{ "cells": [ { "cell_type": "markdown", "id": "486f3cc3-ada8-4fc2-9055-bf55597b8ab0", "metadata": {}, "source": [ "# Flattening and glacier gridpoint selection tests:" ] }, { "cell_type": "markdown", "id": "0fbca102-4775-4389-b48c-bc0f04900593", "metadata": {}, "source": [ "- the tests take too long inside of the OGGM framework and would need all the flattened datasets. Therefore it is done here:\n", " - We check if the climate is the same of the flattened and unflattened files,\n", " - we also check if GSWP3-W5E5 is equal to the flattened W5E5v2.0 in the common time period!\n", " - did we always select the nearest glacier gridpoints? (this was wrong for around 11900 glaciers in v2022.2, but now work in v2023.2 upwards)\n", "- created test GSWP3-W5E5, ERA5 and ISIMIP3b files for pytest\n", "\n", "**Currently updated to check if RGI6, RGI7C and RGI7G glacier gridpoints are correctly included**" ] }, { "cell_type": "code", "execution_count": 14, "id": "65269134-d855-49fd-9005-725bd2f19194", "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import time\n", "from oggm import utils\n", "import sys\n", "import pandas as pd\n", "import os" ] }, { "cell_type": "markdown", "id": "f78e7cfd-ede7-4ffb-a820-9e2299e8a075", "metadata": {}, "source": [ "**Let's get all glacier longitude / latitude to check later the distance to the nearest gridpoints:**" ] }, { "cell_type": "code", "execution_count": 38, "id": "b700c805-2c0c-4097-a638-0cc543a7fdf7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RGI60-01.00001 (-146.823, 63.689)\n", "RGI60-01.00002 (-146.668, 63.404)\n", "RGI60-01.00003 (-146.08, 63.376)\n", "RGI60-01.00004 (-146.12, 63.381)\n", "RGI60-01.00005 (-147.057, 63.551)\n", " ... \n", "RGI2000-v7.0-G-19-02738 (-3.254146774869713, -71.1422615)\n", "RGI2000-v7.0-G-19-02739 (1.161198746966389, -70.2348605)\n", "RGI2000-v7.0-G-19-02740 (2.039157613598415, -70.63090700000001)\n", "RGI2000-v7.0-G-19-02741 (2.929238357467267, -70.5055405)\n", "RGI2000-v7.0-G-19-02742 (4.329046288919026, -70.37280200000001)\n", "Name: coords, Length: 683902, dtype: object" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get the dataset where coordinates of glaciers are stored\n", "frgi_6 = utils.file_downloader('https://cluster.klima.uni-bremen.de/~oggm/rgi/rgi62_stats.h5')\n", "odf_6 = pd.read_hdf(frgi_6, index_col=0)\n", "odf_6['coords'] = [(lon,lat) for lon,lat in zip(odf_6['CenLon'],odf_6['CenLat'])]\n", "\n", "#### glacier complexes\n", "frgi_7C = utils.file_downloader('https://cluster.klima.uni-bremen.de/~oggm/rgi/RGI2000-v7.0-C-global-attributes.csv')\n", "odf_7C = pd.read_csv(frgi_7C, index_col=0)\n", "odf_7C['coords'] = [(lon,lat) for lon,lat in zip(odf_7C['cenlon'],odf_7C['cenlat'])]\n", "#### glaciers \n", "frgi_7G = utils.file_downloader('https://cluster.klima.uni-bremen.de/~oggm/rgi/RGI2000-v7.0-G-global-attributes.csv')\n", "odf_7G = pd.read_csv(frgi_7G, index_col=0)\n", "odf_7G['coords'] = [(lon,lat) for lon,lat in zip(odf_7G['cenlon'],odf_7G['cenlat'])]\n", "\n", "# this includes now all cen lon/latitudes from the three versions, there may be \"duplicates\", but that just means that the long test takes a bit longer \n", "odf = pd.concat([odf_6, odf_7C, odf_7G]) \n", "odf['coords']" ] }, { "cell_type": "markdown", "id": "98fca757-3883-4ffe-9cd8-bfec6adccf01", "metadata": {}, "source": [ "### We start by the ultimative test that should work at the end (after reproducing preprocessing levels 3)\n", "- todo: once RGI7 gdirs are out, need to repeat this with these ones!!!" ] }, { "cell_type": "code", "execution_count": 71, "id": "92e4dd10-45e9-4b24-a9e4-a94e210a8b6e", "metadata": { "scrolled": true }, "outputs": [ { "ename": "ValueError", "evalue": "No objects to concatenate", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[71]\u001b[39m\u001b[32m, line 14\u001b[39m\n\u001b[32m 11\u001b[39m odf_prepro = pd.read_csv(filename, low_memory=\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[32m 12\u001b[39m li.append(odf_prepro)\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m odf_prepro = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mli\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 15\u001b[39m condi1 = np.abs(odf_prepro.cenlon - odf_prepro.baseline_climate_ref_pix_lon)>\u001b[32m0.25\u001b[39m\n\u001b[32m 16\u001b[39m condi2 = np.abs(odf_prepro.cenlat - odf_prepro.baseline_climate_ref_pix_lat)>\u001b[32m0.25\u001b[39m\n", "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/pandas/core/reshape/concat.py:382\u001b[39m, in \u001b[36mconcat\u001b[39m\u001b[34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[39m\n\u001b[32m 379\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m copy \u001b[38;5;129;01mand\u001b[39;00m using_copy_on_write():\n\u001b[32m 380\u001b[39m copy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m382\u001b[39m op = \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 383\u001b[39m \u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 384\u001b[39m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 385\u001b[39m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m=\u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 386\u001b[39m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m=\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 387\u001b[39m \u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 388\u001b[39m \u001b[43m \u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m=\u001b[49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 389\u001b[39m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 390\u001b[39m \u001b[43m \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[43m=\u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 391\u001b[39m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m=\u001b[49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 392\u001b[39m \u001b[43m \u001b[49m\u001b[43msort\u001b[49m\u001b[43m=\u001b[49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 393\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 395\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m op.get_result()\n", "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/pandas/core/reshape/concat.py:445\u001b[39m, in \u001b[36m_Concatenator.__init__\u001b[39m\u001b[34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[39m\n\u001b[32m 442\u001b[39m \u001b[38;5;28mself\u001b[39m.verify_integrity = verify_integrity\n\u001b[32m 443\u001b[39m \u001b[38;5;28mself\u001b[39m.copy = copy\n\u001b[32m--> \u001b[39m\u001b[32m445\u001b[39m objs, keys = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_clean_keys_and_objs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 447\u001b[39m \u001b[38;5;66;03m# figure out what our result ndim is going to be\u001b[39;00m\n\u001b[32m 448\u001b[39m ndims = \u001b[38;5;28mself\u001b[39m._get_ndims(objs)\n", "\u001b[36mFile \u001b[39m\u001b[32m~/mambaforge/envs/oggm_env_2025/lib/python3.11/site-packages/pandas/core/reshape/concat.py:507\u001b[39m, in \u001b[36m_Concatenator._clean_keys_and_objs\u001b[39m\u001b[34m(self, objs, keys)\u001b[39m\n\u001b[32m 504\u001b[39m objs_list = \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[32m 506\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs_list) == \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m507\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mNo objects to concatenate\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 509\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 510\u001b[39m objs_list = \u001b[38;5;28mlist\u001b[39m(com.not_none(*objs_list))\n", "\u001b[31mValueError\u001b[39m: No objects to concatenate" ] } ], "source": [ "import glob\n", "sum_paths = ['/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/W5E5/per_glacier_spinup/RGI62/b_160/L3/summary',\n", " '/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/W5E5/regional_spinup/RGI62/b_160/L3/summary/',\n", " '/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/ERA5/per_glacier_spinup/RGI62/b_160/L3/summary/',\n", " '/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2023.3/elev_bands/W5E5_spinup/RGI62/b_160/L3/summary/']\n", "for sum_path in sum_paths:\n", " all_files = glob.glob(f'{sum_path}/glacier_statistics_*.csv')\n", " \n", " li = []\n", " for filename in all_files:\n", " odf_prepro = pd.read_csv(filename, low_memory=False)\n", " li.append(odf_prepro)\n", " \n", " odf_prepro = pd.concat(li, axis=0, ignore_index=True)\n", " condi1 = np.abs(odf_prepro.cenlon - odf_prepro.baseline_climate_ref_pix_lon)>0.25\n", " condi2 = np.abs(odf_prepro.cenlat - odf_prepro.baseline_climate_ref_pix_lat)>0.25\n", " assert len(odf_prepro.loc[condi1 | condi2]) == 0" ] }, { "cell_type": "code", "execution_count": 76, "id": "e2be981e-765b-4796-8a6e-027c21f77d8f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2023.3/elev_bands/W5E5_spinup/RGI62/b_160/L3/summary/'" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum_path" ] }, { "cell_type": "code", "execution_count": 78, "id": "d8b9430d-56ab-47a1-b202-8a97977bbcfd", "metadata": {}, "outputs": [ { "ename": "AssertionError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAssertionError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[78]\u001b[39m\u001b[32m, line 22\u001b[39m\n\u001b[32m 20\u001b[39m condi1 = np.abs(odf_prepro.cenlon - odf_prepro.baseline_climate_ref_pix_lon)>res\n\u001b[32m 21\u001b[39m condi2 = np.abs(odf_prepro.cenlat - odf_prepro.baseline_climate_ref_pix_lat)>res\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(odf_prepro.loc[condi1 | condi2]) == \u001b[32m0\u001b[39m\n", "\u001b[31mAssertionError\u001b[39m: " ] } ], "source": [ "import glob\n", "sum_paths = ['/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/W5E5/per_glacier_spinup/RGI62/b_160/L3/summary',\n", " '/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/W5E5/regional_spinup/RGI62/b_160/L3/summary/',\n", " '/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2023.3/elev_bands/W5E5_spinup/RGI62/b_160/L5/summary/',\n", " '/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/ERA5/per_glacier_spinup/RGI62/b_160/L3/summary/']\n", "\n", "for sum_path in sum_paths:\n", " if 'ERA5' in sum_path:\n", " res = 0.25/2\n", " else:\n", " res = 0.25\n", " all_files = glob.glob(f'{sum_path}/glacier_statistics_*.csv')\n", " \n", " li = []\n", " for filename in all_files:\n", " odf_prepro = pd.read_csv(filename, low_memory=False)\n", " li.append(odf_prepro)\n", " \n", " odf_prepro = pd.concat(li, axis=0, ignore_index=True)\n", " condi1 = np.abs(odf_prepro.cenlon - odf_prepro.baseline_climate_ref_pix_lon)>res\n", " condi2 = np.abs(odf_prepro.cenlat - odf_prepro.baseline_climate_ref_pix_lat)>res\n", " assert len(odf_prepro.loc[condi1 | condi2]) == 0" ] }, { "cell_type": "code", "execution_count": 79, "id": "84e9e4f6-b9f8-4781-9a88-12dc69a70926", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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rgi_idrgi_regionrgi_subregionnamecenloncenlatrgi_area_km2rgi_yearglacier_typeterminus_type...reference_periodtemp_default_gradienttemp_all_solidtemp_all_liqtemp_meltdem_needed_interpolationdem_invalid_percdem_needed_extrapolationdem_extrapol_percdem_invalid_perc_in_mask
180145RGI60-19.014971919-01NaN-179.917000-67.4006000.0711995GlacierMarine-terminating...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
186438RGI60-10.050381010-06NaN-179.91825966.2673030.0782009GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
186449RGI60-10.050491010-06NaN-179.91552766.2761080.0632009GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
186450RGI60-10.050501010-06NaN-179.92880266.2618260.0462009GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
211310RGI60-11.032281111-02Taillon-0.03968342.6954190.0832011GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
211311RGI60-11.032291111-02Gabietous-0.05745642.6951290.0872011GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
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" ], "text/plain": [ " rgi_id rgi_region rgi_subregion name cenlon \\\n", "180145 RGI60-19.01497 19 19-01 NaN -179.917000 \n", "186438 RGI60-10.05038 10 10-06 NaN -179.918259 \n", "186449 RGI60-10.05049 10 10-06 NaN -179.915527 \n", "186450 RGI60-10.05050 10 10-06 NaN -179.928802 \n", "211310 RGI60-11.03228 11 11-02 Taillon -0.039683 \n", "211311 RGI60-11.03229 11 11-02 Gabietous -0.057456 \n", "\n", " cenlat rgi_area_km2 rgi_year glacier_type terminus_type \\\n", "180145 -67.400600 0.071 1995 Glacier Marine-terminating \n", "186438 66.267303 0.078 2009 Glacier Land-terminating \n", "186449 66.276108 0.063 2009 Glacier Land-terminating \n", "186450 66.261826 0.046 2009 Glacier Land-terminating \n", "211310 42.695419 0.083 2011 Glacier Land-terminating \n", "211311 42.695129 0.087 2011 Glacier Land-terminating \n", "\n", " ... reference_period temp_default_gradient temp_all_solid \\\n", "180145 ... NaN NaN NaN \n", "186438 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "186449 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "186450 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "211310 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "211311 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "\n", " temp_all_liq temp_melt dem_needed_interpolation dem_invalid_perc \\\n", "180145 NaN NaN NaN NaN \n", "186438 2.0 -1.0 NaN NaN \n", "186449 2.0 -1.0 NaN NaN \n", "186450 2.0 -1.0 NaN NaN \n", "211310 2.0 -1.0 NaN NaN \n", "211311 2.0 -1.0 NaN NaN \n", "\n", " dem_needed_extrapolation dem_extrapol_perc dem_invalid_perc_in_mask \n", "180145 NaN NaN NaN \n", "186438 NaN NaN NaN \n", "186449 NaN NaN NaN \n", "186450 NaN NaN NaN \n", "211310 NaN NaN NaN \n", "211311 NaN NaN NaN \n", "\n", "[6 rows x 69 columns]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "odf_prepro.loc[condi1 | condi2]" ] }, { "cell_type": "code", "execution_count": 55, "id": "be110727-d2ab-47ff-9deb-ee72334bb21b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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rgi_idrgi_regionrgi_subregionnamecenloncenlatrgi_area_km2rgi_yearglacier_typeterminus_type...reference_periodtemp_default_gradienttemp_all_solidtemp_all_liqtemp_meltdem_needed_interpolationdem_invalid_percdem_needed_extrapolationdem_extrapol_percdem_invalid_perc_in_mask
32589RGI60-19.014971919-01NaN-179.917000-67.4006000.0711995GlacierMarine-terminating...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
38882RGI60-10.050381010-06NaN-179.91825966.2673030.0782009GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
38893RGI60-10.050491010-06NaN-179.91552766.2761080.0632009GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
38894RGI60-10.050501010-06NaN-179.92880266.2618260.0462009GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
63754RGI60-11.032281111-02Taillon-0.03968342.6954190.0832011GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
63755RGI60-11.032291111-02Gabietous-0.05745642.6951290.0872011GlacierLand-terminating...2000-01-01_2020-01-01-0.00650.02.0-1.0NaNNaNNaNNaNNaN
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6 rows × 87 columns

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" ], "text/plain": [ " rgi_id rgi_region rgi_subregion name cenlon \\\n", "32589 RGI60-19.01497 19 19-01 NaN -179.917000 \n", "38882 RGI60-10.05038 10 10-06 NaN -179.918259 \n", "38893 RGI60-10.05049 10 10-06 NaN -179.915527 \n", "38894 RGI60-10.05050 10 10-06 NaN -179.928802 \n", "63754 RGI60-11.03228 11 11-02 Taillon -0.039683 \n", "63755 RGI60-11.03229 11 11-02 Gabietous -0.057456 \n", "\n", " cenlat rgi_area_km2 rgi_year glacier_type terminus_type \\\n", "32589 -67.400600 0.071 1995 Glacier Marine-terminating \n", "38882 66.267303 0.078 2009 Glacier Land-terminating \n", "38893 66.276108 0.063 2009 Glacier Land-terminating \n", "38894 66.261826 0.046 2009 Glacier Land-terminating \n", "63754 42.695419 0.083 2011 Glacier Land-terminating \n", "63755 42.695129 0.087 2011 Glacier Land-terminating \n", "\n", " ... reference_period temp_default_gradient temp_all_solid \\\n", "32589 ... NaN NaN NaN \n", "38882 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "38893 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "38894 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "63754 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "63755 ... 2000-01-01_2020-01-01 -0.0065 0.0 \n", "\n", " temp_all_liq temp_melt dem_needed_interpolation dem_invalid_perc \\\n", "32589 NaN NaN NaN NaN \n", "38882 2.0 -1.0 NaN NaN \n", "38893 2.0 -1.0 NaN NaN \n", "38894 2.0 -1.0 NaN NaN \n", "63754 2.0 -1.0 NaN NaN \n", "63755 2.0 -1.0 NaN NaN \n", "\n", " dem_needed_extrapolation dem_extrapol_perc dem_invalid_perc_in_mask \n", "32589 NaN NaN NaN \n", "38882 NaN NaN NaN \n", "38893 NaN NaN NaN \n", "38894 NaN NaN NaN \n", "63754 NaN NaN NaN \n", "63755 NaN NaN NaN \n", "\n", "[6 rows x 87 columns]" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "odf_prepro.loc[condi1 | condi2]" ] }, { "cell_type": "code", "execution_count": 56, "id": "4196bbd2-bfa1-4c26-9b1e-8622b69aef96", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['rgi_id', 'rgi_region', 'rgi_subregion', 'name', 'cenlon', 'cenlat',\n", " 'rgi_area_km2', 'rgi_year', 'glacier_type', 'terminus_type',\n", " 'is_tidewater', 'status', 'grid_dx', 'grid_nx', 'grid_ny',\n", " 'geometry_type', 'geometry_is_valid', 'geometry_area_km2',\n", " 'inv_volume_km3', 'vas_volume_km3', 'inv_volume_bsl_km3',\n", " 'inv_volume_bwl_km3', 'dem_source', 'flowline_type',\n", " 'apparent_mb_from_any_mb_residual', 'inversion_glen_a', 'inversion_fs',\n", " 'run_dynamic_spinup_success', 'temp_bias_dynamic_spinup',\n", " 'dynamic_spinup_target_year', 'dynamic_spinup_period',\n", " 'dynamic_spinup_forward_model_iterations',\n", " 'area_mismatch_dynamic_spinup_km2_percent',\n", " 'reference_area_dynamic_spinup_km2',\n", " 'dynamic_spinup_other_variable_reference',\n", " 'dynamic_spinup_mismatch_other_variable_percent', 'used_spinup_option',\n", " 'dmdtda_mismatch_dynamic_calibration_reference',\n", " 'dmdtda_dynamic_calibration_given_error',\n", " 'dmdtda_dynamic_calibration_error_scaling_factor',\n", " 'dmdtda_mismatch_dynamic_calibration',\n", " 'dmdtda_mismatch_with_initial_melt_f', 'melt_f_dynamic_calibration',\n", " 'melt_f_before_dynamic_calibration',\n", " 'run_dynamic_melt_f_calibration_iterations', 'error_task', 'error_msg',\n", " 'dem_mean_elev', 'dem_med_elev', 'dem_min_elev', 'dem_max_elev',\n", " 'dem_max_elev_on_ext', 'dem_min_elev_on_ext',\n", " 'dem_perc_area_above_max_elev_on_ext', 'terminus_lon', 'terminus_lat',\n", " 'main_flowline_length', 'inv_flowline_glacier_area',\n", " 'flowline_mean_elev', 'flowline_max_elev', 'flowline_min_elev',\n", " 'flowline_avg_slope', 'flowline_avg_width', 'flowline_last_width',\n", " 'flowline_last_5_widths', 'baseline_climate_source', 'baseline_yr_0',\n", " 'baseline_yr_1', 'baseline_climate_ref_hgt',\n", " 'baseline_climate_ref_pix_lon', 'baseline_climate_ref_pix_lat', 'bias',\n", " 'melt_f', 'prcp_fac', 'temp_bias', 'reference_mb', 'reference_mb_err',\n", " 'reference_period', 'temp_default_gradient', 'temp_all_solid',\n", " 'temp_all_liq', 'temp_melt', 'dem_needed_interpolation',\n", " 'dem_invalid_perc', 'dem_needed_extrapolation', 'dem_extrapol_perc',\n", " 'dem_invalid_perc_in_mask'],\n", " dtype='object')" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "odf_prepro.columns" ] }, { "cell_type": "code", "execution_count": 50, "id": "d24e1282-2b3a-4bcf-9e09-680881198329", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/www/oggm/gdirs/oggm_v1.6/L3-L5_files/2025.6/elev_bands/ERA5/per_glacier_spinup/RGI62/b_160/L5/summary/'" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum_path" ] }, { "cell_type": "code", "execution_count": 8, "id": "9e172aaf-2dd5-469e-9a0f-fed4d4fd8855", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "amount of glaciers that do not take the nearest climate gridpoint:\n", "0\n" ] }, { "data": { "text/html": [ "
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rgi_idrgi_subregionnamecenloncenlatrgi_area_km2rgi_yearglacier_typeterminus_typeis_tidewater...reference_periodtemp_default_gradienttemp_all_solidtemp_all_liqtemp_meltdem_needed_interpolationdem_invalid_percdem_needed_extrapolationdem_extrapol_percdem_invalid_perc_in_mask
rgi_region
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0 rows × 68 columns

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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [rgi_id, rgi_subregion, name, cenlon, cenlat, rgi_area_km2, rgi_year, glacier_type, terminus_type, is_tidewater, status, grid_dx, grid_nx, grid_ny, geometry_type, geometry_is_valid, geometry_area_km2, inv_volume_km3, vas_volume_km3, inv_volume_bsl_km3, inv_volume_bwl_km3, dem_source, flowline_type, apparent_mb_from_any_mb_residual, inversion_glen_a, inversion_fs, error_task, error_msg, dem_mean_elev, dem_med_elev, dem_min_elev, dem_max_elev, dem_max_elev_on_ext, dem_min_elev_on_ext, dem_perc_area_above_max_elev_on_ext, terminus_lon, terminus_lat, main_flowline_length, inv_flowline_glacier_area, flowline_mean_elev, flowline_max_elev, flowline_min_elev, flowline_avg_slope, flowline_avg_width, flowline_last_width, flowline_last_5_widths, baseline_climate_source, baseline_yr_0, baseline_yr_1, baseline_climate_ref_hgt, baseline_climate_ref_pix_lon, baseline_climate_ref_pix_lat, bias, melt_f, prcp_fac, temp_bias, reference_mb, reference_mb_err, reference_period, temp_default_gradient, temp_all_solid, temp_all_liq, temp_melt, dem_needed_interpolation, dem_invalid_perc, dem_needed_extrapolation, dem_extrapol_perc, dem_invalid_perc_in_mask]\n", "Index: []\n", "\n", "[0 rows x 68 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('amount of glaciers that do not take the nearest climate gridpoint:')\n", "print(len(odf_prepro.loc[condi1 | condi2]))\n", "odf_prepro.loc[condi1 | condi2].groupby('rgi_region').count()" ] }, { "cell_type": "markdown", "id": "357459ce-bdf0-40cf-a5c4-0c92e2a7bb8b", "metadata": {}, "source": [ "### The same can be tested in a more complicated way for the different flattened files:\n", "- a short version is inside `test_shop.test_glacier_gridpoint_selection`" ] }, { "cell_type": "code", "execution_count": 23, "id": "0db08ecc-8f37-44fb-872f-c6262054aea0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['GLIMSId', 'BgnDate', 'EndDate', 'CenLon', 'CenLat', 'O1Region',\n", " 'O2Region', 'Area', 'Zmin', 'Zmax', 'Zmed', 'Slope', 'Aspect', 'Lmax',\n", " 'Status', 'Connect', 'Form', 'TermType', 'Surging', 'Linkages', 'Name',\n", " 'GlacierType', 'TerminusType', 'GlacierStatus', 'IsTidewater',\n", " 'IsNominal'],\n", " dtype='object')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "odf_6.columns" ] }, { "cell_type": "code", "execution_count": 42, "id": "b066f494-d6d9-4c9d-86c0-ca7a6278f585", "metadata": {}, "outputs": [], "source": [ "def test_glacier_gridpoint_selection(path_l, short = True, print_stuff=False, include_rgi7_points=True, res = 0.5):\n", "\n", " #### res=0.5: for W5E5 and ISIMIP3b have 0.5° resolution, for ERA5 it should be 0.25\n", " for p in path_l:\n", " if 'inv' not in p:\n", " with xr.open_dataset(p) as dt:\n", " dt = dt.isel(time=0) # we only need the lat/lon anyways\n", " else:\n", " dt = xr.open_dataset(p)\n", " if short:\n", " # select three glaciers where two failed in the\n", " # previous gswp3_w5e5 version\n", " coords = [(10.7584, 46.8003), # HEF\n", " (-70.8931, -72.4474), # RGI60-19.00124\n", " (51.495, 30.9010), # RGI60-12.01691\n", " ]\n", " if include_rgi7_points:\n", " coords2 = [\n", " ( -141.670274, 69.166921), # in RGI7C, not in RGI6\n", " (-66.855668, -67.535551) # only in RGI7G, not in RGI6 or in RGI 7C\n", " ]\n", " coords = coords + coords2\n", " else:\n", " if include_rgi7_points:\n", " coords = odf['coords'] \n", " else:\n", " coords = odf_6['coords']\n", " for coord in coords:\n", " lon, lat = coord\n", " if lon <0:\n", " lon = lon + 360\n", " # get the distances to the glacier coordinate\n", " try:\n", " c = ((dt.longitude - lon) ** 2 + (dt.latitude - lat) ** 2)**0.5\n", " _lon = 'longitude'\n", " _lat = 'latitude'\n", " except:\n", " c = ((dt.lon - lon) ** 2 + (dt.lat - lat) ** 2)**0.5\n", " _lon = 'lon'\n", " _lat = 'lat'\n", "\n", " # select the nearest climate point from the flattened\n", " # glacier gridpoint\n", " if 'inv' in p:\n", " _c = c.to_dataframe('distance').sort_values('distance')\n", " lat_near, lon_near, dist = _c[[_lat,_lon, 'distance']].iloc[0]\n", " # for a randomly chosen gridpoint, the next climate gridpoint is far away\n", " # for glacier gridpoints the next gridpoint should be the nearest\n", " # (GSWP3-W5E5 resolution is 0.5°)\n", " if print_stuff:\n", " print(p, dist, lat_near, lat, lon_near, lon)\n", " assert dist <= ((res/2) ** 2 + (res/2) ** 2) ** 0.5\n", " assert np.abs(lat_near - lat) <= res/2\n", " assert np.abs(lon_near - lon) <= res/2\n", " else:\n", " dist = c.to_dataframe('distance').sort_values('distance').distance.iloc[0]\n", " if print_stuff:\n", " print(p, dist, lon, lat)\n", " try:\n", " assert dist <= ((res/2) ** 2 + (res/2) ** 2) ** 0.5\n", " except:\n", " print(p, dist, lon, lat, c)\n", " assert dist <= ((res/2) ** 2 + (res/2) ** 2) ** 0.5\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "f196b5fd-f2b9-4da8-ad4d-f657d168b4c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_glacier_invariant_flat.nc 0.05099656851200873 46.75 46.8003 10.75 10.7584\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_glacier_invariant_flat.nc 0.2438121613045622 -72.25 -72.4474 289.25 289.1069\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_glacier_invariant_flat.nc 0.28779506597577154 30.75 30.901 51.25 51.495\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_tas_global_monthly_1901_2019_flat_glaciers.nc 0.05099656851200873 10.7584 46.8003\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_tas_global_monthly_1901_2019_flat_glaciers.nc 0.2438121613045622 289.1069 -72.4474\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_tas_global_monthly_1901_2019_flat_glaciers.nc 0.28779506597577154 51.495 30.901\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_temp_std_global_monthly_1901_2019_flat_glaciers.nc 0.05099656851200873 10.7584 46.8003\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_temp_std_global_monthly_1901_2019_flat_glaciers.nc 0.2438121613045622 289.1069 -72.4474\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_temp_std_global_monthly_1901_2019_flat_glaciers.nc 0.28779506597577154 51.495 30.901\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_pr_global_monthly_1901_2019_flat_glaciers.nc 0.05099656851200873 10.7584 46.8003\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_pr_global_monthly_1901_2019_flat_glaciers.nc 0.2438121613045622 289.1069 -72.4474\n", "/home/data/download/cluster.klima.uni-bremen.de/~oggm/climate/gswp3-w5e5/flattened/2023.2/monthly/gswp3-w5e5_obsclim_pr_global_monthly_1901_2019_flat_glaciers.nc 0.28779506597577154 51.495 30.901\n" ] } ], "source": [ "### for that to run need the latest OGGM \n", "# select directly the OGGM w5e5 files\n", "# TODO\n", "short = True\n", "from oggm.shop import w5e5\n", "d = 'GSWP3_W5E5'\n", "\n", "path_l = []\n", "for var in ['inv','tmp', 'temp_std', 'prcp']:\n", " path_l.append(w5e5.get_gswp3_w5e5_file(d, var))\n", " \n", "### TODO--> once updated, change this to include_rgi7_points=True\n", "test_glacier_gridpoint_selection(path_l, short=short, print_stuff=True, include_rgi7_points=False)\n" ] }, { "cell_type": "markdown", "id": "be191a35-e3a0-46eb-b676-8d91aa0b021a", "metadata": {}, "source": [ "- w5e5 files:" ] }, { "cell_type": "code", "execution_count": 27, "id": "7e8cd5e7-a227-4b6f-89d1-1a02819c0ca9", "metadata": {}, "outputs": [], "source": [ "w5e5_files = []\n", "#folder_w5e5 = www_lschuster/w5e5v2.0/flattened/2023.2/w5e5v2.0_glacier_invariant_flat.nc\n", "os.listdir('/home/www/lschuster/w5e5v2.0/flattened/2023.2')\n", "\n", "w5e5_files = []\n", "w5e5_files_l = []\n", "w5e5_files.append('/home/www/lschuster/w5e5v2.0/flattened/2023.2/w5e5v2.0_glacier_invariant_flat.nc')\n", "w5e5_files_l.append('/home/www/lschuster/w5e5v2.0/flattened/2023.2/w5e5v2.0_glacier_invariant_flat.nc')\n", "\n", "folder_p = '/home/www/lschuster/w5e5v2.0/flattened/2023.2/monthly/'\n", "for p in os.listdir(folder_p):\n", " if '.nc' in p:\n", " w5e5_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "w5e5_files_l.append(folder_p + os.listdir(folder_p)[1])\n", "\n", "folder_p = '/home/www/lschuster/w5e5v2.0/flattened/2023.2/daily/'\n", "for p in os.listdir(folder_p):\n", " if '.nc' in p:\n", " w5e5_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "w5e5_files_l.append(folder_p+p)\n", "test_glacier_gridpoint_selection(w5e5_files, short = True, print_stuff=False, include_rgi7_points=False)\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "f9e2dbf4-b500-494a-8ed4-0d02a5abfd1e", "metadata": {}, "outputs": [], "source": [ "# do the long test only for three files in total\n", "test_glacier_gridpoint_selection(w5e5_files_l, short = False, include_rgi7_points=False)" ] }, { "cell_type": "markdown", "id": "c2320a47-2658-4abb-a315-22f657cd122d", "metadata": {}, "source": [ "- isimip3b (including secondary files)" ] }, { "cell_type": "code", "execution_count": 45, "id": "7763e7ff-06a1-4e25-9af2-61c0e1ca307f", "metadata": {}, "outputs": [], "source": [ "isimip3b_files = []\n", "isimip3b_files_l = []\n", "#invariant files are in monthly/daily\n", "#isimip3b_files.append('/home/www/lschuster/isimip3b_flat/flat/2023.2/isimip3b_glacier_invariant_flat.nc')\n", "#isimip3b_files_l.append('/home/www/lschuster/isimip3b_flat/flat/2023.2/isimip3b_glacier_invariant_flat.nc')\n", "#isimip3b_files.append('/home/www/oggm/cmip6/isimip3b/flat/2025.11/daily/isimip3b_glacier_invariant_flat.nc')\n", "#isimip3b_files_l.append('/home/www/oggm/cmip6/isimip3b/flat/2025.11/daily/isimip3b_glacier_invariant_flat.nc')\n", "\n", "folder_p = '/home/www/oggm/cmip6/isimip3b/flat/2025.11.25/monthly/'\n", "for p in os.listdir(folder_p):\n", " isimip3b_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "isimip3b_files_l.append(folder_p+p)\n", "test_glacier_gridpoint_selection(isimip3b_files, short = True, print_stuff=False,\n", " include_rgi7_points=True)" ] }, { "cell_type": "code", "execution_count": 44, "id": "d2f30de4-2b51-43e3-a525-481a8f128fda", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/www/oggm/cmip6/isimip3b/flat/2025.11.25/monthly/cesm2-waccm_r1i1p1f1_w5e5_ssp126_tasAdjust_global_monthly_flat_glaciers_v2025.11.25.nc\n", "worked\n" ] } ], "source": [ "# do the long test only for the last file in total\n", "test_glacier_gridpoint_selection(isimip3b_files_l, short = False)\n", "print('worked')" ] }, { "cell_type": "code", "execution_count": 46, "id": "59c1c24c-48c5-48d4-84e0-c706bfc63587", "metadata": {}, "outputs": [], "source": [ "###daily files\n", "isimip3b_files = []\n", "isimip3b_files_l = []\n", "folder_p = '/home/www/oggm/cmip6/isimip3b/flat/2025.11.25/daily/'\n", "#folder_p = '/home/www/lschuster/isimip3b_flat/flat/2025.11.25/daily/'\n", "\n", "for p in os.listdir(folder_p):\n", " isimip3b_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "isimip3b_files_l.append(folder_p+p)\n", "test_glacier_gridpoint_selection(isimip3b_files, short = True, print_stuff=False)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "215ea410-f1cb-4ae3-81b1-89c5a8c4d27c", "metadata": {}, "outputs": [], "source": [ "# do the long test only for one file in total\n", "test_glacier_gridpoint_selection(isimip3b_files_l, short = False)\n", "print('worked')" ] }, { "cell_type": "code", "execution_count": null, "id": "af3a6937-b542-447c-9c9f-eac2b8138d3d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "65f65c12-4afc-41a5-b65e-9982b068662d", "metadata": {}, "source": [ "- isimip3a" ] }, { "cell_type": "code", "execution_count": 151, "id": "3debbc2f-859a-45ae-98a5-14208227bc9c", "metadata": {}, "outputs": [], "source": [ "isimip3a_files = []\n", "isimip3a_files_l = []\n", "folder_p = '/home/www/oggm/climate/gswp3-w5e5/flattened/2025.11.25/monthly/'\n", "for p in os.listdir(folder_p):\n", " if '.nc' in p:\n", " isimip3a_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "isimip3a_files_l.append(isimip3a_files[-1])\n", "\n", "folder_p = '/home/www/oggm/climate/gswp3-w5e5/flattened/2025.11.25/daily/'\n", "for p in os.listdir(folder_p):\n", " isimip3a_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "isimip3a_files_l.append(folder_p+p)\n", "test_glacier_gridpoint_selection(isimip3a_files, short = True, print_stuff=False)\n" ] }, { "cell_type": "code", "execution_count": 94, "id": "c0d79082-01a8-4ddd-952a-d9ba69f9b2e8", "metadata": {}, "outputs": [], "source": [ "# do the long test only for three files in total\n", "test_glacier_gridpoint_selection(isimip3a_files_l, short = False)" ] }, { "cell_type": "markdown", "id": "c2560cc2-d6e7-447f-850f-c93287087fa4", "metadata": {}, "source": [ "- era5 monthly/daily" ] }, { "cell_type": "code", "execution_count": 69, "id": "0cf901d5-34af-4e92-b758-550fff57a9c2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['/home/www/oggm/climate/era5/monthly/v1.2/flattened/era5_tp_global_monthly_1940_2024_flat_glaciers_v2025.11.25.nc', '/home/www/oggm/climate/era5/monthly/v1.2/flattened/era5_t2m_global_monthly_1940_2024_flat_glaciers_v2025.11.25.nc', '/home/www/oggm/climate/era5/monthly/v1.2/flattened/era5_glacier_invariant_flat_v2025.11.25.nc', '/home/www/oggm/climate/era5/daily/v1.2/flattened/era5_glacier_invariant_flat_v2025.11.25.nc', '/home/www/oggm/climate/era5/daily/v1.2/flattened/era5_t2m_global_daily_1940_2024_flat_glaciers_v2025.11.25.nc', '/home/www/oggm/climate/era5/daily/v1.2/flattened/era5_tp_global_daily_1940_2024_flat_glaciers_v2025.11.25.nc']\n" ] } ], "source": [ "era5_files = []\n", "era5_files_l = []\n", "\n", "folder_p = '/home/www/oggm/climate/era5/monthly/v1.2/flattened/'\n", "for p in os.listdir(folder_p):\n", " if '.nc' in p:\n", " era5_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "era5_files_l.append(era5_files[-2:])\n", "\n", "folder_p = '/home/www/oggm/climate/era5/daily/v1.2/flattened/'\n", "for p in os.listdir(folder_p):\n", " if '.nc' in p:\n", " era5_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "era5_files_l.append(era5_files[-2])\n", "print(era5_files)\n", "test_glacier_gridpoint_selection(era5_files, short = True, print_stuff=False, res=0.25)\n", "\n" ] }, { "cell_type": "code", "execution_count": 66, "id": "78b91d4d-e419-4efc-b9e5-6447ca1279b3", "metadata": {}, "outputs": [ { "ename": "AssertionError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAssertionError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[66]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# do the long test only for three files in total\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mtest_glacier_gridpoint_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mera5_files_l\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshort\u001b[49m\u001b[43m \u001b[49m\u001b[43m=\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mres\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.25\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[42]\u001b[39m\u001b[32m, line 52\u001b[39m, in \u001b[36mtest_glacier_gridpoint_selection\u001b[39m\u001b[34m(path_l, short, print_stuff, include_rgi7_points, res)\u001b[39m\n\u001b[32m 50\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m print_stuff:\n\u001b[32m 51\u001b[39m \u001b[38;5;28mprint\u001b[39m(p, dist, lat_near, lat, lon_near, lon)\n\u001b[32m---> \u001b[39m\u001b[32m52\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m dist <= ((res/\u001b[32m2\u001b[39m) ** \u001b[32m2\u001b[39m + (res/\u001b[32m2\u001b[39m) ** \u001b[32m2\u001b[39m) ** \u001b[32m0.5\u001b[39m\n\u001b[32m 53\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m np.abs(lat_near - lat) <= res/\u001b[32m2\u001b[39m\n\u001b[32m 54\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m np.abs(lon_near - lon) <= res/\u001b[32m2\u001b[39m\n", "\u001b[31mAssertionError\u001b[39m: " ] } ], "source": [ "# do the long test only for three files in total\n", "test_glacier_gridpoint_selection(era5_files_l, short = False, res=0.25)" ] }, { "cell_type": "markdown", "id": "cd6c83ec-7f5f-459b-8ae5-02b9acfc85fd", "metadata": {}, "source": [ "- era5 land (these are just older flattened files, where all gridpoints remain, but it is probably still good to double-check)" ] }, { "cell_type": "code", "execution_count": 156, "id": "5464dda8-632d-47f2-9c8b-44136f4b4191", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['/home/www/oggm/climate/era5-land/monthly/v1.0/era5_land_monthly_t2m_1981-2018_flat.nc', '/home/www/oggm/climate/era5-land/monthly/v1.0/era5_land_monthly_prcp_1981-2018_flat.nc', '/home/www/oggm/climate/era5-land/monthly/v1.0/era5_land_invariant_flat.nc']\n" ] } ], "source": [ "era5land_files = []\n", "era5land_files_l = []\n", "\n", "#era5_files.append('/home/www/oggm/climate/wip/era5_daily_lily/flattened/2025.11/gswp3-w5e5_glacier_invariant_flat.nc')\n", "#era5_files_l.append('/home/www/oggm/climate/wip/era5_daily_lily/flattened/2025.11/gswp3-w5e5_glacier_invariant_flat.nc')\n", "\n", "folder_p = '/home/www/oggm/climate/era5-land/monthly/v1.0/'\n", "for p in os.listdir(folder_p):\n", " if ('.nc' in p) and ('flat' in p):\n", " era5land_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "era5land_files_l.append(era5land_files[-1])\n", "\n", "# this can't work for HMA ;-)\n", "#folder_p = '/home/www/oggm/climate/era5-land/monthly/vhma/'\n", "#for p in os.listdir(folder_p):\n", "# if '.nc' in p:\n", "# era5land_files.append(folder_p+p)\n", "# add the last one inside for the long test\n", "#era5land_files_l.append(era5land_files[-2])\n", "print(era5land_files)\n", "test_glacier_gridpoint_selection(era5land_files, short = True,\n", " print_stuff=False, res=0.1) # resolution for ERA5-land is 0.1°\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "643f79c8-8728-4b0e-a92c-3cf8c24df61a", "metadata": {}, "outputs": [], "source": [ "# do the long test only for three files in total\n", "test_glacier_gridpoint_selection(era5land_files_l, short = False, res=0.1)" ] }, { "cell_type": "code", "execution_count": null, "id": "3354e61b-4b17-4cb1-8fa8-5cdc2be07e80", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "71b74a10-0d46-498e-96ef-0b7cb74c6a4a", "metadata": {}, "source": [ "## Is the climate the same of the flattened and unflattened files? TODO: update to 2025.11.25" ] }, { "cell_type": "markdown", "id": "e5ded5be-1b32-45a3-b9a0-836d5cae13b8", "metadata": {}, "source": [ "- gswp3-w5e5 / isimip3a : " ] }, { "cell_type": "code", "execution_count": 83, "id": "b5209208-8171-4e96-b3dd-7aaeac480e0f", "metadata": {}, "outputs": [], "source": [ "# check for every glacier gridpoint if the correct climate dataset \n", "# was used by comparing it to the unflattened file:\n", "# this takes some time (we only do this for the monthly files like that, it would take too long for the daily files)\n", "\n", "# path_clim = '/home/www/oggm/climate/gswp3-w5e5/'\n", "for version in ['2022_missing_points', '2023.2']:\n", " for var in ['pr', 'tas']:\n", " fp = f'/home/www/lschuster/isimip3a/flattened/{version}/monthly/gswp3-w5e5_obsclim_{var}_global_monthly_1901_2019_flat_glaciers.nc'\n", " ds_flattened = xr.open_dataset(fp)\n", " fp_unflat = '/home/www/lschuster/isimip3a/monthly/gswp3-w5e5_obsclim_{}_global_monthly_1901_2019.nc'.format(var)\n", " ds_unflattened = xr.open_dataset(fp_unflat)\n", "\n", " for p in ds_flattened.points:\n", " # get the point\n", " ds_flattened_sel = ds_flattened.sel(points=p)\n", " # select longitude, latitude and tas of that point\n", " lon_p = ds_flattened_sel.longitude\n", " lat_p = ds_flattened_sel.latitude\n", " var_p = ds_flattened_sel[var]\n", " # select the same gridpoint from the unflattened file\n", " # the unflattened file is in -180, 180\n", " if lon_p >=180:\n", " lon_p = lon_p-360\n", " # check if the unflattened and the flattened file have the same climate\n", " # data inside\n", " np.testing.assert_allclose(var_p.values,\n", " ds_unflattened[var].sel(lon = lon_p, lat=lat_p))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c510cab9-dd17-4d4a-ae2b-377a1fbeb1e8", "metadata": {}, "outputs": [], "source": [ "# the same test for the data directly in www/oggm -> they should be the same:\n", "path_clim = '/home/www/oggm/climate/gswp3-w5e5/'\n", "\n", "for var in ['pr', 'tas']:\n", " fp = path_clim + 'flattened/monthly/gswp3-w5e5_obsclim_{}_global_monthly_1901_2019_flat_glaciers.nc'.format(var)\n", " ds_flattened = xr.open_dataset(fp)\n", " fp_unflat = path_clim + 'unflattened/monthly/gswp3-w5e5_obsclim_{}_global_monthly_1901_2019.nc'.format(var)\n", " ds_unflattened = xr.open_dataset(fp_unflat)\n", " \n", " # check for every glacier gridpoint if the correct climate dataset \n", " # was used by comparing it to the unflattened file:\n", " for p in ds_flattened.points:\n", " # get the point\n", " ds_flattened_sel = ds_flattened.sel(points=p)\n", " # select longitude, latitude and tas of that point\n", " lon_p = ds_flattened_sel.longitude\n", " lat_p = ds_flattened_sel.latitude\n", " var_p = ds_flattened_sel[var]\n", " # select the same gridpoint from the unflattened file\n", " # the unflattened file is in -180, 180\n", " if lon_p >=180:\n", " lon_p = lon_p-360\n", " # check if the unflattened and the flattened file have the same climate\n", " # data inside\n", " np.testing.assert_allclose(var_p.values,\n", " ds_unflattened[var].sel(lon = lon_p, lat=lat_p))\n" ] }, { "cell_type": "markdown", "id": "10e748dc-ea9c-41cc-bcc0-19793f76b568", "metadata": {}, "source": [ "- w5e5v2.0: check if the flattened file is the same as the gswps-w5e5 file over the common period" ] }, { "cell_type": "code", "execution_count": 99, "id": "f8796137-8253-4286-b397-1401da61e78c", "metadata": {}, "outputs": [ { "data": { "image/png": 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fvn3ZsmULsbGxDB8+nF69eu1xfFZWFueccw5paWnUrFmTCRMmcP7551OjRg3i4+OpW7cuV199NT/88ANJSUmkpqYyZMiQP8xToUIF4NcttfZlx44dec/3tGVXYmIiycnJuzwkSSru5k38msO+uZRKoW3cVGkcT5zdgbjYEvVPkWKlYtWarDvmMQC6bRzJzO/eCziRJGl/lJS6F6x9JUmSJCloJea3s6mpqRx33HGkpqYSCoV45ZVXOPXUU/d6zJdffpm3rdZ1111HzZo1dzuudevWnH/++QBMnTqVmTNn7vJ63bp1gV+L4a1bt+51zd/OkK5Wrdoe79MkSVJJs2DaGOp9PpiyoSx+TupE22uHE2+TOHBtew1gQvWzAKj7w61sWrc64ESSpL2x7pUkSZIk5acS8RvajRs3cvzxx7N06VIAnnrqKQYPHrzP4+bNm5f3vGPHjnsd26lTp7zn8+fP3+W1Vq1a7fG138vNzWXJkiUAtGzZcp/5JEkqCRbPGk/NT86hfGgncxLa0fS6j0kqUy7oWPqfDhc9zvKY+lQhjZWvX0Y0Egk6kiRpN6x7JUmSJEn5rdg3itPS0ujXrx9z584F4KGHHuKaa67Zr2Pj4uLynufm5u51bE5Ozm6PAzjyyCPzno8ZM2aPc0yZMiVvC64jjjhivzJKklScLZ83lSofDCKFHcyPb0nD6z6lTLkKQcfS7ySVKUd4wItkR+PYvG0nIyYuCjqSJOn/sO6VJEmSJBWEYt0ozsjI4KSTTmLatGkA3HXXXdx22237fXyjRo3yno8dO3avY39fCP/+OICjjz6alJQUAF5//XWi0ehu53jttdfyng8YMGC/c0qSVBwt27iDKSMeohLpLIprSu1rRlGuQsWgY2k3Grftzqfd3uKSnFu55/NlLNu4Y98HSZIKhXWvJEmSJKmgFNtGcXZ2NgMGDODHH38E4IYbbuC+++47oDmOPfZYypYtC8DQoUOZNWvWbsd98cUXfPjhhwDUqVOHDh067PJ6QkIC119/PfDrtl7//ve//zDH+PHjefnllwHo3bs3Xbp0OaCskiQVJ6s2Z3DuixO4LeMC3kkcRPWrRpFcsUrQsbQXA07oR8/GVdmZE+bGd2eQkxsOOpIklXrWvZIkSZKkghSK7uk04CLu9NNPZ+TIkQAcc8wxPPHEE4RCoT2OT0hIoFmzZn/4+j//+U/+9re/AVC+fHmuu+46jj/+eCpVqsS6dev4+OOPefHFF/O26HrjjTc4//zz/zDPtm3b6Ny5MwsXLgTg8ssv5+yzz6ZMmTJ8//33PPDAA2zfvp0yZcrw008//aHo3pv09HRSUlJIS0sjOTl5v4+TJCkIa9euYdDr81i5JYsm1csz/PLuVC2fGHQs7Ydf0nYy8PEvuTX8ErUbtKDHpY8GHUmSdlHaaqPSVPdC6ft8JUmSJGl3CrM2KraN4r0Vx7vToEEDli9f/oevR6NRbr75Zv7zn//scessgPj4eB544AFuvfXWPY5ZvHgxJ554IosW7f7efsnJybz11lucfPLJB5TdYlmSVFxsXLuSnc/3Y1xOc15IvpbhVx5JjeSkoGPpAEz54jU6T7yBcDTEopPfp0WX44KOJEl5SlttVJrqXih9n68kSZIk7Y6N4v2QXwXzb6ZOncpLL73EuHHjWLFiBRkZGZQvX54mTZrQu3dvrrjiit2emf1/7dixg2eeeYYRI0awePFisrOzqVevHieeeCI33HADDRo0OKDcYLEsSSoeNq9PJe25fjSKrOQXqsFl31GrTv2gY+kgTHnsDDqnf8OaUA1SbppI+eRKQUeSJKD01Ualqe6F0vf5SpIkSdLu2CjWLiyWJUlFXdrmDWx8pi+Nw0tZT2VyBn9GncNaBx1LByl96yZ2PNGNWmxgUsUT6XrjO0FHkiTA2qik8/OVJEmSpMKtjWIKdHZJklTibUvbzLpnT6RxeCkbqcjOcz60SVzMJVeswpYTniYSDdF16+dM+/K1oCNJkiRJkiRJymc2iiVJ0kHL2J7G6qdPplnuQrZQgfRBI2jQvEPQsZQPWnU/gYl1BgNw2IS72JC6PNhAkiRJkiRJkvKVjWJJknRQMnPCPPbaOzTJnk86Zdk0YDiHteoadCzlo05DHmFxbGNyorE8+9H3RCLesUSSJEmSJEkqKeKCDiBJkoqfrNwwV745ldGr6/FLwi1cd0p3WrQ/MuhYymcJiUnEnf0G/YfN5peV5ak/fjkXHdEo6FiSJEmSJEmS8oFXFEuSpAOSk53FXcP+y+gFG0iKj2HwhVfSovOxQcdSAWnYtDVXn9QdgAe/mM+CX9IDTiRJkiRJkiQpP9goliRJ+y2cm8vPT53NDcuv5rC4jbw0uAvdDqsSdCwVsPO71adP82qcEBlL1kt/IiszI+hIkiRJkiRJkg6RjWJJkrRfIuEw0546j07bvqNGaDOPHVuOI5tWDTqWCkEoFOJfpzTi7wlv0C48m+mv3Rp0JEmSJEmSJEmHyEaxJEnap2gkwuRnL6JL2pfkRmOY3fMJOvQ5I+hYKkRVq1Zj5REPA9D1l7eZPe6TgBNJkiRJkiRJOhQ2iiVJ0l5FIxEmPncF3TZ9TCQaYkaXh+nYb3DQsRSADsefy8TK/YkJRan27Y2kbd4QdCRJkiRJkiRJB8lGsSRJ2qNoJMKEl26g+/r3AJjS4V46n3x5wKkUpLYXP82qUG1qsInFr15GNBIJOpIkSZIkSZKkg2CjWJIk7dHQb2ZRefV3AExseSddB1wfcCIFrWz5FHaeMpTcaAydtn3P1M9eCDqSJEmSJEmSpINgo1iSJO3W82OW8Mj3qzk7+6+MbvVPup11W9CRVEQ063g0kxv+emX5lKmTWL0lI+BEkiRJkiRJkg6UjWJJkvQHI7/+nge/mA/Apf26cPQgryTWrrqc/0/urPQoD2Wdzs3vziQciQYdSZIkSZIkSdIBsFEsSZJ2Men9xzj1xwGcE/tfrjumCdf0aRJ0JBVBcfEJXHn+uZRLiGXS8s08/8OSoCNJkiRJkiRJOgA2iiVJUp4pnwyl86x7iQ1F6V9vJzcf3yzoSCrC6lcpy9/7t6ZuaD2Hf3cBi2eMDTqSJEmSJEmSpP1ko1iSJAEw9fNXOXzqHcSEokysOpDulz9DKBQKOpaKuDM61eXfVUfRI2YuCZ9cwc4d24KOJEmSJEmSJGk/2CiWJElM//pN2k28hdhQlEmVTqLLVS8RivGfCdq3UChEiyFPs4FK1I+s4edXvZ+1JEmSJEmSVBz4G2BJkkq5n0d/QOsfbyA+FGZK8nF0umYYMbGxQcdSMVKxak3W9nkMgG4bRzLzu/cCTiRJkiRJkiRpX2wUS5JUiv20ZCNjv/uMhFAuU8v1osN17xAbFxd0LBVDbXsPZEL1QQDU/eFWNq9bHXAiSZIkSZIkSXtjo1iSpFJqyvLNXPr6FP6VfTrPV72TttePIC4+IehYKsY6XPg4y2PqU4U0Vr5+GdFIJOhIkiRJkiRJkvbARrEkSaXQglmTuPLVH8nIDnNU02oMueJWEhKTgo6lYi6pbHnCA14kOxpHwvY1jPxpTtCRJEmSJEmSJO2BjWJJkkqZJbMmUPOD0/hP5EF6NSjLCxd0JineexIrfzRu252vOg7ltOx7+etXa1i2cUfQkSRJkiRJkiTtho1iSZJKkRXzplL5gzNJYQdVEiM8e14HyiTYJFb+OumUM+l0WE125oS58d0Z5ITdglqSJEmSJEkqamwUS5JUSqxaPJuy755OJdJZFNuEWteOonxypaBjqQSKiQnx6KD2VEoKcXTqy0x+7bagI0mSJEmSJEn6P2wUS5JUCqQuX0D8m6dSjS0si2lItatGkVKxStCxVILVrliG53qkc1P8B3Rb+SLzJ38bdCRJkiRJkiRJv2OjWJKkEm7dmqVEXz+FmmxkRUxdKlz+GRWr1gw6lkqBbv3OZkry8cSGolT4/Gq2p28JOpIkSZIkSZKk/7FRLElSCbZ+WyZ/e/M7yke2sSZUgzKXfEbVmvWCjqVSpNnFz7OWatSJrmPeK1cHHUeSJEmSJEnS/9goliSphNq8I5vzX5rIV1tqcUPifcRc+CnV6zQKOpZKmeSKVdjc7yki0RBdtn7O9C9fCzqSJEmSJEmSJGwUS5JUIqVt3sg9z7/DwnXbqV4hkb9ffja1GjQPOpZKqVY9/sSkOhcA0GjCXWxIXR5sIEmSJEmSJEk2iiVJKmm2p29h7bMncn/abRxTbilvX9adhlXLBR1LpVzHIf9icWxjkqJZvDHyIyKRaNCRJEmSJEmSpFLNRrEkSSVIxvY0Vj51Ms1zFxAJxXL3wK40qV4+6FgSCYlJxA96hYGRh3hydVNeH7886EiSJEmSJElSqWajWJKkEiIzYztLn+pPq5zZpFOWDacNp1HrbkHHkvI0aN6Bc048DoAHv5jPwnXbAk4kSZIkSZIklV42iiVJKgGyszJZ8NQA2mTNYEc0idST3qBph6OCjiX9wfndG3B082q0Dc9j40tnkJWZEXQkSZIkSZIkqVSyUSxJUjGXk53FnCdPp/3OSeyMJrD8hNdo0eW4oGNJuxUKhXjk1KY8l/gfeuZMYPprtwYdSZIkSZIkSSqVbBRLklSMhSNRbhsxnfT0dLKi8Sw65gVa9/hT0LGkvapeuRKre94PQNdf3mb2j58GnEiSJEmSJEkqfWwUS5JUTEUiUW774GdGztrEVeFbmXncW7TrPSDoWNJ+Obzv+UyqfAoxoSjVvrmBtM0bgo4kSZIkSZIklSo2iiVJKoaikQhvvzGU96euIiYE/z6nK12P6hd0LOmAtLn4aVaHalGDTSx69Qqi0WjQkSRJkiRJkqRSw0axJEnFTDQSYeJzV3L+sju4K+5tHhvUgRPb1go6lnTAypavSMbJQ8mNxtB523+Z+tkLQUeSJEmSJEmSSg0bxZIkFSfRKBNfvpHu698FoH3Hbpx2eJ2AQ0kHr1mnPkxueDkAqVM+Y/WWjIATSZIkSZIkSaWDjWJJkoqRCa/dTvc1rwMwseUddB14Q8CJpEPX5fx/8kjynVyfdQU3vzeTcMQtqCVJkiRJkqSCZqNYkqRiYsKb99B9xXO/Pm9yM93Ouj3gRFL+iItP4Kwh11IuIY5Jyzbzwg9Lg44kSZIkSZIklXg2iiVJKgYmDn+Q7oufAGB8g6vofv49wQaS8lmDKuW4p39rktlBje9uZPHMcUFHkiRJkiRJkko0G8WSJBVxwyet5P1Zm4lEQ4yvcxE9Lnoo6EhSgTizU12eqv4pA2N+IP7jK9i5Y1vQkSRJkiRJkqQSq1g3iqdMmcK9995L3759qVu3LomJiZQvX55mzZpx0UUXMW7c3q9EWb58OaFQ6IAeDRs23OucX375JWeffTaHHXYYZcuWJSkpiXr16nHqqafy7rvvEolE8vE7IEkq6T6esYY7PpzFiPDRvNzqVbpf8ljQkaQCEwqFaD/4X2ygEg0iq/n5Ve/BLUnWvZIkSZKkghKKRqPRoEMcjF69ejF27Nh9jhs8eDAvvvgiCQkJf3ht+fLlNGrU6IDW7du3L1999dUfvp6VlcV5553HBx98sNfjjzrqKD755BMqVqy432ump6eTkpJCWloaycnJB5RXklR8rVm9gpOfn8GWnDjO716ff57ahlAoFHQsqcDNGjOStt9fBMDM3i/Rvs+ZASeSVFSUttqoNNW9UPo+X0mSJEnancKsjeIKdPYClJqaCkDt2rU588wzOeqoo6hfvz7hcJjx48fz6KOPsmbNGoYNG0ZOTg5vv/32H+aoU6cOs2bN2udaDz74YN7xQ4YM2e2Y66+/Pq9Yrl69On/5y1/o2LEj8fHxzJo1i4cffpgVK1YwduxYzj77bL788suDfeuSpFIgGomw+Y0hfBhK5YU6d3Jv/xNtEqvUaNt7IBPmfEH39e9RZ8ytbG7dk8rV6wQdS5IKnXWvJEmSJKkgFdsrik8++WQGDx7M6aefTmxs7B9e37hxI0cccQQLFy4EYMyYMfTq1euA1wmHw9SvX5/U1FQqVKjAunXrKFOmzC5j1q1bR+3atYlEIlSqVImff/6ZunXr7jImPT2d9u3bs3z5cgAmT55M586d9yuDZ1VLUukz6cOn6TrzLjKj8Wy44HvqNWkbdCSpUGVmbGftv3vQMLKS6WV70uHWUYRiivVdUyTlg9JWG5Wmuve340vT5ytJkiRJu1OYtVGx/W3bZ599xqBBg3ZbLANUrVqVRx99NO/P77///kGt8+233+adxX3GGWf8oVgGmDhxYt49mC666KI/FMsAycnJ3HTTTXl/Hj9+/EHlkSSVfBvXrqL5zAcAmN74SpvEKpWSypYnPOBFsqNx1N4xl0/GTgk6kiQVOuteSZIkSVJBKraN4v3Rp0+fvOdLliw5qDmGDRuW93xP229lZ2fnPT/ssMP2OFfjxo13e4wkSb+34s1rSWEHi2Mb0/mcvwUdRwpM47bd+a79v+iX9TC3f7uZZRt3BB1Jkooc615JkiRJ0sEq0Y3irKysvOd7OgN7b7Zt28ZHH30EQMOGDfe4hVfz5s3zni9dunSP8/2+aP/9MZIk/WbGN2/TaftocqMx0P9J4uMTgo4kBarvaRfR8rCG7MwJc+O7M8gJR4KOJElFinWvJEmSJOlglehG8ZgxY/Ket2zZ8oCPf//998nIyADgggsuIBQK7XZc27Zt6dmzJwCvvfZa3pZdv7dt2zaeeOIJ4Nezr/v27XvAeSRJJVv61k3U/vEuACbXPpcm7Y8MOJEUvJiYEI8Oak9yUiyHrfmUCcPuDjqSJBUp1r2SJEmSpIMVF3SAghKJRHjooYfy/jxo0KADnuP3228NHjx4r2NfffVVTjjhBJYtW0bHjh35y1/+QseOHYmLi2P27Nk88sgjLFu2jKpVq/LWW2+RkOAVYpKkXT399Wy6hhvQIi6Rwy94OOg4UpFRu2IZnu2Vy5E/DCW8PMT8yX1o0eW4oGNJUuCseyVJkiRJhyIUjUajQYcoCI8++ii33norAAMHDuSDDz44oONXrlxJw4YNiUaj9OzZkx9//HGfx2zatImhQ4fy8MMPs3379l1ei4+P54YbbuCGG26gbt26e50nKytrl+3D0tPTqVevHmlpaSQnJx/Q+5AkFQ8Tlm7i7BcmAFFGXNCMLq2bBR1JKnKmPHYGndO/YU2oBik3TaR8cqWgI0kqZOnp6aSkpFgb/U9xrnvB2leSJEmSdqcwa98SufX0mDFjuP322wGoXr06Q4cOPeA53nzzTX7roe/rrOrffPrpp7z11lt/KJYBcnJyeO+993j77bfZV2/+wQcfJCUlJe9Rr169A84vSSo+MrNzuWPkLADO6VrfJrG0B80ufp61VKNOdB1zX7k66DiSFKjiXveCta8kSZIkBa3ENYrnzJnDgAEDyM3NJSkpiREjRlC9evUDnueNN94AIDExkbPOOmuf42+55RYuuugi5s+fz2mnncaPP/7I9u3b2blzJ9OmTeOiiy5i5cqV3HbbbZxxxhmEw+E9znXHHXeQlpaW91i1atUB55ckFR/TX7uZa9P+TdPy2dz+pwO/t6BUWiRXrMLmfk8RiYbouvVzpn/1etCRJCkQJaHuBWtfSZIkSQpaiWoUL1u2jL59+7JlyxZiY2MZPnw4vXr1OuB5Jk2axPz58wHo378/FStW3Ov4UaNG8dhjjwFw4YUX8uGHH9KzZ0/KlStHUlIShx9+OK+88gp33303ACNHjuTZZ5/d43yJiYkkJyfv8pAklUyLf/6JLmve4PTYsTzUNYOUMvFBR5KKtFY9/sTEOhcA0Gj8nWxIXR5sIEkqZCWl7gVrX0mSJEkKWolpFKempnLccceRmppKKBTilVde4dRTTz2ouYYNG5b3fH+233rppZcACIVC3HfffXscd+edd1K+fHkAXnnllYPKJkkqOXJzsuHja4kLRZharhed+p4XdCSpWOg05F8sjm1MRbbz0YjXiUT2vb2pJJUE1r2SJEmSpPxUIhrFGzdu5Pjjj2fp0qUAPPXUU/t9f6X/Kycnh+HDhwO/3ufphBNO2Ocx8+bNyxtfp06dPY5LSkqidevWAHlnbkuSSq8pw++jSXgJaZSjwQXPBB1HKjYSEpOIP/MlhoT/yv2/dGbY+OVBR5KkAmfdK0mSJEnKb8W+UZyWlka/fv2YO3cuAA899BDXXHPNQc83atQoNm3aBMC5555LXFzcPo/5bUxubu4+x+bk5OxyjCSpdFq9eDYdFv+6HeOCdrdTtWb9gBNJxUuDFh059sQzAXjwi/ksXLct4ESSVHCseyVJkiRJBaFYN4ozMjI46aSTmDZtGgB33XUXt9122yHN+fvtt4YMGbJfxzRq1AiATZs25Z1lvTubN29m9uzZuxwjSSp9opEIae9dTVIoh9mJHehy2rVBR5KKpQu6N+Do5tWoEf6FZS9fTFZmRtCRJCnfWfdKkiRJkgpKsW0UZ2dnM2DAAH788UcAbrjhhr3eJ2l/bN68mVGjRgHQtm1bOnTosF/HnXLKKXnPb7zxRrKzs/8wJhKJcP311+e9dvLJJx9SVklS8fXZDxOplrWCndEEKp31HKGYYvvjWApUKBTikYGteDvxQfplf8201/8cdCRJylfWvZIkSZKkglRs94E655xz+PrrrwE45phjuOSSS/LOWt6dhIQEmjVrttc5hw8fnlfQ7u9Z1QAXXnghTzzxBPPmzePrr7+mc+fOXHfddbRv357Y2Fjmzp3L0KFDGT9+PAA1atTg5ptv3u/5JUklx7r0TO4cnU4o6xEe6pbLiYe1DDqSVKxVTynP9J73UPena+iW+hZzfjyR1kecFHQsScoX1r2SJEmSpIIUikaj0aBDHIxQKHRA4xs0aMDy5cv3OqZ79+5MnDiR2NhYVq9eTc2aNfd7/hUrVnDqqacyc+bMvY5r1KgRI0eO3O+ztgHS09NJSUkhLS2N5OTk/T5OklT0XPHGFL6as452dVMYeVVP4mK9mljKD5OePJ+umz9lHVVIun4iKZWrBR1JUgEobbVRaap7ofR9vpIkSZK0O4VZG/nb6f9ZtGgREydOBOD4448/oGIZfi3IJ0+ezLBhw+jfvz9169YlMTGRhIQEatasSd++fXn22WeZNWvWARfLkqSSYcrXb1Nm3gfExcBDA9vZJJbyUeuLnmZ1qBY12MSiV68IOo4kFUnWvZIkSZKk3yu2VxSXJp5VLUnFX9rmDeQ82ZmqbOWLxn/lTxd4L1Upvy2Y8h2NPz2duFCEKZ0eofMpNoylksbaqGTz85UkSZIkryiWJKnEWfDGDVRlKytj6tDnjKuDjiOVSM07H8PkBpcBEJ76Oqs37wg4kSRJkiRJklR02SiWJKmAzR73CV23jAJge9/HSCpTLuBEUsnV5YL7eLH8VZyfeRu3jPiZcMTNcyRJkiRJkqTdsVEsSVIB2rljG5X+eysAE6ucRqvuJwScSCrZ4uIT6HvR3SQkJDJx2WZeHLs06EiSJEmSJElSkWSjWJKkAjTjjduoE13HOqrQavDjQceRSoUGVcpxT//WxJFLzrf3sWj6D0FHkiRJkiRJkoocG8WSJBWQefPn0vmX4QD8ctQDVEipHHAiqfQ4s1Ndnqr1FdfFjqTcJ5eQtnlD0JEkSZIkSZKkIsVGsSRJBSAnHOGmLzdyfvadfFP5HDoce3bQkaRSJRQK0XPwvawJ1aB2dD3LXrqASDgcdCxJkiRJkiSpyLBRLElSAXjhh6XMX7uNBWXacfjF/wk6jlQqpVSqys7TXiUrGk+HjPFMeuvvQUeSJEmSJEmSigwbxZIk5bMVi+cw8r/jALj7pFZULZ8YcCKp9GrS/ghmtr0TgM5Lnmbu+C8CTiRJkiRJkiQVDTaKJUnKR5FwmB3vXs6nsX/hxjoLGNixTtCRpFKvy8AbmZzSl7hQhOpfXcnGtSuDjiRJkiRJkiQFzkaxJEn5aPIHj9EqZzYAg045iVAoFHAiSaGYGFpf9hLLY+pTJrqTF977lHAkGnQsSZIkSZIkKVA2iiVJyifrVi+h9ZxHAfi5+Q3Ubtg84ESSflO2fAqhs97grOgDvJDakMe/WRh0JEmSJEmSJClQNoolScoH0UiEX96+hvKhnSyIa06XQbcFHUnS/9GgeQcuP/1EAJ7+fjHfz18bcCJJkiRJkiQpODaKJUnKB9O+eIUOGePJjsaSePqzxMbFBR1J0m70b1+bC7o3oEtoPnWHH8falV5ZLEmSJEmSpNLJRrEkSYdo68a1NJr8DwCm1r+Yhi07B5xI0t789aQW3FvuPZqyivRh55GdlRl0JEmSJEmSJKnQ2SiWJOkQ3f/tSt7IPZbFMY3oeN69QceRtA+J8XGknD+MNMrRLHch01+6JuhIkiRJkiRJUqGzUSxJ0iH4YeEGRszYwBPhM0m74GsSk8oGHUnSfqjdsDnLjnoMgG4b3mfa568EnEiSJEmSJEkqXDaKJUk6SDt2bOfukTMAGNKjIZ0aVQ82kKQD0uHYsxlfezAAzSfewaqFM4INJEmSJEmSJBUiG8WSJB2kWa/fzDMZt3J08lr+3K950HEkHYQuFz3KnIS2lAtlkjt8MDt3bAs6kiRJkiRJklQobBRLknQQFkz5jq7r3qNNzHJu6lmRcolxQUeSdBDi4hOoceFbbKQic3Jqct+ns4OOJEmSJEmSJBUKG8WSJB2g7KxMEj6/kZhQlMkpfWl/9OlBR5J0CKrWbsCKgZ9xfe71vDVjM+9NXhV0JEmSJEmSJKnA2SiWJOkATX3rHhpFVrCZZJpc8FTQcSTlg07t2nJL3xYA3P3xLBYsWRpwIkmSJEmSJKlg2SiWJOkALJ8/jU4rXgJgaee7qVS1ZsCJJOWXq3o35sSmZXk89Djl3jyR9K2bgo4kSZIkSZIkFRgbxZIk7adwOEzmB1eTEMplZpludDrx0qAjScpHMTEhHji1JR1jl1E3+guLX7yQaCQSdCxJkiRJkiSpQNgoliRpP40Y+zPbs8Jsj5ahxrnPEorxx6hU0lSsWpP0/i+RHY2l444fmDj8/qAjSZIkSZIkSQXC33BLkrQf1mzdyT+/W8eZ2X/juyPeoGa9JkFHklRAmnU8muktbwWg04LHmT/524ATSZIkSZIkSfnPRrEkSfsQjUa568NZ7MgO06lBFU4+7rigI0kqYF0H3c7U8kcTHwpTadTlbNnwS9CRJEmSJEmSpHxlo1iSpH2Y8tmLHLHkcVJis3no9HbExISCjiSpgIViYmh++WusCtWmBptY8fIQIpFo0LEkSZIkSZKkfGOjWJKkvdi8fg1Npt7LZXGf81Tzn2lSvXzQkSQVkvLJlcg943WWR2tyX1o/nv5+cdCRJEmSJEmSpHxjo1iSpL1Y+ub1VGIby2Ia0v2s24KOI6mQNWrdlSmnfM2UaAse/3Yh4xZtDDqSJEmSJEmSlC9sFEuStAczvnuPzunfEo6GyDn5SRISk4KOJCkAZ3RuwNld6hGNwpPvfMz6NcuCjiRJkiRJkiQdMhvFkiTtxvb0LdT84Q4AJtc8m2YdewecSFKQ/t6/NRdWmcfr4dvZ9Np55GRnBR1JkiRJkiRJOiQ2iiVJ2o05w26hJhtJDdWg3QUPBx1HUsCS4mO5dMAJ5BJLy5w5TH3lpqAjSZIkSZIkSYfERrEkSf/HjAVLaLHhCwA29fkXZcunBJxIUlFQt0kbFvf89cSR7mvfYvrXbwacSJIkSZIkSTp4NoolSfqdrNwwt45aTd+sRxhZ80ba9jo16EiSipDD+w1hQvWzAGj8059Zs3RewIkkSZIkSZKkg2OjWJKk33nmu8UsXr+dcPlaHDP4rqDjSCqCOl36JPPjWpJMBjvfOo/MnTuCjiRJkiRJkiQdMBvFkiT9z9K5k5k95gMA/tG/NRXLJgScSFJRFJ+QRMUhb7KFCjQJL+G/bzwYdCRJkiRJkiTpgNkoliQJCOfmkjPyGl6Jf5gH6/zEiW1rBh1JUhFWs14TVh/9H57JPZXrl3blw+mrg44kSZIkSZIkHRAbxZIkAZPefZDmuQtIj5bl2IGXEQqFgo4kqYhre/TpZPf+K2FiuXPkbBau2xZ0JEmSJEmSJGm/2SiWJJV6qcvm037hUwDMa/tnqtdpGGwgScXG9cc25cgmVQnnZPLTy39mx7atQUeSJEmSJEmS9ouNYklSqRaNRNg0/CrKhrKYk9CWLgNuCDqSpGIkNibEE2d34IUyz3Bh9jvMe+FiopFI0LEkSZIkSZKkfbJRLEkq1SZ/MpS2WdPIjMZT4cxniYmNDTqSpGKmavlEap5wK7nRGDpv+y+T3v930JEkSZIkSZKkfbJRLEkqtTZu3ECzGQ8AML3xldRv2i7gRJKKqxbd+jGl6a87Ehw+52EWTf8h4ESSJEmSJEnS3tkoliSVWvd8vYrbsi9jUnwXOp/zt6DjSCrmup37N6aXPYKEUC7lPrmEtM0bgo4kSZIkSZIk7VGxbhRPmTKFe++9l759+1K3bl0SExMpX748zZo146KLLmLcuHF7PX758uWEQqEDejRs2HCfuXbs2MEzzzzDscceS506dUhMTKRGjRp07NiR6667jq+//jqfvgOSpIP1zdx1jPr5F76lK2Uv/ID4+ISgI0kq5kIxMRx22TDWhGpQO7qeZS9eQCQcDjqWpGLOuleSJEmSVFBC0Wg0GnSIg9GrVy/Gjh27z3GDBw/mxRdfJCHhjw2A5cuX06hRowNat2/fvnz11Vd7fP3777/noosuYsWKFXsc0759e2bMmLHfa6anp5OSkkJaWhrJyckHEleStBvpaZs56+nvmLetDFf0Pow7/tQy6EiSSpDFM8dRb+RpZBHPZ93e4twTjwk6klRilLbaqDTVvVD6Pl9JkiRJ2p3CrI3iCnT2ApSamgpA7dq1OfPMMznqqKOoX78+4XCY8ePH8+ijj7JmzRqGDRtGTk4Ob7/99h/mqFOnDrNmzdrnWg8++GDe8UOGDNnjuG+//ZZTTjmFzMxMKlasyJVXXsnRRx9N9erVycjIYN68eXz22WesW7fuIN+1JCk/zBt2E29nf8ujKddy03EnBB1HUgnTpP2R/LD8Ee6aAKnjMmncYhPdDqsSdCxJxZB1ryRJkiSpIBXbK4pPPvlkBg8ezOmnn05sbOwfXt+4cSNHHHEECxcuBGDMmDH06tXrgNcJh8PUr1+f1NRUKlSowLp16yhTpswfxm3YsIGWLVuyadMmOnTowJdffkmNGjV2O2d2dvZuz/TeE8+qlqT8M2f8F7T+6mwAZh//Fm2OODngRJJKomg0yi3vzWTk9DVUq5DIqOuPpHqFpKBjScVeaauNSlPdC6Xv85UkSZKk3SnM2qjY3qP4s88+Y9CgQbstlgGqVq3Ko48+mvfn999//6DW+fbbb/PO4j7jjDN2WywD3HHHHWzatImyZcvy0Ucf7bFYBg64WJYk5Y/MnTuo8PXNAEysdIpNYkkFJhQKcd+ANjSrUZ6WOyax9JkzCOfmBh1LUjFj3StJkiRJKkjFtlG8P/r06ZP3fMmSJQc1x7Bhw/Ke72n7rS1btuRt0XX++efToEGDg1pLklSwpr9xB/WjqWygEi0GPxF0HEklXNmEOJ47vTHPxD9J98yxTHr11qAjSSqBrHslSZIkSQerRDeKs7Ky8p7v6Qzsvdm2bRsfffQRAA0bNtzjFl6fffYZO3fuBKB///55X8/IyGDx4sWsXbuWYrrDtySVGEt+/okua94AYFWP+0ipVDXgRJJKg8Pq12VB138C0GPNq8z87r2AE0kqaax7JUmSJEkHq0Q3iseMGZP3vGXLlgd8/Pvvv09GRgYAF1xwAaFQaLfjJkyYkPe8bdu2TJ48mb59+1KhQgWaNm1KrVq1qFGjBtdeey3r1q074BySpEOTm5NN9ONriQtFmFquFx37nR90JEmlSOeTLmNi1YEANPjhJtauXBRwIkkliXWvJEmSJOlgldhGcSQS4aGHHsr786BBgw54jt9vvzV48OA9jps7d27e8++//56ePXvyzTffEIlE8r6+YcMGnnnmGTp06MDMmTMPOIsk6eC9PnYhP2UdxlbK0+CCZ4KOI6kU6nDpMyyKa0pFtpM+7FyyszKDjiSpBLDulSRJkiQdihLbKH788ceZNGkSAAMHDqRTp04HdPzKlSvzzszu2bMnTZo02ePYzZs35z2/8sorCYVC3HfffaxcuZKsrCzmzJnDhRdeCMDatWs57bTTSE9P3+N8WVlZpKen7/KQJB2c5Rt38Mh3q/hb7kWMPv5LqtasH3QkSaVQYlJZyp3/FmmUo1nuQqa/dE3QkSSVAMW57gVrX0mSJEkKWolsFI8ZM4bbb78dgOrVqzN06NADnuPNN9/Mu7/S3s6qBtixY0fe88zMTF5++WXuuusu6tWrR0JCAq1ateLVV1/l8ssvB2D58uV7zfTggw+SkpKS96hXr94B55ckQTQS4Y4PfiYrN8IRTapwas82QUeSVIrVbticZUc9BsCKXzYwauaagBNJKs6Ke90L1r6SJEmSFLRQ9LeqsISYM2cORx11FFu2bCEpKYmvvvqKXr16HfA8LVu2ZP78+SQmJrJ27VoqVqy4x7Ft2rRhzpw5ALRr126PW2xt2rSJOnXqkJWVxeGHH860adN2Oy4rK4usrKy8P6enp1OvXj3S0tJITk4+4PciSaXVpA+eIGfGe/ydy3jpxkE0qFIu6EiSxGvvf8jfpyRQLiGWT647ksbVygcdSSo20tPTSUlJKfW1UUmoe8HaV5IkSZJ2pzBr3xJ1RfGyZcvo27cvW7ZsITY2luHDhx9UsTxp0iTmz58PQP/+/fdaLANUqFAh73nfvn33OK5KlSp07twZgJkzZ5Kdnb3bcYmJiSQnJ+/ykCQdmA2pK2gx62GOiJ3Dvc1X2iSWVGScP+BUujWqzI7sMNe8MYWdGRlBR5JUjJSUuhesfSVJkiQpaCWmUZyamspxxx1HamoqoVCIV155hVNPPfWg5ho2bFje831tvwXssj3WvrbK+u31SCSyyz2eJEn5a9Vb15JMBotim9LlrDuCjiNJeeJiY3jqnMNpXC6L27f8jVnPX0wJ2+RHUgGx7pUkSZIk5acS0SjeuHEjxx9/PEuXLgXgqaee2q9Cd3dycnIYPnw48Ot9nk444YR9HtO6deu85+FweK9jf/96XFzcQWWUJO3dtK+G0XHHD+REY4k57Rni4hOCjiRJu6ienMR/jk3iqJif6Zr2BZM/fDLoSJKKOOteSZIkSVJ+K/aN4rS0NPr168fcuXMBeOihh7jmmmsOer5Ro0axadMmAM4999z9Kmp/v83Xb0X7nixZsgSApKQkKleufNA5JUm7l7Z5I/XG/w2AKXUH07htt4ATSdLutel5IpMbXQ1Au5n/ZMmsCQEnklRUWfdKkiRJkgpCsW4UZ2RkcNJJJzFt2jQA7rrrLm677bZDmvP3228NGTJkv47p1asX1apVA+DTTz/d49nVy5YtY8aMGQAcccQRxMQU62+/JBVJC964gWpsYWVMHQ4///6g40jSXnW94J/MLNOVpFAOiSMvJH3rpqAjSSpirHslSZIkSQWl2FZs2dnZDBgwgB9//BGAG264gfvuu++Q5ty8eTOjRo0CoG3btnTo0GG/jouNjeXWW28FYMWKFfzzn//8w5jc3FyuvvpqIpEIAFdeeeUhZZUk/dGE+atI3jQTgO19HyepTLmAE0nS3sXExtLgkjdYSzXqRn9h8YtDiP7v34uSZN0rSZIkSSpIxfZmQeeccw5ff/01AMcccwyXXHIJs2fP3uP4hIQEmjVrttc5hw8fTnZ2NrD/Z1X/5vrrr+fdd99l2rRp/OMf/2DBggUMGTKE6tWrs2TJEh5//HHGjx8PwIknnsjpp59+QPNLkvZuZ3aYv3yymLXZ9/HXlusY3L1f0JEkab9UrFqThf1fovLHA+m4YywTht9P93PvDjqWpCLAuleSJEmSVJBC0Wg0GnSIgxEKhQ5ofIMGDVi+fPlex3Tv3p2JEycSGxvL6tWrqVmz5gGt8csvv3DKKacwderUPY458cQTGT58OBUqVNjvedPT00lJSSEtLY3k5OQDyiRJpcUDn8/jhR+WUjM5iW9u7kWFpPigI0nSAZk4/EFqzX2Z68I38bfLz6VTg0pBR5KKnNJWG5WmuhdK3+crSZIkSbtTmLVRsd16Or8tWrSIiRMnAnD88ccfcLEMUKtWLSZMmMBzzz1H7969qVatGvHx8dSsWZP+/fszcuRIRo0adcDFsiRp7xbNGEvCT48RRy73ndbGJrGkYqnroNt4otnrzAw35Nq3p7F5R3bQkSSVMNa9kiRJkqTfK7ZXFJcmnlUtSXuWk53Fyoe70Ti8jK8rnUPfG54LOpIkHbTtWbn0f2ocSzfu4KyGGTxw2UBiY2ODjiUVGdZGJZufryRJkiR5RbEkSfttyjv/oHF4GVspT6ez/xp0HEk6JOUT43j2/I6cnzCae3+5kkmv3xl0JEmSJEmSJJVQNoolScXWyoUz6Lj0BQAWdbiTKjXqBpxIkg5di5rJ9O/YkMRQLt1WPM+sHz4OOpIkSZIkSZJKIBvFkqRiKRIOs33EtSSGcvg5qROd+18VdCRJyjddT7uGSZVOJiYUpc5317J+zbKgI0mSJEmSJKmEsVEsSSqWJn/wOK1yZpERTaTq2UMJxfgjTVLJ0u6y51kSexiVSWfTa+eSk50VdCRJkiRJkiSVIP5WXZJU7PyyaQtN5jwJwM/Nr6d2w+YBJ5Kk/JdUtjyJ57zBtmgZWubMZeorNwUdSZIkSZIkSSWIjWJJUrESjUa5+7PFDMr+K6OSTqbLoNuDjiRJBaZukzYs7vkwAN3XvsXEMZ8FnEiSJEmSJEklRVzQASRJOhCf/fwL385bT3xsXZpc+Byxcf4ok1SyHd5vCD8tnci41bm8+X0sn7XJoH6VskHHkiRJkiRJUjHnFcWSpGJj68a1vP/xhwBcdXQTmtesEHAiSSocXS5/mgl1hpCeGeGqt6aSmRMOOpIkSZIkSZKKORvFkqRiY9EbN/Bq+C5uS/mWa/o0DjqOJBWa+NgYnj63I5XLJbAkdQOfDHs06EiSJEmSJEkq5tyvU5JULMwaM5IuaV8SIUSf404iMS426EiSVKhqVyzDf85oSZV3TqTVqhVM+aQCnftfFXQsSZIkSZIkFVNeUSxJKvJ2bEujyujbAJhU/QxadDku4ESSFIyjWtYlrUFfAFpNvYfl86YEnEiSJEmSJEnFlY1iSVKRN+uNv1A7up61VKPt4H8HHUeSAtV1yEPMSjycsqEsQiOGsGPb1qAjSZIkSZIkqRiyUSxJKtIWTP2eLuveBWB974coV6FisIEkKWCxcXHUvuQt1lOZBpHVzH/hIqKRSNCxJEmSJEmSVMzYKJYkFVnZWVnEj7qR2FCUKcnH067PGUFHkqQioUr1Omz60/PkRmPotO07Jo34V9CRJEmSJEmSVMzYKJYkFVlDf1jB81nHs4JaNL7gqaDjSFKR0rJbX6Y0vQGA+nOfY9bytQEnkiRJkiRJUnFio1iSVCQtWreNp0cv5t1wH2ac+jWVqtUKOpIkFTndzv0bn1a8gAFZ/+DK4XPZmpEddCRJkiRJkiQVEzaKJUlFTiQc5u/vTyQnHKVP82r071Av6EiSVCSFYmLodcXjJFSux5qtO7nlvZlEItGgY0mSJEmSJKkYsFEsSSpyJo34F/9afxn9EmZx34C2hEKhoCNJUpGVUiaeZ8/rSEJcDHELR/HT2/cHHUmSJEmSJEnFQFzQASRJ+r21KxfRdt7jlAtlcnErqFOxTNCRJKnIa1MnhaeOCtNv/OPkLophzk8dad3zxKBjSZIkSZIkqQjzimJJUpERjURY9/ZVlAtlMi++FV3O+HPQkSSp2Oh7/IlMTulLXChCja+vYuPalUFHkiRJkiRJUhFmo1iSVGRM/ewF2mdOJjsaR9kzhhITGxt0JEkqNkIxMbS+7CWWx9SnKltZ98r55OZkBx1LkiRJkiRJRZSNYklSkZC2eQONp90HwNSGl9KgeYdgA0lSMVS2fAqhs4axI5pE6+yZTH7NnRkkSZIkSZK0ezaKJUlFwtyPHqES21geU49O5/4j6DiSVGw1aH4487r+euJNjzWv8fN37wacSJIkSZIkSUWRjWJJUuC2ZeawZeUcADZ1vpmExKSAE0lS8db5pMuYUHUgABN++IrVWzICTiRJkiRJkqSixkaxJClwb05YydWZ13B52cfp0Hdw0HEkqUQ4/NJn+HvyP3gg83SueXs62bmRoCNJkiRJkiSpCLFRLEkK1M7sMC+NXQpAv2P7EhsXF3AiSSoZEpPKcsmFl5NSJp6Zq7by4Kg5QUeSJEmSJElSEWKjWJIUqO+++pCYHeuoV7kM/TvUDjqOJJUo9SqX5bFB7anGFk6YeilTP38l6EiSJEmSJEkqImwUS5ICk5WZQZepf2Zc4o38rW068bH+WJKk/HZsyxo81mQm3WLm02LiHaxcOCPoSJIkSZIkSSoC/I28JCkwMz59lupsZmsomV59+gYdR5JKrB5D7mdOQlvKhTIJDx/Mzh3bgo4kSZIkSZKkgNkoliQFIic7i3pznwdgWfNLSEwqG3AiSSq54uITqHHhW2ykIo0iK5g57NagI0mSJEmSJClgNoolSYGY8fmL1I6uZxMptO9/fdBxJKnEq1q7AauPehiAtms/Ylva5oATSZIkSZIkKUg2iiVJhS6cm0vNmc8AsOiwIZQpVyHgRJJUOrTvM4iVMXUoF8pk7hfPBx1HkiRJkiRJAbJRLEkqdDO+fo160VTSKEeb024OOo4klRqhmBh+aXYBAImLPiMajQacSJIkSZIkSUGxUSxJKlSRSJQpP89lZzSBufXPo3xypaAjSVKp0upPV3BL5AbO3PEXflqyKeg4kiRJkiRJCoiNYklSofrv/PU8uPVY+vEMLQfcHnQcSSp1KqRUplynQeQQx+s/LQ86jiRJkiRJkgJio1iSVGii0ShPf7cIgJN6tKNipSoBJ5Kk0umC7g0A+G7eL6xZvzHgNJIkSZIkSQqCjWJJUqGZPuF7YtZMISk+hkuObBR0HEkqtZrWqMBNtWYzOuFGVn70j6DjSJIkSZIkKQA2iiVJhSbpu7v5MPEeHms4marlE4OOI0ml2lEtalM3tJEWqR+RuXNH0HEkSZIkSZJUyGwUS5IKxdzxX9AqZzbZ0Tg6n3BB0HEkqdRrd8xZrKUqlUhn1levBR1HkiRJkiRJhcxGsSSpUIRHPwLA9KonU72O205LUtDi4hNY1ugsAFJmvxZsGEmSJEmSJBU6G8WSpAK3cOr3tM2aRm40hnqn3Bl0HEnS/zT/0zVkR+NolruQhdNGBx1HkiRJkiRJhchGsSSpwGX892EAplfqR+2GzQNOI0n6TeXqdZhZ8RgA0sY8E3AaSZIkSZIkFSYbxZKkArVk1gQ6ZIwnEg1R86Q7go4jSfo/UnpfA0D7rd+xecMvAaeRJEmSJElSYSnWjeIpU6Zw77330rdvX+rWrUtiYiLly5enWbNmXHTRRYwbN26vxy9fvpxQKHRAj4YNGx5Qxi+++GKX4//+978f/BuWpGLo8wk/szpalenJfajXtH3QcSRJ/0ezjkfzbpmzGZT9N96ZtT3oOJL+D+teSZIkSVJBiQs6wMHq1asXY8eO/cPXs7OzWbRoEYsWLeK1115j8ODBvPjiiyQkJOTLus2b7/+WqTt27OCqq67Kl3UlqThavH47jy2tw3+ij/HFQJvEklRUxR13NzNGzGT9xJVc0bsxcbHF+nxSqcSw7pUkSZIkFaRi2yhOTU0FoHbt2px55pkcddRR1K9fn3A4zPjx43n00UdZs2YNw4YNIycnh7fffvsPc9SpU4dZs2btc60HH3ww7/ghQ4bsd8a7776bFStWUL16ddavX7/fx0lSSfHs6MVEo9CnVR2aNmoYdBxJ0h6c1K4W938+j9S0TL6du44T2tYKOpIkrHslSZIkSQWr2DaKW7RowQMPPMDpp59ObGzsLq91796dCy64gCOOOIKFCxfyzjvvcOWVV9KrV69dxsXHx9OmTZu9rhMOhxk9ejQAFSpUYMCAAfuVb+rUqTz55JMkJiZy//33c9lll+3/m5OkEmDN0vnE/vwOcfTk2j5Ngo4jSdqLpPhYLm2XQMrkoVQd9SS0/SroSJKw7pUkSZIkFaxiu6fcZ599xqBBg/5QLP+matWqPProo3l/fv/99w9qnW+//TbvLO4zzjiDMmXK7POYcDjMZZddRjgc5s4776RJExskkkqf1Z/dz7/inuPVKm/Qvl7FoONIkvZhQIeanB37HZ0zJ7Bi/rSg40jCuleSJEmSVLCKbaN4f/Tp0yfv+ZIlSw5qjmHDhuU939/ttx5//HGmT59Os2bNuO222w5qXUkqztatXsLhm0YBULXX5QGnkSTtj1oNmvNzuZ4ArP32qYDTSNpf1r2SJEmSpINVohvFWVlZec/3dAb23mzbto2PPvoIgIYNG/5hC6/dWb58Offccw8AQ4cOJTEx8YDXlaTibtnHD5IQCjMnoS0tu/UNOo4kaT/F97gCgDYbPmdb2uaA00jaH9a9kiRJkqSDVaIbxWPGjMl73rJlywM+/v333ycjIwOACy64gFAotM9jrrrqKjIyMjjvvPM45phjDnhNSSruNq5dRYf1HwEQOfLPwYaRJB2Q1kecwoqYupQLZTL3i+eDjiNpP1j3SpIkSZIOVoltFEciER566KG8Pw8aNOiA5/j99luDBw/e5/i3336bL7/8kooVK/LYY48d8HqSVBIs/vhhkkI5LIhrTpsjTwk6jiTpAIRiYljb7HwAai58k2gkEnAiSXtj3StJkiRJOhQltlH8+OOPM2nSJAAGDhxIp06dDuj4lStX5p2Z3bNnT5o0abLX8Zs3b+amm24C4MEHH6R69eoHkfpXWVlZpKen7/KQpOIgbdNa2qaOAGBnj5sJxZTYHzOSVGK1+tMV7Igm0SCymjk/fhp0HEl7UZzrXrD2lSRJkqSglcjf4I8ZM4bbb78dgOrVqzN06NADnuPNN98kGo0C+3dW9a233sr69evp1q0bl19++QGv93sPPvggKSkpeY969eod0nySVFhG/jibmZHDWBJ7GO37HPgVLZKk4FVIqcykWufwZO5pvL4oKeg4kvaguNe9YO0rSZIkSUErcY3iOXPmMGDAAHJzc0lKSmLEiBEHdZbzG2+8AUBiYiJnnXXWXseOHj2aV199ldjYWJ577jliDvEKujvuuIO0tLS8x6pVqw5pPkkqDNsyc3h8Wphzc/7K4hPf9WpiSSrG6g38J4/lDuKDRbms3pIRdBxJ/0dJqHvB2leSJEmSglaifou/bNky+vbty5YtW4iNjWX48OH06tXrgOeZNGkS8+fPB6B///5UrFhxj2OzsrK44oorALj++uvp0KHDwUTfRWJiIsnJybs8JKmoe2PCCtIzc2lcrRzHHd406DiSpEPQpHoFejauQiQKb01cGXQcSb9TUupesPaVJEmSdOCmrtjCso07go5RYsQFHSC/pKamctxxx5GamkooFOKVV17h1FNPPai5hg0blvd8X9tvjRw5koULFxIfH0+rVq0YPnz4H8bMnTs37/ns2bPzxnTr1o1GjRodVEZJKkoytqcRO+YhqnAM1/RpT2xMKOhIkqRDNLh7fZKWfcPhE54is/cHJJUpF3QkqdSz7pUkSZJUmk14537mzZnJvbkX0LVRVc7tVp9+rWqQlFBi2p2FrkR85zZu3Mjxxx/P0qVLAXjqqaf26/5Ku5OTk5NX0FavXp0TTjhhr+OzsrLyjrvsssv2Of8HH3zABx98AMCrr75qwSypRPj5kye5IjqC48pMpEG7vW9bKEkqHo5rUZUOCa9Rk41M/uo1upx2TdCRpFLNuleSJElSaTb181fpOv9fdI+LMiHSkq+WdWX6snW0TbqT9TWOolafK2jQomPQMYudYr/1dFpaGv369cs7e/mhhx7immsO/pdYo0aNYtOmTQCce+65xMWViF66JBWYzJ07OGzhKwBsan0RcXGxASeSJOWHuPgEljX69eSflNmvBRtGKuWseyVJkiSVZnN++py2E28lJhRlYtWB3PPn27jxuKacXn42h7GG7uuG02B4H+befwSTPx5KZsb2oCMXG6FoNBoNOsTBysjIoG/fvvz4448A3HXXXdx3332HNOfAgQP58MMPAZg+fXq+3Htp9OjR9OnTB4B77rmHv//97wd0fHp6OikpKaSlpXnPJklFzsT3/kW3ufexjipUvH02iUllg44kSconm9evofwz7UgI5bKw/8c063h00JFUypXG2qi01L1QOj9fSZIk6UBlZmxnQ+oytq5fw4LENvyydSepaZkcufxJDts5hx1tB9O5/5VBx8w3y+ZOpsp7/Ukmg+nljqTdTR8T+7+TXcO5ucz+YSSRKa/SdscE4kIRANIpx9xqJ1Kt7600btoiyPgHpTBro2J72nB2djYDBgzIK5ZvuOGGQy6WN2/ezKhRowBo27ZtvhTLklSS5WRnUX/u8wAsa34p3W0SS1KJUrl6HSZXPJYuaV+RNuYZsFEsFSrrXkmSJKl0ycnJZt32XH5JyyR1607KLvyY5PVTSMj4hQpZ66gS3kAl0qkH1ImGGJD1Orn/a/V1jV9Dy9g5MO02xv8yh26XPkFMbPHe/XHd6sWUfe8skslgXnwrWl7zbl6TGCA2Lo72xwyCYwaxfs0ylnz9HA1XfEAtNtB9wwiOe6ULKfU3c3aXepzcrjZlEor396MgFNtG8TnnnMPXX38NwDHHHMMll1zC7Nmz9zg+ISGBZs2a7XXO4cOHk52dDcCQIUPyL6wklVDTR71AVzawiRQ6nHp90HEkSQUgpffV8MlXtN/6HZvXraZyjbpBR5JKDeteSZIkqWRbvXg2az+5hwoZq6mUu55K0TSOzno1r/n7ePxnHB/74x+Oy4gmsjG2Kn0bJVKuci1qVSxDuei1TFhSn+7r3qHHL8OY/uhiml31DuUqVCzkd5U/0nbsZMcrp3MYm1gRU5faV35EUtnyexxfvU4jql/0MOHc+/l57Eesnvlflq2vR3jFFqau2ELks5toWLUC1Y6+ksPadCvEd1K0FdtG8ciRI/Oef/fdd7Rr126v4xs0aMDy5cv3OmbYsGEAxMbGct555x1yRkkqycK5udT6+VkAFjW+kO57+SEtSSq+mnU8moWfN6NZ7kIWfPksPYY8EHQkqdSw7pUkSZJKrmgkQtp7V9M5e+b//2II6sRuJZJSj1opZUgLHcd4GhNTsR6JVepToXpDqtZuRHKlatSPieHZXWZsBpzI5E860G7qXzk84yeWPHE05S58n5r1mhTumztEWblhLn9zBhUzB3B3wtskDBlJSpUa+3VsbFwc7fqcQbs+Z9A5PZMRU1fzxaTZnJbxPYkbc+H9kSz4qDnprc6jdd8LKVs+pYDfTdFWbBvF+W3RokVMnDgRgOOPP56aNWsGnEiSiravZixjU04r+sXtpM2pNwUdR5JUgNLaXMicac/z2aoydAlHiIuNCTqSpINg3StJkqTiZPrXbxKZ9QG1Tn+I2g2bBx0n380e+xFts2eSHY3j564PkVyrGZVrN+L7anV+t2V0jwOet0v/K5lfqwnVRl1M4/Ay1r18PDMvGEf7xnXy9w0UkEgkys3vzWTiss2UT+xB+iXX0qpe1YOaq3pyEtf0acJVvRox58eXyZ70Cu22jaN57gL4+W9sm/kgD7YezjX9jyA5KT6f30nxEIpGo9GgQ2jvCvOm1ZK0PyKRKH/6z1gWrNvGn49twDXHtwk6kiSpAGVm59Dzoe/ZnJHDc+d34oQ2NpcUDGujks3PV5IkSb+JRKKsvLc1DVlDJgmsPesLGrbsHHSsfBMJh1n6QBeahJcwocbZdL/q+Xxf45cVC8h8fRCvZfXmndCf+PeZ7enfvna+r5OfotEo3770V/6+pCnrY6vz2kVdOaLJwTWJ92Tj2lUs/voF6i4bwYZwOa4u8whj/9KnSJ0UX5i1UdF515KkYuPbeetYsG4b5RPjOP+Iknc2nyRpV0kJ8ZzVtT4Aw8YvDzaMJEmSJKnEm7JiC+dn/RmAJLKp+O6pLJjyXcCp8s/o8ROomruW7dEyND/j7wWyRq0Gzal2y4+sbnI+2bkRrn9nOkM/n0gkHC6Q9fLDxLfu5fg1T/N+4j944rTG+d4kBqhasx7dB/+T2n+dQ+6Zb3H3ya2KVJO4sJXedy5JOijRSITtn91J69AyBvdoQErZ0rklhySVNud1q09yKIMmy99mxYIZQceRJEmSJJVgn8xcw+podW5s8CEL4ppTke3U+/RsZo0ZGXS0Q5YTjnDv+Gx6ZT3O1+0eo1K1WgW2Vvly5XlhSBcu73UYyeyg74QhTH98IDt3bCuwNQ/WlM9epPvixwBY0XQwJ3Up2AuUYmJj6dK2BSe2Lbjvf3FwyI3inJwc5s6dy9y5c8nKyvrD65mZmdxyyy3Uq1ePMmXK0KpVK5566qlDXVaSFJBZP3zIwJ0fMCLhXi7tkv9ndEmSiqa6lcrycuU3uTf+ddZ+85+g40iSJEmSSqjc3DCf//wLAAOOaEvdG75mVmJHyoayaP7dpUz9/OWAEx6adyevYvmmDBLKV6bfyYMKfL3YmBB3ntiSp48KUy+0nk7bR7Pq8WPYkLq8wNfeX7PHfUK7ybcBMKH6ILqde0/AiUqPQ24Uf/jhh7Rt25bevXvv9vUBAwbwxBNPsGbNGrKyspg/fz433ngj11577aEuLUkqZNFIhPgf/w3AzzUHULmKjWJJKk3KdL8YgDYbPmdb2uaA00iSJJVMa5bOY8X8aUHHkKTAzPvxY0bkXs91Zb7iiMZVKFehIs1uGsW08r1JCIVZPv5D3pq4IuiYByVjexpTvhkORLnumKaUS4wrtLV7nXQOi/q9yRYq0Cx3IdEX+rB45rhCW39PlsyaQINvLichFGZa+d50veI5QjFuiFxYDvk7/dVXXxGNRjnttNNITEzc5bVRo0bx1VdfAVC3bl0GDBhAnTp1iEajDB06lJ9++ulQl5ckFaK5E76kZc5csqNxND71jqDjSJIKWesjTmFFTF3KhTKZ+8XzQceRJEkqcTakLif59T7UfKcvqxfPDjqOJAVi5/QRNI75hSMrp+XdOzYxqSztbxzJx3Vu4bacy7jrw9k88/1iotFowGkPzMwPHuaJ8AO8UO55zulav9DXb93zRDIGf8WKmLpUZzO1Rw5k+levF3qO3/yyYgHJH5xNhdBO5ia0pdU17xATGxtYntLokBvF06ZNIxQK7faK4ldeeQWAZs2aMWfOHD744ANmz55Ny5YtAXjppZcOdXlJUiGKjHkEgOlVT6Za7YbBhpEkFbpQTAy/NLsAgFoL3yAaiQScSJIkqWRZMfwWKoR2khjKYe0nfw86jiQVuqzMDFpsGQ1Ahc5n7/JabFwc/S+9myv7tADg0a/m8fGwx4iEw4Ud86CkbVpH62WvAlC5/UkkxAVz1Wydw1pT6fof+DmpE2VDWRw+/nq+Gf6fQm+6b83I5oYRc1kfSWZ5TH3qXPUhSWXKFWoG5UOjeP369QA0adJkl69HIhH++9//EgqFuO6666hQoQIAKSkpXHvttUSjUcaPH3+oy0uSCsn8Kd/RNms6OdFY6p1yZ9BxJEkBaf2ny9kRTaJ+ZA1zfvw06DiSJEklxtzxX9A5/Vsi0RAAHdO+Zfm8KQGnkqTCNfeHkSSHMlhPZVp07fuH10OhELf2a85fT2rJP+Je47Rl9zL1yXPJzckOIO2BmTfiHySTwdKYhnQ86bJAsyRXrEKrW75kYrUzWB2tyh0zqnHLezPJyi2cpntmTphLX5/CpI0JXJ90P2Uu+ZiUStUKZW3t6pAbxRs3bgSgTJkyu3x9xowZpKenA3DSSSft8lqbNm0AWLVq1aEuL0kqJJn//d/VxJX6Ubth84DTSJKCUiGlMrOrnQhAzni3n5YkScoPueEI947P5v1wLyZXPZVp5XoRE4qy+bN/BB1NJczKhTOY/WBv3nzpMUb9/AtpO3OCjiTtIjLrfQCW1ui31y2ILz3qMFp07kNuNIYuaV8y6/FTyczYXlgxD9i61Uvo8Mt7AGw78q4isb1yXHwC3a55mR+PHcmWmIqMnL6Gc1+cyMYtWwp03XBuLs+++hpTVmyhQlIcz158NDXqHFaga2rPDrlR/Nt9iX9rGP/mhx9+AH69N3GDBg12ee23q4vDxWQ7AEkq7eas2cpH6U1JjVah1km3Bx1HkhSwmsddR240hl+25bB607ag40iSJBV7w8avYML6OO6Lv45mFz1P5ZPvYVW0Gu9sbcHsNWlBx1MJkblzB7nDh9AmawbfLd3BNW9Po+M/v+GOJ19lwmt3sHjmuGKzha9KpoztabRM/wmAyt3O2ef4Lqddy+wjnyErGs/hGT+x5IkTSN+6qaBjHpQVH/yNpFAOc+Pb0O7oM4KOs4uzerXn9Yu6kpwUR/1Vn5Dzny4smzOxQNaKRiJMee4ybk69mSviv+DFwZ1pXrNCgayl/XPIjeLfmsATJ+76l+bTTz8lFArRq1evPxyzefNmAKpV8zJySSoOnh29lNfCJ/Bw8/eo17R90HEkSQFr0KIj19V+h6tzbuStyWuCjiNJklSsrd+azuPfLATgz/2aU6l8Eg1bdubxlu8yInw0j369IOCEKilmvHYzh0WWA9C+0xE0qV6ecCRKk/Vf0X35szT58CQ2//MwJj9+FlNGvUjapnXBBlapM3fMe5QNZbEmVIOmHY7ar2M6HH8ui/sNY1u0DK2zZ7H+qePZtG51ASc9MCsWzKDT5lEAxPT9B6GYYO5NvDdHNq3Kh1d147rEUdRiA9Xf68+M/w7P93UmvHE33TaOJBIN0bfH4XQ/rEq+r6EDc8h/G/v06UM0GuWpp55i3rx5AHzyySeMHj0agBNPPPEPx8yePRuAWrVqHerykqQCtnj9Nj6f/QsAVx/jltOSpF+dekQHAIZPWklmjlcdSJIkHazlr17OY5GHObZmJmd3qZ/39euPa0FsTIjvF2xg6orNASZUSTBrzEi6r/u16TOz1/PccPoxfHtzb8b+pQ9NOx/LjLI9yYgmUpWtdEn7ks6Tb6X8k82Zf193nv9iMjNXbSUSiQb8LlTSfb6mHO/mHs2SemccUDO1dc8TWTfwAzaTTJPwErY+fxKrNhad3a+Gj53NkmhtppftSYsuxwUdZ48a16hIlWu/ZU5Ce8qFMmn3w5XMu78nU/99KhOevYz3Pv2ckdNWM27RRhavXk/a5g1EI5H9nn/yR8/QY9nTAExqfiudTrykoN6KDkDcoU5w3XXX8cILL7B+/XratGlDpUqV2LJlC9FolLp163L66af/4Zivv/6aUChEu3btDnV5SVIBW/LeXzklVJ6sFv3dBkSSlOe4ltWpnZJEbPoKfvrha4459k9BR5IkSSp25k/+lq5pX0As1D+yIrExobzXGlYtx9mdapI97R22D38Vbns/wKQqzrZs+IVa398EwMSqA+l2zNl5r9WrXJZ6p10GXEZ2ViazJ3/N9tlfUmP9OBpFVpCSs44Hx6yDMeupUi6BO6qOo2GdWjTucSqVqnkh2P4K5+Yy98dP2Dl9BJGEChx29kNUr1w56FhFSlpGDm+sqEhO+HK+OvGPO9XuS5P2R7Cq7Cgy3j6TBzNPZ/YLk3jjkq40rRHs7zNnrNrK0MWVeCH0MF+dVfR3akypUoOyt37DpOcuoevmT2mZMwdygO3w0up6fPu/E0b6x/zEkwlPkxmNZ3NMZdLjqpCRWJWcMtWJlK9JesM/UbZ2c2okJ1G9XByrpn5Bh+l3Qwgm1DyP7uf+Ndg3qjyH3Chu2rQpb7zxBhdffDE7duzI21a6YsWKvPPOOyQkJOwyfu3atXzzzTcAHHPMMYe6vCSpAK1ZOodjNwyjX0KEhe1tAEiS/r+42BjuOWwBx8+9k8Xjm4KNYkmSpAMSzs0l7su/ADCp4ol07XzsH8Zc2zWFKj+/QsLOXGaP/Zg2R51a2DFVzEUjEZa/ejGHs5UVMfVof/FTexybkJhEmyP7w5H9AVi7ajFz5vzMCRtqMW7xRrbsyOTY3JeotG47kam3szC+KZtq9aLxCddSvU6jwnpLxcryeVP45YdXafzL57Tl197JpEhzjv3PJG48vjlDejQgLrbobUMchK/mriUnHKV5jQoHfbFKvabtWHvtJFa8PoO167dz5vPjeXXw4RzeMJjboEajUR7+Yj4Apx1enyYN6u/jiKIhPiGRLtcOY8nsCWxdvYCcrWuIbltHo7KdODKrKuvSM6mdngFAUiiH2tF11M5Zl9dQZgNcsiCR/0bSAOgf8yNPJjwDIZiSfBxdL9vzf4dU+A65UQxw5pln0rt3b0aNGsXatWupVasW/fv3p/Juzoj5+eefOffcc4Hdb0stSSo61nz2IHVCEX5O6kK7DkcGHUeSVMR06XMquXPvplnuQhZOG02zjkcHHUmSJKnYmPLBo3QLLyGdcjQ599+7HVOrXmMmVB9A9w0jiB9zP9EjTimS97bUH039/GXKTH+F8JG30Lb3wMByjBw3k07bl5AdiiP3tBdIKlt+v4+tWa8JNes14TggJxxh+uLVzB89kOrrx9I4vIxmuQth1ULWvfghqRd+Qe2G3rIMYNP2LOZ+MZSa89+gaXgxDf/39TTKsaJid97hFLatDfPPz+by2aQF3NcjSusenni7/ccXaBuqTr92/Q5pnppVUnjvih5c9NpkNq5eSMVXr2fWsQ/StteAfEq6/2aN/ZQ2K75gduwJ3HR800Jf/1CEYmJo3K4ntOuZ97Ueu4zoTWbGP9m8bjXpG1aSsWkNOVtTiWxbS9yO9VQu04rmOyuwblsm1bO2AjArsSPtrnmLmNjYwnwr2odQNBr1xgJFXHp6OikpKaSlpZGcnBx0HEmlxNpVi6n8UlcSQmHm/2kELbr1DTqSJKkImvz4ILqkfcXklL50uWlE0HFUwlkblWx+vpJKk83r1xD3bBeS2cHEFrfT7ew79jh249qVlBvaiTKhbGYc9Twdjj17j2MVvGg0yrOjl1D/u2s4JXYCWdF4lp34diC/V1m8fjsnPzWW2Jwd/Lt7Fn867bx8m3tD6nKWT/iYGrOfp35kDatDNUm87Cuq1W6Yb2sUJ1mZGXy/aCsfTE/l+/nr+VvMKwyO+4acaCyzy3WD9mfTqveZJCaVJRyJ8u7kVTzy1Xyuzn6Ny+NGMaXCcTQ459FS+/3btG41Kc+2JS4UYfUF46nbuNUhz7kjK5dJT55Pnx1fkB2NZVb3R+n0p4vyIe3+iYTDLH2gC03CS/ih5oX0uvI/hbZ2UZOZuZO0jalUrdmA2Lh8uX61xCvM2sjTzyRJu7XikwdJCIWZk9DOJrEkaY9Sel8NQPut37Fp3eqA00iSJBUPi9/5C8nsYElsIzqfcetex1atWZ8ZtX9tDlf46SEi4XBhRNRByAlHuP2DWfzrqwX8JedyABJDOdT+4kKWzZ1cqFmycyPc+O50MnMiHN6kHv36n5uv81er3ZAuA28g6ZJRrAnVgEgut749ns07svN1naIsGomwcNpoJj59ETsfaspLb7/DN3PXkRuJMrnKqUxofhvbrpnF4X/5gsP7DSExqSwAsTEhzu1Wn+9v7k2bmmWIREN03vYtZZ/vxvg3/05OdlbA76zwLR79FnGhCAvjmuVLkxigXGIcPa97hWnle5MQCnP4hJuYOOLRfJl7f0z/8lWahJewPVqGNgP/UmjrFkVJSWWoUbexTeIiykaxJOkPNq5dSfv1HwMQPWrvBaskqXRr1vFoFsY1IyGUy6Ivng06jiRJUpH389JUamyaBEBO30eIjYvf5zEtT7+LbdEyNA4vY8ZXrxd0RB2EbVs38eGTNzNiygpiQnDHqZ3Y+efVzI9vRTI7KPfeIFKXLyi0PJNevZXOa9+jYpk4Hh3UnpiYUIGsU71OI0JDPuWahPsZszGZC16eSNrOnAJZq6hYu2ox41+/k5X3taXZJ6fSbeNIKrKd08tM44reh/H1Tb146qYL6H7OnVSuXmeP81Qqn0jPa19myYBPWRDXnHKhTHosfpzUhzoxe9wnhfiOgpe8+NffQ25udHK+zpuYVJb2N45kYpVTiQlF6TbnXia8dDPRSCRf1/m/crKzqDHl11sKzGoweK9/D6Sg5fvW01u2bGHmzJls3LiRnTt3sq/pBw8enJ/Ll0huvyWpsE147mq6r32LBXEtaHbneO9/JEnaq8kfPUOXGXeylqpUvWsecfEJQUdSCWVtVLL5+UoqDcKRKAOe/ZEFqzdwe5NVXHTp9ft97PhX/kyPlS+wMqYOde6aRaz3eCwy1q5azM5XT6dRZDkvRfrT6Jx/c2zLGgCkbVrHlmeOo2FkJatCtSl75TdUqVG3QPPM/vFTWn19ATGhKBN7v0m3PqcU6HoASzZs56znx7NxezYX1FrNHZecRdnyFQt83cKSkZ3Ld1Pn0XD0tbTKnElM6Ne+x85oAnNSepHY6TxaHdH/oK+YjITDTPn4GZr8/C8qkw7Ae9Wu58jz7qR2xTL59j6KorWrFlPz5U5EoiE2XDaVGnUb5/sa0UiECS/fTI81rwIwpcKxtLn6DZLKlMv3tQAmvvcvus29j80kk3jLLMpVqFgg66jkKszaKN+u8x49ejT33HMP48aN2+9jQqGQjWJJKmK27MjmxdSGxNGM+J432SSWJO1T234XsmXGQ5SNZjBh0niOPKJ30JEkSZKKpHcnr+Ln1WlUSCzLSWddfkDHtjn9Dr5/fBrP7DyRc2b8wumdCrbZWJDGv/JnGqz6iBUNB9H+9L9QtnxK0JEO2uKZ40j58HwasYWNVKT3wMtp+r8mMUBKlRpkXfIJa188nnrRVF5//RFOv+ExyicWzBasaZvWUf2b64kJRZlU+ZRCaRIDNK5WnmEXd+PFF57g7s1PsPCpt2ly4+cF1ogrLOFIlPtGzeXdyavYmZ3D2MTVxISizEloy44Wg2h57Pl0Tql8yOvExMbSdeD1pB19DhPfvo0WG77g36uac8+jY7ju2CZccmQjEuNK5skhy8e8SU1gfmIbWhVAkxggFBNDj8ueYNLIRhw+8x/Epy3jolcn8vTgI6hSPjFf18rYnkbjuU8DsKjFVXSzSawiLl9++z906FCOO+44xo0bRzQaPaCHJKloefWn5fw3uzV/q/wo7Y4eFHQcSVIxkFSmHJ+2+Q/dsp7m2bn5W2RLkiQVlNyc7ALffvT3tm5cy5IvnyKGCDce34zqFZIO6PgKKZVZ0OcFpkRb8MR/F5ITLrzs+WnV8iV0W/EitaPr6bHsaTL+3Y6J7z5EdlZm0NEO2M/fvUvtkQOpxhaWx9Qn9+JvadrhqD+Mq16nEdnnvs+zobO4Z3NfrnhjClm5+X+v6WgkwpJXL6U6m1kVqk2bi5/J9zX2plXtZK48+QhyiKdN1gwWPDmw2N9vd+T7bzLsxyVkZIdpUKU8E9vdR+qFk2h95zi6DryeCvnQJP69lMrV6HbtK6ReOJH6DQ5jZ06YR75cwEcPX8zPoz/I17WKiirLPgVgW5P+Bb5W14E3sOD4YdwUcxvjV+7ktGd/ZPH6bfm6xswPHqYqW0kNVefwATfn69xSQTjkRvG8efO4/vrriUajtG3blo8++ohRo0YBv14xvGTJEiZPnszQoUPp2LEjAEceeSRz5sxh6dKlh7q8JCkfpWfm8NqPywC47timXk0sSdpvxx73J7JCSfy0ZBOL1uVvoS1JkpTf1q1ewob7W/HAvx9i6YbthbLmgndu4+7oC7xY4SWG9GhwUHMM7tGAquUTWbV5JyMmLcvnhIXjP5O3c2b23/glpiapoRpUZSvd5j3IxofaMvmjpwnn5gYdcb9MfO8RWo+5grKhLGYlHk7l60dTs37TPY6v36wDR17yCOUS4vhx8SZuGT4139/r5I+foeP2H8iJxpLZ//lArtRu3vlYVvR7hcxoPO13TuDnp84qNp/p/zVz9AecPuc63kp4gH+f1pzvbz2a008/h9oNmxf42i0b1WfElT149Mz2nFh2PmflfES70Rcz/V8n8cuKwrvXdUFbsXoNFXM3kBuNoenR5xXKmm2OPJkXrj6Z+pXLsmrzTj5+9nZmj/04X+bempHNw8sO4+twJ1IPv5mExAM7IUgKwiF3AJ566inC4TBVq1Zl7Nix9O/fn/r16+e93qhRIzp16sQVV1zB5MmT+fOf/8y4ceO47rrraNDg4P5BJEkqGDPef4SLc4dzeLUo/VrXDDqOJKkYqVOxDMe1rAFE+WzMj0HHkSRJ2qNoJMK6Ny9jY6QcM7ckcOozP/LDwg0FuuaiGWPpsvHXRkTNY64kLvbgfi1bNiGOG4+qxe1x73DEVyeTuXNHfsYscMs37uDD6WuYGm3OuosmUvX2n5nY6i42UIna0fUcPv1uLnl8BF/OXltkd6OMRKI8/eH3tJ/zCLGhKJMqnkiLW74iuWKVfR7brm5Fnr+gM+Vjc+i/4HamDL0k365qX714Nq1n3AfAlMOuounhvfJl3oPRqudJLDh6KNnRWDpt+55pT19AJJz/V1AXpHWrl1B/9A3EhKKEqjXnjO5NCIVChZohFApxeqe6PHT9hUyofha50RgO3zGOiq8cyfhXb9vn//9zsrPYmLqcJbMmMOuHDxnz3895aexSHvlyPneMmMbMh/ux4L6upP6jGRn3VGf1P1qwce2qQnp3v/pkQQbds57mH7WepnL1OoW2bpPq5fnomiO4rOZibuFNmn97ERM/ePyQ5x06egkzMmvxWJW/0/GkK/IhqVTwDvlGCGPGjCEUCnH99ddToUKFvY4NhUI8/PDDTJ06le+//55XXnmFiy+++FAjSJLyQcb2NNoufo5ecekc0bQbMTGF+49fSVLxd+nh5bhl8W3UnbORbWnz8n0bNkmSpPww6YPH6ZY5lcxQPFVr1GHb2lxuePU7nuywhiPPvDHfd9eKhMNEPruFmFCUKRWOo3OPPx3SfGd2a8SW78dTk41M+PAxup97dz4lLXjPfzOTcCTKMS2q06FeRQC6DfoLO3dcxYQPHmH+0mWM3pTM6Den0r5uCvd0i6Fj5x5QyA26PcnMCXPTuzP4YnYGs2Ou4cKmmXQb8uAB/Z05smlVXuqTS9ex04jZNJXxr1ajxyX/PqRcOeEI7388kmvIYU5CW7qe949Dmi8/tO9zJtOydtB+/I102fo5E5+7nK5XvVgsdq/Lyc5i8+vn05JtLI5tTPtLnw00T3LFKnS/+gWWzr2UzI9uplX2LHqseI7Vj3zEvNa3MDvlaDbtyGLLtgyuXXkT5cJbSYlsJYUdVAWq/m+eb8OHc1/On/PmvTtxOmVD/9saPARlo78wefhfqHrjO4X23j79OZUwsbTrXPgnNlQul8CtV17KlGd/oHP6t3Sb9XcmbFhE18ueIib2wO8H/cvWDF77aTkAt53QgpiDPCFIKmyH3ChevXo1QN620sAuZ9bk5OQQHx+/yzGXX3453333HW+++aaNYkkqIn7++D90J53VoZoc/if/2yxJOnBdWjdn5YdQLpLJxC+ep9vZdwQdSZIkHYTN69ew4LP/EMpKg1As0Zg4iImFmDhCoRjWp7RjbdXuxMbEkBjOoOm6UYRiYgn9b1xMbBzExBETE0tmhQZkVmtDj8OqkBR/4L94z2+pyxfQZvYjEIIZza7n8bMGUX7kDPrPuo4j581h0lNTaH/FSyQmlc23Nad88gxdcxewI5pEg3MePeT5EpPKsrLttdSc9XeaLXyBHduuo1yFiocetICtWjyLu+YPoHFcH7r02fXeuWXKVaD74H/ScmcOm8Yu5eVxy9ixZg7tP7uNOd+0I7bvPbTofGxAyX+1ef0a/jF8NF+kViQhNoYTzric7ocf3BWQ3Y87g4mbl9Jt7v30WPUiE4ZXpfvZtx90tqf+u4gnN3RiYtI/eeL8vsTGHfKv/fNFxxMuZHJWBl2m38GKXzYw9ut53HpC66Bj7dPUV26ie85ctkXLUOa8N0gqUy7oSAAc1qor0RY/MOXzl2gw5QHqRteyYObb/Cfn//89fCRxMeVC//++0OFoiC2hFLbFVCRSri6n1K1NlXIJVC2fwPS0f1C2XAXKVKzBzq3rOPyna+iy9XMWThtNs45HF/j7WbBqLYvXpZMQG0ffgHY2TEwqR6cbRzD+tdvosfIFuq99i+mPraDF1cMpU27vF0b+XyuGXck9pDOm3qUc3bxaASWW8t8h/8TIzMwEoHbt2nlfK1fu//+Hc8uWLVSvXn2XY5o0aQLA3LlzD3V5SVI+yNy5g8aLXgEgtc1V1I1PCDiRJKk4CsXEsLb5BTSY9yC1Fr5BNHJbsbhiQJIk/SoajTJiymrGff4GT0af3+O4F3JP4oHcigDUYQM/Jt2/x7Fv5h7LX3MvoWn18nx4zRGUTwyugRUJh9ny9qXUDmUyN74NXc6+i9jYWB4+43Am5pxAeOFcum4ZxbxHj6P6pe9RpUbdQ14zbfMGmsz8FwCzml5J99oND3lOgMNPuZrVs5+jbnQt40c+Qo8hD+TLvAVp7af3Ui+USecKW2jfYPfbNKeUieeWvs0Z3KMhP37wJOFlsbTOngmfDWTGdz1JOekfNGrdtZCTw8qFM4h9ZxB3RrKYl/Qg9w7uR/fD9r3V9N50G/QXxr+ygR4rX6DrvIeYMqoqnU+69IDnmbx8M09/vxiA8wYOoEb92vs4onB1OfVqvkyoxW1j4oiOXk6ZxESu6dMk6Fh7NOObt+m+9i0AFvd8mMMPK1qN7VBMDJ1Pvpztvc5k/Ij7yNmewfkN6lO5XCJVyycwN/0J/h979x0eVbW+ffw7M+kdSEJN6KHXEHqXJlWKFJWmIqKi2Cj2whFQEVQUFUGlq0hRUFEUkBJC6L0TWmghlYS0mXn/4HfyyoEACUl2yv25rlxOMmuvdU8Ggk+evdd28/TBo3hJvEqUwbu4P74WC75ARaDTDbPdeE/t8P0/ExK3mtN/zKBK/Ta5vttg9O+T2Oy8gt/9H8fb9d52WrgXJrOZZo9+wLafK1N3+6s0SNzI0WntKPboT/iWubvbp546vIuQKz/T1MFOw5Cn83ybcpF7cc//Z1a8eHEuXbpEYuL/3w/fz88v4y/CkSNHbmoUR0VFARAbG3uvy4uISA7Y/ctnNCGGC/hSv/uTRscREZECrGaXJ0g8MI1A2zn2bvyFOq17GR1JRERE7sLxc5eYsPI4W09GA/Vo7d2HgGIu1+8Ta7disqWD3YbJlo6rewh9PMtitdtxTXVmx8VWmO1WTP/3Yf7Xf9M9KuKd6MjRS1d59Ycwpj/czLATybb+MJmmqXtIsjvjPegrLP+3tajJbKbpw2+wZ21NKqwbTY20/VyY2ZbjfedTuU7Te1rz0KLxNCGeU+ZyBPfPud1WHJ2cudDgecrtGEetk98SF/MC3sV873ygQU4f2UXD2D/BBO6dX7vjeD9PZx4Y9jLnT/XizLI3CY75jfpJm7H90Ilt3h0o88A7lKlUMw+Sw8HQ3yiz+jG8SSTSXJJZg2pR/h6bxP/VdNgUtnweRdOopdTdOpY9HiWo26b3XR8fHxtF2tz+BPEgtRo2p3vd/NUk/q8u9/dmgvtx3vv1EB+tPkCVhK107vmQ0bFucuZyLKU2Xf/zucV/AE07DzU4UeY8vIrR7LHrOxR0ueGZCtmes+LAD3n7i0C+i23DlB1nebBRwL1EvC27zUZg5G+UMsVQp4IxVxP/r0Y9n+RQyYqU/O0xqqYf5c05cxjw6IvULON1x2Ov/Pw65U12dro1p0HjjnmQViTn3HOjuHr16ly6dImjR4/SvHlzANzc3KhatSpHjx7l559/pmXLljccs2zZMuB6Q1lERIyVlppC4IGvADhV/XFKObsYnEhERAoyT+/ihPl1pUnUUqxbvgA1ikVERPK15GuJ7FrwOpXPLOFEymRcHYvzQscgHmgxG4dM7q/YGBh8w1c6ZDp/DaDu6RjGfbmUp46OJWzhgzR95K2cewF36eS589Q69CmYYG/Nl2hyi6sE67brx6kyVYhbPIgAeyRJS3qyM/IDGmSzWXQgMp6J5xrwlsNunO97HUennK23G3R9nIhdM6hgO8OWn96j6eMf5ej8OenSyncJNNnZ5daM+vVb3fVxpcsHUXrMIk4d3kXUL28QfHU9jeL/5NJ34bxZbxlPdahJSa/c+z3Gtl++pO62V3AypXPYoRq+I5ZSJgeuNP8vk9lMyKiv2TY9hkYJaynz93PsDmxKvYql7+r4I3NG0ty2nS9cLlKiR/6+jdgTrSuTmJxC0Ibn6LxjK+Hplwnp85zRsTKkptt45vt9JKaM4xWv1bR87BOjI+U539KBlL5vFLZfDzHl98N0rl0KLxfHOx+YDUd3/UOQ/SJJdmdqtHkwV9bIjupNOnPObzXzv5/Dd3GNWfLFZmY81JB21f0zPebIjnU0TPwHm91EsR7v5mFakZxxz6fvtWzZErvdzoYNG274ep8+fbDb7XzyySd88803JCYmcunSJd5//32+/vprTCYT7du3v9flRUTkHu1c9RWluUwUPtTrOdroOCIiUgiU6nD935M6iaGcP3XY4DQiIiKSmX0bVnD5/UY0PTsbP1McL5fexZ8vtGZE60qZNomzo2FgMd6tF0M181lCjk5n/6aVOTb33bDa7Ly44gQDU1/jD/cehPR7KdOx5avVx2v0P+x1boibKYXimyby2Z/7r19ZnQV2u503f97HXltF5gR9Se1WOX/ynMXBgZjG119L0JkfiI6Lz/E1csKpw7toEPcXAB53cTXxrZSvVp/gl37m6AMr2e0Swqz0rny39TxtPljLpF8PEBd9KScjY7fZ2PLtKzTaPhYnUzo73FsR+PxfObId+f+yWCzUeWYRoa5teCL1eYbN28uxS1fveNy2n7+gUfwa0u1mkrt+jKerc45ny2ljOlSnRJnKADTc/Sbbf/3a4ET/33u/HmT32TiiXCtSbdQCnIrohRTDmlekkq87CVcTWLl0fq6tEx22CIADXi1x8/DOtXWyo2ylWgx5bjLNK5cgMdXKuO/WsOmHW5+IY7fZSPv9DQC2+3SmQo1GeRlVJEeY7Fn9v5z/ERYWRrNmzShevDhnz57FxeX6D9ArV65QrVo1YmJibjrGbrfj6urKtm3bqFGjxr0sXyTEx8fj7e1NXFwcXl533uZARORuWW12Bn+wiJ4J3xNYoxHNH37d6EgiIlJI7JvUhtopu1gX8BRtH5tkdBwpJFQbFW56f0XyTvSlcxxfMIaQuD8AuEwxzjR9iwadhuTattB2m41tHw8kJG410XiR9vg6SparnCtr/a8v1h9n8m+H8HB2YPXzrSnr43rHY9LTUgn7egxvn67LEXsA3eqU5sMH6+HqZLmrNVdsO8ZzSw7j6mjhrxfbUOYu1swOu83Ggg+e4bOYpvRoHcIrXfPf71q3fdSXRvFrrm/JOva3HJlzy/HLfPDHUbafiqGDeTufO07nstmXqxYfrjkVJ825OOmuvpg8/Iir2B133wBKeDhRwsVGcXdXHJwyb6qmWW2s+vptHjg//fpaJQfR+InPMFvu7r3Prqsp6Tw0awt7zsZRxtuFn55qTmnvW/+5iTx5CM9v2+JpukZo4EiaPfp+rmbLSXabja0zhtEkegVpdgsHWn1GvQ6DDM207Y+FTFp7ge32aswZ1oj21Usamsdom/YcovxP3ShJDJGD1lC+esMcnd+Wnk7UxKr4E82uFjOp3zH/bUMO138WvPnTTvrte4KG5mOE+fYleOQXODg6ZYzZu34pddYOJ9XuwJVHN1O6fDUDE0thkpe10T03igG+++470tPT6dq1K6VL//9tMbZv307//v05efLkDeP9/f2ZO3cunTp1+t+p5BZULItIblmx6xzPLd6Fj5sjm8a1x935nu9IICIiAsCWf37js993sd+5AZtf6YiLY+7+Yk2KBtVGhZveX5HcZ7fbCV82g6A9k/HhKja7iXC/3tR45EO8fHLmnqu3cy0xgciPWlHZepLDDtWp+PL6XL9q7+ShnTw3L4w91gDe71uX/iFZu+fm4q2neX3FPtKsdkb47uOxAX0pFVDltsckxF4hcXpjlqc3xdR2PCM71LmHV3Bnaw9fYvg34Tg7mNkwth3+ubgVc1adOH2aUrMb4WZK4VjvX6lSr0WOzW2321l7+BKmn0bQLm19puO6pfyH/faKADxh+YVXHBcRiwdxZh8SHYqR7FSMVJcS2F39iKzYl+UnTew4eobFThNJqjWQJgPG51jmO7lyNYUHvwzFNWofE90WU3HUT/j43ti0TE9L5ej7baiRdoBDjjWpMnb9DY2rgsBqtbLz4/40il9Dit2Rox1m58pV93fj7LF9eM/rgCspfF/zMx4ekD+blnlt5/tdaJAUyh6XYOqMXZOjJxHt3/wrtf4YRDzuOI8/hrOLW47NndPsNhth89+k6YnrW5Hvdm1M5VE/4OFVDJvNzt73WlMvfc/1e1o/9ZXBaaUwycvaKEc6AkOH3vo+HcHBwRw6dIi///6b/fv3k56eTtWqVencuTNubvn3L7+ISFFgs9n5fO1xAB5rUVFNYhERyVGNWnTm+CYnouOSWbnnPP2Cc36bPhEREbl7xy4l8MqyfXQ98w+NHa5y3FIRa7fpNGnYNs8yuLp74vLwAuLndqRa+iHCZo2iyTPf5Np6aakppP/4OEscTjKr9AQebNQ1y3MMbBxIJT8Pvpw3n5cTphA/+3MOdZtN9ZDM78u8f+EEmhJFN8cd+LfM/aum2wb5EVy+GNtPxTB39WZeejD/3O5v2qYrhKV8xDPljjMkB5vEwPVbG1YviX3Ccs6eOMDVK5EkxV4kLf4StquXMCdF4ZB8hYCSlUlPdudKYgq+yde35/bhKj62q5B6FlKB/9vpuduxCuy3V8DV0YPLA1bRvlbZHM18JyU8nJk7LJi0T5+movUsh77ohdOY1Tdszbtt/us0TTvAVbsrXg99U+CaxHB9u+36oxexY1pvGiZtpNKaERxydqN64455miP5WiIpiwbjabrGQcdaDOjdN0/Xz8/8+k4ldW5r6iZvZ+eahTTo9EiOzX11+/cAHPJpQ+N83CSG6/cRbzrkXXb+XpkaoS9R79pWTkxvy9VhSwiPcee1q8/wjPOv9O33ptFRRbLtnq8o/ueffwAICQnB1fXutlBJSUkhLCwMgNatW9/L8kWCzqoWkdyw44/5nN0wn2/Mffl2/DC8XR2NjiQiIoXMZ2uP8cHqwwSXdeOn0e2MjiOFgGqjwk3vr0juSL6WyLd/7WJqaBxpVjt+jil8XP0gjfu/bFiDac/f31P3nycA2NrwfRr3HJkr64TOGUuz018ShzupT2zGr0yFbM91/tRhrn3Xn0q2CFLtDuxu8DYhDzxz07iTB8IJ+L4TDiYbe9rOoW7bvGk8bTl8lvj5Q2hr3p1vtj89ejGBTtP/wW6HX59tRc0yxv9st6anExd9ifgr57kafZ6UuIukx1/CfvUypmtX+MFzMEkOxXi6XRVqlzXuvqkRB7dR7PueeJPIbpcQar6wCkcnZ3aeukLi7J60NO9jW8PJNOo5yrCMOSElOYnD03tQ9doenjO9zLMjRubp9z3skyE0iV5BDF6kjfgH/7IV82ztgiB01nM0O/ctkaaSFB+7ExdX93ueM81qY9h/vuK+1LUE3z+Uei2yfgKPUY7sWEeJn4dSglii8GGc06v8FV+W5zsE8VyHqkbHk0KmQG09bTabMZvN7Nmzh5o1a97VMcePH6dq1aqYzWbS09PvZfkiQcWyiOQ0u83Gsf+EUNV6jNCyw2g24mOjI4mISCF05WoKi6aM4iHzaqJ6LSAoD69YksJJtVHhpvdXJOft2/gz3n+N42y6D4PSXqV99ZK806sW5YoZfwVX6OyXcDy1nhfsY/hiVI8cbyIe272J8kt74Giysi34fRr1uPdmdGJCLEe+eJgGiRuB6/euDRkxA4vD9R267DYbBye3pmbqXna4taTh2FX3vGZW7J3UljopO9nq05XGYxbl6dq38ta3v/DtITNdapXii8HBRscpcA6Fr6H8ykG4mlIJ9+pI9VEL6D5jM2euXGVCpZM8/vgzuXZP8bx0LTGBt+csZfE5X4q7O7FoRFOqlfLM9XW3/fwFjXaMw2Y3sf++OdRp3SfX1yxoEhNiSZzaAH+iCa0wimbDJt/znOsOX2LYN+H4ejixZcJ9OFgK1p/hC6ePcu3bflS0RbDPVoFhDu+zftx92qlRclxe1kY58rcwu73mHLg9soiIZMPe9Uupaj1Gkt2Zar3GGh1HREQKqRIezjQpfpXipqvErf/M6DgiIpJLrFYbsVEXOHNsL4e3/c3JA+FGRyryoi+dI3xaf2qvGUyAPZIqlvN880ApZg9tlC+axABNhk1hZoXpnE7z4cn524lLSsuxuVOSk7CseApHk5Ud7q0I7jYiR+Z19/Sh3gs/s6XcYwA0vbiI/R92IT72CgA7fp1NzdS9XLM7Uar/RzmyZlY4dnwDgIYxv3Pm6O48X//fIg5u442Tg5nj+D7PtatgaJaCqnpIB460/Zw0u4WQ+D/59cPHOHUlkdI+7vQfPKpQNInh+pb0r454iLrlvIlOTOWTzz4i9KvRJMRF59qapw7vpOb2639fwgIfU5M4E+6ePpxuNAGAeifncOHMsXue85fd5wHoWqd0gWsSA5QKrIrvc2sJdb+Pp9OeZWzXmmoSS4FnyJ9gm80GXL8XgYiI5C27zYbT5qkA7CnVh6b+eXu/HRERKVq82zwFP6+mXuzfXLl4lhIlda9iEZH8Kj3dSlxyOrHX0ohNTMHhxJ+kxkdjTboCSTGYkmOwpMThlBbHMVN5ppsGE5uUSnxyGkech+BjsmbMlZdb7sr/Z7fZCF8xg6DdUwjhKja7iXC/3tR45EPa+ZQwOt4NzBYLUweG0GPGRk5HJzFz7neMHTEccw78vnDHvAk0s0UQgxflh3yRow01s8VC08c/YvuvtagZNp66yeF8MnMyHQaOIWDbewDsqvAozSrk/dbP1Ru1Z9dfzah/LZSLP79NwItL8zzDf0WveocKJjvFvDypUS5//dkrSOq1e5BtCVE02jGeB9J/4yNTF6YNaF7obh/m6eLI3EcbM2bhVsafnkdA5GWuTFvB1jrPE9xrdMZV+zkhKTWd0KWfMtCUwj7n+jQeOiXH5i6Mgrs+zoHd33Em2YUNfx9j4tAq2Z4r+VoiIfsnEmkOoUfdJjmYMm95ehen6Us/sSwpjeLuBe8e4SL/y5BG8alTpwDw9jbuPg8iIkXVgdDfqJV2gBS7I5V7jTc6joiIFHJBDdty5NcggtKPcPS3zykx7D2jI4mIyP/Y8vUL1Dy7iM3WWjyZ9vz/fdXOEeencPpX8/ffrtmucjo16f8+MxGLJ272FNJMDhQjAc9/3sbasleO/nJfbu9yZASXvn2Exql7AThhrkBa12k0adTe4GSZ83FzYubDwWz48jlGXVhG6NyjNBt+b02bg7s20/jsd2CCiGYTaZBLJ6kFd32Mo2WC2P7LF3wU14bNsxYx2zGRs+ZSNBj4Rq6seTc8738Dlt5Pw/i/Obk/jIq18r4Zc/JAOA2vrgfAp+vreb5+YdOo5yjCkmKpc3AaU+pcoHHF4kZHyhU+bk5882hzdq+dCBvfJsAeSYm9b3H8wHdca/cOtVv2vOc17HY7ry3fx9K4Ppx0K8GI4TnbhC6MTGYzPLKEUV/swHbQSvcTV2haKXsnfxz4ZykDTX/Qzmk7foEv5nDSvGUymdQklkIjyz8FT58+fcuvnz9/Hg8Pj9sem5KSwvHjx3n99dcxmUzUqlUrq8uLiMg9sv/zPgC7/HrQpEwFY8OIiEiREFd7GOx6hQoR35Oe9hYOjiqoRUTyi51rFtH07GwAipkSAPB0ccDHzZH9aXVxMJtIdfIh3dkHm4sPJtdimN1L4FAskJ8qNMPb1QkfN0e8nY/i6OhEXPRl4j6pR0XbKbb+8jmNez9r5MsrMqITUxm9cAdvJ8eQZHJmT9VRBPd/BUcnZ6Oj3VHtst5ca9gIdi2jScSX7F7biHrtHszWXMlpVkavuUab9Ifp4BtNs85DczjtjarWb4V35RAazNvOltM1aJ8ylU97lKacq3uurns7les2Z/sfbQm+uo7YVW9DrV/zPEPMr+9QEdjh0ZqGBjSqC6MmAyeQlvoCbQvA3+l7YTKbqX/fQFJbPsCWnz6kxpHPqWw9CWsGs3Nzc3z7fEBAldrZnv/HbWdZuuMcZpOJdo+Mx7eUrna/GzXLl2ZQ40AWhJ3mrZ/3s3J0y2xtG23fuwSAk6W6UFK7zYrkGyZ7Fm8U/L/bRf/3cJPJlOXFZ82axaOPPprl44qavLxptYgUbofC/6L6qj6k2S1EPRpK6fJ5vxWWiIgUPcnXErk2pTrFiGdHsxk07DzY6EhSQKk2Ktz0/ua9+NgrJE9vhD/RbPEfQLX+7+JZzO+e7xm4Zf5bND02jUsUx/PlPbi6e+ZQYrmVhOQ0Hv46jD1n4wjyTGP2wKoEVC54F2eEfTqUJleWE4c7iUP/pkzF6lmeY+LKA3y98ST+ns788XxrfNzy5uS05DQrM9cdx9PFgcdbVcqTNW/n1OGdlFvYjksU48rgddSuUj7P1j6xL4xKSzphs5s4NeBPKtYMybO1pfCJjbrA4e9fJfjSUhxMNoalT6BK056Mvq9qlrffPrEvjD0/vMtrqUMZ1bkBT7fL/hbKRVFMYip9P1jGU9b5lKvdiqYDxmbp+MSEWMwfVsXVlMrRB1ZStX6rXEoqUjjkZW2U5f/zt9vtN3xk9vXbfTg7O/Pyyy+rSSwiksc+2evApLRBbPTrryaxiIjkGRdXdw6V6Q2A046vDU4jIiL/dXDe8/gTzVlTaeoP+4hiviXvuUkMUL/vy5zHj53WSizeuD8HkkpmkpOu8umXX7DnbBzF3Z34fMR9BbJJDFB/xEyOOAThTSJJ8x8iOelqlo7fs2sbCzYdAmBy3zp51iQGcHG08HzHoHzRJAYoX60Bs8tPoW3KR7z/z8U8XTv2t4kA7PRsoyax3DMf31I0eXo25wb9xVLvYaxLr8PXG0/S9oO1rFr9G+lpqXc1z9X4GBx/GsYD5g186ruMUW0q53LywqeYuxOTapyin+UfahycTszl81k6/uC673E1pXLGVIYqdVvkUkoRyY4sbz39zTff3PD58OHDMZlMvPvuu5QtWzbT40wmEy4uLpQuXZoGDRrccZtqERHJWfvOxfHrkauYTT0YOLCt0XFERKSIqdhlNN/NOs/c+I58cTGBqiV1dZmIiJH2b/6VJldWABDXYSrl3HLu9zQuru5sv38FzyyLwHNzHL1apuo+frkgNSWZw5/2ZnxSOKnOI+j36GtU8S+4/746u7jhPWwRMV+3o4r1OOFfPU6jZxdevz/mHSQmxFJixUOscjTxU7UPaF+9ZB4kzt+69BrM+1PX8c+Ry2w9GZ0n97U9FBGJ/9UD2DBRopvuTSw5p3z1hpSv3pASRy4zceUBYi+dpc3mFzgb5kd867eo27ZfpsfabTYOz3qUYHskFylB/aFTMZuzvjuqQHCfMZyYtIBKtgjCFo+nyejv7vpYh4PLADhX9n4C7uLnuojknSw3iocOvfHeHsOHDwfggQceoGbNmjmTSkREctzna48C0KNeGSr6Gne/JBERKZpKBVZlU9VxHD9wkbmhp3j3gezfW0xERO5Ncmo6TmteASCseE+atOiW42t0DalJzS3RHDgfz6d/H+XNHgXzKtf8ypqezt4ZAwm+tpVrONH//vuoWdbb6Fj3rGS5Kuzr8Blefw4hJPY3Vv+xks5det7xuH3fPU8T+0UumH0Z2U1XqgEElnCjf0gAi8MiWLviOxqNfhZzLt8TdNqGSNamTOW5yhd4ukajXF1LiqY2QX60eK4Vf/+2hLRtjlSwnYF1j7F7yxf4PPA+5as3vOmYsCVTaZrwN2l2CzHdvqS6X2kDkhcODo5OXOswCf4YREjUCo7t3kSVenf+mRt/5RI1E7eCCUq3eDgPkopIVtzzqRtr167l77//pmLFijmRR0REcsGpg9t48sjjdDaH6x4sIiJimKHNKwCwdMdZEpLTjA0jIlKETf/rGI9ee5bfza2oMWR6rqxhNpt4pWsNShJN9fBXiTxxIFfWKYrsNhvbPh9OcMJaUu0WjrSdSc2mXYyOlWNqt+zJlqov8nTqszyzwYGdp2NuO37vhhU0iVoKwOX2H+HlUyIvYhYIo9tW5Gfn1xkX+xa7PupFYkJsrq21PzKO1fsvkmZypFNPNYIk9zhYzHTq3h/zczvZUnIQqXYL9ZLDKbvoPsJmPEps1IWMsUd3baTh/skA7Kj6LNUbdzQqdqFRq3lXtnu2w2yyk7byZew22x2P2bJzO+ftJThhrkD5GsF5kFJEsuKeG8Vt2rShTZs2uLq65kQeERHJBZd/m0xd80lGFttGkLb6FBERgzSvXIL7i0cy2T6NfSumGR1HRKRI2ncujlkbTnDGXhJLv9m52lRrWdWXz4stZIB5LeeXvZJr6xQldpuNsK+eoUn0z1jtJvY2+ZB67TLfcrWgav7wa6TX6EWa1c5TC3Zw5WrKLcfFx0Xj99cLAISVeIA6rXvlZcx8r3QxD5KDnyTV7kDDxA1cmtY6107aWP3L91iw0qNuGd1iRPKEdzE/mo76gouD17PLrTkOJhtNon7COqMx360/QPSVy7iteBQnUzo73ZrT+KE3jI5caJTt/yFJdmdqpO1n+69f33H83IjitEmdxroms/IgnYhkVYHeDH7btm288847dOrUiXLlyuHs7IyHhwdBQUEMHz6cjRs33vb4iIgITCZTlj4qVKiQ6Vyffvopffv2pWrVqri5ueHi4kK5cuV44IEHWLx4Menp6bnwXRARub1zJ/bTIG4NAJ4dxxucRkREijKTycSw8lfoYdlC2cPf3tXZ5yJFnepeyUlpaanMXvwjVpudbnVL07Fm7t/H1afrm9jsJoIT1nJkx/pcX6+w2zLvNZpeWADA9rpvEdz1UYMT5Q6TycSHD9ajkp871rjzhH4xivS01JvGHfr2GUoRxTlTSWoP+9iApPlfo55PcqL790ThQ0XbKdzndmDvPytydI1juzfxwvmXWe00jufalM3RuUXuJKBKHeqP/Y19HeZxwlKB5enNefO3kwyb9hMWWyqRJn8qPT73ru53LnenVEAVdle8/u+Px/aZJN5mt6jLCSlsPh4FmOjQSLcfEsmPTHa73W50iOxo3bo1GzZsuOO4IUOGMGvWLJycnG56LiIiIstbZnfq1InVq1ff8LXXX3+d//znP9zpWxkSEsKSJUsIDAzM0prx8fF4e3sTFxeHl5dXlo4VEdn68cM0jlnJbtfG1Bv3p9FxRESkiLsaHwNTa+Bhusbe9nN15Y9kSVGrjYpS3QtF7/01Qujc12h24lPm0IseL87Cz9M5T9YNn9afkLjV7HeqQ83x/+iX9dk0NzSCxFWvMcrhF7ZUfZGmDxf+q+OOno/B6YumlDddILT0EJqN/DTjud1/f0+9f57AZjdx6P7FhWr77dxw6dxJYr/pT1D6Eax2E+FBL9Bk0Gs58vdx5/v30yBpM9u8OtDohZ9yIK1I9ljT01kafpwpf50m6moqpSwJfDegItXqNjE6WqGTfC2RBR+9wPSEDgxuW4exXarfctySvzbzyp+XqBHgx4qndQ95kbuVl7WRQ67OnosiIyMBKFOmDA8++CCtWrUiMDAQq9VKaGgoU6dO5dy5c8ydO5e0tDQWLlx40xxly5Zl7969d1xr0qRJGccPHTr0pufPnz+P3W7H3d2d3r17c99991G1alVcXFw4ePAgn3zyCeHh4YSHh9OhQwd27NiBh4fHPX4HRETu7MLpo9SP/g1M4NxurNFxRERE8PAqRpjf/TSJWop1yxegRrFIplT3Sk46c3Q3DY9/ASaoU79xnjWJAQL6/oeU2X9TK3Uvu9f9QL32A/Ns7cJi6Y6zvLFiPzCI4vW7M6Bf0fgeVi1djO1NxlJ+6ws0Oz+Xnasb0aDzUGKTUnllM7xsrYdzmZo0U5P4jvzLVsTrxXWEf/kYIbG/UeXILN5Y1JpX+7fCxdGS7XmP7tpIg6TNWO0m/Lq9nnOBRbLB4uDAg82q0aVBJX7YdpbqpTypVsXX6FiFkourOwEPvE3CvO18veEk/RsFUMHX/aZxtba8SLhzBKHlPgTUKBbJjwrsFcXdu3dnyJAh9O3bF4vl5v+ZiYqKokWLFhw5cgSA9evX07p16yyvY7VaCQwMJDIyEk9PTy5evHjT/ZjHjRtHiRIlGDVqFJ6eN9+Dw2q18tBDD/HDDz8A8Pbbb/PGG3d/1qfOqhaR7Aqb8ShNon5iv1M9ar3yj9FxREREADh1aAflF7fDajdx6dEwSpevZnQkKSCKWm1UlOpeKHrvb16yWa0cmtKGmql72eMSTJ2xa/L8qt7QL5+m2fn5RJgDCXhlJxaHAnvtQp4LW7+K4avTSLI5Mqx5Bd7sUROTyWR0rDy1ZeaTNL24iKt2V6If/p2pO+ys2BVJJV83fn2mGS4uLkZHLDDsNhth309i+j4XtlirU7ecN18ODqa0t+udD76FXe93oX5SKOFeHQl5YUkOpxWR/MxutzNkzlY2HL3M0xXO8/KTI254/sLpo5Sa0wib3UTUEzvxL5u1XW5EirK8rI0K7F4/K1eupH///rcslgF8fX2ZOnVqxudLlmTvf1TWrFmTcRZ3v379biqWAaZMmcLYsWNvWSwDWCwWPv/884xtwLKbRUQkKy5fPEO9yz8DYG/9ksFpRERE/r/y1Ruyz7k+FpOdiNUzjI4jkm+p7pWcEv7TR9RM3UuS3RnfgTMN2fq55oNvEYc7FWyn2f7z53m+fkG1b8MKGvw9hK8tUxhUvwRvdC96TWKA4Mc+5oBTHTxM1/BY2IM/d53AbIKp/eurSZxFJrOZpoNe5dnhQ/Fxc2TP2Tgmf/wJh7b+keW5ju78h/pJoVjtJkr10NXEIkWNyWTize41WOA0iZcvvMTutT/e8HzE+nkAHHSurSaxSD5WYBvFd6Ndu3YZj48fP56tOebOnZvx+Fbbb92tEiVKULdu3XvKIiKSFbO2xfNM2rP84XI/tZp3NzqOiIjIDdKCr59tXj1yGcnXEg1OI1Jwqe6VO7l49ji19l8/oWBPtWcpU8GYXRy8i/uxP+gZvkjvwSsHA7mWajUkR0FyaNtfVFozAidTOi5eJXi3b0PM5qLXJAZwdHLG/9FFXKI4xYlnpdMrjG5ZmgaBxYyOVmA1r+LLL8+0pKNfLP+xfkSlVQPZuuSjLM2R+Md/ANjh05GAqvVyI6aI5HNVSnphKV0HgGL/vEFqSnLGc74RKwG4WvUBI6KJyF0q1I3ilJSUjMeZnYF9OwkJCSxfvhyAChUqZGsLr1vlyU4WEZGsiE5MZX7YGdbYgnF84BNDrhgQERG5nTrt+rPdXJsZaT35bU+k0XFECizVvXI7dpuN8wuewsN0jcMO1QnpP97QPMH9xzHf81GOJTgyZ9NJQ7Pkdyf2hVFm5WDcTCnscQmm1ugfcHB0MjqWoXxLBRDd9Suu2Z2wOnrw1H3VjY5U4AUUd2P6E9044tkYJ5OVxvveJmzG8BsaPZnZffICKYmxpNvNlOqRtVsNiEjhUuuh94jCh0B7JDt+eA+AM0d3U8V6nDS7haC2DxucUERup1B3DtavX5/xuEaNGlk+fsmSJSQlJQEwePDge9ra59KlSxw8eDDbWUREsuKbjcdJSrVSq4wXbav5GR1HRETkJg6OTmxp9R2zrd34Nvyi0XFECizVvXI7v+w5zzdxDblo98G572eG3xfY2cHCS52uX9E8c90xrsTGG5onu85dvMyirafZFhFNmtWW4/OfObYXryX98SKRQ441qfLMMpxd3HJ8nYKoeuOOpIzeS8BL/+h7kkPcPX1o8MIKQiuMwmY30SRqKcemduDKxbO3PW76utMMSH2dD6t8Q0CVOnmUVkTyI0/v4pyod/22d3WOfUlU5CnOblgAwAHXhhTzK21kPBG5g0LbKLbZbEyePDnj8/79+2d5jn9vvzVkyJB7yvPBBx+Qnp6e7SwiIncrPvYK/UIf4CnLcp5tE1gk718lIiIFw8CQAJwsZnafjWPXmVij44gUOKp75XZiElN5+5cDrLC15IcWq6hQo5HRkQDoWa8MPfwvM9v+JkcXvGB0nCyJjbrAlplP4vF5PT5euo5+X4TS4J0/mfrFl4QtnsTpI7uw2+6tcXzhzDEc5/fGl1iOWypR+qlfcPPwzqFXUDj4+JbCxdXd6BiFislsptmwyexp9QVX7a7UTN1L2sw2HNu98Zbjd56OYe3hy1jMZgbe3zGP04pIftSo51McdqiGuymZk9+/RJmzqwBIrd7b4GQicieFtlE8bdo0tm7dCkCfPn0IDg7O0vGnT5/OODO7efPmVKlSJdtZwsLCmD59OgDlypVj1KhRtx2fkpJCfHz8DR8iInfrwPKplOcC/Z1D6Vi7nNFxREREMlXCw5ledUrwgHkjkT+/a3QckQKnINe9oNo3t03+eQdXElOpVtKTke1rGh0ng9lsYmRIMZqYDxF8aSnnju8zOtIdXUtMIPS7V7B8Wp+mFxfhbUpkRLEdFHNz5GpKOkHnltLk0GQCF7bh4jtVCZ/+ENtXfU3M5fNZWifqagpvL1qLqz2RM6Yy+DzxC97FfHPpVYncrH6HgVx56HfOmMpQiig2/PQ5K3adu2nc9uWf4kMCvRuUpYKvmvYiAmaLBVPXDwAIifuDz1LuZ4atD9XaDjQ4mYjcSaFsFK9fv57x46/fd8ff35+ZM2dmeY758+djt9uBezur+uLFi/Tr14/09HRMJhPfffcdbm633xpn0qRJeHt7Z3wEBARke30RKVqSrsZRLeL6VSFRDUZj1r3hREQknxsRdI3pTp/T4dK3d9ziUET+v4Je94Jq39y0Z+0SXjw0gC6WrUzuWwcnh/z165/arXqx2yUER5OVC8teNTpOptLTUtn60zQSPqhLs5Of4Wm6xnFLRfa0ncOjL3/E9tc68sszLfGq3pZ9zvVJtTtQiihCYlcRHP4i3jNqcOTdRry/ah+bjkWRnGbNdK24a2kMmb2V36LL8IzTRByH/0yJkjrxV/Je+Wr18Xp2Az97P8x/Ugfw3OJdTPrtIFbb9X8vDm9bw+PRU1nn/ALPtixpcFoRyU+CGrZhq09XDtoCOGILYG/Vp/HyKWF0LBG5A5P9v1VhIbF//35atWpFTEwMLi4urF69mtatW2d5nho1anDo0CGcnZ25cOECPj4+WZ4jISGBdu3asX37dgCmTJnC2LFj73hcSkoKKSkpGZ/Hx8cTEBBAXFwcXl5eWc4hIkXHlgVv0/ToR5w1labUK3twcHQyOpKIiMgdHZkYQlD6EbZUeJqmw94zOo7kY/Hx8Xh7exf52qgw1L2g2je3XI2P4epHIZTiMlv8B9D0qa+MjnRLx/eGUXFJZ8wmO0d6riCoYVujI2Ww2+38uT+SCkt7EGQ7DkCkyZ9zDV4kuNuITE/IvZaYwNHw1SQdXEPJy6FUtEWwy1aJB1InAuDiaOYjnyUULxmAf/0uVKzZGJPZzLXEBF6Z8wvLznnj6+HEDyObUcnPI89er8itWG12PvzjMDPXHceBdD7y/502w97h1FcDqZu8ja0+XWk8ZpHRMUUkn4m6coUOn4QRm2JnxkMN6F63jNGRRAqkvKx9HXJ19jx28uRJOnXqRExMDBaLhcWLF2erWN66dSuHDh0CoGfPntkqlpOTk+nVq1dGsfzSSy/ddbHs7OyMs7NzltcUkaIt+VoiVY7OASCyzijKqUksIiIFRFztYbDrFSpGLCY97S2d6CRyG4Wl7gXVvrll/7yXaMJlIk3+1B3ygdFxMlW5ThPC13QmJO530n5/DXv9fzCZjb/yOTwimsm/HWL7qRjGOdTEz+ESh4NG0qDPi5Rxuf2V8q7untRt2w/a9gMg6sJpog8dps8lPzYejSIxIZaO8ctwTLDCsWlE4UOEVyNcky/xdspRol3GM+7RR9UklnzBYjYxrkt1apT2IvqnF+kZ/ysXP1lNXa6QbjdTtucbRkcUkXzIt0QJvhrWlN1nYulau7TRcUTkLhSaRnFkZCQdOnQgMjISk8nEnDlz6NWrV7bmmjt3bsbj7Gy/lZ6eTv/+/Vm7di0Ajz/+OB98kH+LMxEpHHb/PIMmxHIBX+p3G2l0HBERkbtWp/MwYnZNpiRX2Pn3Yhp0zv4WuCKFmepeuZNDW/8k5NJPYIIr7T6kjIe30ZFuK6Dff0j5+i9qpe5l99ofqHefcfcxPHVwO9G/vMakmE7ssAfh4mgmrenzWFp8TNNsbpvpWyqQ9qUCac/1q5SPnz3P9g3P43rmH6om7cbXFItv/BoAknBmQtdaVC+jq+klf+lZrwzHeIYLy8IpxWUAdhTrQuNKNQxOJiL5VeOKxWlcsbjRMUTkLhWKRnFUVBQdO3bkxIkTAHz66afZvr9SWloaixcvBq7f56lLly5ZOt5mszF48GB++eUXAAYMGMCXX36ZrSwiIncrNS2dgENfA3CqxhOUcnYxOJGIiMjdc3F1Z2eZ3jSL/A7HHbNBjWKRm6julTtJSU7C9ffnMZvshPvcT0jr7J1EkJdKBVQhtMwAmp2fT1zod6S37Y+DJW+vKr549jinlrxGcMxvlDfZGesYy4r6XzGmQ1VKeuVcXWUymagSUIYqD70OXH+/9m9fS/yBP3CLPoBDyzHUatwxx9YTyUlV6rUguvR6dn8zjBLJpwno/ZbRkURERCSHFPhGcVxcHJ07d+bAgQMATJ48maeffjrb861atYorV64A8NBDD+HgkLVv0ciRIzMK7h49ejB//nzM+WDrJBEp3JbvOs9XyS/xpOtfdO/5jNFxREREsqxil9FYZ8+ldsouTh3cTvkawUZHEsk3VPfK3dg5/zWa2s4QhQ9Bgz82Os5dq9n/bd78xIt5V5szacdZBoQE5sm6cdGXOfDjOzSIXERjUxqYYId7K0r3nMikanVyfX1nFzdqtegGLbrl+loiOaG4f1mKj/sTu82WL7aJFxERkZxRoP9VT0pKolu3buzYsQOAV199lXHjxt3TnP/efmvo0KFZOvaFF17g66+vX9F333338eOPP2a54BYRyap0q43P1x3jmL0cMW0n4+LqbnQkERGRLCsVWJU9Hi3YbK3Jqp0RRscRyTdU98rdOBgZx6lT1682P93kLbxLlDQ40d3zLuZLQPsR2DDz0Z9HuJZqzdX1ktOsrF88FT6pR7Pzc3ExpXHAsTaHui2l4csrKV+tfq6uL1LQqUksIiJSuBTYf9lTU1Pp3bs3mzZtAuC5555j4sSJ9zRndHQ0q1atAqBOnTrUr1//ro996623mDZtGgDNmzdnxYoVODs731MeEZG78evuU0RcSaKYmyMPNcmbs+9FRERyw7Wes3go7TU+O+ROQnKa0XFEDKe6V+6G1WZn/NK9jEsbwcSyM2nQOWvN//xgcLPylCvmSmx8Aqt++zlX1rDa7CzZfpb2H65j5d4LeJNIhDmQXa2+pMaEDVQPuS9X1hURERERyc8K7Gm/gwYN4o8//gCgffv2PPbYY+zbty/T8U5OTgQFBd12zsWLF5Oamgpk7azqTz/9lLfffhuAsmXL8v7773Py5MnbHlOtWjUcHR3veg0RkVuxWa3UWvkAUx0DiGv8Ku7OBfbHuoiICM2CSlPZ7wjHLyeydMc5hjavYHQkEUOp7s0fftx2hrLejjSvWsroKLf0zaaT7D4bh6eLAyMG9CmQV/s5O1h4o5UHNVePwGdHItHNGlPcv2yOzB114QxHf/+cdWdNfHm1BQCbvToSWjuIxl2HU0FXxIuIiIhIEWay2+12o0Nkh8lkytL48uXLExERcdsxTZs2JSwsDIvFwtmzZylV6u6KwLZt27J+/fos5Tl58iQVKlS4q7Hx8fF4e3sTFxeHl5dXltYRkcJt5+q5NAgdTQKu2Mfsw8vH1+hIIiIi92RuaASfrNjMUO/tPDPugwLZ8JDcU9Rqo6JU90L+fH+3RUTz2Fd/s9DhXRIbPEbj3s8aHekG504cYM+3z/N26sOM6dOWgY0L7g5DNquVE++FUMV6nDC/fjR5ena257LbbBwIW03y5i+pE/8PTiYrp21+PGCZwci2VRnavAIujpYcTC8iIiIiknPysjbSaZP/5+jRo4SFhQHQsWPHuy6WRUSMYrfZ8Nw6HYB9ZQfSTE1iEREpBPrU86fH72MplpLA3o2tqdO6l9GRRAoN1b1ZV7usN2+VDafW5QjY/Tqhl4/S5LHpmC3GNxntNhsx34/ifvMu/HwgOGSw0ZHuidliIbndW7BmMA0vLePssecpV6V2luaIj73Cwd+/wv/IQmrZTl//ogkOO1Qnoc4QNndph4u2SxcRERERyVBgryguSvLjWdUiYrzda3+k3vrHSbI7k/LMbor5lTY6koiISI4Im/EoTaJ+YqdbcxqM/c3oOJKPqDYq3PLr+2u32djyzcs0O/M1ADs8WlPzqYW4uHkammvrT9NpvPdNrtmduDJ4bZabqvnVnskdqJscznaPtgS/tOKujtkfGcf8LadpvPtVepuuX/meZHdmX4lOlGg7isp1W+RmZBERERGRHJWXtZH2cRMRKYDsNhvOm6YCsKdUHzWJRUSkUCnV4RkA6iaGcv7UYYPTiEhRZzKbafbYVMIbTCLV7kDDq/9w+qP2RF04bUie9LRUQueMpeGe6/eM3l31qULTJAbw6P4fbHYTwVfXcWTHukzHJV9LZNvPn/P0J9/T7ZONLNp6mvmpbTllLkdY9XGkP3+Qxs/OV5NYREREROQ21CgWESmA9oeuonr6QVLsjlTpNcHoOCIiIjmqfPWG7HOuj8Vk59TqGUbHEREBIKTXUxzrMp8YPAlKP0L6F+05eWB7nmY4c2wvx6e0pNnpL3Ew2djmeR+NBryapxlyW6XaTdherAsAab+/ht1mu+H5cycOsuXLp7g2pRqNdkyg8aUfcTCb6Fa3NC8/PoTA1/bSZOArePmUMCK+iIiIiEiBokaxiEgBlLLh+i/Nd/n1wLdMeYPTiIiI5Ly04BEAVItcRvK1RIPTiIhcV7PZ/SQ+8jtnTGWw2a08+v0R1h2+lOvr2u12FoSdInzuq1RLP0w8bmxrOIXg55fg4OiU6+vntYC+E0m2OxJ5zYn1+yKwpqeza80i9kzuQOnvmtH0/AKKkcBFSlApqDabJ7Tns4ca0rSyLyazftUlIiIiInK3HIwOICIiWbP9VDTDYx9juEMAg3qOMzqOiIhIrqjTrj8XNr9FKS4TvvobQh54xuhIIiIAlKtSm7hn1vH24nWcPOPBo9+G83bPWgxuViFX1ruUkMy4JXtYe/gyXjyMfzEzVQZ9QKPAqrmyXn5QKqAKnzdYwvtbEimz8gRfL3uQ+vZj1580wR6XRtiCH6V22wdpVQgb5SIiIiIieUWnWYqIFDAz/j5GPO5cqP8spQrxL4dERKRoc3B04mTFAaTYHTl0RPcpFpH8xbtESSaPfJB+weWw2SH8l1ls+XwE1vT0HF1n5x/z2fjRQ6w9fAknBzPPdguh+cvLikQd8HDnFvi4ORIZn8LatJrE4sGWUg9z9pFN1B3/F/U7Plwor6YWEREREclLuqJYRKQAORARydrDlzCbTIxqW9noOCIiIrmqWvcxtPqoHpei3alzJpb6AT5GRxIRyeDkYOaDfnWp5ZnEoNAvcbmUxq6pp6n61Pe4e/rc09wJcdEc/OZpGsf+SgPgYIkQ+g1+hmqlPHMke0Hg7erIR/3r8X34GcpVfQWXujNp6uZhdCwRERERkUJFVxSLiBQgyT8+wTKnN3k6KJ4Kvu5GxxEREclVxUv40bJeEABzN0cYG0ZE5BZMJhPDuzRjf5MPSLY7Uv/aFi5Mb8fFs8ezPeeBLb+TML0JjWN/xWY3EVp6CC+Nfq5INYn/q331knw5uBG9mtbARU1iEREREZEcp0axiEgBEXFwGw0TN9DAfIx+TYOMjiMiIpInhv7fPT+P79nMlUuRxoYREclEcNfhRPT4nit4U9l6AtPXHTi2a0OW5khNSSb0q9FU/20gZeyXiDT5c+j+xTQb+SnOLm65lFxERERERIoyNYpFRAqIqN8mAbDDvTXlawQbnEZERCRv1AvwYabPfFY4TuDorzOMjiMikqnqje4jZdifRJgD8SeaMsv6svOP+Xd17JGLCWz7sBfNIudiNtnZ6tMVzzFh1GzaJZdTi4iIiIhIUaZGsYhIAXD22D4axP0FgFen8QanERERyVu+NVoBUDFiMelpqQanERHJXJkK1Sj+7Dr2uATjZkph8z+r+XrDCex2+y3H22x2vt5wgu6fbmT61Y5cwZsdzWbQeMwiPL2L53F6EREREREpatQoFhEpACJXvYfFZGeXaxOq1GthdBwREZE8VafTUGLwoiRX2Pv3YqPjiIjclpdPCWq++DvLyo3jw/T+TFx1kFeX7yPNarth3MXTR/loxsdMXHWQ1HQb7kGtsT67i4adBxuUXEREREREiho1ikVE8rkLp4/SIPp3AFzajzU4jYiISN5zcXXnUJneADht/9rgNCIid+bg6MQDj03g1W61MJngp7Bj/DF9BPGxV7DbbGz/5Qtc57TmqSvvUcPxIv/pXZs5w0LwL66riEVEREREJO84GB1ARERu7+Cvn9POZGWfc31qh3QwOo6IiIghKnYZjXX2XGql7ubUwe2UrxFsdCQRkdsymUw83qoS5Uu4E794JN0S1hLxSRjH3CoRfHU9AIcdq/HVoCYEVC5vcFoRERERESmKdEWxiEg+dik+mVFnO/BU6rPY2r1udBwRERHDlAqsyh6P67dfuPDXDIPTiIjcvY41S1Kv70tcojgVbGdoeHU96XYzoeWfpPK4jQRUrmV0RBERERERKaLUKBYRycdmbThBcjpcDLifOk3uMzqOiIiIoRybjQTA/3IoCUnJBqcREbl7Veq1hBF/c8CxNscslTnRaznNhk/BwdHJ6GgiIiIiIlKEaetpEZF8Kjommh+3HAMsPNO+CiaTyehIIiIihqrVvDuvb3qVRTHVeH3XBYY2r2B0JBGRu+ZftiL+r24yOoaIiIiIiEgGXVEsIpJPHV7yLn+YR/OU707aBvkZHUdERMRwJrOZqq37k44D34VGYLfbjY4kIiIiIiIiIlJgqVEsIpIPxcVEUfvsIvxNsXSoU15XE4uIiPyfPg3L4eHswKnL8YTtO2J0HBERERERERGRAkuNYhGRfOjA8g/xNF0jwhxI/Q4PGR1HREQk3/BwdmBslbP84zwG59/GGB1HRERERERERKTAUqNYRCSfSUyIpfqp+QBENRyN2WIxOJGIiEj+0qZxMGVNV6ibGMr5U4eNjiMiIiIiIiIiUiCpUSwiks/sXTGdYiRw1lSaBl0eNTqOiIhIvlO+Wn32OjfAYrJzavUMo+OIiIiIiIiIiBRIahSLiOQjyUlXqXLsGwDO1xmFxcHB4EQiIiL5U3rw4wBUi1xG8rVEg9OIiIiIiIiIiBQ8ahSLiOQjm/9cgi+xXMCPet1GGh1HREQk36rTrj8X8KMYCexd/Y3RcUREREREREREChw1ikVE8onUdBuvHQjg/pRJ7At+BydnF6MjiYiI5FsOjk6crDgAAJ+932C32QxOJCIiIiIiIiJSsKhRLCKSTyzbeZbIuGSiPIJo2WWA0XFERETyvWr3P0WK3ZGq1mMc3h1qdBwRERERERERkQJFjWIRkXwgPS2VH/8OA2Bk60q4OFoMTiQiIpL/Ffcvy4oyz9Er5R2+OuJudBwRERERERERkQJFjWIRkXxg129zWJg0kjddvuehJoFGxxERESkwqnUdzW57FVbuOU/U1RSj44iIiIiIiIiIFBhqFIuIGMxmteK3awZOJis1KpTDzcnB6EgiIiIFRr0AH+oF+JBqtfHD1gij44iIiIiIiIiIFBhqFIuIGGzXn/MobztDPG7UfOBFo+OIiIgUOCOCvfmPw2y6buhNelqq0XFERERERERERAoENYpFRAxkt9nwDv8YgAPlBuHlU8LgRCIiIgVPh3oV6eoQTgX7Ofb+vdjoOCIiIiIiIiIiBYIaxSIiBtq77kcqW0+QZHem+gNjjY4jIiJSILm4unOoTG8AnLZ/bXAaEREREREREZGCQY1iERGD2G02nDdPBWB36X74+JYyOJGIiEjBVbHLaKx2E7VSd3Pq4Daj44iIiIiIiIiI5HtqFIuIGGTH7l2US4sg2e5I1QfGGx1HRESkQCsVWJU9Hi0AuPDXZwanERERERERERHJ/9QoFhExyIfhqbRM+ZifKv8H31KBRscREREp8BybjQSgzuVVJMReMTiNiIiIiIiIiEj+pkaxiIgBtkVEE3riClct3rTtOcToOCIiIoVCrebdOWUOwM2UwoHfvzQ6joiIiIiIiIhIvqZGsYhIHkuIi2bNoumAnb4Ny1HWx9XoSCIiIoWCyWzmdK0n+Ti9D1PPVMNutxsdSUREREREREQk33IwOoCISFFit9k4+tVQxqf8Q4B7L7p0nmV0JBERkUKlQfcnGbXnL65eSWfTsSu0rOprdCQRERERERERkXxJVxSLiOShsIVv0zDxH1LtFhrdP4wSHs5GRxIRESlUPJwd6NuwLADfhUYYG0ZEREREREREJB9To1hEJI/s3/wrjY5+AsDOWuOp1qi9wYlEREQKp8HNKtDevIOHj73A+VOHjY4jIiIiIiIiIpIvqVEsIpIHLp2LoOQfo3Aw2Qj37kTjfi8ZHUlERKTQquLvwQuef9HWvJtTq2cYHUdEREREREREJF9So1hEJJelpiQT/e0gfInlhLkCtZ+Yg8msH78iIiK5KS34cQCqRS4j+VqiwWlERERERERERPIfdSpERHLZoh+/p2rqQeJxw+mhBbi6exodSUREpNCr064/F/CjGAnsXf2N0XFERERERERERPIdNYpFRHLRil3neHOfL4+kvcLxVtMpV6W20ZFERESKBAdHJ05WHACAz95vsNtsBicSEREREREREclfCnSjeNu2bbzzzjt06tSJcuXK4ezsjIeHB0FBQQwfPpyNGzfe9viIiAhMJlOWPipUqHDbOZOSknj//fcJCQmhePHiuLu7U716dV588UVOnTqVg69eRPK7IxcTGP/TXgAatulJg/sGGJxIRESkaKl2/1Ok2B2paj3GkR3rjI4jki2qe0VERERERCS3mOx2u93oENnRunVrNmzYcMdxQ4YMYdasWTg5Od30XEREBBUrVszSup06dWL16tW3fO7YsWN07dqVo0eP3vJ5Ly8vFixYQPfu3bO0Znx8PN7e3sTFxeHl5ZWlY0XEGAlx0ez6ZBBvJvWjTOW6fPdoYyxmk9GxREREipzwaQMIifudcO9OhDz/o9Fx5B4VtdqoKNW9UPTeXxERERERkVvJy9rIIVdnz0WRkZEAlClThgcffJBWrVoRGBiI1WolNDSUqVOncu7cOebOnUtaWhoLFy68aY6yZcuyd+/eO641adKkjOOHDh16yzEJCQl069Yto1geMWIEAwcOxNXVlbVr1zJp0iTi4+MZMGAAmzZton79+tl85SKS39ltNo5+NYRW1i187XIO7wHhahKLiIgYxLvN0+xdfojvr1Sl4tUUfD2cjY4kctdU94qIiIiIiEhuKrBXFHfv3p0hQ4bQt29fLBbLTc9HRUXRokULjhw5AsD69etp3bp1ltexWq0EBgYSGRmJp6cnFy9exNXV9aZxb7zxBu+++y4A77//Pi+//PINz2/evJk2bdqQnp5OmzZtWLdu3V1n0FnVIgVL6Lw3aXZ8Oql2Cyd7LKFao/ZGRxIRESnSes3YyO6zcbzcuRpPt6tidBy5B0WtNipKdS8UvfdXRERERETkVvKyNiqw9yheuXIl/fv3v2WxDODr68vUqVMzPl+yZEm21lmzZk3GWdz9+vW7ZbGclpbGJ598AkCNGjV48cUXbxrTvHlzHnvsMeB68R4eHp6tPCKSv+3ftIrGxz4GYGet8WoSi4iI5ANDm1cAYP6WU6RbbcaGEckC1b0iIiIiIiKSmwpso/hutGvXLuPx8ePHszXH3LlzMx5ntv3W2rVriYuLyxhjNt/62zps2LCMx8uWLctWHhHJvy6dO0mpP0dhMdkJ9+5E434vGR1JREREgK51ShPolk6nqyvYuU7/Hy6Fi+peERERERERya5C3ShOSUnJeJzZGdi3k5CQwPLlywGoUKFCplt4bdy4MeNxmzZtMp2vUaNGuLm5AbBp06Ys5xGR/Cs1JZnobwdRgjhOmCtQ+4k5mDL55ZmIiIjkLRdHC1NKr+Vtx+9wC/vY6DgiOUp1r4iIiIiIiGRXoe5irF+/PuNxjRo1snz8kiVLSEpKAmDw4MGYTKZbjjtw4EDG4+rVq2c6n4ODA1WqXL8n2sGDB7OcR0Tyr2mrdnA1xUo8bjg/vABXd0+jI4mIiMi/VOz8NFa7iVqpuzl1cLvRcURyjOpeERERERERya5C2yi22WxMnjw54/P+/ftneY5/b781ZMiQTMedPXsWAHd3d3x8fG47Z0BAAACXL1++4cxvESm4Vuw6x8ytMQxMfY0DnRZRtnJtoyOJiIjI/ygVWJU9Hi0AuPDXDIPTiOQM1b0iIiIiIiJyLxyMDpBbpk2bxtatWwHo06cPwcHBWTr+9OnTGWdmN2/ePOOM6FtJSEgAwMPD447zuru7Zzy+evUqzs7ON41JSUm5oZiOj4+/69wikreOnLnA+J/2AvBku+o0bV7N4EQiIiKSGcdmI2HNRmpf/pX4uGi8vIsbHUnknhTkuhdU+4qIiIiIiBitUF5RvH79esaPHw+Av78/M2fOzPIc8+fPx263A7c/qxogOTkZACcnpzvO++8C+dq1a7ccM2nSJLy9vTM+/ns2tojkL/Fx0bjOaccL9u9oW8WH5zsGGR1JREREbqNW8+6cMgfgbkrmwK9fGh1H5J4U9LoXVPuKiIiIiIgYrdA1ivfv30/v3r1JT0/HxcWFH3/8EX9//yzPM2/ePOB6gTtgwIDbjnVxcQEgNTX1jvP++2xpV1fXW46ZMGECcXFxGR9nzpy529gikkfsNhvHvhpCgD2SHg5bmfZAZSzmW9/PTURERPIHk9nMhWqPAFDm6DzsNpvBiUSypzDUvaDaV0RERERExGiFauvpkydP0qlTJ2JiYrBYLCxevJjWrVtneZ6tW7dy6NAhAHr27HnH+y95enoC17fUupPExMSMx5lt2eXs7Jzp1lwikj9sWfAWzRI3kGp3IL7XbIJ8SxkdSURERO5CrftHEnfgEw6kleHcwQia1apkdCSRLCksdS+o9hURERERETFaobmiODIykg4dOhAZGYnJZGLOnDn06tUrW3PNnTs34/Gdtt8CKFeuHHC9GI6Njb3t2P+eIe3n56eCWKSA2rfpFxof+wSAnbXGE9SwrbGBRERE5K55eBVjRr1lPJn2PHO2RxsdRyRLVPeKiIiIiIhITioUjeKoqCg6duzIiRMnAPj000/vqtC9lbS0NBYvXgxcv89Tly5d7nhMzZo1Mx7/94zsW0lPT+f48eMA1KhRI1v5RMRYl86dpPSfT2Mx2Qn37kzjfi8aHUlERESyaEDLWgD8dfAiZ6KTDE4jcndU94qIiIiIiEhOK/CN4ri4ODp37syBAwcAmDx5Mk8//XS251u1ahVXrlwB4KGHHsLB4c67c7ds2TLj8fr16zMdt23btowtuFq0aJHtjCJijNS0dKK/HUQJ4jhhqUDtJ2ZjMhf4H6MiIiJFThV/D1pW8aUcF9ny+0Kj44jckepeERERERERyQ0FusORlJREt27d2LFjBwCvvvoq48aNu6c5/7391tChQ+/qmLZt2+Lt7Q3Ad999h91uv+W4b7/9NuNx7969sx9SRAzx3m+H+SSxIxcogfPDC3F19zQ6koiIiGTT6KpXWOf0Ah2OvEVy0p3vuSpiFNW9IiIiIiIiklsKbKM4NTWV3r17s2nTJgCee+45Jk6ceE9zRkdHs2rVKgDq1KlD/fr17+o4Jycnnn32WQAOHjzIhx9+eNOY0NBQZs+eDUCbNm0ICQm5p6wikreW7zzHt5sj+NXWlAP91lO2Ui2jI4mIiMg9aNSiE5dMvhQjgb1/fGN0HJFbUt0rIiIiIiIiuenO+0vlU4MGDeKPP/4AoH379jz22GPs27cv0/FOTk4EBQXdds7FixeTmpoK3P1Z1f/18ssv8/3333PkyBHGjh3LsWPHGDhwIK6urqxdu5b33nuP9PR0XF1dmT59epbmFhFjnTi0i4+WHgC8eaZdFdrXDjA6koiIiNwji4MDJysOoNTJGfjs/RZ7z6d1SwnJd1T3ioiIiIiISG4y2TPbLyqfM5lMWRpfvnx5IiIibjumadOmhIWFYbFYOHv2LKVKlcrSGseOHaNr164cPXr0ls97eXmxYMECunfvnqV54+Pj8fb2Ji4uDi8vrywdKyL3Jj72CnEft8TFlsgnpf7DWyMfwWLO2s8fERERyZ+iL53D/bN6OJvSONx9GdUatTc6ktxBUauNilLdC0Xv/RUREREREbmVvKyNdMr8/zl69ChhYWEAdOzYMcvFMkCVKlXYuXMnU6ZMoVGjRvj4+ODm5ka1atV4/vnn2bNnT7aKZRExht1m4/isIQTYI7GanHihXwc1iUVERAqR4v5l2eNzHwAJ/3xucBqR3Ke6V0RERERERP6twF5RXJTorGoRY2yZ+zpNT3xCqt2BiF5LCWrYxuhIIiIiksOO7vyHqit6kG43c6TLQmo2u9/oSHIbqo0KN72/IiIiIiIiuqJYRMRw+zb+TMjxTwHYWXuCmsQiIiKFVNUGrdnm1YGruPLZn/s5H3fN6EgiIiIiIiIiInlCjWIRkf9x8exxyqx5GovJTrh3Fxr3fcHoSCIiIpKLao38ljHeH7MqqQZPzt9BcprV6EgiIiIiIiIiIrlOjWIRkX9JTbexb/5YihPPcUsl6oycjcmsH5UiIiKFmau7J+8M7Ya3qyO7z8Qy5acN2G02o2OJiIiIiIiIiOQqdT9ERP7lP6sOMDr2IZZwHy4PL8TFzcPoSCIiIpIHAku48emgBrSz7GLMwUFsXfKR0ZFERERERERERHKVGsUiIv9n+c5zfBd6iiRcKDZgJmUr1TA6koiIiOSh1kF+PFEtGW9TEg32v8ehrX8aHUlEREREREREJNeoUSwiApzcH8aRZf8B7IxuX4X7apQ0OpKIiIgYoOkjb7Pdow1OJiu+vz7O5cgIoyOJiIiIiIiIiOQKNYpFpMiLj72C45KhjDUv4P2SaxjTIcjoSCIiImIQk9lM9ZFzOWkujy+xXJkzkNSUZKNjiYiIiIiIiIjkODWKRaRIs9tsHP9qMOXs57mAHx0fGYfFbDI6loiIiBjI3dMHx4cWEo871dMPsvOrkUZHEhERERERERHJcWoUi0iRtmX+mzRI2kSq3YH4nrMp5lfa6EgiIiKSD5SrUpuTrT/GZjfR5MpyNq6YbXQkEREREREREZEcpUaxiBRZ+zesoPHxTwHYWfsVghq2MTiRiIiI5Cf12j9IWMVRLExvx8itfuw8HWN0JBERERERERGRHKNGsYgUSRfPHqf0X89gMdkJ97mfxn2fNzqSiIiI5ENNBv+H9dVeI9FqYdT8HVxK0P2KRURERERERKRwUKNYRIqc1HQb3/6wBA97IsctlajzxNeYzPpxKCIiIjczW8xM7V+fKv4eXIpPYsWsd0hNUbNYRERERERERAo+dUZEpMiZuOoAMy/V5lHT27g8vBAXNw+jI4mIiEg+5uHswJeDg5npPIMR8Z+xc9ZTRkcSEREREREREblnahSLSJGyfMcZ5oaeAuDRgf0pW6mGwYlERESkIKjs50HJVsMAaBL1E1uXzTA2kIiIiIiIiIjIPVKjWESKjJP7w6i+oivVTad5tn0V2lcvaXQkERERKUDqdxhEaOATANTb9RZHd20wOJGIiIiIiIiISPapUSwiRUJ8bBSOS4ZS3XSaiT4/81yHIKMjiYiISAHUZOhkdrk1w9mUhufyYVy5dM7oSCIiIiIiIiIi2aJGsYgUejarleNfDaac/TwX8KPKY99gMZuMjiUiIiIFkNliodITCzhjKkMporjw9UDS01KNjiUiIiIiIiIikmVqFItIoRc2/w0aJG0m1e5AQq85+PiWMjqSiIiIFGBePiWwDlhAot2FSimH+HbpL0ZHEhERERERERHJMjWKRaRQ27dhBY1PfAbArjqvUrVBa4MTiYiISGFQoXpDDrb8hD6pbzNxpwvLd2oLahEREREREREpWNQoFpFC6+LZY5T56xksJjtbfboS0meM0ZFERESkEGnUcQD3tb0PgPFL97DvbKyxgUREREREREREskCNYhEplFLSrby07Aj7rYEcs1Sm7hOzMJn1I09ERERy1vMdg2hbzY+a6Yewze5MzOXzRkcSEREREREREbkr6pqISKE0ceVBNpyz86zlNVyGL8fFzcPoSCIiIlIIWcwmPn6wLlNdZlPXfohzXw8kPS3V6FgiIiIiIiIiInekRrGIFDqrN25h3pZTmEzw0cBgypULNDqSiIiIFGLeHi7w4Dck2Z2pnbKLbbPHGB1JREREREREROSO1CgWkULlxL4wWv/Zg/ccvua5thVoV93f6EgiIiJSBFSsGcLBJlMAaHphAdtXfW1wIhERERERERGR21OjWEQKjbiYKJx+GoqrKZXaHvGMvq+a0ZFERESkCAnuOpzQ0kMAqLH1FU7sCzM4kYiIiIiIiIhI5tQoFpFCwWa1cmLWYMrZz3MePwIfX4DFwcHoWCIiIlLENH5sGntcgnEzpeD802Dirlw0OpKIiIiIiIiIyC2pUSwihULY/DdokLSZVLsDV3vNwce3lNGRREREpAiyODhQfsQiIk0l2ZseyMvLDmC12Y2OJSIiIiIiIiJyEzWKRaTA27dhBY1PfAbArrqvUbVBa4MTiYiISFHmXaIkVx9exfM8zx/Hkvjoz8NGRxIRERERERERuYkaxSJSoJ2/HEWpv57FYrKz1acrjfs+b3QkEREREYKqVGVy3/oAfLb2GOs3bzY2kIiIiIiIiIjI/9ANPEWkwEpJtzLqh8P4pI5gjNsf1H1iltGRRERERDL0ql+Wg6cvUjP8FUJW7yCi2K9UqNHI6FgiIiIiIiIiIoCuKBaRAmziyoPsOhPLTufGlBj1Gy5uHkZHEhEREbnBS/fXppLbNdxMKVh+GExcTJTRkUREREREREREADWKRaSA2vzrfNaGbcNkgukD6xNQwt3oSCIiIiI3cXB0osxjiziPHwH2SCK+egib1Wp0LBERERERERERNYpFpOA5sS+MBmHP84vTq7zZ1JF21fyNjiQiIiKSqeL+ZUjs/S3JdkfqXQsj7NuxRkcSEREREREREVGjWEQKlriYKJx+GoKrKZUzrtUZ0q290ZFERERE7qhKvZbsbfgOAM3OfM3OP+YbnEhEREREREREijo1ikWkwLBZrZyc9Qjl7Bc4jx+Bjy/A7OBgdCwRERGRuxLS6ym2+D0IQMVN4zh+9oLBiURERERERESkKFOjWEQKjK3zXqd+UigpdkcSe83Bx7eU0ZFEREREsiR4xGeEurRiZOrzjPj+EAnJaUZHEhEREREREZEiSo1iESkQ9v6zgpCTnwOwp+6rVGnQ2uBEIiIiIlnn6ORMlad/4rRXA05cTuSFH3Zjs9mNjiUiIiIiIiIiRZAaxSKS70XGXiPu72lYTHbCfboR0vd5oyOJiIiIZJufpzNfPBKMk4OZkwe3s27hZKMjiYiIiIiIiEgRpEaxiORrKelWRi3YwWPJzzHP9RHqPPGV0ZFERERE7lm9AB8+6lyC5U5v0PboFHb/tdjoSCIiIiIiIiJSxKhRLCL52rsrD7D7TCwuru60HfEBLm4eRkcSERERyRHdWzVmn9/9mE12Km54njPH9hodSURERERERESKEDWKRSTfCl/xOf7bPsRisjF9YH0CirsZHUlEREQkRzUc8QUHHWviRRLWhYO4Gh9jdCQRERERERERKSLUKBaRfOnEvjBq73iTZx2W83nto7Sr5m90JBEREZEc5+Tsgt+j33OJ4lSwneHIl0Ow22xGxxIRERERERGRIkCNYhHJd+JionD6aQiuplT2uITQceBzRkcSERERyTW+pQOJ7jaLVLuFhon/sGXe60ZHEhEREREREZEioEA3irdt28Y777xDp06dKFeuHM7Oznh4eBAUFMTw4cPZuHFjludcs2YNw4YNo0qVKri7u+Pt7U1QUBD9+vVj5syZXL169bbH//777wwcOJBKlSrh5uaGi4sLAQEB9OrVi++//x6brg4QuS2b1crJWY9Qzn6B8yY/yo+Yj9liMTqWiIiISK6qHtKBnbVfBcB2fC3rDp43OJHkF6p7RUREREREJLeY7Ha73egQ2dG6dWs2bNhwx3FDhgxh1qxZODk53XZcTEwMw4cPZ8WKFbcdt3PnTurXr3/T11NSUnj44Yf56aefbnt8q1at+Pnnn/Hx8blT9Azx8fF4e3sTFxeHl5fXXR8nUhBt+fYVmkZ8RordkTO9l1GlfiujI4mIiIjkmYVzpvPGkYq4ubjwy+iWlC/hbnSkfKWo1UZFqe6Fovf+ioiIiIiI3Epe1kYOuTp7LoqMjASgTJkyPPjgg7Rq1YrAwECsViuhoaFMnTqVc+fOMXfuXNLS0li4cGGmc8XFxdGxY0e2b98OQO/evenXrx+VK1fGYrFw5swZ1q9ff9ti+Nlnn8143t/fn7Fjx9KwYUMcHR3Zu3cvU6ZM4dSpU2zYsIGBAwfy+++/5+B3Q6Rw2PfPckJOfg4m2FP3VULUJBYREZEipu+Q0fzw5RZ2nYll5LztLH2yCW4uzkbHEoOo7hUREREREZHcVGCvKO7evTtDhgyhb9++WG6xLW1UVBQtWrTgyJEjAKxfv57WrVvfcq4hQ4Ywb948nJ2d+eGHH+jZs+ctx9ntdqxWKw4ON/bXL168SJkyZbDZbBQrVow9e/ZQrly5G8bEx8dTr149IiIiAAgPD6dRo0Z39Vp1VrUUBZGx1/jk48m8ZfuMvcU7EfJc5r/kEhERESnMLsQl0+uTdTyZMoe6xVJp+PxSTOYCfdegHFPUaqOiVPf+9/ii9P6KiIiIiIjcSl7WRgX2tw0rV66kf//+tyyWAXx9fZk6dWrG50uWLLnluI0bNzJv3jwAJk6cmGmxDGAymW4qlgHCwsIy7sE0fPjwm4plAC8vL55//vmMz0NDQzNdR6SoSUm3MmrBDhZfa8yLPtOp88QsoyOJiIiIGKaUtwtzurrxiGUNwQlrCVv4ttGRxCCqe0VERERERCQ3FdhG8d1o165dxuPjx4/fcsyMGTMA8Pb25plnnsnWOqmpqRmPK1WqlOm4ypUr3/IYkaJu8vJt7D4Ti4+bI+OH9sHFVffiExERkaKtVnBrdtQcB0DI0Y/Z+8/t7ykrRZfqXhEREREREcmuQt0oTklJyXh8qzOwU1NTWbHi+i9cOnbsiIuLCwBWq5UzZ84QERFBcnLyHdepVq1axuMTJ05kOu7fRfu/jxEpysKXf8aIvQNpYD7G9AH1CSjuZnQkERERkXyh8YMvs9WnKxaTnXJ/P01kxGGjI0k+pLpXREREREREsqtQN4rXr1+f8bhGjRo3Pb979+6MgrhOnTrEx8czZswYfH19CQwMpGLFinh7e9OxY0fWrVuX6Tp16tShefPmAHz77bdERkbeNCYhIYHp06cD18++7tSp0z28MpHC4fjeLdTZ+SZlTNGMrXKOttX8jY4kIiIikm+YzGbqjvyaow5VKUYC1+YN5FpigtGxJJ9R3SsiIiIiIiLZVWgbxTabjcmTJ2d83r9//5vGHDhw4IbxjRo14uOPPyY2Njbj66mpqaxZs4b27dszZcqUTNf75ptvqFixItHR0TRs2JCPPvqIdevWsXHjRr744gvq1avHyZMn8fX1ZcGCBTg5OeXMCxUpoOJiLuOydAgupjT2uITQZOgkoyOJiIiI5Dsuru54Dl1MNF5Utp5g35ePYrfbjY4l+YTqXhEREREREbkXhbZRPG3aNLZu3QpAnz59CA4OvmlMdHR0xuMpU6Zw9OhRunTpwtatW0lOTubSpUvMnDkTb29v7HY748ePz9iy638FBQURHh7Ou+++S2JiIi+++CLt2rWjVatWjBo1irNnz/LSSy+xc+dOmjZtetvsKSkpxMfH3/AhUpjYrFYivnqEsvaLRJr8KT9iAeZbbJMnIiIiIlAqoAoXOn1BrN2dWVG1+GZThNGRJJ8oyHUvqPYVERERERExWqFsFK9fv57x48cD4O/vz8yZM285LjExMeNxcnIyHTt2ZOXKlYSEhODs7Iyfnx9PPvkkK1euxGy+/q2aMGFCpmfw//LLLyxYsICrV6/e9FxaWho//PADCxcuvOMVAJMmTcLb2zvjIyAg4K5et0hBsXXea9S7toUUuyNJD3yDd4mSRkcSERERyddqNu/GL+1W84cthP/8epDQ41eMjiQGK+h1L6j2FRERERERMVqhaxTv37+f3r17k56ejouLCz/++CP+/re+76mLi8sNn0+ZMgXLLa5qbNmyJX369AHg4MGD7N2796YxL774IsOHD+fQoUM88MADbNq0iatXr3Lt2jV27NjB8OHDOX36NOPGjaNfv35YrdZMX8OECROIi4vL+Dhz5kxWvgUi+dqeDT/T+OT1X2LtqfsaVeq1NDiRiIiISMHwSJva9G5QFqvNzsQFq7lw5pjRkcQghaHuBdW+IiIiIiIiRitUjeKTJ0/SqVMnYmJisFgsLF68mNatW2c63tPTM+Oxn58fDRo0yHRs586dMx6Hh4ff8NyqVav46KOPABg2bBjLli2jefPmuLu74+LiQoMGDZgzZw6vv/46AEuXLuXzzz/PdC1nZ2e8vLxu+BApDM7FXuPpv62ssTUkvFg3QvqOMTqSiIiISIFhMpl4r3cd+vqdY651LAnfDiA56earOqVwKyx1L6j2FRERERERMVqhaRRHRkbSoUMHIiMjMZlMzJkzh169et32mH9va1WuXLm7Hnv58uUbnvv666+B67+4mThxYqZzvPLKK3h4eAAwZ86c264nUtikpFt5av52zlxzYobfW9QZOdvoSCIiIiIFjquThRf7tsFsgqrWY+z98jHsNpvRsSSPqO4VERERERGRnFQoGsVRUVF07NiREydOAPDpp58yZMiQOx5Xq1atjMd32hLr3887ODjc8NzBgweB6/eFKlu2bKZzuLi4ZKx56NChO+YTKUzmLZrP7rOx+Lg58vngRri4uBodSURERKRAKlOhGufu+wyr3URI3O9s/fF9oyNJHlDdKyIiIiIiIjmtwDeK4+Li6Ny5MwcOHABg8uTJPP3003d1bPny5QkMDAQgIiICu92e6djjx49nPP7fovi/BXR6evod10xLS7vhGJGiIHz5DB4//izTHGfy8YD6lCvmZnQkERERkQKtdqtehFcdA0DDA+9zYMvvxgaSXKW6V0RERERERHJDgW4UJyUl0a1bN3bs2AHAq6++yrhx47I0R9++fQGIj4/nr7/+ynTc0qVLMx63bNnyhucqVqwIwJUrVzLOsr6V6Oho9u3bd8MxIoXd3n9WUG/nmwCUqliLNtX8DU4kIiIiUjg0eegNtnu2x9FkpeTvT3Dx7PE7HyQFjupeERERERERyS0FtlGcmppK79692bRpEwDPPffcbe+TlJkxY8bg4uICwAsvvEB8fPxNY+bPn8+6desA6Nat2w33bQLo0aPHDfOlpqbeNIfNZuPZZ5/NeK579+5ZzipS0Bze9jeV/xqBkymdHR5taDJ0ktGRRERERAoNk9lMjZHfcsJcgRLEsXf+eFLSb7+1sBQsqntFREREREQkN5nst9t3Kh/r27dvxtnO7du3Z/r06ZhMpkzHOzk5ERQUdMvnPvjgA8aOHQtAtWrVGDduHHXr1iU+Pp6lS5cyc+ZMrFYrXl5ebNu2japVq95wfGpqKvXr1884q7pOnTqMHj2aevXqYbFYOHDgADNnziQ0NBSAkiVLsm/fPnx9fe/qtcbHx+Pt7U1cXBxeXl53dYyI0SIObsPn+174cJU9LsFUf/5XnJxdjI4lIiIiUuicO3GQTXNf543kh+jduAqT+tQ1OlKuKWq1UVGqe6Hovb8iIiIiIiK3kpe1UYFtFN+uOL6V8uXLExERkenzEyZMYMqUKZner8nf35/ly5fTrFmzWz5/6tQpevXqxe7du2+bo2LFiixdupT69evfbXQVy1LgREYcxuHbLvgTzWGH6gSM+QM3D2+jY4mIiIgUWv8cucywb7Zis8N7vevwUJNAoyPliqJWGxWluheK3vsrIiIiIiJyK3lZGxXYradz2qRJk9i0aRODBw+mQoUKODs74+3tTUhICO+++y5HjhzJtFiG6wV5eHg4c+fOpWfPnpQrVw5nZ2ecnJwoVaoUnTp14vPPP2fv3r1ZLpZFCpLLCSl8vmgpxexxRJgDKTXqZzWJRURERHJZ6yA/Xu5cHRM2Lq18h0Pha4yOJPmQ6l4RERERERH5twJ7RXFRorOqpaCIT05j4JdbOHA+nt5eh3llaG/8ylY0OpaIiIhIkWC321n62Xj6Rn3BZYphemI9vmXKGx0rR6k2Ktz0/oqIiIiIiOiKYhEpgK4lJvDS7NUcOB+Pr4czzz0xUk1iERERkTxkMpnoMnQCEeZA/Igh6puBpKYkGx1LRERERERERPIpNYpF5J6lpaZwZEZfXr84hpouUcx9tDEVfN2NjiUiIiJS5Lh7+mB5aCHxuFE97QA7vxppdCQRERERERERyafUKBaRe2KzWtk94yHqXQvDzxTL1PtLUbOMtokTERERMUpAlTqcbD0Nm91EkyvL2frTdKMjiYiIiIiIiEg+pEaxiGSb3WYj/IsnaBS/hjS7hSNtPqNGk85GxxIREREp8uq1H0hYhetXE9ff8y5HdqwzNpCIiIiIiIiI5DtqFItItm35dhxNLi8BYE+j96jbfoDBiURERETkv5oMeY9dbs2xA/NWreNyQorRkUREREREREQkH1GjWESyJWzxezQ7/dX1x9XHE9zjSYMTiYiIiMi/mS0WKo9cwBj3ycxLDOHpBTtIs9qMjiUiIiIiIiIi+YQaxSKSZb9sP4nPgQUAbAl8giYDJxicSERERERuxdO7OC8NH4SHswNbI6J5/5edRkcSERERERERkXxCjWIRyZK1hy7x/E8HGZj6Gr+Xe44mw6YYHUlEREREbqOynwfTBtSnlimCoTv7E778M6MjiYiIiIiIiEg+4GB0ABEpOHYcPsmT8w+RbrPTtkENOj04EJPZZHQsEREREbmDjjVL4l4lgnJnovDd+SZHK9Slav1WRscSEREREREREQPpimIRuSvH94RSeVEL+tn/oEMNf97vVxezmsQiIiIiBUbTYZPZ5doUF1MansuHE33pnNGRRERERERERMRAahSLyB2dPbYXn6UD8CaRh922MmNgXRwt+vEhIiIiUpCYLRYqjVzIGVMZSnGZ818PIj0t1ehYIiIiIiIiImIQdXpE5LYunTuJeUEfShDHcUslyj39My7OzkbHEhEREZFs8PIpga3/fBLtLtRK3c22r581OpKIiIiIiIiIGESNYhHJVNyViyTN7kkZ+yXOmkrjPeJnvHxKGB1LRERERO5B+RrBHGn+PgBNLy5i66pvDE4kIiIiIiIiIkZQo1hEbikxIZYLM3tQwXaaSxTHPHQFvqUCjI4lIiIiIjmgQeehhJYdxjprPUZv8WB/ZJzRkUREREREREQkj6lRLCI3SUm3smjOdKqlHyYWD64NWEKZCtWMjiUiIiIiOajx8Kl8W2EKF9PcGDlvOzGJul+xiIiIiIiISFGiRrGI3MBqs/PC97uZeL4RH9oe5lKP+ZSvEWx0LBERERHJYRYHBz4e1IjA4m6cjUni2zkzSE9LMzqWiIiIiIiIiOQRNYpFJIPdZuPNpTtYtfc8ThYLTQe/Q1BwO6NjiYiIiEgu8XZz5KshwbzrNI/nr7xN+JznjY4kIiIiIiIiInlEjWIRybDl6+e4f/doPE3X+HhgfVpW9TU6koiIiIjksuqlvKjZtBMAzc7PY/uvsw1OJCIiIiIiIiJ5QY1iEQFgy/w3aRY5lxaW/cxsGsf9dUobHUlERERE8khw18cILf0IADXCJnByf5jBiUREREREREQkt6lRLCKEL/2YpsemA7Cl0rO07PWYsYFEREREJM81fuxj9jo3xM2UguOSIcRFXzY6koiIiIiIiIjkIjWKRYq4nau/o+HuNwHYUvphmg551+BEIiIiImIEi4MDASMWEWnyp5z9AhFfDcKanm50LBERERERERHJJWoUixRh+zesoNbmF7CY7Gwt1o0mI2YYHUlEREREDOTjW4prvb/jmt2J0Kul+GztMaMjiYiIiIiIiEgucTA6gIgYY0/ERfzWPI+TKZ0d7q1o+NS3mMw6d0RERESkqKtctzmrE39neVgiXzYMMDqOiIiIiIiIiOQSNYpFiqBjlxIYOnc3JVNf5nWf1TQavQAHRyejY4mIiIhIPtG5WTDtG9twtOhEQhEREREREZHCSlW/SBFzLiaJwbO3EpOUhnPZOtQbswRnF3ejY4mIiIhIPqMmsYiIiIiIiEjhpspfpAi5cvEs8Z+2pmz8Lqr4e/Dt8MZ4OGtjARERERERERERERERkaJGHSKRIiIhLpqYr3pSw3acD11m4zz8CYq5a7tpERERERERERERERGRokhXFIsUAclJVzn9WU+qWI8TjReWhxZRupin0bFERERERERERERERETEIGoUixRy6WmpHJzxILVS93LV7kp070UEVK1ndCwRERERERERERERERExkBrFIoWYzWpl54xHaJC0mWS7I6c7z6FKvZZGxxIRERERERERERERERGDqVEsUkjZ7XZ++24SIXGrSbebOdTqU2o272p0LBEREREREREREREREckHHIwOICK5Y8bfx/j0SG3sjk0oFfIAjToMMjqSiIiIiIiIiIiIiIiI5BNqFIsUQvO2nGLqn0cARy53/oLuLSsZHUlERERERERERERERETyEW09LVLIbFs5i5SV4zBh49n7qjJcTWIRERERERERERERERH5H7qiWKQQ2b12CfXCx9HIwUqxyo3o06G70ZFEREREREREREREREQkH9IVxSKFxMGtfxK0bhSOJivbPdvTe8jzmEwmo2OJiIiIiIiIiIiIiIhIPqRGsUghcGJfGGV/HYqrKZU9LiHUHb0Is8VidCwRERERERERERERERHJp9QoFingzp3Yj9eS/niRyCHHmlQdvQxHJxejY4mIiIiIiIiIiIiIiEg+pkaxSAF2KToO+7y++BLLSXMFSj/1M67unkbHEhERERERERERERERkXxOjWKRAiouKY3B3+3mvZQHiTCVw3PEz3gX8zM6loiIiIiIiIiIiIiIiBQAahSLFEBJqekM/3Yrhy8msM29DZanNuNburzRsURERERERERERERERKSAUKNYpIBJTUlm0yfDOX/6ON6ujsx7rAkBft5GxxIREREREREREREREZECxMHoACJy96zp6eydMZCOV9dS0Xk7cUPWU62U7kksIiIiIiIiIiIiIiIiWVOgryjetm0b77zzDp06daJcuXI4Ozvj4eFBUFAQw4cPZ+PGjVmec82aNQwbNowqVarg7u6Ot7c3QUFB9OvXj5kzZ3L16tU7zpGYmMhnn33GfffdR9myZXF2dqZkyZI0bNiQ0aNH88cff2Tn5UoRZ7fZ2DbzMYIT1pJqt3Ct3bsEV9Q9iUVERERECjPVvSIiIiIiIpJbTHa73W50iOxo3bo1GzZsuOO4IUOGMGvWLJycnG47LiYmhuHDh7NixYrbjtu5cyf169fP9Pm1a9cyfPhwTp06lemYevXqsWvXrtuu82/x8fF4e3sTFxeHl5fXXR8nhUvorDE0O/cNNruJnU0+JLjr40ZHEhERERHJU0WtNipKdS8UvfdXRERERETkVvKyNiqwW09HRkYCUKZMGR588EFatWpFYGAgVquV0NBQpk6dyrlz55g7dy5paWksXLgw07ni4uLo2LEj27dvB6B3797069ePypUrY7FYOHPmDOvXr+enn366baY1a9bQo0cPkpOT8fHx4cknn6Rt27b4+/uTlJTEwYMHWblyJRcvXsy5b4QUCVsWvEOzc98AEF77NZqoSSwiIiIiUuip7hUREREREZHcVGCvKO7evTtDhgyhb9++WCyWm56PioqiRYsWHDlyBID169fTunXrW841ZMgQ5s2bh7OzMz/88AM9e/a85Ti73Y7VasXB4eb++uXLl6lRowZXrlyhfv36/P7775QsWfKW86Smpt7xTO9/01nVRduWX76m6fYXrz+u8DRNh71ncCIREREREWMUtdqoKNW9UPTeXxERERERkVvJy9qowN6jeOXKlfTv3/+WxTKAr68vU6dOzfh8yZIltxy3ceNG5s2bB8DEiRMzLZYBTCbTLYtlgAkTJnDlyhXc3NxYvnx5psUykOViWYquP/ZfYEyoKwdtAWwpOYgmQyYaHUlERERERPKI6l4RERERERHJTQV26+m70a5du4zHx48fv+WYGTNmAODt7c0zzzyTrXViYmIytvh65JFHKF++fLbmEfm30ONXeGbRTlJtPsyvOYuJ/RtjMhfYcztERERERCQXqO4VERERERGR7CrUjeKUlJSMx7c6Azs1NZUVK1YA0LFjR1xcXACwWq1ERkZitVopVapUxtczs3LlSq5duwZww5nZSUlJREZG4uHhQcmSJTGZTPf8mqRoOLprA8uW/UFqelM61SzJ2w82VJNYRERERERuorpXREREREREsqtQd57Wr1+f8bhGjRo3Pb97926Sk5MBqFOnDvHx8YwZMwZfX18CAwOpWLEi3t7edOzYkXXr1mW6zpYtWzIe16lTh/DwcDp16oSnpydVq1aldOnSlCxZkmeeeYaLFy/m3AuUQunU4V34Ln+I902f8Gzpg3wyqAEOlkL9V1VERERERLJJda+IiIiIiIhkV6HtPtlsNiZPnpzxef/+/W8ac+DAgRvGN2rUiI8//pjY2NiMr6emprJmzRrat2/PlClTbrnWv+dZu3YtzZs3588//8Rms2V8/fLly3z22WfUr1+f3bt338tLk0LswpljOC/qSzHiOWqpwohhj+LieOv7kYmIiIiISNGmuldERERERETuRaFtFE+bNo2tW7cC0KdPH4KDg28aEx0dnfF4ypQpHD16lC5durB161aSk5O5dOkSM2fOxNvbG7vdzvjx4zO27MpsnieffBKTycTEiRM5ffo0KSkp7N+/n2HDhgFw4cIFHnjgAeLj4zPNnpKSQnx8/A0fUvhFX4ok5ZtelCKK0+aylBj5M57exY2OJSIiIiIi+VRBrntBta+IiIiIiIjRCmWjeP369YwfPx4Af39/Zs6cectxiYmJGY+Tk5Pp2LEjK1euJCQkBGdnZ/z8/HjyySdZuXIl5v+7P+yECROw2+23nWf27Nm8+uqrBAQE4OTkRM2aNfnmm2944oknAIiIiMg0E8CkSZPw9vbO+AgICMjeN0IKjKvxMVz5siflbWe5SAmch62guH9Zo2OJiIiIiEg+VdDrXlDtKyIiIiIiYrRC1yjev38/vXv3Jj09HRcXF3788Uf8/f1vOdbFxeWGz6dMmYLFcvM2vy1btqRPnz4AHDx4kL1792Y6T926dRk8ePAt13vvvfdwdnYG4Pvvv8/0NUyYMIG4uLiMjzNnzmQ6Vgq+5ORrnJrRi6rWo8TgRcpDSykZWNXoWCIiIiIikk8VhroXVPuKiIiIiIgYrVA1ik+ePEmnTp2IiYnBYrGwePFiWrdunel4T0/PjMd+fn40aNAg07GdO3fOeBweHp7pPJ06dcp0jhIlStCoUSMAdu/eTWpq6i3HOTs74+XldcOHFE7pVhvPfr+Pf5ICSbS7cKXXAgKD6hsdS0RERERE8qnCUveCal8RERERERGjFZpGcWRkJB06dCAyMhKTycScOXPo1avXbY/597ZW5cqVu+uxly9fzvS5O22V9d/nbTbbDfd4kqLHbrczYele/jh4iWk8zKG+f1KlQea/4BERERERkaJNda+IiIiIiIjkpELRKI6KiqJjx46cOHECgE8//ZQhQ4bc8bhatWplPLZarbcd++/nHRwccmUeKTrsNhsr537Iz9tPYDbBp4MaEFy3rtGxREREREQkn1LdKyIiIiIiIjmtwFdscXFxdO7cmQMHDgAwefJknn766bs6tnz58gQGBnL69GkiIiKw2+2YTKZbjj1+/HjG47Jly97w3L+3+fpv0Z6Z/87j4uJC8eLF7yqnFD5b5r1Gj5Of4etYk3M9FtK5VimjI4mIiIiISD6luldERERERERyQ4G+ojgpKYlu3bqxY8cOAF599VXGjRuXpTn69u0LQHx8PH/99Vem45YuXZrxuGXLljc817p1a/z8/AD45ZdfMj27+uTJk+zatQuAFi1aYDYX6G+/ZFPYjx/S7ORnAJiqd6Ff44oGJxIRERERkfxKda+IiIiIiIjklgJbsaWmptK7d282bdoEwHPPPcfEiROzPM+YMWNwcXEB4IUXXiA+Pv6mMfPnz2fdunUAdOvW7ab7MVksFl566SUATp06xbvvvnvTHOnp6Tz11FPYbDYAnnzyySxnlYJv+6+zCdl3/c9paNlhNH34TYMTiYiIiIhIfqW6V0RERERERHKTyW63240OkR19+/bNONu5ffv2TJ8+PdPtswCcnJwICgq65XMffPABY8eOBaBatWqMGzeOunXrEh8fz9KlS5k5cyZWqxUvLy+2bdtG1apVb5ojOTmZFi1aZJzlPXDgQIYOHYq/vz/Hjx9n2rRphIaGAtC1a1dWrlx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SpDfeeKPc5a+8KTY21vO4ustqFRYWeh5Xdbmu8PDwct/CBgAAAFC9E/l2bT+SJ8mift3amB0HqHf+PO+VmPsCAAD4O2epQ9GWsqvPRMclVdou3xKnCLddhfk5DZQMQE0FbKF40qRJcjgcateunYqKijRz5szT2mzbts3zeOHChUpPT5ckXXfddTWeYLdo0cLz+NChQ+rdu3elbU/9djSX1AIAAAC8a82ONFnkVrdmCUqOofiEwMe8FwAAAGYqyM1Swv8ex8QnVtru0eR/a+3BQk2OPkvev1EKgDMRsIXik5e72rt3r4YPH15t++eee87zOC0trcYT5m7dunke79y5s8q2J5eHhoaqY8eONeofAAAAQM00WfEXrQ1frZWJf5TU3+w4QL1j3gsAAAAzFeZlKkFSoRGh6DBbpe1io6MlFSq7qLShogGooYC+R3FD6NOnj2y2sjfAJUuWVNrO4XBo9erVnnXCwsIaJB8AAAAQDAy3S21z16qxJU/t27Y1Ow4QUJj3AgAAoCJFeVmSpAJL1V9ATIgqO5bMLnLUeyYAtROwheIZM2bIMIwqf8aPH+9pv2jRIs/zbdq0qfF2YmNjddlll0mSFixYoEOHDlXY7vPPP1deXp4kadiwYXV/YQAAAABOs3f7OiUrR4VGuDr2uszsOECDYN4LAAAAM9nzywrFRdaYKttdVLJEU8NeVru0jxoiFoBaCNhCsbfMmDFDFotFFotFEyZMqLDN//3f/0mSnE6nxo4dK5fLVW55RkaGHn/8cUlSQkKCxowZU6+ZAQAAgGBzYtNcSdKeqB4Kj4gyOQ3gX5j3AgAAoC4chdmSpJKQqgvFzdzpGhyyUYm5PzZELAC1QKHYCy699FLdeuutkqTZs2dr8ODBmj17ttavX6/p06frggsu0IEDByRJL774oho1amRmXAAAACDgxBxeKkkqbjXQ3CBAgGLeCwAA/IWz1KH1r9yk1R/9zewoAe9ESKqmOa/Slpj+VbazRCdKksIc2Q0RC0AthJodIFBMmzZNeXl5+vrrr7Vo0SItWrSo3HKr1aq//OUvuueee0xKCAAAAASmooJcdSrZJlmkZr2uMTsOELCY9wIAAH+wf8XH6p23QMpbIOlJs+MEtP3hHfW8c4RuTG2uW6toFxabLEmKKM1tmGAAaowzir0kMjJSc+fO1QcffKDBgwcrJSVFNptNLVu21G233ably5dXegkvAAAAAHW3e913slmcOqrGatnhHLPjAAGLeS8AAPAHRVlHPY+dpQ4TkwS+vGKnJCkuMqzKduGxjSVJ0S4KxYCvsRiGYZgdAlXLy8tTfHy8cnNzFRcXZ3YcAAAAwKe8+enXCtnyodq2aKqrfv+S2XFQj5gbBTb+/wIAAG/44Lvlun3ltZKkjN9vVXJqK5MTBa4XP1uqz9Yf0B0Dz9W4q86utF3a9jVq++kVylacGk042IAJAf/UkHMjzigGAAAA4NdmHYzWi87hMi56xOwoAAAAAEy2x95IGUZZYSUv42g1rXEmLjvwmtZFjFXfzM+rbBfTqIkkKc7Il9vlaohoAGqIQjEAAAAAv3Ukp1h7jhfIapEubJ9sdhwAAAAAJjuaU6LM/xWKC7PSTU4T2MJK8yVJIZEJVbaLS0yRJJUqVPl5OfWcCkBthJodAAAAAADq6se18zXI+oNKmvdTfFTV98UCAAAAEPjaH/9WrSzHleZuoly72+w4Ac3mLCsUh0YnVNkuPCJKPd3vKssRqsWuCMU3QDYANcMZxQAAAAD8VuIPb2m67SX9IWKe2VEAAAAA+IBR+VMUaXHowdIHtTPiXLPjBLRIV4EkKSy6UfVto2IlSVlFjnrNBKB2KBQDAAAA8Esup1PtC9ZLkhqdc5XJaQAAAACYzWEvUZKRI0k6aiQps5CiZH2KcpcViiNiE6tt2yi67ApQORSKAZ9CoRgAAACAX9qzeaniVag8RatDj0vMjgMAAADAZJnp+2W1GLIbocpSrDIL7GZHCmgxRqEkKTI2qdq2tzln662wl2RLW1jfsQDUAoViAAAAAH4p64dvJEl7onspNMxmchoAAAAAZstJ3ydJCrc4tcT2sH6z9y/mBgpgpQ67oixlhfiY+OrPKO7s3qPLQzYpNGt3fUcDUAuhZgcAAAAAgLpIOLpMkuRsO8jkJAAAAAB8QeGJ/Z7HrawnVOI4amKawJZfVKJZzqsUbynU9XHV36PYGZ4g5UtGUVb9hwNQYxSKAQAAAPidvJxMdXTslCxSyz7Xmh0HAAAAgA9wZh+SJB2yNFUL46hindkmJwpcec5QPescoZjwUN1Ugys8uSPLzjq2lvD/BPAlXHoaAAAAgN/ZvfZbhVrcOmBtrqatO5sdBwAAAIAPsOQdliSlx3SRJCUYuTLcbjMjBay8klJJUlxEzc5HtESVFYrD7BSKAV9CoRgAAACA3/lvQXddZn9JC9s/YXYUAAAAAD5ibsQQ3ecYp0Md7pQkRVocKirMMzlVYCrIz1NjZSs5omaF+NCYZEmSzZFbn7EA1BKFYgAAAAB+xTAMLd2doZ+N5mrV6wqz4wAAAADwEZuLG2ueu6+iO16sIiNckpRz4ojJqQJTxL7vtS5irF4oeqZG7W2xZYXiKGdOPaYCUFsUigEAAAD4lX2ZRTqUXaywEIv6tk0yOw4AAAAAH3Ekp0SS1DQ+QjnWeElSfiaF4vrgLCy7hLQ9LKZG7SPjG0uSwtz2essEoPZqdvF4AAAAAPARhxe9pTfD5mpb4+sUHc6UBgAAAIDksJdoSPGXOmpNVNPYQdoV1kHHS2JlL3aaHS0guYtzJEnOsLgatY9o2UOdS2ZIoRH6qf5iAaglPlUBAAAA4Fdi0ubp4pA1Sko43+woAAAAAHxExpF9mhD2ruxGmGwx4/V282e1YMdxTYzopr5mhwtARknZvYZdtpoVihNiImWXTXK6VexwKdIWUp/xANQQl54GAAAA4Dcc9hJ1LNwkSWp87tUmpwEAAADgK3LT0yRJJ6xJslitSoouu0dxRj6XOq4PVnueJMmIiK9R+5jwUIVaLZKk7CJHveUCUDucUQwAAADAb+zesFBnWUqUqXi1636B2XEAAAAA+IjCjAOSpNywFLWQlBRjkyRlFlKUrA8hjrJCsaWGhWKLxaJnwj9QE+dhFR5pJiWcV5/xANQQZxQDAAAA8Bt5276RJKXF9ZE1hEuVAQAAAChTmn1IklQUmSpJ6lOwUMts43TVnmfNjBWwwkrLCsXWqIQar3Oh5QddHrJJJf8r6gMwH2cUAwAAAPAbycdWSJKM9peZnAQAAACAL7HmHZYkOaObSpJiw0PU0npCeSVHzYwVsDaEnqfdrgi1SOxY43WKQ+Mlh+TIP1GPyQDUBoViAAAAAH4h6/hhtXf+LFmktn2HmB0HAAAAgA+xFZUVhK3xLSRJEQllZxZHO7NMyxTIPrJcq92lA/Rhiz41XsceVlYodhZk1mMyALVBoRgAAACAX9i8Y5cSjXaKCZU6pLYyOw4AAAAAHxJjPy5JCk9qKUmKTiwrFMe7c03LFMjySkolSXERYTVep9SWIBVK7iKK94Cv4B7FAAAAAPzC3GOJusHxnP573gyzowAAAADwMRN0j+5zjFNEm7IzXGOTmkmS4o18OUsdZkYLSLbiDIXLofjImheKXRGJkiRLMYViwFdwRjEAAAAAn2cYhpbtLruP1cVdmpmcBgAAAIAvsTtdWlHYQlIL/a1p2dWHGiWnym1YZLUYyso8puTUluaGDCAOe4mWhdwrhUh5xm5JUTVbMaqsUBxakl1/4QDUCmcUAwAAAPB5uw8cVlF+tiLCrOrVupHZcQAAAAD4kGO5dklSeKhVjaLKznANCQ1TriVWkpSXccS0bIGoIPeXewxHxyfWeL2Q6CRJkrW0wOuZANQNZxQDAAAA8HlZy6Zqc/jr+i7+N4oIu9rsOAAAAAB8SOahnRodMk95Me1lsfwyX9gT2l6hjnyp0G5iusBTmJelREn5RqRiQ2teZsrrcL06r22pLsmN9WX9xQNQC5xRDAAAAMDnRR9cqlCLW4mprc2OAgAAAMDHuPav0fiw9zTaPavc86+kvKBhjmd1ILy9OcECVHFe2T2GCy0xtVovIS5WdtmUXVRaH7EA1AGFYgAAAAA+rbgwX51KtkqSmva8xuQ0AAAAAHxNafYhSVJxZJNyzyfHhkuSMgocDZ4pkNkLygrFRdboWq2XEGWTJGUX8f8D8BVcehoAAACAT9u97judYylVupLVqlMPs+MAAAAA8DHW/LJ7EDtjmpV7Pim6rDCZWcClp73JUZAjSSoJja3Veo3CSvVy2GQluPLlLB2k0DBbPaQDUBucUQwAAADApxXt+E6SdKBRX1msTGEAAAAAlGcrSpckWeObl3u+f8E8LQ//gy7a9YIZsQKWsyhbkmSvZaE4PiZGN1qX6fKQTcrNOl4f0QDUEp+yAAAAAPBpTU6slCSFdBxschIAAAAAvijWfkySFJ7UsvzzNqtaWDIUXZxuRqyAdTwkVf919df+2J61Wi80LEz5lihJUkE2hWLAF1AoBgAAAOCzjh36WW3dB+QyLOrQl/sTAwAAADhdkuuEJCk2pU25523xZfcsjirNbOhIAW1HZE89WnqftrS4rdbr5lviJElFuSe8HQtAHXCPYgAAAAA+a+WBYm0oHa1ecXkaltTE7DgAAAAAfExJcaEaKU+SlNysbbllEY1SJUkxrpyGjhXQ8kpKJUlxEWG1XrcwJE5yHlUxhWLAJ1AoBgAAAOCzvt/n0BzXYCWe18HsKAAAAAB8UHq+Uw/Zn1Wr0Gy91qhxuWWxic0kSQnuXBmGIYvFYkbEgOMoyFG4HIqLrH2huCQsXnJKzvyMekgGoLa49DQAAAAAn+RyG1q+p+zDg0s6Na6mNQAAAIBgdDTfqc1GB22LHyiLtXzJI6FxU0lSlMWuooI8M+IFpNsPPaOfIkbp7BNzar2uw5YgSXIXcjlwwBdQKAYAAADgk3Zv36gh9q/VNTxTPVommB0HAAAAgA86mlssSUqNjzhtWVR0nIoNmyQp58SRBs0VyMKdBZKksOiEWq/rCm9U9t+SXG9GAlBHXHoaAAAAgE/KXv+J/ho2XRuj9yg0ZITZcQAAAAD4IOu+pRodslJxtkskXVBumcVq1c6QDjKcDoUXFqq5OREDTqS7rFBsi25U63U3dxirkQeu1g2J7XWRt4MBqDUKxQAAAAB8UvyR5ZKk0jYDzQ0CAAAAwGc1PfytbgibpVWlYZJuOW35hKSXteVgjqZYW+isho8XkKL+VyiOiK19oTg6LkF22ZRd5PB2LAB1wKWnAQAAAPic/NwsdXTskCS17D3E5DQAAAAAfFV4UbokyRrfosLlydFll57OLKQw6S0xRqEkKTIuudbrNooKkyTlFJV6NROAuuGMYgAAAAA+Z/ear9XT4tJBSzO1bNvF7DgAAAAAfFSs45gkKSKpZYXLk2L+VygusDdYpkBmLylSpKWs6B4dn1Tr9Zs6D+sfYf9WWGaMpH5eTgegtgL2jOK8vDzNnDlTjz76qAYMGKAOHTooPj5eNptNKSkpGjhwoP7+978rMzPTK9vbt2+fHn/8cfXq1UsJCQkKCwtTYmKiLrzwQj377LM6fvy4V7YDAAAABIPSn+ZLko4kXVBNSyC4MfcFAADBLtGVIUmKTWld4fLBBbO1PPwPOm/npIaMFbAKcrMkSW7Doti42l96OiHMpZtClunC0lXejgagDgL2jOK1a9dq+PDhFS47ceKElixZoiVLluill17S+++/ryuvvLLO23rvvfd07733qri4uNzz2dnZWrVqlVatWqXXXntNM2fO1ODBg+u8HQAAACBYtMgq+9AgvMsVJicBfBtzXwAAEMxKivLVSPmSpKRm7StsE2uTWlgylF58tCGjBax8u0uLXf0VFeLS1SEhtV4/plFjSVK8kS/D7ZbFGrDnMwJ+IWALxZLUsmVLDRo0SL169VLLli3VtGlTud1uHTp0SJ999pk+//xzZWRkaOjQoVq7dq3OPffcWm9jxYoVGjVqlNxut6xWq0aOHKnrr79ezZo104EDB/TOO+/oq6++UlZWlq6//npt27ZN7dq1q4dXCwAAAASGQ/v3KNV9QqUKUYfzrzI7DuDzmPsCAIBgdeLIPrWUVGSEKy4hscI2obEpkqRIu3eusBLscizxerT0PjWPjtTVdVg/PrGJJCnM4lJeXrbiEmp/+WoA3hOwheJBgwbpwIEDlS6/5ZZbNGvWLA0bNkwOh0PPPPOMPv/881pvZ+LEiXK73ZKkN954Q/fff79nWZ8+fXTTTTfp0Ucf1SuvvKLi4mK98sorevPNN2v/ggAAAIAgsehIqF60T9FvmmVrQh0uZQYEE+a+AAAgmOWmp6mlpBMhjdW6kjNTw+NTJUnRzuwGTBa48opLJUlxkWF1Wj8iKkbFhk2RFofys45TKAZMFrDn9IfU4JIHN9xwgzp37ixJWrZsWZ22s3LlSklSUlJSuYnyqZ5++mnP41WruO4+AAAAUJWluzNUoCg1PvtSs6MAPo+5LwAACGZ7wjrrBvuzejfpoUrbxCSVFYrj3DkNEyrAFRQWKFwOxUXU/TzEXEucJKkw57i3YgGoo4AtFNdUbGysJKmkpKRO6zscDklS27ZtK20THx+v5OTkcu0BAAAAnK7U5daqn8suCXdJx8YmpwECB3NfAAAQiA4VWrXZ6KDclPMrbROb1FSSlGDky+V0NlS0gJW857/6KWKUHsv9W537KAwpKxSX5J7wViwAdRTUheKffvpJmzdvliR16dKlTn2c/FZ2WlpapW3y8vKUkZFRrj0AAACA0+1a+53eN57Qg5Hf6qxmcWbHAQICc18AABCojuaWfQmuWXxEpW0SklLlNiyyWgxlZxxtqGgBy1WcK0lyh0XXuY/i0LK5nr2A+0YDZgu6QnFRUZF2796tV155RQMGDJDzf98geuihh+rU3+9//3tJUmZmpiZPnlxhm+eee+609gAAAABOl7ftG/Ww/qwBMYdltVrMjgP4Lea+AAAgGLQ/9IVGh8xTB1vlBcfQMJt2WNppg7ujcvLzGzBdgCrJkSS5wuv+xd4ZLZ5Tp5J3tLXRFV4KBaCu6n4ReT8yY8YMjR49utLlf/rTn3TbbbfVqe+77rpLy5cv17vvvquxY8dqw4YNGjp0qJo2baoDBw7ovffe06xZsyRJTz75pC6//PI6bQcAAAAIBsnHlkuSjPaDTE4C+B/mvgAAINgMyP5c7cPStMUYUGW7h+MnadexAn1gSVGHBsoWqKz2vLIH4fF17iMitpEcyldOEbcrAcwWFIXiyvTo0UNTpkxRnz596txHSEiI3nnnHV133XV6/vnn9dZbb+mtt94q12bQoEH685//XOOJst1ul91u9/yel5dX53wAAACAv8g+cVTtS/dIFqnN+deZHQcIGMx9AQBAoEpyl93jNq5Jm6rbRYdLKlBGgb3KdqheqON/x2yRdS8UN4qySZKyi0q9EQnAGQiKS0/fcMMN2rp1q7Zu3aq1a9fqo48+0rBhw7R582YNHz5cc+bMOaP+d+zYoXfffVdbt26tcPmqVav09ttv6/DhwzXqb+LEiYqPj/f8tGzZ8ozyAQAAAP7g57VzZLUYSrO2UeNmrc2OA/gd5r4AACCYFBfmK0EFkqTEpm2rbJsUU1aYzCjgDNYzFVZadvnukKiEOvfRxb5V/wj7l3odesdLqQDUVVAUihMSEtS9e3d1795dffr00a233qrPP/9c7777rvbu3avrr79eM2bMqFPfy5YtU79+/fTVV1+pefPmeu+995Seni6Hw6GDBw/qn//8p6KiojRz5kydf/752r59e7V9PvHEE8rNzfX8HDx4sE7ZAAAAAH9i7P5ekpSecqHJSQD/xNwXAAAEk4wjeyVJhUaE4uITq2w7tPAzrQh/UF12vNkQ0QJauLOsUBwa1ajOfaS4j+umkOVqn7/OW7EA1FFQFIorc+edd+rmm2+W2+3WAw88oKysrFqtb7fbNXz4cOXm5io1NVWrV6/WHXfcoSZNmigsLEwtWrTQ/fffr6VLlyoiIkJHjhzRyJEjq+03PDxccXFx5X4AAACAQGa43Wqds0aSFNPtCpPTAIGFuS8AAAhEuen7JEkZIcmyWKsudcSGutTckqmwoqMNkCywbbCepfmuXgpJrPtVoGyxyZKkSCe3HgHMFtSFYkm6/vrrJUmFhYX65ptvarXuN99847mk1oMPPqjU1NQK25111lm64447JEkbNmzQli1bziAxAAAAEHh+Ppyuta6OSjcS1bEPhWLA25j7AgCAQFOcWXY1krywlGrbhsSWtbHZa/eFOZxuknGbflf6qEKbnVvnPiITyv5/xLhyvRULQB0FfaG4cePGnsf79++v1bo7duzwPO7Zs2eVbXv16uV5vHPnzlptBwAAAAh0i/eV6IHScXqs1UxFREabHQcIOMx9AQBAoHFmlxWKiyMr/hLbqcLimkiSIh0Uis9UXnGpJCkuMrTOfUQnlB2bxhn5XskEoO6CvlB88lvRkhQTE1OrdUNDf3kjdDqdVbYtLS2tcD0AAAAA0pJdJyRJl3RqXE1LAHXB3BcAAASa+dFDdYP9Wf3YtvpbXkQ2Kismx7py6jlVYCtxOCVniSQpLjKszv3ENCor3EdZ7CopLvRKNgB1E/SF4k8//dTz+Oyzz67Vum3btvU8XrZsWZVtlyxZUuF6AAAAQLArKS5SRtpWSQaFYqCeMPcFAACBJq0wTJuNDopo2q3atrFJTSVJCe6cek4V2AqyjuiniFHaET5KMWF1Ly/FxSfKaZStn5d13FvxANRBwBaKZ8yYoZKSkirbTJo0SV9//bWksgls//79yy1fvHixLBaLLBaLRo0addr6l112maKioiRJ//73v7V169YKtzNv3jx98cUXkqTmzZurR48etXw1AAAAQODavfYbzQt9VLMjn1XHlNqd6QgEO+a+AAAgWB3NLTsGapoQWW3bhORmksrOYC0q4L64dVWUV3bp7lJLmKwhIXXux2K1Ks9SNvcryKZQDJgpYK8DNWHCBD366KO66aabdPHFF6t9+/aKiYlRfn6+tm7dqg8++EArVqyQJNlsNk2ZMkUhtXxjS0hI0J/+9Cc9/fTTys/P14UXXqgHH3xQgwcPVqNGjXTs2DF9+eWXmjp1qtxutyTphRdekNUasPV5AAAAoNYKf/xOklQS314Wi8XkNIB/Ye4LAACC1Y0503U0JFrNIntW2zYqJl67jBbKM6LUNCdXUTHxDZAw8BTnZUqSCizRijvDvn4X/Ya2ZkozItur3ZlHA1BHAVsolqSsrCxNnTpVU6dOrbRNixYtNG3aNF1++eV12sZTTz2lrKwsvfbaayooKNDEiRM1ceLE09qFhYXp+eef1x133FGn7QAAAACBqsmJlZKkkI6XmZwE8E/MfQEAQLApKsjVPfpcCpPy456ttr3FatXoyDd0OKdYn7tj1bwBMgYie0G2JKnYeuZXgrLEpMiRma3swtIz7gtA3QVsofjbb7/V3LlztWLFCu3Zs0fHjh1TZmamIiMjlZKSoh49emjIkCG65ZZbPJfQqguLxaJJkybpjjvu0FtvvaXly5dr//79KioqUkxMjDp06KABAwbo3nvvVadOnbz4CgEAAAD/d+Jwmtq698ttWNS+7xCz4wB+h7kvAAAIRicOp6m1pAIjUrHxiTVaJznGpsM5xcoscNRvuABWWpgjSSoJOfNCcaNomyQpu4j/H4CZArZQ3LlzZ3Xu3FmPPPJInfsYOHCgDMOoUdtevXqpV69edd4WAAAAEIzS1s5RY0l7wjqqU3Kq2XEAv8PcFwAABKO8Y/slSRkhyappyTI5JlySlFlgr6dUgc9ZlCNJcoTGnnFfA0uX6qqw7xW97xrpgrFn3B+AuuGGQQAAAABME7J3oSQpM/Vik5MAAAAA8BfFmWWF4jxbkxqv89uiD7Qy/AG12vlWfcUKeO7iHEmS03amdyiW2pfu0U0hy5WQ/cMZ9wWg7igUAwAAADCF2+lUu/x1kqT47leanAYAAACAv3DlHJYklUTWvFAcG+pSM0uWrPlH6ytWwDtmTdV8Vy9lxHY9476MqCRJUkhJ9hn3BaDuAvbS0wAAAAB827YjuXrF8XsNDNuh23sOMjsOAAAAAD8Rkl9WKHbFNq/xOpboxpKksJKMeskUDNZGD9CHpW31cOtOuvoM+wqJLru3tM2Rc8a5ANQdhWIAAAAAplj2c7YWu89TWPurNMoWbnYcAAAAAH4ivChdkhQSX/NCcWhc2dnH4Y6seskUDPKKSyVJcZFnXloKjUmWJEWU5p5xXwDqjkIxAAAAAFMs2XVCknRJp8YmJwEAADgzuzYuljUkVB3OvdjsKEBQeD7sQZXk7tOfOlxR43XC41MlSdGlXOq4rgqK7ZKkuIiwM+4rIq6sUBzlyjvjvgDUHfcoBgAAANDgCvKyddmhf6mfdbsu6ZBkdhwAAIA6Ky7IVafZ16vDF9fKUVJsdpygY7jdys/lDNFg82N+hLYYHdS4aYsarxOdWFYojnPn1FOqwPfE0XH6KXyk2uasPOO+ohNSJElxBmcUA2aiUAwAAACgwe1ZM0/3hszWS+Fvq3VyjNlxAAAA6izr+BHP42MHd5mYJDiteWucol9pp5/WLzA7ChpIgd2p/BKnJCk1PrLG68UlN5UkJRh5cjmd9ZIt0EW6CxRuKVVE5JnP4aIblRWKY40i/n8AJqJQDAAAAKDB2X+aL0k6nNjP5CQAAABnpjDnuOdx1iEKxQ3tgiPvymoxlLDsWbOjoIFkHPpZfw79QKMjFismvOZ310xIStVed1NtNDoqJzen/gIGsCh3oSQpIi7xjPuKT26qPiX/VBf7DOXa3WfcH4C64R7FAAAAABpc88xVkiRb58tNTgIAAHBmSnJPeB5n5hWamCS4OagzBY38Q9t1T+hcpVlbS3qpxuuFhtn0m7A3lFXo0Lel4eIGOLUXaxRKFinKC4XisNBQlYQ3lsPuVHaRQ4nRNi8kBFBbnFEMAAAAoEEd3rtDLYyjKjVC1KHvNWbHAQAAOCOOgkxJ0grXWVoRcr7JaYLXvMSRZkdAAynJPChJyrel1HrdpP8VIzMK7F7NFAxKigsVbimVJEXHJ3ulz4ToMElSTpHDK/0BqD3OKAYAAADQoA6tn6Pmknbbuqpb/Jl/Ex0AAMBMWxsN1oiSaQqXQ72zisyOE1QK83MU/b/Hm40OpmZBw3HmHJIklUSl1nrdpBibdh+nUFwXBbmZipDkMiyKjon3Sp8jLV8rMWyHXAfCpNZXeKVPALXDGcUAAAAAGpRt3yJJUm7z/iYnAQAAOHPZxU4VKULZitOBTArFDSnnxGFJUpERrkOFfNQdLELzy/6/u2Ka1Xrdu4pnaFX4A0rZ8a63YwW8wtwsSVKBJUrWkBCv9NnHtVk3hiyXMnZ6pT8AtcdfTwAAAAANptTpUmLhz5KkpHOuNjkNAADAmcspcihZufrS9pSm54yS4eZmuQ0l3UjSjfYJmu3qpwG5X5odBw0kvPiYJCmkUYtarxsf6lRTS5ZUkO7tWAEvv1Sa7+ql9SE9vNZnqa3szGR3YabX+gRQO1x6GnVWaHfq7eVpum9ge4WF8J0DAAAAVG/zoVzdYn9ZfSOP6INzLjI7DgAAwBnrcegDnRv2g8617pUkZRw/pOTUVianCg7HiwwdMxrp1tDFspeukOH+uyxWPqcMdHGOskJxZFLt/50ZUWX31g0pyvBqpmCQEdZMvyt9VGc1jtNlXurTGd5IkmQUZXmpRwC1xV9N1IlhGBo5ba1++v5drf3PA2bHAQAAgJ9YtuuEDFmV3LGPQkL53ioAAPB/HfNW66aQ5Z7fM/ZzCdWGcqLArgyVnZEYbilVfl62yYnQEJJcZUXeuCZtar2uJSZFkhRm5wzW2sorcUqS4iLCvNanEZkoSQop4d8uYBY+mUGdWCwWPdTD0IXpb8h63NDa/3bS+Tc9ZHYsAAAA+Lglu05Iki7p1NjkJAAAAN4R6cwt93t++h5JV5gTJsjE7l+g34Ws9vyem3FEcQlJJiZCfcsvKdWl9pfV1JKlj1p2qvX6tvgmkqRIB4XJ2sorskuS4iK9V1ayRpcVikPt/8/efYfHUV19HP/Odmkl7arLqrblBu7GjWrTW0LvCQFCKCGQ0JJAQgmkECCUkEILJcAbCL1XA8Y24IptbHBvstXb7kpbtbvz/jG2E8dNZWZH5Xyexw/y7sy9Pyxppdkz91yfbmMKIbpGVhSLbjvs4ENZMPgKACZ8fSerF84yOZEQQgghhOjN/M31PNJ4IX+yP8IRQ71mxxFCCCGE0IU7EQBgk2UwAPGmjSamGViK62dzo/2lnX9vb64xMY1IhTp/hEay2ewciTs9vcvnp3mLAMhKSKG4qyrXPs4a50V8v/Xvuo1pzdBu7HB2+HQbUwjRNVIoFj0y7Qd/4Cv3ETiUBPnvXkrDNvlFWAghhBBC7Nn6BW8zSGlhsn0zRTmZZscRQgghhNBFltoGQGPWgQDY/ZtNTDOwOKO77jMb8dWZlESkSo0/AkCxN61b52fmaoVib9KnV6SBI+LHqXRgt+vXetqZqXWacv9PZwYhROpIoVj0iMVqZeSVz7LJMphcfPifPodIqN3sWEIIIYQQohdKrNU60NTlH2pyEiGEEEIIfcSiEdyKVrhSSw4CICO8zcxIA0p6TNtnNqEqAMT89WbGESmQWD+bX9ue40T7V90635NfwobkIFaqgwmFQzqn698sUa17gur06DdmxcFMifyNCyz36DamEKJrpFAsesyd6cXx/X/jI4Ph8XWsfORi1GTS7FhCCCGEEKIXUZNJKnwLAEg/4FiT0wghhBBC6CPQrBUmE6pC9rBpbEoWsimeZ3KqgSMr3gLAJttQAJLtjWbGESmQXjufy2zvMj2xpFvnuzO9nJR8gHNit9Mc1jlcP2eLaYViS5p+hWKvJ4tGsmkMq6iqqtu4QojOk0Kx0EXJ0FFsO+Zh4qqFxS1OnpgnLaiFEEIIIcR/VK1ZSiHNRFQ7I6Yeb3YcIYQQQghdtPm0wmRAyaBw5HSOjD3Aj8M/JhSLm5ys/1OTSXJUHwCfFf+IC2M3MS9Dfs/s7yxt2j7Uyczibp2vKAp5GU4AmtqjuuUaCOxxrc2+Nd2r25jZ6Vob646ESjCW0G1cIUTn2cwOIPqPMYedwsuh1/jjJ0Es761h1CAvhw2XOyiFEEIIIQTUfvUOFcDatHGMS88wO44QQgghhC7qnYM5KfIkY3LgpXQ7njQ7/nAHVS0hRhVlmR2vXwv4mvAoWkFeGXYUc9dtwB3NNjmVMFpaWNuH2uot7fYYeRkOqn1hmttjesUaEFzbC8W2dP2+z9LsVn7leIE8tQV/w2gyygbrNrYQonNkRbHQ1ZnHzuCsg0pJqnDt/y1g25YNZkcSQgghhBC9QPrWOQCESo8wOYkQQgghhH584Q7CuEhkFAFQnpMOQFVTu5mxBoTWBm0vaD9uCrO1VrjNQVkh2t9ldWir+NPzyrs9xjXRx5nv/AkZq1/UK9aAkJbQXtecGfoVihVF4TTLPM6wziPUvFW3cYUQnSeFYqErRVH43WljmFmS5JHkb4j/8zTaA61mxxJCCCGEECaKdCSYGypjVbKcgkknmR1HCCGEEEI3raEOALLTHQD8RP0XXzkvJ2v542bGGhBqlSKOid7DLem3U2gPcbZ1Nge3vmF2LGEgNZkkL6EVirOKBnd7nCxrjCKlFbWtVqdkA8NyhrMgOQpH9iBdx223aN0XIn7ZY1wIM0ihWOjOZbdy75ljqbA0MjhZxbpHvkcyIfsLCCGEEEIMVIs3t3Jv7Cwudj3AkAOmmB1HCCGEEEI32Vve5x7bo8zsmAuAx2UjR2lHad1sbrABoCGssl4tpck7ngJrG/faH+NH0WfMjiUM1BZoxa1EAMgvHtrtcRJp2naJSlAKk52lqirXx37MubHbSCsaqevYIZvWESDa1qTruEKIzpFCsTBEfvFgWr7zJDHVxsTQ5yx8+pdmRxJCCCGEECaZu057A+bw4fkoimJyGiGEEEII/Xial3GO7TOGx9cCYM0dAkBau7RQNVpjm9ZmOj/TiSevBIAsQkTCQTNjCQM112wEwEcGae7Mbo+jZOQDYAs365JrIIjGk8QSSQCy0uz6jm3XCsXxNvl8CGEGKRQLw4ycfBTLJvwGgOlbH+erD541N5AQQgghhDBF07ezcRLj8OF5ZkcRQgghhNCVNbJ9y7W0HADSi4YBkB2tNivSgOGt+pBrrK8yQVlLljeXmGoDwNdYY3IyYZQtljKmRP7GTZl39WgcW2YBAK6YFCY7KxCKAWBRwO2w6jp23KXteayG5PMhhBmkUCwMNfX0a5hfcA4Ao764gU3fLDA5kRBCCCGESKWmmi3c134Ty5yXc3hFmtlxhBBCCCF0ZY/5ALC4tUJHbukIAAqT9STicbNiDQgVDZ9wg/1lDoytRLFYaFW0VYltzbLvbH9VG4jRSDax3AN6NI7TWwhAeodPh1QDQ7j2W9Y4L2KW8+e6d4lKbi8UW8Ituo4rhOgcKRQLw02+7G+sdE4gXYkSe/lKfMGo2ZGEEEIIIUSKbFr4NgDbbOXkZOeYnEYIIYQQQl+uDj8Atgytc0pBSSUx1YpDSdBQvcHMaP2eM6rtZ2rN0op+bVYvAKFWWVHcX9X6wgAUeVw9Gic9exAAnmRrjzMNFOFAK06lA6ei/w0wSrp2nWiN+nQfWwixf1IoFoaz2R2UXvZvvrQcxJWRn3D188uIb9/PQAghhBBC9G/Kxk8AaCo6zOQkQgghhBD6S09ohWJHZi4AVpuNeotWuGzeusa0XANBxva2wU6vVvQL2rViU8xXb1omYazBm17g17bnmKCs79E4WfnFbEgOYm2yhIS8T90p0aC22jdsydB97LrKc5gS+Tt/9Vyv+9hCiP2TQrFICW9eEdmXv069vZR565u4673VZkcSQgghhBAGSyYSDA0sBCBrzAkmpxFCCCGE0F9Gsg2AdG/Bzsc2po9jTmIsde1SgDLSjtWg7twSAKJObVV3vK3BtEzCWMNbPuUy27sMoWerxrPzSzk6dh/nx27BF+7QKV3/1tGufb9FbJm6j53pyaERL81h3YcWQnSCFIpFyowqyuK+s8cDsPqLN1nw5qMmJxJCCCGEEEbauPJLcggQVF0Mn3Sk2XGEEEIIIXSlJpNkqe0AZPxXofjjEbfyg46bWcIos6L1e/GOGNlqAABPvlYoXll2Ht+P3cyCzGPNjCYM5OnQbgJIyyvv0Tg2q4XsdDsATe2xHucaCBJhHwAxAwrF3nQHAK0h+VwIYQab2QHEwHLi2EHcNTnEOSv+SHyJjbWlIxkxaabZsYQQQgghhAGalr3LMGCteyITnT3bR0wIIYQQordpi8aZFn2cbNr5JL905+PlOekAVLWEzIrW7/kaa8lTVBKqQnae1no6UTCOeUk7uTGPyemEEdRkkrxEEyjgKRrc4/FyM5y0hjpobo8C+hc/+xs1pLXZj9v1/7fKtQS53fZPMkMx4CjdxxdC7JusKBYpd+7pZ/C1+2CcSgfeNy+hqWaL2ZGEEEIIIYQBMqvnAhCrkNXEQgghhOh/fKE4YVy02gtxOR07H99RKK5rajUrWr/na6oGoFXxYLVpa6HyMrXPQVN71LRcwjgBXzPpiva5zRs0pMfj3Rz/OwucV2Ff+2aPxxoQIj4Akk79b8TITrdzie0DzlI+JRaN6D6+EGLfpFAsUs5itTLsiv9js6WMAlpofvIcohG5w1IIIYQQoj8JRuPcGLyQ33Z8j+Kpp5kdRwghhBBCdzvapO5oYbvDMHsTi51X8mTLhWbEGhC22co4Onovv8u8dedjhdYg51g/ZUrzWyYmE0Zpqd0IQCuZpLkzejye1xKhUPGR8Nf2eKyBoN5SwILkKIKZPS/S/69Mbx4JVQEg0CJ7jAuRalIoFqbI9ORgu+AFArgZGV/N149cippMmh1LCCGEEELoZP7GZlYlSvjQcxalg0eYHUcIIYQQQncd25Zyj+1RLrK8t8vjRSXl5CkBPATxtzSalK5/awjBBrWE1uxxOx8rVHzcY3+cC8PPmphMGCVQr3WlbLHm6zJe3JULgNouhcnO+NB9CufGbmPb0HN1H9titRJQtOJ/W2u97uMLIfZNCsXCNKXDxrDlyL+SUBWm+N5l4Yt/NDuSEEIIIYTQyZy12puiRwzPR1EUk9MIIYQQQhigcQ3n2D7j0OSiXR5Od2fRhBeAhi2rTAjW/zW2aS2I8zOdOx/z5Gt7FXvVAIl43JRcwjjR5ioA2hyFuoyXdGsFZ2uoSZfx+rtApAOArDSbIeO3K9rexyGf3FwjRKpJoViYauyMM1g0/DoANn2ziC/Wyw8CIYQQQoj+YNzKP3KW9TNmDnGbHUUIIYQQwhDxYDMAMfvue3Y22osBCNSuS2mmgSK/6j1+an2Vccr6nY95c4tIqgpWRcXXXGdiOmGEeVknMSXyN2YNuUGX8SwZWqHYHm3RZbz+LhDeXih22fdzZPcEbdrraKxN6gNCpJoUioXppl1wK4+W3cNNHZfyk38tZWuL7FcshBBCCNGX1WxazZkdb/FH2+NMG7z7G6dCCCGEEP2BGtIKTB3O7N2ea08vBSDWuCGlmQaKysZZXG9/mRGx/6zYttkd+LevSgw0VZsVTRik2t9BI9mk5euzR67Do61MTo9Jobgz7m+5ikXOH1MUNKZLQsTmBaCjrdmQ8YUQe9dvC8WBQIAXXniBG264gRkzZjBs2DA8Hg8Oh4OCggJmzpzJPffcQ3Ozvi88s2bN4uKLL2bYsGG43W48Hg8jRozgrLPO4uGHH6a9vV3X+foDxWLhoh/8iHGlXlpDHVzxzwWEgm1mxxJCCCGEEN20bfHbAKxzjCLLm2tyGiH6N7n2FUII81jCWoFJTdu9UBz3DAbA6tucwkQDhyum/Vyze4p2edxn8QIQbJEVxf1NXSAMwCCPS5fx0rza105molWX8fq77GQr+Yqf9LR0Q8aPObwAJIJSKBYi1YxpKN8LLFy4kPPPP3+PzzU2NvLZZ5/x2Wefce+99/Lcc89x/PHH92i+1tZWLrnkEt54443dngsEAqxbt45XXnmFgw8+mAkTJvRorv7IZbfy6IUHcd5DH3JDyx2sfjiTide/gWLpt/cyCCGEEEL0W7bNswHwFR9hbhAhBgC59hVCCPPYoj7tg7Sc3Z/LHQJV4A5uTW2oASIzrhXp07IH7fJ4yJYNsSoiPikU9zdn1P+VmbYEFbYKoLTH42XklbI+WUytUkBxz+P1a2oySYYaBAXSPbu/3unhiyFXc9X8UzgzZwwHGzKDEGJv+m2hGKCsrIwjjzySgw46iLKyMgYNGkQymWTbtm28/PLLvPrqqzQ1NXHKKaewcOFCxo8f3615/H4/xx57LEuWLAHg9NNP56yzzqKyshKr1crWrVv57LPPeOWVV/T83+t3BnnSeOSEDCrf/hpHe4L5/7yZ6ZfcbXYsIYQQQgjRBfGOGMOC2u/FueNPNDmNEAODXPsKIYQ5HDEfALaM3TuouEvHMHfxGDYmhjM6xbkGAm9SWwXqzi3Z5fGIMxdiEA/UmxFLGERNJjkx9iHptijb0n+ty5ie4mEcFvsTAN/G4qQ7+nWppEci4SBpSgKADE+eIXPYPUU04ac5ohoyPsC3NQHeWFbNT48ejtspn28hdui33w1HHnkkVVVVe33+nHPO4fXXX+f0008nFotxxx138Oqrr3ZrrmuuuYYlS5bgdDp58cUXOeWUU3Z5fvLkyZx++uk88MADJBKJbs0xUBww5WgWbrmFqSvvYPqWR1j20XgmHHuB2bGEEEIIIUQnrV/6GaMI4SODynGHmR1HiH5Prn2FEMI8aXE/AI7M3QsneSOmclLHr1DicH48icMmXfP0Egm1k0UIAG/BritLF5d8n/u+ns4hmYcy3YxwwhCB1kY8ShSAvOLBuoyZ4bThtFmIxpM0t8dIz+m3pZIea/c3kwbEVQvp7ixD5shOdwDgC8UMGR/gzre/Yf7GFjJdNq4+arhh8wjR1/Tb31CsVut+jznttNMYOXIkAHPnzu3WPPPmzePZZ58F4He/+91uF8r/TVEUbDb5gbM/U8+6nvl5ZwAwbN71bFm9xOREQgghhBCis1pXvA/AhozJWOV3XyEMJ9e+Qghhnitsv+OQyEOoFbvfHJef4STNbkVVYVtryIR0/VdLQzUAUdVO1v+0wY0XjueL5BiqYplmRBMGaarZBEArWbjSM3QZU1EU8jKcADQHjStO9gchv7ZvcJviNmyryNJ4Fbfb/skxDU8bMn4kHOT4rQ9xrGUxn3xba8gcQvRV/bZQ3FmZmdovDZFIpFvn//WvfwXA4/Fw9dVX65ZroDvo8kf4xjGWDCWM9d/fw9/SaHYkIYQQQgjRCcHGKpKqQmLoUWZHEUL8F7n2FUII/dWFLdSQh8fj3e05RVEoz0knnQjb6uV9LT0FmrYB0KJ4dyta5bq1VYlN7VL460/aGzcD0GzN13Xc36t/ZqHzKtQ17+s6bn8TbtP2BA8qbsPmyMXPJbYPOCT8qSHjr//qEy6xvsvjjvu5peE6mur23pFHiIFmQBeK16xZw7JlywAYNWpUl8+PxWK88cYbABx77LG4XC4AEokEW7duZfPmzd2+CB/o7A4ng370b+rIp1StZd3jF5FIGrc/gRBCCCGE6Dl/qIPL/RczOfowZYfJ9iFC9BZy7SuEEPqLxZMEY1qb/R0tU//X7YmH+Nb1Q5zf/DuV0fq9Ksdwjo7ey5+8t+z2XLGtjfOsnzCx6U0TkgmjRJq2AtDuLNB13GxLiALFR4e/Ttdx+5v2uIUFyVFstI8wbI50r9bCPzMZMGT8tlWzd348ybKejZ93bysWIfqjAVcoDoVCrFu3jvvvv58ZM2YQj8cBuPbaa7s81vLly3deDI8dO5ZAIMC1115LXl4e5eXlDBkyBI/Hw7HHHsvs2bN1/L8YGHIKSgie8Qwb1GJ+4zuRe95fbXYkIYQQQgixD59vaCKpQk5BMYMK9L3bXwjRNXLtK4QQxvI313Gv7RFusj1PpmvP7fYtbq3wobZsSmW0fq8hpLJBLSGQM2a35wrVBv5o/wdnt//LhGTCKKpfW0UeTR+k67gxZy4AifYGXcftb6rTR3Fu7DYeLfi1YXNkZBcBkKW2k0wkdB/fUz8fgHpFe122b/hA9zmE6Kt02TSoM3sidZWiKDsvZHvq6aef5pJLLtnr8zfddBMXXND1FQ/ffvvtzo+TySSTJ09m3bp1uxwTi8WYNWsWH3/8MXfddRe//OUvuzzPQFY57hDeSn7Iyhe+ZuWcjRwwKIvTJpaYHUsIIYQQQuzBvDXaXk9HDJcisRBmkGtfIYRInfbmas62zaGVTCwWZY/HKDmDoR6cbdLiVE+NbVEA8jOduz2XmVsMQLbqR00mDdtPVaSWpV1b8atm6lsoTqTlgQ+UYJOu4/Y3gXAHAFkuu2FzZOVqq8Wtiorf34wnR7/V4+FgG8Niq0CB6qm3ULjgWkYFFxMJteu257UQfZkuPylVVTXkj9EmTJjAwoULueuuu1CUPf9Cty8tLS07P7777rtZt24dJ5xwAgsXLiQSidDQ0MDDDz+Mx+NBVVVuuummne269iUajRIIBHb5M5B9d0IZV82sBOC5V15j3bJ5JicSQgghhBD/S00mueqb83nB8VuOLQ6bHUcI8V/k2lcIIfQX8mn7DrcrmXs9Jq1gGADeSHVKMg0UxVvf4mfWVxijbtjtuex8rVDsVDpoD7SmOpowyIPpP2NK5O80jdJ5e5sM7QZXW7hZ33H7mUBEW9BnZKHY6UwjqGrbmwRa6nUde/2ST3AoCerJZeLxF1FPLmlKjDXz39Z1HiH6Kl1WFN9+++37fP6dd95h8eLFAIwePZqpU6dSWFgIQH19PYsWLWLlypUoisLkyZM56aST9Ii102mnncbkyZMBCIfDbNiwgRdffJHXXnuN888/nwcffJDvfOc7XR43GAzu/DgSiXDsscfy9ttv71xhnZ+fz5VXXsmYMWOYMWMGyWSSm2++mVNOOWWfF+d33XUXd9xxR5fz9Gc3HDcSy6bZXFP3G3yvZ9FU9Bl5RWVmxxJCCCGEENtVrfuaCrWOAqWJxCjj9q4SQuydXPsKIUTqxNq0QnHQ5tnrMdmlwwEoTNTJ6lYdjWiaxbn2L1gQP2C359LcmQRVF24lgq+xmkxvrgkJhd5q22I04iUvX98VxbZMbdWqKyYrivdlwtqHWOR8nRW+C4Bxhs0TUDJxEyHo07cVePuaTwGo8hxEocXC5tzDKWx+ncg378JR5+k6lxB9keGF4jvvvJPFixczfvx4HnvsMaZMmbLH4xYtWsQVV1zB4sWLOfnkk7ntttv0iAaA1+vF6/Xu/PuUKVM477zzePbZZ7nooos49dRTeeKJJ7j44ou7NK7L5drl73ffffce23AfdthhnHHGGbz88susWrWKFStWMG7c3l9Qb775Zq6//vqdfw8EApSVDeyiqNWicPn5Z1P/0EOUJ6tZ9cQ5ZN34KQ6na/8nCyGEEEIIw9V99TYVwFrXWMa4976yRghhHLn2FUKI1Olo01YgRvdRKC4sH0lSVUhXojQ1bCOvqDxV8fq19Jj2b+/w7rlo2Grx4lbraG+pxciilkgNVVWp8Wkdi4o9abqO7fRqi9ncHT5dx+1v7JFm8hU/aVZju8AGrVmQaCQaaNR1XGvTau2DisMASBvzHfjsdYY0z5WbeIRAp9bTe/Pxxx/zm9/8hhEjRjBv3ry9FolBu4CdO3cuw4YN44477mDWrFlGRgPgwgsv5OyzzyaZTHL11Vfv0k6rMzIz//MGWH5+PhMnTtzrsccff/zOjxctWrTPcZ1OJ1lZWbv8EZDlzYXz/kWbmsYBHd+y7NHLzI4khBBCCCG2c1V9BkB76REmJxFC/C+59hVCCP0lglqxMubw7vUYh9NFg5IHQNOW1amINSBkJrSW0mk5ey4Ut1uzAYi01qYskzCOv6WR+5QH+LXtOQqzHLqOnZ5TyvpkMZvUQl3H7W9sHdr2IJY0r6Hz/LXgDiZHHmZD5nTdxgxG41zQdg0zovczaPrZAIyYfhKNqpfFiWGs2rxNt7mE6KsMLRQ/9NBDKIrCTTfdhNvt3u/xbrebm266CVVV+ctf/mJktJ1OPfVUQGul9f7773fp3P++07m0tLTTxzY26ntHzEBSPmICG2c8RFJVmNryJgtevNfsSEIIIYQQA140EmJ4aDkABRNPNjmNEGJP5NpXCCH0pYS1YmXClb3P4xa7j+Bf8SPZFjZub8+BRE0myUlq//ZZuSV7PCbsyAEgFtB3n1Nhjubq9XzHuoAzbJ/jtOvSIHWnzPJxHBP7Ez+K/IxE0tjVsn2ZvaMNAGu619B5ElmlNOGhJZLUbczFW1qJJyHuGULJIO3mEleam1srX+Sqjmv5YENEt7mE6KsMLRTv2Jd4X62m/tf48eOB/d95rJf8/PydH2/ZsqVL544ePXrnx4lEYp/H/vfzNpu+P9AGmvFHncOCymsAmPTNXXz75XsmJxJCCCGEGNjWLZ6ltVTEy5AD995FSAhhHrn2FUIIfVkiWrGStJx9Hjdv6LX8Kn4ZKzr2XNQUXdMWaMWldACQXbDnf9MvSy7igtivWJ5xeCqjCYO0NVQB0GrN033sbLe2Qjmpgi8U0338/sKVaAfA7t73jTE9lZ2u3VDTquPnYv5GrfvD9KG5KIqy8/GjDiwG4OPVckOJEIYWine0s/L7/Z0+JxDQ2hi0trYakul/VVdX7/w4IyOjS+dWVFRQXq7tLbJ582ZUde93HW3YsGHnxyUl8othT03//h0syTwKu5Jg7YePUb19nwohhBBCCJF6bd98AMAmzzTZ30mIXkqufYUQQl9Pea7m4Mhf2Fp57j6PK8tJB2BrSygVsfo9X8NWANrUNNLcmXs8JpI/gS+SY6iK7fl50bdEW7RCcZtT//bQdqsF7/biZHNQCsV7k7a9UOzMMLZQPKZjBbfb/snQra/pNuZRX13Nw/YHOK5g1xrVUaMKUBQI1aymvl7a1IuBzdB3cYqLtbsyXnnllU6f8/LLLwMwaNCe95jQ20svvbTz47Fjx3b5/DPPPBPQCtwff/zxXo979dVXd3582GGHdXkesSvFYuHAK5/h7+lXcl34h1z+zGLCsX3f2S6EEEIIIYzxaaCUjxKTUEecaHYUIcReyLWvEELoqzGiUEsubm/BPo+ryE3HRZRw48YUJevf2ppqAPBZ9l6wyst0AtDUJoW//iDp0/aQjaUXGTL+Xyz3sdB5FbENcwwZvz9wq0EAXFm5hs5T3rGJS2wfMMT3hS7jtQdamRhdwonWRYwbsuuNBnkZTp7NeoRPnDeyZc5zuswnRF9laKH4hBNOQFVVHn30UV588cX9Hv/yyy/z6KOPoigKJ510Uo/mfvrpp4lE9t1f/oEHHuDdd98FYMiQIRx++K7tSGbPno2iKCiKwsUXX7zHMa699lpcLhcA119//c4V0f/tueeeY/bs2QCcfPLJu+zZJLovzZ3JqZf/hhy3i29qAvzi5eWoSf32LxBCCCGEEPvX2BblseZxXNZxI0NnXGB2HCEGHLn2FUIIc+xojbpjNeLejFS3sNp1Cb9tui4Vsfq9za4DOSr6Jx7K/fVejym2+DjP+gljG99OYTJhFFtQW+2ZzCo1ZPwcS5ACxUekVVaV7omqqqxKlrEqWUZ6Vv7+T+gBW4ZWiHbGOt+hdl82LJmFTUlSoxQyqGLkbs9bi7WbJ50bP9RlPiH6KkMLxb/61a/IysoimUxy/vnnc9ppp/H6669TXV1NR0cH8Xic6upqXn/9dU4//XTOPfdcEokEmZmZ3HzzzT2a+ze/+Q0lJSVcfvnlPPPMM3z++ecsX76cefPm8fDDD3PYYYdx/fXXA+BwOHjsscewWq1dnqe8vJw777wTgBUrVjB16lSeeuoplixZwqeffso111yz80I7KyuLBx54oEf/X2JXJd40/v69SbgtHRy56lYWPHub2ZGEEEIIIQaUeesbARhdnEVehtPkNEIMPHLtK4QQ5ris/RF+Zfs/8pS2fR5XUDYcgDx8hNr1KX4MZPUhlY1qMaGcA/d6THGylj/a/8EpbS+kMJkwSnq4DgB7tjGF4qhD22c8HmgwZPy+LtyR4ILYrzkxdjcZ+cZuK+LI1PahTovr81oZXvMpANs8B+3x+aIppwMwKrRUXp/FgGYzcvCSkhLeeustvvvd7xIIBHjrrbd466239nq8qqpkZmbyxhtv6LKXUUtLC48//jiPP/74Xo8pLS3lySef5Jhjjun2PD//+c9paWnh7rvvZs2aNfzwhz/c7ZiCggJef/11hg8f3u15xJ5NG5rLw1MaOGL5PJIbP2f5J+MYf9Q5ZscSQgghhBgQapZ+SAkWjhhRaXYUIQYsufYVQojUUpNJzkp+gM2WpMH5+30e68nJw48bD0Hqt6xmyOhpKUrZPzW2RwHI38cNiu4cbUtDr9qakkzCWBkdLQCk55UbMn6HKxfaQW2XQvGeBMJxAGwWhTR712827Io0j7ZiOSO5e+ea7shpWgSAMuSIPT4/eNRB1CgFFNPA0i/eYuJx39dlXiH6GkNXFAMcfvjhrFixgjPPPBOLxYKqqnv8Y7FYOOOMM/j666+ZMWNGj+f94IMPuO+++zjjjDMYN24chYWF2Gw2MjMzqays5Mwzz+Spp55izZo1HHvssT2e76677uLzzz/nwgsvZPDgwTidTjweD1OmTOG3v/0ta9eu5eCDD+7xPGLPDj/tMhbknIpFURk656dUrV1mdiQhhBBCiH4vmUhwXtVv+Nz1M072bDE7jhADklz7CpF6yz76FzV3jODbL98zO4owScDfgk3Rtj/Lytn3HsUADdZiAHzV6wzNNRBUbH2da20vcwB73/PZu33VYyZhIuFgqqIJA6iqynEd9zAl8ncyK425ySLp1r6HreEmQ8bv6wKRDgCy0uwoimLoXO5sbR/hLLWtx1tMBnzNVHZor7nlBx23x2MUi4WqPK0WFf/23R7NJ0RfZuiK4h3Kysp46aWXqK+v59NPP2XFihW0tGh3AmVnZzN27FiOPPJIior025B+5MiRjBw5cmeLre6YOXMmqqp2+viDDz5YLohNoigKE694jFV/Ws8BHd/Q+sIFBH46lyxvrtnRhBBCCCH6rU3fLKASPyHVyYhJPb/ZUwjRdXLtK0RqRcJBBn1+C4U0Uz/vr3DwiWZHEiZob63HA4RUJ+lp7v0eH0grgfZ1RBs3GB+unzuweRbn2RaxMDFxr8dkeXOJqVYcSgJ/Yw2uculy0Ve1BGNE49CIl8IcryFzWDK0Vaz2SLMh4/d1iS0LWOi8is0MAXp+0+G+7LjxxqV0EAq1kZ7h6fZYG5d8xARFZZsyiNLSvXe/co/9LnzyEkN8n5NMJLB0Y4sWIfq6lBSKdygsLOS8887jvPPOS+W0YoBwOF3kX/pv6h+ZQXmymuWPns+YG97Fakvpl7kQQgghxIDRsOw9KoG16ROZ4HSZHUcIIYQw3PLXH2QaWjFhZPsiopEgTtf+C4Wifwn6GgEIKJmkd+L4WFYFtIPSutnQXAOBu0P7/nN6B+31GMViwad4KKCFQHMthVIo7rNq/REA8jKcOGzGNEe1Z2mrWNNjLYaM39fF2pooUHz4lXbD53JneHbe5BFoaehRoXhVXZBEcjix/DHsa3frkdOOp+3jNPIUH2uWfsbIyUd1e04h+ipDW0/PmTOHOXPmEA6HO31OJBLZeZ4QXZVXVEbgtH8SUe2MDy9g4ZM3mB1JCCGEEKLfyqzWfmePlMtqYiGEEP1fONhG5ZrHdv49XYmyZv77JiYSZgn7tUJx0JrVqeOtuUMASGuvMixTKlStXcZXHzxragZPQivmuXOK93lcwJoNQKi1xvBMwjjh9fP4m/1BrnQa91rryi1lXbKEKnX/beQHoo6gttd3xJph+FyKxcIF9geYHHmYJktej8Z6rnkEZ8buoOGw3+3zOIfTxduFV/Kj2A281yDdScXAZOhSy5kzZ2KxWPj666858MADO3VOdXX1zvPi8biR8UQ/NXzC4Sze+nuGLr6ThzaX0LS8hu+O3/cvj0IIIYQQomtC7X5GRFaCAiWTTzY7jhBCCGG4f83fTHv8KE62f0WDdwKRps1sqg4zzuxgIuU62rS9TMO2zhWKneWT+PfSmWxTD+zTXy/l/5pBObAxt4Chk49P+fyJeByvGgAFvPn7WiMIQXsOJDYQ89WnKJ0wQrJuBSdbF7JUtRs2R/rgqcyM3YsbK98YNkvflQj5AOiwZ6ZkvoB7ME3t7fjCiW6P4Q918G1tAICDK/dfcHYd/CNm/Xs529b6ufakbk8rRJ9l6IpioEv7HOlxnhAAk797BU8f9BpfJkfz85eX802N3+xIQgghhBD9yrqFH+BQ4tQoBZRWjjU7jhBCCGGo9micv35eywPxs1h60puEjr2HH3b8gie3Fst7WANQvF1rfxy1ezt1fO6wKfwyfjmPtB9OItk3v16ikdDOj0MbvjQlg6+5DpuSJKkqePP33noaYHbRDzk/9mtWZhyconTCCEl/NQAx974/3z2Rm+EAIBhLEI51vzjZX6lhHwBxe+dujOkpb7r2+WgNxbo9xtJVa8hQQwzNd1OQtf8tkmaOKMCiwOq6Nra1hvZ7vBD9jeGF4q5KJpMAWGXTcNFDP/vOFI4YkU+kI8nvnn6DlvptZkcSQgghhOg3wqs+BGBr9nQUS6+7rBBCCCF09dS8TbSGOhia5+b0SWUcOiwPp81CtS/M2nrj920UvcuCnFOZHvkLs8p/1qnjB3nSsFsVOhIqtf7Ob9HXm9RsWLHz47Zg0JQM/kataOhTMrHZHfs8NlQ4kS+To9kWM75drjCOvV1rHa5mlRg2R4bTtnP/46b2qGHz9FkRbQFW0tn9/YK74ujkF9xu+ydpVZ91ewzXgodY5ryMX7rf6dTx2W4HpxcHuNH2b9Z/am57fSHM0Ove0dmyZQsAHk9qXnhE/2W1KPzlvImc6V3Ho9FfUPfEeXTE5Ie9EEIIIYQe7gqfxpWxa4lP+IHZUYQQQghD+VsamTj3Ug6xrORnRw/DZrWQ5rBy6LA8imli+aLZZkcUKdYUUagjF5u3c8Urq0VhqNfKMGUbNdV9cyFDy+b/FIrn2A8zJUOwWSsU+y05+z02z+0EpPDX16VFtNbhtux9txrvCUVReNb+RxY5f0xo8yLD5umrLDGthbPqSs2K4gmxpVxi+wB349Juj5HfvBCropJXNrLT55ztXcXVtjfIWfvvbs8rRF+l6x7FVVVVe3y8traWjIx9370VjUbZsGEDt956K4qiMHr0aD2jiQHKk27nmtOOwPL8HzkwtoIFj13JtKufMjuWEEIIIUSfVu0Ls7xJYaVlGndPmWl2HCGEEMJQ3776Bw5jOaWuVsrH3bjz8Us8Szjc9UtWf30gfPe7JiYUqbajJeqOFqmdcW/iXsY5F7FwzW9gzAiDkhknVrcagBfiM1kSKjAlw/q08fw0eh8Hl7v5w36OLbM1c771Y8rqc4GJqYgnDODtaADAnV9h6Dw51nbyVT/VrbWGztMXNZHNqmQZiQzjVnX/t2RaNrSCEm7p1vm+pjoqE5sAqDio83upF087Azb8mVHh5bT5W8j07P+GFCH6C10LxUOGDNntMVVVOe6447o81g9+ICsThD4Gj5rEssMfYNzcHzOt6VUWvjyWqWddb3YsIYQQQog+a87aRgAmlHnxpNlNTiOEEEIYp7WxlnFb/wUKtE67kcH/tVXaiCnHwvJfMiK2itbGGrLzi01MKlLp0Pr/Y7ythor45cDu74fuSTijDCKLSDRvNDacQWZzEB93hFirlrKtxZw9POvDsEkdxMSc/Resiju2cZf9CTb5BwO/Njyb0F8ykSQ/2QwKeAoHGzpX2JYNcejw1xs6T1/0f+4L+bz2JB6snJCS+ZR0rUBri7Z26/yNSz5kErDZUsbgorJOn1c+fDxblWLKqGHll28y6YSLuzW/EH2Rrq2nVVXd5c/eHt/XH6fTyc9//nN++MMf6hlNDHATjjmfBYOv1D5e8TtWL/jQ5ERCCCGEEH2XZ/49/Mz6CieVdZgdRQghBqxls55n/vO/NztGv7fmld/iViKst1Yy4dgLd3musLSSDdahWBSV9Z+/ZlJCYYYp7Z9yme1d8pONnT5HzR4MgCOw546Mvd0n/mKeSJxIEgsz298hFo2kPENjm9ZGOj/Tud9j3TmDAMhKdq/YJMzX2lIPqCRVhfxiY1cUR125AMTbGgydpy8KhOMAKbtB2OLWPheOmL9b53es1/Y2rs+Z0rUTFYXqghkAJFa92625heirdF1R/NRTu7b0veSSS1AUhd/+9reUlOz9Ti9FUXC5XAwaNIiJEyfut021EN0x/aI/sOT+bzmo/TPy3ruM+pJPKCytNDuWEEIIIUSfEu+IcVjLq5xkD7K66HtmxxFCiAFrwjztZuiab4+i+MCDTU7TPzXVbmF87UugQPDQX6BYdl9v0TDoSCq3bcS6/gPgJ6kPKUyRkdD27HRl5XX6HGd+JayDrHDf26O4I5FkU1MQFYXH7PeTrkTZtvWHlA4bk9IclVtf5TrbBso4Bzhgn8d68rQV/l41QCIex2rT9W1wkQI10XROif6TERkxPnC4DJ0rnpYHPlCCTYbO0xcFItrNwVlpqfkecmRor6uuePcKxQXN2j7T9sojunxu5rjvwkfPM8z/pbxuiAFF16/0iy66aJe/X3LJJQCcdtppHHjggXpOJUSXKRYLo654lo0PHMHQ5Gbeee42jr7hGVx26/5PFkIIIYQQAKxfPpdRBAngZtj4rl98CyGE6Ln2QCs7brEPNFUjDY+NseHVO5mmxFhjG8W4mefs8ZjcSafAticY0baQWDSCw2lsMUP0DllqABRwezu/V6+nVNuXuDDR9/ZA3bZ1C8fzJVscFTRYCxic3EprzbqUF4rHtX7EGNsyliSm7fdYb14RSVXBqqg0N9eRW1iagoRCT7X+MCoWXN5Cw+dS3PkA2CJSKP5fTwavRnUkscZeAozft3fHDTjuRNcLxS0N1QxJbgFgcBf2J95hxJRj8H/kJpsAq5d8wqhpXd9SVYi+SNfW0//r008/5ZNPPtnj3sVCmMGd6cF14b/5J9/lWt853Pzqil3apAshhBBCiH1r/fp9ANZnHITNLvsTCyGEGVrqtuz8OBhsMzFJ/1W3dQMTG14HoGPGr/a4mhhg2PjDacJLhhJm7cIPUphQmCUaCZGuaC2QM3M6X8AqqhgFQBZB/C2db1ndG/jWzOFvjoe43/Eofqd2a0qoPvV7LWfEmwFweQft91ib3YFfyQQg0Nz3ivMCav1ae/NBnjTD57JlaTd9OKPNhs/Vl6jJJBVqDcMsNWSku1MyZ9r2G3Ay1a7/frN4W5BfdVzKy47TyCno+m10doeTdVkH06JmsHrd2i6fL0RfZWiheMaMGcyYMYO0NONfzIXorOIhoxh+4YMkLXZeW1rNP+ZuMjuSEEIIIUSfkV07D4COIUeZnEQIIQaubUoJ85Na29V4QPZTNMJfFrVxY8cVfJp2HKMP/e5ej7NYrWz0HgJA+4q3UxVPmCjQon3PJVSFTE/nV9elu7NowgtAw5ZVRkQzTKTmWwB87qFE3NrK3GTr5pTn8G7fbzgjb+9bHP43v8UDQLC5xrBMwjhFa/+Pv9kfZKa6wPC57NllrE2WsE3NN3yuviQUDGBTkgBkeHJTMmdGUSVHR+/lyMh9dCSSXTp3blWMfyWOZuWYX3R7/sbDf8vk6CP8tWFst8cQoq8xtFDcGcuXL+fWW2/lxz/+Mffccw/V1dVmRxIDwCGVedx68gFYSWD58FesmP2K2ZGEEEIIIXo9f2sTw2KrASiffLLJaYQQYuCqb4+yIamtlEm2962ViX1BVXOIfy+u4c3kobjPeXSvq4l3iE/6IVfFfsqd7adK17IBoL21HoCAkonF2rXtzD7IOJU/dZzNlmi6EdEMY29ZB0AidwRqdgUAjkBVSjNEIyG8tAPgLehcG+mgTSvkR3x1huUSxslt+YqTrQsZbKk3fC5b5REcF7uXW5NXGD5XX9Lu11ZYx1QrrvSM/RytD0+Gm42U0IwHX6ijS+d+uVHLO31o94vah4wZjsViZX1DO1uag90eR4i+xNBC8aJFi5g6dSqHHHIIPp9vt+cfffRRDjroIP7whz/w2GOPcfPNN3PggQfy0UcfGRlLCAAuOmQwDw5ewKW296iYfQ3b1q80O5IQQgghRK+2ceE72JQkWyylDKoYYXYcIYQYsOr8Ud5JTuP2jotYmrb/vTpF1/zl49XEkyqHD89j6pD9rxgdP+1IZimH8G2LwoZGeVO5vwv5tD1M2yyZXT53SdkP+WvidNaEvTqnMlZ2SGsznVY8Gmd+JQCZkdSu0m1t1OaLqVaysju36vP9wh9xfuzXrHZPMTKaMEh6WCvw27PLDJ8rL8MJQEswSjIpN/zsEAq0ANCuuPd705RebFYLWS5tiyNfKNbp85rqa5je/BqVlmqmdeJn99540uzbf/arzPl6XbfHEaIvMfS7+6233mLx4sVkZWXh9Xp3eW7Tpk389Kc/JZlMoqrqzj9tbW2ce+65NDbKHbHCWIqicNzFv2a17QCyCJL413m0+VvMjiWEEEII0WttrqoioKZTm3uw2VGEEGJAK974EsdbFrFGLWNFcqjZcfqVqrXLuO6bs/i+9SNuOLZzN0W5nTamV2qrlz5eZfzKN2GubZljmR75C3dn39nlc8tytJXEVc0hvWMZJhGPUxrfBkD+0HFkDRqmfRxP7SrdQKPWhbJV8aJYOreSuy3/IL5MjqY61rdWcAuNN67VB9x55YbPleN2AJBUwRfu2irW/izSpr1XHlRSs5p4hx/aP+Q3tqcJ167u9DlbFr/L7+xP8Vja38ne/vnsru8XbuEL5zVM+vKnPRpHiL7C0ELx7NmzURSFE044Ybfn/va3v9HR0UFaWhqvvvoqfr+fF198kbS0NPx+P4888oiR0YQAwOlKJ++H/6aBHCqSW1n/6PdIJhJmxxJCCCGE6HVUVeVPzYcyMfooscNvNjuOEEIMaGXNn3OR7SNGKFtpDnZ+tY3Yv8a37qBYaeYsz2omlGd3+ryTh1i5xvoqgxfcZmA60Ru0RKCOXKJZFV0+d3C2nUqlGkfdEgOSGaN282qcSgdh1UFR+QgKKkbx847L+XHspwQjqSuohVq0FcUBW+dXCuZnaqtEm9ujhmQSxkkmEuQltdX73kFDDJ/PbrXwiuu3LHJeSWCrdJ3cIdqu7Qsetqa2UHx8ci4X2z4k0bCm0+fEN84BoDF3ao/nHz9mDMVKCyOiK/G3NvV4PCF6O0MLxTv2Gx43btxuz73xxhsoisIVV1zBaaedRmZmJmeddRZXXnklqqry/vvvGxlNiJ3yiivwnfIUUdXOxNAXLHi6+5vdCyGEEEL0V5uaglT7wlitdiaP6vobo0IIIfTjjjYAUKo0UeFbYHKa/mPTNwuYGPgUgMwTulbwPXxIBjfYX+bo4Lv4m2VVcX/Wur0Vqje96yvWDkiu52Pnz7mq6fd6xzJM06avAai2lWG12cjM8vKR81jmJw9kqy+cshxr3JOZGb2PZwp+2elzypVGLrB+TGWdvM/c17Q0VuNQEiRVhdwi41cUA+RZ2shXArQ3p7atem8WjMOqZBmNjs7tC66XiN0DQLyt811nB7UsBsA5fEaP5y8ZOpotljLsSoJ1X7zW4/GE6O0MLRTvaB+dm7vr5uHV1dVs2LABgHPOOWeX54477jgAVq/ufFsBIXpqxKSZfD3xDgAO3voPvnr/nyYnEkIIIYToXb74dgugMnlwNukOm9lxhBBiQPPEmwG43PYOvw7fa3Ka/sP37p1YFJWvMmZQOe6QLp1bPHgkmywV2JQk67543ZiAolcYtPVdbrE9y8SOpV0+N7d8JAAFahOxaETvaIb4Sh3O5bHr+LTokp2PlWVrrZy3tqSuUFwfgs3qIDpyR3b6nLL4Zv5gf4KjfC8ZmEwYobV2MwBNSjZ2hzMlcwZtWheJiE9u9tlhfeZ0Tozdzb9Kbk3pvB0OLwCJYOe2iWys2Uy5Wk1SVRh60HG6ZKgtnAmAsuY9XcYTojcztFAci2l32AWDwV0enzt3LgDp6elMmTJll+cKCwsBaGtrMzKaELuZctpPmF9wHkHVyVNfbGFVbcDsSEIIIYQQvcaY+dcz13EtF+SuMzuKEEIMaMlEghz1P2+cemmnIyZtVXtq3bK5TAzO01avnXx7t8aoK9q+imntBzomE71NSfOX/Mj2HpWxtV0+N6+glKDqxKqo1Fd1vqWqmVa22vkwOYVo5X+2FpzuruE86ydEN36RshyNbdrr3I520p2R5tXeZ86M+4yIJAzkb64jplppteWnbM6IQ2trHg9IoXiHwPb9mrPSUnujcMK5feuHUHOnjt+yRPu5u9E2FE+OPl8z3gmnADA8MF9+zxL9nqGF4vx87Ztyx+rhHT766CMApk+fjtVq3eW5SES7m87r9RoZTYg9mnzZX7it6O+8FTuIy59dTKvs9SSEEEIIQSwaYURoKWWWRg4YVml2HCGEGNBam2pxKIldHvO3yJvaPRX64E4AvvIcQ8UBB3VrjOztbyqPaJM3lfsze8wHgMXd+b1yd1AsFuqtgwBo3dY3br5b19AOwLCCzJ2PHR2bzR/t/yCv6t2U5Ri17SWus73EcKo6fU5mXgkA2aoPNZk0KpowwMq0KYyM/pNHylLXNaPDpXVFVYOdb3fc3wW270Oe5bKndF41XSsUWyKtnTo+sVFbmNiU1/P9iXcYftBRtJJFFkHWLpql27hC9EaGFoonT56Mqqo88cQTJLf/MG5ububVV19FURSOPvro3c7ZUVTesbJYiFSy2R3c8oNTKM9JZ2tLmF8/+xHxDikWCyGEEGJgW7f4Y9KVKE14GTJ6mtlxhBBiQPPVa0WSJry0kAVAoKnWzEh93srlixgfXkhctVB0SvdWEwMMn3TkzjeV1y2WN5X7K1eHDwB7Ru6+D9wLv6sYgHDDer0iGSaZSDCz8TmOsyxieN5/9mRWcgYD4GrfmrIsk/wf8jPba5Qmqzt9Tna+9m/tVDpoD3Su4CR6h1p/BBUL2TqtDu2MZLo2lyXUlLI5e7vDNz7ALMeNTPWntv2yJV17fd1xY87+FPm0rQBcI2bqlsFqs7HeczAAbV+/pdu4QvRGhhaKf/CDHwBaq+nDDjuMG2+8kUMOOQS/34/NZuN73/vebud88YXWsqSyUlYqCHNkux08/oPJHOpYxx21V7L48Z+YHUkIIYQQwlSBb94HYHPWFCz/0xFICCFEarU3aYUZnzWXgMULQLClzsREfd9dixJ8J/o73i7+KaXDxnZ7nP9+Uznw9dt6xRO9THpC26rMkdW9AlY0swIAtWWTbpmMUrd1PTdYnuev9oeoyM3Y+Xha/lAAPJGalGXJjGst99OzB3X6nDR3JkHVBYCvKXVZRc/V+rWuo8VeV8rmtGRo39OOSOfaHQ8EGZFahllqyLB0pHReW0YeAK4O/36PrfWHOTn8Gy7p+AVDJ+uzP/FOY8/mifiJPBOYqO+4QvQyhhaKTz/9dM466yxUVWX+/Pk88MADrFuntVX5xS9+QVlZ2S7HJxKJnauNDzvsMCOjCbFPI4syueHQPPKVANMbXmThaw+ZHUkIIYQQwjT59Z9rHwzbvSOQEEKI1PrWPZ3xkcd4bNCdBG1aa8aoXwrF3fXlhmY+X9/MGkslk8/5ZY/Hs4w8kYCaRlVzSId0ojfKTGqF4nRv9wrFO1fjtnW+hbJZGjd+DUC1tRSb/T8rirNLhgFQmKhLSUtnVVXJTmorgne0k+4sn8UDQLBZCsV9yXeq7uFv9gcZoabuhgprdjlrkqXUql1vK99fOeJtAFjTvSmdNz54JkdH7+XXzpv3e+yXG5ppJ52WQTPI8uj7uTvg8NO5W72Id1tL2dDYruvYQvQmhhaKAV544QUeeughDj/8cIYNG8YRRxzBE088wW9/+9s9HltfX4+qqpx88slGRxNinyYd/32+LLscgAnL7mDN4o9NTiSEEEIIkXrN9dsYltC2hxk89TsmpxFCCFEXiOInA2tOBRGHViiOt8l+it2hJpM8+f6XAJw3pZzS7PQejzl8xjlM63iMX7Sdw6amYI/HE72LmkySpWrFggxvQbfGsFQcwn0dZ/Eyx+oZzRChmm8AaEkfssvjBeXDAUhXorSmoPV9sN2PW9H2/fYWlHbp3DarVjgKt0qL/r5kbHgRJ1sXUpCmpm7S4cdyfOwe7rZcmro5eznX9kKxPSM7pfNmenPZoJZQFdn/ivL5G7UV4NOHdm87gH3JcNqYNlR7DZn1bb3u4wvRWxheKLZYLFx99dV89tlnrFmzhtmzZ3PJJZfs8djvfe97JJNJkskkBxxwgNHRhNivaRf/kaXph+JQ4uS8fSmNNZvNjiSEEEIIkVKbFr4DwAbrUPKKyvZztBBCCKM1tGntOIuyXKwqOJnbOi5itWu8yan6phVzXuOvDRdxi+N5rj5qmC5jZrndTBqqFRA/XiVvKvc3AX8LNkVbQZuV271CcW7lJP6SOINX2g5EVVNYBOsGa9MaAGI5I3Z53Oly04BWPGnats7wHK312wAIqU7cmd4unftmwRWcF7uFtenSOravSMbj5Klaq3Fv0eCUzZub4QSgqS2asjl7u7SkdmOM053aVdbZbjsAraGO/b5Onrzq5/zS9jyHlyqGZDl2ZA6HWlbgXPyoIeML0RsYXijW0+bNmznqqKM4+mhpeSdSw2K1MvzK/2OzpZx8Wml58hwiYbkjWAghhBADx2ct2fwzfiybSk4xO4oQQghgctUT3GF7ipFspqXkSJ5JHM9qtcLsWH2OmkySNu8unEqc0YMyKMzSbx/Mo0YVAirLV67UbUzRO/gSTqZF/sqpyXtxutzdGqM0Ox1FgXBHgsb23l2QymrfCICj6MDdnmuxFwHQVrve8BxtTdXanJaur2pszZvM/OSB1MbS9I4lDNLSUI1dSZBQFfKKylM2b26G1l49GEsQjiVSNm9v5la198HTslK7ojg7zcZ1tpe5zfIkgYBvr8fVblnDjMQCLrO+w4TBhYZkObYsyf857uLCwGP4mmSrD9E/9alCcTAYZPbs2cyePdvsKGIAycjKxv79F/DjZmR8DXOe/FWvv+NTCCGEEEIPqqrywrYcbo9fQvoR15gdRwghBDChbQ4X2T5ikNW3803tpvaYyan6nuUfv8Dw+DqCqpMRZ9yi69jHDnUy23E999ddTMDXpOvYwlyt4QT15NCU3v0V6A6bhcmZfmZallG7NXX7r3aVmkxS0rEFgNwhY3d7/uNBV3B+7Ncsc0wyPEukVdtfuM3W9VWN+TtfJ3t3UV78R0ud9n3RpOTssje20TKdNt5y3MJi55X4UnADRG+nJpNkbi8Uu7PyUjq3y2HnMus7XGT7iPbmvbeN37b0QwA22IeTYVAxe1DFSDZZBmNVVNZ/8Zohcwhhtj5VKBbCLCVDR1N11N94K3EwP9t6BE9/sdnsSEIIIYQQhltd10ZjW5Q0u5WDBqf2LnIhhBB7lp3QCo8ZeeUUODqYrKxmUOsik1P1LclEgqz5dwPwdcm55BZ2bc/T/SkvLkax2bErCdZ98YauYwtztYa0mzK86fYejfMbHuFpxz3E1n2qRyxDNNRuJlMJE1ctFA8ds9vz0bJD+DI5mvVtPfu36IxvMg5hRvR+Xhz08y6fO9jawPess6io/dCAZMIIwUbtBgWfLT+l8yqKQr4lQJ4SoK1ZVo4GQ0HWqaVUq7lkeFPbehogoGRqOXwNez9o0zwAWgqmGZqlbtCRAFjWvmfoPEKYRQrFQnTS2CNOp/64vxPGxe/eWcXn6+WuYCGEEEL0b2sXfsBUZRWHDsnCabOaHUcIIQa8WDRCDgEAcorKKY1t4GXnnVzhf9DcYH3M0g+eYWhiM21qGgeeqe9q4h1qCmYAoK6WN5X7E8u2Bdxie5aTmNejcYLuMgDiTRv1iGWINW1pHBe9m1vTf43DuXtr9rJsrZXzttaQ4VnqQ7BFLSKRO2L/B/+Piuh6fm9/ksNaXjEgmTBCtHkrAEGXMa2E96XN6gUg1Lr3VawDRSBu44TY3RwZ/xvOtIyUz99u9QAQ9jfu8Xk1maTMvwSAjJFHGpolZ6K2DdOItoXEohFD5xLCDFIoFqILLj1sCGdMLCGRTLLw/26neuMqsyP1epFwkG0bvqGprsrsKEIIIYToospv/8aLzt9yqWu22VGEEEIAzXXaKquYasObW0hGjrZHqDfpNzNWn5KIx8lbfB8AK8u/jyfXmEKEZ/x3ARgW+JJEPG7IHCL1nPXL+ZHtPabH5vdonIRX21fcFtiiRyxDrGuKsFYto6V45h6fH5wR51zrpxxS/y/DszS2aW2j8zOcXT43LVt7ncyIt+qaSRgnEgoQU63E3INSPnfIrnVR6vDXp3zu3iYQ6QAgK82Goigpnz9sywKgo23Pi7Vqt6yhiEY6VCuVBx1taJbhE2fQhJcMJcyaBR8YOpcQZrCZHUCIvkRRFP5wxlimbn6E88LPs+m5uQSvm4M702t2tJRTk0l8LQ201G6hvXELkZZt1CSymW89iLpAhDZ/M0/6f4SXdkqBsOpgw5lvUDnuELOjCyGEEKITwsE2RkRWgALFB51kdhwhhBCAv2Erg4AmSw7FFgtZeSUAZChhIuEgrjS3uQH7gM/mfcr0RAN+xc3oM282bJ4Rk4/G/4EbL+2sXvIJo6YdZ9hcIoVCLQDEnT3bksORXwmbIDO0TY9Uhljf0AbA8ILMPT5flh7nbvvjxKJWEvH7sNqMe5t5XM0LlNiqqeRiYHiXznXnaMVGryqF4r7i5fTzuCR6OLcdMIzpKZ475syFCMTb9tHueIAIhLWbnLJcxreX35OowwtRiLc37/H56qUfUgxscIxkVIbH0CwWq5WN2YeS1/oOwRVvwRGnGjqfEKkmhWIhushlt3LU939J0+PvMSS5ha8euZAJ17+Oxdp/2jHGohGaarfgb6iiMaKwVhlKfSBCs8/PjzbfiCfeSG6yhWylg/++NPogMZl/dxRs/5uKyxkFBRKqQpoSw/r6FUSGz5c3L4QQQog+YN3CDxinxKkjn/Lh48yOI4QQAgg1ae04/bY8ioEsTw4x1YpDSeBrqqWobJi5AXu5jkSSOxbZaI8+yG1T4VRvrmFz2ewO1mUezOS2WbQuewukUNwvKBGt2Kim9axQnFWstVDO66jpcSajjNn4BD+ydjA267I9Pp9fPGTn609dzSaKyrtWwO2Kqf4PGW5bxzJO7PK53nzthppM5IaavqLOH0HFQmGuN+VzJ9JzwQ9KSLYctG2ezUeOW9gcHw3MTPn8cacX2oDQngvFVfXNDFGzaDV4f+Id7AecBF+8g6v5G1RVNWWVtRBGkUKxEN1QUDKE1Sc/Qdbb5zIpOIcvn/kVB19yt9mx9ktNJgn4W2jy+anuyKIuEKGhtY0pq+/GGaonI9ZAdqKJXPwUA8XA1sRB/K7jhh0j8AfnGlxKB2z/WdhCFq3WPNod+aieCVw/fARFWS4KPS5qkrPILSghEY+R/PshDE5WMf/p65n+40dN+hcQQgghRGeFVn0IwJbs6RRZZMcaIYToDWI+bc/EsDMfAMViwad4KKCFtmYpFO/Pq19tY0tziFx3PsecbOx+hgCMPB4Wz2JQ/Wzj5xIpYYtuX5WantOjcQrKDwAgDx+hdj/pBq+G6yo1meQ77S/hsQdZn3HhHo+x2mzUWfIpUeto3rbO0EJxVkJbye3OKe76ud7cnQVtf2MNLgNzCn3U+rU9YIs8u++NbTi3tgDGFpZCcSJQx3BLNWGKTJlfdWmvs0q4ZffnVJV7W2dwQ3Qqzx8+ISV5Rh56CqfOaWN5vIwP6tsZWbTnbgtC9EVSKBaim0ZNOYaFW29j6te3cfCWR1j64VgmHvd90/LE4wmagh3U+sPU+4N4Vz6DGqjFHqwlLdJAZkcjeclmPEqUBYmDuPy/ir+rnO+SpsR2GS+m2miy5GDJyOc7ZYMoynJR5HGxPPhnPNl5ZBVUkDuonBxXOjsuj8YDJ+wySv7Oj5YfcTd5c65gev0LrJx7EmMOlxYdQgghRG9W1PQFAPYRxu73JIQQovM+9ZzGFZFKLhw3iEnbH2uzeilItBBqrTM1W28XjYSY/dGbwBB+PLMSt9P4t8SGH3I6/1rwPh/FJnFnc5CyXFnJ2Nc5Yj4ArO6erUb35OThx42HIPVbVjNkdGpWxHVWU8M28gmSVBVKK8fu9bgWRzEl0TqC9RsMy5JMJMhWfaBA1vbVwV3x3zfUBJprKZRCca+WiMd5KPRL6uxeSlxTUx/AW8bqZBl1yZ51DegPEiEfAB02cwqim4ecxy0bRjHRO4r/fYXc0hyiLhDBbrUwYWhq9rJOd2eRU3kQrGlk1qp6KRSLfkUKxUL0wNQzfsaCmuVMa3qFEZ/fwOayUQw+YLLu8wSjcer8YcJrZxNu3krcV42lrRb79lXA3ngTXyUqubLjuu1nqKxyPrBb8XfHKmCvNcLInEwKPS6KspzMb/8J6e5MXLklZOSVk11UQXbeIIotFoqBY3YZZGi3/h/GH3UeC755l2TDOu6eG+afB3XgSTdnjwshhBBC7Fvd1vUMTm4loSpUTvuO2XGEEEJs19AWw08Gmbn/KZYE7dmQgKhf9lPcl2Vv/pWHY7/nnfSZHD391ZTM6cnJ543Sn7NgUwsfr27g4kOHpGReYZy0eAAAR2Zej8d61n0J61uTnBrNord9ZdSvX04+UGMppDQ9Y6/HhdylEP2KRMtmw7L4WxvJVhIAZHejUAwQsGZvv6Gm97b6FpqWhm0cZFlLXLWg5KS+WBsdcQonzCtilC2TvnAVtOjVP5MItTL9+7/RfWw14gcg7sjSfezOcOUUs0FtpCjq2O25RWu3AioTy3JIc6RuO8hjDizk0zWNfPJtLT85Urq4iP5DCsVC9NCkyx/mmz+tY3j0G55+/T2urRiHN333H2B7kkwkaWmsxle/hfbGrURatqH6a7AG60gL17FeLea22A9oi8QBlW+dl5KuRPc4VrHiwWpRKMh0UpjlYmHsRFwOO2pmMTZvCWm5ZWQVlpM3aDBT3Zl8sMvZ43v6z9ApYy/9Oyf/dQGbWiLc+sZKHjp/YkrmFUIIIUTXbFn0LkXAevtIRubk7/d4IYQQqVG3vR1nYdZ/2nEuyT2VlzePYbRzJCasveoznJs+BiB38Dhc9tS9qXz0AQVSKO5HMhJaodiV1fNC8aqSM3mnuZYxQScpaITeJcHqbwBoShtC6T6OS3rKoQXs/i2GZfE1bCMbaCWTbGf3WhG/mHsVy7f6OM81JkXvgInuaqnZSD7QrGRTaEt96SIvQ3tPtzkY28+R5lu9aBZTvr4NgA0rTqBy7HRdx1e2F4qTTnMKxTveX28Nduz23JAvf8UC5xIWem4CDk5ZpqNH5OKy/52ZDctprp9PbuG+XiGF6DukUCxED9kdToove5FrHn+ND/wVbHh+KU9dPIV4LExz7Wb89VWEtq8CJlBLbcLDs9bTqA9EaWgLs9x2CXl7Kf6SbKEtFgfA7bCx0jaadFuSsKuQuLsIi6cYR3YpGfllFBcNYW1hGVbL9mXDHJqaf4AuSE/P4P7zJnHWI1/y5vIaThxq48Rpe29hJIQQQghzPBs+mN9Ff8f3x+Qz0uwwQgghdjq38UFOtMUps94GaCvrtg06lmc2DOMKtXsr7QaK7Og2ANyD9e8Cti9HjSzgg/fe4LDN/6Y9MIKMLGln2pedm7wTV4efx0p6/l5GeU46oLVQ7XUa1wAQ9uy7TbMjbwhsgoxwtWFR2pu1sX2WbLr73dOcN4UFVduYETNhz1vRJcGmKgBa7QUUmjB/XoYTgJZgjGRSxbLzfdbeJZlIYH3/lzv/3rjsXd0LxZaoVijGZc4e6vlKgOtsL+MJWIDDdz6uJpNUtH1FvuJjSHlqC7VF2RmMd9SSm2hj4Revknv6T1M6vxBGkUKxEDrIzh/ETy/+HnMe/pK565qou3MEpUojJey4dP+PFcnBfBWbufPvNdZcvEqIVmse7c4CYmkFqJklWL3FOPMrmTX8MAqzXGS67PzvDsB90cTybH42owzP3Ds45L0vqCueQ1GZtOoQQggheotEUmXu+lb86lCGTUnd3dlCCCH279jYp7htEarSbtv5WO721U9N7b1/9ZNZEvE4gxJ1oEBu+aiUzl2Z7+bPrscoVWtZ+uXRTDz+opTOL/QTjSfYEvMAHrweb4/HG5YZ5wjLcrKrNwBjejyenjIC6wGwFe77+8U5fCbnf/5rQvZy3jAoS6S1FoCgPafbY+xcJSqvk71erGUrAEGnGWViyHZZed/xS/IVH/7mRWTnp2b/265a/PpDTE1o36dfJ4fwhc+LvmVisHW0AWBJ8+o8cudk26L8zPYqobhzl8e3rv+aclqJqnaGTToq5bkai49i2NYN2Ne/D0ihWPQP/bZQHAgEePfdd1m0aBGLFy+murqaxsZGwuEwXq+XAw88kJNOOolLL72U3Nxc3ecPhUKMGTOGTZs2AVBRUcHmzZt1n0f0HqOLPfzp7PFc/fxXRFUbKBBWHTRbcvHb87RVwOmFdGQP4+8HTqIwy8Ugj4v8jBXYbTZ63rSo7/jxkSPYtGgLnniQrc9dSsEvPsFiTV3rLyGEEELs3YpqP/5wB5kuG+NLvWbHEULsh1z7DhztgVYyFK31dG5Rxc7HBzmiTFZWk93cQKq2Fepr6rdtoFiJE1NtFJZWpnRuxWJhW/4MShteoGPV+yCF4j7LF9Lan1oUyHT1/C3VkWziGcfdVDUV09uKDYUxbVWnt2LfK6cHFZfxZXI0SrtWSHfa9H9vZ0XmDG6IPsBxQ/O6XU4faqnj+9aPKKkZDByoYzqhO7+2grzDbU6B1uGwM8jSgocgW5pqemWh2N/axPAV9wPwdvFPuXrjdBx1Fn4cS+i6X69fTadGzUFxm7MVUUZOEQDpSpRIOIgrzQ1A7bKPKAfWOw9g9PbHUil/8mmw9XFGti/eJZcQfVmfKhRnZ2fzgx/8AEXZf8uHhQsXcv755+/xucbGRj777DM+++wz7r33Xp577jmOP/54XbPedtttOy+UxcBx8rhBjBo0A1pfxp9XQJY3j1KLZZ/7uQxEdoeTtHP/Qfi5YxgTXcb8f/+B6RfcanYsIYQQQgC+2X/jHtsCthSfic1qMTuOEGI/5Np34Giu3UIGEFDTycr8TxvIIeEVvOy8k3XNw4FLTMvXmzVXraYYqLUWUWHCnpcZ406GWS9Q6fucZCIhN0r3UW0Nm7nF9iw+eyEWy8k9Hi+3VNvgoyhZTyIex2rC1+aetARjHB65n0qlhn+PmLTPY3PcDtIdVkKxBNWtYYbmZ+iepy4MVWoh5HR/j+/K2GrOtT/FiuaJwLW6ZRP6cwRrtA885m2n4Ld48SSDtLfUmZZhX954923OVKNssZZx/MW3UHL/51T7wizY1MzMkQW6zfMn1zV80xLgqWFTdBuzK7KysulQrdiVBP6WelwlQwGwVc0DIFBkTveryrGH0PBaDgVKC8vnv8v4I882JYcQejLlnZ94PE5jYyONjY3E4/FOn1dcXMzTTz/NU0891anjy8rK+MEPfsCf//xnXn31Vb788ks+//xz/v3vf3P22WdjtVppamrilFNOYfny5d3939nN0qVLefDBB3G5XGRmZuo2rugbKvMzqBwxBk9OAYpF3lzdm7Lh4/l69M8BmLjmz2z6drHJiYQQQggBUFj1NufYPuOI7CazowghOkmufQeGtsbt+zZad10ZnubR3hTOjPtSHanPCNWtBaDVac5t3COmHEebmkYuftYt/cyUDKLnovUb+JHtPc5RPtJlvIKSocRUKw4lQUP1Bl3G1MP6hnbCuGj1jMbt3nfhV1EUzslYzg22F2nesMSQPI1tUQDyM537OXLvnB6tjXFGR6sumYRxoh1x7fsip8y0DO02bTfsiL/3FYrX1rdxxzcFHBm9n+bj/4bd4eTw4XkMopkNOv98CUS0LgqeNLuu43aWYrEQULTXoPaWRkDbn3hw+1It1wFHmpZrU662Z3Jk5dumZBBCbym7Ve3bb7/lkUceYdasWaxduxZVVQHtF4rhw4dzzDHHcMUVVzBmjD57chx55JFUVVXt9flzzjmH119/ndNPP51YLMYdd9zBq6++2uN5E4kEl112GYlEgttvv50nnniCtra2Ho8rRH809awbWX7vR4wPLyT5ymVEK7/A6UwzO5YQQggxYAV8zQyPrQYFyqZ8x+w4QohOkGvfgSPcvA2AgH3XjYsyc4sB8Kh+1GRSbljeg2/UISyMn0ZZ4VgmmDC/w+liReY0DmqfTcvSt2By6vdUFD0XbdMKFSGrZz9Hdo7VZqPaUki5WkPz1rUMqhipy7g9ta5Bey0fXti51cGn8SkTbF+yoGo0TJ+he57JNf/HUFsDQ7gU6F7r+PQc7XUyKymF4t7uBm6gNhritQPMWS0KEHHkQAw6/A2mZdgTVVX5zZvfkEiqTBw9iknTJgNwZsY3/NF1DZvWlgP6rW4NhLUFflkucwrFAO2WLHKTfsL+egCq1iylAj8R1U7lRP1fbzrLNfokmPMGg5vnyu9eol8w/Cs4mUxyww03MH78eP72t7+xevVqkskkqqqiqirJZJI1a9bw97//nYkTJ3LdddeRTCZ7PK+1E218TjvtNEaO1H4Jmzt3bo/nBPjzn//MkiVLGDlyJL/85S91GVOI/kqxWCi56AlayaQysZGvnv6F2ZGEEEKIAW39gnexKUm2KsUUD+4db1YKIfZNrn0Hjri/FoCIa9e2kt7t+yemKTGCwUDKc/UFX0QGc1/8HAIjzzItQ3K41va9oO5T0zKInuloawYgYtenUAzgc2rtdUP163Ubs6eyVzzN72xPcGRa51Y5RzO0lfpqy2ZD8hzc9iHX2F5nkNLc7TE8udrrpFcNkOhCd0uRWomkSn1bFBULg7L1b2PeWR0u7YYsNdhoWoY9WfTxK6ib5uC0Wbjl5P/stT1i8lEkVIUhySrqqzfqMlcykeTFxHW84rgdD+bdCBjcfmNONKB9/39VE+ax+MnMzTgBpyvdtFwjp3+HZepw/h0/nFXbetfXiRDdYXih+IILLuDBBx8kkUigqiqjR4/mkksu4aabbuKmm27ikksuYcyYMaiqSiKR4KGHHtrr/kpG2NEeKxKJ9HisLVu2cNtttwHwyCOP4HA4ejymEP1dXlE5mw6+C5/q5rkqLws3tZgdSQghhBiwOtbOAqA67xCTkwgh9CbXvv1Au7aaJu4u2uXh9AwPIVVryepvrE15rL5gS3MQgMG5btMyDDvkdBKqQla8mdr6etNyiO5LhrRCRYfDq9uY4QytvW6iWZ/ijh7KGj/l+7aPOdDRudWUqrcCAEf7VkPyeJPa+0QZ27sndGuM7TfUWBUVX3PvaycsNA1tERJJFZtFIS+j+63GeyqZrhWKLaHeUwAMB9son3cTzzt+z/0Hrqcs5z9FUk9uIevtIwDYslCfVsjBdh8jLds4yLKOzAzztheJbr8xJ96ubYv0UV0af4h/j9WTbjctE4ArPYO/Dn2E++Pn8PnmoKlZhNCDoYXiF154gRdffBGA8ePHs2DBAlasWMETTzzBH/7wB/7whz/wxBNP8PXXX7NgwQImTpyIqqq8/PLLvPDCC0ZGA2DNmjUsW7YMgFGjRvV4vKuuuopgMMiFF17IzJkzezyeEAPFpOMv5L4DXuSdxHSu+/cy2rbvgSGEEEKI1FGTScpavgTANepYk9MIIfQk177d1xCIcM3zS7n4qYVmR+GZzMsZF3mczSN/tNtzfiULgLaWmlTH6vWSiQTFrQsoponBOeatPsrOH8SNeX9navTvzNrU8xs2ROopIa1gmXB6dRtTzR4MgDOwRbcxe6owqmXJLO/c9oCu/KHa8WH9X386YlG8qraa0ZPf/T3GbXYHrWjFrkCz3FDTW7Wu/ZLXHLdxd9o/sVoU03IkPeWsSpbRkNSve0BPLX/hDopopI48jjr14t2eby06DADrRn26VgT92o0xMdWGK828m6w+KLmao6J/Yln2cSSTKvM3aq/DB1fmmpZph6H52r9LfUB+pou+z9BC8WOPPQbAiBEjmDdvHlOmTNnrsVOmTGHOnDmMHDkSVVV59NFHDckUCoVYt24d999/PzNmzCC+vd3Itdde26NxX3jhBd59912ys7O57777dEgqxMDyi9OmUZqdRrUvzB9fX2R2HCGEEGLAqd74LcVqPTHVyvCpJ5gdRwjRQ3Ltqw+HEufgb+7g0o3XEQ6auwdzXVuUAG6yc/N3e67Nlg1A2Ne79lPsDRqqN/JP6++Z7byOYo95+ywCDB87FRULn6ySFcV9kTXqA0BNz9FtzMTQY7ghdiVPW8/Ubcye8Lc2UYBWiCkeNqFT53iKhwGQn9B/pW5rYy0WRSWuWvDmFu3/hH3wW7wABJvlhpreqv2LfzDRsp4haSFTc7SNPIsTY3fztPMCU3PsULtlDROqngagetotpLl3X+HrGattbzC0bRGJRKLHc4YC2utAQMkAxbyivZo9hI1qMfURO5s2ruPA8BKy7XHGlXpNy7RDbpqFPPwk/NVmRxGixwwtFC9fvhxFUfjlL3+J273/O0/cbvfOvY2WL1+uW46nn34aRVFQFAW3282IESO44YYbqN/e6uemm27iggu6/8Lf2tq682L7j3/8I/n5u1+0CSH2LdNl54FzJ3CsdQnXrTqXpR/80+xIQgghxICyYu061iRLWescgzvTa3YcIUQ3yLWv/jzudL5rnc/h1pU0VK01NUtDIApAQZZrt+dme8/k1o6L2WYrT3WsXq+pahUA9ZZCbHZz26QfPaoQgC82NBKKRE3NIrrOHvMBYEnXbyVb7pCxvJI8gtn+nhVB9VK7fhkADeSQ5e3c/2dBmdby1ks7bX59txPzN24DoEXxYrFaezTWM9nXcE70Vra4RuoRTeisqa6KCS0fAJB+xE9NzZK7ve11U3vveJ2uffFGXEoHKx3jmXT8RXs8ZtjEmbSraWTTxsYVX/R4zkib9r0cspi3mhjAm6793PaFYjQteonnHHfxtPshHDbDd1Tdr4m+D1js+jGnb73b7ChC9Jih31GxWAyAcePGdfqcHcd2dBjfenbChAksXLiQu+66C6UHd8b8/Oc/p76+noMPPpjLLrusx7mi0SiBQGCXP0IMBFMG5/DDikbylACDv/w1TTW9p/WSEEII0d+90ljG8bF7mDftYbOjCCF0Jte+3adYLDTYtAKOr3adaTmSiQS3hf7Ab21PMsgV3+35dYUn8mziOKrUQhPS9W7B7Z+3Flf329bqZURhBrdnvMYc61WsnfuK2XFEF/3Z/VOOj/6R9iH6dV4p394OPRCJ4w+Zvw1XoGolAPXOwZ0+x52VTSta+3u9b6gJtWgr9QLW7B6P1ZA7hYXqAdRFzdv7Vuzdurfuw6HEWW07gFFTzd0GJ9etFSeb22Om5gBYOfcNJgXnEFctuE+9F8Wy53KO3eFknXsSAM3L3uvxvLH2VgDClowej9UTg5NbuM72EhPqX8axTSuAhwZNNzXTDo6sAgDS4q0mJxGi5wwtFFdUVADg9/s7fc6OC8Md5+rhtNNOY8WKFaxYsYKFCxfy/PPPc/rpp7Ns2TLOP/983n67+5u8z5kzhyeffBKbzcYjjzzSo4vuHe666y48Hs/OP2VlZT0eU4i+4qCL7mWDdSjZtFH9zKWoyaTZkYQQQoh+LxZP8uWGJgAOG2X+G+lCiO6Ra19jBJzFAEQaNpmWobWpluMti/ie9WPyvFm7PZ+T0Xve1O5t1OYNAIQz9HufqbsUReFAb5JCxUds1btmxxFdtCWSzhq1nPRs/Vb/pjtsHOPewPess6gxuWsBQLJhNQBBz7Aunfd7z284PPoAG9D398ioT2tnHXT0fBV3Xi9bJSr+I9jm48DqlwAITfmJyWkgz5XgQ8fP+YxLiYTaTcvR0REj49NbAFhScAZDRk/b5/GNo3/IVbGf8lh4Zs/nDmrFz6ht9zbXqVQUr+FntteYHviAIcFlAGSPPsrUTDu4PFpnncx452tfQvRWhhaKzzzzTFRV5ZVXOn+X5Msvv4yiKJx++um65fB6vYwZM4YxY8YwZcoUzjvvPF599VWeeeYZNm7cyKmnnsrTTz/d5XGj0SiXX345qqrys5/9rEsrp/fl5ptvxu/37/yzdetWXcYVoi9wOF1Yz3qcqGpnfGQRC1/+k9mRhBBCiH5v+fotxGNhct0ODhy0ewFCCNE3yLWvMaKZWgFbbTWv41FrnTZ3i+LB7th9NVyJI8xUZRXupmUpTtb7OQLbP285Q80Nsl362O8AMKRlHkkd9pEUqePbvuI3263vXtc3Wp7n9/YnCW2cr+u43aG2aYVZJX9Ul84LF05kq1pIlU/fm1WWZB3NEdEHeK/k2h6PNcxSw/etH1FYM6vnwYSuVrz9NzwE2aoUM/7o882OQ6Y7k8FKPblKG76mWtNyPLdgG/dGTuNbhjDq/D/u9/iR00/k3eR05myN0x7dvftIV4TiKjVqDiFHXo/G6Slnljb/iPhavLQTVF1UjjvM1Ew7ZOZoNw15VCkUi77P0ELx9ddfz9ChQ3n00Ud58cUX93v8yy+/zKOPPsqQIUO48cYbjYwGwIUXXsjZZ59NMpnk6quvpqWla/to/P73v2fNmjWUlZVxxx136JbL6XSSlZW1yx8hBpLBB0xm6chrARj3zb1UrdNvz3IhhBBC7C4+90GWOS/ntpyPsFh6vkpQCNG7yLVvzyjZ2kpUZ7t5hexgsza3z7rnVXUHtM/nRedvOa7usVTG6hO8EW2P0/SiESYn0YyYdgJB1UU+rWzQYR9JkRrJRIKfRJ/gautrZNt7VoD5X23p2ircWOMGXcftjhuTP2Vc5HFsE87u0nk7WmhvbQnpmqc+pFClFmLJq+zxWCOiK/md/SkmNr6pQzKhl3g8TvnafwJQc8APsdpsJifStp1oVTwABEwqFDe1R7l/1jreSU5n2Qlv4MnJ3+85FbluKnLTiSdV5m9o7tH8y7zHc0j0r7w55JYejdNTbs+u/98b0sbu8YY5M2TlDgIgTYkRDraZnEaInjH0ldfj8TBr1izOPfdczj//fP71r39x8cUXM2XKFAoKClAUhfr6ehYtWsQ///lP3nzzTSZPnsyLL76Ix+MxMtpOp556Ki+++CLBYJD333+fCy64oNPn3n23tlH5Mcccw1tvvbXHY4LB4M7/vvDCCwAUFBRw1FG9o0WCEL3V1HNvZuU9HzEmuozIv39Exy/m9ZpfBIQQQoj+Jq9uHmlKjOIS89tyCiGMIde+3efKHwqrIStSY1qGSLO2T2e7Y89vFDs92j55btknbxfJRJJBiRpQIKesayskjeJ0pfNNxhQmBefStOQNhk843OxIohPa/C1catP2/Yy6H9J17HhWBfjB6jOvawFAezROtS8MuBla3LX22gfa67ne9iKFmwuBe3TL1NimtYnOz+z5+0EOj/b/lNYhr5O9ybvfNPCXyA38yPUJp37nx2bH2anN6qUw0UzYZ87P/gfeW0FbJM6YkizOnVre6fNOLY+S9L1G7POFcGD3b+4LRLQOClkufTsodJU7u2CXv4eKDzYpye4yMr3EVBsOJY6vqYY090izIwnRbYYWiq1W686PVVXlrbfe2utF5Y5jFi9ezNChe28HpCgK8bh+d+7l5//nImvLlq79QhaLae1UnnrqKZ566ql9HtvU1MT552utM2bMmNEvLpaFMJLFaiX/wifx/+NwvooU8/4na/jpCfq0uBNCCCHEf7Q0VFMZ3wAKDJn6HbPjCCEMIte+3ect1vbqtMbDqKqqy/7MXZUMaCuaommFe3x+x56pmQlpf/jfGvxB7o9fxFBLPZeWDzc7zk7xYcfB8rnk1XxqdhTRSW0t9XiAkOok3ZWu69i2vKGwFdxBc9vvb2jQ9mLNy3CS7XZ06dzB9ha+a3udzf4y9CwUH1L7LAfYmhmsXAkM6dFYaV7t9TNLbqjpNVRV5bE5G1inllJ36O9wpWeYHWmnoD0HEhuI+htSPve6pXO4duX36bCewznf/RXWLnR8OjK7iYn2l9haUwx0v1DsD28vFKeZu8Lbk7NroThnzDEmJdmdYrHgU7IooIX2lnqokEKx6LsMbT2tqurOP//79z396cwxO47TS3V19c6PMzJ6zw8jIQQUllYy/8T3uCl+OX+es42vquSXeSGEEEJvGxe+g0VR2WgZTF6xrCgWor+Sa9/uKxgyjgMjTzIzet/ON05TzRrUCsXJjD2v8svc3v7QqwZk39v/sqk1xouJI3k+64e9qkPV0INPJ6kqDE+sp7Fms9lxRCcEfVqxKKDo3yI/Y5B2E0NOzLyuBQDB5a/xjP0urnJ3/QaG7GLt/6EwUY+aTOqW6Yjg+1xte4NCpec3wWTmlQCQrfp0zZhKZv0MMsqXGxpZWR3AZbdw4cG96zok5tS2eki0NaZ03mQiQeKdX5Cv+DkndzOTB+d06fzKqScSVy2UqTXUbF7T7Rwnbn2AVxy3c0Dgy26PoQe7w0mbmgbAv9TjGTq296woBpjtOoqn4sfTlEgzO4oQPWLoLSG33367kcPr4qWXXtr58dixY7t0bmeK1oMHD2bLli1UVFSwefPmrsYTYsA7ftp4Tt2U5I1lNVz/wlLe+ck03G632bGEEEKIfkNd/wkA9QWHsve+PkKIvk6ufbsvzeUgPcNDqD3K1pYw3vSurbTTgy2s7Stt9RTv8XlvnlYotisJ/L4mPLl7Xnk80Gxp1lqiD87tXdeQeUVlzHEdwepgBgXrGzmteLDZkcR+RAJNALRb9S8U55Vrq9AK1CZi0QgOp0v3OTpDqV7MEdYVOO1dX31fUDaMpKqQpsRoathGXlHnW+XuS3ayFRTIzNvza1+XxsrXxnAqHbS1+cj0dK0AZ7b3V9Zy5XNfcet3DuTSw3q2urq3cL1yEffZLWwecy05XVzFbrREWi74gWBqC8VL3nqUKfFVhFQnFef9qcvnZ3lzWe0YxaiOb9m6+B2KB3dvlWthZCNjLOtYbI1063w9BSxZZKphNg86EZu9d32dvJV/OXN9TdxvGWR2FCF6pN8Wip9++mnOO+88XK69/3L1wAMP8O677wIwZMgQDj98131hZs+ezZFHHgnARRddxNNPP21YXiHE3t15yhg2blzPjW1/ZOWTQ5h2zTNmRxJCCCH6BTWZpMK3AICMA483OY0Qojvk2jc1ynLSaGqPsrU1xNhST8rnv8V5M9ta6/jLqKl7fN7pSqeNNDIJ42+ulULxdpEti5lu2cABnkPMjrKb5VPv476P1nLMJoXTjjA7jdifWJtWKA7bDCgUF5QSVJ24lSg1VWsoGz5e9zk6I823Qfsgv+v7eTucLuqUXIpoonnbWl0KxaF2PxlKGABvQVmPx0tzZxJUXbiVCL7G6j5XKE58+Qh/sC2nauUp0A8KxZu+Xcyk8BdMsCjUTtGvXbleOjwVrKoppymZui4sbf4Whiy7G4DllVdwcDdvImotPgK2fIt906fA9d0aw5XQWtE70rO7db6e7vbcysr6CN8fOd3sKLvZcYNDSzBmchIhesbQ1tNm+s1vfkNJSQmXX345zzzzDJ9//jnLly9n3rx5PPzwwxx22GFcf732QulwOHjsscd22VNZCNF7eNLt3HWEixnWr5nW/AbLPn7B7EhCCCFEv7B59RIKaCGsOhg+pffs9ySE6Dy59k2Nsy2zecZ+F+nf/MuU+evbogRwk5+998KGX/EC0N5Sl6JUvd+4zU/xguN3zIz2vr2AjzpA23dx3vpGIh3SLry3SwSbAYg5vLqPrVgs/Ml9A+dEb2VTTP/xOysvsgmAjNIx3Tq/xaGtqGur3aBLntYGrRV3WHWQkenVZ0yLNk6w2dw2391R3vQZF9g+xeLbZHYUXTR9qK2WXZZxOCVDR5ucZncNI77HibE/8qLr7JTN+c3zt5CHj61KMZPOubnb4+SM1W4AHhZcQryjewXM9OT2QnGm+YXigw+ZQUbJAZx8UO+7QSI3zUI+PsKttWZHEaJHzN2N3GAtLS08/vjjPP7443s9prS0lCeffJJjjpE3xoTozcYc9l3mrzif6fXPUzr3l7SMPZycghKzYwkhhBB92ufVKs93nM+YXDglrXe15RRCdJ5c+xpviLWRg60rWNBYmfK5Y/EkzdtXqhR59r5y/M2Mc6hpCXC0UsiBqQrXy3nCWwFIKxpmcpLdHTgoi/IsKxXtS/l6SQZTpx++/5OEadSQ1v497jSmaLKt6GgWttSzOaBixgLzcLCd4mQ9KFA0rHsrmoNpJRBbQbx5sy6Z2pq2AdBqyabYos9ap8c8P2NVfZjLHUPp+rppcxVGqwAYFlpGIh7Hauu7b+s31mxmfOuHoID7yOvMjrNHeZnavvZN7alZKVq1dhmTal8ABVoOv4MyV3q3xxo24QgCb7vJIsjq5XMZNfnoLo+RoWqF4vSs3G7n0Mv5U8s5f6o+7ez1dmzL89zmepiFm04GDjU7jhDd1m9XFH/wwQfcd999nHHGGYwbN47CwkJsNhuZmZlUVlZy5pln8tRTT7FmzRqOPfZYs+MKITphwsX3sclSQR4+tjz9I9Rk0uxIQgghRJ/2QZXKY4nv0jj1l2ZHEUJ0k1z7poY1ZzAAacHqlM/dVLuZx+338Rv7s2Sn2/d63LL8U3gucSzb1L7VTtUoajJJUUJbNZhTdoDJaXanKAp3Z77Is44/oi76h9lxxH587D2L46N/ZEX5DwwZvzxHKwptaQ4ZMv7+VG/4Goui0komOfnd2w847tEKOVb/Fl0yhVu179+AVb/XtLqcqSxSR1EX7V37nO5PJNROAdqq9jMsc6jfps+qbbOsf+s+HEqCb+1jGDn5KLPj7FHu9pbCTe1Rw+dSVZXP3nsRh5Jgedo0xh91To/Gs9psrM+YTJuaxqZ133b5/GQiQYaqvRalZ8nvFPtiycgDwB5tNTmJED2TsluPli9fzty5c9m4cSNtbW0kEvtuq6MoCk888US35xs5ciQjR47c2WKrO2bOnImqqt0+H2Dz5s09Ol8I8R+uNDfJ0x8j9vJJTAx9wcLX/8LUM35mdiwhhBCiT4p0JFiwSVsdc8TwPJPTCCG6S659UyO9cCisAG809e1KA7UbOda6hBqlAEVR9npcboa2+qklRaufervmuq3kKVESqkJh+Uiz4+xR+ugTYM4rDG6ei5pMoui0alLory6Wxhq1HMv2m0b0dkB6gPOtH1OyJQ9M6Ang27ICgFp7Bdnd/DpsGXkBR6w7gIq0ETyrQ6aYrx6AkEO/FY07XiebU1D801Pt5lX8d9Pd5qrVFA/una9r+9MeaGV07csAxKb+xOQ0e5dvbecjx8/JjrWRTGzGYuC2HR+vauDW2kN53VbEg2d1ffXvnmyZdgfnvFPFeF8+J3bx3PY2H1mK9nthhsf8FcW9mSMrH4C0mBSKRd9meKF41apVXHrppSxYsKDT56iq2uNCsRCif6ocO535S3/C9I0PMXr5H6iecCwlQ6WxmhBCCNFVK7/6nO8kZ7MmawrDCjLMjiOEEL1aTskIAIqS9SQTCUPfMP5foWatfXLAlse+1vmVOdqZqqzC3hgFhqckW2/WULWKPKDeUkCxc+8tu800cvpJhD9zUKg0s+GbhVSOnW52JLEXvlAHANnuva/q74kRyjbOsj/BxubBwK8MmWNfmltbaFUzaMvqfnv9gpIKqtQaVJ8+N6vM95zAjdE8Th1VzCRdRoRRlq1caP2Q3OqtwAidRjWeb+uuq0KDdWtNStJzK9/6C9MJscVSyrijzjU7zl5lZ+dSaNG6iPhaG/HmFRkyT6QjwZ1va5/fKYceR1mlPk3Rp4wZSfydGpZt9eEPd+BJ6/xrV3ubn3Y1hzQ6yJbtifbJ6SkEICPhMzeIED1k6K2KGzdu5LDDDmPBggWoqoqqqmRkZFBaWkp5efle/1RUVFBe3jv7zgshzDflgtv51jGWOjWb+97+inhCWlALIYQQXRX/6l/c73iE292v7nOFmhBCCCgoHUpcteBQ4jTVVaV07g6f9kZ1yJm/z+MmtX3Ci87fMr366RSk6v2CNWsAaHKWmpxk71zpGaxxHwRAw5LXzQ0j9unI5he4xvoqhWqTIeN7S7WiZWGi1pRttl5VjmVi9FFWT7il22OUZWvts2t8EV3ep6kLwVa1EFve0B6PtcPIyNf81v40oxve0W3MVIjWrdv1gea+2Xq6I5Hk9i3jubvjPLaN+1lKb7rqKofTRQCtSOpvMq6byKev/QOldSOFWU6uOWqYbuOWZqczNN9NIqkyf21tl871WfM4JPpXjnM8rVue/sqdrd1AkKX6TU4iRM8YuqL4tttuo7W1FYvFwo033siPf/xjBg8ebOSUQogBwGqz4bnwOU77x3Iaa2xUztnIT47U75cpIYQQYiAoavwcAOtwfdqbCSFEf2azO6ix5FGsNtC8bS0FJUP2f5JOkgHtDd5YeuE+j7NlFQDglPaHAMSbtEJKOKPC5CT7Fi2fAau/JL1+idlRxD6cHH6LInsja7nQkPGLykeQUBXcSpSmhm3kFaV2Ac26hnZAobKo+/uRFmQ6+an9DQZTTX31KErKe1bgbWzTVibnZzp7NM5/s2dpr6NpsWbdxkyFju0/B5rxkosPZ5s++0Cn2jtf17ImYKM542x+dvKRZsfZL7/iIUsN0t5SZ8j49ds2MOObWznKkeDzQ17E7dS3VHNl3gqm+v9C/ZyDYfw/O31eIKJ1UMhypWzX0j7Lk7u9UEyIjlgEu6N3djARYn8MXVE8a9YsFEXh2muv5e6775YisRBCNyVlg7npFO3O6wc+WsuKqr71S74QQghhpobqTQxOVpFUFSqnfsfsOEII0Se02gfRombQ2mLMisK9sQe1fTrJHLTP41zb2x+641IoBvjEdgQ3d1xKY/lJZkfZJ++waQCUhteYspJUdE6WGgDA7d33yv7ucjhdNCh5ADRWrTFkjr2JxhNsaQ4BMLyw+9uRWCwKZ9vmcoZ1Hq1bv+lxrpn1T/Nz2wuU09DjsXZI2776L6OPvU4+ZL+U0ZEneLf0WgCyI1vNDdQNajLJY59pN/BcfEgFLnvvXU28Q5tNu3Ei4jOmULz13z8nXYmyyTGCI4/Qv3A+siyfwZZ6ylq+7NLPl0B4e6G4C+2qB6qs7HwSqtady99kzNeJEKlgaKE4ENB+iTrzzDONnEYIMUCdMamEk0YXcJHyNq6njyYSajc7khBCCNEnbF74FgDr7cPx5u17hZoQQgjNc8PuZ1L0MRbZp6Z03rSIViSxefa1QzG4s7XXc09S2h8CfNFeyPOJo3ENO8LsKPtUceBUOlQrufipr95odhyxB9FIiHQlCkBGtnG/N7U4tO/x9tp1+zlSXzVrl/Kp7af83fU3Cnq4etfn1G5oCdf3/Gv5mND7/MT2JgXWth6PtYM7R8vnUX26jZkKm5pCBEmjdPShAAxK1JFIJExO1TUr573F71qu40T7Mr43rXd3etgh4tAKxR0B/W5W2OHb+e8zue1jkqqC7Tv3olj0L9MMm3ICMdVKsVpP9cZv93/Cdu7NH/KK43Yujvyf7pn6G4vVymuWY3gyfgItEbnZS/RdhhaKy8rKALDZpE2BEEJ/iqLwh5MquNL+LsOTm1j21LVmRxJCCCH6BMvGTwFoKTrc5CRCCNF3lOR5ANjaGkrpvM64dhN+Wm7ZPo/LzNWKTB61jXhHh+G5ejNVVdncFARgcF66yWn2zZWewT0ZP+ek6B9Y3iotK3ujQItWJIqrFrI83W/NvD9Bt/Y9Hm9K7Q0DLZuXU25pZKitBUVRejRW2K3tCR5v2dyjcdRkkhxVW/WblV/So7H+m3f7WFmEiISDuo1rpECkg6Z27UaFSWPH8qv4ZfwgdhO1vtT+LOop9Ys/M8mynouLNpLtdpgdp1M6XLnaB+36FooT8TjOj24CYFHuKQwbf5iu4+/gzvSyzjkagOolnd+X2+qv4iDLOkrVru1tPFA9mnkNd8Z/QGMyy+woQnSboYXi448/HoCFCxcaOY0QYgDz5hZQM/M+AKY3vsTXn71mciIhhBCid0vE41S2LQLAM/Z4k9MIIUTfUZajFRy3tqT2zflT43cxLvI4aZXT93mcN7eQpKpgUVR8LfUpStc7NTc3cVLiY6ZYVu/8vPVmgSEn8606mK9rpUtWb9Teqn0/BZQMQ1b97ZDwaqss7f7Nhs2xJ7HaVQAEMnu2pzBA0rP9/yFQ1aNxAr5mHEocgOyC0h7n2iHLm0tM1Voe+5r6RhGsbs1inrP/ntvSXsaTkcb87O+wQD2ALa1Rs6N12saVCxgXWUJCVSg76edmx+m0SNZgvk1W0JR06zru4lcfoDKxiQBuRpx/t65j/69AiXZjsGPL7E6fo0a0ziRxhxQ+OyNn+40PzcGYyUmE6D5DC8U33HADmZmZ3HvvvbS0tBg5lRBiABs/80wW5J0BQNGn1+NvHthvigghhBD7sv6bJWTTRpuaxrCJM82OI4QQfcZQWzPP2O/il3U3pGzO9micYCxJADeF2Z59HmuzO/ArmQC0NfeNAohRmjYs5V77YzzkeBinrffvgzm2VPvcfr1N2ob3RiGfti95m8XYokloxGmcG72Vhx0XGzrP/3K2rgUgmTuyx2PZ87Ric0a4pkfj+Bq0PXgDuHGl6VekUywWfIr2/dbW1LOMqRKo+prDrN8w3aZ9ngbnav8em5v7xopogOaP/gTAsswZFA8ZZXKazqsaeQknxe7iTdepuo3ZUFPFyG8fBGDVqGvIzh+k29h7kj/hJACGB5fSEevczQXK9kJx0imF4s7Id1vIx0d7q/4tyoVIFUMLxRUVFbz66qu0tLRwyCGH8NFHHxk5nRBiABt3yUNUWUoooIX1T12OmpR9IYQQQog9+ag5hymRv/NY8W+xO3q2D50QQgwkg/K8HGFdwYTkt8SikZTMWefX5sl02nA797+t13Ou87ml4xKakplGR+vV2mrXANDs1K9lrZHGFzn4vvUjvrP1PrmW7YWibY0AhKzGFk3yy0awQD2A5b7UtiDPCW0CIK3kwB6PlTWoEoC8jp4VYduaqwFotWT3ONP/+nPW9ZwVvY1a277b+fcWHY3rAQi6tdXaE93NnGP9FOv6vvE+e93WDUzwfQxA1tGpu9FKD3k6rxRNJFV++mYVj3WczBrbCA460/h/j6FjDqaVLDKUMOu++rRT51ii2pYXuLzGBetHvu97mEWuqxi6/hmzo+jO39rEwgfPZ/6/fkskJF1P+jPDNw8+6qijWLp0KYcccggnnHAC2dnZDBs2jPT0fbf+URSFjz/+2Oh4Qoh+Is2dSfS7j9Dx+mkc1D6bRW8/xpRTrjQ7lhBCCNHrzFnbRCNeCg3aC0sIIfqr3IJSwqqDNCVGw9b1lA4bY/icoY0Ledx+H9tcI4H9bxcwL/t0FvhamJoY2KuA4o0bAAhlVJicpHNGFHm4zfYsDuLUVK2jeHDPV3YK/azPnM5N0buZXpLLnQbOU5GjrRRtao8SisVJdxj+ti0dsSjFiWpQoGDouB6PV1A+AgCvGiASDnZ7NXCkVeuK0G7Tf0/obd5pLG5opC5q/L+vHuw+bc/qeI5WhJ+aWMo19sdZWn0o8CMTk3XO5nfuo0hJ8I1jHKMnHmF2nC7Jy9Ruqm1u16fN918/Wc/8zX5WOM7g7MvvxWY3fq9mi9XKV9knUtPYTKIuSWduB7F3aIViS9q+O5kIjZqm7WWthJtNTqK/VW/9mem+d8H3Lk33/INlI37EhNOuw5WeYXY0oTPDfyJ+8cUXXHjhhTQ1NaGqKi0tLfvcs1hRFFRVRVEUo6MJIfqZ4ROP4Mtll3HQ5seZtWQVxUeEKfGmmR1LCCGE6DXaIh18VdUKwBHD801OI4QQfYtisdBgLaQiuZXWmnUpKRRH61dzrHUJK0l06vi8DH3f1O6rHIHNACSze77naio4Xemssw1heGIdtau+kELxfsSiEWw2OxZratqKN8ZsrFXLGO/Vb6/cPfGk2znPNZ/y+GZqNw+mcsRoQ+cDqNm0igolQUh1Ulg6rMfjZWUXcJL6EOuiXt5rSzKsm2/JxP11AESceT3O9L92vE429ZHXSU9oCwDOQq0In1Y0AlZDdmSrmbE6JeBrZkztq6BAfPpPzI7TZQXJRmY5biStvQNY36Oxln21gEc+rgEc/O70MQwpSF0RtuXQW7n15a8Zvy2LiztxvCOuFYpt6V4jY/UbFrf2OmWP9L+tV531XwEQU23kKT7y1v6Jpnv+wVejrmLSmTfisvf+7T1E5xhaKP722285/vjjCYVCqKqKy+Vi+PDheL1eLBZDu14LIQaoKd//Ldc/PJ43azwsf3EZ//rRdCwWufFECCGEAFg37xWetj7EvIyjKM892ew4QgjR57Q6i6kIbyVUvzEl8yV8WvvWsKuwU8dXOANMU1ahNCjAEAOT9W5ZIa2A4izoeeErVVq8o6F5HbGqJcAlZsfptZrqqrA+cgib3BOY9PO3UzKnL6S1nc12G7/671LruwxnPUs3Hw8pKBRXNzTRkhyGxelmvA6Fd8ViQc0ZSkdtgK0tYYYVdK8N/hzvKdwYLeesMaUc1ONUuzrAsoUfWGfhra4DevdrhJpMUhjX2nDnlGl7++aWa/8tStSSTCRSdsNEd7ywrJmlHZdzpnsZR8042+w4XebNzsVj0X4O92SFfGtjLcVvnsdrdjcvj/gTp0809qaT/3XECO0G4a+r/bQGY/t9LYskbbSrLuxu/Vu/90e2TO3f19nRanISfamqypuR8VQlkgw96VrC1d9QtvLvDKKRt1Yu4WcbPuXHMyq5YFq5FIz7AUMLxXfccQfBYBCn08l9993HD3/4Q1yu1O6zIYQYWGx2B9dfcCqzHprL/I0tPDF3I5fNqDQ7lhBCCNErqMtf4DDrN9i8xr/xKIQQ/VHEXQrhBSRbN6dkPqVda78ad3euUHx44F1+4XyUBVtPAU4yMFnvpRVWakCB7O2Flb5AKZ4Aza+T2bLC7Ci9Wv3sxxhNG9nBuSTicaw249sHV9S+xzXWbylLnAYcYOhcgbRSaF9PdHv7dKMtiVVwX+xOzhhTwv06jVmWncaq2gBbW0PdHqMuCNvUfBx5g3VK9R9jIl/xI/s/WVx/DHCp7uPrqaWxhlxCJFWFosHa115h2XA6VCsupYO6mk0UlfXOYncsnuTJL7ZSl5zGkcdf1qsL2nuT5ckhplpxKAl8TbXd+rdWk0m2PHkxE2ghanVx3anTDUi6b4VZLg4sSCOtcRlff+VmxuEz93n8T223Ud0e5rWhh6QmYB/n9BQA4I77zA2isxp/hKeCh2KzHMaKg44h7eDjiX33Kha+9TdeX1dBoz/KnW9/y+zZH/KTykbGn3qttKTuwwxd1vv555+jKAq/+tWvuOqqq/6fvfsOb6u6/zj+vtqWbEm2vHf23pOVAGWvhL3Kpi0tUFooZZTZsgqlQCmUMgNlFkgZZc+Ekb33cGzHe8uyrC3d3x83hF+a5aGrK9vn9Tx9Gizdcz5JbEW633O+RxSJBUFIiNJMG7efMpoxUgWHfnkGO9Yv0TqSIAiCIGiuvbWJsZ5vAcg87FJtwwiCIPRV6cqZt6aOxLT8NPkaANDZ87v0fF1q/21/2FVtzfXYJaVAlVvSdwrFrmEzACgObkWOxTROk7w6m3bu/nVDdWKKqWNbP+MG41uUBreqPlfIXqz8IkGLUbY1egEY1sOdv/syW7+Wh41Pkr35Xz0eo6lDaQudtatNdDwZ7EpRxxxM/vNE62sqaZbtNEiZuwswBqOJep2yeKh55yYt4x3Q+2tqqfcEyE4zM2di1/4NSzaSTodbUlpEdzTX9miMJa/fx0T/YoKykdDpz2FLc8YxYdfdZn2bt813k7Ly6YM+1xMIA+BIMaodq1+wOpWfR3usXeMk8bVq15FVI/PSSDEpCz1MZgvTz7qBN288g/tOH0eBM4VLg68yY8tDdDw4lsWv3UvA59UyttBDqhaK29qUb6YTTjhBzWkEQRD2ct60Iv6Y/iFjpAqk+T8nGOj5SlZBEARB6A82f/4CZinMDl0pQ8YdonUcQRCEPsmUOZhWORV3KDE7o6zBJmXe9K7dZDfuKoCkhAduobiiQ+KC0K38yXBtn9rZUjxyCkHZiB0fNTs2ah0naf0zOmf3r5srE/PnZAkrN/8NqS7V59K7lHO1rd7ELEYpa1B+b8Oy4/ezMkRXz5n6b8lpXtzjMU5sep4bDa9TqGuOW64fmB25AKT2gTaxG2LFTA0+xW0Fz+3x9VZzAQCdddu0iHVQcixGyUcX8Uv9e/xsRhZmQ9/bTfwDj15pv+xrq+/2tdtWf8PkLcpe/dWjf8eQ8drt0E0deTQAxW1LDrgYKRaT8QYjANhFobhL7BnKa4pD7iAWjWqcJn7qN33PEKmGyYV7n6dtMui4YEYxX90wG9v406gniyzamLnlQToeHMuS1+4l4O/UILXQU6oWigsLlX770X70AyIIQt8gSRIlFz9FK3YGxSpYNe93WkcSBEEQBE2lb30TgMYhZyLpVP0YIAiC0G+Zx57K5ODT3Bi5KiHzOSJKkcTqKurS8y3OXQWQftb+sDsq2qN8HxvLxuxTtI7SLUaTmQqjUiSs2b5a2zBJKhCOsqAxhU+jyqm1vrotCZnXGlWKqeZd51CqOleO0trWGaxRfa5oJMIbbefyhekGRqT54zauJVv5PrYHerYDE+C4wMdcbXiPbEP8cv3Amp4HgD2W/IXi8mal0JKfteciBX9aKQBy8/ZER+qSdQv/w9TISq4xvMM5kwu0jtMrPmMGAMH2hm5d5/W0kfLuzzBJUVZZD2P62b9XI16XDZ9+PEHZSC7N7Ny6er/P6/C08ZbxTl4w/pk09Y9l7xccmbm8Ez2UedETcHv7T3H08O0P84X5Rk7Vfbff55iMemacfQMZt6xnyZjbqSeTLNqYseVBPH8ey8I3HycQFrXBvkDVO0SnnnoqAAsXLlRzGkEQhH3KzC1i52F/BmB63ats+O4DjRMJgiAIgjYqN61geGQrYVnPsGMu1zqOIAhCn1XksgHQ2hmic9eOG7XEojEsslIkceQUd+ma1N27WtxqxUp6FbsKK6WZVo2TdN9Hw//IuMCzfB6drHWUpLS+pp1ITGaHrBT6Yq3lCZk3LdYBgNWZqfpcrqIRAOTGGohG1H2NqavcQqoUoEBqJj+vMG7jOvOVYndWtPs7MEEpYKfLSnHekR2/XD9wZCodGpyyR/U/49764fVsUKZtj6/XDz2XC0K38pZ5rgapDk636G8ArMuZgyND/Z8bNQXNSqE42tHY5WtkWWbN87+mUK6jnkwGX/GC5gt1LdZUtlrGAVC38sP9Pq/T3cQU3TYO0W3EbIp/6/f+yGgyc4f+N/wpchGtQUnrOHERCgYYFFI6FuSOOfygzzeZLcw4+3ek37xud8E4m1Y+XV3G7Ie+4sXvK0TBOMmp+gp14403kp2dzUMPPURFRYWaUwmCIOzTxGMvYGn6KegkGddn1+FxJ/8ZNIIgCIIQb7ULnwdgg20Grpz433ATBEEYKOwW4+4z+6ra1D3eptUfZmLwacYFn8WVV9qlaxyuXTvl8BEMxn8nXl+QXv4BZ+oWMsbWoXWUbssfPIYOrKyt7l/nHMZLy8r3uFL/AQtjE5ga+Af/NKu/+C0WjWKXle+l1PQc1efLLhhMSNZjkqI01qh7BnNT+VoAagxF6A2GuI2bXTQMUF6H2lubun19W3MtekkmKkukZ8b/bFtnlvI6qZdk3C09K2YnyqUVv+cl4/2MMe25O9tROoHvY2NZ57ZolGz/tq/5jrHB1URkHSUn36B1nF7rTCtlQ6yEtmjX/6zfWlHNDfXHsig2GveJ/8DhUv+1oys6i2YBkFK1YL/P8XuU+6YdUt85uiEZuHadp97iDWmcJD4qNizBLIVxk0rh4DFdvs5sse4uGC8eexff2E6gwRPkzvc2cOefH2DJ6/eL4yGTlKqF4pycHD755BPsdjszZszgmWeewe12qzmlIAjCXsZc9ndqpBxyaWLr87/QOo4gCIIgJFQkGuONxmK+jk4gNvFCreMIgiD0eX8yv8QXphvwbfhY1Xnq2wOAhNmWjrGLRZw0ZyZhWTkL0t1cp2K65HVY02s8bHqK0ezQOkq3jd91DuD6mnZiMVnjNMkna8d8bjO+wtyseppxUN6i/s3mjvZW9JLyd2HPyFZ9Pr3BwG+t93FY4DHKQ+mqzhWoWQ9Am21wXMe1pjpoQflebqrqfnvw9sZqJZfkiGsB+wcGo4k20gDwNPe8PbbaYtEo48NrmaVfR4HLucdjg3Z1t6hs7Uy61wr3F7vO5LUfTV7JCI3T9N7W4b/g5ND9fGY9uUvP397o5Y53N1CPi5VHvsTIGcepnLDrsieeBMAw35r9FusCHUpLdp9OFIq7I9sqkUUbHner1lHionWL0m66MmV0j3bDmy1WZp71Wz79/bHcM3csRXYjvwi9xIzND7Dmnz+Ld1whDlQtFA8ePJi5c+fS2dlJU1MTV111FZmZmeTm5jJ48OAD/m/IkCFqRhMEYQCx2dPpOPFJorJEsK2Wj1Ylpj2VIAiCICSDb7Y1807nWK433c7Yoy/QOo4gCEKfV6hvZ4iujlDDVlXnaewIAJDr6HrrR51ezxP6n/KH8OW0BONfYOkLciLK2a7phcM1TtJ9Q7NSucn0Js9zJ1Xb12odJ+nkezco/z/2CACq23wEI+q2suxoVc4l9clmzJbEtDP3Zk2mhix2tgVUncfQqryGhTOGxX3sZoPSBt9T1/0zdDtblOJtu169QvkD1t9xZvBO6vW5qs3RW4215aRIIcKyntziPf+OCtJTOMPwLVfLr9PUoP551l1VV7mVie1fAuA85nqN08SHy6b8G9zsDR70uQF/J8+/+Cz+cJTDhrq46sihasfrlkGjp9GME6sUZNvyz/f5nFCnUij2i0Jxt9zifYBllqtxlL2jdZS4MNStAMCX3bujMMwGPT+dWcLnN8yiceg5AIxs+4pYVLShTjaqfmr433bTsiwjyzKNjQfv6S9J/aOfuyAIyWHk9GN4becz3Lrcgv29bUwanEeuI/la9AiCIAhCvL21QtmVMWdiPiaDtmdjCYIg9AehtCLoBNyVqs5j2PIhzxpfpF4+HDiiy9d94jibTXUejoukqBcuSbmb63GinOmZWzpa4zTdZ9DrOMq8hZHhzSzf/D0lwydoHSlpNNVWkEszUVli4syj+N03NzBcLqeuvJjSYeNUm7dZn8XlwT9TmibztGqz7Kk4QylIV7aqu2Pa6VV23Zvzut5WtKu8KQXEPFvxtXa/s0HArVzTaXTFO9ZuO50zWdHaQmNAr9ocvdVcsYFcoE6fS7HRtMdjRr2O643/oVCuY8OOc8iJ4xnTvVH54cPkSTHWmycydsJhWseJC1eq8mff3IWWwmueu4b7OudTmnI2c8/5O3pdctU3JJ2O9wp/x3tlMQ7xDGHsPp4T3lUoDhpEobg7wuZ08EHM26x1lLjI61gHQOqQmXEZz2y2MOW82/Dd+0/sUicVW1dROmpqXMYW4kPVQvEll1yi5vCCIAjdctbpZ/Jq3fesq2nnxrfW8OJl09El2Zs2QRAEQYin9pYGhmx+ijwO56wph2sdRxAEoV+QMkqhHszealXnMTWvZ5Z+FUuk4m5d57IpN7VburD7qb9pqNiIE2gkg2xbmtZxesTtHANNG4lUr9Q6SlKpWvcNWUClvoTBaU5OMK5iaLSMVRVrVS0UtwUltspFGFPtqs3xvyaaa8kxvEFOWSFwnypzxKJRCiJVIEHmoPj/+X0/4mbO//YSzrUOpbvvQKMe5dzgoDkz7rl+0J3in1a8dcqO71ZzMfv6V6DVXEhhoI7O+q1A19oiq6ndH+bBuklcJFcx+JArtI4TN/nRWr4w3YDUpgc27Pd5qz55iRnN8wGYOfsEsu3JuTklY8oZrN6+mvD2Vm7ax+MxvxuAsDFxr3n9QSTFBW2Ar0XrKL3W0lBNgdxATJYoGT8rbuMaTWa2mkcyJrSGhvULRKE4yahaKH7hhRfUHF4QBKFbjHodj5w7kbP+9hnHl/+ZpW9MZeb5t2odSxAEQRBUs/nzeVyvf4PTTMsZmn+x1nEEQRD6hZQs5TxPZ0Ddsy11XqVYErN1rzXqYLObiG4jkUYrkBy7zBKlo1YprDSZClD/NFl16AsnQ9ObONr2X5AYiIIVSwFodo5jMNBuLYGOMoIqt4Bv8ymFxHSr6SDPjJ/B+kbONLzLttahqFUorm1qYXlsCoOlekYPiv/u+6zsPEI0U9XW/V3RXzjP5MbgMM4bVMq0uCdTjNVV4NJ/TWp1MzBIpVl6qUVp2x2wl+7zYX9qMQSWEW1OjvPYX1lSycpQEb7cm/lodte7YCQ7p8NBtq6OsKwnFo2i0++9C72ucgtDFt0MwOK8C5l55FmJjtllhw9TFmBsqPXQ1BEkK23P4y1CoRCdspmoqW8uttKKZFP+XA2Bvn9G8ZqGKC+Hfsf0tFaucsa3s4MnazLUrEFXvSSu4wq9J3rPCYIwoAzNTuWJCZX81PAFEzf/lcrNYpW2IAiC0H+lb3sLgOYhp2ucRBAEof9wFihnDuZE65BjMdXmsfiVs1H1jvxuXXdSx9u8brqH4sq31YiV1CJNSmGl01aicZKeyx6htHksCW0nGolonCZ52FtWASAVKqXDkFNZsKFrLVN1XnPNIq7Vz2emvFrVef4/Z4FyHm12tPttm7tqmxt+E76GG9Mfw2CMfxG8aFf77KoetM+u74RqOQuLq3vdFLpjQmApdxtfZHD9x6rN0VspnnIApMx9n3MrZyg/A2ZPRaIi7VcwEmXed0qOnx0xuF8dKenMzAPAKEXpcO/dVjgcCtL+r0uw08kWwwimXPZIoiN2S2aqmQuydnC/4RnKvn1zr8e/cp3HmOALLBh6swbp+i59ahYA5lDfLxSvqPPzZWwyO4ZdGvexrUOVlvR5nrVxH1voHVEoFgRhwDn0zGtZZ5mCRQoTfvNKQsGA1pEEQRAEIe7KNy5neGQrYVnP8GMu1zqOIAhCv5FdpBRxrFKQtmb1Cjm2sHJD2pxR0L0LrcquFp2/77c/7C5Du1JYiaYn6Q7BLigcOp5O2YJVClK1bbXWcZJCJBKhMKjsmswapdxkNmYpxTObt0LVuTMavucG41tMCy5WdZ7/L6dkJAAOOqnevl6VObY1dgAwNEedc0iLrREeNv6De9pv7faCmqYOpW3+/+50jCd9Wg4ApmDyvk62hI24ZRu2vOH7fDwlV/m6078zkbH2adV//8kN/seZkdbMqRO6t7gp2ZnMFjzYAHA31ez1+PIXf8/IyCY65BTSLnwJo0m979t4OTV1K+cbvkK/+b29HvP4lQVK9gR2UegPzHalj4k14tY2SBys2ukGYFJxetzHLp14FF7ZQmUkg6Y2T9zHF3ouoYViv9/Pt99+y1tvvcVLL72ExyO+GQRBSDxJpyf34udpx8bQaBkrXhKr5ARBEIT+p2Hh8wCst80kI7ubRQZBEARhvywpNrZLxayNDaK+qUm1eTKiSgEjLauoW9fp0nbtakniAohanjJcxEWhmwkNO1XrKD2mNxioNCuLEZq2JK44mcy2NvqYHnyCC+V7KBo2EQB74SgAskPqnhWu8yu7w2RL/G+Y74811cE682QAGv9ziypz1NZUoyPGsGx1CsW5menM1X3LIboNtNRXdevauS1Pc5PhNQp06u3MM9mVQrE1nJy7/8LRGD/3Xc3E4DO4xh6zz+dkFI0AIFfl7hYHE4tGyV37JOcavua3xWWYDP1vX1q75ADA21q/x9dXLF/CjOoXAdg6437yB41MeLaeSBtzLACD2pfs9b3T7g8DYLcYE56rL0tJV15T0qLtGifpnWgkwmHVz3CUbhUTC+L/74MjPZOzHK9xYfgPrKztfscJQT0JeeWuqqrioosuIj09ndmzZ3Puuedy2WWXUV2955u55557junTp3Psscciy3IiogmCMEBl5ZdSNuNeAKZXz2Pz0s80TiQIgiAI8RMJhxha/4HyHxMv0DaMIAhCP3Rz9j85LXQvZRF1TsINBnykoyyuT8/pXhvlHwogKeG2uOdKdivcVr6JjSerZJTWUXrFkz6GJtlOQ/PA+zvcl9VVboKYoGgaeoMBgJzSMQBk0Ya3w63a3IbgrrGtGarNsS+ppz1AVJaY3LmQDd9/GPfxzy+7kU3myzg0tjruYwMYTWYaJaW7QXN1986RPjn0Cb80vE+WKaxGNABS0pWz39OSdPdfdZufSEzGYtSRY0/Z53NyikcQlSWsUpDmbhbj42nj4g8pjVXhlVMYfep1muVQk9egLBQJuBt2f62pI8hVn3RwdfjXLMw6nyknXaZVvG4bNvUYfLKZTNyUb1y2x2NnNTzKC8Y/U+xXp5tBf5WaVcL86OG8Ez2sT9e1dm5ZwdXSW/zd+DjDc+yqzDGpVFnQuKJSvMdJJqoXipcsWcKkSZN49dVXCYVCyLK83x+WU089lbVr1/Lll1/y6aefqh1NEIQBbvKJl7HMcRx6SSbto6vxesQ/UIIgCEL/sGHhfDJx04qdsUeerXUcQRCEfmf3+Ztt6uyGaGmqo1M2E5INOF053brWsntXy8D6fNPuC9PmUwpLJS6rxml6p2n6zUwL/oPnQkdrHSUprNqpfC9PLHLu/pojI4tW7PhkM3WV21Wb2xx2A2BIdak2x74MGjOD5ZlzALB8cWtcz6uWYzHywzsxS2GyiwbHbdz/1WpSznbtqO/6OdIBfyd2OgFwZnevm0J3pGUq3XbSZbemu3H3p7zZC0Cpy4ZOt+/zfk1mC9dZ7uHw4GPs8Gv3mufdtgiAzY7DsDsT+3OSKAGTslAk0qEUimMxmRveXENTR5AdWccw/edPaBmv28wWK9usEwBoWP3RHo8NC6znKP0aHJI4pq870rMLuD78K+4Ln09HMH6v14nWtOk7AMrNI3cvzIq3qSXKwov1O9TtCCJ0j6qFYrfbzZw5c2htbSU3N5cnn3ySdevW7ff52dnZnHjiiQB88MEHakYTBEEAYMRlT1FPFtZYJy+884nWcQRBEAQhLjZt2oBPNrM1+8Q+cU6WIAhCX1OUruzwqmr1qzJ+XSydMcEXODnlJSRd927d2DOU4owjNrCO+6ot38hvDW9xtm01NrM6NzcTZWxJNiCxsdZDOJp8RaxEO3XLzfzJ8DwzXcE9vn5D1tOMDj7P5qh6Z6KmRJSfI2Napmpz7M/w8x6gklxeCBzJv5fHb8doU10laZKfiKwjf/DYuI37v3zWQgAiLeVdvqatUSkchGSDqkXH9Czle8YshVXdkd5TllXP85Xpt/xS/+4Bn9eePY1qOYvKVu2KeqZmZedpJFu97yWttaeWsj5WSmtYObf387f/yaatW7EYdTx+wSQsRr3GCbvPXzQbgNSqBXt83RpTFimY0xLbRaGvsxj1WE3K90FbZ0jjND0nVS8HoCNzompzTMuW+cJ0Ay80nU/A36naPEL3qFoo/tvf/kZjYyOZmZksWrSIq666ijFjxhzwmmOOOQZZllm6dKma0QRBEACwO100nvwcx4f+zMMb0/h0Q/3BLxIEQRCEJNbWGeL2ukOYFnySjBPUOddOEARhoJsSXc0Xphs4a+uNqozf4FEKYnZH989FtWcqhWKrFMTn7dtn5XVHZ/lSrjPM5zJd/Nv0JlpJhpU0s4FgJMa2+oFV8P9fHncLh4cXcZHhc0YX7Vk4zMzOByTKm9W70WyLKkdO5fMAAQAASURBVH/+KY4s1ebYn/SsPL74yQe8HD2Wv3y2HU8gPq2YG8pWA1Cjz8dktsRlzH2JOIoB0Lfv7PI1nqYaAFql9G4vkumOFFsanbLye3fvmjOZ6Js3M0jXQLb5wDsTS102AMpbtCu2ZHVuA8BWPEmzDGpbNfTXnBK6j29sx7J5+Rcctf5WPjLfwp+PcTE8J03reD2SN/kkAIYH1hHweXd/3SYr30spaYk7l72/yEqRyKaN1va++94ry7MWAMvgmarNUVRQgEPnxyyFqVj7nWrzCN2jaqH4/fffR5Ikrr/+eoqLi7t0zQ+F5LKyrrclEQRB6I3x02Zz+hFTALhl/jqaOoIHuUIQBEEQktd7a2oJR2VK83MYPniQ1nEEQRD6pUxHGkN0deQEK1QZv75d2R2Wa+9+EceW6uCR6LncGr6C1k71zvhMNuEmpf2w19a1+0/JTKeTuCvtHRaZr8G7eJ7WcTRVufZbdJJMrZSDK6dwj8dKM3cVyVQsFNvlDgBszsQXigEuOnQwg7NstHSGePzzLXEZs7N6AwAtKeq+TzS6lPFtvq63F/W11QHgMahfpLrT8nvODN5JI8nXLtnmrQBAnzX0gM+baKnnt4Y3Gbz9pQSk2lunp42CmPJ3lj9ymiYZEiEzVdlJ3NDQgP2DqzBKUSrtUzjtiKkaJ+u54uETacBFlZzFuo3Ka0I0EsEuKUdq2OzJ93OR7J6J3sZSy9VIZV9rHaVHPO5mSmNK94qicUeoNo+k01FlUzoQtG39VrV5hO5RtVC8fbvyJn3WrFldviY9XXkj4PEM7BWTgiAk1vXHDWdkbhpT/N+x5Z8XJeUZNYIgCILQFYuXKueEnTWl8CDPFARBEHrKVTgMgOxYY1zPDv1B4baXeNb4EEdGun8DTdLpeMt2Hq9Gf0JzqG+3YO4Og1tpbxtz9o9FUgWpEnlSK3LtKq2jaMpb9j0Atal7t7Udb6rlSeOjzNlxlypzB8JR5oT+xDnB20nNKlFljoMx6nXcfvIoTtAt5bxlZ1O1ff9H+nWV1KwUnIPpw3o91oGk5g4hJkvI4a63RQ65lS5vPpP6RaodjpmskEfQFNj3GcBaygwqxXV7wcgDPm+IsZnrDP9hcttHB3yeWsordlAh59BIBq7sAk0yJEJmqhmQOb3mQfLlRmqkHIZd8Zyqu97VJul0PDnqZY4J/YVP6pVd0V5P2+7HU52Jb7ff1/kNDgDCHU0aJ+mZnWuV95w1Ug6ZOereSwjkKQtLLHXLVJ1H6DpVX80CAeWNgNFo7PI1nZ272hukpKiSSRAEYV/MBj1/PyWHvxsf53DvJyx9+xGtIwmCIAhCt5VvWMI/3Ffxrul25ozP1TqOIAhCv5WVP4iQrMckRWmqq4j7+BltazlGv4oiXXOPrnft2v3U4h043ZLSfEp7W2P2EI2TxIexWOl6ldG+QeMk2kppXA1AJG/yXo8VOs2cpF/K5OBSVRZ7u31htsmFrJBGY0+1xX38rjpqZA6/dHzPEKmWlvm9b3dv71C6OBpzR/V6rAPJGH4II4PzOC1wV5fP2o51KIXikEX9IpVS/INmb3KdJxrweclFee3PLh19wOdmFCmF5NxIrSYbHlb5Mjk69Ff+UPyvhM+dSIWhMiosF3KKfglhWU/nqc+oeoZ2okwbVQrAN9uU77fO9hYAfLJZ1bb0/VXIrJzrHPH27L2b1jrKlgBQlzpO9bnSRxwOQIlvvdislSRULRRnZ2cDUF5e3uVrVq9eDUB+fr4akQRBEPZr6NARrBp2LQDj1v+Z6u3rNU4kCIIgCN3TsPAFAKJp+WSkiYWXgiAIatEbDDTqlFa0LdXb4j5+SqARAIOjZ/dGhptbOUS3gUDjwDnWKzusnDPqOMgOvL4id+ShAJSEywkFu74jsz+RYzFK/BsBcA4/dK/H8wYpx9fZ6aStuT7u87f5lAKiM8WIJGm769Q59yHCsp6JvkWsWzC/x+PIssyH4cl8EJ2Oc7C6bXOzHDYkg5mYDHXurn0Pf+Q4j8MCj7F68C9UzQYwXlfOJfpPsFQnV+vTunJlcYgHG+m7zpzfn5zi4URlCZsUoKWx6y2+42VjrdIRdFh+/9596khL3f3rFcN+zfDJszVMEz+HDclEkmBHQxsNrW46ve10yma8knYLY/qyqEUpFEudfXNH8bPM5SfBh9g59peqzzVo3KEEZSMZeKgqE/ffk4GqheIZM2YA8NFHXWt/IcsyzzzzDJIkccQR6vVBFwRB2J9p59/OBtMErFKQztcvJxJOrpWlgiAIgrA/4VCQYQ0fAqCbdKHGaQRBEPq/NpNyA7+zfnvcx7aHld0oKa6iHl1/rvdlXjPdS0bFB/GMlbQ87hYyUAoWOaXq7pJMlPzSEbRjwyRF2Ll5udZxNFFbsYV0PIRkA4PGztzrcYs1lXqUBRsNFfHfee2v38Kv9fM53bgo7mN3V8mIiazIOQuAtAV39PheRbM3xOOBE7k28hsKh02IZ8S9SJJEYbqycLGqzdela+o7ZWrI6vFrX3dMDSzibuOLFNV9qvpc3eGu3gxAvaHgoK2NzRYrDbsWLTVVblI92//aVNsOwOh8e8LnTqTCIeNYbj+GJVlnMf3827WOEzfpNhN/db7FavPP2LnwZZqsQxgTfIGL0p7TOlqfJNuUn0V9oFXjJN0nyzIrq9opkwsYPFr9s7fNFis7TMrxB/XrF6g+n3BwqhaKL7zwQmRZ5pVXXtm9U/hAbrjhBtasWQPAJZdcomY0QRCEfdLp9bh++hwerIyIbGHZy/3nDaAgCILQv21YOB8X7bTgYMysM7SOIwiC0O/5bcr5bdHWivgOLMu4Ykr7R3t2z86Ii6You7skX0vcYiWzxl0FkhYcpDkyNE4TH5JOx07zCABati7ROI02dpRvpyqWRblxCGaLdZ/PaTIrPyMdNZvjH6B+Pdcb3+LMqDbnv/6vUefdSxtplMaqWPH2wz0aY1tjBwDFGVYsRn084+3TFYaPed30J1jftV3QTbva5WelmdWMBYCUqhR1jElW1KnriLAmNpjG1K4temk1KWcDd9RuVTPWXqKRCE83Xcgbpj8y1hlJ6NyJptPrmXr928y4+jl0evV/bhIp1+XEJgWRyr7C41f+HlOtou10TxhsSjtyUzC5XlO6orLFR5svjMmgY3ReYhZ+1OQdxxuRI1nRkZ6Q+YQDU7VQPGfOHI466igikQg/+clP+Mc//kFjY+PuxyORCLW1tbz55pscccQRPPbYY0iSxBlnnMGhh+7dUkYQBCERcouHsXXynQBMq3iarSvFyiZBEAQh+cVWvQLAtpyTMJrUv7kmCIIw0AXTR7A+Vkp9KL6t/js8bVglpViSkVvSs0FsSqHY4O+b5+R11+ZYMUcGH+bh9Du1jhJXXpdyTqBUt0rjJNr4onMwR4Qe462x/9jvc3xpgwCINsW/BXzYqyy0CBidcR+7JxwZWWwd/WsARm5+HHcP2m3XVW6jgCaGZSWmtewQfQMzdZvQN3Wtteg5rf/kZsNr5OnbVU4GBnsuACmh5FpQ85U8lTmhe1gxtmsbFzpTlX8nos2JPWqgumw92ZKbcVI5ReIIyT7LMfZ4AIZ0LKXdp7SIt1sMWkbqs4x25RhWa8StbZAeqFs2n0eNf+fKjLWYDKqWDHeTZ/6KmyI/5+1m9TtICAen+t/622+/zaRJk2hra+Oaa64hLy9v97kekyZNoqioiPPOO4/vv/8eWZaZMWMG8+bNUzuWIAjCAU055eesSD0SgxTju49fwx+Kah1JEARBEParramOsd7vAciZdZnGaQRBEAYG9/grOCV0H69KJ8V13Na6SgA65BRsac4ejaFPU25WmkJ9b1dLT1S0BamQ8wjmqd8uMZFMpTNZHhvOWn+W1lE0sarKDcDY0tz9PkfOGAKAub087vPLncrPT9jkjPvYPTXl9N9QrivBQSffvPtst68v3Pg031mu47LQKyqk25vsVIqYRk/VwZ8bizE3/BFXGd4n0yyrHY2UdOX7KjXSpvpc3VHR0gnAoC4W8+WMwQAY2yvUirRPzduXAVBlHITeIAqLfdWwyUfRKVtIx0Pmkgd4wfhnTgr8V+tYfZIxexhvRw/nK6ZpHaXbdGVfMlf/PbMsOxI25+RiJwDbG724feLoR62pXih2Op0sWrSIW265BbvdjizL+/xfSkoKv//97/n666+x2cSB6YIgaEvS6Rh62dNcb7iVuz2nct+HiT/rRRAEQRC6atMXL2GSomzTD2XQmBlaxxEEQRgQijKUVrhVrf64jtve1ohXttCqz+zxGCaHUii2hZOrAKKWihbl/NNS177bE/dVudNP56zQXTzQfiyB8MBavBwMh9lc6wZgUtH+21Km5I4gKBvxhcLxD+FXCsUxS/K0xTQYTXQc+xAXhW7mN2WT2drQ0a3rUz3KmerGnBFqxNuLObNUmddfc9DnejvcpEhKsSA9W/0dqrYM5Zx5h+xWfa7uqGxSzlsf5Ora/fHgqLOZFXyEPxqvUzPWXkI1awFodyTme0lQh9FkZqttMgBHNL/JUfo1FEcPvrBD2JutYDQ3hH/Fo6E5Wkfptow25efZUDI9YXO6Us0Mc5kZJ+1g48Y1CZtX2LeELPcxmUzce++93HrrrSxYsIDly5fT2NhINBrF5XIxadIkjjnmGBwOR9zm9Hg8fPjhhyxbtozly5dTU1NDU1MTfr8fp9PJ6NGjOemkk7jiiitwuVw9nsfn8/Hxxx/z2WefsXz5crZv347X68VutzN8+HCOP/54rrrqKnJz97/6URCE5ORw5XD6uZcz/7ml/GtxJUePyuaoEdlaxxIEQRCEvTzQMJ3M0O+4YMYghmkdRhCEhBKffbVTlK60nG7o8BMIRbCY4nOLZbt5DKcFn+fIIanM6+EYNqfy95AWdcclU7KbWvEM+Xo/I61XaR0lrvIdFlw2Ey2dITbXdzCxyKl1pIQpX7OQpYYr+c40maKM/e/ad4w9jpGfvoApamBTTEank+KWQR9QFlrIKclTKAYYf8jxpGxzEd3YwJ/+u5GXLp++u3vjweSGlI4FzpJxakbcLS1vKACZkYO3yXY3VpEGeOUUUlPjd494f5xZytm+dnwE/D4sKdovNPG4W/g2cgE7TTnkOJd26ZqCwiJ2yuW0toaQZbnL3wu9ZWtVNlTIOYn5XhLUEyqZDZu+xyQpZxTHzIk5o7a/yUg1AeALRQmEowk5Bz4e/J0dlEbKQYKCsUckdO4/meYx0/w+i1ZeAlP73k7s/kTVQvHChQsBmDZtGikpKdhsNk466SROOmn/b/ACgQBLlyr/EM6aNavHcy9dupTzzz9/n481NTWxYMECFixYwEMPPcTLL7/M8ccf3+051q5dy2GHHYbX693rsdbWVhYvXszixYt55JFHePrppzn33HO7PYcgCNo6YlgWlx5aykffryTw+hW0/epp0rPytI4lCIIgCLttrPWwts6HUT+Fh485Rus4giAkmPjsq50Mm4n3zLcznJ00VnxO8fCJcRm33qOcEZjRi8X0aS7lM4tTbkeOxZB0iTlvTivHdL5PptHNtpRLtY4SV5IkMa7QwdItVWwrK2Ni0RStIyWMe+siRko+CizhAxa+Cl1p6HV6gpEYdZ4ABc74nRluDLkB0Nt6vshGLX84eRRfb2li07Yyli7yMePQow56TVtTHS6Us38Lho5XOyIAWcXKblMX7fi87VgPUADuaK4FoE2XTmoCstmdLkKyHpMUxd1cS27R0ATMemAN5RsYJkVxSj7SUtO6dE1huhVJAm8wQrM3RFaaWeWUivyAci64Y/DkhMwnqKdgyimw6f7d/y1Z1F+o0R+lmQ1Y9VHSou20eLwUuPrGn2PF+u8ZJUVpIj3hr4O6wqnQ9j6O5pUJnVfYm6qF4iOPPBKdTsfatWsZPXp0l66pqanZfV0kEunV/EVFRRx11FFMmTKFoqIi8vLyiMViVFdX89ZbbzF//nyam5s57bTTWLp0KRMmTOjW+B6PZ/cH5cMOO4xTTjmFqVOn4nK5aGpqYv78+TzzzDN4PB4uvPBC7HY7J554Yq9+T4IgJN7NJ47kzDVXMi66kZXzfobzhvf6/Y0WQRAEoe94e2U1AMeMyiHdZtI4jSAIWhCffbUhSRJp+giWWBh3zfa4FYobPUEAcuyWHo/hyCrgL+GzacXOTf4gDlv8imfJxutpIxM3ANklo7QNo4KLpY94zvwIK1afCEe+pnWchDHWrQDAlz3pgM8z6HUUu6zsaOqkorkzroViS1gpqhrTkq9QXOKyccf4VuZsvAH3Z+mEpqzCZD7wa0Zd2RrSgTopi7wE7NgFcKRn4cGGnU4aq7ZROmr/54j725RCcYchIyHZJJ2Om4y3UNlp4O5oKsnQD6O9Wtml22gspKuHD1iMen5t+5KS4GYatznJmnyYegF3aa6vIhM3MVmieGT/Oht+ICoYPJrP9EdwbPQbAHQpTm0D9VGSJPG18Tqyja1sqyoFV2J35/ZU+7ZFAFRZx5CV4PvdOWNmwToYHNxMOBTEaErMQhdhb6q3npZlOaHX/eCoo45i586d+338nHPO4Z133uH0008nFApx9913M3/+/G7NodPpOOecc7jzzjv3WQg/7rjjOPHEEzn99NOJRqNce+21bNu2LWEtQARBiA+LUU/KaQ8Rnn8akzu/Ydl7TzJt7jVaxxIEQRAEwqEgZy6/kHTDOMZNuEvrOIIgaEB89tVWuyUffJX4m3bEbcxDyx5llnEr4cjVwMgejWFJsTLPcDbeYIQrfVEcXTvqsk9qqNhEKtCGnfT0np/rnKzSC4air5DJ8mzUOkpC5Xk3AJA25JCDPvdXhv8yyvQp7auvgKHxO6f1HtN1BDtruano0LiNGU9zTjiR4KZbKZJrWfzmn5n50zsP+PyOqvUANJpLSWSftAZ9Hr5IC81NDZQeYC1HuL0BgIA5cYX5rfaZbPB6aA4kx2aAcKNyhrQ3taRb1x2rX85Y/WqW7VwFCSgU76ipZ110Ai5jmPEJWnQgqEfS6fhq7P3YV13JDN1m9Nbkarffl3ToHWRHW/G3NWgdpctM9crCrGBu4rsDFA2bgJtUnJKXresXMXzykQnPICiS41/B/ycWiwGg1/euh3tXrp87dy4jRigtUL755ptuz3HooYfyxhtvHHC39Jw5czjjjDMAKCsrY9WqVd2eRxAE7Q2dcDgrBinnXY1adQ+1FVs0TiQIgiAIsH7BW4xmB+cZFnDYyEKt4wiCoAHx2VdbAZvy2iu3VsRtzJLONRytX02WMdCrcVy7zspr7QzFI1bSclcrn80aDfkaJ1FHwWilSFkcrcTf2aFxmsRoaagmX25QdiuOP/iOrEKTlzG6SnSNG+KaY2Mwk2XySNIykmGv6d7SHBmUj78egDHb/kFLQ/UBny83bgbA70hsa9G/lvyDmcEnWK8/cLdJuUMprIRTshIRC4DMVGX3WpM3mLA5D8TYriw6iqUP6dZ1nbsKy5Gmsrhn2pdV3gwuC9/EP4c+kZD5BPXNGpaFHR8AplSntmH6MJ/BCUDQ06htkG4I+LxEZQn7sMQvitLp9VSkjAWgddPChM8v/CjpCsWVlZUAOHpxFk93pKUp5z0EAr37AHYgRx314zkhZWWJ+QdbEIT4m/bTP7LJOJpUyY/7lcuJ9rI9viAIgiD02qpXANieezIGo2g7LQjC/onPvipJV27Om7xVcRvSEWkBwObq3QKg0eYWDtFtoKNx/zvO+4NQk3JOpsdWrHESdWTlldCME70kU7lhidZxEqJqnbKgpUpfiN158N2lUqZS+EzpKI9bhlhMxu1TFlmkW41xGzfepsy5hu36IaRJfra/ccsBn/uVPIUnI6cRKD06QekUhS7lxOGqNv8Bn/eu46ccGvgbG4dcmYhYAEzWlXGZ/iOM1YsSNueBOHzKfXFTzohuXSenD1au88TvZ+BANtZ5ABidZ0/IfIL6Dhnion3X6eAWq/h77amgSWmdH/U2aZyka+ra/Vzg+x0TQs8xaOKRmmTw504BwFS3TJP5BUVcW0/vr91VXV0dqampB7w2GAxSVlbG7bffjiRJjBkzJp7R9mnLli2sXr0agJEje9bOqSuCwR9XpfV2p7QgCNrRGww4LnieznlHMjq8nsWv3s3Mi/+kdSxBEARhgGptrGFs52KQIGfWZVrHEQQhiYnPvuqxZA2BrZDmr43LeLFoFJfcBhI4cnpX+Px54Dkmmb5nSaUOpk+MS75kpG9TCiMRxyCNk6hD0umoThlJpn8x7WVLYPoxWkdSXaB8MQAN9nF0pQGvLX8EbABXIH4LNjo8bq7VvU0bqThTTojbuPGm0+sJHXs/fHwO01rep2zt9wwZv+9dYe94htEQKWb+mMTuGivKsAJQ1eo74PPqfTK1ZGJ1FSQiFgAzAt8w0/gKi+vCwDkJm3df5FiMnHANSJBR3L1/q83ZQ2E72H3x+xk4kKqaGkASheJ+xJFihPxJbGzVM2TswVv+C/sWtaRDB9DZrHWULlm90w1AcW421pQUTTI4hh8B5U9Q5F2HHIshJficZEER10LxoEF7vymXZZnjjjuu22NdfPHF8Yi0F5/PR01NDe+//z4PPvggkV07An/zm9+oMh/AggULdv961KgDHMYhCELSyx80imUTbmXa2jtwlL3DhurfMKYwcefnCIIgCMIPtn7+AjOlKNsMwxg2eprWcQRBSDLis29iOPKV9qBZ0fq4jNfaVEumFCUmS7hyino1VticAT6I9ZFdLT2VuqswYszqXqvWvsSfNR52LkZXv1rrKAmxxuciEh2DrvjwLj0/u1RpW5kbayAcCmI0mXudoaO5mt8a36ZTtmAy/q3X46lp9MzjWf7dUUzt+IrAf3+PPHbhXjfa2/1hGjzKYp6h2QfezBNvI3VVvGa8B/1OK/DFfp/X1KHky0rr/d9fl6VmA2Dwa1/UafF0sCg2nkFSPUMPdJjzPqQXKYXlnEit6oWWgM/Lm56f0mx2IGUMjC4HA8XMq57UOkKfF7NmAqDzt2qcpGtW7WwDYFKxU7MMgyccwV8/OJtlseE82OanyGXTLMtAFtdCsSzL3fr6vlgsFn79619z+eWXxysW8+bN47LL9r/L4uabb+aCCy6I23z/35o1a/jggw8AGDdu3ID5sCwI/dnUudfyUq2b+6rHUfzmet675nAsxoGxY0IQBEFIHlllbwPQOuwsjZMIgpAsxGffxMsuHsGGWAlVcjaHdvqw26y9Gs/dUEkm0Co5yOxlsStqzYQ20Pm0L4Co6WexP2AOVvPYqO5vUugrrKVTYCdkd2zSOorqojGZx1tn4A1P4cOZBz+fGJT23D7ZjFUKUlW5maJhE3qdo9OtnC/pkdLoC7esC856kI7nZ7HYV0jVuipOmLDnXuyKih0cottAu20IdktiW2nnONMo0W/EG045YBHzYveTtBn05OpHAjkJyaZPUwrF5mBLQuY7kHJ3lGvDv6bAmcJ3Kd37rsstHUVMlrBLPlqb68jIVm9XdtWWFQyTZIzESM/MVm0eQeiLdDalUGwM9o1C8SlrfskJJi9t9nuAcZpksFhTWZB3GWuq3KzY6RaFYo3EtVD8wgsv7PHfl112GZIk8ac//YmCgv3/AyVJEhaLhby8PCZNmnTQNtXxMnHiRJ5++mmmTVNnB0YwGOTKK68kGo0CcO+993b5uv/fssvj8aiSTxCEnpF0Ok6+9Gb+9ug3bG3w8tAnW7j9lNFaxxIEQRAGkLK13zMkuoOQbGDkMaLttCAIByY++6rHlubkIuPDtHaG+KA9zJhe3tvyNlcD4Na7yOxlNsmWBYAhoH0BRC2+UIQabwzIpzg/X+s4qskfcxhvf3U4a8ND+H0gjC3Bhb5EKmvy4g1GsJr0DM/p2v1BSaejzlDAkOgOWnZuikuhOOhRFlh06vtGa928kuE8MeO/PLSwgcKPyzhydOEeC9qDmz7mNdO9rNNNBs5MaLac4mEApEp+3K2NODNz93pONBrlzOjHGAwxGq33JCybxalkSQ23JWzO/Slv6gRgcFb3/yGxpNiol1xkyS00VG1XtVDctmMlADXmoWSIFrGCsIdY1mjejh5Om34ck7QOcxDhUJDhwQ1YdGF2FuVpmmVqSTprqtwsr2xl7qTEHT8g/CiuheJLLrlkj//+YSXz3LlzGT1auyLK3LlzmTp1KgB+v5+ysjL+/e9/85///Ifzzz+fRx99lFNOOSXu815zzTUsX74cUP5sTj311C5dd//993P33XfHPY8gCPHjSjXz4FnjuHLeUnSLHme94wTGHjFH61iCIAjCAPHB1g4yIj9hUIaFw1yJ2XEhCELyE599tVGUnkJrZ4iqVj9j8h29GqvT006HnILXlNXrXIY0ZQxLH9nV0hOVLcqZp06rEYe1/xZPs3KK+Iv1euraA5xc18H0QRlaR1LNli2bScfD8IJSDPquF6FarEOQ2wM0tXfGJUeoQykU+w29+5lOpMuOmci/Vi+gus3Pc9+Wc/VRQ3c/Fm1QdqN3Oobu73LVWFJsNJFOFm00VW3dZ6HY3VKPS4oBkJ6VuIKFNUNZYJIWcydszv2paWhAR4xBmT1bcfTHnMf4rDLKAwxBzZ4ect1aADrTB0bnEEHoDl3JTG4IwyDZxpVahzmIio1LGSaFacdG4RBtdhP/YEaBmVrdEgZt/gRI7uMe+itVl/189dVXfPnll/s8uziRnE4nY8eOZezYsUybNo3zzjuP+fPn89JLL7Fjxw7mzJnDvHnz4jrn/fffz7PPPgvAtGnTeOKJJ7p87S233EJ7e/vu/1VVVcU1myAI8XH0yBz+PngxfzC+StYXv6W9tX+f/SUIgiAkh1AkxrxNOm6LXEHopEe1jiMIQhIRn321UZhhBWTqmnu/I21p6lGMCz7HW8Me6PVYZodSjLFG3L0eK1l5Nn/NQ4anuNS2SOsoqhtXoBQs11a7tQ2ispzVj7LKchW/NLzXresWjr2XY0J/4StpelxyRLzKTvyQyRmX8RLBajJw04kjGC1VMPbrK2mqrdz9mK19OwBS1khNsrUYleKvp65sn497mpRuCm3Y43LGdFc5XEqudLmdaCSSsHn3Zfamu9hkvoxjgl/26HpHTglhDFS2xGexxP7Y27cAYMgfr+o8gtAXuVJNALR4gwd5pvZat3wHQIVlNDq9tkcqTs4x8A/TY1zmf4mO9v67wDGZqVoonj17NrNnzyYlJUXNaXrsoosu4uyzzyYWi3HNNdfQ2hqfb8J//vOf3HrrrQCMHDmSDz/8EJut66vBzGYzdrt9j/8JgpCcjrzg91RLeeTQwrYXfqF1HEEQBGEA+GpLI62dIbLTzBwxrLeNSQVBGAjEZ191nRF4h03myxi14S+9HqvBEwAgy977I7lsGUqh2J4EO+XUEqtaxtmGhRwurdU6iuomFNgYLlXh27ZA6yiqynKvB8Be2L3OhD/swvyhfW+v+ZXXyYjZGZ/xEmTOhAIeTv0Xs6VVlL9x0+6vZwcqALAXj9UklzdF2bkbbt6x78dbagFw69ITlgnAmZVHTJbQSzLulvqEzv2/0v1VmKUwjh7uqC51WQGo2NVpQQ2xaJSikPJ3mDlsqmrzCEJflWEzYyKMNdBAWOPFJwejr1G6AfmytG+SnZlfQq2Ug06SqVj9tdZxBqQBf5DAnDlKq9jOzk4+/vjjXo/32muv8atf/QqAkpISPvvsMzIzxQ08QeivrKkOOk9+koisY2rHFyz/79NaRxL6mert61nz5b+1jiEIQhJp/vIJJktbOX1SfrdaMgqCMLCJz77qSU1zkCKFSPH2fkf0D4XiXEfvd9Sl5ZTyUPgcHgqfQzQm93q8ZKRvUwoWYYe2newS4Qj9Rj4138TpVQ9qHUU1nZ42SqLKLtiicUd069pBWT8Uir1xySL5lQ4BsZS+1eZbp5PQn3A/ANPbP2LrygV0etrIRemAljek9+c390TYXqzka6/c5+OBtjoAvMbE/nkbjCau093M6cG7aY5ot9EpFo2SF60BILOkZ8c3jjY18LDxH5xacW88o+2hrnIzqZKfoGykcKjYUSwI/8tp0bPJfCmLLdfS3lijdZwDyu1QFmZZh8zUOImiNk15TfFu/07jJAPTgL+zlJX147k/lZX7frPSVe+99x4XX3wxsViMvLw8vvjiCwoLC3sbURCEJDdi6tEsK74CgOHL76K+at+tlAShu3yhCPe9/hnmr//IuoX/0TqOIAhJoKWhmnOan2C++S7OH5z87awEQUge4rOveqw5gwFwBGt7PdaltX/keeODDIrt7PVYzowsnojO5fXoUbh9oV6Pl4xsncqfkzFriMZJ1Fc05lDl/+XaftuWsWLdd+gkmXoyycov7da1gx063jXdxqehi/B523ud5T37+ZwdvIPq4rm9HivRhk85imWO4wCIfXQTNdvXANCMc5/nAyeCPmMQDbKT1tC+25tGPcpu3qA58QuONqcdwip5GM1+7RbUNNbswCKFCcl6coqG9WiMQoeRM/XfMCP4HXIsFueEim1Nfl6IHM/XlqMxGE2qzCEIfZlOr6ddSgPA06ptl4IDaWuup1BWFuiUju/ewiy1RApnAJDauELjJAPTgC8U19T8uLIjNbXnrZ2++OILzjnnHCKRCC6Xi88++4whQ/r/BxVBEBRTL7qXrYbh2Omk+eXLiUWjWkcS+oFnvylnYmAZ+VIz0cX/1DqOIAhJYNvnz2OUomw1DKd0pPYtogRB6DvEZ1/1pOcrN/Wzo429vjk/PryGo/WrybQZe53LqNfhtCrjtHT2z0JxZkj5vk7NH65xEvWlZ+VRK2UDsHN9/zyTuWP79wDUpo7p9rVOh4NiXRN2yUdd+aZeZ6kIO1kmj8SYObjXY2mh5JwH8clmRoY3Efj0TwA0mEo0yxMZfz4zgk/yoHzJPh+XvY3K86zZiYwFQGaq0sGhWcMzRZsrNwJQr8/tcQE2t0Q5f9qOj/bWxrhl+/9Wtqdxd+QSvhx2myrjC0J/4NE5APC5GzROsn+bK2r4KDqNVfpxODIS/7q7L9mjZwEwOLCJSLh/vm9NZgO+UPzmm2/u/vW4ceN6NMb333/PnDlzCAaDOBwOPvnkE8aM6f6bWkEQ+i6jyUzKuc/il00MD6zjvU96385PGNiaOoL8c0EZr0ePwi75Gde5mPqd27SOJQiChuRYjJyytwFoG362xmkEQehrxGdf9WQXDSUmS1ilIC29aDMYDPhIxwNAek5xXLKNT2nmUN16PE3J3f6wJ/ydHeTQAkBO6cD4PqyzKoWgjvKlGidRh6VhFQCh3Mk9ur7BoHQ2cFf1vlDc1hkG2L3Yoq/JLhjE2tLLABgfWM5d4YtZV6Dd+8eiDKU1eHWbn9g+WuG/6bycQwKPs33oZYmOxlT9Vi7Xf4SueknC5/5BZ90WAFotPX/tT7Gl0YALgPqKDXHJ9b821in/Ro3KS1NlfEHoDzoNTgCC7eos2IiHRa2p/DL8W14e8YTWUXYrGTmFDjkFmxSgYtNyreMMOP22UDxv3jwCgcABn/PII4/w4YcfAjBo0CCOOGLPbfZff/01kiQhSRKXXnrpPsdYvXo1J598Mp2dndhsNj744AOmTJkSl9+DIAh9S9GwCSybdB9zQvfw++91bG3o0DqS0Idteuk3XBT9D678waw3TUQvyZR/kjxv4ARBSLyydYsYFKsgJBsYeUzib6IJgpCcxGdf7ZnMFhol5eZ8c9WWHo/T2qCccRySDThdOXHJdnP4CV413Ye0s/+d91ZfuRkADzacSbIbRm2hHOV8WWPDGo2TxJ8ci1HkU3ZVOocd0qMxOmxKkS3U2POfwx+c4HmLi/WfkKn39XosrUw893bqUI4dkJAJDz9Vsyx5DgsGnUQoGqOhY+9/s+q9MepwkerKT3i2wwILucP4LzJrv0r43D+Qm7cD4E8r7dU4zaYCALy1W3sbad9qVmAlwOh8hzrjC0I/EDSmAxDpaNI4yf6tqnIDMKnYqWmO/09vMFCeoiz8a9yyWOM0A49B6wBqueuuu7jhhhs488wzOfzwwxkyZAipqal0dHSwbt06XnnlFb77TvmgZDKZePrpp9Hr931Oxv6UlZVx/PHH43a7AbjnnntwOBysX79+v9dkZ2eTnT0wPsAIwkB0xJwreaFtGZu2NPGb11fzztWHYTL02zU5gkoqN6/i0MbXmWWMsXHqXALtl8Hi6xheM59Q8AFMZovWEQVB0EDLt88zFFiXdjhTMrIO+nxBEAYG8dk3ObSa8sgNNeOtLwOO6dEY7Q2V5AHNugzydfH5DBEwZkAIwp7kvVnZU621OyiRJRqM+djj9OeV7FJLp8EOyPH2fsdssql1+3g0fA5TdNuZO+7QHo0RSR8M7aBvK+91nisjr2E1BqnR/aLXY2nFYk2leubd/OObJbwSPYaXs7XbBWrQ63gi5SnGRDbQtvkf5M04bo/HmzqUts8/tIFOJNmWBU2g9zUnfO4frI+V0BqdTmr+1F6N02krhtBawk1lcUr2o/aWBp4N3UTULNGZsSPu4wtCfxG2ZEAnxDq1e005kFg0SvPOLUBGUhWKAdaMupFfLqpnSng8PXsnIPRUvy0UA7S2tvLMM8/wzDPP7Pc5hYWFPP/88xxzTPc/yH3zzTc0Nv7YQuC3v/3tQa+58847ueuuu7o9lyAIfYMkSfz5rPEc/8hCdPWrWfDyVxx76R1axxL6mLb3bqFEirHKeiiTDjmRcChI0+K7yKKNFZ//iykn/0zriIIgJFgo4GdEk3KsgXHKTzVOIwhCshGffbVXZZ9CVYMZf8hGT2/z+1qU9tDthkzitafuh5uVsrf/FYpXmKZyQfAFzhqayr1ah0mQ4rGHwpdQKNfT3tqEox8tHFtV7eHN6JFszp3DedaeFTRN2SOgAuydlb3KEvB3YpWUwmVqenx292tl6vEX8Gr7GMY1d2peECgwtFMYbWZZ3TZgz0LxzzyP02owk2Ps2dEIvaFPUxY1mYMtCZ/7B68EDqM8PJFXx87o1TjR9MHQBsb23i+W+F9Vm5fiABp02eQ7MuI+viD0F3KK0mVGp+HikwOp2rqaj6RrqTZnkZvd+w4c8TR4zFRqFi2BnW6toww4/bZQ/Mknn/DBBx/w3XffsX37dhoaGmhpaSElJYXs7GwmTpzIKaecwjnnnIPVatU6riAI/Uh2moXHjrVzyMd3oi+PsXHxDEbPPF7rWEIfsfH7D5noW0RE1pEx535AOQN7e9FZZFU9Q8qaF0EUigVhwFmycgWDZAthyciYI+ZqHUcQhCQiPvsmh00jr+bR6m2cKxcxt4djhNqUQrHfHL/iX8yaBS2g8yfnzcreKG/2EcSEK07nOfcFjowsHjddzgqvi581hjisH9VqVu+6KTyxyNnjMZxFo2Ap5ESqe5XF09qIBYjIOux9vCAmSRKPnDtR6xgA+KyFEFxNpLVij68Hg37O4TMwQFvqwwnPZXTkApASbkv43ADhaIydrUqL80GZtl6NZckZSqxMIhrwxiPaHrwVyhniDdZhcVvMJAj9kTdzPG9XHoHXMJLeLf1QR+OmbykB2s25FBq612VIbROKnOgkqHH7qWv3k+dI0TrSgNFvC8UjRoxgxIgRXH/99T0e48gjj0SW5f0+fumll+73/CZBEAa2WYfMZNmS45jm/gjnJ9fQMWoJaX38A6agvlg0ivFLZQf6iszTmDFi4u7HhpxwNXVPv82XvsEY6twMz3NqE1IQBE28uM3MF8FH+f0MK780GLWOIwhCEhGffZNDUbpShK9q6/l5pj6/H4+cQsiaG69Y6FIzATAGtNspp5bKlk4ASly9K6z0NVtKL+LrtXVMr/dz2Eit08SPZdv7jJdSmFw4usdj5A0axY5YLuVyHpPbO0h39GxnsretkWzAI6WSMUDamidCzFECbWBo37nH19saa8kFQrIeR3rid8lbncqucXtEm0JxTWMLmbEWPMZMctJ6d8xUypiTGPmVA6veyur4xNtN36gcNxFwjYnzyILQv/gGHccNi7OYocvgEq3D7INcvQwAj2uSxkn2lmo28KuMFUzwfE3VYjd5x1+odaQBQ7zbEQRBUMmoy5+kVsomX25k0wtXax1H6ANWffQ8wyLb6JQtDDn7nj0eyy4YxB+HvMFfIufy8tLerZAXBKFvaeoI8tWWJmR0HHtYMq5JFgRBEIoyrIBMe2t9j8d4z3YW44PPsXb07+KWy5CmFEBSQq1xGzNZXFl3F382PM0wa4fWURJqfKEDgHXV7RoniZ9QMMA17od4z3w7Ux2eHo9jTXVwYcqTXBG+kfL2SI/H8bUrrfY7dPYejyHszZBZCoDNX7PH1z1Nyn+7JQc6feJ3t6VlFgDglN3IsVjC52/f9AVLLNfwH9Md6HRSr8Yqzk4nhBG3L4zbF4pTQkWGdysAlsLxcR1XEPobl80EQGtnfH8G4yXLvQ4Ac2ly3ls4IqWCY/UriG3/SusoA4ooFAuCIKgk1Z5B+wl/JyZLTHd/yKpPXtQ6kpDEguEw2SuUNltrSy8lM7dor+dceMhgAOavrKEz2PMbH4Ig9C1fLlqCFAszqdjJ0OxUreMIgiAI+1Bq9rLRfDnv+i4jEu7ZjcEGj3Imao4zfjtkLbt2yqVqtFNOLQF/J0dGF3Ou4WsKXAOrmDchx8KxuuWMrZindZS4qdy4FIsUph0bhUN6d0btD617y5s6ezxGyKO0avfpB9b3ltpSc4cA4ArV7vF1X5vy3+16bbqwpWcpjZQtUhhvhzvh8/vrlAKsL6X33SSsJgM5djMAFS0973Dxv0LBAEURZSd47ohpcRtXEPqjdKsJMyEMnXVaR9mL19NGSbQSgKJxR2icZt/0pTMBcLWt1jbIACMKxYIgCCoaNeN4lhZcBEDpoj/QXFupcSIhWf1rcRWXBq7nPeloJpx96z6fc+gQF0NdFqaGl7HkszcSnFAQBC3IsRjTF1/DYvM1XDW4/50vKQiC0F9k5hRgIIpBitFYU96jMRo8AQCye9l69P+z5g3nwfA5PCWfEbcxk0FDxWZ0kkyHnEJGZp7WcRJqdI6FZ0x/5erIS7Q11R78gj6gdev3AFRaRiP1stVzaaYNkNnZ2PPFEWGvsgM/YHT2Kouwp8zC4QBkya2EAv7dXw+5lWKKz+TSJFeKLY2rY7/n9ODdNAcSf6tcai0DIOgYHJfxrjV/yL9NdxNc925cxgOo2rIKkxTFg43comFxG1cQ+qMs2thiuZT/Rq4iFo1qHWcPFWu/QSfJ1JFFVn6J1nH2qXD8UQAMCpfh8/af7inJThSKBUEQVDb5kofYrh9COh1898afD3j+mzAwtfvCPP7ldsrkAvwnPYY11bHP5+l0EncVrWSe6SFKVz6oSVssQRASa/va7xgUqyQNPzNnHKZ1HEEQBGE/dHo9DbpsAFqrt/ZojIc7buR544PkG71xy+XMLubJ6FxeCRxKKNJ/3ju2VW8BoMGQ3+vCYl+T5nRRJSk7IKs2fK9xmvjQ1ywHoDO79+clHhtZwGrzz5m94Q89HmO9fRZnBe/g69zLe51H+JEru4Cdcg6r5KHUNzbs/nrUo7TsD1oytYrGutRDWSUPo8Wf+NdJm7cCAH3m0LiMN1xfx3TdFmL16+MyHsDGDgt3hS/mv2nnDLjXXEHoLodL6eail2Q8bU0ap9lTR9liAGpTk/es8dziYTTgwiDF2LFmodZxBgzxyi4IgqAyk9mC8axnuCd6Mb9pOJGXl+zUOpKQZJ7/dCnt/jAjctI4a8reLaf/v3HHXYpfNjE4VsGW5V8kKKEgCFpp/fYFANbbj8CRrt3NM0EQBOHg2sxK8c7XuKPb13a0tzJJ2srR+tVkZTjjlsmRYkS/68zLtjifV6mlQOM2ANpTDvzeub9qSBsNgK98mcZJ4iOnYwMAtkG9Py8xPSMTp9SJw1/V4zHqwjaWyyPxZ47tdR7hR5JOx5XOZzgzdDcVgR9b7Os6lTOho9ZsraKRmaqcKdrsDSZ+7mA1APaCkXEZL5qu7Ew2unvW3WJfVreZmRc9gW3Dfxa3MQWhvzKZLXiwAtDeUq9xmj19FRjBU5FTcJeepHWUA6pOVY6h6Nj2ncZJBg5RKBYEQUiAklFTyD/+emR03PvBRnY0xW+XgNC31ZZv5qpVc/mz4WluOX7Q7ht5++PIyGJ9+jEAeL99KhERBUHQSDDgY0TzJwAYp/xU4zSCIAjCwfhthQBEWyq6fW1rvbKY1IN1v91lekKnk5iU0sihuvW0NTcc/II+QmpVivEhxyCNk2gjkjMBAHPTOo2T9J67uZ4iWWmhXTK+9+cluopHAZAXqelxB6Y2XxgAp9XY6zzCnorSleJJVduP5+e+5LiKmYHH2Tn0Iq1iMVO/hcv1HyFXL0/ovAGfl1yU42VyBsVnYYI5W9mZnObr+WKJ/7Wx1gPA6DxxbrcgdIVHUt7LdbYlT6FYlmXebszngcgFOKedo3WcAwoXTAfAWp/Y1+SBTBSKBUEQEuTSQ0s5fGgmurCP7164hXAo8StVheRTO/9WUqQQY2xuZo8s6NI1ztm/BGB8+9e0NtaoGU8QBA1t/OoNnHhpJIMxh8/ROo4gCIJwELKjGABjR/c7CHU0VgLQpov/GZ0PyX/lVdN9hKtXxn1srVi9yp+xPnOIxkm0YR+i3EDN923WOEnvVa77BoAqKX93u87eyC0ZSUTWYZWCNNVV9miMEY0fcYn+E4rk5LnB318UZewqFLf8WChu8Eapx0VaZr5WsZgd/Io7jP/CWZvYNqd15cpueg82nHH4/gdw7NqZnB2Jz70CORajtPYDRkmVjMqxHfwCQRDw6p0ABNuTZ5FedZufls4QRr3EmPzkXvThGjWLoGzEHYgSi4kjHBNBFIoFQRASRKeTeOiscbxhuY+LfC+x/F89PzNJ6B+2rVrI1I4viMkSlpPu6/JZP8MmzWKbYRgmKcKWj8WuYkHor3RrXwOgLP9U9AaDxmkEQRCEgzFmKu0+U33dvzkfaFVaj3qM8T9moNOYDkDQnTw3K3st3AlAWv5wjYNoo2TMTGKyRA4tNNf37aONFgRHcG7wdj4u/HVcxjOazNTpcgForOjZGa2z3O9wt/FFCkNlcckk/Ojw0LcsNF3HTzb9eD+kaVe758xUs1axkK1ZAEi+xJ4nWtmh46nIqXyRckLczv7NHaTsqk+ng/bW5l6P11hdxv38jfdMtzEsy9Lr8QRhIPCblPdeIU/vfwbjZdv6pRyuW8fUXAMWo17rOAdUOvYQpsXmcUngd2wXXTkTQhSKBUEQEijPaSU0TdkNOm3nc2xZ/qXGiQStyLEYoY9uBWCF81iGTjisW9e3jbkYgJLyN4hGInHPJwiCtprqqxnjU84dLDjyco3TCIIgCF2RWjSGT6NTWBAb3+1rw26l9W7AEv8zOoOmDAAiHY1xH1sLwUiUOf47GRl4AdeIQ7WOowlbmpOdeqXVec3G7zVO0zvLawMskUdhGX1i3MZssShnV3fWbunR9bZoOwCmtKy4ZRIULruNYl0TTv+PCxx+7X2MWwyvkG3SruualKr8XZv8LQmdd2MgnQci57OwND4LJUB5fWjGCUBDxcZej1e3VWn9WqUvwmyx9no8QRgIwmalUBzzJnbxyYFY173My6b7uU73b62jHJTRYGBMkbJ4cnlFm8ZpBgZRKBYEQUiwKSdfyXL7MRikGOYPryMUDGgdSdDAmi/fYExoHUHZSNGZ93X7+nHHXYYHG+6ohSVrN6iQUBAELf1na5BTQvfxfOrPKR4+Ues4giAIQhfkDJnIz8M3cL9vDoFwtFvX6rxKi9uILTfuucIWpZ213Jk8Nyt7o6rVT0wGvclKliNV6ziaebf4Zo4KPsxX0YlaR+mxWExm9U7lBvCk4vS4jRtIU86ujjVv79H1qXIHAFanKBTHmz1POT83M6K85nV2uDlT+opfGD4g056iWS6DXXntTQkltlBc3qR0Ryh1xbelc6OxiFo5g6aW3i8Q8letBqAlbUSvxxKEgaIxfQpvRWdRoS/ROspuGW1rADCUTNc4SddMLVXeF6yq6EcdcZKYKBQLgiBoYOjFT9CKndLYTla8eqfWcYQEi4RDpH9/DwAr888nt3hYt8dIsaXxwriXOSl0H8+tDcU7oiAIGpJlmbdWVLNZLsZyxLVaxxEEQRC6yJFiJM2sHBVQ3eY7yLP31BmK4pFT0Nnz4h/MqhSK9QneKaeWyhalsFLisiFJksZptOMYdhjlch7rajq0jtJjVeWb+E3keU4xLmdEblrcxg3nTWJBdDzbIt0/8zUWjWKXlTaXac747/Af6LKLlXbx6XTQ6WmjrVFp1d8pm7GlOjTLZXEq3yupkcTuXJPr1pKFm0GZ8d2pO2/Y3zg0+HdW6Cb0eixLs7IwPZo9ptdjCcJAUVd6Or8LX8Vi00ytowAQ8HdSGlaOU8gfM0vjNF1zhLOZj0w38evNF2sdZUAQhWJBEAQNODNz2THlNgCmVDzLzq2rtQ0kJNTHCxZij7ppI40x597V43FOnTUdkPhySyNVrd27GSkIQvJaV9PO1gYvZoOOUyaoUDAQBEEQVCFJEoXpKbhop6a+e7sfHjf/jPHB53YfLxJPujSl2GUKtsZ9bC0Y173Ka8Z7+KnxC62jaGpcoROAtTXtyLKsbZgealz3JZcbPuZqyycY9fG7RakbdxaXhG9mXuiobl/b0d6CXlL+PO2u7heahQNLc2TgRukE0Fi1lY5mpVDcpkvXdOFHqisfAIfsTui8t7T+gWWWXzFaKo/ruCVZdgAqmjt7PVaWbxsAqSWTej2WIAwUGTYTAK2dybGxo2L9IkxSlBYc5JUM1zpOl4wYNoIRUjVFci3N9VVax+n3RKFYEARBI1NO/hlrLVMxSREa3/49sVjf/HAvdI83GOGuxTA7+AjfT/0bdqerx2MNzkrl8KGZWGU/X3/xYRxTCoKgpfb3buER4xNcPtSL3WLUOo4gCILQDX8MP8wKyy+xbHyzW9c1tCvH0eQ44n/+o9GuFLtSQv2jUGxuXMsh+o0MMvSPHdI9NTrPzhWGj7k98Beaaiu1jtMjsaplALRndP9c7wMZlKm08d3Z4iMSjXXrWk+r0qq3U7ZgMlvimktQNOmVNs/ttdvxtynns3sNGVpGIj1TKRTb8REM9L642hXt7hZcKOdh5w6K727dH1pZV7T07vfS0d5Koay0CS8c2Tfa1QpCMshINWEmRMyTHG2T27YuAqDKOhpJ1zdKgo70TCr1xQDsXPO1tmEGgL7xXSEIgtAPSTodmec9yfuxw/hV+8W8uUKsjhoInl64g2ZvkAxXFseeMLfX4/1ypJ8l5qs5ecNvCQbErmJB6OsC/k7GN77L6frvOGmwQes4giAIQjdFU5UCiNzW9cJdLCbT2BEEIMce/8KUIX8sD4bP5WXdqXEfWwspXuXPVp85VOMk2kox6fmpaSGn6RdRu/E7reP0iMu9DgBT6Yy4jptrt2Ax6rDGvNQ0da+VcKdbKRR7pPi1whb21JFSAECgaQfhdqUI6TP1fAF1PKSlZ/KLyO+YG/wjLb7uLS7oqYYdSkvnJtJJtcfvjG6AoWY3r5v+xP2Nv+rVONWblcUcDbhwZubGI5ogDAiF/i1ssVzKI+3XaR0FAGP9CgAC2ZM1TtI9jekTAQiVf69tkAFAFIoFQRA0lF86goZj/04j6dz7wSYaOwJaRxJU1FRbyY6FbwAyvz9hJCZD7/8ZnjHjUHySlQw8rPv85d6HFARBUxu++jcOOmnAxehD+8cNfUEQhAHFWQKA2dv1RaCtTbW8b7iJ54wPkZVqinuktNxhPBmdw/zAtLiPrQVXsBqA1Ly+0TpRTc320QAEKpdrnKT7/J0dlEaUdrv5Y4+I69g6ncS/Tfew1vIz3Bu716K8wVzCWcE7+Jv9t3HNJPzI4xzNythQ6oIWYh3KbrtwSpammSSdjjXWQ1ktD6XZF03InJ6aTQA0mQrjPnZBXg4zdZsYQQUed8+7L6wO5nNp6EbmZ/0yjukEof9LTVeO/XDKHuRYYhafHEhBx3oAUocmx5nJXaUrUvKmN6/UOEn/JwrFgiAIGrv00FLGFTjwBCI8/+Y7WscRVFT+5q38Xf8Xnkh/gxPHxmc1rsFoYkfxWQDY1syLy5iCIGjHsO5VAHYUnIreIHYUC4Ig9DWW7MEA2AO1Xb7GXV/BKN1OJujLMRr0cc/k2lV87gxF8YcSUwBRSygYIDem7PjMKhmlcRrtxfInAmBtXqdtkB4oX/cdBilGIxnkFAyO+/gRi9LK2F+/tVvXNYfMLJdHUu2YGvdMgqJ67C85I/RHPtAdib5T+XmO2bI1TvXja2WLNzFnioYblbN/vbaSuI+das+gBQcADRUbezzOmiaZr2OT8A07LV7RBGFAcO5qZ2+SIng73JpmafAEuCL4W/4QvpzS8fFdmKW2/PGzARgU3kbAn5hjAQYqUSgWBEHQmEGv4/7Tx/KI8Ulu3vkL1nz5utaRBBVUbFrOlNYPABhy1MVIkhS3sYeccDURWceo8AbKNyyJ27iCICRWc20lY31Ke7fCIy/XOI0gCILQE858pR1yVrS+y9d4m5Xdx269Oq1XU80GxuhrOEy3jta2vn1OcUPVNvSSjE82k5lbrHUczaUPVVo2FwW2JMWOpe7wbFfOS6yxqXNeYtAxCACptaxb17X5lCKh0xr/3f2CoihDOYu9qtXPs/ZrmBH4O7XDLtQ4FRxu2MwV+g+JVK9IyHxGt7KjPpYe/4USAI1GpcW3p2ZLj8fYWOcBYFSePS6ZBGGgSLGl4ZPNAHha6jTNsmqnm41yKSuyTo97m3u15ZeOohknJilKxdpvtY7Tr4lCsSAIQhIYW+gkr0BZRZqz8A90erp3jpKQ/NrfuwW9JLPSdgQjpx8b17Gz8ktZm3Y4AI1fPhnXsQVBSJxtXzyHXpLZZBxN0bAJWscRBEEQeiCnSGmHbMdHe2tTl64JttYA4DWps6NOkiSeNz7AK6b76dzV6rSvat2p5K/X56lSXOxrikdNIyzrScdDQ/V2reN0S7RBKV4Fc9Q5L1GfNQwAa0d5t65Lq/ueS/SfMIa+9efZlxSlpwBQ0+alriNCAxk4MnI0TgXHBD/jduPLpNUk5sxvh185b92cO0KV8b1WZTFNqLFn38uRcIjjGp/nBN1SRudY4xlNEAaEdklZYNHR2vXFg2pYVaXcY55U7NQ0R09IOh1rHUfxdvRw1jX17a44yU68qxYEQUgSE376ALVSDrk0s+7l32sdR4ij9d++xwT/UsKynqzT71dlDtPMnwEwtvljvGKhgSD0OXIsRn75fAA6Rp6jcRpBEAShp1JS7TTjBKCpqmu7uGIepU110Kpe69UOvRMAv1vbm5W91epuo1F24k4p0jpKUrCk2Kg0lAJQt2mRtmG66XfBKzkk8DiGKT9VZXx7wUgAsnadad1VpQ2fcbfxRSb4FqsRSwAKHEa+Mv2WVdJPaWlSdtplpZk1TgXRlEzlF76uLfLpDVmWeSF8HM9ETsIxSJ3FEpF0ZVe9wd29xRI/qNm+lmt0b/Ow8SmKXanxjCYIA4J313uvQLv6rykHUrLxKS7Uf87MbFnTHD1VNuUObgj/ik9atD+ioD8ThWJBEIQkkZJqp+XIBwCY3vAmW1d+rW0gIS5i0SiWr+4CYGXWXIqGjlNlnjGHnsJOXQEWgiz9+r+qzCEIgnrWVLXwWvAwNsiljPzJxVrHEQRBEHrhG+tPeD5yAtW+rp01b/AqhZKYLU+1TD6jcl5rsL1BtTkSYYHhcKYHn+TTMeosvuyLWh2jCct6Wmt3aB2ly+rbA9R5gjRILkYPG6LKHDmDxgCQSxMBn7fL1xmDyqJbyZqhSi4BzCYzNl0YoxTlX9Id3Gp4hSxzROtYkKoUIQz+ZtWnavaGeDV4GPdFf0p+qTo7ik1ZylEIqb6dPbq+abvSgrvKNBidXh+3XIIwUPiMTgBCnkbNMkTCIeZ6XuNe4/NMdCXB62wPTClV2mWv3NmGLPfNYndfIArFgiAISWTc7DNYbj8WnSRj+O91hENBrSMJvbTyg6cZGi2jQ05h2Dl/Um0eSadj3ZR7mRV8lD+XlYg3T4LQx7y1qp6noqfxzKh52J3qnFEpCIIgJMZXRdfyx8jFbAllden5Jr9yA1HvUK9QHDQrRa9Yh3Y3K+OhoqUTgJJMh8ZJkkfVpBsZG3yOebGTtI7SZat3tcEckWvHauragorucrpy8WADoK58Q5evM4XbATDYxPsxNbUYlde7Ibo6fm74AJdd+9bG+jSlUGwOtqg+1w+vZQXOFCxGdYqw9oJR1Mvp1EXSenR9pHYNAO2OkfGMJQgDRrl9Om9GZlEvaddav3LTcqxSkA45haLhEzXL0Rtj8u1YDJDj20ZldZXWcfotUSgWBEFIMoN/+hhuUhkcq2DFa+oVFgX1BcJRXlwXZEuskPWDLycju0DV+Q4/+hRajblsaehgeaVoPy0IfUUgHOW91Urb0bOmiFaagiAIfV1RhnL+ZlWbr0vP90YNtMtWzBmFqmWKWpRCsexTf6ecmiqadxWKXdoXlZLFiMGlBDGxtrq9zywWtSx+jGeMf+Fcp3pnZks6HQstR/OvyDHs9HT9zyUl4gHAlJapVjQB8Fp//GzsJhWzRfufaYszF4DUsPqfpZsqNjJZ2srYdPXO3MwZOZ2ZwSe43H8dHYFwt6+3tSo/n1KuOl3RBKG/W1/8U26MXMV603jNMjRvVs5cr7CM6rOdAcwGPa9b/8JH5ltoXP6O1nH6LVEoFgRBSDIZ2QVsm3gLdXIG87Zbdt8MEfqeed9X8H7HMK6wPMKkc29XfT5HipE5E/MBePvbdarPJwhCfKxc+D5HhhZSapc4dIjYvSIIgtDXFTlTSMdDqGFbl55/bewGJgSfxTTiWPVC2ZTdzQa/+jvl1BIOh3jR+wteNt7L4DT1iit9zfCcNEx6He3+MFWtfq3jdEl247ccq1/JaHtA1Xm+GPQ7bo9czoZg14u+tqhSKLY4utYRQOiZiL1496/dunQNk/zImq4UitNibtXnSt/yBvPNd3FZ6FXV5rBbjLhsJgAqW7q2cGk3WaYguB0A52B1zlAWhP4uI1X5+WvxhjTLoKtZDkBn1iTNMsSD3zUWAGnnYo2T9F+iUCwIgpCEpp72K24reIGPw5P4wzvr+szKcOFHbZ0hnvhK+WD1m+NHY7GkJGTei6Zm86zxIe7adiYtDaIliyD0BanL/s7fTH/n/uwv0OkkreMIgiAIvTQ2vJZVlqu4puG2gz43GInS2qncQMxxqPd+Ube7pWqranOorbFqGyVSA1N1W8l2id2ePzAZdNzq+JR3TbfRtPRNreMcVCQcojS4FYCcUYerOtegTKX1dHlT1xdf2+UOAGzObFUyCQqDq3T3r72G5Fgo6chUdjmny+1EI+qe5WnxKGeKy66hqs7zQ/eFiuaun9MN0Fy/kww8RGWJohFT1IgmCP2ey2bCTEjTM4qzO9YDYB08Q7MM8ZAy+BAAstvXapyk/xKFYkEQhCQk6XTcceY0LEYd321v4e0VO7WOJHTT2pdv4oLwfxifa+H0Seq2nP7/xpTkUmT2YZHCbP34HwmbVxCEnmmsKWeMX1nlW3TkZRqnEQRBEOIhPX8YADnRRmLRA+98bfQEATDpdaRbjapliuVP5sHwubxrPF61OdTWslNpg1qvz+uz7RPVMiqllQm6HYR3LtE6ykH9cF6iBytFwyaoOtegTBs2/ATrt3Tp+QF/J1ZJ+ZlMyxCFYjXZsofs/nXAnByFYmdWHpeHfsfpoT/S5u9+q+ZuzeVXFnWn5A5XdZ7LeYfvzNfiWv1kt66r3bwMgGp9ISm2np1xLAgD3fD279liuZRr62/VZP721kZKYtUAFI2bpUmGeCmZeJTy/7Eq2lsaNE7TP4lCsSAIQpIqcdn4zU+GcY7+K6b/9zhaGqq1jiR0Uc2ODRxS+yK3GF/j3ile9AneIdg+9hIABpX/W/WV0IIg9E7ZF8+jl2Q2GcdQOHSs1nEEQRCEOMguGkxE1mGWwrTUH3jBp7d8KR+ZbuIRy9NIknrvGVPyx/BkdA4fhvpuC1F/vdLK221R7yznvkpXoPy9prWs1zjJwf1wXmKleaTqBf+R0k42WK7grpYbuvT8tkCMM4N3clX4BtLsydEOub/KKB65+9cRa3K0+TYYTayyzGCtPISWTvU+R8eiUfKitQBkloxWbR4AV4qOAqkFfduObl33HeM4KvgwbxXerFIyQej/UhxK95PUqFuT+XdsWApAtZRHelaeJhniJT0rj506ZRNOxeqvNE7TP4lCsSAIQhK74rBifmH5nGLqKX/lOq3jCF1UP/9WTFKUtZapjDtiTsLnH3/8pbhJJZcm1i14K+HzC4LQNXIsRkHF2wB0jDxH4zSCIAhCvBiNJhp1ys3BluqtB3yur7GcUboqBuvqVc3k+n/n5PXVY23kVqXQEbAP0jhJ8nENnwlAcWjrQXexa01XuwIAb9ZE1efKH6QUIzPw0N7afNDnuwMyK+QRLE85BEknbpmqKSt/EP+JHaH8hy15dm9nppoBaPYGVZujsWYHFilMSNaTUzRMtXkAjFlKa+u0zspuXbex3ke5nId1V7tXQRC6z5aeA4Az1q7J/F8HhjM+8AyvlN6nyfzxVm9XupD4yr7XOEn/JN71CIIgJDGj0UT05MeIyhJTPZ+z9mtR9Et2m5d/wRTv18RkidRTtHkzZkmxsSn3NAB0y57VJIMgCAe3ZeVXFMdq8MsmRh97sdZxBEEQhDhqMyo7Nzrqyw74vHBbDQA+s7qFEpfVxGipgunyGrydXT+vNZmkdCiFDsk1WOMkyad4xCQCspE0/NTs2KB1nAPK9qwDEnNeoi3NSRPKzuD6ioP/ubT5lPPCnVaTqrkE0Osk/p56HdMDT9A47Fyt4+w227ieK/UfEKperdocTRUbAajX52Iwqvu95igYAUBWuKZb122sVQpbo/Ptcc8kCAOF3aW8F7RKQQK+7p0THg+rqtx4sJE3vO92k9lDkfK+wdG8UuMg/ZMoFAuCICS54ZNnsyxH2WmWueAWfF5tVqIJByfHYsif3AbA8vQTGTxW/Zsf+1N0zNUAjPUvp2bHJs1yCIKwf+2LXgRgg2M2qfYMjdMIgiAI8dRpVdrjRVoqDvg8uaMOgLA1R9U8KWYDb5nu5mXT/bQ3dG9nWbJIDypH8dhy1d2B1xcZjCYqjcqZrw2bF2mcZv/avT7qI6n4ZRNFY49IyJyNJqVVeUf1wT8TReo3can+Y2br16kdSwCOGJmPx+hi7NBSraPsdmLoM24zvkJKrXo/R766zQC0WopVm+MH2btaW2fiprPD3aVr/J0dXOf+M1fp32N0jlXFdILQv9kdGYRk5YgFd4u6nWP+Vywms3pnGwCTivrHUQrZE47n0cgZPOCfSygS0zpOvyMKxYIgCH3AuIsepJ4s8uVG1v7rJq3jCPux6rNXGBXeiF82UXq2tq1dCoeOZa1lKjpJpvLzf2iaRRCEvQXCUfwtVQBYpovdxIIgCP1N1KEUAAyeA59RbOxUbhzKaeqfHefWOQHwttapPle8RSJRaiMOmmQ7mSWjtI6TlNzpYwGIVCfvTps1tZ2cG7qDk22vkZFdkJA5O1NLAQg3bTvoc821S7jL+BKnBD9QOZUAcNdpY1hz53EMzkrVOspuYYtybIDsbVJtjuW6cdwevpRt+eofU+XIyKSNNADqyzd26Zqqzcs5Tf89Vxo/JsthUzOeIPRrkk5Hu6Tsyve2JrZQXLV9Lc/E7uB3prcZmZeW0LnVUjx4JPNM57MwPIqNdR6t4/Q7olAsCILQB9jSnDTMUgqP0+pfZ/vqbzROJPyvcCRKxpI/A7C68EKyC7Q/Oy0449fcEr6CG+uPIRBO7rPKBGGg+XRjA5cGfsc5picZfcjJWscRBEEQ4ixSeAgvRI5nIVMO+DxLQClGGJ35qmfq0DsB8LUl9mZlPNR5gpwXuo3Dok+TXSh2FO+LrmASVbEsqjr1WkfZr9VVbgDGFbsSNmcsQ9lpbXLvOOhz5c5WAMImp5qRhP/HbEiu71fZphSK9T4VC8XeTP4VPY7QsJNUm+P/azQoizLaa7Z06fltO5TFJrWWoaplEoSBokPnAMDX1pDQeRs2LGSGbjM/MW/GqO8fJUCdTmJKsbI7enlFq8Zp+p/+8V0iCIIwAEw4+hxWpB2FBHz1yX+IREWbjWTy2rIqrvD/mv9Ksxh7zh1axwFg0qxTWZB6MjU+PR+t73s7RwShP3trhdI+c+bUqej0yXWDTBAEQeg96/BZ3B25hPmBAxeKHWGlGJHiKlI9k9+kHHMQ8TSqOk8kHKK1sXvnYR5MRYtyrnJxhhWdTorr2P2FY+bFHBF6jLs8pxGNyVrH2ad1lcqN8klFzoTNackZDoDD14WW637lxnPM0j/adArdp09TjgEwBdUrQpQ3K69ngzITs1u3IW0My2LDqevs4utC/VoAfOmie4Mg9NZa2yG8GZlFs5zYXb1y9TIA2l0TEzqv2g4pNHCcbhmGdW9oHaXfEYViQRCEPqTkwse5WLqHe9t+wgvfVWgdR9ilIxDmsc+3USYX0Hbc46Q5kuOsUYNex/nTlbaHLy/qm2fRCUJ/VN9Qx/ptZQCcOaVQ4zSCIAiCGorSlXMd69r9hPezwFOWZVpjVtplK/Zs9QvF4V2F4qhX3ULxx/+8CesTE1j8rzuQY/FZ3FrR5AWg1CXOy9yfIdlppBj1dIailDd7tY6zFzkW4887z+dT041MT09cPuegybwaOZrXwrOR5QMXyvQB5TxHUpLj85yQeCa7Uii2htUpFIdDQaa0f8okaRuDE/R6tnLMzZwduosFB+lw8QNHu7Lz2FAwXs1YgjAgfJH/C26MXMUOQ2J36Ge61wFgKp2e0HnVdqitnqdNj3BK41Nxe48pKEShWBAEoQ/JzC3itJOVc2z++tlWqlp9GicSAOZ9vpKWzhCDM22ct6swmyzOnV7E+Yavuaf+F5StW6x1HEEQgPKPH2ex6WoeyvyAEpc490sQBKE/ykozk2PoZCxl1DXuuzDrDUaYE/wjE4LP4ipWf+dWxKq0+5U6m1WbIxyN8WDDNBbEJjCz7DGW/e1CQsFAr8cdsuZBFpqu44zoJ3FI2T/pdRJjC+yAzMbK5GsvXrNjIxl4KJEaGTokce3D80tHcFv0Sp4LHUNTR/CAzzWG3ADobKJQPFClpOcCkBZpU2X8+srNPGx4kldM95FjN6syx//6YedyRcvB7x/FIhGKw0qb9qyhU1XNJQgDgctmAqC1M5SwOX3edkoj5QAUjpudsHkTYfCEIwjJejJxU1fZtXb6QteIQrEgCEIfc/bUQmYOziAnUs2qedeLFVQaa6gu4/Jlp/GA4WluOa406c7+yE6zcJZzK6N0VTR//aTWcQRhwJNjMQor/oNJilIyZKTWcQRBEASVSJLEv0138575dtq3Ldnncxo8SgE1zWLAajKon8mWBYAx0KLaHOtr2tkZtrOUsURlienuD9n+8DG0NfXuGBRrRznFuiZcqaY4Je2fLjN+wWrzzxn76QV43OotCOiJ+o3fAlBhHILJbEnYvCaDjsJdO/x37Gr5uz+WcDsAxrRM1XMJySnNpZwXny67VbnX0rJzEwB1hnwkXWKOn/lhYWpNs/ugz60p34hVChKQjRQOHadyMkHo/9KtJiwE8XvUe+/1v8rXfodekmnARXbBoITNmwgWayrlRmV3du26rzXN0t8k191sQRAE4aAkSeKBUwbzrukOTvO8xor/Pq11pAFt51t/wCYFmJDSxDFjk2s38Q8sh/4CgHHNH9PRrt5ZS4IgHNyWFV9SJNfSKZsZfczFWscRBEEQVOQ2KwUHX2PZPh9v8Ci7G3PsiSma+fNn8kD4PD43H63aHEt3NAMSO4ddxIYjn6VDTmF0aB2+J2dTuWlFj8dND1QBYM0dHqek/dOgsdOx42NweCuBR6ex7uu3tI60W3TnUgDaMiYkfO4RGXpGSZU07Tzw7iNbVCkUm0WheMBKzy3hstCNnBO6A28wEvfxA/VbAWhPSdy9g9I0mW9M17EgdAE+b/sBn1tXvpmwrGencRB6gzFBCQWh/5rR9h6bLZdxWsW9CZuzo0zpJlhjG5OwOROp1TUZgGil6JoYT6JQLAiC0AeV5uewadAlAAxZeS/u5uRrLTYQlK1bzJS2jwHQHX8Pki45/1kdPfMEKnRFWKUgGz8WCwsEQUueRfMA2OA8itQ0p6ZZBEEQBHUFbMo59LHWin0+rt/0Lh+ZbuJa+bWE5DEUTuKp6Gl8FVGvUDd9ya9533Qrc507GH/UWbSe9wE1Ug4FcgMZr5/Mmq/f7vaY0UiEvKjyeScRLbr7slEzjmfzia9TLeWRTSvjvr6CZY8mx+7i9F3nJRpKpiV87iuCL/GR+RayNr98wOfdzLVcGboBU8HYBCUTkk1KSgpLDVNZKw+huTMc9/GlVmXhUNCRuF1+Tmc6aTo/BilGfcXmAz53oTyBMcHnmT/0vgSlE4T+zZSmHPthCavTzn5fWjr8NMkOgq7++Z7JPOgQALLbVmsbpJ9JzjvagiAIwkFNPv9OynUlpONh27+u0zrOgNT5wa3oJJkVqUcyYqp6OzN6S9LpaBh+IQA5W14W7coFQSP+zg5GtXwOgG262E0sCILQ38nOEgCMHVX7frx1B6N0VeTpEnPzMGPXOXnNXnXOyYtFIgzxr2acroLhRTkAlIyagvVXC9hoHEua5Oepz9Yy77tyZFnu8riNNWWYpAghWU9O4RBVsvcno2eegOt3y1icfS4xWWKa+wMCj05j7YL5mmUK+DsZFFYKZPljZiV8filTaVNp9pTv9zmxmMyiQAmfx6Zgz8hOVDQhCblSlbODW7wHPtO6J2zeCgAMWYk7pxug0aB0uGivOXCheGOdhxBGCkoSm08Q+iuzXfn3JDXiTticzzKXacF/0DLl2oTNmUjFE48CoCRaiceduJbe/Z0oFAuCIPRRJrOF4EmPKB/+2z9m3cJ3tY40oKxbMJ/xgRWEZD25ZzygdZyDGnXCz/HJZkpjVWxa8onWcQRhQNrw5aukSX5qpWxGzTxB6ziCIAiCysxZyo6xNH/NPh+XvMou2agtJyF5Mq0GRksVjPEvJxaNxn38is3LsePDJ5sZNPaQ3V9Pz8pjyO8+59niP/NRdDp3vb+R299dTzjatcWLLTuVwka9Phe9Qf2znPuDFFsaM3/19B67i1/79FtuemstnkD8d0keyMqdbTw570WMUpQWHOQVJ74AZc0bAUBGYN+LNgA8gTCxXesXnCniLOyB7BjjOq7Uf0CgZl3cx84MVgOQlj8y7mMfiMeqtLoONGw/4PM21noAGJVnVz2TIAwEqenKezy7fOC27/EiyzLbG70ADM1xJGTORMvMLaZGykEnyVSsWah1nH5DFIoFQRD6sJFTf8KyrDMAyPjqRvydHRonGhiikQi2BXcDsDLnLAoGJ387F7vTxXrXcQD4v/+nxmkEYWAybvg3AJWFc9Dp9RqnEQRBENSWlqcUxLIidft83ORrAECy5yckT3qKng/Nt/Ki8X48bU1xH79p/VcAlKWMxWDcs9BmNqdwxWW/4JYTRyJJ8MXiVSz9yxm0tx48h2/XmZ6t5qK4Z+7vfthd/GHR73g9djRvLK/i+EcW8u36fZ+bHS9yLMaiZcs455+LOOPJ7/lbZTEbYiVsG3aFJsf1ZJaMBiAvWks0su9zZ9tb6rlM/xGnm5ZiMojbpQPZqeGPuM34CoaKBXEd19/ZQS5KG/icQYk9OzS8q9W1vm3Hfp/T1lTHU4HfcZ/hWUbmpiYqmiD0a2muXAAcdBIOxb9Lwf9q8gbpCESQJCh12VSfTytvF9/G7OBf+Tw4Wuso/Ua/fefj8Xh4/fXXueGGG5g9ezZDhw7F4XBgMpnIzs7myCOP5MEHH6SlJX7b0z/66CNOP/10CgsLMZvNFBYWcvrpp/PRRx/FbQ5BEIT/Nebiv9BIBgVyA4ve+LPWcQaEz775loxoEx6sjDznj1rH6TLXkb/is+hk/t42k8aOgNZxBGFAqXX7uaz9Su4KX0zx0T/TOo4gCP2I+OybvLKKd+1ixENnh3uvx61BpUhqSi9ISB6T2YIH5aZhe3Nt3Mc3Vi8CwJs7fZ+PS5LEL2YP4emfTuHv5ic4zP8VnsdnUbX9wLv2av0GVsaG0u4UNwN7IsWWxklX3M4bPz+UUpeVjvZWSt88jqWPXRD3lo2RcIjl7z1F+T2TGPXfOawvr8WolzhnahGWq75k5oV3xnW+rsopHEpQNmKSotTv3LbP5/gbtnGn8V/cqH81wemEZBPIGg/A6G1PHfT1qTsq2oJcErqJ+7gCpysxnSR+YMxS2vbbOnfu9zk1m5cyUbeDWcZNpFrErnpBiAdHRg4xWQKgvbVB9fma1n3Jt+Zf83frc1iM/Xdxesao2VTKuazYmbizn/u7flsoXrp0Keeffz5//etfWbhwIWVlZXg8HsLhME1NTSxYsICbbrqJkSNH8sknvWvBGYvFuPLKKznppJN45513qKmpIRQKUVNTwzvvvMNJJ53Ez372M2LiTEhBEFSQas+g5vD7+Uv4bH61ffruVkGCOvyhKHctijIr+AhfT3wUZ2au1pG6bMj4Q/lH3j18FRnHv5ftv+2aIAjx959VNbTKdjYVX0DBoBFaxxEEoR8Rn32Tl8PpYh6n8qfwT6lp8+/1uDOi7CyzZRYmLFO7pLQh9LbWx3VcORajxLsGAOeI2Qd87rFjcnGe+Sj1ZFIk12J/+XjWf/v+fp//AUdwRuiPVI6/Lq6ZB5rpgzL46LpZ3Dm6nnxamN72Af5Hp7H267d7Pba/s4Mlr99P431jmLryJgbHKjAS5eaJAb75/dE8eNYEhuRnxuF30TM6vZ46fR4ALTs37vM5QY+ycMOnS0tYLiE5TT7/TjYbR2Onk9ir59HR3hqXcSvawiyITWBJ1pkJ31mfmj8cgMzQvo9CAPBWrgKgwSrOJxaEeNEbDLRLyr8rHS377jATT97q9RRKzRSavKrPpaWppekArN7pJtLFo0yEA+u3hWKAoqIiLr74Yh577DHmz5/PokWL+O6773jjjTc4++yz0ev1NDc3c9ppp7FmzZoez/OHP/yB5557DoBJkybx2muvsXTpUl577TUmTZoEwLPPPsttt90Wl9+XIAjC/5p0zHnsGP1LAjE9t8xfS/SHw5WEuHv+u3LqPQHSnFkcd/JZWsfptp/OLAHg1SU7xfeJICSILMu8uVxZnHH2VNE2UxCE+BOffZPXW65f8Fz0JHZ697z9EotGccnKLghnTnHC8nQanAAE3PHd1VJdth4XbkKygUETZx30+UPGH4Lhqq/YYhiBg05GfHYJS958eJ/PrWzpBKCkH7dQTJQUk56zL76WzSe+To2USw4tjP/68h7vLna3NLLo+ZsIPDSaGZsfIF9upBU7i0t/ReS69Vx83oXkOiwq/E66ry1F+Tnz1W3Z5+Mhj/L79xv655mOQteZLVYyr3iDRjIoiVVT9tT5cTnXfUez8lo2ODPxr2W5pWNYGRvKt9HR+IP7Pqfc0LgegFCm6N4gCPG00HgY/47MpjVkUH0uuUk5rsPnGKL6XFoalp3GZeaveVB+mIpNy7WO0y+o/92pkaOOOoqdO/ffTuOcc87hnXfe4fTTTycUCnH33Xczf/78bs+zdetW/vKXvwAwdepUFi5cSEpKCgDTpk3jtNNOY/bs2SxfvpyHHnqIyy+/nKFDh/bsNyUIgnAAd506hm+2NbOxuoUPPnqf004+TetI/U5LYw2bv34dmMiNx4/ok21cThqXx7Pvf80pnZ+w+ms3U44+U+tIgtDvbVn6GQ91/IE3Tcdx4tjjtY4jCEI/Iz77JreidCvrazzsbPXt8fXWtlZa5DyycZORk7hFRH5TBoQh0tEY13HXVbeyInoo2TYDh6Z0rQiSmVtM6vVfsvwfFzO14wtmbPgji5u2MO3nT6I3KLerYtEY1S0dgESpyxrXzAPZ6Jkn4B+3nMUvXs/0hjeZ3vYBDY8upuLIhxh/5ME/H9S6/Tz7TTmLli3mA+mf6CSZWimHqlFXMOGUq5lpTb7zTSvyT+bjjUU4GMXMfTwe7VQKxSGTM6G5hOSUmVvMtjkvYn/nDCb6F7P4+euZ+bPHejWmdcfHnKarY2zaSXFK2XUOVw6X6u7DE4gwps3PyFzjXs9xeZW27CnFkxIdTxD6tVdc17G0opXHdXlMUXkum6cMAH1W/+5iptdJnJ6ykvGB5SzZ8CVDx+772BOh6/rtjmK9/uA37+fOncuIEcoPzTfffNOjeR599FEikQgAjz/++O4Pyj+wWq08/vjjAEQiER555JEezSMIgnAw2XYLdx+dxfumP3DM0iupq9z3Smmh57b/+zYelx7i745XOW1CvtZxesRi1HNH7vf8yvAehqX/0DqOIAwInsUvMk23lbMzyrCZ++06TUEQNCI++ya3IQ6ZsdIOQjV77uSuD5o4PvQgx5lexGhM3FmQYYsLgJi3Ka7jft7k5Dfha/h+0l+6dZ3FmsqU377F4pKrAEirX8wvX/yOjoCy462xtpw1+ov53HwjBQ5zXDMPdCm2NGb+6hk2n/g61bt2F1d/8RQ3vbUWT2DfOw4rN63grafuZtaDX/H8d+VsCuXw75SzWT7tL2Tfup4Z59yEJQmLxADh4afyz+ipLPLt+3Oc7FPaC0fMzgSmEpLZsEmzWD/lHgCCO1fywer9L8rqipn1r/A309+ZIG+KR7xukSSJQbt2Mlc0+/Z6PBjopDCqdEDKHT4todkEob/LsCnv89p8IdXnygoqr1P2wv7fGaAzezIA+pplGifpH/ptobir0tKUHvGBQKDb18qyzLvvvgvAyJEjmTlzX2sSYebMmbs/lL/77rvIsmj1KQiCOuYeNoGY2YFVCtL4+rXI4ny4uKnatobJTcprfums89HpJI0T9VzxsdcAMCGwjJodGzROIwj9m7+zg9GtXwBgnX6xxmkEQRjIxGdfbRzh+4L/mm9jRuU/9/h6g0f5e8ixJ7b4GUtRzonV+eJbKF5arhTZpg/K6Pa1kk7HzMv+zIoZf+Nq+UY+3eblzH98T1Wrj+adGzFKUcw6GYNBLLZSw+iZJ+D63XK+zb2IOyKX8cbyKo5/ZCELNv94luLmpZ+x+sETKHnjaE6ve4R8uZ5DBrt48fLpnHvT00w9+WcYErjgoScGZSlFsvJd7X//ly6gtIKPpXT/e1jov6aedhX/Hv4wl4dv5Ia3N7Chtr3HY+WEqwFwFo6KV7xuKXHZMBChrmHvM+qrtqzCKEVxk0pOwSAN0glC/5WRaiKFAB3u+Jx3vj+dHW5yaQYgd8h4VedKBqlDDwMgv2Otxkn6hwFdKN6yZQurV68GlA+73VVeXk5tbS0As2fPPuBzf3i8pqaGioqKbs8lCILQFTq9HtuZfyckG5jgX8LKj57XOlK/0fzOrRilKGtSZjD2sFO1jtMr+YNHs9airBLe+ekTGqcRhP5twxevkCr5qZVyGDVDtJ0WBEEb4rOvdizZgwFwBmr3+HqDJwhArj2x57e25R3OA+HzWGQ+PG5j1tXXk+LehkEnM7kkvcfjTDnxEv72i1PJsZvZ2uDl9cdvxbfsVQBazQXxiivsQ4otjcOv+jtP/vwESlxW6toDeF65lOV/PYtN9x7KyA/PYqJvETFZYk3q4Tx14SRe+/lMZg/PQpL6xgLaQS4rw6UqxngWEvDvvaPSEHQDIFlFoVjY05nnXcHhw3MJhGP8/MXltLS5uz1Ge1szGXgAyB2kzU6/M4L/YbP5UkZv2Ps8+Oq6Oipj2VSbhyLpBnS5QBDi7pTWF9lkuZypZY+rOk9t2ToAWrHjzMxVda5kUDphFlFZIl9upLm2Qus4fd6Ae+X3+Xxs27aNv/71r8yePXt366zf/OY33R5r48aNu399sA/b///xTZsS32JEEISBo2TkJFaWXA5A6bI/0t4a390CA9GmJZ8wqfNborKE87T7tY4TF7GpVwAwsv49Av59r6oXBKH3zBteB6CyaC66LrSHFQRBiBfx2Tc5OPOHAZAdrd+j20/hxmf42HQTc4LvJzRPtGgmT0VP43t5bNzGrF32Dp+Zf8/btgdJ7eURC+MLnbx79eFcnLWNG+UXmO7+EIBAWkk8ogoHMX1QBh9fN4ubJkU4WbeEqZ7PGBXeQEjWszT9ZKov/JrJN/6X0WP73hmmLpuJt0138U/jIzRU7P3a9Lr1fK4I3YCn6CcapBOSmV4n8bfzJjHMZeLqzr/T8I9TCAWD3RqjoVzp5NVEOqn2ni+o6Q1bei4GKUaqt3Kvx74OjWZ26FHeG6NuIUsQBiKdVfmZNwZaVJ2ntqWD5bHhlJn7f9tpgDRHBhWGUgB2rv1a0yz9wYAoFM+bNw9JkpAkCZvNxvDhw7nhhhtoaGgA4Oabb+aCCy7o9rjV1dW7f11YWHjA5xYVFe3+dVVVVbfnEgRB6I5JF9xNpa4IF+1s+dd1Wsfp0+RYDN1ndwCwwnUKJaOmaJwoPsYdeTb1ZJFOB+s+eVHrOILQL9VVbmVMYDUAJUdfoW0YQRAGBPHZN/nkFCuFYpsUwN3SsPvrFs8ORuqqyDR2r9jQWy6b0uq6xRu/eSMV3wPgz4jPjclch4Wbf3EZK22zdn9Nzhgcl7GFg0sx6fnluXPYfOLrrDNPYnHuhbT/YiXTr3uV4uETtY7XY5JOR71Ref1qrdq7ULw+XMAXsSmYssT3mrA3h9XIs3NzOFW/iNGhdax6+ufdut5TrXzPNZkO/G+omtLyhwPgCtXs9dimOmW384gCsaNeEOLNkJYFgDnUpuo8yyJDOCt0F/NH/EXVeZJJs3MCAKHyxRon6fsGRKF4fyZOnMjSpUu5//77e9Qqp6OjY/evU1NTD/hcm822+9der/eAzw0Gg3g8nj3+JwiC0B1mixX/8Uo7oeltH7Dhuw80TtR3rfz4RUZENuOTzQw+616t48SN3mCgovQcAOzrRaFYENRQ8eVz6CSZDaYJ5JeO0DqOIAgDmPjsqx1Lio0mlJ0kTVVbd3/d7FeKxnpHfkLzuFIkxkgVDPGuiNuYuW0rATAPiV87a2uqg4nXv8OikqvYahhO6WHnxG1soWtGzzyBcbd8zcyrniQrv1TrOHHRblV2pgfrt+z1mNsXAiDdmtxnLQvaKRk2nrJZjxKTJWa0vMOSN7tejAk3bQPAa9OuO0J2iXI2ci7NBHw//vssx2JsrFPOXh6db9ckmyD0Z2Z7DgC2iFvVecqalJ/rIVm2gzyz/9CXzCQk6+loV/f854FgQBSK586dy7p161i3bh1Lly7ltdde4/TTT2f16tWcf/75/Pe//+3RuIFAYPevTab/Y+++46OoE/+Pv3aTbHovhFBC7yC9SFcQxY6nJxYE5U5P9Cz3tSCe2NGz3unpHSqiYkURESsovTfpvUMIJKSSnt35/ZEfKxxJSNlktryfj0cej83OZz7z3g8fNjPzmflM5TuSgYGBztcFBQWVlp0yZQqRkZHOnzOvyBYRqap2fUawKvYaVjra8+KyLApL7GZH8jjFpQ4+2pTLLkcjNjYdQ1ySd0151+rSv5BhhLGysAlbDqebHUfEqxiGwdep8SyydyGv42iz44iIj9Cxr3s6GdAQgNzUPc73wkrK9r0Co+v32btx/gV8F/gYU42nKSkprnV9GcePkOwou3O8efdhta7vTFY/P/qNe5E2j68h8f/fmS1SG6WRzQGwZu49Z9nIgjlca11CjE3HzVKxrhffyKoWdwPQfcvzbFv5Y5XWC8jaD4A9pmWdZTuf6LiG5BACQOrBHc73jx3azRLjdj60vUjLON8ZYBKpL8HRCQCEO7LrdDsHTmQB0Cqh8os6vUmDPjfQueg97s4dq/PeteQTA8VRUVF06tSJTp060atXL2688UZmzZrFhx9+yL59+7j66quZPn16tesNCgpyvi4urvwAq+iMZ1cEBwdXWnbixIlkZ2c7fzRdl4jUVPtxb/JX2zMszoji3wv2nH8FOcuMlQeZndWSMbbX6HLjk2bHcbm4xCY81XY2k0vH8fGaFLPjiHiVtQcz+SK7PXdbJtHpsj+ZHUdEfISOfd3TqeCyweDi9P3O92LsZc+pC4+v38HxyJgG2I2yu8qzT6bWur6Dv/0KwAFrU6LiEmtdn0hd8k8ou+Dgf5/RWliQx9+t03nN9jaRgeWtKfK7vrc+y7rwoQRY7CT++CdSD+0+7zpv+93M7cX/R2mby+shYfksVivH/cpmscg8/PtA8fFda4iy5NEoIBdbgJ9Z8US8VkRM2QWDkUYuDnvdDGaWlhQzK/tGFtgeoHVo4flX8BKNE2IIDwunxG6w5WjdDsR7O58YKK7IrbfeyvXXX4/D4eCee+4hI6N6t6iHh4c7X59vSq28vDzn6/NN1RUYGEhERMRZPyIiNRERFs7T13QC4O2Fe9l5VFNxVFV2QQn/+rXsgO+vw9sTGuqdV+Td3K/siubZG1LIKSwxOY2I95i5tmyw4/IuDQmx+ZucRkR8nY59zXU46TKeKbmZNX7dACgqzCeasmm2oxPrd8YaP39/sixl7ZyTfqzW9RXtXQLA8egeta5LpK5FNS6bejeh5MhZ72dnlE0FX2L4ER4RXe+5xLNYrFY63PURe/xaEkMOuR/cSEFRxcfShmGwKjOMXx3dSWzmmme511R2SFMAik78PrhdeGQjABnhbUzJJOLtImPL7ij2tzjIyUyrk20cO7iTYEsxiZZMGjas39lqzGSxWOiZXPZ3e+0BnfOuDZ8eKAa4+uqrgbKD2R9/rNp0Iac1btzY+frIkSOVlOSsK6O9dTotEXFPl3ZqyFXtwnjc+j6O6SOxl5aaHckjbPj4cW4omkX7eBs39Gx8/hU8VK9m0bRJCKVD6TZW/vyF2XFEvEL+qWwSN/2HBmTwhx7a7xMR96BjX/OUtLqE9+yXs6awrB1Pppa1UZERQFRMQr3nybVGApCXUfs7iuNOlj3r2K9F/1rXJVLXGjTvCEAcWeRmnXS+fyqj7MR9jiUMi9XnT5VKFQSHhhN22+ccpCHPFYzioa82YxhGuWXTTxVzqqgUiwWaxITUc9KzZST05Wt7f3aV/j4DRFD6VgDsCZ3MiiXi1QKDQvjR6MfnpUPIzK/9Yz/Kk35gCwAp/o2x+vnWzABXhu/mW9tj9Fpzv9lRPJrP7/3Ex8c7Xx88eLCSkufq0OH3q8B27NhRScmzl7dv375a2xERqa3HhzflD35LaF+ynbVfvWx2HLd37OAu+h1+j8cCPmVKt0z8/bz3z6XFYuGJ5C18FfgUbX97HsPhMDuSiMfb+svHPGj9hC+Dn6dXcpTZcUREAB37mqlJdNnAwOGMfAAys7PY4WjCAWsTUwal8vzL7rwoyjleq3pyC0t4uuAG3iy9mqZdL3FFNJE6FR4Vy2vW27i7+K8czPr9DtCC7BMAnLJ656wGUjcSm7bm+K2LWUo35m46xtuLzn32NcCxvZuY4Deb68K3EWTy1M45HW7mgZIJ/FTazfleg4Kyu4vDk7ubFUvE600Je4RHSv9MuqNuZissStkGQFZIszqp3521bBRHZ+sBmudt1DnNWvDeM99VdPToUefr802L9b+aN29OUlLZsx0WLVpUadnFixcD0KhRI5o1a1a9kCIitZTQqDlbOzwAQMdtr3P8SPkHMFLmyKxJBFpK2GrrwgVD/mB2nDp3wbCbyDOCSHYcYeuK78yOI+Lxgrd9DsCRplfprhQRcRs69jVPk+hAOln20Sl7IfbSUg5YmnJp8YtMSvi3KXkKA2MAKM05Uat61h3MZLGjMzMjx5HQqH6n0BapqRUJo/ne0Ze9Wb8/J7IoNx2AfD8NFEv19G6ZwFNXl92p/tlPi9mwaPY5ZQr3LuOhgC8YY/2hntOdq1ls2YVLB9LLLlzKzkwnySj7W9C4fW/Tcol4u5hQGwAnT9XNHcXWjD0AFEe3rpP63VnzzhdSbPgTQw5H9201O47H8vkzVzNnznS+7ty5c7XWtVgszum7duzYwcqVK8stt3LlSudV1VdffTUWi6WGaUVEaq7XH/6PHf7tCbMUkPLJPbrKqgJ7Ni6jR9Y8AAIve84nBnnCI2PYEncpAMUr3jE5jYhnO3ZwJ52KfgOg2UW3mxtGROQMOvY1T8OIIGbZJvOm/+ukHTvA8ZxCABpEBJmSpzQoFgAjr3bPyVu9v+xZcL2bxdQ6k0h9aR4XCsD+9N+fp16aWzYNdWFApCmZxLPd3CeZBy4o5Rvb32n1610c3LnhrOX29LIL9QvCzL+gpllcKH7Y8cs+QGFhIUd3rAEglXgiY+LPs7aI1FRsSAAhFJKTnVUn9Uec2g+ALbFtndTvzgKDQthnK3vG+rEtlV/QKhXz2rPf06dPp7CwsNIyr732Gt9//z1QdoX0wIEDz1q+cOFCLBYLFouFsWPHllvH/fffj9//n/f93nvvpaCg4KzlBQUF3HvvvQD4+/tz//331+DTiIjUntXPj6Dr3qTY8KNb/nI2/Pyh2ZHcjuFwUPj9Y1gtBusiLqZVt0FmR6o38UPvBqBz7lLSU6o3HaWI/O7AL+8BsCWwKw2Tfe8gTUTqn4593Z+fvz8nrGUn4DOO7DZ9oDilwVCeLxnNhsDa3T0Ws/UDhlo30LdpsIuSidS99pHFXGxdR/CB+c73HPllA8UltiiTUomn+8uoERyzNSPcUoDls5vIzkx3LgvK2QeAEdvKrHhOsSEBrAv8C4sDH+D4gW3szyhgsb0ze8J6mB1NxKuNz/4X24Jup8nu6S6v23A4SCo9BEB0U9981nhWbNl0+o5Dq0xO4rm8dqD4ySefpFGjRvz5z3/mww8/ZNmyZWzcuJGlS5fy9ttvM2DAAB588EEAbDYbU6dOdR70VkebNm146KGHAFi7di39+/fn888/Z+3atXz++ef079+ftWvXAvDQQw/RurXv3f4vIu6jWfuerG9yGwBNV04+6+BFYNPCr+hU9BvFhj8NR00xO069atGpD9sDOhBgsbP7p7fMjiPikRx2O8mHZwNQ2PGP5oYREZ+hY1/PkGlrCEDe8b0M3DWFn2wP069wsSlZ8psMZKr9StYbNb+gqTD/FGNy/sv7tpfoG19y/hVE3MQFxnbes73C0NT3ne+tCR/G7cX/x9ak601MJp7MFhhEg/Gfk0o8TY0UDv73RuylpQBEFRwGIMQN7vSzWK2k+zcAIPPwDhYVtGRMyURWX/CMyclEvJsRVDZjheX/X5jkSmnZOcyzd2OjowVJLX1zoDiwxYUAJGRtNDmJ5/I3O0BdysjI4J133uGddyqeRrNx48ZMmzaNYcOG1Xg7zz33HCdOnGDatGls2LCBG2+88Zwyd9xxB88++2yNtyEi4ipdb36Ww//4kXBHNp/N/ZE7b73F7EhuobTUTujS5wBY3/AG+jYz/yCuvuV1GQvrHqblwS8oLXkG/wCb2ZFEPMqO1T/TwTjOKSOYDhfdbHYcEfEhOvZ1fwWhjaFoA6Un9xOdv5+21iPkBptz7X5saCAAGXlFNa5j74ZFdLTYSSOapGbtXRVNpM7FNCnrr4mlRzAcDixWKwfssfzq6E6vhHYmpxNPFtugMXuu/YCCWdfQpXANK967jz7j/0WSPQUsEJvsHt+VOcGN4dReCk/sZltGEwA6NNTzuUXqkiU0DgD/wgyX1703w87fSu6maUwIi4NDXV6/J2jSZTAsh2T7IbIz04mMjjM7ksfx2juKf/rpJ1555RVGjRpFly5daNCgAf7+/oSHh9OyZUuuu+463n//fXbu3Mnw4cNrtS2r1cp7773Hd999x9VXX01SUhI2m42kpCSuvvpqvv/+e959912sPvCcSxFxf0HBoWRe8Q7Dil5mytZo1hxw/U6KJ/py/VHuLLib7+lP+xueNjuOKToPv5UMIih0+LFi/YbzryAiZ9m6aR0Fho1tMRcREqZn3IlI/dCxr2ewRzYFwD/nEBElZbP6BMc0NiVLfJBBR8t+Guesr3EduTvLngF3KLwrFv17iwdp0Kw9DsNCBPlkpKUAkJVfdld8dEiAmdHEC7S6oD/bepfNTtbv2AxWzfg7gZYSig0/GjRxj5k2iiKbA2A5uYcTqccADRSL1DW/sLJHkNiKM11e9560UwC0Sghzed2eIi6xCRusnZjr6Mv2A0fNjuORvPaO4rZt29K2bVvnFFs1MWTIEAzDqHL5kSNHMnLkyBpvT0SkvnTpMYBhBzbx2ZrDPPrVJr6/byCB/tWfgtBb5BeX8uq8XZwwGpFy8ZtExsSbHckUgUEhfHDBe0xZVcSALTCwj9mJRDxHXlEpk4/25Knit5gxvLPZcUTEh+jY1zMExDWH/RCWf5RYx0mwQERCU1OyJBgn+C5wErkFwcC9Naoj9PhqAEqb9HNhMpG6FxQcSoo1niTjBCcObCW2QWM6ZMwj0ppDgtWc/5PiXXpcPp4VRzfRL+UD+u3/NwDH/BqS7CYzdvnFtYSj0DJjMasD5rDbvwmNozebHUvEq9kiEgAIKclyed3HUo7gTykt433zbuLTPmr7b2ZtOMpf04Ppa3YYD6TLPkVEfNTEy9oTFxZI85OLWf7J82bHMdUHv27iRG4RTWKCubVfstlxTHXZ4AFgsbJkdzr70/PMjiPiMX7Ykkp+sZ242DguaO97U9eLiEjlwhu0BKBJyT5CLGVTPsc2NGdQKiKuUVkmSwGFBdXf3yspLqJl4TYAEjpd5NJsIvUhPbBsut1TR3cAcEPuR7xme5vE4sNmxhIv0ueO1/gkZgKdCt9lWNE/+LjBw2ZHcgpPKjtWiSMLgBJbJFarxcREIt4vOKrs2eDh9iyX133prr+zLXAcQ0qXubxuT9I9ORqA9Qddf9e2L9BAsYiIj4oMCeCfFxbwru0V+u99nYM7aj71nCdLSz3EzSsuZ4r/O0y8ONmn76wGaBITwtC2CdgoYf6vP5sdR8RjLFm1CoA/9GiMxaITLSIicra4Zp14tuRm3iq5CoAcQgkJNWeqz4jIGIqNsn3erPRj1V5/36ZlhFiKyCKM5LbdXR1PpM4VhJVdHFyatgeAcCMXgJAo35xZSlzP6ufHFX9+igbx8ewxGmNt6j7TdcX/z7OScyN1katIXQuLSQQgysjBcDhcWndC4UFsFjsxDZu5tF5P07NZNBYcZB/aTGlJsdlxPI4GikVEfNiFQy5nY3AfbJZS8r+6B4fdbnakerdv5t+JsOTTI+gol3VrbnYct/CnDnaWB97LDdsmUJh/yuw4Im4vZf8O/nnidr6xPc61XXSCUUREzhUTG8/H1qv4zSi7szjDGmNaFovVSpYlEoDck9UfKD65YykAB0K6YPXz7YssxUPFtgIgKGcfdrudCKPsmCcsOsHMVOJlIoIC+OiOPjw4vA13DHCfcw2xCY35xhjo/N3SsIuJaUR8Q1R8Q+bZe/C1vT95hUUuqzcvN4tE0gFo2MK3H4HVOj6MxYF/41vr3zi4fY3ZcTyOBopFRHyYxWqlweh/k28E0r5kK2tmvWZ2pHp1cMd6eqTPAaB02DNYrPqzCNC7e0+KrUFEksemn943O46I2zu44D0ArMFRNIqLMjeMiIi4JYvFQtOYECwYbHc04XhgM1Pz5PpFAZCfmVrtdaeVXsrFRS+xr9N9Lk4lUj8srS/h3uJ7eMsymtzMdPwsZc9oj4hpYHIy8TZJUcH89eLWxIcHmh3FyWK18t/ohzlphAMQ3bKHyYlEvF9IaAT38hCPlv6ZjALDZfWm7N0CQAYRRMUluqxeT+TnZyUzuOzREunbl5icxvPojLiIiI9LbNqaze3+CkCHLS+TlnLA3ED1KHPOY/hbHGwIuZAOfS81O47b8PP352CzGwCI2vqhyWlE3JvDbif58GwAijrdaG4YERFxa93DMwmjgAdL7ubz5s+amiUvoOw5bsXZx6u1nsNhsPpAJnuNRrTs3LcuoonUuYYtOvKt40IWZcWSnVH2fyDPCMIWGGRyMpH6cUF0PrGWXEoNK030CAGRehEbWnbByMk8191RnH2obKA4NaCpy+r0ZHkJZd9nfkd1R3F1aaBYREToef2j7PJvQ7ilgMMf32t2nHqxdfn3dM1fQalhJfaaKWbHcTttLv0LxYY/bUp3sfs3XYknUpHtK38kyThBrhFMx4tuMjuOiIi4sWuK5vBf2+tc7becxAhzB6SKbWVTX9tzT1RrvZ3Hc8kpLCXE5kfHJHOesSxSW0lRwdj8rBSXOjh09AgAOdZwk1OJ1J8etsMAGBYrQcGhJqcR8Q3RIf6EUkBWdo7L6iw5sROA3PAWLqvTk4W16g9AUu4mk5N4Hg0Ui4gIfv7++F/zBqWGle55i1m55GezI9Uph92O7dcnAFgXdxVN23Q1N5Abim3QmE2RQwDIXPS2uWFE3Fj+6rK77rfHDiM4VCcYRUSkYkZ0MwAaW07QwOSB4r0Jw3m+ZDTbA7tWa730ZR/wRsC/uCNhJ/5+OqUknsnPamFk5H5u9/uBgj3LAMiz6sIH8R2tYmwAbI0ZZnISEd/xTP5zbA26g9A937isTlvmHgCM2NYuq9OTNbtgEHbDQpJxgnQfmjHTFbRXLyIiALTo1JfFze5lXPFD3L/Ej9zCErMj1ZlfV6wkviSFPCOIltebO+2fOwvrfycAnTPmkZ2RZnIaEfeTl5tFx6wFAET0vc3kNCIi4u4C45oDcIXfKtoWbzU1y8nGFzPVfiVbaFmt9UIOzOdKv5VcGHasjpKJ1I/b+JYnAj4iI+Mktxf/H1/FjDc7kki9uWD4rey//mfa//l9s6OI+Ay7rezCckeu686vLbV3Yo69H7bkXi6r05OFR8ZwwL8ZAIc2LTQ1i6fRQLGIiDhdeMtk9kf3JzWnkJd+2ml2nDpRVGrnyWVFDC56jXmdXyEusYnZkdxW217D2G9tRrClmPW/fml2HBG3s/WXGYRYijhkSaJtz4vNjiMiIm4uMun3uz2iw0NMTAJxNXhOnuFwkHzqNwAi2w2pg1Qi9acoouzCjaJTmfzq6M7RmH4mJxKpPxarleYd+xAYZO7fIhFfUhocW/Yi/6Rr6rM7eDN3IH8tuZf4DoNdUqc3SI+6AIDi/StNTuJZNFAsIiJOQQF+PH9tZwB+XPkbW3/zvj+qH604yJHMAoIiYhlx1Wiz47g1i9XKrp6TGV70D5450B7DMMyOJOJWXk+9gPHFf2Nr+wewWLVbLSIilUto2sb5Ojo+ycQkEB9kp5NlHw2yfqvyOkf2bSWOLIqMAFpcMLDuwonUA2tcKwCSKbs7PjokwMw4IiLi5YyQsoFiv8IMl9R3OLOAErtBUICVRlHBLqnTG5S0uYI3S69mdnEPs6N4FJ3REhGRs1zYKo5H2qUx3/YQoXP+RHFRodmRXCbn5Ak2/fIZYPC34W0JtvmZHcnt9b/4Ko7ZmrEvPY/le11z1aOINzh0Mp/lB3L4xehB1xG3mh1HREQ8QFhENCuajGdlwg0kNG1rapZGxfuZG/g4D2T/o8rrHNv4CwB7bW0JCg6tq2gi9SIsqR0AQ/w2cq11Cc0smk5dRETqjl9YPAC2ItcMFB86dJAmluO0ig3GarW4pE5v0LTn5bxc+ke+OtGIwhK72XE8hgaKRUTkHKOvuBS7xZ9mjkOs/2Sy2XFcZtsXT/AvXuStiA+5rkdjs+N4hLBAf67t1giAmct3mJxGxH18uf4IAANaxdEwUlfviohI1fS74xX63v2O2TEIi2kIQJSRheFwVGkdy6HlAGQn6Dl44vkSmnV0vn7N9jYd8labmEZERLxdQHjZQHFwSaZL6gvc8ilLAh9gsv0Nl9TnLZrEBBMXFkiJ3WDL0Wyz43gMDRSLiMg5ouIS2ddjEgDdD7zL4V2/mRvIBVL276B76kwAGve7Hj9dbVdlt/RqyOsBb/L83lGcOHrA7DgipnPY7QxdfhsP+n/BjV0izY4jIiJSbVHxZQPFQZYS8k5V7SRao5zfAAhrM6iuYonUm9jEJuQZQc7f/cNiTUwjIiLeLjiqAQBhdtcMXloz9gBQGt3CJfV5C4vFwsDGAQyxbuDoxvlmx/EYGigWEZFydb/8z2wK6onNUkrOl/dU+U4Dd3Vs1kRsllK2BHal8+DrzI7jUdo2iqVNUA4hliL2/vRvs+OImG7byu/pZmxjnN9PXNxRsxOIiIjnCQmLpMCwAZCdnnre8sdOpHPCHkqREUDzbkPrOp5InbNYrRzzb+T8PSA8zsQ0IiLi7YLjmvKzvQe/OLq7pL6IU/sBsCW2c0l93uSP/guYbnuJpjvfNzuKx9BAsYiIlMtitRL3x3+TbwTSsXgza77+l9mRamz3hkX0yP0Vh2EhaOTzWKz681ddBReMBaDloS8pKS4yN4yIyQpWfwTAttjhBIWEmZxGRESkZrKsZbNi5GaknLfs6pQiril+lpvjPicsIrquo4nUi2+T7nO+Do6MNzGJiIh4u6jEFvy55G/8vejWWj8713A4SCo9BEB0006uiOdVotoOACA5f4vH3/hUX3SmXEREKpTUvB2bW98NQLvN/yA99bDJiarPcDgo+aFsGu11UcNpdUF/kxN5ps7DbuEkkSSQwZYFn5kdR8Q0p3Iy6Zi1EIDIfmNNzSIiIlIbuX5lA76FmcfPW3bV/gwAujZvWKeZROqT0bSf88760KgEk9OIiIg3iwj2x///PwYvI6+4VnWdPHGUCPJwGBaSWmqg+H8173whxYY/MeRwdN82s+N4BA0Ui4hIpXr88TF2+bVhZukgnp9/yOw41bbxl8/oULyZIiOAJtc9b3Ycj2ULCmZ3o2sA8F+vqVvEd22f/yEhliIOWRvRtoem3hQREc9VEFA2UFycc/6B4t/2HQOgd/OYOs0kUp9aRfsRbCk7WR8erYFiERGpOxaLheiQAMLIJzPnVK3qOr53EwDHrAkEBYe6Ip5XCQwKYZ+tDQDHtiwyOY1n0ECxiIhUyj/ARslt3/O8YwyztmTx647zn0hyF6V2Bx+tT2e3oxHrk0aT2LS12ZE8WvIlE3AYFjoXbeDQ7o1mxxExRej2LwA4mnytprEXERGPtil2JM+V3MReW+XPtss4cZTZOTfylW0yvZrokQviPVoH5zpfa0p1ERGpax8bj7IlaDyO/UtqVc+po2V3yaYFNXNBKu+UFdsNAMehlSYn8Qw6uyUiIufVsWk8dwxoDsATX28mLy/P5ERV8/naw3yV2ZLRfq/Q8abnzI7j8Romt2VzSB8AUua9ZXIakfp3ZM8WOpRswW5YaDlsvNlxREREauVoo0t5x34Fu0mutNyBDb9is9iJ9S8iOkIDxeI9WrZqxw7/9qyOGqkLAEVEpM4V+5ftRxVln6hVPRvtzXmj9BoOJV7iilheKbDFhQAkZOlGl6rQXpCIiFTJ/cNa0z/qJK8UTGLzB/ebHee8ThWV8tq83QBMGNaOiPAIkxN5B8eF9zClZDSPnBhGQbHd7Dgi9erHbWl8UnoRa0MHkdCoudlxREREaiU2tOzZrCfziiotV7y37K6X49Hd6zyTSH2yBQbR7vGV9L7/U7OjiIiIDyiylc1eUXoqrVb1LMlvwiulN5Df/gZXxPJKTboMBiDZfojszHST07g/DRSLiEiVhNj8ebRvKH2sO+h9fCa71i80O1Kl1n36NNcWfEWbGH9u7lP5XRJSdRf0v4LvI2/gYGEI325MMTuOSL1xOAze32bwWOl4jo/4j9lxREREaq1BUAmdLfuIzNhUabnYk+sA8GsxoD5iiYiIiHil0sAYAIy82g1c7ksrm+mxVYJmeqlIXGITngl8gOHF/+C3E7rR5Xw0UCwiIlXWech1rI0YjtVi4P/dfZQUV373gVnSUg7Qa/9/mBTwCVO6nMDmrz93rmK1WpwD7x+tPGhyGpH6s2LfSY5mFRAe5M8lHRqYHUdERKTWmuVv4dvAx7nt5GsVlsnNzqBF6V4AmnYdVl/RRERERLyOIyQWAL+CkzWuIy/vFM1zVtOQk7SM10BxZTJbXMNeoxHrDmWbHcXt6cy5iIhUS4tb/kkWYbSwH2DtZ8+aHadc+2dOIsRSxA7/9nS/5Gaz43idG3o24cqA1fw97UG3v7NcxFUO/TKVbpbdXNWlIUEBfmbHERERqbWQ6EQAIuxZFZbZv/5X/CwGRyyJeuyCiIiISC1YQ+MACCjMqHEdx/b8xgzbFL4LmkT0/3+MiJSve3LZVN/rD2aanMT9aaBYRESqJSahEbu7TgSg2963ObJni8mJznZg+1p6ZHxX9sslz2Cx6k+dq8WE2hgTs43e1p1kL9YUvOL9crMzuCblNb4OnMytzbLMjiMiIuIS4XENAYgycnDYHeWWydu9GIBjkd3qLZeIiIiIN/IPTwAgqCSrxnVkHSw7D5sa0NQVkbxar8bBjPH7iesPP0tpSbHZcdyazp6LiEi19bzqbjYHdiPIUkLWzAkYjvJPLJkhe85E/CwG60MH0q73cLPjeK3wAXcC0DlzPtknj5ucRqRubZ//IcGWYg5aG9O2q57PKCIi3iEqtuyO4gCLndzs8p+VtzSvCd/Ze1PSQtNOi4iIiNSGf3xLfrL3ZDWdalxHyYmdAOSGt3BVLK/VKjGGh/y/4GrLYg5uX2t2HLemgWIREak2i9VKzB//TaERgLUgk29XbjU7EgBbls7hgoLVlBh+xF87xew4Xq1tj4vY69eCIEsJ23/UXcXi3cJ3fA5ASrNRmqVARES8RmBQCDmEAJCdnnLO8sISO1PTOzKh5H4a9R9d3/FEREREvEpw027cWfIgr9n/UOM6AjP3AGDEtnZVLK/l5+/P/uAOAKRvX2JyGvemM10iIlIjjVp05Pse73JV8TM8Mf8YJ08VmZrHYXcQsOBpANbHX0OTVp1NzePtLFYr6e1uAaDxnk9x2O0mJxKpG4f3bKZ9yTbshoVWw+4wO46IiIhLZVuiADiVkXrOst8OZ1FiN0gID6RpTEg9JxMRERHxLjH//5nCWfkllFbw2I/z1lFwAIDgpA6uiuXV8hJ6AOB3dLXJSdybBopFRKTGrrz8Klo3jCErv4Rn5m4zNcs3m1KYkP8nfjT60fqGZ0zN4is6XXoHuUYwjY1jbF02x+w4InXiyIL3ANgS3JP4pGbmhhEREXGxU/5RABRmnTtQvH/zcppbjtG7WTQWi6Wek4mIiIh4l+gQGxaLQRj5ZJ4qqPb6pSXFJNmPAhDfXDfIVEVYqwsBSMrdZHIS96aBYhERqbEAPysvjOpMkKWEppvfZNPCr0zJUVhi5+WfdrHHaMzeIW8Sk9DIlBy+JjQ8im3xIwEoXfWOyWlEXM9eWkqLo2UXQZR20ZSbIiLifVZEXcmzJTdz2L/ZOcs6bXuFBYF/4+aAhfWeS0RERMTb+FktrAq8hy1B4zl1tPo33Bw7uBObxU6BYSOxSas6SOh9ml0wGLthIck4QXrKAbPjuC0NFIuISK1c0CSKt5ov58GAL4lb9Cj5p7LrPcPHi7dwNKuAhpFB3DGgeb1v35clXjyBVY52vJvVg2PZ1b8aUsSdrd+0kVIDcgil49AbzY4jIiLicrsbXsm79ss5wNkXWpYUF9GisOwEZmKnQWZEExEREfE6BdZQAPIyz53N5Xz25tr4v5I7+TBkDFY/P1dH80rhkTEc+P8XRB7atNDULO5MA8UiIlJrfUY/RirxJBkn2DRjYr1uOys9lVFLLud5/3d5+KImBAVoR6k+JbfvwauNXuc7e18+XX3Y7DgiLjVjl5WBRf9kWrt3CAoONTuOiIiIy8WFlT0r72Re0Vnv79u8nFBLEVmEkdy2uxnRRERERLxOnl8UAEXZJ6q97o4cf760D2ZL01tcnMq7pUddQKlhJePIbrOjuC0NFIuISK2FhkdxYtBzAPQ69gl7Ni6tt23v+OLvRJNLn8ADXNWzZb1tV353a79kAD5dfYgSu8PkNCKukVNYwo9bUnFgZWj//mbHERERqRMNbEV0sewlJH3LWe9nbl8IwP6QLrpjRURERMRFCm3RAJTmpld73b0nTgHQMl4XsldHeq8H6VL0Lv8uHml2FLelgWIREXGJLhf9kXXhQ/GzGDDnr5SWFNf5No/u20r342XPRc4fPBk/ncQyxSUdEmkdWsgf8mey4ZcvzY4j4hILVm3AXlpM64QwujSONDuOiIhInWiXt4o5gX/nyuP/Puv9oJRVABQl9TEjloiIiIhXKgksGyh2nKr+QHH84R/pa91G2xgN61VH59atySeILUezKSyxmx3HLalHiYiIyyTf/AY5hNLKvpe1nz9f59s7PmsiNoudjUG96DzomjrfnpTP5m/l6YZLeSTgM0LXvml2HBGXaLv0PlYE3su9LVKxWCxmxxEREakTQZENAAgtzXS+57DbaZ6/CYCYDkNNySUiIiLijRzBsQBYCqo3UGw4HNyV808+sz1LW9vJuojmtZrEBBMXFkiJ3WDL0Wyz47glDRSLiIjLxCU2YWfnhwFou/tdjhyv/tVxVbVj7S90P7UIu2Eh/Irn6mw7UjXNR0zAbljoWLyRgzvWmx1HpFYO7fqNdiXbiCaXC3v3NTuOiIhInQmNaQhApOP3k2YHtq8lkjzyjUBadO5nVjQRERERr2MJjQMgoDCjWuudPHGUCPJwGBaSWnaqi2hey2KxcE/0ar62PUHx0jfMjuOWNFAsIiIu1fPav/JN2A1cXfw0j323D8MwXL4Nw+HA+OlxANZFX0aLTpoSz2yJTVqxKbTsROKxX94yOY1I7RxdOA2ALSG9iEtKNjmNiIhI3QmPSQQg0siltKQEgBUZ4dxR/Dc+i7kT/wCbmfFEREREvIojri0/2nux1dq2Wusd31s228sxawJBwXpGcXW1iyymm3UPwSkrzY7iljRQLCIiLmWxWuky9nVS/RqyeFcaczamuHwbi1evI6n4AAWGjWbX1/0U11I1fr3HA9DxxFzyT2kqF/FM9tJSWqZ8C4Cjy2iT04iIiNStqNgGOAwLVotBVsZxAJYfKeQXRw/yOo0xOZ2IiIiId7E3H8JdJQ/wseWKaq136ug2ANKCmrk+lA+IajsAgOT8LRgOh8lp3I8GikVExOWax4Vy38WtAfhmziyy0o65rO4Su4Mnl+YxqOh1vm//DxIaNXdZ3VI7nQZewxFLIuGWArb8NM3sOCI1sm3ZHBLIIIswOg79o9lxRERE6pR/gI1sSzgAuSePYRgGq/eXTYXYu3mMmdFEREREvE5MaNlsLSfziqu1npG+C4DCiBYuz+QLmne+kCIjgBhyOLpvm9lx3I4GikVEpE78aWAL/h71E9Mcj7N7xv0uq/fT1YfYn56Hf2gMl1xzi8vqldqz+vlxpGXZHZgx2z7SFXrikYrWzgBgR9wIAoNCTE4jIiJS97KtkQDkZRzjyP5d3FrwEQP9t3NBkyhzg4mIiIh4mbKBYoOS/Cwc9qqfNwvJ2QuANaFdHSXzboFBIeyzld3UdGzLQnPDuCGvHiheu3YtTz/9NJdccgmNGzcmMDCQsLAw2rRpw7hx41i6dKlLt7dlyxbuueceOnfuTEREBDabjfj4eIYMGcKrr75Kbm6uS7cnIuLObP5W+g+7GodhoVf2j2xZ8k2t68zNzmDdvM8Ag/uHtSY8KKD2QcWl2l16FzlGCBuLG7LxQKrZcUSqJTsrg045iwGIHTDO5DQiIlWnY1+pjZ/Dr+WZkps5Zm3IsQ0/8Ff/2UwM/pqgAD+zo4mIiIh4lZggCzsDb2Oj7Q5ys9OrvF5C4SEAIhp3qKtoXi87thsAjkOrTE7ifvzNDlBXBg0axJIlS855v7i4mN27d7N7926mT5/OmDFjeOedd7DZbLXa3osvvsikSZOw2+1nvZ+ens6iRYtYtGgRr7/+OnPmzKFr16612paIiKdo1/NiVq0cRZ/0r4j+9SEKe1xEUEh4jevb8sVT/NMxnUvChnFJ75kuTCquEhWXyCPtv+Hz39IYtfYEXVskmR1JpMrm7sxlRvHTXB+xlXFd+psdR0SkSnTsK7W1MeFavjt+jMZGHFGHlgOQndDT5FQiIiIi3icwMIhcAgiklOyTx4iMSTjvOnlFpdxT9BdaW4/ycOse9ZDSOwW2uBBSPyYha6PZUdyO195RnJKSAkBSUhL33XcfX375JatXr2bFihW8+uqrNGrUCIAPP/yQsWPH1mpbn376KY8++ih2ux2bzcYDDzzAd999x6pVq/jkk08YMKDsQdmHDx/m0ksvJSsrq1bbExHxJB3HvMwJYmhkHOe3GY/VuJ7jR/bS7cjHACT1vpYAP6/9E+bxbrywbCqXuZuOkVnNZ66ImOnLdUfYbiRj7/83LFZ9x4iIZ9Cxr9RWbFjZxQMZecU0ytkAQGjrQWZGEhEREfFavz/243iVyu9Pz2Ot0Y6fgi4jOia2LqN5tSZdBnPciGJbSSLZeYVmx3ErXnsGrF27dnz++eccOnSI119/neuuu45evXrRt29fHnjgAX777TfatGkDlB3sLl68uMbbeu6555yvZ82axauvvsrIkSPp3bs3o0ePZsmSJYwaNQqA48eP8+6779buw4mIeJCwiBhS+pd9T/Y8OoO9m1fWqJ5DX04iyFLC9oCOdB12kysjiot1bRJFp0YRNLcfYPm8L82OI1Ile06cYsOhLPysFq7upjvhRcRz6NhXaispsIgulr2wZx5JxgnshoUW3S8yO5aIiIiIVzrlFwVAYfaJKpXfm3YKgFbxYXUVySfEJTbhhtD3mVByH78d1aNyzuS1A8Vz587lhhtuwM+v/GfqxMXF8corrzh///LLmp3IzsnJYevWrQB0796dyy+/vNxykydPdr5esWJFjbYlIuKpug6/ifVhg/C3OLB/cy/20tJqrb9380p6ZP4IgHXEs7rTz81ZLBYeSd7DT4GPcsHGp3D8z9SUIu7o2JyneCXgLcYkZ5EQHmR2HBGRKtOxr9RWt5xfmBP4d/52YhIA+wJaERYRbXIqEREREe9UGBAFQElO1QaKS3bO4wa/BfSOzKjDVL6hR3IMAOsOZpqcxL349Jn2oUOHOl/v3bu3RnUUF/8+pWaLFi0qLNeyZcty1xER8RVNb3qTwzTgP/kX8cGKQ9VaN++7x7BaDNaFDaFtT93d4Al6XDSKHEJobKSyZclss+OIVMpeWkrbI19ynd9SrkwuMTuOiIjL6dhXKhMQcfaz8dJj9XxiERERkbpSHFg2WGnPS69S+RaHv+IfAe8wwLGuLmP5hO7J0YDBvn17zI7iVnx6oLioqMj5uqKrr88nLi6OmJiy/9j79u2rsNyZB+Nt27at0bZERDxZXFIySy/9iVmOQbw8bxdHswqqtN7mRbPoUriOYsOPxFEv1HFKcZWQsEi2xZfdaWRfrWknxb1tXTKbBDLIJJyOQ28wO46IiMvp2FcqExSVePbvLQeYlERERETE+9mDy/apLXknq1Q+Ov8gAMFJHeosk6/oG5vPmsC/8GLKOEpLdFHraT49ULxo0SLn6/bt29e4nrvuuguA9evX8+OPP5Zb5plnngHA39+f8ePH13hbIiKe7I+9m9G7WQz5xXaenbUGw+GotLzdYfDR6mPscSSxvsEfaNSi5t/VUv8aXjwBgC55K0g9tNvkNCIVK1k/A4Bd8SMIDAw2OY2IiOvp2FcqExZTNlCcawQzougFknuNNDmRiIiIiPfKi+7Ij/Ze7Lc2PW/Z0pJiGtmPAhDfvHNdR/N6zZu3JogSQi2FHNy+1uw4bsNnB4odDgcvvPD7nWk33FDzu0cee+wxRowYAcA111zD//3f//HDDz+wZs0aPv/8c4YMGcKXX36Jn58fb775Ju3atat1fhERT2S1Wnh+VCdG+q/jyYO3sv7HaZWWn7X+CF+cbMYfrK/QdvSL9ZRSXCW5XTe22i7Az2Kw/6d/mx1HpFzZGWl0ylkKQOyAcSanERFxPR37yvlExSYBEG4pwD++NTHRMSYnEhEREfFemS2u5K6SB/g+YNh5y6Ye3IXNUkqBYSOxSat6SOfd/Pz92R9cdmd2+vYlJqdxHz47UPzaa6+xevVqAEaNGkWPHj1qXFdoaChz587lvffeo3HjxrzyyiuMHDmS3r17c+ONN7Jo0SJGjRrFihUruPPOO89bX1FRETk5OWf9iIh4i1YJ4dzaIpcGliyarX6anJMnyi1XUGznlZ93AfCXoW2Jjo6uz5jiIkXdygbe2hydRXFRoclpRM61Y/50Ai0l7Lc2o2XnC82OIyLicjr2lfMJj46lxCibknxQY589TSQiIiJSL2LDbABk5J1/6uP0A5sBSPFvjLWGj5CRs+UllB0P+R1dbXIS9+GTRwCLFi3i0UcfBSAhIYG333671nWuXr2aGTNmVPispnnz5vHee++RnZ193rqmTJlCZGSk86dJkya1zici4k663/QUB61NiCWbnTPuL7fMmi+mcHneVzSL9Oe2C5vVaz5xnc4X30Q6UTgMg+WrVpodR+QcUbtmAnC85SgsVp/cNRYRL6ZjX6kKi9WPAIsdgEGR5V/EKSIiIiKuERMaCBgUnso6b9nCY9sByAppVqeZfElYq/4AJOVuMjmJ+/C5s2Fbt27l2muvpbS0lKCgIGbOnElCQkKt6vzyyy+56KKLWLBgAZ07d+brr7/m5MmTFBcXs3fvXp5//nlKS0v573//S79+/UhJSam0vokTJ5Kdne38OXz4cK3yiYi4m8CgEApGvAJAr8zv2Lb8u7OWnzx+hO673+TvAR/zj85HCArQFXOeKsAWyHfd/sOFRW/y9vZAs+OInGV3ajbfFHRll6MRrS6+3ew4IiIupWNfqY6Vje9gl38bOvYZbnYUEREREa+W4DjBrsAxfFc0DsPhqLSs9eRuAIqjNe20qzS7YBB2w0KScYL0lANmx3ELPjVQvH//fi655BIyMzPx8/Pjs88+Y9CgQbWq8/jx44wdO5aioiI6duzI8uXLueaaa4iJiSEgIIAWLVowceJEvv32WywWC9u3b+fee++ttM7AwEAiIiLO+hER8Tbt+oxgVew1AITP+z8KC/Kcy/bM/DthlgL2+LWk52VjzQkoLnPJkCE4rAGs2p/BruO5ZscRcfpyQwpv2a/mHy0/JC5Rd7GJiPfQsa9UV9/xr9Lm8TVERMWaHUVERETEq0XGJGCz2AmylJCfV/mjV972v5nRxZMoaHttPaXzfuGRMRzwbwbAoU0LTc3iLnxmoDglJYVhw4aRkpKCxWJh2rRpXH311bWu97PPPiMvr2xw47HHHiM0NLTcchdffDEXX3wxALNnzyYzM7PW2xYR8XTtb32VNKJpYqTw28eTADi8eyPd074BoPCip/T8DS/QMDKYYe0TsOLg5wULzY4jAkCp3cHX648C8IcejU1OIyLiOjr2FRERERFxXyGhERQaAQBkp6dWWM4wDNalB7DC0ZGGLTrVVzyfsLPBFfyn9ArW5sSYHcUt+MRAcXp6OsOHD3c+Q+mNN95gzJgxLql7+/btztfdu3evtGyPHmUPyXY4HOzatcsl2xcR8WQRUbEc6fcUAIcP7mPnsRzSZz9GgMXOxuA+dOp/pckJxVXu6Gxjke0Bxm7/E3m5WWbHEeG3Vb/QO28hDYINLmpXu6lYRUTchY59RURERETcm8VqJcsSCcCpzIoHitNPFZNTWIrFAs3jyr9IU2qmqNddvFB6Ez+kazYd8IGB4uzsbEaMGMG2bdsAeOGFF5gwYYLL6vf393e+Li0trbRsSUlJueuJiPiybpeM4dlGb/NQyZ288cFHdMtbit2wEHXVFLOjiQv17NwR/AIIsxSw9cd3zY4jgmX5m7xpe4PX4udi8/f6XWIR8QE69hURERER8Qyn/MoGiguyKh4oTtm1jof9P2N0+CaCAjTjoiv1aFp2J/GWo9kUlthNTmM+rz4rlp+fz+WXX8769esBmDRpEo888ohLt9G8eXPn6yVLllRadvHixQBYLBaaNWvm0hwiIh7LYmH8DaMIC/TntvzpAKyLvYLk9j3MzSUuZfXz42ir0QDE7fgIw+EwOZH4suyTx+mcuxSAhP63mpxGRKT2dOwrIiIiIuI58gOiASjKTq+wTOHeZdztP4c/+v1aX7F8RpOYYJJD7fQxNrFrx1az45jOaweKi4uLufbaa1m2bBkA9913H88++2y165k+fToWiwWLxcKTTz55zvLLL78ci8UCwHPPPcfRo0fLrWfq1KmsXbsWgL59+xIbq1vaRUROS4wM4pFL2/JYyXh+MvrQ4g/PmR1J6kD7S++k0Aighf0AO9dqJ1fMs2P+dGyWUvb6NafVBf3NjiMiUis69hURERER8SxFtrKBYvuptArLGOllj3ApiGhZL5l8icVi4bWgqcywTSF/w0yz45jOa+eAGj16ND///DMAF110EXfccQdbtmypsLzNZqNNmzbV3k67du0YN24c06ZN4+jRo3Tr1o3777+fgQMHEh4ezuHDh/nss8/45JNPAPDz8+P555+v2YcSEfFiN/dJxmAEYfHXEZcUZ3YcqQORsQ1YHT2c3lnfc2rpf6D3MLMjiY+K3v0lAGktr0OHWyLi6XTsKyIiIiLiWdIiL+D7zGwKLA0qLBOSsxcAa0Lb+orlU4ob9oQ9SwhMXWt2FNN57UDxrFmznK9//fVXunTpUmn55ORkDhw4UKNtvfXWW+Tl5fH555+TlpbGpEmTyi0XGhrK1KlTGTJkSI22IyLizaxWC2P6NTM7htSxqEF3wZzv6ZK9gIwTR4lJaGR2JPExB7avpU3pLkoMP1pfPM7sOCIitaZjXxERERERz7K/xWhe2t2dPwQ25roKyiQUHgIgvHGH+gvmQ6LaDoA9r5GcvwXD4cBi9doJmM/Ldz+5CwUGBvLZZ5/x66+/MmbMGNq0aUNoaCj+/v7ExMTQr18//v73v7Njxw5uuukms+OKiIiYpk33wez2b43NUsrmXz4xO474oNRF0wDYEtqH2AaNTU4jIuJZdOwrIiIiIlJ7MaE2ADLzistdnn8qi0TKpqVu2PKCesvlS5p3vpAiI4AYcji6b5vZcUzltXcUG4bhknrGjh3L2LFjq1R26NChDB061CXbFRER8VZHej7GI4sOknaoC4scBlarxexI4iNK7Q7yj+8p+6XrzeaGERFxER37ioiIiIh4lrKBYoO8U9nlLk/Zu4VWQCYRRMcl1ms2XxEYFMJ2W2val2zj2JaFNG7VyexIptEdxSIiIlKv+l50NXsCO3A4s5BFu9PMjiM+ZPHuNG4vuI9rrf+i05A/mB1HRERERERERHxQ0/xt7A4cwysn7y53edahrQAcC2han7F8TnZsNwAch1aZnMRcGigWERGRehVs8+MPPZoA8Nny3SanEV/y5bojAHTr1osAW5DJaURERERERETEF4VGxRJgsRPpyCl3+SLbIAYU/ZOfmj9cz8l8S2CLCwFw5Bw3OYm5vHbqaREREXFfN/duRLNVT3DNgWWkHFhIUrO2ZkcSL5eZmcHabXuBUP7QQ88mFhERERERERFzRMaUTScdZimgqDCfwKCQs5bvTc/niBFPeJP2ZsTzGY17XkHXX/2xhcexuMROUICf2ZFMoTuKRUREpN61bBBJ99B0Iiz5HPz5LbPjiA/Y9dN/Wep/Fy9GfU2HpAiz44iIiIiIiIiIjwqPiqPUKBuey0o/ds7yvSfyAGiZEFavuXxNfEwUcx+5ilWPXeyzg8SggWIRERExSUn3cQC0SfmaosJ8k9OIt4vZ/SU2i53kps3NjiIiIiIiIiIiPszq50eWpewi9tyMs6c9Li0p5t7MKTzg/yWto3138LK+NI4OwWKxmB3DVBooFhEREVN0vmg0J4ghlmw2z59hdhzxYvu3rqK1fQ/Fhh9th99udhwRERERERER8XG51kgA8jPPHihOPbiTK6zLudPvW5Jio0xIJr5GA8UiIiJiCv8AG3ub/gGAkI0fmJxGvNnxxe8DsDWsH9HxDU1OIyIiIiIiIiK+Lt8/CoDinBNnvZ9+YAsAR/0bY/XTHcVS9zRQLCIiIqZpdekESg0rHUq2sH/rKrPjiBcqKS6i9fHvy37pdou5YUREREREREREgAPh3fje3ps0I+qs9wuPbQcgM0SPzpL6oYFiERERMU18UjM2hQ0A4MSvb5ucRrzR1sWziCWbk0TSadAos+OIiIiIiIiIiLCq6Z+4u+R+tgZ2Pet9v5O7ASiJbmVCKvFFGigWERERUwX0n8AbpdcwKW0Yp4pKzY4jXsa+4RMAdjUYSYAt0OQ0IiIiIiIiIiIQHWID4GRe8Vnvh5/aD0BAg3b1nkl8kwaKRURExFSd+o3g6+hx7CmO5usNR82OI14kI6+Yu7Nu5umSW0kY/Cez44iIiIiIiIiIABAbZsOCg9zcbOd7hsNBUukhAGKSO5kVTXyMBopFRETEVBaLhVv6JAPw8cqDGIZhciLxFrPWH+G4PZzViX+kZYceZscREREREREREQGgffZSdgeO4e4jjzjfO5l+nECjCIdhIamlBoqlfmigWEREREx3XY/GXBywmYdOPsGO1fPMjiMebvdvS9jw0uUs+OELAP7QvbHJiUREREREREREfhccHoW/xUGoPcv53p5TNjoUvc/o4P8QFBxqXjjxKf5mBxARERGJDA7grthN9MrawNpl/4U+l5gdSTzQ9hU/ULLoZboUrgVggl8GMZ1HcGPvpiYnExERERERERH5XWh0AwAiHL9PPb037RR2/Ahp0MKsWOKDNFAsIiIibiFqyF9g9nd0yV7IyeNHiG2gu0Dl/AyHg82LZhGw/FXal2wFoNSwsiFqGI0ufZQ32nczOaGIiIiIiIiIyNnCYxIBiDROUVpSgn9AAHtOnAKgZXyYmdHEx2igWERERNxC664D2TW3DW1Kd7Hrx7fpd9tzZkcSN+ZwGPy8LZXgOXcyuHgRAMWGP7/FXU6jyyfSq0V7kxOKiIiIiIiIiJQvKrbsjmKrxSAz4zixDRrTb+c/6BKQRnDQ/UAHU/OJ79AzikVERMRtZHUcA0Cz/Z9jLy01OY24o5LiIr5eu49LXl/MXTPW882p9uQbgaxsMJrsO9fR+94PaaRBYhERERERERFxY/4BNrIou3M4NyMVgC6nlnGt3zKSw81MJr5GA8UiIiLiNrqMGEc2oTQkjS2LvzQ7jriRwoI8Vn3xEmlTOrH+63+y58QpIoL8aTpoDEX3bKTvX/5DfFIzs2OKiIiIiIiIiFRJjjUSgLyM4+SfyiKRNAAatuxiZizxMZp6WkRERNxGUEgYvzW4ir7HP4U178FFN5odSUyWl5vF5jn/pOXu9+lDJgA3Biyh4fB7ubVvMuFBASYnFBERERERERGpvi2B3dl8qhERpYEE7t1CKyCTCKLjEs2OJj5EA8UiIiLiVhoNn8DWD5fzWU4XYjLyaRITYnYkMUF2RhrbvnmZdgc/pi+5ABwnlgNt76DLVX+lY6jmYRIRERERERERzzW74f38vO04z9haEXLoRwCOBTQl2uRc4ls0UCwiIiJupUmrztya/A5LdqcTueoQj17WzuxIUo/Scot4b+l+uq68n0stKwA4YmlISqe76HrFXTQIDDI5oYiIiIiIiIhI7cWG2QA4mVdMyfGdAJwKb25mJPFBGigWERERt3NL32SW7E7ni7WHeWB4awL9/cyOJHUs9fAePlp9jHc3nKKo1EE3y6W0DT7GyR730G3EWBr7a4ppEREREREREfEeMaE2LDjIyc3FlrUHAEdsG5NTia/RQLGIiIi4nYvbJdAywqBf3nesm5/OhZeONjuS1JHDezZzbO7zdM38iTj7MIpKb+OCJlHcPXQ0yW3/SnM/q9kRRURERERERERcrl/mtzwQ+Dy/7RtMSWE+dsNCcFJ7s2OJj9FAsYiIiLgdfz8rzzRcyoUH32fHulWggWKvs3/rKjJ+fIGuOQtoYjHAAl1DM/n4D725sFUcFovF7IgiIiIiIiIiInUmKCQcf4uDwKJMrit4FOxF/NJpiNmxxMdooFhERETcUpsRf6Hkv1NpV7KNvZtX0rJzX7MjiQvsXLeAgl9epGv+CpoDWGBjcB8CL3qYbr2GmR1PRERERERERKReBEYmAGAryqDY7iDQP5Ck2EiTU4mv0UCxiIiIuKW4pGTWhw+g+6lFpC98SwPFHswwDFbsPcm/F+5h0IF3uNN/BQ7DwobwwURd8igXdOlndkQRERERERERkXoVHNUAgGhyAGgRH4afVTOsSf3SQLGIiIi4rcB+d8K8RXRO/5Hc7AzCI2PMjiTVYDgcbFzwBR9tKeSrY/EA7LNeTtcoSBr5MD3adDU1n4iIiIiIiIiIWcJiEgFIsGQx2/Z3dgRcBQw0N5T4HA0Ui4iIiNvq0O8yDv7ShGTHYVb9OJU+f3zU7EhSBfbSUn77aTrR696gq+MAOfbOzPWfxI29mvDnwRfRKOpmsyOKiIiIiIiIiJgqKq6h83VX614KgvJMTCO+SgPFIiIi4rYsVivHWt9E8s4XabDzYwzHw1isVrNjSQVKiovY8N1/abjpbXoYKQDkGUHYGl/A0hsHEx8ZYnJCERERERERERH3EBQcwikjmDBLAQABDdqZnEh8kQaKRURExK11uOxOTu34F7tL4kjfdZCe7ZqbHUn+R2GJnVVzptJ28yv0Jg2AbELZ3vRmOlz9EH1jE0xOKCIiIiIiIiLiflb492S4fQkAMcmdTE4jvkgDxSIiIuLWIqJiebLTLKavy+TKDVn01MWVbiO3sIQZKw/x3tJ9XFywl8EBaaQTxZ5W4+h81X30jYg2O6KIiIiIiIiIiNv6KOouhp9cgsOwkNSio9lxxAdpoFhERETc3vX9OzJ93VJ+3HKMtNwOxIcHmh3Jp2Wlp7L9m5f45nAwnxX2A2BV5HBWtEqi2xV30jc41OSEIiIiIiIiIiLur0NAKgDHrAk0CgkzOY34Ig0Ui4iIiNvrmBRJt6ZRpB3ezbJ5s7hm1GizI/mk9JSD7J7zAhcc+4p+liKSHAmsixvKXUPbclXXJAL8RpgdUURERERERETEY7SypACQHdiQRiZnEd+kgWIRERHxCA+2TKH/8fs5sSkO+1XX4+ev3Zj6knJgJ4e/fZ6u6d/Rz1ICFtjj15KcPvfy4yVD8PPzMzuiiIiIiIiIiIjHaWvsA8AWrLuJxRw6wyoiIiIeodegkeSsCCWRNH5bOJOuw3RXcV3bc+IUW76awhWpb5FkcYAFtgd0oPjCB+ky+DosVqvZEUVEREREREREPFbTP77M6l+70vaiW8yOIj5KA8UiIiLiEYKCQ/kt8Wr6pn6Mde17oIHiOrPlSCb/XriPH7em0pMYrgl0sCmoB36DH6JDnxEaIBYRERERERERcYHI6Dh6X3e/2THEh2mgWERERDxGk0smwIcf06lgLbt/W0qLTn01BbUL7Vj1E8ULXmLFqQR+KL0ZgOgOg9nRdR5duvQ2OZ2IiIiIiIiIiIi4ks6sioiIiMdo1KIjm4J60aVwDa1nX07O1yFcFjSDRtEhJEUFMaxkAfGBpQTFNSWiQXNikloSERmjO2ArYTgcbFkyG79lr9KheDMAzf2C2dPhXsZf1Im2ieEmJxQREREREREREZG6oIFiERER8SiBwx7j8HcTaOhI5YQRxdHsQo5mFwIw3vYBnawHzip/yggm3S+etMBkZrWeQqOoIBpGBtOSw8RHRxHfqDm2wCATPom5HHY7G3/5hLDV/6Rz6W4Aig0/fosdSaMrJvJSi44mJxQREREREREREZG65NUDxWvXruX7779n6dKlbNu2jbS0NAICAkhKSqJ///7ccccdDBgwwKXbNAyDWbNm8dlnn7F27VpSU1MJDg6mQYMG9OjRg4svvpgxY8bg5+fn0u2KiIj4irY9L4Ke27GXlhKWnspXRSGkZBWSklVAxrZBbMhNIqIolRh7GtHkEmYpIMxxiNK8Uj5dfchZz1zbYzSyHsBhWEizRJHpH8+poESKQ5OwR7cgt9MYGkYGkxQVTFyYDYvFYuKndp1Su4O5m46R+ePzjCuaAUCBYWNjg2tpftUj9G7c0uSEIiJSXTr2FRERERERkZqwGIZhmB2iLgwaNIglS5act9yYMWN45513sNlstd7moUOHuPnmm1m6dGml5TIzM4mKiqpyvTk5OURGRpKdnU1EREQtU4qIiPiO/FPZpKfsJzt1Pxm5+ayz9eJYVgEp2QU8lvJXWtr3EWQpOWe9PY4khhW/7Px9VuBkGlhzyQpoQH5wQ+zhjbBENyE4timRDVsS16wToYHuff1dUWE+363azuurcjiUkU9DTjI3cBK7Gl9H26sfIiahkdkRRUTOS8dG59Kxr4iIiIiIiHepz2Mj9z6jWQspKSkAJCUlcf311zNw4ECaNm2K3W5nxYoVvPLKKxw9epQPP/yQkpISPvnkk1pt7/DhwwwZMoT9+/fj5+fHLbfcwpVXXklycjIOh4P9+/czf/58vv76a1d8PBEREamCkLBImrbpCm26AjD4rKWrMRwOMtKPcTJlP6dOHKDo5EHIOkxaaTDd/KJIySrgRG4RzThGjHGKRsWpULwRsoEjZbXscSTRsfhlIoMDSIoK5kH7NCJs4IhoTEBME0Ljk4lu2IK4hsn4B9T+5Hx15Z/KZtM3/6TF7veJsCdzqOQhYkJt3DLgQgJ676BfaGi9ZxIREdfRsa+IiIiIiIjUlNfeUXzFFVcwZswYrrvuunKnukpPT6d///7s2rULgEWLFjFo0KAabcswDIYMGcLixYuJjo7m+++/p2/fvuWWLS0txc/Pr1rTV+qqahEREfMUlzpIP7KbrGP7yE87QEnmYaw5RwnKTyGi6Di7HYn8ufA+Z/n1gX8mxnLqnHrshoXfrB15PuElkqKCSYoK4sL8hURGRBLeoBmxSS2IjEnAYrW6JHd2ZjrbvnmZdgc+JpocAI4Ty8+Dv+a6/h0JsXnt9YIi4sV0bHQuHfuKiIiIiIh4F91R7AJz586tdHlcXByvvPIKV155JQBffvlljQ+WP/74YxYvXgzA1KlTKzxQBvD399omFxER8Uo2fytJzdqS1KxtucubA5sKSziWVUhKVj67Nz+AI+sw/qdSCCk4RnTJCeIc6dgsdvJKLaw7mMm6g5kA3Bn47FmDyvlGIGl+8eQEJHA8ojNb295LUlQQSVHBNPE7SUKDRgSFhFWaN+PEUXZ98yIdj3xBP0sBAEctDTja8S4uuOIubg0KcU3DiIiIW9Cxr4iIiIiIiNSUTx+5DR061Pl67969Na7nzTffBKBt27b84Q9/qHUuERER8SwRQQFEJAbQNjEc2v3fOcsddjvpx48Qm5nNv+3xHMsuICXzFAd3duVkYSox9jRiySbEUkSy4wgUHSEztYTXDu9y1rE+8M8EWU6RQQQn/RI4FZRIcWhDjIjGWBp0wNJqGD9sOYax5l2etH4AFjhgbUp6t3voeuk4Gpkw7bWIiLgHHfuKiIiIiIhIeXx6oLioqMj5urwpuqri0KFDrFq1CsB5hTZASUkJR48exc/Pj8TERAICAmoXVkRERDyW1c+PuKRk4pKg41lLvnO+Ksw/RVrKfrJT95OfdpCs4hBu9G/C0awC0jKzCcwtASCGHGLsOZC3B/KAE7B4Z2fG/FL2rOFABnF5+Gb8e43lgotH06yG+zgiIuI9dOwrIiIiIiIi5fHpgeJFixY5X7dv375GdZw+UAbo3LkzqampTJw4kS+++IL8/HwAQkJCuOSSS5g8eTJdu3atVWYRERHxTkEhYTRp1ZkmrTo737vqjOWGI5XszDTSj+4l58RBik8exJF1GNupFA4ZyTQqDaZ5XCh3Dm5Bz1bXVOuZkCIi4t107CsiIiIiIiLl8dmBYofDwQsvvOD8/YYbbqhRPdu2bXO+zsjIoEuXLqSlpZ1VJj8/n9mzZ/Pdd98xbdo0brnllpqFFhEREZ9lsVqJjG1AZGwD4MKzlvUAtHchIiLl0bGviIiIiIiIVMRqdgCzvPbaa6xevRqAUaNG0aNHjxrVk5GR4Xw9ceJE0tLSuOWWW9i8eTNFRUUcOXKEKVOmYLPZKCkp4fbbb2fdunWV1llUVEROTs5ZPyIiIiIiIiLVpWNfERERERERqYhPDhQvWrSIRx99FICEhATefvvtGteVl5fnfF1YWMjtt9/ORx99RKdOnbDZbDRq1IhHH32U6dOnA2XPb3r88ccrrXPKlClERkY6f5o0aVLjfCIiIiIiIuKbdOwrIiIiIiIilfG5geKtW7dy7bXXUlpaSlBQEDNnziQhIaHG9QUFBTlf+/v78/zzz5dbbvTo0fTs2ROAn3/+maysrArrnDhxItnZ2c6fw4cP1zifiIiIiIiI+B4d+4qIiIiIiMj5+NRA8f79+7nkkkvIzMzEz8+Pzz77jEGDBtWqzvDwcOfrrl270qBBgwrLjhgxAih7RlRlU3AFBgYSERFx1o+IiIiIiIhIVejYV0RERERERKrCZwaKU1JSGDZsGCkpKVgsFqZNm8bVV19d63rPnBrrfNNknbk8LS2t1tsWEREREREROZOOfUVERERERKSqfGKgOD09neHDh7Nv3z4A3njjDcaMGeOSujt27Oh8bbfbKy175nJ/f3+XbF9EREREREQEdOwrIiIiIiIi1eP1A8XZ2dmMGDGCbdu2AfDCCy8wYcIEl9Xfs2dPgoODAZwH4xXZu3ev83WjRo1clkFERERERER8m459RUREREREpLq8eqA4Pz+fyy+/nPXr1wMwadIkHnnkEZduIzQ0lEsvvRSArVu3snv37nLLORwOvvnmGwBCQkLo3r27S3OIiIiIiIiIb9Kxr4iIiIiIiNSE1w4UFxcXc+2117Js2TIA7rvvPp599tlq1zN9+nQsFgsWi4Unn3yy3DKPPvooAIZhMGHCBEpKSs4p8/zzzzuvqh43bhyBgYHVziIiIiIiIiJyJh37ioiIiIiISE157cOCRo8ezc8//wzARRddxB133MGWLVsqLG+z2WjTpk2NttW7d2/uvvtu3nrrLebNm8eAAQN44IEHaNOmDWlpacyYMYMZM2YA0KRJkwoPukVERERERESqQ8e+IiIiIiIiUlNeO1A8a9Ys5+tff/2VLl26VFo+OTmZAwcO1Hh7//rXvzh16hQffvghq1evZvTo0eeUadWqFXPnziUuLq7G2xERERERERE5Tce+IiIiIiIiUlNeO/V0ffPz8+ODDz7ghx9+4LrrrqNRo0bYbDZiYmIYOHAgr7/+Ops3b6Zt27ZmRxURERERERGpER37ioiIiIiIeA+LYRiG2SGkcjk5OURGRpKdnU1ERITZcUREREREREyhYyPvpn9fERERERGR+j020h3FIiIiIiIiIiIiIiIiIiI+RgPFIiIiIiIiIiIiIiIiIiI+RgPFIiIiIiIiIiIiIiIiIiI+RgPFIiIiIiIiIiIiIiIiIiI+RgPFIiIiIiIiIiIiIiIiIiI+RgPFIiIiIiIiIiIiIiIiIiI+RgPFIiIiIiIiIiIiIiIiIiI+RgPFIiIiIiIiIiIiIiIiIiI+xt/sAHJ+hmEAkJOTY3ISERERERER85w+Jjp9jCTeRce+IiIiIiIi9Xvsq4FiD5CbmwtAkyZNTE4iIiIiIiJivtzcXCIjI82OIS6mY18REREREZHf1cexr8XQpdhuz+FwkJKSQnh4OBaLxew4ch45OTk0adKEw4cPExERYXYccVPqJ1JV6itSVeorUlXqK1IV7tpPDMMgNzeXpKQkrFY9Scnb+MKxr7v+3/IFantzqf3No7Y3j9reXGp/86jtzaX2N48r274+j311R7EHsFqtNG7c2OwYUk0RERH6IpbzUj+RqlJfkapSX5GqUl+RqnDHfqI7ib2XLx37uuP/LV+htjeX2t88anvzqO3NpfY3j9reXGp/87iq7evr2FeXYIuIiIiIiIiIiIiIiIiI+BgNFIuIiIiIiIiIiIiIiIiI+BgNFIu4WGBgIJMnTyYwMNDsKOLG1E+kqtRXpKrUV6Sq1FekKtRPROqG/m+ZR21vLrW/edT25lHbm0vtbx61vbnU/ubx1La3GIZhmB1CRERERERERERERERERETqj+4oFhERERERERERERERERHxMRooFhERERERERERERERERHxMRooFhERERERERERERERERHxMRooFhERERERERERERERERHxMRooFqmFtWvX8vTTT3PJJZfQuHFjAgMDCQsLo02bNowbN46lS5eaHVHc3COPPILFYnH+LFy40OxI4kYOHTrE5MmT6dmzJ/Hx8QQFBdGkSRMGDhzIE088wZYtW8yOKCYrLi7m3XffZcSIETRs2ND5d6ht27aMGzeO5cuXmx1R6tCJEyeYO3cuTzzxBJdddhlxcXHOvydjx46tdn0//PAD1157rXOfpnHjxlx77bX88MMPrg8v9coVfSU/P59Zs2bxl7/8hV69ehEdHU1AQACxsbH069ePJ598ktTU1Lr9ICIu4srvz/379/PAAw/QqVMnwsPDCQ0NpXXr1tx9991s3bq1yvWsWLGC22+/nbZt2xIWFkZgYCANGzZkxIgRvPPOOxQXF1epnoMHD/K3v/2Ndu3aERoaSkxMDL169eKll14iPz+/Wp+tLnhj2595PFfZz5AhQ6r1+VzNHdt+6dKl3HLLLTRv3pzg4GCioqLo1q0bTz75JOnp6VWuJz09nSeeeIIuXboQERFBREQEXbp04YknnuDkyZPV+mx1xRvbv1mzZlXq+82aNavW53M1V5+7c8U+c2lpKf/5z38YOHAg8fHxBAcH07JlS+68885q/Ru6e9/3xrb3lH4P7tX+RUVFrFy5kjfeeINbb72Vtm3bYrVane1VXe6+v+ONbe8p+zvgXu1//Phx3n33XW666SY6dOhAWFgYNpuNhg0bcumllzJ16lQKCgqqnKXO+r4hIjUycOBAAzjvz5gxY4yioiKz44ob2rBhg+Hv739Wf1mwYIHZscRN/Otf/zJCQ0Mr/X657777zI4pJjpw4IDRsWPH8/4duvfeew2Hw2F2XKkDlf2733bbbVWux263G3fccUel9Y0fP96w2+1192GkTtW2r2zcuNEICws77/dNRESE8dlnn9X9BxKpJVd9f/73v/81bDZbhXXZbDbjjTfeqLQOh8Nh3Hvvvef9/9WxY0fj4MGDldY1Z84cIyIiosI62rRpY+zevbvKn68ueGPbV+W8AGAMHjy4yp+vLrhT2xcXFxvjx4+vNFODBg2MxYsXnzfPypUrjcTExArradiwobFq1aoqf7664o3tn5ycXKW+n5ycXOXP52quPHfnqn3mtLQ0o1evXhXWERgYaLzzzjvn/Wzu3ve9te09od8bhvu1/9ixYytdvzrcfX/HW9u+Kp8JzN/fcaf2nzp1quHn53feLK1btzY2btx43s9Wl31fA8UiNdSyZUsDMJKSkoz77rvP+PLLL43Vq1cbK1asMF599VWjUaNGzv+ko0ePNjuuuBm73e7cOU1ISHD2FQ0Ui2EYxjPPPHPWH/mXXnrJWLhwobFhwwZj/vz5xksvvWRceOGFxgMPPGB2VDFJcXHxWYPEXbp0MaZPn26sWLHC+Pnnn40nnnjirAsNpkyZYnZkqQNnHhA0bdrUuOSSS5y/V+eE46OPPupcr1u3bsann35qrF692vj000+Nbt26OZdNnDix7j6M1Kna9pUlS5Y4y/fv39+YMmWKMW/ePGP9+vXGTz/9ZNx5552G1Wo1AMPPz8/4/vvv6/5DidSCK74/P/30U+c6kZGRxtNPP20sXbrUWLNmjTF16lSjVatWBmBYLBbj888/r7Ce559/3llPeHi4MXnyZOPnn382li9fbrz//vtGp06dnMs7depklJSUlFvP+vXrjeDgYAMwwsLCjOeee85Yvny58csvvxh/+tOfztq3zMnJqUmzuYQ3tv3pMn/5y1+MzZs3V/izb9++mjSZy7hT2995551nnRydOnWqsWbNGmPp0qXG008/bURGRhqAERUVZezcubPCeg4dOmTEx8cbgOHv7288/PDDxuLFi43FixcbDz/8sPPC7ISEBOPw4cPVbTKX8sb2Pz1gdvXVV1fa9yuro6658tydK/aZS0tLjQEDBjjLjho1yvjhhx+MVatWGf/617+c54esVmul+1Oe0Pe9te09od8bhvu1/2233XbW39zBgwefdaFDVXnC/o63tr2n7O+4U/ufPr9rs9mMUaNGGf/5z3+MRYsWGevXrzdmzpx51r5AfHx8pd/Xdd33NVAsUkOXX3658fnnnxulpaXlLk9LSzPatGnj/E+6aNGiek4o7uy1114zAKNdu3bGxIkTnf1EA8Uyf/58Z38YM2aMUVxcXGFZzVbgu2bOnOnsJ/369Sv3b9HatWuNgIAA50meik5uiud64oknjG+//dZITU01DMMw9u/fX+0Tjjt37nSeSOrZs6eRn59/1vK8vDyjZ8+ezpNQZt+JJjVT276ybNky44YbbjC2bt1aYZnZs2cbFovFAIyWLVtqJgNxa7X9P5GXl+c8oRwWFmZs3rz5nDLZ2dlG586dDSi7My83N/ecMsXFxUZUVJTzBNKGDRvOKVNSUmL06dPHmW/mzJnlZjp994S/v7+xfPnyc5b/4x//cNYxefLk837GuuKNbe8O7VoV7tL2q1evdm63S5cuRnZ29jllNm/e7Lzo8fLLL68w06233uqs64svvjhn+eeff17tfaO64o3tf3rAzOy2rYyrzt25ap/5vffec27r7rvvPmf57t27nXeLtWrVqsJjOE/o+97a9p7Q7w3D/dr/s88+M95//31jy5YtzrsvBw8eXO3BSk/Y3/HWtje7XavKndr/1VdfNR555BHjxIkTFeZ98MEHnVnGjRtXYbm67vsaKBapQ99++63zP+i9995rdhxxEwcPHnRO37hw4UJj8uTJGigWwzDK7jRv3bq1ARgXXHCBBvakQg888IDze2POnDkVlrv22mud5TZt2lSPCcUMNRko/stf/uJcZ8WKFeWWWbFiRaUnVcTz1KSvVMV1113nrHfdunUuq1ekrlX3/8SZF2xNmjSpwnLz5s1zlitvKtiNGzc6l48aNarCer755htnuQcffPCc5atWrXIuv/POO8utw263G+3bt3deQFbZxYj1ydPb3jA858Tp/zKr7SdMmOBcPm/evArrmTRpUqX7sceOHXPOZjFixIgK6xkxYoQBZXcJHjt27Dyfsv54evsbhucMmJ1PVc7duWqf+fT3cExMjJGXl1dumSlTplQ6COzpff9Mntb2huE9/d4w6rf9y1PdwUpP3t/5X57W9obhufs75TG7/c9UVFRkNGzY0ICy2ULKm8a6Pvq+FRGpM0OHDnW+3rt3r4lJxJ1MmDCBU6dOcdtttzF48GCz44gb+fnnn9m9ezcAjzzyCP7+/iYnEndVXFzsfN2iRYsKy7Vs2bLcdUQADMPgm2++AaBdu3b07du33HJ9+/albdu2AHzzzTcYhlFvGcWzaN9XfMXatWudry+77LIKyw0ZMoSgoCAAvvzyy3OWu+rv+ezZs52vx40bV24dVquVMWPGAJCVlcWCBQsq3J47c7e29yWuavvT9QQFBTFkyJAK67n00kudr7/66qtzls+ZMweHwwFU3O8Bxo4dC4DD4WDOnDkVlnN37tb+3uR8+y+u2mfetWsX27dvB+CGG24gJCSk3HpO91mAr7/++pzl3tT3Pa3tvU19tb+reNP+jqe1vbdxp/a32Wz0798fgOzsbE6ePHlOmfro+xooFqlDRUVFztd+fn4mJhF38cUXXzB37lxiYmJ4+eWXzY4jbmbmzJkAWCwWrrjiCuf7GRkZ7N69m4yMDLOiiZs5vSMKsG/fvgrLnd7htVgstG7dus5ziWfZv38/KSkpAOe9cOn08qNHj3LgwIG6jiYeSvu+4ivOPIHToEGDCsv5+/sTExMDwIoVKygtLT1reevWrbFYLEDV/p7D2fsApy1duhSA0NBQevToUWE9Z37XL1u2rMJy7szd2t6XuKrtT9cTGxtb6YWxZ25j8eLF5yw/3e+h8v0Yb+j34H7t703Ot//iqn3mqvbZxMRE2rRpA5TfZ72p73ta23ub+mp/V/Gm/R1Pa3tv427tf7489dH3NVAsUocWLVrkfN2+fXsTk4g7yMrK4r777gPgxRdfJC4uzuRE4m5WrlwJQLNmzQgPD+eTTz6hc+fOxMbG0qZNG2JjY2nbti0vv/zyWTsR4ntGjx5NREQEUPZ9YrfbzymzYcMGvvvuOwBuuukmZ3mR07Zt2+Z83a5du0rLnrn89NX4Iv9L+77iK8LCwpyvs7OzKyxnGAY5OTlA2d2oe/bsOWt5ZGQko0ePBmDu3Lls2rTpnDpKS0uZMmXKOeXPdPp7uVWrVpUO/HjDd7m7tf2ZZs6cSYcOHQgJCSE8PJzWrVtz2223ue3dTNXlqrY/Xc/pMhU5cxtn7rP873uRkZEkJiZWWE/Dhg2d+8Ge2u/B/dr/TIsXL6Zr166Eh4cTEhJC8+bN+eMf/8js2bM94u628+2/uGqfuSb1HD58mLy8vHLr8Ya+72ltfyZP7/dQf+3vKt60v+NpbX8mb9jfcaf2LykpYcWKFUDZRVqnL/Y6U330fQ0Ui9QRh8PBCy+84Pz9hhtuMDGNuIOHH36Y1NRU+vfvzx133GF2HHEzDoeDHTt2ABAXF8d9993HzTffzJYtW84qt2vXLh566CEuuugisrKyTEgq7iAuLo6PPvqIkJAQli1bRq9evfjwww9ZuXIl8+fP56mnnmLw4MEUFxfTvXt3XnnlFbMjixs6cuSI83Xjxo0rLdukSRPn68OHD9dZJvFcGzdudF6c0rlzZw0Ui1c7s3+feaLpf23YsIFTp045fz906NA5ZV599VW6d+9OcXExAwcO5Omnn2b+/PmsXLmSDz74gJ49e7Jy5UpCQkL48MMPiY2NPWv9wsJC0tPTgfN/l0dHRxMaGgp47ne5O7X9/9q2bRvbt2+noKCAU6dOsWfPHj788EMuuugirr322koH9zyBq9r+dD25ubmsX7++wnrOvIv1+PHj50z9fXo/5nz9Hn7fj/HUfg/u1/5n2r9/Pxs3buTUqVMUFBRw4MABvvjiC6699loGDhzI0aNHK/5gJqvKuTtX7TPXpB7DMM5a78x6PL3ve2Lbn8mT+z3Ub/u7gjft73ha2/8vT9/fcbf2nzp1qrNvX3/99ecsr6++r4FikTry2muvsXr1agBGjRpV6bQA4v2WLFnCu+++i7+/P//5z3+c05yJnJadne18ztDmzZv517/+RcOGDZkxYwYZGRnk5+ezaNEi53Mxli9fzu23325mZDHZVVddxbp16xg/fjy//fYbt912G/369WP48OE8+eSThISE8Prrr7NkyZJKp6cT35Wbm+t8feZdKuU5fbABnHXyUQTKpsoaP368c3aD5557zuREInXrsssuc17N/+qrrzpP3pzJ4XAwadKks94783v3tAYNGrBkyRJef/11goODmTx5MsOHD6dfv36MHTuWTZs2MX78eNatW8dVV111zvrV+S6H37/PPfW73J3a/rSQkBBuvPFG3nnnHZYsWcKGDRv4+eefmTRpknNwefbs2Vx99dWUlJTU5uObylVtf2ZbPv74485joDOlp6efc6Hj/9Zz+ndf6Pfgfu0PZc9VvOqqq3jzzTdZuHAhGzZsYMGCBTz//PPOE+fLli1j+PDhbjtwUJVzd67aZ3Z1PZ7e9z2x7cE7+j3Ub/u7gjft73ha25/mLfs77tT++/btc/7dDgsLY+LEibXKcmae6mbRQLFIHVi0aBGPPvooAAkJCbz99tsmJxIzFRcX8+c//xnDMHjggQfo1KmT2ZHEDZ05pVFhYSEhISEsWLCAm2++mejoaIKDgxk0aBC//vorF1xwAQBff/01q1atMiuymKy4uJgPP/yQb775ptyprY4fP86MGTOYP3++CenEExQWFjpf22y2SssGBgY6XxcUFNRZJvFM99xzD2vXrgXgtttu48orrzQ5kUjdatKkCXfddRdQ9jyy/v37880335CTk0NhYSErV65k5MiR/Pjjj2d9v1b0/fnrr78yY8YMjh8/fs4ywzD45ptvmD59erl39FXnuxx+/z731O9yd2r7044ePcqnn37K+PHjGTBgAF27dmX48OE8++yzbN26lW7dugFl5wk8+dyAq9r++uuvdx7P/PDDD1x++eWsXLmSwsJCcnJy+Oabb+jfvz8pKSmV1nO67/tCvwf3a3+A1atX88033zBhwgQGDx5M165dGTJkCBMnTmTr1q1ccsklQNn0l0899ZTL26S2qnruzlX7zK6ux5P7vqe2PXh+v4f6b39X8Jb9HU9s+9O8YX/Hndo/Pz+fUaNGOS8oeeONN0hKSqpVljPzVLcvaKBYxMW2bt3KtddeS2lpKUFBQcycOZOEhASzY4mJnn/+eXbs2EHTpk2ZPHmy2XHETQUFBZ31+/jx42nbtu055YKDg8+6U+vzzz+v82zifvLy8hg2bBhTpkwhIyODhx9+mO3bt1NUVER2djY///wzAwYMYO3atVxzzTW8+uqrZkcWN3Tm905lJ8CBs56LHhwcXGeZxPNMmTKFd999F4BevXrx73//2+REIvXj5ZdfZuTIkUDZo0GuueYaIiMjCQ4Opl+/fvz000/07NnzrEfOhIeHn1PPP//5T6666irWrl3LoEGDmDdvHtnZ2RQVFbFt2zb+7//+j4yMDF588UUuuuiic+4OqM53Ofz+fe7J3+Xu0vanRUVFVZi1QYMGfPnllwQEBABlJwE9mSva3s/Pj6+//ppWrVoB8OOPP9KvXz+Cg4OJjIzkmmuuYdeuXdx1113OAc3y6jnd932l34N7tT9U3vfDw8P54osvnM9anDp1apX+repLdc7duWqf2dX1eGrf9+S2B8/u92BO+7uCN+zveGrbn+bp+zvu1P6lpaVcf/31bNy4EYC//OUvjB07ttZZzsxT3b6ggWIRF9q/fz+XXHIJmZmZ+Pn58dlnnzFo0CCzY4mJduzYwZQpU4CyP5JnTkchcqb/PfA+fRVqeS6++GLntGNr1qyp01zinp588kmWLFkCwHvvvceLL75Iu3btsNlsREREMHz4cBYsWMDQoUMxDIOHHnrIuQMqctqZ3zvnm5bozFkPqjLdkfiG//73vzz22GMAtGvXju+//177OuIzAgMD+fbbb3nnnXfo2rXrWY+WSUhIYNKkSSxZsuSsWT+io6PPqmPTpk08+OCDGIbBsGHD+PXXXxk2bBgRERHYbDbat2/PSy+9xNSpU4GyqSz/98LT6nyXw+/f5578Xe4ubV9VLVq0YPjw4QDs2bOHlJSUGtXjDlzR9gDNmzdn7dq1TJo0iaZNm561rEOHDkyfPp23337bOd2in58fERERZ5U73fd9pd+De7V/VURGRnLjjTcCZf8Gp2cfMVt1z925ap/Z1fV4Yt/39LavCnft92Be+7uCp+/veHLbV5U77++4U/sbhsHYsWP5/vvvgbJnJL/55psuyXJmnur2BQ0Ui7hISkoKw4YNIyUlBYvFwrRp07j66qvNjiUme+211yguLqZFixbk5+fz2WefnfOzZcsWZ/lff/3V+f6Zf2jE+wUGBhIfH+/8/fSzbcoTFBREXFwcAGlpaXWeTdyLYRhMmzYNgDZt2nDbbbeVW87f359nnnkGKHte2fTp0+sroniIxo0bO18fOXKk0rKHDx92vq7s+0l8x6effsrdd98NQHJyMvPmzXP+bRLxFVarlfHjx7Nhwways7PZvXs3R48e5dixYzz77LMEBQWxe/duZ/kOHTqctf7777/vfD7oU089hZ+fX7nbuf3222ndujUA06dPP2sQKCgoyPlcuPN9l2dmZjqPMTz9u9wd2r46ztz+0aNHa1SHu6ht258WGRnJs88+y8GDB0lLS2Pnzp2kp6ezdetWbrvtNux2O/v37wegffv2Zw2Kwu/7Mefr9/D7foyn93twn/avKnfr+zU5d+eqfeaa1GOxWM5a78x6PK3ve0PbV5W79Xswt/1dwZP3dzy97atDff/87T9hwgQ+/vhjAC677DJmzJiB1VrxMG199X3/apUWkXKlp6czfPhw9u3bB5TdOTpmzBiTU4k7OD3dw759+xg9evR5y58e1IGyq510V45v6dixIwsXLgTAbrdXWvb08tN3FovvOH78OBkZGQDOZ8BUpEePHs7XO3bsqNNc4nnOPIg7X/84c3n79u3rLJN4hjlz5jBmzBgcDgcNGzbkl19+qfGJNBFvER4efs4MMXa7nd9++w0ou8vify+m2L59u/N19+7dK62/e/fu7N69m4yMDE6cOEGDBg2cyzp06MCSJUvYs2cPpaWlFe4feut3uZltX1U1HWRzdzVp+/LExcWdU27Lli3O4+nevXufs06HDh1Yt24d2dnZpKamkpiYWG7dx44dIycnB/Cufg/mtn9VuVPfr+m5O1ftM/9vPV27dj1vPU2aNDnnvJAn9n1vafuqcqd+D+a3v6t44v6Ot7R9VanvV97+jzzyiPO5yIMGDeKrr75yTtd9vjx13fd1R7FILWVnZzNixAi2bdsGwAsvvMCECRNMTiUinujMaU9O78SUJycnh/T0dAAaNWpU57nEvZy5Q1haWlpp2ZKSknLXE4GyaQeTkpIAWLRoUaVlFy9eDJR95zRr1qyuo4kb++WXX7jhhhsoLS0lNjaWefPm0bJlS7NjibilBQsWcPLkSQD++Mc/nrPcVX/TBwwYAJRNNbdu3boK6zjzu75///6Vbs/T1VfbV9Xp8wWA82+vtzpf21fVzJkzna/Lq+d0v4fK92N8qd9D/bV/VblL36/NuTtX7TNXtc+mpqaya9cuoPw+62l935vavqrcpd+De7S/q3ja/o43tX1Vqe83q7Dcs88+yz/+8Q8AevXqxdy5c6v8HOF66fuGiNRYXl6e0b9/fwMwAGPSpElmRxIPNHnyZGcfWrBggdlxxEQbN2509oWbb765wnLTp093lnvmmWfqMaG4A7vdbkRERBiAkZSUZJSUlFRY9ttvv3X2lXvvvbceU4oZ9u/f7/z3vu2226q0zl/+8hfnOitWrCi3zIoVK5xl7r77bhcmFrPUpK8YhmEsW7bMCA0NNQAjMjLSWLt2bd2FFKlHNf0/URmHw+E8VgwICDD27dt3Tpl77rnHud3vv/++wrqKi4uNhg0bOv/vORyOs5avWrXKWc+dd95Zbh12u91o3769ARhRUVFGcXFx7T6gi3h621fFvn37DJvNZgBGy5Ytq71+XTGr7avixIkTRmRkpAEYbdq0Kbfdjx07ZlitVgMwRowYUWFdI0aMMADDarUax44dq1GeuuDp7V8VWVlZRmxsrAEYISEhRmFhYY3qqS1XnLtz1T7z6e/hmJgYIy8vr9wyU6ZMcdbzxRdfnLPck/q+t7V9VbhLvzcM92r/8gwePNi5XlV40v6Ot7V9VbjT/o67tf/rr7/uLNe5c2fj5MmT1cpSH31fA8UiNVRUVGRccsklzv+k9913n9mRxENpoFjOdNlllzkP5ubPn3/O8mPHjhmNGzc2AMNmsxlHjhwxIaWYbfTo0c7vjSeffLLcMhkZGUaHDh2c5X766ad6Tin1rSYnHHfu3Gn4+fkZgNGzZ08jPz//rOX5+flGz549DcDw9/c3du3aVQfJpb7VpK9s2LDBiIqKMgAjNDTUWLp0ad2GFKlHNfk/kZ6eXuHJ39LSUuPuu+921vnEE0+UW+6nn35ylunSpYuRnZ1dbrmJEyc6y40ePbrcMgMHDnR+Vy9fvvyc5f/4xz+cdUyePLlKn7E+eHrbz5kzp9KL9lJTU41u3bo563jllVeq8Anrh1ltbxiGcfTo0QqXZWRkGH379nXW88svv1RY9tZbb3WWmzlz5jnLv/jiC5cPxrqKp7f/Dz/8cM5+45lyc3PPOmdm1kWrrjp356p95vfee8+ZZcKECecs37Nnj/Oi4FatWlX4/eIJfd8b295T+r1huF/7l6cmg5WesL/jjW3vSfs77tb+06ZNMywWiwFlF1+lpqbWKE9d932LYRgGIlJt1113HbNmzQLgoosu4vXXX690Hn6bzUabNm3qK554kCeffJKnnnoKKJsmasiQIeYGElPt2rWLPn36kJWVRVBQEPfffz8jR44kODiY1atXM2XKFI4cOQLAiy++yMMPP2xyYjHDjh076NGjB/n5+QBceeWV3HbbbbRo0YLCwkJWrlzJ66+/zqFDhwC4+OKLmT9/vpmRpQ4sXbqUPXv2OH9PT0/noYceAsqmGRo/fvxZ5ceOHVtuPRMnTuSFF14Ayp57/cgjj9CyZUv27t3Liy++yIYNG5zlnn/++Tr4JFLXattX9u7dy4UXXsiJEycAeO211xg2bFil20xISCAhIcEF6UVczxXfn19++SX33HMPN954I4MHD6Zp06YUFhayadMmpk6d6nxG6GWXXcbs2bOx2WzlZrn44ov59ddfgbJp7u677z569+5NUFAQe/bsYdq0afz4448AhIaGsm7dOtq2bXtOPRs2bKB///4UFBQQFhbGY489xtChQykoKOCzzz5j6tSpALRp04a1a9ee81zT+uJtbd+sWTNKSkq47rrr6NevH82aNSM4OJj09HQWLlzIf//7X+cjYwYMGMD8+fMJDAysfsO5gDu1/T333MPChQu54YYb6Nu3L/Hx8WRlZbFkyRLefvttUlNTAXjmmWd4/PHHK/xMhw8fpkePHqSlpeHv78/f/vY3rrjiCgDmzp3LK6+8QmlpKfHx8axfv57GjRtXub1czdvaf8iQIWzevJlRo0YxYMAAWrZsSVhYGNnZ2Sxfvpz//Oc/zmORtm3bsnz5cmJiYqrVZq7gynN3rthnttvtDB48mGXLljnz/elPfyI6OprVq1fzzDPPcOLECaxWK3PnzuWyyy4rtx5P6Pve2Pae0u/B/do/NTXV+Tf1tBdeeIGdO3cC8P7775+1bMCAAbRq1eqcejxhf8cb296T9nfcqf1nz57NH/7wB+x2OxEREXz++efn/T5u3rx5uc9Hr/O+X6PhaxFxXqFR1Z/k5GSzI4ub0h3F8r+WLFliNGjQoMLvE4vFYjz++ONmxxSTzZs3z4iLizvv35+LLrrIyMjIMDuu1IHbbrutWvsiFbHb7cbtt99e6bp33HGHYbfb6/HTiSvVtq+8//771d73dac7FkX+lyu+P2fOnFnpOhaLxbj99tvPO+VkRkaGMXTo0PNmiI+PN+bNm1dpXXPmzHHeDVXeT5s2bYzdu3fXuN1cwdvaPjk5uUqf47rrrjMyMzNr23y14k5tP2HChErrCQkJMf75z39W6XOtXLnSSExMrLCuxMREY+XKldVuL1fztvY/8260yn4GDx5s6ixY1WlzqPzcnav2mdPS0oxevXpVWEdgYKDxzjvvnPezuXvf98a295R+bxju1/4LFiyoVp7333+/wrrcfX/HG9vek/Z33Kn9q/u3HyofG6jLvu+PiIiIuJUBAwawdetW3njjDWbPns3+/fspLi6mYcOGDBkyhHvvvZdu3bqZHVNMNmzYMHbs2MF7773HDz/8wNatW8nKysLf35/ExER69erFTTfdxFVXXVXp1ZMiVquV9957j+uuu46pU6eyZs0a0tPTiYuLo1evXtx5550V3s0gIuKrBg4cyEsvvcSvv/7Kjh07OH78OFarlaSkJIYOHcq4cePo06fPeeuJjo7ml19+Yc6cOXzyySesWbOG1NRUSktLiYqKomPHjlx22WWMHz/+vHclXXnllWzatIl//vOffPfddxw5cgSbzUarVq24/vrrueeeewgJCXFVE5jGndr+gw8+YNGiRaxYsYJ9+/aRnp5OTk4OYWFhNGnShAsvvJDbbruNfv36uboZTOGqtr/zzjuJjIxk0aJFHDhwgLS0NMLCwkhOTubyyy9n/PjxJCcnVylTnz592Lx5M//85z+ZPXs2Bw4cAMruyLn66qu5//77iY2Nrc3Hdhvu1P4vv/wyv/zyCytWrGDnzp2kp6eTlZVFSEgISUlJ9OnTh9GjR3PJJZd4zbGIq/aZ4+LiWL58Oe+88w6ffPIJ27dvJy8vj6SkJC6++GLuu+8+OnbseN56fKnvu0vb+2K/B/c7XvSV/R1wn7b3tf2d09yl/U+ry76vqadFRERERERERERERERERHyM1ewAIiIiIiIiIiIiIiIiIiJSvzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIiIiIiIiIiIiIiLiYzRQLCIiIm7NYrFgsVh48sknzY4iIiIiIiIiIiIi4jU0UCwiIiIiIiIiIiIiIiIi4mM0UCwiIiL1bsiQIVgsFoYMGWJ2FBERERERERERERGfpIFiEREREREREREREREREREfo4FiEREREREREREREREREREfo4FiEREREREREREREREREREfo4FiERERkz355JNYLBYsFgsAOTk5PPnkk3Tu3JmwsDASEhIYOXIky5cvP2u9EydO8Pjjj9OxY0dCQ0OJjY3l6quvZsOGDZVuz+FwMGPGDEaOHEliYiI2m434+HiGDh3KW2+9RXFxcZWzFhYW8tJLL9G9e3fCw8MJDw+nd+/evPnmm5SWlp6z/tixY7FYLCxatAiARYsWOes7/dOsWbNK869Zs4bRo0fTuHFjAgMDadSoEbfeeivbt2+vdD0RERERERHxHCUlJSQmJmKxWLj00kvPW37Lli3O48p//OMf5yxPTU1l0qRJ9OzZk5iYGAIDA2nSpAk33HAD8+fPr7TuzMxM3n//fW655RY6dOhAWFgYNpuNxMRERowYwdSpUys9lj5w4IAz2/Tp0wGYNWsWI0eOJCkpCX9/f4YMGXLezygiIuJqFsMwDLNDiIiI+LInn3ySp556CoBDhw4xbNgwdu3adU45Pz8/Pv30U66//no2bdrEyJEjOXr06DnlAgMD+eGHHxg6dOg5yzIyMrjqqqtYtmxZhXnat2/PDz/8QHJycqVZU1NTufTSS/ntt9/KrefKK69k9uzZWK2/X5c2duxYPvjggwq3DZCcnMyBAwecv58elJ48eTIJCQncd9995Q5Ch4SE8MMPPzBo0KBK6xcRERERERHP8PDDD/PSSy9htVo5dOgQjRo1qrDsgw8+yGuvvYa/vz+HDx8mMTHRuezjjz/mzjvvJC8vr8L177jjDv7zn//g7+9/zrJmzZpx8ODBSrN269aN77///qztnnbgwAGaN28OwLRp01iwYAEfffTRWWUGDx7MwoULK92GiIiIq+mOYhERETdy/fXXc+TIESZOnMiiRYtYs2YNr732GhEREdjtdu644w7279/PFVdcQUFBAc899xxLly5l1apVPPXUU9hsNoqKihg7duw5VzPb7XauuOIK5yDx4MGDmTlzJmvXrmXOnDlcc801AGzfvp2LL76YU6dOVZp11KhRbNu2jb/+9a/MmzePdevW8cknn9C+fXsAvv32W955552z1nnuuefYvHkzPXv2BKBnz55s3rz5rJ+ff/653O399NNP3HvvvXTs2JFp06axZs0aFi9ezAMPPIDVaiU/P59bb7210qu4RURERERExHOMHz8eKJsZ68MPP6ywXElJCTNmzADgsssuO2uw9osvvuDWW28lLy+PFi1a8Oqrr/Ljjz+ybt06vvrqK0aOHAnAe++9x8MPP1xu/Xa7nT59+vDMM88wd+5c1qxZw7Jly5gxY4bzbucNGzZw4403nvczvf7663z00UcMHDiQTz75hLVr1zJ//nxuvfXWqjWKiIiIKxkiIiJiqsmTJxuAARiBgYHGypUrzykzd+5cZ5n4+HgjLi7O2LNnzznl/v3vfzvLzZo166xlb775pnPZmDFjDIfDcc76jz32mLPMww8/XGnWgIAAY8GCBeeUOXnypNGgQQMDMLp06VLuZx48eLABGIMHD66gVX53enuAMXLkSKOoqOicMs8++2yFn1tEREREREQ818CBAw3AaNOmTYVlZs2a5Twm/Prrr53vp6WlGZGRkQZg3H777UZJSUm5658+FrZarcaOHTvOWb5r165KM06bNs25/fnz55+zfP/+/Wcd21Z0TC4iIlLfdEexiIiIG7n//vvp06fPOe9ffvnlzqmg09LSeOaZZ2jZsuU55caNG0dQUBAAS5YsOWvZv//9bwDi4+N58803nVM6n+mpp56iXbt2ALzzzjsUFRVVmPXee+8t9xlKMTExjBs3DoDNmzeTnZ1dYR3VERQUxPvvv4/NZjtn2V//+lfn+//7uUVERERERMRznb6reNeuXRU+Run9998HICEhgSuuuML5/ttvv012djaNGjXirbfeKndaaSg7Fm7UqFGFdy63bt260ozjxo2ja9euAMyePbvSslFRURUek4uIiNQ3DRSLiIi4kcqmqerSpQtQ9szeP/7xj+WWCQ4Odh7A7tu3z/l+SkoK27dvB+CGG24gPDy83PX9/f2dg7yZmZmsX7++wjw333xzhct69OgBgGEY7N+/v8Jy1TF8+HASEhLKXRYeHl7u5xYRERERERHPdv311xMZGQn8PiB8puPHj/PDDz8AcOutt541GDxnzhwArrjiCgIDAyvchr+/P/369QNgxYoVleYxDIPU1FR27drFli1bnD+nn5+8cePGSte/8sorKzwmFxERqW8aKBYREXEjbdq0qXBZVFQUAHFxcURHR5+3XG5urvO9LVu2OF+Xd8fymc5cfuZ6/+v0ncfliYmJcb4+M0dtVLa9M7fpqu2JiIiIiIiI+YKDg7npppuAsucN5+fnn7X8o48+orS0FIDbb7/d+b7dbue3334D4L///S8Wi6XSny+//BKA1NTUcnN89913XHHFFURGRtKwYUPatm1L586dnT/fffcdAOnp6ZV+ntMXgYuIiLgDDRSLiIi4kZCQkAqXWa3W85Y5s5zdbne+l5GR4Xxd0V25pyUmJpa7Xk2y/m+O2qjJ5xYRERERERHPd3r66dzcXOeA7mmn7zLu06cPHTp0cL6fkZHhHECujv8diDYMg/Hjx3PFFVfw3Xffnffi5IKCgkqXV3bht4iISH0r/6EMIiIi4rX0HCQRERERERHxJN27d6dbt25s2LCB999/nzFjxgCwatUqtm3bBpx9NzGcfRHx+PHjue+++6q0LZvNdtbv06ZN47333gOga9eu3H///fTp04dGjRoREhKCn58fAGPGjOGjjz7CMIxK6z9dXkRExB1ooFhERMQHnDkV9PHjxyste+Y0W2euJyIiIiIiImKW8ePHM2HCBBYtWsT+/ftp3ry5827ikJAQbrzxxrPKn3k8axgGnTp1qtF233nnHQBatWrF8uXL/x979x0nV1X/f/w1M9t3s7tJNtmUTe8FkkACCS303ntRCUVEQUVR1J8NvihIUVBQ6QREgVCkV+ktjRAIpPe6yZZke5/5/bEaiaRndu+W1/Px2MfjZu6557wXkp0987nnXFJTU7fabns7ckmS1FK59bQkSe3AlyfE06ZN227b6dOnb/W6eHJVsyRJkiRpV1xwwQWkpqYSi8WYPHkyVVVVPPbYYwCcccYZZGZmbtE+KSmJESNGAPDBBx/s9rhffPEFACeffPI2i8SxWIxZs2bt9hiSJAXFQrEkSe1Ajx49GDZsGABTpkyhvLx8q+0aGhqYPHky0PjcpH322adJ8qSkpABQU1PTJP1LkiRJktqWrKwszjzzTAAeeughnnzySUpKSoCvbjv9HyeffDIA8+fP59VXX92tcf/znOOKiopttnn22WdZt27dbvUvSVKQLBRLktROXHHFFQAUFBTwve99b6ttrrvuus3Pd/rmN79JcnJyk2Tp3r07AEuXLt3h85skSZIkSYLG7acBVqxYwTXXXAPAgAEDmDhx4lbbf//73ycjIwOAiy66aPPq4G158cUX+eyzz7Z4bdCgQQA8//zzW91eesmSJZvn25IktTYWiiVJaicuv/xyJkyYAMCDDz7IEUccwVNPPcWsWbN48cUXOeOMM7j++uuBxon2L3/5yybLcsABBwCwYcMGfvjDH/Lxxx+zePFiFi9ezIoVK5psXEmSJElS63XIIYcwePBgAPLz8wGYNGnSNh9vlJuby0MPPUQoFGLdunWMHTuWb3/72zz33HPMmjWLadOm8dRTT/GTn/yEAQMGcOKJJ7Jy5cot+vjGN74BwNq1a5kwYQIPPPAA06dP59133+Xaa69l3333pbi4uMl25JIkqSklBB1AkiQ1j0gkwgsvvMDJJ5/MBx98wJtvvsmbb775lXbDhg3j5Zdf3nzXdVM499xzufHGG1m6dCm33347t99+++Zzffr0Yfny5U02tiRJkiSp9brkkkv4yU9+AkA4HGbSpEnbbX/66afz7LPPMmnSJIqLi7nrrru46667tto2HA6Tnp6+xWvf//73ef3113nttddYuHAhl1xyyRbnU1NTefjhh3nxxRd9TrEkqdVxRbEkSe1Ip06dePfdd3n44Yc59thjyc3NJTExkc6dO3PooYdy5513Mnv2bPr06dOkOTIyMvjwww/5/ve/z7Bhw0hLS2vS8SRJkiRJbcPXv/71zcdHHXUUeXl5O7zmpJNOYtmyZdx6660cfvjhm+fCqamp9OvXjxNPPJE//OEPLF++nMMOO2yLaxMTE3nxxRf505/+xNixY0lLSyM1NZWBAwdy+eWXM2vWLM4666y4f5+SJDWHUMwHA0qSJEmSJEmSWoHXX3+do48+GoDHH3+cs88+O+BEkiS1Xq4oliRJkiRJkiS1Cg888AAAnTt35pRTTgk4jSRJrZuFYkmSJEmSJElSi7dkyRKefPJJAC666CKSk5MDTiRJUuvm1tOSJEmSJEmSpBZpzZo1VFZWsnTpUn7yk5/w6aefkpKSwpIlS+jRo0fQ8SRJatUSgg4gSZIkSZIkSdLWXHDBBbzzzjtbvHb99ddbJJYkKQ4sFEuSJEmSJEmSWrS0tDQGDx7MVVddxYUXXhh0HEmS2gS3npYkSZIkSZIkSZKkdiYcdABJkiRJkiRJkiRJUvOyUCxJkiRJkiRJkiRJ7YyFYkmSJEmSJEmSJElqZywUS5IkSZIkSZIkSVI7Y6FYkiRJkiRJkiRJktoZC8WSJEmSJEmSJEmS1M5YKJYkSZIkSZIkSZKkdsZCsSRJkiRJkiRJkiS1MxaKJUmSJEmSJEmSJKmdsVAsSZIkSZIkSZIkSe2MhWJJkiRJkiRJkiRJamcsFEuSJEmSJEmSJElSO2OhWJIkSZIkSZIkSZLaGQvFkiRJkiRJkiRJktTOWCiWJEmSJEmSJEmSpHbGQrEkSZIkSZIkSZIktTMWiiVJkiRJkiRJkiSpnbFQLEmSJEmSJEmSJEntjIViSZIkSZIkSZIkSWpnLBRLkiRJkiRJkiRJUjtjoViSJEmSJEmSJEmS2hkLxZIkSZIkSZIkSZLUzlgoliRJkiRJkiRJkqR2xkKxJEmSJEmSJEmSJLUzFoolSZIkSZIkSZIkqZ2xUCxJkiRJkiRJkiRJ7YyFYkmSJEmSJEmSJElqZywUS5IkSZIkSZIkSVI7Y6FYkiRJkiRJkiRJktoZC8WSJEmSJEmSJEmS1M5YKG6FNmzYwAsvvMCvfvUrjjvuOHJycgiFQoRCISZNmhR0vLhYvnz55u9pR19t5XuWJEmSJP2Xc1/nvpIkSZKaVkLQAbTrcnNzg44gSZIkSVKTcu4rSZIkSU3LQnEr17t3b4YOHcprr70WdJQm85vf/IZTTjllm+c7duzYjGkkSZIkSc3Nua9zX0mSJEnxZ6G4FfrVr37FuHHjGDduHLm5uSxfvpx+/foFHavJ9OzZk5EjRwYdQ5IkSZLUjJz7SpIkSVLTslDcCl133XVBR5AkSZIkqUk595UkSZKkphUOOoCCVV1dzZ133skRRxxBt27dSEpKomvXrhx55JHcf//91NfXBx1RkiRJkqQ94txXkiRJkr7KFcXt2Keffsopp5zCihUrtni9oKCAN954gzfeeIO7776b559/ntzc3IBSSpIkSZK0+5z7SpIkSdLWuaK4nVq8eDETJ05kxYoVZGZm8rOf/Yx//vOfzJw5k1dffZUrrriChIQEZsyYwSmnnEJdXV1gWe+44w4GDhxISkoKWVlZjBgxgssvv5xZs2YFlkmSJEmS1PI595UkSZKkbQvFYrFY0CG0Z5YvX06/fv0AuPDCC5k8efIOrznwwAP58MMPGTNmDK+99ho5OTlfafPKK69wwgknEI1Gueeee/jmN78Z7+jb9OXvaXu+9a1v8cc//pHk5ORmSCVJkiRJCopzX+e+kiRJkuLLFcXt0HvvvceHH34IwEMPPbTViTLAsccey5lnngmwUxPweMvOzuaiiy7ioYce4sMPP2TWrFm8+OKLfP/73ycjIwOAu+++m4svvrjZs0mSJEmSWjbnvpIkSZK0fT6juB167rnnABgyZAh77bXXdtsecsghTJkyhRkzZlBfX09CQvP8lenRowdr1qwhLS1ti9fHjBnD8ccfzxVXXMGRRx7JypUr+cc//sE555zDySef3CzZJEmSJEktn3NfSZIkSdo+VxS3QzNnzgRgwYIFhEKh7X5deeWVANTV1VFcXLxFP5MnT97h9TvztTVJSUlfmSh/2aBBg3jkkUc2//mOO+7Y0/8skiRJkqQ2xLmvJEmSJG2fheJ2aMOGDbt1XWVlZZyT7JmDDz6Y4cOHA/D+++8TjUYDTiRJkiRJaimc+0qSJEnS9rn1dDvU0NAAwKhRo7a4M3lHevbsucWfTzvtNMaPHx/XbLtq+PDhzJ07l+rqaoqKiujSpUugeSRJkiRJLYNzX0mSJEnaPgvF7VDnzp0BKC8vZ+TIkbvdT1ZWFllZWfGKtVu2tX2XJEmSJKl9c+4rSZIkSdvn1tPt0JgxYwBYunQp+fn5AafZM3PnzgUgOTl584cAkiRJkiQ595UkSZKk7bNQ3A6dfPLJAMRiMf74xz8GnGb3ffDBB3zxxRcAHHTQQYTD/nWWJEmSJDVy7itJkiRJ2+fsoh06+uij2W+//QC45ZZbmDJlynbbz5kzh+eff745om32zDPPEIvFtnl+8eLFnH/++Zv//J3vfKc5YkmSJEmSWgnnvpIkSZK0faHY9mYkapHef/99Fi9evPnPhYWF/PjHPwbgwAMP5NJLL92i/aRJk77Sx5IlS9hvv/0oLi4G4KSTTuKcc85h0KBBRCIRNmzYwCeffMLzzz/P1KlTufrqq7n11lub7pv6H6FQiIEDB3L66aez3377kZeXR3JyMuvWrePVV1/l/vvvp7y8HICzzz6bxx9/vNmySZIkSZKannNf576SJEmSmpaF4lZo0qRJPPTQQzvdflv/ixcuXMgZZ5zB559/vsM+rrvuOn71q1/t9Jh7KhQK7VS7b3/729x2220kJyc3cSJJkiRJUnNy7vtfzn0lSZIkNYWEoAMoOIMHD2b27NlMmTKFp556ihkzZlBQUEBDQwOdO3dmyJAhHHTQQZx22mnss88+zZrtueee46OPPmLatGmsWLGCwsJCKioqyMzMpH///hx88MFcfPHFjBw5sllzSZIkSZJaF+e+kiRJkrR1riiWJEmSJEmSJEmSpHYmHHQASZIkSZIkSZIkSVLzslAsSZIkSZIkSZIkSe2MzyhuBaLRKGvXrqVDhw6EQqGg40iSJElSIGKxGGVlZfTo0YNw2Pue2xrnvpIkSZLUvHNfC8WtwNq1a+nVq1fQMSRJkiSpRVi1ahV5eXlBx1CcOfeVJEmSpP9qjrmvheJWoEOHDkDjX4jMzMyA00iSJElSMEpLS+nVq9fmOZLaFue+kiRJktS8c992Wyj+yU9+ws0337z5z2+99RaHHnroHvf76KOP8uCDD/LZZ5+xadMmcnNzOfjgg7niiiuYMGHCbvX5ny23MjMznSxLkiRJavfclnjntKZ5Lzj3lSRJkqQva465b7t8qNPs2bP5wx/+ENc+q6qqOOGEEzj//PN5/fXXWb9+PTU1NaxcuZK///3vHHTQQVx33XVxHVOSJEmSpK1x3itJkiRJ2pF2VyiORqNcdtll1NfX07Vr17j1e/HFF/PSSy8BcNhhh/HMM88wffp07r//fgYMGEA0GuXaa6/lnnvuiduYkiRJkiT9L+e9kiRJkqSd0e4KxX/605+YMWMGQ4cO5ZJLLolLn2+++SaPPfYYACeddBKvv/46p5xyCuPGjePiiy9m6tSp9O7dG2jc+mvjxo1xGVeSJEmSpP/lvFeSJEmStDPaVaF45cqV/PKXvwTgrrvuIikpKS793nrrrQAkJCTwl7/8hUgkssX5nJwcbrrpJgA2bdrEfffdF5dxJUmSJEn6Mue9kiRJkqSd1a4KxVdccQXl5eVceOGFTJw4MS59lpWV8cYbbwBw5JFHkpeXt9V2p59+OpmZmQD885//jMvYkiRJkiR9mfNeSZIkSdLOajeF4ilTpvDCCy/QqVOnzXdCx8OMGTOora0F2O4kPCkpifHjx2++pq6uLm4ZJEmSJEly3itJkiRJ2hXtolC8adMmvv/97wNw0003kZOTE7e+586du/l46NCh2237n/P19fUsWrQobhkkSZIkSe2b815JkiRJ0q5qF4Xia665hvz8fA488EAuueSSuPa9evXqzcfb2n7rP3r16rX5eNWqVXHNIUmSJElqv5z3SpIkSZJ2VULQAZrae++9x3333UdCQgJ33XUXoVAorv2XlZVtPs7IyNhu2/T09M3H5eXl22xXU1NDTU3N5j+XlpbuQUJJkiRJUlvWGue94NxXkiRJkoLWplcU19bWctlllxGLxfjBD37AyJEj4z5GdXX15uOkpKTttk1OTt58XFVVtc12N954I1lZWZu/vnxHtiRJkiRJ/9Fa573g3FeSJEmSgtamC8U33HAD8+fPp3fv3vz6179ukjFSUlI2H9fW1m637ZfvlE5NTd1mu5/97GeUlJRs/nK7LkmSJEnS1rTWeS8495UkSZKkoLXZrafnz5/PjTfeCMAdd9yxxfZX8dShQ4fNxzvaVquiomLz8fa260pOTt7iLmxJkiRJkv5Xa573gnNfSZIkSQpamy0U33bbbdTW1tK/f38qKyt57LHHvtLm888/33z85ptvkp+fD8BJJ5200xPsvLy8zcerV69m7Nix22z75buj3VJLkiRJkrQnnPdKkiRJkvZEmy0U/2e7q6VLl3LeeeftsP3111+/+XjZsmU7PWEePnz45uP58+dvt+1/zickJDBo0KCd6l+SJEmSpK1x3itJkiRJ2hNt+hnFzWHcuHEkJSUB8M4772wK3TJTAAEAAElEQVSzXW1tLVOnTt18TWJiYrPkkyRJkiRpTzjvlSRJkqS2qc0WiidPnkwsFtvu169//evN7d96663Nr/ft23enx+nQoQNHHHEEAP/6179YvXr1Vts9/fTTlJaWAnDaaaft/jcmSZIkSRLOeyVJkiRJe6bNForjZfLkyYRCIUKhENdee+1W2/zoRz8CoL6+niuuuIKGhoYtzhcWFvKTn/wEgOzsbC699NImzSxJkiRJ0s5y3itJkiRJ7ZOF4jg4/PDDOffccwF47rnnOOqoo3juueeYOXMmDz74IOPHj2flypUA3HTTTXTs2DHIuJIkSZIk7RLnvZIkSZLU9iQEHaCteOCBBygtLeWll17irbfe4q233trifDgc5pe//CWXXXZZQAklSZIkSdp9znslSZIkqW1xRXGcpKam8uKLL/L3v/+do446iq5du5KUlESvXr04//zzef/997e5hZckSZIkSS2d815JkiRJaltCsVgsFnQIbV9paSlZWVmUlJSQmZkZdBxJktSCxKJRpi7K55GZ+QzvkckVhw0MOpIkNRnnRm2b/38lSZIkqXnnRm49LUmS1ApFGxr47M3HSZn2Rz6tHsiL9efz0ufrOHPfPHIzU4KOJ0mSJEmSJKmFc+tpSZKkVqS+rpaZz9/Nit+OYfQH32Zo/XzOirxLt7QQsViMtz+ZH3RESZIkSZIkSa2AK4olSZJagZrqSmY//1fy5t7N2Nh6AMpjqczpeRaDTr6GX0z7iNGzfkbJBz1h4rsBp5UkSZIkSZLU0lkoliRJasEqaup5dPpKEt76PyZF/wnARjKZ3/drDD/laiZ0zAFgn70r6PFJId1riijasIbOXXsGGVuSJEmSJElSC2ehWJIkqQUqKVrPkx/N545ZNWyqrKMHh3FkyoesHnIhe5/8XSakZ27Rvke/oSyKDGRQw2IWv/s4nc/8YUDJJUmSJEmSJLUGFoolSZJakMK1K1j83E3ste4pekdHsKnuavrlpPPtiXvTdfQF5CVu+9e3wt7HMmjZnaQufgGwUCxJkiRJkiRp2ywUS5IktQBrls5j9Yu/Y0zhC4wP1UMI+idt5C+nDeWY0f2JhEM77CPvwHNh2Z0Mq5pNSdF6sjrnNkNySZIkSZIkSa2RhWJJkqQALZ8/i8KXbmB0yRv0DEUhBPMSh1M74QfsfeiZDAiHd7qvXgP3Ymm4L/2jy1nw7hT2O+27TZhckiRJkiRJUmtmoViSJCkAs1dt4s9vLabbgr9xfeLrEILPUsaSMPFHDBt/LKHQjlcQb836vGPov/Jukhc+D1goliRJkiRJkrR1FoolSZKaSSwa5YsPX+D52au4e3VfAFJCh3JcZj5dDr+SvUcfvMdjdD/gPB5aupRXyyZwV3UdmSmJe9ynJEmSJEmSpLbHQrEkSVITizY08Nmbj5M67XZG1i8gOdqTB8I3c8qYXlw+cQADu54Wt7H6Dh3DJR2vZElBBW/O28CpY3rGrW9JkiRJkiRJbYeFYkmSpCZSX1fL7JcfoPPsvzA6ugKA6lgixV3H8/ZZ+9Mzt0uTjHv8Xt25483FvDRnnYViSZIkSZIkSVtloViSJCnOauob+PDlfzB41vWMja0HoCyWyuc9z2bQyT9m/269mnT840Z0Y/pbz3Pg4r9RUXYX6R2ym3Q8SZIkSZIkSa2PhWJJkqQ4qaip5x/TVnLve0sZVLGGvyetZyOZzO97AcNP+RETOuY0S45h3Tvw+5T7yIut4+P3nmLf4y9plnElSZIkSZIktR4WiiVJkvZQSdF65j57K9NWVXJ71fEARDLH8u6g3zD2uG8wISOrWfOEwmFWdTuKvHUPw9znwEKxJEmSJEmSpP9hoViSJGk3Fa5dzuLnbmLvdU8xIVTDiFga/+p8PN84dG9OHdOTpIQjA8vWedxZ8NzDDCv7iKqKclLTMwLLIkmSJEmSJKnlsVAsSZK0i9YsnceaF29kdOGLjA/VQwiWRPqxad/v8uzRxxBJCP5XrEGjD2Ldc13oHipg1vvPsM8xXws6kiRJkiRJkqQWJPhPMSVJklqJBfllfPzMHZyz7mZ6hmIQgnmJw6md8AP2PvRMQuFw0BE3C4XDrMg9ku7rHyX6xT/BQrEkSZIkSZKkL7FQLEmStAOfLl/Pne+u4vW56+kV6sbZSSE+TRlL4qE/Yvj4Y4OOt03Z+54BLz3KkJIPqKmuJDklLehIkiRJkiRJkloIC8WSJElbEYtG+eLDF+C937O6MonX664iFIK9Ro5i8T7vM2rYiKAj7tDgfQ9nw0udqCPEqk8/Yfz+BwYdSZIkSZIkSVILYaFYkiTpS6INDXz25mOkTvsjI+sXADA4nMCkUel87YixDOyaEXDCnReORPjbiPu44+MqzlyZwfj9g04kSZIkSZIkqaWwUCxJkgTU19Uy++UHyJn9Z0ZHVwJQHUvk0y4n0/ukn3BtnyEBJ9w9B+47mjs+nsrrc9dT1xAlMdJynqMsSZIkSZIkKTgWiiVJUrtWXdfAU7NWs+5ff+FHdXcBUBZL5fOeZzPo5B+zf7deASfcM+P6diInI4mN5VXMmLeUA0YODDqSJEmSJEmSpBbAQrEkSWqXKko38tIHH3PLLNhQVkMa+3FGyvPk9z2F4adczYSOOUFHjItIOMQ1PT7lyBW3sejNw2Hk34KOJEmSJEmSJKkFsFAsSZLalZKifOY9cyvDVv2DkdFObKj9Hd2zUrnskOHkjv2UfsmJQUeMu+EDB9JpZTmDit+mvq6OhMS29z1KkiRJkiRJ2jUWiiVJUrtQsHY5S579HXvnP834UA0ApZEs7jihG8dM2IekhLb77N4h449j05sZdKKUz6e/ysgDTww6kiRJkiRJkqSAWSiWJElt2pplC1j9wm8ZU/gi40P1EIIlkf5s2ve7jD76G/RKaPu/DiUmJbMw+xD22/QSFZ88BRaKJUmSJEmSpHav7X8yKkmS2qUF+WX89e3FlM15ifsTn4UQzEscTu2EH7L3oWcQCrfdFcRbk7z3afDuS/QvfItoQwPhSCToSJIkSZIkSZICZKFYkiS1KQs+fou3p33MjSuH/fuVUbze8WR6HnQBw8cfG2i2IA094ERK302jCxuZN/MNhu1/dNCRJEmSJEmSJAXIQrEkSWr1YtEoX3z4Arz3e0bWzCY3ls6fQncwcWRfvj1xIHvludVyckoaczIPZGzp65R8/CRYKJYkSZIkSZLaNQvFkiSp1Yo2NPDpG4+SNv1PjKxfAEBdLMKijofwwqn70q9v32ADtjRjLuDPb6QydeP+7B+LEQqFgk4kSZIkSZIkKSAWiiVJUqtT3xDlg7dfps8HP2VMdCUA1bFEZnc9hT4n/oRxfQYHnLBlGnnQyVz4VioVZQ18urqE0b2yg44kSZIkSZIkKSAWiiVJUqsyc3kxP5zyKQkb1/KvpFWUkcrnPc9m0Mk/Zny3XkHHa9FSEiMcNrQrL3y2jpfnrLNQLEmSJEmSJLVjFoolSVKr8f5nC7n0iSVU10XplN6XVwf/jgOOPpMJHXOCjtZqnDAih8rPX6T/rMeIHfsQoXA46EiSJEmSJEmSAmChWJIktQqzXn6QUVN/xpiGH5I85FD+fP4+pCcfFXSsVmfigEwOTfwTqfW1LJ7zEQNHHRh0JEmSJEmSJEkBcAmJJElq8WY8/UdGTf0BHUJVfDdnFvd8fSzpyd7vtjvSMrKZl7E/AAXTnwg4jSRJkiRJkqSgWCiWJEkt2tR/XM+4z35FJBRjescT2f97j5CU4K8weyI69CQA8ta+SiwaDTiNJEmSJEmSpCD4KaskSWqRYtEoH93/I8YvvBWAqbnnMe67fyOS4EriPTXkkLOojSXQK7aW5fM/DjqOJEmSJEmSpABYKJYkSS1OtKGBaXddzoRV9wLwUZ9vs/+3/kIo7K8u8dAhqxNz08cBkD91SsBpJEmSJEmSJAXBT1slSVKLUt8Q5adPfUrBuhUATB3yUyZc9DuLxHFWN/hEALqtfjXgJJIkSZIkSZKC4CeukiSpxaipb+C7j37ClFnr+FH9d3hv/3sYf97Pgo7VJg0+5BzqYhHq6utZtnpN0HEkSZIkSZIkNTMLxZIkqUWoLC/hyTt/yiufryUpEuaPF+zPwcedE3SsNiurUxeu6fkwR9fewkuLqoKOI0mSJEmSJKmZWSiWJEmBK9lYyIo/HssFm+7hV0mPcv+ksRw7slvQsdq8/UfvDcBLc9YFnESSJEmSJElSc7NQLEmSAlW0fjWFdx7FsLq5lJLOhJMu5uBBXYKO1S4cPaIbkXCIJWsLWLVuQ9BxJEmSJEmSJDUjC8WSJCkw+asWU3nXUQxoWEoRWRSc8TRDxx0ZdKx2o1N6EjfkvMKs5MtZ/a8/Bx1HkiRJkiRJUjOyUCxJkgKxatFncP+x9IqtJZ8cKi94ngF7jQ86VrvTN68naaEaOq14OegokiRJkiRJkpqRhWJJktTs5q8uIOHvp9GNAlaFesDFr9Br0KigY7VLAw4+l2gsxJD6BeSvWhx0HEmSJEmSJEnNxEKxJElqVh+v2MjZ983i2tqvszAyiNTLX6db70FBx2q3crr3YUHScACWv/dYwGkkSZIkSZIkNRcLxZIkqdl8MH8tX7tvGqXV9RT2OprcH75PTm5e0LHavdL+xwOQteylgJNIkiRJkiRJai4WiiVJUrP45LW/kffooXSuz+fgQTn87ZL9yEpPCTqWgL4HnwvAkNq5FK5dEXAaSZIkSZIkSc3BQrEkSWpyM5/9M3t98D36hNZzXdd3ue/CsaQlJQQdS/+WmzeQBQlDCIdiLHH7aUmSJEmSJKld8BNaSZLUpKY9diP7z/8dhGBG9nFMvOIuEhIiQcfS/1g9+Bs88+kcVhcNYf+gw0iSJEmSJElqcq4oliRJTSIWjfLR5J82FomBqV3OZt/v/p2ExKSAk2lrBh95MX9tOJkXVyVSVF4TdBxJkiRJkiRJTcxCsSRJirtYNMq0e65kwvK/AvBRr2+y/7fvJhxxJXFL1atTGiN7ZhKNwWtz1wcdR5IkSZIkSVITs1AsSZLiqiEa49qnptNh7fsATB10NRMuuZVQ2F87WrqTh2VxcvgDEt+/JegokiRJkiRJkpqYzyiWJElxU1sf5YdTZvPCZ0W8FPopfxxfzgGnXBZ0LO2k4/rEuCzpz9SVRCgp+gVZnXODjiRJkiRJkiSpibi0R5IkxUV1ZTl33f1HXvhsHYmRENeed5hF4lam16BRLAv3JTHUwIJ3pwQdR5IkSZIkSVITslAsSZL2WFlJMUtvP5bvFVzLeUnvcu83xnLC3t2DjqXdkJ93DABJC18IOIkkSZIkSZKkpmShWJIk7ZGNBevI/9NRDK+dQ1ksla8ffxiHDukadCztpu4TzgZgeOVMykqKA04jSZIkSZIkqalYKJYkSbttw5pllP71KAY1LGYjmaw//UmGjz826FjaA32G7MOKcB5JoXoWvPtE0HEkSZIkSZIkNRELxZIkabesXTqX+vuOpk90FRvoROl5zzNw1EFBx9IeCoXDrOtxFACR+c8FnEaSJEmSJElSU7FQLEmSdtmi5StJfPh4esQ2sDrUnfpJr9BnyOigYylOuuzXuP10ddlGKqrrAk4jSZIkSZIkqSlYKJYkSbtk9qpNnPXwfP5RfxhLw31J+dZr9Og7JOhYiqP+I8dzRur9nFf7c95eWBh0HEmSJEmSJElNwEKxJEnaaR8uLuCCe6eyqbKOt7pdQqfvvU1Ot95Bx1KchcJhxu49AoCXPl8XcBpJkiRJkiRJTcFCsSRJ2imz//UosYdPo6G2kgMGdObv3xxPdnbHoGOpiRw/sjsAH89fSnVVZcBpJEmSJEmSJMWbhWJJkrRDM5+/m5HvfYcDw3O4sft7PDBpHOnJCUHHUhPaOy+LO9If4L3QZcx77+mg40iSJEmSJEmKMwvFkiRpu6ZNuYV9Zv6EhFCUmZlHceK3f0dKYiToWGpioVCILjk5JIYaaPj8maDjSJIkSZIkSYozC8WSJGmbPnr4l+w/9zeEQzGm5ZzOPt9/nMSk5KBjqZlk73sGAENK3qem2u2nJUmSJEmSpLbEQrEkSfqKWDTK1Hu+y4SlfwLgo56T2O879xOOuJK4PRm87xEU0JEOoSrmf/h80HEkSZIkSZIkxZGFYkmStIVoNMbNT73D4DWNz6Wd2v97TPjmHwmF/bWhvQlHIizNOQyAms+eCTaMJEmSJEmSpLjyE19JkrRZXUOUH06ZzV8/ruTCup8ydeSvGf+N64OOpQBljDkTgMGb3qWutibgNJIkSZIkSZLixUKxJEkCoLqqghvvf4xnZq8lIRzi0rNPZ/yZPww6lgI2dP9jKCaTbMqZP/WloONIkiRJkiRJipOEoANIkqTglZduZPmfT+Xq6nnMSfgFl19wDkcMyw06llqASEICH/aYxNQVZaTkd2avoANJkiRJkiRJigtXFEuS1M6VFK1nzZ+OYWTNbCDEtccPtEisLWQd9j0eaTiKfy6ooSEaCzqOJEmSJEmSpDiwUCxJUjtWuHYFxX8+iiH1C9hEBmtOmcKIA44POpZamPH9O5OdlkhRRS3TlxUHHUeSJEmSJElSHFgoliSpnVq7fAHV9x5Nv+gKCujIpnOeZfA+E4OOpRYoMRLm1EHJfC3yOkVv3Rl0HEmSJEmSJElx0GYLxaWlpTz22GNcffXVTJw4kYEDB5KVlUVSUhJdu3bl0EMP5eabb6aoqGiPxpk8eTKhUGinviZPnhyfb06SpD20fMk8EiYfS14sn7WhXGq/8TJ9h40NOpZasNNz8/lN4oPsv/pBog0NQceRJP2bc19JkiRJ0u5KCDpAU5k+fTrnnXfeVs8VFBTwzjvv8M4773DLLbfwyCOPcMwxxzRzQkmSgvH5mhIu/vtyftvQl0GJ6aRf+gJdevQNOpZauCEHnETZO6l0CW1k/sw3GLr/0UFHkiTh3FeSJEmStPvabKEYoFevXhx22GHsu+++9OrVi+7duxONRlm9ejVPPvkkTz/9NIWFhZx88slMnz6dUaNG7dF4r776Kj169Njm+by8vD3qX5KkPTV9WTGXTJ5BWU2Uu3r+kvvOG07HnG5Bx1IrkJySxpysgxhb+jqbZj0FFoolqcVw7itJkiRJ2h1ttlB82GGHsXLlym2eP/vss3nmmWc47bTTqK2t5brrruPpp5/eozEHDx5M375996gPSZKaymdvPcG8t56irPZr7N+vM/ddOJYOKYlBx1IrEhl5Knz4On3X/4tYNEoo3GafYiJJrYZzX0mSJEnS7mqzn+5FIpEdtjn11FMZMmQIAO+9915TR5IkKTCzXnqAYW9/iwvDL/PLnrN56OL9LBJrlw076FQqY8l0o5BFs98NOo4kCee+kiRJkqTd12YLxTurQ4cOAFRXVwecRJKkpjH9qdsZNe2HJIYa+LjD4XzjWz8mJXHHHypL/yslLYN5mQcAUDTjiYDTSJJ2hXNfSZIkSdL/ateF4gULFjB79mwAhg4dGmwYSZKawNS/X8d+c35NJBRjWqeTGf39J0hMSg46llqzYadQF4tQuGE9sVgs6DSSpJ3g3FeSJEmStDXtrlBcWVnJokWL+MMf/sDEiROpr68H4Kqrrtrjvi+66CJ69OhBUlISOTk5jB8/nl/84hesWbNmj/uWJGlXxKJRpt73Q8Yv+gMAU7tdwH5XPkQkISHgZGrthk48i4Oid/PdiouYu6406DiSpG1w7itJkiRJ2pF2USiePHkyoVCIUChEeno6gwcP5uqrr2b9+vUA/PSnP+X888/f43Hefvtt1q1bR11dHUVFRUybNo3f/va3DBw4kLvvvnuP+5ckaWdEozH++sQLjF31IAAf9buC/S+7k1C4Xbztq4mlp2cwenA/AF6ekx9wGknSlzn3lSRJkiTtina9rGj06NHcc889jBs3bo/66d+/P6effjoTJkygV69eACxdupSnnnqKJ598kurqai6//HJCoRCXXXbZDvurqamhpqZm859LS12tI0naOfUNUX7y1Bye+iTCwvDlnDcqiwnn/DToWGpjjt+rO69+sZ6PPptL7KhB3oQgSS2cc19JkiRJ0taEYu3g4XKbNm1i9erVAFRVVbFkyRKmTJnCP//5TwYMGMDtt9/OiSeeuFt9l5SUkJmZSSgU2ur5F154gdNPP526ujrS0tJYsmQJ3bp1226f1157Ldddd902x5IkaWtqqiv59aPv8tiCBiLhELeetTenjckLOpbaoLKqGj6/8TAmhL9g+Tlv0HfY2KAjSWonSktLycrKcm60Dc59JUmSJKn1a865b7soFG/L3/72Ny688EJCoRD3338/kyZNapJxfvOb3/DLX/5y8/HPf/7z7bbf2l3VvXr1crIsSdqmyvISltxxKplVqzm/4Tp+df7hHDNi+x/OSnti9k3HMLpqKh/1vowJF98SdBxJ7YSF4t3j3FeSJEmSWo/mnPu2630Cv/71r3PWWWcRjUa58sorKS4ubpJxLrvsss13Xb/zzjs7bJ+cnExmZuYWX5IkbUvJxkJW/vFY9qqZRZdQCXed0NEisZpc3ZCTAOi2+rWAk0iSdsS5ryRJkiRpa9p1oRjglFNOAaCiooJXXnmlScbo2rUrnTt3BmDNmjVNMoYkqX0qWr+KwjuPZGjdXEpJZ+VJj7LXAccFHUvtwOBDzqEuFqFfdDmrFn0adBxJ0g4495UkSZIk/a92Xyju0qXL5uMVK1Y02Tjbeo6TJEm7K3/lIirvOpoBDcsoJJvCM//J0LFHBB1L7URWpy7MSx0DwOoPHw84jSRpR5z7SpIkSZL+V7svFH/5LueMjIwmGaOgoIDCwkIAevTo0SRjSJLal5WL5sADx9IrtpZ8ulD1tRfoP3L/oGOpnakeeAIAXVY2zco0SVL8OPeVJEmSJP2vdl8ofuKJJzYf77XXXk0yxj333EMsFgNg4sSJTTKGJKn9+GJtCRc/toDyaBIrwz3hklfoNbBp3sOk7Rl4yDk0xEIMbFjCmqXzg44jSdoO576SJEmSpP/VZgvFkydPprq6erttbrvtNl566SUA+vXrx8EHH7zF+bfffptQKEQoFGLSpElfuX758uV88skn2x3jhRde4P/+7/8ASE1N5aKLLtqF70KSpC19vKKYc++ZyuKKVP6v0w1kXP463XoNDDqW2qlOXXvybOZ5fL/2O7y6vC7oOJLULjn3lSRJkiTtroSgAzSVa6+9lquvvpozzjiDgw46iAEDBpCRkUFZWRlz5szh73//Ox988AEASUlJ3HPPPUQikV0aY/ny5Rx22GFMmDCBk046iVGjRtG1a1cAli5dypNPPsmTTz65+Y7qW2+9lZ49e8b3G5UktRtz3v0nT/9rKmW1hzCub0funDSOzJTEoGOpnas44Cc8++wXrJhXysWHB51Gktof576SJEmSpN3VZgvFAMXFxdx7773ce++922yTl5fHAw88wJFHHrnb43z00Ud89NFH2zyflpbGbbfdxmWXXbbbY0iS2rdPXn2YER/+gOtDDWT07s9VFx9LatKufcgrNYVjRnTjV899wexVm1i7qYoe2alBR5Kkdse5ryRJkiRpd7TZQvGrr77Kiy++yAcffMDixYtZv349RUVFpKam0rVrV0aPHs2JJ57I2WefTVpa2m6Nse+++/LII4/w0UcfMXPmTNatW0dhYSH19fV07NiRESNGcMQRR3DppZduvttakqRdNeOZO9nnk18QCcWYlXEIV19yIUkWidVCdM1M4bietfRe9wrz31hNjzO+FXQkSWpXnPtKkiRJknZXKPafvaHUYpWWlpKVlUVJSQmZmZlBx5EkNaNpU25m/7m/BWB69vHse+XfiCS02fu81EpN/cdvGL/wFuYmjmT4zz8IOo6kNsy5Udvm/19JkiRJat65UbhJe5ckSbvt0zcfY9wXNwAwNfdcxn3v7xaJ1SL1PfhcAIbWfkHhupUBp5EkSZIkSZK0MywUS5LUAi1d9AUD3rmKcCjGtE4ns/+3/koo7Nu2WqZuvQayIGEI4VCMJe8+FnQcSZIkSZIkSTvBT5wlSWphiitqmfT0Oh5oOIbPk0axz+X3WSRWi7exz7EApC99MeAkkiRJkiRJknaGnzpLktSC1NZHufyRj1m5sYYnMyfR88qXSUxKDjqWtEO9DzoPgKHVn1G8YU3AaSRJkiRJkiTtiIViSZJaiFg0ynMP3sSny/LpkJzA/ReOpWNmetCxpJ3So98wFkcGkBCKsvi9KUHHkSRJkiRJkrQDCUEHkCRJjaY9dgNnrrmFfkmDKDv3OQbldgg6krRLCnsdS49l97F85Ur2CzqMJEmSJEmSpO2yUCxJUgvw6dtPMW7BrRCC+sEncuiwHkFHknZZ7pFXss+dY6kvSOGYyjqy0hKDjiRJkiRJkiRpG9x6WpKkgK1YMJt+b11JJBRjRvbx7Hf+r4KOJO2Wfnk96Nsth/pojNfnrQ86jiRJkiRJkqTtsFAsSVKASorWE3nsXDJDlcxLHMHel99PKOzbs1qvY0d2A+CjT+YEnESSJEmSJEnS9rj1tCRJAamrrWHVPWczMraOdXQh99IpJKekBR1L2iMnDkrhuHevod+qfMpKFtEhq1PQkSRJkiRJkiRthUuWJEkKyJ+ffp0e1YupiKVQfdbf6ZSbF3QkaY8N6JVHakKMpFA9C957Mug4kiRJkiRJkrbBQrEkSQH420fLuX12iFNrr2f+oX+l34j9g44kxUUoHGZN96MBCM97LuA0kiRJkiRJkrbFQrEkSc3sg/lrufb5uQCcd8xE9j3s9IATSfHVdf+zABhWPo2KspKA00iSJEmSJEnaGgvFkiQ1o1WL59D3sUM4hFmcPqYnl0/sH3QkKe76j5zAmlAuqaFa5r//dNBxJEmSJEmSJG2FhWJJkppJycZCYv84h54UcE36i9xw2ghCoVDQsaS4C4XDrOx2VOMf5j4bbBhJkiRJkiRJW2WhWJKkZlBfV8uKu8+md3QN+eTQ5ZLHSUlKDDqW1GQ6j/v39tOlH1JdWR5wGkmSJEmSJEn/y0KxJEnNYOY932Hv6o+pjCVTcfrfyOnWO+hIUpMaNPoQpkRO4Ht1V/Leko1Bx5EkabcUltdw1WOf8M2HZwYdRZIkSZLizkKxJElNbNoTv2d8wRMALDjgFgbsfUDAiaSmFwqHmT/6F/wrui8vzS0KOo4kSbslIVrDuM+v56xF11BTXRl0HEmSJEmKKwvFkiQ1oc+mv8U+n/8WgI/6XM6YYy4MOJHUfI7fqxsA/5q3npr6hoDTSJK067Iy0jkr8jZHRz5mY8HaoONIkiRJUlxZKJYkqYmsKKrg4permNJwKDM7HMH4C28MOpLUrPbp3ZEJGfl8q/7vfPHhK0HHkSRpl4XCETaGsgEoLVgTbBhJkiRJijMLxZIkNYGy6joueWgmhVUxpuT+gJFXPkoo7Nuu2pdwOMRV2e9zZcKz1M96JOg4kiTtltKEjgBUFq8LOIkkSZIkxZefWEuSFGcN9fU8fc/1LNtQQrfMFO65cBwpyclBx5ICkTHmDAAGb3qXutqagNNIkrTrKhM7A1BbYqFYkiRJUttioViSpDibce+VXFj8R+5K/hP3fn1fcjNTgo4kBWbo/sdQTCbZlDN/6stBx5EkaZfVpOQA0FCaH3ASSZIkSYovC8WSJMXR9H/+ifHrHwWg0/7nsVev7GADSQGLJCSyqNOhAFR++nSwYSRJ2g0NaV0BCFUUBJxEkiRJkuLLQrEkSXEyb9qrjJ59LQBTe13KvsdfEmwgqYVIG30aAAOL3qahvj7gNJIk7Zpwh8ZCcaS6OOAkkiRJkhRfFoolSYqDtcsXkPvypSSFGpiVcQj7Tbo56EhSizF0/AmUkE5nSpg//bWg40iStEs2Dj6HEdX3c3P6j4OOIkmSJElxZaFYkqQ9VF66kZqHz6YTpSyODGDY5X8nHIkEHUtqMRKTklmQPZGNsQzmLZgXdBxJknZJp44dqSCVgvLaoKNIkiRJUlxZKJYkaQ9EozHuePQ5ujaso5BsOlz0BKkZmUHHklqc6sN+zbiav3Dz2lFEo7Gg40iStNO6dEgGoNBCsSRJkqQ2xkKxJEl74JbXFnD3shzObfg/ik6aTG7egKAjSS3S/iMGkZqcwoayGj5ZtTHoOJIk7bSc1BA3JNzLbdGbqKwoDTqOJEmSJMWNhWJJknbTP2cu5a9vLwHg0jNPYsi+hwWcSGq5khMiHDGsKxDjvY8/CzqOJEk7LSM1hdMj73NU5GM2rl8TdBxJkiRJihsLxZIk7Yb5M/7Ffs8fxT6hhVxx2ABOGd0z6EhSi3dW7zLeT/4+5352KbFoNOg4kiTtlFA4THG4IwBlRRaKJUmSJLUdFoolSdpF+SsX0eXFi+kZKuRnnd7m6qOGBB1JahX2Hb0PnSijGwUsmv1e0HEkSdppZZFOAFQWrws4iSRJkiTFj4ViSZJ2QWV5CRUPnU1nSlga7svwyx8iHA4FHUtqFVLSMpjXYQIARTOeDDiNJEk7ryqpsVBcV2KhWJIkSVLbYaFYkqSdFG1oYP5fL2BAw1KKyCL1wimkd8gOOpbUugw/FYBe+a+5/bQkqdWoTe0CQLRsQ8BJJEmSJCl+LBRLkrSTpj34Y/apeI/aWAIFx99P9z5uOS3tqqEHn0Z1LJG8WD5LvpgWdBxJknZKQ1pXAMKVFoolSZIktR0WiiVJ2glTX/kHE1bfD8CnY65j6H5HBZxIap3SO2QzL2N/AAqmTQk4jSRJOyfcobFQHKneFGwQSZIkSYqjhKADSJLU0s1etYnL30/lt6H96NxzEONPvTLoSFKrFh16Enz8Pj3WvhZ0FEmSdkrZkDMZMas/g7t0459Bh5EkSZKkOHFFsSRJ25FfUs1lD89kU30S/+z/G8Zd+qegI0mt3uCDz+JvDcfwk+pJLMovDTqOJEk71DG7IxWkUlBeG3QUSZIkSYobC8WSJG1DVUUZT95zPRvKqhmcm8Ft5+1DJMHNOKQ91SG7M28P+DFTo8N56fP1QceRJGmHunZIBqCgrIZYLBZwGkmSJEmKDwvFkiRtRSwa5Yu7vs6VFXdyc8pD3H/hODqkJAYdS2ozjh3ZDYCXP18XcBJJknYsJy2BGxLu5c7QzZSXbgw6jiRJkiTFhcuiJEnaiqmTf8qEsreojUUYeezF9OqUFnQkqU05angu4yMLOLbwI1Yu6kjvQXsFHUmSpG1KTUni5MhUMkJVrNqwig5ZnYKOJEmSJEl7zBXFkiT9j1kvP8iElXcDMHvvXzF8wnEBJ5Lanuy0JP5fhxeYlPAaaz58LOg4kiTt0MZwRwDKCtcGnESSJEmS4sNCsSRJX7L40/cZNvUaAKZ2PYf9zrgq2EBSG1Y98EQAcla+EnASSZJ2rDyhsVBctTE/4CSSJEmSFB8WiiVJ+rfCtSvI/OfXSQ3V8lnKWMZ+886gI0lt2sBDzqEhFmJQw2LWLl8QdBxJkrarKrkzAHUl6wJOIkmSJEnxYaFYkiSguq6Bux97kuxYCSvCefS9fAoJiUlBx5LatE5dezI/eW8AVr7/aMBpJEnavrqUHABi5RsCTiJJag0K81fx0b1XUbR+ddBRJElbUV3XwA+nzOaud5ZQ3xANOk5gLBRLktq9WCzGT576jHs3DOWy0K+JnP8Ymdmdg44ltQvlA44HoOPylwNOIknS9sXSuwIQqbRQLEnasfoHTmDCmgdZP+WqoKNIkrZi5cLP2OuzG1j59mQi4VDQcQJjoViS1O7d9eY8np29loRwiMu+dgF5A/cKOpLUbgw4+FyisRBD6uezfvWSoONIkrRN4cxuAERqSgJOIklq6aory+lWuwKAPhs/CjiNJGlr8tcs55DwZ5yX+C6hkIViSZLapU9e+xvHv3MKg0OruPbkERwwMCfoSFK7ktOjLwuThrEm1pmPZ38SdBxJkrapYvBpDK9+gGtTfxZ0FAC+WFvC2wtc3SxJLdHc9/65+bg0lhpgEknStnzYMIwjan/PE8PuCDpKoCwUS5LarSVzpjLkg6vpE97Ar/M+4Wvj+wQdSWqXPp5wJwfW/InJq3sGHUWSpG3q3DGbSlIoLK8JOgoAjz5wO3c99DCrCzcFHUWS9D/mLFtLTSwBgNxoIdWV5QEnkiT9r4XrywAY1C0z4CTBslAsSWqXCvNXkf7U10gL1TAneQz7X9a+7xyTgnToPsOBEDNWFLOhrDroOJIkbVVORjIAheU1RKOxQLNUlpfwm/o/8FjS9SxdWxRoFknSlqpqG/jdmlEMrZnM3xuO4Nr6b7C8sCzoWJKk/7FgXSkAg3M7BJwkWBaKJUntTk11JYX3n003ClgV6kHvbz1BQmJS0LGkdqtndiqje2UTjjXw7sdzgo4jSdJWdU5P5MaEe7krfDOlGwsCzVK0bjkA5bEUlpS23+epSVJL9Ob8DVTVNdCzYzpPdr+ahxuOYfGmYG8wkiRtqbx0Iy9Xf42nk37F4M6JQccJlIViSVK7EotG+fSvFzO0bi6lpBM771GyOnUJOpbU7l3WfQkzkr/N0I9+FHQUSZK2KjkxgeMTpnNE5BNKNqwKNEvJ+hUAZISqqVs5I9AskqQtfTLzfSI0cMLe3RnUNQOAxRvcelqSWpI1Cz8hM1RJ73AhHbPcelqSpHbjvcdvZb+Sl6mPhVlx+J/pPXh00JEkAaNGj6NTqJyh1Z9RvGFt0HEkSdqqTeFOAJQVrQk0R1XhfwvV41feG2ASSdKXVZRt4uoV32Fa8hWcNiDMkE4R9g4tgRUfBR1NkvQlpSsbd7Rbl9wv4CTBs1AsSWo33pi3nu991of3G0Ywc+iP2euQ04KOJOnfevYfxuLIABJCURa/NyXoOJIkbVV5QmOhuHrjukBzNGz6b6G6U83qAJNIkr5s3rtPkBqqpTqczpCBg9g3Oofnkn/JSWtvDzqaJOlLGtbPBaAia1DASYJnoViS1C4syC/je49+wqZYBi+P+Qv7n/PToCNJ+h+FvY4FIGXRCwEnkSRp66qScwCoL10faI5Q2X8LxbnRDdTV1gSYRpL0H5G5zwCwqscxhMJhOvcdCUCP+tVEGxoCTCZJ+rL0TQsBCOcODzhJ8CwUS5LavI0F63jqgZuoqG1gfP9OXHvK3oTCvgVKLU3PA84GYFjVLEqKCwJOI0nSV9WnNhaKY+XBFoqTqjZsPk4MNbB+5aIA00iSAMpKihlePg2AruPPA6B7nyHUxhJIDdWSv2pxkPEkSV+SW7McgMzeewcbpAXwU3JJUptWW1PNunvP4v/V3sEvOrzAXy/Yl8SIb39SS9Rr8GiWhfuQGGpg4btuPy1JanliGbkAJFQGe0PTowmn8LO6S6iNRQAoXjU/0DySJFjwzhSSQ3WsDPek/4j9AEhITGJtpAcAhcvmBBlPkvRvJcUFdKUYgJ6DxwScJnh+Ui5JarNi0Siz77qE4bVzKIulctTpl9IxPSnoWJK2Iz/vGAASFz4fcBJJkr4qIbOxUBypLQ00x5uVA3i04QimJowFoGq9K4olKWgJ858BYG2PY7fYxaw4tS8AlevmBZBKkvS/lq9dxxsNY/g0NJQOWZ2CjhM4C8WSpDZr2mM3sN/GF2iIhVh66B30GbZv0JEk7UDuARdwZ/0p/LrkFMqq64KOI0nSFqoGncyw6gf4ecrPA8tQU99AYXktANHs/gDEipcGlkeSBCWbihheMQOAbgect8W5muyBAISKvKlHklqCOeVZXFL3Y27rfUfQUVoEC8WSpDbps7eeZNyCWwGYMfgHjDrsrIATSdoZ/YaM4umOF/NpQ2/enL9hxxdIktSMOnfMoooUCspqAstQuCGfU8Lvc0DCQqoHHs8v6i7ilfChgeWRJMHriys5rfY67k2+kL7Dx21xLjF3CAAZZd7UI0ktwaL1ZQAMye0QcJKWwUKxJKnNWbHgE/q9fSWRUIzp2cez/3m/DDqSpJ0UCoU4bmQ3AF6ekx9wGkmSttSlQzIAxRU1NERjgWQoWTmHPyb9hZsT76bDoAN4pOEo3i3vGUgWSVKjF+es44tYPyrHffcr5zIGHcj1dV/jT/VnBJBMkvS/Vq5r/LxpkIViABKCDiBJUjxtrKjl8aee4EdUMzdxJKO+/cAWzwaS1PIdNyKX+e9M4ahFH1NZ/jBpGVlBR5IkCYBOqYncmHgvXdnExoIx5OT2aPYMlYUrAShJ7ELfnHQAVm2spL4hSkLE33slqbltqqzlvUWFAJywd/evnO/Vfxj3NxwPlY1ts9OSmjuiJOlLfp9/EdFkKEp5DsgLOk7gnEFIktqMuoYoV/xjFn8pPZAfJ/2cbt+cQnJyatCxJO2iET2yuD75Ec4Iv838954OOo4kSZslJEQ4OjKLIyKfULJhVSAZ6jeuBqAqJZfumSnsm7CME2LvsT5/TSB5JKm9m/f6g/wu8lfOyFnJwK4ZXzmfnpxA96wUAJYUVDR3PEnSlxStX00nSulEGb37Dgw6TotgoViS1Gb89tlZfLikiPSkCN+85Ft06uoWfFJrFAqHWZl7JACxuc8GnEaSpC2VhjsCUFkcUGG2dC0A9RndCYdD/D7pLm5P+gvFi2cEk0eS2rn0eY9zZuRdzsxZsc02B3Qs4aTwhxQtmt6MySRJ/2vdolkArA3nkpru1tNgoViS1EZMe/x3nDN7EnmhAv547hiGdssMOpKkPdBpv7MBGFr6EdWV5QGnkSTpv8oTOwFQvXFdIOMnVTY+Uy2U2bjt9caUXgBU5i8KJI8ktWcbC9YxvOoTAPIOumCb7c5ueJE7ku4kc7E3wkpSkMpXfQ5AYWr/gJO0HBaKJUmt3px3n2XfuTcxLLyKm0as5MjhuUFHkrSHBo0+hHxySA9VM+/9Z4KOI0nSZtUpOQA0lOYHMn5GzXoAkjo1FohrOvQBIFa8NJA8ktSeLXznURJCURZHBtBr4F7bbpgzGICUUn9WS1KQQgXzAKjqODjgJC2HhWJJUqu2atGn9Hnz2ySEoszIOoYDzv9F0JEkxUEoHGZ51yMAqP/Cu+4lSS1HQ2qXxoOKgkDGz64vBCCjS+/GFzoPACCldNtbnkqSmkb6osa5SkHv47bfrucwADpX+bNakoKUWbYYgMTuIwJO0nJYKJYktVolxQXwj3PJpIL5CcPY6/IHCYV9a5Paiux9zwBgSMn71NZUB5xGkqR/y+gKQEJV8xeK6xuiXFP7TX5WdwnZvYYCkN5tEAAda1Y1ex5Jas+K1q9mWPWnAPQ++Gvbbdut/ygAekTXUVNd2eTZJElfFYtG6Vm3HIBOfUcFG6YF8dN0SVKrVF9Xy4p7zqFXbC355JBz6ROkpKYHHUtSHA0eeyQFdGRdtBOz5swJOo4kSQAkZHYDIFJT2uxjF5TX8HZ0FE/EjqRzp8aVzZ17N65S696QT0N9fbNnkqT2avE7/yASirEoYRA9+w/bbtvO3XpRFkslEoqxbtncZkooSfqy9RtLear+YN6PjqTn9h4X0M5YKJYktUof3H8Ne1d/TGUsmYrT/0ZOt15BR5IUZ+FIhPv3eoRjam/m6eVJQceRJAmA2iEnMbT6QX6S0vyPPFlX0rjDRm5mCuFwqPE4bwC1sQhJoXo2rFnS7Jkkqb2aubaGxdEeFPU9YYdtQ+Ew6xIbP7coXvF5U0eTJG3FgqI6rqu/kF9n/ZbklLSg47QYFoolSa3OP6at5Jrl+zI7OoAFB9zCgL0PCDqSpCZyyOjGbTVfm7ueuoZowGkkSYLOWZlUk0xBWU2zj122ai6nht/ngLTVm1+LJCTwh9QrmVR7DcsqU5s9kyS1RxtKq7l1/T4cWXsLecddvVPXbErvD0BN/vymjCZJ2oZF68sAGNKtQ8BJWhYLxZKkVuWjJUX86tnPWU8nPpj4D8Ycc2HQkSQ1of36dqJzehLVleXMmrsw6DiSJNGlQzIAGyvrmv0mpuQVb3J70l84t+7pLV5f2O0k3o6OZmlJrFnzSFJ79fLn+cRiMKZ3R/I6Z+7UNSv7n8sltVfzSuTwJk4nSdqawpXz6UAlg3MtFH+ZhWJJUquxZukXPPXIndRHY5w8qgffOXxI0JEkNbGESJhfdJvKrOTL4e0bgo4jSRLZqYncmHg/DyTezMb1q3d8QTyVrAGgLq3bFi/37ZwOwIqiiubNI0nt1LIZL5NMLSfs1X2nr+kwcAJvRPdl1ia3O5WkIJy+7NfMSbmUgxpmBB2lRUkIOoAkSTujdFMR9Y+cw62xVfTJ+R7fPPNaQqFQ0LEkNYOBg0eQtqaGgUVv01BfTyTBX2ElScEJh0McEfmErhSzaMNKuvbo3WxjJ1bmNx5k9tji9aEdKjk5/CE5K+YCw5stjyS1RxvWLONXxT/j6uQUKoZ8utPXDeiSAcCSgnJisZifaUhSM4o2NJBXtwJC0KWPvy9/mSuKJUktXkN9PcvuOoc+0VVsoBPnnn8JKYmRoGNJaiZDJ5xACel0poT5018LOo4kSZRGOgJQWbymWcdNr14PQGLHXlu8PjS0kj8l3ckxhQ81ax5Jao+WvvN3wqEYq5P60a1r7k5f16dzGsdGPuaihqfYkL+qCRNKkv5X/spFpIVqqI0l0LO/heIvs1AsSWrxZtx7JaOqZ1AVS6L0lIfo0qNv0JEkNaPEpGQWZh8CQPknTwWcRpIkqEzsBEDtpvxmHTervgCA9C5bFoo79xoKQPeGfKINDc2aSZLam+ylLwBQ0v+kXbouMRLmZ8mP8+PEKWxY9HFTRJMkbcOGpbMBWBPJIyEpOdgwLYyFYklSizbj6T8yfv2jAMzd/3cMHHNIwIkkBSFpr1MB6Ffwph+AS5ICV53SBYCG0vXNNma0oYGcaDEA2d36bnEut9dA6mIRkkN1bFi7rNkySVJ7k79yEUPr5xGNhRgw8fxdvr44pQ8AFWvmxTuaJGk7qlbPAaAofUDASVqeNlsoLi0t5bHHHuPqq69m4sSJDBw4kKysLJKSkujatSuHHnooN998M0VFRXEb8+WXX+a0004jLy+P5ORk8vLyOO2003j55ZfjNoYktSdzp77CqE+vA+CjXt9k3+MvCTiRpKAMPfBkymOpdKWYhbPeCjqOJLUYzn2DEU3LaTyo2NBsYxYXrCEx1EBDLETn3C1XFCckJrEu3Lj9aeFKiw+S1FSWv/t3AOYlj9yt3c6qsxsLFKGiRfGMJUnagcSiBQDUdx4acJKWJyHoAE1l+vTpnHfeeVs9V1BQwDvvvMM777zDLbfcwiOPPMIxxxyz22NFo1Euu+wy7r///i1eX7NmDWvWrOGZZ57h0ksv5e677yYcbrO1eUmKq1XFlbz16jMMDzUwK+MQ9p90U9CRJAUoOSWNOVkHMrb0X2ya+SSMOzLoSJLUIjj3DUaoQ2NRNrGqoNnGzK9O5ke119A3tYprt7Jd3saUPHpXraVi7cJmyyRJ7U2n5S8CUD5g17ad/o+ErkNgLaSXLY1nLEnSDnSsWAJAcg+fT/y/2myhGKBXr14cdthh7LvvvvTq1Yvu3bsTjUZZvXo1Tz75JE8//TSFhYWcfPLJTJ8+nVGjRu3WOD//+c83T5THjBnDNddcw4ABA1iyZAk333wzn3zyCffddx9dunThhhtuiOe3KEltUnlNPZc+NJMFVSexKacfP/z2dwhHIkHHkhSw6Ohv8Ls3cvh44yFMicUIhUJBR5KkFsG5b/NLyOwGQKSurNnGXFsR4+3oaEZ1yt7q+aqMPlA1nVjRkmbLJEntydoVixlcv5CG3dx2GiAzbzjMhq41K+MbTpK0TQ3RGJNrD2dorB8TB48LOk6LE4rFYrGgQzSFhoYGIjsoKjzzzDOcdtppAJx22mk8/fTTuzzOwoULGTFiBPX19YwdO5Z3332X1NTUzecrKyuZOHEiM2fOJCEhgXnz5jFw4MBdGqO0tJSsrCxKSkrIzMzc5YyS1Jo0NDRwxd+m8sr8TXTpkMxzVx5I96zUHV8oqc2rrmtgn+tfp7K2geeuPJC987KDjiSpmTk3+irnvsGYtmgtF94/lR45nXjzR4c2y5gPf7ScXz37BceO6MZdX9/3K+enPnoD4xfcxCfpBzHmxy82SyZJak/++vYSHn/1Lc7uls93vv+L3eqjdFMRmbf3bzy+aimZ2Z3jGVGStBVLC8o5/PfvkJIYZu51xxIOt/yFB805N2qze0HtaKIMcOqppzJkyBAA3nvvvd0a5/bbb6e+vh6AO+64Y4uJMkBaWhp33HEHAPX19dx22227NY4ktRfT77uKS5ZeRbeEMu75+r4WiSVtlpIY4bChXQF4aU5+wGkkqWVw7huMnOxMqkmmoLym2cZMWPUhp4bfZ0Ty1p+LHBt8LBfV/pjbwxc2WyZJak9enLOW5bHuZI//xm73kZndmQI6ApC/dE68okmStmPh+sZdgAZ17dAqisTNrc0WindWhw4dAKiurt7la2OxGM8++ywAQ4cOZfz48VttN378+M2T8meffZY2uohbkvbYjGf/woR1DzMuvJA/TahkTO+OQUeS1MKcMKwjp4TfZ/jHvyIWjQYdR5JaDee+8ZWT0fiM4LLqeqrrGpplzMGrn+b2pL8wrvrDrZ7v1mcIb0XHMG1TB6LRtvvfXpKCsLywgs/XlBIJhzh2ZLc96uuvnf8fR9XczOcNfeKUbtfEolEWznqb6sryQMaXpOZWtPQTRoSWMbxLUtBRWqR2XShesGABs2fPBhonu7tq2bJlrF27FoCJEydut+1/zq9Zs4bly5fv8liS1NatW7GAvWf9CoCPek5ivxMuDjiRpJZo4sCO3JR4LyfXv8rSL6YHHUeSWgXnvvGXmZLADUkP8GDiTRSvW94sY6ZWrwcgoWPeVs/ndUwlEg5RXRdlQ1nzrXSWpPZg9Uu3cFfibVzcczWd0ves0FDbawKLYnksKgrmZ/Wc5+9k8HOn8MV93wpkfElqbkMW3cOLyT/n1Dofz7I17a5QXFlZyaJFi/jDH/7AxIkTN2+dddVVV+1yX3Pnzt18vKPJ9pfPz5s3b5fHkqS2bvkb95EcqmNe4gj2v/gPQceR1EKld8hmXvp+AGyY9njAaSSp5XLu27RCoRCHhT/lsMinlG1Y3ixjZtYVApCW03ur5xMjYc7oMJcrI/8kf8mnzZJJktqL7iue4djIDI7qVrnHfQ3skgHAkg3BrOgdOvs3AOxb/EIg40tSc8upWAJAWt7IgJO0TAlBB2gOkydP5qKLLtrm+Z/+9Kecf/75u9zv6tWrNx/n5W39jt7/6NWr1+bjVatW7fJYktSWxaIN9FnVuJ1hxV7fILwTz9qT1H41DD0ZZn1Aj7WvBx1FkloU577NqyyhI93rC6gsXtfkY8WiUXKihRCCrNy+22x3QegVRiXOYPqyEbDP/k2eS5Lag5ULZzOgYRl1sQhDDj1vj/sbmlnDtyLP02d1LfDAngfcRc8kHMfZdc80+7iSFITammp6NqyBEOQOHBN0nBap3a0o/rLRo0czffp0brzxRkKhXX+AdVlZ2ebjjIyM7bZNT0/ffFxevv27xWpqaigtLd3iS5LasvnTXqNHbD3lsVRGHHFB0HEktXCDDzmL2liEPtFVrJj3cdBxJKnFc+7bNCqScgCoLclv8rFKNxaQGqoFoHP3ra8oBqjKaDzXULi0yTNJUnux5oNHAZibug9ZnXP3uL++2Qn8LPFRzqp5hrra5t1+uriilnvLDwBgE9t/T5ektmDNks9JDDVQFkslt2f/oOO0SO2iUHzqqacyZ84c5syZw/Tp03n00Uc57bTTmD17Nueddx4vvLB722xUV1dvPk5K2v6zKZKTkzcfV1VVbbftjTfeSFZW1uavL9+RLUltUdHMJwGY2/EwUtM7BJxGUkuXmd2ZeWljAVg7dUrAaSSp5XDu27zqUjoDEC3b0ORjFf37OcjFZJKSmr7thp0aP/xKLl3e5Jkkqb3ovuolAGqGnBqX/nJ79qcylkxiqIF1y5v3MQ0fr9hIYSwTgGzKqa3xmfaS2rbiZbMBWJPYl1C4XZREd1m7+K+SnZ3NyJEjGTlyJOPGjePcc8/l6aef5uGHH2bp0qWccsopTJ48eZf7TUlJ2XxcW1u73bY1X3rTTU1N3W7bn/3sZ5SUlGz+auvbdUlq36pqG7ii8Awm1V5DysHfDTqOpFaiZvBJAOSueiXgJJLUcjj3bV7RtK4AhCvWN/lYZRtWAFAc6bLddqndBgGQVd26/ltKUku1fN5M+kZXUhuLMOTQc+PSZygcZk1C481RRcs/j0ufO2v9nDfoH/rvIxM2Fq5p1vElqbnVrpsLQEmHgQEnabnaRaF4W77+9a9z1llnEY1GufLKKykuLt6l6zt0+O+qtx1tqVVRUbH5eEdbdSUnJ5OZmbnFlyS1Va/NzaekJsaS7AmMHDMh6DiSWokhh5xNbSzCxvpElq9t+pVcktSaOfdtGqEOjduPJlYXNflYixOHMKn2Gp7PuXS77Tr2GgpA9/q1xKLRJs8lSW3dug8fA2Bu2jiyOubErd+S9L4AVK9r3hXF+y6+k6eSr6MmlsgD9cdSXOF7haS2LWXjAgBiXYYGnKTlateFYoBTTjkFaJzMvvLKrq1IycvL23y8evXq7bb98p3RrW07LUlqSk/ObPz5ePqYPMLhXX9mnqT2KatzLt/v+Thn1F7HSwtb1zMtJSkIzn3jLymrsVAcqdt+8TweVlSl8HZ0NEXdD95uu269h9AQC5EWqqEo31XFkrQnYrEYbxdk8El0IPXDTo1r33UdG1e2JRQvjmu/21NTXUn/2oUAnBG6hf+r/wb50dZ1k5Yk7aoHo8fz27rzSRp0WNBRWqx2Xyju0uW/2zatWLFil64dPnz45uP58+dvt+2Xzw8bNmyXxpGktmr96iX8btX5XJPwGGeM6RF0HEmtzMGjhgDw8pz8gJNIUsvn3Df+ooOOYUj1ZL6b8OsmH2tdSeNzortnbX8776TkFNaHG7fE3rCyeVepSVJbMz+/jLs3jeOc6G8YctQlce07uVvjyrbMimVx7Xd7ls35kORQHcVk0jGv8T26oNxnFEtqu6rrGnhhU1/ubTiRvKH7Bh2nxWr3heI1a/77HIYdbYv1v/r160ePHo2FjXfeeWe7bd99910AevbsSd++fXctpCS1UUvfeICeoSIOTV1K75xd+xksSUePyCUcgpVr1rB63bodXyBJ7Zhz3/jLyc6khiQKypr+Q/a++S9zavh9+iSV7bDtvV1+xhE1tzA3PLjJc0lSW/biZ41zjEMHd6FDalJc++7UZyQA3etWN9ujAjYteA+AFWkjye2QQBc2Ul7sTbeS2q7FG8qJxiA7LZEuGclBx2mx2n2h+Iknnth8vNdee+3StaFQaPP2XfPnz2fq1KlbbTd16tTNd1WfcsophEJurSpJsWiUHiueAaB86FnBhpHUKuVkJHNb52eYmfxtVr9xT9BxJKlFc+4bfzn//rCpqq6Bipr6Jh3rhI2PcHvSX+gbXbnDtnU9xrIk1pOlxbVNmkmS2rJYNErFrClkUs4Je3ePe//dB+zFCbU3sF/NnRSUN8/P6+S1MwCo7j6OMzc+wIyUKxi++N5mGVuSgrB+4XROCE/loM7lbX5usifabKF48uTJVFdXb7fNbbfdxksvvQQ03iF98MFbPuvn7bffJhQKEQqFmDRp0lb7uOqqq4hEIgB897vfpaqqaovzVVVVfPe73wUgISGBq666aje+G0lqexbNfpc+0dVUxZIYduQ3go4jqZXqltefxFAD2ctfDjqKJAXCuW9w0pMTuCF5MpMTb6J47ZImHatzQyEAmbl9dti2b+d0AFYUVTZpJklqy5bM+Yhf19zK+8lXceSg7Lj3n5ycQkXH4VSTzOKCpn/WfSwapU/lHAA6Dj4IMhofU5BQVdjkY0tSUNIWPsefk/7E12PPBx2lRWuzheJrr72Wnj17ctlll/Hwww/zwQcf8Omnn/L+++/z17/+lYMOOogf/vCHACQlJXHPPfdsnvTuisGDB/PjH/8YgJkzZ3LggQfy+OOPM3PmTB5//HEOPPBAZs6cCcCPf/xjBg0aFL9vUpJasY0fPgTAF1mH0CGrU8BpJLVW/Q85D4Ch9fNYv7ppP6SXpJbIuW+wDgl/xqGRTynfsLzJxigrKaZDqLEw37l73x22H5xWwXciz3LAKnfbkKTdVTD1MQAWZ4wlPT29ScYY0KXxURBLCiqapP8vW7P0CzpRSm0sgb57H0hCZi4AKbVFTT62JAUlbdOCxoMuQ4MN0sIlBB2gKRUXF3Pvvfdy773b3kIjLy+PBx54gCOPPHK3x/ntb3/Lhg0beOCBB/jkk08499xzv9Lmkksu4Te/+c1ujyFJbUlNdSVDC18FIHnfCwJOI6k169KjL/MShzOsbi7L3nuc3PP+X9CRJKnZOfcNTllCJ6jLp2rjuiYbo3jdcjoApaST2SF7h+37ZNRzSOLjlFelEov+hVC4za4RkKQmEYtG6ZPf+JlFbMTpTTbOoamLOSJhCtmfj4Lx1zbZOADTNqbzvZprmZhTxvdT00nO7gZARl1xk44rSUHqWr0UgA699w44ScvWZgvFr776Ki+++CIffPABixcvZv369RQVFZGamkrXrl0ZPXo0J554ImeffTZpaWl7NFY4HOb+++/njDPO4J577mHGjBkUFhaSk5PDuHHj+Na3vsVxxx0Xp+9Mklq/L95+gn2oYAOdGH7gyUHHkdTKlfQ9DhbNJXPZS4CFYknti3PfYFUldYY6qCvJb7IxSjesAKAonEPmTrTP7T2EaCxERqiKwg1ryOnWq8mySVJbtPjT9xgUW09lLJlhhzRdoXho4gbGJbzJnIJNTTbGf8xcVc6s2GDGDusPQHqnHgBkRZt+bEkKQkXpRrrHCgDoMWhMwGlatjZbKB4yZAhDhgzZvMXW7jj00EOJxWI73f7444/n+OOP3+3xJKm9+OeqdObUH0WfvgM5NKHNvhVJaiZ9Dz4PFv2eoTWfU5i/kpxuvYOOJEnNxrlvsOpSu0AFRMvWN9kYVUWrAChL6rpT7VNS01gXyqE7BRSumGehWJJ2UdG0xxkEzMs8gH0zsppsnA55w+Ez6FKzssnG+I+ZyzcCsG+fjgBkdfl3oThWRn1dLQmJSU2eQZKa05pFsxkMFJJNTk63oOO0aO4/JElqVoXlNTy6LI1f119E3sk/DzqOpDagW+9BLEwYTDgUY8m7jwcdR5LUjkTTG4u3kYoNTTZGw6Y1ANSk7lyhGKAouScAZesWNkkmSWqrYtEoffNfAyA88rQmHatb/8atULtRSEXZpiYbp6S4gG8U/4lTwu+zb+9sALI7d6chFiIcirGpoOkenyBJQSlZ8RkA65L7BZyk5bNQLElqVs/OXkt9NMaovCwGdu0QdBxJbcSKIZfwi7qLmLxxZNBRJEntSDijsXibVFPUZGO8k3oUk2qvYVGfc3b6moqMvgDUFyxuolSS1DbNmzODrrFCKmIpDDv4jCYdKzsnlyIaVyyvXTKnycZZPvstvp7wL36U/Aw5HVIAiCQk8Ez4KO6vP47CqvomG1uSgtKwfi4AFVmDAk7S8lkoliQ1q5T3b2K/0DzO2Kdn0FEktSFDDv8GjzQcxWsrYhRX1AYdR5LUTiRndwcgUlfeZGMsqMrk7ehoIj13/tlq0Y6NKyeSSpc3USpJapueWZPJ/jV38nCv/yMlLaPJx1uf1PjYnJKVXzTZGJVLPgAgP2vUFq/fm/Vdrq//OusbMptsbEkKyqPhE7ms9gdsGnRm0FFaPAvFkqRms/TzaVxQ/SiPJN3AyYNTg44jqQ3p3TmNET0yaYjGeH1uftBxJEntRGjQkQypnsy3wtc12Rj5pTUA5Gam7PQ1KV0HApBZuapJMklSWzVvXSkFdKTTqOOaZbyyjMYbe+o2LGiyMTILPgYg2mv/LV7v0iEZgMKymiYbW5KCMrUoldei4+gyeL+go7R4FoolSc1mw3sPAvB5xgFk53QLOI2ktubUIWl8LfI6qe/dEHQUSVI70TkrgxqSKCyvJRaLNckYR26awmnh9+iRtvP9dxh6GEfX3MT5Db9uslyS1BatKKoEoG/n9GYZL9a5cUvUhtKmudm1rraG/jWNRejcERO3ONc1PUIXNlJe7I22ktqWkso61v/7ZsvBuU2/O0RrlxB0AElS+1BfV8vA9S8DEB59fsBpJLVFx/SL0PujB6ktiVBSfD1ZnboEHUmS1MblZDSuxqptiFJaVU9WWmJc+6+uLOfq2MOQBCUZP9rp6/K6d2NhrBdUQ3FFLZ3/nVPSf8WiUab99TISajYSGXEyww4+o1m2GlbLVVdbw0/KbmRlQlf6ZB3QLGPWjrqAEXOG0zOrC681Qf/LPp/K4FAtm8ig96Att54+a+N9/D7lUaYuPg+OGtsEo0tSMFYunM13Is+wOm0YHVJOCDpOi+eKYklSs/jivafJYRPFZDJi4hlBx5HUBvUePJrl4d4khRpY+O6UoONIktqBlMQIN6Y8xEOJv2PjmvhvG1q4djkAlbFkMrM67VKuHlmNW1Uv//fqOElbWjZ3BuMLnmBs6b8Y89H3ePSmb/H9xz7htS/yqa5rCDqeArBh9WJOiEzj4sgrdM1unuf29uvRnQpSWV5YSX1DNO79F89/F4AVqSMIRyJbnszoCkBCVWHcx5WkIFUueodrEqdwSeSloKO0ChaKJUnNomHWPwBY2PVYEpNc0SCpaazreQwACQtfCDiJJKm9ODD8ORMjn1GxYUXc+y5Z39hnUbgzofCufYRzTtpMbki4l6p5r8c9l9QWbFqzEICNZJJPF56rHcezs9dy2d8+5vLf/ImZfziT2f96lJpqb7ZoL4pXNd7wsy7S7atF1SbSMzuV5IQwtQ1RVm+sinv/tesb/55XdvvqiuFIh1wAkmuK4j6uJAVq/VwAqrIHBxykdbBQLElqciXFBYws+wCAnAMnBRtGUpvWbfzZAAyvmEF56caA00iS2oOyhMaVvlWb1sa976qilQCUJHXd5WvHhz7n/IS3SFg9Nd6xpDahtmAxAEs7jCX3Vwv55eXf4JKD+tEtM4WjG95hbOnrjH7/cmp+N4AZt53Dp29OobamOuDUakqV+YsA2JSS12xjhsMhrunwCn9P/C3Fc16Na9+xWIwfVV7IuOo/kzhu0lfOJ2d3AyC9rjiu40pS0NJLG9/jI92GB5ykdbBQLElqch/MmEF+rCPLwn0ZsNeEoONIasP6DhvLqlAPkkN1zH/vyaDjSJLageqkzgDUl6yPe991G1cDUJWSu8vXRjv2AyChZHk8I0ltRmjjcgBqM/sQCofZp08nfnnicD786eGMOfV7TO1yNhvoRCaVjCt5hVHvfpOqG/sz/fbzeG/uCuqaYJtgBStWvAyA6ozezTruqISVHBj5gtrVs+Pa7+qNVWwoq2FjuBMjBg74yvn0Tt0ByIxuiuu4khS0HrWNP8879tk74CStg4ViSVKTu3dJFhNrb+ODCffs8pZ5krQrQuEwa3ocBUB47nMBp5EktQd1qV0AiJXHv1AcLmtcpdyQ0X2Xr03uOgiAzKpVcc0ktRWpFY3/NiI5/bd4PRwOMWzs4Yy/4l5yfrmYucc+zrScMygkmywq6Fr8MV9/eA7jfvsvfvrUZ3w84wPq62qD+BYUZ8lljdv9hzr1a9Zx6zoOBCBctCiu/X68onGHpRE9s0hN+upW2lk5PQHoGCuhob4+rmNLUlCK1q+mE6VEYyF6DhoddJxWISHoAJKktm1JQTmfrNxEJBzh6PGjgo4jqR3I2e9s6p5+hMLyaipr6khLTgw6kiSpDYtldIUCSKgsiHvfSZWNxedQVs9dvrZjXuMz2XLr18Q1k9RW/JELyKjdn0kDJm6zTTgSYfj4Y2H8sTTU1/PF9Nf4ZNEKclYlU1hey9MzlvL/Pvs2ZS8msLDToXSe+C0GjjqoGb8LxVN2dePPy9TcQc06blLuEFgJmRXL49pvx49uYHLiHFZnXwwc+JXz2V26E42FiIRiFBXl0zm3+bbclqSmsm7xJ3QG1oZzycvIDDpOq2ChWJLUpN5+/z0SqeegQd3p2iEl6DiS2oEBex3A8a9MZt7GCH9dWMhxe+36KixJknZWJLPxGY9J1YVx7/uu5ElUbzqIb/Y7cpevze07DIAsKthUuJ7snF3fvlpqq+oborxT2p36aDd+2mfwTl0TSUhgxAHHM+IAOLchyvRlxcyY/j4NCxPoSCn7Fz9HxdOvUT14KSmp6U38HSjeYtEoHRsKIQQdew1p1rGze4+EGdC9bgWxaDRuO7H1Knyf/pHlzOq89fMJiUk8FT6SjXUJTKysZxvNJKlVKV85B4CC1P54+8vOcf9PSVKTiTY0cMKnVzI9+TtcNKA06DiS2olQOMwhezWuAnjp8/yA00iS2rrkrMZCcbi+Ku59zyrvxDvRUWT3+OqzJXckLT2TDXQCYP2KufGOJrVqazdVUx+NkZQQJnc3bmhOiIQ5YGAO3z//VDr8fAlzDn+YTWSQHqpm+ecfNUFiNbUN5bWMqbmbg2rvJLd38xaKew4YSTQWIosKigvWxqXP0pJi+jY0bqXde9Th22x3d+Z3+U3911lfnxGXcSUpaC8mHcvRNTfx6cDvBB2l1bBQLElqMnM/fIFuFBIJxdhv3ISg40hqR44d2fih/bJ5syjbGP9nRkqS9B+RQYczpHoyl4Sui2u/tfVRCstrAOietXs78xQkNW5ZXbJuadxySW1BwfLP+UbkVY7PXEY4HNqjvhISk9jrkFNYnrYXAJsWTY1HRDWzFUWVxAgTyu5JUlJSs46dkpZBfrgrAOuXzolLn8tnv004FGNNKJec7r232S4nIxlg8/uNJLV28zZUszDWi4799wk6SqthoViS1GSqZj4CwLzOR7r1lqRmNbpXNven3ckL4R+SfvsQZv7+dD5783Hqav0ARJIUX12yMqghiaKKWqLRWNz6LVy/mm+Hn+W0hI/olL57RYtn+l3H8OoH+DDl4LjlktqC+qXv8n+JD3FR7Jm49VnVZRQACes+iVufaj4riioA6NMpmM8uCpJ7UxjLpKAgPje5Viz+AIC1maO2265reoSubKS8eF1cxlXz+vYjH3PGXz+kpr4h6ChSixCLxVi4vgyAwbkdAk7TelgoliQ1ifLSjYzY9A4AWeO/EXAaSe1NKBQi97DLWRXqQTgUY2zZG+z97mWU3zCQaXdexPyZbxCLRoOOKUlqA/5TxG2IxthYWRu3fktXz+eaxMf5ceIUQqHdW/HYsXsfKklheWFF3HJJbUGsqHGVfVXGtlda7qqM/vsDkFM+P259qvlkz32EOxP/yDEJMwMZ/8XhtzK25i7eDe8Xl/46bGj8PqI9999uu3M33cv0lCsYsPjBuIyr5rOhpIqXP8/n4xUbmbvWx71JAAXrVvGL+j9zccKr9O/ioqWdZaFYktQk5r7xCGmhGlaFejBk320/D0eSmsrIg08h75dfsPDk55ja5SyKyaQjpexf+DSDnz+DM295mj+8vpClBeVBR5UktWKJkTC/S/0bDyfeyKbV8SsQVRSuBGBTYpfd7qNv58YPyJYXVcYlk9RWJJU1/vuiU7+49dl71OGcXHM9R1f9lo0V8btpRM2jY8EMToxMY2BkQyDj981tfKb84g17Pjepr6ulf/U8ALqO2P6OErH0xveYhMrCPR5XzWvt9Kd5P/l7/Czh78xfuynoOFKLkL9oJmcnvMOFSW+QnBAJOk6rkRB0AElS25Qx73EAVvc5lV5h70uSFIxQOMzgfSbCPhOpr6vls/efo/aTxygrKeLjjal8/MYi/vTGIm7PfoJufQYx8LBvkNOtV9CxJUmtzPjQF/SNrGTOhmUwdPvbfO6suo2rAahKyd3tPgak1/LbhPvpXlAK/CsuuRR/dbU1JCYlBx2jXcmqXgNAatcB8eszO5vyzntTW1jB7NWbOGxI17j1raaXWbUKgOSuAwMZf2DXDACWxOEm1kXLV1IcHcjA8Dr6DB273baRzMb3mOSaoj0eV82resUM8kKFfCvhRabNTIbxrgqXKlc3Pue9KK0/fQLO0ppYKJYkxd3alUsYXjuHaCxEvyMuCTqOJAGQkJjE3oedCYedSUV1HbfP28Azs9cwd9FiTqp6hsiCGPXzb+Wz1H2pGX4mww87j/QOWUHHliS1AuWJnaBmJTWb4viMx9K1ANSld9vtLvK6ZDMk4Q0ASooLyOq0+6uT1TQ+fulBxkz7AdP3/jX7nfGDoOO0C7FolNz6dRCC7J6D49r3qF7ZLC2s4NNVFopbm671jT9z4/13YmcNyA4zOfEm+leupariE1LTd//ZmtM2RLi27udMHNSZhyLbX1GXnNX4HpNWV7zb4ykY6YWfbT7OLFkQYBKp5QgXNO7uU9NxSMBJWheXeEmS4u7JRVGOqrmZBzp+l269grkbV5K2Jz0lkVPH9GTyRfvx8tVHMWPoNSxMGExCKMre1TMYN+snhG4dxMw/nMHHH7xKfYPPM5YkbVt1cg4AdaXr49ZnUmVj0TmU2XO3+0jvkEUBHQFYv3xuXHIpfqLRGKs+eZVwKMZ+c64NOk67sbFwHRmhKqKxELl94vtB8sSORdyQcB8jPv1tXPtV0yrZWEg2jSt54/13Ymd1yspkdGQJvUMFrF3y+R719fHKTQDs27fzDtumdeoOQGbDxj0aU80rFo3Sq/q/xeG82qXEos5ZpezyxQAk9xgRcJLWxUKxJCmuYrEYT89azaJYHh0P/lbQcSRphzp37sL48/4fg38xg1Vfe5+Pen2T1aHupIVqGFv6L5586VX2v+ENrn3uCz5ZvsEJuCTpK+pT/71Stzx+heK06gIAkjrl7VE/BYmNhebSta42amnemL+B35Udv/nPJUXx+/ujbduwsvHfQkGoEymp6XHte3jnMOcnvMnYsjf8nbEV2bCi8Xm+hWST3iE7kAyhcJj8xN4AbFy1Z4XiRctWADC2T8cdts3M6QFAx1gJ0YaGPRpXzWfd8vlkU040FgKgQ6iKdSsWBpxKCla0oYG8usaff537xedRMO2FhWJJUlx9vGIjy4sqSUuKcOzI3d8mT5KC0GvgXky45FZ6/nIuC078J9O6nMnU5IMoqqhl8ofL+ee9v2HN9cP56P4fsWrxnKDjSpJaiozGLWYTKwvi1mV2fWNf6V327Alr5emNhYe6giV7nEnxde97S8mnMwujjcX8xdNeCjhR+7CYXpxecy33dYr/Vt99RuxPbSxCR8pYu9ybM1qL0jX/vnkgsUewOTL6AVCXv/t/d/JXLeaV2gt5LenHjM7L2GH7jl0af/4khhoo2Ri/9zA1rbXzPgBgceIglkT6A7B+0cwgI0mBy1+1mLRQDbWxBHr0d0XxrvAZxZKkuKp5/sfckbiahQO/SXqybzOSWqdQOMyQsYfD2MN5rSHK+4sLeeaTNZw4bwZ5sXXkrboXHrmXhQmDKR5wGoMO/wadc/dsxZckqfWKZDbeIJlcUxiX/hqiMSbVXUNurIhbeo/cs7469oNNkLBpaVyyKT4WfvoBQ1c+zrzwoazqNIHBm56kYdHrwEVBR2vzlpbArNhgBnSP/+9uySlpLEwcwOD6hayb+z49+w+L+xiKv7JNBdTGIpSn9Qo0R7TzINgISZsW73Yfqz97i25AKDGVtJSUHbZPSk7hSY6kuD6Zwytq6Ziz20OrGdWv/BiA4uyRROoqGFCylOrVn+3gKqlty1/2BT2ANZE8+iUlBx2nVXFFsSQpbqory9m78EVOikzlmAG+IUtqGxIjYQ4b0pU/njuGET96mZn7/I7PUsbREAsxuH4h4xfcRNZf9mLWTcfy7CerqKytDzqyJKmZpXRsLBSH6qvj0l9heQ3zG/J4n9F07rTjZ0xuT1KXgQB0qFgVj2iKk7J/3cr/JT7E3V2eosvoxu2n+2yc6nbFzWBFUSUAfTqnNUn/G7P3AqB+pav7WouXko9naM1DzBj2/wLNkdK98caC7Mrlu91H/fKpABR1GrPT1/y1w3e5of4CNtQ1zb8Jxd+8yixmR/sT6rU/DV0bV04mF80NOJUUrKmhvRlW/QAP9bkx6Citjku9JElx8/lbjzI2VMU6ujB8/PE7vkCSWpn0DtmMPfnbcPK3KcxfyZK3HqbjkmcZXL+QovJavv/4Z6QlfcExI7rx9Z757L3/4SQkJgUdW5LUxBL6T2TQSw+TmZ7Gx3Hob11JY8E5t0MykXBoj/rK6jkEgMT6sj3OpfjIX7mIUaVvQwi6HnEFeQP3ouatRHJDRaxYOJs+Q/cJOmKbNnzVo6RGqhmU3rNJ+o/0HguFT5G90dV9rcWKokqihOmR2yXQHDl9RsJ70LN+NQ0NDUQikV3vo/gTABL6Ttj5azKSWVJQQUF5zS6Pp+bXEI1xS8lhVNYewuvjD6F6yfvMXDCYz2rz8N1D7dmi9eVUkUKX3oODjtLqWCiWJMVN0pzHAViedxLdd2NCI0mtSU633uSc9wvgF6xaOJt1c9fSZ1EqK4oqmTl7FrfN+wFF/8piUZej6TTh6wwafTChsBv6SFJb1CU7gzoSKK6spa4hSmJkz37eVyz/mO9EnqEmZSRwxB71lTtoH0ZW30c5aXxWXUdmSuIe9ac9t/zF39MtFOXzpNGM3PsAAF7rcDyLN8XotLqaPkMDDtjGnVT2OF0Ti1mYeGqT9J879ACYBX1rF1FXW0Oi21+2eP9ZZd67iVaZ76xufQazIZbNqlgXuq3Pp2ePXbuZoaJsE33rl0EIeo06dKevy80I040iyovzgaa5gULxs3hDOZW1DaQnRejfJYOSjCPY57ko1MLZNfVk+Bg4tVML8htvihzUdcfPZ9eW/KRKkhQXBWuXM6KqcWutvEMvDjiNJDWvXoNH841Tj+ftHx3K0985gMuGR9lIJp0pYXzBEwx+7mRWXz+Cjx64hjVLvwg6riQpzjqmJREJh4jFoLiido/7i6yeyjWJUzih4Y097qtDWiopGdkArCis3OP+tGdKNxUxMv8ZABrGf2fz6yv3v5ab68/l5ZV+wN+UqivL6UoxAF17N01FvueAvSglnfxYJ5Yu3f1nzap5VFdVcFf1j7gj8U/0zQr2hveExCS+nv03zqi9joVlu35Tz7LZ75AQipJPF3LzBuz0dRdsuoepKd+l/+LJuzymmt+8pctIppaRPbOIhEN0Sk8iN7PxhpT/FMqk9qahvv7/s3ff4XFUVx/Hv7NVvfcuy5Ll3hvGNqb35oTeIfQakkAKIS9JaAkBAiFA6Am9g+mm2ca9y5ZlSVbvvUtb5/1jjIODi8ruzko6n+fxE7E7c+8vtrSrnTP3XO5qvoM/mp4jN2po3XhGIykUCyGE8Ig9Xz6HUVEpME8gdexkveMIIYQuFEVhRlokF198FSG/KWbbon+xMfQYelULqWoN8yueIvmlI7jnb4/w4uoymqW9mxBCjAhGg8KfAv7DS+b76KjYMfQB26sBsAclDH0sID06GICy5m6PjCcGL3/ZY4QovZQbUpi8+Cf7Hl+YrbW8XVfaTJ/DpVe8Ea+uvACADoIIj4rzyhwGo5GfJ7/CUfaH2dAmq5r8XX1ZAdMMJRxl2EZkqP7/Xllx2uv1noauAZ/bWfQdAFWhUwZ0njtYe/0x9jQNeE7he4mbHmKH9Up+Zvpo32O5CWEEYKOkvFzHZELop7o0n/mGnfzEuIKU2Gi94ww7UigWQggxZKrbTULpuwC05/zkMEcLIcToYLZYmXr0Ocy6/R1ctxeyYfp9bA+YSYcaxCsNadz9wU7m3Psl/3j8r2xc9jQ9Xe16RxZCCDEEc5RdLDLm0dNYOuSxTN11AChhSUMeC2CpcRUvme8jNO9Fj4wnBsfhsJNR/G8A6iZeheEH2/XkxIeQEQrzXZvYuW2dXhFHvNbqIgAajIle3RJkfHoiAFsr27w2h/CM1upCAOpNSX6xTUxWrFasLmnoGPC5K3ozeNW5hJ7M4wZ0njE0HgCrTQrFw0FU2w7MiovIxMx9j12qvs9O6xWkbXtYx2RC6KepZBsA1aZUDCbpzjJQ8jcmhBBiyHZUtfKhbQGnmUzkHnup3nGEEMLvhIRFMvuM6+GM62lsbuaXuzp4f2s126vaOK7hOXKaqunZ8Ds2RCzGOv08Jiw4DZPZondsIYQQA9BtjgZXCba22iGPFdRXD4ApKmXIYwFkmluYb8xjQ5P3955U3W4Avyi4+Jvlm3djdKVjMjqZevLV+z2nKAoPBv+HOY6PWbuxGmYdoVPKkc3WoLWC7gj07s/C1JQIALZVtHp1HjF0fT76nuivOcouvrLcQffuBODbfp/ncqv8pyGLTufPWDb3yAHNaQnXulcE2VsGdJ7wPVtfD+l796FOHL9g3+Nh8ekYK1XCO3brmE6Ig+u2OfnjsnyOmxDPMePjPT6+rUbr6NMaMtbjY48G8lu7EEKIIXt7ax1Pu07jqXHPEh4Zo3ccIYTwa7HR0Vx5ZCYf3HgkX966gJa0E6lW4glSbMxu/5wp31xB25+zWfvEzyjauhJVVfWOLIQQoh9sVq3NnaujfshjRTgbAQiOThvyWADmOO2iWUh3hUfGO5T1T1xF5z0pVJfs8vpcw4mqqjyxvp2rHbfz+rz3CAgM/tExxuyjAYhvWO3reKOG0qKt+LeFZnh1nqmJAfzH/Gfe7LiAzvaRU3xr6bZTWD/C9kDd+z1hD8vQN8deiXExjDHUkWwvGdB5hfWddNqcBFuM5CaEDujcoChtBXyoq21A5wnfK89fj0Vx0UoYiek5+x6PzZoJQKq9FLdLti8Q/ufV9RW8tqGSG17ZTPEgWusfjrlFu0nCFT3O42OPBlIoFkIIMSR2p5v3t2p7qC2d6ZkVD0IIMVpkJUQy76q/kXRXAQWnvM26mLNpJZQY2pjX8AY73r6PYx76lr9/WURFc4/ecYUQQhyCM0jb71TpbhjSOKrbRYy7GYDwhPQh5wIIT9YumsU6ajwy3sG0dNtJb/yKMLpp+OZJr8413KwrbSGvuh2rycB5R0444DFZc0/DrSpkustorCnzbcBRIqC7EgBjdOZhjhya2MhwsowNRCjdlOd959W5fOlnL23kpEdXUtLo+Yv8egns0m6gMUSP0TmJJnHMJACi6KCtqa7f5xXvWMcUZQ+zUkMxGQd2yT80WisUR6lt+7pCCP/UWrQWgIqAcft17kgeOxmbaiZY6aN2717sQviTuNX/x1LDCoyObj568UHstj6Pjh/TvQeAwNTJHh13tJBCsRBCiCHZ8t0nLO77mtQQlYVjZTWxEEIMhmIwkDv7WObe+Dwhv9nD1oVPsSn0aD5gMSVN3fzti0J+9tcXKfjTPNa9/gAtDd690C+EEGLglJBYAEy9Q9vjsbWxFoviBCDaQ4XiuPTxAMTQRleH91rhLttew7+cJwNgaJQVxT+0+6PHSFEaOXtGCtEh1gMeExGTQLE5G4DSdR/6Mt6ocY9yAz+x/R4l50Svz1UTMhGAzj1rvT6XL9S197GpvBWXWyW/duD75/qrKJt243tQgn+0Kw0OjaAO7dpKXUlev89LzHuSD6x3ca3hvQHPGRmn3fRvUZx0tI2cFfAjkaFmCwA9sVP3e9xktlBp0rqQNBRt9HkuIQ6lprSA03vf40HzU/wn4EFu6X6UTS/d6bHx7bY+kl3aa3l81nSPjTuaSKFYCCHEkFjW/YNHLE/wQOynA75rVQghxI+ZLVamHXMeM29/l8d+dzsP/XQqC7NjONv4HbnOXczddS+h/5jE1gdOYONH/6K3e4S1/xNCiGHKtHePx0Db0ArFNfYAjrc9wPXGu7FYAzwRjfDIGFoJA6C+zHsF3Hc2V7POrRWlM3vzcDmdXptrOKko3MqlzQ/zteXnXD3jxy2nf6glQdtz0lDytS+ijSout0peu5WNai4JKRlen8+RoF2sttZv9fpcvlDx1TOUBVxAWcAFWIs+1juORzidLnrdBhyqkZjUXL3j7NMQoN0k1FGV3+9zkjq3AxCSPX/A8wUEBvOWejRPO0+hucuzq/yEZ8V27gQgMGP2j55rCdW6h/RVbfdpJiEOp3zlywDssk5FnXsdAHOqXqBg/eceGb+qsoxmwuhSA4lPyfLImKONXNEXQggxaK2NNUzq1u6OTlx4qc5phBBi5Amxmlg6M4V/XzmXpdf/mbXZt1NkHItZcTGtdy2zNvwC94Nj2fDwOazevhunS1rFCSGEXgIitdaditM2pHHqOl0UqqlURc7xRKx96k3JALRV7/bouN8rL97JpJo3aVBi6FIDCaOH0p3rvDLXcFP72d8A2Bk0h8yMQ7e3DZ+srXTN6twg+0x6WF1HH3aXG7NRISki0OvzhY2dB0BKT/+Lff7MWPLlvq9D6zfomMRzajtsHG97kCnOl4hN9p/iQk+o9jrhbujf63VjTRlJagMuVSFj6lGDmvMfobdwr/NCGpze/9kQg9PV5+BF+1F84JpPysQFP3reHadtaxDQPDJec8TIEVv+EQDd2acz46TL2RB+AkZFJeyTG+nqGHoXg53dYcyz/YOfxf57v5bsov/kb00IIcSgFS5/HrPiosg4lswJP76bUQghhOfEJKUz78Lfk33XJsrP/5Y1KVdQo8QRrPSR1fYdl7xSwPz7v+KeD/PZVVgs+4sJIYSPBWQdSXbfS5zn/uOQxqnt0FZzJYR5ZjXx9zqDU2lWQ2lr807r6ZpvnuFP5ud5OvwF9gRqe2w27fzKK3MNJy0NNUxt0lZfmhfefNjjx05fQpcaSCQdFOet8Xa8UaWxeDO/Nr3MxSGbMBoUr8+XMWk+TtVAHC00VJd6fT5vcrtcjOn8b3HY3D0ytkEpb+4BICkqBKMfdUhTYnMACGjf06/jK7dpHQjKTBmEhkcNas6YEAsATV1Du9lJeE9edQfPO0/i/qBfEpOQ+qPng7IW8LZrIZ86Z+iQTogDqyzazljXHpyqgZyjLgBg3OX/pJZYktR6dj13w5DnKKrXuqylJ8YPeazRymfvgAaDAZPJxIMPPuirKYUQQnhZdPHbADSPXapzEiGEGF3Sx01j/lUPk3jXbgpOfovPM35JaFAgjZ02nv9uD6Evn0Dlnyax5vk7qC6RPSKFEMIXYsKCcWCio8+JzTn4laDBpZ9xvfE9Zpn6VyDor7UT72Gm7Sk+Mx/j0XFBKyJlVGl76jon/ZSeJK31qbV6ZOzNOhS7lz1KgOKgyDiWCfMOvy+u2WLlhYTfcLTtr3zeHOeDhKOHvXwD15g+4kzlW5/MFxQSTrlJayFctWOlT+b0lpLt3xHJf7c7CbY16JjGc8pbugFIjz50S3hfC0qdwjb3GHY5E/p1vL1Uu6mkKXLwe3PGBxtIoJnO5tpBjyG8a3tVGwBTUyMO+Hza5CO53XEdT3UcQWefw3fBhDiE6lVa2+n8wBlExmrdd8Iiomk98XHcqsLsto/Z8tlLQ5pj995CcXZ86NDCjmI+KxRbLBZUVWXhwoW+mlIIIYQXleVvYKxrD3bVSM4xl+kdRwghRiXFYCB3znGcd/mtrPvNsTxzySyuyHURQztp7mrmlz9J8kvzKPjzfNa98QCtjXLhRwghvCU80IzZqK1SbOqyD3qc1Lrl/Mr8BlMceZ6KBkBanLZHcdneFXSeVLD+cxJppFMNZNKS84iasJh6NYLC7mDcbtXj8w0Xfb3d5FS8CkD79Gv63Q4xYvoZlKhJrChu9ma8UcfVXAJAT8iPV+J5S1XUPFa4JlPYOrx/Dpq2fQpAA9pq1SjnyCgUp2x/nHctv+c01Tc3D/RX/MSjOMP+J37bdQ59jsPfeBTVsgUAY/q8Qc95UftTrA24iYzifw96DOFdPUUrGKPUMCU57IDPRwRZSAzXupEU1HUe8BghfC2hUuuq0jfujP0enzDvRNYlXQxA8prf09DSPug5ri+9mZfM9zE1sGnwQUc5nxWKk5KSADCZTL6aUgghhBfVrXgegJ3B84iKS9I5jRBCCIvJwLET4rnrsjNx/LyQ9VP/TJ51Om5VIdeRz9z8ewl5fCL/+fvv2FXboXdcIYQYcRRF4Y8Br/Bv8710lm8f9DiBffUAmCKTPRUN+O+KubKmbo+OC9C1Xiss7Io6moCgEMZMW8JR7ie5o+9Sihu7PD7fcLH9438RTTt1xDD1+Ev7fd7inFgANpe3yqowD7J0lGtfRGb4bM66Ob/hEseveb9jnM/m9IawmhUA7Ek9G4BotRWHY/A3xPiLsLZ8phuKSQzwr3bLsaFWQgNMuNX/tsc+mN7uTjIdWgeK5ClHDXpONUjrYGDsaRz0GMK7zqv+M19Zf8FC88E7Rk1KCCJHqaSq2LM3mwkxGEWVtdQ5g7GpZsYtPv9Hz8+87C98bVnMZbZf8qv3d6OqA7+pqq+3m0mufBYZ88hIltbTg+WzQvGiRYsA2LRpk6+mFEII4SVOl5v2+jIA1KkX6BtGCCHEj4SGRzHnrBuZ/OtvaL5mK2uzf06xMQuz4uK92kjOfWoNO6oHf8euEEKIA5upFLDQuIO+xpJBjxFu11bqBUaneSoWAJnhJl4wP8Drtuvp6fLce0BfTxfjW7S9iINnXwSA2WRkZrq28nBdyehcFet2q2worKRDDaR07MWYLdZ+n5saFcTF4dt41Pgwhd+9572Qo0xYbzUA1tgsn835fYvY7VVtuIbp6vqujlaybfkApC6+HLtqxKioNNdV6Jxs6MJ7qwAIih+rc5L9KYpCVmwIRlyU1h66cLu9tpuL7L/hEePlJKRmD3pOQ6hWKLbYRudrtr9raagmUdW+F9ImLTjocVc4X+dz6x0k5T/jq2hCHNSHuzo4z34Xv0h7g/Co2B89b7EGkHLly+wxjuGb3Y38Z235gOeoKtqGUVFpI4TouBRPxB6VfFYovummmzAajfz1r3+lo0NWMAghxHC2qriJa3pv4BTlcSYd9RO94wghhDiE2KQM5l14N2Pv2kzZeV9D6lw6+pxc+Mw68itlxYAQQnhStzkaAHvb4Fr9q2430W7tIn14fLrHcgGEh4UwzbiHTEM9dWUFHht35zevEar0Ukss4+eesO/xuZlRgMquokKPzTWcfFvYyF/aj+Y4/snE028d8PmnhhZxinE9zvxlng83SsW5agCISPHd6t7suFCCLEYC7C2UVg/PLUC2FFbwsXsuOw05pIydRJNBe51rrR34BX1/orrdJLi0fxNffk/0143uV9hlvYzwrU8d8riNlV2sU8dTNOaSfre3PxBLuLYSL8guhWJ/VLnjOwAqDMmERUQf9Dhz0mQAIjp2+ySXEAejqirLtmuvscfMOPhrbHZ8KHeelAvAex9/REXh1gHN01a2DYAaS+aQXgNHO5/9zc2cOZPHHnuM8vJyFi9ezOrVq301tRBCCA97e7N2J/asadOxWAN0TiOEEKK/MnJn8PwVc5mWGsF42zYinp1P6c51escSQogRwx4QA4Crs35Q53e0NxOkaC1QY5IyPBVrn3qT1s66vergbSsHqqIwD4dqpCz5VAxG477HF0W2sMl6LbeX/AzV7fbYfMPFv1Zqq8pPnT2OsLCIAZ9vzT0egJSWNZ6MNWq1tzQSjtZ2PT4tx2fzGg0KzwQ9waaA62jZ8JbP5vWk5dVGbnHcyKtTngPgjzEPMb7vOYqt43VONjTNDdUEKTbcquLT74n+Cg6PxKK4MLcWH/K4jWUtAMxMjxzSfIGRCQCEOluHNI7wjp6yDQDUh0485HGxY2cCkOoow+V0ej2XEAeze88e2ptqsO7dIutQLp2fwS+Sd/Ca4S5sb1yJw97X73kctVrHi85Q33ULGYl8tmHwFVdcAcC4cePYtm0bCxcuJDU1lSlTphAZGYnxBx8m/peiKDz77LO+iiqEEOIQOjpa2bhzNxDK0pnS0kMIIYab0AAzL10xm/K/3UGSo5GWN39CmeFdMsbP0juaEEIMe66gOGgFQ3fDoM5vqS0jHGgjhIigEM+GAzqDUqFjN30NezwyXlOXjV80nMAf3fN4+4Qj9ntu3PhJKO/3YcVB5Z48UrOnemTO4aAofzNq6QqMholcviBjUGNkzzkRx0ojydRTvWcHyVmTPBtylGmoKCAcaCKCmJBwn85tiEiBOlCrh+d2fCuLmgBYmK21DTVHpdJbWUNte6+esYasqWIXMUCDEkNCQJDecX7EmpALJRDRXXrQY9wuF4vL/06QIZPZqbOHNF9otHYjUYTajup2y8o8PxPUqK2adCVMP+RxKVmT6FUtBCk2Kkt3jqr33v4o2LCc9jUvknXOn4lJ8OwWH0Ohut1U7ckjKXMiRpPPSnZe1frVo6yz/oePoy4hxHrSIY81GBTOOfscev71KNnOYta+dCfzrnqkX/MEtu3tXBM3YYiJRzeffde98MILKIoCaIVfVVWpqKigsrLykOepqiqFYiGE8CMFnz/HCuOfeT/oVCYnn6x3HCGEEIMQFmgh7fr3KHr8BLJdxaivn035ee+RnjtD72hCCDGsfb/Ho7m3aVDnd9ZrrVxbjDFEeCrUDzjCM6EDjK2D30P5hz7YWoPLrZKamkZm2v6tsgMCg8m35jLBnkftti9H1cXqts/u41XL53weeT4pkacMaozgsEh2Wicw0Z5H1cZlUigeogIyuajvcRYmwV99PLc1fTbUvUx0W56PZx666uoqTM0FGA2pHJGltbtNjNC6itW293/Flz/qqi0CoNmSRILOWQ4kJmMSrIYkZxVul2u/jg3fqyjcwmV8yDlmK+bE3w1pvsi4JAACFTudnW2EhkcNaTzhOarbTUqvtmVERPbcQx5rNJmoNGeQ4yykoXjzqHrv7Q/187uY68gn7/lKon715QF/rvSw7ukbmVf3MmvH/Yp55/9W7zhDprrdpNd+iklxk5w9pV/nxCVnsmnOn5m5/lZmV75AwfpTyJ1z3OHP69NupglJ69884sB8dmtQWlrafn/S09NJT0//0eMHOi4tzX/u7hBCiNEubPdbmBUXyakZ+24AEkIIMfyER8YQd/3H7DGOIZp2gl87a8D7AQkhhNifOVwrNwTaB1co3h0wheNsD/JS3B2ejLWPOVZryxfcXeGR8b7dqK1wOnt68gGf74ibA4ChYvRsP1ZfXcK0ti8ByFh4/pDG6kheBICl/Nsh5xrtKlr7qCcKd/xkn8+dOGkhAOnOMnq7u3w+/1BUr36Nz6138GroY4QGmAGYQjH3m55mWum/dE43NE1dNsrdcXSGZuod5YASM8bjUI0EKTYaag68qrhhp/baUGodh9liHdJ8QSHhvKUu4SnnKTR32YY0lvCs+qo9RNOOQzWSMXHeYY9vC80GwF693dvRhpWOtmay7VrBfbJtCxve+ovOiTR5K95nXt3LAASXLdc5jWcUbV1BktpAj2pl/KKf9Pu8mSdfzobw4zEqKqGf3EBXx6Fb4Xf39lHhiqJVDSEp+9Cr7cWh+axQXFZWRmlp6aD/CCGE0F9VcR65jnxcqsLYY67UO44QQoghCo+OJ/q6TygxZBBDG4GvnEll8fBb7SKEEP4iMEpbkaW47IM6v6oLitQU7HHeKWaFJI0DINZeNeSxyndt5MW2S/mP5V5Om3Lg9Xgh4xYDkNqxedTsU1yy7GHMiot88yRyZiwe0lixU7VWjTndm7HbhvfqTb1VNPcAkBbt+xbD8cljaCICs+KibOfw2nPaVPYNAK6E/65KTDK2c57pG8Z3fKdTKs/41LiExfZH2DLpLr2jHJDZYqXGmAhAQ8mBfz9XKtcD0Bk30yNzPhZyC/c5L6TRMbSis/Cs7c0Gfmb/Oc8GX0VAYPBhj3fHaR0oAlt2eTvasLJn/SeYFDcOVVtFPCX/ISqLtumaqb2lkfivbt3335l9u0bE3tIt614DYFfYEQQNcLuHcZc/SS2xJKv15D93/SGPLWrq4zz7XRxvfoHIGH/sDTF8yGYDQggh+q3qm+cA2BE4i9ik9MMcLYQQYjiIiEkg4tqPKTWkE0srG175I+XN3XrHEkKIYSkgcx5j+15iqfPPgzq/bm8r18SwAE/G2ichYwLtahA17kh6+4a2Yqx25YsABIeEERVy4Lxjph+FQzUSTzO15YVDmm846O5sY2Lt2wDYZl835PHGTD6CJiIoVFPYuXvk//150+yyJ7nT9CoTrI0+n1sxGKgM0vZObC8aPoVip8PO2G5tX+Xoqf/dXzI0Vuv8GOn0/d+lJ5W3aDcPpMccvvCml+aADAB6avIP+Hxih1bkCsxa4JH5YkK0AnFTp6wo9ieb6xx84Z5F+diL+3V80Lgl/NXxU55znOjlZMPLty1RPOo8i+XJ17LDOg0rDj764HWcLv1uZFvxyn3E0UKlkkS3GkCI0kvF7uG5n/333C4XmfWfA2CY3P/VxN8Li4im7YTHcKsKc9o+Zv03Hxz02MK6TgByEkIHF1bso3uh2Ol00tjYSGNjI84RcLeEEEKMVG6Xi4yqDwFwTDpX5zRCCCE8KSoumdBrPuYNy1n8qudCzn96LZV7L54JIYTov9jwYJyY6La76LEP/BrHpOrXuMH4HlmmBi+kg/CoOBYqz3Om/Y9UtA1u1TOA2+kks+YjANQpB/9sEBQSzh5zDgDV274Y9HzDRd6yfxBGN5VKElOPGVrbaQCD0ch9Oa9zlv0eltdaPJBw9Dqy6zOuNX1IWkCvLvPb4rSWmKa6LbrMPxjFW1YQRg9thJA15ch9j0ckaq2ao9U27LbhW1Dct8o8yverzPurOW4eH7rmUWSP/tFzTXWVpKi1uFWFjGlLPDJfQjAk0kxnS61HxhOesa2yDYCpKf1bmZk+fhaPu87ig85s2nscXkw2vLxfFcTDzp9iXHAzMRc9y+X8gfsbF/DUihJ98myt5uaqJdznvJCeU/7Bc9G3sdR2N2vbh/f+4IUbvySeZjrVQMYvPGtQY4yffxKrU67g/xwXc90KKw2dB+6qUlTXBkBOvBSKh0qXQnF+fj4333wzEyZMICAggISEBBISEggICGD8+PHcdNNN7NixQ49oQgghDmLX2k9IoJEONYhJRw/9oocQQgj/EhOfwlE3PUlabDg17X2c99Qaamoq9Y4lhBDDSrDFSKBZa2nY1DnwQuzC9g/5pfkNUhXvFIoVg4GMmBAAyobQPWLXmo+Ip5kOgpmw+JxDHluWejr/dJ7Gys6kQc83HLicTtIKtVXWNeMvx2A0emTcI8alALCicHD7Xguw9fUQpzYDEJOWq0uGgHHH8G/nsbxjm6PL/IPRmvcpAHtCZmE0mfY9HhWbhF01YVBUmmrLdEo3NJ3tLXzmvIK3LXeTHmE6/Ak66Zh8OTc5buZj27QfPVex7RsAyo1phEfGeGS+S9qfYk3ATaQWv+yR8cTQuV0uFlQ/yxLDFqYk9W/1e3igmeSIQAAK6jq8GW/YqGzpobSpG6NBYX5WNAmpYzn9dG216yPLC8mv8e3fU01bL3e9twMVAwGLb2XcrKNx5J7FJnUcG6uG9w3b7RtfB6AgYmG/WqUfzJzL/8q6uHNp7nHyq7e2o6rqj445deetfGu5lUUG2Y97qHxaKHa73dx+++1MnTqVf/zjHxQUFOB2u1FVFVVVcbvd7N69myeeeILp06dz22234R4le9gIIYS/696gfVDYFXUMAUEhOqcRQgjhDXGhAbz6s3mMiQ7k2u4n4F/HUFdZrHcsIYQYNhRF4W7ry/zH/Ge6yjcP+Pwot1bMCovz3jYv6dHaRbuypsEXins2vgLArqhjCQg89Go867yreMB5Pu/XDe8VMoezYuMWel0GWgllyilDbzv9vYU5WgGovKaW5tZWj407mtRXFGJQVHpUK9Gx+tywMGb6Iu5yXsErHVNo7hoeq3Aja1cB4Mrcf7WqwWikyaCtcG2vL/d5Lk+oL9tFjNJBpqGe0GD/bT2dFaddeylu7PrRc93l2ur0hshpHpvPHRQLgKFneLcVH0kqi/O4UXmDJ8yPDmjV5LxYO8cZNtJYMLz3EveUgvVfcLxhAwuSzYQGmAE4e0Yyx0+IJ8VdQ+ULl2Pr802B1u1y8cXz/4e9r5upqRHcePRYAKanRwKwuWL4vte73Cq/bT2VOx1Xocy+akhjWUwGHjlvGhaTgfW7K/ni47d/dExiXwnphgYS42KHNJfwcaH4ggsu4JFHHsHlcqGqKhMnTuTyyy/nzjvv5M477+Tyyy9n0qRJqKqKy+Xi73//O+efL6vWhBBCbz12J7e2LOV3jssJOfIaveMIIYTwoviwAF69OJcl5h0kqfU4nzuFhupSvWMJIcSwMY0ijjTupK9xYK0MuzvbCEMr3kYnZXojGgAnu7/lS8vtTN4+uH2Ue7s6mND2DQDhcy867PGz0iMxKFDe3LNvD+aR6O+b7Rxr/wvvTn+OwGDPtUCMCw3gybAX2Gy5mtJVb3hs3NGktaoIgDpjIopBn134wgLMZMVqBcltVW26ZBiI9tZGsh0FAKTNOfVHz7eZ4wDobhyeheKOGm3P70aTf3c6GBMbjIIba1cV7Z37F4sfdi7liL6/0zr9Bo/NZwjV/l0tvdLBwF80FKwGoNwyFpO5/1sQ/ET9jH9Z/kZc0WveijasRO94hqctD3Nd4H+3wVAUhXvPHM9/rA9wgn05m1+8wydZ1r9+L5e2/5O3rffw8E8mYTZq70szUiM53fAdl7c9TmtjjU+yeNq60maKuyx8YjmBKfOOG/J4OfGh/N+SKD6y/JqF66+jonDrvufaWxuJowWApOzpQ55rtPPZb0evvfYab7yh/UI7depU1q1bR15eHs8++yz33nsv9957L88++yzbt29n3bp1TJ8+HVVVeeutt3jtNXlBE0IIPX26o44aeyArI85gwowjD3+CEEKIYS0+IQnTFR9Ro8STotZhe+ZkGmvK9I4lhBDDQq9VWznraK8b0HnNe1u4dqqBhIRFejrWPjHBFrIMtYR17RnU+flfv0Kw0ke1Es+42cce9vjQADOzEswcZdjKri2rBjWnv9tU3sKWijbMRhOnHr3I4+NHRMViUtyoxV96fOzRoLde647SHpiia45ZyUFMV4qoz/f/n4M1FT1c5riDZ60Xk5A69kfP9wTEA2BrG9jrnL+w7b2RpyMoVeckhxYWYGZFwC9YZb2Fut3r9z3e53Cxo7qDGmIYP36Sx+azhGmF4kB7i8fGFEPjrtoEQFvk5AGdZ02eAkBEZ5HHMw03LqeTrC7t7zFy8gn7PRcTFkz9/LsBmFPzbwrWf/Gj8z2pNH8D03c/CkDXxAsZE//ffafDg8zcHvABl5i+oHzrN17N4S3Ltmv7m58wMR6LyTOlx3OPmkVXYBKBih3bG1fhsGtdOWoLtc49dcQQFvHjfdzFwPisUPz0008DkJOTw6pVq5g9e/ZBj509ezYrVqxg3LhxqKrKU0895auYQgghDuDtzVUAnD09BUVRdE4jhBDCFxJSx6Jc9iG1xJKq1tD7zMk01VXoHUsIIfyePUBrf+furB/QeR17W7g2Gz2z1+TBhCbnABBjqxrU+f+sH8+t9uvZPvaGfq/O/LnlbV6wPEhQ3kuDmtPfrfr0DazYOXN6EnGhAR4fP2SidmE7s30dqmzRNnCtWmcUW4i+RcGzla9513o3Ewr/qWuO/vh2Twcr3VOomnTgNurfZf+K3L7n+SLsbB8n8wxjm1YodoZn6BukH9qtCQB0VOXveyyvuh27y01MiJW0qEO3/x+IwMhEAEJdUij2FxGteQCYUmcO6LzY7FkApDrKcDmdHs81nOzZvopwuukgiLHTFv7o+RknXMSG8BMwKiohn9xId2e7V3LY+npwv/0zrIqDbYFzmPOT2390TH24VuDvLV3jlQze5HTYOWnbzVxq/IzTJ3mucGswGom/5HnaCSbbWcTGl+4EoL1C+9loCPBeF57RxGeF4m3btqEoCnfccQfB/dj7ITg4mDvuuGPfuUIIIfRRX1HEbRU3cb7xS86e7t9tmYQQQnhWYvo41EuXUUcMae5qup8+meb6wRUWhBBitHAHayuyDN0NAzqvt7kSgA5LnMcz/VBs+ngAEmiir+fH+14eSkNHH1/t6eI995GMP6H/e88FjNVW2Sa0bhrQfMNBdclObqq5k1XWW7h6jndWtGTPPo4e1UoMbZTmb/DKHCOZpasaACV6jK45onLmA5Det8uvC/6qqrKiUNujdlH2gfd9jIyJpw8rNW29vozmMcFd2uutKUbf74n+6A7VMjrrd+97rPe7p/mX+a9cGbPTozfzh0Rr13wi3W0eG1MMnt3WR4ZDu6khfvwRAzo3KWM8PaqVQMVO1Z4d3og3bDRv/xSA4uCZB23fnXPZE9QRQ4paR96Lt3glx+aX7iDLVUoroSRf8uyBb7ZLmQNAWNMWr2Twpl2rl7GQzdxifpd5WZ79XTYuOZM9c/4EwJzK57WV3/XazTM9ETkenWu08lmh2G63AzBlypR+n/P9sQ6HwyuZhBBCHF7pV88xy1DIRcEbSI0+/I0+QgghRpakzFxcl3xAA1Eku6p59MXXaO6y6R1LCCH8lvH7PR77BrbHo7NNuxGnb29LV2+JikmkUw0EoL5892GO3t/7W2twqzAzPZKMmP5/NsicobWoTndX0TTCbjiq+vghDIpKTWAOY9O809rYGhBEUdBUABq2fOSVOUayO42/YH7fY7gm6Lv6NX3CHGyqmQi6qC7JP/wJOqnck88lXc+w0LSLuWOiDnhMYrj2GlLXMTz3HY+2a/t/hiZm65zk8NRoLWNAe/G+x8Kqv+E442amh7Z5dK6IuGQAghQbPV3eWVUp+q+iYCNWxUEHwaSMmTigc40mE5XmDACaijd6Id3wEV6jtft3pC8++DGRMTQd8zcA5jW9y/Zv3/Fohvy1nzK3+t8AlM3/MzGJaQc8Ln6idmPdGNvufS2Wh4veLW8CUBR99ID20+6vGSdfwcaw4zAqKqGf3EBU23YAjAkTPD7XaOSzQnF6ejoA7e39f5Pp6OjY71whhBC+pbrdJJe/B0D3+HP0DSOEEEI3yWMmYrvoA35t/iUvtYznwmfW0dpt1zuWEEL4JXO41rozyN48oPM+DzmLY20PkjfmZ96ItY9iMFBv0laNtVYNrFCcs/JmrjF+yDmTwgZ0Xnh0PKWGDAAqNnt3/z9fam+uZ3LjMgAMC27y6ly9qdoF7pCqFV6dZ6Rxu1XKW/uoJZrkxERds1isAZSZswCo8+N9ims3fsA1po+4I/hDgiymAx6Tau7gAdPT3Nj8Zx+nGzqbw0GhO4FKdywxabl6xzms4GStQBjdp21PoLrdpPdoK0Qjxx3p2blCInjLfRRPOk+luXN43gQwkjQXrgOg3JrT760efqgtbBwA9urtHs01nHR1tDLWpt2YkzLrlEMeO2nhGayL/QkAbd88QXuPZxYvdvbaMXz2GwyKyoaIk5h+wqUHPTY1eyodBBOo2CnLX3/Q4/yN3dZHbtu3AITM8N7145wrnqKOWJLVerKdRWx1ZxGeOd1r840mPisUL126FFVVefvtt/t9zltvvYWiKJx11lleTCaEEOJgCjd/RapaQ49qZcIxF+kdRwghhI5Sx07m2mtuJjbUSkFdJzc//RHtzQPbf1MIIUaDwKhE3KoCroFdYKzoMlCsphCUMNZLyf6rLUhbydLXUNTvc0p2rGWxYyW3m97kxIkDbynYEK3tl2gv8d8C2UDlL3uUIMXGHkMmkxac5tW5EmeeCkBOXx69XR1enWskaeyyYXO6MRoUkiIC9Y5Da+RkAJyV/rvCz1r+DQBdyYsOekxceBDnmr7hWPca7LbhVVCsarNxif3XnMDjRMd7pwuAJ8WNmQRAoqsOh91GZfF2IumkTzWTOXmBR+dSDAYeCb6F+50X0GD3/IpAMTDvcjTH2R5kc86tgxsgXrvJIKh1l+dC9dO61+9ny4Mn0dmu737XRZu+wqK4qFbiSR5z+JWnUy57hCcsV3BF703c/YFnWnbfs2wXl/bexkfGo8m9/IlDHmswGikN0HI271rpkfl9IX/Ve4TRTRMR5M49wWvzhEVE03LCY3zpms7svn9wluOPpIyf67X5RhOfFYp//vOfM2bMGJ566ineeOONwx7/1ltv8dRTT5GZmckvfvELHyQUQgjxv9rXvATAzojFhIRF6pxGCCGE3rJiQ3j1Z3OZGNzOn1p/ReMTJ9He0qh3LCGE8CvBGXPIsb3I6Y57UVW13+fVtmvFloSwAG9F26cnMpft7kxqe839Pqdh1YsA7AiZT3jUwAvF5jHayre4Zv8tkA2E3dbH2NJXAGiZcvWgVnsNRFr2FD40Hsv/OS9hQ9nAVquPZo2FG3jc/Ci3BX+G2eizy6AHZU7TbpiIbM3TOcmB2W195PRoe2PGTjvpoMdFxiRiU80YFJWm2nJfxfOI8uZuANKigjy6v6+3xCVl0q0GYFZc1JTspH6HtmqvxDIOi9Xz7xcxIVYAmmSrGd1tre6kSE0hIXfeoM4PnHAiN9tv5M/OSzyc7NBsfT1Myn+Y6T2ryf/0aZ/O/b/e78xlke1hPhpzV7+ODwwOZf5Fv8etmHhvaw0f59UOaf5Pd9Ty5qYqGpQoYi96ltDwA7fz/6He+JkA2OsH1vVFT85tbwGwJ/ZYjKYDd6LwlAnzT2L9Ef+kkUgyo4MP2vlCDIzPfkMKDw9n+fLlzJgxg/PPP58zzzyT9957j+rqahwOB06nk+rqat577z3OOusszj33XGbMmMGXX35JeHi4r2IKIYTYq6+3m9zm5QAEzpLVxEIIITRj40L5x0/GEWroY6xrD/VPnExHm1ywFkKI78WGB+HEhM3pptPm7Pd5F7Q9xQ3G90gK8P7F+fopN3C6/c+8zTH9Ot7ldJJV94n2H1PPG9Sc6Xv3Kc5wlY+IjhTbPnmGWFppIIqpJ13h9fkUg4HVE+/mZdexfF3a4/X5Roqeyq2calzHYsM2vaMAED9eu2Eiw7HHL1fiFm/6kiDFRjPhZE48+CotxWCg0RANQFttia/ieUR5UxcAGdH932ddT4rBwGeBJ/O48wxKO0CtWAtAe+wMr8yXEKyQRBOdzUMrkImh6bE7KWrQvlenpkQMaozM7Il84D6C9Z1RtPX4btug3Ws+JljRXt9C9yzz2bwHsrKokQo1nrTpx/b7nOlpkVx/1FjMOCl7526a6ioGNXdTXQWfvP08ANcuzmJO5uGLxACG2Zczp+8f/Lrv4C2q/Ulfbze57Vq3mPDZ5/pkztuPG8cdJ+Zy39mTfTLfaOCzQrHRaCQrK4uNGzeiqioffvghS5cuJS0tjYCAAKxWK2lpaSxdupQPPvgAVVXZuHEjY8aMwWg0HvCPyct3JwghxGi28+vXCKObOmKYcMSpescRQgjhRzLGz6T9p2/RSig5zkJqHz9Z97ZiQgjhLwLMRkKt2vWKxs7+FX37eru5SP2QX5rfICHU++0+M2K0AknZ3pV1h5P/3QfE0koroUxa/JNBzRkdn8p9AbdxvP0B1tW6BzWGv1BVlap8be/IPZkXeGVV34EszI4FYGVRk0/mGwnczaUA9ISk6ZxEkzxmAn9RLuMi+68pqO/SO86PtO/4DIDSsDkYjMZDHtth1r4fe5oGV0TRy/ht97PBeh1nOT/WO0q/rcy8hb86z2VndxgJHdpND4FZnm07/b0rOp5gdcDNJBe/6pXxRf+U5q3mL8Z/cHnwahLCB/ceExpgJjVKa7mfX+u7LQt6d3y47+tc2w4aa8p8NvcP1bT1sqexG4MC87NiBnTuzcdk82TY81yvvk7VS1ejugf2e4vqdlP9whU8qj7A3RGfctuxOf0+d0L2WJqUSKrbeqnv8L8biv7Xmrwi1rpzqSKBnJn9uwFxqCwmA9cdlcXcMdE+mW808FmhWFXVfX/+978P9Kc/xwykhZMQQoiB+awcvnJNoyTljMN+QBRCCDH6ZE6cS8vSt2gjhHHOAqofP4Wujla9YwkhhF/4neUVXjb/mZ6y/rVZbq7VCi29qoWwyFhvRgMgPToIgLq2bmyOw++lbNv0MgCFMccNqSjaOW4pxWoK68uG9/vFiqImbus4j9Pdf2XCabf5bN4FWTFkKPXMa36Xuuoyn807nJk6tLbIani6zkk0isHAjtQL2aDmsrXa/wrFMfXfAaBmHX3YY3sCEgBwtFZ5NZOnBXaVE6u0ExGi/57V/ZUVq93cs7WsgVpnKL2qhcxpR3llLleQ9h5k6JHtZfTUWfANZxtXcbp185DGOS6ykauMH9Gx9yYQb1NVlefaZ/Gi8zia1HAMikrRitd9Mvf/Kv/2JZ42P8TVcfmEB/Z/qw3QCpFjzvgNdtXEtJ41bHzvsQGdv/6th5jatwGbambJGZdiMfW/DBdiNTEuIQyAzeX+//vS28VurnL8kn/PfFOuHw9jPluSe/fdd/tqKiGEEEPU0NnHs5WJPO3+FV+evkjvOEIIIfxU1uR5FLtfx/DuOeQ68tn12Cmk3fwxwaERekcTQghdTaaYCcadbGosAY467PHt9WUkA02GGFK9vNctQGyIlQ+sv2ccZdSVfEn6uGkHPba7s40J7StBgYh5Fw9p3rmZUbyyroJ1pcO3C0VFcw+3v6Gt6Jsx6wjCowa2SmkowoPMPBn8NLnOXaxfm07CUt8VqYer0J5KACxxWTon+a+pqRF8W9jI1so2Lpmvd5r/am5tI8TZAgpkzj18VzFHSBJ0gKGj2gfpPCfCpuUNSsjWOUn/ZcUEk0AzTUXFnOv+PeNiA/gsOsErcxlCtD3ozb3SuUBPprqtAPTFThnSOMcZNjDf/DLrS1uBC4ce7DB2VHfwWdcYVlqyiZz+E15eV4W7ZS5HeH3mH7MUf8rxxk2EhU4b1PmZE+ewNut65pX8nfHb7qNmxokkZYw77HmVRduYvPMvoMCWcbcwb/ysAc99fuQu0pr+g2HNbJj80GDi+0SP3cmXuxoAOHmaf3TuEIMjhWIhhBA/8v6WGlxulelpEWTFheodRwghhB8bO/VIityvwfvnYra1ctu/v+ORq04gyCLbxAghRq8+azQ4wNFe16/jv2/d+n0rV29TFIVgkxury0lrVcEhC8XfbivG6Z7BBHMdOTOOGtK8czMiudC4nLkNBXS0v0xYeP/26/MXTXUVND59AeG9lxCTkMttx/W/laSntCYthIpdmEq/AqRQfDhxTm2f1fAk3/9bHcysBBNnGFYxubgN+KfecfZZVd7NLbbHODauk2cSDn/B3xCRDDWg9rV5P5yHuJxOEl11oEB0aq7ecfptvLmGtQE30aEGMsX2DNPSvfdeYQrTCtCB9mavzSEOL6ErH4DgMQffK7w/AlImQyVEde72RKzD+mJXPQCLc2KZe/RR3LLuS9SKDmraekmK8N0qfrfLxZhOratL+OQTBj3O7AvuZtcDyxnvyKfilStJuOPbQ66addht9L5+FUGKjR3Wacw59zeDmnd8uJPZxm0UNPhub+nBWLduDdHOOgxR6UxJCdc7jhgCn7WeFkIIMTyobje2NU+RRBNLZ6ToHUcIIcQwkD19ETWnv86Vyh/4vAKuenEjvXaX3rGEEEI3jkDtIr7aVd+v4517W7f2BsR5LdP/agtMBaCvvuiQx71c4ORmx018csTrKENc7ZwQEcT1lo843bia0s1fDmksX+toa6b9X2cw053HYwFP8dLlswfcytITIvde8B7btQmX8/Btw0ezzvYWItH25YxL95+i4KR4K49anuBy+yu0t/lPMU7b+1ohK3dav47vzj2X3L7n+ZN1+Nyw0FhTikVxYleNxKf4zyrzw0nMnIBLVQhTeomjjZkZkV6bKzBSKxSHOP2/5e1I1d7SSIqq3eSSPmloe1HHZWurWVOdFTgd3i86hm1+gjnKLo4bF018WACzM7Qbwj7Oq/X63D9UsmMNkXTQpQYydvpRgx7HaDIRdv4z9KhWJtjzWP/6vYc8fuN/fkeOs5AOgom9+NlBt2JOmrwYgDGOImx9PYMawxci1t7PKust/D5uJYqi6B1HDIEUioUQQuxnT94abux9ki+tv+C03DC94wghhBgmcmcs4m9XHEewxcjqPc38/V9P0tfbrXcsIYTQhTtYKxQbuxv6dbzSWQOAMyTRa5n+lz0sQ5u7peSgx9S297J6j1bIOnNGqkfmrQmfCUBP4QqPjOcLfb3dVD5xBlmuEpoJJ/TCF4kL12d/07HTFtFBMGF0U7R1+Pwd6qG+ag921UgrYYT60er1qLhkapR4ACq2r9I5jUZ1u1lVqN3Ysiinf6tV42Mi6cNKXXufN6N5VFNlAQD1hniMpuHT/cYaEESjQWtzvz7gBubEqV6bKyRaex+KcEuhWC8VO7TXhSolgfDo+CGNlZieS7cagFVxUFWc54l4B1Vbvpurep/nVcufODrDCsBPcwz8n+l5pqy81qtz/6/GrZ8AUBQ8HbPFOqSxksdMZMekXwKQs/sp9lQf+He7/PztzC5/BoDCWX8gPmXsoOdMyhhPC2FYFCeleasHPY43dba3MKFrHQCZM47TOY0YKikUCyGE2E/Tdy8AsCt0PuER3rtLVQghxMgzMz2KF66Yw3mWVfyy4bcUPnqGFIuFEKOSMVRbkWWx9W+1oKVHa1GthCd7LdP/MsVoq+kCu8oPesy6rz8kiyrmZEaRGhXkmYkztNVRkY0bPDOelzkddnY99lMm2vPoUgNpPetVUsdO0i2PyWyhOEQrtrdu/1S3HMNBkTuFXNuL3Bb9hN5RfqQ2ZAIA3SXrdE6iKSvYxDL7lTxoeYaZ6f27DpC492aJpi47Nufw6CTTU1cMQKvVd6+1nhKk/LeDQHqK97q/hcdqY4cqvfJ7vE66S7T3x/q9rxNDYTAaqbRkAtC0Z9OQxzuU8tVvA7DbOomIWO2GgyXjk7jIuJw59nXUlBZ4df4fCq3Riu32tEUeGW/20ttZFnYuZ9rv4bZ3C3G43Ps932N3csPHLVznuJUVEWcy69SrhzSfYjBQHqT9rtG22z9uKPpfu799HavioMKQzJhJQ2uRLvQnhWIhhBD7OOw2chq0iw3GGRfonEYIIcRwNDsjiktOWkQfFqb0bWD338/y63ZZQgjhDdYIrVAc3M89Hh+w3sKxtgfpzT7dm7H2E5yo7dkabas64POq28207few3Porbk7Y6bF5k6ceC0CWo5CernaPjesNqtvN5icuY3rPd9hVE+XHP8vYqUNrA+oJzsyjAYisWalzEv9W3tKDG8O+wpc/cSRMB8DasEXnJJr6LR8To3SQG9RJgLl/rVIjg8z8yfICL5nvo7Gq2MsJPaPaFsBq1wSaIybrHWXAWoMz93091G0ADiUsPIo33UfxpPM0mtqlUKyHrpYaXKqCI2GaR8ZrDxsHgKPauyuKg8s+B6Az7dh9j8UkpLIrYCoAFStf9ur83+vt7iSnbwcASTNP9siYisHArCv/TntACtur2nni6z37PX/vx7sobeomL+RIpl79jEfm7EvQ2oZbar1b4B8s0673AKhOPsmrr0nCN+RfUAghxD47v32bKDpoIoKJC8/WO44QQohhasL8kyg5/ll6VQtTe9ex6+9nY7cNn7aEQggxVEFRSbhVBdXt7NfxJZ0GitUUouN8t8otNl1bqZTgbjjga/SevNVkuCuxqWYmLz7LY/MmpudQRwxmxcWezd94bFxv+OrFe5jT+hEuVWHHEQ8zccEpekcCIG32qQBkOoppb2/TN4wfq2jRblRL99RqeA+KGDsfgNSefFS3+zBHe19Q5bcA9KT2f/WdoigcZcxjkTGP9po9hz/BD3yhzuECx+8omXSz3lEGzLz0KbYGzafojA+9Oo9iMPBw4M3c7zyfRrvv92EX8Nu+i5hkexbTrMs8M2C8tjI1uHWXZ8Y7gPa2ZnL7tgOQNG/pfs91j9Vugosp/8hr8//QtoLdbFWzqFISSMny3E0hCeEB3HPGRADWf/0+RVu1lb6bVn3G52u3AfDQOVMJD/LMz014jnZjWmp3nl+8T/xQe0sjE3q0le9JR8hCo5FACsVCCCH2cW99BYDi+JMwmS06pxFCCDGcTVpwGsXHPkOfamZazxp2/v1sHHab3rGEEMInQjJmkG17iZNt9+F2H3ovSYfLTWOX9vqYEB7gi3iAtspnq5rNp+7Z1DQ0/uj5pu9eAmBH6ALCI6I9Nq9iMFAVpq2m7C781mPjetozK0u4bfd41rvHsXHK3cw44RK9I+2TkJbNHYF3M9P2T1ZX9Oodx28dVfwXHjP/ncnGMr2j/EjG5Pk4VQMxtFFfffB9wn2hr6eLnF6twJM4Y2A3Q7Sb4wDoaarweC5vKG/235sHDicpYxzTfvUp2dM900r3UGJCtT1dm7rsXp9L7K++o4/6Dhs2JYAJmZ65eSxg8umcbvsjNzhv88h4B1L03TuYFRflhlRSx+5fnM1efD5O1cBY1x6qind4LcP3vqgN4hz73Twx8VWPr3Q9fWoSf8jYwcvmP2L+4Frqq/aQvvxqPrPewW+m21kwNsZjc42ZciRtajCFriRqGps8Nq4n7P7mVSyKi1JDOunjZ+odR3iAFIqFEEIA0NZUx6Su1QDELbxc5zRCCCFGgskLz6Do6KexqWamd39H3qM/weGQC05CiJEvOjQIF0acbpX2Xschj22ur+JPxme5wfwh0cG+u1lTMRi4M/Jv3OC4ldJu637POR12sr/fkmb6+R6f2512BC5Voau5xuNje8I7m6v400e76CCEjUf9m7lLvXdxfbCCJpxAF0GsKPpxkV9oJvWs5TTjWpKDD32zhh4CgkIpM2mthKvy9d2nuGjjFwQoDhqIIm3c9AGd2xOotdl3tlZ6I5pHqW43Dc2tAKRHD79CsS8lBCkk00hHS53eUUadbZVtAGTHhRJkMXlkzLFjstiuZlHZBc1dXrpxt+ATAGoSlvzoqai4JPIDtdeWqlWveGf+H1hVpBVVj8hJ8PjYiqJw5tJLaCKCDHcllmcWE0Mb7cZILjntOI/OFRAUwmUxr3Gh47dsrD3075K+Zi5cBkB96kk6JxGeIoViIYQQAGxY+w0OTOwxjmHMpLl6xxFCCDFCTF58NgWLn8CumtjQHsptb+ThdPlX6ywhhPA0i8lAxN7Wg42HuSjbVl3IhaYvuci0HINB8UW8fTKigwEoa95/H8qdK98jmnZaCGPiQs+1nf5e7PwLmWr7F9d3Xkqfw+Xx8Ydi21evs+OdBwG4YkEm1y3J0TnRgS3KiQVgRWETqup/hVC9Oew24txaET02LVfnNAf2SdbvmNP3D75wzdA1R/fOzwAoi5g34NV3rpBEAJRO/7zp44famuvZoFzMGuuNpIZLS+VDuarjcb4LuIWkolf1jjLqBK39G+9Yfs+loes9Nmaw1bTv5oiCuk6Pjfs9u8NFZGcBAJHTzzjgMX3ZWvvpuErvtp9uaGqmrr4WRYEFWZ5b3ftDEbGJVC96AIBIOrGrRpxnPEVAUIjH55qeoXV02VLR5vGxB6u5y8bFHddyo/0mkhdfoXcc4SFSKBZCCAHA42UpzLb9ky1z/qp3FCGEECPM1KPPYdOJH/BX9UKW5dVx+5vbcB2mFasQQgx3d5pe42Xzn+ktPfRqwe5GbSVeuynWF7H2kx4ThIKbuv9pPe3coq34KYw9EbPFeqBThyQjKY7A0EjsTve+1VP+oGD9F+R8eyO/N73I3WMK+d0p41EU3xbv+2tuZhQ3m9/nHz23U1G4Te84fqehqhiT4qZPNRMdn6p3nAOKz5lNA5Fs0flnIL5xDQDG7GMGfK4SngKAtafWo5m8ob5c25/VqEBAgO/a/A9HriDt/UjpkY4FvhbesIEZhmKywjx7Y+3ZYbv5s+lZbFvf9Oi4AOvLWjm27wEuMd5PzowfrygGGHfU+ZSr8Sy3TWRPXavHM3yvfNWrbLZew7/CnifSi11aph59HuuizwRgc/ZNjJ26wCvzzEiLBCC/tNor4w/Gpzvr6HJbKUs8gdQx4/SOIzxECsVCCCEobuhkW1U7dkMgRy04Uu84QgghRqD58xfwjwtmYjIofLq1jA+f+h0up1PvWEII4TWTlD0sMO7E0bjnkMc59rZs7Q6I90Ws/Sy0f0e+9QpO2f2bfY91dPeQ3KEVHqMXeGdfXkVRmJsZBcC6kmavzDFQpfkbSPr4UgIVO9sCZnPRZdf7fIX3QARZTBwftJtphhJqN3t3hdZw1FK5G4A6YwIGo1HnNAc2LTUCgLyqdt26rdS39/CJfQrb3ZlkzRnY/sQAAdFaET7U1uDpaB7XVVsEQKPFM/u+jmRKiLb3tLnXv/ZFHelUt5s0m/baFZUz36NjzzGXcKHpS0Irv/bouADLd9WjYiBx/IKDvt6GR8dzd/q/udd5IR/t9N73lVLyNUZFJTjG+z/ns69/jupLVjPvov/z2hyz4g18abmdl1vOo6er3WvzDMSybdqNQadOSdI5ifAkKRQLIYTg47V5gMpR42KJCfH8igEhhBAC4PiJCTx+/jSesPydM+sfZ9PjF+N2+VfLUSGE8JQ+i9Yu0NlxmD0eO7SWrY7gRG9H+pHI2CQCFTtRtqp9j32S38RC2yP8Ouhuxk7xzgoZgFMiq3jbcjeLNl7vtTn6q6ZsN8FvnEMY3RSYxpN949teWUntaV0piwEIrPhG3yB+qKe+GIC2gBSdkxxcVmwIN1g/4R/cR0XhFl0yrCxu4UHnedwV9zgRsQN/DQqJywDA6Or1cDLP+/6mne4g/1xh7k9M4dqNSwF2/7iRZ7SoLsknnG7sqon08bM9OnZAyhQAorqKPDqu6nbz5U7t95jjJhz6hrdTp2rF22XbvdOq3u1ykdmxAYCwCcd7ZY4fMhiNJI+Z6NU5EuPjCTXYMCsuSrd/59W5+qOptoK7qq7mBuN7nDLJ9zc4Cu+RQrEQQoxyLqeT8zZfyCeWO7k4Ry7WCyGE8K4TJycRMe8SnKqBOW0fs/HxS6RYLIQYkZx7W3fSWX/I48zde1u2hvl+ZUZMurZ3a7y7AYdd20v5nc3VODCROuf0Ae9XOhDjM5KYaShiXO827LY+r81zOM31VbhePJM4Wig1pJF43fsEhYTrlmcg4qZrK0Cze7Zh6+vROY1/UZtLAOgL8d+ioNGgcLJ1G0cbt9KUv1KXDCuLtNbCC7MH1/o+OnMKuX3Ps6Tvr3633/j/MrWXAeCOyNQ3yDAQEJEAQIjTey2CxY/VFawGoMw8BovVs+3R43O0wnOqs2Lf+70nlOxcz7t9V/Any4ssGHvoPYGPmxBPkNFNfONqSop3eSzD90rzNxBNOz2qlbEzD9wCe9hRFKpCJgPQUaR/obj4m/8wwVDO6YHbSI32/J7MQj8julC8ceNG7rnnHo4//nhSUlKwWq2EhISQk5PD5ZdfzqpVqzwyzx/+8AcURenXn2+++cYjcwohhKfkf/cBcbSQZGhh3vQpescRQggxCsw8+XK2zn4Al6owp3UZG564AtWtT8tFIUYC+ezrp4K11p3Gw7TuDNrbstUa6fuVj7EJ6fSqFsyKi/qKIqoaW1hX2oyiwJnTvNu2MS1nOq2EEqjYKdnume/Rgerq7qbl6TNIVWuoI5agK98nPHr4rJDJnDCbJiIIUmwUbfhC7zh+pa+3G4dqhEj/Lgp2RE/Tvqje6PO53S4XrsLPCaSPRTmDKxSHBweimIMAqGvX74aP/gjp0dr8m2PH6JzE/4VGaTcuRbilUOxLzspNALRETPb42Ilp2XSqgVgUJ1VFWz02bsPGd4lROpgY0kmg5dBt/sMDzTwf8Sz/ttxP/bfPeCzD9xq3fgxAUdBUrAFBHh9fL/bEWQAE1m/SOQmEl3wIQOuY03ROIjxtxBaKFy1axOzZs7n77rv54osvqK6uxm63093dTVFRES+88AILFy7k0ksvxW636x1XCCF0Y9v0MgAFMSeMqF+khBBC+LdZp17Nlpn34VYV5ja/x/onrpRisRCDIJ99/ZcxTCs4Wm2HLhRHOLQVfYGxaV7P9L8MRiN1Rq3dbEtlAZUfPcgKy638KmEzSRGBXp+7LHgqAK35nt8z8XBsThfXvJrH631zaSEM+wVvEZ88vApIisFAafhcALryP9M5jX+53/Azcm0vYJtysd5RDikgQ1vlF92+w+dzl+St5nH3vay23sL0lNBBjaEoConh2srHmnb/bj8d69Da3YYlj9M5if8Lj9VuFAqnW7oV+FB4Sx4AhpSZHh9bMRiosmjvcc17Nnts3Jiq5QA4x57Yr+ON404AILn6E49/9guu0joz9KYu9ui4eoscp20Dkt6zU9fPy3WVxYx35ONWFcYsvki3HMI7THoH8JaaGu3NPykpiZ/+9KcsXLiQtLQ0XC4Xa9as4aGHHqK6upqXXnoJh8PBK6+84pF58/LyDvl8ZqZ/38kohBhdOttbmNi+AhSIPOJSveMIIYQYZWadfh0b3C5mbvkdc5veYeVTwRx57WMoiqJ3NCGGDfns67+se1t3BttbDnqM261ykv1+YtRm/p053VfR9tMWkAI95fTUFZJc8QFphkZmpkX4ZG5b8nwoXEVQ7TqfzPc9l1vltte38l1xM1ssp3PmRXcwaWy6TzN4ijL2GNj0GbENq/WO4jdUVaWipQcXRlLiIvWOc0jJE4+ENZDuLKenq92nbc+btn3CWKAseDLTzeZBj3OlcRmp5rWw+xrI8s/CfHefneXOaWQY6hmfPl7vOH4vLDKWN12LaVLDOLOzh0RZVOB1TpebIlskEUoU8bnzvTJHR/g4aNqJs+bQv8P1V33VHrJdxVrhcMHSfp0zbvG52DbdRZq7mpL8DYyZNNcjWfp6usjpywMFEqef5JEx/UXmpCOwfWgmUumgsmQnqWM9v+K8P8pWvEwCUGCZyIRk+T1/pBmxheLc3Fzuvfdeli5ditG4f9uDefPmcfHFF7NgwQIKCwt59dVXufbaa1m0aNGQ5500adKQxxBCCF/Ztfwl5ih2yg0pZE8b+mugEEIIMVCzz7yRDW4XOdvu528VY/nmo1387pTxUiwWop/ks6//Co5Kxq0q4HYe9Jimbhtt7kA6lBRiIyJ8F+4HbGEZ0PMdEXs+IM1dTa9qYcIxvlkpEjNxCRT+hazeHTgddkxmi9fnVN1uPn7mD6wsmYzZGMzTF89i0mH2VfRnY+aeSsvGe8i3xxPa3kVcuOwZ2Nxtp8fuQlEgJdK7K+OHKi5lDA1EEae0ULhjDRPm9W9VnieEVmur7+zpQ1t9l0Mls415rGn0/J6jnlLR2sedzquJCDKzNXL4/rz7isFo5K+BN1PfYWOBzUSi3oFGgeLGLm62X0+I1cT27GlemUNJmARN7+DuqPHIeGWr3yYeKLSMJze+f9tnhIZHsSV4DtN7vqN+zSseKxRvKm/hA+dlzLeWccY4fW688xZLQCAFlmxyHfnU7fhWt0JxVOkyADrHnq7L/MK7Rmzr6WXLlnHOOef86IPy92JiYnjooYf2/fdbb73lq2hCCOE3QgreBKAm/UwUw4h9SxBCCOHnZp99C18c+xlb1GyeXVXK/Z8UoKqq3rGEGBbks6//Cs2YSrbtJY6zPYDLfeDXtO/39IwLDcBk1Of3cXviDD51zSbFUQrAzvBFhIT5ZhVm5sS5dBBEiNJL2Y61Pplz7fO/5LSaR3nF8ice+elEjswe3kWjqLhkLot+lZscN7OyuE3vOH6hcfc63rPcxf1BL2M1HXrPTH9QHaytcG0v9s3PAEB3ZxvZtp0AJM88dUhjOUO1/WwNHdVDzuUt5c1a++T0KFkZ218xIVYAmrpsOicZHbZVtgEwKTkMg5d+H7BO+ylT+p7mZvuNHhkvsETb8qA19dgBneeaeBYAqTWfeayV8rel3bzuWsKq8XeNyOublYnH8apzCZu69OmSUVNaQI6zEJeqkLX4Al0yCO8aeT81A7BkyZJ9X+/Zs0fHJEII4XvVJbuY4NihtYg55gq94wghhBjlfrJwCn88U1uh+N3K5ax87k7Zs1gID5HPvvqIDglEVYy4VWjuPvCF9p6SNfzZ9CwXWFf4ON1/mSaeyU2Om7DvbTpnmX6+z+Y2mkxsD1nIx6455NV0en2+da/dx/zKZwDomXQhp0wbnu2m/9eROXEArCxq1DmJf+iu3sk0wx4mGSv1jtIvtrhpdKtWWloO3qbe04rWf4pFcVGjxJOSNXFIYxnDtf1srT11nojmFbV1tVixkx4drHeUYSMxCFKURjqa6/WOMioUlFcDKlNTIrw2R3ZaEp1KCE1dNho7h3YDQFdHC7m9WwBImtu/ttPfG7/op/SqFlLUWvbkeWbbhJVFTQAsHOY3fx2Ma871/Nr5M95rStVl/q931fCuawGbA+cTk6BPBuFdo7pQbLP99wXxYHdfCyHESPVmscrl9l/yTvglxKdk6R1HCCGE4OJ56dx/YiIvW+5lUeWTrH3ul3pHEmJEkM+++jAaFKKC967I6rQf8BilZisXmr5koXuTL6PtJz0mmMWGbUQpXTQRwYQjfdtScOfs+7jecSsfNcV7dZ7Nn77A7F0PALAm7WrmnvMrr87nS4tyYgGVysKtuF0uvePoztFUAkB38DC5mD3vOibbnuXenjN9NqWt4AsAqqLmwRC3+7BGpwEQam8Yci5vmbDrYXZZL+cnvW/qHWXYuKbzcVZZbyG++HW9o4wKF+66gY3W61gcUOy1OYIsJjL33iyxq7ZjSGOtKazjWdfJrDHOIi17yoDODQ6NYFfoPACqNiwbUg6Apvpq5jW8TpZSzYJhvJXEocxIjwBgd30nnX0On8//cpGZ2xw3ULzkSZ/PLXxjVBeKv/32231fjx8/3iNjHn/88cTFxWGxWIiLi+Ooo47i/vvvp7W11SPjCyGEJ7jdKm9vreNr93RMS+7QO44QQgixz3lHzWBXznUAzK96hjXPjZwL+ULoRT776ueXxld51fwnbCXfHfB5d7u2T6A9OMGXsfaTGBbAEtN2AMpij/bJPsE/NHdMNAAbylpwH6RF91DVlBaQveZODIrKupizmXfZA16ZRy8zUiNYbr2Dt923UrJjjd5xdGdqLwfAHZ6hb5B+mpiegKoYqG7rHfIqv/5KatJW8ZlyjhvyWGHx2sr8KJf/rmgP6qrAoKgERMpuu/3lDNIKbkq3//67jhR9vd2kO8uIUTrIGJPj1bkuDV7Lf8x/ho3PDWmcj/fYecB5Pl/PfHxQrZ67593OybZ7+V3TcUPecqhs3Yfcbf43TwU9ua9l+kgTFxpARqSZSZSwa9dOn85d0thFfm0HJoPCiZPkNXSkGrWFYrfbzf3337/vv8855xyPjPvFF1/Q2NiIw+GgsbGRb7/9ll//+teMGTOG999/3yNzCCHEUG0oa6GypZcQq4kTJup3UUoIIYQ4kHkX/p61Y28DYH7FU6x54dc6JxJi+JLPvvoar5Qy35iPs+nALb/N3VqhWN27x6ceDAaF3KAu7KqJ2CXX+Xz+iUlhBFkMRPZVUFxW7vHxnQ47Ha9cTqjSS4F5AjOveWrE7V9oMRvp2rt6tnHrxzqn0V9Ij9Zy2hw7Ruck/RMaYGZsbAgA2yq8f7NNVWsPl/Xdxj3OSxk79+QhjxeVqP09R9JJb3fXkMfzhmi7tn9ySGK2zkmGDyVEa2lv6m3SOcnIV75zHWbFRQthJKZ593s0J6CNI407CarbMOgxnC43XxVoHQSOHT+4biCz5iykzJxFVVsf26vaB50FwL3nawAa444Y0jj+7kHzM3xo/R2uLa/4dN6tKz4kV6lgQVY0kcG+vZlQ+M7I+s14AB5++GHWr18PwNlnn83MmTOHNN7kyZO56667+PDDD9m0aRNr167lxRdf5Pjjjwegra2NpUuX8sknnxx2LJvNRkdHx35/hBDCk7o+uZtfmV7jgnEKgRZpPyiEEML/zLvoD6zNvAmA+WVPsOalu3ROJMTwJJ999WWzaiuyXB0H3uMxsE973Bypb4vc3Bteo/Vn60mfMMfnc5uNBp4L/iffWG+nZd2rHh//35+uJMJeS6caSNgFz/t8xbSv2NKPAiCseqW+QfxArEO7ASMsybsr8zzpZyGr+NJyO8Fr/uL1uVYWNbFHTWZ78nmERUQPebywiGh6VCstaggN9dUeSOhZdlsf8W5tVWxsWq7OaYYPU6hWAAywNeucZORrK14HQGVgrtdvZApMnQpAdFfRoMfYsW0DM2zriQ9UmZkeObgcFiPH7C0yL9s2+NcN1e0ms137+wudMPQOCf5MTZ4BQHCDb7crmbHzXj613sm1sXk+nVf41qgsFH/77bfceeedAMTFxfHPf/5zSOPdeuutbN++nXvuuYdTTz2VGTNmMHfuXC655BI+++wznnxS693ucrm46qqr6OvrO+R49913H+Hh4fv+pKYOkz1VhBDDQm93J3Mb3uJ60wecmSn7VwkhhPBf8y79E2sy9rahLvk7X745tN/bhRht5LOv/lyBe/fK6z7w3p3hDq14ERij7//34NAI4lOy9AsQPxEAS/Vqjw67qbyFP67u40Tb/Ww78p8kZY7cIlHyzFMByLbtpLuzTd8wOurpaieGNgDi0j3Tat8X0iMsZBlqCW7a4vW5VhZprzsLs2M9Mp5iMHB26H+YYXuaavfQC8+eVl9ZhFFR6VUtxCSk6R1n2LBGaC1mg50tOicZ+Qy12s99T8xUr8+VkDMLgBRXJba+nkGN0bfuOZ63/IW/h7+C0TD4Pc7PzjHzkPkJztt0Hm7X4K5PlhVsJpZWelULY2cdO+gsw0F07pEAZPTlD/rva6DKdm0kw12BXTUxYeGZPplT6GPUFYp37tzJWWedhdPpJCAggDfffJO4uLghjRkREXHI56+55hquvPJKAGpqanj77bcPefyvf/1r2tvb9/2prKwcUj4hhPihnV++TIjSS40ST+6c4/WOI4QQQhzS/MvuZ23a1ax0TeKGTfE8u6pU70hCDAvy2ddP7F2RZer58R6PqttNjFtbqRURn+HLVH4nfPxRAKR3bUN1uz0yZkefg1te24pbhaOmjePI487yyLj+KmXsJKqVeCyKi6J1h1/RP1LV1lRT7o6jhTDCozxTCPWF6HHzAcjoK8Dt8szPwIE4HXZOKvoD5xq/ZnFWqMfGjYkIB6Cm/dA3COmhtXI3APXGhBHXdt6bQqK1bcoi3N5vhz7axXVqe84GZc7y+lzxKVl0EIxZcVFZtG3A56tuN6kNWqtn8/iThpRl/oRMTjBsIkutoHDT14Mao36Ltt1CUeAUAgKDh5TH32VMmEOPaiWMHioKvX9TEUD9V9pNpjuD5xAeGeOTOYU+RtW7Y2lpKccffzytra0YjUZee+01Fi1a5JO5r7nmmn1ff/vtt4c81mq1EhYWtt8fIYTwFOvO1wEoTzkdg1HaTgshhPB/8y5/kE0LnqYPK39cls+Lq8v0jiSEX5PPvv7DFKZdaA+0/XiPx/aWBgIUBwDRiaN7lduYqQvpU81E005F0XaPjJn35BXMbf+MlIgA7jlzkkfG9HdVUVqx0bZr9BaK9ziiWGx/hMuj/qN3lAFJHz+bPtVMGN1UlXivveeebSs4jRX8xvwKk1M9d9E/MTwAgLr2Xo+N6Sm9DcUAtAak6JxkeAmLSQa0vacddpvOaUauzvYWUl1a6+WUiUd6fT7FYKDKou0r3rpn84DPL9+9mWS1HptqZtwRpw0pS0BgMAXh2v/nto2vD2qMwMoVAPSk+Ob3XD2ZzBZKreMAaNi5wuvztTfXM7nhQ23uedd6fT6hr1FTKK6pqeHYY4+lpqYGRVF47rnnOOOMM3w2/4QJE/Z9XV3tf/t1CCFGh/qqPUzs0+46S1typc5phBBCiH5SFG45YQLXH5UFqLR/fDfr3nhQ71RC+CX57OtfAiK0QvGBWnfW2IOY1PcMS42PYg0I8nU0v2INCGKPVWsTXLf9yyGPt/GDJ1nQ9gEPmJ/myZPDCAswD3nM4SBo6tkATGr6jPa20dkutqJFa6WaEuO51bK+YLZYKbNkA1Cf79kW7D/Uuv0zAPaEzMJoMnls3CNd63jJfB+5u/1vm5AiZwJvOhdRF3OE3lGGlYjoBN50LeYJ5+m0dHbrHWfE2lXVyDOuk/nKMJ/oeN/czNAZrhUbXbUDvymlbt07ABQETSc4NGLIWUxTfgLAmIblA26nbLPbyOjNByB++olDzjIcdMRq+xRTtcHrc+Uve5QgxcYeYyaTjhzaTQHC/42KQnFTUxPHHXccJSUlADz22GNccsklPs2gKIPv1y+EEJ5S8uVzGBSVfPMkkscMn/2ahBBCCEVR+OUJ43hgSj03m95jbv6fWf/W3/SOJYRfkc++/ickJhmXquA6QDvluo5eugjCFqHj3sB+pDN+DgDGiqEVyWpKC8jd9AcANqRfxaQps4cabdiYdORpvGM5nYvtd/LattHZLra8WSsUp0cNv5sv2qKmAOCu9F4BIKJ2FQDOzCUeHTfR1MUiYx4xHTs8Oq4nfOOcyC+d19I0wbfvh8Od0WTiAevNPOg8j8Y+6UbnLZubTNzrvJA3x/zZZ3MqCZNpUUNo6nYM+NzIquUA9I3xTGF2/JGn00EQcbRQsOGLAZ27qaKTebbHuNn4OzLGj473+qAxWueQhI6Btw0fCHtfL2NLXwGgZcrV0rZ/FBjx/8Lt7e2ccMIJ5Odrd5fcf//93HDDDT7P8f38AElJST6fXwghVLebpPL3AOjK/am+YYQQQohBUBSFc867nLXx5wEwZ8f/seGdR3VOJYR/kM++/ik8dRLZtn9zTN9fsDv3LxbX7t3LMyEsUI9ofidk3GIA0jo3D3qfYqfDTsfLlxGi9LLLPIHZF/vuwrs/UAwGnMffy2Y1hxdWl+Hw4l63/urkwt/xruX3zFb9r2B5OKY0bX/SyDbvtJ5ub21irL0AgLTZp3p07IBorX1+mK3eo+N6wnC+eUBvMSEWAJq6pPW0t2yvagNgSkqEz+YMmH0xM2xP8fve81BVtd/nNdVVMM6p7fk9ZsFSj2SxBgRREK69/3cOsP30yuImegnAlHPcqClkpk9dwoOOc/ll7xW0dtu9Ns83a9biUqGBKKaeeIXX5hH+Y0T/BPX09HDKKaewebPWb/+3v/0td9xxhy5ZnnrqqX1fL168WJcMQojRbXt5AyvsudSpUYw/Vu6kFUIIMTwpBgNzr/kn62K1NmUzt93N+vf+oXMqIfQln339V3iQFYNBW4nV3L3/hfbI4ne51/QMi5QtekTzO1nTl/Cs62R+a7+MipbBtTnd8NJvyHXuolMNJPzCFzCZLR5O6f/OmJZEbKiV2vY+PtpWo3ccn0vtLWC6oZjYMKveUQYscfxCdrtT2GBLw+YcWAvW/ihZ/xEmxU25IYWEtGyPjh0anw5AlPvH+7Hrye1yYWopwoKD9GgpFA9UUjCkKA10NPvfDQAjRvkaQulhamq4z6bMSYzAoCi0dNtp6Oz/TQCFa5Zp/2vKITYpw2N5rNO0ovPYpq9wOZ39Pm9lUSMAR2Z7br91fxcRE8+nURewTh3PlkrvdA5RVZW/bTOy0PYoX895Cos1wCvzCP8yYgvFdruds846i++++w6AW265hT/96U8DHueFF15AURQUReEPf/jDj57Py8ujuLj4kGM8/fTTPPPMMwAkJCRw1llnDTiHEEIM1VvbmrjLeQX3575FaHiU3nGEEEKIQVMMBuZc9y/WxZyNQVGZteW3bPjgSb1jCaEL+ezr3wwGhZgQrWDV+D8XY2Mb13KB6Suy1VI9ovmdwOBQPkm6iS/cs1hX2jbg8wvWfc6cCu37b/fse0jKGOfhhMOD1WTk2pmh/MH0Alkfnzvo1dnDkcvpJN7dAEBMaq7OaQYuKXMc55sf4beOK9hV2+nx8R27tZaxtdHzPT52dNIYACLooqe7w+PjD1ZTXQWfmG4nz3olSWGjY69yT7q+8++sst5KXPEbekcZkZrrq3jC/lu2Wn/G5FjftfcOMBvJjAkGYFdNe7/Pe7ZtFsfY/sL2iZ69GXHCgtPZwjhedBzLxuLafp3T0lDNfY03cofpVY7MGl3XOGekRQKwubzNK+OvLGqioK4Ti8XKiUs8u02B8F8mvQN4y/nnn8/nn38OwNFHH82VV17Jjh0HbztjsVjIyckZ8DybNm3iqquuYsmSJZx00klMnjyZ6OhonE4nBQUFvPzyy/tyGI1Gnn76aYKDgwf3f0oIIQbJ5nTxwd67yc+emaZzGiGEEGLoFIOB2dc9w7onXMxtfp/pm37N8pjpHHvEXL2jCeFT8tnX//3c+BppljycJb+FlNP2PR7QWweAOSJZr2h+Z+6YKDaWt7KutIVzZqf2+7yOPgdffPEROcCG8OOZferV3gs5DCydmYZ1zTcEOu3sXPMJExeconckn6iv2kOS4sKuGolNytQ7zoApisLUlHC+3t3I1opWpqVGeGxsVVXpbmvErSoEjj/WY+N+Lyw8im41gGClj6aaUtKyp3p8jsFoKt9FHNBoiCFlFHYYGCpnUCx0AN2NekcZkap2riIaqDImkx4W6dO5b7R+wmzrW1SuvwBy/3jY43vsTlbtacamJjN5/iKPZjFbrLw26Rle31hJ86425uYe/v2/ZMPHzDKUEWKCuPDR1S1gXpKJ3i1ricr/Dk641+Pjr/ziXYwkcM7sDMID5Qab0WLEForfeeedfV9/9dVXTJky5ZDHp6enU1ZWNqi5XC4Xy5cvZ/ny5Qc9Jjo6mmeffZbTTjvtoMcIIYS3bFz9Jdl9eVSFTmXB2NHTkkUIIcTIZjAamX3986z7x2W8Ux/PW8ua+XtILadMSdQ7mhA+I599/d84yphq2MX6xj37PR7m0C68B0T3vyA60s1NC2WLYQdjCj8FHuv3eXe9t4P3u46nMDyLe6+4yHsBh4mI2ETWxZzM3Ob3cKz6O4ySQnFrZQFJQL0hnlTT8LzkOTU1gpW7aykvLYQFnit2lzX3cHnPTcQYr2DFPC98PygKTcZYgt2VtNeVgZ8UirvrigBosSaTonOWYSk4FgBTrxSKvaGndCMADaETSffx3AmhRlKamqhrzO/X8SuLmrA53aRGBZITH+LxPKdOTeT1jZV8uqOO/zt9IibjoRvhuou+AqA+dj7D77agoZkZ2cNPLH+np82K0/EHj26zUbJjHb9t/CUXWuIxzl3rsXGF/xuevzX5kZNPPplnn32WNWvWsGXLFurr62lubkZVVaKiopg6dSonnngil112GWFhYXrHFUKMUsFr/8Zb1jV8E38dRoPn7x4WQggh9GIwGpl940u8+fZ2XJuquPm1LRhwcdIUuRwohCfJZ9/Bs1ljoBdcnXX7PR7tagYFwuN9fXnYf81IDuIl8/2YnG7qKm8hIXXsYc95d0sV72+twWhQuOyCiwkN9+2qLH+VdOLPcf/nfab1rqV891bSx03TO5LXdddrN2O0WJMZrrdfLAos5RrrVTTuiQF2eWzc7/fyzE5PIyjIO90e2i1xtPS20tHe5pXxB8PZXAJAb/Bw/Y7QlyksHgCrrVnnJCNTUNM2AFwJ030/d+o0KIXo7sL+Hf/lb3jcXE1t2tUoiuLxPPPHRJMUpDKtdwV5G61Mn3vwVcuq20162zot1/jjPJ7F36WPm0GnGkio0ktx/gbGTl3gsbGbv/gbY4DmsPHMjBtdLb1HuxFbKFZV1SPjXHbZZVx22WUHfT4uLo4rrriCK664wiPzCSGEpzXXVzGxez0okHnkOXrHEUIIITzOYFB4YOkU3G6Vb7bsIuWtU9lS9wumHy+rysTIJ599/Z87OBbawPCD1p2d7S2EKr0ARCdm6BPMD4WERVJoHkuOs5CqLV8ctlBcXbKLuPeuIU25jKVHL2RmuhSJv5eaPZUtwfOZ3rOaus//Rvq4l/SO5HWuZm2/776Q4bvd0phxUwn8wk6aWkN7SwPhUXEeGXf1bm0rqkU5sR4Z70Bezvorr2+q5eeBOXiubDE0lo5yANTI0bbm0DOs4QkAhDhadU4y8qhuN6m9BQBEZPt+65yEnJmwAlJdVfT1dhMQePAbSFxOJ5NaviDS2MGO1Bu9ksdkNPC3yLeY1/wu69eWwCEKxRVF20mnGZtqJnv28V7J488MRiOlgROY0reJ5oJVHisUN9aUMbXtC1Ag9OjbPDKmGD4OvYZfCCHEsFf05fOYFReFphzSc2foHUcIIYTwCqNB4S8/ncr9yauYbChl4nc3s/XL1/SOJYQQEKKtyDL/oHVnS41W0OogmKCQcF1i+auWmNkAuEq/O+RxToedzlcuZ4GyjUdCX+aGJVm+iDesWI+8CYCpTR/T0lCtcxrva7IZqXDH4o4avt8LETEJVClaca4ib5VHxrTb+nig9Ce8Yfk/jkrx/ErA7yVEaO1oa9t7vTbHQIX1VgFgiTt8dwLxY0FRSQCEuaVQ7Gl1lUVE0YFDNZIx0feF4rikTNoIwaS4qSrccshjCzd9RSQddBDMOC8WZkOmnw1ATus3OOy2gx5Xu/kjAIoCJhIYHOq1PP6sJ1a7vmusXu+xMYuXPYRFcbHLPJGcGUd5bFwxPEihWAghRrjYPdq+da3ZS3VOIoQQQniX0aBw9LUPsynkKCyKiwkrbmDbV2/qHUsIMcqZ967ICvhB6872xkoAmg0xumTyZ4FjFwKQ1LbpkMdteOnX5Dp30aEGkXDBE4fdz3A0Gj/vRIpMYwlQHOxe9qjecbzuKWUpi+yP0jXtKr2jDEld6CQAukrWeWS84s1fE650M9ZQy7hM77W6T4oIAKC2vc9rcwxUvFNbSR2ZMk7nJMNTeKxWKI5QO3A5nTqnGVlq8lcDUG7KOORqXm9RDAaqLdpNNS0lmw95bPuW9wAoDJuP2WL1Wqbx806mmXAi6GLXdx8e9LiAihUAdCUffNXxSBecfQQAiZ15Hhmvu7ONiTVvA9A3+3qPjCmGF/ktWgghRrCSHevIcpVgV42MO+YyveMIIYQQXmcyW5h68xtsDlmERXGS++11bP/2Hb1jCSFGscBI7UJ7qLNl32MFgTOZ1PcMjyXeq1csv5Ux8zjcqkKqWkNTTfkBj9m17jPmVDwLQOHse0jKkCLQgSgGA60zbuafztO4p3oGfQ6X3pG8RlVVypt7AEiPDtI5zdA49+5XGtiwzSPjte/4FICSsDkYjEaPjHkgY9RKXjLfx9XVv/PaHAPR1tXLvxwn8bZrIQnpuXrHGZYiYhJ53XUUT7pOo6WzW+84I8ravgx+57icLUnn6ZahM0J773TXHrrYmNTwDQBK7slezWM0mSiOOQYA27a3DniM3elmd08IjWoYMVNP9Goef5YxZTFuVSFZraeprnLI4+Ute4IwuqlUkph6zPkeSCiGmxG7R7EQQghoWPk8Y4AdIUcwIyZB7zhCCCGET5gsVibf/BZbHj2L6d3fkfPV1eQZjExeeIbe0YQQo1BIdCIuVcH1g/2ka9v76CIIa3Sqjsn8U3hkDHtMmWS5SijbspyYpCv3e769tYmIT27AqKhsCD+B2af+TKekw8P0Ey7m1m0p1LT38f7Was6dPXz37z2Uth4HnX3aise0qOFdKI7MmQeFMK13Df/3xnd0KiE4XW5mtH7C5K7vUNxODKoTo9uBQd37terkj0G/popYnG6Vn9re5VznB5hwMVPtBgXUMUd7NXdcaADpxjw6nL5fHXkg5a02HnOdTVyQlaWjtD3tUJnMFh603Ehzt52j+xS8t8P16LOyMYC1ruOYPGWyfiGSZ7K1bhNFfRHMO8ghFYVbSXNXY1eN5Cw4y+uRQmeeA5+9w7i2b7Hb+rBYA/Z7fktFK3fYriAm6GrWT5rv9Tz+KjQimlJjGpnuciryVhGTMPjirsut0lmsrXCvGX8FqV68oUj4L1lRLIQQI5TT5Sa4fiMAhmkX6JxGCCGE8C2zxcrEm99ha9B8AhQHQcvvZHVRnd6xhBCjUETqeLJt/+Yo20P7VnTWdWh7eCaEBeoZzW81Rc8EwF62dr/HVbeboueuJpFGqpV4cq94Uo94w4rZaODyBZkAPLOyFPUHNyyMJI2717DBei0vBD5CgHl4X+ROnzifHlVr7/rllt28tamK97bW0Fu9g+ndq5jWu5YpfRuZaN/GeMdOxjl3M9a1h9qmZvY0dlPe3IOjt4tYWomkA4viop1gso4406u5o5O177MwuunubPPqXP1R3jIyVpjrLSZE+15s6jr4nrFiYNxulR3VHQBMSYnQLUfIrPM50/5HHuo67qDvDTXrtM5MBYHTCA2P8nqm3DnH00gkYfSQv+q9Hz2/sqgJgCOy4zCM8i0nPhpzF0f0/Z3P7FOHNM5nO+v4Wfd1XKb8iSmnXOuhdGK4kRXFQggxQq0oauTKvrs5LqiYfyyW/YmFEEKMPhZrAONvfoevH/8ZdzUfR/NLW3n+8tnMGxOtdzQhxCgSGmDBbDJhc7pp7LSRGhXE3PKnmGyqIYbrgGy9I/od24yrOOn9Gai2XD79weMfbigks30PTsVA56lPkuyDi9YjwblzUln95Xtc2voeed/czJQlP9E7ksd11hSSo3QQbxr+7XEDAoPZcdwzdBZ8y4UpU3AFRGA2GEjqPId13dNRjGbtj8mMYe/XBpOVPyTMQrGGYTYqBNrGssd2FUazBaPJTFRCOuGhEV7NHRIWRacaSKjSS1NNKcHjpnt1vsNpq9pNplJLZmS8rjmGu6RgNz1KPR3NdZAta4o9obyskNOcn7HTnEN2XIhuOcbGhWA0KLT1OKjr6CMx/Mc3r61rtBDgHoMt8wSfZDIYjeyJPZbYxjdpzF8Fx+zfmru4YBsQxMLsGJ/k8WcJ4+dTs2MbmyvbBj2Gqqo8vaIEgCnzjydQui+MWlIoFkKIEertTdWoGEiefjxmi1XvOEIIIYQurAFBzL/lJbL+vYmqwkaueGED/75oPDNz0vWOJoQYJRRFISbESnVbL41dWqF4aucqMk1l5Jku0jueX5o0eQa73muGhl5auu1EBVuoaO7hNx+XY7PfwwOzujh7lnfb6I4kYQFmrknYzbz6beStfRxGYKHY3rQHgK6gkdHOfdKRp8ORp7N/Y9UxAxhBn5somo0xhLor6agvA50LxblFT/O19WPW9FwLzNI1y3B2Q9djzLIuZ23xbTBPxzbJI0jT9uXcZ36WXeYJmIz6reAMMBvJig2mvL6FwooaEidn7fd8c5eNRxqn87A6ndXHL/FZrqCjbmHRf46gpSmZjQ7Xvi4R7c31/KPlapqt4ahpG32Wx1/NSIsAYFtVO3anG4tp4Cus83bmUVpZhcUUxsXzMzwbUAwro3t9vhBCjFDtHZ18k18NwNIZKTqnEUIIIfQVYDby1MUzWZgdw5HOtWS9fAQFG5brHUsIMYrcYnid1yx/xFX8DQBR7kYAQuPlppUDiQq2kBOvrbJaX9qCw+Xmlte30GVzMi0jljPOlq11Bir95J/jUhUm27awJ2/t4U8YZoxt5QA4IzL0DTLKdVi01bu9TZU6J4GQ7goAzLFZhzlSHIozcO/Kza4GfYOMIK6qTQC0R+pfeP+V4RV2Wq/AsvnZHz33ZUEDqgqTksNIivRdC/dJ4yfhCEuny+ZkRWHjvsf3rP8Yo6LSYwwlPk5Wt2fGBHN14Ff8Q/kLJXnfDWoM1ye/Zo31Jv4vcxexobLIaDSTQrEQQoxABZ8+yQrTdfwm4ksmJoXpHUcIIYTQXYDZyNMXzeSG0BVEKF2kLLuI3Ru/0juWEGKUyKGCeYZduJv30NvVQThae9yoxEydk/mvpXHVPGT+J+bVD7PhhV9xdM3TRAbAw+dOw2hQ9I437CSmj2Nr6GIAWpb/Tec0nhfSoxUmzTEDWXUrPK0vMAEAV1uVzkkgxlEDQFhSjs5Jhjc1OA4AY2+TzklGjsi2HQCY0vRf6R4cGYdJcWNpyv/Rc2WblxNCD8eO9237doNB4ZTJiQB8sq1i3+OOIu2zW13MET7N468UReHkgB0cZ9xE265vB3x+VfEOpnZ9R5Bi44gFi72QUAwnUigWQogRKKLwLaKVTqakhKMochFFCCGEAAi0msi+6V3yLZMJUXpJXHYhRVtW6B1LCDEK2AK0FVnuznqaassA6FYDCA2L1DGVf5sV0cNS40qm1rzG3IpnuMn0Hv+c30mKD1c1jTShR98GwNS25TTWlOkbxsOi7VpRMCRR9vzWkys0mWY1lK4+h645+nq6iKMFgLi0XF2zDHfGUK1QHGCTQrEn2G19ZDi0PWETcvUveAanTgUgprtov8f7erq4sfoONluv4ZTkXp/nOiM3mKfND/H73WfT19OJ6naT2qp1wwjMPdbnefxVX/xMAMw1GwZ8bvWnD2FQVLYFziE9d6ano4lhRgrFQggxwlQUbmWcswCnamDssZfrHUcIIYTwK0Eh4aTftIxd5omE0UP8++dRvG2V3rGEECOce++KLEN3A+0NWovcZmM0ikEuyxxM+vTjAIihDaOisj78ROadcJ7OqYa3nBlHscs8EYvionjZyFlV3NfbTaz6fVFwnM5pRrfKSTcy0/YU/w44X9ccdeUFAHQQTHhUnK5ZhjtrhLZKPMjRonOSkaF4yzdYFQcthJE8ZoLecUgaNxuAFFc1fT1d+x4vWLOMIMVGixLJ2HG+b5E9aUwqk0yVRCqd7Pr2LapK8klSG7CrRrLnnODzPP4qNHsBACldeQM6r62pjimNywAwHnmzx3OJ4Uc+kQghxAhT/e3zAOwMmkVMQprOaYQQQgj/ExwaQepNH1FgHk8Y3cS+ew57tq/WO5YQYgQz7F2RZelr2rd3Z4dZiheHEpOUTqWSBEC1Es+EK5/UOdHI0DvrOgAm1LxFT1e7zmk8o7a+nrXu8RSrKUTGJOodZ1RLjAwEoK7d9ysQf6i1UisU1xsT5YacIQqO0n6mwl2tOicZGdrztfbJpSEz/OJ7MzohlRbCMCoqFQWb9z1u3/EhAOUxi3TJqRgMlCccD4C6811qNn0EQJF1IkEh4T7P468ypx6JUzUQTzN1lcX9Pm/XskcIVOwUG7OYOP8ULyYUw4X+r0ZCCCE8xu1ykVmt3RHmnCx32wshhBAHExIWSfKNH7PbNI5wuql593d06twmUQgxclnCtRVZgbZmHO11APQG+nbPv+GoJvdSKpUkuk/7FyHSptsjph5zPquNs7jXcQHvbKvXO45HlPYFc4Hjd9wc9aRfFF5Gs8TwAABq2/p0zWFr3ANAR2CKrjlGgrCYZAAi1A7cLpfOaYa/kLp1ADjTjtQ5iUYxGKixanu7t5VqhWK3y8WYVq3jUtCU03XLFjNX60wwvnMNQSWfANCRvFC3PP4oKCScMlMmANV5/dunuK+3m+yyVwFom3q1vG8KQArFQggxouSvXkYCTXQQzMQlUigWQgghDiU0PIrEGz/h7oA7uKT3Nv61okTvSEKIESowUlsZG+Zq4ZOwc5jY9ywbsn+ucyr/N/fcO0m9exc5MxbrHWXEMJpMFB/7LK+7lvCv1dW43KrekYasvLkHgLQo2b9ab4khJv5tvpd31VvpbNevVfFWxvOwYylVicfrlmGkiIxN5DXnUTzpOo22rh694wxrfQ4Xl3TfwmX2XxE/52y94+zTFTEeALVOa19cuOUbYmijUw1k3NyTdMs1dsoRVCkJBCp2tnZH86TzVKKmn6FbHn/VHDkNAEfZun4dv+bbzwhTO6knmqknypaFQiOFYiGEGEF6N/4HgF3RxxIQGKxzGiGEEML/hUVEM++Uy1Ex8MyqUho69V0BI4QYmcJiknGqBhxuqG3vpZtAwmMS9I4lRqmfzEwhPNBMeXMPX+QP/1XFFU2dAKRHS6FYb8FBgUw1ljDWUENLTaluOVbbMnjUtZS+nNN0yzBSmC0B3G+5gb86z6VR347iw97m8lZaXVbyg+eSnj5G7zj7uNKP5F3XAlY7sgFo3fw+AIVh87BYA3TLpRgMVCadCECs0s6TlksZO2mObnn8lTF9Ln2qmbaOjsMeq6oq9+ZHs9D2KOum34/ZYvVBQjEcSKFYCCFGiC6bkz+2HMdTzlMIO+JKveMIIYQQw8aJkxKYmhoB9m4+ffffescRQoxAEcnZ5NheYrHtYfY0dgP/bdEqhK8FWUxcOjuOC43LCVh2vd5xhuyMgl+y3no9RzjW6B1FAM2GWADaG8p1y1DerL3OpkfLDfSeEBOiFZOaumw6Jxne1pQ0A3BEVjSKouic5r8ip53ObY4beK5tOqqqklj3NQBqjn6rib8XN09rP73EsJVjMgMxGvzn781fxM89h8m2Z7mx4xL6HIduD/9NYSNFDV10W+M46oSzfJRQDAcmvQMIIYTwjE/yatnuSKYz5mqunrFI7zhCCCHEsKEoCnctjib9zfOI2NNNVfF8UsZO0juWEGIECbKaCbSY6ba7uK39frpMASSZxgGyT7HQx8XTIghf9yKWPheFm74mZ+YSvSMNWqStijiljYbIKL2jCKDDEgd95fQ1Vegyv9NhZ0zbWlQlnvSoQF0yjDTJQW7sSj0dzXUwNkbvOMPW5C13c6fJRFLSzXpH2U9WXDAmg0Jnn5M1Jc38oucXHG/awm0LluodjTET5/DV+wtZ1ZvBEfK9d0ApsRFEhgbT0Gkjr7qd2RkHfy98/+vVgJnzZqcSFmD2XUjh92RFsRBCjBBvb64CYOmMZL+6M1EIIYQYDmZNyqU2cBxmxUXd+3fpHUcIMQLFhlox4+R04xouMH1NfLisdBP6iU3KYFvEcQB0fv2IvmGGwO1ykeDS2mdHpozTOY0AsAVpbfVdbdW6zN9QWczz5vv5wvIr4kOlraon3Nz9KCustxFR/K7eUYat7s42lvR8zrWmD5mZEqp3nP1YTUZyYgPJUqr5z+frqCGGwvTzCI/SvzCrGAxEX/ofwpbczOlz5DX+QBRFYUZaJACby5oOetye7at5pO5SnrP8hcuPSPNVPDFMSKFYCCFGgJqy3ZxXeQ8LDXmcNSNF7zhCCCHEsBR8yp9wqwqzOr+iaOtKveMIIUaY63mDb6y3AWBTzUREy2pioa+oY7Xvx2md31JTtlvnNIPTWFuGVXHgUI3Ep2bpHUcArtBkAIxd+hSKm6sKAKgzJmAwGnXJMNI4AvYWDLsa9A0yjO3Z9CVmxUUtsSRl+F/B826e5EvrLxlTpd0McNwE//kdZWpqBLcem4PZKKWsgzk9pIBPLXcwa8PPD3pM6/K/ARAeHklyVIivoolhQn66hBBiBCj/+jnONK7mjtBPSI6Q1kpCCCHEYGRNnsfm8GMB6Pvk9zqnEUKMNGOpIlnR9idsNESjGOSSjNBX1uR55FmnY1RUKj75m95xBqWpQitw1xtiMZktOqcRAKYIrVAc0Funy/w9dcUAtFmTdZl/RAqOA8DYe/DViuLQund/A0BVxCy/fP9XY3MB+IX5TS4wfsmxuXE6JxIDMSY5jlxDJendeahu94+er6/aw9T2rwAIO/o2X8cTw4D/vSoJIYQYENXtJrXifQB6x5+jcxohhBBieEs660/YVSOTbZvJW/Ge3nGEECOII/C/LRzbzbE6JhHiv9R5NwIwqe59OtqadU4zcN31WlGwRYqCfiMgJp1mNZQ2pz5tn9XmEgB6Q9N1mX8kMoRqRUNrX6POSYav6Ma12hcZC/UNchBBqdP2fX2V9UtSo2V7jOEkY/IR2FUjMbQdsENI6UcPYVZc7LRMJnv6Ih0SCn8nhWIhhBjmdm9YTopaS49qZcIxF+odRwghhBjWkjJz2RJ/NgAB3/4Rt8ulcyIhxEjhDv7v6pzeAP9p6ShGt8mLz6bMkEqI0kv+ssf0jjNgrqa9RcHgVJ2TiO8F5R7DTNtTXO+4VZf5AzrLAVCiMnWZfySyRGj7Tgc7WnROMjx1tDWT5SgCIHXmCTqnObCkcbP2fd0SP1/HJGIwAgKDKTVnA1C745v9nuvqaGVCrdZS3DHnBl9HE8OEFIqFEGKY61j3EgA7I5YQHBqhbxghhBBiBMj+yf/RqQaSb4/j0y0lescRQowQxtD/FocdwUk6JhHivxSDgYZJP2O5azrPlcficP24ZeVQlRXt4A/v7+CrgnrPj+2MYo1rAn2xkz0+thicxPAAALpsTjr6HD6fP7xP2xs5IH6sz+ceqYIiEwEIc7XqnGR4Ktn0BUZFpUpJICHVP78vYxJScaLt6R05W7oVDket0dMAcFas2+/xHcseJ4weKgzJTFki/7biwKRQLIQQw1hfTxfjm5cDEDjnIp3TCCGEECNDVFwyr87/kFscN/LA11XYnZ6/aC6EGH0s4Qn7vjaES6FY+I8pp93Indbf8nlnBh/n1Xps3LrKYtY/cj6p/zmS6nVvc+1/NrOjut1j4wO85jqa8x2/o3eSfB72F0EWE+GBZgBq2/p8OrfqdhPv0vZGjkrJ8encI1lYjPaeFaG2H3D/U3Fo5VXVNKph1ETO1jvKIVWf+wV5R79E1oyj9Y4iBsGSMQ+A2NZt+x5zutyEFmnbFdaOvxKD0ahLNuH/pFAshBDD2I6vXyVU6aWWWCbMO1nvOEIIIcSIceGS6cSEWClv7uG1DRV6xxFCjABB0dqF9nY1iM7xsqJD+I8As5GL52UA8OyqUlRVHdJ4bU11rP3ntUQ+M485bR9jVFSOCijG7nRz4yub6fTgKtOKlh4A0qKCPDamGLp7zc/ypeV2end/6dN5Gzt7uMtxOX93nkV82jifzj2SRcQm86pzCU+5TqO9u0fvOMPO0+1zmG37Jw1H/F7vKIeUPn4mkxedoXcMMUipU5cAkOEspauzDYBPdtRxdu9v+YNyPVNPuUbHdMLfSaFYCCGGsTWl7RS7kyhLOV3uChNCCCE8KNhq4pZjxpKq1BP0+S/p3vthWwghBissJhmHaqRdDSY2MkrvOELs56J5aWSYWjit7h/kr/t8UGN0d7ax5vk7MD42jXn1r2JVHORbJlNwytuc8otnSY4IZErrcvKeuNgjqxI7enrp6ekGIC1aCsX+JMXYQpahFltTqU/nLW+187Z7EW+EXoLFGuDTuUeygMAg7jVdx0POc2jqHdqNJKNNW4+d/NoOQGFOTpreccQIFpuUwWbDJN5zH8mu0mpUVeWZlSXYsBCx4HICgkL0jij8mEnvAEIIIQanvqOPh6vH85D6F74+bYHecYQQQogR57zZqRz5+d/IVCtZ8+Z9zL/iAb0jCSGGscjETMY7XsLpVtgQKUUt4V+iQ6w8EL+cuc0fs2VFG8w7od/n2p1uXl1fQe4XFzNf3Q4K7DGOofvI3zJ58dkoBm2dytOnxZDzxj8xd7hY99ZfmXvOr4aUuaFoE7sDLmM3GYRYTxnSWMKz+oISoRfcbVU+nbe8WVvtmi43DnhcbIiVzj4njZ12xsbpnWb4WF+oFeyyYkOIC5ObF4R3PZ/9OB9uq+H2RgvGghK2V7ViNZm4eF663tGEn5MVxUIIMUy9t6Uatwoz06PIjI/UO44QQggx4phNRlpm3QrAlPIXaGmo1jeQEGJYs5pN/OH0yfzm5FxiQ616xxHiRxKOvw2Aqd1rqCzadpijweV08t6mEo5+6Bvu/mAnT9mOp1qJZ9Osv5L5m41MWfKTfUVigIkTp7Ap5xYApu98gOJt3w0pb0dNkfaFSYov/kYNSQTA2Fnj03ltZRs40pDH+DC7T+cdDZKD3KQrdbQ31+kdZVgJ/+5PrLfewA0Ra/WOIkaBGWkRAGyuaEX58Ea+tPyC23MaiQ6R3zvFoUmhWAghhiHV7aZ67VsEYGPpjBS94wghhBAj1vQTL6fYmEWw0kfhm3/QO87/s3ff0VFVexvHn5n0HkgIEHrvSJUqTbooAopgoQiKiorl2q5eAVGxYy+ggB0FEZAiXXrvvfceQgqEtJnz/pGXuXBJQhJmcmaS72etrDXk7Nnnmc3OzDnzOwWAh3uwaTk92qqS2TGATJWrVk+bA5rJajF08u+Psmxn2O3avHCSjr7dULumvqfjFy6rWIif2t35kKJe2aaG3R7J8tZITfr+R5sCm8vXki7/aQ8rMe58nvOmnDsoSUoMZJ/Y3XgVKSNJCricv0XF6kd+1E++o9U+ZUG+rrcweDZpjJb4PafwA9PNjuJRos6vU5QlTuWji5sdBYVAw3JFZJVdYQdnqX7SSlW0nlbnpnXNjgUPQKEYADzQ/q0r9Mblt7Xc7xndUSvC7DgAABRYVi8vXW49XJLU4OwfOnlot8mJAABwHZ+WT0mS6sbMVlzM9UW+XWvmavfolqq3bIgq2A/rIZ+FeqlTZS15oY0ebFZePr7Zn7VksVpVcdBEnVIxlTZOa9+3A/N8v2KvuIz736aFlc/T8+E6AZEZ92INTTubr+sNSTomSfKL4oAcZ0vzj5QkGRfPmJzEc5w/c1wV7EckSRUadTY5DQqDGiWCtcZvqD72+liStDmwmcpWrWdqJngGCsUA4IHOr5goSToc2lBhwUHmhgEAoICr06q7tvvVl6/FphN/vmZ2HAAAXKZmsy7a71VJAZZU7fprjOP3B7ev0ZZ3O6rGnN6qkbZDyYaPVpXsp5CnluvxttUU6Oud43WERRRXQrexSjO81ODiEq2d8n6esgZeyigKekdUzNPz4TphxTPuh1nMdi7PBwLkRbH0U5Kk0Ohq+bbOwsIelHFjYq+kcyYn8RyHN8yVJB2ylleRYiVNToPCwMfbWyk+Yf/9d8unTUwDT0KhGAA8TGpKsqqdy9jY9GnwgMlpAAAoHPy7jJIkNYxfoANbV5qcBgAA17BYrYq75VFJUpUjk7T/VIymfPUflZ/cSbdcXqN0w6o1Ed2V8Og6NRvymcIi8nY51WqN2mlD1WckSRu27dL2E/G57qNoasb9b0NKVslTBrhOZHRFnTdCdNAoqYSLF/NlnQlxMSqiRElS8XIUip3NGpxRKPZNzvvl4gub9ANLJUlnIm41OQkKk/NF6jke12zKmezIGQrFAOBhtv8zRUWUqHMqolot7zI7DgAAhULlerdpZdgd+iS9p95bm2p2HAAAXOaWzgN1SKU1Lb2Zen6ySF8dLSObrNoQ0lanHvxHTZ76QVGlKtz0epr0fU1vlfxc76Xdq6G/bFRiclqOn5uakqzi9owzGyPLUBR0NwFBIWrvNV7dUt/WqaT8WefZw7skSecVpuDQIvmz0kLENyzjoJDAtFiTk3iOkhfWSZL8qrQ2OQkKk1K93tbaInfo0L3zZLFS/kPOMFMAwMNYtvwiSTpQoqu8fXxNTgMAQOER/dA4fWHco7n7L2nlgRiz4wAA4BI+vn6a12aa3kx/SAkKUnTlW3Sg7wo1fH6aylS5xWnrsVitGvrQfSoVHqAj55P07z825/gyxadiYvWHrZVWGnUUUaKM0zLBeUqEBUiSTsUl58v6Ek7ukySd84nOl/UVNgFFMy6dHJp+weQknuHcycMqaz8hu2FRxUadzI6DQiSyRBndOuwXVajVxOwo8CAUigHAg8SePalal1ZLkoq3GmhyGgAACpfykUG6v0lZSdK7c3bn6z33AADITw/fVkkj76qlXx9pqh8HNVH16jVcsp7wQF99fn99lbGe18A9j+X4fsWHLnrrxfQhGhn+NmdMuanoMH9J0qn4/CkUp5zbL0m6GFA6X9ZX2IRGZoxrESOObeAc2HA4Vt+k36F//NoorGgxs+MAQLbYkgIAD7Jl2Qz5Wmza71VJFWo2NjsOAACFzlPtqqi17269cfYpbZr3g9lxAABwCR8vq/o3L69mlSJcvq76ZYvo3drH1MC6X/V3vKf9W5bf8DnHYjOuZ1w2ItDV8ZBHPVL/0kLf51Vmx5f5sr7l3s31QtqjOly2R76sr7ApUqyUfklvq29sdygxOcXsOG5v8UlvjU5/QKtuedvsKABwQxSKAcCDfHiyllqljNGuhiPMjgIAQKFULMRPj5U7qVusB1VszbtKT+N+xQAA3KxmfV7RpsDm8rWky3/aICXGnc+2/bnTJ+SnVJUtSqHYXUX6pquS9ZT84g/ny/o2XIrQZFsbeVXifrCuEBAUrLesj+mj9N6KuWQzO47bW3Uw4z2seaVIk5MAwI1RKAYAD7HndKK2n0jQSUsJNW/F/U0AADBL7Xte1QWFqoxxUhumfWZ2HAAAPJ7FalXFQRN1SsVU2jitfd8OzPbytm33valdfgN1++W5+ZgSueFVJONSxYHJp/JlfUfPZ5xlXi4iKF/WVxhFhvhJkmIucqBkdk6eOKpycasVbE1R4wpFzY4DADdEoRgAPMSf6w9JktpWj1JEsJ/JaQAAKLxCwopqT7XHJEkVd3ympEsJJicCAMDzhUUUV0K3sUozvNTg4hKtnZz1/YrDk0/IajEUHMn9aN1VQGQZSVJo6jmXryv5cpLaXJyl5tbtKlfE3+XrK6xKB9pV3nJK8edPmx3FrR1fO10/+r6jyYHvKdjP2+w4AHBDFIoBwAOkp6Wq3/qeGuvzoe6v4Wt2HAAACr36PZ7VSUuUiumCtkx5x+w4AAAUCNUatdOGqs9IkurvzPx+xYbdruK2jEJVkdLV8jMeciG8eAVJUqQ9Jtuzw53hzJHdetvnO431GaOiQRxY7yr/SvpI//g9r9ADM8yO4tYsh5dJkuKibjU5CQDkDIViAPAAO5bPULTOqbHXXrWoW9XsOAAAFHp+/oE6Wf95SVLtg+MVF8OZFQAAOEOTvq9pU2Bz7TdK6fW/jyghOe2a5efPHlegJUU2w6LiZdk/dleR0eUlSYGWFCVccO1ZxRdO7JUknfEuKYuVr7tdJdU/4367RuJZk5O4L8NuV5n4DZKkoGptTU4DADnDJycAeIC0jT9LkvYU6yRfPy6jBACAO2hwxyM64FVBIZbLWj/tU7PjAABQIFisVlV45EcNDXhXKy+E65U/tskwDMfymCO7JUlnLZHsH7sx/8BgXVCoJCnm5GGXriv5zH5JUpw/lyJ3JSOwmCTJkuT6y4l7qpOHd6mEYpRqeKlyo9vNjgMAOUKhGADcXHzcedVOyLhsTUSLAeaGAQAADlYvLyW2HqWnUp/U0EMtdPxCktmRAAAoEMKLROqjB5rK22rRrG2nNGXJRseyxFMZRcHzfqXMioccOuJdQdvt5RWbmOjS9VguHJIkpYaWd+l6CjtLSJQkyTc5xuQk7uvExrmSpAO+1RUYFGpyGgDIGQrFAODmdi/4Xv6WNB2yllXlui3MjgMAAK5yy23dFFP+TqXYpDHz95kdBwCAAqN+2SJ6uXNVPe01Vd0Wd9b+zRkHUKefPyhJSgri7FF393mZD9Ut9W3tsVZx6XpC4vdIkrwiK7p0PYWdb1gJSVJgaqzJSdyX9UjGfdXjizc1OQkA5ByFYgBwc6F7JkuSTlfowb12AABwMxaLRS91qS5JmrNpv/Yf2GtyIgAACo5BLSuqbdhJBVhSFTB9kBLizmunUV5TbK0UX7yJ2fFwAyXDAiRJp+OTXbaO+POnVTVlhySpdIMuLlsPpIAiJSVJIbYLJidxT4bdrvKJGfcnDq7RzuQ0AJBzVBwAwI0d279dNdJ2ymZYVPn2QWbHAQAAmahXJlzPVzymf3yf08UpT5kdBwCAAsNitari4O91SsVUyjij/d8O1KyUevpX2mNKrXGv2fFwAyXDM+4hfTL+ssvWsWfFNHlb7DpkLa/oCtVdth5IIZHRkqRwe5y5QdzUgXOXdH/KKxplG6DKDdqaHQcAcoxCMQC4sel7k/V6Wn/ND+2pYtHlzI4DAACycHebpiqqBNW7vFo7V/9tdhwAAAqMsKJRSug2VmmGlxpcXKJaJ6dIkspFBJqcDDdSN2WTFvo+rwcOvOSydUyMb6juKW9offV/uWwdyBBerLR+SW+rcbauunQ5xew4bmfVoVjtNcpoZ5m+8g8IMjsOAOQYhWIAcFN2u6FftyboB1snpdw+yuw4AAAgG2Wq1tOGyDslSdYFI2TY7SYnAgCg4KjWqJ02VH1GkjTKZ4LqW/apTFEKxe6uSEiQKllPKSr1qEv6T06z6Z9957XFqKxqze90yTrwX0EhYRpleUxj0u9VTFK62XHczqoDMZKkZpUiTE4CALlDoRgA3NTqQ+d1Iu6yQvy81alWCbPjAACAG6jY6w1dNnxVPX2XNi/4xew4AAAUKE36vqaDfhmXFv7Tb7jCAnxMToQbCS+RcWW0SHuMSw6iW3XgvJJSbSoR6q86pcKc3j+uFxniK0mKucgZxVez22xqv/8t3ee1WM3LcTYxAM9CoRgA3FTs3PfUx2uRetYKkb+Pl9lxAADADRSLLq/Npe+XJBVZ/Y7S01JNTgQAQMFhsVoVMWSmNga10upqL5odBzkQGV1ekhRgSVVc7Fnnr2DhG3rP+xs9WD5eFovF+f3jOqUDbapgOaW4mDNmR3ErR3avV09joYZ7/6C6ZYqaHQcAcoVCMQC4oUuJ8Wp3ZqLe8flWfcpdNDsOAADIoVr3vq44Bau8/Zg2/fWV2XEAAChQwooWU4MX/lLTvq+aHQU54OcfpFiFSpJiTx50at92m011zv2l3t5L1CracGrfyNpLSR9psd/zCj4w0+wobuXs1vmSpP0BteXr529yGgDIHQrFAOCGdi78WYGWFB23lFT1xu3NjgMAAHIoNDxCu6s8Kknav32tktNsJicCAAAwT6xXMUlS4lnn3qd436YlilScLhoBqta0i1P7RtZS/TPuv2u/6IIzxD2Y77EVkqRLJZuZnAQAco9CMQC4oYBdv0mSjpXpLouVt2oAADxJvZ7/0kCf9/VK0v36fuVhs+MAAACYJtGvuCQpJda5heLYjdMkSXtCmsjPP9CpfSNr9sCMwr/1EoXiK2zp6aqUtEWSVLQ2J3sA8DxUHwDAzZw+tl81kzM2MMu1e9jkNAAAILf8A4J0R+eukqQvFu9XfFKayYkAAADMkRBSWdvs5XUuxdup/ZY8vUiSZFTr6tR+kT1LSEbh3yf5vMlJ3MehHasVqku6aASoUt0WZscBgFyjUAwAbubQwu9ktRja4VtX0eWrmR0HAADkQY/6pVSteIhCU05p0dRvzI4DAABgip01ntGdqW9roW87p/V5fP92lbcfU5rhpSotezmtX9yYT2hGoTgg1T0LxfFJafpry0lN/fp1nR5RWevG3CfDbnfpOmO2LZAkHQisK28fX5euCwBcwbmHcgEAbopht6vUkWmSpEs1epsbBgAA5JmX1aKRzbxUf87zMvZZdOZEVxUvVdHsWAAAAPkqOtxfknQq/rLT+jy+eopKS9rjX0e1i0Q6rV/cmH+RkpKkkPQLJifJYLfZdXD7Kp3b9Je+v9hE8074ym5I93tdVE+fcyoR/7fWz/xGje563GUZEs8ckt2w6HKp5i5bBwC4EmcUA4Ab2bL/qA6mF9NFI0A1b3/Q7DgAAOAmNLm1mQ76VpO/JU1HpvzH7DgAAAD5rkTolUJxstP63HLWrv32aF0s38lpfSJnQiIyCsXh9jjTMsTHntOG2d9p7Zg+ih1VQZX/7Kpmh79S1KnFshtSlahgFWvcU2si7pYkVdr4lmLPnnBJlnSbXc8m3K/6Kd8otPlAl6wDAFyNM4oBwI38viNRv6S9pD51i+id0CJmxwEAADfBYrXK0mGkNPseNYydpSN7NqlctfpmxwIAAMg3pX0vaqHv8yp68aIM+1FZrDd33lLspVS9e66JRhtNtLxjKyelRE6FFS+rn9NvV4zC9GhKmgL8fFy+Trvd0M5TCdq0eb0abX5NVVJ3q6Hlv5eTTjL8tCeooVrXaKJHW7ZV6SKBkqS01MY6+M5mVbQf1vqfnlbR5/5werZtJ+J1MSVdof7hqla+rNP7B4D8QKEYANxEcppNM7eclCR1a1zV5DQAAMAZqt/aQZv+aa76SSt1fvprKvfiLLMjAQAA5JtixYrL13pKkhQbc0pFo0rdVH8Ld52R3ZBqlgxV6YgQZ0RELoSEhGukHlFqul09L6WpjIsKxfGx57R/9QxtPXVZX5yqrpiLKQrSZW3y2yNvi12HrWV0qlhLhdTuoiqNO6i+f+B1ffj4+sl252eyTbtLjRIWaMuiSbqlXR+n5lx1IEaS1LRihLysFqf2DQD5hUIxALiJ1WtWKij5jILCSqlZpQiz4wAAACcpeuebsk26XQ2Slmv3uoWq3vh2syMBAADkC18/f8UoXJGKU+ypwzddKN67aan8FKgONYs7KSFyw2KxqFiwn07EXda5iykqU/T6Am1eGXa71k75QKH7/nScNextr6iY1DcV6OulZpXKa2X4R6pWr5nKl6um8jnos0r9Vlq9uo+anvlVxZe+qou3dlVwcKjTMjdbM1S/+8bpbMSrkho5rV8AyE8UigHATYSvGKUVfms1r9Qr8rLyBTIAAAVFuRoNta5IVzWOmyX7vOEyGra96csuAgAAeIoL3sUUmR6ni2ePSGqR536Sky7q2ePP6hk/6Xjphc4LiFwpE5guv/iTio85LZV13m3T1s/4Uk12vpXxD4t02FpGl4o300+3N1bjihHy8/ZSXoqxtzz0nrZ9tElfJndS1IIjGnl3HafkTU1JVrXLmxVoTdHBCiWd0icAmIFvJwDADZw7fVS1k9bJajFUs2kns+MAAAAnK3vPG4o1QjT/cmX9s/uk2XEAAADyzUXfKElSSuyxm+pn98q/FGhJUaIlRFWr1XJGNOTBy5c/1CK/fynwwGyn9ZmelqqSWz6TJK2JuFsnB6xV+de3q/njX6pl1aj/LxLnTUBwqOL7zNQcexP9sOaoNhyJdUrmA5uXKtCSoliFqnw1ziYG4LkoFAOAGziwYIK8LXbt8a6uslXrmR0HAAA4WfHSlfXtrTM1Jv1evTP3oGx2w+xIAAAA+SI1KONsS3v88ZvrZ8dfkqQjka24OouJUv0zbpdmv3jWaX1umjVOpY3TuqBQ1R7wiaLLV3Na35LUsmox3duwtAxDemvyCqUkJ910n3E7M85qPxRcX1Yv5iMAz8U7GACYzDAMRR2cKkmKq3qPyWkAAICrDGlbS6H+3tpzJlHTNp0wOw4AAEC+MEKiJUk+F/N+VRV7eroqXVguSQqq290puZA3tsBikiTrJecUitNtdk3flaDD9uLaXXGAgkLCndLv/3r1jhq6N3CTxiU+ro0//+em+ws9tUqSlF625U33BQBmolAMACY7sG2VKtoPK8XwUfX2A8yOAwAAXCQs0EdPtK2sptadipg1SMmXL5kdCQAAwOUsxapom728jtgj89zH3o3/KELxSlSAqjXt7MR0yC1LcMalxH2SY5zS34wtJ/VTfF3d4/WJ6vZ62Sl9ZiY80Fd9GpVShCVRDY9O0KGd6/LcV/LlS6qcslOSVOKWjs6KCACmoFAMACaLWT5BkrQjpLnCihYzOQ0AAHClAbeW1Ke+X6qNfbU2T/3Q7DgAAAAuZ63RTXemvq1P7H3y3EfcpmmSpL0hzeTj6++kZMgLn9DikiT/lJu/12+6za7PFu2XJA1qXVVBQUE33Wd2GnTur02BzeVrsSl16lDZ0tPz1M/+jYvkZ0nTORVR2Sp1nZwSAPIXhWIAMFFauk1lzv4jSfJqcL+5YQAAgMv5BwTqSJ2nJUnV9o1VQtx5kxMBAAC4VonQjMLu6fhk2e1GnvqIPrNIkmRU7+q0XMgb//CMe06HpN98oXj93J/U9MIMFQuQ+jUrd9P93YjFalWpB75UohGgaul7tH7yO3nqZ9vpZM2zNdTu8NbcLxuAx+NdDABM9M/eGHVMHq3h1qdU67aeZscBAAD5oP5dQ3XEWlpFlKgdk0eZHQcAAMClSoT5y2KRUm02nb+YkuvnHzx3UQMvP6P3bX1UtQXfnZgtOCKjUBxmxN1UP+lpqSq1brRG+3ynMRU3KMjP2wnpbiyqVAXtrP0vSVKd3Z/q1JE9ue5j6rlSejTteZ1ozrY8AM9HoRgATPTHhuO6pAD5NLhf3j6+ZscBAAD5wNvHV7FNM+6/dsvxXxRz8ojJiQAAAFzHx8uqqf6jtNPvYcUdzP19YefvPKMDRiltLf+wQsMjXJAQuRFWvJx+Sr9d36V1UXJq3i7dLEmb53ynMsZJxSlY9bo/5cSEN9a457Pa6VtHgZYUxfz6uAy7PcfPTUpN1+ZjcZKkZpWYjwA8H4ViADDJhYvJWrj7jCSpV8PSJqcBAAD5qV77B7THu7oCLSk68MfrZscBAABwqSAvmwItKUo8m/sD5ObvzPjupEPN4s6OhTwICy+qkcZgfWLrpfNJaXnqw5aeruKbPpUk7arQX8GhRZwZ8YasXl4KuedzJRs+2nYxTDM2Hs7xc7fu3KXi9jOKDvNX2aKBrgsJAPmEQjEAmGTvrI81zetlPRaxWTVKhpodBwAA5COL1Srb7SMkSQ1jZujo/u3mBgIAAHChi34ZRd602GO5et75M8c18OQI3WVdofbVo1wRDblksVgUEeQnSYpJzP2lxCVp0+xvHWcT1+nxgjPj5ViZqvU0qdkM/Tt9sEbM3p/jy6Iba8dpud8zGh30qywWi4tTAoDrUSgGAJMU2feHalmPqE2pnF/eBgAAFBw1m3XRopA79XzaY3p39WWz4wAAALhMWlAJSZI9/niunndwxR+6w2uNngqYq+ginL3pLsoGpamS5YTizp/O9XNt6emK2mze2cRXe6BDU9UoGaoLSWl6468dOXpO0bOrJUmBZeu5MBkA5B8KxQBggiO7N6pq+l6lGV6qcvtAs+MAAACTRD/wpWYYLTVr+1nHvc4AAAAKGiO0lCTJ52LuCove+/+WJMWUau/0TMi7Vy9/oIV+LyjgwN+5fu6mOd+prP2E4hSs2nf/ywXpcs7Hy6p3e9VRWctZ9dr5tLYsmpRt+8T4WFVK2ydJKtOgU35EBACXo1AMAPns8qVEJfz5vCRpR9CtiijO/YkBACisqpcIVY/6GV+cfjR7iww7VxoBAAAFj0+RMpKkwOScF4ovX0pU9UvrJUlRjXu6JBfyJsW/mCTJnng2V8+z2Q19u8tLS2x1tat8P4WEFXVFvFypWzpc75TboFZe21R86au6mHAhy7YHN8yXt8WuE5biKlG2Sj6mBADXoVAMAPnoYsIFHfyki+qkbFSS4aeAdi+aHQkAAJjsuQ5V1dd7iT442U/blv5pdhwAAACnC4osK0kKTz+X4+fsXjFdAZZUnVIxVazV2FXRkAe2gMiMB5dyVyieufWk/o4tqae9/6Pa941wfrA8qv/Q6Izir2K044fns2x3ec9iSdKJcOYjgIKDQjEA5JP4xCQd/7SzaqVuU6IRoCNdf1S1Ru3MjgUAAExWukigupeKV5QlTkHLRslus5kdCQAAwKnCoitpq72CNtvKy243cvSctJ2zJElHirWRxcrX2G4lOEqS5JMck+On2OyGPl2YcdnmwS0rKCTAzyXR8iIgKESx7d6XJDU+N1W7187PtF1kzFpJkrViq3zLBgCuVqA/YdevX6833nhDHTt2VOnSpeXn56fg4GBVrVpVAwcO1PLly52+zl9//VUdO3ZUiRIl5O/vr3LlyunBBx/UqlWrnL4uAJ4j5mKK+ozfoMmXGypOwTp99++q0YR7mQAAgAzV7xmhRCNAlWyHtHH2t2bHgYdh3xcA4O4ioyvo7rS39ETqMMVcSrlhe1t6uirHZXx+Bd9yl6vjIZd8wopLkvxTzuf4ORv//l59L3ytiv4X1b9FeRcly7s6t3XX2vCusloMBfz9rFKSk65ZHh97ThXTD0qSyjXsbEZEAHAJi2EYOTuEy8O0atVKy5Ytu2G7JdkSFAAAYPlJREFUfv36ady4cfL19b2p9V2+fFn33HOPZs+enelyq9Wq119/XcOHD8913wkJCQoLC1N8fLxCQ0NvKieA/Hc6PlkPfLtaB85dUmSwn359oJKqVKhgdiwAAOBmVk98RU0Pf6mTluKKfHmrfP38zY7kdtg3uh77vgAAT9Hk7QU6k5Ci6UNb6JYy4dm23bxzlzTpfpW3nlXQvw/Kx9d9zj6FtH35DNVe8JCOWMuo3Ovbb9jelp6u42/XUzn7Ma0q95iaDXw3H1LmXnzsWaV92liRitOqMoPVbNCHjmXztx7WlEkT1Cz4jAb8+2sTUwIoDPJz36jAnlF88uRJSVJ0dLSGDRumKVOmaO3atVq1apU++ugjlSpVSpL0ww8/aMCAATe9vocfftixo9y2bVtNmzZNa9eu1XfffadKlSrJbrdrxIgRGjt27E2vC4DnOHl4j3Z9erfOnjunkmH++n1IU4rEAAAgU3XveVkxCle0cUYbp35kdhx4CPZ9AQCeomRYgCRDpy9cvGHb2Yelu1Pf1Ogqv1EkdkNBRaMlSWH2Czlqv3nuRJWzH1OCglSrx4uujHZTwopG6UiTEZKk8KPztOdErGPZisOXNNd+q/bVGGpSOgBwjQJ7RnG3bt3Ur18/9erVS15eXtctj4mJUYsWLbR3715J0pIlS9SqVd7uLbBo0SLdfvvtkqQ777xTf/755zXrjImJUcOGDXX06FGFh4fr4MGDKlKkSI7756hqwDMd3btZfr/0VHGd1zyv1qoxdJLKFA00OxYAAHBjayZ/oCY7RilWofJ9bquCQ3O+31AYsG90PfZ9AQCeYvGY/moaN1tbqj+jpn1fzbKdYRhq9+ESHYq5pC/ub6A76pbMx5TIidjz5zRrzGM6Z4TrqZFj5eN9/TbIFfb0dB19u57Ku/nZxFcYdru+++o9vXesumqWKaY/Hm8uL6tFnT9eqt2nE/X5/fXVrW602TEBFHCcUewEM2fOVO/evTPdUZakyMhIffjhfy8dMWXKlDyv64MPPpAkeXt768svv7xunZGRkXr33YwPwLi4OH37LfccAwq6g9vXKOiXu1Rc53XEWkb1B31KkRgAANxQg+5P6ZglWkWVoCUzfzY7DjwA+74AAE8R4OenAEuqFH8i23aHjp9QTMw5+XpZ1bpasXxKh9wILxKpEfZB+tTWU+cvpWXbdtO8iSrvAWcTX2GxWtXtwWfk5xegzcfi9P3Kw4o9d0qdYyboVssuNa0YYXZEAHCqAlsozom2bds6Hh84cCBPfSQmJmrhwoWSpPbt26t06dKZtuvZs6ej6v/nn3/maV0APMPejUsUMaWHIhSvA14VFfLYXBWLLm92LAAA4AF8fP104rZ3dFfKKL2wu7LOJaaYHQkFAPu+AAC3EJZxOwSfS6eybXZm0Vfa4DdEH0TMULCfd34kQy5ZrRYVDfKVJMVczHp71W6zKWL9x5KkHWUfVGi4ZxRZS4T56+Wu1eUlm87P+0B7536tZ7yn6t2AHxQZzKXQARQshbpQnJLy3w+xrI6+vpF169YpNTVVktS6dess2/n6+qpp06aO56SlZX+kFQDPtHP134qefp/CdEl7vKsr8sn5KhpVyuxYAADAgzRpe5cspRooKdWmzxbtMzsOCgD2fQEA7sCnSMZBRoHJZ7JtV/TYAvlabCpeumJ+xEIelQtMUyXLCV04n/X/56a5V84mDlRNDzib+Gp9G5fVV0Un6QXrT2q87xNJ0tmIxianAgDnK9SF4iVLljge16hRI0997Ny50/G4evXq2ba9sjw9PV379vGFD1DQLNtzSoFzhinYclnbfW9RqWFzFVYk0uxYAADAw1gsFr3UJWPf4Z81G3T88F6TE8HTse8LAHAHQcXKSZLC085m2Sbm9FFVSdsjSarY8t58yYW8eT3lAy30e0F+B+ZmutxuN/T+riIan95ZW8sN9LjvyKxWi2r0eEkpho+8LIYkyadyG3NDAYALFNpCsd1u1zvvvOP4d+/evfPUz/Hjxx2Ps7r01hVlypRxPD527Fie1gfAPc3bcVqDftisQanPa0VQe1V+ZraCQ8LNjgUAADxU80qRei16veb7PKczf7xkdhx4MPZ9AQDuIrxEeUlSpBErW3p6pm0OLp8iq8XQXu+q3MbLzaX4ZVxG2paYeeF/zvbTWn3OT2O8H1adPiPzM5rTlKlyizZWfFSSZDcsqtSoo8mJAMD5Cm2heMyYMVq7dq2kjHsoNWzYME/9JCYmOh4HBwdn2zYoKMjx+OLFi1m2S0lJUUJCwjU/ANzX36u36PGfNyrVZleVmg3V6Nnf5R+Y/fsBAADAjbRt00F+lnQ1TFyk/VuWmx0HHop9XwCAu4gsUVY2wyIfi02xZ09k2sbvwN+SpPOlb8/PaMiD9ICMM4Qtl64vFNvthj5dmHFVkYdbVFBYgE++ZnOmRn2Ha01kT62t/LTCIoqbHQcAnK5QFoqXLFmil19+WZIUFRWlr776Ks99JScnOx77+vpm29bP7783ur98+XKW7UaPHq2wsDDHz9VHYwNwL+umfqJWczqoibapR/1S+vz++vLzztt93wAAAK5WqW5zrQ9tL0m6PPs/JqeBJ2LfFwDgTrx9fLXaq77m2hrpbPz1BxJdSoxT9aSNkqQSt/bK73jIreAoSZL35ZjrFm2eO1Evx76mpv6H9XDLCvmdzKl8fP3U5MkJavrQG2ZHAQCXKHSF4h07dqhHjx5KT0+Xv7+/Jk+erKioqDz35+/v73icmpqabduUlBTH44CAgCzbvfLKK4qPj3f8cKkuwD2t/vVtNd76ugItKXoier8+vPcWeXsVurdVAADgQiXvflOphpfqpGzUtqXTzY4DD8K+LwDAHX0Q+aaGpD2no+lFr1u2d+V0+VnSdMJSXOWr5+0KGMg/PqEZZ9f6p1xbKLbbbCqybozaem3RsDKHPPpsYgAoDLzNDpCfDh06pI4dO+rChQvy8vLSpEmT1KpVq5vqMyQkxPE4u0tqSdKlS5ccj7O7VJefn981R2ADcD+rvn9VzQ59LklaXbyPWgz5SharxeRUAACgoClVsYZWR/VU03OT5b/kDdlbdJPVi6uXIHvs+wIA3FV0WIA2KU6n4pOvWzbtfGlNTRugJpWLq5SVA/HdnV94CUlSUNqFa36/ef6PamA/okQjQDV7vGRGNABALhSaT9yTJ0+qffv2OnnypCwWi8aPH6/u3bvfdL+lS5d2PD5+/Hi2ba8+OppLagGeybDbtWrcM44i8arSg9VkyFeysAMDAABcpMo9I3TJ8FcV235tmjvR7Dhwc+z7AgDcWckwf0mGzl6Iu+b36Ta7pu+36UdbR0W2HmJKNuROUNFoSVKY/b+FYrvNpiJrx0iStpd5QGFFi5mSDQCQc4WishETE6MOHTro4MGDkqTPPvtM/fr1c0rfNWvWdDzevXt3tm2vLPf29laVKlWcsn4A+cew27Xm6yFqdmKCJGlVxafVbPCHFIkBAIBLRRQvra3l+ivJ8NPyzbuUZrObHQluin1fAIC7a3Xpb+3yG6j2e0de8/sNRy4oLilN4YE+alSuiEnpkBuhxcvrx/T2mpjeUen/v326ZcFPqmA/nHE2cU/OJgYAT1Dgqxvx8fHq1KmTdu7cKUl65513NHToUKf137hxY/n6+kqSlixZkmW71NRUrV692vEcHx/uzQB4Epvd0L+nbtGZUxlnT6yp8Yqa9RtlcioAAFBY1L333+rh/bnGJLTVpHXcxxXXY98XAOAJgkOLKMCSquDkM9f8/uzSb3W/10LdVdlH3l4F/ivrAqFIRJSG2x7Wp+k9FZuUKrvNprA1H0mSdpS5X2FFo0xOCADIiQL9qZuUlKQ77rhDGzdulCS9+uqreukl5x7JFBISottvv12StGDBgiwvwTV16lQlJCRIknr06OHUDABcK91m13O/b9av60/qhfTHtKTJWDW572WzYwEAgEIkKCRcD7S/VZL0yYJ9upSSbnIiuBP2fQEAniKwWFlJUnjaWcfvDLtd9Y+M19s+3+nuokfNioZc8rJaVDQo4yCymMRUbVo4SRXth3XRCFAN7k0MAB6jwBaKU1NT1aNHD61YsUKSNGzYML355pu57mfixImyWCyyWCwaMWJEpm3+9a9/SZLS09M1dOhQ2Wy2a5bHxMQ4dtLDw8M1ePDgXOcAYI6U5CRN+eo/mrH5uLytFn3Yp7Fad7nP7FgAAKAQ6tO4rMoWDVSlS5u14o/PzY4DN8G+LwDAkxQtWVGSFGnEypaeceDbkT0bVdo4rRTDR9VadDczHnKpXGCaKllO6ML5Mxq5s6ReTXtYays8rrCI4mZHAwDkkLfZAVylb9++mjdvniSpXbt2GjRokLZv355le19fX1WtWjVP62rXrp369OmjSZMmacaMGerQoYOeeeYZRUdHa9u2bXrrrbd09GjG0XDvvvuuihThPhuAJ7h8KVH7PuuhPsnrlOazTyX7fqb2NdnQBQAA5vD1tmp0gzi1WD5KF/cEKPZsbxWNijY7FkzGvi8AwJNEFC+jdMMqb4tdZ88cU1SpCjq9ZqrKS9odWF+3hISbnBC5MSLlPdXx26QJy17W1tN1ddCvs17o3dbsWACAXCiwheKpU6c6Hi9atEh169bNtn25cuV0+PDhPK9v/PjxSkhI0OzZs7V48WItXrz4muVWq1X/+c9/9Oijj+Z5HQDyz8WECzry+Z2qm7pNSYaf6nV8QHUoEgMAAJM1a3uX9q+qpMq2A1o9ZYSaPjHW7EgwGfu+AABP4uXtrdOWoiqhGMWeOqSoUhVU9Ph8SVJyxc4mp0NupfhFSCnSmROHJdXVgOblFR7oa3YsAEAuFNhLT+e3gIAAzZo1Sz///LM6dOigqKgo+fr6qkyZMrr//vu1fPnyLC/fBcC9xMee04lPO6lW6jYlGgE60vUn1bmNSx8BAADzWb28dLn1fyRJDc78oZOH95icCIUN+74AgJsV5xMlSUo6d0TnTh5W1fS9kqRKLe4xMxbyID2gmCTpZZ9J6um7VoNaVjA5EQAgtyyGYRhmh0D2EhISFBYWpvj4eIWGhpodByjQzp85rrix3VTJdkhxCta5uyepSr3bzI4FAABwje2jW6t2ymatC+uoxs9ONjtOvmHfqGDj/xcACoe5nz4uy7k9Sms0RJGpx9Rkxyjt8a6maq+tNTsacmn1D/9R04OfSpJWlRqgZo98YnIiACgY8nPfiDOKAeD/nb5wSbHfZBSJYxSu2N5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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['font.size'] = 20\n", "j = 0\n", "plt.figure(figsize=(20,20))\n", "#plt.rcsize(20)\n", "for var in ['tas', 'temp_std', 'pr']:\n", " if var == 'temp_std':\n", " var_w5e5 = 'tas_std'\n", " else:\n", " var_w5e5 = var\n", " for version in ['2023.2']: #['2022_missing_points',\n", " \n", " if version == '2022_missing_points':\n", " path_w5e5 = f'/home/www/lschuster/w5e5v2.0/flattened/monthly/w5e5v2.0_{var_w5e5}_global_monthly_flat_glaciers_1979_2019.nc'\n", " pathi = f'/home/www/oggm/climate/gswp3-w5e5/flattened/monthly'\n", "\n", " else:\n", " path_w5e5 = f'/home/www/lschuster/w5e5v2.0/flattened/{version}/monthly/w5e5v2.0_{var_w5e5}_global_monthly_flat_glaciers_1979_2019.nc'\n", " pathi = f'/home/www/oggm/climate/gswp3-w5e5/flattened/{version}/monthly'\n", "\n", " ds_w5e5 = xr.open_dataset(path_w5e5)\n", " \n", " ds_gswp3_w5e5 = xr.open_dataset(pathi+'/gswp3-w5e5_obsclim_{}_global_monthly_1901_2019_flat_glaciers.nc'.format(var))\n", " # let's only look at the common time period!!!\n", " ds_gswp3_w5e5 = ds_gswp3_w5e5.sel(time=ds_w5e5.time)\n", "\n", " # this here is the main test, for all months and for all gridpoints, check if they coincide:\n", " # yes, they do!!!\n", " if var !='pr':\n", " np.testing.assert_allclose(ds_w5e5[var_w5e5], ds_gswp3_w5e5[var_w5e5], rtol=1e-6)\n", " else:\n", " # prcp is in daily mean values (kg m-2 s-1) and has much smaller values than temperatures, so datasets should be more similar\n", " np.testing.assert_allclose(ds_w5e5[var_w5e5], ds_gswp3_w5e5[var_w5e5], rtol=1e-9)\n", "\n", "\n", " # visual test just for HEF:\n", " lon, lat = (10.7584, 46.8003)\n", " c = (ds.longitude - lon)**2 + (ds.latitude - lat)**2\n", " plt.subplot(3,2,j+1)\n", " plt.plot(np.arange(1,13,1), ds_w5e5[var_w5e5].sel(points=c.argmin()).groupby('time.month').mean())\n", " plt.plot(np.arange(1,13,1), ds_gswp3_w5e5[var_w5e5].sel(points=c.argmin()).groupby('time.month').mean(),ls='--')\n", " j+=1\n", " plt.ylabel(var)\n", " ax=plt.gca()\n", " plt.xlabel('month')\n", "\n", " plt.subplot(3,2,j+1, sharey=ax)\n", " plt.plot(np.arange(1979,2020,1), ds_w5e5[var_w5e5].sel(points=c.argmin()).groupby('time.year').mean())\n", " plt.plot(np.arange(1979,2020,1), ds_gswp3_w5e5[var_w5e5].sel(points=c.argmin()).groupby('time.year').mean(), ls='--')\n", " j+=1\n", " plt.xlabel('year')\n", " \n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "id": "d8d163c2-1180-4c48-bcdf-b447780916ec", "metadata": {}, "outputs": [], "source": [ "# check for every glacier gridpoint if the correct climate dataset \n", "# was used by comparing it to the unflattened file:\n", "# this takes some time (we only do this for the monthly files like that, it would take too long for the daily files)\n", "\n", "# path_clim = '/home/www/oggm/climate/gswp3-w5e5/'\n", "# don't run this ... \n", "\n", "run = False\n", "if run:\n", " for version in ['2022_missing_points', '2023.2']:\n", " for var in ['pr', 'tas']:\n", " if version == '2022_missing_points':\n", " fp = f'/home/www/lschuster/w5e5v2.0/flattened/monthly/w5e5v2.0_{var}_global_monthly_flat_glaciers_1979_2019.nc'\n", " else:\n", " fp = f'/home/www/lschuster/w5e5v2.0/flattened/{version}/monthly/w5e5v2.0_{var}_global_monthly_flat_glaciers_1979_2019_.nc'\n", " ds_flattened = xr.open_dataset(fp)\n", " fp_unflat = f'/home/www/lschuster/w5e5v2.0/_script/{var}_W5E5v2.0_*.nc'\n", " with xr.open_mfdataset(fp_unflat) as ds_unflattened:\n", " ds_unflattened = ds_unflattened.resample(time='MS').mean()\n", " for p in ds_flattened.points[:100]:\n", " # get the point\n", " ds_flattened_sel = ds_flattened.sel(points=p)\n", " # select longitude, latitude and tas of that point\n", " lon_p = ds_flattened_sel.longitude.isel(time=0)\n", " lat_p = ds_flattened_sel.latitude.isel(time=0)\n", " var_p = ds_flattened_sel[var]\n", " # select the same gridpoint from the unflattened file\n", " # the unflattened file is in -180, 180\n", " if lon_p >=180:\n", " lon_p = lon_p-360\n", " # check if the unflattened and the flattened file have the same climate\n", " # data inside\n", " np.testing.assert_allclose(var_p.values,\n", " ds_unflattened[var].sel(lon = lon_p, lat=lat_p))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "32409940-cef3-4d72-a081-abd48bd8d062", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "b89b56af-280f-4209-a144-3ac1355bc73c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "1110d56f-7d87-482f-b4c6-e8f2a33fc2db", "metadata": {}, "source": [ "## Create climate test files:\n", "- this is for pytest in order that during the tests only a small part of the climate datasets are downloaded (just the nearest 4 points of HEF, of one glacier from RGI19 and two glaciers from only RGI7)!\n", "- but these test files can not really be used to check if the right glacier gridpoints are selected, for that we use just the entire `inv` file which quite small (~100kb)" ] }, { "cell_type": "markdown", "id": "da6b4a16-f413-4845-bb84-521a4a146f9d", "metadata": {}, "source": [ "#### newer test files for 2025.11.25" ] }, { "cell_type": "code", "execution_count": 47, "id": "3c701238-36c5-407e-a35e-4a4fd10d4de2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ERROR! Session/line number was not unique in database. History logging moved to new session 4277\n" ] } ], "source": [ "# HEF location\n", "lon, lat = (10.7584, 46.8003)\n", "# RGI60-19.00124\n", "lon2,lat2 = (-70.8931 +360, -72.4474)\n", "\n", "lon3, lat3 = ( -141.670274+360, 69.166921) # in RGI7C, not in RGI6\n", "\n", "lon4, lat4 = (-66.855668+360, -67.535551) # only in RGI7G, not in RGI6 or in RGI 7C" ] }, { "cell_type": "markdown", "id": "28e06b1b-70ac-4488-8c72-8143345fd667", "metadata": {}, "source": [ "- GSWP3-W5E5" ] }, { "cell_type": "code", "execution_count": 3, "id": "1f9447f1-9b62-4897-9e9e-8b2007b347fc", "metadata": {}, "outputs": [], "source": [ "test_clim_path = '/home/www/oggm/test_climate/gswp3-w5e5'\n", "for version in ['2025.11.25']:\n", " v = f'_v{version}'\n", " for var in ['inv','tas', 'temp_std', 'pr']:\n", " if var == 'inv':\n", " ds = xr.open_dataset(f'/home/www/oggm/climate/gswp3-w5e5/flattened/{version}/monthly/gswp3-w5e5_glacier_invariant_flat{v}.nc')\n", " # we just take the entire invariant file inside because it does not make a big storage difference\n", " # same invariant file for both, monthly and daily\n", " ds.to_netcdf(f'{test_clim_path}/flattened/{version}/monthly/gswp3-w5e5_glacier_invariant_flat{v}.nc')\n", " ds.to_netcdf(f'{test_clim_path}/flattened/{version}/daily/gswp3-w5e5_glacier_invariant_flat{v}.nc')\n", " else:\n", " ds = xr.open_dataset(f'/home/www/oggm/climate/gswp3-w5e5/flattened/{version}/monthly/gswp3-w5e5_obsclim_{var}_global_monthly_1901_2019_flat_glaciers{v}.nc')\n", " if var != 'temp_std':\n", " ds_d = xr.open_dataset(f'/home/www/oggm/climate/gswp3-w5e5/flattened/{version}/daily/gswp3-w5e5_obsclim_{var}_global_daily_1901_2019_flat_glaciers{v}.nc')\n", " \n", " c = (ds.longitude - lon)**2 + (ds.latitude - lat)**2\n", " p_nearest = c.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to HEF\n", "\n", " c2 = (ds.longitude - lon2)**2 + (ds.latitude - lat2)**2\n", " p2_nearest = c2.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 2\n", "\n", " c3 = (ds.longitude - lon3)**2 + (ds.latitude - lat3)**2\n", " p3_nearest = c3.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 3\n", "\n", " c4 = (ds.longitude - lon4)**2 + (ds.latitude - lat4)**2\n", " p4_nearest = c4.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 4\n", " \n", " p_nearest = np.concatenate([p_nearest,p2_nearest, p3_nearest, p4_nearest])\n", "\n", " ds_test = ds.isel(points=p_nearest)\n", " ds_test.to_netcdf(f'{test_clim_path}/flattened/{version}/monthly/gswp3-w5e5_obsclim_{var}_global_monthly_1901_2019_flat_glaciers{v}.nc')\n", "\n", " if var != 'temp_std':\n", " ds_test_d = ds_d.isel(points=p_nearest)\n", " ds_test_d.to_netcdf(f'{test_clim_path}/flattened/{version}/daily/gswp3-w5e5_obsclim_{var}_global_daily_1901_2019_flat_glaciers{v}.nc')\n" ] }, { "cell_type": "markdown", "id": "b537ab55-e2a8-4142-a49c-84b1b84d3fb2", "metadata": {}, "source": [ "- ISIMIP3b" ] }, { "cell_type": "code", "execution_count": 48, "id": "dbe241d2-60cd-4885-a63e-9f588937138d", "metadata": {}, "outputs": [], "source": [ "test_clim_path = '/home/www/oggm/test_climate'\n", "for version in ['2025.11.25']:\n", " v = f'_v{version}'\n", " for var in ['tasAdjust', 'prAdjust']:\n", " for ssp in ['ssp126', 'ssp585']: \n", " ds = xr.open_dataset(f'/home/www/oggm/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_{ssp}_{var}_global_monthly_flat_glaciers{v}.nc')\n", " c = (ds.longitude - lon)**2 + (ds.latitude - lat)**2\n", " p_nearest = c.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to HEF\n", "\n", " c2 = (ds.longitude - lon2)**2 + (ds.latitude - lat2)**2\n", " p2_nearest = c2.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 2\n", "\n", " p_nearest = np.concatenate([p_nearest,p2_nearest])\n", "\n", " ds_test = ds.isel(points=p_nearest)\n", " ds_test.to_netcdf(f'{test_clim_path}/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_{ssp}_{var}_global_monthly_flat_glaciers{v}.nc')\n", " ds.close()\n", "\n", " ds_h = xr.open_dataset(f'/home/www/oggm/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_historical_{var}_global_monthly_flat_glaciers{v}.nc')\n", " \n", " c = (ds.longitude - lon)**2 + (ds.latitude - lat)**2\n", " p_nearest = c.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to HEF\n", "\n", " c2 = (ds.longitude - lon2)**2 + (ds.latitude - lat2)**2\n", " p2_nearest = c2.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 2\n", "\n", " c3 = (ds.longitude - lon3)**2 + (ds.latitude - lat3)**2\n", " p3_nearest = c3.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 3\n", "\n", " c4 = (ds.longitude - lon4)**2 + (ds.latitude - lat4)**2\n", " p4_nearest = c4.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 4\n", " \n", " p_nearest = np.concatenate([p_nearest,p2_nearest, p3_nearest, p4_nearest])\n", "\n", " ds_test = ds_h.isel(points=p_nearest)\n", " ds_test.to_netcdf(f'{test_clim_path}/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_historical_{var}_global_monthly_flat_glaciers{v}.nc')\n", " ds_h.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6dbb3162-eb05-45ab-97d5-9bbf6d4d0d99", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "9ff2a9d3-24b3-4a62-a27a-1660f30409e5", "metadata": {}, "source": [ "- ERA5" ] }, { "cell_type": "code", "execution_count": 3, "id": "094ba4cc-8e69-4b2a-9b8e-a49ae2a46628", "metadata": {}, "outputs": [], "source": [ "test_clim_path = '/home/www/oggm/test_climate/era5'\n", "for version in ['2025.11.25']:\n", " v = f'_v{version}'\n", " for var in ['inv','t2m', 'tp']: #'temp_std', \n", " if var == 'inv':\n", " ds = xr.open_dataset(f'/home/www/oggm/climate/era5/monthly/v1.2/flattened/era5_glacier_invariant_flat{v}.nc')\n", " #ds = ds.drop_vars('time')\n", " # we just take the entire invariant file inside because it does not make a big storage difference\n", " # same invariant file for both, monthly and daily\n", " ds.to_netcdf(f'{test_clim_path}/monthly/v1.2/flattened/era5_glacier_invariant_flat{v}.nc')\n", " ds.to_netcdf(f'{test_clim_path}/daily/v1.2/flattened/era5_glacier_invariant_flat{v}.nc')\n", " else:\n", " ds = xr.open_dataset(f'/home/www/oggm/climate/era5/monthly/v1.2/flattened/era5_{var}_global_monthly_1940_2024_flat_glaciers{v}.nc')\n", " if var != 'temp_std':\n", " ds_d = xr.open_dataset(f'/home/www/oggm/climate/era5/daily/v1.2/flattened/era5_{var}_global_daily_1940_2024_flat_glaciers{v}.nc')\n", " \n", " c = ((ds.longitude - lon)**2 + (ds.latitude - lat)**2)\n", " p_nearest = c.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to HEF\n", "\n", " c2 = ((ds.longitude - lon2)**2 + (ds.latitude - lat2)**2)\n", " p2_nearest = c2.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 2\n", "\n", " c3 = ((ds.longitude - lon3)**2 + (ds.latitude - lat3)**2)\n", " p3_nearest = c3.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 3\n", "\n", " c4 = ((ds.longitude - lon4)**2 + (ds.latitude - lat4)**2)\n", " p4_nearest = c4.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 4\n", " \n", " p_nearest_all = np.concatenate([p_nearest,p2_nearest, p3_nearest, p4_nearest])\n", "\n", " ds_test = ds.isel(points=p_nearest_all)\n", " ds_test.to_netcdf(f'{test_clim_path}/monthly/v1.2/flattened/era5_{var}_global_monthly_1940_2024_flat_glaciers{v}.nc')\n", "\n", " if var != 'temp_std':\n", " ds_test_d = ds_d.isel(points=p_nearest)\n", " ds_test_d.to_netcdf(f'{test_clim_path}/daily/v1.2/flattened/era5_{var}_global_daily_1940_2024_flat_glaciers{v}.nc')\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7b3ea6e8-6e50-4789-a1d2-0e61f87a3ecb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "366cea04-dd5d-4ea7-a756-9b42b4350b43", "metadata": {}, "source": [ "#### old test files from 2022 and 2023.2" ] }, { "cell_type": "code", "execution_count": null, "id": "19fa195b-f4e7-4c5d-8166-d96a43e0e1e3", "metadata": {}, "outputs": [], "source": [ "# HEF location\n", "lon, lat = (10.7584, 46.8003)\n", "# RGI60-19.00124\n", "lon2,lat2 = (-70.8931 +360, -72.4474)\n", "\n", "#test_clim_path = '/home/www/lschuster/isimip3a/test_climate'\n", "test_clim_path = '/home/www/oggm/test_climate/gswp3-w5e5'\n", "for version in ['2022_missing_points', '2023.2']:\n", " for var in ['tas', 'temp_std', 'pr', 'inv']:\n", " if var == 'inv':\n", " ds = xr.open_dataset(f'/home/www/lschuster/isimip3a/flattened/{version}/gswp3-w5e5_glacier_invariant_flat.nc')\n", " # we just compy the entire invariant file inside because it does not make a big storage difference\n", " ds.to_netcdf(f'{test_clim_path}/flattened/{version}/monthly/gswp3-w5e5_glacier_invariant_flat.nc')\n", " else:\n", " ds = xr.open_dataset(f'/home/www/lschuster/isimip3a/flattened/{version}/monthly/gswp3-w5e5_obsclim_{var}_global_monthly_1901_2019_flat_glaciers.nc')\n", " \n", " c = (ds.longitude - lon)**2 + (ds.latitude - lat)**2\n", " p_nearest = c.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to HEF\n", "\n", " c2 = (ds.longitude - lon2)**2 + (ds.latitude - lat2)**2\n", " p2_nearest = c2.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 2\n", "\n", " p_nearest = np.concatenate([p_nearest,p2_nearest])\n", "\n", " ds_test = ds.isel(points=p_nearest)\n", " \n", " ds_test.to_netcdf(f'{test_clim_path}/flattened/{version}/monthly/gswp3-w5e5_obsclim_{var}_global_monthly_1901_2019_flat_glaciers.nc')\n", "\n", "#www_lschuster/isimip3a/flattened/2022_missing_points/gswp3_w5e5_glacier_invariant_flat.nc" ] }, { "cell_type": "code", "execution_count": 33, "id": "0276a3cd-8ee2-428d-8672-40d3094e494e", "metadata": {}, "outputs": [], "source": [ "# HEF location\n", "lon, lat = (10.7584, 46.8003)\n", "# RGI60-19.00124\n", "lon2,lat2 = (-70.8931 +360, -72.4474)\n", "\n", "test_clim_path = '/home/www/oggm/test_climate'\n", "for version in ['2022_missing_points', '2023.2']:\n", " for var in ['tasAdjust', 'prAdjust']:\n", " for ssp in ['ssp126', 'ssp585']:\n", " if version == '2022_missing_points':\n", " ds = xr.open_dataset(f'/home/www/oggm/cmip6/isimip3b/flat/monthly/mri-esm2-0_r1i1p1f1_w5e5_{ssp}_{var}_global_monthly_flat_glaciers.nc')\n", " else:\n", " ds = xr.open_dataset(f'/home/www/oggm/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_{ssp}_{var}_global_monthly_flat_glaciers.nc')\n", " c = (ds.longitude - lon)**2 + (ds.latitude - lat)**2\n", " p_nearest = c.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to HEF\n", "\n", " c2 = (ds.longitude - lon2)**2 + (ds.latitude - lat2)**2\n", " p2_nearest = c2.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 2\n", "\n", " p_nearest = np.concatenate([p_nearest,p2_nearest])\n", "\n", " ds_test = ds.isel(points=p_nearest)\n", " ds_test.to_netcdf(f'{test_clim_path}/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_{ssp}_{var}_global_monthly_flat_glaciers.nc')\n", " ds.close()\n", " if version == '2022_missing_points':\n", " ds_h = xr.open_dataset(f'/home/www/oggm/cmip6/isimip3b/flat/monthly/mri-esm2-0_r1i1p1f1_w5e5_historical_{var}_global_monthly_flat_glaciers.nc')\n", " else:\n", " ds_h = xr.open_dataset(f'/home/www/oggm/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_historical_{var}_global_monthly_flat_glaciers.nc')\n", " \n", " c = (ds_h.longitude - lon)**2 + (ds_h.latitude - lat)**2\n", " p_nearest = c.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to HEF\n", "\n", " c2 = (ds_h.longitude - lon)**2 + (ds_h.latitude - lat)**2\n", " p2_nearest = c2.to_dataframe('distance').sort_values('distance').index[:4].values # 4 nearest points to glacier 2\n", "\n", " p_nearest = np.concatenate([p_nearest,p2_nearest])\n", "\n", " ds_test = ds_h.isel(points=p_nearest)\n", " ds_test.to_netcdf(f'{test_clim_path}/cmip6/isimip3b/flat/{version}/monthly/mri-esm2-0_r1i1p1f1_w5e5_historical_{var}_global_monthly_flat_glaciers.nc')\n", " ds_h.close()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "96089864-663b-4968-8532-20402fde26b5", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "23621827-f66b-42f9-ba5a-de07e4c5601a", "metadata": {}, "source": [ "### Check of Sarah's downscaled flattened data:" ] }, { "cell_type": "code", "execution_count": 13, "id": "b8c778f1-af8e-4be3-9432-04b59a157669", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "id": "790868f0-85b6-4c00-b204-deff42af7c8a", "metadata": {}, "outputs": [], "source": [ "sarah_files_l = []\n", "\n", "folder_p = '/home/www/shanus/ISIMIP3a/flattened/daily/'\n", "for p in os.listdir(folder_p):\n", " if '.nc' in p:\n", " sarah_files_l.append(folder_p+p)\n", "\n", "folder_p = '/home/www/shanus/ISIMIP3b/flattened/daily/'\n", "for p in os.listdir(folder_p):\n", " if '.nc' in p:\n", " sarah_files_l.append(folder_p+p)\n", " \n", "def test_glacier_gridpoint_selection_sarah(p, short = True, print_stuff=False, max_dist=0.0417):\n", " # file path (ISIMIP3a or ISIMIP3b)\n", " if 'inv' not in p:\n", " with xr.open_dataset(p) as dt:\n", " dt = dt.isel(time=0) # we only need the lat/lon anyways\n", " else:\n", " dt = xr.open_dataset(p)\n", " if short:\n", " # select three glaciers where two failed in the\n", " # previous gswp3_w5e5 version\n", " coords = [(10.7584, 46.8003), # HEF\n", " (-70.8931, -72.4474), # RGI60-19.00124\n", " (51.495, 30.9010), # RGI60-12.01691\n", " ]\n", " else:\n", " coords = odf['coords']\n", " for coord in coords:\n", " lon, lat = coord\n", " if lon <0:\n", " lon = lon + 360\n", " # get the distances to the glacier coordinate\n", " c = ((dt.longitude - lon) ** 2 + (dt.latitude - lat) ** 2)**0.5\n", " # select the nearest climate point from the flattened\n", " # glacier gridpoint\n", " if 'inv' in p:\n", " lat_near, lon_near, dist = c.to_dataframe('distance').sort_values('distance').iloc[0]\n", " # for a randomly chosen gridpoint, the next climate gridpoint is far away\n", " # for glacier gridpoints the next gridpoint should be the nearest\n", " # (GSWP3-W5E5 resolution is 0.5°)\n", " if print_stuff:\n", " print(p, dist, lat_near, lat, lon_near, lon)\n", " print(lat_near-lat)\n", " assert dist <= (max_dist ** 2 + max_dist ** 2) ** 0.5\n", " assert np.abs(lat_near - lat) <= max_dist\n", " assert np.abs(lon_near - lon) <= max_dist\n", " else:\n", " dist = c.to_dataframe('distance').sort_values('distance').distance.iloc[0]\n", " if print_stuff:\n", " print(p, dist, lon, lat)\n", " assert dist <= (max_dist ** 2 + max_dist ** 2) ** 0.5\n", "for p in sarah_files_l:\n", " print(p)\n", " test_glacier_gridpoint_selection_sarah(p,short=False,print_stuff=False)" ] }, { "cell_type": "markdown", "id": "ac17f112-7517-415e-bc17-c7299c3ebd50", "metadata": {}, "source": [ "ok, it looks good in case of sarah's files: (I just checked the first ISIMIP3a /3b files but that should be sufficient:" ] }, { "cell_type": "code", "execution_count": null, "id": "b6053ef3-893e-4eed-af90-f6ecba73f91d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/www/shanus/ISIMIP3a/flattened/daily/gswp3-w5e5_obsclim_glacier_invariant_flat.nc\n", "/home/www/shanus/ISIMIP3a/flattened/daily/gswp3-w5e5_obsclim_tas_global_daily_flat_glaciers_1979_2019.nc\n", "/home/www/shanus/ISIMIP3a/flattened/daily/gswp3-w5e5_obsclim_pr_global_daily_flat_glaciers_1979_2019.nc\n", "/home/www/shanus/ISIMIP3b/flattened/daily/ipsl-cm6a-lr_r1i1p1f1_w5e5_ssp370_prAdjust_global_daily_flat_glaciers_2015_2100.nc\n", "/home/www/shanus/ISIMIP3b/flattened/daily/ipsl-cm6a-lr_r1i1p1f1_w5e5_ssp585_prAdjust_global_daily_flat_glaciers_2015_2100.nc\n", "/home/www/shanus/ISIMIP3b/flattened/daily/mri-esm2-0_r1i1p1f1_w5e5_ssp126_tasAdjust_global_daily_flat_glaciers_2015_2100.nc\n", "/home/www/shanus/ISIMIP3b/flattened/daily/mri-esm2-0_r1i1p1f1_w5e5_ssp370_tasAdjust_global_daily_flat_glaciers_2015_2100.nc\n" ] } ], "source": [ "for p in sarah_files_l:\n", " print(p)\n", " test_glacier_gridpoint_selection_sarah(p,short=False,print_stuff=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "75d250f4-4e86-4cb6-8332-78ef0b35b214", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:oggm_env_2025]", "language": "python", "name": "conda-env-oggm_env_2025-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 5 }