{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":101849,"databundleVersionId":13093295,"sourceType":"competition"},{"sourceId":12872884,"sourceType":"datasetVersion","datasetId":8143188}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false},"papermill":{"default_parameters":{},"duration":582.336608,"end_time":"2024-08-28T10:39:00.603263","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-08-28T10:29:18.266655","version":"2.5.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# ADC 2025 Wavelet Transform and Basic Training","metadata":{"papermill":{"duration":0.015294,"end_time":"2024-08-28T10:29:20.933047","exception":false,"start_time":"2024-08-28T10:29:20.917753","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# READ THIS BEFORE YOU PROCEED\n\nThis notebook contains a series of procedures to prepare and model the data, with a CNN training procedure that we think might be useful for the competitors in this competitions. Please note that the approach we've taken is not *THE* approach— it's simply ONE possible approach. Our aim is to assist participants in exploring different ways to preprocess and model the data. Please feel free to fork the notebook and save the model/data for your own exploration.\n\nTO use this notebook you will need a binned version of the data, see the calibration notebook for more details.","metadata":{"papermill":{"duration":0.014491,"end_time":"2024-08-28T10:29:20.962454","exception":false,"start_time":"2024-08-28T10:29:20.947963","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"This notebook was prepared by Angèle Syty from the Institut d'Astrophysique de Paris, and Orphée Faucoz from Centre National d’Etudes Spatiales (CNES), with modifications from Gordon Yip and Tara Tahseen from University College London.","metadata":{"papermill":{"duration":0.015478,"end_time":"2024-08-28T10:29:20.992703","exception":false,"start_time":"2024-08-28T10:29:20.977225","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"## Task overview","metadata":{"papermill":{"duration":0.01461,"end_time":"2024-08-28T10:29:21.05165","exception":false,"start_time":"2024-08-28T10:29:21.03704","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"The challenge's primary objective is to process these exposures to produce a single, clean spectrum for each exoplanet, summarizing the rp/rs values across all wavelengths.\n\nThe exposure are subject to noises and the images or spectrum are not perfect. The Jitter noise has a complex signature that the ML model should recognize to produce a better spectra.\n\nDifferent techniques are possible and are up to the participant imagination to produce a novel (and hopefully better) solution to this task.\n\nHere outline our approach :\n\nThe goal is to suggest **one of the many ways** to pre-process data, to help the machine learning model, using a wavelet transform. \n\nThe first step consists in extracting the flux, for each wavelength ie each column of the images, as a function of time, by summing for all columns of all frames the flux in a rectangle of a given size centered on the spectral PSF. \n\nThen, the light-curves are normalized using the stellar flux out of transit, to make sure that the wavelengths variations in flux are due to the planet atmosphere properties and not the star or the instrument transmission function. We obtain data of shape (N_time_steps, N_wavelengths). \n\nThe wavelet transform extracts the low frequencies (mostly the transit light-curve) and high frequencies (mostly noise) in the signal. Here, we chose to keep only the low frequency coefficients cAs. The obtained wavelet parameters are then normalized using only the training dataset. We obtain data of shape (N_wlts, N_wavelengths). \n\nFor the second part of the approach, to retrieve the atmopsheric spectra, we use a 2D CNN using the wavelet parameters and the associated targets for training. We use Dropout layers to predict uncertainties with a MC dropout method.  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B1NY4feU+56oVL6OBPP05h399jIfx5CX85Httl7/PEw9f2/z0ECpxoyeeydvcwjLYD5JCjIJq5Bj10WqFyKVzOdf3TNfx1w91+QfDeOOwdXZZdAl3DfaZpl1v3LwvgfLiCMPlpHHHG7WTX3QHirbCZnbywR1dhm0h+t0USY7HHacjBxxBle0AwVCoj1rDXS6iullqqe5fPPDj/L0C6/IhMk/2Zpq22yxuey92y6y0frr2ppCabSfHlDBy5vqdbbfkCRpiPdE9TdGPjsHs2iI+ZSIJn9qeG+gmHnvz5J7i9Yy4ZyPp6FOSAH2GwxZz8dUij/W1Jiz+zffi8b7Y1odhc9H5HuibNX7qXU7dPMOy8dk0pyPjIfPdh5e/3xMPef3eYrt48Y1pynmNTBd83EwYXa8jIfN9/7DvLELOXy2gp9pMpX5ufS75PF4Q3NNmnXsMdg6swi1B30j24THCJUH73+YjDz2ZzLyuCNtjfLagy5adiv+0l/Vers0i8jS1g8+6CgZOeJ0Eu1AgTSxRPu8/H3BNRrg1BXLy1XAeVRWVsrjz70gr779nq2p1rljR9l/z91kr113sjX1odF+ikCZlzfVa+y+KXEa4r0Q+gYp+CbJm4f7HEw7fzTtU8P7AsXMe3+W1Fs0g8nmdDyZblyok5bncYPh8jdu7SN5Peq6u/xNMwuhnUb3H/72o0HmVowKcCJyGtJtHP1eMU4RvIgZTUE71dYx1J6ya0Y785muWfRPyY4RHarGz6y1vV51mYu3TVab1aF/PhRLHsG8BJl83qST7hwm/KY8HW7w41d0zqnqdd1UxSyDsrIF1+aWnh2GDpORxx0tI48/2qwHqieTXPYED00NxlK24NrscvDQn8nIM0i0A4XSRBPtdTzkGps1xKnz9tkQu62hMPP4+Iuv5H+vvSk/TZtua6rtOnhb2We3XWWdnj1sTX0oipONgimWz6da6DdF5oE27rsmnbcNs1oKB9EAOC85Kobzx2vYaJmXtkRe32CaBZ5vRl+z8jyHQh2bGS4fY1aPkfFotXZM0aEh/82I7tr9c68acFr1ox4PMGlXddyv2SyybfD9WQby9j7T7w1tsVbePmvbrZlXtFNo3RRrGSRorqVfiE2J1i6T8xbuk+1cau0W7hDT2VRluC+V0fugln1mI9g8w3Ey6ZbF2z6Z3bDG9pkO6E0u7njCc051fjM67xFJn9+hshbNaqTd1QVLLbh1/XnO1Xv/uza77pY7HHyMjDzxWBl1wrFa6XPtgVA53BZUu4K3DIp+wVwxT6IdKJimlWg/dq5fMEecj8O2YxT8DEZ2kPH+CjixPL9tlixdJk+/9Kq8+cFYW1Otd4/ucuDee8gOWw+yNXlQlG/7YpxTNnT+wb/sMYr0+Op9WlnuUL8hMk+Jr/CXjj6AyjycyhvPvJ+L9Pw2JHNKGsl5abDDaOjz5+2/kbyEiFHwf4tzH9+MEAxTD/OtdRf5nIMdK3bIDPaTtkuG8zTdMuwbqKW/bU7fK9IarNYytsqgS2bcQN4yaUyXFEn3PZVjN8zkcykYM25cnUMGYxRUHvef7liSmuq4z+j4SYm4Wpht67jfgN1fVvu1S8OtROYRnZspRvoGq6F+gUgfI7RiijHbpXu9Al6fjLqFOgXFVBtG6lOObxvMopaxkppT9HXjBM01+/k1rk/N9jpJOs8ZjJmuS0ZvvTSdYt+7Xl3a97Q3ITMnN7HIuqs29eE6V3Yd3DITOie7jDI/o9mlX/AjXO/aTFmT7PbCKVPlyl5oAr7clctkxyNOlpGnnCCjThzurVumzQmXraR2pePZpWGX3mLwvofIyDNOK1ii/YjDDpVu3bramjwg0Y4S00QT7bkccsy2aYfLw+nNdb55mEK1vA5mvDv2U3nl7Xdl2szZtqba/rvvInvvupN06tDe1uRJ0b7ti3VetTDT1g/uH/SIxvtlJjt1OQ/6TZMm2V2YE+yNEzyh3iubcfNxjvMxRpExh9RIjit6GNHPtYIcZgOfO752FJ96e03ysJ88zNVcbVmnYeqyUSb7qut8wmoZoNbzZtvNIl3f2sbxBF0y6Kvi9pli0+xfO+2fwQZ5/RzQsXS//pphEy0ZM9vacdKpbVxzXLWMkTVvvHwPWSu7z9peJ9Ncx8mFxzanNIvXy8yvjvsNy/Z9kkTnYJeByLzc+yFurlqXVB3Tz6xG6tKNV6sU24aZZu1n1tJwfcIdw9uF6z1J9ZE2ZaoibUExRf/gWLxlTN/qQ9X26vpk2maL2TJj1rZxivEzec/X9v407WHeeo26OG5OunSTs2VXH24zq6F1v1OwqLluBVPRedmiKdgVU7RtZmkKfpj1UBLdrZdr6IVTfp1JtJs67avL6v47Hne2jDztZBl10nF+XQ0xdXH9grrktsH7HiQjTy9gov2II6Rbt262Jj9ItKOUNL1Ee50PN812SU1Zjp/1fOowf7NJXbeLqsM4KcyeN19eeP1teWfsJ7am2vr9+8pBe+8uW2yyoa3x5G/XVsyAed9HphpsxxnKYH7aJe77AFXsh1dw9gTU5TzoN0jBN2b2GzH9umGS7PaKdh04aey67KhAiuKfmHqaQxGd9iRmXnWdXAMfVFG8f5CkPl6TnN6zTortsxw2uK1B7HbZzjHcP2bboCrNuBmd/1AfV8xkbCftPmxbuj6mKdwe19erSzNEbGNQFWqLq7Oy+hHH9I0bN6K2c5OuOZZuENnOfC+V6huqOBnuNzZZY0WPPxC3TZqdpTo/pjrNdnnj7cPtJnYuti5oiutTi7hx071mSf3D+6/Dvp0UyTMj3evsBHPyluH5adlFMNfouv1gVw3X7trCTJsugw/+0tWrcDlOeNsafcPjaLh+/iKe1xg3jqkK1Uf7BOt2GTSH6k0x3O7qwrTO1YcaQ/vzi9on1G7Y9aA9WKld0M0WaowdEm4Ll9O915N4feL6Bquu3dEVW1fre9ibj87JhK7qX/naOZq/+DWVfnirQV+znV03BcsUQ+uOmYabi52bW9c5unVTjkQooR78/FahP8/ZsldfpuUKvZBK6+1S28rLZMczL5ORI86QUScfb/qmZtuSukTram4/eJ8DC5poHzt2rGy33Xa2Bmh6mlai/Zg5thT9wpqJDDtmezqzPv3Z9rfq+jLn5e1Rc4w3Pxwnr7z9gcyaO8/WVDt8/71kn113lBbNm9uakPp4u9bHPmpoiH3WQYOcmzRK5LTlNFH3DVrwjZkX+jqYJHv41jERxfRaNfhc6nH/BdhV7kN6I9R5kEKcuyzGzGn3hZh7KSjwcddp+Cw3Mt1zPY6Y7dMOmaLRq055D2FTnXbQZEn9Y7YLqlKN6dWbplTtVvRrrtumls2CDm77lF+73Vgp2mu0Rft5664q2uQq0v27UaPNW081jkpqcyuRDYJVW4g0B8y+UzQG80q1cQzTNYv+sTLdvmayJZDyuCIJm9rmm+p1C7ZLs22mgiFS76u6RVfsWlJ3V5dUGSNu27rwBojbV637j5Eq6WbqI23BatILaBduTrrUdS1qWROVZsWu2wj6uTpbDha23nBLT7hvUK1lXbF1pt58SM2NHezDcfX2gy7j+iRVufVIv/Bcgz7KFoI2XcTUKbcezEHLunDrjq0P9w0t/SZ/3aRrbFN1QetC9W7b2tToFxrDMev6wTZE21O8BZPFdfLqtNq9h5O6mAa/zRRtoy6D+YTocbikuv51r2bUdWn+0jf0HnZ/+evCq0u4dg3DFoJ1j919EjcnZZPlwVy9MPdbd/UmeR5Z16S6Jtu9OpNkN3XNTH2ZJtqDpHu57Pibv8mos0bLqJNP8PcXOyFLxw8zq9H+yeuD9x4qI08/lUQ7UCBNLNFe89YkNb7ApmKaMzlVafrENkUqa91FJnOI421Xl01zenvU3HbKjFnyyjvvy/uffGFrqm220fpyyN67m6vZU6qPt2u9f0rU9/7qws6x3qeaZoelcNoCuUzW+8Yo/I2bhr5HzTeJ9hvM2OHr+QSl3F0e55H1UIU8BynGLsAu/SFzHLjOX9cKcECqrvPJeLMsxq/3r/mFVujjqcP4wSYZbJuub9rNaxvba89ie7MWVMVsGO6QzXso6BvZJlgN14fKphi3TaRORefj+qWdp22L7RtuU9pulzW4thAzVqjSFFP1M4WkRTLdLtyg6+FllKuMtEf3FebaYpqM6PE4qerDajRHKsxqLWPkRXS/3np0tyYvY7/vcFKeN7t90B7D7CNNeyCDPqZL6n7Vu/EKbqXGNrYtqAq3RZimNO21MXPwIjpE0twyoC+Fe12i3PeIYbF93Tx06UJXw+umwpZtAtPU2X5ujGhf5ZamQ/Ui6Gv+t2V/xS/rMp1w/2Dp88fzS2YZjGc++Mz2VrRNy7pqt/PTI7bO/+Bz6+HxzdIK6myl6RcqVy88mvD1l9E+unQpGrPQdbfi/68rwbrfyS38ckpJzXbFWwTVwT6CGtvo1vV9ZouhLhkx71FbtuNUr2qi2l+68B8UGmKP0/DKiaoqL7w6L/R8aQI9sdb/2cjvauvt+9e9rkFdwFv3F6mZqdj5mOn5c9QVLfvrrt5PpJfZW8GYdl2v0NCkulv3yhrN/ES7v9Tke7nscvMjMuq8c2XUKSf6+0xF9xcVV+fmbg3e64CsEu3muDJU1rYTiXY0eSTa4w4/41OSpl+6MZKaMt2Xk6J/jeqYfpn0iZXJWHGSO7363kfy2ntjZc78BbbG18z7B+bIA/eRvXba3tZYmU4vrToMYjbJy85rSjlsgfaXN6H51etUa9lZsZ+2fE7QfIPjvsnxxjVfY+z4Oe8mD/M0Q+RhnHTy9s9V3cZJ3ipmjAIffp13YDbLYNuM+nntGQxVqzq/lna7fMwhGCsvgyUrwJC1czst1M69cYOh67APs0kG26XrV9trFWr3S97HWjbxRfvZldhtXaXbJrpMx3YI5hnaIGnbVO1x/W0h2u64+qRzFyq79jDT14twmyvb1er6oMJK0SfcL9yeVB8qJ40TZrdxfaPLGmLatRismxV/kSRUFzu2bU9qs+WUc/HUaIpUmNW47dOMaUSTIGn6x+1D5xy3iX7vYcIrB+1asCuuzh1zaOmaAmYfwQbm/3gpG5IFY8UIzSNpaXhls6pLV1a2EKyHpWkzY0cb7HlzbUGzK9sKVx87RgrRhJdbd6+VK5s5+KvVBW8fZjd26ZLoZt2Gu0o4vB7uY5cmhWATmEGYQXVhl064r9lei3bdVOkHv+jKSVwfv0P10jKbJLXbshvL1rvV6oLjrXt1fkJWi7Zdl7YYbKMLLZtz4jPvdrdqtqkOU+0+uGr9zzutpmTatFjd152bUJOp88OvCJa2g66bsOuZcePY7bToHZepMhW21jU60bdThoJkrXlrmg9u1X/7agLavo/twqxruxGajibVq9YmvLenHzrvKu+9ulbL3uT19OoxVGk/fQt7W5n/vEq/XtfckH5d7XRC5n9/bvqfLs0c/Xlqudw7jnL9Ty9O1zYvKry6iopyE+WacHfrzTQqzNLUa7LdW+7+3Psy+le/klGn1pJoN3TPYWZyaQ3ec38S7UABkWiPO/yMT0mafunGyOqUZ9g3b/tTGfTPdEyv28Sp0+WVdz6QsV9+Yyurbbv5QDl83z2lZ7cutqY22R5LSLbnIevzVhcF2ke9zN1TyufIqY9jyPs+6jhesFkBjrng5zEP45sh6j5OsGXJvGe8MZKGSTFmyj62HLtZirHC0nZJ05jpsefrdUg5jlef8y5qGSDcHP0Zpk77dhuFNq7TOKmExq/TuBluZ16TFB1NU6pBvHrbltSj1veKbY/2S7cfs/CWWjT9Qn1r259rD/ql6u/Vx40VrtNydD1WqF+NPqG2MFMXajOr4X6uPlznCa+Hx4h0C9oc08dGVLjOz1DV7B+3nRHTrsVg3VvGbVvbuFrvIsysp9vGllOyHaLjqri6dAmQuP6pxtfVcJ2Oq5miKNMnuq23bqp06bfV+BHTrUeXNdTWHpKqj9aH21w5vIyrCy9TibZHt9PzFn5NtD7aR0XLmg2sTdxr7fYXjnB9HDcnt99wObyeqs4MkdwWvN41lvaD6W+XXrgris12pmiX2qarTlKbNruyfrBM0c5BwyRbq+uN0Ha2plqwnS78cvx+bJ2Ob+cf0Ca7bXLZzsXwyxomOWzKQWOw7qq8kn4w6yahHG63S7duIhhfO9TO9Ar1D8axdbY6JdOsff2SrYjQt6Erht6P0bepS1Dr0tVpklrrwtyc9Hys9d57a22yXRPsejF7pVenSXSv2pwvU/b6r/WWXrNZDy91OHPevKV+MMs0zNy8Zbn30V/aZLpbelHhlSu8ZTOvUuv8shcVZdJcE+uaiPfKzbyyRnOvoyk3L5dyb1nmxd5fTpEzL/utjDr1JG+0bOls0hu8534ZJdqzSbA7JNoB7/PA+yJa29eTkkWi3cpqfyqD/pmO6XW75vZ7ZMKUabbC175tGzly6D6y09aDbE2msj2WkGzPQ9bnrS4KtI96mbunlM+RUx/HkPd91HG8YLMCHHPBz2MexjdD1H2cYMuSec94Y1RP2i5jpOxj12tsmmassJTdatk+k2PP12uQdhyvLafdZHIcdqmiP8sEbdlOItI/281TCg/klbMe126QarvgtUgzsPap0RzaLtTmF5PrkrkGuwxWU23g1ZumUHswn1Cdih1D+4bqU/axxfCYNfrafqY+3C/4kCzcL2h2694ybi5um3BbXDlpW+1vi27boJ+/CApmoR9C66ZvaN0sQ3XKFL0PbmzXrosaXH2k0WxnCmbVH8MKyrZPuC1g20zRdLLrrlKFyknV4RWrxrbKridVx2wbiEuGhPoHRTduZCyzGqnTMWOTLHFjeGVdDZ0PLYUKnnBZ2ZWkOqX9alTG1zk1jie0rmW3nm4ZrYsT1xZXp+ctPKZy5Wh/1y9aHxV+LaKvi667CK+7cpTbXzSBrrE29Pyf8Lr2DW1rUgiuviq07riyrTermvl061o27bqqZS34VX5nXfoLLfjjm6Lfbstei20P13sfdE66Yta9UK5di+aDW/Hrq/dhPoTK/sK16zJVot0Pf10LWg7mYar9Ov8qbFtn2mxfE2YLw43nJ+b9bbQtKNu+2kdbTF9TzpS/jRtDx9Pt3RBuWV2o5leFPkb66Kp597m3ov2oy/DbU3Ppfp1LstvEtSak7boO5vahR2wS6N5E/V9A+In1Sq+s4SfZbb23SaWWvaUm273/vTq/3h2nH6HzoKEfUtA5uQS7v/TXddYVXjlIrGvZq9P2Zt6H5uXl3tJPtFd4GzTzPmiSXZPvmmiv8MrmyvZmFTL0h2ly5u8uq0Oi3dtZbby5Dd5jXxLtQAE13UR7usNOe0pqOV2ZnM6MTnmO42S0D5Vhv4zHs0Ld9Yr2q8fcbddEdt1uKxm2314m2R4v231l2T8sl21ryOdYKeRzvnk99mzkYb8NNveIvM0jx3HyMY+CntM8jp2Heebln72Cni9PXsb3xsh0nJT9shgjKpdj0G1znnuu7LgFG9/S8c3PMTE/zJh952H/eT8Gb7xcxtRtU21eyzHX/PzVsVL3Tz/PyLaumHKbSH8V19fUxfXzwlRH2sK0X8oxHVt2dTX6e+vROrMargv10UWt43thVm2dMtXhdVNhy/aD1iX1CT6EhPpE200mRNdDdaa/Lmydaw/vx0nqE6LrKftrm1nxlzX6hdtDbUE5VOdoVdz+DK++RlOoIiim2l7FJUMi/c2qrYvOJdzmqS5546bKs5gxbM9gA6/gxtZl7H5UBvXhbaPjRKXr69Z1GS67ZbhehctOXJ1TW1vc2NFtXMI7FU12RRNeceuuzpWjfRy3r2ii3STNq+v0HtjRunCYr8f6OeqFKWsYWrYL1yfor/V+o79wbV7ZsXXht4Pfx9aF2kyd2ZH3v+7Hr/TDzMuvMrTsKkwXV65e+kVtNDU12vxTEZpLiNb5h2o+6P9+2DZHiy5x7sYwdbpq63SpTWZVy/ZYNHns14Xq/SH8Om+pfbLhttOP+vKYeZj/qqUbUpvi2v2qhPc2TH4f6pomn/1am0y39S7JrmGu/LbrZiy7D52ZzlET5+ZKda+st4bRhPoab0UT7X4i3W/XepN89+pMkt1u6y3MvM1b0pS9Nt3OW9N6rYvS2ejh6FXrOm9NsGti3SXctazJ9RbesplX6a+LTbyXSXMv3FXuze0V7s0rNNHutTf3ll5Zk+2H/DRLzrpcE+0ne1tnS89YeoP33FdGnkaiHSgUEu1x0p6SWk5XJqczo1Oe4zgZ7UNl2C/j8axI9+fffFfeHfeZuYp96003trWpZLuvLPuH5bJtDfkcK4V8zjevx56NPOy3weYekbd55DhOPuZR0HOax7HzMM+c/9kr6LlS3vg57cJunMk8U/YJzyGDcaKyPUfR/mm399rqMKWa0gxS6/xTtWfzA4k7DjeWt63ZXD9E27Llts8nO2AwrttHHXYUt12wGh3PrgeL5PXqQlDhC7dHmny2Mvxam2Js51C/aH8Vrov0C/dxbaneX+m2dUzR+5ByPpF6V+eqY8sp6szCNWi9K1u6rlWp+pj2yLpfsEuPKdr1cF8zli50aSNgy6beL5qC6xPuG7udRrjeMuOZxtC6XSYJt7u2SJ/wNq4c6VItpm9YXF0c9+UnVXczjhc12mtuYGpqVicL5hXpqPWuLVxOJa49WlfbOLWN4bZ3denaVLisouthqdrixosulZZdxHGJrnDCKy75pXXRvuG6KN2fS6CH10OJdfP9kl7R7vqZPrbelnVp1rXsD2SazAfTbNv0f9vfhffR1Ad1dtWUvV3ateo2008L0Tq/PppoT+qvTLVdMU3mg1b6VWZh6wx3LNrmV/oJcj2koFPQX/u4Nl2aLlr2F4arc31Mu7fU8Kfv1qvr/bK/HpxC28/Mw/vf39KGXa+djuL3D7b1/jP7MP/5daq2McN9ff72Pj9Z7mjZT6779eZtautdIlvLJoFtyrrmjWcH1IUet85TE+f6VtG2NYkqLxJe+OdJ2/QKd61zV7VrmKvfvXXdxvvfH8/7qOtmG2/pn4Nk/vy88D64K9Vdgl1vJVN9NbuYRLsm1bXOJdlbmHJ50K+5t6Fe5a4Jd3MLGZNk98re8mcz58jZf/hdikS7N0BdeftVg/fYp0aivS5J9Tgk2gHv88D7oh39GtJokGjPRIb9Mh7PinRftXq1NG/WTMrj7vVYQ7b7yrJ/WC7b1pDPsVLI53zzeuzZyMN+G2zuEXmbR47j5GMeBT2neRw7D/Os8z97BT1HIWY/OezLbJrB9sHxxPTNdIxUdOxszlcmfdPNty7S7rOWfZjmmD7Z/JASO4a3vQ6RavxMmOOq47bpuDklDe3WkyozU8fX3K+KbBvTL+D6xzRV14cao+thqc5t0jZ2mVRn6fZursGcQ6LHYRaRfkGdF9H2pHW7dHXhtqSyXSpXn1TnyinqdRku+wXzv/1QXQ7XOeG6pO1t2WQ7tOzarKCvxxRdPy2G20Jl1ydc5Zh6bQg1ptq3iuvvmPrQUoXL6UTHNMUMt9WvP6n667iZzsHjd00xTjpuP65fuv5xbdG68Dh1HSs6RrRvuC66VNH+UeHtw1KN4crhZbg9TvTfFl2P+/cmWu/W4/qGk+du/9FEuysHt5Px/jefF1XVSXNvaTbXMB00dBFa2r5mG41wP7OZXbdF1+bqzIotunH9cWwXU6fzsG36wRyCXx+mdaZKm+y6EaoL2L5BH6/VnBJb77iyP7YXpo9b99uUX7Z9tN3rZJZ2PbyNLt02fptX7637h23767rWmjqfK9rNjVDRCL8bdFQdx/XX8cw8tM785wuPFyfoFyzDG/h7DO9Xk80ZJdptuw4X3odLtLtzoGVNqK/2Tr4myt1V66u9hlXeMnxVu/Y1CXpv6f1vtveaTVn7ue21b5hOw0/8+4l1l2jXY/ET7n6SXZPqLb1lC6+Dn2QXk3TXdT/5rle5++WkZLte2W6S7mVyzNz5cs4ffx+faPe2y4m3/eDdNdF+Col2oEBItMdJe0pqOV2ZnM6MTnmO45imPO1HZTTnkCy715TFAKZrHXeY7XGllc+xUsjrfD35Hi9jOe7XbN5Qcw/J2/nLwzj5mEtB3w95HDvHeZqtY4fIYNw8HkbKwVJPMEN221qH8Dqk3VetA6Smr1HasSMyfU1Nvwz71iab+dXgbRe3qfkhJcMxU3VzP+fUeWq6YV03Tscbs8bQti65MkNu21pEjscvRbd1K0mVPq1K9f4y1ZFtg66RbZL6Rnn10X3E7VPrXH3K/qH6VGO4PtmMYRbeh5TbKG13y2g/+yEYL7QM9zXl6LoV7RsWN4br7yKVoE376cJ8MDUBs6rttj7S7NdXJdf7N0Cu2Ve5fbjxosx2kbZUfZPEjWnX4zYP8iLRBEmosyl6H+K2TyH+x8MMxgiO2/ZNd8xxbTXqQuOkGys6MbOq24SWSWO4pccUbVvQHBSq6+IkjalS9I8bL9jWVdQiKQnmlc1q5HXXPkGVlkMRFby/NXTd+2CyyHoFu1+fMBljL/Qpk7avSQ7bdhO67i18WuctTJ3fbpZuvPB23v+Glt3S/G/bdF37h5g1t63rb8tuf7ZTUO8XfH6T32bC1dr1KL+fbbe9TcLbng5lFnZF+5rupt1P5Joe/v8+LWs//c+cJ123dd7SXTFv9qvdXbtXNglhW+mv+/Xuo+P3MUWzCLe6d4Iu/TZ/LLcPM++Ytky4bm67ZH5S3dFEs3u7uhb9qMlqU/YW4WS2Px/LK7jkus7V/RLCXNHuVfpJdT+xvsp7/2qy3b9K3T+26qVuX32MuvQT7d62Xgd3HpQ/Nztfb3LuFwB+gt3NtfoK9pZepbl9jIZJpvvruvSvfvfbdF2vctc+LtFe4cUxU2fIOdEr2r3+ufPG8P7XRLsm2Wu7dUxdkGgHvM8D74up+/rR6KRPtAcf0kt7etK0BU217CPTfjXY/ik3q63dqeN+w7IdolbegFmPmeMk8vppkPcTUi3vQ9sBCzjleHnYYTF+6WrQOeXjnAYfCiTHsZM2r/tYqQ8zx/llq67/vmQq02M0Van2V8d5BJuFtk87VIb7yffX6pyGi9vY/RCUz3lmK3xceZ5H3LimWMf9ZLpt6HVP6p30fnDlmPFSNkUqglVbyPj95vVL1Tc6R7Nql+GmYCWp0luNrBteXbg+KLqCtttiwNWFG2xdUOUKWh9U+oJ1bfMXQcGULdMvXGG57V17TBfD1Gu762c7uvok2m6LhlsPLcPcWEF7iOuvGTInqIt0dvVGTLsTV1+jKq5P3HheXbTafLnJNPESs70TUx+XJgskNUX7eeumKrqM4doDtlyjv1eR6hzXEDeG3d68tK5B6+wyWETKdtWXtFJNq83cIu2x3W1l0OYV3HGlPD77+tZ4mcP1oXJSIs4r67oJux5mkuh2Dmb3utQ6L0ydH8FV7SZ77HfTdZNCcKHsIqjzwjSF1nV/wXvLNOrSfrCrWh+kJ8x2frGarQu1mTHdugk3hq0LCeZtymZhhMvV/L7+Jm4b29HW+2X/g1noMbp1b2lqzbqlq7qtrffno/Xa3w+/3u9sF6Zk6mzZfDRlp7rkiqEaU468AwL+fEzBLP1x/a3N0i9mzHV3y7j9mnuxm/ern3CvXmqb+WgS1+Y/Wx+Ma6fkzqM7b/pLirXee9Mk0r2yLtd679vKtX6d309fIt2qenvzv7/wtzXhr2soM1Pvgz81nZM/L7/eJd79OetDXM0tYsrL/Qe6eqFl/yGo5aF+rk+FlOttYyr8uvKKCjnwlY/kzN9cIqNOi1zR7m2XO72ifW8S7UABkWivTdrTk6YtqSkP/WJ5/TPZJKOXONt9RxTibZSXMYt1XjkqgikUxSSK4bWIatA5FWrf3rjFcqrzcH7Tj1CfB9oA5zXr85fnCeb8+jXAOUspOpHwDz+pJql9iuYAspTnc5/xeyF5v9VFW6rTeyp5TF+owhSjHWpsUC22v+Xawn3C5SSun+NWQpVBUQtJnSOrKdrM+Qq1BUVbMAvvg1uGRc91eKxwU8rXxPY3zXZpynG0Pdxoy0n9dUX7+WvJXP9wo6vzF8m8Sq2P9q/RV+sifUKL7ETHSkX72WJYjXxLJl+DQkyX+H4ZbO0LxojZwlSnG8lrM83p+njcGLV0qxbTUat0nPAvUrTSdI3pr9K1OdocHGOoby2bmQ5JfXQ9spFJqNV4kWtyiTddRpNw4Ta/YJcedz7MfrUcqlOmrIlJm2TXaltnIm7O4XbTFF7XCl1qfUi0PrZfaMUUw+2uYOvMvty6KSRLGj+mPcx0rU7GuvGS0yfaZtdNUT+4FVPriZbj1kP1ugzz1k2V/8EPV+ckrUSE26LvEdPkj+cvdWEq/bL/wX7ME28KLsku5eX2bWnXXVmX5brUdlNZPS+lZRP2PWyK+j5da/4Cw38dvHOkdWZd22ydbdONgtfSX9Uav83/P2Bn4M9F/zdLv6wf/HXvf3M83nqFtyyv0Aq/zlt3Sz3mMj0uXW/m9amo8Nq8sNuUeeu73PKYjLrgIhLtQIlq4on2sAxPg+mW5SnL+BRnMW4288ho/1nsO06NzXMcT6UdIg/jp1TL2IXcdaZK7tO21OYboxTOeUl/Oc9w7nU+xOoNi+MsebNoiIlk/R7J8yRL+j2qGuh1KwoN9V5IPufVxfD2mY7lCbqm2cY06YcMx003ZrgqeszptlMpz5HWp9om+JCC1xbuE9c12G+kMbyd4ZWjdeHmGrS/dtClv0jJ9YtKOideOeUY2hZtjOkcVGlBt/HXkoXrXSHU0RRD65mqMabSckwipcax+EmdnKWYdorqkFCPGnMLibYlrda+F9Mnk26BFJ21WufikshJ7Daxm8ZUmqrYzsmSutTSP+U5zOR1dn3CfUMbmcScXQ9V++dD96vLyHq0LpTMDPoES28R5rYJ+ti68NJtFKy6gvLKuh6uCjN9o43h/q4crlN2JWlftTDz8PsnJWTN2Hbd0HVX1IJdCboEBY9XNl3s0gjV6W80gnqPKWu7rQwvbdFvt8UaXH8vzOsffhM4bvvQ0izsMhc1dufei94yCFPhhyubVV2W+0s3J0dXw+9L/WAS7TZMfXWdSbi7Oi+8j9Xrpmw/2NWg3jFziCy16C31v/C6/wsCmzg3CXWt85PoQdkl2iu0TpPttt3W7/SH22TUueeRaAdKVBNLtM+xpbAMDz/oluXpyvj0ZjGu6Zph/0LsPyp20xzGU7VunuP4aaUZu5C7zUbJfdqW2nxjlMI5L9nTXB8T9/ZRVOenDpPJy/yzHSTPJ60xfMthDqERHEdBZH5esnor1No5i8GCrnkc0/D612nIdNulakizo4xObLp9eswYMR1Mlau3y1BdUnMsrzHolKJjUnWkj1tNNT/HNAWd7TKFcN905y7dPsNjZCNpE7eiS5dIcUvb5uZnEi35SLbkwptLuvMVcHP3F8HSSLO9OUavPdwlTffa6cY6XqpBtM0W03L9wp29uca9HEGXDAZONa+Mkmpu/5G+wbbaHmlTwXvaC7d7k7g0BbPqt8WEabJLJ6k+pk+4PsxUhfvZD9HxVVAV02a2scWwYBy7jOuTko5pk7KGrrtlda3h6sN9UjHdXLvrr0XTYNddpaVtSfV23a2mpP1sp/B7Iokdy7CF6PHVRar3r9a7cHNxXZPqvGXQz83HteuctU6XurDr7sp11+4S7G7p1Zv0ly2bbZVZ16W/SKK7M3Qu0aVfrk622zCJdS/Mui513dab9XAfTba7si7LZKdz/ygjzz5bRp12iu4oQ96YGSLRDhQWifZALachtjnLU1enUx2zTUbDZDs3u8wLb7C8jhdV0ME9tYwfbY7+m1bo6Rn1spM8KrX5RpjpF/kxlPgprpcDCHYRty/3iVzEJ7LeplbAHTWKbzm8Y8jXYYT//YiOmernpWz3HTdOwV6GzAfObgp5nLAZKpPxstxnxuNG1LpJNmPavpmOmbKf15BRW7iTJi9sMS3dPqOO8XRbs3ktY2S8D+0X6ptyM6+hRlsm22XKDqDzNp+z+sF98obalCZoUn6BqC/eXLJ6Hd0x+ItamWPUffireZFyvpnux/WLdvbmGvdyxPZNJ65vJq9zeP+h/qZo1835DEmam126uuA8uXpb5+qj7coUtY9Z89iCqzeCxpqCMS2zP1MwqxmJjhGWrs1I3+5vbvsE5cg24T66rG2fQbMtmIVuF6xUC+pC9a4uVBUv1Cf8nkjJ9a914Fp4+0m3P006h9uC96i3NEW3VEGhus3Qudp56kLLZj20NLeLCV3Nrmr8Qsnj2tJyc7L7d3PWpas3dV6YpLldN1GdRK9R7xLsJvz6nU4+X0aeOZpEO1Cimlai/di5thQj9izk89TYsbIaMof9N+TLWvB9N+CxqaTj038MbdHw2hp4eigA85oW+Qtb0u+7+p583P7cJ3JJn8g8KeA5qPPQRfS65HMq4X8/ouOm+nkp2/3HjVOQ01lEr1FK3hwzmmYdjyXvp6AuA6Y/xrQjmsba9hk3fi3bBM21jZ2B2O8xY+oy3lWGHdPtN+N9pRM3SLp/l1J9gagvdTzoTDdLd+h14g2UdqwMdhR0ifb1Jhv7dTaDMfNFk3M1hOpqtNu51ZiiVxGZd/ie1X5FULBLT1AM1QVi+ofFbWuKKfrHiRsjLOVQWezD8PqbTSLbBavhetc3nVAHU3Tr0XonVb1l6uIaspWPMdKxnzNJ78uYclAVbvPoavRY3bp5r9p6s4zUBeu2HFrUKjKN6nl6y6BNy7rihftlgmkPhanThS5tgj3Yzq/f8egzZOSokVkm2jNHoh0orCaWaJ9nS9kowOkp9Ckv6pe0EbzdwufX/YPoNN5PJ6TCa15ieL1KTlP/HDP/zOgH7zzkcirMv1c5jtEg6mnCwW6K6QTlfy61H6bXkNfd5nu8qFwHr+P2wWYFPThkq9AvR3T8yI8BvphJFHJewb8R+eBNNGmudiWuzsno3+hM+njy8e991mNk2T/b8TPu7/UzXdP0z3SsfJzHbM9L1rz3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t+abOl693rxj2uuM0TFQjAD2/XGS3z5urVdzgXHcU/PPW5vmH/fBOMYpnTGKTzqKBTKd7/E6de413tv0McNnb6f4ZAfGOlPnUDL5Hq8TjGMc96Hb+1TM8Av/AGPQmZL1cxljjDHGGLO340S7MTuJPS3RPpUI0I2tbojB+GZc9MkI+epv4sH4pnzKl266e1vMP09vtTlXire/iR+zlnXCtvfd24hx/Do2/bFexG4qzqnzplihDxY5LpqrX9+U73FMYGo928vU/GPm6Yzj7Nclpo7BanGPj6OQXX98+/nGcY71RR/31HGdx2rrHY+P0brGx3F8PMbrEv188qW1Tc09Ph6r9RdlKj75wthq61TMqNGHXIzXNRUj2vPmR0F7XgyL2C6iM8XUcQG9v3l+cN7Gx2IM/PR289bYxzFPZ96/DbFIPFPnD/Rxap7+ugRj//PWpvhWOrayG9uMgQ50+3nA+BhNxTaee2rt47VO2Y1jGM8t+vUuci56HfkcHwfFJzlsVvudYYwxxhhjzL6AHx1jzD4K/jz+m9/8Zrnvfe+bksLHmcRN8bIvRcUjZW5yk5tkr3K7292uPdM9bprLueeey0fQ9Nz61rcud7/73ctZZ52VkmlfawFxCMSMZ85jTszdg9iw3rjxT8lyVlun6OcU8o0Y8MV5+PP3ww47LEcL2w984APLV77yFT5eYcz22G3vMVzrcTnkkEMG8yGm3/iN3xgcFzxyCHHhGtpRtNbxozl2lFve8pZ8/MNawHHB8Rmf8/G1jX9H0MO/BYDjguOjfxN6JFP/7w3o31z/b2R8DSzCqaeeynp7HyEzXhfOM86jvqgXcU0dx9Wum/E1sxo7eu6n4oOvD37wg/zyyl35uwmP08A8eAwGvjBTaC3jf+v9sV7E9oILLuDvhCkdXCMrsT3nbUeut3ms9Ltxe85FD/6N6HfLSo8SmTp/iEe/p/DlnPiSTnxZJ34n4rji8SR4fNf2gOsK5wXnpH+MCq7dRcF1gXOMc90fe5wvnLeV2N7fa2u5Tscsei7Eor8/wfb+zjDGGGOMMWZvxIl2Y/ZBlHjFM8Jxw6znoeImF4mQcWJ56sZ+6mYcCQv5QoENnnW7u9HzdftYFkmmLLrOlVACZXvZUbvtYUePyxRKuCCBrOtpnMjZEZQcBbo2d0bSfUdBogj/TlZDiSMlqKc+eBj/e0PRv7m1guP+spe9jMm+/nnJqzG+vnF9KNGnD392JvAHv1Ns77nfnvjW8rtpXgJY4Jj3vlEW/cBjJVusb97vhJV+L+2M84Zrdd6HKGtle88Frm2s58QTTxwcr3Gid5Hf33r2OPRwbLHGd73rXTm6GHqWOfzgOsUHB0tLS4xvUZSAHrPIhyg7ylquU31Ysui5EIv+/lyJlX5nGGOMMcYYs7fhRLsx+yD6ElQkGJAg6IsSXf2Xoo53vIFx8gm70WA79ofyV3/1V6m1e0ASYCoO7GbErsZ5LLLO1egTldvDjtptDzt6XKZAwgWJdSTYcd773dtrRTtPERsSNUhY/+iP/uh3JdmORDiu7dXQ7lDsHP3v//5vHpcnPelJgw8esHMd6xkffxQkmdcKduriHD/lKU8p5513XkpXZnx99x/47Mqk3zx2xbnf1b+b8NcEU76RIOzP/xQr2X7f933f3N8JK/1e+m6ct0VZy7nAuHRx3JDo7RO8q/3+xgdQsNHr3iLnZwolp/VveUeuIfxewbU9Zmd8SDKPtVynY1Y7F2LR35/GGGOMMcbsLzjRbsw+CHbdIunX77YV2pn7ile8gjv3wPhRAwAJDOhBH360u7lHO/9e9KIXpWTHWWTHd5/sVOwCMSCWsbxntXUugpJc4xiQQEHydWrXJdhRu0VY63GZBxLrSLD/yZ/8CY/Rfe5znxzZeSABhJ3aq+2wXSnxuBaUENNOdaFHw/Q7ZrWjFUmos88+e/CYmPGjZYQePbE9u9BXAkkv8Ku/+qusV2N83HCMEac+NMH6xn/hAnA8cFxwfHYVq517XddTCVYk/fBB0G1ve9td9rsJ8enDpvGOW80/byfuIrZg3u+E1ZKx383zNo+d+Trx5Cc/udz73vfmcdDxW+33N9pI+o7XPv63vRI69uPfx1gDztOi6IPV8flf5HVue1nLdboIU+dCbM/vT2OMMcYYY/YHnGg3Zh9DCQHcXOMGfIySV3ichZKCSHThmdy6iUZCBDvYIIM+/GD3Lnb69QlD7fxTAnIKJZj7m3QlZF7ykpewD+BrkUdsaK7HPOYxzR+Smc9//vOX7TAes9o6F+XXfu3XmPTp/yz/5S9/Of+KQL6UgOgTnYvY7SiLHJepc7ESSKwjiXXGGWfwmllLfABz4rocJ36QpNEHQ5gDc0GmXc5ax65A1zauA13biA3nA+evv7ahi9hf8IIXlO/5nu8ZfDiDndpIXj/60Y9uccvPzvyQAjEgOb0o/brwu+HhD3/4IB6sD+tEnDon2hm82r+nlRhfayirnfsppq5rxYeYsSt8R383jZn694F/s0gYItko+vlX+jexiK0+OJn6nbASu+q8CSRIpz7gWAn9W9recwFdPJ6kt1Gitv+9s9rvb8QMnT7Rr2PSs9LadA2MP8TAGrbnEVDwg+9CgR+cT7G9fhZlLddpz6LnQuicYx7Z4PxgzvHvT2OMMcYYY/YHnGg3Zh8DN7xI5uLGex64+UVyC4mAzZs3s33HO96Ru/Bwk42kJpLweFSFwKMv8Cf5/fN3kZD67Gc/u2pSBwlIJI7gHzfj0H/HO97BJJt8IfmxyE05bDEnULxI0p122mmrPp5jkXUuApKqX/rSl/iBhuLHXwhgt6V8IU4kFZFYQeIGSc5F7HaURY/L+FysBJIqSK5gl+j4Sz6nQIIZu7fn7VyFv/e85z1sK0YUHI+Pfexj7TpCvIgT8WMcyWGsY1c9omB8bSM2JVwVk9BO8KmkE3a6n3zyyS1u+cH6Fk10LYIeIbMIeMwMCuLBhz+4Jvt4pq4bfFiAfxer/Xtajf5aw+OsFjn3Y1aKT/9mxucPZdHfTWPG/z6m/s2O55/HIraID+vvdbAz+aEPfSjH57ErzxvQX2jgmtmeneg7ci5wLMY2+DeEf0v9Wlb7/S0/+J0rP3iNw7UHlIBfbW34d9z//kHB6xM+FBkn4FcC5/9DH/oQ45Sfyy67jLvDdzZruU57Fj0XPeNzjvMz7/enMcYYY4wx+zrrlvAARmPMmjnppJPKq1/96uwVPtt0bwCJBiR7V0p27QvsL+vc2eDRA0iYIEm6M5PFZteCD3buda97cbfpzki8GvPdxL+/jTHGGGOMMXsD3tFujDFmEj2GaHsePWCMMcYYY4wxxhizP+JEuzHGmAFIsONP//FoBeyI3p5HDxhjjDHGGGOMMcbsj/jRMcbsJPbWR8cYY4wxxhhjjDHGGGPWhne0G2OMMcYYY4wxxhhjjDFrwIl2Y4wxxhhjjDHGGGOMMWYNONFujDHGGGOMMcYYY4wxxqwBJ9qNMcYYY4wxxhhjjDHGmDXgRLsxxhhjjDHGGGOMMcYYswacaDfGGGOMMcYYY4wxxhhj1oAT7cYYY4wxxhhjjDHGGGPMGnCi3RhjjDHGGGOMMcYYY4xZA060G2OMMcYYY4wxxhhjjDFrwIl2Y4wxxhhjjDHGGGOMMWYNONFujDHGGGOMMcYYY4wxxqwBJ9qNMcYYY4wxxhhjjDHGmDXgRLsxxhhjjDHGGGOMMcYYswacaDdmQa666qryV3/1V2Xr1q0p2THOOuus8tGPfjR7xhhjjDHGGGOMMcaYvR0n2o1ZkEMOOaR8+MMfLre73e3Km9/85rK0tJQji/GFL3yhPOpRjyr3ute9yp3udKeUGmOMMcYYY4wxxhhj9nacaDdmO3j2s59dzj777PLYxz6WyfJ3vOMdqybcv/KVr5QTTzyRCfrTTz+9PP3pTy+HHXZYjhpjjDHGGGOMMcYYY/Z2nGg3Zjs47rjjyhOe8AS2P/OZz5Sf+ZmfYcL9jDPOoKzn3HPPLSeddFI54YQTyutf//qybdu2cuihh5anPe1pqWGMMcYYY4wxxhhjjNkXWLe0vc+/MGY/BzvUb3WrW5XrrrsuJZWjjz66fPOb38xeKRs2bFimc/LJJ5fnPe952TPGGGOMMcYYY4wxxuwLONFuzA7wS7/0S+W1r31t9hYDj4s555xzylFHHZUSY4wxxhhjjDHGGGPMvoAfHWPMDoCd6evXb98/n1/7tV9zkt0YY4wxxhhjjDHGmH0Q72g3ZgfBs9pf97rXZW9lsJv9a1/7WjnyyCNTYowxxhhjjDHGGGOM2VfwjnZjdpBnP/vZZd26ddlbGexmd5LdGGOMMcYYY4wxxph9E+9oN2YNPP7xjy9veMMbsjfNwQcfXM4991x+WaoxxhhjjDHGGGOMMWbfwzvajVkDz3nOc1Z9Vjt2szvJbowxxhhjjDHGGGPMvot3tBuzRh73uMeVN77xjdkbctBBB/HZ7E60G2OMMcYYY4wxxhiz7+Id7caskec+97lzd7U/5SlPcZLdGGOMMcYYY4wxxph9HO9oN2Yn8JjHPKacdtpp2av42ezGGGOMMcYYY4wxxuwfeEe7MTuBZz/72dma8aQnPclJdmOMMcYYY4wxxhhj9gO8o92YncSjH/3o8pa3vIVtPJv9nHPOKccccwz7xhhjjDHGGGOMMcaYfRfvaDdmJ3HyySeXdevWsX3SSSc5yW6MMcYYY4wxxhhjzH6Cd7QbsxP52Z/92XLGGWeUr371q+XGN75xSo0xxhhjjDHGGGOMMfsy3tFuzE7kuc99LnezO8lujDHGGGOMMcYYY8z+g3e0G7OTufbaa8uBBx6YPWOMMcYYY4zZM9m8eXP5yEc+Uu5zn/ukxBhjjDE7ine0G7OTcZLdGGOMMcYYszfwqle9qjzlKU8p3n9njDHGrB3vaDfGGGOMMcYYY/YzsJv9+OOPLxdccEH567/+6/Kwhz0sR4wxxhizI3hHuzHGGGOMMcYYs5+B3exIsoPnPe95rI0xxhiz43hHuzHGGGOMMcYYsx9xzTXXlFvc4hblwgsvTEkpZ5xxRnnIQx6SPWOMMcZsL97RbowxxhhjjDHG7Ee88pWvHCTZwTOf+cxsGWOMMWZH8I52Y4wxxhhjjDFmPwG72W92s5uVSy65JCUz3v72t5eHP/zh2TPGGGPM9uAd7cYYY4wxxhhjzH7Cy1/+8skkO3jWs56VLWOMMcZsL97RbowxxhhjjDHG7AdMPZt9jHe1G2OMMTuGd7QbY4wxxhhjjDH7AdjNvlKSHXhXuzHGGLNjeEe7McYYY4wxxhizj3P11VeX448/vlx88cUpmc9b3/rW8ohHPCJ7xhhjjFkE72g3xhhjjDHGGGP2cV760pculGQHJ598craMMcYYsyje0W6MMcYYY4wxxuzD4Nnsxx133MKJdnDaaaeVRz3qUdkzxhhjzGp4R7sxxhhjjDHGGLMP86d/+qfblWQH2NW+bdu27BljjDFmNbyj3RhjjDHGGGOM2Ue54ooryi1ucYtyySWXpGRx3vSmN5XHPOYx2TPGGGPMSnhHuzHGGGOMMcYYs4/yspe9bFmS/UEPelB52MMelr3KM5/5zHLYYYdlr/J7v/d73tVujDHGLIgT7cYYY4wxxhhjzD4IdrOfeuqpbK9bt6489KEPLZ/85CfLe97znnLMMcdQLl74wheWr3/96+W5z31uuf71r0/Z5z73Oe5qN8YYY8zqONFujDHGGGOMMcbsg+DZ7Jdffnl55CMfWT7zmc+Ud77zneUud7lLji7nete7XnnOc55TzjnnnPKCF7ygHH300Uy8e1e7McYYszpOtBtjjDHGGGOMMfsYV199dTn//PPLWWedVU4//fRyu9vdLkdW5/DDDy+/8zu/U84999zyK7/yK+X9739/jhhjjDFmHv4yVGOMMcYYY4wxZj/jpJNOKq9+9auzV4pTA8YYY8za8I52Y4wxxhhjjDHGGGOMMWYNONFujDHGGGOMMcbspfzmb/5m+fEf//HypCc9qfz93/99So0xxhizu3Gi3RhjjDHGGGOM2QtZt25decITnlD+4i/+gs9kv9/97lf+4A/+IEeNMcYYsztxot0YY8x+zxVXXFF+7Md+jAVtY4wxxpg9nac97WnlwQ9+cDn22GPLLW5xi/KKV7yiHHXUUeUZz3hG+fa3v51axhhjjNldONFujDHGrJFvfOMbvKn9z//8z5QYY4wxBrz5zW8u73nPe7K3Z/PBD36wfPWrX83ed4/nPve52VqZyy67rLz73e/m+xBw8MEHl4c//OFsn3322ayNMcYYs/twot0YY4xZI6973evKy172snLdddelxBhjjDFvfetby6tf/eryUz/1UynZs8GO8Pe+973Z++7xne98p9znPvfJ3nzwmJgXv/jF5YQTTkhJKTe96U1ZX//612dtjDHGmN3HuqUg28YYY8x+CR4XoyQAdt0ddthhbC/Ki170ovL85z+/fOhDHyp3vetdU2qMMcbsvyDJ/rM/+7PlzDPP5KPZpvja175WTjzxxOzNB49GecQjHsEv/NxVCeTPf/7z5e53v3v59Kc/zcewjNmdsV555ZXlJ37iJxjHaaedltLV+da3vlWOP/74cuc737l8+MMfTul8TjrpJH4QIpwaMMYYY9aGd7QbY4zZqfzZn/1Z2bhx4+AG75prrikPetCDlsn1bPQHPOAB/AKvrVu3ltNPP73c6U53KgcccAC/4GvTpk1Mgn/xi1+kDXaN4wYWN5/nnXceZQI+4Ov2t799+eY3v0nZli1buNscO7zgD34f9ahHla9//escXwnc6D7zmc/kjbJiwfNQsdNM4AvIoHPVVVeVu93tbn7OuzHGmP0eJKuRZP/1X//1uUl2cPnll5d73/veLHhkCx7dcvHFFzcZyq1udSu+puO1/x73uEf5h3/4h7TeuSDhrPcXUywS6/d///eXSy65ZM2xHnrooeX3fu/3ylve8paFHyMDXvKSl/A9yG/91m+lxBhjjDG7FexoN8YYY3YWH//4x5cOOeSQpVNPPTUlS0tf/vKXl4499lhsk1r6oz/6o5QuLZ111llLRx11VNN98YtfvLRu3bqluClfes1rXrP0hje8YSluVml3pzvdaSlutKn35je/mXqoe+LGfunII49ces5znsP+5s2bl375l3+Z9nHzS59x47p0xBFHLJ1wwglLcaNMvbh5XoobZBa0waWXXrr0Ez/xE7T9yZ/8Scby1Kc+dWnjxo2UYxx87GMfW3rsYx+7tGHDBq7jzDPPXNqyZQvHjDHGmP2RZz7zmUvHHHPM0tlnn52S1bnRjW7E11y9ho954hOfyHEUvNfY2Rx99NFLH/7wh7O3MngfsTtihf+b3OQmS+eff35K5nP66adzvs9+9rMpWZ0+ThRjjDHGrA3vaDfGGLNTufWtb80/vcZjVLCTHXzhC18o3/72t0vcxJaPfvSj7Vnmn/zkJ7nz6kd/9Ef55854bAt2pP/t3/5t+cVf/MXyv/7X/+IO9yc/+cnlS1/6UvuCsh/5kR8pxx13XDnjjDMGz0X/m7/5G+4sv//9788+dpK95jWvKT/3cz9X/u3f/o0+n/WsZ5W///u/5y407ESf91z117/+9bQ/5ZRTyvve9z7Ggp1i+FI3/Bn8//2//5d697znPbkDH7vd73vf+3Ln3oYNGzhmjDHG7G/g9f6Nb3wjv5Tzlre8ZUpX5u/+7u/KhRdeyPYDH/hA1mMe85jHZKvwC0B3Ju9617vKDW5wg/JDP/RDKZnPWWed1f6ybVfHisfPnH/++fxrwZXQY3ri/r7c7na34257FGOMMcbsXpxoN8YYs1PB881xo4okOm4OAR4Xg+T7E5/4xPLxj3+cN9NIcP/1X/81k9S3uc1tylFHHcUENpLsBx98MO0AHtly85vfPHuVm9zkJnycDG4izz33XMouu+wy+sOXh+FPt8Hb3vY2PioGCXY8tkYgloc97GED+x48Muad73wnn7OKx8wgBoEkPhL9SOojmWCMMcaYGfigGs8zR5J4UZBoB3i9xwfYU5x99tnZKuWYY47J1s4BH9w/9KEPzd7K/Mu//AvrlWLF5gBxvetdL1vbDzYifN/3fV/58z//87mPpcP7KiXZBR63N28jgTHGGGN2HU60G2OM2ekg2X3RRReVz3zmM7wx/MhHPlLucpe7UI6d5F/5yleYbMfN4f3ud7/Bl4XhOe14fjp2gP3BH/wBd4k/+9nPztEKEt+PfvSj+cxW7E4H2GH2qU99igl4JOqRLMcOeCT+P/e5zzHprvL2t7+9bN68mcl5PEt1DGJGjLg5xg783va9731v2bZtW/nGN77BOYwxxhgzA3+JdtBBB5VHPvKRKVmdf/qnf2L9wz/8w6yn+Nd//VfW2+t7EbCj/SEPeUj2VgbvacBKsf7Xf/1Xtkq5173ula0dA++TsHEBGxjGfPazny3/5//8Hz7H/XnPe14r2Czgv64zxhhjdj9OtBtjjNnpYEc5dql/4AMf4K423AgiYX7b29623PCGN+RNKr4oDTeOSL4D7MTCo2Owsx27xHDD+/znP59jU3/Kfcc73rHc9a535S52fAkqbOEbX7oK4A+7uS699FLupMdNeV/e9KY30e6CCy6gfg8S6bDFI28e//jHL7P953/+Z+5m15+5G2OMMaaCJDN2Yi8KPvRWYhqv61N84hOf4JeVgmc84xl8vd9Z4JFw+Au2RR4bA/BoPDAvVqAd+ngP8QM/8ANs7yh43wGmEu1PecpT+Nd5SLT3BfPji+GNMcYYs3txot0YY8xOB885vcc97sE/r/7Yxz7Gx7bc4Q53oBw3fv/+7//OP9M+4YQT2mNesCP953/+58ud73xnJriR6MbOciTr8dz2Mdht/tM//dPc4YZ5sHsLj3TRY2awkx1/bn3Tm96UyX4k3qfKgx/8YOr3HHHEEYwNu9XwHNYpO+xmX+km2xhjjNnfwOv35Zdfzke8LQo+eBdTr6t4nX/CE57A9nOe8xwmkncmp5122uCZ6iuBv3b78pe/zPa8WLGBAI+OwffAvO51r8uRHQfvY4B2/ffgeOCxe1NlZ34YYYwxxpjFcKLdGGPMTgePdkESHDfc+AJRfDHXjW50I/65N3a54Tns+NNyJOORfAd49ioeBYOk+q1udSs+Wx3oS1Kx+3z8THTtXv/jP/7j8t///d+8Ee//VBq76LFrHrvXkRwX2D2Hse/93u8dPEdVHHrooUyy47Ex73//+1NaOeeccxgfkvqIDeCDBPjHTnhjjDFmf0VJ6Bvf+MasF0GJdvxFW787G1+EjseuIHGN12skj3d2kh2Jczw2ZtFEO94XgJVixYf1iPWFL3whddeKEu1T3ymDL2CfV4wxxhiz+3Gi3RhjzC4BO9iRsP6P//gPflkY2gB/mr1lyxYmqZGM1xeNQh83ky94wQv4vFE8Dx1/Ho5H0OBRM0hkI9neg7Gf+Imf4BeT4iYcz4Hv+Zmf+ZnyS7/0S/SDLzF94xvfWF7xilcwhn/4h38oJ510Eu2mePrTn84b5kc84hHlxBNP5I63U045hR8UIJHwtKc9jTfaALvfEdvLXvYy/rn2OE5jjDFmf0Dfe7I9O9rxfS1g/OgWfCiOHeJ4Tf3d3/3dXZI8xmNj8Ki6W97ylilZGXy5OxjHeu211+6yWA855BB+l40+3DfGGGPMnosT7dsJkixIFuE5gcYYY+aDm+z/8T/+BxPpeg47wM0sEuooeM66gBxfgHqnO92pvPjFL+YzSd/ylreUU089lc9Ex43m+Hcvdq8jEQ4e/vCHc9d8D3aa/+mf/il31X/xi18sj3vc47jrDMD3b/zGb7RE/xg8muYd73hH+a3f+q3yvve9j7vd8KWseDQNdqphPoHk+2Mf+1j+ifgv/MIv8ItSjTHGmP0N/GUa2J4d7Upe41Es2iGO8oY3vIGJcIAP7PGdKzsb+MeXqy8K3o+Acayvec1r+Jd6ALHiy9h3JnjP5ES7McYYs+ezbqn/W3qzIjhUSNLgOcD4Ehw/m9cYY4wxxhhjKvirtGc961n8gPp+97tfSueDx77hcWwAH7b/1E/9FNs9+kD8U5/6VPtel50B/loOf1l30UUXpWRlPv/5z/NL3cFqsf7lX/5l+bmf+zm2dwY/+IM/yJ3/mzdvTsnOAX/Zpy+ZBU4NGGOMMWvDO9q3g69//evl3/7t37JnjDHGGGOMMUbokWr4fpRF0G52MLWJ6a1vfWu2yk7f0Y5ns+Ov5xZFz2cHU7H2f8121VVXZWvnAN9HH3109owxxhizp+JE+4LgmXsPfOAD+aV5eON0t7vdjTsWUI4//ng/JsAYY4wxxhizX6NE+6L3Rvi+FIDvW5l63MxnP/vZbM3nO9/5Tnn+859f3vve97KPHdr4HpWzzjqL/XmcccYZk7vS5/GP//iPrOfF+md/9mfZKvwS+JW4+OKL+Z0xeJwdvlR1NfDBhb483hhjjDF7Lk60L8jtb397PjIG3yZ/8MEH840c3vCg/Pu//3s55phjUtMYY4wxxhhj9j+UDF5kRzueOY4vEAc//uM/znrMOeeck61SbnGLW2RrBnzgi9HxbPinPOUp5Rd/8RfLFVdcUe5xj3twk1S/I74Hu9m3bt1KnUVYJNavfOUr2VoZfACA767Ztm1b+b7v+z7G3T++ZQy+YBaPjPGOdmOMMWbPx4n2BTnssMPKscceWw488EDuYscbHexkQEGS/YADDkhNY4wxxhhjjNn/uPOd78x6kR3tSFzrCz7xheJT9F8qqkT7F77whfYs9NNOO41fWn7CCSeUL3/5y+UBD3hA+d//+3/T3/d8z/fM3RGP3eyLJtnBIrGee+652ZqBDwoUq3jlK1/JL3JHgv0ud7kLY1y/fv5tuT608PeDGWOMMXs+TrQbY4wxxhhjjFkzN7zhDfmFpUiGz+O5z31u+dmf/dnya7/2aympMpTxDnQkooXGsGtdXzSKR7DgMTGf+cxn+BfI/TPXkaSf+n4t7BDHjvZFEu07GqvAI23GX4p64YUXluc973nll37pl8p5551XzjzzzPKoRz0qR5eDL2EFqz2OxhhjjDHffZxoN8YYY4wxxhizU7jnPe/JxPfZZ5+dkhnaoY6k8VOf+tTynOc8hwW71bHzG8nynj/+4z8uT3/607mbGwlv6GzcuLH85V/+JcdhCz70oQ9xl3gP5sKu9jHvfve7y01vetNVE+1TsSLBfstb3pJyfKjQ08eKR8PgL6CPPPLIFqv4+Z//eT4y5rWvfW153OMeV17zmteUww8/PEeXo4T+ne50J9bGGGOM2XNZtxRk26wCnveHL8zBM9nxZs5/vmeMMcYYY4wxM97znveUBz/4weUlL3kJE9Q7i69//et8jOc4wf2xj32s/MAP/EA5/fTT2472l770pdyFjiQ2dsD3ICF/hzvcgUnzXQVive6665Y9Vx7PYv/hH/7hcrOb3ax85CMf4WNkkPhf6ZYc68UXsOIxOTubk046afB8eKcGjDHGmLXhHe3GGGOMMcYYY3YK2JiERPuHP/zhlOwckJweJ9nBe9/7Xtb9Y2PwTPX73//+y5Ls+NJUJLZ/+qd/OiW7BsQ69eWtL3zhC8sf/uEfliOOOII76n/1V3+1/NiP/ViOLgePuEHMj3/841NijDHGmD0ZJ9p3AHzr+9VXX509Y4wxxhhjjDHiiU98IneYI+G9q/mv//ov1j/4gz/IGs8/v/baa7lbfAwS13e/+93bl7bubo477rj2rHXE9z//5/8sj3nMY9if4m1vexsfL/OgBz0oJcYYY4zZk3GifTvYsGFDOfTQQ/lngP/xH/+R0lIuv/xyyowxxuw6tmzZUn77t3+7HHDAAXxGK75EbEr2hCc8oRx//PHlG9/4Rlouxo7aGWOMMWYIdrU/+clP5g7uXQ0S7di5jl3feBwMnn/+t3/7t+1Z6j1nnHFGechDHpK93c8b3/hGvs/AexbsrP/7v/97figxxYtf/OLy9re/vfzv//2/U2KMMcaYPR0n2reDgw46qH1pzjOe8YzyR3/0R/zSm3vc4x4rfrO+McaYtYNdcaeeemp5wAMeUN785jeXX//1X5+U7cls3bqVN/nYbWeMMcbsy7zsZS8rmzZt2qXPQv/yl7/Mctvb3paPYcFc/+t//S8+y30Mvm8LO9of+tCHpmT3g0fK4P4Rz0LHhwH3ve99c2QIEvD/5//8n/I3f/M35S53uUtKjVkQPGt/2za88axtY4wxuw0n2rcTfEs8EjnXXHNN+c3f/M3yzne+kzsNxt+Qb4wxZufyuc99rhx88MG8iX70ox9d7njHO07K/uqv/qqcc8455cY3vnFaLsaO2m0PF110Eb+c7eyzz06JMcYYs+/ygQ98oHz605/ml5PuCvBFqOCud70r65XAh/PYNHXrW986JXsu97vf/fjB/ErPbzdmLvhr+1NPLeX0051oN8aY3YwT7dsJHh3zJ3/yJ9yViJ0IH/rQh9pz9owxxuxa8KfW69cPX7qmZMYYY4zZM8CmpKc+9anZ23kceeSRfDTNve51L26Gwg76lcCXpWIX+d4A7jO/m4+4MXs5559fyimnlPLudzvRbowxuxlnJowxxiyMPmD8gR/4ASa4UW5zm9vwC8/wAaT4zne+w0dsXf/616cO/nT8l3/5l8vXv/711JjxxS9+kV/ypees3/SmN+VjYOQPzzLFs9Of+cxnlquuuqrc7W53459eo/QyPV996lnri8QzZXfllVdyjt7uaU97Gv0JxXfiiSdytxyOx5TuJz7xCT439txzzy2ve93rqIPnsxpjjDFm+7n00kvLZz7zmfIv//Iv5Stf+Up5ylOekiPG7KcgqY7y1reWsnlziTe6TrQbY8xuxol2Y4wxC/OOd7yj3Oc+9ynnn39++dM//dPymte8ho9uedSjHsW/9gF4/MoP/uAPlj/4gz8oD37wg5k0/5Vf+RU+mgVy/Am5+Ld/+7dyz3vek88ixZemveENb+CjuPB8VXwPBhL72LH2qle9qjz2sY/ll1Ljmewvf/nLWXoZdKA75rLLLisPf/jDJ+P5yZ/8ycnkP4AdnuP6ohe9qNz97ndnbLB7xSteQX8Y78GOPTy+Bn/uDV38uTeOEb7EDF+YjS9lQ8yI8Ud+5EfiHuit5X/8j/+R1sYYY4wxxqyRa64p5cwz6zPa57zHNcYYswtZMsYYYxbgggsuWLrNbW6zdPe7333pm9/8ZkqXli699NKlH/uxH1u6733vu3TJJZcsPec5z1lat27d0mmnnZYalY997GNLhxxyyNKjH/3opS1btixdddVVS/e///0pw5jYvHnz0i//8i8vbdq0aemjH/1oSpeWTj31VOp+/OMfT8m07MQTT1w67rjjls4//3z2X/rSl2Irz9LrX/969gXiO+qoo5be9KY3sT/P7pRTTlnatm0bZeBtb3vb0vr167lOAH3Yjdeh9fU+pYu5jDHGGGO+mzzxiU/kex0Vs5eD96vf/vbS0g/+4NLSrW61tHTQQUtLV19d5cYYY3YL3tFujDFmIbAT/Qtf+EL5xV/8xXKDG9wgpaVc73rXK2eeeSZ3peNxKH/zN3/DLyXDzu4eyPAlZB/84Af5+BQ8MuajH/0oZf2XmG3cuLGcdNJJ3Kn+nve8J6U7Br64Go9zwXdpPOABD0hpBbvwL7nkkvKYxzwmJTPwyBh82fWxxx5LPaxL3P/+9+eOdKzz29/+dkoLd6f339mBnf63utWtqIMvQTXGGGOMMWaXgkfGxPvYeGNad7X7PagxxuxWnGg3xhizEFdffTUf5XKTm9wkJctBYhtJZSSc8VzzHiTO8QgWPLMcyefzzz+/XH755eXAAw9kUvttb3tbK5/85Cf5jPOvfvWrnHNHwSNbkDS/4Q1vWA466KCUrs4VV1zB573iQwR8GNDH9t73vjfuW7bx2ezwLfSM+R48bx7PmkccxhhjjDHG7FKQaL/qqlK+93tLOfRQP6fdGGN2M060G2OM2W30X5iq5POb3vSm8shHPnJQnvjEJ/JLzs4777xBMnt3gUQ64sMO/sc//vHL4vvnf/5nflhw4YUXpoUxxhhjjDHfZbZsqTvalWiP99JOtBtjzO7DiXZjjDELgUehYMc2dqKP+d3f/d1y29velmPHHHNMOeusswaPVQFIXGOn+hFHHMHd7scdd1w5/PDDywte8ALuWp8qeMzMYYcdlh62H+yiPzRuMi6++GLutu85++yzyy1ucYvyvOc9LyUzEOMJJ5xQfviHf5g78KdiwwcA/SNvjDHGGGOM+a6C97va0Y730N7RbowxuxUn2o0xxizEHe94x3LrW9+6vPGNbyyXXXZZSgsf73LaaaeV448/no+MedCDHlQ+8YlPlPe///2pUYEMz0u/173uxcfPIMkN/f/3//5fOeecc1Kr8va3v52PjnnOc56Tkh0Dj4vBM+A/97nPlX/4h39IKb/tq7zlLW/hs+Lvec97pnQGkvNIsuOxMeN1IFY8ex3Paf/Wt76V0sVYv349k//40AExGGOMMcYYs9O44AI8z7CUm93Mj44xxpjvAk60G2OMWYgb3ehG5fd///fLRz7ykXKnO92pvOQlL2G5973vXb72ta+V3/zN3+Su95//+Z9nAv3Rj350OfHEE5mE/7Vf+zUmpg855BDufkcCHM8/hz12m8MffEMXj43BF5QiEf8Lv/ALOfuOg0e//PiP/3iLBx8UPOEJTyi/8zu/U376p3+a8U/x9Kc/vdznPvcpj3jEI9o6TjnllPKjP/qj5ctf/nJ52tOeVo466qjUXgwk8G9+85vzy2P/7M/+jB8AGGOMMcYYs1M499xSjj66Jtmxox2PjjHGGLPbcKLdGGPMwjz84Q9nkhg70n/913+dBe1//dd/Lfe9732pg53t2An+W7/1W+Xd7343k+avetWrmNz+1Kc+Ve52t7tRD9zjHvdg4h5J+Oc///nUxTPbofuP//iP9LVWkNB/xzve0eJ53OMex/q5z31uecMb3sAPB6bo7d73vvcxtmc/+9ktUY5jsb3gMTjPeMYzuKP9V3/1Vzm/McYYY4wxOwUk2m94w1IOPBB/2lkK/gq1+44kY4wxu5Z1S/7bdWOMMcYYY4wxZr/ipJNOKq9+9auzVx+tZ/ZicP6e+MRSLrmklNe9rpTHPKY+s/2d76w73I0xxuxyvKPdGGOMMcYYY4wxZm8GiXbtaF+/vibXt2zxjnZjjNmNONFujDHGGGOMMcYYs7fTJ9rxjPbrrnOi3RhjdiNOtBtjjDHGGGOMMcbsreixPxdcUAq+rN872o0x5ruCE+3GGGOMMcYYY4wxezNIql95ZSlHHFHKunWlHHKIE+3GGLObcaLdGGOMMcYYY4wxZm/m8strgh072VH70THGGLPbcaLdGGOMMcYYY4wxZm/mO98p5YADSjnooNr3o2OMMWa340S7McYYY4wxxhhjzN4KntGOHe0bNpRy4IGzne3e0W6MMbsVJ9qNMcYYY4wxxhhj9maQaJ/a0Y5kuzHGmN2CE+3GGGOMMcYYY4wxeyva0T5OtCPJvm1b7RtjjNnlrFsKsm2MMcbsdWy74Cus1x1+VL3JWJ+fIV97FW8sllBDfsCGUrbGzYZ29SzFTQfk122pfQGbb3+7rNu4sZRDDglB6PCVcqksXX1N+L22rDs4bmAwD+z157i4sdGf5+LPdjdtqv3ror9+XW1vg6+YF7EAzB1+ljZvKVuvvLZsuP7hjHEp9K677OpywMEby/qDN5Vtm68r1121uWw87EDGsvXqzRHfAWXdAevL5tA78Ki4kQIxDeaAPefCvFHWH3m9snTZd+p6Nm8u266MdcQ6122ImLEG/HkxbBEe4o36umu2lANijm1bt5Utm7dyrnXhc8OmmHdDPcbrwve6TRvKtohNxwhct6X6oH34Qyh4u7Fly7Zy4IFhj7liQtSQXXH1dfzk/5CDN5SN4fvKq7aUzdctlesfeSDeqMQhjTXF+IYYw9q2hWx9xAOuQ2zhqPqMYxPj8HfoQZgnhcGV11xXNkW8GyP+reEbASIuqMAnJACHAwXAV1wNjE2+tmLuqHXLClWMoEiGGjoYwxzwjnZEVOeLDmrOH3L8gF+0D4gBrI+2KJxvXTkwYo/DUK7jWBzLaG8Mgw2hvzn1Yc9Y4z+c+i0hP3DdetpsDQ342xBjG8PmmrgOr87jCjbAONBaUcMOvjGH/IPNETjiVBywxSHF+EExMcYPjhr/0uDn2ugjfvjQfNeEDHZx+rmO9eEPaB6UO/3iEyAyxhizizjppJPKq1/96uzh9Um/pc1eB95/vv3tpTz1qaX87d+Wcpe7lHLmmaU89rGlfPCDpdz2tqlojDFmV+JE+4jNmzeXD3/gfeU+P/ojVVDv+2Z3hkCyMYvozGOlswBfU+OcI36sdgoVyzy11WKFXa8jP3lTPDk/hnrxvBjoY8Je9HNhnnl++wmaTZRJXdANohqMBb3dmLFfMLYH0uFY/NBx6te80jxAfnu9qbnAPF9T+tLtQiFj3YHPzqg/H6Rz1Kk1IOtUBoz1wFhXfV1z6AzGQz7v30Fnkk7QqERz3aFHZsfsrSDRvvTlT5X1t7xjdJDk3hSXQ5zrSy8sZcvmsvStb1T5poPjl/w1pVx1RTVE4nsp5FdeGfW2uBjW8zpa2npd2fbZz5b1hx9a1h17kzoGeZRtF3+zlG9dUtYfc8NSkIjHDqFrwifmw+6hq6+ufSS0jzii9lGQhEetRLx2Gl0RsWzYULZ+58py7XnfKgff4Ra0X9p8Xbnqvy8sm445vGw85shyXYxfff5l5dDj6ocJmy+8rKw/9KBywCGbymVfuKBc/443q5c2kpmbtzJZvi3q9Zti3o0byoZbn1C2felLpRx741Iuu6xsOf+bZenaLeWAQw9kknnd+vVMmi/FerZedS2yvuXqb15RNh12YNly7XXlykuvKesO2lgOCL+HHhnzHhxrj393SLgfcL1DypZvXMrDVI/junLN5dfS70GHH1g2X3ltQd4dCfvLL99cbnDUQWX9AfXf4QEx7+VXbC7nXnhViUjLTW90SDns0I3lvPOvLJdefV25w62uz0T7tdduZdL7kEM3la3hDIn3jQeGRcx1xXeuDT/rynrEH7pI3J970VXlJkcfXDZkMj4CK1+75OpyvY0HlMMP3xSHGClgnI4lfl6CxDtOIX5DoN4asaK+JurrQhqrbYngq5E0jiYSzEg0I/mNRHRMz4QyloYxrAeHBMluJMuR6D44rjEksZFUZ5I59BhhyK6NYw8bfBiwJfzAFnPD56YYv/6GA5icvgofBMV/V8TEh4XuwbHGy+K8hCv6QiwoSIRfGbpHxrV3ddTXxpyI5eBY8BFRLopr8eK4RnAksLJDcPyi3hQd2CL5fVXMd+l128qhMbY1tNZTIy6hmPug0MGslyOOWPRVUWP+G8QxviyO2402ro/5a+zfivgQf3S5JuhdHOfp0LC7PPwfFmvAuuEdOjiG3/+LTyg/+bL/i+mMMcbsIpxo34fA+8v/9/9KOfnkUt7//ppY/7d/K+V//s9S/umfSrljvE82xhizy3GifcRLXvKS8mevfEX5z9e+kDfDTLzgFpQZBBCHC3IctahaTZ3uUEKnKSwA/dNRMLZBDJofpJ7mYBIoytSciGkdkhFoywc6QHrRp0hyEX3awEceByAZd2SGfOuWKuP8CeaETHOuR1/zqQ7Whw/p9OgYay7oIVFGefQ1n9bWYsNY6AEmzeQbuqkDuJ7Q3Zb++/UBxooxyNDOinNmuwpqjTga0kfdjSkurjnH+vgI5NGWHLqQYe0AcXIdUXLqeiyw1tSB77YejCk2KIP0TzuMoZ02/TGinmyi7mNF0pDHDt3oc/70o7UNzk364TmEbvrDddHPqfl6f8iCAcx3AJJ70cYYdxFjHswffvr4+nOnaw8F87Mf4zHH+mNvWdbd8k4wMnsxTLR/CYn2O9RzuzET7d9Gov3asvStC+r1s/FA9oeJ9pBfeXm9PvLaZKL9rM+W9Yd1ifawD41hoh271mG3MxPtt89E+xYk2i8om445YnmifdtS2XzR6ol21OuYaN9YNtz6+8q2//5SKTfuEu3XbKYPxI5EOzPFsU4l2q+6+Ipy4OGb4pBtZaJ9/UEby/qI/bAjD452rD30sct9w5GHlM3nz0+0X3vFteU6HEIm2reUGxx1YBwOJMVxWNaVy6/YUr524VV8pbvpsYeWww7dUM47byLRHjEdctgo0R4xXHGZEu31NCDR/rWLrio3HiXav37J1eWILtGO13jowg4Jd5xgJHmBEu3XRr0lBsaJdiSjq8n8RLtmxo7w1RPt1RZt7PxeKdGO5DlmvjImRgJ8VyXaER/Wuj2Jdvg5Oo7xpZloR6J+WaI9YsCxWinRjrmRaL+vE+3GGLNLcaJ9HwLvMV/2slL++I/rTvZb3rKUz362lB/6oVI+9KF4Yf3+VDTGGLMr0X2gCbCb/YUvfGE56/NfKO/6539PKW4be6LP9x96ExL1oA8gQz9lesPCLARqjGfNRjdH3JAOoC7sII/SJymBatqhpD/pAeigDdkY6mE8+/StArnmiYIkpfotHtQ9miMM4BtJzZZkj0IfaIOwpb+MVz41PydFSZnWhDm1FsYYMhQmo6EfSFc+WdBP2bZ4I4K7eRJ96kZhPFEjKSbgS/MB6qLGD/jtgQ8MSN6ND/xID/S6UdoxjRqPuZAagD2OJ6AcfjLmwbnIvpLsaGM+6QL245jRJ5JmiC/1APqURRtyzKvkORKF9I9OB/xLl/PkONed+iqcH6U7h5pb4/31gvZ1m+uOZIjwQY/89iDmDUiLBVw/fGVcsNE1Sdv0bfZ+eG2zkXW9hnjDKJGupTHNZHYt8YPWuYzGOEdXppgjr/GhSCX1MEU2h9MhtTtkEOt4cBmpEJNxPvyQPWS1NYOCHA89/Kc25f046dszqjSscxgx1/XOsxixTKkmdTmwLOgQYTDkdT7opoMMYDy3aoH4qDoeCDRdPy3afX+M3EzpTExB6HMFp/PstotwAj/b40u6uFpkizAlx3GbySQdsj3HwRhjjDEL0j+jHS/I+jJUblAyxhizO0CmySSvetWrygUXXMD28197WvyMF6eWvBHd7SGbuDWEHjoqul3s5MuSQOhDjoKkZSYEUZAAVJ8JwWiS9MO72NBpd+CoU4ny0MGON7SZVMRpTh0mSuUQtYrG+zFUsN9Q29QR0dbdtOJhibb05auZ9XrhF0lklDZn55/HBXOEHuRIjPMYplzJUviEvB8D7dgEjA960e7E3Inf02xCl3GF/7YGHM/arDGhTr/oY1w7+xs5Ic4vipLGA52EfqLGGPRwjJo8itaK66b/QAEwSY5jjnFUaat5oI/jBx+YX/6ZqJbfQGOKEdcQ2u0ayvkBP4iIPmLjPKrRzPm1dtZRAHdiJvDRHz/obdxU7aGPsQ3RhxxxoY1YEDeea30tdgdHzeMRwA5xwhZvKGkX60ZyHvFxbTEPfSvGkJt9A10HcXpn5zU6vPYorOC6mgevxxxnPaHbuWpgDs6T9D7U5nU34a8HH/6NPgDs3YJBv3fHqWBT2wT1yJ5DcEK56o5Rn/P1/lRAqzshmxro6FREv2sOrVmvKvbqNO8ELa4oM7vKTE/J+CpjyT7Arupe0Pvv25VlgmXzajnLNYeM7cZoHHXTTae9b8XYy3rfSoSLefNCp9cD83SBPtiBjmx1PnhMR0jWD7VTH7WaY1PJjTHGGLMgSrQfeGDt68tQ+VecfmU1xpjdgbJN+z3XXHNNOeWUU7JXyn/+91fKu//5X6M1fkHCIcPt4OCWMesRSh72oEvT9ME7/yjU7ZKQsmvZBFTzTleOA+hzF3LK0O/vaGcBJNFvukhCKubUwxATWCGXXu9btWxb6WT9unDHrXUgYYoC/63Em4AeJthzHsDkd9T0DQF8wyeEUTifjlNnBxQXVPGDxwm6skNT7ZHtgPEYfIaflgzP8Xa+soZvHTuA8aaT9H3q9/0ocI34lLDmmtDP4yafPE7dXAIyJtzTvjqsJcOuY2mLNj5A2Cp9xBNj+CACBowDujmuOXGc9YaOxxzyqPWhj2IEmIPHLuAO/hjnec+5Za8PLKgbMvqBPGrGDLsYlxxxIT4WyDEEnfCNfv/vxOz96BoDvB56+vOcOoDXTtfvoXw01nSzRh/zqvSo38/Rt8f6ACLKpdOpNbMcR18FqO7MOZXsR34qIRyPQ6HTwXPbAUW9ba6DVb8ufFCQTVDjTR95Wnp1oM8WFEJNwGeSONpTf8ZOt/nBBF9W0nhCNYBCbU0OB7Cbsk0z0u/OhupwbNif+NuANt7rNUK9yTtT+dUYhjQ86acD8Uq3c0noJ36M11znqivt/UNNsqnzQVJc/dYPiaQ5ZVHHZ0eq1xnPb4wxxphVwAtrPpKwPaIQiXbI8fhCY4wxuwVkvUzwyle+slx44YXZq/zOK1+fd4Jd0mZw94fbQpX+lhD9vgYaDxnVo4+Ctv7GHWAuzLmMkUxZBTLWT99NB+PRrne12U+bwVx9O4A5xyWXv4AJ4JC3oW6u3kbjM8VoYo04prDhJFlAP0eOt7WEjo4PS54Xzd3rEfSzyZ3V2deatQYWjGGQClFA56eVoK1PJYAtk8hRa5z+o08z/GCjjjP2jB/omDABnXq9b9n2Maotf5w3+r09fWYf4y2m3k5jkMc4Etm0CRmT8mjG8evnQfIaNX2lT7ZDXwls2LakOKqwYZ1t9tMn/NEPgAxV1pJLxr82CBl2uGMHPB8TE/NJH3AtfQwjOCfWhBjMPoHO+YB1IYIczax1jfD6VSfo5bqWumEi/5CjTR/Z1lhPL5saD5qULvAj+0B+ZYsK3dpbBqOZnGe2kKrDZoXtHEc1U61gfOxSemPdDqyFw2Hbh6RDhvRqTcIvR4efw2hHkW6aB7Mk8hS9Z7THM3GOFRwoBjC2BRhrck0wpThCKr3qvDBWcze2U4J8NaQztb5556RnbKdjgVpjqvuW6HX60eWaxhhjjFkY3E8hoY4kO5LtADvb0UYCfqU3PsYYY3Yaymzt12A3+wte8ILszfjcV79e/vqfRrval71ADW5Rs4BeT7egU2MBzZAMjIYSfy0ByMHaxNxIMOpGWFkIjLckZUDbnAM6/RjkXEPnE/qaX4W+UaSbMjT7+GkvndHlBL9MPo+RPWy7dQ78p08mSSmcwfkCHosYp3r64TpyHHYck36tqmvYYpwKtQ9aPNHn2tCPWsla+qADNGb6qOlL9jnOOVKGOTQffUMW4y0O2ESNNTA+xAS9reH2ujouPY5F3SeyAePAfLCDHnyii37Ioa94agBRpJ8FNnijpl3rzQc+rGAj/GAtocPYo+ZYyOUHfpEAb8c1ZYqjxRDwuKCPeVBCR1tUuZs2+kzEo5/rhRjI/+BDC8wRNW2iRkG8eLyOjltbU63MvkCe06zqNTcF5LhGoug6UE0whmHooZu68+jH4GfgKxnPs5LObAFZj5gQN9FobDZNNAZjGAhBWyNK1wahQvuUN1fB1G5tKqfbBtpdX/HM4qqMusN+dKCvHdID0McYmhwbe5qB8fHolLsxvUxt+Jk/0zSw7e3HzBuTXDWQjo5HP7araHNmDfq0fjunqcCkf8pWi2/mZaaLejU7Y4wxxnTgjcFVV5VygxvUex+AF2Psar/yyto3xhizy1GWar/m5S9/ebnkkkuyN+TkV78xWyBeqPo7wgF5S8g7y0zmLYT0ZN+fkt4nSn/bKbuoBzYJVXv9HsgnfFI0xx/08OINdZYusdn8oQbRVgaActV90bhqkLrNNusWT4wzviiMBTFABzVkGVPT72LUMWy+AzQpR40f3XwpHoI5s0lSCfP2u6Nhj3n0uJIBmiNsWZA4Vjv9DdYrX1EG64qxwTkIeh8kbLjenBO1EuTaQQ59DVM/5ChwozX08zBhjQ8CJuYZrDtqDsk2x5sd6mxDzjeD8NPN1fwhntBF4dzQBTGmR82gSIb5kaDnWqJP/5lkZ4EvyHpfZq+H15YKyGuiXXMg2hDrchFQ4TUUpV1LYtwPOE38QFmmH0iOIc3fxyFbtVU3lWjILWSaA1W0NUQ5x2uXDc4rQXXYTz3T7Riqz5BvjdM3arU1EMh25GP5Y0Zqf6AWndbHcHZmljPtgR3pxrrBfla1e5mYPjbZmDIIYEKzse2UWa+zCrDbDvV+ymzPro1aD5/RDtRHrSIbwA80sr0ok/rhWHPpEljNr/SNMcYYswPgBReJ9qOOqn29yUGi3TvajTFmt7HfZ5mwm/0P//APs7cc7mr/4MeihRemKKzyRUpJO92uqo2aCbzsN7oXt/ZChzp1etVmG+Nsxg8lBVn3vpCchB50osm4MMAfMaQ5UI/a1D0gmkhKhkg2guOSUaGqaK35/N5qFj/ajvKuzR3a6Hel0bcTjfPb0XNOJXf7tTBmrB0iyDCOvsZTB11c6vILGZ8BDv8hU7KbbRzbqJHcRcIWfepDF2MAellkB6ibc7Zd4hzAj2jnfK0fg0pKc17oo4RM8cmGXz5KZ1WHLqKGnuLQOugr0fHQunBMOUf6gilpjaqLxDQfyZJ9lR70eXyjPiB16TtkqLE2zY/jCXgt5LnkGkMmHcWFcSbGIYshJOExB/1hPNr88tecH7boY0zrgK2umfZoGcUQdgDqZt+B538Er6lsg7w8BrR/PxODU/o9suE8o/k1hnrseyrWKSZir6KhP7qnaDQPtJdNFQLqpy7HU4+xooYM3Wx0LHMniQaa3+Wa8xiqzvyNPczW2TPUaiFDzMeyLaefD+3xA1dWC70NqzGhL5/TESxHLiYOeR2bmKMHw2OVVUzIlE0vG4eD/tSaYEO5BudMPhaP/U35NsYYY8wC4A2MdrT3byiUaDfGGLNbyIzT/gt2s4+fzT5muKs96e/40R7e3cf/SOrplnJ8a5m0u/m0EXItn0hSShc1fNMEdRYiw4AJhmwPfOuU00Ftqk1fmdxtsclPlLbOrg8k065kFMrkN/yBFitsQ7f5A5oPPlLG3dewzwKYZM3SbAOMy2ebIwsTspn01fo5hjZ8Qx/+oQvgK9qcP0VsKH6QA/Sf9DG2daSMa0Ad9tpJDVmfjKZqyrJJuaag/yiylV0D/Si0h13otV3bdFbHIGsfkEQbSXyuI3XQbuvMdl+YNIf/9DFYR0K9kOna5VjUNMm+5pQu6vaBSo7r+YICcn1BKuoqjP+x5myDLZvDHusOros2x3Mu6WB6rcHs5cQ55XWPps5vd33xGkQJ0eB3R9S4TtCnLGs+uii68sXrJsF4ileEPrPd03x2TuSTMczkg8fXcCxRM+eoQ/EDl7zacoMmfdcuSTvWGOvHWdcOf03kvFmRQQI+bOtjXXJC2lSXAkO1QLqO7R50myh9N52sOSd913GFsKRvUg1k02xhknqgqXYBsNnpzAMqK6nRYyp07gfHYZ79lFx2/byQreSv150HjkHvQz5RZK/x7qonTac/qAF7Gmws3xmP49LLejdDj8v7xhhjjFkFvNCulGjv36AYY4zZZWQmav/k6quvLi960YuyNx/san/7mf8Srf7WD228WKHOwoSObk0lF6M27zhDFy+CbOcLH9Ugi6rJ0pZ96GIOjSmRKtKf9KgLe8hUg/SJWgmqgZ+Ec4cNx6KtGqWPGzV3gWNO6EMH8lqx0V7wm7DqCq2r+R2PYX75yPkFkmfQ11oAx2ETYzhOslGiuBEy+dIxgw1F8aMdl7BpieuAMbGRdcAkdhSNKV5WmiNr+OmT0VSKgj7nhw/JNUe04V+wneP0FXU79rIFqjEO3znWzws5Y4p5t2yp/gD8aR4eOwC7HuhsiJI+uCN+U21zTsxRm+UA6CGBnnNSN3fEg3Zc04Y+NHfUiBk6+ICgrT8K2sqkQQdzUheCaHMu6Jt9D5zfPN/qqzkFdHVNUrHvqwYjWfO/AFO6K9rHWLtex9T5McRW7yfN0Ki7qKNkuGrXpTVhdcR++qFCNz4BNKt2b5OkaBmp0pLwZEK5G6+RxM/4f2gXoJsiDlG5Ju77cFYllccmo9kqmq9WZEpvcvrtiEmq4yX3LrbDHXVR4G4qXiC5atrEsZHdPDDWx7KS/vCvBaqWJP3ImJXGjDHGGDMB3kQo0d5z2GF+RrsxxuxGkMHab3npS19aLr744uytzHP//E3xEzeJWZDoawnBlOHmXUnBptuD8QOqTtOFGPpRaJI2KWpwLgmzMHmc+rSdiIn+ssgf548CMeOIgjZ1IE99FH4JZ9RtV3rKNQ9jBylHV/4BE+AUTpSO9oHB2If6SKJ2j3vR/DpeSgBzLdFucQV9rCh8DAuA74wPOlwrSFt+GWi2GR/0wga6mJbHX+OItzY5znUHSFa3+UOGJDPtszDpDD/QybXoMTF4HEs/h2CcIUOCGm3OFQWxtRhxnDQvdOEn0BjXHSC+9qWnkEUNlMRWsh01wqCf0MHc0Nc6gc4F5GqDDQfWGnLGl2uVb8DYYRdjiAc70jVO/SicK/1SlihW+YWvjTHndfiwIGSYk/Jow6ds0W7n3Oz16Pri+e5oMlxjGksZxbimUEZARPXUFbq2+2u8Hxf9+DxdtTHOku2MZx2/awANiKps2Vwhn3yqNtSoGj9o0+tEW/MB/NMauYXNhNc008jYKMlYMdp2P3fOIOp9T3lDu9cRg+R7OBrvrha9WM1WR0NlNTTbVCwN+Rkrod/JVvI15QIy9DmWCtIbkEaoZF/1pF2l66Lq7Sd9BZKjHutzjjgHA9uuo/l1cHGt6A+oxtBXbZJercrnGBpjjDFmObjvufrq4Y521N7RbowxuxXcXu+X4NnsL37xi7O3OtjVfvoHPpy97uavJfwgU7Zi/CLW6ZOJW8s+cTgGY0pqoq7CUYF9358Dh+IH50ONBCp8I4EasbQXYK0j48OLNMZanGhn0WXERFe2qRc2TICnH8iYAEZfdGOYoyXcMZRjHEecqLMNGfUwPqtqYjqArd5gcN5uTsUGlGiFHZLHSkgjLiRw5Rf9lszXOuETpSkl6KOkDdeVNjSBLNotSR1CHUvFxkQ26tRBXPKjZLd0dYxb7CB1pcfjEX3KMBzjkMOG11X6J6GrDwDoU75gE2OogWJu/fCH5DUe1aIkP3xTJ8CxRmHcAXaxa06oSA6b/gMJzq+1pa927IL+vLAOfTxWRh+owAeOp+KE3uBYmb0e/tvQtZTXSCPky2QB9dNmArkb+AY0m7Cbkm0PDLHGOYh2UbcwmlpnwOS0hvo4pT+QzeqBu7Eq6tViW2W8Js3nKK3mexW2x1zJ+9VOa384VmLKz6Jojt7FCkepseiUi65hzCJ2g+tlAab013DojDHGmP0bvLD2X4Yq/Ix2Y4zZrXTZqv2LP/3TP114N7t47mveVLZ1z4Ot9H20u74Sf5Izadjrg5ENbzOzNHHeesoed/G6k+ccKNlvoB9lnCgFfRyQt6yAZOhHGy/Wg4xBjg/0JoAcc/Tz8I5abf0IPR2jdqyCXle14uecGbMKoAnmCyTrfUJBd/Vac5sb7ajHMWtO2kGns5EO9DXfsn9OIWeyF+NRxscDyWTNAR9MeKNgHLGEDkrzjzZqKlSd9oWxqct5otZ5J6oD+kRDMvQxV/hBwpo+ErU1v3aYo49d53xWevSxxv7LUDG3Evco8s050Q8d+ELs/HJWjEXRBwYAMh0b7kKHD5SQY7c6PxjBGqECHXxgEraKg/PAX7b5nPbOP2zb8Td7P3GOdZ2CrjlrR4PXP66BvA56vTaW9P6m6HXHrDTWE3r9Du1Js16m9jg2yJvx2En0xyJAH1FQNdNeMWNLtRmyS2HXnIem6ukfKaLWVJga7I9TQ/ElfRzQXsnvVMxDUbWaUJtPKq80r1jNO+JbyX7M2JNsIe+P9dhnb7fIfNKZqztnYBxfz6o+jTHGGLM6eF+ER8T4Ge3GGPNdZb/MMl0RLzR/+Id/mL3F+eK555fT/+EjeceIF6p8AeOLlvoqQZODkPVJvVSpSc5sN9IXXiB7mz5R2OYDXUJRMiZts6+66SftTh7y3p90s2CIw0ieIh74jjaToRjX/OmDa4o2x5EMhWxrrXnJQS9ou5tzAtrkGHS5fthDln0hv1qbaoAYpUtxtuFHx7PNk6DPsfSDvhK5XE+uscWbetRNWYsfNdYbNXzCj+yhj3gg56NN8hjIFmNwQVX4DziGWu3Q5xqjtER+tDkHxnIOxIBnrbe5wxaq/U5vJs8hREEF24yXyeuExyZgYj/AmrTrHjFtuSaE8A8/UWvHOfxzd3zGCzv4Z/wxzmnjB/zKFwrF0Wcs0UeCnbbQwXqgE23J6CPibTGnH84bXcWj6wYfEnDtOZfZd8D55o96bgdnWNeXrrGqnHXKed1Jnoz7Y6bGV/LT5k/URzV21enqkS10M9YbA9VmO/Mhu+YDBXqDmGq7/ero4LKyXRsQDJ/EDShJl3rut8BUnD/R41+grrLc4xCapHLvC7T+ii66wZEPxMPDEbKVXMmG1UB3pg03K9HGO/ucmqDufaiNOQbrVLujXS/8WW17XzTr7DSGeuyytwO97uDSGQEfvLxab2bbQz9ROG/8qDpTmsYYY4yZBC+4enRMz8EHV7kxxpjdQmaf9i9e9rKXlUsuuSR7lQc96EHlYQ97WPYqzzjxEeWwgw/KXuUFf/mWMtzV3t+KiqmbQ8hWsONuXjCybUlFoLvPrDHG8d5X6ipLQFDTgL0hnRx3t70roPn7u/Ex4zEmZeGru7yYZI1CVfzAeGfX7tRjvoEca4yx8TqVyIZZmw/j8pOwGz9kqvVIj7Zdn3FE0byixQDCWfPTM+5j0tRFbDoefIxJB3xrzdy9nX5QKxmseDSmhDHlGQsT3zjGkEsP86ZvsC50MCS5YlKMAn1ck00kf2hGGwltJc85F52GLBPfsOU1HW3FxLnoIGtUkEUfyX/FgrUxMQ6djEv2WCPmwJiOiY5Z04mCudsxiL5K08/YpGP2bngtZLv/MA+0362JrgOVRlwPgNcIdnPXLq8rddiOemAXoI+xMZDPGxuzfl1c+qEX8VG7t6EfiFIW7RlzfENMfSRkl+ss4XWsf3j2wCfIRG4np7b6qKM01/0caKOrItieJYhrXEsD00qdZJk4kKyegvgRjV6GgqXRc/Y1NkYv5VPHRyKZqV6uuVxnnOCeovqXxQyIV5pDY+hrTXFEa2OE5L0t/nWQ6EDey3D5AVT0z94M6QPW8aPXgwyXFGoV0MYhiI5s+sM+1jXGGGPMdoA3BddcU8rmzaUcffTwRVaJ9qk3Q8YYY3Y67Z5rfwG72U899VS2cXP90Ic+tHzyk58s73nPe8oxxxxDuXjBrz6+nPOuvygn/+Kjy/UPP4wyPKv9ze//UH3xUtEtIpN38QLGF7F5L2SS68UPdcjoB2PyF20mj1AgT7s2Z9AnZTVO1fgh2yrASKA5ejSXCmT4wcYMjUOuWJGwgn1LuHbxMk6M9zZZUFF2QB2mjMKK4u7na76VJE3kq+mDbLc+lFQgh2w0rjk4CD001QcjOf3XbiV9DEr+85J+A231Q48VjiHaKBhLuZKEfSycO/tyA5nOA2yZoI8aCWzNjRpzwLYlwVHqcB3XOYNe1CjYbQ59jDEhDuVmVOfl89Zzfn4IANucR4+YwQ/u4Ecn5xf0nR8UaP5+HsoD+ox40EfCHb4wB+RqQ46Y0O/1aItd79ClsyhdDGYvhyc1Tml/TifO7+QpT1uCayXLFBBrfJ7OPFbS579FlK4NmsnoCyiFhKFPVfwYK8pX1P0Qe31InFftrDsFtAa9tp6ZdNZUIx3RdbdzYzYAAP/0SURBVPzX/C5CVa42yw05Q/xYlihfFgNas/YyJmKCT4hhtYJlRUojRbmVuD/6/ZQYH4cwcjXJIjpj+kMF+2U+8pxKrnocH+hlbMcP1OM5WEdDOj2jbtWXkTHGGGMW55vfjHuyuJ86rOYtGki049ntxhhjdguZvdp/wLPZL7/88vLIRz6yfOYznynvfOc7y13ucpccXc71DjuEifaz3/ma8nu/8rhy9JFHlOe/5s1l29buTpB3lVF4xwi5xlCPbiObWWcPkBgESjTyZhc+o78sibAtG4ESkAT6va78gJA3O+hITzqoO79tXKQOm6jlL9tUx4/UG8SRbcVClfjRYkMtfdQqGpc9EqSww7ydPtSkCppfIV0gX6nT6vyBuHlMoRd9jFMmOcQp6yflecpxJXK1654xUhAlYBI823AziCmK/HBOiEYyQN1oM7meepgXSEfyGky04QN1CBkbSF3ADzFQ40dAPdlGW9coYPI6zwd8wDf6TKRnHOyHTzzSBT6gyzHUUXHqaChePtJGOrVJuI4sGNuCL1sNuLM9a46FAR8JEzroI3GPDwA4L9aGuSJOfviQsYWa2Q+ZPO+64Dog4jXeQduU8ZpKZ2O9KVbSwdACLhpTupKN6wH94juFwTGZPEBDYBpqWFJdVucrZcuWm/pD8gMCkQrzEuO9FFFKr/fLXwfZns+0Bmx1SkHXJFNWq88VjB1N0PtZfpzms+KHCI3QmYgBligYwpy9p15d7X68h8e8s6e/2mygLz+oVUCvO7YzxhhjzCrgRRiJdiTZx29mvKPdGGN2K13mbN/n6niBOf/888tZZ51VTj/99HK7290uR+Yxe4E6/JCDy28/4ZHlK3/95+WJP/2A8vf/+skcAdvihQslu0SHthfiRS+qyde4nIt+oKACMNaPR8GLZ0t8Ygzt0OcLa39au8lgJ9iGbvqFTf+CzH4Wzd/8dj7B4O48fXANmkN2AmNZNCXjkUw+8CNs4auPDeg4yIZ20IU82z3tmIYfJqdTF7LmB1XWtA9djoFo89iioJ/+OB/66EYbydtmE6ANX7CbXAdkUdMu2kyWp8OBftTcKR5oxzboxwHjj6Jd3Dh+WqtsIOY6etsQUj/0+vgBE9mylV6gxD5ivw47xXMNvQ7nQQw5l5LjQDqo1AbSRXIciXvFDx3YKyHPBD6AbdhgfSh8Vnvoy4921NN2QzTkP4T9vGav48wzzyyb8SeyA3RO4zzj/zzdA3hddgPja4FD6o+vkXF/gqnrarXrDdcsddBBALVdLZYvon05aFTLH32SY6ANpaxbO31ItfehJuv40bkjTTUbGEdBV+ukLNukGRFJBUaHGhXEONadfTFqtRh8UWo6GtuAbumzsBJ0exHGJUuTLr5ec8g42pnNDMl637Jif2Q05WMwUzam9ZbPIdAfHcrG2NfU43VEPzKeQ2iu+V6MMcYYs8PgRRaJ9sMPT0HHrn5GO980jYoxxuzHZPZs/+DgeJF5+ctfXm5961unZDXwItHfFq4rBx90UPmNxz6s3P9e413wekEJfd7NL7clVIsfTMCijR8oaa+EouS8uc0x+kWSMccGQEd6OK0Yj5pJyhxr8tQDaMLnMt+9TsTU0DiqzjcYzBP1UucHY73ugNDTTTzrLIxJBT4hjlq6gLFN+QyopzmjtPl7e/VR4DtjpyjafWKWRJs6UcNUyWaet9qsMcpX2g52a9M5pAl8wl+MMxENO7Sj1heOtmeV5xhBnTImw6GHOaJI1usgAc35M4bB8YjCtUA39Yn0AvqNfvsS1NDffE3YIbGtpHenzxhQoB+xIBFP/yFDOWBTjoUYsfELUUPOsBCLCAFiw5fH0jYMmNSHfs6HPsY2HpjjoX9tvKFE0Zq0ix++dd4gN3stT37yk8trXvOaaI3PY9/P9uBcq53XKuD1nW3QDQ3+DVApivxBptKjPq+90dhY1vuq/wBqFT9yZAV6P1lLNDDuO9HGXNCDeKA3oyZXO//BQDU6UFH4JNuwqstZGiyVQCdltK/NZVA+c4Reg3PCNhr9SIqDKu1/inE8g0T9BOPRKe2VPMxiGqJHyQzG1JkwWDnKIUPzCcsUaWTeIZgIYwDGUWCuGqgv6thskl5PSF96vb0xxhhjVgAv5Ei0H3FECjr06JhV3u+sCSTyTz+9lNe9rj4n3hhj9mPWZ21WQnf0PUwIxosVCwUUtzZr/Ig2E9gUZB0FMg6P/UYfyUnUmIO+0kYvjhhDspC2kEV7kJgMaAubTk6fsOnbqdNeeNOX+pwnLxP6kn4mLemj86OYe6gPW7ThV3MlzUcWxUY5RJ28kT4wn+IjoYMEc1uPfIRM9oqFc3S2rZ8+uCYM9PH2sih6ZjnXnvacB3qYB+0ASWS0ERdjQ1s20IkiX0yqh5/BGjhQQVJZu8cB/KhIl/0Yp176U+xoH7CxrpHXJuxigHGArHEd0kAFVdSaF354rcLfhmhnAp7PWs8YgB4nw37ayqdi27Bp1mZyHnEFqOk35e3DgGgL6PADkQCxSZcxYY2IBfNhXVE4Bjl8QGb2Rt761reWz33uc+X3f//3y2Z8CKPrUvDUxg+cf13bvD7QjoI2vw0zZbyWpVcr+oC8h+apB3BNjueeR28nIOP8qFMWtN3mlKV/jUM14tIuY1YsqZAVGYemWOUbZZ4OQLMfD31+kapQ/GxGnV+wWn/KdLjPW8lxrjEV0WcTY0Nl9lVSVJWjwBf6lCXsp0Jvx5rtKhjYoGS/Jd6zEup2ZpynzlR/1H5q5ED2GlN+hGLsx1o7DWXfmHIUsm3hDLr9b94xMEXReHVVP+CpbTH80Af6iBWnW20pDH1V0EZhLKHA2KKmbRSA8b5vjDHGmO1AO9r7NzhgV+9o3xLvw+P9eHniE0t56lNrwn173h+vBHzo+8J2hj9jjNkNjDIIZlUGyUIR/faLX+14gWOd/T4xSCAbvQjChonPADaaSwlB6kd/TJs7oS0Su5DDRiWBGDpIwoq+zfmoVOekabQRj3YCj1EMjFnAJuUt0T2KpYG5osLcWnd/rAd+QR4TErWOJRKrzR626YfHgxNEUZVtxUjd0IMvJdcGHygE8MVjFX2dU9TtmGW/rbtWTZf2iAcxoo85UaLPR6HIINBc3C2e/iBjYjm/fFSxSFe+SLTb7vOcB+3+jcq2eGOkY4nENHwoVtQqjCsKE9/RZxxRcwd51Exo06qiBDeALnQQM+ZG4XlCDKHD2HJexc4PoiCHPtYYbZ6TjBv6aGIMj4ahTLbhEwXJe8xJxSg8DqGDer3k0TZ7Jc973vNYf+Mb3yh/+aa3zM6/rm2cW16DOO8hk5jj6Hd6DejWqtoGtE1dyQYm0VFRf4o5ckr7MU3FHyMbhcwaP6pgHF5az5DCoI6SXdInz5tarzCin2Ksl2N9+MN95yIUxtPiR4iZrmY9heyizkxvHwLa6tLHMidVMBNna+AjO1kNXcx6/UcIku6MHdmTtimsVUvnD5AM4c+7HtCbRV1pdlmP6Y+vPuABOtYS4Y/5JENp9BNEmz5mbhoQTYiNMcYYsxJ48b344uWPjsHrLRLt2GXOjV87Gcx70UWlnHFGKY96VCl3u1spf/EXpVxxRSrsBP7u70p5//vrXMYYsxeAzJWZB3+Zr3Tb1/2yb3eZcUiZmIyxJhvbZ593o9uywJf8xbiSl9TNMfpFH+NRYIOEpHb8tuQz9FGjieST5sh5cmgZ1FUdvuhThJEe8wEYe5TxzkZ0ez9q0w6D6Hc2gH6QOI262UQb/hQDan0IAWd8NE8AG/mDHZKuOk70hTFM3vnSceV5kX0UxhE2SGxjhyyBTqC4oMOdp4gnfTBe6MQY5LDXGOVYW/fGhjs+UxeFSelsg5Zkhlr65ljK+CYpGrCTLeLkukKumOhDcYdNk2efzy6HTrR1/JsO+lErJhLj1Ev52EYfIGD9fBRMtJHshhw62EXO3e7hSn5QBBPmMai14nqLqp2L9mFNgjbGeM7Cn2oaxRhi07lCm+sNHey62BpvNjlPN7/Za3jb295WPvvZz2avlBf835fH/QPOL8590l0qlRjjtZo6rDv9ZpB6vNY6/Xk03WyLqbbqSZ+dvhiJmhXs8T8ErNvI0IZ6defwAMqzndDHWBFd+BuHRlkK5V96cjG2mWD6cMx2Tre6P5ZjYmgUNanHRL5qUro/Db3Lrkn6eFozlSbnygKgNtbReJ+U75nSF7JdzvzHCo3l6E/p9jK1MdeUvUC77yu2sQ3oY8cxlQ5q/PYd28x8TXkzxhhjzFzwQjvvGe0HHVTra66p9c4E837uc6V85Sul/Pqvl/LIR5Zy4YVlpyTa4RvlWc8q5bWvrd8JZowxewG41zFz0W3fGB22/rYx4QtCJu+UpGSSVb6gGwV6bIYOC5KkQa9LP9ALRRTYUI4kJdpwgLor0mUWIQqTr4gj2lABnBs6kKeQ+mh38jZfrWpsALpAA6KTaw71Sfqmf42Bzs/ALlAyFnMrUUqgk/MwTvjO0vvu52L86Y90egC62DWNmj5RUk6y3/yhjzEU6UeBPuOAfBSLSiP0FQ/kWmvzF/DaCD88NhBErUR104FttCEb7IzPcSStJdPO8JakhyzbTPLDb2cvvyzRZkI9/epaRKGPjBO6G7BrPP3g3OEDAtpBR/6yQAZbJOl5zYL0M6Yd2xhn8j7mwfowX78mJvwxZ+02GBJ/mL2Uk08+OVuV8y64oLz2b/4xexPoep0HxnldzVgxuStk1+tO2U3NLxn0V4tvRFPPBn8OYogSwrHeMlaat7fvGMtW8LCcUFaY/VEa++AhWcXzSqFPsfx8jvuVsd9prUVYPcDV1gigIS3EMmWxWIxVq18fJLKd8jE91/zZVjsnOAfjy9QYY4wxOwntaB+/59m0qcp2RaId91pvfWsp97xnKfgevHvdq8Zx+eWpsEbwyJvPf76Ur32t3oMaY8xeQGakzDTxgrQsISjijrK9iOHuUu3QkZw3nWijZGKQyUwMpA18Up/KHdHXXSsq7Bzn/OiEPmwYE+RQAmjneIsVutmkYipTL9vNLtsoSlY2PY0nXEeU9jiPKFwLB/EjfaCtAKAXbcacNiRkXA+a0s9CXawl2pqTfoOBj2zrWDJhjoRtJ0cTtOOGeDMxyzH8kB7Go8COL+rwC6Kmr0x0MzaNqwR69jOKEuX8MlLIQOqhhj+uKedjO2skkLmW6DMBHTUJXa4BBeuALuyCKV9tpzeIfmvKXwI9+sJcAfxrftrBbzT5jPNoMOme83NtKUMt34whYAxCeknfhhl0+YEB+iGAb364gHhibvrCHIgN/kPO84EYAuhoDfAnPemomL2Ot7zlLXw2+5gXvO4dZTP+WoH051gF1wvGElxXukZ1LbAOWVZNX3ok271tbz/wlYxlA389I3n/F0MifbSRMBkkPzGA0ruaNx2gfvyQTmc7DrN2QwE6CWRNbxxvZ1/DznG1c1xqsqZuFPZjcO7hCsXB8+KTJgrD8ehy7WCef9CNoTlPVTHK/3iesW2exrmsMkymdCAby/t+Hc9nsHcxSwci9SUT6MNGcvgZ67RzNR6YYMp/f1wWcGGMMcYYgBfQeTvaN+T95K5KtP/Lv5Ryn/vUNwEnnFDKgQeW8pnPpMIawJq+/OV4Yxf3b+edN3yTYIwxezDIUJlFYKIOd5BRmCjH3SbqbLdbwpRTN2slAqU3eJFQX+MCdlE4lnImdQO6kQ/oRZtzcKDq0Qz2mZyEoOmggK49HpP/tmYkVqEDcoxEm93Qacnt7MOH1o82S7cGJaAJ9LIpW8XAsfCBxKlkqCmLNw6MMWUY5vpznkb6APSRflBDDhdtPthCEAV+ev9cL/ynneSD+aAXfSWGCeZHrT6Aj5yjkXNBrmS24HoxJ2zSVvo4z3gTAlmTR6HvLga2VUTKr9scVcZCX7m+3g6+Mbf+dA+7yNt8qYO4AXaxc4c6jm+Oc7d+gD7kOKdal9akR+PoC065ZgD/AeLsbSVH1cfCZ87jw43++AawQ9x8frt8m72BbXFd/u7v/m72hlzwrUvLq9/wluyJvDZwTeCyYB1F1xqvlxGUxXgz7WxQqjCrlI8Z+x3ryKfaJHWyi0efQELTbqi5QkN+Ri4G89F/KvS6AqooGGtmqcfHXHUGvY7moErVqbE2hRjCDub00cU0ftRNmg+ASvVX67EO+xifSLRX//Xjh36q2h7qT80N2g74UGdraDZg7EL95SY18b+CKwJ7xT3+DTWeq6f33Y4PGc7YdFDix8Auivp9EZw/g+vlguOkjnIOlNlAp7OcKZ/GGGOMWQW8Nq+WaL/22hTsRC65pO42v+Md64v9xri/us1tSvnIR1JhDWBNX/pSrTHPznzuuzHG7EKcZVqRvB0c3yHOuxOE3kC3a4OWuEx0J90cQl86kI180V/IqR4/mGiszZmvjrF/+gj/9CPDCSjeFnWfoOx0IWcJGcXxg34zds5Tm5XOdtAO2hxpgGHGnXot3oC6IUc9mAMN9DF/rzvy3fSnkP0ovgbk6aAltHv9vh/tJu50dNxR89hJKdDa2M4xFCTrW8I+C3TZ7GwEu51MfhQ72v1x0rHtgV8kqPs1yY/WjXHUHI8CG/minI2UQydhLF29bCz6/W51JtnRVj+ueRx/onjQRBt1jKFgNzz1Qgi3kAn40IcB/bEyezynn356+TJ2tszhhS99Vdk8+H4FFFRZr0S7vqPN6zL6uDymbHX9gn58rCu99mFRIB3V0lHCGFXK1q1fxzLFQJqmrdHHQb9NoflmEh1xQRUF4mYWHei1klK0pYMm5sEYfTZj0v7JpXhyHbStFaBGxi7XPf2yOCWYcCsQr2xmyf2hAcXjiTpWGCL9XxOghTJp0wmHEQyh2goKvW/NNwXWxUe1RHsqRkBf0Mu2ijSW6XZ1Hes9Rz86s/HZ2NhWtFOSoJu/8Y0xxhizPWATFB7XcsQRKejAfQ/e8+2KHe1nnVWfAX/jG9c+5rrd7WqiHS/04xf77QG2SLQD3Nd99au1bYwxezjd3b9ZRrvb6277+GIxfsHo+svGo83kn+TwhaJDr37XhK6yFMwSpD8kf9jEj2gzGZS2YjB/jLGf9GP02/nmfOOxQPZK9mJOzgtyjKiNOm3Rhk0fA9cgeZdAlgnr1Oc82VZ87XhEjUpy1iGjPX70hYpRQPTbGrKtMR1zymEHOr0mQjveREgfutg93a8VMvoOoIe10kc6Qd10Uw8wyZzwmfShw2MPH2h3/mXf1hbIf1tLoHkhQxuJZpQ6GCXs4YtyxZw/Wrwaiz70INeatXsfcC00rnocRwl92LRYQtbGQF/nGN5Q8S8Wct52AgLZt3XGGI4NH88DGeaImBSr1kF5xsvrT/ZmTwe72Z+FL0NagW9cdHF51etzVztOeTvvY3T9ZBfwelwQ6Q6urTlIF7V0+7nQZl+yfBxHHz+rbp5odh5qO3zQTR+Pmr0ySH9ZTduAsR3pFTjtKmq1waR8P88KMLKRatthvoyVfC5P9k6ykovtZOUlrhhFQy5YdwtYxHq1pfTx6dx1U8xFY6phM14rurwG0R6NTTGer/lcwNYYY4wxHVddVV+EDz10+Yvwrnx0zGc/W+e84Q3rvJjnVreqj45pj3TcQXAvePbZpRxySClHHlnbxhizF4AMlpmH7hh5O5ht3plK3jG4O8yaurVZgRwCFYF2jvWJQyUMe3/LdCUTkEelFzqS9hhgQjT6THhmYT912EfdyXowZ7MLMIf0uF7FBOAj635OJT8hQxs17dIn4Hj6kTv5oL8mDNAHKWPsGReGNAwoSyEqzDs4pyGkDPHUbkswC9r3xDjtc+1o9jrtETlRdNww3vtVm3ZdLNTL46Vjy37oQ0Y55sx1yJ+QHuxYB9jxyx3rQb9u+QbYjdDGAu6EQNIb88VceOwK3vzoOOlaYyywS3/sp46S9YhJu2n7DxbkW20dB9pkjfH2wUPIEAP68ofkP/zzi1gDPaoGevAJP3g0DRLy7diYvYHVdrOLF73s1eXaazdnr0PXM6/Jrj+G4hzT5SFdXqNoxA/I+uun9zfvuqJ92iaDndbNP3rZ5jjGasUyBXVrs5GmQ5vsRNWrD1Q40EvGjiuz2MdE9LGWeaOVwYxkUj/U5h3OarHKLHNsp2KHBOrz5xPVts6e7ajYqt1lVN3qfzXmHtZVWMR3pWoqJiDbqQ80JKF+GqCCXEXIp8pKTB1n+TXGGGPMdnDllbVGUnoMEu140d2ZiXa+8YmCHe1ItB99dJVjnhvdqN57XXRRle0o8HHBBXWHPPw70W6M2UtYn7VZiHjhwIsH7w5Ht4MtQZjJu3annDXkA1Iuu9aX3RjJ4TttmGyUfcA+9Dpfg3nVDjnt5BPkWlh1ctlzzQF8yi/X2c0/6Q/2ORd0kRSlryjtOKnOgmE240cfJ/SRrFUsoMWntSU6FjSFfmcDNC8TsLDNOWCHfnZrzLlLWnNrrPkMgXasg34qzhMgCQxgj2PABHO0uZYoWj98ICaAttaKcSaYU4ey0KM8a8YInzGu5Dx1o5Yt2kgy8znoPelXc9IOPtMvdov3/iDnjnvMqeMCPawtddFmMj3lYClt2Uaddn08mEPPfpeuvhQWYJxrhk2M408l4R9yJtrDjkS/9wtX8IekPGqo6YMFs0ezyG52gV3tr/yrN9aOrh9eL1EITnzK0WzXSyB91VQQkgVdk7q9nfypbr6Cvj2GsdTmDPiOqouxV1NSdLlZNw91lml0zBmT2cQwRfiR03ThNSjrwlht6VPMkw/oYhjGMXtsDMCxUn/uBwQQjyYddVdlhWWuMrgYiGfKDb86pgN6i8Yu00X0odsn49VCjcM6jo36tTmX3ocxxhhjdoCVEu276tExeOb7+eeXcuyxs2fD4z3C9a5XyqZN8ab8G/XNwY6C9++XXVa/YBU+v/KVHDDGmD2bzFyZaeKFQQlH0r1QQM6bzf7WEOMoIaMdyHEleToX7DDRJz9hwwSpRNHni1MU1iFsfkXaQae7+SXazatJ5VN9Jl8jLq0FCVNOo3mBbHNezhEy+eadNZJYvR7aUdBWgpWkvK2JwiAa8k9yDsaUcsyheAl0APzAUXNWUVxq97AbP5BAx/GmnvwFmrfJuzj0KBOOYQ44Q0m9VgLoKAYSekhy9yi2fq1Ax00Jc9Y5jrgZeyaVN8QbGdRIMhPMH37pI2p0sQOdyWroR02b9Mf1QC/GaBO11s41RJNz5ppZAujDTo+4wfXDx8mEHneMRx9zKi68wQuTeu1EAzEpBq1Pa4W9kqP8UtWwwU58+ET8GyN+6OHaP/DgaosY9Xxu6ONNpRL20KFN2GIMMiTodfzNHs1b3vKWhXazixe99FVxL4EvfOrPLy6+rNZ62nH99IyvI/Whh6J+LxeQUQ69KiKjKRrjuYn81mqA5gTR7qcYjPU0P9HgfGPHXX88ROAXtmrj5/RcbflTpG8kxmup/Up2Rrb1kOfcUeb6niLnq8CwGs8eQDNjnttePp5b3bG3vr9spjCCDLbL7FLQ++11JFc9o0qkr/H8bUym/Iz9k2WCyvI5h8wxW3kuY4wxxkyz2o523Ift7C9DvfrqUr71rVLucIfqX29MkBQ/8MCahN+uN2IjlGj/nu+pz55Hoh3+1uLTGGN2A/19lZlCiUbWaOCQxYsI+0gYQhZ9JSvbL37VQb3zj0bYQM6EYvppwBbj8F27tZ8d2ktHwC989X5EL0vbAfKJONCF71gPSz8HwJj0o/Rx8pnhkAf0IduUwabFqAI9dbOPah5NL9Ax1rFBm846NAY0f9ONfr8+6SqhTTvVaI7bAP0o/Voxh/rQw1yQMRmMNmQ5F+MIKMvjjZ3XqCHDGxUU6DK5HYyT3DwGUbi2QLWQHWJDm/4CxhMy+WE8Oa+Ok8b0oULvG7osuPYTvrGKAjlAclvHU/poI3nOmDEm/ZwLIaGNMZIxyqd0NS/XHzJ9wSLa9I05o8b8tIk+Eut6XAzaGNMHD6Fq9mywm/3kk0/O3mJcePE3y6u0qx30b/57cP7176jXGeuqD11ep9342IY6XQ15+/c4QfvehTDZGnrNdWtUX1BLVfjEl4suS14rBv27ACOVFpfQ/L0N4JwYG8kBRBiKwma6aORyaTkaW6YbCkiij2dpye3QX26TMq03kR5O5QGwj76WN3YyMiXt10mosqAzoQeG3mZMfaAgF3NcNTA+FdeqZKy9KfqIpUazPCawbKoUMI4ssuxlgP7jhw4rqxjUuEAfY5KjZtEA2lmy22pjjDHGLEifaB+/mdhVz2hHov3b3y7l9rdPQYLnqWNH+85ItH/nO6Xc8palHHZYTepjo5QxxuzhxG9cszh4oVAB+SKmRGR7UdN41uoKJQVJ90LYJzQH8wDdhkaNBCHQnSoTiiD68EE5ZH0f7Sh4ke1Pu2JG3cek/uDFEXLZhhymfXKGY5gPCVrYKymKH7KFz4iNcaSMyVX0QdRKkHJu+M8iewI/6QtyxdkfC4A+40A/dLDrWbZ98qv1Uw/xo89Hj2DuKPAF+xYXqowbc0AHcs4RMHneH4MA+kj2yl5rUJtzIUncyTU/5O3chx/NTdJfI+wxD+dCF76jXLd5OD99w1f47tem9SJ+llw754wCPSSruVs9fSmJz3lhm76Y5EaB/5Bj1/mWa6sM82od7Xjl/PAv4EtvrDaHLWzgHnLo4rjomgJY55Yo8KdjhjHEyH60OZeOsdlTedOb3lS+9KUvZW9x/uBlf1Z3teMa4bUYqM1/T3mtkBwX0oe8HxrYdEg+bxzXZq8zqQd5rQejY1X1EdcobD4VnWtEZ/aolMnpeBzi//znt4zmO/VQotd8sa5jK1Ef09KcNcYxQUOimiCO/8IW/wnF0CQp4JKjNJ/oo5JsMDgfqE3RxwBwhuitE/fuBzOlDmXxox/DkFy0R79EreY85IPrRl27UQ9mjp76M/lAY6ieDI+5aPOMbPr5ORQ/UEtN9fjYsp+6JButT4Y9Y4wxxsxh3o52vEHZ1Yl27Gjv2Zk72pFov/nNSzn44HqvuNYvWDXGmN3AvFtsQyZu8lryE+iFI2re8WafCUrpQabxKpndUcbhZ7Kvs2tK0EFJW8kpTnvKs01GttDr7/6VAIUe5maGJdqKl/qSpU4PXYWcu5WjRLP5I7AJJR2HPg4COXzKb/Sxfo73PrpxkvaMM8dbQXI3oA8VNTEWDfrDXOimb4zj0SL0AZ3oc105DgGSyPwgAbZ5jKgIEY4XbFVwPKCH84nxaMM/9aPg2CvWjfHGA2IlwZkk7udGoFHoE3OOQLx4s4Qx7tJGAjznYQxpAx3N0Se+mRBHM9qIETqwwTDl6Rt+Yac1wo6PZkFsYatkNvr0mQ54jhALfOa8mEePkFE7VOqHErAL/3icDdgaPvW4GF5fAfzhuGGeTVHDBjslUCMmxMLEPXTDF2BMIUANe5xLnR8eFyqza/ZMsJv9937v97JXOeyww8pv//ZvZ69y8lOfWP7nD9w1exXsan/Fm/+6Xl74weufnXo9oKjPum8nbHb9xkjG6z1kfQGqOVdXg/FYwCa7+BHjzS4LqY2awBYhg3gkGswXDEzUDh1qtUEa1gqEuHkZ+Rv2O/v4fxjfcobhj/wmVBm7oSqev85JltkOp52lnsczQK+pjudYAaiOffWCWXJ7htxjbN5UkrdxuMkyz2bMdIq8ulGt9hQ8JiMH8/QhnxcXT0kbrB56XbRRMMJSVUjXNMYYY8yiINGOF9Td+Yz2886riW88Q70Hj3lBYvzCC+MFf967hVWAHe4D8egYJNrxDHgn2o0xewmZkTLT6IUhXrSUbJwE4yvdHqYfqvR6K73wTPmMPkyW3QmP9ZJelzqwR+KSA1kAaoypL6LPNad/JmRTF9BXtuWDpJ+puCBDgR+VlgSeAInRfgwulbyVr8E6AGSikymetqYoXFM0GQv89r7zfLOGkuwxhiQuDSlqx4Jqoa/Y2pxReh3Qz8Ex1BEPYpryIcNmE3qQtWcjBEok0z6KEuBMmmOtMQ/HQqzziT7l3TxMcGNujKX/FmOOAdhAl4nxqKXDWNNOCXb0MU4djMFH1IoHtN3nAXwzSR7jAraQoca6GSeAbuhhTfCJx9dQJ/pYO9cQOjpv9IN5NbHZE3nDG95QvvjFL7J91FFHlec973nl61//ejnllFMoE997/M3Lu170zPKJ972zPOQn75PSUk59zevLNfgLCILzjusg6c89xP3YPGCDsogukB6uNTB1vUFEOXTT/wIsGsKMMJg0yvlyqJ++qY9CmkznjkTz46sD/fB4xdu1tgUO1zx389Y3eQpCtlLM28M4nnEfvrvfeg3ojXXFPPmiyF61frOKfr36EGU6rS+mx+AHIytZGmOMMWY7ueqqWu/OZ7T/53+WcrObLZ8T8x1/fCnf/GZ3r7YDYLc8/qIZX7Z66KHV12Zs8jLGmD2bqXs5MwC3g3iByNvDdgeOOgqTgNBJOcezzTtJtPMws88fVY83qznGJGX0kQCkj+z3KCHZy6FPMKACoAMfKmmj+DkXatinTDFRN+vmHzL5TqDPHd9ZaA/9kX1fa2xAPweqrFuCFbbS0RzZniTs++StGlo7ajYVSxb4VTJ6EAOUO3+cP8cVi2TwzTcUaIcNE90Ac6Kgnf6RVKb/aDc/6RegraJjqyI4R85J//DHgayybsGD9AmZ5qP/nIe71qMeJKKjhm99uajWx13/WmPQYkt96DEBDjn6QYs1fdMGfVSpA9qzq1OXSXOU0Ees2M0P35hfiUyYcK5AcUEXc0CfYKKQSc/skWA3+/Of//xywxvesLzwhS8s5557Lp/Vfj38OeqYPMd3uf1tyztf+/Lyn2f+bXn4gx5QvnXZ5eWlb34nT3deYGjU66kHffqg4ogcazaosz3PRteWbKTX62qs6aCdtTr4N4AmZQn9jNudDEAetkyI4vcD1xfynIqwnc95x3iOVTfLZZT3cwcMHaq1OwN6s4qMH2PS2zC8TrcOpkUqtnHUUZTsxc52hpp6gEMc7oRzoG22aTay6z1It85cma1rPjjGshnazvxDvq3TI9npZ9D8g+M1l6ESerqkAHz0GmirP55TpTeQzlh3GNusg3OGHnUoqbX0m1yDxhhjjFmMRZ7Rjke97Ah8wxBF92B64f6P/6hfVKr7MIH5b3WrUi65pO5C3xEwR7z3b4+hwTPavaPdGLOXMPqtaIboRQo1XljUTnmfFOyTxTKrt5DRDwFf8LKPwuSi2qhF6ClZSf+wRY2Seu1uHzFlka9WhNqq5Y8Oun62GxoXnZ5eYLUulhjTOgbrAbke0GKFf4Ba7aCtHe1swLfmaboYjCIZ59QYUBs1HQXZpmnqt2OR0F8wiJMGXVvAFoRMdv0bDSbtZZtwLTlnG4s2ZYnexADJ1VfcPZwbNohZY6GnY0n/UVoyOuWQKQbUkGse7UKHDmNmh6KG1gxdPE9dupiHY+kLPukv+xzPWOgz/WB4M/6kMfr8ECKa3L2OgfSHY6OEudaL44wxxgnbqCFjO+zbMcx1868GsFacnyoyex4f+MAHypOf/ORyzjnnlGc+85nlUOxkWQ1cJsEdb3vr8tY/f1n59LteX8676Jvlms3xppzXZypIsZF9Xf+hp+ZMNRoU4lpDFbWUmvKINl/S2yQzDYzVummE7rJHsKjfDDWedpoTnZEpH7ciu16vR/6jmphips5GHiccL3Q7mm3AaXsBGPc7MFRL/uyMNQ/nzc76Zc67ODumZGS5+TJ6lbl+OhA7bMa6ko1DnuszBqbC6/UxXkvV1Bjnqc1Gbzf2Me4LxTrWgaDXW20+IB35UukVu6YxxhhjFmHeM9rBWne0403X175Wysc/XneZS/aJT5Tyvd9bfffgjcNtblPKRRfVe64dAf6RaL/BDWof9wFOtBtj9hJGvxXNcvK2EXd+/R3kshv76FMk+WicWQEQcjRbP9GuXO3CFUoewqglTgPGM9Klz5FfAvs81UymssFug8nI0MNc9AubDsVAEAfavQ76WeBLqoJ+o6a880W/8qNahI7WrGQp/Yce1qrSzKJNOj/0L2KcL/Y4BtBFyXH6iXZ/vFV4zDIO0GxD1uIC0I3C9adOnwSmD8yB5G/U9IsxzQtfaHfzq90eCZO6tTPswx/U2YYNasyJRkJ/UgI51vthIjz6tM02gU/IAMazr3k1pmuZruUnoF70dY1THn3ODQcpU7vfLa/jpDbstsI+2jzGQH6iQAW+9IEBgTwG6AvryvWaPZL73e9+5elPf3o5GM94XIg896h4jku57fccX/7kt55cDjown//frjVcIABtgH60dX2gJ73st+sYYFuwrsfxzQXoZbIf083Fuoures42Y27dNm+Vo52JaIzLXxXX4ZAxWZ92Q+Q0kH0TwS4qmXXtltDVfGNirn66odqsU+2rYqcerItDnB8cpDGX2Pmp/ocfRKhJXVqnLX8GFPX6ucYU9THAR9+fR0Y5F/iQH+mirzBk3euB3utUu7/q+mMNZj7rQD/c1jWyERDDdz+n6P4FkPXpBLo6jtJZ/sWssymhOu6jGGOMMWYHwIvwrnpGO3wjwf2IR5TywAeW8trXxot9vNrjsTB4vCO+CHX8Xhj9O96xlAsuqLZ607M9wEaJdqxLj45xot0Ysxcw+q1o5hO/7AevEfELH4kXvnDolhGHU23Qt6GH8ah5pwt5Hn62A/hSG0lA7riNok+CW2IwA+EjAbo5Zct2JnNbCbESRUrajoEOE5BUTjv4H8XVrwMxtxdPOqhNwKSp4gNR84W402Ez4wLUZyN9RxPr1xyqFRfm4PFBG2OqAZxrLsiijZh1PKXf1pnjoO+j3T7USBlsNU2bD6S/HgxDRD+wjfnlF2j3tWyxOxyMry+eU9iETLETjKOC/5wDtZLUWC/7SDqnL9hjXl4T6RtfRgpbnKOWvI5x6uWcnH8EdpjjcTPUyZgVi2LULnQdR87LRtpEwdx4NE0vx9w6X0Br4nGKNty3uFKPXw6LBmKIhmInUcMH5umfB2/2cnjCk76d4Droa9H3J8yIrh1dR7qUhK6xHsjGcwHJoD+YG+1eP9o5HRnPGQJajM0aElY/Q/vlBjXh3Cktm28OA5tsZ0zLXIwENYpZQn5wOPBjmYNh8r7tcl+mN0FnN0XvAm6hXv2vYtgTTsahzPqr+6kaMz3aZld+ei9afy8D6PeycUyVsVXVG9uuhHSX0gD2nGsUbOvSQKn/aaRrjDHGmO0A78HwjPaNcX+Dx6yM0Y72HU20f/WrpXz60/VLT888sya7/+u/6lx4Fnu+J2mgf9vb1piwq31HwLzYRT/e0Y5ntPfvP40xZg8E2Skzj/ai0f0ylwy/4Nmc84seenhBG7zwSFd1Jgahw8RjyPECAvgCAlsUtKPQrPPXXmSiZqJR+gBj2Ybe3BekTs6EJ/qSyVfUFGUf8SJhSr9VVIF8ziVFv2mvWHhscIxkkzpMpqYujyEK+iipk8O1L1n64fEMmY5pI9rQaT47v7SVjeyzzCYLuj7nyXMI0JcftmMd9A8R7DrQbcc7dRop620Gfrr2wG/aNFnUsgOcL4+JYmWdhTpRmAxPH+1DDNUplz3bqMd+ZJPgmEunj1nylszvbJCcV8KfegnaKP3jbWCnDxRagV/M1fnFtYUEewuhm8/svcSpHpICnWddlwTXSw4ss5sxSDwPCHlvN6XHa1lzdMpqT40JDsmn9kDjp2RBs+t9tx8DcaPJOj/BshAw3PvqGSuP+wwz/WelhDBXkjKA44shqnfy+fRKmnc0fyJN+K7t5XotUZ/0Nr06uhqbNeYwGF9NecT0UpqX8bDk/Sz9jvlJdxiOMmeqasfjMtLoJlGzE203Q++z3mhWY4wxxiwC3rwgqX300Xghr6VnLYl23F+9//2l3Pzmpfzu79bHx1x2Wf0iVDw3HV+GOp4PfSTGb3Sj+hz3ftPUomBN2NGONQE/OsYYsxcRv3HNXJicwwuHSgd3hYcMLyTceQ7deEFoiULIZd+9uPDOH7eokKMkbbexdKGT8MUrTxV88ssqO+STCc/03eIIWRtL2O7nz7k4D3yFbf+CCf22xtQB0MGuYtRMlGahSvpA3fujD9jnPDyO6Y9rhBxFcpVkvAtZx7JfO4riVaJWcUgH8Sgm7IAmkIUflhwb2KHqYoGMSWnp1qrS+ac96lC47trsR3uZ39DXfLDlc88hTjnni4prQlvHC/poJ0pA8zhG0TGT38HaUKePgZ/QQVJacbTrE8BH6DFmdFOON1H4ZnjU8EX70NP8SpZDH7L2YU2eI6E4MYa14hrDmvQoGfjZHMcRY4pP60ThXwQgRvgIX7DbdBAc1z7ON2oUs4+Q14uILq/t+mNGryN0/QK0VWTLa3Rk19tMMYgldFe7wWj+Zn5bon88N/qT04ew/j8Eskl9MBqAokSym7f0HF+Hv6xSjBoLmlkO1wJpVWrj0Zq1Z8zmmTntmnOhTjjU3mmF1hg5QW/K7dgO/Xm6KzG5tqzHrCbHmmYxzDxPzdHTj8/Thbw/NGhKd+rqxRjl0WBM8YP26SOrBv3X5lxkvpqeMcYYYzrwJgWPjrnhDVMwok+0L3tjtApIbr/vffXLTX/oh0o55phS/uRPSvnMZ+pu82OPTcUReFNwi1uU8qlPrf4+eArEec45sw8PnGg3xuxFZObLTIIkYX/Hh7ZenNodJXRSSS8iTUeHF3Xo0B9KJjBZcoygVhv0cvhMv0ykhqyfR4lDyhCHxqNgHoy1+UDKmSgWGUtLgKLAF/yEXDZsJ21OkGNKgFKUteITtMn5enOgxKsStFofFaCMEm3EyWRudAkaUWTf1ptykm3ESDewx3phEwIdM55LtEHaaJ3wi3npB0n6tEPp182EbsiYeMZ4rp9+Ol3oyQ/PR8pJ16avjBNgbdCnrNPj8Y9a6+/bMNW8nC8qHiP4RrzwlTLEDd3BuYMD6IYedLmWAHpIcPPa7GSYYGu8IeLc3XGgP7SjMDGeHLCpnneuIWzgD+3rNncfMGEunLP0AZi4z7lQZMO5YnxLtLlYEULo+FfgPkKcT5xeXY/zGI+3bjZ0zQ6ulYT/ThLoqSwyJwp9L2cWglqdP83RM+h2umE/OUXvA03UndlcQqepjRtw0vll6LXZGHRHY+hDpA8T+LPNgWZ0UqcRnYlpgtBOP1om9dJe+s191xg8yKTJg3ntMWnevERjPF83QzDsLe93wEE/d6c6ZTUOc3r++WyfXmrnJIvain5p22trjDHGmBF4H4Qd7fMS7XpGO5LU0M33TQuB3etnnVUT7Xj++4MeVMof/3Ep73pXKfe+dykrfZeSEu3bM5/AhwLnn193xQM9ox2PjjHGmD0cZ5kWBXftfI3IFwolDtGHCOPasQuY4MNA6hO104a3mGijILGYfcjhX1kDymiQRJuqkneFNvIThTEGkMun/Mo+qwr6EQvtpBtFu5OZYM1192Cs7VjOGpdXvw4dE7qEDvryhXmzUCGhHmRoh1zrAfKH4XFMSoTTLxKq3Zjca53s40cUxa6YKUMNP+mDOp1uVai+umkqIeBxiRo++/UAymTfG6cM69Ax0Tkg0ee60Oza8k2T0G3rEDGgxxNxTONR85gFmK+fl6RffhCAOuWKhzHkuUFb9vqwgfIudl1HtItxluhDHzLMo3h6YNf/O+NxgP+0xxywg45ixBiasMXaeZ6iQE8qZu8G10C7vgJcEqBdAx0Q6brjdYOSA/zeC9lkW321ef2NxsbMk3PORG0+3ihgHBpfFy6yICbBZvyAWoxJu+1+Z1wYSpusKtFp/3ZSv49HNhriB4S1PRPG/50J5l1S/Dl39dPFHcNIaEPcwuo7AZoxWwUTdC41IXzM4klzjlfxOC4M4UebJsc5FkLo43BArC9elQvVoJdtG4xUuLbWrizXqmhcjPtjxn76vf+zWWMd0a4jQ4/SH88DKWS9HO16DGE1mwdIjzbxg6WKKtGptjO5PKg/rmcxDOdCbygxxhhjzIrgRViPjplCO9qRaOdmsu3gvPNKueKKUu585+rnsY+tCffb376Uk0/u3luOwJsFPL8dz3bfkV3o+LJVgA8P4Ms72o0xexFzfjOaZSAxN3X7RznI20dWocdkHvQhQA1ZFLxQtL50xqQuauqLlMEnd+MmTKgiaZilgdPb2yeKC3ZoMuHd+cO4kpBt3alfDdI22tqtrTiZuUgd+GWcadviQxv6adOAH8wBMBZ9zpE+mt+Ac8IP9Ds55oRvivAj2tDheqQfMInb+dR5UVJeetKhXuoiHhwX1PST+mz3YO6QYVf2YB6BNnRyLs6TvrErG351nqHTXy9oS0af8IE40ifjU5I7+9KFCueKWmPwpaIYoMu1dnOiqA24gz3QsdD8fBOXdtyx3vuINueJGgW2eOPE2EKOneuInfHnPJDj2kKtDwoIfGSf8YYNnu3e4kz/vE6jIDzNz/HUM3s/OK8C57mnH0O77wP2IWcHPyps4rrsHDbdTm8e47mmbOSa1z8b7JKuOeikn8Ew4HzZBmj3c/ZJcVY14TwJVDRBrr+pjmz0z6nOV5vTpJ+YVK5F6+dxaInkkeI4CYxuvwa1a9UZqznyB/iBRrbHzOTVYzdVdoYRDcZXQbpTc+chJ/Nik/34mIxjQH+eD9CPTV0Py/yFkmSy7eMdo6GpuIwxxhizRvDiPW9HO16gcQ+1adPsEZ+LAr8XXFB9I7EOP6g/8IFSXv/6Uq5//SqbAvNiR/vll5fy9a+ncDu4+OJa68tQ8Tx4J9qNMXsJc34zmhntNrIr8aLDu9Gp28TUQeaBqngxQ0NgbJyQTVrSDzrZ5BTo9CXhnW2U8Z0x5Exqh5xt6IQcen3cnC9l9AWgn5eF/NIudJX0BRqjftpCh2L8gAxjWCvsozCeHGdcOEZZC/qAPdq1mulJl046un573EzqNn2VQHOwGzXag4QySsoVK2yavw7ISehgjH3opy/K0MZk6UeP/qGvnKMlgiGPgqQ1kmJ9HPQdtcYZD3xCFoUJZZwj9OFbcwC0dd3BPmKIqvmDbySo0cebGCW5oROi2sZaouYbtPQBG8acsQC94ZKMJeamD7QxlrpwrgS9jpkS6pQrZgC/odOuQzqq8WidPBbpG/FhTi4AffjLmPhFq1hjjpm9G/0b6cnLYEB/nZFUQkUX3ZjasOntUHdqQ3+B9MGUPyE9+ZZNVg32occeaa5kA6JdxTnIsWizm3oyTLtaVVnnKZmffF62loFmHUMyNhuDMMnYHKQOqrYjP/T6KOiStkuzEMa+A9rAto3VxD76zfcIiuWzZ1p9OQvoLfuQIOOaQvKpcXkZ12JqGWTOZBBraMp2IIsDP6XTXI8G2c3BcYIe7V59ZLqsb4wxxpgJ8KYIz2ift6MdYBc67vHaG6gFwXPSYXfrW8eLd7x6Y1f7Xe9ad6vPS7ID6H7/99f6Yx/b/nn7RDt8+NExxpi9iBV+O5oKXhTil7sSeVPMvUMX/VjXhh1tUYDaKCudmoyJoEZ/PL90Uk/qjbE+GMkYG5jjH+Pt8QAYRxtyxC59JDoTiPQiq2H0my2gUi3jXfZ9DZBEZSIVyB5Eu8kBfKGffhUnaGsM2rmIMTZzLFUpZKIWBW2Isr0MGUXNmFVCV2vuY6S/KEjCw2ezybiV3B7YYxwiHG/VqQPgr+3QT5kYzJ1rkIztrJXc5lSaL+RIWoPeD2OEThaMoamEdvsQBOtDjXnQCPRGrfeHdVAeNZLrilFzy6bFEr4QA3S0JvZzDhJtzU2d9GH2ftq/hzi3k8Q4hqA3uCYArq2UD4aiAxv6HNuMmDtv0s/Z665mNwVdpb8+rJiDXcY8nHJAP+ekzigm+EKduvPcNlZzP6Cm0pvJdtkGqY8lab07ckjn0buC+3FMrT8VLIzlYHtiWkW3j2Ml1f5DihbGAnFAZWo5YJ4cLPvnM8E47l5/XtsYY4wxK4AXYOw6xxeVzgOJduwGH/xl8CpA93OfK+UOd6iJ7u0BbzjueMdSjjiilH/917xP3A7w6Bj40I52xK91GmPMHo6zTIvAO8L4xd7uUPtabd0WRn/ZnazGombyES80aEM+0kdbXY53L0pKDKK0T5AxDgP4ixfDQaKyp5uD7TZJNLPfEqWi0+v9Qr+tI8e1BsTc1oOY4C/6TQaiPfZXG7Wwn7Lel5LOTI52OkB68Ms25s4YlXxlyXhA30atYzvoYzzsSPro1wXQlv/eHnLtMKcObNCNWjFqLdptLT3NzX43L867bFkC7txGI+XSly/6gy/4iJrzRBtvnrSzQXFHs7bRAKEHW8qiHsQJf4FU0edYtgdrz2sTbSBdrZF+MU8UoD5t04b+QtZ2tAfNX65j46bopB309MGFjhvfXMaYfPOYmX0CXTsEbRXRtaGrrtosKcS1wWsk27xWOgZzBfTR2QLJ+jIBpW0MNXSzCfq5FU+MUYxCPSkHIeNz01sckGVbZJ9Vmi5Rr7bBIKQOdLkrHMbSX+a/Fi4bzRweaQXVuVyx4NdKGtTRFaBarzVvp/tyT3pO+3wYDXVgjaS1vIy98XBHvZI72Yz11B77lByHA2B8EG90xjayWi6vI5BrDH25U1s6kouxPz1qh5dB1FPHsbcZjy/zlzWQKv3XpjHGGGNWAi+0eI76scemYAI8emV7Hx2D+6bPfraUH/mReFGOV2WU7QGPq/mFXyjlox/dvp3oWM9FF9W2drTDFwrWOfXGwxhj9iDWZ22mwC/x8e9xvsjgsMWAXmuYQETNHxTNCKUmggHKSIeZBclRmuNa9S9qbEdRDE23s2n+RqTpbEj2EuR8QGvRfE0naqlN6kCWbTajphgxpX5rR03baNMkdeuPHEONtUYbJoD2sI2q2QSQMybItMsZMhS0q4i21AsUC3y089cf2yyIodnUqsZHQ3ZJ8wHUhn366NG6tE5A0/jRfOeY+s11+m3zYTzX2fpRc97UYzvmpK+EamGHpDTHo68PcbReCJmo7uJtf20Ag9Sb9B/93h9tod/51jlgN3TZTzAP3HHXemcPPcQM3SaLmo/EgQH8QRYV+4HiEIM4zF4Nz3GeZzEhGp5uDEoQbV0npFPsrxNdZ70q+xCkkPpZa6zpJBjL0h6xAqDSqZFuGFB/rAN6GX1nO6hzwG48AR7rMnKG7si+mYKBeVWS+xmzR4QMDFJfVJ8zXboJnV5raBGEUR8zml23AZGm0/Bw+m6eaKA9GJ4DfI2nk+1oeY2J8AaMzfrY57HS8NR80m9jnYPeF44l+tAb+0FfYz29fSOF+niitxvrj/0ZY4wxZjtAQhwJ6BvfOAUTING+vV+GiuT45z9fyg//cAq2A71Be9KTatL8ne+MNwDxDmC1NzgAOpdcUsrGjaUceWSVwdfhh9dnvhtjzB7OKPNkhsx5IUByjy8SeAHRIURf+r3dlA/cVqIE/YsN73DRh3+8CEonf7R50dWLZOoQJCk7fwNkN2tSl4lRlPDDZKb8xdh4DugQOejnluOQMQbpgO4yo1h6aAfQb+0omEfHtcUApNQxaauSyXbFndXQDew7B5xPdaJY5EcO+nXqOGrnNueHnebOPmqM07/aUJCf1GMiO2XQ005yyLhDH034iEaLP+AObujCd/Q1RJ2QCYzDpx7nQsWcW3NhHj1bHcCc8YZu76/vcxwF3fSHMfg6oFsDUKIcUAR9+IgObTAetdZDW9UYh230mUCHXc6Nsfi/zYsxltBjDPCRxew7DD4gQsmqP9+8njo9FV0LtE0oRyPHeqDW6wJdy1NMXWuSaff5WKV3n2MQrcPjusJ2mUvq17FmIOB/4K8fzzbikDh1ucRunTBjF3opbq5Uj0A6e1msCU4Fd0dPjMOOU+T8/QcSXXPQ7kkr/lx+OMJ3DC2fF/q1YBytocr0QiDtR6a1hiyfe75vwHVmJ6/gAfPmhJno20J2M3u06vP+p/R7TR7DqGeSGXn2Biz7UGdEPzo9tzHGGGOWcdlldbf6jW6UggmQpN7eR8dgNzv83u52KdgBsCP9/vcv5VnPKvwwYBHwBgOPjsGXqSLZjvcPKFjDoj6MMea7yNT9mmngtm+UPOHdbsAbRhT01UYJ2t/gZ93ukGU7KqAlCUPA5CBIfT0Wo3ayipoy6EbNF6BoKhnZbFKP7TZZ1YeIPzQWhRVqJCSjSCY/TR9IBqiUQBZxYD1Kii5TCwGzLFmaLxQoYRxz5xiBXGOQR1s7nTFXS7hGH0na3keaNVkjxwdQMSocQxSdDzmJWjIlgyGHbotZaCwKY0rQlx3doo0afkPABDh8RdGXpwL4xjohU6Ic88KPdnOj6Hgo+Qgg65Pb8ok+5odPxoWhzo+OtfTbOkKRj56BTozhC0b5JaMhhw2uY8TGGOE3C/y0Nog+5865UBhj6nKe8Cv9AzCOGuuFIH5gfiYs4SOOC49ZoBrHij5DD/8+ldw0+y48vRPnuF3b23P+Q58Vath1toNreTuhv47ezWioETbzhlZlkTjpfEJvypTHslaN7ODn8ulmSfWaOJ/pDlgxTAzOVxjE0jFlIVWOzXM5xx/JMaksMjdUxmrqzwtB8t5uPFf1O/a8nOprOBMv4ah7a2n0mu3c1WpGyqdml0zXQutn3TNlb4wxxphV+MY36jPM8WiVeezIjvYPf7g+9/1610vBDoBNTg98YE2cf/rTszcEK4EYoX+b26QgwJsQ72g3xuwlIINm5oFf6EwyArwodEXydvc41g0oi8IvDO1kvJ2MwrvbXp60tnRRRd38B72+YCJR5Dj95xyQwY7z1i5tZMckLBocyIIqlTlnlhyaxRECxph9VNoZneaD9QImsbv+TJG9+oFF+pN/NqNNO9QROz8QQDfXwTp9wL6Pkf2IibGiriNNvZHjJAbb2rLmWG+U7WXyAPEhXiaLM0b6Qls+cy6ti8cOYwmaTBZjvJOP4TgbnS78o0Yf8pgX57p3o/iYRI824sF8gKZho9iBxpH0VjIfNebhl58iwZ7xwg4J7y2bq1wxMlEe+vj3ARn0NT/AcaPv7Pe0OaGjkn60FsTIuMMnr220YwzxXRdvNBlv+jH7DrwG2GB3GbieAfQIBBIuiGz7uvkL1Jes/7fcywY2WYMpux2AO4jbHONajPuVJu2HFY9k6KI9FSdFQ98KZ7bs4Tiorqp8wuvIBJ3lPsbUHewr6a3uYwwsxvGhPxlzoBmmbMBq0S0fr5aSS2dad8a8sV6+kj0ZHcu2pnmLn2BqvlXnNcYYY8xyLrigJtKn3o8JJdoX2dGuN2sf+Uh97jtsdxTEdOc7V3/4YtX+PYTm6WUAMV54YSl3ulMKEnyxqhPtxpi9AGeZVkKJxVbjh17Asmairn9xgLwrGGp2KKBvg9GLyxjOAV9KQmY8fZKQL1DQiTG8oC1LILZAcgx68ptjCIlrRYk+E5Noj6D/KNgVDDR3A+3UYTf72NmMhGgbz3ZvLxuAphKzKoyTA/F/F5vm4HisC0X68o+523GJPnXQhCz1NE7f8AdUB80+0PohO2BTLdDlfCMO2FgTy4oDfcaAQehj/pgTY7BvieFcI2OFThTFgIQxk9opow1sQyZ4/MIHE9iBjlPzl+vEzgH53Yo3YVFIzomC5DT04UOxbYh18LjCJ3wHOs6aA3aYHzsaeBxgg7libGv4wDhA3Hy0S9pDB/b4NYVrRx8EcBd9yDEP44ZtyNCGDQqPRfT1ZpL9sEGMOA8HHlRrxmH2CXitJLgm1IVcRedbuv11CyBHX+NZVVJPvtRejV6/RzK4HcWwTDsFTBjn7x38nHo8xyRcE+rabYSsJaD7OLLmfGyNSBtoabwtQb8XA6jVXdYpo85wvE+Ay4UY9ydJ8+qmWiCWbOUcGq8MnvGOHzEmm16X/docAFnvD8zmrKCtfi8XnCfbY/ox2K78IUHq1Cbp230cYGpexD6ToTX7olX0evu+3YDPLALnHbqT+kEvl1lnPtfOGGOMMSOUaF/pvkaPjll0RzsS2mefXcrNb17KwQencAc57rjq55OfnL1ZQBzf/nYpr351KRdfPHwTgfs3rOn7vz8FAd6sONFujNlLcJZpJZh4jF/q3JFcRbURMiYwlbwLPd5loo8Xr1RmUjJqJR27m3vq1kbWoBuXvInQzw5fiKLQn0on61+oIGM/bYHsGF/EhYQl+nr0iHRbvJ2/fpw79RMdizFIcLZYNR5tzI3EKl1AnmOKtfmKNmx5LnKsAb3wo2Ou86Dkc6/LMehJV2PRhi7niMJ4g6bf6fWFxw02GReT0/o29ZD38eu6UBIdMq49ffex9klywA8jAiaSMRa6qhlzjCt2kr7gk2vJMSX2FZeOGfpMTuc8aHNt6KcvzYUkOJG/AG+ElEhHga3G0N5wYE1osx/2TJbnGjH3hhjD+JZr6xz8NnyU9Ic4t1xT7egHMYnQhw3GFCP0UdBmzLCJmh/yRDyA5yFqvMFTIt7sG+i8sx2lv1xICPTvTXrsjxRjjMnY5ivb+vdD5wH60hFjG9DsRkzZQxVy1aBTGTznOsebaOSKDPSznrfe2ml+8Dx4NkO9Wozsgpn3WasRoqlkfZtqubskFVA1t62xEHBNC7maMOfhgzwG58Y4AYY2bsjfJ2BSdyZs02R7exmc8w5J21rT+1i7n7Mfm2rrkgOQoUg0HgMQ9f7H6OzPdKpkam7Qt40xxhizAHh0zCI72nGftei9D77AFElt7Ebn/eEaXqHxnPWf/ulS/v3f421AvAtAwRetPuMZpTz5yaX8x3+kYoIvQsVz58eJdj86xhizl9DdKZqV0W0iXmSizQSrCFl78ZGe6tBrSfWA4s4X/WQNF80PO1WVsijQoal0Ao7JHxj3EyYgQ64Eq2AftcYCxqS44K+bj3faUSBHYR/0/ZQpNq2RpF6z7+cEod/HRB8ga9rpOOQ8tMkmG7IRGMc5wHzopn8CXRlHG0lY+Fd8JMdl2+xlK91OfzBHQLuQS0XQPP1EzaQUCpPMqdDPybh0PKNQnk6ZnI4+dLQOzQvYT39M0EdpiXXpVtUQ1nHNA+gH8mxTN2VA8zImjGMNUSDHznclztvaYBdFx1u/jhQP6/CnRD3mRB9jqOEESXYm5zFn6sAn+lqr7LAexYb5qJt2Zu8G55PXULZ1bdULLcH1kAVDuI5IXiM98IEP0gYfJkonZJpPcwrprCTXGGSSD2rI+f9yaB91DDIFDqXOZVZD2mA3qvkEhjAe8qqGxDsOUfygAL+XWFXSF7u1OaM7ZjXG0ML/tMePkIYO9kzTfop0Qe2YK6ersJOCwcA0/Skcsy3iQUh0U0MbuVxurJgP2rS+LgdkjUplTO8J45pH+ugun62CK1Vjk/5DgHHJp3xpjKc0R/u1qgk9tHtfaqNWW0jWfEVd2zWhPq1fpe34dciNMcYYYxZk0UT7ojva8QKNXeZXXVXKHe6QwjWA99wPe1gpn/lMaV9m+tWvlnLGGTXxj0fK9HH953/WL1E99NAUJP4yVGPMXoIyDWYKJumyjJGML2jxYtTuGNFXgRx60aafaDKpI38YV52FdjgtaENPcjTjhQgMXkRzLiQlNU8j7ZjI1KlO/WWELhKgbOa8y/Q0D5qYCzY5HxO1sEk/XC/6gXZgo0DWPnjo/FM1+kyIKlboZhMNrZFxZA04T7SVTGdsiAsl2kzKplwOGWOU5ifHOIy2ahURba4h190IOXxhrRjnnB2MPeRM9nbrb8cT646iHeLwz2MaYzovTTdr2SsetHX8GEPX1znAzm6AGGRD/4gNa0sZahQlorUejtVm9R/+eAwDxQ4FnROMcQ746WT0n44QC3bBc76QyQa06ytkiBP+Ma7HyMAHauqFHNAe+iHjcY02dsxjHh0PxK45zN4Pr6W8nuYS18GUSi/DtQJfYz1dq4sCPz1T9rpOO5oWxBM21IZYZuN5BMT0n915ej053fRSR8nuMXOOz9imqnW6aTdtvSBz4pryuXwNVdAezTL4XpBaj03Q3xr6l1yN33WL08cz9lmZaaA11plaTwPK004XAqYr+u+Y9/HI1CUwls2608FKuoalGGOMMfsfeJ75oon27dnRfvXVwy8k3VEQ1/d9X02cf/zj9R4NMWPn+j3vWcrnP5/3cgkeMYNnw7e/pg7gQzvap950GGPMHoQzTauCX+Qo+cKFigm67DNp2utEwQsBdPgigFLFrT2ge1GBkpKN8s+694M+9KJqyVCNxZxMiMq2p/OpF6f2Ypy1Eo/9i5ea0kWNcbqLtl4AmdTM4yJdxpbIpo+1xYk618EXWeikHsG4SoI52EWNRugjqYoa8yoGIJ8tHtj0a+3nCh3p9cd2MI98d3OQ1EHyWX6pi5J+6BN9zB8yXj/R11xx/i7787+of05Hm/SheIF0FQdqzMckcueLMcA2Co5Bv9a2fshR0I4f0JUf2oae7AHF8YM2oY8kN2SwZ/Ia7ehwLHeaKx4MUgd1nit+MIEm/IRccWk+6cOf2jrPmBsyPvsdMVcTzod+W0fQ3qilLxSO5Xxm7wfntNUqI3SNkBgfqLQLqJasiHzPBFknus7EIJagH5/bjtLcjvxNkrvPR6E0MMbxUOjctaWI1l/uqCWgw37uVCGs8uGodk1PQtVOP5sDCw1HPTyMnR0Fc+aBXTZ7oD11WlYKV0hla3g+75ot8Woxm2HZcZ2gV1EbPhkTe4uxUqir+apj+XN7Ju3ozRRLL4Nf9PsiPdR9u6fJxwPGGGOMmc8iO9q35xnteCHH7vOb3ayU618/hWsAceFxobe4RSkf/GD9a+RPfKKUW96ylHvco5QvfGEY16c+VcqNb1zv8wR84Bnt3tFujNkLcJZpJZj0wwvW+EUr+hzT4ctxvACwGS9OvIPt5FM+BkC/B+Phf/CCGW36HetOIDMlStmu1aQ9Y5dCjLc7cCVJEyVNmSAWzXGFu5x1bFBjHGU0L816215HdupjzpgbpcWJWkV60V523roxrGtgD9I3gSz1NV8Pj2f6p3nnYxk5Jv1+jj4GtBEXxtcfUK664U1Z17GwXY9nnqc+fWANWiPssjQ/UQjamhMylaDNrz5K/FACHEgFCW2AcRUB/4iFiWzIo+iNEnZM0Labk3FKN2CsOd4mRB0+GHu0oU+7mIc+0j9kuNb45aqhg3HIWGIcMWF+ziFynM30Y/YNRqd50Ae6vICukaaE6ywV8vpgkrldK6jRz7bkov830SO9kW8C2TI76ddqRTpXdAubZf6i3+mRqT7XVLstRsqzMXKrR3+AtqSZqBI2DGf9bDd8c92MKhOmM79rokY6OCxsD50vsuNfKqi2hvnZ12xhjCt+oNCxbDnLBJMiMk8uxjGspK8x1H27R94k770P2tEZzhz9sbOgF43tjTHGGLOD4I0IyiJfhrq9O9r/67/qM9LbZqU1gkQ7Eusf+lCNA89rv+td6455PDoGcWEtuI/EM9uxo328Hj+j3Rizl7DCb2NT7w5xJ9kV9JGgazvZUza8fYwSY7RB8g/60O305WtgF7S5os0XlxynfY4B1bqFVUx9ErJnbnITRBuygY3asIkiG9YpkxyxMEGbMBbEAR2M6zJDH1V32SlB2sYyTnQpx5y5JtGvEXr0AbuMRXYsgM5qE+iFXONoqwxiga8oknNdadPcoR8dJnRxTYxsx8CP5PQbba4n2tk/9md+qn5pDOTUzTmpk002UNJXmytlnCeOiY5VW1eOAdrITrKoEQfnRjvXzAcdR6H7qJV8x/PT23WKQehGH+cEtebQnPCl5Dttce5SH3PxOCLmsMHjYVAYC3xEoT7qTLDTfyA/Kpyns1Ps9IU6Oihm30Dnsj+nPO15vdQOpEOgzvH+Wqh69Wcn5/WcbTC+fnStoWiMvpNxjGN7dcMGVuv47yqAHLq9evORVT8GIfq0iQJnvQKdx4+Mjf9kumHJexGoJmmTB6L+zFodkt83gVbYLC3VZHuaN/gM+BWoPhRJtNDsA8t1aXlj/wyKY7OBsa5GVGPOts6oBtNlOSDkN9kUv4PYrxqwkI+B0Qo0/RFjOdxJNhvDU+7Vqhq9Th8C+k23G0Cz11N7SiZ6nX5s6thr3rySySxqY4wxxqwZ7A6/9NLFHh2zyJeh4o0Cyqc/vXMT7fCDx8ecd159LA0S7Xe7WynHH1/KNdfUDwvAd75T1zNOtGNtfaK9f0NjjDF7GP39j1kGfoFvy1/kumUcM5Zlv73QyTZfDCDnWNbIcrCthBBI3fYCMpK3/kTNrEm2AV8skbjs0PytnbXkTFrnpSF5G4P/rAFDwo9uHtaKFUSb5vIDskZ8knMsfTVSj+M5t1CTPhBPN9bT+9fxoSp/pDzbgG3IoBvHon+RF+2YQi/XwDZqkH02u5ri+NH/RUCXEMbw+ngjsg7+ee7yeNBHFMWiRDfkSlhXQZVpnfVHHecxGh+rqPkXCNnt4dwxwC8jjZoxgGyzhF+G2MXDuaPoLxskQwyMP2Pox1D660djuhaafhb5og7Gs8ackCOBBz/6IKDpqqCKWnZm74endnQ+dc6bONvqN3mHfPQ6zQ+uIwlRZc1/KwH6kvVMyYDsGp1f2chUqujnmESzZHRP6KS4n6YmkaNUF0HfnmKWwF2myjhRs4MfQzBvxgov+BLUcbK+p4aZ8gl3PXSTC0uXRG3UA3n81x2GQIq1Qj3lZ4r4DV1utBE/K9RdQR9Ulfg5pTchG8Y+Y9Cesssixv3hMaj0Omr3sgExKeRjP80mjVDj1zC0Wae8Bz6mrgNjjDHGLMiVV9b3Q0hCrwQS7dDDF5yuBnx+6Uul3PnO9R5qZwA/d7xjTZR/9KOlfPOb9YtWjzqqlIMOKuUrX6l6kINjjql1D9aIR8cse/9sjDF7FjvpN+e+Dn6ZI/GZyU+CO8fu8LU2xlG6O0uO9TeTGMuaVdRMMmbNKXKMukiSIqEZAyj0C3n4ZSI4ClXRz8IXRfVzXI+Bob8cI+mLTbRRaneA5mEcSKxGzYRm1HxcCHxHRV/wn764tkTr1Bpg045pB0XwF3407wDZo4K/rAFi0zHj/OlHIBmL+Og350abOtmnT61BttFubzZyLtjRf9TyoaQuC+TRR8KXftJONdceBTFt2FTbXHeMyw+PrxLz8Al5+MWf4OF4tviC3gZFa8QuAs2v2ESLN2rGAxmOUbQhw85xIbsNB9YaXZ1fxoU54AO2UWF+lA3hA+tD2RhvpjAnCpRYpy5Qclz66KMoNqC16biisJ814kPhPKnD8XzMDOKhXfozeze6fnt4GebvpQG8MKsN27BFHeB6UVvQN2S45nK8Lz3qT9Uo7VqPur+e029dQtoIdGF7QIxriP7UznpMzXIuR7KcviXpm+6szxAhbxMHanK87k9uS+nGemiest7Vtm11gl696lYZ5b3BMuoxUQH9YRVNp/VrC4dI9D7mgWGVQ/CB6Ei/TT2S993REIFMRaxm00txtKb0VfeHpMpmxxcFlx1LdHq5Ci/LhLJYOGrA45ZtNSTrC4AftOWPY/FD40DtXmaMMcaYOeiZ5UhCj9+Y9ByIe6O4J8KO8ZXAGyk8NgaJeTwnfWeB97o/+qP1sTGnnFLKkUfWZ7ajRqL9q1+tc198ca2RaO/Xgzae0Y5d+dgBb4wxezD9/ZMZw9/t+JG/5NvdI2o04kUgh2q/LwJt6MWhbkXyhJkB6ISMSUHZRNUKxg6oBaCvUgVhsi19oZ3+OAQ91GGL0mSIJccoy8uhJU5jAPMpbgK/2ZdMiVD2Ndb7xwTq5/zsS55jNcjaZgakG2MJEeA4SswpG8GxmEMJLfbzmLY44ZODdUx+FI/WTCUUkOP8QKHTkf+WuE19zg+bqHAuGFO3drQVz0a88clktvwywZxJYfShB9SGPk411wY5fGIs9WkXhQluxIZYosAvaviALfXpIP7HvKkL+41IdMN/9BEj4tHcSGhLFz40D2OG3yi0yTGer2w3WdQqm0L3wIPjjdYhtebcKKGHAl/QQTzsp5zzqA59zcESMvqPuQ+MN3Aomge6KLAz+z78vRjgeuX13rXZHcvQV0GV7VZTOs3AR4dkuB47HSV9q0/I2OtqyFKPuqirnAnPJkONqo6rbrR2NvI1Av9RRv0o7VChX9uqpVJNZkL60FgP9bKgok6NWbpVVlUoykbTSx2gWrBPneqv99tT9bp21tKvMjRmsikkh86B0Mv/xkiCum83olPHemltU57iwWh0en21qjzb/FEFYx99zXb8QN2S6/EjrsyoYxYU6LR+1uhPFPmgfVfLS5WlffxoJcdYst8z7htjjDGmA+/l9CiV1Xa044X24LgHWu0Z57jX/fjHS7nhDWtiG3Y7CyTuH/Woulv+Lnepj5LBHJvingyPlMF6VtrRjvjxPtpfiGqM2cPB/ZCZC+78ssxuB4dJm/5WULqUi6l21gO9KWK89znWV0IcKGHZ6wxssl7mB+1elmujLC4PrbUh3dRHGzrUizZe/ETzGbX0IKI8/aItvb4tX4iByX3INRZQnjXF/VgcB/Sb3yhtHZ2e/LYSvpAE1hq0a59yzBMFYzrOzT/0Q4/ZhihcZzfOdaARP9Bmcjt90VakDccyDunIjrapBxmALhPonS5ioW36Q0wtG5K+6W9DVySPNvWilq7mYz8KfIJwy+MtXSS3lcBGP2nJmtZOX5kMX4eyYSPl6w6ImiXGGBPGQx/Jc9hhDLKNB1Z/spePsGnjm7LAL/ptPPr8ECHXYfZDcEHyCuL/Df0uy6Ha7hR4IWdf7fE4/63g2hrJ+1r09n3d67GfIpbsxP+ItpFjeERLmzr1+KOXBzN/6iTZrOPq1IqNlOHfMuVcasqj5HAM1Qb1aqtVVTUbVUR9/hcyDnEkoFrKsia5ntZXHaDZ9PlfqmcBbOs/ymutMdYs+E/LrO0N8YM20e5RH3VfQG1X+yn/KoDt7FR57dR2Re1W0qD3B9p4FDxfHkBDlwPsaiIcdV3n+pBVeepSliXa8NPsMZZ6CIF91FGqDUbQ7/ynDuaVT+j1Y7AxxhhjzApghzreu2Jn+ErgRfmQQ1bf0Y5EO56fjkT39a6Xwp0A5kd50pNKOe64Un7nd+p9IHbOY7f9N75R9ZRov9GNai1gi/tCbLDChwXt3t4YY/Y8cD9j5sEkHG718MIQbfyCn2KefABeDEYvCEqUr48XjQM21TbJOUnne1lSMMaYzJlDiyvqtpY5cKd2gFioj07ECzu8qNFX9DkeMGYkSUfJSr3oSY8xQi/6KDyOWQRsKMO8UWNNmFOJVoAxtDkvaviFfhTOkTb0G2tBH4lZ+kl5PyfajD/q/hi2+Ef6OD5MzIYvgnjQjzqTxdRXfGxDJ9qQwZ5tyKPgk3sel2hrlzkKdDlP18cbCvpNfbRVY3e2fCLBrXVhZ7kS3ih8XAv8xjjPa/juk/NaC2AMGROOIfqMB7qoozAG6IWdxllDnnNiDH4YR8azCbvKEUtNhtd+vLlC/JobbdgfeFBZF/brtHsdvg7Gn0WGHna4s4bPOAY85iHA42K43pDL50GHRsFxgs+o4UcF85m9H5x7/dvlNYoaBddsXgcYp06OA1wjvHajRsE4/q1qXNc82xIGnCOLfPS0sax71B/XMWlNkGdhP303HTRrmzX0859z01OpSvRJ3Sav9TIZQJcxYF4sDXqQ53jCbhcr52AGdyaTnuZnm1KprIt/qjFPjHOekEevQn1pB+inn9kUkFWLdlolzzaEg19b0cdcSOhyNGo2cxzNGK66KKmLWTBG3daucupL1gr+G8riN15rV3/T9i02/pe6kkXFIjnr0Otkklfrynh8pjOTo8YpRGF7BTnaLSkedeujDq02lgWgHtjCDvLsU579+J/HC21jjDHGrMBll9V6ZyXa8Xz2c8+tX1IK/Z0NnsuOR9Pc9a61fyju0+J+8JJL6mNhLrww3gTEO4Sjj67jPbi3xH2hd7QbY/ZwdA9kJskMBkoPEzch4819dwiV6AG0SVsmINMGyCdkLdHXzdWPC/oe+WeSYWwDefhU4rEh/2HT66oo0YrCeNOH7nQhYyIKSezOd2/TF8F+6gKuGTZod3b0l3IdJ8Xcr4Xt7rggJvnD4dm2tSy9/12lbLm2CrjeKLDDMZQ/xajjwaRwHgPKo4YOdFWkh93tKIBytGGX/scfLAD1WTaEduhRN+dHwTkA0WYCCP/FfNjdLT3u9A6fLCHD1/sN+tCJOdgOf2xjjOcNPsI/kteUQ1c2MYbkNRLUUTgea4uRmS76KpuQBA//8sGSY4w5ysbQQY0PkXjcsI48Jkic0wfmijEkvZmMR6IcY4glyoHxBu8g7HQ4JHRzJzuOE+KFjR4DgxqPnWHSHnOlT3zAoDYeHcN50I85EAf8mH2DuBazMSuS6VyjzyG0o8bvDCFd6kk3Za2d9XhM9XhsSh+or5hQ4/cYf1fmGJr83YYGxJ2c/fg/xlGqmxynryihgP+qrKvTts0nn1HTR8hQ4QcSoLLhv3UUug4ZC4dm/QM6W7RrGDmMHzkljUr59uWb4yUFv8NCP/pVr9OFUqylzp1j6LcYqxHaiLWO43jU2NmHj6AlzSEbFcgI2tSDvNbSr/5yLFQRG49PNav9aNEmZZJzPWr3Bf4gr8OcB034QbvWUShTwRwpy3pcZrpVR3FxDDbRQD9+m+Y4T90g2V1LnwCv7QOk38q6Er/lW7/6TXvIsr+h+ZrpwldrQ542iiGGjTHGGDMP3Nsqcb5aoh0s8ugY+LvoolLufvd4McYr8k4GPhGHXuTj3rEce2yNa/PmUs4/v5Sb37wm38dAVzvajTFmD2YX/Pbcl5i4y+sTMHrx4QtFyvJFA18ShxxO/aq4pBtv+krOCrSVEBZwxH4UzMnsRQA9JFBRc54ojC/HYIOiO1bIe79aCwf6fgAb+tMYCvqpg3HOn/O0Ofq2YqGHKmf8EGZbBSLBdgrkRz6A7DmW46jl/4Tb1Vo6NI7S5lKNsUA+qJ/jlEGkdh1H0rrgQwjW1ZZJFPkFKWsJcBQlq9mHDmQaDzmSyChIcoeMCWkkhHl8oR72kGEevMlAXQNI3YxJcSChzDrejEAG+/BXd4jHGO06W/rPOKmPMdRREDD80Bd0Iy74RkKcBT6QvM4ENn1GG7ZMnKeN4laiG/1M7FcZ2iFvSXnowlcUxpDjBx/GJD2T6oxDtkjORw3fGEcbNWOGn9CDT86dMUBu9n7wTxzXiAro+yxx7nH++Q8qCwxZV/R7G/DfAYaafVcwRl+JZBrHv1uBvmoV0OunPySJe536O4ItjiEJDMFAL9r132z+LslSY6+lriXlnS0T1ihshy7HoIvhbqwN1T5t4Tp1YEBxzoNBJqxzrO/TJgqO0CWXXVP/mArjIWOhTfQhhi76Whvs0UcbalnXEu0qqnOhjR8pq/OyV3XQhz/Ku4IlcJ5qHyL6zWlbnJCpUCa/UUsX/qBRf+acKKnDGNnHWLUFSDgDVCr0iXb8oG7aqUimOVq76URfuirRqcnzKJRVm3gVaHpMrKNEG8lyyJkgzzLwEf34zVpl9CVd2NZ2/M9adjOdOkffjiFjjDHGrIR2tK/2mJd4beUO9ZWS1Fu3lvLlL9cvJP2BH4gXabwa70QQw1TB7nnsUr/22vqs9tvcpsrH4B5RO9r799rGGLOHsZN/e+5j9L/g+UIwdbjwSx5jqPij1luvK1v++h2lfOvCmZxMtbMoSYHEIvoawp0o5uaLXbTRp27KWh3DetFRvPJZhVWHdepTL+Uo+AH/cIMMCPzRJXTSH0rfl518sV0rHZ6qB7uEfrokJ+y4rpApNqD1MEmrMZVU4rw5hkTy98aLMxKv0mM82YYP+oEsCttRqKN+6jMxm32COuJp3bQjUUOf80Rbc7EdhccsUA0wznXBJu3Qhh8WjCMZHG0ljnXcZMukccoA1o3H0jCJHHJ8EAO/TDofWOWYCzvAudMcBQnqKIwXPqONsfWhy+Q1xvJYMJGNNuaMwmOUYzpe0FGCHW+GoMe5NV/IGHfoo9BnFOhgvYhBfrV+9GEDv/IFOWyhgz50AGXR5npi7RpDHzZcbxxPHoOQmb0f/juL60ygLziWRf/O2cSPrGWLG4xLv9WNdzppW5OnCW07nX4M8Hf3hFyy8VjfZ3uqxmDWIVOitvvBqiWk8e8PYtrPCsepGFXXxjB/0D7aAPpZpErYjh95+NCUrB6n2RiJLsTytSliq7/6oo/hNKFeTERT6tc+farPUht9v8aHDqaOftqliDVatUY/2oxBRf6ibv9RcTaWdkBJaMxb9XK8KxoHlMWPlpCXLIv0MM5+lNlYlaVKbTMOyKtU43VsXCt+9Kpv/AZs8UdBH7Xm5VgUJsXVV1v6HF9XfaHGOHVShjplVafqV3+qp+cwxhhjzBxwn7zojna8uGMn+dSjY3i/HwU7yp/znFLuda9Svv/7q83uAM9s7xPtt9OmuRG6t/SOdmPMHs66JWzhM+Skk04qr371q7NXynUfe3e25tBefLoXIdwdblsqS1uvK0sf/+ey7nZ3LusOPyoG8jDzjr42WxIZFewAfKKPsc5tTVhjLG04Fj9QUxQ/Yl6ix5r0pxbN/8/em8BbdlT1/tVzks5MQiAMgSQChkkZlEEFBBERwQFFBkEc0CfiE30K6ns88Ikf/D+R58RTFCRRFJWHMigoMogDM4IyTxHIQEJChk53kp7uf31/tVadtffZ597bne70vZ36ddepqjVX7Tp7n73OvnXQDxuK3fnoxcUs+G2fYpcTHx50ayOjRPDmUvbYRRnAq0oGbLsutRJZtBPaWFynxeWIvpWaGMj6G4yNP5p2qx5qJgJdT4UPYgDVlpB0BcWS5JVEDz3HOD7aeQzMURwDXwetDehKxnzHfGs+jR9xhH8SworDdaHv3+ttYPLBizpiieSxrUHZV8LZ5PFJIhEaSWvJmy592ujBFw8a7GR7v/EibmxiPwCPBDdy4U+qbj9oGEUGu5L1dBIy8KFp3Oi7L+BhVBmTbQRDkzM9YhKrPjGLPf5SwDtVVsW6FvOG4060D6a3gdmxzhCJTnD+i19YnvzAu5eNd7p7Jdh5SZe2qy+3E/mesnTlJZXOGrV+2eV7O8a564Zd2hdy6frry77PXVg23fUupXzy46Ucd2zZcJszTYC1tb8smc+ly79cylVXlg23Pr2+H4hjl+njjyeFzEa54YZ6M3PyKdY3Hvtd8v6Cx/6TnAPgU3NjYTcN+67bVfZcfEXZ9tV3kv7Snr3lhs9+qWy99Yll02knlyXj33DxVeWYO9r1xHztvuzasvmEY8rGY7eWnR+/pBx/rzvWOSFGxmK291+/p2zcZut/y+ay8dyvKksXftbiPqMs2U3W3osvL/tv2FM2Hb/N4uC9YbpkOm2c+6/freHsuWJH2bR9W9m7e1+5/is7y0Zrb967r2w96Vizy7ZQNn4rW04+ruy+5Krq2/Sp9157vcWwVDab7N5rbzCzS/b23G9Tv6eccuo2extuLFdeeX055bTjyvU795RLL96h+M+83fHl2OO3lssu2lF2XLe7nHO3W9kpdEPZb/OB/haLd/+NzOGSYsD/DVftsundaIejXhf27l0qX7hkZ7nt6cfa1G7UWEjUX3zZznLCsZvL8du32KHYVzZv3mD3lPult8dsI8expmb9cHhutLHvMXtbbG42WYF3/Z59ZYvpMCZgbP9x1A3lRlPiye+okdhjSujttZdjbNxMM30uDTdag61XkNttsXM+3MJ4jc4ZFh30txntpM2byg1md5f5Jb7rrX2s2aNcw7nbYCblFxvY32kyJ9v7AVliQuoY4x1vtq60Of2KHU+cI3usvRDH7Al2i8l8XWNzc5zNL2dem035uHafzYnJIb/Djvl2072B+TDmrcz2NWb3Vls22Zhs+RntKrNxyuaNmqt9Fju40uYRu9ea/olWbzMbjNtMl83WPu/pTy0P/q3fkGzH2sNb3/rW8ra3va38x3/8R7nsssvs40Ndgx1HP0488cRy7rnnlgc96EHlcY97XDnlFLvWdaxbjO9/e2pgHYHPlM9/fil/9Ef1SfSp7VYCJNGf8pT646Mvf7k+qzVwzPfY5+P//b9L+c3fLOUv/7KUb/zGKpPlDhf+4i9K+aVfKuXv/q4+Sf8Hf1DKt397/eycceGFpXzLt5Ty3OeW8vSnz/M7Ojo61gh6oj3hkCTaodmUalI1tXZTSjJGbeNxNwu8K8CLZKHrS0A0q+lD0sWukipErM1oIxs+PAnQdKRPcWL4Cf/wROd2GlrqKxb66FBZTZKdCxwX+bATsrJtCBuqoXkdfOQjqeykGqMVxSFC1bJ+Ta5VfogDqIElEqgkdtUxKekEZMltWxs5xQwZiy4r/9G3WnJuS/J+bIIvW0bbz3h8/CEv/y6juUHG7JGcBsg3v85nblvcViNLkSwkq/GFDE+Yy7zLiW9tbnoxwfxSA3SQ3YQfqyXnx48nv9EP3xoDSvYStmNOkNEcGkS3micMmrzZR4cCVHs7gC0bj45pjEXzipzbkEHoVjTv1PBs/hrN5RBdIhbisvUBHfvyw3pAD1nYpmvzs+G4E0o56daV1rGuUM8FFef/xq+WJz/wvLLxrHtYz47z5i22TKyORPsVFxvd5FuifUddAy3Rfr3aS1bf8K4PlmO+6QGzRPttb2cmzZatc1KMS+xb+ZUry4bbnFHfj9ghgc77jUQ7SXb6tE862XztrIn4nGjHHjdD6JBo37q17Nuxq+y5xBPtJkei/cYLLytblGg/pSxdt7PceNFXyjF3vJX0d19+bdl0PIn2bWXXJy4u2+9xB1vedX0r0W4ySzdEon1L2XDOOXaD8rlSSLTv2FH2XnSZ8Xcrea73jg2DZDR6+3fVeak+ttmU7S83XLnT3i/byhazvfXkY8vGY/h9BVOyc8nmk44ruy/+Slmy/gazhbl9115vb+ulskWJ9uvL3n1m18rOHbvLKbfi9xk2lf179pVN2zaXndfeWC69+Dod0zNvt70cYz6/fPGOcq3JnvPVpynpS6J9/779ZbPFq0S7xblhK9thbSw3Xn29DoUS8uZzn8l94eKd5Ta3PtamdlM7TZBoP/G4LWW7leuv32unzo2eaLfDH4l2xV+T7MzFjTfuK3tIKm/aYHI2XsMNpkOfRDsydhSV3IZ9PTFae7fRqZUwt2L/lTg/xoTYVsW8aVxKtJscrtHhTMXT3tjdY0RW3R7zQxL6JFtD11t7lwWHPEl3kuYkq6/ea+czhbdBCfKt1uEfspGgp5DEPtZ4JNqvsDlFD1v43ab1bPMoK3VMJPlJmm83H/XMWgvJdyXGrb1DiXgbMx3DqVs2iq9EO2vA5K4yGyebDfxXj8X87ze7G8q1JnuC8Uj0Y4JCPHd7+lPLg3qifc2Bz8kveclLyqc//enyqEc9qtznPvcpt73tbe0Ux1HruCXgmmuuKZ/4xCfKO97xjvK5z32u/MRP/ET5pV/6pXLmmXwx3bHe0BPt6xh8pnzWs0r50IeKvSHtQm6f6RaBRPqP/Eh9+INEev3QUMEx/7h97n3IQ0p5xjNq8p77yyxzOPGe95TyuMeV8ud/XsrDH17KF79Y920f+4f+rd9ayo/+aB03MXZ0dHSsQfREe8IBJdrbid9qmtHPsxlTK5616XsipAFek4NHO9GQV9vprabpt735IoQsfdk1vvruk7bsGV06yGAvkGSzHyA9GvYSOnZTpYQ2F3mzpyfMw59kaltWFA99dYxjbURI0pqN5KnKttrbZnv2pLrRRObF7TT7XkNsbfg5JqPv9zmIcTX9KlJr+vhrhCpHmwrIpnfi2KJDAo352Iis+45kr/SthH3ZhEVMLhuxBT/PrWxQeT/80pcN7Bstxhh2I7kf/qRnx4Ifjv38p0q57R3LhmNP0HGVDPoAu7nGP7Y0PrepdkrQK/b6BUmNeQaZMXkl6VrC3G1rjoD19VcFxIAVZGUtyZpd2vKHrNV6it/7yCPDOBQPcbguMLqeaD/hVk7oWE9YmGhnLSrRboyrL7M1wRPtX6rrgi+DSLTzBDugDZ1kuL1n+ZJu/5cuLxtvfVopn/qEnjrfcNszq0wk2q+4opQrrygbzrh1fa8AkuusM5LnPDFEQp0n1iPRTh9ZEu6RaOdmiJo/4bW2Eu0Xf7km2vnT2d17yg2f80T76aeUpR07y+5LvlK23Z6/kDL2ZdeUTXqifVvZ9clLyvbz7lCfMDcscf6xePbfuKds3GLvSxLt555byuc+qxuXpWtJtH+pJuL9iXa9HynW3md0aj3Rftw2C3lfueGK60qJRPup28vGrWYTf6azmSfaL73KPKNfk8Ak2nminafd93jSfd+epbLzuhvLKafavG7epKffN2zZVHZde2O55KId9jatT7STaL/84mvLjh27y9l3O62+jffYsbHCU/w10W6uTVeJdj3RXt/vPMnPk+8XkWg/47iyZSs0G5+tl4u+tLOcuJ1E+2ab4ppgV22Hhqfgscm1icPCRyNihr/X4txiY+UHXkXbvb9s9rbGarIkkxm/nmS3VkugW03CHEmSzFtNribabUmYym6TV2Ld+iS1OYI8KR4JaZL0N5ofeLe1OSfpTvIc8JQ6T8jPnmjHSiTaLV6rd1nsJNrrE+3VHvLH23h4ov1q1r2pIatEu4FXRkP8yPPEeXvaHQEDT7EzZjNZrrP2drOHfXCqHZNrjHaq+eXLA8ZDQp+n1omcsWH/CqMdZzZ2GuF4m4z6RHsdBXNz16c/rTzgN1+MyY41gIsvvrj8yI/8SHnPe96jpOoznvGMcsIJ9pmh4xaNN7/5zeXFL35xee9731te+tKXlic/+cnO6Vgv6In2dQyS5/Ge+5M/0YMbC4HsT/1UffLd3rf6EATieP/iL5byD/9QE/bbt1fazYVLLy3lLncp5Wd+ppQ//MNSPvYx/nTGPizwaSEBuUc9qpTv+Z4ab0+0d3R0rFHEPVPHcuAk34pN2fikzy1j/kzChQuR0AGqvd/0J2jcZQ5kDGqnflwQp2KRbrThJ5kmm3Qk72WA2ic5oR/sJCwbJNT6A54k0ENnNnjJq7aycZP66gwmyEj8q0JOcDnJOoLnUJI9ZOLDAQgdkj7BC1rItzZ6Tmu+edEAa0fy6pgtqyP560mE2k/+QfORbQJrQMvxBiIZHLqBoOk4W6GWDZcPmcFT7x5b6xtyW+OzfpSwSdvsLn3pi0o0zn3oAlkv5paCjCd8pJvk4l+bD6A2xx0RXtL4KXk+IjZsgEazkmOgRMxNhrbLUmKekJMP6Emv4+gFxznXbT35S6yFKJu3lI1nnC4RQXpRHNA4L4RO0Ba15WdUgjdHp2TekDSTAzNmfT+pUdd1yDR6tL1jNa3atBcfj56It7ZoQuXRD1pr69xoBbL0ZnMpPiV8NRpd50Fzfeogy14lV1kzK1lo1NJxgcQLWvXl3Toc5+u/84b+6Iec9kF3WtArzYr0Z7qztvNpu4xCU8l0D3lQRrL6V1F5tU+iXTTryD61ZCqt2qiysxqeta2R+zRqmxpbMz8UoDFbPZMb0pqOdfBtZ9ZKpzYiXyZUWeRmdLXRsYLMrD/ieb9jbYCnlx/60IeWY445pnz84x8vP/uzP9uT7B0Cf9Xwlre8pbzgBS8oT3nKU/TXDh0dHTcjrryy7nHOBXk5wF/0Y6gk4c8/vyaxl3sq/nCB/eVJrPNlwe1vXxPoU+PhoRUemuk/htrR0bHG0e9jVoV8oveTuk7uTo8TPRVPU1ICiFDgcfc4QLLlTSUrwp4uMLRz3wrJTTIIwYtkZzOf/LSLlMnRnrwouYxsDqHUusUUTzfoiU7a2FJhx1YYs6fN9WS7aNmXy88BmSxncDt6Urrxkm7MY55n5PYxL+4HGzGe5td96S6etAD94BmIN45f2CC5rvkOHQlWNeThUdpYjYE+Cd2cCI9YAtA51kB2SFRbHx/hJ2xE3AHZtMLTuA2miz5PlSMqeyaTSxsf4mabuLWn+Yay4UHfWsoJvscmunNzQcE+xXSjlqzpyH61rb9yoEAD2KKNTtDClpLtVmv+8AEf2aSvRxzhO9q8WUEXcEzDR2yVM/Bl9tR2f9JznY71j1g/Ga1rjVgzsb4CNGNtWVtJycH7VsRZP5Bp4zrWe/RZqHGOzhjpybeWZU1QNnp6FZq8qhkHghclUzOCB2K88Z4x6JUXFxHgaSzerdWsT20lfDV+A3FQIaRZaDqC85q+14LLSbXRZ3zRfRgZSSK9AmvFuA31VOQyVoV0uPIjIMQpBhI6cFSafqWlZVTthKy9UA9ea1NwM82O2klA82aFfc4jlEDEIFiTXpT60qoK6wQfH8FTPzqG1GzxR8mvrRC7NSgkzDPUc1KtagKdNkswkvfQapuexzay1XFksGvXrvJ93/d95cEPfnD5q7/6q3LGGWc4p6Njhp/+6Z8ur371q8vP/MzPlFe96lVO7ejoOOwg0U5yeqVrJnwS7VdfXfvxwYX6fe+rCXh+BLXd796MILHOnuz/+Z91LItiQC4S7YcLzEf6zNjR0dFxMBjft3WsCL+IKXlnbS5aqqFRpymFFoh2nLzj/B06ujgaUXWgCdUqn/RJalan1T+Ajf7UBQIfjTbiRX/g22A6upE3vbr3L20S6u4jx5XG3Z48D7uS9XbogBZPQuhRRTxK9tamGvoTeZrWDhuhR+KXAoKniheTIeHq5BYz/RhDJIkj2QxqVsZ9GJRcNz59PggouRtj9oQae4XTjbmQLYr1K2NmH0RyDxolbIaIks1mWzomh11+5DNikklejE89KHxYsbrpWgHi2TG28da/Ugg6MRqIKRLU6IrtMrJlUAw07IUEd0veuS8gG9bWFxIcS8ZmtBChjz3mDAL6cQyxGX+pIAVXihhlDx5tK7RV0PE+8yhAt6r1DTGOjqMAHF8OcKyR6DvUHNEqsSLooadu4kd7jp4w8Jfay62z5tdfBv5nmFlwfkbuj3kCtAmdkLV6lrSFPmM1tL4zxwJB8jI0F53KnoMTs8kaj5UgpuaMRu0zQ5N+Y3kjY8a2Q1L16EttSly0ygj2lFwg+xyL0a9lNhtBA1EHsh/abL9CI7RjPdBLoq2faWBMH9TRcYRcjWHIFK0yGmdWj8YWHQNzI1rtCrkfstRZpuPI47/+1/9aTjrppPLKV77SKR0d03jCE56gJ9rZs/2SS/xHyDs6Og4f+CxzIE+0n3pq3X4lfy7l/uyd76xPld/tbk68mUECnT3a2XbxDndYnGiHzvY4U0/lHyqwHST2+z1iR0fHTUDKOHWsClzD8oWMJudhaIMTsvWVDIReKTWhaHVsQRK3k7qz9DaJQyCa64/BRUbyZifYoQeghb0B3K94VqST5HL8rg+l7dVntPrUetBNPyU625PsYV/jZb9b5CpLdDrYdHvt1rzpWRnMZUJLovOCbI1HOkpMw8DOhD4yOYGOGAVZ7KgNrI9IfIGCvJLEWU8vtVAB+NLj+Jg9+WJ+sO1xRiIehD2APcWHvBuUvehbO5LvSlw7D2h+sQXNSo4NhL3gA2hRZCf0HKKHbI4/2QC0Y25MRxYa32vFFzJuJ3yGbCS/9++t84YleJKzdovN6fQhxZyErGor2GltP+Zhg3iBbDitY/0jDqUd6op0bGMNt2Oej3tuG7LMiDWv65iyq36qQW6DpucYsL0zp+O10Aa7CoSs12ZHpinxZa2QbCbfteV9qmA1EWs0+UY0QHfW1FisLB6FccY6wPXAnO6UvAHyjJNkFshnDJL2NK3I3kg1+xixFmIlucpn3/XYSoURD0fdfFoj2xvY9s6AZhjrCE22ntPndLyMGUEP8mTfO41nL7U99BV1x5HFu971rvKHf/iH5dd//ded0tGxPHiy/eu+7uvKC1/4Qqd0dHQcFugex+5p+O2gA0m087tB/CBqgN8O+uAHSzn77FL4QeOV7BwO4PPrv55v6+qT7csl2nmi/XAkwmM+ud798A/zq8+H3kdHR8ctBvW+rWMxxhcbzrei+Yk3zr9xIs7iQVMi0PskNJQotz52oKl4QpEkfPbZeOimRJFsI+dFyd3oT0AJTuNJhnjQr6QZfQZPmbpv6iVr8tNs6lQ6FyM9aQ3J2jkZmxFJLsHkxHaZkV/Ria3JExvtqAMmE8khas2T6yn56nMMYl6UwAUuqwSxdZEXmQ7FsNkv8NI1GpXmfxQD/KDRZh60HYvRNvkP0rDFC/qAp8vjw4PmzBhaH+4XxBP7QPOPDOOx0pLsVnjamzHE/ujQ9BS6x8l4NbZkO8YK5Bu7Lk+JOKjbfCXIr9tQLAbGw5MI0m0DrfYUu/Ea3YBf1fi0Nnqbt3nfimJCz+uwo3ZVVQz6KwCr5RY9L1k3xhexAtpBl+2OowasD62TWCjAFkisDxDHPGSoWEMgdMf6rU78kGH9au0bwscYi+gDP45wB8IuYtEOhGqrky1vz0imq/Od20i29KRxCEJObrQfussiEmIhX2sY6s5gfX1BK1Veahq1irtw+In+CPyAqVxLBwJUQ8hTh6q1ownGJiXaiNTV2IxWMRuXKiFNlVTFS7ShhdqPaPxXTar4lKBQ5wY0s86LVUOXPcuJL4cjGLOZAqmTZ6W2a1+vsxdhaNfoM5Y1UyeBuSHG8TyCSiJmdRdYqGj6IzvL6XTcPPj93//98qQnPal8PQmQjo5Vgj38+WHUq67iR7I7OjoOG770pZooP+usuWvoHODz2xrct33lK5XGhZz92T/ykVIe+9iaxD5S4EsAfpT34Q9fPJbYOubGG+vn2kMNvoB429tKectbSvnQh+r83FLB2JnjL36x/oBu7It/S56Tjo4DwIK7/44hOKHYCT9O+nGCoVI7TjhRG4IuFeqJCwa0dsJy+y1pTFKEC4jzKJIzhO3WTjX0pu+08N3iz3Yr2TnzaL6WTI7bbdeTUlXUtjIkr2SXask1DOggR28QD8WhvkFx00j9SphBX1JkfXSQsT70IAP6Lbnm/earVtWnd6gi+T7g57b3qfUlAx0H7fAhvifcdUyNrwSf6zBXOZ7QGSNsSs+PaRaDlZPIJPkZA3T0iDEQvrIt2tA1T9amDnuRkDS6jmHrW1Ovbhv6ps2S4a8X6l82uO1m12zmOBWL0SMGzVXE55Adh/wHH72I36E+tb0gR5u4wkboav5cV7XzO9Y3dHxZa6wTURz0WQcsCIeOvRqzNjpqizGD7CaDkqOM5EDo5wIGa89K9DMG8hR7ab6dHhjwolB5nW2BoAeYD2h+biAhPvvdDfgiy239ayYjuL16HhDLXlww5FucQa+y6MYVoSXfRzFFj7q27TWI7nsAmau2uCo1HYdU8IsIbUfE39qB1AyMQhRE83Fnu2AmL6HWmgL0RbwMrIRd/var/o/xVtCWnHoJCiHiqDohGwhaFMC8RDtQ+/m1gna2DXIs1PTDZ6Yvh5X4HTcfXvva1yrR3tFxIOAHUs8+++zyute9zikdHR2HHHwQ+cxnSjnllJp8nvrgMsb27VWW7Wbig8xll5Xyuc+V8t3f7fdtRwj45j6fetFY4PFjrXw5wBcMhxo8xc4+8di/4II6R+MPfLcUMO53vauUb/3WUh7xiFJ+5mfKYd0bv6PjKMMRPJuuV8QJ1y4AqulTRe0F5DbXCy4aovFibX0T60KijRA02R7z0wUoNWULQiRTRKrP1QnNDjJBnT0lF8mHmhgB8zfd6OkfF7t8QQ7bKMR2HQHasj1nzQAtyQIlQF02VFqC1Apzx4+fio5tqxlz9McJ82ZEgt71WnJhy9sqldRkBTeqyl50DB2ML0rYE0IXwEM50UIHiGdQ39rx5LXk8cdT5mabeQ87Yll7oKuGFafH3MCTTatjPjVXST8nxB1aD7KT1oP0vFi7JtGQiTWBD2y6BpVi836T9b5gbWQkR3fUlz1qK/nJf4BIkzUB4gu9DMXpY+44etCOdTrmavJix7uB40/xbjTGyyHsLVxDI4VYV4Hl1tekTa8zjyZ0K6LyAr/FVqtB7AO/IWAIvWzfbQ++kANjE9YfmFUnEUZd0BLZQc8yKYQKO68EzRsD/ZFt0EKYs1WRVZY7FGCBiVVhys/y7obc3GtT4HVGTAf14EuCCQQ3bOf+mEadS8a4P0aLyctwZDcBKznuuFnwvve9r+zYsaM8nKcLOzoOEA996EPLv/7rv3qvo6PjkIP7HBLtt7pVTVCvBvwYKk+FxxPtJKvPP7/uzX67280u7GsVxMdT+STCx/eBhwJ84UCy/dnPrk+1r/QB8mgGa4TkOl/kPPWppbz5zaX8wi84s6OjYyVERqxjCuOLzeS51mRiC5MQp1bhBaUggJGRSFRLNPOMQBKyxTDhPHgDFnrQQ28EeFECtPE9SBJhoT7J3J54lE1kkUt9R+zfXpOng6AM3pcfK9k/PNk3RCwCtfcbzYB9PlC0H8m0EnMlOZdV38eEmF6Mp7Yh6oyg5XjCNiUS38ipb+0MqcGkcp5qGE6XjvXxwYckSqDZTDbCdwvOanRFCroVqnwMw8cACHkNC358OJMtAzaiHZCdZCvGFvOhLwCMb30lgaJkKGbo3g8+9IiTsUPPcwAyX01siVIRttqcW5+S4w7aYE47jm74cc5rDLRDn9bGGHNrJLWzrWUwYbViNWsvRBSHt5dFEpqKbzU+x5iyA2m54Y/dTPidSxSrS7J9QYyZHDEtcwzGZur1a3WYxUC9IB5Hk7RGVZv30yysPoQ5LB/FAixQGpBHMsv5yXO6nFxgLLManY61hc9+9rPl3HPPLcccc4xTOjpWj7vd7W7lMyQBOzo6Dg/4bBOJdt03rQLjJ9p37Srlj/6olEc+cnYPttZx0kl16xiS7YyBLwv48eXdu4f31AeDN7yhlHves5SHPKSUyy8v5Za6/RXzGn/p8HM/VxPsP/mTpfzxH/cfiu3oWCVWeVa+haKdRPJFJ7VJsC93Uy5epmHP+1QtaQhGuiD0VfHiwrTDb9RzcNl8Igy9SEYC47ckxMDWjKYfLG28ZC8ACRtuH8mhjmGs77IVrlsNiTKE8/CRY83jWITmI6G5wQaFdoBOFEf41Bj9QwglvmCJ5LbkXDb8hiyILygk5zRAP3yAkAe0s96Al2IJvyCaIYu+Cja8Bk039K2Gl/dml47LO/TFC41Ihic7vFa4LcHqiCXo9GFrbFaSjYGe2l4HPd53U8e/jQ9bhvjANf4yg/3u5a/jqEFbN2M4va0Ljv/EsR+T5voj3bG/oE/aHvGmdEMt8wamxrrpL5Xqm6k2x/4Tq8IJiEUZI+ihFzJNNhmMeJynykiz64q/BJ/a29nMcphLwOc5mrJh8ug0texvJF9ZSXYSM6X4ay/kp1wHH8imlWyao4bElLuBrtcx1IHOTCw354C8dOwl5JqNCYznAJ1sP9hjGyvZXs7nGMgeiHzH4cO1115bTiKh0dFxEGDtsIY6OjoOE/iAcDCJ9niiHf0PfICTfSnf+I128V0HV19ivM1t6hcEkVj/i78o5a53LeU7v7N+gXAwYC4o7353Kfe4RymnnVbn6lOfqvSjGTH2PE7m9T3vqfPNX7WxXc/97lfXGXN0tM9JR8chwCrPyrd0TJxM4lo0PjFliA7fTlacsOhzwpJuGDBIfWQjLnayQdtK+FnkL6PJjGRFH9MiCel+IraIQTF7UlUIeq1qbTYHcUGMAqxWYphibWQpajs/ZLNfmO0LjbCPrsXMn4wpgUrfeZF8dRPSo90SrcFAnkLfSmynEvL41BchJhM+dAwpoQesHb6FaMP3Nk3F5X3ksR2xgrw+mq7VA39JXzzkkw1o4UcyAejoG6BLz/tA2/xYHb6ibvOR9CPONp8oGnz+WhJe8JZs0jYbqoHbE2h7wTcY2w/xsBPxNG/eb3oJkjU5xZH4zUbHuofWhR/jhtw2wOOYc9jjfRMiquGlNQEt+FNrZeDLoff1hKz8TtADjW+lmfW+Kq+B4nKhKZtzNJOVPDacJ/Xa1g+eQhioVVn3ovYAjWGYCem1drNAJLErX21eqH0cY/MuOoRovFTmsl/+Gi/btJ67mpIdFUdrmmJMt+Dt4CeVFTHhXRjT6Q/sJmfEMuC7sipevF/5AyvCFD1689LJoGNeZgZ4wQ+t5eSHliuWk+/o6Ojo6OhwcM/Dj1TelCfaSZqiG0nUtQ4+3EWinafayQX85V/Wv85+61tvWhKY+fzwh0s555y6Pc3JJ5fyyU8evL21Dn2gtMK4+RHYvBUPfyXw539eyrd9W91uiHk/99z61wTvfOf0/XZHR8cA6+CMuhYwvnV0jE+8U2K0Ke3u0TqxH27oK9HRBCrEszJIJng7JwsXwm3nbEP4G8c9Qn0S0JbGWEymJuwJiQ7wu5RO2GDKb9BywpjxSd/7cTJHBnr+IECfElDTdbEdc60EGA3rSx4Zb9PVnGaeVQCfOj4GZNrch4BBsaNjNBWXCxHipz8YY9IfI2QjrhZn0pfx7CvLUKNM02qN3el8EBHP/YccCBubNg/n2PRqUivJhj46jI8fYVU/xYO8aivQQNQg+wYtrqBTWxyQaEdMsk8tRoVUQs+BPX1JY3p8ESA+Rcqp33F0II5lrAs/1lrXvnZEchqIc29bsw7aOu84LXhyYW2trfweWQHjD6TjtQ84V02Q23CoQ0/xWJsa0tge/UYb8Vo/DBuYH9lKsuoOdcNtOyeCZs7tJbPVnhH0PzMMsNx+Ux2LJDczR8DaEcMoRs2jGRrYGov44ZBMyOW2IZr1iwgHxOha3ThNj5R+ojtyv7aHEgN+6jSz1lDbCQNt69CnwI4ygBFCZ7QSJRu08fxnDHyOELxJ30L90gY/+V0TcY/fScv56ujo6Ojo6DCwhcf119enr1f7mTQS7TzRTjL13/6tlK/5mlJue1sXWAfIT7QzB//xH6U85jGlbN1aygc/6EIHgYsvrtvR3PGOw0T7+DP80Ya//uu6R3/+8WrWB3/twA+hxgfTM84o5S53KeWjHz08P0Tb0XGU4QAyBbdQtDvxfPtoJ5wgKZFH8X67U0Um6zigSdbqlnR05SZP7W1oOsEjA6wfSSPB5YLdGuN6Aoo7yc3ZHZWUJJ3b83ZgyzAYi9fST3rIi5b0WnILWpIFER/kYEGTXyOk+GrfhbKsxkkHovul2WTNBiXGQhV6YDBuo2X/g/H7OGSPGr7Hn+UAfQpJ8JBVEdOK98NXPH0PQrYKz+iRWBbPoKfWKd73qiHiFKpe/cKFNrxKrv59WyDkNU4DbfNRtwzyOF22KjqgAdn0EuDYoxs0RGXDCvX4BxvhK2YPLmwHxIPmBX7zbzxvdhwl0GG2l9EyaRivD2FqPVh7zoYTqCiTay3VYCwz6d+gmMMw/VotxsxHbZHcdaXwMefb6wWQeAq9YU5vSmgKEUetZmPzJ8uzmVF/GHrqqBl2aiVYezzcwXEwZDYJ/xnX20PxOcQXDogNXCXHuKxde5kLaOYis+aljDKKBfkgNdaEDepEbpjTHQH6SjJjZLmJoc4FkrvoRl/XGG8HIp4psx0dHR0dHR0OfrSTZPOZZ3JBdeIKOPbYug0I28WgyxPc/NDlegHjzE+0f/GLpVx3XSnf/M2lPOABpXzoQweXBOZDHPuRMz98ccFT3OMn2qm5V8X+4fgh1psbjIetcZ7xjLof+3OeU+cU+vvfX7+Q4Sn2APf0fCnD3vVHw/g7Og4zyIJ1rAROODrJ+kWMSj/EWbutIRkrQaeOttTRMYKSidisLCUW/Sm8SnPFsMeJLXQFCc0g+og3dcENWktuhp77ET1sB8+Ks2cxLFlzk4XnslEDbChe5ESY8dGXPfxQy6j+t5jaWIzXbCCTaMjSJQGLDS56qqNNQc34yChRi2/0rYqEbdiSPWRgBpDNfUN7GtyQx4xtEtuygx+DkuE+Xmwp8b0SPIbmFx+U6IPUjmR6+CQmCiIxRxEbT6lvsgtmyHCMckySswJPCfNkt8UTtA1m3seqcRod+y12K4rDaxBPyUMLHdDWist5pS8GAOMItJhNPvQzkEUGwKdAU3F7MSbs9w8JRw+m1sMAvh4DA3FfdMvZCJ7WYyxSQ6zvqEGsfZVEy4j+IKbUHgPxzB6ZG44nIWKYsr2cPzClUo3VDlBcQ0F1rURyGuTwFTrjp6F5sOJMVFQ4Zy8CrMRufoIe3SQDqu1KHPMinFVhrLwKTNnGSrYU/Sw752q0jsbsOsZ5esYUD1rQ/Qw5CWSIYNFchY1In4/lgj81h1AydZGPjo6Ojo6ODsPVV9fEKE+jr/azCfdJyJNoJ5lKzdPg8QFirYMYSbTzJP8NN5Ry0UV125Ov/dpSnv70Uv793ytvDH3Q8zIF6CTa+fFvEu18GcEXGGyxQ1IfPveNv//7pTz4wXVblfX+VDdj+vSn61P8JNtJoP/rv9ZcCvuzn3hiKbe73WxdsHZ48v3LX+730B0dq8By91QdkxcdP0HvI+FiPLqcqDgpKalrU0oyD7WsHwlAMUyeqiUTrJae1dmf+k5HR0kepwnQgg6ybsigXvV5VdsTnfpjbiUhJeaof+Ldnvlrtg3yBb3KNMi+A79x8lUMlKojORWjhd3QpZZt2uhAj2KAxRxJBFloeoFgJOS8LVg7SBSOD6Dd5traEU/4oa2+IeIBxCueyQ3idGRa08cndNfJ9gTvQ1eiunarrOlm+aChE3OnZHeKO+iSjWK0vK1LwFj+UhHyo/lY8vEiKZa9cPT5okWrwPltfsE4TkBbxfvhI/qgOqjtjTYfQPZpuL2wic+oNfbUV+1ykdRHJr/H9H50Xx1HB7R2WA+1q7UQ6wGIH/B25kMLmSaadQxZvq21bGMVCHl8Zd1BfIZRdw6mK5EsNxWTmmNfXq8GSW0As9euEyN7Lfk9BVMZ/EXUIvuBLJsxHk+4TL4RoavzFkXEKjr3V1nWnXSVaNVWbVfZCYVqXM0pc0Aitbks3FVD01mNsiNsHIDKwenExKyA1dicPA4dHR0dHR23FHBfRSKZZDo199b54kj7iitqUvnss7kIO2MFIHf725dy1VWlvPSlpdz97nXf7fWE00+v4yfhy4/B0j7vvFK+4zvqFw9veUulxXxFO76YyPesAWiRaI8977/6q+sWKjwxD/9tbyvlZ36m2nnWs+qXFOsdfFHBmiDRzo/kvv3tdY4+8pE6D2wXE0AuEu3MR8xvR0fHJMg2dawa6YQ9vl1siTsKPGpgtRLqrhMn+wZr6+JIXSnqixYE5EPOQRs7IasiRm2D5Ec/UkmxNukGpUuzPu0kX5H7tGtfepPyjvAvmAxJzuYjLm6uS1/yVjSHIHjU3uYJZD31z4k9bCS9HEs89Sz1pIO+xgubFwM0PfHu/YASwYawERcUimzAhwng8+WC0RWHy4GwA5Bpc8EY3C+yJMOp81jg6UsL70OnKZvGi6e+40l7fQjzODPghW/mRvNsMvlDG3rIUYy3pPjwN7IluhpeJb7GRsGGlcyjTVc8+u5PsNr9Vjo2KkdQmxcrHAc1vc9Yms3kL45XzBV2VfChQPR/oNOxzuHroB5YL4447ipOa2AtpDLoVwkh1ooqe5GsI7ensBp+k/EaPzTFE2WGJmqN8XhanNTeHusPCHPMSdKK8PgH4YxjC0RobcwzxGEaT0cdT+14ynyIsBnNbMPh7IShwEQ4DfO6FfJVmw30xzFOxTJlM4vlQ0gdvCDNh+te5xkD/XENsDmvBqVS4YffwLz8GPXLoMXxdnR0dHR0dAzAxf+SS0p5ylNqsvdpT6tbfIxx6aV1+xe291juA0wG902PfGTdy/yNb6xPZ7NFyHoB4+S+99a3rtvGfOxjpdz//nUMJMm//utL+e3frnOY8YUv1Ln87/99+kl07huxR3KZLWPwg12e8maLHr7U+IVfqD8OylPf+H/ta6ufsa/1AuK+8MI65jvfuW4h9OY31y8X+NLh4Q+vyfdYW9Ts0c6++MxJR0fHsiAL1bESIoE4QJx0vHCObSdaJzQdlwWpWeUzAYSNEWS/NoWxLn3RojZwQmxtYkGe4nLea0/9KkFtaMlUcR2hS+W1ksKewMzBab6ybsBkkliFySl2T4KCqAXjY4onvjOgRZG4dyKJPLf1gPUVk/Ha1jHETzvJakxGi8R3JGw3+7YroQukG7UV7GNqA/Ppc6lYrM348pzEViv4C5+yz1wmhA/ZRp++F80JbeyYj63baq3j6L7QkQ3v4ytoMecRW/iSOPKJJuIEkFPxNnJBi/UUdSDblA6I2pCaM5gsepKnhB8bD4AUY4cWPvfzYQpZ64ffxkfXaR3rG7HmMnI/3sft/RXH3Wq9B5Ns6I3thY7Od45Yk9gAuZ0xRZuCvgz0ttnWU8L5i0Ct4dr2aCoGnYRJoUXCI+BL47OKeZPbqjuZ6B5BX+sq3hk0DIrbHZth+FWl6lX5dHzyNAZNSlZXlYpx35HDwXZMa9QZmnYaiYd+2Mi2Bq6yD68JW+0Rb6DnCL/BH8hYoDlW8e0lyw3kEzRe6tptGPdB2Aj5LDOmRTv+QiDogHVSqf7XF46QDeReluvo6Ojo6LjFgc9c//f/lvIP/1DKD/9wKe96VylPfnLdwiSADNt+3OlOB/ZEOp9Hv/u7Z1uCPOQhtV5vYPsbnsbmhzn5soAxkBR+4APrPu3/+Z982KiyJNYvuKA+if67v1vKG95Q6RnIkFxmn3fsME/3u199ap0vPf7pn+qXHT/xE6Ucf3z9S4BXv9qV1ymYH9bQ131d3Srnv/yXOneveU2d2x/6IRdMYDuZU04p5ROfmM1vR0fHJPJta8cYcQKJxEok9UDc5IOp8wy6OSEDJGd62FGh7aUht7FB8e74QkgfXvAHcKLJzG5qrR7HJFS+rCcXPNXcTENX4Ubfil2AqnzMSVKUn6ZZQZ94KSD0REvtZifpQw97yEYSVaSwV6uWqRGPmvEas2VNDNCQ40NKPo4RB/vvKzFmMrCRb/55cbt5jOi2+PFpJfqSS7L0lbhynuSczrf0bS94K7QbnKZm+HOgm1zMYDJzcghaafOOLbcXvEDWFdxe+yIhgTE1+waqZhMku3ks0KVnzTl/htBvLOvrODPPjhgLstjQE/zWZwsaPfkPHZnkU2vC9TrWP1h/c+AYe7FDPrm+KsObtBNiPYF4n3BuEM1KticfJh82xrZWA8wltfrXQ95pPmtXfcmv4E/yoWRYIDYby6wOk7NtQaI2hsZL016yTbpBC3HBO1kWQHZHGlLSqdcubIUSAlHVpDPbW1V3ITMGCvyVjvfQhST6DLLVZMJm9L0hVOqQNgSxZWgLLoOqMLoCFom1qTDIqrsaepxhTJ+yOxVW1ss86OL5mGKeYo3Qzon0Sp3NSKtNPtpj5MtyR0dHR0fHLQrsmf3Wt5bypCeV8rzn1cQnT6+TcA/wV8n8UCdJUq6/fg1eEchxb/nsZ9ens3lqe7W6awXES6KdZDoJ3wc9yBkGfhSVuYntYwBPX7/pTfUJ7TvcoZS//dvRX3VbTaKdp7sf9jDPARhIKt/1rqX88z/XY8A+8N/0TfXp+Xvdq5R/+7f6pPt6BeMm0c6XC6wJ5uahDy3lF3+x7tvPFkMZsc74koYvOCbvuzo6OgI9y7QqcAHiZEzxi5FOzlaonVQBzU88cQIn0Sdx7zdkW4ZI5AwueFKszbF8w7hvWEEm2KrsRFlvkq3njFkE4dPLADnO1MYGBZtqpxNxOB7AaWOWTBpRCVwvALpO9l4DnewxELaspqmnnCeWOWotFmS9YCeOQ7uASLj6asdoZFNxeCzNbGtYcRvhC4TPJmcIny2R7TZBuMB31lWJWANOF9w3fc1l0J2mvtNiPl1G68LKBvMpUWvzbOKSkV3D4XqBsAPmYpuCxzA+VhErVcy5fh8BmI/YPicQY1HcJh9J0bDDFyCtbfSe0Tl6MFhno+Ma63HuXBBrw/ltbQBvhy7rSesqivNyAdjIaywALWTGmPIbaPaybk0yD2mOOR/Wn5Sd0JUf/FmV2dg0WhtirRYjBFBwpbmwkJHcjEGrlkqLaVnii7UpLPf+Taxqx15k3F5SMMtYGCDLKUkswlC7mV2tUcOqRScEIY3JU7TA1Cwiu2B2F9oBY72VZFVbY6gX29nNYzl7HR0dHR0dRx3i8x7burD3+M//fE3qsjUMT66zVUncx5Eo5glrEu0HCj4Ls03IX/5lfTp7PYIfRH3nO+tcsJ0J4EPGPe5RyiMeUZ82j2Q627/wtPqP/mgp97lPKZ/9bP0B1QAyl11Wk+bf+I2ze0cKW9GQmP+7vyvl536uHg/497xnTc5zDNBfbyDm+HKBRDtjYuudV7yibi3E0+yDh/0SSMCTaF+P4+7ouBlh76qOZcFJhDs+6nZCMQJN0SqlofVRkmI9UYedqkijQja9n9vo1IZdVIPuvINCshdxgQmT8WTaLJmQ4XG0uTBEAjRDY6bBixvJOtFWAr02a6Is6El2HASs4MMSOxpWkqow8MuLySg+T8xNyYvn9pTMcZ0ogKol99TxMkboxTy5TNjBRlzUW6y5dv3AWEb9ZEtAB3/wrISOtlIBLq+4vGQoFrNlerEeBnbg8WEvvhigj5hk0UXWeXNjoB/F5QPSg07bStiTCLUVjkc89R82Q8+7jUd8kSCVQSqrFbt9AFuUwOtYX9Dxj/U2Aa0Hb2fE+gGxhii5L4yUgwzCxtiWYLRMDzT+CFmWZhaTzrReo47tZtfRbrRlZFdCqI5jXBFTCqmfYhgMhbbmZsLZxFxOTXnGCmwhrK5kC6woYsYmIh8g+OM6sJyPRbzFT/kfZqxmgg2L3gYdHR0dHR23SPA0+3/7b3ULlDPPrDS2hiGZzBPt/DAnYBuUiy8+uEQ7H2zYHiW2SFmP4In2L32p/lhn/rKAMf3Kr5TyvveV8vGP1/u9z3++bgFDEv3Rj657sec9xpHhCXj2KWeu44Mf9Td8Q/1hUH5wloR0gOPBhxgS1ev1wwzzQLI99vhnLfC0Ol/osKXQ1NpALhLtkQPo6OiYxDo9ux4B6KRrhSqD/uT5FaKVOPmG/gDw7SSFjORSfw6uix3ZckTydjmdAZJc+FSTmqeVa7tSTb+SK6KW0PjkWjXqa8Blchgt9iwZba9DJo9J405jhZWTpCRfx7bpaj5TrOjm+RNC3uhcVJDn4iGbRlcSvIoMk+XJDrqQIubwqVjDv8sEpJPsRGzUcfEK34gNbCc9gJ7kjB5yQO0kx9PgEU8koHUhHdqKHtrVZNilODHqxjMtxTHQtkIfGZrBA97OtLALIMue17GetHUQdIrFjs7YBn1MZXs5RvXdXsfRg3bM03EX/LjHe8eXQgM6sW7GaPYoVC4ruvfHyDT5nZAZA5EJ9zOYADIyFcJOqy9DtLEgE/KBCfnl4PEPhkFnPF8hMOHKLehV/BReQ24Hsg/xF/iEzn/kVUPiZYbo7U8DmXIJ8vYnqSmFej70TkKTG5JblzPrApGGsLGInzEvA2U4ZihRxpijmeqUXEbwWxJ/6E6ANCYjHWMb81fy2dHR0dHRcdSDiyRPaZPYffzj69PTgPs0tizhRypJjiL3nvfUrU1INN/SwGe7xz62bm/yXd9Vf7w0AO+ss+oPn/J0Nj8Wy5yy1QtJdLaWIcnOE+6BG24o5a/+qpSv+ZqqH6D9hCfU44GN005zhoFtVngCfL0m2skz8GQ/+63HOguMPjvPgS85GDd/MdDR0bEQZNg6DgqcVDkRWaHiJMuJieTsiidcFAwSo+39VhuwIRdGUyJUHThunza1lUiYzsF5akZScUpuCi7XxPEFsr7RtC2O2Vahb3WOZRxX9CMu+pQY01getKRz6IaM6cSfhTW71FZkUwReals+kLUKvp6KtjgkZy/BjwR+xIiCk9o4w27IxD7gDaFraPEaFJfJyQawOmhzgIYd9Ed8/GpejNf0kTMQI0+uQwvf1DS1lhyhxw+/xhxL3gs+ND6TwaZs4NefiifhLZqVbBdINxLi1ka/jcFoKgAaxWmT8+B0lRmp2WgxJ+S44WmdujKx8kOy8YO0HUcfYp1oDXiHYz1YQ/nYJ7lot663g6++rasmb8jtsNtoyQ80+BFLQDr0E49uyENv8lEb1PR+2B3A+9Id6VnJIUz9dUczN8dCuVmvMGNVjM2liEUdQ5VtJcc5CGAGLLVE7hhhg2bSF4kXaFbTdC8h3vpDtzNPIbccQrbpeGmwzsLYGxJ/ZAAOZc4usMBnYxlyZxZrK/rRC+nlxghrqDePxreGTI0EoY1jG2PwV3NqDc0s8t3R0dHR0XFUgvswfuz0f/7PmsgkkQy4XlLY8oSn2dkGBfzjP9btU044ofZvSWA+SIqzPcwLXlB/yDMAb+vW+qT/m99ct4N5+9vrFxXwTj+9Jt3/9E/rBxnm/W1vK+W97y3lcY+bv5/FNk+zH3fc7FgAkuwck/X6o6DETKL9VreqY45xxRijPwZ05pAvJ/grgY6OjoUYnU06BmgnmjjZ2EmpnUuDZwgaJy3aSvI56IsPzwrJDE7qOilbiSexKZEUlB34juYHvhq1hIySmtiYOpwzO7EVTEsCjHXEd19A7SgGqdmLSCE3lJE+cjEG6YQsSG3FvanKxUk+9MRH1kqLwys1kXGe+AmiWS07VjI/LqAcB/gcC7HtJXzrmCBn7aDXgTi8LfoIoYdN2vH0NbI5jmjHD7uCPAfNB/HJWO2DkAn5sCV4X769hAwmEY0EecQUstaGTakvKQ5krIjvtdDsey2YzpJ/yw1dNqiNn+ehrWfrYwcELeTaXNAWp9L0Hgp9EEwHvNi/XTFaiVjRBYq7NjuOAsTxbMc1H9y0PoIc5wCgdZoQdEC79ZN8Xksgy8V61l9hhG6CYszxBUayTSb81qoh25m0N0Iel2xGmUa7Htg5Uc1wx4v9l3ay2UJgehSslfbFZS0bm5CD7jgE66PfRGlofq2d5xl4LIKLqJlEAvEke3xnS+zyU7sTOvNG5C4UEhptwu+E+IoInYGuO5lyleWCHmOL45hlAH1kx3am5OZoTgje4C8AvB7rZIR8yGTZ0buxo6Ojo6Nj/YPrHg+Ise0GP0LJth1x7aTND5SyHQpPV/O0ev6wwfYeJH35AVSe0iYxzDYf63WP9ZsC5oWH5Uio8zQ2/TxX8L7lW0r58pdLecMbajKcrXiQYWuZRz2q/rjplVeW8vd/X8ov/3Ipd7tb3VamfX43hN2xfYAcyXtsr0ew7niq/9RTh2NeDXgKHjB/HR0dC9HvZ5aDLn6jEyu3kJDa09W1GpyA0fPrpgCL/jgxiE6TS/qCMxQDxfkRk/wFze0O7DskX9FuhBVf0mlt6GZ14yYzn/xZWYqEksuoRiaSl5IXw/6bbOiLthywwzJ0WwNg15uAhA128U0SlWNA0liJY+JwOYCs+G5bPHshnLAhO4wfHnQrOSmkRJyPuxqo4MsB+R3FC21uvPSttPkwSA54HGrCt6KEcNiGj3+r6dNW8hhZqqh5IdbkCzttfG4j6HEssSmaDAisEUUUprBhZYPJ1i9RxK3ALv1sI7ErP2SsP54zjdVj4Kn6GDtAJ44FCB/Nj/Fk23kaI3RekHNbFGQ38eM1bhsh6Vsz++hY5/C1IUQ71kutap/1UXuN0forINZyloc2rmOdBsa08VpW7SVjZKZikji0OYnEG4gtozOwN5KDZ7HrvKAiolgaB03vChobhDRI2bcyHrejJYizHRDyXmd2PU95Z0W4ZjIw5yuh8niZEprRMlfS9jKj1XNsHPZA1mmQcm0uhzBFWl3tZDtMLDKT6aiN+4uQv6AAY1n6y+kvx+vo6OhYq/joRz9adpPo7Og4GLC3+o/9WE0Ev/WtdjG0qyHlkkvqPuFsVfJVX1WTxRkklb/2a0t5//trApkntdkeZbztR0e9t/zWb61bvTznOfWp7dgWBh4/cMq9MLwnPrF+YfH61w/3Z18J2GHPd34MVffa6+xTDfHyRPuBJtqZn55o7+hYFQ7gnXULhE62nDgptK3EBRHSGBKxFxcVWg3dppt+JBcb3Af8MKy2I07ekShEVzHYiT2e2g2ZRTB7JCDaH5Bn+xvrFhpLSlZi123LZrVL8n02Bq+xFbFUoVrahwN8UYDLREIUhK58MZbgYdf9wG8JUoNkjE7MkQhnDkjUAtxRALrNpkFxW0EPGY7D5tEHGfm0mqILjxvDjor5oo5xaB4cLQnuBRmeXIh5yeNtMBp0IHmebgg957FVC7aV4LdxZ0g3ioGYFXcMwqE52lNtw1cczm9tt6H4nGdzr2PvMnWNmBzjwQ41ovA1166HjNYMcsyx9XnKXXMXMlYzTs0bXfQdrCGNBV2Dxm99QQ697/bCpoZA3+mMmfnbZzdFzTdCyGBTCh0r4Prrr7dD5cdprWLqWDYSx9uK3lO1O8C4P1OcYdJ+ooX9WIvChE5G6KOT9Zw+9+XWGOETP9lG04na+MiFP8Xl7SCtBskFUKq16UfDa+SSbKDpIybReaFIsC8Hpatddc6COclxzmIbQtNLw2Wn3A6mbYTsIz/RjYJ/ZTmAaMYbjA+xedFlEeJYiTJD7QVt7GoAwrEqzqyL4oC8gLUqLKcLbxj/TfPVUcrOnTu91dHRcaixbdu2cu6555aXvvSlPeHeceDgBzo/9rG6xzfbxPDjp4B9wElc8lT1OMkOoJEgJtHOj6Du2FHKfe/rzI45sLXLr/1avZf8vu8r5U53cobhvPNKuf/9S3nTm2pC/g//8MD3uufDFXb47MexG37oXPvgno4fie1PtHd0HDYc4DvrFoy4W1VtJe4Mx+fVSEQOkHSlH7eWIZfbBmQGJ+zEy0AuEpEroP0EW05mAo9HT6c1W+E74gTWnrqIQBuHp4SrQeO1euwzMEUPHbUp9iK/UQx6Ut2F4Cnh7rwG7ysRTNvkpRL00KftBQQdoBd2+YDTeGED22msYDAmo2V7zEt0dVEzO2E3Cseg+YWGnCs1W/BCxqBEderzg6fSSfrU2A77gDqOleB0+amJIihKriNrhW1jIo72PCP8SJZntAQ6uthIbXWIy20I1UdtWh0sJ5lBp/HiuvBCB7L66lW+9mFPcwhTc49uCHasBG4sH/OYx5QLLrhg7Sbc21qKGni7rRGOfYbxpTdaC5DCHrU3KzLdyxjNX+KN19vc+rP+mIb6nJwhYnLWVAgDLORPMJI7JY8n3De4uqpsKutAb7zKmEukj7r0ERkOfSQUPGorWXbW5pzlTWHsaISRzxqn64Rq4h8IxmpTxyxkJlgNwzlZgAh5OUMjZNGxi6kvDQJwKMNj2r7Sd83xcajyi6123BT8y7/8S/ne7/3e8jGSOR0dHYcUJNm/4Ru+oTzzmc9U+/d+7/fKnkiWdnQsAhdvyh/8QX0ynSQw247wFDvr55/+qT55TTJ96uLNvQs8krrveEcpN94423d8Sv6WjJgPtoh53etKed7zZjRq5hk62/Ccf37tH8wc8lcGd75zKf/wD6P76XUAvpCn8NT/wSTama+eaO/oWBYH+M66BYOkoRKEdmLhXBx3u9Doq9h0xokadtxFStaKWPYSF9uBkCMSQarRMflmM53E5ctlW+KyYnDD6+3mKdsTrN1iAZlncPm2dQyIsUOjhGrQhWQv9mEHuVZxuzF3nOwVn/NbfJC8VizIBs+Kx6m+tgOh77ZUqpgQ7eAHXQzDJqMrDkgurONs7TznEU8VdH4qMZZ4Sl37lls//GAr5pDkZR5L/gKlxWlys2Bd3gCv+fe45Y9iMiFHRZG8QzpeBpBgjSvizb5NfikS/BnhL3zqWFm7mQ8ePunzQpvicwuynXF8MUagOfbS7FjBhdy4zfYhwngRd/bXsSw22vx9//d/f3na055Wvvqrv7r8yZ/8iR1aju1aBAd+ERIv1lA08hoDWkveBrFuG3FCPiP6U2tYtlItjPTrAq5NYcwHyCRb4xiEEU3irgOsOQsjZJ0wiC9hAVnqVvQlXehGTF6J3NoThpzXgAy0TI92pme+Y2x+anomSHOY10uGjTcxijmECepN9kKZQpBlcxWGs0i0s+m5OXBukKlXsgEm+7w4I5rebRj3hxgF13FI8MhHPrJcdNFF5R73uEd5whOe0BPuHR2HGM973vPsurChfPGLXyz/5b/8FyXc/+AP/qAn3DuWx1VX1S1K+KHTu9yl2Em6lFe+siY8+XHTJz+57iE++WHFaOzTfvvbl/LCF5byvd9b93HvWAyean/IQ2rN/MW8ck947LF1/tj3vt0jHgCwxbY9bPPDlyVr/a9+x+CHTPmLHJ7kn1pvi4AsT8EDEu1Tn+M7OjqEnmlaNdJJSHem49tRaqfFyTypqKMEcMjESxYyXtglSSobqzhELdGY0Ppu358u1pPr3qrF/QElcgkhaK4L8kUoTshhQnF70q3VyS78iKfpWq24NzkNG1bp6XRJ1DrsgTYnVsK+EtJOo4aOXH7iXbLR9xKYSxaGvEN2HRGLSPZCLRol6ahpL+MxCzCtKMFuxwQWc0vJ4wrbLZltBM0Xsp6ERydiQkby3g9ZbGILvo6h2x8Dv+jmsYOISWNItvCV4+XJcSA5EHasZgx0NcYU1+D0E3oOPXlvNX7GMYmBvBVsgaibXwMiod/G4FA/4uhYDX7gB36g3PGOdyyf/vSn1T7vvPPKn/7pn66dhPt4nei4c/xZq36sc6z08/GnHWsFUzIXfK8XrZfsO97Lsmd9eLSh6z3oGNuKcxbI9kJuJF7fBkZULUrtRwnkdthVFT6sho5/RCkhRp3jMjRzko8OgknOFIO1Iem3YcGkrcNh7TwtbqfJAsk7IcxZrS+VPe4WiiG3FyFkNpp+M2+lXv+yc1CFxQrZRXC7VFNi0Ddv2lA2j+YVZMqki2UdVwxsLCduPNhRArkdtmafG2aQXhYGJqS5rM0EJwZC0aux7Y6bjuc///k2zUvlL/7iL8o973nP8sQnPrF8kqf3Ojo6bjLudre76eGDwBe+8IXyjGc8o5xzzjnl93//98teftiy45YLrnF83uSp87i3ombbF3Cve5Vy8sl125L/+I9S/v3f65Yy3/VdlT8FLq72Gbx853fWpC5J+aktZjoqmK9cDgeYf74w4UdFv/IVJ64TRKL9Nrc58Plh7XI/wxdHcx8EOzo6Aun2tmNZtBN1OqEMTi7RXnCyCrLE6EzIwWuJRbdHvzKc7mhyjtyELj06Xm/YOAhBQ6Eg60nKlmA3ZrSpa9u1aatEXMB5miNfUnmuRPeSAwWiOdSEj32vgXyhP1uuepq6bRnjNXLys8KyDpfIx/w0f8akrYScC0IG+qIE4AM574aObABq1w3Z1k8IeYmnmLNvEs6N5z6kZ/yIXbJWTI5tXpbsA5ieKA354AMlsKEZ8twHgmY1TWmGWNSKweqIa2APJRfMtVguH3IgxgANO2LZS+jKntfhF8hnlBHwE0ks5rLZSmj6HasFT7W/4AUv8B5/cflJ+5z/5HL3u9+9vPrVr7YpPcLzqbU0caxjDSm+4Ees8KAF3RDrJZGGa2U0zvG4Q7+tMevTjpst+HlNjvsZIQ9U53YCblRbI5dGS7yMMEMdLMXjzVrNYASZyPLZpLWlk2Jd8i9Paw/Qd6UgLmWu8wxtmCJZh35T9a1H7EVi0XHb4ykClVMxnoqMzItrIObGNtU32cpzZtOtfbrZFe1r9+wr1+2N8/cBYGpQK2Dev/dGpnK3tocCYQPqSFVYbj5DnvDVjnFMGXIsw+pYBb71W7+1PPCBD1SbL0M5R/PXSD3h3tFxaPDLv/zLs/O+gyfcf/zHf7zc5S53Ka94xSt6wv2WBC6CUUiE80OnP/IjpXz605XGZ8B//df6FPXZZ/Ohuu4dftFFpbzkJaWceWYty4Gn3X/pl0r5zd8s5Z73rDY6jhyYf7YBuu66ehyX+yC01nCwiXZkWYf8ZUV/or2jY1n0M/RBgZOKnWjivKQLq7dpkPDRiQcZL3TnnmBrSlUn5CSWZNtJLMtHO+SsDy38OfgQGB8EayKsylVt6EPZBmvP9uamttL8ey1TZgu9eDJdeiQm4FWxIVhy7kcCLuhNgTpiiRpitENOXe/Ek/Bc9LIOYN7bMXGaROwFWfGdHlBMlDTuwfFDx3gkdeXPeU1HHa8NQYMUCWeAfKgrFuNpDOLOZNFXoo6CEfzUqgG9y75Y9r3zLWXpks87L2RddwBzgs/sV+TmXP9rPznSmPlvtJDV9jjuS0xsWS2byFiR/5ABUWd4DLJlkCr2ojPSiXE1efS9nccLnxLxtjF2HAie8pSn6Kn2jE984hNK3vDUJE9P1vPMzYw4vkI6tnGcg5cPu9aDtwfLYdCZYW7NuE3ZMV7UAfrhI9qUQG4PbFs792nSV6mkMdqcj30I9KM43F6c81W7XiW5I+cPkew0v+1l6B713Dem+BTjTVkXL+sIAyNeA2vnGOjWnhDhL7smXWZw/TPQH6shUstQdnogQ2BKXw5YY+fe/WWXvhCdIdphmmrQrk0ht5dDkzPj2RcY21wJY/0x4lPFSnJDp7WDzoHE0rE6/E9+aC+B9wEJd74c5a+SPvOZzzino6PjQMF2Md9HonQCF154YfnhH/7hcte73rW8/OUvL2v+x+Q7Dg3YAuZv/7aUH//x+nT63/xN3SbmssvqPuwf/GDd/iWSm+ecUwpfiKJD8p0nhVcCe2r/0A+VctJJTug4YuAYkmjnuP/nfzpxnSAn2g8GrGOeaCc30dHRMQnPanWsjHwr6DUnWDWjHgF+3HVmuSYL3wQo+Y5acjScH8gJ2uY78UHzJyYNrwPeb35rt8I69f+IbH4jnoxMC3/hnyRniyEBWtCpW/icqOnAF6Gi8UHo2X8lo5mPEEbQiqpIvBpP46Tpc0c718gMLhKNUXWrs1rTjXmjoxogE3IZRkOuzR8InRGURMemtZHliwPpQBOx9sMONPVpx9zR3Fc27LEL/n77QNfkXYd2JNSbbrVfJVxOtZjqzfrAatl0xNgUjwgiVxn0Q9ZqkvHRDtt5bqLW8Qh7CfJjtdZWJakBPfoaD3Pp/dBRm7GnvxAIfx2rxubNmwdPtWd89KMf1X7AJNz//M//3A5FTPzNhEX+4jgP+NaObqMv0J/DSG7sl77TDngOFq1J7Kx2vU7FE5CNaieSxQO21w0LXM6sTGAVY56bl4XGDG3cQx31dKpY2d8hwXIxHgCIdrfFvMfKlMmgLcdbNSYUBiTvrGR3ER96FMChWPlwVOnxGsg+bqYjetSDp9q/7uu+znszkPTjdzZIAvK7GyQFjzZ84AMfKNu3b9d5jvJr/PCg49JLLy1nnXVW40VBHr3VAHtZ96EPfWi5jqcaHW94wxtEp85ABtmIJ/pjfYAucRJvx9pE7NW+CJ/73OfKj/zIj2irmT/+4z/uCfejGVzTSJg/9an1KXaeUv/TPy3l6qtLedKTSvnFX6xPtD/96bN92Lkf+73fK+VVryr24bruJb4c0KGwZcky667jZgLHgB9D5elurh3r5f3NWmVd8uXPwWwdA847r36B1BPtHR0L4RmnjknortHbwuj2T10/OUXyTjAa5EgI6oYyncTaFiQG+fCiC2iyIz1HyGSMuo0gW2bHT5zthtb6QxXrGS+oPJFW3cSzaQbFVG8kNASZrDS17EOCWpHc1pgrr0F0r2eWHfCcH3oDfWPQly5dq+Pp9UhOAz6sEAn+A5gZxxJPr0MO3SpYm4GQQR/bkvUSNkNffeizOR/YVmKb+LwfMSJLm3FEwU7UDd5udvDlfhpqf8OZdy6bHvk9ZePtz62UiEl1iiHrt1gdkrdVoThCjrVRS5Onoh1xud4Aja/OTC50mw4EQB00ml6HzyZqjaDFnvVC2AEIU2wc8itibTdk+Y7VYOqp9gwS7uxd+jVf8zXlr/7qr+ww3ZxzjC/3p7Xi7RZDrAkrsbaWWwNtfSaEi7YmEx9aHm+wxjKBaFOP/QTCpt6PhiaGjjdByEXMYU91EtzgMkA6VImWMEgg0DQxFUAf/lhtYDuEozkUzqZUJzbttnZmZoZCYGzEMHM9u+bFWEJM5zM11F0ZzeYEFhhqIY1Y2yyWrXYeGo1krr/IXQjO80cU62abtW3Xc9UV0Q7NzGsw4pRck4Xv85sjaPyEpl8PsPcqpuQ7bhoWfTEK2FLmggsu0DYXP/RDP3TUJNxJln/TN31T29LskksuKS996UsHSe9TTjmlvP/976/nAS/Io7dcsj0S429605vKjh07mi4Jcf5SYJwU/8mf/MlVJcr/8R//sfzu7/6u944esKbud7/7HbXlqU99ajl5FU8h89cjyLJ906te9Sq99zqOItg5QEnWP/qjUh72sFLe/Ob61PkjH1m3kCGhaecMOyGU8oQn1PsiQM2TwY9/fCn3va/fL/Ur4boBx4ovTfirhX/5l1LWy1ZRrNfLL68/CLuav6KYgp3/7OK6fr5c6Og4AthgHxCHdzq3YPzYj/1YednLXuY9O1++2z6UDy54tNttol0QrdANkZCFxrTGViMkhtu2I9TWRwaEDh+6QkaJQBNoTzUn5D4+pvryXS/iwZ0dZijedtkN7k/UeGl2a71Be3vzwTDsmA7xQt+z2+Wxt79KuP7czbTT2ZZmAzawqVhdjrbkKVVWNP2gptFaXIA2NOOLjA1itJp+9q3KZbGlMRt0DMMuxdr0kRcPIcPYd8QJLfzE/ufEEPazDjR48cOhEW+zZTXi0ZYIBOS4kFmND82XQb6sxLhqy1RkpHYAT5KjF7YjzhQ7a0DizI3Fh2gdBy0pmhgSnqRxPW0vhO+9tga2bLMmfiI+4wFoEUzjeV9zgS0fX4AxyhY8txM61NiJ+EVDBllkKE7fvKXSFIPR4nhTePp/23GlbD/IDxkL8E//9E96WvBoxqc+9anyjne8w3vL4173upf2Mn3c4x7nlEODSOyB81/8wvLkB969bLzzPe3Y2lrYvLWuuKsvs7W5pyxd+aW6JLYcY/0bS9l1Xe3zAZE1f/2u+j5hXdnaWOKm6DOftLVxXNlwxpmywTl6yfhLX7YPp1ddWTacfnp9qoi1hDzn8G3bzNb19Qew+AB7kq0tbENjTVPHB/GtW6sOf3JqH9T377qx7Ln48rL1bncuG268QTHc+LkvlS2nnVA2nnZKKTt2lhu/dFXZdvvTTHmp7Lns6rLphGPKxm1by65Pf6kcd97t65wQk/lYouzZWzZwE7B1S9lwzleV8rnPlHLGGWXpuuvKvksuK0vX7y4bt1vMFseGzZukzxds+3bZeGxce768w/hby77d+8qeq3aW/cdsK5vN7tZbbS+bjrX4/Vy0+aTjym6Lh0lf2lffr3uvtbHaNWzzKcepDX3v3qWya8eN5aRb2bxutrncu79s3Lq5XH/djeXSL+woFkK5zZnHly0nbCtXXnRtufrq3eWce56uJwL272Y8+8umk44pSzfuU5wbttSYsb9hUz0ncJ4i3osv2Vlue5vjbGo36FK6yd77F126s5y4fUvZvt18Xr9P4bMEOOzExn7y+xHGn1W09+yxsZvfLRYvP+zKFKMru8ZHet++Jdnnh1V3E+OmDWUPNJOFv9tiZVawSZKdJ9qhH2fye6zB+Nimfo8JoLOFMZkMK2Ufx8HoW0z2ZObK7F9va5zvS3aZ3WOMfpwN5Bq/2eEMbaFZ4Qq7odxgsifYxF5v87/b7HCF3mYDPt7m60o7ltfunW1hQ2wMkHhociUgjh34MQJyRhZ/p9naar6Jb6eNdbsFvstqcMqWjdqD/mTzy1iJZYfFd7zFud/a+40GrrK5Pc70rjO9Ey0exgiPkdiZu5zz9KeV+/+fX5fs4cKv/uqvls9//vPeOzrx13/913ZPbeetFbDJzh084f4//sf/KHe60530eZhCQno9gafFSYS/8Y1vLMcff3yjfexjHyvnn3++Et/f9m3fpi097ktyK2FKN2MRnwT8Yx7zGNl9znOeo6Q+8wjufe97yy8Yy0WfRP1rXvOa8s53vrPFhA0S9e9+97vLbW97W9HWE1g7z372s8uuXXYN7BiAvyZhayf+CpDfvznSGN//9tTAAcKukeWjHy3lQQ8q5f/9v1Ie8Yj2+UifEe19rq06+Ny4xa5uXGA7jg5wfEmy8yO1fElr5/I1D+5DfuEXSnnb20p53/tma/VAwJ70/E7ARz5Syu1u58SOjo6MnmhPmEy0c1up62G6KEaTC2WePbtJFIKmCykd5JwYutxxg0jUhFzWEdDlFj3TDFMXaeyQMWgBIFblRBkc6powyCCJpKQTMGZNqYZN2jWBojYxkSQlgUkSalmYjYhNTknm7LWm9UVPgeQPJo1OjQ3aejEYTfyQ8z4fdpw803ddYsZ+mwfXSWIVUh7qt8peNA7azkeOJAdJXRBjrR0ryEGzGnpO9CMbawAabdF9ruXLCn213VYL2hCJ9BgbMm2MBrVTP3wjH7axR99tbcg0gI1sEzpzbXa1Rz96m7aYa2tHzBFnxBx+mx2rTUcyth6ano6TzwOqGk/tqoE+/Bin7MNqQhXExzGJmEAbjxXW4NZjD3minXMI55KOIXjCnYT7d3zHdzjlpiHObUCJ9gfdo2y80z3q+2vLtrpkrvmynZ9210Q7FKPrS6Hr2V7J1geF9UY/3kdGW6L9yY+XctxxZcNtzqw86Fjhx3++csUs0Q5IrAMS7bRJqPMnwJFoJ9GALHTOFazVSLQ7b//OG8qeS748SLTf8NlLy9bTSbSfWsp1u8qNl35llmi//Jqy6fhtNdH+mS+V4776DrbMbU5Y4znRvsXO0/Y+2HBuSrTv2FET7TfuLZuO22rhLJnuRs3p/n37y/4bbI4sxN2XX1s2n3BM2WtyLdFuc7H1FE+0k9w2wc0nbZ8l2hmTYe81jHV/2Xzq9pZoJyG9c8fucvKtjjNduw5Yf+OWTUq+X3bRDoV+WxLtNq4rLrrW7kt3l7Pvfrourfv37LPx7CubTjym7L/R5tDsbdhGon2j+dql+IlHXxS0RPuxdk9rc2vXW5LgX7zkunLS8VvL8cdvsUOxV7Q9JPut3ms1S4FDw3xwiUZvt9mClxPtN9xQE+2MB3mSzZvFm9lbLtFOUp2E8lbkjV5nETmL02oS2CS4bZSTiXaS5/i9wexuM/p2W7dXK9HOX6gtn2gnrX6MzVkk2q/Zi14FSXssEAPjXC7Rfp3Z2so4rE2i/TgjXl8PfTl1y8ayY5RoJ6F/AsfH2oyJOb7GYjrWfA4S7aYPb5PJnfODTz3siXaeSl3tdiG3FLBFGE+43/nOd1byd70l2qfAFwhgpUQ7a4G9tUmmj5Pb4yT5ciBJ/uIXv7g8//nPL9/+7d+up+W59i1KtP/sz/6s5povfSKJ3xPtRy/uc5/7lF/4hV8o3/M936PrxpFGT7TfRPC5z9Z6+ad/qk+z86OmcVyn5nINHPOOQwSO78UX179kIHnNXzKsdfBwEFsYsUf7n//5wa1Hzuv3sHsufpj3EN3XdXQcbeB+qWM56NzjJyCdiOJW0wonV5G8zkBWF1cJeDvBbiirjG4rK00IHSqXkV/HuD/AyIdh9mFprFOfqJvB+BoH9rk9d/lIUPLEMfx5Fwnwo/gdtzIXVkfMHs+GjZvdl9MDsMlGRNub0mPORKNtcaHa4jMaBRoyI7OtLxnsuKxsolvZaigumtb2pFFL/ALpezsQ8oyXmNq4sDVuR98QPMYT0JzhwHmK1ccbffEdsYawFfZUu5zopt/sVBm9ossYqUkmNhiXmLKvPC7aMQlZRj6xZ+tFY/I+9qKO2EDTdbvQ4UvWID+GkKPA15caoWNQbO6POifiKfHlBu0YW9juuFnwoQ99qPzv//2/tbXMYUWs0QxIma51kOG8Kd05WTAht1rEmgSTMUVtPDUjtlo10M8qY37DBCPpRXNZILScYLjwul0/Bqi0zJmb7hTXJCQ/sDDsGqZ9zzAzTWJ8Tl0Yx6VDZvXU8phhnrlInKvJRjOW+a3tDfmrzTnk6RnK0MvcipCZ56weWXfKzpA2Hfn8fM96oQ9lLSSebsnYu3dveeUrX1n+4R/+wdb+TVk1awMkz9/+9reXn/qpn3LKYpxJgszAdjNjsFUMW6Gcx960qwRf5LCP92q2kHnRi14k+0fTFjLHHHOMvsw4msupp57qo10ZbDv01re+VWvy8Y9/fD/XHQ3gHMl7+y1vqQlHHsLIx1UXvlHpOLpw4on1y5V//Me6Htb6dZP7/i99qf4Y78GCh4cYM0/Ed3R0TKJnmpaDLoZxQfSTpmjpBEpSWBdOptLlOcEqeelyIdPgcjl5K5icqwg6WZPgjqRjsgFPT0YmP8QwiDlg/bDh4MPdTGqDmXC+fNLGprcpSlxiX1IY8AZoRCujWKJu+tYOm5KHZ3QQNiUTcRgkT18dK25DCXkR3ZTrB01weQoXBbroKYls7X3Yhm38KJL3ikQ8jUi4t3FZO+adGjpPZTup/mUAdBPMckA+Ut345iOS3TzprfURvnwO4i8OMpCDhy512JM+fXSt8BcIir2OgRtoRaAYjR/xhA9oteFVXW+sFX50tdqxAnR80cc+JWK3opiw73NHX210rGaOYw0038h4zdyHjORNT2ufGBACxEjBfo2z+kIXHew7n+PEX2GEj47Djsc+9rHlXe96l/4snn1sDwtiLa4KrIWM6Cc66yOgNR2LhZq1s8ziGfPG/Wy7YUyzfpNL+tEU2/ljVRC8pjphC5nJWObjHX81mzEwMRajT3GTc1ZGroQpmiNPJW09xa+OF/eQxCRXz3c0aDtDyJLLwHXQHeoHJokDMId4oxzIap1DuBqFnrvtCk+86i/GSnywHH+8vMcYs6fEiSHKggnuuBnA3uVseXLxxReX7/u+77Nju9LKWNsgocm+68985jOVFD0SwDd/HfDc5z7XKdPgqfXf+Z3f0V9+HS1/ZcG4+YuIo7Xww+9X86OCK+BBD3qQEux84fPN3/zNTu04KsD9yBvfWLeG+fEf50+CnNFxi8Fxx5XCb1f9x3/Uv1xd6+C+mi+HbkqinftqEu3veU/9UVS+ZODJfv11ZUdHB7hJ93pHPQY3e9xsWD/uBBvPCXH3TdHdf+WKlxMB2SYJwYyciBwAe6NDpYSjFURzUp/EohGVSPcbpPETSdCh1BKxjZdC+KwJ2ZaID3nVoVurapFOxOK1xkTfbcQYSapCiyQ2QL3ZGwEddMMeNcKc1Emey6bXAm1vBtDheIzHi47iw6a14fv4WzxRy36iNzgNOxGbamhhfxnoeCJHx+VDH8CLuKFnXm4DZJnXPM5mTw6sSTvpyTc+gmZ1lvE5qT+A67TQiTbIPgE2wk7MgWTRtb6a9iL7JOgNSowbJOe6CGJHOtiBBqBF02VjrGEf2+ETSJ+6VocSz3jGM8wV75mjt/Ak1kpgfX3Xd31X+fd///fyute9rjzgAQ9wzuFCrIdAHG+ja10k2BgEyOLFurA6eFOLY2yHfqY1XYPR9R4b6+TzdfBCLouGKWiZnjEI0W1MAXK2B6KfbLCliUy4TFxDGqI7IgvNnjUm+JA07DxHNF1WZNoxF95sthBw1SCNEfSZXr1+ZdR+pQUruwCMe5EPgI3GHwkOvc2Q6c36hJ/oj+nSX2R8ZD16zMNClYSZr7HXimlqxcwX630muxq/w6gdNreix8E4zCBZFue1o7HwFPVWtqlaATyV+7/+1//S1iUkek87jS2q1jciyc4T5Stt9RKIJ9njyfaME044QYlj9no/ELANDNvIsDXM61//eqdOg+1luL6ylQxP0HesbbDP+nI/bMpfNPzN3/xN+Zd/+ZeeYD9awZPBdu4sz3xm3as67l86bhnggw9frrBfOV+2sB7WOsibHIon2m9/ez5ElfJLv1T3qLd7Pu3/3tHRIfSrwYrgZi/d8NmNi+4II9EXd7LQB7JGbwl2EohWBytkQ1xPZhtkC7vwnKYq9RvoW4k7W+qE9tyc6KGbbGDP4+Bf7e838bokGg2I5vaaHrBa5r1PRV9PUjvg6UMossZ0+zMYPZKeIGzxQaXF7nrSt6JEOTzYpiu6tZVYtjp8uCkh5APhJ3SzMMdDMbuOQrB2iKCL32wD2Txuwe2Eb2r6oQfkP+YEG7RdbwzJYstKi8HlND+uK56XrAMfu247bsSFFkOFvlgJXsyD2YEi0yL42sF+fpqeL1D0JQpS7hfE0+oBxVEtqcZNHNvm2xBrgSIdBOEnmYhXPpwOTYl76OgB7Li97KNj1fjwhz+shMEikHBj39GPfOQj5bWvfa199rQPnzcHOKYDpD6HmuM9OObeFr02VTc7Y3uBJB+y47VEfy6eEcb8ZqtWgmiLfGQTdKhcptWp3WCC0nM6RpLvsbTOEbLtxeDSjmEvuoMvGUYiy4MvimtrYAOkGAS1kVFvIdoUIDeSbefAEZoK5if0ApU1zcyWm4Q1Mj1Pb7W1GAO99jKNASt1iJXuSr7G3OiNdcZ05gr7UUByP8DYVmCRfMeB41d+5VfKbvZhXYBb3epW5YUvfGH5whe+UP77f//vSiYfDWB/c5Kc7I2+2iQ7YLuck08+eXIeSJiztzr7t7O3+hjsAx97wY/B0/Qk/H/0R3+0fO5zn3PqNGILGX54tWPt4rOf/Wz5sz/7M+8NwW/S8IDB+973vvLoRz/aqR1HHbiAX3BB/U2e7/s+v4fruEWCv5jiy1ES2As+V64Z8IUyMd6U3/7gw963fEvd7/2VryzlqU/lpFjKv/+7C3R0dPQrwqpgJ5N8R9iSduMTKUJZ0PuDE270rRY7ajsUQVdx6A6fulZDBC8SkV4L2LIy9j1G85nlDKJXGtwN9COp22CcgRodSdt/6oDrho2WcHX5SMQKrtfiSnZkg9pKsOTHSpsn2lYjA5zc5iHiQABe1gMxxhar94Fk5gxX2ejSCFuCyytZHONE3mQigRxfyIgex9D1Il7FbHT1iQmaVST3leB3eQVC2wvxY4/tUkAblx0D6IgH1IdATazIuh3oagPoKWkuEZchlmiHLlDTxyI5s68PpNYGxKnkfO0Kee4F18uIuVDctSkfUfjGnbFIDris5GlXasfqQbJgCiRFv/d7v7d8/OMfVyL+QPaxPSQYnEdArAk7zrGwYk1x/PM6CX5GrCmgL0NDx5rSSQg7oTPmZ4RMtj9A0s0i4aOxrYENSvBEbgIV0Zdc9L2NevtC2ErYgRewPl/cVlZmOLDhzRqS9ULMx5gT0bBmX/DR8WZqA32JNwFR3dySjgtlZj98gnG8cBSfy0eoIUVftKGaoT6tHag2ZtBXjq6TOWEbeegxD0Gfm09jN54IFSOp5iTo1LPn2AM1JkThVBkwm9msUdszirsQgrqIFvTcngLxoEcZywYNhO2Om4YvfvGL5Q/+4A+8NwRPrJPQ5Qn2X/zFXyzbt293zvoHT7J///d/v54eP5Af3yY5z9P8PH1OUn0KbAMD+AHTnGwnwc51b7l94NG9//3vr+OyHGILGf4arGPtgs9C46fZ+dzDlzsf/OAHtWVex1EMLmh8ifne99Yn2W/K08Ed6x/3uU9dD5/+dF0baxkkxPky+dhjnXAQ4HPwIx5Rt03ivvDnf74+4f53f+cCHR0d48xExxz81o9zpu5a0+1hnEfjpjtqnWCDmdsOF6sN40nEZbARBYTPkQkhyeVERtzUZ4jmZRJK8FKG/Bpa3Zfbe1XG4xVPLdqtVauBPa8lY23o2Wf2i4zkwk6tBMkF3wGNbtSB3JcNvQxlwFgPSNSI8jcB0dFzRcWcP3CHHnUyjhxjjnbU0cauisuAmHtk8of6Jmsl9OeAHrXLKClfdezVCq8VtOsaqdvDzOwusB0+Y9wRC0V2jY8MRSR7iT4EyQHvC8jCy+MPHUOjU1PcJpCeGtVks5kgW95WPN7uWBW4eRz/6TtrhqQGCfa/+Iu/KHe9612dc3OCY5nffyAObjrILIlYL4G2DscY6dHPstnOQhsLsJx88KzO78/FGI1HazzFSt1EvDGag3ZdCHdeR0oWtkRCzjC0sAgmNfa1YEwjMckNaArC2wH6khkpr4gqP/YJpmgrIumM1SdHa8RFbpCPEoi26ImRZbK9RXQw5TfkVzP2bBugkvVoIjOWW4SpWFer27E82Apm/DT76aefrgQ7T7DzpPfRlGAP/NZv/VbZtWuXEp3xuZfCD1FGcvyqq67SE++Zzw+WfuYzn1l2L3cS8O94xzvKWWedpafeQ5cvLC677LIVdUniH8d+viuALwieyhOCHWsSn/jEJwZPs59zzjnlVa96lf6S7wlPeILWRMctAOzPf+GFpTzqUX1v9ls6OK8/6EGlvPOdTljD4EtcfsT5piTaAWv+G76hlF/8xWqPferf/vb6lPtBfZju6Di6kLJZHdPQbaQXYDUfoMYfoqLfeBST5UQT6kEW34nw6apP5bRIroqnlyFG/pXkRsdqLOkJRKv1Yc9K9FXsJW4O6lYxznF9IeznvmhWREOeQtP7Eaf6tKkNJHfxEzYjWSqa1eED5LbgfflPyxUyTzPyY6YBZCImwRsRD+OL8aILufG8D19PeDoNhN3aqX1vDmICmkNiMvnGM0ES5PJvXfmyNrQoIOjNlfuSPyuyS3Pkc5B8p/DiiHY8CU5fSfSN5o61QpxGs7Lk9Qyp7T60kkw3thgaxAok523xaNOwOs8nNPWdxpPnzZ/VkegHmheX46l3fMD3cVQXRoPuYqKJ7gR9wQCwVcciBL9jVWBrgQDnjx/4gR9Qgp0bziOTYHdoqazmWJpgWyNS8trbAWQmzY3kMvJaCtsZ2d/Ad6ozIA3IThiL4nZKvwHe5GBmyOqy5QSpeduqLCbk92bGWNBkDu6tNrJPbPLpfTDuJ0xOy0h2SmQKU/HbFdRbCQviCRIaoSVaNpH0xpbVT8Qxf4qyCEhOhNgwZSnLBz9ouY42MstHtDw3++s4cLD1yB/90R95rz7B/hu/8RtKCJNgP/am3mCvYZx//vn2NuScMywkyEl288Q48zDmQ4O3Gox9hO0AifIxDZCI37lzZ9vOJhL3U0/e4+NAYuq4+fCCF7xAx/3ss88ur3zlK8snP/nJ8qQnPcmuOcuf1zqOMnz5y/zpUCmPe5xd0uzY9+N/ywX3sQ9/eP1RUN2PrsFPMcREOVSJdtY79+KMne2T7n73+oOoV1zhAh0dt2zYu6NjRehkycUzLqB+8oyLapAH8JPZABOC0ucwwMvyLgsPO+Er6Nm26CRAJ3iBnFx0sQYfX9Uf61ZeizHrikzS1RomVt2H/shOJK8F4ymekYzgtBgLJ/AG9LyEKYG+EVScRB02hLDbXmo1mEevuUAqYWwYzGVuB4ymeMImtZXQyzFozNFPdMXtRXpRnEatpDJ9n4+okR8cWz9OsuO6YXcsC/SFA7K02Q4GnaBZoVtfZroRK4i4Qg9on3Ua3s+80AtW2B7AmBFToPWprYQdwcccdBXi8hJocQKrx3PRsSze+973am/aTfaB6ilPeUr51Kc+VS644IIjm2DPGK+Z5SDZWAsTiPW/HPIaHKzHg8RgfYKRzfa+SljJr9gLxjEmu/+BSebB+6riBaHQt7qZctkxxDdbM/Wh4KJhDA5p8ics0BliWiibXZQYCeoghgEWB7CUPCC1YWRjkeZCVwmh2+KzQsJ/NbpjHIyOkAaAjRqD1dZYPF8jLJqEjkOC5z//+WXv3r1K0r7kJS/RdiXPfvazj+oEe0fHzQH2Zn/3u99dXvaylynBzrZBfC7quAUhLnZ/+IelnHtuKXe5izM6brHgHPCAB9T9z//zPw/gw9DNDJ42Z3ubW93qpifaA3yOprB9zs6dpVx++eEZf7zvonR0rHGkLFTHPLgT5OTh05RvDHmDi00yDxmnkbyjVrKWvqQNreEIY1E7P05WFJGiHUlBl5tIEvJ0hTcqn6rZNwTfaHrC3fpBqciyvNTku8IRDYMzG3Xs1oSGkOC15HJ7BM2p8YNHPZALewbZNt7YRySe0NP+3lbr6XmY9hLm2hPqVnFc1DfQV9saso2MtaG7iOqIS/sBG3KctDUPsQ7wz9icFyVoSmY7kG+JakPIWaV2BKI2tinWjHj51XDawW8FfrKba/mwutGsQBsXs1Gf1JJUg9ZYkzHIjtuK+aHfFKldftMWJ9FnfVrtqrXttkJXY6DhcqJbQ3aQMxvQVXgxNF34tK2ELwShi2c0EGuhY0U897nP1Z+yf9o+oP3xH/+x3VvYzcVaQRz3OfjxBiHDMW9fXMHzdTJ+AmVgEjm3A7ChdTQQGulbO2SigPCbaWPfczB5kGVa7COE/QAilCC1OGpV4/C2m0zaCWNqPUeMyTI1ZcBouuKErylIr16bEKxDdIWwKTvUlSDJbNM7M1oozjCeompkaIdmFmntJsRoxlIVJL5rXe0kswPAz9rjtnSTcp2XirHdaV+1l+1OoemYYLSznawf9LHNTK+8oYT4RhrrTSH77jhw8GObb3vb28pv/uZv6sn2n/7pny7H8LRZR0fHTcaePXuUYOeHbTf37UJumeBafNllpfAbGE98Yilbtzqj4xaNM84o5da3LuWtb62f69ci2O6ILdTudKf65cChBA9e7dpVypVXOuEw4DOfKeWXfqmU6693QkfH2sVGrzsm4bd7LTGbbiXjLj0SdmOQJOQEpoRO0Gh4oZ3voAd3/QZ4knH7SsBm+XToWiIRZDtGix/BxJZ0wqbL0x74DrlsDx3vN7/WRzdfSMIOsuKlpPJei0N+w1eyOYeQMZC8VTFdyY90iIfCPMNv8bgcpkKPtuYUhr1AD/0A/E30XS7sQceHIAMGo6HLuNpYsGv9sKl58LFvtA/kmz3ZnOcqahV0rS+73hc/5hKfVoLH1jn6cgG6seOp9EgkUjOGTeY7aOEzENuqKCbjqbaiMbisEoujmAD+EI+1HjQQdtCJeUKGfmwnhHnkaFNHvA3Bcz5gPjWnblcBUCFbm22MUQPFHbKmR8I+sTsW49prry0vf/nL9afsd77znZ261hAHn3VAO/pjOE9se9EaGcmOukKjse7ohK4j1mcg8wJjmYwmHjJOcFfLYsLVDAuYblNcYvV4ea0J5DGChmxtRUJ5Fp81nDdvY6VBVImYopk2iXdvAhOYsjQ13RFDe2qdrjWnLEyoDzEX1zyqGzloQH6RzlwURlgkK/pyzlcAvlCf8+mAHvMy5Uac0fod25KNMdGxKP6IacpWx8Hhhhtu0F7j/DDntm3bnNrR0XEocLe73a1s7YnVWza4T2GPfn5Q8mEPm93/dNxywYcfkuxf/dWl/L//V5/sXov4yldqkpq/wiDmRR/aDgYk73kvsJ3S1IfymwLs8QXB059eyv/5P6W8+c3OmACyh9p/R8dBoF8ZVo14w/oJiRMTJEokgwWj64nJfOIKWbswtxLyBtmiH7XTGqRcm4HGt5qkYVzklUwMWas9EYm0P8duqpUmuG/orR91VRIUgXjYhDeyURtejekZrl8t2v+Il2JoPNo+JgA9CggVuiLZi5LBTgeiAxpepGc+lfD14xBPhvPBKY5DHD/FYLQ4LoLzQMQjWDu+2Igg0I+kMsCHxlG7sgttnFwOf9SSJxmOEnRXJnkOT8lup8WYVKytsa0SZqM+xY4P1kTzRFdoa0cx2bqKPjoag/sWXFuJfKMRLzSS8q4vNHH61qFmTFobTg9Zr+QDu9RNzmKJvuaZfvCcj52ID5kWa8dyOPHEE9dwgj1gx1LrIx3TOL6xhrSOoIWM1dGM9aG21219WB26IjkfjH1kaP05fYoP8hps15GMsU2vA7I5oYddWIxL5zO3k2OwduuGCfRyTC4gUpQQDpN6SXaTekb8hdQAJhvuZm65ZnmCHfkcj0N2VKrBObsGbGSQeIdSz3OVJhgxJLOdgVc6Vmb8aIxiS/qp2eBm2oev0A7ZsVX6zac1Rt6E+oVCHdsQPgOJkfXH8vSjLIL0kxFkYyzEWSOpyHainf3n9pRsx8HhvPPO6wn2jo6OjsMBPjzceGMpr399Kfe/P9+81ItfRwc/iMqPWH/4w2tz+xjiYf90njo/HNsd8Zdz2P3oR51wCME9+1//dZ1btrx5+cvnf3SVNg92fuhDpVxzzdqb/45bHOL+qGM5jJMfgyS6Y0CyDm9uJfKsKAFpwE4kc3RRHtkJnTlgj5qX0MFGu711Ps0RXa9ZD7hwyJmOKOjGSYmaZuszDhK66LiPgS9DJJUE6mQr19J3Oelbgac/YTK65LxormlbzdzoaengA7cTMtGnS5IZW82eQ32Twx8xS8X7+sLC+dDptzEanbiVOLZax9dpsgk5ZFE2GvY1L9ZWct9qxhDJffnDJn7QcbR2tm1t7CMbyWvRrEQsQP6orSjx73I56a648Yl/77e1Z2105+yh4/7EixpYrbEY9AUAbeSp0Ec27BsUrxc9nW6x7bULpmJAiQLcpzdFVwxmP3zzZHqMI2RZN/viS48EaPJrulP8jqMAdmzzumxtB12VTPd11ZDaef1R6GbRRQi9kB3Yd8zRPCbRR/rZaYt9rG9AN/MlMqErOTWMFPKOyYT/EMO9zcNP1RubE7K4YaCf5IllaDpsj2A6wxjmMWNjk87y8suBEOeHNbMXsQRlXGeM7dBXStwacz6mDCyDLB5DHpuYMrloLsfxILWaGDf4xvTBmhNJRqYOzZyPjo6Ojo6OIw0+4LB9xfvex49h9G1jOmbgPvixj61/6fB3fze87z7SYN1SSEIT53nnOeMQArv3utfhS7S/5z2l3OY2pfzgD5by8Y+X8oUvONMQ43vnO0t59KNLedWrnNHRceTg2amOhcjJQUACT29m7w9uDoNoNXeO+e5RTV5cBrtByz44SWF/cJvp9qSQ9KUXsklevvOhdT3R3E74lS/aVlMiRtWwTIembLocfBKjaifIftCopajejG7I4wXMKbbZBkVwP5xUdZHChtvjyW4ldEPG+CSFKEqcWo04RTLUztf4IPPifdFS3ErkZzhdwB5918/Q1i7Bi8pelBA3wIoLbsxtJLZlzgRk29HaVke8occ49+2pbRD8+CKEEmtjMx8AjY8v5MIPxb+00Ncs8NAjXs0HIui4PclYJTkaFZJRwwo+Im7R3Z/2rXcd6O5XPPHdh3yj4zZDh3mDluc4ZEBrG12xUrNOXL8Gbv+94IeSt7vpOHrAEpmC1pOvOdVUSTjW2xhBD1k34y/zaHIIJpvZ12rgqs1CMiUsZ85jHupOKThtkmXE5XxkSDYLT+iu2pYXKq+nksBzXw6MsOhwZq0pmRXMjjATHsczZSa2spnCAbk9EKzCcJ7fwxHH1KFgLg7bmDs6Ojo6Og4HuCd55StL+ZqvKeWe91z8YaPjlgme6ibZzvYxPHG9lsDn1Fe/upTHPa6Uk05y4iEE99S8J0i0H9iH6ZXBXH7iE6V83dfV+HfsKOXii4d+oD3rWXX7nu/9Xid2dBw59CzTgUIJWxr+xo4ErmAXW11wrYjm9Ej4TV2LkUuiShpiQzR4zhemDGRs0A2zpMxObOvR1FOrxax4aztue6HMPDnNbA3/DN/aSn5GSbbhRfK0+HhES/rIa5xWK+EMz21ILMlGEz/IRIJaMGa2K58GTEUCPwrgIhAymQ5kHxhN8bmcjNG3SjT6CWEjxkSRiMsp2W7t2GddNpCzejBvgaC5rWZvBPTlm0KV5acUDDlGikguSzf4dNP4kZFcyAJr1wSN0ZjX8dzGunB7amd96JHolhz6RgubAJpksGeVyMGD5j5mTInOfNExmaDJ9liv46jB4HCmjo67YepwB2/A9HbjGVgzsX7gxxrTOoI06gtZf4rvCD/ZH9AXu1aP6YGwt0gfcI0Cy9mBPsFyzYrgj+UIQXU0aHs9wIy41M7h036BQprgNVLzZY0gWlNkU1w01Dr/lRkJ5uG1rWJMib7UhdZoTKp5S9O20E6zICSLA8x8zvvIulP6Y1rWHSNkp+xMIc/x1BcfmYLs+JhMxbJcfB0dHR0dHUcUfH55xztKufe9/b6nX7U6HPFB51u+pf7VA4nhtQLW7Sc/WZ9of8IT6sNmhxq8H+5xj1IuvbT+IOrE58KDAnZItH/qU6V827dVH8wzcxw++ALs//7fujf+H/9xKaed1t+bHUccZLA6lgVv0uVOFJlH2wpv+njj6z1uL+ouesNneT8kOllbPbqIKzEQfSWGQ6/KN9fWr4nR+jSwnlveX1Pp9YaYUjGzXlFlqPXixXzhQz4dimOsDdCLYl3F5nJqpzFZPUi6VAW7ALgMsmEjgF264tP32AC8sIeeapePuFy08mWAhsHa2Y9A3+kSsxeeyMaOEuiV1I6bgI4RY65CrtlHN89BjNULtgWrGQt9bLWn7ZMMdI3JbOAn5MVDz9thW0lrQ5szp4ec4rbirIrWqHDZ+sR70pWPsB/2gNtscswHNq0fY49xUBSj0UgQtmPpukLWsdLmCBmvWffNdiVLVqDjdTQ71jc4zpMIeuKHbFahPVgLiTlYe7SNRzWgO8ZxqLvIloE+OgM9o2kN16YQ/YDaTgjVsW2zqXM9/Cw/B+jOCxsTczSVjBaaf6/pj2kB64vU6GOBikjajqdzhpledpehKZswX0n+iozaVkbOqkRg1hv7yj5kzwWwG2ecA8HQ7xCYXsSfmoMxqsxMMtsafy6YRBos8oOxew3GVuhnWcB6qnM/z+vo6Ojo6FiTuOqqUj73uVLuele7kK3myttxiwJr4qu+qj4x/ra3OfEIgw9ZrNuf//m6bg/n2j399FJOPrmUD3xgdg9/U0H8l1xSype/XMrDH173wv/ar63bNwVI7r/sZaU85jE1hsh3dHQcQfRVuGpwQhqflLg7NFrboiLDaHHzyAlCIkFICNUpE7KRdaq/oaj3kJNsLVXK2i3xycnO+WLRrt2ALEnOdQe+R4ikpU7U0hwB3fBBnWWyXWsPTvbWHphzO8iEnMYUQtRWWqxW086xo9fYiS4gW6vWri9WeS24vzyPDakNP+YmdLjQ5PgjvijoD3w5mh2DxhAyLq++2wT01aVO/jIW6QlOa31Ds0FtJfphXz6pjSY9L8EPmUCsR42ByscoOjQvplb7MZ9Ow16WU0xWAclCc4R/6UY7hA0tlo51Dx3adGwFjrs3M4I2kKftayrWX+P7+lFzZBN6LmNa9DMW0q2Y+QaPRYlb2kNm6rodl59HGB75c8TbawCPv20p4rXEQjbMuWyg6WSIRGLV4PpK7La/EBsBspU5WzE+qgk3zJXIJodoJOzlO+kEVa1ZJw9DNiYxNT6HNOwlJMYW4uo9bZnr9kxXmDcgQKYMZCewaAjLYWyTfpjJPiOGRS7GvrXMpOCMbBgsMtTR0dHR0bFWQJId3PnOte7oGIN9xNmnnX3E+cxzMB/GDiV42vsVr6j7l//2b5dy61vbZ7CVPkEeBLB54onV/rvfPRv7TZ0D8ihvfGMp55xTyq1uVcrmzaU88IGlvPe9M9v/9m81Ef/kJ1d+R8caQM80rQSdiMZ3hAGjN37A20EKtXZCC/nUJ+E3TvrphOQnj9Hd6OxcFTaGqFt++LYeUZLsIFVvPJIRkdBQW62KeOIs6w+RY3PMApxB27hYXPAG/JTMCcRWB1k+68lWbWps8UVHzGN7WtzkqXw+FGr2hb3ou7i/eJ+2leafVEGG0WTbgB1K1gmeaCNMxdFioXad+EY2xhZxqO86g6fUoz3y3576ZgzwrNI3zdYG7pNX6pagUl1XjI6T+pUHNcQa5INCx+TzWKJNDKHoVYtVcdbmYGw1gtrMxwEZ6XrdfAfouF5Avp2umDrWPfIxX+6YNtZYJq8Ha8f6FKCN5KOr9ZZkJ9tWT8U08OGYEGuI8+JCjGNcztgIiGbzi1Shq9hLHl40Yz4GtiaMrTK2wRTRDrXsP7CCyXDJeStEpw5BYMaatRaGPbIT3ebH64wpWsYkf0CctrDCNAihmWVpL4ppymbQVuMPLCeH3/C9KIaOjo6Ojo4jjki03/GOdmFb7RWw4xaF7dtLOf74Ur7ylVL28vtxRxjvf38pL35xKc97Xilf//WHZ9uYAE/y80UDW9Sw3Qv39Wwjc1PAFwVveEMp971vfc8RP3u180UG+7Izx295SylnnVXK/e7X35cdawaRxeqYgt6o+bZv0a2g9ZUEMXnu3FFDN/SpMsYnAHVNLpIUwFUFaqcrkYF++AEjezU57jTZtN7GjSYWCQb4XitpOdOPfbmRn4tTgGYlkpsy5fYUoxX0InEaJjz+OUg2ySseK5xUQcSgpLAh+tTYjMQsyXYlZd2PkvHIWDu2egHSoY0dq+nTxD8n7rAPMb4cCLkAMtDbGF0nfvQz6CB4WR/boifaps0efyDbCf8u39ScnhPXQth3ZF3BeDG/GdlGa4euFdGwa4W2+dA6EZvjQ4MKWT9e4gXdio6F8TRPOUYfg0guXzsuB89qyZjPrC/7zgcuLsBjbqlV3I/Wjbc7jhL48W+wfhzfOP6i1aoi8WP9BObMQQii1WP5jLyuUnNFZJPRbvrJEL6TbP1S1RptnCLWegyxky1hJDuni3x8ESdHtciX20InSAFEx6ZApie+wvc2iC/82hd/gWyztWtjLDpGHtpYds6Nywad7qJpBcvxwOyL64oQz9TwNQpFsmNaQF96tvZBIo6fdzOyzebH5Fv8RlwUX5P3eoxmw+vAIvmOjo6Ojo4jAi52JNq59yGpt9JFv+OWCdYF6+Paa0u58UYnHgGwXrnff/nL65PgP/ADde0eznW7bVspd7973eqFBPtv/mYp551Xyj/+Y733Phhg58MfLuU+95nlSphfvtD4p38qZdeuUl73ulJ+8if70+wdawq+WjsmMXXXOEXkhBWlErxQWd1UnKYkpBPjrlqwdusjm3kJTT/s1apBNjLR2tIB6LjeFNI4uI2e3UoHsOWJa5kK/tinQTznIz8Yq2NAj9p0Ipna/Hs7ZNvJemRTWxF4AU2dRshSeztIDa7Q5gsYDXLEI/Vsw+qwo+Q48sgaWhzeB/EEfvBAS/omOXSwJzF7kR8r0EO+1VZA+0ICPaeFHTCgpZKgox7yA6CLfWq3E/YU36xZh0EjE9ENf1GM3mwYcpwaW+0K0EaxVhsGyY/4smt91RRHjFnFaR3rGzr23p48qMbXOqBJHeusVgO0NcT6iJL63mzAXtgGuS2kfrNtmJMbw2WRQy/Ly06ypRhTf1mYHUxl96EadsPWRIywGjnsuNospCnjDuvWJC0N+l47Bh6zHyHpNIyVBxaEKfFKS3/5NYLoCbI6LzZNWwaIb/AQD1D1gOUPCH5Q52dvdcjzNRVnns744iSS9WP5g42ho6Ojo6PjsIGkJT/AyB7c7EPd0TEGH3YoZ59dn7Y+kol2cPXVpfz7v5fykIfULV0ON0iEs63LF79Yyq/8Sim/+qs1Ef63fzv9kN9q8LGP1fpud5vNL2O5053qk+7nn18T7I96VJWD39GxBjCVTetosNs93fHxMvGmhRRP9ILIPpDsVMLT9fSGD/0QTggSScb4pi4jqwdaItQYLUlroE1l/fFTgJW2f0b3uoa30cKwBh3oYtlL+JUQxCBU3TnED13KNnWNR32pur7Uw5ZBctZnXPgSCyH06HsBzK1i9Bpymw8DcpFwDj+aW4+NJ5zhM9eab6OFPSkgb0WGvZJ95EymxWO0GF/+wVNs6MltituUHjYM1PhXTFawtZEn2rGhgFNtaDG6rabn9HhKXGPwInhb9s12zBtAlxKiGm+t9SOntVNl1LT1Yce2qhMLDbeNKscdGmPmLwPwl+00v7WajQX9IAIZm9HpStUa2Ih4QLNvwB+y9cVh8ppn7CQ9AE0/LpvlO9Yt8hqKdRFrDmjt02AtIJt46vpaAbDCXojJlpWwGXStLZcF2SeQmr2E7SzbfLiOviSszeYvMLYbwESzaXWOZ6wTYtGAL/naVR1+x/EmU4PQYPPFofouK1r9CyrBjkdtVSW148tGeqMtcSpfzXrqAyEOsm/a1OHLKvzmGFsY1pdK6At16zQRh4ymx8bvGy1e+uPkO6ip4gpM8NPjy4GZRSJbmrc6TQOij1zkJ+X9yAmK2dsAKWTHtpkD9IbDm9kcuRP2m07QZdf6cbiqn6GX0WGW0DiOAPSxeEdHR0dHxxFFJNq/7dv8ArvoKtZxiweJ9iP9RDu46KK6Zn/oh+qH6sO9ZvHxuMeVcu65pbzylaV8x3eU8uhH1/3U2Upm9Fl7RSBPop2473KXWfx80XWve5XynveU8pKXlHLve9ctazo61hDivqhjCpEM5E2ek3q6DbTCuSJkhKgNiKjrNCV6En9V8JNJ1HFywSeJQ/XNJnXwlFD0NqDf/Abd+9rqxOWxSYkEC8i2NE7aVkgKR2IVNDm3o2a04dFPtgTTDZ+N7rSBnIPEjIr5JVGOPWq+wdTcomeQqr3Iv/cp+hbVCJHgUQYHGaNpzPgOBWqaHrvk3D6s8JfnCsbe3Ubz5K0SvzFmK7TjGAH5Nx0dO2uj1xK/XohHxbr7fY83sULG25u31rlQ30rOToVPbEOP7XFoMwYV9wOsak+zNx7jtFrx0UTIZCjSM3vEx9zGWKAzBwGJGY+1Q7vBdMK3YqVtpcXtMUKLOaOIFrC+5s9p8uP6sQ9diyd80LR++O44SuHrgMPszdUhhE2RNdLWidd53cRaBSGbaQFoQc/6QP0RDUzRw7Tq6LjcOBbxF+gDk1fyeCQy6LdYQ44XivWzP29SiRws6WRCxTBNW3VUaq++KjZ/6hxYW9LQwp6H0hCyc8ip39pqZmu1ImIqxi4ivpq0V1Mest2mkohqjvsOyY/8LIcQzTZWwpTsci7hZZ3V+BrY806tZtrL+ezo6Ojo6FgT4Mlcto4h0d7RsRyO9BPt+jxqhW1jeBL8nvd0xmEGn4fZPuaP/7iUF7yglOc/v5RnPKPu2X7ddS50AGAMn/50KaecUsrtb+9EAzmB7/quUj7xibpNzfd8TynHHFP9d3SsEZC56lgEEnYBvXHzm9dvEtudd7StDN7k1oYeJInTsRJykdAEorkdEHphR02LK+tQjxOPqmq9YeOmsoTdLEPb/ZMcoNRkATpeoLeeByIR7Bg14gfWbomTFo/3gUSrJUG6UZKckPXSMdB4rJBUba6tQQIVHolWJWaRawJuw2iKywpJdx6vi/GKLskK2rLlCWNKbEkzgPexw3iV7M5yRoeHm4iRC8O+vbWt42i1/ETc3g+bQDyrsRE6AdlxP8EXHV3jhaxsWEFG/cQL2Jj1Fw+0iRmbamN/o/2vpfmTfNjAXjRpWwl9dCgBxek2Qilko5a++wIal7Vlu5Lm9CVfu5IDcQwlZ0V8XpJux9GB8eGMtQD0JZAfc62hJMx7PZYEyHroSDZqQ6iO7YBYrwMYLeihM6Wbfbj8UjwG3GTdjs5zVjdblTzpf85PgvOkNiUWuhNmJ2kDuEDEZFULT+dTq2V/ZmgWfvXbnoIW3XXGgWKjzRPdIZ8uJZLh9ZVrnhqih1tEIobQS+FZvyqJp9aMVpGfb6/tkYkB4GVtYZGwY05+hKye41wNiHgF940f32ViX2dnmzja4a/2ZkC82Y5J7ujo6OjoWC/gqdwtW+qTtB0dy4FEO4nlnTudcASA7wsuqE+Vs25vTrCty8/9XCl3uEMp3/AN9XPfRz5SP5geCPih089+tm59k/dfJ6/AVjF/8RelvPOdpTzhCc7o6Fg74P6oYyVE8pIbR04UusO0omTmCDnZRx0nlEguxs0n9H1jfaNJ3u1nhB3Zpzhf9NBJaHwSp2HXbQjGV/xjOqI18e6dKhey6qeCH2oh2RS9UoWQz1CILgtibADRSHYDJZawYe2w7WoCerJvROhRIEGPRC99ELrqB9HQ4kfe6pyEo+SkMbx4ur79dYAYlQfyuNGLMQUfxBxS+BKAOhLE8Cjoim56EaPmxuqQC8CXD2wg7zIMQLLE6wVa+Gg/Buv2mk2rpW9otccg/9bmSfWIJZ6u1xhDlxKgDT2ayQ8x00dHsRuZInhDvg3MOToqDum6X/V93lCNeNYI3vCGN5SzzjqrXHrppU4p9pnsuvLQhz5UhXYAGWTROZz4tV/7tTnfi0BMX/M1X1M+8IEPOGUNQMshjrHV+XA3umOwLgeCznPkdsbYHoA2pufuIluBMZt+pk24nIPW+zJ+sBF2shg6U/ELmTYvIzPZJ80Q8VqJbtdr15ewM1KdIXphzOAkXadG0g2IJ5VVI7vxGAmRZoQc9ZTrOpx5x0tOUiJ7ni2EuWAvEGuYSokv0lH83gZjX2D11gLOn5iHAWmBGWRy6ejo6OjoWJPgIso9xF/+Zd0So//gYsdK4MdHjzuulAsvdMLNDNbrP/xDzR+wZ3rkLm4O8EEXf1HIL9zxjnWveN2LHwBiu6Zv/dZqNz5EU2OXLxHuf//+nuxYk7gZ33XrEJE4jcQed4y6K7QX3uf5jrk9UZdk242oX6DVtqIEqSHUdeJwX/kEBN/NNl/sga0kKnLEkXQzIsZo08lyOfaGStMTfsGXfS8DuE35qbz6BBttlx2rZGR7MWeqsQG8ltzIkObaCr7bfFubL0DiMbt4ah2ETPjUdiLWZg6h5YsPKtA1ZwZ4apqckxrQjeMQTGgkgNGzZi3oGl/b19A3XiS10Q9/9AOS2VLpYZvjztPwMR9xPBlf1lcb/85vdo0eyXfakJPLBsVrNaXBCdiWvitLNmiQvN+UvU0sitNjpZYJXhBxnpLnBsWPfdq8uD3acbyUlIdmJXzGMW1+nB59xYKCIeojhPvd737llFNOKZfwJ2+OHTt2lKuvvrpcddVV5ZOf/KRT+au4Syz8pXLmmWc6pWMeHN+Mcd8Rh33AXk6XdvQnlLWuFmEkt6ysYW5JTsg3G/6FqJWB1Eo+2psGIDuUb2+LhWbyFiwBE3Z5WWwxmGSzM5NpkKEZUS1exnKZkPlZLo27xTcf6MEj+7LOoDsMxF8TbSi8LA5lyKtFXO+XCxPe6ocxPfappbnI7pGYh46Ojo6Ojjlw8WKf6Ne8ppRv/Ea/j+zoWAasEe7Z2Pbk5gbrlS1rXvWq+kT5eefxQc+ZRwiRaJ/6ILgcLr64lCuuKOVhD3NCAmNinpVz6Z8aO9YePGPVMY2pk4HRIFMiGQioF73JtW0BDVPKIpEIbSedsY3o47M59Rok2UnfUzTgdCU/zZbp6s++SeIbwlOFteQbnWzP2uFz7Bv5oRGhPoFoQF4yVqQ70o9+1g95EOJ0VeyFpLr4zsSuijoiCeE7IP/AaZGoz/RICucTeRw78WulL0HCV9DUtyKfVtBr+6mHrNEjpuwjks8B6HqC3vmqrYR+yOIjtpFpcQbCtsl7gr89GRl2pGN1HcACOC/+UgO9sBO8TIu2akCbOvqG8Vila4Wq6QEIDsjiZTvWjm4ef8ybbNK2MrB78+KEE04oJ598cvkHnjhwvP/97y/3vve9y5Oe9KQBnfbZZ59d7nrXu7an3uMLMQpPooOpp+R54pwnz4OGTNZd7in5eJI+ZJ/2tKc1+gMe8IDy4Q9/WF8YHO4n7VdEHEcqi1ONqUMLT+8LZCYwRYemNWXrM9jhL+QXriOnj/mhl/3RDF+B1hdTJEF0Kqcl1iRkcl5I5+QcWmvTkAP13J3kG1JTWMQLelRzel4D2lNjEd0Y8DwWIejRDhitkp03YC5CNlw1w3SgDWVkbtHQA3M0dzUyL1rIZp1xHCthzp8hTFBPmYM2OL4J09SZneAPbdcWPK782cYiPx0dHR0dHWsKXK94UOp3fqfuAc2PSvZEe8dK4IPbrW9dyhe+4ISbCdzbX3NNKb/3e6X8/d+X8uu/XsqppzrzCIG5INFu94yK70DAGPiy4KSTnNDRsX4wzsJ1rAr11rEmBu3kEfeMnEgouomE73VGJD9NrN49Oz+exI2ndQdALiH8zCHRjN+SMGBwY5vakvFicelZxSxLc5ysjCKYAPIxrgGSnQHw4c2IWfq0Kc4MF0AJaCvyZQwKbR0D5GlLsvIkH20aE4A8mBcD3YhFZu2FNlBi2o8TwAf8eEq82aL2uCKWgKtGkrvxZct5sh8dh+Tsg10k2sMXbfXx5+3gU4edOD6iBbIPp6PXxoKtSpaoSNB8PDSbnBcAKaC4As6XLA0TbOOI+Cq5xQEG9sSsNOZDcnSwibKXiDNstK2bKMDqQWw3P44//vjybd/2beVjPCXjeM1rXlMe//jHl/POO6+86U1valu4IIMseMxjHqPkd02SLpUXvehF5Zd/+ZeVUCfpDUjYB0jSk7y/7W1vq4T4T/7kT7Yn5JF75jOfObn9SyTTf8duLpDlafvPf/7zSupj693vfrfsYuM7+NO9IwqOp62DOLwBlkYQ4VMCIttLW7fBc7kmD99LrN3QCUR/TEe42aE74gddCN5IJnw20PcSCDPYG/jI9hMQkZi9jEKoISUbZq/SZrYHVqNjvOaaOr78dFqzkTHqguTZ7FmvGZ0hUzBRbVfAUyLX/o81JVubzWxLAdPPClN+E6k158XmMCUStDEv4ptHlRR/wuBivSGQy7LRXk4/xzrhuiHbGE7fvJatFm9N21wuno6Ojo6OjpsF3K+9/e2l/NmflfK7v1uTfh0dK4H7eraPSX+1fLOAbVb4kdD/7/8r5cd/vD4JnvMQRwq8b9hr/YYbnLBKfPCD9UdQ+5dbHesQa+Cdt5aRb/8mbgV1d28FVmNbw8kD6MY/yTcFqyNxuTDxN9YZw22jLx9IjmUrfQD5m8lVHZdzO8si+VM9l5gY9vVULA35HdtP/bApe26j2TZee3qdYnR4zKES6vCn5hFespERpjLy+OP4RHI884Bo7jNiab4cMZawAcJW2J9E8oUsSX3BbIV+9EHrZ55j3Mev4oKeeAMxtxsyjDPGIhhN3ehTuwGNKxuL4xKyQMretDpKHofYLgOd4t3Kdx2IjW4QLfOh1Ur0NYBHPOIReiqcpDZJ9WuuuUbbw5AwZwsZto+BToKb5DvJ+Xe84x3l/PPPdwvVxq34MGcgAc7T7iTsAbok7Ene037xi1+sxDly4L73va8S7b/1W7+lfsYFF1wgW5FExzf6f2Y3G/F0/JqBDjXrqx3gGYI0OG8Y2prw92usLaAq9Tm38EFVayneN47cBoP+Al7YzYAVBSAif7XbINqI3uK2OvgZ0ZUcMnUMYzHBTQUkgh5qQaHRdH0rGdGwyXme5Dw6GPO2Gx74hA7BacEbfEkMoh826Y5EBjTEqK0v8/ZCDK3ASrJAcrUp0MZVQ2LWLwC8M0Idfa2z+hSCH/UCkw6XWsYoLEq2o1icsJz9ZcxWG2oMLYSvEdnRvsKY6WdM6Eya6Thi2GQ3tvtiy7uOjgMEa4c11NGxbsGHAD7vPvvZ9YcXH/3otZG07Fj74IMRT5KzfgYfJg8T8MF2MU98Yq35q+jnP78mqKc/pN18wD/3ncR42WVOXAHIUvgB1dNP7++7jnWJvmqXQyQVSUq0kyQnKz9hQY99wJGTjPdpu1g9WVB7G4S8kjzRtzr4QG0KhqwgQ7/RgffFm4FbXBIKSnqEj4AnWQS3FRaVJDB+TXRU26QLJI2sCgmtKj2D80DYnjspGn0UZ0XWpYxk6Mc8h/+Qlw/sUlkbMjx0KJKD6fIZeR7gSweak9qT8NRW4mIVvmXa+siFHfHclvoWKzeqTYe5MwQ/7IltLzx5TRGsry8Nkq1I1EsfPfrWjFjVR8ehOUHAIH2PJcWrJFjQKBoP8+r0kJG41YrJeQK2kHd96XqbH0lV34B8+AWtH3a8H/QsCzS2kDWo7bIN6FnFuoCtJ9nDP+P3uWlmkr0jgNhznSfMY092tochEc7T4kEn6R5PqwfYxoX3KfQrr7zSqUVJdZLxJMOzLk+kX3jhheWxj32s9KI897nPdc0heIqeZHuWxQ77x69JWHw6njqkaU3EmtG69j4ln1NWBbeZ5ce69BXHAqzaV0KoNLNGwE42tRqzC3y3cOfCroQpLVsN3jJMC4wwE4rr0kqYFIEYDEyOZBZZzUMndv5pCkdzMuwNkW0rDG9PYWyH/oCWO2NDE0EsOHQrgjhXUl1uHCsB3Wx/RV/uLB//m+K/4/Di1re+9dr7UrVj3YC1c8YZZ3ivo2MdoX5AKGX37lL+9E9nyfatW2cXso6O5cA6iUQ7ONgPcqsF9t/73ron/I/9WN2XnR9jXQvrlRjiWnAgnyn27KlPwbMFT0+0d6xD9FW7IjhB2clL5yk/SXLCUCKPvhUSNrnAi6QsBTn0lQylGEJX+3o731niiY96OkSiWx2JTMHq6OM3TkTSbwa9dnjCsiXUlbz1UgW8rsm1agfajF7tWxnHB6DFuBvN+jLjfSDbhkRSO+iRFAXQGBu2Nbfexh7JYyXNrKDK9io6FozJ+lEjG7ZVQTRo3khuG9H3qa9JOafFj24CfMYco6/xEAvyTo9ktuxV0gxuaDwP2IQWRXFC93WEoebbdeXPeXEM6UNXoR26sHhxZP/eVvrJ9Op2MI6sY9igY4Bdi6vNmflFzNdV5Zuc4glb0ImXJnRH+MrHSLIYxA726Vu3zYXTwiZtFeJABzniRMTpkofglTdT44ggJ9TZ4uWBD3ygnhwHJMx5Mh0ee7mzpzuIBDtPuZM8Z+uWeKIdREIeet42JvD617/epqN+eRIlPyGf8dSnPnVOFr/Z3pqAxTVDbmfYsc7rJeSiHesTsCycPYMRJOprdYxBDCNoXVpBJtqU4DVdpwVyN+SRjZgDI7UGREIv5KVa26oa32oIqdtcuPwkQi4KL81GNhbthDQmXDQTq4WpywQlKwZ9yljwRtAQrcRQQ7UNfUJHcP7Ulwdjio1S4tCjgNw+IKxSKYYQyGpqt0EOEVTq3A79he5NaIHJOR36WXShzY6bHfe4xz3KZZddVv7zP//TKR0dq8cHP/jBcs973tN7HR3rDNyX/MqvlPKiF5XyrGeVcv/7+z1YR8cqwGfCm/OJdtbrm95UypYtpbDV6FpbqweTaOfBLpLtJNqnPrh3dKxx9CvGclh4px53kcGjbyc4NaM2GsleatmxQn9wooi28+z/AEaekbwV+s0OQui632bToGaSUyIp9Q1L0jN9480SBdTeTs0ZbcGyQZ+SLigk5rwxK61fm00vAD3kQOi1bVMANB9zJH9DR/aslp7L0A8ZxFSi4W3JSnDW926IVXp0YICgWWlzTNvqXEjiq8YutctE8py2gC1HkzOZ7DfaoQIazYihJ1+mG092Z16AvgEK60HHbMBHZENZohi9HtMqIyn6+nKB+at01bFOogbw5C8KsjRrDFU3QBvn6LusiwmSheA8AI04FIsIxuYLAW8PDBjC7xEECfWXv/zl2uKF7WECPO3OVjLw2J+dBDxPiLHVDEl0nlqPpHwGSXC2fAmb2Ack6u985zsP9oRfDsRCUj32iV8X8GWg4xpLA7S+E7RGWCuxXhIPTK0LrTfXaY4SRF+AsDdlN2DqC7mYzrqKmwql+EI0QXyUgPFoRhdMxTrpPNmBn2Ugu9+FpkPH9ebcNl4VgF9F/HUg70bwGXSv4fjZyMBXhjPdKRvyE7rjuQsMFcO70b2eUQZNAfszwSESeYHnOWSXClcEOVmIOgsOl6Ma+2zmFgBe49ucRHtKT1M2Jjoa2RQjhnEsC1Q7jhC4Xtz97nc/8j923bHuwEMAb37zm8s3f/M3O6WjY52BJ4MvuIAnTkr5hV9Ye4nLjrUNPqydckope/dyQnTiYQQfwOy+sPzgD1a/awnMBQ+EUV9xhRNXAOP58pdr3f8yqmOdol81Vg1uCa1QcTdI4YTBhTcK/ZZUNIFI4KgbDYCRQGpLP5XYjgPIZyQPDWETdGO4AAC3CElEQVRPNGuTSIVGH13v18SpichebCVT2/D0xDxFcm4TSMYASWTrQ9J+ix6H683D7ci+KdUAKi3sUquditTsJWxK18cnutf6wiLmBprD2fpWF/Bk++C4oFdZgmTtJZ4Gpy8+NDogdA1hV3LYrV0hYt5vF1QJ0PYn22XbSiR8RQsZo+8zHdmzgpz2RDXjzDVy7Ql5A5XsIeMI3UDIav6szvq0W1xeMpxX/3li3WxT2lxhNGxQy4+1WwzWjj6F8TEe0Zg0yqwSsAOajNUg6EDHyW1rS5qwEzat0EYnimIyPd4Pm7aUsmWry1IRn7ePIEios6XLeHsYtpCB9pa3vEX7sAfYuoWn3AGJ9+/+7u8ebB0DSK6/7nWvK+973/va9jQk5X/2Z3+2/XAqIIlOUp4fOB2Dp9mJ63f58ScHcsivyeS7jqe3tU68DeCJZmuBYx5FN04L1kCsjbxGsElXNCuZF+eaQPiLdiC3Rz4G52CD/oJEdq0zodeSqV4NZEBsKQVy27Eh08Zf9o78SdJJLU7VI7tGWmrnzyH0h0Jmq+kzDtTdRHRnrjE2smV9/jXAR0/vdREGY/WpEmZaXAu9OULQR17VF88KLhmDukYchzivLbWG3AYDaexbNW/BYET5rs2BTLZZeS44wliPFTqOZzmE7MA0rnwSeK0yQ6sxr/apoOoODFTpoUbHWsCTn/zk8opXvMJ7HR2rw8te9rLyVV/1VeXhD3+4Uzo61hG433jjG0vZubOU5zynlG3bKn3RB4eOjils317v5b/yFSccJvD5a9euYjd3pTzpSZ6rWWPg6X5Aon3+Q/M0SLQDEu39vdexDhF3ph2rRrzR/XZSd72U2lVRItRqJUmMEAmAdpKAGcWhhE+CZI0fiRowdZKBpiRSvV2uSXRDTlCaHW5tZ4kIdMKW+RA92w6/Hl+LxYv0Y0wjm61voC0axWmBJoc97MY4TZC54CIRSSZo+G0xG+hvgu8Xkzx/tLGv44B9A3UkagGmsCdZ2sgbT3wjhC/qmEtiCjq0Zsto8UWH4ne5qDUPVuLCF7FKxnnq12rmw+zBH9irLH2JEHJxnHLcERs8aIrXxo9e0MN2g9Hgo5uT2tGWfMRKbTS1zX5LfLt/1eija0Vwexmyb7FFHMGPuo0n4qndBrkxHnIhq7bFR5Edp5Pw37sn2fL6CIOE+imnnDK3xQuJcZ5kP+2001qyHD4/Zhr7rJ977rnqI8M2MQES9ne84x3L/e9/f9kP8MOmz3ve88RHn6fc8fEcbiRGwNdrX/taJeaRpfCE/Bvt5oPY4BMztqYS9TcrOOZt7YF0XPMxVpP1keSbGsyJ9TCwDT/pibcCxjJTOkHLscr+lHgizPEcrjTUHenBHPAN9Be+J5JwFhnYWqTrSCZAdHOcw5gdimmB7UnylJH2tUTClNzyiPhqRNaJ/sS8wRp7oL9gJAOsRmaM5suVa4wHhrCR495gRqbogfDTZHySBja8XoSV+B1HBs961rPKRRddVH77t3/bKR0dy4Pthl74wheWZ7OndUfHegDXb/Zj/5u/KYWtsngK+WUvK+WZzyz2AZuLWi0dHasF64U90tnKhS1QFn62PgTgPp21yxYrdl+4Jtcqc3HiiaufC2Ryor2jYx3Cs2YdkxicCGhbaUlDg9qczCZOaE3Xecud9MJmlhEt+YcXCdmMKbsTJzCSAEt2ItYT7tYn5VBvhmlBcR0jSXZsQr7FtNpKFlBs1VZF1Aljg2N91Vagq1RSq8F4/LLhAlxkALbCRovL6UpqO01Pb1JMDxOyBYwX7dANVoPphD8BOeymRLhsYjspo4NN8ewl7Kt2OdrEKd2RvhAODPCyrUiCDxDyVjN/8aRpJKHxQVzhh7p9QZB8WVtf1ugfaqYjm1bCDhATW1bGdgNhu9lPbeJS2wo6LU70jaYvYOC5/2y3xRA2DPBJ5MeXJRHXGgJJa7aBmdonnQT4eE90kuX1PbpUdu7cqT4yOVmOPLSp7WWQC31K1qOdde573/vKR8iO7RHz2MaRQ6wFjr0f/0CsF7V9LWW07og+hnSpvd3W8AR0vvG1OH5vTuqFbaun/FAynRfMGC2W/pxdF2/DCn7UwQcj1UlIJitVhGqENwkTmuQbLdPrGWYCCCGXbURbdOcLtVHXrZptCjPG/YxgZZkcZ0X1OW+mUsb0GFs2k2XUnvOxGGP7hxLYXo19wm0hm8JsvoYDacehVmpMDXWK1nHkwPn+RS96Ufmpn/qp8q//+q9O7ehYjB//8R8vX//1X1+e8YxnOKWjYx2ApN5P/3QpP/mTpbzvfaXwl6OPfWwpm+MBpY6OAwTJZdYPyeXDCf4S/rWvrT+AuhZ/sJd4uA8655xS+Avs1d6Hx9YxfIHQ0bEOMbr77xiAJMnkrSa3gn7SGLOlY1Ay0Op2Z261TnxWB0nKuW9AXjpjfWA07EciFAz4qDh9Ei67VJPtkTxj720B22EOkpJEVuMj/LSMDk1PXgY/nppuRhZAc4ROJL/cv3y4ruxF30r+MyhtC2C08EsZzEfQISQ6JRLs2MN/9DmW+uuDKl5ljc8T0EFjfNrWxYA8MtEGsZ0LZMXTjNl/s6VibXj4jwuN4oBHx3jIyI8MQXQ+fdcXy2oVjwVRbAUdUOV+2Ax7FI2d+Q0ZxDd4qdTKoUNB3gpEzMl3lRDExx5MA0+7ByTnfjGQ13I4E8/5itFiBcFrOsiMIF01aqVj43bCT/bZcfQi1gvQey2tF3isgbZeMibWxlgu3stgubU0Zi2SnYoDktGXDzEaIyH8WKm6wfNaKvaSVNR0HReYtRs9OPXVIrOXZMTgYibhjYR5ykwdXugOYcRpxhydeBRT7SzEAmsNbc4W+XXAHc5vUlnBCWzpq3fgyG4mbSzwv4A8Z2ORXAB5yliOPiXbC5kZbegtetRDTsdawQ//8A+Xn/7pn9Y2ZT3Z3rEcfvAHf7B85CMfKX/4h3/olI6OdYL/+I9SvvjFUv75n0t53vNKOeusUm5zG7uIrXRF7OhYALaO4Yl2to4ZfV4+pCCR/6EPlcKPT6/lL4Z42p5YI/+xHJivyy+vc3jyyU7s6FhfIAvVsRBx6zdxkdXdpPHivNnuJknAWM2FWYlbCXodCCXHFA/bkDNPtj3BM3nChj46eSmp7TaarZE/TA1iMEjPID/uq4lYw+Tjz8ObjPpWZx3QYq16tRn2fUwZEncd5ClKiGc5o2E3ntAOH6KZnLuRneC15LDbIQZsQ28xGpBRsRfJeKzUSvLThuftuGCob0VjSvaiOaB5Wwlvq8OWYrO2YrWiLy9Cdm9tj/3GGEnOh2y2j73QiX3rSfRDG/gdIWy0ARh8bL7Lrkw3O5JPbYrsQ6ft8wiaPIjaaDEfgXassGOgzxYwTdcQ7awnO0ZocxSIeBxT4+44ujFaAgJ9SqyzQOu7gvpWsj7rLJcpZLtTMtL1doPTsrya0V8Qa46jtVPM1KJVAl+2VoQ960czvWVBFXV5Knxm/TzODBMJseQ66SY+L83MjD9rhqDVyI1cimu82bgWYyWR8XByv023FzC2F92ZWvsqYCGQjbJQOPmeoRKbbsIgbv2rWCmWMZDPOrTD1tSXK5MwpbGNXHesTbzkJS8pT3ziE8uDH/zgvo1Mxxze8573lG/8xm8sH/zgB/Xjube73e2c09GxTvAnf1JsEZdyr3uVwheKd7lL/QHHjo6DRTzRfrj3aP/CF0q59lr2Da33+GsVJNqZi7l78wXg6XeuJWt5TB0dy6Cv3OXQ7pq5MxzfBnJTOXVj6TTE5+4cnRA83f1akcqcsCPsGT/HsxxyrEokZl1AO8vU5+Ddk4W14Ja5mam6atcX9VWn5gx0zGKMt2HkBVuSwY4boK8EbTIocxO6MCJpHkndgWzSYV4iaQ7QkQl08emlPUkvphV0xnpxwQh6yBokHjpZ1hA+clv+vU87nqAHIRNJ5PABsCvdibe0eFbCLiWQ22YvH/v6Q7r4MIrV9IUm4PUYLTYEq67q8EU/ZMImaHoJ6OhLB/Q5FmGP4vZAs+e1LuL0I2aTjb+4kEzH0YuJ4xtrT+uINmtjgVwuGaydoLcvURPG9uhHif4iiGdlym58mWgYmBjIeTv8uWBLNsOOdpOJdiVPISdlK5IesBj44VOljyVoL0OFCtE4h6jX9AfxAXWxVZ/Gn/8s7vIZzAP/x/PmCHLzbUC29nmZt5llc3uImb8QQTaLRxtJ2jGc6Eed4cNJ1ofIwxz7ijKFKfpyfhbZCaAXMrJjL4w/LddBPIlscqwFrZqBHbAono4jD5LtPKnM73Z87dd+rRLu/8l+xh23SNxwww3lb/7mb/SDuQ94wAPK3e52t/Lud79bdUfHugEXrh07Svnbvy3lUY8q5S//spSXvrSU//E/6tPIHR0Hi7x1DOvscAC7F15YynXXlXL/+9cPY2sVkWhfzVwgQ6L9DndY22Pq6FgGG+xm5zC989cffuzHfky/lB/Y++431MbgDe63hdDizjJuDbMYCNb4BBE6IttLJAPZ0kJteEkHefUp6DpEj7aV0GnyAesHX8miSELXtpIO6oceyRUp1G1lnG6LxZr7JS/exqqrZLDkJmLPbXiCvBlCJ4AfT5SD0JXt0DHom02jaesaqxGDTY0+9Ni6RHQYwPsxByS1yArAjuyAkro0XEe+Pcaw46KzhtGlZ2Uudvw4nSexRXZajEvjMdBnLuNpc4TbuJGDbrKyYzzpO5tG0CJ2dDVWp0ne+pobEHJVJo5rRU2CgJpq86RIk2EtULl9MmKbNs/4GhNth5rez2MO2Rgn+pQsI5sG4tYxN4jPOJC3KjJyOlZJP+JANnjSM77pbNh6TCnb+5+krUfUtVZx/ot/tTz5G+5VNp711XUtbN5aV+zVl2v7p6WrLqv0Ldusv7uU6+0DKe/5+CuRXTvre48PxLbGlmh/8uP6kLzhNmfW95yVpY2byxK/mH/l5WUDewZyE4bd668noFK2mX1+UIs+uieeVJau3zXj33BDKXv4QV7ziSzgBs/e8/t33VD2XHJF2Xq3O5UNJre0d1+54TOXlK2nHV82nnFaWbp2R9l9yVVl2x1Pk/6ey68pm44/pmw8dlu5/pOXlmPvecc6J9i2+BnD/hv3lI1bbUxbt5YN59gH3M9+ppQzblOWzOe+S75U9t+wp2zabu8BU0N3g50T9+/bb/Q6L3suv9Z8bCv79uwru6+4ruw3X5ttHraesr1sPGZL2cAPUhs2nby97LnsarWXTB/svcbGbKFsPvk4a++S3X37lsqu63aXk0891hgby9Ke/WZnc9l17Y3lS1/cod+3vu3tjy9bTzimfPkL15Rrr91dzr7nrctGi2X/bpv/vfvLJuMt0TZbG7ZtKhs3byp7vrKzbDB7zDGJ//0W70UXX1duc8Zxdog22VD4ErGUi7+0q5y4fUvZvn2zHQrzbSp79y7V2uztN11OE8izPKh3795nh2zJ7Gyww2T2WS679trh22S6dRs2dDfb3DGHe2ycm6y9mxqnht2yt1T2WTnGzuGbjW5HqHAmutEMmFnhRnNKewt8k7XQJLfb6FvN5sl2LG80nzvNNuv7BqMfY/Tj7Jx2DWvWgKnN9oIPu0KXXbY+T7Y5uN50kEeTGI63yb5y795yja2z0NtKvNKz06TVxLfH4sAffjiy8MBOs4Ufxn+9tY8z/i6OifVP3rKx7LA4T+bYmD6x4OdEjrkpQMP+VXacttuAd5ge8TBGBIgI3+c+/Wnl/v/n163VsZZwnd3Qk2R/1ateVT760Y+WM844Q78Lsqk9mNBxtOOaa64pn/nMZ/SD6mwpxP3TAx/4QOd2rDeM739vUakBPsP927+V8rCHlfLnf16T7RlcFzs6DgbcLzzgAaV813eV8qu/eni+uOGHe/lSiC+IPvnJet+7Vtfshz9cyrd8Symf+xw/AOPEBeBe6TGPqX9Z8hu/0b/06liX6In2hOlEu52s5k5YMWVOD/5yYs2G1THlItGviYlBMjD7RJ4+SVaM0pcNpwO66sdt8AJgH5kWQ00OVHW3Z/wNkoOObG2TaJePwCY76eGek3yOTSF5XCB4jZbabRwzPzPQzrLO5yKCnnQN6lsdqnxogtYQ+gi5nsbpBT5JCm7yw3+eozAePNnwGmh8uUbOZeOCR0x57qHFOFqsyBGH9TW31oePHvps+yJbJhO8AHaDRg1CV/2guZ02HpPx+apJOgnx4nC58IWKXkaJduZv85ZqQ3attPVM12kAWiS7FfdIjoJxzY/TaBC3xm50JdyNjozm1PUZSwBexICMfAK3Zz42kHjtifZ1Ca01x/kvfmF58jfce5RoN1xzuX1Y212WSLizRrdsrR/ebthhcibhycmy67raJlFEop3k46c/Ucpxx5YNZ5xZ11tOtF9xudFTon3XrqpL8vzGG2eJ9pNOron2nTvreoSOf8APFgHn7d91Y9lzyZfL1rueVTaYDSXaP31x2Xr6CTXRfs21ZfeXri7b7nC6KS2VPdbeePy2sum4Y8r1n7q0HHv3O9hbifgtHhvLINFucW4496tqov02t1HSflGinR863nc9XwZYqIsS7aduLxu3eaLddJVov/Tq+tayeeVjxTjRvo/ksPF27dhdTrqVzc1mG/PufWZns9FuLJddtEOn4Nve7viy5YRjyhVf9ET7PU5XUnb/jSTX9+nLhUi0o7tBifbraiyKvybaL75kZznj1sfa0IeJ9hO2bynHe6Kd6SKJvnHThpo0R9emD3lOQfusv8dsRaJ9IwEalGjfukkJdv7xBQLJdfh7zA51JNpZhySWSbSbuXKM0TQe43BGGifacUHCm0Q7q3Ov1XuMvsUYixLt2+3cdrWvZUzlRPv1NqCTcqLd7B1j622lRDvrgSsF/q8zG8RtU6OYGROJdpuS1j7e4ti5TKL9WvNzgvlEHpvYJ9F+nA3+uolEO77P6Yn2NY8LL7xQe3Jffvnl9j6oa6nj6MeJJ55YzjnnnHJ/nqDsWPe4RSfaOW+RYH/2s0t505tKuc99nNHRcRPBvQHbuTz4waX87u/OPvcfSnDPwZdDX/u1pbz4xfoMt2bBk/1nn13KBz5Q6+XAvdKDHlTK935vKT/zM/ahkE+FHR3rCz3RnjCXaH/PG70F8onLpkzTZjROaGKltt10is9NY6Cd+KJ2fTW5s7eaREwgEoTYkZi9mA2aOmRKHloPvYB8SELdBtlIPOlR46/eGA+WgdGhAT3R7slNmZCYyxIjhac9AxGXg8TFhhhX8tuejB/4nempTSHrQTTMJfOKb82N1dJNffwQS7ODDasQixpINmQMmgcH8QP5doUQlV0r4tmcaFz0Q9/ksU0/eAHFaoCOvuScHzVgvMiQaFefuXU+4wSK0WjZDjpqww+ZgMvxY63YbbJOly2rsM+4jMaxqSwYtdYXLUqEWN/a/Gt2+NHYLdtkRn0V4kXYoR9FlbK6JlDlqpbB6MQQtBin5pKmtZkfzbfJ0s5P0BGLio9d+9EbHxry+vLCdBkjffu/YduxpRx3ksQ71hd0/nAsTrR/2dbm7vpEO18I6on2lGjXWjA7u6zPzRbrw9bPXKKdNWX6S7aelq4wm1+5omw4/fRZop0EOut0tYl21uQxxxBh0Z972jrev/P6ZZ5oP70sXXNN2X3ZNWXb7U8zpVGi/dOXlmPP80Q7tonVxrN/956ycYu9DyYT7ZcpEb95+zbNFfM5SLTb/Owm0X7CMWUfT3VfueCJdtMjmb77QBPtprvfxsgXATzRftnFO/QWP5NEu43riouurYn2u59uU5cS7TzRfiNfJOz3RPvG+kQ7sSj+5RPt+Yn2nGjfs8eOo8lxOAmcevlE+0bjM9zDn2ifeqId3GBBbjP6cba2rjYaZig50Y7MiSnRjt1jTf5AE+3brM9YiJl6UaKd+E/avKnsMLunWM3T/ONEe/VYytVGO9YUcqKdY8Xo8N0T7R0dHR2HH7foRDufDf/X/yrlggvqD6H23xfoOFTgL1wf/ehSTj21rq/43H8ocZnd39zjHqW8+tWlfPM36zPcmgVfPNz97qX8/u+X8shHOnEBmDu2IfuVXynlCU/QfVJHx3oD91MdBww/ielklj6M0KcLWSJZbgquq8RskpGd4EF3Xv7go2Rj6BidJA405LM/2kpeIu/6VOrTDptjOS8kmBot2zWaEpluRzCZZqNikGQX3CZZDEwOgIzHQY0MNbq0scWJVmOFDpu2q0aCVTwr8oM/K/KHQ3caffjyA5znlfwofmxBkJMqrzF6X3OET48LYDviiToM82UANppfQxyHGJti3+d0K2G3wWkhD6jDpmKJGKmsVuLbEL4UoxWS+ejhk74nqHXsxLc4Ijkt+1bHWOTDwDELu+G7jduR+0vEFcWBOnMjO1YUM03Ti/ig84VBHBtkVUa+gHSMpzVJfNiJFI8ZDnsdRwdi/S1EWmtt7VkRmZqG2Yh1592K4Ae8Hz4HvIRxTFlu3M6y9Bu7ttu5VO8BKyaTv2wQRv7qV7O1VW3Sd5q6QTO4rqow02pruFhD6EVYjprUrkn7rCNX3gYh553ml1PyNGAgPzK0ALI3wlityUwIz1Em7EXME6yG8Bn1WDamYCWE3lh/1h9yxnIZy/HGkKy9UIdeHlPmrwZZjvXZbIywWnsdHR0dHR0HDR6yYI9rkqGUjo5DBT7g3fGOpVx9td8/Hwa8/e11G5av+ionrGFwH3PKKXXrmOUQn8nZz/32t1/9B+WOjjWG0S1yxwC80SduwHULqGSiNZUUNsQ5QLW9KKswII4wptHHl/sjmSialUgimr2lpue1fCBTdeszZ0mnJRadLlQrKh6jwjUd1XA8SSuZ5FUgaRmJ1Dw/0JQMqlq1OJCLWEyuJo2SbiDkst0Yi3hW+FAkvtnHBe0k3toMpiV79OI8F1C8zg8bxNUSWo5MQ0Z2ed4OhF1ipJ36smkFPz7PdYKtNHsuQ0E3ksNtEAYlitFPtBp0bYI2J4Foex0+w34U0X0Nj2zUlss4oPHEq5Jk+ufyYSvaig9Qe4GveYEuS7U0PXhWgOZsNG9hl3jTWqoyVpqs0+hqLblNEHYy7WbCG97whvLQhz5U+9seCeAX/7znzzrrrHLppZc652gCB93XAYjjrXatjGDFOlojEKMOpHbQJRZ0twfCdryfB3ZGgBdlqj8BsYJtPnJiWmiq1oAX9EDwVXsnVbJX/wvSHpjwjt4z0bYiZfWsskboUAcfjuKtTMm5jjCyAyQpX1RVzzutHeLY1l/VDJAdIDMrwljc0PwkN4rbO801SHznDkyGn3AXiP5kGHQGhBnwnVnqQzBDzcdIt0U0rBo0H94GuR0Y64wx5uf+YL4M0R3KWE+CI+GOjo6Ojo6bE9z7fOEL9Yck+/YUHYcSfOA6XIn2+Bz1d39XfzD0pHXw19nMx8knl7KaH1HnN634DaueaO9Yx/DsQMfyGL3B252qvURSWPeMkSR0eSU1o49Onm4YUUcBJic76FAbXfrObwlC72MzEqW6cTVZYspJSnQmb2qr/JKSliEbL1401og70c1m1QOha/SWKA44D8iO9dsYHNFXja7LjGPGFPZl0ugkt4gPESXMPc6w1+I2nv5iIOzRN17EK3gb9mAMVrf4aEOnjuMOz+yho21e4Bua3QTk+SFG5MN+2GQczGcbk/O1L7n7aLIuA50an5Hoy3LEludEfURcF72whX4UyfqXMa5fE2YO1htxmb5ZkBkBm4rf2vqiyLCPbTLcd6yXGINi9T6Atsn3r0M/nvwHCgYdk431GjalTz/sSLj2iTP8A+QVk8t6dUvAJz/5Sfusd3W55JJLyuc//3n9eN1RA61hzoNxXKlZB9E2IBNrA7mgD2A86MjxpZ7kx5igsU7Rm7PpssEL23qfuSx9taN2WDt3gRKUyCsu1xljKmZoLUanBcZ9wYnNVvtKbQYnZHpOWtf4reGPqGMq2HNwP7zqSXhHJc/62A0Ti0yBmZ+qOwxrlhyHLhqd5EY/iurtWaMiTiWBpCY0ew61nZDpmMn9hiRLwX4r9hKHJPjjAOhKL9Gb7AjQYjhjfjYbvJErAdqUbEbTN8GQlW97wX/YmNLt6Ojo6Og4LOBDANv5kfjjRyv5bNbRcajAh55ItM8+mB46sC3lhz5UyllnrfzjomsBvL9ItPMXJCvhS1+q23Mi39GxTtGvKAcETpKjE6USlRTuIJlOq5XYq2zdPSoZCM2I7USb5APqq+F1glyPfAdEz3a8rSTlhC1hZmtD2zubDvJWZDLGE/atRIwRSyQx87hot0IMUwh5Q3xRIB/AeJrP6Dsi8YUqJT+BrTt28wVf9lwXdpgJeyRyPPlTE1612T5gKWmPTIwLPaup2Ac4tmAZI8+DfFm7Hfc0D0rIO8Jn8xXwGNhPOkA/21GMzIG1m19e8JdKRhw/5GANYnYeTNHxtWRuZNThPDVdXrIWB2Kx5uChF0l52fBC1fxGfNH2PnsQZ795LF7JNvOHXMQEJOv6oYNS/CVGzPnNCJ5mf+xjH1v+8R//sdz97nfX0+S/9mu/Vp71rGfp6XLmGBnwtKc9Tf0o9APofOd3fmd7Mp0CLYDdsJd1sX2/+92vfPjDHy5nnnlm04Eestu3by8f4AdqHFPxQcNmjjHo0c/xHhlwrL0erxmLbx5JTqBv1ZRoQ5Y3aM0l2nJtYpiKI0jZFrSQTWbm0OQn7M5hZGgcX0brWiPaKc7Zk8kVlTXrizcKqYlnurc1NbXpGCuPakfuDjRG6iDLti8GDgiuE6rug7UvON3eDbXhmPIEbYoOFtHHGHvJevDUX4WxKZHV0g4Ew3jncVPtd3R0dHR0rArcs/z939cnZ7/924/I/UHHUQw+F5Jov+Yavz8+xLj44prE//qvXx9rl/ngyfuVnmjnszljO+GE0vdm71jP6FeUlaCb59GtHycAkXhxnmjed9IQbiduxkFODoLxTX90VWdesiF6TYZS+JHRaidkXK8lUSv0VB+Ff94OHVlsdkaARsFfjqONS9q1OUYe+wBGF6/aHfhNcdWkqtXyDyHxQkcJXurajVhbSDEPWSbQ/CIfeokWhSqQ41bb65yMhxxQcth5zX7Spx9Jd8VAvND2SWV2HI2GYSollamRsxK6YlL5Ogv7lJykBqJXv/rRU0dbGyanZ9wl73YC0DSmZKsBu96Xqr2ET/Hw6ePN+iEnWSvQVGpXiD4Im6EbiLHzZZKesocf46Yt7mHHd3zHd5TXv/715SEPeUj56Ec/2p4mf8UrXlFe+9rXWthLkiFJzdPmO+xDPzR0LrjggpaEB6973evKz/7szzb+L//yLytBztYwT3ziE8tP/MRPiIcNbJEEx/b73//+cu9731tPtD/nOc+Rze///u8XHflXv/rV5Zu+6ZsGyfZxfIB4Hv/4x4v2ohe9SF8gfOxjH1MfW29/+9sHNm42xLEW2iLxpi8Ur4S2ZtqLw+g2lmYjryeB/pg2AdlwhA1q6JSgjT98y3X1MUzWhj2jOX/VyLF4k0olGg10wra1Fa93hezX2vGlJYi4sjy0UBnYSQj+KsF1q356cUXrsv4CcrkKmwOZPEeGljifuajhWx9fVboyec3+xwhOaA2xWM9dz7BYdAD0pDtnYDGyKO1VuhJWK4vcWDb8ZvqB+O7o6Ojo6Fg1uFbzueuLXyzlv/7XUv7bf+NDen9ytuPQg8+RPG0eW8cs8znxgIGtiy6qSfwHPaj6Ws0H3yMJ8g68z+zeVPEvmg/oJNpPPHF9jKujYwEia9exELy5eZOntsAJwire/JwQIFPrqWvoXgK53RDEEMZgwsITS5LzkxSJ8brZh0EVcYTdmZ1IyHuHl1p7Qpf9t6vuxNIwX0rA1057rb4digd9bDeq9xM87mFybAJ8k6lvM02fJ9hBPHGucWUfVuIYKPkTPqyGx0vzJ0IFTezBC34kY6WLgNURcwYkkuCSp0ATsfa5qESSHIwvtLLvvuTHaTFGxh62B/6d7ypqKHZknB7yJMHRzwk9eAN7Bvpmk2TQbC2Fbzr2wnYukCUHyV5VIIpRaxD29bR7EA36IVQ1jM6xBU7TMYBu/bAbdrJtEGu1+bEamtZJ6CMTStC8KR3r5zk5Arj//e9f7nrXu3qvlPPPP7+84x3vKMf7nwDyFPodeRoigWT9wx72MLXhn3baaUqej4ENbJFUH4Ok/Itf/OLyvOc9r9z3vvcVjUQ6CfTf+q3fUh+M4wPZ/yMe8YhyhzvcofzUT/2U+sieffbZk/EcVuhYt4PrdcCPv/hjnqHpOdJyGWJEyGt6OWA/ZKd0xv4DWQeZSTloTp/gL+Uk+BQmbeYYJ+LNUGjJBuIrqExiHMYiG0F3n/PTucJ4jZ+/vmjXwmWQRVYhPgmimlKdjNYFs/xKowIHGdphRYx7UWyrGVdHR0dHR8chA58f2G6DPdn/+Z/rE7bf//1+v7UWr6Qd6xo8WMW95uWXO+EQgXX8vvcVuwnk5suJaxy8v/gxVPZfX+73yhgb95Ek2tfDk/odHQvQV+9KiMQfaNdfb0R/2QszvChg6tYyaCYTYmObJDsXgfhUPGmIuRZztYm1+aTCuF/FhWYrYjMYc2BDtr2f5UD235DkYzyqp2SB9yPBPsUOt21fXyNoLrxEv8Hk9GUI8iODU3MMTduzuJ2whb5MGH9KrwE5+F7PjXEZyJfrReI99KmCPwAyUae4kI2YAzmW4AM+EKhdE1JZpcL9skZyohoydqKWPx+7EDUyrhc+1YcuZZHaXzAA5OIYww49ID9R6EehzwdnhBLkI9VrELEVC9u8fIEfaVoFSKzzpPtzn/tc6S73w6s87X7hhReW8847zykV4/5qcOqppyrONYGFh9QXxfiYx3pTlXhaMyP5WEdRj8F6A01ngSD8KHMwHdRkC34Uqgl5Z7dk91gmhxDtJBN6+nKNpt4z1CJXBC0huhKNcYMIowYVnVZ5syLUIoYWywyiVSe8iFZh7RDMCoZRdx5uJoc9xiRv0p91VnS4Mqb8LROe+Vyd0yaWjS0IeSTSXseYps5jLCf7RoRefc2+8Ojo6Ojo6LhZwIXxX/+17m/9l39ZyvvfX8ojH2mXpH5F6jjEYE1t3VqTy2yXssrPbisi7LztbaV88zdXH+sBzAdzAXgSfxEYX0+0dxwF6Kt3RfiFl3MaJa7DnASCBqIG0Y6LNhXySkpaJ59okRnrDi72RqArmtNbwtZ4spUNONwGSZAqSSKFsl91BU/31WeX3bIhejPKzL7RsOu2Z0nULGsQPcUk+ZCxOhLA2ZbgvHZSNRvMGUlYYsYkSfJcx1iC35D4UQPZFkHuWgyQ2pPy1gmfwWvx04HmNfxmM+g0jSbfVmI+QlY+IDkt2hqr06grw+ie6FcM1FYkB98R8hKBZ42YZwG+KzTZbABYH14U2CFjlRJqFn+luAxox8uhubPSEDaslo7rqulyitdLyLv5GZwO0G9frgBvSz9gbc2B1YN4MJzl1g4iwf7Sl75UT4VTxk+0LweeSuf9zRYu73vf+8oJJ5ywBvZMvxnQ1o46XicM+BmsQ0ptVpmR7JQa0Fp2hH3ZskJb5xPnhUzohNy4n+Uhjc9z2ZbMuc4CDJLgvBdyH4RdYG24EvFzIT9MKg3aYjqflzhf0ncoHsXLf7edaNKFk/3y1hyJVj9shUbbHVBZu1b2Gk/ruy2XUj/MQ8MX8nUumpQQpgVvV9qMEbYA7dw/EGQ12nioZeYrMHaBBLSgx9ksbEyj/nXSGNkOWKQ/rzmN1coNoOOhag4HZa+jo6Ojo2MKceGOwr3Wu95VCn/NyedrkpT53qyj41Dj9NPrD4Ae7AfIKbCOP/jBUh784PW1j3kk2pf7gVjoeeuYjo51ilGGrGMOg7tBv0iDQRLTMHUeaOcPmOjW3iwxC81OlPIhjgGa87xbS6ZZraKOSIMkSsTc4jZIntvzIY0khHbySLJKvreu2wfoR9e3/5Aqyde8RzdQPHWOajq/+qoGaBMLzTCYdKvVymt814XVaAAZtyU1e1EC1vUDuc38q49MJUm3vgwBKT6A6bihk+0yJ9aPJDl8TZ7XNKHnoh8HHdElmwqgzl80RLCh03xB8zkAwcvQsTB61EByCeFHNuN4TQA+stRqm1z4Dxp+WtyGbIum1gdtp6tPbGHTmoydEpCs8eL9k23Lf/RrVWPwv4Zofjy25pvG2gBPn7/pTW/SvuvsrR77uB8M2A5m586dssX2MfxIagYJ+Dvf+c7aWz1j3F//iOPLsbZClzVAo61VSiUNEGtmkOz2dn2puoGQXw5ZJnQjhhxAyEVsgRZD5c8lj8NmADG9r4DbHBiMBLq96LwJrNaYddauWvJndKowExj7BCIlupqcU2qnxu2g6eMRvImIxDIvAB3mgCWi6Nk80HVOdmYKdClN1FlT7oSRzdxFZTAmg8+cMFJtEH2B3Wpz1g5EzHFsKg9KlAq4LYmfDAyl5pF9LScHQjbkiHc898tjXhjKAZno6Ojo6OhYDlw499atL/VX0mwZ8wM/UMpmux/r6Dic4EMRiXaeaM/3tDcV7M/OXy2zbcyBffA6siDRzvuRRPsiwI9Eu+75OzrWJ/rqXQ46cU3ddjpNT/RZPT6/tb7JYSOSkMhz8qBE8mYSEwx0Gib4+BmcaHPbEYnMMRQLMY5sDOzh00ojhU60M28EjdcTodGmjjGpjjY8l83Q+FL8zKXms3bbPuQUaEoaWUM0BLwviDBDc0UDm1bHxXDwo6ah5woRK0U8o1NrbK7f9mdHBz5jACYX9prdKZiOxhayrh8+mq7bg89FSV8OOAtZyUUZofEdcVGTD3i1S7slk6CDrBdtzTk2Ei/kAeTGcnr4ot9sU3jxftQthloJojldlb14Vw19EUQzZDJ/bSGS3fHjpqvdOgZ5tovhqfjAa17zGtHGSfvYZiZ+SBXw46jIx37r6w/LHNC2/tJxp27rob3M0HSovR1rNIB+kxsB+iIe0BpMPrP78AMty4CwSaXzHGJWj+0FJGMlQkkiEreiKuu2p8Up9qJupTmnYsH4WoI3A1HkndX8UU+bqRBvXoBTTCSzYxy1XRst7IOEm2tofWtkf1HXZH5tB+hm0YxGH8UZstBG5hKmLC6i/v/t/Qe8rVdVr4/Pk5wEElpCCT2hJ7QA0iwI3AuI/MALCJcSuDEURSlylQ9SP4hIuwoiTeCKQqRZgL8KigoC0i5CEFAhVAMGQy8h/bT9H893jjHXWO9ea+99zgnJ2fuM55y55pyjzTnf913lHevd75pgRlO7PFYec/n4HeknRv3dgyT/POvFCja0hqIoiqLYCLwxcy/rRz6yte98p7VPf7q173+/tXvda3a+UxQ/SjgHO/30fixeHJCj+Mxn+g+LkozeTORbxyzbHjt39ufoFa5gHwrrU2Gxeal3mLUYLwDTGqyNPhLmvBDwWkChH4UXQxXrKPloNf144ciyIJKUxHezHmwCMSLBzJW7OYYSwHbib7pxZTJ2ihdBvbY4ShJ4eyA/+mHHOMwp2dDEzMbjh1RnP5ZqRFshko+xLWLF3FRTcmyzGT8G6nJqSiSCJKbvt1fJ8aY+xAHtDytxdQPj4iYft9GYyCxuxNF6Ir61vSkiPnLNxRPsStSb4SHbXWeFqylifkqIW42cmqvdgdiaQ4qpdVob5GclMJXI24EYgD8/YiqdtTlmqCHiYZsT+mNbRByTay7et/FJpimpFT6Avje6nMJYUSuex6DEGtheMeawDYiDnHnLyeZtNVeiDHvrz/lYnzWjzx+kNRf6xPR9cwnBD5dyX/SrX/3qI7mdIfnND5SS/Ga7ctX5L/3SL+nHR0mAr8fUn8KV8a985SvdYh5uM/Onf/qnmhe2D3nIQ9oHPvCB8eOomxLbrbNjEFI7XjMAuynDz+u5OPuKxdCxuSTWVL7QLk12ql+0jmUsm4OeC5NAC0y7aKaY8/COXg8MrKK9Icx0kfmQEdDnH8n1LutNsSRGsGj5471RzDtPY3XTsF9joMRyqwWTMfYuerZbvo6NsHg2nbG91yBGzJtz72exbz5FURRFsRDelEjaPe95/X7sfJb+sz9r7YQT+o+g7s3nlKLYFzjGrn/91j75yZR32E84rv/93/mRLE7+NtdxnG8ds4zzz+/PW75IqC/Dik1MHb0bZnqyaS9qcVZJnZM4GV4gSGDOXWFtbboSmSziBPRlx+7BCKKeMHyTPr/g5uQjbcV2/fSFOfTEGrpJfOYkG5fL3HWRxNS8jajlG/EM7McLZ8Q3iDvm6/barlaUEE62xJAOO/zcLttkHdtfIa2Nr+bpfVw0NjUJX6vRaf7WUOwgfK1oDQoymycoWW66SGxjq3Xh5/aAnnXxxhtzGuu3WEque0zspPO+5mZI7raRNB9ztsJwkUBXgt39qUhEQ+wL+qGXPXICGMhDp7n19tyXODF3rRs5vlaIJX9pDY8d40Zs7MKX9sD6rC22xx6b57hKF/u0XTIa13TxJYNskFkdPrEdLwG4qpzEN7d0IZn9lKc8Rbd1IUEexC1f+tWxK+2kk06Szamnnir91CdikjSHqX+2RfepT31q7ur2uKc7JeYVLJrfVDaNiRx9zOcSRfs0HzeGrWtWR9uLjqkk4wE7F42Gju0hnCFbK3GsUkc7dMHUX8fmRLaUyRhjjYzhzSFCluIiVz/LJNTah+mcn9XhFyLrD7XB8ZJDriYpfbjZHLu/YoRAdAOmN0vS93rYxrjJdS6MSheMEAsYPoaaPEzs5+Kq6o25LxAQWZlzpRNzoOllSpaHf9gHhIl5zI2xhPDNMcTEeZU+gWmUzra2Z4lH2ORxeRaKyZhDvoAcnXbeP0VRFEWx1/BGwue8v//71j74wZ6Q/NM/5c83WzvxxNYOO8wNi+JHCJ8Zr3e9fgX3t7/twv2E45pE+1Wu0o/rzcTeJNpZX1FsYi65LNOmJk4705kjSR1ePP1P99WPpE2ASm/0JPkC66cw6kMk/KKORKCgthJnn9IFc8EM6+ez1Bh7zscJmScOlECIcUcMjz+XXDBd7gfhn8c3tplMxftCSeF5OzESn8wh5myeJGVHXHS0va+k7aE95pgbPu6HLkbHRTLXRWyI+NS7LRZXnjMfzRXMVmskuYstMX08SowvO+wzJuDq/Bgragh/fGIOYzsgt3bYZD9gG0nnyfQu7IVY+MY6UYdNngsgjr77sc9meExHP6rLsRUJbM0x+eOLTvNDZwNEclz7w+00995VH2KOqkNphFzxrcR+0bimw13xfB4aK9qGh1cj5lZsHZbuTz82BnSsqEqKOZtEHHeCdhQj+wdz9k620zFqJY7jORb5WlFM11HJl1qSGdN4MZcs1tjeBjcZoAu/MPT+qukGy+QiDTDiGnNz6HKqQ+bGcqNsu+ZYEzZgO7emJdPLvYVLcFnEmvft/ZlsUWs1aZhVu2gR8UXAuphZWG4kLjbTyGv1Yxvk2Fm/1phhxzN5OkZRFEVRbJjzzuNejK39/u9zVUhrv/3b/arir361tR//8X4eVBSXBNe6Vq/PPLPX+8tFF/V7mB9zTGtHHOHCTQK3g+Ev0rk1zLKTCp67XITIFfv5Q3dRbDJSFqpYTDzBeTGgeJ8nPkUiq0noSeVtkoqReKUvd3tY9aJidkoMZrm3xxkreuw0gPcNJZam8eh7TM3RY8d8ISekTKYk+Ig9++ChZOqi5JVs3R58rG3mq1vCrJqTEeaxJl1l7euAGH+6dvrMgS428cUGlba5NWSKHSXa7gO6otn68UVI9OlqHOy9xoaY2yMZbHYxppKz2OBrBXvNe7KNueLaurLV/Nwev7iKPCMbsyWxjw3s2unjeZ+1ag0Uk0cctiE2Gtu3Z8SLGh8dg0zKoFbb9GD6vv+J4TKLucIVr/qX0DgUHwufnTv6nCJ+6MM21oCePhFlS6FPAfQeF732FzLiuAxUmyD++kDbzO2Qa0zra06IkHtNE3n0Y27F1iCOkUC73mTxHBbe1vHAMaCDpNvMHROu0/FlbYm8DmI86jz2sJvYZ8Im+W2bm2fqy8aK5tebc2DGPLGbzhEQZTE2VmSaYim0+inOqnjJYYKuPA9z1faQ1gd9TJeFymQxjF5zsg/y2DdpLsMkv7ShjrYJ+6tXv42SXMM/qhRvFrD7QvdhPuqKMVaSxZhqZFuvIY2kdtZlwi7bL2NxjNn90WMLzM3fa2EdbPNYsvcSTOeSdcvs4u22V/P3bNe+oXh/Gn/aL4qiKIoNwRse5dWv7vdh/9jHWnvc41o7+eTWXvGK1m5+89Z+5mfsjabeaYpLAI6za1+7ty+uRDuJaBLV3AKJ8/vNBEn2a1yjte99zwULYH11RXuxBdhkz85Lk/yGTNtKnCVm4g2eomSjyyGSuKCEYigjUNSG/L1exkh2JmRvRcmiBWNlH81vGmOaeIw5JuY+nMRY3l4L/GI89+nTJYaVhR96sszaw442MvelH/7qxji0TR4JK1X0SfRbUwkctgMyq8cblgznkc4CYouPxgUfP4gvEWTvctl7IoxCUizmGMQcYo2hn66P+GGnftzX3RPIkP1BMb0fcQLr98SSlYgp6LOfelJkDmIwB82FLyXCLm4yYI9jDmkufAnBlwhuNY4d9IwR42iO3h9jh85sp2vI88vt8YWE2SOP7c6cZTeJU2wx0v4dxwz7PR0jgErHq5elJL+wy/YcU3H8ZT0l5NkGQq+SZL1h/5ItfqhCHbpkMsdUrnFdOI0xxsy4zKpF2inYyG7EyhNIEUKczMYmofZ2yJLnfId2njf23s1LnRIeaTN3lthDGmUNNmY1xxouUq0Tco0pyzW7L9ok9NcZYhXYkybPsebjzh21YvEY3WqV7d5OqCiKoiiCHTta+9CH+psJP4J6n/u0duSRrT3sYa398z+3dt3r1htNccnAccYV7dQk2seH3f3gggtaO+ecfu935Ro2EWwHnn/8MPGybRGJ9rqivdjkbLJn56VBvAhQpyd7vDhEolCE3m314mAlQtAd4WhEx4iEpEi+2GAbJYhxsxwfXnDjRSnbi4mcGN7uiVTTr/JZcDqdbdT2fshTrduLCB+bavqiOelKkG1oR4nYSd3bJpeP1WNObgtxdTRV2AFy7COexom214MsCJ+Q0bd2JIE1htcaKh8nTtjlAkoGR9+K9hP+CuQgtz7y8BtzMXQVbrKXrfdjvblPiavSQ65ws5iSuk5pFB2zdFhzPn5Np+1t/TFvZKzD9TFGEGugCh2V2sQJe2tjg/0IEQ2rx7YyYhtGDPZB7B9kkodvsSUYx8kC4hhbZeJyiGMmk2PGMYNJtl3mF2WqC7J/wBAiy1w4dAY+9E0390VYbi9imR65dEk/pmCNhXPs9K49uo2qaZjkHvA1Qma9qYvssiowAXqfq9V7vLWD5mVN6f79injVc2OtpsfayCLWJiLkSEujuqJXUyvfFnrsrBVzsXdnegX6dF+t+jJ2Ayxz2YdQRVEURdEhCcmtNU46qbWXvKRfRcsbNLeLOfzw3l7rzb8oLk6uetV+y5SvfW3/P+DgH4n2447bfMcx8yXR/t3vumAB3KM9bh1TFJsYz5QVG4cXSCvjhY0361lz7sxUNtjS9j4vkIQg6agr3EPuyUGgr8RljJNiBJHkXIDMwh/Gi7rVC17gJYkEJH2zmcqGH9WIkSdkhG3MebQd/CSe2Y1blgzcN4agEWuVyvradiRNu3iVf77aOwiTSMRqLhTvB4qrgayebEPkEVeJZCP7Dt3uXiK+1mMlvgRReMZ1uf3vYzq6fzpXiYP74YQd9hEfAX5qGzlGXLFNEXL2JvLe7NBnnskfYrtHjN273A27sDWJ1tS3FbcO0j5FrTX69mG7AjpdgR/+1NEmurWx0UA+7tiWvSsb+tqedPHxuQYho8ZRV/vTdhQvxyw2NexH7etUDyb7XZhMx0hvzuSQ7MUGDpJVYxpzMZ2wW6Qzlk5jyJPBqnk7U9mSsYK11DyXYxrTFa4dFZKFOeM/YrAd1gmwKnGbumpOJwRS8MWDeoPVSeDuvNrOG4JOEswtYMY08o+CMcaqdXRCOtWOw3LRvN14mW9m2RcMxJil3zuLLWfSsO7v/YtZay5FURRFsSb88ORZZ7V2j3v4OVRRXIpwDN7udj3Rzm1i9xcS7eee2xPtmw0++x177MZuHVNXtBebnHr32TD2RM/P9UggTu6pKyIJm4mkn4r18ZOvFcwjERlEfBnnQjXZbfEipLPeNU5RpbMie/cBRFTZd7RNY+2k6e1FttSjrf95lBljbYYG9n74K4QeeqWY3l9GjKtbwVDYRt5W7f0AOT7D1gk5MC+1XTb2kfVjv0/3xSDFCMI24mMjPXWKj53m7fK5HzoNTC5MTiJaeiur7DKuW9MmwdgqsR0SxAidtk9aZ4yjOXpb66BtRXXE83rOH5BPbEDhrI+95iDpLHYw5u4GUQ+sj09yKTYz7F/254IdiigfC8vIeto6PlK83KdeFC9sQufm6xL2Grc3hfWlImSXzDEny/PL8aIdgRcFyowYvRKKYcVVqvMYA5+vlSwF5FLRwD9iLQKTVXp5RxBJoi1bF2VWxRi+vUQYuqPt0A/Z6kT9/PTRMpZ8umhClq5a2ByK1Zsdd6WKKNN6YIJ1r7r3OpuNONnVhLN3frbxfOQ8Nu2snXl10NCe+kyJCHmcoiiKotgr/vVfe0Lzjne0NxR7R1n1YaAoLiHi+ONY5K8sLo5E+7e/3RPRm/EWSMx3rVvHIOOLMuq6or3Y5CzLEhYwkqjxQpBfzGibXLce6c1RZGYNEqch6w+uswfF9ngeavgMwsbk+YV0VVLSSAnfnhSwohq/5AsjkWsoYamG2nh0+mmzTq4p1iaufhxVcQGfaHpjbp7IrJ9l4YBOa0Wf5iO9FfqSWVs/eul+krv92Pb2oC83sLdCX3Y4IMKWebNW5CioLQ7fMmM/YhhUETviSe9vjophfjFA3meytVpz7CIX2Jsr9yYnHvNgXHQY4WNF8wJrM5bmQGHuLkOnH6zFNvnK37qKj5g5eTx00YbYfgNraz2GbD1erCvsZeP33lV87OjQZE3YWNF8EUbfOlbN9CitaJ94G/IcsAP5+BcN4Rvziu1B8DEfLxCyMQ9JOxGv2BrE/lx1bBvI8nERSOZ19HUM+XGJn/5qwo9L/kpkjGPF3Xoc74Qu2nHsRZnaha+KyedeS+z/bu/nK7KwwzderyCa2CVbvReEDp8FzF1ZbO1Zl9f7Xpgflb8rdDUQW/JeY9MfHIIdMn8Ll1Drh14Vrteq6IOMohN4H1UEyW1HP+IsuXnEF6JGNzOdHmd9/Vd7GROld2O7aRup5X09dqIds2D7zWbUob/IB8b2SMRoi5iON9blNfLxHXGvRJilo2xNxjgehH5uzyzSXFKtYpObO/YcdIhXa4qiKIpiA7zvfa3d6lb9NjFFcSAQiXZuibI/cJ7wla+0dp3rtHbYYS7cRPABj0R7XNE+/QBOn9vKkGSv52+xydnoedXByaInP+gs0MpQWyOfFYYeWZRAMVJc9c0gzn6pkQ27ZLsU7D0hE37UzEFMYoz4MyYW3Xf4R+3Qlc6KkjquDx8v2yJJJdspjLh6Hgsh0YrdXGI2xsHAZbQp2JGERRw+srMHNx0JrYgbjDGMiCd/a8ytg1hmyzfTYQ8j4Rsx3Wck77w/kub0rVDnOan2vmyNsNV2pc04VuhjOxJvXmMy2sk+5jLiJGTmdujCJpKNYDol3wC9xmAONJkz9gok2x4r+aPCHhvspUPofvKlG/3e7bU9aL3WjNi5RBy1aRLb2tq2YedzHKXYEmi/+j4WXusYs/0cx1Uwji+XD33IJrHiNYXCcw2boTfyaweMeIaOu6SD6Gc7HbvehFCF7aSWesTF19seM9zFsHPoz429D2R32pMhtJ20rejMj5WT+NEfzG07+6/XNivjqWsPYU4TmxBYX+6UsElIvEDeSa9tQXS97uNEmbHKD9wsNGOOmkVn2VyyOLcjVT8vmxU0/kwYjwHjLxovZoMq2muxIMRA/smA5mzdM/KYWbuR8YuiKIpiFbzBUT760dZuetN+K86iOBC4zW36LVFItu8PnGv8x3/Mbhuzv5/jL2mYL4n273+/X5U/hfVxtfu1r937m219RZFI2a9iFeO+zpQ4FbS2nvSTU0MllHtzhtuOFwmrcVPyIZIyVkayEcIeuevWwk17IhFf4iAMUE5BluTpzHvmmf2svfCFzm2UbDVivimeAmpuGfQRz22VTDaZtuNkrEiUKcFq9opnNWZcZZrHV+Lb55DjMAxfYkSBSGALk2Evlfuyn+JPvBhDc2MsglE8ToAur33E5ErYNEf5uS99Smyj2I8g39SO+7bHPdwVM8UJW2AbEEvj+Riaj7SGy1Swc9uYp7aBr0e+BvGxQ0fsmLPAZsXUJhvzYDwrsX+CGCNvF/mksTROxAlSf27fGcRRrPCnNnvNX4JeIu6Yo5TFVkD7PO3PaFKzv8c+d0bXGmETx8bUFv043q3DWNkE+zh2IftPY4GOS2JMdOpO4qxyXzAO1So7Y07mfpIxvnozPFZfxiSg6Wbm4ev6MEMmN3uIeaVtMqYa9nPMEsA59BxSuBH6ETvG6hX92fBDmJgPvsgik/URjypCEy0iaunR9rIey8aXfCgZ0JsJxc+DTAYMl+za2wuCQRavM/lQr2W2xtQGeY5LZlUURVEcjMSbbX7TXQ9+SPGzn23t+OMr0V4cOFzmMq1d61qt/fM/u2Af4XlwxhmtXe969sFqnQ9qBypXuUr/geKvf90FCdb3zW+2dv3rb971FYUzyYAVcyjRuCcl9XiTt6Kr6xK8DkwTjKqorbibHkjyIuaFhDpi0Ybph4mRUAWXK1kYEM8LerUj6Yi61/xI5SzOAsa4MRG6JD9cZnF1Cxmbj2IF2j7MB7vkH3PUVaC+bTTHDH2XEVM+FkN2VhSOeU/s6Md8I/kVvygPSkQTL4/dVSN5HnJtf4ohG2wZw5raV9bWB7Vko7F9XMYZiX6TYTvmjB3Fj5+YH7XmZzpEIUemOSX7EcuQvbV37bS2jUXZs6vb4Esbwk9jUxC6L0W+JmIc7R8ZuBw7xrQSPuzz4c84Zic5NuFnBUZ871PHMaC50LdiZtbo9lP9bluHNUXMDbCTn8Hz4lDb53p+pBi0cY65DUwesfgxVpoxZrF10H7Vg7odb+vYWMCQJ/0yW1C4HH8DxOvU3mBTiKuX5xhDL1jXquNZQVazbPrIQxd+czGtvcB3gWgeN5jbrMjC0eshWjjnRddFX3JMR19rLnm/Zbu1fBbZrWW/DPnwsMR5X2LC4i8tYOFRKjY61qLQ+zrPoiiKYgvBVa8f+cjyezpn0HO1Lx82uGq2KA4UyA9wlfb+Jto5n+DWMZvxh1ADtsUVr9iv7p8+p1nfN77R2g1u4IKi2LxMs1HFKuzNelXSLmMvEPEaMV4sXEafZN6qU8aISe2nqPK1tndHQ/7UHhOUBE3EuLJJScsunD0OGYyBZhxyqNmZjSdpSa726xitjESR91cxiadkLTLuzzuxH7oFKHyMEX7YWpsufh63q+1ByXPWTD/k1NgZxEMWa9D4fY0DN5Us2kFsT4piYDA1MqS37a+kb8Q2uxhPTzfi+DxIEhMv5kU9rqAngYw9jMnNfAO2hcZiHLfTeNbmGEOk+SO3ju4NJ6GVBDEYM74UAPyRj7UYSnb7OKFDTYl1xJo13y6RXSTDtYYUI9apbYcOP+yoI4AR4w4bK/iQmAdMpXO7GFxda8c48RwZ8y22BOP54sRhAPk4Arr5+Mq2KHlNCT3HXRx7gQ5B10G0w2c9cryl9oxrVajDZ7haQ+N6N47nteJBfLkL1px9oUpfgllMiX0ckRUhc0wlrcfT/bfzOhcxVWt86t5dSMQcc5oaM76qVfR3NFP0/0aPtWiaI3zgfdLKU9UqLN66NgOzHLGd5BxN6j52lyyYssjzXpQCD0mOG0WH9eitZqqJsSIm23HqiY0K7S4abGQNRVEUxQEOL9p8BvnCF1o75xwXXkyceWZrD3pQayed1K+AXesNAt3nPtfaEUe0dvWr9zelojgQ4JyaK9o//vF+nO7rBx3O47/61c2baOc5ybaoRHtxEDDJTBSrWPQmLZE9RB2nj9mW9pwvNvSR06VvRRU1DUg+atqDEpOmn85FciMSqhBhgrkXsDxGkpOgynZzPpDspYvBPA1v8+gn5yZXgtTLnt1tJeac/aytH1WdbqNpokxohP7CS1JVIuvjD/iwfmyGzIpkBt+aamxK4O0YT7U5SWwPGivZK+lGwx5k6zrNHVmswdtRGDdUwuJqLjRJ9rIe92G+MWetz0qsFxttL9PH+COpRnG9BvO2xqFvRXNhPJPp6nxHx03EjH7Ss27f7krExZigNbofwzBXZKvmYEV2obOCHfbEkw2YbPthszlxtbrioXIfzc1lEPs7YkjlYwt8LNbYVjGWg32MUWx+5valdm5vBuz/YcNx4k1Ano9F6b2vY28SK+s3SoyxKtYyIn7Uyc/H7pXLh3k0DKnWGs9sMR8u9L2jyh7cfUSZxgw70LayOr9+LmOYzGIN0RL3njh2JVXqDoZ6SZDwW8CiH+dcRt7MqzBdVi9qLxpJc87GBt2RMF/sNHVZBXHjy5QRYplfEq4XN7PYduleEKGdbva9GbcoiqI4APjWt1p74ANbe/Wr7UXcXsXXfJPcAOH/9rf3i5A+9anWXv/6yblDAhuu9P3rv27tyCN7or0oDhQ4v73a1fpfXMRFdfvCt7/d/8qDpP1efGY9oGBbXOlKrX3ta6ufy2ybuHVMUWxy7Egv1mS8APBi5kUiexgqk6FS3/Xjg4D7SI5PyGVs4qRXzNB5m6L4tqsWvaAilx1tittErXjUNu7wD5nXSuha22pO6HWLmJwctb5O0sM+/GE6L/Xx9XZOho4EqKlIpELMS7dckaDbAnKKkq8+Rtbx1wD0Nf/Qs32t5oXaRbJFFjGIh28UxBGHOaPPoFexB8U321hLnhdXhI95WD/s+wC9T1NXq9OmtoJNJIMjEY5s+Dsx7tyHzKgDBjCwiyu92daY7drRa8VVY7ZWZJqDyUNGjOm2gOFvJeaX5qNpYjMwgfpRG1HjP0r0CUDHbLDTeq2r/WyM+WFndRxvwPZEj5yH3fFDKz6+fHy8ub8aKDY9Oma04+fhGGI/xzEX6Pmf9n/2z7ZxzEQblV43JvHkn2oxsVkTxunVINwt5jbNwQSymSVOB2E7fc7mKWhbZMEEn0J66JhP7/Ur1VeFCFMpulI/YOpzDPsx5UjEUyG0/9iMsNboV8X3di9d2+O6DCJ4MO1O9Hn+Y4wFDDljQQi2hcBx+VibEQnk4Wpl4iWG3kOrT9tKxAtf1S508wFSN1+MKUPvR/JckGjGWJlZMnw6KjJvGMwtb4M53C5HwDQOkVgXj71VFEVRbBp4DScByO1duKJ8f+Azf5znkFB81av61exPe1prv/d7rf3nf7phAtsvf7m1e92rtfe+t7X/+T9bO+YYVxbFAQAfmK561X5F+g9+4EKnfxCaP/Ypi+Aq8MMP71eEb1Y4T1l2RXt8kXCd67igKDYvlWValyUvdJlhkmzjjJJ6enYZJaMz1mwYuOGyF91ITIrkH4mhMZDphl2KpdrbnnScXaFu+Jn0opPs7mdFfl3fT7anJ9ymm/pPr3hUf2JHkDCL7RPrnYtH2w0lp41vOGPfqy6nTjB2JHHDh0QaEE/F1hgJHsmtH/Mb47iP5Mi8P8ak7yX8IcagzxssjJhGxMtFmE8k7dVlPyT9OAaMsAm9CtsSG+xNzzaQr9vyRqiYBvKIEXayXUTIY8yojfgQAYiHiTU0lxAQw4vGscK4KszbRGNNkPxkjg2N1B5xXK51+FyKzY3v0l7nYwi8H10aiMbxSz21MeL4GnYQtq4LsJk7Ho3wW0sHU110MUlmC8mu0ziwyD/MrO7TiIGysbVzN88JFo0lstM8qzQMvsw8y22sufcfdHPbdFmQTt7U7qz/i1Ywb7sAd19Mf99brO6j5THDLtsv8u0+plk0YWfE8sZ6MQOFNIPh7/WUpXJXUM1izFsvWnOQ+/JbdwcURVEUByTf+15r557b2ne/2xNl+wr+T31qj0XSnivl73rX1u5//9aOPrq1N71p9RXBnBe87GWtnXdea+9+d2svfGH/8cmiOJAg0Q48V6afdzh2+ZLoz/+8tQsvdOECuAqcY/sKV3DBJoTP78sS7dwW53KX67d/KopNjmfRivWJFwLq3HYknvR5GCJrRGKAOs4+1U59QZ9ErHeJmxOHoFgUZMRGuIB8K5CIM4wjHuNRomsNJUO7vifeUdK3OtxAMdEmoeYZ8h6uyzK9r9iK4euj4BBtihKrLqfoik2CAnKrNAjd8PO2kuOpAPGQx33dcQ2dz3u1n5UYMpLPkpkwbCIJnOeCk7Ylxdrar24Tdsg0Jysxlmon5NpGLosYyMccTDY+fHpsfjiVK9khxmOeBCIu9ppfBAZiuZ6Yox9+PgayqLUu70PWKaGddKA+8/G557FiPbE9sz7KmC+1FcmjznqDpr44QW9F83H7Yotg+5LjRmUmGujYiEI/issC9Tl+Msio3I74McYyPO5IHsZzL47tkEUbqObimrcd90M9bKm7bDDpR3c+eel17uaxKTFPI8xEmFkdX6TGl6oDmupbmVvHTNwbTvZPYuHy7pftrI42SM//eRn2TKFLZzpaUTKzkFONEdvEjGL4sOr9vB2i7rreZ3YYRm9IBhFnsKiZHQy6c1HW04N3VdlDn4f9y3JqGuhl09E6c+1FX8sP2bwuCoSN2l5nGEu1FxHCoiiK4sDmrLN6gpCrdS+6yIV7Aa/3FJLkr3xlax/8YL/NRtxvnST7Na/Z2mc/288JMjvsPOd972vtV3+1tZve1C8UmnwQKYpLE45HEu0c43yZlEH2nvf0Wy/92q/1W6csg+R0JNo36zHO83PZrWP4i5XLX76+KCu2BHakF0tJJ9jW6W0YLwrIrE1/yLwOWdblBI6Lhi4SuxBJQI1vJeQQMaSn7t1V8wuf8SLssjzOFMld736zk38/mbbxIzmu02bsJPd+IiSh6wl7x2Qd97fWygr3Au+xh54+ySZvS65ibT5oKQGLDgNihcwKhH3I5j58uY6ibtRWlIylTRyzlz+2Hldj07dCuJy8VRxqTzBTy37XvB1tUGxswp6CvRUS4UPmcmrg1jDDx+XyI5bVgEwxQu++2m/I3A77kBFXfsTHppt0exq+/ejH+PEXADGG6cZxYjbat91ARTIVYlqJQdxX48Y6xji0zWbIvJ/ts3+sAyR3W+mTvNg6+GG4itjvvaP/vZlkqlKd28uY2oTfVL4esrMSr02KYf9320Mcq8YIZ2bzS53ZDDzGcMomI5AxUWsKdLAZr5VGchmxA++P27qETLW3oz8HrwXx+h+FB4aObeFFVddFH8I+o6F8G0XcuU3rY0a/1yZz26Bvi5mAlsZb2O41hEv0O/O96FLlArneiEzT9AK5jnYm5krhGaB2MgydsEZSCdbNoRl27HYV2i6TnzfmYluHrgo23oaIE7KQF0VRFAcwvJiffrq9gNs7AIn2ta7IXQsSb697Xb8innuzk2i/7GVbu8pVesKdRCU/lBiJfMZlzHe9q7Wzz27tkY+sJHtx4LLsinbaf/d3rd3kJv3WMp/8pCsS+sBkZStd0c5rxfSvX7iinUQ7t8cpik1OJdrXIr8IBnMvjPbmrn6WWTvbxHt9litBmPpK/IXMaqCf6Wf8vQ2y9XaALOJEX3P0WEMeY0QA7Ho7JH3iVpb5Ki61lVijxkZvQo1N6apZ34tE2IeBEToSpgNk3U5JDdqMo9KTtV2fErv0zW/WDnwsksIuV0xisX3jg5n7R1yhs3/62PuY1CMR3s16xgMdMorrGYM3z5HEtjeWMXerhbflTxt/q6WKfirEYvyow5Y2sWNdsvU+ciY79j0Qj/jW1Dzz3NC5XqbuR3+sP+kkj+JjwRjfS/h76fvWCjol+N1ec6Xt2y18iKdtOR9n+CLXlxeuZ9vH3LQGK9ofpiu2Dr5rRyOOySk6PkJvJY4LFfTdTP3ecB3HULS96Dg1os5kO0rIprVKkgUTWz26rWtGf6C+C3JNGce/y6KWLl4z6fLQ1aqR8y/k1GwHfPy1BP14XaGygpkerOyx4k09UM3sXedttey/Xr6wkV7aGXKwwhysJv6QGe7mJujxGWqrfUz3V5ti/7LvTO5+lPHQGTpiqdFlFI6KaPc43SZqzS1k9qDtFP34Z52ooa+323e7XqCPx7+QzdqhA9X8t9LH7DYUiDY+Got+shvzto5s3C5seWWVHbVse1/FbfAP+9D3fTWzL4qiKDYBXGnOj5CS8N6XK9r5fPLRj/ar03/sx1r7m79p7UtfmiXYSaCTiCQ5d/753Yc3k098orXHP761n/qp1o46qsuL4kAkJ9oDfSCy8g//0Nr97tfa7W/f2jveMZNneI7wRdNWSLTzFyok2bllDsR6K9FebCEq0b4R9OS3F7dFCRuIF0IqNd3em70gs+K5XAmVpLSaM02J3IZuTgSF0zjbJTbtGMPbcz5SeJ+mPRBmjG/0LMa8DcUTlSMRTpGBEbdNCbtIoIaN7K2vNnJfY8QyeU9quC12IzGKX6df3e52XcDDkOkfMvU9NnE8WS4d/fBRa5tNZ5cN09emJD0JXNEtWEvXUZsfNclb6WM/MJ7VjMHY9PkASJ/b0eSkrrYPdnKwtvXHNkOfkr2Y0Kago0hutfyIGzFSMlzJZeJQu4x6l33QJWneJ9tlO+0DrIrpGIc3OearYnKtwdqhZxxixDyxUY3MamAt4S8/0421Wxnz3+nb32y1DmxMprnu6LpkoyI9+8P2hcXrMitaN3NjPLeLGDa3FVvjCvuCsZgrc9PcmTd95sg8zKbY/Oh578/PvE85zuirtjJkWW4yjkE9R1wG1PSJGz48l5FFHTahD79oZx1lapv6KwwTrxUhB5Y17NH11zTJ+I8thMx9u01Xjdcg043XQ8yIpXgu8PgaI8WVmgeQvXXMXnXMFT9skkyvpdT47PYaMx5kqo6ZR22VhwsbOnPztEFGTC/dz+XuZ10rnrS1sRkDTbdhim4nf+zc3mLtsQfsutwLBlbbf2bQ7RVvvmArPY/Rt8IRhqz79LrbWj/ioMcndMi839syslg9vuzwkpi6y6PvUx5lxNQ/6l6w2y0dPj3uiOV6ZPoDi/BxOevCF79dqvFF1+Pgg82uLLMIapvMXrF7XOsTp28nszEl4xdFURQHOPba3T7zmdb+v/+vtXPO2fdbx/zbv/VbSpBwJKH+kY/0H0Uk2c551vHHt/b978+Sc5xnPOtZrV33uq0985mtbd/e5UVxIBKJdn40mOMdqPktAm4Xc5e7tPbjP96T7pxXT+FLqB/+sN9K6bDDXLgJ4ZyNv1LJifbgK1/pXyJUor3YAlSifZ8h+0EJ8gmhyeOe1fFCGnBmGXhSeEayj3t5K4bVup+4t8NmZCWsPfx6JRDJRgZuh4I64uViMiVGTa+50aDKNtaPeYcPJYi4mZiDMhjExx5fBZOJ2mPNbp9jjTGTvgvUVg+b+CIAtASPQ1KVsTUH90eOCcX2SyRquiNCbKwosdt1aLu8pyAiYd8T8qQP7J/NoyeHra8xTEZ7xGUO1sc+ksbMTb72qMSxzzdqoI2t7K2EjvlEXzZ9zjrW+KIjkszYRiJcevOxMUctf9MxzxHDdMSgreR68g37SGDzoYAPvTFHZHwoILEfa8GWvny9yD+PYbW2MXMwufYdxdqht6JtbOP0L0woPqbGspova2RH8p6EvfdtPPUtft8/xaZHx4vtd+B1II6fgT/3OM6DYeOybE9bx36qdaxgQ9sLRB3jBvgFYb+sgJ6vExmVN0PGa4za2I+5pdKNRltV1k/9ZGT/tTyXuZwvq3hd7HPrehKi3ab3ZUrXbPrcJLYSbXvoJi7rfSFhbzBEb/FgfRNoEyIgtsu6D7UV7/du71MNUpv1aX60zUjm+NP3x+4701GrIEWW+2PsHpWpxnRBNr3Z2/YQsrGpQ5eKbfFVsij48U6ivj3MdH2u6AFZn55tM/XdJ4o9oOcZ02PO2t0+1bLr+0d2VvekeG+z/pkcP8b0cU0XcWIulPDVmO4T/enciqIoigMYXrT5rM5tXu5zn/65n0Ti3kKc005r7W53a+2Od+yyf//31m5zm55k5zMWiXYS8CTnsOcWNf/0T/2+7je7WbcpigMVfuTzyleevwc7H3T5EVR+f+Ba12rtlrfsXyZ9+cv9GM/wBRaJ9hvcoB/rm/V4Z94k2skb5EQ7f6nCawfbqBLtxRYgZSWLpSx9IUsvgLwY6kwe2aTNi6gyBsRBlglb9I4+UHhb9pPxEXEmSnwlIjklDdxWyUpimg3zjxdrZOEL8UJNP+SEoMYk5jZ8ogb8GAMHI+TMP+xDRkVbCR7i+Zxl433p3Z6YsvU+NS/IoZ7T4Y/O+opjfRhJd58fYIOeNWuO+DIXYrn/mJvVrAWIrySy21FiPBXTZxm1xvd1SOdjhX60Tc0XKdqXyLGzgg2FxDQJZvn4PGTj/sh0hba1qaMN+GDPPLT96HtszRkbxvYYGs/eyNERI+RKlrs/E0aOnj5xNV8vtJVQtzg7L+jj095Bkt9sibXDPhxT8Kcgp5/XgA9xKOioscUGneZB28dCr21DDMZhTBs/ticlth1EUr7YGrBfdXxOyCLpQ2B1HH+D0Ic8apq0e3M05nwN2aQS/lFijlGmPnr+T+R6nYpub9NRUzraVnvbFVYUpPe7U7eLMnQJ6aK2MuJTkSrt9RgrajR5LqpD1dsxXNdbP/C+4kexWD2cHoaMbvex/2nsbke329hj/xd910VM+tjPxqBGlu2tMKaJSSy7utdD5nL/RzxeXSSzh9DPySKW67KMhy6LpPTENoo99Ho2ZpSh88I8c6zoS2/tuBo94vSEuY9nD7TQ9WS7F9lRd93cHKJvxf5b3W2IOxuv2+axeSUOObJub52iKIriwObb3+41V5Zzte3nP9/7G4UXez63/8u/tHb/+7d2hzu09tCHdnlOtHPrGJJx/Jgkn+ff9raesONWM3G+VhQHKhyjN75xv/1LwHkBtz/iecNfc1DzFxxrJdpveEMXbGIWXdHObaf48uz61+/P96LY5NS70lrwAqcXufRCFzKSdAsJHyf8lcy2F1P5R+1lvJhgY30lA9GZiBdgJTStxkwJYvScllo7ksn46SzVbSMW/sSTjY+rOVK5nlo6isnlYx94KMQhrvCaD0PE1JxMT1vJXeJYiZhA20yEauKZTLZWE5M2MRgPG+ldR5yIDxEXYr6av9dAzXZQTCuxrWSHjxXkjKUEt68n5q21WBuDGJc29uarpMjcWO6ntsWJmrhWjyvWd+4wd/NHNrc+rmDnimviMI9eVmw76+prs+lXY2Nj9sEhh5rMxmB/gK2hxzc/fIjLmL4NdLU8cq0fP1sbx7HPT1eHX3RBrxXHCmOanXyYj8eTzc6LevH5KbHNm2Yk0pFtO7Qnu5GTqGdcYlGzfs0BudXcBsbGl/0F5/YYxGK7kESnHTU+JO4vtA/ciu8yinyYB2Oab8zlorBlHGtfcE4fv9gC8JyzfT2F51d/cUiFymuYtqNgq4q4NBzpo05yCNlUl/vMM/ejPWT++qJC1WVd50jkMv5r6TwP7Xk55F1HTXOQdSLG62IX2Ty7IOJ51d8OvC2kc6E2VVfO5u0yNaJpDx6oSzvJTP7qq2P/2Wzm0+UIYoyOixRxtCkxjnXoqyt76xNPbe/T9jEQdp+wmckRIGO5WjK1VLR6n0fq4R99K7KVzh6iH3ah02Nv97onpbtuBpKZz2w+aofcHqI95muP0bdXx5H07v+Sb9hYA7to5zpiDVu1LR5t2fQ2RXOxfk/Uuz0yK8hm8l4XRVEUBzC8qJ91Vr8/O1fscquXL37RlRuEGPyYKredufOde6LxpS9t7Sd/srW7393OJTgBM7gH+zHH9EQ+n/Hf9KbWbn3rfvXrOJcuigMUjtFItHPMA4ll7kt+jWv0e5Nze5llifb4kulWt3LBJoXtME20s1b+WuUCO0+PRHs9p4tNTiXa12KaYFGmFZBNXvxWYfrpC+QictKFZCDEuD17MoNwi5JJwFlqoHgeV1hbZ82p5DVMYw4bgzmpeAzZum+204shbasjfsTV2NaWabKhkATuitRXx0p+gXX7YevziVrzM1/83aTbU6NjfAq2bt+VXlkd8Q85dF6m4jEimTuKxxz29N02xsQOE/TMMdfMN67UDj+WrW1ufoqDrdVAQtoT1iS5t9k/JcHl74WYc34rSpZ3uZUu6TVJdJLlSSeYMz4UzcV1jE2yGjljRQLdx+6JeZLe+NjYY23WJ6bJ4vYtikHSm0Q4SXPslfifja+5kXTXeKYniY4BepLyStBboR/J9tiO6EKuudoYGpfY2BEqrbnYvLAv4zWIfa9jzrq0IY6J0Elp4BO2QNvtRlIVws/i9NupRN+L+4wC0rkvhecEsiB8o01cZRzNlr7VSr7yGqMY1nYf1Xr9s/+m457iYaM42EZRXNfFWFT0aZqqx+pFY4YNdcRAHfPAPsbDNGp7mN2LnaIgZooMlxWbnvc9puJZ3adhbcK4jKSrtomBRvasBTltK30siTSNvilsDBMoHrVk2HhftfvIv/e7X485lkexoaJNbaohIxZTpA8xbtioeBDaWU7btsIsntdDbw8RnzZ4KPnx6kVRDFPwb8RM/d1q9VhztdpeZMc27yXm1fvRnq8p9ko6bKIQv9+TPeTdJny6fL7o/u6ul60V+18URVEcyNhrd/v613uSnQQht8DYl0T7pz7Vb53Bvae58pf7NH/gA/0K37iinXKLW7T26U+39q1v9WTkiSfWvdmLzQHHL3+VwfMl4Ap1bpcSf7lBov2yl+33Kud5kfna13pintvLbHa4PQzn4nEbKCDRzvq4NU5RbAEq0b4m/sSnIktAUd9q3U/cFCGbIlfXq0Q7194W1s61zvJTYka1lZ4tsIKf10owIUdG28Am/O3/sIdInA69y7EZxeUUxadt8hgf34H1VaEnJm3so5g89EPG+GFv/Yzk2IbeauG+JE+VtEp2YTsSrR4z2+jMnWJtYkmOka8Pf9lgS9vK0GGLDntELgs/5sPY0vmD7L0Iq/GJQsJI2BjYRHI6MgwRk/iKg263bTquWMffas0R9Z7eRs6xGXMiCU3M8Nc4FpOr05XUtj76SEYD+jGeFZ+X7CNW2LN5GGNXv1pfArOPq/ZXlYiBDXOQzGr+USMjwY6/JmMQm7Ljol5feJ61L1QsXV1PjGFjcptbj2V69c0PH+TS8cUCa2cezLnYGsQRE/vUj2Edy6ktFf2osafOBVvXc7yO147oe6ws8+fJ6EO2oQRhM6fvz5HQ9eRsb1NHQlkP3u/6FTNJfeZOO2KobWVujT1OTzB3mfzBdWHedT1WzKGPhcr9Je/6bkA77Nwn1Ur0EjPZ0+xd7PqaZCe9z8seIpGPDoewl44+OrOxyuRmMjazyfpyzKbX9jjTRSz8tQm7LuoYR3r8unePOepeoo3vaHtfsdR2vfpuh161F9rYTov7RTz5UuxBa0my2Zg9tvpuS1uJcOtHchu/nix3vVnTjoR5l7mtZF1OIbne2yHr66ImkT50yGIctV3m/j1uH6MoiqI4gLHX6/Zf/9WvaCdBSKJ90dW4a8Gb7mc/269WP9QvdloGiXWS8mee2dq55/Z+3Tam2AzkW8fE84O/4uDWS9z+iHwLV7VzCyaS6pxXZD7+8daOPbZ/CbWZYZ1xRTvP4eB73+u3x+GK9qLYAtQ707qQSTR0Zq7TS++nDxC0cxm47xxZ7yhZ6rbKCngRxKTtdQ6psazMMgk9luzMkKJYyFyPvRLnc4HMjyQx9tgkneZmhL9iMR9sQuZlyBjTiubiulgXNTaAnKSw5ul2wfBJdRTiag3YeVzpws/0eezQa53WnovrZYwRtRfijGS1x9XZv48/7sPu4/GmOPw9FkTyGvK4UWK7oSMW9tSAG3FJCud50mZuvFEp2W1FduareXisXVzFHfqkI47mHXH8am9qbCD0FI1vPuhY/ojFfKJvZazFdIrpV6szBjJsYi7MjRgRR/sVfyvo8dP2xd/icBW75uc6+VubctGFvc+V67qlDH0fBz+tkRhWrL3CDzYxn2ILYMcLxwPHTTw3Qccmx5AzXmusrZrGpA3WHFEkTzqQyB4oMW7404+i547LF9kPO7fp2UuXr5g7dshT4l19HjyBaqV3eZjVst/tMbrA217k6ON4W5tK7S4j4RnmJKL7X9B4LKvE8LfarzYf4akVs/d7nN6WPnxdqX8+B+RI1fb1u5lqkumyiL5i4UC/63qfh65gPZHA19qMEVeB6Hdramxo+xK6zGsEIQcfUf1hQ9tqmmhmcWa2s9L1mpt6s/jYR8zu63IV+4fc2mHf/VnjzI72iKfi4+U+sa3Dq2Lo8KOfx5iLpTaJcU+k0zZ51OHT9f1Kd129TpG8F8V0efhbsyiKojjQyVe0c1X6l77kbxobfBHHbppo5/NalAxX837hC/2qea6GpT+1KYoDEY5TEu0klLlymw/EXM3OvcnzbxHQ5nZMcS4OPEc++tHWTjhha3yxxP3o+UsUtkW8VvCDykcf3a92L4otQCXa1yLufy54E/cSyeFV+AcKfbCIsh4eUwkZLxIzzmQMxU0y2Xs9iDHz+PhEO7C+TKhpRFz6xJPS5+BtlYR83TbiYKLkbOgAIQWQ07a42TcTvqqJFe2wxTfagbVje0mHj4/P/gr7kGUbxfEiPW2qkBuxeebsaOY3wYgXOquV6YiS9CpJNxJUXs8lgENuMmUjrK3sRySiXa/tbgU5feAe6bG90M8VZB4n7BWDWMRGTuxkEzoS8mGLPs8BHR8OlFGzogS71YrvMrXNRkl2sycBrvWEn8WjDnvNE10UkxGXgl465uWyuFo/fEjOxxcJjCM7GzPmUmwBOA6ncFymEjJVyDi+qL0/IO0oYS9zOkM+STbtB8tky+xFkmPDaw8ia3c3PUg96kGS0wzbudLlNOdtXG7Pta6LtpVh6i3Xu6E9x7xNV6reiLnqn9cZmSHHrIvctuskS52QRXzh8k7yzSaTdpRILKMechn1tmr69uhdk+d2b1BByOZsQqb2vDyTZfPt8Jn5QrRH3xpjnNyWdr7Osfqu631eDSndH4nrk/2s2D/peum+JMqtb2XUWR99dF5Hkl6+bhc66qIoiuIAhhdrEu1xRTu3euFe0twGYqNw4QtJtrhNzDI4z+NqX8Z817v6eDe6UZcXxWaAW8NweyTuy865LH+dwV+BXO1qXc/xz9XtkWjXhyEr2H7sY60df/z6f/VxoMPzlTVwdT6vHawP+O2FrbC+onDWeDcr5uBFgRIvBqq9DUrYqOFlisnixXJOH30vJEZlw3jsnsmHh0Xjjn6WW5uieXuieeC6cUprxJcKsU5AhR0v7kHERamzZtaNLGJZmdtO6N1GoLM3jgC5YsSbScQB+t6WDr+kG23ma4WEasg0hxjXito+rnQxh4hhhH2MKT9vUzNPFeaabalD7u24NUmsC5vQx5hjPVZoR1z6LAmkdnv0JJNJFEdSOoh95KZjH1DPbQtj1Mis4Mt4HAOK6b66gt3HjLnJ3mXYULT/cu22w8dCYk98Et0kuUOntWNrResyeVyljg3bUVewe+IcNH4ujEEsmzd+svM1x/bCbmxvgzmQ3OcYLrYIHMQL0PHg+5n22OXWiGxgFImj301E6EBy72f5Wiyzz/KloVhXtps35Ok9CLNeub23aaidZGP8Xokcf07eS1d7Q+1ejVCSuy4Vqv7Q60jk9vZodlfvwwiVZFOHkfSVuLfVQoe8d8N8ZsfLhpozu5m9P45+r2elS0MH83orrswyjUWdmI7RobXA1guotgdqEtXqer/3ZjVMZblWsYexX4zenm0biLaeOtT2GO0obNbexnemlzy1VayT5So8eLsoiqI4gOEz+De/2a9oJ/HND5aSSMy3x1gPkvL8tSlXw693tS63nGCcv/u71u5ylz7W3AehojhA4Tjl+OaKbW6vxDnxaafNbhsT5fa37wnouK0KzyNuJcPzjC+WtsIV7UBSndtORf6Av2q5+c23zvqKg546ktck3rjtya+zQD4wpHoVocPP2+o7kcyWnBeVXFD4C2yQfUXSDdssg+jja23FtnYk26MIbKPtNZXmmeNI6PLcp4YkA+xo6kdFTT43ntfMS6CjTTwJOnk7jHGpPB7d0faCUG9S2AMyKq8BXfSndpqAtdHHi36OoTl7f26yEHJD/vTDBl/XMz+p+EIFgctjLMYlKRxfuMT+mwO5Fb0RoWdehtbuRbhdzDvi5G00ZNYnKT3e3LChRu/xRqzEGA+7GIc5eVxqYlIr4W2FJHfEka3VPsSYO7Yk3COZHmuQoduErbZZxMPGCrrwlQ55ZtovNje2v+N4iDrv4zge4zgJk2EzOR6IgY+KyzJD50pqfKJkwibLp765DnKfpnys1mvLMtyGYuOtslQMK7EdAmRML48ZuIzHSFzL2OqclB3IsDdX40lb9+svx0rHzlykW0mbC4do9UaP0RujneQxz5ArhLfznOOKdm3SKKLLmUOIpPbOMFvCnI+XTCwt1iOsOeztQbsDeWLOPoEcTX+lW23V+zM5dawF1LQH/O2Vt+snZf7q/371eZfHuDNk05tzOtpAH79u1+cVBexVe9gWRVEUBzAkyEkIkmDnVhBXvGJrhx8+/4OP6/H97/c4XK0+/QyVQccVwdyjmntZP+5xriiKTQJXa/NlEX/BkRPtAcf49a7Xj/P3v98+DNmnIcrb396fY/wY8FaAdXIbHL5AiDX++7+3dqtb1RXtxZYhsmrFUjgbjNO/RfgHgpG0DZAv8nP7OUyGWGf1doo5PmRM/elT3H4gZy9GzHfE4ZQ1xZLeivQUa4810k72WpfZKJQ9yI4aG6uVTJfSibb7BWP7mL/axGGsGAfbsPcamW594jYDxqYAuuin8ZTADV0Qeo+nNThj/W4TV5jTZ77TEkQMrv6G0JNcll2aU+yPsX2txLB5TpEEG66m44rumEso2DZ6c+KYQY7Mx479hOnYFmOw+T4+zA07YsY8gT5veNhr3q47xD5Mh08+BnSrFp+Txo/CnHyekczXWi0urjG1GIPxiEE/1jLmgA65NTUWbZdrTm6n2IxrbeYFkXhHfuhhXVZsDXScAAdUkNpxLOrAoY6S0LHjcajUjj7HWeqD4iWGb7LJIA9d9s32NNVfsSHDPunn/LyG3J7DFJ7V7EntoCdOxdg2BrIYgpq+28185xnm0TAigdrbi4nxqac2QzYULhiT7s1IqgfhJ1FuU0djSNKEF5DjDpeMuY/YXvaWZf5qrxEwq1jF2AdeAto5diS4IbaV2layr7+iiqjj9i5hFfLM8LFGjhHkfrRjTPpT+6IoiuIAhgQ590rnanQ+BHDv5ctcZv6WEOtBop17Vq+XaAdusfGSl7T2jne0duc7u7AoNgmcA8cV7Vyhzi1i8u8MUFO4TzvHOOevPMdo3+EOrd3kJus/RzYLXNEeP/pKzRd2JN+3yvqKgx7PeBWL8Q8Ic094XgBts1GmYCa5+Y0PF+6rM1qSff00dcRWvIhvsmirSr65BsWjPz2VpY3OfNEr0YgojTtdj+aQ5j1iUwzs1bUYimedSFgqoQrEwQ+5FfnQx9F1Ann4GJoLspgfhK0R+jHnFGfOjmSvEclZ4T5jLVby2JBCDJt5oa8R31wYx0qMG4n1iK9kstlp+rRZAwlrmxuJ6bHdPJ7imB1+h27vdlz5DeEvW19bxKHEvpPc4sY20G1TGMd82VegW6tYmzHJuARaE7XPSzEk6L5jHCeS5ciIyW1YFMPHQa85eDxhsu2HtXaYfQAHZXyw93Fo8wOm1NsP70XztBgUtkvsU6o9fmW8rsQn8W9zDBva/Dmp5m7jIkeGHT6xf4otAseJ7VcdsxP8kNGxeoh14rjmNSVeX4Lwt1pXPud40UcUvvG6FPVaDH8CGOGTZX5M6nFe1MFFbjGeKdHL39sL6Fdxh958cVfbHdgu4N2Y27Y0R28ZEx8wO9mGfRDxqKPtqJtkU5NZot4fXdmrGMd6Y9vNIylm1kjWaR3M2ZtOWq7WMz/mAqRYpp190QC09WOy3l6Psaxk3GMEXRHqLu892vNrm83FniUDZCEHXA5h3VbnEsT3Pnk7Zebt+y1jIKaSa0r2Dx0QfxK6KIqiOBCZJtrjiva1bh2DnBKf2/hBxLiifS2Iz8U/P/uz/Srgum1Msdng/DMS7VzBzW2QrnENVzoc0xzfXNHObZVe//rWPvOZ1p761P7c2irwpcG3vtV/0+Hf/q1fsc8XdfWcLrYIZJuKpfBE58OAfRDgSa8nPh8aKPEiQDtY9MIQ9mGHjZdIXCqulWwWdWaa7JyL6TDX3vDKaiUVw2Zq62tT32yVjMLe2hE+E9uBuQA+2ZBkZsRDLts8viEfMLkSzI78rCikPUxj5zWE7ShmxzjIYx1qo45xXDfGN8K+d7xQ4WNlbAu2FXYU2sgNJbZdN7a9Q1x9iLSicXLS2YixY3wNjSz5MAeIfa++2VMrnrWZIzFyPM19e/cDxdIAdOb7xNEc3JekduwzzQWd2yumFa0ZiOF+FBLaY3/jZ5CIl435kPjXLWTSGmXrkIiXnxUdSxYLGevAJ/ax1o+NtUMW47F/iI294mGfxsCO29JozpN9UmwttNtj39v+JvPHscChouPamDs2nDlZtK1GPrWPY38aL/rLWKZP4Q+5zGH6znQc2kB7KgPJ3XnEsIbmbM2FfimZTAM9T98pec1z/gZ9D9IT+hPcNZlZuBTPQSLpAh0sEU8UfYCIP53NNAT9Oe8F058yjbGM9cbeyFjrsS8xpmsGwkSZkm0X+QaSpwB8NRO2IZ765l03HXvZOEVRFMUBAm9CF1zQ2jnnzK5GJ1nGFe3xY4+LwI97M//lX7a2c2e/+h3b446bf2NYBjYbsSuKAw3OWbl1DM+PT36y/7YBf6WRj2fa3KucL7Ce//zWnvGM1h7/+H7vdp1jbxH48WNuN3XGGT3RznbhllBFsUXYQs/WHyE5GazTPwqnhbkd/YA+IGMzh63bxwtqJEKFx1l19mz9uKqXBKKIWICPyZU0jLGorK0rfD2efBfFB2Rh1ytBjDEOmJK5K/FrXfRRghhHTP0DfKzSvClmP9aQGDIfM8cK3zFvagV1ucN8x/YmSRxjmJ2StFa0Hm9Hgpdtp9DuGzHGHKitUOWkdowte59LrAGQS6bgbkdtYwaaD3Kz03yxtT4y7CIeiWs+pAIm+MnXBbKzOuYWcTUW80RHMtpgDbFuiPnF/dKpd5OgNj8S2JqH2cjH4ip5Tjxi2xjce45tKT/aJufqc8A+1k087DS+yai7wtomJ5ZsfDzWHPsQPbeAGfuRYv6xzWIN/HUAc2B7xPq1LYhfbHrYzX449c6UoTRoY5PtOMasH8fknL2hY8aL+r0ax9ci0E31ESN0oY+4Dj0lrblqfKIbyN+bevCOMXPpsn61udeJ3p3FEclmLnHeQ8xMXadp0FRDorkYc+1Aopkck+hRE0Z9xaUB/Qp05LOQ/Ur6PESOBcMdkjNrUzMZz9lCDrQGvin2iew6HW6jYbGLsphZ5PyHCEH4RR0mq+q0cVWFwqA54tDAdKIPpuMEI0YYFEVRFAc23O6BW7+QGORFn0TZT/1Ua//6r/08YBEk5h/zmNae+MR+Ze+XvtTazW62ta7WLYpFcP7LbWG4kvvNb+7JZkqGc9mf+Il+VfsrX9na3e/enyv4biX4Qo6/gPniF/vV/ZVoL7YY9kwulkJyL079lESIsz/a6Kaks0MSeTopdf84QeUHRJXcowPEwo/iMrl4IhFWJQPdnjFGIhsZILe+kpIWg+Ri6JFjpjlRh6+RMwV5bWqbTslTmm4XiU/disHac/Nl7GQHGtvtlBTtYqH5otfkrFBHcUOqkXA22CaMM9ZgReP6vNATT3qrx3yszvfmlhg986S2MSjMh/FibnkM9X1dMPYPcifmpdoK48S2oI6kchf0EnPAR2Nbrf1nMvoku0kYhw5/kuIj6W3+3Msdn0h6ZyKebilDwtzlmg/by+Liwziau1Xce17j+PYOFMfsiaW5WIkPANo3VkeSkPkRV7d4MZ0S3GbPNmD+7I9I7keSXHNzH2JQiBd95qyrky1ObBvJrA3YMAnJrDBf+REYnTHdPsUmZtG+9P0MqTnaq/Y/x9NQ9iq6Qx54f61jKOuineNMfYfKGmaHdrc9/1YNMYbuCt0UJNtIbw/okdPXuO7olQg5tn6fj6Gm20USZrcRW22vN8RclAHSEWZ0+jYIxqbL29Cgm0Vz28va6rt+3nMezOZ85ezNVcwr1OPBxXmc3l4aSKBdNrep56JIe7M7+jizxxh3kV/oY/uqPTWMAMFEP1XTXzbHsJ2/6U5RFEVxQMKbD/dWJqEe947mHO+hD+1XqHI7mPk31v6ZnCt53/vefiX7Bz/Y2he+0JOK0zf0othqcHz/zM80fUH1la+09oAHtHbEEa5MXPvarf3N37T2vve19oY39IQ0bKXnRyTaeT1gWxx7bGtHHunKotj82LthsT6LXtRCFh8gpv0g9WmuUocgdgVxrIykpumxGS+sURskEZe94A45vhaLorEo2cfb2MeYxAVUMT+vOslnzN9lAx8rJ+hjDKnsYcwniDZ2JG2Zt/vwwSz0Wlv2g9B5Ejf6rGXMEaytfsx3ogtI6EaSNpvMgcLKSIZT+5w1rieuGSrWoS8ArM/DmFfMxWvZEcNttB5DSWpvQ6wt5qrtYlArmR1xPR7xI2EeMbW+5KvJWRsZ8CddSoKbvb4gwc9sYh7owlZx1ejjB8Qmma61WJuiuTMuPtaPBLliMQefhwptmviavVRW6x7stnbkEPE0zxST/mGHd5mS++ZHXOl97sXWII6FOfz40XHjeuphiyJsrEZsdb96Vx2pBPrcD0YsQzZGlgWhywy71fZfPPPsduFFHOPWMdd8Rfq2+CIrQrpubgSNx+1hcO6iOUw+Fcslbq4dWHciGTBsTEs1hm6c5dkuE2vK8ZGRbJUm9EsmIPkCXY7K+ueuzk+EDvs8vyXmiTzCPCGLcPSndjHWGDKNnQlf1EtMFGuRfpawnu3niJWNwzfKIvJ+Ytus55NltOWn3up+1DDVFUVRFAconENw5To/asj5AvCZmyvaSaKRTNf5W4L+u97Vr4D/hV9o7VWvau3003uivSi2OnyWut71+hXrXMH9kId0WXwoBNo8j0g681ziPu5Tm60A6+N+9X/7t/32Mfe9b193UWwR6mheC72gxYva5MVt7sWO9uRUUWfpXit5mO39NFJyA1Wo58YE2h6LJKL86IPHASUsQ29xFdtlZr9iH2xWvnB6W/n4R9RXPMWcjkVFHDVcb2XEBMZI44af6hRba6dPHYlU76sYYyzwOKrQx3geizrGi/Ezmp/7MW9s8/rUzn4RBzltl+FHAlnJ/ohpJcb0Sn0K7uFH8hZ96MIn9ChlY/WqbR8+jGl9ahLDgdYUcSTofY1Boe+JdJCt9ZXwt1iKh979Yj7U0hPD0JuctfkwTJGd6yO2fmSVvsVRwtraIwZ+JlNCmz5jWaW42IedgZ1iTSCW/MyHNRBrx0VdRxzGxS+ujtf+slpX/JtvrDNfya6YMU/zjTGKrUUcW8GkK8JmbvfHceLdgOMnkN+CgNkmWCTDH3mUpXTdeefusMPZfWDqQj+mI13vKMEa8sHUuU9n1TzGGicBvNuHSbrsPtrJONsaGi7U2Tcw3biW3fQzExLiVF4DSivRzaxa1njoKJlv/cn0esiJ7yLWMgnddF6zxPc8i2LlOSz2mjEznd0XfRlrxVrqu94EJjD3uVipE6H2MmRRFEVxoMHnbRLtt7iFv/B74RYwd7hDa3/xF6s/49P/xCda+/Efb+1hD+u3jjnzzEq0FwcHPD84j/2d32nt2c/u92dfRn5ObUW4P/1d79r/ooUfROVHjivRXmwh6mheF04Hrcy9xkXH5P3M34rL4qydpN/QUdmHESUNzS7O7uXCg8lJOI5+xLC2XlyxDXn4OYqDIITRppgukqB0jz66/7K17MHkMUclH0k8Osw1mOqAGPjyIQv/iAHotFZihNztY8zcHnh7jMc8KSZXTGJgANlvCj5U7q95+lXfQ44/cX2O46pwGXhJeoh1ZbROk0fsSAILq3Vlt8cSyMwm7CHbj7ZBbPafjg3iWonEchDj60uBGIOakuwjAT+2vY+Dfrpt1I65TOw1VoxJPJfN6b2tsTxurEHxYu3Wloxm2FkdSfLQ00Yfb74xXviKpOP2OYw9Nw8rgA9zoa8vMVxebAF8f4P2d9q3cTzoWKBvx5Ns8HG/OF6Gn+vDbxn5OBsxDLVTP4Mu24qYj2P6Kx+2vW0PW1Q8TfL03GWEksweSM5T+w2585XwG6Pba9kRMzBV6kmlPnJ/GgfhLxsVWY46EzPUFebeXsSczta1aG3Lljvz7Qb08R9Xa5tg6jv6XkeM6SzVcxvS3TnMJKTQWGvYRXuRb2deMz8bY06wIIrpMYkSLOvHPiPS3DbKxsZ0+0GIFqhWQTiNoV5RFEVxwELSPBLtGd4ITjyx3xaG28dkLryw31aG+1Tf+tat3fjG/fYRxxzjBkWxxeH5wRdR/E4Bf519MMI2oPBXLde8Zmv3vne/hc6iD5FFsUnJmapiCieWOrmMMiF0yi7Q9hcNEXJjLiGYwdZjxKllzlRofOsrZsTFxpvsvqFzIf0oIDF9s736tVs71j7QSG/9iL/itouYrn+sxceQa/i7TZDXrXbozV5xgRr/LPN4sYbox3bWnMN2EclePk74zPkSj775MJ5UoXe5sPmPNbh+9JNccw59xCSxm+ZBgnesbULeZhp7EmvOz3VhN9bnRWJryM/nqC9eAt+OY/1uQ40PiW1dlT5hzMFq1hX9LAdtf69h6JmHj5378aWNxreiuXlSnBhKtFuM+DJjxANiWRlfbFiRCF8aiJJ9rDHmWmwNxjE1hf28TOfE8ZHN1js85o4pa+fjDab+2R673J+A5sgjttsh7TZzvl4H0rkenc9BCeQxn6kTmMxMcrJ5kM2jncdZxTJ5wkxiOsuspc9jr4FizAViHd50op/XN7WZQfo8biOz2KhLXWdVtqI967tSgpk0M7/MxTbrEV7TGkjjb3BTLiR85+rJNNlMlLBBTZn1Jw4bYl98iqIoikuUH/yg32f9hBP6G0FAGxk/evrVr7rQ4YdPSdDf4AatXelKrf3Kr/SkfNyDuiiKg4drXau1d76ztRe8wM/zi2LrUEf0uqTTRz5D6HPE9JTSNqOSJnRJ4Fk9sglsYnQm1IcQyoTwVZ30SlR6HI2Z/a2OJjZKZk58GZsfojTCM4cXMa+ReGQc4vm4mr88Z+1InI65AXJvSk5xO/l7jZhuJERj7GhjkGVjLKtHXAoVurDNmF7bxO0Uj9gBcvrEd5tI4gYhl63phr/VY34GdtJh4zHk6/4xf8lpu1xXoONnKOmcdR6bvm6JkmKqipgGbXRRhy19XQ2/c2YHmjuFMZizteO2KxR8tW+MGFPJeW8rtjW5ajzLQF170BXjjG9F8e0YjJgxVly1PubF+L2pOJqLrwG0vah71XVcie/jx5qIz3j0FciIxLyS+RYvxgfZFVsT37dU6XDohJDK6ziO50h2g2nfyL5zbSvTYyyO2UzYDNOuP/Jy29uhI9G+pGRyf6yrV6uIOSzTB+uOk+poL4k5l3RNTcyjrDsf0Y10lfWa9rMRNb005jLmQuY5LhpnzbETKej8FGbXxW9gahO65/BXgGV/DTAvz23cFo293nyIQcEulqd2KrBsRkVRFMUmhytQX/3q1m57W3sDSO8atEmkU3/xiy50PvvZfm9mbpmB/uST+489HnWUGxRFcdDAOfotb9nacce5oCi2DnZ0F2vDB4f48OB1PqvkQ0KUIM7ISeiB+qZXbUVJxbBPfqEbuL0qb8OqsTxZGfoYH7LfmD/2JtNYeR6pxBiq6cecieW6EZpY2NCegtzKWHuXzkDg/mrn4qhrD9lGfZ+T5BBt74c4bw/Z9KqPSwwrY360SciyjcwotpWYxFFFjV/YIrMYqt0GW95IQh9xpHb/YNgYiultGvqBQivIGSOSxfox1hgDvXW1Bk9SA7LhIwMvUvS2toG1udJEanuIH3odt54hJn1ra534GroHo8ljjhBr4QdYsQv7uXnQt3bYxFXtoHVa0ZcgyLA3du6wrtsIa8f+omZcCuOMsd039nOQwxSbl9i/QXS1f2Pfc0x4O44tZH57lYF0duyETcgGtP3A0fMt6XT8Jd30AAtbdGEX0JUeXW8fceRhdvibzGx1hTX/9SOoOOhhVEMmOyvDzpkMB/2qbR8vQOZzG7d4kV1H4VNg9fM21Np6M0l7CAl8LeBVHl6YfHol9MIrzNNYMdUp2W0aYazPw6DPcRaMuCEmy1u9PgNZlMzwmcxjUYz1WGv8AJuwy/MOGeT2mpjhsrEiRtT+bjAHviHbl/UWRVEUlyBckf7gB7d2neu4wOHNhEQ7n9+5/7Leq63ErWa4N3Mk2rmfO1e18nk9v2EXRbH14TlP4bWinv/FFoNznWIp/uRfBR8YSEb6B4cg+kowhpwYJPuyXUrGBLKx3SFfxrQyF8+gzvNRDLcd9m4rGbhsKh99g5jRjxe7QHLGIY6PF/YeqkMnCnZUVsd883jDJmIaYRd1QEJWSVQwHWollLtkxJhbjxXJ6CQdsZTsTf6MN+botpp3F4mYIyjRbKCXn5WILzt8TRZJ5T6ZbpOTcEBTcwp7R1eEo8TXfLDhdjOr9osRfmNtE0acaDu6jznzYSxq4jEWMbCzQjyN7XOUu88VGfFiTOJgc6j3mS9/TYF+l98DnkLinivRI762obWZz7h63efLWIwDjLVrR5eBfK0wBlexax1WFANbX5/mZ338Yy15O8T+LLYOcfhAHEs6fv040IHsRZU9hA92aiNzH9lZGa9DyNwhx482jLEmZJnHnyWzrSQ96kPtQQlmxHvMlkI7hxmTh1C4DzXqPG7YWFzEiokMO8ZK+rkEujdjOj00QhdEcVHQ1ycPr/q4ffBeZZcYU3NKyMcYdmpEr4Osy2d0v4nQYdvK3tXzI87I3thMo0WIkMc+1dooySGPMR9npsE+222U8CEu7bkY04BmlMefn0snZMvmssrHDENGHSUzndciu+hPj4GiKIriAII3q1wy3HP9utdt7XOf84txjLPPbu1DH+r3Zr/KVbqsKIqiKLYgZKGKZUw+M3RMyLnf+EBhHSUR/YRQHzY8CagkH8k+bPHjgwZtt41zSOyUFPSYiuFytd0nbp8hcM4lY33mQ0wFIg4JRuYZczCUnESWxoaREPY4mWE31VlbSUz3pWj9qND5HGINuKofc0FgxJrHGmRoxWNRK5lq4wBqzd/kWo8JItlKHN0PnXHwt75srBASfcyXucdc9COZtIlh8tCp+DyUDGfcJIsxNL77B4yBrebjc9V8Yu4+j9g+gnmZvcY3G9lhQ9vHoU2ymbYSgYzp4+Q15fnEvCFiUkC1FfSakxeS4xTsQclyR/PweIrlddjGeJq3lUNsvswHGXOE3WZDe/vh3TdiRgx0FLabEutmf5jZAuuO/RxXsOt4s4IdfeIGEUvxsS+2FrZfg2jG8Qn5GKWhv8Jwvb5osrZ0EDVYO+LkuGGr48nLekxtIkYeDpDLdMUO5bAJo16PR5MPlaTeMf8RPonH3KM/t27Duvkq8lkLzE5rtaaV8WUBuHz+C4DObB690ave9lD5oWPNPo00RpDkaaoidyeqQf4eIaMlbCReNCb205lqW+j/zCiZDxasULJF8kXbFxZKFwTALs+Zdt6NEOpF40PoI062Q9SLf7GyAMQRA8JnTlgURVFsPnhjOOmk1j796dZ27uzl3e9u7ctfbu1JT/LP60VRFEWxNfEsVrE+ceZntc4qUz/grFFnlEkfsjjTzG3FkUEvSgrSdJtITGayDcgmPqy4jxLrEZsKH9rIcryYo+nl5/YQZ87U3hSRuKR4JTRn00UJZcwzaiCmkqYui7FjTOFtJaRdp7i0WW/YWl/yKeFvOsWl+Hial1WaM8XGkF3abtQaR4b2PxXZUaYfEtGZH2vzboeYhuQunMaeGXfz2D7MS7dysZpCAppaRhSzUdLYt6F0TiTiIWKN+fscsYmkdMwz2uEf21vbxlAsL9lGsc2G+Yw/ATUZRfdmT31gu8d6uBJeMb1P0W1rjLj6PsbxaYxb3IQAG9rEUT9q5Ib8Ta/1Rr+ris3OdEd6P44B+mESx/EgbIxh70R31THVq3WJsaLGfzr+XJ/AYUvT2lGHWR472tRRArWzwJgbf6JbRLLPm2Ca5J278n0BffqzcccUlrCePpOXlOcoIk7oe7WQPOTwN0aIbLCAHDsPT3utcafIfg2Hdaax7nj2qrcuMcY0zqKxs03fz6tZNh9sQze1WeZTFEVRHODozcDKQx/afwyVH0B92ctae8ITWvv5n+8/fho2RVEURbEF2cg510EMHwCipNO++GCQPyAomUfBLoon+KSzfiRXSfANG0fJ0mXjoLMSV1viP5KKPob86FObTAlqmlyF7HEVznSMFVdWo4t4FBKfMNf2/hjD2lpDiFgXWIfYGijsKWC15oTc56N5IqfQRuxtzYuxEGIPUSMz5uZvMm0nn1fUEPGGf2B9jetjqu9tblOSx1YC2McNnxHT5SM+MVgrNszRSvwgKMnjiJPnpH0S2zHiWM0+YB/iM25dYXJtG+tL5/LY9pqfFfZx7EPFMVm+dUuMI50VJbZNxm1X0I/5GLRZQyTCtc3Dx2xjDnFVvcahzXysv+PCPkbMjfkTkxjIlYgngMsPs7EOv6xCzba71dqeh7bzdppPQCxtV2LLYSY//Ihex9j4My/27272sbWLgwP2fcAxARwvcXxNiSvIV4Fv0uXjU1/imD5KP6jnwS7GN8ZV4yNkNLCL2hrJZ4bp8vjhqya63s2uo+m61YIJedi5KXjH3Poa5v1X3fZjLs7ioNMZZCuIpeI/2kZO/M/L9w6mFVNLm2/VvIKIH2NShe1aY+d4I4bXU7I82ov8Ya0xM9gtihXkOOijH/UiH1g0PrY7eXSnqe/C/lRYFEVRbD6ufvXWbnSj1v7gD1p7/etbu899WnvBC/pn/KIoikuZ8847z1tFcfGTMg/FatLZns6kUz/OxuPMXEkcZF7iLD0nd0B+HidnBGiOM/usJ9YkQYlcSVC3I2HNOMg1F3SMQ9uLYkc8YoUvYEuFLQ3Xxdxd3WPYXLCLhLmuZDS5KvRWM7dIvhMDXWwfjUtBiI55EwuRyRSbAsRF5m3GijVIlNoDfGxsrbF39RDJZc0niomYKwlpiO2LP0nlYNU42Pi8tW9MFL6yxcTXr0S42SjRazoll81WfQzsgdDokIuIwThprPHlBRW1tyGOEcAeHTLdQsb6FG6fovumMyAFPD5FX+QY8eecigPE8hJ9bTcr3L5F/j5XZBF7zMflYxyPH4l97S+LiY75Id9xUWs7rbDm7ZeRudB+bO3vP/yx9gvP/r32pa99vY+jsSwWa2Z+jBnbiG2tNZiN7gXfY8yvqdj05H2pY9wIkY4vjjXkrstI721n7nYoEDF1LFk1+s7cGE40p7aJlYU6H8Obs+fiBLdRFfYZydKcFo2FiJL9p2apr82yaMBJdy2YRr4tDSx0d2HW0d42sZ4l9efloGFWi2ckXU/YRzFMt5brlGwb7Xn/WY/WvG45eUuFX/hSa94SzCyzD+TNLdMcBHJ76pzhAEjPjWyKNPq5fvOO89pH9lzU/BV/0NvTvTk/laIoimKTQkL9Lndp7dRTW/u6fV7/nd/pP6C66LNIURTFJcx73/ve9tCHPrR9/vOfd0lRXHwsOXsvlqKkXj6dBGuPRMiiU8ZkLzVts5/7oGEK+kNEI8eydjq5lV5jWh0x6c+yINYfwRyX60SZxGRirCmXwPtKlkYiNeuJ580sFm47N3dH8zP5mCdxsKPO25namUs4uVzbzUug9XmsMamoXR5qJWLR0UkM88lcxraz/tgeyLFJdlOUcN9l25E1oE9xIOJGkp716C8P0Fth7ejUz/GtHWunHnMFa7O+ue0W43thv0byG1t98TAZQ9vAiuzMP7aZ5mQFOV9OxP3TBxZD86FJmzheC4uh7eHz5YdTaXPVedjyBUvMD2zsn7/LHdu/fO5L7eb/85faKc95SfvS17/d42gubDOzJyRxYvsJi6dtEf1iSxDH2CpMno8ljgXteq/jeaO/vki1KfthYw/T0IrjbZCNG4V/9Kd1fJmlGJOxhQZVayT66WYb5CrWdnN5qO3z1nMmFM6hh7ZtR1x22Imo9Dzu7U7vaAY+DYmsLdew1RzT/eODiO/0Ka/4y9BMh9m4X7seDWsgJ3ncZfOx6LnLjEkff22xJM9Tmn2JsjrJGwx7M8hjTu17cn4x2IZWMXpzKYsiLXq16v1ZtD3WzkdRRrb2wDyncfAJv+zP7iR6zD/8wmbpOsLPHnK8u2y/bPu73Re2Z13wg/ZPuy5svHvANE70GW+qK4qiKDYZfOZ5ylNae+QjW/v1X2/tqKPsBX76TlQURXHp8HM/93Ptc5/7XLvZzW7WHvawh1XCvbhY8bP+Yjk6bfRmPvULGYVTymQzkojgp6n5rD2KbL2MU1nbJVLHqegC5nQYe60xiJdOh0k4jnkzj7TLlXzyMuK4DaE0JW9EgmggpYM9epMpDON6X/NBaG3ND5sYM0Bn9uGrbZLn5GOPuXs8NbGLuGFv9bAF18/Ftr7mY23qMX+3ybZK8lobc9klHSAf8V0G2Ch2JIm9jZyYGsdko5aR971WYQ6+HtokucfYxCau6Uec0NO2Ogp+4Su9kRPYmpvJuaI87otO4VYySqzLyIqNw7qk9/kF9Md+tja+6qMjhrU1D3yQuz82fAmhq82tz3pJ2nN1vW5Dg9zQ9uzlOb90Utttcd74t+9tN3/wY9sjnvPS9qUzzzIji4m/VX3evs/yPtbYEHWxuWH/ehO0j8GF0R0NPwbi2IRhY4zjzPswYq7B1Cb6a/lmHePlMUFznMb1OhubnUypu8R9Oyu7drc95/NXIiabTifMkv2MbrxQ43FWJdrBHeauXJfZzJbW6iFnP+raq5m9bIfDfJxFm1jy3kzWG2OtXRaEScxo1VI2yFpzzLEpi2yWrT3I88ryRbFgKl+1CxPRz2ME6CjXO2R7u5GVH9pr/dt3nt+ef9HZ7UO7LxoJ92AauyiKotjE8OZx9NGtveQlrT35yf0z/eo3/aIoikuN3/7t32579uxpb37zm5Vwf/jDH96+xO9KFMV+QuapWMr0w4D340OCzgr91JAKebioPbEfp5HRN0asnLRUMIk7tK0gjiR72MkMnZS9DZFUFNRRqEw39FbUNuJsPeYS9qDwMbdut2J9ypBpLV3Xban9QaFSPJAN49PxmnEVJwpEzPHQbbQOa5OEHfOg8lrjug31kBtjbPTorB9F1+K5LzVJ4fFFg4JZIVYUQ2L/AMk4JJ5iHYrphCsN6R3aSg77HOST4s/tjywzSJbH+vCLcWMuEAl2rRdB2PgaIeZAP2KA5DaG5odOwh4vz5XtpHGIl2LKwfry9VjUKszH4/LjprEWkuzyszZxYx1hb+X+d/2JduKNricxCfc3vOu97WYPeaxuKfMf3FJGw+LHuOajOUUfJaXYOnA8TfZrdOP5G4cA6NhwW/zoj+e5oVhObkPEWARxsj19jeWgi/GsHonoPESWw7SdbQmd+wFPy8TK+Re0HWd9L3rzlebY25lVYUOwaM4ZYqV4rIWSN4OwfqyTLT/CTQyRz20PQybTeIlZrEltzGLNhKvmFswPu2rJq+5FvwB8tk3MJOvNwVqRwnbqA8jmtp/D3KYxcx/7ZfEW6RbZRjzp3IDNSzN0/HHF3Q69rPeaEu7/v53nt1fuOqf98+6LxqHq7vKLdlEURbGJ4XOVfneJW03WK3tRFAcW97nPfdptb3tbtUm4v+lNb2rHH398+1//639Vwr3YL1JWoVgTJffsA0L+kDD6nBb6qaHOLOP0MnSJ4R8+QZZbe84u9zMLTq1HIjLJaY5EY8b6kkPYZxva3leCf+L/3e+09rWvmNjkI0uRaiV2aec5WntsSxdNwoo8r4g9ruR3R1VhlzH76fbTfEJuRclhmrFdkm4O9w358LP+mCOEn9VjW4VvIpLSGu9QiToTO8jzlT1Ne4ir2EX4Rd9gXuNKdZOP7efxxrjIw9/Q/sJ3ey/40ueWMKvWyjqI7duD2LQ1lsfkw7V8sXdiLpFQ13ySXuPQtxKJe+ndNvB5PvvRD3FBZ8+elfamv3t/u9mDfrk9+nmvaGec9a2uGMl9/Hz9OalabEE41vxYEtR+/EEci8G0nw63hUzt94d8bCus9UO25rxcR4Vd1IJ+tO25sXNP23X+DpPOZIMFohkLNsQCkUA+me/sNi0zkK01ZNatdl/LcwlpvrPNanOwUHkpG428DzNYkzyH3M6sNWY+fBaBb5QMbstcp7rwRXaIDyhZCjpnP9lx17b3nZvn3z4xzrX3kXfuvqC9ZndPuMc7fFEURVEURVFcEvzWb/2Wtzok3N/4xje2E044oZ1yyintjDPOcE1RbJzKNK1JnChSe3ucpZNgDH2cXqa+mtGfQAwlGa1wMhrJSkEsEpaetJwbh75V4R9oTm4TseIkN/rysRLxxkkwdW4bOb5E9qA+41DAZOf+sLVvf2MmCr3mjSPF+hrXZZorULN+1pkS03PbJNmHb8TVldNdNZur6+jHfBXfi4h4veo+Pq6K62VnOvS677jbQbaJcVS7PWCDGeOiQz7GQkHT25QxR7NRAtjjYB8J4XFlNzYkjj1hjP9ISGesz9wjqa05IKdWI8U0YhuxPxTKdRFXiW/+egB7i5fHUxwreQpje1phDvwYK8RV75Hw1o+iWl9XsdO0ONy6Bhm21FwOig99yQwL+z/udPt2m+Nv2PuJXbt3t9f/zT+2Ex70y+0Xn/+KdsZ/2XEaXyDEtmD7VbJ9C2H7dMDx4s2AQ3EqA47RVc+dSSyc9Rxfgp4TiYiJfKoLXDeXkFSTBysxpaSeEq6rrvbmYcjCyFqIpvHoR6Axl5nPupjpqs23iAgNtBkr+UnUmxMm0sVGC1lvXgqVbbT++QEWhRgWk7lMumuTA68zz0Vwg6DpeEu/vpB4sS6Gnk5Bfd+AbBa8l0QfTG2Gnwvvc9gRvTHhHLN6x+4L2gsuOrt9JCXc92GzFEVRFEVRFMWGufe9791ud7vbeW/G7t2726mnntpufOMbt0c+8pGVcC/2isoyrcc4U9zDg/f9rFHYqSAno0rCRJvStQsJ2zBS35OUOZmjk9wIRK2JzNp5HuEXyVLpU+Iz26rtJeSasyc+w0kqbIiJzMcFk227znFt2y34Uxt80aWxhclI2o7xMiFzeZ5TyILYpiHGluHCB52Stza+bLANY0Ntk+UENn/LDmN7GWMOgfvMiUw29p+BT07Wurg7UUyQ44b/mB8OuZ3qWBdJ7GFnBV9k6IF+XO0fMtn52iKhTSI79PqhxJi3xx2+VuvWN+iJg96JtYctfSWsrUQ8zcfGnku+2wNh8JsbFzl21o7xqJUQd53ioPJ4ume71+b37Ec+iGAL4ZYyr3vne9pNH/TL7THPe1n7z69/s/sSUMdMtys2OTpWFuxMHX+mE15T6bi1RtZFE+ZimYLn+LBdgI55K8tsQr5ojnGbKVTTcQPFj7YV7JP6EJ7PWS8b9QT3UD/k8O3t8Cv67TvSPBfeXz2YrsdsNcU0lvo+n2E+cevTXxn6WE4OTz+YGxZbL8E2W++cjdH7M2HED7vcngNDj61mGme6jgyJ7r1hVdgkiEijXid0qGchTLLAacxxvlrIIh0yXi0pobe9aNuop/TzmiD2axBtavyva6/r06vaM2fb8/Uvd57ffnfH2e2juy9qu3YzclEURVEURVH86HjOc57jrdWQcH/d617XbnKTm7RHPepRlXAvNsQ2O2GanisdtDzmMY9p//f//l/vtbbr//21nSF6ApPTxHEiSx2njjDdhFm/YPOS5CHJp3huw25QM/mNxLfrIScpSSIFipmSSvTH3B35ml5+Hm96ZbLkyWbMwcAsriamw4/akGDl3tqgs2xray0YW6FWLNo+d2Rav48dic8hxzaNK53ZDjviWB1zISlLP7aHbE0XCeYYX3PwPrHQ047axlyxsffs2mXR7J/ZrNj22bPH+ramkWCwsofaXFba7r5s0/FnRrpvvfnI19pmYpsEG/xtWJvbym7iWyxsrL+HPnobu4+Za/OVHVPuY/fxbA4aw9qMudvKIRZr+DKuxSV+xOKfx0ASbc2Ttdi+UzzmiGzXTvdjvB5X65GPFdaDnPXtNlu2HzKtL9v5NmV99IlDrb7ZMT9bp2R5nsRCr382J/4xFvayZYw+91P/5h/b17/7fZOszXY7Zk+5939vT3vEg9pxV79qO+QaN2jbrn+ia4vNRL6K+9QX/XZ7+EkPbduueFXr2ZHBawBceB4HSVu56ILex8eOmf5ljY4g6Zsdn5JLb30OqrN/0O8relm/Chc5x+AFF7Z20UVt2xEmV4Lb/WG7vRbxekihjf8ua6PHljrGjdenHTs4MNvKjl1t97kXtu1Xvrz8FfYH57VDjzi8HWJlj+m59cthl7+M3HdfsKNtO+xQe9k6pO0658J22JWOsGWzTfpzledI2804JrPXhm2Xv3zb853vtUOufCXNY8+5F+gHUrfZ2NqUY3vaGnfxTDf3C3fay+uheg3ZdZGtw8ayDw3tsMNtbYeavfsccpntbY/NTTCuyXfZfJnnoTbH3TYOYsrOnbvbZQ63MbWLtml6O3buaedesMt6rV3uiO222Q5p5523s+3YtdKOviL3dWWXEIDvC3l9tVdemxNfMJjIxrL49g///tq80n54/s525GVsvgjlumJj7GzbTUB85gG77bUTG2qNYfTXqf46v9tKHxtd1++0sW0FbZe/B2GnsU2uLYeP18Tabf78w+4QhUDea3RsA3S7zB/FoTYh1qfxmYfZEZ+/Cdpl/V0mJ+4ObO243W4b8yLeK4jIdrGaPwJiXTusT2qb2MyW9RxqOt41LzDZBazbZMwFGUtlHzMntu4O019IDAsmOysam3njZeNfZO3DNFbMs+sPtw7bgXgXWhz6Z/3w7PYH//Q+k67PNa585fZbL3hBe8QjHmH7bHmCviiKoth3pue/+vxQFAcgcWzmY5T2VL5IBovkWQaL5ItksEi+SAaL5ItksEi+SAaL5ItksEi+SAaL5ItksEi+SAaL5ItksEi+kXot3cc//vFhMy1veMMb2je+8Q3ZrsWhhx6qK9yf8YxntOOOO86lRTFPJdoT0w8ar/ztZ4yEBSeY8SRki9mjTr5nsl66HT6zJ3o/eU82S/3WkhGvJwJy/JmNj2entfPxk1+aA2Ve1pMEWW+i1bKwY0Vho6RQsrF/q+YgWcTyuStOsgmZ+mimMu9r/Qv8FKv3hyz69m+u76XHmsmKgwMS7o/8n/dtT3/cL7bj7vSzLi02EznRfqc73amddNJJ868fXqayjdhsVLbVYm0mv+Lg4brXvW575jOfWQn3oiiKHwHT899XvepVc++50wKL5IvKj8J2o3aUrWq7UTvKpW27UTvKerZFcSCxffv2kXA/9thjXVoUnUq0J7761a+273znO+3hD394+9znPufSoii2Iocffnh77GMf2170ohfpm+lic/GJT3yiffazn20nn3yyS4qi2Kpwf8yXv/zl7Z73vKdLiqIoiouDOv8tiqLYNyqfUCyjEu0L4McQSOIURbH1OOqoo9oTnvCE9qu/+qvtqlflViPFZoXX6UU/XlMUxdbgx37sx9rTnva09oAHPGDur1iKoiiKi5c6/y2KotgYlU8o1qMS7Qu4/e1v30477TTvHdhw4kk5hHvwentvZJe03yLZJe23SHZJ+y2SXdJ+i2SbxS/LfvjDH7Zb3epW7bzzzvNnxWKucIUrtCc+8YntSU96kt4ci83Pv/zLv7Tb3pYfZN4cbOTY3tvjf1l/f2SXtN8i2SXtt0h2Sfstkm0Wv0WyRTb8mNRTnvIUf0Ysh9f03/qt32r3ve99XVIURVH8KNlM57/7Sn4/mvbXKhu13agdZTPZbtSOcmnbbtSOsplsN2pH2Uy2G7Wj/ChtySeccMIJ7cILL1R/GZVPKDZKJdoXcPrpp7cLLrjgYj25/FHFKoqDHe7d+7znPc97qznyyCPb4x73uPbUpz61XfnKV3ZpsRXgy5XPf/7zc6+PlL19Hd0b2f7EKoqDmR07drTrXe967etf/7pLVnPTm95UCfYHPvCB9ZwpiqK4BInz3+nnltxfq1zathuxK4qiWAR/PfnCF77Qe6shn/D4xz9eF4tUPqHYCJVoL4pi0/KDH/xAP5R37rnnumTGZS5zmfbLv/zL7elPf3o75phjXFoURVFcGvADe9zHchE3utGN2m/+5m/qR435kqooiqIoiqIoftSslU+47GUvq3wCifjKJxR7Q53NFEWxafn93//9VW+K/AL4ox/96PblL39Z+npTLIqiuHTZuXNne+5zn+u9Gccee2z74z/+Y11JyQ/xVZK9KIqiKIqiuKT43d/93VX5hMMOO0wJdvIJL3nJSyqfUOw1dUV7URSbEr59JklzzjnnqE+C5pRTTmnPetaz2nHHHSdZURRFcenzh3/4h+2XfumXvNfaNa5xDb1W86UoJzNFURRFURRFcUny3e9+V7c1jET7oYce2h71qEe1ZzzjGcozFMW+UpcOFUWxKXnpS1+qJDsJ9oc97GG6V/cf/dEfVZK9KIriAGLXrl3javarX/3qujLoK1/5SvuVX/mVSrIXRVEURVEUlwq/93u/pyQ7CfaTTz65ffGLX2yvec1rKsle7Dd1RXtRFJsO3hB5A7zb3e6mBM7xxx/vmqIoiuJA4rWvfa1+jPrJT35ye8ITnqAflCqKoiiKoiiKS4v46/h73/ve7dnPfnblE4qLlUq0F0Wx6fjwhz/crnCFK7QTTzzRJUVRFMWByBvf+MZ2v/vdr13+8pd3SVEURVEURVFcenzgAx9oV7nKVdrNb35zlxTFxUcl2ouiKIqiKIqiKIqiKIqiKIpiP6h7tBdFURRFURRFURRFURRFURTFflCJ9qIoiqIoiqIoiqIoiqIoiqLYDyrRXhRFURRFURRFURRFURRFURT7QSXai6IoiqIoiqIoiqIoiqIoimI/qER7URRFURRFURRFURRFURRFUewHlWgviqIoiqIoiqIoiqIoiqIoiv2gEu1FURRFURRFURRFURRFURRFsR9Uor0oiqIoiqIoiqIoiqIoiqIo9oNKtBdFURRFURRFURRFURRFURTFflCJ9qIoiqIoiqIoiqIoiqIoiqLYDyrRXhTFlmb37t3tbW97W7v97W/ftm3b1g499ND2vOc9r+3cudMtiqIoikuTCy64oP3BH/xBO+GEE/Q6feSRR7Y3vOENbWVlxS2KoiiKoljG1772tfa4xz2uHX300XofPfHEE9vpp5/u2v3j61//envSk57UfvInf7J95zvfcWmxEf7yL/+yHXPMMdonl7vc5donPvEJ1+wd3/3ud9ud73xnxaH8n//zf1xTXJr8+q//ervXve7V/vmf/7k+sxZzVKK9KIotzQ9+8IP2J3/yJ+2+971ve+1rX9tuetObtmc961nt/e9/v1sURVEUlyb/9V//1f7iL/6iPfaxj20vfelL25WudCWdvHz2s591i6IoiqIolvHBD36wfeUrX2mveMUr2uMf//j2mc98pv3Gb/yGvsjeV0gccg51/etfv51zzjm6cOmqV72qa4uNcL/73a999KMfbde4xjVcsm9c5SpXaf/0T//UTjnlFJfMc+6557a73vWuSsK/4x3vcOnBwS/8wi9cal8+MObDH/7w9rM/+7PtkY98ZDvvvPNcUxzsVKK9KIotDR9M/uqv/qo985nPbI961KN0ZcHVr3719uY3v7m+eS6KojgAuNGNbtTe9773tV/91V9V+fM///P2wx/+sL3zne90i6IoiqIolvHQhz60/c3f/E172MMe1l72spe1pz/96e29733vPn9hzTnS7/7u7+rc6dWvfnV7zWte0655zWu6ttgbjjjiiHb44Yd7b98hmcxf/hUHDocddpiecx//+Mfbhz/8YV3Yd/bZZ7u2OJipRHtRFAcVN7zhDds973nP9rGPfUx/hlcURVEcWNzmNrdpP/ETP9E+8IEPtAsvvNClRVEURVGsBwlZrqSG0047TfXewlXRT3va09pznvOcccXwVoBb7FznOtfRmrYS3JaGvzg466yz2j3ucQ+XHhzwl5Csm1snXVpwwQi3PPx//+//tac+9alt165drikOVirRXhTFQQUfFO9///u3r371q+2MM85waVEURXGgcPnLX173vPz0pz9d94MtiqIoir3k+OOP1+9T8YX13v4FLxciPfe5z203uclNdDuMrZJkB+43/73vfc97Wwf2EX/FzV8dXPayl3XpwcFRRx2ldfPZ8dLkDne4Q3vEIx7R/viP/1gJ9+LgphLtRVEcdHCfdn5s7wtf+IJLiqIoigOJH//xH9c9YbnnbFEURVEUG4ekI4l2znX4vaq94d3vfrduhfHgBz9Yt9vcSnA7nf25b31RLIMvO7iNDPAD/3VV+8FNJdqLojjo4JfBv/3tb6suiqIoDiy4+u4973mP7tPOD7oVRVEURbFxuCqdH8/8j//4j/aNb3zDpetDcvCtb31r2759e7v73e/u0tWQrH75y1/erne96ynBSDn00EPbjW98Y/0eFnziE5/QLTXQcWsT+twznr9YCx/ayy58wvbe97634mJ79NFHt6c85Sn6bDCFzw1cvX+nO91p2FMYn6Qn88WXe9cDP/IaNhR+1JJ5H3PMMerzJcX3v/99zY1b2SE77rjjdEU8MIc/+qM/ane84x3n5vfiF7+47dy5Uzb7Q2zfa1/72mOOt7jFLXQf/ilf/vKX261udathl38M9c/+7M90cRnyn/7pn9b9w/mygVjIWC9XYLP9GJP78oc9Np/61Kc80jyL5he3/Mt/QfH85z9/bB+S0Bxfb3nLW4YfNXOc/tXFRo4vvkD6mZ/5maFf9GOoi+LQRoYus69zzXAP/RNPPLG9//3vb//5n//p0uKgxA6UoiiKg4bvfOc7K/bhiXfIlbvd7W4r5557rmuKoiiKA4EvfvGLK9e85jX1Ov2oRz3KpUVRFEVRbIQ3vOENeg+l/O3f/q1L1+drX/vaynHHHbdygxvcYOUb3/iGS+fZsWPHyi/+4i+uHHnkkSvvfOc7V3bt2qXzqWc+85ka74UvfKFbzuIde+yxK695zWtWTjzxxJWXvvSlK6997WtXbn3rW8v+xje+8cpXvvIV9+i89a1vXTnkkENWTjrpJJ27MQY+yDh/+8EPfuCWKyt79uxZedGLXrSybdu2lSc+8YmaC3N83vOep/gnn3yy/L/5zW+unHrqqZI98IEPXDnrrLNGOeeccxTrYx/72MoRRxyxcpe73GXlPe95z8pRRx0lewprwPb1r3+95nG7291u5Qtf+ILG/+QnP6ltht1b3vIWxQrwwZftddppp7l0OayNNWLPfmQtzP/tb3+7ZIyRtzH88Ic/XLnTne4k3V//9V+7tBNr+umf/umVZz/72Ss/+7M/u/LGN75x5dGPfrTsDz/8cB0jD3jAA1ae8IQnSBfnyuwvtn8m5se2wY+5nXnmmYrPdmHfZd72trcp1oMf/OCVxz3ucdofjPGQhzxE8ul22Zvji23PMTKVw6LtePbZZ6/8yq/8iuynxxHs7VwXwedWbP/qr/7KJcXBSCXai6I4qIgPnnxwWOtDZFEURXHJw0kTJ1OcMJ9wwgk6EaovRIuiKIpiY8RFRUcfffTKMcccs/LiF7/YNetDEpFkIonmSD5P+exnP7ty5StfeZUNbWQ54RmyRYnNfPHTb/7mb7p09mU7upzk3blzp5LB2Odk9rI5c47H5wiSpQFJaPyzLBOxGJ/E9ac//emV888/X8nTSLSzvu3bt6+8973vda8OcmJPLxDYm0R7fAYizjRxjI7k7yJd3s7TRHusCR3bmYQzsD1zAvnv//7vJYf/+I//WLn2ta+tBD2J+iDPj3PqzAc/+EFtlzve8Y5z+zm2OUn4173udYoBbNd73vOe0uVjdG+OL2BfEiPLGYMvXaZyyNvq5S9/uUs7ezvXRTDeonGLg4u6dUxRFAcN/Bnly172Mv3pF7+kf+GFF9afdRVFURxA8CfQ/Dk2Pyr15Cc/uX3uc5/Tn28XRVEURbE+73rXu3SP9Sc84Qm6/Qu3bFlZ43YXmbPOOqudf/75Olfidi9rwRgf+tCHRmzuC88tM7hFyxRuRfPsZz+7XelKV3JJ0493PuABD1CbOOedd57ab3vb23SLlvvd736yCYhx+9vfXu2PfOQjquHUU0/VnP/H//gfcz+Iyf3lTz/9dOn3Fs4ZuZUItwE54ogj2mtf+9r21a9+VT+6yfq4Pcx/+2//za07N7vZzVTvz61j2P5veMMb2pWvfGWtJ8PtS6573et6b+9hnz760Y9uhx12mPpsz1vf+tZqs63vcY97qA1Xu9rVdNsdbq+Sbz0U8yPWXe96V5d2bnjDG7ZrXeta7Ytf/KLspvzkT/6k9jfrALbrne98Z7UXbbO9Ob6mMD63mDnyyCNX3QKJOA996EPVxiaOu8zezjXD7W2AWx8VBy+VaC+K4qAhPnjyIYN7+PHmWT+IWhRFcWDAyRQnxJxg/+///b/bLW95y7Zjx476QdSiKIqi2AD5oqJTTjml3fzmN9+nH0QlCRtJxikkEklUktzmHuvcp5x7h+/evdstVnP44YcrWTklktO875PU5XPAv/7rv0r2jGc8Q3PI5alPfap03Gsczj333GEfCc6Lg2tc4xpKNF/SkNTmd8RIBh911FEuvXjg/uOUzHWucx3V0/3N+NyTfgoJ7G9961vtv/7rv5T0z/uGJDsXsHEhG8fGlLj/eWY6H9iX42tKbMerXvWqmtcUvhRgLnwpsOie/xud6yLii4zi4KYS7UVRHBTEB0+uRHj4wx+uDy98gKofRC2KojgwiKvZuWKNK6s4AeTEvH4QtSiKoijWJy4qesQjHqEffeQL6739QdT1ICn70pe+VD8Meeyxx2q8+9znPu3444/XD7DuDSS0eZ8nYc6VwlxdTBIX/vqv/1qJ90UlrlI/55xz2hlnnKH2JQlz/Yd/+Ad9mUHSli8Spleg7wskshclqX9UXPGKV/TWxuA44guRu9zlLtr2i/YN+/C2t72te+w9F8fxtd525IdrF33xUxQXF5VoL4rioCA+eP78z/+8PhDxTT2Jdq6C4MNdURRFcenByVlczX7yySfrz8U5AeTKpo9+9KPSF0VRFEWxmLioiIuJHvzgB+uKXBKVe/bs0ZW7ewPJ0l27dnlvNVzd+6AHPUhJ/A9/+MP6cpwvy//7f//v7d3vfrdbrU8kbo855ph22cteVoWrkGHR7UemcD7HGi9JPvWpT7Wb3vSm7bGPfayS6x/72MfaRRddpC8G9pf44uFAhQQ1iXCuFufK9R8V+3t8rbcdOeb4i0nWcsghF29KdNEV8sXBRyXai6LY8sQHT+7TxhUe8adg3Fvv3/7t39qZZ56pflEURXHpEFez8wXofe97X8m4Pyx/MsxJLK/jRVEURVEsJi4q4t7SJ5xwgmQkoXlffc973qP+ekSC8jvf+c6GEqkkRLmf9d///d+3Zz7zmUrqv+Md73Dt+kRSktuUXOEKV1DiM271sZG/ZmOu3Isd9uae2Ot9kbAMblnzy7/8y5o325sLuLgwYHqbkX2FLw5YE4nsr33tay6dcWknceMiiLg1y4+afT2+mCe/B7BsO3K/ffb/bW5zm7nfAbg4iPG4Ar84eKlEe1EUW56/+Iu/0AdPrjq41a1u5dKmD6F8iPzHf/xHlxRFURSXNFyt/opXvEJXsz/sYQ+b+7EvEu38IOppp53mkqIoiqIoMtw3+4UvfKFuYfKoRz1KCWvg6vZb3OIWugr4m9/8pmRrQZKbq8u/9KUvLb2vO4lOrujO8Ug0k7QEEuZTuI1H3BIm4PYrf/VXf6X2Ax/4wDFnktdcZfzWt75V7/9T+BIgfpASH+7jDX/yJ3+y6hY5JNRzcpokNnMliR9f4MctazYC6+CzyvSe8yTgud3J/sLtfrh9Hldcx61zAi46+IM/+AO19+Z+5WsR23GjXP/619ePgn7ve9/T9p7Og3mj2x/25fiawhdM/Fjrou2I7E//9E8V86STThrH3cUB48RvBvC8Kw5eKtFeFMWWhg+Kz3nOc/Rm+gu/8Atzb6Zc4cG9C1/ykpe0D37wg+1JT3qSfsSlKIqiuOT40Ic+1F71qlcpIcDJdobXaH5b4/nPf377yEc+0n7t136t/iy3KIqiKBySey9/+cuVPOavdW9961u7pico73nPe7bPf/7z+kL77W9/u/7KdxlcHX67291OSc61foicBPiv//qvj2Q190n//d///YXv4wHv39x2hfmS2Ob8jIuhfvEXf1FX4Qc/9VM/pS8LSGjzZftTnvIUJd1JMpM85ar7v/3bv3XrnqS/293upivasWd9JFK58vxqV7uaflQ14DMFVxoz/0c+8pHtTW96k/yf/vSnSx+3FCGBvCiZTZKXZDNfGnCbO/wZj+QzV17z19NcLZ1vSxoxKbTXguQ9t6Thi4bnPve5OndlLWyDZz3rWZIB+zonj/OXBdPPSGutKb7ImF7hz/3XuW0L5Hjc2udpT3ua9jNf7Nz97nfXXyMyxyc+8Yn6ywRu+YJ/EP6MnecMoSM5nXUbPb6Y86J1c77PD+dyGyI+P772ta8d24g+P676mMc8pv3cz/2ce3T2Za4Zvpzix4c5RivRfpBjB0lRFMWWxT6o8U64Yh+8VuzNz6Uz7IPayhWveEUV+5CwsmfPHtcURVEUP2rsZHTFTpD1Ov2QhzxkxU6EXNPhNfmVr3zlymGHHbZywxvecOX973+/a4qiKIqi+NSnPrVyhStcQe+jb3nLW1w6g/OfBz/4wSvbtm1buf/977/y9a9/3TWLIQaxfvM3f9Ml83zyk59cue9977ty9NFHy47Ce/S9733vlc985jNu1TnnnHNW7nKXu6xs37595V73utfKMcccM3yOO+64lde97nUrO3bscOsZu3btWvmzP/uzleOPP37YM94pp5yy8rnPfc6tZpx99tkrv/Ebv7Fy1FFHyfaQQw5ZucMd7rDy1re+VbEyfI444YQTZIf97/zO76ycf/75Kx/96EdXrna1q43x+Mxx2mmnudeMM844Y+Wud73rGOdBD3rQyplnninbI488UvL73e9+K9///vdXvvSlL63c9KY3HTE532ScteBzD+ensfYjjjhCa2ONL37xi0ese9zjHhqDQjvk2HNOC4zFmKHLa3rRi16k/Ra6Bz7wgQvjYYNthvU++tGPHv5sh1ve8pYrp556qrZlwLGUx4g5w/Oe9zz5he4xj3nMygUXXLDh4wtbfMKGWMTMxDwvd7nLDbub3/zmOramx8W+zHXKRz7ykZXDDz984efZ4uBiGw92sBRFURRFURRFURRFURQHKVxFzO1YuCr5/e9//7gH+r7Ald33uc99dAvPD3zgA+22t72ta4pia0Fa9QlPeEL7wz/8Q/0mwk//9E+7pjgYqVvHFEVRFEVRFEVRFEVRHOTw45D88CS3wOB2M3VdZlGsD7dBJMnOLYl+4id+wqXFwUol2ouiKIqiKIqiKIqiKArdu/oFL3iB7sN96qmnVrK9KNbg9NNPb6eccoquYuc5c3H+wGqxOalEe1EURVEURVEURVEURaEfUX3yk5+sH7rkR0Uf97jHjR+m3BvOPvts/agqP8j57W9/26VFsTXgB1bf+MY3ttvf/vYqb3vb29qVrnQl1xYHM5VoL4qiKIqiKIqiKIqiKATJ9pNPPrmdccYZ7YgjjmgPfvCD2/e//33Xrg8J9p//+Z9vX/3qV3VFPO2XvOQlri2Kzc9zn/vc9qY3van94z/+Y3vLW95SSfZiUD+GWhRFURRFURRFURRFURRFURT7QV3RXhRFURRFURRFURRFURRFURT7QSXai6IoiqIoiqIoiqIoiqIoimI/qER7URRFURRFURRFURRFURRFUewHlWgviqIoiqIoiqIoiqIoiqIoiv2gEu1FURRFURRFURRFURRFURRFsR9Uor0oiqIoiqIoiqIoiqIoiqIo9oNKtBdFURRFURRFURRFURRFURTFflCJ9qIoiqIoiqIoiqIoiqIoiqLYDyrRXhRFURRFURRFURRFURRFURT7QSXai6IoiqIoiqIoiqIoiqIoimI/qER7URRFURRFURRFURRFURRFUewHlWgviqIoiqIoiqIoiqIoiqIoin2mtf8/E9MfFk18CjsAAAAASUVORK5CYII="}}},{"cell_type":"code","source":"import matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\n","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:06:16.404351Z","iopub.execute_input":"2025-08-26T21:06:16.40468Z","iopub.status.idle":"2025-08-26T21:06:16.417565Z","shell.execute_reply.started":"2025-08-26T21:06:16.404648Z","shell.execute_reply":"2025-08-26T21:06:16.416375Z"},"papermill":{"duration":0.682433,"end_time":"2024-08-28T10:29:21.778336","exception":false,"start_time":"2024-08-28T10:29:21.095903","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Import library\n<a id=\"import\"></a>","metadata":{"papermill":{"duration":0.018066,"end_time":"2024-08-28T10:29:21.814676","exception":false,"start_time":"2024-08-28T10:29:21.79661","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.models import load_model\nimport tensorflow as tf \nimport random \nimport os\nfrom tensorflow.keras.losses import MeanAbsoluteError\nfrom matplotlib.ticker import ScalarFormatter\nimport pandas as pd\n\nfrom sklearn.model_selection import train_test_split","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:06:16.446001Z","iopub.execute_input":"2025-08-26T21:06:16.446365Z","iopub.status.idle":"2025-08-26T21:06:39.729597Z","shell.execute_reply.started":"2025-08-26T21:06:16.446324Z","shell.execute_reply":"2025-08-26T21:06:39.728419Z"},"papermill":{"duration":11.775222,"end_time":"2024-08-28T10:29:33.607461","exception":false,"start_time":"2024-08-28T10:29:21.832239","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Setup Paths and Read Data","metadata":{"papermill":{"duration":0.017896,"end_time":"2024-08-28T10:29:33.646024","exception":false,"start_time":"2024-08-28T10:29:33.628128","status":"completed"},"tags":[]}},{"cell_type":"code","source":"path_folder = '/kaggle/input/ariel-2025-new/'\ndata_folder = os.path.join(path_folder,'data') # path to the folder containing the data\nauxiliary_folder = '/kaggle/input/ariel-data-challenge-2025/' # path to the folder containing the train targets and wavelengths informations\n","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:06:39.731984Z","iopub.execute_input":"2025-08-26T21:06:39.732708Z","iopub.status.idle":"2025-08-26T21:06:39.739588Z","shell.execute_reply.started":"2025-08-26T21:06:39.732672Z","shell.execute_reply":"2025-08-26T21:06:39.737632Z"},"papermill":{"duration":0.025357,"end_time":"2024-08-28T10:29:33.689486","exception":false,"start_time":"2024-08-28T10:29:33.664129","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data_train = np.load(f'{path_folder}/data_train.npy')[:,:,:,10:-10]\n# data_train_FGS = np.load(f'{data_folder}/data_train_FGS.npy')","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:06:39.740829Z","iopub.execute_input":"2025-08-26T21:06:39.741254Z","iopub.status.idle":"2025-08-26T21:08:03.936836Z","shell.execute_reply.started":"2025-08-26T21:06:39.741214Z","shell.execute_reply":"2025-08-26T21:08:03.935287Z"},"papermill":{"duration":158.169758,"end_time":"2024-08-28T10:32:11.877053","exception":false,"start_time":"2024-08-28T10:29:33.707295","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"data_train.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:03.938595Z","iopub.execute_input":"2025-08-26T21:08:03.939081Z","iopub.status.idle":"2025-08-26T21:08:03.9496Z","shell.execute_reply.started":"2025-08-26T21:08:03.939033Z","shell.execute_reply":"2025-08-26T21:08:03.948521Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"We create a directory to save the outputs of this notebook, and define the hyperparameters of the model","metadata":{"papermill":{"duration":0.017601,"end_time":"2024-08-28T10:32:11.913695","exception":false,"start_time":"2024-08-28T10:32:11.896094","status":"completed"},"tags":[]}},{"cell_type":"code","source":"output_dir = './output_wlt_transform'\n\nSEED = 42\n\ndo_the_mcdropout_wc = True\ndo_the_mcdropout = True\n\nif not os.path.exists(output_dir):\n    os.makedirs(output_dir)\n    print(f\"Directory {output_dir} created.\")\nelse:\n    print(f\"Directory {output_dir} already exists.\")\n","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:08:03.952796Z","iopub.execute_input":"2025-08-26T21:08:03.953163Z","iopub.status.idle":"2025-08-26T21:08:03.977858Z","shell.execute_reply.started":"2025-08-26T21:08:03.953138Z","shell.execute_reply":"2025-08-26T21:08:03.976419Z"},"papermill":{"duration":0.027216,"end_time":"2024-08-28T10:32:11.958715","exception":false,"start_time":"2024-08-28T10:32:11.931499","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Extract the main characteristics","metadata":{"papermill":{"duration":0.018671,"end_time":"2024-08-28T10:32:11.995658","exception":false,"start_time":"2024-08-28T10:32:11.976987","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train_solution = np.loadtxt(f'{auxiliary_folder}/train.csv', delimiter = ',', skiprows = 1)  \ntargets = train_solution[:,2:] ### <-- it should really be 283, but we have omitted FGS1 for now, hence 282. \nprint(\"Targets shape: \", targets.shape)\nN = targets.shape[0]\n","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:08:03.978942Z","iopub.execute_input":"2025-08-26T21:08:03.979291Z","iopub.status.idle":"2025-08-26T21:08:04.172054Z","shell.execute_reply.started":"2025-08-26T21:08:03.979263Z","shell.execute_reply":"2025-08-26T21:08:04.171208Z"},"papermill":{"duration":0.158066,"end_time":"2024-08-28T10:32:12.208136","exception":false,"start_time":"2024-08-28T10:32:12.05007","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Split the targets and observations between valid and train\n","metadata":{"papermill":{"duration":0.017898,"end_time":"2024-08-28T10:32:20.583919","exception":false,"start_time":"2024-08-28T10:32:20.566021","status":"completed"},"tags":[]}},{"cell_type":"code","source":"cut_inf, cut_sup = 39, 321 \nl = cut_sup - cut_inf + 1 \nwls = np.arange(l)\nprint(wls.shape)\n\n\nN_train = np.arange(data_train.shape[0])\n\nlist_index_train, list_index_valid = train_test_split(N_train,test_size=0.33, random_state=42)\n\ntrain_obs, valid_obs, = data_train[list_index_train,], data_train[list_index_valid, :]\n\ntrain_targets, valid_targets = targets[list_index_train, :], targets[list_index_valid, :]\n","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:08:04.173137Z","iopub.execute_input":"2025-08-26T21:08:04.173529Z","iopub.status.idle":"2025-08-26T21:08:11.327285Z","shell.execute_reply.started":"2025-08-26T21:08:04.173489Z","shell.execute_reply":"2025-08-26T21:08:11.326456Z"},"papermill":{"duration":0.113969,"end_time":"2024-08-28T10:32:20.715863","exception":false,"start_time":"2024-08-28T10:32:20.601894","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('Train obs shape: ', train_obs.shape)\nprint('Train targets shape: ', train_targets.shape)\nprint('Valid obs shape: ', valid_obs.shape)\nprint('Valid targets shape: ', valid_targets.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:11.328212Z","iopub.execute_input":"2025-08-26T21:08:11.328479Z","iopub.status.idle":"2025-08-26T21:08:11.334683Z","shell.execute_reply.started":"2025-08-26T21:08:11.32846Z","shell.execute_reply":"2025-08-26T21:08:11.333412Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Data pre-processing","metadata":{"papermill":{"duration":0.017856,"end_time":"2024-08-28T10:32:12.031996","exception":false,"start_time":"2024-08-28T10:32:12.01414","status":"completed"},"tags":[]}},{"cell_type":"code","source":"from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\nfrom concurrent.futures import ProcessPoolExecutor\nfrom tqdm import tqdm  \nimport pywt \nimport joblib","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:11.336024Z","iopub.execute_input":"2025-08-26T21:08:11.336429Z","iopub.status.idle":"2025-08-26T21:08:11.495813Z","shell.execute_reply.started":"2025-08-26T21:08:11.336389Z","shell.execute_reply":"2025-08-26T21:08:11.494434Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Aperture photometry\n \nThis code extracts a star's brightness from Ariel’s 4D data by summing light in a small window (aperture) around the center of the image. For each time and wavelength, it adds up 7 pixels (a=3) to measure the flux.","metadata":{}},{"cell_type":"code","source":"####################################### APPERTURE PHOTOMETRY #######################################\ndef apperture_photometry (obs, a) :\n    N_samples, shape_t, shape_wl, shape_x = obs.shape\n    flux = np.zeros((N_samples, shape_t, shape_wl))\n    for i in range(N_samples) : \n        for wl in range(shape_wl) : \n            flux[i,:,wl] = np.sum(obs[i,:,wl, shape_x//2 - a: shape_x//2 + a + 1], axis = 1)\n    return flux\n        \nflux_train = apperture_photometry(train_obs, 3)\nflux_valid = apperture_photometry(valid_obs, 3)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:11.497027Z","iopub.execute_input":"2025-08-26T21:08:11.49874Z","iopub.status.idle":"2025-08-26T21:08:17.42325Z","shell.execute_reply.started":"2025-08-26T21:08:11.498708Z","shell.execute_reply":"2025-08-26T21:08:17.421883Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(6, 4))\nplt.plot(flux_train[10,:,100], 'k.')\nplt.title('Aperture photometry')\nplt.xlabel('Time')\nplt.ylabel('Flux')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:17.424437Z","iopub.execute_input":"2025-08-26T21:08:17.424717Z","iopub.status.idle":"2025-08-26T21:08:17.861758Z","shell.execute_reply.started":"2025-08-26T21:08:17.424695Z","shell.execute_reply":"2025-08-26T21:08:17.860635Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure()\nplt.imshow(flux_train[0,:,:], aspect='auto')\nplt.colorbar()\nplt.ylabel('Time')\nplt.xlabel('Wavelength')\nplt.title('Aperture photometry')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:17.862933Z","iopub.execute_input":"2025-08-26T21:08:17.863343Z","iopub.status.idle":"2025-08-26T21:08:18.277366Z","shell.execute_reply.started":"2025-08-26T21:08:17.863285Z","shell.execute_reply":"2025-08-26T21:08:18.276395Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def get_star_spectrum (flux) : \n    flux_OOT = (flux[:,:10,:].mean(axis = 1) + flux[:,-10:,:].mean(axis = 1))/2\n    return flux_OOT\n\nflux_OOT_train = get_star_spectrum(flux_train)\nflux_norm_train = flux_train / flux_OOT_train[:,np.newaxis,:]\n\nflux_OOT_valid = get_star_spectrum(flux_valid)\nflux_norm_valid = flux_valid / flux_OOT_valid[:,np.newaxis,:]\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:18.278395Z","iopub.execute_input":"2025-08-26T21:08:18.278706Z","iopub.status.idle":"2025-08-26T21:08:18.816526Z","shell.execute_reply.started":"2025-08-26T21:08:18.278683Z","shell.execute_reply":"2025-08-26T21:08:18.815492Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure()\nplt.imshow(flux_norm_train[0,:,:], aspect='auto', vmax=1)\nplt.colorbar()\nplt.ylabel('Time')\nplt.xlabel('Wavelength')\nplt.title('Aperture photometry')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:18.823353Z","iopub.execute_input":"2025-08-26T21:08:18.824469Z","iopub.status.idle":"2025-08-26T21:08:19.26438Z","shell.execute_reply.started":"2025-08-26T21:08:18.824434Z","shell.execute_reply":"2025-08-26T21:08:19.263178Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(6, 4))\nplt.plot(flux_norm_train[0,:,100], 'r.')\nplt.title('Aperture photometry, normalized by the star spectrum')\nplt.xlabel('Time')\nplt.ylabel('Flux')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:19.265565Z","iopub.execute_input":"2025-08-26T21:08:19.265853Z","iopub.status.idle":"2025-08-26T21:08:19.489723Z","shell.execute_reply.started":"2025-08-26T21:08:19.265831Z","shell.execute_reply":"2025-08-26T21:08:19.488672Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"####################################### CHARACTERISTICS EXTRACTION #######################################\n\n\ndef wavelet_transform(flux):\n    wavelet = 'db4'\n    N_samples, shape_t, shape_wl = flux.shape\n    cAs = [] # Low frequencies\n    cDs = [] # High frequencies\n    for i in range(N_samples):\n        cA_list = []\n        cD_list = []\n        for wl in range(shape_wl):\n            signal = flux[i, :, wl]\n            cA, cD = pywt.dwt(signal, wavelet)\n            cA_list.append(cA)\n            cD_list.append(cD)\n        \n        cA_stack = np.stack(cA_list, axis=-1)  \n        cD_stack = np.stack(cD_list, axis=-1)  \n        cAs.append(cA_stack)\n        cDs.append(cD_stack)\n    \n    cAs = np.stack(cAs, axis=0) \n    cDs = np.stack(cDs, axis=0) \n\n    coeffs_all = cAs\n    return coeffs_all\n\nclass WaveletNormalizer:\n    def __init__(self):\n        self.cA_min = None\n        self.cA_max = None\n        self.cD_min = None\n        self.cD_max = None\n\n    def fit(self, coeffs_all):\n        \"\"\"Fit on training data only.\"\"\"\n        cA = coeffs_all\n        self.cA_min = np.percentile(cA, 2, axis=(0,1), keepdims=True)\n        self.cA_max = np.percentile(cA, 98, axis=(0,1), keepdims=True)\n\n\n    def transform(self, coeffs_all):\n        \"\"\"Apply normalization using stored training stats.\"\"\"\n        cA = coeffs_all\n        cA_norm = (cA - self.cA_min) / (self.cA_max - self.cA_min + 1e-8)\n        return cA_norm\n\nwlt_train = wavelet_transform(flux_norm_train)\nwlt_val = wavelet_transform(flux_norm_valid)\n\nnormalizer = WaveletNormalizer()\nnormalizer.fit(wlt_train)             \nwlt_train = normalizer.transform(wlt_train)\nwlt_val = normalizer.transform(wlt_val)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:19.490888Z","iopub.execute_input":"2025-08-26T21:08:19.491188Z","iopub.status.idle":"2025-08-26T21:08:26.988862Z","shell.execute_reply.started":"2025-08-26T21:08:19.491166Z","shell.execute_reply":"2025-08-26T21:08:26.987765Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(10, 6))\n\nplt.subplot(1, 2, 1)\nplt.imshow(flux_norm_train[0,:,:], aspect='auto')\nplt.colorbar()\nplt.xlabel('Wavelength')\nplt.ylabel('Time')\n\nplt.subplot(1, 2, 2)\nplt.imshow(flux_norm_train[10,:,:], aspect='auto')\nplt.colorbar()\nplt.ylabel('Time')\nplt.xlabel('Wavelength')\n\nplt.tight_layout()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:26.99014Z","iopub.execute_input":"2025-08-26T21:08:26.990526Z","iopub.status.idle":"2025-08-26T21:08:27.774493Z","shell.execute_reply.started":"2025-08-26T21:08:26.990502Z","shell.execute_reply":"2025-08-26T21:08:27.773431Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.figure(figsize=(10, 6))\n\nplt.subplot(1, 2, 1)\nplt.imshow(wlt_train[0,:,:], aspect='auto', vmin = 0, vmax = 1.2)\nplt.colorbar()\nplt.xlabel('Wavelength')\nplt.ylabel('Wavelet Coefficients')\n\nplt.subplot(1, 2, 2)\nplt.imshow(wlt_train[10,:,:], aspect='auto', vmin = 0, vmax = 1.2)\nplt.colorbar()\nplt.xlabel('Wavelength')\nplt.ylabel('Wavelet Coefficients')\n\nplt.tight_layout()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:27.775555Z","iopub.execute_input":"2025-08-26T21:08:27.775895Z","iopub.status.idle":"2025-08-26T21:08:28.535621Z","shell.execute_reply.started":"2025-08-26T21:08:27.775872Z","shell.execute_reply":"2025-08-26T21:08:28.534651Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range (train_targets.shape[0]) : \n    plt.plot(train_targets[i,:], alpha = 0.4)\nplt.title('Targets')\nplt.xlabel('Wavelength')\nplt.ylabel(f'$(Rp/Rs)^2$')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:28.53683Z","iopub.execute_input":"2025-08-26T21:08:28.537286Z","iopub.status.idle":"2025-08-26T21:08:29.353027Z","shell.execute_reply.started":"2025-08-26T21:08:28.53725Z","shell.execute_reply":"2025-08-26T21:08:29.352107Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"######################################### NORMALIZING TARGETS ####################################################\nscaler = RobustScaler()\nscaler.fit(train_targets)\njoblib.dump(scaler, f'{output_dir}/scaler_target.pkl')\ntrain_targets_scaled = scaler.transform(train_targets)\nvalid_targets_scaled = scaler.transform(valid_targets)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:29.354173Z","iopub.execute_input":"2025-08-26T21:08:29.354622Z","iopub.status.idle":"2025-08-26T21:08:29.412742Z","shell.execute_reply.started":"2025-08-26T21:08:29.35459Z","shell.execute_reply":"2025-08-26T21:08:29.411809Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range (train_targets_scaled.shape[0]) : \n    plt.plot(train_targets_scaled[i,:], alpha = 0.4)\nplt.title('Targets')\nplt.xlabel('Wavelength')\nplt.ylabel(f'$(Rp/Rs)^2$ scaled')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:29.413778Z","iopub.execute_input":"2025-08-26T21:08:29.414047Z","iopub.status.idle":"2025-08-26T21:08:30.730264Z","shell.execute_reply.started":"2025-08-26T21:08:29.414028Z","shell.execute_reply":"2025-08-26T21:08:30.729149Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Train 2D CNN\nThis part is to demonstrate how one can go about training a simple 2D CNN model. \n","metadata":{"papermill":{"duration":0.022849,"end_time":"2024-08-28T10:32:22.792923","exception":false,"start_time":"2024-08-28T10:32:22.770074","status":"completed"},"tags":[]}},{"cell_type":"code","source":"\ntrain_data = wlt_train\nvalid_data = wlt_val\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:30.731282Z","iopub.execute_input":"2025-08-26T21:08:30.731616Z","iopub.status.idle":"2025-08-26T21:08:30.736111Z","shell.execute_reply.started":"2025-08-26T21:08:30.731594Z","shell.execute_reply":"2025-08-26T21:08:30.735039Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from keras.layers import Input, Conv2D, MaxPooling2D, Dense, Dropout, BatchNormalization, GlobalAveragePooling2D\nfrom keras.models import Model\nimport tensorflow as tf\nimport numpy as np\nfrom keras.optimizers import Adam\nfrom keras.metrics import MeanAbsoluteError\n\ncheckpoint_filepath = f'{output_dir}/model_cnn.weights.h5'\ninput_obs = Input((train_data.shape[1], train_data.shape[2], 1), name='input_obs')\n\n# Simpler CNN architecture\nx = Conv2D(32, (3, 3), activation='relu', padding='same')(input_obs)\nx = MaxPooling2D((2, 2))(x)\nx = BatchNormalization()(x)\n\nx = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\nx = MaxPooling2D((2, 2))(x)\nx = BatchNormalization()(x)\n\nx = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\nx = GlobalAveragePooling2D()(x)\n\nx = Dense(700, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\nx = Dropout(0.2)(x)\nx = Dense(500, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001))(x)\nx = Dropout(0.2)(x)\n\noutput = Dense(282, activation='linear')(x) ## note that its 282 not 283! you need to process the FGS1 as well!\n\nmodel = Model(inputs=input_obs, outputs=output)\nmodel.compile(optimizer=Adam(0.001), loss='mse', metrics=[MeanAbsoluteError()])\nmodel.summary()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:30.737048Z","iopub.execute_input":"2025-08-26T21:08:30.73728Z","iopub.status.idle":"2025-08-26T21:08:31.101245Z","shell.execute_reply.started":"2025-08-26T21:08:30.737263Z","shell.execute_reply":"2025-08-26T21:08:31.100512Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n\n# Save the best weights (only weights, faster and lighter)\nmodel_checkpoint = ModelCheckpoint(\n    filepath=checkpoint_filepath,\n    monitor='val_loss',\n    save_best_only=True,\n    save_weights_only=True,  \n    mode='min',\n    verbose=1\n)\n\n# Early Stopping if no improvement after patience epochs\nearly_stopping = EarlyStopping(\n    monitor='val_loss',\n    patience=30,  # Number of epochs without improvement before stopping\n    restore_best_weights=True,\n    verbose=1\n)\n\n# reduce LR when a plateau is detected\nreduce_lr = ReduceLROnPlateau(\n    monitor='val_loss',\n    factor=0.5,  \n    patience=10,\n    min_lr=1e-6,\n    verbose=1\n)\n\n# Train the model\nhistory = model.fit(\n    x = train_data,  \n    y = train_targets_scaled,\n    validation_data = (valid_data, valid_targets_scaled),  \n    batch_size=32, \n    epochs=100,\n    callbacks=[model_checkpoint, reduce_lr, early_stopping], \n    shuffle=True,\n    verbose=1\n)\n\n\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:08:31.102265Z","iopub.execute_input":"2025-08-26T21:08:31.102663Z","iopub.status.idle":"2025-08-26T21:54:18.971658Z","shell.execute_reply.started":"2025-08-26T21:08:31.102629Z","shell.execute_reply":"2025-08-26T21:54:18.96945Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.plot(history.history['loss'], label='Training Loss')\nplt.plot(history.history['val_loss'], label='Validation Loss')\nplt.legend()\nplt.yscale('log')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:54:18.975486Z","iopub.execute_input":"2025-08-26T21:54:18.976149Z","iopub.status.idle":"2025-08-26T21:54:19.382232Z","shell.execute_reply.started":"2025-08-26T21:54:18.976083Z","shell.execute_reply":"2025-08-26T21:54:19.381293Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Then, we perform the MC Dropout to obtain the mean prediction and the uncertainty associated. We choose to compute 50 instances.","metadata":{"papermill":{"duration":0.022555,"end_time":"2024-08-28T10:35:17.964097","exception":false,"start_time":"2024-08-28T10:35:17.941542","status":"completed"},"tags":[]}},{"cell_type":"code","source":"nb_dropout = 50\nscaler = joblib.load(f'{output_dir}/scaler_target.pkl')\n\n\ndef NN_uncertainity(model, x_test, scaler, T=5):\n    predictions = []\n    for _ in range(T):\n        pred_norm = model.predict([x_test],verbose=1)\n        pred = scaler.inverse_transform(pred_norm)\n        predictions += [pred]  \n    mean, std = np.mean(np.array(predictions), axis=0), np.std(np.array(predictions), axis=0)\n    return mean, std\n\n    \nspectre_valid_shift, spectre_valid_shift_std = NN_uncertainity(model, [valid_data], scaler, T = nb_dropout)\n\n","metadata":{"execution":{"iopub.status.busy":"2025-08-26T21:54:19.383498Z","iopub.execute_input":"2025-08-26T21:54:19.383844Z","iopub.status.idle":"2025-08-26T21:57:17.033441Z","shell.execute_reply.started":"2025-08-26T21:54:19.383815Z","shell.execute_reply":"2025-08-26T21:57:17.032272Z"},"papermill":{"duration":62.331658,"end_time":"2024-08-28T10:36:20.318519","exception":false,"start_time":"2024-08-28T10:35:17.986861","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Look at results on validation dataset","metadata":{}},{"cell_type":"code","source":"wavelength = np.loadtxt(auxiliary_folder+'wavelengths.csv', skiprows=1, delimiter = ',')[2:]\nuncertainty_fixed  = 0.05*spectre_valid_shift #spectre_valid_shift_std\nuncertainty = uncertainty_fixed\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:57:17.034918Z","iopub.execute_input":"2025-08-26T21:57:17.035319Z","iopub.status.idle":"2025-08-26T21:57:17.051014Z","shell.execute_reply.started":"2025-08-26T21:57:17.035258Z","shell.execute_reply":"2025-08-26T21:57:17.050074Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def plot_one_sample_valid(ax, p):\n    ax.set_title(f'Result for sample {p} ')\n    line1, = ax.plot(wavelength, spectre_valid_shift[p][1:], '.k', label='Prediction')\n    line2, = ax.plot(wavelength, valid_targets[p][1:], color='tomato', label='Target')\n    ax.fill_between(wavelength, spectre_valid_shift[p, :][1:] - uncertainty[p][1:], spectre_valid_shift[p, :][1:] + uncertainty[p][1:], color='silver', alpha=0.8, label='Uncertainty')\n    ax.set_ylabel(f'$(R_p/R_s)^2$')\n    ax.set_xlabel(f'Wavelength ($\\mu$m)')\n    return line1, line2\n\n\nnum_samples = 16\nrows, cols = 4, 4\n\nfig, axs = plt.subplots(rows, cols, figsize=(15, 10))\nsamples = [1, 2, 7, 15, 20, 25, 30, 35, 40, 45, 46, 47, 6, 5, 8, 9]\nlines = []\n\nfor i, ax in enumerate(axs.flat):\n    lines.extend(plot_one_sample_valid(ax, samples[i]))\n\nfig.legend(lines[:2], ['Prediction', 'Target'], loc='upper center', ncol=3, bbox_to_anchor=(0.5, -0.05))\nfig.suptitle('Validation dataset')\nplt.tight_layout()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:57:17.053378Z","iopub.execute_input":"2025-08-26T21:57:17.054402Z","iopub.status.idle":"2025-08-26T21:57:19.634614Z","shell.execute_reply.started":"2025-08-26T21:57:17.054359Z","shell.execute_reply":"2025-08-26T21:57:19.633608Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"predictions = spectre_valid_shift\ntargets_plot = valid_targets\nstd = uncertainty_fixed \n\npredictions_concatenated_plot = np.concatenate(predictions, axis=0)\nwls_concatenated = np.arange(predictions_concatenated_plot.shape[0])\ntargets_concatenated_plot = np.concatenate(targets_plot, axis=0)\nspectre_valid_std_concatenated = np.concatenate(std, axis=0)\nresiduals = targets_concatenated_plot - predictions_concatenated_plot\nuncertainty = spectre_valid_std_concatenated\n\nfig, axs = plt.subplots(2, 1, figsize=(9, 8), gridspec_kw={'height_ratios': [3, 1]})\n\n\naxs[0].plot(wls_concatenated, predictions_concatenated_plot, '-', color='k', label=\"Prediction\")\naxs[0].plot(wls_concatenated, targets_concatenated_plot, '-', color='tomato', label=\"Target\")\naxs[0].fill_between(np.arange(len(wls_concatenated)), \n                    predictions_concatenated_plot - uncertainty, \n                    predictions_concatenated_plot + uncertainty, \n                    color='silver', alpha=1, label='Uncertainty')\naxs[0].set_xlabel('Concatenated wavelengths for all planets')\naxs[0].set_ylabel(f'$(R_p/R_s)^2$')\naxs[0].set_title('Prediction vs target, validation dataset')\naxs[0].legend()\n\naxs[1].plot(wls_concatenated, residuals, '-', color='cornflowerblue', label=\"Residual\")\naxs[1].fill_between(np.arange(len(wls_concatenated)), \n                    residuals - uncertainty, \n                    residuals + uncertainty, \n                    color='lightblue', alpha=0.9, label='Uncertainty')\naxs[1].set_xlabel('Concatenated wavelengths for all planets')\naxs[1].set_ylabel('Residual')\naxs[1].set_title('Residuals with Uncertainty')\naxs[1].legend()\n\nplt.tight_layout()\nplt.show()\n\nprint('MSE : ',np.sqrt((residuals**2).mean())*1e6, 'ppm')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-08-26T21:57:19.636119Z","iopub.execute_input":"2025-08-26T21:57:19.636434Z","iopub.status.idle":"2025-08-26T21:57:21.070277Z","shell.execute_reply.started":"2025-08-26T21:57:19.636413Z","shell.execute_reply":"2025-08-26T21:57:21.069202Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}