Xm/Fd4nQ6U96RV155JZqbm/HMM8+grKwsq7SU2WwWkk/PPfccNmzYgLlz58Lr9cJgMIhMmra2NthstpSilmRvAMQiK2MsJ9mmdGwehwwtPQuFQqFQKBSKAUYuTuPTAWwNwAiAPEkcQJ85jSVHcCOA1ax9VvAKgE0A4oyxFZzz6zJFGnPOHwDwAABMmDAhZRs5Eox2zydqRS6yEggEUqqV0zbBYBDr1q0TleIB4PHHH0draysuvPDCLl0TmYKCApx77rk499xz8dNPP+Hll1/GCy+8gLlz54qITI/HA71eD71ej0QiISJUOedYvnw51q1bBwDYdtttcfHFF2PKlCmYMGFCXrq1zz33HHbaaSe4XC7U19cLPc9kMimKAxoMhrTpsslkEg0NDSIySy60pI3K7Ep0eG+Szb7yxWq1wu/3i1TOTOdH18ZkMsHv9wuN3cbGRjQ3N4t0eCo+Q+m0pBX68ccfZ3UaZ4Mxhnnz5qG1tRULFizAggULhF1ptwPSTwjdbjccDodwElPRIRm73Y7Jkyfjj3/8Iw477LCsEZsbN27EmWeeiR133BFPPvlkysKNrFdMzhObzYZIJCLkZShCW574NzY2YuPGjaKAl0wmG6TFkZ5y7PWkbWmx2WwptkaLYHQdSL8Z+L0/IyeVwWAQ14qeVZPJhHg8jiuvvBLjxo3DjTfeiN133x3Lli3LuXgURRwDwLx58/Df//4Xw4cPF0VGy8rKUFBQALfbDY/Hg5EjR2bto0KhEJYtW4Z7770XmzZtwt57740HH3wQe++9t9hGa7fZWLp0KR599FFcfvnlmDJlCgKBAPR6vXCkDBs2LEXqhK6P3J66ujoA7QW2tLqsfenk6ynbytZmOao9FAqJz2OxmHCQySnrVqsVO+64Ix544AGceuqpOOmkk/D8889nLfbKGMPhhx+OyZMn45ZbbsHixYvx/vvvY8aMGZg4cSI++ugjvP/++0Lyorq6GieeeCLWrl2La665Bq+99hquuuqqjBGf4XAYS5YsweLFi7Fq1SqUlZVh8uTJmDhxIiZOnIjRo0enLMJmgnOOL7/8Es899xyef/55rF27FiaTCVOmTMGMGTNw2mmnIRwOY926dR3eedpibjqdDoFAAKtXr4bFYkFlZWXad4U8lrFareL5pQjm3qI3+610dBbRnytWqxUNDQ1oaWmBxWKB0+lEIpEQ47qGhgZce+21+OGHH3DOOedg0qRJGDFiRN7fs88+++D999/H22+/jauuugrnn38+7r77blx++eWYNGmSkH+gRctEIoFEIgGPx5PiDO6Mb775BosXL8azzz6Lyy67LK+o5lAohAULFmD8+PHYfvvtsXHjRlHkk7JU6LkH2q+dXq8XchXyQpiWdH1Gur9t7hv6p3iCQqFQKBQKhQJAbk7jiZzz8b3ekgwwxnScc3JWbwCwEMAnAO4D8CSACQAuYoztyDn/pie+M9cCKwSlu5eUlAipCnkADLQ79xhjKCwsRENDAx555BHsv//+2GWXXXqiyYKtttoKl112Gc4//3z89ttveO211/D++++jvr5eTDzIgZhMJpFIJLD11ltj1qxZOPDAA/OelBDr1q3D559/josvvlikJ5KzkCIaI5EICgsLRVSnTCAQQGtrK5xOZ4diXFoGQop/b6HT6eDxeFJsJ9N2FLlNzimHwyEKQsnSA3RtrVYrxo8fj2HDhuGTTz7BWWed1a123nbbbTj88MPxxhtvoKysTBSLGzduHMrKykRqeWNjI+rq6rB69WoEg0HU1dWhoaEBfr8fdrtdODA8Ho8ojlVYWIiJEyeKiL1s9zqZTOK0005DIBDA/fffD5PJJCb6nHPhVJQ1Lz0eD0wmE5qbm4VTRXYeM8ZEBXij0djBgd8VG5SL6A2Ewo50zWWHsewID4fDKQ5OeSFHm3FA9tja2opQKISTTjoJo0aNwrXXXouDDz4Yr732Gg488MCc2kWO44KCArz11lt45513MmpAW61WbLXVVhg9ejS23357jB8/HltvvTXcbjeeeOIJLFq0CHV1dWmdxfmyYsUKzJo1S+gYNzc3o6mpCSUlJSJ6nhyhZrMZgUCgw3WyWCyw2+0iQk/bXAth1QABAABJREFUx3W2YDkQydTmcDiMxsZG8TstbjmdTlgsFjDGxGKrz+dDNBqF2+1GU1MT9tlnH9x88824/PLLce655+LBBx/s9JmxWCw477zzcNJJJ+Gmm27CwoULxd8nTZqEE088EUcccQRGjRoFoN2J++yzz+Kaa67B1KlTcdlll2HatGkwmUwAgPr6eixduhRPPPEEmpubscMOO+C+++7DMcccI7YBkFLPIBMff/wxLrroInzzzTcwGo04+OCDMXv2bGy33XYIBAKorq6Gy+WCwWBAMpkUC6uBQEDI5dA1BH5fCCPHZqZ3hTYrip7noSJbkSvxeByNjY0oKirCiBEjYLPZUFhYiGg0KmQXIpGIiDieP38+Dj74YMycORMvvvhiXt/V1NSEwsJCMMZwyCGHYMKECXjrrbdw880351QEdNSoUdh5550xfvx47L777thhhx1SsiEikQjeeecdLFu2DJ999hnsdjvOPvvsvGTQQqEQjj76aHz//fd46KGHsN1226GoqAjxeBzhcBgejwcFBQXCRhsaGuDxeOByuVLeo9mOr+0z0v1t8zFyX9VTKBQKhUKhUPQ4uTiNP2WMbcs5X9HrrdHAGDsUwIGMsUIAZ3LOFzPG3ADOB/A85zwB4AvGWBJAjwnf5jOhCgQC2LRpU0raKE3w2trahJOKigC1trbi3XffRW1tLfbaa6+0Vah7itGjR2PWrFmYNWtWyt9bWloyRkali/jsjP/973+44IILAAAnnXQSHA4HdDpdyiSfiiOZTKa0UcZykbzOIuwomjOTRMBgJx/700ZjUxStHNVOWqtVVVUYNmwY9t9/f7zyyiv4+eefReRxVzAYDJg8eXKKPl5zc7NwXBPkUB45cmTGBQmaSOcL5xzz5s3De++9h/vuuw/bbLMN6uvr4ff7hfSEzWZDcXFxSsQxPY8UIUopwDRptdlsMBgMGDlyJBobGztIFJAN5sNAi5AHUs9Du1hmsVhQVlaW8jdZhzyd/AJ97nK5cOihh2KvvfbCgQceiOnTp+PJJ5/EpEmTcmqXwWDAHXfcIX6PRqNYv349EokE2tra0NLSgtraWqxcuRIrV67EJ598ktZ5M2nSJPz973/HAQcc0IWr004kEsHixYtx8803w263Y/bs2eCco6CgQDhQSFrF5/OlRM5ppXWo+JUcqS2T74LlQCBTmy0Wi1g4SiaT8Hq9ANozY6i/DoVCSCQS0Ov1sFgswtEcCoVwxhlnoK6uDnfccYfIgMmFiooKLFy4EGeffTaampowceJEWK1WtLa2oqioSGzHGMPUqVOx7777YubMmbj++usxf/587LTTTmhsbERNTQ3i8TgOOuggzJo1C5MmTerSe2bx4sWYOXMmRo4ciZtuugmTJ0+G2+0WfRLJNVHRMIfDAYvFgkgkgra2NtFvORwO4ZRra2uDw+FAZWVl1qh0bVYUPc9b4oJrd2hsbERtbS0AoLS0FKWlpSnPs8lkgtlsTlns+etf/4oFCxbg7LPPxk033ZRVHgdoX4S46qqr8Oijj+K0007DvffeC6PRKCLlDz30ULz55puoq6sTC+vxeFxIj+h0OtTW1uKrr77CF198gRdeaE/2s9ls2GeffYRm+LfffotoNIoRI0bguuuuw7Rp07JKr2iJx+M4+eST8f7772PRokU49thjYTabUVpaipqaGkSjUVHUMx1yXYVMtpmuz0j3t8379m3FXYVCoVAoFApFCrk4jfcA8D/G2Gq0axoztCtG5F5+uQswxg4DcCOAOQBmAXgKwPEAFgEYDWAhY+wYADsDGIt2qYp+wW63w+l0wuPxiMkfEY/HhT5eOBxGKBTC5MmTcfLJJ2PZsmX4/vvv8fDDD2OnnXbqr+Z3mdbWVlx//fW4//77UVxcjHvuuQdutxvxeFykMZLzctSoUcKBHggEUjQYqchWQUFBXs60geiE60m0xe7SpW9q03KDwSDq6+sBtE9+bTYbRowYgVAoJGRDrrrqKrz11ls45phj8O677wpHwmDktttuw6JFi3DWWWdh+vTpYIyhpKQEFosFfr8fDodDyHSQBiNF5pHeMvC7/WgjpEi+IhKJ5FzULRODLUJe1niWSSaTwjFfXl6eso3dbkd5ebnQ+LRarbjllltwxRVXYPLkybj11ltx6qmn5t0Wk8mEkpKSrJrG8Xgcq1atwo8//gij0YitttoKu+++e97fJZ/nM888g+uvvx5r1qzB5MmTcdxxx0Gn0+HXX39FZWUlLBYLotEojEZjB6cHY6yDI7UzpzAt+gy0RbBsEhSZCorJ0iYAhMYwSS2Ew2HEYjHEYjEMGzYMbW1tcLlcYIwJmaKrrroKq1evxtVXX419990XEyZMyLnN2223XU7blZeX45577sH//vc/fPLJJ/jyyy/hdrux995747TTTkNVVVVO8hPpeOihh3DxxRfjsMMOw1133QWj0QiDwQCbzQaz2ZxSKI90/mlBVFuYU5aNSSen0xmZnmcFxEJnYWGhWIimfpoWM0wmk8haKSwsxPTp09Ha2ooHH3wQn332GR599NG0NpdIJPDQQw9h3rx58Pv92HvvvbFkyRKsX78eTz31lNhOr9fjsMMOS9mXZB7S8eOPP2LVqlX46KOP8N5774ExhrKyMkyfPh077bQT/vSnP+X9zuKc48ILL8Trr7+O2267DSeeeKJY0AeQ1u5oLCdnVWhlUWgcQ8Xw0vUZW2r0O8ktDYTj9FRbFAqFQqFQDC1yGVFO6fVWaNhc5O4cAJdxzt9hjNUBOJcxdiqAVznnMxhj5wOYCWAYgDM45zV93U6gfcAsR/vIg3Q5XRtodxZRevqtt96KP/3pT5g5cyb+8Ic/4Oqrr8bFF1+cl4Zwf5BMJvH111/j3Xffxf3334/6+nrsv//+uOyyyzBu3DgYjUbEYjGhY6zX64VTnaKygfaJOk0QLBYL3G53yqQ4F13PweaEyxd54mU2m7F27VoYjUYUFRWlTMRkm7HZbCgtLQUAIbPgcrngcrlQU1OD1atXY9SoUXj66adxxBFHYOrUqfjnP//Za9Huvcl9992HG2+8Eccddxzmzp2bMiElqRiSA/D5fGhubhaLE8DvBRa1E2Dgdx3mnoz8HOiT4lylEUKhEAKBgCgCKkcc0zlGo1H4/X7U19ejrKwMjzzyCObPn49LLrkEH3/8MRYuXNjjDqyioiLsueee2HPPPbt9rA8//BBXXnklvv76a+ywww547rnnMHbsWOj1egSDQfG82O32FJ1Z2X4608MfLKTTm++MUCiEhoYGIbUgO/y1i2HhcBgtLS1IJpNoaWlBcXFxSjHB22+/HcuXL8cpp5yC//znP73iUGeMYb/99sN+++3XY8d88MEHUxzGFFlMz4rf7xfjAYfD0aGPkW1FHlcMRhsa6BgMBpSVlaXIPckOfOD3zAEqoGq323HttdfiD3/4Ay6++GL84Q9/wPz588XiJQB88cUXQpbkwAMPxIUXXohhw4bhrbfewty5c7Hffvvh0Ucf7VC0MRc8Hg+qq6txyCGHiDbS9/r9/i4tct5000149NFHMWfOHFxwwQUdpCaGDx8uxnQE2aNcu0Ari9LW1ga/3y8CB4aS/Y4f3zPqfj1xnJ5qi0KhUCgUiqFFLmEqswDYOec18r9eblcbgHM2O4xLADwPIApgEoCHGWNVnPNFaJepOI5z/l1vNIIcIjTQTQcNmLOlh8qTwZKSEhQXF8PhcGD//ffH008/jSOPPBLz5s3DQQcdhH//+9+oq6vLWE26P9i4cSMef/xxnHXWWaiursa+++6Lv/3tbxg7diyee+45XH/99fi///s/uN1uGAwGVFZWorS0FCUlJTCZTKivr0c8HofVakVJSYmIsiK015AmGXKBpHT0dNGxgYbVaoXL5YLFYkFjYyNaWloQi8VSJmLaa0TRZE6nU1xPzjmCwSDKy8sxatQoDBs2DLvssgvuuusufPPNNzj11FNTIt4GA48//jjmzJmDww8/HLfffruQTGhoaEBbWxuam5vh9/tRW1sLn8+HUCiEgoICOByOFB3Vzmyss+d7sEBSLtn6FbI30uNN1++Rg7S4uFhID6Szw2AwiJqaGtTV1SEWi6GyshJXXnklLr74Yrzyyis44IADsHLlyp49yR7gu+++w5/+9Ccceuih8Hq9uPfee/Hvf/8bu+22G9xuNyoqKrDddtvBbDaLyLqGhgZxvTp7X8jksn2+x+wNQqEQ4vE4DAZD2sUTuY30f7PZDI/HA4fDIQotyscj+RhyHtH5NTQ0wOv1iohru90Oo9GIO+64A6tXr8bMmTP75qS7yYMPPogLL7wQU6ZMwYMPPoji4uKUYoeBQAAmkwk2m01k5Cj6B9l+rVYrnE6nuC9U3Njn86GpqQn19fVikczhcMBsNmOvvfbCQw89hL333huzZs3CtGnT8PPPP+Occ87BAQccAK/Xi/nz52Px4sXYfvvtodfrccIJJ+Dpp5/G2rVrcdhhh+G777o3hGWMdXsctGTJEtx44404+eSTcd111wGAqNFBNQi0het8Ph98Pl+K/WoX/em9UlJSkrYIssxA6O96mldffRWvvvrqgDhOPseoqqoSdqX9V11d3a12KBQKhUKhGFzkEorwI4AHGWMGAI8AeJJz3tobjWGM7YR2CYwk53zV5j9b0R5J/P7mbZYAuATArM0F8vIeXWZynHDOUwbe2hS7dNvT4DjTgJ30VCkqj5x5VqsVfr8fZWVluP3223HooYfi0ksvxUEHHST2Gz58OKqrqzFy5EiMHDkSY8eOxU477YTy8nLE4/Gsjj7SzkxHJBLJuG88HkcymcQvv/yCJ598Eq+//jp++OEHAO1yB5MnT8akSZMwefJkEZnT1NSESCQitJzlyF+v14v169eDc47y8nIR/RKJRESErPa6y1Eq8mfk/ByI6dud0dkiQLrPGWNCn5i0ON1uN0KhEEwmk3DwZTo2RQmFQiH4fD44nU4R0dTc3Izdd98dF1xwAe68807MnDkTCxYsENeeiiWmIx6PI5FIX5umq59Fo9Gsetpy6utLL72EWbNmYf/998fdd9+NWCyGcDiMYDAonFtutxs2mw16vV44vYxGIwDA5/PB4XAI7dB016+z+9UT9teX9pyLlAs5yAOBgNBjp2hteUHH7/enyKO4XC6YTCZR+I2kaRhjMBqNKCgoEP3Nueeei9122w0zZ87EgQceiLvuugvHHnusaEM2G4nFYp32W5nIdEyg3fbef/993H333Xj77bfhdrtx5ZVXYsaMGWhtbcWvv/6KYDAotJpJsiQcDkOv16fo81KUIvVdmWCM5RTZPRAK48n9MWMs5bzoOlAbAQiZCUrxD4fDKYUBLRYLEomEeKZJX5xzjkgkgqKiIlF8knOORCKBXXbZBZdffjluvvlm7LnnnjjxxBPTtjUajaYUqZPJZj+UHZOJbO/aWCyWYl8kSTFlyhTMmzcPkUgENpsNkUhERBeTbJNOpxNyQrIefTAYFL/nu2ClfZ8qUtE+l9pxHj1nPp8PDQ0NIko+FosJyRXOeYrdjxw5EjfccAPGjx+P+++/Hy+99BIMBgPOPfdcXHLJJULaIplMCpueOHEiHn74YcyaNQuHH344HnrooQ7a69n6tc4+y2az2s/eeust0Sffcccd4JyLxYza2lrU19fDbrcLmTXK5CGdcnpPcM7h9XrF8endRn0HjQ0z9Y10L+R3z2Dn9ttvBwAcccQR/X6cfI6xZs2ajJ+p/kWhUCgUiqFFp05jzvlDAB5ijI0HcDqAbxlj/wbwIOf8g55qyOaidw8AeBnAfoyxOzjn/+CcrwOwjjGm2+wk/gJASVe/hwbt6QY92mgNWf4g3fZaR0w6WQW9Xg+j0YhgMAij0SjSmik912g0oqysDKeddhoOPfRQvPPOO/j5559RU1ODn376CXV1dfj8889TJuWVlZXYddddMXHiREyYMAG77rprB83FTBNnoL0QUTpJh7a2Nrz88st4/PHH8emnn0Kn02HffffFhRdeiFGjRmG//fbDNttsk6Kv6HA4kEgkUFFRkeJopCrt5NS0WCwpkwb5mmqvu16vF9dJnlykc3wNxsGr1lmYSCTg9XpRXFycNqVUp9NBr9ejrKwMgUAATU1NiMViGDlyJAwGQ0bnIzm05Ikb2WVxcTECgQAOPfRQNDc3Y8mSJfjnP/+JadOm4cwzz8Q222yTsf2FhYUwmUyIx+NYsWIFzGYzqqurRbGcTBIrmc4PAFwuV9Z0WpPJhLa2Ntxwww245557sMcee+Chhx4STvSioiLYbDYRGdbY2AiHwyH0jTds2CD0U00mE+x2e4rDT0tndtUTdtdXmtyZnjv6TAttSzrQcvvIgWgymRCNRoVNkaOZti0oKIBer0cikUAwGITFYoHD4YDH40FlZSWcTiduvPFGnHXWWbjrrrswduxYjB07FqNGjcJWW22FsWPHorKysoN0SDb7oT6Pngf5mqZzrsTjcTz33HO488478fXXX8Pj8eCSSy7BEUccge222w6NjY3YsGEDSkpK4PF4UiQWKOqWZCgsFouIptVKxnTnnnT2DuoLsskhyJk02qJW1K87HA4R2U/ZIaRdDLQvRgaDQZEdALS/i6LRKAwGA4xGI/R6Pf7617/i3//+N+bMmYP9998fY8eO7dAeq9Wa8d1XXFyc8b4UFhZ2KHYpk23RwWazwWQyCX3bOXPm4LDDDsN5552HtrY22O12jBgxAsDv44Hm5maxqEVyQuREDofDaGhoANBe1C/fvqEnok63ZLTXxmw2i4haub+hBRIa5ySTSVHIl/o7umdUcHXWrFk4+uijRQ2B0aNHw+FwiO80m80wGAwwm82oq6tDZWUlXnzxRZx99tmYNm0aFi5ciFNPPRW//fYbfv75Z6xatQq//vorVq1ahZ9//hmMMYwbN070kVtvvTXGjRuHUaNGiUVRoD2jLVeps+XLl+PUU0/FDjvsgEWLFsFgMCAQCKQsDNpsNlRWVorFLnIqk+QK9U+0cEsyY3KRx1xsUo7Gl8e8CoVCoVAoFIr+IyfRM8aYHsDWm/95AXwD4GLG2Nmc8xO60wDWPpK0o12f+K+c81cYY3sAeJwxZuac3wcAnPPkZk3jMwD8pavfR06MXCZicpGxdJERWk3dTFFhFoslxWlBEWnhcFhE6jocDhQXF+Ooo45CMBjEihUrUFNTg1GjRmHixImIRqP4z3/+gw8++ACrV6/G119/jVdeeUUcc7vttsOiRYswadKkvK/Hhx9+iKVLl+Kll15CKBTCNttsgxtuuAH77rsvqqqqUFRUhDVr1sBms6GxsREej0dMCpqbm2EwGNDa2prydyowpi0iRk6DrrClaBhrnYVer1dUbu+sKJ3VakUsFkNLSwvsdjvKyspydj5qFzXIkTF79mwcffTRePTRR/HAAw9g0aJF2HPPPXHGGWfguOOOE86xtWvX4ssvv8QXX3yBr776Cv/973+Fo4wxhsrKSlRXV2P06NEYNWoURo0aBafTKaLr2traEAwGEQgERDHEww8/HHvssUfWc04mk3jssccwZ84c1NXV4cgjj8ScOXNgNpvh9XoRiUTgcDjgcrngcDhEtGwymQRjTEQfUtRST0TyUbGk7shW9KU9awsm5rItRdalk5LhnKc4KdIVgXM6nSKt22KxwOPxoLGxEdFoFC6XC3fccQeeeeYZfP311/j555/x5ptvimwFoL3f3GabbbD77rtj9913xy677ILx48d3cD4EAgEsX74cX331Fb744gt88cUX2LhxIzweD8aPH4+tt94aW221FcaPH4/x48ejuLgYjz76KO655x6sXbsWW221FS6//HKceeaZwhkeCoUQjUZhsVhEhL7ssHY4HOCcw+/3C61OikLMtWhiLvckn/vWX2idylpdcCDVMZduf1p8pAjGaDSKtrY2OBwOmEwmUbxy8eLF2GuvvfCXv/wFH374YVoHsd/vx3fffYdvv/0WALDDDjtg++23z7qQGg6H8fXXXws7+vXXX1FVVYWxY8diq622wpgxYzB27Fihh078+uuvePXVV/Hmm2/i448/Rjwex+GHH4558+aJAnZjx44V/QTpq/v9flGU0263p8h3WK1W8R7oCS11RXYyFTt1OBziHQH8PnZJJpNiccPlcol7SsEIo0aNQiAQQH19vXg2wuEwfD4fdDodSktLRUZMYWEh7HY77rjjDixYsADnnXceZs6cmbJI4fF4MG7cOBxyyCFCcunFF19EY2Oj2MZgMGDkyJFigYvkg+TFrtbWVni9Xni9XjQ2Nop/33//PcrKyvC3v/0NdrsdTU1NYhHa4/GId6der4fNZoPRaBTXgM4faH/eySlOhWgpqh7IbWGUHPCBQCCtzrdCoVAoFAqFou/pdGbLGLsTwBEA3gNwE+f8P5s/upUxtirznrnB22eWfsbYcgAuxpiRc/45Y+wEAM8yxkKc8yWMsYPQ7iw+nXO+oqvfRwPfnkDrAE0XcUXOBBqwl5aWisgit9uNjRs3Ih6Po62tDXq9Hpxz2Gw27LjjjigpKYHb7YbZbIbdbsekSZMwZswYkVa/ceNGfPvtt/jPf/6DV155BQceeCBOPvlkzJo1C1tvvXXWtq9atQqPP/44nnjiCaxbtw5utxvHHXcc/vznP2PfffcFYwy1tbXYuHEjnE4nxo8fL9IOQ6GQmOhSGiTJIFDkjVz0iKrF9/T1HqxonYUlJe2B81TBPRs6nQ4jR46E3W4X23fmfKTFDHLoA+0OOYPBgKqqKgQCAVRUVGCXXXbB9OnT8cknn+D555/HmWeeiUsvvRSjR49GU1OTSFc0m83Yeeedcfzxx2PChAlIJBKoqalBbW0t1q5di3fffVc4wdNBkb5+vx+33XYbhg0bhlNOOQVnnHEGhg8fnrLtf//7X1xyySX44osvsNtuu+GFF17AhAkTEAgEkEgkEAgERGQUTfJlJ5scoUnXhyaycuSjDDnXc8kw6I5Db6DbMy1ukXMrHaRBCfzuFCDNT/q/yWSCw+FAKBQSac4ulwsjRozANddcA6/XC4vFAp/Ph7q6OtTU1KCpqQnr1q3DN998g2XLlmHx4sUA2p+Vgw8+GFOmtNdnffLJJ/H2228LJ8vYsWOx3377obq6Gt9++y1aWlrw8ssvi8hNmb333htXXXUVJkyYAIfDgcrKSrS0tCAYDAqHT0FBgZA+cblcGa8VFTMDtjxHX0/IqJAkEi060nHo/Ug2Nnr0aKHh7nK5RAo+FZkdN24c5s+fj7POOguzZs3C3//+d9Gvvf3227jjjjvw8ccfd1jkZYxhr732wtlnn42jjjpKOJDr6uowd+5cLFu2TKTUl5WVYdy4cfjss8/w1FNPpRxr9OjROPbYY7Fx40Z8+umn+O233wAA2267LY4//ngccMABOPLII0XBxHg8jtbWVhgMBhFxqUW72Ex9WGfXOteCsYrMkA436RPTYiBFa6frn0lqrKCgQNwvnU4n3rG0qAhALCxqx4Z0DJKYGjZsGG6//XbssssuqKurQ3V1NXbYYQdUVlZi9OjRIpsjHo8jGo2KcVdtbS2WL1+OH374AeFwGK2trVi/fj2+/vpreL3etJJPZrNZ1NZwOp3Yf//9cc4552Ds2LEoLi5GOBwWY4tkMol4PC7emeQEp//TNaNIYnLAk2wRLZzk0yeSBJLL5VJ2rVAoFAqFQjEAyCXS+FsAV3POA2k+260H27IJwIEAXgEQ45wvZ4ydAuAuxtg7AL4G8GfOeVN3vkSn0/Va6ma6NF6qIt/Q0ICmpiYRUeR0OoUDLxwOC0cwyQgA7Q6SYDCIUCgkIpJoIs05R2FhIY466ihMmTIFM2bMwPz58/HMM89g6dKl2G+//TBz5kwcdNBBYuDd1NSE5557Dk888QSWL18OnU6Hgw8+GDNnzsTkyZPBGBPp/KFQCDqdTjiJXS4XiouLRUo6TbaosEmmiYEcmShPyIYy2skoSU9kQ3YQUKX3TMfTIksKNDU1dYgc1el0Qot1xIgRuOyyy3DppZfijTfewPPPP49vv/0WlZWVmD59OnbeeWfsscceIiJLvvc6nQ61tbVYs2YNTCYTEokEmpubUVJSArvdLooMRSIRYVsffPABnn76acyfPx+33XYbDjvsMMyYMQPbbbcdrrvuOixZsgQejwe33HILzjrrLPG9sp6i2WwWzieHwyGimcnRSU4p2WkuT+a1zpfOdGS3lIj3XIjH46irqxN2pyUUCmHjxo0Ih8MYNWqUcJ6Qoy2ZTMLtdsPj8QhngtPpRFtbm7gHOp1OSLS43W6MGTNGOGMsFguCwSBWr16NTz75BF988QXeeustPPHEEwCA8vJynHbaaZg0aRK23XZbuN1ulJeXA4BwUJeWlqKxsRE//fQTvv32W/zyyy/Ye++9sf3226OkpAThcBicc8RiMZSUlAidXlqMIAmYdNjtdpSXl2/RjrvOMhlycV6Shj+9T+gdQ9HawO/Pk06nQ1FREbxer7ADet+EQiEcfPDBOOuss/Dggw8ikUhg0qRJuOeee/Djjz+isrIS5557LiZNmoTdd98diUQCn376KX766Sc8/fTTOOWUU1BeXo7p06fD4XDglltuQTAYxNSpU7HDDjtg4sSJGDlyJEaMGAGDwYBwOCzkAX744Qf861//woIFC+DxeLDHHntgxowZOPzwwzFs2DDU1LTXB9br9eJ9TZH169atE45qshnKfMgWhZ2NgaB3PdiRHZSRSCSn66l1AFPhRwBYvXo1gPYFYHIqA0hxtvp8PrS2tiIYDMLj8Yi+1Wq1Yt68eSJjIZlMijEjOW1dLhecTicaGxtRVlaG8ePHY8KECUICyGAwIJlMorGxEW63G01NTYjH42hqakJxcTESiQTKy8tFFkUkEoHb7UZLSwsSiQRisRgcDofo++rq6oSGPT2H9KzL+vdA+/MrXxvqC3LJush2fRUKhUKhUCgU/QvLNBlmjO2SbUfO+X97pAGMsc3RxmCMPQ0gDuA8AEHOeYwx9g8A13POV/fE902YMIF/+eWX2dqT9u+dFcbKNHGmNL54PI6GhgY0NjaKatIej6eDZi9Fq9XW1qKurg5OpxNjxoxJidyl1OmmpiYUFRUhEokgFovBaDSCMYYHHngADzzwAGpra7HVVlth0qRJaG5uxhtvvIFoNIptt90Wp5xyCk444QQRyUwpi8OHD4fJZILf74fZbEZLSwsqKioQj8eFBjMAMVmg6E6txqkW2l5OZ+yJ697XDmjG2Fec8wmZPp8wYQJfvnx5h793dh7ZoEi/bNcv037y9ZHvAS1QAL/blNlsFhFQciEaKqrY0NCAcDiMYcOGwWw2C4cr2aHBYEBTUxN++eUXmM1mVFVVpdiC3+8XTt2GhgZhTwaDAevWrcPTTz+Np556Cl6vV0xSzzvvPMyaNUtEPpEzmFJYgfao6VgsluIYJkex3+9HQ0MDPB4PXC4X/H6/KApot9vBGOtgm51FGgNdt7vO7ECn02W0r0y21VskEgmsWbMGLS0tqKysTFmoINtKJpOoq6uDz+cT+qvUDwIQzoxwOCyup8/nE5HhZMvJZBINDQ1obW0V0WoOhwNlZWViG+rnIpEIPvzwQ4wcORLV1dWoqamB2WxGOByG1WrFyJEjU+QA5MUC2dYjkYiIlItEIkIagaRNmpqaUFdXJ6rFl5WVpdizfA3kY+biQO6PhbNsfVc225KfJ227tX2T1WpNKQ4rby+/I4PBoChISfrO9Dd619XV1YlMFZPJhIKCAlitVrS1tcFoNGLOnDm49957AbRLUJx55pk4+eSTodfrO0Q4hkIhhMNhvPLKK3jxxRfx5ptvgnOOQw45BPPnz0dhYaFYILBYLKJwXVFRkVhUk++30+kUDkeKMtdG3FPB0tbWVoTDYSSTSeGMpmuXrf9JZ0fyZ/Qd6bYbSO/Fvu63OkNbyFF7PTP1++lqYcjjoLa2Nnz99dcwm83YdtttU/oL2k/riK2qqsrYX8jPHQUbyNkc1EeGQiHU1dWJ86BxJb1nS0tLUVxcDK/Xi9bWVuj1elGcORgMorS0FFarVbzHKaqfskgSiQTC4TA8Ho+Q2aBnXbZ50oDW2mQu79NsDKR3IvC71nWurFu3DgCEHFhX6Ynj9FRb8r0GA5XOxvMKhUKhUCjayRYCcHuWzziAA7J8npXNRfWKACwHkASQAADO+fGMsScB3AXgc8aYAcAfAMzt6nf1FZmifuRUU4PBIDTyyGlFhVbISUeTFyoAJA/OaSBut9tF0TmajBqNRpSUlIAxhiuvvBIXXHABXnjhBSxcuBDLli1DQUEBjjnmGEyaNAlTpkyB1WoVRepogkGOFbkYidVqRWtrq5ACKCkpEc4Wl8slCpbIjpmhHp3ZE8iTWTn6vDvRN/Ix5AG/HCFPEbzJZBIejwcGg0EUASKHRktLi3C2JRIJrFu3DslkEuXl5aLoE00mQ6EQEokEHA4HbDabuP9kywaDQaSdX3PNNfjzn/+Md999F+vXr8cZZ5yBrbbaSjinKSKP9iXnEmkp0gKNLBWgJZ0dqsim9KSTQkm3TVlZmein6P6QsxiAiPwGICLOOecpixM6nQ4ejwfBYBDr1q0TBdTk6MtwOCycDwcddBAAiDTxaDSKgoICFBQUCMev0+nssIAnOzQomq6lpQUAhJ4y6W0XFRV10MFOZ1fp5F/IebolZFbkmslA5yxHHsrI/QztI+uC22w28T4ymUyw2WzCRigSUy66dfPNN+Okk05CU1MTdtppJ1HkliIgKYIZaF+wMplMOOaYY3D00UdjzZo1aG5uxj777APGGOLxOAoKCmA0GoVECWUyUMQwOfrI4UKSKCaTKaU/AiC+n4o/NjQ0IB6Pp9W7TiddkCniVfuZttgj2XeuRdCGOpn0uHNBlrawWCxCk9tsNovFdNqOFrTIqUsF5ORFlnR9FWkJa/sdnU4Hv98vCisC7UUTjUYj7HY7EomEGDsUFRWJ+hn19fVioTYej4sF4UgkIhy+5Byna0MLLuFwOCUbTo7SBn6XfaLnTl48kfuE7krdDDa666DtyeP0VFsUCoVCoVAMLTI6jTnn+/fGFzLGjgFwE4ANm/8tZ4wt4Zy3bf7eExlj0wEMA7AjgCM55+t7oy09SSaHHICUInhlZWUpE+VEIiEisuRJ9ogRI4TjgiJG7HY7CgsL0draCrvdLlKp5Yk0fV88Hsexxx6Lww47DD6fD263Gz6fD83NzYjFYqitrUUikRAphVTMiSYMNGGntNnGxkYEAgFxbi6XS0zytZHG6SIQBrp+60BDnmjJjmPZkUxkm3zJ90K2sUQikTaKLZlMoqioCEB7BC/tzxhDcXExGGOIxWKIRqNi8cDpdKYUe6RoJfoeikqh9GybzYaysjLo9Xo0NDTAbDZj06ZNMBgMKC8vx9FHH43y8nK0tbWhqampg2OJzosm4+RMpkhFumZ0rcrKyoRdynYon5t8XJX23Q7nHHq9XmiwayP05Cg67fWjaGLSiaV+hJyCFGFJ+1ssFuh0OrjdbuEEJmkTcsaZTCYRRRoKhYQWst1uF1r1tMBgtVoRj8eFJjNFDpOjj9prsVhQWFgIvV4v7J2i+EKhkHDWkBNcvgZ0HtT3y9GtQ8mG0jmDM70HZNI9h4wxtLa2iuKV5Pg3mUzCiZZIJKDX61FfX49oNIptttlGOMbkqGCyBbIROk4ymURFRQVKS0sRCATEgi7nHOFwGLFYTOjzFxQUwGAwoKioCJxzxONxUeDL7/cLaZOysrKUfpiuA9m1x+NJiWjVnrf8/GRbxMr2mWxzA7144kAlXTSx/JkMXW+DwSAcxVtvvbW4z7Q9OVcBdFjQJDui76U+hPohueAove/Ihq1Wq9D/pr6qsLBQbEsRxUB731RcXAyTyYRgMIhoNIri4mIRxd/U1K76lkgkUFdXh3g8LhzftEBMC3hy30k/6VpQQVrZPuUFolyL9m5JPP300wCA448/vt+P01NtUSgUCoVCMbTIpRCeEcC5APbd/KcPAdzPOY/l+2Wbj3U8gDM45/9mjB0LYA8AlzPG5nPOWwGAc/6PzdubOeeRfL8nh3b0+D7p9IwJvV4vojzKyspSIo1IUgKAKBREg/vi4mI0NTWJCXMikUBjYyNisRiGDRsGh8OBlpYWGAyGlKIhgUBAFEshJxo5RzweDyKRCFpbW+H3+9HY2Ijy8nI4nU6YTCbhmJF18CiCzufzoaioSESBaYvF0OSmK9evp/cbaOR7HnJELO2b74Qr23caDIYOn8sFvyiqWKsH7HQ6hcO2oaFBRN7JBYFIk5GioGhSWlJSImzEaDSivr4ebW1taGtrw+rVq0W0HjnnKioqAEBE2pNkCkWEAhDRf2STcuS83+8XEav5XJ/ejIofTPacra2yNrw2gld2ojY2NgqbJeeZwWAQ9hKPx+H1elFYWCgWIcaPH4/6+nrU1dWhqalJRA9TZgbprVPKtsPhgF6vF1HyoVBIRC0DEBrHlJFBTmiSlCCpFYpQ5pzDYDCgoKBAyG8A6CANQ/2fHAlI9p3u+d0S0UpQyNcim9M4U20Bm80mnPa0Df2trq5OyJVwzlFQUIBAIIBhw4bBaDQikUggmUyKCGHKkJDvJS1WJBIJUcA1EAgIeSen04loNIqamhoRJUySKBaLJSU6kxzNJBel7ZPJFrQ2Il+DTNeU0v21pDuOfO3kn4r0dGcsol0YIOmxWCwGu90Oo9GIUCiEWCyWskhKi07ae079A8mX0b1zOBwoKChI2YcW44D2MV5paanIMKNsm0AgAIvFgra2NrjdbsRiMdEfUzvr6+uFE7i6uhr19fVYt26d6EdJCsbhcKS8S8nhTddAr9dnzRwg5O0y2eiW3Efed999ALrvqO2J4/RUWxQKhUKhUAwtcqlQcR8AI4C/b/79lM1/O7OL3+kCMA7AvwG8CMAL4DAAJwJYzBjbDUB8s2Zyx9LPWwBa6QGisbERGzduFCmPpPFZXl6OqqoqMbmlCQlNeqm4kHYAbzabU+QACIqIoQkISWXQxFhuC20fDAZFSrc8OaZJBX3PUEs97C1I1gT43fnS204BOYqNnMIUKWqz2VBXVye0N0mmhGxQnijK0cyUFh4IBDpIHNDv5HyxWq0oKSlBNBqFx+MRDmidTodwOIy6ujohxwIApaWlKVFN1A454slisaRIu+SCNhpZ2XR2KMpMzqigAonFxcVCW5r6KVlmh/qg5uZm0fftuOOOGDVqlJDzIU1qxlhKRFtVVRWCwSCKi4uF0y8YDIp+j54fWW5H22atrI62kFMikRBazvnIl8jP75aK/Gxke7Y6K5Sn/dzj8YhFIrpHWkKhEFpaWmCz2RCPx8UCKRU+BNrvF2lp0/dQir3BYBCyKTqdDnq9XkjxcM5RX18Pi8UCv98vFm7dbjcqKirEYgVFlAPd75N7op8ZCjbXV+RS3DFdBHm6dzS9s9JJk1CWmN/vRzQahc/nQ1VVlVhUkxdeZOcvLW6RM9fpdAoN8EgkgkQikSIRRPs5HA783//9HzZt2iQWZYuLi5FMJmGz2YTkCdmgLPGS7fpQf96Z7Q6WjDPSyk9HVVVVH7dGoVAoFAqFon/JxWk8kXO+o/T7+4yxb7ryZZsL290BYCZj7FfO+ceMsU8AVAI4nDH2KIC9ADy5efvBX2lhM3a7HRUVFRl1HymN1uPxIJlMYtOmTeCcw+12w+PxiAgj0o2rr6+H3+8XBb4oykR2yADooLPY1tYGp9MpHDIGg0GkAJPzDvjdoUeRxhStp52UytGvAIZc6mFf0hsTLu3kj45PTj6DwSAipciBEolEhDwK7StrMJLzRY4wlSeUsoOktLRURElpnU9UtA743R4pIhVA1om1HDmqtcnOHAJy+4ZiOm2+UNpyW1ubcMI5nU6x+KXT6VBcXIzm5uYUjWLg93tIDl9K/S8oKEBVVRUaGhoQjUbR0NAg9EIpmpnsgIogkpNE7tNkaEFBGw0tF0zTZoxotWoVvyM/G9mclZ1JdchRxHTfyJlPWsYAxPsTgNCGBSDkSux2uyg8R6n3FG1M8kuytrbX60UwGBTSEtRHOZ1O/N///Z9wyplMJlG4VmsPPWUbqp8ZWOQqL6PtL9K9o2nxQe77ZFskZ20ikUB9fT1cLpeQmUj3XfRcJBIJtLa2igVZWmwxmUyIxWLi2dFKRYRCIZSUlCAej8NsNkOv14vxHX0PFamksaXcR+ZzfQYrNTU1W0ShN4VCoVAoFIqeIBencYIxNoZz/isAMMZGY3Phui7yMYDxAE5hjDHO+b8APMEYmwFgGOf8zm4ce0BBzietQy6dLiHp3kWjUTHZtdvtKemAlKrr9/tFamQwGITb7UYoFEJ9fT1KS0tTJrKyg4y+jxw0pI8nRynTPpFIREySgcyT43SRNSo9dvCQafIn31eKhqIIPiqKSFFJlBIej8fBORcOQwDweDywWCxobm6GyWRCfX29cPLQd5Jdk6OIHLbaNlDEJ6X9pkM7ic/0rGWb8MoOHJXy3TmRSERICjidTjgcDuHU1RbKlJ0Pct8EAJWVlWhpaRGRb6FQCIWFhdiwYQMACFkA7XFo0SKZTAoJiXSLAelsQasHn4lcIg+HGrk+G10pNKld/AEg+pq2tjb4fD7odDqUlpYK+3M6nWhtbRUZMiRRQs5fu90Op9Mp9GZLSkpgtVpFHyS/Z51Op3DquVyuXo/gVf3MwKIni6OmizQOhUJC9qaiogJ2ux1bbbUVbDYbhg8fnvV49I5LJBIIhUJCMoUyazZs2ACz2Swi5AsKCsS5kCQPOZwTiQScTmcH2Q1Zq13u98iRmu/1URk7CoVCoVAoFIOXXJzGlwH4gDH2GwAGoArA6V39Qs55mDG2DAAHMIcxtjWACAAPgPS5cIOUTNFDsmNLHoRTNJTJZBLp2FQQhQbblFLrdDpF1WrSfE1Humrrfr8fDQ0NCAaDKC0t7TDw1xZhy+Ys0UbWbIlRJ4OJfCdnmSZ/2lRnSuEmvURZEoUmrnJUoGyn0WhUaMO2traKhY10DhK5mJ3D4RCRgvlKTBDpIr86m/Bq9WiVTWeHig0CSHHYGgyGlGKa6aLV6F4D7RIVVGgxFosJZ7PJZILBYIDFYhE2rV2IIzkDWdudIIcvRfolk0k0NjamSK90xpYeWdcVsmnvymTT+wdSs3A62ycUCmH9+vVYu3atiCovKSkB0G4TtKBUXFyMYDCI1tZW0ZdZrVbo9XoAEO9LyrhJJBJCyoLOR84CIu30TOfRXaeY6mcGFp3ZbD6kkwiT+0ySkDCZTBg9enRebaSFEfqO5uZmtLW1oaSkBOXl5aKoLZDa34bDYaxduxY2mw3l5eUoLi5OsX+5X+1sDCsvqNHzpUVF0isUCoVCoVAMXjp1GnPO32OMjUN7dDAArOpucTrOeTNj7EEAKwCcDSAMYBrnvK47xx1opJssZEKrj0dRHeREI+cLTTBogC4P2MvLyzt8VzoHmclkEsdKF5Un76OcJYOLziZncvQ7YyyvyTEVpHK5XDAYDEJH1Gq1oqCgIEWjO13kXFFREaxWK4qLizNOLtPtJzuSe8IGOztn5cBpJ1dHWDYJB/laU5QdRaGTpA45c202W4d0atnZrC28KSP3i1q0+sWkAy8vcnRGT0YeDhVyjc7Opw+yWq0YPny4KI5IWTaUvWCxWIQzjvqreDwu+jr5OPJPyvQBfu9jyNY459Dr9VnvvXKKbdl0J9MgXZ8l95ndkUHQLu7KMk5yYWLKxCDddovFIv4WjUaxbt26lMJ9RC79njxGVEUaO/Lcc88NmOP0VFsUCoVCoVAMLTp1GjPGLADOA7A32qODP2aMLeach7vzxZzzKNojmP/V/itPdrbPYIMmC8lkUkQyaStokwOFPrNYLGLyC6QWcZIdZ8lkUlRub2lpgcFgQFFRUYpmo1ygRCYajYooLZvNltI2oL3aNQ3u6W8WiwWc806jqFQaYv/S2eQsnfRCrjDGUFJSIlK+GxoaAABlZWUpUXmxWAwbNmxAYWEhOOfCBjnnQodUO1EmWQpKF5e3IYehXB1efoayTbq1TnLtZ6FQaIu3Vfn6pHs+M517JkdYLg6UdPeErjc5J9avXw+DwSC0iYF2x3KmhQdy+NL/5f40m6Nfq18sy6Nor422QCn1eT0ZeThUyLbgqL1/6ZC3ASDklAwGAyoqKkShTJ/PJ5zDgUBAyCppIzHpXsvHZYwhkUikONS0kHwOkNmuZYkU2U4z7aMYXHR38ZxsIF0fo7UX2T455ymLZjLa/YD2sVtpaSlisZh4VwJAQ0MD/H6/0O8G2iXHSMYCQMoiGrVXa/vpbFl2LGeydW3/PJTGiZQJMRCO01NtUSgUCoVCMbTIRZ5iKQAfgIWbfz8JwGMA/twTDeCcd0cfeVDAGMs46aCJb7picjSgphR9inYiJwbpzlHRsqamJuFYrqioyBr1QftSsTCfzycmCPIgXq/Xdyj0kg6aLKiIq75HvidkL5nQSi/kCxVklBcQ5AJ2jDGsW7cOP/zwA0pLS7HddtulRO5lglJn0zn/yAYzpcpmO242e8y1kNeWRD7PZ6Yos1wiy7J9t8vlEkUVCwsLhU0yxlIkKLRQnwe0OxDl85A1abXIfRhFO8tF8WTpH+21yXZcRXay9TXa+5cOeRsAKdHi1E/QsS0Wi9BVD4VCwi71en2HiE5tXxMKhRAMBuFyuTJmQHQGvZOpjbm++5RtDSwy3Y/uvjeJdH2M3K8B6e1e3p7Q7icTi8WEjQOAz+dDKBQSdiqP+bQZbtlI931dycwZSuPEJUuWAABOO+20fj9OT7VFoVAoFArF0CIXp/F2nPNtpd8/YIyt6K0GbalkS/NL91k6jVlZX5gG77KshdlszikNsLNiYd1JxRzKaYiDAUpnlSOfuhLxk0mSIJlMwu12Y+uttxaFpnIhlzTY7hbUyvTZULLVfJ7PTM6vrl43Wa6HIuBKSkpSCp1lilTT9kly9DDpXefj8MvW5yoJip5Bmzov09XnXauNLdsovQc7s0uz2Sx0tnNtSybSFZpV9rPlkc2W8yGX/jedHWXaPtNYTZupVlFRAeB3B6220CRlr3W1dkC+DKVx4pboNK6qqso4XqyqqsKaNWu6dXyFQqFQKBQDi1ycxv9ljO3BOf8cABhjuwNY3rvN2vLIVDgkXeqzNj2fkCP85MG2vD9FTnW1bZQ+2dUoEKUH2zv0VjpnT0f8hMNhhMNhDBs2LK/jyVIumSauXZEIyGaPPeUIGEzk+3ymc0rQfcjXDmWNWJ1OJ7Rnc0HbJ1Eb5OjzfO6l7Cgh+RIlQZE73e2POrvW6eyOttfr9Wm/M1e7jEQiSCaTiEQiohBeV88jXaFZhSITufS/mcaD6SD7I6kKcgaHw+GUZ0e7wJvu+evL+hVqnDi4yeYUVtkTCoVCoVBseeTiNN4VwKeMsbWbfx8JYBVj7Du0axHv0Gut20LJ5PztDJoQmEwm1NfXo7i4WKRbpzt2Pk4UFTE1sOmtdM6eiPgh26HiOg6Ho8tR66rw4sCiP+9Huj5JLp5H+sQul6vLfZWyt67R29IuvXlfMr3fEokEvF4vSkpKco5aTxfx3ttRmgoFIb9nc5G0oG3ldzL9LvelQ0lzWKFQKBQKhUKRnVycxlN6vRVbAPkMstNNWnNxrlF0SF1dHTZt2gQAHaL1upo6rp2ky1IYahLc//RWOmemiJ98nL1kO36/H8lkEi6XS+zTmfNHW6iuswWL7kinKPInW18la1l3lWz3MxAIoK6uDmVlZXA6nbDZbMJZSc4Ol8vVLaeiWiDrGr3RH3Vn4VLetzOHb6YoZ6/Xi9raWgAd36uZ7JSilIeSRqti4EDRxYFAIGWxFsj87MjvZKvVioaGBsTjcbjdbmG7meoH5IpyOisUCoVCoVBsOXTqNOac1/RFQwY7+UwatXIQQH6RVcXFxSk/0x0734G6dpKuIvAGFn2dzpnP/SebsVgsIi1W+1mmCaz2ueksbV3ZZd/SWRpzd6NM872fcmSnrG3bVbojTTCU6Y3+qDtSDz1hkyUlJSk/s7VNi1p8UPQXoVAIfr8/ZbE2W58m22ooFEI8HofBYEirodzVRSG1iKJQKBQKhUKx5ZBLpLEiBzobZGv1iUk72GKxQK/Xp510ZioKRVqs2aL8Mu1Ln9GEQo6gkgtRqUnw0MZqtSKZTIp/2WxNdixSxDDZEWNMPBOy3RFycbRMNkvPijYCUd5ePq4c5aToOpnuh5zNkK2fyXRMWUNY28/Ix7Pb7aioqBD3muxH7ms55zkVwssW0dzZOSiHctfo7B2kJdOznW5f7T2R9yWdaq3jrLNj6vX6jDrb2d6H6d7tMtQfqshLRa7kmlHDOU/73Mjv7GQyiXA4DIvFAp1Ol7Lgoy2IR8fsbFGoM3seDIXuqqurUVOTPiamqqqqx77n9ddfHzDH6am2KBQKhUKhGFoop3EPkW9RLa0mpF6v77C/PFmQB+m5RFVlmyAzxsRAX3ss+nu69mRDTYR7j964tp0dU6/XQ6fTCSkA2Ray7aud4Gonv9p9GWNwOBxZ7VUbtdRZ5JJ2+6Fum109/0z7yQ6FfJ1hnfV7sv1oHRfpjp+rtm62PlPJnGSnJ54frZ2kO2au0cvp9ic7okWEdFGO2c4jU5u0x0+HTqfLms7PGOtywUbFlkFntqdFKyGRzoFM+6V7buR3bTgc7mCbXR3n0b6d2TO91wcyNTU1eS96doWecpz3xHEGshNfoVAoFArFwEU5jfuAdFEj+Ubyys6RrkZxpHPwDIaIEEX/0BO2EQqF0NLSAr/fD4/Hk3OBKRk56jldlKEWZdN9R75pyF25N9kc07keL9/vVZGhPUtfpqtnu9e9dV9tNlvWPkr1SYp8kDNwuivJJMtH5ZKVQfREv6sA/v73vwMAzjvvvH4/Tk+1JRtVVVUZ+9aqqiqsWbOm175boVAoFApF7zDoQqwYY+WMscr+bkc+0KA/FAqJv1FqdigUQjKZ7PQYNpsNLpdLDOBJ/zUfaOIeDAZT2tGZ1IViaNITtmG1WmEwGBCPx1PsP9926HQ6+P3+FNsFICILtVIVKsK450h3jQm5X8qFrvRd6fot7fE6u9f52nK271TkTzo7yWZX3SGbTfTWfWWMZeyjAPWeVeSHbC9WqxUul6vTAINMzxPJR1HEca7v4WzPirLn3HnmmWfwzDPPDIjj9FRbsrFmzRohJ6X9l0kORKFQKBQKxcBmUEUaM8aOADAbgIUx9iqABzjnm/q5WZ2SKao4n+I9PVF4SEWHKPoanU4Hj8cjIu27SibbVQV3ep9s17gvCjT2R7+l+sqeJZ2d9Mez25v3VdmMojforDgskS4bTSbf7DZlzwqFQqFQKBQKYBBFGjPGDgBwC4DzAZwMYAKAw3PcdwZjbDljbHlDQ0MvtjI9NOjXRmVQBElfDcpVBGbv0N/2NdDJZP/5kMl28410HWwMBNvq72vcH/3WUOgr+9u2+sOuevO+DgWbyZX+tq2hSGfPU77v4S3Bnqurq4VeufZfTxa7U+QGSVdk+lddXd3fTVQoFAqFQpGGQeM0BlAN4A7O+Tec858A/B3APmwz2XbknD/AOZ/AOZ/g8Xj6oq050RPONEX/M1DtayiwJUxsszEQbGtLv8ZDlf62LWVXWy79bVtDEfU8dYSK3aX7p7R1+55s0hVKvkKhUCgUioHLgPdWMsaojY8AeHnz3/QAwgCGA9BxzjljzNlPTcybZDKJQCCQk5axQjGUUM+GIhO9pYGrGByo+69QtEPvyaH+LHz33XdZI1dVNLFCoVAoFApF92EDedDJGDsUwIEAXADO5lJjGWPVAO7knB/NGDsFwLDNv0c7OWYDgL5czi4B4NX8TQdADyABoLe9Y+m+v6/p7zb05PdXcc4zhk71g30R/X2Nie62o6eejcF6PTLaVz/aFtHf15RsoxBAfT+2g+jv60Hk2o6BbFvp0J5XX743szFQ7ruW/mxXb9lWf55Tf9/nbN/f28/CQLru+djWQL5nqg0Drw1Zx/MKhUKhUCjaGbBOY8bYYQBuBDAHwCwArZzzE6TPSwDcCeArAGcAOIFz/kN/tDUbjLHlnPMJQ/X7B0Ib+vv7+4KBco6qHQOzHT3BQDkX1Y6B2Y6eZqCel2pX39Gf59Tf13Oonnt3vnso3zPVhoHXBoVCoVAothQM/d2AdDDGigGcA+Ayzvk7jLE6AOcwxk4D8AqANgAWAEcC2AHAcZzzVf3VXoVCoVAoFAqFQqFQKBQKhUKh2FIYqJrGbQDO2ewwLgHwPIAYgD0APASgknO+HsA/AExTDmOFQqFQKBQKhUKhUCgUCoVCoegZBlSkMWNsJwARAEnJEWwFcAbn/P3N2ywBcDGAC9AeiRzvh6bmwwND/PuB/m9Df39/XzBQzlG1I5WB0o6eYKCci2pHKgOlHT3NQD0v1a6+oz/Pqb+v51A99+5891C+Z4RqQzsDoQ0KhUKhUGwRDBhN481F7x4A8DKA/QDcwTn/h/S5jnOeZIydC8DDOf9b/7RUoVAoFAqFQqFQKBQKhUKhUCi2XPo90pgxxgDYAcwE8FfO+SuMsT0APM4YM3PO7wOAzQ7jU9Fe9O4v/ddihUKhUCgUCoVCoVAoFAqFQqHYcul3pzFvD3X2M8aWA3Axxoyc888ZYycAeJYxFuKcL2GMHYR2Z/HpnPMVXf2+kpISXl1d3TONHyIkk0kkEgno9XrodANVBrtv+Oqrr7ycc0+mz5V95YayqfRksy9lW4OXgWDvyrb6h4Fw73sbZVuKfMjnmVC2NXDY0vqyoTKe//XXXwEAY8aM6ffj9FRbBgOd2ZdCoVAMJvrdaSyxCcCBAF4BEOOcL2eMnQLgLsbYOwC+BvBnznlTd76kuroay5cv735rt1DSyZVwzhEMBmGz2TIOFDuTOWkPKB/8MMZqsn0+1O0rV7kb2abINrYUG+kO2exrqNtWb9EbfZf2mFp77w9bV7bV93DO0/Z1RCY7GGzvU2VbA4uBZj/d6Q+VbfUOXZEmzGUuMJhQ43lFb9KZfSkUCsVgot/f+pvlKcA5/zsAG4D7GGMFmyOOPwHwLQAT59zbXYexomswxmC32wfcRFUxeFE2pRhKKHsfuqh7r1Ckop6JwQndty3BYaxQKBQKhSJ3+uXNzxgbzxjbkzFmlNvAOT9+8+93AZjOGPsrgD8AiPVHOxUKhUKhUCgUCoVCoRhszJkzB3PmzBkQx+mptigUCoWib+lzeQrG2DEAbgKwYfO/5YyxJZzzNgDgnJ/IGJsOYBiAHQEcyTlf39ftVCgUCoVCoVAoFAqFYjDy2WefDZjj9FRbFAqFQtG39Gmk8ebI4uMBnME5PxDAywBGALicMVZA23HO/8E5vwHANM75D33ZRkXX4JwjEAh0SSdNsWWibEIx2FA2qxiIKLtU5IuyGUU+KHtRKBQKhUKRif6Qp3ABGLf5/y8CeA2AEcCJAMAY240xtsvmz6N93zxFVwgGg2hra0MwGOzvpigGCMomFIMNZbOKgYiyS0W+KJtR5IOyF4VCoVAoFJnoU3kKznmMMXYHgJmMsV855x8zxj4BUAngcMbYowD2AvDk5u3Vkncv0BuX1WazpfxUDGz6orq6sgnFYEPZrCJf+mKYouxyYNMX79N8UTajyIe+sJeB+JwoFAqFQqHonD7XNAbwMYDxAE5hjDHO+b8APMEYmwFgGOf8zn5ok0KiKwM3nU4Hh8PRC61RDCZk22GMKZtQDCqoOnxPHEehALpmC9p9VF+qyAfG2ICyGdUfDjxUHzN0GD58+IA5Tk+1RaFQKBR9S587jTnnYcbYMgAcwBzG2NYAIgA8APx93R6FQqFQKBQKhUKhUCi2JB5//PEBc5yeaotCoVAo+pb+iDQG57yZMfYggBUAzgYQRnvRu7r+aI9CoVAoFAqFQqFQKBQKhUKhUCja6RenMQBwzqMAPmCM/av9V57sr7YoFAqFQqFQKBQKhUKxpXDhhRcCAO66665+P05PtUWhUCgUfUu/OY0Jznmiv9swVOGcIxgMwmazKc25IYi6/wpFetSzoegusg3pdLr+bo6ij1B9h0LROeo56Tv+97//DZjj9FRbFAqFQtG39LvTWNF/BINBtLW1AUCPFH9SDC7U/Vco0iM/G6o4kKIrKBsamqj3qkLROeo5GfhUV1ejpqamw9/JyV9VVYU1a9b0casUCoVC0R8op/EghXOe9fNcVu5tNlvKz1yP3RVUJMHAI9P9B7puX329n2Lo0B0bybdPy/Zs9ER7FAOL3njn5WJDmVC2NXjpzn3vKbpjz5zzrPalbG/wMJDHYwPhOVFkp6amJsUW9ttvPwDAhx9+CED1BQqFQjGUUE7jIQi96BljHSKgemPyrBh4MMbS3n9C2YFiKMMYU9FPim5BNqQm1ls+8j3O9l5VKIYy6jlRKBQKhWJwopzGCoVCoVAoFAqFQqFQbEFstdVWA+Y4PdUWhUKhUPQtymmsyBlVuEKRD8lkEqFQCFarVRWCUgwKVB+nyAVlJ4qBjrJRRXdQ9rPl8MADDwyY4/RUWxQKhULRtyhPjiJnqHBFMBjs76YoBgGhUAhtbW0IhUL93RSFIidUH6fIBWUnioGOslFFd1D2o1AoFAqFglCRxlsQvR0ZoApXDE26aldWqzXlp0Ix0OmJPk5+XlSE/ZZJX74LVcSfoiv0lI2qjKGhSbZC2YFAQL3fBhEzZswA0P0o3544Tk+1RaFQKBR9y6B2GjPGGFcVuwQUGQCgRwpMaCerXSkOpRwogx/ZrnK5//IkMx97Uc4RRS7kayfJZDLn7XuiAF5P98OKviEfu9LaSW/2Xfn2vwoFkHtfls12k8kkGhoaEI/HASj7G6zQPbZYLAiHw916F6r32+Djp59+GjDH6am2KBQKhaJvGVROY8bYvgCGAYhyzl9QDuNUco0syXbZOOdiMKmdrMqfZdtPJpcJb1eOSyjnYtfI59FJZ1eZ9uecC1mKdPtkWjigSU0mW+msvdnspyv7KQbutctkJ5na21kf1Fn/k4lM+1Fkvc1my7i/srveoTtDgu44Z/O1Sfosl/deuoyNgfpsKtLTVbvsztgoV9LZLrU3FAohHo/DYDDAarUimUxmXfxXdtc1ujuV6ez9RffY7/cjmUwCyD5PyNYeeTyYbjs1nlcoFAqFYstj0DiNGWOHAbgZwD8BbMMYa+Ocv7v5syEXcZxucMUY69GV/3zlBdKlMXZVokA+lhpI9i/5RF8yxlImFZnuXa62Qtup6GMFkW/aNW1nNpsRCAS6nWZNNmmxWKDX6zt8rtPpVEReP9OVyN/upPPnu28+Kf/KnhRAbjbTFSkJ7XtXfl607/IhNswe9GgXnijSONN4PFf7SbeoT/2tQqFQKBSKLY9BoRfAGHMBuATAuZzzOQC+A2BhjI0FAM45Z1lmhoyxGYyx5Yyx5Q0NDX3T6C0AmqzmOvlIV/gs32NkO9ZARdlXKuRkzuasydVWaLuhWoxF2VZHcrGvdNtHIpEe6VPIJsPhcLeO099sybbVlSJO+dpVd/YdTO+3rrAl21Z/kYvNdMWuso3RuvNM9BbKtvKH7rFer886Hu9qv6SK5ikUCoVCsWUzWCKNdQBiAIKMsREATgKwPQAjY2wj5/zMbJHGnPMHADwAABMmTFChEr1EtkjRfKP7LBYL/H4/TCZTj0QH9iZbon11V6Ozs/1zjUCnlNhkMtlj6biDiS3RtnqCrtin2WyGTqeD2Wzu1nfLUVuZGAwR8luybXW3CFhv66tn6/8yvTMHU0GyLdm28oU01bt73+R3YTKZ7NZ7tTMGcn2BoWpbfXFPumo/XelvE4kEvF4vSkpK0mbsKHqOnXbaacAcp6faolAoFIq+ZUA7jRljuwOo5ZyvY4x9AGAhABeAZZzzuYyxMgCPMsYO4Zy/1a+N3cLoygQ1XRqtrG+bT4ptOBxGMplEU1OT0GBTKbp9R3eLL3W2f64p1zqdDjqdDm1tbT2Spj2QJ8OK3OmKfUYiESSTSUQiERgMHV99ufZ5ZIfZUrW72u8peobuFjTs7eJzOp0uYx8UCARQV1eHsrIyOJ1O8XdlU4OTrtoSvauoP9K+C9M56HpKykQVXxx49MU9yeXdlg65v82kc0y2TH2e1+tFbW0tAKCsrKybLVdk46677howx+mptigUCoWibxmwTmPG2KEA/gHgJMbYBs75LYyx+wHMAPABAHDO6xhjazFIZDYGE9oJar5OZNrebDbD5XKJKJlcj5GrBpuid+hupF62/ZPJJAKBAACkpEpmsg+5sFh3UZPhrjHQnO1dsU85iiqdZnp3nHJa2+1Jm1X0HLnacXf7v65ANkSLpNq/y+9SxeCBbCjf+5buXdWVCPVMZNu+qxrdNpttwEfBDwbS9VNd7ZMyjbf6knS2XFJSkvJT0T9UV1ejpqYm7WdVVVV93BqFQqFQDFQG3OiOtVMGYB6AUzjnHwCwAADnvBnAbwAeZoxtzxg7AcAuAH7qtwZvoVit1pQJar5aZ7R9JBIRA9V0x6DUTe1EWdbZo580+NVuq+h5uqtlmG3/QCCA1atXY9OmTSm2INuHfK/JBnrCWWmz2eByuZQzL08GmmZhV+xT7kvS9UXaPg9on7zn0udoj6e1WTqOKiTVv+Rqx/2h5Uo2pNPpUFFRIZwroVAILS0taGhoUO++QUg+dR3kfoLeVXJ/lO1YXR2jacdjsoMxX43ugfJ+GOyk66e62ieFQiHU1dWhrq4urW30xbiabNlisYjv0uv1KCsrU9IUfcC0adMwbdq0tJ/V1NSAc57235o1a7Iep6qqCoyxtP+qq6vzbotCoVAoBi4DLtJ4c1E7P4AfOOfvMsYqANzGGAuh3Tn8FIBHACxCu87x6ZzzX/uvxYObXCOv5Mhf0hjmnAtNMu0kJp1+qDZKJplMoqGhAfF4HEDn0X1y2q7dbleRLRLkfO/q9cg3kpS2p0jwXPdLJpPw+/0wm82w2WwpesXJZBIOhwNWq1VMQslpnOn4crtzOe/upq0PVfoj8rIr5NOfke3F43FEo1GYzWYxgbZarULOwu/3A+jYPyWTSZEFIfd36fSMVYT7wKA70XraqEzZweZwOMAYy2p/csRwJBKBxWJJcZjQe5E+l//u9/vR3NyM+vp6jB49WshWDLQMAEV2OosG1vYTNpsNnHO0trYiGAzC4/GkldYB0mseZ3tHpxvT5ZNtIZ+LyqzoWbRjo3yebXksyBiD1WoV8g/y2JvunXzP6Xc5A0fuYwCI96HD4QCAtP2Ptl+icVcgEFDvwX5g/fr1vXIcrVNZJpPN9lRbFAqFQtG3DDin8WbCAMYwxuYA+D8AHwFoAbANgDM45/MYY48ACHPOt8zy453QnYg1GoRmc9qmG0jS5IP+7vf7sXHjRgCAx+MB0F7corGxUThPwuGwiI7Q6u2FQiHE43EYDIacUzdlJ4zP5+vQ7nTbDnay3Wu6l/k6pbQTkXz2pwlBS0sLotEoTCaT2E8+brqoJYoidrvdsNls8Pv90Ol04JzD7/fD6XSCMQaLxSIcydnaRe3w+/3weDwdJuLZJlydPUNbiv10Ffn8GWNikpgLudhsLp9pHW2ZnC2dPQdkS7LDhjEGn88nMh10Ol3KhJgWMTJJAsh9IbWVnH2dpZXT9UnnROrMSTAU7LKn7Af4/RpbLJYUR4jWyZHtuqZzpoVCIdTX14t3m9VqTfs+pXOhdxZlzXDOU54pej9qHSs6nQ4ejwfJZBI+ny9lYSOTky/T9VO21b+ku1/yPUknPxEKhVBTU4NAIACdTpdW/5UchQBEhCq9X+n30tLSFI1s+u5gMAi/3y+imznnOY3HtOdCDsJMtpfNtgZaBkZvjA3y6dPovsTj8U51qrXHlcdEVGTObrenRC3L42fZ5uiecs7FdwYCAbS2tsLv94s+jnMu3lfp+p9M72F50U71NQqFQqFQDB4GjNOYMbYjgAAAC+f8e8bYlQDOAVC8uVoyGGOTAZwGCKkKRTcIhUKIRqOIxWIpEXJy5Ei6SASgPRoqHo+jrKwMxcXF4piNjY3YtGmTmKBk0t7z+Xzw+/1wOBxwOBw5RYna7XaUlZWlRLMMFX3HziLKuls1Xb6v5JDIdk8o+i2ZTGZ0+pNTBQDKy8thsVjEJKi4uFgU9qEIKSA16om0tGWN2EztiMfjCIVCWaNBVUT64INS8yORiMhaSDeBjsfjaGpqQmFhYUYnbygUEk43OQvCZDKJPlDWH9VGg2bSLZa/S/t/eZ907VbFzXof+Ronk0nU19ejtLRUZKvIUXWZkO91PB5HY2MjCgsLUVpaCqC932xoaBCLaOnsj2yrM53+dHZFzkKXy5WykJat31dRyAOPdLrqch+TzkFotVpRVVWFYDCI4uLiDosgwWAQiURCZH0ZDAaxcOFwOETGjhZyHDocDjidzryjhLs75lBkprOAinSa/IR2TGSz2cRiFr37LBaL2Fa2OXkcSJjNZkSjUSSTSZjNZrEvZdfI+xGZMjryXXxWKBQKhUIxMBgQXhTG2BQATwKYBmApY2w65/xTAO8B2I4xdunmTQsBFDPGCvqpqYOCRCKBuro6JBKJrNtZrVYxwQiFQmJy7fP50NDQIAaFpPVpsVjEADMUCsHr9cLhcKSkSxYXF6O8vBwej0dEntbX14tJDPB75ExNTQ3C4XBKuq/P5xOOHa3Wmk6nEw7moaZ13JkWZz66iZn2t1qtaGxsREtLSwftO+29oeg3ihhOh9VqRWlpKUpLS2G1WhEMBlFTUwO/3y/sTa4Mb7fbEQ6H4fP5EAgEhLYj3d94PN7hPsvtkCcutA9dt2w6j0PBfgYCdJ0zRVzJ+ua0rdlshsFggNFozDqBXr9+PTZs2IDm5uYOz4F8LKfTiWQyiQ0bNmDt2rUwGo2IRqOiL8z2DFH/SLYJ/B4JKj9/JKeSSTtZJp2OsiI9nelCZ9LHl99ftJ3f70cgEBBawXLkd7q+QHas1NTU4JdffkFjYyOcTiccDoe4zwaDAcXFxR0K2skOXIr8y+TIle0nXRvsdruwmWzvwXTvjFw1uhW9Qzpd9XA43OH9SpBz0Ol0oqKiAgaDAYFAAJs2bUIwGBSOX7rH9D4sKCgQ9krZPNo+hsZzVqtVjKtIAoxsRLYV7fMlPxOBQACJRELZVjeha26xWLLWXtC+V2i/aDSKhoYGFBYWwu12Q6/XY8WKFQiFQjAYDOCco66uDsFgsEMR4kzF8mQHNgA0NTUhEAggEolkHHf2hya8QqFQKBSK3qPfI40ZYyUA5gKYyTl/jzH2IYD3GWNxzvmjjLH1AK5jjP0fgD0BHMc5b+3HJg94vF4vamtrASBtKiNBTlgaLNKkoq2tDXV1dWJ/GhjmkkKo1+tRXFyMxsZG8bO2thaBQADV1dVikiJHzhBUsAMAKioqAECk0FG6uFa/Nl9d5MFKX2jKpotuoUkrRegB7VHDsoOMijfRtZejYLTpsDRBBZASLRcMBlMi3OPxOBoaGmCz2RAMBsE5Fw4/IPU+p9MppkkV2YocOZPuvFW0Z+/T2XWWPwfa7cPlcsHj8YjIukwFoHQ6HUwmEwoLC1M+k/sHcqT4fD60tLSIRTXGGDweD5xOZ0oxMq2tyc9ErvbSWTReushCkmqh46sI+XY6k9DJZF/y+4scrvS+kBdNaTE003eQLUWjURgMBtEXy7rXDocDkUhEHIPS/bO9ozLp3Gb6e6Z09XRyAdSGzq6Rou+RozobGhrQ2traoQiiLIGT7n5po9ctFot4l5LNkHMvEomkLPCTszocDqeN/pRrSMh9o7Yt8t8DgUBKzQmV4ZMf8rXMNrYym81pC1UHg0HU19ejuroaFRUVWLNmDTZu3IiKigqMHDlS9FPZvlf7XbFYDE1NTSI7zGKxiNoTisHBnnvuOWCO01NtUSgUCkXf0u9OYwA+AD+gvcgdOOcfMsZeAHALYyzKOX9qsyN5OIAQ57y+/5o6OCgpKUn5ma34it1uR0VFhfjMZrOJCGPZoauNRLDb7SgvL+8wcEwmk6ipqUFdXR2SySQ8Hg+CwSAMBoOYmOt0OjidThgMhpT2pCvYQTp7Wuew7EzMVxd5MJKpgBtFsPXE5EyexGqLpDgcDpGKTRF7tK1Op4PRaBQRMuFwOK3OsM1mw7Bhw8T30AJCKBRCa2srAoFAis1RmyhihbalSNTO5DMAiG21E+Z0227J9tNfZJK8SbedXAiRkKUgMvVhJK1TUFCAWCwmNLaB1IUQWXpl/PjxIipOLjpG9lxYWIhYLJZi6zR570wyRaYzPcp010m7eKace+3I0iFAbnIhhLwtLUKQTZIGv7xvJnmTeDwOt9uN4cOHpzj2kskkXC5Xyr2iY8japMlkErFYDM3NzSgqKhIRp+n6S60jp7Miatq2p3tnqL5u4CBrWMfjcTE2khc4LRZLSnE7OTKYFtHI4etwOFIKJhYXF8Nut2PUqFEAOpcQoEUykm6h8R5B+8sSUvJzR5lAgFqcyERXZMbk/wcCAWzcuBFOp1MEdJAGdTLZXtTVarWKe1pRUQHOOSorK1My9eSFpEzvXpJzIvmT5uZmjBw5EhUVFXkVXE5XNFktJPQtN99884A5Tk+1RaFQKBR9S7+/uTnnEQAmAPMZY39kjN2DdifyNABTGWMlnPME57xGOYxzQ6/Xo6ysTOjk0UCTBvRy2mG69DKDwYCysrIUJxvp09bX1yMQCKRMXuW0RZpY6/V6WK1W6PV6jBw5EkVFRWkHv/LEhJzJFJ1KERXBYFCk3kWjURFNQ1HIbrc7bRG0oUBn6e/5QLYQiURE2rYc0ZLOViKRCJLJJJqbm0WqrVb2JF3qo3ws2j4ajWLdunVobW1PJCC9a7IJWmSg1PJsqbB0fJvNljb9X7bZoSZ10pfI9knXOd1kORQKiTRqrX1ojwOk9mHpHMOyo8XlcqGkpASRSAQ+n0+k6jqdTtHnkFOusbER8XgcsVgsbfqubC89mXor94e0eFZWVqacexLalGetTciyDtrnmFL6qZgY3Vvqv2jhQGt38nGsVivcbreIvKRtLBYL3G63kKUAUiPEyblHEgQbNmzApk2b0NTUBOB3p9DGjRvFO43+ni6aMFNfn4tEEV3DofiuHEjIdkX243K5AEDYIkUBU5YPvUO1Nitjs9kQj8exceNGrFy5UkhbyPYqR8bT4ju9D/1+f0rRYnnRip4vin6Wnzu73Q6HwyG2l20333dqZzI0fU1PticfmbFMzzM5c+V+QH5vVlRUiKysWCyGESNGCE1i2XFM0LtHG4VsNBoRDodRUVGBsrKylIyMfPoPOmev15v13BUKhUKhUAxc+jXSmDGm45wnOeenMsZuBbAH2h3ItBT5FwBtGQ8wxJDlGbrjsOgs5VH+PjkiiyJNgVRpATkCmBweFP2i1eeTHdfpvstsNiMSiQh5Amqfw+EQEYQkfQFgyEct5BI5lk9BJDmiKR6PIxKJiKgjKqhD0VBUnMzlconfKTJTjgrMFHUk21dRURHWrl0rUr8bGxthMpkQDofTRu/F43GROivbmjYSL9dU7nR/VwVbuk+ukY1yFFsux9EWNyP7JuczQcU2Q6EQjEZjikPRbrejoKAAJpNJOJ4pqp6cz1RsKl8JHHL40HMh26TWTsmJQ30iY6yDrIuiI51FBefyjtMeQ3tv0hWBJWhbi8WS0kfK30kLEbJ8BcljmEwm0Z+SLndxcTEKCgqE/ch2k8uzRG3Kta9XEgI9QzrprGzIdmWz2dJmMGj7ONkOZY1uLUVFRUgkEkgkEmKhAvjdJuW+DGiXACOHKMmpyG2Rx2XZMka0MgrpzjWXd2pnMjR9TU+2h7LmkskkOOd5j+Ptdjuqq6sRDAY7SG7J94X0qak+BN0Pi8UiinhGo1ExTiMHNDn6g8EgWltbUVdXB4vFguLiYqxbt04ch4qBFhcXZ8zgkiOM5XFlb0qsKdJz7LHHAgCef/75fj9OT7VFoVAoFH1LnzuNGWPjARQBWA5AjCw555drtvsL2iUp7ACifdnGviRb9IJ2UCkPXjubFNLAnyaFJCWRSCQQjbZfToPBgFgshnA4DJPJJBx28XgcFosFfr8fGzZsQGVlJVwul3C60EAwGo2itbUVsVgMnHMYjUZwzkUEVyAQQHNzM9xut4g0JKdxQUEBotEo9Ho9IpEIotGoiHKRr4nZbBbV6G02G7xeL6LRqBj4ZqogPVRgjImJHkUpAkipyq6d9NAElxxiMuR0s1qtiEQiwrEhFxGjSOBwOIzi4mLYbDZxXykanO5PIBCAyWSCw+GAXq9HIpEA5zyl+I/f74fRaITf70ckEhFSF+SMpskvYTQaEYlEwDkXCwwulwuBQAANDQ3weDxicso5B+dcTHAo+p4m3EajEXV1deIz2XnZnciiLd0eO7s29DnZp/w37b6cc/Ec0/3SwhiD2WwWcjSJRAJ2ux1Go1HoPlI0HvWN0WgUZrMZPp8PiUQCjDEh5ULOHSpeFovFhKOusLAQDQ0NiMViomgZ9VFWqzXF+ZxpwkzRYLQd6ekmk0l4vd4UmR2KxifN5c7eCZ1Fkg4G5HPMZ1FL3jedbZFjwmQyiZ8kcVRWViYcGDJmsxl+v1/cCyoGW1JSAqPRCMYYDAYDfD6f6EccDofoV3U6HUpKSkShqWg0Kr6fsi9CoRDcbjdMJhOampqEfq3BYIDFYkEoFIJerxf9K0U2UtvltqaTI6J3vVYPO9t11zou6Zpu6XTWd2W6Btn268yxKI/FqC+jxddkMim+02g0ikJmZrMZdrsdZrMZjDG0tbWhubkZlZWVQu7LaDQiFouhvr4eHo9H9HVutxvxeFwsWshEo1EkEgmxCK/T6RCLxdDS0iL6YHnh2Ov1ijYbjcaUwrbyMytLRsm2Re9aOnZnDDQJlXTa4JnOI5sjOJFIIBwOA/i9OKG2/9IiLz4C7WMuOj6NlxwOBxKJhBgr04IBYwzhcBgejweJREKMdTZs2IC2tjbo9XqUlJTAZrMJCRPqCxsaGpBIJGC1WhGPx8XCA9lGY2MjNm7cCAApNUz0er04F7/fj/r6ehFs4vV6UVpa2mGMnytDoW/qLbR9QH8ep6faolAoFIq+pU+dxoyxYwDcBGDD5n/LGWNLOOdtjDE95zzBGLMAOBjAPABHc86b+7KNA5lMg+l0EUOMMeG4AH4vJNfQ0CAiS0gnNhgMpgzk6GcwGBQTmIKCAuh0OoTDYZFeRtGgRqMRBoMBzc3NKCkpgcVigdVqRVtbGzZu3AiTySQcgFarFbFYDN999x18Ph/0ej1cLpdIzTSZTGJwSGm9cvSOx+MRkTLNzc3CATAQIlJ6i1wGy/KkFUDKBFaeoAK/O7TSRQ0nk8mUaCGKNCbnGV3vRCIholPC4TC8Xi+Adh1tiuSkya6c/i/bpd1uF4sUVJTMbDbD7XaLNEty+FqtVtTX1yMYDOK9997DAQccIBxsgUAARqNRtJmiSMlmvF6vmOCQZrZer4fNZkN9fX3KZ5S+PVBSYwcrmWw2XV+Vz2KYLHfidDpF2q3NZkNDQwOam5tRXFwsFiWo39Pr9TCbzfB6vVi9ejXGjBkDi8WC+vp66PV6FBUVCac02XUsFhPfq9frRdRWVVWVSOsmh7I2kpjaBPye1s05h9frRTgcRiKREIs2slNiKE6M843ko3cV2RGAFJuiBYNkMoloNCocv7Sv9hqHw2GxYGm1WhEIBFL6NzoOkOqwpm0jkQjWrVsnFjFJ/sRut4vIvh9++AEvv/wy3G43pk+fDpfLJRbkSG6HijiuWrUKLpdLOA4pqjAQCKCxsREGg0EsxFE/R7rwlH6eyeEmn788nhiKdteTyI5RIHvEOvUZ9H6Vawg0Nzdj06ZNSCaTcDqdsFgsMBgM4r3q8/ng9XpF/1JSUoLVq1ejvr4efr8fZrNZSEjQPaX3Pv2MxWJCeoAyLGhhi9rLOUdLSwuamppSbJPGA4yxlLElAPHsydehK47fXHXg+4pM9STyhTL8KNtA+9ylewZpH8o8oXGT0+mEz+dDfX19Sl9C4zmSk2tubobBYIDL5RKOfwBisYHuIUWU+/1+tLa2YsOGDSgpKYHL5UoZ85MmcTweh8fjQWFhIdauXYuWlhYAv4+vsvUnKsNBoVAoFIrBR585jRljRgDHAziDc/5vxtixaJejuJwxNp9z3goAnPMwY+wnAH/gnK/vq/YNJOTiZvLgSx5MZ4oYkge3sjOCHMYULRcOhxGLxURRFXIckhOPIgxKSkrE5NhoNIpJLDmKo9EoRowYgdbW1pSJNtA+maAUODn9srW1NcXBWV9fLyZCVqsVTqdTOLUTiQQikYjQLNbpdCKygaJpcpmY5BvNNthIN0mjiaw8QaWCgRQx4vP50NraiqamJlGYyel0Ci1Cn88notBpkE92RRqyoVBI7CtXcaf2kBMtHo+jtbUVbrdbfAdVWScnC2E2m9HS0iL0HL/66is8/vjj+PTTTxGLxeByuTBjxgycf/754lgAROo3ae8BEFImFPksT1boM20BvlzZ0u2qp0nXV8k2Qg7edITD4ZRJs2zrRUVFKCwsFJNbcuZRBOivv/6Kp59+Gq+99hpWrFgBnU6HMWPGYMSIERg+fDjGjh2L3XbbDZWVlcIpQs8I9Z/ywkpDQ4PQWqdoLlmLnbRzSWvZbreLgmi0SBIMBuF0OtM6JYaSXaWL5OsM2Y4AdIiYTSdnok3TJ+RFNYoalnWMaRvqO7RFqkiWwmKxiOKztM26detwww034Omnnxb7PPLIIzj55JNxzjnnAIB4R5aVlXUo7Eq2FAgEUF9fD5/Ph4KCAhgMBjQ0NGDUqFFwOp0i2yMejwtNWhltpKui+2idX1qZI7m4odYe5Z8kBRAIBOB2uwFAZEiQVrBOp0NpaakYP7W2toIxhubmZrS2tmLx4sVoa2vDsccei/32209kfZEDOZlMoqmpCVarFWazWWRIUIFG6teA3xfrSVaFFvaTyaRYqKe+LtMzS9eD+sp8pH22VGhMRGPtzqD3IS0C0WIp1YCg/ooijePxOOrr68E5R3NzM9auXYvW1lb88ssv8Pl8CIfDGD9+PEaNGoVwOAy9Xg+/349wOCyKKNI7jfSQy8vLAaQWS6ytrUVraysqKirEAgRpusvY7XaUlZWJfUmjXxVJVCgUCoVi8NHX8hQuAOMA/BvAiwC8AA4DcCKAxYyx3QCEOOff9XG7BhS5RF7JmofZNOYoCsTv9wsdRRq8Ujo+ORX9fj9MJhMMBoOYQLjdbjQ2NsJisYhiKnLqYmtrK2pra1FRUQGfzyekAywWCxKJBGw2mxjwer1eJBIJFBYWYrvttkNhYSFsNhtqa2tF+rfNZhNRy4WFhSLdl2QHKNqZor9yjVQYaDp5PY120ipHy2rtg5y7FClJEZRUrNDn88FisSASiYjoXgDCMUYONIom/+mnn1BRUSEmvOTUo0UEig5taWkRESkejyclWpAcNF6vV9jnN998g3fffRdvvPEGVqxYAZfLhbPPPhuHHXYYbr31Vtx222348ccf8eSTTwKASAFvaWlJ0fujwo6BQCBlIk/yFvkWUZR1Q7d0u8oVrZMzUzRRpr6KHKqys0G7P0kLkNY2RUsZjUbRR1Eq7MaNG7F8+XIsXrwY7733Hn7++WcwxjBp0iTcfPPNaG1txcqVK7FixQp89NFHItKdnMlbbbUVhg8fjvHjx2PPPfcU0j4UTerz+YSMD7WdJvTkYCwsLERzczPi8bi4NkD7YlpDQ0PW6zmU7CqfSD5Z+15b4DLd4qG8PdmVrCNMkZZ078gZRo7a0tJSEdW3du1aAL/3b4FAABs2bIDP50N5eTmSySTeffddrFy5Et9//z1WrVqF77//HowxnHbaaZg9ezYAYP78+ViyZAmWLVuGww47DPvttx8OP/xwlJaWikUTSiW32WxwOp0wm82Ix+MoKipCSUkJmpqaUrS7dTodPB6PWGzWks5Ro5w3qch9WFd0iWWsVqsYc9ECJtmfvPgQCoWElElLSwvi8bi43yQPBfyeQUMR5haLBa+//jreeOMNvPXWWwCA0tJSvPPOO9h+++1xzDHHYOrUqaItiUQCzc3NQnaFMrtoHFdTUyMydKqqquB0OjF8+HCRNUbyP3LBNVmWigIRtNdOLlKay+L+YIhC7eqCHr3btNrn6c6ZnO001h0+fLiIBCanv9XaXmckFoth/fr1WLx4MV555RWsWbMma6bUHnvsgZ133hlTpkzBLrvsAp1OB7fbnSJF0dLSgkQigcbGRjgcDiHbFIvFUFRUBIvFAovFArPZLMaNWrkcuci1zECTH1EoFAqFQtE5feY05pzHGGN3AJjJGPuVc/4xY+wTAJUADmeMPQpgLwBP9lWbBiraaCktFH3p8/lEpeTOCtpxzuFyuWC325FIJESRk59++gk//vhjikOPIhj8fj82btyITZs2AWiPWG1sbITX6xVRyx6PBx6PBwUFBSguLhbV5UtLS0X6LE3GSWdNr9djxIgRIvJ12LBhQustmUzil19+EXptI0aMEJMziljmnAtnOBVnIzJVds41mm2wR/iliyhLl+5JUZuU+mowGOB2u1FbW4vGxkY0NjZi7NixANqdrrW1teI6B4NBNDY2oqSkBF999RU+//xzFBQUYNiwYSgpKYHdbkcsFoPBYEAwGMTatWsRDAbh9XpRV1cHAGhpaYHX60UkEhFax5RSS0Xu1q5dC51Oh3322QcLFy7EiSeeKO77fvvth4ULF2L27NmYPXs2brnlFjFJIceJXExMdhbRRB5AXk4Tura0wAJ0LUpyS0Tr5MzkkCJng/xsyY4Fcgyni06j5/qXX35BPB5HOByGwWDAhg0b8Msvv4i+7KeffsIPP/wgpFb23XdfnHvuufjTn/6EYcOGdWi73+9HTU0NVqxYgRUrVuDHH3/EihUr8Oabbwpnsl6vx5gxY3DggQfioosuQjQaRXl5OWKxGEwmE5qbm1MKU9GiFjnyyJlCchkGgyGrzWTS0UynZzsU0D57FH0JpM/Aochcun4ulws6nU7oTJPcDWkSkz1u2LABr776Kj7//HPYbDbssMMOGDduHKxWq5C0SSQSWLNmDb777ju88cYb+P7777Fp0yb89ttvor0jRozAtttui4MOOggzZszAyJEjxWeLFi3C7Nmzcc899+DZZ5/FSy+9hLlz5+KUU07BLbfcgtLSUqGFDLTr/8diMZEFYjAY4HA4xPs1F9I5apTzJhW5D+usYJs2ClQLOfFlCRWgY4R8S0uLiBptaWlBMBgUzlfK6IpEIvD7/Vi9ejXee+89/Pe//8Vbb72FaDSKyspKnHfeefjrX/+KiooKPP7441i8eDGuu+46LFiwQDiPR48ejVWrVqG4uDhl8T8TtKAqa9DSogadfzAYRH19PQCI9tIiCyFH6muLf6ZzDg+GhYx89KvT9dPp5MLkIsO0mEAO3Gg0Kt4t5IwlG3njjTewbNkyfPDBB2CM4aCDDsJJJ52EwsJCuN1uuFwuFBcXo6ioCE6nE8888wyeffZZ3Hfffbjvvvuw9dZb48ADD8TRRx+NfffdF+FwWCzeAqmF+yg6OpFIwOVyiXoA2sUQqh1AtkGSFbKcSaaCyEPtvdZXHHjggQPmOD3VFoVCoVD0LawvtTs36xWfCWAHAI9zzv+1+e8fol224tfebsOECRP48uXLe/trciafQngERTiFQiGMHj06ZZCeDr/fLyLeKisrhXzAkiVLMGvWLKHXmAmShCgpKUFJSQk8Hg9KSkpgMpmEA7m+vh4NDQ2igIaMy+USqeDV1dUYMWIEpkyZArfbDYfDIYpEMcbQ2tqKVatWobCwEGVlZXA4HCLilSYtNTU1Qle0qakJFotFRO5VVFR0a7JBUSDkYJfhnEOv13/NOd8l0/59bV9a+5Hbr3XOydEfZrMZjY2NYtvS0lKEQiE0NjaipqYGNpsNlZWVcDqdiMfjaGhogMPhQGlpKXw+Hz799FM8+OCDePnll/Nus8PhQHFxMQoKCmA0GqHX62EwGMQ/o9EIh8OBQw45BEcccQQ8Hk9KtXeZSy+9FIsWLcKdd96JqVOnCmdiWVmZkDmhAowtLS3CcUeLD1RkRqtLKz97mRxW2RYWurrgwBj7inM+Id1nA6nvSlfMLpdIY9pWa5fyQkcwGBTOFDkKnK5/bW0tvv32W3z55ZdYvnw5vvrqK+HELywsxMSJE7HbbrvhgAMOwG677Sb0HjNBjl8tkUhEOKFXrFghnDWHHHIIZs+ejdLSUiGHQo6Q4cOHCw1TchTLzx1F5FF/lw/avimTln0mBpJt5TvuoHOnZ49sQdtPk221traipqYGI0aMEIVU5eycUCiEDRs2oKamBuvXr8eXX36Jf//73/j+++8BQEQEaqH34G+//SY0rysrK7HbbruJfzvttBMKCgoAQDh705FIJKDX6/HBBx9g2bJlePzxx7HLLrvgmWeeERqyAIQjifooKvipddD5/X74fL4Uh6d2ETXTmELOnuiK42Yg2VZnZCtili3SWN5P+yyme1/I7xUAHf6WTCaxbt06GAwG6HQ6eL1exGIx1NTU4LfffsPKlStRW1uLDRs24LfffhMZP8OHD8cxxxyDo48+Grvvvnva/vXLL7/EkiVL8NRTTyEYDGKXXXbBscceiwMPPBA77LCD0DGm6GJqN8EYS/kM+H0x0O/3p0ii0LMYDAZRXl7eYbE2EAjA7/cjmUzC7XanRNtqn19tn7Z5gS3jmKu3bKuzcXmmwAL6THtu8nkFAgHU1dWJMUq68QVhNpvR3Nwssld0Oh2+/fZbLF26FG+88QZaWlpQXV2N0047DaeccgpGjBiR0h5avNeydu1avPrqq3jttdfwr3/9C/F4HGPHjsV9992HXXfdVdgzOa9NJhOKioqE7ZJ9AKmLxDTuTCQSaGhoEIEetEhvs9k6yE9p+/ZM46reCOLI1m8BA6/v6g26Wpiwv447mOjMvhQKhWIw0afyFJv1ipcB4ADmMMa2BhAB4AHgz7qzQmC1WlFWViaizjpbqZeLRAHtxeauueYaLFy4EPvttx+uuOIKUXmZol5MJpNIhSS92ng8nlFvNJFICFmL5uZmbNy4EatXr8Zvv/0m/q1atQpvvvkmotEo3n77bdx3332wWq2ora2Fx+NBIBCAXq+HTqdDQUEBXC6XcCiTNlpdXR0CgQCCwaCIHLNYLCKaobtRU9kiRzdP2tJfgAFCtuixQCCA1atXw2q1Cue61+sVk0Cr1QqLxYKqqiohF0G6ii6XC9FoFJ9//jkWLlyIZ599Fk6nE/PmzcPee+8tNPWi0ajQ5YzH4yJ9sbi4GIWFhWKxQQs5UPLllltuwcqVK3HZZZfB4/Fg5513RjKZhMlkQiAQEPqMcrowPSOyA0kb6aONhNUWsFERMaloJQa00e1yH0XOetmZQv+nol+ULis7jH/99Vc8++yzWLp0KX766ScYDAbstNNO+Mtf/oJdd90Vu+++O8aNG9djk0uz2YztttsOO++8s/jbww8/jJkzZ6KmpgZ///vfYbPZUF5ejk2bNsFgMAgt5cLCwpRCfxR1RZHGXSmupM1A6Swqj6IBu+oI7E+07zS5XyOte7KVdJD9yVrpslPs008/xT333IMPP/wQwWAQJpMJe++9N2655RYccsgh2G677RCJRLB+/XqsW7cu5Wd9fT2OPPJI7L777pg4cSLKy8u71HcB7RHskydPxuTJk3HMMcfglFNOwV577YUHHngA++23n4i6lzWzAQj9UtI3BSCKLZLzmIqJ5mJnsi11FmW7pULPZGf9R7YoYzlLQpb5ouww2dFP0hS03QMPPICHHnpILEZYrVaMGjUKY8aMwQEHHIAxY8Zgl112EY7iTPJcjDHsuuuumDRpEm677TY88cQTuOWWWzB37lyEw2GMGTMGLS0tKC4uTiluS07axsZGURh07dq1IhNo5MiR4n1qMpmEvBU5s9MttIdCIdTV1SGZTKKgoCCjtjORThsa/TTmyuQc7qzvNplMCIVCQt8c6BhhLiMXStUuiFFWgcFgwGeffYZ77rkHb731FiwWC4455hhMnz4dkyZNSusYzsbIkSNx7rnnYtasWWhubsbrr7+OefPmYcqUKZgzZw4uvvhiABBO7JKSEiFLIi8uUaaG9n5SloRerxeFRh0Oh7AVeTGL9kkmkwM+ylyhUCgUiqFKn0Yaiy9lzIR2KYqzAYQB3M05/7ovvrs/Vo5J2zVfZxM5F9IRCATg8/k6VFWmtDeKAKEBKWkKt7W1Yfbs2Xj99ddxyimnYP78+TAajSnHzTRg8/v9GT/TRjhpkQeGS5cuxcyZM/GnP/0JCxYsEEX0WlpaRGRYWVkZjEajiIiRnQVUtI0qRFNEsnzdyCmV6Xp3RYaiNyONuxJxDmS3Ebr/BEVp6vV6VFZWwmAwiEIoVFTQZDIhHA4jkUgIXWOr1Ypff/0Vc+fOxcsvvwyLxYJzzjkH559/fkpEHAAxkUxHQ0NDRicPaSpnQrZRLU1NTTj88MNRX18vovRowcNoNIoCQvF4XGhlh8NhNDY2iolzKBRCIpGA1+uFx+NJiYhJF/2Uzb6y3S9gYEWDdrX/7+wctceVo6845x36Lvp/Y2Mj9Ho93G43wuEwotEoPvroIzz22GN4++23kUwmseeee2LatGk48sgjxXMfCASyyj1kilSn78/Ud0WjUaHVTXz88cc4+eSTAQB33XUXDjjgAPGs+f1+bNq0CaNHj8bw4cOF45Y+p2eK2pqvU1e+rrImON0L+Z5oo/l0Ol2P21Zn9pPJRjrbT5sxIUPPI9mNXKRJ1vSljBq6ti0tLWhtbcXChQtxzz33oLKyEocffjgOOugg7Lrrrh36MplsTuFNmzalXQijNmTq18LhcIcsoR9//BEnnHACNm7ciPnz5+PEE09Ec3Mz7HY7CgsLEY1GodfrsXLlSjDGMHz4cJGBEwqFwDkXjhyHw5FiW3K/lS5CkpxBXcme2BIijTuD3rVyX0a2l0gkwDmH1+sVYzFakLTb7YhGoyJDymw2i/dgJBLB66+/jiuvvBI1NTWYOnUqTjvtNFRXVwsd/kz9WltbW8Z3bTgcFrrFQLvtX3rppXj++ecxceJEXHvttZg4cSJ0Op2QnrLb7bBYLCKClN6RTqdTyBwAEM7qcDiMQCCAqqoq+P1+uN3uDs8BPZMAsjrks41h+ivSOFM0dLqocnls0NDQgI0bN6KiokJo7NOzRzIT4XBYFBgmBzzJVpAUBEWlf/HFF7j77rvxxhtvoKioCBdccAFOO+008V5qa2vLOK4KhUIZ322BQCDls7a2Nlx00UV48cUXsffee+OGG25ARUWFKIYdCoUQDAbBOYfb7U4peqzVM6bnhBZYSE6D6poUFBQIRzTtQ3bicDhUpHEPc+ihhwIA3njjjQ6f5RMRnO04uR43n2MMdlSksUKh2JLo60J4AADOeRTAB4yxf7X/yjPnDm8BdFWnjTHWYaBEg1STySSiQwmSEbDZbKKYhdvtRklJCcLhMH777TdceOGF+Pbbb3HjjTfi7LPPTjsQy+bAyPZZtkGdrD85ffp0BAIBXHHFFbDZbLjpppvEwNlqtYp0copkILkLihyjgbjdbkdFRUWHSXA4HO4QOaptd2e6dFkGrQPGVjNFmNPfLRZLynnY7XbhHE4kEjCbzTAajSnXy+l0Qq/Xi0i1eDyOCy64AI888ggMBgNmzpyJc845J602LAARjZKpvdnOJZv9ZHLKAO0p4y+88AL23ntvnH/++bj33nsxYsQIEVVMcgDRaFQ4js1msxjQUnQTae7R36g9er0+ZXJFDitARcQQWlvUSlTI2pa0sCAvJNHCkF6vRyQSwYoVK7BkyRI899xz8Hq9qKiowPnnn4+//OUvQmtbRqfTdSvaM9O+JJkis//+++Pjjz/GMcccgzPOOAPXX389ZsyYAQCor68XqbiMMRFlRRHqcpSW3+9Ha2srAoFAh2KMuUyQO4t4G8x629rIYhnGWNrP6VrTdaFrE4/H0dTUhPr6esyYMQP/+c9/MH36dCxYsCDFeZ/t3dZV2wKQMQqQsmpk/u///g8ffvghpk2bhgsuuAArVqzAVVddhWSyvZBoLBZDfX094vG4kPkhO6PFD22UPkH9PNCx3+pK5PtQg8ZjWtsjhxc5GaPRKJxOJ5qbm4VGMecciUQCbW1tYv+mpiZceumlePHFFzFu3Di88sor2GeffdJ+bzp0Ol1G29K+h4uKivCPf/wDf/zjH3HxxRdj6tSpuPHGG3HSSSchFovBarUK6YhgMIhPP/0Uy5cvx8qVKzFt2jQcccQR4JyLvtpsNqO+vh51dXUpBY9l5yU5C8lZ3JWF1M3PXb+MuXLpP7Vje8aYcK7TswlA6KHT7yTR0dLSgmg0CoPBAJfLBYvFIhawly9fjnvvvRcvvfQS3G43rrnmGpxzzjkdFpqoTkg6OrMR+boXFBTg4YcfxoEHHojZs2fjmGOOwW233YYTTzwRTU1NaG1tRSQSQUlJCcxms5D20i6gk+3pdDrU1dWJhVGSN/P7/YhGo2KBnsZo9J4cbFkxgwEakwyE4/RUWxQKhULRt/SL05jgnCc632rw092CM7LzhQapVPxO1s/z+XzC+cI5FwXjwuEw3nrrLVx99dXw+/1YunSpWO3tL2bOnInm5mbceuutKCoqwt/+9jcMHz5cpOSR9htFN9PAndJvAaQ4BoBULUGXy5U13W0wO1QIecJCtiHbCNCxCBkV5zEajSnp3UajES0tLSLaJR6P44cffsCpp56KmpoanHnmmZg9ezYqKyvF9e8OkUgETU1NKC8v75EIkjFjxuCpp57CH//4R8yZMwd33303xo4d22GSHwgEEI/HYbVa4fF4hB4lTWxIBoXsLF0UKE3+tpRq8D1BKBRCS0sL/H6/mAhq/0YT5UAgIIoxAe12Sc5Tk8mEf/7zn7j11luxZs0aHH744Tj99NMxefJksRAyEBgzZgzef/99nH766ZgzZw5WrlyJq666SkiwUGqybHva55I0QePxOILBYI/LAgxWR2Bni2Fmsxl6vV5cU4rIMxqN4JwL56per4fdbkdTUxMef/xx3HjjjeCc47HHHsNxxx3XK23/5ptvUFBQgOrq6i4fo7i4GC+++CKuvvpq3H///Vi3bh3uvfdeYS9AezTe1ltvDYPBgHg8nlIoNpODThW+S41G72p/nE5CgSIoKSqVbJMWrkmrPxAIgDGGJ554Atdffz3i8TjmzJmDCy64IGO0aE9y3HHHYffdd8f555+PCy+8EO+99x5mzJgBo9GIzz77DB9//DHeeustbNy4EUC75MTXX3+NCRMmwGAwIBaLgXMuah6QZBMVUZaR+7vBOM7Kpf9M90wZDAYhlwakLuLLzl2SzKLCqBaLBcFgEP/73/+wePFiPPfcc3A6nbj66qtx9tlnC5303oQxhpNPPhm77rorzj33XEyfPh3vvvsurr766hTns8/nw4YNG0QWRSAQ6BA5nEwm4fF4APz+zNA1JQkXisSn6zcY7UShUCgUiqFAvzqNhwraSUa+aJ2DAFIqL/t8PiFH4XK5RAQyYwxFRUV49dVXcf7556O0tBQfffQRKisru39SPcA111yD1tZWLFq0COXl5Zg5c2YHHdNwOIzi4mLh1AsGg2hoaBDnSoNSuZAIpY2SRly6SfJgdajIZHJI0d/TOdjIFinK0WAwoKSkRFy/SCQCn8+HhQsX4o477oDH48E777yDSZMm9Uib29rasGzZMjz88MOor69HYWEhdtxxR2y//fY44IADsPPOO3d54vCHP/wBTzzxBE455RSceuqpWLhwIXbeeecUBxSdqxwlRsWICgoKUnRoyTlOzjxZI1Z20mdzPgyGavA9gawZLU8E0/2Ncy4WuQCIaKWWlhbcddddWLZsGcaPH4/XX38df/jDH/r5zDJTUFCAl156CZdffjkWLVoEr9eLa6+9FmPHjoVer8+qywv8vohDNiXbV3ciWwc76Z4ZWSuWFhyoyBa9+wBg48aNaG5uhslkwrBhw1BaWoqbbroJixYtwoQJE/DYY491y6Gbrc3z5s3DU089BcYYDj74YNx4440pEgH5YDQasXDhQmy33Xa46KKL8Mc//hFPPvkkhg8fDrvdDo/HI5w4jY2NqKurQ0VFBQoKCjKmGmcrMiUXwuuNNPCBQmcZRvlCzjGq+yAvMsqLQIlEArFYDO+88w6WLFmCb775BocccgjuuusulJSU5K1J2x1GjBiBN954A3fffTfmzp2Ljz76CIFAQMgGHHDAAZgyZQoOPvhgeL1e7L777pg/fz4uueQSlJWViYVlkkyhyGPteIO26wtneH+Ry9ieFk+1hV2p/w8EAohGowgGg1i5ciXOPvtsbNiwAbNnz8aFF14oCtD1JePGjcPHH3+Ma665BnfddRe+++47vP/++4hGo4jFYtiwYQN8Pp/IXIvH4/B6vSgpKREF9ORCpVrHOZ03PT+yU1mhUCgUCsXAQzmN+5FMEzWKfCTHluxsoMEVDbZIi06urq7T6RCJRPDdd9/hoYcewgsvvIBdd90VL774IkpLS0VBvM7wer1CA5dSYKnCMRVHIedafX19yiTeYrHg+OOPzyorwBjDggUL4PV6cc0112D77bfHYYcdJj6nSVg4HBaFNWw2mxhwRiIRrFmzRjiV5UJltP9gddTJ0a+5QOdsNBrR0NCA4uLirM5M0i6kNMGioiKsXLkS9913H5544glEo1Ece+yxYlLbXdavX48FCxbg2Wefhd/vx1577YVzzjkHP/30E/773//iww8/xMKFC2EwGLDDDjtg0qRJOP300ztUA++Mo446Cq+//jr+/Oc/44QTTsAdd9yBSZMmiUrlwO8V3W02GxobG4WMCwDhyKRnUr7+wWBQfM4Y69T5kK1g0paItlCbHNku/01e/AF+Lw732GOPYdmyZbjkkktw9dVXZ+07ukosFhOFmSg9l7QaqV9zu905T14NBgNuv/12jBw5ErNnz8Yuu+yCq666CkBH56fcH5GDWC7YSBIDADqkIMt0RY99MJEuei8UCgmHhdVqRXNzM4qLi+F0OoWGP0kzGY1GmEwmWK1WzJ49Gw8++CBmzpyJv/3tb70Sqf7bb79h1qxZWLVqFc4991yYzWYsXrwYhx56KG677TYcddRRXT722Wefja222gpHHHEErrzySjz66KMdnIxyOnwm5AUMrXSMNiugtLR0i7QroGMflS/prltbWxsMBoO4/vLzTHIDkUgEjz32GBYsWACXy4Unn3wSRx99dMp7pLt0pjMvo9PpcNFFF+Gggw7CLbfcglGjRuGQQw7BHnvsgWQyKWoIVFRU4LTTTsPjjz+OI488EjqdDuPHjxfF+wKBgJAfkqV3qKAeLRhmkgkbqHSlj02nb0yZEDpde9Fmm83WYRzi9/vh9/vx0ksv4brrroPdbscbb7yBPffcszdPsVPMZjPmz5+P3XbbDSeddBKWLFmCU089Vcw5SKeYag/QvaZFdSBz0VaSr2hpaUEwGOwgz6RQKBQKhWJgoZzG/UimKMR00TA0wJSrDodCIfE3chhzzvHWW2/h7rvvxltvvQWXy4VLLrkEV1xxRVZHBLFx40Y88cQTeO211/DZZ59l1aHtjGeeeQaPPPJIVqejTqfDvffei19++QV/+ctf8PHHH2PEiBGieKDb7U4pSAZAFD9atWqVGKSTs5iuTX8UeOxJZBvI5DjW2o/dbkddXZ1ILSXtZy0Uwe12u1FbW4u1a9fipptuwhtvvAGr1YrTTz8df/3rXzFu3Lhun8f333+Pu+++G88//zw45zjiiCMwY8YMbLfddinbrVu3DmvWrMF//vMffPHFF7j//vuxePFiTJ06FbNmzeqwfTb23ntv/Pvf/8Zxxx2HM888ExdffDEuuOACUfCOnJWlpaUpDhc5wh3oGGFMDidyBMjOYK1TBkBKtM2WPCGiKFDSZgRSJ4ZaCRmSRSkuLhbP+Ndff417770XxxxzDK677rouO60CgQDWrFmDmpqa/2fvusObKtv3fbKTJp3pogvKVECW7L1BZIkgiCxlqiCoiKgon4KAKMgGkY0D2XvvvfcstJTONE1X9jy/P+r7fidpkqYs8fvlvi4u2uackzPe8477uZ/7QXp6OtLS0pz+z87OLrVvaNmyJebPn4/o6Gifv3fMmDE4fPgwfvzxR7z11luoVKmSW6UdaSd2ux16vd7JZ5ZkTZQWKHraasmnDV8JF082FO6CfcTrngREyXaErOK+a9HR0bDZbBg/fjxWr16NCRMmYMqUKc9Erbd+/XpKFK9evZoq47t06YJRo0Zh8ODBmDBhAsaPH//Y6vHWrVtjxowZGDduHGbNmoUJEyY4FQZ1lw7v6jPqahXjOnZwswK8FTP9t+NpZn0FBATQLAm73U77fp1OB4PBQNWXubm5mDZtGrZs2YJ27dph2bJliIqK8vgdFosFGRkZSE9PR1JSEnJycmj/VVBQALPZDLPZTAMp5HelUolx48Zh4MCBPl9PjRo1sHbt2hLfz8X48eOxatUqrF27FhMmTEBeXh4iIiJgNBqprZBUKqUZb6R92Ww2Oh64s9F6kcfEx+ljPb1jgYGBkMvltDAcuV+kkLNer8e0adOwbt06tGjRAqtWrfJYM+LevXuYMmUKCgsLqSc6y7IQCATUm58owIOCghAQEICoqCiEhIQgJCQElStX9mkdwEWvXr1QtWpVbNmyBe3bt0e5cuWQmZkJhmFgsVgQFBRELYI8CTZcs9+4CmNuJpIfzwavv/76C3Ocp3Uufvjhhx9+PF/4SeN/EBKJBDqdroT6yVUNYzQakZmZiYKCAlSrVg0KhcLJj9ZisSAtLQ03b97EggULcPr0aUREROC7777DyJEjS/VCS05Oxo4dO7B9+3ZcunQJAFCtWjV88sknqFmzJi2AotPpIBaLYbfbnVLKyGSP+JbK5XIcOXIEH330ETp06IDVq1ejQYMGHr9fJpNhw4YNaNiwIfr27YvFixfDarVCJBLRyS9RthClIqnqLRaLqbLBWwE2rvcl+f9FVur54rnsTpHni+qMWJrweDzcvXsXgwcPhsViweTJkzFy5EgolUrYbLZSz5GoQ2/fvg2dTge9Xo/CwkJaVb2oqAjJyckICAjAsGHD0Lt3b7dFzAAgODgY7du3R/v27QEAmZmZmD9/PtauXYs///wTffr0waeffopq1aqVel4AkJiYiKNHj2LIkCGYOXMmHjx4gB9//BExMTGUrCTeqBEREZToAkB9J0lRIOJvTMhGYo8SHBzsRMq4kvz/Ng/R0sg+T+p3QhBYLBZoNBrodDqUK1eOekgDoJkQpB8hVigOhwPZ2dkYNmwYypcvj/nz5/v0ThYUFGDfvn1ITk5GSkoKkpOT8ejRI6hUKqftxGIxYmNjERMTg1atWiE2NhbR0dEQiUS0HzMYDNQPNzc3FwsWLEDLli0xd+5ctG3b1qd7xzAMFixYgDp16mDEiBHYt28fjEYjVCoVJBIJ7YNJOwkICIBCoUBYWBhkMhmCgoKgUql8yix40f3YuYSLN59mvV4PlUrlNgsA+G+bIX17WFgYNBoNDcIQoio1NRXBwcGIioqCWCxGWloaPvjgA5w8eRJfffUVvvnmG5/aFMuyuHnzJnbt2oXr16+jVq1aaNasGerWrVvCPsBsNmPChAlYsmQJ6tati0WLFjkFGapWrYrt27fj888/x/Tp03Hq1CksXbrUidwtC95//30alGjXrh2aN2/udG8IHA4HcnNzaf/NzUriEjSerFKIUvD/I7xZdJC+TygU0kAQV01rtVppsJEodYuKinD27Fl8+eWXyMrKwrRp0zB27FiP85SHDx/i3XffxZUrV0oEtiIjIxEXF4fo6GhIJBKIRCJaoJX8fvbsWXz++eeYP38+xo4diyFDhjwV24sKFSqgb9++2LRpE7755hsIhUJotVpIpVLqqQ2A/kzGVqK+JsFDro2WtyLFLwLc9bGuGYAEXOsFUryZ+IsDxWOQ0WiEUqmkxDH5e0ZGBj744APcunULEydOLOEdzP3uVatWYcKECRCLxahcuTLsdjtYlqUkv91uh91up9ZjZD7DRXBwMD777DMMGzbM53vBMAwGDhyIL7/8EiaTCWazGTweDxKJBGFhYbBYLGBZFnq9HmKxmGbvuBbHI/UjgP8G1Enm4r9ljvRvxaeffvrcj5OQkOBx3E1ISHhq5+SHH3744cfzAfNvV2SWFa+++ip74cIFt5+Vdi+8LTy97etwONzuSxbXcrncbZTdarVS8jQ9PR05OTlISEiglbllMhkkEgkOHTqEiRMn4tatW0hISMDYsWPxxhtveJyIpaenQyKRIDc3F+PGjcPFixcBFKtO6tWrhzZt2iA+Pr7EfoWFhR5Vw3l5eSWIyjt37uDzzz+HVqvF7Nmz8dprr7nd1263IzIyEocPH0a3bt0QFxeHt99+Gz179kRYWBhYlkVkZCRsNhtN+yQpoXK5nP7sWoiD609bVFREUwbJ/8RvzR08PWuGYS6yLPuq2w/hvX15g7f2U1rb8rTostvtHsm/3NxcnDp1CoMHD0ZwcDA2btyIKlWq0G28qfIePHgAu92OyZMn49ChQ3SBRNSAxFNbJpOhUqVK6N69OwIDA2m1bHfIzMx0G9zIy8vDhg0bsGPHDphMJnTo0AEjRoxA1apVna7TkzLHYrFg7ty5mD59Oho1aoSFCxciLi4Oer2eLmrNZjNdEIrFYhQWFiIiIoISnIS8EovFyMvLg8PhgEKhQHh4OL2/LMs6kTGPUykeAHg8nsf29SzaFheE6CfvhWu6M/dzbtV0AFQ9q1arERAQAKVSCZ1Oh/v37yMoKAiBgYGUpCVFy4DiqvJDhw7FkSNHcOTIEcTHxzupKLl49OgRRCIRNBoNhg4diuTkZADFhEpERATi4+MRExODcuXKoVy5coiOjqZWE94qdhcVFTmReampqZg8eTKSkpLQo0cPzJgxw62tgcVioYWACP744w98+OGH+Prrr/HOO+/g0aNHiI+PR0JCAr2HhEDgkgM5OTlISUlBQEAAKlSo8NSL4gGP37bKOkfgkiveAnlc0phcr8FgQFZWlpMNEyn6qtVqkZubS58FKax479492tdcvnwZ7777LtRqNRYuXIg+ffrQ73z06FEJQsZiseD8+fPYunUrzpw5g+zsbABAVFQU/VksFqNOnTqoX78+Xn31VYSGhuLzzz/HzZs3MWTIEHTp0sWjd7HFYsGVK1cwZcoUBAQEYMaMGTTt3GAweCSRiZc/F/n5+WjVqhUEAgF2795N6xdwrRBMJpOTVz0ASiJzz5HrH8rtu3wl8FwDTM+i33oSlHU+xrIs9cwmCk0u9Ho9tFotGIahBYZJhgUhysjYYTKZYLPZsGjRIixbtgzly5fHwoULUadOHbfnk5ycjNu3b+Pjjz+Gw+FAnz59EBMTg6ioKAgEAlSsWNGtVU9ubq5TG2FZFhcuXMAvv/yCO3fuIDExER999BE6depU4rl6s2kxm80ICQlx+tu9e/fQpEkTjBw5EuPGjaP1AUQiEbV3MhqNMJlMUCqVsFgsdKwg9SXIvXNXf+J5j4mlwV2GncFgoN7E3LkmmV+SMU6tViM8PNypTyNFqu12O/h8PkJCQrBq1SqMHz8eAQEB+Omnn9CmTRu355Kfn4+RI0fi5MmTqFOnDsaPH+/03E0mk9v5j8ViwcOHD8Hn86HVapGfn4+NGzfi7NmziI2NxfDhw9G3b1+PHufcY2ZmZuKll17C2LFj8fXXX9PrAUBJZL1ej4iICMhkMqdnSzIfSJsj6mjXzCzy2fPEs5rP/5tA3sv/9e/8J1Ba+/LDDz/8+DfBrzR+DvBWzdzhcJQgd4n6jRSKCAoKQlxcHKRSKcRiMWw2G8RiMViWxY0bNzBs2DDw+XwsW7YMb775JoRCoVuVAQEhXgYMGICcnBx8+eWX6Ny5M2JjY2n1d3cwGAwelSsWi6UE2VOzZk2sWrUKn332GUaNGoVPP/2ULji4sNls4PF4aNu2LXbs2IFJkyZh2rRp+P333zFy5Ei0aNEChYWF9B5wU93ItdpsNphMJrrYI36l5D6T7bmL6xdJ3fC4imdyne5SvUm7c1XvGY1GbNiwAR9//DGqVKmCjRs3lkjF96ZQysrKwpdffomkpCSMHTsW77zzDj3/5ORkjwQuwzAe21ZaWprb9O3w8HCMGjUKH3/8MVauXIm1a9di7969GDZsGMaOHUsLjnk6X5ZlMXnyZNSsWRPDhg1D165dsWrVKlSrVg02mw35+fm0GrxcLodOp0NRURFNhef6E5KFMgCqkOFeG5do+Df6zpamYOWqE7n+ziTbgEsMGAwGGAwGWK1W8Pl8hIaGQqPRIC0tDWazGQqFAkFBQVi9ejX27duHuXPnom7dutBqtR5JYz6fj6KiIgwbNgxZWVlYtGgRGjduDLFYjDt37ngMSADeSSTXfq1ixYpYtmwZFi5ciD///BN37tzBwoULnQIVQHG/5dpm+/fvj+3bt2PmzJlo1KgRpFIprFYrNBoNpFIpNBoNgOL+ks/n03c2LCyMKvaexHf3RWhr5F1wveeuFi4BAQGIjo52Ig6kUikiIyPpO0l8M8nnJPBALIokEgliY2Nht9tpnxYSEoKDBw+ibt26Jc6LvLM3b97E4sWLcezYMaqQa9CgAYYMGYKmTZtCqVQiPz8fV65cwZUrV3Dt2jXMnz+fXlNgYCDmz5+Ptm3b4s6dOx7HEpvNhr59+6Ju3boYPXo0hg4dirfeeguff/45RCKRx2ftcDhKtC2lUolly5bhtddew9dff41ff/0VYrEYarUaOp2OZlAAxZkmDMNQ2xiRSETteYiPKAmYEQIP8D0V/0W3SPEGd/MxMocQCATU718ikTgpQ0l7y8/Ph1gshsFgoO3ZbrfDbDYjODgY2dnZGD16NE6ePIl+/fph7ty5Xt/LAwcO4LvvvkNUVBTmz5/vVKjxwYMHHtuWyWQq0UYaNWqEhg0bYv/+/fj999/x0UcfoXr16hg3bhxatGjhRNx5Ao/HK9EHV69eHT179sTatWsxYMAAhIWFQSwWw2KxwG63IzMzk5KiJpMJMTExNLhIgrFEGU+CQWR8fRH6LFe4OydChrrONbnjosPhQGRkpNN1CYVCagdGrEvGjRuHDRs2oHnz5li5ciUEAoHbZ3Lq1CmMHj0aarUa7733Hnr27Akejwe73U630ev1bsdMoVAIhULhNB9r06YNTp8+jZ9//hlff/01tm/fjq+//rpEX2m3253GxPj4eLRr1w5//fUXPvroIwBAUlISVZQnJiaCYRgapCWqfPL+EGEHCbRzx4h/2zzp34hWrVoBAI4cOfKPH4ccww8//PDDj38XXry8sP9HcK22TWAwGJCamgqtVguBQEDTAInCWKPRwGKx4P79++jduzdYlsXu3bvRr18/j4QLF0lJSejVqxcKCgrw22+/YdiwYYiNjX0m16hUKrFo0SL06dMHP/74I0aMGOGV0G7dujWOHz+OzZs3Q6lUYuLEiXj77bcxZ84cnDlzBjqdjhYHJMXxwsPDERwc7HFxRVRrxO+N/P8ipkX6CkIEkwUtSft0p6g0Go3UW1GtVuObb77BmDFj0LBhQ+zatatM3q3nz5/H8OHDkZ6ejtmzZ2PAgAHPZbIfEhKCcePG4dChQ+jTpw+WLl2KUaNG+VxEqFevXjh8+DB4PB5ef/117N+/Hzwejy6MSDBCKpVSL+icnByYzWZqJUCsVwj54s3vm5Aq3tr6iwaykHNNvSXtjLxHZLFPiDvu/kCxojMnJwcymQyJiYmIi4uDQCCATCZDSEgIoqOjoVAoMHXqVHzzzTd48803fUqX1Wg0GDx4MDIzM7F48WK0atXKyS/4aUIkEmHs2LGYMWMGcnNz8dprr2HVqlU+ZaPMnj0bQqEQU6dORc2aNREYGAitVguWZaFUKhEWFkbvK3lnBQIBtTshRB73/bbZbFCpVD7ZxryocO2juL6X3DamUCgQGBhIM0lIP00KnXKPx+PxEBcXh+XLl2PkyJGoWbMmTpw4UYIE4eLq1avo378/zpw5g9dffx1LlizB77//jpkzZ6J79+40MBQSEoLWrVtj3Lhx2LRpE06fPo2FCxdi3Lhx2Lhxo8/WJQBQpUoVbN26FcOGDcP69evRqVOnx1p4N2zYEF9++SXWr1+P3377jfqiGgwG5OfnIy0tDfn5+dS7nRDGhFAWiUTURkCj0dD+k1tA1hfIZDKqJPxfgFQqRXBwMJRKJS1IqdFooFarkZaWBo1GQz1cWZaF2WymhVLJnMLhcCA1NRXdunXD5cuXsWzZMqxYscKjhyzLspg9ezYmTZqE6tWrY82aNU6E8eOCYRg0a9YMe/fuxc8//wytVouhQ4dS4nfq1KnYt28f8vLyynTcTz75BFqtFsuWLUN6ejry8vIglUrB5/MhFAoREBAAgUBA56hEiV1QUAC1Wo2srCwavOaOJa5w7fteFDAMg4iICJ/mmtzrysvLg0qlotldHTt2xKZNm/DFF19g165dboPsVqsV06dPR+/evSGVSvHTTz+hV69eT2XO2rhxY/z2228YP348Hj58iK5du+L9999HWlqa1/3eeecdpKenY9euXTQTQqlUIjQ0lGYBWq1WWCwWOBwO5Ofn0/6Fa13Cxb9xnuSHH3744Ycf/x/x72XN/gdgt9uRm5vrpBoAiifNRH0bGhpKJ4pisRgSiQQSiQRmsxmDBg2CVqvFtm3bnKwFPIFlWezatQuDBg0CwzBYv36918X104JYLMbPP/+Mb775Brt27UK3bt2Qm5vrcXuGYdCpUyccP34cGzZsQHh4OFavXo3Ro0dj+/btMBgM0Ol0UKvV0Ov1JSbqRKntbdHxoi5MfAUhYAhxzi0G6AqxWAypVIrQ0FD88MMPmDt3Lrp164aNGzciODjY5+/csGEDunfvDqlUipUrV6JZs2ZP63J8RlBQEL799ltMnjwZp0+fRu/evZGenu7TvrVq1cLJkydRr149DBkyBFOnTkVGRgYMBgMtLEQ8LWUyGSIiIhAaGkrJAQA0A6CoqAh6vd5jG/o3kyrEn5CkkLoGI0rz/nQ4HLRKfEREBPUjB4qLD+r1evTv3x8rV67E6NGjsXz58lIDDxkZGRgxYgQljL15pD9NNGjQAAcOHECTJk3wxRdfYODAgbSQoieUK1cO06dPx8mTJ7F8+XIn5b9CoQCfz6e+zkAxIUwyTgIDA2mhRe5912g0yMzMpErlfyPc9VFcYolcK1G+2mw2FBUVobCw0KlYICGOSVZAv379MHPmTAwaNAg7d+70WmTs2rVrGDRoEEJCQrB161ZMnToVbdu29UndHRQUhNatW2P48OGPFWSVSCSYMGEC7Xc/+ugjjBgxoszPdOzYsWjXrh0+/vhj3L59m/ZV5cqVQ3h4OMqVK1dC3Wc2m1FUVASLxULvoUQioUGwsgZR3QWY/s0gcwjS1xO/8fDwcMTFxSE8PJwGFeVyOQQCAXJzcxESEgKZTAaxWIwrV66gS5cu0Ol02Lt3L/r37+/x+7Kzs/HBBx9g6tSp6NixI5YuXVrCEuJJwefz8cYbb+Dw4cNYvnw5hg4dCgBYvnw5hgwZgpo1a6J169aYOHGiTwRyzZo10alTJ2zevBlWq5V6EyuVSkq4JyQkICYmBhKJBCkpKbQNljYOcudj3gLg/zRI++C+K+R89Xo9dDqdk3UDAKq4XbhwId555x2wLIv9+/dj0qRJbjOk0tPT0bNnT8ydOxd9+/bF3r17n0pRYi4EAgG6du2KEydOYOzYsdi7dy+6dOlCSX136Nq1K4KCgrBr1y7ExcWhYsWKKF++PAIDAxEaGkptySQSCQIDAxEWFkb7ezIfcp2f/pvnSX744Ycffvjx/wl+0vgZgEyA7XY7nQi7I5dyc3ORnZ1dYsJOCocQEovsr9frkZeXh5ycHPTt2xfp6enYvHkzatWq5fV87HY7Nm/ejFatWmHgwIFQKpXYsGGDT0Tz0wLDMBg5ciRWr16N27dvY9myZT7t07FjR5w4cQJbtmyBUCjE5MmTaZVuQt653lfuJN4TqfciL0x8AZdgAjwrdxwOB/Ly8sCyLKxWKxYsWIDevXtj2bJlZUqDX7x4MUaOHIn69etjyZIlSExMfKrXU1b07dsXK1euRG5uLn788Uef94uIiMDu3bvx3nvvYcmSJfjqq68gEoloYSPiv0iIHNfFIVkQEssKT23o30yqEPUPSb12XeyR+0CKH3LVtwzDQC6XUz9HrVZL31dCpnTs2BFJSUn466+/MHPmTLd+nVwcPnwYLVq0QHZ29nMljAnCw8OxatUqfPfddzh9+jR69OhRqiqrb9++6Ny5MyZPnox79+5BLpfTwoBisZgWczMYDMjNzaWqWa6Sm3vfw8LCUK5cOa8FLp8nuIEF7s/e4K6PIkUUSUYN+RtX6Zmamgq1Wu2kRiOE55o1a7BlyxbMmDEDS5cu9ao8T09Px9ChQxEcHIzffvvNo43Os0bNmjWxefNmvP/++9ixYwdatGiBffv2+bw/j8fD6tWrIRQKsWjRIgQGBkKpVMJut0Mmk1FrHR6P51RoiowX5DOSKv//BQ6Hg/ZbNpvN49yAEIMmkwlyuRxBQUFQKBTU3iQgIACZmZm4e/cu8vPzYTAYMG3aNPTq1QsymQyHDh1C/fr13Z6DwWDA+PHjUbduXWzYsAHjx4/HN998U2of+CQQCoVo164dJk6ciM2bN+PmzZvYtGkTPv/8c8TGxuL333/HtGnTfDrWkCFDkJeXhwcPHiAjIwMajcbpvRYIBIiMjKTvc2BgIMLDw6FQKBAdHe3RyoQ7H/MWAP+nQewUuO2GnC9QXCRXrVZDq9UiJyeHtrMZM2Zgzpw56N27N86dO4cmTZq4Pf7Vq1fx+uuvIykpCUuWLMFPP/30TO1f5HI5xo8fjxUrVkCj0WDPnj0et5VIJHjrrbewf/9+qFQqSKVS5OXloaioiGZkcft20i5IwOr/U1/jhx9++OGHH/9r8JPGpcDXBTEXZAKcm5vr9L8ruaRUKhEREUEJK6KwcjgctMCSzWZDbm4uCgsLaRrzpEmTcPnyZaxevRqNGjXyei4nTpxA06ZNMWzYMFgsFixYsAAbN25EXFycT9fuqoJ+UrRt2xbNmzfH+vXrfVb5MgyDzp074+OPP0ZqaipSUlJo1XKiNuaCO4n3ROq9yAsTV5DFLlfB4i29k+xDVLFWqxVmsxn37t2jhXbc+Qd7Os7UqVPx1VdfoVu3bvjrr788+hI/bxDF8MGDB3HlyhWf9xOJRJg/fz5mz56NPXv2oFOnTrh58yZVv5PCaTk5OTT4o1arqUcgj8eDzWaDwWCgZKCveJz+5HmDqwpy186INQVQXBxTrVbTAJlMJqPewnq9ntqiOBwOrFy5EgMHDkRoaCiOHz+Obt26eT0Ph8OBGTNm4I033kBERATWrFnzVAjjrKysMmcY8Hg8vPvuu/jzzz+Rn5+PHj16ICkpyeP2DMNg3rx5NAWdFFPU6XR0gR0QEICIiAg6BnBJLNf7ziVjXgRw04p9STH2FDgltgDh4eFOGTU8Hg8hISHUzkOpVEIikUClUkGlUsFkMtG+iVhIeAvQ5OfnY8SIEbDb7Vi2bNk/RhgTiEQijBgxAnv37oVSqcQ777yDsWPH+my3ExkZiZ49e2LLli2UtCHvpUQioW0LcF/Ui1heuUsZ/zf0UY8Do9FI2w+x5uCq27lBELVajcLCQhoYs9vt1KM9JyeHvr9arRbdu3fH7Nmz0atXL5w5c8ZjMN5kMmHgwIFYuXIl+vXrh7Nnz2LChAnPPbAokUjQsGFDjB49GmvWrMGgQYPw559/4v79+6XuS9qUxWJBYGAgnbe6IiAgAOXKlUNkZCStu+ApqE38o72NOf8UXN8FbkAVcPZpDwgIQHh4OMLCwqDRaJCeno5bt25h+PDh2L9/P77++mssX77cY9HM3bt3o2fPnhCLxdi+fTu6du363K6zefPmiI2NxdatW71uN2zYMJhMJvz1118wGo3Q6XS0Lyb9u8lkopYkJIPGb0/hhx9++OGHH/9u/POzsicA8xxm248zqSGEpFKpdPrflVzi8/mQy+XQarVIS0uDTqeDSqVyUrER/2IejweJRIIZM2Zg165dmD17Nrp06eLxHHQ6HSZMmIAePXrAbrdj+fLlOHnyJN566y2ffI/v3LmDYcOGoW3btmjTpg06dOiA9957D506dULXrl3Rs2dPfPjhhz4tNFzRp08fZGRk4PTp02Xar3PnzgCAjRs30gmpqwrDdRJPqpz/W20oAOfFrjsC3B0hw1VbE8/BY8eOAUCpynSCa9euoVOnTtS7uDQl39NCYWEh7t275xNpMWjQIAQFBfmslCJgGAYjRozAnj17kJOTg3bt2uHkyZMAQNV4fD4fKpUKDx8+RGZmJgoKCiCTySgBSCxkuLYopVme/NOLJF8IIU8qaW5BRfJuCQQCWCwWpKamIisrCwaDASzL4tGjR7Db7VAqlQgKCsJnn32GSZMmoW3btjh8+HCpWQ42mw2DBw/GtGnT0Lt3bxw4cAAVKlR4omvX6XT44YcfMGjQIIwZMwa3b98u8zFeffVVbNy4EQ6HA3379sXFixc9blu+fHl8/fXX2LZtG3bv3k3TdIn9kNFopH7ZRI31b8l84KYV+5JirNfrqacp1z7Ik/qYkBF8Pp++iyaTyYmkmDNnDjQaDaZMmeKVeLNYLOjXrx/S0tKwcOFCVKxYsczXq9frcfXqVWzatAlz5szBoUOHqDXQk6BGjRrYt28fxowZgz///BPNmzfHgQMHfNq3b9++KCoqwsGDB0uQbVw1MclY4toZ8Xg8p1oAXNL0n+6jnhVIkcXIyEiEhYU5zQ2446VKpUJBQQFtk0T1rtVqcf/+fdqOr127hjZt2uDKlStYunQpVq1a5dHuidiJHT16FPPmzcNPP/1Upv5MrVZj586dOHLkyFP3NR8zZgykUilmzpxZ6rYkgyQnJ4eKHUhQkAtfiV9uu3xRiGIuXN8FV5sFrkKaKPsFAgEEAgEtXnfo0CH88ssvHgMELMvil19+wdChQ/Hyyy9jx44dzzULECge87t164ajR48iPz/f43Y1atRA06ZNsWTJEmRkZCA0NBQJCQkAioOxOTk5Tr7per0eRqMRYWFhbr2g/fYUzwfXr1/H0aNHaRFC7j/y/HxBnz590KdPnyc6lyfd3w8//PDDj38GL4ZsyUcwDFMbgBBALsuyKSzLsgzD8FiWfWaMIJnMkLR1T3BN0ebuR6LtLMtCp9PRnzUaDRQKBfVuFIlEYBjGSW1GVBhCoRDfffcdVq9ejU8//RRdunRBTk6O23M5deoUPvnkE2RnZ6Nv3754//33IZFI8PDhQwDA7du3PZJ/ly5dwrFjx3D27FnI5XI0a9aM+pSSAhd2ux12ux23bt3CwIED0bBhQ7Rt2xYFBQUe7w9XIVe7dm0EBARg1apVSExMBMuyHlPwzGYzXagolUrUqVMHZ86cwdChQxEdHU0LcJBzzM3Nhc1mo8fk8XgoKiqiRQcJyGSfPCNXPO14xOOqtsiilijxJBIJ7HY7JcZZlqWFe1iWpdciEolgsVhoYMJkMuHOnTsIDAxETEwM0tPTPS7QdDodpk+fjvXr1yMoKAhff/01OnbsiAcPHgAA7t+/77G4z/Xr13H9+nXY7Xbs378fOTk5iI+Pp/+Sk5Pd7nfr1i08ePAASUlJSEtLA8uyqF69Ol577TWIRCLUrFnT4z3q2rUr1q5di/3795fw6C4qKqKqfVfY7XY0btwYR48eRZ8+fTBixAhUrFgR8fHxtNgReV+Dg4MREhJC7zepGk7aHuDcpjy1Z7Jo+qcWSWQRDHg+RwLXNsu9PrFYDIZhqKqKWMWQ/q6goABBQUGQSqXo3bs3jh07hrFjx+Lbb7+FXq/3SEjdv38fPB4PP/zwA7Zt24YxY8agf//+yMjIwKVLlzwqbXNycpCRkeHxWvbv34+DBw9Cr9ejRo0aSElJwUcffYSXXnoJ7du3R2Fhocd9tVptib9NmTIFkyZNQteuXbF48eIS6cYOhwNCoRAjR47EmjVrMGHCBLRs2RJisRg6nY4WioqIiIBcLodYLIZCoaBEjKdMAJZlad/krvL881KHksACgbu25OlcSut7yfEB0PdLIpHA4XBQuwCVSoX58+ejY8eOqFOnDqxWK4Bi4oJ771iWxWeffYajR4/i/fffh0gkwuXLl0t8X3JyspNv+bVr15CVlYWsrCxkZ2e7JVKEQiEqVKiA6tWro2nTpm6vhajuPF2jxWIBAAwYMAB169bFt99+i7fffhsdO3bE1KlTPWZ1SCQStGjRAuHh4di/fz/effdd2O12qvQjimNy78i9EIlE9Lm4erW7PpN/M5Hjru0R6xwSrODODcg9slqt0Ol0sFqtEIlE0Ol0kEgkkMlkyMnJgVQqhdVqxTfffIPff/8d9evXx7JlyxASEuJRJW6xWNC9e3dcunQJI0eOREREBA4dOkQ/T0tLK0GmWa1WJCcn49y5c0hOToZKpaKfhYSEoEmTJqhdu7bHtkWuwxNcM83efvttLF26FIcPH0ZiYqJHuwzyXjocDkRFRdG5oF6vh0gkQlFREUJCQmg/TdojGS9dQa77WWR8PUlfSPblnh8JLpB3yG63QygUUtuS7Oxsqqp99OgRPvjgA2RnZ+PPP/9Ep06dkJmZWUKwYbPZ8PXXX+P3339H48aN8cEHH+DevXu4d++e03YpKSkez7WwsBBJSUlgWRZZWVlQKBR0fsbj8ZCZmel2PzLmEDRv3hwLFy7EunXraO0Kdxg0aBCGDx+O3377DYMHD0Z8fDx0Oh0sFgv0ej0CAgJoQUli4cUlhl2fC/fvnubdvhSg9YT/tYyJxwGxqHtSvP/++0/lGB988METH8cPP/zww4/ni38NacwwTFcAPwC4CUDLMEwOy7ITWJZ1PC3i2N3Egyw0nmTSQsg5g8GAwsJC6PV62Gw2PHr0COXLl0dMTAykUinEYjFVYJlMJkgkEkomL1myBD///DP69euHb7/9FiqVqsQkXKfT4fvvv8fq1asRHx+P5cuXo06dOiXOR6/Xu50Q3rhxA4sWLYJWq0WtWrXQqlUrJ3L52rVrTtW969evj1OnTuH06dO4c+cOPvnkE1StWtXtPeBOThUKBV577TXs2rULU6ZMgVgs9qpg5V5nly5dMHXqVIjFYhQUFEAkEsFsNkMoFJbwx+QW5XI4HGBZlj4L7sL4RfadJao7hUJByXNSbAUAVb4CoNdMfLCFQiG9XolEggcPHqBmzZoQCoUQi8UliClSKPGbb75BTk4O3njjDYwaNapEOiVRbLmDSqWCQqHAtm3bcO/ePSiVShw/fpwuCOLj41GhQgUkJiZCIpHg1q1buHXrFg2AREREoHHjxmBZFmfOnIFKpUKPHj282hJ06dIFu3fvxty5c7Fu3TqnzwwGg1cCjsfjITExETt37kT9+vUxZswYrFu3DkFBQQgPD0d+fj4tjMQwDE1fDgwMpEEJm80GjUaDkJAQ6hvKVby7+vyR5/hPoCyLdKJE8bYvwzBQKpX0enNzcxEcHAyRSAShUIj33nsPJ06cwK+//koLQwmFQq/ZDmvWrMHGjRsxaNAgDBo0iP69qKjIo6evwWBwa7ljNBrx+++/48iRIwgODka3bt0QHh6OevXq4cqVK7h+/Tru37+Pnj17ol27diX61JycHLftJyYmBtOnT8f06dMxdOhQLFiwgGZCAKAqdLFYjAULFqB9+/YYOnQoFi5cCKB4oc8tkEfuMyEgfOmTyhIAeBI87oKce10BAQGIjo6m5AsAGtxzDV4RlTt5d8j7QlSyNpuNenBOmjTJ6fkQVTLB3LlzsWnTJowdOxavvvqqx3GGKOj379+P48ePw2w2QyAQIDo6GlWqVIHVakVwcDCCg4MREBAAlUqFR48eITU1Fffu3cO2bdtQvXp11K9fHy+99BKioqIgEAjw4MEDj4EOo9Ho5Ctfr149rF+/HkuWLMGyZctw+fJlTJs2DR07diyxL4/Hg0gkQq9evbBy5UqoVCqEh4dDo9HAYDCAx+NBoVCUIPZdiRny3hKfbVLky7WP+rcRL2QcdO1/AdD3i7QF0g6B4nGS2OsYDAaoVCrqaRwUFITDhw/jk08+QWpqKj7//HNMnDgRAoEAhYWFbp+z1WrFiBEjcOnSJQwZMgTNmzenAQ6CnJwcJCQkICMjA/fv38ft27dx9+5dmM1m8Hg8xMfHo02bNqhYsSLy8/Nx6tQp7Ny5E4cPH0bXrl3Rvn37Es8rPT3dq5WNazDi/fffx4YNG7B48WIsWrTI4/hA7g2ZcwUHByMvLw8ymQx5eXnIzs4GUBzg59bjAOC2z39Rvf+5/XFAQAAt2Gmz2ejYDxTfB0LWarVahIaG4uHDh+jVqxeAYssJ4m/NMIxT36TVajFq1CgcPXoUr732GgYOHAgej+f2XfM03wKACxcuQKPR4MqVK9BoNGAYBpUrV0adOnUQFhaG+Ph4j9fIVcY3bNgQFSpUwMGDB9G/f3+PY3SPHj3w9ddf0zbtcDjAMAzKlStHxRl8Pp/eN+Kp/qI947/v84slbX+GIAHmJz0G8GQBxf+1DBY//PDDj/8v+FeQxgzDiAAMBzCRZdktDMO8BGAGwzC/siw79G/imGE9rGwYhhn+9/4eJ1BPCi5JBMDtgkUmk1HCGChO9ZNIJBAIBFAqldDr9VAqlVQBQxYjw4YNw44dO/D6669j8eLFbidfJ06cwKeffoqMjAwMHz4cQ4YM8Vm9odfrsXbtWhw8eBAKhQJ9+/b16T5JJBK0adMGlSpVwrFjxzBp0iR06tQJ/fr1K/W7e/bsifXr12Pfvn1l8m577bXXMGXKFKxbtw5dunRBYGAgJYclEgm0Wi1diBPClRQtIymTQEml3JPgWbYv4u9JiEiDwUCvj0zcuNel1+uh1Wpp2yPqa6BYBTxw4EC33/Pw4UNMmjQJR44cQY0aNTB9+nRUr169zOdrt9uxZcsW3L9/H23atEH9+vVhNpuRkZGBO3fuQKvV4vjx4zhy5Ag994oVKyI2NhYNGjRwWszGx8dj27ZtWL16NcLCwjwWj5FKpRg9ejQmT56M06dPo3HjxmU+78jISMydOxdvv/025s2bh08//RQWiwWRkZFO7xvxiZbL5fQd12g0yMrKosfhpngDT0boPe22xW0rnkDO33Whx93XZrPBZDLRbZRKJQwGAywWC0wmEy1auWPHDsyaNYsSxqVhz549VEH64YcfPv6FArh58yaWLl2KvLw81KxZE/Xq1aNEilAoRP369VGlShWcOXMGf/31F44dO4a3334bNWrU8On4YWFh2LBhAwYNGoQRI0Zg5syZeOutt0ps17BhQ8ycORPjxo3D119/jbFjxwL4b8CHmyEB+N5enlQR+jzGRQJu2zEajbRIKVF8isVi6stLSAZXj2dyr/Ly8rB8+XJ06tTJY8ExANi6dStmzZqFN954Ax999BG1n3FFSkoKLRDGMAzq16+Pdu3aIT4+no7frvvGxMQgJiYGjRo1Qn5+PhwOB86fP08LvPL5fJQrVw4hISF46aWXEBcXh7i4OOrx6gkikQijR49G48aNMXPmTLz33nvo3r07vvvuO7cZE3379sXixYsxc+ZMjBkzBlarlc4jiMc4AKo6JfcVKG5nRPVNiLCnRew8z7blDr5kfZjNZtoOiSqSeBUbjUYEBQUhLy8PfD6fWtv8+OOPiImJwd69ez2ORwQ2mw3Dhw/Hrl27MGjQILRr145+ZrFY8ODBA9y9excXLlxAVlYWDYoolUo0atQI1atXR35+vpOdSmRkJKpWrYpHjx7h+PHjWLduHbZs2YK2bdvitddeg1KpfKz7pVAoMHLkSEybNg3nzp1Djx493G5HggqkPZE5llQqRUhICGw2G2w2GwoLC5Gfn4+wsDAoFAqIRCIa6Ob2A+5I/dJQ1rblLiPDF3Dn9FxBApl/2Ww2p0wkADh79iwGDx6MiIgIbNmyBZUrV3Z77IyMDAwaNAgPHjzAzJkzERYWVub78PDhQxw6dAgnTpyA3W5HdHQ0OnbsiPz8fFy/fh337t1DcHAw8vPz0bx581LHFYZh0LVrV8ybNw85OTmIiopyu51IJMJ7772H77//HhcvXqTjVkREBBQKhRPp7ct845/C3+Slb8U9/gfw2muv0Xn3kxwDwBMdhxzDDz/88MOPfxf+FaQxy7IWhmGSAJj+/v02wzDvAljGMMwUlmW/8kQY/739LwB+AYBXX331mUhmjEYjCgoKoNPpIJPJnBZp3AlreHg4Jf6CgoKo2oUUjVIqlYiLi0NeXh5u3LiB4cOHQ6VSYcaMGfjwww/dTixXrlyJr776ComJidi8eTNeffVVShSWhkuXLmHJkiUoKChAt27doFAoEBMTU6Zrj4+PR9++fZGRkYE9e/bg/PnzGDlypFfv3FdffRWxsbHYvHlzmUjj2rVro1y5crhw4QKGDBkCuVwOnU5HFwOkMBOfz6cLZi4hQRYCT1Nh/Kzal8PhoAX/yIKSLLyIsojrySiVSilZT/5mt9shl8uRnZ0NnU7n9pmcP38e/fr1o0TfoEGDoFary3y+JpMJe/bsQXp6Ojp06EBV7mKxGImJiZDJZKhZsyasVitSU1NhNBpRqVIlSKVSXLhwoYT6qXz58hg8eDC2bNmCGTNmoHv37hgyZIjb5zZgwAAsXrwYP/zwAzZt2vRYz7ZHjx54++23sXDhQtSrVw+NGzcusejhplKS9z0kJAQAnFSwTyvt9nn0Xa7wRnhz2xUhn2QyGcxmM11MFxUVYf369Vi6dClGjhyJUaNG+fS9R48exdSpU1GvXj1MnjzZ4yLaZrNh7dq1uHjxIuRyOfWRLFeuHAIDAxEYGIjMzEwcOnQIUVFR+Oqrryip74qgoCC0b98eCoUCf/zxB2bNmoWaNWuiV69ePhESISEh+PPPP/Hee+/hs88+Q/Xq1d2SzsOHD8e1a9ewYsUKvPTSS2jTpg2AYuKKXBPJkPAVZQl8uVNVPc+25dpHcZVzBQUFMJvNdCwkpJ1KpUJ+fj7EYjHkcjklXBctWgSNRoOvvvrK4/edPXsW48ePR8OGDTFt2jS3/cGdO3fwxx9/4PLlyxCJRGjbti3at2/v0c7GHRiGQWRkJNq0aYO3334bGRkZSElJQXp6OtLS0pCSkoJr167R7cuXL4/x48dTxaYnVK1aFTt27MCCBQswd+5cnDp1Cnv37qWFyAgaN26MHj16YPHixWjWrBnq1q2LoKAgmEwmGAwGOl6Q4CkZLwEgKiqKBiWfxKv+b2L/H2tb7lBa/+tq+yQWi5Gfnw+RSESJUKLEVKlUGDBgAE6cOIHevXvj559/9uhdTGC32zFq1Chs27YNU6ZMof7FGo0Gixcvxt27d2mRYaVSifr166NSpUqoWLEiwsLCaHs9depUiWMTH1KlUomoqChs374de/bswe7du1G7dm106tTJY0aGN7zzzjtYsWIF5syZg+7du3vMvFMoFFTNzgWPx4NAIEB2djZkMhlMJhPCwsIQEBBAvaIBIDo6GgDo+BkeHu5zYV6g7G3rcQO43MCDazaXw+FAeno68vLyIJVKERMTg23btmH8+PGoUaMGNm3a5JF0TUpKQv/+/aHX67FmzRo0a9YM+/fvL/V8WJZFTk4O7ty5gyNHjiA5ORkikQjly5dH06ZNnb6vWbNmuHv3Li5evIg//vgDGzZsQKNGjdC1a1evquXXX38dc+fOxd69e/HKK6943G748OGYPn06duzYgXLlylFVMTeL0VO2lbvrehxS/0nx91zu6Vb69sMPP/zww4//UfhMGjMMkwCgMsuyBxiGkQIQsCxb0uzxKYJhmIYAMlmWTUOxLcUvDMO0YFn2IcuyuQzDfANgNMMw0SzLumcFngPIJJKkqgOgxTKMRqOTwoJhGEgkEmg0GrcTe4fDAbPZDKVSiY8++ggMw+DIkSMlvFoJ7t27h2+++QYdOnTAggULykQ6HDlyBIsWLUJcXBw+++wzVKxYETt37nyMO1CsPnj33XfRtGlTLFy4ENOmTcPMmTPdpowDxYuPnj17Yv78+cjJyfG4nbv9atWqhVu3bgEonsRzCWKyqOYWBnJXIA54tind3uBukuxugu2qbjEajZDL5SWUhUajkdqeKJVKes12ux0Mw0AoFOLbb78FALftaNasWVAoFNi1axdd0JUVKpUKEydORHp6Ojp37ux1wSEUClGpUiWfjhsYGIj+/fvj6tWr2Lp1Kxo1aoSXX365xHZSqRQfffQRJk6ciNmzZ+Pjjz9+rOv48ccfceDAASxbtgxNmzalvqDc50XSvA0GA2w2G6xWa4mF2IussCHwFEAh7ctdX0LeH1LAjVjqiMViBAUF0bb366+/okWLFj4VVwKKCz0NGjQI8fHx+Omnnzx6agLA3r17cfz4cTRs2BBAcSAlNzcXKpUKWq2Wvu8dO3ZE7969IRaLPZLGBLVq1cLLL7+MAwcOYNeuXfjPf/6D5s2bo3nz5qWeu0wmw6JFi9CqVSt8+eWX2LJlS4ltGIbB7Nmzcf36dcyfPx8DBgyAXC53usePo7rzFc9LVcXtx7htyrXflcvl1L+fWDJxSXNSNJBkjoSFhUEgEGDKlCn46aef0KVLF48q4wcPHmD48OGIjY3FkiVLPBKis2bNgl6vx8CBAxEVFfXExacYhkFsbCxiY2OdziUuLg7p6el48OAB/vjjDyxevBiTJk0q9XgikQjjxo1DpUqVMGrUKJw+fRrdu3cv8Z2LFy9G3bp1sWTJEuzYsQMFBQUICwtDfn4+vedyuRwBAQH0vgKg8xMy5/BmZ+ANfxdvfKEUe6X1v4QYlsvl4PF4MJvN1BYMAA2A8Xg8jBs3jgbX+/fv7xOxNXv2bGzatAlff/01Ro0ahT179oBlWSxZsgQPHjxAp06dUK1aNVSpUgXJycmPXeQzISEBH374Id566y0cPHgQR48exfTp0zF8+HBUq1atTMcSi8V47733MHXqVDx8+NDjOQUFBVEPW4FAgPDwcNqmSMBFIBAgPT0der2e+mtHRkZSsp54/5KCoM/SsulxMzK4fRG3PVmtVmoDQd6fPXv24JNPPkGrVq3wxx9/eKz5kJycTLNRNmzYgJdeesnj9+fl5eH+/fu4f/8+kpKSkJKSQlP7o6Oj0b9/fzRt2hSnTp0qQVALBAJUr14d5cuXR1RUFA4dOoSTJ09Co9FgwoQJHr+zcuXKiI2N9VrgFSjOtGjYsCFu3rxJ1eQ8Hs9prUPsPMjvnsAl9Z+nddff7/G/t0K2H3744YcffjxH+LRKYBhmGIrTwUIBVAQQC2AxgLbP6sQYhukMYDmAfgzDZLAsu4xhmBgAGxiG6f13IbwrDMNEAlACeG6ksSuJZDAYnBYg3MU/116AQKPRUO83QhxLpVJKRuXk5CAoKAhJSUn45JNPPBLGAPDtt98iICAAP/7442MRxjVr1sRnn33mlaQpC6pWrYpvv/0WH330EVasWIFJkyZ5XGR17twZ8+bNw4EDB1CvXj2fjm+1WnHq1Cl06tQJarWaqqdIAQ4yWecKz90pRrgLiOetdHCnfHFHZpNzFYlEVHEcFBTkRCw5HA6qRCYqKW4KckZGBoYMGYILFy64VX9fu3YNJ06cwBdffPHYhPGVK1cwadIkWK1WdOrUySth/Djg8/l4/fXXsX//fq+VvQcOHIjLly/jxx9/RIUKFdCzZ88yf1dwcDA6dOiAPXv2wGq1Qq1WU6UU8N9nw+PxEB4e7mRJwwXxOSZk14sITwEUro8jUQ6RNkeulfRrxKddIBAgNDSUEgIajQYdO3b06dpZlsXYsWOh1Woxd+5cjwtuoPjd2bNnD2rVqoXhw4fTv6ekpKBq1ar0nB0Oh8ciYp4gFArRuXNntGzZEtu2bcPBgwdx9uxZ9OvXD927d/faRwYHB2PChAkYP348tm/f7jYFUyQSYciQIfjggw+QnJyM2rVr0/ZD7j8hXYlf9tPC81JVeSpy50n5Sd4j8n5x/65QKJCXlwcAuHv3LsaOHYuzZ89iwIABmD17ttvv12g0ePfdd8Hn87FixQqvilChUIiaNWvizTffxIULF3y6Pq1WiyNHjqBatWoeU89dIZFIUKlSJVSqVAlmsxlr165FcnKyz/0tyRpyV58AKCbq3n33XUr2iUQiBAQEIDw83Gm8M5lM9L4SPG5GhJvslhdKsVeaypGrhiwqKoJcLkdgYCCsVisl5nQ6HcRiMQ4cOIA+ffqgb9++Ps0Ptm/fjh9++AFvvvkmPvroI/r3S5cu4ebNmxg0aBA6dOjwlK60GOHh4ejbty969OiBL774An/88QdatmxZoh5BaSDbe+t7IiMjqf9yQEAAEhISqBUWsVojNQoMBgMNTCoUClowtbTx82nC14wM13mga+CBtCmbzQa73Q6WZRETE4O7d+9i4sSJSExMxO+//+5x/EpPT0e/fv3gcDiwfv16j/3HnTt3MGfOHGg0GgDFzyIuLg4NGjSg9SDi4uJ8nqvGx8dj8ODBsFqtVGzhDRaLpVTylmVZ3L17F23atIFCoaC2emT941pfxBv+Fwpv+uGHH3744cf/OnxdlX4AoCmAIgBgWTYJQITXPR4TTDEiAXwDYADLskcASP7+3m8BrAJwgmGYNxiGGQsgHkDBszgXTyCkH7coAPEEdF3suypYgGKiOCoqyklpTNL3SHT+9u3bsNlsXtVPhw8fxpEjRzB27NgypdSeOXPmmRDGBEFBQXjrrbdw/fp1nD171uN2lSpVQmJiIvbt2+fzsU+dOoXCwkJ0794d4eHhkEgkSElJQXZ2dokCCzabDSqVCkKhkCq/yULA1aeW+zyfNUh7cSVVyDkSkHM1m81OimOdTkf99AwGAzQaDV2wEdIsOzsbc+bMQcOGDanCbe7cuSXOZdGiRVAoFD57znLhcDiwceNGjB07FoGBgViyZAkSEhIe76aUAm4RQE9gGAY//PADGjVqhI8//thnIsgVDRs2RG5uLq5fv46ioiIagCALRaB0UoL4HJOF3z8JLpHKBWlznhZrhAAkvp+uvuCkHZIMi7y8PGi1Wty/fx8AnBSX3rB+/Xps27aNWux4w759+2AwGEooLgkIKVZWwpgLmUyGvn374rvvvkNiYiJWrlyJkSNH4uTJk14LgvXp0wcvv/wyvv/+e6f+novWrVsDAM6dOweHwwGVSuVEmJJxwFs7fxw8L1WVVCqFXC6nikKg5LvCVRcD/yWouPeCfBYXF4e9e/eiffv2uHv3Lv744w/88ssvbokZk8mEUaNGISsrC0uXLi21L+IGg3wBy7I4fvw4cnNzceLECVy6dKnMBeLatWsHmUyGrVu3+rzP2bNnERMT49Uu5a233gLLstiwYQPUajWysrLgcDigUCigUChgMpmg1WqJKpiCvM9lDVCQvsFoNJJ9XyjFHvf83IFcd0BAgNPcjetXazQacfToURQWFqJLly6lfqfJZMJnn32GwYMH45VXXsEPP/xAP7PZbPjtt99Qrlw5ak3zLCCRSDB69Gjo9XrMmzevzO3TYrEAgFe7kujoaFgsFkRHR5d4z7ljRUREBCIjI0tkU7jOxZ5VdkVZUdo8UK/XIysrCyzLQi6XIzQ0FCzLYv78+UhPT8fq1as9kq2ZmZkYMGAADAYDfv/9d4+EsclkwoIFC8Dj8TB48GBMmTIFq1evxrfffoshQ4agVatWiI+PfyxxA8k+IOpfd2BZFvn5+dRyyxPu3bsHjUaDatWqQa1W08AWIYwlEonXuQUXj1MMkWVZ6PX6f12BTj/88MMPP/z4t8LX2ZqZZVkL+YVhGAGAZzJa/+1NrANw828rjGgAvzIM8yvDMJ+hWH38AYBaABoBeOdv+4rnBlfSj0x6PClaSHqeVquFVqsFj8dDRESEk4+byWRCXl4eDAYDhEIh7t27BwAeJ5c2mw3ffvst9X31Fdu2bcOaNWueGWFM0KFDB8THx2PVqlXUt9MVDMOgY8eOOH/+vM8ezDt37oRYLEabNm3o/SeTYK6aGyhOeU9OToZGo/G6OHFH4j5LuJskuxaz4xJ8UqkUQUFBCA8Ph8lkQmFhIdLS0lBYWAigeDFAUt2JN/Znn32Gb7/9Fo0aNcKlS5doNW8uUlJSsGvXLgwYMKDMiqSUlBSMHj0aP//8Mxo0aPBMCWMAlCQi6Y+eIBKJsHTpUkRHR2PIkCHIyMgo83cR24M7d+5QpazBYEBOTg7UajVYlvVKSpCijJGRkY/lLfm04elc3QVQuCALfIfDgaysLCeCzWQyQa1W03crKCgIoaGh4PF4uHnzJgD4ZDmTlZWFTz/9FA0aNMCYMWO8bqvX67Fv3z7Uq1fvmbY1gqioKAwaNAhTpkyBRCLB999/j59++snjQpXP5+Obb75Beno6LYjmivLly6N8+fI4evQo9Ho9dDpdCdLFldx3hxd10UzUZjqdjhLnru2vNEKPbKPX6zFr1iyMHTsWdevWxeXLl9GnTx+32zscDgwdOhSXLl3C7NmzfcpcKStpfOvWLahUKjRp0gSVK1fG1atXcfz4cepN6wtkMhk6dOiAM2fOUI9Xb2BZFmfPnqV9kidUrVoVdevWxbZt26DVapGWluYUsCJKT0LgkYAqGTvL0vYA90HOFwmlnR+5XgBOcwOTyQSVSoX09HSEhITg9OnTEIvFTkXs3OH+/fvo2LEjli1bhg8++AC7du1yClzt378fKpUK/fv3f+aZJxUqVEDnzp1x+vRp7N27t0z7krmaN9I4KioKWVlZCAoKooEv8j6T8TkoKIgWRgNAg0QvGlHMBXce6O19IOOlRqPBoUOHsHv3bnz11Vce31GVSoUuXbogLy8Pa9eudWuvRfDnn39CpVLhgw8+wGuvvYYqVao8tTk6IbkLCgo8bqPX62G1Wkv17CZe282aNaOBThIwyMnJoZ7XOp3OqZ8n95XURHjc8et5Cz3+F1CWdaK3YzzpcZ7Gefjhhx9++PH84evs9SjDMF8AkDIM0x7A+wC2P7vTgglARYZhJgKoDuAIitXE1QF8xrLsNwC2MAzDY1m2zAoXbxMVTwQK9+8Mw/jsvcXn8xEQEACtVovU1FRIJBKnFC6JREIL5AH/VWo9fPgQQHFqmTvV2pIlS5CUlISff/6ZTta4IKQzF8SSIigoCJGRkVi7dm2JbW7duuWxArdarfZYII2QRly0bNkSa9aswZIlS5wqgHPRsGFDLFq0CNu2bcOAAQNKfM6yLF1gsCyLXbt2oVWrVrDb7bBarQCKLSuImpv7XGQyGQICAkolg8tSUOpxUZqKgrRJd5YBPB4PMpmMthfuYoZ4fjocDrAsi1OnTmH06NHIy8vDd999R4snuhI0OTk5mD9/Pvh8Pt58880SCwl37QcoXlROmzYNDx48gEgkQrNmzVC5cmWsX78eAHDy5EmPRKndbvdY8CYvL89rcSg+nw+BQACtVluCpHn48GGJ406YMAGffvopRowYgSNHjrhVJpL244qXXnoJgYGBuH//Pg1EKJVK6PV62Gw2aoUCuE/rJkWoAgMDqfrbnc/r04S3Po2b/un6/aUt2iQSCXJycsCyLFiWhd1uh9FohEgkov7hXBJALpfTAFBERITbgFFWVhYtnjdixAgYjUZ8//330Gg0uHv3rscAxs6dO2GxWNC2bdsS7fXs2bO0z3QHbwvvtLQ0j6r0goIC1KlTB6NGjcLevXtx+PBhxMTEoFatWmBZtkRhsoSEBDRv3hwLFizA0KFD3RYdatGiBSX3CMlKAhEOhwOBgYGQy+Uenw3DMDTNG/ivCv9JCWRP+7Ms67Xdun7m6ont7n+73U7/EfsmElxlWRZ8Ph8LFy7E1KlT0a9fPyxduhR8Ph8WiwXZ2dklnue0adOwfv169OvXD/Hx8bh+/XqJ8ywsLHTqYxQKBbKzsyEWi5GWluaVQD5//jxu3ryJ4OBgWCwWBAUFISYmBg8ePIBKpUJhYaHbNsaybIn2/Oqrr2LHjh3YvHmzR6KJBJ7u378PtVqNZs2aOQVF3RHVffr0weeffw6BQIDY2Fgn4ofrxQ4Uj+UPHz6Ew+FAdHS0U9E8T2Mh9znz+fxn7j/qrT2X1ia9gVgH2e12OBwOyGQy+v7ZbDaYzWZq5bF//360atUKMpkMWVlZbgnf9evXY+LEiRCLxZg2bRoaNWqEO3fu0M8LCwuxYcMGVKpUCQqFAsnJyU7737p1y2NmQUZGhse+69y5c7hx44bbz8LDw1GzZk0sXboUlSpVKlHc2FMmBCHh5HI5hEJhic/tdjsiIyORm5uL/Px8mM1m5Ofnw2QyITw8nAYhZTKZExGv1Wq9zpufpO96nH7L3WfceaBer4dWq6WqYjLfioyMhEAgoIU6J0yYgEaNGmH8+PGw2WzIzc11um/5+fl48803kZaWhm+++QYKhQIPHjwocT6nT5+GXq/Hrl27UKdOHRgMBqcxafXq1R6vv3z58h6LV1ssFtq2yLWlp6dDIpHQuQkXJNAUFhbmdcw8deoUQkNDERERQd8Zck/JfXXt9x0OB82k5H43t7/xdW3mbU7jbb//z3hapPHTOMaQIUOe+Dh++OGHH348X/ga7v8cgBrAdQAjAOwC4Ll0+WOAYZhaDMNUYhimBsuydgBfAHgJQBjLsr+wLPsXgOMAaP7y4xDG/yQkEglVhBJVBiHz+Hw+IiIiIBAIwDAMHj58iIiICLdp1gUFBVi6dCnq16/vc6oj18O4WrVqZapW/bhISEjAyy+/jFOnTiEzM9PtNlWqVEF0dDS2by89BnH37l08ePAALVq0QFBQEORyOU0TdFUKE1VLhQoVvHqkvmjwpJAiZLLZbEZ4eDhVdebm5sJkMkEikWD69Ono3bs3QkNDcfLkSbz//vseFT0ajQZbtmxBt27dvJK1XFy+fBmff/457t+/j0qVKqFXr16oUqXKc5mUkwWnr2n7sbGxtDDf0KFDvaZkuoLP5+PVV1/F1atXwefzqeqKBHRKS611fYZcmwduWv7zArl3j6Pu4hKZUqkUubm5KCwshMlkcpthQQrg8Pl8j9XjgWIiZciQITh+/Di++uqrUgtB5eXl4cqVK6hbt67X6u/PCnw+Hx07dkRcXBw2b97stR2OHj0aZrMZU6dOdft5y5YtUVBQgJSUFMTFxTkFIIKCgqBUKkt9p553dkRZQHzmSbtwfVcIUa7X6+n4R2ycSJBh5syZJQhjT/j9998xf/589O/fH127dvX5PAMCAnzqTxwOB5KTk8Hn81G+fHlKjJQrVw6JiYlUAe9r3xQUFIQGDRrg/PnzXj3agWIyCQAaN25c6nHffPNNMAyDvXv3QiaTUbsYbn/jcDig0+kgkUicAqquSmSybVnUx88Cj6OoJ+RUXl4eJam418ElyEmwlczHGIZB5cqVUbVqVWg0GiQnJ7v1JweK7Xk++ugjjBs3DpUrV8bSpUvRqFGjEtuRbKtOnTo9NwKLYRh8+OGHEAqFmDNnjscAqSt8URoTL+60tDSUK1cOsbGxCAkJoYF7V3VpaTZILyLI+0DO2WAwoKCgAJmZmUhLS4NQKMTcuXOh1+uxZMkSt8GEwsJCvP3223j48CFWrFiB6tWre/w+m82G3bt3IygoCC1btvS4HcuysFgsj0WyE8sJ4hPvDqQ/Ks2e4vTp02jUqBEEAgECAwMRFhZG5wMkeAAUv18kw5IE3Ynv9ZO0Cdcxxo/S4Ws2Z2nHeNLjPI3z8MMPP/zw4/nDpxGXZVkHy7JLWZbtzbLsm3///NTyYhmG6QTgDwDvAFjNMMy7LMueAnAQQA2GYT79e9MQAEqGYR7fsPIfQkBAAKKjoxEcHIyHDx/CZDLRauZkcabVamma18OHD1GpUiW3x/rxxx9RVFSEzz77zKdFiGvRO18JY65f3eOiXbt24PF4mDNnjtvPGYZBq1atcPjwYWq34Ak7d+4EALRq1QqFhYVUnRYdHY3w8HAnQo5URycEhTe8SKnenshId16A4eHhlEC+fv06Zs2ahTfffBOnT58utRjdunXrYLVafVIO5OXlYc6cOfjpp58gFArRpEkTNG/evIQdiCuIkvBpQaFQlMnrtVatWnj//fdx8OBBTJo0qUzf1bBhQ9y4cQMFBQUoKChAdnY2kpKSaDDCG1zJVPLsAJSalv9PwpUkIkUWeTweQkNDYTQaUVBQQEmXwsJCGAwG2Gw2ZGVl0fc3Pz8f0dHRHtOwMzIy0KdPH1y+fBmzZs1Cv379Sj23jRs3wuFw+FxAymAwwGQyPdX2x+fz0adPH5hMJq+etPHx8XjzzTexZs0at4rXFi1aAACOHj2KjIyMMgU0CJ73opnbNrhEgC/jgzvykfTdYrEYer0eYrGYkpbff/89Jk+e7BNhfOzYMXz++edo2bIlpk6dWiZSTi6Xw2AwlNrvnz17FgaDAQkJCSXUl2FhYahUqRJMJhP27dvn84K4bdu2sNvt2Lhxo9ftTp8+jfDwcI9zAS5iYmLQvHlzbN68GWazGQUFBUhNTaX3XqvVQq1WU2Vo+fLloVAoqMc0Vx0KgL7varX6HyOOvaWheyK1jUYjLBYLCgoKYLFYoNFoSvS7hPgngQtuMUoyxpL5hjvS+N69e+jcuTM2b96MTz75BN9++63b4OvDhw+xdetW1K9f/5kGuwwGQwmlfGhoKEaNGoWUlBSsW7fOp+NYLBYIBAKv7xy5DrItCWDJ5XInYQTBi2BJ4WmOx7Is7cu4n3H7VxJYsFgsSEpKwq1bt7B161b89ddfGDNmDKpWrVri+8xmM959913cuXMHv/zyC5o1a+b1/C5evIj8/Hx06tTJo8LXZrMhPz8feXl50Gg01H/aVxAi2FugihDK3khjjUaDO3fuoFq1arDZbJDJZAgLC4NOp4NOp6MFmbVaLVJSUpCSkkILIwYHB1NrPldrrBdpHv6/iDfffPOpHONJj/M0zsMPP/zww4/nD6/2FAzDXIcX72KWZb0zUz6AYRglgK8BjGZZ9iDDMEcAHGIYxsay7CqGYdIB/IdhmOoAGgN4k2VZ7+xiGeBaMflZgVhS3LlzBykpKVAqlahQoQK1HeB6tZpMJty4cQM9evQocZysrCz88ssveP3111GtWrVSv/dxit4RpU52djaEQiGqVq362MrkwMBANGnSBEePHkVqaqpbL9JWrVrhjz/+wOHDh91eM8G+fftQs2ZNKBQKmM1m5Obm0iI2BoMBhYWF0Gq1dPHCsiwlObwtWMjCFPCcmvtPw7WSN/dvPB4Pv//+O0QiEX7++edSyVwA2LFjB1q2bIny5ct73c5qteK7775DQUEBevfujS5dumDTpk2lHt/hcODGjRswm82Ii4vzqjr1FSEhIfT9KU2ZStCpUyfY7XYsXLgQXbp0KXXxRqBUKuFwOHDv3j20a9cOarUaFosFeXl5VLnuWtzLkwUFeU6kHT4vH9Cy9m2u1igmk4mmqxIFmsVigcPhAMMwVImlUqlw8+ZNBAQEoEaNGrh7965XP+MvvvgCOTk5WLlyZalerQ8ePMDmzZtx+vRp1KhRw6N1DhdXr151Imv5fD5EIhFEIhGEQiHEYjFCQ0MRHh5ewl6iNERFRaFx48Y4ceKE1+JYffr0wbp16/Drr7+WCJiFh4ejRo0auHbtGrKysiCRSBAeHu5EyvL5/Gc+JpUF3LYBgPrxRkdHl2pT4NquiCKRZNyQ4kyxsbG4ePEiJk2ahL59+5ZKGOv1eowaNQpVqlTBkiVL3KbTu4KMGxqNBteuXQMAj4SoXq/HyZMncf36dYSGhnosNqtQKNChQwccOnQIx44dwxtvvFHqeURERODll1/Gjh07MHToUI/bXbt2Da+88orP7aB79+745JNPkJ2dTVPpybWkpqZCLBZDKBTSYCrxHSXzD+6zlEqltNil0Wh85nYU7sBVQrvCnZUTUHzeIpEIwcHBEIlECAsLg9lspv2uTCZDZGQkxGIxzaQg/bfVakVBQQHu37+PpUuXolatWiUKEJrNZgwdOhQ6nQ7r1q1D48aNcenSpRLnZ7FYMHPmTEil0mda/M5qteLkyZMAigttcoN1DRo0QOvWrbFt2zZ0797da9YVy7K4e/duqepP8p7l5ubSviogIID6kAcGBr5wClBPczyDwUD7Mtc5FgmmsCwLnU6H0NBQVK5cGTqdDu+//z5iYmIwceJEt9+3YMECnD17FvPnz0fbtm29ntuePXtw/fp11KpVy6NXP6llQc7faDSWaunlCqFQCKFQ6LUuRFZWFgB4LaxNsh/atGkDpVJJA7RqtZqKGQjxK5VKnQqFc+0rXPFvmIf74Ycffvjhx/9XlOZp/PpzOActgJsA7gEAy7JHGIbZBGA6wzAWlmX//JtIjgVgZFk252l++fOcqJCFV0JCAoKDg2EymZCeno5y5cpBoVBAJBKBx+Nhx44dKCwsdEugrlixAjabzSeF6O3btzFv3jxUqVLFJ8KYpK5mZWXBarVSO4DU1FSfSTp3IBNQT4vzatWqQSAQ4Nq1ax5JY7vdjgsXLqBbt27g8Xiw2WywWq0wmUyIjIyETCajPp86nQ6BgYGQSCRQqVRQKBSIjIz0WgiP+/+/BUQJZrFYwDAMWJb1iVQDilUrvhBm165dg1qtxkcffYT69ev7fG4ZGRnU+zclJQXZ2dklFt9lxaBBgzB16lR88cUXmDhxYqlqaoLPPvsMv/zyCw4fPuwTaZyRkYEpU6bg1VdfxSuvvIKCggKIRCIEBQVRFQ2Px0NRURElgsm7A3huR8Sb+nkRgdy+zRfCx9WDkJCZ3L/FxcXR50rIAeIdLhaLcenSJVy6dAnTpk3z+D3p6eno2LFjqYTx0qVLsXv3bshkMrzxxhs0LdobkpKScP36dSQkJCAiIgJWqxUWiwV2ux0WiwUWiwUmkwk3b96k/ZFYLEZycjIiIiIQFRWFqKgoj8/Ibrfj7t27iIyM9ErA/PLLL+Dz+Xj77bfdfl6jRg2cO3eOEnuEuOAq0oEXZ/Hs2jaI2tCXAIjrvtwgqVQqpVYCAKgX7Pvvv19qoPLPP/9EQUEB1qxZ4/ZZXLp0CSdOnIBGo4FGo4FarXZSrIpEInz00Uclvsdms+HixYs4e/Ys7HY76tatW+o1KhQK8Pn8MlkhlStXDjdv3qQp2+5QtWpVnD171mcP39zcXPB4PFSqVAlBQUEoKChwqp8gl8tpQVVS8MvhcLj1/ufxeAgPD6fBsH8CnuoNkICwq6oVKHnePB6vxP0lJDFQbDOhVqtht9tpUOPDDz+khctcsXTpUiQnJ2PNmjUebUNYlsUPP/yAW7duYfLkyc/UDox7be7mOKSfLi2YvHHjRhw7dqzUgqS//PILQkND0aRJEye7NbFYDIZhfArU+4q/yccnPpCnOR4JILj7jGTUyOVyKBQK6k9+/vx5nD9/HgsWLHA7rup0OixbtgwdO3b0KoIAgA0bNmD+/PmIiYlB69atS3xO6lQUFBRAKBQiODiYWmbl5eWVKUvl7t27sFqtHuuLAMDx48cRFRXlNeh7+PBhSKVSNG/enM55QkJCIJPJIJVKIZVKYbPZIBaLERgYCLFY7LEYNhf/1nm4H3744Ycffvx/gFfSmGXZ1Gd9AizLmhmGEQH4gWGYNQA6oZhEXgjgQ4ZhDrAsmwvgmZzL85qokIl0SEgIYmNjqYqRFJCIj4+H0WiE3W7HggULUL16dTRv3tzpGGazGStXrkT79u0RGxvr9fvS09Pxww8/ICIiolTCmGVZqFQqJCUlQa/XQyaT0dRVlUqFjIwMZGdnP3bVbzJh9HSPhUIhqlSpgps3b3o8xr1796DT6ZCQkACdTofExETw+XxaRAooVvDJZDKqjiIKCFJMLzw83O1C5nkUwnsWIJNztVqNkJAQmEwm5OTk+JQGK5VKPRbD4eL8+fMICAhAnTp1fD4vg8GA9PR0KJVKVK5cGfn5+Xj48CHu3r0LnU6HBg0alOqZ5w4VKlTAzJkz8Z///AczZszAlClTfApmyOVy1KtXD8ePHy91W4fDgZEjR8JsNmPBggUICwujSjtCtlitVkrg2Gw25OXlUY++spIrXIXy01ZnlbVv4yqtiMoKcC50JxAIKAlD/qZQKFCjRg0AxX6+AQEBHslSoHhRXRq5dufOHezevRtt27bF4MGDERAQgBMnTnjdJzMzE+fOnUN0dDSaNm3qdD9d+z+73U6JxNTUVKjValqgqG7dum69SYHiNGK1Wo1BgwZ5fF579+7F3r178fnnn3skxitWrIi//voLAoEAFouFWgMQr3KiNH4SPM0sGlcVXlnIUe6+NpsNRUVFlFzg8Xh0PDSZTJTIK43gstlsWLp0KRo0aOCW1GVZFvPnz4dOp0NMTAyioqKQkJCAxMREKJVKhIWFISEhoUTRzoKCAuzYsQMqlQoVK1ZEixYtEBoaimPHjnk9n4KCAmi1Wrz00ks+3xfyfLVarcf+sHXr1ti6dSvu3r3rU2bR5cuX8dJLL9FCk8QnmthjkQJYhOwqKiqCWq1GeHi42zbiLsPlRQCxn/KkavV23gaDAY8ePYJEIoFSqaSqTb1ej+DgYPTp0wc5OTnYuXMnGjRo4LRvRkYGfv75Z3Tu3NktyUewZs0aHDx4EEOHDkXLli1L7buA4j43KSkJdrsdSqXSq9qTC4Zh0LRpU6c+mYvU1FTExsZ6VeLfv38f06dPR6NGjbwWqbpz5w527NiBDz/8EBaLBTweD2azGTqdjrY1Ymn0NNrN32NQmRl3176PFOcjfyMgGTPuwB0/SWAYKFYRx8bGui3cDAC//fYbCgsL8cEHH3g9v1WrVmHlypVo0aIFKlWqVGKMslgs2LVrF+7duweJRIKgoCD6jpK5uK9e1UDxXE4ikdCx2hVmsxnHjx9Hz549vY4Xhw4dQvPmzZGTkwORSASFQuH0HrIsC7FYXMIqDvA+F/m3zsP98MMPP/zw4/8DfGIBGYZpBGAeigvTiVA8idOzLOu+zL2PYBiG97df8iCGYWYAaPT38YlMbSCAIo8HeAp4XhMVkrpHJqh5eXmQy+VgGIZWY5ZKpTh9+jQuX76MRYsWlZi4bdu2DWq1GsOGDfP6XSqVClOnToVQKMTEiRO9LvDz8vJw9+5dFBYWIiAgAEqlEnFxcfS7IyIiYDAYkJWV5bOK1RXEe83bfa5RowZOnTrl8XNSSbpu3bpUbREQEACLxeJUmVuhUFC7CqKs4qbYviiTUk8+ktzPiUehTCYroVQinxP1KyEUUlNTfSKNJRJJqd66NpsNly5dQr169XwOGLAsi/v374PP56NChQpgGAahoaEIDg5GZmYmsrOzsWXLFlStWhV16tQpM8kaEhKCr776ChMnTsR3332H6dOn+7Rfs2bNMHv2bBQVFVE1pzssXrwYBw8exIQJE6giJyAggFb7NhqN0Gg0sNvt4PF4lHTiWlAYDAafSWBPKdZPA576Nm7b87Q4NBqNUKvV9Hdiq8FVIXM/UygUUKvVWLduHYYMGeK2gCcAGszx9gwcDgdWrFiBkJAQvPvuuz61kaKiIly8eBHBwcFo0aJFqfeeFB6NiIhAYGAgKleuDIPBgEOHDuH27duoX79+iXfOarVi3759iI+P91jUKDMzEz/88ANeeeUVfPrpp263AYBKlSrRd7hKlSoAii0EzGYzVW4/KcqSRUN8Oz21h8cJbrjbR6PRQKVSUYW1VCqFQCBAeHg4UlNTKbngrRAXAOzevRtpaWmYPHmy28+Tk5OhVqsxZswYtG/fHgDw6NEjr97Ad+/exb59+8AwDLp164bKlSv7dJ3k2AzDlBrM5YIQKEVFRR5J41atWgEorktQGmnMsiwuX76MJk2aoLCwkL6zrrUJuFkR/1b/UFf1uid4areEoDMYDLDb7dDr9cjOzsbw4cMpYewu4EPam6d2BxSn769YsQIdOnTwGjzjorCwEAcPHqT2AEBx/y2VSpGcnIywsDCEhoZSWw1XeCPjUlNTvWblmEwmfPrpp5DJZJg+fbrX93vu3LmQSqUYPXo0QkND4XA4aBsj91IkEj01Zfrf11VmY3p3fR/3b74E5IivMff3M2fO4OzZs5g7d65bIYbZbMYvv/yCJk2aeM1QWLt2LVauXIlOnTrh008/LeE5XVhYiE2bNiE3NxetW7fGrVu3nPpmYi3jq9LYYrHgypUrqFOnjsfgwenTp2EwGLxaqaSnp+Pu3bvo3LkzioqKEBUVhdDQUDpX5foUk7mNXC5/rIC6H3744Ycffvjx4sBX6eh8AH0BrAfwKorJ3CqP84UMw1QFEArgAgC6kmFZdoLLdgNRbEkRAKBsFR+eAR5ncUUWLCKRCBKJhC7eSBVhiUSC0NBQ6HQ68Pl8WK1WLF26lKpd1Gq1E3GxaNEilC9fHq+88grOnTvnNuXRYDDg22+/RVFRETp27Ihz586V2KagoAA5OTnQ6/XIzMyEQCCg5InBYEBmZqbT9kKhEAKBABqNhvqiuUIqleLevXtu7wM5Xl5enttid3w+H4mJifjrr7/w8OFDBAcH089YloVMJsOFCxcQEBCA1q1bw2QyQSQSwW63Oy0euZXQxWIx+Hw+5HI5ZDIZTCYT9TkG8EwtAnxpK66LGu4+LMtCrVbThX90dHSJRY7RaIRKpQLLsggJCaEEZ3JyMl2skKIk7iAQCFBQUOC0SOXi8uXLSElJgcFgQFhYGC5fvkw/y8/P91hMRa1WQ6fTISQkpEQ7Im0sOzsbd+7cwd27d6FQKBAcHIyoqCi3vpAErkVf2rRpg61bt+Lzzz/HW2+95ZHUlkgkUKvVqFmzJhwOB/bs2UMVYiQtm+DOnTv46quv0LFjRwwePJgqH4kCFCgmrSUSCWw2GwoKCiCTyRAcHEzbFpcEdrcwJQoo4n3Mbb/PksDhHlun01HizrUYDYFUKqW+zsRrl2VZSKVSSCQSmM1mqNVqBAcH03u/dOlSmM1mjBgxwm3hKuC/hXZYli3RPoBiP+Jr164hKSkJrVu3dlLoaTQat+1Vr9fj0qVLlLS7f/9+iW28qeoDAwNx9epVAKAe88eOHaM+thkZGQCKVcaFhYVo164dMjMzaUCKwG63Y9KkSWBZFp988gnMZrPHtFyikL99+zaqVasGlUqFoKAg8Pl8aLVaKBQKqm5zDS65ayfuVMVlUZrb7XYYDAaP5LKndk2yOHzdJzg4GAaDAWKxmBafIu9ObGwsHdPIu+QOaWlpmDNnDuLi4lC1alU8evSIfvbgwQMIBALs378fDMMgMDCQZrBkZGS4zWax2WzYu3cvMjMzaZbNo0ePnI7rrXhUYGAgkpOTERQU5BRoAYr7i9u3b7vdj/Rp9+7dK/FM+Xw+oqKiIBaLUaFCBezduxe9evWin7vr1zMyMqBSqdCgQQMIBALYbDaqNjYajbDZbNDr9dQSBCjOzomMjPTa/xBVvlKpfOxsI0943D6PKEc9HYP8raioCGlpaYiLi6OBKqFQCIVCAb1eT+dewcHBGD58OFQqFf766y9Uq1atxFxlw4YN2L17N0aNGgWWZZGenk4/u3HjBoDifubnn39GVFQUmjVrhjNnzgAoLqbo6fpv376Nhw8fAii2/yHzFeKxnJmZiZSUFADF7SI+Pp76NLvrQ4Hi7Jq0tDTodDrk5+cjKCgIaWlp9HOuVdSMGTNw//59zJkzB1KpFHq93m2RtYyMDKxfvx79+vWDUqkEj8eDXq+nSl6HwwGRSASlUkntslyfR1kDT3/3ZWWuwsjt+8h3u/aH3tqeJyu1KVOmICoqCj169HDyeCdYvnw5VCoVvvzySzpuEJw/fx4WiwVpaWn4/fff8fLLL6NWrVo4cOAAHjx4QPsYvV6P5ORksCyLxMREFBQUlChyCBTfG7PZDLvdTv3ZXSGXy5GRkUHrS1SsWJGeV2BgIAoKCui2u3fvhlQqxUsvvYTCwkK39mX79+8HUFzgmowVJJBOzkkikTjZoZDfST9UmtrYj2eDUaNGvTDHOHr06BMfxw8//PDDj+cLn1cALMveZxiGz7KsHcAKhmEuA3BfBcIDGIZ5A8D3ADL+/neBYZiVLMsWkWMzDCMB0AHANwB6sizrebX2mHheExOyaFYoFLQSs1arhUwmg0AgQFhYGLRaLfR6PSQSCbKysrB582aMGTMGcrkcWq2WLtKuXbuGy5cvY/LkyVT56OqnZrVaMX36dGRkZKB+/fpgGMbtYjczMxNCoRBqtRpCoZDaNphMJo+LEKBY3VBYWIhXXnmlxOJRIpFQxZwrUlNTIZVKPaq8zGYzateuDaC42niTJk3oZyzLQigU4tKlS6hbty6189BqtQgLC6PPkixciD9heHg4AgMDnYoucRcqz6sAoie4LmC452AwGGCz2Wi7cVdcTSqVIjIyEnZ7sQiHTPDT09MpqS+Xyz0u8gMDA8GyLGJiYtx+HhoaiqNHj0IkEqF27dpOgQIej+d2QaHVaml7c0csFxQUQKFQQCAQ0AIqOp0ORUVFePToEQIDAxESEuJERBK4LprEYjFatWqFAwcOYMuWLejUqZNbBZbVakVAQACaNGkCsViMy5cv4/XXi63aCWEFFBM4w4YNg0KhwKxZs6BQKCg56nA4YLFYEBAQAKFQCLPZDJFIRBf3QUFBtG1xlX2uhJrNZkNqaiqsVitiYmKgUCieWqaDtzbsbXHsqTANSds1GAyUANbpdJBIJODz+SgqKkJ2djb0ej1VnP3yyy9o3bo1qlev7pGkJVXUo6Ki3GYupKen48yZMwgLC0P58uWdSNecnBwkJiY6bW82m3Hs2DHY7Xa89NJLHm0NvFnflCtXjhYgCgoKgkAgQE5ODoKDgyGTydCiRQuwLIvffvsNNWrUQP/+/ek94qb6/vTTT7h58yYWLlyIFi1aeH2upK+8d+8eHj58SEl84hlN7CnUajVsNhtVAXvKTHCnrCtL2/Jmh8El6Lz1l4QUIB6WIpGIKswICexwOKhFAlEk6vV65ObmIiwsjLZVuVzuMeB179493L59G5MmTSqhaJfL5QgNDUVSUhIqV67sNCYdPny4RMp/UVERTp48SckTg8GAW7dulfhOb17oOp0ORqMRDRs2LKFALygoKNFmXSGTydwWWyPjOykUS4o4Au6V2IS4bNCgAQ1GiUQiZGVlITo6GoWFhbDZbGAYhvbfXOsZ0sZc1bkajYbOC55GQVNvcP3uso7NrgVJSWq80WikbYXP5yM0NBR2u51mVAwcOBA5OTlYt26dW4Wx2WzGnDlzkJCQgOHDh5dom2azGYGBgdixYwe0Wi2GDBni9O6564NNJhOtGUDAJXaBYlK/atWq9Fyzs7ORkpKC3NxclCtXDlWrVnV7H0ixTRKIbdq0qVNfRYLMx44dw6ZNm/Dhhx/Sfo2Mma5YunQpWJZFjx49aAE8uVxOtyV9iKfMqdzcXKqMJT7IT8OWydP45e4aSICemw3k2m9x51vczw4ePIjjx4/j+++/d5u9Z7fb8eeff6J69epo165difPKzs4Gj8fDtm3bEBQUhFatWtHx7dChQ26vjVgmubMqsdlsMJlM0Gq1HvuYoKAgNG7cGLt370ZERAT69Onj9L6TOgEsy+LkyZNo1aoVypcvD7vd7nYsPXbsGCIiItC2bVsUFBTQ/iQ0NJT6plssFggEApoNSGAymUoIJVzn4H7S+NnhrbfeemGO0bdv3yc+jh9++OGHH88Xvs7WDH/7Dl9hGOYHhmHGlWFfAADDMEIAbwF4j2XZtgC2AogDMIFhmKC/yWiwLGtCcVG8lizLug+fv0DgKvFcIZVK6aKZkEiEPCZkJjmGTqejk/KRI0eWONaqVasgk8nQu3dvj+cxZ84cXLt2DWPGjPFaVdlut9OCOUQx4gtCQkJgMBiQlJRUJoUQ8ez0BrLYdrdgt1gsuHr1KipWrEi987RaLSW0CgsLoVKpqHKNC0Lcu6rWCMniSRH5rEEWNe4myUS9GhUVRYlFVxBLAFLAKDo6GmFhYUhN9c36uzR7Crvdjlu3bqFatWpevRAJWJalFiK+QCAQIDAwEOHh4QgNDUVAQABMJhNSU1Nx9epV3L9/v1TP5YiICDRr1gwqlQozZsygBLo7SCQSvPrqqzh06JDbtjtv3jxcvXoV3333HaKjo2E0GpGfn08rf3MVwXK5HGFhYdSPVq1W0/efpI0SooILjUaD7Oxsr6rF5wHib+paKd5dPyaVSikpnpOTg9zcXOh0OggEAgQEBECr1SItLQ1//fUXHj16VKoShSwaPVnm3LlzB3q9Hg0bNix1AWm323Ho0CEUFRXRYphPCtInuhYZunv3LtRqtcfU3YsXL2LWrFno1asX3njjjVK/h3hgEyKcFNYj999qtUKlUtEFOADa57kDaaePew9KI+i89VcEpK/VaDQoKiqCxWKhpLBKpaLELDlPLmFJ9ieKNW/2FGvWrEFwcDC6du3q9nO1Wo1Hjx6hXr163i4ZKSkp2Lt3b6k2PaUhNzcXDMOUSg67gvQp7hSLXLRq1QomkwknT570ut3ly5fB4/EQHx8PvV5Pg7wkoyQkJIRagigUCqpmd4XrmBkWFoZy5co9tj1VWeBpvC7r/uQ9CQ8PR2JiYon5EMkCyM7OpoTxzp07PbaZBQsWIC0tDV988YXHYEZycjJOnTqF5s2bey36yrIsMjIycPToUdreSwMhaCtWrIhy5cpBr9cjKSmJKlI9ITk5GQDctk2SGREfH4/PP//c6/drNBqsWLECnTp1gkgkQnp6OgoLC+lzIgVRAdBMOu54YjKZaMFHQhi7PmeWZaHX65+5ZQqZ+5Hv5vZbBQUFNFCn1+tpceW0tDT89NNPiIyMxMCBA90ed/v27Xj06BGGDRvmtp9kWRYHDhyAXq+n9/FJQMaF0orMaTQaXL16Fa1bt/Y4179+/TqysrLQoUMHj8dxOBw4fPgwGjduDLPZTPtobtHD/Px8aLVat+8vWQ+RdvJPz8H/v8E1IPW4x3jS4zyN8/DDDz/88OP5w1fid8Df234IQI9isreX1z3cIxAAMQrcDGAHACGAfgDAMEwDhmFqsix7h2XZdA/HeKGg1+uRlZXlNn2M+JzyeDyqqmQYBmKxmFosEOXehQsXsHDhQvTq1Qvly5d3Ok5RURG2bt2Knj17evQK3bt3L44ePYqBAwd69STjTsyVSmWZqnpLJBLExcXRAlK+gGVZ6t/sDWFhYR4tLg4fPkzVyDqdjhZgAUBV20RhzDAMTbsHnIl7Lp6UZHmW8IWgAf5L9BUVFUGr1SIhIYEqU0pDaaRxZmYmDAaDz76e6enpyM3N9WlbLhiGgUgkQlBQEGrUqIFq1aohMjISOp0Ot27dKnVBFB8fj6ZNm+LMmTPYsmWL1227dOmCBw8euG1j+/fvR+3atTFo0CDIZDKEhoZSv2KRSERVUuSdtlqtEIlEcDgc1C+bQCKROLU58pxCQkKQmJiIl19++R/11ub2SwSeyBqiMlUqlYiKiqKp7UVFRVRVFhQUhF27diEuLo6quD2B2Eu4I40tFgtu3bqFhIQEnxSNV69eRVZWFho1avRU32OieOWS+8eOHYNIJPJY2O6rr75CeHg4pk2b5vZzVzAMg7i4OCQnJ8NsNiMmJgbBwcHg8XgwGo00tVwkEtF2SKx2PB2P9BnPi3xxBelrw8LCEBgYSPuYnJwcp0CDa9sLCAhAeHg4pFIptS/xpBh/8OABjh07hr59+3r0yLx+/ToA0OwVd0hJScGZM2cgl8vRqVOnx7ziYuTn5yMiIqLMnp2kf+danLhD48aNIRAIPKoRCS5evIiXXnqJKrilUinCwsJou0pLS/OJkHUdMwUCAfWgftbwNF6XdX+JRILCwkKnIrHc90EikSAsLAxjxozx6mEMFFvqzJo1C23atPGqON+xYwdCQkJKbU+PHj3ClStXaBZDWcAwDJRKJapUqQKZTIYrV67Q9u4OSUlJCAsLcztv3LZtG27evImvv/661MKTq1atgl6vx/DhwxESEgKRSASLxYKCggJkZ2dDq9XSorwFBQU0cMQtbBkcHEyz2sRiMf2f4HmRiO4CwaTfIrYueXl59L3k8/m4evUqDh06hE8++cRt22RZFrNnz0ZCQgLatWvn9nsfPnyI+/fvo0mTJk9FsU/m7qXNkbZv3w6Hw+G1cOOuXbvAMAzatm3rcZu9e/ciJyeHvgMajQZ5eXk08KFQKBAWFkZrO7gGoElggWuf9KLOwf8X4alwY1mP8aTHeRrn4Ycffvjhx/OHr6uAXACWv1XA/2EYhg/Ae6UaF7Asa2UYZhaA0QzDPGBZ9jjDMCcAxAB4nWGYVQCaAvijLMf9t4BMjCQSCdLS0ighERwcDJFIhIULF0KhUGDu3Lkl9t25cydMJpPX1CCNRgOGYZx8D91Bq9XCbrcjODjYJwUpF8TH1Z01hiecOHECqampGDNmjNftFi5cCKPRiObNmzv9PSsrC++++y4qV66MHj16UIKxsLAQEokEDMMgLCwMDocDMpmMFhckC0TXKt5cX7VnQdyVVlDqacJoNCI3N5eqIqtVq4bNmzeXqBDuDjqdzuszJCT+hQsXULt27VLV6KWpgn0BIb4CAgIgkUiQmprq0VuQC6JgLE3p99JLLwEoThN1Tevlqn6InYnVakVBQQGSkpIAFAeIlEolAgICnGwoSMEpTyCEbGBgIKKiov6RFMzSCpm5eoOTlFyyLXmPyCKfKP5FIhGKiopQWFiIihUreiWWWJbFnDlzEBYW5raCO4/HA5/P91mBFRkZCYZhkJyc/FSL7JCAGOkfSFX5hg0bevwes9mMl19+2WuBPy7S09Npwb3bt28jICAA5cuXpwQ9yQAhRT11Op1HZagrylIAzxMex76H29cKBAKwLAuxWAyZTAaZTObx/FmWhUajwcGDB7FkyRK89dZbHu/zihUrwOPx0KdPH4/nQfo+b31SaGgoZDIZCgsLqZ/s4yIwMBBZWVlIT0/3uRAey7LYuXMnhEIh6tev73E7k8mETz75BDabzet2VqsVJ0+eRM+ePel4oFAoqAdubm4uCgoK3HrVusJ1zHzWcO2bnuS7yf46nY4GXkg9A+C/74PJZILVasW1a9fw7bffeiSMgeICYUajEf369fP63WKxGAzDlDqvInMvo9FYKuHnCcT6hfQL7mA0GnH69Gm0bNnS7eeXL1+GRCJBt27dSv2+e/fuITo6GvHx8QgNDaWe0Lm5uTCZTDCZTJDL5VRNHBYWRu1oyBjJfa7E9slsNtMxoywe7E8CEggl/RoZv41GI/WKJjZUUqkUAQEBNIOrR48ebo+5e/duXLt2Dd98841HIUZaWhrEYnGpGRC+wmq1AvCelZGSkoL9+/ejTZs2Hq3ICgoKsHLlSnTs2NFjNsGZM2fQv39/1KhRA507d6bEsEQiKREIJDZ8nt5l7tjyohSm9sMPP/zwww8/vMNXpfFBANyZnBTAgcf4vuMA9gEYwDBMC5Zl7SzL/g6gHIByLMvOZlk2+zGO+4+BpHrLZDKPNhXAfysxm0wm6ucoEAjgcDhw7NgxHDx4EBMnTnTrXbZp0yZUqFABderU8Xgecrnca/Egguzs4tv7OCRLbm4u7HY7atSo4dPE/vbt2zh69Chq1KjhMaUPKFZg/PDDD3j99dedUo7NZjPee+896HQ6LFmyhPr7WiwWsCwLk8kEvV5PlSl8Pt8twcFNvXdNX33aIAWlnhVsNhtUKhWsViscDgdCQ0ORmJiIiIgIdOnSBUajEfv27Sv1OLm5uV4tTGQyGTp16oTU1FSvBeoIyqpCs9vt9Dm6A1lMe1sQAcWqrZs3b6JXr15e3w/gv4SBu6yAKlWq4NatW9i3bx8tEmm1WpGRkUEJUpvNhtzcXFocj8fjoaioCLm5uU7vPfHuI++iO1XV80Zpad/chbNWq0VWVhZ0Oh0MBgO1s8nPz0deXh54PB7MZjMlXrRaLfh8vtsil1zs3bsXp06dwvDhw932HwKBAFFRUUhPT/dJJRsTE4MWLVogOzsbKpXKpwBDadBqtcjJyXEqPnnq1Cno9Xq0b9/e436JiYllIh9XrFgBh8OB7t27w2g00v6JLLS5tjSEcPWVTHkaCq6yqP64xV21Wi20Wi19FuQ9JgpqkvZtt9vpNWs0Gpw+fRpjx45F/fr1sWzZMrffY7FYsHbtWrRo0cJr30V8jO/evetxm6CgIHTu3BmxsbG0COLjonz58ggODsbBgwd9Dp6dO3cOt2/fxrBhwzxaGWg0GvTr1w/bt2/HF198ge7du3s83vnz56HT6dCpUyfqG8+1DlAqlYiOjkZcXBwAlLAP+CfxpJYUXJC2SLKi4uPjERwc7GQRRrLDSJE6T7UWCM6fPw+RSIRq1ap53a5evXrIy8srtR8ICgpC06ZNIRKJ6DmUFWazGVlZWYiMjCyRmUZw/PhxmEwmj8rnhw8fonz58j4Fosh8wWg0QiAQ0PGeBF9CQkIQEhICuVxOiWJXGwgu3CnKfc2wehYgbdBsNtOaIYQYValUSElJAZ/Pp+8PFyzLYsaMGahQoQJee+01j9+hVqtpoPNJQea/xO7DHQoKCnD48GFUqVIFH3zwgcdjLVmyBEVFRfj000/dfn7r1i306NED0dHR+PHHHxEREQEejweBQED/B0D7dYfDQefq7uC3pfDDDz/88MOPfx98JY0lLMvSHMq/fy7zivRvpfJvAK4CmMgwzHCGYQYBCAfgPUfzBQVZ5LsSRZ5AJtjVqlVDSEgIWJbF9OnTkZiY6NbLOCMjA6dPn0avXr28TjaJarS0VNesrCzw+fwy2VIQOBwO1KhRwyeVcWZmJrZu3YrY2Fi8/vrrHs/dbrdj6tSpkMvlmD59utNnX375JS5evIiFCxciLi4OGRkZyMnJcVLSFhYWUpLCarVSqwZCOjkcDpoySdRMzzIlzltBqacBUpSIqKgEAgGCg4MRHx+Pjh07IiQkBNu2bSv1OKRYoDfUq1cP5cuXx549e0ptV2UhjR0OB/Ly8pCXl4eCggK3XsQmk8lJ6eoOer0eZ86cQUREhNegBAHXc9EV48aNQ3x8PN555x1cuXIFZrOZEsYMw6BSpUq0eBtXlWuz2ZCfn+/kTelqT8FVVf1T8CXtmxtQIe9NRkYGHj16BIvFQgvvqNVqBAYGIiIigiqnpVIptFqtx2Pb7XZMnjwZiYmJ6Nmzp8ftiJ80sSkoDRUrVkSTJk1gMBiQkpLyRJYMNpsNDx48gEgkciIHDhw4gOjo6BJFzrh4+eWXkZKS4pNHqc1mw/Lly9G+fXskJiYiODi4hGWDwWCgHsYk4Oir9/zTIF/KQjxzPUFzcnKQnZ1Nfb6JB7hOp3NKW8/Ly6Pel/n5+Rg/fjyUSiU2b97ssY3u2L5Ept8AAQAASURBVLEDubm5pXpGh4aGIiIiwitpDBSrNZs2bYoGDRqUeo3ewOfz0b59exgMBhw9erTUNqjRaLBt2zYkJiZ6zAxKS0tDt27dcP36dSxevLhUr/BDhw6BYRhUr14dQqGQFvLkBiMImazT6UrYB/yTeFJLCi64fZhAIIBCoYDJZILZbIZWq4VaraaWWefOnQNQeobKuXPnUKtWrVKDfjVq1KBFe0tDQEAAmjZt6lYkUBpYlsWjR4/A4/FQt25dj+/5nj17EBsbi5dfftnt5ykpKR4JZ1fk5ORQH+zQ0FDqdS8QCGAwGGC1WiEQCGAymaBSqZCRkeHkYfwigtTG0Gq1EIvFVCnNDXrl5eXh3r17uHHjBuLj492qyInKePz48R7nQWRMe1qFJIlowFObNJvNOHHiBEQikVcfbo1Gg19//RVdu3Z12060Wi369u0LsViMWbNmOdVCEIlE1GaPBGJSU1OpytjTeOW3pfDDDz/88MOPfx98ZXr0DMPUZVn2EgAwDPMqgMdabbAsm88wzFIAtwCMAGAC8A7LsqrHOd7zQmkLQS6RxLIsVf2S37kTe24a3vLly3Hz5k0sX74cVquVppwBxQTvb7/9BqC4ejtRCQPFZDLXh45E7UkBsZSUlBLnaLFYUFRUBIlEUmrxHXdQKpXUosLdZ4To0Wq12LRpEyQSCdq1a0dJAndYv349bt68iYULFyIkJIRe/x9//IHVq1fjvffew5tvvgmz2YzQ0FBaUIVUQxcIBJRcycjIgEgkAp/PR0JCAhQKBYxGIywWC6xWK00fLUvKdVlRWkEpbiouALc/k8k21yaAkPzE35RYdAgEAkq6BgQE4LXXXsPOnTthNpthMBjcLnJIgEMoFHpURZEU/RYtWmDt2rXYunUrLZKi1+tLFNxz1ybcwWQyQafTwW6302rlarUaQUFByMnJofdOr9dDIBDQ84iIiChxH8+dOwe73Y7mzZt7VLna7XZ6DEKQ5OTkUP9rcj/Dw8Oxc+dOvPbaa3jvvfewYcMG1K1bFyEhIZBKpU5V4bkWJLGxsdQDmfQRpI0Bxf2Ga9/wvJVU5Du550TALVREUp5FIhEtolVUVISgoCCIRCJEREQgMzMTRUVFYBgG0dHRcDgcEAqFEIlEKCwspMXjXP2tN23ahFu3bmHevHlIS0vzuIglC9K7d++WUACaTCY8evSoxD6kcCRJy1cqlWW+xxqNBo8ePYLZbEZcXBwNkqjVaty+fRvdunVDerqzzb5EIqFtq3bt2mBZFtu3b0eXLl1oarM77Nu3D+np6Zg8eTICAwNhs9koEcOyLGw2G1iWhUwmA8uysNvtThXvfSWPnwSEeC4NLMvScUgkEkEmk0Gn01HyhVgDOBwO8Pl8BAcHUxJPKBRix44dGDVqFHg8HrZs2UI/d1cscunSpYiJiUF4eLhbX3KgeMwsKipCTEwMbt++jeTkZHq/dDqd28CNL+RFVlYWrFYrbDYbHA4HJWCBYi9PtVqNhIQE3L9/n74rQHE/TXy8geL37a+//gLLsujYsaNbtd3169fx/fffQywWY+3atahdu3YJBTO3SCNQHNioW7cuDAYDzGYz9V/XarXQ6/UICwsDj8ejlk6k7yVtjpzbs2pbnuZPDofDY98EwON7TLbjzrNI8AoAbX9cX2ZCoJO2TeYkMTEx1LajsLDQacw0m824fPkyBg4ciHv37nm8PyqVClKpFImJibh8+TLq16/vFJj3VAOiQoUKpQaaXOdOhOyPiYmB0Wh0S/zb7XbcuHEDvXr1clt4qmrVqnj48CGaNm1aog2azeYS74RarUb16tUhl8uh1+thsVgQFBQEsVgMpVJJ7RwkEgkiIyMRFhYGq9VK25unAsUAnul8zBPsdjs0Gg0KCwthsVgQHx8PHo9Hs4YIOR4cHIwqVarAaDQiISHBqYg0UNz+pk6divj4eLRu3RrXr193O7bdvn2b9u137twp8/m6vv8mkwkMw1BVN7e/dDgcuHjxIoxGIzp27EgDwK4QCASYN28ejEYjxowZ49Q32u122O12vP/++7h//z6WL1+OxMREyOVymn2k1WrpvIv4hpMMSolEArvdTvt/1/eGG8DntrV/QmXuhx9++OGHH36UDl9J448ArGcYJvPv36MBeDbYLQUsy1oAHGYY5ljxr+w/nyNZCkqbzLgusrm+kmRB4wq9Xo/p06ejfv36bpXEAQEB2LNnD+rVq1dC5RYfH4+QkBD6O5lUhoWFoWrVqm69NTMzix+fN0/ikJAQqrIzmUy4fv06taSIjIz0WgimZcuWePjwIT755BMAwLx581CxYkUAcJvW9+DBA6xcuRIdOnTAu+++S6///Pnz+OKLL9C2bVuMGjUKRUVF1COPkNM2mw02mw3h4eGQyWQoKiqCTqcDj8ejKaokRY5M4jUaDS3E8izhqXI24LxYAkB/djgcyMnJQUREBPUoJNsqFAr6vAQCAcLDw2mFb5vNRkkZo9GI7t2747fffsOZM2dKLFwJyAIiISHBo4cdt6CZ2WzGunXrMHLkSLz88su4e/duifZDihgS6xV3qFKlCvR6PQoKCtC5c2dUrVoVBQUFOHjwINLS0sCyLCpWrEh9v8PDw6lHqKuf5KZNm5Cfn4+RI0eifv36qFu3rtvvLCgooMQwWZxwfbC5yqCEhATs3bsXHTt2xJtvvont27ejdu3adNFDUm6BYh9THo8HoVCIyMhIp+90ff6ufcPzXhh5+z5SpJN4LpPzJIXJSCoqUcKWK1eOFqO0WCxQKBSIiopCYGAgDUQAzrYiJpMJs2fPRq1atdCjRw9cv37dow9nmzZtkJSUBLvdXsJr89ChQx59GcnC+Pr164iPjy/hG+n6jLiQSCS4efMmbDYbBg0a5OR3vW3bNggEAgwYMMCpvwWK3wvSzzZu3BihoaE4e/Ys+vXrB6vV6rGP/f333xEVFYX+/fujoKAANpsNIpGIvjdms9mpLyP3vzRfauDJ2lZpBJ2nfbjelUKhEFKpFBqNhnqeRkREUCUpj8eDxWKBTqfD7NmzsWDBAtSpUwe//fabk+rRVUGXkpKC48ePY8KECV4LAsbGxiIuLg5qtRqXL19GWFgYHXtUKpVHpR/Z7tixY7h06RICAwOpKtdisZQoMFpYWIiWLVuifPnyOHfuHGQyGapVqwaVSgW1Wk2vJSQkxMlTduPGjcjIyMDYsWPRunXrEu10/fr1+Oabb5CQkIAtW7Z4VIJyic2ioiKcO3cOb731FoKCgihhzDAMLexF0u656eSuqsjSAp7PAgzDPNZ3kn1c/btJWw0ICACfz6d9dk5ODoDi4CPJziksLERISIhTxo1CoXC6t3fv3oXFYkGzZs28qmYTExNRoUIFiMViTJ8+HRKJxMmD2lsAhlibXbp0CQqFArVq1UJUVBQEAgFu3LiB6Ohoum1hYSEyMjIQHR2Nxo0beyy6tn79evB4PLz99ttux3iTyQSj0Yhq1aqVKJJHAuxcaDQaREREQC6X00LIkZGRkEql4PP5MJlM1G6KBB658wDXZ8wVTjxuG3hcEEsci8UCPp9Pr1UikUAmk8HhcFD7HFL/IiUlBV27dgWfz3fqf/ft24dbt25h9uzZUCgUkEqlbsc2EnCsXLmy276rWrVqUCqVsFgsuHDhAsLDw52KEHOJfZPJhFu3biE6OhrR0dFISEjAm2++ST9ftWqV09zIk4eyWq3GmjVr0KdPHzRp0sTpM5ZlsWLFCqxfvx4TJkygYzG5bq1WS4t4k1oEpK+SyWTg8/m0SDO37gN33CKfA4/vve+H7yDrshfhGLdu3fL4zickJDxxnQE//PDDDz+ePnwljSsAqAMgHsAbABoCeOLS7CzLlsxL/5eBEJQAnFKMuepC122Juu/HH39EdnY21qxZ43YAvXPnDu7fv4///Oc/pZ4HmXR5sxHIy8ujEzpvYFmWppoxDOOzJcWtW7cwYcIE8Pl8zJ07lxLG7mC32zFhwgRIpVJ899139PpzcnLQt29fREdH4z//+Q8UCgUsFgv1LCULN6lUSsk8hUKBgIAAqsrVaDRUjRwQEOBEspK//VNw1y6kUqlbywSyjWtlc4PBQIkBkmJJ/t6kSRNIJBJs27bNY+EkonwrzZ6CoH///jhw4ADmzZuH+fPnu93GF3uKzMxMqFQqNGvWjBJzwcHBeOONN/DXX38hJycHV65cQVRUlJNC1xV37tzB1q1b0bx5czRp0sRJne8NRPXkzUsvNjaWEsddu3bF2rVr0bJlS0gkEuTm5kKtVkMqlUIoFNJ2xCXzyIJUqVQ+lgXMPwFuUT+ywCOVzklRQNLWCgoKIBKJnO45wzAICQmB2WyG2WwuQTisWrUKGRkZmDVrlk8Bmxo1amDv3r0+FXTkok6dOrBYLLhx4wZEIhFq1qxZ6j5msxlXr16FxWLB4MGDnfosq9WKq1evokmTJiUIY1fweDy0aNECx44d8+oT++jRI+zduxcTJ06EUCik18dt60TpTTIvyPMhfRjw/BbZhHTzRlS7gtixEOIuJCQEFouFjn8ZGRkYNmwYTp8+jeHDh2PGjBmlpv7//vvvlARzp5x0BUm1vnnzptuApTsIBAK0adMGCQkJuHz5MkQiEcqXLw+5XA6BQEAL+tlsNhw9ehR79uxB1apVaf/MMAzCw8ORlpYGu91e4v1PSUnBb7/9hsaNG6N169ZOn7Esix9//BGzZ89Gs2bNsGDBAp+tA44fPw673Y4uXbrAYrEgMDDQqUAVN5vlfw2ufvHcQCuXRJZIJDAYDAgICKDvV3Z2NipUqOD1+MTC4tVXX8WNGzdKPZ9atWpBoVDg+PHjXgsXcsEwDKpUqQK5XI6LFy/i+PHjYBiGWlcwDEOL5xElqzd/ZYfDgfPnz6Nhw4Yeg8LJyckAUOr1A8UkpVarRWRkJPW8J/2TUCiEUChEXl4eRCIRzTpRqVRux8AXoQga8VYXCARO4goyLyBq6pycHGRkZEAmkyEnJ6fEvWJZFj///DMSEhK8Wi4BxUpjkrHkDSKRCFFRUcjMzIRWq6X3iZDVIpGIZkm5e7aHDx/Gvn370LlzZzRv3tyrx/ry5cthsVjwxRdflPjs0qVL+Pjjj9G5c2dMnjwZVquVZraFhobSgExoaCj1f+aqr4H/jmkOh8MtOfy8Ch/6UQxuvZh/+hgkiOcOfrW5H3744ceLCV9J40ksy65nGCYYQGsAPwJYhGLy+P81jEYjVKpiZw2u3xfwX5KYqDGMRiPUajV0Oh1ycnIwd+5cdOvWzWPl7t27d0MgEHgtrkFASF13ab0AqN+vQqGg6c+ugzMhVS9fvgyDwYCgoCBUqlTJpwVnamoqvv/+e4SEhOCnn37yqAgkWLNmDS5evIjZs2c7kZeDBg1Cbm4uNm7ciLi4OJpmGRERQe0pDAYDVUByCTuuSpK7UObxeE5/I/v8E2mRrhWlyc8BAQGIiopyutdkW7vdDp1ORy0SuKm8KpUKer0eSqWStoH27dtjy5Yt+M9//uOWuCQ2J8HBwT6ds0wmw8iRIzF16lRs2LDB7TaENOZaNxCQtEyVSoVXXnmlhPKFLIpjYmKQnJxMSW137c5gMGDRokWIjIzEgAEDfDp/LiQSSan+zLGxsdRz9p133sHx48cRHx9P93Mt8sJVj+t0Oqro96ZufZHALerHbZ+ELABAySeLxULV7jKZjKZHk30KCwudrESMRiPmzp2LJk2aeM1S4KJGjRrYvXs3Tp065VFJ5w4Mw6Bhw4awWCy4dOkSNBoN4uLiUK5cObfbFxUVISkpCTabDUOHDi1B0t24cQMGg8Gn/hcAWrVqhS1btuDmzZseSZ2lS5cCAN577z0YjUbodDpKXJCxghCsVqvVqXgjITqIV21ZiNzHhcFgQEFBAXQ6nc9ZGuTdIHYner0eOp0Oer0e9+7dQ58+faDX67FixQq89VbpCUssy+KPP/5A27ZtER0d7RNpHBUVhdDQUFy4cAEdO3YsUz9fsWLFEgFP1z7jjTfewMWLF3H58mVqkRQWFobAwEAaXCEZDkDx+Dtv3jzI5XJ88MEHJVL2p0+fjnnz5qFv374lvP1Lw6FDhyCVSlG7dm1kZmZCIBAgISGBPqt/WsXnbkx4WuD6xXOVwMR2w2AwUGW6RqOBTCaD3W6HVCpFWloaatWq5fX4586dQ/ny5REZGekTaSwQCNCkSRMcOnSozGR9uXLlEB4ejpycHOTm5tIgJbGvIKrxevXqubWdAor77P3796OoqMhrv0XsXXwhjY8ePQqgWN2u1+vB5/MRHx9P+y1i9ySTyVCuXDnk5eV5HANdleH/BEjbIPNzMo8kBUiJdYXFYqEqcKDkvbpz5w6uXbuGqVOnlho0v3PnTqmBR4IKFSrQ9qrVap2snng8HliWpYQ9F0lJSVi5ciVq1qxZIjPLFY8ePcKGDRvQv3//En2dVqvFgAEDEBUVhU8//ZRmKZAxyGg0UmsMkp3kblzgZsgQpTEXvlog+fF0cPfuXacsqsc9BoAnOs7TOIYffvjhhx/PH76SxkQR3AXAUpZldzIMM+UZndO/ClKplE6MXYkkQiZHRUUhICAAUqkU4eHhEIlEuHHjBnQ6HXr37u3x2ITgTUlJQZ06dbyeB0nl/+2339C8efMSn/N4POqDS85PLBZDKBRSX0CiYpPJZKhatarP3qBFRUX4888/ERYWhrlz53pUtxCYzWYsXLgQTZo0caoIf/36dRw6dAiTJk1C5cqVYTabqbcaSY0nvmtcH2DXRQghVrnnziXDXrS0ONe0c9ffSXotsa4ghbHIQkev18NgMNAU8K5du2L79u04evSoW9KtSpUq1EP0888/9+kcW7VqhSNHjmDp0qWoVq1aCRWnXC6HUqlEbm4u9bbj8/lgWRYWiwV2ux0hISFo1aqVxzZFFFQajQb5+flu1e07d+5Efn4+Jk+eXEKB7Q16vR6//vorjEajx4JmmZmZ2LJlCzZs2IDTp0+DZVlUq1aNvjMRERFufT+5RAVRvJX2DvxT4Cq9uM/BnQKe3F/uZ2QBTTxAiYUFIWZdSWOg+F5cunQJu3fvRufOnUs9x8TERFSuXBl//fUXHjx4gA4dOvhEbgDFC9FmzZpBJpPh/v371HtbJpMhKCgIwcHBlDBSq9UQiURo0KBBCcLYarVi3759iIiI8Gh94orbt2+DYRiP1hunTp3C3Llz8dZbbyEiIsJJ4U2IVcD5vnN9zcm5u+vznhW4HsW+ZmkQgkCv10MkEkGn01FFaHBwMAwGA2JiYtC+fXufzoHYyVy7dq2EV7a3fdq3b49169ZhzZo1jxVg8gY+n48GDRogISEB+/fvR0pKClVvElUggdVqxbRp03D//n188sknJayjbDYb1qxZg9deew0//fQTGIah75cvOHDgAJo1a4aYmBhYrdYS75+vIH2Dr8EIbl/iDXa7nap8nza4fRN33GQYBnq9HoWFhdDr9TRYSPpo4svrbQxhWRZnzpxB27Zty3RO9evXx969e5GUlIRXXnmlTPsKhULExMTQoPv169cREBCAwsJCFBQUIDAw0CkYQaDX63Ho0CEcOXIEer0e1apVQ+PGjd1+h06nw8yZM1G5cmWvanabzYYRI0bgt99+Q0xMDDp06ACZTAaFQoHg4GDk5ubSewmAznHJPXU3Bj5Pdam3eRVRoROfb5PJRMdEYr0QHBwMPp+PAwcOAChJchHStrQgNABaLM4XMAyDqKgoaqVjs9mQl5dHLUXMZnMJm52bN2/ip59+QlhYGD744AOvWU65ubkYNWoUZDKZW5Xxjz/+iNTUVPz666/0veLxeFAqlTAajXA4HMjKyoLRaERiYqLHvsKdoMOPfw4jRozAkSNHnvgYAJ7oOE/jGH744Ycffjx/+EoaZzAMswRAewAzGIYRA3j21Xj+BSDpu6TQDAGXTCaTaGKlQIqxAKBFzNyhf//++PPPPzFt2jSsW7fOK4HL5/Px5ZdfYsyYMZgxYwaEQqHT9nw+H9WrV4fNZsP169cBFJO3pIgKUYXJ5fJSq4lzYbPZ8Oeff8JisWDq1Kk+kWXbtm2DWq3GrFmznBRXf/zxBwQCAfr164fQ0FAwDAObzYaIiAjIZDJYLBYnItG1yJivINu+KGlxriSQ6++utiYEPB6PLiCJZQePx0OPHj3wxRdf4Ndff3VLGlevXh1vvvkm/vrrL3To0MEnUoxhGHz11VeYNm0ajh07Bj6fj5deeom2MR6Ph1atWmHz5s1U+cV9tkKhEOXLl/eJjAgLC3O7MM7Pz8fevXvRpEkTn9qo2WzG0aNHcerUKRw8eBAmkwmtWrXCG2+8UWLbefPm4bPPPgPLsqhevTomTpyI5s2bo3bt2rRSOSmeZLfbnewpuAEJHo/3QiuMPSm9XBXwnv4mlUppkSlCBAL/9Sx3zXSQSqXYvHkzBg4ciOHDh2PKlCmoXbu213Pk8/kYN24cdu3ahQMHDuDixYtITEykC+nSbD94PB5effVV1KtXj6reHjx4gOzsbKpiZxgGMTExiI2Ndau4P3r0KAoKCjB48GCf2qxKpcLKlSvxxhtvoHz58iUsU3JzczF48GAkJCTgu+++o+8qj8dDQUEBDQQRwss1wEUUXY/b5z0uXLM0fAXpw3Jzc6niWy6XIyEhAStWrED//v3xxhtvYOfOnT4RCgsWLECnTp0wduxYjB492qdz6Nu3LwoLC7Fp0yaIRCK3fcqTIjIy8v/YO+/wqIr1j39OdrMtvdMDSFMRGxbsXlFBRYoFRRQbKIiIHVFRsYK9IgrCT0HRC15FxXbtYrn2Liol9CSkb8/unt8fccazm93NpgeYz/PwkGR3z57ynjkz33nn+9KvXz9yc3OprKyktrZWivtQ93y/++67+frrr5k8eXKYv7Hgq6++orKyktGjRzcqG1fXdZYsWcJvv/3GCSecwMaNG7HZbFRVVTVp+X9jJyOMbUk8TCZTzGdtY4XqSGJNBgsrEZEdKawHxPfpuk5OTk7MAr1QlxFXVlbG4Ycf3qh9Es+mdevWNVo0jkTTNDIyMsjIyKBHjx71Xg8Gg3z00Ue8/vrruN1u9t13X0488UQKCgpiZiM/9thjbN26lXfffTduWzpjxgyWLl3KpEmTuOqqq8jNzSUUClFVVSVXSeTl5clJCmPGaaxnYFtml8brVwmB2LjSC5Bexrm5uSQlJeFyufjf//6HzWarN9ncp08fhgwZwpIlS7j00kvjnsshQ4bIfnKsmg+xEDVIYlnEbd++nddff52CggJmzJgRc9IS6gTuKVOmsGPHDp5++ul61j0bNmzg0Ucf5fTTT2f48OHSWx/CM4fjfYegLSc3FQqFQqFQtC6JisZnAsOA+3Rdr9Q0rTNwbevt1s5BKBRi48aNVFZWAuG2CEIgFu8T3rMej0f6vkH9qshGHA4HV155JTfccANvvPFGWHGyaPTp04fJkyfzyCOP0KNHD1lEzIgoVCYECCFEig5vrIFGLN588002bdrE2LFjE8oGDAaDPP300+y5555hg7FQKMSLL77I8ccfT1JSEj6fj5ycHAKBAN27d8dqtaLruqzILMSVaMJWQzTlM61JpAgk/jdm8djtdtxut4wpkeEl4kcUJBG+z2PGjGHBggVs3rw5ahxcdNFFrF69mjvvvJOlS5cmlLWbnJzMjTfeyNq1a2XhsH322UcKHZqmYTKZsFgsBAIBamtr0TRNZh03d3nyK6+8QjAY5LTTTov5ntraWr788ktWrVrFe++9h8vlIicnhzPOOINTTz2VAw88sN5+bN68mVmzZvGvf/2Lq6++moMOOgiXy8XmzZullYfb7cbn81FbW0sgEGjQg7WjEivTK1aRNePfPR6PjEefzycLBYn7EZBtoZHs7GxeeuklJk+ezMyZMznrrLOYMmVK3Hgwm82ceuqpHH/88Xz22We8//77rFu3jh9//JH99tuPQYMGNShiigzVnJwcWc29uroaXddl0aNolJeX89FHHzFo0KCEM5znz59PMBhk+vTp9V4LhUJMmjSJHTt28Mwzz5CcnCzPqd1ul5m8QkQ22hbEyqQU16MtLCqitZdiX0QxpMjXAoEAXq+X2tpafD4fNptNro449NBDmTNnDldffTVnn302y5cvb1BIGThwILfccgs33ngjvXr1SsjWQtM0Jk2ahN/vZ9myZRx66KGcfPLJjT8BCWA2m+tNmAaDQT744AM2b97M5MmTY2bZv/POO1gslqiCciy2b9/OlClTeP311znssMMYP348qampWCwWHA5HmJ9oQ3EihFSr1Up6erp87hg/E83SKdGs0XgF9mIJS8bvSzS+I5+fwmPa7XbLbNLa2lo2bdokhdh4ovHq1asBGi0ap6WlkZ+fX694Ykui6zpbt25l9uzZFBcXM2DAAE4//XT5rI+V1bp69WrefPNNrr76agYPHhxz+4sWLeKxxx5j0qRJXHDBBbKf6PV6CYVC5OTkkJKSQnZ2NklJSTL5oCMRq18l/o+26qasrIySkhIKCgqkcCyeOdH6xhMmTODSSy/lww8/jJuRPmTIEF544QW2bNmS8DMlESoqKtiwYQO9evXiuuuuiyvm+v1+pk+fztq1a3nkkUeiev7fdNNNmEwmuQJNTLSJYpqiTSgoKJAr26IhxjyRVl4KhUKhUCh2ThISjXVddwMvG37fBmxrrZ3qiERWkA+FQuzYsQOTyURmZibZ2dn1MhkEQujctGkTFRUVdOrUSVasrqmpiVmYq6qqiqFDh7J48WLmzJnDIYccIsW98vLyqAXADj74YA455BC+/PJLkpOTo2Yn6LoubR4EwppCVIyPhtVqlZYbULcc+3//+x/7778/hYWFcbOOXC4X27dvZ8aMGfz555/ce++9Ycf98ccfs3nzZtlp9fl8lJaWyteF+CCWx4mMu3gD4Xh0xGILuq5L70eHwyEzuVJTU0lPTw/LmBJesyKj3GKxyKz3HTt2MGrUKJ5++mkWLFgQdQmiELiuvfZannjiCSZOnBj2uvBRjEbfvn1JSkpizZo1eL3esIxjcRxCxAak/52u69JLMRK/3y9tIKLx/fff89FHH3HooYdSW1srPROhLkteCJrnn38+GzZsIDU1lX/9618cdthhjBgxQnoOGpd9i/N98803EwqFuOaaa+jZsycVFRWyqI8QwIRfaUlJCWazGZ/PFzezKPJ8RFvGHW35LE1cwdFQvBv3K1IAFPY0kW1XIBCgoqKCQCAgV0SI6vIej0eKr/DPZJMo1hZNtHjkkUeYNWsWy5Yto7y8nGnTptU7h06ns54/5P7778++++7LypUr2bhxI59++imff/45ffv2Zb/99sPhcODz+eIWVxH3jVHsF39zOBxs2bJF/n3VqlUAHHDAAVRVVcns5Eh0Xaempgafz8fy5csZOnQoWVlZ1NTUyHiHOkH5nXfe4a677uKII46Qnre6rpOSkkJubq7MiKyqqpLXSIht4nqI9kDc50Y7odYkWmzFetaJ10pLS9F1nczMTDp37izFh4qKCmw2G6effjpOp5Obb76Ziy66iIULF4bFQjQR6pxzzuG9997jmWeeYc8994y6tF5YEhg599xzcbvdfPHFF5hMpqg+tjU1Nfz5558xz0G8QrCBQKDeMzMUCvHrr79SXl7Oueeey0EHHVTPWsNkMlFdXc2bb77JIYccIuNJbDOaB6qu66xYsYJrr70Wt9vNzTffzHXXXYemadK+QNQsEAJpcnIy2dnZ8jpFPvtEXIls9mj2TdHE3ZbIGo2VNR8ZX5HinnG1VigUwuv11psIimxfrVYr69evl/dQeno6mzZtCutHlZaWyvbnvffeo3PnzthsNukzHOte27FjR9hKoIKCAv744w8pHBufV5F4vd6wvo6RYDBY715wuVysW7eOyspK8vLyuOiii9hrr73Cirwa/egFVVVVzJ07l169enHVVVfFtEBZvXo106ZN47jjjmP27NlUVlaG1VFIS0uT4qQQkUV7H+8+aa0+V7xnn3G/jW2piA1xjsQkXGZmppx4F4Vdv/vuO8477zx5vlwul4yRo446ivz8fJ555hkOO+wwSktL6513qJs4TU5OZs2aNVEnnKO1IUYi++tQN0G7bds2srOzmTRpEl6vN2oSivD9vummm/jqq6+47bbbGDx4sLR/E6xevZr//Oc/TJw4keTkZEpKSuR1Ngrt0WprGBH9CeHX39qTmgqFQqFQKFqfRDONd3siO7xer5dAIIDNZpMFgsSgV2TBio6p8A0WHVK73S5FY4g96O/WrRsOh4O7775bLuW94oorgLpBVawO+gMPPMCYMWOoqKjg2muvreeh+MEHH8QsVFdRURHTrqC4uJiBAwei6zrfffcdTz31FIMGDWL27Nnouh532flHH33E1KlT8fv9zJs3LyxTTNd1HnjgAVJTUzn11FOlB+L27dtlp7VXr17U1NSQmZkpO9DxMpig/jVLJOuqtYi3n9EG5MZMDbHU2LhEXNM0mX0sBAKRQZ6SkkKvXr0YPnw4S5Ys4ZZbbqmXyTdw4ED2339/vvvuO1588UUuuOCCMDElWsao4LLLLiMnJ4cnn3ySl156ifz8fC6//HK6d+/O3nvvHdOi5LPPPoua9Qx12VnxlvK+9dZb2Gw2rrnmmqh2An369OH666+nqKiIJ598klGjRmG1WsOysyMRcfzCCy9w+eWXs9dee8liSrquk56eLkVDh8OBrutYLBb5nkQHwcZl3MZ7PdryWSC+90ITaWhfjVmD4r1+vx+/3y9F44qKCvLy8khNTaWyspJQKITJZCIlJUUWs6yurpaF8qLx1FNP0blzZx599FFCoRDz58+vVxgy1vXq1asXnTp1Yv369axYsYK3336boqIizjnnHI455ph6S22NxPN5XbNmjVxS/v3337Nu3TrOPvtshg8fTnV1dcyidrW1tXTq1Inly5dTVVXFxIkTpdekGFwXFRVx6623MmzYMM477zy5ZN5sNstzLSZYRFHLlJQUeV6NGLNHRVG8tijmGW37Yl9sNlu9161Wq1zGLGIkKSmJzZs3U1VVRXp6Om63m2OOOYbLLruMxx9/nNzcXB577DG5rVhFoxYsWMAhhxzC448/zhtvvFFPbOzevTvZ2dn1PnfEEUdw9tlns3r1avbaay+GDRsW9npNTU3cGBG1B7Zt28ZTTz1Ffn4+Q4YMYeDAgfzyyy907txZvld4GJeXl3PhhRdy5ZVXRt2mz+ejvLycjRs3cvnll4fZZ4RCoXr9gpKSEqZOncp//vMfDj74YGbPnk1hYSFVVVVS9BIkJyfjdDqprKyUdhlCzImMmch7P1pbEO1vLYGwZYnE+H3RLHU0TZP74fV6pRAs/LSTkpLwer3S0zgvL4/y8nJ8Pp/0Ns/MzJQThIKUlBTMZjMul4tPP/2U0aNHy/0bNGhQTIuTYDAYFneDBg3ip59+olOnTqSlpcV87kFdNmcsb2G3283BBx8M1CUKLFq0iNWrV5OSksLkyZO57rrrombAVlRUhO2PrutccskluFwu/v3vf0e9R6CuoPHEiRPp1asXy5YtIysrS04MOhwOmckurBucTiepqalkZGREfSYm6nvdksTy7TciRE8xcWyckCsrK5P1GP766y9cLhcHH3yw7N/n5+eHTWxeeOGFzJkzB6fTSd++fWOu2urfvz9r167l6KOPrjdZutdee0XN/BVE3iNvvvkmixYtYt9992XixIkyRqKdiz333JObb76Z//73v9x+++1yNYwoCCl+vuGGG+jevTsjR44kEAiQmpoqJ1RycnKkzYzx3otGrDZE0br07NlT1nCIZGddGadQKBSKjoMSjZuIMUPG6PllXDYsRCGRRZiWlobFYpEFNiC+PYXgiCOOYNiwYTz00EOcccYZsuhULFJTU7niiiuYNWsWTz75JNdcc02LiKShUIjVq1ezfPly1qxZQ6dOnZgxYwYmk0lmvUXi8/m48847eeaZZxg0aBALFiygT58+Ye/xer2sWLGCE088MWw5amlpKW63G7PZLLP9xPmuqamJOeCMhVGka8znWhvRwY4spCgEOHFOIsU1qBNthDgjBs35+fkkJSVx5plnsmrVKl555RXOPPPMqN89a9Ys3nvvPa6++mpeeumlmIPJSDRN49JLL6VTp04sXLiQCy+8kDPOOCOmWNgcNm7cyCeffMIFF1wQVTCGOl/Qp59+mosvvjihpetQN6C66qqr6NSpE7NmzaK6upq8vDwqKipwuVxycOh2u/F4PKSlpWE2m+WSXIHRc1pcJ+PrsZZxx1g2G9vkvBUwDrCjiZSiuryYqAmFQpSXl8tMrMrKSsxms7TxiDfZAHVxM336dLp3786MGTM4/fTTee655xpVOLBXr15cc801nHXWWTz55JMsWLCAjIwMzjjjjKj2I4La2lrWrVvHX3/9JcUkt9stV0+4XC5KS0vp3Llzo2wMlixZQmFhYb0CpLquc+mllwIwc+ZMiouLpYgUicPhIDs7m1AoRHZ2dtQlvSK2RAGn9hyQG/dFYCzoJya8PB4PwWCQsrIyamtrSU9PJysrS95Tw4YNIxQKMW/ePA499NAGC9bl5uYyd+5cLrjgAmbPns3dd9+d0P6azWYuueQSdF3n6aefJjk5udEFztatW8cVV1xBdXU1gUCA//u//yMlJYV+/fpx2GGHccABB5CZmRnmYRytKK2Rt956C4ATTzwx7vuWL1/O5ZdfTnV1NXfccQcTJkyQBR3dbrd8ntntdjmplZycjMvlolu3blLkM4qvse79aM/Vxj5rm4vxedeQDYawbhFxB/8UZTMWcRSCr8h+7NSpExUVFQSDwXoi3rvvvovH42HUqFFN2v899tgDqIuZaJntjeG3337jP//5D++//z6hUIjRo0dz/vnny0LGifDqq6/yxhtvcMMNN7DXXntFfY/T6eTMM8/E7/ezdOlSOXEj7umCggJSU1Plsy1aHziSWB76rUlD32n00Ya6yZjc3FzMZjPr1q1j48aN2O12MjMzWbBgAWlpaYwYMSLm902YMIF7772XRYsWxS1s3bdvX37++WeKiooaVTdE4PV6KSsrk33wgw8+mCuuuCLmSkXBwoULeeihh7j44otl0kkkzzzzDN9//z1z586la9eudOnShcrKSvk8TEtLa9Szpq3bC0XdhE+srHtRzLG53HTTTR1iGwqFQqFoe5Ro3ERi+eIahUnRKU1OTsbv98sMMZH9AyRcJf3222/nyCOPZMaMGSxevLjB9/fs2ZPx48ezePFili9fzumnn95k4bimpoYvvviCt956ix07dtCpUycuu+wyhg4dGncG++eff+aaa67h559/5tJLL+WWW26J+v533nmHqqoqTj/9dDnotVqtmM1m0tPTqa6uJjk5mbKyMlwulxTtGjt7LiqoJ+Lf25bEKjgmBrxerzcsA9koUBpFdLEtq9WK3W7npJNOolevXjz55JMxReOMjAzuvfdeLrjgAg4//HBmz54dd+BjRNM0Ro8ezdFHH83TTz/NCy+8QGpqKsOHD2f//fdvkYkKn8/Hm2++SVZWFqeffnrU9wivvs6dO3PzzTcnvO0VK1bwxRdf8PTTTxMIBOR9GwgE5H0psmjtdjvl5eXS2884WPd4PBQXF0sBpnPnzmHX07iM29ipj7zuf5+v8GqHrUy8AbamadI6QRQCDIVCVFdXk5KSQk5ODrquU1FRIa0qIgvhxeK8884jLy+PyZMnc/zxxzN//vyY2VKx6NatG3fccQfffvstc+fOZcGCBXzwwQeMGjWKPn364PP5+Ouvv/jzzz/ZuHEjf/31l8ycEqKryOrNysqiU6dO9OnTh+HDhycsxqxZs4ZPP/2UmTNn1ov3pUuX8s477zBr1iwKCwvx+/3ymSDEeDGwTkpKwu/343a7w5ZBRxNk2rKYlJGGVmqI+yAUCsnMQ6iLeXG8ubm52O12afdSWVnJbbfdxo8//siVV17J0KFDw7J2o3H44YczefJk5s2bxyGHHJKwsGc2m7nmmmu45557mDdvHklJSRx77LEJffbXX39l+vTpWCwWFixYQOfOnfnqq6/44osv+Pjjj/nuu+8A5PNKeBjHW3IeCoV47bXX2HfffeNOBC9evJiJEydywAEHsHDhQnr16oXT6cRkMpGXl0dxcbEU9YS4k5SURG1tLVarVa4QiCwA29aCXiIZoNFoKN5FLIqsV3Gc0Yo4pqamyvNTUFAg26/ISatXXnmFrl27xvX9jYcQBd955x3cbjfZ2dnk5+c3qu/x119/sXz5cjZt2oTdbufkk0/m9NNPlysqolkWCGpra1m7di0bNmxg/fr1PPjggxx44IFMnjw56vuDwSAXX3wxP//8My+++CJdunTB6XTK/my0fmoitSES9b1uSRrq50Ur4ChixWKxkJubS1paGr/88gvvv/8+F198cVyv4C5dunDyySfz3HPPceqpp8b83l69emEymfjpp59iisaBQICNGzeyfv16iouLKSsro7y8nPLy8jDbiyOOOILLLruswaKwn332GVdffTUnnngi9957b9T77ocffuCaa67hmGOO4fDDD8disVBdXU1VVRV+v18WpVbsvEQriN1e22mpfVEoFApF26JE4xYmmkefseqwx+MhEAjw5ZdfAiRc0b2wsJAZM2Zw6623MmPGDC666KIGP3P88cfz559/8sorr/Dbb79x4YUXxl3CbUTXdX788Uc++OADfvzxR4LBIN26deOGG27gsMMOi9tZLS0t5fbbb+fll18mOzubRYsWMXLkyKjv3bFjBzfddBO9e/fm0EMPxefzyczG7OxsvF4vubm5BAIBtm3bht/vl0vjXS4XPp+P3NzcBjvP8I//ntfr7fBZEMYBr8gmFn7OJSUlchl7QUGBLFwUCAQoKysjKSlJ+jteeOGF3HzzzXzwwQcxBZLjjz+e9957j+uvv55p06ah6zonnHBCwvuanZ3N9ddfz4gRI5g9ezb//ve/+eijjzj++OPZe++9mywe//rrr7z++utUVVVx/fXXR82+DIVCzJkzh99//50XX3wxoareggULFrDnnntyxhlnyEGtMQtSiGOFhYW4XC458RM5WWG32ykoKAgT8ncW4g3qxXJ2QGZaAzKrLxgMkpubS+fOnVm/fj39+/fnjTfe4M4770zou4cPH85rr73GpEmTGDt2LIsWLWpwFUU0DjjgAC655BLWrVvHypUruf/++wHkCo+kpCT22GMPhg0bxp577km/fv3IzMyUcWm0p2gMv/zyCzfeeCPp6emMGzcu7LVNmzYxbdo0hgwZwjXXXIPL5SItLS0sroS4IDyKRWZfpD9+Ryna2dA+CVsKh8NBWlqazOQWgkNaWpr0zPV4PGRnZ8us6vnz5zNw4ECeffZZrr/++gb35ZprruGrr75i2rRpfPnll0yaNCmhAlMWi4Xrr7+eOXPm8Nhjj1FaWhpzMkrgcrm47rrrSE1N5bHHHpMxeuyxx3LssccyfPhwAoEA33zzDb/++iuHHXZYg2K02+3m+uuv57vvvmPOnDkx3/fuu+9y2WWXccQRR7B8+XJycnKkX7awusrPz8ftdssVDmJiym63U11dTXV1tbwmxuvW1oJea4jUxueimIQxPm+MwqbItLZYLPh8PrniSdgTGXE6nZSXl/Pll18yZMiQRu9XWloa++yzD1988QVffPGF/HtWVhYDBgxg//3359BDD435bKyoqGD+/PlYrVauuOIKTjzxxLjn7K+//mLx4sWsX7+eDRs2sHnz5jC/2s6dO/PQQw9F7Sfpus4VV1zBq6++yj333MMxxxyDyWSSfQrRVzJ6NovfG7L7akjwN04ktJRlmLGfF+27hahstVplxrk4hqysLDIyMqiqquLHH3/E7/dzzjnnNPidU6ZMYeXKlTz33HNMnTo16nusViv77LMPn332GSaTieHDh+Nyudi4cSO//vorr732GkVFRXJi02q1kpubS05ODv369SMvL4/c3FwKCgrYY489GjxfRUVF3HTTTQwcOJDFixfXqxUAdSsdxo0bR2ZmJrfccgsOh4Pk5GRqa2uprq4mKysrob41NH1SSNH6fP/99wBxLQTbajsttS8KhUKhaFuUaNwEYnWWjX8PhUJs2rQJs9lMVlaW7LwKQenxxx8nMzOzwQGrkcmTJ1NWVsajjz5KVVUVs2bNittx1DSNKVOmMHDgQJYuXcrMmTM56aST4grVuq7z888/8/LLL7N27VoyMzM54YQTOOyww7BarQwcODDuPq5evZrLL7+cqqoqLrvsMi677LJ6nsqCQCDAxRdfTFlZGfPmzcNisUhbj5qaGpnhqGmaFIgBOnXqRG1tLTU1NXJZfEFBQUOnL2bRnY6KcQm4sShefn6+HCTbbDbcbjdOpxOLxUJ5eTnp6emkpqZis9kYO3Ys8+fP58Ybb+TTTz+NGS/9+/fnxRdf5Nxzz+Wqq67i0UcfTTgLT7DXXnsxfvx4tm/fzjvvvMPSpUvJzMzkkEMOSXiJbiAQYMOGDXz++ef8/vvvFBQUMHbs2JjLtxcvXswHH3zArFmzGiV0l5WV8eWXX3LNNddIsUV4morBlc/nk68J+w8hOJhMprA2oDFidVvS0KA83qDe4/FQVVUl41DcNxaLhY0bNxIKhejfvz8+nw+bzcall17K5MmTWbhwIePHj09o//bZZx9WrlzJ2LFjOe+887jllls46aSTGn2cSUlJHHHEERx88MF89tlnVFRUkJSURL9+/ejdu3fCk2WJ8u6773LrrbfSuXNn/vOf/0gvY6jL2psyZQqhUEiuChH3a1pamowlMXnldrupqakhLS1NZj+KgbqYELJarWHV69uDhtpP0T6bzeYwUVzsu9VqlQUUIVzQGzBgAAcccABvvPFGQqKxxWLh+eef58477+SFF15g6dKlDBs2jDPPPJNjjjkm7metViszZsxg/vz5vPjiixQVFcX0lA0Gg7z22ms4nU4WLlwYdVJD0zR69OhBjx49GD16dIP7vn37di6//HL++OMP7r77bi688MKo7/vkk0847bTT6N+/P1dffTU+ny8s+1wU1svNzQ0T9oRgI0TUWMvX2zpjvTVEao/HIy1xHA5Hvdg09snEa2LZ/Z577smJJ57ItddeS2ZmZpg1yrx58zj77LOZMGEC99xzD2PGjGn0vt16662yNkNRURHFxcVs376db775hs8//5zXX3+dsWPHRi2y/OyzzxIIBJg6dWqDVjlVVVWce+65lJaW0rdvX/bdd19OPPFE9txzT3r16kWvXr3IycmJKuTpus6NN97IwoULmTRpEueccw5ut1sW1hV9suLiYsrLyyksLJT9uZaY2DJOJLTURH5DcSZEZfF8j9x3j8fDjh07ZHHUfv36NfidQ4YM4ayzzmLJkiUMGzasngWb4IwzzsBut/PJJ5/wySefyL+bTCZ69uzJscceS58+fejduzdZWVnymjX23DidTul3LVaAGdF1nSeffJKZM2eyzz77cM8997DHHnvITP2ysjJZw8Fms8ksZ5GpH43WuJaKlkH4WH/44Yftvp2W2heFQqFQtC1KNG4CorMcaREgBjDCW6+ioiKskIjH48HlclFSUsJ//vMfrr/++kZ1rjRNk35Qjz76KMnJydxwww0NCsdHH300+++/P8uWLeO1114jNTWVESNG0L9//7D3FhUV8c4777Blyxays7O54IILOOKII6SIJrw/oxEMBnnggQd46KGH6NWrF0uWLInpnSe44447+Pjjj5k5cyYHH3ywFOjE4M5qteL1erHb7VitVqqrq+WSybS0NAKBAJ06dUrYDzWR5ZTtRUNZOyIrRth3GD8nSEpKIjs7m9TUVLKzs3E6nXTu3JmrrrqKq666iuXLl8e0qRDf8cwzz3DGGWdw5ZVX8vTTT3PQQQc16jg0TWOfffZh77335pdffmH16tW8/fbbvP3222RnZ7PPPvuwxx57hC3hFNXgf/rpJ/766y/8fj8Wi4Vhw4bFzWp/8803WbJkCSeddJLsiCbKu+++SygUqlcQSwh6RgFCTAKJc230boX4vontnXXTnIGc8DQOBAJhRSe7deuG2WzG6/VKITAlJYWxY8fy/PPPc/vttzNy5MiEhfS8vDxWrFjBuHHjmDVrFpqmMXz48MYd6N9YLJYGRcPmoOs6ixYtYt68eQwaNIiXXnqpXvvz0EMP8cUXX7Bo0SJ69+4tB9yRVjIWiyVsGbUQOYSgHM0XHxIXaFo6BiPbz8g2K/J+EceQlJQki4s5nU4cDoe0qTCKoEOHDuW+++6jrKwsoYk9u93OHXfcwbRp01i0aBHPPfccb775JoMHD2bixIkMHTo05rPRYrEwdepUCgsLee6551izZg2nn356vQnOjz76iC1btnD77bdLn9rm8PPPP3P55Zfj8Xh48MEHY2Yxfvvtt5xxxhkUFhby+OOPyza/urpa2h1kZWVRUVGB1WqVk6jCdkWQkpIiJ8NEf6W9aA2RWtg4iXvE5/NhNpvDMpCN7bTRbz45OZmHHnqIyy67jIkTJwLItiMvL4/ly5czefJkrrrqKv7666+4z85YOBwOevfuHWa5EgqF+OKLL1i+fDn33nsv3bt358wzz5SrHd5//33++OMPxo0b12CNAF3Xufbaa9m2bRsvv/yyLGIcWQgvGlVVVUyZMoWXX36Z888/nzvuuAO73Y7f7yctLQ2v14vD4ZBxE1lMtyUm4VtjIkHEWSx/12g1JCInF3JzcykpKSE7Oztm0kMkd911F2+++SZ33XUXCxYsiHqvmc1mRo0aRb9+/di+fTtms5nCwkJMJhP7779/E462PqFQiNtuu42NGzfyyCOP0KNHj7DXa2trufbaa1m4cCGnnnoqN9xwg0xsSUpKkjZCJpMJv9/Pli1bZFHcwsLCmM/19rAiUSgUCoVC0TYo0dhArE5m5GvGKvZCkHE4HNhsNsxms1zS17VrV7Kzs+VSLyE8PfLIIyQnJzNp0iQ8Hk9M/0yn0xl1n6688kq2b9/OSy+9RG1tLVdddVW9Dmqkl2JSUhLjxo3joIMO4qmnnuKFF16gd+/eHHnkkbjdbr744gvpnXfaaadx2GGHYTabw7bj8XiiZi1VVFTw8MMP8/PPPzNq1Chmz55NSkpKmOde5JL+lStX8sgjjzBmzBjOPvts6fEcDAbDBr42mw1N0zCZTKSmplJWVobH48FsNuPxeGRRm8jzFO9aAh1u+Zwxayey0x0IBNixY4cUmMxmMzabTQpJDoeD/Px8KeABMvs4OTmZY489loEDB3LrrbcyevRoWRQpWtxpmsZTTz3FmWeeyeTJk3niiSfqZdpUVVXFFHK9Xq/chz59+tCnTx8qKyv56aef+PLLL/noo4/45JNP6NKlCxkZGWzbto3y8nKgbknv3nvvzR577EFhYSEWiyXseIz88MMPPPDAA+y3335MmjQpZkFJt9sd1ft61apV5OXl0aVLF7Zv3y4zbCoqKggEAtIbNBgMym17PB55zlNTU0lPT8dut0eNtXhLsRuKzZbEOECO9r26rse9F3JycvB6vWEiv8lkIi0tDZPJJLNnxX169913c8QRR/DQQw8xY8aMqNuM1q4lJyezaNEixo8fz4033kh1dXW9DLuqqqqoS2zhn3hvCi6XC5fLFfU1Yzz7/X7uvfde3n77bYYOHcq1115LVlZW2DLwb775hrlz5zJ69GjOOussnE4nW7ZswWq1kpqaKlegRC71FjESDAbZsWOHLCwama3bGE/UlrIDiBWvkW2WpmnYbDZpwVRaWiq9rwF5THa7Xd5XIhYcDgcjRoxg7ty5vPnmm5xyyikx90fch4KMjAymT5/OxIkTWbBgAStWrOCSSy6hsLCQKVOmyIkhp9NZ7zn5r3/9i9zcXB555BGeffZZ/vWvf8ms8bVr1/Ldd9+x1157sf/++8vM3kiE3UY0RPwUFxfz3nvvMW/ePHJycnj00Ufp3r171AKyv/76K2eddRZ5eXk8+OCDpKamyuefz+eTvup+vx+Xy0V5eblcCSDqJojjFP69QlRt70mslkLEpPBdN/YXxGRJTU0NKSkpsn0KhUIEg0EptFdWVlJTUyN98CdOnMhdd90lPf1TU1N55plnuPXWW3niiSf44YcfuO2226KKpMbCatGoqqoK+33vvfemf//+rF69mtdee43777+ffv36MXDgQF599VX69+9Pz549qa6ujukTHwgEWLhwIatWreLaa6+lf//+sh0T92AsvvnmG8477zyKioq4+uqrmTp1qpy8sVgsYX0wr9cr/xbNds14TaLFlhBko8VeLL//liLWNo3WQICMF9EWJScns2XLFulFb2THjh31BHSoO5YLLriAhx56iKVLl9azZBNFnQG5MsG4TaNncSTxJnu8Xm9Y/+jZZ5/lo48+4rLLLmPgwIFhz5rKykomTJjAhx9+yGmnncaECRPYa6+95L1RWloqLSlycnIoKyuT56mhCaf28tpXKBQKhULR+uxUorGmaQOBZKBa1/W1bfzdYT9Hq2IvCtMIWwWHw4HJZMLpdLJ582ZCoRB+v59ly5Yxbtw4unbtWq+olpH8/PyonVOAOXPm0KlTJx599FEyMjK45557wjp1sZZjDxs2jMGDB/Puu+/y3HPP8fzzz1NbW0tGRgaTJk3imGOOiZnx4Ha7621XZArX1NTwxBNPcN5550UdOBiP47fffuOKK67g4IMPZs6cOeTm5pKUlCSL+0QWERPby8/Pl1kQmqZJX0dxHYzsbANjY9ZO5L77/X5qamrqDQ5EES/xs/APFRlnaWlpFBUV8fPPPzN69Ghuv/12vvvuO4YMGUJeXl5M4TcnJ4fly5czcuRIrr76al555ZUwz1eLxSJF/kgKCwtjWoV899130rrivffeY+vWrey1116MGTOGffbZhxNOOCHmdTNaqqxZs4Y5c+bQt29fVqxYQXJycswsWuGzaqS2tpYPPviAYcOG4fF4KC8vl4W5AoEAZrNZ2jkIqxTxe1ZWlpwMiSfAGLNu2iIWY31HcwZyIrs42rm12+0yk7S2tlbG0j777MPYsWOZN28el112GV27dq33WV3Xo7Z52dnZLFu2jMsvv5x77rmH9PR0Lr74Yvm6xWKJmeXUpUuXqN8lEIPfaOy9994xPY29Xi+aprFs2TIWL17MH3/8wQ033MB1111HKBQK25+amhqmTp1Kt27dGD9+POXl5TgcDgKBAJmZmdjtdkpLSwkEAmiahsPhwGw2h93zXq9XxqAQccT1iyWYGzHGQUvFYCwhSIhSVqs1bP/FBGl5eTnBYFCeI+PkkN1ul88EITAPHjyY/Px83nrrLcaOHRtzf7p37x6zCOq1117LjBkzeO2113jssce4/vrr+e2335g9ezbHHHMMGRkZ9T5z7LHH0r17d+677z7eeecdpk6dyoABA3j++efl0u2+ffvG3J9u3brVi71gMMjXX3/NRx99xH333cevv/4K1BXxW7x4Mbm5uQSDwXpZjL///jtjx44lNTWVhx56iD59+sjno4gDkT0u4kZYFCUlJcnCqXl5efKZaFy509L+sYnSUm2gECDFpCn8M4llfI8o3Bm5nL68vJyysjJyc3NlNmVNTQ0PPfQQV155JTNnziQvLy/MquL//u//GDx4MNdffz3Tp0+P6r2ek5MTN7NXWGhEMnz4cE488UQ++ugjli9fzh9//EF2dja33XYb6enp+Hw+Bg0aFPWzP/74I3fffTdDhw6tt+IsJycnalup6zrz58/n2muvJT8/n8WLF9O1a1e8Xq8sZhrZn7LZbNTW1sqCzrH6pLEwTi7Fek43RzBuSmwZ+5UQXrS6uLhYZtjuvffe9Z5Vdrs9Zp/94osv5ttvv+WZZ55hwoQJYRnmKSkpUdsfqBON4xUAjbdKyOl0kpWVha7rvPnmmyxatIjTTz+d2267TX4v1E2CjRw5krVr1zJnzhwOPPBA7HY7O3bsoEePHtTU1OB2u+W9ZTabyc3NlcWv/X6/fJbsbP1rhUKhUCgUzWOnEY01TRsB3AZsAYo1TXtN1/VXW/M7oy3vjfxbtOW6UFcMDuoKkIhZfKvVypIlS/B4PI1eTh9JpFVFMBjkxhtvbHBJItSJD2PHjuXoo49m2bJl5OXlMXr0aBwOR72q0rEIBoPcd999PPDAA/Tt25dly5Zx6KGHNvi533//XRYeu/vuu8nNzQ0bDMcSA6BucGgUJEUGlfg5Eb/P1ii80hLEs86w2WxyQGG1WmWmFIQXAhLF8cxmMz6fD7fbLa0qjj32WObMmcOKFSsSKuzTpUsXXnjhBUaNGiW9+hLx9ouHpmn069ePfv36cemll8pBCNRlYjU0ENF1nXfffZfrr78eq9XK888/T3p6eswsv1isXr2aqqoqRo0aRW5uLsXFxYRCIbmsOTs7O8zH2OgXWltbG5a5F+uaCT/RjkYixYsSeb/f70fXdSoqKmQRt8rKSkwmE9OnT+fll19m9uzZzJ8/v1H7Z7fbefbZZ5k0aRIzZ86koqKCq6++us3v1VAoxGeffcaSJUt4++238fv9HHjggSxdujSmx+j06dPZsGEDixcvpmfPnjLDKzU1VS73DoVCdOrUidTU1LCl8uI7Q6EQ6enp9V5riGjtWmvGoMfjkc84UXzM6BsbCATIysqS3uput5ucnBz5PuEpCnVFA3fs2EHPnj0ZNmwYK1eulMJ5UzCbzYwePZoRI0Zw11138fjjj/Pjjz8yZ86cmKJN165defjhh7n77rt5+OGHcTgcpKamMnPmzIT3w+Px8NZbb/H222/z/vvvU1FRgclkYsiQIcyePZsTTjiBfv36xWzn1q1bx7Bhw9A0jX//+9906dJFtjHGfRDXNCkpSRbgNJvNWK1WysrKCAQC0jJGvE8UINzZPUcT8dEVXuyBQEBO3ANSFBWCsdfrpbS0VD43586dy7XXXiutKoRwrGka06ZNIycnh2nTpnHKKafwzDPPxCzkJOoxGH3O42Gz2TjrrLM46aSTeP311znwwAMbtERwu91cddVVZGVlMW/evITaiqqqKiZPnsyKFSsYPnw4d9xxB6mpqTidTrnSBuq3+cKPXExkNRbxmY5uWxAKhfjrr7/4/vvv6dq1K0VFRQ36SUeiaRpz5szh2GOP5cYbb+SZZ55ptX1dv349P/30E99++y1//PEHP/30E2VlZeyzzz48+OCDMrEC6oTlk046iYqKClasWMEBBxyAxWKRq3eMfSiRae1wOHC73TLZRWURKxQKhUKx+7JTiMaaph0KzAHOANYBU4CDgIREY03TJgGTgHr+XvGItrw33pJfMaBJTU2V4qbwiANYv349zz33HMOGDWPPPfdMeD9iESkcP//88xx55JGMHDmSE088scGBYadOnRotXodCIV566SUeffRR/vzzT8aOHcs999wTM/NCUF1dzZ133skjjzxCSkoKS5culf57QowTQkK0QXo08SqaTUhDHdvWKNbR1PgyEk/ME4N+j8eDz+cLO08iE1Ysw3U4HDgcDn7//XcpbA4cOBBN0zjhhBNYsWIFc+fOTWif+vTpw/PPP8/ZZ5/NcccdxwUXXMDEiRMTElFcLhfff/89nTt3jprFaTabExZjQqEQK1eu5IEHHuCXX36hR48ePP/8800ubrZq1SosFgvHHnsswWAQk8mE1+tl69atOBwOfD4fDodD3s9ms1lmOodCIex2O+np6W0yCG6J2DLS2OJFRp924a9ZU1MjY8tut+N2u6moqJDnKjs7m4suuoj58+czdepU9tlnn0bto9VqZcGCBVx55ZXce++9/Pjjj9x///2NP9gmUFlZydKlS3n++efZsGED6enpnH/++Zx33nlxi4A++eSTPPfcc1xxxRWccsopuFwuOWmTm5uLy+UiJyeHjIwMKeyKiR6jJ77T6ZSCkfG1hmhKu9aU2BLtlNVqlc+4UChEcXExBQUFpKWlkZKSElaQzel0UlZWRl5ensx+FPsrfNhdLheVlZUMHjyYZ599li+++IIjjjgioX2KhdlsZtasWRxwwAFcccUVnHnmmcyZM4fjjjsu6vMqNTWV2bNn88wzz7Bq1aqEJ2F//PFH5s6dy8qVK3E6neTm5nL88cdz/PHHc/DBByd0bjdv3sywYcPwer28+OKLZGdnU1ZWhsvlIikpKWYWYuRzIy8vT/4uXheWBTtzuyUQxyXsJozHavSkDQQCVFZW4nA4yMvLk+KXmFgRljt9+vSRWbkbNmzg/vvv5+qrr2bixInous55550nv/u4447j1VdfZcKECZx22mlMmzaNQCDA1q1b2bRpE6WlpWzdulXa5AwYMIARI0Zw9tlnJ3RPpqenM27cuAbfp+s6t9xyC+vXr+eVV15p0PcY4Pvvv+fss8+mqKiIyy67jJNOOonU1FSZkZ6WlibbGa/XK60SjAVQG1OEMzIuU1JSGpWdapwEi6SxsWW8B4yFbY2e62ISbMOGDVRWVpKWlobP54tZIDMehYWFXH311dx555288sorjBo1qtHbiMaaNWtYuXIlq1ev5qeffpLHlJyczJ577smwYcPYZ599OO200+qdtxkzZrBu3TpefPFFDjvsMLZs2YLdbper9+x2e1R7DOVTvGtw1113dZjttNS+KBQKhaJt2SlEY8ABLNB1/RcATdPeAp7UNC0PKNN1PRTvw7quPwU8BTB48OCE18FF6zDF60TF6lzbbDZMJhNr1qyhtLSUDz/8kNtvv52jjjqK/fbbr0HBNR6apnHzzTczatQoXn31VVauXMmVV15JSkoKY8aM4dxzz2XAgAFN3r6R0tJSrrzySj7++GMGDhzIwoULGTFiBEA93zeBrussW7aMm266ie3bt3POOedw7bXXMmDAANlRN4oJsTJZXC5XmDAhjl0IFJF+e7FojU5wU+PLSENinnFCQnjpQngWjxiUud1ukpOTSUtLw2KxoGkaXq+XUaNG8frrrzNhwgTuu+++hETDQYMG8fHHHzN37lwWLlzIggUL6N+/PyeddBInnHCCXLa9detWWRX+119/5ddffyUUCpGcnMyMGTNk9lZjCIVCvPPOOzz++OP8+eef7LHHHjz22GOMGTOmSfeM2+1m9uzZPPbYY5xyyin4/X40TSM/P79eUZxQKITVasXv90uRPpEM45amJWLLSGTciMF5ZJtlFAeFT7vIRioqKpKWHt26dZPLblNSUrBYLJSVlXHBBRfw0ksvMWLECF544YWEstuNJCcn8+ijj7Lffvsxa9YsDjzwQIYPH86kSZOaNJBviIqKChYsWMAzzzxDdXU1hx56KFdddRXHHXdcXNuLQCDA9ddfz6OPPsoJJ5zAjTfeGOYFqmkaHo+HkpISkpKSZFEyqH/PG69NrGKrsWhKu9aU2BKTCGazmby8PDRNi+olLQoqRRMixP0lxJuMjAzMZjMlJSUMGTIEm83GvHnzmi0aC0455RQGDBjAeeedx6RJk8jIyGDo0KGMHj2aI444Iuy8mkwmJk6cyEUXXdSgQPbzzz/z4IMP8sknn5CSksKIESMYO3YsQ4YMkdmtsbyyjaxbt47hw4dTXl7Oq6++yj777MPWrVsJBoPSKgcIEz3FvkXGUKRvsfBThrpVT40V75pKS7dbAtH26rqO0+mkuLiYvLw8aesCdeehe/fuOBwOcnJypF+tOG/iswCZmZlkZmbidDpJS0sjGAwyd+5crrvuOiZOnMj27du57rrr5PcPGDCAN954g4suuoi5c+fK50dBQQF9+/blmGOOoXPnzmiaxltvvcV9993Hgw8+yKhRo6TPdnPYuHEjN9xwA59//jmTJ0/m6KOPjvv+mpoann76ae644w6ysrJkfP3xxx9yBZywmhH3pPCnF+1RU553jZ2cjMQ4CRZJY2Mr2j1gFMbFJIPNZmPw4MH06NFDxlIi9280Jk2axMqVK5kyZQrLli1j6tSpTZ7k3rBhA0888QT/+c9/0DSNQYMGMXbsWAYOHMigQYPo0qVLTEswXdeZPXs2Tz75JKeffjr9+/eXEyaR4xXRdni9XvnsEsUjFTs3hx12WIfZTkvti0KhUCjaFq0tizI1Fk3T9jYIxRm6rldpmmYGcoHngFN1XfdomtZD1/WNiWxz8ODB+tdffx31tdY6F1VVVVRUVFBRUcFLL73Eu+++y/fffy+FqYMPPpijjjqKo446ikMOOURaD9TU1MT0j6uoqIjamdN1nW+++YaFCxfy+uuv4/P5OPTQQ7nwwgs5/vjjMZlMfPXVVzGXP1ZXV7PXXnvV+/sHH3zAzJkz8fl83H777Zx77rlhg0+/309WVlbYZ77//nuuueYaPv/8c+kJOHDgQGkzEQqFpGgslg1HZqiI4io1NTX1ROOGinjFoxmf+0bX9cGxXo8XX/EIBoMxM411XUfX9aivG2NWCICimJawWaipqZGFaB577DHmzJnDoYceypIlS8K8go1Es3woKipi1apVrFy5kh9++EH+PT8/n5KSEqBuAHLAAQcwePBg9t9/f1544QXefvttjj76aC6++OKYmUFVVVXS/iIYDPLuu+9Ksbh3795cf/31jBw5MqoPs8fjiemx7Pf7+f3331myZAkvvfQS5eXlnHLKKUybNo39998fq9Uadk5dLhc1NTWyEJ5RIBPflUjGVaz4EtcrmvUNQFJSUsz4ampsxWvXxJJ1YYkQ7e9CxBSDzJqaGpxOpxSzysvLyc3NJS0tTd7TVVVVbN68mUsvvZRNmzbxwAMPyImDqqqqmKJ/RUVFPS/OdevW8cQTT7Bs2TJqa2sZOnQoF154Ifvuu2/Y55riaVxeXs6iRYt49dVXcblcDB8+nOnTp8usYq/XG/Meqaio4JJLLuGdd95hypQpXHvttZjNZime19TUyM+WlpbKyUNh5xDZ1kXLnAyFQjL7OJHBe7x2LV7blehzUVxf4dPscDhitk3CX9dsNrNt2za6du1KcnKyvMeEyCmOV9QCeOCBB7jjjjt49dVXOeGEE+rtj5jMiEZZWVnM1zZt2sT333/PW2+9xTvvvEN1dTV77LEHl1xyCd26dYt5nQF69eolf16zZg2PPPII7777LpmZmUyYMIHp06dHzSZ1uVwxLTGCwSCbN29m+PDh+Hw+KehZrVZZnFFYUIiitKJtMlrmRJ578VwUGZZCDBTnuzVE46bGVjxitVvimIUXtljtIFZ4RRbGFW1tKBSSfv8pKSlh4rsoUFlVVYXNZqO4uJjbb7+dzz77jLVr15Kbm8v27dvl6hiRXZ+bm0tycjI1NTVRs9I3bdrEE088wXPPPUcgEODkk0/m0ksvDfPI/uqrr+r1nQQ+n499992XkpISli1bxvz58zGZTMycOZNRo0bV81YWlJaW8uyzz/LYY49RUVHBcccdxy233MLee++NzWZj+/bteL1eiouLCQaD5Ofn06tXL1JSUggGg9KSwufz1buvG3q2Ga+R8bPx4i5aIWPxfDSZTM1qt6JlGou2STzbhEWVsOlwOp2MHz+eP//8k19//TVsUrmkpCTm86u8vFxey5qaGp599lmefvppSkpK2GuvvbjkkksYOnRovf5DNE/jrVu38thjj7FixQosFgsXXXQRl156Kbm5uWHvE57GkYRCIW666Sbmz5/PGWecwT333ENmZqZcteZyudB1XU74RrYXog8UjV3F07i1+vPtgdGOJJLPPvsMaL5g2xLbaWgb8Y5jZ6Oh+FIoFIqdiQ4rGmuadiJ1lhRjdV1fE/GaFXhF1/XhmqadCxwHTNN1vUFD3tYSjePZC/h8PsrLy8MGLDt27OCzzz7j66+/5rvvvpMi8t57782qVavIz89vkmgs8Hq9UqRevHgxW7ZsoXfv3kyaNInCwsJ6HU9BpGjs9Xq57777WLp0Kf379+eZZ56JWhTIKBqXlZUxe/ZsFi5cSE5ODtOnT+fEE0/E6/WSnp5Ot27d5KDX6/VitVqxWCz1BIlIsSraz031Ou1oorFxgBNLBIj3OaCeGJOcnExZWZkUGnJzc/H5fKxcuZKLL76YLl268NJLL9GnT596243nE7xt2zb8fj///e9/Wbt2LeXl5RxwwAEccMABFBQUhIl3uq6zdOlSbr31VqxWKzfccEPUrNOqqiry8vJYsWIFL7zwgozXKVOmMHz48LAMzWj7Gikab968mRdffJHnn3+eP//8E5vNxogRI7jkkktk/Obk5NTLTjcOMIVVRSwhP5roK2joesUSa9taNI7VbjXkfRwMBuVkjhBTunfvjtlspqamhpKSEmw2G9u2bWPWrFm88847XHjhhTz44IN4vd5GicaCr7/+mpUrV/L8889TXV2N3W6nb9++snBPeno6NptNFj/s0aMHPXv2pGfPntKnUVBSUsJzzz3HihUr8Pv9nHrqqUybNq3eqoxYovG6desYO3Ys69ev5+GHH+bUU08lGAxSUVGB3W6XxyDsGmpqaigqKpL+5LEExsiYaKwHdWuLxpH7FK8okrCoKCkpYfv27eTn55OWlobNZpOTWBDelrtcLnbs2MGwYcOora3lm2++qXePNlU0LikpkQKuz+fj7bff5sknn+Tnn38mMzOTMWPGcPLJJ9e7Nn6/nx07dvDxxx/z8ccfs3btWlJTU7ngggu44IILSEpKijlhEU80/vbbbxk7diwmk4k33niDzp07y+J4oVCIjRs3YrfbZbyITGObzRazXTKuEBD+xkIoEuzsorG4T8TkixCPxYSyMSMbCPtZ9C8i210xOeNyucjKymLbtm188cUXnHvuudx6663ccMMNYaJxJLFEY8Gff/7JokWLeP7553G73ey7774MHTqUoUOHhgmNRoLBIF9++SVffvkl77//PsFgkKFDh3LrrbfSpUsXfD5fPaGxuLiYxx9/nIULF+JyuTjllFO4+OKLOeywwzCZTPI8+Hw+aTMkJkWFRYXoR4hzK15vSPxtqP/cGNHYSFOfifH6VEb7HDGZUFZWht/vp7q6mrS0ND7//HMmTpzI/fffz2WXXSa3m6hoLPB6vSxfvpxHHnmEzZs306tXLy666CK52qm6uppNmzZhtVqprq6mpqaGH3/8kWXLlgFw9tlnc80118TsA0UTjQOBANOnT+eFF17g4osv5sorryQjI0P2AYWlWyAQoGvXrvj9fux2Oy6XSyZyxPPVVqJxxyOe2HrMMccA8OGHHzbrO1piOw1tQ4nGCoVC0THpkKKxpmmnAjOAa3VdX61pmqYbdlTTNBuwBNgMHA2MFxnJDdFaonEsEUh0SK1WqxTirFYr69ato6qqirS0NHJycti6dSv//e9/ueOOO+jVqxdvvvlmWIX5SBIRjQWBQIA333yTJ598kp9++onMzExZKChSoKmurqZ///5s376ddevWce+99/Lnn39y/vnnc8kll7DHHntE/T6/3096ejqLFi3itttuo6qqinHjxjFlyhSSkpKkqBcKhcjPz8dsNkuxJxgMSk9j4yBGLHFMNAuyMQJyRxWNo8VRoqJxpJApsrO9Xi9dunShtraWzMxMKisr+eijj5g6dSrBYJAlS5Zw+OGHh223IdE4VmZvZWVl1KWSf/zxBxdddBHr169nzJgxXHrppVLc+f3333nxxRf59NNP8fv9HHTQQZxzzjkyMx6ImwVoFI1ff/11nnrqKT766CN0XWfIkCEMHz6ckSNH0rNnz7B7MSUlJepxiEFzvOzOWPe7oKNnGiciRsaLOyEaC3GrqqpKFnizWq0yK3Lbtm3U1taycOFCnn32WQ455BCeeuqpmEt144nG69evJy0tDZfLxcqVK/n999/ZunUrXq9XZhL6fD659Ni4tNhkMtG5c2e6d+9OamoqH374IaFQiJNOOomzzjpLDmQiiSYaf/zxx0yYMAFN07j99ts5/vjjZTGtUChESkoK2dnZBIPBeveiw+GQmaKxBL/WmgxrKdE48rWGRONgMCjF3JqaGilUiKxjUYxJTGR6PB4+/vhjTjnlFGbMmMEtt9wStt2WEI2N+//pp58yZ84cfvzxR+x2O8OHD+df//oXa9as4auvvuL777+XEx1iVdCoUaOkUON2uxstGn/xxReceeaZZGVl8dRTTzFw4EBCoRAmk0l6YJeWlobVRxDthbBbiNb2iHYpluAHO79oHC9DX4ieQvgzrmYS9hNlZWVkZ2dL6xjRh3A6ndhsNhwOBzU1NZSXlzNt2jR++OEH/vzzTyoqKposGldWVgJ17dvKlStZuXIlP/30E1BXdPbwww9nyJAh9OvXjx07dvDOO+/w7rvvUlpaSk5ODqeddhpjx44Ny3g3isbbtm3j/vvv57nnnqO2tpZjjjmG6667joEDB7Jx40YcDgeFhYUyfoQlkc/nIzs7O+y4jJnG0SYedjbRONZknHF/XC4XFRUVcmWR2WwmGAxy6aWX8ueff/Lzzz/LzzZWNBasX7+eL7/8kqeffprffvst5vFC3fNqzJgxXH755XTp0iWuJ3akaOz1epk4cSKrVq1i0qRJXHXVVVRWVpKVlSXvCZFtXFtbKy24RDujRON/UKJxy29HicYKhUKxc9LhRGOtrjfyHVCj6/qRmqYVAOdSZ0nxIrAeqAK+BtKAU3Rd/yPR7Q8ePFj/6quvGr1f0QbHxuVc8I/3m3GAFq3D6nK5KC8vx+/3y8GLKJz0+++/c9ZZZ9G7d29ef/31mBW44w2coa7jH+0YPvnkE+6//34++OADUlJSOP/88+natSvr169n/fr1rFu3jk2bNlFbWwvUWQ/MmzeP4447jmAwGLPzunr1aq6++mq+++47jjnmGGbNmkXXrl0JhUJYLBa6d++Oy+WiqKiInJwcmRlorOouhBRjoah4fqtG789oA+i2HhxD7E5mogOqaL6VjRFtjOdGbAvqYq6kpIT09HTMZjPV1dWUl5dzySWXsHbtWp588knOO+88ua1o8SPweDwxB8+ieFc0SkpKuO+++3jyySfZa6+9OP/881m2bBnffvstDoeD8ePHM2nSJPbee+96n/X5fHHPQXp6OgsXLmTSpEn06tWL8ePHc9JJJ6HrOn/++SdZWVn07NmTQCBAQUEB6enpWCwW/H5/vWW48cQ78ZrNZsPr9YZdp5agNcSXaDidzriiN8Ru8yIFm5qamjBBKz09nWAwyNq1a2UBuPT0dD7++GMmTZpEWloay5Yti5pxHi+2amtrY8ZWZWVl2EBe13V27NjB+vXrWbt2LRs3bmTt2rWsW7eOLVu2cPLJJ3P11VfTs2fPuNsNBoNhE3dPPfUUV1xxBf369ePhhx/GZrPRr18/6Ykp7CeAelYNZWVl2Gw2eY7iTfq1hjDT1qJxtLZp3bp1bNu2jT59+tC5c+ew1SWiSKDb7cZqtTJ+/HheeeUVvv3227AMcL/fHzNGfD5fTEHH6/XGnIT1+XysWbOGhx56iOXLl8v2r7CwkBNPPJHjjz+eY445Juo18/v92Gy2qNuNjB+A9957jzFjxtClSxceeeQROnfuTI8ePTCbzXJCKxQKSasTYTEk7JmE4BNtlYOYjIrVPhmfL41ZIdEQbSkai9fiTVaINkpk+BcWFpKRkSHFQZPJhNVqldY6VquVbdu20alTJ2k9AfDLL78watQoFi5cyOjRo2N+p8/niyvsRXtGbN68mddff51XX32VTz/9lGAwSG5urrTS+de//sX48eMZM2ZM1LjVdR2z2cz8+fOZOXMmXq+Xs88+m6lTp2K1WsnOziYvL4+ysjJZBFFM4pSWlsp+a2QmutEOS8RRojYTrUFDsRWrPx9p0wL/2FNEa5tKS0vx+/1YLBYyMzPxer2sXr2aU045haysLM4880zGjx/PgQceGLONcbvdMdsmcV/qui5XLIhJJavVSn5+PhkZGTIj2Cjaxuvri9oRUPdcHzNmDO+//z6XXHIJt99+O1arVdpx5OTkyL6U3W6nrKyMiooKfD4fvXv3lm2L3W6PagVmPLfx2FlEZSUaNw4lGjcOJRorFIpdiQ4nGgNompYKfAn8BmQA7wG9ACvw9N/Zx5OA/+m6/n1jtn3AAQfoX3/9daM7NdEGKS6Xi23btsksM1Fkw0isys2iY+Z0OiktLSU7O1sKWG+//TYXXnghPXv25K233oq6LK0porHA7XbLZdXLly+XA/ZevXrRvXt3+vTpQ69evSgsLGS//faTmVLRRONt27Yxc+ZMnn/+ebp168bMmTM555xzpBhsPG6ReZiSkiJFE+P5iCbANTRwjOWnCR1fNDbGQUMd9FhCZuQ5MIroYvk4IDOsMjIy2LZtm5yo0HWdadOm8eGHH9K/f38mTJjAueee26AdRFNEY2Ep8N///pepU6dSWlpK3759ueiiizjttNPo1q1bzO9sSDR+//33OeOMMxg6dCgvv/wydrsdXdepqqqipKQEh8NBXl4eVVVVctJCiFVms5lQKBQ3u1ucf6PHbDyRoKm0lWjckHgE4efAePyxVgMYs/6rq6v59ddf8Xg8dOvWja5du+JwOPj5558ZM2YMmzZt4sEHH6xXILGlRONI4rWVDYnGycnJfP7558ybN49ly5YxbNgw6S1aVFREz549SUtLkzYwolCXOB7jfSkmxhrKJt4VRWOos6HZsGEDPXv2lM8VIahnZWVRUVEhC1Ru2rSJk08+mf3224933nlHbqu1RGMRA0VFRXz00Ucccsgh9OvXr8FBbGNE45UrV3L22WfTr18//v3vf8tJ0tTUVCnYRBbtAqJ6+scj1jVpaLJoVxCNjQirnNzcXMxmM8nJyWzZsgWTyURSUhIVFRV06dKFqqoqmY3ZtWtXmbFuMpk48sgjSUpK4oMPPoh5zzZFNBaIlRLCa7tXr15MmDCBwsLCsLiM5Mcff2TatGl8+eWXDB06lHvvvZd99tmHUCjE5s2b0TQNm80m/fiNBStLSkrQdZ3U1FQZe8ZJalGIrq0m4+PRGNHYuIJH/B7NGz5a31G0Qzk5OVJsdrvdfPLJJyxdupSVK1fi8Xjo06cP5557LuPGjatXlDUR0TgaLpcrbhHThvr6a9as4Y033mDp0qX88ccf3HbbbUyZMoWUlBS5okNcTzEpIiagIvvpgqZmhjf02Y6EEo0bhxKNG4cSjRUKxa5E9N5NO6Bp2r6AC7Druv6TpmmHUpdx/Lyu6/f8/Z67gfOB1XpdBeVGEwwGcbvdLVIR2G63y4JuQgiIFPfEP1EJWrwvsvCEsJNISkrimGOOYfHixZx//vkMHz6cN998M66I1xQGDhzI008/zT333IOmaXJppdPpjDlIMeL3+3nssce488478fv9XH755cyePVv65omsa5HpKs6XEOjEeRHHnIgAFynAa5rWpMreHQVjhfGGjj9eNXKPx0NlZSVOp1NmdYpYFFlCmlZXCVtk0gg7EYvFIgcb//d//8fMmTO5+eabOfHEE5kwYQInn3xyXDGuKQwdOpTPP/+coqIi9t13XzRNk5ntTWH16tWMGzeOgw46iMcff1xmUGmaRkZGRtjSTTFoc7lcBAIBkpKSsFgs0ls7FkbRT/iLijjeGRH3XKKdc+Pxm81mAoFAmMe4aO9EbIqf7Xa7PF+6rjNw4EA+/fRTzj//fKZOncq3337LQw89FPfctxelpaU899xzPPfcc/z222+kpKQwdepUbr31VrnU3WQyyUkIIGzgb7wvRVFK0QZGI5FJpIa8tDsq4thSU1Pp06dPmHji9XoJhUJUVFQQCAQIBAJUV1cTCoW47LLLuPPOOzn//PO5//77Y3rxtySFhYWcd955LbrNqqoqFi1axIwZMzjwwANZtmwZnTp1wufz4XQ62b59u/SdFyKfuLdEeyZEHuMka2NjQMRnPIFqZ0DcBw1NvqSkpMjsYWHbkZycLDON3W63zDwOBAL4fD6Z6VtbW4vP5+PCCy/kqquu4uOPP45pY9NcsrKyOPvsszn77LMbfK/b7eauu+7i4YcfJisri/nz53PyySfLcwKQnJyM3+8nEAjgdDplW+T3++nWrVtYIUAhJov2SdO0sHZ9Z0KI3VAX4+K5JY7V+JyJ7FMJr1+fzyd910tKSujevTu33347EyZM4N///jdr167llltu4ZZbbuGoo45iwoQJnHnmmTEF4dagtraWDz74gFWrVvHGG2+wcWNdHfDBgwfzxBNPMGHCBMxms3y+G6+n0bZJFLxTKBQKhUKhiEeHEI01TRsGPAC8BJyqadoCXdef0DRtTyBk8DT+C7BommbSdT12Km0cjMVAYpHowDxah0vM6MM/4p7NZpOZDmIAKLKKjBWbRdYRwIknnshLL73EmWeeyahRo/jvf//bKgO9eF6xsVi3bh3jx4/nm2++Yfjw4Vx44YXstddeuN1u6YvmdDrRdZ3q6mpcLhd5eXnSJ89sNjdpMBKZhbWzD3wbMzATInA0sVJkrItBr4g74X0pqKqqkjFrs9lk8RNN07jgggs466yzKCoqYvHixbzwwgusWrWKQw45hGXLlsWs0t5UsrKyYnr/NYZvv/2Ws88+m969e7Ny5UrsdjsOhyNMVIiGUZApKSkB6rLFxKRGZFa38VqJwWaikx27AsbjNy5hjTVpk5KSEua/KQaqwt98yZIl3HzzzcyfP5/33nuPGTNmcO6557bNwcRB13U+//xz5s+fz8svv0xtbS2HHHIIDzzwAGeccQb5+flhy7Tz8/NjPi+M92V5ebkUJGKJxolMIhlFkZ1psixSoDFOVoh2XCyFt9ls1NTUsGPHDsaNG4fb7ebRRx9l1apVzJw5k4svvjjmOexobN26lXnz5vHUU0/hdDo57rjjmDNnDllZWTKOiouLKS4ulhnrYjJHvC78dtPT08M8e6HxMSAmD3d2XC5Xg5nXxnbc+DdRKBPq2vza2lq52ioQCMiJHWFbdNFFF3HPPfcwa9Ys3njjjXYV2Xbs2MHIkSNlEcVHH30Ui8XCtm3bMJvNFBQUSPsFu90uJ2TEBKnInk1KSpL9MPF3o9i6s07GR06KGJ/zov1NSkqSK/XEBLvxvWKVknjWud1ugsEgvXr1YuLEiRQWFrJmzRreffddli9fzkUXXcQPP/zA/fff3ybH+P7773PllVfy+++/Y7fbOe6447jmmmvo168fwWCQgQMHyuOMNn4RhVp3tgkBRdN56KGHOsx2WmpfFAqFQtG2tPvIS9O0XGAWcLmu6+9pmvYh8L6maUFd1+cb3ncxMBk4r6mCMRCzI2WkKQNzo9cphAuBIqtW+BSK15KTk+VyWuFt5vV6cblcpKWlMWTIEB5++GEuueQSpkyZwqJFi9o9u+yVV15h0qRJACxcuJADDzwQk8lETU0NPp8Ph8OB1+uVFcpFlrXD4ZBZUtC0ok8is1v8vLPTmIFZrIx1kW0trAKM5yUyFo3ZtWLyRAhewpvW4XAwffp0ZsyYweuvv87UqVMZMmQIK1asYPDgjrXKSlgdZGdns3z58rAsRFHMDqILcGKCRtd1mcUvzlO0rG7jtRLv29knLRLFuGzXmFEbry0SqwLEwNvofyz8Ne+66y6OP/547rnnHiZPnsw999zD1Vdfzbnnnhszuz0QCPDFF1+watUqNE3jpptuapG2wOVysWzZMlksND09nXHjxnHaaafRt29f8vPzMZlMYe2WuHfEfVVTU0NKSkqYp7G4L42+2bFIZBKpPTJFG5pEjRTnxM+RArrxf/E5sV1xzkRWuigIV1ZWxqWXXsrAgQP597//zXXXXccTTzzBHXfcwemnn97uz8NYrFmzhgceeIDnn3+eYDDIqaeeylVXXcXBBx8si4yKZ6Hf7yc5OVlOWEVm/0cTtWD3aX/iYWxnIvsRke24yKoVq5SEMCj+OZ1OKisrcTgcsv0R1+Txxx/nrLPO4oILLuCFF15o8dU3DaHrOhs2bOC0005j/fr1zJ07l0svvZSUlBRqamrw+/0Asp2Gf55ZYgIiJSWFsrIyaVkgzpnIPhXJCzsrke2U8biNqwFF1nVkkUjj6xaLRbbf4vOigFxNTQ3BYJARI0YwcuRIDj30UPr06dOqx+bxeHjppZeYP38+33zzDb169eKZZ55h1KhRcmVKcXExO3bsAOr8sgOBAP369cNkMoXdC0DUCczWrtegaD/222+/DrOdltoXhUKhULQt7S4aAzXAL8AfALquf6hp2gpglqZpTl3Xl/6dcXwqcL6u67+09g41ZWAezz4Awgd8oiNWUVFBaWkpNpuNTp06ySJJYlm3pmkcddRRTJs2jYcffhifz8esWbPo27dvcw+x0fh8Pm6++WaeeuopBg8ezNy5c+nSpYsUwtPS0vD5fFKAFEV8ALlk0ih8ivdB4sK8yJI1ihQdVTSIR1MEc6ibZBC+nJWVleTn54cJVJHnMfJvOTk5lJWVYbFY5ODK4/FIsUsMkM1mM6mpqYwbN45+/foxbtw4jj/+eJYtW8aJJ57YMiehmfzxxx+MHj0ah8PBY489RteuXYG6gaPIchf3kTjfkQXvIPpqgXhZ3eIzu2KGcSxxsKysjG3btgHISZuGKC8vp6ioiJSUFLp3747P5yMzM1NmuIlBfe/evXnxxRf573//y9NPP820adO4//77ufbaaznnnHNITk6mqqqKd999l1WrVvHf//6X8vJykpOTCQQCfPbZZ6xYsaLJg9v169czf/58lixZQmVlJYMGDeL2229n/PjxpKWl4XQ6MZvNuFwuXC4XJpNJTsbouk5JSQlut5vU1FTpTWpcMWK8B6Nlx0a2BQ21he2RKdrQJGqkIGHMWBREOzbjSgjj/VRTU8OGDRtkIS+/30/Pnj25/fbbmTRpErNmzWL8+PE88sgjzJkzh8MOO6zlDrYJeL1eysrKKC8vZ9u2bTzzzDO8+uqrWK1WzjjjDC6++GJ69epFMBiUgnBJSQl2ux2bzSZ9dFNTU3G5XGFCXrS4MP6+s9qVNBdRQ8KYIRsZX9HE9lAohN/vp6SkJEwc83g8VFRUUFNTw5YtW+jRo0dYpvfBBx/MDTfcwB133MEVV1zB448/3uTz7Xa7qa2tlb7KwsNbWNJs3ryZ33//nTVr1vDrr7/y119/sWbNGsrLy0lNTeXOO+/k7LPPlvuXmppK9+7dKSsrw+/3U1ZWRigUkm2F+D8YDErvZWNbFJnJ3pz6C+1JvHYqctJXrADxeDxh7xWCsrAdAuTE/Pbt29m4caOcnA6FQrz55psAjBgxolWOaf369SxcuJAlS5ZQXl7Onnvuyb333su4ceOoqanB5XJhtVpJTk6WBX6DwSDr1q2jqqoKk8nEgAEDok7aRdppiHbcuCJyV+zn7I7897//Beqs4dp7Oy21LwqFQqFoW9pdNNZ13adpmgWYq2nac8Aw4FfgCeByTdNWAuuBcbquO+NsqsVoysC8oSyxaIPmnJwcQqGQXC7m8XhklrGu62zdupXq6mpOP/10dF3nkUce4ZVXXsFisdC/f38GDhzIwIED2XvvvRk0aFDCgk5j2bBhAxdccAHfffcd5557LjfffDOBQEBaHJjNZkwmE506dZJLIo2CQX5+fr0szUQy72IRS6Bvqhjb1jQ0wRCLiooKqqqqZIc+HiKLz3gujN6hIqNNbEtkhAobAeGHvN9++/H2228zduxYxowZw1NPPcWYMWMae8gtyoYNGxg5ciQACxYsoE+fPui6Lov9CeuIgoICNE0Ls5NIZKLCOLmxsy7TbQqxBt1i8NwYK5vs7GzZtrlcLoqKiigoKKBnz54EAgF0Xae8vJzy8nIAhg0bxsEHH8znn3/OwoULmTp1Kvfeey89e/Zk9erVBAIBsrOzGTp0KCNHjmTo0KG8/vrrTJ48meOOO44lS5bUK0oUi1AoxPvvv8+TTz7J22+/jclk4tBDD+Wcc87hlFNOkaKCEONMJhMbN27E4/GQkZEhvTE1TZOZsTabTa6yaIy3ZVPbgrbEaB8hCjY1lEWcSLtu3K7T6ZQintPplHGRkZGBxWKRPqyDBg1i6NChLFu2jFtuuYVjjz1WZn2LSR7xs67rHHDAATz00EMcdNBBLXIuvvjiC+677z42bdok41dkDgsyMzO54oormDhxorRyqaysxOVyYbPZSElJwev1ylVFHo+H/v37k5aWJlciCSEP4sfFzmpX0lxEH824cilygi+a2A6wdu1aampq6N69O/n5+YRCIbZs2YLZbJYZpjt27CA3N1cK+KWlpZxwwgls2bKFRYsW0aVLF2666aaE93fz5s2sXLmSV199ldWrVyf0DIe6NnfAgAEcd9xxdO3alf3224+BAwdSXV2N1WqVE+dmsxmbzYbZbCYnJ0fWyjAWDHS73bJmRXp6uvyOyEnSaGJ4Y+ovtBeJJnskJSWRm5sr6z3AP31HYZ9WW1uLx+NB13VKS0vJyMhg69atbNu2jYKCAvr3709NTQ3vvvsu+++/f9wivo3F7/fzwQcf8PTTT/Puu++SlJTEyJEjOe644zjggAPYe++9qa2tpbS0FKi7fiLTXEwOdOrUSRb9M05EiXsg0iJIbAcIm0xJlN118mpn4Y477gCaL9S2xHZaal8UCoVC0ba0q2isaVqSrushXdcnaJo2BzgUsAB3//2WCYBP13V/u+2kgWgZGKICsxANor1PZHjYbLawQY3JZCIvL092smw2m1zmLKp5i6VyF1xwAcOHD+eHH37gzz//ZN26dXz44Ye88MILcnv77bcfw4cP56STTmKvvfaK2XkLBAJs27aNL774gq+++gqz2Uy3bt3o3r07WVlZ9O3bVw4qXnvtNS6//HIAbr/9dg488EC5xFMUk6mtrZWDDZPJJAdqwnvRbDZLKwBN0+p17oPBoOykRu5ztCrpsQT6nUGAgcZ5GcM/8SREu6ysLGpra+VgL5pA7vF4qKqqwuVySVsBq9WKy+UiKysLr9crhTsg7PyLTBOPx0N2djbdunVj0aJFXHXVVVx44YVs2LCBK6+8Mmp8iaXW0QgEAvj9fj755BPeeOMNfv31V8xmM2azOaxImJiECAaDsjCW8J0MBAL88ccfBAIBHnzwQdLT06moqJDHEAgEwore6LqO2WzG4/HQpUsXgsEgNptNVk+PdY8Iv0Px3kiaOjBqblXoWJ9vTqVzQbRBt67rmEwmaeEhtmX8P1rWv2jbxGvJycmyCGlycrJs17p37y6vk8PhYMSIEQwfPpyPP/6YefPmUVpayqWXXsoxxxxDv379yMzMJCMjg5qaGk4++WQWLFjA1KlTOfnkk3nxxRcZMGBA1GMTWfovvPACjz76KH/99Rf5+fmMHj2as846i+7du9OzZ0+Sk5PloDkQCMgsM4fDIe8jY+FSsTpEZDACUowQ50i839j+i3PVkQpOxYstIT5Etq+RbboQbI3icrT4EO2W2G5VVRUlJSWEQiFycnLo37+/zMSFugx3m80mt3H66aczatQoFi1aRFFRkcwYFPe9yOBctmwZRx99NJdccgnXXnttzEJ6tbW1cX2St2/fzk033cRLL71EQUEB++23H3vssQfp6elkZWWRlpZGr169SE1NJScnh5ycHDIyMqQAZSyEaLfb6dGjhyxStm3bNjkJYRTSxfvj0dhVUbuSuCNizzjBl8h5sFqtVFVVyUxTq9WKzWaTfvg+n49AICAnXY3PjhkzZlBbW8v999/Pxo0bOffcc8nJySE1NVUW2BOsXbuW1157jZUrV/Ltt98CsNdeezFlyhQyMzPlqhifz4fFYqGmpoZQKETPnj054IADKCwsxGKxyP4V1E2i6Lous5NFNrqxHRHXVYiK0UReXdel17oogiw8okV2spGO1E5FEs0fvaHne2SbJfqO6enp5OXlUVNTI8+RqD0ivI0zMjLIzs6mtLSU//3vf1x//fWy7ff5fDH3M1bfqKamho8//pjvvvuOzz//nG+++Qav10t+fj4XXnghkyZNonfv3mzbto3S0lJKSkro3LkzOTk5sn9SUlKC1+ulR48eeDwefD4fnTp1ktc+2uqPyL5jZN+8MYkXu+vklaLlKSwsjHnfFhYWsmHDhrbdIYVCoVAA7SAaa5rWH8gGvgZkuoWu69dHvO88oBuQArSoaNySg6V4nSUhvIkieJHvEWKC0YOtoqKCsrIykpOTpWXF5s2bCQaDDBgwgKysLEaMGEFmZiYpKSmUl5fzv//9j88//5yvvvqKe+65h7vvvptu3bpx8sknc8opp3D00UezadMmPvvsM1avXs2nn37KX3/9BdQNoEKhELW1tWH7npGRQZcuXfjtt98YPHgw8+bNw2QyUVRURDAYJD09nZycHCmGRBN9bTabFAK8Xm/UzmQoFGLHjh0EAoGo51DTtHrXy2QyRR0IGTu8HW1AHCmoNSZbR3xWFLoBsFgsQH1RPTJjxlgcT8SiKJJiFGHEIEGcu0AggNfrxWw2U1VVJYtR3X777cyePZuKigrmzp1bb2CRnJxcb2BUU1PDW2+9xauvvspbb71FdXU1qampDBo0SB6DsMkQ4nAwGJTisdlsltsNBoMMHjyYW2+9lW7duslBnVFgEiKeEPaKi4uprq4mJSUlLBs/WmwJhHjdmnSUzHhxDkTmbCIYvbCjtW1i8qG0tJTc3Fz69+8vBUXhfe7z+cjIyKCkpEQujxZ2BIcddhiHHnqo3B8xYaBpGiUlJRQXF5OSksIhhxzCG2+8wRlnnMHIkSN5+eWXOfzww8P2taqqiscee4xHHnmE7du3c+CBB3LHHXcwaNAgLBYL6enpDBgwQHqzWywWOnXqJEUjkclntVrDskrFftvtdiwWCzk5OXL1hIgtY/sm2mzjOW9qW9AexBIojSsa3G53WLFSqBMsjJ6iIpNfnB/hPVpbW4vP56OgoCCs8KaYkDQW+fR4PKSlpXH11VfL9kOIYOK5VF1dzbhx43j00UeZP38+K1eu5IEHHmD06NH1zqPwFI7E4/Hw4IMPMnfuXILBINdccw3Tp08nOzubv/76S2aaB4NBObEi7gdd12VmcXJyMhaLRVrheL1ecnJypChosVjCBEeTySTb+GgIoayxq6J2JnEn0ViPJpjGaltTUlLIz8/HYrHg9/uprq4mLy+P9PR0eX+L+zEUClFaWkooFCIjI4P8/HxcLhcPPfQQycnJLFq0iH//+99y20lJSeTk5JCbm0swGOSPP/4AYPDgwVx33XWccsopDBo0SArRwuO9srKS9PR01q5di8vlYsCAARQUFFBTUxMmhItjKyoqorq6WrYpIgZELIo2yiiqQ514bDKZ5HkSdirGbG1xHIn2uToC8YThWK9FPm8j+47iWSD+ZrFYsNvtsm0pKyvjxx9/JBQKcdRRR8m2Q9zrsfZn+/btbNiwgQ0bNvDTTz/x6aef8sMPP0gf9/33359Jkyax5557sv/++2O1WuWERNeuXbFarWRkZFBZWSkLHGZnZ+P3+2U7mpubi6Zp5OTkYDab5d8j+8XiHDRlEinyvR25763YuYgnCqvYUigUivajTUVjTdPGAHcBW/7+97WmaYt1Xa/WNM2k63pQ0zQbcAJwCzBa1/WKttzHxhIvA0NkL6SmpoZVaTa+Ljqmokqz6OQFAgEsFoscBIjMqYyMDNLT08nNzZXFzOx2O4cccojMhnjxxRf56quveO6555g/f37Ysvzs7GwOPvhgxo4dy4EHHsi+++6LyWTC5XKxbt06Kioq2LJlC2vXrmXz5s0MHTqUiy++WGZBFxYWUlhYKJdpi4GEMTvFKJaLrK9Y/rBisC+ykZvDruo12xiMGTORSzCNSzFFFrJYgisQ2X9CbBbZzGlpaXTt2pWlS5eSm5vLo48+yvbt21m4cCFWq1V+PhgMsmnTJv744w/WrFnDBx98wHvvvYfP5yMvL4+RI0dy5JFHMnLkSHJycuSAJRgMUlpaSk1NDWlpaWEFfjweD5s2baJ79+7k5uaG7bOIeagTa4wFJ6FO2PP7/VgsFrKyshp1Lltb1G1qZnxHyBaMbNtEFrsQXkSmF9R19NPT03E6nRQXF4ctLRfZTTt27MBms1FRUUF2djY5OTny3LtcLiwWC/n5+TLDXnhzp6en07t3b1atWsXYsWM56aSTePbZZxk5ciTbtm3jscce46mnnqK6upqhQ4cyb948TjrpJILBIGvWrMHv95OVlSX3R4h5RsQ9UVNTQ0lJibTbyc/Pl7EoslxF3IljN7ZvHTFLTxC5lD0a0QTKyBg2Fis1TkrF8p41Wsd06tQpqh2Mx+OhpKQEQE6kAjKbW1jqCIzWIgUFBSxYsIDzzz+fK6+8krPOOouTTjqJhx9+mMLCwpjHqus6//nPf5gxYwYbNmzg9NNP58477yQ9PZ1AIMCWLVukcNO/f39qa2sJBoPS1sCYSe73+/H5fHLCRGQXA+Tl5dG5c+d6BQI9Hk+jsv0SbRPao5Bia2Fsn8X9KzJvY7WtorhZUlISlZWV8tqI14zvLSkpweVyUVBQgMPhkO2VruvccccdTJ48mfLycv766y82btwYtkLM7/dz/PHHc+qpp8rMuVAoRHFxMenp6SQnJ7Np0yYyMzPJzMzE7XaTnp6Ow+GQxdfExK6YjBHXrEePHuzYsQOTyYTf7w97/rpcLkpKSsjNza0XV9FqHMA/9jAZGRkdfiKhtYhs24wZ/yIxIjU1lU2bNuH1etm+fTuffvopJpOJgw8+OGxbgUCAH374gY8//phffvmFDRs2UFRUJJM/BDabjUMOOYQJEyZw8sknM2TIEHJycqioqCAjI4OKigpsNhsmkwld19mxYwdJSUlydZhYoSUm2sQYQ1hzxTo2Iy01iaT63gqFQqFQ7Nq0mWisaVoyMBa4SNf11ZqmnUadHcX1mqbN1XW9CkDXda+maX8AR+u6vrmt9q+pGCsuR4pLRo+waMVDhKAqBsGio1pQUCA7jl6vl+7du8sCaDabjdzc3LAleUZPOpvNxoQJE5g0aRJVVVV88803fPjhh+Tn53PkkUey33774XA4KC8vl+9PSUmRgxcxUBUFnsTATGR/pqWlyaV4sYgUlKqrq9m2bRudO3eOWnRMHLuaRW48QiwwVkKHf7KuohVQEkKrKAiUkpIiB6kCo8iRlpYWJrDMnj2blJQU7r33Xr766ivGjBnDli1b+O2331izZk3YEs3CwkLOOecchg4dysknnyz/Lr5PDDRqa2txu92ykJgozCeyU3v16iUzp43Fa4wDYU3TsFgsVFRUSCuB/Px8abchMvoSpTXsToxCR1OX/HaEbMHIwp7CukAsFfd4PBQWFkrBBZCCiChSaPRNFOKrmKwwZn2azWbpkexyucjMzJQ2FKLt7devH2+88QbnnHMOZ511Fg6Hg9raWmpraxkzZgxTp06VxRKFb64YhAu7FmGT4HA4pEgM0Zd3i9htqM0SGf8iHjsqwjqkuV7+xsKSQoQWbU40uwVjUSpj8dTI94gsXuP3iefSpk2bsNlsUmyzWCx4vV7S0tJIS0vD5XLRv39/Vq5cycKFC7nvvvvYd999Oe644zjooINklrHVasVisWA2m3nllVd4/fXX2WeffVi5ciUnnXQSbrebqqoqzGYzmZmZbNq0ieTkZPx+P6mpqTJexCSWKA5bU1MTNnEsVveI5348IT5RISbRNqE9Cim2FpHts/H3eG2ryMY09s+EQFdWVobT6QwrMmuz2SgtLZWTS8FgEI/Hwz777IPf7+fYY4+VxVfF86i0tJT169cTCAQoLS0lNTVVxrCwJRF+2GazGb/fT0FBQZhgLL6rtLQ0bCVWeno6aWlp8u+R7aU4xoaus1i5JCZZ23vFS0fC2DcR/ycnJ8s6JLm5uWzevBmLxcJrr73Gpk2b+P3332UBQ7HaoEuXLvTs2ZM999yTUaNGyVUUhYWF9OvXj+3bt+NwOMjPzyctLU16pYt4S05OJiUlhcrKSrZs2UJeXh7Z2dmkpKRID/1Ei6hGY1eaRFIoFAqFQtF6tLU9RTrQF1gN/AfYAZwMnA08qWnawYBH1/Wf2ni/mkW8rJZoBSeMmbjRrBvMZjN5eXmUlpbi9/tl0TmLxSI9GUXmiSieJzJ9RSGX9PR0mYV36KGHUlNTIzOgLBYLeXl5sliKpmlSZBTLZHNzc6MWa/F6vfj9fkpLS2XGTiSRA3tR7Ccau1OhsdYgMvaM5zOWR6lYRiv88RwOB507dw67DpGiYCgUoqioiNzcXGw2GxMnTqRHjx48+OCDPPDAAxQWFrLHHntw1FFH0a9fP/baay969OhBVlaWXCIrBttpaWn14kZUrk9NTZUZi3a7nfLycvLy8uQ9YMycjjym1NRUKRB7vV5SU1Pl9oRQ2xhaw8cx8nrtrAO9SPFJTIBlZWXJdiPS7kJcI1GYR3w+NTVVTp5FXiexBHfHjh24XC6Ki4vp1KmTLHonrB/sdjt5eXm8+eabTJ06lV9//ZXevXtzww030KVLl7AidXa7nUAgQG5uLtnZ2TLDT4h4QuAV2dJG/11R7DNRjJMvrW130hzEqpXG0pAIGZkNGonI/Ezk/hSTBlarlYqKCgKBgIwbEUOBQIBQKEReXp6MPXFcZrOZ6dOnM3r0aO666y4++ugjXnvttZjHdfPNNzN9+nQ8Hk9YpqfD4cDpdNK1a1dSU1Pl38U+iIkrkWkt2mGjNYeYTI0mlBsnUhOlI7QJbY1oc0SmbeQzS5zXaCtGjJMbLpdLTj4I4d+Ykex2u6VNhfDgDoVCMtMckN7dYiIpKyuL8vJymQ2fm5sr49Hj8ZCbm4vD4ZB+7n6/n9zc3LA2IhQKUVZWJlfKRF5b0Wcz3jfx2qhYK2d25j5YIiskmrNtcd8bbUKSk5NJSkrirLPO4u233+b8888HoFu3bvTt25fTTz+dIUOGMHToUPk8NHr/i76Orut07tyZqqoqNm/ejMPhIDk5WdZTMK6+83q90m9fFPiF6AXt4mFckaDYfZg/f36H2U5L7YtCoVAo2pY2G8Xqul6radoDwOWapq3Vdf0TTdM+BboCp2ia9n/A4cALcTfUAYknLomBLvwj6hkzcdPS0mSROGNGCfzj4ymqegvBeMeOHXLZfnZ2dpiwIYq4VFRUSN9QXddlMb7Kyko0TaOgoECKchaLRXZohbAXmXkqBhx5eXmUlZXJQjGxsvDEIMRut0tvy511YNIRiLX8uKnCphBbXC4XNptNCmYQfXDp8XgoKysD6pbH5uXlMWrUKA455BDS0tKkGCgKQME/1dqFcOJwOEhLS8NisVBcXCw99wBpHZGcnExFRQUul0vGcm1tbZgNiiAYDFJWViaLTEH4slLhN2ssztjYc9TSMdsSQnRHzBb0eDw4nU5pCSDaNSPCI1R4E0ceg3FgbhQRxZJrISAHAgE5ESUEFUB+/913383WrVvp27cvaWlpuN1uWdhOZM4Lf+vs7GxMJpMU8/Ly8sLuM+ErapyQEW16rBUkRqJZwzTWeqAtiOZj2hIkkq3f0H0m7CnEtcjLy5PZkSLrTsSGx+MhGAzK7Rmzxf1+P+Xl5WRlZTFv3jzKy8vRdR273U5lZSUlJSVygspkMtG/f3/pbyqulzH7MNIn1FgwUExc2e123G63FLiNz0Xj/9HOR2M9Rjtam9DaRE7IxIojMdkk2qVIhAWOuEYi21estEpNTZWZ6EI8Tk9PJzs7W15j8Vmr1Up1dTXl5eXyWZednY3P55MrgiC8UKjVaiU5OTnMXgP+6eeJSX5xLA6HA7fbjcvlIj09XU7qGlewRBOHGzoPOyNNXSFhJFbfyu12h7Uf4v4XFjOnnnoqJSUlfPnll2RkZFBYWIjNZqOqqkpec6fTSWpqKuXl5VRVVZGWlib7R5mZmbKGg9/vx+/3k5SURL9+/fD7/WH7I7yUu3bt2qx22rgiQcRRe69aUrQ+/fv37zDbaal9USgUCkXb0tapT58A/YFzNU3TdF3/GHhe07RJQBdd1x9s4/1pEeINej0eT1hhIJFFCeGFW4yDllAoJIVkm80mvWXFMnAhnCQlJcliX2KJbkFBQVjxstzcXNLS0mRhDJ/PR3JyslxCLjJmhAhstVrlINfn88mOrRjQaJoWJqyIbAwhzkU7N7vKAKU9idW5N4oFjfXg9Xq9Yf+MBXqMEx2ALGYGyEGuyPRKTk5m8+Y6JxmjH6wQopOTk6msrJTidHFxsfT0FN57YhAt4tLlcqHruix+Fc0Pu6ysjK1bt+LxeOjRo4cU/kTGcTx7gcbQkt7G7ZXV1ZI+yJGWKAJRFE8UZYpEtCuR/uXGwbmIQaPXe0pKihRoCwsL5USD8H3Pzc3F7/djMpmkiGS326VALNrU2tpaOWkHdYKDKJymaRputxtd16murpYTIGKfxPE1VNhUxIk4x5HXuynWAx2dePdHYyZJok2wis+KlS/CJ7+2tjZM8C8tLcXpdOLz+WTmqcgQhbrrIDxnhR+/iCkxeSoyRMV1ESskogn8RgFZZPqJ+0Ks9hGZ89E8rSMFZ0XjaeoEnBD0rVarbCuMdhXGCQGoE9cyMjJkn0v8Tdd1GXN+v1/2r7Zs2UJ1dTUZGRn07dsXn88n41AUqxMIW4vk5OSwdg/+ad/y8vLQNA2Xy0VNTY3MXAVkrBvbFWC3EQKbukLCSKy+lfH6G7OO3W637G+npaUxaNAgadkmJjLKy8sxm82ySKbP5yMYDOL3+1m/fj1ZWVmyQKiw0RGTFH6/v9518/v9cqVVpKAM9dvgWG1y5IqE3XGFws5Mz549KSoqivpaPI9+saJmxIgRzfr+lthOS+2LQqFQKNqWNhWN//YrXgrowA2apg0AfEAe4GzLfWlpYi0RiywMJJbSiU5apNejGDiLvxkHL0JEhrrOsliWLcReMVC12WwEg0E5oMjOziY1NRWbzUZycjKlpaVUVFSQnp5ORkYGWVlZcrmv1WolLS2N6upqNm3aRGpqqhxIi+xBo1ApBjLiWMUS8MaKa2rwHJ9YnXtj3BkHjmJQG+u86rous5fsdrsUOGpqaqToJ+JVDLKFP6gYJIvBthAKxXJb8X5hBSAKo/n9fpKTk7FYLKSlpZGZmRkmuMA/HuEpKSn4fD5ZqTw5OTns2IV4lJ6ejslkqpdtJO4nq9VKMBiMGZeJxF1reBs3heYsxY2XUdSY5a2R2zLGpSgSJjJpIxGZ7dnZ2WHHEDk4F22ciD+Hw0FmZqYUUcT7IvdfTHiJzHOxL+K6WywWOfiGOsHGmI0u7FGEd7bwXDZm20cW/zPGltfrDfNUjXadmmI90NGJdX9EPuuMcRarKGpxcTEej4eePXuGibdiosFsNmM2m/H5fLJArHjNYrHISSYx0eRwOGSxWGEh0LlzZyk2i1jo0qWLtN4RKyrEdwuLgViI4xSTH0K0EcKk8LQ2WlVEno9o7O7PxEgx3iiUGf2D430W6mJSFIUT2yotLQ27B41thLhORj9up9OJxWIJ+76ysjI2btyI1+uVE1m5ubl07dpVWg34fD7pty2+RwjVYj/FSjBjuweEicNAWJ9xx44daJqGz+fDZDLV+yxQL26N52FXoaEVErHuMeNzNNrkg/H6Q3g/V1wXu91OZmamzAYXwnFtbS2aphEMBqV4GwwGycjIQNM0MjMzSUlJkcXuhO1JSkoKZWVlYddQIArN6roeNqEqiOfvHSvJIPIYFR2foqKiRvfXAO6//36g+UJtS2ynpfZFoVAoFG1Lm5ss6rpeoWna08CvwCWAFxiv63pxW+9LY2jqAE74eho9zSL95DRNk/9SUlJk9pP4mxGR4WTs9ApBt6amBp/PJ7cplr0lJyeTnp4uP28ccAnfPpE9LAQU4XVsMpkwm81RfWgBuYxWiINNFdd29wFyQ8Tq3BtjJDKDPVr8CERWuLAOgX+KEUbGqLCZEAKa8Hk0m82kpqaSkZEhs2pEnJeWlsplsCaTSQ5shQgMyEkKqPMb1TSNtLS0sKw8ccyR2X4ejwePx0N+fr4Umo3HajKZ5H0SSzAVYkC0TMJYomZz47Q5n2/OUlyjbYdY4pzIvkR7j9iW8Zwbl1vHEkxFFpZok4zfYRQIo3khG7ctYtBkMlFdXS2zzcSEBtQNiC0WC+np6WiaJgfiIt5E1qrT6SQvLy8s9u12uxy8G0UJYxyIdlQUABTnQ/wfS8wQcdkRaUpsGkXhyJhqaOIwMltdTBaJQXEsGx5d1+UzSoi0ULfc2zgBW15eLsV/sdrFbq8rGmu328OKYgpRNxQKhcWm0+nEZDKFvTcyi8/oLypiTUw4iOxBIWZHnrtoGEXSWOewOf2RnY1o7Xe8Z1skkc9Oo12SeNaICdP09HR5rSNXFhhfA2RRNL/fT2VlJXa7ndraWllU2Bgjxm2KFQ/GNkz8brzm4jXhey3axWAwKNtw0dZGWjDF6isYz9+uQrz7KBaRq2UaujfFJLyu67Lfk5mZKZ8XgLS5qa6uJjc3l8zMTEpLS9E0jW7dusnrKyYNhFe6WJ0AxGwr/H6/nGgTBTWNtjiRbXAiGcSNuYcUCoVCoVDs3rRLZR5d1/3AB5qmfVz3q16/4touguicxlrSLIgmuIpOb0PL44WQLJb2ioGxyMYTPwvPY0B2fI2DUyGyQV3l9wEDBuB2u8nKypLbFoVeBGIgIjIvxPcr2p7mWh/E+rwxU9CY4VlbWyszkzVNY8eOHfL9Yumu0QtWxIio2C58SMUgSiwHN+5DpLWJuBdEMRoxyRFv8BMrLhO1CjAuRW9PmrMU1+i3Gi/jOBELi1hx0lD8JdI+GAfzQFi753A4CAaDlJaWkpOTU297QhC3WCyyrRVEtqFiwC7EGuO+C6EosnhdtDiIFIp3x6ytptwfxqxskc0pPPPFtozZyMZzK9oRl8tFamqqtCIxTpYJGye/3x+2ykC8P96xCD9bMdFgrBkAsb1hjaJcIt7FsTCKpB2h3WlvmruEPrJdi8y4Fb7C6enpYVmewks9OzsbQBZZFHFgNpvp3LkzoVCI7OxsnE4nbrcbiH29jc8c0U8T1ziyXY61KkFYkgkfZ0XjaWyCgzjPxcXF5OXlkZ+fD4S3UcKWQvgY5+Xlycl0sRpCrIyA8FWIVqtVFlI0Xu9Iyxu73R61oGpkGxzZn1coFAqFQqFoDu1azl3X9WB7fn9bIAaAYkmzGChEZuTFG1xGDjSM4rAYLBsHQkIEcTqdcqms6HCKyvKioxvN91Xsn3hPRUWFFGGaK4y0pD/s7oxRXGtOtkis6xHpLyq8GEWWlljiKwqKORwOcnNz5SSD8PIURRUFwuNaiJdiQkV8T0OIe0HEtVj+KzyZI49BHFu0be9sVgENLcVNhHgCTGsUxYmMr4a263K52L59uxxgR8ZGWVkZW7Zswe12U1hYGCYkCr9HkT0ufCiFWF5SUkJ+fj5paWnY7XY6deoU9Vw0RujbXYXi5iLEOaNwV15eHmYfEu/cGq+nsKyAfyZaxXYDgQA7duyQYp4oHCYQ8Sn8jUWxRrHk3OFwyOdoU66z0eJAFDlr6LmnfEbDae4S+sh2zXhNRGFOYRkgsniN977X65UZoSLrF6L7yBoL5okYEl7HkVmponio6BdG2pTFWrVhXOkRbT8UdcSbBG3KZI64h3NycuTqK9H+iOspVvUFAgHKysqwWCz1/MzFtowFp10ul4wF476K2DX2d6KJxrH2t6XqGCgUCoVCodi9aVfReHfA2ME3ZipFeoLGG5RGDjS2bduGz+cjNzdXvsfo4yiKpYisKiHgFRcXEwqFyMjICBMBIwen0bw7ExnIJpK90VH8YXd2Wkrgi3U93G4327dvx+Px0KtXL5l9J7KI8/Pz5WSEEPiE5UVVVZX08ow1KDN6Dhs9HqMRWRk+8nOxjsHlcrFt2zbS0tIoKCiIW8xqdyGeANNYsSqaWBEtozeRODWuyBCFEFNSUqSgIhADdrPZXG9CwuFwUFpaSnV1NQ6HQ7aHwWAQp9MZliUvJi/EdxtRQnDLYhRmjZM7kR7kgUCA7du3x/ReNW4nUVJSUqSQF62dEfGp67pceWMsNCvaOGFrIrbZWG/Yxjz3xD2qhJ6WIVa7Fq1IYWQGfKdOnWQWKPyTHRxZEFOIg0IgNsZQaWkpbrdbTlhFZhKLLHjxLGvI6su4ussofEd77+5MvD5S5HMwEeFdtFlerxen0ylFf6/XS35+vvQpFvZdIitY1Isw1lTweDxUVlZKe6RYE9ji90T6SY05foVCoVAoFIrGoETjVkaIsrH8GSEx+wnjQEOIHUZPPoEoJqRpGp06dZJLGEtKSgiFQvVEGIHISojMhhH7E2kTEI1EsjeUhUXLYJyMaE6mUazr4XA45MSCGBgb7VWixYPRN1cMcBoagEHdMtt4yyjFANqYMW9c5hmZtRj5WajLMFQDp/g0NqMvmrAR+bdE73fjioxevXrJay0ysQRms5nCwkIZ75H73717d2w2m4xFl8slBZ5EYqAp95LK9IuPiAmjcB8pjiQlJYVdu2ge+pGx1alTpwZtAMTzzijaRJuEEm2IsQ3zer1SmDNm+EUKfIlcc/Xcaz9itWvR+jlG73Pj9U1LS5N1JMSkv3H1mNHSxjixaXx2GYmWSRy5osz4fyyiCd+KOhozCZrIpI6YLLJarWEWOsIuRGxHPGtEG2Ysoic+Z7fbcTqd+P1+SktLycvLi+tDHRk/iaBWLCgEzz33XIfZTkvti0KhUCjaFiUadwBidVhFh9Q4qElKSqKgoCBsSaMRo0+aceAhPNhEBlMoFKK4uBin00mnTp3QNI2amhpZJK8pAkhDGXpKXGk5jNlGzckoiSaAiAkEYWMispjS0tISGoBExkHkEnAjkYPnaDFinFyJdpyx4i4lJYXevXuHbUPRckQTNiL/lmjWbrQVGfDPkmCjuCIyP4W3scjsMhZqdDqdpKWlkZaWltAkhiCWX2081OqJ+BiFWfE8i4bwiI23HeGF3pCIErk6xzjpFFlfwFh8T6ycMFpAiQKJUL84mthGQ6js9Y5HtGsi/hbp/S6uu9VqDVvFJWpEGCcujc9UQNpWRE7uR642ixSxE4mXaMJ3LBL1rN9VaMwkqLFtCQaDUe2ujLUZxLPBeG09Hg9+v59AIEBBQYHs84RCIVJSUsJWSCQl1RV3FVnikatmImnKMyYyhna366/4h+7du3eY7bTUvigUCoWibVGicRthzKSMzCiJlVViXCopBtwiW8pisdRb8gbICtvG7432N+HJJgY2xmJnkYMZI83pbCpxpXWIFT8NVQ+PluGUkpIiK8kD5ObmRrUgEJ8z/t3tdlNTU1PP8iTedY8cPEfeG+I7hC2GGJAbj81YjMaI2A/xWrTzoQZPsYk3ySOuRSzRpbEYJ0FitZXGtq6srIxt27YBddlbIl4BmXlnLNppbN8ijzGaQGA8zlhE+udGxmW82GqoQFF7xGW8fWrK/hjjJzLrLdY9Gw9RKDPSCz2ycJR4bzTv18gM0ch9dTgcuN1uSkpKACgoKAjzzY60bhLXvCnnp6mfUzSfWLEu4lIIfDabDV3XpYe2iKn09HR8Pl9Y22S0IRB9t9TUVFnEzOfzyUnTyO1HKwIr9rMpRUmjoQos/kNkTYhE2haBcfJJ9Mk1TcNms8lrXVFRISc8Q6EQaWlpsh6EQNM08vLyZBsZr/1tiZUKyq5i9+XFF18EYOzYse2+nZbaF4VCoVC0LUo0biEaM/gzDqBFhzVaJ95ms+F0OsMGIVDX4fP7/fXENaPvWbwBuRBoRCGo1NRUuQQzGAxKYS7aMcU6zkSOP/K4FYkR71yJ1xo7CBRxB/WvizHuxPuM7xeZnUIgFgMQ8fnI6yuKkoVCISk2Gl+L/Kz4PVYBo0jiFYhrieJxHYmmHEtTxcuGfDXb4rwaB8vG7xR+7rm5ufXiOHKyAP45zkgh3NiupqamyrYwVhsVWSBSxJcahNcnXvyI89bY1SeRWcuR1ynWM0tMSBg92I14vd6w9ixyZY6gsc+wRO4TYxZrohnxuwPNaV+a+lkRlyaTKazgndPpJBgMSkswo5gofjY+r4ztlpiINfapjP839IyKlSHa2DZ4d7MriHduorVNkdY0se5xTdOksGxcbWAymcjJyaGsrIzs7Gz8fr+0KBHif+T2En12NLX+QnPaLsWuw7x584DmC7UtsZ2W2heFQqFQtC1KNG5jGrNEzOv1hmXCQXgBMbFMW3SARfZDpPdrNJKSkkhPT4/699YSQHbHwmM7A5HXRcSdx+ORhaHgn2W6IntGLNE1bieWL5/I4on8LvG7MUMwchC3uwxyOxodwYfVuNTbaG9gMpmkDQ+Ei3vx2phYnsvGgqQNfb64uBiAzp07N6ut3NWXC7dU8VQjxjbGmAHsdrsTEntiXdtIQSWWNUkiIk9jr2vk89t4bIr2R9iRZGRkyD6TuL5GYdn4TIx8FkZbYWMshBZp22SkpTJEVf/rHyLbpsh7NtZ51nVdWkqkp6fXW7Hg8/kIhUL4/X65DWPfJ5FisK31PFDXX6FQKBQKRVNRonEb05gBQDyPO9E5jSy8Iv4e+R3KT1iRKMYl5JFFdiJFs0RjqSkCcEtncKp7oHF0hAzaUCgkB+lms7nZolosz+VEB+nRPOObyq6+XDiR+ElkYqKh+1acR2Ohvcaez5aM9cZe18jnt/Jf7xgYM8AzMzNlpnCk3zHUPRedTifp6ekJZ8w7nU7pZxtPzFOTpy1P5P2e6D0rJtHNZnO9Iq2A9DCOTN6AxIvBNrQPCoVCoVAoFG2NEo3bmMYMAOJlJhgLrxjfb7fbcblcYVmgRuEFGt8hNQ7a42XEKDo2iWayiLiLlrHeVNFMDNKam0GTyDEY4xWQ3oXKU7tlCAQClJWVkZubi9ncuo8Qj8cjB+nZ2dmUl5eHFRRqLM1dSSFsfIw0NUOso4lB7ZH5bGxrRMHDSCGmofs22rLy1iDR89PY62qMSTEx4nK5otpoKFqHaBYhIu7S09PD4i7a9Y0s0hhZzDNy0kMUQjM+p2LRESbvdnWiXdNo97uxrYk2kSV+NhZTjWzjYrUfHe15oFB0NAoLC+M+ewsLC9mwYUPb7ZBCoVDsRijRuI1pqQGAcaBpzHwB6mW8GIWXpmQxGQftannbzktjM1liFTozFhhraxI5BmO8AlF9JhVNp6ysjK1btwJIX/TWwnjNxDJxr9fboUSUpmaIdTQxqD0z3eIJww3dt8bz2Jr7nej5ae51Vc/btieaRUisuIt2fSMtmIyvx4rt1rQCUzSOaNc02v0u/PKb0lY2NPnV0Z4HCkVHoyFBeFe0+VIoFIqOghKNdwGiDW6i/ZzIsvxoWTFKbNs12BUyWWJ5ETYUr9EsXhTRiawsH0lOTg7wTzG61sR4zTpqO7Qr3FfQvscReW0jn0Md4b5tq/MT6bOtaH2MmcLG1TWNibtY7VNHbbcU8Yl3v8e7pikpKXTu3DnsNaPntbqvFW3N8uXLO8x2WmpfFAqFQtG2KNG4hRCFvNqDhoquRL4eb1+jZUN0lEG7onm0RSaLqBLeWsTzIhSDMZENJPZHxW7jaCiLymw2k5+fj6ZpMduSpsZAQ+1oR7yWu0qGWEMFoOJ9rrnEy86MJrK0R0ZRW13nxvpsKxpHtFgWmcLxfIkberbF6iep/tPOh4gRYz/C+Fq8axrtNaPntbqvFbHo2bMnRUVFMV8vLCxs0nZbaoK/JbbTFskGCoVCoWh5lGjcRjSloxjvM63V8YysIq/YeWnO9Wvr2GvqNhuKV03TVBzHoSFvxbY+dw3FXVP3p60/117bbQ6x9qmpE6LNaUMSua9bkvYSoRVtT6zz3px2r7WeMypGWofm9o1ifT6R2GqNfVLsGhQVFbVKAtLixYsBOP/889t9Oy21LwqFQqFoW3a6Kiuaph2hadox7b0fuxq6ruNyuYA6H0VVgEexM2DMyhMx3J5Z/7sCwre6PdoAdQ0VELtwpogP4TurULQUIuYa2+6pmFQ0hOqnKNqTxYsXS7G2vbfTUvuiUCgUirZlp1EGtTrygaeBJZqmHW94rcMex84yoBBL0t1ud3vvimIXoa0HRyqG25/mtnfqGnY8OpLIoeJD0ZK0RGyrmNx1aY22T8WLQqFQKBSKnY2dxp5Cr+u1lWiatgRIAe7TNG2Wruuv6roeV6HQNG0SMAmgR48erb+zBoz+oB25EvquUsypPWjP+OrINKXCeHPYFWN4Z4ut5rZ3u+I17KgkGlttfR/HQ8XHzsHO0m61RGyrmGxb2jK2WqPtU/GiUCgUCoViZ6PDZuhGomlasla3VtUG/AHMBG7RNO16TdOu+fs9UY9H1/WndF0frOv64Ly8vLbbaeo6hunp6R2+gxhrObCiYdozvjoybR37u2IM72yx1dxrvitew45KorHVkZ5hTbUQULQtO0u71RKxrWKybWnL2GqNtk894xQKhUKhUOxsdPherqZpnQF0Xa/9O9v4ZaCTrutvAK8DtwHpf7+nw3lAqAGFYndFDY52P1R7t+uh7mPFroqKbUU8VHwoFAqFQqFQdHB7Ck3TjgSe1DTtcV3Xn/j7zyZgD03TRgJjgYeBczVN+0zX9bfaa18VCoVCoVAoFAqFQqHoCKxatarDbKel9kWhUCgUbUuHFo2BGqAc6Ktp2tW6rt+v6/rXmqadAywGLtJ1/WVN0z6jzrKi3diZMhGauq8NFQPZmc6Boul0tDhQcdf2tNY5V21Tx6M9zp2KA0VHQ8XO7oO61opdiZayV2mJ7bSmzVVhYWHMe7ewsJANGza02ncrFArFrk6HFY3/9i/2AE7gW+BQTdPOBz79+/f/03X9e03TNF3XX22/PVUoFAqFQqFQKBQKhSI6PXv2pKioKOprhYWFrfKdTzxRt1B3ypQp7b6dltqXaMQThdVEkEKhUDSPDms8qdexBlgNfAm8AJwBvAb8+LdgbGrPfVQoFAqFQqFQKBQKhSIeRUVF6Loe9V9rZcK+9NJLvPTSSx1iOy21LwqFQqFoWzqUaKxp2r6apvXRNG2A4c97AJ2oy4o+ANgAHAag63pQb2g9qkKhUCgUCoVCoVAoFAqFQqFQKBKmw4jGmqYNoy6beDzwvKZpk/5+aSFwIfAscCXwANBH07Ts5nzfhRdeSH5+PgMHDmzOZiQmk4n99tuP/fbbj1NPPbVFtqlQKBQKhUKhUCgUCoVCoVAoFG1Nh/A01jQtF5gFXK7r+nuapn0IvK9pWi3wFpABXKrr+ipN01KAL3Vdr27Od55//vlMnTqV8847r7m7D4Ddbuf7779vkW0pFAqFQqFQKBQKhWLnoT18ixXxUUXyFAqFonl0lEzjGuAX4A8AXdc/BFYAdwIH6Lo++m/BOEnXdVdzBWOAo446iuzs8GTltWvXMmzYMA488ECOPPJIfv/99+Z+zS6Pruu4XK4Gq9Yrdi3UdVfsDIg4DYVC7b0rijZGtVGKaKi4UHQUVCzumrSHb7EiPhs2bIh5TWIJ/AqFQqH4B62jdFY0Tfs/wAI8BwwDKoCPgcuAi4AqXdebPfLXNK0UEE8IC9CXOsEaoN/fr/mAFKArfwvZCXAg4AZ0YDtQ+fffc4Edzd3vZtDa358EmIAgEOv67ErnoFDX9bxYL0bEV1vS1uc41nVv72st2Fn3I2Z8tWNsCXbGc5pI+9QW+9GaJLofHTm2otHc89ta176jXPdI2nO/Wiu2WuOYEo2L9r7O7fn9Hem7GxNbO9s1a402qr3Pwc60Dy3Zn+8IxxxJR9unjrY/0Lr7FDe+FAqFYmei3UXjv7OHQ3//PIc6wTYfmP73W+YBl+i67m+F7+4JvK7r+kBN01KBUmCN4S1WXdf31DRtDDA7yia26Lp+4t/b6qrr+hZN03oD7wPH6bq+VtO0r3VdH9zS+54o7f39HWEf2vv724KOcoxqPzrmfrQEHeVY1H50zP1oaTrqcan9ajva85ja+3zursfenO/ena+Z2of23YeOcMyRdLR96mj7Ax1znxQKhaIj0i6expqm9Qeyga8xzK7run59xPvOA7pTl/Xb4qJxBElApa7r+0W+oOv6y8DL8T6s6/qWv/9f97cn8/7A2pbfTYVCoVAoFAqFQqFQKBQKhUKhaD3a3NP476zdV4E7gIXAZZqmpf/9munv/22app0K3AJcpet6RWvv198+yes1TTvj733QNE3bN5HPapqWpWma9e+fc4HDgV9bbWcVCoVCoVAoFAqFQqFQKBQKhaKVaFPRWNO0ZGAscJGu68dRJx53B67XNC1D1/UggK7rXuq8hI/Wdf3HVtqXF4DPgf6apm3WNO0i4BzgIk3TfqDO53hkgpvbE/j67899ANyj67oQjZ9q4V1vLO39/dD++9De398WdJRjVPsRTkfZj5agoxyL2o9wOsp+tDQd9bjUfrUd7XlM7X0+d9djb853787XTKD2oY623oeOcMyRdLR96mj7Ax1znxQKhaLD0aaexn+LxiuBF3VdX6xpWhJwJHAysE7X9Sc1TTsY8Oi6/lOb7ZhCoVAoFAqFQqFQKBQKhUKhUCiANs401nW9FngAGKNp2pF/F8D7FPgeOErTNDt11g6lbblfCoVCoVAoFAqFQqFQKBQKhUKhqKNNM42hzq8YuBgYBCzRdf3jv//+IXW2Fa1aPC43N1fv2bNna35FhyMUChEMBjGZTCQltbmN9S7FN998s0PX9bxYr++O8dXW7MrxHC++VGyFsyvHQWugYusfVOy0LCq2moeKx9io2KqPipeWQfXn46PirHnszm3X2rV1Us4ee+zRIbbT0ttqbxpquxSK1sDc1l+o67pX07SlgA7coGnaAMAH5AHO1v7+nj178vXXX7f217Q7xskAXddxu904HA40TQOQ/7fH/kSjrfenqWiaVhTv9d0lvlqaxkxedYR4bi3ixdfuEFvNiYNdJQZai90hthKNn125DWkPdofYag4NxWW0eBTs7nGpYqt+/CT67NtV+t2therPxycYDOLxeLDb7VFF4909fhpCtV2K1qKhtkuhaA3aXDQG0HW9QtO0p4FfgUsALzBe1/Xi9tifXR1N00hJSWnv3VAoWgQVzwpQcaBoOip2FB0JFY+KxqDiRdEWJCUlqThTKBQKBdBOojGArut+4ANN0z6u+1UPtde+KBQKhUKhUCgUCoVCoVAomsYNN9wAwN13390httPS21IodkfaTTQW6LoebO99ULQN8ZZgKhQqPhRGVDwoWgoVS4r2IhQKqdhTtCuq/VM0l1AoFNeqQqEw8vnnn3eo7bT0thSK3RHV8ivaDLfbTXV1NW63u713RdEBUfGhMKLiQdFSqFhStBcq9hTtjYpBRXPxeDxUV1fj8Xjae1cUCoVC0Q60e6axYvfB4XCE/a9QGFHxoTCi4kHRUqhYUrQXKvYU7Y2KQUVzsdvtYf8rFAqFYvdCicYdmEQrwbf0tptSiVnX9QaXvaniHS1Pc65JU5cpNrUid0P7Cv8MaiLfq5ZUdiya2zYlcj0b2140pU1r6HMNoeIyNh3pvCYSS635vI1FW8dPU9vuXYnWeH41tL14sZdI36mxqOvcPrRHG5IILfksFdtTdBxaK+6MbVNjiuK15n2gYk+hUCjaByUa78a0pc+ZetC3DzuTD1lrCNyKnYvWEndbAuUL2fFprYkwIztTm6rYeRFx5nA4VJwpmoV6dimg8c8uTdNUvCiaRLdu3TrUdlp6WwrF7ogSjXdjhM8ZoDKAd1GEDxmoa6xQNAfVXipAtamKtsEYZ6mpqe28N4qdGfXsUoB6dinajiVLlnSo7bT0thSK3ZGdVjTWNE3T2zv1bCdH+Zzt+igfMoWiZVDtpQJUm6poG0R8qfZG0VzUs0sB6tmlUCgUiqaz06150zQtFUAJxs1H+Jw1Z/lRKBTC5XK1+9JxRXSED1lHXN4qYicUCrX3riiaye5wLVuivVR0XHRdT+hZ1pHbVEXHQ8RVY9tGFWcKQXOfr+rZpYCO1absDn3G3Znp06czffr0DrOdlt6WQrE70v5PjkagadqpwEJN05ZpmnaSpmk92nufdjYSHRjHIvJBL5Y7eTyeqO+tqamhpqZGdQxameZe15bch0SvdWTsiNgKBoOqM7mT4fF4qKyspLS0VF631hwUtGa8d4R7aVdgZzuPYgm32+2u91pLxXJHOicdaV/ai7Y4ByKuIvtIkRPurS2iqOvd/jR0DaK9rus6paWlVFZWRu1ntyVNnQBRdBzaU6w1xk+s/n/kfql2a+fk+++/5/vvv+8w22npbSkUuyM7jT2Fpmn9gHnAmcBg4HDgBE3T5um6vqaBz04CJgH06LFz6szxClk0pshFc73NIj2x4i138ng8FBcXA3Uz3LuqL19HiK9ErmtrF3Ay7kMi19putxMKheQ/r9dLdXU1TqdTdhx3d9+1jhBbiWC323E6nQQCATweDykpKfXaCmP8mUymZn1fQ/HenMI/u4v/Y2vHVmPOY0co1BRvCXdjvSBjHU9Hiq3W3Jedpd1qyjlobKyKeDL2kUKhEKWlpQQCAfndre032tjnc0dlZ4ktIyJmQqEQTqcTiH6No8Wj2+0mEAiQlJQk+0rtlSm6q8RQPHbG+IokXhsVr51p7eewMX4ix46x9qsjPTMVbUfPnj0pKiqSvxvjsbCwkA0bNrTDXikUuzc7jWgM2IBPdV1fDazWNO0A4CTgUk3T7td1fXOsD+q6/hTwFMDgwYM71HRlorOn8R6cjXmoNuRpFW9/QqFQ2Od1XUfTNBwOB7qu1/us3W6noKAAQL6nLWkr8aEjxFc0wSPyfBs7ZQ11CuNdq1ivGT0Yo70nFAqFfaemaSQlJVFdXU1SUpL8vM1mw+v1hsVoQ/sT61jivSb2oSMTGVuxzkNTj6M55zXy+/Py8qQoDPEHBbEGnInuT+S2I/c1sk1szHHuLr5/ibZbDbXbsWKkMT6azbleLYVYwh2NyOde5P5EijixnskdKbZa0+e0JZ6JTY27hj4brx1JhGjXNl5Mij6Scb88Hg+BQACz2RyzvWzoOBqL8Xo3Z7vt/cxs7XarNRAxk5qaSnp6er14E/ETLQbEz0JwTkpKapF7tiltbEN9PEF7x0hzaO3+fGv1ZY3Ee55GPsuMz6+Weg43FD92u71euxirLU70OdWR7ndF8ykqKkLXdY455hgAPvzwQ/maupYKRfuwM4nGvwN9NE2brOv6PF3Xv9XqWo6xQD9g865YHE80jsYHZ2SDGe21WKdBdDib0uhqmhZ3YB0tA1qIgY35vo6QdbazIM6Ppmn1hLhIISNeDDX1eyP/Fm/bIn7gn6xnq9UqB1Emk0keQ+SxNPa2FjHU2NjbnWlMJnqs6y/aBk3Twq4ntKxIJbz5YtGQGGQ81shjidy2ip9wEm2f460uibyf26tQU2OOpTFZTrHir7HbaWkiJ+121WzBRGnK9YjXtsRqQxvqs0VrLxvaZmOJ13dTtC7GmIl3DaPFo/ib6E83d7VhPOI9F2Ptn6JjYLxe8fr6iU6ONpaGYgcSix/jOEH8vrs/pxQKhaIj0KE9jTVNO0DTtEM1TTtc13U/cCMwRNO0MwF0Xf8GKAfG//37LiUYGxED8GgdznivxSLSP6o5Be1CoZBcemcknt9xPOJ5SyoSJ9ILrKWLoYiYCQQCjfZIE7Hh8/lapTBHLB9JRWwaul+b6znXlHYqUSK9Fhsq+NLUtknROu1zZNuUiO9iS3gzqmfN7ktj40e8H4jZtkTzdo9GY57Fqq3a+RFir7CYiBZ3sfrk4vd4MdNS7ZiKtY5JrDFWLJra1xJJPh6Pp1HPVWG30xzfbfUs3rXo168f/fr1i/uenj17ykmCyH+FhYUJb6cl90mhUMSmw2Yaa5o2HHgAeBM4S9O0abquL9c0LQ84WdO0TrquPwJsAQZommbVdd3Xnvvc0YiXoRLpH9UcP71Yn23qrHWsrDOVgdw4WtMLzOjJaDabZQcz0UzB1l6mHc1H0oi4N8RATtFwBl2kB6egPTznItu2xrZfHckmYGejLbKCXS4XxcXFFBQUkJaWFvU9LeHN2FrH0tr+tIrGYYwHQWOvUSLvj+bt3lxauq0yrsJRz77WI1b/O1YcNadP3px2zLif6rnYMYkVCy1ZJyLadyUaT9HsdhpLSzyL1Rix4/DUU081+B5hQdHc7SRKS25Lodgd6ZCisaZpg4D7gYt1XV+tadq71HkX/4c6EXkLcK+maUcBBwGn7uyCcWQHsyVErXidTqvVSlJSElarFWjewERsy2azRX1dZAEm+iCPtXwqEXFKdRr+oSUFkchBt7GTmJWVRUVFhYylhgiFQtTU1OB2u2XsJEoi2V7wTwzF6pAk4q+7uyGyc8U5ttlsciBivN5WqzXsfm5tEVG0hTabDU3TZBaMsaCQsf2KNliP/JtxmaTI4lEiSmK01jL3aMIeRBdfREGo1NTUqM+sRCcyoh1LY54hsYShWPEItGoxUkV0jPHgcDiixk9DfS6bzYbT6YzZz4G6NjTS2z0ajYmxWEu6jfvbmL5OZDGqWP3OXaH/1F79wXiTrLH62pGFgRvTJ4/VJidy/JHjBNFvSrSvlej3KJpOtFiIjLGm9GOjTSAZvysYDFJWVkZ2djZ+v7/eM1jUHom0X4nsdydisdMS/QpVNE+hUChajw4pGgNW4Na/BeMk4A8gC3Dour4DeF/TtEOAnkC1rusl7berLUO8LIOmilrxOp0+n49QKITP58NsNof5pjUWsS2v1xs1c8LpdMpM1OY8yBMRp3aH6s6J0pLiTuSg2xhbQsTz+XwJCYcej4eioiJZ1EUUS0wEj8dDcXExAJ07d27y8RkLuijCiTbZFHm9ja+3tlem8fuAqAWFjPvgcrnq7X+8CTSVFdoxiBzwde7cOWq8Qd01czqdpKenRx2ENmciozEDz1ixY3yeGrcHqFhrByLjIVr8NNTn8nq9Ufs5kSTi29kS4kZT2y3jKpyWXHHWEWkvESle5mWs+BCTmaIwsBBwm0Mixx9rnNCYvtb/s3ed4VFVW3Td6S29QkJC70V6tSAIgigdFVE6YgFBAVGkoyBFAZUO0otURRRpgkiVJgLSS3qbzEymZPp9P/LO8c7k3skkBAg66/veJy93bt/3nH32XnvtQLDuwYJPy7o02L18741rn9nZ2UhLS4PZbKbnINusVmuhZIMQHta48qh6IwRQGMOGDQNw/+xevuMkJiYKJqcSExNx9+7dB3pNAQTwX0WZDBqzLPsHwzBJ//+3G8AthmFM+L8GM8MwlViWvQPg5iO8zFKFUAft+5n8fC1e/GEx+MoO+1PSRv6/QqGA1Wq974ncn+BUwGl4MPD1XIvLUlcqlUhMTITFYkFERESRv+cyQZVKJQ0y308JJfk2AqyYwuB+twTcseRhB9wJw4/LSvdmbXLfo68O9Hw2EyjJLRvwbt7DtTcuA49hmCLf2f0kMvyZQ7jsd1/Xwd3GZ48BPBxw7YFlWZ/vROi9l+Y4UVI/hc/vKu4xvL8t8l8u+/rf4j89Kn/Q38Z3QPHkIYrL6PXn/oXWCcXxtQJ+98NHcWxMCOR9KRQKGhjmHov451ymMYGvuc+bwfywfKxAs8+yg+vXrz+w4wgFhQHfjatL65oCCOC/ijJXH8n8/4tnWTaT/H+GYeQA4gBIGYYZAGA7wzBBzL8o4uPduKmoRk6+4KvBCwnAAZ4sUT5wm2J4N2LgbuM2+eC7J7FY/NACdKXd7C2AAng/V+/3722rvpqjiUQihISEoFy5cpBIPPNWRHaAa5PcpnYl+S5Ko2FWWYDb7S5RJUBxUdQzLirgzrJsoXd4PyAMP5vN5nFtxWna4+ueiG1zt/1bbKYsgO+b5oPQ2E0YeCaTib7rkjbs8QdC18G1CbPZjPT0dBrA8TUecW3vfub1/yr8bbRZHPC9B++/edttab47X36K93m5dsc3796Pr+M9npLqn3+L//So/EE+WxGaU0jzxMzMTN7AHbdJdXGbhd3P/YtEIgQFBSEoKKhImw/43fcP8n6FGiF6Q8jGiuN7EXkxwhr29qXEYjFiYmIglUo9KrncbrfP8dDbNwvMewEEEEAAjz/KBNOYYZiqAEIBXGJZ1srdxhasFGwMw1wFMBZAawBvsCxrfOgXWkL4WuywLCu4cPClLSe0H+Bbs45M5uSajEYjWJb10Pc0mUyUBRoUFASlUgmLxQKj0QiXy0X1i8k2on9G9K28nRg+PUrvxXZJnM2HEUD7t+J+nh3LslAoFGBZFjKZDCaTyYP5qVKpeMvR+M5J7EMul0MikcBoNCI7OxtRUVG0XFYul0Oj0VAmFN+xhTTBCcudrzTO1/UUxd54FIsjl8sFi8VSbNmV0vxOuM+V6B17ax8WVYpI2KLc43G18LyfO5f57HQ6odVqERER4bOs1pftebO1WJYFy7KFxkm9Xg+TyYTo6OiH/r7/DYtv8sz5SmBdLlexNPsJE9LpdMJkMgnama950d9tQmOA2WxGVlYWoqOjAfh+R9wFv9Di3h+NRyEU9U3/G+yH3ANXbqa4Y5+/4zkB97n6o/3r73H5fB7uNq7t5eTkwOl0UkZ0fn4+DAYDTCYTwsPDERQURMdCm83Ge02kA31x4N3n4nFHUd9PSefFko4x+fn5hfxtAJDJZHA4HLDZbLBYLIiJiYFCoUB2djado4xGY6H3T/xuX+Ooy+Xi9csJfOmt/xvGkLIIoefqPc4VR6LQ11xLwB2DyG+JfZF1HJlnyTbicwIFY2Bubi5cLhfV3OZrvseyLFwuF9WLL+5Yyb0fX9v5GPcBmw0ggAACeHB45EFjhmG6APgMgBZABsMwn7Ise4lhGIb1nDnUAHoDeIFl2b8fxbU+aHAnV6EFMV+jIG/40h8lzgE5BgnEEWfXYrEgKSmJMk7IAlmlUlFH22g0elwX8I9DzD0v9/wsy1LHhwSggYDu8OMKEggxm830XQKAwWCA2WxGRESEh+asELgLKRIYJmwLrk0RrT8C7wZYvrQZvcvbfTmuZVnTUSwWP7TyT74GKcA/rCiJRIKoqCgaYM3KKpCVj42NLVTyTAL/JMDBBfcdCz1vrj5sVlYW0tPTAQAxMTG8QeGiSiG544+Q5rFSqYTJZILT6YTFYilztvA4wJeEA/e9c+cAX8E1siAWiUSF7Kw0rpHb9LGoMUCtVntcgzcsFguysrLoN+StCVqWx5myhvspfb+f5+zLjyrOcUnDKrvdDrPZTMdN7nYS2OPTKVUqlTCbzTRIzGX8cccx7jXxPauigjfefS4C8A2h5yn0d+44yP2NzWaDVCqFXC6HWq2GSqVCdnY27t69C7VajYoVK0IikRR6/8A/4yhfWT43AQHw2ylfvwCh3wbwYOE9zpVk3PM1J3L9HuAfXz0yMhJisZjOwyaTCUajkc7FxL5UKhXMZjP1jwi8A7gWiwVms5nqxfP1mLhfBDS0AwgggAAePh6pZ8gwTCsAcwD0ZVn2PMMwiwC8D2AQWzjV+B2A4yzL/mt0jL2dS+9AF+DpMHg3IxMCn/4o+a93gx6WZZGTkwMACAoKgkqlQkJCAiwWC8LCwjwW9tzJmRtoBgpYKmazGVKp1KOjL58TE9A/K7vwZyHEhff7NRqN0Ov1cDqdCA4OLvJ8ZD/CbuJKnXCPzWVi8TXA4h7HbDZDLpcjODgYCoXCo8EMFyqVqhBToizr25L7uF/4o4tosVgo05Yb5JDL5bDb7dQeyAKCJJeI3Xg3pSNMOPIb4J+EFXcRUlRgg2js+dLCJucnbE+u/QCeOn5kfFOpVB5JDpFIhKioKMri+q+juFqagO+AnZAeK3eRSt4hkaBwOp1Ub9XfxqpcZjofK0qo6SM5n9FopCzhogLFXKhUKkRHR9MkVVHjZgDCEIlED6QZsD/nFfKj+BJjQmMWCQQ7nU4aRCFBGK4sBABacRMeHl5ozOQmPgFQH4vMnd7X5M0wLSrQHbDJf+DPeCf0PH01x5TL5cjJyaF+itlsRlhYGCQSCSIiImiwPiIiAm63mwaaw8PD6TjIlYjzfvfe11dUozS+dx54/48G3uNcScY9IX1qrq9F5s/s7GwaGA4KCqK/5RKKHA4HGIah/hapKAP+kQ3yrjQj+sh84xIffI2fQt9hYA1Z9vHEE0+UqeOU9rECCOC/iLJAJ/icZdnz///3ZADLGYaRsyxrAwCGYZoB0LAsu/aRXWEpg7sQJosF4ugTBiXf5F+SiZKr/cidlE0mE7Kzsz2aXQEF5T3BwcEIDg6G2WxGZmYmrFYrEhMTKZsFKHAeuNdIWCo6nY4ujnwt7InDEdC5erAobrDHn4WQrwCfWq1GdnY2zGYztFotKlWq5OGQ8l2f2WyGTCajjq1KpSrkYHIDgXxBGC7zOS8vD8HBwTQ54n3NpCN4TEwMPRa5fm5S5d8Kf1gaKpUKJpMJdrudyoWIRCLYbDbIZDJIJBK68FUqlR7vmGsX3GC+N9OYsKS4tmO1Wn0yjyUSCW3Ow3c+b1Y012a53wGxlezsbAAFDGnv8xHbKG6wtCQB1rKOkjB7fC0WvRfEZPHqzUom71Cj0SA0NBRyuRxarZbOMb6uxe124969e8jIyIDb7UZsbKzgNXo3fRSJRB7seTJPkbmUO3d7z8fkXojGp5DsBdc+SlLCyz1fSff9t6MoGynOcbjvyzsxxpdgIyDVXUQqiVutRfw9hmEglUqRkpICiURC9dtJYp8EZbhatikpKTQ56s0M5pNkEvoeuYmVf/vc5y/8Ge+EnqfQ391uN1JSUqDX6xEbGwuJRAK73Y7U1FTIZDKYzWZYrVZERERQPWESkCM2ReYrkUjkkfDkkzwh6wm+bdzqCu79BVib/z44nU4kJydDIpEgLCyMjivEj+MGiYnfwq36E4lEkEgkkEql1P/Lz8/3WMMRwhHg2YOCOy55B5e5ySyh8VPoOww0vCv7mD9/fpk6TmkfK4AA/ot41EHjUwAuAQDDMGIAcgCJAIIBZDMMEw+gBoD9j+wKHwC4C2HCbiNsE28GJRfeHcC5ENJ05S6ygcJOoUajQUhICJWfICwvwugizgO3EQdxUrkLbbLwIcEhsqjmLq6IE0LKzAEgMjISVqsVkZGRgZLIB4DiBnuEWHZ8LCa+Rh0KhQKVKlUSbNbibadarRYZGRn0+jIzM6HX66FUKqnGMfBPCXtRQQDv6ydsYm7QmRt0/C+WuPmTfGIYBlFRUbRqgMsqBuBTK9o78UC2SSQSOgaQgDIJuGRkZCA4OBjx8fEejF9vnU6CoqR8AM/FO58khUKhgEqlKpSk8OfYvsD3zRUVSC7rgeaSJiyFnpnL5aKBKolEIihZxH2HhKUpxJ7zlrawWCyw2+1wOBw0KOMdYBFKEhH2vLesDd/c7X0N5JvhHrMoSYP7kVHwtegOwDe85XO4MiV8v7VarR4JMDI35uXlIS8vDyqVqlCSlCRIvCstiCYtscGsrCzo9XoEBwfTuZUblCF63iRRp1AoPMZkrh3wyUMJfY9ardZD8ieAosc7X4kavkoXkUgEq9UKiUSC0NBQREZG0sQUSZYRSRugYAwg/pdKpYLRaPTQmpbJZDTAzBeMI/rr5Pq57z4gjfP4wl8/gWufWq0Wer0eoaGhkMlkyMzMRFhYGF33MQzjIaFD7E6j0SAiIoJWLOTk5MDhcMDlckGpVMLlctF1Ibc/hHf1GLE3bqKDK9ekVP4jBUb8TIIAoziAAAIIoOzgkUbpWJZ1ASA14wwAPYBclmWzGYbpB6AhgCmPU9M7f+Bddk8aEXC3FRdkYiYLEzIJkwW2TCbzODZhIHCdD6PRiOTkZNoQRa1WUzYmN+hMmnKQcmESzPYODnGDjYThSRpLkXJ2k8lEmZ98TLAA7g/FdboIy468E+LccRcdFosFWq0WeXl5iIuLAwBa6hYcHOzRxI5rcy6XC0lJSXA6nYiJiaHMBwAICwuDWCyGRCKhTbK4kgeA72Z3ZKEvxBL0vkfC/uJjLf/b4S9LgwSOue+R2AFphkJKHbla0b5Ktwm7mIxTpMmhVqtFbm4uVCoVypUrB8AzAAd42jB34etPAyfud8CyLA2+uN1umlTgO7aQjIIv8H1zRSVvyrpGX2kze7wDVdxn5m0z3EoZbpk+YamT8clbJ1mlUiE0NBQikQh6vR4KhaLQffCNId4Lbi6r2TuIDfyTwOUuvGUyGQAgKysLLMuiXLlyvHO8L91nf+Fr0R2Ab5BAq81mg1wuR2hoqODzI0kyMvdwfRyNRkMDd0IBRRLII4lyp9NJZSoAIDQ0FBaLBSEhIR7BRpJAJcc1mUyQy+UICgqirFQARQavhcCV/Pmvs9a5QTlf3xHX3/auVgIKz12kbD8kJMRD2kmtVtOqBJLQ4r5Tom9MGnDGxMRALpdTxjIZNzIzM6l8DrfKT6VSeSTYSEIdwL+m4eF/Cf76CVwfhvt9c0kaJEFEbN7pdMJqtSInJwfh4eHUDyPjk1wup8kOu92OzMxM2gOnZs2aCA4OpslfiURCk7/cajNyLO9klrefSRBgFD++6NevHwBg/fr1ZeI4pX2sAAL4L6LMUDtZlnUCMDEMk8wwzEwAHQAM/LcFjAHPrD9ZcPpbRsltUMUtbSWlaG63G7dv34bb7YZUKkVISAhkMlkhBpL3ZMyVCdBoNFSXSqVSISoqCiaTyaNDr16vh9VqpYEePj0u7mLfYrHAZDIhPz+flt4B/yy+IyMji/0cyzo7ryygJE6XUqmkDiUfk87tdiMvLw+ZmZnQaDRISEgAAMoCJOWU3gup3NxcpKen02QDsYPIyEj6HhMSEjyYn1yWuvcijI8xQ66P2JN3QNO7WZCQ/hvRsuXa1X/R3oTGJavVSisJSLM4Msb4Kt0mwXpSMqlQKBATE4PExETk5OQUkq8gSS+5XO7RmI8bKOY2cCLBQ+8KC/KuWZZFdnY2Hb8iIyN9Br1LUt7O980Vlbz5rzFqvLWpSQKTVKKYTCb6TrhSECRAnJWVRVmYJDHElQEwmUxQqVQ06Wm1WmG32wsFSogEU0xMDJ2TuMwoPlYzYfZxk2ikSRBhIUdFRdGkChnP+GypNFh/vhbd/2X4EwAlAXe32w2JRFJIrosLso075nAT9VFRUQAKbIprr94gjV69S8P1ej0tGSdyTU6nE7m5uYX8I5vNRv9LKrSID8bVHeV7Bt5/40r++JLa4M6L/9aAsj9BOcKmJP6uULUNt8qBNPt1Op24ceMGQkNDERcXR8czmUzmMb9x/WkyRoaHh9PqK7vdTpnkpCcJ93o0Gg1iY2MLMefz8/OpxAWfrAnXNoQY9wE8OvjrJyiVSjidTuTl5SEqKop+38SWwsLCPOyWOw6RCj2Xy0XHHlK5YzAY4HA4oNPp4HK5aPLLZDJBLBbTdaP3OoB8G0KJrNKSEQqg7CAlJaVMHae0jxVAAP9FlJmgMVMQhZECePL//23HsuyNR3tVpQNfGnHcYIX334XYKgaDgZYgMgxD2SdBQUFgGAYiUYH2qFgshlQqRXh4OFiWhcvloosQ0miFW4btdrs9gngmkwkul4uel2VZaDQaD6YwgVgsRmZmJsLDwyEWi+F2u2nQhmVZj0Akt5ERCRiLxWK4XC66AAfgUWrMh7LOzisOitIRFApS+tqvKLsDQFlupASNnIebACCLJK6OWfny5REUFETZ4SRoQxbERA/XZrMhJCQETqcTarUa5cuXh9PppLIRRBeXsE9JUIckKUhg0OFwwOl0QiwWQyaTwel00gQHlwlvNpuRk5ODyMhIMAxTqKkWWeyRcnUua9lqtRaSXRBii/pqUMKyrM8A8+MUcOazIe5zVCgUyMnJoQsN7+eiUCho53cScFAoFFS+JDQ0lAbWgoODIRaLqWMXGRmJ4OBgaiPZ2dmIjIykCQMyXsjlcigUCkilUloFIRKJ4HA44HA4IBaL4XA4YLVa4XK5YDQaIRaLoVQqqcYoCVoSkOCby+USXDxzG4EWhaKSN2WBUVMSLdOSjj8Mw9A5hIwvOTk5tIGmWq2mwbygoCAEBQVBoVDQOSwiIoIy8mQyGUwmE5RKJaRSKf1OTSYTwsPDERYWhpycHBrU5QaBybjjdDrpXEdYnDKZjLKGSWMfm81GqxjI9RN2s8vlgs1mo8FDq9UKALRcHPDUHyXzIvCP7i35O1/Sytez5TJWi2OXDwtF2cKDgFBAnnstDMNQ5i8JCpNgCGmiSkAS8VlZWZT1zjAMHdtEIhEt1SZzDLEpYl8RERFQKBQ0geFyueg4RZpOuVwuylQnkmFkXiTJirCwMCqnwb0fkihhWRYOhwO5ublwOp00gQGA+nbk9+TerFYrDVpzx2vu8yxKBuVR2F1p2hYfE5ILbvI6NDSU2gh5Dy6Xi/osMpkMIpEIubm5MJlMtHyffJ96vR52ux0qlQparZYGgxMSEgoRMEJCQqDVahEaGgqXy0WbJapUKoSHh9PAnlQq9UiqkvnRbrfTOZL4ajKZrNCzexjSFWVtbHoQKKkuOLENvvUf2SbUI4YLhmFgtVqRlZUFkUhEK/qAgoCxyWTCzZs3IZfLERcXh+DgYFqNSiobiGa6w+EAADoesCyLyMhIOi4ZjUYolUoYDAba2JEktfxp3M69B5I4CSQsAggggADKHspM0JgtmPXsDMNMB/DHvyVgDPA7SXyMYS6EGkIRHVBSSiSVSqlGFXF0ExMTaemQ9wSs0+mQk5NDAyUul4s6HGShYzKZPIK32dnZYFmWLt75rjcrKwtpaWmwWCxISEiAyWRCRkYGIiMjqeOj0Wgok4y7ICHleSSTTRjOZGFDGNTeTlRJSsf/a/DFlCXPOzs7mzKbyALIarV6BDgsFgsyMjIoY1MkEiEuLg42m40uVLhsAZFIRG2ALHaCgoKQkJBQ6D2SJIJCoUBubi4cDgekUikYhoHBYIDdbkdoaCiAgoAOkXQhgRLCdOWWYBJdNsKiIPdLyjxtNptHQJkbKPaWjyEojoRMWUpolPYijbCMg4ODIZVK6XfNdy6xWOzBgiRJh+TkZNjtdtjtdohEIqqjnpmZiWvXriEvLw9Nmzb1kKogQV8iK0EC0GQ7SQIAoExjq9WK/Px8SCQSGoTMz89HTEwM1XwkbDAu08sbQk1cSor/wsLZ1z2S75Gr90kSP+S7cbvdHokhoCCZZDKZIJFIqN5ifn4+fTdyuRxyuZzaikKhoE16LBYL1WIncwupeuC7ZrFYTPfT6/VwOBwoX748ZX0S2yPBX679kWAN2Z6RkYH8/HyPxqBEg9n73P6MHaXRQO/fDG4Q3Tv47v2cuM3AyBzJ9/xFIhGys7ORlJQEtVpN2eQEEokEDoeDBl24sNlslI1HJFaMRiNsNhuCg4MpQ16v19NEFUmmWa3WQvMQCcqQQCR3bGIYhlZ1cb8ph8NBEyaEAUiOS+Y/Ii/Fl7QgPtnjIIPi3X/DXxSVvONWv/DJ5wCg41pkZCStelEqlTT4Rt5/UlISJBIJ4uPjqUySVCqlPjAJBJNALmEIh4WFUb1rIkNCGMTEdyMJLnKtXDsFCmxVLBYXGkdIY0XuvMrV/OaT4QiMP6ULvqq6okDGAe67IHYSFhZWaB2Qm5tLq2nCwsIQHBxMCUcajQYGgwFutxtRUVHU3yOVWRERETRpf+PGDSiVSsqCdzqdsNvtlKRB5kE+X8C7BwEhjwCFSRkBOwsggAACePQoM0FjDtaw/4H2zd4LEz4dRy4TCfhn4gwPD6fM0KSkJNy7dw8APBYqFouFLiy4IKxj0niDsIJJGZLVasWNGzfgdDpRuXJlGqwLDw+njgUBccwVCoVHF+/8/Hzk5+fTzrvcBX92djYNEpJFttlspos8EhAH4NGRl48B8W8vaSptOQSuzRFGAWG3kEWC0WiE0WiExWJBdHS0RzkZWahwpR4IS89msyE8PJw6nqQ8jtgEKV3zfmdcTc/8/HwYDAZaLsswDHJzc5GVlYXc3FxIpVI4HA5Uq1aNLpQIW5TL5AsKCoLRaITVakV0dDSvk+nNKPKWJOB75uTaSdDJ14L03yw34P3MiEQAN2hP3gcJcBC2MBkDjEYjFAoFDdYSmwgLC+OVqiHJDJvNRtnsQUFBkMvlSE1N9ZAj4D57wo4nzVlI8NlgMCAvLw8syyIsLAw6nY4urL3HFLKII98OCaz4+03+F2VNhCReCLzHc2I7eXl5uHXrFsRiMRISEgrNfySx5XQ6odVq4Xa76TdLbIkw/gwGA2UDE2kShUKB9PR0yuokNpSbm0uDh4TxDIBKS5AmZS6XC2q1mgZ0AE8ZE1K1QBa/xBb5KhPy8/N5tdf9TYaWJLjwX4K3ZJYvcOchIe1pUhlB7FKtVtPAnE6nowkIMi9wq1jcbjcYhoHD4UBqairsdjvS09PBsizi4+OpLRmNRoSEhFBWvcVioSxBhUKB6OhoKrHDMAwlBHiz38k3RQI+9+7do3q53vM6917lcrngd8uncV9W4W9VUHFB7pv4KyRgTM5FmOHc3wL/9FrIz8+HRqPxSGyRgGxISAgMBgNycnKQn59PG92RKgelUgm73Q6LxQKdTgez2UyTZ8Q+CUOdBLddLhcN/JGx0vvayO9J0oDbGJQr1cOdH8n34i0dFMD9wzsxUZz9uHMqSWAREgZXtstutyMyMpJKQSUnJyMuLo6OKaQawmKx4NixYzh48CBOnTqFhg0bYvHixbh16xatDpJIJEhMTER4eDhyc3NpspRUwHrrqxNbJecG/ulBAPCTMgLNGwMIIIAAHj3KXND4vxAwBgoHrby1Fb0DV9zyXW7X2/LlywMAQkJCKBOBBFdIiRoXEomEOgvEieA2QiCMKpvNhpSUFNy5c6dQd1wSACFM1czMTLAsi4iICBqA5Db24UpwEP0rlUoFu91OgzlkoRUVFeWRfSbmUByW578F97Pw4QtUeTcDI4EObiOW/Px8GnzhgjRFJGXgRGeWMHazsrLouySMYZfLRYMr3HJ/Io8iFothsVhw/fp13L59G3/++SfOnz+PjIwMJCcnIyYmBgkJCYiKiqJyGGShXr16dVr6K5PJqC2ThTF3ced9DyRhwmUtcIPZ3AQOgEIMB64DK/ReyoLcwIOC99jknQjgBsNIlYBIJEJeXh7MZjPCw8OhVqsRFxdHpUq4x65VqxbVNyZyBBaLBZmZmVCpVMjKyoLJZEJeXh7u3buH1NRU6PV6qseZnp4Ok8mEKlWqoHr16qhevToqVqyIWrVqoXbt2pS5qtPpEBsbC4lEQr8Vb31jIrNjt9vpu+Yr3ebCO2BalljnxUVJA95FLfK8x3NiQyS5QMq8SZKKsNkJu44sbEkwxO12Q6fTUTYfwzA4fvw4FixYQMcoIl3CZSvXqVMHVapUQaVKldCyZUvUrl27kI62XC5HSEgIdDodLl++jNzcXDz11FOoVKkSTZCQ6ydJUHIv5NmReZ2rt0waAykUCg+78zcZWtLgwn8dfKw1b3vl054m1Ssk0ZCfn0+P53a7aWIdAJVHIWOE2WyGw+HA0aNHcf78eaqxTSQrAFD5gNjYWI9maOXKlUNUVJSH1j/ZTo7PlQszmUyUcQwUJPyBfxqGkiQauS7uPRMJF/I3bzwu89qDStpytdf5KpNIJQwZx0hDYaCg0aHVakVYWBhmzpyJFStWoF69eqhTpw4qVaqEypUro0qVKoiOjobVaqUMYb1ej2vXruHcuXO4evUqjEYjqlevjjp16tAANCFdBAUF0bGLSJOQah6DwUBJJyRBwk1qkGOR3iZEQoVIRBGmMfDP96LRaKi2cgClA28CA1A005b7DvmIEAaDgfox33zzDX13wD9kkJCQENpk0WAwIDU1FSkpKXA4HFCr1ahVqxa2b98Oh8OBNm3aoHPnzlCpVLRiglQrEqkeb3Y7ASGHkHmd7xvlI1Fx7ymAso+WLVuWqeOU9rECCOC/iDIXNP6vgDjfvmLkDocDaWlpiIuLo6XcTqcTcrmcTqoAEB8fj/z8fLqQcLvdiIiI8FjEksk3IyMDf/zxBy5fvoyzZ8/i3r17yM7OhslkgtFopKwlLtRqNWrXro26deuiRo0aqFixIpo2bYrExETKDHY4HLT8V6lU0gBfdna2B+NHIpFQh4WUMxF2qne5kvf/fxwWK6WJ+1n48AWquAs+YneEHadQKKgGNdGf9g7mcQMfhOEkkUgglUppSSRhLSQlJSEpKYkuSnU6HUwmE2XSkODxlStXKDMHKLDlOnXqoGnTpsjIyEBSUhJOnz6N3NzcQvcok8kQGhqKkJAQhISEUD3uwYMHo23btpQJ4838IQwIQJgBSJ4dULjRTUAWhR/cUlYSxCBMLMJIBwCpVAqxWAyDweDB2CWJLKlUij179mDZsmW4cOECTQ74QkREBGJiYhAbG4vWrVtDpVLh5s2b2Ldvn0en5ODgYNSpUwfvvvsumjZtSvVqk5KSEB0dXWisIbIJpBs4YU7zVYCQMdY7APU4s86LG/DmMjYB4XsWCj5JJBJUrlyZVrAQmRxyLLfb7ZHEJHMoGcNycnKQl5eHlStXYs6cOYiOjkZERAQ0Gg1tUEdklvLz83H58mWsXbuW6g+LxWJUr14dCQkJsFgs0Gq1yMnJgVar9bBDsViMJk2aoHXr1qhXrx6eeOIJxMbGUiZXVFQUDVYDoOcnYyAJfgOA3W6H1Wot9hznK7jwb25Wdr/gS2jwJRm5IMlvMp8YjUaapCf9IchcSgLKly9fxvHjx3H79m2cPXsWly9fprqkRBqsOFAqlahcuTJq1aqFp556Ck2bNkXjxo0hkUg8JE8YhoFMJqN+XVRUFB27yNxH5m8S1CxXrpwH4/9xHKu4IGNDcRJdxUmQeX973KaYpG+IVCql/5VKpcjLy0NeXh5+/vlnTJs2Dc2aNUN6ejoOHjxI58bQ0FA0bNgQFStWxN27d/H3338jIyODnlehUCAxMRG//vor7HY7gAKfqX79+mjZsiXatGmDJk2a0HmYy5w3Go3Iy8uj0nZRUVEIDg6mNhEcHAybzebBLCbMY8CzkpFv7AnAPxRlZ3zzgPeYxfW1yPsjfT68K1eBAru8cuUKPvjgA9y+fZv6vUTmjfvvsLAwxMXFoVmzZujRowfatWuH1q1bQyKRYOrUqVixYgV++OEHLF++HIMHD8aLL77oUXnjdDqRk5ODiIgIOidy16BKpZJeLzk36Z+Tk5NDqwO598v3TP6LFVyPE2bOnFmmjlPaxwoggP8iAkHjMgK1Wo1y5cp5lHjfvHkTqampsFgsqF69Om2sYbPZ4HK5cPfuXQDAqVOnsG7dOjAMA6VSCYVCAYZhqJyEy+VCdnY2Ll686MF8iI2NRd26dVG3bl0EBQXRhS1ZVAcFBSE3NxeXLl3CpUuXsHv3bnz77bd0/7i4OLRr1w7169dHt27dqDYsHwhLLDIyEhqNhga4uVq0ZrOZas4GBQUV+v//NdwPq8ffQBVxxkjwXyKRQK1W8zaiICwbs9kMhUKBrKwsrFu3DmfPnoVWq0VycjJSUlJ4ZVGAgmAh17YiIiLw+uuvo06dOqhZsybq1atH5VC8YTKZkJSURAPSer3e4395eXnQ6/U4evQoduzYgblz5+L555+nbB2SuCBsjKCgIEHWAndBRPYlJXfkOZRmyevjBj7Wi9PpRHJyMm3Qw/1eib51cHAwQkNDIZPJKAuKW7VgtVqxevVqrFixAtevX0e1atXw/vvvU5kapVIJjUZDWXHBwcGIjY2lUibeOqIEOTk5uHbtGi5fvowrV67gwIEDeO211/D+++9j+vTpcDqdEIlECA0NhVQqhdvtps2w5HI5QkNDaRkud/HiXcILgDfw8riw84qqTvAHJSkj9bYn7vdFvlmuHAzRIicgbCWLxYKQkBB8+eWXmDNnDnr37o01a9ZAKpUCAG2k6Q2izfj333/j4sWL+Ouvv3Dv3j2EhISgZs2aiIqKoo33IiMjoVKpcObMGRw5cgQLFy6E0+mEVCpFs2bN0LhxY3Tv3h1RUVG8AThvCSY+Fp8vcHshFBVcKGtjVFlZ5PvLWvO2S26Fzs8//4wvv/wSDoeDNrQj/yNyFampqQAK3kPLli3Rs2dPtG7dGk2bNqXJDrfbDZvNRv00p9MJo9EIrVZL+0/o9XrodDpotVpcvXoVhw4dwrZt2wAU2E/z5s3RqFEjtGvXDs8++yxUKhWys7Oh1+tpg1KulAr3v6Q5sTfj/78IXwkybjKMECP4npNCoaDfN2GfEwYwwzA4ffo0xo8fjxYtWmD//v1UpuKvv/7CxYsXcf78eZw/fx67d+9GlSpV0LFjR9SuXRs1a9ZEzZo1kZiYCLFYDJvNhgsXLuD06dM4deoUTp8+jZ9++glAwRjz9NNPo27duujcuTOqVauGqKgoWlXmDb7vQSaTeVRxACg0RhdlJ2Xle3+U4HsGJak88n5HXNkQ0sxVIpHQpDUAD+LDpk2bMGnSJISFheHAgQNo06YN3UbkK/hgt9s9tk2bNg0ff/wxduzYgWXLlmH8+PGYPHkyevXqhQEDBqBFixYACvx1qVSK0NBQD5IGCQBzK0otFosHcYTcJ7E9buUXF49zBVcAAQQQwOMI5j+iBkHRpEkT9syZMw/1nEV1mOdzqMxmM3JycmjQlJTcE8fNZDLhr7/+wueff449e/agRo0aiI2NRX5+Pmw2G9VEy8/Ph8PhQJUqVVC3bl1aDtegQQOPkkYCUhYsdK05OTk0iHz8+HHs378fBoMBYrEYrVq1Qtu2bdGuXTu0aNGCOi+E8SHkQJLnk5mZCZPJhNjYWKpLyxc09nbghZzSB+GoMgxzlmXZJkLbS2pfRX2HQvdSEtsCQBl7QjIMXFkULtvbZDIhNTUVq1atwvLly6HX65GQkIAKFSogPj4ecXFxHv8m706j0dCGeXxwOBw+Oyb7epck2A0UsGl69eqFQ4cOYcSIEXjnnXfo+YnGI1f6xNdzZRiGlutyGTdFXU9J3+X/twna18Meu4Tug++ZZGZmIjU1FWFhYUhMTPTZLId0kCd2lZ2djaVLl2LFihVISUnBE088gQ8//BDdu3f3sAmhoB8A2uRJCNzrsVgsGD16NFatWoVWrVphyZIlUCqViIqKovdCmKshISEejTi5AT7CzClJk5ZHsYguyrYOHz7Ma+u+4P0N+fssuPsJfWMEpAqGJA/MZjPVdQX+CXzp9XrMmDEDy5Ytw4ABA7BkyRK/7Ycsun1dLx9MJhOOHj2Ko0eP4siRIzhz5gyCgoKwaNEi9OnTh7JKyTm4DGq5XA673V7oWfnSgy7qWT0qpnFRtvXHH3+UaCx9EOB7l3zX5v03wgy+cuUKWrZsicjISFSqVAlisRhisRgSiYTq96tUKjRt2hRt2rRBvXr1fL4L7vzlDZfLVWgby7K4ceMGTp48SYOGFy9ehNvtRkJCAvr27YuuXbsiMjKSNrMi5yd2UVLb4H63fN/6w/a5iG352Jf373w24CvISWyB6KlLJBKP50pA/CrSrJXLQL969SpeeuklmM1mnDx5kkrLkf2EAndku69tEokEWVlZOHHiBA4ePIh9+/bhxo2CXuKVKlXCk08+ia5du6JVq1ZUwz8sLIzq3npXKpAxitg/gELfR1FjfVn53n3hQfvzfM/Al53545MCnlVdZJ3lXQVF+jmMGDECa9asQdu2bbF27dpC677iBI25cLvduHbtGpYtW4b169fDaDRCLBajXr16qFatGurUqYPmzZujWbNmsNvtiIiI8BjLvOdDbkJGLBbDaDR6SDbyPQ/v51iWbAsoW/78w0bPnj0BANu3bxf8DZG8vN/j+HvM4h6rLKOosSuAAB4EAkHjUsL9BO+EOsuaTCYPp4A7Oe7btw/vvPMOUlNTMWbMGIwdO9ZjcvcVgNPpdB7MSe9tQgwlg8FQiAnqdDpx7tw5HDhwAL///jv+/PNPAAUs5OHDh2Po0KEICgqCWCwu5GASx8flctGGMjKZzENmg3Tp5j4jkmHmK6Ej8PXMgZI7F48iaCwUmJFKpdDpdIiMjOR9376OabfbodPp4HK5PBoOcs9B9FwtFgvCw8NhNpuxfPlyfP311zAYDOjcuTPGjRuH+vXr0/24ZdfeyM3NFWR35eXlISQkRPB6fb0vh8PhYZc2mw1vvvkmdu7cieHDh+OLL76gz4dlWeqg+mKnkgUf38KINFMTWiwVNab6WrA/CCezJGM8WfgKLWy8NaHtdjvS0tJQvnx5yGQyyqTkNswhpYk2mw0ikQhZWVlYsWIFli1bhtzcXLRp0wZDhgzBs88+K5hII02hvJGbm+sz0Mk33m3fvh1jx46FTCbD7Nmz8dRTTyEoKAjJycnQaDRU8oTsy/0eCOOUNB7iPhuy6CY2V1aCykXZ1unTp4vNDCtq3BK6X+68J8Rc5x7HaDRCr9ejXLlyAAq+odzcXCgUCmg0GrhcLgwbNgwbNmzA0KFDMX369ELnzsnJERybTCYT1Zblg6+Ass1mo4GVu3fvon///jh//jzefvttTJ48mTaKJXIVJIhE9E9JMyvyPIh+LpF48n5u3EQfGcsAUBYh0XwWwqMK7AkFSx7E9RC75LMtl8tV6JxkTCfNVr39LqIJm5GRgR49eiA3NxdHjx6l9kjKr4VAtLb5YLFYBCup8vLyBG02Ly+P7mc2m3HgwAFs2rQJR44cAQA8++yz6NKlC7p16waxWAyWZWnylC8QxO1z4b2dG0wkbEan04ns7GzExMRQn9EX4cAX7ieR6itoLISi/EOXy+VhN9z7J71FiN/JtTGXywWxWAyz2QytVovQ0FDYbDZIpVL07dsXv/32G3766Sc0b97c43xarVbQJwd8B43z8/N558U7d+5Qv/zw4cMwm82oUKECFixYgIYNG9IxjbDoVSoVbZyo0Whobwoiv8IdX8j/JwFRbxv17n/yoL/3kuJB+/N8448/60EyHpGx3Wq1QiaTgWEY+nfut0Z8Ku5+t27dQt++fXH58mWMHj0aY8eO5Z0XjEaj4JrPZDIJjk1ms5luM5lMOHbsGM6dO4czZ87g3LlzlD0cEhKC5s2bY8iQIWjfvj3tt8OyLHJzc6l8FPfZSSQSmEwmShoi/VYUCkWhwLO/vlVZTNT/m4PGzzzzDADg8OHDgr/xJ2jsz3H8PWZxj1WWEQgaB/AoEJCneAjgC7IQcDWluCBsFdLNlmRxRSIRJk2ahAULFqBixYrYt28fmjZtWmj/kk6gQkFsss0bEokEzZo1Q6NGjTBnzhxkZGRg//79+PbbbzFx4kRs2bIFU6ZMQbt27ai+LZHRsFqtyM7Opk0UyIKGPBPSzd77Xridvm02W5ENqbjswLKqv1aSJlPcUuTY2NhCvxOaOLlNo6RSKS8rjWVZ2Gw22szl66+/xrp166DX69GpUyeMHz/eI1jMha9AkRCEStD8BdcZVqlUWL16NaKiorBkyRLk5eVhwoQJiIyMhMPhoNdByo35nE7CiOU2PCNBVBLUAYTL4srSwqgk4JYMEoeeyzr3TuQ4nU4oFAqquU622Ww25OTk0P0iIyOh1WqxfPlyLFu2jNrTmDFj0KJFC2RkZPgMPPiykSKC8YX+1qtXLzRq1AhDhgzB0KFD0a1bN0yYMAEhISFQq9WIiIigJeck0WY0GmEwGBAeHs47dvOVTD4unb8fpuwK99mRuY5b0QB4Vj4YDAbcvXsX+fn5qFKlCqxWK/290+nEkCFDsHnzZowaNQoTJkwQTMQWZ27joijbIsetVKkS9u3bh48++giLFi3C+fPnMXv2bNStW5eOI263mzYCEolEsNvt0Gq1iIyMpHOjy+XyKA33bljGMIwHE0skEtGGtMHBwZQFWZKExYMAeUYPWzKD79vjvi/u81Gr1bTBpt1uh0wmQ0hICJ1b8vLyMGXKFNy4cQO7d+9GXFwcPU9R7F1fQXyRSCS4nctU9wZ3fler1ejatSu6du2K5ORkbN68GVu3bsXo0aMxffp0dOnSBR9//DG1Hb7vgIxdYrG4EIvWbDYjOzsbUVFREIvFHqXv5DofFUpShVUUuHO8RqOBWCymthseHk6TxuS3RJPVarUiPDwcLMvShptmsxkrV67EwYMHsXDhQrRu3drve/Bnu1AQsnLlyhgwYADee+892O12HD58GCNHjsTLL7+MCRMmYNSoUbSZdU5ODipUqACGYWhzTtKo2m63Uz+ckDW4MkzEZwcKV0mU5fnuYcAfGQ/uGES+TcLyJtDr9bTBIdHM5wvmkqD/999/j/feew8ymQzff/89r81xr1Fo/CEVFEL7kfceFBSE559/Hs8//zyAgiByZmYm/vjjD5w5cwY///wz+vTpgy5duqB///6oWrUqIiIiqP4xaa5NQGyH9Fbh80cBz/GdVOL+l+VQAggggAAeJAJB44cAvoULl7kgNGGTYEVoaCiCgoJw584dDBgwAH/++Sdef/11zJo1q8zpFsbGxuL1119Hv379sGvXLkyYMAG9evVCp06dMHnyZMTHx9OFMCkJJ5IYxFEvytHibvfFAOPqfpFz/hucWG4HdcI0Lg6IZInD4aABBwJilzKZDDabDd9++y2WLl0KvV6PF198EZ988gkqV67s87mXBYjFYsybNw9RUVH49NNPodPp8M4776Bq1aoeesa+AnreWmukgRVhBXozy7yd/7IIPm1GvqAECahzEzLc5+H9rPh0ER0OB7KyshASEoLw8HCYTCbMnj0bixcvhsFgQJcuXfDRRx/hiSeeeAB36h8qV66M3bt30+tKSUlBv379KBNdLpfD4XBQxk3jxo0REhICq9VKZSu4AT2+hlreGtlCC5v/ggYkqaABChZ/xPa8F4VEnik8PBwSiQQxMTEwm820YgUo+HYtFgv69++P3bt3Y8aMGRgyZIhfzy47OxupqamoUqVKqevlKxQKfPnll2jZsiVGjBiBnj17YvHixWjfvj21C8LU1Gg00Ol0AAoCc4SNT6SAyNx17949KBQK2rDMG6R5KWkwRI7Ffd7/hrmvuPAel0jiiySSrVYrbt++jcqVK0OhUEAmk1EdfIlE4jGebd++HVu3bsWkSZPw1FNPPZL78QcVKlSgeu2HDh3CmjVrsH79ely+fBnLli2jurje8gmkaTJh2QrZCxnHSDWAP1rcZRne7H1fjTz5qozIfiaTCVqt1iNYLZPJcOTIEXz77bcYMGAAhgwZ8jBuqRBkMhk6dOiA48ePY+jQoZgyZQouXryI+fPnQ6PRwGKxeDDryT0GBwcX+oZ86V8/LgnSsgTuMyP25v2sTSYT7HY7TCYTb4KT2KXT6cQnn3yCdevWoXnz5li3bh0qVKjAq2X9ICESiVCjRg3UqFED/fr1w6effopFixZh/vz52Lt3L7p3744333wTYWFhcLvdUKlUHqQhcgwSMCbfo3cDYtKjhASMA7YXQAABBPDgULYjP/8S8AVU/JngdDodsrKyAAB//fUXunXrhqCgIGzfvh3PPPNMqQbuzGYz1Sm+ceMGrl27hnv37iEvL4+yXQj7l2TDy5cvj2nTptGSDy4YhkH37t3RuXNnLF68GLNmzULr1q0xZMgQjB49mjJZiFNASrK8F2oEJWENezOSH/fFDQHXWSdNwPwB9xmS8lOtVkuZoyKRCGazGSdOnMBPP/2E9evX02DxhAkT0LBhQwAoxDIqq2AYBuPGjUNcXBzeffdd5ObmYtWqVZTRB/huiuTdBMxsNlNWIJ+e4ePgtJJr5C4++K6VMD24tuWrKRrfAjI1NRXXrl1DfHw8jhw5gilTpkCv1+OFF17Axx9//EiDxVwoFArMnz8fzzzzDN58802MGjVK8LdxcXH44YcfoFAooNVqERQURJuLkoAeaXJFwH02ROMQ4E9SlNUmZqUFi8WC7OxsOqeQ+yQNpLjBdgBIS0vDxx9/DK1WC4ZhqJ4owzCw2+0wGAy4fv06FixYgLfeegu5ubmC53Y6ndizZw/Wrl2LU6dO0XdUoUIFVK1aFU2aNKGNYcuVK3ffgfs+ffqgbt266N+/P3r37o2RI0di9OjRiI2NRUREBA3SkAa3RAeVPA+SmCKNjkgzSAJu81yRSETlLIgcTF5eHjQajUfg578G73GJJKaNRiMsFgtMJhNlupEmvZGRkYWkCY4dO4aPP/4YHTp0wAcffPAI78h/iEQitG/fHu3bt8err76Kfv36oWfPnti9ezcUCoUHs5/M/5GRkR7NibkBVaL5To6t1WoBFDTjK+tJZF/gztvAP7IffGMwYVwTW+H6VC6XC+Hh4ZDL5bh58yauXLmCa9eu4ZtvvkGLFi0wd+5cv64nLS2NasTabDaa3LDb7bQJ9tNPP41evXp5vBN/EB4ejq1bt+KLL77AlClTcPnyZWzduhXx8fEA/mE0ezd9BfjneO9KBr7mnwH4JhTw+aDcZ03GfjI3yGSyQuSj/Px83L59G0OHDsWlS5fw3nvvYdq0aYXksx4V1Go1xo4di/79+2PmzJlYs2YN9u7di9dffx0jRoyAXC5HRkYGDR4HBwdTCRRC1oiKiqLrRDLHkQbLAdsLIIAAAnjweHw9vTIMb8YYX5kWHyONC6fTCZfLhbCwMEilUvzwww9gWRbnzp1DTExMoW6zxYXb7caJEyewY8cO/PHHH7h+/Tp1SqKjo1G7dm00btwYoaGhtMs3cY5JhvfgwYN47bXXMHjwYIwbN473PHK5HKNGjcKAAQPwySefYOnSpbh58yYWLlyIuLg4D+1UiURC9dP4NPSKyxomjhc3M/+oGgU9SpB7djqdNOAZGhqKtLQ0WCwWWCwW6HQ6bNu2Dd999x3OnDkDiUSCLl26YPz48TRYXFrIzMzEt99+ixMnTlA753aQJzrLX3zxBerWrXvf5xsyZAgiIyMxcOBAtG3bFsuXL8fzzz9PA1fcBpPeEhXchWNUVBSys7PhdDopE4u7GPAVgC4r4I473BJbX+CyaZVKJQ0q+GoqZjKZaCDh008/xbZt29C2bVvMmjULNWvW9CvIcPXqVdy8eRN16tRBYmKiv7dYYnTr1g0dO3aEVqulTHwS2HM4HLhy5QqGDh2KTZs2YejQoZStp1arCwX0hHSvfS1sfAXlHxdwbYVo6xLtU5KcIoEO7n1arVb6zDQaDR3b+/bti0OHDtGu7BKJhJZjE0mBTz75BK+88orgNRmNRmzcuBGLFi1CamoqEhIS8N5776FmzZq4efMmrl69ikuXLuHw4cM0kBwWFob4+HiUL1+eNveMi4uj/y5fvrxfQeWaNWvixIkTePfddzF//nysXLkSiYmJqFixIipUqICKFSsiNjYW8fHxVLtRKpXCarVCIpEgKSmJBrCCgoIKjU/ecyBJxBIpl0ctS1HWoFQqwbIsnE4nLBYLypcvD4lEgooVK4JlWUilUhiNRpjNZjidTgQFBSEnJwfDhg1DVFQUli1b9lg+z86dO2Pfvn3o0KED3nvvPWzZsoVqbev1euTl5SEqKgoSiaSQRAzxNUUiEZVJk8vlvOPe4wi+eZuviog0Zib2k56ejvPnz+OPP/5ARkYGbt++jfT0dNy5cwdWqxVAQRD2qaeewtq1a31qFhOcPn0aQ4cOpQlJuVwOmUwGuVxO/2ez2TBjxgzMmjULHTp0QI8ePfDSSy/5lEDhQiQSYcyYMWjYsCG6du2K+fPnY8aMGR6a19wgnC94J8r9kWP4L8E7wEnY/d7kF26TPG8Q+ROFQgG9Xk+rA91uN7KysmAymSCTyfDZZ5/h2rVr2LlzJ5WJKGuIjo7Gl19+idGjR+PDDz/EokWL8Ntvv+Grr75CaGgobeQukUig0Who41syXpOkKF9CNGB7ZQvt2rXDvHnzfPpJ/vj17dq1K9VrCiCAAEqOxy5ozDBMKwBOAOdYlnUW9ftHAT5dS2+QCY4bkOUyW5KSkmiTCb1eTxufkU7xJcW1a9ewatUq7Nq1CykpKVCr1WjZsiVeeOEFNGjQAAkJCahWrRrvvqQpBsHYsWMxc+ZMrFy5Er/99hvWrl2LBg0a8O4bFRWFpUuXomXLlnjnnXfQqFEjvPHGGxg9ejSkUimVqCAOg7emnC/WcFFl3XxMEnLcxxHF1ankPksS1CHBWY1Gg507d2Ls2LGw2Wxo0KAB5s2bhz59+hTqssyHv/76Cy6XC/Xq1Sty0XLjxg2sW7cOO3bsgMvlQtOmTREcHEyDbyTIJBaLceLECfTo0QNr166lAaP7Qbdu3VCjRg28+uqr6NatGz788ENMnjyZ2prQ98rV8RWJRIiKivJgYnnv680yLWvgOtb+OtgWi4VWPKhUKtooh4xF3raYn5+PrKws2ixq27ZtGDVqFGbOnOkhL+ALBw8exLBhw2Cz2QAUaObVqFEDjRs3Rv369VGvXj3ExMQgOTkZSUlJuHLlCrKyspCUlITk5GRotVrExMQgLi4O8fHxSEhIoIG/xMREwXFUqVRS1hXg2Vm8SZMm2LBhAzZv3ozRo0d7BFckEonHt8jVJCyKrUXwb9CAzM/Pp8zr6OhoGmi6e/cu1Go1KleujODgYLAsS3U/iYam0WiEy+Wi8goLFizA3r178dVXX+HNN9+k5yBs46KQmZmJpUuXYs2aNTAYDHjiiScwadIkPPfcc4XGKoPBAJVKhcuXL+PSpUv4+++/kZaWhnv37uHEiROFqitq1aqFV155Bb169SrUHNYbGo0Gq1evRu/evbFv3z4kJSXh7t27+O233zyOq1QqMXDgQLz77rsICgpCWloadDodypUrh8TExGLZRmAB7RtqtZrKUISHh8PlcgEo8HHI2BUZGYlKlSph0qRJVMfYV7NEf3Dnzh1MnDgRbrcbkZGRiIiIQHBwMOLj4xEZGYnIyEhazaLVapGamkplD3Jzcz2CkS6XC1KplBITEhMTMXbsWMGGsk2aNMHs2bPx7rvvYvHixXj55Zfp/bIsC71eT30/kvAiOu5Op5OO8cQHAwqPe48DiL/I9SW53wrfd2M2m3HlyhXk5eWhXLlyyM3Nxe+//4633noLdrsdSqUSlStXRpUqVdCxY0dUqVIFVapUQYMGDagfRd6dEDZs2ICPP/4Y8fHx2LZtm4cf7u1T3LhxAxs3bsS2bdvw008/IS4uDq+99hr69u2LChUq+PUc2rZti/79+2P9+vX44IMPEB8fT9+xv6QKrl9OmkHy7fdfkF7ig3eAk1SAkHWe0+mE1WpFREQEndO8/Xu5XO7ReFClUiE6OpoGky0WC7Zv347du3dj5syZJQoYu91ubN++HV988QX0ej1CQkIQGhoKtVqNyMhIhIaGIjQ0FOXLl0eNGjVQvXp1v5IgQqhZsyZ27dqFH374AW+//TZeffVVbNiwAdWqVaPJKHJdRIKCT7qD2Jov2a8AHg0mTpyISZMm3fd6aOLEiaV0RaV7rAAC+C+CKcsBDm8wDNMJwEoArwI4xrKsk2EYhi3GTTyojqXcS/B2kHx1yyW/JQFipVKJ7OxsWo4mlUohEokwa9YsrF+/npbfGo1Gnwtn7racnBxs27YNGzduxNmzZyESifD000+jT58+eP755z2c5JycHMFFh3fQmODIkSMYNWoUdDodpkyZgvfee6+Q48jtxn3nzh3MmDED69evh1KpRO/evTFgwABUqFAB8fHxkEgksNvtSElJQUhICGW1CDmxpOSbdLMmz5Wv2Q2A+2IaP6huy77gbd7e91tUcyen04nc3FyEhoYiJycHWVlZUCqVsNls2L17NyZNmoT27dtj1qxZVDKALKL5kJeXB4lEgm+++QaffPIJgIIOyW3atEHz5s3Rvn171KxZk9r+0aNH8dVXX+HAgQNQKBR4+eWXMXToUFSsWJEeU6/XeyzIU1JS8MorryA1NRXLly9H+/btBa/HbrcLLuZdLpdHgsFsNmPEiBFYv3492rVrhw0bNhQqRwb+sR+TyQSDwQCJRMIrSVHc7tjA/XWKv59O3iXZj0gCEKYoANy7dw8qlQrlypUD8E+TFm7zrYyMDFy9ehXdunVDq1atsHv3bnrf+fn5gmNXRkYGDh48iHfeeQe1atXC1KlTcfPmTVy6dAkXLlzA9evXBYPOISEhNDgcGRmJzMxMpKSkICUlBQaDweO39evXxyuvvILu3bsjNDSUd1wDPIPGALBnzx706NEDX331Ffr27YugoCBeuRzSFIk75hRHWudBLHwehm1xmcYsy3pUxHDZsqTCxGg0erCHgIKA55EjR/Dmm2+ie/fu2LBhQ6Gmi0L2k5ubC4lEgt9++w1DhgyBXq9Hly5d8PbbbyM+Pl5Qv9hgMPgs9bbZbEhLS0NaWhquX7+OHTt24MyZMxCJRHjmmWfQv39/dO7cuVDlEEmG8YGwF+/du4ekpCQ6RwcFBaFv374YPHgwzGYzEhISEBkZ6TFPl6Qxlj/7lhRlrUu80JhH2KJRUVFQq9XUVoECnWuXywWGYaBQKBAUFIQjR46ge/fu+OCDDzB58mTBZ0cCZkJwOp24cOECXnnlFdjtdiQkJCAnJwc5OTlwOBw+70UmkyEiIgLh4eGUKc2yLBwOB/2WWJbFlStXEBsbi2+++QZNmzblnRNZlsUrr7yCn376CevWrUO9evUgkUhgsVjgdDopg97lckGtVkOn08Fut1MGssViQXR0NP1mvccyX76Ir6Dhg5gThWyA+E9kriLa6t73wr1ms9mMmzdvQqfTITw8HG63G++//z4uXryIY8eOoVq1ah7yOXywWq28iXWHw4GRI0dizZo1ePrpp7F48eJCiSiheyE+3K5du3D48GEAwCuvvILp06cjLCwMdrtd0J93u93Izs5GzZo10bFjR6xevdpDlophGDpvEbYseU7e8xi3KSffGMv1WcsSWeNB+fPkffERgri+QF5eHjIzM1GuXDlERUV5SAsRn4rMjeS5k/dKJGb+/PNPPP/882jZsiV2797N61sIaRqzLIsffvgB8+bNw+XLl1GvXj00bNgQBoMBer0eOp0ORqMRer0eBoPBo2ozPDyc6hZXr14dDRs2RNOmTcEwDCwWC4KDgwWfD3dOvHLlCp577jmIRCKsXr0ajRs3htPpREREBE2chYWFITExESzL8lZw8a0BvfEogsllbV582CBrwH/7OR8Fihq7AgjgQeCxCBozBaN9GICtAOawLLuXYRgVy7IWzn/9Ch4/7MALULgzO8uytNlYbm4uQkJC4HA4IJPJoNfraaCClMDJ5XIsXLgQ3333HWX8paam+mR25uXlITs7G6tWrcK2bdvgcDhQvXp1dOnSBY0bN0aVKlV497ty5Yqg08uyLBISEni35eTkYNmyZdi/fz+aNWuG2bNn06ASAI9/E1y/fh2fffYZduzYgdDQUAwZMgQjRoxAdHQ0tFotUlJSaKY9MTGxkLPJsixlqXovYNxu930tnIW2P2gnkw/e90LKuInupdC1OhwO6vgTdqjJZMLNmzehVCqxdOlSrFq1Ct27d8eSJUs89M8Ik4gPp06dwurVq7Fz5060bt0aLVq0wF9//YU///yTsgwjIiLQuHFjJCUl4erVqwgPD0fHjh3Ro0cPXmfy7t27hZgLeXl5mDdvHlJSUjBjxgx06dJF8D6F2DUWi6XQIoZlWWzYsAHjxo1DWFgY1q5diyeffBIulwtarRZKpRIqlYpKsWi1WjidTqjVathsNtqcSwjkXQpp2PmyPZFIVKoLZOCf8ae410P2y8vLQ0pKCuLj42nTHJZlqXNOnk9ISAhkMhl0Oh3EYjH69u1/gy36AAEAAElEQVSLEydO4MSJEx7jRkZGhqDW3tdff4358+ejRo0amDp1qscCwGg0IiIiAsnJybhx4wYt1SxXrhzkcrng2ETuPTMzE5mZmbh37x4OHjyIGzduQCKRoGnTpnj55ZfRunVrjwAxeTbc5Ibb7UbTpk0REhKCDRs20HOThBoZk8kCmywCuY3JhPQyufDFzPK17X6CL3/88Ydf5+CCOzZxxyWRSESTVVarFSzLIi4ujn43DocDLMtCp9NRlhoZu5OTk9GzZ08oFAocPXq00Hih1WoLvSeCmzdvYseOHfjqq6+QmJiIzz//nL6/9PR0weRARkaGT8YwXxItOTkZBw4cwMGDB5GTk0M7yA8YMIDaotPpFBybzGZzITv4+++/8dlnn+Gnn35CZGQkBg8ejEGDBiEiIsLjXfjSqixp0PhxSnYVda1C+5pMJmRlZdFmpmazGWq1mmpqk6RDUFAQsrOz8dRTT6F8+fI4fPgwDeTwQafT+WyouGbNGsycORPBwcG0ITC5zuzsbIjFYhqYkUqlCAkJoYx8IX3tvLw8j4qJK1euYNq0aUhPT0ffvn0xadIk3u9Ep9PhhRdeQHBwML7//nt6LQaDATKZDGazGVKpFG63Gzk5OdBoNDRQqtPpaIM0vsS7r6C6r6BOaY1bXAjZCLfZr91u9wjShYeHw26300CpyWSi8lXEP9Fqtdi2bRsmTJiAhQsXYuDAgfTYOTk5gmMTkZzgQqfT4d1338WJEyfQvXt3DBo0iNevL6rhc4UKFZCWloYtW7Zg48aNCA0Nxfjx49GmTRvExcXx7ud2uxETE4OPP/4Y8+fPx9atW1GvXj1oNBrajJQEwU0mEx3bNRoNWJalNgAUfFckaKzRaATnrKISpg87sPco/Hmn0wmbzQaZTEbl4UiFQV5eHk3IEJ+K2J9YLIZIJEJOTg7154ODg/H000/j3r172Lt3r2AFFV/Q+M8//8S8efNw5swZlCtXDv3798dTTz3l8X64yVQyHty7dw/37t3DrVu3kJGRgTt37tCEb+XKldGzZ08888wzqFq1quAz8J7Tr127hl69ekEkEmH58uVITExEdHQ0QkNDkZ6eDpFIBLFYTJ9beHi4R+BZSA6stOBrzvSFkvrz/wZ06tQJe/fuve8AbqdOnQAAP//8s1+/9xU0Lu6xyjICQeMAHgUei6AxAcMwXwEYCSAaBYzjTBRIVSxjWfasj/2GARgGAAkJCY3v3btX7HMLPSd/NHK9g8ZGoxHZ2dkACibzqKgoREVFwWw2w2AwwOFwIC4uDg6HAykpKcjPz8eiRYuwZcsWyjTOysoSDBprtVrMnTsXmzdvhsPhQNeuXfHqq6+ievXqAAoCzuXLl+fdd//+/YILa4vFgqZNm/JuMxgMqFSpErZv344ZM2ZALBZj8eLFaNasGQAIng8Azp07h1mzZmHPnj2IiorC/Pnz0b59e1gsFlrSV65cOd7gH1C4PJ5sux8HtDhB4wdpX2QbSTTw3acQSKCD22CJBEZnzZqFNWvW4I033sD8+fML2ZLdbuc9pt1uR58+fXDs2DG88MILGDx4sMe+t2/fhtFoxKlTp/DHH38gKCgI/fr1w4svvojbt28LSl789ttvvA4vsf3r169j+vTpGDBgQKHfOBwOwePyNdciuHDhAgYPHozbt29j6tSp6N+/P1JSUqBSqVChQgUaRCLfOGGpxcbG+pSJcblcyMnJgdPppCV+BEXZpbeT6a9tFWU/3GoG70W7r6Cx2+3G3bt3kZmZicTERLqI+f+1ejBL1Wo1cnJykJGRgf3792PcuHGYM2cOhg8f7nHcjIwM3neyefNmjBs3DvXr18e0adMKMfdSUlIEk12pqamoVKmS4DNwOgsrGd28eRN79+7FL7/8Ar1ej7CwMHTu3Bn9+vVDbGws/V2NGjU89lu6dClGjx6N3bt3o2PHjsjJyaELZmKH5HslTFqRSER1pMlih+975mvy4v2+fAVgihN88batu3fv+nUOcn/eQQDvfci4de/ePej1epQvX55+N0QX3Pv5uFwu9OjRA4cOHcKRI0d4myVmZ2fzBmYsFguGDx+O/fv3o127dpg+fbrHtaempgoGUP7++2+f37Sv0nKDwQCTyYTvv/8e+/fvh1gsxvTp0/H888/DZrMJ6vYZjUbBQOMff/yBzz//HIcOHUJ8fDxGjRqFQYMG0fsRCkwBJZ/7SjNo/DDmRH/u0bvKiMyFpLEUSRKSZFhmZiZUKhWuXr2KcePG4erVqzh+/Dhq1aoFvV4v+Nz1er3gu9y2bRtGjhyJ+Ph4TJw4sZB/lZycTH0zb9y7d09wzMvKyiokJWY2mzFv3jz8+OOPaNiwIRYsWMBrf1euXEHnzp3x8ssv45tvvvHQsCUBKZZlPZJhhJmanZ0NtVqN8uXL+y2BUlQSqrTGLe9z8lWbEXY2dxvpVUAkS4KCgmiwjsz7XOmQTp06oXr16ti/f7/H+J2ZmSmY0MnOzkZYWBj9/1evXsWQIUOQlZWFoUOH4qWXXhJ8Br6SRDqdzsN+/v77b3zyySe4evUq2rZtiy+//JLXP3K73YiKikJubi5q1aqFevXqYerUqahcuTJ9DhKJBCEhIdDpdJBKpXA4HAgLC4Pb7eaVBygqaPcoqiB84UH5875gMpko053YHAnMkm+E+28SBDMYDLBYLDAajcjNzYVIJMLGjRvx9ddfY+nSpXjhhRcEz8mtZrh58ybmzp2LX375BREREXjppZfQvXt33rEtOTlZMPhL7I5lWWi1Whw9ehTfffcdLl26BIVCgW7duqFfv368fUn4/L9r166hZ8+eEIlE2LZtG5o2bUrtUKvVIjMzExaLBTExMUhISPCQKST9b3z12bgf+YpA0Lj4eOaZZ3DkyJH7Dho/88wzAEArKYqCr6BxcY9VlhEIGgfwKPBYiJExDCNiGEYEIBHAPAATAHwPYBmAywBGMQwTygiM7CzLLmNZtgnLsk2K2224KJDSIaHyH18ICwtDREQEZTTK5XLKMrbZbHA4HLDb7cjPz/dr4DUYDJg9ezaaN2+ONWvWoH379ti1axcmT54suCjxBYvF4lG+WRQYhkGvXr3w/fffIzIyEoMGDcKhQ4eK3K9evXrYsWMHjh49iri4OAwZMgT79++nJVjcwBsJUpGAVnZ2NnJzc5GdnU3/ZjabPZ4Xd58HgdK2L77rJXbmjx4sF/n5+TCbzbDZbNBqtbh37x7Gjh2LNWvW4N1338XChQv9bqBiNBrRt29fHDt2DK+//jptBsZFdHQ0unfvjlmzZuHgwYPYtWsXevXqVWL9M6VSiZEjR+K5557DxIkTMX/+/FIrPapTpw6OHz+Onj17YuLEiRg8eDBl/DudTvr8yYJaqVQiOjq6SF1Lq9UKp9NJg6r3Y3elZVuEHVacpkWEWSKRSFCuXDmo1WoYjUYPGySLavI3jUaD9PR0zJw5E82aNcPQoUP9Otfq1asxduxYPPHEE5gxY8ZDaa5UtWpVvPvuu1i2bBkWLlyIxo0b47vvvsNrr72Gy5cvC+7Xr18/hIWFYeXKlRCJRAgPD0dMTEyhYJBKpaKsYpVKRQPGhP3P9z1zZRq4TC7v4wptKw582VZR5yBJCO71c22Mm0yIi4tDaGgoQkJCaDMgAFQCgPzeYrHg888/x969ezFnzhzegLEQkpOT0bVrVxw4cAAjRozAvHnzHpqmr1gsRqtWrfD5559jz549qFq1Kt5//318+umnRUoPCKFRo0b4+eefsXfvXpQvXx5jxozB0aNHS/nKHxwepM9VHHC/M8IcZf7fANVms8FisUCr1dJgTEpKCsaNG4cOHTogPT0da9asQa1atUp8/iVLluCdd95BzZo1MWPGDMGEfGlBrVZj0qRJmDhxIm7duoVOnTph+/bthebMZ599FqNHj8bmzZuxZ88emEymQmMRwzCQy+XIzMxERkYGrS5Rq9UefSb8AVf2AACVpynJXF4S2/LlP5FeBcHBwZRJy21QFhkZSZOIer0eM2fOhMFgwFdffeU3q5FlWWRlZeH48eNYt24dpkyZgu7du8Nms2HLli00mFEaqFWrFjZv3oz33nsPv//+O5555hls3bpV8FmHh4fjs88+w/Hjx9G+fXuMGDECZ8+eRVBQECIiImCz2ahMl81mw61bt6hsnnfS2JvQUNJ3/CjxoMcuMk8SHWOSQCX2RqoHyfxFZJxSU1NpojEyMhKnT5/G119/jSFDhqBDhw6C53M4HDh16hRmz56Nzp0747nnnsOxY8cwevRoHDlyBB07dvSZhCwKDMMgMjIS3bt3x6ZNm7B582a0a9cOu3btQpcuXdCtWzds27bNZ/UiUJCc3759O9xuN/r06YOLFy/CaDTC7XZDoVAgPj4eiYmJNGDMHdfz8vJ8JnaLWqc/CFv9/7EeixhLAAEEEIA/KNMDGsMwUQzDiFiWdbMs6wbwDoAqAJqwLLucZdlTAH4G4AJg9Uee4n7hHdDjlur5C1K+S8rA8vPz6eLFZrPBarXC5XJBLBbTybKo42/atAnNmzfHl19+iWeffRbbt2/Hp59+6rNk2xdOnTqFsWPHYsyYMdi5c2exJtPExERs2rQJ1atXx9tvv409e/b4tV+zZs2we/duREVFYdy4cUhOToZUKkVkZCREIhFcLheSkpKg0+lgNpuh1Wpht9vhcDio48VdHJB3RRhwxQ24PirwLXAIG8o7CMkNkrtcLmRlZXmUUpMmGiSYM3PmTPz444+YMGECZsyY4XfWXa/Xo2vXrvj9998xfPhw9OrV66GxQqRSKZYtW4ZevXph3rx5mDZtWqk5d8HBwVi7di0WLlyI48eP4/nnn8fKlSuRnp6OnJwcD2mH/Px8BAUFFRlkJ4kOIgdSFuyO2xjL3wSK2WyGXq+H0+mkerDcIJ/ZbKb2lZeXRxuerV27FmazGd98841fCYk1a9Zg4sSJ6NChAz788MP7arBSEojFYjz55JOYM2cONm/eDIVCgSFDhuDYsWO8v1er1Rg0aBB++OEHXL58Gbm5uQgLC4PNZvNIXAGgOqAmkwlGo5EuWoSC+OTvGo1GUK/cOwDzIFDUOUhQmXv9xMaILAe5X5L8MxgMMBgMyMzMhMlkok1gs7KycPfuXezbtw8zZsxA7969/U42AMDZs2fRsWNHJCUl4fPPP8eQIUMeiX4hUFARs2bNGvTv3x8bNmzAoEGDkJKSUuLjtW3bFgcPHkRiYiKmT59eauPe4xrQ8YavhDD5FgmrWC6X08AgUPCtEdkPt9uNb7/9Fj169MD27dsxatQoXLx40Sf70xdYlsX06dMxefJkvPjii5g4ceJDbUzYtm1b7N27F3Xq1MH777+P9957r1ACY+rUqXjiiScwevRorF27Fj///DN+++03KiljMpmo7NTFixeRnJxMnyGfvn9xQJJOJSFbcOF2u/2yYX+SpkS6hFvtARRIQ0RHRyM6Ohq3bt3Czp07MXr0aNSuXdvnOX/77TeMGDECnTt3Rs2aNdGhQwe8+uqr+OSTT7BlyxY0bdqUMsJLG1KpFMOGDcP69etRtWpVvPfee3j99deRlpbG+/vBgwfj+vXrGDduHE6ePImuXbuiTZs2+OGHHwAUyJWRKrXMzEwkJSUV6Vv7esdEz500P32QpI6yBjJPEoZxaGhoIbsk4zPXbyC9XVQqFdRqNWbPno0aNWpgzpw5vOe5du0a3nzzTTRo0ACvvPIKli9fjuDgYHz44Yc4fPgwRo4c+UDGpDp16mD8+PE4deoUJk+eDKPRiDFjxqB169ZYvXq1z31r1KiBbdu2we12o2vXrjh79iyys7ORnJwMAB764+R7Jv8m47o3sYj4qf4kwe93PPI+JgD/GDkBBBBAAI8BymzQmGGYbijQMH6VYRgy8KYB+BJAJYZhyEzZGEBlAA/FI/cO6HEXyv7A7XYjNTWVNhmQy+U0GEgYHSaTCTk5OUhOTobRaMQff/yBNWvW+GQLT58+HRUqVMD+/fuxbNkywZJGf3H06FGIRCJERERg3759xV5choeHY+3atXjiiScwYcIEpKen+7VfdHQ0duzYAaPRiDfffBPZ2dnUASAdxA0GA1wuF20GmJCQQB0vrjPhzdp7GOzF0gDfAkckEkEkEhUKQnKDM1qtFmlpaUhOTqbviwSy8vLykJ6ejh07dmDs2LH44IMPihVYWbhwIS5duoR169aVKivGX0gkEsybNw8DBw7EihUrMG3aNJ/N+ooDhmEwbNgwXLhwAU2aNMFXX30FoCBQToJ/SqUSQUFBVPvS1wKHjAlqtbrM2F1JEyhE85EEzcm9kO/QZrN5BAqzsrKwefNmjBo1CjVr1vTrHJs3b0bdunWxZMmS+2K8lAYqV66MNWvWICEhAePHjxcct4YPH47g4GA8++yz+P7773Hz5k36XM1mM9UdJAxGAAgKCoJMJqOBCb55g8s2KsvgBpX5AnckmaBQKCjjmjCrTCYTHcuzs7Oh1Wrhdruxb98+iEQiLFmypFj3P3v2bCgUCvz8889o2bLlg7jdYkEmk+HDDz/EwoULcffuXbRv3x6nT5++r+N9/PHHOHPmDLZs2eLzt4SxTd4FeTfegZkHsUh+FPDFICXMYhIA5Y5VRP5Fq9XC5XLhu+++w+TJk9G4cWOcPXuW6g+XFCtWrMDXX3+N/v37Y+nSpT7lBR4U4uLiKOP0+++/pwFAAplMhjVr1kCpVGLChAno27cvOnTogFq1aqFx48bo3r07Fi5cCIPBgODgYDgcDqSnp1NN1vtBaVVLuFwuv2zYl59OKtaIHVksFhgMBo+ksdvths1mw969e6FWqzF+/Pgiz7lw4UL8/PPPCAkJQe/evTF+/Hhs2LABJ0+exJUrV7B27VqfkjilgYoVK2Lnzp2YNm0a1U3WarW8v42Pj6eNZ5cuXQqGYdCnTx9s3boVYWFhUCqVqFChAhITExEREVGkb+3rHXN7DOTk5DxWpI7SBNcuuQQQYoNmsxkqlYrOo2q1GiaTCbt370ZaWhoWLVok+A2tX78ev/zyCzp37oxFixbh7Nmz2Lx5M4YPH15ktVxpICQkBAMHDsSBAwewfv16VKtWDVOmTMGpU6d87lejRg3s378fNpsNn3zyCU0u6HQ6uv7hPjfvZ5idnQ29Xu9BIiJjf1FJ8Psdj7yPiQJCWwABBBDAvwJlMmjMMEwigE9RoFlcG0BvhmHELMu6WJY9DKANgCYMwywC8DGAt1mW5feEShklKfPmgkx4QEFQihs8IMwr0iVbLBbjypUrGDRoEGJiYrBt2zbB41qtVjz55JO8+lElQe3atWGxWJCeno7GjRuXiFWi0Wjw+eefw+Fw4KOPPvJ7v3r16mHPnj3IyspCr169cPHiReTn59MO4iEhIbBarZStLZFICjkQDMPQd0UCeI9L6ZzQAofP9kgwU6VSISIiAqGhobQbOnd7aGgo1XUtiiHjDZ1Oh1WrVqFbt2547rnn7vPuSg6RSIQpU6ZgwIABWLFiBQYNGkQDcqWBChUqYMSIEbDb7bhy5YqHrjNhN2i1Wuh0OmRmZhbJkikqofSgZVO4KEkCRa1WIzExkS5siQwF0V63Wq00yBsVFYXIyEjs2rULEokEw4YN8/vawsLCIJFIHnnAmCAyMhJz5syBy+XCpEmTeJMTcXFxOHz4MGJiYvDuu+9iy5YtHrqgBGShR/6t0+mQkZHBu3B/mPZQmuAL3JFklfc4HRUVhZiYGDqWR0VF0QaLAGhVhL/Q6/VUYqZy5cqlcj82mw0nTpyg7MqSzhPt27fHmjVroFar8dJLL2HTpk0lvqbXX38djRs3xvjx45GRkSFoI2azGenp6TTZRd6Nd2CGb5FM5kTvgHNZtEdyrQqFQnA845aBe/8mPz8fKSkpyMjIwMGDBzFy5Ei0bdsWO3bsKKQTXFwcPXoUkyZNQqdOnTBr1iy/5Z8eBMRiMUaPHo2aNWtiyZIlhd5lzZo1cfPmTSQnJ+Pw4cNYsWIFxowZg0aNGsFsNuPnn3/GokWLULFiRQQFBcHhcNDmxN62wScNJoTSqpYQi8WCgR5vexYCaToWHByMSpUqISoqykM2gMg0kSBeRESE3/7/E088gU2bNmHGjBl45ZVX0KZNG8HGhg8KYrEYQ4YMwdatW5GVlYXhw4f7lM1RKBTo168fjh8/jmeffRbDhw/HmjVroNPpYDAYaBUMme+EfBxf71ipVCImJgYxMTGIjIwsM8n1RwkuAUSlUlE9X6vVStn/QEEi//r16wCAxo0bCx5Pr9cjLi4Oc+fORadOne4rCXY/YBgGbdq0wapVq1C+fHlMnTq1SMJH7dq18eWXX+LUqVPYsGEDoqOjERISAgA+K9HItyyRSAqRiIq6xtJO1v//WGVv8nxIEGpcXpLjlMVjBRDAfxFlMmgMQAtgIIC3UMAubgmgD8MwMgBgWfYWgPYoCBg/w7LspYd1YcVlFntDqVRCoVDAZrPh5s2bSEtLK7T4kUqlCAoKwl9//YV3330XMTEx2LdvH+1yzQeihVxaaNCgAf33/bC3EhMT8eabb2Lr1q3FEp9v3rw5DRwPGjQIycnJYBgGCQkJVAtarVYXcga4Cxdf7+pxZFrx3Q9h8xFN54iICISEhNCFFMMwUKlUcDgclB3kqxM3H5YtWwaz2YxRo0aV2r2UFCKRCNOnT8dnn32GI0eOoFu3bkhKSiq147do0QJAQWfp+Ph4qNVqD5au0+mE0+mE0WgUZMn4G2zxDvA8CBDWCinP4yZQirpOkUhEJSnUarWHNIXBYIDT6YRer6fBZLvdjg0bNqBr167FYlDFxMQgKyurtG65VJCQkIBx48bhzJkzWLBgAe9vqlatimPHjuHFF1/EjBkzMHbsWFgsFrhcLjidThgMBrjdbohEIpjNZlgsFkRERCA2NpaX6VNS3fJHDbIw4waSuMksUulAGsYRWSaiLUvGI5fLVWwN4IMHD8LpdNKu2PeLO3fuYPjw4fj444/x3nvv4Y033kCXLl3QvXt3DBgwAKNHj8aMGTNw8uRJv4JjlStXxoEDB9CyZUu88847GD16NDIyMop9XWKxGAsWLEBGRgY++eQTpKen+xXMJe/GOzDDt0j2tr+yao9cNpnVahWc433N/0qlEvHx8TCbzfjggw9QpUoVbNmy5b59qNzcXLz77ruoUqUKbTD3qMEwDIYPH47r16/j119/5d0eHR2Nli1b4vXXX8ekSZMoI3bevHk4efIk/vzzT2g0GojFYmg0Gg9NUYL76e9RUohEIsFAD7keMncLfS9KpRKhoaFUcoPo1EskEsjlchqIEolEPhselnU0atQIc+bMwbFjxzBlypQif69UKrFz5060a9cOkyZNQlZWFlQqFZxOJ7RaLYxGY5HPVghc30IsFt/XmurfAm51DsMwiIqKQkhICMLCwqBQKGi1gkwmw7Fjx1C/fn2fwVC9Xk8DrWUBSqUS48ePx5UrV7B58+Yif9+3b1907doVc+bMwY0bN6DT6WCxWGhCms/u+L7lgG09GowZM6bUjlMWjxVAAP9FFC969JDAsqyJYZiLLMtaGYZZCWAwgBb/37yJYZjKLMveBqAv4fFLfR9f3YPdbjd1bBmGQUREBKRSKfR6PTQaDfLy8qDRaBAbG0snuSNHjmDw4MGIjIzExo0baVkzQXp6OmVnud1u2rhLr9cDAG7cuCF4rXfv3oVQx+mUlBQYjUaP+1WpVLh37x6sVitdENjtdpjNZtoNWiaTCS74n3nmGXz//fd477338PPPPxcq1XQ6neBrOPHEE09g69at6N27N1566SXs3LkTVatWhcPhoE0CtVotwsPDYbPZIJfLkZubC6fTSZu2CIEEVUuzHMkXirIf78Z9VquVal+TwBMfiM5cRkYGcnJyEBoaiuDgYKoZp1AoYLVa4Xa7aXZfIpHQ7s1CILaWnZ2NpUuXom3btlCr1bh37x7S0tJ4ux8DwK1bt/D7778DKAjA2O12VKtWDSKRCCEhIYLBxKKCpydPnvT4/9WqVcOECRMwb948vPTSS1i8eDGaNCncSDY/P1/wnHa7vRBrISwsDFWqVMGZM2doVQC387tGo6ENK4ECR5/IzJB3aLVaKavX277Iu+XTsHsQIMkRLquQnIvLPub7VrzHLbI/WVSTRBcpBdy0aRNyc3MxePBgmEwmwWvKzc31eC7BwcHIysqCTqejOuZ8yMzMhMFgEDym0JhGziEEUsbojapVq6JZs2aYOnUq6tatW4ih73a7Ua5cOXz77beoW7cuPv30U/z111+YPHky1aqvXbs2KlSoAJVKBZlMRpu68HV4Jt/Uw9Z0vh+wLEttg9gaAFpSC4DKdXC7wZNmr0ajESKRCGFhYQgLC6Nzma/AU2ZmJn1G33//PaKiohAXF4fs7GykpaXRa/DG3bt3kZuby7vt6tWrWL9+Pfbu3Qu5XI4+ffpAoVDAZDJ5SBwYjUZcunQJx44dQ4UKFdCxY0dUrlxZsAkPwzCoXr06Zs6cia+//hrr16/H5s2b0aNHD/Tq1Qt16tTh3Y8EqbioVasW+vXrh02bNuHpp59Gx44dERMT4xE0UyqViI2NhUql8ng35J0Q9iWfn8K1P64OpFKpfGDjk6/jkuv3hjebjOt7ke/KYrHQeyQSMcQnsNlsEIlEEIvFGDVqFMRiMbZt2wa1Wo28vDxBKQmTySQYpLl+/TpEIhGmTZuGnJwcTJw40cMHu3r1quB95uTkICcnB2azGWfOnIFIJEKFChUQFxcHt9sNnU7Hu5/D4fA5Z3KfXd26dRETE4MvvvgClSpV8hn4tFqt1Ld8+eWXMXPmTEyfPh2//PILZDIZXC4X5HI5goKCaBNn4J9kNPFFH3Wghqtx6j0fc6+PfCOk4RjLsmBZFk6nE7m5uR4+FWHaCn3vWq2Wjk3ERyBjTmpqquD7unXrlk8JE1/fCWlyzAfS8JHgiSeewMsvv4xvv/0WUVFReP/993n3c7lcCA0NhVgsxvz581G3bl0cOnQIDRs2BMMwVNpFLpdDIpGAZVkP/4KPXU++U+LX8qGsyzIRFDUe+roPoX1JMNRisVB/TC6XQ6vVejRDT0lJwR9//IEZM2bQaji9Xl+IDKLVaqFWq6HT6Xwm5W/evCnoc+Xm5lKJLm5jaPJfIbsD4FGpR1C7dm3qKz311FO84xDXL//iiy9w5swZvPPOO9i2bRtiYmLgcDiQk5ODjIwMVKtWDcHBwbBardQWSaKaVCGqVCqfyaUAAggggAD8Q5kMGgPA/wPGzP//uxrAAAC1GIbZAKAtwzB1WJbl96YfAIqacLgLZjLhC3UWJkEXEuy02Ww0uCeXy3HgwAEMGjQIkZGR2LZtG8qXL1/ofGq1GqGhoQBAnUIiQwDAp2bVlStXBIMo4eHhVOJi27ZtlLELFDT2cDqdOHXqFA4ePAir1YrevXujYcOGsFgsdKHhDY1Gg08//RSvv/461q5di7fffttju0gkEnRaWrVqhT179uCFF15A9+7dsWPHDrhcLoSFhdEGXMSZIUxQspj09c4YhhG83kcB7rVyg47EbrzvhTjgMpmM6mJLJBLqNJnNZuh0OiiVShqAIceQy+W8QQMuFAoFLWV0uVwYOXIkXYRVrFgR0dHRvPt99913UCqVuHHjBm7evAkAOH36NKpXr45q1aoJ6nJHRESgRo0afj0fgvr162PWrFn47LPPMGDAAEyfPh29e/f2+I1cLhdMDMhkMt6FWsuWLbFv3z44nU5kZWUhMjLSQ2KAsEGAf5rKEc00ADSwQAJkXHDfLWnqwfe74sKXVpvQtZBt3oEX7jjFd1yVSgWVSoXw8HCIxWJEREQgJSUF27dvR9WqVfHss8/6ZCdynydQYE/ku42OjhZM9mRlZQmOW1evXkVCQgIMBgOuXLmCjIwM1K1bF1WqVIFIJKLjIh8sFovg9rfffhsffvghxowZg127dnkEjcjYKBKJ8OGHH6JBgwYYMmQIhg0bhrFjx6Jq1ao04EjOA4D+zbuZFGFc3c+C9H4gdFyykOWzIdLpHfjH7snYS+xHpVIhMjKSjtPcKpD8/HzKzObOAb4CKKTpjc1mw7Fjx9C9e3eavIyKiqL/9kZaWhrvItVkMmHTpk24desWqlSpgp49exb6HbehrMvlwrFjx/D9999jxYoVSEhIwMCBA3kDwGazGVKpFFKpFGPHjsUrr7yCFStWYMuWLdi5cycGDBiA4cOHIzw83GM/m83GWxEydepU7N69Gxs2bECjRo1oVQnLsvT79TW28/kpBIRFajKZaKKJzJGPQsaJa1tccMctwuLX6/UwmUyIioqC2WxGVlYWoqOjPXToSbKGBNJ++eUXXLt2jTYLAwrmCyFfhK9hFRd79+7F8ePHMWzYsELznK8qsZs3b+LixYs4evQo7HY7fdYikQiRkZGoU6cOKleujMqVK3uMfxkZGYLjodFoLGTDb7zxBubMmYNbt275LGuXSqXU9iQSCcaNG4eRI0fiwIEDqF+/PmW5k2SJXC73CHhZLBYqGfYoQcYYEiDm+oTecxs3qU7shDTx1Ov1CA8Pp8mK8PBwwcQedxuxI/Jt5+XlCc4zarVacNwC4HM+vXXrFmJjYwX387bZ0aNH4969e/jiiy/w1FNPoXnz5rz7EhuoVq0aKleujEuXLtFgcHp6OkwmE02CR0RECM4fhJmsUCjoHPiobaO0wSUWFDU/+/LVSJUO+Q1pik78eJfLhR07dkAkEuHVV1+l7yg8PLzQnGk2m1GpUiVERkYKJjkA0GbjfLh16xZMJhPOnTuHP//8E0BBFWqjRo0gFot516ZAgd0Jra/GjBmDgQMHYtWqVZg0aRLvvmT+ioiIwKpVq9C5c2d89tlnmDlzJiwWC1JSUpCTk0P9ca1Wi6ioKDoekv4aQEElG6lsKm0EAtHCKK3eN+Q4xalUfhjHCiCA/yLKRM0GwzBVGYZpwjCMB32RZVn2/4FjM8uy3wBoAKAVgE4PM2DsD0hpEdeZzM/PR0ZGBm7fvo3MzMxCpTTeXZoVCoWHhvF3330nOClzQRgNxW22kp+fjx9//BH79u1DdnZ2oe1kgUWu9erVq5g7dy5++OEHxMTEICEhAZs3b8bBgweLXFS2b98eHTt2xLx58wQ7OAuBK1XRp08f3LhxA1qtlgZsuI2WQkJCaDCmLGsy+gKfDpf3vXC1KrVaLSIiIhAREYHIyEh67/n5+ZBKpTTARxhL/mgsZmVlYeDAgZRp7K/Oo8vlwoULF3Dz5k3Ex8ejYcOGYFkW586dw4EDB6gWW2mhXLlymD59Opo1a4bx48fjs88+u+8GeS1atEBWVhYuXrwIi8VCF5hms5ku3ry7MpN3RZ670OKBqyX6IHTUvOGrsRoJ6JHAS0pKCm7fvk21r7ng2h8JEJBklUgkgsFgwLFjxzBkyJBiM8xIAiIzM7MEd1jAhr9w4QKWLFmC2bNn48cff8Sff/6J1atXY/78+Th27FiJJUCCgoIwe/Zs3Lx5E59//rnP3z7//PM4fPgwoqOjMWHCBJw4cQLlypWDxWJBVlYWcnJyAMBDL/NxgL9SPiQww53TyPdA5E0Iu5iwu8PDwxEcHEx1rQH4LVFx6tQpWCwWtGvXrsT39vfff2P06NG4c+cOOnTogDfeeKPI8nOxWIynnnoKn3/+Od544w3odDpMnToV06ZNw7Vr13zuW6FCBUydOhU7duzAk08+iaVLl6J169aYPXs2rRLyhYiICHzyySf49ddfceLECepvED1MMkb5KsUvSuPxQTQFKk14lxwrlUqf35TVaqWBK2KPmZmZ2LBhA2JiYtC1a9f7vqbk5GQsXrwYjRo1Qs+ePf3ax+1248iRI9i8eTMOHDiAypUrY8SIEfj444/x+uuvo02bNhCLxTh69ChWrlxJK2suX75coiB+165dERoaijVr1hRrvzfeeAPx8fGYO3cu5HI57HY70tPTcfv2bRiNRuTm5lJWX3R0NKKjo8uUPq33uMTnF5rNZty7d88jyUeSEsTPIk1kH5U8BcuyyMzMxOnTp/HXX38hNTUVFoul2LYgFovx6aefoly5chg4cCBSU1OL3Kddu3Y4cuQI/ebCwsJQqVIlWrUmxIYHChiv6enplFhC3sG/Cfcjd0eqP4B/SAlAQbDdZDJRGT7S4HTbtm1o27ZtkWvD+5GnMBqNOHDgALZv345vvvkGp0+fpomr06dP45tvvsHGjRtx4MABn1VlfKhVqxZNnt6+fbvI3zdv3hwTJkzAjh07sGLFCmg0GtSrVw/Vq1eHRCLh9e1UKhUdi8rqPBZAAAEE8LjhkTONGYbpAuAzFOgYZzAM8ynLspf+Hyxm/x84FgOoigJt4/Ysy158lNfMB24ghSx+lUolNBoNLBYL1XLkBmosFgvt3h0UFISbN2+iV69eVMPYX309Erzx9/dutxtnz57Fli1b6EJ127ZtqFChAqpWrQqxWIyKFStCqVTC4XDg3Llz2LJlC+7du4fy5cuje/fuqF69OlwuF7Zt24Z9+/YhJycHLVq08KmXO336dDz11FOYMmUKli1b5te1EjRv3hw7duxA586dsXjxYixbtoyWx3MXkFxmVFHl92UVZFHMhfe9kEWZw+GggU3uPtyFkl6vR3x8PGW8FGUnGRkZHgHjhg0b+nXdOp0Of/75J/Ly8lCjRg1UrlwZDMMgJiYGKSkpuHXrFmbNmoUGDRrg5ZdfFmTHFBcajQarVq3CjBkzsHLlSty5cwcLFiwosbNINLwvXbqERo0aeRyHPHdvNhuBPzZXVm1Rp9PB6XQiKCiokKQH937JYpqUJlutVqxYsQIymQxvvPFGsc9LgsZ8iSsh5Obm4tixYzh79iySk5MBAOXLl8dzzz2HOnXqICwsDJcvX8bJkyfx008/4eDBg2jVqhVat26NqlWrFkvXu02bNhg0aBBWrVqFp59+Gm3bthX8bdWqVXHo0CEMGzYMX3zxBdLS0jB16lRoNBoolUq4XC7Y7XaIxeLHRoaiOFI+XPsn4w+3WSWRXSALYDJ+m81mj/J2f+ayQ4cOQS6Xo1WrVsW+J8LY2rhxI6KiotC9e3c88cQTxTqGRCJBu3btkJiYiKSkJOzatQsTJ05E27ZtMXDgQEEJH6BA63/ixIn48MMPsWDBAnz99dfYunUrfv75Z0HWF8GgQYOwYcMGfPbZZ+jYsSMSExM99KTNZjNNSHHhS0KLC9K48HECl30MFLDLuHZLqpBIUCcpKQk//fQT3n///fvWMbbb7ZgzZw7kcjnGjh1bZNLM7Xbj/Pnz2LZtG1JTUxEdHY2+ffuiYsWK9Dc1atRAjRo1ULt2bVSvXh0pKSm4efMmfv/9dyxZsgQJCQlo2rQpatSo4TdZQKFQ4JVXXsGSJUtw5coVvxviymQyyjY+cuQIOnXqBIlEgkqVKsHtdlOmsUKhKHa/hIcF7rgEwKN0nYxFxF8nfzObzR4BPIlEAoPB8FCCxqT5mVarRVpaGq5cuYIbN27wyjPJZDJERkbSXh9PPvkkZc4LITg4GLNmzcKbb76J/v37Y/fu3T4D/c899xyWL1+OX3/9FZ07d0ZcXBxdHxQVnCRVjxEREVQLm8/HfZxxP3J33OoPuVxOpXVyc3NpNQGRSjl+/Dju3bvHy9DlgmVZ6PV6nxVWfLh27RoOHjyICxcuwOVyITIyEl26dEH9+vXpvVksFly8eBGnTp3Cpk2b8N133+GJJ57As88+i5o1a/p1nvHjx2P37t2YOnWqX0msUaNG4ejRo/jqq6/w8ssvo3r16khMTKQSHCEhIR7zLdHMDuC/h8TEREEijlwup31rAggggOLjkXp4DMO0AjAHQF+WZc8zDLMIwPsABrGc9DnLsi4A1/4vSeF/ZOEhgluaSyASiWhpDAFxVjUaDRQKBRQKBViWRUpKCnr27Ino6Gja9M5f5t2KFSsAQFAygCAvLw8///wztmzZAp1Oh/Lly+Ott96CRqPB+fPncf78efz666/49ddfwTAMYmNjYTQaYTKZEBsbi+effx5PP/00daQlEglefvllhIWF4dChQ9izZ49P1k6FChUwcuRIzJ49G8eOHUPr1q39uj+Cp556CqNGjcKcOXNw5coVNGjQAAqFQtD59H4n3EXzo+xozrd49/6bt06j970Qp5ssZkgJLtmfPBPCPsvLy8OiRYsAwGeTMpZlMXz48GIHjJ1OJyZMmACTyYRGjRp5BIRFIhESEhJQs2ZNsCyLn376CXPnzsW0adMKOdlOpxN5eXlwOByIjo72m4UrkUgwZcoUVKtWDZMmTcLatWsxfPhwv/b1Ru3atREXF4fJkyfjzp07GDFiBOLi4jzei8vl8mArkb8T5vHjxm5Qq9WoVasWbdYmBNKEiuh7ikQi3Lp1C+vXr0ePHj2KDHjxgTCd+BjO3jAajfjpp59w6NAhuFwuVKlSBX369IFEIikkD9CgQQM0aNAAaWlpVIf2119/hUwmQ6VKlVC1alVUqVLFr8XFmDFjcPz4cYwZMwY7duxAYmKi4G+DgoKwdu1aLFy4EFOmTEHz5s0xfPhw6HQ6ZGZmIjc3FxEREQgNDS2zQRYu+AKQBN76sd7jFJE+IcEl0lzKaDTCbrfTMcxqteLIkSNQKBR+BfEsFgt+/PFHPPnkk8VmNTqdTsyePRunTp1CmzZt8Pbbb+PYsWPFOgYXUqkUL7zwAtq1a4edO3di586dOH/+PHr37i1Y+k1QvXp1fPPNNxgyZAh69eqFhQsXYtq0aT73EYvFmDNnDtq3b489e/Zg4MCBAP7RaSUNK73xuCZRi0J+fj5MJhOCg4PpfEoCfyRBT2QrWJaFXC5HUlIS3G43Xnrppfs6t8vlwtChQ3Hz5k1MnTq1yPHv8uXLWLNmDTIyMhATE4MRI0bAZrN5yJ94QyqVolKlSqhUqRLatm2L06dP45dffsH27dvx448/okGDBmjSpAkaNWpUZLC/d+/eWLt2LSZOnIht27b5Pb8SaYvPP/8cHTt2hEKhoAlGoGD+JXNiUUmJRwHvcYlo8BK9a9KvgCtlwtVXt1gstG+EP01e79y5gytXrhS7ibTL5cLWrVvx448/ejCIw8PDUbNmTVSvXh2VKlWC3W5Hbm4udDod7ty5A5ZlkZubi1u3buHMmTOYOXOmTx1/oEAWasmSJejXrx82bNiAIUOGCP6W+P1nzpxB586d6d9JQ1yDweCx/uD6Q6Tyim9++LfA1xxZFLgBZ8Jqj4yMRHh4OG38ptPpYLVasWvXLgQFBRU5bq1fvx4ul8unLI43rl27hrlz50KlUuHZZ59F69atkZaWVmhsUqlUaNGiBSpXrozw8HAcO3YMJ06cwNmzZ9G3b1+/Kn+io6MxevRoTJ8+HStWrPBpe0DBGmLp0qVo06YNhgwZgl27diE0NBQxMTF0vHkUEkoBlD346m0SkBMJIID7Q1lYsX7Osuz5//97MoDlDMPIWZa1AQDDME0AxLIs++ODCBjzaVEVR5+KQChzTlg7pASJOKYkGEsWMaNGjYLVasWhQ4eKNdGvXbsW8+fPR+/evfHCCy/w/ubGjRvYsWMHDh06BLvdjsTERPTq1QsNGzakQYsOHTqgQ4cOuHHjBiQSCe7cuYM7d+5AqVSiVatWaNCgAY4fP15oMcAwDN1v48aNeOGFF3wGQt566y2sW7cOs2fPxq5du4o1iLtcLvzwww+oXbs2qlatSrUwhfSUuWxb8l+unuyjArkOrtae2WxGZmYmoqKiIJFI4Ha7admXrwAksTvSxAX4h+FHkhJGoxFDhgzBwYMHMWfOHDRo0ECwgcX69etpd21/A8YAsGzZMly8eBG1atUSZBBLJBJ06NABtWrVwmeffYYvv/wSYWFhyMvLg1arhdVq9Sg1CwkJQb169VCvXj00adLEr1K71157DTt37sSPP/5Y4qCxSCTCr7/+innz5mHVqlVYvXo1unbtiilTpiA8PJw2iOMy3Mk75UpPPE4gwSaJRMK74Fer1YiNjaV2SUp4FQoFli9fDpPJhI8++qjY583Ly8P06dNRv359PP300zhy5Ajv7ywWC44cOYKTJ0/CZrOhVatWeOmll2iQ5vTp04LnKF++PFq2bIm+ffvi77//xtWrV3Hz5k388ssvNFAdGRmJ6tWro2bNmmjbtm0hfUm5XI5FixahR48eGDZsGLZt2+Zz4SsSifDRRx9h/vz5uHDhAtXDDAsLg1KpREREBNxuN+x2O3Q6HSIiInjHTX/ZoY8K3oFI7jxIxjeRSEQlKcjfpFIpLBYLkpOTERYWRlmMa9asoeOXL6xduxbZ2dnF/sZdLhe++OILnDp1CoMHD8aLL75Yat+qQqHAq6++ikaNGmH9+vVYvnw5du/ejbFjx6Jt27Y+z9OwYUO8+OKL2Lp1K8aOHVske5SMzVqt1sMOuUlFru0ABe+DMN7Lul0VB3yBKBIMBP7RjmcYhjaHrV+/PoCCBWazZs1KdF6WZfH+++9j+/btGDRokE/Ge35+PjZv3oxff/0VsbGxePvtt9GkSRNIJJJiJSwkEglatWqF5s2b4+TJk8jJycHZs2dx+vRphISEYMaMGbxNhQlCQkLw1ltvYd68edixY4ffUhoymQzz5s1Dnz598NVXX2HAgAGFfmO1WukzL2tJCW//nEg2cZu3mUwmJCUlITQ0lOpXa7VaWCwWhIeHY9myZRCJRLz3zkVeXh4GDhwIhmEwceJEv6/RYDDg66+/xpUrV9CmTRvUr18f4eHhRZbY37p1i2pop6amYvLkydi+fTtNJvlCx44dUatWLXz//fc+A3ehoaGoW7cuTp48SRubajQammAOCwujCQOGYei8QOQ98vPzUalSJWg0mjJnG48a3IAzWXu6XC7YbDbEx8dTKb5ff/0V+/fvxxdffOFz/XL06FGMHz8e7dq1wyuvvOLXNRgMBixduhRRUVGYOHEiHUuLkhKsUKECXnnlFfTo0QNLly7Fxo0bYTab0b59+yLP+eabb+LMmTOYNGkS4uPj8fzzz/v8fXR0NL744gv069cPK1euxKRJkzzmLqKprlAoPKRoSP+I+60oCSCAAAL4r+NRB41PAbgEAP+XoJADSAQQDCCbYZh4ALUA7H9QF8DXGMZXsxguihNcJhIVQUFBsFqtyMrKgtPppM74iRMnsG7dOr/LewBgz549+Oijj9C+fXvMmzePd/G3c+dOLFy4EAqFAh07dkS3bt2QmpoqGNjTaDSoW7cumjRp4vd1MAyDZ555BuvWrcP+/fvRqVMnwd8qFAqMHDkSH330EX7//Xc8+eSTfp9n27ZtuHbtGubNm4eQkBCEhYXB5XKBZVnK+LRYLJDJZDQQQ8rhAE/W26Ps7s1dxHNLJoF/mmVpNBoP7UkhlhhxikhpFgm4GAwGiMVi5OTkoGfPnrh79y5WrlyJ/v37C15Xeno6Jk+ejDZt2vi9mASA33//HRs3bkS3bt380uWsXLky+vTpg59++gkWiwXBwcGIjo5GXFwcQkJCKEPmypUruHDhAn7//XcwDINKlSrhiSeeQKNGjVCjRg3Bb+6FF17AjBkzcOvWLVSoUMHv++AiPj4eX3/9NcaNG4cZM2bg22+/RYsWLfDOO+/AZrNBpVLBZrPxMivLGvwdp3wxEblNhWJjY+FyuaBSqWAwGLBx40b06NEDtWrVKtZ1uVwuzJgxA9nZ2VizZg0v+9/hcGDv3r3YunUrDAYDGjVqhO7du/ul9e4NtVqNJk2a0LHN4XDg3r17OHfuHAwGA65evYrjx49j3759mDNnTiEGcmJiIr755hv0798fI0eOxOLFi32eTyQSoWnTpjh//rxHQ1GVSgWn0wm73Y6cnBxachwVFUXZWcS2yjo71Jfdk7GJLNZIgy21Wk0rbEwmE77++mvs2rULU6ZMwWuvvVbkOY1GIxYvXoynn34aTZs29fta3W43Fi5ciGPHjmHAgAH3zTIVQo0aNTBt2jScOXMG69evxwcffIAGDRpg1KhRPiUwBg4ciB07dmDr1q1FPofg4GDI5XKqJeodAAM8k5MWiwVOpxOhoaFUw/x+7KokifUHBb6EPV+5OGkY6HA4ULNmTcjlcpw7dw59+vQp0XmnTp2KlStX4v333/cZJDl37hxmz56N3NxcdOrUCT179ix2/wlviMViVKlSBd26dcOgQYNw7do1zJ49G19++SWmTJnic98+ffrg0KFDGDduHNq2bVuoCaMQunTpgp49e2LOnDlo06ZNIXIDaSCnVCpht9uRmpqKuLg4yGSyIrX+SxPc6geGYTxYr2Rc5TbEIzq75L9EHxv4h1yQmZmJbdu2YfDgwT5JHS6XC++88w7u3LmDjRs3ekiO+MLt27fx7bffwmQyYdiwYXj66ac9tvurfx8XF4f27dtj3759ePrpp1GuXLki93nppZcwe/ZsZGRk+JQMa9myJTZs2EArQ8h/4+PjqVY9UPCdkfmASFiRZLNQfwWufTzuSaz7ARnHbTYb8vPzaaNhm82GBQsWoFmzZhg2bJjg/na7HR999BESExOxbNkyvwKlLpcLq1atQn5+Pt5///0S+bAymQxvv/02Vq9eje+//x4GgwFNmjTx+S7FYjG+/vpr9OrVC2+99RZ27txZpETUCy+8gN69e+Pzzz/Hiy++iIYNG9IEqVarhdPppHNbbm4uXReKRKJSk8MLwD/06dNHkARS3OOUJkr7eAEE8F/CI52dWZZ1sSxLomYMAD2AXJZlsxmG6QdgNIBdLMtmPKhr4Gv64m8jmKKaH3AbbpBmbSqVior0BwcH49ChQ1iwYAGGDx+Ol19+2e/rPn36NN5++200bNgQS5cu5XUONm3ahIULF6J169bYunUr3n//fVSuXNnvcxQHNWrUQPXq1bF+/foiy8xfffVVlCtXDnPnzvW7pMhms2Hq1KmoU6cOOnXqRJ0BhmFgMploCaHRaERqaioyMjIoC4sEXwnrjfz+UYEsctVqNb02tVqNcuXK0Q7AZDtxuvgaLQL/sHusViv9PWkMdODAATz55JMwGAw4cOCAz4Axy7IYN24cnE4n5s2b5/fCLi0tDTNmzED16tUxYsQIv59Bhw4dMH/+fHz66af48MMP0bNnT/Tv3x/dunXDs88+i2effRbvvvsuvvnmG0yZMgV9+vSBTCbDrl278Mknn2Dv3r2Cx+7cuTMYhsGePXv8vh4hJCQkYOnSpahXrx5WrlxJ3x0JfpH3w23ORKoKykq5nL9NWvxpkgX8w+AHgPnz5yMvLw/jxo3z+3rsdjs2btyINm3aYO3atRg0aBAvq/3MmTN46623sGLFCiQmJmLIkCF45513ShQw5oNUKkXVqlXRpk0bjB07FitXrsRnn32GjIwMTJ8+nWrFc9GiRQtMnToVv/32G2bPnl3kOZo2bYpLly5Bq9XScUcikSAiIgLBwcGIi4tDuXLlEBERQYN8Wq0Wer0e2dnZhRotljV4M+65sFqtMBgMSEtLg8lkgk6no2MwkQy4cOEC5syZg1dffRUTJkzw65xr1qyBXq/H2LFj/b5OlmWxfv16HD58GK+99hq6d+/u974lAcMwaNq0KaZPn46JEyciLS0NAwcOxJQpUwQbdTZo0ACNGzfGmjVrimwURbTik5OTCzUCIuMPsR3gH01WbpD/fuzqfho/+QNyDyVtmEUa5BK9WgKSvHc4HKhXrx7OnTtX7GObTCaMGzcOc+fOxYABAzB16lTe35nNZsyfPx8ffvghpFIpPvnkE7z66qv3HTD2hkgkQq1atfDOO+/Q4KMviMVifPXVV8jNzS1SG9Ubc+bMgVqtxowZM2hQxlumyWw2Izk5Gbdu3aLSQ2Rsexh+F7FNEmjljqvcv3NBpOTi4uKQmJiIkJAQqNVqREdHo1y5cli+fDmAAi1WX5g1axYOHTqEGTNm+CW/Rsal+fPnQyqVYsqUKYUCxsVF165dERwcjHXr1vn1/bz00ktgWRa7d+/2+btWrVrBZDLhzp07qFChApxOJ3JycpCUlAS73Q4A1D/l+kkxMTEIDQ2lgWE+PEz7KIsg451SqURkZCRdrzgcDuTm5mLixInQ6/X45ptvfErrrVq1Crdu3eKVfhPCvn37cO3aNbz++uvFqnL1hlgsxsCBA/Hcc8/h8OHDmDp1apFrQZVKhbVr1yI6Ohqvv/46kpKSijzPwoULERQUhJEjR0Kv18NsNlPNeuJbaTQahIaGIj4+HhUrVhSUXCPJjH9bY8aygLfffrvUjlNaxyLHCyCAAEqGR800pmBZ1gnAxDBMMsMwMwF0ADCQZVljEbsWG9yAGMMwHs3TyN/UajWdyMnk6x1IK4pdyGX6EHYVcQzUajXS09PxwQcfoGHDhpgyZUqhxZ/T6eRtlvTXX3/h7bffRmxsLD7++ONCHWhJ2eSlS5eQmJiIChUqYNOmTXT73r17BR0KlmUFM7Imk0lQey86OhovvfQS5s6dix9++AFt2rTx2O49aQ8aNAiffvop9uzZg+bNmwuyXRiGoRnpW7duYd26dcjPz0d6erpHwx6FQkFLUCMiIqBSqXgdBfKuCOtICKXNhuE7nlgs9rA9wpbiK1Pna7QIFAS+lEolnE4nHA4HRCIRnE4nFi9ejBkzZqBWrVpYu3YtKlSogNzcXLpfenq6B5Nyz5492Lt3L8aPH4/IyEgcP35c8F52796NmJgYZGZm4uDBg7Db7WjRogV27dqFkydP0uvjfk8AUKVKFUGJiR07dvh0cl944QWqs/bLL79g3bp1lO0aGhqKGjVq0N8qlUo0atQIP/zwA1577TVBh9BmswkyMex2u8f1fPDBBxgwYAB27tyJl156ycNx9y719sUO9daALS6EbNaXvXqz7rgsQS68GXsulwtWq9XjWi0WCwwGA1iWhcvlwsqVK9GuXTvUrVsXDoeD7puTk1PoOqxWK7777jssWbIE2dnZqFmzJj7//HM8/fTTdLFw8eJFWuo6e/ZshIaGYsiQIahWrRouXLiAW7du8d7juXPnaEM8Pnz++eeC22JjYz2aQjVs2BB//PEH3n33XXTp0qWQVEXLli3Ro0cPrFmzBnXq1MGrr75a6JhEM7Np06Zwu934888/0aJFC6jVaqhUKkgkEvqdEI1MboKIsGZsNttDYxiXdpKDzHdut5sGypxOJ/1eLl26hCFDhqBBgwb46quvaODBF7RaLVauXInWrVtDKpXi6tWrHtuvX79eiN3HsiyWLVuGo0ePomHDhtBoNPjpp588fnPy5EmfWqW+ghmZmZmIi4vj3UbknYYPH47Dhw/j+++/R1JSErp16waFQlFoPOzduzfGjx+Pffv2FdlUMiYmBjk5OTCZTB4MPaLxGxQURNmThMHGZVjej13dT+Mnf8Ct9vJVii0ks0G0151OJx3HiA6vVCoFwzCoWbMmfvjhB48qAKPRKCivdfbsWfz5559YuHAhsrOz0a1bN7z++us4e/Ysjh075iELce3aNWzevBl6vZ7K3Vy9erWQvQIF5eRCuuoOhwOXLl3i3Wa1WnHv3j2Pv9WtWxe//vorWJb1qZNctWpVDB48GMuWLUPHjh09mgM5nU5BnzYsLAyzZ8/G0KFDsWXLFvTv359qsIpEIjruh4eHQyQS0e+C63fxoTTHHl+66tyEpzdIoiE/Px8ymQwulwv5+fnIysrC6tWrMWjQIJQvXx46nY43cLdhwwasXLkSnTt3RvXq1XHq1CmP7SdPnvSofLLZbNi4cSPOnTuH4OBgVK1aFb/88gvvtfHNpwRBQUG04oCgcePGOHToEHbt2uXhG3FB2KwVKlRA9erVsWvXLvTr1w9AwdzvPTYRffbjx4+jdu3aCA0NpZrGqamp1AcnklFcuQDy3oWagxZlH48SJfG5yD5C8oekygYAbZZuMBiodFNaWhrKly8Pp9OJXbt2YcOGDfjggw9Qs2ZN3kBsWloa8vLyMHfuXLRu3Rq1a9f2kJYgyRtvnD59GocPH0ZCQgIyMjKwY8cOj+1Xr14V7JWTnZ2NCxcu8N577dq1sXfvXqSmpmLo0KGFEmXeY+wXX3yBQYMG4eWXX8aSJUvQqFEj3nM6HA6EhYVhzpw5GDRoEObNm4c333yT2g2R+YqJiaHzG5En4nuP/lYV+8KjrrQpqyitBBA5TmmNDXxrnwACCMA/lJk6IKYAMgBPAngNwCssy158lNdUHDYNYViQ8hhuN2bisJrNZmRkZCA7OxsWiwWvvvoqRCIRVq9ezRsc5sPdu3fxyiuvQKlU8ja7YFkWS5cuxaVLl1ClShW0bNnyoZV7NWnSBBUqVMCOHTuKzNz26NED0dHRWLx4cZELhvT0dHz22Wd48cUX0aVLF5qJd7vdyMrKQnZ2NvLz8+lCWCaTITo6GhKJhAbwSDYagGCJ3KOCN1vHe5vFYhFkHBLWntlshsVigVarxRtvvIFp06bh+eefx+7du4uUaNBqtZgyZQrq1avnlw5efn4+9u/fj++++w4WiwWdOnUqtMAgzrLFYinVBaFcLseTTz4Jp9OJkydPCv6uQ4cOuHPnjmCQsbjo0aMHKlWqhLlz58JutyMzM5M678TGCIuGsPi4QVrCPPb+7cMASYIRm/d3XCMsdu61Eha70+nE+vXrkZWVhTFjxvg8jtFoxNKlS/HMM89g+vTpiI6OxoIFC7BmzRq0bduWd3wiyYjXXnsN1apVK8Fde4JlWTidTuTn58NoNNJyfT7bjIuLQ7169ZCeno7jx4/z/uatt95C48aN8fHHH+PEiROC5yXyCRcuXChyjCfjFylJJU3jHidwmTskgBAWFga1Wo3c3Fykp6cjKSkJ6enp6N+/PxQKBbZs2eI343XBggXIz88vMqBKwLIsVq9ejR9//BHVq1cvlpxFaUImk6FDhw549tlncf78eezevZvXrtq3b4+oqCiPJK8QYmJikJeXR8cVANTX0Gg0dPwRYt3eD7zHlNKGv9VexKfyTriT71sikdAmZhaLBSEhIQgPD6dSNXl5eX7NEcnJyZQ1HhQUhK+//hqjRo3iDR7+/vvvWLx4MaRSKUaOHImuXbv61XiXZVlYrVbYbLYSz5n169dHuXLl8Ntvv+HatWs+fzty5EgkJCTgk08+4a2qEMLrr7+Odu3aYfLkybh79y5tDOt2uxEZGYno6GiEhISgUqVKNFhExraH4Xd5n4v4SBaLhf5bCGR+NplMSElJgcFgwPTp0wHAZzXNyZMnMWbMGL98KLfbjRs3bmDu3Lk4f/48unTpgkqVKhXZFJU7hxVlH9WqVUNsbCx+//33QhJofOjSpQv++OMPZGQIF3UmJCSgfPnyOHHiBFQqFUJCQhAfH4+YmBhUqFABGo2G9hghSRuDwUCrbBiGKZTYIX4vt1Lr3wQhX8tisSA7O5s2FSbjsslkQk5ODtXS1uv1mDNnDqpVq4YPPvjA57m++uor2Gy2Iv0xgoyMDCxYsAAajcYjaX6/YBgGNWrUQN++fXHp0iUsXLiwSH+3UqVKmDdvHlJTU/HRRx8VOR69/PLLeOGFFzBv3jyanCFyWE6nE9nZ2dTv9uXn+jvPlLXqwccB3IaZ93uc0joWOV4AAQRQMpSZGZotgB3AdACdWJa98aivqagJhRsA4iuB4zqrZrMZJpOJbn/vvfdw/vx5LFq0yCcjhIusrCz06dMHTqcTU6dOLZQBdrlcWLBgAbZv347q1aujWbNmRTphbre71CZCkUiEHj16ICUlxWdjKqBgET106FCcO3euECPDGx9//DHsdjsmT55MJR2ioqKg0WhoYx8hkAAegIcerPMX3uWUXBC7IoxDvvdJpCsyMzPRq1cvbNu2DRMmTMDy5cuLbPiXlpaGvn37wmQyYdasWT4XLna7Hbt27cJPP/2E69evo1GjRnjjjTd47dfhcFBGhT8MwuIgLCwMDRo0wNWrVwUXOe3bt4dIJML+/aUjhy6RSDBq1CicOXMGP/zwA1JTU5GcnEyTQ0RjlC8xwV043G9ZeGnAX0dZoVAgKCjI41pJIFAqlWLlypVo2rSpT13yW7duoV27dpg7dy5q166NDRs2YM6cOWjZsqVgAEGr1eLkyZNo2rSpX53qhcCyLHQ6HS5fvkylIRwOB8RiMVwuFy1Vz8nJKbSwqFKlCqpWrYrLly9j8+bNhY4tFovx0UcfoWLFihg2bJhgx+bo6GhUrFgRp0+fRlpaGj2Xd4LRG9zKgscF3ECByWTy+BZEIhFlemq1WowYMQL37t3Dxo0b/S6JzczMxJIlS/DUU0/5rRW6ceNG7NixA507d0b9+vUfebKwbdu2ePrpp+k44j33SqVS9O7dG8ePH8eNG75dIFLtoVKpaLA4Pz8fZrOZ3icJSBDpJu85xlfC8lHCl+xJUSDPIigoCFFRUYiKiqLNxLiMbKLBfv78ecFj2e12fP3113jmmWfw119/4a233sKyZctQt25d3t9fvnwZ27dvR506dTBmzBhUqlSpyOslAYmUlBQkJycjKSkJt2/fRmpqKnQ6HXQ6nd/vRyQSoU2bNlCr1Zg+fbrPHgNKpRLTp0/HnTt3MHHiRL/9QIZh8M0338DlcmHChAk0aUEapJbF4J8/8y434UL6QiQlJWHLli0YNGiQYPI9KSkJ/fv3R0JCAkaMGCGYINBqtfjhhx8wZcoULFy4ECaTCW+//TY6duxYaFwiQcWkpCRcvXoVt2/fxrVr13D9+nXcuXMHN2/ehE6n88mCbdOmDWw2G1avXl3k8yESFT/++KPgbxiGQYsWLXDixAkaAM7Pz4dEIkFISAhiYmIQEhJCE1RcuQAizcdFfn4+lWEqa+NPaUHI11KpVHRsAgqSX2S9SKokGYbBokWLcOfOHSxatEiQpQ0UVJ/++OOPeO211/xaT9rtdiqvVbt2bb+SWsXFU089hcGDB+P27dv44osvCiX2vNGoUSNMnjwZ58+fx6hRo4qsBF26dCni4uIwevRoJCcn0/Fdq9XSJCGf3Xkfx59k1oOWYwoggAACeBxQtjy7AqxhWfbyo74IoOgJheuIkn8TrUpuaRxxokwmE5RKJbKysvDtt9+if//+6Nixo9/XM2XKFGRmZmLDhg28Duwvv/yCPXv24NVXX0Xjxo2LnAhZlkVmZiYyMjJgNBpLxXFr2bIlypcv79P5JCBsY1+sKp1Oh3Xr1qFv376oUqUKLUXlLgK5gWPvRTCffnBZA3Es+a6Nb7HjfY82mw0OhwMffPABzp49i/Xr12Py5Ml+LdzGjx+PjIwMrF69usgmjGvWrMH27dtRrlw59OvXD61bt+bVZyRNvkgJPgkglyaaNGkClUolqEkZHh6OZs2aYdeuXUhPTy+Vc7744otgGAa//PILwsLCKJOd2BhpuugdmOEuHO4nGFJa8NdR9mb/uN1uGI1GmM1mnDlzBnfv3kXfvn19HmfRokXQ6XRYv349Vq1ahWbNmhV5fadPn4bb7cZzzz1XvBvjwOVy4eLFizhx4gSSk5MhFouhVCrpIoKMGSKRCLm5ubwNO+rUqYMqVapg+fLlvGxEtVqNVatWAQAGDx5My029QXSNy5Urh/DwcJjNZuj1eppAJFUQ3PG3LCQXigsSKAAKSlf1ej39Fohme40aNXDp0iXs378f48aNQ6tWrfw+/ubNm5Gfn+93R/gbN25g8+bNaN++Pd58881HHjAmaNeuHVq3bo3jx4/z2l2vXr0glUqxdOlSn8cpX748srOzcefOHeTk5FDpK2LjJFBMyjG9E0BA4SqJxw1qtRqxsbEepcWkmoDcE9GfJ1Ie5DdVqlSBVCrFn3/+KXj8JUuWYObMmXj22WexaNEivPzyyz4Tq5s3b0ZUVBTeeOMNv7WL7XY70tLS4HQ6ERUVhZiYGAQFBcHpdMJgMODy5ctFJhC4kMvl6NSpE/R6Pb766iufv23Tpg1GjhyJ7du3Y9euXX6fo1KlSpg6dSr27t2Lv/76C0qlEg6HA3a7/ZEGVsgcRfzZ4jBZSeBbJBIhIiIC0dHRWLlyJQDfLOPp06fDbrdjw4YNgkn63NxcrFq1CgcOHEBsbCz69++PKVOm8PpcBoMBZ86cwd9//427d+9Cr9dDLBbTwGz58uUhFouRnp4OrVYreF2RkZGoX78+fvzxxyLZxpUrV0bdunXx3Xff+fTVWrVqhZSUFFy7dg1GoxEymQxGoxEul4v6FQCobjuRrCASAVxwq5Ye1/GnKPD5WqQKj5BeyPOOiIigdvc/9q47vqnqfT83zU660nSXlrL3RvYGBWUJgshSZCOKLAX5iiLgQEBRxIECMlVABAEZMkRlyEZkjzI6k7RNs+f9/VHP8Sa9N0mhVfzZ5/PhQ9skNzc3557znvd93uepVKkSJBIJPv/8cwwaNAjt27cP+D5ffvklYmJiMGrUqJDOa/v27bhx4wYmTZpUrjFGs2bN8NxzzyE7O5u3+O6PHj16YOzYsdi8eXMJCSl/aDQafPHFF8jOzsZHH30Eu90OoPg6JiQkUGIRMcoLlSnMxyoOlWhRgQpUoAL/n/HAJY3Zf1H/BzcQDWSQBRQzftRqNSpXroymTZuicePG2LNnT0itYwQnT55Ely5d0KxZM97Hjx49isTERDz77LMhbZA9Hg8N7oxGI3JyclBUVBTUvCAQRCIR2rdvjytXrgTUYQOK2cb+2lv+iIyMRHJyMgwGA/R6vU8iwm63U+YeYVgR51y73V5i06BQKGCz2Uokx//p1qNAGxrC0OOeN3ej7/F4qBEecYIvjTvs9evX8cgjj1C9OiHY7XYcPXoUHTp0QOvWrQW1iYl+JDHtIwyGsr62EokE0dHRNFDkw+TJk2Gz2TBkyJD73siyLEtZREOHDkVaWho0Gg2VSeEaT/kH4dyNw/0aPJUXuPeKEPvQZrNBp9MhNzcXNWrUQGpqKj7//POA80W/fv0AFGsVh4ro6Oj7Yqg7nU4cPXoUmZmZqFatGrp27QqlUkm1TIHi70QikVD5Bz6XeYZh0KZNG4hEIhw8eJD3vdLT0/Hxxx/j2rVrgsZs/fv3R3Z2Nnbs2AGxWAyPxwOn04no6OgHvguiNCCJSavViuzsbLhcLnovkDlOIpGga9euSE5OxsaNG0v1maOiogAgJEd4AJRl2aNHjweK+cgwDLp06QKGYfDHHyXr4zExMejevTvWrVtXQquUi6FDh0IsFmPx4sX0b2S9IFqiwZhW/8biBBd8ayc3GSU07ysUCppcyM3NDXh8oFgWhatZLIT09HRYLJZ7Yu4Rw7CIiAjExcUhLS0NqampiI2NhcFgKNWaERsbi8GDB+OXX34JOve+8MILqFOnDpYuXVqq2K9jx44AiovWYWFhkEgkkEqlpUqslHXsZbPZkJubi9zcXOj1+lLNq9x7QSwWQ6fTYd26dRg1alRAiS+TyUQ7U4Rgt9vBsiwGDx6MCRMmoFmzZoLSQ4QNr9Fo0KZNG7Rs2RJpaWnUMDUqKgrp6emQSCQB4x+gWK+fXONgGDFiBC5duoQDBw4IPqdXr14AgJUrVyIvLw9ZWVnIyMiATqejz7FardDr9dDpdLh16xZ9bz5CR2xsLKKiov6188+9gFvMI9fKarXC6XRSeSqRSASHw4GUlJSQvruMjAw0adIk5HvvyJEjqF69ekhF/PtF3bp10alTJ5w4cSLonhAABg0aBIZhBLXcufj222/BMAy6desGk8lEiz6keMHttAmVKcz33PKWY6pABSpQgX8DHpyd1P8z8CVeyIYuJycHc+bMQU5ODt58882Qjmc2m5GRkYG6devyPu7xeHD27FlBAwE+EOMqjUaD2NhYyho4d+4cbt++fc+t0W3btgXDMPjxxx+DPler1foEnP4QiUTo378/9uzZg8zMTGosaDIV+yPGxcVRmQ6SgI+IiKCJVj4Xbf9NxIPeemS3233a+LhyCFlZWcjLy4PH40FOTo6g6QkfvF4v9Hq9oNEFF6dPn4bT6QzoCH7r1i1qxqdQKGiSFCgfswghcwuCGjVqYO7cuThz5gwmTpx4XxvT999/Hzt27MD06dPRrFkzn2SFv4RIoM/6oDL7+KR2iIwCmcNIooUkUqZOnYo//vgDn3/+ueBx27RpgxYtWmDdunW0QBUMZPN97dq1Un8OlmXx+++/w2QyoUmTJqhRo4YgM5BoiIrFYsF5VaFQoGHDhvj5558F37Nt27aYMmUKtm/fzqtv3LdvXzRp0gSvv/46TCYTZeGT+ZXIy8hkMrrBIcWvB22c8IHoGAOgrB6JRELNTf2lOBITE/Hee+/hypUrmD17dsjvU6NGDQDA3bt3Q3o+SYCFmmQmn+XKlSs4dOiQIHO8LCCRSBATEyOop0sKXatXrxY8RtWqVTFx4kRs2LABZ8+ehUQioTJYubm5sNlslGlFjJb0ej0tDJGugXs15ST4c159YGJJkowirfJAsRlSfHw8lEollU5RKBSIjY1FXl6e4LGIPE6gxDIXrVu3hsViCche9gdJMPPNj+SzeL3eUhEMgOJiVWxsLD799NOAcy/DMJg4cSIyMjJC6hDzP++wsDDI5XKo1Wof80sCMj8EMqIi3gf3m0BWKBSIj49HfHw8tFptqQoi/gWId999F2KxGC+//HLA10mlUh8T2EAIplsMFM8NcXFxKCwsFCwUkKJnsPclsUgoa2/fvn2RnJyMpUuXCj4nNTUVHTp0wLfffovY2FjKvuReY2J8FxMT4/N3Pomc/69axoFAiqukU5LIVHATvsTcukmTJjh48GDAAjrxdklKSgrp/QsKCnD16tW/Vd+/c+fOEIlE2LdvX9DnymQyVKpUqYTBuz9OnDiBZcuW4fHHH0e1atVgNBpp4thoNOLWrVvIzs6mRrGhMoUrWMUVqEAFKsCP/85KXYYIRQeQm4RRKBQICwujLsNGoxF16tTByJEj8cUXXwTU1CO4dOkSdaUVetxqtZYqaUwCEcK4IyYmGo0GOp0O58+fx507d0odxMfFxaFp06b48ccfg7IFtVotCgoKAjJcnnjiCTgcDpw4cQIRERE+lWSVSoXw8HAqP0GkKEjikqs1K8QEfRCDBO4Yk8vlPm18XDkEoieXn58P4K/ESijIz8+H2+0OKWl85MgRREdHCyalb926hR07doBhGJowLm+QBFUgdOjQAbNnz8aWLVvw7rvvlvo9dDod5s2bh9mzZ6Nfv36YMmWKz8aYq4NIWMeBzP/KgtlXHsx4PqkdAD6bPJFIRCVH7HY7OnbsiE6dOmHu3LkBCz/Dhg1DZmYm9u/fH9K5xMTEIDIy8p6SxtnZ2ZQJnZCQEPC5RDc4Li4uYGKxffv2uH37tqBuMQCMGTMGCQkJePvtt0t8LyKRCO+88w5u376N9957D263G263myaCiGYpcbPnFr/+LgZWabXtuWPQXwYhMTERtWvXptqgJpMJ+fn5PknLjh07YuTIkVi2bJkgi9sf95o0DiVRAxQXZo8dO4aMjAw4nU6cP3++XDsC4uPjBZPGNWrUQLt27fDZZ58FXBtfeeUVVKpUCZMmTcLx48dLFHtIctjj8UAkEtE1xGKx4ObNm8jOzr7vwsSfxdZ7FsW81/nM33yYW5jwb4knv1utVty5c4cyILVabUD2W2mTxjVq1IBWq8Wvv/4a8ucg5yj0PUdGRoJhGLrGhwqZTIZnn30W165dC1rA79atG2rVqkW1iktz3haLBfn5+bBarcjLy0NeXh41QyMJejI/+IMbe5VF8V4kEiE8PBzh4eH3lJB0u93Izc3F+fPn8fXXX2P8+PFBk3ESiaTMfRuSk5Ph9XoDSmtJJJKgzPBABQm+440dOxYnTpwI6EkyZMgQXL9+HadPn4ZIJEJcXJxPAZ+wn9VqtY+ZK0mW8hUWHlSUpecLAcMwdG4SiUSIiIig+xoSP3q9XkRHR6NDhw4wmUwBTZ9zc3PhcrmCxjsEJ06cAIC/hWVMEB0djebNm+PXX38NiTldtWrVoPHfzJkzkZCQgDFjxkClUkEmk0Gv14Nl2RKyJ6Ux4qxgFZcNnnnmmTI7TlkdixyvAhWowL0htN1UBQD81WLP3dgLJRqJaQEJkAibjPw9MTER8+fPx7Zt2zB58mTs2rWrREtjQUEBDcBIEEekGq5fv+6zqO3atQsMw0Amk+H8+fMBJR88Hg+MRiNNQBLWLgAazMTGxsJsNiMvLw9FRUWIjIyEWq3GrVu3eI+pVCpx5swZ+nv16tVx4sQJfPXVV2jQoEFAEweWZXHnzh3eFnG3242mTZsiKSkJBw8exKBBgyAWi2E0GuH1ehEWFka/A66uMfcxshkhn80/CORqsZUXggWeRNaBaMBxx5hcLodWq6WPk2MRdo9MJqMthVWqVKHsE7PZLJgwycvLQ2ZmJoBipoz/5oTbGmaz2XD27Fk0bdoUFy5cQE5Ojo/GYlFREW7cuAG5XA6Px+PDfiGbFafTCZZlBRlYHo8nIJOKL4C0Wq2w2+3Iz8/HnTt3eF/ndDrRt29fnDx5Em+99RY0Gg3VyvV6vYKGWleuXMHKlSuxZs0a2O129OzZEx9++CFYlkV+fr7PRtRsNiMiIgIMw9CxBhTfE8QkiJtIv9+xxn2P+z0WlwnOncuUSiU8Hg91bC8qKoJKpaLjj7RWPvfccxg0aBDmzJmDJUuW+JwjQatWrZCYmIiVK1eiTZs2uHv3riC7j8w3CQkJuHr1KjIyMuh1MxqNuHDhAu/rzGYzcnJykJmZCZlMBrvdjosXL9LH/Tf1Xq8XTqeTbiaE5jWFQkE3Y9988w0efvhh+phUKvXRLR08eDAWL16ML7/8Eu3bt6eJd6BY17hHjx5Yvnw5Bg4cCLlcjsjISISHh0MqlcLpdFKmMSl48Um7sCwbMAlyrxsdYgwY6ngiY5BlWR/9foZhEB4eTo9J5n2pVEqLg/n5+bDb7Zg5cyZ++uknjB07FocPH/aRvOFLHMlkMmg0Gly7dk0w0afT6eicQ+a3rKwsOJ1O5OXl8c4TLMsiKysLOp2OJkFIF8bJkycRGRkZMHnvdrsFE9kpKSk+6yIXKpUKFy9exPXr13mLFsOGDcOYMWOwdu1a9OzZ0+cxMjbkcjnVbZ4xYwaWLl2KmJgYqp9uNptx+/ZtyOVymtwh8hUKhYKyQ4lp6b0w/v6cNwJmpQKtf4HmM6HXEdO/oqIimmzhFhHJXEae58+mdjgcMJvNiIyMxMWLF2nyzWaz+XwXRBLlzp07cDqdgkm6nJwcmqCoXbs2fv75Z5w9exbR0dEAIDi/AH+tkXzMdqVSidzcXMjlcuh0Op84Kjw8XHDdY1kWR48ehUKhQEpKCj777DMqnUY+pz969+6NBQsWYOXKlZg6dSrvcV0uV4mx6vF4qJGXWq2GSqWCQqGgxSRS2Oe7h7ixF/nOyqp4H2jMkfHiD2KiNXfuXCgUCowfP77E96LX60vEsw6HA0ajETqdjld2gmgPX79+nfe8bDZbiTVKpVLh7t27UCgUvEVZp9NJdaSF5h/yPV+/fr2ElEVYWBjtBiFo27YtoqKisHjxYnTo0IH3mJ07d4ZCocDq1auxePFiKJVKar5JCudqtRpmsxlFRUVQKpWIi4ujj9vtdsH904OWqCvtuhgqyBgg66FGo4FYLKZxFfEHaNCgAcLCwrBnzx7BrikS50ilUsFxwI27f/zxR0RGRqKwsBBnz55FQUGBYDzmdrsFzabJvo0PCoWiRNdVXFwcnE4n1q5di7S0NN7XEaSkpODYsWMwGAw+Y8Lr9SIyMhKXLl3Cr7/+ismTJyM+Ph4ajQZOpxN2ux0Mw0Cr1dL5RqVSCd7vFSg/PPPMMxgxYkSZHKcsUZE0rkAF7h0hJY0ZhpnC82cjgJMsy54p0zP6BxFqwMINboVeExYWViIYNplMyMrKglwuR1FREbRaLRYsWIBnnnkG69evx8SJE32OYbVaaZBw9epVREREoGbNmrRSzQ1kMjIykJ6eTt26W7VqJXj+P/30ExiGoRssbuWXm0AmsNvtVCM4OTmZ95hOp9NH80+r1WL//v04ffo0OnfuLMhmJedbWFjI6zTudrshkUjQv39/fPbZZzAajbSlnLC3zWZzCUaJSCSi3w03mUyCVSHDkn8K3ECabLqAvxIxfIntsLAwqNVqMAyDq1evIiwszCewJGZtfNBqtbh58yaA4u8gJibG53Euu+bw4cPwer3o0KEDkpKScPz4cZq0cLvdVMOYMFG5yZ+ioiIUFRUhLi4Oer1ekA0hlUpLbGC44NMLvHbtGtxuN6KjowXZQGQTN2/ePNy+fRvz5s1D27ZtKXvLfxwcPXoUixcvxtatWyGRSNCjRw8MGzYMXbt2hUgkQm5uLrxebwkWKPmeyN9kMpkP2woAZS/cz8aIm9wNNP+U5niB5jCGYWjSKTExESqVCklJSXC5XJBKpVCr1Rg9ejQ++eQTjB07Fk2bNgWAEg7eY8eOpfIMGo1GUM8xNTUVlSpVgt1ux+XLlxEfH0/1JHNzcynzzx9cEyeHw1GCFcw1XGNZFufOnYPX60Xjxo1hs9mQkpIieH0qV66MatWq4cKFC3jyySfpYxaLxWfT+/jjj2Pjxo1YuXIlOnXqVKIIOHfuXLRo0QIffPABJkyYgIKCAlSuXBkikYgmYsg8S6ROuCAJMLlcDrvdft+yAlyQAhvfWOAmOcg5kK4NuVxOxxB3LJHPQBKTQLHOKknK5efnQyaT4a233sKgQYMwa9Ysairo/55c1KxZE0ajEQ0bNuR93Ol0QqvVAgBl8VapUgWxsbG8yR6n04mMjAy67pFWX4KCggIUFBRg7NixgteObML5YLFYeNc1AFR2wOl0lrhfrFYrGjVqhPT0dKxatQpPPPGEz+PcsdW4cWN89tlnGDp0KF577TXMnz+fmsORgjXpwOEWschcHOqcJPScP/9+z7RBofksUNLPf651OBz0fzIXk8S/2+2m66darYZGo8Hdu3dhsVgQHR0NvV5P77+4uDifNZP8bLVaIRKJaJFo3759OHjwIFq2bIm2bdtCq9XSMZmWloaff/4ZDoeD+k8IJVeA4uIGKUD5x0kmkwkxMTFwuVy4c+cOLRIDxWuOkL8FSTQDxfPSJ598gtWrV2P06NE+xsFcdOrUCd988w2+/vprzJgxg1eXWSwW02tFzsPr9UKj0VCzNnK9yPuT7zfYXHWvxXuhcRno/Qh5gHRHkIKDXC6HXq/Hli1bMG3aNN71RqVSlZAS8Hg8CA8PR4MGDXhjS1I0IIV+f6SmpqJq1ao+f8vIyMCuXbuQnJzsUwD1B8uygrJhJ0+eBFDcveMfI9lsNloU4eKZZ57B+++/jwsXLvDOs9HR0ejbty927doFh8OBuLg4SmiwWq0wGAxUcoGYCpJ5Ry6Xl4htH2QEWhcDIZTne71e3L17l+rWEwNM0vmZn5+PypUro3Xr1vjxxx/x8ssv8x6XrFf16tUTjGMIicjpdOLGjRto06YNHdtFRUWCRVGGYXjJPEDx+so3foDiIom/DnhcXBxSUlJw6dIlREdHC8aATqcTderUwZo1a2Cz2Xzen2VZyOVyrF+/HmKxGM888wySk5OpXItYLIZSqURYWJhP0f5+2eIP+jh9EBGKfnVpjkPiurI4XlkdqwIV+K8h1B1nMwDjACT/+W8sgO4AljMMI2wr/P8UpW1fIQk1wp7its4MHDgQ3bp1w+uvv06ZUXw4ceIEmjRpwvueNpsN165dE9Tl5MO9aM0WFhaGbJTCMAzat2+PzMxMmpzkA0lWBmv/JBIV+/fvpxsXpVIJg8FATU/42t24TCMiYfEgmm74yxaUtrXyjz/+QHp6esiO7cBfwWYweYpTp07RgI8Lr9dLE8ZCG4B/UtOYC6lUijlz5sBkMuHjjz8u8fiFCxfQsWNHtG/fHgcPHsTkyZNx+PBhLFu2DF27dqWJfOJuTwok/iw2kUgEpVIJh8NRbjIDQm7koeBeWsHJRpdslIlhS2RkJBwOB5599lnExsbixRdfFGw5HTZsGORyOZYvXx7Se9auXRtAsexOWSMrKwsWiwVVqlQJWe+2WbNmuH37dkD9U7FYjDFjxuDGjRvYs2dPicdr1qyJZ599Fl9//TUMBgPsdjvy8vLgdrthMplgNBrhdrt9ikZcEGZlaQ2eQgFZo4LBX7+b2xVBjEi5LFA+/UqXy0XHSatWrfDiiy9izZo1Pol/IVSvXj0ga5OLYPIUBQUFuHDhQkjtsuXRSk3WPiGJCpFIhFGjRuH06dO0pVgI/fv3x8yZM7Fz505s2bIFGo2GximJiYnQaDR03BC9Y8JE/qe1/O9nPiOv5zMhttlscLvdEIvFlAHJTVyp1WpUqlSJynXwISoqCjKZzIdt53a78fPPP0MsFuPgwYN48803sXPnTsryi42NRWpqKk6dOhXyZyAxoRBIMtZoNIZ8TIKUlBQ888wzMBgMWL58uaBWt0gkwuDBg3H79m1s3bo1pHMGiguzJFlvtVphNpt95Ci4LfcPEriyOuR3s9mMpUuXIjIysgSJQwihaAvfC1JTUxERERHUECyQ9wi5F0KVHAGAp556Cmq1Gu+//77gc4YOHYqCggIcOnRIcD0Si8WIj4+HWCym6wGJF/8tOsahrouhgis7Z7PZIBaLER0dTdcCEks7HA5oNBpotVp06dIFp0+fFow9SEFKKLnLxalTp+ByudCoUaMy+0wsy8JgMCA7OztoXFm/fn3YbDbe+IiLKlWqAACvrrHL5aLdN1qtFk6nE/n5+TSu47sfHlQD6v/P8C90389xyupY5HgVqEAF7g2hroYpAJqwLDuVZdmpAJoCiAPQHsAz5XRu/zpwNfaMRiOys7PpRkAmk0GtVlNH7PDwcKrB99Zbb8HlcuGNN97gPa7JZMLly5cFWSXXr1+Hx+MJ6N5cFnC5XLh9+3bIz2/atCkUCgUOHz4s+BzCLg0kpwEALVq0QHJyMvbs2QO3200dl4mZBLmWJpMJJpOJBgfc5MWDEqwKmSQKnVswDW2ilS3EMhAC2QgHcoU3Go24du0ab8GCBGeEbcgHEkRyv5OyQiiaxlzUrFkT/fr1w/r161FYWOjz2GuvvYYTJ07gnXfewfnz5zFv3jzUqFEDcXFxUKlUdEPE1Uu02Ww+BoVckCIAV2M7FC308kYoupHc81Sr1UhMTER8fDw1NiRmjGTTLZVK8dJLL+Ho0aP4/vvveY+p0WgwcOBAfPPNNwG1gbnPj4+PDylpXJoNu9vtxp07dxAZGVmCXR8IhEEdTH++c+fOqFGjBr788kvex2fOnAmlUon169fDbDbDbDYjPz8fOp0Od+7cgcFg8Nmocr8LMqaIwdM/oQ3Jp8nN1cAmyWMydiQSCXJzc+F0On0+B7mHAGDChAlo3LgxnnvuuaBFyerVqyM/Pz8kFkugY+n1ety4cQMymYwWKAJBSGKCC7PZjIsXL4bMsNFoNBCJRAGLqgMGDEBERATWrl0b9Hj/+9//0KtXL8ybNw/Hjx8Hy7K0M6GwsBBZWVnIysqi8k0sy1J5CrPZTGUGyno8lYcuKBDcVFShUCAqKooyHrkGnzabDV6vl84dRD7AH4Rpx5Vvun79OsxmM/r164eXX34ZaWlp+P333/HSSy/RWKdx48a4cuVKyIUdsVgccB6Ty+WQSqUl1q1QUa1aNTzzzDPQ6/VYv3694PPatWuHtLQ0fPDBB0GPyU1IEkkgwtgnEjUmkwkGg4Fq45d2DSxPk0WuIRkZo9euXcPWrVsxfPhwKi0SyjmGSqYoDUQiEerVqycoD0AQaNyQ76g0msvh4eEYOHAgvv/+e8G4vEuXLoiPj8eWLVtgNpup9IVKpUJcXBwUCgWv70Mgmbr/AoiMFjHi1Gg0SEtLg1gs9jHBNZlMtBBDErw//fQT7zEzMzPpXigYfvnlFyQkJAh6nxDvBoPBEHTMeL1eFBYW4s6dO7RwEMw8PSkpCdHR0UGLUqQLhi9W3L9/P/Ly8jBkyBDaOaLVapGcnEy7d/yNOHU6HQoLC/8VxsIVqEAFKvCgItRgLA4AdzVwAYhnWdbm9/f/NMimxGAw4NatW7h58ybdjJDFlMgqiEQi6HQ63Lx5E0qlEm3atBE0nyBBoVALbKVKlaBSqbB9+/ZyTyDwtSwKQSaToVq1agFbMxMSEhAZGVlC/8ofIpEIjz76KA4ePIj8/Hzk5+fDYDBQNifZpOh0Ouh0OhrEkhYoYjz1T+NeAhgiXUGeT8yNSCK2oKAA1atXxy+//OKjNRwMv/32G2rWrBmQnXz27FmwLMtrsEjGgs1mg91up5qP3OusUqkglUphNBqRm5uLrKysMmPlkCRmaeB2uyGTyUq0xt2+fRsPPfQQ+vfvDwA+8gB5eXnIzs72YaORwJRrLsUFXxGAm7T4pxCK6aPFYqGfl9tVwbIsdDod3dB4vV6oVCrExMRg2LBhAAIzg6dNm4bo6Gi88cYbIRWfUlNTgxaTAH69ayGQdlOz2Rx0g8OFVqtFZGRkwG4QoPh7r1KliuCxY2Nj0bdvX+zduxfVq1dHbGwsZDIZYmJiUKlSJcTGxvokZP2LXqT1UqVSlZgXygvcRA/fuCZ/I50cQPHmWCQSoaCgANnZ2bh7967P5yCbXJVKBbfbjYceegh5eXlBN6qdOnUCAPzwww9Bz5volfPpYJPkBdHpDoYTJ04IfvcsyyIzMxOXL1+G1WoNaArJhVgsRnh4eED2KDH4CWXdFYlE6N27N5Vh4o4LjUaDqKgohIeH0++IMNdIso+sp2U9noguaFmDr4ARaKwSdrHVaqUs8wULFqB9+/YBC65E2oQgJiYGDMPgyy+/xIoVK3Djxg2IRCI0adKEjjmJRAKWZUsVLwXqxmEYBtHR0SgqKrrnBCVpyRVqKQeKx1D9+vVDMpskCZ1q1aohLCyMxhFer5cW84HiLh/SPcFdW8hzA3W+3K/JYiBwGe6EZZyUlIQaNWpgw4YNIa1RZrMZ27dvD2oqRq55ae+t2rVrB5XsCLSWx8fHQyQSCXpJCIFISgh1aYSFhcHlckGpVCI2NpZqyBLZGNJpZbPZYLPZ6HrwTxM2HiT4z0/ENBYA1QC32Wxo0KABFAqF4HdYp04d2Gy2oIx0oHh/VK1aNcGuQMLUzcvLw/Xr15GZmYnbt2/Te5RlWRQUFODKlSvQ6XTIzc2F2+2m81ww01mWZeFwOIIW7IkGe/PmzUs8RtavnJwcnw4TsVgMkUgEs9nsc5/5d5xUoAIVqEAF7g2hruDrABxjGOY1hmFeA/ArgPUMw6gA8DsTlTOYB1BkiGxiYmJikJaW5qMVK5FIYLVa4XQ64fF4aGCtUqngdDqRnp6OGzdu8CY2yUInFHBGRkbi6aefxtWrV7Fjx45SnXNpk27BpAz8ER8fT3UF+SASidC8eXMcOHAg6Ll06dIFJpMJd+/epU65d+7coZtflUpFtdTkcjm8Xi8MBgPMZjN0Ot0DUWW+lwBGLpf7bI5tNhtycnJw8+ZNqlk5Y8YMSKVSzJs3L6RjmkwmnDp1StDshOD06dOUaeoPor0mEong8XjgdDphs9mQlZWF7OxsGAwGeDwexMXF0eRYTk4Ozp8/j9u3b5cqaVcWsFgs2L17N7p3717i2hcVFSEmJgZisRhyuZwmksiGx99EhvxdrVZT1nswkPmhrIx+7gX+0jqB2vb8pSyIsY1IJEJUVBTdcBPHeYVCIcjYA4qLW1u3boVIJML//ve/oAnY+Ph46HS6oEmSQHqP/mAYBjVq1ADDMLh8+XKpCklarTakhGAwzbSWLVsiPz8fOTk5tHWX6IH7yx7xJcZCeaws4Z/oEYJ/8lgulyM6OhoRERFISkoqca4kiUBYlEDwomS9evVQu3ZtfPvtt0HXi7p16yI8PJy3IKlWqxEXFwedThfQgJMgMjIS+/btK3ENCgoKcPfuXeTk5CAmJgbx8fGwWq0hF8bkcnnAdclgMECn06FmzZohHe/s2bNQKBRo3ry5z7UOCwtDamoqYmJiKPOWgDAuY2JiBMcT0Wa/l8Ir16y2LMFNupDko8ViEUx8c5NW0dHRWLt2LTweDz7//POAySz/+16r1eLRRx8FUBxz9O/fHxMnTsS0adOobmxeXh6io6NDlovyer1BJZxIIi8/Pz+kY/pj3759YFkWXbt2Dfi8YKxngt9//x1A8T1JivVms5nGXOR/YhzLvcb+35dQUSEUk8VQwC0m+P9sNpupBFNcXBxWr14Nl8uFwYMHB53zNmzYgMLCwqBSFhEREZBIJCHNNVxIJBK0bds24HMCFb1UKhUaNGiAn3/+uVSx/pUrVxAbGysY71+5cgX5+flo3Lgxvf/y8/Ppe3DXJvKzTCaj0iXl1dXwoEOtVlO9eX8QCT0uU1ahUECr1aJ+/fqCSeF+/fpBLpcHlXdiWZb6v/CBSJjExsaiatWq1Dj17t27OHv2LE6dOoUTJ07g4sWLKCwshEKhQGpqKtLT02khOFjSODMzE1arFT169BB8zqVLl7B+/XqMHDmSlxHdrFkzqNVqnDt3jv6NxONA8TXmMtyJB4lWq6XdDnzXprSybRWoQAUq8F9DSEljlmXnAhgDoPDPf+NYln2DZVkLy7JDyu/0SoJhGOWf5/RAzO7cpAtXWy8yMhKJiYl0ESVt7JmZmcjPz4fFYqEsooiICFSrVg12u503kUJYkYE2l61bt0bz5s2xadOmkFgi95Jzl0gkpU5QECf6QOf00EMPITs7O2greseOHcEwDH799VdaVSYmJkDxptBfPsDtdkOtViM+Pj6kc7+fzXEo8G+Z5Xt/PukKmUxGE2iEMUWcqO12O6pUqYKnn34a3377LTXWCITDhw/D4/EETBobjUbcvHkTjRs35n2cmE+QYJewiSIiIiCVSuFwOFBQUACWZSGTyaDRaFCnTh1oNBoYDAZcvHjxb00c//jjj7DZbOjTp0+Jx4xGIyIiIqDT6ZCVleWz+SFMGplMVkIqgCs/EQwPijwKF3wt3kQHlWEY6ghPrgf5DKRbwu12U4f0mJiYgEljoJiV9vrrr8Pr9WLWrFk+bd/+iI+PB8uyAdv9SfK1NJDL5ahWrRosFktAaQB/aLXakBI2Op0uoORLy5YtAQC//vorJBIJ7HY71TUOhbEeymP/JLjn5XK5qFEUN8FHNm8kWUO0pYNtOgGgT58+uHr1alBmVVhYGFq2bInjx4/zJsGSk5Mhk8lC0kju1q0bXC4XfvzxR3g8HrAsi99//x2bN2+G2+1GlSpVULlyZdrSHmpySKlUBlzXL1++DACoVatWSMc7d+4c6tWrB5lMRrsDrFYrTdIQGSeFQoH4+HifBEKg8XQ/XRJcU9rSItTNPJEKIted20niL/GSkJAAqVSKr7/+GqNHj6bsYCH4M42BYsb7W2+9heeeew6tWrUqEVvk5uaWqsBO4sdAUCqVkMvlQedYPuj1epw4cQItWrQIaDgLgBoMB8O5c+cQExMDmUyG69evIzc3F2q1ms59JPbidk+QtUUkEvlo/gsVFe7XZJGAu84RRqfVaqUSSzabjUo3paWlYfny5fjjjz8wbtw4wViQZVl8+umneOihh4IyjUmxls9oOhiCjc9gaNeuHXJzc0vVhXb16lXUqVNH8PFjx44BKC4YAL4+LcBfcwnwl+8DYbDm5ub6dG89CLJdfxf45ljy+cnjpAOVkGEUCgVq166NP/74g3cejIiIQOfOnbF79+6ARQ5isitkAk6SxqRrICYmBomJiWjWrBmqVq1Ki4s1a9ZEs2bNaFHAbrfD5XIJdsJycfXqVchkMh9jYi68Xi+WLFmC2NhYTJ06lfc5EokE7dq1w6FDh6gUZFFREeRyOb22hG3MZbkH6swKRbatAvyoXLlyCTNkhmHw008/CZodVqACFfh3IvgODQDDMCNZlv0CwAnO395mWXZGuZ0Z/3k8BuBJhmFkAD4A8BvLsmXvQFEKkMUGAHW1l8vlPkGB0+mERCJBSkqKz2Nms5kmAatXrw6geJNIDA0KCgroJlomk6GwsJAmBA0GQ4kAoVevXrh48SI+/PBDtG3bNuAmhBt8hMJyAUCN5/ig0Wjwxx9/lPg7OcfffvtNcONHXHZ37NhRwsyB2/qkVqvRuHFjHD58GG63G/Hx8ahUqRKtcpvNZmpK5vV6IZVKIRaLodFo6HUk58A1yONeJ+73KRRc3Qu4hnBkg8R3PcjmBvir7dDj8VDDP6B4E6vRaKiEgtVqhUQiQe/evfHxxx/jm2++wahRo2A2mwWTMLt27aItcFeuXCnx+NmzZ3HhwgXqVsxtjWNZVtBYRSwWg2EYyrCy2WwwGo0ICwujTFTyneTn5+PKlSs00RJIr5HPLMpkMsHlciE/P18w8WOxWBAdHQ2v14s1a9YgOTkZ1atXp4xZkgAyGo3QarWQy+X0mFqt1odhXFBQAI/HA7PZDI1GA7PZDKlUCpZlfe77QAkAlmUfqCQfYYrLZLISY5RsAt1uN6xWKw3IFQoFndPIBsPj8dBiAHm+EDQaDV599VXMmTMHr7zyCubMmUMTLFyzTTLGzp07B5vN5nMPENy8eTMkmRK+8aFWq5GbmwuRSCTIcK1cubJPMYsUOxiGoYUcf+Tl5aFBgwaCsgNxcXHQarX49ddf0bt3bx+mm0wmg8fjCaoR/ndCqVQiMTERcrmcalqT8Q6gxAaY+xh5jVQqpfMzmePcbjdtDSfrBJlbhMy6gGJ9e5lMhq+++qqErIC/FmPNmjWxd+9eHDhwAA6Ho4T8RWxsbEhF1uvXryMlJQUZGRn47rvv4HA4YDKZEBERAbVaDbvdTo2ARCIRcnJy4HQ6ERcXJ6jhTQov+fn5JYoXLMsiNjaWzrtJSUk+iWi+tYllWZw9exY9e/ak8y9puyesTvLdEK12r9fr0wkkxEIjSb/yYAwHGtPc9Zh7bv7xC5lzzWYzGIZBeHg4fT5JDLIsS5n8b7/9NqRSKSZNmuQzV+n1+hIbXbVaDYPBgIKCAkHjzMLCQp95IisrC1WqVKF/C1Qcc7vdcDqdEIlEJZLTEonER95LLBbDbDYjIyMDVatWFVz3iHY7wZ49eyASiVCzZk3cuXOHt3OIwOl0wuVy8a7HHo+HjoVLly6hdu3aSExMhFqtpmw+Mh+TtYJhGB+JEjKGSJcBX1GBFDtCGW9C44cb15JzVigUlHFJ1j5yDLfbTTummjVrhlmzZmHu3Ll455138PLLL9Pj6vV6SCQSXLt2Dbdv38aIESPo95ubmyu49kmlUhgMBt5CpdVq5Y3DQsWhQ4d4/56YmIjk5GSIxWJs3boVPXv29Dkfoh3LhcfjwbVr19CuXTvBov6RI0cQGRlJCybE48Dj8cDlctF5hiTqANB5B/BNlHJjXqGYO9i693c3nt7POkyS4yzL+uh8EzkbIj1lsVjoOli5cmUUFBTg1q1bSE5OLnHMjh07YufOnfj22295WbwXLlyg8YjRaPSRbCLvRQqr3J/lcjkd2+ReJF4MHo/Hp1BHfgeK7zN/aTGXy4WMjAxUqlRJcH3fs2cPLl++jIULF1KjVv9rFxUVhfbt2+OHH36gbGOXy4X4+Hja4UT2LeQ6c+91MgdwQT7bP9kF+G/FrVu3eO+Hr7/+ukyOP378+DI5TnkdrwIV+C8hpKQxgP4Mw9hZll0HAAzDfATgb3U0YBjmEQDvAhgHoCuAF//8ufS0izIENxi1WCzIy8tDXFwc1dglbVsWiwVxcXF0IRSJRNTwQKlU0rbGW7du0Y0JN9lJ3oewREil1x9SqRQzZsxAYWGhYAvQ3r17aatYYWEhEhMTadIkPz/fZ+FkWZYGAlqtlia3/SESiXh1AWNjY7F161aYTCbeYAco1jWuUqUKfvnlF0yYMKHEcbmJzy5duuC9997DnTt3kJaWRrXiSCDDMAzUajUYhqGO3STBxYXQZrS8NsehBrXc8UReIxKJqMwJ0VMsLCyE0WikwRHRBa1bty42bNiACRMm+JhNceHxeHDmzJmArCOtVou7d+9Cq9WWMFjs378/ZZj4Y9myZfTaud1uXL9+HXK5HDExMZQ1x4XL5UJeXh4qV64saM4BgPexW7duwWQyQaPRCI4th8OBypUrY+3atbhw4QKWLFmC9PR0AKCafTabDS6XCzExMahatSqysrIQFRVFE0NkcwsUB9gejweZmZk0CFer1SgsLIRYLKYM8gdJPSfQuTgcDrjdbhgMhhLsd/J5yIaGJJNtNhukUinVhLTZbJBIJJBIJLTQFajg0rlzZyiVSjRv3hx9+vTB4sWLsXPnTsTGxsJut9OkTZUqVbBq1SrIZDI0aNAAP/zwg8+96vV6kZeXB41GA4/HE/A9+bSRSaCbk5ODatWq0TmbC6lUSplelStXxrFjx6DRaBAZGQmGYUrMhxaLBTabDenp6QFZFq1bt8bhw4fp/afRaOgcTK41H0ozrriJl/sZj2QjS8BNBPgf2z9JYLPZoFQqaeKOvMbj8VCGn0ajobq9ZK4PpONZu3ZtPPbYY9i7dy8WLFjgw/I0m80+jKcaNWpgxYoVyMjIwMMPP8yr53rgwAEcOXIkoHYwYbMrlUro9XowDIPIyEgolUqYTCY6dhiGoZ+bJAGEjPbUajXCw8Ph9Xp5i6VarRZ37txBdHQ0ateu7XOd+cZWRkYGjEYjKleujNzcXHp+5JyI4R0xpCL3NZFLCjROyLp6r7jXIkigzbzVaqVMN/K5yDkqlUqaHCTJYrlcDpPJhJMnT2Lz5s2YPHkyjbsIZDJZiXb/+Ph4uFwutGvXrsT3RLBv3z66RjudTphMJqSnpyMtLQ0ABJl1wF+a2/6xDgBBCR+dTof09HRq0OkPvV5PO4SuX7+Oq1evomfPnujSpQsA/vWU4ODBg3A6nT4xCIHX66WxVHZ2NurXr4/w8HBIpVKfMU8StgzD0K4vUvBLTEykHTpC4MZnocJ/viP/gL+YvgAoq9XpdEKlUtF2dpFIBIlEgszMTIjFYkyZMgXXrl3D22+/jY4dO6Jbt24AivWJGYbBqVOnAABdu3al446YcfEhPj4ely9fRp06dUpc199++y1gIr9Lly64desWrl27Bq1WC71ej4YNG0Kr1eLWrVuCMigqlQodOnRAo0aNcOHCBYwePZrOcW63mzdpfPPmTTidTjRp0kSQPXrs2DE89NBDiIiIoIVy0jFDGK2AbzxL4gWGYXwK6+R++6/ozZLvnqyJKpWKriMajYbOI2SOl8vl1AwvIyODlwH+2GOP4bPPPsOBAwfw3HPPlXg8NTWVanSnpqYiNTXV53GyRgPF3wN570BdOMnJyahUqRJOnDhRYm8YHR2Nzp07+zz/yJEj8Hq9ePzxx1G3bt0SxyssLMS6devQpEkTjBgxgnfNIIVm4m3w66+/olu3brTQZbFYaCGXxLUxMTEICwujets2mw0ymYwaDZJ4vSxJQhUAnnzyyQfqOOV1vApU4L+EUClv/QE8wzDMUwzDfAnAzbLsyHI8Lx8wDKMAMBDAXJZlD7EsOxuAE8DwEF8/hmGYEwzDnAjVpKasQNpjCgoKaHukWq1GdHQ0wsPD6cYuKSkJMplM0NSJLHbB0KlTJ3Tv3h179uwJaEAHgAb/gcyHXC4XZZfcS+KByG8EawHu0KEDfv3116BGSJ07d4bb7caJE8Wkd64mHXHCJuDqqpFqPmnP9dcJJriXlu+yHF98Oo1kA8bVO42JiUFCQgI1OnG73cjPz0ffvn1x5MiRgK2Ip06dgtFoRKtWrQSfQ7SjQ22L5oNYLKbFiWAoK3M8Puh0OsybNw+tWrXCwIEDSzxOkr/h4eFwOp2Qy+V00wwUM0etVivsdjs1kktOTkZ8fDzkcjlkMlmJ9kw+EHfs0rBUynvuIjp05L7gaj2STQRpJdbpdFSDkjCwlUolHYMKhaJUepv169fHV199haysLDz55JMlWndJEkiIpZefnw+Xy8W78Q0F3KTGqVOngrZkk81cIPkX0lIaSJ4CKE4iXbt2DTdv3vTpdiDsI/J93E/LbrCWy3sdW6HqLFutVuTm5sJisdDWViInxDAMNdAk80Qo0hQETz31FEwmU1ANf9IK+/PPPwtey3bt2oFhGDidzqD3ZmRkJCIiIhAbG1tCf5r7nizLBl3LgODyFBcvXkStWrVCWnsJ6yo2Nha3b99Gbm4uDAYDvebkng0LC4PdbofRaITZbKafpyw7IEIdW8HkJ/zXPQIiR6HT6WiMoVKpfOSpiCmhXq+nn1+n02Hx4sWIiIjApEmTQvosRJ88UCcMF4QtHGwO4CJQoSjQa4IhJycHixYtgkaj8WGZBgK5D4W6ich7Z2dnIzIyEjdv3sSdO3dw9+5d2onmL3skk8mo5wQ3HhO6JwMZtgqNrVBbzAPNXw6HA1KpFFKpFGq1Gp9//jnS09Mxc+bMEuf6888/o2rVqiUKD0Ig8cW9SnJVqlQJarUaer2eFuJDRatWrWA0GoNKwAGgjGchYoDJZML58+dRv359mEwm5OXlUYkAkUgEjUZDr69QPE00rouKiqDX60t0/P2TKI+Yi2+eI2siMee8ceMGbt++TeMsh8NB/2/Xrh0A+Oj4+p0zBg4ciLNnz/KSM4C/uj6FijVkfJfGvLOgoAButzukue748eOIj48XNB197733YDQaMWvWrKBzYYMGDRAdHU0LXKSIz4XBYEBOTg6dk7gm5OVl+lqBv3Dnzp2gOYi/8zhAcaGDT0qD/LtfKaAKVOD/OwKu0gzDaBiG0QBQABgF4GUAJgBz/vz73wKWZW0A5gHYwTAMWdH+AEDL4Jy/873+M5Zlm7Es26w0gXwwkEUoPz8fOp2O6uVxF2WJRAKHw0GZSNwqZ0xMDLRaLdRqNVQqFdLS0gImjUPVW5o6dSrCw8Oxbt26gEkHkjQuKipCfn4+jEYjXC4XbeN1uVywWq20FfteERMTE9SJun379rBarZS9IYRWrVpBLpfj+PHjkMvltFpPEnqEXewPsokhwYLdbi8zPdCyGF98myhyzna7nerwkXY2u90OrVYLt9sNr9cLh8MBr9eLPn36QCQSYe3atYLvtXfvXmpAKASSdCas3HsFad0OtuEOpFvLB5JcCwXvvvsuLBYLFixYwBuMkqQxGfNEa5wc32KxwGq1+jBiuE7NDocDsbGxQQ3xuLqgoWp1ltfcRSASiei5A/DRfSQ/y2Qy+h0SUxFiNEnaUInBS25ubqkKAA899BBWrlyJ33//HaNGjSrxeGJiIi9LGABlrpF2ykBJDiEwDEPZ1MEM9YiMSiBNUZI0DqZnSpiHW7ZsQW5uLmXp6fV6FBQU4Pbt2ygsLBQc46GMn0CJlz+PcU9jixQIyFzEnbv89de5ryHGicBfBj8xMTFQKBRwuVwhyYwQPPTQQ0hPT8eKFSuCFqU6duyIwsJCQdamWCyGWCwOKdFLGEmBEtxkXQ0lOSQWi0uYbHJx6dKlkAt3Z8+epUaPGo0GcrkcRUVFMJvNsFqtlFFM2ndJYZIUTgjKwhQo1LF1r1qS3GIXkWkC4LOGKpXKEsa42dnZ2LlzJyZNmhRU25eAJOdC1RImiaZQND4J7iVpHGz9s9vteOedd+D1evHyyy8HZPb6vw4ITCYoKCiA3W6n8W5MTAxSUlIQExND5wTiuwD8dS+QdZNPS58LoWIBIDy2gs13BP5eGICvREJUVBS0Wi3tMJoxYwZOnz7tYzTmdDrx22+/BWSQ+4PsAUItPvCdd61atRAVFYWaNWvSa+NyufDLL7/Qf3zzXIMGDSCXy6kWcSBcunQJYWFhgvPOiRMn4PV60bp1a2rKbDAYYLfboVAoaGdKsNiaew8/SMm78oi5uPMc1weHQKlUQiwW00IXkcIh66lKpUKVKlV8ZOL80adPH0gkEmzcuJH3cZI0FopRSfwU6jzk8XiQlZUFiUTC28HDhU6nw61bt9C8eXPe41+/fh3r16/H0KFDQ1rvRCIRevbsiT179uDmzZswGo0+nSbAX8QaMn9zTcgDmb5WoGwwbNgwDBs27IE5DlDsqdKhQwewLMv7LxR/iwpU4L+MYBmzkyjWMT4J4ACKk7SP/vm3EwFeVyZgGCaWYRhyjndZli1iWZZkBq4DkP75vMcBdGH+5p5wsgi5XC6fzQs3WCI6qCTpmpeXR12lHQ4H1f365ZdfcOXKFcG2waioqJBZfMQYITs7O6DxBtnIA8UbBaIRazKZUFRUBKPRCLfbLRi8h4Ls7GzcunWrRDuUP3bt2kXb4UMB0U3jMthIAplsQLkbE8IueVCDBb5NFDlnuVwOhUKB8PBwyOVymljKy8ujSWOZTAa1Wk115vbu3Sv4XmazGRKJJGA7VmRkJEQiEbZv335Pxjvc48hkshJatP4oDbvB6XTihx9+wK1bt0JKaufl5SEhIaGEzAYBSY7k5+fj7t27lAFpNpuhVCqRkJAAuVwOh8MBs9kMvV5PWetisZiXUcNXBCDfJxmrD4rxBjl3lUrF63hOdIodDgdNGNrtdp+CjVqtRt++fVFQUIDp06eX6v0ffvhhTJ06FXv37i2hA1y7dm1cvHiRt6hAzi8jIwMWiwXZ2dnIzs6GXq+HyWQKycyJ6FkDENQrJSDJiEAbXDLfBmt1bNasGSIjI3H06FGqZZyXl4fCwkIqp0PGFlByPIUyfgIlXkoL//cn19tqtZaYuwwGA7Kzs2G320uYrXGNRkmhy2azoXLlyrDb7di+fXtI58MwDKZOnYqLFy9i2rRpAZ/bqlUraLVaav7JByIN4PF4Qio+EG1nj8dDW90dDgesVisdq8GY07m5uTh8+HDATbJUKg15joiMjKTFNJIIJ/ORUqlEZGQk1Wkncg6k04mLv3NuCjXR5w9S7OEW6vzHIbdQQXSzyVzvL0ERCPXq1YNCocDmzZtDej7pGtuwYUNAYyouwsLCSt1tE2x8nTt3DjqdDuPHjw+ZDfv9999j/fr1aNmyZcAYadeuXQCA5s2bU1ZuTEwMZUcSCTageK4gbHCuXEFZx2Glme+8Xi9MJhNMJlMJY1uZTAa9Xg+n0wmLxYJhw4YhJSXFpxAvFouh1Wpx5MiRkJnDpJBYGvNVf0RGRqJp06aU/c4HPqmKrKwsn+KKEBwOB7Zs2YImTZoI3iO//fYbgOI1TKvVIiEhIeQCDBfcgvWDFo+XNbjzHImbDAYD3a+Eh4ejdu3aSE1NRWxsLKKjo2lnhNVqRUFBAerWrUuZtXzQaDRo0KCBIJucdGQdPXqU93GyR7179y70en3Q+T83NxdmsxmpqalB7zmSjBMyVzQajfB6vWjSpEnA43ARHx8Pp9OJ+Ph4JCcnQ6PRlJBXi4+Ph1gspoUs0lUjFosfSBPhClSgAhV4kBFwxmRZNp1l2SoA6gD4CMCZP/99CKCkKFEZgmGYvgA2AniKYZgwlmVdfklhEQARwzADALwN4Dr7N7oEcReh1NRUwcCHVDuTk5N9WFYkUZCfn48bN25gypQpqFq1KmbNmsX7fklJSYJMKT6QoDJQKzVQnIyOj49HUlISkpOToVAoEBUVhYiICISHh1PG0r3A6XRi8+bNUKlUeP755wWfd+zYMXz99deYNGkSqlatGvCYR44cgd1upwwPwgbR6XSQSqU+EhXcjQlJjD2owQLfJoqbiCQ/E904t9uNoqIi3LhxgyaRSftno0aNcOrUKcENa7Vq1eBwOAKye6tWrYpBgwbB4XBgzZo1gi1vwSCRSJCWlha0aBBqscBsNuObb77BjRs30KZNG7Rv3z7oazp27Ig7d+7gxo0bvI8TBhAZO2TzTjRHU1JSKJsiLCyMJpWioqIE27r5igCE4US0ge8lWVKe8B9vSqWSah5HREQgPT0dKpWKmhtaLBa6ybbb7RgwYAAmTZqEjz76CKtXry7VexONOn9ZlUceeQTAX0kKLipVqoQWLVqgVatWUKlUiIyMhFQqpY7aOTk50Ov1sNvtvKxJwi6w2WyoW7cuatasGfT6kNcJIdQWT6lUiscffxz79++HWq2Gw+Gghbro6GhoNBqfseU/nv7u8cOVA/Dv5PCfu4jrOumi8TcaNZlMtK2VJL569eqF6tWrY/bs2SFLcvTu3RtTpkzBzp07cfDgQcHnKRQKTJkyBQaDgSY8+CCRSHhlKggD2Ww205bX7Oxs5OTkUPkNvV4Pg8GAwsJCiEQiaLXagN+Ny+XC9u3bERUVFVAmoXnz5gHPmYv+/fuDYRjs378f8fHxiI2NRXx8fIkCBim0Ej1X//nr7xxboST6hJjP/oU6fzkq4nNACjnh4eFo06YN2rVrhw8//DDkZF9sbCwmTZqEQ4cOhfRdxMbGYuLEicjNzcWSJUtCeh9iwBpKoYtASMeW4OzZs1AqlYIyA/5Yu3Yt5s6di6ZNm2L9+vUBv5PVq1cjPT0dXbt2hUKhoOOfsHXJ90DmLX9Cxb3IgJUl7HY78vLykJeXB5vN5nM+5B7PysqC0WhEQUEBevTogT179vhINs2ZMwfXrl3DW2+9FRIrPzw8HJUqVcL58+fL1NBUIpGgbdu29J9/LOX1erF69WpEREQElSjZtGkTdDodxo0bJ/icY8eOoXbt2lTPWKVSISwszIe97V9kFJIjuddxUBbdEGWNQOfEnefI/BsTE0NjTuZP8061Wo34+HjEx8eDYRiYTCZqvPj444+jsLAQ+/fvFzyH6OhoQQPelJQUtGjRAj///DNv56dcLkdiYiJNbAeT5pBKpWjUqFFALW4CnU7n483ij4YNG0Kj0eDAgQNBjwUUX+uNGzeiW7duqFSpEpVc4o4vboGayERyC1n3I/1VgQpUoAL/RYS6Un8JoDaAD1CcMK7z59/KBQzDpAGYDyD3z/ca8GfimOUwj/UARgJ4DsDjLMteL6/z4QN3EfJ3j+fqgjocDmpop1QqERcXR6UmyL8FCxbgxo0b+OyzzwSZAJUqVUJmZmbIbBTSSl2aVjii6yMWiyGVSn0cZ+8Fe/fuhV6vR9++fQVbNc1mMxYtWoRatWph8uTJQY+5f/9+iMViakpCJEJu3LhB9Ru5rsMPYoIY8A0wSeLKX9dNiK0aGRlJ9e0kEglNMCmVSoSFhaFx48Zwu904efIk73sTNnswyZCUlBQMHz4cMTEx2Lp1K3bv3n1P2sPEgCLYc4LBarViy5YtMJvN6Nu3L5o0aRLS64ghx759+3gfJ0ljsmEnxyTfB5EGKSgooHp9hJkrNLb49LS532NZskDLE6SIFB8fT7VCyXUhRQliGGi1WjF79mx07doVU6ZMCakVlqBRo0aQyWQlHORjY2PRokUL/Pjjj4IMUGLCRzZiCQkJSEhIoBqSer3epwUNgM/PSUlJSEtLC/pdcDfEQiDnGApznug4f//993S8REdH844p/8Ts3z1+/FuJVSoV3WD6b/z9GT5k4wYUb0xFIhEiIyMRGRmJmJgYKJVKaLVavPrqqzh//jy++eabkM9rzJgxqFKlCl599dWAMg8dO3ZEeno6fv75Z8FCKsMwkEqlYFkWDoeDdrPYbDbo9XoUFRXB7XZTOZaIiAhERkZCLpdTAyOtVovY2NigCb0jR47AYDBg1qxZAWUMHnroIdy8eZPKngRCcnIyOnbsSA0jiSElYdoSeSN/qRDgrw02kUl4kOamUJnP3HHINV4kyRiSPB89ejSys7OxYcOGkM9h3LhxSEhIwKJFi0JK7NapUwdjxozBjRs3sGzZsqDsdS5buizAsizOnTuHunXrBp2LWJbF0qVL8cEHH6Br16545513AnZK3Lp1CwcOHMDAgQOp9r0/W5ckjLkt9v80uOuwXC5HXFwcjce5zyGPJSUlQSwWw2w2o02bNrDZbD4dXB07dsSzzz6LNWvWYNGiRSGdQ7169WA0GktFALlfHDp0CNevX8eTTz4ZkGnscDiwfPlyNGvWTFC6jGVZHD9+nJqe6fV6GI1GiEQin64S0olCYgSu1FpZ4EHq1CII9ZxI4thqtcLj8dB5l4DEWIQEYjKZcPv2bbRt2xaRkZH49ttvBY8dGRkZcM/32GOPISoqCl9++SVvclkqlSI2NhaVKlUKKrFFpKVCgU6n8zF290dYWBg6deqEAwcOhLTHuHLlCm7duoXWrVvD6XTC4/EgLy8Pubm5cLvdtFhI2NBkL0tk50gBvCy8IypQgQpU4L+CUJ1n6rEsy+0rOcAwzIXyOKE/YQAwAsA1AE8BaAUADMN8y7Is6c25ieKk8nMsy5bJuQRjkHE3UYR9S5zSuU7P/j8rlUooFAqaqDAYDMjPz4dGo8Hvv/+Or776CuPGjUOzZs18Ng1E1woo1pZ1Op04efIkatWqBZ1OFzAAIxvjq1evltgAeDyegK8VYsaQlj4+KBQKHwmCjIwMHD9+HA0bNoRarRZ0wl66dCkMBgO++OILsCxb4rxcLhdN6gHFib/mzZvTYN7lctFNPAnEyAY7ISFBkC1VnhtiIVYj9z393cHJz9zzJeOKbOBJgEaOT5JiXHa1TCZDp06dwDAMfv31V7Rp06bEuRCx/zNnzggyfLnn1rNnTxw/fhxnzpzB7du30aRJE8Fg0eVyldr1nCCQhEVcXBx27NgBs9mM7t27IyYmht4rYrFYMKnicDhQs2ZNpKenY9euXejbt6/P41FRUfReJvIvLMvS+ycsLIwyV1mWpU7XgfRXibYr+U6433V5sfeCMW7uZbyTMUvOmbwHSSqRzRG5fmRT/vbbb2PAgAEYOnQofvzxRyQnJ/scV0g6okGDBrhw4UIJBnzbtm1x5MgRZGdnC24oWJYt8RiZd+12O4qKinwSxlwQCQ4+hIWF0UQ2GW85OTm4cuUKIiMjcffuXZ/nE3ZOID1ioLiY1aZNG8TFxeHbb7/F448/DqlUCo1GA4/H48MoBv7aaAb6nu9FFzUUkOMSaQO5XE7Px+v1BrwPuKx08nqv1wuXy1XCtHTgwIFYsGAB5syZg06dOgluMC0Wi8/nfPXVVzFixAh8+OGHaNmypeD61bhxY9y9exdbt25F9+7dfY7Bsiwdk0Trl2wkySaeJMD8QRjK5Djc9mGy3nORlZWF8+fPo2HDhkhMTCwxhgji4+Mp+33fvn20UAoIFyX69euH559/HgcOHKDsfaB4jHu9XkgkEt7viyRZyb3Nh3+K1UfGif/cSc6Hq63OZR2T/7kdAlarFS1atECjRo2wePFiPPnkkz7jzGQyCSZ4hw8fjgULFmDdunXo1auXz2NFRUUl7r309HQ88cQT2LhxI4qKitC6dWve8UMkUcLCwmCxWEIqThGcPn2a9+9OpxMFBQWoWrUqb0cRmZM9Hg/ee+897NixA71798YLL7xAvSz4QFirLMuiY8eOyMrKgkwmo9JZRDuaSBqRwhHxkBBCecVj3DHLjdNJzEg+EzFHJrJLRLecdNp1794dkZGR+O6771CrVi36HU2fPh0mkwkff/wxpFIpunbtKngu+fn5iIiIgFgsxvHjx33uYZvNFjCRLMQeBYq/Q6HHlUoldu7cifT0dFSpUgU5OTn0MbFY7BOrbdy4ETqdDq+99hqNrf1x7do15Ofno0WLFnTO5Ep0kfPhgmVZSKVSiEQiWpS7XwjNCf8kSnNOhMlrtVpp4ZUQXkjMQoqzRJ7C7XajS5cu2LZtGxYvXuxTmNTr9ZTsQxKiXHDngMcffxxr167F559/jsGDB8PtdguyowOBMKH5oFarkZGRQX/PzMyESqWif+MjrDRs2BCbN2/G3r17eQ2rgeKxpVKp8MMPPwAoNrEViUQoKCiA0Wik5AUSk5K4g0hhORwOKBQKmM1muN1uGAwGOs/+E/NTBSpQgQr8mxBq0vgUwzAtWZY9CgAMw7RAOWoasyxrZhjmHMuydoZhvkAxo7gVAAbABoZhKrMse41hmFYsywqL9pbNucBqtUKhUPgsHMTsAfDdoBAQhiGRTxCLxbBYLNRJnmEYeL1eTJ8+HVWqVMEbb7xRYpMcHR1NGRok+Xfr1i20adMGVapUQUREhOB5N2zYEFKpFBKJpIRm4mOPPYa0tDTe1x0+fLhEkodAJpMJBsW3b99GrVq1UFRUhPPnz+Pw4cOoXLkyZs+eDYlEgoYNG5Z4zS+//IJdu3Zh9OjRPhtcLjweDw2O8vPzcfr0aTz//POIjo5Gbm4uTV5UqVKFXn9SIff/zggIO8bfDKUsQJIo/u9LWNwEfAGm//mSz+N2u6HT6RAXFweGYWA2m6khE/BX4YKwAbVaLRo0aICTJ0/yjhHSmmyxWAQ1gf1bWseNG4ejR49i9uzZ+Omnn6DRaNCmTZsSn/POnTuCDIWbN28Kvl9mZiZlr/jD4XDgxIkTsFgsWL58eQnzmezsbMEWuYKCAqrxvWbNGsr4B4rv7bCwMISFhUGtVlOJD7lcTo1+CIj2Jxk75NrzMcRFIhHv90i+3wcpAA10LoHuDbKpJqaeZC602+2IjY3F8uXL0bdvX4wYMQL79+/3mRvdbjdv0qtVq1ZYtmwZGjdu7COJ06pVK+zZswc3btxAly5deM/L6/UGlCr57rvvYDAYcOXKFWRmZqJatWpo0KABRCIRnE4nHnroId7X3bp1izLzSSJFq9WiRo0a8Hq9JcY6GRdEl1AIZAPdr18/rFq1ChkZGVQORaFQwO12QyaT3VfHBEna3q8zPRkj3DWP+zmExhB3I0wY36Q9lRgm+WPOnDno378/tm3bhmeeeUbwuFzmYr9+/bB9+3Z89tln6NKli6AvwKOPPgqtVks7erhrTmZmJmX8ki6QsLAwOm4rVarEe0yguIggJAFQVFSEZs2a0d8LCgowefJkVK5cGePGjRN06yY6ly1atIBMJsMff/yBfv360ceFmMz9+vXD9OnTsWHDBtphARR/d3K5HPn5+dBqtSViDblcTrVnQwWJje4ncRPKXMhd6/hAikLAXxt/MqcTkHtBLpdDqVRixIgRmDRpEnbv3u2ToIiLixPUNh8+fDgOHTqENWvWYMSIET7mTyKRiFfXtWvXrkhOTsb777+PypUr46mnnirxmf/44w+Eh4cjKysLN27cQFRUFJWqAYo7furXr4+aNWv6fO9nzpwRZKkTmZ/hw4fzro1paWmw2+0YO3YsduzYgalTp2LGjBlgGAZWq1XwuB6PB2vXrkXnzp3RokUL+tnJdWcYBna7nepmp6enw2QyQSaTldu6F+px/WMuEoPr9Xq43W4fiTlSgBCJRIiIiIDFYkGnTp2wY8cOfPDBBz7fw8qVKyGRSLBkyRJIpVKMHTuW9/3btGmDiIgIZGVl4fjx4+jQoQO93xiGCVl32h+ZmZmC88+mTZvgcDgwZ86cErGXy+Wif7Pb7Vi3bh1atmyJ/v37U+a4P86cOQOguAPCbrfTWNLr9SIvL49K75FOFBLzOJ1Oqv0ezDtACNzvOdicUN7gG3OlOSeVSoW4uDi6RyTJYYvFArvdTvV5pVKpT6dbhw4d8O233+LXX39F9+7d6fFIZ09SUhLsdjuioqJ85nJ/WZLq1avjlVdewdmzZ/Hkk08KalIfOXJEcD/odrvRqlUr3scyMjJQu3ZtAMXrxKefforGjRujUaNGAMBrnNelSxfMmzcPJ0+exMiRI3mP6/V6IZVK8eOPP6J27dpISEiAx+OhHgMulwsulwtqtZrui/xl4UQiEeLi4mhymXjjVKBsMXXq1AfqOGV9rApU4L+IgDtJhmF+ZxjmHICmAA4zDJPBMMxNAEcANAv02vvFnwljhmVZO4BVAK4AqM0wzPo/zyWqvBPGwF8sQSHWGEno8Uk5kOomYR6R55DW9lmzZuHmzZv4+OOPgxpUpKenQ61WB3TP5YJhGGg0mqCaxveLwsJCHDlyBFu2bMHEiRMxdOhQvP322/B6vZg2bZpggGixWPDSSy8hPT0dL774YkjvdfDgQbAsiw4dOqCoqAhisRgul4u2cpEgnwSyNpuNl61DGCfl0drm8XhCOi63RZgw2fzPl0/vjWv8R15HNPhMJhMsFgskEgkaNGiA3377jZfRyTAMKlWqFFSewh8tW7bEhg0bkJaWho0bN2LFihUhG/3cK1wuF3744QdcunQJH374Yancyrno2LEjnE4njhw5wvt4eHg4TYAQLW8yrrRaLRITE6FSqXDnzh1kZ2dDp9PBZDL5tGAK4Z/WbyxLEFYWULzxIVq8FosFNpsNMTExtKDw0Ucf4eTJkxg/fnxI7KIWLVrA7Xbjjz/+KPHY0KFDYTKZBHWpubh16xZ2795NE64EMTExaNWqFZ544gk0atSo1N9HaTSNg5lUEQwcOBB2ux2ffPIJNbUE/rq2ubm5yM3Nvae2dT5d7bIEtzVdCETH0O12w+l00g2a0Nzct29fPPTQQ3jrrbdC1pwFgPnz50MqlWLBggUBv5+HH34YtWrVwsqVKwXbeMnmv6zNmYgMgN1ux+TJk0NKnshkMjRo0ADHjx8P6T0iIyPRo0cPbN++ncYfpP02Pz+fGvH6g8vE8gc5hv91fVBaxEMxVSMtyQaDAQqFAu3atUPNmjXxzjvvhMx8ZBgGr776KoxGI5YsWRLy+T399NN4+OGHcejQIWzbtk3wecSH4tatW7BYLFSDdPDgwahfv35QyRMubty4gWrVqgkWU00mEwYNGoQdO3Zg/vz5mDlzZkjJ18OHD+PmzZt4+umnERkZSSWLCAhDn9zvubm5guPq7waf7AoxsybdBQB/gU6hUKBfv34wGAwljMREIhE++eQT9OvXD++++y7WrFkT8Dzatm0Lu92OEyfK10v81q1bOHnyJAYMGBDUMHj16tXIzc3FtGnTAo6Do0ePIjIyErVq1aJSRUAxk/XixYu4desW7HZ7iZg2mFzXfw1Ew5jcQyShSTwzSCHe6XRCLpcjLS0NlStXRr9+/RAZGYlNmzbxHpckYwMx04HiMTh27Fjs27dPUMaurFBQUEAN6wJBqVTioYcewqFDhwLOyWazGYcOHUKLFi2oB0RCQgJq1KiBunXrIjk5GbGxsQgPD6dxu78kE/mdSDg9SESO/y/o1atXiY6cf/I4ZX2sClTgv4hgu+aeAHoB6A4gHUAHAB3//LlHWZ0EwzDVGIZpxjCMj+PanxrGDMuyFpZlPwLQEMWM4x4syxaW1fsHAjGFEdqQEKd4shHjbtRJoERa9vLz82lAcPjwYaxZswbjx49H27Ztg56HSCRCgwYNcOrUqZA3OdHR0YJt1/cDlmWxf/9+TJkyBePGjcOSJUtw8uRJxMTE0KTxqlWrApqfvf3227h79y4WLlwYstHerl27EB4ejvbt20Oj0VAtU51OR1uTSCAaKGFCvpfyqC4TQ5DSwv98uYG1Uqn00Yvzf53ZbKbsnqKiImRlZeGhhx6CxWIR3JjcS9IYKE68DRkyBH369MEff/yBBQsW4OLFi6U+TijweDzYtWsX8vLysGjRInTs2PGej1W5cmWIRCJBwyy1Wg232003lUSzlzAVyAZILBbD6XTCarXi1q1bQbUq/7/BX5uQ3EsAfAyPHA4HWrVqhUmTJmH9+vX45JNPgh6bsH35xmynTp0QHh6OEydO8BZCvF4vzp49i7fffhtTp07F8uXLMWnSpJBNxEJBaTSNQ01It2rVCs2aNcO2bdtgMBhgMBhoUl6hUFBTnHtJYBK23L0amQZDqElpu90OqVQKqVTq4x7v/zqShH7llVdw584drFixIuRzSUxMxKxZs3D48GHs2bNH8HkikQjjx4+H3W7H559//rdJLrAsiy1btuD06dN4+umnAzKX/dGsWTP8/vvvISf/Bw4cCJ1Oh+3bt1MtZpvNBo1Gg7i4OMr25q4xQolX4hlQWFhYIjn8oJh5hlKU42pyMwyD1NRUPPvsszh//jy+//77kN+rTp06eOqpp7BmzRre4pYQHn/8cbRp0wY//PADdu/ezTvupFIp2rRpgzp16qB58+aoXLnyPV1bp9OJu3fvChZYTSYT+vTpg6NHj2LZsmWCzFg+rFu3DhEREejWrRvcbjfVtCVj02KxQKfTITo6mpork+4JLjweD3Jzc//x9ZNo9qvVapjNZtoN6G+oZbPZ0LNnT0ilUt7xIhaLsXLlSnTu3Blz5szB5s2bBd+zRo0aiI2Nxf79+8tM49cfLpcLW7duRUREBEaMGBHwuQaDAYsWLUK7du145cy4OHbsGJo3b47w8HBqOkv2OkTTnaw33PWB3KMMw5R7MfPfCkL0CQsLoyxhmUyGsLAwShRRKBTo2rUrvv/+e95CDEkah0IWGjx4MB5++GEcPXoU58+fL9PPwrIsCgsLcfHiReolkpCQEPR1RPIm0J7iwIEDcDqd6NChAyIjI2nil5uE55qH/38ga/wbcfny5Xs2UC+P45T1sSpQgf8iAs6mLMveCvSvLE6AYZieAL4F8C6AlQzD1Pvz78yf58AyDBPGMExNFCeM+7AsGxrdtmzOL6ApTHR0NCIiImh7j9Dmy2azweVyweFwQCKR4Ny5cwAQ0ATHH926dcPvv/+OyZMnh2TGkp6ejjt37oTsSBsKbDYbli5dis8++wxKpRJDhgzBvHnzMGfOHMyZMwcDBw5EnTp1ArKofvrpJ6xatQrPPPOMYFs4F16vF7Nnz8aqVavQr18/hIeHIywsDBqNBlKpFGazGbdu3fJh5AViH/EZAZUVArVrB4L/+QoF1iThQjbwCoUCcXFxSE1NRUxMDJVbaNCgASIjIzFkyBDegJDoYvsbj4X6GTt37owXX3wREokEn3zyCT766CNBbc57AcuyOHjwILKystCxY0efVrxQkZubi2+++QYDBgxA+/btaWsb33sZjUafdnkiKUM2RYQxI5fLERUVBYvFApfLBYlEwpvMfxDYNGXtME5YZGq1GlKp1IdxrFKpfMavXC6HVqvF7Nmz8cgjj9BEYCBER0ejUaNGWLlyZQk2FzF4zMnJwbp165CdnQ2guNPh8OHDWLVqFebOnYtLly5h4MCBmDNnDmJiYrBw4UJBdnlpYLFYsGPHDgAQbOUEgOvXr0MsFgeUDvKHRqNBXl4ebVcl7F3upkeI7RkIRD+4vBITZM6SyWQ+Y91/7CuVSkRGRlL9dK/XS82yuM+3WCwoKipCx44d0bZtWyxZsqRUSaXRo0ejVq1aeO211wLORZUqVcLAgQNx+PBhLF26tNwTV0VFRXj33Xexdu1atGjRolRzmdvtxqlTp6g2bDDk5+fTAk1+fj5cLheV2RCLxYiNjaUFMXK9uS283LFGkmVEt9Q/gfmgGeZxQT4Dl+0aGxuLqKgoyujr168fatSogXHjxpVqHZwyZQo0Gg2GDx8e8prHMAyGDBmCpk2bYsuWLVixYgVv0owkju7nmp45cwZer9dHnoSLbdu24dy5c1i5cqWgdigfjEYjvvvuO3Tu3Bm5ublU1oFo2gJ/FX5EIhG0Wi2cTic1n+KOX71ej6ysLF695b8T3M4/bvHTZDIhLy+Prv8mkwlisRidO3fGzp07eedhiUSCxYsXo3Xr1pg9e7aPpqv/e/bo0QPXrl3D9OnTsW/fvjKNE8xmM5YvX46cnBz06tUraOFh9uzZsFgsmDt3bsBxd+XKFfz+++9o3bo1FAoF7ZwkWu/p6elISEigcTXXfIyLUDoD/qtwOBzUiJX87vF4aKzFsix69eoFo9HIa4hXrVo1MAyDVatWBX0vhmHw0ksvITExEVu2bMH+/fvveRzq9Xr8+OOPWLt2LRYuXIilS5di6tSpWLhwIXbt2kXNuwOBZVn8/vvvAErqYhOYTCbMnDkTCQkJaNSoESQSCY0/yHUiceo/HX//1zF27NhSFSTL+zhlfawKVOC/iFA1jcsFDMO0RnGyeDDLsqcZhlkGYAqAZ1lOVMayrAfAZYZh6rIsqxM43D8Cl8sFmUyG/Px8uiHjOggXFRXRVldicpKfn49HHnkEhw4dwttvv42EhASMHj066HuNGzcOZrMZixYtQkZGBj788MOAshbdu3dHZmYmNm3ahKtXr6Jz586oWrXqPX9Wg8GAV155BTk5OXjyySfRp08fGiCGylotKCjAlClTUL16dcycOTPo861WK0aOHInNmzfj8ccfxyuvvAKDwYCYmBiIRCLExsZSrVnCCAX+2tD+W8AdN4CvDi7XCV6pVMLj8cBkMlEmIdcoUKvVQq/XIzU1FWvWrMG4cePQuXNnfPPNNz5M3Ycffhiff/45Nm7ciFmzZt3TOaempmLGjBn45ZdfsHv3brz77rtITExEy5Yt71tv7sSJE7h27RqaN28uqFHKB51Oh127dmH37t2UlV+zZk1MnjwZjz76KKpVq1biNZcvX0ZeXh6aN29OWTNEV44YZxAQDd/4+HgUFBTQcegPrukOYSnfr7ZsacE14CsL/T/Cao+IiIDT6fTREfUfv8Bf2rNvvvkm2rZti2nTpuHrr78O+B7z5s3D5MmT8eKLL+L9999Hy5Yt6WM1atRATEwMfvzxR5/jEObgmDFj0KxZM1oYmDNnDubNm4f3338f7dq1Q0pKSqk+L8uyyM7OxvHjx3Hy5Em4XC40a9YMTZs25X2+2+3GDz/8gPbt24c091itVowZMwZ79uzB//73P8TExMBoNFIjPCKxU1RUVKJ92l+vmE+/mE9rvyxBEo1msxm5ubmIj49HeHg4PWeWZWlxjow/wubjtrWT56vVavq8cePGYejQofjhhx9K6DEKQSwW4+2338awYcMwadIkrFu3TpBl3b9/f3g8Hnz99dcwmUwB9afvB9evX8eyZctgNpsxbNgw9O7du1QJwcWLF+PYsWNYsmRJ0DF18eJFPPnkk7h79y7eeust1K9fH3a7HfHx8T7zDvd6c1vGyfjxn7vI/w9acph7zkDx5yLzs9frpR04pFWbdOsQSSu5XI73338fw4cPR69evXDo0KGQWPkajQYbNmzA448/jnHjxmHjxo0hna9IJMLIkSNRqVIlbNu2DdevX8ezzz577xeAB3q9HufOnUP9+vVRv3593ufs2LEDjRo1Qo8epWsW3LhxI2w2G/W1kMlkcLlcVH+VawArEolospUkb7gxGZHiIP9z9bH/znHGHUNkXhWJRCgqKkJOTg6sVitSUlLgdrthMpnw6KOPYteuXbh8+XIJrxCgmC3+7rvvonv37pg5cybWrVvHu+Z36dIFaWlp+Prrr7F69WqEh4ejU6dOqFmz5n19fp1Oh1WrVqGoqAiDBw9GnTp1Aj5/z5492Lx5M6ZOncr7ebj47LPPIJFIMGLECDgcjhJm3GROJ1s3ch9yu7VIFxcZB3z38N89Bh4kcOdbkvwk49HpdEKhUKBDhw6oWbMmPvzwQzz11FM+r69RowbGjh2LTz75BEqlEjNnzgwolSWTydCnTx8cO3YMv/76K7KysvDoo48GLIxz4XQ6sXfvXuzduxdutxvh4eFISEhArVq1ULduXSQmJiIxMRFRUVFBv9MNGzZg69atGDVqFO/cxbIspkyZgqtXr2LVqlVQqVSQSCTIysqCVCqFWCz2Keb469wHwj81/1SgAhWowL8J/2jS+E+8w7IssYB+DcByhmFkLMs6AIBhmGYAEliW3f6gJYwB+GxSyEafgAQAYWFh0Ov1iIiIQFFREaRSKVQqFRYuXAgAmDx5MjQaDfr37x/wvRiGwfTp05GcnIzp06dj+PDh+OSTTyiDyx9hYWEYMWIEfv75Z+zcuRNnz55FSkoKUlJSkJSUFLIhBcuyuHr1Kk6cOIGIiAi8+uqrQYNRIbz22mvIz8/HqlWrgiYzSHL69OnTePHFFzF48GC6MbTb7TRBwk2a/n8BN7Am14lsxux2O3Jzcymbx/91Go0Gd+/eRd26dbF+/XqMHz8evXv3xhdffIEBAwYAKE4i9ujRA9u2bcO4ceNoy3JpIRaL0bFjRzz00EPYt28f9u/fj61bt6JWrVqoV69eSMZKLMvCZrPh6tWrVMNVr9ejZs2aaNy4ccjnsn//fkyfPh1WqxXVq1fHxIkT0apVq6A6yIcOHQJQvJEjSRNihMW9n8nPYrEYDMME1GjjBv/cJMzfWcgoC4dx7qaOfCbCHAuk+0qkUoie+1NPPYUVK1Zg165d1ECJD1FRUfj0008xduxYvPjii3jjjTfQpUsXygJPS0vD0KFDcezYMWr4UqdOHSiVyhLfs0KhwCuvvIK5c+fi0KFD6NixY0hmQzabDadOncLRo0eRnZ0NmUyGli1bol27dgHZMseOHYNerw9JMy0zMxMDBw7EqVOnMHHiRPTp0wdGoxExMTE+m2juNecWMPzHFN8Y40vk3w9CNdbjss25rzWZTDCbzVCr1ZQdxB1X5F4pLCxEmzZtkJycjE8//TTkpDFQzCJ+5513MGHCBLzxxhuYP3++oGnRk08+iYiICHz++eeIiopCx44dS6UbGwhOpxM7d+7E0aNHkZqaildffTWorqg/Dh48iA8//BCDBg0KGhvs3r0bzzzzDBQKBTZt2oTatWsjIyMDZrOZmt/xFRPI30wmE038c9edsh5DZQnumAfgU1wha2ViYiKUSmWJ9Y1oiGo0Glqoevzxx7Fp0yZekyZ/VK1aFe+//z5GjRqFmTNn4vnnnw/pnEUiEbp3744aNWpgxYoVWLRoEeLi4oLqanq9XmRmZsLr9SI5OZk3EeT1emniW4hlfO3aNVy+fBlvvfVWSOfLxZdffol69erhiSeeoEVsolfMZcLHxsb6jBn/uQsojk256ye3wPl3jjf/eZOMd6lUCrvdDoZhcPfuXUilUuTl5dF45IcffhBMssbHx2PWrFmYMWMG1q5di+HDh/M+r1q1atSMbOXKldi2bRsSExPRoUOHgNJuQHHMVFRUhLy8PFy7dg1nzpxBVlYWNfMdPXo0UlNTAzItzWYzXnrpJdSqVQuTJk0K+H4WiwWrV69Gp06daKGJSA3JZDJIpVIftjmJ0yMiIuD1euk19o9F+O5h4O8dAw8SuIUVi8Xi48vAlb7q0qULli1bhuvXr5cgAk2ePBkOhwMrV67E1atX8cEHHwR8T7FYjJ49eyIlJQV79uzBsmXL0LBhQ5+CPR8yMzOxe/du6PV6NG3aFH379qXFV64RXig4duwYFi9ejE6dOmHUqFG8z1mxYgW2bNmC//3vf3jsscdoUZAkjKOjo+F2u0sY0oeCf2r+qUAFKlCBfxP+6aTxMQDnAYBhmDAAMgBpACIA6BiGSQFQG8DesnzTstQxdDgckMlkEIvFkMvl1MXVbrdTp+7c3FzodDoaSIlEIsTHx0Mmk+Gdd95BYWEhRo4ciYiICJ9g3263855rv379YLfbMXfuXAwaNAjvvfdeiQ0p1wihadOmqF+/Ps6ePYsjR47g6NGjOH36NK0Gc1upuQxCoJhJffbsWWRmZiIxMRHjx49HeHg4bQ8nKCoqEjQWAooDoH379mHLli2YMGECqlSpQluu3G53iXak33//HU899RTy8/OxdOlSNG/eHGKxmOqohYWF0dZIPvxdWpVlhWDnS1gwZrOZbmo1Gg2VKSHjjWjGkcQ6Ma0bNWoURo8eje7du0OpVMJoNOLhhx/Gli1b8NVXX2HIkCE+7+dvIsYFd3xw0bZtW5jNZmRmZuLChQu4evUqIiIiEBERQZPder2eSmi4XC4UFBSgoKCAtuOJxWLExMSgQYMGqFOnDh0jeXl5gudTUFCAzZs3Y+nSpahTpw5ef/11VKlSBUCxfIHL5eJ9ndfrhcfjwcGDB5GUlITKlSvD6/VCIpFALpfT+7m0YFmWN+mvUCj+1nHJ3YDcy/uSzR93w0c2isSIUqlU0mNz5z3C+jCZTFAoFBg7dix++eUXTJo0Cbt37xaUbyBJyffeew8vvvgiXn75ZcTGxqJHjx4wmUx0/vR37XY4HMjKyuI95qhRo/Dmm2/i4MGDaN68eYlCS1hYGC5fvow7d+4gIyMD165dg9vtRlxcHFq3bo2OHTvS4gd3HEokEuTn59PfN23ahIiICNStWxdGo1FQQuj06dMYMmQITCYTli9fjscee4y6ylssFnofsyxLk1vk+rMsC5ZlS4ypsmAVC40R8nf/sUAeU6lUSExMpO9Nxr7X66WvtVqtuH37NiwWC9LS0mC32+F2u+nryfHI2HG5XBg8eDDeffddXLhwATVr1qTnY7VaBdtXzWYzmjdvjlGjRtFk8IABAxAXFwebzVZCl7dDhw6QyWT46KOP8OOPP6JZs2a8bFPu9+wPm80Gne6venZ2djZ++OEHFBQUoHHjxhg0aBAYhinRru5yuQTb8/Py8jBt2jTUqFEDs2fPLjGHkWQQy7L44IMPMHv2bDRs2BAzZ85Es2bNEBUVRWMRh8MBvV6PmJgYalLGHWP+4I457uPlwcAq7bxEGGFkfgZ8ixNSqRROp5Oy9FwuF5XQINeDrG1yuRxVqlSBQqHAkiVLMG7cOIwePRqrV6/mjSusVqvPNWjVqhWef/55fPDBB4iKiiqxhnLhv2ZqtVq88MIL2Lp1K06ePAmz2YzKlSv7FFlZloXb7cb333+PjIwMKmchFouRlJTkY6wMFHfMGAwGtGjRAh6Ph9fPYvPmzQgLC8PDDz8suMZbrdYS88jZs2dx7tw5zJ07Fx6PBzqdDnK5HJGRkVSqyOPxwGw203WBsI6584DQ9+3PsCzPzhxyfP8xxI3dZTIZ9X3Iy8uDRqNBZGQkNBoN6tWrhx07dmDixIkljm2xWOB2u6kZ5Ztvvom0tDQ0b94cZrOZN4lbuXJldOnSBXq9HidOnMBXX30FpVKJuLg4ek5k3icxS2FhIS3IAsXSTnFxcahbty7q1q2L8PBwFBQUQCwWC+rbLlmyBDqdDp999lkJGSOPx+Ozfm3YsAFGoxHDhw+H2+320d13OBxQq9X0mtrtdphMJqp3zpUt8ng8lIXuXzAk+Kc10ssC9xrncV8nl8tp4thms8Hj8UCv10MikeCRRx7BsmXLsG3bNjz//POw2+0+JKDp06ejSpUqmDNnDvr27YuZM2cKFiKsVivEYjGqVq2KIUOG4OTJkzh//jzOnDkDjUYDq9WKmJgYOvdZrVacP38eOTk50Gq1GDZsGKpUqUJN4YHidZi7JvqDO/dkZmZi+vTptHPRbreXWO/OnDmDWbNmoUuXLpg5cyYKCwuRlZUFkUiEhIQEOiYjIiLo/MVdw0hMLoR7JVj82/aZFahABSpwP2AelEmPYRgxADmArSzLdmEYZiiAxgBeZ1nWVFbv06xZMzaQE3lpN0ZEw40b4BJZioiICNoOqdfrqZwAcWdnGAZ2ux0XL17EiBEjcOPGDezZs4fq/BK2Ix8KCgpw+fJlDB48GC6XC6tXr/apDOfm5vK+jmVZfPPNN/jpp5/w66+/gmVZxMXFITIyElFRUQgLC0NKSgoiIyOhVquxadMmZGVlYfjw4ejQoQOqV6/Oe9yioiLBx4DixEyrVq0QGxuLffv2+TC6vF6vTxJpx44dGDp0KCIjI/HWW2+hUaNGsNlsYFmWaqYRtgMJlPg2GmXdcsSyLMLCwk6zLNtE6DnNmjVj78UVO9B9SIIenU4Ht9uNqKgoulH0eDw0iUd0nokOJWFji8ViHD58GI8++ii2bduGRx99FLdv34ZIJMKoUaNw6tQpHD582Gfz7Z9c4SIjI0OQZXv27FnEx8cjIyMDu3fvxi+//AKn00n/+YO0sqWlpeGRRx5B1apVeTfsQtrfDocDM2fOxLZt29CvXz98+OGHPhsQl8slmKD0eDyQSqWoVKkSOnTogDVr1vi0+t+P7rXQeAs234pEopMsyzbje6w8xpbQ8wmDiCQquIYixBzLfyxy5z2ZTIa8vDzcvn0bKSkpCAsLw4EDBzB06FDMnDkTr7/+Ou97k40uUFzA2r17N9atW0f19tq0aYMhQ4agZ8+ePgH+yZMnAwb8ly9fxpw5c5CXl4dZs2ahcuXKuHTpEs6dO4cTJ04gJycHQDED/6GHHkK3bt1QtWpVGAwG1KtXj/eYVquVJjNNJhOaNWuGJ598Em+88QZYluVlNW/atAkjR45EXFwctm7diqpVq8JkMkEkEtGkQkREBJU64vtuAs1lQR4LOLYCrYvkvbnzKfd3vnMlMhvkfiRa7ETSgquxC/y1lsrlcpjNZmRkZKBly5YYPXo03n//fXpcs9ksuC4ajUYq4TNy5EhqWtWiRQt07doV/fr1Q3JyconXLV26FAsXLkRUVBTmzZuHxMREn8eFipNutxtnz56FRCJBTk4Orl27hu+//x5arRYvv/wyFAoF0tLSeF9rs9nQoEED3mM+/fTTuHDhAg4fPuyTMCeQSCTIzc3FyJEj8cMPP2DAgAEYP3484uPjkZycDJFIRLV8yZyWkJAAhmFoQofLqCJz3r2ulfc6b5V2bvKPrYRAxhLLsjCbzQgPD4darYbJZEJGRgZkMhkdB4WFhRCLxdi4cSNeeOEFjBw5EkuXLi1xHUwmUwkmutfrxbBhw/DDDz9gxYoVJYpZ3OcJ4YMPPsCXX34Jr9eLMWPGIDY2Fr/88guOHDkCo9EIuVyOVq1aoVOnTpDJZDh27BiOHj1Ki2RJSUlo0KAB9u/fj0aNGuHll1+G2+1Gs2a+X4fH40G7du1Qr169gJIaLperxHo7ceJErFu3DufPn4dGo8GtW7dQqVIlWsTX6XSIjY2lxaCIiAhafLBYLFSzndsVJpQc9v+Oy3psWSwWFBYWUtkc7tpG3js8PBwqlQpGoxGXL19GVFQUlaRavHgxPv74Y2RmZpZo4zcajTQmNRqNeOSRR5CTk4Pdu3dTI1A+nD9/HjExMXA4HNiyZQv++OMP3L17lxbYuf/EYjFSUlJQtWpVVK1aFSqVCnXr1uU9blFREW+Hw88//4ynn34aU6dOxZw5c0o87vF4fJJuzZs3h8fjwU8//QSGYaDT6WCxWGi3EWGaE+Nvbicg97pydaNLOweVR9Eq0JoI3HvMxV37uGM72Gf0X99Jh05eXh50Oh3y8/Oh0WigVqsxdOhQREdHY9++fbBYLLxr1NGjRzFkyBBYrVYsWbIE3bp1K/GcmzdvlpBnKigowDfffIPVq1fDZDKhSpUq6N27N3Jzc7F582YwDINHH30Uzz//PG/HamZmZsDOGvKY2WzGY489hpycHOzatQvp6enweDw+91VBQQFatGgBlmWxbds21KpVC1arFTqdDl6vF2KxGDqdDmq1GrGxsfB6vXS+F7qu/rjXsfVPxPMPGoSKz0QOUch8PFSU1XFCOZbQZ3kQEWzuqkAFygP/NNOYgmVZNwAzwzB3GIZ5C8DDAEaUZcK4PEACTyLCz1c9F4vFCA8Pp8kBYq4RGxsLl8uFhIQEbNq0Cd27d0e/fv3w22+/hdRG3bBhQ+zcuRNPPfUUBgwYgK+++iqo+zHDMKhVqxbatWsHnU6HPXv2IDMzE4WFhcjPz4fBYMCJEydopVej0WDBggVo2LChIJMvFLz88sswGAzYuHFjwBbgDz/8EFOnTkXjxo2xZMkS1KhRA0qlkrrpOp1OyrAlQStXwxD4q70o1JajUJktfyZS+bMV5QAuI4ZsxkgCgCQDAFC9bI/Hg/z8fHg8HhQUFMDj8SAiIgJisRiVK1eGQqHAnj178Oijj9L3ePbZZ/Hjjz9i69atePLJJ8vs3CtXrlzCdODKlStITk6mCWSJREI3EUajkTc5Egh6vR7jx4/H6dOnMXPmTEybNq3Uwd/ly5eRm5uLtm3b0utNriPDMCFLn5Q3O+rvBLlvyFgjTHECIotCNoYE3PkuIyODMpOjoqLgdrvRtm1bDBo0CIsWLcLgwYODalVLpVL06tULvXr1QlZWFr788kt8++23mDBhAl566SU88cQTGDNmTEia10RW57XXXsPcuXMpi08sFiM1NRWDBw9GgwYNULly5Xv6/nbs2AGHw4F+/frxPs6yLObNm4d58+ahdevWWLx4MerWrUvHq1Qqpfe10+mkSZgHaSz568QH08z2b/2OjIxEZGQkvF4vwsLCfIzXuAk+AoVCge7du2P16tWYO3duqWSIwsLCsGrVKly+fBnff/89tm3bhvnz52P+/Plo2rQpevbsiT59+tB1tkGDBnjzzTfx+uuvY9q0aXjjjTeQkJCA/Px8FBQUwGg0oqCgAPn5+cjPz6cyOgaDwSchSFqHJ06cCLVafU9O3R9++CGOHz+OFStWCM6J27dvx6hRo2AymfD+++9j4MCBdB0kbG1irFhUVETPU61WU8Yidw15kGUouCCJrGCMehKXES1dbrJOLpeXkNZRKBQYMWIELl26hGXLliE1NRUvv/xy0PMRiUT45JNP0L59e0yaNAlbtmzhLUoEQosWLdC0aVMsXrwYS5YsoefYvHlz1KtXD/379/cp6LZp0wYsy2Lv3r3IzMzE6dOnsX//fkilUowZM0ZwDTx69Chyc3Pxv//9r1TnZzab8fXXX6N3795ITEyEVCpF1apVfWQHCLjXmUhYCEFItqmstdj912aFQgGz2YyioiJYrVYkJCTwdgUBxesGabMnLP1u3brho48+wp49ezBo0CDB942MjMQ333yDrl274sknn8QXX3wRlMkok8l4jxkoriEFz1BhNpsxc+ZMpKenh+QrcuTIEZw9exZvvfUWNdUkxoZcUzsiQcLnJ8KVLCKfhc8s/P+LRADfZ+EW27l/DwSRSES7WMncLZfLERERgXbt2uHTTz+FXq8XvFdatmyJn376CQMHDsSoUaMwffp0PPfcc0Hj5OjoaIwdOxa1atXC+fPn8f3339PCbcuWLfH000+DYZiQJQ754PF4MGbMGFy5cgVfffUVb5LZ6/Vi5MiRyMrKwuLFi+FwOGgHBSlSRUZGQqlUQiaT0WIyWeO4xq6AL/mhAuWL0q4z5X2csj5WBSrwX8QDkzRmilcxCYB2f/7fhWXZq//sWYUO/yDBPyDgtt8aDAa43W7KUCaO2Z9//jl69+6NQYMGYe/e0BQ50tLSsH37dvTp0wfDhw/H1q1bBZlx/oiNjS3RUpmRkYH09HTYbDYYjUZER0eHZA4TCHv37sXGjRvx0ksvCZqzAMCuXbswZcoU9O7dG6tWrUJ4eDjcbjccDgfi4uKoHrTD4YDJZILT6aRahVxTHwKhliPCkCObiFCD1T+Pw98XXQ7gMyQiyfFAxnn5+flQqVR0Q5STkwObzYYOHTpg9+7dPu/RqlUr1KpVC1988QUeeeSRkPQc7xUMw0Amk4WkcxwMly5dwpgxY5Cfn4/FixfjmWeeuafjkIpz48aNYTAYfBi2QuwwPvMWvqLFvxXkfpFIJCgoKPD5vriFDCJBQJIDZN67ceMGrl4tnrqJRI9SqYTT6cSQIUOwc+dOPP/889iwYUPIhitJSUl47rnnMGPGDBw5cgTr1q3DV199hVWrVqFz587o0aNHUA2+qKgovPbaa1i3bh2io6NRv3591KpVC3fu3OE1SAwVVqsVH374IerUqYOGDRuWeNxut2PkyJG0MPjCCy/A7XbDYDAgLi4OKpWKsnAJ24wUFvmYWA9KYSJYS2ew5A/ZzHEZ1uHh4ZDL5cjLy4PdbsfgwYOxbds2fPDBB/dk2FmzZk3UrFkT06ZNw6lTp7B//35s374dc+bMwYIFC7By5UrKPqlZsyYWLFiAV199FS+88ALv8SQSCTQaDeLi4tCwYUPEx8dDIpGgXr16SExMhFarDWg8FAw///wzPvnkEzzxxBMYPHhwicedTiemTp2K5cuXo1GjRvjss8+ozmpeXh7VeSTXkvzONR0EwLuGlAblZRwU7LgkIRWMDSSUKCBSKlx2PDdh+MYbb8BgMOC1115DcnIyhg4dGvScw8PD8f7772PIkCF47rnnsG7dulJf17i4OMyfPx+HDh2CTCZDkyZN6H3AF38xDIO4uDg0atQIjz32GBwOB5xOZ8DCyrfffovw8HB06dKlVOe2adMmmEwmjB492sfIjCRluNq1hNVNvj8yN8THx5eYJ4Tmh7IuYPDpFhOpFrlc7lNAIUllMga5YFkWdrsd9evXR1xcHHbs2BEwaQwUmwVv2LABvXr1wosvvogvv/zyH48PFixYgOzsbGzYsCGk2P7jjz9GREQEhgwZQtceYq5JiAxisZjOe1y5Ku49qFKpfBi4JIkaExMDsVjs493xb4f/2Caf1el0BmScCx1LKpUiKiqKykUYDAa0adMGH3/8MXbu3BlQ8z4lJQWbNm3C9OnTsWDBAly6dAlz584NyfxVKpWiW7du6NKlC86ePQuZTEb9bISklUIBy7KYPXs2fvzxRyxYsAAdOnTgfd7ixYuxY8cOzJ8/Hx07doTBYEBYWBhkMhmsViuVXyLzEumQtFqttLPBarUiLy8PHo8HkZGRZV6MrzDR4wcxTH1QjlPWx6pABf6LeGCSxmxxpOFkGGYugOP/poQxEDo7QiQSITY21qcNl2EY5Ofn09bYyZMno1evXli1alVISRWNRoOvvvoKjz76KPr374+1a9cGNdIIBBLs32/wxrIs1q9fj/nz56NevXqYOnWq4HPv3r2LESNGoF69eiVc78k1jY+PpxrSLMvCYrFAqVTSgNM/EOBjPAAlGcihfnd/BgTCfaZlDC47w3+jxj1XYtwWHh5OJSru3r0LtVqNoqIiJCUlQS6Xo1u3bti1axc+//xzPPzww/QzjR8/HpMmTULTpk3RvHlzdO3aFW3btr2vMVSeOHnyJEaNGgWlUomvvvrqnhJ+LMti3bp1eOWVV1CtWjXUq1ePJvK4kieke4BPmoGgqKiIt2jxICKUAJfcN8T1nhgdAfwGbEQKJzExESqVCikpKZRNWlRUhOjoaEgkEkRHR0OlUmHOnDmYPHkyUlJS0Lp1a7z88su8bZNC59a6dWu0bt0ac+bMwerVq/HFF1/g4MGDmDVrFh3XQtBoNCGbVoWCM2fOYOrUqcjMzMTChQt5r+mSJUuwadMmjBs3DsOGDUNiYiI8Hg/dtJFxRu5tYobnzwZ90JhYZJwIjSOh5A8pSJJNtM1mowZsXJmO2NhYxMbG4oknnsAbb7yBpKQkjBgx4p7PNz09HZMmTcKkSZNw/fp1jB07FsOHD8dnn31G17pKlSph0aJFuHz5MnJycqDRaBAdHY3Y2FhER0fzGpbdvn2baqjfD6xWK2bNmoVq1aoJMmLWrl2L5cuXY9y4cZgxYwYkEgktLpBCIRdKpRIJCQn0d6PRSFm4oc5VfHNGeRkHhXpcLiudjz1GEgVAcdxAmPBkTPKNWfLY8uXLkZOTg4kTJ6Jdu3aC8iJcpKWlYdGiRRg3bhx69eqFadOmoUuXLqUqkIaFhaFTp04hP5+LQMXYnJwcLFq0CN999x2GDRtWqnNyOp147733ULt27RIFMe79LRaL4fF4aOxIxhiB/zUXKoCVR2GML76zWCw0YUmSxeSz2Gw2H5Y0aYMXiURUM7tly5bYsmULfvnlF7Rt2zbg+zdt2hSfffYZhg8fjl69emH27Nm0UPV349tvv8XatWsxYsQINGkiqLIGoJgJOnv2bOp54d9tFBsb61M89wdfbEAMcoHiwhXxRiFJ6AdhXSsLcON1sra73W5IpVLExsaWmH8CSYKRa202m6kUTEFBAdLS0lClShVMnz4dKpUqoGGsQqHAhx9+iNq1a2PBggXYvXs3evfujeHDhwvKt/l/ntKYUgeCTqfDK6+8gn379mHs2LGChI/jx49j9uzZ6NevH4YNGwa9Xk+7ZmQymY9O/Z07dxAXF4f4+HjauUSgVCqh1Wp94o1/Ys36r+HMmTMAgEaNGj0QxynrY1WgAv9F/PN0pZL4kmXZP/7pkygtSJDATS6RYAEoDpByc3Npkphs8O7evYu8vDwoFApERkaiX79++PTTT/Hzzz+jR48euHv3bkjvn5ycjK1btyIqKgpPPPEEDh06VG6fNRSYTCa88MILeP3119G6dWt89913grIUxPTIbrdj5cqVJfSOSWDKbWlWKpWIjY2l2nPcNiQiXSHERlIqlT4JPv/v7kEB33lxA2uTyQSz2QyJREIZPwCoxAIxB9Pr9VCpVHjiiSfw8MMPY+LEiTh8+DA9Zp8+fbB161aMHz8ehYWFmDdvHrp3745evXph8eLF+O2331BQUBBQl7E8wbIsLl26hOXLl+Ppp5/G0KFDodVqsXHjxpBZ9VwYDAYMHjwYY8eORcOGDbFmzRqf4kNYWBjkcjny8/NRWFhIg3iSMLbb7XA4HJDL5bQ9kyRtSjuGuPPEn9e3XAchCXADaVYTcFtP/f9GijoKhQLx8fGIj4+nz5NKpUhNTUV0dDTS0tIo8ywsLAwqlQqjRo3CoUOH8NxzzyEzMxO9e/fG0qVLS60lptVqMWXKFPz222+oX78+5s+fjz179pTqGPcKl8uFVatWoX///rBarVizZg2vnilhIT/yyCOYMmUKqlevTo0WicY3YdsSfU2iX84de8Bf114mk/msLf8GcMc50cAk/1QqlY9+OJFXiI6OhlKpxLx589ClSxeMGTMG7733XpmcT9WqVbF582bUq1cPo0aNwpEjR+hjMTExaN26Nfr164eOHTuiYcOGSE1NRXh4eLkyib744gvk5OTg9ddfF0zEHDhwAElJSXjnnXeQmJiI8PBw+lyVSoX4+HhajCaJrvDwcJ91kiTEQp2r+OYMsoaWNSsw1OOShFR2djZN6nFBOhy0Wm2JY/nHZv6QSqVYvnw5GIbB4MGDcerUqZDOvWPHjlizZg0kEglefPFFNGrUiHYWLF26FLt378aNGzcEDTLLGhcvXsTUqVPRoUMHbN26FRMmTAhJjoCLZcuW4fLly5g4cSJYlqV+CXzXjySKQxlXpADGnd8C/f1+wBdHWa1WyoLlrnEkuQf8NYZiY2OhVqshlUppbDphwgRUrlwZAwcOxPXr14OeQ8+ePak8xbhx4/Diiy8GNPcta3i9XuzcuRMzZsyghdpAyM/PR+/evbFgwQIMHjwYL7zwAoxGI+7cuRPy+JXJZFCpVIiNjfUhQJDrHR0djYiIiJAYr/9WcAu9UVFRgixXMscaDAbk5eUhLy/P5x4QiURUpg4onieJ50xaWhqeeuopvPbaawG/G4Zh8Nxzz2HPnj0YMGAAduzYgV69emH8+PH4/vvveT1HyhIsy2L37t0YPHgwDh8+jLfeeotXTxso3i8/88wzSEpKwgsvvAC5XI6kpCTqaWM0GmmSnTDdiRRReHg4LUIDxdeOyKpIpVLKci+rGKq81sJ/O1588UW8+OKLD8xxyvpYFajAfxEPDNOYgP23qJAHAZ/TPAFhyRBjFZVKRTd2NpsNI0aMQOXKlTFgwAB07twZmzZt4jXM8UflypWxfft2GuTNmTMHffr0KZfP5w+Xy4WrV6/iypUruHLlCr7//ntkZWXhpZdewsiRI6mMBB/eeOMNHDlyBIsWLUJcXBx1KVcoFLDb7SgsLITBYEDt2rVpJd5qtVImC2GDkmtOEsgAf+VXiIH8oIErBcAwjA8Dx2az0U0HYTQ4HA461hiGAcMw1PAlPDwcUVFRWL58OXr06IEJEybgu+++owy5hg0bomHDhpg2bRru3LmDnTt34sCBA1i1ahU+//xzAMVsIq1Wi/DwcNqKHRMTg4YNG6J58+b31ZbtD4PBQA1/jh49SnXMatSogeHDh2PMmDEBx5QQ9u7dizFjxsBgMODll19Gz549ERERAZ1OB4VCQY8pEong8Xh82ibJhtnr9UImk8Fut/NK0ZQG3HniT5SrZnZpXKLJfcJlXHNBxidfstxut/vco6SVkHRYpKenY9CgQZgwYQKmTZuGqVOn4sqVK3j11VdLPY5UKhXeeOMNzJkzB/PnzweAoIzj+8G1a9cwf/58XLt2DU888QReffVVQaPGL7/8EjqdDmPHjkVKSgocDgdsNhukUil1RyfJPXId9Xo9CgsLERUV5ZPM4OqblzWzxev1BjWLuR/4s6S1Wq3P5+GOR7vdDq/XC6PRCK/Xi7i4OKxduxbPPfccXnrpJeTm5mL27Nn3fa6RkZH4+uuvMXToULz//vsQiUTo3LnzfR3zXuB2u3Ht2jU6Nzdv3py3gMKyLH7++Wd06NDBh8Wp1+uh0WjoXKXX62nygPs8ktQjybJQwTdnlNcaGupxSbGKsO/82WMkuRIWFlbCaNHj8UCn0yE+Pp7XmE0mk0Gr1eLTTz/FlClT0Lp1azz55JN46aWXgna1NG/eHNu2bcOJEydw/PhxXLlyBRcvXsTu3bvpdyqXy/Hss89i3LhxAf0d7gUsy+L06dP47rvvcP78eSiVSgwZMgTPPPNMqTqHjEYj5s+fj2XLlqF79+7o1q0bWJaFXq/3kWMK1OrNlScgRUPuNebrzClrPWMhkPMm0ggEJK4iazuJy9VqNV27ioqKoNVqsWbNGjz66KPo1asXDhw4IGgOTNCoUSN8++23WLFiBZYtW4YDBw7g2Wefva/uiWDIyMjAN998g23btiE7Oxu1atXCsmXLqIEhH86fP49BgwYhMzMTH330EUaNGgWLxYLs7GxcunSJGvEF0+d1OBwAiuNGPu10l8sFhUIBl8tV5vfBgwLueA5UTCFzq0wmE9RtJ+siKQySjsNdu3Zh6tSplOCxcuVKn+4Sf9SsWRNvvvkmZsyYgU2bNuGLL77ArFmzsHDhQvTr1w9DhgyBVqu934/ug8LCQrzzzjvYv38/6tevj+XLlwfsznnppZdw/fp1LFy4kDKrU1NTUa1aNSgUChQUFCA6OprKfZBrwe1yIHOUUqmkMpDkeyDGjMD9x1D/lv1kBSpQgQrcLx64pHF5o7w3xwT+wS9hYCiVSlitVuTm5sJqtSI2NhZxcXH0fEjA0KlTJ2zduhXDhg3DI488gjVr1oSkxxMbG4stW7ZgyJAh+N///geDwYARI0aU6ed1Op34/fff8fvvv+PmzZu4ceMGbt++DY+nWO5XJBKhRo0aWLduHZo2bRrwWN9//z2WLVuGnj17onr16jTQJMym8PBw6HQ6XL16FRKJBOnp6ZRRK5fLfZIRpOocHR0Nl8v1r6/8+icU/SU1iNGRTCZDYWEhvS4k+UQSx4SxRwKrDRs2oFOnTnj22WexZcuWEkyPSpUqYdiwYRg2bBiKiopw7tw53Lx5EzqdDnq9Hnfu3EFOTg7Onz+PgoICsCwLjUaDbt26oWrVqj7jubQ4d+4cVq1ahZ9++glAMTujRYsW6Ny5M9q0aRMwGA6EgoICvP766/jss89Qu3ZtbNiwAYmJibDZbJBIJLBYLDTJ6fV6oVKpfBLGFosFMpkMUVFRkMlk1PQF4Nc45mpmkmQFnyQEzya5XDWz7yXAFRqH3J/9j0nYsCS5ToJ3tVqNsLAwsCwLpVKJmJgYfPTRR4iIiMCnn36Ky5cv44svvhBMwgpBLpfj7bffxowZM8otcex2u7F+/XqsXLkSERERmDNnDp5++mnB57tcLrz33nto1aoVmjVr5jNm1Go1TCYT7ty5g5SUFB+mNpEkIq3TXEkUbiKZu6nkurLfS8eEx+OB1Wott82P/zlz29f9TfSUSiW8Xi8kEgmMRiMUCgXCw8Px6aefQiKRYNGiRSgsLMSiRYvuuzskPDwc69evR58+fbB48WK43e5yKzhkZGTg8uXLlEmWk5ODwsJC5ObmwuPxQCaTYfr06YKvv3r1KnJyctC+fXu43W7o9XqYzWYq0xAXF0cLW2KxmHf9UyqVSExMLFVS7kHcFBNGGZHI8f88fIluq9UKo9FIky9ccLtISBt5586dcfDgQXz66adYvnw5tmzZgjFjxmDatGkB2ZEymQxt2rTxMSW2WCy4fv06rl69ikOHDmHZsmXYv38/3nrrrbK4HHA6nTh06BC+//573LlzB9HR0XjppZcwaNCgUs2lHo8Ha9euxdtvvw29Xo+hQ4di+vTp1EsCKL6mdrvdx7ATAF3juHMWKeITiRCylpDuHH/8XRIFYWFhvElehULBa+pJZDfIvaZWq9G4cWNs2bIFPXr0QO/evUPqcpFKpRg3bhx69uyJRYsW4eOPP8batWvx2GOPYdiwYWUSsxoMBuzevRs7d+7E5cuXERYWhnbt2mHGjBno1q1bQB3jb7/9FuPHj4darcbXX39NfQLCw8ORl5cHh8MBu93uM8/wFZPtdrtgYYDg7yoQ/JMIdTxztdqFdMnJ9yaRSFBYWAiNRkOT+a+99hpatGiBl19+GW3atMGqVavQrl27gO8ZERGBZ599Fh06dKBmdCtXrsQ333yD559/HgMGDCj9B/4TRUVFOHHiBK5fv47r16/j0KFDMJlMmDBhAoYOHRowYbx792588cUXmDJlCvr164fs7GxERkbSMSWRSBAXF0evBSlMcQuEwF/zvcVigVarLVezzQpUoAIV+C/gP5c0JptjvsAwEEIhQPsnj7jgJoyJEy4AmkTxP45Op0NiYiK2bt2KkSNH4oknnsBHH32EkSNH0mSg/+sIwsPDsXnzZkycOBHvvfceDAYD5s6dSxdNmUwmGJhUqVKFd0N0+/ZtnDx5Ehs3bsSBAwfoQp2eno46deqgb9++qFevHurUqYPq1auXCEz5XHavXbuGiRMnomnTpjRQJXrFYWFh8Hg88Hq9qFq1KiwWC6KiomCxWCASiRAREQGWZSlLhOvo7HK5AmptAsG/z7IuKtwLgZ6PccBNuoSHh9OEJDFpIYlhtVoNs9kMuVwOl8vlM2bq1auH1atXo1+/fnjxxRexY8eOEkwPwtQCQI0vuI+Rc7PZbNizZw+++eYbfPfdd3A4HEhNTcXjjz+Ofv36UedxoJgJz6efRtrWnn/+eRw+fBjR0dGYNm0aevbsiQYNGkAkEgW8fh6PRzAwd7vdWLduHWbOnAmDwYDRo0fj3Xffpfci97jk2hHpCXJ/EVYCd6Prz04KlEjlap6RcUoSyDybijLXHLjXscxlBgMlx6F/oYsLwkByu91QKpWw2WxQq9W0jVAul0Mmk1HJhTlz5qBKlSp4++238dhjj/mw4AmkUqkgC7levXqQyWS0YDZ37lycPHkSkydPRuPGjQMWG2rXro3k5GTcuXMHmzdvxu3bt2E0GlFUVISCggJYLBb6u9VqRd++ffHOO+9ApVIJJmRYlsXmzZtx69YtvP3223TsiEQiSCQSsCwLg8GAoqIi2O12nzlXLBZTbT6uHiJXu91/vPt3tpTWmIVI/5RXMdVf45E7frggayhJMLEsC7fbDZlMhsjISHz66adITk7GwoULYTabsWLFCp+5SyKRCI4R0qbqD6VSiU2bNmHChAlYsmQJEhMT8eyzz/o8J9B1SU1NFdSFTE5Oxk8//YQVK1bg2LFj9DySk5ORnJyM+vXrIyUlBZUqVULLli1Ro0YNAMVzmn9C/OeffwYAPPTQQ9Dr9bh58yaAYj1QjUYDt9sNk8kElUqFiIgI3oR6WFhYqWOf8sT9jjehpIy/pi5Q/D1bLBYqM6BQKGgxy2q1wu12g2VZ+rhGo0FERAQWLlyIAQMGYP78+fjoo4/oejJhwgRaqK1WrVrADgmLxYK0tDR07twZY8eOxQ8//IApU6ZgwIABGD16NKZMmcLLttTr9YKsv8TERLhcLnz++edYtWoV9Ho96tevj9deew09e/YMaIrHF48dPnwYkydPxunTp9GmTRusWbMG9evXp2tiVFQUNZ2KiYmhhs6kjd5kMtHkF0kqqtVqqNXqEkl80mVRnnPOvRxXJBIhISGBFjy5HTZEUzUsLIx2CLZu3RqrV6/GoEGDMHDgQGzZskUw8VutWjV63dPS0tCmTRucO3cOc+fOxVdffYU9e/bg+eefx7PPPlviGAzD4Pbt27h+/ToyMjJoe77ZbIbRaITD4aA/nzp1Ch6PB40aNcIbb7yBIUOG0CQbF2QdBornm1dffRULFixAixYt8Pnnn0Or1VIjaqVSibS0NEgkEiQlJdHPwcegJZ0w/oUBrjY6mYv/jrnon2pevRd97kBjViwW0+R9Xl4eLc7o9Xo4nU4MGzYMzZs3x9NPP41evXphy5YtePTRRwGAEnr4UKtWLTRr1gyDBw/GlStXMG3aNLz55pv44YcfMGvWLDRv3pz3dcRszu1246effsJPP/2EP/74AxcvXqRa1kBxEbxZs2aYM2cONUIX+py5ubmYOnUqGjVqhFdeeQURERHQarWUNczXOcJlEXNB5nu32007BwjKqjhVYXxXgQpU4L+E/1zSmGyOywNCySPyfhaLBXl5eYiLi6PtcURfyV96oLCwEJmZmUhOTsb27dtpO+PevXvx0UcfBQ22pFIpPvnkE8TGxuLTTz/Fhg0b0KxZM7Rt2xYNGzZE27ZtBQ1RTCYTMjIycPPmTRw7dgz79u3DtWvXABQHu0OGDEG3bt3QoUMHujEhLdeluVZPPfUUwsLCsHTpUkRFRUEsFlMGC7k+RDe2Tp06tB2J+/1xF///b9Vjwm4hgZFQkMNlvBK9SpVKBblcTjfD/u27LVq0wKuvvopXX30Vzz//PD755JN7CoAUCgX69OmDPn36wGg04quvvsL27duxdOlSLFmyBCkpKahTpw7q1KmDlJQUNGnSBOnp6RCLxXC73fj+++/x8ccf4+LFi0hOTsabb76J4cOHl8lm4syZM3jhhRdw7NgxtGrVCmvXroVKpYJOp0NaWpqPMYt/Qpi70Qg2rvhYbXyJfq6ECvDgm2YInavQz1yQeY3Mbf6GemTuI8Wn1NRUjB07FmlpaXjppZfQtm1bbNy40YetFwpUKhXWr1+PDz74AMuXL8fOnTvRuXNnjBo1infzY7PZsGPHDuzatQuHDh0Cy7KIjY1FZGQkZUklJSXR31u1aoUePXoA+Kv9lg9erxcLFixAvXr1UK9ePVgsFnqtyEZPLBYjKSmJMvX4zo2YLBKGsdDmkzsG78WYhRjulCX4Ns3Bxj/3cSF28vz586FWq/H666/DaDTiww8/REJCQkAGXTDI5XJ8+eWXGDVqFGbMmIE//vgDjz76KGw2G2w2G5UVsdvtcLlcSExMRNWqVVG1alXepMSFCxewbt06bN68GSaTCTVr1sS7776Lxx57DJUqVaIsar7knRB++uknJCUl0eJqeHg4xGIxXStv376NwsJCJCUl+WysiRwAYboLFZv/P8J/DBITYsIazc3NhVqtpvcgMTrjXi+RSEQTaefPn8d7772Hl156Ce+99x5d+1q2bFkqWZ0ePXqgZcuWmDlzJj7++GP89NNPWLhwIerWrRv0tSzL4tdff8XKlSuxf/9+AMBjjz2GcePGoWPHjmAYxqfoGwx3797FK6+8gq+++grJyclYtWoVHn/8cUilUuTn58NkMlGWn8PhgNvtptIOxJSKjDf/e9Z/zuKarD6o6yD3HAlTkUil2e12JCUl+XQgVa9eHW+88QZeeeUVPP3009iwYUPIY6FBgwbYuHEj9u/fj0WLFmHOnDlYtmwZnnnmGZhMJty4cQPXr1/HrVu3SshJED14pVJJWfcqlQrPP/88nnzySdSqVQs2my1g8QAoHk+jR4/G6tWrMXjwYCxcuBBerxdRUVFwuVyQSCTIzc2FQqFAamqqULGbglwb/30Wd116kApX5YXyivVIJ5JGo4HX64VWqwXLsvB4PKhRowa2bduGQYMGYciQITh06BBN1IaCGjVqYOvWrdi8eTNmzJiBAQMG0G4DboGcyODs378f3333HfR6PWQyGWrWrIlOnTqhevXqaNq0KerWrYv4+PiQ9hYsy2LChAmwWCxYvnw5HA4HHA4HVCoV3G431TAmsFqtMJlMPmbBXHDNGkkBqDyLVBWoQAUq8P8d/7mkcXlsjgn4kktCiSaSHM3MzERUVJSPYy4JtF0uF0QiEeLi4rB+/Xq8/fbbWLJkCX755RcsXbo0oFsuUPxZ582bhyeeeALfffcdfvnlFyxcuBAsy0Iul6NZs2bUvCkjI4MmivV6PT0GabUcMWIE2rdvj6ZNm5bJojt58mScO3cOGzZsQFJSEmX2+DsHE3aC1+uF0+mkwabb7YbBYKAt3GRjSBJz/182xyTQJoERdxPGrbSTBBwZP6RFUKPR0M2v2+1Gfn4+oqKiYLfbMWDAAOh0OnzwwQeoVasWJk+efF/nGhkZiQEDBmDYsGHQ6XTYtm0bfvvtN1y8eBH79u2jbAeZTIbq1aujsLAQd+/eRY0aNfDOO+9gxIgRZaJtx5WiiImJwQcffIDHH38cEokEFy9epA7KcrmcbsQCOcoHYyX4t2/7B6Z8hY1/g3TK/RRhyDxLkp5E9oPbFm6z2aDT6QD8pTHZvn177NmzB0899RQeeeQRvP766xgyZAgSExNDfm+lUokZM2bgueeew4oVK/Dxxx9j8ODBaN68OcaPH4+2bdvizJkz2Lx5M3bs2AGz2Yy0tDS8/PLLeOqpp1Dp/9i7zvA4qqv9zvZd7a5Wq+4mG2PApmNjMKbGppneS2wghhBCNx8QeknAtBB6L6F3cGg2EFooJoCDKSYQwMbdklYrrbS9zXw/5HN9dzSzTStpJd33efystWXmzp0z5557yntGj2bHCgQCOTfdWnj77bfx/fff44orroDP54PFYskop6cS+GyNo/LlQwQyZbAQ3uq+hNamOZdMqa+Zd7RTgEGWZZxwwglIp9O47rrrMGHCBADd5bbUQb2+vh51dXWYMWMGDj/88LzGa7Va8cgjj+DMM8/Ek08+iSeffDKv37lcLowfPx6bbbYZRo0ahU8++QRfffUVrFYrZs2ahbPOOgvTp0/v1bqpKAo++ugjTJ48mckMVSyRgyuRSPRoLKXF8z8cHDZ8sIooBcim4KuQyN4g3lqiRQmHwywAS5m2FRUV2HbbbfHiiy9i8eLFeOCBB/D444/j/vvvR2VlJaMpOPDAA/N69qqqqnD//fdjzz33xLXXXotDDz0UZ511Fg477DA2vo6ODvYMybKMjz/+GE8++SSWL18Or9eLCy64AL///e8zdFa+6OzsxK233oo777wT6XQaf/jDH3DFFVegtrY2g7Kpq6srg+6LKh74YCA5L/OV8XLRUdlANhVfNUM9IpxOJ+LxOOtLMmfOHBgMBlxyySU455xzcO+99xb0vO+www54/vnn8fnnn+OGG27ALbfcArvdjnHjxmHixIk49NBDsdlmmzE9U1VVBYvFAkmSEAqFeuWU/Otf/4onnngC8+bNww033ID29nb4/X4YjUbU1tbC5/Ph119/hclkwuabb45kMpnBBa0OzOjZS+V2zzcG+/qs8bB6rSsm81gLJpOJZY6TIzSRSCAejyMSicBoNOKpp57Cvvvui8MOOwyLFy8uSOdLkoSjjz4a++67Ly699FI8/fTTeOutt3D55Zdj6623xquvvopXX30Va9asgc1mw/7774+jjjoKM2fOZHZ0LBbTrb7Rw8MPP4y3334b119/PbbYYgtmL5KOoVej0ZhRASfLMjo6OuDz+TB69OgMuSSbIRqNZqwDAn2P+fPnl9VxSn0sAYHhCGmI9J3LG1OmTFGWLFlS8O96M0/EocxzmwLAqlWr0NXVhbq6OtYFnYyJ9evX45dffkFDQwPLouro6MCaNWvw+9//Ht9++y3+8Ic/4MYbb9TMsNJrdtPZ2Yl33nkHX331FRYvXozvv/8eADBixAiMHTsWI0eOxFZbbYWxY8di3LhxmDBhAjN6kskkPB6P5nFzZRrzGVULFizA8ccfj7lz5+Laa69lDUfoOPw8KIoCWZYzHMQUYW5paUFjYyOcTifLFgXA/p/NUMrGMwvolx1JkvQfRVGm6B1XT76KlR9qmhgKhVj2Bxk9lK1DcxcMBpljija99LcsyywTzePxwGg0IpVKob6+HqeccgpeeeUVPPTQQzjppJMAIGumkrpxFY/29nZNeYzH4/j888+xdu1a/PDDD/jhhx+gKApOOeUUzJw5k2WXZ5sHPaTTabhcLiiKgqeffhqXXHIJ/H4/Tj31VJx33nmw2+3MeSnLMsss45sJNjQ0sHnSaz6YL7JxpufiUzcYDLryVazuKha9XRv4TRLPZWm326EoSkb3ap4jsbq6Gu3t7Tj55JPxz3/+E0ajEUcccQROP/10XedbR0eHruM/EonggQcewMMPP5xRNmm323HAAQfgkEMOwaGHHqp5j7M5jePxuCY9RXt7O2bOnIlkMol//etfjB+bf27pGkKhkGYZb19kw/S3bCWTSaa31Zl3hV4jcTcC3QGGcDiMcDiMFStW4D//+Q+am5sRCoWwYcMG9lyvXbsWoVAI1157LS677DJW3qqF9vZ2ptMURcGvv/7KdBkF5Ww2G2w2G8xmM9auXcv4Gv/73/9i7dq1WLFiBdatW4fNN98cs2fPxlFHHYXKykrN0nAAWTON0+l0hmwtWbIEu+22G26++Wace+65iMVijE+WqIjo74qKCnad/ZVpnG1d7AvZyqWbaG0kB7vapqB1leSQvkc9L8h+oiBPMBiE1Wpljvmuri4WkP3nP/+JF154Ae+//z7a2tqwxx574J133snQJ9maDzY3NyOZTOKaa67BggULcl77jjvuiJNOOgn7778/xo4dq/kdygzWgqIoeO211zBv3jy0tLTgmGOOwemnn45JkyaxrEWz2cx4s6m8u7a2lnFlGwyGjMA9VefQM61F6VQs+kJv5ZIf+pxfw4LBIL7++mtYLBZUVlYye5dK6A0GA+bNm4c777wT55xzDm655ZYMHRePx3Wf9/b29gz7uaOjA5WVlUyGslUkZHMaZ8s0lmUZP/74I3bddVccc8wxePDBBxmffFtbG8tglmUZK1asYPc6FouhoaGBcUIXeq/7O9NT716Hw2G4XK5vFUXZXu+3pZSv3j4TsiyzueP1OgVviAaM8N577+Gkk07ChAkTsGDBApahrAbRQWlh/fr1+Pnnn3H55Zfjm2++AdCdELD77rvjwAMPxPHHH6/pHM7lNFbLwK+//oqpU6di9913x4MPPgiTycSozMhmJLuc9DEf6Fu1ahU6Ojrg8XgwduxY1uyObIb6+npGEVnIfq+36O91cSAgSdKAUcCUGoPpWnL5IgQE+gLDLtN4IEGLFm0gRo4cyZpBqTdy9fX1SCQSiEQi+N///oeRI0fCYDBgq622wrvvvovLLrsMDzzwABYvXownn3wSW265ZV5jqKyszMi+6urqgtlsZo7hUCiUtclLb9HS0oILLrgA22+/PU499VS43W6YTCa0trZmZCHyRhVFiW02G2tU43Q60djYyBzJQH4Z3oRiSrhLDZ7jLYuTmm1m1U1HyPmmfo+i89Q1mDYBxAVdWVmJaDTKstnvvvtuBAIB/P73vwcA5jguJaxWKyZOnIhddtml5McGuu/nOeecg6eeegq77rornnrqKeyxxx6IxWKsq7LH40EikWDzpSgK2+zxTewCgQBMJhNsNht8Ph/q6+vZ5qkUmSLDBXrUMalUCuvWrYPJZGK6RpZlWCwWeDwelkV5yy23YPny5XjjjTewYMECvPTSS9h+++1x5pln4phjjsmbjsDhcOCUU07BCSecgNdeew0tLS1wu9048sgj4XQ6GRd4KRCLxXDCCSdg1apVeOWVVzBq1KiMz8nZ6Xa7YbVaWcklL1v5bmDy0R8DCT47KFu5dj7Pld1uZ04Km83GHBpNTU2sWRPpOaq2WLFiBa6++mpcffXV8Hq9mD17dl7jliQJm222WQantnp+6fN9990XbW1tbIOcSCRgNpvZ97NxSRaC+fPno6qqCsceeyyATVmztAYAYA5jgnpeC6FPGOzIlaXPO9nVzo1oNMqqdsgxA3RnmsqyjPXr16OtrQ2yLKO+vh6HHXYYDjzwQCiKgrvuuotl582ZMyfv8Xo8Htx+++347W9/i3Xr1rH3yTlCGD9+PCs3V9MV5IP169dj3rx5eOONN7DTTjvhkUcewS677IJ4PA6v15uxaSb6HSoPt9vtMJvNbD6zPd+DlSqMdKrNZmNZs+SwslqtGDlyJBRFgdVqZbzGPM3SpZdeira2Ntx1110A0MNxnC/60gbncfnll8Pr9eLaa69FKBRiFStOp5P1D6HMar/fDwCoqamBzWbLqH4DBt+93hgk7NPGw0Cmg1evMaDeWs6/zx+Lz6J1uVyMA590hSzLmDFjBh566CHMnTsXhx12GF599VVdx3E2bLvttliwYAGWLFmClpYWTJs2jdH8FJpNrAVFUXD++efDZDLh5ptvZr1qqqurMyiCqFEgBTbon8FgYJUWJpOJUfHxNgOtlwL9h8WLFwMAdtttt7I4TqmPJSAwHDF8dhElRG8265FIhGU31tXV9ThGOp1mEX1qzhQIBFhGGjWPuuqqqzB9+nRcdNFF2G233XDLLbfgt7/9bdYyey2UYtHPFy0tLdhvv/0QCARwzz33sIwwh8OBVatWwWq1wuPx6F5DKBRiTQxdLhfcbjebu3x4VnmUQ6lcvo5rvQ2wOnJO2TB+v585PamhFJXDWywWZsB2dXUhHo+jpaUFjz76KObOncscx+ScGAxYsWIFTj31VCxbtgwXX3wxzj77bCiKgmg0ypr/1dbW9qBGIOcdT2lCzZGIQ43HYOIjLhVyOfTy1YW8A7mjo4NlvPMbKHKiUqAjnU5j4sSJ2GOPPXDeeefh+eefxz/+8Q/84Q9/wOWXX47TTz8d8+bNy/teWK3WXnUEzwVZlnH66adj8eLFuPPOO7HvvvtmbO7oumhDw5d3Az058HOhHAJf2cAHsPR4B4H8niu+8WcwGGQNmqxWK8uSSiaTMBqNqKqqYtzDF198MSRJwrnnnguHw9Gn9x9ASeh1eMRiMTzyyCN44403cPnll8NoNPZo6kO6zOVyZTj9wuEwWlpaWNBrKEOthwptdMQ3/TSbzSxTNxqNwmQysUaDFRUVjB9TURQkk0mWJSpJEg4//HC8+uqruPTSS7HPPvv0CBrlws4775zBvZ6tEV4hkGUZf//733HFFVcgmUzi3HPPxcEHH4zRo0ezsnabzQaTycTsL9LNwWAQXV1dsNlscLlcLNM4X2chrSH52ssDGQwjnaooSkZlSEtLC0KhENrb27HFFluwKgCq6qKmlCNHjsSdd94Jk8mEu+66C7FYDH/7299KrhdKgY8++gjvvvsu/vKXv6CpqQmBQIDRklgsFpZN7vP5UFVVhcbGRhYc+PXXX1FbW4vGxsYMx/pgCqhvlK2SNx5Wg6+y0tNJemu5VhNlanZKwZxgMMiCHERB5/V64XK5cMwxx8BsNmPOnDm9chwbjcY+S/h48cUX8d577+Giiy5CY2MjW8OzBTppT0N7oFgshlGjRiEWi2XYG0N93StnXHbZZQCADz/8sCyOU+pjCQgMRwxap7EkSZIyQHUEvdmsOxyOjHJVProPAH6/H83NzQCA6upqjB49GpIkIR6PM2dzbW0tPB4PDj30UOy4444477zzcNZZZ+GSSy7BzJkzcfDBB2OPPfYoyjjoK5DDePXq1Xj00Ucxffp0dHZ2orGxMYO6gyLFZIjS3NTX1yOdTiMWi7FS0t7cfjUP7UAgX8d1vmONRqNYs2YNgsEgy+AmQx7ozkxqaWlhpaTBYJA54h0OB+677z788Y9/xO9//3skk0n89re/7f1F9jEWLlyIP/7xjzCZTHjggQcwe/ZsxnGdTqfR3t4OoNvo5cuOszWn45tn8JkhgzWjpjfI5dDLRxeqHc+UReX1etkGkzbfpA8pwykejyMWi8FiseC4447Daaedho8++ggvvfQS5s+fj8ceeww33HADZsyY0ReXXxAuv/xyLFiwAJdddhkOOeQQ5gz2+XxIpVJIpVKMc1bP6VKIbJVD4CsbeF7rfBon5XPtxIPd3t7ONoZUMh0MBlnzKpvNBq/XC4fDgdtvvx2dnZ0444wzUFVVhZkzZ5b0OvsCoVAIjzzyCG6//XY0Nzdj+vTpOO+882CxWDJKdIHhqZfUKNQmIxuCstboGaU1IxAIMCcqOWKIBqmyshLJZBLxeBzLli1jpdh0X2666SYcfPDB2G+//fDOO+8U7DguNZYuXYpLLrkEn3zyCfbaay/cfPPNGRl51DCXHC5GoxF1dXXsmeWd6MFgEJIk5XQS6jWFzufeDGQwjHSpuoolFoshEokgHo8zvRYMBjOunQKADocD11xzDSwWCx566CEsXboUTz31FEaMGNGv15INiqLg2muvxahRo/Db3/4WyWQSsiwjEAiwKjXeYWcymTBmzBhEo1F0dnbC5/OxTFOXyzUsA+r5Ipt+5jPbgZ5rOb/G81WFlN2+atUqyLKMhoYG1NbWor29nVFw0TO844474uKLL8bNN9/cK8dxX+A///kPLr74Yuy88844+uijmW7JFnig5t5ElROJRNDZ2cn+DoVCBQcNBQQEBARyY9A5jSVJqlUUxTeQY9DarGdzYPKfEX8e/77NZmMOUeIMps64VHrT1taGtrY2VjZJGUWNjY247777sHTpUrz77rtYtGgRFixYAIPBgKlTp+KAAw7AAQccgPHjx7MxJJNJJBIJzbFShpYWEomEbrltIpHQzQppbW3FYYcdxhzGkydPBgBmRPO8a2So0oaYuGWB7vmur69nJbn5Oo3LtYw7X2cwf53U4I4yC/gMPpvNhtGjRyMajbLSLpo/MqYSiQQr/6+rq0NFRQX8fj9qamogyzKef/55HHvssTjzzDNZwyk1spWbq7Nzeagz5Hgkk8mCym1TqRRuvPFG3H777dh+++1x8cUXo7GxET6fD3V1dcyoJl5iAKzskoIRNDdksNtsNiZXlCFKwQniayxXB11fQW/DQ/OR7XNgE2UA3VvKUiNHHzmIge755+Wgurqa3Tuj0ch4yo844gj85je/wddff42rrroKJ598MnbddVfMnz8fkyZN0ryObLJFDgEtEI+eFpLJJNLpNL766is8+OCDeO655zB79mzMmzcP6XSaZezV1NTA7/ezQIbVatWUJ55TVwuUPU96rBwCX7mQj0NTa4OnNwd2ux21tbWoqqpCPB5nWcXBYBB2ux3V1dUsC9DlcsHj8UCSJDzxxBM45JBDMHv2bCxYsCAjmxPovpd66x6NUQ8kB1pIJBK6ay1lRvNob2/HQw89hIceegiBQAC/+c1vcN9992H//fdnaxc1sqVXtRyRvnI4HGhsbGTfpc+yXUs5rY+FQG2T8fqHXy/pPV7mqIlgKpVi82M2m2E2mxkllM1mQzgcRjqdhizL8Hq9jEaCqr/IkbHlllviySefxJw5c7Dffvth0aJFur0gADBntRayyWW2zxKJBL7//nvccMMNWLhwIaqqqnDrrbfi6KOPhtvtZjQ5pEuqq6tht9vR3t7OnLZUrRSLxZBOpxn3KTnHyUlI37FarUyetZ77fNfOgQyGkU4lbmug225oampCIpFAZ2cnPB4PUqkUHA4Ho2moqalh2Y/pdBoWiwXz58/HlClT8Kc//Qm77LIL7rzzThx55JGa502lUlm5xrM9lyS3iqLgiy++QGNjI8aMGQNAX0YWLlyIJUuW4PLLL0dDQwOzcywWC7PzKaOVqrRIXiwWC8aNG4dkMolQKMTmAeiutODlSqDn+sZzf6sDJOp1j1/jidOYfy4kSWI0hpFIhDVV93g87FhVVVWYPn06rrzySvzlL3/BoYceiqOPPprxBlMwzGg0wmw2Y9KkSdh6660hSRKSyaSurZ9IJHT1D9HC6SGRSOD666/HPffcxa9c8gABAABJREFUg4aGBlx//fVoamqC2WyG3+9nulcNWZZZBRPNpbphJQXw+LnMte4JCAgICOTGoHIaS5J0MIDzJEnyA7gXwEf9lXHMG0C847fQ3wKZTkyn0wlJkhAMBtHa2sq6v9P3CNQkzOFwwOFwwO/3I5FIwGQyYcSIEZgwYQKOP/54yLKMt956C0899RR+/PFHXHPNNbjmmmtwyCGH4MYbb8RWW22V1aCrrKzUpYcgh4csyzjrrLPw+uuvY+7cufjDH/6Auro6TaO3paWFOYyfffZZjB07FoFAAKlUColEgs1FTU0N47EyGAwwGo3MuUeN72jhp9/Q57mcwlqZK/1t0PbmfLyxE4vFMq7FaDSyYxsMBng8HrZB5bk1ge7sKmqcQ019ZFlmjgeiKnnttddw2GGH4eyzz4bVau3BcWyxWHQNybq6Ot2mLW63W3cjmM04Vc9Ba2srZs+ejQ8++ADHH3887rnnHqxduxbNzc1ob29HVVUVHA4HjEYjampqMhqE8NxmBoOB0VfwRn0sFkNLSwsL0DQ2NvZwamW7n8V+Vm6gucmm62hDRJtKyhKh6yQOcnK+0DFDoVCP0nly1rS0tLCGhQ0NDbDb7fD7/UilUrDZbOweTpo0Cffccw9ee+01PPLII5g5cyb++Mc/4qqrrurBCZmNIzIUCunKnt1u17z+zs5OvPjii3j00UfxzTffwOFw4OSTT8bcuXMzmjr6fD7YbDZUVVUxhyfpNCp/B3py7qnlhM+GBKA5pnJr4JFNfop9DkhPUXUKn+1otVphMBiYXuNL7quqqvDEE0/g2GOPxfHHH4/33nsP22yzDTtuNp0GZHcaV1ZW6uq8bAE2r9fLytbXrl2LO+64Aw8++CDC4TAOPfRQzJkzB9OmTWOyEQ6HmUOAGvdR1iOQ2S2e1oZyDyoUC7X86NlksViMcdTX1tay+eJ/T30myO6w2WxIJBJYv349c7xbLBbWfJDKnimDmIIClZWVLPOtqakJTz31FGbPno1Zs2bh7bff1s04djqduvJTX1+v60xMJpOatAdLly7FddddhzfeeAMejwcXXnghzjnnHNY7wmKxoKGhAZFIhMmMy+ViupUcxzRPiqKgvb0dlZWVqKyshCRJTA9ZrdaMOa6pqWGNcnnZo3nJpxFVofZ1b6FlP6qfd3K0k6OU1jSXy4VYLIZYLMacrslkkv3/xBNPxC677ILf//73OPnkk7FkyRLccMMNPe5bXV1dTt53PTgcDrz++uu44YYb8PXXX8NgMOCII47AvHnzMHny5B7Xkk6ncf3112PChAmYO3cujEYj+04ikWDOS4vFwjLKfb7uPB1qGLzZZpux9YhkxWq1snUaKJ+M44GwufI9p5rCSWtdI6j1ltPpxLhx4zK4pYmarrOzk+k7j8eDrbfemvUzueyyy3DNNddkHVdTUxNmzZqFAw44APvss4/mnjBbY0e73a67j/zggw9w1llnYcWKFZg7dy6uuOIKmM1mRp1HMtTU1MQSY/j5ILuiq6uLrXFUHZgrS1n0JREQEBAoHoPGaSxJ0i4AbgVwCoD9AZwB4KOBoqjoDXgnptqJxjtfeAOBIskVFRXMico3SWttbYXdbofL5cL06dMxZswYjBw5Es3NzXjppZfwt7/9Ddtttx1OP/10XHzxxcwxXSiIt/Pxxx/HlClTcOONN+Lmm2/G4YcfjrPPPhvTpk1j4+YpKR577DHstNNOWLVqFeOqCgaD6OjogN1uRywWA9BtiBBfFwCW2UObCCp1TqVSCIVCqKury1nOWO5l3IWAHCOFclcDm3hBgW5HGWWSuFwumM1mtLa2wuPxsIzj4447rk+b4xWC9evX46OPPsK//vUvvPbaa+jq6sJNN92E448/HgaDARMnTsxo0BIKhdhm2GQyaZbJUxaa2oCkBhrqRi8C+tArT83VkArINOTtdjtqampYdrIkSczhQ3oR6H4ObDYbRo4ciVNPPRVnnnkmLrvsMtx33314/vnncdlll2HLLbeEy+ViXbYpU7nYhmCKomDJkiV45JFH8PzzzyMSiWCHHXbA5ZdfjgMOOAAOhwPJZJJxMxPXZSwWQ3V1NcaNGwej0YhwOMxKm6miQiswoZ5fviRzuIN3ons8HkZv0tbWBqA7+zEUCrGgmNFoxJgxY/DCCy/g4IMPxkEHHYQPP/wQ48aNK+r8iqIgkUgwuU8mk4hEIohGoxg7dmxe66uiKPjkk09w9913Y8GCBQCAWbNm4bTTTsOkSZNY1lhraysqKioYLUdNTQ1CoRBz3pBDTlEURmFFzp1cm+ShvonmOeqJjkENg8HAnA7kOCXaimz6n0r2g8EgIpEIC3in02nY7XZst912uPPOO3HuuedqUlXIsowNGzbgl19+werVq/Hrr79i5cqVqKqqwsSJEzFp0iRMmDAhb05jLWfxqaeeCqfTyZIByInEN9cimg4ArPKGrp2SBKqqqjKyjOk5o0ZVNMfUIBDQ52YtF2ciId+x0RzxzlGax9raWuZ8pv4RFOwZN24c7rvvPtx1112466678Nlnn+Hpp59muiccDuOnn37CihUr8PPPP+Onn36C3+/H1KlTsffee2Pq1Kmaa1YymcTzzz+Pm2++Gf/73/+wxRZb4P7778eyZcvw1FNP4eWXX8bUqVNx/vnn47DDDmPHePbZZ/HDDz/glltuQUtLC+Mvbm5uZuOliqxwOMwyqQFk8MVWV1fD7/cze4vscrFG5Q81hRMlOKjlkdfTANj/yZ4n6jWiwTIYDKz/i8FgQH19PZxOJ7bccktmMxPNCDliyWG7dOlSfPXVV3jsscdw3333wel0YubMmZg1axZ23313jBkzRtdZnA2BQACXXHIJHnvsMWy++eZ44403sPvuuzOaDbL9iFqPAnkkXwAymuCRjNI8UuPqmpoa3UCboFEREBAQKB7SYPG5SpL0RwBbK4pytiRJowE8BOBrAP8G8IWiKOuz/PZ0AKcDwJgxYyavWrWq5OMrZB7VHXH56DItkOqszGAwyLp9A90NUrxeL3O+rly5kkWe+SZL5DgLhUK46qqr8Pe//x0VFRV4+OGHcfDBB/cYWyKR0HVI/u9//8Mll1yCt956C0cffTRuuOEGpFIp3HLLLXjllVcQCAQwduxY7LLLLqirq8PLL7+MQCCA+++/H9OmTYPX62UbM8q2C4VCcLlcqKury+iUqzUffBdrMto9Hk/OLBYt9CLT7T+KokxRvden8sWX2qodJVTqprX558vg1EilUhkO09bWVjQ3N7PSLqCbX3vevHl4//33sc8++2DmzJmYOXMmttpqK93GLpFIRNegTKfTcDgcaG5uxsUXX4xPP/0U22yzDXbccUdsv/322HnnnTFixAg25s7OTjz33HN49NFH8fXXXwPozubaY489cNVVV2HChAkAwLq6A2AOOdqwUfCFz8AD0EO2tMrjByJjWC1f/aG7ioFa36mzHLM5oKiUluSVbxRDWX/EUUfZMnQOmvdwOIwNGzawpi91dXWw2Wx4++238de//hWffPKJ7vldLhdOOukkXHrppaitrc2aaZxKpeB0OvHxxx/j8ssvx+eff46KigoceOCBOO6447D99tsjnU6zDQzpMLo2v9/PsvDGjRvHmrnxjbbUc6Yle/zzrbchyrUGGQyGQSFbQG66p0gk0iODVJZldHZ2sjVmzZo1kGWZ6TSqUlm9ejUOOuggSJKEvffeG9OnT8cuu+yCHXbYQVMOotEovvrqK3z66af49NNP8eWXXzLuTz0QXYqew6etrQ1z5szBBx98AI/Hg1mzZuHwww/HjjvuCIfDgXQ6zdZ2avITDofh9/sxYsQIVh7vcrkQDofR2tqKmpqaDI5wyk7mmzCpZUv9eanWxYGWLTWlE782ZnOUF7JmEsg2I6dxa2srW19kWcZPP/2E008/HRaLBTNmzEB1dTVWr16N999/nz3/QLfjY8SIEejo6GC6FAAmTJiAOXPm4Le//S1GjBgBn8+Hn376CT/88AOWL1+On376CT/99BN++eUXeDwe/PGPf8RJJ50Ep9OJioqKDAcTyUQwGGTBNB6yLKOlpQWtra2w2WzweDzwer1Mr/H6F9gkZzSnRFWhtsVyVYPlWGtLLlvq0vVC7EfeiUVOOaA7EE/rFjlZyV4laqJXX30V8+bNg6Io2HbbbREIBPD9999nHH/kyJHweDz473//C0VRUFlZiSeffBL7778/+04ymcQee+yBpUuXYtttt8W8efMwe/ZsGI1GpFIprFmzBq+//jruvvtuLF++HBMmTMCTTz4JADjiiCNQX1+PF198ET6fDyNHjszIot9qq63gdrsRjUZZf5WGhgYAyNAVpDtoDqxWa4YNr4dyqrYaSHuewOsjyuxX6yd+rikwQXsAOiY1Ua+qqkIikWCVKHQOHuFwGM3NzYhEIqitrUV1dTU6OjoQj8eRTCZRVVWFZDKJTz/9FK+++io++OADRsdjNBqx0047YcaMGZg1a5ZmNju/j4zH41i0aBHmzZuH1tZWzJkzB5dddhmTA6Ih4wMS5EDnHd985alWY8GWlhasX78eI0aM6BG0zbZPAvrPnucxZcoUZcmSJX1y3lJj7Nix0HsOmpqasHLlyh7v055thx126NW5S3WcfI5VzHUOFLLJloBAX2EwOY0nA3gUwLsATgZwF4AogEYA3ymK8mg+VBV9paiLnUf1JoUWNSr7I8cxb6QCYLxhAFgDIMoapcWQ76Qbj8fR0tKCn3/+GVdccQWWLl2Kv/zlL5g3b17G+bWcxsFgEDfddBPuvPNOWK1WXHjhhTj++ONZxLmrqwvLli3Dhx9+iM8++wzLli3D+vXrMWPGDJxxxhmYOnUq28DwmxbesafeWJORRJ1xq6urM75TaEduNUrpNObRF/JFshUOh3s4Skh+1Jt/+p3edfKlcJShQHKXTCbh8XgQj8cRDodx66234t1338WyZcsAdHO0zpgxAzNnzsSMGTMyGrxkcxonk0k8++yzuOyyyxCNRnHggQfil19+wQ8//MCM2vr6euy4446oqqrCq6++ikgkgu233x4HH3wwZs2aha222gpOpxPRaBShUAhOp5NRUQBghnN1dTVrfqfuXq3e7BTqPACEkaml7/KZVwCMv13LiaOmm+F/r5bZzs5OtLW1oa6ujmWXdHV14eeff8aaNWuYjqQqjZaWFsTjcaxevRrPP/88KioqcNFFF+F3v/sdo2ZRY9myZZg/fz7efPNNjBw5En/4wx9wxBFHMAoYWZZhNpsZpyVxPBKVDp2f51FVl5nScfg5KEb2cjlajUbjUkVRdtL6vJxkC8h9Lfxzzq8LwWCQrS3E20rOVOLyr66uxrfffovbbrsNn332GdskOJ1O7LLLLpg+fTpz7H366af4z3/+w7gbJ02ahF133RVVVVUwGo2Ma5t0ssfjwffff497770X69atw0EHHYTrr78eW221FRvjd999h6OPPhrNzc245JJLsP/++zMKhOrqagDdAbvKykoEAoEMihTi5+V1LE9t5XK5MjbIaueenr2hppUpFOWmt4iDV0sHaa2VhHzWTDXIaUwNLuncVqsVnZ2dsFgs+PXXX3H33Xdj8eLFCIVCqKysxO67747NNtsMW2yxBbbZZhuMHj0aFosFRqMRS5YswYYNG/Djjz/iww8/xMcffwyDwYDKykp0dHSwc1utVkyYMAEjR47ErrvuiuOPPx6jR49GLBZjz4XP52PBEwrc03XyTk9CZ2cnWltbYTAYYLFYYLFY2DPUV9yghTiNeRQrW73Z91CVlsvlykhYADbZ5WrnvN1uRzAYhCzLeP/993H33Xdj2bJlmDRpEn7zm99gxIgRGDt2LLbeemt2zGg0in//+9+49dZb8eOPP+LFF19kjuP7778f5513HuOobWpqgsvlQjKZxKpVq2Cz2dDQ0IBAIIAXX3wRN954I6tCqKurwxNPPAGv1wtFURgNADVGdrvdGfRKFJwCkHfwJRvK3WnMoy/teb3PtOaHT6IhvnE+oE6ZxnwwiCia+OQjfn/p8/nQ1taGxsZGyLIMj8eDQCCAZDIJs9mM6upqpstaW1uxcuVKrFy5Ej/++CP+/e9/44svvoCiKKipqcHMmTPR2NgIAGhubsb69evh8/kYZRwAbLfddrj11lsxZsyYjEAvNZD0er0IBALo7OxELBZDU1MTozXjr1PLqQ506/y2tjbNTGNhz/cOZJcPZ5TbHAinscBAoKydxpIkTQVgBpBUFOULSZKmAdgRwO6Kopy48TunAdhTUZS86ucHi9OYshnIWOvs7GROB6fTyYx3PiuPzzQCkPEZbRbC4TBaWlpw/fXX4+WXX8bRRx+Nyy67DC6XC1arlXFGms1mKIqCZ599FldccQWam5sxa9Ys3Hrrrdh8880zriEQCGD58uVoaGiAw+HA+vXrUVtbyzJWacPKl1fxm1niqOKhlWmcT7ltvhiMTmO9bGIyGAH9jEWtbCtyNhOHLHWKp3JdMjapo3wsFsM777yDt99+G4sXL0ZrayvLOjj22GNx9NFH6/J7/ve//8W5556Lzz//HHvvvTduuukmWK1WtLa2wuVyYf369fjqq6+wfPlyfPfdd1i/fj32339/HHXUUdh8881ZcICMVLpGymxyuVzseshw5huLqK89V1as2rGpxnA3MrX0nd4zq0a+Tht+00P3TVEURl1BJZikawwGA0KhEFpbWxn3OzlIiBZClmW4XC74fD5ceeWVeP3119HQ0IArrrgCJ554ItNDGzZswPz58/HUU0/B6XTi1FNPxR/+8AdYLBakUinGRxsKhVgzNho7UQhRII+XOZJLtfzwc5IrEFaM03jjub9VFGV7rc/LSbaA/DKNtWSIaCIoCEVrJm18FUVBXV1dRjD2m2++wbfffov//Oc/WLp0KZYtW8Y4PSdPnozp06dj+vTprFqGNq2UWRePx9Hc3IxRo0ax4EMoFMKtt96KO++8E6FQCKeccgquuOIKfPHFFzj11FPhdrvxyCOPYNddd2WNSGmt5+lLqNybAr8k5ySn2RzDhQYSgb5ZFwdCtkKhkK6OyTfTWP09vUxj0nckl7ROSRsbSSUSCcap7vf7GQXGunXrYLFY4HQ62b2n+04UGcFgEH6/H//73/+wcOFCBAIBbLPNNth2222x2WabYbPNNmPUXvT7QCAAq9XKKG8oeEKBK96RpOX47OrqQjQaZU6cVCqFyspKtp4Od6cxvy4RVRzNYzgchs1mY1zmGzZswIgRI5BMJtHZ2Qm73c4q7BKJBMaMGQNpY08TCrTya5Ysy0gkEjjyyCPxww8/4MUXX8T06dMxceJEbLXVVnjppZcQj8cZJ/LKlSvx1VdfwW63Y8cdd4TZbEY8Hocsyzj//PMBADfffDO8Xi/a2trgdDpRV1cHABlyUllZyehuKBhV6PzqZW8Lp7F+FUS2oJ5eFjIdUx1s5zPfqQqC1gSySagBsN/vZ85cquJ0u90wGo3M6VtbW4t4PM6SNQKBAN566y288cYb+OKLLxCPxxGLxdDQ0ID6+nqWCV1bW4sJEyZgr732wtixY2E2m5m9HolE8OuvvyIcDmPcuHHwer1Ys2YNkskkGhoaYDKZMjLYswXUcwX8su0bh7s9nwvFOEzfffddAMDMmTN7de5SHae3xxJOYwGBMuY0liRpfwCPA/g7gOMlSboF3ZQU3wKYLknSbxRFeR9ACIBDkqQKRVHC+kfsO/BGZG8Xn2g0ikAgkLFxADZlE5FBwHOF8pmiaj5R6gxOXIgVFRUwm8245557sPnmm+Omm27CSy+9pDkWs9mMZDKJyZMn46abbsLWW2/NDEwe8XgcAFjmFfFOUWmRtJEvi/g7aXNLhrIW/xldYzgczuCuGs6cVDwfLw/KcHS73bobumycs8QXS1x8VGpIaG9vR3NzMyorK3HSSSfhoIMOQlVVFb766iu89tpreO+993DRRRfhT3/6E/bYYw8cd9xxOPTQQ1FZWYlIJMKy1N1uN+6++26ccMIJ8Pv9zDBsaGhAY2MjdtllF5Zpxzt+gG4Z6+zsRGdnJwBkZNuFw2HWEEOLv1o9b2oeOa1ghLrhoEB2qDn3snGBUjCJPldvjOg9rcZvPLcvXzZN3OcAmFOQHLlGoxFVVVWw2WyIRCJIJpOora3F888/j7feegvXX389zj77bNxzzz248MIL8cMPP+Cee+5BKpXC7NmzceaZZzJnIJ2nvb2dyQVfTk4Zx8QfKstyBne4ek74zB+i4OkLbHwe0n1y8D5ArpJxfv3jQc82ca7W1dUxfWaz2RhvNqGiogI77rgjtt56axx66KFIJBKIx+PMoTJx4kSYTCYmZ8AmfUtOOEmSMoKhJMdHHXUUpkyZgpdffhlPPvkknnnmGcRiMey888645557MG7cOEiSxNZl/tpI7ikL66effkJlZSWjYyLwFE9q3a83RzyGKqdxtmunwE4uqNfMWCzGskUJZMuos/lI/1GwlSq+nE4ncyBXV1czXUhyk0gkWOCjrq4OVqsV1dXVmDZtGnbYYQcoisLOaTabYTKZmG5UNjas6+rqgs1mQ2NjIwwGAwtUUUY08XwnEgmmE8k2k2UZa9euhdVqhcvlYvpMT4YKoSUaKqAMbWATx2o6nWbBd7JdyIYgvQMgwy6mKimaQ6IzUhQFLpeLJXoAwOOPP46TTz4ZxxxzDPbbbz+0trbiqaeeYrqJnNQjR45kjRmtViva29uhKAq8Xi/uvPNO2Gw2loVaUVHBAvDkMKZ7SNzVhYDXJcPZTi8EueaJ/5z2flpBMK1n0GazoaWlhTUQJvuDgpIAWM8PPhhWXV3N7KTVq1ejo6MDVquVBbsdDgejyTr55JNx7LHHwmw2IxAIMOd0PB5HIpFgDcv9fj/Wr1+PaDTKmmkC3fp59OjRLEhFSQe0l+Ed3DQHfTHPAqXHddddB6D3zt5SHafUxxIQGI4oO6ex1L2aWACcAOBcRVFekCTpBQA3A3ABeBjAdwDOkyRpLoDJAI4dKIcx0PsmH3z0irq9UyYdLa7kECMHKkWWJUlCdXU148gkY4AMAMooCAaDrEM8ZcudeeaZOOigg/DOO+9AURSkUikkEgmYTCZIkoSuri5MnjwZ++23H4LBIOLxONLpNMLhMOsmDoBtgqg8k8oaaVNC10WvlMVF36NrUYN3hlKUj98M5sqaGirIFt2UZbnHnPCfkQGp/g7v8KDNSywWYwZoNBplzpbKykq0t7fDYDAgEAiwJlCTJ0/GuHHj8Kc//Qlr1qzBM888g5dffhlnnnkm5s2bh/322w/fffcdVq5cieOPPx7nnHMO43Ql5zCNJx6Po6qqijUHIlkbMWIEy64HuilXSFZJJioqKtj1qR0CerKldiqojUq1Y1MgE2pHr3pzQ8a91tzTxoM+539LmZTE20fliSSzvB4hZw2NhTLuycFjMpmYw5jOabPZ4Pf70dXVhVAohC233BKPP/44PvnkE9x888049dRTAQBHHXUUjjzySEyaNIldj8PhYFkvAFj5t9VqzZATalJGm3PahPMVAORU4LOyaaPOf7dU2Kgn9Ul4ywy51lS1DAGbHDJUTg9sWm/4wCa/ZpB+oPWKNs0ejwfhcJhxPFKjOdINdA+7urqY4xcAyzi12WyoqqpCKpXCNddcg7PPPht33HEHHA4HrrzySkiShHQ6jUQiwQKrdB2UTUVO846ODiQSCXR1daGuro5toCnYAHTLYCgUygi8aM2R+nlUP7dDZT3VunZCNrtBbYvRK8kIrV8+nw8AMnQQyVYsFkNFRQWMRiOqq6tht9vZPeNlEeiWR6JOSafTbH2jcQQCAUaFQhUMJItqmhJgE/csVSzw9z0QCLDKCMpiJWcRsCnJgLfN1POoXk8pg5/OPRwcMvz10/pFwQTSPclkEl6vl60Zfr+fyRzZNkQFF4vF2D0lpxvJFNldXq8Xr732Gg455BC8/vrrOOyww7Dbbrux41BlIDmO+cxlqsZqa2tDbW0tXC4XEokEk0Wj0dhjXSVd19DQoGkD6dnrpEvyCVgJ5J4nPdueB/8MUsM7AMy+iEajGRnIVVVVTH84nU5G2UR2Ne0L1q9fz/aNFouFcV5XV1dDluUM+wzoDpjT/oGo7bxeL5N7ysantZPsR7fbzYLyFOSnPYl6L5mrAklPrwt5FBAQEOg9ys5prHSvCnFJkn4AsJ0kSQsVRVkqSdL56OYxbgbwVwC7AtgWwNWKoiwfsAFjU/Sz0E0XbXL5LB+j0ZjRyZuMUofDwTYEZOzR7wwGA5xOJ2uIA4AZe/S9SCTCjAgqV7JYLKivr8f48eNZ9Lijo4NxW5FRkEqlYLVaYbFYkEwmkUwmM7Kt3G43K48DkLGR5SPgfIkbZQFSdqtWSSgt8Py86mUJaWWmDaUNMKB9jWTg6XWF15s3tfOEZIg2v+TMojJoj8fD5IY2msFgEOvWrYPH48H48eNx5ZVXYt68eViyZAleeeUVvP322/B6vXjvvfew++67Y9WqVQgGg4wHlGgvKCszmUzC7/ejo6MDFouFBVCo6R4ZxOrSc0mSdJ1s/Byoy/d4mgD1M8xncAn0hNrJXogOVH+uzg4nRwVlcPK6kYIcvJ6Ix+Mse8vj8TDZpd+l02nWjIhkjTgjge7NzPHHH4+TTjoJX3zxBaLRKDbffHN4vV52XsqeosZ4RBVAOo+6fVOGqFo3q8HzP/NNPnubDTNUdF4xa2osFmMO+MrKyozP+CwpXleEQiFEIhG4XC40NDQwmTKbzYjFYjCbzRnZWwaDASaTiTWpoww/4mTky2ndbjeCwSBSqRQmTpyIxx9/HG1tbQgEAojFYuz7Ws0NKyoqEI/HWUYi0C1jyWSSOQ+DwSCjC6Br55+dfJr+8JvpoSI7QE66A93P+XkiRy3BbDbDYrFkcBvzjg1JkuByuVi1FdCtJ8kJQk0Na2trmQwRpYGiKEgmk8zZAnTLMwVv6+vrEYlEWEDfbDYz5y+wqX+F1WpltpzJZGI2JjljPB4PRo0axShOSDZIxpxOJyorK5FKpZBMJnUb3hIcDgerPuOzALVslVzVA/2JXOfP5pwi+UmlUli9ejWTE5rrRCLBbN36+nrGC+twOJgd7PP5EAgEIElSBs80JUgQLUAgEIDH40F1dTUCgQAWLVqEv/71rzjzzDORTqcZh3sgEEB9fT2jcKL7SbJpMpkY7RitSxRY0Fun+IzqfMDrEr2qOIFM2SPdoPWZ+vN0Ot1jvwh0zzc9g7xNS05/2qclEgkWUAfAMoxDoRBLFLFYLMyes9lskGWZ0dREIpGMClgKjvHUg9FolAXFSE/R/pOqXM1mM6uQUcsYjY/WRJIhvT4p6nnVe67V8ywgICAgUDjKzmnM4VsAhwIYL0nS94qifC9J0sUAXgDwlaIonwD4ZEBHuBF6joFc0MumooVZzd1ImZ4+n485ZtVlrdQ1lhbwYDDIGulUVVWho6ODUVmQc4waGADdvFV8lh85kEeNGsWyXMhoIUdMIpGAz+fLaHYHaGeh5CrHVjtO8jU8e5vtPRjQV9eodi5UVFQglUohEokwJxuwaTNAmz7qCE6OMyqx3HbbbbH99ttj/vz5GR3cZVlmDaWATbQXdXV17HpIrjbffHOkUimWpcV3gKcyW74ZoPpa1Dxoueav2Gd4uELL0dsbRyfPuZoPiIu7vr6e/ZbnrAY2ZdpbrVa0tbUhnU6z5oj8hpnkI5VKYfz48YwfHgBzckSjUZZdSNk56XSaOWB8Ph/jAq2oqGDZXzQOtfNOvcFWv1dODpaBQDHPozqbSEsXqINLlPlEckeyVFFRgcbGxoxKhEQigWg0iurqapatDoAFP+PxOOOQJUqSUaNGseAHHSORSECWZXR0dDDnsLoRLPGQ0sa8qakJoVAIyWQSzc3NqK6uhtvtZtnOfPCBkE8QQjh39KHndKc50yrf5+WW+NWBbjlbuXIle6YbGxths9ng8/lQWVnJekrQ2huPx2E2m9Hc3JzBnVtRUYHKysoeZdrt7e1Yv349a2JGDh+fz8eqx4hjFIDmddH4a2pqMjKQs4Ec5WporbVDzUbz+/3MqVtfX88cb2qQk85oNMJkMrG1Z82aNZAkCW1tbRg1ahTTAVQtWFVVhUgkgpEjR6KtrQ0rV65EXV0drrvuOqxfvx6BQIBxElPlFl+dR/ePnIYU+A+Hwz3ozEpBU8PvW9Tgj68OkAnkBz19ThQ0ajuBAgNOp5Pt2wBkUGe1tbWxIANV0VBjdZvNxvRMY2Mjcwan02n88ssvSKfTLGhJwXJKFCKdRrorFosxqkQ+qKam9SE+Za01UUBAQEBgYFG2WllRlEWSJP0GwLkA7pIk6RdFUZZIkrQIwJCwOmjBzGacqzfCPJcidZ7ls6DIgKdySuq2nkql0NHRgerqambYknHKd9qlTBriwaPsOso8XrNmDduUkHOlvb0dwWCQ8fBRWRwfAec334FAIKP7L29kkkHDc+oWMpfFcl4NBuR7jbzDKdvn5JBdvXo1zGYzvF4vM0bJaUGOEHL4h8NhWK1WlhFP5WoAmFxRZguVu5I8kGFrMBhYWaXD4YDH40EymWTOOcpup7FQQ8dwOMyceXog+YrH4z0CK8NBRvoLvXES5wPaIPH8oFrOU9qMWiyWjE2vwWBgG2MqBzYajYwHmZq6GI1GNDc3I51OZ+hj4nns6upCJBKB1+tlHeXNZjOWLVvGKAVqamoYtcHo0aNhMplYua+am5kPjmgFLniZH0oOlv4AH9ACNskQrZW0OVY3WASQ0fCJ54ik46RSKVa6SxtbfqNNVTxEF5RKpRAOhxlFBbCp/J+cN+RA4Z3cdF5y+tG4SW+uWrWKZdFTZiE9H2r6E1mWM/oBCBSGbE53vc/oHlLgijhjeWod0jN+vx/Nzc0AwGhHDAYDKisrmUNn1KhRGU5ZviloOBxmf3s8HrS1tTFHC1/JRfJH2X58wE3L4ZstkED6NlcwS2utHWzrb67AHdk9RClBgSb19WnZ+WQ3UbCRkjooi51olKg0n57vYDCItWvXIhgMorKyklXa8LYZ7zDmE0B4/QR022kU6M8lE72BXn8CgcJQLMWC+nmOx+MsKcTr9SIWi6G6uppxEBPvOVUD+nw+uFwuxs3d0dEBu92OWCyGRCKBWCyWUS1F1BU87zev30hnqRtw+nw+to6q939qOrThHFAXEBAQGCiUhdNYkqTNAXgALFMUhbWCVhTlIkmSbgJwOropK9YAOBzALQMxzlIjnywf9XeIuoJKsOk7WhkCHR0d6OrqYoYnbZYBsA00LdiUyUIbEmqsQwYl7/SoqqpCMplkEeGampoMp446U5hvskFZX2QE0LXR32TYFhpp7msnVjkg32vMldHDfx4KhVi2DO+80HI48A7/mpoaGI3GjM7b/KaXstCJb5gymJxOJ2w2G9auXYsNGzbA5XJlOAe1Gu9QRlYqlUJbWxuqqqoyZFdNaUKBFZ5vrZD5E+hb5ON44GWR51wl0AadPuczjHmOa3qlLFH6HOguz2xtbWWc7w0NDRlOHpJJ0nO0mW5tbWXBLtJRPA8on23FN+5Tb/Z4uaXv8tn86msGyqvEuxyQLUOOz6wiOhFg01oHdM8v8VtTQCoajaKlpQUAWLYxcUBSEDYWi2U48AwGAzo6OuD3+zFq1CjE43G0traysnPiEuX1m5pWh6/MoXWe5La2thYOh4M1DSKHlZ4NkU9zVAFt5KqGAvQdOJFIBM3NzazxXWNjIwtUjB8/HkC3zBG3MLCpJwTfLJioxhoaGtj9IycM74SjNS2ZTMLj8bBgF+kcGr/f72cVYfQc8Neab4ZpvhQ6WmvtYFt/c9lRJpOJVfYB+s8iXTefHEE6yGazsX+pVIrpFd7ZS/zGo0ePRltbGzweD5xOJ7xeL9rb25mesdlscLvdLMDPO+GcTmdGYIzscuK55mWi1Mi2BgrkD7V8FZsdTjYy9bABugNYkUgE1dXVLDhOVQ8UlCLqHGqQbrVaWdN2chiTs5cPmlOwitZKsl9CoVBGxWAqlWK84LT/4/eFtN8FIALqgwAPPPBAWR2n1McSEBiOGHCnsSRJBwOYD8APoFmSpOsVRVkmSZJZUZSkoih/kiRpHwDbAdgCwL6KoqwcwCH3KdQRVTXIcKDmPPxiql5AaWNJCzltKIhDjwxMYJNzgs9soOwFs9nMqAKampo0SxydTmfGBpp3hDQ3NyMajWLcuHEsi1htQNJmhDbi6kizcJbkj1wZPXzmC80zyQo5f7UcDna7HV1dXejs7IQsyz0cxuSsoxJIChTw8kybFdpwVVdXM45Oys7jDUwyGClrnt/kUOk2dbUHup8BcrgMNZ7OwQr15qaQsnmeJkXrc9I3vN7iPyfE43E0Nzejo6MDEyZMYOMgDuR0Oo2uri6WER+NRlnmVmdnZwanntfrhSzLbPNkt9sz5JyHFg1FPhshPQfLUCvxLga8PGWTJV5GeJD+o2AUZe7yNCE8zRNPOcBnR6l1o8/nQ0dHBwBg5MiRALodS4FAoMe4KeiQSqXg9/tZwzTSqdRstLOzE5WVlWzjXVlZmdF9XmtOiA6DxpVt/oaTQzkfG4J3yPJ0DvznNHf0GX8PqVkxOepsNhtCoRAcDgdr9hQKhRAMBuFyuVi2OP2jNZT0XWVlJfs93atQKMSyU7UoWYLBINLpNKPeoaouui6DwcCCIernJxelC51nsGQL9wb5ZEYX+ywRlzU5U/meI1rn5rPXeRDvvtfrZWsRsMlRy1cc0npD9hU1vevq6oLNZkNjYyOjF8iXRoKvjuCp6XhorYECvQclcYRCIdTW1uZN+2EwGJiNTI0Yk8kk2traUF1dnZFpThWFAJjTuKKiAvX19QiFQhn0dWSDU+NH6n9C79tsNqxZs4YF2hVFYbY8rbdk1/NyrKZfUvchEShPbLnllmV1nFIfS0BgOGJAncaSJO2G7qzhEzc2u7sXwAUA5iqKkpQkyaAoiqwoygcAPpAkyaQoSmogx9zXUHcz1wNlDhNvldbmkDJBKesylUqhpaWFcUpR5glFemmzQw6ScDgMn8/HspMrKioY/QRtcsiIyJYxyJej6RmQ6ixXdaaxcJbkD63MFvXntHGmMmdAuzkXD5IRv9+PUCjEjEvieq2vr8+QDbpPRC1BzjZFURgvI8kAcfvx5yL5Ike01+vF2rVrASAj6GCz2ZijRMsxIzCwUHdVJ4dKPkY/T5Oi9X2eR1GrWQrJg8fjgd/vRywWQ3t7OyoqKpjjpq6ujmXcp9NpxucHdG+EKJhFDj2TyQSXy4XW1taMpqSFzofWc5brOIOtxLsvoJYn/lXv+2pKJKfTybKtiPdRzROp5eTgs5T5dc1gMGDUqFEANjUY4hv/AGCBNGDTGub3+7FhwwYAYKXh5NwxmUyorKyExWLJeb/VdkO2KiY9R+FQ5xvlbQi9EvlcWZFaQQqeZqK+vp7dR56LmD8n/wzTGqcoCsLhMKvwMhgMTC9RYClbWT/d70QiAb/fz35LeowcReqAh/r54SldUqkUc0jxFGjDxf7KJzO6kOaldI9p/ul+2Gw29vxRJYO6bD8QCDBagEAgwPRITU0NKioqMhoGA5vWWTV9DfUzIQcvNdCm+8rTIuVDI6GuytCag+EkM/0JPmM4Go0WRPvB3xPiMaYgghaIpoLoTeLxOAwGA8tUdrlccLlcsNvtaG1txZo1ayDLMttbOBwOFlStqqpiAVsALGBC4+f3k3r7RSFP5Y/XX38dAHDIIYeUxXFKfSwBgeGIAc80BnCToihLN/7/agAPSZJkVRQlriiKLEnSzgDqFUV5A0B64IaZHbmcU3pOVfXv+EUy2zFpM8BHXvnv8/8nJ2EoFGLZeG63m2V3UhYTsKl5FJ2jtraWZTjwjrm2tja2uSCjVFGUHptXSZJQX18Pt9vNnHtkDNMY0+l0huOFPx4/fjpmKcCXSWndm6GSoaonQ1rvk0zxxiP/PdpEjhw5EkajkX2fMhFsNhsURWFNL8iIpI04NV8Mh8NwOp2MExbolmM6DgDW9MflcrHzVldXo6mpCUBmVkuxXLDFPrO9wXBxYvPXyes0vmyen19yXKk3LrxM8rpJDb3PeAft5ptvDr/fD6vVCovFwnj6+HPHYjHWuMjhcMBsNsNgMKCqqirjmmw2G2pqajKanGnJndb7vdkIDaYS7756vvj5U68nWufkN9f8Wqd2mEmShHQ6jdbWVgSDQUYtwINfh+hc9Go0GjFmzBiWRR4MBlkFDq/PSE/KsswqPKqqqliwgvQn6bh8qmsK4bvUcxQCQ5tvNB8bIltWpKIomvYZz20LZDbD46t6eHuGX68o+5PWS7WeIR3Dr5WUBEA0P4RAIICuri5IkgSz2Qy/38943PnsaC3bir8+yhbknYwCm8DfC3ol/aO3RvEBBHU1Cf83NdIkpFIpGI1GVFVVweVywev1MkoxavDKUxoBYAkBXV1djOKCGhyS/kmnu7dT9fX1TJ/x620+toq6KkOg/8BnDOea+2z3MhKJIBQKsUA+/13qPRIMBpndBgAtLS0ZfR7UgSiHw8HWLZJxXk9S5js1web1m9belX9/qOzNhjpuvfVWAL130JbqOKU+loDAcMRAO40/B7AMACRJMgKwAmgC4AbgkyRpFICtAPwTAJQh6HHRWwAlSdL8jC+xzMUpp4bL5cLYsWMBbOIbVpfxG41GSJLEsvto88Nnb1JZndPpZBla6pIhPlsiV6ZBPB7vwYOsdT167xeDwZS5nO0ae2NAqX9rNBqzzkV7eztaWlrQ2NjINgrUuAUACyCQPNC9r6ury8ic4Tc4vIyQQUo0FYqisDJORVFgNBrZd/TAOwb0niGB3qOQeeWfXcqu47OgAP2MLV4mqSGYFqgUW51pTuXh5GiprKxkPO+U1Ull2263m22sSX7MZjOcTidMJlOGM4DkVa331HKnfp+uaSg753KBX8OKKVdWrye5jidJEsu85B07/H0g2aJyWuK/VsubJEk97p06O9fpdDJ9xTukSSbVXNZOpxPxeJxllPIUAPnOT74yRdfDf3coZq/rBYJzzVG2eeQrV/hnnKiWtM5pNBrhcrk0nTZEiwN06zySI16eTSZTjwqKRCLBms2qq7yIl7uqqgodHR1IpVJob29n67S6h4TaOU7foWdGUIL1BD8Xenap1vzSPGrNJ/83bz/RZw0NDaziz2KxoKGhAeFwGOl0mmXFq4/JB8uo8kFN2USOQKPRCFmWGTUAHUt9HXw/AtKhaptMyErfQE+/5KP3KaFHiyaL1j6yc3hQwgb1kOAbqZvNZlZtyMuJ2+1myR38uHkOcHoGtIJz6utU0woJ+RIQEBAYGAyo01hRlDSAro1/SgACANoVRfFJkjQbwI4ArlEUJahziCGHQpqYFerslCSJbWBo4c2nhBXI5NvUcoTw51CXueUa50Dw5A3FjXJfgzIFiEcP0C5P5DOtgE2ZV7Rx1soK5u8DcacR9zEZrPncK/W5BcoLfAYU7/wrJFMyG9TOZ8poIbobrSzLVCoFi8WCurq6Hk6AfMuP9eROyGNP5EMTUOzx9O6RntypYbfb0djYmPVY+YCnCMomUwB6UJXwgbf+gJDR/FHKYHM0GmUl2uTUU59Da81TZ9rzMBqNqK+vZ80X1fzE/Lnz4ZUXclEctOY3X5oGtb7gf8/rr1xcwbkyUbNVHJDcqa9juFQlDDVoySNfhUUc52rwFa2VlZXs942NjRnNE9XHz7XWFkJZMpgSfAQEBASGMgY605hhI1dxSJKkNZIk3QBgPwC/G04OYyD/Jma9cXZS5JY3FvU4hrX4NvMpkdYbp1YGRr4GRCmb4Q2mMu9Sgr/32Zop8dkIPA8nbUgJvSlP1JMR9THF5nVoQUu38E3LiMc6GxWFXvMh9bHVr2pdwwestHQKzw1JmdHDhQO2r1DqgF2+x8s3MKHHZ6y3/hSyLpEc8U3MaEyiSVT5ozeyq9Zb/DrHHy/XOXLZS/wab7fb2frNo1RBuuGCQm3PUs+vlt7g+fz57/FUSzwliRpqGzibza9+FckWgwvZ7q3NZsuwY9R6SqvBLFFy0THUsskfm3i8i92ziQQfAQEBgfJA2TiNpe4VxQxgj42vMxRF+XlgR9U7FOPkzOXMLIWzM1sWMUFtLBST1azVeZxKwYs5pog49x7Z5lDdjdlgMORsdGIwGDK6LRcCXkb4jU9vjtkblDIoIaCPbDosnwy4bN9R663eNuHRylDlnxNqDCqQP/gS+FIerxTfyyZberoz33VJlmW0tLQgFAqhoaGBldqKtWzwoDf3SysbT2udU9NIkAM4Xz3DyyOAnI3KBHKjUNtTb90p1sbgewFkkwOSsVAoxHqR5EufwY+Z7DG99VTYR4MLes5fddAByK8ZezgcRktLC2t+rZZNrcaKudZGvUQAsUYKCAgIlAfKxmm8ka84IUnSXwB8OdgdxkDpy3BLAWrAo5XplG3hLgVydSbPBRFx7j34xjxq8Bx41Pwmn0zivpYbNY9eX0EEJUqDXE0msyGfDC3iKebLI4tFPk5qrWwrvrFaueh2gUxoOWhy6aps8qe3/uS7LpHjhzghBYYXisk+5Z0zxMGea61Vr/Fa63e+tDsC3cj3GeezvLXuTz42hpbeyld2+AxPyjTWgrj/Ajx4vVKonip0zdR6RoQ8Dj08+eSTZXWcUh9LQGA4omycxhweHyoN7/rLyVlI9gJ1yuWjwlocs32xcOfiYMsFEXHuPXiuTTW0OPDyyfrta4Ovv3j0+vp57Y0zdTCht7zruZo1EU8xzwNaLPIpt9XKtqLnRASwyhdacphLV2XLTM/WjDUfObTb7WhoaGAZgNkaOwoMPfS26gHIb62lBlOkO7XWb0FPURj0nnG17Z3r/uRjY2jprXxlh/+enp0HiPsvkAm13GpVAPIgXmM1hYXaZtN6brTkW8jj0MPo0aPL6jilPpaAwHBE2TmNh4rDGNAuM+wLh1EhTppcmab0Sl3myQCgMdNrMWV2pdg0CZQGah68WCzGslNsNhskSdI1GInblaCWm2LkmzLg6fz88SmjVG+jlUtllIoaprcYLpnM+WyM1fdMS9fobcC1NhjFyoDBYNDUYWred61xZst8741MlkqehyP4dUlLDvU2p6Wac/W6qOaQdzqdTA9kWw/70gwS8qOPgTA/tdZMnpuWmk4pisLWQovFkpUrNJfzUpIk9lxo2XpCRjYhm62rXtOzOb+0mmJqBUa19Bb/Pa21qtD7Rby0enZebwJaYv0qP2S7J4qi6O79rFYr4ztW2+dayT/5BLX4PSiNS62PAAyLBItywtixY7Fq1SrNz5qamgo+3vPPPw8AOO6443o1rlIdp9THEhAYjig7p/FgRS4nQDaHkd5v8zW+eCMz1wJLG1ctGI3GvLkaC3WAlcIALXUjvKGObNeoztzMhweP/y1/7EI4RbMdUy/T3Wg0Zs0wLlYu+lsGCnlOBzOKaVyoReWjni+aMy154OVZSx4Kne/e8tcK9N3zle24ah3C3yPip9aSTYPBkFWP5OvkV8uH1iZ6uOiBoYhi71ehvyO5cbvdPdbCiooKBINBRlvhdrt7/L5QGRN6TR9ac6Nne+eyVdTQ0g9a9lQ2Z1w2J38+NqDeeil009BBtntJn2nZOW63m1UtyLKMUCjEvqslI/nonXz2C31d9SrQE6tWrSpp0PS+++4D0HsHbamOU+pjCQgMRwincT+hL0vfi3HS5INi+B0JfdFUTGxs+gZ6PHj93Riu2BK1wSIXffWcDgVo6ZNi56sU8tBb/loefc35LbAJvVln+0JutMbTVxUNopFn6TFQc8qvycWgUN0p+kXoI9vc9HZNz9fm6cv701f2k9BHgxd8NjAFLJxOJ3Mi66E3z0OuKiEBAQEBgYGFcBr3EwYbH28uR0eu6+kLQ1QYEn0DNQ8eob+dsXrl2rka4Qm5GPwopX4shTz0lr+Wh7qZlUDfoTdy1Bdy05/rvlpfC6dN7zFQAUlaC7Nx0rpcrpKteYPNPu1P9NXcFBJM7Mv701f202AJ5gv0BMmbLMuscXpFRUW/NqIWMiMgICBQXhBpT2UORVEQDof7nWuPosvFdnp3OBxwu90lNUTJkBEb4P5BtnvYn3JJshiJRDQ/F3IhwGMoysNArQPDCeUmN4Xec7W+pk24nt4UyI2+sGPUkGUZ4XCYUUPlg3g8zjhGBQYn1Db2QOn4vtJ7/fHsCOSPYuQrGo0iFArBYDD0eZWUkBcBAQGB8obINC5zDFS0vrfdbEXmyuBHtnvYn3JJMiiMSYHBCL7TeLHQ4nwWGNoopm8A/z1RgdF79Icdk0/zKDXEmjj4obaxh1pmrtgDlBeKka/e7gMLgZAXAQEBgfKGcBqXOQZq45ets7uAQH/KpeACFhjMKIUuFQ7A4Yfe3nOxCR8cKMYxQzqlXLLiBQqHel0QOl6gL1GMfIk1RKBYvPTSS2V1nN4eq6mpSXe9bWpqwsqVK4s+toDAYIFwGpc5ynnRFpyJAgICgw2yLDO9NVga05XzOjAYMRhkQNzz4QGtoBLx3dpsNhiNxgEamUB/Qut5Fza2QKmQbT0RciZQatTU1JTVcXp7rGxOYfHMCAwXDFqnsSRJkjJICB6LVSgDoYiynVM93epyp74Yr1DGfYPezms4HC5pKWVvxiNkZHBhIPQEr7u0yjRLPSYhzwODXOsXyUEpZUCse0MH5TLvPGVFX1TZlMt1liv6+5nW28r0JS1RX8mAkK3yQy7Z06OukCRJrG8CReGxxx4DAJxyyillcZxSH0tAYDhi0DmNJUlqVBRlg6IoymByHA9FiHK64Qtx7wUGK4TsCggZEChnCPkUAIQcCPQPhJwJlBrCaSwgMPRQnnWZOpAkaQ8A70qSdCYAkON4gIc1bFFu3eYF+g/i3gsMVgjZFRAyIFDOEPIpAGySg3Kl0BEYGhD6RkBAQEAgFwabJRIE0A5ggiRJFwDdjuOBHZKAgICAgICAgICAgICAgICAgICAwNDBoHEab8wojgIIAfgKwHhJkk6RJGlzSZJG5Pjt6ZIkLZEkaYnP5+uP4QoMIwj5EugrCNkS6CsI2RLoKwjZEugrCNkS6EsI+RIYqhg7dizjqVb/a2pqGujhCQgIlDkGjdNY6cb/ACwG8DmAZwEcBeBtALUAcyxr/fZBRVGmKIoypba2tr+GLDBMIORLoK8gZEugryBkS6CvIGRLoK8gZEugLyHkS2CoYtWqVawhsPrfypUrB3p4AgICZY6yboQnSdJUAGYAKUVRPt/4dhOARgASgCkAlgLYDcA3gqpCH7IsIxKJwOFwCH40gX6HkD+BoQgh10Mb4v4KDAcIOe97iDkWKAZCbgQGIxYuXFhWxyn1sXg0NTXp8oE3NTUJh7zAkEHZOo0lSdofwOMAHgVwvCRJtyuKcufGv08DMBPAPAB+AAdIkuRVFKV9wAacBbl82X3RfEB9zkgkgq6uLgDI2vCAfqcoCjNURHMEAR6yLCMajcJut+ctG7z8OZ3OvhyegEDBoGwLLZ2XTVeq9Wq+EDq1/KC1TvdWb/XV2p9rnRbyNXSQTYZ6Kz+EfO3DvoLeNW58v6w8ZVpj5W0io9Go+bti1wqB4YO+WIP6CmINEsgGh8NRVscp9bF4ZHMKi+dAYCih7JzGGykmLABOAHCuoigvSJL0AoBbJEkyAvgUgAfAGYqiLJQkqQLA54qidA3YoAcBSFnmqzT1DFyhAAWi0aju5kdv81eo/AkIlBLZ9BbJbDGbeiHXgwf5yIAa5X5/hSNq8KMYuSw1ylXOI5EIAGh7YcsIvE2k59jLNcfCthbQQrk+mwSxBglo4d577wUAnHnmmWVxnFIfS0BgOKLsnMYbKSbikiT9AGA7SZIWKorytSRJ5wO4G0CLoihHAoAkSUZFUcIDONxBA0mSClrQy91QERg42O32jNd8QPInNkYC5YpidF6helVgcKHc9ZZYpwVKgXLVYxvlOj3Q48iFfGyicp1jgfJGucuNWIMEtPDCCy8A6L2DtlTHKfWxBASGI8qq7EuFbwFUAxgvSZJJUZTvAVwE4C+SJO0IAIqilL0xOVhR7ptlgYGDwWBARUWF4FcTGFIQOk9gsEHIrMBQxka5lgd6HLkgbCKB4QqxBgkICAgMD5SthaMoyiIAIQDnAthGkiSnoihLALwFoNc1e3PnzkVdXR222Wab3h4KAGA0GrHDDjtghx12wKGHHlqSYwoICAgICAgICAgICAgICAgICAgI9DfKwmksSdLmkiRNkSTJxr+vKMpFANoAnI7uDOMLABwOINDbc55yyil46623ensYBrvdjq+//hpff/01XnvttZIdV0BAQEBAQEBAQEBAQEBAQECNsWPHQpIk3X9NTU0DPcRhh6amJt37MXbs2IEenoBAQRhwp7EkSQcDeAXALQD+LknSNhvfNwOAoih/AvAigJUAxgPYV1GUlb0975577gmv15vx3vLly3HAAQdg8uTJ2GOPPfDjjz/29jSaUBQF4XC435qcCAj0NWRZFjItILARQscLaEHIhUCxELJTniDbR5bLnkVDQKAHhF4ZXMjmGAa676fev5UrVw7s4IchVq5cqXs/AGR18gunskC5QRrIhUKSpN0APALgREVRlkqSdC8Am6Ioczd+blAURea+b1IUJdXLc/oArNr4pwXABADfb/x7i42fxQFUABgJ4Kc8Dz0ZQATd1BnN2JQNXYPubGkeBnR3hE6j7/natM7f3xjoMZTy/E2KotTqfaiSr/7EQM8xyXQVgNYBHAdhoOeDUOg4dOVrAGWLMFjntK+QbRzlqOPLWba0UC73WY3ejKsv5WIg56uvZGsgr2mg5U99/nLUKf1x7kJkayDGzd8X7wCcX42BltvBNIZS2vPlcM1q5DOm4aJX9NCXYyrVujhQ8ybOW77nzaq7BAT6AuXgNN5CUZTHNv5dC+AhAMcpihLf+N7OAOoVRXlDkiRJKeGAJUkaC+ANRVG2kSTJCcAH4H/cV6yKokyUJOlIAH/WOMQ6RVH233iskYqirJMkaTMA7wOYoSjKckmSliiKMqVUYy4UA33+chjDQJ+/P1Au1yjGUZ7jKAXK5VrEOMpzHKVGuV6XGFf/YSCvaaDnc7hee2/OPZzvmRjDwI6hHK5ZjXIbU7mNByjPMakxUGMU5x3a5xUQKBSmAT7/5wCWAYAkSUYAVgBNANwAfJIkjQKwFYB/AkApHcYaMAAIKIqyg/oDRVFeQTeFhi4URVm38XWFJEkfAtgRwPLSD1NAQEBAQEBAQEBAQEBAQEBAQEBAoO8woJzGiqKkFUXp2vinhG5Kh3ZFUXySJM0GMA/APxRFae6HsXQB+FWSpGMAQOrG9vn8VpKkKkmSrBv/XwNgOoD/9tlgBQQEBAQEBAQEBAQEBAQEBAQEBAT6CAPeCI+gKEpKUZQQgDWSJN2Abofx44qiBPvifJIkPQvgMwBbSpK0VpKkUwH8FsCpkiR9g26e48PyPNxEAEs2/u4DADcqikJO4wdLPPRCMdDnBwZ+DAN9/v5AuVyjGEcmymUcpUC5XIsYRybKZRylRrlelxhX/2Egr2mg53O4Xntvzj2c7xlBjKEb/T2GcrhmNcptTOU2HqA8x6TGQI1RnHdon1dAoCAMKKcxD6m79acZwA8bX2coivLzwI5KQEBAQEBAQEBAQEBAQEBAQEBAQGB4oWycxgRJkk4B8KWiKN8P9FgEBAQEBAQEBAQEBAQEBAQEBAQEBIYbytFpLPVxwzsBAQEBAQEBAQEBAQEBAQEBAQEBAQEdlJ3TuK9RU1OjjB07dqCHUTaQZRnpdBpGoxEGQ9lQXJct/vOf/7QpilKr97mQr8Ig5C8T2eSrHGVL3L/Bg1LJlrjnAmoMNr1VDITcDwyGg2xlg5C7vsNQtOeFvJQPhrvuKgZCfvPDcJCt5cuXAwDGjx8/ZI9VjsgmW6b+HsxAY+zYsViyZMlAD2NAwQcKFEVBJBKBw+FAN6002KtAT0iStCrb50K+9KEVoOLlTxgI2eWrnGSL7qWW/gCEDilHFCtb6udWfc/FvRYYLHqrN5BlWVPXEcRz0DcYDrKVDbnkDhCyVyyGij0v9nTlieGuu7JBL2FR7Anzg5Atgb5CNtkadk5jgUxIkoSKioqBHobAMIWQv8ENcf+GH8Q9FxiOEHIvMBAwGAxC7gTyhtBTAoMZJL8i0CEgUH4QYRwBAQEBAQEBAQEBAQEBAQEBAQGBAcOll16KSy+9dEgfa7BBZBoLCAgICAgICAgICAgICAgICAgIDBg+++yzIX+swQaRaSwgICAgICAgICAgICAgICAgICAgIMAgMo3LGHpE8YS+4Pzpq3PmOm4x5xyI+REo/l4qijIg8pMNQkb00Zs5L/bZzPa7bPLTG9nKNh6BgUFfyE+u8/WVDAjZKi+Um93QVzov22flNgf9jd7aE4NJ/wgMHvSVnVvsOYvVMX31fAmUH/pi/RooP4SQOwGB7BBOYwEBAQEBAQFNCENaQEA8BwICAuULoZ8EBAQEBPoSgp5CAEB3BC4cDg9IpFsPNCZZlgd6KAJlgGJltBxleziiFPdBlmWhEwSKRil1gZBFgYEAyV1frGdCpvsHxeohcX8EBhOyybmQZQFCf8mC2AsOLowaNQqjRo0a0scabBCZxgIAgEgkgq6uLgBARUXFAI+mG/yYnE7nAI9GYKBRrIyWo2wPR5TieY5Go+JeChSNUuoCIYsCA4G+lDv+2MLm6jsUq4eEzhEYTMgm50KWBQj9JQtiLzi48NRTTw35Yw02CKexAADA4XAAAOx2+wCPZBNoTPQqMLxRrDwIOSoPlOI+kH4qJz0lMHhQSl0gZFFgINCXckfHFGtl36JYPSR0jsBgQjY5F7IsQOgvWRB7QQGB3kHQUwwy9FV5hSRJLPJGZSJ8yUgpy0fyvQYak8EgxLScQLKQTqfzkgn+fvdGjkge8uVuo/MCKOh3AvpQP7uF6KNC7p/WcRVFQTQahd1u19UJouRx6KLQtU/r+3oyWAgVEskYALY+ibLHwY9ypcPKpdN6K3v88Q0Gw7C3ufj5LHRu1fdK796p9VC+sifuz/BDf68tvH0fiUR6tf/LZvOpZVnYboMThdy3fL+rpQ9LIR+F7iEFBhbnn38+zj///CF9rMEGkWk8yNDXlA18mQgA9n9ZltHS0oL6+nq43e5enaOvSkQURUEkEoHD4RCLQh+CZCQUCrEFPNt95O83AAQCAXR1dcHpdPbpBkiUIpUe6jntqznWOm4+JWzRaBSBQAChUAi1tbWasiX0xOBEoWtfIbJJskWOM8p40QpSaMmh0DWDHwNNhyXLMpM3oFvObDYbYrFYhmyp5a+3sifKxDOhtlcKmRv1XOY7t/msW0CmjAjH8fAAySOtTX1tt2jZ9wB6rI+llj9BjTM4kU13ka1N8sLfYz7bNx+9KeRj+OHrr78e8scabBi0TmNJkiRlGKb1FFNeIcsyc5LkWui1ykTsdjvLrCoF+qpERGzc+wckG7ShzVVSpKY+CYVC6OzsRDAYRGVlZdZNUm8gSpFKD/Wc9tUc22w2hEIh2Gw29l4+JWx2ux2hUAipVArRaFRTDwg9MThRqKzl+32qgKCNSC6HkZYcCl0z+DHQ95APXEQiEaRSKXg8nh7ypn7t7bhFmXgmtOYz37nNda+y/U5r3VI7iYWDf/iBZE+W5X6593r2vSzLeSeK9Oa8Qg8NLmSzudX6Su8e2+12ltEuy7KuH0LrtwICWhg7dixWrVrV431JktDU1ISVK1f2/6CGAAad01iSpHGKovyqKIoy1B3HWtFkSZLgdDqhKArS6XReWQdaThK9SDWVDBHo/xUVFWhsbITdbmdlUsVmPfBUGNmuFQArEcx2LAJv7IsMwuzozWPDy4j6PiqKkjH3vIxIkgRJklBbWwuHw8EMjXA43CfZC6IUqfSgOSX50XqWc0GvkzZl1hmNRsRiMciyjFgsxo6v1k1aMBgMqK2tRTQahdVqRTgc7iFXwvgceBSjf3jZ0/s9r3/UsqnWTYRIJIJQKAS3281kRWuzQiA5pHJJklm1bJJM5xOsFRh45Fov8qHT0kO235Jc8s6ZVCoFk8kEm83WQ+/R39l0cK6xqm23fHV4sXOg/p262qO/1+hs5yMbm1BIVpvevSJdwa9FatuI1i1e3+TjdMlHtrJdq0D5INuej880LhSFrLdq/ULvGQwGyLIMk8nE5K8Qqqhc1V38eYvdnxQrz73R7f2Nchur0WhEXV2d5v3l9ZV6zdGSr66uLibj6jWJ1618IpwW1Vi2dajYdTobykk+BLqxatWqjPu59957AwA+/PBDcb96gUHlNJYkaWcACyRJelxRlMuHg+M4G/LNOiiFk0SrLKrcsh4MBoMoWykzaJUjGQwGuFwuVobUXxkUAuWLfDMS8gEZl+FwWFOuCnGSCAx98LKmFzTVQq71T5RTChQC3sFIgYu+2twMtO023Ko9cpVbU2BJPRfqdVCsXcMXxQToewP1M6peJ3tzLIGhBb29N6+v9OxxQiE2v5AnAYGBwaByGgPwAfgvgNGSJN2hKMp5w9VhDOSvZAs1NPPNIO6tM1pwiw59qMuOjEZjhnypN8kCgxO95VrkSyKB4jfH/DhERvHQRqn4PSkgSs1X8j2eWmb1PheUFQKFgNd92cxbyrYCNjVkzBcDrRsHmgqkL6Gll4otty5mHVRnMAsIFAM1rRywqRlZofpmKD/vAvmhlGtOb+RJ8MIPHmyxxRZD/liDDYPGaSxJkgFAFEAHgEcAnChJ0rUAXgQQVRRleZbfng7gdAAYM2ZMP4y2f9BXWQf5ZqH09vwD3XimVBiq8lUKqMuOtJoc9Edzj8GKwSJbvc1c0yqJLMU4RBaCPgaLbOmh1NmShR4vl8zS58NRrw122RoMiMViaGlpAQA0NjYW9AwMdMZqb7Imy122tPSIlo2Tb3CgN+cXTrrCUe7y1V+QJClDZqPRaNH6pr+zpMsVw1m2cq05heit3sjTQFfZCOSPBx98cMgfa7Bh0IRZFEWRFUVpAfA9gCSAqwHsCuB9AHVAd3M8nd8+qCjKFEVRptTW1vbXkMsKFCHmu+HqfcdqtTJ+x3x+VywcDgfcbndBhi1lg5VTgvlwka9i595ut8PtdrOMPLvdzjjaSK7UctaXcjeYUI6yRRlu/L2he8zrDC0ey75+dvlxZBu/kK3ylC0eue5Tsfda77j5HC/fsQ13lLtslQq90Wm5fqulZ3nYbDbU19ejvr6+3zKGy8H+KnfZylePpFIptLS0IJFIZL3PpTi/0Ff5o9zlq7cguUulUgX9zm63l1zfFKtPykEPFYOhLltqFLKvy6U3e6vDaD212Wx523kCAgKZKGunsSRJO0mStKskSVO5t60AGgCMBjAJwNcAjgKA4UxVkUuhUnQtGo3qHoMyf+PxOIvAtbS0YP369QiHw3mPJd8FvZhGZTRGKssU6D/wc98bo42yj0OhEJPHWCyGQCAAn8/HyodyyasWBqsxWY7Q0yla94ayCPgO7+pnlOQn2z3NxzDMdo/5cegdr1jZEuh70L1Np9Pw+XwIBAK690l9r7Wgda/17n8+x9M7hgh6DU/0xh7JpQ+15JSXK+oN4HK5NGVWlmUEg0EEg8G8kgXykVVhf+VGvnrE7/dj3bp1+Omnn7LqOSD/e6RXek2yFA6HhV4a5vD7/diwYQP8fr9ugF8NkquKigpdfVMMtPQJ2QDZZLTUekjsG/oG0Wg0731dLr3JH6uY+0Tn5ptr91YX5iOrAsXj9NNPx+mnnz6kjzXYULb0FJIkHQjgbwAWAThekqT/UxTlWQBPAbgB3VnGZwH4EsDFkiTVb8xEHpbgSy7sdntenGpqUMav1WpFOBxmEWk1cnEC9SVJveDGKg2K4ZPm576Qe6xVDqSWR+oS39nZCYfDwWTLarUWNF7RIKF00CvjyqVL9LhctTjyCKRTZFlGKBRi56RNDf1tMBhKIns8z7bgNSsf0L0NhUJIpVIZndqzgeTHZrMhFosx/VEsl6jWsbOtp2o5ow1OKBRCXV2dkLEyB69nnE5n3verN/ZILn0oyzKcTmfG5yRnfB+AbJvsfMrJCynXFfZX6VBdXY1wOAyj0QiTyQSbzabLqR4Oh9HS0oLa2lqmE7XuO693amtrma1EMiSaDg9+FGO782tYdXU1gG75y/fZz/W9YvvTaOkTtQxryXmp9dBQ2Dfw96Bc7A273c5sOZI/er8Q8LZ6KpVCJBIpuE+Tej0thUwPFYrNcsVPP/3UJ8dqamrSvadNTU1YuXJlv41rsKEsncaSJG0H4FYApymK8qkkSf8E8AdJkl4FEAKwEsBdiqK8J0mSCcCFiqLEBm7E2ZEtKqYoiq7wan2m1+SCV8Za3EDET0XH0FpUJEnq8fuKigo4nU7WKAjQ5h5SFCVjQ91XThnBjZWJfLK5tRAKhdDS0oL6+nq4XK68zsXPvZ7RpjUeoqWwWCzsc/6V/u90OtnGPR6PQ5ZlxONxGI3GrEYd/5yojZJinz263my/zYahwGPKz6Va79Cr1jzoPaP0PukF/nikU5xOJ6MyURRF0/GhZXjq3Q+eEoW+I0lSBs92bxpiDIX73FcoJhuED1zG43FNOdM6LslPKBRiWR9k7PNrFMmU1WrNkL9sY81nPSU5I7m12WwwmUxZNzi59E82CLnLjlwN5NTzx+sZPe5FvWPy8lXoeMiuUn8eiUQQCoXgcrky5JPkjJx/iqJojpV0LDldSC7VY6JAC9BTR2phuNhfxdoNWrLFf8avM0ajEaNHj0Z7ezuqqqoQi8U0Ez/oeJIkZWRWatnzvN7hnTSkr8hO5+91oddIxxMoDr21HfVs4ULWsLq6OgCZ9lG2sarXN/qM5E49Jl5+siUZaekTtaNRvceQJKlgPUQ6Vs8JOBSCYeXowJQkCbW1tRm6TG+9pPWIdJO6UiIUCrGAbjans9ZxfT4fUqkUPB4PW0/5fQTJB3/OfAIJQ0FuhiOyOYXF2pYdZek0RjcFxTUbHcYGAD8B8AIwKoqyRpKkKxVFCUqSZFIUJQWgMHKmQQZ+0dWLjmk59NSOZT4bS+vYBoOBOYSAbiefwWBAdXU1jEYj+43D4cjgoyUlzGdJGAwGBAIBRCIR3WhxMSg2oi1QGHI5TLM5BrWMRd6QlGUZra2tCAaDaGxsZOVuFRUVaGxsZIt4V1cXUqmUbtaVFvgNfzFdcvnf8DI/HMHPZTgc7qF3euPwUhtkfMY5P+92ux21tbWIRCKwWCxsXHa7PSMDWe0MpPeNRiP7nNcb2Yw90V25f6AlP5Ikwel0QlEUmEz65on6tyQ/ZrMZHR0dsFqtGTIRj8dZFjIFC2j94uWZv/cA2O+Je19rzLRu8vrQaDRmbJbUMpWvjIn1rvTg1yiCw+FAfX09+3+23xL4e5PPOQla917PiWG1WjMqL0ifpVIphEIhpNNpttklzkaHw8E22W63mwXqIpFIxjmLya4bTkGOXM+e3n3Uu1bSE/zv29vbkUqlkEwmYbVaWXWVOtuyoqICDQ0N7DO6/1R9Q8clvcOXTPPnHC5O/6EIuo+87ULv5XJE0xqklhuj0ciCCbR/A5Dh5JMkiekdNahCg/iRqTKQRyHVEXSdakej1jwUimz6zmAwlI2jNRf0rr8cHZi5HPz8tfD3x2azsfUKANv/qekr+DWP3uedz1SZmEgkkEqlYLVaM4Jw/F5RXdGolxWtXheGa7NjgeGJsnQaK4rypSRJqzf+XwawXJKkEDZxMFcBCG50GA95qKkn+Fc1SKFpbU61HM56nZ4rKiqY4RmPxzM28OpMPSrHVTuB+GixluOmGAyFMqJyAO+gLQV4ueO7LWuVSkajUQSDQcRimcUBaicl/YvFYpBlGW63uyAnXq7yI1mWM0rZ1b8ZLEZkf6BUBqk6cEXyR0aaehNuMBiY7kkkEjAajYzGorW1FQDQ0NAAAEwfBYNBAJkl2Wq9wRuM6g2X6K7cN+itM16vygbQXrMA9HAQk7yR84UCUrxuontPvyfHW6EwGAxsrVMHXfKVMbHelRZ6NogkSXC5XBkZcrnsFf7eFKIX87n35MQIhUIZeo6+H4/HEY1GEYlEYDQa4XQ6EYlEmO7jK0L4c5LjxuFwlKWToZyQ69nLp4w+G8j5S9QUZOdQhYXafubHsGrVKtjtdjQ0NPRwxFHSBm+fCwwdFOLg5J1hAODz+QD0pKvRWveA3LohGo2itbUV4XAYFRUVPfaKQG5qFC27oC/kdqjru8Hk+NaCXrU0ABYAJVuOZFqWZbS1taGuro5VzNL+Etjki6AEOC35pN+oKxr1ZFDYZALDGWXnNJYkSVK60UJ/A7AAGAnALEnSKQDOlSRpLwChwdz8Tu3g1QOvTHMtpmqFxm8WgMwsYTI2KQIdDAbhcDhYpl82B7V6Adb6LkULKfpcCmU71Bf+/kKpjTL1vVVzUPF8o1arFY2Njey7WrDb7RmZX+Tc1YNehqDT6dSlSaHme/ymrxR8p0MR2bIF8p0DWZbR0tKCUCiEhoaGHrQoeptwPguZvmMwGFBTU9Oj1NZqtWpyhWbTG2o9rEXLITI9ewdZllmJIJBd/6uzIvX4rrWgVW7LZxrzeo93rGRbxwoJrKkzVih7WV0pwcu0Ho8pINa7UqMQGyTXd4u9N3prjDprSpZlKIoCr9fL1k2C1WqF3W6Hw+GAzWZDKBTKKCGn9Zb0sp7jRmx69ZHr/mo5drXAB0r5Khqr1QqTyZRxf8leAaBbqUDnJgdRoX0HClmvh4NtMxTBr5nkDKuvr2c2tVoutOTFarUiEon06BOg/l1NTQ1sNptuJSCtufyekx8nbxeoEwdKKYMiy778oLaX6P7wdhzJDa1/4XAYy5cvB9CtI8ku5DOE+YCpwWBgupSvsubB7ze1KsN4CJus/7DDDjsM+WMNNpSF01iSpM0BeAAsU3MTb3QKxyVJ+hHARQCmAzhJUZRgvw+0xMh3A1OIg09v80uKlc+64jMUtLJZsp1XvQCr/45Goyyq7Xa7YTKZSqJsxcJfnlDLnRYHFQUw3G53Th5lg8GQ8Z1c9zwcDqO1tZU1naLzZMu4UfP/ZYsu60FkpeY/ByQTRB+hBr8JD4fDGfqJKhX475hMpoxsYfq/lmxl0xvq8eei5RAoHHxWXS4nrFbgkwIFuShq1M8v/d9kMvXIKNfaKOv9Pl+oM1bUVAF8JlU+DUXFeldaqG2Q3vBcZqtWyIZsGUyUIUWZw2SzAcjIkqJMeqPRiFgslvG7UCiEYDCYQVFAG3I+01ggO3I9e7wzIptO0lsfqW9De3t7RiUVb6/orUMjRozoUaWjHhutmfmOhwfPA5rtewLlCb5HBDnD+NJ+ctaRg01r3SOZ4/sEaNE6mEymjFc9aOk9tV2glk1hXw9+ZFtjtTJ8gUxZoeCHVpaw0+nM6B1B+00tOdXTh/S50+lk48tm9wubrP9w++23D/ljDTYMuNNYkqSDAcwH4AfQLEnS9YqiLKOMY+6rFQCOAXCQoig/DMRYSw3e0aaObKVSKfh8PjgcDsb5mg/4ZlNkFPCbBcq64jNOKFpMWcGliO6qI3f82ARKh3LhvKR7qyhKRmYdb6jmarqRDYVkxwDo4VzSyujjN31WqzVrxp/eeHpzTUMB2fimU6kU/H4/qqur2aaAqCS09IDB0M2f7vf72W/VjRrz3ajz48uVKawVYFNn6AknS++grpbJBq3AZygUQiKRYE3ltPRBsbzBhSKbztVa97Jl/WWTPZHdV3oQnzndt2xO+2LtFS350LuvlF1MDaacTqdm5jBRFhCouoKyialZMWUnkz5W/07QFfQe6soU3t5WZxPz66M6y42nyqH7pM6q47/L9xHh72EhnMv8ebX0EV0bVR/mE+QT6D1KbcfnWm95Z6w6k5hkmeRWr9KP6N2I87/QNYzkmeiftCr9Cq38Eyg/ZFtjtewlIJM6UC0DFRUVGD9+PHuPZIzWP3W1TjY7Ecjk7+bHBaDgfaFA/2Ls2LFYtWqV5mdNTU39PJrhgQF9CiRJ2g3ALQBOVhRlHwAdAC4AWIYxjxcA7DdUHMbApg2MwWBgizhl4fn9fqxcuRIrV67UzczLBvXxyLgl/jRgk2ONONAikQii0Sj7LXHKkqFaCChTtBCHt0DhoAWZ76pdDHhDsbegzDqSKwLJoHrTHA6Hc2ZqqeVZDb5ckz83bfypAZb69zSmeDye9fh644nFYj2uaThB714D3Tpsw4YN8Pv9ALTvvxoU0FLzXecLtRzTfcr2fJCM0Lh4WaMxC2qK3iGfe89/l7KOyIlSW1sLi8XCqgK09IH6Pa3v8PKhp1Ny6cJsOldr3ct27erPcuk5gdLC4XCwJoelgpZ8hMNhbNiwIYNfFOi+352dnVi1ahWam5sBgGUO09pCFRVaa2ksFkMoFGLZgpSdLEmS5u8Eeo9suke9bmVbf3ibnPYBtP5RJjn/XQCMP7bYceYC7+DxeDwlbWItoI9S2fH5wm63M0ev2jam7E+qFNWjpohEImhubs6o0qPf5yN3WrYjVUHks3YKDA5kW2P1/AS8TKplgP+N1voXDAaZ7AWDQSxfvhzBYJA5lCmLWM+OpHMUsy8UKC1mz56N2bNn636+atUqKIqi+W/lypUFHauU4xrKGPBMYwA3KYqydOP/rwbwkCRJVkVR4gAgSdJUAE5FUZ4YsBHmgd5QK1PGCACWcVJdXQ1ZlhlnHWVw8lGxbAsp/cZms2VsfhVFQVtbG1KpFDweDyRJYmMnxwiNhc9Gpiie3jmzfcYfWyvy19u5y3XOoYxcJbR688M3fwIyMw9ybaCzzbksyz1kmf9MLSPhcBgtLS2oq6tjTl+++y2Nkc+0UY+dNll0fLfbzc5N5+ej1fQePx71mKkjvR60jsejr2RPb+6zna9UtO98Nky2eSV4vV4A3ZtPysIiY1CWZc2MN7PZDLfbDYvFknEfgU33S0tW9T6jBhhaXb356+LBy0I2vdabeS1H3VRqXZrteNmeLzXPIWVf1dTUsMwmg8HA6Ep4WaQ1T+s1nU4zhxy973K5YLVakU6n2Xho40HvEY0NjU2dWZ9LHxYiPyKzahNyyU+27MpC1jCeqiKf8+b6jOSQl01aU0KhUEY2IclWOp3OWMNIvni5VCOdTvew8+hvq9Va9POcS6+Vm+4q5jp7o7u17Bt6Tz3vPAVWLBbrIZcUILVYLBkJHWrbid8Mh0KhHplzWjJCx1CvzXyfEwrQ0e95/cM3hFSj3GRgsIOvrtGTTT2dB+S257OtNWazGS6Xi8mLxWJBRUUFG5NWM2sCcfbzMqknd3rnV3+vWLuBxjOcUazu7u0enN+z8feHZFbL1uf3Z2qQ/W+xWNjeQZ1sBGzSt2TD0d8kg5FIhP0zmUyMsoX2FgQ9W5/e1+uLw19LsRjuMpsNa9euHfLHGmwY6PDd5wBeAQBJkowArACaALg3vjcKwJYA/jtQAywVJEnK+s9oNDLF2traCkmS0NDQgMrKShiNRkiSxLqCRqPRrIqG3zhRtiX9C4fDCAaDzFik9ysqKlBfX88a4RGfGjXm4A0WdSZWOp1GW1sbkskkIpEI+y79UxQlZ3ZXMaDjlsopNhhBG0z1fc61EKm/y2ceqH9LRiEtnKFQqIeRR/eBl2WSW/pHjp5IJIJ0Os3KIOkcJDvkSA6Hwxm/lSQJoVAIkUikx3Fp/CS3/LnVBo36mIqisGwK/nfZQJs3tazn89tiwZenDgT4bBh+DikLgO4tLwdOpxOJRIJlYVFGOAUBeD1Czn+73Q6z2cwyfOmYdL/oXlOjFv4zciharVZIkoREItEjc1mWZQSDQZZ9wMsJXRsvQwLFIdeapwbJQzgcziiNVq+RVP7N6yJeFmmdkySJ8dSRnFGwwmq1aj7ziqIglUoxGens7MyQHXV2FK1penpASy9n+4x/ngqZu6GMQtZ5PrNTb+7Ueko9r3rfVX+mtm/47/BctMpGugi3283khb/fNTU1aGxsxNixY2EwGJh8kZzT+dXjpu8QNzZlXDmdTibTWteqXq+Hu3wVCl7P0Nzyuomfc6PRiNraWng8Hthsth73hIKoiURC1w7hbTSj0cgc0HQcAOy4oVCI8dAC0NwD0PoJgNnipO/4Umyhf/oPkiQx/vtibLxs9nw6nYbP50M6nc74P33XZDJl6AyeL93hcMBkMiGRSLDfhcNhpNNpAGCZ6Py+lJ4FLRniZTbXWqfWXWTHl2J+hDznBm+f56q+ymdfn0ql0NLSwpIBtO4DJQwA3TQU/N5BSx7U+pJklv6ura3FuHHjWBIeUSaSrlbblOq10mg0sjW5WJ+F8FMIDCUMaKaxoihpAF0b/5QABAC0K4rikyRpNoAdAVyjDIGmd/mira0N69evBwDU1dWx93muOq1sBd4pRs5lQJ87lByN/Hs8Jy1/PGrMQZFhcuo5nU7U1dXB7/ejpaWFGap1dXUZPKR81LuUmVSFdEIfrsiXX4zuvxb4Bj0A2OaUvh8KheDz+VBbW8uyhfVAskAOF6fTicbGRqTT6YxzkJHClyVRxgQ5dihzh3e2ZDsnkCkn5Ewk55P682zgn1Pi5OprpNNpVgpYKvDZw7mM51xZ7eoMJppXcpiQDNK5wuFwRgCCjFL+XmplFdPnfLMK+owv7+X5GK1WK8vyi0QirPFnfX295rkKnWPBRdt78M17PB5P1rmMRCIsG9lisWDkyJFIJBKM1kLdUIwCpbIsw+VyIR6P91gjZVlGW1sb26TYbLYMrkag5/qVLWtPC0JOCkch63w+HOSFPOfZuD/572jZW/y6WVFRgdraWqZrgUxuXLKXaFOeSqVYRjxPR6B1nnA4jFWrVsFms2U0E6Kx8ZmC6mqMXOu1QCb4e6a2a6nHATmCgczGqmRb81UUJK/ETa2WS9IXxGtNWctWq5XJXTqdhtFoZMfmOT31SsIrKipYFjuNXashlUD/oVT7Gb6XhMFggM/nY9Q3ANj/6+rqWLKG2s6mV3K8kczy8gVs0h88F3s2W7JY3Sua4vUv1HoKQNa5z7av55PFSMfo7Zn8fj+Tz/r6+owkEL7ZOclDrn2byWTKkF+Px9PD7qLx6e0De+uzEH4KgaGEcqCnAAAoipICEJIkaY0kSTcA2A/A74aTwxgAampqMl4JvMKkrD6CejG12+1s06FuzEHO4nw3VXoKk0o+KioqUF1dDaC7pKStrQ2hUCjDCFE3ZSAjlW8AUky0N5cTS6B3jjACNZGjEiFFUTI2voVwIWs1fzEYuhs/8oEJt9uNRCKBtrY21NTUZESCqVOuVlMYHmQMqBt1EGKxGAKBQEY5sNbvtRw8es9pX8JoNLIM3VI5ngoxaPhAQbZyR8ooqK2tzXAW86AgGB3P5/OxpihaTjrKKtYrpVV/n15pgxwKhZgTBui+b2o9yBuoelBTBBEG+4ZGXU0yEMjVvIeHw+Fgm4H29naEw2FUVVWxz/hX0l+VlZUs85wvzW1tbYXH40EymUQqlYLT6WQONi0uWVq/+CYtPJVTtvs/2OVkIJDvOq/3bKqhpjrKl4ZI796R3lA3OlOPW60/+U05OV8og52CWrW1tXC5XD10HG1wJUlimfEOh6PHGma3dzeRJC5wsg/5cQnkD3691JMNrXWJd0zwVRS8Q1nrd3Rcyuw0mUzM8Q+A/TYYDLLABNHw8DRzWvYCH5y1Wq2MkoCn3SmHJsvDBeoGsED+Oo0H9ZIAuh1vtD8jujD+/+R4owoFciJT0IHsJZ4GjP8cAEsCoWpEQH9tK0T30D62q6sLNptNUDf1I6LRqGYjzGLmnnSYw+FAY2Mjk0e+Ipoyh202G+rq6ph8anG8U0IR2elazwvZbeT8TiQSsFgsmuPnkxVo/6HWmb3RgcJPITCUUDZOY6n7iTQD2GPj6wxFUX4e2FH1P4xGI4vC5VvOwDtWqLwsEokwIvCGhgb2XSqD4vnQ1AYlKc1UKpWR7cmXx9fU1LAyIyrBA4BEIpGxQQF6ZrGqs02B4jbQ/CZMQBuFGlq8o5U2Hn6/n2X2Op3ODO5h2vi63e68spZ4WSCeUPX7QLd8Efd2NBplGYOJRAKJRAJer5c1kNEDyRnRVqjB8w3SZl3r90BP+eSf0/4CjZHPsC1W/mlDSBvLUhg0dA/5jHE9RKNRpltkWWbcwx6PJ0MfaWUVa+kV0pV6Ged8kCMUCsHlcmXIMdAzS1lv3Frc34N9Q0N0MX2tT/nMOT6YCWSvdlBvng0GA2uuQnLCrye8LqJqgmQyyUpp6Vytra0ss6WmpiZjPdJzJqozXoDMgGg2DHY5GQjku87n45CnEmdgU8PUbMfmP9fiR5dlOWN95I+ldhLzDjgKfqo35UC3bFCVGZXeEnjaC7/fzwK4TqeTNRNS05NVV1fD7/ezcVOQVKAn9Byl6vWSd/jS30C3jGg5aPOpoqDjkVOED0oRLYDdbkc8HkcqlUJHRwdqa2tZeTUdMxwOIxaLsUqsfJ4LcsqQUxoQGXL9Ddqb6QWjqa+MlnxR1ZzdbmdOOXo1mUwZVav8/2mvR1nwkUgEnZ2dCAaDrKqVHMvUfIwoAHhKlJaWFtjtdng8nqy2ZC59q/5uLBZDc3MzDAYDo08UAYz80Jugj1YAv9BguFbSTmVlZcZv+MoZSkQjGVOPA+iWXaKfUlc00zH5dZ2c3xaLRZOTGwDLkLfb7czup/0GBZW17NV8IfwUxWPatGlD/liDDWXjNFa6d+4JSZL+AuDLoeAwLkWknkqNKisrc2ZDEm1EfX09ywpVZ7/wv6PycNq8kJFAPD4tLS1QFAVutxu1tbUZGySXy8WcOfzGvba2lo1Hb5x8po/BYIDZbEZLS0tGRqlAaaA20nJlLlA5ayKRYFxm5BRWG4O08bVYLKiqqoLP50NVVRWSySQzNvLNlCCDVZZldHZ24ptvvsGkSZOYI4cyqWhD1NbWluEM1gt+0CvJO7DJIaQnr7xjiz+O1hwORIl5KRxPpdoQas1FRUUFGhsbs2ZfkqEWDAaZsUjjUhQFjY2NGSXbdP9TqRQ6OzszskCJjzESiaC2tjbD4cuPz+l0IhaLwe/3s6xUHvnMK19OzGc+a22GBlOmFm0E+wrqEsB86RwIes56k8mE0aNHw+/3s4xiygyh552/Z1QZYbVakUqlWMliKpVCIpGA3++HJEnw+/0YNWqUbvBAPS7SRXwWTDweh8ViYbQZJCfqSpvBIB+DAfk8v6T3+KwiPah1G62Ffr8/g+qBgo7pdBpdXV3M+cPTJ/EUTtR0iirBeIcPyYjL5WJrIlERAJvWLqvVCrvdzrKciaeRKFZSqRTbrJKTkR93NmjpdP69crfReqN39dZFdYaxulIuW3CT1i+y37Wy5uj7FNRatWoVRo8ezbLjyGlMVV18Vidx2JLNQtnrZL+YzWZEo1HmRKTz8vdY69kRGXIDD7W9obd+qilRKKGB5IunIiGZTafTWLduHdxuNwKBAAuChsNhBAIBtpekhCA9qhOn08loyKqrq5k8W61WdHR09NgTFIJcFX0DbYuXM3pj4+s59/XmW60/eHoLj8fT41iyLLPqCb75Z2dnJzo7O9m6xgfSyH70+XwZep13TieTSUSjUVRVVSEcDsNsNsNkMjGqFq1EPEoq4JNF+Otob29HMpnEmDFjNPcWwo7rO9xwww1D/liDDWXjNObwuDJEGMN5pV1sZoff78f69euZQ46aDZDxznMq8vygRqOROWHT6TTjuCKDPxqNorW1lRkffr8fXV1d8Hg8qK2thd1uR319fUZpI3HJmkwmVFRUoK2tDa2trcz5xi8oADKaaqi59YBuXti6ujp0dHRk5YcdTM6XckeurBMqZ6VM81QqxRbESCQCi8WCjo4OVFdXZ5R/r1mzht3fdDoNl8uF+vp6XWcPDwpGLFmyBI899hgWLlzINsnbbbcdJk+ejN/85jc49NBDUVtbC4fDweSSsiP1uEV5/lw1Z596s57vHA10iXkh2Rp6KMWGUJZlxmXe0NCQ4eQNhUKoqqrSzL5UFAXt7e3o6OjA008/DZvNhmnTprENh1bGN2UPhEIhlqHc0NDA7v3q1asZF+3o0aOZ/ABgetHpdGLDhg0sK0aduaKV6aXWN7mcA7xBPZgytQwGQ5/qVnUJYL50DgS++oWyoEhOyOiXJAkul4s5jH/99VfY7XY0Njay9yijijiN16xZw+75+vXrIUkSk83W1laMHDkSZrMZZrMZ6XSaBc8aGho0yzaj0Sja29vR2dmJyspKWCwW5twhxw49H72ttBHIRD56kS8Bz+VkUOt5vjJFTfVADaaIE5E4Pmk9Upu0sViMNa8zm83se7IsM5mjsloKaMRiMdTU1GRkVxGlCn8t6gxmWtPVFWB64JMP6FyDiQe5N3a33rrIv0/H13p+yQFCVX9Wq5XJRUNDA+LxeIZMkZ6i0m2iUvL5fCzQCWyygTs7O/Hqq6/igAMOQENDA6NtIhufbCOya0wmE4LBIAKBAABgzJgxrKKG5+/UenZEhtzAQ21vaK2ftDZSJjJPuUPyRfaP0+lkFaShUAgrV65EfX09O0YkEmFNw6gajK/KCgaDaG5uZv1sqJFYY2Mjq0qkZ0OSJHR1dTFbip4VAKxBba69nVZFH29nDbQtXs7oi6CPXkaxeh/F01vwFdD8521tbUxWvV4vmpub2R7SZDJl3Hf1eSkAR8kiVOmzevXqDKqLeDzOnLp6CQB8YIbA7wV8Ph8CgUCPYAw5xU0mE5NtWh+Er0JgqKLsnMbl6jDOd1j8IshvUPR+T5tdreOEw2G43W60tbXBbrejo6ODKVlqfmGxWFhWiizLGR1za2pqYLVa0dLSgnXr1iGVSqG6uhqtra2orKxEOp3Gv/71LyiKAq/XizFjxsBisTADEwBcLhfa29vh9/vZhtxmsyGRSMBoNKK6uhoulwuxWAzJZBI+nw8Oh4MZOul0Gg6HA2azGZIkZWTX0MJCnZyrq6t7zBPNg3oTQ5/pKeZc92swKfRs15JtDrRki18geX4y9Xfsdjtr6kIgR4rZbEYqlcK6detw7rnnYtSoUTj33HMxcuRIOJ1O+Hw+lnHl8XggSRLMZjNisRhzpgGbyu3+8Y9/4IEHHsBHH30Em82G448/Hocddhi++eYbfPjhh3jmmWfw97//HWazGbvssgv23HNPnHHGGczwDAQCrDyXjGkq2yVH0/r162GxWOB2u2G1Wlk5Pm2c6FptNltGhnE27t5iMn3LRb3RhpAfj9rpqSVb/GaTnPEkI3QsaoypKEpGpjg568LhMFasWIFLL70UH3/8MTv2+PHjsddee+GAAw7A6NGj2XElSYLVaoWiKKisrITD4UAwGEQqlWKGZ2VlJctY3rBhA3MkkuOX7rnH48G4ceNQVVXFOI7V2XPUwIpkiC9N5mWXuEbputUBkqGUqaUnt/noH0VRkE6nWTakJElIp9Psferkrv4tn8VBG49gMAij0cjum6IojKeYHLsU7CIdlkwmEQgE0NHRAbfbzcZMjmKfz4dvv/0Wv/zyC1auXInly5dnbHDVsFgsOOecc/C73/0OI0aMYJsGCqqRky+RSLCu9OTcCQaD7PniM7C0rj0fx2YxmVaDae3TQzF6lNY60glax6CMPKDb8edyuZgMrl+/HnV1dUgmk0gmk0gkEuw3kiTB6/WyoCpl+qbTaXR2drImUq+++ireeust+Hw+BINBdHV1IRgMZmSfOp1OTJ8+HXvvvTd23XVXNDU1AQACgQCi0Si8Xi9LADAajUgmkwgGg0y2KKmAni0A8Hg8bOOcTCZhNpuzzhXJCM0RUXNYLBbd3xRrp5QavN4tVG9pOUr5bGBFURiFg8fjYVyZNM9kF7e2tjI5CQaDSCQSzPlGJdiRSASBQIA5M+he2mw2OJ1OlmVMmcIfffQRrrzySqxfvx533nknLr/8chxzzDEAwAIP6XSaNX1ds2YNq1Ck8uq2tja2rpF9xMs8Pz/Z9MlQsq/7AsXaebTf4Z1OlKxD/L7U10aSJGaTBINBptNobUyn02hpaWFylEqlUFFRwfpHeDwejB07FjabDWazGW+88QauvPJKdHV1YeTIkaipqUFDQwPGjx+PUaNGYeTIkRg7diz8fj9qamqYQ5D+RSIRJJNJJlt2ux0VFRWorKxkvOs+nw/ApueM1nQ+E1pRFGaLq52R5KDkm5PSK+8s18Jwk0ktXaaWS76qMh/7gd/78A2s1Y3QrVYrKisrYTabEQqFWG8a2nNQ4CoWi+HLL7/E/Pnz8d///hfbbrstdt99d8yYMQO77747O14sFmN2Fq1FbW1tWLp0Kdra2rDddtux58TtdqOyshJdXV1sjaTgGf1fPQdEXaauzJYkCaNHj2a9m/hr7uzshMvlQnV1NcveJ53ON57NtfZl0xXDTWbVOOqoowAAL7/88pA91mBD2TmNBzvU2WWkJLXK0HinnZqfiqJYBoMBJpMJPp8PyWQS6XSacRQHg0HmuKCIMG3K6Tyy3M1XReWMHR0d+OKLL/Daa6/hjTfeyHAQA0BlZSXGjx+P+vp6jBo1imWpUPYpKXoqRzrzzDMxY8YMFr2mbC7ixPV4PBllwvQdcgIB3cYyZY7RZr7Q5g8CPSFJUo/5452FZLABmVyJwWCQZZPTop1Op7FmzRq2KXn55Zfxpz/9CS6XC9999x1ef/11nHjiibj88ssxevRohMNhpFIpBAKBjNKfSCSCNWvWIBKJ4IMPPsDDDz+MX3/9FaNGjcKf//xnnHLKKayMcr/99sNFF12EaDSKf//73/jggw/w/vvv44YbbsBzzz2HF198EaNHj8aGDRswevRouFwuloVKsiPLMpYvX45ff/0VdXV1GZy5ZChRxgJt4CibWU/2qGlIb1BsWV1vn4dsmR16WRu8nopEIqw8rLa2lkXeSaai0SgqKysRDofh8XjY+eh6k8kkPv/8c1x88cVoaWnBww8/jKlTp+Kdd97BO++8g2eeeQaPPvooHA4H9tlnH2y22WYIBAIswzQajTJnPxmIZrOZ6b3KykqMGjUKdXV1OPLIIxGJRBjPY0dHB8sgcLlcMJvNGU2qCOru4fRMULZPNBrF2LFj4XK52PVRoAVAht4aTNkvuQJwubKC1DJN+icYDLJNhcFgYKXyVHZPxjpfDUPZ5bSehMNhRKNRdHR0YMyYMcypG4vF0N7eDgCMUofGwHeHB7qDGQsXLsQ333yDpUuX4ttvv2XOHqPRiPHjx2PSpEk46qijsNlmmwEAcw7G43GW9bls2TLceuutWLx4MebPn4/JkycjFoux9XjUqFEZQbnKykqmi+g6ibZCS4/oVU1oYShnWmXTdbQZyzerh68MoM2dFoUSb4PwDos1a9ZgzZo1LBBGeoDsI6B7/mtqahg1BVWG/eMf/8CiRYvw7rvvIpVKYYsttsD48eMZR6jdbkdVVRX7/1dffYW3334bb7/9NiRJYoHS8ePHY7PNNkNjYyMbY3NzMxobG2G329HW1gafz8fWPz4QajQaM/7mob5+h8PRowqD53vP5jgeCGjZOE6ns9cBWr4kmq9SokB0IBCA1+tlusnr9bJAZzKZZMGCaDQKo9GIRCKBf//73/j4448ZxdI+++yDCRMmsOZ1FGSitTYejyMWi2H+/Pl44YUXMHHiRNxyyy244447cPHFF+PLL7/EFVdcgfHjxyMWiyGdTiMej6O9vZ05hykbNBQKoa6ujgXUSHcQrZOWjhHl//0LcgADm7LkiUN/w4YNTI7GjBnD9L3VaoUkSXC73ejo6EBXVxfMZjM2bNjAqkfNZjMCgUBGoLa+vh4WiwU//fQTbrnlFrzwwguYNGkSZs6ciXXr1mH9+vX47rvv8Oyzz7LxGY1G7Lfffth///1x2GGHoaOjg3Gq2+12uN3uDFoU4k8mOii1k1eWZXYdwWCQ7ZNpf0ngG4XydpaavmC4y2mhzslC7Ydc1Qd0ftI5iUSC2XFerxeSJLFqrNbWVtx77714/vnnUV9fjwsuuACfffYZHnzwQdx3333wer2YNm0aZs2ahYkTJ2LlypX48ccf8cMPP2Dt2rX48ccfWUXhAQccgPnz52OLLbaAw+GAyWRCOp2G3W5HTU0NsyfNZnNGhQiwqVKNEu7IPq2urobJZGJyrFV16HQ6YTKZMmgtCqnqAUQldTb4/f4hf6zBBuE0LjH0ssui0Siam5sRjUYxbtw4uFyuDCJ4vnMtTwNhs9nQ1tbGPh81ahSL3MZiMZjNZsZhR5tTgizLaG1thc/ng8vlwoIFC/Doo4/is88+g8ViweGHH47f/e53qK6uxq+//ooVK1bgl19+werVq/HTTz/hn//8J3NckwOcHIkOhwNffPEFFi5ciKlTp+LMM8/EXnvtxRpz8A5JUtDE50eUBzwfF+9Q5xsNOZ1OVv5UTGangDZ4g41vEkabF2roQp/F43G43W7E43FceeWVePXVV7Hvvvvi4YcfRjKZxA033IDHHnsML730Es4//3ycd955zKnX1dUFi8WCr776CgsWLMDHH3+ML774AoqiYPfdd8f8+fOx77776t5fu92OmTNnYubMmQCAzz77DCeccAL23HNPzJ8/H9tuuy3LpKLMH1qsaSPW1NSExsZGhEIh1kSBZAvoNmopY5EoEvrSAB0oZ082ygTeGOczigGw59PhcDB6GT74pS67pwzPeDyOlStXMp6xN998E1dddRU8Hg/eeecdTJ48GQBwxhln4IwzzmBBLXKavPfee+w+2e12Fhjzer0wGo0sw45oMoLBIBv3W2+9hQcffJAFCgKBADMaid+Tzzjkr5mCWLTxBjZtdHjw93Go66dcdBtqmSa5UG9W/H4/WltbWTNLcqQ2NzdDlmXm0KdKhWQyCavVip9//hldXV1ob29nDVXWrl2L1tZWJBIJrFu3DkB3eT5tYr///nt8+umn+Oyzz/D111+zgOauu+6Kiy66CFtvvTUmTZqEMWPGZA0EEU8oYdasWTj//PNx5JFH4v7778eRRx4JYNNG1uVyobOzk9EEUDYOALaR1+s3wPPp5eJR7U3Vw2BHIfQvWvQC9DutZzgajaKzsxPhcJhl3EWjUeZoIV1Dzv9kMskc/R6PBx988AFef/11LFy4EJFIBCNHjsQ555yDY489Fttss02GnRYKhTJkYc6cOVAUBd9++y0WLlyId999FzfffDMA7UAwwev1Yuedd8bBBx+M/fbbjzkRzWYzqqurWWADQIbjV/3c0mZYb/4KBR9YKxeHTq4x8YkbbrebBUfJqUC2QltbG9asWcOoshRFQVVVFcxmM3744Qd88MEHWLFiBdatW4eff/6Z6RFq5nn77bdjypQpOPjgg3HyySczvVVdXY1gMIgnnngCDz74IDo7O3HppZfiwgsvhMViwaxZs/C3v/0N8+fPx+LFi/G3v/0N06ZNQyKRYIEwh8OBqqoqVFRUwO/3I5FIoLOzkzlRLBYL61vBB/V4DOWg1EAgl4NI6zkjpyxVOVBglO4HH2AlKhLKzLRYLKivr2dNOj0eD1snI5EIFi5ciPPPPx+dnZ247LLL8H//938ZuiEej8NgMGDDhg1Ys2YN3n33XTzzzDNYtGgRrrnmGuyzzz445JBDMG3aNLjdbsYdq7aTiBuWgviRSIRVmlZWVmZQGgaDQcRiMUZ/odY5WlQqQk6LgxY1Q6GgICNvg/BJIhRUowoLu92On376Ceeddx6WL1+Ok046CX/+85+ZTRcIBPDPf/4TH330Ed555x28+eabGeerr6/HpEmTMHfuXGy99dZoa2vDVVddBQB46KGH4HQ6kUwmEYvFmE6jPU1nZydLMgC65ZtvvOd0OuH3+7FhwwYA3T4LPumNbC89nwRRdWr1ddLDYKKxExAQTuMSQy8Sx/OT8e/xTgmKMlOkjBwjTU1NLHOTSiIp86O9vZ1FZalEiJRVLBbDN998g0cffRQffPABAoEAJkyYgOuvvx6zZ8/OaC6w9dZbA+jO1qIFJJVKsXI5SZKQSCQyOodHIhE89dRTuO2223DKKadgwoQJ+O1vf4tJkyZhyy23hNfrRW1tLSu95BcmKsGjOaDuz+S4IaNDj3e2WNBips7uHm7g+crIeI1EIiwAQc12CBaLBZ9++ikuvfRSrFu3DvPnz8d5553H5vDuu+/Geeedh6uvvhrXX3897r//fpx66qmorq7G0qVL8fHHHzNjdcqUKbjyyitx6KGHYtttt2XjyRfTpk3D4sWLccIJJ+C8887DnDlzcOWVVzL6C+LQrqiogMVigdlsxsiRI1mGPD1DPCgTmsro+OyxvsBAOXuybf55Y5x3clGwh54ZKrElUJCnpqaGBbeAzOYtsizjwQcfxG233YapU6fi2Wef1eQvdzgcOPDAA3HggQf2+CwWi2XIJA8y5oDurPhnnnkGZ511Fg466CA8+OCD2HXXXdmYYrEYUqkUc3DT7yiLmuaCz26h/xNFDk89RK/ZNi5DIZsgl+NILdMUFK2oqGCBBlmWWSWBxWJBe3s7c5IA3cEbKrVNp9MIBAKoqamBwWDA+PHjYbfb4fV68a9//QuPPPII3nzzzZwZheQkvvTSS7HPPvtg6tSpPTZJPD1APjjuuOOw00474ZRTTsFxxx2H8847D9dffz3bLFitVphMJsbrTfB6vaxhkN76Q9meVCLOy5aaR7UU/OaDFYU4Mnm7TC2n6lfSV0C3g58c9W63m1V7kW6jzNtUKoXnnnsOX3zxBT7++GMWJDvhhBNwwgknYOrUqQVXlGy//fbYeuutce2112LDhg1YtGgRVq9ezb5DFGWEVatW4Z133sGiRYtgMpmw5557Ytttt8Xmm2+OnXfeGdtvvz2r8uJB100UMBaLBfF4nOlKXu8XA62S3WzoD12px83Jc6Xy3NB8oIbuOZXnUyKEy+VCc3MzQqEQrrjiCixcuBCSJGHs2LGYNGkSDjzwQGyzzTaYOHEittxySwSDQTz33HN44okncM011+DGG2/E4YcfjunTp8PtduOFF17Am2++iSlTpuDuu+9mNjrQ3QD04osvxowZM3DaaafhuOOOw+mnn47rrrsOiUSC6QWav1GjRsHhcMDr9bJrp74n1NxaKzNOz04ZCuvZQCCXg0iris1gMKC+vp7RjPC2DiEej7PKqWg0yhzGHo+H8QoTlZPX68UPP/yAq6++Gq+99hp22mkn3HvvvRnyxcNms2HcuHEYN24c9txzT1x99dX417/+haeeegqvvfYaFixYgPHjx2PWrFnYZ599sMcee6CioiKjijEajTK7m4Lvau51euaoHwqtg2p9oZVVzDs/1fy5AvogPZGNPlMrWK11DKKL83q9rGeEzWZj9JVkX//5z3/GQw89hNGjR2PBggXYe++9M47n8Xhw2GGHYfbs2ZBlmVWFjR8/HhMnTkRtbW2P8Xo8Hpx77rk49dRTWZYyyQKwKQM6FoshHo9jyZIleOWVV1BTU4NLL70UQLez2mq1Mlu0uroa0WiUVZkQ/SGwqYo825wScgUohxKNncDQh3Aa9xNo4ec7dqsNOz7jk5ylxNVKXLGU0UAOYrPZzJqBkaIGgP/+97+49dZb8fLLL8NoNOLII4/Eaaedhj333JNFoXOBL7vQgsPhwOmnn45TTz0Vr7zyCu644w5cc8017HOj0YiGhgaMGDEC9fX1aGhoQE1NDTbffHMcffTR7DqolEWd3UHGRiEbjlwYTA1dSgUtI4s2iLzzhLJIqUM3NcBbvHgxrrjiCnz00UcYP3483n33Xeyyyy49zjNhwgQ8+eST+O6773DFFVew7Ci3242ZM2div/32w6xZszSdhYVixIgReOedd3DBBRfgkUcewZdffokLL7wQhx9+OONwA7qDINFolDkDybGpboRnt9sZV5bBYMh4Tvui9G2gnD35Nrex2+3MOCN9ROA59Phj0dzRhpS47err63H//ffjtttuw5w5c3Drrbf2qYFkNBoxZ84cjBs3DieeeCJmzJiBs846C5dccgm73xR0o80UOYLJuWmz2RAKhWC3Z/Id884TcuqRLMmyzAJhagyFbIJcsqOWaZoHyu4MBoNsI+hwOFhGHP9bajZmMpkYDz9lmEuShMWLF+Ouu+7Cl19+ierqalx66aWYPHkyo2EihwlxdY4YMYI5iakKoVSYMGEC3nvvPVx++eW444478OGHH+K2227DlClTAIDpUKJPUVcv5HJ28/PJO/a0nAbDEcU26tLSW2rHIVWbELUN2VhAd2aww+FAXV0d1q1bhzvvvBPPP/88C8rPnTsX++67L2bMmMHkLZlM9upaGxsbMXfu3Iz3tOQ5nU7j3//+N958800sWrQI77//Pvvs0ksvxSmnnMKCGurrJwoPopDx+Xzs+L2xlfjGtfmU7PaHrtRyhvK0MAAybFB+POrfer1epFIpxst611134a233sJ1112HM844g/XhIF3Aj+Hcc8/FOeecgy+//BLPPPMMXnjhBTz33HMAuuf8pptuwrnnntuj0oEwefJk/Otf/8Kf//xnPPDAA1i0aBHOPPNMTJ8+HVarFWvXrmXViZRhTDZ3Op3OqCbTs3fIic7rrqGwng0EyK7IltmpRVvI7xXVezIKpNIxqeLO4/Gw5A+q5kmlUnj55Zfx5z//GfF4HDfddBNOPfVU3WC8FoxGI37zm99gzz33xL333otXXnkFTz31FO655x7cddddkCQJ22yzDbbaaitMmTIFe+21F3bYYQdYLBZEIhG0t7czijACOYwtFgtz1lEiAtmSNBdae0KyK4RclhZ6wWottLe3Y/369YhEIhg1ahSAbooSCnZ8+eWXOP/88/G///0PZ511Fi666CKWXawHg8GAyZMns4pEPZx66qmQJAnz5s3Ddttth3nz5uGkk05i8mY2m/H999/j9ddfx2uvvYZAIMDkZcOGDXj88cdZRQ7fhI/8NkC37dXR0VHQs8LPod7ciYajAoMJwmncj9DanFAWL0/PQEYDb1gQhyhl4VI5ODkxKGP3o48+wkMPPYT3338fbrcbF1xwAc4++2yMGDGiz66LnNLHH388AoEAvvvuOyxbtgzr16/HunXrsGHDBqxYsQKffPIJ41B+9913ceGFF2YsMLzhQMaAVsfyYjiP+fkGBldUr9isDnKK8tnb/CaAjFcyytRZDoqi4Nxzz8W9996L+vp63HHHHTjttNNyOjumTJmCt956C6tWrUIkEsGWW24Jg8HAOEdLBavVinvuuQeHHHIIzj//fJx22mn46KOPcNttt7GScIvFAq/Xm+HUI2OVuNMAsGxIXq6IN5ICGfS94QAqsacmlPQeUdMAmzaZFRUVGdx1VPLq9/vh8Xjw3nvvYf78+TjkkEPw0EMPFZzVWSx23313LF26FFdeeSXuvPNOLFiwADfddBMOPPBA9ixQuRx/fdRQj6gviMKFb85CmaDkhKbnye12i2yCjeAd7Gp6gFAoxLpR22y2Hs8YOZk6OzvhcDjw0EMP4f7778eKFSuw2Wab4Y477sBJJ53UYz7zafJVSthsNvz1r3/FQQcdhNNOOw2zZs3CgQceiIkTJ6KxsRETJ07EjjvuCI/Ho7n26wWh1BU2vO1AlRQim6pvoHbQExUYbzv89NNPuO666/Dmm2/CYDDgiCOOwNlnn41dd911QDMvjUYjpk+fjl122QU33ngjotEoli9fjltvvRU33HADfvnlF9x5552a2Xh03VQBIMsyTCYTe8bILqUASL4g2zXfkt2+1pXEva/mtLZarXC73cxhTtmOatomvsrGYDDA6/Wira0NVqsVdXV1+Oc//4kZM2bgoosuyms8kiRhp512wrRp03DHHXfgl19+wZo1azBt2jQ2B3pOY6BbX95999044YQT8H//938sc66urg7bbrstDjroIBxxxBGwWq2M3oD45MmZ4Xa7M7Ks9ehbSAcNx/WsFCB7nOgitMDT81VUVGQ0q5ZlmfVXIMcr31TMZrOx/RSwSYcZDAaYzWZcddVVeOKJJ7DHHnvggQcewIQJE9jesxg4nU6cdNJJOOmkkxAMBvHFF19g8eLF+Oyzz/DWW2/hxRdfBNCd6T537lzMmjWLOQppv0fge/JQQJ9fI8mWsNvt6OrqYrYBZYDy+zsRVM0P2TKJKRscAGvaS8lG9D3i//V6vfB6vYhEIjCZTIjH4yz7GAA+/PBDnHLKKXC73Vi0aBFmzJjBdEqpMHfuXOy555645pprcN111+HBBx/EOeecg2QyiRdeeAE//vgj7HY7jjjiCMyZMwd77bUXXn75Zfzud7/Dcccdh3/84x899qe0R+B7s1BVqtY8atlkw5lCrLeYMWPGkD/WYINwGheIUpVlkSFGDrjW1lZGxk5djSnTmEA8aERh4XK5GJfjG2+8gXvuuQdff/01RowYgRtvvBGnnXYa3G53r6+5EHg8Huyxxx7YY489ND8Ph8O49dZb8Ze//AXjxo3DaaedhnXr1mH06NGwWq3MUUzQ4ghScxDms4FWG76DqaQuW/Q8W+kLdRl2Op1wu92QZZlF6CnqTyX65EQjo3TZsmUsKnzuuefiL3/5SwZPcD6gru99jf322w/ffvstrr32Wtx+++344Ycf8PTTT7OO4h6PJ6MhDIFvhAdkBnXouVSXqQ43UPYtZRurueqAzAxjel5bW1uRSqXw4Ycf4uyzz8a2226LJ554IiNrtz9QXV2Ne++9F7/73e9w1lln4cQTT8RBBx2Ea6+9FjvssEMPTmOfz8eCG5RlGAqFWLkn0QSR4czznfGvagyXbAK9gJ7agawoCtPDwWCQ6Sk+IOF2u7FmzRocdthhaGlpwS677MKc/v3pGM4HBx98ML755htcdNFF+Oyzz/Dqq69mPCsNDQ3YYostsNtuu+Goo45CQ0MDZFmG0WjU3LBFIhG0tLSgtraW6R4+00pkU/Ud+HWAOrabTCYkk0mEQiE8/vjjjIPxT3/6E04//XTU19eXNCBaKtjtdmyzzTZ49NFHMXr0aNx4440IBoO48cYb0dTU1EO2yO6sqqpCR0dHBo0KX2FSaOaxnv7j6cJIX/S1riR7ird5iOKNrwKgwJYebRM59aj0OhKJYNmyZVi9ejWuu+66osZmMBiwxRZbYIstttD83O/349tvv8Uuu+zSw2E7ffp0/Pvf/8batWvx3nvv4d1338X777+P9957DxdccAEmTpyI4447DocccgirIiM9C6AHr7feKzB81rNSo1BnOz1zfDPaNWvWMA5qsrENBgNrWE6B8FAohLa2NqTTaZaFuWjRIlx00UW47rrrSh5wdLlcmDFjBmbMmMGeLeop8Prrr+PPf/4zbrrpJhx++OE488wz4Xa7GUUiUeXRNQNg/XPUvMZkS/DO7mL2hALZM4n5Ck2quuAbylZUVMDn82HVqlWQZRk1NTWw2WyskiUcDsNsNmPhwoWYM2cOmpqa8NZbb/VpAtvmm2+OJ554At999x0uueQSXHnllQC6E0geeughHHzwwaiqqmLfP+aYY+BwOHDiiSdi//33x6uvvoqxY8dqyo0WjZVeoI0H6UoKhAi5zB90/4bysQYbys/KLXMUU/4iy3IPJyVxoVksFhiNRkYLkEql2IY6kUggEAgwJcXzVsmyjG+++QYPPfQQFi1ahPb2dmy55Za4//77ceyxx7JNtVZWX0tLi26Zbnt7O1u8yVChsYfDYcb3o/W7bKUmpKglScIFF1yApUuX4uabb8aWW26JHXbYAT6fj2VcR6NRVm6kznZRFIU5lWlhamlpYZxfBHU2LK/w8+mqXU5O5WyGJr9YkTOGjCmLxQKXy8Wco5QlQ6WUtGkwGo2s6ZIsy7jnnntw2223oba2FgsWLMBee+2FdDrNzkNBCy2Q80cLgUBA97Pm5mZ2TJ/Ph2+++QbTpk1jzxhfyqZGMplEdXU1rr76akydOhUnnngizjjjDNx8880ZvIP8Yk+v6vcI1IXcZrOhsrKSbWrz7chejPxsHEO/WxPZ+Mxow0xc13RdoVAog4/dZDKx0knSX6tXr8YZZ5wBp9OJ5557DkajkfHc6pVnNjc3637W1tamq2M6Ozt1dRMAbLPNNnj33XfxwAMP4KabbsLUqVNx5JFH4v/+7/8wevRoeDwedHR0sCAKAMYpT5k8QLfO7+zsZNkG1C1ckqQMHUXUFcUgl4yVk25Sj5XXR1qZtOoqGporfmNIDUusViuuvPJKxONxvP3229htt92Yk1mvRJAvt1aDaAW0sH79+qwlw9loLaLRKKqrq2E2m3H77bcDAGsA+cMPP2DdunX45Zdf8PPPP+Pmm2/GzTffjIMOOgizZ8/GtGnTYDB0N1xMJpNsfMlkEpIksQ20LMvsM4vFwuhz1PNPWZTlxjWqJ9OKomQdp95n+ephrd9ly/JOJpOsgoCa3FG1088//4wLL7wQH3zwAQ455BDcddddTOdks6mAnusLj2x6rb29PWOTy6Orqwter1fzs1Ao1ON355xzDqqqqnDZZZehpaUFDz/8MOrq6hgFTDgcxtq1a1njLd6hC3TbWxQ4JCoGHtREVx0wUmf28hiIAAivZ/igeiqVYiXxbrebNYirqalh12Oz2VBdXQ1Jkhg3q81mQ01NDWRZxtNPPw2Xy4W99967RyZdZ2enrv3T0tKiGXBUFAXLly/HokWL8Mknn+Cbb76BLMsYP348brzxRowbNw6yLGPs2LHsNxUVFTj00ENx6KGHIhaLYe3atXj//ffx2muv4ZprrkEkEsFxxx0Ht9sNr9ebcV6LxQJFURjtHV9Zw9s/uXRLqZ/3oYJcznayqdSNxSiIYbPZ4PV64fF44HQ62fsAGJcsT5WSTqexYsUKXHLJJfj+++/x17/+FSeffHKGbLa2tuoGu1taWnRt73g8joaGBs3Purq6UFlZifr6ehx55JE48sgj8fPPP+PRRx/FCy+8gOeffx577bUX5syZg2OPPZZVPBL/MgWQab9HTbppP0hcufQZn2GspVMGk03Vn+CrS2ito7my2WyMmsFkMqGlpQUej4fNO+2f3G43rFYrWltbsW7dOowcORKKoqC9vR3vvvsuzjvvPEyYMAHPPfccLBYL2traAHTb+nrPQldXV1Z7PhvdUyKRwIQJE/DSSy/hq6++QlVVFTbbbDMA3euiWi/vscceePrpp3HSSSdh//33xyuvvKIZtLNYLBk2vlYDXd4m49c9SZJEsF9gSEA4jfOEmndYvSnLtihpdbymrEcyooi3mLKYI5EIOjo6MngyQ6EQWltb8cYbb+D555/Hjz/+CKvVikMOOQSzZ8/GzJkzmVGebRE0m80ZmVqdnZ1YsWIFVqxYgW+//RYbNmzAypUr8euvv8JqtWKnnXbCTjvthC233BL77LOPrnFLr8FgEIlEIqPRHp9haDQa/5+9645r8vrez0smCXsjCCrurRWl2rqtC/fee486cdW9tda6rXvViXvvvUddVXChgKwAAUJCEpK8vz/43ts3IQkBd399Pp9+LEnefd9zzz3nOc/B+vXrUbduXYwZMwZHjhxBQEAALbmSSCRGTf2s3TvT+2zpb1Nn7Vtq5GHN0eROViTIQOQEfHx8jLYjjU+ysrLA4/Go1hSQkwx5+/YtwsLCcOvWLbRt2xZLliyBi4tLrmMS1qU58Hg8i/eTYRiLTFODwYDo6Ghs2rQJBw8ehFarhVQqRZs2bdC9e3dUqlTJ/M0BjIKZzZo1w7JlyzB8+HAsXrwY8+bNg4eHB7Raba5z476XpmVaJCsMfFzta2tj7X/NAD8rFddakMnOzo46O4RtzXXCiYNE2LjkfSUlk/369YNMJsPZs2cREBBA92tvb28xQCcQCKyOEWvfWQvSMgwDoVCIESNGoEuXLlixYgU2bNiAAwcOIDQ0FGPGjIGXlxfS09Ph4+NDtXFFIhGkUimePHmCx48fo0SJEqhYsSIA0KTLx8a3Zpu450iqRUhQV6lUGjV6Nfdeke7V5HoJa2/16tW4ffs2Nm/ebFS5Yk1r39p3AoHA4nekiscSrOnYkQAvF2KxGKVLl0ZgYKBRQPDdu3fYtGkTtmzZgqNHj6Js2bIYNGgQ2rRpQ204CTCSgCCphiDHMK2YIPjapXTMjeu8msRYQn5lqcj+80r6E8aeTCaj45hhGBw/fhwjRoyARqPBqlWr0Lt3b6Nz4PF4VqsorLGQieaopeu0dK3WbJ6lpFXPnj3h5+eHgQMHom3btti7dy/Kly9P5c7Iubi5ueUKAHPnAnOa/KSyCTC+t9x7bjqXWvKnPyVIgo/baJJU8JHPPD096bvE1WEmvjoAOq54PB6cnJyQmJiIkydPom3btmYDbaZ+NxfcpIJer8ft27dx8eJFXLhwATExMQCAUqVKoU+fPihcuDCWLVuGHj16YPLkyfjpp58sjgMej4dKlSqhUqVKGDFiBLp3745FixbBz88PrVq1gkgkos+ZK58jl8tpRQ3Riyf3zdyxPkXfh28Z1sZyXmtFblUOAJrAIXODnZ0dJTKQBBeRHSF2hiR+IiIiMHjwYKSmpmLnzp1o1KhRrmPa2dlZ9OdJdeLp06dx6tQpCAQCdO3alRJJrMkrmaJEiRKYP38+wsLCcPDgQaxZswb9+/fH/PnzsXLlStStWxf29vZU8oD07+GShohPqtVqYTAYoNVqIRAIjGzUl7Ap3wLM3QuyDiSJItPviB1LTExEQkICAFApOiCHgcwwDJXuAUCrWI8cOYKwsDBUqlQJO3fuzLWWJMF/c8jMzLQ6n1pLwpJrZRiG9pcgIDItpmjQoAH27duHbt26oXnz5ggPD0fFihWNKk9Mk59cGSuydrTka3Cr6P4bl7aDNEQ/efLkv3Zf3xq+2aAxwzAMW1C6yQfAdEIvCLgBKqJtRYwcVzPM1dUVqampyM7OxvPnz7F48WKcO3cOBoMB1atXx4oVK9CuXTuzgT1bkJqaii5duuDp06dG1+fv74+iRYuiWrVqUKlUuH//Ps6fPw8gx9BXqFAB1apVQ0BAAJKSkhAfH4/o6GgkJycjISGBsqO6dOmCiRMnmmXRODo6Yvfu3ahbty6GDh2Kixcv0vtKdNRIBt0ay1QqlcLX1zffwZt/S8MEMmGTycre3p5mh60x5wiTG8gJ7GzYsAEzZ84En8/Htm3b0KJFi89y/gDw4MEDLF68GJcuXYJAIEDbtm3RoEEDmhzZsWMHateujX79+qFBgwZ5Shz06tULSUlJmDVrFsRiMZYuXUoCsjTZQJiyZMwRJi3RzzbV7/0c+N+x9Hn97mPCluw319khThRXWiA6OpreO8LeGjp0KO7cuYOdO3eiSpUqH3yeGo0GUVFRePToEd6+fYt3794hPT0dLi4ucHZ2hkAgQOHCheHq6goXFxf4+vrC19fX7L48PDwwc+ZMjBgxggaPjx07hpYtW6JHjx5wcnLCvXv38Pz5c9y+fRsPHjxAeno6vQfh4eG00zgJkn7MRfO3bJsIk5w4xunp6UZSHkSHkVSJkN9xr1MikeDBgwf47bff0LZtW3Ts2PGzXsPp06exf/9+hISEIDQ01GiR9KEIDAzEzJkzMXHiROzduxcbN27Ezz//jGnTpqFTp07o06cP/P39aXAgNTUV/v7+RvIBJIhljrVJ3sOvUWvU3Lj+1OybvJqYWTtX8gymTp2Kbdu2oXLlytiyZYtF6YBPAb1ej4iICNy/fx8PHjyAWq1G0aJFUaRIEbi7u6Nq1aqU+WorGjRogHPnzqF169Zo0qQJ1q9fj++++44Ggl1cXMDj8XKVeXMDxoRxzB2DXLYf1x5yG2Ka4mP40wWFaeCb60syDAN3d3ekpKSY9aOInSPbAcCpU6egVCrRuXPnAp2PRqPBwYMHsWnTJrx9+xZCoRDff/89+vfvj2rVqhmxOkNCQjB58mRMmzYNd+/exapVq/J85/l8PrZs2YJ27dph9OjR0Gg0aNy4MWUUkueQlZUFrVYLnU4HT09PaLVaSgYBzCfRC/Ie/xdotg2m/W+4ASuGYeDh4UH9WZVKRWVWjh07ht69e0MqleLYsWM02W0LlEolzpw5g127duHOnTvQ6XQICgqCUqnE8OHDERgYiI4dO2LQoEH5ttuurq4YO3YsRo4cicOHD2P27Nlo3rw5wsLCMGnSJGi1WiO5KsJ659psUzvO1YF2cnL6f9Pk/HOBsH7Jv9wEMHl/+Xw+XFxckJKSgj179mDWrFmoUaMGduzY8dGeh16vR2xsLF6+fImoqChERUUhMTERLVu2RNOmTT8oGBscHIzTp08jNDQUrVq1wt69e40avpvaODJ/cBvIcisSTcfol5zrvlWQdfu/eV/fGr65oDHDMA4sy2Z+iYBxQWHa1IbooonFYroA9Pb2NjJ4pERXIBDg8ePH6N69O6RSKcLCwtCtWzcEBAR8kK6jWq1G37598fLlS0ycOBGlSpVCsWLFIBKJ4Onpmev3qampuHHjBl69eoV79+5h+/btUKvVtNOou7s7ypQpg7p168LHxwdxcXHYvn07zp49izlz5qB79+659lmiRAls3LgRHTp0wIABA2iDFqIVB+S9uDPHeCGw5pT+mxp5mJbJcJ1K03uQlZWF9PR02sBFLpdj1KhR2Lt3L+rVq4cNGzagcOHCH9QcIy8QtuHNmzexZs0a3Lx5E46Ojhg8eDB69OhBGep16tShwZXdu3ejV69eKFy4MPr06YN+/fpZLQceN24ckpKSsHbtWhQuXBhhYWGUmSGTyZCRkUHZYSTQSRzUtLQ0qFQqeHl5wd7ePleZ5qda4Pxvv9ZT6B8Z1hb05haK5kr8xWIxLW9LS0vD3LlzER4ejlmzZqFVq1YFPrfdu3fj2LFjePv2LeLi4owYOi4uLnB1dUV6ejrS09Oh1+eOtXfu3BlhYWEWS79J8HjIkCHYsGED1qxZg8OHD9Pj8Hg8lCtXDh07dkRwcDBKlCiBbt26YfDgwdQR1mq18Pb2/qjBr6/NNhV0cU8CxYT56ujoSN+f9PR0JCcn027VXDYyj8fD8OHD4ebmht9///2zsTJUKhXmzp2L3bt3w83NDefPn8f8+fNRq1YtdOjQAc2aNftoTr+9vT26deuGAQMG4Pbt21i9ejU2b96MdevWoW7duhgwYABq164NpVIJtVpt1DyISKiYjjPTqpOvDebG9aduEmNu4ZYX08fe3h4ODg548OABpk2bhpcvX2Ls2LGYOHHiJ0/iqFQqPHz4ELdv38a9e/dw9+5dOhcTv+jcuXNGDaScnJxQpEgRBAUFoUyZMqhWrRqCgoKsHue7777D5cuX0bJlS3Tr1g39+/dH9+7d4erqShe83MQ9dxEMgH7OZepyYWoPv4U+EqY9DdRqNW2KR/RVyTVwx5Ver4dMJsOOHTtQpEgRfP/99/k6rkKhwMaNG7F7924kJyejfPny+O2331C/fn36rpB5mMDT0xNr1qzBunXrsGnTJrx69QqbNm1CiRIlrB5LLBZjz549CA0NxS+//AI3NzcEBwcb/YZUNarValqVRmyKpTmJy7ojDcy4shbm8F+5tnVwG56ToJS5d4lIpZCKMbFYjK1bt2LIkCEoVaoUDh8+bFHixhTXr1/Hzp07cebMGWRlZcHb2xu9evVC8+bNUbJkSeh0Opw7dw7btm3D4sWLsW7dOnTv3h0DBw7Md3JVIBCgffv2aNy4Mfr3748FCxbg7du3mDdvHnx8fKgNSktLo8G4/499Rb4GEAlNbiW0QqGgVQgksUaaci5cuBCNGjXCmjVr8vSZYmNjsWLFCty+fZtWsZLKM5FIBKFQCIPBgHfv3iE6OhparZZuS3zKc+fOITg4GNOmTTOS6skvKlWqhHPnzqFZs2Zo3bo1Dhw4gHLlysHOzg5OTk5UFowLgUCArKws+Pn5UaknwHps4j98vQgMDLQ4ZwUGBuLt27ef94S+MnxTQWOGYVoC6MYwDAtgG4CnLMtGf+HTMgvuQlutVudiJRBtR7VaDZZlaedYgpSUFGRkZOD9+/fo2bMnvLy8cObMGdoZ19ZmZOagUqkwZMgQ3L17F2vWrEHLli3pd6QExRRubm6oXbs2OnToACCnJDctLY02SklKSsrFeG7fvj0mTJiA4cOHo2XLlmab8jVp0gQTJkzAggUL0LRpU8qgIQaX26TKtBke0cGyBFvE6b81mCvx5ZaEm2sIkZ6eDoVCAalUCpFIRKUaUlNTMWHCBOzbtw+zZ8/G+PHjP1pAVKPR4I8//sCdO3eQkZGBjIwM2oSP29jDz88Ps2bNQu3atc02SHB3d8eQIUMwZswYnDp1Cps2bcKsWbOQlJSE6dOnWzw+wzBYuHAhkpKSMGfOHFSpUgWtW7emQSzCYhCJRFRjy87ODnK5HHK5HOnp6VSjV6vVIj09HY6OjlAqlfD09KQ6pN+KjIAlWHNsVCoVbYRBxgVxFIGc4I9UKoWPjw8yMzMRHx+PmJgYbN68GZ06dcKYMWMKfF6RkZGYNGkSihQpgmrVqqFIkSJwcXFB+fLlERgYaGRriENJAv5yuRzXrl3D1q1bcebMGUyaNAn9+vWz+Iw8PDwwb948jB49Ghs3bgSfz0eNGjVQtWpV8Pl8o+Tcnj170LBhQ8ycORPTpk2jwRtPT0+zDiVpLKlSqWhTs7zwtdkmczaFyLiY2mBTtp6npycNMHETLuTdMdWWVSgUePXqFZ4+fYoFCxYYSRx9Sjx+/BiTJ0/Gu3fvMHjwYPz888+IiYnB4cOHcfjwYYwcORJhYWFo2LAhWrRoAX9/fwiFQgiFQlpGKxKJIBKJ8hVYZhgGISEhCAkJweLFi7FlyxasX78evXr1QkREBG0QSxJ9RC6BK5fBlfzgNuz62uySuXH9qce6OftmmsQ3hUajgVAoxOrVq5GcnIyTJ0+iXr16H+RzEcTFxWH37t2Ij4+nc41MJoNCoYBcLqfHYBgGpUqVQuPGjVGrVi1899138Pf3B8Mw0Ol0iI2NxdOnT5Gamoq3b9/izZs3uHnzJg4ePAggJ8DcqlUrtGjRgrKITVGsWDFcvnwZw4cPx6pVqyCXyzF//nw6tsRisZH+ODfgTlhVXP9KrVbTOd7d3d2sPfyWIJFIKItMpVIZjSXu/yckJGDLli24cOECJk+enC/plD179mD27NlITEzEjz/+iAEDBqBGjRo27YPP52Po0KGoWLEiZs2ahW7duuHKlStWK8yAnATwgQMH0KRJE4wbNw6XLl2iCT7yTkilUioTxL0fxN/hjidzDaEyMzNzNdYyxadOGH3rIAFT0mhYqVTScWgavCfNGB0cHLBr1y4MGDAA9evXx549e+Ds7Jwr6WAKuVyO6dOn48CBA3BxcUH79u3RunVreHl5GemmCwQCNG3aFE2bNqUNX9esWYMtW7Zg/PjxGDhwYL7nHEdHR+zatQu//vorZsyYgStXrmDevHno3LkzNBoNXFxcqGQct0G1aQXJ564M/LeB+x4bDAakpKTQpD4Ao0Ax8M97TRowKpVKvHnzBosWLUJoaCh27dqVSz+YC7lcjmXLlmH79u0AgHr16sHOzg5arZYy59PT02mQOCgoCI0aNULRokUREBCAokWLws3NDQaDAfv27cOSJUvQpk0b9OnTB9OnT8/TDlpCqVKlcO7cOdSvXx/Tp0/H77//TtfOXFtG7hdZ3xLy33/4tmEtKPw1+dNfCt9M0JhhmJIA1gDoCKAagFoAfmIYZg3LspFf9OQ44GaHyWKZW7ZH/iUNo0h5kVgsNsrQu7q6IioqCgMGDICDgwNOnjxJA8YfgoyMDPTq1QsPHjzA0qVLjQLG+YFAIDDLSOaifPnyGD58OIYMGYLXr19bLFMfMGAAFixYgMuXL8Pb2xtyuRwODg7QaDRUwoN7P8l1ANYZCpacUnMdu78VmCvxJQ6VRqMx2/FaqVQiLS0NSUlJkEgk0Ol0EAqF+Pvvv7Fjxw6MGTMGEyZM+GjneO3aNYSFheHVq1coU6YMXF1dERAQgJIlS9LFpJOTEwIDA9GoUSMIBAKq22cJfD4foaGhCA0Nxfjx47F+/Xq0a9cO5cuXt7iNnZ0d1q5di5iYGPTq1Qvnz5+nY5Cwjsm7SprCZGVlwdXVFVlZWVRL/MWLF7Qchc/nU+f8W5YRsAUSiYS+41xnnBsI9fDwoA4VkMNUYVkWQ4cO/aB3a8eOHRAKhThw4ABlycTHx5sNItrZ2cHR0RHe3t60m3jdunXRpk0bTJ8+HePHj8eBAwfw66+/omzZshaP6enpiYkTJxp9ZtpItFq1avjtt98wbNgwFClShDaVITqYpotpmUyG+Ph4utj7Fp1Kri2x1m0b+CeYQgKZJMCgUCiMyvMIa00qlVKmMRlXZcqUQeHChXH8+HGMHDnyk15bdnY2Fi1ahCVLlsDb2xs7duxASEgIgJxFypgxYzB69Gg8f/4chw4dwpEjR3Ds2DGr++zWrRuWLl2a7wSct7c3JkyYgKpVqyI0NBT3799H1apVaVLLwcGBJlJJI0YgJ0AcFRUFe3t7mnj7luzS5y5TN9Xe5QbdhUIhrS7RarUoX7486tWr98HHfPfuHVatWoXdu3fTxq2urq5wdXWFn58fvLy8aPVE6dKlUb16dTg7OyM+Pj4XS5DP56NIkSJwdnbOxe5LTU3FpUuXcOjQIWzduhXr16+Hr68v2rdvj/Hjx+eqTnN3d8euXbswZMgQbNu2Df3794erqytkMhlNwpEGtl5eXjSARe4bACpVQRoG6nQ6aDSab2LsWQNJehHtftIAUKlU0mQHSTQvXboUFSpUwLBhw2za999//43Ro0fTd3zx4sXU7uQXNWrUwLp169CuXTssXboUkyZNynMbNzc37N69Gz/88AP69++PEydO0OZSJAjn7e1txBgmQSPAWKKCBDe5yQLudqbgvu/f+hj5lLC3t6cBeNPqSxJQk8vlNIGpVCpx48YNDBo0CHXr1sXhw4etavETXLhwATNnzkRqairGjBmDYcOG0e3evXtncbuKFSuiefPmePPmDaZPn44ZM2ZQGcX8ws7ODmFhYahduzZGjRqF3r17Y+vWrQgLC0NwcDDS09PB5/OpD2G6ziFrILVa/Z8EQAHBlfhQqVSIj48HAOqzchuIKpVKyvLV6/VQKBSIj4/HnDlz4O7ujs2bN1sce0QOccWKFVCpVGjfvj1Gjx5tRBpKTk62ShjgNsLj8Xjo3LkzmjZtinnz5mH9+vW4dOkSli1bhurVqxfoXhQtWhQdO3bE2rVrIZVK4e7uniuOQOweuUfu7u7/Se78h389vpmgMQAxgGssy14HcJ1hmKoAmgEYzDDMEpZlYy1tyDDMQAADARg1ZPoUIItq0pGZGBru4sRgMEAoFCI1NZWWnwoEAigUCvp9VlYWunfvDo1GgwsXLtBgyIcgJSUFLVu2xOPHj7Fq1aoCB4zzg6JFiwIAXr16ZTFo7Onpie+++w5RUVEICgqijRCAnAmGaM+aMle4mURyb3U6HdRqNWX1mXNKP3ZZ3OccX3mV+HIZMERzkHT2VqvVyMzMpF1yhw8fjuLFi1tl7OYHMpkMYWFhOHDgAAIDA7F7927Ur1+ffp+WlmaWbZ5fTJkyBadOnUJYWBiOHj1q9bdisRjh4eGoXr06unXrZtSUjTRzA/65d46OjjAYDBAIBJRt4+zsDKlUCj8/P2i12lzP4FOyGz7l2LIleUJYLcQBkkgktDxer9dTeR0iU/Ps2TM4Ozt/kI6xUqnEoUOH0Lx5c5vLKs2hXLly2Lt3L8LDw7F48WLUrl0bQ4YMQVhYmFWd9LzQp08fXL9+HZs2bUL9+vWpzeI2TAL+0ZglwWRr3aC/BGwdW1ybwi1DJkxjc44yV/OT+56QChvSIZ6MPSIbQxJb/fr1w4wZM/Dw4UNUrlz5k1x/ZGQkBg4ciIcPH6JVq1aYOXOm2XHBMAyCg4MRHByMmTNn4tGjR0hPT4dGo4FGo4FCoQCPx4NWq8Xz58+xdetWCASCAi2eAVDtyUePHqFs2bK09JHMf9zkIAFhWnE//5Ksq/zYrU9Rpk58CC5TioD4DaSU3mAw0OaxJEiYVxWTrXj9+jVWrFiB8PBwurgdPny40T1JSUkpcF8KU7i5udGeACKRCGfOnMHBgwexYsUKFClSBF27djW73axZs7B//37MmzcPq1atopIMWVlZSElJgUQiAZ/PN2qQRwLIJIlEkozcirBPgQ+dE601X+TaMu6cqFKp6JjS6/WUbS0WizFs2DCoVCrs3r3bpus+ePAgRowYAUdHR6xcuRKdOnVCbKzF5YtNqFOnDjp06IDly5ejVatWVpOjBEWKFMHGjRvRvn17TJ06FYsWLcol48JlFhN7Qggu5P5xmwdzkwWW3uWvXZbic/rz1qrVSICUNBnmJqXT0tJw5coV+js3Nze8fPkSvXv3RkBAAHbv3p1nwFgul2PixIkIDw9H2bJlsX37dtqrIT8oVqwYtm3bhiVLlmDJkiVITEzEokWLCuS7hYSE4OrVq9i6dSumTp2KFi1aYObMmWjSpAlcXFxocNz02qzJrH1N+JxjqyAg5CxTDWMuuMFjIGf8JScnY9asWbhx4wY2b95sVhIuOzubMsqTkpJQr149TJ48+aP1CHB2dqayGHPmzEHz5s0xYMAATJkypUB2platWli2bBliYmIQEBCQa64gMj46nQ5OTk7g8/mUoJWZmZmLRPIf8o/Q0NB//b6+NXxLQeMIAMUZhhnCsuwalmUfMDmzbCcAJQHEWmqOx7LsOgDrAKBatWofVQuZLIJNtWS5DikxrmQhTSQCfHx8kJiYSHWoSKOgJ0+eYNiwYUhISMCpU6dscgDzQkJCAkJDQ/Hq1SusXbuWdn/81CDB7tevX1v9XaNGjbB48WIqqE+6UpPgusFgMGr+w5WuAHLubWJiIpKTk2mw1BKr72M7GJ9yfJnCXDmvLdpJUqnUKPEwceJEREVF4cKFC1YXOjqdDps2bYKzszPKlStndoI3GAzYtm0bpk+fDqVSiTFjxuDnn3/+ZAtHFxcXzJo1C0OHDsW2bdssLoQJ/P39ER4ejvr166N79+7YsWMHChcuDMD8veOWHAE5mpGk/JYsoD6XNMXHGFuWst+mizfTa8rMzIRMJqPSC8A/JWqmjA+CS5cuoU6dOh8UcDl8+DAyMzPN6qDnF3Z2dujYsSM6duyIWbNmYeXKlThw4AAmTJiALl26FEgXnmEYLF++HA8fPsSoUaNw5coVFCpUyGwgj/z7NTqP+Rlb5thhpFrGHOOesKFMbSwZUx4eHnBycgLLsjTZl5SUBIPBAAcHB/Tt2xeLFy/G6tWrsW7dugJdX1paGi3hT0lJQWJiItLT05GamkoZmVKpFDt27EDlypVtsld8Ph/fffddrmsiQT+WZeHi4oJly5bB3t7eJtafKby9veHj44MXL17AYDDA2dmZzokkYMz1Lcg94y5SvnQwJj9j62OWqXPHIwkEe3t7G7GJCcOdSA84ODhQX4HP5yMlJQXZ2dkwGAwFtmMRERFYvnw5Dh8+DKFQiH79+mHIkCEWm3N+Cjg6OqJdu3Zo27YtmjVrht9//x0dOnQwa/M8PT0xdepUjBs3Djdv3kTJkiWh1+vh5+cHPz8/GpgiiWfiY4lEIqNk/ufQcfzQOdFahRCZE1mWpUljmUwGnU6H7OxsODk5QSgUws7ODkKhEL/88gsuX76MzZs3o3Tp0rRhqjno9XrMnDkTy5cvR40aNbBly5aP2mRz9uzZuHDhAlq1aoU1a9agYcOGeW7TunVrjBw5EsuXL8ePP/6IJk2aQKPRwN3dHVqt1ohZzA1icuX2SEUgsVF5gfzGNPj8teBT+PPcZKlpMsJaAJ2sg7RaLVJSUij7MiYmBmq1ms4VycnJGDlyJDQaDQ4cOGAkKWEOJ06cwNixY5Gamorhw4djzJgxVnuE5AWGYTBu3DgUKlQIYWFh6N27N3bu3Fmg8c3j8TBkyBC0b98ePXr0wIwZM1C+fHkULVoUarUaSqUyV6UTsTu2+uJfSlruc64VzYGMQ9MeLeReOjg4wGAwIDs7m86JZBvSZ4mQjohPqNFoMGjQIDx8+BCbNm1Ct27dch2XZVn07t0b58+fR/Xq1bFhwwZaLfOxUatWLVy9ehVz5szBunXrcPnyZWzZsiVPvXdT1KxZE0BO9WStWrVyfU+qC7lJUm4g2ZRE8h/yj3Hjxv3r9/Wt4asOGv+PTSwEwGNZ9jrDMFMAdGUYJoVl2b0sy95nGKYRgO4ALnzK5niWdm066ZtzmhmGMWoikZ6ejoSEBAQEBMDf359KCGg0Gjx58gRz5szB06dPsWfPHgQHB1ODzoVCobA6yXPF4uPi4tC5c2fEx8djy5Yt0Gq1uHnzptntkpOTLWabIyMjLUpkZGdnm3VUiMbly5cvjc6JC4PBgEaNGmHBggV4+PAhmjZtSo1wcnIy0tLSEB0djdKlSxsxVYm8BwBaTufu7g61Wm2V1fc1CtRbG7rc6+TCUjCQfE4aCqhUKmRmZtLxee/ePaxfvx6DBw9GSEiIUVMdAlJ2s379esybN49+LhAIUKRIEVSoUAFlypRBQEAA1q9fj3v37iEkJASDBg1CxYoVaUk1F5GRkRYXzampqVYzzqZj58cff0TNmjUxd+5cVKtWzWIZklarpYGezZs3o1+/fqhbty7Wrl2LUqVKQSAQ0LJbotVKFsRJSUlQqVQoVqwYeDwe0tPToVQq4eHhQRNApAmEKavN2vP8FE6qteNZYvYQe0TeNVNbRs6TYRijDt5KpRLZ2dlgGIY2AXJzc0N0dDSioqLylBQwlXzgQi6XY+vWrShVqhRKlChhJEnz5MkTi+91fHw8SpcubXG/bm5uGDlyJOrVq4fFixfj559/xsKFC9G3b1/UrVvXokNpMBjMjlkHBwds3boVdevWRb9+/bB//36IxWLqLIrFYohEIlrq/ikbKH4s2DqGTMs/zQX9uIs4pVIJhUJB50Fiz0jAmKt5TAJ6BoMB7du3x65du2hjHAKDwWBxLnn79i2kUimuXr2KKVOmIDU11eh7sVgMV1dXODs7o1GjRhg1ahQ8PDzw4sULqwE9S8cDgLt37xptGxwcjMaNG2PNmjVISkrC+vXrzW5Hgi3mULFiRbx8+RJOTk50ESKXy40Wd0DOO5uUlAS1Wg2pVApHR0cj3cevJSBjyeaRzz+0pNi0aRTwT8kokHMvkpKS6OdSqZQG/4isgsFgQFJSErKzs5GdnZ3rfTD3GQFX+/Xy5cvo1asXJBIJBg0ahH79+iEqKgqvXr3Cq1evcm2bkJCAMmXKmN1vZGSkxSqz2NhYFC9e3Ox3GRkZRgzrwYMHY/Dgwdi6dSvat29vllHfr18/bNiwATNmzMC5c+eg1+vh5OREAwkqlQp6vZ4mGDMyMox6cVjyU8h31vAhc6KlfVs6H2tJCnMN3by8vGiFll6vh1AoRHp6Ovbt24dFixahW7du6NixI5U9MfdOp6WloWfPnrh79y7atGmDMWPG0CauAPDmzRur8nNyudzidxqNBm5ubrCzs8Pu3bsxYsQIdO7cGUOGDEHfvn0tJj50Oh34fD5mz56N69evY/DgwZg3bx6qVq1qxDbk8/m02axGo4Grq6tRoiA/yVEyB3CTNsCXT3LlF/ldanJ9K26gkujHkv9Mx6taraYkI6KdD+TI6Pj6+qJChQpQq9UYPHgwnj17hj179sDPzy+XhjEZZ2q1GpMnT8ahQ4dQpkwZbNq0icpgmMPjx48tygSkpqYayQQAOUzhBQsWUJbw9u3bzdooIvdmDllZWdSubNy4EXXq1MGwYcOwa9cuFC1aFAzDQCgU2pykMId/u7ScOXATqqaa4+RdJH6bTqeDTqej0mIymQwuLi7UZjk4OMDR0RHx8fHo0aMHYmJisHPnTjRt2tRI+z85ORlisRjnz5/H+fPnMWbMGKp7/ejRI4vJjfj4eItzG5C7MSgXqampKF26NEaMGIHg4GBMmjQJDRs2xOzZs/H9999b9LlMK7icnZ1RpkwZXLt2DYMHD6a9lrgwjSVwA8lkDsnLLn5rEpmfE0WKFLEqkfMxKvD/Q/7w5VcTFsAwTFMAfyJHw3gfwzDtWZY9BeAsgOYMw5DIxHsAdgzD5C3e9AkgkUjg5ORkVOpoyzakG7NpY7yVK1fiypUr2LhxIxo1alTg8yKB4UWLFqFly5aQyWT4888/8eOPPxZ4nwVF0aJFzS6WuAgJCYGjoyOOHz9OSwOzsrKg1+vpBKZSqWiggeu0cYOnQqEQ3t7eH6W09GsHCeSYOmDkczJ529vb00ViYmIihgwZgoCAAMyaNcvq/mNjY7F06VLUr18fZ86cwbJly9C/f394eXnh5s2bmDt3LgYNGoQ3b97gt99+w969ez+bEWcYBr/88gt0Oh0WLVpk0zZt27bF2bNnodFo0KFDB2zatAmPHj1CcnIylEolZaoTLdbAwEDamJHokGu1WuoIEU2vlJQUxMfHU0mVrwlEi5FIA3DfG5LIIg6Nvb290WJQIpHAx8eHlilnZGRAo9HA09OTBhJSUlKQlJSExMREhIeHA8AHaYA+efIEL168QMeOHT+JM1WhQgVs3boVy5cvh6urK+bMmYPu3btj165duRZAeaF06dJYs2YNbty4gSlTplDbJJPJ6OJOpVIhPT0dMpnMprnhawFXSgn4Z54zV51BHGfCWDGdB8m4Is46kaUg3zk4OMDDwwM+Pj602aTBYEC/fv2g1WrzxTTOzs7G4sWLMWTIEHh4eGDVqlUIDw/H2bNncejQIdy+fRunTp3Cnj17MGfOnE/SaI9hGHTv3h1169bF/v37sWzZsnzvo3z58nj27Bnt2i0UCuHm5paL3U/uHynb5VY1mZsb/m0g440bgCLBUlIyCuTcJy8vLyrVRHyGzMxMGmxOTk5GdnY2eDweXF1dC+xDbN68Gd7e3rh+/TomTpyYZ++Hz4HatWujQoUKWLt2rUU7RyRV3r59i3Xr1sHDw4POG8TOy+Vy+r47OjraHLQx57d9KZhKLul0OiQmJtIgib29PVJSUpCeng61Wk318sViMYRCIdRqNc6fP49x48ahdu3aWL58udXjRUZGolmzZvjrr78wceJETJgwoUAVLrYgMDAQe/fuRfv27bFmzRoMHTqUNrO1BKFQiL179yIgIADjxo3D/fv3odfr8f79e6hUKqSlpSE5ORmvXr1CVFQU4uLiIBaLqX9e0OSUtTnlW4S1MW7qW3ErH+zs7KgdMoVEIoGzszMKFy5M3zeRSAQXFxdUqFABPB4Ps2bNwsmTJzFnzhyrvpdarcaAAQNoY9eDBw9+lCpWU9SsWRNLlixBVlYWWrdujbt37xZ4X66urti2bRtkMhmmT59Omf/JyclUIqYg+LeNPVtBxqFpo1IiMUPkHVUqVa7xqNFo4OTkBG9vb/j5+UEmk6FDhw5ISEjAwYMHLVYva7VaLFq0CMWKFUPfvn0/a5A0JCQEO3fuRJEiRTBmzBisXr3aLEnKEmrWrIlbt27hxYsXRlUk5vxcAjI/ajSa/xc+2KfEu3fvUKdOHbAsa/Y/a03rTFG3bl3UrVv3o5zXx9zXt4avMmjMMExFAEsA9GdZdgyAfgB6MAzDA3ASwFYAvRiGCQcwB8BSlmUt09c+7bnabCBIaTefz0exYsVoIC8rKwtKpRJz587FkSNH8Ntvv6Fz5875Ppc3b95gw4YN6N69O8qXL48OHTpg1apVCAgIwJ49ewosCv+hKFq0KN68eWP1NwKBAHXr1sW1a9eQlJSEjIwM6HQ6ODg4oESJEihUqBDc3d3pPSQ6X9wOpikpKUhLS4NMJqPBMlsD+d8iTB1R089JIAHIySZLpVL8/vvvePHiBVauXGmV3cWyLKZPnw6GYTBr1iyUKFECLVu2RFhYGJYvX447d+7g8ePHOHDgAC5fvowOHTp89oxpQEAABg8ejHPnzuHUqVM2bVOtWjVcu3YNpUqVwpw5c3DixAlaaqVSqSASiSjLwdnZGT4+PuDxeFQbWigUUjYpgbu7O3x9fb86zVoAlGVOyoq5jDhTcIN/pn+T4JRpwIUwsdRqNc6ePQtfX1+LrDlbsH//fkil0k+qGcUwDGrVqmUUPJ44cSLq1q2b7+Bxp06dMHDgQGzatAn79++nn7MsS+8PaQ5l7d5/DeDaS9OgIylNzmuRZi5YyXWiiR4211bo9XqaiJFIJPQ4RYoUQbNmzbBu3Toj9oolvHnzBv3798fWrVvRpUsX7Nq1C3Xq1EHp0qXh6+tb4G7atoJlWbx48YKWz/br1w+1atXCzJkzLbKNLaFixYrIzs5GYmIiXFxc4O7uTtnZZN4jkEqlcHJyos8NsDw3/NvAZcATGSFPT0+4uLgYBWi42uskyExkUQgTPi0tDdnZ2bThY0ECnAkJCbh8+TLat2//QXrsHxsMw2DYsGF4//49Dh8+bPF39erVQ2hoKFauXInXr19TO69QKCAWi2Fvbw+BQGBToNCcPfkabSA36Uual5IKJRJUIgwyZ2dnPH/+HBMmTECZMmWwe/duq3bl5MmTaN68OTIzM7F48WK0bt36k1+PWCzGvHnzMH/+fDx+/BgNGza0WFlIUKhQIZw9exZVqlTB6NGjceLECfj7+8PDwwNubm7w8PBA8eLF4e3tDYFAgOTkZGRkZNB/8xsY+VLyAJ8ShMXJHePc4DDXt+LOk9ZsNZl3SX8WOzs7Wqml0Wiwbt06LFu2DL1798aAAQMsnhsJGN+4cQOLFi3CqFGjPkiOIi+UKlUKhw8fhpubGzp37ozjx48XeF+VK1fG4sWLceHCBaxZs4b6oh8S8CXr9n/L2LMVxBfjjifgn6ongUCQKzlNJMlcXFzA4/Hg5OSEq1evokWLFtBoNDh27JhZ+QaCXbt24e3bt580WWYNvr6+2Lx5M9q3b4/t27ejS5cuZithzaFWrVpQKBS4f/8+ZDKZRf/YHP6/+GD/4f8XvsqgMQARgBn/k6SwA/ACgCsACcuyySzLXgBQA8BEAMEsyz76gucKwDYDIZFIaECKNJnQ6XQwGAy4f/8+/vzzT4SFhdncgZmAlByFhIRg8uTJePHiBdq3b48NGzbgyZMnOHDgAG2u8yVQvHhxyOVy3Lhxw+rvGjdujLdv3+LRo0d49eoVzSbrdDoIhULI5XKIxWJaTpOenk7LQOzs7ODq6koDNFlZWVYN+9fEfCkIuBIUhClFQLQGY2NjERcXRye6y5cvY+3atejfv3+ebNAjR47gwoULGD16NPz8/Mz+xtXVFcHBwR+tiU9B0Lt3bwQFBWH06NG0229eKFSoEM6cOYOOHTti9erVOHjwIGUfkZI3wqIxx5okyR6FQgGVSpUr2Ap8PeOLMCqIhqwtjjZ3sW+OEUOaAfF4PKqlWqhQIURERKBOnToFdsRfvHiB8+fPIzQ09LMwQEjwePXq1diyZQs8PDwwceJETJw4MV/7+fXXX1G1alUMGzYMsbGxNMCnUChoU05nZ2eIxWKqlf01JrJsXchag7XtuN9xpSlevHiB169f02ZQpGQ3IyMDDMMgKSkJV69etXrc6OhoNGnSBHFxcVi2bBmmTJliU+f4j4nbt29j5syZWLp0KYCc92X48OFo1qwZJkyYgIsXL9q8L9L8b8WKFdTGZ2Zm4t27d0hMTKQ2n7D87e3t4ezsTO8dN6j3b06ekjFFkqKkORvXHiuVSiQkJNBSUWK/SUNicr+4JeBHjx7F33//nS87FB8fj5EjR8JgMKBDhw6f5Ho/BLVr10b58uWxefNmq7+bO3cu9Ho9hgwZgr///htKpRIMw1DmdVpaGhQKhZFfpdPpaLCVu7AmSXyiffylmH0GgwEKhQIKhSLXe8BN+hLGOp/PpwxxroazwWDAgAED4OnpiYMHD1rV5Hz8+DH69euHEiVK4PTp0yhfvnyBz1+v1+Px48dYt24dxo4dizNnzuTpW7Rr1w5bt26FVCpFmzZt0KtXLzx9+tTi793c3HDixAk0atQI48aNw+XLl2myj2jrurm5UX/CwcEBYrHYbG8DU5j6Q+YCrN86zLFXLa1BuHOhOf+RNCQjY5X8TXriODg44P3795g2bRrq1auHefPmWZWGGT9+PA0Yt2vXrkDXR/xBWxEYGIhDhw6hfPnyGDRoEJo1a4aNGzfaHLDjolevXujUqRN+//13MAyTi/Bier/+Q/4gFovpe03WQMQWymQymlTNysrCkSNH0Lt3b0ilUly8eBEVKlSwuN9nz55h2bJlqFWrFurUqfMZr8gYRH9+ypQpuHPnDurVq4ejR4/maUOJrnFMTAzs7e3pPTDn55r6Wabv9deyJvwP/+FD8FUGjVmWvQvg8v/+38Cy7GsAmfjf+TIMU5RlWR3Lsq9Ylk36gqeaC6aGgzvZA/80lCCMWdKkh0z4wcHB+T7mokWLsGHDBvTv3x937tzBnTt3MG/ePDRp0sRIA9gUb968wc8//4zhw4dj9erVuHjxImXpfky0b98exYsXR+/evRETE2Pxd6GhoWAYBseOHaPOqkAgoME8UjpDnHducyAi3s9lGYnFYqpZaIpvzWk1HVfEGU1OTqYLYi5SUlIgk8kgl8uRmZmJN2/eYPDgwQgICMhTzuHdu3eYOnUqvvvuO/Tu3ftTXdJHgUAgwPz585GRkYFu3brZzHixt7fHxo0bUbJkSezYsYNqYhMpBhIcJQvNpKQkKBQKyiIk5bkk+WMqQfC1jC/CqODxeBaZFaYLaq68iblAIiltIxpfBoMBkZGRSEhIQI0aNQp0njqdDsOGDYO9vT0GDRr0oZedLzAMg3r16uHQoUMYOnQowsPDceHCBZu3F4lE2LFjB7Kzs/Hzzz8D+EeCiCvvQJqxceUrbMXncDjzWsjaAmtSFdzvuNIAzs7OcHR0hFarpe/dnTt30L17dxw/fhwDBgxA7dq1LR4zKysLvXr1gk6nw8aNG9GgQYMPug8FRenSpSEWi40Scnw+Hxs2bEDx4sUxatQoI41uawgKCkJYWBi2b9+OwYMH03eQG6QhwVCyCCdSV6TihtjCf7NURX7HKUkOkkoSf39/ODs7w8PDA4GBgUhISECPHj0wdOhQlCpVCgsWLLBpv2fPnkXjxo3x5MkTLF26FEWKFPmAq/o0YBgGoaGhePPmjVWdwCJFimDXrl14/vw5OnfujOjoaHh5eVH9SYFAAHt7e+h0OjpnpKamIjExEbGxsUbzBfHbNBrNF2X2kUbJJOHCBZ/Ph7e3N5Uy4VZVED+d2Ovly5cjLi4Oq1atyrOp4Zo1ayCVSrFnz54CNUDUaDS4fv065s+fj169emHq1Kk4e/Ys1Go1Vq1ahWXLluVZgVGyZEmcPn0aY8aMwZ07dxAaGmq1KksikWDv3r0oW7YsRo4ciaSkJGqTo6Ojqa0hxyVJ87zeP1N/6N8oD2COvWopiZqX3TIdd0qlEu/evcPbt2+prz9r1iwIBAJs27bNKoNz+/btOH78OMaOHVuggHFiYiJ27tyJgQMHon///li3bh2ePXtmU4DWzc0Nu3fvxowZM2j1YrVq1dC1a1ccOnTIpgoiIOfeBgcHg2VZWrFE7g2pDkhPT//i/va3Cq42LwkWR0ZG4tWrV3jx4gUUCgX0ej2ePXuGsWPHwtvbGxcvXrTaXO7Zs2cYPHgwXFxcMHfu3K+C1R0aGooTJ07A19cXAwYMwJgxY6z61H5+fggICEBERATs7OxozMHc+6tUKhEfH59rLU7wtawJ/8N/+BB8dY3wGIZh2Bwkkr+R0wzPD4CAYZjeAEYyDFMHQOanbH6XH5CFmWlXV9MGLCRDSpwlwswgbM7nz5+jZcuWNh/3xYsXWLt2Lbp27WrUsCwv3LlzB8uWLYODgwOKFi2KO3fu0ECJh4cHKlWqhEqVKqFy5cooUqTIBxl8Z2dn/Pnnn2jUqBG6deuGkydPmm0+UKhQIXTt2hUbNmxA/fr1UaxYMaSnp8Pe3h6enp60nI1AJBJRprFpsINlWcrGIk2CuCD7+VqdVi5bjGVZJCcnQ6vVQigUwtPTk7KrhUJhrknIYDBALBajcOHCAHImswkTJuDNmze4cOECnJycLDprer0e/fv3B8uyWLp0qVEzna8VJUuWxPr169GtWzcMHz4cGzZssGm8MgyDrl27YsaMGXj9+jVKlSoF4J8mOK6urrSDeFJSEry8vIwCgUSugWVZ2NnZUZYat3Tuax1fXJAFNZBTysXtbs5t2EJkKkjwijhMarUajx7lFHt89913BTqHZcuW4cGDB5gzZ45NGqAvXrzAkydPkJmZCaVSieTkZLAsS4PfLMuiVq1aaNy4sdUGi1wwDINRo0bh3LlzmDx5Ms6cOWNzg66goCD8+uuvGDJkCDZs2ICRI0caNXmzs7ODVqulMif5HRfcxi0f2jTMEj52g1CVSoXExER4e3vnarzFHWNSqZQukFUqFXbs2IGZM2dCIpEgPDzc6nzIsizGjRuHJ0+eYOfOnQgICPigc1ar1Thz5gyePn2KFi1aWGXRmMLFxQUbN27M9blYLMby5cvRvHlzjBo1CsuWLbPpPs+dOxdATlKYz+djwYIFVP+Z+BlqtdroXczKyqINrsw1qvr/CFLOzJWrUKvV1Kbx+XxkZWVh0aJFWLx4MaRSKVauXIl+/frZFAybMmUKNm/ejAoVKmDFihUoWrSoxd/r9Xpcu3aNHp9owwI5c4VYLIZGo6ENh168eIHbt28jIyODNiAqW7asxWbFeYGUEV+4cAF9+vSx+LsWLVrg0qVLaN26NVq1aoUzZ84gKCiISqmRYDCQY49EIhE8PT3h7u5OJT6AnLFPZJ8+N4gEgr29PU0KA9bfA6lUCh8fH4hEIigUCnqNYrEY2dnZ2LhxI6pXr54na+7Jkyc4fPgwhg0bZpW4YQ7v37/HunXrcOvWLWRlZcHBwQHVqlVDSEgIqlSpAoFAgH379mH37t14/fo1xowZQysTzMHR0RFhYWHo27cvevTogb59+2LBggXo2rWr2d8LhUKsWrUK9evXx/z58zFnzhxkZmZCKBTSuScjIwMODg42V6OY+kMkwPpvR15zqrlm1sSnlEgkVGaP+/vU1FTcvHkTx44dw/z58+Hr62uxUWJERAQmT56MWrVqYfDgwTaft06nw7Vr17B+/Xo8e/YMQI5vJxKJcOXKFZw9exaurq5o0qQJfvrpJ5QoUcKiz21vb4/+/fujf//+iIiIwP79+3Ho0CGcOnUKTk5OaNu2LX755ReLTdEIyDUWK1YMer2e6q0nJiYiIyPjX5eE+BIgcYzU1FQq0SMUCiEQCHDr1i0MGDAA3t7eOHPmjNXGna9evULLli0hFAqxdevWAiXNPhXKlSuHEydOYOHChVixYgWKFClCyR7mEBISgitXrsDZ2dlIC5/77gLIk+n+La0J/8N/sISvIiLEMExxAC4AnrIsaxTN+l9QWMMwTASA8QBqAejJsqzl9plfAKaC8oSJkZGRATc3N/D5fIjFYmRmZuYKfBKNx4CAADpB2wKWZTFp0iRIpVL88ssvNm9z/Phx7Nu3D0FBQZgwYQJcXV1hMBgQGxuLu3fvIikpCY8fP8b58+cB5ASRq1WrBj8/P9SvX79A2q0lSpTA+vXr0aVLFwwfPhybNm0y62QsWbIE58+fx5w5c7B7926qC0iadpHAMZGoUCgUtDzVFNYWy1+z00oy51zBfm43W7LIJGxrssgh5bdZWVlQqVS0+dT27dtx+PBhTJs2Lc9GiEuWLMH169exZMkSGnT+FtCsWTNMnz4dM2bMQKFChTBr1iybAsddunTBjBkzcODAAUyYMIEmGgwGA7RaLaRSKQ3KuLu7Q61WQ6FQ0C7gRLvWwcGBjlHg6x5fprC2oOYGism/GRkZRr8TCAT466+/YGdnVyAZnCdPnmDx4sVo06aN1eafer0e169fx/Hjx/HixQsAoAtZsVgMDw8P+Pn5wdHREVlZWTh9+jSOHj2KokWLok2bNmjWrFmeOqMikYiey/z582ngzhb06dMHx48fx9SpU1GrVi3aRRnIcRQJS51oZOcH35LDSZxp4kCTRlPu7u5GSSjCnia/1Wg0mDBhAg4dOoQGDRpg48aNKFSokNVjbdq0CXv27EFYWBh++uknvH79ukDnHB8fj7179+L48eNQKBQQCoU4evQofvjhBwwaNOiDg9EhISGYNGkS5s2bhwcPHmDFihWoVq2a1W0YhsHcuXPBsiwWL14Mb29v9OnTB2q1mjIhSZk4sTVisRhKpdKoCsAS8+XfAp1Oh5SUlFzji4BUImk0GvD5fKjVaqSnp0OpVMLFxQX79+/H1KlTERUVha5du2LhwoXw8vLK87hPnz6lgZC+ffti0qRJVnVCWZbFpk2bcObMmXxfI2m8mpGRgaNHjwIAPD09UbVqVVSoUAEVKlRAoUKF8pzzgoKC4O3tnWfQGACqVKmC8+fPo1GjRvjpp59w4sQJlChRgiYmSAUXYf15e3tDKBTSe0CSeQDovf9cYFnWyIcilUHc74F/bBXXb5dKpcjMzERSUhKUSiWkUinUajX279+Pd+/e4ddff7V6n1mWxezZs+Hi4oIRI0bk67wjIyMRFhaG7Oxs/PTTT/jxxx9RpUqVXBUKnTt3RunSpbFkyRJMnDgRLMuiefPmVvft4eGB8PBwDBo0CGFhYXj//j3mz59v9lqqV6+OoUOHYvXq1WjSpAnKly8PgUBA7QqXcWcLviV/6HOCq8lO7g/pQ0HsN8MwVJJCIBBAJpNhwoQJKF26NEaOHGl13/369YODgwN+++03m55VUlISDh48iAMHDiAxMRHOzs5o37496tevT5vGqtVq3Lt3DxcvXsTevXuxa9cuBAQEoFWrVujQoYPVBqKlS5fGlClTMGvWLFy9ehV79uzBn3/+iffv32P37t1W36vU1FQ4OjrSNTOQY3syMzOh0Why9VwgfUqIz/Rv09AuCEwDncT2kc9IBRNZU/v6+iI9PR2RkZE2B4yjo6PRsmVLGAwGbNiw4YPWkSzLYt++fTh69CiCg4PRokULq0lZW8Hn8zF58mRqA0uVKmVRsjEkJAR79+5FYmIigoKC6Oekebq3tzd9V52dnel7zPW9yPrwPxuYf3Ts2PGr2s/H3te3BuZLE3UZhgkFMA9ACoAEAHNZln1KGMec3x0DUBZAc5Zlnxf0eNWqVWOtdXO1pgtlDsQIc9k+BElJSUhISICPjw88PT0p84xk60lmKjMzE3K5HGPHjoVarca1a9cA5GRWLS1CoqOjcenSJYwbNw6//PJLrsZ5jx8/zrWNTqfDhg0bcOXKFZQoUQI//fSTWUdeKBSCZVmkp6cjOjoa7969w7t372gAxMvLC8WLF0dQUBD8/Pzg7OwMlmVp4MkcSHnxhg0bsHDhQqOmIHq93kg39/jx4+jcuTMmTpyIMWPGUN04Utbt6ekJBwcHGkg1d+9ZljX7LAkDOS/nwdJ3DMPcZ1nW4mq/atWq7L179/LtmHDHV2ZmJuLj42nXbrLgFQqF0Gq1dLGmUqkoI4EEMx0dHSm7JysrC9euXUPXrl1Rv359hIeH0/uUmJiYq6Tt0aNHaN26NZo2bYoJEyZYvIaXL19abP7y5s0b8Hg8GAwGvHnzBkqlEt7e3jSAZkn/mPteGAwG6PV6o/Ozpgeo0WhQrFgxsCyLuXPnYvfu3ejSpQsmTZoEvV5vMfBkMBjg5uaG2rVrQ6VS4cqVKzTwTkpqxWIxeDwezTATlhrR9yXOgOkCypbnb45lYmdnZ3F8VatWjb13757ZfRXUjpNFCReE1UqYG9y/ydgipYAk4TV8+HDExMTQTtnWdOvi4+Pps9ZqtejUqRPkcjkOHTqEJ0+e5Pq9UqnEtWvXcP78eSgUCri5uSEkJASVKlWi4zA1NTWXLVOr1YiMjMTTp0+RkJAAHo+HcuXKoXr16ihTpgy95wKBIFeZ3YYNG7Bv3z4sXLgQvXr1MnsdGRkZuRi0ycnJqFevHjw9PXH+/Hlqm8jChTjphI1nylogwQtr9qmgix5rtqug86I5ZgUZL6R8T6FQQCaTwdvbG15eXlSLNyMjg9qsR48eYfTo0UhOTsasWbMwcOBAi4vc169fQyAQ4Pnz5+jevTtq1qyJZcuWwc7ODg8ePLA4Z75//97IaTcYDPj7779x5coVPHv2DHZ2dggKCkKFChXg4eGBx48f48GDB9BqtahWrRqaNm1qlgUfHR1NF9SmEAqFRpJTf/31FxYsWIC4uDi0adMGc+bMMZsI0Ov1dD5lWZZW6Ozfvx8hISE0+UF8D8JcJbIf3HeXaL2TKgJL99XSnGkL8rJbBfW5LDXM4totrp/FDfaaCwgSyYG0tDSo1Wr88ssv2LNnD0qVKoWlS5eiatWqFlmxb9++pXPSiRMn8Msvv8DJyQnz5s2Do6Oj1YDx7du3cevWLVy5cgXVq1dHtWrVoNVqkZ2dTSUesrOzjRqwkfMljbCAnHEhk8kQFxeH+Ph4xMXFUb/M19cXvXv3hlAohKOjo0X26eLFi3Hr1i08fvzYbIBHq9Uasf7evHmDFi1aICsrC/v27cP3339P50Ygxz6r1Wq4uLjAYDAYaWlz5QisBa2sjbuCjC2lUom0tDR6/0yPT8YPkQEg50tsFkkuEC1jkUiEKlWqQCwW48SJExYD4G/evMH9+/cxYsQIjB8/Hl26dKHfPXjwwKLf9PLlS0RHR+PPP/+ERCJB7969jWxNWlqa2e0UCgWOHz+OmJgYNGjQAJ07d87l15lKpeh0Ovz22284ceIEOnbsiHnz5uXahmVZ2Nvbo2rVqnB1dcXu3buh0+lQqFAhGrwkvovps2NZlo4rcz6ONXzugF5e/rwlnysvf8va92RMkf/nVovo9XoA/9h18m6TBIy9vT2GDx+Oy5cv48KFCzRJ//bt21z2Z/r06di9ezc2btyI0qVLWzyfq1evQqfTISEhARs2bIBarUaJEiUQHBwMNzc3i0xy8pyfPXuGx48f4927dyhRogTatWsHLy8vWrlnDlzi0d69e/Hbb79h7NixVAu+ePHiubYZOnQotVvctQKxMaRJJ/mMNNj18vKiPSYsEYwKOu7yGgefyp/Pb5wC+OdeEZ8LALV9Op2O9loyHZM3btxAy5Yt4eXlhcOHD+fqcRMdHU3tR1JSEnr27In09HRs3rwZmZmZFm3lu3fvLK4HX79+DYVCgfPnz+P169dwc3NDWloaDAYDvL29UbNmTVSsWNGiPbUUWFar1Ubnr9FoMHbsWERHR2P58uVo1qxZrm3+/vtv/PTTT1i2bBl69OhB/XmVSgWZTAZPT09IpdJcc51KpUJCQgIAwMfHx4j0Zut4yyNGUaCx9S2BELO+Fnxt5/OpYG1sfVGmMcMwNQEsBtCVZdm/GIZZDWAMgL5mZCf2ArjBsuyrj3kOH9LFl8sIdXFxyTUZkYnR1dWVLoC4hjkzMxMikQgsy0Iul8PT0xM3btygBlgsFls0uCzL4tdff0W5cuXQp0+fXM6/6WJeoVDg999/R2RkJMqXL4+GDRuavd5nz55Rg8vj8VC0aFEULVoULMsiPj4efD4fERERuHv3Lm1sJ5VK4eHhgVKlSqFw4cIICAigJbQEZGE8YsQIHDx4ENu2bUOXLl3AMAz0er3RdbZq1QpdunTBr7/+iho1aqBu3bqws7OjixMg5+Xl8/lmF3jWHFUS0CCO28fOPOv1eqhUqnxnFE3Pgaurx81QkrFBHAAuG1kqlVIn3s7ODhERERg1ahQKFSqEzZs3Gy0M+Hy+0T1XqVQYOXIkvLy88tQ8Jg6tObx58waJiYm4efOmUeCQx+PBxcUFxYsXh5+fH2WEJicnIzk5GREREcjOzoZcLodcLgfDMOjYsSN1in18fCyeT3JyMnWYZ86cCQcHB2zYsAEajQZTp061uJgnY6BLly4YNWoUXr16RcuotFotYmJi8P79e7x58wZqtRrjx48Hj8ejJYOE4WduMWSLU2eOZVJQ5MV8srad6bam8hRcxj7RL+TaMsI0bty4MR1T1gIoQqGQyq78/vvvePnyJTZs2ABfX1/cvn2bLgiSk5Nx+vRp3Lx5E9nZ2QgICEDv3r1RoUKFXPf88ePHZsdIqVKlUKpUKTg4OOD27du4d+8eHj9+jHLlyqFbt26UAWy6MBo4cCBu3ryJpUuXolOnTmYlIQhrmAtvb28sW7YM3bp1w7x58zBlyhRaISCVSuHg4EArJMg9JUxbLrsI+HQyFJZQEDtobhvu+OHxeNRWiMViowZ4pKHNtWvXMHDgQJQsWRKHDh1C5cqVaYd4cyDNyyZNmgR3d3cqKUBg6V1KS0uDo6Mj5HI5bt26hevXryMlJQXOzs4oWrQoypUrR89dLpejcOHC8Pb2RkREBB4/foy//voLderUQevWrY0Y61qt1mJSKyEhwWisfvfdd9i2bRvWrl2L8PBwPHjwAL/++iu+//77XNtyt/vjjz/www8/YMiQIdi3bx9Kly4NrVZLqx/I/SSammQeMK0iKOhcR+bUgvpJBQ1Ic2VZuM+Va7eIn0UaUBJkZWUZ9ZIgzB87Ozu4ublh/Pjx2LNnD21+SaSeLPlcPB4PQqEQt2/fxpQpU1CpUiWsXLkSbm5uuH79utUy/VevXuHKlSuoWLEimjdvbvRsb968aZGN9fr1awQFBdFgEpDjT7q6usLHxwc9e/ZEcnIyXr58iVOnTuHixYto3rw5XVybQ/369XH69GlERkaiSpUqub4ncksExYsXx7FjxxAaGooOHTrgyJEjRtr15H5lZGTQ5rGurq6Qy+WU/f0hCYmCgPibXLvKHT/k+sjvSOJFr9cjPj4eUqkUnp6edJG4a9cuvHjxAn/++Se1a+YgFouxYsUKBAYG0gA+gYuLi8VKl507d+L06dPw9PREnz59cklaWBqTrq6uCA0NRVRUFE6dOoWoqCgMGTKEPvuMjIxcAWGBQIDJkyfDw8MD27ZtQ3JyMv744w+j5JXBYICLiwuWL1+Odu3aYc2aNejUqRPc3d3h7OwMpVJJAyaOjo5GPjfDMEbVJoQl/W9i2X3IWCb+E2Ehcn1I4u+TuZ8kXe3t7eHq6orly5fjzJkz+O2334ySQvb29kZroePHj2P37t0YMmQIfvrpJ6vVJvHx8dDr9di6dSv4fD4GDhxIk6BRUVEWA86RkZEoXrw4qlSpgipVquD+/fs4efIk1q1bh44dO1olenDHc5cuXXD79m2sWLECwcHBKFGihNmxQggDJOFjKj1ImnOS4BwXZN761iWauPGK/FSrkfUi8cFIUghArgpokhBUq9W4c+cOOnToAC8vLxw/ftxsU3Q7OzvKgO/fvz9SU1OxZcsWVKhQAZcvX7ZYHScQCCw2Wd++fTseP34MlUqFSpUqoVSpUtBoNHj37h3evHmDgwcP4tixY6hatSq+//57FC9enL6TpLrbHGJiYozGHp/Px+zZszFkyBBMnToVderUyVVJXblyZTg6OuLKlSv44YcfwOfzERgYSN9broQkn8+nc429vT1NYnPJN8C/yxZ+apjKkn7IfoCPU635Mff1reFrkKdYyLLsX//7/+kA1jMMI2JZVgMADMNUB+DAsuy2T3HwD3mRzekHEhB2KLcDM3GyiMQAGXju7u4oWbIkSpcujSNHjhixLi1h7dq1SEhIwKpVq6yWAwE5DKslS5ZALpdj2LBhNCiXHzAMA3d3d4SEhKBx48bIzs5GdHQ0YmJiEB0djZcvX+L8+fN0cePk5IQhQ4bkYjkwDIO+ffti4sSJuHfvnsXGf7/++isuXryIadOm4dChQ3BxcaELZG6HeHNBO2vBOK6u4acw4ISV+iEgZUHWHBzu2ANyHCYSRE9LS4NUKsXs2bORnJyMS5cu5akXNm3aNERFRSE8PBwuLi4WmS2WkJCQgNOnT+P8+fPQarXw8/OjzgZhgkVHR1ONRlMQeQFfX1+UK1cOUVFR2LVrF/R6vdmFrSUwDIPx48dDKpVi2bJlUCgU2LFjh9UgZseOHTF27Fhs3rwZ1atXx8OHDxEREYE7d+4gNTWV/s7R0RGdO3em2XlfX998ywxw8TVpjZomWoguOGCsy6fT6ejYcHV1RXZ2Nt6+fYukpKR8PadXr15hxowZuHnzJjp16pSrNEwul2PhwoVQqVSoUaMGGjRogKysLKvsFWvw9fVF69atERoaimvXruHIkSP47bff0LdvX7NyOyKRCBMnTsSIESMwd+5czJ8/3+ZjNWrUCL169cKqVatQrVo1NGrUyEg+hhvQI7aK6ENymcbfKkigQCgUgsfjgc/nQyqVQiaT0SCMWq2Gr68vsrOzMXv2bJQvXx5Xrlyx+brnz5+Pt2/fYsuWLXlKjgA5gd1nz57h0KFDeP78OViWRcmSJdG2bVtUqlQJBw4cMPseCoVCVKxYEcOHD8ehQ4dw6dIlXLt2DW3atEHTpk3znHvNwd7eHqNHj8b333+P5cuXo0OHDujTpw8mTZpk8fqdnZ2xY8cO1KtXD1OnTsXq1atpUEYqldIEDpHIIckH0zkyvyyJjxH8KWgiFbBNloU0MstrW8KyEolEOHToEFavXo0hQ4Zg2rRpNp/PmzdvMHz4cBQuXBhr1qyhVVDW8Ndff+HQoUMoVqwY2rRp80Fzhins7Ozg5eUFLy8vpKWl4datWyhbtqxVZmFISAgA4OLFizbb7KCgIBo4btWqFQ4dOoRq1apBo9HQYJVQKIRcLkdGRoZR82dzz+ZDyBq2gLwHJABuzj8nASYyLqVSKdXzJElhstj/7bffqL2wFoA7fPgwXrx4geXLl1v1ObjYv38/Tpw4gaJFi6JHjx759gd4PB66du1KeztMmjQJjRs3tqoFzzAMevfujbJly2Ly5Mno0KEDtm3blmsuDA0NRbt27bBhwwYq70Qq3LjgBpFJ01edTgcnJyebdY//P4H0ujEYDLSpcFZWFk3GExtB1joGgwFPnjzB77//jpYtW2LgwIEW9/3+/XuEhYWhSpUqGD9+fJ7notFosH//fmg0GvTs2dNi1Uxe+O677+Dp6Yl9+/Zh06ZN8PHxQc2aNfPcjmEYTJ8+HZ07d8aUKVOwfft2s79LSUkxIqtwx5ydnR1iY2OhUChQpEgRWnGpVqupjfncgbr/vSMfz9ij4L0tSAKV2F2dTkeTE1yCGalUyszMxMWLFzFs2DB4e3vj6NGjFgO8QE4VcM+ePREXF4cNGzagUqVKBbo+lmVx8uRJ3Lp1C2Kx2EgWRSwWo1SpUihZsiQCAwNx8+ZN3Lt3D7dv30a9evXQrl27As0lnp6emD17Nn7++Wf06dMH4eHhRrabx+OhWrVqiIyMNGK4c9dLSUlJkMvlcHV1pbbOzs7OqBLxW5KY+5rQrFkzXLp06aPsB8BXt69vDV86aHwbwFMAYBiGB0AEIBCAEwAZwzD+AEoBOPupTuBDXmRTBh7XkJBggE6ng1qtpmUzRCNNJBJBIpFAJBLRyYw417GxsVad/lu3bmHr1q3o2LFjno2nYmJiMHv2bAgEAkyZMgXFixfHiRMn8n2tphAIBAgKCqIaP5GRkShatCji4+MRExODkydPYuXKlZg3b14u57ldu3aYP38+1q5dazFo7ObmhhUrVqBDhw4YPnw4pk6disKFC0OtViMrKwt2dna0EylxGgisBeO4ATFu2fjHAmEKWIK1clvToB0ZL+aYLVwNbYZhaBA5NjYWiYmJOHHiBC5cuIBVq1bluTC8fv06/vzzTwwfPtwmJ88U586dw8aNG2FnZ4dixYqhfv36KFy4ML0+Ig+RkJCAChUqQKlUIi4uDpmZmfDw8ICHhweeP39ulGDQarXYsmUL9u7dC4PBgKZNm9p8PgzDYNiwYbC3t8eCBQvQp08fyqIwB09PT5QvXx6rV6/G6tWrAeQwVFu2bImQkBDUqFEDY8aMwYIFC9ChQwdIpdKPshD62I3HPgRZWVlIS0tDZmZmrveJC9JFnZRM63Q63L9/HwCsNuPh4uHDhxg4cCAkEglmzpxpVMJLsHnzZmi1WkyZMoWOn1evPrzIhM/no27duggICMCWLVuwdOlS1K1bF5UrV871PCtWrIg2bdpg/fr1aNiwIRo0aGDzcWbMmIEbN25gxowZaNWqFTQaDQ2ikCAf8I+mvb29PR2ffD7/m9bdS0lJoZIIRF5HoVDQ950EzHk8Hnbu3Ino6GicPn3aZlt879497NmzBwMGDDDL0DVFeno6xowZg5SUFLi5uaFp06YICQmx2HAxMzMTr1+/RokSJeg5ubi4oHfv3mjWrBl27dqFPXv24MaNG2jVqlWB9Y4rVaqEs2fPYv78+di8eTNu3LiBY8eOWbQrlSpVwtKlSzF06FDs3r0bAwYMoBqEjo6ONCBP5pePUclgmtQoyHz5IYlUa4v9/Ja+E73n+Ph4DBs2DHXq1MGCBQtsPpf09HQMGjQIPB4P69atyzNgzLIsnRu9vLzQpUuXT6rr26hRI0RGRuLgwYMYNWqUxd+5ubmhYsWKuHDhAsaMGWPz/oOCgnD06FG0bNkSbdu2xenTp+Hh4UFLvlmWhYODA1QqFdzc3ODg4GCx/4U1ssbHCigTKTLyPpGkHalak8lk8PDwMBqfKpUKLi4ucHR0pNtdv34djx8/xoYNG6wmiVQqFVavXo3KlSujSZMmeZ6fTCbD+vXrcf78eRQvXhw9e/bMxQrOD6pVq4agoCDs27cPx48fx40bNxAWFmZ1mx49esDLywtDhw5Fhw4dcPbs2VzXuGTJEpw+fRqbNm1C+fLlkZWVRSXiyDvFDTyRZ2uuEutDKha+RViyUYSFmJmZSavaSCDP0dGRVmqS/969e4dhw4bB19cXq1evtnrvVq5cCY1GgxUrVuQ5nrKzs3H8+HGkpKSgU6dONmm5W0NAQAAGDBiAPXv2YMaMGejatSt69OiRZ3LVzc0N06dPx88//4zt27ebXbMoFAoqj8a1vWTs+fj4wN7eHu7u7nTNKRaLIZPJcund53fuKAj+RwrLf1bZCgoaryBsWL1eT99NsVhM/TK9Xm/ExH7z5g0NGJ85c8ZqIoE0TycBY0tr+rygVCqxatUqXL9+HR4eHvjhhx/MVhIzDIMiRYqgSJEiaNeuHQ4dOoSLFy9Co9GgU6dOBTp2mTJlMH78eMybNw+LFy/GlClTjL6vUaMGFi5cSJMRKSkpdIxJpVKjaidL4+k/TeP/8G/Ap7GWNoJlWT3Lshn/+5MBkAYglWVZGcMw3QGMBnCIZdmET3UO3JLO/IIEfbidyzMyMuhk5OTkRDUF5XI51eFNTk6m7B21Wg2lUon09HTs2bMHPj4+CAwMtHjMx48fo2PHjvD398ekSZOsnp9SqcTvv/8OgUCAmTNnmtWJ+pjg8/koXLgwatasCW9vbwgEArOLJIlEggEDBuDUqVNUv9kcQkND8csvv+DUqVMYOHCgUdCIsPUyMjJyMT9IMDgvDb2CPvcPAXGoTcunyGKWey1kPBFtPa5zzh17DMPAw8MDzs7OcHV1BZ/PpxIjpg1UzOHPP/+Eq6srxo0bl+/rUSqV2LVrF0qXLo0VK1agUaNGCAgIsHpfpVIpSpQogSpVqqBw4cIWGX59+vRB8eLFER4ejiNHjuT73Aij/cSJExg3bpxVll2lSpVo+TEAqn9WpkwZlClTBhqNBm5ubrT5FMkgc5+J6TP6lkCCljqdjuroEXCvy93dHQEBAXB2doZAIICdnR3VJZPJZDYd68CBA5BIJDh79iy6d++ea0GRmZmJyMhI/PTTT3k2QisoihUrhnHjxqFs2bI4e/YsunbtiuPHjxuVgQPAgAEDUKFCBfTt29es3rIlSKVSDBw4EG/fvoVWq6WMUMI4JmOE26TrWwcZJ66urvD19aXVDSqVCiqVikoKODg4wMPDA/b29kbSFbbixo0b4PP5GD58uE2/v3z5MlJSUtC2bVvMnj0bLVq0sBgwzsrKwuXLl/HixQucPn0aUVFRRnbDy8sLI0eOxPDhw6HT6bBy5UosWrQIp0+fRnZ2ts3XQCCRSDB79mxs2bIFkZGRWLx4sdXf9+zZE82aNcPSpUuRnZ1NJYy4OvekTJf4IObsq622iuyDJDoK6id9inmW62+ZA7kXRA6F+FwnT56ERqPB0qVLbQ7S6XQ6hIWFIS4uDqtXr84zURAfH49p06Zh1apVCAoKQs+ePfM1xgsCoVCI9u3bU1/S2rNt2rQp7ty5g7i4uHwdo1ixYlizZg1kMhmuX78OrVZrtLC3t7eHg4MD7cdgKUgukUgsJiEs+Uh5gZT8k/eVOz4sjRUyRoiN0ul0EAqFcHV1peNm9+7dcHZ2zjMgsXHjRshkMowfPz7P8X7y5En07t0bV65cQffu3REaGvpBAWMCV1dXDBw4EFOmTEF6ejqOHTuW5zaNGzfGkiVLEBkZSZtfc+Hr64t+/frhyJEjVKqL+KRyuZxWXnl7e9OgCFdejQvyHPL7bL9VWBp3hIVImkfy+Xw4ODjA3t4eaWlpSEhIQHp6Om3Kunr1arx9+xYbNmzIs7LmyZMnqF69utV1JMGCBQsQExODZs2afZQGY0BOlWnPnj3RuHFj7Ny5E5MnT4ZcLs9zu1q1asHPzw9v3741+/2gQYPw119/oUuXLkhOToZUKqVNyJRKJfR6PSQSCbRaLd2GJLBTUlKM9pXX3PEx8D/bps/rd/kBWbfmN9DNXS+Sd1OtViMpKQmZmZng8XhGhKVZs2bRudJa0zsgp2Ll+fPnmD17doECxizL4sGDBxgzZgxu3ryJHj164LvvvrPYW4ALoVCIDh06oEmTJrhx4waWL19u05rXHBo2bIimTZvizz//zLUOqF+/PliWxd69e40IgVxWsTl79x/+w78NX80IZ1lWx7JsJoAYhmHmIydgvJVlWcUXPjWbwV2kEePr6ekJZ2dnCIVCSKVSCIVCqkPL5/ORmZmJmJgYLFy4EHfu3MHixYstMo1evXqFdu3awcnJKU/nwWAwYO3atUhOTsbIkSMLXHJUEKSlpeHZs2cICQmxaEQHDx6MgIAATJ061epimwT9EhMTERoaihs3btDJj/xrbgFMFhBfWwDP2mLJFGQ8kYYYXCfH0qJfKpWicOHCaNOmDRo2bIj58+fncpi4yMzMxMmTJ9GqVSubJmlTHD16FEqlEj179sxTAiO/EAgE6NmzJ4KCgjBt2rQCBY67deuGMWPGYOvWrVa1mknDj8jISPz666+oWrUq1q5dizp16kAsFuPGjRvo0qULWJalAT7TZ/I5HNFPBTs7O3h6esLFxSVXYJN7XXw+H05OTpDL5YiNjYVIJEKJEiWo1mde0Gg0uHTpEho2bGjRJkVGRgLIyf5/Sjg5OaFPnz5UA3LhwoXo168fPT6Q8w7u3LkTLi4u6NKlC96/f2/z/ok+9vPnz5GYmAgg514mJibS5JC1wN63BjJOTDuZi8ViODg4IDAwkH5OmLDBwcEQCoUIDw+3+TiPHz9GqVKlbL5nly5dQlBQEEqXLm3Vqddqtbhy5Qq0Wi1q1qwJV1dX3Lt3D9euXTOSqWEYBjVq1MCCBQswcuRIiEQiLF26FH369MGhQ4cKlABo2LAhevTogfXr11PmvjkwDIMZM2YgIyMDK1eupMEbovUvkUhoh3lrTCpz84lKpco1n5gmxr8m5PXukHthMBiQmJgIg8EAR0dHPHjwAJ6envmSupk2bRpu3ryJGTNmoGrVqlZ/e+/ePfz88894/fo1hg4ditmzZ+dqmPmpEBAQgCZNmuDZs2fYtGmTxd+1bdsWLMvi0KFD+T5GzZo1ac8EoVBIx7vBYKCyBGQ8ElkT04SttaR9fnwkLkyDzdzxYTpWSMCJMMPEYjEkEgmEQiGEQiHS0tKQnJyMuLg4yq625h9lZGTg999/x/fff4/q1atbPc9Dhw7h119/RdmyZbF582b06dPno79fpUqVQsOGDXHjxg28ePEiz983b94c3t7eFqUBunTpguzsbNy6dQt+fn70vrm6ulL2HQnYSyQS+Pj4mGXVkefwby/R5lYJWkvcZWVlwd3dHW5ubvD29oajoyO0Wi2Sk5MRExMDmUyGy5cvY/369ejduzd++OGHPI8dExNjU/XLnj178Oeff6Jq1aq0d8jHgkAgwNixYzF27Fj8/fffGDp0KG0KZg3m9IgJBg0ahC1btuD27duoXbs2oqKiqHYsOSaRlCFwd3eHr69vrooH7trqU60T/2fbvsgC1HR9SP7m6uyKxWJ4eXnBx8eHEmIA4Nq1azh8+DAmTJiQZ+KBZVls2rQJhQsXNttEzhq0Wi1OnjyJ4cOHY+bMmTAYDJg3bx7at2+fryQzwzBo0aIF+vTpg+joaKxatQqvX7/O17kQdOzYEYmJibh69arR55UrV4a/vz/Onz8Pe3t7FCpUCP7+/mYJg//hP/yb8dWsBJgcCAH8CKAbgM4syz7+wqeVL5gusAiDzM/PD66urrRhnLOzM3VkxWIx7ty5g99++w29evWyyGaIjY1F69atwbIsDh48mCcD79ChQ/jrr7/QvXv3AuuAFhQkuGetdNje3h4zZ87EixcvsHXrVqv7a9SoEe7fv49y5cph6NChuH37NrRaLSQSCTw8PJCUlITY2FizLN2vzYhbWiwRHWPTZi1kPJEydrJwIXICKSkptAxToVAgNTUVKpUKGo0GEyZMQEZGhtUS3BMnTkCtVqNdu3b5vpb09HScPHkSNWvW/GgsBVMIBAL06tULNWrUKHDg+JdffkHXrl2xYMEC7Nixw+pvHRwc0LVrV/z555948eIFVq9ejdDQUISFhaFx48a04YhSqYRQKDR6JrYEAL8GNrIpI4uAjDeNRkMZ7oDl62JZFqmpqWBZFlWqVMGdO3fyPPbVq1eRmZlp1cF8/vw5xGKxTUyZj4EiRYpgzZo1mDFjBhQKBcaMGYOoqCj6va+vL3bv3g2lUokuXbrYzGQgNvrx48cWt/mag3L5BbFRRC+e2F4yjng8Xq7rdHR0RKNGjXDw4EGb3gm9Xo+nT5/arJkXExODN2/eoG7dunnul4xNwniqU6cOKleuDJlMhkmTJuHq1atG74ydnR2Cg4MxatQozJkzB76+vli7di2GDBmCp0+f2nR+XEyePBm+vr4YN26c1cAzaeK4cuVKyOVyODo6Uv1WlUoFe3t7+g4rFAokJiYaNUwF/mGEEr3MD5kvP4V2oy0w9+5w7asp+4ckK27duoXvv//e5oXpnj17sGLFCnTu3BkdOnSw+tuoqCj8+uuv8Pf3x8qVK/HTTz999nc7JCQElStXxooVK3Dr1i2zvylWrBiqVq2ar2QNgZOTE8qXL4+IiAha9kwSF6TPgqmOdH6YpQWtAjMNNnNZeaZjhfxNqkC4c52joyPc3Nzg5uaGhw8fIj09HW3atLF67D/++AOpqal5Vj/s378fK1asQM2aNTF37lyaWPwUaNOmDaRSKX7//fc8tcwFAgG6du2Kixcv4t27d7m+r1y5MkqWLImTJ09Cq9XSZy2XyyEWi/H+/XvExcUhJSXF7DooPT0d8fHxtOLm3y5Nwa0StDS/c5Os5DcSiQSBgYEIDAxE4cKF4ezsjMmTJ8PT0xOzZs3K87hKpRKpqal5skPv3LmD2bNn48cff0StWrUKfJ15oXHjxli2bBmysrIwd+7cPKtxiLSjJXTo0AGnT59GUlISfvjhB9y+fZvOdW/evKF2n4Do3ZtWs5Exqlar/5XMd3OEFoVCAbVaDYZhkJmZCbVaDUdHR5rQzMzMREZGBtavXw8nJyf8/PPPeR7n2rVrePr0Kfr372+z9FJycjJ27NiBOXPmYO3atRCJRBg9ejRWr179QUSRatWqYcyYMWBZFqNGjcKVK1fyvY9GjRrBwcEB+/fvN/rczs4Obdq0wZkzZ6BUKql8DPE1iD+l0+mgUCjyvb6ztCb7D//ha8OX1jSmYHPeFi3DMLMB3GVZ9uUXOg+zn5OOrfkBMdykSQ1pCAPklDuqVCqkpKRg2rRpKF68OBYuXGi0WExMTASfz4fBYECfPn0gl8uxfft2ODk54cmTJxYbbdy9excHDhxAkSJFkJWVhaNHjxp9n5CQgGfPngEA1TLiNl548OCB2f36+flZLNEm8gqRkZG4efMmfvjhB9qJGoBZR7RkyZIICQnBkiVLaNM0U2i1Wtp1euPGjQgODsbvv/+O6dOnIzAwEGq1mnYtLVSoEH1+IpGIBrq4z5QEWC1pqhW0O+2HgjRuIefABUk+kH95PB7EYjGVEyABKYlEQgMARKKic+fOWLt2LQYMGED1p4Gc8lmGYbBz5074+fnBy8sLMTEx9Pvo6GiL5xoVFYX379/TpneVKlWiQZKoqCjcu3cPLMsiPj4eLMuiUKFCVIfK0jsUFxeHN2/eWDxmq1at6Lvy9OlTozIo0hHYHNRqNXg8HiZMmIDXr19j8uTJCA4OhpOTk9E9J5N8XFwcRCIReDwenJ2d0ahRIzRo0ADZ2dlQq9VISEiAt7c3tFot7OzsoNPpkJKSQp0IwgrPi+EHfNyGCPlxNrh6kuYC3KSMWigU0vtC3iPS4MzNzc1o4ejv748TJ05Ao9FAIBAYMTO52LdvH6RSKYKCgqht4OL169d49OgRvL298ejRI6PvFAqFRbZKamqq1RJIa9ISJUuWpGXazZs3x969ezFs2DB06NABZcqUoTIcs2bNwtixY9GzZ0/MnTsXOp3OYrLEYDBQCYTk5GQ4ODhAr9fTho9kPuBqbnK7zxfU9nyKxbi1scWdFwnrlZTekqoaEigQi8VGshzkfoSGhuL48eN49OgRZTyR8WmKiIgIqFQqBAYGmi2tj4mJQVJSEv375MmTsLOzQ6FChfD27Vuzdk2v1+PBgwdQKBQoVKgQEhISjMZZQEAA0tPTsW7dOhw+fBgVK1Y0khrw9/eHt7c32rVrhypVquDIkSMYN24cQkJCUK9ePRqQMgXDMLkWWj///DMmTJiAuXPnWq2M+OWXX7Bnzx5Mnz4dq1evpk1ulEoldDodXRQrlUp6Pzw9PWEwGMDj8egYy8jIMJK3KAjj/UO0G62NLZZlLY5nS9sR+0rGHPCPfqi9vT1evnyJt2/fol+/fmYbmsXHxxs926dPn2LYsGGoXr06WrRogb///tvscR89eoS0tDSEh4eDz+ejdu3aRoxxa2xPpVJJKxFMIRAILB5TKpVa/O77779HYmIiRo8ejUmTJuWqAqpbty4aN26M+fPn4/bt20a+gbVGb3q9HkKhECEhIdi5cyeSk5MhkUio3igJ2pK5giRUrc3RwMexW/nRjCRji9v/QqVSUX1wvV4Pg8GAU6dOQSQS4YcffqBl7ykpKUbvbVpaGpYvX04bnlp6JsuWLcPVq1dRtGhRlCtXDocPH6bfyWQysz4ygbVn4uLiYrHKp1atWjh9+jR27NiRq3Tc9H7Vq1cPy5cvx5o1azBixAg4OTkZ/b5Dhw6YO3cuoqKiUK5cOep/kkAxYfKTNQWBSqVCdHQ0VCoVlbHgglQ5SCSSAjUW/RpBxhVhspJ5HfhnziTvhFgspr4A1xYDOZ6cICEAAQAASURBVHrS9+/fp82cLQVUZTIZhEIhZVg6OzsbzYM3b96k/5+cnIzFixfD3d0dLVq0wI0bN6za03Pnzpn9zhor2NXVFSdPnqR/169fH8eOHcOUKVNQr1491KlTx+x2dnZ2SE9Pt7hfrVaL4OBgnDt3Dm3atEGbNm1w/vx5aDQaOteZkwmwdH3kORGbZQlfW5IjrzgFV/aLjDGWZY3md+7amDQATUtLw7Fjx9CqVSsAoOON9OMwxeLFi+Hs7IygoCA8fPgw1/evXr2icYqkpCRcvXoVz58/BwAEBgaiSJEicHd3R0xMDHbv3k23k8vlVskZe/futfhdaGgorl69ijlz5qBu3bqoV6+eUSNgSw0EWZaFr68vGjVqhCNHjiAsLMyouqRly5ZYsWIFjhw5gtatW0MkEkEul8PNzQ3Ozs5gGAaJiYlIT09HiRIlqP1kWTZPu2ZN4///O3r37v1V7edj7+tbg01BY4ZhzHXLSAdwn2XZhx/1jHIkKT5puiW/CxGyTX4nDm7ZDNHTI1IBbm5uYBgGU6dORUpKCvbv35/LSSOlcrt27cLdu3exYMEC2vjOYDCYDRq/f/8ev/32GxwcHFC4cGEjx4Hg8uXLFs+ZlEmZQ3p6usXGe9nZ2ahQoQJWrFiBYsWKYdKkSUYabZYM9eTJk9G2bVvMmzcPq1atyvU9N3NcrFgxjB07FnPnzkWTJk3g4+MDd3d3GAwG2gWbPCMej2fW+OZlnAvanZYLS8Ho/G5DwO3+TZxPHo8HT09PWlKsUCjA5/Ph5uYGgUBAtRwHDx6Mw4cPY+bMmUaTskQiQXJyMu7evYuBAwfmulZr1y4SieDg4ID79+8jODjYqLRtzpw5uX5PSvoZhrE4fk6cOGH1mAzDoGfPnti6dSv27t0LvV6PatWqAQAt8TMHuVwOkUgEkUiEKVOmoGXLlti1axdGjhwJAHTxR8ZYfHw8BAIBPDw84ODgQJ+LQCDA06dPoVQqodVq4eTkBJFIZFSOy2UuAObHl+lz/BIwbaZheh7k3eEGBPV6PWJiYujikGh+ubi4wNPTE9WqVcP+/fsRERGBqlWrUp1jLog0RZ06deDj42P23Mj7V7169VyBjoiICIsVFgqFwmpJ5oULFwDk2JPs7Gzw+XzqyKWmpho1Xqlfvz5Onz6N/fv3o0uXLnSBU7NmTfz8889YunQpNm3ahEGDBlmUZNHr9fQe6HQ6eHh4ICsrCzweDw4ODuDxeMjMzMzV0RrI22n8WI2iPgSE0QnAiCUFgHYtt7Ozg0qlQmJiIm2KRM6XZVmkpaUByGFGAsBff/1F7YOl4CVJTtWqVcusvImHhwd9JqTjfKVKlVCuXDncv38/1/NiWRY3btyAQqEAj8dDcnIykpOTc+03KCgIAoEASUlJOH/+PAoVKkTn66ysLMqKL1u2LIKCgnDq1CncvHkTkZGRGD16tNnGtiShzEX9+vXRvHlz7Ny5E/369UOFChXM3odixYphwIAB+OOPP9C1a1f4+/vDwcGB2lAS/CKJC7L44eoLmzbyLSgD0BbtxoLs15rPZS6pQljTpESZ6wuQ+0IS4j/88INZ/4nP51O/RSaTYdiwYXB3d8fq1atpObQ5vH37FteuXYNarUaLFi3A5/ONCABkrJvDy5cvLc59YrEYxYoVM/udTqezyCgkTfsWLFiADRs2YMyYMUb+mKOjIzp06ICFCxfi7NmzuZqYWtJf1uv14PF4qFWrFtatW4e4uDiULFkSSqUSGo0GPj4+4PF4UCqVUCgUVMOcECYsJVM/ph2zpckV+ZyMDe6cRpJbdnZ2uHv3Ln788Ucj39ze3t7oXi5duhRKpRKTJk0y+04DwM6dO3H16lX4+fmhUqVKuSTD4uLiLD5nALk0NrmQy+UW575y5crh5cuXCA8PR4MGDYyeq1KpNBp3Dg4OqFevHo4dO4ZRo0bl0lju0qUL5s6di9OnTyMwMBAikQgGgwEuLi6Ii4uDnZ0dFAoFPD09jZ6nRCJBQEAAbZBIQILFJMEA4LPJuHxqcJN2pv6g6ZgkzE+ZTEblPgwGA54/f4558+ahSZMmaN++PdRqtUU2p6urK0QiEf766y8AQOnSpY3uNWH4qtVq/PHHHzAYDOjXrx8EAgGys7MtMt4PHjxocS52d3e32Pw4OjraKODm7++PChUq4OHDh/D09ET37t0t7jMqKsoiIYoke0qXLo1du3ahTp06GDt2LH755Rejigdz76ClxuOkiadSqSyQT/Up/LG8mqmbA/eaTNe/ZC4lyWLT+Yac+8WLF6FQKNCxY0ejsWZuDDx58gSXLl1C586drb63Xl5eSElJwfbt28GyLOrXr4/atWtjx44dSEtLMzs3SqVSur4zByK9pNfrodFoqC44+Wzo0KE4cOAALl26BCAnkAzk2HtLbOb09HRIJBK0a9cOBw8exM2bN2lFJMuy+OGHH+Dl5YWTJ0+iefPmlGVsmgzSarXIysqiPa1M7525+amgDQ7/P+C/oPHXBVvpTNUADAbg97//BgFoAmA9wzDW2/PmE586YPw5YLqg1mg0VCPN1dWVshI3bdqEEydOYO7cuRYn38TERMybNw8hISHo3Lmz1eNmZWVh1qxZ4PF4KF++vNnslmmZqinkcjni4uJoMCM/WLFiBRQKBcaPH29zU48iRYqge/fu2LVrl03l7aNHj0bRokWxZMkSaLVa8Pl8+Pr6wtnZ2agcjgRTTeUA8tJUK6ie3ofCkgaVucZ3BFxWAmG9ajQaKn3i7u6OMmXKoF+/fti/fz9u3LhhdMwTJ07AYDDQCTU/OHfuHLKzs9G0adOCXrJFqFQqxMTE5CplI1IVpDmeLRq6XJQrVw6NGjXCpk2bLDIZ9Xo9HB0djViTUqkUer2ean8RiQqNRgN3d3e4uLgYBWCsSVQUtJHFx4Sl8l8y5nQ6Xa5SKZlMhri4OGRkZIDP51NNSA8PD6MAvrV3+OrVq8jIyECDBg0s/oaw3YsUKfIBV5gbJGhBSvZIybS5MjJXV1fUr18fSqUSBw8eNGJ5derUCS1btsTmzZtx9uxZq8ckpX9ET1Wn0yEtLY0yIYnmKgDKDiULH2soaKOojwmizZyUlETPg9tghTRjMxgMSE1NzSV9IBKJ4OnpCU9PT5QpUwZubm64e/dunse9f/8+nJycbJIuef78OVJSUvDjjz+a/Z5lWdy9exfv3r2DnZ2dVTYIwzBwd3dHsWLFIBQKERsbS8utTSESidCqVSsMGDAABoMB06dPx5YtW2zWOh49ejScnZ0xcuRIq+W8kyZNgkgkwoYNG5CWlmY0Hkh1EynNJ029uGP+Y0mj/G/7L948ICsrC5mZmfSauSC27dq1a5BKpXlqeMpkMvTt2xdyuRzr1q2zmJQk+7579y6Sk5NRr149q7/9nPDx8UHPnj0RFRVlVobCy8sLP/zwAw4dOpRvX49Ij128eBEGg4Ema7jzIFf+hCRTP4dcWEGkVrKysiCTyaBQKJCSkgKDwYCkpCRERkaicePGFreTyWTYuHEjWrdubTYYwbIstm/fjs2bN8PX1xfBwcGfde63s7PDgAEDkJycjAMHDuT5+06dOkEul+PMmTO5vitWrBiCg4Nx6NAhREZG4smTJ8jMzER2djYKFSoEFxcXo2QxITOQBJavry8N7BgMBshkMqSnpwMAldj5t8HceoIEKDMzM5Geng6ZTGb0/hkMBsTFxdFS++XLl9scjIyNjQUAs8kkvV6PrVu3IjExEb169bLYDNYSsrOzC9TslSAkJATe3t64dOmSRVY9YfzbgsqVK2P8+PE4ffo0bt++DW9vb/j4+FAbZE0KztRGfIhP9TX4Y4B1u8f9ztx9IUn/EydOwMvLyyITnIsVK1ZAKpWiUaNGVn+n1WqxYcMGsCyL8ePHo3Xr1maT93nFJkx/T9jlpGqbOzb5fD46dOiA6tWr48qVK/lq+Pr999/D09PTqBIEyAk4t2zZEhcvXqSyMoTQRapUPD09Ubx4cUroIn4wAbF7aWlpRp8XVJLp/wPMkTgKup+vcV/fGmz1XvwBVGVZdizLsmMBfAfAC0BtAL0/0bl9szA13mKxGNnZ2cjIyIBWq4WjoyMSEhIwZ84cNG7cGEOHDrW4r/nz50Oj0WD+/Pl5GpRVq1YhJiYGEydONMsUSUtLs6hxRyAUCqFQKBAbG4sXL17kcmgs4dWrV7h8+TJ69OhhVOpoC/r27QtfX19MmjQpz9/a29tj8eLFiIiIwPbt2/N0CEyfRV6Mqs8R1COTtl6vp/+aTiS2LnxIgAbIcbzt7e2hVCqRkpJCHdYJEybA29sbU6ZModuxLIujR4+ifPny+dYjTklJweXLlxEcHGyRMWoOMpksz99kZ2fj9evXSElJwevXr3ONPdIcr3Tp0jh48CBlVtiKESNGQKFQmG34otPp4OTkBCcnJxrsIpO+vb09XF1d4enpiaysLKSkpOTSo7MFX7pBozXtLDLmyLVlZWXR35PSY39/f3ofSIBQp9MhMDAQPj4+Vu3LyZMn4ejoaLXDcmxsLNV+/1jXS6QHtFothEIhHBwcIBQKodVqkZGRYZbp4OXlhdq1a0Mmk2HChAnUoWUYBmFhYahUqRLmzJlDZX4soVChQoiNjcXr168RFxeH169fIzY2lpbqEgaus7MzHBwckJmZmec7/6USW1zY29vD29sbXl5eRudhMBhoCSBhUri5udFECjehShiharUawcHBNiUN79+/jzJlytjkXF+9ehX29vYWGSsvXrzAixcvUKZMGZvfX5FIhKJFi8Ld3R1yuRxRUVEWyyiDgoLQt29f/PTTTzhx4gTGjx9vU4MWZ2dnjB8/Ho8fP8Yff/xh8Xfe3t4YOXIk9u7di/T0dCrvpFQqKaOYMNhJsIYbzLeWpPwWwU3Yca/HYDAgOTkZcrkcN2/exHfffWdVf1GhUKB9+/Z49eoVVq1ahXLlylk97t69e/H+/XvUqFHjg3TY9Xo9nXM+FneiatWqaNSoES5fvmx2rmzTpg3evXuXK6GcFwICAuDv74+7d+9SljFX0osE7ok9s9YQ7GOjIM1FiT1zdHSkQYjjx48DgNWg8erVq6HVajFu3Diz3x86dAjbtm3DTz/9hPLly3+RZHH58uXxww8/YP/+/VabIgM5gb2AgACLWtedO3fGkydPEBcXB4FAAIlEApFIhLS0NPD5fKSlpVEbolKpkJSUhKSkJMo8z8zMpKQOkiglc8a/EeaCQSSYBOQEt7RaLVQqFTw9Pankw5w5c3Dp0iWsWLEiXzaFNCc25z8dOXIEERER6NChQ7763LAsi4yMDCQmJiIxMdHmng6m4PF4aNSoEXg8HiZNmmT2mVuTvDCHsLAwlC9fHosXL6bjiTu/xcfH55J24VakcOUpCupTfQ3+GPBPTwlza3+uTST9cGQyGSWIkCTZ2bNn0a5duzz1iWNiYnDw4EH07t3baoUoy7LYvXs3YmNj0bNnT7OJCr1ej8jISDx9+hRv377NMzGhUqlo3x5CYCHPnRt4ZhgGoaGhsLe3zxUAtgY+n4/69evjwoULuchFbdq0oclnct/I3EfOw93dHWq1GmKxmK7JAdCGvGlpaUbVw//BOtq3b//R9vM17utbg60ejBcALk0mG4A3y7JZJp//B+QEiU1ZoP7+/vD19YWLiwuUSiVevnwJjUaDUaNGWV0AP3jwAI0aNbJaugbkBA8vXbqE0NBQi929uSXQluDg4ICSJUuicOHCkEqlSE5ORkJCQp5OHdF7y08QkUAikaBv377466+/LGoncUEcHuKMmoLbNK4gC4hPDRKcS05Opv+ShT05T9PGd5ZAtI0JK5ZhGCOmtZ2dHa5cuQKZTGbEArl16xYiIiLy3QAvKioKW7duhVAozDdD2dZMMinFtBTAEAgEaN26NQBY1M61hHLlysHHx8eo0RmQo+en0WgwY8YMSCQS8Pl8ODk5wc7OjsrLEAa3nZ0dZbabPh/ybJVKpdkgzJdu0GiNFUHeFXd3d/rOkAUOj8dD0aJF4ezsbFT2RrYJDAxE1apVce/ePYvH9vDwgEKhsNokTCAQQKvV2szKzAspKSl4+/YthEIhdexJoyZHR0fweDzExsYiIiIi17b+/v5o1KgR7t69i9WrVxud44IFCyAUCjF79myrxyclmRKJBM7OzihUqBAKFSpktMDgBo+5TiYX3CDY52YlWGKmEHYYl72qUqnoopLIb3h6esLLy4s69gqFgj5fwg4tV64cIiIi8lwweHh44M2bN3S+sYaoqCiULl3aYqlramoqhEKhxfnSEhiGgbe3NwoXLgydToc7d+7g9u3bZudIoVCIfv36Yfr06dDpdJg1axbevn2b5zHq1q2LMmXK4Nq1a1Z/N2rUKPB4PJw7d46OKcIyBv6ZH9zd3eHg4EDHl7lGOV8DW+pDwGVOKxQKREVF0flVq9VCp9OhcOHCePDggZF+vylYlkVSUhIaNWqE+vXr53ncx48f0+ZwBQVh7qtUKqSnpyMpKcmmMW4LWrduDV9fXxw7dizXGG3atCkKFy6MoUOH2jQuuWBZFnw+H2KxGAkJCUhKSjKyEeZ8L9NqFm7Q4mPBVgY9YcJyk+7e3t5wcnJCZmYmZUNaazp99+5dhISEWPTPHzx4AH9/f4wdO9Zme63X6/H27Vs8e/YMDx48wI0bN3Djxg1cuHABp06dwrFjx/Dq1Sub9kXQunVraLVavHxpvVUMWatY8sNJAD0xMRGenp7UppMELCnNBnL8ei8vL5rMSkpKgkwmo6X8JFGanJxsVLHybwfxPSQSCezt7XP570COXImvry+6dOmSr32/evXKbO8QlmVx//59VKlShcpB2QKScMvIyKC+74f4Zg4ODihXrhwiIyPNrgdIBZ+tEAqFCAwMhEajoevtvPxrcxUpH+JTfS0sUdL3xlwPBcIkJsFKrh45uV8qlQp6vd6mtfKpU6doryVLYFkWFy9exN27d9G8eXOL82NSUhLUajWcnZ2Rnp6OiIgIJCUlmfWnlEolwsPD6XkSHXTy7E3nEYlEgmLFilntd2IOiYmJuWSIAFD76enpiWLFikEikdBxTCq6UlJSkJ6ejpSUFNrjhkhSZGZmQqPRmNXd/g//4VuAraP2TwC3GYaZzjDMdADXAexkGEYKwDrV6l8OcwtqUtpO/iXOEJGqUCgUNICXF/uSMBnzwpMnT6DT6aw6BKaayZZAdOj8/Pzg7u6OtLQ02tTMEkqXLo3AwEBs377dqv6aJZBydKJ/aw1btmwBj8dD69atodPpEB8fj/T0dKMmSyQD+LFKcD8myGLKw8OD/ku0Ycl5kmsgk7mlRZWdnR08PDyoDhopbycT1YkTJ9CjRw+EhIRgx44ddLs//vgD3t7eNPhqCyIiIjB+/HjY2dlhzJgx+S5vs6SVzYVAIECxYsVQqFAhFC1a1KIjFhkZCQB5ssDMgZRLctGsWTOsX78eFy5cQJ8+fagWncFgQFpaGmXoaTQaOuaIHAgX5NkCMFsOZokN97lgjRVB3hWSvCAMMUdHx1yatABouTvRZ65SpQoiIyMtLjh//vlnBAYGYu7cuRYXiOXLl0d2djYeP378Ua43IiKCJo9MbQDRs3R0dMTdu3fx+PHjXDauXLlyaNeuHf7880+qiwzkaL936dKFNnCzhHbt2iExMZGyaHk8Hm2sp9PpkJiYaOR0cjVpuePiSyYb8ip7lMvlePfuHXQ6HV0QGwwGvHz5Eu/evaPvCGGsOzo6QiQSQalUUn30cuXKQafT5cnEHTlyJGQyGQ4ePJjnecvlcqtSAY6OjtBqtQUuuXV0dERQUBBcXFxw8OBB7Nixw2LDqnLlymHWrFmQSCSYN2+e2V4DpihUqFCev3Nzc6MlmCkpKUhLS4NcLqfBODJ3kK7p3IUjN6CXl2zTtwayQEtNTYVOp4NQKISfnx/Gjh0Lg8GA0aNHW/RnnJyc0KlTJ5w6dcqmslYibVKQoAGRzUlJSQGPx4O3tzfc3Nyg1+uRlJREE8ofAh6Ph4YNG+L9+/e5kmMODg7YsWMH9Ho9unfvnicTlSA6Ohrv379H9erVqexOamoqtRFc3UYgp5yTsNsSEhIgk8mMqlq+RMCQBOoTExONKtFIIJT4N9aaAiclJeVJlCAkEltBAsUPHz7Ey5cvIZPJkJ2dDYlEAm9vb7i6uuLp06d48uSJzQxdci15Jdn1ej0ePXpkUS4vMDAQ9vb2iIqKohVZAGhSVigUQiQSUVkKqVRK1y8eHh6UTUtAmlSaVqz8m0GCjGq1mjZ9I1U3QM54adKkCeLj43Hx4sV87fvZs2coW7Zsrs/T0tKgVCrzJB9xERsbi7S0NGg0Gri4uFBJgQ9dSxkMBuoHmYIEM23Fw4cPcfz4cfTr1w/Ozs50HiNkDm9v71zr56+RSPQxYMt1Ebvs6uoKPp8PV1dXul6RSqX44YcfjBoYWsKVK1cQGBhotUo1PDwcd+/eRZ06dSxWa2RnZ1NCU9GiRVGqVCmIRCK8fPkSR48eNfKnMjMzER4eDoVCAYlEYjR+iA9nbkzFx8fDz88vz2sieP36NS5duoRevXoZ3cusrCwsXLgQP/74I5o3b47s7GyoVCokJydDJpNR39bV1RVqtZrOd9xqVR8fHxQtWtRoTFqr/vwP/+Frg03Wn2XZ2QAGAkj733+DWZadxbKskmXZbp/u9L5+mCuBMVcK8vLlS1oGLZVKqTHKK0iq0WjyZJsCOY6mUCi0GkQjASFbwTAMPD094eHhgfT0dDx79sxiQNjOzg69evVCbGysxW671kCMOtHksgStVott27ahadOmKFOmDFJTU/HmzRu8e/eOsjtFIhF1HL7GklsSnCPNCsi/XGfMXPARMJ+kIItVmUxGS0UNBgMuXryIjh07olKlSjhy5AidqK5evYr79++jb9++Fll4pnj06BEmTpwIR0dH9O7d22LjjLyu2xY4OTnRbveW8Pfff1tt2mgJBoMBmZmZZhs39OrViwaOBw0aBJFIBEdHR7i4uFDGMWGE8Pl88Pl8OsZIkAb4p4kQKX/jso+5DRA+dSDQnDNiKysiKyuLZsu5DT5YloVCoUB8fDyePXuG+Ph4GvQoVaoUWJa1KBkikUiwZMkSvH//Hr///rvZ33h5ecHPzw8PHjz4YCdKLpcjKSkJxYsXtypHExAQgGLFiuHRo0e4d+9eruOOGjUK5cqVw+zZs420+Dp27AgXFxcsWLDA4jk0bdqUlsfxeDx4eHjQhEFMTAzi4uKohiYp3TU3LmytPPgUsLYYsbe3h06ng1wuR0pKCg18Eza+t7c33N3doVQqKZOSJE8J60IoFFLbRDprW0LdunVRrlw5bNy40Wqwl8hBubq6WvwNsa+2VLdYAp/PR5UqVdCsWTNERERg2bJlFvUaPTw8MGXKFGRnZ2Pu3Ll5HtfT09MmSZ+GDRvi3r17kMlkyMzMREZGBt6/f2/EoDQNyJgmU7+W5Gp+F1CWEm+ECeTn50eZ1lqtFkWLFsXPP/+Ms2fPWu3A3r9/fxgMBtpwxxoYhimQrcrMzMTff/9NJcu8vLxoyb+Pjw+kUinS0tJw48YNm8aBNVSvXh1OTk5mfbOgoCBs3boV8fHx6NWrl00B3Js3bwIAateujUKFCtHqNGIjlEolEhIS6JxHKqm4DX+4VS0fK2CYn0QskaTw9vY2sm329vbw9PSkMkqW3meWZSGTySiT9mMgISEBL1++RMmSJdGhQwd07NgRrVq1Qp06dRASEoIqVaqgZs2aKFq0KF6/fo379+/bdK2kMiqvpMDLly+hVCqNGsNywePxUKJECbx+/ZrKKhE5KRK0U6vViIuLw7Nnz2jvgMzMTJqkJQ1SiX9BAsvWyBH/RkgkEmi1WiQkJEClUlEfRavVokGDBnBzc8PWrVtt3h9hbJsLGpPKisKFC+e5H6LRfurUKdjZ2cHb25sms4n/+yHIzs626Nvz+Xzo9Xqb7en8+fPh6uqKVq1aQS6XU5IV8S/M6dt/LXPdh8Jc3xuuRJDpb4nubkZGBuRyOSVWAaBrnQYNGuD58+e5KjG50Ov1uH79OmrXrm3xNydPnsSOHTtQrlw5tG3b1qL/TapTSOJNLBajRIkSKFKkCN69e4dt27YhIiICGRkZ2LdvH5RKJdq0aWMUx2BZFtnZ2RAIBLmOk5WVheTkZIsNY81h/fr1EIlE6NWrl9Hnf/zxB+Lj4zFy5EikpKQgOzub2i5uM3StVmtEGpRKpRCJRMjKyqLVhNxn9LVoYv+H/2ALbIogMgzTj2XZjQDucT5bwLLsxE92Zl8BuF1Ryd+m4EoBkO8JowfIcUBJ6ZZYLKYl3xKJBGKxGNHR0WbLSYAcJ4AYHlPR7Tdv3hgZnhs3biAwMJAyMC0xlMRicZ6aVKaOJZlkExMTsXLlSpQtWzbXxBQUFIRSpUrB398fmzZtgqenp5Fht+YM6vV6yrx++fKlkeYpYVgQHDlyBDKZDK1bt4Zer4dQKISvry9l5yQmJlInVqPRUE0i00XJly4lMoXp2CJjiJSji8ViOh6J5iGZrBiGoZOWTqeDq6sr7t27hz59+qBYsWL4888/YTAYaInOzJkz4ezsjGrVqlksSX348CH9/+fPn2P37t1wc3NDz549ER0dbbZRii04deqU2c9JoNsSuGwfrVaLV69eoUKFCoiJiYGLiwsSEhLMbmfazZwEJBiGQUpKSq7gd9euXaHX6zF48GC0b98ee/fuRWZmJi3lysrKogswd3d3OsaINh/wj3aVt7c3fXbkc24nbS7L71OAOCPkeASmLDBzIGXtJBhOtlcqlVQfNC0tjTYJ1Ol0dKF57949izp8VapUQYsWLbB9+3bUqFEjF6NJLpejWLFiuHr1Kh48eGBUGqxWqy0+Z41Gk6tklzQ4A2A1MM+yLMRiMdzd3REREQGZTEad2MqVK+PChQto3Lgx1q5dixEjRmDgwIHg8/nw9PREr169sGzZMly4cMFoscayLIRCIQQCAZo0aYLr16/TRBZ5VwnTw9XVFZmZmUhKSoJUKqUawOSdJ9dHnPz8JP4+BsgYJsxzU9vv7+8PiUQCNzc3er4SiQS+vr6Uhc6yrFGHcqJFJxKJ8P79ezq+nj17hpYtW1qcE4GcZk3Tpk3Dnj170Lx5c6Pv3r9/T3XjgJz7RgLR6enpRhp1xEkn2unWKmRIwyZzcHd3R2ZmJsqUKYOXL19i7dq1KF68ONzc3FCiRIlcwe0WLVogPDwcixcvhkgksqhB6OrqiuTkZGRmZuaar7jb1K1bF7Nnz8aVK1fQo0cPKBQKuigmi0jyzpsei2VZagsYhrHaDPBzwJLNMgUZZyTBAuSe400XcGKxGEKhEL169cK5c+cwYcIE1K5d26hihmifOzo64scff8SZM2cwYsQIADljy1zwlsh+WQoukm255070XXk8Hj1vU/1EOzs7FCpUCDKZDH/99RckEgnc3d3pM/Lz88Pff/9t9nhSqTRX8q5UqVK4e/cuLly4kCt4xOfzMX78eMybNw+DBw/Gli1bLNoZZ2dn3LhxAw4ODihbtixNhhFfhesHAzlj1cXFhY49ImkBgD6XjxXAsTYeuCDnyg2GKRQKGhQTCAQoUaIEgBxfm/sOp6SkQCAQIC0tDWq1Gk5OTtRnjo6ONkrEE+3N169fU/atpfO+e/cuoqKi6PZPnjyxeP5CoRCenp6IjY2FQqEw0i3nwt/fHy9evACQkzyKioqif/P5/FzBlOvXrwPIaXpnyQaXLFkSd+7cgVKppHJ7YrGY9oFwcXGBQCCATqdDTEwMSpQoQRPoer0ePB6P9t4g+tFAzrvHfSZfMwqSJCI2WCQSgcfjUR+B+z7o9XoIBAL4+vqiRYsW2L9/PzIzM+m6zRLJIzExkfrsPj4+ufyke/fugWEYJCUl5WKbEwkvIMe3jo2NpWxUvV4PtVoNtVpNrzk7OxsKhQKurq5W5U4sVQ3J5XIIhUKzvhwZc2lpaWZZoyTJDOQQWY4dO4bx48fD09OTNqg2GAwQiURGvinZlsxzpvja1oO2gGvryFzJHZfcuZ38lly/s7MzsrOzKVkByJlv6tevj6lTp+L48eMYPHgw3Rd33nvy5AnS09NRuXJlyGQyREZGGh33yZMn2L9/P0qVKoUKFSpY7HOiUCgoQ9d0TAYGBkIikeDdu3c0ecEwDIoXL45Xr14ZSTdx+41otVqwLEuD3mTe5fF4tDqCxEhMwbIseDwe9u/fjzZt2sDR0dGoSm7x4sWoX78+KlasSN9FEmcgNo1lWVo9R+YfIulibl5iWfaTrwP/w3/4mLB19dmOYRg1y7J/AgDDMKsA5F7pfOMwnThIVg6wvICxs7ODUCiETCYzYvJyAzPu7u5QqVRwd3c3Kk329/eHTCazyB5zc3OjpUGmjQ0kEgl1ruRyORITE1G7dm36uxo1apjdp1gsxoMHDyzeA4PBkKuzKZCzUIiLi4NMJsO9e/fg4+OTixlbq1YtdOzYEb/99huePn2KBg0a0O+t6cJpNBoULlwYPB4PycnJRk4jKekn2LZtGwICAlCvXj0IhUL6nVQqpawWlmWN2FXmHAVuQuBzOAwFPQZhIZNyVVdXV3ptDMPAzs4OLMtCrVZDKBSCx+PhyZMn6NGjB3x9fREeHm40du7fv4/Lly9j0KBBVrOvzs7OAHICbzt37oS/vz+GDh0KBwcHnDlzhn5viipVqiAoKAhyuRznz58HANSpUweenp64dOkSfY90Oh30ej0d+4ULF8aPP/5o8Xy4DPpbt27BYDAgNDQUpUqVQnZ2NgICAsxul5aWZsRGjo+PB5AzHk0TGwREp2vw4MHo1KkTli9fDpFIBGdnZ6hUKmi1WjAMQ51QIOe9Iskj8l9mZqYRk5wsSMmzI8ybjwFz44s7/glI917iaFka/zwejzZ04L4/JDhIpDnImHN2doaPjw/VDOU6nKaYOXMmHj16hEWLFuH06dNGTtSPP/6Ihg0b4tGjR0hJSUHv3r3pd1Kp1OKY3bp1qxF7nDClfX19IZFIrOrkvXjxAkKhkGp0pqSkGAV0AgMDIRaLERoair179+Ls2bOoX78+FAoF+vfvj82bN2P16tVYv3690X0mwZ1OnTrh4MGDuHDhAtq1a0cblREZB5lMRlkjxHaZjosv6VgyDAO1Wm12LiSl+aaMfzLmybhxcHCgjDPCtLe3t4dGo4Gnpyfev3+PgIAAvHjxguqJW0KnTp2wd+9e7N69mwbwCQhDkywqihcvThMY7u7uRqxAvV5Pg3Gurq5W76012ad9+/bl+owspnU6HZVeIihevDjatm2Lffv24Y8//sCkSZNyLZBJQIcEPbl69OR77rk5OTnhxo0b6N69Oy19JprdQM5inDDZiQYgYR6RZJctMlgfG6a2h8tEtTZnku+47wX390QGzMHBgbLk7ezsaHBh7dq1+P777/HLL79g586ddDt/f396z2rXro1Lly6Bx+PB09MTvr6+Zu8RKZWtVKmSxfO9e/cugBy/IzMzkwaHJBKJ1YQEl41OGgARiMVii8m5jIyMXNIstWrVwl9//YXIyEizAZnatWsjIyMDy5cvx7x587B48WKzz0AgEODWrVuoUaMGDYDZ2dlRqQ0SuCE9AIgdJfsicyGZh1xcXD5oHuSeY37Gj2lzsqioKBgMBjg6OsLDwwP+/v40qcUN1pGgKJEuKVq0KH0/STM97vlkZmYiICAAVapUschKnjFjhtHfJLBLYM5/FggEcHFxQVpaGu7fv4/g4OBcSSGDwUDtj6enJ7RaLf3bYDDk8pvevHkDLy8vfPfddxbXJeXKlUN4eDjevn1LWdrkfpLAS0BAAJRKJdLS0pCUlER9dRL8Ie8UN3nNJdv8G0ECd05OTtRGE/+Kz+eDYRgIhUIwDAORSIR27dph69atuH79Opo2bUp9SXNwcXGhVZrBwcG5KmyIPTA3/iwF9eRyOVxcXKgdIdU7jo6OcHJygl6vtzieSfLfHKKiomBnZ2fWdpG1Clenlgvumo+wjIcNGwZnZ2cIhUKkpKRQeQAyt5F1KrGlH3Oe+xLBZq4vTv4ln5ljsAKg1W2E3EZYsuRvovsvFotRunRpnD59GqNGjaL74r6XZC6rV68e1eUm3z99+hQHDhxAUFAQBg8ejBMnTliUMSRJA/IecEH6TbAsSxNQQqGQMubJWpFlWdy6dQsMw1AfzcXFhRJYSLC7Xr16cHZ2RlpamsXqMx6Ph8OHDyM7OxsTJkwwsrlLliyBTCbD9OnT4e3tTSsoyNzt5ORE301CUCE9ccRisZG/YjpmSPXnfzCPIUOGfFX7+dj7+tZga3q/HYDeDMN0YRhmKwAdy7L9PuF5fRWwpSuqVCqFUChERkYGlQfglhiTAEFgYCANEgA5QTI/Pz+r8hQ6nY5mTa2BNJayRd/1Q0rpSNkekeQwxwwtX748SpYsiaNHj+aroQGPx0OhQoWs3o/Xr1/j/PnzaNGiBRXCJ01vEhISoFAoUKhQITg4ONBybktlSN9aSUhKSgri4+PNlheSxb9UKkVUVBSaNWsGV1dX7Nu3L1cw59dff6XlXHlBp9Nhx44dcHJywvDhw/O1sHN1dUWNGjUQHBxs5DRoNBrIZDK8f/8eCQkJkMvl+WZt3L9/H05OTpQJlB9wS7atoUePHlSqYuTIkdT5dHBwoHIVhCFFgmNkTJHSfGIHCD53WRxXKoCAWypsGiQzV+5mer6k3I/YNBJIIWyGSpUq4f79+1bPSyKRYPHixYiOjsaiRYtyfS8QCFC3bl08ePAgV4WFrUhISKCllbaCYRha4kYYC1wUL14clStXxq1btyjz3cnJCf3798eZM2csNvhr0aIFSpUqhfnz50OhUCAlJQUpKSnQaDR4//49Xrx4gdTUVHh5ecHb29vsYok7zj43TLuNcxtImZsDuM2uuI2muF2uFQoFYmNjkZGRgaSkJPB4PJQuXTpPeQog5zmNHDkSb9++xdGjR83+hlRVmAZbueDxeHB0dLQatDOH/JRQx8TEmLVvJUuWxE8//YRHjx5h9erVZvdJ7GZeusZ8Ph/16tXDjRs3oNPpaKkp9/3mNgmKj4+HTCajchbmbMGXgjmbldfvzdlUIqlCyke5MhwSiQRBQUEYNWoUwsPDceTIEbP7JhVP1pp7AvkLHGRnZ1Pf5XO/z5L/Y++6w5sq3/Z9spPupHtC2VAom7KRIZTKRqasT2QIyEaGIFNA9pQtICDK3kNAmSoCAgIyWwot3SNt2uyc74/6vp6kOWlaQIo/7+viApr05JyT9zzvM+7nfhQKVK9eHXfu3OEdDPTee++hZ8+e2LlzJ5YtW2b3PTk5Ofjjjz9Qs2ZNqtcIgBaWMjIykJ6eTlm29uBoH3oZFGf9cCWBAFCCh0KhoP5jaGgoL4OcFKBLItX1qqBQKBAUFIS8vDxcuXLFYRehp6dnkbbu2rVrqFu3rsN1SQZR5+TkICAggO6bQEHSjwynCg0NhYeHB+0Q4utgJP7E2ywZ4IysDrFJxBYTjVOSPDWZTNBqtTCbzXQ4rJubGw4dOuTUOdy/fx/+/v6FkmJksOfLyqhw/cKXgclkcihPAaDIOQNnzpzB8ePHMXz4cJqsS05ORkJCAh4+fEi7jQirWS6Xw9XVlfoy/wYUZeu4smIkXgFAE73kGFqtFhkZGXQAda1atXDx4sVCnS8Ely9fRuXKlQsR2YxGI7Zu3YqgoCAMGzbMofQh37FtQWyLvZkkQIF/pdFoeMkkz549g6enJy/JiQutVouNGzciJiYGFStWtDrX5cuXo127dmjSpAnc3NyoxAQpjObk5FAijj3fw9a+2cZab2K+zduCnj17vrLjlMZjvW1waP0ZhlEyDKMEIAcwGMCnAHIBzPrr5/9qOOOAEscyKCgICoWCalFyNWm5jjV3eryfnx91PO2BqzfkCL/99hu8vb2d0qsq7vAyW3h6esLX1xf5+fl48eKFXVmFrl27Qq1W4+LFi8U6dlBQkENN4y1btkAoFKJ58+bUwSLJvPT0dDx79oxuRkUlrJ0pCJQmqFQqypokVU4uGIaBWq1G+/btIZVKcerUqULi/3/88Qd++OEHDBs2zKlA7YcffkBSUhJ69epVovsUEhJCHWK1Wo3s7GwkJyfTlk5XV1dabHE2cWw0GnHr1i3UqlWrRM6rs0ljwFrj+OOPPwbLsjCZTEhOTqYSAwS2uo0+Pj7w9PQsNckYArlcXmjoIoE9LV0+Z4YwZYnzROxatWrVrPTb+RAVFYUBAwbg66+/pkPiuCBdCiXRR9fr9cjIyICPj49dRp0jEJYPy7JWLXAErVu3hqenJ44cOUJfHzRoENzd3Xl1moVCIaZOnYq7d+9ix44dtB3V09MTQUFBCA4ORkhICHx9fQvpnTnCP+Vo2k4b5w6QstfCTNYR6YAhevMuLi7w8/ODt7c3HYQjFApp90R4eDju37/v1PW8++67qFSpElatWmX3/WT9OdI0Bgo6KopTuHrx4gUOHTrkcDgWF6mpqTh48KDd41evXh19+vTBhQsXsHPnzkKvkyDfWV3jZ8+e4fjx40hLSysUZJGBLZmZmVROQSaT0ZkFb2uyhg9FJaHkcjnGjx+P6tWrY/To0XaLx9WrV4dEIqHMKj4UR9OYdKk4O0vgVaN+/fowm828iXKgYN97//33sWjRIrssetLpU7t2bWrLuLMkCKPR3lwCYrOkUinvPvRPgUh9EZ31gIAAWmQiA4j9/f15n3Xiuxc1CK8oe1YccoU9uLi4ICoqCmazGVeuXOFNyHh4eDhMGpOkW926dR1+XuXKlQEU2EK1Wg2z2Yzs7Gzk5+dbaci6uLjA19cXISEh8Pf3dziU9G2HMyQUezrybm5uEIlE0Gg0dJDpw4cPqY/5zjvv4MiRI04NF79//z6qVKlS6OdJSUnQarXFnv9hC3IOLyth9LJJY4vFggkTJiA8PBy9e/emBQ4yY4R0akkkEhiNRmRlZdGuTI1G80aGCb8J2K430m1kOx+HSHkEBgYiKCgI7733HoxGo10Guk6nw7Vr19C4ceNCrz1+/BharRYxMTFFxj2OpHecxYsXL/DgwQP4+vrydjI/e/aMtwvVFidPnkRmZiZGjx5t9fN169YhKysLM2bMoCQIoofv4uKCrKwsK0KXMwUw4iMT//lNDrou7SDs8ldxnJc9VlhYGO2qsffHtqvw34iivLXrKNAxvg7gRwAeANr/9TPH9Iv/IYhEItqWxq3scat5JMlJXsvJyaHMWj6HgEwk5msfIiAtT86wVkqih8mdxE4GNwGw0rniokKFCpDJZMV+QCUSiUMty19//RV169ZFREQE3Nzc6CASuVyOsLAwlClTBkFBQXBzc6Maa3xJFWcHgv1TKCoBxF1jRJ7CZDIhPT0dUqkUfn5+uHPnDpKTk7FixQqUK1eu0DFSUlIAwK78iD2Q9iE+VlJxQFpRRSIRgoKC4OXlBaVSSQcEODsxmbQylSQAMZlMWL58OcRisd37Yw8DBgzApEmTcObMGaSkpNABeLYMKW4yFoDVs16a4MihsR14ZrFYkJqaSgd9kjVKOg64LVVyuRxubm5UFse2vdYeJk+eDKVSadUeTuDj44OoqCgcO3bMKfYpF2SQSkmfbRL42nsWJRIJatWqBbVaTVld7u7uVCeVT3O5e/fuqFy5MtatW4fY2FhoNBpkZ2dDJBLRAId8ZlEDgcj3QOSTXrejabsuuAOk7NlZ8n6SgCEdM2TtiUQiKJVKKrvk7u6OMmXKIDAwkCaoi4JAIEDXrl3x+PFju0kSwqqJjY11eJzAwEBkZ2dDo9E4TPwZjUZcu3YNly9fhslkwq+//lokA5icx+PHj3n3+K5du6Jx48Y4fvx4oe+csGccDaWxxdOnT2mS3hZk4KC3tze1oyKRqNTZKGdhMpmQkpLi1N5hu0ZJcah58+ZITEy028Ejk8kQERHBO9iTICgoCNnZ2U7ZKaLtbTQa38i0dKIz6qioxzAMbe+11zVC9CfJ4DvyzJIhVCKRyKqgyAUJjsl73+TaUygU1JciBBEyqIjMJSBFJXsgRUNHhcmgoCDEx8fjypUrvO8pbqcD37kIhUKqN2sPvr6+yM7O5n2d/LyohE/ZsmUBgNo1oVAIpVJJO1EIiByRwWCAj4+PQ9/+bcfLkFAIq93Ly4uSEQwGA7y9vdG7d2+kpqY6xTZOT0+3yyYm9tFeEbw4eBVMY1Jw5mOGkiKvIzJVdnY27t69i65du8JisUAikdBZCcHBwfDy8oKPjw9UKpVVscLRQN9/E/hiSZlMRm0EmZVABtGRWEUgENA5I/bm3XBnBNiCrDNnWMTO+HiOkJ2djXv37kGpVKJ69eq8LOQXL1443ZH66NEjqFSqQlJkRJ6I5BvS0tKsBi6S2SRknTlD5rAdbP2/sjZLgn79+r2y47zssZ4+fQqWZdG8eXM0b96c+nPkj6O5Fv8WOLT+LMuWZVk2HEBVAGsA3PzrzyoARWshvAYwDNOIYZj6DMP8s9OAOOAaBe6/bRMyxJkmjihJvhIDHRYWBqPRyLtBSiQSfPzxxzh37hydVm0PDRo0QGxsrMONtrjQ6XRISUlBXFwc4uLi8OLFC+Tn59PBdD4+PggNDbVrrBMSEqDT6YotH/D48WOUL1/e4XskEgnVo+XqOBFNVbFYTL8DIqNBBpCQ+18aW0CcrTSSNqO0tDTK9nvx4gVkMhnq1asHsVjMu1ZatWqFJk2aYPbs2U6x1/r27YuIiAh89913OHXqVImui6BcuXKQy+U00U0cD71eDw8PD6cZoYSRyB3U5yzmzZuHixcvYu7cuU6x8gkYhoFIJEJoaCi8vLzoAARb2Qay7kibbmmsGvM5NFwNdltmArcarlarkZqaipycHKjVaqjVaiuZAlLVd8YWKRQK1K9fn1fO4v/+7//g6+uLZcuWOZWg4x7X29ubStYUF8Su8zFqHj9+DG9vb6viS5cuXcCyLI4dO2b3d4RCIebOnYuHDx/i/PnzEIlEMBgMlIFLnEXC2CZdFPa+K+4AlH/C0eRjSHFZ5vbWO2HQcROT9tryyHAg8vvOBt6E1WWva6BRo0bw8/PDpk2bHDpyERERqFevHvR6PVJTU+0md7VaLU6dOoW4uDhUqlQJMTExcHd3x6VLl4qUT9HpdGjZsiVvsZZhGERERMBgMBQaBlO2bFm4ubkVaessFgtWrlyJ2rVro1WrVrRwxb3XLi4u8PT0pMEKaSO3J4XytoAr2VRUoMZdo6QwAxQM1m3RogXvfhAREYF79+45TPD27dsXfn5+OHHiRJEMdKJLShJqzhZLXxYsy+L8+fM4c+YMKlWq5FCT78CBA5g6dSratGlTSGsXKGArA8DVq1cRHx8Pk8kEV1dXu2vJ1lfmytz8U7BnczQaDU00kWIdSWQQGa7s7Gyo1Wre1mbSReXIvnzwwQeoWLEiFi1axLuHvUz3n9lsxosXL/Dbb79BIBCgQYMGhTrMCCIiIsCyLK+MUvny5RESEoIffvjB4WdKJBK4uroiOzvb4XdJ5oaYzWakpaVBrVbTgmhp9cNLiqJIKI4knfR6PUwmE7KysuDv708LqiKRCG3btkWFChUwf/78IotMRN/aFqGhoQgODsbt27df6p6/iqTx9evXYTKZeGde1KhRA0ABQYgPZHgw2XuzsrKQlpZG5SBJl4BWq7XS9X/bJVCcBZ8/RvZ9sVgMs9lMX1er1cjLy4Ner4dMJqNFMnvPtbu7O50pQTTdCapVq4by5cvj4MGDRfrdTZo0KfH1sSyL27dvQyqVokaNGrzf58mTJyGVStGsWTOnjuvn50dl47j49NNPIZfL8emnn9KB2VwChYeHh1U8mJeXh+Tk5EKFe67dI4OttVotfR9Zm/9G+/gf/j1w1npuA1AFwEoUJIyr/vWzfxQMw0QD2IsCuQzys3+cKso1yo6SfaR6RBzTjIwMpKWlUa1Nog1mr6JHMGjQIAQGBmLevHm8RqRRo0YQCoXFloOwB71eT2n8ubm5kMlk8Pb2RlBQEJRKJcLCwuDv7w9PT0/eFsv79+8DgN1WKT5oNBqnqoIMw0Amk0Gn0xWaUsoH0jqWkZFRapN5RB+XL/Diau+RYBko2MTFYjFtOW7dujX27dtn18FkGAZLly6F0WjEihUrinRCJRIJPvroI9SrVw9Hjhx5qcQxcaqVSiW0Wi2Sk5ORnp4OkUjklN4UF3Xq1EFsbGyhJIsj7N69G19//TUGDRpUbC2ip0+fIiQkBCkpKTToccSakclkdpN5pcEZ4LNX9n4uk8ng5+eHsLAwmtgUCAR4/vw5Hj16hMePHyM+Ph7x8fFITU1FSkoKleRxpE3ORZ06dfDs2TO7AbWLiwsmTJgAlmWxZMmSYjFlQkNDIZVKERsb61RrJxckkWMv0ZeTk4Pnz5+jatWqVj8vV64cqlWrxquxCwAxMTFo0qQJNmzYQAe3kGIjcTiJ1ilhUWZnZxf6rvj0Wt8UbFkS3KQ2WT9yuZyyo9VqNZKSkpCTk4PMzExoNBpkZmZCrVZDKpU63Q2TnZ1tNYCEC5FIhNGjR0MikWDBggW4d++e3WMwDIPIyEi4ubnBaDQiJSWFrjOz2Uz1WcViMVq1aoUaNWpAoVCgWbNmkMvlRe65Li4uqF27tsP3kPZ220KLQCBAZGRkkUnjo0eP4tGjR+jSpQvKli0LqVQKmUxmVYAgASNphSb2621l/1ksFmqfvLy8aPHUHsMasF6jpBi2ceNGPHv2DGPHjuX9nIiICKjVaoeyWWKxGC1btoSnpycOHTrksJBAGK1EG/xlGFfOMpVZlsXp06dx+fJl1KhRA126dOH13b799lts2LABMTEx2LRpk11/pGzZsvD398eVK1doksYWtt0QOp2ukMzNPwXbvU2r1SI1NRWpqam0eMCVqiD7u7u7O0wmE+9QTsK4deS/S6VSzJgxAx4eHjh+/LhdVnFJQhiWZZGbm4vU1FTk5eWhUqVKVoOw7aF8+fKQyWS4ffu23dcZhkG7du1w6dKlIpM+Xl5eyMrKomxFMjyWuxbIYDudTmcl3fW/2IrtSNKJ231KuuiI9rGLiws++eQT3Lx5s0ipLqVSycuKJ/rjRXXeOMLLJo0JO7RKlSr02bFFUFAQvL29HSaNGYZBpUqVEBcXB7lcDqVSCR8fH7i5uVGWNoBCuv7Owhl96tIMex2Dtp1gxJ8nAz+9vLxoMZUkg/ni6hEjRgAA1qxZY/VzgUCA3r17Q6/XY//+/Q7PsaSD31iWhV6vh9FoRGRkJC/ZKC0tDVevXkWLFi2c/iwicWFrzwMDA7Fw4UJcunQJ69atAwAraUzy7KakpPDO+SC/Y0++1NYW/i/ax//w9sBZtm4Ey7LcKPlHhmHsR2GvAX8lhr0ATADwfyzLnmcYRgHAhIIEcj7DMAz7klae/Lo91h330PYm2cvlcrv6vkRGQCqVQqlUQqfT0QQhYeU9efKEsje40Gg0kEqlGDVqFKZMmYI9e/YgJiYGAGgClKBSpUq4cOECoqKiIBKJSuwc5OfnQygsmHxPzhP4W4uPL8gxmUyUcXHjxg14eXlBo9HQ9ztyZoG/pRBCQ0OtdMHMZjNlkrEsC7PZjNTUVLAsSw22TqcrtMFZLBYoFAoEBARAIpHAYDDQv51hV71qh6Go4+l0Ohqs2F5Lfn4+0tLSoFKpIBAIaNu3UqmEyWSiLUNSqRQNGzbEiRMncO3aNYSEhBRKwISEhGDSpEmYPXs2jhw5wluF5SZkY2JioNfrceTIEWi1WhgMBt6WSoPBgMePH9t9jbQyurm50aDE1dWVJrwdDRziOtqkpefQoUOoUaMGVCoVL4tHq9Xi6dOnmD59Opo2bYpJkyZRvTSj0cirJ2g0GmkwFxcXh9DQUKSlpUEsFlsN1LAX8HGngHO/d24y7Z+clstnu0hLjb2fE9iep0wmo84mmVJNkiqEgSSVSvHs2TPehFR2dja976RwdvHiRbRq1YpqrhIIhUL0798f69evx4kTJxATE2P3nhsMhkK2yd/fH/Hx8bQl2xFIopjoVjMMQydLc5N5hKXl4+ODzMxMK2Zh06ZNsW7dOvzyyy8IDg62KwMze/ZstGzZErNmzcL8+fNpqzZh4xIHnzwfHh4ekMlkYFmW2gfutGp7e86rBndf5O5f3H2Sez7kfCUSCbU/JClD2teJw+3h4QG9Xk9bTEnC3JHOZ0ZGBiQSCZKTk+Hu7m7VNZGZmUmdbZFIhGHDhmHz5s1YtmwZqlatynt/SPGKDOaTSqXQ6/VgWRYKhQKVKlWy2s+AgiQMXzKaICwsjNceurq64uHDh/Q5uXXrFrVt7u7uyMzMROXKlbF9+3akpKRYBUjcpN+yZcsQGhqKd955B5mZmWAYBllZWVCpVPR7IHaHJJNlMhnVo+UGOaVFrglwvGcSNpm7uzttyc/Ozoavry9vwZSsURIcr169GrVr10aLFi3od5Cbm2s1Q4J0Pt24cYNqttqDXq/HO++8g+PHj2PPnj1o37691T5uq9FJ5MsMBkOJ2cZms5lXBsjDw4Pu4Tdv3sT169cRGRmJqKgoZGdnF0qusyyL3bt34/vvv0fjxo0xf/58mvS1fZ+npyeioqJw/fp1hIWFQSKRUKkdX19fOhSWPO9ubm70nnJbcm2P+7pg66/L5XLaxk/2O7FYDL1eD09PT6SlpcFoNFI2bfny5a2+P66skFAoxNWrV9G6dWsA9luzRSIRJk2ahClTpmD79u3o1KlTsTWtuQkEo9FIk9tisZgOf7bnA8nlciv90NDQUFy7dg2NGjWCu7s7ZUsT1K9fHxs3bsShQ4cwZMgQ3vPx9PTEs2fP8OjRI3h5eVGdeqlUCrPZTFmdQME61el0UCqVdB8jhRPu9058+pLgddqtkqxN298hBS4AdGYCAcMw1A8m3zP33rVu3RohISFYsmQJXWe2SE9Ph0KhwPPnz+1KZLm4uEChUODq1atOzfPggrCXiV+Wk5MDhmHg7e3Ny7KXSqWFimdXrlyBUChE2bJlHRI+KleujF9++YW3+KZQKFCxYkWcPn0a3t7ekEqlEAqFyMzMBMuy1OeQyWRgGIayagl5iwtuzEXWECEZAa/fT+dbWyVdz0SWjeuPkbiDXEt+fj7V4VcoFJT1mpKSAl9fXxofi8Viave4vrxKpUL37t2xe/duDB482EqbVyKRoEmTJjh//jyqVq2KvLy8Ehem7e2LROosODiYSmzZQiaTYf/+/RAIBKhRo4aV/56Xl8e7Z5J1c+/evULyKTExMYiOjsbSpUvRtGlTlClTBt7e3rRzSCgUQqPRIDc3l+7tEomESuUJhUJ6X8m6VCgUMJvNyM/Pt7IJfLHYf/gPpQHOJo1vMAwTxbLsLwDAMEwD/IOaxn8lgzP/SlSfYhjGD8BmhmFSAJgYhtnAsqz9HmcADMMMATAEgFOi6PaSO1wjTliTBEVtLEKhkA5WslgsMBgMVOKBYRgkJibarYgGBgZCLBZj8ODB2LVrF9auXYtBgwZBIBCgXr16VslPgUCAqVOnwmQyoUmTJg7ZDykpKfD29kZKSgoePXpEf65UKiEUChESEmJ305JKpVTrzhbEQFssFiQmJqJBgwa01QiAwyEMFouFsh9q1aplNfHebDZTY84wDPR6PR1cIpVKYTQarZLbBESYnHw3JOAu7mAsZ1Dc9WUP3I2C3HuSlCHJK247to+PD91spFIpLBYLRCIRBg8ejNmzZ2P//v2YPXu2XSd80qRJ2L9/P9avX48ePXrYHRZlm+Rv3749ZsyYgZMnT6J169bo3Lmz3etYv349b4W6bNmyNEmo0Who4gIAfv/9d4fJfO45enp6QqlU4tmzZ2jevDnkcjlvS+azZ88wevRolC1bFt9//73V2iLJOnsgkhTkGPXr16dJUcLW4wZGtr9rD+S+FEf37lWsLeIg5ufnQ6FQWJ0zOVfus0Ley12LBEKhEK6urvD29oarqyvy8vKQmZmJ1NRU+vPg4GCkpqby2kV/f3+aOGjWrBkkEgkePnyI3r17o27duoWCmoYNG8LFxQWLFy9GQkICunXrVuiYcrkc4eHhhX5+9OhRnDp1itoDvvMhv5uSkoIHDx4gIiICXl5eCAgIQJs2beh7f/rpJ4SHh6Nbt27QarVWLLSOHTti3bp1uHLlCgYNGmTX1kRFReH999/Hli1b0KdPH/j4+FDdR5VKBYPBQAtlRGOTKxfyKoMZZ9cWuW+2n6/T6ZCbm1soUCEBGveeEwc5Ly8PKpWKdooQbTiSBCSJGEfPiEqlot0VKpXKKjkfFRVVKFnfsmVLjB8/ng7Q5H6fBAqFAn5+fsjNzcXhw4eRlpYGPz8/tGnTBt7e3rwBbFhYGI4fPw6BQIBmzZrBzc0NqampuHTpEipVqoRy5crxdtzk5uaibNmyNPljNBopA4sEHrVr18amTZvw7NkzRERE0N8lSacHDx7gypUrmDNnDnx9feHv7w+NRkP3cvKMpqamQqfTITg4mA4oe51sz1dhtxyB7BVE1oS0hgsEgiIDboPBgO3btyM+Ph5Lly612gOUSqXVPtSoUSMIBALExcWhbt26vMk+8t1FRUVh+vTp+O233zB79mz6nDqaI7Bz507KCJdKpfD396fn8OjRI7uJDtLuzqcNGhwcjJ49eyI1NRVbt25Fw4YNMW/ePHpvuGuSZVnMmTMH33//Pfr06YMpU6bwzgwgxZ5GjRrh4MGDtKVZIBBQrUwfHx8oFApajCC2gCQ69Xp9IdtYnOC4uHaLPAcEpHDNvaYXL17QgZgSiQQSiQQqlQoajQZeXl5W50sY+0CBLNGePXvwf//3f4iIiECDBg2sfAyChg0bIiUlBUuWLMHt27cxZcoUqw6JU6dO4fnz55DJZHalUoh+4rVr13D9+nW4ubmhSZMmCAsLw5MnT3hnNOj1eqtBfZGRkdizZw/1ZWx9P8JWvnz5MkaNGsV7b5VKJSwWC9LS0hAUFET3QVKQYhiGJlOIbqpAIIBCoYBGo4G7u/sb75DhQ3HXlzPgrjl78xaI1jyRMyT7PimiDxw4EHPmzMGNGzfQtGnTQsf38/NDYGAg7t69azfW6tixIxiGwe7du1GjRg2r63r69CmdEfPs2TM8ePAAAoGAzhggtosUc4ltYBiGdwCZSqVChw4d6P/v3buHffv2oU+fPujSpYvD+9qgQQNcunQJOTk5hYZMkgQcKaaazWY6UJfcZ6AgviD+B8uyvP66PZ+qJH66s3jd+6K9NUmug7uvkeeRzC0h2tBubm70GG5ubtTu2X4Pn332Gfbu3Yvt27dj0KBBVsd+55130KtXL5w7dw5Nmzbl7STV6/UIDAxEQkICrl27BoVCgcjISPj4+ODevXuFZHsyMzPp4LsuXbrw3gOz2YyTJ08iOjoa0dHRVq/du3ePN1Yk55mYmFiosCkQCGiRefbs2Zg2bRo8PDyonfP29oZcLkd6ejpSUlLofCuxWEzXISHj2d4Dsidyc03c93Fjt9JU1P8P/5twuGszDPMHwzC3AdQBcIVhmKcMw8QB+BmA4xG7rxAMwwgYhhEACAOwBMA0AIcAbABwF8AYhmE8+aQqWJbdwLJsXZZl6zqjH+aMKHlxW1gIQ9NgMNCAVygUIjAw0GGCFygwWMOHD8fTp095h7LUqVMHvr6+OH78uFPnAxQMxoiMjERISAjq1q2LqlWr2k0UFQfx8fHQaDRWAa4zePDgAUQikd3EDxcCgYAGvUlJSbQl7mXa/p39Lv96vdAzU9z1RUBYhaQSbhvIE01moMApJExjiUSCtLQ0ZGVlIT09HTk5OdBoNDCbzZDL5WjZsiX279/Pez0ikQgLFixAdnY25syZ49S5CoVCzJ49G+3atcOZM2eckqowGo28U5BJ8rUkYBgGlStXRlxcnMMqtkajwfDhw2E2m7Fv3z67wVxR0Ol0SEpKgp+fHzw8PODi4kJZ7CR4L855F3f4YknXli2cme7tzHtdXV3p1GDy3AUFBaFq1aooX748BAIBgoODndZXl0qliIyMdMgyBwoGydWvXx/Hjh1zqO9ui+joaISGhlqxqvnAsiwt4NlbK0lJSYiLi0PDhg3t/n5QUBBq1KiBkydPOvycmTNnwmQy0VY3iUSC3NxcqnEqEAhouyXXHpB9SSaTvRKZk5fdF8nwQ8JSIcNSiYY89xxJR0hKSgqysrIow4XYb7lcbuU4O4Ps7Gy7BS9buLu7Y9WqVahatSq2b9/OK98DFARKPXr0wLBhw9CzZ88iO2Tc3Nys2PI5OTn4/fff4eLigsqVKzt1HUSuigwq5SIyMhJAAQvZHogeeGRkJA2OCXubaPgTGSfCOE5JSSm2ZEtx8arsFuBYhz0lJQXJyclwdXWlgS3fc0HkILKzs/HNN9+gTp06aNu2rcPPVigUKF++vNPT3sPDwzFhwgQkJCRg3rx5TrWYisVihISEICAgACaTCc+ePeOV2QD+Tgw48/yvWbMGLMti1KhRdvcdi8WCadOmYe3atRg4cCAWL17Mq+XORaNGjQAUFNFIQCsWi2EymSiTjcyVIPtIUcPCnJVvepVrCyjY78gwI5IAJTZOrVbzylMABZ0jSqUSo0ePLlI+qUaNGvjoo49w7do1fP3111avMQwDd3d36g/awmg04ocffsD169dRqVIl9OzZsxBL2BkQm0Qk5GwhEAjQunVrXLx40aFv5enpiaysLJQrV476Qunp6VCr1Va/R4amkpkuwN86/Pa0pv8J21QUXoftIgVTvrUtEAiQn5+PhIQEKicEFNxnT09PDBkyBL6+vliwYAHvZymVSmRnZ/N+RosWLSAWi3H69Gm7rzMMg7CwMDRp0gQBAQF0QGhycjIlPJUk0W+xWLB9+3aoVCq0b9++yPeTQWy//fYb73vInkt0mklC2M3Njf4N/D3ckU+qgishSfA6h6S/atvlDLjXQ+wyWWPELwMKuucEAgF9Th3lP0JCQtC/f3/aBcWFTCbD5MmT8ezZM6f2zeDgYNSvXx9msxk///wzjh8/jpSUFGRkZFBboNVq8fjxY7i4uPBKmxBcu3YNZrO52DKE7u7ucHNz4x08HBQUhMWLF+PKlSvYs2cPsrKyaCGUzPkICQlB2bJlERISAqlUWuR+Zm/92aI4sdu/EePHj39lxymNx3rbUNQO8B6ADgDaASgLoDmAFn/9O5r/114NGIbxYRhGwLKshWVZC4ARAMoBqMuy7EaWZX8FcAKAGYDuZeUpCLiC+XyObHEfZKlUSpPGqampiIuLQ15eHsqUKVNk0hgA2rZtC6FQyJsUFgqFiI6Oxo0bNwoJ1POBYRi4ubkhLCzslQ3EIe3b1aoVb07iw4cPUbZs2SKZwDKZjLJGSHuNxWKxKzzvLJz9Lv96veiIykkQRjtJ/PIFmEQHkThuWVlZMJlMMJlMyM3NxcOHD6m27LNnzxATE4PY2FiHWphVqlTB8OHDsXfvXly4cMGp8yWJ4xo1auDIkSO8zifB3bt38dtvv/Emjl8GlStXhsVisWLKc2GxWDBhwgTExsZix44dRQ5Y5AORHwgNDYW/vz8yMzMd6mMTe2HPFL1JrbTiTPe2fa+98yYJcxcXF3h4eCA8PByBgYFwc3ODt7e305rGAFC3bl3cvn3bYZDKMAw6deqESpUq4euvv3ZKcgL4W96CXAffvTeZTHj48CE0Gg1CQ0PtBgwkWW07XZmLdu3a4cGDB7yOJ1DAuP/444+xc+dO/PrrrzAYDNR5J8wie4UwwtR6U4MW7Q3Es9VUVigUVgEb9xy9vLzg4eFB9fNYloVcLqctevakeRwhKyvL6UKQTCbDwIED0axZMxw8eBBff/01rzMvFovtdq/wQS6Xo0mTJjCbzTh37hzy8vJQq1Ytp5JvBH5+fnYLLWXKlIG7uzuvDunNmzchlUrpoFGj0Yjk5GRkZWUhISEBarWaMnHd3d2RkpKC+/fv0+ezNOisFwV7Gn86nQ6pqalIS0uzCoC5uqq2swBIQePYsWOIj4/H1KlTnUoMVK9e3emkMVDQLTVmzBg8evQICxcudEqLnSQNy5QpA7FYjISEBGRnZzvcK4pKrl29ehUXLlzABx98gICAALu/P3r0aGzZsgXDhg3D/PnznV7zNWvWhFwux88//wydTgeJRAKZTAY3NzcoFAoqQ0Da4rlsKb7PeFNajqTjSalU0uQdkanJzc11OHPBy8sLX375Je7du4fly5cX+VnR0dHo2LEjjh49WsiXJ2xPW5klg8GAgwcP0oJlixYtSlxwDwwMhLu7O/7880/e97z77rvQarX46aefeN9DNI0Jay8zM5PqFtt2QLq5ucHPzw+enp5WLFrb7zs9PR0vXrwocrgo8Pbozjrr4wMFeydJphObRZiILi4uGDZsGM6cOcM7OJjsq3zScW5ubmjcuDEuX77sUEtdoVCgWrVqaNq0KeRyOTQaDSUDFWdPIzh79iyePHmC3r1725WmsUXlypUhk8mcShr/8ccf0Ol0VsMWyb0jQ1+Jhq89u8NlH78N68kZOHo2iPwa8HfXbVBQEJXRTElJoXJfRfli48aNAwB88803hV6LiopC27ZtcefOHV6dbS4CAwPx7rvvIioqCoGBgVRG4tq1a3jw4AEePHgAhmFQsWJFh3uUXq/HzZs30bx5c15GMR8YhkGZMmUc+u4ffPABoqOjsXXrVvocEV13o9FIu+xcXV2tSBG2IH4XgCILFMWJ3f6N4HYsvOxxSuOx3jY49BBZlo139Od1nhjDMJ0B7AHQm2EYslO9ALAMQFmGYRb99bM6AMIBvBbxIT5H1tGDbM9ok83fYDBAp9MhLy8PAoEAKpXK4ZAVAqIjd/78ed73tGvXDmKxGHPnzn1jA25iY2OhUqmKZGhxkZiYiPPnz1vJWdiDTqejTgtp6XAUhDgLZ43yX6+/MhoEqTJ6e3vbZba7uLggICAAMpkMubm5MJvNcHV1hUqlgoeHB0JCQuDm5gYPDw8olUooFApIJBKqU1wUe3PUqFEIDg7G5s2bnT5noVCIrl27ok6dOjh8+DCvXifwtwYanxP7MiDfOZ822smTJ3Hu3Dl8+umnaNmyZYk+48aNG7RaXadOHeTk5MBkMsFoNEIsFkMkEhX6zoi9sFeAeJMV4+KwJ7jtUWSQUUpKCjQaDU28ZGVlURvGff60Wi2CgoIQHx+Pw4cPO3VujRs3hsFgwI4dOxy+TyQS4eOPP4anpydWr17tlDMKFCTkyHVzE8fk3+np6fj555+RkpKC0NBQu+2d169fx+HDh1GtWjXe1m0AdK05GuQCAFOmTIFAIMDJkychkUjg7e1N7SaxnUUNWC3JkJfXARcXF/j5+UEul9PEEOma4AaJRqMREomErh2S8EtPT8ezZ8/g5uaGmzdvYurUqU4ljhwFyPYgFAoxePBgtG/fHj/++CNvIrYk8PDwQJMmTSAUClGmTBmqmeoILMvi7t27WLx4MW7cuGE3ucgwDCIiIni1k588eYLy5ctDJBLBxcWF2iWSwCFsT6IvW758eVSsWBGenp4wm81IT0+nHSulNXHMXe8k2JJIJPD19UXZsmVRtmxZOtiH+1zYDjcjReaTJ08iLCysSJYxARnW6Wz3BFAgRzBy5Ej88ccf2L59u9O/JxKJEBoaCrlcjpSUFBrkm81mWCwWmM1mp5iYOp0OixYtQkhICHr06GH3PQ8ePMCePXvQvXt3fP7558Vi1onFYppMJzJnZCgbUHDvyf5ApCmK2vvelF3T6XTIz89Heno6MjIy6JyIFy9eQCgUFpnEbNu2Lbp164Zly5Y5VSQYMGAA6tatiy1btljpscvlcohEIqSnp8NsNkOv1+PFixd48uQJcnNzER0djcjIyJdiQJJCnaNrIkNeCQHEHsRiMXQ6Hby9van/6eHhAW9vb4fJOe65237f3t7eCAwMdCp2eFvYd0X5+MDfCSSyjwIFfhRJhBoMBmi1WgwbNgwCgQCHDh2y+1nkdx2RkNq2bQuDwYA9e/YUee6kCFSmTBl4eXmBYZhiy/vdunULmzdvRvXq1e3KatiDWCxGcHAwr14yABoHm81myGQyZGZmIiEhAfHx8cjIyKA+KtEsJjE53x73tqwnZ8B3LcTXTUtLQ35+PtW2J7NdsrKykJmZSWOqonwrwjY+evSoXR3tsWPHQigU4saNG06dt1AohL+/P2rXro2yZcuiatWq8PHxoeQDMsjTEe7cuQODwYDevXs79ZlckOJOUeSVfv36AQBWrlxJi4wpKSlITExEeno60tPTafcc0W63RXEKpK+T+f424MGDB6/sOKXxWG8bSqWoFMMwYQDmAUgBUBXA+wzDCFmWNbMs+xOAJgDqMgyzFsBUAB+zLJvxOs6Fz5F19CDbM9qkTSssLAyhoaEoW7YsvL294enp6TSzomLFig6rYD4+Pvj8888RFxeHY8eOvZENUKfTFavF2Gg0Yvr06WAYBp9//rnD98bGxiIkJIQmjAmTSKPRUFF6R+BjVTlrlP96/ZVF1sSRJlpHts42eV2v1yM1NRXp6elUa5fIOxBHmyS93d3dKTuWT+eOQCaToUqVKnY3/KLOu2/fvvDy8sKePXt4g9ioqChUr17dYZKtJNBqtdizZw88PDxQr169Qq+zLIstW7agTJky+OCDD4p9fIPBgDlz5qBRo0ZIT0/H3r170ahRIxoYkSFnpJ2LC3vtRsRhlclkb1XFOD8/H2q1GhkZGfSZIQxMoknITQqSBPPIkSNRt25dDBo0qMjkKVCQaG3dujXmzp1bZNeFm5sbPvnkE+h0OqxatapYLHZu4pgkkViWhdFoRFBQEGrWrIkyZcoU+r1Tp05h6dKlCA4OxsiRIx1+BhkE5WjQC/C3dqper4dWq4VWq6UDcYiWMV+QaY/h+yZBOiGIxjFhDRM7TUCSIgaDgQ6tISwgmUyGxYsXo3fv3li0aBEaNGiAixcvOvzcbt264aeffnKadQ4UrIHu3btDJpPxSj2VFF5eXoiOjubV/ScwGo24fv06tm/fjjlz5uDBgwfo1KkTZs6caff9ZcuW5X0uiK4/aZFUqVS0kEiKWkTvXi6XQywWw8/PDzqdjibIyJ/iMDz/SaYfd72TYMtgMNDrJDIuts8FYfyQc9VoNFTXmeg4OoPu3btDJBIV2Vlji+bNm+O9997DiRMnnOomIyBzJchsCaLLSWwWaTHm20dYlsXFixehVqsxffp0Xh3mKlWqoEaNGvjtt99KNIyP7AWkMKFWq2lhkXQccPVBi5KmsCfR9dd39NoMHbmn3t7e8PX1pQV5pVIJqVSKli1bYteuXXYH3HGxdOlSDB48GDt37kS3bt2sBqTaQigUYujQoQCAffv20Z8zDIOgoCAYjUY8fPgQsbGxyMnJgVKpRM+ePV+JBuovv/yClJQUh4X0M2fOAABatWrF+55Hjx6hQoUKUKvVVAKAO2jLmWfLXueKn5+fU2zWf4p9R9ZHSVGUjw/8nUAiesFk38/MzER2djYd8knuK1+c06BBAwiFQocM8dDQUERHR+PMmTO8kke2IHFG2bJlC2nbOsLDhw+xaNEiBAcHY/z48U77K9nZ2Xjy5AmVqbDFjh07MHLkSLRr1w5dunShhTR/f3+EhYXRPVClUlnZf0dJutLE5iTdBiUt4vJdi1arhclkgqurK3x8fKBUKqn2LgBasKlZsyYkEglvcYILwjbeunVrode8vb0RGhqKuLi4YkvOMAxDOxjr1KmDunXrFilFZjabcfv2bYSFhRW7s9RsNmPx4sXIyMjArFmzeN938+ZNDBs2DJUqVcLChQvx9OlTiMVi+Pr6wt3dnXbcEJY73zNf2ogfbxJlypShfo7tH6lUSvfKl8XQoUNL5bHeNpSOqLMwMgAMAjAcBezihgB6MAwjAQCWZZ8AaI2ChHELlmX5S+IviZIE6PaMNjchQAI4Munc2eRumTJlkJ2dTafZ2kNUVBS++OIL5Obm4ujRo9Sh+6dgMBiKNRl6zZo1uHfvHlavXu1Qoy07Oxvp6ekICAigDIfQ0FAIBAKkpKRAp9PxyomQtpvc3Nw30v74siDTvn19fakcB3EqSPsV8LfTTmQpCGPEEXx8fKzYLs5CIpGgS5cuSExMxKVLl+y+h7QmvsoKqcViwcGDB5Gbm4v333/frpN3/fp13L59GwMHDix2Yu3mzZto0qQJFi5ciPfeew+HDh1Cq1atrIYQkCKDs4waUkQiBZW3pWJMWPwmk4neZxK45OTkFEoKElaHt7c3tm7diqCgILz//vu8EiIEDMNg6dKlcHV1xfz584tkagUHB+Ojjz7C06dPrYLuoj7D3jA8hmHoIDxb7UqWZXHz5k1s27YNtWvXxmeffeawVRkoSAh4eHg4zYKWSqUQi8VUooGrkcZle5dWFigXJFEkk8nAsixcXV2tdN2IVjMZNEWG/fn4+NBBPhs3bsTu3bthNpvRtm1bjBgxgne/69evHyQSCYYNG4a7d+86fZ5isRgRERG4efPmK096ikQi3uc7NzcXZ86cwdKlS3HkyBEwDIOhQ4dizZo16NWrF++wtLCwMGRkZNhtKVYoFJQtJJVK6RrKyMhAfn4+BAIBZQCSdkkANKgmHSseHh50H3EmIfymmFlcXe+iQPyBuLg4mEwmes3VqlWDVqst0i4R+Pv7o2PHjjh37pyVvXMGffr0QXBwME6fPl0sv4MMluQOFOP+cTTwLz4+Hi9evMCoUaNQoUIFh58xYcIExMfHY+/evcW6LgKhUEjbm/38/ODq6koHoZG97mWkKf76/yuTBLNFfn4+bbt3c3OjBXlXV1eEhIRgxIgRyM3NxcqVKx0eRyKRYM6cOVi2bBni4uLQvn17h9r2Pj4+aN26Nc6cOWPlfykUCgQGBsLDwwMqlQrly5enMwReFnq9HocOHULZsmVRty7/SJrDhw+jTJkyvEk7oEB6LCAgAPfv38f9+/cpczkvL4/q279O6Zt/in1H9uXXCWLTiP0mdlepVMLT0xMikQhqtZp2OvBJMnl6eqJOnTo4d+6cw8/r0aMHgoODsWHDhmLFh84MGSXIysrCF198AU9PT0ybNq1Y6/f69etgWRZNmjQp9NqRI0cwePBgtGjRAsuXL4der8ejR4+QlpYGsVhMi6Wurq5WUjMAHM6DKE1szpfdW/muRS6Xw8PDA76+vnQmicViQXZ2NjQaDXJzcxEUFISIiAi0adMGBw4cKPLZDQkJwXvvvYeDBw/aJR+FhYXBYDDg+fPnJboWcj3OMNxjY2Oh0WhQp06dYn/Gnj17cOPGDSxcuJC36P/o0SN06NABHh4e2LBhAzQaDR4+fEgZ2YmJiVRejsj15ObmIjc3l0rOcH3h0kT8eJOIj4+nBB7bP46kAP/Dm0GpXLEsy2oA3GZZNhPAZgAPAUQB6AYADMOE/8U6zv7rPaUKRW1AWq0WaWlpyMnJgVwudzqYICy4olgrtWrVQnR0NHQ6HY4ePfpa5AH4UJyk8cWLF7Fr1y50794dHTt2dPhewiarWrUqNcJyuZwGK4TNSoIP0mJisVjw7NkzJCQkID8/v1RU94rjTBMGDhnuQBwhbtttWloabQ3VaDR48OABvL297bbZ28LHx4cyzoqLWrVqoXLlyjh06FCJEs/FhVqtxrZt2/DgwQO8++67vJPjt2zZAk9PT4cTdm1B2MVNmzZFWloaVq5ciQULFlglJ7hMyuIEQ6WJxVBcuLi4WLW5AwUJJy8vr0LDRbjsNxcXF6xatQoMw6Bz585Frg8fHx8sW7YMT548oQPiHKF27dpo1aoVTp8+XSyZAW7ymARB9uy02WzGn3/+Sdfa2LFjndZ99/T0dFjY48Ld3R0hISFwd3en2uxcqQBHbWylTY+Wyzgm08v1er3V+ZPEMdE2Jj8n+6BOp0OjRo1w7NgxfPTRRzRhb0+D08fHB5s3b0Zubi66dOmCr776ymlGS/Xq1ZGZmWl3+NzrQFJSEtatW4fLly8jLCwMAwYMQL9+/fDOO+8UuV+SwS+xsbGFXnNxcUFeXh48PDyQnZ1N/QqdTgcvLy8rDVHCfiHyITqdDnK5HEKhkN5/kuArKmh9UzatuMEWsdU6nY4W7GvXrg0AxbIbH330EfLy8hzKg9mDVCrF6NGjodVqcfbs2RIXKfiKXrbIzMzE06dPER4ejvfee6/I47Zp0wYRERFYtWpViQeQke+E6OzatvdqNBoqb8QHPubVX50sr43ObsuIJgV5oOC6wsLC0K1bNyxevNghe5igVatWOH78OMqWLYshQ4bgq6++4n1vt27dAKBQ4dPd3R2BgYHw9fUtsXaxLfR6Pfbt2we1Wo3u3bvzrqMXL17g6tWriImJ4X0PaXGvVKkSVCoVZWXb7kXOtGCXtj3MFkKh8LXbOG5HYVpaGpVtIoUjFxcXhISE0Pc7Yly2atUKjx49cpikk0gkGD58OHJzc7F58+ZXXjjNz8/HmTNnIJFIMH36dKeG1XJx9epVKBQKOgSW4NSpUxg1ahQaNmyIrVu3wsPDA0+fPoXZbKZFQdvzIDJNXMmi0k4cel17K7cQSXIQaWlplLggEAhgNBrh4+ODbt26ISEhwalOQdLNaY9tHBAQAKlU6lDG8FXh5s2b8PDwKLLD1hY3btzA7t270apVKwwYMMDuexITE+kQx82bN1Mf1tfXF1KplO4Z3t7ecHV1hdlsRkZGBp49e4bU1FTaTVja195/+A9F4dV4JK8BLMvqGIZh/vp7K4CBAKowDLMTwDsMw1RjWdY5OpeTcOSQ29tY+VrqHB2bVJxUKhVNBprNZqqTavuZ3M8lSeO4uDjUr1+/0CbJxbhx49ChQwdMnDgRZ86cQcOGDWk7Z8OGDREeHk7/HxoaSp3TjRs38rLpsrKy7LZvA38PwtDpdFaTkglsWcSJiYmYO3cuqlevjgULFvAGzuQ4hBUUEhKC9PR06PV6mM1maLValC1blp6/XC4Hy7KQyWQ0oSoUCuHl5QUfHx+IRCLqqDr63l5nxZlsHgCKrMBrtVrk5uaCYRga2BMmj1wup4liboD/+PFjRERE0JZWexAIBFRDimVZZGdnWyWZHU0Mr1ChAnUEp02bhqFDh2Lfvn1YuHAhmjVrBr6pwLGxsXSAhS28vLxQvXp13s/Mz8/HsmXLYDAY8Pnnn1tNYTYYDJR9ERsbi7Nnz2Ls2LF0Gj3fd0kYj2q1Gl26dMH58+fRsWNHzJ07F/7+/tDr9XB1daXJeu5wTEffH2khJuBqBNu+9k+gpJ9HkoBCoRBSqRR6vZ4+MyzLFnpmue3ger0ekZGROHToEFq2bInRo0dj7969dPimveeuQ4cO6NmzJ3bt2oX27dujUaNGVq9XqVLFimUzefJkxMfH4+uvv0a/fv0cMpQ3bdrE+1pycjIiIiLo/3NycjB37lykp6ejS5cu+Oijj+zeQ7FYbNdukSnmjpJu5DosFguEQiFkMhni4+MpY4vsDSTYIX9zz8PWjrxs8Mf3+47WrL1EFimYchNAtsUFV1dXK91TANQm6/V65OfnY9iwYejcuTP69euHKVOm4MSJEwAK7h35zLZt26JevXqYMGECvvzyS5w9exarV6+2CrIJfH196RDVunXr4uuvv0ZcXBwqVKgAmUzmMHnriJknl8t5AxWRSARPT08sXrwYbm5u2LZtG22dzMnJ4e2uSUtLo6xWsufevXuX2k/yXXGZxmQQUl5eHrRaLZVu4d5XbndAbm4uWJalNo0k+rmSAlzY2jRyL18GjtYcYaeWxH6R4xKNVaVSSX8WGRkJiUSCO3fuFNI+ZBjG7p4ZFRWF8uXL48cff8TYsWMLnZOvry/vvlezZk38/PPP2L9/P/Ly8gqxZ27cuMGbWKlWrRoaNmxo97X09HSrxEpmZiamTp2KoKAgjB071qF/yO2EGDx4MMaMGYPdu3ejffv2YFmWl81o69fp9XoYjUa4ubnB09OTFvNt1woB3/fN7aywPT4AhwvAWbvF9dfJ+gJgVYQg/hZ32PKECRNw/PhxTJ06Fd9++y08PT15JRRMJhP8/f1x7Ngx9O7dG1u3bsWYMWPAMAydO0Hg4eGB9u3b4/jx4xg7diyqVKnCe42Ohlt7eXmhQYMGdl97+vQpfvvtN+zduxdqtRodO3akA3x0Ol2hwWTExnbr1o13aBkpXtWoUQMhISG08EZ008lzIJVKrQoB9uIl2z2sNDA9ubCd2eAsSnIdMpmMFvRkMhklc7i7u8PFxYUWoj08PArZKBcXF0gkErz33nuYP38+rly5gsGDB9PXbffDkJAQDB48GOvXr0fXrl3x7rvv2j2nmjVronLlynZfe/r0aSF/Pjs7G7NnzwbDMPjyyy9540VHklK//PILmjVrZjXI7NSpUxg2bBhq166NGTNmIDs7m/rhYrEYXl5eyMvLg6urKywWC/Lz82nHk8Vioex3Pz8/uwnZomJ/R77Vq16zr1PyiZyrXC6nOQgiN2c0GqFQKCAUCtGhQwdIJBLs378fTZo0cehbV61aFb169cJ3332HCRMmIDAwkL5WrVo1PHr0CLdv30Z4eLhVEUwgEPDGgwqFwq4PR0AK6QRxcXFITk5Gz549ERAQwMtMrly5spWdTUhIwIoVK1C5cmWsXbvWbqI+LS0NvXr1glqtxp49e9CsWTOa58jPz4eXlxfdD8geRqRApFIpXF1drY5bnLX3H/5DaUOpYBozDFOeYZi6DMNYUblYlmX/ShznsSy7BkAkgEYAol91wrgkcLaSTiqeubm5SE5ORlJSErRaLR0iAcCpVhQSYDqrj1ehQgWsWLECgYGBuHr1Kvbv348NGzbg66+/xvTp0zFmzBgMGjQIw4YNK5bmniM4wzQ2mUwYNmwYDAYDNmzY4BSDj1QqIyMj4evri4CAAKukCvD3vSZtkFqtFgaDAWaz2Sox/qYrfiS55oxeGqk6k4Qx0T4jgY5MJoOvry+8vb1hMplgsVhw9+5dqySYI5CBTampqSW6Fh8fHwwfPhx37tzBwYMHS3QMRzAajdizZw/Gjx8PX19fbNu2zSphbIsNGzZALBZj0KBBTh2f6PtdvnwZixcvxsKFC6FSqShbj7Bsgb8DW6I3+zYyh4sLLovMHsOPK5VCdGyJVIxcLkdERARmzZqFQ4cOFTnoDgAmTpyIsmXLYvz48UV2SEilUnz++efQ6/U4cuTIK2ErvXjxAhMnTsSTJ0/oEMXiOnVeXl5OM41Jki8hIQEpKSlU0sWZbojiMFJetzYoFyQpbE9nlpwLkU7IyMigTCBSTDAYDEhOTkZ+fj7Kly+PUaNG4fTp0zh79qzdz1Mqldi8eTNWr16N+/fvo02bNtizZ49D2xoYGAgvLy/88ccfr/z6ubhz5w4+/vhjaruKq7UHFOz7YrHYLlvH1dUVRqMR6enpePLkCdLS0uDp6Ql/f3+IxWKkpqZCrVbTZI2bmxukUilN/nELYGQtlYZW3Zdt0SXPlUAgsMvYrFq1KpVxcgYMw6BXr164e/dusX6PIDo6GuXLl8eOHTuQkfHqx2+YTCasWrUKer0eY8aM4U342UObNm0QHh6ODRs2FDthYTKZoNFo4OXlxZtcc3Fxgb+/f4mKDK9y+DDX79PpdEhJSUFKSoqVH8hlPBNWfkhICMaPH489e/Y4zTQnCbykpCSHA7369OkDAMVmsBeF7OxsfPPNN5g8eTI2b96MqlWrYs2aNZgwYYLD3zt48CDq1KnjUCqOdH0EBwcjMTGR3kM3Nzdq6zUaDfVTgYLCla2uLCHQ8A2L+l8DYegLBAJkZmbCYDBQkktKSgr10fkKOkBBgbFSpUo4depUkZ/Xq1cv1KhRA8eOHXslNikvLw8LFy5EdnY2hgwZwpswdoT09HTEx8ejRYsW9Gc//vgj3n//fVSpUgW7du1CREQEgoKCqIQLIRO4u7tDo9HQwa9arRb5+flITU2l7G178nHO4HVo+JP1bwtHw7RL+jm5ublWOskCgYDuiQKBAGKxGGKxmA4RFAqFaNu2Lfbv3++UXz1ixAgABXKTtqhVqxZ0Oh3u37//Sq7HHs6dOweZTMZbYLUHvV6PIUOGwGw2Y+PGjXZtUE5ODjp16oT4+Hh8//33aN26NY0JVSoVRCIR7Vaxna3g5uaGgIAAuu+VBr/qP/yHl8UbTxozDPMegP0AFgH4mmGYiL9+zgA0cSxkGKYSCrSNO7Es65yC/2uGM2LmhLmQmZmJtLQ0Oq2ctCwQRqczCUyi/1icBG9YWBhWr16NPXv24NSpUzhx4gSWLl2KzZs3Y9myZRg/fjxSU1Px0Ucf4cCBAy+9KTqTNP7yyy9x9epVLF68GOHh4U4d98mTJwgODqbtzWKxGN7e3jR5DPw9uCstLY0yXiQSCaRSKf0ecnNz7TIg/skWOdIab8sQ5nsvSRhzz5ucMwCqDWoymfDkyRPk5eU5nTQmrJCSJo0BoHXr1oiKisLWrVuLHABWHKSmpmLx4sU4d+4c3n//fWzevNlhMJOcnIxvv/0W3bp1c0qa4+nTp2jWrBkePnyI7777DoMHD6asO6VSCV9fX7trpLiOZ2lvweQDcTaJHIw9cJ85Mm3bx8cHvr6+dI0PGTIEjRs3xujRo4vUNpPL5Vi+fDnS09MxderUIu1RWFgYRo8ejfj4+CK1/OwhMTER165dw5o1azB69Gh8/PHH0Gg0mDt3Lho3blzs4wEFQV1xNI2BAtZyQEAAKlWqRKfH2wu0uSiOE/qqtUGLK7HDfS+xxbGxsUhJSUFSUpKVNrtAIIBEIqESO+PGjUNoaCimTJnC+3kMw6Bnz544ePAgqlatirFjx2LYsGG83wPDMIiIiMCdO3deG7Pn2rVr2LZtGypVqoQtW7Y4ZZPsQSwWIzw8HA8fPiz0Gtn7JBIJzGYzMjMzKfMzKysLGo0GIpHIqn2ctKaSpCphssnlcitNzTeJl23R5e73qampVgxZmUyGqlWr4vbt28W61g4dOsDV1RXffPNNsc9HIBBg8ODBsFgs2LJlyyvbCxITE3Hw4EFMnz4dDx48wODBg60Yes6e20cffYQHDx44HKJlDyKRCO7u7tDr9dDpdHZtFUm+liRYZl7R8GHbBKVMJoOfnx/8/Px4/XcS+LMsi0GDBiEsLAzjxo1zWsaDJDB++eUX3vf4+fmhffv2uH79+ivxndLS0rBx40YMGTIE+/fvR0REBDZv3oyFCxc67OQCCpLB9+/fL1LW6/79+3B3d4evry99rshAU0Jk4PrXXDax7c81Gk2J2bxvO2z3RVLUI4U8Uqgng2XJc1WU3EObNm3w66+/FrmehEIhpk6dCqBAWuBlbFJ+fj4WLVqExMREjB071qGf7gikIPfOO+8AAK5cuYIuXbogPDwc27ZtQ2BgINUeJ9016enpEIlEyMnJoRrNpMuCDH4rW7YswsPDS9wd8zo0/EmnrC3sDdN+GWi1WqSkpNDEOfdzSKFHqVRCLBYjJycHz549Q15eHpo2bYqEhASH9osgKCgIPXv2xO7duwt1RVSsWBEKhaJExVZnkJWVhevXr6NJkyZOS8jl5+djzJgxuHXrFlasWFGIuQwUFHG6d++O27dvY/369WjdujX93dzcXCQlJSEnJ4fXxyR2zZk4/z/Yx2effYbPPvvsX32stw1vdKdmGKYRCpLFA1iWfQdAFoBxQEGymLzvL/3iBwCqsSzrvBDda4Yz+nrEMCuVSvj4+CAsLAxBQUGwWCxQq9V0Y9i5c6dTAUy5cuVw/vz5EjmYDMNAJpPRARu1a9dGx44d8fXXX6NWrVpYvnw5fv3112IHjbm5ubhx4wZmzZqF1NRUh0n07777DitXrkTfvn3RtWvXIo+t1WqxaNEiHD58GFWqVEFOTg6MRiNNDMtkMqSlpdFhXSKRiE6CJwlmUplPTU1FamqqFVOXfMY/zTwuTlBsj2Fsq93s4uKC4OBg6jT9/vvvTukUk2nIzg4FsgeGYfDJJ59ALpfj4MGDuH79+ks5oCaTCT///DO++OILpKenY+jQoZgwYYJD9lRubi6GDRsGk8mEsWPHOvU5H3zwATIyMnDw4EF07NiRMogBUIedDIt4mTWSl5eHpKQkylTgBgqlOaFMnE1bNhYXEokEWVlZ0Ol00Ol0UCgUVCuVsLVcXFywZcsWmEwmDB48uEj7Ur16dYwdOxbHjx/Hhx9+WGRBo3379qhSpQpOnjyJvXv3OuWgxcbGYtGiRZg+fTr279+PS5cuwdPTE927d8eSJUsctgo7QnJyMu7evVvks6fVaqHX6yGTyei68/Lyomxt20DbnmZkcfCXTX4ljD1y/s4+D1qtlurtkmKem5sbwsPDaeKGJMhJiyn5OSmIzZw5E9evX6ft03wICgrCnj17MHnyZJw+fRpdu3blHfZTo0YNZGVlUXbUq8TFixexd+9eVKhQAevXr3fIDrMHk8kEtVqNxMREPHz4EAKBgA5C4oIk4jw9PeHq6orw8HBIpVJIpVI6PV6pVEKr1SI+Pp4+S/YSZq+a4fQy4Oov2oJMlyfyGvZet1gsVO85Li6Oyr6QToi6desiLS2tWIUmFxcXdO3aFUePHsWBAweKfU0+Pj7o3bs37t+/jzVr1pR4SDHLsnjw4AF2796NiRMn4vvvv4dYLMbgwYNLXOhq2bIlxGKx09f1xx9/4M8//4SHhwfEYjGkUmmhZOHL4FUz+2wTlKSjw1YqiWvXBAIBvL294e7uDj8/P4waNQq3bt3C119/7dRnVqxYESqVCvv27XN4HYRtPHfuXBw9etSh9jMfdDoddu7ciY8//hgnT55E06ZNsXr1agwZMsThMESC9PR0TJs2DWKx2KEW9qNHj3DkyBHUqFHDSsaO+EmAtdRIXl4eJTu4uLhYDUOUy+W04640+j+vC8Tnsy0Ik4R7Xl4e8vPzaSLez88PYWFh1Bba0/fnolOnTjCbzZgwYUKR+3NgYCCio6Px6NEjLF682Kk4ICcnB3fu3MHly5exevVqTJgwAUOGDMHjx48xYsSIIosTfGBZFj/88APc3d1Ro0YNAMCAAQMQGBiIffv2oXLlytDr9dRuksGbOTk5VKKOaPcDfw9+8/Pzg4eHx0sN5X4dOsNkloAtuJ1arwJyuRx+fn7w8fGBVCotRAQhe6JSqYS7uzslffXs2RNSqRRr16516nNGjBhBO0O5EAqFqFGjBm7evImtW7fi4sWL+PXXX/H48WPcvXsXjx8/xvPnz6m/6yyMRiNu3LiB9evXg2VZK3a6I6SmpqJTp044cuQIpk6dirZt29p935o1a3DhwgWsXLkSMTExAKx9i6CgIPj7+9N5SvbwqgsA/2to3bo1Tdb/W4/1tqE0aBovZFn297/+/TmAjQzDSFmWLRAzY5i6APxZlj3Ksuzrn7b1isFlWZAklJubG7Kzs5GZmYmGDRuic+fOmDZtGuLi4rBs2TKHk0KnT5+Obt26YcSIEdi9e/cr0RVUqVRYuHAhNm7ciJ07d0IikaBFixYON1iNRoPbt2/j9u3biI2NBcuyCAwMdKiP9d1332H06NFo1qwZ5s2b5/CcTCYTduzYgTlz5uDFixeIjo5G165dkZWVBa1WiydPniA8PJxWRwHQjZE4XMDfDqzFYqFSDLYbta1u6D8Brs5tUeCeH0koEUecbPCk3SgqKgr9+vXD+vXrcffuXWzfvp0mhu0hKCgI9erVw+bNm9GrV69iJzcIlEolFixYgDlz5uDIkSO4evUq2rZta8UkZ1kWBoMBRqMRRqMReXl5yMzMRFZWFp4/f46zZ88iLS0NWVlZYFkWFSpUwMCBA6FUKh1+dlZWFkaMGIF79+5h9erVTrXFpaSk4MaNG5g+fTreeecd6qB5e3tT3Whu4s52jZRUc9NWw684+tb/NIizSYI6wrIG/tYnzM3NhdFohMVigUKhoFV4EiQbDAYkJCSgTJkyWLBgAT755BNs2bIFH330kcPPHjZsGMRiMZYuXYq2bdtizpw5vOwVhmHQvn17eHt749KlS7h9+zbq1auHgIAAGAwG6PV6CIVC6PV6Omzmzp07cHV1pZPEmzVr9tJO+qVLlzBx4kQYDAbMnj3b4XsPHDgAo9GId999lyYxCMODYRhqq4iWNEl6AH/rFxe1/uxoSPJG5kQqx/ZY5HNs9d+dsZlcW8Ut5pHkQX5+PlQqFQ1gCLNPJpPBw8PDiuXfqVMnqFQq7Nixo8iJykKhECNHjkTNmjXRt29fjBo1Cps3by70vpYtWyI9PR2HDx/GL7/8gmrVqqFx48YltoFAwf06e/Yszpw5g+rVq6Nv375OBQwHDx7EsmXLaBsp0R3mYtGiRYV+9uOPP6JMmTJUm10kEtF1ToamksQBKVBwC6YkcQH8/V2W9gAnPz+f6qja08IlNojInnBlrMhz9M4776By5coYMGAAfvrpJ6dlQ8aOHYtHjx5h/PjxePjwISZMmMCrb2sPTZo0QX5+Pvbv348ZM2bg//7v/5z+XbPZjJs3b+L8+fNISkqCq6srevXqhSZNmhS5RzoCy7KYOXMmzGZzIY1nezhy5AgGDBgADw8PTJs2jepkO8vyIp/pyH4RZt+rgj0fytam2ZNLIOuL6N43b94cs2fPxvvvv1/kPRcIBBg3bhymTZuG7777jlf33M/PD4MGDaK610eOHEGDBg3QvHlzhIeHF6m3eu/ePWzYsAEZGRlo3rw5+vbtWyzZMZ1Oh4EDB+LBgwdYtmwZlEol1UPn4vr165TosXjxYnh7e8NiscBgMECj0SA9PR3e3t70fIlvQxLG5Hy590cgECAnJ4dX0/rfCHJfXF1dCxVZdDodlW7iFjcAoGPHjtiwYQMGDBiAo0ePFpr5QFC1alXMmjULM2fORI8ePYosctSsWRNubm44fPgwlixZQrsbuXNiRCIRZfNymZXe3t4ICwtDw4YNUaNGDacKFHw4fvw4rl27hlGjRlGbmpGRgT59+tBnRyaT0WSrQCCAyWRCZmYmvLy8qB+alZVFE6KvIj4GihevOYt/imFP1hCRBSNDqRUKBS2kkTk5Pj4+YBiGzlgaP348vvjiC8TExCA6Otrh55B1bO+ek8GaP//8s0PGcVBQEKpWrUr/bXt/WJZFQkICLl++jBs3btDh9h9++CHt0HOEpKQkDB48GCkpKfjmm28oo90e4uPjoVKp0KpVKxrnyOVyqNVqmEwmhISEUDvLh/8lu/Y6QNZKzZo1/7XHetvwppPGvwK4AwAMwwgBSAGEAXAHkMYwTDCAKgB+eGNn+ApBkixyuRze3t6QSqVgWRY7duzAtGnTsGLFCqjVaoftjw0aNMDmzZvRv39/DBs2DJs3by6Wfh0fGIbBRx99hFu3buHOnTsAUChxbDAYcPXqVdy8eROPHz8Gy7Lw8fFBq1atEBYWhnfffZfXweUmjLdt28abbGBZFkePHsWMGTNw//591K1bF8uXL0fz5s0p+yM3Nxc5OTlITk5GQEAAPD09abWPVGm5zikJEvhY4aXdsHMdlry8PGRnZ0MkEsHHx8fqOuVyOZRKJdavX4/atWtj6tSpaNiwIb755hs0adKE9/gTJ05Enz590KpVKyxduhTNmzcv0XmGh4ejS5cuSE1NxQ8//IDt27fT8zIajQ7Zl3K5HP7+/ihXrhx8fHwQGBiIWrVqFelUJSUlYeTIkcjIyMA333yDVq1aOXWuREOwZs2atE2by4xlWdYq0CSvE0kQAFZJPFtwA1MXFxcEBATYHQz2JgoWzoI4mySxxH1OiC1TKBTw9/eHQqGgQTc3SZOamoqUlBTIZDL0798fR44cwcyZM9GqVSuH0jSkZbply5YYP348Ro0ahebNm2PSpEl2BzxJJBJ07twZ9evXx759+3Dp0qVC641I1cjlcnTu3Blt2rSBXC5HcnLySzvv27Ztw8KFC1GhQgUsX74cAQEBDt+/ZcsWlC1bFs2aNQNQkBz29PSkCVMCkuSyDTC5SRU+21WcgoTZbEZ+fr7dBJy9YzhjM7lJAx8fH5pAJuwzci0ymYyy04k8jO1xdDodOnfujF27dmHBggUOh3wRNGnSBLNmzcJnn32GRYsWFUo2C4VC9O7dGzExMfjyyy9x//59/Pnnn6hRowbq168PhmEoIyw+Pp4ydMhzT4aJqtVq3L59myZA7t69izp16qBr166FtHTtwWQyYdGiRRAKhWjXrh1cXV1pAEeSvMHBwYUcVK1Wi3PnzqFnz57w8PCATqeDr68vlWAijDWLxUL19WQyWaHkP5fpV5r3QQKFQkGlPuwluEkwTAbR+Pr60uuSSqUwGo1wd3fHV199hS5duqBWrVoYNGgQJk6cWGTBwMPDA1u3bsXMmTOxfv163LlzB8uXL3f63BmGQdu2bVG1alVs3LgRy5cvR2BgIGrXrs27VtRqNX799Vf8+uuvyMnJgZ+fH3r06IGQkBDUqVPH6c/mwzfffIMTJ05g7NixaNiwIS/rk2VZLF++HJ9//jnq1KlDOzLUajXtkuAOlwOs90Gub1iU/eIbxmgLvmKXLWzZr7afTYYmm0wmyljkgrBie/XqhYsXL2Lu3LlYunSpw88EgA8//BBHjx7FtGnTsHr1aoSGhtp9X9myZdGoUSMkJibihx9+wNWrV3Hp0iX4+fnRwdW2SeoXL17g2LFjiI+PR3h4OCZMmFDsDhmWZfHpp5/i9u3b2LRpEy/h4/Tp0zQZTToT09PToVQqKaOfFAXJd+aMb1Oa/Z/XBVu/kgsim8JlgQKgtnzBggUYNWoUunbtilOnTlkNwuSCyNSMHDkSHTt2xLx583jXHsMwaNy4MerVq4c///wTjx8/hslkgslkQlZWFhQKBUwmE4xGIwICAhAaGkoL+K8qaZKUlISvvvoKtWrVKtSBKpFI6GA72+StSCSCt7c3RCIRLVqR7rfiFLH+V0A6Qsi/09PTIZFIrFjPZFCo2WzG2LFjcfToUYwfPx5RUVEOpVGePXsGAHbXmZubG3r06IEuXbpAr9fDYDDg0aNHCAwMpEXu1NRU3Lt3D2fPnoXFYsHFixdRtmxZlCtXDiqVCg8fPsS9e/egVqshkUhQq1YtNGjQAJUqVXKqcBsbG0vlhb777rsi986srCx4enrCaDTSTkoyDyI7OxsKhQK+vr68xIr/8PIYM2YMABRbNuttOtbbhjeaNGZZ1gyA0AkYANkAMlmWTWMY5gMAtQDMZFm2ZH18rxlchxiAFXvB9jXgb2eBOAISiYSKqs+fPx8sy2LlypXo2bOnwxaxtm3b0mB47NixWLVqVbHYLnxgGAaRkZGQSqW4fv06AKBx48Z4+vQpHj16hGfPnsFisUClUqFly5aIjIyEv78/GIZBZmYmr+P+008/4auvvioyYWw2m9G1a1ecPn0aFSpUwK5duxATE0MTeFKpFBqNBr6+vqhbty6tnvr4+FgFXNyBeABoMAC8HUGxI8jlchoQa7VaSCQSmnABCq49MzMT3bt3R+3atfHhhx8iOjoac+bMwejRo+1+R40bN8aRI0cwevRo9OnTBwMGDMC4ceNKxDgjWqGVKlXC48eP8fDhQ4hEIojFYmg0GjrZViwWQyaTQalUQqlUIiMjo9htbbGxsfjkk0+g1Wrx/fff804Qt4effvoJHh4eaNasmVXbP19ijASVJHnq6+vrsO3INmHHZdoUN/n2pmEvsOOyEhmGQVpaGk0YcFt+VSoVHcAhl8sxb948tG7dGh9//DGOHTtWpN0qV64c9u7di7Vr12LlypW4d+8eJk+ejHr16tl9f2BgIEaMGEGZARKJBBKJBH5+fq/Nodu3bx8WLFiANm3aYOHChZDL5VQ2xh7i4uLw008/YfTo0ZQRmpmZSQcIkoAtNTWVtkfbOqTOJFWKE5ALhULeBJyzx3D0+WSdk4QN0dIja5/7DOTl5SEtLQ0+Pj5WQwGJrvmxY8fQq1cvp85hwIABtAPBYrHYTYq4u7ujXr16aNq0KX755RfcunWLlw1DkkcMw8BiscBsNkOv1yMxMZEmX5s1a4Z27do5vd5Onz6NhIQErF+/Hu3atQNQsGc5ansECoa/aLVatG7dGjk5OZDJZJTlr9VqaceEo/N4G9l+DMPQ74DvddIxYrFYKBvN29sber0eYrEYQqEQ5cqVw+bNm7Fz5058/fXX+Prrr9G3b1+MHz/e4eR2sViMefPmoUaNGvj888/RsWNHzJ071+n2WAAICQnB9OnTsW/fPvzwww/Izc1FnTp1aNKaZVmkpaXh6dOnOHjwIFiWRcWKFdG9e3dUqlQJAoGASm6UFDk5Ofjqq69osXXw4MG879Xr9Rg5ciR27tyJ999/H2PHjkVQUBCys7Np8ZAkNXNycmgxRSaTFUokA0XbL2eZfXzFLkewZxfJtHvCTges/Xs3NzeUKVMGcrkc77//PjZu3Ij/+7//K3J2hEAgwPLly/HOO+9g5cqVWLRokcMEd1BQEAYOHIiePXvi2rVr+Pnnn+mA4dDQUNSsWRPlypXDpUuXcO3aNSgUCkRHR2Pw4MEligE2btyIAwcOYMKECXZtI8uyWLduHSZPnoyqVati8eLFqFKlCjIzM5GcnAwAtChjG+/Y+k+k4M6VsHpbbM6rhKNrJq+lp6fDZDJR252Wlgaz2Yxy5crh+++/R4cOHdCpUydcuXIFgYGBdo8VHR2NPXv2YODAgRg+fDjmzZvnMMkrkUgQGRlplYiOjY1F5cqV7b7/VQ1Pt1gs+PLLLyEQCDBx4sRC+1VeXh71hQg5gUvI4MLV1dVqMPOrYhq/7SAxMdkLSdcRIa8R20GGByoUCupHbNiwAU2aNMG0adMcSlU4ShoDBTad+ONAQVLWVku4TZs2yM/Px5EjR5CdnY3Y2Fgqx8IwDMLCwtC0aVO0bt26WEWBO3fuYOLEiZBKpdi/fz8qVapU5O9kZ2fDy8uLDlg0GAwQCAQICQmBQqGASqUCy7JISUlBbm4uAgICHJIZStqd+h/+Q2nCm2YaU7AsawKgYRjmOcMw8wG8C2BQaU0YA38nhrgGGShwirhJI6lUSo0EqeIRfWFuG+qsWbNw9uxZjBkzBs2aNaOOrD288847mDBhAhYvXowpU6bgs88+szJEjrSsMjMzeQPJ/Px8qh11584d3Lt3j15TuXLl0KJFCwQEBNDPIo4gaaG2xaVLl7Bp0yY0btwYX331lZXzSGA2m2GxWLBt2zacPn0ao0aNwpQpUyASiZCVlQWBQACWZSGTyejfrq6uyMzMpM4B13kg7ankHEkwQH4fwFtltLlsYpZlaaAlkUiQkZEBs9lM35Oeng6z2QyxWIzq1avj/PnzGDlyJKZOnYobN25g69atlEFnMBjoccPDw7F3714sW7YMW7duxblz5zB79myqLcZFVlYW7zAYbht9SEiIVfCdkJBgl11KEi9qtZr3HtjqbN67d4+ukS+//BI1atSwu/4IQ8IWP/74Ixo2bAiDwQCTyURZqCTxQ/R5SQEoPT0dBoMBrq6ucHV1pYEPH/4NDBrus0Kc9by8PLuMD6DARgiFQqrLJ5VKaYKdOKs1atTA9OnTMXHiRKxcuRIjR460OgbfIM2PP/4Yfn5+WLt2LcaPH48OHTrgww8/pI6jTqezsiuknRIoWF+kJc8esrOzeQf0kQGm9mAymXDs2DHMmDEDDRs2xLx588AwDB1aY09iAAB27NgBgUCAmJgYiMViaLVamM1muq6IJAXwt7wRYP198CVVuLaCfG+2P7cHgUBg1yY60pYtCvY+n8sGcnRODMNQtgt5f926dVGmTBns3bsX3bt3t/t7hLXCxfTp0/Hw4UNs3LgRvr6+vENPFAoFGjVqhIiICKSnp9OfKRQKBAUFwcXFxS4bNDY2tlCSkdhAkUjEu7YMBgNUKhU2btyI4OBgNGzYkP4eabvkg9lsxtGjR+Hq6ooWLVrA3d2ddqCQNl1uEox0BgCFC6dc5pEjlIYBeVw4Oh/ueiN+FEmiu7i4UF+AFAtmzJiBJUuWYOfOndi5cyf69u2LsWPHIjg4mB6TFHgIOnXqhAoVKmD06NFUd79r1652nxU+/eKYmBgkJCQgNjYW58+fR2hoKCQSCRITE5Gfnw+JRIJ69eqhZs2aNKFM5lmQriN70Ol0vPup2WzG/v378dVXX0GtVqNz584YO3YsfW7I2iFIS0tDnz598Ouvv2LChAn49NNP6X1gWRYeHh7w9PSkQXF8fDzS09OpJJi7u7uV70XgrG1yBL5ilyOQriFiY4CCdUI6PUhBKC8vj0ouuLq60iTBpEmTcPr0aYwfPx5HjhwpxKC2ZWr7+/tj0qRJmDlzJg4cOGBXC5HrNxHUqlULtWrVQmZmJs6cOYN79+7h8OHDAArsY61atdCwYUMIBAJeGYqcnBze+SfXrl3DF198gejoaAwfPtzqOyf79YgRI3D48GG0a9cOGzdupHrpZN/y8PCgA72499T2OyXzEQBQsgdQuGjwOvxyvvX1189fSyXZ0Zom7HidTleoqEd8T5IwJnNbfHx8YDAY6P369ttvER0djQ8++AAnTpyAwWCw2yFQtWpV7N27F/369cO4ceMwbty4Qi35Op2OV0ebKwVki5ycHF7fSKPROPTnuX7xgQMHcPv2bYwZMwYeHh6F4l6JRAKtVkuvUSgU0oGtxDfRaDS0mGjLmCV4m2K+ksK2w5asMZ1OR/cgmUxGcxQWiwVGo5HubcQXkMlkyMjIQFpaGkJDQ/HJJ59g6dKleO+99woVlzIzM5Gfn09zBXK5nNqj/Px83mKWTqfjtU2+vr6oVq0aGjVqhPT0dGRmZiI4ONiq05Evx2EymazW87Vr1zB37lyoVCrMnj0b5cqVs5tXMRgMVjFxZmYmTah7enrSIgXpAgP+HlZNYkHbWJ1LJOTrrinKD/4P/6E0odQkjZmCp0MMoOlff7diWbbk07leMew9vGQDJ0422eRtW9uJ8SBtM2RADQArp4GIzjdv3hwLFy7EjBkzeI1GSEgIPv30UxgMBqxcuRK+vr7o1asXfHx84OXlxVt9Bhw72l27dkVoaChYlsXt27dx69Yt1KxZExEREcjLy0PFihXt/l5eXl6hz/zuu++wadMmNGvWDN9++61DSQqj0YjZs2ejUaNGmDhxIt20CDOTYRg6qZt8nsVigUgkKpTUIP+3ZUZynbN/2hgX9Xlk47Cnt0eSRMDfDp67uzsMBgPMZjMd2kZA7o1QKISvry+2bt2KiIgIzJkzBz169EC3bt2sEv9cLFmyBF27dsXw4cPx4YcfYtSoURg/frxVIq9WrVq8FdXAwEDetZeSksLbtp+UlERbju2Bq1f1008/YdKkSfDx8cHevXvh7+9v1dLPhdlsLpQMefr0KeLi4tC3b196L5VKJfR6PeRyOfR6vZXTQZjdEokEPj4+EAgEdltcCRiGoTIptnibnADbcyUBBBlQRuDm5kZtCilIkInBRCOSFNWMRiNGjBiBS5cuYcGCBejSpQvVMAMKnlM+Tfd+/fqhd+/emD9/PtavX4/ff/8dgwYNQp8+fdC/f3+HVX7bic5cxMXF8eolZ2Zm8ur0XblyBVOnTkWdOnWwf/9+q3VgW8giMJvN2Lt3L9q2bQulUgm1Wk3XFPeZF4vFVnr4zuBl15Yj5uarOp5QKKTyL3zHVSgUVgEfGfyZlZWFdu3aYcOGDTAYDHa12vnWz549e9C4cWOsW7cOhw4dok4/QXh4uENNPEfD4ZKTk3nZNfHx8bw2z2g0IikpCTdu3MC8efMQFBREX1OpVLw2DSi4jydOnEDdunUp28VoNMJgMNDhKxKJBAKBgCYbyJ5oe9+538k/DUdry9l1Z2/PJAOTuNIo5HUPDw+aGCTa2Z6enli9ejWmTp2KL774giaPR48ejTlz5kAkEqFcuXKFAuCQkBBcuXIF/fv3x4IFC/D48WPMnz+/kK/TsWNH3vMnXRCrVq3CpUuXAABVqlRBTEwMypcvj2rVqtn9vaysLF7blJOTY7WeCC5cuIBZs2bhwYMHaNKkCebPn1+oMGw2m2ny4Pbt2+jatSvS0tKwYsUKtG/fHlKplK4XsVhM1xZJ4igUCgQGBsJkMkGlUkEkEjklIQEUn43lSBfU0e/b/g55Brj/J+8hMjSECJKbm4sxY8bg888/x8mTJ9GlSxf6ez4+PnbtDxnsunXrVvTt27eQjxQUFOSws6Bdu3Z0AOLVq1fRtGlTWvx69uwZr/1Rq9V2C/VPnz7FypUrUa1aNXzzzTeFfJVr166hX79+eP78OebOnYuRI0daybtpNBqYzWYqO8ZN2tmDraQMKci9SX/oL5v+8i2axQTDMLzSUVw/ymKxQKPRQCgUUh+KFOJr1KiBOXPmYNy4cZgxYwYtWNuDn58fzpw5g//7v/+jjN7Ro0fT7zIgIIC3QHnnzh1eSYKkpCReibHc3Fy7xVkChUKB8+fPY8+ePThw4ACio6Mxffp0MAxDYxru/eLOQyFD4qRSqVWxgvxN3k86EF62w+x1rtFXfWzu8XQ6XaF5BWQWjslkQnJyMoRCIUJDQ6ler0AgoPaL6JOrVCrMmjULP/zwAyZNmoQ2bdpYSeWQTt/09HT4+PhY+Ts9evTg9X/u3bvHy7ivXLmywxyGI19fq9XStXfgwAHMmjULlStXxrfffgulUsnr5xkMBivbrVar4evri7i4OISGhtodpEgSyURKzJEEE7e75j/W8X94W1FqBFjYAhgAzAEQXZoSxnwgiSFXV1c6vVyj0VDtXbJhEZZPYmIikpOTKTs0NTXVSrsqLy8PDRs2RN++fbF8+XKqLewIn332GT744AOsWbMGTZs2pca2atWqaN68Obp3745PPvkEjx8/Lta1EamK/v37o0aNGsXeeJ3VMCZYuHAhUlJSMGfOHOqw+/j4UGOsVqvx7Nkzyjwjk3FJwoULkmwhAQVhWr0NE5q507vtgTiNRBPUw8MD3t7eVkMzfH194eHhQVlWGo0Go0aNQkREBCZPnszLgCRo3LgxfvzxR/Tq1QsrVqxAdHQ0EhMTX8flFgtarRZffPEFevXqhbCwMBw9etSpoXe2IDpE0dHR8PPzo23Lubm5lIFNkpyk9YibMAZezzTltxWEUcplf7i7u0OlUtEhOD4+PrRazzAMZs6cCQ8PD/zf//2fXSY4H+RyOWbPno0DBw4gNDQUc+bMQc2aNfHZZ5/h999//8cSX3fu3MHYsWNRvnx57Nmzx+kW2zNnziAxMRH9+/enGrP27BJhEv8vOJQWi8Vqorder6cFiufPn8NsNkMul0OlUqFXr16wWCyFJnQXBV9fX6xZswZqtRrDhw8v0gb+U1i3bh1cXV3Rp0+fYv3e77//jhcvXqBdu3awWCxQq9UQCARQKpVQqVSFnjnijzhaT8QHMZlMVpI99sCyLPLy8koF+9jenkkS59xr57JjNRoN0tLSkJ2djYyMDAAFgV14eDi2bNmCP//8Ez179sTixYsRExND32MPSqUSGzZswPjx47Fv3z507Nix2K3bbm5umDJlCnbt2oW1a9diyZIlaNmypcOhyMVBbGwsLbDpdDrs2LEDR48etdtJRKBWq9G2bVuYTCacOnUKXbp0gVQqhUQiKbQ+uEmacuXKwcPDgyZ3igMSaDsq0rxukGQdGQRLbLtCoYDRaERGRgaioqIQERGBTz/91GFXH4FAIMAXX3wBk8mETz/9tETPDcMwqFy5Mvr37+8wIVcUcnNzMWzYMAgEAnz//fdWiV6WZbF27Vq88847MJlM2LRpE3r37o3c3FzKHrdYLDR5bjKZwLIs7ZJxdO5ubm60wFwa9ra/ztd+y9xrAnlOSPLOXlxE7BWxXSQ5mpuba7XWOnTogKFDh2LlypXYu3evw8/18vLC7t270b17dyxYsAB16tTB9OnT8dtvv/1jcRHLsvjjjz8we/ZsRERE4P3338epU6fQp08frFq1inc9cItY5FnUaDTIyMhAbm4uLdBzBzCmpKQgJSWF3i+yt5nN5lKzb71O2A71JDExIRiRvEROTg4yMjLsMt65A8FFIhE2bdqE9PR0TJgwwe5nxsfHW3VcaTQa/PLLL1iyZAl69+6NihUrokGDBti0aRMvs/1VIS8vDxMmTMDw4cNRt25d7N+/vxBRICMjA7/99hvi4uKg0WgKrYns7GyoVCoqwaTRaKifSt5LGMZEZpQLrg8CWPv0pWGf+w+vHmFhYbRwZfunJLmK0ohSkzTmYBvLsnff9EkUB2STd3FxsesIkERfQEAA/P39oVKpkJGRgeTkZNqeodVqoVar8fz5c0yaNAlKpRKjRo2yapewB4ZhsGTJEhw9ehQbNmzAF198gU8++QRt27ZFSEgINBoNDh8+jA4dOuDWrVuv7R4QEA204iSMExISsHTpUrRv3x6VK1em7+dqXhoMBmRmZuL58+ewWCxWiWE+EEeBsEKdce7fNGw3Glvo9XqaWOG2rnODOG6QTPT4CMMzNjYWmzdvLvI83NzcsHTpUmzfvh3x8fEYMGAA4uPjX92FFgMmkwm7du1Co0aNsHTpUnTt2hWHDx+2yzR0BufPn4evry9laXEHuAFAYmIibU/38/NDQEBAoeLE/1JSj4A70M/W8eY6QeTe8D2bOp0OSqUS8+bNw++//44mTZpgzpw5xQpgGjVqhIMHD+LChQvo27cvfvrpJ7z//vvo3bs3Hj58+Equlw9arRYjRoyAl5cXDhw4UGhAkSNs3boV3t7eePfdd+Hu7o78/HxkZGQ4tE+lKUFXXBAbTJ4xe8lI2yCPJIgNBgOysrKonJJAIEBQUBAiIyOxatUq/Pzzz8U6lypVqmDRokW4ceOGVTv+m0JGRgYOHjyIDz74wKEUhT2cPXsWQIEGoEKhgJubGw2s7QUwzoAwk4paj0DpSO4R2NszbRPFBBaLBRkZGUhJSUF2djZSUlKonAyRGTObzfDx8cGGDRuwePFiXLp0Ca1atXJomwQCAcaOHYvt27cjKSkJrVu3Rs+ePTF//nycOHGC6r86AsMw8PT0fKXBhUajwdy5c9GyZUv8/PPPmDZtGs6dO4cOHToUuXetWbMGGRkZ2LBhA8qVK0fvU2JiIrKysvD8+XOo1WqagFer1XTNFFX85kNpKMbm5eVRW0QSqkQnNSQkBGXLlgXDFAyOJr4RnzwEF6GhoZgyZQrOnz/vcNj164TFYsHEiRMp09g2+Tx37lyMGzcObdq0weXLl1G7dm34+flBJBJRiReiMUv0TwnLmHRa2kvAlEb8tf7/USYJsZt6vd5qMKM928L19Qmjkeur+vn5YcmSJWjcuDFGjhxZpMa5VCrF6tWrsW7dOlSvXh1bt27Fe++9h06dOuHYsWOv9Tt79uwZunXrhg4dOmDHjh2oX78+tm/fjvv372PZsmV2faiEhATo9XorUgLwN5uWJDpttW1JscfHx4feL2KP0tPTS82+9TpByEJ8nRhk/QgEApp4t/391NRUxMXF4cmTJ3jx4gU8PT0xevRo7Ny5E4cOHSp0zKSkJDAMg1OnTmHAgAGoVasWhgwZggULFuDZs2do164dlEolpkyZgjp16uDu3deT5nn27BnatGmDnTt3YuTIkfjuu+8K+Vcmkwn16tVDkyZNULlyZahUKvj5+aFWrVro3r07xowZg/T0dAQEBFCJCovFgvj4eKSkpND1Q9agPWkvPh8EKB373NuAL774Al988cVbc6ynT59SeSbbP28qf/KqUeqSxuzb4G0UE2TzN5lM8PX1hVAohEqlgr+/P90s5XI51eH09vbG4sWLcePGDcyYMaPI4wsEAjRo0ABdunTBRx99hKlTp2LZsmXYsWMHTp48iTNnzkAmk6FTp064cOHCa7vO/Px8jBgxAp9//jnat2/vVMIYKJBEsFgsmD59Op3YS6rqJJlusVjg7u4OkUhkNxCxl5DgtoA5SsSWJjjaaAD7ATKXhUAY7ISRTRLHcrkcHTt2RMWKFXH69Gmnz+fdd9/FunXr8OzZM7Rq1QoHDhx4uQssBiwWC44ePYqYmBh88sknUCqVOHjwINauXVvklHtHSExMRIUKFZCcnIy0tDRkZmYiLy+P3iulUong4GA6iIs72O1/GWRt6vX6Qo431wkiSU5usYabRCBt823btsXcuXMhlUoxb948NGnSBOHh4Rg4cCC+/fZbpwY9Va5cGQsWLMCpU6fw+eef4/Hjx+jUqROWLVvmUHP6ZfDgwQNkZmZi/PjxvK139hAbG4sjR46gU6dO8PDwoI6mRCKx0uKzBQk0NRrNW5c85n7v9hJJJKjz8fGBn58fZcUIhUK4u7tDLBaDYRikpKRQHbo5c+bAYrGgRYsWGD58uFMJG4L27dtj2rRpOHnyJN5//33s27evkMb+P4WTJ0/CZDJh4MCBxf5dMsjl2rVr1DbpdDqrKejFTdzJZDKrDgFH+2VpCnocJYjt+QRkoI1IJKJt4HK5HPn5+UhPT0d6ejpyc3Px4sULNGrUCBMmTMDdu3dx7dq1Is/lnXfewYkTJ9CzZ0/k5uZi/fr1+Oijj9CoUSM0bNgQw4YNw7p166hf8rrAsiwOHz6M5s2bY926dejWrRsuXryI4cOHF6ldDRSspbVr16Jdu3aIjIykbb1Et16tVkMoFNI2ciLPptFoqM6yozXEVwh7k8VYvqIWmZGRkZEBnU4HNzc3KBQKtGnTBp9++ilOnDiBGjVqYOvWrUXa5v79++Odd97BrFmz6LDpfxLr16/H2bNnMXnyZERFRVm9ZrFYsGXLFrRs2RLffvstjEYjjEYjcnNz4e3tTaVcANCZLACsWMbkXv3bk3IlhUKhgKura6Fkpj0bTchGRD8aAGV8AwWxAJFKycvLw40bN4r8fIZh0KVLF2zfvh337t3DmjVrIJfLMWrUKHTv3t0pG1ccpKWlYfny5YiJicHjx48pOWD79u147733eG1RQkIC3n33XchkMrRo0cJqPbm6usLb2xuZmZnQ6/VUxpCAdFyKRCLa+UviJjJYuDTsW68TRZGPBAIB/Pz8UK5cOdrxBvxtA6VSKXx9fSk79+nTp3j8+DEGDhyIOnXqYMiQIYiLi7M6ZpcuXXD9+nV8/PHHePz4MYYOHYq1a9fi0aNHuHz5MlauXIkTJ07g+PHj8PDwwIQJE/DHH3+80uu+du0aBg8eDLVajf379+Ozzz7jXWMpKSmoXr065s2bhylTpqBTp07w8vJCbGwsvv76awQGBmLAgAHw8fGBUqmkCXhC9EtNTaXFxOISEP4XSUclQaNGjdCoUaN/9bHeNpQaTeN/A2x1qohTQIwWSaiQ1gwfHx8r3UYy8dtkMiEqKgoffPAB1q5di4YNG6JTp04lPq/y5cvj+PHj6NGjB3r16oWJEyfSKe2vCi9evEC/fv1w9+5dTJkyBaNHj3bKID59+hQ7d+5Er169ULt2bWqQRSIRpFIp8vPzYTAYoNFo4O/vTxlVZCAXCRTtaYRxdZzeNKvsVcHe5GVynTKZDPHx8UhMTISXlxedsk4SNm5ubqhXrx5OnTrltMYgALRu3Rpnz57Fxx9/jOHDhyMmJgaff/75a3O8WJbF2bNnsWLFCjx69AgVKlTA9u3bER0d/Uo22ezsbMogFovFVMdYLBYjISEBIpEIXl5edode/Ye/1xv3++cOZiPJYldX10KOK7nHOp0OBoMBPXr0QPfu3fH06VNcuHABt27dwtmzZ7F7924wDIO2bdti4cKFRU47dnFxQb9+/dC+fXvMnz8fa9asweHDhzFt2jS0atXqlV7/o0cFyknOTGDmYtWqVRCLxejXrx8AUBmG/Px8+Pv78xYmuNr5fDraXJQmvTR7AyFtC15EV5zsmcS2y2QyGAwGPH36FBaLBWFhYfD19YWXlxf279+PpUuXYtu2bXT/GDlyJK/GKxcffvghAgICsGjRIkycOBEzZsxAs2bN0LNnT9SvX593cMurBMuyOH78OOrWrevUOdsiJiYG5cqVw5YtW9CjRw/KquKuoaKGcdpqAXP3lqJsH98gxtIAcl2E9Qj8/bxIpVKqT+/m5gZ3d3d4enrSZBeZRyESiaBUKiGTyagW6NGjR9GwYcMiPz8kJATz5s0DUJB8vXv3Lq5fv45bt27h1q1bOH36NPbu3YtNmza9lpbFJ0+eYObMmbh27RoiIiKwYcMG1KlTp1jH2LVrF1JTU9G7d2+4uLhYzcEgA9CIZBMAyjLNzc21aoHmA99QoDcJ4kO6urrCz8/PSoMSAB0o5eLiAq1Wi5CQEEyePBlt2rTBzJkzMXToUHzzzTdYuXIl7zMtEAiwYsUKdOzYEV26dIGvry8CAgKgVCoRGhoKf39/+Pv7IywsDJGRka+0WH3u3DksW7YM7733HgYMGFDo9StXriApKQkLFy6EyWQCwzAwmUzw8PCgtoEk1MuWLUtnFhCWMfde/duTciUFuVc5OTmUpAAU2Ghbe0zIRllZWVaFLY1GQxPKOTk5VPP8jz/+KDSkzBHc3NzQvXt3REVF4cyZM1i6dCl69OiBIUOG4NNPP30p3+Hx48fUBzMYDGjZsiVmzZpVaGCsPbx48QLdunVDSkoKvvnmG4SFhVnpz5Nhwzk5ORCLxfDy8qL+Dhfce8vd20qLvXmd4Moy2s7z4b6HaAOTPASxgVx/DCiQ90pPT0dYWBi2bduGxo0b44MPPsCPP/5Ij0cKD/fu3UOLFi0gEomQl5dXiOBTr149HDx4EO3bt8fIkSOxevVqVK9e/aWv+ciRI5g1axaCgoLw/fff884pAQr8m7CwMPj7+yM6OhoqlYoyrr28vKgGMen0lsvlkEqlYFkWOTk5+PPPPyGRSODr62vVAVASe12a/PXShitXrgDAK0nQltZjvW34LyvyCmEbpBED7ObmZpVQ4U6cJQL+Op2OtiCRSv3w4cNx//59fPzxx6hWrRrKly9f4nMLCAjAkSNH8MEHH+CLL76AVqu1GuDxMrhy5QrGjBkDi8WCHTt22J0OzYcvv/wSIpEII0eOhF6vp4lzsuHl5+fTKp9KpYJYLEZeXh7VvyTMKnsBMnEUHA0tK83g6kqxLIuMjAw6WIYL7nWaTCY6sTYrKwu+vr7UIbVYLGjcuDF27tyJ2NjYYq2nkJAQHDhwAEuWLMHy5ctx9+5dLFq0CFWqVOE996SkJLi4uBSLFfzzzz9j2bJluH37NsqUKYMlS5YgOjoavr6+Th+jKKjValStWhUuLi6UIeXi4oLU1FRkZ2fD09OT6jbyOVz/y7BXuOCCOO+2987FxQUpKSk0YFIoFFAqlWAYBkqlEpUrVwbLslAqlfj1119x7NgxbN26FXXq1MGoUaMwdOjQIqUgVCoVFi9ejO7du2PWrFkYNmwYmjdvjqFDhyI4OPiVXP/Dhw/h4uLicFCHLdLT0/HNN9+gbdu2CAgIoDaOrGupVMq73kiCjmVZK0kaPpSmhIxt8ELOhwQpfHumq6srVCoVlaogewMZSOXu7o7p06djzJgxWLt2Lfbs2YNt27ahSZMmGDVqFKKjox0mf9u3b4/o6Ghcv34d+/fvx5EjR3Dq1Cn4+/ujY8eOaNu2LTw9PSEWiyEWi2E0GiEWi+1KP7AsC7PZTAMsZ3Ro79+/jydPnmDx4sXFv6koGJg0cuRIjB07Fn/88QeioqKQmpqKvLw8qmnP95w6Sqrae9/bZgO5a4hbtCLSFCKRiLL73d3dqU9msVhoZwkJ3vz8/MCyLFq2bIkjR45g7ty5xQrsZDIZ6tSpYxUUX716FcOHD0fXrl3x1VdfvbLrzsvLw9q1a7F9+3bI5XJ88cUX6Nu3b7GLIBaLBcuXL0dkZCRatWpF/Qcyi8Pd3Z22hZNglwTOxB8rar2UxuSibZKJgMwuAQpYjklJSTCZTMjJyYFKpUKlSpWwYcMGnDhxArNnz0bDhg0xceJEjB071mqAMIGnpydOnz6NHTt24MGDB0hKSkJ8fDyuXbuG3Nxc+r4yZcqgX79+6N69+0td1++//441a9bg/PnzqFSpEh2aZsuK3rdvH2QyGdq1aweZTIbs7GyIRCI6EAoAladwc3ODn58f8vPzIZPJoNFoqFwc3zC8/1AAvmSmbZxiS3oxm83Iz89HZmYm7QghjNHQ0FCnZuDYg1AoxPvvv4+YmBjMmzcPGzZsgFwux+jRo4t1HLPZjIsXL2LXrl24ceMG5HI5evbsiYEDB6JcuXJOHSMxMRGdO3dGeno69u7diyZNmiAzM5P6DjqdzipRHBAQQAeCazQaKx31ovzUfzv4hi06AndtarVaK1a7SqWCyWRChQoVsGHDBvTs2RNTp07FyJEjART4qY4GoXMRGBiI5cuXY9y4cS+dOGZZFl999RU2bNiA+vXrY86cOQ4TxgQVK1bEs2fP4OPjA5VKRRPCSUlJKFOmDI2lyZpiGAYVK1ak0h65ublQqVTQ6XTUbhN/lxRRnfGfSpO/XtowdepUAH/PIfo3Huttw/9c0pgkz4o7KZ7rYNmrDBHmJnGCWZalLR9cx1EqldIJ0yzLIjMzkzKRudOnfX19kZqaipkzZ6Jfv37o168fzpw5Q4060bTlA5En4EIsFmPbtm344IMPsHTpUqSmpqJ///6FJq7ytZaRlnPufdixYweWL1+O0NBQbNmyhTIQbGHvvj5+/BjfffcdBgwYgLCwMHofCMNYKpXSwIQ4BUajkQ5Z4TLvyH0j58Vl0hbFuCoNsNfWyN30NRoN1UXkJlBJokKn00EqlSIgIADe3t5Qq9V0MCOpzms0GtSuXRsAcPnyZYdVf741MHLkSAQFBWHhwoXo06cPhg0bhkaNGuHp06d4+vQpHjx4gOTkZDx9+hQ6nQ4ikQgtW7ZE165dERwczBsk/v7779i9ezd++eUX+Pv7Y/bs2ejYsSNEIlGR7T/EoeZ7zTaJk5WVBYVCgfT0dLpGFAoFnRLt5eUFg8FAHQEu01On00Emk9FA3J5jUFSLammqJjs6V0dsdEev2dpB7u/IZDI6oMtkMll1CwiFQmRkZFDm7YgRI9CpUyesXLkSy5cvx44dOzB9+nR069atkO3TarVWyZEaNWpg9+7d2L17N9auXYvLly+jW7du6NmzZyENPML2tYecnByrIh8A3L17F2XKlEFWVhavBAZ5HgnWr18PrVaLPn36ID8/H0ajEUKhEPn5+fDy8qKOJ2E6Eg017toqit1JvpPSYu9s2SuA9bNErokMW5FIJFbDW+xdL8uy0Ov19Dt0c3PDypUrMXHiROzZswebNm1Cz549UbZsWQwdOhT9+vWjenYGg6HQmo2MjERkZCT69u2LP/74A0ePHsWmTZuwYcMG3uviPvv2np+WLVti2rRpcHV1pYkXW+zZswdisRgxMTF25xbYrh9bSKVS9O3bFzNnzsTy5cuxfv16GuCR4VTkfnEnzJP/2yZVyZ7Jhe339rbYLW7nDdcmky4ukUhEC7EWiwUymQze3t5UQiw1NRVeXl4QCATQ6XSwWCx45513cPr0ady+fRtVq1Yt9JkGg8EhO5u7f0VGRmL37t0YPnw4+vfvj759+yI6Otru76nVaqSkpNh9jbCjAVAps9TUVHTo0AGDBg1CZGQkTCYTlXXhwl4yk+DUqVO4f/8+vvzyS7rnAQU+JRliJhAIaPEe+DvYtWf37f3f9r2vY205WiP2YgC+fYv7O6RjCwA8PDwAFFyHTqdDx44dUa1aNWzYsAFz587F3r17sWbNGsryJn4sQe/evem/U1JS4O3tTfWU79y5g++++w5z5szB4sWL0bJlS3Tq1Mlu8i0/P9+uxM4ff/yBr7/+Grdu3aJ6pL1794ZQKKQsVjKE1mw248CBA2jRogX185VKJY1zcnJyIJVKYTQaIZPJqM0A/u4WAUCLm+Tnb1vB6XWAGy8CKGSPCbh2yzaeVCgU0Ov1CAkJgYuLCy1Senl5IS8vD5UrV8adO3fsPusAHM7Gyc/Pp7ZrypQp0Gq1WLFiBSQSCSIjI3l/Ly8vjxYfT5w4gW3btiEpKQn+/v748MMPMWTIEPqM2PpJ9vyYFy9eoHPnzkhLS8POnTtRtWpVZGVlAQC1N9nZ2VYxN9nTyd7H7Yjggtixf8N6LMpnJ+CShYivwnftJI4kaxMA9dWJz8WyLO0kadu2Lfr164dVq1ahXLlyvJ3L3D3KFjKZDEuXLsWYMWMwYsQIzJw5E0FBQRAKhXj+/DmMRiOVKiPPELF1JB+Ql5eH33//HRcuXEBMTAwmTpwIg8HAKzlmMpmoP1ijRg2cOnUKBoMBarUaZrMZbm5uVAqTfK5UKqXPm8FgQGhoqJU8D3mWbfMRzibtHRVQ3yYpuv/wv4H/uaQxqda+TEXHXmXIntNLHGyuU0w2b5IMs1gsVCeOBGYsy8LFxQUhISFQKpVYtmwZBg4ciE8//RQbN26EUCikxpQPfE6Cu7s7vv/+e0yfPh07duyAUCjEokWLaGKtfPnyvPcmNzcXHh4eYFkWqampmDFjBvbt24f33nsPX375JYKCgnjPxzZRAwDLly+HXC7H5MmT4e3tbSU1wR2uQaY2k6CPMILINFhitLkgEyvJ91Da2Q/21g+XsUkSCKQiSjZ3knjKzc0FwzCUOaXT6ZCWlkY1QYl+Uu3ateHp6Ylbt25hyJAhvOdj7/si6NGjBzp16oRPPvkEq1atwqpVq+hrwcHBqFy5Mlq1aoWKFSviwYMH+Pbbb3H69GmEhoaib9++6NWrF/z8/AAUtPovWLAAx44dg7e3NxYtWoSPPvqo0Oc7GkLEna5sC8IO5B5HrVZDIpEgPz8fbm5udGMWiURwdXWl79dqtZTRQLoBbJ97PsfgbWXpEXADHUeOJt812lvPxOnz9PSk94p7DHLf4+LikJaWBh8fH3h4eODzzz/Hhx9+iFmzZuGTTz7Brl27sGLFCloAAQqSIPbs4bRp0/Dhhx9i2rRp+O6773Dp0iXMmDEDoaGhyMzMRFZWFl68eIHHjx8jKysL2dnZqFWrFnr37g13d3c8f/6cJgnIfYmPj0dMTAwCAgJ4bSWRVwAK1sjGjRvRvn17VKtWDWKxGBkZGbS9lAy2Iewi4oCmpaXRILA4+xW3lfhNsuXtFe2I7ZLJZPR18lwRdgbRrrcHi8UCkUgEDw8POpBJJBKhcuXKGDt2LAYOHIhDhw7h+++/x+TJk7Fq1Srs2rULjRs3Rrly5Xjvg7u7Oxo2bIghQ4YgOTkZly5dohIqJpMJZrOZ/psEqmR/MhgMtF04IyMDmzZtwtOnT7Ft2zZEREQUCgYMBgMuXLiAVq1a8TJiZDKZw6SxUCiEh4cHBg8ejGXLlmHChAnw9PSkiXgSIGo0GqSmpsLX15euY25QSIJ57n5JwG3LL23tk47Og6+4Qq7bbDYjLy+P+mcA6L6ZkJCA9PR0VK5cGTKZDAkJCQgICEB0dDSmTp2KU6dOoW7duoWOHRoa6jARS5JzBCEhIbhw4QL69++PrVu3QiqVYvLkyYVsWFxcHC1m2kKn06Fs2bJ4+PAhJk2aBF9fX2zduhX16tWDwWAose7/xo0bERQUhNatW0MoFNJClFAotCqYkvVAbBZZe3wo6WuvA/bWO+B4TyOFPRcXF7i5udHnkyT3vLy8oNfrMXXqVHTt2hVTp05Fly5dcPnyZVSpUgUhISG89sfV1ZX6qBUrVkTTpk0xfPhw3Lp1C5s3b8bevXtx7Ngx1K9fH4MGDULFihXh7u4Od3d3BAYGQqVSASjYn65cuYIlS5bg0qVLUKlUmDNnDgYPHlzIBzabzfQaLl68iOTkZLRp0waPHz+m8YhYLEZKSgot8hHdc6FQSIsGxEfg2nRH7Lk38V2/SXDvBQCH98WRjRUKhRAKhZBKpcjMzATDMNDr9UhNTUW5cuVw9uxZq2QWF45sk0QisdpryKDsRYsW4fPPP0f//v3t/p5er0dycjImTpyI69evo27duliwYAFiYmJgNBod+iy255iQkIBu3bohPT0d3333HVq1agW9Xg+pVEr1nIlPSvY2s9mM3Nxc2pVEWJ727p893/3fCO6120qhOFpbfEVA4pMZjUbo9Xqo1WoYjUaYTCZ069YN9+7dw+eff462bdsWGqoJFBQ9+TqvqlWrBjc3Nxw/fhydO3fGpEmTSnTNIpEIU6dOxZgxY8AwDPLy8mixwhakYwwA6tatC4vFgt9++w0VK1akfjNZm8SH5yasc3JykJeXRwejc4sR5H0ktrRNKvOBYRi7+Yn/Esb/oTTify5pzFeJLA5sK0PEWDgTnNtqWJFEgcFgoEF+eno6JBIJxGIxDAYDGjdujE8//RQLFy6kbc7OtMDyQSQSYeXKlfDx8cGyZcuwbds2VK1aFeXLl0dwcDAqVaqE8PBwlCtXDmazGQ8ePMDDhw9x584dxMbG4uHDh9RpmTZtGsaMGVPsKdl37tzB999/j0mTJiEsLIzeN8L2IRsVMb4kOCdax2RzK+3J4JcBty2dC1sGmC2zUC6XU4F+kUhEnQVyrKioKPz6668vdW4qlQo7duzAo0ePcOvWLVSsWBHly5e30skimDlzJg4fPoxNmzZh/vz5+PLLL9GmTRu4u7tj7969kMvlGDduHCZPnlxoyu2rBmFz+vv7w8fHhw7LIOuPy+wm/7Z3fwn4WJ0laQ0rTeAGOnzPWHGvkVsE4TuGq6sr9Ho99Ho9/Pz8kJOTA29vb4SGhmLHjh04ffo05s6di6ioKHz00UdYuHBhkTbA398fq1evxgcffICpU6di6NChhd7DMAxNZu/fvx8LFixA7969ERMTQzUDASA5ORk5OTlO6xlbLBaMGzcOaWlpGDJkCCpUqACtVksH3AB/S1OQgYtcViRxTEuil1Za1qCjFlygsPY82RsJW4RojpOWScJ2IYGiSqWiDBSJRIKYmBj069cP169fx/Dhw9GyZUvMnTsXI0eOdOoe+vv7F2oJd7TfksQJQceOHfHhhx/i3XffxbRp09CjRw+r91+4cAFZWVno3LlzkedSFIYNG4YlS5bg6NGjGDp0KDIyMpCRkQGpVMr7nTsr20T2jLdd3slWs9lgMFjNmiCQywuGoBL2sU6ng1qthlarhZ+fH6KionDo0CFMmzbtlZyfp6cn9u/fjxEjRmD9+vWIjY3FypUri3WPMzMz0bdvX0ilUuzZs+elJXhu3ryJCxcuYMCAATRBRQp43AIrUHT7N/f+v+nEHfdcAH4WrCObSQpDXNsEABkZGbSo7efnB51Oh/DwcERFRaFRo0bo0qULrly5UiKt9MjISKxcuRIzZszAt99+iy1btmD48OGF3qdQKODm5gaJRILnz5/D19cXs2fPRq9evZwa1rp7927IZDLUr18fycnJcHNzg6urK9zc3ODl5YWEhARKcCH7lVQqhbu7O92f9Ho9LfbJ5XIq4UHkfd7WAvrLwh6TsLgxKJGHIdKFxJ55enoiLy8PNWvWhNlsxv3791GzZs2XOl+hUIgNGzYgPz8fs2fPhqenJzp27Gj1noyMDHz55ZfYt28ffH19sX79evTo0YM+57ZFMkcgQ+9SUlKwZs0aNGjQgMrckLgyJycH2dnZ9Bkkayk1NZXKVNgbLMaV+APefPfV64CjQpft2uN7r8VioWxas9lMcxpqtZraeSINR2ygr68v1q5di3fffRf9+vXDuXPnHBYn+BAYGIgTJ07g/PnztEBP7As5FwDUHrm7u1MZKfLvkuRDatWqBaBgBkDFihUhFouhVCphMBisihqElEUIRSaTCVqtlmrbcwtCFosFmZmZdD94m/yl14kyZcogPj7e7mvOSIn8h9KD/7mkcVFsCGdgy2SxV1W3dZi5/+eyFLmDDghzKicnB4GBgVSPFgBmz56N4OBgjB07Fo0aNcLKlSvRpEmTl7qGGTNmoHPnzvj+++/x+PFj/Pnnnzh+/Dhvi5O7uzuqVKmCmJgYVKpUCQ0bNnTYvsSH7OxsjBs3Du7u7hg2bFih6eakRZ20HLm6usLLywuJiYmQyWRUk/Df6ABwwS1GZGRkICkpCQDo8BmSXLFtuxIIBPDx8bEroZKWloaIiAicPHkSCQkJLxVkEo2nihUr0p/ZKx7IZDL06NEDrVq1QlJSEnbu3InvvvsOOTk5GDx4MD755BN4enq+dMI4MTERs2bNglAoRPPmzdGsWTN4e3tbvefFixcACgJ2V1dXytq3TQQT58fLy8sq4LF1AvgGTpQWiYCSwhnNyeJeo70iCDdhSBzFwMBABAcHw2QyUa1EojM6dOhQ9OzZk3ZdnDt3Dlu2bLFK7PIhKioKp0+fxrlz5wAUfLdk4GGZMmVoUH/r1i1s2LAB27Ztw6ZNm9C0aVN06dIFrVq1wvnz5wHAas3zgWVZjB07Fps3b8aHH36IJk2aWO09pNWXBIBisdhKEsXT05PqzpZEn680rkEuy9UWtsnl1NRUAAX2nyQgCKuNMH5Jx4JCoaABDymkNWjQAL/88guGDx+OKVOm4NKlS9i1a1eJApvioGHDhvjxxx8xePBgfPbZZ7h58yamTp1KJZV27twJb2/vl9q/CUJCQuDv74+zZ89i4MCBkMlk9Pq5nUz21oCz66M06s86A74EICnQc5NcxD9TqVRUssPFxQWZmZnIz89HSkoKGjRogOXLl+Ps2bN2h2tmZ2fj0qVLuHDhAtLS0jBnzpwi91eRSISZM2eiUqVKmDlzJjp37oyFCxeiVq1aRfqp9+/fx4oVK/DixQscPHjwpRPGFosF8+fPh6urKz788EN4enoWkoUqTgeNPVkagn96+I9tcM+XGHb0TBBJBwKTyQQXFxeoVCpkZGTQBClJJvj4+GDPnj1o06YNevTogZ07d5b4GfL09MTw4cMxdOhQ3Lhxg8YJarUaqampVE5Lo9Hg448/Rp8+fWixrSj8/vvv2LVrF95//32EhITQGQMCQcFwbrm8YGYI2Z9JUoQUWUlBk3vPiLRLSkoKlfSyd7//F2AbL5bkHmi1WpjNZsqqJUVU8v0SqbkdO3agSpUqDrtUnIFEIsG2bdvQoUMHTJgwAQkJCXSA765du7Bu3Trk5+djxIgRmDRpUol99/j4eERHRyMlJQWbN29GlSpVkJmZSckDubm5kMvlUKvV0Ov1cHV1hYuLCx3CCICy3IHC9qm0FM5fJxxdI3ftcaWpbN9L9Ivz8/Oh0+kouS4vL48WWRUKBYKCgpCYmAixWAyFQoHAwECsWrUK/fv3R9euXdG3b1906NCh2GQulUqFrl270v/n5uY6XFOvgoXr5+cHV1dX3Lx5E82aNYNAIKAydWTgKWDdcUXgiEhECs6v0+/+6/rfmgpcfHz8f8zpfwn+55LGrwNFMeiI8bXXJkp0PonemaenJ5RKJfz8/GA2m6HVaqFUKiEUCtG/f3/4+/tj3LhxaNu2LXr27Il58+Y5xSTgQ40aNVCjRg36//T0dGRkZCAuLg5PnjwBwzCoVKkSKlWqBLlcXuKWR4LffvsNvXv3xosXL/DFF1/AaDTSAW7EOSWT4HNzc5GUlEQTxSEhIVat7EST8G2WAXAEbjGCtB9y27BI0SEnJwcajYa2zNiCBGiExd2xY0esW7cOnTt3xsmTJwslVl8nwsPDMX36dEyePBkGg4FuzHyFCmfAsiy2b9+OcePG0Za2TZs2ASiQW2nWrBmaN2+O2rVrY+DAgVAoFGjQoAGAwmwprtwMSUgVlWSy54g5M724NIMracKHVzFohBwjNzcXcXFxUCgUdHI9N8jnavWKxWIsXLgQ9evXx4IFC9CiRQuMGjUKU6dOLTJYEolEhSaMq9VqKxZYZGQk1qxZgxkzZmDlypU4fPgwxo0bR1+vVq2aU0M7Nm3ahK+++gp9+/ZF7969odPpIBAI8OzZM8jlcvj5+VENNYvFguzsbKoN6e/vzxuQO4vSOAiGnBPXgbT3/Mjlfw8JJEWznJwc5OfnUzvHTcCQxHteXh4UCgUyMzNpK+LWrVtRuXJlzJ07F0OGDMGWLVte+/Po5+eHAwcOYPLkydi6dSsuXbqEWbNm4fTp07h8+TKmTJniUAO3KMTGxmL37t3YtWsXkpOT0aJFC9rlQe5LXl4e4uPjIZfL7Up+OLs+itLSftPgS0DyJQBlMhl95kwmE/R6PUwmEx00S4JVgUCAChUqICEhAXq9Hm3atMHhw4cRExODjz/+GOPHj6fM3PPnz+PWrVtUA1EgEODXX3/F6dOnnUrmDhw4EGFhYfj000/RuXNnhISEoGnTpqhUqRJatmxp1W577949rFu3Dj/99BM8PDywdu1a1KtX76XuocViwejRo3H06FGMGzcO9erVg9FotGL/A7Aa+sO3JohPRpio9mzXPz38x1GXEBf2nglyPYTNlpeXh/z8fGg0GuTm5iI7O5s+d+SPVqsFy7KIjIzEwoULMW7cOPTr1w87d+50KP1VFAQCQSF5lOzsbN52bEcwGAxYtGgRFi5cCG9vb4wfPx5CoRBubm5Qq9Xw9/eniUmWZWE0GikpQCwW0/tne88IkYE7I4IU3v9D8UHsFOmqIZ1Y5LsgQ4RjYmKwatUqHD58GNOmTcO77777UrGhXC7H6tWrMW3aNCxZsgRLliyhrzVr1gwTJkxAs2bNSnz8P//8EzExMdBoNNi/fz8qVKiA5ORk2rnj7e2NnJwcpKen0xlAxDYrFAr4+/vTjkqS5LP1JUpj4fxVg1uIJ/4Pn9/Odz9I3M3V0mYYBiqVCs+fP4fBYEBaWho8PT0hEolozPnixQs0bNiQysadOXMGQUFBWLFiBd57773Xe+Evgdu3b2PkyJF0zg+Z+WM2m+Hv7w+ZTGbVzczVKCaSmLYkQODvvex172l/JbeL37rylmH58uX/+mO9bfgvafwKYC+ocmaz4jpbAoGADlzy8/ODUChEamoqnj59iry8PISFhUEul+Pdd9/F7du3MWPGDGzYsAHHjh3D5MmTMWLEiFfCoBKJRAgPD0d4eHghNg2foL2z+OabbzBixAj4+/tjwYIFaNmyJW3tI5VJohcHFNwTvV5P9T6Jk0Tau/l0Zv8t4BYjyJRkAu4wI9uWGfI69/siWnweHh6IiorC5s2b0b9/f3Tu3BnHjx9/7bIQthCLxS8lsUKQmJiIkSNH4tSpU2jcuDHmz5+PkJAQXL9+HefPn8eTJ0+wb98+bNmyBUBB692WLVusCiWO4ExRwpFEBUkE8iX0/9dB7q/FYoFcLrfLirRYLMjPz4dYLKaJMK1Wi+bNmyMqKgpLly7FihUr8MMPP+Crr74qUfeDPfj5+WHYsGEYN24crl69iv9v77zj9Cqq//8+23s22ZJCOiUB6SAEkRZq6CBNpEtTUBARaT96EflSFRBpIiggKIiKiggIAqKIIijSE4oh2WQ32Zbt8/tj7rmZvfs8zz67+7Tdnc/rta99nnvvc+fcmXPPnDlzyksvvcTcuXNZtGhRGDoXD3/5y18488wz2WuvvbjiiisoLCykpqYmDJVUjxkgzDsMhEZvNyVFMnJtKCmSso3oOxXr/Ymmuamrq+vn8Ror1Fu9jY0x9Pb2hoVUPvjgAxYsWMBXvvIVbrvtNqZNm8ZVV12V5qe0c+nXv/51Fi1axHnnnRemRTn99NM5/vjjE+Zpj4WVK1fy85//PCwYCrDTTjtx/PHHs8suu4SFdDWvHtg+1cKC8TDaN17jGSDjvTv6jMuWLevnpdbe3h6G4CoKCgqYOXMmbW1t1NbW8sc//pErr7ySW2+9lVtvvRWw7+w222zD+eefzxZbbMHOO+/Mq6++yhe+8AX22GMPnnzyyX5zdzzssssu/OY3v+GJJ57g+eef5/HHH6e1tZVLL72UjTbaiO2224533nmHZ599lsrKSk455ZQRefkp1GB89913c+qpp3L66af3y0mq9TfU01Q369XgHuUbnfcKCgrCeS/qZZRJ7/VY/D0UD2fVtXRTyhgT6qtLliyhoaGBuXPnMmPGDHp6esLi1m1tbeTl5bHvvvuybNkyrrnmGo466ijuv//+ERmOU4HXXnuNU045hX//+98cfvjhXHDBBaGhpLGxkYaGhtDwq4YRLc7Z3d1NUVERbW1tYVFEF6qTV1VV9dt4Vx5KVCfBYyDWrFlDQ0NDaNBTGaVRSJqb/YorrmDPPffk7rvvDuuVTJ8+nc9+9rNstdVWbL311myxxRYD0sclQnl5OT/84Q/DIrEA++67L5tssklSXuzx8Pe//519992XgoICnnzySTbddFNWrlxJeXk5TU1NYS7jTz75JJQxlZWVNDU10dPTE6bLUa/jqONWdENjLHs5Jptuyr021vFYfNHR0UFRURH/+9//WL58Oa2trUyZMoU5c+aE6UJ6eno466yzOO+883jmmWe48sorOeSQQzjwwAO55JJLmDlzZmofeARobW3liiuu4JZbbmHixInccccd7L777nR2doayHawuoPYYdVDQtI9dXV189NFHoY6VDbtDwO/xFyBjBCNNtTMa7jXaMGqNxiIiJodnAlc4a2E73bmPleutrKws3OnTha961Pb09NDW1tYvVPniiy9m//3354YbbuDCCy/k3nvv5dJLL2X//ffPev64KLq7uzn//PO55ZZb2Hnnnbn22mvD4nTLli0bkBtNFcry8vLQ69A1LqgXMqw1FAw372cuI15OY+i/u6wG9agXjRoRFPq5oaGBrbbaiptuuonTTz+dQw45hMceeyxnwo+10GJtbW3cPIDGGH7yk5+E1XIvuOACvvGNbzBhwgT6+vqYMGECO+20E6tXr+bjjz9m8eLFvP3222ywwQYccsgh/XITJjKWJBPeFk8RUyUjatAfqxiO8cnd/Jg2bRpFRUWh/NJ3Xj1rXO/64uJiJkyYQF1dHffccw+LFi3im9/8JrvuuivnnHMO3/jGN1KyKQF2o2G77bZju+22S+r6pUuXcvTRRzNr1iyuu+46pkyZQmdnZ+gtXVlZGRb+bGlpoaGhIXz3VHFvaWkZkqfwYCmSckkuDjVkVJ9DDRSJpn2dOysqKqirq6Ozs5NJkyZRUlLClltuSXd3NzfccAPrrLMOp512WsqeKRE222wzHnvsMZ555hnq6+vDXHpRuv/73/+yatUqVq1axYoVK2hra2P16tWsWrWKd955h6eeeoru7m422mgjzj77bPbee2+mT5/OpEmT6OzsZOLEiaHHtc4F06ZNCz+rrIvqB8MN4XU9fLOJeAbIeHlsFbog1joJHR0dYd9E353Kyko6OzuprKzkkksuYccdd+Rvf/sbu+66K9tss03oCadh/LNnz+b+++/nqKOOYo899uCJJ55IWChYUVdXx7HHHsuxxx5Ld3c3v/3tb3njjTd46aWXuPvuuykrK+O0007jyCOPpLCwcMgGY02HprmvV65cyWOPPcYDDzzAmWeeySWXXBLTK7StrS3MbZyfnx96/cfK3ZjMvJdJ7/Vk+TuevHQ39HSc9T7FxcXU1dWFRe5WrVrFp59+Sn19fai719TU8LWvfY2amhrOOeecrBqO1bv4//7v/6ipqeGnP/0pBx10ECtWrGD58uXk5eUxf/58Jk2a1G+9omPd2dlJR0dHeO2cOXMGGJv0udRwHitVwFiuRZJqaFSSekKq84yud7q7u+nu7mbSpEnst99+LFy4kNdff51//etf/Pe//+W1117j0UcfBaxOtdFGG3HMMcdw/PHHJ/0ObrLJJklFVyWD5557jiOOOILa2lpuv/32sAhZTU0NfX19oUwGmDBhAl1dXWGKKrA83NbWFnrEKi9pgbxYzzSaNtWHC5VT8ebj4fSBrq+10OuECRMoKCigt7eXyZMnhwXi1Pt49uzZXH/99fzxj3/k5ptv5qmnnuKiiy7i+OOPH1ZO91TBGMNjjz3G+eefz7Jlyzj88MO55pprEJFQrmshU02Jp2kgtT817aMak9XjOBsI9JSheR2MQjz11FMA7LbbbmP2XqMNo85oLCJlxpj2XDYYx0K8YkC6iBMRKisr+1UwFRHWX3/9UMB/8sknYV7fuXPnsu2223Lvvffy7LPPcsEFF3DkkUey9dZbc+mll7LNNtvEpaWrqytuoYJoEvjouXhpBLSiahQrV67khBNO4M9//jOnnnoqF198MZ9++ilgvU2nTp3KpEmTwp1SN+xYKwFrNXhVEvr6+vqdy8vrXyFWkWvG8+EiyuoajqXjpP3iXuv+d3mvpaWF1atX09XVxe67786NN97I1772NQ455BBOPPFENt54Y+bOnZvQMNPT0xOXDwbjkXiGPC1+uHr1ar761a/y6KOPUlxczAYbbMCGG27I/Pnzw/8lJSV84xvf4He/+x3bbbcdV155JdOmTaOjo4OKigry8vKoqKigvb09DCvdaqutQqOfpgnQfoTYORdhYOHK6GaP5gmLHlO+rKurG2CsGKsYivEpVv+KCN3d3eG54uLisKp8Q0MDBQUFYeVi3UhSo8WOO+7Ir3/9a66++mquuuoqnnjiCW644YbQ67irqysuLZ2dnXHlYXd3d1yPYl2sRe/1xS9+kdbWVh555BHWXXfd0PNKF9DGGDo7O8nPzw95auLEiRQVFYXVmt10HNF+U3nohuQnU2QwVxD1BopFp26ednR0hJulYA0S7vuk3iH67jU2NmKMob6+vl/R29mzZ9PW1saVV14ZVnwvKSlhp512YvLkyfT09CRMF5EofU5nZ2fcKJ+uri4KCwsRERYuXAisLRKkG51r1qzh8MMPD3NtR1FWVkZ9fT3HHnssxxxzDPPnzw89//S8Gm70fVC5r8/f2to6ID2WIjoeyW42uBsV2UQ8A2S8PLaa91kLsGgfFRQUhGmvtAjQihUrmDZtWuhlKiL09PSwww47sGDBAqZMmUJBQQEtLS10dXWFsktE2G677fjJT37Cl770JRYtWsTjjz8e03CcqIjwvHnz2H777TnllFNobW2loKAgnPe1WGYsqNx69dVXefjhh3n66adpaGjoV6THxZlnnsm5556LiIQbd64O4EaEqJHGTV2h76vWDHHnvWyr6smGqMfLwezK14qKCnp7e0N5U11dHaZsM8aEaSLU2KV9opsBIsK3vvUtvvSlL3Hffff1y8Xa3t4ebhrl5eX1y5s/3PlLaQCbu/j000/njTfeYJ999uGiiy4K9SYtQltaWkpxcTFz584No14aGxtpbGwMiyrrdZr/Mzq+8QpoDmbUGi9IlM871ruislyLdPX19YX1Xmpra8OoQhFh4sSJFBYWsssuu7DRRhsxbdo0KisrWbp0KS+++CKvvPIKf/nLXzjnnHO45pprOO200zjiiCPiRkJocbRYcFNkRBGr0CjAE088wZe//GXWXXddfvCDHzBnzpwwekFEwufRvLCaKqCpqYnW1tZQPgED0uQkkjO5qgulEoM5GSTKMR/P4Ur5c/ny5WGdjby8vHDMioqKyMvLo6qqiuLiYqqrq2lvb2fGjBnssssuXH311Zxzzjk89NBDXHfddf1qj8TSnxUa1ZDoWeNBi+kp3nvvPc4991yeeeYZNtlkE2666Sb23ntvVq9eTUNDA+Xl5eFz6POqPcbtH5dHdf1tjBnU8chj+LjiiiuA1Bhnc/Veow2jymgsIvsAh4tIMXAz8FdjTPJlWtfeJx20JX3O9YyJdc4tnKdGsOLiYmbMmMGECRPIy8ujvr4+TEa/1157sd9++3HXXXdx9dVXs88++7D77rtz+eWXs+WWWw6gp6ysLO4Cuby8PK5hL1GV0ljnXnvtNQ499FCWLl3Kd7/7XQ499FBaWlro7e0lL89W/tVCG0BYMdcVvlEDiXpSaVhSeXl5wv4cbYilUGof6CLMTckR75m1eE9dXV24I6qTonpYAZx44ol0dXVx7rnnhkW+ysvLw1zXm266KVtssQVbb711uFOcaIJMxD/FxcVx+a6vr4833niDww8/nCVLlnD22WdjjOH111/n5Zdf5mc/+1m/60tLS7n00ks57rjj6O3tpaGhIQyd1Fyw6nFYVFTEjBkz+ikora2toUFSr43ynf6Ptdmjx2J5eOomEBB61Ocqhvu+xPrdcN7DqMGnoKAglH+rV6+mu7ubJUuW0NHREYbdV1RU0NbWxooVK5g1a1bocVxQUMAPfvADFixYwLXXXsvChQs55ZRTuPzyy8P8uLFQXV0dV0GdMGFCXINgd3d3P342xnDWWWfxt7/9jZtuuolNN9009JpRA09+fj4TJkwIwy/V2BLlk8FSDUX5LlYO6mzLxXhtRp81Hp0q5yoqKsLcqO4CGWzUhIZJ61xSVlbWL2xa59Le3l5Wr17NrbfeyuGHH87pp58etlVVVcWUKVPCv5kzZ7LXXnuxww47hB6D8VBUVBR3vObNmxdXHhpjMMZwxBFH8Mwzz3DVVVex0UYbhX0ze/Zs1llnHQoLC/vNCbpQKSgoGBAervlW9Vy0PRg4Ljoeej7eAjuRHpNpJMPPsehzeSzqpat9oEbCxYsX09zcTElJCZWVleHcUl1dTUNDQ5jySec8jfrp6+ujq6uL/Px85s2bF3ocH3DAATFzHM+YMSMuj7hpWaKIt9Hx5ptv8uCDD/Lwww/z7rvvUlRUxC677MK2225LXV1duElfU1PDOuusE+qSmkvU3ZBTnUN1My0c66auUEQX3bGKMmUSiebgkcxf+fn5/fQx9/0rLi4OvULVCUKdHHp7ezn88MMpLi7m61//OjvuuCPFxcU0NTXR1NQUbgIp9txzT7773e+y8cYbJzQaV1RUxJ2/+vr6WLZsGRdddBH3338/kydP5pFHHmH77bdnwoQJ9PT0UFZWFvKSRq3pBgoQ1hjReaqyspKqqqow+lEdNlQ3jW5k6vdc14VSiUT8kyiNQFTX14KCmu5DayAoX+Xl2WKFbnSRpoZpb28nLy+PoqIipk+fzsKFC9lxxx2prKzk5Zdf5qabbuKyyy7jhhtu4Ctf+Qpf//rXBxiP58+fH1dnV/6OBZ2HXdx3332cfPLJbLnlljz44INMmjSJ9vZ28vPz+7VRVFQUerCqh7H7jNG6IW5fR/s9ml99LKwRh0t/dD50dQpXdiu077Tukuph7tygMgEI06YsX76cjz/+mOrqah599FF+/etfc/bZZ7Pbbrtx9NFHc8ABB7Bw4UKmT58el7fKysoSRgrGOtfa2srSpUv53//+R0NDA0uXLuXdd9/l7rvvpri4mGuuuYb999+fKVOmAISRrBMnTqSpqYnm5uZw4091Afc9dOeTWGvCVMu20c6nHmMTo8ZoLCJ7AtcCpwK7AWcGn1dmkaxhQT0hYeDuqCrkrmKgCsOECRNCT4boAq+qqopTTjmFww47jHvuuYfvfOc7LFiwgEMOOYRLLrmkn9dCJvDwww9z0kknUV1dzSOPPMLUqVNpaWlhwoQJTJ06NczpqeHG8byFdFe9p6dnQF/pzrMu3MYC4uVndBfyQynuoN4gbkGtOXPmhAUkurq6OP300znooIP44IMPePXVV3n77bf573//ywMPPMDtt98OWAPGySefzPHHHz/iYohRGGO4/fbbOeecc6ivr+eZZ55h++23D+lesWIFq1atYunSpbz//vu8//77HHTQQaFyqZsQmhdX+0urAZeWltLb29vPG0/7ws1XOBgPxTNCRI+NV7hybaRYs2YNXV1dodeVGotLSkrIy8sLC3aq56Tmuuvr6+PAAw8M5d4Pf/hDHnnkEa644gqOPvrotHoD3Hnnndx9990cffTRLFiwIDwe9ThXr0V342eodCXDd6kcj3QiHp2unNP+icq++vr6AV4yBQUFAwypy5YtC38jIlx//fX8/e9/xxjDJ598wocffkhTUxNtbW3885//5Je//GUYwr3//vuz//77s3DhwpSGlLsextdffz2HHXYYfX19VFZWhnOiGoxdr6rW1lYWL15MaWlpmH5CeSve3FBeXs7UqVOTmjOSnV9ynb+i9MXTuWJBPZ10Y17TEmhBnKKiIqqrqyktLaWhoQERob6+nry8vDCUv729ncbGRjbYYAMefvhhDj300DDHcTLF8QZDZ2cny5cvD2Xh3//+dx566CFee+018vLy2HnnnTn77LPZY489KC4uDovoqsd0cXExq1atorq6OsxPWlxcHBoTdG5UT8P6+vp+c2Q8j8lEnpS5jER6eRTRjRU1opSUlJCfnx8WpZo4cWLoga7FzI4++miKioq48847qaysZMstt2TixIkhP02ePJn33nuPW2+9lS222IITTjiB888/f8hFzVpaWrj22mu5+eabMcZw4oknctJJJ1FTU0N1dXVYiDgvL4+mpiZWr14dbpC4735BQQHV1dV8+umnrFixInRCUF1VU5DoBjxYJ5Jclw/ZwmBzd7xibjr3aLoc1SE0L3tjY2O4eakeyboRoZvWujGw3XbbsdFGG/H6669zzz33hHxywgkn8NnPfpbe3l56enrCyC/9vt5667Fo0aIhF3H93ve+xze/+U0WLlzIhRdeSG1tbejYkpeXN8BYrcVENcpInaeiqYbceV6f1z3uro/HOy8O5gCjcA3C2vfTpk0LHR3cdZPycG9vb6izaJoRnTePPPJI9txzTy688EIefPBB7rnnHkpLS9lll13Yd999WbRoEdOmTRvy87S0tHDPPfdwzz33hLWfoigsLOQLX/gCZ5xxBvPnzwcII5PdnOvu5qzKxKiOHgs+esJjvEGyHTqWDESkFPg+8JQx5oHg2E+AV4wxNyTx+5OBkwFmzpy51ZIlS4ZMw2D9NFzlWD3NopOg5lt1iyG5E2Rvb2+4OIl1/qOPPuL666/nzjvvZM2aNcyfP5+NN96Yrbfemp133plNN900Js3d3d0sW7aM119/nddff52qqioOOOAApk2bFobaxkJPTw+LFy/mrrvu4sknn+SNN95gwYIFPPTQQ9TX17Nq1ap+VZRdWt3wYn127Q8NE9XrNadzfX19TO86F2nyKP+7MWbryLGU8VesBZd6pMVSlKJ9p3B32Ds7O0MFoKqqivLycnp6ekIFa82aNaxevRpYa+BS74aVK1fy5z//mUceeYRnnnmGwsJCDjzwQC6//HLmzJkzoN2enp64PBL1zAS7K33GGWfwi1/8gkWLFnHnnXcyZcqUfvRrCJx6wagBRRWaiRMnsnr1ampqasL7a7GC6upqioqKYhZsSdSvI8UIPHj78VcqeCvXoP0ey7CgXlm6WdDY2EhzczPrr79+aMABqzCuWbOGmpoampqa6OrqoqioiJqamjClQVNTE6+88go33ngjL7zwAttssw233357qDwqurq6Ynpqvf/++/z+97/ntdde4/333+e9995jzZo1ocdPVVVV+FdeXs59993HwoULufHGG6mpqQk9L2LlVe3r62PlypVxZWI8/hnJPDSaeCvRc8aSeTpfAgPe9ZaWFpYtWxbKh56eHlasWBEuyJcvX05+fj61tbXhxtLixYv55S9/yZ/+9Cf+9Kc/hR7PixYt4vzzz2ejjTYK76+8FwsdHR0x5WFTUxNHHXUUzz77LDfeeCN77bUXYL1fdUHd2tpKZWVlWNxOQ3a7u7v54IMPqKysZM6cOXR2doaLINeAFU+mDZW39H6JNjfGGm/F4idXNqmB0M3rqx5I+m4XFRWxYsUKKioqqKmp4eWXX2bfffelvr6eRx55JOQhLfIZC+oJ+txzz3HHHXfw7rvvhrmIYxUm3mabbTj00EPZbbfd2HDDDens7OTTTz+lo6OD6dOn09HRQWtrK5MnTw6Lis6aNSv0vtL0aK5nv6s7uPJaF9XKd9p30eOK0TQnRnkkmr9bvf1hrV7S3NxMZWUlFRUVtLa20tLSEn7v6elh5cqVYZ7fFStW0NDQwKxZs6irq6OxsZFJkyb1y5P58ccfc8MNN3DbbbdRVFTEt7/9bc4+++wB/Ridv3p7e7nnnnu47LLLWL58OYceeijf+MY3mDVrFq2trTQ0NIS1QpS+rq4uPvnkE6ZOnUpPT0/oVQ6x1yLt7e3hvOvSrZ7G2cxfOhSkS59PhMHkT7K6vivn1bNSI64mTJjQTyfWXMh6T9cZp7y8nHfeeYcbbriBn//853FTBiimTZvGySefzKmnnhrX0aK3t5eysjJWr17N5Zdfzs0338yBBx7IZZddxtSpU5k4cWIoJ5VGfZ6ioiIaGxvp6enp59mq+n57ezs9PT1UV1f3az+W3IlGS2QauTQvunwX1b9d3tJ+dA31rhE1OjdokcbJkycP4Ac3lY8Wl/vrX//KCy+8wK9//Wv0+bfcckv2228/DjroIDbccMMwrWAsrFq1iptvvpnbb7+dVatWsd122/GZz3yG2bNnM3XqVEpKSsICrAUFBaGnvW5ilZWVxdS9E71fbvRodG2dK7zlYuuttzavvPJKpkmKi2i6q2Sx8847A/Dss8+OmIZs32u4fZANJOKtUWE0BhCROViv4jZjTK+InA8UGWMuCc7nG2MGrSY53JcpnUbjlpaWAfkGW1paWLx4MSUlJUyZMqWf8AYr2DU38JQpUwYI6+7u7rCK9S233MKrr77KO++8w4cffgjA1KlT2W233dh8880pLy8PjcSvv/46TU1NA55t++2354ADDuCQQw7ptytojOH555/nxhtv5IknnqCgoIAddtiB3XffndNOOy0sWqWKaKxFZ1RYu5OT5rA0xrBq1Sq6urrCxX1UoYqV1iHVSPQyQXr4K55h2D0Xy1Dg/i5aFMgtiNDX18fy5cvDvFXqkeZe39LSwj/+8Q/uvfdeHn30UfLy8rjjjjs44IAD+tGjRuM1a9bwzjvv0NzcTGFhYagAlJSUUFBQQGFhIb/73e+4+OKLaW1t5fTTT+eMM86gpqaG8vLysECYGkrq6urC42vWrGHSpEn9CvZEJ+1ly5axdOlSpk6dGjdfW6J+HSlStUB2kWuKwHChC+5YhgXXMKOFwBobG5k1a9aAd14XPlGDsVvwURceDz/8MOeeey4dHR3ccsstLFy4kLa2trCNrq6u0HPq5Zdf5g9/+APvvvsuYL0Q1l9//TBEsrOzMwyfbGlpobm5mZaWFubOncu9995LW1tbGPrteroAMT/HKxQVr+8SYShGYxe5xlv6nIPJNUU8fnLvoXnr3EV5W1sbH374YVhYcdKkSTQ2NtLV1RV6ZRUWFvLoo4/y85//nGeffZampiZ23nlnZs6cyYwZM5gyZQqzZ89mxowZTJ8+vV8uvFhG42effZaTTjqJZcuWcfvtt3PUUUexYsWKsEgirI2iqaioCJ9VZaJWM6+urmb69On9ZHV0cZcK3nLvF89bazTyVrxz8fpHZZNCDazqGa5GfiDc6Nb8r5r38OWXX+bAAw+kubmZE088kYsuuihhKO6KFSu47LLLuPvuu6mvr2f99ddn5syZoQ6kKVmKiopCHqyvrw+9pDU9wYoVK8LNiI6ODmbMmDEgxY8+o24QAAMWx+7iOZ7hId6G4GiaE6M84r4D+lxtbW1hKoCamho6OzvDOhuuQRnWzkXl5eU0NTWFc8eMGTMoLy+nsbExnF9Ux6mtraWgoIB3332Xs88+m1/96lccd9xxfP/73++n76gzxyeffMLrr7/OZZddxj//+U+23357rr76arbddtvQKN3W1sZHH30Upr3RDRE1cqs+qMZk99mj3p4NDQ3h3KtGGsVo8TBPlz6fCCPR9eMZlLu7u1mxYkW4gaUh9u5v3c0uzcmq85NuLn3yySfhNUVFRdTW1jJhwoSwAOKLL77II488wu9+9zs22GADbrvttpj1cxobG7nrrru4+eabWbVqFccddxxnn302PT09zJw5M2a0ous5rV7RyleqD2qBzVg819vbO8DTWJHMxmc6kEvzYrJ8F2/TOXrc3UyCtbnfXd5Sj3HV+dva2ujt7Q3rPDz//PO88sorPPfcc7z88ssYY9hoo41CW4Pr4NHV1RWm4GxsbOTAAw9kr732Yr311mPatGlMnjw5jErMz8/v51RXUlISyufu7u6Qz/r6+gZseg7WPy6f6u+z4cWeS7w1GLzReOwYjXM6PYWI1AErjTF9wMemf/7i94DNgusOAtpE5A8mw6PiKsgjmYw0DQWsVTRLS0vDz26YIFjjm+bpdEPJVJD39vaGvz3jjDNCofbWW2/x3HPP8ac//Ynf/OY33HfffWGbG2+8Mfvuuy/z5s1j2223ZYMNNqCxsZH777+f3/3ud3zrW9/inHPOYfvtt+fggw9mwoQJfP/73+cf//gHNTU1nHfeeRx66KHU1NRQVVUVPlNHR0dYnMjNrxsPJSUltLa2hqHoGvamObo0r56LeGkdxgN0t1fzbsZ6fjf9gi523TQNbpiNjk801+zMmTO54IILOOmkkzjzzDM57LDDOO644zjggANYsmQJb7/9Nm+99RbvvvsuH374YVICcuHChRx77LGst956dHV1sXr16pj5Q900FRpyp97msdqpqakBbL5at1CBevzU1NTE3R12FczRsvgZbUgmRFPztmqotHrx6QK4s7MzXECowdjla81VVlpaype+9CV22GEHjj76aI499thBadtpp504/vjj2WmnnZg3bx4iNhd4Q0NDuKCHtcWgampqqKioYPny5Xz00Ud0dnaG4cZ5eXmhJ5h+dtMtJFt4bDwi2QI22r+aosTtR5VxKgfcfJvFxcVh8aeuri4+/PDDcKNB0w0AHHzwwaHx5Ve/+hV/+MMf+OMf/8jSpUsHFAqqqalh7ty5zJ07lxkzZrDeeuuFn2+77TZuvvlm1l9/fR5++GE23XTT0MCUl5cXeu+0t7dTWVkZU/7k5dmiSFoUNto3Q0lflAzGSwim9ns056O7QHb1PE1BEKsgbVtbGz09PYgIa9asYdmyZaGO9fTTT3Pddddx55138tBDD/Htb3+bU089dYDh+PHHH+ess85i+fLlHH/88Rx77LGh7JgzZ064QdbY2EhnZyeFhYVMmjQplClgeUVp1jyjbu5c5fGenp7QMBzddHM9+ty5Nl5KsXjHRzNizVelpaWhEauzszM0PKhRQTed1DNS0+ZoMcPq6upQ9que0tfXx0cffRTKhMrKSmbPns0999zDVVddxfXXX8/f/vY3jj76aJYsWcIHH3zAe++9x5IlS8K8x+ussw433ngjJ5xwQlg8dvny5WFh2dmzZ4cFCrWdaDHD4uLi0JO9tLS0nz6vmyc1NTWsXLmSnp6eMP+sR3rhpoxxHYkKCgqorKwMN6vcsXR/q8W/NDJGeVg3VKdNm9ZPr3aNZfn5+SxatIiDDjqIZ599ljPOOIPdd9+dr33ta1x44YWUlJSwevVqbrvtNr7//e+zevVq9ttvP8444wy23357Vq5cGRa0jRXJofNMUVERXV1d/fShvLy8UA7HMhjrNbE8jHVeTVfu2bGGeKn6osdVrtTV1QH0c/LR/zU1NVRWVtLS0sLHH39MXV0dH3/8MSUlJfT19TF37lx22WUXLr/8cpYsWcJ9993HM888w3e+8x2uvvpqNtxwQw4++GDKysq45557ePfdd9lpp5045phjWLBgAU1NTaxZsyZcy7W2tvaLoIjSr+s6Ldync95QdHDlU/f3HumBpsccy/cabchZo7GIHIjNW3yHiDxojOkWEXGMwnlAnogcClwB7J1pgzH0N1YOdzIqLy+nqqoqNLCq57Cbg1AnzE8//ZQ1a9YwZ86cAVXQ1XjY3Nzcb9cQ1uaImjdvHhtuuCEnnHACq1ev5i9/+QvNzc0sWLCAGTNmsGLFijA/n3qdXnDBBZxyyiksXryYRx99lOeee46zzjoLsIV+rrnmGo4//niKiopCQappBfLy8ujt7Q2989xnjgcNL3e9BjVtgi7kdIGn6SnGc25ZN19Zsjkre3t7++0Sq3FeeUoNLHr/4uLisCCKiPD973+fO++8k7vuuosf/ehHgOX/9ddfny222IKDDz6YjTfeOMxl3djYCBAWTsjLy2Pq1KlssskmLF++PCwm9dZbbzFx4kSmTJlCXV0dpaWlYXoNfUb1jNY8erFQUFAQhuC6RqeVK1eydOlSgLjF0cZDpeVsYzDDgquYqVFDi+FphIUabqOGwL6+vn5eU+pNM3PmTJ588snQq6GjoyOMXJg2bRpTpkxh9erVTJo0KSxI1tPTQ1tbG01NTUyfPp0ZM2aEHhQrV66kra0tfD+0uNTMmTPD6s3Ku5ozVD+7edgT5Zcb70jWAOp6f8cqAuemtdH8tPqer1ixghkzZoTFg9xcnwo11E6aNIkLL7yQb37zm6xevZr29na6u7v5+OOPef/99/n4449ZtmwZH374IS+//DKPPPJIv0reAHvssQdXXXUVVVVVNDU10dfXR01NTSiPdcGjRkndvFJPP/Wa0ZDd6GIu1UY7vf9Y3EBzF4sujwDhZw2Jbm1tDQumlpSUhAYz1VPcucjVVdra2kJDbVlZGTNmzODKK6/k1FNP5ZJLLuG8887j7rvv5uqrr2bPPfdk2bJlnHXWWTz++ONsvPHG3HHHHWy++eZ0d3fz0Ucf0dHRwapVq6ipqennwaU8Gx0nNRy7m+15eXlhIbSGhoYBefqjOkU0VHe8wX2ndJmhOqnKf91A0DFXY6r2rW4uqF5cWVkZ9nNzczNNTU1UVVWxzjrrhMaIlpYW2tra6Orq4owzziA/P5/bb7+dc889l4qKCubOncs666zD5ptvzgYbbMD8+fNZsGABhYWFNDc3U11dHRoL+/r6mDVrVihHNFpR1xMiEm5Wab5s9aRTw5DOqfpuuHzukX7oPBjLychNneJGqihKSkrC/OsdHR2Ul5eHskO9i1V+aMqVhoaGMDVSZWUlxcXFNDY2snDhQp555hkuuOACbrrpJh5//HHmz5/PSy+9xKpVq9h777056aSTmDlzJlOmTAn1M/3TjTSwUa8qu3TjRTfQXLkTq8ZBPMTLC+35NDkkMqK6OY+ThTqB6Lir44XruFNaWsqiRYs46qijaGxs5Omnn+aJJ57gqquuwhjDhhtuyLXXXsumm24apoLS9aq7idDX19dvw0Tp1bnSLWSn6QyHooO7+tZQc3t7DA3z5s0b8/cabchJjheRWcCVwBvARsChIvJwkJYiL/A8XgF8GVgAHGSMeS8btLrGykQ268HCHtxE7KqYa6ib3r+vr29AyJELVUA1JBJg5cqV/RY1ZWVldHZ2Yoyhu7ubBQsWhLmiTFA5tKWlJTTOKT26QDnmmGM4//zz+fTTT3nrrbfYbrvtqK+vp7OzMyyupjt6qmyATaFRVlZGV1cXJSUlA/IruXCVA1f4u4q7esvqsbHo3eIiHm8ZY4akTEH/XXv1egGYPHlymA5EFwXq4VtbWxsungsLC6mrq+Pmm2/m4osv5s0332T27Nk0NTVRWVlJXV0d3d3dTJw4MfS0Auup0NfXFyqnra2tfPjhh3R3d4fHamtr+3nQaUiR0l1XV0dDQwMrV64Mr1GPiFgoKioK05wYY8I8s5MmTYobjpROBXO0hKdkEzou2v+1tbVhegH1KK2urg5TrKgyWF5eTllZWZi/r7q6mu7uboqLi8PIhaKiInbddVc+97nP0djYyDvvvMPMmTPDFCbqJWyM4eOPPwbsJpamSKmrqwvD0TU/qBqOtJr4OuusQ1dXVz/+cQuoRYupRQ2jww1fHUvQPnD5wE1ZEZVzbuRNdH7RYyp7YKARGWDixIn9vDS7u7tDY5/WENBFrRp3NR9pcXExc+fOpbCwkAkTJoQbpDov/vvf/+btt99mzpw5zJ8/n2nTpoVGPA0PVnms9KjnPNAvBYX2iRt6mWg+jWI88U+8c9oH0QKzxpgBBlQ9pvOMGvLUqKq8FYV6t5eUlDBjxozwfo2NjTQ2NjJnzhwef/xxHn74YS6//HIOOeQQYK3X/AUXXMAxxxxDX18fxhiqq6upra0Nc9ICoUee6ouw1mPa3WQoLy8P51ilVXMyK63us7gySY2Jo21jKxPvgRoQ1OhgjKG8vDycs7T/lBfKysqoqakJDc1q4NANeeUp9ULOz8/vlxv75JNP5sgjj0REWHfddRERli9fzqeffhrKnsmTJ9Pc3BzOSzU1NWFtgI8//phZs2b143Xth6jeXV9fH85v6qGnckdlUCxj+mAYD/InHXD1oqjsd/NNu3qxFolWfUW9eDWfsK47dSNSI2f198XFxUycODH0BG5sbAznwokTJ/Ld736XPffck8suu4zf/va37LPPPnz9619n4403prm5mfz8fCZOnBhu+Ov6VXk++gy6Ia/GRFc+axRkvOhCV65HdSp1ivFIDlGju9vfeq6iooIpU6ZQXFwcpi2KehqrR7sWYa2srAzPuZvRfX19FBYWkp+fH6ZWOfHEE/na177GBx98QENDAzNnzqS5uZlVq1Yxffp0Kioqws0PN+2YylA3xVJra2voZBSNzFZZrLmaB+Mtj8zhV7/6FQD77bffmL3XaENOGo2xuYuPB94FvghsByAivzDGdAXXfAAsA04zxvwn3QTFExi6mztcY5CbfzfqORzNv5ifn8/kyZND4RalSfPF6g6ehuJrjh/dFcvLy6O7u5v29nZqa2uZP39+mAtqxYoVYX6z+vr6UInQitD19fXU1tbS1dXFhhtuGE7kxcXFoRKsRugJEyaEQrmjoyOsyhzdwYw+R1QRjdX3qrDqYm40Vut2MYJ8fwM8CpK5p/aVOw7qWdnS0hIqcm6Imnosub9fZ511WGeddUIvOc3xV1ZWRk9PD8YYVq5cSX19fb/8nEB4nd6rqqqKSZMm9Sve524MuNW5deGinhHxnlXzJ+tCurS0tF+O41i/y8/PH1TBHO77PhZ4NZ2It+mhMlZEmDx5Mq2trTQ3N4djqnkg6+rqwkWRbqCpp58WRNSFujEmzLVeXV1NQ0MDs2fPDjfM1PCrId2at1LDvadMmUJ7e3tYREpzqFVXV1NVVRUqw0DIqyr/3DD3WPw23lNWJHo3YnlT5ufnh8b8RO90NCdkcXFxWByvq6uLqqqqsMZAWVkZra2trFmzhpkzZ4bzs+Z3rKyspLe3NzTuqNFOi69oChxjDOuuuy5gDYJNTU2hwa62tjZcOLuRNbrhqjJajdz6Xfl+KDmLR9LnYwnufOHOZ1HDgn5WntG+zs/Pp7CwkKamprCIk84HrnzXxaqIMHHixPC+aixRebLbbruxzz77cO+99/LYY49RXl7OmWeeyS677MKaNWtYvHhxmG7Hja4xxoTGHs1rXFVVFeYt1nQIrqwrLy+noKAgjEZTw2FFRUWoH+o75PJWdN6P169jBYmeJZ4+CmuN7CpvVO/QcHzXQ66lpYUlS5aEuczr6upYvnx5OFdoW11dXeTl5YXGGC2sqMa9ddZZh/z8fN5+++2QDp2bdN0wb968MHwc1nr+aSFXbUt5Sw086nHqrjf0/Umkd3nEx1B5S+HqAa63pJ5z00jk5eWFRj83NY2IDCgurfJN5zeVZ8qLmmZr+fLlNDY2snr1alpaWlh33XWZPn06e++9N5///OdZtWoVm222GUVFRWEajFmzZoWb9fn5+eFc5hblVmjxbfd4LLmTSP64feCNxMkj+i4nkvfRczr3aX+7urrKrdLS0lBPnjNnDh988AElJSVMmDAhjLjo7u6mqqqK7u7u8Hd9fX3MmDEj3GwzxtDY2Eh5eXnoOBJNp1RUVBRumCo9+fn54brSfVa35kiizVAv67KD6667DkiNcTZX7zXakJNGY2NMq4j8yxjTISJ3YT2KtwMEeEBEZhtj3hWR7YwxA0tIjyFEd/zi5RuC/mkdVNlzE73DWi8UIPT2dO85adIkenp66OnpCQsLtLS0hEJaF0NAWGV32bJlYRGPnp6e0ONY76k53fR3Q/FY0UVY1HgSzdE72rxgso1YfNTX18eHH37IqlWrmDhxYrhA0d1kTVmhRgv1eFi5ciUTJkygoKAgLHqoBVJcj2C3HV2ITJs2rZ8Sq+Os4ZPahnpStLW10dDQED5DvPtGz+VKyonR6LGVa1B+ACtL1INOPyvUYy/qdaK52CZNmsRnPvMZ1llnHVauXMmyZcvCyt6a11a9lSsrK8NwdCBUSt10LuopWlxcHMqsKN8lk1svVo5yNTq4HkXj0ZicDJKRA24FcPX8U28n7WuNSoC10RmaM7K0tDTcFOvu7g4XKuqZHJ2HdZHc3d0djmFlZWW44NHUB5rzsa2trZ/hzuXheLLM9ZrRzQ2/0FmLWBt2yXigRWW2epZqqhk3RFULnMHa6LDu7m6WLl1KXV1daMh1i4qpB/yRRx7JDjvswDvvvBN6qgLMmjWrX87PqCyIFgDVdCjuBr7yO/QPcde5tKqqKmGo7ViP5BopdExVhqj80YLWM2fO7DcGQGjM1/B5CdLC6bn29naampro6uoKc+arAbqhoSHU3bUAlDEm3IhXejQNmW6Wa7oBNRarB/SyZcuYPHlyTMcVd/5JJH88MotE9TlUruhaDNbKLrC5sjU1TVFREY2NjdTX14fni4uLQ11Kow9bW1tpaGgIU39NnDgxTF3iRgbm5eWxYsWKfrUbXD7SiNloyqlYqai83Ek9knFISDQvRsfE1VvUMai9vT0sMF1UVBTOVbrRPWfOnFD2tbW1sWTJkjBFYXt7e7jxphv7GiWtxmN9DuVxIPQw1rWq6sjRaBClExLXVhnvjhseHrGQk0ZjgMBgLMH/HwHHARuKyE+BnUVkI2PMqmzS6CJdHoSxJlIVZtG2YglAV/i3tLSwatWqUNGYMWNGqAxokbmysjIKCgr49NNPQ2Gr3i26e6gL5hUrVoQGZVVeOzs7wzxZmltS87656SbUoDMY1qxZw6pVq8JwmFjGkvGazzgVRRijOR0LCwuprq5m6tSprF69OgxbVe87LZKhC2PNi9fe3h56OhUWFtLR0REajHXToqenh8bGxtDLzt3MUKOf8pzyJRAufNVDWPkxVt7S6GLGDXnTRZL7zPGK4SWD4bzz45VX4yGWYjZYv7a3t4dGE7fqvOa0dasmq1dfrA2Srq4uZs+ejYiEiqj+V0/3VatWhYsr9SDVQp260FdDnXrkqFHaXYQp3+mCKdH4t7W1sXr16jAyAwhzwWm/aB7AaD+ORBbkIpJ9x3p7e8M0OuqxBAONGu586hpLVKapd4x6ZKohRQ2AygednZ00NzfT2NgYeoCqV2fUIBxrDtcwyKgRSESYNWtWmD9SDTW6ONLCfdH7wVpZrHN0rOcfzxhu/YnoOLqfdWyUR91NTn0nP/300zB1jaZX0g0h5YPly5fT2tpKfn4+dXV1/QzTbiE6sOP8wQcfYIyhoqIinJP1GZcvX05HRwfTp08PF+qaMkw/qwFH59TBcoaPZmQ6usfVQ3Su6ujooLq6ul/7qptXVVX18xR1546GhgZWr14dynU1fHR0dFBbWxvKCS26OGnSpH7yP5pqzNWjo95zrmG4t7eXxsbG0JM+msYFEuswPqIq/YjW59Dxg7VyX3XnWOvH5uZmli1bRmVlZT/jW3NzM11dXfT09JCfnx96ik6ePJna2lpaW1vDOhG6uVlWVhY6IzU0NLB06dJ+myTapvJgrLoDQ4U36g0Pw930ifdOt7a2smTJktB5o6ioKCwQrOvHvr6+fhtTynO9vb2hTuWu6dQw3dHR0c8RDWxqOqBfYUVNG6dORSLST0eOp3u79pFY6T795piHR3/khNFYRNYDqoE3jDFhaWdjjAkMx23ALSLyS2BTYFEuGYwhfR6EsQwe8YRZdAewvLycKVOmhAbB5cuXs2zZsjA0ceLEiQMMG7DWcFJSUhLmN3YXWaoklJSUMHXqVKqrq0OvLDf8380npW2o4hD10Ik3IZWW2uq+q1evpq2tbYCxJNZzjxekoghjdDGg1dfjjZNeB4Q8ADYfaFNTU7izqxsQ+fn54YJHlUk3xYpCcynqn55Tg53rVTVt2rSYimIsY0qskLdYO87DwXDe+fHKq/EQS5YN1q+q9Glxn46ODurr68MQNN1w0OiG6D3Ky23hUTdXoxoIFa6RV4urqReXu6EhIgnD26IREepxOtgix/VGVHomT57cz2MjXj+OpbDMZN+xFStW8L///Q8gLNoUyxAWL1InagiZPHlyP489NydkQUEBNTU1IX9oLm0337DSrhtT0XkZCNNA6caqbqapjHCfvbW1NXy+yZMnx+0L3cBwc/p5WAx3w07Hw11UuqlmXIOLbh5oupyenh5qa2upr68PPdrdAnNabEq9RmfNmhVG3+jGeyw+1nBzTS3gejert5d6vWsRzuh75EZKxMNYMP5lOrrH1UPU21J1ahfl5f2LXUeRl5cXhmYXFxeHepC+22p80UKtYHNcR2nRTSTduNB5Rb2iYW1BO/3e2toaGiQnT57c75kSRTsqfERV+hHd6NY1Htj0JGpUra+vH7CR0NzcHG4+q+6uG1lq5NNc6DrmKis037ZG57S0tIROHbpp5hZFdzcdNA1OLN1sqEY6dShqbW3tVwjNIzHibToPhkTvtEallpWVsWTJEt5++20mTZrE3LlzqampCWVXFJprvbq6OpRluuGu864WHJ40aRKFhYX98ibrM7hRGBUVFQM2Stxor2T5ZLj95OERC7NmzYqrQ82aNYvFixdnlqBhIutGYxHZF7gKm8f4UxG50hjzRmAsNoHhOB9YD5uiYjdjzL+ySXMspMKDMFkFPZmdfui/sCkuLqa+vp5JkyaFBX1co66GIJWUlJCfnx/mM+7o6KCrq4uGhgZqampCuurr68OCHWvWrGHFihVh+KUqF67nggpqVaKjnsbxJiSRgUUCPSxSwXPxFgPRCdPdgHANn+oZpYUxmpqamDhxYljwQHPnlZeXhxsLWhTBDaV2Uwy4Hu59fX3MmTOH5ubmUNF18+zp4idejk81arsexq7X8UjgvYZHDtc4qwbReP2qBe/UMKI53JWfdOxdHohnOKyrqwuLWWmRjGj6G7BeM6WlpaF3Q2tra2hM1AIgeXl5odeN8qC+K9FnjUeTi1jGBF20ZaN4YzaR7DtWW1sb/nfnvejmkuud5Pazm88T+hvedePADdfVHNfKB5pfWxevahB2DY2lpaVh/ln9nfKzbsy6OfqixicgpvFJoWGfSv9oNfKlCyPdsHPnGVibQ7GqqqqfZ7kaTtzCVRq2HZ1vS0tLqaurC3mptLQ0XBBrnuzoIre8vJxp06aF12h+/5KSkn7y0TVqK//09PSEcjYZjAXjX6bnaXcu0gg8GOjJ5l7nGnDVkUM3ROvq6sLfajodLSjd0tJCX18fs2bNYvLkydTU1PSbo1zovKRyR+WYblj09PSEhWN1PlOdLRlDsQuvG6UeUc9a3ejW+UnXeLA2kiZWZKbqXGA3C3SNpXNPaWlpGI0KhOtC9Th2nUJcKM9VVlay4YYbxnQC0XnXjTzVz4n0I1f26rymDkXxnAM8YiORfpQI8d5p3YRy69z09fUxadIkqqqqwrSZEyZMGBAdV1xczJQpU0K9LZZDhHtv97vya3t7eyjz3FQsQL9CwoOt+aLv11BlnodHIiQyCo8mXT2rRmMR+RxwLXCkMeYfInIrcBZwgnE0LGNML/CWiHzGGNOQJXITIhUehMkq6EMRZm4ORzXmTpgwIWTStra2sBq4LnR0wazKhC6OosVgVNH46KOPwvRcAh54AABOD0lEQVQWrrderJ1jDfuOerAmUjKT8YgZj1CeG4nAicdL0ePRBY4LzeOo4dldXV1UVFTQ2toaej6od5zyh3r76m8gtlHNHXv1nEqUJzb6LFFPz2SKHiQL7zU8cqhy5m4WxOtXN1+oeqzEWpAn41Wg7bqFh2K1qd40EyZM6Ff4Q2WYBMXumpqa+imssTb+kpXbw1FW3b4bS0j2HdMisa5xJZZMiB6PJ9NcuHynaQNceacL+J6enrBCvM5jxhiWLVsGEIZk6n10Luzo6AgXXm6oZDzjUzz4eTK9cHkH1uZQjL53Kk9aWlqorKwM60JAf56Dge+tRiPotbH4Un+j55RvovOt5k/WDQ39G0po+Fgw/o2WeVqNH5WVlf02noAwXQ5Yb2KVQxqKXV9fz8SJE/v9pqKiol96CuU9N9JKda3i4mJWrlxJXl5e6JleWlpKV1cXhYWFQ36W0dLnowmDzWnuGk+vibV5HdW5okVA1WOzo6MjzFer68No25pWRVM7ufOYrmddJxCXNhhYjyYez8RaG+vmvxr6PJLHcFIvxHuno8cLCwuZO3cuYPWY6EapoqOjIyzAqLItlj6mPNXX1xfKMiC0W+h8qbndW1pawrSDqnvFilCOwqejyD3cd999Y/5eow1Z9zQGrjHG/CP4fDFwh4gUG2M6AURka2CKMebXuWowThWGo6AP5p08mHebHndDPtx76jndyY71+6lTp7J06dLQGy9R2/Ho8UpmZhEtmhRvxzmZvGFRPlGjWllZWej5EOVpd7HiehpHd6Jdr8CoAhzlpcEUR7dN9QodSU5jj9Qg2fFLVj4OhR/q6+vp6emhubk5DOtXlJeXM3v27LBNXQzp4skYQ0lJCVVVVeTn5/PJJ5+EIXNjzXg72hCPB5LhDTdHbdTzXWWbbgyo91XUS1h/19bWFnofu56f6n0V9bJPBj6XY2YQ1a3i6TMa7eIiKqvc37tQXnM9j9021Hij592xj8qYkpKSMK9y9D7FxcVJRWtF62V4vSwzcB02orwTLzd6aWlpmPKkoKCApUuX9gsT12tEZEAhY2CAzuUa4JSfvTEudzDUOc0dW1ff1zkoVjE66M93GsVQVFREU1NTGG3jyol4elkyc22yc2A82eW9QYeHVKVecKP/hqrzulGgLjTdk25CaLSORgS6fOamqHCP67Gh5OxPtk+i+qFH+jBjxowxf6/RhmwbjV8G3gAIUlAUA7OAKqBBRKYDGwJ/yBqFSSIVRoKogh4tVOFClbrBvJNjTaruffWzGkQ0Mb0W1gCbLxJshe2ioqLwPrqg6e7upre3l1WrVvXzhorVdqxjsUKu/cJ4LYbLW4m853RX1fX0jSqYauRobm7ul0vV5Z9oAa78/Pzwvu5COLqwdvmgoKAgJq3RPK3xwseH6sEZ9W6Ih0T9Ptwx8cbEgX2Qn5+fVB7e/Pz8pDwpE20+RFNQVFZWsmzZsrDwZ1R+RcP89V5AGNZbXl7OsmXLWLZsGW1tbRQVFYUKbLy2o/wevS4en4wX/hnpu5dsBEUsqHeeOx9rm5pup6qqKkwpEa8Ioevx7npHuR5eKi91Dh5s/I0x/QqpxXqWeClM3PPxMNb4ayR6RFS3cvlB+zDqYex+dmVaPJ5THlG+jHqLJ4qqiRpp1ENUPYyhP7+7943nbDASb6t4fJWKgr3DQbp4OdH7M9x2NU2EGx2jMsIt2hv9XlhYyJo1a1i6dCmffvop5eXlrLvuuv3uUVpaSkNDA11dXRQVFYWFFqO0uvOwMWbYupFHehBPT4rmzI8FV99Xb82pU6eSl5dHb2/vgNQTbrSpRjG4UTFacLO+vp7KyspwHtSNiUTRWy5iRZrFQqy5OArd0I3nRDVWeHa48ifeu673HOy+saBRWLE2F93ih270TCwa3Hm6o6MjtDkob2tBUY2e0I16sHYJ9/fuc8VaL8R7TpfvY/0mkc1lrPBWruGhhx4C4PDDDx+z9xptyKrROEg7ofF+AqwCGo0xDSJyFLAFcIkxpiXOLcY9RrpjGF0kaLVStwCLeovqzmD0t2VlZUyePDksZpaKhcFYLeyUK4h6+rr8E6tStlY9hv6L1Sj/uLvHI1mAutVuU+3tkqpddo/RgcH4MFpQJpl7adV79eCaOHEic+bM6efll0zbQ73OI/2IyodY8jCZMU4m0iaWfE00/u3t7QPm52QR9SIdDxjJe5UosiFVuX4H8/6NemMlmrui5xJFocWjPx21I1JRsHesI57X3WCpuPT6mpqa0NM81j00X3FeXl7S+V+948bYQaKIAy0WDfFlWTJRXok2tBIheu+RFN8cCznYRxMSzUdRXdm9JipbojqWm5vb/R9PT8qk/jwWUjaNFtx2221AaoyzuXqv0YZsexqHMMb0AK0i8pGIXA3sARzvDcaJEW93NlmFLyqUS0pKqK6uDsOYSktLw9266A6d+9uCggKam5vDfHkjVTi9YS81iKeAuTvDbkg+xC6O53oax7sW+vPjSMbQ9dID4hZ3GQ7ivTNjoVK8x0AMFgKp+XCTvZf+F5FwkVJVVcXUqVPD69zwXrdtIKZc9PIu84gX6h+VD7HkoYtE4cFRWRLNDT+YfI3CXfAPJgsTLcxiRfuMxZDLkbxXiVIzpGrhqKG3GuYbKxoinn4XTSsV5a1EBpR49Cs9qZz//CI7NqLvZ6xxjrdZ4OpbOl46/0Q9PjXtUn19PV1dXUm/C34jc+wgXsQBxN+wgP4bjdHIicmTJ8dMCaA6j+u9HO+esVLgjMTwO95kTbbXLLF0HEXUMclFvM0v5ZeKigqMMQPkY7Lr0HTCp2zyGM/IGaOxWKlTCOwQ/N/VGPNOdqnKDNIh+NesWcOqVavCnT4NfRwMQwnhdxWRqOKR7AI13vP6XFWpwVAVsESh/LGKFCQap0TndGGjdGlBsViVlIfqdZ4o72MieC+FsYl4IZDKJyUlJYPmto7m1wbo7e1NyhtevUP1fVq1ahWtra1hrlul0fNcZtHW1sayZcuYPHlyQrkSa2yiMiaRnIu1eB5upW416rl5cOMh3sJsMM/ZseQNmq73arCF41B1uqinlc6NmsIkel2stFLQn68SGVCSXfimwts0FQV7cxEj1dtVR29tbaW2tjap+gqD6VTRcPCysjI6Ozvp6emhsbGx35wzGPxG5tjAYO9wvHReJSUlrFixIkyF5KYSGGz+SeS9PNhmxEgMv+PNoJeLaxY3fzasdQCCtTRGNxh0vnMRi0/c2hDxNkw9soPZs2ezZMmSmOdmzZqVYWo80oWcMRoba4nqEpHLgb+NF4MxpEfwl5aW0traSnNzM21tbUydOrWfsHVzNCYbWpto9ziRd1YUsZ7Xh8KlB0NVwIbiXZJozNxzel/XOLdmzZp+lW01V1+sSsqJjB2DPcNIwuQ8xgbcVCduQaCh8Hqsa2PloYz1u2g6gdbWVnp6emhra+uXx9Yj9YgWdYX+xpXhIlkZE2/xPBIvvvb29pgbD1EkigKJYqzJvmzrE0PV6aKbpMuXL6evr4+qqipqampCr+Ko99Zg6QxGqk96b9P4GKnerjp6T08PHR0dMe/hbmyph2g83m5ra2P58uXU1tb2Cwd320k2NQV4Q8xoRqy6JJAcn+r1yjMFBQWUlJSE0X7KZ5rPOPo7SOy9PJg+P94MvyNBNubtWGs7VxZFNzZjpadwPZRVxoFdCw4WqRqPn7M95493LFmyZFh5sT1GF3LGaOzgXjPOOC9Vgj8qzMvKysIKyfGUh0ShtVGDbryE9rEQb0cw3vP6xUl6MFQFLN5mQaxxjFckzz2niMV3kydP7he6n4gXh+L9lmjDIpFi4ZXVsQk31UlnZ2docKupqaGqqmpARfBYvBGLpwYL6+zo6KC4uDhMsQKWl7VKfbJ5bD2GD9ewo/ONm2tPN1OHCpcf1DAdi29cHokXSQFDW/BoJXE1AsWbZ4di9Blr3qDZ1ieS1elc3nHnxvr6+nCMV65cSU9PT7hJoNfl5+cnjNAZKdJZV2AsYKR6uzsXROuFuDq3MYbW1tYwWtA1JGue46hBJpoCR9vx4zg+EI1cSJSeKwrlkZKSkjCNhGsoVrhpUNyUATonxoPfjEgdsrFmSWZtB/03NhPxXHFxMeXl5THzsbtwPZijRugoXfH6JNvpPDw8Rjtyzmg83gzGkDrBHxXm0crtsRYU0Qm8t7eXlStXUlNTE+a6TZTQXhHLCzmeEI/1vD4ULjcQS6EbzBu9r69vQJhlIiObtlNZWRmmQdF2R+J5HiuFQFScZNuY4JF5RHlRjTGq0MbyyNHv6h0f671IlG9Uwzqrq6sHpMYYah5bj+EjlmHH5QeVLSonko2ecMc+USqneNcNZ8GjEJF+RiAv0wYi2/pEsjpdPG9VNfR1dnZSXFwcGo5jeYrGSkWWigWxu9nmPbcGIhV6u8qHeHpKRUUFFRUVtLS0UFFRMSAfrbtxD7ZodSyahmqo8157oxvROS5Weq54cK9xZYk6DdXV1TF58mSMMf1kV6y5TnWckpKSuFGqHrmLWBvisebWeDYF3diMBbUZqOwqKCiI6ZQE/YuAujYNF4PN+cYYli9fHqZb8bpS7uORRx4Z8/cabcg5o7FHf8Szocc6nqwwV+8F916qIK5cuZKlS5cChLvKauwrLi4O0wtE23fDcMvKyvp5qUS9KGJBczXGurffEYyPTOyxRPlK29Qx6+3tpaGhIVQCogokMIDvop6Ymv9KEVUYEj2nMQYRiWk8SVS8UX/nMToQHctYqQdi/UaNvzrWtbW1dHR0hJ40UUNe1BAX7/7xvHZipaTQ+7rvjve4GR6GIvNcw05vb2847tG5Rv+7eUbr6ur6yYd4PJHIw8r9noi/Yt0jkWxyeSfbBtJcRLrercHmoaHey9WRXB7UxbEu1hN5imr6gebmZtrb25kyZUpSHs4uXDmq8+J45qtM+67E04tUd9acn8YYysvLB0RIqEGvqqoqLKqoc148OZLoGdO1ETVYv44VfSzTzxlrzeS+z4PNUYPxSFlZGVVVVf1SqfT09NDe3k5xcfGAe7rRW5rqws3Bng2Pz0RjMlb4LtWIFanl8hYQyplY/ZuIt9RmoM5oOgdGZaDybHFxMXl5eTH5LRZdsZ5F9fJ4141Dn8WcRm1t7Zi/12iDNxpnGYkmq6FOcu4CWUTihvRHDR3uxKAvQ21tbdhGfn7+oIqj5rzSCcTN95lot9EjO0i0mIgiPz+/Hy9F+Ue9oXQyTlTcUKEKg7tr7P7O9RCMp5BEnyX6m1i0uvcZSh945B6SySkZ6xpXnrmpfFz5GYuXohCRmGkJoveMRZ/nu/RhsEUKxB+PaOoH97p4PBH1OHQXxC5fJOKvoXotuu1H5bPnreH3wXB/l8wGZNR7s729vZ+OpIh6CYrIgDF2ac3Pz6e+vr4fTw3Fmy/qKa/8GKvNRBhPfDecZx1sEwhi60VqJI4nK8rKysI0FLEiDxK1G8+jONYc5pG7GGyMEs0vifRg5Yn8/Px+G1e6xmtpaaGsrGyAB7zKjqinsZvHNpHulur3aywZA9PxPsa7ZzJ6sM5RUahsiTcfxYr4gtgyEOw6s6+vj87OzqSisRI9Syx6xsuG1mjCj370IwCOO+64MXuv0QZvNPYYEMI7efLkId9Dw0x0F3o8e6mMNySjWEQxGH8MNY9xsr/xodxjB8nklNRzifgs3qJlOMXxEt3TI7tIZk4SEerr62N6sI805cBw7uUxdhCVF27uUBfDkR95eXn9cqcPBWOtCOJoRjIp5GJhuJEHieawoepfHmMbw5VLykcuP3mZMzqg4zcco7srW2LJknj85G5YuUhFDnkv00YXctXQO56Nxj65UI5DQ81ihRTFOj4cRIvgaIGDoVSXLy0t7Zfv2M3fmQja1ljaCc41pJJXYmGoRZRGki9vOM/i8nOUTz1GL5LhO9c4l0imDZWv3NDyZHgp3e/geMRQ+zTZOSkRXyXTpobyxlrcDJcPPP/kHuKNSTz9yZ174s2Bw9G9RoqxVgRxtGIkepHLN8nKObA8qR6hmeA5L8cyh0R9PdJxUF7RYoxDRTZkjue99CPWWmuoRl6VX1HeiMUzqRxTpb23tzfjc7CHx2iBNxrnONRrqb29PanjqYDuEK5Zsybp36hAH6qym87n8LDItT4eDn8phvMsbntDWVB5jB0MxnND5SsNLdciM8lcn0vv4FhANvo0mTYTLYiHS7Pnn9xDvDGJJ2vcuSfeNSOZGz1GN0Yy9sP9rc5fra2tGeE5L8cyh0R9PdJx0CKZyeo/uQDPe+lHptdaqRxTpX3FihV+DvbwiAOfniLHES8kI53hPdHwtuF6QLi/c3P2ufBhSqlFrAITudbHI0ldMtRncQsyeu/i8Qv17tO/qBwbKl+5aS+SkY+59g6OBYy0T4czr420zWR+PxpkuMfAMVF+0nQTieabeHOgWxvCI7vIdLGuZPSieDSNRKfKZCq58STHslHszUWivh6OHu3OlaMx/eB44r1MQXl8uHwxkugKSO2YuumiOjo6RhVve4xuzJo1K+4cMWvWLBYvXpxZghLAG41zHPFyH6YzJ2I019Bw88AOltMoVlseI0OsfJq5lj9zJGM+1GdRj4iqqqpR4xHhkXqoR0xzc3NM/htOIbJki7oM5/4eg2OkfTqceW2kbSbz+9Egwz36j4kxZkj85MogN7Q2WhvCI3tIJjd5KpGMXhSPppHoVJnUwceTHMs0/0QxWPG74a7l1Ht0tBVJHE+8lylEeXyosmSkNWZSOaYu7Z5PPDKJREbhXJOx3mjsMQDR/EDD3VUejbvRox25sJseL79UMhXmUw3Pgx6KeLwwUr6Mdd/B7ukrNWcfuSobckGGewwdqeCn6D0SyYlEMiYbc+1YQy6+h/FoGk5OTz8HpRe5yD9DhfKIK5f02GDyx2PsY7BC04lgjInJVwovf3IPs2fPZsmSJTHPzZo1K+XtPfHEE2P+XqMNo9ZoLCJixvjMlC6hOdT7JrObJyID7pufn59UtVI/OQwfsYoFaJ/n2usRi0eG8tvhIFke9Mh9jFRODJcX4rWrxz2PZRapkiFDGbfhGliG8ztXhnvkDgYruAnxI6qSvW+mZInXuRJDdZVMvYfJjkcqacoGD4wXvss1/hnuWkDvG2sNmA19frgYL3yXLiTSgYfL48o/ibzh4yEda1vPI4NjyZIlGbUrpHLDLVfvNdow6uK1RaQu2zR4eHh4eHh4eHh4eHh4eHh4eHh4pAa33nort95665i+12jDqDIai8i+wE9F5EFgh+CY3x7y8PDw8PDw8PDw8PDw8PDw8PAYpfjZz37Gz372szF9r8GgRfJi/c2ePTsjNLgYNUZjEdkWuA64CPgvcCrAWE9RkS309fXR1tZGX19ftknxGAGMMbS1teVcqgoPj1yFf2c80gU/r3qMFCqfPDyGCj+3eWQKntc8koXXi3IDs2fPjmugFJG05C32SIzFixdjjIn5Fy+/dDoxaozGwJbAH4wxLwF3AZNE5DsicqCITEv0QxE5WUReEZFXGhoaMkLsaEZfXx8NDQ2sWrWKNWvWZJucnEcu85dWt21vb09bG145TB9ymbfGKtL5zuTSu+J5a2QYzlhqtfCxPq963kofkpVPuSJnUg3PW8NHLN7JpTkpF+D5a3AozyQy8mVi7THa4HkrNoaqF3mZlR5ozuJ4f4sXL842iR4OEnkhp8sTeTQZjf8K7CAi1wH/AF4CGoGdgL0gfqoKY8wPjTFbG2O2rqvzKZEHw5o1a+jp6aGgoCDnKsvnInKZv8rKyqiqqkpr4navHKYPucxbYxXpfGdy6V3xvDUyDGcsS0tLqaqqGvPzquet9CEZ+ZRLcibV8Lw1fMTinbHMK8OB56/BkQzPZGLtMdrgeSs2hqoXeZk1fCTyJvaexKMLibyQdUMl1QblghTSn3KIyDZAIdBtjPmriJwKbAE8aYy5NLjmRGBn4G6fqiI1UMFdWlpKXt7g+wrGGNrb2ykrK0vqeo/MIVF12lRBlUJXOXR5wqcd9xhNSOc7E+tdUfh3ZnQh0VjGQl9fH2vWrEl6XvXwiAWVT4nU3ZKSElpbWykpKckgZR7ZRDLzR6y5bahyzGPsYLhrt2R4JhNrD4/RDVcnGgqveJmVGK+//nrcOWDWrFneQ3ucIJFnuG4eDBWSq8wjInsC9wL3AEcA1wJ3AEXAD4G7jDFPi8gRwCHAscaYQRO9iUgDkMlEILXAigy2l43284B8oBeIFa80lvpgljEm7hZxFvhLke0+Vigdg/FEpujINoZKR1z+yiJvKUZrn6YLqaZjuO9MsnTkMm/FQq6McxTDpSvdMjGb/ZUu3srmM2Wb/0bS/kh5LZf6fSi8NZrHbKTQMZ8ILM8SDYpsj0OyNKRSn8+FZ45iMJoyraePxj4aCVI1L2ar39Ldbjz+G6vPm8p2k+WtXJrLfdujo+34vJVrRuMgxUQRcDvwhDHmZyKyBfBd4CngTuAkYDugBdgKOMwY83qWSE4IEXnFGLP1eG0/F2jIdvuZQK48o6cjN+lIBXLlWTwduUlHqpGrz+Xpyhyy+UzZ7s/x+uwjaXs8j5mnIbs05MIzR5FrNOUaPZCbNEWRLRp9u6O/3dE6l/u2c7PtnIuRNBadwJvApiJSYYz5B3AmsCewL/B/WM/jF4B9c9Vg7OHh4eHh4eHh4eHh4eHh4eHh4eEx2pDLOY3/BewPrCsi/zbG/FtEzgF+BrxqjPkz8OesUujh4eHh4eHh4eHh4eHh4eHh4eHhMcaQc57GCmPMb4FW4OvAxoHH8SvAb7E5cEYLfjjO24fs05Dt9jOBXHlGT0d/5AodqUCuPIunoz9yhY5UI1efy9OVOWTzmbLdn+P12UfS9ngeM4WnwSLTNOTCM0eRazTlGj2QmzRFkS0afbujv93ROpf7tnOw7ZzIaSwi6wHVwBvGmI7IuWuASqAT+Aj4JrC9MWZxhsn08PDw8PDw8PDw8PDw8PDw8PDw8BjzyLrRWET2Ba4CVgKfAlcaY94QkUJjTHdwzS7ApsAGwC3GmP9kjWAPDw8PDw8PDw8PDw8PDw8PDw8PjzGMrBqNReRzwF3AkcaYf4jIrUCJMeaE4HyeMabPub7AGNOTJXI9PDw8PDw8PEYVRERMtj0EUoBsPsd4bTubEJF8Y0xvtunwGFsYr++Tx9hAtvh3vLWbLaTzeb0ek3mMpT7PhZzG1xhj/hF8vhiYJCLFAMaYPhH5bOCNDOCVRw8PD48sQETKs01DLkHnKY/sQEQk2zTkOkRkGsBoV9RFpASy8xwiMlXbzjTPichntO1Mtuu0v62IzMhS258D9g4+58JaZVgQkcIst79OdAyzwMdbicgWmWwz0v7mIrKNiKwLo18ejlXk4pyeSzRlay4SkbpMtRVptwwy/76KyOcCeVGQ4XYrID3Pm60xDNrOyjgGbWdlLIO20zaeSbQ9R9tOpazItiL2MvALsB4FQDEwC6gKjk0H5gOvgJ/oUwURWV8nn/GMbAiRdEJEdhGRnXOAjlkiskHkWMYVLxHZW0T2y3S7sZArfTJciMhuwHkiUpplOjYXkQ1FZMMs07EQOFFEirJJR0DLNiKyvYhsm21a0gkR2VFEjhCRgyF39AER2VpEdg6MXDkDEdkbuDkqd0YbRGQf4FYRuTfQCTMmO0VkB+ApEfkqZHaxLiJ7Aj8RkfUz0V6M9hcBjwFzAv08k/2+J/AMcB1YB5Jh3GOKiKyTatqGSMPewKkiUpWl9g/BrrF+LiKXichBkDk+FosZwK+Bm0RkO/dcutsP2tkfuAc4DzhLRGozQYOIfD4X9HGFiGwsIluo4TwXEOhzn40YOLJql8hhPSMrc5FYp72fisiDwA7BsUy0uw/wAxF5KNBvM7L5Fsx7jwClzrFMPO/+wF0i8mCwdp2ZwntnZQyDdrIyjkHbWRnLoJ20jWcSbX8WeF5EroTUyoqsCmdjTK8xpjn4KsAqoNEY0yAiRwHfAB4zxnyaLRpTiUCJOFdEDsriQuBAbEqQ6dmYnMUaNi8Vkf1FZOMstf+iiBQZY3pkjBiORWQP4EagK8t0HIJdaP5YRK4P3uNs7IzvDlwLtGWqzQS05ESfDBfBxHsN8AdjzJos0/Er4KvAwyJyfJbo2Av7rv3LGJPt921P4HFgH+ABETldgt3tsYRA8fw+sBlwjNhNDD2XtXcoUMbvAI4DzhCR07NFi4tAabwVWwPi7ci5nJc5imDcrwTuAwxwM2R0Id8CNALri8hZmWo7WHBcDJxmjHknw3OniMjkoP2jjTHPAaonpV1nDN6py4DPAy+KyBnDuMd+wMPAYyJykYhMSTGZydCwG3AL8LazztFzmTBCTMAWDv8KcBC2ZsweInIiZIaPjcVH2IX708D5akjN0Hs0E7gQ+CJwGLAOME2cyIVUj0Xw/tRj54X7A11Uz2VlzR28Dz/GvlfnicgB2aDDRUDTA8C3gYvEFr3XCONs9VNO6hkBMj4XiXVEuA64CPgvcGqG2t0Tu367E3gLOJPAmTCNbYqITALOBk4wxvwJUKeQUr0mTW1vANyG1W9eArbHbnDNS8G9szKGQdsZH8eg3ayNZXDvtI1nkmgA/gPMEJGbIHXjnW1P4xDGmB5jTCvwkYhcjTUY32uMackyaSlBoAjfDkwC9gKOksBlP4M0zAMuBc4xxvxtON4bI2x/N6zBug+7IPheMElnElsDC4BnRaQkMByP6jDzwJh2N3C4MeZFESnNNG8FdJRjFygnAQuBfwMLRORMyNxCP1iU3AEca4x5WkQqRKQmS5skOdEnw0UgMx4FrjfG/ElE6kVkdiY3fAIFoAL4GtaI8jXgROACETk1U3QEtGyKXehcZox5PuCrWgk8ZTJIhwRy64vA140x5wMHAwdgPdsy/v6nC2K99M4CvmKMOQ94HSgRkfUge5svIrI51qB5nDHmOOBBbLHeXMC6wEPGmGfEhqcfICJfhlG1WVWBNcafa4x5Brga6BWRb4jIPBGpTHP7AqwBWoFXgXVF5DgRWU+CtB9pbPcyoNcY80JgwP2miHxHrKdgWp87mJNagX8bY54SG5V2t4jcEdCRNh4PjLtfAS4wxvwNu+Bab4j3WAh8Bzgd+BJW59s34Y9SCOfdOhg7T/w+mCfWF5G5kLF3sADoAVYZYz4BHsIabreUtSn/0goRKRDrpZ4PrMDqEqeLyMkicmxwTTr7oQzbB4sDGuZjC6/f6MrDVDYYGMqXA/djDbX/p0baTK+5AERkAXbT/2is4fxN4LOZpiNCUxFwMnCeMeYQ4LvAhiJyJ4SG40ynUKki2GTJJT0joC0rcxGwJdZZ5CXs2n1SMA8dmK52xUYzHgZcbox5zhhzEdYZ6ph0tKcI3ttGrLHt98G8+zMRuQu4QUS2SuN6rQT4szHmBWPMTcDPsfLyVAmiq0aAjI8hZG8cIetjCekdz4QI7BxrgCbseJeIddTcWFIQZZIzRuNgEVyEdZ3/EnCEMeZfWSYrJQgG6iLgy8aYc7DKxI5kYMclglKs18NfAsPPFSJysQQh/BmYENcD7jTGXIpdFH0P+K7YEL5M4THgy8DfgH8Gx7oz2H46MAGYBiwPFPQfYz0xr5HM5pErAAqBfGNMO/Az4PdYBeewDNJRAVQCTSIyEWvM+Ql2kyLTHhaF5EafDBctWM+LbcWG3/8U67nzRxH5SiYICBSAVmyaoioRKTTG/AU4Avi2iByXCToClGDHcIqIbI3lq+uwiknG6Aj6pBO7ANxURCqMMf/E7uTvDWTFCztNEOzCv11sqPOR2E2Dm5xFZjY2XwS4zRjzWvD979gNoTk5YJT9CPuuzMB6oi/AplP5BeT+ZhVA8M6fZIz5nVivkd8CHwKTsd5pW0D69JbgHXsLeAGbSu0B4AtY+V2XrraDsfk8dnH3CFZfLAAmAmcAm6a6zRjowM5R52E9hf4E/AEr/44SkeI09ftq7CbMU8H33wB7qYEvSczGbnK+ZqyX/a3ADsEaI+3vpfNurQQ+CNp8HGvIvlpELo5cl1IEOg/GmJVYI/HlIlIffH8GeAfYKh1tx6Chx9hCho8Aq40xdwMfAzdg+Tld+Tv13v8F/gG8iN2w/xF2I+pPwOfSsYAXkcJgzEuAt4HzgYtF5NsicnZwTSbX3mXYdde/jY0U+x2wk4jUZZiOEMZGaL2DlTMYY94ETgDqROSK4Fim56g87Fow1/QMdy56kQzORcBfsbLzOux79BLW23knrPNbytsNePQK4DfBehbsuztBr3GOpwwikhe8D7OwOv0FwC+BHwbtnyki1Wnq5/8C6+mayhjzKlbf6SZwRBhBuxkfQ8jeOAb3zeZYQnrHMyGMMX3GmGXY5+zGRowtwOoC9SNtO2eMxoFQ7AIuBxYZY97JNk2pgjHmPeB67M4lxpjnsbsOmVD+XawGVovN6XUT1uN3OTZs/gsZmBC7gM3BLgiNMb8A/h/2BZ6f5rZVQJUDXzDGnIHND/Uh8L5Y79ySdNOQSoj1oM0zxjyINYQvxYaAPAWcizWeHpUBOmaJSIExZjXWQPstEVnX2CiBP2ENW2nPtxpshBQaY36N9Vb6E3aC/CXW0/cD7AI07QXdxBZ+KTbGrCKLfTJSGGP+h5UVrcCzwC+NMSdi0yFcIdaLJVP4FNiVILzIGPMK1nvmdMmQp68x5q/YTZn1sO/Z49h37wRsf3wmE3Q4+BdQgzXwFBhj/g18CxsKtVmGaUkLArnyLHaT8TfAT4wxB2L7fLrYELhs0PUP7LuN2DRHy4FlQHPglZSpULRYWAlMx24e/NQYc54xZjusIfLsLNI1KMR6RMwXkfmB/ASbZuhLxphvGmPOxcqCMM1PitueJ/1zpq8LTMEabrfGzrGfS2PbmwYG822x+tKLxpjvGGNOwc7xafHUcdrfODD0nQ9sCEwyxvzQGPMzrNFitjGmMw3PPh+YZWx6Ognk2cfYENOdZZA6HI4B7B7snK86Xwf2XcgL3su0eWpHjHCfYGXWrdhQ1cOw64AN0yWbxUbzPS42DR1Bux8DZweG4xXYHMe7i/W+SicN+zuHC4DPinUQ2Q+4F9hb0pAH3mlfvXtPwzoiPQv8KOiDXwFTcQwYKWhXi5R1B+/GL4ApxpjfYHM6X0rgKGQy4HEsa4toPo31ONN5aiX2nWg11qM3k7k23cKa/wZ+KCKzAzpXYA0cUwd719NBUzDXPEOO6BkSu1bFLCzfpm0uctrdxhjzd2w6g/eAJ40xlxpjvosdu51T3K67ifGxMaY5mIcI2i8KrjsI2DVVRjdtNzC29QGnYef8rY0xdxhjXsYa/HqBjhQ+75YiskBEtg9sXxcA20ngTBT0fSPD0HOyNYZB21kZR7ftTI9l0HbaxnMIbW/jHC7G6q0zgI2wTpJfGGnbOWM0dnBvsPAd9XAVSGPMg8aYNmdnpY9AYRGR7URkVgZo+ACbHuOXwF+NMRcZY24DTiEQHmlov0aCfHLGehnMEpHbnEuexu7KpKVgSdD+5KD9XmO98d4ITn8Pa1gVY8waY0xHOmhIB1QxxnpL5Btj7sHuiv/UGHO7MeZ1bOj0VpLGqqli87vejFVkwIYf/gub33NdY3P53Yf1wEsLjzt03IQVkgQL3NOAu4LJ4iNsepgNgNq4N0oNLXtjPQF0QfQolufOEJH1MtUnw4WITBenwFvQd7cC+xtjviciEhhsH8BOvhmBMeZWrNfMbSIyIdgg+DOW3zLmAWKMeQEb6vvlgKbegI7fAZ2ZoEEVLWPMb7EG/a8DG4v1OP57QEu2vV1HDOc5r8Z6UN+PXfBj7G76h2RRj1GjprEedTp/9InNW36dWA/ZbND1X6xH/LHABhJ43mGNF6uzQVMycGTnWcA9gVwnMFK+5Cww3gZcfSqVbX8Tm45BI6DuxBrff4xNm3YD1oskZWMbaft2EdkvMBxvCFziPPe7QGsqnztG+3eIrZHwEvBHrFzRjYaJWG/AVBrbBoy5segJLnkbGzk0O8E9FmGj1n4IoQGKYLG6GLuR0ysiRwNflTQUMHVouCuYI3+AjUTZD/g00D9fxur+6XRQKAc2F5EDja0Hcx/W4+g2sYZ53ehN51xVjk2DoYvUJ7GL2B8BZxljvordcPs4ze0fErT/JnadcZ6IVGMjPSdh6+iMGBIpUhYgH7uZewBwOFY/PVplWjoha4tozoNw45XgnWoCeowxa4L34TLJQKFGWVtYc93AsHMXVrY+ImsL4f0TG0mSVh09Bk3rBTR9B/u+/gQ7V2ZFz5CBtSq+Hpy6Gzunp2UuctrdF3go4OdXsJs8RmzqH7A6Z5mkyAFH7CbXw8AXg/Vsd8SYmAfkicih2IiN91JhdIu2Gxz+H7Zf54jItcGxrYC5WLkyYgR89xPsRuLDInKIMeZ32GiefZzx/gT73Emn0MzWGAZtH0gWxjFW28HhtI9l0HbaxnOIbT8mIl8MTt2PdTD4FTYC9URsqoqRbRYbY/xfGv6APbBFMKqD7/nB/4Lg//9hQxD3we6AT0s3Dc7xp7GejkrbV7E70ZLi9vcFnsMKrKuDY5OwC5Hbneu+D3w7Dc/vtn+ZMwbfwRry3sbmmb0Xu0DKS3UfpJG/9gPex3oD7aB8FbnmsGCsK9NEw75B324fOb4u1qvi8YDHv4gNkZmUSTqCc0XO54OxocY1aRyXmdg8SjtFjs/D7jz+KhN9MgL69wzehbnB93znXIHz+UjgNWBmmuhYgPUiXuCOYXDuAaw32SnYjYH3gOlZoMPlrcOx6QnWSePYrI/1LslzxyU4dw3WsH8D1vjyCdYjMOs8lYLnznM+Hx/w3cbY9CSvAOtmkJa48wPWSPBgMJ+8AnwmG3RFPh8BPIk1Bl4eyKYNsz2mcZ5ha6xhZ1vshscxWENLXoQHvhy8aynr33htB+c2w3oN7hV8LweqMvDcBZHnPjHVzx2n/WOBm4NzVVgd6blA5v4X2DgDzy70n3uuAf4cHJfIPfbBetEswnoRPRg5X4s1nJ6JjfZL+XsZg4aHguMV2JDYN7EbAEdi5/1ZqaYhaO+z2AiYC7Gb+Z/DehptgtUVf4v1ptwyHe3HoOFGbBj0DOzG5nbOdQN01jS1vwvW01zXQy9hQ/w3S2GbmwPPY+ffbzrHb8AaaQ8Ovh9AoFulsf/3x0YEbB98j74vJdh0ITdiw9XTOk8F7+xk4C/AbsGxMuf817D6ysHBO/ovYEaWaTo0kBWbkEE9I6BLN1cOc3jrD1gj8TbYuWjv4FxK5qI47W4RtPttbDTbuViHs/sDebZJip55Ftbr9SGso9MRrF2r5wX/d8cW93oW2Cjd7Qbn18XKylsDnkzJvIeNMP+P834uCvo1HztXLcTO848AS0hSTmVzDLM5jtkcy3SO5wjafgzrXDUDK+N3Dc4VACUjbjNVxPu/fgP5eayi8ChW0a4OjrsLgG8FTP6XVL64g9HgnP9V8ALdHDB0yl6i4P67Bsy8DdZD5GXg9ODcBKzi9jPsAuEdYIMMtr9x0O8HOden3GifZh7bCrsA0DGch81rXBmc/zKBcSVN7VcHffjT4HstdsF3BnahWYItRPNrbKhXWhYpceg4GhvSWh0cy8OG56R0sohDzxzgEeUpbD7IW7GeLRtiF/5p7ZMR0L4HduG7mMBgEuOaIqxC/QZpWmxgFz3/whrfHgHW13F0rjkBuyh8ONN0BOck+F8Q8P2/0kVH0M6Bwfv8c6wi8FWgPHLNLsH7dwspVMgyzIO7YzcCvhbpZ3fsL8EqnU+RhrkzDl3rA5NdmuJc93PsZmRK57Oh0hX5vC12Y+3STNE1zGfZCzja+f55rKKvC49i7Hz+m1SPe5y2n2XtJn918D/lhq4knrsgmDt+nQ5+H+zZg2P52LmtPsPP7vbB1Bi/r8HqsrsH37fEGmmPwzooFGANhqsD+TkvDf0Xi4Y7sMb3ScGx07F6wGPplFlYXedcbOTX8Vj9/++snUMrScHCcYg0PIbV0ZSG/Ay3/wtsDRNtfwOgNoXtCVb//i1W97wl4L/1gu+b63XpfG6Hln8CzwffJ2N14e9gDUjVwTV/J7PzVDk26o9gXH6K9TI+Jzh3IHZ+ehDYNEs0/cShaSZ28/1PZFDPcGj7NjYfbEXw/TNYuXikc03K36M47T4dyLICrHz+Cik0oGM31rbByuvTsDaBI+jvlLEeKV5zxGn3i5F284N3JmXOPdhNLTXq5mENms/hOHcFfb0ew5hvszGG2RzHbI5lJsZzJG07/1Omt6aMeP/XbyC3DSacGdgcZj8isujATpC9qX5xk6HBuWYbbMhvqoVHPrYyrjvB7UnEEAUchFWq5meyfewuzJTgc1Hwf1R4GDvPU4n1HKkCzsMqxn/HGshLscajdBqxCrApTe4K+Os5rDfQz7EKem1wXSkRD80M0fEw1jugJrjuqHT2h0OPFr35AtbD7zSskesebK525b+09ckw6d4NG/r8GWzhvieBHeNcux1p8mLFLsB/T2Dcx4bjHYpN4F8W4/ribNMRyLH10jg2NdgF6UbB9xOCd+z/EcPLhDR5cGWABz+P9UL4Mtar8HvBMZ0zXUPoBKA0Q3QdgA1PvYXAsz7efIH1lsqIh/FgdMWjMZf/cAyD2IXAE853leUp8/IdrG3WesgMMFhm8Lnr9Hgm22fthk1aojiSfPYpg/y2kCC6A7th/EHwPvwAqxPNCs7dQJoMPwlouB1rtFUa8lItm7FeiBsSRA8EbTyKNWIuCuTprwi85NP0/MnSsEcW2/91utp36LgQa5D+PHZj600CrzLsmiQj8jh4h/6N3ej+A9aAfjt2DageaScTGLMzRFM+diPqPKyX48nYSMhLgUud6/JygKaLlSZsOp6M6BkR2hZh85Fvxlr9Z2tsVN0WWWj3/XTJz6CNEv2PY/QLjs0O/qc8YnaQdtMWEUCw0e98fwKYEHyeMxrHMJvjmM2xTPd4jrDtlEcC52JO41EPY/OW/cCszQW6ArhZRCYaY3pEpMgYczF2l/e9TNMAtoiaMeavxpgnUk2DsXnkfoI1PLmVGjcVW4hBr3vUGHOfsfkXM9H+ZkEe1HZsMQiMTViOCd6w0QJjC6pthjUev4Y1nC7FGmk7gO+ZNOYGNzY32otYL8wdgV8ZY75tjPkCdifx28F1a7SPM0yHhpedE1x3fzr7A/rlD38Gu3D42BhzizHmEuwYHRvQ0p7OPhkm8oFjgj4qxxbY+AysfX/EFgzZzRjzkjFmcZro6MHy8Pwgz97OWE/eG7CbJIjIFiKyZXB9uvoxWTo2CuTYu2miQ2mpYG2+7rux3uC1WA9SzYu/T3B9xvJMpxjbAN83NtfhbljvwEOC4xhjjIjsJSKTjTGrja3OnFYEuVtPwkbFLMHmJZ8Z0CLOdQtFZHNjzC/SLWeSpSv4vIuIbBH8JmdzXDs0L3UOF2ALEOWLyPHYgr1lxuaEz0jbwenjgDtFpCzVfZjEcx8H/Ch47tZUtj1Y+9jce8dhcyxn69nvEpHyaNsisrnYYoVzjTGfBIdLsXnmTzPGnAo0Yx0nAL5lbJ2HVNI/GA2nYGXYN4Pn7DNr8zSnov1FWGPsV7F5FE80tvjPj7GRVd/DhrQ/CewoaSgAOEQadk41DUNo//dB+ynJ3Ssim4nIetK/gLdbMHNL7BytRcp607nGcOjZJJATC7DG9JdM/yKaxwX0/NDY/MFpg0NTtLBmjVlbWPN5bH5RArrSWiAwSZpewI4lxpimTOgZUZjYtSpewdaqSBsfJWj3t9g1Qrra7RARMbYuxI+wXvAbishPgRdFpDpY82aq3Z8Af5a1tSBSAmfOW6bfg/y26wCFwZz3cxGpHO58m60xDNrOyjgO0nZaxhIyM54jbPuxlLc9Uquz/wut+/tgd02/g5MzFet5uAFwHTaNwDex+YzTEVoyFBquJsXejkH7l2FzykyKnNsI+GXw+Rhs7qqU9kG2208zf7n5edQD6IRgTN/E5sr7bjCuadsZj/YZVkFeN/isXlnfAs7JVH9kk444tMwN3rO/AgcExw7FekOnxTM2hc+ifbcX8CnBzjTWm+qLpNnjLmjrEKzX/F+A/xccW4hdCG4bvLsJPdAySEfa+yNo91SsN8zRgXy7H5vT+Z7g/BGZoiWNz7grVrHdIPheDFyFNSTrNReS4VzN2HxplVjj9ZXYiIbZkWtOIk35vUcbXUOgfx42aqGQSO47Z/wfwy7s/0JqQ1PHZdvZbj8VbWM9qT7C1sJ4Azghcl7nsK8AF6WJd4dCw/9LcdvCWm/w/YNjC7CRQsdjN3tfZa3uMZEU13HINg3ZbB+rG/0HG0H2KnBycPzzWGPFh9j5eHesfp7WuhUx6PlqcLwQx8MZG8FzHRlY98Sg6YTg+LHBe3N28P1Q7Jw/YTzSFLS3HtYTdEDaGNJYqyJH23UjpX6JjdrYbKy261zzEGvz9iedQjFbY5jN/szltkc6nrncdnj/VN9wPP5hDQgfYA13P8DuVH6O/vlUyrC7qitIQ1hQtmlI0H5hcL4Gm2vuZNKQWzbb7aeRtzZwPkeNk9thvRkOCr5XEQlVyBAdrpA+CpsWIi3FlnKFjiRomYn1tv5J8PcmGcrVlsLnuwwbuqcL4IyFu2MXedcC+zrHfgHskuE+yBU6JgBfwqbJuN45/hvshsmoS0UQ0O++s9Owi9oTCAzg2FCzvxIxymSYLteotS3WkH09Ngz6s6Qp3G400TWM5zgYWwDtj9hNmK8TpJ2IPNdLpLh433htO9vtj7RtEhsKvxK59ljs/J/S3O65QINz/8uweo7quJ/F6oP7ONekO39wVmnIdPvY6J4XWVtcaGegD2uonopNi5HSImXDpOeUyHVpKaI5RJqOCb7vijVo3EOKC2uOJpqCdvfFrkefwRZ41nRohc41Ka9VkYPtunpNPnZzcTkpWjPlcrvB919j00ckPd9mawyz2Z+joe3hjmcut93v/um46Xj7w+7g3u58PwvrLbHAOfZF4GPSV7QpqzQM1j52h6QbmzIgHcVIstp+msZ0X6CdoMhbcCxajbQ6+F+YDTrc71hF7A+kL3dgTtAxhLEpwRr7diHIeTia/rB5mf9MlnLkYr257sEW6Nsf6x0ye7zSEdDiGleOwS6E0pbrNM3Psk8gpyucYwdii0h9mSDXPTZ38zFZpitaWO5bQd+vIkOevLlK1zCeoxDrDaH5Nb+A3Zi5koh3F9bjNGU1D8Zr29luP5VtM9BQuDXWYeC44PvuWMN0Ouf/XKDhq9h5qco5tgPWUJ22HPu5REOm28d6wt8BzHCOPQz8j/6G6ozk5k1AzyfAl4LvG2LrbGSqaGwsmh4J+uiI4Hs+NlImpYWhRhlNn8M6k2wRfL8VuDseD5EiPTxX241xfd14aReryyctr7I1htnsz9HU9lDHM5fbHnD/dN14PP0Bm2I9J+Y7x87GekxUB9+3J71ej1mlYZD2JwbfU7rblUvtp+F5yrH5q07Ghrzd75xzq5qnrAr0COkoxxpJ00JPrtAxRFpSGhKajT9sztTZWWq7GuuN9idsTsLNxjMdEZpOwHrjZbSqdwrp/yzQhvXQO5n+i/4DsOFVf8IWs1iWzrlzELriGWhvxBqJMuWRlJN0DfNZCrHhv8cF3/OAnbDplU4Njm2LE8nh2x7d7aeybWIbCj+PNRSug/UqTHdKgKzREHnfH8JGMk1grQH7btI8Z2ebhmy2j62f8QC2iPjN2JSAu2IjkCaSwWJuCejZJaCnEqsPZ3RjOQ5NCwOa0rpmGS00YQ1Bxznf67COTsXOsc8SRLmRomiyHG93a5yovnHQ7jbAwtEyhtnsz1HS9rDGM5fbjv75QnipwafYYkW7i0gtgDHm/7C5zk4Nvr9gjHlzDNOQqP2Tg2vONsb8Z4y2n1IYY9qwhqGfYo3fJSJyf3CuB2xRB+CIIPl5NunYPLimzxizYizTMQRaNgO+KCIl6aIjnXCS7B9m0lf0LiGMMauMMTdjvboPNca8Np7piOCP2PDolBZ3yiAqgIOw1cq/CBytRYqMMb/E5v2/EBsZsn2a585EdB0rIhUBXUZEioOCGrsABxtj3hjndA0ZxphubBqNg0VkB2OLH/0Z+Ce2YFcpVklOR+Gbcdl2tttPRdvOnHQrNtXabSIyQWxx4z9jQzeLjDErjDGNqX6GbNIgIvPEFjsthLXrNmPM4cH3G4ETROQ0rDE+ZQX3coWGHGg/L2jvWGze4gVAEbaOyAvYoodtJs3F3JKk5yVsIchOY0yHSUMRzWHQ9GJAU0oLmY5GmgK8jDVYIyL5WG/oWdg0g4jIdGA+diMKE1iGxni7G2q7KUQutzsP6/yRjnunYwyTbTsd/Tka2h7ueOZy2/2Rbqv0WP1jYFj8FthcIl9jbfGoc0ljIa5s0zCE9s8di+1nmN9qgJ8TeLViPau/QIZCqTwdo4MW/+f/cvkPmyZoU6xXWE1wbFvgaeB01uY4Lc8xuipcukhQiGI80JWC5yoJ6P8hsKNz/FmCgqa+7bHV/nDaJkbhPOfcA1hv31OA04D3gOlpoDurNJAgF7RzzQnYTbaHSU/6uazSkK32E4195LpjgKcIohrT9Zdr9HiaUkZvAXZj+I/B96Ow9R3SWpfAtzv6283WM/m2x1/bxpiwqqpHkhCRDYwxbwef840xvSIixhgjIltglcdqwGDdxQ80KfYGyzYN4739bCHwoL4W65GTh114LfV0ZJeOXKPFwyMXISL7You1NWJzLn5H5bKILAjO/RAb2r0ttrhQr0mzkjJEuhY4dKXVoyxX6UoVAs/oI7Ee/I8CncA52BC7Zb7tsdf+UNoWkYOxPP5J8PcK8CNjTLNzzQnYApqbAZcYY/6dYnqzSkPgVXs/cLMx5gUR+QL2Xe8CvmuMWR25vtgY05mq9nOBhmy1n2jsnXVHCbbWwQ3YgtT/Gmm7o4UeT1PqISI/ApZiaTs+U3T5dkd/u9l6Jt/2OGs7E5bpsfJHcsWvaoH1sYrxnLFGw3hvP9t/wDewqTiymsvU05HbtPg//5dLfyQo5ADh5vUMrBL0ESmseOzpyt0/bJjwLsCD2PzwW/i2x3b7ybTNEArnBeeL00BnrtAwWC7obYAtg88py12ZKzRko/2hjD02DDzlHu65TI+nKeV0SyAX38Om0Fjft+vbzfa9fdu+7eifz2mcJESkHBtadybQJWtzmPaKSIFZ693TY4x5xxjzU2PMB2OJhvHefrYReOnsDexhsug57enIbVo8PHIU1xhj/hF8vhiYJDYfuwTHZgClwF4ms54/nq4swRjTZYx5BvgScILzvL7tMdr+ENquwm7+g/VK/jXWKPRFABHZRkS2DM53pYncrNJgkssFvT3wv+D6lEdlZJuGLLafzNhvYoz5rzHm4xS1OZro8TSlCMaiC7gcWGSMece369vN9r19277tKHx6iiFARKZhE+aXAD8AOowxRznnNwN2AO7EFiBIeedmm4bx3n62ISIlxpgOT0du0QG5RYuHRy4hKN5QboIQUWAq8CvsJktDINfnAR8aY97zdOUmXR4emYKI7I6tT3GtMeb54D04HBtt9mVsgecHjDGfjnEaSoATsbnN7zfGPBccfxb4cibe/2zTkI32c2Hsc5keT1PqIWLTLPp2fbu5dG/ftm87bHeM2dQyBhGpweYTXGOMOUpENsXubj5vjFk+HmgY7+17eHh4eAwNIlKA3fT7pTFmVxH5EjaH8XnGmDZP1+igy8Mjnci2oTJXaAjay2oe7FygIdPt58rY5yo9niYPDw+P8YWCbBMwWmGMWSkipwDXishbrC1+lTFjZbZpGO/te3h4eHgMDcaYHqBVRD4SkatZW8ghqwZQT5eHR+7AGNMhIj/BFjQ+T0TmYw2FdUDreKEhoKNJRO4A/oMt9NwBHJUpg3Eu0JDp9nNl7HOVHk+Th4eHx/iC9zQeIUTkG8C3gd1NlnKZZpuG8d6+h4eHh0dyEBHB5hh8M/i/a6bzcsWCp8vDI/cgIkXYnLVqKLzJZDgHdC7Q4NCSj01t2DfoxWOUhky2n0tjn4v0eJo8PDw8xge80XgECMKlfgZ802SpEE22aRjv7Xt4eHh4DB0ichzwN2PMv7NNiwtPl4dH7iHbhspcocEjO8i1sc81esDT5OHh4TGW4Y3GI4TkQPGrbNMw3tv3GNsQkbuxefSWG2M2TsH9egH1iP/QGLP/SO/p4THakM0iEong6fLw8PDw8PDw8BgK/HrRI13IBd7yRmMPDw+PBBCRHbG50H6cIkHdaoypGDllHh4eHh4eHh4eHh4eHtmEXy96pAu5wFt5I23Uw8PDYywjqL7c6B4TkXVF5Hci8ncReT4otuHh4eHh4eHh4eHh4eExjuDXix7pQi7wljcae3h4eAwdPwS+ZozZCjgbuHUIvy0RkVdE5C8icmBaqPPw8PDw8PDw8PDw8PDIFvx60SNdyChvFQyDQA8PD49xCxGpAD4HPCwierg4OHcwcFmMn31ijNkz+DzLGPOJiMwFnhaR140x76Wbbg8PDw8PDw8PDw8PD4/0wq8XPdKFbPCWNxp7eHh4DA15wCpjzObRE8aYXwC/SPRjY8wnwf/3ReRZYAvAKwEeHh4eHh4eHh4eHh6jH3696JEuZJy3fHoKDw8PjyHAGNMMfCAihwKIxWbJ/FZEJoqI7gTWAtsD/0kbsR4eHh4eHh4eHh4eHh4Zg18veqQL2eAtbzT28PDwSAAReQB4CZgnIh+LyJeBLwFfFpHXgH8DByR5uw2BV4LfPQN8xxjjlQAPDw8PDw8PDw8PD49RCL9e9EgXcoG3xBgzPOo9PDw8PDw8PDw8PDw8PDw8PDw8PDzGHLynsYeHh4eHh4eHh4eHh4eHh4eHh4eHRwhvNPZICBGZLSJvZJsOj9xHPF4RkcVBzhyPcQQRuUBE/i0i/xKRf4rItsHxO0Vko+DzBBH5sYi8KyLvBZ8nBOc2F5GXnHscPgwaznc+x5VlInKZiOyW4D7Hicj3h9q+R/ohIs+KyNbZpiOKgH/3dr5fIiJnx7n2xUHulZPPOF4x2sZDRFrjHP+RiBySaXpGA0bbGMeDO3eJSJ2IvCwi/xCRHbJIU8r7VkQOVL3CI/Pwa0WPZOHXih7phOev9MEbjT08PDw8UgoR2Q7YF9jSGLMpsBvwEYAx5kQnd9JdwPvGmPWMMesCHwB3BufagWOMMZ8B9gJuFJHqIZJy/uCXgDHmImPMU0O8t8c4g4gUDOHyzYG9B7sIwBjzuWER5DHmMUSe8xiFyOAY7wq8bozZwhjzfISG/AzRkC4cCHijsYeHh4eHRxrgjcZjHCJSLiK/EZHXROQNETlcRD4rIi8Gx/4qIpXBzszzIvJq8DdgESsi+SJyrYj8LfD8OyUbz+SR0ygQkZ+IyJsi8oiIlOkJESkVkd+KyEnB9/8nIm+JyJ9F5IF4XngeoxJTgRXGmE4AY8wKY8z/YK2XkYisB2wFXO787jJgaxFZ1xjztjHmneD3/wOWA3XJEiAi3wFKAy/nnwSH80XkjsB7+UkRKQ2uDT3uYsnHyH33CTyg/Y51ihHMQ/+NJUNEZNfAQ+51Eblbgsq/zm9PEJEbne8nicgNzj1/JCJvB/feTUReEJF3RGSb4Pry4L5/Ddo5IDh+nIg8LiJPA39M8jmKsLx8eMB/6iW/UcD/74vI153rW53P3w6e8bWAh9375gXPccUQutUjAcYKzwW/+0xwr38GOtr6kfMiIt8P5t2ngPphd9wowhgb47+IyGec7zqfThKRx4Jx/4uIbBr53ebAd4EDAv4oFZFWEblObDGc7UTkIrH6/Rsi8kMRkeC364nIU4FMelVE1o3ce8A6I8m+PVVErnW+u17RRzm8fLsERu2A5iuDtv4iIpPFrlf2B64Nru9Hn8fgiDWG4teKHumDXyt6pBOev9IBY4z/G8N/wBeAO5zvE4D3gc8G36uAAqAMKAmOrQ+8EnyeDbwRfD4ZuDD4XAy8AszJ9jP6v9z4C3jFANsH3+8GzgYWB+eewnqOAnwW+CdQAlQC7wBnZ/sZ/F/KeKEiGN+3gVuBnZxzzwJbYxd5j8b47aPA/pFj2wBvAnlDpKPV+Twb6AE2D77/DDgq+Pwj4BCgKI58PA74PnAQ8DwwMdt9PBb/EsiQEqyn+gbB8R8DZ0b4qQJ4DygMjr8IbOKM+ybYjfK/B/cVbKXhx4Lrr3L4oTrg3fJg7D8GJg3xWY4Dvu98vySgqRioBVY6tLYG/xcF15QF3yc5z7gAeAC4INvjNJb+xhjPfQ/4UvC5CCiN8NfBwB+AfGAasAo4JNtj4Md4SM/yDeDS4PNU4C1n7C8OPi8E/hl8Po5ADjFQJhngMOf7JOfzfcB+weeXgYOCzyUE8sm5NtY6I5m+rQPedX73W+Dz2Mruv3L6/FbW6o7Goeu7rF2T/Gg88HIa3xG/VvR/GfnDrxX9Xxr/PH+l7897Go99vA7sLiLXiM1hNhNYaoz5G4AxptkY0wMUAneIyOvAw8QO89oDOEZE/olVImuwSoOHh+IjY8wLwef7sQsAgF8C9xhjfhx83x74pTGmwxjTgl0geIwRGGNasV7EJwMNwEMictxw7iUiU7EL2OONMX0jJO0DY8w/g89/xyoQLuYRWz6CXYh/G9jHGNM0Qjo84iOWDJmHHbu3g+P3Aju6Pwp47mlgXxGZjzU4vB6c/sAY83rAP/8G/misxvg6a3lgD+DcYH57FqtEzgzO/cEY05iCZ/uNMabTGLMC6zk/OXJ+N6ycbA+eyW3zduyi/MoU0OHRH2OF514CzheRbwOzjDFrIud3BB4wxvQaG73x9BDvP5oxVsb4Z9gNToDDgEeCz5/HzpMYY54GakSkapB79QI/d77vIjbn8evY+e4zYiNt1jHGPBrcu0Plk4N+6wxjzGqS69sG4H0RWSAiNcB84AVsGo2tgL8F/bYrMDf4WRfw6+BzrDncY3jwa0WPTMKvFT3SCc9faYA3Go9xBArblliF4Aqsp0ksfANYBmyG9QAoinGNAF8zxmwe/M0xxjyZBrI9Ri9MnO8vAHtpuKPH2EdgmHjWGHMxcDrWk8XFf4DNRSSch4LPmwfnCBa9v8F6V/4l2oaIzAjCUf8pIqcmQVan87kX6zmTLN7D7kRvMITfeAwd8WRIMrgT6013PHCPc9wd9z7nex9reUCALzjz20xjzJvBubZYjYnIaQ7/TUuCvpHw34tYo07JEH7jkRzGBM8ZY36KjeBYAzwhIguH8BxjHWNljD8BVopNP3E48NAQniOKDmNMb9BmCdaj9xBjzCbAHVgD96CIrjNE5KIh0PAg1vj9BWzkkcH22b1On80zxlwSXN8dXANDl6EeceDXih4Zhl8reqQTnr/SAG80HuMIFM52Y8z9wLXAtsBUEflscL5SbBGOCdhd5T7gaGz4YhS/B74iIoXBbzcQkfJMPIfHqMFMsUXQAI4E/hx8vghoAm4Jvr8A7CciJSJSgS2a5jFGICLzpH8+zc2BJe41xph3gX8AFzqHLwReNca8KzYv7KPAj40xjxADxpiPnIXJD2Jc0q3yKkm8RWz5SED/F4Afi5NT0iPliCVD3gJmi82DDXaO+lP0h8aYl4EZwe8eGGK7vwe+psqkiGwx2A+MMbc4/Pe/yOkW7CbDUPAH4HhZm291knPuLuAJ4Gfii6OlGmOC50RkLraw6M1Yj5pNIz9/DptnOz+I4NhliPSOZoyJMQ7wEHAOMMEY86/g2PPAl4I2dsbWFGgeAp1qIF4R6GSHBLS0AB+LyIHBvYvd/JDBseg6Y0uS7FvsHH8A8EWsARlsjudDRKQ+uP8kEZk1CP3DkbceAfxa0SPD8GtFj3TC81ca4I3GYx+bAH8NwoQuxr4whwPfE1v84g9YZfFW4Njg2Hxie0DcifUAfFVE3sCGy/rFq4eLt4DTRORNYCJwm3PuDGxhsu8GIW+PA//C5rF7HVidaWI90oYK4F4R+Y+I/AsbwnhJjOu+DGwgIu+JyHtYL94vB+cOw4azHud4XW0+RDp+CPxL1hbCSwhjTBex5aOe/y92Yf6w+GI76cIAGWKM6cB6+T0chMX2AbE2CcCGb79ghp5C5HJs6O2/ROTf9C/QOBw8gy185xbCSwhjzO+wcvGVYM4+O3L+euxGy32uh77HiDFWeO4w4I2AdzbG5pF18Sg2Z99/gnMvjbC90YSxMsZgU1IcEdCkuATYKphvvwMcO5QbGmNWYb2L38Aa/f7mnD4a+Hpw7xeBKZGfR9cZVyTbt0F/volNp/LX4Nh/sBvITwZt/gGbvzkRHgS+Jbbwnp+bhw6/VvTIJPxa0SOd8PyVBsjaKB8PDw+PzEFEKowxrYHXynPAycaYV7NNl4eHR3YgIrOBXxtjNh7BPX4N3GCM+WPKCPMYs/A8N/bhx9jDw8NjdMKvFT3SCc9fycN7qnh4eGQLPwy8Gl4Ffu6FtIeHx3AhItUi8jawxht2PDIBz3NjH36MPTw8PLIKv1b0SCc8fyUJ72ns4eHh4eHh4eHh4eHh4eHh4eHh4eERwnsae3h4eHh4eHh4eHh4eHh4eHh4eHh4hPBGYw8PDw8PDw8PDw8PDw8PDw8PDw8PjxDeaOzh4eHh4eHh4eHh4eHh4eHh4eHh4RHCG409PDw8PDw8PDw8PDw8PDw8PDw8PDxCeKOxh4eHh4eHh4eHh4eHh4eHh4eHh4dHCG809vDw8PDw8PDw8PDw8PDw8PDw8PDwCPH/AVJXzNuPsZfgAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "global_objective.corner();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.38/doc/refnx.analysis.rst000066400000000000000000000001671450471266700172100ustar00rootroot00000000000000refnx.analysis ============== .. automodule:: refnx.analysis :members: :undoc-members: :show-inheritance: refnx-0.1.38/doc/refnx.dataset.rst000066400000000000000000000001641450471266700170070ustar00rootroot00000000000000refnx.dataset ============= .. automodule:: refnx.dataset :members: :undoc-members: :show-inheritance: refnx-0.1.38/doc/refnx.reduce.rst000066400000000000000000000001611450471266700166260ustar00rootroot00000000000000refnx.reduce ============ .. automodule:: refnx.reduce :members: :undoc-members: :show-inheritance: refnx-0.1.38/doc/refnx.reflect.rst000066400000000000000000000003631450471266700170070ustar00rootroot00000000000000refnx.reflect ============= .. automodule:: refnx.reflect :members: :undoc-members: :show-inheritance: :special-members: :exclude-members: __dict__,__weakref__, __repr__, __module__, __init__, __abstractmethods__, __copy__refnx-0.1.38/doc/refnx.rst000066400000000000000000000003551450471266700153650ustar00rootroot00000000000000refnx - Neutron and X-ray reflectometry analysis in Python ========================================================== Modules ------- .. toctree:: refnx.analysis refnx.reflect refnx.dataset refnx.reduce refnx.util refnx-0.1.38/doc/refnx.util.rst000066400000000000000000000001531450471266700163350ustar00rootroot00000000000000refnx.util ========== .. automodule:: refnx.util :members: :undoc-members: :show-inheritance: refnx-0.1.38/doc/requirements.txt000066400000000000000000000004271450471266700167750ustar00rootroot00000000000000nbsphinx jupyter-sphinx sphinxcontrib-bibtex sphinxcontrib-jquery jinja2 sphinx_rtd_theme tqdm corner periodictable pandoc scipy numpy sphinx<7 # sphinx-rtd-theme 1.2.2 requires sphinx<7,>=1. pandas numpydoc h5py nbconvert ipywidgets cython jupyter matplotlib pytest xlrd refnx-0.1.38/doc/testimonials.rst000066400000000000000000000010421450471266700167500ustar00rootroot00000000000000.. _testimonials: Testimonials ------------ Please cite the *refnx* paper if you use it for data analysis in your own publications. Its full reference is: "Nelson, A.R.J. & Prescott, S.W. (2019). J. Appl. Cryst. 52, https://doi.org/10.1107/S1600576718017296." Please `let us know `_ if your work should be included in this list or `fork the repository `_ and add it yourself. .. bibliography:: ../testimonials.bib :style: unsrt :all: :cited: :list: enumerated refnx-0.1.38/examples/000077500000000000000000000000001450471266700145575ustar00rootroot00000000000000refnx-0.1.38/examples/CurveFitter_EMCEE.ipynb000066400000000000000000000232271450471266700207700ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of MCMC non-linear regression with EMCEE and refnx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`refnx` is a package that can be used for non-linear regression (curvefitting). Here I demonstrate how it can be used to analyse Gaussian curve dataset, with Bayesian MCMC sampling of the posterior distributions of the parameters. This is a very robust way of estimating parameter uncertainties. I will also do the analysis with the `emcee` package for comparison\n", "\n", "The first step is all the imports." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import emcee\n", "import corner\n", "from scipy.optimize import leastsq\n", "from refnx.analysis import CurveFitter, Parameter, Parameters, Model, Objective, process_chain\n", "from refnx.dataset import Data1D\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First step is to load some data in." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = Data1D('gauss_data.txt')\n", "plt.errorbar(data.x, data.y, yerr=data.y_err, fmt='.k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the fit function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gauss(x, p, *args):\n", " # p is a Parameters instance. A quick way of getting all the numerical values out\n", " # is making it into array. However, there alternate ways of access:\n", " # e.g. p['bkg'].value or p[0].value.\n", " p0 = np.array(p)\n", " return p0[0] + p0[1] * np.exp(-((x - p0[2]) / p0[3])**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up initial parameter guesses and lower and upper bounds. The last step is to create a `refnx.Parameters` instance." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bkg = Parameter(0.1, 'bkg', vary=True, bounds=(-1, 1))\n", "amp = Parameter(20, 'amp', vary=True, bounds=(0, 30))\n", "mu = Parameter(0.1, 'mu', vary=True, bounds=(-5, 5))\n", "wid = Parameter(0.1, 'wid', vary=True, bounds=(0.001, 2))\n", "\n", "# to get numerical values out of p0 you have to use np.array(p0), or refer to each Parameter\n", "# by using p0['bkg'].value or p0[0].value.\n", "p0 = bkg | amp | mu | wid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse with emcee\n", "\n", "To start with we'll do the analysis with the `emcee` package. Then we'll repeat the analysis with `refnx.analysis.CurveFitter`. \n", "\n", "The following functions have to be defined for `emcee`. The log-likelihood, the uniform log-prior and the overall log-posterior probability." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bounds_varying = np.array([[-1, 0, -5, 0.001], [1, 30, 5, 2]]).T\n", "\n", "def residuals(theta):\n", " resid = (gauss(data.x, theta) - data.y) / data.y_err\n", " return resid\n", " \n", "def lnlike(theta):\n", " # log likelihood\n", " return -0.5 * (np.sum(residuals(theta) ** 2))\n", "\n", "def lnprior(theta):\n", " # uniform prior\n", " if (np.any(theta > bounds_varying[:, 1])\n", " or np.any(theta < bounds_varying[:, 0])):\n", " return -np.inf\n", " return 0\n", "\n", "def lnpost(theta):\n", " lp = lnprior(theta)\n", " if not np.isfinite(lp):\n", " return -np.inf\n", " return lp + lnlike(theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets fit the data with least squares first." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result = leastsq(residuals, p0, full_output=True)\n", "best_fit = result[0]\n", "best_errors = np.sqrt(np.diag(result[1]))\n", "for mean, std in zip(best_fit, best_errors):\n", " print(\"{:<12g} +/- {:<10g}\".format(mean, std))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up the walkers for `emcee`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ndim, nwalkers = 4, 100\n", "pos = np.array([np.array(p0) * (1 + 1e-2 * np.random.randn(ndim))\n", " for i in range(nwalkers)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the `emcee` sampler" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnpost)\n", "a = sampler.run_mcmc(pos, 1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Discard 100 burn in steps for each walker and flatten the chain." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "chain = sampler.chain[:, 100:, :].reshape(-1, 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse with CurveFitter\n", "\n", "Now we're going to do the analysis using a `refnx.analysis.CurveFitter` instance, it should be a lot simpler than the direct approach above. First setup the curvefitter." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# first setup a model\n", "model = Model(p0, fitfunc=gauss)\n", "\n", "# an objective is composed of a model and data\n", "objective = Objective(model, data)\n", "\n", "# a fitter is constructed\n", "fitter = CurveFitter(objective, nwalkers=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First of all do a least-squares fit, to get a starting point for the sampling." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_leastsq = fitter.fit('least_squares')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now do the MCMC sampling with CurveFitter instead. There are 100 walkers, we do 1000 steps on each walker. We parallelise using 4 threads. After the sampling discard the first 100 steps of each walker and take every 5th step" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fitter.sample(1000, pool=4)\n", "res_sampling = process_chain(objective, fitter.chain, nburn=100, nthin=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following plot shows the posterior distributions for each parameter" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = corner.corner(fitter.sampler.flatchain)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what about the fits, are they good?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(data.x, data.y, yerr=data.y_err, fmt=\".\")\n", "\n", "saved_state = np.array(p0)\n", "# plot a selection of the samples\n", "for pars in objective.pgen(500):\n", " # could also use:\n", " # >>> objective.setp(pars)\n", " # then to calculate the model:\n", " # >>> model(data.x)\n", " plt.plot(data.x, objective.generative(pars), color=\"k\", alpha=0.02)\n", "\n", "plt.plot(data.x, gauss(data.x, p0), color='r', label='sampling')\n", "objective.setp(saved_state)\n", "\n", "# the leastsq fit overlies the sampling\n", "# plt.plot(data.x, gauss(data.x, best_fit), color='g', label='leastsq')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following fit parameters are obtained. Lets compare them to the least squares output." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Curvefitter.sampling\")\n", "print(objective)\n", "\n", "print(\"\\nleastsq\")\n", "print(\"-------\")\n", "print(best_fit, '\\n', best_errors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.38/examples/analytical_profiles/000077500000000000000000000000001450471266700206035ustar00rootroot00000000000000refnx-0.1.38/examples/analytical_profiles/brushes/000077500000000000000000000000001450471266700222565ustar00rootroot00000000000000refnx-0.1.38/examples/analytical_profiles/brushes/brushes.txt000066400000000000000000000002221450471266700244660ustar00rootroot00000000000000For a detailed explanation of how brushes can be analysed with refnx please see https://github.com/refnx/refnx-models/tree/master/polymer_brushes refnx-0.1.38/examples/auto_reducer.py000077500000000000000000000017631450471266700176240ustar00rootroot00000000000000""" Auto reduce reflectometry files """ #!/usr/bin/env python import os import sys import time import argparse from refnx.reduce import AutoReducer def dir_path(path): if os.path.isdir(path): return path else: raise argparse.ArgumentTypeError(f"readable_dir:{path} is not a valid path") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Auto-reduce reflectometry files.") parser.add_argument("file_list", nargs='+') parser.add_argument("-p", "--path", type=dir_path, default="./", help="path to datafiles") parser.add_argument("-s", "--scale", help="scale factor", type=float, default=1.0) args = parser.parse_args() files = args.file_list pth = args.path files = [os.path.join(pth, file) for file in files] print(f"Path: {pth}") print(f"Reducing against: {files}, with scale: {args.scale}") ar = AutoReducer(files, data_folder=pth, scale=args.scale) while True: time.sleep(10.) refnx-0.1.38/examples/experiment.mtft000066400000000000000000006562021450471266700176460ustar00rootroot00000000000000(dp0 S'params_store_model.params_store' p1 ccopy_reg _reconstructor p2 (cdatastore ParametersStore p3 c__builtin__ object p4 Ntp5 Rp6 (dp7 S'displayOtherThanReflect' p8 I00 sS'parameters' p9 ccollections OrderedDict p10 ((lp11 (lp12 S'theoretical' p13 aclmfit.parameter Parameters p14 ((lp15 (lp16 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p738 tp739 bsg663 Nsbaatp740 Rp741 sbsS'plugins' p742 g10 ((lp743 (lp744 S'default' p745 a(crefnx.analysis.reflectivity ReflectivityFitFunction p746 S'' p747 tp748 aatp749 Rp750 sS'settings' p751 g2 (cView ProgramSettings p752 g4 Ntp753 Rp754 (dp755 S'usedq' p756 I01 sS'current_dataset_name' p757 Vc_PLP0000708 p758 sS'transformdata' p759 S'logY' p760 sS'quad_order' p761 I17 sS'current_model_name' p762 Vtheoretical p763 sS'useerrors' p764 I01 sS'fit_plugin' p765 g746 sS'resolution' p766 I5 sS'fitting_algorithm' p767 S'DE' p768 sbsS'history' p769 VSession started at: Sat Jan 10 19:38:55 2015\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aDE logY\u000a[[Variables]]\u000a SLD0: 3.47 (fixed)\u000a SLDback: 2.07 (fixed)\u000a SLDfront: 0 (fixed)\u000a bkg: 2.5460e-08 (init= 1e-07)\u000a iSLD0: 0 (fixed)\u000a iSLDback: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a nlayers: 1 (fixed)\u000a scale: 1.02776490 (init= 1)\u000a sigma0: 1.50865496 (init= 5)\u000a sigma_back: 8.96316867 (init= 5)\u000a thick0: 9.43233911 (init= 25)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a___________________________________________________\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aDE logY\u000a[[Variables]]\u000a SLD0: 3.47 (fixed)\u000a SLDback: 2.10012647 (init= 2.07)\u000a SLDfront: 0 (fixed)\u000a bkg: 2.6717e-10 (init= 2.546023e-08)\u000a iSLD0: 0 (fixed)\u000a iSLDback: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a nlayers: 1 (fixed)\u000a scale: 0.99914376 (init= 1.027765)\u000a sigma0: 0.03420463 (init= 1.508655)\u000a sigma_back: 9.40460955 (init= 8.963169)\u000a thick0: 9.01947491 (init= 9.432339)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a___________________________________________________\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aLM logY\u000a[[Variables]]\u000a SLD0: 3.47 (fixed)\u000a SLDback: 2.10017649 +/- 0.002931 (0.14%) (init= 2.100126)\u000a SLDfront: 0 (fixed)\u000a bkg: 4.8409e-08 +/- 9.12e-08 (188.44%) (init= 2.671706e-10)\u000a iSLD0: 0 (fixed)\u000a iSLDback: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a nlayers: 1 (fixed)\u000a scale: 0.99929591 +/- 0.004452 (0.45%) (init= 0.9991438)\u000a sigma0: 2.32914227 +/- 2.665627 (114.45%) (init= 0.03420463)\u000a sigma_back: 6.57880693 +/- 4.670020 (70.99%) (init= 9.40461)\u000a thick0: 10.6526384 +/- 2.336976 (21.94%) (init= 9.019475)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a C(sigma0, thick0) = 0.987 \u000a C(bkg, sigma0) = 0.903 \u000a C(bkg, thick0) = 0.872 \u000a___________________________________________________\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aLM logY\u000a[[Variables]]\u000a nlayers: 1 (fixed)\u000a scale: 0.99929595 +/- 0.001250 (0.13%) (init= 0.9992959)\u000a SLDfront: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a SLDback: 2.10017651 +/- 0.000823 (0.04%) (init= 2.100176)\u000a iSLDback: 0 (fixed)\u000a bkg: 4.8418e-08 +/- 2.56e-08 (52.92%) (init= 4.840928e-08)\u000a sigma_back: 6.57815664 +/- 1.312517 (19.95%) (init= 6.578807)\u000a thick0: 10.6529414 +/- 0.656648 (6.16%) (init= 10.65264)\u000a SLD0: 3.47 (fixed)\u000a iSLD0: 0 (fixed)\u000a sigma0: 2.32948952 +/- 0.749114 (32.16%) (init= 2.329142)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a C(thick0, sigma0) = 0.987 \u000a C(sigma_back, sigma0) = -0.971 \u000a C(sigma_back, thick0) = -0.967 \u000a C(bkg, sigma0) = 0.903 \u000a C(bkg, thick0) = 0.872 \u000a C(bkg, sigma_back) = -0.820 \u000a C(scale, SLDback) = -0.535 \u000a___________________________________________________\u000a p770 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com.apple.print.PrintSettings.PMLastPage 2147483647 com.apple.print.ticket.stateFlag 0 com.apple.print.PrintSettings.PMPageRange com.apple.print.ticket.creator com.apple.jobticket com.apple.print.ticket.itemArray com.apple.print.PrintSettings.PMPageRange 1 2147483647 com.apple.print.ticket.stateFlag 0 com.apple.print.ticket.APIVersion 00.20 com.apple.print.ticket.type com.apple.print.PrintSettingsTicket ^Graph*@@??WDashSettings#  !0\26Normal@ Geneva<HHHH$$0\26Normal@ Geneva<HHHH$$4444440 ,SNormal@ Geneva<HHHH$$4 4 4 4 4 4 homeawds1Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit: Macintosh HD1H+a/global_fitting_motofitsNq examples1M^a/(dBA ^Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit.global_fitting_motofit Macintosh HDJUsers/anz/Documents/Andy/programming/refnx/examples/global_fitting_motofit/ motoMPIawds1Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit: Macintosh HD1H+a/global_fitting_motofitsNq examples1M^a/(dBA ^Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit.global_fitting_motofit Macintosh HDJUsers/anz/Documents/Andy/programming/refnx/examples/global_fitting_motofit/ RecentWindowsGlobal Reflectometry AnalysisMOTOFIT_globalreflectometry.ipfReflectivityReflectivity PanelScattering length densityTable0: 4Misc_EndXOPState_StartData Browser PGizmo anz ectometry.ipfRePeakFunctions2ctivity PanelScAbelesg len` ble0:base64  HDF5(d SOCKIT b@p.XMLutils^b;'.rsrcZIPdy/programming/rGenCurvefit  ng_motInterpolate@ gb`.jobtieasyHttp` 4XOPState_EndV_FlagV_fiterrorV_gausspoints1@bloadMotofitPackage() plotCalcref() Motofit_GR#init_fitting() Motofit_GR#init_fitting() Motofit_GR#init_fitting() Motofit_GR#init_fitting() Motofit_GR#init_fitting() _________________________________________________________________ Global Fitting e361r_R vs e361r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 6.411011 +/- 0.00241586 w[5] = 0.000000 +/- 0 w[6] = 0.000013 +/- 4.91189e-07 w[7] = 4.000000 +/- 0 w[8] = 10.649409 +/- 0.293464 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 212.174845 +/- 0.19969 w[13] = 0.462346 +/- 0.0144887 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e365r_R vs e365r_q w[0] = 2.000000 +/- 0 w[1] = 0.899760 +/- 0.0318868 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 3.572482 +/- 0.0230132 w[5] = 0.000000 +/- 0 w[6] = 0.000016 +/- 3.82877e-07 w[7] = 4.000000 +/- 0 w[8] = 10.649409 +/- 0.293464 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 212.174845 +/- 0.19969 w[13] = 0.382092 +/- 0.0418078 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e366r_R vs e366r_q w[0] = 2.000000 +/- 0 w[1] = 0.894982 +/- 0.0712748 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = -0.551582 +/- 0.113992 w[5] = 0.000000 +/- 0 w[6] = 0.000016 +/- 4.32547e-07 w[7] = 4.000000 +/- 0 w[8] = 10.649409 +/- 0.293464 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 212.174845 +/- 0.19969 w[13] = 0.176707 +/- 0.0828097 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e361r_R vs e361r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 6.357716 +/- 0.00463148 w[5] = 0.000000 +/- 0 w[6] = 0.000013 +/- 4.77725e-07 w[7] = 4.000000 +/- 0 w[8] = 9.835674 +/- 0.266634 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 211.781262 +/- 0.190209 w[13] = 0.383379 +/- 0.015601 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e365r_R vs e365r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 3.481883 +/- 0.010562 w[5] = 0.000000 +/- 0 w[6] = 0.000017 +/- 4.05502e-07 w[7] = 4.000000 +/- 0 w[8] = 9.835674 +/- 0.266634 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 211.781262 +/- 0.190209 w[13] = 0.423765 +/- 0.0167166 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e366r_R vs e366r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = -0.560000 +/- 0 w[5] = 0.000000 +/- 0 w[6] = 0.000016 +/- 4.05176e-07 w[7] = 4.000000 +/- 0 w[8] = 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O![?QĖ]x:`(L@Z#Az_n'7ۉ W兠AFTͺ_1?#?/p?K?OD奇(HǶ&=xz(/cDE*Z\Ya ^x[\}?t0zW*2^E?$v!X?bQU=տwzL-9ӾckjNkb`7zU0~%'^?%*ڕ쿻ɨߚѿ _W?Gcᰙ@ҿonY@H/O?#?&2Yx?)?xLO?UE?hЩ?7C4? 5 @0N&@[?yȿ}?#i? old_genoptimise@ 3 !iterations@@popsize$@recomb?k_mffffff?fittol?GCF_continue?,hƈtƈt thosebeingvaried ????g!@@@ A0A`ApAAAA  ƈtƈt(limitsdialog_selwave ????h@@@@@@@@@@@@@@@@@@@@kƈtЈt(limitsdialog_listwave ????i 457101114151619206.361.3314e-0510.649212.170.462353.57251.5827e-050.382091.5511e-050.1767160000000006.362.6628e-0521.298424.340.92477.1453.1654e-050.764183.1022e-050.35342Parametercoeflower_limupper_lim %+28BISZ[\]^_`abcdhrx~awdtՈtlimitsForThoseBeingVaried ????j@@2_7NbA+C$l?ף@98MC?8w>oՈtՈt*GENcurvefitlimits????k@ p@@>IL5@pz@$?@z@ ?)t?@C?n? WM_WaveSelectorList@ 3 ! *5// Platform=Macintosh, IGORVersion=6.370, architecture=Intel, systemTextEncoding="macintosh", historyTextEncoding="macintosh", procwinTextEncoding="macintosh" Silent 101 // use | as bitwise or -- not comment. NewPath/Z motoMPI "Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit:" DefaultFont "Geneva" MoveWindow/P 5,44,505,339 MoveWindow/C 2,860,1846,1074 Table0() SLDgraph() reflectivitygraph() globalreflectometrypanel() reflectivitypanel() KillStrings/Z root:gWMSetNextTextFilesTextEncoding Window reflectivitypanel() : Panel PauseUpdate; Silent 1 // building window... NewPanel /K=1 /W=(873,44,1464,628) as "Reflectivity Panel" ModifyPanel cbRGB=(43520,43520,43520) ListBox baseparams_tab0,pos={22,266},size={205,72},proc=motofit#moto_GUI_listbox ListBox baseparams_tab0,fSize=12 ListBox baseparams_tab0,listWave=root:packages:motofit:reflectivity:baselayerparams ListBox baseparams_tab0,selWave=root:packages:motofit:reflectivity:baselayerparams_selwave ListBox baseparams_tab0,mode= 6,editStyle= 2,widths={80,80,20} ListBox layerparams_tab0,pos={22,341},size={544,197},proc=motofit#moto_GUI_listbox ListBox layerparams_tab0,fSize=12 ListBox layerparams_tab0,listWave=root:packages:motofit:reflectivity:layerparams ListBox layerparams_tab0,selWave=root:packages:motofit:reflectivity:layerparams_selwave ListBox layerparams_tab0,mode= 5,editStyle= 2 ListBox layerparams_tab0,widths={60,60,21,60,21,60,21,60,21} CheckBox usemultilayer_tab0,pos={407,308},size={116,16},proc=motofit#moto_GUI_check,title="make multilayer?" CheckBox usemultilayer_tab0,fSize=12,value= 0 PopupMenu coefwave_tab0,pos={236,270},size={176,20},bodyWidth=139,proc=motofit#moto_GUI_PopMenu,title="Model" PopupMenu coefwave_tab0,fSize=12 PopupMenu coefwave_tab0,mode=1,popvalue="coef_theoretical_R",value= #"motofit#moto_useable_coefs()" ValDisplay Chisquare_tab0,pos={252,304},size={132,20},title="\\F'Symbol'c\\M\\S2" ValDisplay Chisquare_tab0,fSize=14,fStyle=0,limits={0,0,0},barmisc={0,1000} ValDisplay Chisquare_tab0,value= _NUM:1 Button Savecoefwave_tab0,pos={421,265},size={68,31},proc=motofit#moto_GUI_button,title="Save" Button Savecoefwave_tab0,fSize=12 Button loadcoefwave_tab0,pos={495,265},size={62,30},proc=motofit#moto_GUI_button,title="Load" Button loadcoefwave_tab0,fSize=12 GroupBox group0_tab0,pos={14,28},size={555,74},title="Dataset" GroupBox group1_tab0,pos={14,107},size={554,48},title="Plotting" GroupBox group2_tab0,pos={16,160},size={554,80},title="Fitting" GroupBox group3_tab0,pos={16,245},size={554,297},title="Model" Slider slider0_tab0,pos={8,544},size={564,16},proc=motofit#moto_GUI_slider Slider slider0_tab0,help={"adjust a parameter by moving the slider"} Slider slider0_tab0,userdata(whichparam)= A"Ch[s4G@>Z+/TPcJF?4AL@r#LcATKnLDffo0BllCVAS,ai@ruF.BlnV]@UX=hCghU#Ec>H-@;U'IEc6.R0frl`Cb7@" Slider slider0_tab0,fSize=12,fColor=(43690,43690,43690) Slider slider0_tab0,valueColor=(43690,43690,43690) Slider slider0_tab0,limits={0.5,1.5,0.1},variable= root:data:theoretical:V_Flag,vert= 0,ticks= 0 Button loaddatas_tab0,pos={32,50},size={108,43},proc=motofit#moto_GUI_button,title="\\f04l\\f00oad data" Button loaddatas_tab0,fColor=(65280,32512,16384) PopupMenu dataset_tab0,pos={163,62},size={192,20},bodyWidth=145,proc=motofit#moto_GUI_PopMenu,title="dataset" PopupMenu dataset_tab0,fSize=12 PopupMenu dataset_tab0,mode=2,popvalue="_none_",value= #"motofit#Moto_fittable_datasets()" Button Savefitwave_tab0,pos={382,56},size={167,31},proc=motofit#moto_GUI_button,title="Save fits" Button Savefitwave_tab0,fSize=12 PopupMenu plotype_tab0,pos={24,129},size={133,20},bodyWidth=104,proc=motofit#moto_GUI_PopMenu,title="type" PopupMenu plotype_tab0,help={"you can change the plot type to whatever you want."} PopupMenu plotype_tab0,fSize=12 PopupMenu plotype_tab0,mode=1,popvalue="logR vs Q",value= #"\"logR vs Q;R vs Q;RQ^4 vs Q;RQ^2 vs Q\"" SetVariable res_tab0,pos={199,129},size={160,19},proc=motofit#moto_GUI_setvariable,title="resolution dq/q %" SetVariable res_tab0,help={"Enter the resolution, dq/q in terms of a percentage. Use dq/q=0 to start with"} SetVariable res_tab0,fSize=12,limits={0,20,0.5},value= _NUM:5,live= 1 Button Addcursor_tab0,pos={430,123},size={79,29},proc=motofit#moto_GUI_button,title="Add cursor" Button Addcursor_tab0,fSize=12 Button Dofit_tab0,pos={30,182},size={111,48},proc=motofit#moto_GUI_button,title="Do \\f04f\\f00it" Button Dofit_tab0,help={"Performs the fit"},fColor=(65280,32512,16384) PopupMenu Typeoffit_tab0,pos={147,195},size={150,20},bodyWidth=150 PopupMenu Typeoffit_tab0,mode=4,popvalue="Genetic+MC_Analysis",value= #"\"Genetic;Levenberg-Marquardt;Genetic + LM;Genetic+MC_Analysis\"" CheckBox usedQwave_tab0,pos={307,187},size={99,16},proc=motofit#moto_GUI_check,title="use dQ wave?" CheckBox usedQwave_tab0,fSize=12,value= 1 CheckBox useerrors_tab0,pos={307,205},size={111,16},proc=motofit#moto_GUI_check,title="use error wave?" CheckBox useerrors_tab0,fSize=12,value= 1 CheckBox fitcursors_tab0,pos={423,205},size={140,16},proc=motofit#moto_GUI_check,title="Fit between cursors?" CheckBox fitcursors_tab0,help={"To get the cursors on the graph press Ctrl-I. This enables you to fit over a selected x-range"} CheckBox fitcursors_tab0,fSize=12,value= 0 CheckBox useconstraint_tab0,pos={423,188},size={137,16},proc=motofit#moto_GUI_check,title="Fit with constraints?" CheckBox useconstraint_tab0,fSize=12,value= 0 SetVariable FT_lowQ_tab2,pos={73,53},size={142,19},bodyWidth=60,disable=1,proc=motofit#moto_GUI_setvariable,title="low Q for FFT" SetVariable FT_lowQ_tab2,fSize=12,limits={0.005,1,0.01},value= _NUM:0.005 SetVariable FT_hiQ_tab2,pos={68,77},size={147,19},bodyWidth=60,disable=1,proc=motofit#moto_GUI_setvariable,title="high Q for FFT" SetVariable FT_hiQ_tab2,fSize=12,limits={0.005,1,0.01},value= _NUM:0.5 SetVariable fringe_tab2,pos={255,77},size={213,19},disable=1,title="layer thickness spacing" SetVariable fringe_tab2,fSize=12,limits={0,0,0},value= _NUM:0 SetVariable numfringe_tab2,pos={281,53},size={193,19},disable=1,proc=motofit#moto_GUI_setvariable,title="number of fringes" SetVariable numfringe_tab2,fSize=12,limits={0,100,1},value= _NUM:0 Button Addconstraint_tab1,pos={36,52},size={119,29},disable=1,proc=motofit#moto_GUI_button,title="Add constraint" Button Addconstraint_tab1,fSize=10 Button removeconstraint_tab1,pos={36,95},size={119,30},disable=1,proc=motofit#moto_GUI_button,title="Remove constraint" Button removeconstraint_tab1,fSize=10 Button allon_tab3,pos={300,68},size={100,20},disable=1,proc=motofit#moto_GUI_button,title="toggle on" Button alloff_tab3,pos={413,68},size={100,20},disable=1,proc=motofit#moto_GUI_button,title="toggle off" ListBox plot_tab3,pos={26,96},size={535,345},disable=1,proc=motofit#moto_GUI_listbox ListBox plot_tab3,listWave=root:packages:motofit:reflectivity:plot_listwave ListBox plot_tab3,selWave=root:packages:motofit:reflectivity:plot_selwave ListBox plot_tab3,mode= 5,userColumnResize= 1 TabControl refpanel,pos={3,1},size={575,571},proc=motofit#moto_GUI_tab TabControl refpanel,tabLabel(0)="Fit",tabLabel(1)="Constraints" TabControl refpanel,tabLabel(2)="thickness estimation" TabControl refpanel,tabLabel(3)="plot control",value= 0 SetWindow kwTopWin,hook(moto_GUI)=moto_GUI_hook String fldrSav0= GetDataFolder(1) SetDataFolder root:packages:motofit:reflectivity:ft: Display/W=(0.1,0.3,0.9,0.9)/HOST=# /HIDE=1 fftoutput SetDataFolder fldrSav0 RenameWindow #,FFToutput SetActiveSubwindow ## EndMacro Window globalreflectometrypanel() : Panel PauseUpdate; Silent 1 // building window... NewPanel /K=1 /W=(406,356,962,1012) as "Global Reflectometry Analysis" TabControl globalpaneltab,pos={5,7},size={544,573},proc=Motofit_GR#globalpanel_GUI_tab TabControl globalpaneltab,tabLabel(0)="Datasets",tabLabel(1)="Coefficients" TabControl globalpaneltab,value= 1 Button adddataset_tab0,pos={20,35},size={72,31},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Add\rdataset" Button adddataset_tab0,fSize=11 Button removedataset_tab0,pos={97,35},size={72,31},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Remove\rdataset" Button removedataset_tab0,fSize=11 Button changelayers_tab0,pos={174,35},size={72,31},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="change\rlayers" Button changelayers_tab0,fSize=11 Button linkparameter_tab0,pos={326,37},size={100,30},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="link selection" Button linkparameter_tab0,fSize=11 Button unlinkparameter_tab0,pos={434,37},size={100,30},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="unlink selection" Button unlinkparameter_tab0,fSize=11 ListBox datasetparams_tab0,pos={17,72},size={526,499},disable=1,proc=Motofit_GR#globalpanel_GUI_listbox ListBox datasetparams_tab0,fSize=12,frame=3 ListBox datasetparams_tab0,listWave=root:packages:motofit:reflectivity:globalfitting:datasets_listwave ListBox datasetparams_tab0,selWave=root:packages:motofit:reflectivity:globalfitting:datasets_selwave ListBox datasetparams_tab0,colorWave=root:packages:motofit:reflectivity:globalfitting:M_colors ListBox datasetparams_tab0,mode= 10,widths={164,156,103,217},userColumnResize= 1 ListBox coefficients_tab1,pos={17,72},size={526,499},proc=Motofit_GR#globalpanel_GUI_listbox ListBox coefficients_tab1,fSize=11 ListBox coefficients_tab1,listWave=root:packages:motofit:reflectivity:globalfitting:coefficients_listwave ListBox coefficients_tab1,selWave=root:packages:motofit:reflectivity:globalfitting:coefficients_selwave ListBox coefficients_tab1,mode= 6,widths={60},userColumnResize= 1 ListBox coefficients_tab1,clickEventModifiers= 4 Button do_global_fit,pos={184,600},size={80,40},proc=Motofit_GR#globalpanel_GUI_button,title="Fit" Button do_global_fit,fSize=12 Button simulate,pos={276,600},size={80,40},proc=Motofit_GR#globalpanel_GUI_button,title="Simulate" Button simulate,fSize=12 Button savesetup_tab0,pos={451,600},size={70,20},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Save setup" Button savesetup_tab0,fSize=10 Button loadsetup_tab0,pos={451,622},size={70,20},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Load setup" Button loadsetup_tab0,fSize=10 Slider slider0_tab1,pos={22,589},size={517,16},proc=Motofit_GR#globalpanel_GUI_slider Slider slider0_tab1,userdata(whichparam)= "row-4;col-5" Slider slider0_tab1,limits={-1,1,0.2},value= 0,vert= 0,ticks= 0 ValDisplay Chi2_tab1,pos={223,42},size={100,17},title="\\F'Symbol'c\\M\\S2" ValDisplay Chi2_tab1,fSize=12,limits={0,0,0},barmisc={0,1000} ValDisplay Chi2_tab1,value= _NUM:3.78398563933925 EndMacro Window reflectivitygraph() : Graph PauseUpdate; Silent 1 // building window... String fldrSav0= GetDataFolder(1) SetDataFolder root:data:e361r: Display /W=(10,44,560,342)/K=1 ::theoretical:theoretical_R vs ::theoretical:theoretical_q as "Reflectivity" AppendToGraph e361r_R vs e361r_q AppendToGraph ::e365r:e365r_R vs ::e365r:e365r_q AppendToGraph ::e366r:e366r_R vs ::e366r:e366r_q AppendToGraph fit_e361r_R vs fit_e361r_q AppendToGraph ::e365r:fit_e365r_R vs ::e365r:fit_e365r_q AppendToGraph ::e366r:fit_e366r_R vs ::e366r:fit_e366r_q SetDataFolder fldrSav0 ModifyGraph mode(e361r_R)=3,mode(e365r_R)=3,mode(e366r_R)=3 ModifyGraph marker(theoretical_R)=8,marker(e361r_R)=8,marker(e365r_R)=8,marker(e366r_R)=8 ModifyGraph lSize(theoretical_R)=2 ModifyGraph rgb(theoretical_R)=(0,0,0),rgb(e361r_R)=(18724,65535,0),rgb(e365r_R)=(0,43690,65535) ModifyGraph rgb(e366r_R)=(65535,34327,0),rgb(fit_e361r_R)=(18724,65535,0),rgb(fit_e365r_R)=(0,43690,65535) ModifyGraph rgb(fit_e366r_R)=(65535,34327,0) ModifyGraph fSize=12 ModifyGraph standoff(left)=0 Label left "R" Label bottom "Q (\\S-1\\M)" ErrorBars/T=0 e361r_R Y,wave=(:data:e361r:e361r_E,:data:e361r:e361r_E) ErrorBars/T=0 e365r_R Y,wave=(:data:e365r:e365r_E,:data:e365r:e365r_E) ErrorBars/T=0 e366r_R Y,wave=(:data:e366r:e366r_E,:data:e366r:e366r_E) ControlBar 50 PopupMenu plotype_tab0,pos={167,7},size={143,20},bodyWidth=100,proc=motofit#moto_GUI_PopMenu,title="Plot type" PopupMenu plotype_tab0,mode=1,popvalue="logR vs Q",value= #"\"logR vs Q;R vs Q;RQ^4 vs Q;RQ^2 vs Q\"" Button Autoscale,pos={9,5},size={73,24},proc=motofit#moto_GUI_button,title="Autoscale" Button Autoscale,fSize=10 Button ChangeQrange,pos={87,5},size={73,24},proc=motofit#moto_GUI_button,title="Q range" Button ChangeQrange,fSize=10 Button Snapshot,pos={320,5},size={73,24},proc=motofit#moto_GUI_button,title="snapshot" Button Snapshot,fSize=10 Button restore,pos={395,5},size={73,24},proc=motofit#moto_GUI_button,title="restore" Button restore,fSize=10 Button refreshdata,pos={472,5},size={73,24},proc=motofit#moto_GUI_button,title="refresh" Button refreshdata,fSize=10 CheckBox appendresiduals,pos={10,32},size={100,15},proc=motofit#moto_GUI_check,title="Append residuals" CheckBox appendresiduals,fSize=10,value= 0 EndMacro Window SLDgraph() : Graph PauseUpdate; Silent 1 // building window... String fldrSav0= GetDataFolder(1) SetDataFolder root:data:theoretical: Display /W=(10,365,560,591)/K=1 sld_theoretical_R,::e361r:SLD_e361r_R,::e365r:SLD_e365r_R as "Scattering length density" AppendToGraph ::e366r:SLD_e366r_R SetDataFolder fldrSav0 ModifyGraph lSize=2 ModifyGraph rgb(sld_theoretical_R)=(0,0,0),rgb(SLD_e361r_R)=(18724,65535,0),rgb(SLD_e365r_R)=(0,43690,65535) ModifyGraph rgb(SLD_e366r_R)=(65535,34327,0) ModifyGraph fSize=12 Label left "SLD (10\\S-6\\M \\S-2\\M)" Label bottom "distance from interface ()" EndMacro Window Table0() : Table PauseUpdate; Silent 1 // building window... Edit/W=(5,44,510,251) ModifyTable format=1 EndMacro o#pragma rtGlobals=3 // Use modern global access method and strict wave access. #include "MOTOFIT_all_at_once" refnx-0.1.38/examples/global_fitting_motofit/README000066400000000000000000000002031450471266700221570ustar00rootroot00000000000000This example script takes a set of output files from the corefinement setup of Motofit (MotoMPI) and runs it with the refnx packagerefnx-0.1.38/examples/global_fitting_motofit/e361r.txt000066400000000000000000000063741450471266700227170ustar00rootroot000000000000000.010109 1.0034 0.0094945 0.00050463 0.01065 0.97225 0.0086708 0.00053146 0.011192 0.98372 0.0083194 0.00055644 0.011733 1.0342 0.0082585 0.00058513 0.012275 1.0154 0.0076366 0.00061382 0.012997 0.99216 0.0070227 0.00064589 0.013539 1.0046 0.0067381 0.00067643 0.014261 0.99416 0.006279 0.00071221 0.014983 0.67153 0.0048674 0.00074614 0.015705 0.35359 0.0033644 0.00078377 0.016607 0.22776 0.0025849 0.00082294 0.01751 0.15822 0.0019988 0.00087322 0.018593 0.10422 0.0015267 0.00092319 0.019495 0.076868 0.0012544 0.00097347 0.020579 0.055009 0.0010107 0.0010234 0.021481 0.040873 0.00082455 0.0010737 0.022564 0.029023 0.00066089 0.0011237 0.024008 0.018142 0.0004868 0.0011989 0.025633 0.011538 0.00036445 0.0012739 0.027077 0.0076838 0.00028001 0.0013492 0.028521 0.0057328 0.00022902 0.0014226 0.029965 0.0041516 0.00013134 0.0014978 0.03159 0.0034595 9.9397e-05 0.0015746 0.033034 0.0031528 9.0775e-05 0.001648 0.034478 0.0029971 8.4669e-05 0.0017233 0.036103 0.00316 8.3229e-05 0.0017983 0.037547 0.0031109 7.9381e-05 0.0018735 0.038991 0.0030434 7.5547e-05 0.0019488 0.040615 0.003049 7.2983e-05 0.0020237 0.042059 0.0029684 6.9403e-05 0.002099 0.043503 0.0027884 6.4658e-05 0.0021724 0.045128 0.0024531 5.8821e-05 0.0022492 0.046572 0.0021525 5.3315e-05 0.0023245 0.048016 0.0017791 4.6874e-05 0.0023997 0.04946 0.0014628 4.1274e-05 0.0024713 0.051085 0.0011376 3.5329e-05 0.0025481 0.052529 0.00088726 3.0275e-05 0.0026233 0.053973 0.00069964 2.6122e-05 0.0026986 0.055597 0.00047966 2.0985e-05 0.0027754 0.057041 0.00033789 1.7035e-05 0.002847 0.058485 0.00022997 1.3635e-05 0.0029222 0.06011 0.00017335 1.1489e-05 0.002999 0.061554 0.0001763 1.1306e-05 0.0030743 0.062998 0.00017343 1.096e-05 0.0031495 0.064622 0.00022163 1.2167e-05 0.0032245 0.066066 0.00022976 1.2117e-05 0.0032979 0.06751 0.00024547 1.2264e-05 0.0033731 0.069135 0.00028256 1.2876e-05 0.0034499 0.070579 0.00032159 1.3489e-05 0.0035233 0.072023 0.00031782 1.3131e-05 0.0035986 0.073467 0.00033734 1.3304e-05 0.003672 0.075091 0.00033519 1.3005e-05 0.0037488 0.076535 0.00031096 1.2254e-05 0.003824 0.077979 0.00027131 1.1229e-05 0.0038975 0.079604 0.0002403 1.0362e-05 0.0039742 0.081047 0.00018693 8.9476e-06 0.0040495 0.082491 0.00016258 8.1858e-06 0.0041229 0.084477 0.00010162 6.2806e-06 0.0042231 0.086462 6.5975e-05 4.9174e-06 0.0043234 0.088628 4.7223e-05 4.0454e-06 0.0044251 0.090433 3.9405e-05 3.6043e-06 0.004522 0.092599 3.7471e-05 3.4425e-06 0.0046238 0.094584 4.4874e-05 3.7049e-06 0.004724 0.096569 6.2564e-05 4.3059e-06 0.0048242 0.098555 8.3657e-05 4.8985e-06 0.0049226 0.10054 8.8552e-05 4.9385e-06 0.0050228 0.10253 9.7242e-05 5.0707e-06 0.0051231 0.10559 9.9722e-05 4.9951e-06 0.0052751 0.10848 8.3608e-05 4.4476e-06 0.0054219 0.11155 6.1624e-05 3.7079e-06 0.0055739 0.11462 4.2962e-05 3.0089e-06 0.0057259 0.1175 3.238e-05 2.5415e-06 0.0058727 0.12057 2.1377e-05 1.9997e-06 0.0060248 0.12346 2.4942e-05 2.1205e-06 0.0061715 0.12653 2.9365e-05 2.2547e-06 0.0063236 0.1296 3.6973e-05 2.4845e-06 0.0064737 0.13248 4.5666e-05 2.7125e-06 0.0066224 0.13555 3.933e-05 2.4552e-06 0.0067744 0.13862 3.6904e-05 2.3321e-06 0.0069245 0.14151 2.7705e-05 1.9706e-06 0.0070732 0.14457 2.3851e-05 1.7881e-06 0.0072233 0.14746 1.693e-05 1.4673e-06 0.0073738 0.15053 1.4813e-05 1.3432e-06 0.0075221 0.15359 2.0346e-05 1.5527e-06 0.0076741refnx-0.1.38/examples/global_fitting_motofit/e361r_pilot.txt000066400000000000000000000003551450471266700241170ustar00rootroot00000000000000stuff value hold lowlim hilim smearedabeles log10chisquared 2 1 0 0 1 1 0 0 2.07 1 0 0 0 1 0 0 6.3024 0 6.0 6.36 0 0 0 0 1.3798e-05 0 0 6e-05 4 1 0 0 10.208 0 0 30 3.47 1 0 0 0 0 0 0 3 1 0 0 212.71 0 0 420 0.44599 0 0 1.3 0 0 0 0 3 1 0 0refnx-0.1.38/examples/global_fitting_motofit/e365r.txt000066400000000000000000000051601450471266700227130ustar00rootroot000000000000000.0099283 0.30595 0.0040195 0.00049641 0.010289 0.26833 0.0029101 0.00051446 0.011011 0.19821 0.0022815 0.00055057 0.011372 0.17949 0.0020772 0.00056862 0.012094 0.13725 0.0016884 0.00060472 0.012997 0.1036 0.0013326 0.00064985 0.01408 0.077181 0.0010638 0.000704 0.014983 0.057562 0.0008426 0.00074913 0.016066 0.043969 0.0006914 0.00080329 0.016968 0.032673 0.00055253 0.00084841 0.019134 0.017386 0.0003556 0.00095672 0.02112 0.0085938 0.00022338 0.001056 0.023106 0.0042309 0.00014211 0.0011553 0.025091 0.00195 8.7889e-05 0.0012546 0.027077 0.00081153 5.1717e-05 0.0013538 0.029063 0.00036616 3.1872e-05 0.0014531 0.031048 0.00034338 2.8853e-05 0.0015524 0.033034 0.00051014 3.329e-05 0.0016517 0.035019 0.0007302 3.7709e-05 0.001751 0.037005 0.0009503 4.0907e-05 0.0018503 0.038991 0.0011156 4.2096e-05 0.0019495 0.040976 0.001064 3.9074e-05 0.0020488 0.042962 0.0010034 3.5948e-05 0.0021481 0.045128 0.00087652 3.2201e-05 0.0022564 0.047114 0.00073642 2.8155e-05 0.0023557 0.049099 0.00054692 2.3297e-05 0.002455 0.051085 0.00037025 1.8243e-05 0.0025542 0.05307 0.00024836 1.4376e-05 0.0026535 0.055056 0.00013019 9.9128e-06 0.0027528 0.057041 7.3e-05 7.0576e-06 0.0028521 0.059027 3.01e-05 4.272e-06 0.0029513 0.061012 3.66e-05 4.5898e-06 0.0030506 0.062998 3.5e-05 4.3483e-06 0.0031499 0.064983 7.8e-05 6.4093e-06 0.0032492 0.066969 9.91e-05 7.053e-06 0.0033484 0.069135 0.00012223 7.627e-06 0.0034567 0.07112 0.00013512 7.8193e-06 0.003556 0.073106 0.00014595 7.9306e-06 0.0036553 0.075091 0.00013131 7.3031e-06 0.0037546 0.077077 0.0001215 6.8341e-06 0.0038538 0.079062 0.0001047 6.1649e-06 0.0039531 0.081047 7.78e-05 5.1647e-06 0.0040524 0.083033 5.57e-05 4.2534e-06 0.0041516 0.085018 4.19e-05 3.5824e-06 0.0042509 0.087004 2.57e-05 2.7118e-06 0.0043502 0.088989 1.82e-05 2.2191e-06 0.0044495 0.090974 1.66e-05 2.0659e-06 0.0045487 0.09296 2.23e-05 2.3695e-06 0.004648 0.095126 3e-05 2.7044e-06 0.0047563 0.097111 2.91e-05 2.6094e-06 0.0048555 0.099096 4.42e-05 3.1919e-06 0.0049548 0.10108 5.35e-05 3.4466e-06 0.0050541 0.10397 4.85e-05 3.1832e-06 0.0051985 0.10704 4.69e-05 3.0394e-06 0.0053519 0.11011 3.8e-05 2.655e-06 0.0055053 0.11299 2.87e-05 2.2402e-06 0.0056496 0.11606 2.35e-05 1.9747e-06 0.005803 0.11913 1.88e-05 1.719e-06 0.0059564 0.12202 1.94e-05 1.703e-06 0.0061008 0.12508 2.69e-05 1.9756e-06 0.0062542 0.12797 2.29e-05 1.7791e-06 0.0063986 0.13104 2.56e-05 1.8421e-06 0.006552 0.13411 2.9e-05 1.9153e-06 0.0067053 0.13699 2.99e-05 1.9078e-06 0.0068497 0.14006 2.51e-05 1.7197e-06 0.0070031 0.14313 2.25e-05 1.583e-06 0.0071565 0.14602 1.96e-05 1.4547e-06 0.0073008 0.14908 1.89e-05 1.3971e-06 0.0074542 0.15197 2.38e-05 1.5485e-06 0.0075985refnx-0.1.38/examples/global_fitting_motofit/e365r_pilot.txt000066400000000000000000000003521450471266700241200ustar00rootroot00000000000000stuff value hold lowlim hilim smearedabeles log10chisquared 2 0 0 0 1 1 0 0 2.07 0 0 0 0 0 0 0 3.374 0 0 6.94 0 0 0 0 1.6413e-05 0 0 6e-05 4 0 0 0 10.208 0 0 30 3.47 0 0 0 0 0 0 0 3 0 0 0 212.71 0 0 420 0.44076 0 0 1.3 0 0 0 0 3 0 0 0refnx-0.1.38/examples/global_fitting_motofit/e366r.txt000066400000000000000000000051771450471266700227240ustar00rootroot000000000000000.0099283 0.015845 0.00093441 0.00049641 0.010289 0.012501 0.00064038 0.00051446 0.011011 0.010141 0.00052443 0.00055057 0.011372 0.0092412 0.00047856 0.00056862 0.012094 0.0082654 0.0004178 0.00060472 0.012997 0.0056552 0.00031192 0.00064985 0.01408 0.0045741 0.00025885 0.000704 0.014983 0.0040526 0.00022318 0.00074913 0.016066 0.0036273 0.00019553 0.00080329 0.016968 0.00373 0.0001866 0.00084841 0.019134 0.002566 0.00013591 0.00095672 0.02112 0.0028301 0.00012829 0.001056 0.023106 0.0023669 0.00010651 0.0011553 0.025091 0.0022101 9.3974e-05 0.0012546 0.027077 0.0015573 7.2677e-05 0.0013538 0.029063 0.0013097 6.2009e-05 0.0014531 0.031048 0.00089022 4.732e-05 0.0015524 0.033034 0.00059942 3.6374e-05 0.0016517 0.035019 0.00050994 3.16e-05 0.001751 0.037005 0.00030359 2.282e-05 0.0018503 0.038991 0.0002169 1.8213e-05 0.0019495 0.040976 0.00014288 1.3992e-05 0.0020488 0.042962 0.00010078 1.1069e-05 0.0021481 0.045128 8.83e-05 9.9212e-06 0.0022564 0.047114 9.5e-05 9.8691e-06 0.0023557 0.049099 0.00010321 9.9087e-06 0.002455 0.051085 0.00010571 9.6462e-06 0.0025542 0.05307 0.00011195 9.5508e-06 0.0026535 0.055056 0.00011972 9.5212e-06 0.0027528 0.057041 0.00012241 9.2885e-06 0.0028521 0.059027 9.75e-05 7.9932e-06 0.0029513 0.061012 0.00010275 7.9355e-06 0.0030506 0.062998 8.01e-05 6.7568e-06 0.0031499 0.064983 6.44e-05 5.8373e-06 0.0032492 0.066969 4.36e-05 4.6167e-06 0.0033484 0.069135 4.02e-05 4.3104e-06 0.0034567 0.07112 3.51e-05 3.9135e-06 0.003556 0.073106 2.3e-05 3.0487e-06 0.0036553 0.075091 2.61e-05 3.1695e-06 0.0037546 0.077077 3.31e-05 3.5074e-06 0.0038538 0.079062 3.27e-05 3.4063e-06 0.0039531 0.081047 3.23e-05 3.314e-06 0.0040524 0.083033 2.9e-05 3.0535e-06 0.0041516 0.085018 3.64e-05 3.36e-06 0.0042509 0.087004 3.59e-05 3.2668e-06 0.0043502 0.088989 2.7e-05 2.7546e-06 0.0044495 0.090974 3.22e-05 2.9506e-06 0.0045487 0.09296 3.49e-05 3.0159e-06 0.004648 0.095126 2.65e-05 2.5621e-06 0.0047563 0.097111 2.78e-05 2.5768e-06 0.0048555 0.099096 2.27e-05 2.2814e-06 0.0049548 0.10108 2.18e-05 2.1775e-06 0.0050541 0.10397 2.09e-05 2.0736e-06 0.0051985 0.10704 2.23e-05 2.089e-06 0.0053519 0.11011 2.13e-05 1.9846e-06 0.0055053 0.11299 2.82e-05 2.2387e-06 0.0056496 0.11606 2.07e-05 1.8635e-06 0.005803 0.11913 2.53e-05 2.0174e-06 0.0059564 0.12202 2.45e-05 1.941e-06 0.0061008 0.12508 2.39e-05 1.8745e-06 0.0062542 0.12797 2.19e-05 1.7576e-06 0.0063986 0.13104 2.08e-05 1.671e-06 0.006552 0.13411 2.14e-05 1.6636e-06 0.0067053 0.13699 2.1e-05 1.6069e-06 0.0068497 0.14006 2.41e-05 1.6894e-06 0.0070031 0.14313 2.26e-05 1.6019e-06 0.0071565 0.14602 2.17e-05 1.5344e-06 0.0073008 0.14908 2.2e-05 1.5165e-06 0.0074542 0.15197 2.13e-05 1.4682e-06 0.0075985refnx-0.1.38/examples/global_fitting_motofit/e366r_pilot.txt000066400000000000000000000003511450471266700241200ustar00rootroot00000000000000stuff value hold lowlim hilim smearedabeles log10chisquared 2 0 0 0 1 1 0 0 2.07 0 0 0 0 0 0 0 -0.56 1 -1.0 0 0 0 0 0 1.5481e-05 0 0 6e-05 4 0 0 0 10.208 0 0 30 3.47 0 0 0 0 0 0 0 3 0 0 0 212.71 0 0 420 0.3146 0 0 1.3 0 0 0 0 3 0 0 0refnx-0.1.38/examples/global_fitting_motofit/global_fitting_from_motofit.py000066400000000000000000000313371450471266700274350ustar00rootroot00000000000000#!/usr/bin/env python """ global_fitting_from_motofit.py [OPTIONS] [-- ARGS] Sets and executes up a global fitting environment supplied by Motofit in IGOR. Examples:: $ python global_fitting_from_motofit.py global_pilot_file """ import sys import numbers import time from argparse import ArgumentParser from copy import deepcopy from multiprocessing import Pool import numpy as np from refnx.analysis import (CurveFitter, ReflectivityFitFunction, GlobalFitter, to_parameters, Transform, values, names) from refnx.dataset import ReflectDataset def global_fitter_setup(global_pilot_file, dqvals=5.0): # Parse the global_fitter setup from Igor. # TODO deal with user generated non-slab models. with open(global_pilot_file, 'r') as f: data_files = f.readline().split() pilot_files = f.readline().split() constraints = np.loadtxt(global_pilot_file, skiprows=2, dtype=int) # open the datafiles datasets = [] for data_file in data_files: dataset = ReflectDataset(data_file) datasets.append(dataset) # deal with the individual pilot files parameters = [] for pilot_file in pilot_files: pars = np.loadtxt(pilot_file, skiprows=4) # lets just assume for now that the data has resolution info # and that we're doing a slab model. pv = pars[:, 0][:] varies = (pars[:, 1].astype(int) == 0) # workout bounds, and account for the fact that MotofitMPI # doesn't set bounds for parameters that are fixed bounds = [] for idx in range(np.size(pv)): if not varies[idx]: bounds.append((0, 2 * pv[idx])) else: bounds.append(pars[idx, 2:4]) P = to_parameters(pv, varies=varies, bounds=bounds) parameters.append(P) # now create CurveFitting instances T = Transform('logY') fitters = [] for parameter, dataset in zip(parameters, datasets): t_data_y, t_data_yerr = T.transform(dataset.x, dataset.y, dataset.y_err) if isinstance(dqvals, numbers.Real): _dqvals = float(dqvals) else: _dqvals = dataset.x_err c = CurveFitter(ReflectivityFitFunction(T.transform, workers=True), (dataset.x, t_data_y, t_data_yerr), parameter, fcn_kws={'dqvals': _dqvals}) fitters.append(c) # create globalfitter # setup constraints unique, indices = np.unique(constraints, return_index=True) # TODO assertions for checking linkage integrity n_datasets = len(datasets) def is_unique(row, col): ravelled_idx = row * n_datasets + col return ravelled_idx in indices cons = [] for col in range(n_datasets): for row, val in enumerate(parameters[col]): if constraints[row, col] == -1 or is_unique(row, col): continue # so it's not unique, but which parameter does it depend on? # find location of master parameter master = np.extract(unique == constraints[row, col], indices)[0] m_col = master % n_datasets m_row = (master - m_col) // n_datasets constraint = 'd%u:p%u = d%u:p%u' % (col, row, m_col, m_row) cons.append(constraint) # also have to rejig the bounds because MotoMPI doesn't # set bounds for those that aren't unique. But this is bad for # lmfit because it'll clip them. par = fitters[col].params['p%u' % row] m_par = fitters[m_col].params['p%u' % m_row] par.min = m_par.min par.max = m_par.max global_fitter = GlobalFitter(fitters, constraints=cons) # # update the constraints # global_fitter.params.update_constraints() return global_fitter def _mcmc(args, global_fitter): # sample via Markov Chain Monte Carlo pos = None if args.chain_input is not None: pos = np.load(args.chain_input) # do the sampling chunk_size = 50 n_remaining = args.steps done = 0 sys.stdout.write("----------------------\n") sys.stdout.write("Starting MCMC\n") sys.stdout.write("----------------------\n") start = time.time() reuse_sampler = False while n_remaining > 0: todo = min(chunk_size, n_remaining) res = global_fitter.emcee(nwalkers=args.walkers, steps=todo, ntemps=args.ntemps, burn=0, thin=1, workers=args.nprocesses, pos=pos, reuse_sampler=reuse_sampler) reuse_sampler = True n_remaining -= todo done += todo pos = res.chain # write raw chain in npy format. It is unburnt and unthinned np.save(args.chain_output, res.chain) sys.stdout.write( "{0:^7} steps, {1:^7} seconds\n".format(done, time.time() - start) ) # thin and burn the chain. chain = res.chain[..., args.burn::args.thin, :] res.chain = np.copy(chain) return res def __resample_mc_iterator(args): global_fitter, seed = args gf = deepcopy(global_fitter) np.random.seed(seed) res = gf._resample_mc(1, 'differential_evolution') return res.mc def _resample_mc(args, global_fitter): # do the sampling by Resampling Monte Carlo sys.stdout.write("----------------------\n") sys.stdout.write("Starting resampling MC\n") sys.stdout.write("----------------------\n") start = time.time() # do a single fit first output = global_fitter.fit('differential_evolution') chunksize = 5 * args.nprocesses remaining = args.steps done = 0 mcs = [] with Pool(args.nprocesses) as pool: while remaining > 0: todo = min(remaining, chunksize) # seeding the random number generator seeds = range(done, done + todo) gfs = [global_fitter] * todo results = pool.map(__resample_mc_iterator, zip(gfs, seeds)) mcs.append(results) done += todo remaining -= todo sys.stdout.write("{0:^7} steps, {1:^7} seconds\n".format(done, time.time() - start)) mc = np.squeeze(np.vstack(mcs)) quantiles = np.percentile(mc, [15.87, 50, 84.13], axis=0) params = output.params for i, var_name in enumerate(output.var_names): std_l, median, std_u = quantiles[:, i] params[var_name].value = median params[var_name].stderr = 0.5 * (std_u - std_l) params[var_name].correl = {} params.update_constraints() # work out correlation coefficients corrcoefs = np.corrcoef(mc.T) for i, var_name in enumerate(output.var_names): for j, var_name2 in enumerate(output.var_names): if i != j: output.params[var_name].correl[var_name2] = corrcoefs[i, j] output.mc = mc output.chain = mc output.errorbars = True output.nvarys = len(output.var_names) return output def main(argv): parser = ArgumentParser(usage=__doc__.lstrip()) parser.add_argument("global_pilot_file", help="The name of the global pilot file") parser.add_argument("--walkers", "-w", type=int, default=100, help="How many MCMC walkers? Default=100") parser.add_argument("--steps", "-s", type=int, default=2000, help="How many MCMC steps? Default=2000") parser.add_argument("--ntemps", '-T', type=int, default=1, help="How many parallel tempering temperatures? " "Default=1") parser.add_argument("--burn", "-b", type=int, default=500, help="How many initial MCMC steps do you want to " "burn? Default=500") parser.add_argument("--thin", "-t", type=int, default=20, help="Thins the chain by accepting 1 in every 'thin'. " "Default=20") parser.add_argument("--resample", "-r", default=False, action='store_true', help="Don't do Markov Chain Monte Carlo, do resampling " "MC instead. All MCMC parameters are ignored if " "this option is specified.") parser.add_argument("--qres", "-q", type=float, default=5.0, help="Constant dq/q resolution. Default=5") parser.add_argument("--pointqres", "-p", action="store_true", default=False, help="Use point by point resolution smearing. " "Default=False") parser.add_argument("--chain_input", "-i", type=str, help="Initialise/restart emcee with this RAW chain. " "This file is a numpy array (.npy) that would've " "originally been saved by the --chain_output " "option.") parser.add_argument("--chain_output", "-c", default='raw_chain.npy', type=str, help="Specify filename for unthinned, unburnt RAW " "chain. The file is saved as a numpy (.npy) " "array. You can use this file if you'd like to do " "the burn/thin procedure yourself. You can also" " use this file to restart the sampling. The array" " has shape (walkers, steps, dims), where dims " "represents the number of parameters you are " "varying. If ntemps is > 1 then the array has " "shape (ntemps, walkers, steps, dims). " "Default=raw_chain.npy") parser.add_argument("--output", "-o", type=str, default='iterations', help="Output file for burnt and thinned MCMC chain. " "This is only written once the sampling has " "finished") parser.add_argument("--nprocesses", "-n", type=int, default=1, help="How many processes for parallelisation? Default=1") args = parser.parse_args(argv) # set up global fitting if args.pointqres: dqvals = None else: dqvals = args.qres if args.nprocesses < 1: args.nprocesses = 1 global_fitter = global_fitter_setup(args.global_pilot_file, dqvals=dqvals) # do the Monte Carlo if not args.resample: # By Markov Chain Monte Carlo if args.thin < 1: sys.stdout.write("Can't have thin < 1, setting 'thin' to 1.\n") args.thin = 1 if (args.burn < 0) or (args.burn > args.steps): sys.stdout.write("Can't burn < 0 or burn > steps, setting 'burn' to 1.\n") args.burn = 1 res = _mcmc(args, global_fitter) else: # By resampling res = _resample_mc(args, global_fitter) # write the iterations. _write_results(args.output, res) sys.stdout.write("\nFinished MCMC\n") sys.stdout.write("-------------\n") sys.stdout.write(fit_report(res.params)) sys.stdout.write("\n-----------------------------------------------------\n") def filter_dependent_params(params, output): # filters dependent parameters from the MonteCarlo output # i.e. reject all parameters where expr is not None. independent = [] for idx, param in enumerate(params): if param.expr is not None: independent.append(idx) arr = np.zeros((np.size(output, 0), idx)) arr = output[:, idx] return arr def _write_results(f, emcee_result): # the flatchain is what we're interested in. # make an output array # hopefully the chain has been burned and thinned enough. output = np.zeros((np.size(emcee_result.flatchain, 0), len(emcee_result.params))) gen = pgen(emcee_result.params, emcee_result.flatchain) for row in output: pars = next(gen) row[:] = values(pars)[:] np.savetxt(f, output, header=' '.join(names(emcee_result.params))) def pgen(parameters, flatchain, idx=None): # generator for all the different parameters from a flatchain. if idx is None: idx = range(np.size(flatchain, 0)) for i in idx: for var_name in flatchain.columns: parameters[var_name].value = flatchain.iloc[i][var_name] yield parameters if __name__ == "__main__": main(argv=sys.argv[1:]) refnx-0.1.38/examples/global_fitting_motofit/global_pilot000066400000000000000000000002741450471266700237010ustar00rootroot00000000000000e361r.txt e365r.txt e366r.txt e361r_pilot.txt e365r_pilot.txt e366r_pilot.txt 0 0 0 1 13 17 2 2 2 3 3 3 4 14 18 3 3 3 5 15 19 6 6 6 7 7 7 8 8 8 3 3 3 9 9 9 10 10 10 11 16 20 3 3 3 12 12 12refnx-0.1.38/examples/interactive_fitter/000077500000000000000000000000001450471266700204515ustar00rootroot00000000000000refnx-0.1.38/examples/interactive_fitter/interactive_reflectometry_modeller.ipynb000066400000000000000000000016271450471266700306660ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib qt\n", "from refnx.reflect import Motofit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "app = Motofit()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "app()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } refnx-0.1.38/examples/mpi_parallelisation.py000066400000000000000000000044061450471266700211650ustar00rootroot00000000000000#!/bin/bash """ Using refnx in a highly parallelised environment using mpi. You'll need to install: - refnx - numpy - cython - schwimmbad - mpi4py - ptemcee Usage ----- mpiexec -n 4 python mpi_parallelisation.py """ # Start off by importing necessary packages import sys import os.path import refnx from schwimmbad import MPIPool from refnx.reflect import SLD, Slab, ReflectModel from refnx.dataset import ReflectDataset from refnx.analysis import (Objective, CurveFitter, Transform) def setup(): # load the data. DATASET_NAME = os.path.join(refnx.__path__[0], 'analysis', 'test', 'c_PLP0011859_q.txt') # load the data data = ReflectDataset(DATASET_NAME) # the materials we're using si = SLD(2.07, name='Si') sio2 = SLD(3.47, name='SiO2') film = SLD(2, name='film') d2o = SLD(6.36, name='d2o') structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3) structure[1].thick.setp(vary=True, bounds=(15., 50.)) structure[1].rough.setp(vary=True, bounds=(1., 6.)) structure[2].thick.setp(vary=True, bounds=(200, 300)) structure[2].sld.real.setp(vary=True, bounds=(0.1, 3)) structure[2].rough.setp(vary=True, bounds=(1, 6)) model = ReflectModel(structure, bkg=9e-6, scale=1.) model.bkg.setp(vary=True, bounds=(1e-8, 1e-5)) model.scale.setp(vary=True, bounds=(0.9, 1.1)) model.threads = 1 # fit on a logR scale, but use weighting objective = Objective(model, data, transform=Transform('logY'), use_weights=True) return objective if __name__ == "__main__": with MPIPool() as pool: if not pool.is_master(): pool.wait() sys.exit(0) # buffering so the program doesn't try to write to the file # constantly with open('steps.chain', 'w', buffering=500000) as f: objective = setup() # Create the fitter and fit fitter = CurveFitter(objective, nwalkers=300, ntemps=15) fitter.initialise('prior') fitter.fit('differential_evolution') # thin by 10 so we have a smaller filesize fitter.sample(100, pool=pool, f=f, verbose=False, nthin=10); f.flush() refnx-0.1.38/examples/platypus_reduction.ipynb000066400000000000000000000140031450471266700215550ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example Platypus reduction using `refnx`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from refnx.reduce import PlatypusReduce, reduce_stitch\n", "from refnx.dataset import ReflectDataset, Data1D\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_directory = '../refnx/reduce/test/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This command reduces and stitches multiple files together. The\n", "dataset is saved in the current working directory, if `save==True`. Use of `data_folder`\n", "is not necessary if the data is in the current directory. The\n", "first list is a list of the reflected beam files. The second is a\n", "list of the direct beam run files." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset, fname = reduce_stitch([708, 709, 710],\n", " [711, 711, 711],\n", " data_folder=data_directory,\n", " rebin_percent=3,\n", " save=True)\n", "print(fname)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.errorbar(dataset.x, dataset.y, dataset.y_err)\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can reduce files individually. A `PlatypusReduce` object is created with the direct beam run. You need to create different `PlatypusReduce` objects for each direct beam used." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reducer = PlatypusReduce('PLP0000711.nx.hdf', data_folder=data_directory)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_data = reducer.reduce('PLP0000708.nx.hdf', rebin_percent=3, save=True)\n", "# alternatively\n", "reduced_data = reducer('PLP0000708.nx.hdf', rebin_percent=3, save=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`reduced_data` is a tuple. The first entry is a `ReflectDataset`, the second is a dict which contains all the reduced data. Additionally, the files are saved if you use the `save=True` keyword. You can get the filenames from `reduced_data[1]['fname']`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_data[1]['fname']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following items are present in `reduced_data[1]`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_data[1].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`nspectra` is the number of detector images, N.\n", "\n", "`m_ref` is the 2D offspecular map. `m_ref.shape=(N, T, Y)`. `T` and `Y` are the number of wavelength and y-bins (2theta) respectively.\n", "\n", "`qz` and `qy` are the corresponding Q values for `m_ref`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Batch reduction" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from refnx.reduce import BatchReducer\n", "b = BatchReducer('reduction.xlsx', data_folder=data_directory)\n", "b.reduce()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Event mode reduction\n", "If you wish to reduce event mode data use the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduce_event = PlatypusReduce('PLP0011613.nx.hdf',\n", " data_folder=data_directory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use the ReducePlatypus object to reduce eventmode data. `eventmode` specifies the timebins for the event mode." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_event = reduce_event('PLP0011641.nx.hdf',\n", " rebin_percent=2,\n", " eventmode=[0, 900, 1800])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_event[1].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are now two specular reflectivity curves produced, because there were two time bins specified." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(reduced_event[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is saved in the following files:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_event[1]['fname']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.38/examples/reduction.xlsx000066400000000000000000000210651450471266700174770ustar00rootroot00000000000000PK! k;[Content_Types].xml (̔J1&m /K'g:sZ۷7V)młnr/2eKHh@9_mӇ5gHked|~6# jo⍔X&$(wLFU ߿uzyWyxCnuQW1ZS+ʠ;u ,.Fے,ؚۄ|4h`*ѣrC|i\ܑԠCpJc @ΊRS_,epbnqG?J́ @Z[vcz(U S',?PK!4o) _rels/.rels ( 0 ;]6"nv>֔|{{t x I~$/yOr~`#!KRd47$e5! oPM~Iz%!='lNnV!C:}}PLQ5\d 귍mnAәc&~Dz"u$,؍w19,>PK!; xl/_rels/workbook.xml.rels (j0 }qne:A[&Q-o?mi %] 7϶vVA 殨m|XB7΢ #T,)0ݓl5EC&PJv:? ek]h}C翵]Y9\~j "Q+dc0YEu%a8gÙ1<,@7E49F{,^هuRLs0ɒ0&[?10?äȋ}PK!KGxl/workbook.xmlSM0W|'HVVvu vMa|=fhiIftz#_Vu:ۜ&.%$f5s/NXpªU\'t%*X*tDGHY7}+Nj~VK)=s^<'Y픦q<蔫:f~?tSf FH_Ɨ wwb L ;H6v;xGP5F-cLO dfC;z[.>.PZ7$6w^PƦjSizo (g6܈e1ZWhJ;L^5A;K~3j[n1^f\kmqH5}"6y7kV, 񳣑 k#jn]^go;]xL2pԽ fJب1M5G[ILHuel}h>b^n`jd7)]j׵d :2mg_s$KE4/ќ-M>!p`aZ隶bk43-Cg1b>)2Β<)e0o@ZٍrﮊRgr'P{ ]ΔDž9P`"hZ Ƈ#fW a6FHP('*,軁˒LDUĕ{N.i h P n$Cw=X. 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KThcg絜;f)J IW՞NKYһaz7oO5C>Æ(ʷ7\LS?sO#<Ω#lY_iIԹΙ{Ž$$Iq9//;%l9KԲTn1)(~Cע WkPK-! k;[Content_Types].xmlPK-!4o) t_rels/.relsPK-!; `xl/_rels/workbook.xml.relsPK-!KGxl/workbook.xmlPK-!6}8& xl/theme/theme1.xmlPK-!a`vxl/worksheets/sheet2.xmlPK-!a`v-xl/worksheets/sheet3.xmlPK-!Vo$uxl/sharedStrings.xmlPK-!ө xl/styles.xmlPK-!xEExl/worksheets/sheet1.xmlPK refnx-0.1.38/examples/referenceAnalysisScript.py000066400000000000000000000031211450471266700217550ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt import matplotlib from refnx.dataset import ReflectDataset from refnx.analysis import CurveFitter, Objective, Transform from refnx.reflect import ReflectModel, SLD matplotlib.pyplot.rcParams['figure.figsize'] = (10.0, 10.0) matplotlib.pyplot.rcParams['figure.dpi'] = 600 DATASET_NAME = 'c_PLP0011859_q.txt' # load the data data = ReflectDataset(DATASET_NAME) si = SLD(2.07, name='Si') sio2 = SLD(3.47, name='SiO2') film = SLD(2, name='film') d2o = SLD(6.36, name='d2o') structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3) structure[1].thick.setp(vary=True, bounds=(15., 50.)) structure[1].rough.setp(vary=True, bounds=(1., 6.)) structure[2].thick.setp(vary=True, bounds=(200, 300)) structure[2].sld.real.setp(vary=True, bounds=(0.1, 3)) structure[2].rough.setp(vary=True, bounds=(1, 6)) model = ReflectModel(structure, bkg=9e-6, scale=1.) model.bkg.setp(vary=True, bounds=(1e-8, 1e-5)) model.scale.setp(vary=True, bounds=(0.9, 1.1)) # fit on a logR scale, but use weighting objective = Objective(model, data, transform=Transform('logY'), use_weights=True) # create the fit instance fitter = CurveFitter(objective) # do the fit res = fitter.fit(method='differential_evolution') # see the fit results print(objective) fig = plt.figure() ax = fig.add_subplot(2, 1, 1) ax.scatter(data.x, data.y, label=DATASET_NAME) ax.semilogy() ax.plot(data.x, model.model(data.x, x_err=data.x_err), label='fit') plt.xlabel('Q') plt.ylabel('logR') plt.legend() ax2 = fig.add_subplot(2, 1, 2) z, rho_z = structure.sld_profile() ax2.plot(z, rho_z) refnx-0.1.38/examples/reflectometry_analysis.ipynb000066400000000000000000000172071450471266700224200ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# start off with the necessary imports\n", "import os.path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import refnx\n", "from refnx.dataset import ReflectDataset\n", "from refnx.analysis import Transform, CurveFitter, Objective\n", "from refnx.reflect import SLD, Slab, ReflectModel" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# what is the refnx version\n", "# it's import to record this for reproducing the analysis\n", "import refnx\n", "print(refnx.version.version)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# this is a dataset used in refnx testing, distributed with every refnx install\n", "pth = os.path.dirname(refnx.__file__)\n", "\n", "DATASET_NAME = 'c_PLP0011859_q.txt'\n", "\n", "# load the data\n", "data = ReflectDataset(os.path.join(pth, 'analysis/test/', DATASET_NAME))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# set up a series of SLD objects, representing each of the materials\n", "si = SLD(2.07, name='Si')\n", "sio2 = SLD(3.47, name='SiO2')\n", "film = SLD(2.0, name='film')\n", "d2o = SLD(6.36, name='d2o')\n", "\n", "# Slab objects are created from SLD objects in this way\n", "# this creates a native oxide layer\n", "sio2_layer = sio2(30, 3)\n", "# we can set limits on each of the parameters in a slab\n", "sio2_layer.thick.setp(bounds=(15, 50), vary=True)\n", "sio2_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "# create a layer for the layer of interest\n", "film_layer = film(250, 3)\n", "film_layer.thick.setp(bounds=(200, 300), vary=True)\n", "film_layer.sld.real.setp(bounds=(0.1, 3), vary=True)\n", "film_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "# and a layer for the solvent\n", "d2o_layer = d2o(0, 3)\n", "d2o_layer.rough.setp(vary=True, bounds=(1, 15))\n", "\n", "# a Structure is composed from a series of Components. In this\n", "# case all the components are Slab's.\n", "structure = si | sio2_layer | film_layer | d2o_layer" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a Slab has the following parameters, which are all accessible as attributes:\n", "# Slab.thick, Slab.sld.real, Slab.sld.imag, Slab.rough\n", "print(sio2_layer.parameters)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a ReflectModel is made from the Structure.\n", "# ReflectModel calculates smeared reflectivity, applies scaling factor and background\n", "model = ReflectModel(structure, bkg=3e-6)\n", "model.scale.setp(bounds=(0.6, 1.2), vary=True)\n", "model.bkg.setp(bounds=(1e-9, 9e-6), vary=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# an Objective is made from a Model and a Data. Here we use a Transform to fit as logY vs X.\n", "objective = Objective(model, data, transform=Transform('logY'))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# CurveFitters do the fitting/sampling\n", "fitter = CurveFitter(objective)\n", "\n", "# do an initial fit with differential evolution\n", "res = fitter.fit('differential_evolution')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# an Objective has a plot method, which is a quick visualisation. You need\n", "# matplotlib installed to create a graph\n", "objective.plot()\n", "plt.legend()\n", "plt.xlabel('Q')\n", "plt.ylabel('logR')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Structure has a sld_profile method to return the SLD profile. Let's also plot that.\n", "plt.plot(*structure.sld_profile())\n", "\n", "# and print out the results of the fit. For the case of DifferentialEvolution uncertainties\n", "# are estimated by estimating the Hessian/Covariance matrix\n", "print(objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# now lets do a MCMC sampling of the curvefitting system\n", "# first we do 400 samples which we then discard. These samples are\n", "# discarded because the initial chain might not be representative\n", "# of an equilibrated system (i.e. distributed around the mean with\n", "# the correct covariance).\n", "fitter.sample(400)\n", "fitter.reset()\n", "# now do a production run, only saving 1 in 100 samples. This is to\n", "# remove autocorrelation. We save 30 steps, giving a total of 30 * 200\n", "# samples (200 walkers is the default).\n", "res = fitter.sample(30, nthin=100, pool=4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# now let's look at the final output of the sampling. Each varying\n", "# parameter is given a set of statistics. `Parameter.value` is the\n", "# median of the chain samples. `Parameter.stderr` is half the [15, 85]\n", "# percentile, representing a standard deviation.\n", "print(objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# a corner plot shows the covariance between parameters\n", "objective.corner();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# once we've done the sampling we can look at the variation in the model\n", "# at describing the data. In this example there isn't much spread.\n", "objective.plot(samples=100);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# in a similar manner we can look at the spread in SLD profiles\n", "# consistent with the data. The objective.pgen generator yields\n", "# parameter sets from the chain.\n", "\n", "# but first we'll save the parameters in an array.\n", "saved_params = np.array(objective.parameters)\n", "\n", "z, true_sld = structure.sld_profile()\n", "\n", "for pvec in objective.pgen(ngen=500):\n", " objective.setp(pvec)\n", " zs, sld = structure.sld_profile()\n", " plt.plot(zs, sld, color='k', alpha=0.05)\n", "\n", "# put back saved_params\n", "objective.setp(saved_params)\n", "\n", "plt.plot(z, true_sld, lw=1, color='r')\n", "plt.ylim(2.2, 6)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.1" }, "pycharm": { "stem_cell": { "cell_type": "raw", "source": [], "metadata": { "collapsed": false } } } }, "nbformat": 4, "nbformat_minor": 4 }refnx-0.1.38/examples/reflectometry_global.ipynb000066400000000000000000000125631450471266700220350ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how to do co-refinement of several datasets with *refnx*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import print_function, division\n", "\n", "import os.path\n", "\n", "from refnx.dataset import ReflectDataset\n", "from refnx.analysis import Transform, CurveFitter, Objective, GlobalObjective, Parameter\n", "from refnx.reflect import SLD, ReflectModel\n", "\n", "import corner\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# what is the refnx version\n", "# it's import to record this for reproducing the analysis\n", "import refnx\n", "print(refnx.version.version)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# these are datasets used in refnx testing, distributed with every refnx install\n", "pth = os.path.dirname(refnx.__file__)\n", "\n", "# load the data\n", "e361 = ReflectDataset(os.path.join(pth, 'analysis/test/', 'e361r.txt'))\n", "e365 = ReflectDataset(os.path.join(pth, 'analysis/test/', 'e365r.txt'))\n", "e366 = ReflectDataset(os.path.join(pth, 'analysis/test/', 'e366r.txt'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "si = SLD(2.07, 'Si')\n", "sio2 = SLD(3.47, 'SiO2')\n", "polymer = SLD(2.0, 'polymer')\n", "d2o = SLD(6.36, 'D2O')\n", "h2o = SLD(-0.56, 'H2O')\n", "cm3 = SLD(3.5, 'cm3.5')\n", "\n", "sio2_l = sio2(30, 3)\n", "sio2_l.thick.setp(vary=True, bounds=(1, 50))\n", "\n", "# Each contrast uses the same polymer SLD. We account for contrast change\n", "# using the volume fraction of solvent.\n", "polymer_l = polymer(250, 3)\n", "polymer_l.thick.setp(vary=True, bounds=(200, 300))\n", "polymer_l.sld.real.setp(vary=True, bounds=(0.1, 2))\n", "polymer_l.vfsolv.setp(vary=True, bounds=(0, 1))\n", "\n", "# we're going to share the water/polymer roughness across all 3 datasets\n", "water_poly_rough = Parameter(3, 'water_poly_rough')\n", "d2o_l = d2o(0, water_poly_rough)\n", "h2o_l = h2o(0, water_poly_rough)\n", "cm3_l = cm3(0, water_poly_rough)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "structure361 = si | sio2_l | polymer_l | d2o_l\n", "structure365 = si | sio2_l | polymer_l | cm3_l\n", "structure366 = si | sio2_l | polymer_l | h2o_l" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model361 = ReflectModel(structure361)\n", "model365 = ReflectModel(structure365)\n", "model366 = ReflectModel(structure366)\n", "\n", "model361.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model361.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model365.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model365.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model366.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "objective361 = Objective(model361, e361, transform=Transform('logY'))\n", "objective365 = Objective(model365, e365, transform=Transform('logY'))\n", "objective366 = Objective(model366, e366, transform=Transform('logY'))\n", "\n", "global_objective = GlobalObjective([objective361, objective365, objective366])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# create the fit instance\n", "fitter = CurveFitter(global_objective)\n", "\n", "res = fitter.fit('differential_evolution')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "global_objective.plot()\n", "plt.legend()\n", "plt.xlabel('Q')\n", "plt.ylabel('logR');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(global_objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitter.sample(400, random_state=1)\n", "fitter.sampler.reset()\n", "res = fitter.sample(30, nthin=100, random_state=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "global_objective.corner();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(global_objective)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.38/experimental/000077500000000000000000000000001450471266700154365ustar00rootroot00000000000000refnx-0.1.38/experimental/jax_abeles.ipynb000066400000000000000000000214761450471266700206100ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "# os.environ['XLA_FLAGS'] = '--xla_dump_to=/tmp/foo'\n", "# import jax.numpy as jnp\n", "# from jax import grad, jacfwd, jacrev, jit\n", "# from jax.config import config\n", "# from jax.ops import index, index_add, index_update\n", "# config.update(\"jax_enable_x64\", True)\n", "from functools import reduce\n", "from scipy.optimize._numdiff import approx_derivative\n", "import matplotlib.pyplot as plt\n", "%load_ext line_profiler\n", "\n", "TINY = 1e-30\n", "q = np.linspace(0.01, 0.5, 1001)\n", "w = np.array([[0, 2.07, 0, 0],\n", " [100, 3.47, 0.0001, 3],\n", " [500, -0.5, 0.00001, 3],\n", " [0, 6.36, 0.0, 3]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def abeles(layers, q, bkg=0):\n", " qvals = np.asfarray(q)\n", " flatq = qvals.ravel()\n", "\n", " nlayers = layers.shape[0] - 2\n", " npnts = flatq.size\n", "\n", " kn = np.zeros((npnts, nlayers + 2), np.complex128)\n", " mi00 = np.ones((npnts, nlayers + 1), np.complex128)\n", "\n", " sld = np.zeros(nlayers + 2, np.complex128)\n", "\n", " # addition of TINY is to ensure the correct branch cut\n", " # in the complex sqrt calculation of kn.\n", " sld[1:] += (\n", " (layers[1:, 1] - layers[0, 1]) + 1j * (np.abs(layers[1:, 2]) + TINY)\n", " ) * 1.0e-6\n", "\n", " # kn is a 2D array. Rows are Q points, columns are kn in a layer.\n", " # calculate wavevector in each layer, for each Q point.\n", " kn[:] = np.sqrt(flatq[:, np.newaxis] ** 2.0 / 4.0 - 4.0 * np.pi * sld)\n", "\n", " # reflectances for each layer\n", " # rj.shape = (npnts, nlayers + 1)\n", " rj = kn[:, :-1] - kn[:, 1:]\n", " rj /= kn[:, :-1] + kn[:, 1:]\n", " rj *= np.exp(-2.0 * kn[:, :-1] * kn[:, 1:] * layers[1:, 3] ** 2)\n", "\n", " # characteristic matrices for each layer\n", " # miNN.shape = (npnts, nlayers + 1)\n", " if nlayers:\n", " mi00[:, 1:] = np.exp(kn[:, 1:-1] * 1j * np.fabs(layers[1:-1, 0]))\n", " mi11 = 1.0 / mi00\n", " mi10 = rj * mi00\n", " mi01 = rj * mi11\n", "\n", " # initialise matrix total\n", " mrtot00 = mi00[:, 0]\n", " mrtot01 = mi01[:, 0]\n", " mrtot10 = mi10[:, 0]\n", " mrtot11 = mi11[:, 0]\n", "# return mi00, mi01, mi10, mi11\n", "\n", " # propagate characteristic matrices\n", " for idx in range(1, nlayers + 1):\n", " # matrix multiply mrtot by characteristic matrix\n", " p0 = mrtot00 * mi00[:, idx] + mrtot10 * mi01[:, idx]\n", " p1 = mrtot00 * mi10[:, idx] + mrtot10 * mi11[:, idx]\n", " mrtot00 = p0\n", " mrtot10 = p1\n", "\n", " p0 = mrtot01 * mi00[:, idx] + mrtot11 * mi01[:, idx]\n", " p1 = mrtot01 * mi10[:, idx] + mrtot11 * mi11[:, idx]\n", "\n", " mrtot01 = p0\n", " mrtot11 = p1\n", " \n", "# return mrtot00, mrtot01, mrtot10, mrtot11\n", "\n", " r = mrtot01 / mrtot00\n", " reflectivity = r * np.conj(r)\n", " reflectivity += bkg\n", " return np.real(np.reshape(reflectivity, qvals.shape))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def abeles2(layers, q, bkg=0):\n", " qvals = np.asfarray(q)\n", " flatq = qvals.ravel()\n", " q2 = flatq**2 / 4.0\n", "\n", " nlayers = layers.shape[0] - 2\n", " npnts = flatq.size\n", "\n", " kn = np.zeros((npnts, nlayers + 2), np.complex128)\n", "# mi00 = np.ones((npnts, nlayers + 1), np.complex128)\n", " mi = np.zeros((npnts, nlayers + 1, 2, 2), np.complex128)\n", " mi[:, :, 0, 0] = 1.0\n", "\n", " sld = np.zeros(nlayers + 2, np.complex128)\n", "\n", " # addition of TINY is to ensure the correct branch cut\n", " # in the complex sqrt calculation of kn.\n", " sld[1:] += (\n", " (layers[1:, 1] - layers[0, 1]) + 1j * (np.abs(layers[1:, 2]) + TINY)\n", " ) * 1.0e-6\n", "\n", " # kn is a 2D array. Rows are Q points, columns are kn in a layer.\n", " # calculate wavevector in each layer, for each Q point.\n", " kn[:] = np.sqrt(q2[:, np.newaxis] - 4.0 * np.pi * sld)\n", "\n", " # reflectances for each layer\n", " # rj.shape = (npnts, nlayers + 1)\n", " rj = kn[:, :-1] - kn[:, 1:]\n", " rj /= kn[:, :-1] + kn[:, 1:]\n", " rj *= np.exp(-2.0 * kn[:, :-1] * kn[:, 1:] * layers[1:, 3] ** 2)\n", "\n", " # characteristic matrices for each layer\n", " # miNN.shape = (npnts, nlayers + 1)\n", " if nlayers:\n", " mi[:, 1:, 0, 0] = np.exp(kn[:, 1:-1] * 1j * np.fabs(layers[1:-1, 0]))\n", " mi[:, :, 1, 1] = 1.0 / mi[:, :, 0, 0]\n", " mi[:, :, 1, 0] = rj * mi[:, :, 0, 0]\n", " mi[:, :, 0, 1] = rj * mi[:, :, 1, 1]\n", "\n", "# stk = [np.squeeze(v) for v in np.hsplit(mi, nlayers + 1)]\n", "# mrtot = np.copy(stk[0])\n", "# mrtot = np.copy(mi[:, 0])\n", "# for idx in range(1, nlayers + 1):\n", "# mrtot[:] = np.matmul(mrtot[:], mi[:, idx])\n", "\n", "# for sub in stk[1:]:\n", "# mrtot = np.matmul(np.copy(mrtot), sub)\n", "# mrtot = reduce(np.matmul, stk[1:], stk[0])\n", "\n", " # initialise matrix total\n", " mrtot00 = mi[:, 0, 0, 0]\n", " mrtot01 = mi[:, 0, 0, 1]\n", " mrtot10 = mi[:, 0, 1, 0]\n", " mrtot11 = mi[:, 0, 1, 1]\n", " \n", "# # propagate characteristic matrices\n", " for idx in range(1, nlayers + 1):\n", " # matrix multiply mrtot by characteristic matrix\n", " p0 = mrtot00 * mi[:, idx, 0, 0] + mrtot10 * mi[:, idx, 0, 1]\n", " p1 = mrtot00 * mi[:, idx, 1, 0] + mrtot10 * mi[:, idx, 1, 1]\n", " mrtot00 = p0\n", " mrtot10 = p1\n", "\n", " p0 = mrtot01 * mi[:, idx, 0, 0] + mrtot11 * mi[:, idx, 0, 1]\n", " p1 = mrtot01 * mi[:, idx, 1, 0] + mrtot11 * mi[:, idx, 1, 1]\n", "\n", " mrtot01 = p0\n", " mrtot11 = p1\n", "\n", " r = mrtot01 / mrtot00\n", " reflectivity = r * np.conj(r)\n", " reflectivity += bkg\n", " return np.real(np.reshape(reflectivity, qvals.shape))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.testing.assert_allclose(abeles2(w, q), abeles(w, q))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.testing.assert_allclose(abeles2(w, q)[:, :, 0, 0], abeles(w, q)[0])\n", "np.testing.assert_allclose(abeles2(w, q)[:, :, 0, 1], abeles(w, q)[1])\n", "np.testing.assert_allclose(abeles2(w, q)[:, :, 1, 0], abeles(w, q)[2])\n", "np.testing.assert_allclose(abeles2(w, q)[:, :, 1, 1], abeles(w, q)[3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit abeles2(w, q)\n", "%timeit abeles(w, q)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(q, abeles2(w, q), label='new')\n", "plt.plot(q, abeles(w, q))\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%lprun -f abeles abeles(w, q)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = np.random.uniform(size=100).reshape(25, 2, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.dot.reduce()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.multiply.identity" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.38/paper/000077500000000000000000000000001450471266700140505ustar00rootroot00000000000000refnx-0.1.38/paper/.gitignore000066400000000000000000000001501450471266700160340ustar00rootroot00000000000000*.aux *.bbl *.blg *.fdb_latexmk *.fls *.log *.out *.synctex.gz *.backup *.orig manuscript.pdf reply.pdf refnx-0.1.38/paper/LICENCE000066400000000000000000000443321450471266700150430ustar00rootroot00000000000000Attribution 4.0 International ======================================================================= Creative Commons Corporation ("Creative Commons") is not a law firm and does not provide legal services or legal advice. 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This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 UK: England & Wales License</a>. 2019-02-01 true 10.1107/S1600576718017296 10.1107/S1600576718017296 noindex 2019-02-01 true iucr.org iucr.org 10.1107/S1600576718017296 VoR doi:10.1107/S1600576718017296 J. Appl. Cryst (2019). 52 [doi:10.1107/S1600576718017296] computer programs application/pdf refnx: neutron and X-ray reflectometry analysis in Python en refnx is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is browser based, through a Jupyter notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterized in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule etc.). The model and data are used to create an objective, which is used to calculate the residuals, log-likelihood and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple data sets and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. The software offers a choice of fitting approaches, including least-squares (global and gradient-based optimizers) and a Bayesian approach using a Markov-chain Monte Carlo algorithm to investigate the posterior distribution of the model parameters. The Bayesian approach is useful for examining parameter covariances, model selection and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in Jupyter notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses. 2019-02-01 International Union of Crystallography NEUTRON REFLECTOMETRY X-RAY REFLECTOMETRY BAYESIAN ANALYSIS COMPUTER MODELLING REFNX Nelson, A.R.J. 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ȺPws \$TBmgPԋB >Gm1Q\=Kqϋz5Hl1@GQcݓm]PBݑ|,~c%zB[i "4JwYɋJʝw]A=i3QJE}+kW4W{}3| ^ e)^Ӣth̟0 0J_ kݚdHnU@ŶaSb &IENDB`refnx-0.1.38/paper/components.svg000066400000000000000000001245771450471266700167760ustar00rootroot00000000000000 image/svg+xml ObjectiveCalculates: log-prior,log-likelihood, χ2 Data1D ReflectModel: ModelCalculates generative model of Structure.Applies resolution smearing CurveFitterPerforms least squares fitting, MCMC sampling of log-posterior distribution StructureDescribes the interface,comprised of a series ofComponents GlobalObjective: Objectiveoptionally combine several Objectives Parametercalculate log-prior vary: bool bounds: Bounds value: float background: Parameter resolution: Parameter scale: Parameter Measurement Parameters Slab: Component Slab: Component Componentdescribes sectionof interface thickness: Parameter sld.real: Parameter sld.imag: Parameter roughness: Parameter vfsolv: Parameter refnx-0.1.38/paper/main.bib000066400000000000000000000377251450471266700154700ustar00rootroot00000000000000% Encoding: UTF-8 @article{campbell2018, author = {Richard A. Campbell and Yussif Saaka and Yanan Shao and Yuri Gerelli and Robert Cubitt and Ewa Nazaruk and Dorota Matyszewska and M. Jayne Lawrence}, title = {Structure of surfactant and phospholipid monolayers at the air/water interface modeled from neutron reflectivity data}, journal = {Journal of Colloid and Interface Science}, year = 2018, volume = 531, pages = {98-108}, doi = {10.1016/j.jcis.2018.07.022} } @article{emcee, author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.}, title = {emcee: The MCMC Hammer}, journal = {Publications of the Astronomical Society of the Pacific}, year = 2013, volume = 125, pages = {306-312}, eprint = {1202.3665}, doi = {10.1086/670067} } @article{pauw2013, author = {{Pauw}, B.~R}, title = {Everything SAXS: small-angle scattering pattern collection and correction}, journal = {Journal of Physics: Condensed Matter}, year = 2013, volume = 25, pages = {383201}, doi = {10.1088/0953-8984/26/23/239501} } @article{corner, Author = {Daniel Foreman-Mackey}, Doi = {10.21105/joss.00024}, Title = {corner.py: Scatterplot matrices in {P}ython}, Journal = {The Journal of Open Source Software}, Year = 2016, volume = 1, number = 2, pages = 24, XUrl = {http://dx.doi.org/10.5281/zenodo.45906} } @Article{Majkrzak1999, author = {Charles Majkrzak}, title = {Neutron Reflectometry Studies of Thin Films and Multilayered Materials}, journal = {Acta Physica Polonica}, year = {1999}, volume = {96}, number = {1}, pages = {81}, } @Article{Heinrich2009, author = {Frank Heinrich and Tiffany Ng and David J. Vanderah and Prabhanshu Shekhar and Mihaela Mihailescu and Hirsh Nanda and Mathias Losche}, title = {A New Lipid Anchor for Sparsely Tethered Bilayer Lipid Membranes}, journal = {Langmuir}, year = {2009}, volume = {25}, number = {7}, pages = {4219-4229}, doi = {10.1021/la8033275}, } @Article{Helliwell2017, doi = {10.1107/s2052252517013690}, Xurl = {https://doi.org/10.1107%2Fs2052252517013690}, year = 2017, month = {oct}, publisher = {International Union of Crystallography ({IUCr})}, volume = {4}, number = {6}, pages = {714--722}, author = {John R. Helliwell and Brian McMahon and J. Mitchell Guss and Loes M. J. Kroon-Batenburg}, title = {The science is in the data}, journal = {{IUCrJ}} } @article{Chirigati2013, author = {Fernando Seabra Chirigati and Matthias Troyer and Dennis E. Shasha and Juliana Freire}, title = {A Computational Reproducibility Benchmark}, journal = {{IEEE} Data Eng. Bull.}, volume = {36}, number = {4}, pages = {54--59}, year = {2013}, Xurl = {http://sites.computer.org/debull/A13dec/p54.pdf}, timestamp = {Thu, 11 Aug 2016 11:10:44 +0200}, biburl = {https://dblp.org/rec/bib/journals/debu/ChirigatiTSF13}, bibsource = {dblp computer science bibliography, https://dblp.org} } @Misc{Kienzle2011, author = {P. A. Kienzle and J. Krycka and N. Patel and I. Sahin}, title = {Refl1D -- depth profile modelling}, Xhowpublished = {http://reflectometry.org/danse/}, year = {2011}, url = {http://reflectometry.org/danse/docs/refl1d/}, version = {0.7.9a2}, } @Conference{Kluyver:2016aa, author = {Thomas Kluyver and Benjamin Ragan-Kelley and Fernando P{\'e}rez and Brian Granger and Matthias Bussonnier and Jonathan Frederic and Kyle Kelley and Jessica Hamrick and Jason Grout and Sylvain Corlay and Paul Ivanov and Dami{\'a}n Avila and Safia Abdalla and Carol Willing}, title = {Jupyter Notebooks -- a publishing format for reproducible computational workflows}, booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas}, year = {2016}, editor = {F. Loizides and B. Schmidt}, pages = {87 - 90}, organization = {IOS Press}, } @Misc{ipywidgets, author = {{Project Jupyter Contributors}}, title = {ipywidgets}, Xhowpublished = {https://github.com/jupyter-widgets/ipywidgets}, year = {2015-2016}, url = {https://github.com/jupyter-widgets/ipywidgets}, } @Article{Moeller2017a, author = {Möller, Steffen and Prescott, Stuart W. and Wirzenius, Lars and Reinholdtsen, Petter and Chapman, Brad and Prins, Pjotr and Soiland-Reyes, Stian and Klötzl, Fabian and Bangnacani, Andrea and Kalaš, Matús and Tille, Andreas and Crusoe, Michael R}, title = {{Robust cross-platform workflows: how technical and scientific communities collaborate to develop, test and share best practices for data analysis}}, journal = {Data Science and Engineering}, year = {2017}, volume = {2}, pages = {232--244}, month = DEC, abstract = {Information integration and workflow technologies for data analysis have always been major fields of investigation in bioinformatics. A range of popular workflow suites are available to support analyses in computational biology. Commercial providers tend to offer prepared applications remote to their clients. However, for most academic environments with local expertise, novel data collection techniques or novel data analysis, it is essential to have all the flexibility of open-source tools and open-source workflow descriptions. Workflows in data-driven science such as computational biology have considerably gained in complexity. New tools or new releases with additional features arrive at an enormous pace, and new reference data or concepts for quality control are emerging. A well-abstracted workflow and the exchange of the same across work groups have an enormous impact on the efficiency of research and the further development of the field. High-throughput sequencing adds to the avalanche of data available in the field; efficient computation and, in particular, parallel execution motivate the transition from traditional scripts and Makefiles to workflows. We here review the extant software development and distribution model with a focus on the role of integration testing and discuss the effect of common workflow language on distributions of open-source scientific software to swiftly and reliably provide the tools demanded for the execution of such formally described workflows. It is contended that, alleviated from technical differences for the execution on local machines, clusters or the cloud, communities also gain the technical means to test workflow-driven interaction across several software packages.}, doi = {10.1007/s41019-017-0050-4}, file = {Moeller2017a.pdf:Moeller2017a.pdf:PDF}, owner = {stuart}, timestamp = {2017.11.24}, } @Article{Stark2018, author = {Philip Stark}, title = {Before reproducibility must come preproducibility}, journal = {Nature}, year = {2018}, volume = {557}, pages = {613}, doi = {10.1038/d41586-018-05256-0}, } @Article{Trewhella:jc5010, author = {Trewhella, Jill and Duff, Anthony P. and Durand, Dominique and Gabel, Frank and Guss, J. Mitchell and Hendrickson, Wayne A. and Hura, Greg L. and Jacques, David A. and Kirby, Nigel M. and Kwan, Ann H. and P{\'{e}}rez, Javier and Pollack, Lois and Ryan, Timothy M. and Sali, Andrej and Schneidman-Duhovny, Dina and Schwede, Torsten and Svergun, Dmitri I. and Sugiyama, Masaaki and Tainer, John A. and Vachette, Patrice and Westbrook, John and Whitten, Andrew E.}, title = {{2017 publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update}}, journal = {Acta Crystallographica Section D}, year = {2017}, volume = {73}, number = {9}, pages = {710--728}, month = {Sep}, abstract = {In 2012, preliminary guidelines were published addressing sample quality, data acquisition and reduction, presentation of scattering data and validation, and modelling for biomolecular small-angle scattering (SAS) experiments. Bio{\-}molecular SAS has since continued to grow and authors have increasingly adopted the preliminary guidelines. In parallel, integrative/hybrid determination of biomolecular structures is a rapidly growing field that is expanding the scope of structural biology. For SAS to contribute maximally to this field, it is essential to ensure open access to the information required for evaluation of the quality of SAS samples and data, as well as the validity of SAS-based structural models. To this end, the preliminary guidelines for data presentation in a publication are reviewed and updated, and the deposition of data and associated models in a public archive is recommended. These guidelines and recommendations have been prepared in consultation with the members of the International Union of Crystallography (IUCr) Small-Angle Scattering and Journals Commissions, the Worldwide Protein Data Bank (wwPDB) Small-Angle Scattering Validation Task Force and additional experts in the field.}, doi = {10.1107/S2059798317011597}, keywords = {small-angle scattering, SAXS, SANS, biomolecular structure, proteins, DNA, RNA, structural modelling, hybrid structural modelling, publication guidelines, integrative structural biology}, Xurl = {https://doi.org/10.1107/S2059798317011597}, } @Misc{conda, author = {{Continuum Analytics}}, title = {Conda -- Package, dependency and environment management for any language}, year = {2017}, url = {https://conda.io/docs/}, } @Article{Nelson2006, author = {Andrew Nelson}, title = {Co-refinement of multiple-contrast neutron/{X}-ray reflectivity data using MOTOFIT}, journal = {Journal of Applied Crystallography}, year = {2006}, volume = {39}, pages = {273-276}, doi = {10.1107/S0021889806005073}, } @Book{Heavens1955, title = {Optical Properties of Thin Films}, publisher = {Butterworth: London}, year = {1955}, author = {Heavens, O}, } @Article{Nevot1980, author = {Névot, L. and Croce, P.}, title = {Caractérisation des surfaces par réflexion rasante de rayons {X}. Application à l'étude du polissage de quelques verres silicates}, journal = {Rev. Phys. Appl.}, year = {1980}, volume = {15}, number = {761-769}, doi = {10.1051/rphysap:01980001503076100}, } @Article{Nelson2014, author = {Andrew Nelson and Charles Dewhurst}, title = {Toward a detailed resolution smearing kernel for time-of-Flight neutron reflectometers.}, journal = {Journal of Applied Crystallography}, year = {2014}, volume = {47}, pages = {1162}, doi = {10.1107/S1600576714009595}, owner = {anz}, timestamp = {2014.05.22}, } @Article{Gerelli2016, author = {Yuri Gerelli}, title = {Aurore: new software for neutron reflectivity data analysis}, journal = {Journal of Applied Crystallography}, year = {2016}, volume = {49}, pages = {330-339}, doi = {10.1107/S1600576716002466}, } @Misc{refnx, author = {Andrew Nelson and Stuart W. Prescott}, title = {refnx - Neutron and {X}-ray reflectometry analysis in {P}ython}, Xhowpublished = {https://www.github.com/refnx/refnx}, year = {2018}, doi = {10.5281/zenodo.1345464}, url = {https://www.github.com/refnx/refnx}, } @Misc{Jones2001-2017, author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, title = {{SciPy}: Open source scientific tools for {P}ython}, Xhowpublished = {https://www.scipy.org/}, year = {2001-2017}, url = {http://www.scipy.org/}, } @Article{Wood2017, author = {Mary Wood and Stuart Clarke}, title = {Neutron Reflectometry for Studying Corrosion and Corrosion Inhibition}, journal = {Metals}, year = {2017}, volume = {7}, number = {8}, pages = {304}, doi = {10.3390/met7080304}, } @Book{Daillant2009, title = {{X}-ray and Neutron Reflectivity: Principles and Applications}, publisher = {Springer Verlag}, year = {2009}, editor = {Jean Daillant and Alain Gibaud}, volume = {770}, series = {Lecture Notes in Physics}, isbn = {978-3-540-88588-7}, } @Article{Bjorck2007, author = {Matts Bjorck and Gabriella Andersson}, title = {{GenX}: an extensible {X}-ray reflectivity refinement program utilizing differential evolution}, journal = {Journal of Applied Crystallography}, year = {2007}, volume = {40}, pages = {1174-1178}, doi = {10.1107/S0021889807045086}, } @Book{Sivia2006, title = {Data Analysis: A Bayesian Tutorial}, publisher = {Oxford Science Publications}, year = {2006}, author = {Devinderjit Sivia and John Skilling}, isbn = {978-0198568322}, } @Article{Hogg2010, author = {David W. Hogg and Jo Bovy and Dustin Lang}, title = {Data analysis recipes: Fitting a model to data}, journal = {ArXiv e-prints}, year = {2010}, Xvolume = {arXiv:1008.4686}, month = aug, archiveprefix = {arXiv}, url = {arXiv:1008.4686}, eprint = {1008.4686}, } @article{Hogg2018a, author={David W. Hogg and Daniel Foreman-Mackey}, title={Data Analysis Recipes: Using Markov Chain Monte Carlo}, journal={The Astrophysical Journal Supplement Series}, volume={236}, number={1}, pages={11}, url={http://stacks.iop.org/0067-0049/236/i=1/a=11}, year={2018}, abstract={Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data. In this primarily pedagogical contribution, we give a brief overview of the most basic MCMC method and some practical advice for the use of MCMC in real inference problems. We give advice on method choice, tuning for performance, methods for initialization, tests of convergence, troubleshooting, and use of the chain output to produce or report parameter estimates with associated uncertainties. We argue that autocorrelation time is the most important test for convergence, as it directly connects to the uncertainty on the sampling estimate of any quantity of interest. We emphasize that sampling is a method for doing integrals; this guides our thinking about how MCMC output is best used.} } @Article{ptemcee, author = {Will Vousden and Will M. Farr and Ilya Mandel}, title = {Dynamic temperature selection for parallel-tempering in Markov chain Monte Carlo simulations}, journal = {Monthly Notices of the Royal Astronomical Society}, year = {2016}, volume = {455}, pages = {1919-1937}, doi = {10.1093/mnras/stv2422}, } @Misc{Nelson2018, author = {Andrew Nelson and Stuart W. Prescott}, title = {Online reflectivity fitting with refnx}, Xhowpublished = {https://mybinder.org/v2/gh/refnx/refnx-binder.git/master}, year = {2018}, url = {https://mybinder.org/v2/gh/refnx/refnx-binder.git/master}, } @Article{Well2005, author = {Ad van Well and H. Fredrikze}, title = {On the resolution and intensity of a time-of-flight neutron reflectometer}, journal = {Physica B}, year = {2005}, volume = {357}, pages = {204-207}, doi = {10.1016/j.physb.2004.11.058}, } @InCollection{Millman2014, chapter = {Developing open source scientific practice}, booktitle = {Implementing Reproducible Research}, publisher = {Chapman \& Hall}, year = {2014}, author = {K. Jarrod Millman and Fernando Perez}, editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng}, } @Article{Hughes2016, author = {Arwel V. Hughes and Fillip Ciesielski and Antreas C. Kalli and Luke A. Clifton and Timothy R. Charlton and Mark S.P. Sansom and John R. P. Webster}, title = {On the interpretation of reflectivity data from lipid bilayers in terms of molecular-dynamics models}, journal = {Acta Crystallographica D}, year = {2016}, volume = {72}, pages = {1226-1240}, doi = {10.1107/S2059798316016235}, } @Comment{jabref-meta: databaseType:bibtex;} refnx-0.1.38/paper/manuscript.tex000066400000000000000000001016321450471266700167620ustar00rootroot00000000000000\documentclass[pdf,preprint]{article} \RequirePackage{graphicx} % \usepackage[colorlinks,urlcolor=black]{hyperref} %\renewcommand{\harvardurl}[1]{\url{#1}} %\newcommand{\url}[1]{#1} \usepackage[utf8]{inputenc} \usepackage{authblk} \usepackage{amsmath} \usepackage{siunitx} \sisetup{ separate-uncertainty = true, bracket-numbers = false, product-units = single, multi-part-units = single, } \usepackage[version=4]{mhchem} \begin{document} \title{refnx -- Neutron and X-ray reflectometry analysis in Python} \author[1]{Andrew~R.J.~Nelson} \author[2]{Stuart~W. Prescott} \affil[1]{ANSTO, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia} \affil[2]{School of Chemical Engineering, University of New South Wales, Sydney, NSW, 2052, Australia} %\date{\today} \newcommand{\refnx}{\emph{refnx}} \newcommand{\Objective}{\texttt{Objective}} \newcommand{\GlobalObjective}{\texttt{GlobalObjective}} \newcommand{\Parameter}{\texttt{Parameter}} \newcommand{\Structure}{\texttt{Structure}} \newcommand{\Slab}{\texttt{Slab}} \newcommand{\Component}{\texttt{Component}} \newcommand{\LipidLeaflet}{\texttt{LipidLeaflet}} \newcommand{\Transform}{\texttt{Transform}} \newcommand{\DataD}{\texttt{Data1D}} \newcommand{\ReflectModel}{\texttt{ReflectModel}} \newcommand{\CurveFitter}{\texttt{CurveFitter}} \newcommand{\Spline}{\texttt{Spline}} \newcommand{\conda}{\emph{conda}} \newcommand{\corner}{\emph{corner}} \newcommand{\MixedReflectModel}{\texttt{MixedReflectModel}} \newcommand{\pip}{\emph{pip}} \newcommand{\emcee}{\emph{emcee}} \newcommand{\ptemcee}{\emph{ptemcee}} \newcommand{\NumPy}{\emph{NumPy}} \newcommand{\SciPy}{\emph{SciPy}} \newcommand{\Cython}{\emph{Cython}} \newcommand{\Jupyter}{\emph{Jupyter}} \newcommand{\ipywidgets}{\emph{ipywidgets}} \maketitle \hyphenation{Lipid-Leaflet} %\begin{synopsis} %The refnx Python modules for neutron and X-ray reflectometry data analysis are %introduced. An sample analysis illustrates a Bayesian approach using a %Markov Chain Monte Carlo algorithm to understand the confidence in the fit parameters. %\end{synopsis} \begin{abstract} \refnx\ is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform, and has been tested on Linux, macOS, and Windows. Its graphical user interface is browser-based, through a \Jupyter\ notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterised in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule, etc). The model and data are used to create an objective, which is used to calculate residuals, log-likelihood, and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple datasets, and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. A choice of fitting approaches is available, including least-squares (global and gradient-based optimizers) and a Bayesian approach using Markov Chain Monte Carlo to investigate the posterior distribution of the model parameters. The Bayesian approach is useful in examining parameter covariances, model selection, and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in \Jupyter\ notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses. \end{abstract} \section{Introduction} The use of specular X-ray and neutron reflectometry for the morphological characterisation of thin films on the approximate size range 10 to \SI{5000}{\angstrom} has grown remarkably over the past years \cite{Wood2017, Daillant2009}. Most neutron and X-ray sources have instruments to perform reflectometry measurements, and there is an ongoing need for accessible software programs for users of those instruments to analyse their data in a straightforward fashion, including the co-refinement of multiple contrast datasets. Several programs are available for this purpose, with a variety of different features \cite{Nelson2006,Bjorck2007,Kienzle2011,Gerelli2016,Hughes2016}. These programs typically create a model of the interface, and either incrementally refine the model against the data using least-squares methods, or use Bayesian approaches \cite{Sivia2006,Kienzle2011,Hogg2010} to examine the posterior probability distribution of the parameters (i.e.\ the statistical variation of the parameters in a model). Given the number of publications arising from the reflectometry technique, it is vital that both the experiments and analyses are reproducible. Reproducibility in research is an underlying principle of science; unfortunately, it is not always possible to reproduce the results of others \cite{Stark2018}, because there is frequently not enough information provided in journal articles to repeat the analyses. Even if the datasets and software packages used to analyse them are supplied in supporting information (most often they are not), a comprehensive, ordered, set of instructions or a codified workflow would need to be provided \cite{Moeller2017a}. One example for addressing this reproducibility issue is the set of guidelines from the small-angle scattering community for the deposition of data and and associated models \cite{Trewhella:jc5010, pauw2013}. Here, we outline a new reflectometry analysis package, \refnx\ (version number 0.1 is used in this paper \cite{refnx}), that helps address the reproducibility issue for the reflectometry community\footnote{We do not mean that other programs are irreproducible, rather that the information provided in journal articles is often lacking.} by creating a scripted analysis workflow that is readily published alongside the publication, such as we have done with this paper (see the Supporting Information). The \refnx\ Python package is specifically designed for use in \Jupyter\ notebooks \cite{Kluyver:2016aa}, which provide a literate programming environment that mixes executable code cells, rich documentation of the steps that were performed, and the computational output. By including the analysis, as performed by the authors, in such a notebook, and appending it as supporting information along with the data, readers are empowered to replicate the exact data analysis and potentially extend the analysis, provided they have set up the same computing environment \cite{Millman2014}. Setting up the computing environment is simplified using the \conda\ package manager \cite{conda}, and an environment file (although other approaches are available). \section{Method} \refnx\ is written in Python with an extensible object-oriented design, Figure~\ref{fig:components}, in which the user creates a model of the sample based on what they know about its composition, with refinement of that model against the data. As with \emph{Motofit} \cite{Nelson2006} it calculates reflectivity using the Abeles method \cite{Heavens1955} for specular reflection from a stratified medium. Detailed documentation for \refnx\ is available on-line\footnote{https://refnx.readthedocs.io/} and is distributed with the package. \begin{figure} \includegraphics[width=85mm]{components.pdf} \caption{Schematic showing the relationship between classes that make up a typical reflectometry curve-fitting problem. The key step for the user is assembling materials (\Component) such as a `Slab: Component' (a \Component\ that is a \Slab) and encoding prior knowledge into each \Parameter\ that describes that \Component.} \label{fig:components} \end{figure} The building block of the analysis is the \Parameter\ object which represents a model value (e.g. the SLD of the material), whether it is allowed to vary in a fit, and a bounds attribute. The bounds are a probability distribution representing pre-existing knowledge of a parameter's value, called a prior probability. A prior might be a simple uniform distribution that specifies a lower and upper bound (e.g. volume fraction is in the interval $[0, 1]$), or a normal distribution that represents an experimentally derived value and associated uncertainty (e.g. thickness is $\SI{100+-4}{\angstrom}$). Any of the \emph{scipy.stats} \cite{Jones2001-2017} continuous distributions, or other distributions created by the user, can be used for this purpose. Algebraic relationships between \Parameter\ objects can be applied to permit more sophisticated constraints that can cross between \Component\ objects (e.g. the sum of the thicknesses of several layers is known to some uncertainty). \subsection{Structure representation} The \Structure\ object represents the interfacial model, assembled from individual \Component\ objects in series. Each \Component\ represents a subset of the interface and selected attributes of the \Component\ can be described by physically relevant \Parameter\ objects. The simplest and most familiar \Component\ is a \Slab, which has a uniform scattering length density (SLD), thickness, roughness, and volume fraction of solvent. The simplest models are simply a series of \Slab\ objects. More sophisticated components include \LipidLeaflet\ (a lipid monolayer, or one-half of a lipid bilayer) and \Spline\ (for free-form modelling of an SLD profile using spline interpolation). It is straightforward to develop/modify new components for different structural functionality, a consequence of the program design. To include further prior knowledge of the real sample into the model, each \Component\ can additionally contribute to the prior probability in addition to its constituent \Parameter\ objects. This is useful when a \Component\ has a derived value, such as surface excess, which is already known. To calculate the reflectivity from series of \Component\ objects that form the model, each \Component\ has a \emph{slabs} property that represents a discretised `slice' approximation to a continuous SLD profile for its particular region of the interface. A \Slab\ object has a single slice because it is a single thickness of uniform SLD. A \LipidLeaflet\ is made of two slices (head/tail regions), but the \Spline\ has many thin slices approximating the smooth curve. Each of these slices has uniform SLD, with the N\'{e}vot--Croce approach being used to describe the roughness between them \cite{Nevot1980}. The \Structure\ object is used to construct a \ReflectModel\ object. This object is responsible for calculating the resolution smeared reflectivity of the \Structure, scaling the data, and adding a $Q$-independent constant background (via the scale and background \Parameter\ objects). There are different types of smearing available: constant $\mathrm{d}Q/Q$, point-by-point resolution smearing read from the dataset of interest, or via a smearing probability kernel of arbitrary shape \cite{Nelson2014}. The constant $\mathrm{d}Q/Q$ and point-by-point smearing use Gaussian convolution, with $\mathrm{d}Q$ representing the full width half maximum (FWHM) of a Gaussian approximation to the instrument resolution function \cite{Well2005}. \subsection{Model/data comparison} The \Objective\ class is the comparator of the predicted and measured reflectivities, using the \ReflectModel\ and a dataset, \DataD, to calculate $\chi^2$, log-likelihood (Equation~\ref{eqn:2}), log-prior, residuals, and the generative model. The \DataD\ object has \emph{x, x\_err, y, y\_err} attributes to represent $Q$, $\mathrm{d}Q$, $R$, $\mathrm{d}R$. As is standard for many reflectometry data files, the \DataD\ object reads a three or four column plain-text datafile. A three column dataset represents $Q$ (\si{\per\angstrom}), $R$, $\mathrm{d}R$ (1 standard deviation). A four column dataset represents $Q$ (\si{\per\angstrom}), $R$, $\mathrm{d}R$, $\mathrm{d}Q$ (\si{\per\angstrom}). $\mathrm{d}R$ is the uncertainty in reflectivity, and $\mathrm{d}Q$ specifies the FWHM of the instrument resolution function, for each datapoint. Extending \DataD\ would allow other formats to be read - at the moment there is no standardised data format for reflectometry. One example of this could be a wavelength dispersive file using ($\Omega$, $\lambda$)-data instead of $Q$, such as that used in energy scanned X-ray reflectometry, or sometimes produced by wavelength dispersive neutron reflectometers. In such a case \ReflectModel\ could be subclassed to make full use of this energy dispersive information. Creation of a standardised data format for reflectometry would facilitate ingestion of data, and allow other important information, such as experimental metadata, to be used. An \Objective\ can be given a \Transform\ object to permit fitting as $\log_{10} R$ vs $Q$, $RQ^4$ vs $Q$; the default (no \Transform) is $R$ vs $Q$. Several \Objective\ objects can be combined to form a \GlobalObjective\ for co-refinement. The object-oriented nature allows reuse of \Parameter and \Component\ objects, and this is the basis for linking parameters between samples for co-refinement. For a comprehensive demonstration of multiple contrast co-refinement, see the annotated notebook in the supporting information. \subsection{Statistical comparison and model refinement} The \Objective\ statistics are used directly by the \CurveFitter\ class to perform least-square fitting with the functionality provided by the \SciPy\ package (Differential Evolution, Levenberg--Marquardt, LBFGSB - Limited Broyden--Fletcher--Goldfarb--Shanno with bounds). Additional \SciPy\ solvers can be added relatively simply and it would be possible for other minimisers to use \Objective\ directly. \CurveFitter\ can also perform Bayesian Markov Chain Monte Carlo (MCMC) sampling of the system, examining the posterior probability distribution of the parameters, Equation~\ref{eqn:1}. The posterior distribution is proportional to the product of the prior probability and the likelihood (or the sum of the log-probabilities): % \begin{gather} \label{eqn:1}\ p(\theta | D, I) = \frac{p(\theta | I)\times p(D | \theta, I)}{p(D | I)}\\ p(D | \theta, I) = -\frac{1}{2} \sum_n \left[\left(\frac{y_n - y_{\mathrm{model},n}} {\sigma_n}\right)^2 + \log(2\pi\sigma_n^2)\right]\label{eqn:2} \end{gather} % The prior, $p(\theta | I)$, is the probability distribution function for a parameter, $\theta$, given pre-existing knowledge of the system, $I$, as outlined above. The likelihood (Equation~\ref{eqn:2}), $p(D | \theta, I)$, is the probability of the observed data, $D$, given the model parameters and other prior information. It is calculated from the measured data, $y_n$ (with uncertainties $\sigma_n$), and the generative model, $y_{\mathrm{model},n}$. The likelihoods that are used here assume that the measurement uncertainties are normally distributed, Equation~\ref{eqn:2}. However, other types of measurement uncertainties (e.g. Poissonian) could be implemented by a subclass of \Objective\ overriding the log-likelihood method. The model evidence, $p(D | I)$, is a normalising factor. The posterior probability, $p(\theta | D, I)$, describes the distribution of parameter values consistent with the data and prior information. In the simplest form, this is akin to a confidence interval for a parameter derived by least-squares analysis. However, when parameters are correlated, or two models give similar quality of fit (`multi-modality'), a simple confidence interval can be misleading. The posterior probability is derived by encoding the likelihood and prior distributions and then using an MCMC algorithm (via the \emcee\ and \ptemcee\ packages) to perform affine invariant ensemble sampling \cite{emcee, ptemcee}. At the end of an MCMC run, the parameter set possesses a number of samples (called a `chain'); the samples reveal the distribution and covariance of the parameters, the spread of the model-predicted measurements around the data, and in a reflectometry context, the range of SLD profiles that are consistent with the data. The chain statistics are used to update each \Parameter\ value, and assign a standard uncertainty. For the sampling, these represent the median and half the $[15.87, 84.13]$ percentile range respectively; the latter approximating the standard deviation for a normally distributed statistic. The \ptemcee\ package is a variant (a `fork' in open-source software development terms) of the \emcee\ package that has been extended to implement the parallel tempering algorithm for characterisation of multi-modal probability distributions; different modes can be traversed by chain populations at higher `temperatures', while individual modes are efficiently explored by chains at lower `temperatures' \cite{ptemcee}. Having multiple populations in the parallel tempering algorithm allows the sampler to escape local maxima, greatly aiding it's ability to explore the most probable regions of the posterior. \ptemcee\ is also able to estimate the log-evidence term (the denominator in Equation~\ref{eqn:2}), which is useful when calculating the Bayes factor for model comparison. Parallelisation of the sampling is automatic, making full use of multi-core machines, and can use MPI on a cluster for yet greater parallelisation. Visualisation of the samples produced by MCMC sampling is performed using the \corner\ package for scatter plot matrices \cite{corner}, which gives a representation of the probability distribution function for each individual parameter and also the covariance for each pair of parameters. As will be seen later, the plot for two normally distributed and uncorrelated parameters is isotropic, while covariant parameters show significant anisotropy. An evaluation of the impact of hard bounds can also be made by looking for plots where the bounds are clearly truncating the distribution function, allowing the bounds to be re-evaluated and adjusted if necessary. \subsection{User interface} A significant motivation in the development of \refnx\ has been the facilitation of reproducible analysis by helping the user describe \emph{how} the analysis was performed. A few lines of computer code is an incredibly powerful description, conveying the details with precision that is hard to match in written text, as well as being incredibly concise. Example analyses within the \refnx\ code base are often sufficient to complete the task. These few lines of Python code can be further extended to produce publication quality plots saved and ready to import into the next publication, or used in a loop for batch fitting purposes. While Python is a popular language for instruction and for data analysis, meaning that the relatively few lines of code required to complete a \refnx\ analysis of a set of experiments is not a huge hurdle, a simpler graphical user interface (GUI) is also provided. The browser-based GUI is available for fitting within a \Jupyter\ notebook, Figure~\ref{fig:gui}, leveraging the \ipywidgets\ modules \cite{ipywidgets}. The GUI has a `To code' button that turns the current model into the few lines of code required to perform the analysis without using the GUI, thus providing the desired instructions for the reproducible analysis. The ability to generate analysis code allows also makes it a stepping point for building more advanced models independently. The current GUI is able to use slab based models for fitting a single dataset; a fully functional web-based reflectometry analysis notebook is currently available \cite{Nelson2018}. If desired, it is possible to execute Jupyter notebooks in batch mode or to run the generated Python code within a Python program to complete batch mode fitting of larger data sets. The \refnx\ repository contains a growing set of examples of different uses of the \refnx\ package. \begin{figure} \includegraphics[width=85mm]{./supporting_information/gui.png} \caption{Screenshot of the \Jupyter/\ipywidgets\ GUI; this \Jupyter\ notebook is available in the supporting information.} \label{fig:gui} \end{figure} \section{Example data analysis with a lipid bilayer} Neutron reflectometry is an ideal technique for the study of biologically relevant lipid membrane mimics and their interactions with proteins, etc. Multiple contrast variation measurements are necessary to reduce modelling ambiguity (due to loss of phase information in the scattering experiment) and improve the ability to determine the structure of various components in the system. The gold standard approach for analysis of these datasets is co-refinement with a common model, and to parameterise the model in terms of chemically relevant parameters, such as the area per molecule \cite{campbell2018}. Sometimes a patchy coverage (distinct to low area per molecule) necessitates the use of an (incoherent) sum of reflectivities from different areas. \refnx\ has functionality for all these requirements, such as the \LipidLeaflet\ component for describing the head and tail groups of a lipid leaflet, and \MixedReflectModel\ to account for patchiness. The parameters used in the \LipidLeaflet\ component are: area per molecule ($A$), thicknesses for each of the head and tail regions ($t_x$), sums of scattering lengths of the head and tail regions ($b_x$), partial volumes of the head and tail groups ($V_x$), roughness between head and tail region, and SLDs of the solvents for the head and tail group ($\rho_{x,\mathrm{solv}}$). The overall SLD of each of the head and tail group regions are given by: \begin{gather} \label{eqn:3} \phi_{x} = \frac{V_x}{At_x}\\ \rho_x = \phi_{x} \frac{b_x}{V_x} + (1 - \phi_{x})\rho_{x,\mathrm{solv}} \label{eqn:4} \end{gather} The approach used in \LipidLeaflet\ component ensures that there is a 1:1 correspondence of heads to tails. By default the head and tail solvents are assumed to be the same as the solvent that is used throughout the \Structure. This will be the case when using \LipidLeaflet\ for a solid-liquid reflectometry experiment. However, at the air-liquid, or liquid-liquid interfaces the solvent for the head and tail region may be different, and it is possible to use different solvent SLDs for each. We note that the \LipidLeaflet\ component may also be used to describe other amphiphiles adsorbing at an interface. Here, \LipidLeaflet\ is used to co-refine three contrasts (\ce{D_2O}, \ce{Si} contrast match [hdmix, SLD=\SI{2.07E-6}{\per\square\angstrom}], and \ce{H_2O}) of a 1,2-dimyristoyl-sn-glycero-3-phospho\-choline (DMPC) bilayer at the solid-liquid interface, Figure~\ref{fig:global_fit}.\footnote{The validity of \LipidLeaflet\ does depend on the area per molecule being equal for the headgroup and tailgroup regions, as pointed out by Gerelli \cite{Gerelli2016}, which can be violated if there are guest molecules that insert in the membrane.} Two \LipidLeaflet\ objects are required to describe the inner and outer leaflets of a bilayer, hence, the component contains an attribute which can reverse the direction of one of the leaflets. The use of individual objects to describe each leaflet leads to great flexibility; it becomes easy to model asymmetric bilayers (inner leaflet can be a different lipid to the outer lipid), and one can model interstitial water layers between the leaflets as well. The \Jupyter\ notebook used for the analysis, \emph{lipid.ipynb}, is available in the supporting information. The corner plot (Figure~\ref{fig:corner}) produced from the MCMC analysis shows the covariance between parameters, with an area per molecule of \SI{57.0 \pm0.15}{\square\angstrom}. Figure~\ref{fig:global_fit} shows the probability distribution of the generative model around the data and in the SLD profile. These families of plausible fits that are obtained by plotting a subset of samples from the MCMC chain. The spread in SLD profiles is used to determine what range of structures is consistent with the data. Multi-modalities in these SLD profiles can be due to statistical uncertainties, the $Q$ ranges measured, and the loss of phase information in NR \cite{Majkrzak1999, Heinrich2009}. \begin{figure} \centering \label{fig:global_fit}% \includegraphics[width=100mm]{./supporting_information/global_fit.pdf}% \includegraphics[width=100mm]{./supporting_information/d2o_sld_spread.pdf} \caption{a) Neutron reflectivity from a DMPC bilayer supported on a silicon crystal, measured at three contrasts, with 500 samples from the posterior distribution in grey and median of the distribution in red. Data for the contrast matched (HD\textsubscript{mix}) and \ce{H2O} contrast offset by 0.1 and 0.01 respectively. b) SLD profile of the \ce{D2O} model showing 500 samples from the posterior distribution, as well as the median in red. It is seen that the uncertainty in the reflectivity at high $Q$ is associated with an uncertainty in SLD profile at the lipid-\ce{D2O} interface.} \end{figure} \begin{figure} \includegraphics[width=120mm]{./supporting_information/corner.pdf} \caption{Corner plot for the varying parameters of DMPC bilayers supported on a silicon crystal, measured at three contrasts. The sampling took $\sim$\SI{33}{\minute} on a \SI{2.8}{GHz} quad-core computer for 20 saved steps, corresponding to 4000 samples, with the steps being thinned by a factor of 400. A larger scale image is available in supporting information.} \label{fig:corner} \end{figure} \section{Distribution and Modification} Each submodule in \refnx\ possesses its own unit testing code for checking that the functions and classes in the module operate correctly, both individually and collectively. For example, there are tests that check that the reflectivity of a model is calculated correctly, or that the behaviour of a function is correct for the different possible inputs and code paths through it. Since the test suite is an integral part of the package each installation is testable. In addition, there is a benchmarking suite to track changes in performance, specifically the speed of critical calculations, over time. This development approach is important to providing assurances to the community that the code is tested and works. The source code for \refnx\ is held in a publicly accessible version controlled git repository \cite{refnx}. User contributions may be made using the standard GitHub workflow in which contributors create their own `fork' of the main \refnx\ repository, and create a feature branch to which they make modifications. They then submit a pull request (PR) against the main repository. The modifications made in the PR are checked on continuous integration (CI) web-services that run the test suite against a matrix of Python versions on the macOS, Linux and Windows operating systems. Features are merged into the main repository if all tests pass, and if manual code review concludes that the changes are scientifically correct, of sufficiently high standard, and useful. When a sufficient number of features have accumulated, a new release is made. Successive releases have an incrementing semantic version number which can be obtained from the installed package, with each release being given its own Digital Object Identifier (DOI). We encourage users to submit models for inclusion in a user-contributed models repository (refnx-models\footnote{https://github.com/refnx/refnx-models}). We will work with users to develop a suitable way of documenting and sharing their models. The recommended way of using \refnx\ is from a \conda\ environment, which offers package, dependency and environment management \cite{conda}, using the pre-compiled distributables on the \refnx\ conda-forge channel. These distributables are made as part of the release process using the same CI web-services used to test the code. The matrix of distributables covers the major Python versions currently in use, across the macOS, Windows, and Linux operating systems. Alternatively the package can be installed from source, either directly from the git repository, or via \pip\ from the version uploaded to PyPI.\footnote{https://pypi.python.org/pypi/refnx; the installation command is `\texttt{pip install refnx}'} Building from source requires a C compiler and the \Cython\ and \NumPy\ packages to be installed; further dependencies should be installed to run the test suite to verify that compilation and installation was successful. \refnx\ is released under the BSD permissive open source licence. In addition, all of the dependencies of \refnx\ are released under open source licences which means that use is free of cost to the end user and, more importantly, the user is free to modify, improve, and inspect this software. \section{Comments on reproducibility of analyses} In order for a given scattering analysis to be fully reproducible by others, a general set of conditions need to be met \cite{Helliwell2017, Moeller2017a}: \begin{itemize} \item the processed datasets used in the analysis need to be deposited with a journal article, or be freely available. Ideally the raw datasets, and the means to create the processed datasets should also be made available. \item the exact software environment needs to be recreatable. \item the exact ordered set of steps taken during the analysis needs to be listed. \end{itemize} Each of these points is often inadequately addressed in the literature. For example, the use of different software versions may change the output of an analysis, or the use of a GUI program may preclude recording the full set of steps, or options, applied by a user \cite{Chirigati2013}. Whilst it is unable to meet the first criterion by itself, the use of \refnx\ in a \Jupyter\ notebook can fulfil the other two requirements, providing a little care is taken. As we have already noted, the ordered set of steps to perform the analysis is the \Jupyter\ notebook in which the analysis was performed and this is an artefact able to be archived. The exact software environment can be recreated by noting down the versions of the software packages used during an analysis (\refnx, \SciPy, \NumPy, Python, etc). At a later date those exact versions can be installed in the same Python version using one of: the \conda\ package manager, by installing from the source at a given version tag in the git repository, or by \pip. \conda\ can use environment files to recreate a specific setup. An alternative way of recreating the environment is by using a virtual machine, or other container environment such as Docker; the strengths and weaknesses of various software distribution practices and the relationship with reproducible science has been discussed in detail elsewhere \cite{Moeller2017a}. The usefulness of open-source software in a git (or other version controlled) repository must be emphasised here \cite{Moeller2017a}. With closed source or proprietary software, the ability to return to a specific software version/environment can be frustrated, and different versions can have modifications that can unknowingly change the output of an analysis. In addition reduced accessibility (due to cost, etc) to the wider scientific community can also hinder reproducibility. Moreover, there are important ramifications for verifiability \cite{Chirigati2013}. \refnx\ is based on a fully open software stack, with good unit test coverage. The user can run tests for each component and inspect parts for correctness. For example, the behaviour of the reflectivity calculation in \refnx\ is checked from first principles in the test suite; and can be done now and in several years time. If problems are discovered, they can be corrected. With a fully or partially closed-source program such checking is much harder, as one does not possess full knowledge of what happens inside. \section{Conclusions}\label{conclusions} \refnx\ is a powerful tool for least-squares or Bayesian analysis of neutron and X-ray reflectometry data that is ideally usable for reproducible research with \Jupyter\ notebooks, and has been built with extensibility in mind. Its features include: MCMC sampling of posterior distribution for parameters, structural models constructed from modular components with physically relevant parameterisation, algebraic inter-parameter constraints, mixed area models, co-refinement of multiple datasets, probability distributions for parameter bounds used directly for log-prior terms, and a (\Jupyter) \ipywidgets\ GUI. \section*{Acknowledgements:} We acknowledge Anton Le Brun (ANSTO) for the provision of the lipid bilayer datasets in the example, James Hester (ANSTO) for comments made on the draft manuscript, and Andrew McCluskey (Bath University) and Isaac Gresham (UNSW) for important feedback on \refnx\ development. \section{Supporting information} \noindent \textbf{gui.ipynb} - \Jupyter\ notebook used to create the GUI screenshot.\\ \textbf{lipid.ipynb} - \Jupyter\ notebook used for the lipid analysis example.\\ \textbf{lipid.pdf} - PDF view of the \Jupyter\ notebook used for the lipid analysis example.\\ \textbf{corner.pdf} - larger scale image of the corner plot.\\ \textbf{c\_PLP0016596.dat}, \textbf{c\_PLP0016601.dat}, \textbf{c\_PLP0016607.dat} - example lipid datasets.\\ \textbf{reduction.ipynb} - notebook for reducing example datasets.\\ \textbf{raw\_data.zip} - raw files for the example datasets.\\ \textbf{refnx-paper.yml} - \conda\ environment file to reproduce the analysis environment in this paper. \bibliographystyle{abbrv} \bibliography{main} \end{document} refnx-0.1.38/paper/supporting_information/000077500000000000000000000000001450471266700206675ustar00rootroot00000000000000refnx-0.1.38/paper/supporting_information/DMPC.png000066400000000000000000000214601450471266700221230ustar00rootroot00000000000000PNG  IHDRX{msRGBYiTXtXML:com.adobe.xmp 1 L'Y!IDATx흯GJ" 8@`ud 8H8b@TDB[gg;{zvt̽39?3wT(cO?O>-Xt̙ɓ'+-E@D`}X L ~8|)666 D/^l#ݾ}B[D X)xAeSJ! 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"import refnx, scipy\n", "from refnx.reflect import Motofit\n", "from refnx.dataset import ReflectDataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('0.1.0', '1.1.0')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# version numbers used in this analysis\n", "refnx.version.version, scipy.version.version" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pth = os.path.join(os.path.dirname(refnx.__file__), 'analysis', 'test')\n", "data_d2o = ReflectDataset(os.path.join(pth, 'c_PLP0011859_q.txt'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "app = Motofit()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", 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