process_images_20210828_A2_gulf_stream_surveys.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Ocean Color Imagery - Process Micasense Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pull in all images and process with micasense tools\n",
    "    normalize and align and whatnot\n",
    "    put these into functions and clean up\n",
    "    also visualize along the way and look at distributions to think about whether or not they're legitimate\n",
    "    write out\n",
    "    \n",
    "Pull in all images as modified and metadata with specific functions\n",
    "    Visualize for issues and shadows and bright spots and locations\n",
    "    correct for sky radiance\n",
    "    visualize the distributions of both sky (clouds) and sea\n",
    "    look at the variability of spectra both for specific pixels and across nearby images\n",
    "    filter for orientation and location and issues\n",
    "\n",
    "take the water leaving spectra and convert to chla as well as other future products and just intro general analysis of spectra\n",
    "    produce a csv or .shp or images of ocean color products \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import FloatProgress, Layout\n",
    "from IPython.display import display\n",
    "from micasense import imageset as imageset\n",
    "from micasense import capture as capture\n",
    "import multiprocessing\n",
    "import glob, shutil, os\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import micasense.imageutils as imageutils\n",
    "import micasense.plotutils as plotutils\n",
    "\n",
    "import exiftool\n",
    "import datetime\n",
    "\n",
    "import subprocess\n",
    "import math\n",
    "\n",
    "from utils import *\n",
    "from georeg import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_name = '210828_A2_dyefrontcross2'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect the Exposure, gain, DLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "surface_dir = os.path.join('data', project_name, 'sea')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgset = imageset.ImageSet.from_directory(surface_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls_ed = []\n",
    "exposures = []\n",
    "gains = []\n",
    "for cap in imgset.captures:\n",
    "    if len(cap.dls_irradiance()) == 6:\n",
    "        dls_ed.append(np.array(cap.dls_irradiance()))\n",
    "        exp = []\n",
    "        gain_cap = []\n",
    "        for img in cap.images: \n",
    "            exp.append(img.exposure_time)\n",
    "            gain_cap.append(img.gain)\n",
    "        exposures.append(exp)\n",
    "        gains.append(gain_cap)\n",
    "        \n",
    "exposures = np.array(exposures)\n",
    "gains = np.array(gains)\n",
    "dls_ed = np.array(dls_ed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/clifgray/anaconda3/envs/micasense/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "/home/clifgray/anaconda3/envs/micasense/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "/home/clifgray/anaconda3/envs/micasense/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "/home/clifgray/anaconda3/envs/micasense/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "/home/clifgray/anaconda3/envs/micasense/lib/python3.7/site-packages/ipykernel_launcher.py:27: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "data": {
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4MuPueNVzV9x+BU/+ypO5e8/dHDw9vKayBgpJCFaSe2Tb9/n77oOFBW7PHNp4LDzkEFWL9alPwVvf2ncVKzr/ieuDZRQsg25gFKwlhGgfn1DB0s1AO+yzlEopRae2X8VMfib8/0Khc5vgqPQeip6/m2tp1fuj0fH71Qer0fkaoVkfL/14t+Sn0/eo39DnHDbZXyoIFay9nGDl/BwZJ8PGlRuNRbAB5gvzCATZVP1Kz4ErDgQwASEjgmZjcjv3yLbv8+UEwb+UGihYGm9/u1Kx3vOe1hfRJVKp+pCLaB8so2AZdANDsJYQYkMuIjVYnRIsqK/DijYYjrMJXr/tev66669tXfMwET1/N9diLILJzmEsgksfpgZLIe/nFcGa3mgUrAaYL8wz7o5jifopxYFTZYK1xxCsUcDQLIJlgnVrsQXBOvhgePnL4dOfhrv7+52JC7mIKliGYBl0A0OwlhBaWQQ7jWmHGIKVb06wXvLdl/C2n7ytrWseJgzBqt7WECyDVggtgnt5DVbOz5FNZdmwcgM7Fncw580N+5JGDguFBSbcidjnJtOTrEivMArWiCB6H6x1rvSVYN1+O9KyuEMe0nosPPdcdXF9rsXSIRdBKagsVvueiWk36AkMwVpC6FfIBdTXYbVSsHbmdrIzt7Otax4mDMGq3tYQLINWMBZBhVDBWrkRMEmCcZgvzjckWKBsglvntg7wigwaQd//pKxfWO23giUPPIgibuuxMKpibe3f90YrWNFWDEbBMugVDMFaQohTsLzAQ8r+KlhxUe17vD3M5mfbuuZhwhCs6m2bvQYd/d4pDMFaHjAWQYW8nyfrZNkwvQGALbsNwarFfKE5wVo/td4oWCOCZmNhvwlWcPChVc83xVveooq4Pve5BBt3Bq1gaXsgVNdgGQXLoBsYgrWE0EjB8n21GtVLBSsaclGrYJVkifnCPHu8PW1d8zBhCFb1tv0iQDoG3xCspQ9jEVTIFXNVCpapw6pHK4J14NSBpgZrRJCEYCVpZ9IJwSoc1AbB2rABHvc4+Pznu1vxawKtYOmIdsDEtBv0DIZgLSHEhVwUgkLLSXMz6BWaOotgkxos/fusNxgFK1fMsfHDG7ni9is6PsYgCFajCHLdg2tvIFj9Pn43MASrPRiLoIK2CK7KrmIqPWUIVgzmC/OMp8YbPn/g1IHcv3B/+J0yGB6SEKy456JoNabV3ednZ2HHDrwD2iBYoCLb77gDfvWrhDu0Bx3THlWwjEXQoFcwBGuJILqiVGsR1DexTvosNUsRXJFeAdQTLF3kPSgFa2duJ1tmtnD9vdd3fIxeEqxG77MQ6v2sPb6Oqu1nH6ykaPVd0Y93GqOe9PjDJFjDbhmwVBC2gNjLLYI65EIIwYbpDaYGKwYtFaxyVPvWPaYOa9hISrCa3SdbjWl19/lygmBu/0Ob7leHM8+Eycm+2QR1TLuxCBr0A4ZgLRHU9nGKWgRb9TZqhoYhF/nd4aBYR7AKimDl/XzLyVcv+mDpVc8kNV+trqPba2mlRKXT9cfv5vNphn71wYLOCVDS45s+WKMP/be9t1sEtYIFsHGliWqPQxKLIBiCNQpoNhZGf292n2z7Pl8mWIv7P6DpfnUYG4PnPAe++U2Y6316Z2gRLFYsgkbBMugVDMFaIqhddYrGtPfCIhinYK2fUl3UawlWVLlqpWL1QsHSCT/RurB2MQiLIMSn8HXz+TSDsQi2B2MRbA9hDZaxCJJ1sgChgiVr8633cjSLaQfCscTUYQ0fvbAItn2fLxOsubUbm+4Xi5e9DBYXFcnqMWJDLkxMu0GPYL4+SwR1BKtUbxHstYL1sP0fhmM5LBSqUwSjfWD2eHtYM7Ym9tjap117/e1CT/SS1Hw1PIYhWImuZSkQrN253Zz7k3NZ9Bdbbwy86qGv4pEHPqqOYP3vVf/bke00ZaU499HnhqEHyxVhDdZermDpkAtQClbez3Pv/L2sm1w35CsbHSS1CLaTJPh/f/8/Pn71x5EMh8yuHVvLe5/wXmzLHsr5+4WhEax99iHnTDbdLxYnnQRHHKFsgi9/eRs7tkZcyEU+MAqWQW9gCNYSQe2NT4dceH53BKtRyMVMfoaVmZVMuBMdK1hJ7QatMCoKVqmk3idDsIZ3fICf3/lzLrj2Avaf3B/Xbm7mv3v2bgSCE9Y9Kmyqqc/99p++HcdyWJVdlfjcJVnirtm7OGrNUbz5H97c6UtYEjA1WAq1FkFQSYKGYCkUgyJe4DUlWGOpMVZlV7WlYH3+D5/nSzd+iYNWHNSLy2wL84V5dizu4KyHncXhqw8f+Pn7iUEQLMdRNcnhMW6/HQ49tLOxUAh46UvhbW+Dv/wFDjusjZ2bwyhYBv2E+fosETSyCPZKwYpaBItBkfnCPCszKxlPjTeswYLmdVE9U410DdaQFawktVSGYDU/fi9CLnTC5W9f/lsOnj646baHffSwKhtt9NyFoMAbTngD551yXuJzB6UA5z+dRAEvSx1hDdZebBGUUqqQC20RXFnuhTWzhUce9MhhXtrIQPdJbJYiCO1Htef9POsm1rHl9YMPFfnWzd/iWd981rJcXBgEwRKiZiz861/hMY/pfCx88Yvh7W9Xke3nJb9ft4JWsDTBmkpPmRosg57B1GAtETSyCPYjpl0rRSuzZQWr2JmC1SuCNSoKVpL3eakQrEYpTv0mWJYVn7TYDnbnFMGazky33Na13ViCVZIl/JLfUgGrhW3ZTLqTXZH9pQJjEVROgZIshQrWIdOHAKYXVhR6Aa6ZggXKJthOyIUXeKSdHt80EyJtq/Mux1j5QRAs/ZznlTfeuhUe8IDOx8L994fTToPPfranCUk6pl2HXKzMrMQLTB8sg97AKFhLBI0ULL/kk8uXAKtnMe1aIZjOTMdaBKM1WM0mmj1TsHQNVhcpgr0kWM3eZ9etbLd9YTsrMivwPLflfp2g05j2VEqtMDY7ZqGg1Jrr770+0QTbsRwesu4heJ5TdZxG5+jm+zCTn8ESFpPpyZbbpu10HcEqFCp/P+0SLFCrnHuDgjVKFsF2voudIuNkOHa/Y7FEZd1RL+5kU9lwm/0n9+eae67hd3f/rm/X0m+MpcZ48L4PRjS6EbSBpARr/eT6tt4zz/dCojNo6PvCKHz3e42kBKsZj2lrLLzpJlWQfcQRifZriHPOgac8Bb79bXje8zo4QD1qY9pXZleS9/PGImjQE5ivzxJBIwULYH6xCKR7FnKhFYJGNVhRi+AgFCy9iriUFCwpJUd97Cje+Zh3cpz3Ly336wSdKlitrl9v9/k/fJ5XXvbKxMf++FM+zgbvNVXHaXSObi2C05npqolwIzRSsPTEqZMV8hWZFXsFwRoli2C738VOcdnzLuNphz8t/F0Xv2sFC+CoNUdx2Z8v47I/X9b36+knfvrin/K4DY/r+jjtKFg7cztZLC4ylhpredxhKliGYPVQwfrZz9QDJ5+M9+PW+zXEqafCAx4A55/fB4Kl/s6nM9N4vlKwhAB7eeWbGAwYhmAtEdT2rtAhFwCLeZ9OCVZcTHutRXDb/LaqffZ4e8g6WXJ+LnHIRTeqvl5FXigu4Jd8HKv9r20vriVpDVYupyalO3M7uXf+Xgqy9X6dwHGU5a7dPlhJCdaOxR0AXPLcS5quJAcy4KlffSo7czs5IOF71M33YXd+NyszKxNt69ouXuDVBa5odaZTBctYBAeLe+buAeDy51+eiFi3ix2LO3jhxS/k3vl7qx7X954owfryM7/MDffe0PNrGBTunLmTs79/NjtzO3tyPJ0y25JgRXphJQmOKASFoStY+j6xnDCIPlj6uUIB+OlPVQrg/vt31xPSspSK9cY3wnXXwUMf2sFBqqHnPwtenrSdZiw1xn3effi+Ua8Muof5Ci0R6Judrl+JTnoWcood9UzBylcrWHUx7YU51o6vZdvctkQhF93W3EQHudn8LKvHVrd/jPIhbLv/CtbMTGXlsxAU8Iqt9+sU7apB7ShY+n1/6mFPbRpVrHsCFYNix3Vq7WB3bjcrs8kJVs7P1X0X9efTKcHqRk1dKhgli+CsN8tYaozTDjutL8ffldsFUHev07UZOuQCYL+J/djvAfv15ToGgT/v/DPQu8+1HQULVLJnEoLl+UOswSqfdxS++72GtokXi/EKVqPnareD1vd5P1eEX/4SXvjCxPs1xUtfqsIuzj9f1WN1CT3/mc95jKXGSNvpsAbL1F8ZdIu+hlwIIZ4shLhNCHG7EOKtMc8LIcRHys/fKIR4aKt9hRDPEkLcJIQoCSGOjzyeEkJ8QQjxRyHELUKIt/XztQ0a+sY0NVVvEeyGYMWFXERDBBrFtE+lp1rWotRec6fQq8jQeZKgJhbdTO7bIQ+hvSoh6egU/SRYhaCALeyWfWCEEKSsVOLAlV5ZBJOg1iKov4vdEKwV6b3DIhgqWKXi0Bvr6ntOv6AT8GrvdXEK1lJHr+1viQlWRMFKAi8wNVj9gOep+6D+f9LnareD1vf5A++/Fubn4fGPT7xfU0xPq0TBr34Vduzo8CAVhApWvkA2lSXjZMIUwaVEsIQQRwgh/hD52SOEeMOwr2tvR98IlhDCBj4GnAYcDTxPCHF0zWanAYeVf84CPpFg3z8BzwR+WXOsZwFpKeWDgIcBrxZCHNLjlzU01BGsiIK1mA+A3sW0hwpWtkFMuzfHVHqKFZkViUIuuiVY0SSnTpWDQRMsfc3dpjy2Ql8VLN9LTEBSdopiaYAKVhsWwV4TrKn0VFeBK0sF0cllIIMhXolaWFmRXtG347u2iy3sMHJcwxCs1tDjw7jbPKb9gKkDABJHtQ9TwQotgss0RXBQBOvo+8r1V499bOL9WuKcc9SBPvOZLg6ioOc/C15ZwXLSYR+spWQRlFLeJqU8Tkp5HGr+uwhcPNyrMuingvUI4HYp5R1SygJwEfD0mm2eDnxRKlwFTAsh1jXbV0p5i5TytpjzSWBcCOEAWaAALJtl5mYKVi8IVq2ClXEyZJxMqGBFV7D3eHuYdCeHo2B1OLEdloK1lAlWISgknuC4tjswi6Bugp30uoyC1Rmi1txh12H1W8ESQjDujtdbBMshFzpFcDkgZamb/qAVrIyTYe3YWu6eTUiwhqhg6fMaBavxMaD1ff5BO34GD3oQrF1btV9XibrHHAOPexx8/OPVK8MdQM9/FvNFxlJjZOzMcrAIngL8VUr5t2FfyN6OfhKsA4DonXRr+bEk2yTZtxbfAhaAbcBdwPullLtqNxJCnCWEuEYIcc2wbS/toHaCGA25yOVKQHcWwdqQCz2BnXAnCGRQNdmaK8wxmZ5sOdHU1zw52bsarKWiYEUT2JYiwSoU1PueWMEakEVQSqlCLtqowaolWIVC9wqWDlxZzvB8LwyUGfZEc4+3hxWZ/ilYoO51RsFqH0kJFqg6rKWkYA37e98PeB5MTFT+X/ucJlhJYtqb3ecnUh4P2vNrRYYi+6VSKq+iK7z2tXDXXZWEwg6h5z+LXoGskyXtpMOY9hFTsBw9by3/nNVk2+cCXxvUhRk0Rj8JVlyDjVpG02ibJPvW4hFAAOwPbADeLITYWHcQKS+UUh4vpTy+Fz1ABoVashJnEeymD1ZtyIWewOpBM2oT3OPtYcqdapmm1iuCNWo1WEl6f0QDAnqyatfifEnhea2vX2/XTopXrUWwX32wcn6OQlDoWMHS38VuCRZU94NbbpBSUggKTLqq19iwo9pn87N9VbBA1WHVEqy4kIuljn4QrJSVSvS3dOBUGwTL1GD1BZ4HmUz8fdjz1D1S/7/ZMaD5ff6BC/9HppSrI1g9GQef8hT1Ii7rrlVCqGB5xUrIhT+SCpav563lnwvjNhJCuMAZwDcHe3kGcegnwdoKHBj5fT1wT8Jtkuxbi+cDP5RSFqWU9wO/AY5vsc+SgV5NmpwsN0qNTHg8T3HPTm5ccQpWNEQgjmDNeUrBamUR1NesVYNOBUPP98Jo5k4VrEJBvT+u2/+Y9qhCUiwVu4umbYF2I89bxbTr6HdNEttRsJK+1m5i2qMBLEmgGw1H/358H/LFLiyCZSVlOdsE/ZKPRIZ1NcvdIgjEWgSNgtUaC8WFROoVKIKVOORiiI2GtXK2XGPaG42FhUJjdat2O2h+n3/ozE8pIeAxj6naryfj4NgYPPGJcOmlnU8sqMx/coViGHLhBR6+L0dNwUqK04DrpJT3DftCDPpLsK4GDhNCbCiz6ucCl9Zscynw4nKa4InArJRyW8J9a3EX8PjyscaBE4Fbe/mCholmNVi5fKlj2b1RDVbUIgiV+OKSLDFfmFchFwktgtpyUOxwjpb386wZWwOYGqxG50uKVhbB6DHbrcHSr9W2mzdo7OYziAawtHtdUPkuhsmbHUzg9ER/OffC0hNL/fc/7JX8Pd6evoZcALGBPsuRYNmWjSWsnipYSQnW+qn1zORn6t7nOJhGw/1Bs7HQ8xR3adXOJMmY9qCdP+Nm9yGwsnKvTjL+JMYZZ8Df/gZ//GPHh9Dzn5znhyEXAF6hNGoKVlI8D2MPHBn0jWBJKX3gdcAVwC3AN6SUNwkhzhZCnF3e7HLgDuB24FPAa5vtCyCEeIYQYitwEvB9IcQV5WN9DJhApQxeDXxOSnljv17foNHMIpj3ZMc3rdiY9iYWwYXCAhIZhlzM5mcbRjjXTmo7JjaBSviZcCeWRA1WEMCiV28R7McNu58Eq+0UwXLIRdLjd4KwCXYXIRdQIVidhlzA8lawdHqa/vsfpkWwJEuDU7BqLYLLMOQCKn8XvUA7BCvaC6sVhqlg9ToIZJTQimAlGSdbjmm5HIft+B2/dh8fe/ye4GlPU/9e2mrtvTH0/CfvBSrkoryQkl+CBEsIMQY8EfjOsK/FQKGvIqiU8nIUiYo+dkHk/xI4J+m+5ccvJiZ+Uko5j4pqX5bQysDYmCZYFU9fPt85wYqNaY8oWNoipAnWXEHVnUylp/BLvqq7CbzYFd44gqX93e0g7+fJOBlWpJvHwjeDvrEPotEwVCbwUdLRj5K/dBp2706+fdsKVtIaLKtSg9VPgqUtgu0oWJ7v1ROsxc4JVqhgLeOodj2xDAnWEC2CelGn3wRrwp3g73v+XvXYclSwoPcEq1VEu4buhXX3nrs5au1RDbeTUg5VwbItG1vYyzamvRcEq+mY9tvfkioV+IX1OM6O2a8n2G8/OOEERbD+/d87OkRFwSqqkIvyeFcslpacRVBKuQisHvZ1GFTQ10bDBr1D9MYnJRSKpUqvji5uWrUWQb1aXGsR1ARLr9pPpifDWpRGE81eKlhpO810ZnpgCpaUsupHHwOSEazFMsHS6kk/7IH6fH1TsNqowYrGtPeVYGmLYBsKViADcnmVtKm/i2EwTBcEa1krWCNkEdSLKoOwCO4NIRcwfAXrrtm76u6vUWjFdFgKFqg6rKWsYNWOYdFxrFcEqw7FInzve7BpE4Gw+aV8dLL9OsUZZ8DVV8M9rUr046HnP/lCtYK1hC2CBiMEQ7CWCKI3Pv27HtS8LgpHda2MVrBm87NIZMOQC52cpi2C0HiiGbU1Rn9vF56vFLJWjY2bHqMNgnXV1qvInpfF+g8r/Dnvl+e1qWCpCXxSVadTjEoNVspOJSaTvVCwkoZcaAKVKxOqkGDlOidYe0PIxShZBPX7PJAUwZiQC0tYYVz9coFeEOkF2iFYB0wegCUsXnXZq8J76zGfOKZuO/39G5aCBb0locPA87/z/KoxbPX7VrNjcUfDsVDKLgiW78O558L69XD66XDbbVz+2P9hZ2Gy+X7d4owz1L/f+15Hu2uVyisEYUw7QLG4ZEMuDEYI5iu0RNCIYO3K7erqpiWEusloBas2RKCRghWd7DQjWELA+Hjl906Q9/OknTQT7gT3zt/b0THasQjedP9NeIHHm058E1PpKT7yfx/hpu03cVQ7BCu/tBWsQkFNcpISmYFZBPOdEazFcq84nZCV87pXsJZzyIWeWIYx7UO0COr7S7/7YI278SEXGSfDUmrpkQSu7VIoDV7BStkpvvrMr3LrDpU/9cu7fslPt/wUv+RXkVitoA5TwVrqBOv3W3/Pcfsdxz8e8Y/cuvNWLvrTRdw9ezeetyaWRPm+Iln6uVZ9sML7vJSqL9WnPgVPfzq8/OVw2mlc/Z8pvJ+pp/WfT8/Hwgc+EDZsUDbBs5q1hoqHVqkCX1QrWEVJdqyH12mwV8IQrCUC3T9CR7EXPMF4ajz8fze9JaoIVq7aglWnYJVrsCbTlZWpRhNNfc1RUtgJvMALUwtv23FbZ8coX0sSgqVtiO98zDtZkVnBN2/+ZkiUhGiekKc/B62Y6P360QNLn69dgtXqWvQx26nBcm2X+cJ8W8fvBDP5GabSU9hWkw+h5rpAWQJTqcp3UVsGOyFY46lxLGEtbwWrPMEN7zHDtAiWLciDULByfo6SLIVtIXJ+btnZA6G35GGhuMBEKhnBAnjOMc8J//8/v/kffrrlp6qptRshWCOgYKXt9JKOad+xuIOnH/F0Nj12Ez+8/Ydc9KeLyPv5qrlE9D6s/x/3XC2q7vP/+Z+KXJ17Lpx3XriNfr5YrO6vmOllOaMQSsW64AKYn6+soCVEqFKVUmEfLIBiQZLq73qOQRcQQhwIfBHYDygBF0opP1yzjQA+DDwFWAReKqW8bpDXaSyCSwS6f4SeIBaLotKjpii6WhVKpSoWwVoFS0+wdG2CtghOpadaWgRrr7nT3kd5P991DVb0Wlpdx6w3i0CEJFJPRvQxmi1m107gi0Gxd70/Gpyvl32w9DHbrcHSFsGkxw8C9dMudud3J66/ggqBynulqu9iNwRLCNGyB9xSx95oEdSvdbG4GD6mFazlhmHVYNUiTG2LNJOHCsHv5O+zV1jKCpbne8wV5sL2JnqRYN7LEQTxY2G0t1Url0F4n//MZ2DTJnjJS+Dd767aJm7c78tYeMYZ6mKvvLLtXcM6q5IT9sECKC7dPlh7C3zgzVLKo1Atmc4RQhxds81pwGHln7OATwz2Eg3BGgp+9bdf8YpLXhFLFu7YfQfnfP8cXn3Zq3n1Za/mNd97DTdvv7nOIlgoiEoButcdwYoqWLUx2LZlk3Wy9SEXbrKQi1pbYycIa7DKKYKNYuGbHqONGiytkuhVbNd28QIvsf0NKjU+o2ARnPPmeM+v3kOhGISDa5JjtlWD1aZFEDoj3LtzuxMnCEJlBTyXryZY+Xy5OXeHE7ip9NSytgjqCa5eZBimRXBgIRflBatoHVbOzy27iHboHXmQUraVIliLhgRLK1jGItgRduZ2AoQES7/Pc4vl9zVmLIzWGCepwXp04Sfw6lfDqacqBatm5TF6r/3Abz9QVf/VUzz60TA9ra6hzblBSKKCVFUfrGKxP21VDHoDKeU2rUZJKedQ7ZwOqNns6cAXpcJVwLQQYt0gr9MQrCHgjt138Nk/fJZduV11z33zpm/y8Ws+ziW3XcKlf76UC669gC/d8KV6guUpzzBAsWD1TsGKCRGI1iZEY9qThFz0gmDpGqzpzDR+ya9aYU6KdgjWrDdbVe8R7aWUlDzkvLKCNQIhFz+4/Qec+9NzufquG6uusdUxPd/DtfrTBws6+z7szu9OXH8FEQWr3MogHPS97ghWqybbSx16YqkV7L1BwapV62H5KlgpK9UT8pD385RkqW8K1lAtgs7StQhuX9gOVAiWXiSYW1SfeS8I1tn3b4aDDoJvfSuWjeh77e33382/XvmvXHLrJf0ZC1MpeMc74Ac/gP/937Z3BZSC5VQULN/HKFjDxRohxDWRn4YFdkKIQ4CHAL+veeoAINpwbyv1JKyvMF+hIUAPRtpuF8VcYQ5b2Gx78zaEEEy+Z7Jqch9aBAsWKStF2k7j94BgNQq50NcbVbBsYZNxMmEdTL8Jlhd4ZOxMRTHzZtteMW0n5GImP1M1iXdtl5yfa4s8aIVkEApWoVBdSFwLrUpGB9dWx2xXwYqS0FY2+K4IVm43h68+PPH2FYtgDcHKSwSi43Q43WR7uaLWIjjMlfxoa4h+IlbBKuaWJcHqlTqjx4WeEyyjYHWFHYs7AOosgu0QrPnqvJcq7LfrZh4y9yt4x/sa3vD1vXZPWTXT9V99GQvf+Eb49a/h3/4NHvEIeOQjE+1WIVg1NVhGwRo2dkgpj2+1kRBiAvg28AYpZe1ENG5G1L79qQsYBWsI0BOF2sQqUKRrwp0IU6tq1ZMqgmWnVNxusTuC5TjVCpZjOeFqLlQTrDlvjqn0FEIIXNsl42Sahlz0WsEC2q7Dqo2fLRahVGq8/Wx+tsqO1JGCVa7xGQTBguZ2O/1+6ebHfanB6sAi2KmC1VENVq1F0JO4tttxOtyKzPJWsEbKIpifZdKdDC27/YK+50Xvy3k/b0IumqBfBEtfm4lp7wx1BKusYM3nyv3FulSwnrL1QooipWqvGkDfa+cX1Tn7SrCEgM99Dg4+GJ79bLj//kS7VUIunKoUQd8XRsEacQghUihy9RUp5XdiNtkKHBj5fT3QWcO0DmEI1hAQKliFegVrvjBftVLbiGD5BRvHckg7afyC3VMFa2VmZdXEs0rBKuypur5mxf79qMGCxjVfjVAbPwutCUmtgtUuwdIWtKS2uU6R5L3V71c7Clah0F6KYMrqzCKorSxJMZOfaasGq6Jg1XwXC7KrAvrlHnKhJ5ajEnLRb3sgVF7r3mARHHWCNSox7VpJW2rQBGvt+FogEnKxmJxgFQpQkiVuvO9Grtt2Hddtu44b7r2BYGGeJ2//Ir9b90zYZ5+G16DvtbplSV8JFsCKFfDtb8OuXfD85zdfRS0jVKmCFNlUpQ+W7wujYI0wygmBnwFukVJ+sMFmlwIvFgonArNSym0Du0iMRXAo0L1lGlkE9fNQ6VdSp2AVKxbBXNHpachF7QS2VsGKXl+zWhR9zdGI1k4QTRHU19gOogOHjlhvFhfbixoszwNSg1Owmr23nSlYkkJQSExCXNttW8G6e9f9HPW1/fn+87/PqQ84teU5CkGBxeJiRzVYcWS/K4LlLvOQC3+EYtpr/h77hUYhF3qSupzQK4KlyWjfLIJDjmmPG6OXAjTBWpVdBVTe5+gYkETB+tS1n+Ls759ddezLg+dxWrCbnx56Fic3uYaQYC2qcyYNiuoKxx4LH/oQvOY1cMUVcNppTTdvFNMeFA3BGnE8EngR8EchxB/Kj50LHAQgpbwAuBwV0X47Kqb9ZYO+SEOwhoCmFsHCXNVglbbTVb2UNFnxCzYpy8K1XYKi3VWfpdqY9toJ7IQ7wf0L94fXF11Nbpam1os+WH7JJ5ABaSddVYPVDqL9PaIEqxFm8jNMp6fD36MEK0mPp/D4KQhkgOdJXLc/jUqTkFf9funBtZ0+VYlTBMsx7e28R/fNzBDIgOvvvT4Rwart0ZYEmkQVPJis6SPXDcHaWyyCoYI15EbDg1Cw9qaQC6NgtcZStwiuzKwMa0y1RTA6BiTpg7V9UTkMLn7OxVjC4sxvnMlR3/wVW1KH8ecDHtf0GvS9diFXbRHsV0/IEC9/ObzrXfCJT7QkWI1CLoLAWARHGVLKXxNfYxXdRgLnDOaK4mEsgkNAJxbB2p5SflHVYKWdNEGXClaVRTBXX+NSG3KR1CLYiz5YeiUz42Q6VrBq+3s0u5aSLLHH21O1Yq5JbtIeT6AUoPA19LkPFiSrwcrl26vBguQqj7YItvMeLXrqerbs3pLoHHEBLK0QEqy672J3BGsqPUXezy/ZCVgr6L+7sAZryBbBfke0gwm56AR6XIjW7LaDUVawljLB2r64Pay/ArCEWozNeap9SNQGqBHXB8vzPSxh8Y9H/iNnHHEGx+1McchNW/nK2FmkM80XDSv3+aD8r5eoTUjXcF14xSvg+9+Hu+5qumnUIhiNafd9yyhYBl3DEKwhQFvsmoVcaGgfeFwNlrYIlvzuLYJRBat2AjueGq8LudBYkVnR1z5Y0ZXMTmuwaq0Pza5lvjBPSZa6rsEqeJXBxysn2PUDiWqwQgWrMri2Omb4niVcQe7EIqh7hd0xc0eic3SlYBVENcHqVsEqfxeXq4pVG9M+bIvgIBUsE3KRHL1SsHJ+rurxUVCwlnJM+47FHVUEC1Qd1mKummBFG77HWQRrbeKvvKZE0bH4auolbd/nFxMu8PUEr3qVKrz+9KebblYbcqG/byWjYBn0AIZgDQGu7eJYTqy/e74wX1+DFRNyEfhOGHJRKqYGq2C5gwu5iCpYY6kxHMvpqgar1bVo8labIhgluc0QTuCLUYLVfwXr/tlZnvXNZ7FzcWfdNqGCVe7NlSzkQoBsQ8GyU/glPxGZDAfevBp4kypYYRPsdhoNlwdM3Yw7rGHsgYIFy5dg7ZUWQXfvsgj24jPte0z7MBUsa+kqWHEEK+Nkwntu3FjYkmDdcw/PvbbA9Sdt4J7i2sT3eZ2om3T86QkOOUTZAz/96crkJgbRmPZsKottqfCwwChYBj2AIVhDgBCCSXcy1iIYG3IRR7CKDik7RUpkkKXuUgR1yIWUUoVcxBCsxeIiQSmoq8FKEnLhOCpFtROCpQfetJNGCMGK9IqOa7CSECw9ie9ewao8pif3/YA+7g1/v5Vv3fwtfv/339dto0njYhsKFgCBm7wGy0pBSVAstn6tlYFXXc/fZv+GX/JbnkNbBDsJuSiUe8XZtvopFKyeEKzl2gvL872w3x0M1yJY2zahX3Btl5SVqgu5MApWY5garNFErIKVyoZkJwnBKhbBKxbVfTII4EUvIlWC7z77QR21LEk6/vQMr3kNbNsGl17acJNQpQrc8LuWtjOUfMcQLIOuYQjWkDCZnkxsEYwlWAWHlJUiVVLbdqtg+b4aLAMZxIZcgFrZrU0R1AqWqieshr5mIeoTi5JCD7R6IJ7OTPdXwSqTt25TBAte+U9LDkbB0iuTTRWsfHIFC4DAbStFkMBt6/j6evySz9Y9W1ueoxuLYDHSjDudVjWM3ayO6+/HclWwdJNp27IRiKEpWEEpYKG4MBAFC5SKpRWskixRCArLVsHqJcEaS411tP9IK1hLNKZdSsmOxR2sHatOv8w62bA2OAnBAsgXAnUPfd/74Kc/5T/PXMud68bas4Lng6p/B0awTjsNDjwQLrig4SaaYDkiE7amSVvZqucMDDqFIVhDwoQ7UadgBaWAnJ+rC7nwikWKxeobn/RTSsGie4KlFaxGIQKaYN2/cD8SWZciGMiAxeJi3XGjN+HaxKKkCBWs8urSisxwFCydBtjqfbas8vtZKP9plRyk7L+CpVcHd+V2VT2vVUfogGD56eR9sOwUBOm2jp/LV0h5EptgNyEXtQSr2CMFa7kSLC/wws9+mCv5+rs7iJh2UHVYWsHS9x5DsBpjobDAWGoM27I72n+UFay0k16SCtZCcQEv8BooWO0RrJxX4oS7SvCOd8Czn82lj1yNVwjCvpLNEJK08jmTjj89g23DWWfBj38Mf/lL7CZCgLACUlQWCDJCzb+MgmXQLQzBGhIm3XoFK85u4douXqFyUwwjToM0KSuFXVI3hl7EtDdSCPT1bJvbpq49QgCbFftHCVbHCpY/YAWrQQ2W3ifJ++y6SiEBwE+Hj/UD+riNCFb0c9Grl0lj1AnSbaUIJn2t+vl8vtII8o7drYMudud2M5Yaa4sY6W39Yr2C1YuQi+XaC8vzvfD9SdmpoVkE9d/jIBWs+aK6D+tJv464Xk7QoTRxzoN2MF+Y79geCOBYDpawYhUsgQhjxoeBpWoR1D2w4mqwmo2Fcc9Zu/J85Es7lBJ04YW4Tpp8m+OI3t7LVz8+ELziFWrF88ILG25iOQGOrCyiuJaaUxkFy6BbGII1JEy4E3UhF5pg1dZgRftTCAGplAQ/jWM5OCVVmN2LkIs4exxU0rXumbun7vrCWpSYiWaUkHRKsKI1WKAmtp2mCEb7iLWrYOl9krzPWiEBEqs6nSJUsMpRuDtz1RbB6OeSj9hDkhwTP91WH6x2Fax8JMp+y0xrBSuuPrAVQoJVqPSKc13wC45RsJrAC7zws9cR/MOAfn8HRbAm3Im9QsFKWWp5vlviPF+c7ziiHVQ9ctbJxipYuu52WNDOhaAUDO0aOsH2BdW7Ki5FsNlYWPucQ5E3fuKn7Dfjw9e+BitW4NpuqEglvc/r4+YHGXKhsW4dnHEGfOELDXuZCNvHobKIkhZqwcAoWAbdwhCsIWEyXR9yoX+vq8GK9KcI/w1cUnYKR45VPdcJdEx7uGJbU9QdKljzSsGqjWmH+ommlFT1RFLJdO1fWy9qsNrpg9WoBkvvk5Rg+cWyZSZhXVKnqNgw1OBVq2BF36voCmWSY7ajYHVSg6UJVtbJJlOwYppgJ7ouIPDtqu+iX7RNyEUTFILCSFgEw7/HAYRcQNkiWK7ByhVzQP39cDkgvKd1+bl2q2CBurfHKVjDtAdCJIF0ialYjRSsbCrbdCzU/7oupF3JBZzN8Tdv5T0vOBhOPFHtZ6erHDXNUCFYavukC3w9xyteAdu3w2WXxT4trACHykWlMDVYBr2BIVhDQpxFUCtaUQte2k7XTYzdtKyzCPZCwQonFKl4ghUqWOkYBatmoun7VPm0u1awdA3WAFIE03a6atU67aTLYRXJaqn0BD5tp0PbXN8JVnnwqiVY0c/FqyHqrY7ZVg1WxCLYrjf/iDVHJCZY7dRfgbIgUbIoBdUWwaDYnYKVcTKkrNSyVrBGwSI4aAVr3N17arBghAlWREEdFnr1Hg0aDQmWk1XtN2hsEUylVB3xQ77/bl7BZ/nc447jB4/ZPzxGOwpWqJAVqs8zcIJ16qlwwAENe2IJ28eKWASNgmXQKxiCNSR0YhGsBEYoi2DKTmEF2arnOoEOuWg0oWimYDWyStVecy9rsOYL84liveOuJUkNVq1FUqkzqfAYrZBOQ1Cw1fs2IIugVrBqLYJRBavQRwWrE4ug/gyOWH1EIotgXI+2VhBCkJKTVeftBcESQjTtAbfU4fk1FsEhE6yBhlxoBavc/NYQrMboK8EasoK11AnW2vHqFMGMk6HgNSdY6TTwhS/woG++ky/wYs4/+aFV98m4+UgjCKFIlh53ouceKGwbXv5yuOIKuOuu+uctHztCsHTghSFYBt3CEKwhIVbBamAR1PU8+saUcnuvYPl+ZULR0CKoQy7c1iEXvSJYdTVYHcRjt6VgeTN1NjRFsJITJT2Bn3AnBqZg6XSoOgWrrPZNupOJB7hOarBc2038Wiu9qNTvR645kvsX7o9tWxBFJwoWQJqpqutKp6FUdHCt7qqtO0m0XCqITnBTdmp4FsEhhFzUKljLNeQCRphg+cNXsPT5tU19qWDH4g5sYdfZarNOlmLBCu+/cQTrTL4Nr3gFO447hVfxqbp+ga7ttkWU0ukKsUrqoOgLXv5y9e/nP1//nFXEkpHXKEzIhUFvYAjWkDDhTuAFXlXxeKhg1cS01xIs1y2FIRdWaTQUrNqJZr3q1rs+WEBbdVhtK1jpGAWrDaJURbAGpGDp19OoBmvfiX0pFCwcR1lAkhyznT5YKSu5gqW38coD7xGrjwDgzpk7m+7TiYIFkJLVrQzSaSj53SlYwLJWsHQfLCjfg/aWkIvURHgfNhbB1lgoLPRNwer277NbLGUFa83YmrqAkGwqq6zrkfsgVMaOo277Lp+afy6ccAI3/efFFFH133UEq9AmwSrPX4amYAEccgiccgp85jOqaXIE0ipilSoX5Ug1p1pqCpYQYloI8S0hxK1CiFuEECcN+5r2dhiCNSRoEhUNutCWwdYKVkkpWHYKEWQqj3UIrWC1JFgxMe36//22CEZrsKC9cIF2a7B6omD5qYEqWPr1zORnquyT+n3aZ3wfioXkNWQABG32wWqThBY8FcN86KpDgeZR7X7JZ64w13bIBVCXtKkIVsoQrCaoimkfokVw1pvFElZXSXXtINpo2IRctMZ8obsUQRjdkIulSrC2L26vq78C9T32a/oBQnns+N73OPtnz+ZP6YfBD36As1KN64WC6FrB0vOXYhvErC945SuVRfAnP6l6WNYoWCmxZEMuPgz8UEp5JHAscMuQr2evhyFYQ4K22UVtUZps1dZgBeVEuiqC5SuLoPAVGRJ25xOgViEXGSeDQLA7vxtLWFUTDsdyGE+N1xGeflkE+65geQ1qsNroZ+W6UCqmGHfHQ2LWr94fehCIpiJG35uZ/AxjqTEm3AnVXDfh9QPgt5si2N57VCwP3htXbgSaEyz9/eqZglV0u7YgdRK4slQwKhbBPd4eptJTA4vrHk+Nk/fzBKXAKFgJsDeEXOhFvqUCrWDVIpvKUiqmSKerQyrW/eyrcOaZ3LniOM466AqYmgqfq23IHl3wTX6f1wTLTrxfX/CP/wirVikVKwJpeYhS5aJ0ZPtSUrCEEFPAycBnAKSUBSnlzFAvysAQrGFBD0rRoAtNtsbdyopg3OTeTpXCmHZLqueE0/lAWRvTXrtyKIQIrzdushO3kh/tqQFdKFg1FkFNftqZ2OrzplLKey5ECwUrPV31WCcKlvS1RbC/Me1CVNvtoNomOOvNMp2ZJuNkKBbs9hWspH2w2kgR1NsUChZpO83q7Gom3Am27G4cdLE7X26C3UENliPV31O0D5Y0ClZTjJJFcFAR7VC59y4WF/eKkItuPtegFJDzc30hWNE2AcPCUo5pjyNYGSejnC9umWAFi3yKV/LoC14Aj3gEm068gkJW/a3pe2UzgpU8Ubc8NrURFNUXpNPw4hfDxRdXhV2URBFLVthUirIraLQIliOEuCbyc1bN8xuB7cDnhBDXCyE+LYQYjOxv0BCGYA0J2lpXpWB5c4ynxrFE5WOJ6y+UcoMw5IKygmU5PVCw/JxSq2JWi/UgGlXXNFZkVrCnUD3RjOvd1UkfLD3w6saYnShYhUIlflYTkoZ9sBqmCCYnSsqC5g7EIqiPre0XUE2wZvIzrEivIG2n8YtWewSrDQVLpQi29x75RaVgCSHYuHIjd8w0VrB258oEqwMFq7ZXnK5h7JZgddL0eqlglCyCg6q/gkpT9YXiggm5aAFtpVyuIRdL1SLYUMFysmph1pVw882se/ojeDmf5dpTz4Wf/YxdcmWdfTCWYBXbswj6ZeVqEGNhS7zxjWoi8Pa3AyClRFoeRBUsOZIhF76U8vjIz4U1zzvAQ4FPSCkfAiwAbx34VRpUwRCsIUETlWgN1nxhvqq+CeLVEycScqFvWjid2xgcR9V95or5hqu1UQWrFlPpqb5ZBD3fqyJ9ndZgRW/qja6lEBTI+bn4Gqw2Bgc3LcF3mUj1P+RCH7sQIVg7FytR7VEFKyh2oGC10werTZUvOnhvmN7QNwWrNmlT1zD2SsGSUnZ1nFFErUVwmArWIAmWvs/NF+aNRbAF9OJgLwiWVgs1TEx7ZyjJEjtzO1k7trbuuWwqC36axxUuhxNOwNq1nSfzQ3556nngOFXjpP7XL7sMNNJ2OiRMyRfSygRrAGNhSxx0kCJZX/4yXHutcshYfqiuAWFk+4gpWK2wFdgqpfx9+fdvoQiXQRII8cyYn1MQYp9uDmsI1pAQZxGcK8zVDVZxk3sn5YchF/qmhd05wdI3kpxXbEmwagkgNLcI9qIGK3qD1+pSuzVYSQiWJm21lqS0nW5rcEil1AR+sAqWxVhKEYk6BSujFSynbQXLsZIt43WStFgs2OEkZuPKjWyZ2dKQrOjPu5OQi1qC5fRIwZpKT1EsFZdcjHMSREMGXNsdakz7oHpgQcUiuFBYMCEXLdBLgjWKCtZSjGmfyc9QkqWGCtYrd/2MC+56BjzgAXi/vY4reVJ9HywiClbRrlOw/GJ7BCsolseQUVCwAN76VlizBt78ZnKFRbCKiFJlnHPKFkHbXjoLZ1LKe4G7hRBHlB86Bbh5iJe01PAK4NPAC8o/nwLeBPwGIV7U6UH7SrCEEE8WQtwmhLhdCFEnVwqFj5Sfv1EI8dBW+wohniWEuEkIURJCHF9zvAcLIX5Xfv6PQoiRXXqMswjOF+brLHhpp35yb6eCMORC+uWbX5cKFsCCV2g4mWimYK1Ir2hJsLqJaY+SvjBUo80arCQEq9Ekvl3y4Ljq8xlETLs+tl+w2H9yf6CmBiuvFKy0k6bUJsGy5VjicIF2Gg2H11ysVrAWi4vcv3B/7PbdWATrCFYqaCuCvhHCesBlaBOM1mANu9HwsC2Cw57o9wM9sQgW+mgRNApWR9BNhusIVqnEw87/Np+6/3yuWvF4+OUvSW88AIhpNExEwSrWWwRrQ7eaoYpgBWlsW2Lbnb22nmHFCti8GX7xC0qXXgKWjwwqBMsu17VbdtDoCKOKfwa+IoS4ETgO+K/hXs6SQgk4CinPRMozgaMBDzgBeEunB+0bwRJC2MDHgNNQF/s8IcTRNZudBhxW/jkL+ESCff8EPBP4Zc35HODLwNlSygcCjwWGMytIgFDBKlQrWLEWwSYKliZY0q4eoNpBWwpWTA3WVHqqZR+srhSsmgnOdGa6PwpW+TXE12C1QbBSweAVrKLNvuP7IhDszFUsgroGS1kEU+0RrDIxSYJOQi78glOlYEHjJMFeWgQd1++ZRRDaa3q9VBDtQzRsi+AwQi4WCgvk/Byu7VbVxC4XpGx10++FghUNZeoEJqa9d9i+sB2IIVhf+AJHfupiPjlxOm8++mswOYllqcXVZgQrKNQrWJ30hMw6yp6oAzaGjle9Co48ksl//08cKwcRBcsuK1iBWDrKJYCU8g/l+qwHSyn/UUq5e9jXtIRwCFLeF/n9fuBwpNxFFzyinyPHI4DbpZR3SCkLwEXA02u2eTrwRalwFTAthFjXbF8p5S1Syttizvck4EYp5Q3l7XZKKUd2CSI2pt1rYBFsqmCpgbJkdU+wFvPFhgXdehDtVMHSpKbdcpVaBQsUARptBcsfuIIVFGyyqSwrsytDBUtKGdZgpe00JT8ZwXIcQJTaIlgd9Qor2skJVm43ru12ZNeya5px2ykdEtN9yAW0l2i5FCClHB2L4JBDLpajPRBG0yIYtQePQky7/v4vpZj2WAWrVIL3vpe5Yw7j7Mm3Q+S2Fx0L4wiW9N16gtXmfb7kp9TfcJBWAUOjgFQK3vc+3L9u4fWz36EUUbB002FpLR1ibdA1foUQ30OIlyDES4BLgF+ikhhnOj1oPwnWAcDdkd+3lh9Lsk2SfWtxOCCFEFcIIa4TQvxb3EZCiLN01OUwi9PHUmMIRF1Me61CFBfTbjlqBd6xHEq+C8LHp3uLYM7zO1aw5rw5SrJy84yLaZdSxcG3g9oaLOhMwYr23mhkV2xUg9VujyfLURN4VVScSbxfp3BdVUicttOsyq4KCVbez1MICqGChe+q+rAWEAJsJwiJSRLoRsNCJLOAuK5a2dSD9yHThwCwZSY+6GJ3fjfTmemO+iGJIBueE8p/P4BV6s5BvFwVrEAGSOTQLYKFoEDezw+WYEUUrLzfOPRnqWPUCFZJlqoapBsFqzPEEqzLLoPbbuPes18EpUx4/4PqsTA6Tqp2JrJO6e+sJ6SrXCF+WtW/jgqe9jR2Pelk3rPjSzxk/prwYR1yUbKWDrE26BrnAJ9HWSsfAnwROAcpF5DycZ0etJ8EK24mVMtoGm2TZN9aOMCjUAVqjwKeIYQ4pe4gUl6ooy4H1bwyDrq3VK1FMF7Bqo6/tlLFsA+W9B1wvK4GAa1g5QtNCFaqeciFRFapcXEx7dHHk0KnCEbRbjx2oVCvYMVdR1MFq40IcjvlQylFSqSxZP8JVjoNgW+TdlRPKW0R1MqKrsFSEb3JBjgrVcQK2rQIBi5OKiDJn5W65grByqayrJtY11DBmsnPdFR/BRUiVVGwFFkQgSFYcdAr9lUx7UOwCOr3dZAWwWiKYM7PLcuIdhgtgqVVwqhNcBQUrKVMsNaOR1IE3/c+OOQQFp5+GgQuItLSJToWRsdJ3c6kNgwoOhYmGdPSaZCBo/6Gy+PDyEAIbnrfv/I3dw0fu+fFcJ9yiFlSvbASS+dzN+gSUkqk/BZSvhEp31D+f9cKTD+T/rcCB0Z+Xw/ck3AbN8G+cef7hZRyB4AQ4nJUTOVP2r7yAWEyPdky5EKrJ0JIHEfNXO1UMbQIBsUU2F5XNoZQwSr47NtJyEW5ZulJX3pSeDM+6q7zgQfXEaw3X34uN8//Otz38NWH86nTP9VQmWhUg/XzO3/OyZ87ueVrG0uNMbd4CekIM+qoBqsNi6CewFuljLLZOT5WwjS+TqDsdikyToZV2VVsX1Q+fE0YV2RWKAXCT6sAjgR/9pZTRLSh8Ojvqe36iY6fTkMpomABbFi5ge/e+l1u33V73fZ/uv9PHLHmiLrHk0ATqXDyUJ5gWEF3k+flGnKhU9OiMe3DmGRqgjVMi6BRsBqjlwoWqHv9ZHoSKeVINBrW79FSShHcsbiDrJMNE2X5zW/gt7+Fj36UbGYS/FRVz8xGFkFQ7UbyQRrXrtzP9X0+lZJYVuuVtHRa2QxXZFYo1407QgQL2JO1eN1B/8JVf303PPe5cOWVIcEKROdlFwZLDEI8E3gvsA9K4BGARMquBp9+KlhXA4cJITYIIVzgucClNdtcCry4nCZ4IjArpdyWcN9aXAE8WAgxVg68eAwjHlMZVbCklI37YPlpUm4pVAYspwjSwSKlEnocr6tBIJGC1cQi+LhDHsdpDziNsdQYjuXwf3//P67bqt76WoL1mau/zN9m/4ZjOdwzdw+fuf4zLBYXG15bXA3W8455HieuPxHHcpr+5P08V/z1CnbNzSeuwRKIugldu75zbcEQQQYryCpFq4/QZKXWIqgn/roGiyCt6o8SQDjFtgiItggmfa3qmlNV5Pnsh53NcfsdF/tZHrffcbz8uJcnvp4oaglWOMHwu5vALXcFS382ru0OxSIYKlhDjGk3BKsx+kGwotc0bAVLn39JKVi5mibD73sfrFoFL3uZUmODNMKpvJ6mBMuV8QqWn1bPJYA+xor0ivL4MFoEa7G4yI3j+/PWlefDz38Ob3sboqQJ1tIh1gZd433AGUi5AimnkHKyW3IFfVSwpJS+EOJ1KOJjA5+VUt4khDi7/PwFwOXAU4DbgUXgZc32BRBCPAP4KLAW+L4Q4g9SylOllLuFEB9EkTMJXC6l/H6/Xl8vMOlWFKzF4iIlWWoYcqGsXaq4RU/gZdFVBMue64lF0CuUGlpimilYh646lMtfcHn4++EfPTw2ph1UnPjLjnsp73rsu7jgmgt4zfdfwx5vT8MkKs/36qxhpx9xOqcfcXrL13XL9ls4+uNH43kicR+syfRkXWpYtKFzIoIVKlhZRVKcARAsP0XaTjPpToaNhkMFK72C7c52pWAlHOAsp4AoJZ/g6EbDSQdQfc3RwftFx76IFx3bccuJhhA1n50mWO28vjgsV4IVTnC1gjUki6BeIBikguVYSlU1IRetsVBcwBJW10pTLcGqVVCHhaVoEdy+sL1CsG69FS69FN75ThgfJ7uwCL5AOJWU2WYEK+XKcjuLSlFtOB9Jl0iyPq8Dn5RFMK0SkEcIOT8HdpGvp1/Gh19zDbz//TzqaVk+xkOMgrV34T6kvKXXB01MsMRmkQUOkptiE/xiIaW8HEWioo9dEPm/RBWXJdq3/PjFwMUN9vkyKqp9SWAyPRmGXGii1SjkQknrigmJlLrhB0VHNf1zemMRzHs+Gbv9RsO1yDgZCgUlt0VDLtRFp8MBVddW7PH2sG5yXeyx4iyCSaHJYqGQjGDNeDOxjWyFENilcQKSEaxwhdBPY5UyiFR/J6dKDXJJO2lWZlYy683il/yYGqx0aF9sBeEUEH7y1XshBCLIJD5+HMHqG2otgqnK59MNXNsl42SWXYqgnuBWxbQPUcEaJMECZRM0IRetMV+YZ8Kd6Ch4Joo6glWjoA4LKav7KPtBY8diRMF673shk4HXvQ4oj4eBhBgFKwjUTzXBKkGQJu1UCFbaSYeOmiTQia065MIaGy2CtVhcBEsS+AI+/GG46y6e/b138y0eTEnsN+zLMxgcrkGIrwPfhUhinJTf6eagiSyCYrM4HfgD8MPy78eJzaKVZc+gBaIWQf1vrYKlrV1R5UFP4Et+ShEsuzcWQa9QajihaBbTXouMk6HgCVyX0NYY3rj9CsHSx2o2QY2zCCaFXn0utqFgNSqo15Hl+r1qhpBgBWllE3T6T7BkWcFalV0FKPUqWoOlUgTTWElXEJ1CaItMChFkqxKqWl9z981+E8F3wSpile92lc+n+3NPpaeWnYIVZxEsyRJBabD2nrAmcoAhF6DuwfPF5R1yYQsbgegJweoWo6pgCSFwbXfJxbSvGVsD3/sefP7zcM45sFYFXugxINozU4+FtY4TKBOs2JCLdgiWD9JmwplWC3x9HgvbRa6YA6uI71tqcP/GN7hz/UP5Ci9g+urfDvvyDAaHKZSL7knA6eWfp3V70KQ1WO9C9aaaAZCb5B+AQ7o9+d6OqEUwVLAa1GDFEqxiCr/QOwXLKza2CB668lAcy+HgFQe3PF7GyVCMUY2AKgUricUqLqY9KSoKlpW4BitOwQJl97NTxUQJeVZEwRJB/xUs7XPPOBlWj60GYOfiztgarMRkz/HaJlhWmwoWgUuK/k+iZJBWr6cMYavPR3apYEE50XKZKVhxFkFg4CrW0BQsd/krWJo8dPOZ9o1gjYiCBcPtAdcJdizu4NDFDLz0pXDssfDud4fPWThQcsGu3AubE6ygYUy7k6DdB1QCn8atVWo87PNY2C6UguXj68saG+NTz/48f+EwHvmv74Jrrmm2u8FygZQvi/nprOg7gqQEy5eb5PKaRYwAJtyJ0CKo/22UIlhV22JHFKyCBXahJzVYhULQcELx8AMezsxbZtiwckPL46WdNMWCXRXjGlWw9MQtSQpbXEx7UmgFyy9YyfpgebMNC+qtUnJ1RoYTeFcRLLu/A7RTtmGknYqCtSu3i5n8DLawGU+N41oZKKWqInqbwvaU8tMG1GtNdvyw34ocgELgu2AVQgVGlicYukl3N1iWClaMRRAYeB3WMEIuoGwRLC7vkAvonjwsdwULlhbBKgZF5nKzvOIDP4N8Hr7+dWURLEPHsZesioKlx8LavpUAdqqxgpU4br08HoyJlcrRkXB8GBQWi4sIJ8D3Kyunucw+nMoVeCsm4JRT4Fe/GuIVGvQVul+uEB9FiI/U/XSJpATrT2KzeD5gi83iMLFZfBQw+mmXiCpYjSyCuu9ENJ2tYhF0KBatri2CWsGilGpa1N0oiKIWGSeDX2ykYLkDU7BSdgrHcvD9egWrUR+sxgpWJgyvaAm9Qhi45b4j/R2gbTeAwK2yCO7K7QoJoxAi7AWV9Fqk7bWt8IhSOvF7FPakaqOZcaeQgQuOV1mtL6tZpWL3FsEVmRXLj2DFWARh8LUos/lZUlZq4BPtqIK1XEMuYIQJ1ogpWEslpn1nbidv/yUccsOd8LGPwRHVbS1CgmXnwsf0WFjbtxIqC3dxfbCShyWpe25GTA1kLGwXOT9HyoFidNgKHO7hAL7/6bfB/vvDqafCD384tGs06Ct0sMU1wLUxP10hKcH6Z+CBqOKvrwKzwBu6Pfnejsn0JAvFBUqy1NIiWFU7U57AFwsWxYLo2iIY1hWVnJ6s2CqLoN3SIhgNuWiEbmqwQKlYfsy1tFuDZbVTS1WewMvAhSDTf4Ll+OWVxnoFS78eq0xkrKTXYueRbSpYtRHAzVAhWMmbGXcK6ateceFksvz3U+qRgrXs+2AN0SI4lZ7qOkShXWgFazlbBGGECVaNgjpMpO30klGw8pddzKZfwJ2nnwwveUnd83rMqyVYjSyCTspvGNOu+h22hlUOFLKCrHI4OKNFVheLi6RSgiCAsK1syQFKzO4zDr/8JRx5JJxxBnzrW8O8VIN+QMrLyv9+IfanS7RMERSbhQ1cKjfJJwBv7/aEBhXowWm+MB9aBBvFtFcVh5YniIWCwPNUz6JeWAQJUj0jWEGxAcGKhFxoMtmohqUkS6rhZBcrmdlUlvmiE0uwpKyEcEgpmfVmGypYIsi2rWDJoqvsaW6fCVbKRznsM6zOlmuwcjurXk+YCJiQAJXsfPsWOj+DcHKtt6NcN4YIiV8/USqmKosQacIi77YJZAxWpFfwx/v/yOR71Hc5baf53vO/x4nrT0x8jDtn7uRpX30aV7zwCg6YOqDra+oWtX2IhmURbGbZ7Scm3AnunLlThVwYBashFooLYWPmbqDHg5yv7h2hgmUsgnXYNreNh3/q4XVj5ol/C7jkszlu2Bfmz/u32AJ5TaICq9J3shnBslN+WcGqqFWxJQtNoC2BopRRNb19tsu3i5yfw03BAuD7ai4kgxTYRUX4166Fn/4UnvY0OOsseMITYHp62Jdt0CsIcRmqrVM8pDyjm8O3JFhykwzEZrEoNosVpg6rt9D1VvOF+ZYx7VWT+/IqkL4x2m6xdxbBHqRmpe00fg2pCb3d5VohUD1nxlJjDRUsPbB1Q/oy1jiyFE/2isXKdc0X5inJUkMFS7ShzugJfMlPKZtddq7j608C/d1wSuPKEoioKFjlCaooWwSjBc7NIK18+wpPkEY4yW4Rqu2AEzYB7idKfqqqV1zl8+m+DeAbTnwDa8bWIKUk5+f4xDWf4Ppt17dFsK6951pu2n4Tf7z/jyNBsPQEV69cD8siqBWsQWPcHWe+ML/sFayUneqOYBUWEtvGm2FUGw3D6BGsP+/8M3+f+zvPfuCzWT+5HoB1d2znnPO+wdw+K/nJR1/Bvxz1hNh9Q4Il2iFYbj3BaqOhvJ6riCADfqYqbGgUoBQsZeQqFhXBKgU2WIWKK2h6Gq64Av78Z0Oulh/eX/73mcB+VNo8PQ+4s9uDJ51h5IE/is3iShTZB0Bukv/S7QXszdBq1Zw317AGy7GcuvQ3PUHUN0Zr3B85i2Cp6JCOjL1xChaUU9gaWKz0gNvNSmZGrKg+f+T/nlchWDrSvJGC1Y7VT38+QdFRKkmfBxX93bBKWSxhsTK7UtVg5Wc5dNWhaiNdT5XwWkr2YvsEy3cTEzg1QDsDsQgGZQVLT5S0RSboAcE6br/jOG6/4wA1MfzENZ9gZ25n851qsGNxBwC7c7u7vp5eYJQsgoOOaAdlEZzJz1CSjdtWLAd0Sx56pfCNcshF2kmPVA2Wnif860n/ysMPeDjcfju85FGwci3Z3/yG/3fQQQ331STKt8IpXFOCZYUWwQqZinXUNIMmWH4GArcqIn4UsFhcxHWVjcXXL7PkgLUYfh8BGB+Hhzxk8BdoEAshxGdRMer3SymPiXl+BYosHYTiOe+XUn6u7kBS/qK8w38i5cmRZy5DiF92e51JZxjfL/8Y9BDaIqctgmk7HdpxNIQQ5cl9vUVQ3xidlN8bBStoHnKRFMoiWG/LU+eoJlhT6Sn2FOIVLE0au1KwaE6wJsuCYdhzp4ElSfjJ7Q2hQlJMIf1U3wcVrWBZgfrsVmdXszO3s6oGSxOspNdSsnOUiu0REEUmk71HihRmBqNgFZ2qGqyQABe6J1hRuLbLhDvBrtyutvYLCVZ+NAjWKFkED5w6cKDnBEWw9ER2ufbBgu4J1mJxkbFU9wskox5yMUoKVlWtdqkEz362YgY//zk0IVcQIVgiIcFyihBkcO1KqX6so6YZwsCnNNIfPYKVK+ZwnYqCBRD4FtjduYIM+o7PA+cDX2zw/DnAzVLK04UQa4HbhBBfkVI2+mNeixAbkfIOAITYAKzt9iITzTDkpu6LvQzqoe2Ac4U55gvzdQEXGsqeVpH1S+U6l9AimAp6U4PVSwXLT5FOqzobaKxgNYu5DhWsLgbaNFPV56eaYGm0UrBkkE5coKs97n7RVqSj34NK1IYBrMquClMEw9cTJCdYUkoCkSPohGAlVLDCAbrNXludwC861QqWpf5+ioWkGT/Jod/7djByClaNRXDYIReDRtT2ZhSseEgpWSwu9mRBTt/fR1HBGjWCVVWrffHFcP318KUvqSCGFmhfwSqCv4J0hGDpforCqRyjGSqLjSp9MBqwMQpYLC6SdtXr0wqW7wNWUK1gGYwUpJS/FEIc0mwTYFKohKQJYBfQzNf6RuDnCHFH+fdDgFd3e52JZlBis9hCTCGY3CQ3dnsBezNqLYKNEpmEn6lSBqSlQy7UTypV6lkNVi8mFGk7DX6aVCqGYAXpqoFzRaaxRVC/pm6uyUWR1to+WFBNsPQ1NLQk+S4ylSyOW/cZ8YsWJd+hZPV3JUxY5aSmcmDEquwq7p2/t8pipQMdSqL1oBHIAGyvI4IlExIsrcgOQsEKinZVr7igTLAKhd6n02n1sB1sX9wOjI6CVTvBHWZM+zAsgtH7sAm5iIf+jvRCwXIsB8dyRlLBStvpUDUaBYQKlj0G73wnHHUUPO95ifbV412ByutxXUUo8vnK7xrCLpZDLir3Sa1gCXsm0Tn1XKWYLy/wWaNFWnJ+jrRrAxUFq1gEYXdXdmHQNdYIIaJdni+UUl7Yxv7nA5cC9wCTwHOklI27Y0v5Q4Q4DNArFbciZddfgKQzqOMj/88AzwJWdXvyvR1Ri+B8Yb4u4CJE4CIiE1e9Aq9XnrJu0LMarF5YYjKO8ls7bgndCSCuDxYoBWvb3LbY4/QiTcqVE9Xnj/w/2gurtYLlQqY9BcvzykEXfVew1AsRZTVo9dhqrtp6FVB5PTrQIckKoud74CiCFU1abAUZJH+tYU1h0P8o5sCvtghGP59eoxsFS38Hh43aCe4wLIKtUj37iWgy3nJXsDodNxaL6m+oFwQL1PtsFKzW0NbVyYsvh5tvhm98A2w70b56vCuKCsHSY+H8fPXvUO6Z6KdxI4fXfbCStvuQZbdNYVEdWC9ujQoWi4usKb/AqIJl2UbBGjJ2SCmPb71ZQ5wK/AF4PHAocKUQ4ldSymar5IcBR6A4zrEIAVI2siAmQlKLYO2S7P+KzeLXwDu7OfnejlDBKigFq5FFUDVKrdzQ9CQ5n9fJN90pWP2IacfX3d7VVyxcGYsLuWgQ094Li6BWsFpZBFvVYLXjH68iWMVU/20REZ87wKrMqlAN0a9HqzWBvVi/fw0KQQFsDylFGF3bClKWbSAJ36MwMKTNZsadwC9YkK0QLJ88iBKe1x+L4B/v/2Nb+4xsDdYQQy4Wi4v4JX8oMe17k0VQW87aRa6o7mm9qlHLOtmRVLBGjmB5c0yIDNbmzfDgB8OZZybet6JgVeaYeizcs6f6dyjfo4MMKatiXtKhW4nbfZQVq/yCuodEI+JHAYvFRTKxClawbGuwhOCRwLuAg1ETNIFa01pOjrSXAf8tpZTA7UKILSh16v9itxZiE/BY4GjgcuA04Nc0rvFKhKQWwYdGfrVQilYDucUgKaIx7XPeXOPJhJ+umrjqCftceWxMpUtdDQJVMe29SoUKdDNCdccWAmwnIIgLuWhQg9ULi2BKqslS1zVYbShRUYIVFJ1QcewbdG1YmazoZsNQeT1hk0mRQMEKvPCYyoLa+hL04FRql2ANogarYMNEhWAVS4pAFgq9t3+tzq5m5+LSTxG0hIVtqYnHMCyCesFj2AqWCbmIx96kYPVror0rt6vqb37fiX1bNm6eL8zzij+lVHrgJZeAlXyRKCRYMQpWQ4IFWLLiMlChW8lbluixcHFECJaUkrtm78IvKblqobAQaxG0HH85K1ifQdUcXQska2i29HAXcArwKyHEvihl6o4m2/8TcCxwPVK+DLXPp7u9iKQWwQ9E/u+j8uGf3e3J93ZEa7DmC/Osn1pft42UIP00MhKwoCfs+qaYcksjFdOedtKxvTLslE/gp6tWJqfSU8x5c5RkCUtUDxa9iGlPyYQKVn4W13Ybvv5SMYVMqET5Ig8iYHHRRpbsvhMsTbh1ndXqsdXhc7qGpdJksnVxslaw9H7jCVrdhAQu4XukB+heNPtthWLRqgq5KAQFhFPE83o/edYWQSmlmowkwKgpWJ7vVf3NDcMiqBc8hhLTvhcpWJ2qkropcK9q1KoI1ggpWGkn3ZeFhTlvjvUfXB++jwDH7HMMf3xNc/V7cWGGt/8kBw9/OJx+elvn1PfofKSdaTOCpRfZ/KKt10kJAlSMeeJ2H+r1Lc6pqeawCdaXbvwSL/nuS6oem8yqv/Fqi6BctgoWMCslPxj2RXQDIcTXUIrTGiHEVmATkAKQUl4A/CfweSHEH1EK3VuklDuaHDKHlCWE8BFiCrgfulf0kloEH9ftiQzqYVs2WScbWgTjVq/0qkpUPdE3KX1TdN3ubgbRmPaeWQSDNLZTT7BEkFE2gzJWpFcgkcwX5usSw3oR0677LCVRsJqtlpf8VOICXTWBLzA3pyYf/R5Uwu9G0FrB8hNci67Biu7Xcp/ydomLmMsEru1eWx2gWLDA9sK/ES/wsFKFvtVgBTJgrjCXKAEvV8yxUFSkd1RqsApBoWpyOwyLoA6dGYaCZUIuWqPfCpYlrKpxYlhwrf5YBHcs7iDn5zjroWfxqIMexSW3XcLFt15MMSjWtWqJ4hE/voX1u3z4yn8kL44tQ9/viqKyoNmUYEUW2SYmqo+RNC1WNzWenysn9YnhBob8eeefsYTF557+OQRCLere9jS+RbWCZS/vGqyfCcH/AN8Bwg9SSq4b3iW1Byll02QXKeU9wJPaOOQ1CDENfAql7M3TyE7YBpJaBF8PfA6YK1/AQ4G3yk3yR91ewN6OyfRk05CLuImrLwsI22fPnnJ9U1r2LKa9ZyEXfhq7JnXPSvl1jWX1JDQukrkXNVhOSa1Gu259omFtDVaz1fKg6EBCdcbzPYRTYM8e9V6WEtQ9dYNo3y2oJljadhoX0dsItQpWErSrYIUroMX+K1gFr17BspxiXwiWVg93Lu5MRLC0ejWWGhspi6C2BcJwLIKhgjWMGqy9KORiJAlWjYI6TPSrBkuHVTzp0Cdx5tFnEsiAb9/ybe6avavSHL4WxSLP/O5t3LxhgqNPPbXtc4b3O8cj7+cZS40lJlh1x0hKsMpj39ycGnuLCcaffmLH4g5WZ1fz4mNfHD72g7+rf6sULEcuuRRBIcSdqDl6APhNQiJOKP8bfV6iAiH2Tkj52vL/LkCIHwJTSHljt4dNauB9udwk96AY4T6UC8i6PbmBWi2dK8wx58WHXIQEK6JgFUtFhFOMKFh0dTMIQ4h6FNOuFSxR0+3ddopYDQhWXFR7L2qwNKGL2hU7UrCKqcRWPzWB98PPR1oeQal/VuegXFel1aDV2YpFsE7BEgkUrMALmyq3S7CCBDVeUImLH4yCJapSBAtBASvVH4KlyW3SJEFNsB6w6gHMerN9/Z4khRcM3yI41BqsvcUi2IU60+uQi1oFaxTsgaAW9/ox0a7qZwVsmN4AwB27m5SJfOUrrNuR5+vPPLxt9QqqyVG4eFlDsKIx7dF+m7XHSFqP7FPttikyfIK1ZmxN1WN6gblKwXK6Cw4bIh4npTyuWQKflDwu5mfvJVegiguFeCFCvBMp7wRmEOIR3R42qQav/5qfAnxObpI3iM0d/IUb1GHSnWRXbhfFUjHWIhgqA1EFq+RjOcXQgpZ26epmIISKJZXS7Yktw5ZpkHYdwRKpYtirSUOvUMcFXfSiBkufT71/6k6qB5H/+tkH+MTCDwG4+u9X8/ADHh57DCmVD120Q7BSxTCERKsnWas/diOtGsUqWDU1WMUEFo1CUOjYIpjUDqkH6KDYX4JVKoHvi6o+WJoAjxrBuvG+G5n1Zqs+v2HA872hWwSHWoNlQi5aoh8Klu7xtDcoWGE/q/Ki6saVqtyjIcEKAviv/+Lm9WlueURnpSEVglVQBDlbGQvn5tRCazTxXfew6oZg6fFGj4VJxp9+YsfiDtaOr616TJdIRBUsxyktO4ugELxQSr4sBG+Ke15KPjjoaxohfBwooVS8/0Apgd8G4ieFCZFUwbpWbBY/QhGsK8RmMVm+GIMuMZmeDPtAxVkEde+KaLx2MShiO35400qnu1OwACwnwJG9mUw4Ug26tUlDllPAKlWvCEctgrXoRQ2WPl90QNCrdtduvZHtC9tZLC7ywH0eyPOPeX7sMYIAkBbSziVSGLzAw4p8PtheXyenmtToXld6gj6WGgvVh7geKI3g+V7bFsG472kz6M9D+v2tswh7ndVYBO0+ESytHiZtNqwJ1uGrDgdGI0mwEBSqJrhDSREcYg3W3qJgpexU5wpWP0MuRkjB0gRLpT33DtoiqBdV95/cn5SVYsvMlvgdvv51+Mtf+NATJpho0M6lFaL3Qv356bFwbq7GHkjlHh29T+pjJLWCF2QerMpiY4G5nr+X7SC5gjVyFkFHCHFN5OesmG0k8CMhxLUNntc3tskGP3szTkDKc4DyxETuBrquX0g6u3kFcBxwh9wkF8VmsRplEzToEhPuBLftuC38fy3ilIFiqYiVqlgEMxnR9eRHWAG27M1kwgrUoCtqk4bsAiKoPodeoY7rhdWLGix9LdE6qHAg8dNc9E8XceSaI2P2rCBqrSiWimF8dSMUggJ25POJTu77AV1IrHtdrciswBJW1ep/pwpWIeFlVyyCySwggfCAkmoC3EdEP7sqgpXyE7+2dtCpgnXY6sOA0Qi6qK3BGlaKYMrqjWW5XVQpWCbkIhb9ULD0pL/WojpMuLaLROKX/KbhE+0iVLDKi6q2ZXPI9CHxClapBO9+NxxzDN8+/G5eFLMQmwSeB0JIpOWHFs+oRbCWYGlLfPQ+GeeoaXrOQNcjl987J0+xVKy6vwwSOxZ38MjsI6se0wpWlGA5jhw1BatZTZXGI6WU9wgh9kE11r1VSvlL/aSUfLL87+Z+XugSRREhbBRJBSHW0gMRKWmKYElsFuuB54vNAuAXcpO8rNuTG6gb7PbF7er/TWqwovU/xaCInfKrClO79QtbToBDbwa1kETVFsI69QSrqYLVgxosUVaworVB4UASpFv2HYHq4mDP91pej5rABxWCZXt9nZzq4Ap9nZawWJlZWbX673mACPBkawKkarA6swgmSSmEci8qxyMoDIhg1SpYbn8UrJXZlUB7BEsgOHSlKmwfhaj2UbAIznqzTGemE0fd9xK2ZYeKynJWsKLqTLvvc99DLkZEwdJErxAUekqwdA1WdMzfuHJjvIL17W/DLbcgv/pV9vzlRbHzhCTwPNXSpSCIrcGqI1h24xqspAqW53tVi8G6/msYBKskS00VrKhF0M52P6caNMrJeUgp7xdCXAw8Avhl7XZCkEGJJg8EMpX9efmALnUU8RHgYmAfhDgP1RfrHd0eNJFFUGwW/w28Hri5/PMvYrN4T7cnN6hWrZqlCEb7FxVL1QQrm7a6l7OtAKtHCpaOC5c1BEvYHsKvvos3C7nQg0A3N2Phq9cUHRCiClZbBMtOpkQVggJO5PPpu4IVaWyssSq7qiqBzfNU0EeS70l3NVgLiSwgOqnQLw5HwXJSQV8Ilmu7TLgTiZsNb1/czqrsqjB9cBQsgqMQcjGTnxlKgqDGeGp8ZKLC+wV9X9VNV9tB30MuRkjBgt7bY2stgqCCLmIVrA98AA4/nPwzTieQQaIxKw6aYAF1FsFmClYcwUpaa6vs8tVujmFZ72bzswQyqCNYcQpWavQUrKYQQowLISb1/1GBdH9qsPmXgP2AU4FfAOtRNUd7L6T8CvBvwHuAbcA/At/v9rBJa7CeAjxRbpKflZvkZ4EnA0/t9uQG1aSqmUUwqgz4JR87FbBQ5lzZrEWxVOzK2yzsogqn6AFClarGIigdLyRfGno1rlENlmu7dQ2I20KgBsjogBBVsKJ2oEaIU0Gabu972G7l80lKzDpFUVQrWACHrz6cB6x6QOWaPBWTn2TQ6KQGK0pkkkzYNInzi83tlt2i8tkVBkKwQNVh7conV7DWjK0J1cZRULAKQWHoMe1awRoWxt1xsk52KAraoNDN57pYXMQWdqhudotRVbD0e9RrNWO+MI9jOVVEcuPKjezK7apebLzhBvj97+G1r2U+UIQnbiE2CTxPtXQB6iyCCwv1BCtu4S7OUdMMeT+PlSpWjYXDIi7ajp1EwXIcMWo1WK2wL/BrIcQNqP5N35dS/rDBtg+QkncAC1LyBdRc/kEDus7RgxAHIMTxwB1I+THgG8CLgL90e+h2luemAT1rGN7S4jJDVO5vZhH0I7UtxaCI41YmsZm0mgTUNghtB8LysXpEsEIFq8anLe08skbBsoTFpDvZ0CLY7UqmPl8x8v7pG6pVGktk++hEwUqlImEYTn9DLrwgD3YBz6tMir/5rG9WTQ49D5yUn2ii0I2CpV9rq/c1VLAKgyFYTioIB0zP93DcEjFlfz3BquyqtiyCa8bWsDKjrIUjUYPle1WTEFuoz2jQKYLDSBDUGE+Ns+AMN1K634gSrHFaLzRFkfNzZFO9I6C1CtawanRqocfTnitY3hwT7kTV+7dhpYpq3zKzheP2O049eOGFivm86EXMFWaA+IXYJPA8lTgM9QpW7f+hOcFKrGD5XlWLFN2DaxjQBKs2RTAu5GIixZJSsKSUdwDHJtxc38hnhOAY4F7gkH5c18hDiDcAbwduB9II8WHgg8AXgYd1e/ikBOs9wPVis/gZKrL9ZOBt3Z7coPpm2VTBivQvKpaK2E6l/i6bVhOgrtKXLB+r1COCVSY1tatcimDVD5wrMisahlx0u5IpfReET6FUuRYhwHKKuDLZSmA03ja5RTBSH5lwv06hbRhRglVr3VEWwWTd6bvpg4VdUFayFrxVEawCxWIX6mQb1+W4pSoFK5Mq9U3BWpVdldgiuGNxBxtXblSJj1ZqJC2CQghSVmqwfbDys6xbs25g56uF7k+4nNGtgtWr+iuoECwppSJ8CZwFg0C/1Nv54nydEhWNaj9uv+OUrPTlL8OzngWrVjF/31YgfiE2CZRFUP1fK1jRvlduzdCs5xxxBCtJP0VQ9xLbiSw22oWhE6xEFkEXAhngl/zlaBO+UAhWAv8OXApM0IN6oyWKs4AjkHIXQhyEIlonI+VVvTh4otmN3CS/BpwIfAeVDX+S3CQv6sUF7O2I3mSbxbTXKlgpt3LTGsuWCVYXkra0i1iyN6uGMlCz66CmELZk5cLnophKTzW0CHZbZC79FDheWJStYad8UjLZBCEu6rsZvEApJJWT9TfkwvM9hFNsmopXKFSrOM2g1SVoP6Y9aSS9F3gq5KLPBEtfVypVTbBSbv8I1uqx1W0pWGvH1iKEYGV25chYBGsXNvrVD6gRhq5guePLOuACRo9g6WsZRYtgvxSsKOp6YX3jG6o46tWvDveBzhWsQgEy5a90bchF7f+hPjxJHyP6XCvUKVi2N7TwiHYsgilHKYtLzCaYFCtQKeDHAx8D3gv4QnDcMC9qSMgjpRqspbwL+HOvyBW0ZxE8CXgUKsbQRiVuGHSJqpCLJhbBolWJ1y6WilUTeE2wuhoErCKi1BuCVSyoSXNtM8KSlVeEpwZT6al4BSvI98AimAK70vdDQzhFHJlsoOrIIhglWH0OufACldTUjDB4HjhuwELSGqwuLIJJ3yOcAp7X3xoXfV0pt5Zgyf4pWJlkFkEpZVWq1crMaBAsXfsYRcpODdwiOMwarEl3cllHtEOFPHTyueb8XE/fH02w8n5+pEIu9HX0eqI9V5irG++nM9NMZ6bZsrucJPjJT8JRR8EjHxnuA93VYOlygiQWwTiCFVey0PScgYfjVtvll4KC5abKBCvw2rbPLgE8DEWudBL4U4GrgbOF4JtS8r6hXdngsR4hPhL5fZ+q36X8l24Onohgic3i48ADgK+VH3q12CyeIDfJc7o5uUGFVNnCjh1Uov2FSrKEJSzVkyNS4zOWqVgEO4W0CliyNwXLjXzagbWILNafY0V6RezEshcKVlBMgbMQWiI0hFPALiVbge2EPLhuJHCkzyEXapWwNcFKpUoUS8Xwe9QIUQWr3T5YSdW6QlDAcgp9Izm115VySxRKFYLlpkt96YMFlRqsVvHXe7w9FEvFcMCfzkyPpEUQGKhF0C/5LBQXhpoi+LZHvS120Wc5YZQULE3W8n5+r1Cw5gv1FkFQKtYdM3dUwi0+9CHlaSfSO6sLi2AmUyZYxQQEq0yi4vpgRRd8myHv50O7vO7BNSyCtX1xO2m7PtgqVsEqE6ylVIfVBlYDD5WSeQAh2AR8C1X6cy3sVQTr/9X8fm0vD55UwXoMcIzcpGLqxGbxBeCPvbyQvRX6JjuZnoydjNVO7jNOphxyUVFIxrPqY+zKIiiK0CMFq9EqV8nOUYqJ5Z5KT/G32b/VPd6LGqyg6DRQsLz2CVYCoiSl6gDvpqsVrH6u/uf9vIpgb0Wwytfk+V7TeGVt39P7JUFtyEUrKILVnBT2Avr46QxVCpbbRwVr9dhqAhmwx9vTlCTUrqiuzK5MXLvVT3h+PcEapEVQp6gNU8E66cCThnbuQaEb8pAr5noW0Q6jq2D10yK4dmxt3eMbpjfwp/v/BJ/6lGI8L35x1T7QXchFNq0W1vR46DhgWaqXcS3B0k3pu1KwfA+3rGC5rsQTwyMt2i1QO8+KU7DSrnqflqlF8CAg+oUuAgdLSU4IluULbggpv9DPwyctgLgN9aFoHAjc2GonIcSThRC3CSFuF0K8NeZ5IYT4SPn5G4UQD221rxDiWUKIm4QQJaGiFWuPeZAQYl4I8a8JX9tQoW+WjW6atZN7KaXqhB4hWBNZtQTTzSBQsgqIUm8VrFqfti8WFOGpwVR6KrYPlhf0QsGywfHqFCxpe1ilZBOEqkbDLVTCQAZI5GAVrMDDbhE77nmE19TqNahGyaVwv0TX0IGNspWtsRfQx3ddVTwflAICGeCm1XNddDZoiFXZVUDrZsN1BGtELIK1Me0wWIugVo6GWYO1N2CUFKwqghVD8IeFfsa0xylRG1du5L7770B+6UthuEV0H+jSIpgRWMKqIjmaWLVDsAoiWQCMF3ihXV5HxA+LtMQ1GYZ4Bctd3grWV4GrhGBTWb36DfA1IRhH9bk16BGSEqzVwC1is/i52Cx+jvoQ9hGbxaVis7g0bgchhI0qoDsNOBp4nhDi6JrNTgMOK/+cBXwiwb5/Ap5JTIfqMj4E/CDh6xo69E220U2zVsEKpFoNSpUny5YF2bS6Q3RlERQe9JhgRS2CJVmiZC0iSzZBUL39ivSK2JCLvN99DVahYIFTqFOwpJ3HCpKRt3bIg36+arBy+h9y4SRQsEKC1WKAU9bMNK7bCcEqJrcItrjmXiAkWGlFsDRJSKclUlYG1V5CE6ydueZqVCzBGhWLoBNjERwQwdJR9cNUsPYGjCzB6iYNt8foW0x7YY6JVP2i6saVG/nHG4uIPXvgrLPq9oEuY9rTgqyTrVpwbESwCjSuwdLkq+U5fS+cq2iCNUwFqzaiHapj2qXUClb3ZRejCin5T+BVwAwwC5wtJf8hJQtS8oKhXtwyQ1KL4Ds7OPYjgNvL+fwIIS4Cnk41Q3468EWpOuReJYSYFkKsQ2Xyx+4rpbyl/FjdCYUQ/wjcASyZBiZRi2Acaif3evKqb1bpdGUQ6GZlqCQKEPQmjjTuJlwbnDAWGZun0lMsFBfqIlE932NyrLPVuui1WE6xLkVQ2jlol2AlqMHSz7vRwWoAMe2Om0DBmk42wGkFw0q3R7BSbkBRJCuaVyrZ4AhWOq3OWfv5qNq03p5zdXY10L6CNZ2ZZiY/07J2q5/wSz4lWYq1CA6qBkur2cOswdoboJsEd2QR7GfIxQgqWP2wCMaN+RumN3DstbD4gIMZe9Sj6vZJWamOyaciWKqFR3TBsaGCVSqUa3tTVccAKNCaYEkpVU8ztzJXgeESrIOnD6573C63YiwWCRd/tUVwmSpYSMm19LjeyKAeSWfU2+UmWSUdis3isXKT/HmTfQ4A7o78vhU4IcE2ByTctwpCiHHgLcATgYb2QCHEWSi1bGiTmCiSWwSLFIICfkktubup8qqQG0k66nC1xS/5SKsApV4TrArPVb2V4gmWnkjNeXOszK4MH+9FDZbnge34dRbBwMoj/GQTuHb6YGmSm3bVd8u2JYFV6i/B8j0cp3nsuOfBlFtJRmp6vPIKstWmgpVKSYokm4wUggK2ExAjXPYUFYugep9qPx/Pg4nOFoQbol2LoF5VXZldSSAD5gpzTKWnentRCREqsLUKlp0aWA2WUbAGg1FUsBaLixRLxZFRsPpBsIpBES/wYl0rR95T4OCt8H//+g88omZ+0shWmBTKxaACRaIES/e/qu2DpRbBgjqCJawSAYWWYUl6sab2+EOtwcrWWwSFUHVYvl9xNKRT3be+MVgCEOJVwM+R8i9lQvBZ4EzgTuClSHldN4dPOqP+htgsvgj8D5BBpYwcj4pub4Q49lJb8dBomyT71mIz8CEp5Xwz4iSlvBC4EMC27T5UYLSHVhZB3b/IF1RZnFIxClang0Dez4PlI3ukYOnUoQIVn3bez4fNa2vT2/Rkco+3p4pg9aIGq1AA2/WrBhQpJSVrEYJkoR7RHk9JFaxMeX7gpiU5OotCToq8n2fKbZ6KVygkX0HsRMEqFCohGkktgnbK71uSX/S6QMUTRxUsHVfcDwUttAi2CKzYvridlJUK//ZXZtR3fyY/MzSCpScUdTVYA7QImhqswaDrkIs+KFjaKj4qClY/Ytp1LVXcour6iy4nb8PPHn0gj6h5bq5Q3zurHegxQDd11mhoEQwKZGpqewsFcFyfIuo+34wI64W8dHmuEt5zh2C7KwZFdud3x9ZggSJYxWIl6CIMuViGFkGDKrwe+Hz5/88DHgxsAB4CfBh4dDcHTzqjPgHVjOy3wCTwFeCRLfbZigrD0FgP3JNwGzfBvnHX+E9CiPcB00BJCJGXUp7fYr+hwrVdUlaqqYKVckv4UGURjN4Uw0LcDgcBRX6KyILd0f61iLMI5v18w2Q6PZmsjUXuhVXE88BOBVUEK+fnwPaQfjKC1YlFMF2Ow027kKP3FpOq6ws8UmlJs1TpaA+UljVY5RQvO91eTLu2giRtNNzK1tgLRC2C836EYJU/n34QvHYUrGiqlV5c2J3bzUErDmq2ayLcN39fqHjXYjozzbhb398lnBTVxrTbg4tpNwrWYDCKCpYeA5azgtUwbn1xEfsrX+WS48a4KdgWu1+nARdQYxFsUYOl1ScnFdTFtDupIHQqNCVYerGmvIm+5w5DwdL34kYEK5WqVrAyrpoaL1eLoEEIHyn1wPY04ItIuRP4MYpLdIWkBKuImidmUQrWFrlJlprvwtXAYUKIDcDfgecCz6/Z5lLgdeUaqxOAWSnlNiHE9gT7VkFKGTJNIcS7gPlRJ1caa8bWNPzD1wRLT9LDIv0yN0inu7cIhgqW31uLYL2CFU+w9Ep1bdBF3s93rWDpASE6oMwX5sHxKHnJim/aCbnQn4EmM3pw6XfIheu2tghmym9lUgXLbrMGKyRYCRWsVCpgfkAEK5MRFGYrBEvHFfeD4KVspUolJVgaWsHqRZLgF2/4Ii/57ksaPr/v+L78/U1/x7aqF1VCC2XNxGkYMe3DUvH2FnRKHoJSgBd4/SFY5c9+VBSsfhCshmEVX/867NnDz550LHfsviN2v24UrJBg1VgE83IGmC6Pz9VumNpFsNr5SNPz1YyFmbRAIIZCWho1GdaoVbAyaRs8YxHcC1BC5T7sBk4Bzos817VEn3RGfTVwCcoWuAb4pNgs/klukv/UaAcppS+EeB1wBWADn5VS3iSEOLv8/AXA5cBTgNuBReBlzfYFEEI8A/gosBb4vhDiD1LKU9t83SOFy19wOesm1sU+p25oauJapWCVJ8u9sAjmijmwipSC3ilYwvbxSpWbeG3IRRShglUT1d6LfiieB85UtYI1X5gH26MU0/S40TGARDVY4QQ+qybw2irYr8lpGDvuyqZkRRGsZLYH3egzaJdgpSvf01YoBAWcFqSwF/A8lbSZcZ1qi2CmfxZBUCpWkhTB6ICvFZteJAn+ZedfsITFBU+9oO65a7ddyyev/SS37byNo9dWB7uGISAxFsFBTYxm8jNMupN15M+gt+iUPOjvQT/6YGn1clQUrDBAqodWMd3Pqk6NuvBCOPJI5h9xHFvuuDJ2v25rsOIUrDyzwHTZcVI9l0jV3KM1wYJk4whU7rXptCDtpIdKsOJSBEEpWFGClU074BkFay/AO4FrUDzjUso8AyEegwrM6wpJCdargCOAc+Um+R9is/hn4MUt9kFKeTmKREUfuyDyfwmck3Tf8uMXAxe3OO+7Wl3bKOG4/Y5r+Fx04ur5Xmj56YdFsBQkTe1vDmXLq+7Y3lTByvRXwUq5paoUQa1gxfXkaniMlKRoyeQEq6yQaCLcL4KlBzm3BRmKNplMqmCV2iRY+juZNEVQD95SqkLjfkBfV9pOVxGsbKZ/ChaoZsNJFKwH7/vg8HdtEdSTzG4w680ylZ7iVQ97Vd1zJ91/Ep+89pNct+26OoI1ChbBWW/WJAgOAJ0SLH0v7atFcBkrWLEWwRtvhKuugg9+kI0r57ln7gt1zZznC/PsN7FfR+eUsnIvzDiZqvrQ0MpvV8aFJASr1XuixxmtYOlzD0MVaqVg1VkEU2puYGqwljmk/B5CHAxMImV0ZfMa4L+6PXzSGfXLgBNRRWAAc6jYdIM+w/MgHVWwSvU1WL2yCJb83ilYdsqvup4kNVhRgiWlDJWUbq/FdUv1FkHbwy8me71R8pA0RVBP4PXg0q+AAD2IpdOyIVmQUtUahaQiYQ1WulOCldQiWB6oi32ct+vr0ha3QRGsVdlVQ7UIzuRnGtYwHbnmSLJOlmvvqU/pHQWLYLNrN+gdOiUP2g3Qj5CL0CI4IgrWwCyCn/iEulG9+MVsWLkBgL/N/q1uv04tgmF4Q4xF0EONu9Kp3AwrBEt2TLD0+J/NVBOspWARzGZMDdZeAyn9GnIFUi4AX+v20EkJ1glykzwHyAPITXI30OPuMQZxqLVe6clrdFWo2z5YOV9bBHupYAWJFay4kItiqYhE9sQi6LpUDSgLhQVw2idYAtHyPdaDzljZIphOCyxh9U/B0hPitGhIFnSR8lhWvd6kClanBCupRTC0mvRxkbARwRrL2H09dyuLYFAK2JXbxdqximVlMj2JJayeWARnvdmGKXyO5XDsfsdy3b31CbRhSEutgjXgFEGTINh/aPLQrjK5NylYtrAT3ffbQahgaYvgvffC5z4HL3whrF7NxpUbAfjrrr/W7ddpyEU07KfWIpiTMwBIq17BctP1CpbrJrOCVyyCeiwsE6xgeARL9yisRa2ClS2HXHzimk9w+tdO5/Svnc5zv/XcgVyrwciga19N0hl1UWwWNuWodLFZrKV1bLpBD6AJAlQrWJmy3SvaB6vbmPbA7x3BcmoIlhc0rsEaT41jCatKwQpvzj2wCLppYhSsAsVCsterPgORaBW/YhFUN2jX7W+T1tDS5TYmWPrxrCYVCWuw3Db7YLlucrWuEBTC73W/CZb+DKoVrP4SrNXZ5hbBXbldSGTViqolLKYz031XsAAeut9DuX7b9ZRqsopCy2ltDdaAUwSNgtV/jJJFUC8SjlqKoBCqbqinCpZXo2B96ENKOnnrWwHCBNGte7bW7depghXtBxhVsOa8ubBpsG9VbPShgpUiRsGq3qbhObWClbbDc6ft4dRgbV/czqQ72fB7VatgTWQzPOvoZ5FxMtwzdw/3zN3DvfP3DvCKDUYAXXOcpDVYH0HVPe0jNovzgH8C/r3bkxu0hupdUY6UjipYEdk9ZSsxsVOLYK6YA7tIEPSmEEb37pqvU7Di+2AJIZhKT1WFXITWty4H2kJBJS7eHxNyUSwke726f0g7BEurRem0Wv3vt4KVyQhKJdWJ3q4R5kIFK2PDQnIFS7YR014owMSKMsFKaBEMldk+Os+in50XeOHfyHj58+nXubVFsFEzzkaWlV4RrNn8LAdPH9zw+Yft/zA+fs3HuX3X7Ry++vDw8VGwCM7mZzlyzZEDOdfejI4tguXFql6GXFjCwrXdkUsRhN5/97VFcDI9Cbt3w8c/Ds95DjzgAYBK+BQI7pmrdKYJmxN3GHKh73PaIqjHgC0zW0JnScmqjJGhkp2WFOarj5NOGGak7yXRsXCYNViN7IFQr2ClXYtvPP0bA7o6g6FBiMuIJ1ICiJc720AigiU3ya+IzeJaVIyhAP5RbpK3dHtyg9ZQ/YvU/wtBIQy5CEMU0mpwSlmp7kIurCK+3xuCpX3aJVnCL/k4ltPUIgjKJrinEFGwgu4VrFJJrUil4xQsx6NUEvi+Wr1q9XqSEqxwAl/2cCfdr1PURuF6HozVLCzr93si68BC8hos2aZFMOwtldAiqK0mw7EIOn0996rsKkqyxB5vT6wa04hgrcys7EnIxUx+hmMzxzZ8/qHrHgrAdduuqyZYjUIuBmgRnMnPMJ2eHsi59mY4lvobGAUFC9S9ftQULOj9/Xu+MI9AqPfvo++G+flQvQK1YLp2fC3b5rdV7QMxyYMJEbUIZpxMOB5u2b0ldJYEMQqWm4a5ndXHcceqt2l4zvK9RNuxh12D1YpgRRWsVnMCg2WD93f4XCIk/hrJTfJW4NZuT2jQHjwPpqciClapXsECurIxaIugX+wtwdLHnnAnmoZcgOqFFbUIhgpWFyuZetUukxEsFheRUiKECBUsfS1JCVaS91g/Pz5WTbD6HXIRDW1oRLDGs8mSkbSCRbsEq41AD8/3wl5ugyJYJVkJO5kYS/X13NFmw5pg3brjVg5ffTiWsBoTrOzKvtdgATxw7QNxbZdr77mW5x5TqS1oFtM+CIuglNKkCA4IQiSzPdeiHyEXoCbfegwYJQUrbad7HtM+7o5jLSzChz8Mp58OD35w1TbrJtZVEayGvbMSoq4Gy88hpVT9tsrvtW8tVLYPa3thR41FcEpl8SROERzP9oZg3T17Nzfcd0P4+4r0Ch598KOb7FHBjsUd7Duxb8Pnay2CKZMwsHdAyl+E/xdibfmx7b06fG+Kbgz6BhWvXW8RzEYKR6G7QSDnK4tgqaRsZr24Zq1O6Jup53stFayoRbAXNVhh7VFaIKlErGsFq9G1xB0nVEFKCQlWtkKwUnb/LYI6VCPu9ejHxrLJkpE8v7MUQZ0WldQiqL+7gyJYUFkJDslmH2uwgLAO68d3/JijPnYUb/vx24DmCla3FsGSLDGbn21ax5SyUzx43wfXBV00sgim7MEoWIvFRfySb2qwBoRO7Mv9UrCyTjYcA2oJ/jDRDwVr0p1Ufa927YJzz63bZt3kOrbNxShYHVoEqwhWmRh7gceWmS045cVQX1QIVtgvsGYMqHXUND1nODZVxsJu+mC96OIXhYETp3/tdE7+/Mn8fuvvE+3brkXQKFh7EYTYhBA7UALSnxFiO0K8sxeHNgRrxBFtEBtVsKKyO5RrTLqyCKo7i77BdINo8qG+mbZUsDINFKwurCIhwSqvoOmV1/nCfFsBC21ZBMufwURWLYH1W8GqROE2Dm2ohFxYib4nnaYIZiPf01YoBIW+N/vVx44jWINSsHS/mQuuUe3/3vfb9/Gtm7/F9kW1SBZLsLpUsOYL80hkyyS+h+73UK7bdh2qHaFCI4vgoGqwtEXMpAgOBp18rv20CC4U1SR/OVsE5wpzrBRj8P73w+MeByeeWLdNnYJVG4zRJmoVLFBj7B2772DlhPocC2Iu3L5SgyXqCJauCU9iNYfqxcaMk+l4Ifi+hft44sYncvWrruYXL/0FAsGP/vqjRPvuWNzBmmxjgmUUrL0UQrwReBTwcKRcjZQrgROAR5af6wqGYI04osqAF3ihOhAtHAU1IHUVcmGp4/aMYOn6mvJNOEkNVjSmvRc1WFFiAZU6rIXiQluT+3YIln4+SrAGEXLRLHY8OrgmSXHyApUi2C7BymQsLGG1JJNSSoql4kAJliYMIcHKDs4ieN/8fVxy2yW87uGv46T1J/GyS17Gr+/6NeOp8bqggOnMdNc1WHr/VirQw/Z/GDP5GVXoXkY4sapVsAZkEUx67Qa9QUcWwT6EXED1vX6ULII6IKdXmCvMceYffdi2Dd7yltht9p/cn/vm7yMoBUDva7BAfY5bZrawenIciDQcJpKGm6knWNmEtbZ6bJrMulXn7lTBms3PcvCKgzl+/+M5+eCTOW6/4/jJlp+03C9XzLFQXDAKlkEcXgw8Dykrg6CUdwAvLD/XFQzBGnEoSb6ceFYVclGJPgU1IHVbgwW9afqqFCxROXb5Xyvlh8/XYsqdio1p72agDa1x6XoFS8fcJyVY0ajvZqhYBNUEPul+naIdBct1k60gagWr/Zj2ZBPxsFm2OxiCpT8DiFoE+0uwVo9VLIJfvOGL+CWf1z78tXzzWd9kLDXGD27/QeyAvzK7Ei/wqkJZ2oW2WbWqY4oGXWjov7u4mPZABnWx7r1G0ms36A2S2J5r0U8FS2OUFKxex7TPe3O88Kc74eij4UlPit1m3cQ6AhmESndV8mAHqI1pB/U53rH7DlZPqGN6xChYrlWvYLnJnAoVBasyFnZDsPZ4e8KemQCnbDiF3239Xfh9bIRWTYZh+ShYQghbCHG9EOJ7w76WJYIUUu6oe1TVYXX9LTAEa8ShwwMEosoiWFuD1c0qW97P45S/Sr0iWPq6whqswGtKahpZBHuhYGkPuJ60zhfmq0IhkhwnjPpO2Gh4cqyyatfPPliVQuLGNUVVClYLD7xOftQ1WEGgfppByur3qJWCFV0dbXTNvUJDi2B5VbVf516ZUZXgOxZ38OnrP82jDnoUR609igOmDuAb//QNbGHHE6zyft3UYSVVgR60z4NwLKeaYDWxCEL7TWnbhVGwBotRC7nQGDUFq5cE67Cb7uXwu+bh9a8HER8stW5yHUBYh9UPi+CdM3eS9/OsnVKLGQUq42/lHl1PsJJawfU4E3VzdNoHyy/5LBQXqhZeTtl4CoWgwK/v+nXTfZMQrFoFa6kSLOD1gEn4To5mX+Ku/+iNEDoE3Hwz/PSnybZdXFQpePomryc40ZQ6KFsEO6zByvk5UikHn2qL4A03wK9+1f7xduyAA4+uV7Cyrotnw29/C+efX73Pzbc/nvyfF/nfj6hY9xvv2x+uPYeLUwdw9VTtGZJha7lP43g5nlyvdM0X5kNL3Ze/DAcd1Pw4998PD3xgmzHtAw650Of72tfgqquqt7npJsJraaVgRVPkrPJ36yMfaT7glEqKZCV9rWEBdXmg/u53YcuWJjt0gR07aghWURGstKMUut/9rv672BukSF/7Zj59S4ate57II459UeQ8j+GlhWuw77bqzn3dtofD9efwsfMF6zpbrOZP96+Ea87hCvswbp1utmWafW/6Ty6+fZL9y0PyL//yINhzcqxFkF0bOf98SPdx1PhLXv2xmxqswcC1XbbfuU/iv4GVK2Fhn0Vc28W27NY7tIGMk4Etj4F114+UguXaLvfffETP7hP/eJHDTHqci+ZfiN/gmFt2PwR+fw4XBi4P3Ad+9bdD4KZz+MbEPkx18NbceKP6NxpycfP2mwHYd8U0APkGBKtUUmOAZUE+n5xghRbB8WqLYCfzFE0wowrWow96NCkrxU/u+AlPOjReCYQKwVo7vrbhNsshpl0IsR54KnAe8KYhX85SwbEIsSfmcQF0vrpfxhL8Gi19XHUV/PM/J99+wwZwZ90qBWt6ymb9eji83MKmW4ugm5oiR7WC9brXwa+bLw41xPqD1bXoyXzez5N20mzcCD/6kfqpxpOBJ/PGH+jfjwXO570/qN2uPVgWrD+wBDuqLYLT++7BtuF//ifZcTZuhJ22y0Jhoel2haBA2k7juoJDD1Wfz1VW/0MuNhxsY9vwwQ/Gb+e6sP/+rVcQoylyB25Uj70p4a16wwZI3dvaIqi/p2v2KZLJwKc/nez4nWLjxmoFSyCwhc3GjXDlleqnP3g/ZY7Pl38AX6567rgG+zwUeCj/1dX3/hjgfN6f6Biq/84/f1H/fhqMPQz7s9WT55Sdgp9t5l8/0u+J7yPh9YcYBWtAcG2XP3zhRVx5Y/J9nvexFT23BwI4/jR88SvwxP83UgpW2k5z6yffwT933z2BQ9jC7dzGe3kLb39zs/dwA3A+F4R/w48GHs3bu7gvpNOwbh1sn1fzRk2wHv7gST47MYOfvjfcVt+jN2xUtdSvf33kyjYAO5NZBB3LYd1+FqtWqbHwr35nFkHtbokuvIy745x04En8eMuPm+67TCyCjhDimsjvF0opL6zZ5n+BfwM6XJrbCyFlb1eJamAI1hDw/OfDGWck29ayYNUqeOf7qhWssUyKu++ubNdVyIWfI5VSK0NRBWvXLnja0+Bzn2vveELAXxbv4wufrbEIOhn++EeYm6vf56I/XsQ//+B1/N+rrmbDyg3h79ecdS0HTx/c0esCNaj8cSaA66stgkccdj+7dye3iK1eDU/7msvuoPkoq+uXhIDbb1ePfe1LqdBD32toQnTMA21mZtQKYxyyWRgfb72CGFWwnvUs2L07WfCJbavV7XM/lNwiuGafgB07INd5uVEirF4N3/uzIlhz3lz58xHccAPsiVu76hFO+eIp3HjvDbzsuJfzvie9L9E+12+7nid96Yl8+Zlf4dQHnNrReT9z3Wd464/fws3n3NJ01Rbgs9d/lrdc+W9cf/YfWD+1nief9SuuveRE1AJeBa7tQm4Vxzy4yM9+0p/Zxw9/CC96EZBbaWqwBgTXdinMj3PKKXDRRc23/dWv4JnPhD2zNlm3t/ZAAMtbBdJG5Nb2XB3rBq7t4i9M8drXwubN3R1r/J3nU/pkibnzF9j+rMbbeb7H+g8ewFsf9Tbe/A9v5r9//d988Hcf5L5/vQ/RwFbYCnoMyHrqs7tpu7I2vOAZ+/DtwjO5f6HS/kffo5/7vIAXnlmxids2uOPwpvckU7AyToYVK2BnuVnxz36mCJbuSZkUOgArqmCBqsN618/fxa7crjBYqBadWARHUMHypZTHN3pSCPE04H4p5bVCiMcO7KqGBCHEZwH9mo9psM1jUaQzBeyQUj5mUNenMXpfo70AmYz6aQfanqZDLlJWqu55LaO3i7yfJ51Ssn9UwdqzB9auhTWN70sNsbWYCY+t/804GeXDjlmcPGC/NIzvxJmYYc0aSE3OwvhO1u2bYk2X6zHZeTWgaAVrobjAhDvB5CRMtnHsJCqh53t1AQGDCLlI22nGszDRwqLfqgartgZnerq960liEYwGKYyPq0G/39CfyVxhLrQfuW5n3+2k2GetBXM7+ZfHPz/xeTZYkzC+k1L2/o6vzc/cC+M72XjAVEs738lHHQ2/3cn/3vjvPGDVA9guVkPp0coKFJlDp6wUeFOs2D9gzZr+EKwDDlD/2sVVPa/vMYiHa7sUc1nWrGn9t7B+vfp3fs5ibL/eK1hWYRoAuzjd82N3A4cMpUKWffeNf49+9NcfcdSaozhwxYHNDzQ3h/zqZ/ja0eAeOd3i/U6zao1k1v4ra9ZAkLmPyZUea9d2Rq6i0H9bN22/if0n9yfjZBh3x1mYiTQaDir36BUrq/cvBupemkTBqlUi03YaiUqRbafXmVaw4gjWpp9v4mdbfsaZR58Zu++OxR0IRFjfGocloGC1wiOBM4QQT0FZ26aEEF+WUr5wyNfVL3weOB/4YtyTQohp4OPAk6WUdwkh9hncpVVgQi6WCMIarLI64FjVM6duGg3n/TxuORkoqlbs2QNTHdY/6YLlWoLVCHrFWt9IexHTrqGLeqMK1kSq/WLhpCmCtfUDgwi5SFqz0E4NVidIWa0b0nZ7jk4QtQgO6rwnHHACpz3gNI7b77jE+/Qi5GLWm1WLGQm+E8fueywHTB7AF274Au/42Tu4K/9HoF7ZS9mKYI1Ptkg86QL6XjNW2q/jVXqD9uDaLv7iWKL7vN5mYc7ueUQ7gPDUGKCJ1qjALqrrinuPglLAGV87gw///sOtD/TpTyNmZ/nfE5OFVUR7Yc0V5joOuKiF/ux25XaxcaXygU+4E1X292b3aD33SBJyUXsP0uN5u3VYjdJFH3HAI5hwJ5rGtW9f3M6q7KqmquhSD7mQUr5NSrleSnkI8Fzgp8uYXCGl/CWwq8kmzwe+I6W8q7z9/QO5sBoYBWuJoDbkImVX3wG6iZLNFXO4NQqWlMrK147CU3s9UN0Hq5mvXq9MaStASBx64MXXK3bRGqxOBqtEBKtUiI247mfIhS3sOsLdCGk7XZXWGHc86DwmOWUnr8Fa7gTr3Y9/d9v76NqjbpoNz+RnEtcwZVNZ7nrjXWH8+pe/JHjZ5epvf999K9u5tguFSbLj/SNY+l4zVtq3+YYGPYNru/j5bKL7vN5mccFmog81WBTUCURhxOyhBTU2xb1H9y3chxd47MztbH6Mu++GTZvwHncyV6//JS9PELe+bnJdmCI4X5jvuAdWLaKLlhumNwAwnhoPmzxD83u0ECLRWBinYEUXXtuJnG+kYKXsFCcffHJTgrVjcUdTeyDUK1gjaBHc27AmQc1ZMxwOpIQQP0fVpH1YShmrdvUTRsFaIqhVsOIsgp2mCOb9fNjbQt9gFhYUyeqVgqX92I2gb5yhgtXlRD8KvWK3WFzEL/nk/XzfCFYji2A/Qy7aeY/aqcHqBO30ChtkIbt+jwZJsDqBbdlMpae6ajY86822lcJnCQvHcnAsh5XTapW3TsEqWwSzE/2Ladf3mnTQR9+mQRVSIkPJG29LwcrNO30JucBTJxBeh4NOv1BOtox7j+6eVYXQWmGJhZRw9tkQBPz9/e8EhqxgRey3WsEaT43XKViWsBqqPknawnh+/dhUOy9IiriQC41TNpzCn3f+OfwsapGEYNWmCC41BSsKKeXPpZRPG/Z1dIkdUsrjIz/tkCtQ4tHDUKmKpwLvEEIc3vOrbAFDsJYI9A1NqwO9tgimayyCeoI1MItg+cZ5z9w97Fjcwe787raUmWYIFaxiLhxExt32C38SWwRriEPK6q+C1Q5RaRWG0qgPUlIYi2D3mM5Md90Hq9MUPv33XkuwHE2wxvtPsFK+IViDglVsTB5qMT6uAozyi+m+1MiV8opAyBEjWDKvlJa492jrHpUT2nRB5Ctfgcsvh//6L2bWKQtwEjVq/8n9uXf+XqSUSsHqsMlwLaL2zpBgueN4gRfWeMeNY1EkqkcO6hdVNeFql2A1CrkAeMLGJwBw2Z8vY8fijrqf+xbuaxn2swRCLgzaw1bgh1LKBakaCf8SFU09UJiv0RKBtgD6JR9b2HU1Clkny3xhvu10HlDWudWu+iroFZyBE6zMCixh8ZYfv4W3/Pgt6rEe9cIJa7D8XKXRbL8sgkG9RbDfIRftKlhJYto7rsEacYtg3OczaliVXcWuXDN7eXPM5mc7TuFrRLBkMQ2lVF8JVjYLWD5OMT4NzKD3kF5j8lALIdR23oLbFwVLEyy80UqZbvYe3b2nrGB5DRSs++5TGecnnQSvex1zd6u+J0nI0rqJdRSCArtyu5jz5lg/tb6zF1CDKDmOWgQBFgqqmW+r+2RSN0cji2C7i8F7vD3Ywo793h2zzzHsM74P51x+Dudcfk7s/o8+6NFNj78MQi4MqnEJcL4QwgFc4ATgQ4O+CEOwlgiiFsHa+iuA4/Y7jvOvPp+bt9/MA/d5YFvHzvt5MmllBdArODpKvdMaLH1zru2D1QgZJ8Olz72ULTOVjrNHrTmqs5PXwLEcUlaKXLF7gtVqYGhEsPoZctGWgmU3b0gd2vc6tGa2YxEcBsEa9Hk7wT7j+3Dfwn0d7z+Tn+m4tYH+e69tpeDn1aQsPdafhQJQE3iRnscetRqcZQzRpL4oDpOTMLOY6UvIRZBXk+eSN4BY0TagryfuPdK2tIYK1j//M8zPw2c+A7YdtutIZBGcXAcoV0cvLYKu7SIQSGRVyAWohN1eEaxmIRdtK1j5WabSU7GLx5aw+O5zvsu1266N3VcgOP2I05se3yhYSwtCiK8Bj0XVam0FNqHi2JFSXiClvEUI8UPgRqAEfFpK+adBX6f5Gi0RuLbLYnGRYlCsq78COGXjKQD8ZMtP2iZYuWKOdEoRrF4pWJawcG23rg9WMzz18Kd2drIEyKay5PxcWMjbtxqsoL4Gq68WwQTvaxQtGw0HXSpYVioksY1gCFZz7D+5f9gEtBO0W4MVRSMFq7hYJljjndmQEyO9J6zFMRgAmtQXxWFqCrbnsow5vVew/Jw6ZpDrDZHoFYKcJliS2v5woYIVV4P1gx/AN78J7343HKUWC/W9MYlFcN2EIljb5rf1NORCCEHGyVCSpZDEacu8ttD3RMEKvLpr7rgGq7An1h6ocdKBJ3HSgSe1dcwoHEf1+ioUVO9RyxTPjDSklM9LsM3/AP8zgMtpCPM1WiJopWAdMn0IG1dubJqm0wh5P0+23DCnVzVYUG1Hy/t5Mnb3keudIutke6JglWSJoNQ4Sa1hTHu/Qi5iCombIWlMe8c1WCNuERz0eTvBuol13Dt/b5js1y76UYPlLajvQ3qsfwTLL/lIdza0ZBn0H9qW1w7B8nNjfbEIahJf8sbCxrYNoWfDA0CQV+QjM15/Pk2wZvIzSCkrT+TzSr064gj4f/8vfFj3qmxHwdo2t405r3cKFqgFx0OmD8ESagoYWgSLPSRYMWOTHlc6CbnoZ/NxbQnM54090KB3MArWEkE0pj1OwQKVpvP1m76OX/LbCofI+3myk+qYvVKwIIZg9aCnVafIprIs+otdESw9OBSCAlkr3iITNzDpmPZO6uNaIS4Ktxl0o+FG19JtDVYSMtmtStYJlhrB8ks+Oxd3tizOroXne+T9fMcKVjYLtl1PsAqL6m/XHWtvYtQO9nh7IL2HINeiYatBz9AswCEOU1MQbB3vi0VQf8dAuepWMAtXXw27dqmf7dvhttvgppvg1lvVYHX44fDAB8KDHqTqnP7hH3reuVwra8oeW32v1SEXgQxYLC5WwpPe/37461/hyitVR3P9urSClbAGC5RFcKG40LOQC1ALjtoeCPUKVpwTI4qkClbtmN9NH6xmCla30JbAXM7YAw16B/NVWiLQN7Rm5OkJG5/Ap677FNfecy0nrD8h0XGllOT8XKhgaYLVbQ0WVKslQydYPVKwoEywGkwwGsW0g1qhj1Mfu0EnChZAsVSMHUC7rcFKYocctoI1yHj4ThCuXM9va5tg6WL7ThUsIdTffG0NlpdT718q2z+CNZOfAXcurPcy6D+a1RfFYXyiBN5EXxSsQk79Xa5kF+JdH4bPfRhma6x3Bx6oCNXjHw+ZDNx8M/zhD/Dtb6s4dMeB44+HJzwBnvlMOO449aXuAn4uA6kFAqrva37J5565e9hnfB/uX7ifmfyMIip33gnnnQfPfra6jgjaqcEad8eZSk/xl11/AZLZCpPiJce+pKqUQCtYenzsd8hFJwrWvhP964+nVavFRaNgGfQOhmAtEbSyCAI87pDHAfDjO36cmGDpm2Q/LIK63kdKGWudGyR0DZYeQPSA0g6iBKsR4uJt9X7NPrtOkffzbU12ohaNuAG06xosYxHsGmHtxdw2Hrzvg9vaV9eCdGOnmZqqV7Dy82WCNZbr+LitMJufhfQevMXhLcTsbQjK6kxcfVEcxid8Fdffh5j24rzNJt7Fm/ggU/87pwjS2WfDAQfAqlWwciWkG4whc3Pw29/CL36hfv7rv1Tt0yGHwDOeAU95CjzqUYqUtYlCLqu+l4Gsenzb3DZKssQx+xzDT7f8lFlvlgM4AN7wBiUDf+AD9ZfpzZFxMokdJusm1vHnnX8GOlsUbITzTjmv6vdoyAX0hmDFBTB1HHLhzXL46v61MdKkyihYBr2EqcFaInCtCMFqYBFcO76WY/c9tq06rJyvJkzZdL1F0HUbj2dJoC2CetI+TAVrLDXWUwWrEWItguXPqx9BF+2GXLSyaHRbg+VarS2C3apkncCxnLDeYOQJViQ9rF3oNLNOFSxoQLAWtILVP4I1k59RE9mF0f58lhP8cn3R2ESroieFzEQRvKneK1hS8tbb/413sZkrOJUbvnSjUqWe+EQ4+mjYb7/mg9HkJJx6qiJWv/mNikf/zGeU2vWxj6njrFwJT34yfOhDymYoZePjRVBYyEB6T939W9dfPWifBwHl7+93vgOXXAKbNsH6+lj1dsMq1k1WCFYvLYK16CTkomWj4ZgWIp32wdrjNQ+56BZRi6BRsAx6BcPVlwhc28XzVaPhZirIKRtO4WNXf4xcMZfIJ69vdOOZso0tomB1Yw+ECsHS5xi2RXCuMBcOIIMkWKGC1Yeo9nYtgq0GuF70wRpFi6A+XyPlbpQQTQ9rF9oi2E0PucnJeoKVW1BDhZ1ZbLm/lJLf//33LdMka/G7u38H6QkW582wNCj4i1lwFpFCkGQ6kMkWwFtBtpcpglLCG97Ac2c+z7vT5/AO73yu2KfLY65ZAy9/ufpZWFCq1hVXwI9+BG96k9rmgAPgSU9SxOwJT4DVq9Xjvq+sh/fcAyefjLeYBncnhaAmQbAc0X7MPscAYP/y1/DKTcqi+PrXx15Wu3Hr6ybW8fM7fw70VsGqRVzIRbNxJe2kG/f+KqPXfbB61RczDlEFyxAsg17BjGRLBLrRcDMFC1Rc+wev+iC/ufs3YYfzZtAT7bF0mQREarC6sQdCuQarXHSvfx8Wsqks9y/cz3xhHsdyOppkJyFYcaETmhD3S8FqR21qNcD1ogZrFC2C+nxLgWBlU1mmM9Nsm2ufYPVKwdq9u/qx3IIDIkC4rRWs3239HY/87CM7O3l6M4sLNqWSiUoeBIr5bFmdySZakHPHPcDC8nuoprz97fCRj/CJzL/wjjX/BFvrawC7wvi4sgg+5Snq97vuUkTrRz+C734XPvc5Vad1/PHK2veHP6g4OYDJSd7hns5HUsfjFR5fdVgdcHHMPsdw/N/hoe/bBBs3qnh2N/4eM1+Yb0uJ2n9y//D/vazBqkWcgtVMMeo25KIdBasQFMj7eRNyYbDkYL5KSwRJQi4ATj74ZBzL4Sd3/CQRwcoV1YRpvIZg7dnTPcFKO2kWCguhKjLMcIGsk2WxqFIEJ9yJjtL8apsnx6GZgtUXgtWuglX+DBpZBHtSg5XQIthsoaAf0K9p1AkWqJXrjhSsHtVg/e1v1Y8tzjuQ3oOfoN3ADffeAMAlz72EVdlVbZ37Ug7jf36hUuS6vf8YtEZhUdvf7ETb6xRJnT7YFUolOPdceO974ayzeMPnPwgrvgVb6xXUnuKgg+CVr1Q/QQDXXKPUrR//WD3/mtcosrVqFXzjG5z6+W9ypvwqi084HN7/v8pmKAR377mbCXeCjX9f5IdfhtzKcVJXXqnUswaYK8y1ZxEsq9nQZ4tgj0MuGtVdd0Kw9njqy9BPgmVCLgz6AUOwlgiqYtqbWAQn3AlOXH8iP97yY97De1oet5lFsBcK1s7FnSOjYOmQi06tFl1bBPvQCyuukLgZWg1whaCAJay2Yv6jcG03kYKVslI9j6xvhSVFsCY7I1j9qsFamHcgvT3RIsEtO25h0p3k9MNPb/szvqW8YN+L+49BayiCdT+FIFnoj67B0/2zOsbCArz4xapm6dWvxvvQxylcaMGUUoX6SrCisG044QT188531j//5Cfz4Cvey8nWuVwwf7lSwR77WHjNa3jkN67geTeWWPsfT+WeFHzjQy/nNfvvX3+MCOa8ubYWHXQ9JvTXImhbNhkn07OQC71QV+fmKC+qtUOwerFo1Aom5MKgH+irCUMI8WQhxG1CiNuFEG+NeV4IIT5Sfv5GIcRDW+0rhHiWEOImIURJCHF85PEnCiGuFUL8sfzv42vPt5Th2i6BDPACr+XK/ykbTuHae65ld2530+2gEnJRaxFcjjVYuWKO+WL/CJZetRvlkAu9othIhYuLmW8HSWPah0FylhTBmljXUcjFrDeLQHQ1GYurwZqfs8CdS7RIcOuOWzlyzZEdEWh9zxnYBHsvh7eQhvRc4nuTrsHrimD9/e9w8slw8cUqcOITn2BuoTwVmVTf+VH6/HcuTvOFdcfxf1d+AT76UVWf9Zzn8Mzv3MpK34FXv5pTXuFw16rWKmC7FsEqBauPFkFQKlbYB6vFOKBDtxohdK3UKFhCiLB0ICkGoWCZkAuDfqBvXF0IYQMfA54IbAWuFkJcKqW8ObLZacBh5Z8TgE8AJ7TY90/AM4FP1pxyB3C6lPIeIcQxwBXAAf16fYOGvtktFBZaruScsuEUNv9iMw/6xINiJ99pJ81FZ17Eg/Z9UEh+JrLqRqgVrLk51cOxG+g+WKNAsMZSY6GC1UlEO1QGi0YDi5581g4qfQ+56LGC1Y2VM2WnkEiCUoBtxU84uiVxnWIpEaz9J/dn29y2tptTz+RnWJFZESYmdoKpKWXRi9ZBLcxbkN6T6Dt8645beewhj+343NDjGhyDhvByqdiEvEaws2oCHuQ7DLmYmYFHPhJ27oRLL4WnPQ2IfN7ZnThukbm50ZjlSgmLCzak95C3S/C618FLXgI33MCRvziTxzzodD59xv+y63++GqrHzdB2yMWAFCxQdVj9VrCgsvCaFJpgDSrkYm17rQcNDBqin2LoI4DbpZR3AAghLgKeDkQJ1tOBL0opJXCVEGJaCLEOOKTRvlLKW8qPVZ1MSnl95NebgIwQIi2lbC+uZkQREqziAmvGGnu8AU468CTedOKbuG/hvtjnL/rTRXz9pq9XEazxjLoR9rQGq9wHK7zZDrMPVqTRcL8UrEbhDf0KuZBSxkbhNkOSGqxuCEjUDtmIYBkFqzXWTazDCzxm8jOszK5MvN+sN9v1RET/3UfroObnRKKJ+Hxhnrv33M2Ra47s6tyjpGAsZ+QXXFiZnGBZaVWjU8x12AfrLW+Bu+9WUeonnhg+rD/vlzziGXzvt2JkPn/PA79oVX/3JycpnPQI/vqT7bxw6kBA2ddapepB+zHt0ZCLvhOsVA3BsrogWH7j1iztEiz9vhoFy2CpoZ8E6wDg7sjvW1EqVattDki4bzOcCVwfR66EEGcBZ5X/38Yhhws9KVwsLrasj3Eshw+cWt/kUOOvu//KT7b8hHfz7jDkYiKtBsxe12CNjEUwlUUi2ZXbxcErDu7oGK0IVqOI836FXISKWRuKU6uY9m4bQkftkI0+70JpuARrmGErSaFXrrfNb2uLYM3kZ7qqv4JqklP5v0BkWlsEb9txGwBHrTmq63Mb9B+5+RTsl5xgkVYfTOH/s3feYXKV1R//nKnb0zZlkwAJLXQIIB0FAooNREVBEUUUUVTs2GOsCDZEpYiAKCBYEH4CSpGiiEDoJYCQBBJISN2+09/fH+/c2ZnZKXf6THI+zzPP7t655Z2y997ve875nnKaQd9zD1x6KXzhCxniCsY/71MOOJ77JjXP558aR9bkwqtDr2IwzO2xva4mt00uGsEyxjAULs3kojvQneo5lm/Cqlp0BjqrZnJRaFI16A0SijenyYUKLKWa1LIGK5d6ye7sl28dN9vmPqjI7sAPgY/let4Yc6kxZn9jzP6tKLBGIiMFTS7csGj+Ih565SEGw4OpG+12fxs+n41gxWL2RLOl1WABrBtZV/MIVvYNfK1MLpz3tRRBVMymvdIIlvPdLJRK1qgIlvO5tEoECyjZqn0gNFBxMbjzf5+epjc0BJ7gaNEUwWc3PAtQdgRLa7Dqy+iwHwLua7AI2i9FeKTESYqxMfjoR62N+ZIlE552Pu/u7tw1gI0iNY7AUEbU3+mBtc2kZAQrWDyCFYqFiJt4SdcfEaGvq6/m0SuwEbKSGg0XqKMq5Bxcag1WPUwunAhWLKYmF0r1qKXAWg1sk/b3XCC7ajvfOm62nYCIzAVuAE41xrxYxpiblvQUwUrtrRfNX0TcxLln5T0Z4sfvtwLLubGqVh+sQukC9cLp8bJxdGP9UwRrZHJRzvvqXPBqVYPlRkxWGiUrl5ZKEUxGsEo1uqh2BMthcNDW3xT7Di/bsAyveNlh6g4VHVtrsGpPzvS3IiQC/Xbb0RL/h779bfjf/2wEq2Ni/Vb6Naenp3k+/9Q4st6jVYNJgZVMEXQTwXKiQ6Xarfd199Xc4AJypAgWEVhxEyeeiOd8vtDkX7k1WPWIYGX/riiVUEuB9RCwk4jMF5EAcBJwU9Y6NwGnJt0EDwIGjDFrXG6bgYhMBm4GvmKMua/Kr6XhOCe7UCxUcQTr4G0Opt3Xzh3L70i5CLb72/H77QyOc2NVjRqsuImnLiyN7oMFlDyDmE65AqtWJheFConzkYpg5avBqoKLIJRuZV8PWkpgdY2nCJZCNWuwnPNAImFvNL3tI0WjsM9ueJYdpu5Q9nusEaz6kU88FCIqw+AN275obnnsMTj/fDjtNFi0KOcq6decXG0CGkW+FMGcEaxQ4QjWUMS+4aVefw6ZewgL+xaWtE05dAasi2C+HlbpOM/lOx9U2+Qi4A3UdII2XVRpBEupFjX7KhljYiLySaybnxe43BjztIicmXz+YuAW4C3AC8AocFqhbQFE5ATgQmA6cLOIPGaMeRPwSWBH4Bsi8o3kMN5ojFlXq9dYT9JPVJVGsNp8bRy27WHcueLO1AWizTeeIlgtgeWcEJ2ZvWaIYAFluwgWrcHKk3deK5OLfFa4hah5DVYTpwi2ksDqDnbTFegqOUWwFhGsYTs/gq/NXYpgufVXAIEAtLU1zw32lkw+8VCI0egoBAcZGirBau1LX4LJk+FHPyo6llYRWKsHVzO5bXJKLJUUwSoxGvXDY35Y0vrl4kSw4iaOwRSNYEH+WttC16agL1iyyUUto1eQKao0gqVUi5pqdWPMLVgRlb7s4rTfDXCW222Ty2/ApgFmL/8u8N0Kh9y0pJ/sym0Cm86i+Yv48p1fZmX/SmA8RTAWG5/ZrEYNFoy7ADXapt2h0ghWvuhPsQhW1QVWJRGsWtVgJcV/sRRBFVjF6esqrdlwwiQYDA9WLLCya7Ccn/72UMHPNZaI8fzG53nbzm+r+PjNkiK2JTMewXJfgzUaHcXTNuJeYN11F9x+O/z4xzA1f4NdZyydnc31+ae/R+nnzFWDq1IGF2Drg0aiI8QSsbzX56Gw3VmpKYL1wumDle86lo7bycZ8LoJO2p8bBsODNRdYmiKo1IKaNhpWqkf6ya7SCBbAou1tqsbN/7sZn8dnHzWKYDmpE81gcgGVC6yyUwSbwOSiXjVYmiJYOX3dpQms4cgwCZOoeoqg89PfMVbwc12xeQXRRLRsg4v04zdLBGNLJj064/bcNBYbw9s24u7zMQa+9jWYMwc+/vGiY+nutn3Xmunzz5siOLgqVX8FpCY1CqUJlpsiWC+6Al0MR4bzuuGmk+s8b+fLLcVMLkqNYNWyBxZkRrA0RVCpFiqwWoQMgVVhDRbAwlkLmdI2hZX9K1PCxzG5qFoNVvLGvz/cn/F3I0hPESz3AudcLEq1aW8mkwuvx4vP46tdDZaLFEFtNOyOvq6+kkwunJu7akWwsgVWoKNwBMtxEKwkRRCa6wZ7S6bcFEFf+6i7z+fmm+H+++Gb34T2wn2z0lsC9PRYA45wE3SwLFSDlS6wHAFQyEmw3BTBetEZ6CSaiKaMLkoRWKFYiLk/ncvvHv9d6m/Ifc1v97Wnonlu0AhWcUSkTUQeFJHHReRpEZlo1anUHRVYLUK1I1hej5cj5x8JjEd3fL7qmlyk12B5xVuV1MZyqWcEK69NexOYXDjr50sRrFYfLE0RrJy+rr6SarCcGpBK7Yyz66AyBFaB7/CyDcsAWNC7oKLjq8CqD+UIrLHYGP6OseKfTyJho1c77mjNLVyMJV1gQXOkCQ4O2qiaNxhJTUqFYiHWj65P1S/D+KRGoTosR1Q0awTLqU3ePLYZKE1grRpYxatDr/Lj+3+MMabgtWnvmXvz4uYXWTfirjx+MDxYU4t22CIiWGHgKGPM3sA+wLFJ4zilgajAagTRqH24JZGgZ/krnPI4nLEUusdyW6O64tVX4W9/g1tv5f2D89hzLUxK2JNltk171WqwQgMNTQ+E6kSwvB4vHvGUbtPeRCYXUDhFo9IaLDdistECq5GR1FKY3T2bkeiI69leZ/a80ggWZNbBOD+DHZGC3+FnNzzLrK5ZVYmgNcPN9ZZO6j0uoQ/WaHQUf3u4+Odz3XXwxBPWnt1FSGBoaPx6k6sPW6NwxhX0jTfWfWXwFYDMCFZSABRKESzXpr1edAaSAitUusByUpkff+1xlr66tOC1ySlP+OeKf7oa10Co9iYXrR7BMpakHRH+5MNV71ildrSmVm91rr7azuoFg3a6rrvbzviNjNhHNGqXT5oEXV2wYgV7Dg3xu+Tm4Tt+BR+Owqc/DQvyzBYPD8OLL44/HnsM7rsPXnoptco7k48V09fBRzfh90/NiGBV0+Si4QKrChEsKNzB3pm1a2aTC7AXvUJGHZXUYLkRk9po2B1OL6w1w2tc3ZSlIlhVqFdIjyI5P4Md4aIpgpXWXznHXras4t0oRSg3RTDYEWbwpQIrDQ3Z6NWee8J73+t6LNkRrGaIYjrjGko77zs9sNJNLlxFsJq8BquSCFZ6pP3Xj/yavWbuBeROX9+vbz8mBSdx5/I7OWmPk4qOazA8SE+gfgKrRSNYiIgXeBjrpv1LY8wDDR7SVk+LfpVanIUL4bvftWfvoaHxPITOTvvw++2ygQH787DDeHXBbN747NcJxOE3L+/Owssug1/9ygqs/faDffe1V4IHHoD//heeecYWGTv09cGhh8JnPgMHHAAeD2ZggC9dfjLf+0s/vPe9BL23Eo36GBy0w/B6K3uZzg1tf6i/4VGDdBdBZ6auHAoJrFSKoC9PimCVTS4qiWDdseIO3v+X90947rXh19RFsElI9cIaWsPO03Yuun61arAgt8Bq64rkjUwaY1i2YRkn73FyVY+t1I7BQRAxmEDxBtIOY9Ex2jojrMn3+RgDH/uYnci7+257XXM5lj77dW9KgRX2BrjlhVvY8JcNvDzwMkBGimCuGixjDN+6+1u8sPkFAB5f+zg+j6+h/SAL4Qi/TWObAHcCy7kGORGs4xYcx7VPXcuc7jlA7sk/pzzhzhV3Fh2TMcaaXNQxRbBJI1g+EVma9velxphL01cwxsSBfZI9YW8QkT2MMU/Vc5BKJiqwGsHee9tHCYxtepGnL/w6ALeeciILrzwdfvtb+M9/4N574Zpr7IpTpsBBB8GJJ8Juu8EOO9jHpIknKAFm93yDW+fdw/Hn3cjnZn2BK2f8LGM2sRLSa7CqcdNXCdVIEQR3AqteJhdOml+p0cG37/x2bv7fzTz4yoMTnpvbM5ej5h9V9pjcpgg24iaj5QRWMoLl1uiiWjVYkFtgtXfGGMvzHV43so7+UH/VIljNcHO9peOc5wfE/blpNDpKe1eUsTFbrzthtv+yy+Daa+0E4uGHlzwWaE6BdciC47lr5V2pc+YR845g3uR5qfVyRbA2jm3k2/d+m+kd01P/k+/a9V2ISL2GXxKVpAi+OvQqQW+QLx3yJW567iZ+94TNt8k3+bdo/iL++uxfWb55OdtP2T7vcUKxELFETFMEIWaM2d/NisaYfhG5GzgWUIHVQFRgtQgTTC5mzrQNHB3WrbNXgx12gBJO4J89+LNw8Gch/BlOuuACXp68N49MPq3i9EDIrMGa1TWr8h1WQD1SBPMJLBHB5/E1jcnFz479GT879mdVHYtDM6cItpzA6hpPEXSDM3tejRTB7m54xZaaMDRks5mDAWFwNPd32HEQrIbA6u62DnKRiDXcUGqDU180gHuBNRYbY2ZnPLX9lClpTz7xhE1bP+YY+MpXyhoLNF8N1qRJcOnbLy24niMA0muwlm9eDsBlx13GcQuOq90gq0R2imCh60q2o+6a4TXM6prFIdscwq69u6YMb/IZci2ab+uw7lx+J9vvl19gOf2y1Ka9MCIyHYgmxVU7cDRQnw7VSl7U5KJFSJ8JymnTPmOGdWwqd3bsRz9i6ZSj+cxzZzL7pfurGsGKm3jDa7C8Hm/qZF+pwMrbpLdA7w+/x980Jhe1RFMEq8fktsm0+dpcOwn2h/pp87VV5fuQHcHq6bHnnXyTBM4NVaUW7c6xoTlusLdk7OdqJ39KiWB1dsVT26cYHob3vAcmT4bf/c51aiDYrMJmj2AVw+vx0h3ozohgrdi8AqBghKaZqMjkYmgNs7tnIyJ8dN+PAvY6mC9at0vvLvR19RVNE3QmjTSCVZQ+4C4ReQJ4CLjdGPO3Bo9pq6cFtfrWSfrJriZ25z4fP9j7Oi64/wC+9vAJfOJ1S4G5RTcrRPqNXjPknbf724mGo6mZunLIjmC9PPAyL/Xbim9nFj/XhalQ5GskMsJAeIDZ3bNLGku5Eaxa0gougq0isETEWrW7jWCFBqqWiptLYAW8AQbCA/zrpX9NWP+el+6h09/JnJ45VTm2c9xp0yrenZKH9M+1FIHV3WNS2wPWmOntb4f//Q/uuMNmV5TA6Kj1eGplgQU2NTe9BsuJYKWnEjYzqQhWmS6CTvT6A3t/gC/f+eWCk6oiwqLtF/GPF/5BwiTwSG5B7kSwai2w0uvNWzGCZYx5AljY6HEombTgV2nrpNp9sHIx1j6Vz+94I5cvO4jvPf0OGPtX0QaRhUg/wTY6ggU2TTAaj+L1lO/eEfQGUxeVhEmw/6X7s350fcbz6fVeDgFvIG9U58t3fJkrH7+SBz/yILtOdx8BaMoIVpEUwYRJEE1EGyJyZnTOIOgNNm2jz1z0dbsXWP3h/qql0nR3Zwqs7m5Sjclff+Xrc25z8NyD894olXps57hK7XA+V7cCyxjDWHSMrq40gTU8DG99K/z73zZydeSRZY0Dxj/3zs7M5Y3EeY/cMLltckYEa/nm5czonNG0roHZlGNykR7BOnKe/ex7O3o5cbcTeeCVwiZ2i+Yv4vdP/J6n1j2Vch3Mxkm5rLXJhch4H9AWjWApTYgKrBYhQ2DlShGsAn4/PO/fnc/0Xs0l694BH/kI/P73ZacdNpvA6vB3kDCJivaRfjPy4qYXWT+6ni8f+mWO3v5owPYuynVh8nvzpwj+Z/V/GI4M887r38kDH3nA9WydY3LRTBGsYimCTmSrEQLrvXu8l0O3PbTmF+tq0tfVx9Prn3a1brUjWE4d1NCQ/fv8Y87nvbvnt93ebfpuVTs2aIpgrRkagjlz3AuscDyMwTCpx14PRl8bgje/Be6/37YeOam45Xa+ccD45+7xNEcvtETC6kfXEaxgZgRrRf8K5k+eX6PRVR8nRbBUgTUWHWNzaHOqZhTgkrddkoqE5cOpw7pj+R15BVa9Ilhg739UYCnVRAVWi+AVL4JgMDWLYPl8tgXXX2LHcdyB3+Ht13zduh2mm2mUQLMJrHZ/O6bC3nvpNyOPrHkEsDfu+8zap+h2uURHJB7hydee5Ih5R/Cvl/7FaTeexp9O/JMrpymnKXAzuVI54j9fimA+I5B64PP4WiZdx6Gvq487lt/hat3+UH/VxGO6yBkctDfik9ompZqE1pJmShHbkik1RXAsOgbApElCN4Ms/Mqb4cUHrGvgiSdWNA7IFDLN4CQ5MmLrw9wKrMltkzOizcs3L+eguQfVaHTVx2llUmofrLXDa4Fx11OwYq1YO5RtJm3DztN25s4Vd/K5gz+Xc51qGvcUw0kNbMUUQaU5UZOLFkFEUie1WkawolF7Ybv/yK/ai+ZXvgK3317W/tJP0M2Qxtbua684XSNbYAW8AVcz9/lMLp5a9xTRRJSP7/9xzjvmPP6y7C+cd995rsYSjoWbKnoFxXt+NVJgtSKzu2czEB5I3dwWYiBc3QgW2HNBtdo2lHNspXakCyw3PfpGo6MAzPSF+DvHMvXFB+G66yoSV844oPkEVq5xFWJS26RUSlssEePlgZdbKoLlEQ/tvvaSa7AcUVlqDTHYKNa9L92bd0Ku3hGs9J+KUimq1VsIx8GuJiYX2Jmb4WEbJu/uEbj8cli2zKZ+LF0K80u7WHjEkxIkzRLBqvS9C3gDDEeGAXh4zcPsOWNPV2Ih3yyxEwXbt29fTtztRB585UG++s+vctUTV6XWOWb7Y3Laqofj4aYQrukU6/mlAqs0nFnhfS7Zp+h394VNL3D4tu57DxUivQ6qlDqUah9bqQ3GjFuju41gjUZH6QnB+5acx1RW8rcPXM9x7zqh4rFk12A5vzf68881rkJMDo7XYK0aWEXcxFvGQdChM9BZUgQrHA+nXE7TUwTdsmj+Ii5aehG7/Wq31D7fucs7+c5R3wHqK7A0gqVUG/0qtRCpCFaNUgT9fti40f7e0wN0dcENN8DrXgcnnGCbGnd0lLTPNl+bFVjexgusLxz8harVYBljeGTNI5y4m7vZ23wW14+seYRJwUnsMGUHRITLjruM6R3TWTti0y6e3/g8v3zol3z3qO9OiL41YwSrmVMEW5E37fAmTtnrlFS9XSF2n747p+59alWOm54i6NRg1Qutwao96c59rmuwNq3j9qugd91K3smf2Hfe8VSju1N2DZbze6M//1zjKoTjImiMYUV/a1m0O3QFutgwugEoPYKVniLoljfv9GZOX3h6KhXw6XVPc8EDF7D4iMX4PD4GQgO0+9prlrWTjkawlGqjAquFqEeKYDjZ4il1UdlxR7jmGusU9ZGP2GLmEmp+2nxtDIYHmyKCdfwux1e8D+dm5KWBl9gc2sy+ffuWtF02D695mIV9C1N1VF2BLi58y4Wp5+9YfgfH/O4Y/vXSv3jzTm/O2DYUDzXF+5qOUytYLEWw2YRhs9LX3cfvTvhd3Y/r/P9v2AChUH0FVldyHqHREYwtmfT0t4DHhcAyhtmf+irda+GxX36VO79wPDtW6fPJlyK4xp15Zs0oNUVwcttkYokYo9HRlEX7/CmtkyIIZLQwKZQd4TwXiUfoD/Xj8/jo7egt+Xgd/g4uO+6y1N9/fPqPvOdP7+GhVx7i4G0OZjA8WJfoFYwLK41gKdVCa7BaCOekVkuTC4eMi8qb3wzf/a4tZv74xyEed71PRwA0mxAoFydN8+FXHwYoSWBli45oPMrjax9nv7798m536DaHEvQGczZkDMeaL0VQRAo6JmoEqzVw/v9ffTXz73rguMipwKodGQLLTQTrZz9j6j/u5ZyjYfjYo6paI7XF1GAljRgGwgMs37wcn8fH3J7KeknWm3RjikLnaGcizYlgzeycWZUWDUfOt1bvzvVuIDxQN9dX5/5HI1jNj4hcLiLrROSpIuu9TkTiIvLueo0tHdXqLUQ9IlgOE/LOv/IVW6D1gx/YPMLf/x6CxW/unUhFswmBcnFuRh5Z8wg+j489Z+7partcJhfLNiwjHA8XFGnt/nYO2eaQ3AIr3nwpgmBfq6YItjbO//8rr2T+Xc/jNzpFbEvGeW+7u8Ef9rNi8woufODCnOvOemI57/ziz3nyoHn89OCVnORvr+rnMzRkb27TLyfN8Pmnv0ducAxm+kP9rOhfwbaTtq1ZvXStSI9gFZrIdUy3IvEIa4bWlJUemIvejl4WzlrInSvu5Ouv/3pDIlgqsFqCK4FfAFflW0FEvMAPgX/UaUwTaK3//q0c56a0liYXDhNm7UTg+9+H3l74/Odh82Zbn1Xk6rOlRbCcRsOPrH2E3afv7vp1BbwBRqIjGcvSDS4KsWj+Ir5+19dZP7Ke6Z3TAdv087kNzzWl7XghV7JwPJxaR2lenP//1asz/67n8RsdwdiSSY/ObB/fnnteuodP//3TE9abNgKPXgIre+CIN6wk6Asyt2du1SNYPT2ZmefO/o0puw1jVcbljMUNTqRlIGQjWK1WfwXjESyvePF6vAXXTQms4TVVvQ4tmr+Inz/4c0ajo3UVWGpy0ToYY+4VkXlFVvsU8GfgdbUfUW40RbCFqIfJhUPei8rnPge//S3cfTccdBA88UTBfW5pAivgDRCO2RTBQql92eQyuXj41YfpCnSx87SdC27rNDG+a+VdqWX/evlf/G/T/3jfnu8rYfT1QVMEWx+nDkoF1pZJuni47LjL2PDFDRMfn1/Hq48exdxwkGn/dycvfnMDm87ZxOzu2TURWOn09FgTjtHR6hyjHEp2EUyPYG1ewfaTW09gOUZKpTjjrhlaU5aDYD4Wbb+ISDzCv1/+t00RrEMPLNAIVpPRKyJL0x5nlLKxiMwBTgAurs3w3KECq4WodYpgwQhWOqeeCn//O2zaZB0Gf/YzezXMwZYosDaNbWL96HrX9VfOdtmi45G1j7DPrH2K5q7vN3s/eoI93Ll8PE3wskcuoyfY49rFsJ5oimDr49RBOSmCKrC2LNIFlkc8TOuYNvHxs0sI3PFP5IILmHzoUUzrmJZqRlsPgZU+zkYwOGjTFl1kwgPjNVirB1ezfnR9yxlcwHiKoFuBNRwZZv3o+qoKrMO3PRy/x8+dy+/UCNbWywZjzP5pj0tL3P5nwDnGGPeGATVAv0otRD0jWEVn7Y4+2kavTj8dPvtZuPVW+NWvYIcdMlZzaq+asVaoHALeAAYDuDe4cLZLT5uLJ+I8tvYxPrrvR4tu6/P4OGLeEak6rM1jm/njM3/ktH1OyyhKbhb8Xr82Gq43kYjtWffSS9aZ4tVX4bXXoL/fPgYGrB1gNDr+iETsz1jM3kV2dNhHdzdMm8ZlsWm89uJUBuhg/u8CcHfQ3n0kEuMTKp2dMGmSvSOeOhX6+mDWrPEQWJl0d8PatRW/K0oeitYX/fOfsHgxvO99cMbEyeNq12Blj8P5e2jIfqUaQa5xFcKJYD229jGg9SzaoXSBtWpwFVCeRXveMQQ6OXibg7lzxZ0MhDSCpZTF/sAfku7MvcBbRCRmjPlrPQehAquFqJfJhcfjst3V9Olw441w8cXwxS/CbrvZ+qyvfjV1g7UlRrDAzvruPWtv19tlm1w8t/E5RqOjrkXaovmLuOm5m1jZv5K/Pf83QrEQH9n3I6UNvk4UciVTgVUFjIHly+HOO+Hf/4bHH4dnnrFCycHjsf+fU6ZYATRpkhU+fv/Eh89nxdboqH0MDsIrr3BY9Am6Y5toZwzfL0ucCOzsHBdbs2ZZ8dXZaR8dHeO/5/l7G38nz/Z3QrwDvIVrQZTSKVhftGaNFVYLFsAll+Qsgqp2BGv69In7Tx9nI8gVWSuEU4P16NpHAZg/uQUjWIHSBNZL/S8B5TUZLsSi+Yv41t3fAurTZBhUYG1JGGNS/3wiciXwt3qLK1CB1VLUy+Qiu+C4ICLWuv2446zT4A9+AFdeaW3dTz11ixVYu/bumkqXcbtduuhwDC7c1nEtmr8IgDuX38mvH/k1+/btW1IErZ74PRrBKgtjbNTpueds+GZw0EaeBgfHH/398PDD8PLLdpuZM2HhQttKYe+9bd+6OXPs8gqFyQkHwoMP2t8HNsXpCYatiPN6rYAzBkZGxse5caO9OV+71j6c35980o57ZMQ+jCl67AucX3zY6FpnJ7S12RYRzqOry96Zz5hhf+b7feZMu72SYnAQAoEc6W/RKJx8sg3f/POfeSOR1TShGByckPjQkgKr3deO3+Pn8dceB7aOCNbzG58HYHb37KqOY9H8RSy+ezFQP4GlKYKtg4hcCxyBrdVaDSwG/ADGmIbWXaWjX6UWol4pgmXVW8yZA1ddBZ/4BJx9tk0dPP98Dn/bdP7UueUJrFLFTXZd0sOvPky7r50FvQtcbb/b9N2Y1TWLn/73pzy9/mkueutFJR0/J8bYyEU4bNPHwuHCj0jE3uROnmwjIs7Pri57w+281hyGHg5bpMCKxWBszIoHJwrkPAotGxqybpz9/baj7/PPW6GSjYjNVerpsY/994dzzoGjjrJRhhrZrKWfB7omecGTY0Kho2Ni+KEQxtjvkiO2nPci6++brh3hX/8Y4bxvjiBjyedDISvufD77fRschPXr7WPZMli3zn4OuZg8GbbZxp6nurrGi2tE7H6d7397u32vu7rsT+fR02MFmxORmzatpSNrOcXDK6/Ae98L991nz+W77ZZ3+54e+7UPhexbVu2xtKLAEhEmtU1iw+gGugPdTG2fWrvB1YhSTS6cibRqpggCHDDnALoCXQxHhuvWB0sjWK2DMebkEtb9UA2HUhAVWC1EvUwuKup5c9BB8N//wl//Cl//Op/+0b84pA982z8JOx7bOM/dKuF8BqU4CDrbZUSw1j7C3rP2dh2NFBEWzV/E1U9eTYe/w7174Jo1NuLx6KPwwgvw4ov25+bNVjBVA4/HCq2ddoKFC3nvcD8btg/aOqBZszLEl/MeVNwXzRh7M+0IlM2bx38fHLSvLRYbfzi1RvG4/aL7fPYGORSyQmdoyP4eCIynzkWj7kRTOe9jIGBv4qdMsY+pU+H974dddrGiac6c8dqmLAFbL5zzQFUPL2JFelubFSl5WLYGfvR3WPIll+nKDiMj46Jr3Tr7c+1aa4e4erUVES+9ND5pkEhYhdDebj+TsbHx78PwcF7zHsCKNuezS/85d679DBcssP8TlSqQGjChvuiOO2xa4OiobSh/0kkFt0+vkar05RWrwWoU5dR/TW6bzIbRDWw/ZXukBa91Toqgm/Ozs44gzOicUdVx+L1+Xr/d67nlf7doBEtpWfSr1EI4RhFNGcFKRwROOAGOO47fffFNHH75ncz74Dlw+S02hfDggysea6MoN4LlOC6dfuPpACx9dSmn7XNaSftwBNZ7dn9P/otOOGwdHq+7Du65x4och7lzbQrZ295mow7OLH76w8kbyvdcKGSjLI5xgvP7pk02inDddXy5vx9YCd+ZY7fbbjt7l+7z8ZbQBm4fghmPnQXT+8YFhvPwenOLpuzf+/tLFzZ+v1UK8fh4vZLXOx6laGvLNIAIBMaNH5z6oOnTx/92nktfx82yjo6WuIo754F6OwimH3NwsESB5XxO8+ZVPghHxA8N2e/5unXjqY8bNtjv4aZN4z9XrbI/16/P3M+UKXaioa9vvC6tr88Ksmh0PDrc2wvbb28ffX01FdWDgzCnYzP85S645Ra4/HIbsfrTn6zIL0L65zOjgnvreNxq4maNYC1wl2CQwjFkaMX0QCg9RRBgRueMmpQtHD3/aG753y1qcqG0LM1/lVdS1CuCVbUbKq+Xx4/dh4903skLbV9imwt/C4ccAoceCqecAieeWHAWuxk5cO6BHL390ew3u7QI1iHbHML1z1zPbctvA+xF6YRdTihpH2/b+W0cPPdgPnfQ58YXxuNW2DzwgBVUN95o7wx6e+FNb7I2+vvtB/vsU7GzmyuM4aSfHMI2q4c4f6dPwIoVNmIwNgbRKJGN/XREwb/sObj/ocKRNK93PErg/Nxuu/Hf05enL+vpseLI5xs3cfB4MqOnxtj3zutt+ahqrWgWgTVrVv2PD9jvhSOIZ86EnQv3q0sxMmLTPZ97zkaLnVq0NWvgP/+xP0Ohwvtoa4P5863Y2mGHceG1ww5W5A8Pjwu/Zcuso+vjj9s6uKlT7Xk1+xGP2//FlSs5787n2WH4MXhXwp4XPvpR+MlPXNeqVUsAORGqZhVYpX73HSfBVjS4gNJNLqD66YEO79rtXfxp2Z9KMpOqBI1gKdVGv0otRK1NLpyZm4pSBLNo87UR8cHQGR+Ez3wTLroIrrjCGmN8+tPwlrfYi/uxx7ZETcP+s/fn9g/cXvJ2J+x6AifsWpqgymZ6+zT+88br4L6H4MGrrahautTebIG9sXrXu2wdxVFHNWYqToTNM3t4aTJw+icmPH3dfefzpTu+xNBXHrH5/umpfps325tARyh1ddVO/IjolbQIznmgmueDUo/dkr2wOjut8cjChbmfN8a+sE2bxiPGfr+NkC1fbh8vvjj++z33jP+P56OrC/bay0ahNm2ClSttavCGDTZC5uD3w7bbslnmc+1O3+SUK46GAw4o+VxRrc8nXzPfYLIjQKMFVqnffadeaGuKYFXb4MJh20nbct+H76vJvnOhESyl2tT0DkNEjsUaQnmBy4wx52Y9L8nn3wKMAh8yxjxSaFsRORH4FrArcIAxZmna/r4CnA7EgU8bY/5Ry9dXb5ra5CIPTlpj0Jt0AvvCF6yV++OPw+9/bx833gjbbmuF1gc+YKMUWyObNlnB9Oij9sbIcZBbt87OPK9aZVOKwH5Ye+8NH/wgHHigfey0U1NEY7It6dOZYHKRHiWYM6deQ1Rc0AwRrEbW4NQMkXHr/HScOsZsjLHnA0dwbdyYacKx0072nJkrpdAYW1e1caM97uzZ4PVy6gLYd1845dDyXkK1Pp98ESwRu6xRn380aoOMJUewgpMBWrLJMJRucgHVt2hvFCqwlGpTM4ElIl7gl8AxwGrgIRG5yRjzTNpqbwZ2Sj4OBC4CDiyy7VPAO4FLso63G3ASsDswG7hDRHZudCfnatJyKYLk6YMlYlPW9tkHvv99uOkm22/lG9+wjwMPtOmDb36zrRvq7m4K4VB1QiHby+ivf4W77rKz1g5tbeM3YdOm2VnmE0+0N1ILF9r3rq05nRnduAjWapJAqR7NILBaMoJVbUTGrecPPLD0bZ26tDTKSX9Lp1qfT6F+XNXstVUq+YRfMVo+glVOiuAWIrA0RVCpNrX8Kh0AvGCMWQ4gIn8AjgfSBdbxwFXGGAP8V0Qmi0gfMC/ftsaYZcll2cc7HviDMSYMrBCRF5JjuL9Gr6/u9Hb0MrltMh6pTfFzLSJYMzpnEPAG8psyBALw7nfbx4oVcP319vGFL9gHWJuqmTPtAI2xzl4ez7gbmeNINnOmfcyebYvc58+3gqTRQiQahfvvt5Epx+HslVfg7rttvUZ3NyxaBB/5yHjN1OTJjR1zBRTrg+X3+FvSYWtrQwXWlosKrMIUbMRcgLk9c+nwd7DdpNbMwignRbBWNVj1RiNYSrWppcCaA6xK+3s1NkpVbJ05LrfNdbz/5thXBiJyBnBG8vciu2wuPnnAJzlxtxNrtv+q2LRncfKeJ3PwNgfTHXSx0/nzbX+fc86xxeH//e94cfhrr9n6HMesIJEY7980OmqjP/ffb1Np0q2VRawr1r77WuGy1142pWbu3NpaX7/4IvzjH3DbbbZhpzMl6vVaMThjhjX6eMc74Mgjc3T8bF2yLenTicQjW1YPrC0YrcHaMonF7Cmzks+11jVYzrJGC6xS36OP7/9xjltwHO3+5rPmd0NJESyPRrAUpRC1/CrlUi/G5Tputi3neBhjLgUuBfB6vcX22VR0+DtqmttdiwhWwBtg52ku3bfS2XFH+yiVeNwKshUr7OOFF+Cxx2wK3tVXj6/X1mYduXbaafyx++429a4kX2isZfizz1o3r6VLrbBy0v3mzbP9Zd70JjjsMCuuGtDTqJ4USxFUgdUaNEMEa4uswWowjl9GJZ9rR4c9jdWqBstZlu14Xy/KTRFs97ez49QyrltNQjOZXNSbVo5gicg2wFXALCABXGqMuaCxo1JqKbBWA9uk/T0XeNXlOgEX25ZzPKUAtRBYdcfrtdGpuXPh8MMzn1u7Fp55Bv73v/HHc8/ZPjCOVbjHY4XW/vtbO/nDDrP2zOnRzsFBWzv1979bMfXSS+PPdXbaiNRnPgNvfGPTGE/Uk2IpghU3GVbqQiMFVltb413ktlTKTX9LxzGhqHWKYHpZaj2pxnvUirT72xEkZU5VCOc8rimCTUEM+Lwx5hER6QYeFpHbszwPlDpTS4H1ELCTiMwHXsEaULwva52bgE8ma6wOBAaMMWtEZL2LbbO5CbhGRH6CNbnYCXiwaq9mK6AWKYJNxaxZ9nHUUZnL43Hr0PfEEzYCtXSpNd644gr7/PTptq/U8LCtmervt2mI3d1w9NHwiU/Arrvax/z5LWE3X0sC3gDrR9az36UTe4Wt7F+ZmiVVmhvn5rIR5wPnBv6yy+wchlI9nBZclX6uPT1wzTVwXwVO2mvX5h9LT49NQtivtJaDVWHzZvtzi70W5sEjHjr8Ha5MiJwI1qyuRjWqqy7O/U8rXr6NMWuANcnfh0RkGbZERgVWA6mZwDLGxETkk8A/sFbrlxtjnhaRM5PPXwzcgrVofwFr035aoW0BROQE4EJgOnCziDxmjHlTct/XY79QMeCsLclBsB687nXwyU9ODPxs8Xi9NpVv3jw47ji7zBgb3fr3v+0dxPCwtUbu7LRi66ij4OCDW3a6q5a8Z/f38PLAy5gcWb2zu2dzxHZH1H9QSsnMn299Zt72tsYc/4tfrOzmXcnPHntUfp7/7GdtIL8SZs+2nTly1b28731WgJkGJPLPng1HHOG+t/SWxHeO/A4HzT2o6Hrv3f29TGmbssWkfJ944njqaxPiE5GlaX9fmix3mYCIzAMWAg/UY2BKfsQ04uzVJHi9XhOPqwZTFEVRFEVRmg8RGTXGFE39EJEu4B7ge8aYv9R+ZLVBRJYaY/Zv9DgqpTm1uqIoiqIoiqIoRRERP/Bn4OpWFldbEiqwFEVRFEVRFKUFEdtz6DfAMmPMTxo9HsWiAktRFEVRFEVRWpNDgQ8AR4nIY8nHWxo9qK0dbammKIqiKIqiKC2IMebf5O4FqzQQjWApiqIoiqIoiqJUCRVYiqIoiqIoiqIoVUIFlqIoiqIoiqIoSpVQgaUoiqIoiqIoilIltmqTi0QigYgkGnR4AbbeLs+1Rd/b2qHvbW3Q97V26HtbO/S9rR363taGVnxfOxo9gDqzodEDqAZiTKt9z7YMtpRO1c2Ivre1Q9/b2qDva+3Q97Z26HtbO/S9rQ36vir1QlMEFUVRFEVRFEVRqoQKLEVRFEVRFEVRlCqhAqtxXNroAWzB6HtbO/S9rQ36vtYOfW9rh763tUPf29qg76tSF7QGS1EURVEURVEUpUpoBEtRFEVRFEVRFKVKqMBqACJyrIg8JyIviMiXGz2eVkVEthGRu0RkmYg8LSJnJ5d/S0ReEZHHko+3NHqsrYiIrBSRJ5Pv4dLksqkicruI/C/5c0qjx9lqiMiCtO/mYyIyKCKf0e9teYjI5SKyTkSeSluW93sqIl9JnnufE5E3NWbUzU+e9/V8EXlWRJ4QkRtEZHJy+TwRGUv77l7csIG3AHne27z///qddU+e9/a6tPd1pYg8llyu31ulZmiKYJ0RES/wPHAMsBp4CDjZGPNMQwfWgohIH9BnjHlERLqBh4F3AO8Bho0xP2rk+FodEVkJ7G+M2ZC27DxgkzHm3OTkwBRjzDmNGmOrkzwfvAIcCJyGfm9LRkReDwwDVxlj9kguy/k9FZHdgGuBA4DZwB3AzsaYeIOG37TkeV/fCPzTGBMTkR8CJN/XecDfnPWUwuR5b79Fjv9//c6WRq73Nuv5HwMDxphv6/dWqSUawao/BwAvGGOWG2MiwB+A4xs8ppbEGLPGGPNI8vchYBkwp7Gj2uI5Hvht8vffYgWtUj6LgBeNMS81eiCtijHmXmBT1uJ839PjgT8YY8LGmBXAC9hzspJFrvfVGHObMSaW/PO/wNy6D2wLIM93Nh/6nS2BQu+tiAh2Avbaug5K2SpRgVV/5gCr0v5ejYqCiknORC0EHkgu+mQyjeVyTWMrGwPcJiIPi8gZyWUzjTFrwApcYEbDRrdlcBKZF3v93laHfN9TPf9Wjw8Dt6b9PV9EHhWRe0Tk8EYNqsXJ9f+v39nqcTjwmjHmf2nL9Hur1AQVWPVHcizTPM0KEJEu4M/AZ4wxg8BFwA7APsAa4MeNG11Lc6gxZl/gzcBZydQLpUqISAA4DvhjcpF+b2uPnn+rgIh8DYgBVycXrQG2NcYsBD4HXCMiPY0aX4uS7/9fv7PV42QyJ7T0e6vUDBVY9Wc1sE3a33OBVxs0lpZHRPxYcXW1MeYvAMaY14wxcWNMAvg1mk5RFsaYV5M/1wE3YN/H15K1b04N3LrGjbDleTPwiDHmNdDvbZXJ9z3V82+FiMgHgbcB7zfJIu5k+trG5O8PAy8COzdulK1Hgf9//c5WARHxAe8ErnOW6fdWqSUqsOrPQ8BOIjI/OYN9EnBTg8fUkiTzqX8DLDPG/CRteV/aaicAT2VvqxRGRDqTxiGISCfwRuz7eBPwweRqHwRubMwItwgyZlP1e1tV8n1PbwJOEpGgiMwHdgIebMD4WhIRORY4BzjOGDOatnx60rAFEdke+74ub8woW5MC///6na0ORwPPGmNWOwv0e6vUEl+jB7C1kXRf+iTwD8ALXG6MebrBw2pVDgU+ADzp2K4CXwVOFpF9sGkUK4GPNWJwLc5M4AarYfEB1xhj/i4iDwHXi8jpwMvAiQ0cY8siIh1YJ9H07+Z5+r0tHRG5FjgC6BWR1cBi4FxyfE+NMU+LyPXAM9gUt7PUjS03ed7XrwBB4PbkueG/xpgzgdcD3xaRGBAHzjTGuDVx2OrI894ekev/X7+zpZHrvTXG/IaJ9a6g31ulhqhNu6IoiqIoiqIoSpXQFEFFURRFURRFUZQqoQJLURRFURRFURSlSqjAUhRFURRFURRFqRIqsBRFURRFURRFUaqECixFURRFURRFUZQqoQJLURRFURRFURSlSqjAUhRFURRFURRFqRIqsBRFURRFURRFUaqECixFURRFURRFUZQqoQJLURRFURRFURSlSqjAUhRFURRFURRFqRIqsBRFURRFURRFUaqECixFURRFURRFUZQqoQJLURRFURRFURSlSqjAUhRFUUpGRHyNHoOiKIqiNCMqsBRFUZQUIrKviDwqIkMi8kcRuU5EvisiR4jIahE5R0TWAleIiEdEviwiL4rIRhG5XkSmpu3rIBH5j4j0i8jjInJE2nN3i8h3ROS+5LFuE5HeBrxkRVEURakqKrAURVEUAEQkANwAXAlMBa4FTkhbZVZy+XbAGcCngXcAbwBmA5uBXyb3NQe4GfhucpsvAH8Wkelp+3sfcBowAwgk11EURVGUlkYFlqIoiuJwEOADfm6MiRpj/gI8mPZ8AlhsjAkbY8aAjwFfM8asNsaEgW8B706mD54C3GKMucUYkzDG3A4sBd6Str8rjDHPJ/d1PbBPrV+goiiKotQazaFXFEVRHGYDrxhjTNqyVWm/rzfGhNL+3g64QUQSacviwMzkcyeKyNvTnvMDd6X9vTbt91Ggq5LBK4qiKEozoAJLURRFcVgDzBERSRNZ2wAvJn83WeuvAj5sjLkve0cisgr4nTHmozUbraIoiqI0IZoiqCiKojjcj41AfVJEfCJyPHBAgfUvBr4nItsBiMj05DYAvwfeLiJvEhGviLQljTLm1vQVKIqiKEqDUYGlKIqiAGCMiQDvBE4H+rF1VH8Dwnk2uQC4CbhNRIaA/wIHJve1Cjge+CqwHhvt+iJ63VEURVG2cCQz1V5RFEVRxhGRB4CLjTFXNHosiqIoitIK6EyioiiKkkJE3iAis5Ipgh8E9gL+3uhxKYqiKEqroCYXiqIoSjoLsJbpXVhzi3cbY9Y0dkiKoiiK0jpoiqCiKIqiKIqiKEqVqGmKoIgcKyLPicgLIvLlHM+LiPw8+fwTIrJvsW1F5HwReTa5/g0iMjntua8k139ORN5Uy9emKIqiKIqiKIqSTc0iWCLiBZ4HjgFWAw8BJxtjnklb5y3Ap4C3YJ2nLjDGHFhoWxF5I/BPY0xMRH4IYIw5R0R2A67FWgrPBu4AdjbGxAuM0Xg8WoamKIqiKIqiNB+JRAJjjDR6HPVCRP5ujDm20eOolFrWYB0AvGCMWQ4gIn/AWvY+k7bO8cBVyYaW/xWRySLSB8zLt60x5ra07f8LvDttX38wxoSBFSLyQnIM9+cboMfjIR7Pq78URVEURVEUpWGIyGijx1Bnehs9gGpQy/DNHGzfE4fVyWVu1nGzLcCHgVtLOJ6iKIqiKIqiKErNqGUEK1c4MzsfMd86RbcVka8BMeDqEo6HiJwBnJH8PccmiqIoiqIoiqIo5VFLgbUa2Cbt77nAqy7XCRTaNtmb5W3AIjNeRObmeBhjLgUuBfB6vWqhqCiKoiiKoihK1ahliuBDwE4iMl9EAsBJwE1Z69wEnJp0EzwIGEj2W8m7rYgcC5wDHGeMGc3a10kiEhSR+cBOwIM1fH2KoiiKoiiKoigZ1CyClXT5+yTwD8ALXG6MeVpEzkw+fzFwC9ZB8AVgFDit0LbJXf8CCAK3J1P8/muMOTO57+uxJhox4KxCDoKKoiiKoiiKorQOIrINcBUwC0gAlxpjLshaR4ALsBpjFPiQMeaRuo5za2407PV6jboIKoqiKIqiKM2IiIwaYzoLPH85tmxmnTFmj7TlnwI+iQ063GyM+VLNB1sFRGSpMWb/As/3AX3GmEdEpBt4GHiHmzZQNR56BtoESlEURVEURVFakyuBjL5RInIktn3RXsaY3YEfNWBcNcEYs8aJRhljhoBlTHQNT7WBMsb8F3DaQNUNFViKoiiKoihK1THGcO6/z2X9yHq74L774KKLoMmyh55/Hi65pNGjKA9jzL3ApqzFHwfOTfaGxRizru4DK59eEVma9jgj34oiMg9YCDyQ9VTDWzepwFIURVEURVGqzgubXuArd36FG5+70S5YvBg+8Qk49lhYv76xg0vjqqvgzDMhkWj0SHLicys40tgZOFxEHhCRe0TkdbUeZBXZYIzZP+1xaa6VRKQL+DPwGWPMYPbTOTapa01ULW3aFUVRFEVRlK2UwbC97x2NjoIx8PjjsNtu8K9/wcKFcP31cMghDR4lhMP2ZyQCbW2NHUsOYoVqkvLgA6YABwGvA64Xke3NFmK8ICJ+rLi62hjzlxyruGrdVEs0gqUoiqIoiqJUHUdgjUXH4LXXYMMG+NjH4P77IRiEN7wBPvtZ2LixoeN0BJbzcwtgNfCXZA3Sg1i3vd4Gj6kqJB0CfwMsM8b8JM9q+dpA1Q0VWE2GMYZYItboYSiKoiiKolTEQHgASEawnnjCLtxrLxu9evhh+NCH4Oc/hx12gHPPhbGxhowzW2ANR4ZZPbg69Xhl8JWGjKsC/gocBSAiOwMBYEMjB1RFDgU+ABwlIo8lH28RkTOdVlDYNlDLsW2gfg18ot6D1BTBJuO6p6/jU7d+ipc+8xId/o5GD0dRFEVRFKUsUhGs2Ni4wNpzT/tz8mT49a/hM5+Br3zFPq67Du69F7q76zrOdIEVjUfZ7mfbsWls3DdicttkNp+zua5jcouIXAscgTWHWA0sBi4HLheRp4AI8MEtJT3QGPNvctdYpa9jgLPqM6LcqMBqMpatX8aG0Q2s2LyC3Wfs3ujhKIqiKIqilEVGiuATT8CcOTBtWuZKu+8ON90EN94I73oXvP/9cMMN4PXWbZzpAmssNsamsU28Z/f3cMz2xwAQ8AbqNpZSMcacnOepU+o6ECUDFVhNhhNOX9GvAktRFEVRlNYlw+TiiSdsemA+jj/epguedRZ86Uvw4x/XaZSZAisSjwBw2DaH8ZF9P1K3MShbFiqwmoyUwNq8osEjURRFURRFKR9HYEVCI/DMM9aevRCf+AQ8+yz85CewYAGc4caRvHKyUwQB/F5/XY6tbJmoyUWTMRAaj2AV41cP/YqXB16u9ZCUrZTVg6u58IEL2ULSthVFUZQ64wisqavWQzRaOILl8JOfWCF21lnjdVs1JmKDVlZgJZICy6MCSykfFVhNRnqKYCGGwkOcdctZXPvktfUYlrIVcv3T1/Ppv3+atcNrGz0URVEUpQVxBFbf8mRTYTcCy+eDq6+GQMCmDNaB9D5YGsFSqoEKrCbDiWCt7F9ZcD1nhmU0OlrrISlbKeGYveKsGa5r6whFURRlC8ERWNus3Ax+v037c8PUqXDKKVZobdpUfP0KyUgRTN5fNbOxhdL8qMBqMtzWYDkzLKFYqOZjUrZOnEJfjWApiqIo5eAIrHmrBmG33azIcstZZ0EoBFdcUaPRjZPL5EJTBJVKUIHVZPSH+hGEgfAAm8fy91xwZljGYo1pyqds+TgXmTVDGsFSFEVRSscRWDusHnWXHpjOXnvB4YfDr36FicdrWg+sJhdKtVGB1UQYYxgIDbDTtJ2AwnVYGsFSak1KYGmKoKIoilIGg+FBpo7CrP5Y6QILbBRr+XLO/foRfPT/Plr9ASbJlSKoESylElRgNRGhWIhoIso+s/YBCqcJagRLqTWaIqgoiqJUwmB4kD1fS/5RjsA64QTo6+Pw/3ucR9c+WtWxpaMRLKXaqMBqIpz6q31m7gMUjmDFEjFAI1hK7dAIlqIoilIJg+FB9qpEYAUCcMYZHPLMEIEVq6o6tnQ0gqVUGxVYTYTjILjd5O2YFJxU0EnQmWEZi2oES6kNGsFSFEVRyiUcCxOOh9n7NVjXCcycWd6OzjiDuMCJ96wnnohXdYwOGX2w4uoiqFSOCqwmwolgTQpOYv6U+YVrsDRFUKkxzndMTS4URVGUUhmKDAGwcIOPJ2ZAzJQpjmbP5ua92jj9Edi4ZnkVRzhOeh+slIugpggqFaACq4lwIliT2iYxf/L8wjVYanKh1Jj0FMFaujcpiqIoWx4DoQE8Cdh1bZwnZlaWcXPBYX4mhSF6ya+qOEKLMVkRLE0RVKqACqwmoj/UDyQjWJPns7J/Zd4b21QES1MElRrhCKxQLJSy2lUURVEUNwyGB9lnLbRHDY/2wWh0tOx9PdgX55/zYNqlvxtXQ1UifXdqcqFUCxVYTYSTIji5bTLzp8xnLDbGayOv5VxXTS6UWuMILFCjC0VRFKU0BsODvPcpiHmFm3eqrKQhFAtx3qHQ9tpGuPbaKo5yPD3Q+V0jWEo1UIHVRGSnCEJ+q/aUyYXWYCk1IkNgaR2WoiiKUgKDY/2892l4fr/5bO4oP+MmloiRMAn+sSOs234m/OhHNq+vDAZCA2wc3cjG0Y1sHtsM5BBYanLR1IjI5SKyTkSeyvP8FBG5QUSeEJEHRWSPeo8RVGA1FQPhAQShK9DFvMnzAPI6CTozLBrBUmpFJB5hRucMQJ0EFUVRlNIIPPQI2w3AimMPAMpPEUzd5wj844Q94amn4O9/L3k/1z99PZN/OJne83vpPb+XqedN5ZcP/jJ/BEtTBJuVK4FjCzz/VeAxY8xewKnABfUYVDYqsJqIgdAAPcEePOJJCax8ToJq067Umkg8wnaTtgM0RVBRFEUpjb6b72XMB8NvOgooP+MmHBtXQLfs1wNz5sB555W8n+c2PAfABcdewM+P/TkBb4CV/Ssn1GClXAQ1RbApMcbcC2wqsMpuwJ3JdZ8F5olImT0CykcFVhMxEB5gUtskADoDnczonJE/RVAjWEqNcSJYQW9QI1iKoiiKe+Jxtr9jKTfvBFNnzgPKnxBOv89ZHVoHn/kM3H03PPlkSfsZDA/S4e/g0wd+mk8d+Ck6/Z1E4pG8KYIawWoYvSKyNO1xRonbPw68E0BEDgC2A+ZWe5DFUIHVRPSH+pkUnJT6e/7k/L2wHJOLcDxMwiTqMj5l6yISjxD0BZnVNUsjWIqiKIp77rmHrk3DXL+nh2kd04DKUwQFsfXAH/gAiMANN5S0n8HwID3BntTffq9/gsCKRNTkognYYIzZP+1xaYnbnwtMEZHHgE8BjwKxag+yGCqwmoj0CBZQsNmwM8MCGsVS7PfhJ/f/JON7USmReISAN0Bfd5+aXCiKoiju+cMfCLX7uW+PHjr8HUAFKYJxq4Bmd8+2fRlnzICDD4a//rWk/QxGMgVWwBvQCNYWiDFm0BhzmjFmH2wN1nQgf2PZGqECq4kYCA0wuW1y6u/5k+fz8sDLxBMTu587MyygAkuB+1bdx+dv+zz/fvnfVdunI7Bmdc3SFEFFURTFHZEI/PnPPPK6ufi7J9Huawcqj2DNnzKf0egow5FhOP54ePRRePll1/vJjmAFvAEiiRwCSyNYLY2ITBYRxwLyI8C9xpi6N/NUgdVEDIQHMlIE502eRywR45WhVyasmx6pUKMLxbkAjURHqrbPSDxCwBOgr6tPUwQVRVEUd9x2G2zaxF0HzqQn2EO73wqscu9VHJMLx/xrzfAaK7AAbrrJ9X5yCaxoPJoSWD7fuMmFz+NDRMoar1JbRORa4H5ggYisFpHTReRMETkzucquwNMi8izwZuDsRoxTBVYTMRAamFCDBbl7YWkES0nHcT2qpthOj2BtGtuU4eSkKIqiKCnCYfjzn63wOeEEmD6de3dpoydYeYqgc48zb9I8INmXccEC2GWXktIEc0aw0lIEJ00aTxFspehVob5QIvIFETEi0tuIsdUCY8zJxpg+Y4zfGDPXGPMbY8zFxpiLk8/fb4zZyRizizHmncaYzY0YZ00FlogcKyLPicgLIvLlHM+LiPw8+fwTIrJvsW1F5EQReVpEEiKyf9pyv4j8VkSeFJFlIvKVWr62amOMyVmDBbmt2h2TC9Bmw0ptGk+narC6+gB4beS1qu1bURRF2UJYsQK23x7e/W548EHr8vef/7ApPmwjWBWmCDo1WE4EK5WyfvzxcM890N/vaj8TTC481uTCsWnv7h5PEWyx+qsrydEXSkS2AY4B3OdRKlWjZgJLRLzAL7Hhud2Ak0Vkt6zV3gzslHycAVzkYtunsPaL92bt60QgaIzZE9gP+JiIzKvyy6oZY7ExYolYRgRr20nbIggv9b80YX01uVDScSKatYhg9XVbgaVGF4qiKEoGIyPwjnfA6CjcfDOsWgXnnw877pgSNF6Pl4A3ULFNe0aKINjjxmJwyy2u9jMYHqQnkD+ClRJYLRbBKtAX6qfAlwBT3xEpUNsI1gHAC8aY5caYCPAH4PisdY4HrjKW/wKTRaSv0LbGmGXGmOdyHM8AnSLiA9qBCFD3orZy6Q/1A2REsALeAEFfMOesT3qKoNZgKakUwRpEsGZ1zQJQowtFURRlHGPgwx+Gp56CP/wB3vIWW8iUZCA0kIoYtfvaK240PKtrFgFvYPxadMABMGuWqzRBY0zRFMGennGb9oA3kGdPDcFXal8oETkOeMUY83gdxqfkwFd8lbKZA6xK+3s1cKCLdea43DabP2FF2BqgA/isMWaCok9+Mc9I/l70RdSLgdAAQIaLIIyfALLJMLnQFMGtnlSKYJXEdjwRJ27iGSmCanShKIqipDjvPLj+evjhD+FNb5rwdLqgafe3V+wi2O5vz+zL6PHAccfBNdfY0FMwWHAfsURsgsAaiY4QTt5O9fTAypVNmSIYM8bsX3w1i4h0AF8D3li7ISnFqGUEK5d6yQ5T5lvHzbbZHADEgdnAfODzIrL9hJ0Yc6nTvKypBFbYCqz0FEGwOcLp0SoHNblQ0ql2BMv5fgW8AWZ0zhhv8KgoiqIod94JX/kKvPe98MUvTng6Go8yFhtLCZoOf0fFJhdtvjbrapt+LTr+eBgehrvuKriPwbBNaCrUaLinZ9xFsJVSBHOwA/Y++HERWQnMBR4RkVkNHdVWRi0F1mpgm7S/5wKvulzHzbbZvA/4uzEmaoxZB9wHuFb8jcaJYKWnCEL+CFaGyYWmCG71VLsGy/nOBbwB/F4/vR29miKoKIqiWL77Xdh2W/jNbyDHZPVQZAggI0WwUpOLoDc4sS/jUUdBVxdcdplNWcyDM4ldLEUwVYPVXBGskjDGPGmMmWGMmWeMmYe9p97XGKMX8TpSS4H1ELCTiMxPNvw6CchuWHATcGrSTfAgYMAYs8blttm8DByV3FcncBDwbDVfUC3JG8Hy5olgqcmFkka1I1jpAgugr1t7YSmKoijAiy/C3XfDRz8KnZ05V3EiRs49Tbu/vWKTi1QEK/1a1NYGX/qStYj/+c/z7iNXBCuvwEq0lslFrr5QjR6TUsMaLGNMTEQ+CfwD8AKXG2OedhqBJf3qbwHeArwAjAKnFdoWQEROAC4EpgM3i8hjxpg3YV0Hr8C6DApwhTHmiVq9vmpTagQrw+RCa7C2eqpdg+V855xZvAmzhoqiKMrWyRVX2PqnD34w7yrZgqaSFEHH5CLoC9LX3ceG0Q0pEyYAvvY1ePRR+NznYNdd4Y0TS4+KCSwRqxUjEYjEWiuCZYw5ucjz8+o0FCWNWppcYIy5BSui0pddnPa7Ac5yu21y+Q3ADTmWD2Ot2luSgjVY8dwRrKA3SDge1giWkhJEoXh1vgsTIlhdfTyz/pmq7FtRFEVpUeJxuPJKa2oxd27e1bIFTbuvnc2h8vq9hmIhvOLF5/GlXG3Xjaxjbk/y+B4PXHUVHHKIrQl74AHYeeec48lwavYEiMajRCLWH8PxyIhEaDYXQaUFqWmjYcU9/aF+POKhK9CVsbxQBMs5cWkNVuszGh1lJDKS85EwiaLb17IGC6zAWju81tVYFEVRlC2DsehY6lo0Fh2D226DV16x9uwFyBnBKvP6FI6HafO1AYy72mabLnV1wU03WZv4446DscxjFTO5CATsAyAUNi2VIqg0JzWNYCnuGQgNMCk4aYJ1fL4arFgiRmegk/Wj6xuSIviu69/FTlN34tyjz637sVsBYwx7XbwX5xx6DqfsdUrBdc+77zzOueOcvM8fu+Ox3Pr+Wwvuo9Y1WLO6ZhFLxNg0tonejt6qHENRFEVpXq576jpO+vNJGctW/Gs/5vX2WhFTgAkRLH/5fbBCsRBBnw0vFezLOG8e/Pa38Na3Wvv4tBTGYimCGRGsMHR1qMBSKkMFVpMwEB6YUH8FhSNYfo+fNl9bQ1IEH13zKK8OFTN23HqJJqI8te4plq1fVnTd5zc+z6TgJL52+NcmPPeHp//A8xufL368GtVgpZtcgJ01VIGlKIqy5fPCphcAOHfRuXjEw49v+jJz73kUPnX2eLgnD7lSBCvpg5WKYHUX6cv45jfbOqxf/jKnwOoOdKeW5RVYEdEIllIxKrCahIHwwIT6Kyhcg+X3+m139AakCA5HhhndXN7JcmvAKcrNJY6zicQjTOuYxhcPndhL5NkNz3Lb8ttc7QNq6CKYTMtYO7yWPWfuWZVjKIqiKM1LJB5BEL506JcQETZ8/2v4YtGi6YFQ/RTBoNeqn5mdM4EcKYIOIvCJT8CnPgUPPQSve11qPEFvMBUJg0IRLGkpkwulOdEarCZhIJQ7guXkCGfT6AjWSHSE10ZeK3tGakvH+UzcCqx8BbX5IpjZ1LoGy0nLUKt2RVGUrQPn2iQiEI/zoaVxVu48E/bYo+i2g+FBBKEzYG3cqxXBctWX8QMfsJaAF12UMZ709ECw17doIko4bCZEsNTkQqkUjWA1CQPhAbbp2WbC8oA3QH+if8LyVASrgrzmcokn4qkT5cr+lew2fbe6Hr/W9If6+en9P80rXPu6+zj7wLMn1MulU2+BVY8+WABXPnYlT697esL6Po+PM/c/k20mTfwO58MYwyUPX8IH9vpA6iJcT65+4mqeeK30Tg4+j4+zDjiL2d2zazAqRVGU5iDj2rR4MbuuS/CbD+6JmyZLg+FBuoPdeMTO47f72wnHwyRMIrXMLeFYOCPyNKEXVjaTJsEpp9h6rB/9CKZOzSmwnDTAsZAhGJSUwIpGPJoiqFSMCqwmYSA0wB4zJs4K5UsRjCViDYtgpc9CbYkC69b/3cq37/02AW9gwoUglogRS8Q4cbcTmdMzJ+8+nM7zW0oEqyvQxQFzDuD+1fdz/+r7M9Y1xhCOh+kJ9nDOYfnNOrJ5dsOzfPzmj9Pma+ND+3yoKuMuhTNvPpOx6FjJqSChWIjOQCdfPfyrNRqZoihK40ldm26+Gb73Pa47oIP7D9vOtcBKFzQd/g7Anj+d392SHsECl30ZP/EJuOQSayn/uc/ljWABhMMJgkGPpggqVUUFVpPQH+pncnDyhOWFTC58Hp+twapzBGskOpL6fcXmFXU9dj1wepKtPHtlKnLj8LvHf8epfz216HueimAltowIFsADH3kg57oJk8D7bW9KVLrFeY+Wb15e5ijLxxjDSGSEr7/+63z7yG+XtO3cn8zl2Q3P1mhkiqIozUEkHmH7AY9Nudt7b5acOMD+Lq5HMFFgtfvaATtBW6rASrdpB5tR8dzG5wpvtNdecNhhNk3wM58pKLBCWSmCsahXI1hNjIhcDrwNWGeMmRCZEJFJwO+BbbE650fGmCvqO0qtwWoKjDEMhgfz1mDlsmlPTxGsdwRrODKc+n1F/5YnsIbCQwB0B7snPOec5Iu956WaXBQSWLFErGj/qVq7CBbCIx58Hp+r15rrGI34DkXiEQwm46Ltll16dyl+cVcURWlxEuExfv27Adtc+E9/ItEWdH2eHwgP5IxglXONCsVCKZMLGO/LaIwpvOEnPgEvvAB33FFYYIVMRh8sTRFseq4Eji3w/FnAM8aYvYEjgB+LSN2L6lRgNQEj0RHiJp7TRdCNTXu9XQRHImkRrC1QYKWKc/0T64KcPPBiAqvUGqx8J3PnApArTTSdVIpgbKz4RccFpQgsZ72yBVYDoqDO5+PMqpbCgmkLeHbDs1V5nxVFUZqV9/76fvZeFbG1TDvuWNJ5fkIEy2/PteVkWeRKEYzEI2wObS684TvfCTNmwEUXFUkRJCuC5VGTiybGGHMvsKnQKkC32EL5ruS6sXqMLR0VWE3AQMimpOWMYLmxaa9ziqATwQp6g1tkiuBQZIiuQFdOEwvnJO9EqPJRzRosN/tJf77UVL1C+6uLwGqASHf+Z8qJYC3oXcBgeJDXRl6r9rAURVGag9//nqNve4HLj54G73gHUNp5vlCKYKnkMrkAeGXwlcIbBoO2F9bf/kZww+aJJhfJOqsJAivi1Rqs1uYXwK7Aq8CTwNnGFEkDqgEqsJoAp+anlAhWI00uHIG16/Rdt8gI1lB4aMKJ2MFtimC1XASdk3yx/aSL8GpENOspsF4derWoYK02qQiWv/QI1i69uwDw3AZNE1QUZQvkqafgjDN4apdp/OqEuanFjq25GwbDgxn3NJWmCKZPhu06fVc7zHVPFd/4Ix+BWIzjHxhwHcGKR32aIthYekVkadrjjBK3fxPwGDAb2Af4hYjkvqmrISqwmoCiEaxcNViOyYW//o2GHZOLPWbsQX+on/5Qf12PX2uGIkM566+g/gKrnAhWNSKapQosvyd3vzY3xwB4aeClkratFOd/ptwUQUCNLhRF2fIYHIR3vQt6evj+WXviDUxszOtqN3lSBMuKYKU1GgbYtXdXgt4gj6x5pPjGO+9M4vDD+NDSOD2BzOt6SmBFslMENYLVYDYYY/ZPe1xa4vanAX8xlheAFcAu1R9mYVRgNQFOBGty2+QJz+WtwUqmCLZ5GxfB2nPGnoC1at+SGIoM0R2oTGBV0+TCzX7SRXg1BLcTEatHBAvqX4flfH7lpAhuM2kb2n3tanShKMqWRTwOH/4wvPgiXHcdr/Vk1iL5ve4m0uKJOMOR4ZwpgtWowfJ7/ew5c08eXfuoq+2HTz2JnTfBrs+sz1ieqnGOSIbAMjG/RrBam5eBRQAiMhNYANTdrlgFVhPgRIBypQj6vXlqsJImF41oNOyYXDh9u7a0OqyhsEaw6pkiCPWvw3Leo3JSBD3iYedpO2sES1GULYdEAs44A/78ZzjvPHjDGyZcm9ye551J2Gq5CIZj4QmTYQtnLeTRtY+6MhvacOwb6A/C3n97KGO5I6IiWQKLWFAjWE2MiFwL3A8sEJHVInK6iJwpImcmV/kOcIiIPAncCZxjjNlQ73FqH6wmoFCKYMAbIG7iE7qfR+PRhtdgORGsLa0OaygyRG9Hb87nXEew6mxyEY1HU4Yn1azBcjuL12oRLOc9KieCBdboYumrS6s5JEVRlMZgjLU0v/xy+OY34XOfA+w5uivQlVrN7Xl+MDwIUJUUQWPMBJt2sALr14/8mlWDq9h20rYF9zHgjXLrXnDmHUth82aYMiX1esA2Fs4QWPGgugg2McaYk4s8/yrwxjoNJy8awWoCCplcODe42VGsWCKWchGMxCPEE/HaDzSJI7Dm9MyhJ9ijEawcNCKC5VzMqhXB8nv8OZ0Uc1GJwOoKdLFyYGWpQ6yISmzaAXaZtgsr+1fWfXJDURSlqhgDZ58Nl1wCX/4yfOtbqadyRbCKtQyB3AIrFcEq8foUTURz9izct29fAFd1WIPhQS7bF7zhCFxzTWp5wBsAY/tepffBIhbUFEGlYlRgNQEDoQG84s3Z3TzfDXZ6iiAUv+GvJiPRETr9nXjEw7zJ87a4CNZgeLAla7BSAqtKEaxSZvAqEVgLpi2ofwSrApt2sBGshEnwwqYXqjksRVGU+hGLwZlnwoUX2qjV978PaZNq2dcBt2ZGOSNYTg1Widcn51qabtMOsOfMPfGIh0fXFK/DGgwP8lgfjOy5C/z611ZUkry+JnwYYyNYfkdTxQOaIqhUjAqsJmAgPMCktkk5owXOP3m2k2A0bl0E3d7wV5PhyDCdAduEd/7k+SUJLGMM8US8bo9EGa0PCplcOGkKzRjBclJMq/FdqKfA2nnaznUX6ZXYtINatSuK0uKMjMAJJ8Cll9rI1Y9+lCGuIHcEq94pgvkMiTr8HezSu4srowtnPMMfeh88/jjcdhuQvL7G7DU9GLQvPxg0ENcIllI5WoPVBAyEB3KmB0JaEWauCFYyRRCqkxbmlpHoSCove/7k+dy+/HaMMa7Syfa7dD/Xzj/VoM3XxoMfeZA9Z+7pav1IPJKRbpdNyta1SDNftwIrnogTN/Gq1GBVO0WwVIHlpI6WcgywEaxrn7qW4chwRr5/LanEph2sKAS1alcUpQVZvx7e9jZYuhR++Utbf5WDagosv8ePV7wlX5+ca22ubIOFsxZyz0v3uB4PH/oQ/OIKKyiPOcZOYMfHBRaAP2AIq8mFUgVUYDUBA6GBnBbtkGYjGp8YwXJMLqABESx/MoI1ZT6j0VHWj65nRueMots+ue5JXr/d6zl6/tG1HiZrhtdw0dKLylIsiwABAABJREFUWL55uWuBNRQeAshbgyUiroxF3JpcOJHJqkSwkiK95VIEe21fqRWbV7j+nCqlEpt2sHVjc7rnqFW7oiitxauvwpFHwssvw1/+Ascfn3fVSDxCwFMdgSUitPvby45gZZtcgBVYVz95NetH1jO9c3rx8XT3wne+A6ecAtddR+BNr8uIYAEEggk1uVCqggqsJqA/1J/TQRDGUwTTT2oJk8BgbATLX15ecyWMRDIjWGBvjosJrIRJEEvEOGreUXzjDd+o+TifXvc0Fy29qKQb/6FIUmDlSREEXAkstxGsYnboZdVgVSOClaiPwPJ5fGw/ZXvA9lOrl8CqxKbdYZfeXVRgKYrSOqxdC0cdZUXW7bfDYYcVXD2nyUWiPJMLsBkD5dZg5ZoMc4wuHl37KG/cIb9p3GB4cLyk4uST4fzz4etfJ3D0PyBuX196BEtNLpRqoDVYTUChFMFUBCvtpOZEsxoawQqMR7DAnVV7vmLVWuE2nS+dYhEscCew3JpcVEtgtWoEK+ANMG/yPKC+dv+VRrDApjY+u+FZV31YFEVRGsprr1lxtXo13HprUXEF5ZtcOM7I2ROVHf6OkicAUxGsHPcN+8zaB6Co0cVgeJCeYI8tY/B44Ac/gOXLmXzV9RNSBAMBG8HSFEGlUjSC1QAeXfMoNz53Y+rvVQOrUieKbHLVYDliy+fxNaQGazgyzMyumQDjN8cuXOAcoZMr1F8LnBOyI3bcULUIVtw+nzAJ4ok4Xo8353rVEFiOmUeja7DKFVjTO6bT4e+oq5PgWHSMgDeQ0VuuVHbp3YXB8CCvjbzGrK5ZVRydoihKFdm0CRYtgpUrXYsryF+DVazmejA8SKe/c8J1r5IUwVyTYVPapzBv8ryidd2DkcHMaNqxx8Ib3kDXeT+j8+3TGWHcot3rT7RcBEtELgfeBqwzxuyRXHY+8HYgArwInGaM6W/YILdCVGA1gEfXPsqSe5ZkLNuvb7+c6+aqwUpFsLyNs2l3UgS7Al30dvS6ij4UExPVxhFyJaUIVjmC5Ry/3ZM7Fa0aAssR3O3+dnweX9UiWKXM4FUisESkZDfKShmLjVUUvYLx2rFnNzyrAktRlObl3HNh2TLrnveGN7jaxBiTU2DBeB/OfDgRo2zafe1lm1zkm5hdOGthcYGVPR4ROPdcPAcfzDce8vJl0lIE/XGItZxN+5XAL4Cr0pbdDnzFGBMTkR8CXwHOacDYtlo0RbABfHjhhzGLTcbj0wd+Oue6uWzand/TUwTrWYOVbnIB7q3aWyJFsMo1WFBYHFVFYMXHjTLKuYDlG1e9IlhgU03rnSJYroOgw4JpVmCpVbuiKE3L5s1w0UXwnvfYKJZLYokYQE6BVexcn6+uvMPfUfK9SrF07oWzFvL8xudTk6O5GAgNTBR8Bx1E/PQPc84Ta3kj/0gJLJ+TIthCESxjzL3ApqxltxljYsk//wvMrfvAtnJUYDU5uU5oGRGsRti0p5lcgL05Xtm/suh2jUoRLOXG3ynOLRTBCnqDdRdYhQqLnX04jadbpgYrzUhj/uT5rNi8om71TGOxsYoMLgC2mbQN7b52NbpQFKV5+dWvYHgYzikteJHr2uTmegS2BiuXM3I5KYKFTC5g3Oji8dcez7uPfBE1+fnPeWJyJ7/nFLoHVgPg88ch1nQugj4RWZr2OKPE7T8M3FqLgSn5UYHV5DizKOkpgs7MUiNMLhImwUh0ZEIE66X+l4gn4gW3bVgEq5QarOQsWL4+WGBP9MWiYunP1zyClWb13uZra80I1uT5DEWG2BzaXNI+yiUUC1WcIugRDztP21l7YSmK0pyMjsLPfgZvfjPss09Jm+a6NuVyNc7FQCi3cVe1TS4AFvYtBAobXeQTWJ6OTk46/HW0M8Zu3z0ZYjF8gVgzmlzEjDH7pz0udbuhiHwNiAFX1254Si60BqvJyRnBSje5qLNNuzP7lB7B2n7K9kQTUXzf8SHYwtfzjzmfzx/y+YxtnddQrwiWz+PDI56a2LQXEwKNSBF0IprZF7Cn1j3FcdcexwMfeaBgr5DscdVTYKWbpUxtn1rSfsphLDpWcYogWKOL656+Ds8SnatSFKW5OOsBw4Ub4PWzbuWZ83p59pPP0tvR62rbQhGsogIrPJByGE6n3VdGBKtAo2GAvq4+ZnTOKFiHNRgezOvU/GLPDD7GJVz9+CnwjW/g9Z+dNLnIb+LRKojIB7HmF4uM2t3mR+SdOZYOAE9izLpyd6sCq8nJWYOVdkNd7wjWSGQEIGXTDnDibieyfmR96kT48wd+zhPrnpiwrfN8PUPvAW+gZJv2oLfw7FU5Jhf5KCawcrlIFtpHrhTBR9c8yor+FSzfvLymAitu4gUdEwsdI93uf7/ZuQ1fqkk1IlgA33zDN1kwbQEGvXYpitI8eGJxPvOrX/Ly7t1s+9bX86+nruGFTS/UR2DliWCVk8JeqNEw2AbGC2ct5JE1j+TdR74IFoA30cE1vJ9fvPtuppx7Lm/ZYzZL40fh97b2OV1EjsWaWrzBGFOaqt36OB04GLgr+fcR2Lq1nRH5Nsb8rpydqsBqcgpFsPye+tdgDUeGgcwI1pT2KXzt9V9L/X3d09flFCD1ThEEe1Iu1aa9UP0VuDe58Hv8RBPRigSW1+PFK15XKYLO9yH7u+D0JCll5rAcgeWMpSyBldawuh5UowYLYLfpu7HkyCXFV1QURaknv/sdrBtg8m9+z9kLZ3L1U9ewYXSD680L1mDFi9dg5UwR9JWfIlhoQmzfvn05/z/nE46FJ9xfRONRxmJjeQWWL2Enizd/+xdMWf8/vn7v57m383L8nteVNM5GIiLXYkVBr4isBhZjXQODwO1JS/3/GmPObNggm5sEsCvGvAaAyEzgIuBA4F6gLIFV07wWETlWRJ4TkRdE5Ms5nhcR+Xny+SdEZN9i24rIiSLytIgkRGT/rP3tJSL3J59/UkQqn6JuMLlqsNIjWAFvAEHqF8GK2ghWusDKps3XllPU1NvkAqyYKzVFsFB6ILiMYMXDKaFWicBynqskgjUQqp/AKuW9Tj/GpLZJTGmbUjcnwbFo5TbtiqIoTcnwMCxZAnvsAW99aypqVS2BVeg8H46FCcVCOV0Ea2FyAdZJMJaI8fT6pyc856T9F4pgAQS6g3DDDazu2o4bhs+i8/mVJY2zkRhjTjbG9Blj/MaYucaY3xhjdjTGbGOM2Sf5UHGVn3kpcWVZB+yMMZuAwrMJBahZBEtEvMAvgWOA1cBDInKTMeaZtNXeDOyUfBxIUjEW2fYp4J3AJVnH8wG/Bz5gjHlcRKZRwRvTLOQ6oaWbXIiINTaoUw2WE8FKN7nIJugN5kzLa0QEq5wUwWpFsHqCPWwa21TQcSndYj0fxQRWdg1Wf6g/4/myI1ie2gus9Ive/CnzuXvl3Xz33u8W3XZScBIff93H8XnKO4VVw6ZdURSlKTn7bFi+HO66C0SY1jENqFxguUlZd643OVMEfXYCsFij4nSKmVxAptGF4yro4DgD5xdY9joQDAJTpnDOoRdxwT/ez6z3fhgeeAhmz3Y1TqWl+RcifwP+mPz7XcC9iHQC/eXutJYpggcALxhjlgOIyB+A44F0gXU8cFWy+O6/IjJZRPqAefm2NcYsSy7LPt4bgSeMMY8DGGM21uqF1ZNCfbCcm8t2f3V6H7khV4pgNkFfbhvzejcaBiv2GhHBCsVCzOycCWgEy+0xXr/t6/nZAz/jG3d9w9X2B809iNfNKS+NoxqNhhVFUZqOP/0JLr8cvvrVVFPh7kA3fo+fjaPub4vKjWA515tcNu0d/g4MtoGx24nWcDyM3+PHI/kTrrafsj3dgW4eWfMIp3N6xnPFBJbE0wQWsH7SDN7qu56l/W+FI4+0IlVFVlMhIpdjzTvWGWP2yPH8F4H3J//0AbsC042NSOXiLKyoOhQQbMPmPyf7xhxZ7jhrKbDmAKvS/l6NjVIVW2eOy22z2RkwIvIPYDrwB2PMedkrJfsHnJH8vfiraDC5ZozSIxbg7oa/WuQyucimzdeWWi+dRqQIlhrBGgwPFi0Cdmty4ZzQay2witVg9Yf7gdLq9BohsH567E85/43nF93u7pV3c8zvjik51SQdjWApirLFsXo1nHEG7L8/fOtbqcUiQm9Hb11SBFMRrDwpgmAn+9wKrFAsVHRdj3jYZ9Y+OZ0EiwksTyJTYHl8ER41BzD81+vpecd74YgjrMiaM8fVeJW6cCXwC6wQmoAx5nzgfAAReTvw2QLiiqSQ+lPyUTVqWYOVS71k27LkW8fNttn4gMOwqvUw4AQRmdC23BhzqdNLoBUEVq6i0vQbaiDnTXWtcBXBaqIUwZJrsMLFI1hOVCyf66kxpnE1WL7GRrCKFT+nE41HJxzD5/EVfXT4bc58KcI5m7FodUwuFEVRmoJEAk49FSIRuOYa8Gc64U7rmMaGsSqZXBRIe3euN/n6YEFpk33hWNhVtsG+ffvy+GuPT+jHWVxg2X0Hki9TfFGIBeHQw+Ef/4C1a63IWr3a9ZiV2mKMuRfIL5gyORm4tuAaIu9E5H+IDCAyiMgQIoOVjrOWAms1sE3a33OBV12u42bbXMe7xxizIWlJeQuwb5Ftmp5cjf0aGsFKmlwUrMHy5Xbua1gEq0QXwUJNhmG82DbfDb7zWdUtgpVeg5UjXbSeLoKVRLDc4nx/KvnOV8umXVEUpSn45jdtpOWCC2CnnSY83dvRW1KKYHoDewc3jYYLRrB84xEst4RiIVf3DAtnLWQ0Osr/Nv0vY3lRgRVvR3wRnPl28UUAD2L8cMghVmS99poVWf39rsetVESviCxNe5xRzk5EpAM4FvhzkVXPA47DmEkY04Mx3RhT+EbQBbUUWA8BO4nIfBEJACcBN2WtcxNwatJN8CBgwBizxuW22fwD2EtEOpKGF28gs96rJck1Y5RucgHl9ZYoF7cRrKayaS/V5MJFDRbkv8F3lvcE6iOwnOdSKYIVRrDiiTgJk6iPwCrBSMMhJXBLEM7pGGOsTbumCCqKsiVwzTXwve/BRz4CH/5wzlXqliJYIILlZA2Ucr8SirubDEs3ukineA1WGx7f+P2V+O1rS0STEcCDD4bbbrPRwUm5mxUrVWeDk2mWfFxa5n7eDtxXMD3Q8hpJf4dq4roGS5ZIO7CtWWyec7O+MSYmIp/ECh8vcLkx5mkROTP5/MXYKNNbgBeAUeC0QtsCiMgJwIXYOqubReQxY8ybjDGbReQnWHFmgFuMMTe7fX3NSs4arCyTi0bUYDmh/ly0+dpyippGmFwEvAHX6QjGGIYjw65cBCG/wHJeeykpgs7nnAu3NViOyUU0Ec1o+FtqBKucz6muEaykQC83RdDZTiNYiqK0PA8+aEXV4YfDL38JeUofprVPK0tgOVErqLwGq5Ypgrv27krQG+SRNY9w8p4np5YXE1jEg8molc3KEa99bbFoWj/Hgw6yD6XVOIli6YGWpYhcB/wVGL+xMOYvlRzclcCSJfJ24EdAAJgvS2Qf4NtmsTmu0HbGmFuwIip92cVpvxuse4erbZPLbwBuyLPN77FW7VsMIoLP48vbBwts2N3p9VBrhiPDtPvaCzaTzdfct1F9sLJty/MxEh3BYKoXwapTimD6hdCJyoRioZQRSakRrKYXWBWmCDrbaQ2WoigtzSuvwDveAX198Oc/jxcS5aC3o5eNYxtJmERBRz6HciNYzvU2l6ApO0XQRdaL3+tnjxl7TDC6GAgNIEjesgaJtdm6K4ekwAqXX+KrNAEiMgmbyXaKi9V7sEGeN6YtM0DtBRbwLazt+t0AZrF5TJbIvEoOrLjH7/HnjGClpwiuG1lXl7EMR4YLpgdCfpv2cCyMIGX3LiqHUmzah8JWpFYawXKWO0LNrTjKh9saLCeCBXaGsDPQSTQeTdXNbTECy4lglZki6KSnaARLUZSW5oMfhKEhm8I2fXrBVXs7ekmYBAOhAaa0Tym664ImFwXMjAZCA3QFunJe58tJEQzH3UWwwBpd/HnZnzP6bA2GB+kJ9uR3jY4HU1ErAHz2+h1xfylT6oyIXAscga3VWg0sBvyQEcQ5AbjNGDPR0jobY06rxTjd3unGzGIzIEua33VvSyTgDWT2wWqwyUUhi3ZnPDldBONhgr5gXe3xS7Fpd6KAlUawnBt/txEsn8dXcEYx4A2kat/y7QPGa7Bg/ALmpEeA+7SMcgSWmwaUuY5TjsAqZjJSjFQES2uwFEVpVf79b7jzTvjpT2GPCa2AJjCtfbzZcLrAGgoPccfyOzhh1xMy1q+k0XCu+isoL0XQrckFWKOLXz/ya1YNrmLbSdsCMBgZLGxcFQuCLy0rzGt/1whW82KMOdnFOldi7dzzI/IljDkPkQvJ5VRuzKfLG6HFrcnFU7JE3gd4ZYnsJEvkQuA/lRxYcY/fmxnBmmByUWeb9qIRrDw25pF4pK7pgVCaTbsjRqoWwXJZg1VMZJRagwXjFzAnHx6aK4IVT8SJm3hFKYJlR7CS742mCCqK0rJ873s2anWGO4M1p79jdh3WVY9fxTuvfydrh9dmLK+kD1au+isoL0XQbQ0WjBtdPLLmkdSywfBg3vEAEA9kCCxUYG1NOMYWS4GHczwqwm0E61PA17DFX9dgzSe+W+nBFXcEvIGcfbAaYnIRHSlo0Q6ZJgTpJ8ZwLFxXgwuAgMe9TXsqRdBlBCvffp3IitsIVqUCK1cNlhPBcuqvoLkEVi4L4FKPVWkNlqYIKorSkjz0EPz973DuudCR33AqHUdgbRzLtGp/aeAlgAlZEpW4CBaNYJXiIuiyBgtgr5l74REPj655lHfs8g5gPEUwHybuT4kqAOO11wcVWFsBxvxf8udva7H7ogJLlogXuMksNkdjRZZSZ/weP5FE/j5Yuay5a4WbCFa6AMkQWMkUwXpSSgTLSREs1gfLeQ3VqsGqOIKVVoPlvN/ZEaw2X1tTCaxKHCVFpGT7/XSc/xVNEVQUpSX53vdgyhT4+Mddb5IvgrV60DbQzb6eVRLBco6VjZM1UFIEq4QarA5/B7v07pJhdDEYHmRq+9S825hoAOPtH/9bBdbWg8j/kSs10MEUNvIrRtEUQbPYxIFRWSLaAKBB5ItgOSmC9YxguU0RhIk1MuF4uO4pgiXVYFXJ5MKJbLX52iYYlGRTzRRBv8c/oYjYiWD1dfU1pcAqZO5RiHzNrN2gESxFUVqWJ56AG2+Es8+GHve9UKd1jNdgpVOKwHLO1+k14dn0h/rzRrBSGRYl1mCVcq5eOGvhBIFVaNI0EQukRBVAwmvHpgJrq+BHwI+BFcAY8OvkYxh4qtKdu00RDAFPyhK5HUg5cpjFlRWAKe7we/0FTS5y9T6qFSOR4iYX+VzewrEGRLDyWMbnolomF+k38EXT+xLFBZYbkQZZKYJZEay+7j5eHXq14HGy99esESzI38zaDVqDpShKy/L970N3N3y6tNuv7kA3fo9/gsBaNbgKyC2wBMEr4/cUHvHgFW/RFMHJbZNzPpfKsCg1RbCEidmFsxZy9ZNX0/G9cUONw7c9PO/6JubPEFgawdqKMOYeAES+gzGvT3vm/xC5t9LduxVYNycfSgPIvkl3TC7Sa7Ags/dRrRiODNPldxfBynXCboTJRTQRzbBtzUe1bdqDvqCr+qlqpAh6xYtHPBMiWE5Pkr6uPl7c9GLB46SPyTmuW+ousHyVpwhqBEtRlJbi2Wfh+uvhy1+2KYIlICK2F9boeA1WwiR4ZfAVYKLoca5N2dfNYtejQi6CIkK7r71mJhcAp+59KptDm1NjFIQP7P2BvOvHo34S7WkRLI8KrK2Q6YhsjzHLARCZDxTue+ACVwLLLK5NAZjiDr/HPyFF0COelLV3etSi1gLLrU075E4RrLvJRdqNf7Ho2VBkqGBDQoeiKYLx8RTBegis9H1MiGAlUwRndc1yfVErx4Ci3gIrXysAN6hNu6IoLckPfgBtbfDZz5a1+bSOaWwYG49grR9Znzrf55oQzXV+LnQ9CsVCROKRgq597f7SXI9LjWBN75zOd49y78GWiPkw3rHUJKyTIqh9sLYqPgvcjcjy5N/zgI9VulNXAkuWyApyFIKZxWb7SgegFCfbpj0aj6bqr6C85n3lkDAJRiIjrhoNQ/OkCIJLgRUeoivQVTTS5TqC5Q1O+OyyqVYNVnq6KKTVYIUHaPe1Myk4idHoqKtIXktEsEpI/cxGUwQVRWk5li+Hq6+2qYFFmgrno7ejNyNF0Km/guoILGdCL18EC6wRhdt7FWNMSSYX5RCPWhfBuInjEx9xsRORGsHaijDm74jsBOySXPIsxlT8DXCbIrh/2u9twIlAflsWpapMaDScdkMNxW/4q8VYdAyDKW7TnidFMBwPF61vqjbOBSIcD9NN4WMPhgeLpgdCaSYXzRDBmtQ2iQ5/B3ETJ5qIFj1eOQYUzrr1TBFUm3ZFUbYafvhD8PngC18oexe9Hb08ve7p1N/lCCy/NzOjJh2n5rdgBMvXzmisNMOlWp6rEzEv+MJE4hF8Hp+aXGy97AQswGqcvREBY66qZIduUwQ3Zi36mSyRfwPfrOTgijv8Hn/KgAFyRLDKcOYph5Go9TdxbdOelcLlJopUbZzjubnxH4oMuRKA+VwSHUoyuXApsOImntfEJP37kCuCNbltcqr/yGh01LXAKkX8eMSDz+NriRRBtWlXFKWlWLUKrrgCPvpRmD277N1Ma59WnQhWoj4RLOccX8v7hnjEB14rsDr8HcQ89j5naxNYIhwKfAvYDqsNBBtE3PIz1UQWA0cAuwG3AG8G/g3UXmDJEtk37U8PNqJV31DEVky2TXssEWtIBMtpRFhRimADbNpzjSUXQ5Ghoj2wALweL36Pv2gNVsAbcCWwih3TeQ3RRDSnwEp3IszVB2tScFKGwMrn8JQ+pvTjuqXYa63GMRwqSRFMNyFRFEVpes4/H4yBL32pot30dvSycWwjCZPAI54MgZU9QVtOiqBjqlToGlNKDVY9sg1i0fEIFrA1pwj+BluL9DAQb/BY6s27gb2BRzHmNERmApdVulO3KYI/Tvs9BqwE3lPpwRV3TKjBSkRTDoJQvxqskYid2Slq016gD1a9TS6KRZvSGQoPuUoRhMIpak5RrohULYLlrJvrQhONj6eMesRD0BvMcBHMjmAVoyUEli/I8OhwWduOxcYIeoMpkxhFUZSmZe1a+PWv4dRTYbvtKtpVb0cvCZOgP9TP1PaprB5azfSO6awfXV+dGiy3KYIuDZfS65lrRSzqTUWwgK02ggUMGMOtjR5EgxjDmAQiMUR6gHVQeeTObYrgkZUeSCmfnDVYnuaNYOUbTyNMLkoxXxiKDOXtQJ9NoebOoVgo9TqrLbDc7CN9hnAgNMB2k7ZLCSw3IrwlBJa3Mpt2rb9SFKUl+OlPraXdV75S8a6mtdtmwxtHNzK1fSqrBlax49QdSxZYeWuwXKYIrh1e62q86fXMtSAeh0TcA95I6jXF2GprsO4S4XzgL0Dq1RvDI40bUt1YishkbJPhh7GNhh+sdKduUwTPBq4AhpID2Bf4sllsbqt0AEpxshvNpkcsoP41WEVNLvKlCMbrnyKYbyy5KCWCVUhgpfftqKbAyndRyxbc7b72jBqsScFJqSjnlhLBavO1VZQiqA6CiqI0PWNjcNll8K53wY47Vrw7ZwJxw+gGdpq2E6sHV/O6Oa9j6atL3ZtcFGh87yaC1eHvSN1LFKPW6dwpEZWWIhgjgscXIxx2m+DVeETkcuBtwDpjzB7JZVOB67CW4yuB9xhjNhfYzYHJn+mmdgY4qtrjbTqM+UTyt4sR+TvQgzFPVLpbtzkyHzaLzSDwRmAGcBpwbqUHV9yRPWM04Ya6TimCrmuw8qTlNaTRcJpNezHcmlxAkQhWfLxvRzNEsBwXQaiDwMpT/FytYzhU4iI4FtMIlqIoLcCf/gSbNsHHP16V3aULLGMMqwdXM7d7bs7rWSU27YWuo3O65/DywMskTKLoeNN7StaClMBKSxGMxCN4fNFW64N1JXBs1rIvA3caY3YC7kz+nRdjODLHY8sXV2A7YIucgsg3MWYl0I/IAZXu1q3AchrnvAW4wiw2j6ctU2pM9oxRo00uitZg+fLYtMca12jYdQ1WFQRWyREsT2UCK1dEMxQLEY1HGYuNTTC5KEYrRLAqSREMxULqIKgoSvNz8cWw885wxBFV2V26wNo4tpFwPMzcHiuwsidoy63B6g505zRjcljQu4DR6CivDL5SdLy1NrlIiai0CFY0HsXrj7VUiqAx5l5gU9bi44HfJn//LfCOXNuKcEry5+dyPWo15ibjV8DBwMnJv4eAX1a6U7cC62FZIrdhBdY/ZIl0A8WnH5SqMKEGK55lclGvFMFIiTbtuVIEm9SmPRKP2F5ZVUgRDMVCNUkRLBTByo5ojsXGMtI1yhFY6ft0Q6ukCI7FxjRFUFGU5uaJJ+A//4Ezz4QizeHdMq0jWYM1tjHlIOgIrFC88ghWf6i/YHogwC69tpfrsxueLTpe5xxfq8yXXBGsaCKKzx9vNoHlE5GlaY8zXGwz0xizBiD5c0ae9ZwZ8+48j6ZDRC4XkXUi8lSBdY4QkcdE5GkRuafILg/EmLMA+09gUykrjga4TTI9HdgHWG4Wm1FZItOwaYJKHcjlItjMJhe5UgRjiRgJk2ham/ahsO0zVkoEK18EJV1I1kNgRRPRjHE7NVhOukY5LoJ+jx8p8aLeShEsTRFUFKWpueQSCAbhgx+s2i67A934PX42jG5ICaxtJm1Du7+9NJOLRP5Gw8XagCyYtgCA5zY+xzE7HFNw3VpHsNJrsJzXFI1H8QaaLoIVM8bsX3y10jGGS5I/l9Ri/zXiSuAX5OlTJdaw4lfAscaYl0Ukn7h0iCLixdacgch0qhBEcusimJAlMhd4nywRgHvMYvN/lR5ccceEGqzslLB62bS7NLnweXwIknHCTs1E1TuC5dKm3Wnk7KYPFtgTfj6x0pAIVtb3YTA8mOpJUk6KYDnCp9427WXXYEXHNEVQUZTmZXgYfvc7eM97YOrUqu1WROjt6M0QWKkIlluTC28Bk4vQQEEHQYBZXbPoCfbw3Ibnio63biYXWRGstuaLYJXDayLSZ4xZIyJ9WOvxvIjQhg2m7A6kFK0xfLi2wywdY8y9IjKvwCrvA/5ijHk5uX7B1w78HLgBmIHI97B9sb5R6ThdpQjKEjkXOBt4Jvn4tCyRH1R6cMUdE1wEsyJYfo9/gqCpBcORYdp8bQXzq8GexLNTuJzxNyqCVezGPxXBctsHy1u8DxZAwFOHCFY8mmly4USw0lIES0kjbQmB5Q2SMAliiVjJ26rJhaIoTc2118LQkE0PrDLTOqalUgS94mVm58zqmVyEB4qmCIoIC6Yt4NmNLlIE62VykVWD5QtsEQLrJsAJf34QuLHI+r8DZgFvAu4B5mJrkRpBbxkpkensDEwRkbtF5GERObXg2sZcDXwJ+AGwBluvdnMZ487AbYrgW4B9zGJr+yJL5LfAo0DljRmUogS8AQyGeCKO1+MlloilIhJgT1ildEcvl+HIcNHolUPQl5nC5fxe90bDLm3anQhWtUwuprRNAQpfjOKJOAmTqEqKYHbK6FhsLKMnSb0iWIPhQVfrVqMGC+x77QuUZqerNu2KojQtxsBFF8Gee8LBB1d99+kRrNnds/F6vNUzuQgNpFIAC7GgdwH3rCxWFlP7RsPjEawIkXgEY4ytwfIlWkpgici1wBFYYbIaWIx1+r5eRE4HXgZOLLKbHY3hRBGON4bfinAN8I9ajrsAGypMifQB+wGLgHbgfhH5rzHm+QlriswB+oAnMOZZbDrhZ4APAbMrGINrkwuAyWm/F56iUKqKk/6VPsOSbUCQ6wRZbUaiI0XrrxyC3mCGqGl0imC1I1jVaDTsVmSUZdOeFcHye/34PL6mSxEs1UjDIZ9TpRu00bCiKE3LI4/Ao4/Cxz5WNXOLdByBtWpwFXN75gLjzrPp5BVYBbIynL6Lxdhl2i6sGlyVMs7KR60bDWenCDoZEf5AoqVs2o0xJxtj+owxfmPMXGPMb4wxG40xi4wxOyV/ZrsMZuPUofSLsAf2Pn9eTQdeO1YDfzfGjBhjNgD3AntPWEvkM8BjwIXAfxH5ILAMK8r2q3QQbqd+fwA8KkvkLqw9++vR6FXdcG5Co4ko7bTbGRZP5keX6wRZbYYjw64FVrYrkRPBapjJhcsarKpEsOLubNqrJbBy2bRnR7DANnhsJoFVjpGGg9vaulyoTbuiKE3L5ZdDWxu8//012f209mlsGN1AwiTYe6a95yw1RTBX03tjTKrvYjEW9Noo1/Mbn2dh38K869XV5CIeTRld+AOtFcGqEpeKMAX4Oja9sIsq1CE1iBuBX4iID+sGeCDw0xzrnQEswJhNiGwLvAC8HmP+W41BuDW5uFaWyN3A65KLzjGLzdpqDEApjnOSc05q2TfUQF1SBEciI0V7YDkEfcHcNVhNatNe7QhWmzdTYBljJoiJqkaw0nppZddgOcYdzSawKkkXzdcKwA1jMTW5UBSlCQmF4Jpr4J3vhMmTa3KI3o5eNo1tYiQywlt3eitQmsDKZ3IxFhsjmogWdRGEcav25zY+V1BgpSZma3TfkIpSJSNYzj3WViqwJjHuDu70gIqJsI8xPNaYIeUmT0qkH8AYc7ExZpmI/B14AusGeJkxJpelewgnsmfMy4g8Xy1xBe4jWGCbcB2GtTH0Yh03lDowIUUwkTtFsJkiWNk22rXuZ5EP1zbtVa7BSk8RNBjiJo5PMv/dUmly3sJpcq5qsLwT+2D1h/rp9HemntuSBFYlKYJq064oSlNy443Q3w+n1a4LTm9HLwmTYCw2xjY92wD5BVauFO585/nsjIlC7Dh1RwQp2gurbjVYSZOLVAQraAg3yt6hcewH7A84DuFvBR4CzhThj8ZwXsNGloUx5mQX65wPnF9ktbmI/Dzt7xkZfxvz6fJGaHElsGSJ/ArYEbg2uehjskSONovNWZUcXHFHKoKV/OePJWITI1i++phcTG13ZxmbbaPdKJMLn8eHRzxFb/wdc4ZSIlj5RFu2TTtMbA4N459n1WuwfO0kTIINoxsy0jU6/B21dREs4phYjWM4lJsiaIxRkwtFUZqTyy+HbbeFo46q2SGmtU9L/e7UYDnGSA6O2UMhk4vsrIz0mt9itPnamDd5Hs9tLGzVHoqFCHgDZaeSFyO7BsuJYAUChoGtL4I1DdjXGIYBRFgM/AlbEvQwNI/AqiJfzPr74Wru3G0E6w3AHmaxMZByEXyymgNR8uPMIhUzuah1BKsUk4tsAdIokwuwF4SiNVjhIQLegOub/jZfG3ETJ5aITRBOGTbtaeIo+6a+qjVYnol90dYOr82YTWz3tW8xEaxyUwRrbfurKIpSFqtWwe23wze+AZ5S/MdKo7ejN/V7PpOLQpN/+bIySolggU0TLBbBCsfCNT1XZ0ewUtfkoNkaUwS3BdIv4FFgO2MYE2HLfDeM+W0td+9WYD2HffNfSv69DTa3UakDE2qwcplc+NtTJ7haUZJNuzeYMSPWKJML55huUgTdNhmG8Rv0UCyUIToTJmEbFWZFsHIJj5q5CPrSBFZWBKuWAqtQA8pqHcMhZb9fYgTLcdrUGixFUZqK3/7WWrR/6EM1PUwugZU9QVvo2pQy3crKyiglggWwYNoC7nnpHhImgUdyC8pap3OnR7CiiXGTi2BgqxRY1wD/FUn1y3o7cK0Indj+t0qJuJ0mmQYskyVyd9Ls4hlghiyRm2SJ3FSz0SmAe5v2mkewIiXYtDeJyYVzzKImF5Eh1/VXMC4Us9/z7EhdrQVWPBHHYCbUYMHECFY9arByuUvlIl/6iVvyvf/FcES/RrAURWkaEgm44go48kiYP7+mh3IElkc8zOqaBdjzYSQeIWFbnRa8NuW7HpUTwRqNjvLK4Ct51wnHwzWdlM3ugzWeIihbncAyhu8AHwX6gQHgTGP4tjGMGENtLC23cNxGsL5Z01EoBcmuwco2NYDa12AZY0q3aU+vwWqQyQW4TxF0W38FmRGsdLJT0GotsHLtw4nO5KrB2lJSBN02kM7G+by0BktRlKbhX/+C5cthyZKaH2pah63BmtU1K3UfkZ5y3e5vL09glRrBSlq1P7vhWbaZtE3OdeoVwRJ/NMPkIhg0LdUHq1oYw8NUuQ5pa8ZtBGu9WWzuSX8AkvZ7TkTkWBF5TkReEJEv53heROTnyeefEJF9i20rIieKyNMikhCRCZ2eRWRbERkWkS+4fG1NT3YNViwRmxDBcprL1opQLITBuLdpz3YRbJDJhTOWakew8gms7L4d1RBY2RHMdFKuRzlqsAym7hGsuIkTT8RdHaOYe2IhUjcEmiKoKEqrc+ml0N1t7dlrTHegG7/Hn0oPhPHzqTNJW47A6g/1A7iyaQebIggUNLoIx8M1zXpxRFQgYDIiWMHg1hfB2ioR+SgiOyV/F0SuQGQQkSdI0yPl4jaCdb0skauwlodtWDeR/bHW7TkRES/WS/8YbFflh0TkJmNMei7nm4Gdko8DgYuAA4ts+xTwTuCSPIf+KXCry9fVErjqg+VrZygylLPfUjUYjgwDlJ0i2GwmFy9uepFjfncMI1HbSX7T2CaO2f4Y1/vMG8HKitRVQ2B5xIPP4ys5ggWZF7t6CCywos/r8dbkGA7lpgjWunGloihKSbz0Elx3HZx9NnR01PxwIkJvR2/Koh3GJ+Wc82O5KYKCuL5HmNU1i55gD89tyC+w6hHB8vkg6PdnmFwEgxCP24e38KVMaW3OBq5M/n4ysBcwH1gIXAAcXsnO3QqsA4EfAv8BuoGrgUOLbHMA8IIxZjmAiPwBOJ7MYrnjgauMMQb4r4hMFpE+YF6+bY0xy5LLJhxQRN4BLAdGXL6uliA9gmFM0r0ny+TigDkHcNHSi7hv1X0ctu1hVR+DI7Dcmly0eXMXzTbE5CJHDdZT655iRf8K3r3bu+lttznp79n9Pa73Wc8IlrNOzgiW0xgxRw0WMCGC5SbKWanAisQjRS+KjUoRdGZoNUVQUZSm4Cc/ARH4zGfqdshfvOUXbDtp29Tf2dezgiYXyWuNkz3hMBAeoCfYk9ewIhsRYcG0BTy7Mb+TYLojby0Ih62YcuqHndfU1iap5+ugeZXGEcMY54v8NuAqjNkI3IFIxbb0bgVWFBgD2rERrBVmcbIaMj9zgFVpf6/GCrVi68xxuW0GItIJnIONeuVNDxSRM4Azkr8XfAHNQnpkIFdKGMC7d3s3n7r1U1z+6OU1EVhOpKekCFaOFMGGRbCybsSdSM53jvxOqqt8KeSzCU81RqyiyYWzTjkRrPR8eMemvViUsxoCqxiReKQk18Zsyk0R1AiWoihNw8aNcNll8P73wza565BqwTt3zUxFzBZYKbOHEmuw3NZfOezSuwt3r7w77/P1sGl3BFZ6imBbUAXWVkICG9TZDCwCvpf2XMWzsG5rsB7CCqz9gcOAk2WJ/KnINrnu4IzLddxsm80S4KfGmOFCKxljLjXG7G+M2b9VBFZ6DVauiAVY4XPS7idx/dPXM1SDFuSpCFYpNVi5UgQbZdOedSPuCEa3Ebls6mly4axTag0WTIxgxU18wsxjrnFlC3g3lCqwGuIiqDVYiqI0C7/8JYyOwhcaWzJeSgSrUIqgWwdBhwXTFrBqcBUjkdxJR/VIEQwG7fUzkhg3uWhv86SeV7ZovgksBVYCN2HM0wCIvAGbDVcRbgXWR4H/AV81i81a4FPAY0W2WY3tl+UwF3jV5Tputs3mQOA8EVkJfAb4qoh8ssg2LUF6DVYsEQMmRrAAPrzww4xER7j+6eurPgbnBFhqBMtmfzbW5CKXOHFej1vBmE2xFMFq1mA561Qawerw26m4YnVY9YpgNTJFUCNYiqI0lNFRuPBCeOtbYY89GjqUlMlFtHyTi3IiWI6T4Pn/OZ9Lll7CJUsv4fG1j6eer7XJRTgMgcDECFYwLYKlbMEY8zdgO2BXjPlo2jNLge9Xunu3KYKnAQngKODbwBC2Juq7BbZ5CNhJROYDrwAnAe/LWucm4JPJGqsDgQFjzBoRWe9i2wyMMaliNBH5FjBsjPmFy9fX1KTXYKUiFjkc2A6aexC79u7Kbx79Dafve3pVxzAYHgTcCyznhB2JR1KGF17xFjU/qAVBXzDlcORQswhWrIYRrETlNVhgBVYhp6dWEFjOtuWmCGoNlqIoDeWKK2DDBjjnnEaPJDUpV0kEqz/Uz5zuOSUdd7++/fB5fCy5Z9yefpfeXVh21rLUeOoSwXIElkawtj6MiWFTBNOXjSByLbBtzm1c4jaCdaBZbM4CQgBmsdkMFMwhMnbQnwT+ASwDrjfGPC0iZ4rImcnVbsGG4V4Afg18otC2ACJygoisxjoY3iwi/3D7YluVVOf0RDR1Q51tcgG2puzDCz/M/avvZ9n6ZVUdw+aQ/f5NbZ/qan0nguPcADtCqxHksmkfiYzgFW/ZN/nOa2m4yUWOFMH0C1KpESxjTEsILI94ctbWFcOZodUIlqIoDSMWgx//GA46CA6rfs10qZRkcuHcj2Q1lR8IDbi2aHeYP2U+G764gVc/9yqvfu5Vvn3Et3l2w7O8OmQTlsKx2jYajkQyTS6c1+0IrK2xF5aSouIaIrcCKypLxEuyDkqWyHSK10RhjLnFGLOzMWYHY8z3kssuNsZcnPzdGGPOSj6/pzFmaaFtk8tvMMbMNcYEjTEzjTFvynHcbxljfuTytTU96Teu+UwuHD6w1wfweXxc/ujlVR3DprFNQAkCKyuFq9Yd2QuRy6Z9JDpCZ6CzbKOTYjVYzWJykW3TDoUFVtzEMZj6CCxPZemiQW+wbJt2rcFSFKVh3HwzrFhha6+aoBa8KjVY4dJrsMBOAvZ199HX3cfbdn4bAHetuCs1nkaYXHRoBEtxoXGK4VZg/Ry4AZghS+R7wL+pQn6i4o6ULWo8mtfkwmFm10zetvPbuOqJqybMMFXCprFNeMXruhlv9gk7HAs3pP4Kctu0j0RGyk4PhPrbtPs9ftc27QFvAElOvuRKESxk1V7KmLKpZwQLJjpVukFt2hVFaTi//CXMmQPHH9/okQCVCyxjjDW5KLEGK5u9Z+3NlLYp/HPFP1PjqYdNu9/rz5jA7mj3pZ5XtmBE/g+Rm3I8/g+YVunuXdVgmcXmalkiD2NtDAV4h1lsqpuDpuQl/YRWyOTC4fSFp/PXZ//K3J/OzZlKGPAG+NOJf2K/2fu5HsOmsU1MbZ/qOuKTnSJY62LVQgQ8E1PJnAhWuTSzyYWI0O63luzpVuiOqCgUwWolgdXmays5RTD781EURakrzz8Pt98O3/627XLbBKRMLmLlmVyMRkeJm3hZEax0POLhiHlH8M+VVmCF4/WxaSeZbj4ewfKmnle2aAplulWcBef6v9ssNs8C+TvCKTUjowargMmFw7E7HstXD/sq60bW5Xz+uqev44IHLuCqE65yPQZHYLmlmVIEc0U6RqLViWBl7zfb5CLdoCQbZ5kbS3QnRzybfCmj7b52POLJMBVxkyJYqP+JmzFCHSNY3iCheOk27W2+tpbpgacoyhbGRRdZYfXRjxZft044k2+lRLDS230MRWxrmEp6GzocNf8obnj2BpZvXu6qaX0lhMPQ3Q3GG2AoPJQWwVKBtVVgzD2p30WmJ5etr9bum2P6RClIRg1WAZMLB5/Hx/cWfS/v816Pl6sev4oL33yh65D+5tDm0gRWVp+iRppc5LNpdwRHOfg9fgTJH8FyWYPl9/hd3ewHvIFUL7LsfaQfx6Hd3z7hwuRGYLVSBMtxpyyFsdiYGlwoitIYRkase+C73gWzZjV6NClKMrnIMWnoXJvcugwX4qj5RwHw9xf+DlBzm/ZgEBLeANHEuMmFRrCaGxG5HHgbsM4YM6HHgYgcAdwIrEgu+osx5tt5drYY23pKAA8iMeBC8q1fAm5rsJQG4oipaDxa1OTCDacvPJ2x2Bh/eOoPrrcpNYKVHeGptRtQIbKbHkPlKYIiQpuvreJGw25FRl4XwTw1ee2+9gniuVkEVjwRJ2ESVYlglWPTrgYXiqI0hGuvhYEBOOusRo8kg0prsIbCNoJVDYG1a++uzOycyS3/uyVjbLXA6YPl1DiPpwhqDVaTcyVwbJF1/mWM2Sf5yCeuPgscBrwOY6ZhzBRsy6hDk89VhAqsFkBE8Hv8GTbthVIEi7H/7P3ZY8YeXP6Ye6fBaqQINtLkIpqIppoeQ+UmF0BOgRWKhRAkJYBrLbAKRbCy8+GbRWBVcox0cr3/xdAIlqIoDcEYa26xxx5NYc2ejs/jwyvesgWWE8HqDrozwSqEiHDk/CNTRhf1MLlIuQgmJ7C7Ovyp55XmwxhzL7CpCrs6FTgZY1aklhizHDgl+VxFqMBqERyXGzcmF8UQEU5feDoPvvIgT617ytU2JQusbJOLWANNLnJcECqNYIEVbrkaDQd9wVTan/M51SyClSeiuePUHdm1d9eMZSkXwVhjXQSrJbDKSREMxULqIKgoSv154AF47DEbvWrCGtD0CatyBVY1IlgAR847MnWdquWEWHofrMwIljf1fKsgIp8VkadF5CkRuVZEWnkmsVdElqY9zihjHweLyOMicquI7J5nHT/GbJiw1NZhlX+TnUQFVovgmBy4Mblwwyl7nYLf43fVLysajzIYHmRK2xTX+8+VctDIFEFnDA61jGClXxCc6GO9I1h/PPGP/Pq4X2csc1LjtpQIVjkpgmPRMU0RVBSlvhhjXQN7euD972/0aHLS7m9PtfAotdGwY3JRLYHl1GFB7VMEsyNYfo+ftjZJPd8KiMgc4NPA/smaJC9wUmNHVREbjDH7pz0uLXH7R4DtjDF7AxcCf82zXqGblYrltZpctAh+j5+hAS/PPG5vDrNNLtauhVtvtedxd/Sy16s/49e/eY5dVsXymmYcdhhMmdMPuG8yDOMpgoNDMe6/v8E27cmLxPMvRpnSBttvX7mLINgT/yvPbMPlm8eXvfjqPIJtma8z4A0wsMnPlVdCImGX+Xww4vGWJLDCsQh/+Qv0948vf7nDXtCyBbdHJs6d+L1+fB4fK5ZN4vIXch/nxc2TYGhWRQJrwzovV1yR/7vYt4PJWL9cHJv2J56ApUuLrw/wyjN70rlbf0XHVRRFKYm//tVeoH/yE2tb12CeeQamTIG+vvFlzoTh449D/ybfBBdaB+e8vXmjN3VN+9dLffDYadx6/SweTHpH7b47HHjgxGO//DLccUfh8RmzA1OWfZ7NY5v4t3dnxh4q84UWYWRk3Kbd6TPq9/rtMuBf/4K2pL4LBOCUU2ozjirhA9pFJAp0AK82eDwNwxgzmPb7LSLyKxHpNROjVXsjMshEBKhY2avAahEC3gCP3/R6rr7hQPiSTEgJO/dcuOCCUvf6CQA+9sf8a7zxjfDzq22qazkpgnfdNIfTvg87nhskOLVxNu0AX/h0Bx4Dd9xhbASrwhTBNl8b/zr3HP6ZZu4nnrOZ+4NLMtYLeAM89JdDuOi6zO33+dAhBPa/z9WxAt4AY6/swLu+kLl83p5HwLvci5UOfwd//f67ufzlfGvsCK/7GoGzyhdYt165F3cX8E/ZbvspcGp1UgRDsRAf/KDNvHHHD3j9L99V0XEVRVFcMzICZ58Ne+4Jn/pUo0cDwAkn2MnT3/xmfFmbr41QPMQb3wjbvf5QAvvkPj97PV484uHff9yfH17tLD0cOJwv3TS+Xl8fvJrjFv+rX4Wrr564PBPBaUP0q5sKr1kpc+fCy8ksE8fZt7PTCtA//ME+ACZPbqjA8olI+jTipelRHWPMKyLyI+BlYAy4zRhzW70H2SyIyCzgNWOMEZEDsNl6GyesaMzEGYQqogKrRfB7/Yz0dxIJ+SDaOSFisWEDbLutnXFxSzwR5+DfHGwNL46fmCp42mmwcaOtv4LSBJYT1t+0wUciAWND7Q0zuXCOu3694DE2jc9gKo5gBb1tREe6OPts+Nzn7EXjq1/14I/2Tjj+yEA706bBI4/YyM68eTA22E5bKSmCw3bm8w9/gIMPtqn8DzxhxaPbmrwOfwcDA528//3w/e9PfP6gw0ZZMzatogjWYH+AuXPhvhzacfFiuOGvnoz1y8VJERzZACeeCD8q0hbwr3+19zmeUMUN2hVFUdzxne/AqlVwzTVN01h43TpYn9Xtp83Xxlg0xPr1MG1z4eu1vaYFU9e0Cx+4kB/953ye/9T/CPqCnHtupnhLZ8MG2HtvuKmIcPrT03/i87d9jmvedS2Hbntoia/QHR4PzJkDX74jLUXQ68fvh5degs1p2SkNLpuLGWP2z/ekiEwBjgfmA/3AH0XkFGPM7+s0vroiItcCR2BrtVYDi0nWTBljLgbeDXxcrOX6GHCSMe7zu6pFc/y3K0UJeAOER5MRoHD3hBvqoSE747LttqXs1ctR++zIA6/ck3O76dNh9eryBJYTNRoZsjfToRF/w2uwhoaswHJqkCqNYPnjk8F4mDvXvu/Oe+iPZb5P9rMLMHny+DpdXRAeDdJTgsCKjlnRuuOOdj8zZ0JoNNOtsBgd/g7WjwXp68v9XensiUC4uyKBNTbiy/tdnD0bRoY9YKoksGJhRofI+3rSmTfP/vRF3dcSKoqilM2yZfDjH8OHPtQ0zoHG2PuFoaHM5W2+NkZG7POhUX9RgRUa8aeuab7nX8U/dS07bW+vtXPmWIMIp8YpnaEh6O0tfr7+2Kw3M9D2JO84cF/aK7YbKEy6yYXzuru7myKb0y1HAytMskmuiPwFOATYIgWWMebkIs//AvhFnYaTFzW5aBH8Hn+awOqZEMEaHLT1s6XS19XHmqE15BL3PT12v2UJrKSoGRlOuvGMtjW8Bmt4SBgctPVXQMURLF/Uvh/O++789GbdwNuLUTDj8+npgchosKQarOhY+4TjhUfse1qo8XQ67Z4e4pFg3u9KW1cUwj1liR9H9IeG/Xn339MDsZhArK0qNVhj0ZDr776zjkTcNddWFEUpG2NsmkFXF/zwh40eTYqxMYjH7bU9nXZfO8OD9pYwPBIoeH72e/wZ17ThyHCGwYWzPFvEgft7lc5AJ0uOXFIX19eAN4DBEIqHKnJobiAvAweJSIdYC+NFwLIGj2mrRwVWi+D3+omOJmvuwj0TbqjLFljdfYzFxlIuQOk4AmtzyMbJy4lgjQ7bcUZG2xoXwUpF07wMDsJwOCmwKoxgeSNWSE0QWJGJAisy2pZDYLW5doMMeAOYUNeE40XGgvgYt4UvRjDem7GPbNo6wmULLMcxcWwkUFBgAWUfI52gL0h4zIcxJQqssAosRVFqzE03wV13wfe+BzNmNHo0KRxhlS2w2nxtqet1eLStoNBwsjJSAiuaW2BlH8NZVs69Si1xrkUjkZGKHZobgTHmAeBPWPe8J7H39qU67ylVRgVWi2Bv0pMzOeGeCSe/ck9as7pmAbBmaM2E53p6YHQUNgz3AzC5bbLr/fo9fgRhdDhp6Tra3rAIVtAbhJifcNhDLAabhpIpghVGsDyRycBEgeUsd8gnsKJj7uvSAt4AhHtyHs8fd5/y5o9Oy9g2m2Bn+QLLGWf6hTebqgosbzAVwStFYDnvo6IoSk2IRuGcc2DBAjijnBY+tcONwCqWXeGULKQiVeGhjCbDrSawHFE1Eh1p1QgWxpjFxphdjDF7GGM+YIxpEZP5LRcVWC2C3+MnFkpGsCLdE2ZZhobKyxfu67I+rWuH1054ztnfa5tGmNw2Oadlaz5ExLq8JWuEYqHKU8LKJeANQGT8zdmw2Z53Ko1gSXKfzvvk/JRI5tUj4A0QG2vL+Hy6uyFWssDqxus1KdtYZ3/ZNV+FcNbN910JtIchUl4NljPOyGgw7/5Ty6sgsNp8bSTCHZn7LUBqnUh1erUoitIAYjF4+ml48EEYHi6+fiP4zW/guedsamCTGFs4OGl7uWqwnOt1dKzw9dqZNHTOqdkpgs7y7GM49V/NVtuUKiOIDLdkBEtpTprrP1/JS8AbIDaabDBRiwjWcO4IFsC6TaGS0gMdgt4goZHkSTrc09gUwXCawOq3/eMqjWCRTDXLjihlR0ic+qnsCFYs1FGiwPLR3WNS6YD5UhILkZ3WmI2/I5QUPwnX+8zY3utnaDR/jdf4e1S+iHOwn2tmVK8QXd0JwEMi1GRXd0VRJrJ2rU2xW7fOPtasgSefhKeeglCywbuIbWy4117w+tfDokWwxx6NtXwbGrJ2qYcfDscd17hx5MGJKo2N2UCbP3kr0eZrS12vi2VXOKZLxWqwsiNYTv1Xs0Ww0lMEGzURrGx5qMBqEfxeP7FQmsBKm2WJRu2Jq9waLMifIgiwYXOEKVNLd15r87UxmmbM0VCTizTRs3Gz7UBfaQTLJG/UswWWCWfewNsIVscEgRUf6yxRYAVSIiH9eI7ZhhscA468Aqt9DOJBTKy8i4xf2oiF2uqWIliKwBJfGDw+4mMdFR1XUZQa0t9vIz8XXGAvbABer7VN3XVXaxyxzz7Q2WnF1pNPWq/wG26w686cCcceC+99Lxx99LiCqBc/+pEVhDfd1HBv71yki56hIZiavHy0+9oJJwVWPNSBX/L3WbU14eOThkORIaZ1jLe/yCewnL+bVWCNRkfp9fcWWVtR3KECq0Xwy3g6VLbJhROGL+ekNaVtCgFvIGeKoLO/Tf0x5swpI4LlC9KfLrC8jclITb8RB9g8EAcqj2A5Qsp5n2xH+EhKeDn4TAcmNtFFMBHqKrkGq7NrPLKUr+arEI6DXkGBBXYm0/1uU/iKCLh0gVVpKkabr60kgRWKj0HQEBurMHKpKEr1MQZ+9jPbN2rzZnjf+2yDwXnzbA8ST47rxwknjP++ahXceSfccQfceCP89rdWPbzrXfCOd8CRR0J7jR3pXn3VCqz3vAcOPLC2xyqTdNEzODgusNp8bURGxydBvdH8ZkA+00Eimuki2B0oXoPVrALLyQgaiY7Q5+1r8GiULQWtwWoRJK2GKLsPliOwyslrFhFmdc3KmSLo7K9/MF52iqDTu4lwd9NEsPodgVVhBCsRstt3dVmLexGQ4DAmnLlfT1LUZNdgJcKd+KUEgRXppqMrnrEPmFjzVQhJvg/5viveNmsAEh4tL7rki00puP9q1mClp366+e6PRccgMDRey6goSvPwrW9ZQXXggfDoo7Zz+377wbRpucVVNttsY/tN/f73Nr3wpptsJOuaa+Ctb7X7Oe44uPRSeOWV6o8/HofTT7cpJbm6uDcJ6XVR6b+3+drGr9eAp4DA8kYnA5Rcg1XJvUotyXARbFGTC6X50AhWi5BhLR2elGE4UemsUF9XX8EarMHB0izaHQKmm0R0vAYr4G1MQXLQF8wwuRgYNOCxTXcrIR7qAG8I4wVIXpiCgxMiJLmiRj09QMKHJ+ZO5DkisaMrlrkPSotgjQsSA0xMX/G2Wwt7x02qVIrVeKWWV2Ck4VBqimAoFoLgCJHRyRUdV1GUKnPxxfDtb8OHPwyXXVZ5al0wCG9/u32EQnDPPfC3v9nH//2fXWfffeFtb7OP/fbLFHFjY7B+ffFuuOmccw78/e9wySWwww6Vjb+GZEewHNp8bcTGxiN8hfoFerKuaUPhoQyB1dlpP8JWiWCpyYVSCzSC1SKk1/V4sk58lZ60ZnXNKpgiODLkLUtgOdEMoLEmF1kpgoMD9melAis21gHBQXvjnsQEBifW+OQQAaVahqcEVudEgSUl2I7Hk720gh3RnM972qwIHhoq7wYn27o+m7Y28HjjVYxguRdYY7ExCA5mpMEoitJg/vIX+MQnrNC55JL/Z+++w9sqrweOf4/lvbI3kAQII1BGGgiUvRNWyiaUTQm70AWllKYp8CsF2gJlNVBKU1ahjLJXGWEFSNgrEGZMpp1hx/L2+f3x3mvLspYtyZLt83me+9i6uuOVLEs697zveVM/bqmwEA44AP76V/jySzdu68orobgYLr8cdtwRRo922aczz3SBV3k5jB0LP/gB3HMPNDbGPscdd8Cf/gTnnpt1ZdnDRQuwivKKOnwexZovUEIKPLW0tlDXXNehi2BOjstS9bYAq0VbLINlUsYCrN6iPuSNL0qA1d20+6jSURGLXPjH04bS7gVYocUXsqjIRU2NUJRbRI4k9/JvChZBQU1bgNXS2gIF62iqC+vnH6FbXvtzm9gfzX8MhSXtgVF72fHEJ85trSuBvPU0tAYj3p9T4DJYkeYvSYRE6A7Z4X6BwuKmlJVpp6Gc3LxWN/4tDpfBqqG+1gIsY7LC88+7sVZTpsC//53+kuYisNVWLtv08suuGMWdd7rxWQ884NowZAhceKELwlatcu0bOxauvrq9emGo116DM85wFQz/8pf0tj8FYmWwOlzwi3XhLuQzrbbJfWaEZrD8+6IFWNnaRTD8d2OSYQFWL9HqjevJCbRA2JibZIpcgKskWFVXRWNLx6t07eNlyroVYPl9uAO5rdBYluEy7e7JycmB2vU5SY+/AmisK+yQwWpoaYD8mg792MEFqBA5g+XfF48/D1ZhSfvfKC8PcvIaIMFjgCsNT0E1wabIAZYUuhdTeN/5RPnjwWK9FpOdzLjtOAHX9bMkZFxaLHVNLoPVELQrlMZk3IMPwrRprjvdY4+5jFJPGzIEfvQjl6Vavdotzz4LV1zhgrBFi+CJJ+B733NB1+abu4CsqQmefhqOP95VKtxoI7jvvqyb8yqSmpr2ZoaPwaKxjNxcN6aYWBf/Qt7naxrcQcIDrPLy6GOwsi2DFdot0LoImlSxAKuX8DMdxYPXdrqylIouggAr1q/osD43FwqLXHeu7gRY/nicoSMaU/KFurv84KSguIHycqhdH0h+DizcbPehAZbLkFTTEAwLsMLKuQNtQUFLfWLtyJV8aCzvEGCBGzMVXrUwlqa6otgBVoF7MXU7g5VAl7384oaUzoNVVBq5u2M4/+/T3fFlxpgUufVWOOoo1x1v3jwX6GRaTk7n7ok5OS4IfOYZV51w6FA44QT3Bjd1Kjz+OJx4ogvKBnf9MzITqqtdj0j/d5+fwRo+0n02xexdEVJBd32j61ZeVtBx+/Ly3tdFELAugiZlLMDqJVq8wgnFQ9Z0urKUiiIXEHmy4eLS5m4HWH4/7aEj6jPaRTA3JxcaB5BfXE95OdTV5qUkg1Vfm98xg9Xc4AKs2o6Bgx9Ehf59/KCgJcGS4X7lu4Lihg7rcwpraU2wmyFAY7AwZoClhckFWJpQgFUfcbLsrvK/EBSVJBZg+WOwLMAyJoP+8Ac3TumAA1zQkg3BVSL22QfeestlsI47znUpXL7cFegYNy7TrUtYvABrxCh3Ea+1PnrPiNCLhn6AFSmDFSnAys11w+KyiQVYJh0swOol/C/pxYNXd8pY+G9ipYn3FOvAz2BFKnSRX9zY7QDL70YwaESdy1jkZG7sS07jQPKK6ikrc4FRKjJY9bX5kF/TKYNVHyXACu13XljctQCrKejGdeUVdRwDkFO4PuYHYbiGYAEU1LjuchG05q4Hael+gFUfv2x6XlEd0jgASXIwu1+8JFrBjnB+mfZgbYDW1vjbG2NSbN48+PWvYcYMN1dVSS+bky4nx3Up/Pvf4fDDSWjwZ5aproaBA91T36HIRa4rcjF0pLuIF+uzKfQzLVqAFW0MVllZ9s2/3CHAsi6CJkUswOolmuvdF+yCQas7jdupqXFvloFApD3jG1XmZbAiFLrIL6qDxu6NwfIzbYOG1QI5tDameZLHGHIaBpBXVEd5OTQG81OSwaqrzXUZq2b3gdTQ0gAFNTTW59ESMiyopd6NLSgp0bZ1fibK/7vG01jnPsjzizsGWFKwvu34iWiozYuZwWpqbSSnsLbbY7C0oQTJq4s5FCG3qA5iVKhKlD8PVkFJQ/yNaQ+AAdZnZsYAY/qvpiZXLXDsWFeKPc++yGZCTY3LLoWPkXIZrDKKyxohL0hLQ/TPFf+iXmkp1DS6g4RWEYToY7CyrXsgWJELkx4WYPUSzcEiyA2SU7IGbSqiKeSifXV1cm9aI0pGAJEzWIGiIDSUM6hwUKf74vGzGQOGu2+zfhYmE6SxnNxiP8AqSkkGK7g+N+IYLOj4wdJcVwz51ai0R11+UNCp4mAUDV7lu7zijpknKajpXBY+hro4AVZjSyOBwtpuZ7Ba6kuRwtjRS25REBq7mW4N0Z7BSizA8rsIQve7QBpjuun66+Gjj9zPTBS0MED794XwLnx+F8GCEtfVPdbnSotXLCknp+tdBLMxwArtFmhdBLOfiNwuIitF5MM42+0gIi0icmRPtS2UBVi9RJM355Lmu0mcQr/AJ/umlRfIY2jx0IhjsHIK1yONA7s1fqq1vgSkldLBrrENGSyPrQ3l5BbWUl4OzXVFSWewmpuhoT4QNcAK/WBx5dyrO1RpzMlrhJwmmoKJdUZv8OZuyi0KC4wKql0Al6Da9YG4AVZuUbD7AVZdCVIQO/2VW1Sb8PxfsfhfCMKDzmjW1a+zAMuYTKiogN/9zs11deihmW5NvxYtwMqlEJqLySuqg/xqmoLRP1fcHJDufT5WgFVTA9recSNrAyzrItjr3AFMjbWBiASAPwJP90SDIrEAq5doqiuEghpa8tYCHQOsmprk55UYVToqYoCl+dXkNHbvHbGlvgTya8grdl/m/W5uGdFQRqAwSFmZu/qWbAar7fkPmQfLL3LR4X7a/3ahAVZTayPk17hS7wmor3Vv+rmFYcFEwbqEuxmqQnB9wI1DihVgFdZ1u4tgS31R2wdvNDkFQbShtEM3yu4oyHVl2vPCn5MoKoOVbkwh3S9Db4zphp/9zF2Vuv76TLekX2tpgdpa932hrKzj+2CrN64qUFQLBTU01Uf/bPJ7ZUB7mfbwKoJlZdDaCsGQj5pUfFdJByty0buo6jxgdZzNzgMeAFamv0WRpTXAEpGpIrJIRBaLyK8i3C8icr13//siMineviJylIh8JCKtIjI5ZP1+IrJQRD7wfu6dzsfW0/zqby15a4DOkwUme1VoVNmoiF0EW/PXdjvb0FJfDPk1BArdRISZzGC11peSU7jezTxfX5p0gNX2/IdnsPJrOt5P+98uNMBqbGn0Kg4m9pzUeQGW/1z6NL864a6X9fXQ3CzxM1jFdd3O8Lgrm+tibpPjdSFMdhxUQAugqcSN6UpAZV0lAwe4tzzLYBnTQ/73P7j/flfcYvz4TLemX/PfcyNlsJr9sbz5NVBQHbN3RZP/faS1pS2DFf6Z6n8nSfV3lXSwDFbWGSoiC0KWmV3ZWUTGAIcBt6SneYlJW4DlpeduBKYBE4EZIjIxbLNpwARvmQncnMC+HwKHA/PCjlUJHKKq3wNOAv6V6seUSQ3enEtNeVVA6t+0RpaOjFjkojlvNa31pR3S/IlqCrpuja0FawGoD2Zm8Kiqm6hZvACLhjKKc5MbAxQpwHJFLjp3QfPny4oYYCX4nNSt9wKsoo4BVmv+Olqa8mhIYBhSaJtjBVj5RfXdDkCagkVt3VijSXYyY5+fEQ1/TqKpClYxaKCrBGMBljE9QBUuvdRNxPvLX2a6Nf1e6JQu4QGWf6FOC9ZFnM8xlOuVUU1TaxPrG9dTECjoFJj02gDLMljZoFJVJ4csc7q4/7XARaqaZD+Z5KRzQpgdgcWq+iWAiNwLTAc+DtlmOjBXVRWYLyIDRWQUMC7avqr6ibeuw8lU9Z2Qmx8BhSJSoKqJjYDPcg3enEtNuekJsEaVugyWqnZ4bhtzK9EW9wW+q3NXuDfhFW1t9oOEnlZXB7TmIgXVlJa1AAFymwcmdcyoGawIAZY/X1bnAKuO+toxCZ0vuN4FBlLQMe3Tmr8WcMFKvIrBoW2ua46c9WlsaSS/pJ7qzrF2QhrrCmktjRNgFXTO8nXH+hp3fSgnTlENX2WwkqGDxrMoBec2xiTg+efh9dfhppuyb/KjfihWgNXoBVQteWugIIeGqugX/xpCemXUNNZ0Gn/lnyP0nP7v2RhghQaHVkWwT5gM3Ot9lx0KHCgizar6cE82Ip1dBMcAS0JuV3jrEtkmkX1jOQJ4J1JwJSIz/bSjdictkyENQW/OpcAqIPVjsEaWjqSptYnVdR27tdbndD5fovyrXA2BSgDqartZRz5J/hu8FtSQX+ReErnNXa+KGKrt+ciPPwbL/9t1DrBq2rr+xVO7PgC5dbRIxzLtLflrOp0vXptzCoMxM1gFRQ3dzi41Bgsg33UdiSpFAVbb4ylILINVGaxk+KCiDvsaY9Lo97+HMWPg1FMz3RJD+/te6Bgs/2tQY50LLJrzVkN+TczeFaGfaesb13caf+WfI/ScoeO/sk1AAgjuwrJ1Eez9VHW8qo5T1XHAf4Czezq4gvQGWJGmkguPaKJtk8i+kU8qshWucsgZke5X1Tl+2jHZiU57kl9euz7gxuu1BQ2augwW0KHQhaoSzFne4Xxd0VDrJrVtDwozU1MlNHPjzyMlSc7DFHrMhhYXtLkMVufgwf/bReoiWFebWBK5pkY6HUNVacntPCYvXpsLSxpjB1gljVRX061uoQ0RukOG87sQJhtg+fvHq1roq6qrYsTg4pSc2xgTx7x5brnwwl45IW9fFJ7Bam52Y3MB6r3xwI25Ve67Rm2MAKu2/X1+feP6hDJYoeO/so2ItGWurItg9hORe4DXgc1FpEJEThORM0XkzEy3LVQ6uwhWABuG3N4AWJrgNvkJ7NuJiGwAPAScqKpfdKPNWatuvR9grQDa37Rc4YLUFLkANxfW1sO3dudsrqM5wpivRNUHC6C8mtrW1ZBbT3V1ZrqI+FfQWvPWthdESLJMuP985BbVxSxyodr+t4sUYAUrE/sXrK52Y5dCj9GiLV0qO+5vU1TaFDPAGlDSiKq72ljahaFqDQ3Q0pTb9liL8iIX39CCzlMNdEdokBtPc2sza+rWMKJ8MEVFFmAZk3aXXQYjRsDpp2e6JcYTHmD564qKoLbG9TBpCKyCglaaGgM0NHSOjVWhPug+05pamhLuIhh67myUH8inoaXBMli9gKrO6MK2J6exKTGlM8B6C5ggIuOB74BjgePCtnkEONcbYzUFWKeqy0RkVQL7diAiA4HHgYtV9dWUPpIMa2iA5iY3fxH57jJQqt+0RpaOBOhQ6GJ13eqk5g2q9zI31Q3VSEFNxgIsv+2t+Wvb5pHShuT6KYRmg+YsnMMjix6hMlgJgRaKi5XqapcdDQahtbVz9skPsPwPtUTOl1O4PuIxQtuTSJuLS5q576P7eG3Ja522+a76OzYqaWzbvisBVmjAEyuD5Y8bS1UGS+NULQRYU7cGRRlaPDTiBJjGmBSaPx+eew6uvtp9ezdZIVqANWKE10sCqAsshwIXZEQa21tXB60tOR0yWGX5nT9Pe1uA5QdWlsEyqZK2AEtVm0XkXNwkXwHgdlX9yE/hqeotwBPAgcBiIAicEmtfABE5DPgrMAx4XETeVdUDgHOBTYFLReRSrxn7q2rGauBHVVEBn3wCAwa0Ly0tLoe+fj00NcHAgTBoEJSXE5y/iLN4jR98+gAlS1u5Ke8paqoPAKRDn+q4VqyAF15wH36BQHtH7L33ZtTETYGOXQRdgNW9im+qHQOsnMJaamqGde0gKeK/sbfkr20rEuHP+dFd/vPx631+wsLlb7Wt32TQJswtb78/dL6sTsFRfg11wQAtLe7PEe98OQW1HefSammKOOYrXpvP2fVE3qqOPAZty2FbsuXAHXglwWNGOn74Yw3nz+WWqjFYmkAGq6rOZWKHFA3pNP+LMSaFVGH2bBgyBM7Mqh47/V74GKzQdf7PWlkJBQPb1g0d2vEY7RfS2sdg+UMMQkU7fjaOwYL24ha9tciFl2S4DdgaN6TmVFV9PaON6ufSmcFCVZ/ABVGh624J+V2BcxLd11v/EK4bYPj6y4HLk2xyz3jqqS51mxgE3AQsqypC1sFhTdNYfNfOsO9vqB4+FcjpeFVIFT7/HD7+GD79FBYtgrfego8+cvcXF4OI6wPm3S596y2K84o7zIWVTAarLXOTX0N1QzWBwtqMZQ3aAqy8NW0V51qSDLD87M7Fu1/U6b6HyjpftYuWwQIXUw+IMySsuhoCRcGIhTI6nCfOMQDO2/0kiotPirrd44+HtT1BiWawUhVghf5d46kMukIrlsEyJs3+9S/3GfenP3UtBW7Szn/fKyuLnGGS/FpqmtbG/NwPf5+PNgaroADy8npPBqttDFbv7SJ4HfCUqh4pIvlAcaYb1N+lNcAyUUyfDltsAevWuWXt2vaMUmmpe1dauxbWrIE1a/hGxrHnJT/g6+k/pWDCfzn7hj9w8fqb4aCD2KZkAE+yE5s/tDN8PRBeftkNLF61qv18o0bB974HJ5wAe+8N228Pubkua/bNN7DTTsjRR7PxSSMiZLC6F2CFvglXN1QTKApm7Eutf+WsyRu8C9AcTO69J1ZhkdAv8IkEWNXViQVYuQNraWwNyWC1NnW5i2AgEL/HTqTyuolINMBqIkggv4GamuQGvrcHWGvjbmsBljE94Kuv4NxzYffd4fzzM90aE8Yfb5WXFznAyilcT3VDdczPlfaeCl6Z9oaaiF0ERSJ/FmZ9gNULuwiKSDmwO3AygKo2AtE/hE2PsAArE4YNc0uCvpkHXwMU1tCQB3cOP4bPx/+MR095kIo7XmD0068zbu5s+KfC2LEwbRrsthtsuy1stln0b++BAGy8Mdx1FxxwAH/87zCuPj01GazwACs3gwGWf96mvCpavQp2jXXJjQdLdYCVyPlyR9d1PkZeLSLtY74SaXO84pnpDrAaWxrJK66nujr5ACunoI5GjTynV6jwAOvbb5M6tTEmXHMzHH885OTA3Lnx+z2bHhf6uRUpwAoU1cYNsELf5/2JhiNlsPxz9LoAq3dmsDYGVgH/EJFtgYXA+aqa2BwmJi0swOoFQudcAsgvbmRNbT4ceyyvthzL8U/D5wuq2XREjZtzpKv22w9+8xsOvOwyXh6XA17vsdV1qyEvSE6Otg2A7XKbvUltBxfVUxO/J1daVFdDTqCFRqmhQWsgUO/ma0pCrLnHysvh66/btwOizoPVYZt45yuq7zwGK0cpLGmipiZ+v/FE50sL7zufqGiPNVxjSyP5RfVUVydXKr+mBnILg21VHGOpCnpjsIptDJYxafGHP8Brr7kLdmPHZro1JoLQz4BIY6Ryi+pY37i+7btGpPfJtgArv4baxloaWhqiBlih77XZPgbLz1xlaQYrV0QWhNyeo6pzQu8HJgHnqeobInId8CvgUkzG5GS6ASa+8HLUhcUNna4KlY4u715w5Zs1i8+22YBL71sOH34IuMpreYE8ysqSyWb4QWFdRjNY+cUNNLY0UNtU68rdx5hEMdFj9lQGq7HRlePPK4qQwcJVBUw0C5bI1cOeyGAVhLyGu6u6GvKK690Ez3FUBispyi2iOK/Yuggak2ovveQKW8yYAcfFLPhrMiheBiuv0OsNkGAGa3XdaoCIEw375wj/LMzWACvLM1jN/vyt3jIn7P4KoEJV3/Bu/wcXcJkMsgCrFwgPsPyJYEPvSzrtHgjw5Ozjqc6H1iOPhPXrWV23msFFgykvl6S+bIMLcDI5BquwpIGm1iZqG12AVbc+uTfRWMFKWReLXMR7XvwrfwUlDR0zWK1NABSlOMDyPwDTGmCVNKZkHqz84rq2iZ5jqayrZGixK4dlAZYxKfTqq3DwwbDppnDTTZlujYkh9DOgqMj14gz9rMov9noDJDgGyw+wEu0i6I//yka9uYqgqi4HlojI5t6qfYCPM9gkgwVYvUJ4NqiwpKnjwNSc1Ew1Uj52M2YcCfL553Dmmayuq2Jw0eAkM1heUFjUSF2d66bf06qroaDYBSNr69dCwXrW1yT30q+ujt1FsHOAFblMe4dtYpwLIL+oIWIGq6SsJeEAK5GrhwUFkJ/fvb+5iEJ+bdwAq7C4Mekgp7oa8osaE8pgVQWrGFI8BHDPQUODywwaY5Lw+uswdSqMHu2mABk4MNMtMjGEfgaIdL4YmF/ivZfm+2N7Ix8DgIKauAFW+PGzNXsFvbvIhec84C4ReR/YDvi/zDbHWIDVC9TUeF9c89x4xaKSJmpqXDX2mprEChckYlTZKF4cD9dNHQh33cXIex9jUNEgysuTGI/jd2v0Jq/NxNiX6mooKnUB1ur61QSKapNuh/+8R1Je7qYya2jw+rXnKuTWdwqOcovq2o4V71zgnsNOY7CAktKWhMdxJZrp7O7fvLi0BYSoAZaqugAr5CJBd/mZyUTGYFUGO2aw/P2NMd30xhtwwAEwciQ8/7yrVmuyWvhnQOj7vHs/9d63BUrLNOoYLP8zLZEMVujxs7XABWR9F8G4VPVdr/vgNqr6Q1XN0Kh347MAqxeornZvduQoAMWlzTQ3u3E5iXb7SsTOG+zM8dscz7wf7cq73xvGnx5r4jflh3SrS1XoQFiAwtLGjut7UHU1FJe4YGRN3RrykqxoqBp/DJZ/3rbtwoKOxpZG8osb2raL137oHGD5v5eWRb7SGOk4XQmwuvM3Ly1r7dC2cC3agqIUlSYfYFVXu+ckoS6CEQIs6yZoTDe9/77LXA0f7jJXyYz/NT0m/DPAf5/3P9MKvZ4eAGVRPlfa3ufFXbAEIpZpDz1+pHNnGz+w6sUZLJNlLMDqBaqroTzk/au4tLltfSrftAYUDuBfh/2LB4/7L9s99yH5w0Yy7de3M6KoultftvPyFHLdl98iL8DJxJfatswKrjKiKxHe/ePV17uujrHGYEHnv094cFSQn0NRUeIBVlFpU8QxWKVlrb0mwGorzFHanJIxWIUlTYl1EayrYkiR6yJoAZYxSfjiC5e5KimB//0PNtgg0y0yCYoWYPmfaf6FyND7wrlKhO5ibyIZrGDQHTvbA6zensEy2ccCrF4gvO9ycYkLsGpqEi+93WXDh8M998AXX3DO+zOpXqdd2j00cwNQXNLStr6nVVd3DLDyi5IruBGvGlJoF7TQv0+nDFYgP6HxbW0BVklzxAxWIsdobYX16xN/rXR33F1pace2hWuvfNhCQ4PrRtkd/hXX4pLmuF0Em1ubWVO3pi2D1d0iHsb0e8uWwf77uwGMzzxj5dh7EX/caehngF9G3b/Y5X9OuvsiF7dyF9Lc7/70F7HGYIH77Enbd5UU6c1FLkx2sgCrF6ipgfIB7YOsSrwsQaozWJ3svjtcfjlTvvo3R6++pUu7uv7WQkHAzTdVUtbStr6nhWZW1tSvoTDJCnb+vol3ERTyA/kdg6NWF2AlMtbJv7+ktCXiGKyycm0bkxfN+vWx2xzpMXRnDFZ5uWtEvAAr2ddDQ4N3xbWshRZtoaW1Jeq2a+rWoKiNwTImGevWuW6BK1bAE0/AxImZbpHpgkifW36Wyg+k/PdlgAHlEnUMln+MRMq0+/v0mgyWdRE0KWIBVi9QXQ0DQt6YSkrbs0Fpf9O66CI+23Qaf6i/gNYFbye8m591K8wtBKC0tD0o7EktLVBb2x5gra5bTVFJE/X13a8iF680fqQPlU4BVkt7gJVoBqs4LIPldxEcUC60trquGN1tc6TH0J0MVnm5uxAQL8AqLUvu9eDvV+r9L8Qah1VV500ybF0Ejem+s8+Gjz6Chx6CKVMy3RrTRZE+A8IDrFKv61+O5DBgQPQM1gDvgu+aeldHIVYXQX+frA+wcqyLoEktC7B6gdAsCEBZeQ9lsABycnj2+LmsZDgcdRRUVia0m9+uglyXwSotz0yA5WduyrzMSn1zPcVl7V0suyNesBJpDFayAZaIG/IQqYtgeZl0aFd32hyuuwHWgAQDLL8Pf7J/Az+bG6ubYGXQvWatyIUx3XTvvXD33TBrFuy3X6ZbY7ohkQDLf1+O9dlUUwMDvfd5P4NVklcS8Zy9KcCyIhcm1SzA6gX8vsv+P74/ziWtY7BC5I4cytHchyxfCnvvDStXxt3HL8nqdxEsLU3uC3V3tX2olIZ0sSxpTaot/n5dGYMVLcDy+8DHO19pKRTk5UXsIjjQu5oY6zjx2hwukXZFOkeiAZZ/pbS7QU7444lV6CI8wPL3sS6CxiRgyRI46yzYaSe4+OJMt8Z0U6TPgLIydxFy3Trvdnl7gBXtM8D1ThEEobm1meK8YgI5gYjn9M9VWdl5/Fe2sSIXvYeI3C4iK0Xkwyj3TxeR90XkXRFZICK79nQbwQKsXiE0CwLt3avWru2ZuSXKy2E+O7Pkxkdh8WLYay9YvjyhNvtdBMtK42dZ0iHSVbtUdU/ryS6C0Y4BMGBATtzH050Mll/9KRF+0YmBAwId2hauPevWsV1d1fZ4/AArRhfB8ACrpMRlBC2DZUwcra1w8sluYr8774Tc3Ey3yHRTtAwWwNKl7qd/gSz0syl8bK/fRdAPRKJ1Dww9/nffdT53trExWL3KHcDUGPf/D9hWVbcDTgVu64E2dWIBVpYLnXPJf0MLf1PsiQALYPnW+8Ljj8PXX8Oee7Y3IAJ/DJbfRbCoIJ/S0swFWANCioQk20UsXrBSXAw5ObBmjRv/la4Ayx+DNWhgIO7j6U6ABYlneWpr3Wt1oBfsxQuw/L9H0n+DAe5nrAyWX+lqSLEbg5WT070qicb0O9dd5yYRvvZa2GSTTLfGJCFWgFVR4X4OKHfv3/5nU/jYXn9Mc4cLvgkEWP7xe0WAZRmsrKeq84DVMe5fr9p2aaAE6FoZ7BSxACvL+V9cQ9/QSopzCAR67qpQh4Bkr73gySfdO+aOO8Irr0Tcp20MltdFsCBQ0K1xPcnyAwT/iz+k8Mt9lOddxH2BDw2A8wP5bQERuO593QmwWrW1rWKeH6wMLE9fgJXocxQayObl5MUPsMrjd2tM5Hz+3zXeGKyi3CKK84rb1mXitWhMr/Lhh65L4KGHwmmnZbo1JkmxAiz/u8QgrweC/9kUuh90rEbbNiY8yiTDkY6fzQHWkROP5LK9LiNH7GtxXyAih4nIp8DjuCxWj7NXUpYL7Tftp67zc/MoK2t/00p3v+ZOY1Z23x1efhkKC10m6+qr3aUuT0uLu+oV2kWwILegW+N6ktX2Rby8vY/4gDL3sk9mDFYg4B5+NOXlHf8+scZgNTbGng8qdByXvy+0j8EaMjAv7uPpzhiseMeMdvzwxxqqPcCK360xkfP5f8uYXQTrKtu6B/oy8VrMqOZm9+1ozRpXZruiAr76Cj77zFWGW7zYzXG0bp37Bzb9W0MD/OhHMGAA3Hqru2pkerVoY7DAfVYFAlBe6n3H8D6bQveDjnNAJpLB8seL99R3lWRsN3I7frP7bzLdDOMM9cZO+cvMrh5AVR9S1S2AHwKXpbyFCbAO1Vku9KpT3ur2Kjfl5T2Xdo+Yzdh+e1i4EH78Y7jwQpg3D/72Nxg9usN8G34XwUSzNanmn8/vRhf6ezIZrPLy2N85wv8++dXRuwiC+xArKIh+vpEj2z/QmlqbKKKo7XiDB+XGfTzxJkeO1P54x4x0/EhdGUO1j8EKJDUOqi1wHugFWHG6CPrdA319KoPV3AyffAJvvw1ffumKElRUuIBp7dr2vqqJEoFBg2DIELcUF7sXZ2GhG4PT2tp+QaWszG07cCAMGwajR8OYMe7nyJE2Zqe3uvRSeP99ePRRN+m86fVCq9H6QrvwlZdDUZ67ahgtg9XhfX59/AArN9e9ffSGLoImq1Sq6uRUHEhV54nIJiIyVFUTK4OdIvbpl+U6vKGtc29ouTm5lJe7oVD+fekU9cv2gAFw331www3wy1/CllvCFVdQfdBZQCArugj65xsyqP2l7md8kg2wYikvhw8+aP89vzZ2gFVdDUOHRjhQyPk6ZbD8MVgDEusiWFgI+QlOUp9sgBXaHTKUn3UrzMtPahxUdbUbSzXAu+Iar4tgeAarVwdYTU0wfz489RS88AK8+y7U1bn7RGDUKNhwQ9h0Uxg82AU/AwZAURHk5bkXQV5e+++5uS5jEQy6QGzdOqiqcqW/qqrcsdeta5/dOSfHLaruysDatZGfzJwcGDHCBVt+oFZc7L7hDR7sXvBDhrS3zb+/uLjzbQvUes5LL8E118DMmXDwwZlujUkRf1x0Tki/pdAAa8iQ9h4niQRYecvde2+0SYZDz2EBlulJIrIp8IWqqohMAvKBqp5uh31qZbkOb2heF8G8gMtghfaHTqfQeZ06EYHzzoNp09xElOedx9Bb/slkbqKsbAcKWr0AK9cFWAlUeE8pP5s2eEB7ZDF4oPs9nQGWX/4WvKBjZfwAK975wgMs/xiFhUJeXmLHSFS6M1j+Y09mDFboFdd4VQTHDhzbYV15ecwaLZnR2grffgsff+wat26de6Dr1rUva9e6zHF1tevTM2UKnHEGfP/7btl0Uxc49bSWFheQLV3q+gN9913H39eudZVHg0H3R1+92gWKicrNdVm0lha3tLa6f7IRI1yGJfRn+O8jR3a8bG+iW7UKTjzRFbT4058y3RqTQpE+A/zb69fDuHFQlFcERA+wQnunJNJF0N/WLzpsAZZJBRG5B9gT15WwApgF5AGo6i3AEcCJItIE1AHHhBS96DEWYGW58LEt4AKtSP2o06WgwF3ojvlleNNN4emn4d//JvecC3iLHVl684+Yd7Qbz1EQyNwYrKIiKC5oD7DKCkooLU1uDFa85zz0g8T/261vXN+2rm0MVsicZpH4SYJoY7D8oDtesNKhzaouG1FXB/X1HX96vw/7to4jqWfE03WwNmSboqL2jMjAgW1L/YqBQDllZZJwgJVMBst/PH4X1JhdBOuqGFLUsYtgyl+LfrnPKu8iWW6uW+rr3beL5cvd1YW6OvfcNzS47f0s0fLl8OmnkbvylZe759tfjjkGpk6FffZxt7NBINAe0Gy/ffzt/Rd2ZaX7GQy65yYYbF/Cbzc0uPPk5rrL8OvWued0xQpXkGHFCtcdMpIhQ2CjjdxSWure0AoKXDtqa9vPV1jo7i8r6/zTD9ZGjXIBXCYC2XT64AM45BD3nL74YvsAGtMnRPrcCr0dOma6S2Ow8mK/Tnryu4rpH1R1Rpz7/wj8sYeaE5UFWFmuQwYr0DGD5euJq0IJdakSgWOPZV7+gbxxxB+5+NU/8+dXGhk9BYp/2JSxLoKhY8EASvJLkmpLdbXr4RRL+N8nVpl2/5iR1NW5C/axMlj+OSIeo6oK3niDAxe8xQlVn8KkRa6wQZwxOcOA+wFu95Y4ZgB7MpLB5+7IebnVBLf+BrZZ7C6LhnTvCs9gJZVFLFMKG5XBQQhUfAc5n7Z/Gfe+NLesr+HIl1dz8LeL4d3/c1/Sc3M5fFEuG6zMg8vq2icsq693X7qLitzP5ubIQWj4z5oa9zwnOmmYr7jYdZMbOtSNXzrtNNhqK5g40QUCAwZ07tPTV4i4F22q37waG10WZsWK9uBr6VKXGfz2WzdGLRjsWFmmpKS9W2J9vbucX1PjfsaqPlNW1j5ObfDg9t/HjoWtt3Z/yw037B0FIh59FI47zj2mefNghx0y3SKTYpEyWLECrLhjsLqQwYLO47+M6esswMpykd7Q/CIXvp64KtSVL8NrWsr5DVdw1ONnUPV/+3LxC5/TuM9JrJt8Bbeum4lqbo995wjvXgdQkpd8gDVuXOxtUhVgRfr7h47BCp0brboad0V+wQL45z/h2WddMAXMQFheOA5GbO6qQI4c6a7WFxV1/llUREteIdvsVMQZPynkJxd59xUWui+ga9e6xe+ytnYt/7uviu+eep/jv3yT8z9bBY+tgisnuOBq/HivDGYe+zWs5qkaGPnRL7ho6UZUfjcEbhraPh4nJ6d9/M/q1S5Q8jMZVVWucMOyZdz5zQoKWoLkbKSuY/VV50d8/gLA3wB42lucQ7yF3+Iec3m5C6oaG91jrK93GYpoz9GQIe23S0tdgDR0qCv4kJPjgq3mZpcpGTnSLcOGuW8Y/mC4vhg4ZVp+viuyMWZMao7X1OQCrepqF6x5rz9WrXKvx9Dlyy/dz7Vr2/cvLm7/2w8f7sajjR3rAugRI9zx/dfb0KEwYYILygKBqE1KmcZGVw32wQfh5ptdF9OHH07dc2eySqQAKz+//W29uwFWImOwoO9eKzImGguwslxoSt7vDpabk9sWVPnd99KtK925/O0KN9uIe385jXMnfM4L72/MoU+fwzv8laa/X0z+cUe6Lx9p5neLyM3JJUdyaNVWSvJLki6wEC+oDe8WEatMu3/MaOcKPYa/b+gxaG7m+/I+Uz54Frae68bwFBbCvvvCySfDzjuz608mM2RsKY8+mthjDADflMA3ucDokDuKiyOm755dBH/+H5y4CPa+bhKTqvK5ZvwZ8Pnn8MUXLkBqaqK5ai0D66Fg4bvst/xFSpvWwjmxGhJoL3QwcKD7grrTTjxUO4LWkjKmn9TCr+dfweGTfsReWx/csTBCSQmL65eyx30Hct3hczhyh5Nc0NTayp+ubOJ3v2li+ZpCSgb2sa5eJnXy8lzQPGiQC4wSsXq1+x/88EN3gWPlSrd8+y28/rq7eBBLfj5svLELtjbdtOPP0OCrpcVd5PjgA1dBcuFCdx4/Ixq+tLS4cvxffOHa9/zzLnjMz3fvEzfc0CPvySYzqqvd22e4sjIXYJWVdQywCgroNLa3w7Qx3sW9eBks/zPOugea/sYCrCxXU+Pe5AoKOs407l8V6qlBo10pSBBepv3t0fD17Nup+N03jP/bReSffhL8/DyYMcOVeZ+ckmqcEYVetcsP5FPfXN+WwUpmDFYiVQTBJTlyc+NnsKK1JdKg4ubqtfDx/zj4noWc+k4l/HYQf/cravzgBzBnDhx9dIfxOSuDML6Lr5Wu/s39x9JYVsy7gwrhxFM6bffA/Ou44OkLWH3h2/zs7EG89FwTXy5Y7b50Vla6L4HDhrV3u4oy2djlE10PrIN+uYYbrrqCTQ6YzF47Hdtpu+Xf1rC0HAaMHNd+JSIQoGRIgPUUUlPvpnk3JmUGD4Zdd3VLJLW1Lthataq9/H1+vsuQLV7sLkr4P597rr1CJLjthgxp78IYys/cffmlO3a0qzZ5eS6r/KMfwYEHurF81nerz4v2uVVe7l4u5eVQlNte5MK/L3wMVuhnGiTeRdAKXJj+xgKsLBc651LbGKyczARYy5Yltm1o1qWtTHtuIat3PZSD/3Yw3975Mhs8/XeYO9fNnTVlCvzkJ3DkkSlPx1VXt194LggUuADLG4O1YkXXj9fa2rUAqy24y2kPsFS1LcAqKaHzfFBr1rgr0atXk/viGk5jKdv++0sK1s1n4QLY/rL9oaWFHwosGl0AJ53EX9/ehf8s35WXXt0w6vPQ1ddKV7pRhgeyiVYRXF2T114coQvCx9ZFK9NeGXTZgkhl2v3jjBzZpVMbk5ySEjelxZZbdly/5ZZu4vZQqm4MmR9wff6564boj18bMAC22AImTer8P9TQ4C5arFrlFhGXCeupLogmq0T7DAj9rArNYPnrwrsIhr7PA5TlJ9ZF0AIs099YgJXlIr2hZSqDtWhRYttWV7vvEIFA+xu2X6ZdyWHllnuwwY/2gL/+1QVZN9zgrqb+/Oeuq8oJJ7iB/ikQ+vwV5BZAQ3JjsBItjd8pwAoJOlq0BUXJD+STkwObl1Sw5WtPw6mvwiuvuC9Rnm2B2wD9dy71Y0bwbgl8dcSxbHzICZy49Abeq/+aD866gQ/PgM++Sux5SFQyAVawKRhxu0hl2lW7XgegLcAKxK4imEiAZUzWEmnPTO2xR9f2LShI7Xg002v5RU7jBVj+d4z8nMQDLMtgGROZBVhZLnS8T9s8WCFl2nuqX3NXx2C1jRHzMgwRxxsNGODm0DrnHHjmGbjxRrj6arjySndFdvp0l34aPdqVRs7Lc58Ura0uevMKMlBS4n5G+JYeWpo2P5CPIBTmFnZ7DFZodi6W8L+PH3Q8+8WzNDTXM3w97Pjo23DpHnyyfh78D9f1Z5dd4NRT3SD4wYN5cv4gzpo9guc+2IA1Ze8w9bYduXyvLdlxTA6Ll6xqv4oY4/H4BdO6+lrp7t88kQyWP46wtdUNz+pKDyU/i1hWBoGcALk5uVHnwaoKurLpQ4o7l2n3222MMX1Zba376Iz0GRD6WSXiPh+jfa6Ef56CjcEyJhoLsLJcaHc0/w0tNyc368dg+e0aXTaaotwiygvKo483yslx8/pMner67d17L/zrXzBrVuINLClpD8Q22sh1hdlkEzZfO4HBRdsARRQECijJL0FEup09CR0TFYt//6DSJnjuJWbMeY2j36hn5NX7M2I9rGgBeAi23JLrhl3GV9sfwbVPbdGpMV99Cd8A5YNBcl1xid+88Ju2+/fdeN+28wWDbghTeO+fRNsc6TF8+WVi29bUtPdQihdg5Qfy2/4G0J7xTJRfYb4tM+l1/YykMlhJUW4RxXkdB+/HG/tmjDF9RazPgPDvEqPLRjOydGTbupUr27cNzWD5F3wTrSJoGSzT31iAleWqq9u/uLZlsDLURbCuzlUVjje/Zuib8DFbHcNe4/aiNL80sW5ZI0bA+ee7JRh0A7+WLnU/m5tdMCbiIgl/Ytza2vb5bpYudaWH77oLVHkFaLk2F17chstKqlgwQmDePEbmbEFLyzDq6qRLhbNCy9TG2mij15/kLv7Loa89Afut4/tFRaybNJnGkUOpHDaYlhHDGTP9eHK3m8Q9OwsDACIEeqHnG164CR+e9SFr69e23b/F0C06tKemxhXb63KbI+hqF8EJE9zviQRYoe2prnZxcaLCH09hbmH0LoJ1lZ26B4af2xhj+rJYnwHh3yXe+PEbbVmp8nI3/C/0OP6YZusiaExsFmBlufAvrpC5IhfgvsDHm2Q3NMAK5AQYVTaqwzES/lJbXAybbOKWrmpoYPXCrzh1l0/5zb5vMbnlDQ55tZoZ9a3wnz04DziegeTutjlsvTlsvjlss40ruDFsWMzHFvpYaGlxZY8//NCVS37tNXjhBcY2NVHEMBaOPZw9/jQd2W8/BkaJ5GIFMtXV7VUkAbYavlXUY/jbZyrASrTIRaQAqyvCH09BbkHMLoLh3QOTObcxxvQ2XQmwQi9I2Ris3kVEAsAC4DtVPTjT7env0jrtm4hMFZFFIrJYRH4V4X4Rkeu9+98XkUnx9hWRo0TkIxFpFZHJYce72Nt+kYgckM7H1lNC+zy3VREMZGYMlt+eeELb3N1jJK2ggLUjt+C//JCPjrsCnnuOfa/7PlN/vxk8+SRvHX8t9zCD5vwSVwr5kkvgkEPcZKAbbwzHHefKnX/+uetHGPrYqGaD1+5zhTmGDnXB2RFHwOzZsGQJnH8+ax99mVEs49/73+7GksVIk5WVxS7T7vrGx364sZ7b0LlLusJvV8jDjyq8b34iAVZ3Xw/hj6cgED3AqgxGzmD16GvRGGMyKNZnQKzvEuGfTZHGYMWrImhjsHrU+cAnmW6EcdKWwfIi6RuB/YAK4C0ReURVPw7ZbBowwVumADcDU+Ls+yFwOPC3sPNNBI4FtsJNjfqciGymqi3peozd9fjjro5DIlau7NznOSCBjGWwDj88/lyUixa5ZFC4oiI3PuiGG0h4wttkhI/Vyc8rZO0oN95rWeNUzrkTbmuAko2haKMaNqt9h4k1bzCx+g22fuBFht5zDwAr8jdgbd5wiltq2LWphioqybuw2QVXhx0Gu+8OW2/tKh96T05RA7SS2N/H74ax226d7/vss8SPAS7mC/8gW7Om4zaJKi93XUJ32y1+gBcMdnydrgquYrd/dH5An1V91lb5z9/+vPPiZ0VDrVvX3j5wGawnP38y4vneXf4u07eY3mm9P4nmTTe5/0djjOmrVq92PxPJYIXfV1vb/tnkzxAA7n1eEIryimKe2zJYPUNENgAOAq4Afpbh5hjS20VwR2Cxqn4JICL3AtOB0ABrOjBXVRWYLyIDRWQUMC7avqr6ibcu/HzTgXtVtQH4SkQWe214PU2Pr9tychKf7mnvveGHP3S/H77l4QRyAogIgwfDz37mkiM94Qc/gIMPdl+k49llFzjmmM7rReCCC+Cdd1LevIjy811Saqed3O0zvn8GLV68PWWKu88Pwlryy/ikeHc+GbY7DwCoskHwM7Zf8zzbrn2Jopb1LA1MIBgoo2nwCA6+YSq5u+0cdT6ZggK4+GIXf8VzzDEu8RUpU7T11rD//vGPMXmyey1EysiMGOGSbFtvHf84oaZNgxdfdEFWPPvvDwcd5H4/fMvDWVS1CKXzA9p6+NbsPW5vwE3fc9RR7kO7K4YNc8/rttu626dPOp3HP48cJe284c4ct/Vxndb7r8WFC7t2bmOM6W1GjnTX/7bYovN9Bx7oLopuGGEKxYMOgldfdcOfAfbdFw491P1+xMQjKM4rJkdid4TaZBM480xXw8p0W66ILAi5PUdV54Rtcy1wIWC5wiwhmkj/n+4cWORIYKqq/ti7fQIwRVXPDdnmMeBKVX3Fu/0/4CJcgBVv3xeBX6jqAu/2DcB8Vb3Tu/134ElV/U9Yu2YCM73fv9/a2pqGR2+MMcYYY0xyRCSoqlFr7YrIwcCBqnq2iOyJ+27ca8dgicgCVZ0cf8vsls4xWJE6FYVHc9G2SWTf7pwPVZ2jqpNVdXKELJgxxhhjjDG9xS7AoSLyNXAvsLeI3JnZJpl0BlgVQGjSeQNgaYLbJLJvd85njDHGGGNMn6CqF6vqBqo6DleL4HlVPT7Dzer30hlgvQVMEJHxIpKP+6M/ErbNI8CJXjXBnYB1qroswX3DPQIcKyIFIjIeVzjjzVQ+IGOMMcYYY4yJJW0Blqo2A+cCT+PKRt6nqh+JyJkicqa32RPAl8Bi4Fbg7Fj7AojIYSJSAewMPC4iT3v7fATchyui8RRwTjZWEDTGGGOMMSbVVPXF3jz+KhEicruIrBSRD6Pc/yNv6qf3ReQ1Edm2p9sIaSxy0RsEAgFtabEYzBhjjDHGZJ94RS76mnhFLkRkd2A9rgp5p/rIIvID4BNVXSMi04DfqeqU9LU4snSWaTfGGGOMMcaYlFDVeSIyLsb9r4XcnI+rydDjLMAyxhhjjDHGZIOhCcz7lajTgCdT0KYuswDLGGOMMcYYkw0qUzEPlojshQuwdk2+SV3XrwOs1tZWRCRTMw0L8ef2Mt1jz2362HObHva8po89t+ljz2362HObHr3xeS3OdAN6WGWyBxCRbYDbgGmqWpV8k7quXwdYqpqxmYb7ykzV2cie2/Sx5zY97HlNH3tu08ee2/Sx5zY97HnNfqo6NZn9RWQj4EHgBFX9LDWt6rp+HWAZY4wxxhhjegcRuQfYEzdWqwKYBeQBqOotwG+BIcBNIgLQnImg2gIsY4wxxhhjTNZT1Rlx7v8x8OMeak5UaZto2MTV3YooJj57btPHntv0sOc1fey5TR97btPHntv0sOfV9Ih+PdGwMcYYY4wxxqSSZbCMMcYYY4wxJkUswMoAEZkqIotEZLGI/CrT7emtRGRDEXlBRD4RkY9E5Hxv/e9E5DsReddbDsx0W3sjEflaRD7wnsMF3rrBIvKsiHzu/RyU6Xb2NiKyechr810RqRaRC+x12z0icruIrBSRD0PWRX2disjF3nvvIhE5IDOtzn5RnterReRTEXlfRB4SkYHe+nEiUhfy2r0lYw3vBaI8t1H//+01m7goz+2/Q57Xr0XkXW+9vW5N2lgXwR4mIgHgM2A/oAJ4C5ihqh9ntGG9kIiMAkap6tsiUgYsBH4IHA2sV9VrMtm+3k5EvgYmq2plyLqrgNWqeqV3cWCQql6UqTb2dt77wXfAFOAU7HXbZSKyO7AemKuqW3vrIr5ORWQicA+wIzAaeA7YTFVbMtT8rBXled0feF5Vm0XkjwDe8zoOeMzfzsQW5bn9HRH+/+012zWRntuw+/8ErFPV39vr1qSTZbB63o7AYlX9UlUbgXuB6RluU6+kqstU9W3v9xrgE2BMZlvV500H/un9/k9cQGu6bx/gC1X9JtMN6a1UdR6wOmx1tNfpdOBeVW1Q1a+Axbj3ZBMm0vOqqs+oarN3cz6wQY83rA+I8pqNxl6zXRDruRVXs/toXMBqTFpZgNXzxgBLQm5XYEFB0rwrUdsDb3irzvW6sdxu3di6TYFnRGShiMz01o1Q1WXgAlxgeMZa1zccS8cPe3vdpka016m9/6bOqcCTIbfHi8g7IvKSiOyWqUb1cpH+/+01mzq7AStU9fOQdfa6NWlhAVbPkwjrrJ9mEkSkFHgAuEBVq4GbgU2A7YBlwJ8y17pebRdVnQRMA87xul6YFBGRfOBQ4H5vlb1u08/ef1NARC4BmoG7vFXLgI1UdXvgZ8DdIlKeqfb1UtH+/+01mzoz6HhBy163Jm0swOp5FcCGIbc3AJZmqC29nojk4YKru1T1QQBVXaGqLaraCtyKdafoFlVd6v1cCTyEex5XeGPf/DFwKzPXwl5vGvC2qq4Ae92mWLTXqb3/JklETgIOBn6k3iBur/talff7QuALYLPMtbL3ifH/b6/ZFBCRXOBw4N/+OnvdmnSyAKvnvQVMEJHx3hXsY4FHMtymXsnrT/134BNV/XPI+lEhmx0GfBi+r4lNREq8wiGISAmwP+55fAQ4ydvsJOC/mWlhn9Dhaqq9blMq2uv0EeBYESkQkfHABODNDLSvVxKRqcBFwKGqGgxZP8wr2IKIbIx7Xr/MTCt7pxj///aaTY19gU9VtcJfYa9bk065mW5Af+NVXzoXeBoIALer6kcZblZvtQtwAvCBX3YV+DUwQ0S2w3Wj+Bo4IxON6+VGAA+5GJZc4G5VfUpE3gLuE5HTgG+BozLYxl5LRIpxlURDX5tX2eu260TkHmBPYKiIVACzgCuJ8DpV1Y9E5D7gY1wXt3OsGltkUZ7Xi4EC4FnvvWG+qp4J7A78XkSagRbgTFVNtIhDvxPlud0z0v+/vWa7JtJzq6p/p/N4V7DXrUkjK9NujDHGGGOMMSliXQSNMcYYY4wxJkUswDLGGGOMMcaYFLEAyxhjjDHGGGNSxAIsY4wxxhhjjEkRC7CMMcYYY4wxJkUswDLGGGOMMcaYFLEAyxhjjDHGGGNSxAIsY4wxxhhjjEkRC7CMMcYYY4wxJkUswDLGGGOMMcaYFLEAyxhjjDHGGGNSxAIsY4wxxhhjjEkRC7CMMcaknYj8TkTuzHQ7jDHGmHSzAMsYY4wxxhhjUsQCLGOMMV0mIrmZboMxxhiTjSzAMsYYkxAR+VpELhKR94FaEdlVRF4TkbUi8p6I7Bmy7XgReUlEakTkWWBoptptjDHG9CS7AmmMMaYrZgAHAa3A+8AJwFPAPsADIrKFqq4C7gZeB/YHpgCPA//NSIuNMcaYHmQZLGOMMV1xvaouAY4HnlDVJ1S1VVWfBRYAB4rIRsAOwKWq2qCq84BHM9hmY4wxpsdYgGWMMaYrlng/xwJHed0D14rIWmBXYBQwGlijqrUh+33Ts800xhhjMsO6CBpjjOkK9X4uAf6lqqeHbyAiY4FBIlISEmRtFLKvMcYY02dZBssYY0x33AkcIiIHiEhARApFZE8R2UBVv8F1F5wtIvkisitwSGaba4wxxvQMC7CMMcZ0mTcOazrwa2AVLqP1S9o/V47DFbdYDcwC5magmcYYY0yPE1XrsWGMMcYYY4wxqWAZLGOMMcYYY4xJEQuwjDHGGGOMMSZFLMAyxhhjjDHGmBSxAMsYY4wxxhhjUqRfz4MlIpqTYzGmMcYYY4zJPq2traiqZLodPUVEnlLVqZluR7L6dYCVk5NDS0tLppthjDHGGGNMJyISzHQbetjQTDcgFdKavhGRqSKySEQWi8ivItwvInK9d//7IjIp3r4i8jsR+U5E3vWWA73140SkLmT9Lel8bMYYY4wxxhgTLm0ZLBEJADcC+wEVwFsi8oiqfhyy2TRggrdMAW4GpiSw719U9ZoIp/1CVbdLywMyxhhjjDHGmDjSmcHaEVisql+qaiNwLzA9bJvpwFx15gMDRWRUgvsaY4wxxhhjTFZJZ4A1BlgScrvCW5fINvH2PdfrUni7iAwKWT9eRN4RkZdEZLekH4ExxhhjjDHGdEE6A6xIFU80wW1i7XszsAmwHbAM+JO3fhmwkapuD/wMuFtEyjs1SmSmiCwQkQWq4c0xxhhjjDHGmO5LZ4BVAWwYcnsDYGmC20TdV1VXqGqLqrYCt+K6E6KqDapa5f2+EPgC2Cy8Uao6R1Unq+pkkX5T9dIYY4wxxvQxXm+ulSLyYdj687xicR+JyFVR9v2pd/+HInKPiBT2TKv7vnQGWG8BE0RkvIjkA8cCj4Rt8whwoldNcCdgnaoui7WvN0bLdxjwobd+mFccAxHZGFc448v0PTxjjDHGGGMy6g6gw7xRIrIXrnbBNqq6FdCpMJyIjAF+AkxW1a2BAO77tkmBtAVYqtoMnAs8DXwC3KeqH4nImSJyprfZE7ggaDEuG3V2rH29fa4SkQ9E5H1gL+Cn3vrdgfdF5D3gP8CZqro6XY/PGGOMMcZ0wfz58Je/wMqVmW5JB1+u+ZJ/vvvPTDejW1R1HhD+ffcs4EpVbfC2ifaE5wJFIpILFNO5p5npJunP45ACgYDaRMPGGGOMMT1gjz1g3jzIz4ejjoKzzoKdd4actE7LGtelz1/K5S9fTvDXQYryijLalnAi0gh8ELJqjqrOCdtmHPCYl4lCRN4F/ovLbNUDv1DVtyIc+3zgCqAOeEZVf5SOx9AVXo2EyZluR7LSNg+WMcYYY4wxADQ0wBtvwDHHwIgRcMcdcNddUFYGO+wAU6bAPvvAnntCINCjTVvfuB6AlbUrGTtwbI+eOwHN3Qg4coFBwE7ADsB9IrKxhmRVvCrc04HxwFrgfhE5XlXvTE2z+7fMXjIwxhhjjDF938KFLsg65hi47jr47jv417/g+ONh7Vq4+mrYd18YNw5+/WtYtKjHmlbbVAvAitoVPXbONKsAHvTmmX0TaAWGhm2zL/CVqq5S1SbgQeAHPdzOPssCLGOMMcYYk14vv+x+7rKL+1la6oKrm25ywde6dXDffbDNNnDVVbDVVvDaaz3StGBTEIAV6/tMgPUwsDeAiGwG5AOVYdt8C+wkIsXiymrvg6t7YFLAAixjjDHGGJNeL78Mm28Ow4dHvr+42I3Levxx+PZb13Xwhht6pGm9OYMlIvcArwObi0iFiJwG3A5s7JVuvxc4SVVVREaLyBMAqvoGrijc27gxXjnAnIgnMV1mY7CMMcYYY0z6tLbCq6/CkUcmtv3o0XDCCfC3v0FlJQwN792WWr05g6WqM6LcdXyEbZcCB4bcngXMSlPT+jXLYBljjDHGmPT56CM3zmrXXRPf5/TTobER5s5NW7N8tY29N4NlspMFWMYYY4wxJn1eecX93G23xPf53vdcCfc5cyDNUwq1ZbAswDIpYgGWMcYYY4xJn5dfdt3+xo/v2n4zZ7pqgn6AliZtY7B6YRfB/kZEbheRld74skj3/1JE3vWWD0WkRUQG93Q7LcAyxhhjjDHpoeoCrF13BZGu7Xv00TBggMtipZFlsHqVO3ATKEekqler6naquh1wMfCSqq7uoba1sQDLGGOMMcakx7ffQkVF17oH+oqLXSn3+++H1en7juyPwVq+fnnazmFSQ1XnAYm+GGYA96SxOVFZgGWMMcYYY9LDn/+qOwEWuGIXDQ1uUuI0CTYFEYS19WtpaG5I23lMQoaKyIKQZWZ3DiIixbhM1wOpbV5iLMAyxhhjjDHp8fLLUF4OW2/dvf233RZ22gmuvx6am1PbNqCltYWGlgbGlI8BYGXtypSfw3RJpapODlm62z/0EODVTHQPBAuwjDHGGGNMurzyCuyyCwQC3T/GxRfDl1/CXXelrl0ef/zV+IGuAIeNw+ozjiVD3QPBAixjjDHGGJMOy5bBxx93bf6rSA45BLbbDq64IuVZLL+C4MaDNgaskmBfICIDgD2A/2aqDRZgGWOMMcaY1LvhBlc58MgjkzuOCPz2t/D553Dvvalpm8cyWL2LiNwDvA5sLiIVInKaiJwpImeGbHYY8Iyq1mamlZCbqRMbY4wxxpg+qroabroJDj8cNtss+eNNnw7bbAOXXw4zZiTX5TCEX0Fw/CAvwLIMVlZT1RkJbHMHrpx7xlgGyxhjjDHGpNacObB2LVx0UWqOl5MDl17qJh6+//7UHJP2DNaw4mGU5pdaBsukhAVYxhhjjDEmdRoa4C9/gb32gh12SN1xDz8cttoKLrsMWltTckh/DFZxXjEjSkZYgGVSwgIsY4wxxhiTOnfdBUuXpi575cvJgV//2hXOmDcvJYf0M1gl+SWMKB1hXQRNSqQ1wBKRqSKySEQWi8ivItwvInK9d//7IjIp3r4i8jsR+U5E3vWWA0Puu9jbfpGIHJDOx2aMMcYYY8K0tsJVV7mqf/vvn/rjH3oo5OfD44+n5HD+GCzLYJlUSluAJSIB4EZgGjARmCEiE8M2mwZM8JaZwM0J7vsXVd3OW57w9pmIq3m/FW7m5pu84xhjjDHGmHRZvx7eew8efRQuucSNk7roIlf9L9VKS2GPPVIWYLVlsPJKXIDVyzJYInK7iKwUkQ/D1p/nJRw+EpGrouw7UET+IyKfisgnIrJzz7S670tnFcEdgcWq+iWAiNwLTAc+DtlmOjBXVRWY7/2hRwHjEtg33HTgXlVtAL4SkcVeG15P7cMyxhhjjDEALFwIe+/tqgb6dtgh+dLssRx0EFxwAXz1FYwfn9ShOozBKh1BVV0VTS1N5AXyUtDQHnEHcAMw118hInvhvhdvo6oNIjI8yr7XAU+p6pEikg8Up7ux/UU6uwiOAZaE3K7w1iWyTbx9z/W6FN4uIoO6cD5jjDHGGJMKS5a4SYAHDoT77oP58+G779zP3DRewz/QGx3yxBNJH6rDGKySEQCsCq5K+rg9RVXnAavDVp8FXOklHVDVleH7iUg5sDvwd2+bRlVdm97W9h/pDLAi5YU1wW1i7XszsAmwHbAM+FMXzoeIzBSRBSKywCXOjDHGGGNMl9TUwMEHQ22t66531FEwZQqMHu2KUaTThAluSUE3QX8MVmFuISNKXYCVZd0Ec/3vrd4yM4F9NgN2E5E3ROQlEYlUynFjYBXwDxF5R0RuE5GSlLa8H0vnf0AFsGHI7Q2ApQluE3VfVV2hqi2q2grciusGmOj5UNU5qjpZVSdLOvoGG2OMMcb0Zc3NcMwx8NFHbk6qrbfu+TYcdBC88AIEg0kdJtgUpDivmBzJactgZVmhi2b/e6u3zElgn1xgELAT8EvgPun8pTcXmATcrKrbA7VAp4J0pnvSGWC9BUwQkfFev85jgUfCtnkEONGrJrgTsE5Vl8Xa1xuj5TsM+DDkWMeKSIGIjMcVzngzXQ/OGGOMMaZfuvZaePJJuPHG9FQKTMRBB0F9PTz/fFKHqW2qpTjPDT3K0gxWd1QAD6rzJtAKDI2wTYWqvuHd/g8u4DIpkLYAS1WbgXOBp4FPgPtU9SMROVNEzvQ2ewL4EliMy0adHWtfb5+rROQDEXkf2Av4qbfPR8B9uEIYTwHnqGpLuh6fMcYYY0y/dN99rjvgGWdkrg277QYlJUl3Eww2BSnJcz3jsjSD1R0PA3sDiMhmQD5QGbqBqi4HlojI5t6qfYhdTM50QTqrCOKVUH8ibN0tIb8rcE6i+3rrT4hxviuAK7rbXmOMMcYYE8OKFfDWW3DZZZltR0EB7LefK3Sh2u2S8KEZrNL8UorzintVBktE7gH2BIaKSAUwC7gduN0r3d4InKSqKiKjgdtU1Z9D9jzgLq+32JfAKT3+APqotAZYxhhjjDGmD3nySffzoIMy2w6/DQ8/7MaCdXMcWLApSEm+y2CJSK+bbFhVZ0S56/gI2y4FDgy5/S4wOT0t69/SXObFGGOMMcb0GY8/7ioFbrddplsC06a5n4891u1D1Da2Z7DAjcPqTQGWyU4WYBljjDHGmPgaG+GZZ9w8VNlQiXnMGNhlF7jySvj0024doraptm0MFrhxWL2pi6DJThZgGWOMMcaY+F55Baqr3fxX2eLOO914rIMOgsrK+NuH8cu0+3pbF0GTnSzAMsYYY4wx8T3+OOTnwz77ZLol7caNc+OwvvsODjsMGhq6tHttY23bGCxwXQRX1a6iubU5te00/YoFWMYYY4wxJr7HH4c994TS0ky3pKOdd4Y77nAZtpkzXVXBBIWWaQeXwVKUymDXs2HG+CzAMsYYY4wxsX3xBSxalB3VAyM59lj47W9h7lwXaCUotEw79KnJhk0GWYBljDHGGGNi8yf0zdYAC+DCC6G8HObMSWhzVY2YwYI+MdmwySALsIwxxhhjTGyPPw6bbw6bbJLplkRXUgInnAD33w9VVXE3r2uuA4iYwfpm7TdUN1S3LSY7iMjtIrLSm0Q52jZ7isi7IvKRiLzUk+3zWYBljDHGGGOi++QTeOGF7KoeGM3pp7tCF//6V9xNg01BgA5FLkaWjgRg5mMzGXDlAAZcOYCx145NT1tNd9wBTI12p4gMBG4CDlXVrYCjeqZZHeVm4qTGGGOMMaYXaG6Gk05yXe9++ctMtya+bbeFKVNcN8Hzz485X1dtYy3QMYNVXlDOA0c/wNdrv25bVxAoSFtzTdeo6jwRGRdjk+OAB1X1W2/7lT3SsDAWYBljjDHGmMiuugreegv+/W8YMSLTrUnMGWfAqae6Yhe77RZ1s7YMVsgYLIDDtzw8rc0zMQ0VkQUht+eoamKD6pzNgDwReREoA65T1bmpbGAirIugMcYYY4zp7P334Xe/g6OPdktvcfTRCRW7qG3qnMEyGVepqpNDlq4EV+CSR98HDgIOAC4Vkc1S3so4LMAyxhhjjDEdNTa6roGDBsGNN2a6NV2TYLGLSGOwTK9XATylqrWqWgnMA7bt6UZYgGWMMcYYYzq65BJ4912XBRo6NNOt6bqZM+MWu4g0Bsv0ev8FdhORXBEpBqYAn/R0IyzAMsYYY4wx7e69F665Bs46C6ZPz3RrumebbWDHHeH220E14ibRxmCZ7CUi9wCvA5uLSIWInCYiZ4rImQCq+gnwFPA+8CZwm6pGLemeLlbkwhhjjDHGOO+95wpE7LorXHttpluTnJNPhrPPdpm47bfvdHdfGIMlIrcDBwMrVXXrkPXnAecCzcDjqnphlP0DwALgO1XN+jr8qjojgW2uBq7ugeZEZRksY4wxxhjjxiv98Idu3NX990N+fqZblJxjj3WP4Y47It7dR8Zg3UHYvFAishcwHdjGmwvqmhj7n08GutD1dRZgGWOMMcb0dw0NcMwxsHQpPPggjByZ6RYlb9Ag18Xx7rtd0Y4wfWEMlqrOA1aHrT4LuFJVG7xtIs4FJSIb4Krt3ZbWRvZDaQ2wRGSqiCwSkcUi8qsI94uIXO/d/76ITOrCvr8QERWRod7tcSJSJyLvesst6XxsxhhjjDF9QlOTC67+9z9X1GLKlEy3KHVOPhkqK+GJJzrd5WewenOAFcVmuEIPb4jISyKyQ5TtrgUuBFp7rGX9RNoCLK9P543ANGAiMENEJoZtNg2Y4C0zgZsT2VdENgT2A74NO94Xqrqdt5yZ+kdljDHGGNOHNDfD8cfDf/8Lf/2rK83el+y/v8vGRegmWNtUS34gn9ycrC5JkCsiC0KWmYnsAwwCdgJ+CdwnIhK6gYj447YWpr7JJp0ZrB2Bxar6pao2Avfi+oOGmg7MVWc+MFBERiWw719wEXfksjDGGGOMMSa21lZX0OK+++Dqq+HcczPdotTLzXVzYj3+OKxa1eGuYFOwN1QQbO7GxLsVwIPe9+s3cRmq8Fr7uwCHisjXuO/Ze4vInSlteT+WzgBrDLAk5HaFty6RbaLuKyKH4iqdvBfhnONF5B0vHbpbpEaJyEz/KoBGKdtpjDHGGNOn1dbC0Ue7eaJ+/3v4xS8y3aL0Oekkl6m7++4Oq2sba/ti90CAh4G9AURkMyAfqAzdQFUvVtUNVHUccCzwvKoe38Pt7LPSGWBJhHXhEU20bSKu9yYMuwT4bYT7lwEbqer2wM+Au0WkvNNBVOf4VwHCsqXGGGOMMX3fkiWw227w0EPw5z/Db36T6Ral11ZbweTJnboJBpuDvb2CYMR5oYDbgY1F5ENcduokVVURGS0inQejmZRLZ6fTCmDDkNsbAEsT3CY/yvpNgPHAe15wtAHwtojsqKrLAb9aykIR+QI3yG9Bqh6QMcYYY0yvNn++K8VeVwePPQbTpmW6RT3j1FPdnFjz5sHuuwN9I4MVY16oTtkoVV0KHBhh/YvAiyltWD+XzgzWW8AEERkvIvm49OMjYds8ApzoVRPcCVinqsui7auqH6jqcFUd56U0K4BJqrpcRIZ5xTEQkY1xhTO+TOPjM8YYY4zpPZ56CvbeG0pL4fXX+09wBa6b4IgRcNllbat6yRgs0wulLcBS1WbcDNJP4yYwu09VPxKRM0XEr/D3BC4IWgzcCpwda984p9wdeF9E3gP+A5ypquHzAhhjjDHG9D8PPACHHgqbb+6Cq4nhhZ37uOJi+OUv4bnn4LXXAFdFsLdnsEx2kv5c6CEQCGhLS0umm2GMMcYYkz5z58Ipp7j5rZ54AgYOzHSLMqO2FsaNc+OxnnySbW/Zlo0HbcxDxzyU6ZZFJSJBVe03aTavCN3kTLcjWWmdaNgYY4wxxmTQ88+77nF77QXPPNN/gyuAkhJXLfGpp+DNN/vEGCyTnSzAMsYYY4zpq66/HoYPh0cfdWOv+ruzz4bBg+Gyy2wMlkkbC7CMMcYYY/qiZctcpcCTT4aioky3JjuUlcHPfgaPPcaEr6stg2XSwgIsY4wxxpi+6B//gJYW+PGPM92S7HLuuTB4MFc9XEtZjgWeJvUswDLGGGOM6WtaW+G222DPPWHChEy3JrsMGEDzDdczpQL2vffNTLfG9EEWYBljjDHG9DXPPw9ffQWnn57plmSl9YcdxJ3fg93mvghvvZXp5pg+xgIsY4wxxpi+5rbbYNAgOPzwTLckK9U21nLugRAcOgCOP96VcDcmRSzAMsYYY4zpSyor4aGH4IQToLAw063JSrVNtawrgvmXnwWff+4mITYmRSzAMsYYY4zpS+bOhcZG6x4YQ7ApCEDNLpPh5z+Hm292z5sxKWABljHGGGNMX7F2LVx7Ley0E2y9daZbk7VqG12XwJL8Evi//3MTMZ9+Osyfn+GWmVhE5HYRWSkiH0a5f08RWSci73rLb3u6jWABljHGGGNM33HOObB0qQuyTFR+Bqs4rxjy8uD++2GDDeCww6CiIsOtMzHcAUyNs83Lqrqdt/y+B9rUSW4mTmqMMcYYY1Lszjvh7rvhsstgypRMtyar1TZ5Gay8ErdiyBB45BHYeWf44Q9h3jwotkmIs42qzhORcSk7oEikKjDrgA9QXdndw1oGyxhjjDGmt/vqKzj7bNh1V7j44ky3Jut1yGD5ttrKBahvvw3HHgtNTRlqXb82VEQWhCwzu3GMnUXkPRF5UkS2irPtacBtwI+85VbgZ8CriJzQjXMDFmAZY4wxxvRuzc2u1LiIy2IFApluUdbrMAYr1MEHw403wqOPwkknQUtLBlqXuGhjkkTkPBFZJCIfichVEfbbUEReEJFPvG3O77lWx1SpqpNDljld3P9tYKyqbgv8FXg4zvatwJaoHoHqEcBEoAGYAlzUxXO3sS6CxhhjjDG92U9/Cq+95rIvY8dmujUp99LXL/H8V8+33R5QOIDzp5xPIKf7gWTEDJbvrLNg3TqXCSwrg1tuccFrdroDuAFoK4EoInsB04FtVLVBRIZH2K8Z+Lmqvi0iZcBCEXlWVT/uiUani6pWh/z+hIjcJCJDVbUyyi7jUF0RcnslsBmqqxHpdgrTAixjjDHGmN7q+uvhhhvgZz+DGTMy3Zq0+PkzP2fhsoUd1m0xdAsOnHBgt4/ZaQxWuF/9ygVZV14J5eVw1VVZGWRFGZN0FnClqjZ423QaS6Sqy4Bl3u81IvIJMAbo1QGWiIwEVqiqisiOuN56VTF2eRmRx4D7vdtHAPMQKQHWdrcd1kXQGGOMMaY3euwxl72aPt0FAH3UytqVnLTtSegsJfjrIIW5hTy9+OmkjhlsCpIjOeQH8qNv9H//58a1PfywC7YyI7cbY5I2A3YTkTdE5CUR2SHWxl6Atj3wRgram1Yicg/wOrC5iFSIyGkicqaInOltciTwoYi8B1wPHKuqGuOQ5+CygNvhnoO5wDmo1qK6V3fbaRksY4wxxpje5p13XCGG7beHu+7q0+OuquqqGFo8FICivCL2HLcnT3+RXIBV21hLSV4JEisrJQJ//SusWQMDByZ1viQ0q+rkLu6TCwwCdgJ2AO4TkY0jBRoiUgo8AFwQ2r0uW6lqzDStqt6A6zKZ8AGB/3hLylgGyxhjjDGmN1F1BRgGDXKlxUuidHPrA4JNQYJNwbYAC+CATQ5gUdUivl77dVLHjTj+KlxOjivh3rtUAA+q8yaukMPQ8I1EJA8XXN2lqg/2cBuzg8jhiHyOyDpEqhGpQSTpQDOtAZaITPUqmCwWkV9FuF9E5Hrv/vdFZFIX9v2FiKiIDA1Zd7G3/SIROSB9j8wYY4wxJkOeego++ACuuAJGj850a9KqKuiGz4QGWFM3dfPMJtNNsLaptnMFwb7jYWBvABHZDMgHOhR5EJe6+zvwiar+uacbmEWuAg5FdQCq5aiWoVqe7EETDrBkthTJbNk84e1FAsCNwDRcycMZIjIxbLNpwARvmQncnMi+IrIhsB/wbci6icCxwFa4GZ5v8o5jjDHGGNN3XH01jBnjugj2cZVBFxeEBlibD9mcjQZsxFNfPNXt4wabgtELXPQikcYkAbcDG3ul2+8FTvKKPowWkSe8XXcBTgD2FpF3vaX7VUN6rxWofpLqgyY0BktmyyHANbgIeLzMlu2A3+ssPTTGbjsCi1X1SwARuRdXMjK0Osl0YK7XJ3S+iAwUkVHAuDj7/gW4EPhv2LHu9SqmfCUii702vJ7IYzTGGGOMyXoLFsALL8A110B+jAINfUSkAEtEmLrJVO758B6aWprIC+R1+bi1TbWJdRHMcjHGJB0fYdulwIHe768A2VcWsectQOTfuKxfQ9vaJLtMJprB+h0uWFkLoLP0XVwQFMsYYEnI7QpvXSLbRN1XRA4FvlPV97pxPkRkpl+JJXZREWOMMcaYLHP11TBgAJx+eqZb0iP8AGtIUcdxUAdsegA1jTW8XtG96+jBpmBf7iJoElcOBIH9gUO85eBkD5poFcFmnaXrZHaXAt1IG4dHNNG2ibheRIqBS3BPQnfOhzcj9ByAQCBgEZYxxhhjeocvv4T//Ad++Us3N1M/ECmDBbDP+H0ISICnFz/N7mN37/JxaxtrGVg+MBVNNL2Z6inpOGyiAdaHMluOAwIyWyYAPwFei7NPBbBhyO0NgKUJbpMfZf0mwHjgPa+s5gbA295EYomczxhjjDGmd/rznyE3F37yk0y3pMdU1VUhCIOKBnVYP6BwADtvuDNPffEUV+xzRZeP21fGYJluErkQ1asQ+SsREjKoJvVPlmgXwfNwxSMagLuBdcAFcfZ5C5ggIuNFJB9XgOKRsG0eAU70qgnuBKzzZpaOuK+qfqCqw1V1nKqOwwVVk1R1uXesY0WkQETG4wpnvJng4zPGGGOMyV6rVsHtt8Pxx/f5yoGhKoOVDCoaRG5O55zAAZscwNvL3mZl7couH7evjMEy3eYXtlgALIywJCVuBktmSwB4RGfpvrjueQlR1WYRORd4GggAt6vqR/5My6p6C/AEbrDdYlz/x1Ni7RvnfB+JyH24QhjNwDmq2pJoe40xxhhjstaNN0JdHfziF5luSY+qDFZ26h7om7rpVC594VIe+uQhjph4RJeOu75xvWWw+jPVR72f/0zH4SWRQg8yWx4BTtBZui4djciUQCCgLS0WgxljjDEmiwWDsNFG8IMfuImF+5F95+5LXXMdr576aqf7WrWVkdeMZFVwVbeO/etdf92t7oU9SUSCqtpvIkGvCN3kHjjRo0TqGujTmJXS40p0DFY98IHMlmeB2rZzz0quf6IxxhhjjInjH/+Aqiq48MJMt6THVQYrGTtwbMT7ciSH/x77XxYu63qPrhzJ4fAtD0+2eab3usb7eTgwErjTuz0D+DrZgycaYD3uLcYYY4wxpqc0N7viFjvvDLvskunW9LjKYCWTRk2Kev/OG+7Mzhvu3IMtMn2C6ksAiFyGamgZykcRmZfs4RMKsHRWevonGmOMMcaYGB580JVnv+YakP41L6yqUlVXFXUMljEpMAyRjVH9EgBXKG9YsgdNKMCS2fIVkeaUmqUbJ9sAY4wxxhgTgSpcdRVsthkcmtSQkF4p2BSkvrneAiyTTj8FXkTkS+/2OOCMZA+aaBfB0MFmhcBRwOBkT26MMcYYY6J48UVYuBD+9jcIBDLdmh4XbZJhY1JG9SlEJgBbeGs+RbUh2cMm2kWwKmzVtTJbXgF+m2wDjDHGGGNMBNdeC8OHw4knZrolGWEBVnYRYRfgd8BYXAwhuJ6cvb1H2wRgc1wSaVtEQHVuMgdMtItg6OjCHFxGqyyZExtjjDHGmChWroQnnoCf/QwKCzPdmoywACvr/B3XpW4h0DfmORKZBewJTMTNzzsNeAVIf4AF/Cnk92Zc+cKjkzmxMcYYY4yJ4t57XQXBfpq9gvYAa0jRkAy3xHjWqfJkphuRYkcC2wLvoHoKIiOA25I9aKJdBPdK9kTGGGOMMSZB//wnfP/7sNVWmW5JxlTVuREqlsHKGi+IcDXwINA2TkmVtzPXpKTVodqKSDMi5cBKSL7LY6JdBM8H/gHUALcCk4Bf6Sx9JtkGGGOMMcaYEB9+CG+/Ddddl+mWZFRlsJIcyWFg4cBMNyVricjtwMHASlXdOmT9ecC5uJ5nj6tqp1mqRWQqcB0QAG5T1SvjnG6K9zO0+J0Ce3f/EWTcAkQG4uKbhcB64M1kD5qT4Han6iytBvYHhgOnAPH+CMYYY4wxpqv+9S/IzYUZMzLdkoyqDFYyuGgwgZz+V0GxC+4ApoauEJG9gOnANqq6FXBN+E4iEgBuxI05mgjMEJGJsU6kyl4Rlh4NrkTkdhFZKSIfxtluBxFpEZEjYx5Q9WxU16J6C7AfcBKqpyTbzkTHYPkz2x0I/ENn6Xsyu5/NdpdCF1wA776b6VYYY4wxJtvkaAv/nn8niwYcyG+OSnq+017t41XnUNt4Mns+ntl2bLedK+iYjVR1noiMC1t9FnCleuXGVXVlhF13BBarN8GuiNyLC8o+Dt9QhONVuVOEn0VuA39O4iF01R3ADcQoQuEFj38Eno57NBEBfgRsjOrvEdkIkR1RTSqLlWgGa6HMlmdwAdbTMlvKgNZkTmyMMcYYYzqatOZ/DGtcyjMj+m9xC19TSxN5gbxMNyPTckVkQcgyM4F9NgN2E5E3ROQlEdkhwjZjgCUhtyu8dZGUeD/Loiw9RlXnAavjbHYe8ABuPFU8NwE7A366uAaX2UtKohms04DtgC91lgZltgzBdRM03ZCtV0GMMcYYk2HHz4WKQfz+7YOhINONyaxtbj6RTQZvwkPHPJTppmRSs6pOjr9ZB7nAIGAnYAfgPhHZWFU1ZJtIPdE0wjpU+Zv3c3YX29HjRGQMcBhuXFikwDLcFFQnIfIOAKprEMlPth2JVhFsldmyAXCczBaAl3SWPprsyY0xxhhjjKe6Gh58EE4+GQr6eXSFG4O145gdM92M3qgCeNALqN4UkVZgKLAqbJsNQ25vACyNdVARCnFJl61wk/ICoMqpKWo3wFARWRBye46qzunC/tcCF6lqiyQ2mqkJ16XQBZciw0hBL72EugjKbLkSOB/XL/Nj4CcyW/6Q7MmNMcYYY4zngQegrq5fz33lU1Wq6qqsRHv3PIxX2U9ENgPygcqwbd4CJojIeHEZm2OBR+Ic91/ASOAA4CVcUFaTumYDUKmqk0OWrgRX4Coc3isiX+PmuLpJRH4YY/vrgYeA4YhcgZtkOOkYJ9EuggcC2+ksbQWQ2fJP4B3g4mQbYIwxxhhjgLlzYcIEmDIl/rZ93PrG9TS2NFqAFYeI3APsicv8VACzgNuB271Ke43ASaqqIjIaV479QFVtFpFzcYUgAsDtqvpRnNNtqspRIkxX5Z8i3E0ihSR6kKqO938XkTuAx1T14Rg73IXIQmAfXLfJHwLfJtuORAMsgIG0DyobkOyJjTHGGGOM5+uv4cUX4fLLwQo1Uxl0CRcLsGJT1Wi1/I+PsO1SXNLEv/0E8EQXTtfk/VwrwtbAcmBcF/ZPWpSAMg9AXan1rhxsDDAKeB/VTxEZDlwAnAyMTqadiQZYfwDekdnyAi662x3LXhljjDHGpMadd7qfx3f6XtwvWYCVleaIMAj4Da47YSlwaU82IEZAGWnbk6PeKXIBcAmwGChA5Drgz7jy799PqpEkOAZLZ+k9uEokD+LKHu6ss/TeePuJyFQRWSQii0XkVxHuFxG53rv/fRGZFG9fEbnM2/ZdEXnGS3ciIuNEpM5b/66IdC2KNcYYY4zJBFXXPXDPPWHs2Ey3JitYgJWVBuCqiE/GlTL/I9AswnaZbFQ3zQQ2R3VnXLfAW4GDUP0pqsuSPXii82CBqxG/J7CH93tMCc4QPQ2Y4C0zgZsT2PdqVd1GVbcDHgN+G3K8L1R1O285swuPzRhjjDEmM+bPh88/h5NOynRLsoYfYA0pGpLhlpgQ3wfOxM2XNRo4HRcb3CrChRlsV3fUo+qGPql+C3yG6vxUHTyhLoIyW24CNgXu8VadIbNlX52l58TYLZEZoqcDc70ykvNFZKCIjML154y4r6pWh+xfQpSa/cYYY4wxvcLcuVBUBEcckemWZI2quirAMlhZZggwSZX1ACLMAv6DGzq0ELgqg23rqg0QuT7k9vAOt1V/kszBEx2DtQewtc5yE5R5VQQ/iLNPpBmiw8viRJtFOua+4soongisA/YK2W68uInCqoHfqOrL4Y3yZsCe6f0e5yEYY4wxxqRRQwPcey8cfjiUlWW6NVmjMlhJQAIMKLS6allkI1xVQl8TMFaVOhEaMtSm7vpl2O2FqTx4ogHWItyT+o13e0Pg/Tj7JDJDdLRtYu6rqpcAl4jIxcC5uAoiy4CNVLVKRL4PPCwiW4VlvPDq6c8BCAQClv0yxhhjTOY8+iisXWvdA8NUBisZUjyEHOnKaBaTZncD80X4r3f7EOAeEUro2EMt+6n+M52HTzTAGgJ8IrPlTe/2DsB8mS2PAOgsPTTCPonMEB1tm/wE9gX3h34cmKWqDeCiZ1VdKCJfAJsBCyLsZ4wxxhiTebfdBmPGwN57Z7olWaUyWGndA7OMKpeJ8ASwKy4ZcqZq2/fsH2WuZdkn0QDrt/E36aRthmjgO9wM0ceFbfMIcK43xmoKsE5Vl4nIqmj7isgEVf3c2/9Q4FNv/TBgtaq2iMjGuMIZX3aj3cYYY4wx6ffpp/D003DZZRAIZLo1WcUCrOykykJS3J2uL0o0wFqls7RD6k9my546S1+MtkO0GaJF5Ezv/ltwk5sdiKtBH8SVfoy6r3foK0Vkc6AV12XRrxa4O/B7EWkGWoAz1a8OYowxxhiTba6/HgoK4IwzMt2SrFMZrGTzoZtnuhnGdEuiAdZ9MlvmAlcDhbgqIZOJU6490gzRobMse9UDI1YijDa7tKpGLLGjqg/g5ugyxhhjjMlua9bAP/8Jxx0Hw4ZlujVZp6quykq0m/QROR14EdXPvap3twNHAF8DJ6P6djKHT3Tk4BRckYvXcF3/lgK7JHNiY4wxxph+6+9/h2AQzj8/0y3JOqpqXQRNup2PC6YAZgDbAOOBnwHXJXvwRAOsJqAOKMJlsL7SWdqa7MmNMcYYY/qd5ma44QbYYw/YdttMtybrVDdU09zabAGWSadmVJu83w8G5qJahepzuHl2k5JogPUWLsCajKscMkNmy3+SPbkxxhhjTL/zyCPwzTeWvYqiMlgJ2CTDJq1aERmFSCGwD/BcyH1FyR480TFYpwObA7/WWfp7mS3n4Sb6NcYYY4wxXXHddTBuHBwaaZab3u3RRY9y1WtX4YbZd8/6xvWABVgmrX6Lm8opADyCX0xPZA9SUIU80QDrFFzVvr2B3wM1wHTg8mQbYIwxxhjTb3zwAcybB9dc0ydLs9//8f0sXLqQH2z4g24fozC3kOmbT2fKmCkpbJkxIVQfQ2QsUIbqmpB7FgD/l+zhEw2wpugsnSSz5R0AnaVrZLbkJXtyY4wxxph+5dZbIT8fTj450y1Ji6q6KiYOm8hzJz4Xf2OTNBG5HTeGaKWqbu2t+x2u99kqb7Nfe9W5w/f9KfBjQIEPgFNUtb4n2p0VVJuBNWHrahG5B1fcr9sSLnIhsyWA+wMgs2WY/7sxxhhjjElAXR38619wxBEwpG+WIK8MVjKkuG8+tix1BzA1wvq/qOp23hIpuBoD/ASY7AVmAeDYtLa095BkD5BogHU98BAwXGbLFcArpCB9ZowxxhjTb/znP7B2LcycmemWpE1VsMrGTvUgVZ0HrO7m7rlAkYjkAsW4aZhMCpJICXUR1Fl6l8yWhbgqGwL8UGfpJ8me3BhjjDGm35gzByZMcOXZ+yibIDjlckVkQcjtOao6J4H9zhWRE3Fjin6uHccZoarficg1wLe4SuHPqOozKWt1thN5lMiBlABJv4ATHYOFztJPgU+TPaExxhhjTL/zySfwyitw1VUgSfdAykrNrc2srV9rAVZqNavq5C7uczNwGS6AuAz4E3Bq6AYiMghXsG48sBa4X0SOV9U7k25x73BNN+9LSMIBljHGGGOM6aZbb4W8PDjppEy3JG1W17meajYGK7NUdYX/u4jcCjwWYbN9ga9UdZW33YPAD4D+EWCpvtT2u8gwb92qaJt3VaJjsIwxxhhjTHfU18PcuTB9OgwfnunWpE1VsAqw+asyTURGhdw8DPgwwmbfAjuJSLGICG4YUP8a/iMyC5FKXA+9zxBZhchvU3FoC7CMMcYYY9LpoYegqqpPF7cAV0EQsC6CPUhcSfHXgc1FpEJETgOuEpEPROR9YC/gp962o0XkCQBVfQP4D/A2rkR7DpDI2K6MEpHbRWSliEQKGhGR6SLyvoi8KyILRGTXKAf6KbArsAOqQ1AdBEwBdvHuS66dycy03dsFAgFtaWnJdDOMMcYY01c1NcH3vgeqbhxWTt+9tv3wpw9z2L8PY+HMhUwaNSnTzekTRCSoqiWZbkdPEZEFscacicjuwHpgrj/vV9j9pUCtqqqIbAPcp6pbRDjQO8B+qFaGrR8GPIPq9sk8DhuDZYwxxhiTLjfeCIsWwaOP9ungCqyLoEk/VZ0nIuNi3L8+5GYJ0Uuu53UKrtwBViGSl1QjsQDLGGOMMSY9Kith9mzYf3846KBMtybtqupcgGVdBE0ShnazLH0bETkM+AMwHIj2j9cY4xCx7kuIBVjGGGOMMenw299CTQ385S99tjR7qMpgJQWBAorzijPdFNN7VXajLH0HqvoQ8JDXnfAyXMXEcNsiUh1hvQCFyZwfLMAyxhhjjEm999+Hv/0NzjkHJk7MdGt6RFWwiqHFQ5F+EEya7Od1J9xERIZqeHdA1UA6z923OwMbY4wxxvQ0VfjpT2HgQPjd7zLdmh5TVVdlc2CZjBKRTb2y84jIJCAfqOrpdqQ1wBKRqSKySEQWi8ivItwvInK9d//73hMRc18RuSyk/OIzIjI65L6Lve0XicgB6XxsxhhjjDERPfUUPP+8C64GD850a3pMZbDSxl+ZtIpUll5EzhSRM71NjgA+FJF3gRuBYzQDJdPTVqZdRALAZ8B+QAXwFjBDVT8O2eZA4DzgQFzt+etUdUqsfUWkXFWrvf1/AkxU1TNFZCJwD7AjMBp4DthMVaPWYbcy7cYYY4xJqdZWmDTJjb365BPIz890i3rMljduydbDt+b+o+7PdFP6DCvT3julM4O1I7BYVb9U1UbgXmB62DbTcXXsVVXnAwO92aej7usHV57Q8ovTgXtVtUFVvwIWe8cxxhhjTF+gCp9/Du+95+aXykb33OPad/nl/Sq4Am8MVpGVaDcmnUUuxgBLQm5X4LJU8bYZE29fEbkCOBFYh5uh2j/W/AjH6kBEZgIzvd8TfjDGGGOM6SFLlsDLL0N1tVtWr4Z334U334Q1a9w2hYWw3Xaw446w776w995QkuEL/Y2NcOmlrl3HHJPZtvSwVm21MVjGeNIZYEWKXsL7I0bbJua+qnoJcImIXAycC8xK8Hx4tfTngOsiGLHlxhhjjOl5n34Kf/wj3HknNDe3rw8EYKut4MgjXUBVUgILFsBbb8Ftt8H110NBAey1F/zwh3D00TBoUM+3f84c+OorePLJPj+pcLh19eto1VYbg2UM6Q2wKoANQ25vACxNcJv8BPYFuBt4HBdgJXI+Y4wxxmSbujr48Y9d97rCQjjrLDjtNBg+HMrLobi48zxSM2a4nw0NLtv1+ONuOfNMOP98F2idfDLssw/k5aX/MaxfD5ddBnvuCQf0vzpb/iTDQ4uti6Ax6by88hYwQUTGi0g+cCzwSNg2jwAnetUEdwLWqeqyWPuKyISQ/Q8FPg051rEiUiAi44EJwJvpenDGGGOMSYGWFjj+eBdcXXQRfPONy0htuy2MGuWyVbG69BcUuC6Cf/kLLFoECxfCzJnw7LMwbRqMGAEnnQQPPwzBYPoex6xZsHIl/OEP/WJS4XCVQTfNkHURNCaNGSxVbRaRc4GngQBwu6p+5JdRVNVbgCdwFQQXA0HglFj7eoe+UkQ2B1qBbwD/eB+JyH3Ax0AzcE6sCoLGGGOMyTBVuOACePBBFyBdcEFyxxNxFfwmTYKrr3bl0h98EB59FObOdZmwqVPhsMPg4IPdPFWp8OCD8Oc/w9lnw047peaYvUxV0GWwrIugMWks094bWJl2Y4wxJoOuusplrX7+c7jmmvSdp6kJXnoJHnrIZbKWLoXcXFcY47DDYPp0F+w995xbvvwSzjgDjjvOjf+K5bPPYPJkmDjRnaOgIH2PI4vNfW8uJz18EovPW8wmgzfJdHP6DCvT3jtZgGUBljHGGNPzbrsNTj8djj0W7rqr54pCtLa6aoQPPeQyT4sXd7x/6FAYMsR1N5w4EX7/ezj88Mjd/mprXcZq2TJ45x3YcMPO2/QTf379z/z8mZ+z5qI1DCwcmOnm9BkWYPVO/avEjTHGGGMySxVmz3bB1QEHwB139GzFvZwcFxT98Y8u+/TBB3DFFe7222/DihXw8cdw//2urUceCT/4gRvbFWr5cjjhBPjoIzd+rB8HV+C6CAYkwICCAZluSr8iIreLyEoR+TBk3e9E5DsReddbDoyy70AR+Y+IfCoin4jIzj3X8r4tnVUEjTHGGGPaNTW5CoF//7srPHHrrT1T4S8aEdh6a7eEO/JI131w7ly4+GLYYQcXFJ56qmv/P//pHs8f/wj77dfzbc8yVXVVDC4abHOM9rw7gBuAuWHr/6Kq8frdXgc8papHekXlitPQvn7JMljGGGOMSb/WVhe0/P3vbjLef/wjs8FVIgIBOOUU113wggtc23fayQVdp57q1v/yl5luZVaoDFZaifYMUNV5wOqu7ici5cDuwN+94zSq6trUtq7/sgDLGGOMMel3663wyCOuWuDvf9+7SpkPGOCqBL73Htx0kyslf/PNMGFC/H37iaq6KivRnl3OFZH3vS6EkWbd3hhYBfxDRN4RkdtEpN+M9Uo3C7CMMcYYk17Ll7tqgXvv7SYB7q222sp1cRwxItMtyTpVwSor0Z4euSKyIGSZmcA+NwObANsBy4A/RTouMAm4WVW3B2qBX6Wozf2ejcEyxhhjTHpdcAHU17usT2/KXJmEVQYr2XHMjpluRl/U3NWqeqq6wv9dRG4FHouwWQVQoapveLf/gwVYKWMZLGOMMcakz5NPwr//DZdcApttlunWmDRQVddF0DJYWUFERoXcPAz4MHwbVV0OLBGRzb1V+wAf90Dz+gXLYBljjDEmPWpr4eyzYYst4MILM90akya1TbU0tjTaGKwMEJF7gD2BoSJSAcwC9hSR7QAFvgbO8LYdDdymqn7Z9vOAu7wKgl8Cp/Ro4/swC7CMMcYYkx5/+hN8/TW89BIUFGS6NSZNKoOVAFZFMANUdUaE1X+Psu1S4MCQ2+8CvX5S32xkXQSNMcYYk3o1NXDttXDoobD77plujUmjqmAVgHURNMZjAZYxxhhjUu9vf4M1a+DXv850S0yaVdV5AZZ1ETQGsADLGGOMMalWX++6B+6zD0yZkunWmDTzuwhaBssYxwIsY4wxxqTW7be7ua8uuSTTLTE9wO8iaGOwTLp5EyevFJFOlRG9+3/kTbD8voi8JiLb9nQbwQIsY4wxxqRSUxNcdRXstBPsuWemW2N6gN9FcFDRoAy3xPQDdwBTY9z/FbCHqm4DXAbM6YlGhbMqgsYYY4xJnbvvhm++gRtusEmF+4mqYBUDCweSm2NfK016qeo8ERkX4/7XQm7OBzZIe6MisP+E/qi1FXIseWmMMSbFgkG4/HLYZhs46KBMt8b0kMq6SuseaFJlqIgsCLk9R1W7m4U6DXgyBW3qMguwMmHpUli8GAYOhEGDYMAAaGiAykq31NXB8OEwciQMGQLvvgtPP+2Wyko4/XT48Y+hvDyx86nCBx/Ak0+65dVX3XwkI0a48xx5JPz85+l8xMYYY/qDn/3Mfb49+6xlr/qRqmCVFbgwqVKpqknPzSUie+ECrF2Tb1LXWYCVCU8+6QKkrvr+913A9fOfw+zZLtA69ljYfnsIBNw2qvDxx/D88/D++/DRR26prnb3b7cdnH++y2KtXAmffgq/+AVMmODmKjHGGGO644EHXGn2Cy+EfffNdGtMD6qqq2Jk6chMN8MYAERkG+A2YJqqVmWkDaqavoOLTAWuAwLAbap6Zdj94t1/IBAETlbVt2PtKyJXA4cAjcAXwCmqutbrj/kJsMg7/HxVPTNW+wKBgLa0tKTioXbN8uXw4Yewdm37UlgIQ4e6pbDQBT/Ll7ufm20G++0Hw4a5/RcscOVv778fWlpcJmv33WHwYHjuOZchA3esrbZyy/e/D1OnwujRHdvS0AA77wxLlriAbNSoHnwijDHG9Anffgvbbusu1r3yCuTnZ7pFpgeNvXYse47bk3/+8J+ZbkqfIyJBVS3JdDt6iogsiJfB8r7zP6aqW0e4byPgeeDEsPFYPSptAZaIBIDPgP2ACuAtYIaqfhyyzYHAebgAawpwnapOibWviOwPPK+qzSLyRwBVvSjWkx1NxgKsVFmxAl54oX1ZvRr23hv2398FZGPHJnacTz6BSZNgjz3giSdsfJYxxpjENTfDXnvBe+/BO+/AJptkukWmh5X+Xykzvz+TPx/w50w3pc+xAKvT/fcAewJDgRXALCAPQFVvEZHbgCOAb7xdmlPR5bCr0tlFcEdgsap+CSAi9wLTgY9DtpkOzFUX5c0XkYEiMgoYF21fVX0mZP/5wJFpfAzZbcQI10Xw2GM7rG5saeTI+45k+TPLEz7UEYeP4KK7n+aa48Zz3z4jUt1SY4wxfZC0Kr+66xsOe2UVl562MU8/P8NdOzb9hqLUNtXaGCzTI1R1Rpz7fwx0YxxOaqUzwBoDLAm5XYHLUsXbZkyC+wKcCvw75PZ4EXkHqAZ+o6ovh+8gIjOBmd7vCT2Q3mZR5SIe/exRJo2axIiSxIKll6YNYc9PgvzkwQq+3G4jvt6wLM2tNMYY05tJq3LuPz5i2suruHf6JizcazOsjlz/dMhmh3Do5jaO2xhfOgOsSNFLeH/EaNvE3VdELgGagbu8VcuAjVS1SkS+DzwsIlupanWHg7hSj3PAdRGM+yh6oYrqCgD+Ou2v/GDDHyS+4/4r4Xvf46a71sJbz7qxYMYYY0y41lY480x44Sm45BKOvewyju2jFy2NMaar0jnYpgLYMOT2BsDSBLeJua+InAQcDPzI616Iqjb4lUJUdSGuAMZmKXkkvcySapf826C8i3OrDR8Od9zhCnBcfHHqG2aMMab3U4VzzoFbb4VLLoHLLrOS7MYYEyKdAdZbwAQRGS8i+cCxwCNh2zwCnCjOTsA6VV0Wa1+vuuBFwKGqGvQPJCLDvOIYiMjGwATgyzQ+vqxVUV1BjuQwumx0/I3DTZsG554L117r5t0yxhhjQl11FdxyC1x0kQVXxhgTQdoCLFVtBs4FnsaVT79PVT8SkTNFxC+f/gQuCFoM3AqcHWtfb58bgDLgWRF5V0Ru8dbvDrwvIu8B/wHOVNXV6Xp82WxJ9RJGlY4iN6ebPUCvugomToSTT4ZVq1LaNmOMMb3Yf/4Dv/oVzJgBf/iDBVfGZJiI3C4iK0Xkw5B1vxOR77zvye96Vbuj7R8QkXdE5LGeaXH/kNZ5sLJdry/THsV+/9qPmoYa5v94fvcP8t57sOOOruT7f/9rpduNMaa/e+stN+fi9tu7yextnK4xaRevTLuI7A6sx1Xl3tpb9ztgvapek8DxfwZMBspV9eDUtLr7EpkHqzewb8190JJ1S9hwwIbxN4xl223h6qvhscdcRssYY0z/VVEBhx7qJqP/738tuDImS6jqPKBbPbZEZAPgIOC2lDbKWIDV16gqS6qXsEFZFwtcRHLeeXD00W4Q8wsvJH88Y4wxvU9rq+syXlPjLroNG5bpFhnTn+SKyIKQZWaC+50rIu97XQgHRdnmWuBCoDUlLTVtLMDqY9bWryXYFEw+gwWub/1tt8Fmm7nJjL/7LvljGmOM6V3++lf43/9c8aOJEzPdGmP6m2ZVnRyyzElgn5uBTYDtcNMY/Sl8AxE5GFjpVd42KWYBVh/T7RLt0ZSVwQMPQG0tHHMMNDSk5rjGGGOy38cfu6IWhxwCp52W6dYYYxKgqitUtUVVW3FF5HaMsNkuwKEi8jVwL7C3iNzZg83s0yzA6mP8SYY3LE9BBss3caLLZL36KhxxBNTXp+7YxhhjslNjI5xwApSWujmvrGKgMb2CiIwKuXkY8GH4Nqp6sapuoKrjcNMhPa+qx/dQE/u8btbxNtlqyTqXwUpJF8FQxx4L69bBmWfC9Onw8MNQVJTacxhjjMkel10Gb78NDz4II0ZkujXGmAhE5B5gT2CoiFQAs4A9RWQ7QIGvgTO8bUcDt6lq1LLtJjUswOpjllQvIUdyGFk6MvUHP+MMyMuDH/8YDj4YHnkESqJWDjXGGNNbvf46/N//ueIWhx2W6dYYY6JQ1RkRVv89yrZLgU7Blaq+CLyY0ob1cxZg9TEV1RWMLhvd/UmG4zn1VMjPh5NOcvOh3HOPK4JhjMk+jY2wZg2sXg11ddDU1L40Nrqfzc0uG11e3nEpLYVAINOPwGTC+vVw4omw4YZw3XWZbo0xxvQ6FmD1MUuql6SuwEU0xx8PAwa4K5vbb+8qTJ1yivXPNyYTampg4UJYsAA+/xy++cYtFRXui3IySko6Bl2Fha5kd2srqLp1gwbB4MEwfDhssIH7Ur7hhu738vLUPEbTs37xC/jiCzc9h/0NjTGmyyzA6mMqqivYdsS26T/RIYfA+++7AdCnnQZPPgk33WTzoxiTbvX18NJL8PjjrnT2J5+4YAdg6FAYO9YVptl/f3d78GC3FBe7Lr7+kp/vfubmuuxWdbVbamrafw9damrcuXNy3AJuXOZXX7kM2erV7e3wlZe7YGvIEBecFRa6dgwZ4t4rhg1zF2uKi91SUtL+e+i6wsKOF3BU7YJOujzxBPztby7I2mOPTLfGGGN6JdHwD8R+JBAIaEtLS6abkTKqSsn/lXDW5LP40wGdpjxIj5YWuOYauPRS92XquuvguOPsy48x3VFZCR995OacW7Om81JV5bJVwaDr1rfHHrDzzrDDDjB5cmYvcDQ2wtKlLnO2ZEn7zyVLYO1aF8TV17spH6qq3OPpioIC937T0tKePRs5EkaNiv1zyJD2gNDEVlHR/jp66y0X2BpjMkpEgqrabwa8i8gCVZ2c6XYkyzJYfcjqutXUNdelv4tgqEAALrrIFb047TTXffCuu1w2a9y4nmuH6b6WFpd9WLWqfVm50v1ct859OQ0EXKajvNxlRYYOhYED27MSRUUuc1JQkOlHk1l1de7iQl6ee86amtxz6T+f69e7AKO21gUdK1a4+5YudfMNrVzZ+ZjFxa4bnr+ccgocdBDsuWd2VfLMz3f/84n+3zc1uUCrpsYFjLW17qe/hN+ur3fPaSDgXpNr18KyZbB8uQs6ly+P3CUyP9+9NseNg402cnP75ee712pra3vGrrbWrfOzZqFLWZnrAjlyZHvQ1tde6+++615XtbXw9NMWXBljTBIswOpD2ubASnWJ9kRstZWbJ+vGG+HXv4bNN4ef/MT9PmhQz7fHtFu+3FUE++QTWLTILatWtX/Rr63t3LXLV1rq7mtpccUQmpujnyc/343JmzLFXQXfaivYYgv3hTXVmpvbMxmq7ot1ZaVbVq9u/1JeV9dxaWpqDwiLity6tWtdNqWuruOXalWXlQld/OIQoUtNjQuUVqxw5/Tl5Lgv8LGUlrZ/cT/4YPecbbWVCwj8gCo/P/XPXzbIy2sPWFJl/Xr3evcDr2XLXFbmm2/g669dt8ra2va/nYgLnsrL3d+8oaH9fyIYjP5/Ae7vUlbmujiOHAmjR7us2aabuv+D7bZz9/cGjz/uJpIfNMi9j2+zTaZbZIwxvZp1EexDXQQf++wxDrnnEF4/7XV22mCnzDWkogJ++1u44w735eOSS+Dcc+2KaE9panJfmJ54wo3V+eyz9vvGjHHB76hR7YFEaWn7eJjhw9t/HzLEfQkOFQy2BzJr1risQkOD+2L7wQfw5puu2IIfaIi4rIFfkS4nx53T78IV2p1r1Ci3fUWFW5YubR/7U1PjzrlsmVvWru368+KPN2po6Bj4FBW5L5aFhe2Zk9pa19b8/PaxSv7voUteXvvjGTGivYueH4jl5bn1w4e7paysPUPijz0ymeF/9kXrzqzqgu7aWvf6W7myPWhbs6b9tbl2rVu/dGn7a9Y/7rhx7u/vj4MbPRrGj3fLqFHuNeJ3nRwxwv1v9lRmTNXNcXXffa6b97bbwmOPuTYaY7KGdRHsnSzA6kMB1i0LbuGsx8+i4qcVjCkfk+nmuCIYv/qVK4Cx0UZwxRVufJaNh0iPL76A226Df/zDZVPKy10p/T32gN12c5mR0tL0t6O52QV1n3zSnjWrq2sfP1NT476QLl8eO1ASce0tK3PL4MHtgdjw4S5YEnFLSYkLbvyiDiUl7VmqoiIXPOV6CXs/M1Vf79b3ta5eJnNUXQD2zjtu+fjj9qxqVZULwBobo+8fCLgM2JZbuizmhhu6984JE1zwFd4lVNVl59580y3Ll7v/jREjOi/Nze0Z7Pffdxdgli5178dHHgl//3vPvD8YY7rEAqxO998OHAysVNWtI9y/BfAPYBJwiapek7bGxmABVh8KsC753yX88dU/0vCbBgI5WTR/zf/+Bxde6K6Wbr+96zZ46KF9t+tTT1B11dtefhnmzXPL4sXuy9JBB8Hpp8O0ae1BRbaqq2sPtpYtc5mlDTZwy8iR2d9+Y7qitdUFNV9/7V7zBQUuaCoocFnbjz5yy6JF8O23LnvmE3FB1+jRLmNcXe0CNz9jVlDg/mdWrerYVTWSAQNg331dNdgDD7Tqr8ZkMQuwOt2/O7AemBslwBoOjAV+CKzJVIBl3176kIoaN8lwVgVXAPvs4ypS3Xsv/OY3cNRR7gP9xBPhxz9243RMdM3NrhLbF1+4L16vvuoCqu++c/cPGuQyVGed5cZRjMmC7GWiiorau0wZ09fl5LRfQIhH1WV4v/nGzW/mZ4RXrmzvblpe7jLTO+4I3/te+0Wr9evddv7YwBUrXIC2+eZuGTHCKr0aY3olVZ0nIuNi3L8SWCkiB/VcqzqzDFYfymDt/c+9aWhp4NVTX810U6JraYFnnnFd2R55xAUPu+7qAq0jj3Rdu/qrJUvg+efhlVdc8ORXnlu2zI3p8Y0a5br++cvEidbt0hhjjOmD+mEG62ugMmTVHFWdE7bNOOCxSBmskG1+B6y3DJZJ2pLqJUwaNSnTzYgtEHBd16ZNc1dV5851wdbJJ7uqg8cd54KtSZP6xxXWjz92Y6Yefth18QM3hmj8eHeVeuutXUC1ySbty4Yb9o/nxhhjjDH9TWVfKHKR1gBLRKYC1wEB4DZVvTLsfvHuPxAIAier6tux9hWRq4FDgEbgC+AUVV3r3XcxcBrQAvxEVZ9O5+PLJqpKRXUF0zefnummJG7ECPjlL+EXv3BjiW67zVUevOUWV+L4qKPaxxz443FaW92Sk+MGZJeWuqxXto/VaW11Yyz88UZffeW6TM6f79p+wAFwzjmw994uqLKMlDHGGGNMr5S2b6UiEgBuBPYDKoC3ROQRVf04ZLNpwARvmQLcDEyJs++zwMWq2iwifwQuBi4SkYnAscBWwGjgORHZTFX7Th/AGKrqqqhvru/ZSYZTRaS9u9v118Pdd8Ott7ry7okaMcJV2xo7tn3ZaCOXCZo4MXMB2Icfusdz991uLEWoiRPhT39ykzMPH56Z9hljjDHGmJRK57fOHYHFqvolgIjcC0wHQgOs6bgqIArMF5GBIjIKGBdtX1V9JmT/+cCRIce6V1UbgK9EZLHXhtfT9QCzSdskw+UZmGQ4lQYOhLPPdsu6dW780dKlLuvT2to+l1Jzc/t8RevWuTFL33zjyg8/9pgrwe0rLoaddnJjvb7/fTfIe+ONO8/xlCpLlsA998Bdd7n2BAKw336ukuJGG7VPrjpmjHX1M8YYY4xJkIjcA+wJDBWRCmAWkAegqreIyEhgAVAOtIrIBcBEVa3uyXamM8AaAywJuV2By1LF22ZMgvsCnAr8O+RY8yMcqwMRmQnM9H6P9xh6jSXr3NO14YBeHmCFGjDALV2tMqjqikN8840b1/T6665wxOWXt08wGwi4IGuzzdqX733Pjf0Kn2smlro6+PTT9gpfftl0VRfUXX89HH20y7AZY4wxxqRQpHmhvAIPpwOrvM1+rapPhO23ITAXGAm04opJXNdT7e4uVZ0R5/7lQMa7c6UzwIoUvYSXLIy2Tdx9ReQSoBm4qwvnw6tEMgdcFcEI+/RKS6pdgNUruwimmojrcjd8OOywA8zw/herq11Ric8+67g8/7wLlMB1JdxmGxcc+ZP0jhzZ8fg1NS5Ldv/9bhJlP1sWCLhuf7Nnu2Idm2zSc4/ZGGOMMf3RHcANuGAp1F/iVNBrBn6uqm+LSBmwUESeDRvKY7opnQFWBRCaTtkAWJrgNvmx9hWRk3DR+j7aXmc+kfNlhacXP82f5/85pcf8YvUX5ObkMqLEMiVRlZe7wGmnnTqu9wtQvPMOvPkmvPEG/OtfcNNN7v7NN3fzdq1b55bly6Gx0VX3O+00F4RtuSVMmOAm+zTGGGOM6QHx5oWKsd8yYJn3e42IfILr+WUBVgqkM8B6C5ggIuOB73AFKI4L2+YR4FxvjNUUYJ2qLhP5f/buPM7qqv7j+OvNDMimgruCBiqaaIbmVqa5i6bilklpbmWYaLa5tCGaZS6lZmkupJZi7qGZS1lav1xARdwNcUNQQFzYYWY+vz/Od+QyzHJn5t75zvJ+Ph73MXO/33O+93O/XGA+c875HM1uqG9WXfAM4AsRsbDOtW6S9CtSkYshwBNle3etsLR6KR8tKe1U0LX7rM0Xh3yx/W0y3BF065bWRm20EYzIqjBWVaWE6+GH02PBgjQitfrqaUTrgAPgc59ztT8zMzMrp0pJkwqer7QvVANGS/oaaT3S9yLi/YYaZgnaNsDjrYrUPlbWjYYl7Q9cQiq1Pi4izpM0Cj5eiCbSsOZwUpn24yJiUkN9s+NTgVWA97KXeSwiRmXnfkRal1UFnBYRf2ssvs620bCZmZmZdR7FbDRcd+NdSeuSNusN4Fxg/Yg4voG+fYGHgfMi4o5Sxt4SkiZ1hn2wyppgtXdOsMzMzMysvWpJgtWMc92Be4D7I6K0a1daqLMkWJ7fZGZmZmbWSWRbHtU6BHiunjYCrgVebC/JVWfiBMvMzMzMrAPK9oV6FNhc0nRJJwAXSHpW0hRgd+A7WdsNJNWWa98ZOBrYQ9Lk7LF/Hu+hM/IUQU8RNDMzM7N2qJgpgp2JpwiamZmZmZnZCpxgmZmZmZmZlYgTLDMzMzMzsxJxgmVmZmZmZlYilXkHkKeamhok1eT08iJtAGel53tbPr635eH7Wj6+t+Xje1s+vrfl0RHva++8A2hjc/IOoBS6dBXBPHWWKintke9t+fjelofva/n43paP7235+N6Wh++rtRVPETQzMzMzMysRJ1hmZmZmZmYl4gQrP1flHUAn5ntbPr635eH7Wj6+t+Xje1s+vrfl4ftqbcJrsMzMzMzMzErEI1hmZmZmZmYl4gTLzMzMzMysRJxg5UDScEkvS5oq6cy84+moJG0o6Z+SXpT0vKRvZ8fPlvS2pMnZY/+8Y+2IJL0u6dnsHk7Kjq0h6UFJ/8u+9s87zo5G0uYFn83Jkj6SdJo/ty0jaZykWZKeKzjW4OdU0lnZv70vS9o3n6jbvwbu64WSXpI0RdKdkvplxwdJWlTw2b0yt8A7gAbubYN///2ZLV4D9/bPBff1dUmTs+P+3FrZeA1WG5NUAbwC7A1MByYCIyPihVwD64AkrQ+sHxFPSVoVeBI4GDgCmB8RF+UZX0cn6XVgu4iYU3DsAmBuRJyf/XKgf0SckVeMHV3278HbwI7Acfhz22ySdgXmAzdExFbZsXo/p5KGAuOBHYANgL8Dm0VEdU7ht1sN3Nd9gIciokrSLwGy+zoIuKe2nTWugXt7NvX8/fdntnnqu7d1zl8MfBgR5/hza+XkEay2twMwNSKmRcRS4GZgRM4xdUgRMTMinsq+nwe8CAzIN6pObwRwffb99aSE1lpuT+DViHgj70A6qoh4BJhb53BDn9MRwM0RsSQiXgOmkv5Ntjrqu68R8UBEVGVPHwMGtnlgnUADn9mG+DPbDI3dW0ki/QJ2fJsGZV2SE6y2NwB4q+D5dJwUtFr2m6htgMezQ6OzaSzjPI2txQJ4QNKTkk7Mjq0bETMhJbjAOrlF1zkcyYr/2ftzWxoNfU7972/pHA/8reD5YElPS3pY0i55BdXB1ff335/Z0tkFeDci/ldwzJ9bKwsnWG1P9RzzPM1WkNQXuB04LSI+Aq4ANgGGATOBi/OLrkPbOSK2BfYDTs6mXliJSOoBHATcmh3y57b8/O9vCUj6EVAF3JgdmglsFBHbAN8FbpK0Wl7xdVAN/f33Z7Z0RrLiL7T8ubWycYLV9qYDGxY8HwjMyCmWDk9Sd1JydWNE3AEQEe9GRHVE1ABX4+kULRIRM7Kvs4A7Sffx3WztW+0auFn5Rdjh7Qc8FRHvgj+3JdbQ59T//raSpGOAA4CvRraIO5u+9l72/ZPAq8Bm+UXZ8TTy99+f2RKQVAkcCvy59pg/t1ZOTrDa3kRgiKTB2W+wjwQm5BxTh5TNp74WeDEiflVwfP2CZocAz9Xta42T1CcrHIKkPsA+pPs4ATgma3YM8Jd8IuwUVvhtqj+3JdXQ53QCcKSkVSQNBoYAT+QQX4ckaThwBnBQRCwsOL52VrAFSRuT7uu0fKLsmBr5++/PbGnsBbwUEdNrD/hza+VUmXcAXU1WfWk0cD9QAYyLiOdzDquj2hk4Gni2tuwq8ENgpKRhpGkUrwPfzCO4Dm5d4M6Uw1IJ3BQR90maCNwi6QTgTeBLOcbYYUnqTaokWvjZvMCf2+aTNB7YDVhL0nRgDHA+9XxOI+J5SbcAL5CmuJ3samz1a+C+ngWsAjyY/dvwWESMAnYFzpFUBVQDoyKi2CIOXU4D93a3+v7++zPbPPXd24i4lpXXu4I/t1ZGLtNuZmZmZmZWIp4iaGZmZmZmViJOsMzMzMzMzErECZaZmZmZmVmJOMEyMzMzMzMrESdYZmZmZmZmJeIEy8zMzMzMrEScYJmZmZmZmZWIEywzMzMzM7MScYJlZmZmZmZWIk6wzMzMzMzMSsQJlpmZmZmZWYk4wTIzs5KQtIukl/OOw8zMLE+KiLxjMDMzMzMz6xQ8gmVmZmUnqTLvGMzMzNqCEywzM2sWSa9L+r6kKZI+lPRnST0l7SZpep12Z0iaAixwkmVmZl2BEywzM2uJI4DhwGBga+DYBtqNBL4I9IuIqrYJzczMLD9OsMzMrCUui4gZETEXuBsY1ki7tyJiUduFZmZmlh8nWGZm1hLvFHy/EOjbQLu32iAWMzOzdsMJlpmZlZNL1ZqZWZfiBMvMzMzMzKxEnGCZmZmZmZmViDcaNjMzMzMzKxGPYJmZmZmZmZWIEywzMzMzM7MScYJlZmZmZmZWIk6wzMzMzMzMSsQJlpmZmZmZWYlU5h1AniRFt27OMc3MzMys/ampqSEilHccbUXSfRExPO84WqtLJ1jdunWjuro67zDMzMzMzFYiaWHeMbSxtfIOoBQ8fGNmZmZmZu2epHGSZkl6roHzknSZpKmSpkjatuDc65KelTRZ0qRyxukEy8zMzMzMOoLrgMamEO4HDMkeJwJX1Dm/e0QMi4jtyhNe4gTLzMzMzMzavYh4BJjbSJMRwA2RPAb0k7R+20S3nBMsMzMzMzNrD9aSNKngcWIz+w8A3ip4Pj07BhDAA5KebMF1m6VLF7kwMzMzM+uoJI0DDgBmRcRWBcdPAUYDVcBfI+L0evp+B/g6KfF4FjguIha3SeANm9PK6Xv1VVyM7OvOETFD0jrAg5JeykbESs4jWGZmZmZmHdN11FmTJGl30lS5rSNiS+Ciup0kDQBOBbbLErMK4MiyR1t+04ENC54PBGYARETt11nAncAO5QrCI1hmZmZmVn5PPQVPPAFf+hKsuWbe0Xxs2vvTuPrJq6mO+rfu6VXZi7G7j23jqIoTEY9IGlTn8EnA+RGxJGszq4HulUAvScuA3mSJSAc3ARgt6WZgR+DDiJgpqQ/QLSLmZd/vA5xTriCcYJmZmZlZ+f3gB/DQQ/Dtb8Nhh8HXvw5f+AJUVOQa1nWTr+P8/zufXpW96j3fr2e/PBOsyjolxa+KiKua6LMZsIuk84DFwPcjYmJhg4h4W9JFwJvAIuCBiHiglIGXg6TxwG6ktVrTgTFAd4CIuBK4F9gfmAosBI7Luq4L3CkJUv5zU0TcV644nWCZmZmZWXlVVcHjj8Mhh8DAgfDHP8L48bDWWnDAAXDQQbDPPtCnT5uHtmDpAvp078P8H85v89cuQlUL1iRVAv2BnYDtgVskbRwRtWuRkNSfNI1wMPABcKukoyLiT6UJuzwiYmQT5wM4uZ7j04BPlyuuurwGy8zMzMzK69lnYcECOPxwuOwymDEDbr45JVV33QWHHgqbbAI33QTL84A2sahqEb261z961UFNB+7ISpU/AdQAa9VpsxfwWkTMjohlwB3A59o4zk7LCZaZmZmZldejj6avn8t+hu/VC778ZbjxRpg1Cx58EDbaCL76VdhrL3jppTYLbVHVoganB3ZQdwF7AEjaDOgBzKnT5k1gJ0m9lebN7Qm82JZBdmZOsMzMzMysvB59FNZbDz7xiZXPde+ekqpHH4UrrkjFMD79aXj66TYJbdGyjjuCla1JehTYXNJ0SScA44CNJT0H3AwcExEhaQNJ9wJExOPAbcBTpBLt3YCm1nZZkRRtPAzbnlRUVER1df0VY8zMzMysRDbZBIYNg9tvb7rtO+/A5pvD/vundVplNuLmEbzxwRtMHjW57K/VXJIWRkTbL0zLiaRJrdwHq13wCJaZmZmZlc+778K0afDZzxbXfr31UoXBW2+F6dPLGxtpBKt3995lfx3rOpxgmZmZmVn51K6/KjbBAjjllFTs4vLLyxNTgYXLFnbYKYLWPpU1wZI0XNLLkqZKOrOe85J0WXZ+iqRti+kr6ZTs3POSLig4flbW/mVJ+5bzvZmZmZlZER59NK2z+sxniu8zaFCqLHjVVan6YBl1wiIXlrOyJViSKoDfAvsBQ4GRkobWabYfMCR7nAhc0VRfSbuT6vZvHRFbAhdlx4cCRwJbAsOB32XXMTMzM7O8PPoobLst9OzZvH7f+Q68/z5cf3154sp05CIX1j6VcwRrB2BqREyLiKWkKiYj6rQZAdyQ1el/DOgnaf0m+p4EnB8RSwAiYlbBtW6OiCUR8RppB+cdyvj+zMzMzKwxS5fCxInLy7M3x2c/CzvsAJdcAjU1JQ+tlkewrNTKmWANAN4qeD49O1ZMm8b6bgbsIulxSQ9L2r4Zr4ekEyVNyqqUNPMtmZmZmVnRnnkGFi9u3vqrWlIaxfrf/+Dee0sfW2bRMidYVlrlTLBUz7G6GU1DbRrrWwn0B3YCfgDckm2QVszrERFXRcR2EbFd6mZmZmZmZfHf/6avLUmwAA47DAYOhIsvLl1MdSyq8hRBK61yJljTgQ0Lng8EZhTZprG+04E7smmFTwA1wFpFvp6ZmZmZtZVHH4UNN0xJUkt0755Gsf71L3j88ZKGVssjWFZq5UywJgJDJA2W1INUgGJCnTYTgK9l1QR3Aj6MiJlN9L0L2ANA0mZAD2BOdv5ISatIGkwqnPFEGd+fmZmZmTXm0Udbtv6q0IknQv/+8ItflCamAlU1VSyrWeYRLCupsiVYEVEFjAbuB14EbomI5yWNkjQqa3YvMI1UkOJq4FuN9c36jAM2lvQcqfjFMdlo1vPALcALwH3AyRFRXa73Z2ZmZmaNmDoV3nyz5dMDa/Xtm/bF+stf4Pnnm27fDIuWLQLwRsNWUurKhR4qKiqiuto5mJmZmVnJnXoqXHklvP46bLBB66713nuw0UZw+OElLds+a8Es1r1oXS7f73JO3uHkkl23VCQtjIg+ecfRVrIidNvlHUdrlXWjYTMzMzPrgubOhWuvha98pfXJFcCaa6apgjfdBG+80frrZWpHsDxF0ErJCZaZmZmZldaVV8LChfC975Xumt/7XirdftFFJbvkoqoswXKRiw5B0jhJs7KlQvWdl6TLJE2VNEXStgXnhkt6OTt3ZjnjdIJlZmZmZqWzZAn85jewzz7wqU+V7roDB8LRR8M118CcOSW5pEewOpzrgOGNnN+PVOhuCHAicAWApArgt9n5ocBISUPLFaQTLDMzMzMrnZtugnfege9/v/TXHj06bVz8l7+U5HIewepYIuIRYG4jTUYAN2QF8B4D+klaH9gBmBoR0yJiKalQ3ohyxekEy8zMzMxKIyJtCrz11rDXXqW//rBh8IlPwIS6O/+0TEcfwWpoypykU7LpcM9LuqCBvv0k3SbpJUkvSmpluceSWEvSpILHic3sPwB4q+D59OxYQ8fLorJcFzYzMzOzLqS6Gm68MZVSv/76tF6q1CQ46KA0TXDhQujduvLqnWAE6zrgcuCG2gOSdieNzmwdEUskrdNA30uB+yLi8Gzf2fZQq35OK6sI1vehi0aOl4VHsMzMzMys5R5+OFX422ADOOYY2HRTOPLI8r3eQQfBokXw97+3+lIdfR+sBqbMnQScHxFLsjaz6vaTtBqwK3Bt1mZpRHxQ3mjbxHRgw4LnA4EZjRwvCydYZmZmZtYyDz0Eu+0GN98Mu+8O48fDU09Bjx7le80vfAFWX70k67AWLlsItOspgpUtmDK3GbCLpMclPSxp+3rabAzMBv4g6WlJ10jqDPttTQC+llUT3An4MCJmAhOBIZIGZ6N1R2Zty8JTBM3MzMys+ebNg+OPhyFDUlLVt2/bvG737rDffnD33WlaYkVFiy/VAaYIVrVgylwl0B/YCdgeuEXSxhERddpsC5wSEY9LuhQ4E/hJKYIuF0njgd1Ia7WmA2OA7gARcSVwL7A/MBVYCByXnauSNBq4H6gAxkXE8+WK0wmWmZmZmTXfGWfAm2/Cv//ddslVrREj0qjZ44/D5z7X4st09CIXDZgO3JElVE9IqgHWIo1YFbaZHhGPZ89vIyVY7VpEjGzifAAnN3DuXlICVnaeImhmZmZmzfPQQ3DFFXDaabDzzm3/+sOHQ2Vlq6sJdoARrJa4C9gDQNJmQA9ghY3DIuId4C1Jm2eH9gReaMMYOzUnWGZmZmZWvMKpgT/7WT4x9OuX1n61ch3WomWLEKJHRRnXjJVRNmXuUWBzSdMlnQCMAzbOSrffDBwTESFpA0mFIzinADdKmgIMA37exuF3Wp4iaGZmZmbFu/DC5VMDW1kmvVUOOghOPRVeeQU226xFl1hUtYhe3XuhcpSUbwONTJk7qp62M0jrk2qfTwZaUxLdGuARLDMzMzMrTgTccAPsu28+UwMLHXRQ+nrXXS2+xKJlizrb9EBrB5xgmZmZmVlxHn0U3ngDvvKVvCOBT3wiFbg47zx45pkWXaJ2BMuslMqaYEkaLullSVMlrVSZJKtRf1l2foqkbZvqK+lsSW9Lmpw99s+OD5K0qOD4leV8b2ZmZmZdzk03Qa9ecPDBeUeS3HwzrLpqKtv+xhvN7r5w2cIOu8mwtV9lS7AkVQC/BfYDhgIjJQ2t02w/YEj2OBG4osi+v46IYdmjcLHeqwXHR5XljZmZmZl1RcuWwS23pKl5q66adzTJhhvCfffBokVp2uJ77zWr+6IqTxG00ivnCNYOwNSImBYRS0lVTEbUaTMCuCGSx4B+ktYvsq+ZmZmZtZW//x1mz24f0wMLbbVVqib4+utw4IEpESzSomWeImilV84EawDwVsHz6dmxYto01Xd0NqVwnKT+BccHS3pa0sOSdqkvKEknSpokadKKG1qbmZmZWYNuugn69097ULU3u+4KV1+d1og1o3S7R7CsHMqZYNVX77JuRtNQm8b6XgFsQqrXPxO4ODs+E9goIrYBvgvcJGm1lS4ScVVEbBcR23XUkpxmZmZmbWrhQrjzTjj8cOjRTveM+spXYKON4Mril+F7BMvKoZwJ1nRgw4LnA4EZRbZpsG9EvBsR1RFRA1xNmk5IRCyJiPey758EXgVatimCmZmZmS13992wYEH7mx5YqKICTjwR/vEP+N//iuriESwrh3ImWBOBIZIGS+oBHAlMqNNmAvC1rJrgTsCHETGzsb7ZGq1ahwDPZcfXzopjIGljUuGMaeV7e2ZmZmZdxE03wYABaSpee3b88VBZCVddVVRzj2BZOZQtwYqIKmA0cD/wInBLRDwvaZSk2gp/95KSoKmk0ahvNdY363OBpGclTQF2B76THd8VmCLpGeA2YFREzC3X+zMzMzPrEv77X/jrX9PoVbd2voXq+uvDiBHwhz/A4sVNNvcIlpWDunKhh4qKiqiurs47DDMzM7P2adYs2GabtPfVpEnQr1/eETXtwQdhn33gxhubnNLY7/x+HDvsWC4ZfknbxNZMkhZGRJ+842grWRG67fKOo7Xa+a8hzMzMzCwX1dUwciTMnQu33dYxkiuAPfeETTYpqtjFwmULPYJlJecEy8zMzMxW9tOfwkMPwRVXwLBheUdTvG7d4JvfhH//G154ocFm1TXVLKtZ5jVYVnJOsMzMzMxsRffeCz//OXzjG3DssXlH03zHHpvKyf/+9w02WVS1CMAjWB2MpOGSXpY0VdKZ9ZzvL+nObM/cJyRtVXDu9ayWw2RJk8oVoxMsMzMzM1tu9mw47jjYemu47LK8o2mZtdeGww6D669Pe3jVY9GyLMHyCFaHkVUM/y2wHzAUGClpaJ1mPwQmR8TWwNeAS+uc3z0ihpVzrZcTLDMzMzNLItJeUh98kIpE9OyZd0Qt981vwocfwi231Hu6M4xgSRonaZak5+ocPyUb5Xle0gWN9K+Q9LSke8ofbUnsAEyNiGkRsRS4GRhRp81Q4B8AEfESMEjSum0ZpBMsMzMzM0uuuw7uugt+8QvYaqumWrdvu+4Kn/xkg8UuOskI1nXA8MIDknYnJR1bR8SWwEWN9P82aUuk9mItSZMKHifWOT8AeKvg+fTsWKFngEMBJO0AfAIYmJ0L4AFJT9Zz7ZJxgmVmZmZmMG0anHoq7L47nHZa3tG0ngSjRsHjj8PkySud7gwjWBHxCFB339eTgPMjYknWZlZ9fSUNBL4IXFPWIJtnTkRsV/Cou2O06ulTd8+p84H+kiYDpwBPA1XZuZ0jYlvSFMOTJZVl52wnWGZmZmZd3cKFcNRRqQLfdde1/w2Fi/W1r6VpjvUUu+gkI1j12QzYRdLjkh6WtH0D7S4BTgdq2iyy1psObFjwfCAwo7BBRHwUEcdFxDDSGqy1gdeyczOyr7OAO0lTDkuuk/ztMTMzM7MWWbQIRoyAxx6Da6+FjTbKO6LS6d8fvvxl+NOfYN68FU4tXJaKX/Tu3juPyIpV2cSUuXr7AP2BnYAfALdIWmHkR9IBwKyIeLL0IZfVRGCIpMGSegBHAhMKG0jql50D+DrwSER8JKmPpFWzNn2AfYAV1q6VihMsMzMzs65q8WI4+GD4xz/SyNXhh+cdUemNGgXz58P48Ssc7iBTBKuamDJXn+nAHZE8QRqhWqtOm52BgyS9TioUsYekP5U08jKIiCpgNHA/ae3YLRHxvKRRkkZlzbYAnpf0Emkq4Lez4+sC/5H0DPAE8NeIuK8ccVaW46JmZmZm1s4tXgyHHAIPPgjjxqXpdJ3RjjumkvNXXpn29coGczrxFMG7gD2Af0naDOgBzClsEBFnAWcBSNoN+H5EHNWmUbZQRNwL3Fvn2JUF3z8KDKmn3zTg02UPEI9gmZmZmXU9S5akfaLuuw+uuaZjbiZcLAm+9S14+uk0UpfpICNYjZI0HngU2FzSdEknAOOAjbPS7TcDx0RESNpA0r2NXc9KQxF1C290HRUVFVFdXZ13GGZmZmZtZ8mSNBXwnnvgqqvSqE5nt2QJDBkCG2wAjz4KEr+f9HtG/XUUb3/3bTZYdYO8I6yXpIUR0SfvONqKpEnl3AC4rXgEy8zMzKyrWLoUjjgiJVe1U+a6glVWgZ/+NJVs/+tfgc4xgmXtkxMsMzMzs66gqgpGjoQJE+C3v4VvfjPviNrWMcfAJpvAT34CNTWdeQ2W5aysCZak4ZJeljRV0pn1nJeky7LzUyRt21RfSWdLelvS5Oyxf8G5s7L2L0vat5zvzczMzKzDiICTToI77oBLLklrkrqa7t3h7LPTpsN33smiqkUIsUrFKnlHZp1M2RIsSRXAb0nlEYcCIyUNrdNsP1KVjyHAicAVRfb9dUQMyx73Zn2GkmrhbwkMB36XXcfMzMysa/vxj1Mxix//GL797abbd1YjR8IWW8BPf8rixQvo1b0XdbaIMmu1co5g7QBMjYhpEbGUVMVkRJ02I4Absjr9jwH9JK1fZN+6RgA3R8SSiHgNmEqZdmc2MzMz6zAuuwx+/nM48UQ455y8o8lXRUW6By+8wKcemOz1V1YW5UywBgBvFTyfnh0rpk1TfUdnUwrHSerfjNczMzMz6zr++c80YnXIIfC73328B1SXduih8NnPcthV/2HIvO55R2OdUDkTrPr+BtetCd9Qm8b6XgFsAgwDZgIXN+P1kHSipElZGch6upiZmZl1EueeCwMGwI03ptEbg27d0v0g+N2fPoBly/KOyDqZciZY04ENC54PBGYU2abBvhHxbkRUR0QNcDXLpwEW83pExFURsV1EbOc5t2ZmZtZpTZyYRrC+8x3o5alwKxg8mN9+/dNs8/riVPjCrITKmWBNBIZIGiypB6kAxYQ6bSYAX8uqCe4EfBgRMxvrm63RqnUI8FzBtY6UtIqkwaTCGU+U682ZmZmZtWsXXgirr9519rpqpr9vvwYTPr82/OIX8NBDeYdjnUhluS4cEVWSRgP3AxXAuIh4XtKo7PyVwL3A/qSCFAuB4xrrm136AknDSNP/Xge+mfV5XtItwAtAFXByRFSX6/2ZmZmZtVtTp8Ltt8MZZ8Bqq+UdTbu0aNkirjj6kxw0ZzZ89atpE+KNNso7LOsE1JXXIVVUVER1tXMwMzMz62ROOgnGjYM33oD11ss7mnZp+6u3Z+3ea3PvsAvhc5+DDTeE//wH+vXLO7SPSVoYEX3yjqOtZDUStss7jtYq60bDZmZmZtbG3n0X/vAHOOYYJ1eNWLRsEb2694Itt4Q774RXXkkVBpcsyTs06+CcYJmZmZl1Jr/5DSxdCt//ft6RtGsLly2kd/fe6ckee6QRv3/+E044AbrwDC9rvbKtwTIzMzOzNvbCC3DxxXD44bDZZnlH064tqlq04kbDRx2VplT++MewzjrpPrritLWAEywzMzOzzmDJEvjKV2DVVeGyy/KOpt1btKxOggXwwx+mKZa//jV07w7nn+8kq52RNBy4lFQI75qIOL/O+f7AONK+uYuB4yPiuWL61nmhQ+s5+iHwLBGzGovRCZaZmZlZZ3DWWfDMM3D33V57VYRFVdkarEISXHopVFXBBRekJOvcc51ktROSKoDfAnuT9sCdKGlCRLxQ0OyHwOSIOETSJ7P2exbZt9AJwGeBf2bPdwMeAzZDOoeIPzYUp9dgmZmZmXV0DzyQRl2+9S044IC8o2n3qmuqWVq9dOURLEjJ1OWXw9e/DuedB+ec0/YBFknSOEmzJD1X5/gpkl6W9LykC+rpt6Gkf0p6MWvz7baLulV2AKZGxLSIWArcDIyo02Yo8A+AiHgJGCRp3SL7FqoBtiDiMCIOy667BNgROKOxIJ1gmZmZmXVks2alioFDh8JFF+UdTYewuGoxwMojWLW6dYPf/x6OPTZVZPzggzaLrZmuA4YXHpC0Oylx2DoitgTq+1BUAd+LiC2AnYCTJQ0tc6zFWEvSpILHiXXODwDeKng+PTtW6BngUABJOwCfAAYW2bfQICLeLXg+C9iMiLnAssbehKcImpmZmXVUy5bBEUekBOC++6BXAwmDrWBR1SKA+kewanXrBtdcA3PmtKu9sQpFxCOSBtU5fBJwfkQsydqstF4oImYCM7Pv50l6kZRsNDRdrq3MaWIfrPrmatYt+Xg+cKmkycCzwNOkhLKYvoX+jXQPcGv2/DDgEaQ+wAeN9HOCZWZmZtZhnX46PPww3HADfPrTeUfTYSxaliVYDY1g1aqogHXXbYOIGlQpaVLB86si4qom+mwG7CLpPFKRh+9HxMSGGmcJ2jbA460Ntg1MBzYseD4QmFHYICI+Ao4DkCTgtezRu6m+dZxMSqp2JiVnNwC3ExHA7o0F6QTLzMzMrCP605/gkkvg1FPh6KPzjqZDKWoEq32oamJEpz6VQH/S1L/tgVskbRyx8uZekvoCtwOnZYlJezcRGCJpMPA2cCTwlcIGkvoBC7N1Vl8HHomIjyQ12XcF6X7dlj2axWuwzMzMzDqap5+Gb3wDdt3V665aYOGyhQDLNxruXKYDd0TyBKlYw1p1G0nqTkquboyIO9o4xhaJiCpgNHA/8CJwS0Q8L2mUpFFZsy2A5yW9BOwHfLuxvg2+mHQo0v+QPkT6CGkeUlFJqEewzMzMzDqSCDj+eFhzTbjlllRK3Jql6CmCHdNdwB7AvyRtBvQA5hQ2yKbOXQu8GBG/avMIWyEi7gXurXPsyoLvHwWGFNu3ERcABxLxYnNjLHoES2PVS2O1eXNfwMzMzMxK6B//gMmTU/nwfNcHdVgdaIpgoySNBx4FNpc0XdIJpE12N85Kt98MHBMRIWkDSbXJxc7A0cAekiZnj/1zeRPt17stSa6gyBEsjdWBpBKPPYDBGqthwDkxJg5qyYuamZmZWQtddFFKrL761bwj6bA6ywhWRIxs4NRR9bSdAeyfff8f6q+qZ8tNQvozaURwycdHi5hOWewI1tmkzbk+AIgxMRkY1JwIzczMzKyVnnsO7r8fTjkFVlkl72g6rM4ygmVltRqwENgHODB7FLWLd7FrsKpiTHyosU50zczMzHJz8cXQuzeMGtV0W2tQZxnBsjKKOK6lXYsdwXpOY/UVoEJjNURj9Rvgv011kjRc0suSpko6s57zknRZdn6KpG2b0ff7kkLSWtnzQZIWFcwjvbJuHzMzM7MOa8YMuPHG5QUurMU8gmUNkk7Pvv4G6bKVHkUodgTrFOBHpPmHN5HKG/6s8dhUAfwW2JtULnKipAkRUbhD9H6kKh9DgB2BK4Adm+oracPs3Jt1XvbViBhW5HsyMzMz6zguvxyqq+G00/KOpMPzCJY1orawxaRGWzWiyQRLY1UBTIgxsRcpySrWDsDUiJgGIOlmYARQmGCNAG7INj57TFI/SeuT1nc11vfXwOnAX5oRj5mZmVnHNH8+XHEFHHoobLJJ3tF0eJ18HyxrjYi7s6/Xt/QSTSZYMSaqNVYLNVarx5j4sBnXHgC8VfB8OmmUqqk2AxrrK+kg4O2IeCaV8F/BYElPAx8BP46If9dtIOlE4MTs+2a8HTMzM7OcXHEFfPABfP/7eUfSKSyqWoQQq1S4UIjVId0NRIPno+kq6sVOEVwMPKuxehBY8PH1x8SpjYVXX0hFtqn3uKTepFG0feo5PxPYKCLek/QZ4C5JW0bECjsuR8RVwFUAFRUVDd88MzMzs/ZgwQK48ELYZx/Yse7vqq0lFi1bRM/Knv5lu9XnouzrocB6wJ+y5yOB14u5QLEJ1l+zR3NMBzYseD4QmFFkmx4NHN8EGAzUjl4NBJ6StENEvENWoz4inpT0KrAZrZg/aWZmZpa7K66A2bNhzJi8I+k0FlUt8vorq1/EwwBI5xKxa8GZu5EeKeYSRSVYMaZFcxAnAkMkDQbeBo4EvlKnzQRgdLbGakfgw4iYKWl2fX0j4nlgndrOkl4HtouIOZLWBuZGRLWkjUmFM6a1IG4zMzOz9qF29GrvveFzn8s7mk5j0bJFriBoTVkbaWOymhCkvGTtYjoWlWBprF6jnrmIMSY2bqhPRFRJGk2qOFgBjIuI5yWNys5fCdxL2lF6Kmkjr+Ma69tEmLsC50iqAqqBURExt5j3Z2ZmZtYuXXklzJrl0asS8wiWFeE7wL+QagdsBgHfLKajUgG/JhqNVeFmCz2BLwFrxJj4afPibF8qKiqiuro67zDMzMzMVrZwIQweDFtvDQ8+mHc0ncohfz6EV+e+ypSTpuQdSqMkLYyIPnnH0VYkTYqI7fKO42PSKsAns2cvEbGkmG7FThF8r86hSzRW/wE6dIJlZmZm1m79/vcevSqTRcs8gtVcEjsDZwOfIOUQAiKCBme0dQJDgM1JA0yfRoKIG5rqVOwUwW0LnnYDtgNWbUmUZmZmZtaE6mq47DLYdVf4/OfzjqbTWVTlNVgtcC1p2tyTpOU4nZs0BtgNGEpa1rQf8B+gNAkWcHHB91WkEoVHNCdGMzMzMyvSAw/A66/DL3+ZdySd0sJlC1m7d1H1Cmy5DyP4W95BtKHDgU8DTxNxHNK6wDXFdCx2iuDurQjOzMzMzJrj97+HddaBgw/OO5JOyVMEW+SfEhcCd5BtjQQQwVNtGYSk4cClpEJ410TE+XXOr07au2ojUq5zUUT8ITv3OjCPNAJX1cR6r0VE1CBVIa0GzILipkMWO0Xw28AfsoCuBrYFzowx8UAx/c3MzMysSNOnw913w+mnQ48eeUfTKXWWKYKSxgEHALMiYquC46cAo0kzz/4aEafX07fRRKUetbtcFyYlAezR8nfQPJIqgN8Ce5P2050oaUJEvFDQ7GTghYg4MNvG6WVJN0bE0uz87hExp4iXm4TUj5T7PAnMB54oJs5ipwgeH2PiUo3VvqR9qI4jJVxOsMzMzMxK6dproaYGvvGNvCPptDrRPljXAZdTsC5I0u7ACGDriFgiaZ26nYpMVFYQQXuY0bYDMDWyvamyvXRHAIVxB7CqJAF9gbmkRLN5Ir6VfXcl0n3AakQUVXay2ARL2df9gT/EmHhGY6XGOljDTjsNJk/OOwozMzNrbyqiivGPX8Pr/ffh9OM7c3G20giC6prm/+w8a/ot/K3vuuz2q6bbDhsGl1zS7JdoExHxiKRBdQ6fBJwfWUnxiJhVT9diEhXSOY6K4E8S360/Boq4i0VbS9KkgudXRcRVBc8HAG8VPJ/O8pG1WpcDE4AZpKJ8X46ImtpwgQckBfD7OtdeUUrQvgpsTMQ5SBsh7UBEk6NYxSZYT2qsHgAGA2dprFYFaproY2ZmZmbNsON797LOkulctulleYfSIUx59xk+WPRBi/pWqKK0wZRHZRMJR302A3aRdB6wGPh+REys06aYRKVW7T5cbVFBfE4T66LqG+Cpu6nvvsBk0tTFTYAHJf07Ij4Cdo6IGdmo3oOSXoqIRxp4rd+R8p09gHNIS6VuB7Zv6k0Um2CdAAwDpsWYWJhtPHxckX2tjvb6WxAzMzPL2Rd/D3PX52dPHwDd8w6m/Vvnwn3YZa1PcvjQw5vVr5u6cdgWm7N++990qKlCDPWpBPoDO5GSgVskbRwRhYlIMYlKOhj8Pvs6tplxlMN0YMOC5wNJI1WFjiON4AUwVdJrpM2Cn4iIGZBG9STdSRrJayjB2pGIbZGeJnV6H6moRZHFVhGs0VgNBL6isQJ4OMbE3cX0NTMzM7MivPEG/O1v8OMfQ3dnV02prqlmzsI57DZoN07d8dS8w2lPpgN3ZAnGE5JqgLWA2XXaNJWorECiJ2nQZUvSxrsARHB8ieIuxkRgiKTBwNvAkcBX6rR5E9gT+LdSafXNgWmS+gDdImJe9v0+pJGphiwjrVVLiWcqmFHUDL5uxTTSWJ0PfJs0L/MF4FSN1S+K6WtmZmZmRbjhBoiA49vy59WOa+6iuQTh/axWdhdZZT9JmwE9gLpV8z5OVJRGZY4krVtqzB+B9UhT8B4mJWXzShd20yKiilQd8X7gReCWiHhe0ihJo7Jm5wKfk/Qs8A/gjKxq4LrAfyQ9Q6oG+NeIuK+Rl7sMuBNYhzTd8j9AUflPsVME9weGxZi0QExjdT3wNHBWkf3NzMzMrCE1NXDddbD77jBoUN7RdAizF6YBmbX7dN0ES9J4YDdScYjpwBhgHDBO0nPAUuCYiAhJG5DKse8fEVWSahOVCmBcRDzfxMttGsGXJEZEcL3ETVn/NhUR9wL31jl2ZcH3M0ijU3X7TSNtHFzsC92I9CRpNEzAwaTRsSYVm2AB9COVOQRYvRn9zMzMzKwx//kPTJsGZ5+ddyQdxuwFWYLVhUewImJkA6eOqqftDNKgSe3zlRKVJizLvn4gsRXwDjCoGf07DmkAsD4whYiXSEUxTgOOBTZoqnuxCdYvgKc1Vv8kZXC74tErMzMzs9L4wx9g1VXhsMPyjqTD8AhWm7tKoj/wY9J0wr7AT/INqQyk04AfAVOBVZAuBX5F2mvsM8VcotgiF+M1Vv9ieVnCM2JMvNPceM3MzMysjvnz4dZbYeRI6N0772g6DI9gtbnVWV5F/LfZ1yqJYRFMzieksjgR2JyIuUgbkRKtXYl4rNgLFFXkIvNZ0hzPL2TfN0nScEkvS5oq6cx6zkvSZdn5KZK2bUbf70sKSWsVHDsra/+ypH2b8d7MzMzM8nHbbbBgARx7bN6RdCi1I1hr9V6riZZWIp8BRpH20NoA+AYpN+vjk2gAAH0aSURBVLha4vQc4yq1xUSkZVERbwKvNCe5giJHsDRWvwM2BcZnh76psdorxsTJDfZJZQ1/C+xNKgU5UdKEiCjcIXo/YEj22BG4Atixqb6SNszOvVnwekNJFVC2JP2h/13SZhFRXcx7NDMzM8vFH/4Am20Gn/tc3pF0KLMXzKZfz350r3BJ+zayJrBtBPMBJMYAt5GWDj0JXJBjbKU0EKlwp+91Vnge0eSeAMWuwfoCsFWMSRuUZVUEn22izw7A1KxiB5JuBkaQyrzXGgHckNXpf0xSP0nrkxbMNdb318DpwF/qXOvmiFgCvCZpahbDo0W+RzMzM7O29eqr8Mgj8POfg+rb+9UaMnvhbE8PbFsbkaoS1loGfCKCRRJLcoqpHH5Q5/mTzb1AsQnWy6Sb+kb2fENgShN9BgBvFTyfThqlaqrNgMb6SjoIeDsintGK/xANAB6r02dA3aAknUiaW4n8D5mZmZnl6brroFs3OProvCPpcGYvnO0CF23rJuAx6eMBjgOB8RJ9WHEApWOLuL61lyg2wVoTeFFj9UT2fHvgMY3VBIAYEwfV06e+7CWKbFPvcUm9SVU9VqptX+TrERFXAVcBVFRUrHTezMzMrE0sXAhXXglf/CIMHJh3NB3O7AWz2bj/xnmH0WVEcK7EvcDnST93j4pgUnb6q/lF1v4Um2D9tAXXnk4a6ao1EJhRZJseDRzfBBgM1I5eDQSekrRDka9nZmZm1j5cdx3MmQM/qDsjyYoxe+FsdhxQd3KUlVMET9KCKXNdTbEJ1uwYs0JxCjRWu8WY+FcjfSYCQyQNBt4mFaD4Sp02E4DR2RqrHYEPI2KmpNn19c12mF7n4xik14HtImKOpAnATZJ+RSpyMQR4AjMzM7P2proafvUr2HFH+Pzn846mw4kI5iyc4ymC1i4Vm2DdorG6AbgQ6EmqErIdjZRrj4gqSaOB+4EKYFxEPC9pVHb+StLu0fuT6ssvJKut31DfxgLMrn0LaQ5oFXCyKwiamZlZu3TnnanAxS9/6eIWLfDB4g+oqqlykQsrPekbwL+I+F9WsGEccBjwOnAsEU81dYliE6wdgV8C/wVWBW4Edm6qU0TcS0qiCo9dWfB9APWWeq+vbz1tBtV5fh5wXlNxmZmZmeUmAi68EDbdFA4+OO9oOqTaPbA8gmVl8G3guuz7kcDWpCVK2wCXArs0dYFiNxpeBiwCepFGsF6LMVHTzGDNzMzM7N//hieegO9+Fyoq8o6mQ5q9IEuwPIJlpVdFxLLs+wOAG4h4j4i/A32KuUCxCdZEUoK1HalyyEiN1W3NjdbMzMysy7vwQlhrLTj22Lwj6bA8gmVlVIO0PlJPYE/g7wXnehVzgWITrG8A/wN+GGPiHeAUYHIzAjUzMzOzV16Be+6B0aOhV1E/q1k9PIJlZfRTYBJpzdUEautASF8AphVzgWITrOOAnUjzEAHmASOaEaiZmZmZXXklVFbCN7+ZdyQdmkewrGwi7gE+AWxBxDcKzkwCfl7MJYpNsHaMMXEysBggxsT7QPdmhGpmZmbWtS1alPa+OuwwWG+9vKPp0GYvmE3fHn3pWdkz71ByJWmcpFmSnis4draktyVNzh77N9D3O5Kel/ScpPFKU+LaPUnDJb0saaqkM+s5v7qkuyU9k72/44rt+7GIKiLer3NsATC+mBiLLnKhsaoAAkBjtXbt92ZmZmZWhD//Gd5/H046Ke9IOrzZC2d7emByHTC8nuO/johh2WOlqtySBgCnkvaT3Yq0LdKRZY20BCRVAL8F9gOGAiMlDa3T7GTghYj4NLAbcLGkHkX2bTKEYhoVm2BdBtwJrKOxOg/4D0UOkZmZmZkZ8LvfwdChsOuueUfS4c1eONvTA4GIeASY28LulUAvSZVAb2BGyQIrnx2AqRExLSKWAjez8rKlAFZV2sOqL+n+VBXZtylFDTAVtQ9WjIkbNVZPkippCDg4xsSLzQzIzMzMrGt68kmYOBEuu8wbC5fA7AWz2WDVDfIOoy1USppU8PyqiLiqiH6jJX2NtG7oe1FnultEvC3pIuBNUqXwByLigZJF3XJrNfF+BwBvFTyfTtqvt9DlwARSwrgq8OWIqMlG7ZrqC9Ld1J9ICVizmDdR7EbDxJh4CXip2PZmZmZmlrniCujdG772tbwj6RRmL5zNp9f7dN5htIWqiNiumX2uAM4lJQnnAhcDxxc2kNSfNHozGPgAuFXSURHxp1ZH3Dpzmni/9f12om4ytC+p2vkewCbAg5L+XWRfgIsaef3Gzn2s6ATLzMzMzFrg/ffhppvgqKNg9dXzjqbDiwhmL/AarIZExLu130u6GrinnmZ7Aa9FxOys3R3A54C8E6ymTAc2LHg+kJWnNh4HnB8RAUyV9BrwySL7QsTDH38vrZ0dm92cIItdg2VmZmZmLTFuXKog6OIWJTF/6XyWVC9xgtUASesXPD0EeK6eZm8CO0nqna1V2hPoCMt/JgJDJA2W1INUmGNCnTZvkt4PktYFNiftX1VM30QagzSHNHvvFaTZSD8tNkgnWGZmZmblMns2/OxnsNdesM02eUfTKXgPrOUkjQceBTaXNF3SCcAFkp6VNAXYHfhO1nYDSfcCRMTjwG3AU8CzpJygmLVduYqIKmA0cD8pIbwlIp6XNErSqKzZucDnJD0L/AM4IyLmNNR3pReRvgN8HtieiDWJ6E9aq7Vzdq5JSqNnXVNFRUVUV1fnHYaZmZl1VieeCH/4AzzzTKogaK32+PTH2enanbhn5D18cbMv5h1OWUlaGBF98o6jrUia1II1Z6UO4mlgbyLm1Dm+NvAAEU3+psQjWGZmZmblMGkSXHMNnHqqk6sSmrVgFuARLCub7islV1C7Dqt7MRdwgmVmZmZWajU1MHo0rLMOjBmTdzSdSu0UwXX6rJNzJNZJLW3huY+VNcGSNFzSy5KmSjqznvOSdFl2foqkbZvqK+ncrO1kSQ9I2iA7PkjSouz4ZElXlvO9mZmZmTXo+uvh8cfhwgthtdXyjqZTmb0gW4PlIhdWHp9G+qiexzzgU8VcoGxl2iVVAL8F9iaVRZwoaUJEvFDQbD9gSPbYkVS3f8cm+l4YET/JXuNU4KdA7aK2VyNiWLnek5mZmVmT5s2DM8+Ez30ulWa3kpq9cDa9KnvRp0eXWZpkbSmiorWXKOc+WDsAUyNiGoCkm0kbmhUmWCOAG7I69Y9J6peVlhzUUN+I+Kigfx/q3yDMzMzMLB+XXAKzZsHdd4Pq29vUWmP2wtlef2XtWjkTrAHAWwXPp5NGqZpqM6CpvpLOA74GfEgqP1lrsFLlj4+AH0fEv+sGJelE4MTs++a9IzMzM8tHdTW8+GKadrdwIey4IwwbBj165B3Zit57Dy66CA45BHbYIe9oOiVvMmztXTkTrPqyl7qjTQ21abRvRPwI+JGks0j17McAM4GNIuI9SZ8B7pK0ZZ0RLyLiKrI6/xUVFR79MjMza08WL07rl/75z/T94sVpyt2zz6avhVZZBbbbDvbZBw48MCVcef/y9Je/THGee26+cXRiHsGy9q6cRS6mAxsWPB8IzCiyTTF9AW4CDgOIiCUR8V72/ZPAq8BmrYjfzMzM2sqCBfCrX8HGG8OoUfDYY/D66/DBB2mU6uijU+L10kvw1ltw662pSt+yZXD22bDttrDRRvDtb8OUKfm8hxkz4De/SbFuuWU+MXQBHsGy9q6cI1gTgSGSBgNvA0cCX6nTZgIwOltjtSPwYUTMlDS7ob6ShkTE/7L+BwEvZcfXBuZGRLWkjUmFM6aV8f2ZmZlZKUybBrvskhKU3XeHP/4R9tij8dGoww9PD4B334V774UJE+DKK+Gyy9LI1gknpDZrrdU27+Pcc9NUxrPPbpvX66JmL3SCZe1b2UawIqKKNH3vfuBF4JaIeF7SKEm1Vf/uJSVBU4GrgW811jfrc76k5yRNAfYBvp0d3xWYIukZ4DZgVETMLdf7MzMzsxL48MM0vW/RIvj3v+Ghh2DPPZs31W/ddeG44+DOO1OSdumlsGQJnHQSrLce7LUXXHEFvPNO+d7Hq6+mTYW/8Q0YPLh8r9PFLVy2kIXLFnqKoLVrSgX8uqaKioqorq7OOwwzM7OuqaoqJVd//zs88EAavSqVCJg8GW67LT1eeSUlbZ//PBx2GBx6KGy4YZOXKcqyZSmJmzQJpk6F9dcvzXVtJW988AaDLh3ENQdewwnbnpB3OGUnaWFEdJl69JImRcR2ecfRWmXdaNjMzMysQd/7Htx3H/zud6VNriAlU9tsA+edl9ZtPfssjBkD778Pp52W1mvttFOq+Pfaa/Dyy/DrX8Pee8OgQfDzn8P8+cW91llnwSOPwO9/7+SqzGYvzDYZ9giWtWMewfIIlpmZWdu78EI4/XT4zndScYu29PLLcPvt6fHUUyueGzo0TSt86CFYZ52UPI0aBT171n+tW2+FI46Ak0+Gyy8vf+zt3J+f+zPnPnIuUaZtShcsXcAbH77Bf4//L5/d8LNleY32xCNYHZMTLCdYZmZmbaemBs44I40cfelLMH48VFTkF89rr8Fdd6WS7/vtt3z91GOPwY9+lBKtoUNTnFtvvWLfF1+E7beHT30KHn64/e3JlYORt4/k3v/dyz6b7FO21+i3Sj9+s/9v6FnZQNLbiTjB6picYDnBMjMzaxvLlsHxx8Of/pRGfC69NN/kqhh/+1uK+f334YIL4JRTUtXDG29MUwKrqtIo2IABeUfaLux23W7URA2PHPdI3qF0Ck6wOiavwTIzM7Pyq6lJhSX+9Ke0Luo3v2n/yRWkUa0pU9LarG9/O41wbbppKsW++eZwzz1OrgrMnD+T9Vf1OrS2ImmcpFmSnis4draktyVNzh77N9C3n6TbJL0k6UVJnX/OZRtxgmVmZmbl94c/pGTk0kvhhz9sXhn2vK29dtpj6/LLYZNN4Je/hDfeSNMHt98+7+jalZnzZrJen/XyDqMruQ4YXs/xX0fEsOxxbwN9LwXui4hPAp8mbY3U7kkaLullSVMlnVnP+R8UJJfPSaqWtEZ27nVJz2bnJpUrxnJuNGxmZmYGc+fCmWemEumnnJJ3NC0jpWmNJ5+cdyTt1oKlC5i3dJ5HsNpQRDwiaVBz+0lajbSH7LHZdZYCS0saXBlIqgB+C+wNTAcmSpoQES/UtomIC4ELs/YHAt+pszfu7hExp5xxegTLzMzMyuvHP05rmH772441cmXN8s78tJHz+n2dYLUDoyVNyaYQ9q/n/MbAbOAPkp6WdI2kjrDWawdgakRMy5LCm4ERjbQfCYxvk8gKOMEyMzOz8nnqKbjyyjTyU7cKn3UqM+fPBPAIVmlVSppU8DixiD5XAJsAw4CZwMX1XRfYFrgiIrYBFgArTbfLwVpNvN8BwFsFz6dnx1YiqTdp+uTtBYcDeEDSk0XeyxbxFEEzMzMrj5qalFitvTaMHZt3NFZmM+dlCZZHsEqpqrlV9SLi3drvJV0N3FNPs+nA9Ih4PHt+G+0jwZrTxPutbwi8oZLoBwL/V2d64M4RMUPSOsCDkl6KiJKXvPQIlpmZmZXHTTel/aQuuAD69cs7Giszj2C1D5IK/wAOAZ6r2yYi3gHekrR5dmhP4IW67dqh6cCGBc8HAjMaaHskdaYHRsSM7Oss4E7SlMOSc4JlZmZmpVdTA7/4RZoWePTReUdjbWDmvJlUdqtkjV5r5B1KlyFpPPAosLmk6ZJOAC7IKuVNAXYHvpO13UBSYUXBU4Abs3bDgJ+3bfQtMhEYImmwpB6kJGpC3UaSVge+APyl4FgfSavWfg/sQz3JZyl4iqCZmZmV3r33wgsvpH2vuvn3uV3BzPkzWa/venST/7zbSkSMrOfwtQ20nQHsX/B8MtChNvWNiCpJo4H7gQpgXEQ8L2lUdv7KrOkhwAMRsaCg+7rAnUqFdiqBmyLivnLEqYiGpi12fhUVFVFdXZ13GGZmZp3PF74Ar70Gr74K3bvnHY21geF/Gs7cRXN54htP5B1KpyFpYUR0hOp+JSFpUnPXnLVH/hWDmZmZldbjj8Mjj8B3v+vkqguZOX+m11+ZUeYEq4idliXpsuz8FEnbNtVX0rlZ28mSHpC0QcG5s7L2L0vat5zvzczMzBpw4YWpqMXXv553JNaGZs6b6QqCZpQxwSrYaXk/YCgwUtLQOs32A4ZkjxNJdfub6nthRGwdEcNIZSd/mvUZSlrotiWp5v3vsuuYmZlZW/nf/+COO+Bb34K+ffOOxtrIsuplzF442wmWGeUdwSpmp+URwA2RPAb0y0pLNtg3Ij4q6N+H5bXvRwA3R8SSiHgNmEqZSi+amZlZAy6+GHr0gFNOyTsSa0PvLkhbL3mKoFl5qwjWt9PyjkW0GdBUX0nnAV8DPiSVn6y91mP1XKvz+OADeP99GDy45deYPRv+8x/o3RvWWQfWXRfWW88VnszMrPUmT4Zx4+D449P/LdZl1G4yvF5f/7mblTPBKman5YbaNNo3In4E/EjSWcBoYEyRr4ekE0nTEcnKNLa9KVPgb3+D/v3To18/WLwYZs1KCdDChSn5WW89WHNNePJJuOeelBhVV8Nee8EPfgB77w1NvYe5c+H559Ni43vuSQuP61aO3GUX+Mc/vBDZzMxabvHitN/VWmvBeeflHY21sY83GfYUQbOyJljF7LTcUJseRfQFuAn4KynBKmpn54i4CrgKUpn2It5H6T32GJy5Us2P5aSVk6Ctt4YzzoA+feDyy2HffeFTn4Ijj4Q99oDttoOKCnjxRbjvPnjwQXjmGZg5c/k1ttsOxoxJiVkEvPtu+m3jueem/wzPPrsc79bMzLqCn/wEnnsu7X+15pp5R2Nt7J357wCeImgG5U2wPt5pGXibVIDiK3XaTABGS7qZNAXww4iYKWl2Q30lDYmI/2X9DwJeKrjWTZJ+BWxAKpzRPjdi+MY34KtfXT7l7/33oWfPNGq19trp+zlz4J130qjWZpvBRhst7//978P48XDZZfCjH6Vjq64Kq68O06en51tsAfvsA1tuCVttBdtum6YD1nXooWmfkp/9DA44ICVhZmZmzfHII2nt1Te/Cfvtl3c0loOZ82YixLp96vlZw6yLKetGw5L2By5h+U7L5xXutKw0R+9yUtW/hcBxETGpob7Z8duBzYEa4A1gVES8nZ37EXA8UAWcFhF/ayy+TrHR8OzZ8K9/wUMPpemAe+2VRrcKE7KmvP9+Gg1bbbU0HbFXr7KFa2Zmncy8eWmWRUVFmhXhyoFd0qh7RnHHi3cw6wez8g6lU/FGwx1TWROs9q5TJFilcv/9MHx42hTy4ovzjsbMzDqCJUvgwAPTOt5HHoGdd847IsvJiJtH8PoHr/PMqGfyDqVTcYLVMbl0nCX77gujRsGvfw0PP5x3NGZm1t5VVcHIkWnN77XXOrnq4mbOm+kKgmYZJ1i23IUXphLwxx2XpnyYmZnVp6YmlWK/80649FI49ti8I7KczZw/0xUEzTJOsGy5vn3h+uvh9ddTIQ0zM7P6fOc78Mc/wjnnwKmn5h2N5awmanh3/rtOsMwyTrBsRZ//fEqurroq7dVlZmZW6Pe/T1VsTzsNfvzjvKOxdmDuorksq1nmEu1mGSdYtrJzzknl3U84IVUmNDMzA/j3v2H06FQU6aKLmt7s3rqEmfO8ybBZISdYtrKePdPUj9mz4aSTVt702MzMup4334TDDktrdW+6KZVlNyOtvwJvMpwHSeMkzZL0XMGxsyW9LWly9ti/kf4Vkp6WdE/bRNw1OMGy+m2zTRrJuuWWVB3KzMy6rkWL4JBDYPFi+MtfoH//vCOydqR2BMtVBHNxHWk/2bp+HRHDsse9jfT/NvBiWSIrE0nDJb0saaqkM+s5/4OC5PI5SdWS1iimb6k4wbKGnXFG2rj41FPh+efzjsbMzPJy1lnw1FNw442wxRZ5R2PtzMcjWJ4i2OYi4hGgRes5JA0EvghcU9KgykhSBfBbYD9gKDBS0tDCNhFxYW1yCZwFPBwRc4vpWypOsKxh3bqlqYKrrgpHHAELF+YdkZmZtbWHH06l2EePTpsKm9Xxzvx3WLXHqvTp0WX2w21LlZImFTxOLLLfaElTsimEDQ05XwKcDtSUJNK2sQMwNSKmRcRS4GZgRCPtRwLjW9i3xZxgWePWWy/9xvLFF12K18ysq5k/P+13tfHGcP75eUdj7dTM+TO9/qp8qiJiu4LHVUX0uQLYBBgGzAQurttA0gHArIh4sqTRtt5aTSSUA4C3Cp5Pz46tRFJv0vTJ25vbt7Uqy3FR62T22itND/n5z2HzzeEHP8g7IjMzawtnngmvvZZGsfp4dMLqN3OeNxluTyLi3drvJV0N1FfAYmfgoKwARk9gNUl/ioij2ijMhsyJiO0aOV9f6dKGqrEdCPxfRNROoWxO31bxCJYVZ+xY+PKX4fTT4de/zjsaMzMrt4cegt/+Fr79bdhll7yjsXbMI1jti6TCP4xDgOfqtomIsyJiYEQMAo4EHmoHyVUxpgMbFjwfCMxooO2RLJ8e2Ny+reIRrC6uuqaa2QtnF9f4yovot2QBPb/7XT6qWsDCk75e3uDMzCwXmjeftY47hthkY+acdQrMfyfvkKwd8whWfiSNB3YjTa2bDowBdpM0jDQ68zrwzaztBsA1EdFg2fYOYCIwRNJg4G1SEvWVuo0krQ58ATiquX1LwQlWF3fChBO4/pnri25f+Sn480tw6Ok/4WcP/IQLd6b+AVczM+uwrrgbTnwTdj0e/nvFJnmHYx3AwNUG5h1ClxQRI+s5XO/+OhExA1gpuYqIfwH/KmlgZRIRVZJGA/cDFcC4iHhe0qjs/JVZ00OAByJiQVN9yxGnogtvIltRURHV1dV5h5GrQZcMYoNVN+Brn/5a0X26Latiz5/8gU3+8RSv7fZp/vmTo1m6mufmm+VJVdX0nfUBq858j1VnvEefWe/T88MFrPLhAnp+tICKxcuoqKqi27JqKpalr92qq+lWVU3VKt1Z1qcnS/v2YmmfXizt2/Pjr8v69GJJ314s69OTqh7dUU0NioCAZX16smS13ixZrQ+L+velqtcqed8GK4GBj73AAaf8hslf3YvHTjss73CsA6jsVsnhQw+nX89+eYfS6UhaGBFd5ocsSZOaWIPVITjB6sIJ1vuL3meNC9bg/D3P54zPn9G8zhFpLdYZZ8DAgWlD4u23L0+gZrayd96Bf/wDnngCJk6Ep59Om8AW6tsX1lgD1lwzFSjo0SM9undf8fuFC+Gjj+DDD1f8On9+82Lq3x823DA9Bg5c/v2aa0KvXunRuzestRasvXZ6fWtfPvwQPvWp9Of09NPpz8zMcuMEq2Mq6xRBScOBS0nDcNdExPl1zis7vz+wEDg2Ip5qrK+kC0lVQZYCrwLHRcQHkgaRdqJ+Obv8YxExqpzvr6N75t1nABi23rDmd5bgu9+Fz30uFb/YeWf46U9TxalKzzw1K5nFi+H992HuXJgzB/79b7j77pRYQfpB+DOfgW99C4YOhU98AgYNSglOz56te+3qapg3b3nCtXgxVFSkPfIgHauN7d134a23lj8eewzee6/x66++OvTrl95Dnz4rfy38fpVVUjzV1VBTk/quvz5ssEH6uv76ac8+ec5yq3zve/D22/B//+fkysyshcr2k3DBbsl7k6p2TJQ0ISJeKGi2HzAke+xIqtu/YxN9HwTOyuZR/pK0Q3Pt8Mur2a7NVoSnZz4NtDDBqrXTTum3nCefDD/5CfzlL3D99ekHPTNr3NKlKWmaMQOefx6eey493n47JS1z58KiRSv322EH+NnPYP/902hDuX6pUVGREqB+/VrWf+FCmD4dPvggvY/Fi2HBgvSeZ81Kj48+SscWLEjtZ89OX2uf134tRu/ey5Ot2uRrwAAYPHj5Y9VV0/2qTcSqqlISuXBhSkhXXbXrjqz95jdw7bVpZsJOO+UdjZlZh1XOoYaPd0sGkFS7W3JhgjUCuCHSPMXHJPXLSksOaqhvRDxQ0P8x4PAyvodObfK7k1m/7/qs23fd1l1ojTVg/Hg49ND0W/Rtt4Uf/zj9JtS/AW1/an/AnT8/PT76KI0+zJqVvhZ+/8EHabSisjI9VlstTe1ae+00HaxXr/RDaa9e8MlPph/811gj73fYfNXVsGRJei/d6tm9ovb84sXpa+1j8eL0w/k776T7NTuryFk79W7JkuX3dPbsdL8XLkyPDz5Ij0KrrAJbbAGbbJKm3K6xRrrPa6yx/PtPfSptAN4R9O4Nm23W+utEpGS0oiI9II2qzZy5/DFjxorPn3kG/va3+qc5SuleR6Q/o7q6d0+JVt++6euqq8K66y5P3NZeO/1dWG21lHzWJnO9e7f+veahpibtdXjBBTBiRNqWw8zMWqycCVZ9uyXvWESbAUX2BTge+HPB88GSngY+An4cEf+u2yHbEfrE7Pui3khnNfmdya0bvarrS1+CXXeFU05Jo1lXX53+wz7iCE/byct778G//pWm+7zwArz4Irz5ZsPtu3dPP0iuu276IX6LLdIPX9XVsGxZ+qF22rQ0Pe3991de8wPpB+rttoMtt0wjmZ/8ZPoBtVu39MNx797pB9fmiEijOW+/nR6zZqWRh4j0mDcvJTCzZ6d2tUnMokUrPpYtSz9Y1yaGixal9h98kK4DKb7evdP7rk2oWrNWc/XV0/1cZ530g3nt9VdfffmxdddN93rTTT3Ftj61CVGh2pG1LbZovO+HH6aNcqdNgzfeSL9gqE2QYXkC1atXOjZvXkrKCr9+9BG8+ir85z+NT3tcfXUYMiT9kmnbbWHrrdOf7ZprpmSsPf47uHQpHH883HgjjBoFl1++PIk1M7MWKef/5MXsltxQmyb7SvoRUAXcmB2aCWwUEe9J+gxwl6QtI+KjFS4ScRVwFaQiF02+i05qSdUSXpj9Al8c8sXSXnjddVPBi3/9C047DY48Mk07ueSS9EO3ld8bb6Tk9p570m/xYfkI0+c/n34g3WCDlOTU/oZ+nXXSn93qqzfvh8DakYX582HKlLTu5vHH0zqhm25quF+fPimBW3/9Fb/C8jU8b7+dkrja9T81NY3H0r17Kp5QW9ChV6+UvNQWV+jVa/moUm3C1bv38pGh3r3TsdrpahUV6Yf6VVZJyVjt93Wf145urLdeen0pJXLLlqVkqbXroKx1Vl8dhg1Lj1JYujQl5bXFQN5/P41gzpiRPrMvvgi33gpXXbViv8rKNF1x443TY4MN0rUWLkyJ/HrrpdHJT32q/Il2TU0qjHLXXXD77fC//6Uppz/8YftMAs3MOphyJljF7JbcUJsejfWVdAxwALBnNr2QiFgCLMm+f1LSq8BmwKRSvJnO5vnZz1NVU8U2621TnhfYbTd48kn4wx/gRz9K052OPRbOOy/9YGGltXAh/POfcOWV8Ne/ph+Sdt0Vzj0X9tgj3f/u3Uv/urUjC6usArvvnh615s1LP2y+/HJKXGpq0mPevDRlbubM9IPps8/Cgw+mH1YhJTy1Veg+9an0A/Lqq6fkZcCA9Fh33fR+pPTo27f5yWE51d4T63x69EjJUGPTNCPSLzqee275qOqcOekXB9OmpV9+vPtuulbv3umzMnv28l8idO+e/g4MGpSKlmy2WRoNHjo0/ftZm9zNnZvWwD72WHq88076Zcl66y1P+mu/r6pKfx9fein9MmTWrPRLhC98AX7xCzjM5djNzEqlbGXaJVUCrwB7knZLngh8pXBDL0lfBEaTqgjuCFwWETs01jerLvgr4AsRMbvgWmsDcyOiWtLGwL+BT0XE3IZi7Mpl2sc9PY4TJpzAK6NfYciaQ8r7Yh99lBKrSy5JPziMGgUnnliatRld1bx58PDD6fGf/6Rkdtmy9IPU17+e7u9GG+UdZfPUFjLoqOtYzJojYsVfCCxalBKgZ59NX994Iz1efz39MqIx662XilJstFFK1N55Z/m6wLkF/wWuumoayd5iC9h771QkpSOumTTrQlymvWMq6z5YkvYHLmH5bsnnFe60nJVpvxwYTirTflxETGqob3Z8KrAKUDsR/rGIGCXpMOAc0rTBamBMRNzdWHxdOcE69W+nMu7pcXx01kd0Uz2L+svh1VdT8Yvbbku/Td1jD/jmN+Hgg7tu1a5ivP8+TJ2aHi+8kEaqHn883cNVVkmjUzvvDLvskn5o8r0061w+/DAlXS+8kJKm2lHdfv1gq61SYtXQ6O3SpWm0SkqjX+1llNfMiuIEq2PyRsNdNMHa5Q+7UBM1/N/x/9f2L/7OOzBuXFon9PrraUrLccfBN76Rqqd1RRGpTPc//pHWL02fvryM9YIFy9tJaS3b3nvDXnvBZz/rNT5mZmadlBOsjskJVhdMsGqihn7n9+Nrn/4al+9/eY6B1MADD8Dvf582Tq2uTonDN78JBx1UnjVD7UkETJqU9p256670m2lIe/VsumlKPNdZJ6052nTT9Bg82FPozMzMuggnWB2T6wF3Qa+9/xrzls4rbYn2lujWDYYPT4+3306JxjXXwOGHpzUFhx+eFnlvsEGqMldZubxkeLduKdHo0ycVOFhvvfY9Na66Ok3xq10b8dprqcres8+m6nYjRqTkcs8906J2MzMzM+uQnGB1QZPfmQyQf4JVaMAA+OlPU8XBv/0tjWqNG7e88EFTatcX1FbdGjQoPQYPTlPq+vUrX+yNeeYZ+OMfUzJVd6H6dtvBFVfAyJFpPYWZmZmZdXhOsLqgp995mgpVsNU6W+UdysoqKuCAA9IjIlUgnDEjJSfV1el8RUX6fuHCtD5p3ry0Zqm24tajj6a9uKqq0jUl+PSnU9nyHXZIVbQ237z5m90W6+23U0L1xz+mEaru3VO1rhEj0mL02tLJa65Zntc3MzMzs9x4DVYXXIN1wE0H8MaHb/DsSc/mHUr5VFenxOyVV+D//g8eeQT++99UCrnWwIEp2ap9fPrT8JnPpCl7xVq2LG3SWVvh65FHUqGKiFQ2+eij4ctfdjJlZmZmzdbUGixJ40h7w86KiK2yY2cD3wBqtzP6YUTcW6ffhsANwHpADXBVRFxa+nfQPJ1lDZYTrC6YYA381UB2H7w7fzzkj3mH0rZqk6GXXlr5MW9ealNZCcOGwec+lzZL/sIXVt4nZvFiuP9+uPVWmDBheV9II2Nf/jIcdRQMKfP+YmZmZtapFZFg7QrMB26ok2DNj4iLGum3PrB+RDwlaVXgSeDgiHihpG+gmTpLguUpgjn4x7R/cPnEfKr31UQNb897m2HrDsvl9XPVvTsMHZoehSLSaNeTT6bphY8+mkrIX3bZ8umF66yT9qL56CN48800NXGNNeCII1IiNnRoSq76dJlCP2ZmZpaziHhE0qAW9JsJzMy+nyfpRWAAkGuCVQxJw4FLSXvlXhMR59fTZjfSfrrdgTkR8YXs+OvAPNKeuVXlSuacYOXgoyUfMe39abm9/g4DduCAzQ7I7fXbHSkV2RgwIJWHh7Q55xNPpE19//WvlFytthpsuGGq9HfggbD77p2/lLyZmZnlqVLSpILnV0XEVUX0Gy3pa8Ak4HsR8X5DDbMEbRvg8VZF2gYkVQC/BfYGpgMTJU0oHHmT1A/4HTA8It6UtE6dy+weEXPKGqenCHa9KYJmZmZm1v4Vsw9WliDdUzBFcF1gDhDAuaSpgMc30Lcv8DBwXkTcUcrYW6KpKYKSPgucHRH7Zs/PAoiIXxS0+RawQUT8uJ7+rwPblTvB6lbOi5uZmZmZWduJiHcjojoiaoCrgR3qayepO3A7cGN7SK4ya0maVPA4sc75AcBbBc+nZ8cKbQb0l/QvSU9mI3m1AnggO1732iXjKYJmZmZmZp2EpPWzNVYAhwDP1dNGwLXAixHxq7aMrwlzmlgXpXqO1Z2OVwl8BtgT6AU8KumxiHgF2DkiZmTTBh+U9FJEPFKSyAt4BMvMzMzMrAOSNB54FNhc0nRJJwAXSHpW0hRgd+A7WdsNJNWWa98ZOBrYQ9Lk7LF/Hu+hmaYDGxY8HwjMqKfNfRGxIJsK+AjwaYCImJF9nQXcSQOje63lNVheg2VmZmZm7VAxa7A6kyLWYFUCr5BGp94GJgJfiYjnC9psAVwO7Av0AJ4AjgReA7plVRP7AA8C50TEfaV+H54iaGZmZmZm7V5EVEkaDdxPKtM+LiKelzQqO39lRLwo6T5gCmkT5Wsi4jlJGwN3ptmRVAI3lSO5Ao9geQTLzMzMzNolj2B1TF6DZWZmZmZmViJOsMzMzMzMzEqkS6/BqqmpQVJNTi8vVi4raaXhe1s+vrfl4ftaPr635eN7Wz6+t+XREe9r77wDaGNl3QC4rXTpNVh56ixzTNsj39vy8b0tD9/X8vG9LR/f2/LxvS0P31drK54iaGZmZmZmViJOsMzMzMzMzErECVZ+rso7gE7M97Z8fG/Lw/e1fHxvy8f3tnx8b8vD99XahNdgmZmZmZmZlYhHsMzMzMzMzErECZaZmZmZmVmJOMHKgaThkl6WNFXSmXnH01FJ2lDSPyW9KOl5Sd/Ojp8t6W1Jk7PH/nnH2hFJel3Ss9k9nJQdW0PSg5L+l33tn3ecHY2kzQs+m5MlfSTpNH9uW0bSOEmzJD1XcKzBz6mks7J/e1+WtG8+Ubd/DdzXCyW9JGmKpDsl9cuOD5K0qOCze2VugXcADdzbBv/++zNbvAbu7Z8L7uvrkiZnx/25tbLxGqw2JqkCeAXYG5gOTARGRsQLuQbWAUlaH1g/Ip6StCrwJHAwcAQwPyIuyjO+jk7S68B2ETGn4NgFwNyIOD/75UD/iDgjrxg7uuzfg7eBHYHj8Oe22STtCswHboiIrbJj9X5OJQ0FxgM7ABsAfwc2i4jqnMJvtxq4r/sAD0VElaRfAmT3dRBwT207a1wD9/Zs6vn7789s89R3b+ucvxj4MCLO8efWyskjWG1vB2BqREyLiKXAzcCInGPqkCJiZkQ8lX0/D3gRGJBvVJ3eCOD67PvrSQmttdyewKsR8UbegXRUEfEIMLfO4YY+pyOAmyNiSUS8Bkwl/ZtsddR3XyPigYioyp4+Bgxs88A6gQY+sw3xZ7YZGru3kkT6Bez4Ng3KuiQnWG1vAPBWwfPpOClotew3UdsAj2eHRmfTWMZ5GluLBfCApCclnZgdWzciZkJKcIF1couucziSFf+z9+e2NBr6nPrf39I5HvhbwfPBkp6W9LCkXfIKqoOr7++/P7OlswvwbkT8r+CYP7dWFk6w2p7qOeZ5mq0gqS9wO3BaRHwEXAFsAgwDZgIX5xddh7ZzRGwL7AecnE29sBKR1AM4CLg1O+TPbfn5398SkPQjoAq4MTs0E9goIrYBvgvcJGm1vOLroBr6++/PbOmMZMVfaPlza2XjBKvtTQc2LHg+EJiRUywdnqTupOTqxoi4AyAi3o2I6oioAa7G0ylaJCJmZF9nAXeS7uO72dq32jVws/KLsMPbD3gqIt4Ff25LrKHPqf/9bSVJxwAHAF+NbBF3Nn3tvez7J4FXgc3yi7LjaeTvvz+zJSCpEjgU+HPtMX9urZycYLW9icAQSYOz32AfCUzIOaYOKZtPfS3wYkT8quD4+gXNDgGeq9vXGiepT1Y4BEl9gH1I93ECcEzW7BjgL/lE2Cms8NtUf25LqqHP6QTgSEmrSBoMDAGeyCG+DknScOAM4KCIWFhwfO2sYAuSNibd12n5RNkxNfL335/Z0tgLeCkiptce8OfWyqky7wC6mqz60mjgfqACGBcRz+ccVke1M3A08Gxt2VXgh8BIScNI0yheB76ZR3Ad3LrAnSmHpRK4KSLukzQRuEXSCcCbwJdyjLHDktSbVEm08LN5gT+3zSdpPLAbsJak6cAY4Hzq+ZxGxPOSbgFeIE1xO9nV2OrXwH09C1gFeDD7t+GxiBgF7AqcI6kKqAZGRUSxRRy6nAbu7W71/f33Z7Z56ru3EXEtK693BX9urYxcpt3MzMzMzKxEPEXQzMzMzMysRJxgmZmZmZmZlYgTLDMzMzMzsxJxgmVmZmZmZlYiTrDMzMzMzMxKxAmWmZmZmZlZiTjBMjMzMzMzKxEnWGZmZmZmZiXiBMvMzMzMzKxEnGCZmZmZmZmViBMsMzMzMzOzEnGCZWZmTZJ0tqQ/leha10n6WSmuZWZm1t44wTIzMzMzMysRJ1hmZl2MpMq8YzAzM+usnGCZmXUBkl6XdIakKcACSZWSdpL0X0kfSHpG0m4F7QdLeljSPEkPAms1cf0DJE3OrvVfSVsXnNtG0lPZtf4M9KzT93RJMyXNkPR1SSFp0+zcKpIukvSmpHclXSmpVwlvjZmZWUk5wTIz6zpGAl8E+gHrAn8FfgasAXwfuF3S2lnbm4AnSYnVucAxDV1U0rbAOOCbwJrA74EJWXLUA7gL+GP2OrcChxX0HQ58F9gL2BT4Qp3L/xLYDBiWnR8A/LT5b93MzKxtKCLyjsHMzMpM0uvAORExLnt+BrBVRBxd0OZ+UmL1T2AasHpELMjO3QTURMRR9Vz7CmBORPyk4NjLwIlAADcDAyL7D0fSf4GHIuLHksYB70bEWdm5TYH/AUOAV4H5wNYR8Wp2/rPATRExuGQ3x8zMrIQ8D9/MrOt4q+D7TwBfknRgwbHupORqA+D92uQq8wawYQPX/QRwjKRTCo71yK4TwNux4m/z3ij4fgNgUgMxrg30Bp6UVHtMQEUDcZiZmeXOCZaZWddRmOS8BfwxIr5Rt5GkTwD9JfUpSLI2qtO/0FvAeRFxXj3X+gIwQJIKkqyNSKNTADOBgQVdCpO4OcAiYMuIeLvxt2ZmZtY+eA2WmVnX9CfgQEn7SqqQ1FPSbpIGRsQbpFGlsZJ6SPo8cGAj17oaGCVpRyV9JH1R0qrAo0AVcGpWWONQYIeCvrcAx0naQlJvCtZXRURNdu1fS1oHQNIASfuW8D6YmZmVlBMsM7MuKCLeAkYAPwRmk0ahfsDy/xe+AuwIzAXGADc0cq1JwDeAy4H3ganAsdm5pcCh2fP3gS8DdxT0/RtwGWlq4lRSQgawJPt6Rnb8MUkfAX8HNm/h2zYzMys7F7kwM7N2Q9IWwHPAKhFRlXc8ZmZmzeURLDMzy5WkQ7KpiP1JZdnvdnJlZmYdlRMsMzPL2zdJ0xRfBaqBk/INx8zMrOU8RdDMzMzMzKxEPIJlZmZmZmZWIk6wzMzMzMzMSqRLbzQsKbp1c45pZmZmZu1PTU0NEaG842grku6LiOF5x9FaXTrB6tatG9XV1XmHYWZmZma2EkkL846hja2VdwCl4OEbMzMzMzNr9ySNkzRL0nMNnB8haYqkyZImSfp8W8cITrDMzMzMzKxjuA5obArhP4BPR8Qw4HjgmjaIaSVOsMzMzMzMrN2LiEeAuY2cnx/L96DqA+SyH5UTLDMzMzMz6xQkHSLpJeCvpFGsNucEy8zMzMysA2poTZKkUyS9LOl5SRc00Pc72fnnJI2X1LNtom7UWtnaqdrHic29QETcGRGfBA4Gzi15hEXIJcGSNDz7Q58q6cx6zkvSZdn5KZK2LTjX0AfpQkkvZe3vlNSvDd6KmZmZmVlerqPOmiRJuwMjgK0jYkvgorqdJA0ATgW2i4itgArgyLJH27Q5EbFdweOqll4om064iaQ2r0zY5gmWpArgt8B+wFBgpKShdZrtBwzJHicCVxScu476F7c9CGwVEVsDrwBnlTZyMzMzM2uxl16C++/PO4qVzJw3k3teuSfvMFqkgTVJJwHnR8SSrM2sBrpXAr0kVQK9gRllC7SNSNpUkrLvtwV6AO+1dRx5jGDtAEyNiGkRsRS4mZRlFxoB3BDJY0A/SetDw4vbIuKBiKjKnj4GDCzbOzAzMzOz5vne92D4cDjjDKipyTuaj/3+yd9z8M0HUxPtJ6YClS2YMrcZsIukxyU9LGn7ug0i4m3SyNabwEzgw4h4oLShl56k8cCjwOaSpks6QdIoSaOyJocBz0maTBrQ+XJB0Ys2k8dGwwOAtwqeTwd2LKLNANIHoBjHA3+u70T2wTwx+77Iy5mZmZlZq0yZAv37wwUXwNSp8Mc/Qu/eeUfF/KXzqY5qFlctpnf3/OOpoyoitmtmn0qgP7ATsD1wi6SNCxMNSf1JAxqDgQ+AWyUdFRF/Kk3Y5RERI5s4/0vgl20UToPyGMGqL6upm1kW06b+i0s/AqqAG+s7HxFX1c7rdIJlZmZm1gbefx+mT0+jV7/+Ndx5J3zhC/Dii3lHxuKqxQAsWrYo50hKZjpwRzYT7AmgBqi7Dmkv4LWImB0Ry4A7gM+1cZydVh4J1nRgw4LnA1l5zmcxbVYi6RjgAOCreQwHmpmZmVk9nstqk33qU3DaafCXv8Arr8BWW8Fxx8Ebb+QWWm2CVfu1E7gL2ANA0makdUhz6rR5E9hJUu9szdKeQP7ZbieRR4I1ERgiabCkHqSKJRPqtJkAfC2rJrgTaV5oo9MDJQ0HzgAOioiF5QjczMzMzFpgypT0deut09cDD4RXX03J1vjxMGQInH025PD78Y9HsKo63ghWfWuSgHHAxlnF7ZuBYyIiJG0g6V6AiHgcuA14CniWlBO0uGKfrajN12BFRJWk0cD9pJKQ4yLi+drFaRFxJXAvsD8wFVgIHFfbP/sg7Uaqkz8dGBMR1wKXA6sAD2ZT/x6LiFGYmZmZWb6efRb69YMBA5YfW2stuPhi+M534PTTYexYqK6Gc9t266KOPEWwkTVJR9XTdgbp5+va52OAMWUKrUvLo8gFEXEvKYkqPHZlwfcBnNxA33o/SBGxaSljNDMzM7MSefbZND2wvvXvAwfCjTdCnz7ws59B375prVYb6cgjWNY+5ZJgmZmZmVkXEZHWYB210qDKchJceSUsWABnnpmSrJPr/V17yXXCNViWMydYZmZmZlY+b74JH32URrAaU1EB118PCxfC6NHwmc/ATjuVPbyOPEXQ2qc8ilyYmZmZWVfx7LPpa1MJFkD37vCnP6URrKvapuaCpwhaqTnBMjMzM7PyqU2wttqquPZ9+8KXvwy33ALz55cvroxHsKzUnGCZmZmZWflMmQIbbQSrr158n+OPT+uxbr21fHFlakeuvAbLSsUJlpmZmZmVz7PPLt//qlif/SxsvjmMG1eemAp4iqCVmhMsMzMzMyuPpUvh5ZeLW39VSILjjoP//AdeeaU8sWU8RdBKzQmWmZmZmZXHSy9BVVXzEyyAo4+Gbt3guutKHlYhj2BZqTnBMjMzM7PyaE4Fwbo22AD22y+Vbq+uLm1cmYjwPlhWck6wzMzMzKw8nn02lV7ffPOW9T/uOJgxAx54oLRxZapqqqiJGsBTBK10nGCZmZmZWXk8+yx88pMpyWqJAw+EtdaCa64pbVyZwlErTxG0UnGCZWZmZmblMWVKy6YH1urRA044Ae66C157rWRh1XKCZeXgBMvMzMzMSu/992H69OaXaK/rlFNSsYtLLy1NXAUKEyyvwbJScYJlZmZmZqX317+mr5//fOuuM2AAHHkkXHstfPBBq8MqtMIIltdgWYk4wTIzMzOz0vvzn2HgwLRpcGt973swfz5cfXXrr1Wgo08RlDRO0ixJz9U5foqklyU9L+mCBvr2k3SbpJckvSipBH9QBk6wzMzMzKzUPvgA7r8fjjgiTe9rrWHDYI894LLLYNmy1l8v0wlGsK4DhhcekLQ7MALYOiK2BC5qoO+lwH0R8Ung08CLZYyzS3GCZWZmZmalddddKRH68pdLd83vfS+t6brllpJdsjbB6t6te4dcgxURjwBz6xw+CTg/IpZkbWbV7SdpNWBX4NqszdKI+KC80XYdTrDMzMzMrLT+/GcYNAi237501xw+HLbYAn71K4goySVrk6r+vfq31ymClZImFTxOLKLPZsAukh6X9LCk+v4QNgZmA3+Q9LSkayT1KWnkXZgTLDMzMzMrnffeg7//PU0PlEp33W7d4LTT4KmnYNKkklyyNqnq37N/e50iWBUR2xU8riqiTyXQH9gJ+AFwi7TSH0QlsC1wRURsAywAzixl4F2ZEywzMzMzK5077oCqqtJOD6x1+OEp0brnnpJcrgOMYLXEdOCOSJ4AaoC16mkzPSIez57fRkq4rAScYJmZmZlZ6dxyC2y6KWyzTemvvcYaqSphbQn4VqpNsPr17Nch12A14C5gDwBJmwE9gDmFDSLiHeAtSZtnh/YEXmjDGDs1J1hmZmZmVhqzZsFDD6XRq1JODyz0xS/Ck0/CzJmtvtTHI1jtd4pgoySNBx4FNpc0XdIJwDhg46x0+83AMRERkjaQdG9B91OAGyVNAYYBP2/j8DutyrwDMDMzM7MOrqYGnnkGrrwyfV+O6YG19t8ffvhDuO8+OO64Vl1qhQSrahERwcrLldqviBjZwKmj6mk7A9i/4PlkYLvyRNa1eQTLzMzMzFpm0SI49lhYe23Ydlu46ioYMQK22qp8r7n11mkD4xJMEyxcgwWwpHpJq69p5gTLzMzMzFrmlFPg+uvhgAPgj39M+1TddVf5pgdCuvb++8MDD8DSpa26VOEarMLnZq3hBMvMzMzMmu/aa9Pjhz9MSdZRR8GAAW3z2vvvD/PmwX/+06rLLK5aTGW3Svr26AvQIddhdSWSxkmala0vq+/8VyVNyR7/lfTpto4RnGCZmZmZWXM99RScfDLsuSecc07bv/6ee0KPHq2eJri4ajE9K3vSq7IXQGcq1d5ZXQcMb+T8a8AXImJr4FygmH3DSs4JlpmZmZkVb+5cOOywtO5q/HioqGj7GPr2hd12g3vvbbJpYz5OsLr3+vi5tV8R8Qgwt5Hz/42I97OnjwED2ySwOpxgmZmZmVnxfv7ztNbq1ltTkpWXL34RXnoJpk1r8SUWVy2mV2Uvelb2BDxFsJM5AfhbHi/sBMvMzMzMihOREqvhw2GnnfKNZf+s4ngrpgl6imC7s5akSQWPE1tyEUm7kxKsM0obXnGcYJmZmZlZcZ58Et58M00RzNumm8KWW8LFF8Ps2S26RN0pgh7Byt2ciNiu4NHsNVSStgauAUZExHulD7FpTrDMzMzMrDh33JHWXB14YN6RJNddB++8A0ccAcuWNbt73REsr8Hq2CRtBNwBHB0Rr+QVhxMsMzMzM2taBNx+O+y+O6y5Zt7RJNttB1dfDf/6F3z/+83uvqhqET0rey5fg+Upgu2apPHAo8DmkqZLOkHSKEmjsiY/BdYEfidpsqRJecRZmceLmpmZmVkH88IL8MorcNppeUeyoqOPhqefhl//GoYNg+OOK7rr4qrFrNpjVU8R7CAiYmQT578OfL2NwmmQR7DMzMzMrGm33w4SHHxw3pGs7IILYK+9YNQoeOutoru5yIWVQy4JlqThkl6WNFXSmfWcl6TLsvNTJG1bcK7eHZwlrSHpQUn/y772b4v3YmZmZtYl3HEHfO5zsP76eUeyssrKNFVw2TL4wx+K7labYNVOEfQaLCuFNk+wJFUAvwX2A4YCIyUNrdNsP2BI9jgRuKLg3HXUv4PzmcA/ImII8I/suZmZmZm11quvwjPPtI/qgQ0ZNAj23BPGjYOamqK6uIqglUMeI1g7AFMjYlpELAVuBkbUaTMCuCGSx4B+ktaHRndwHgFcn31/PXBwOYI3MzMz63LuuCN9PeSQfONoyte/Dm+8Af/4R1HNaxOsVSpWQchTBK0k8kiwBgCFk2OnZ8ea26audSNiJkD2dZ36Gkk6sXbzsohoVuBmZmZmXdLtt8NnPpNGidqzgw+GNdaAa64pqnltgiWJnpU9PYJlJZFHgqV6jtXNdIpp0yIRcVXt5mVSfS9jZmZmZh+7+254/HE48si8I2naKqukqoJ33QVz5jTZvDbBAuhZ2bPDrcFqpDbBKVm9g+clXdBI/wpJT0u6p/zRdh15JFjTgQ0Lng8EZrSgTV3v1k4jzL7OamWcZmZmZl3bnDnwjW/A1lvDKafkHU1xTjgBli6FP/2p0WYRsUKC1at7r444RfA66tQmkLQ7aenM1hGxJXBRI/2/DbxYtui6qDwSrInAEEmDJfUAjgQm1GkzAfhaVk1wJ+DD2ul/jZgAHJN9fwzwl1IGbWZmZtalRMBJJ8HcufDHP6bRoY7gU5+CHXaAa69N76EBS6uXAnxcor1XZcdLsBqoTXAScH5ELMna1DvoIGkg8EWguPmUVrQ2T7AiogoYDdxPyphviYjn6+zCfC8wDZgKXA18q7Z/fTs4Z6fOB/aW9D9g7+y5mZmZmbXE+PFw221wzjlpBKsjOeEEeO45mDixwSa10wFXGMFqf2uwKmtrB2SPE4vosxmwi6THJT0safsG2l0CnA4UV3LRilaZx4tGxL2kJKrw2JUF3wdwcgN9693BOSLeA/YsYZhmZmZmXdPbb8PJJ6d9r37wg7yjab4jj4TvfCcVu9hhh3qb1E2w2ukarKqI2K6ZfSqB/sBOwPbALZI2joLqbpIOAGZFxJOSditVsJbkstGwmZmZmbVTy5alBGXpUrj+eqioyDui5lttNTjiiDQKN39+vU1WGsHqgFMEGzAduCPb7ugJ0gjVWnXa7AwcJOl10pZJe0hqfNGaFc0JlpmZmZktd/rp8J//pNGfTTfNO5qWO+GElFzdemu9pzvIFMGWuAvYA0DSZkAPYIWSihFxVkQMjIhBpHoID0XEUW0cZ6flBMvMzMzMkvHj4ZJL4NvfhpH1rsroOHbeGTbfPBW7qEftaFVHHsFqoDbBOGDjrHT7zcAxERGSNpB0b2PXs9LIZQ2WmZmZmbUzzz0HX/86fP7zcOGFeUfTelIaxTr9dHjxRdhiixVOd5A1WI1qqDYBsNJoVETMAPav5/i/gH+VNLAuziNYZmZmZl3dzJlw8MFp7dItt0D37nlHVBpf+xpUVsK4cSud6sRTBC1nTrDMzMzMurLZs2GvveCdd+DOO2H99fOOqHTWXRcOPBBuuCEV7SjQiYtcWM6cYJmZmZl1VXPnwt57w7RpcM89sNNOeUdUeiecALNmpfdXoL4pgh7BslJwgmVmZmbWFX3wAey7b1qf9Je/wG675R1Reey7LwwYsFKxi/pGsDraGixrn5xgmZmZmXU1r7ySRqueeQZuvx322SfviMqnshKOOw7uuy+970x9a7Cqo5pl1ctyCdM6DydYZmZmZl3J/ffDDjvAe+/B3/8OBxyQd0TlN3o09OwJY8Z8fKi+ESzA67Cs1ZxgmZmZmXUVl1wC++8Pn/gETJwIu+6ad0RtY9114bTT4Oab06gdyxOsXt1TYlWbaHkdlrWWEywzMzOzzq6mBr7/ffjOd2DECPi//4NBg/KOqm19//uw+urwk58A9U8RLDxu1lJOsMzMzMw6s2XL4Nhj4eKL01S5226Dvn3zjqrt9e+fNh2++2547LGPE6lVKlYBPEXQSscJlpmZmVlntWhR2kD4j3+Ec8+Fyy6Dbl34x79TT4V11oEf/YjFVYtZpWIVJAHLR7A8RdBaqwv/DTMzMzPr5H73O7j3XrjySvjxjyFLJrqsvn3hrLPgoYfYaOIrH08PhII1WB7BslZygmVmZmbWGdXUwBVXwC67wDe/mXc07ceoUTB4MEdc8nfWWdbj48O1UwS9BstaywmWmZmZWWf097/Dq6/CSSflHUn70rMn3HQT/efM51e3L4AIwFMErXQq8w7AzMzMzMrgd79L640OPTTvSNqfnXbi5pFb8dUbn4VrroFvfMNFLroiqb6/HB8CzxIxq6WX9QiWmZmZWWfz1lupWt4JJ8Aqq+QdTbt06/6DeHSLvqnwxfPPf7wGy1MEu5QTgGuAr2aPq4HvAv+HdHRLL+oEy8zMzKyzueqqNPXtxBPzjqTdWlSzhHO/vlnaG+uII+i9cFk63oGmCEoaJ2mWpOfqHD9F0suSnpd0QT39NpT0T0kvZm2+3XZRtys1wBZEHEbEYcBQYAmwI3BGSy/qBMvMzMysM1m6NE17++IXu95mws2wuGoxC9dYFW66CV55hXWOHkX3qg43RfA6YHjhAUm7AyOArSNiS+CievpVAd+LiC2AnYCTJQ0tc6zt0SAi3i14PgvYjIi5wLKWXtQJlpmZmVlnctdd8M47Lm7RhMVVi9O0wD32gHHj6P7wv/nDX2DRkgV5h1a0iHgEmFvn8EnA+RGxJGuz0lqiiJgZEU9l388DXgQGlDncVmtoxK7g/CclPSppiaTvF3HJfyPdg3QM0jHAX4BHkPoAH7Q0TidYZmZmZp1FVRVcdBEMHgz77pt3NO3axwkWwNFHU/Ozn/HVZ+HzV/w138BWVClpUsGjmDmfmwG7SHpc0sOStm+ssaRBwDbA4yWIt9yuo86IXR1zgVOpf9SuPidn1xxGugc3ACcTsYCI3VsapKsImpmZmXUW55wDEyemaW8VFXlH064trlr8cWl2gG4//CFX/eWnnHjz/8F2F8P3vpdjdB+riojtmtmnEuhPmvq3PXCLpI0jsnr0BST1BW4HTouIj1odbZlFxCNZQtjQ+VnALElfLPaCwG3Zo2Q8gmVmZmbWGfzzn/Czn8Gxx8LIkXlH0+6tMIIFIHHmwX15+vObwve/DxdemF9wrTMduCOSJ0iFHNaq20hSd1JydWNE3NHGMTZkrRaM2LWcdCjS/5A+RPoIaR5SqxNNj2CZmZmZdXRz5sBRR8Fmm8FvfpN3NB3C4qrF9KzoucKxHj16cdV3v8AVG2wLp58O1dVw5pk5RdhidwF7AP+StBnQA5hT2ECSgGuBFyPiV20eYcPmtGDErjUuAA4k4sVSXrTVCZbGqhewUYyJl0sQj5mZmZk1R0QatXrvPfjrX6Fv37wj6hBWGsECenXvxQKWwo03pimWZ52Vkqwf/SinKBsnaTywG2nkZzowBhgHjMsKQSwFjomIkLQBcE1E7A/sDBwNPCtpcna5H0bEvW39HnL2bqmTK2hlgqWxOpC0iKwHMFhjNQw4J8bEQSWIzczMzMya8tOfpsTqN7+BYcPyjqbDqDfBquyVyrRXVsINN6Qk6/e/h5NPhn798gm0ERHR0FzQo+ppOwPYP/v+P4DKGFpHMQnpz6RRvyUfH23llMnWjmCdDewA/AsgxsRkjW144ZmZmZmZldDvfpfWXX396ykJsKLURA1Lq5fWO4L18UbDlZVw3XXw7rvtMrnqihoYsesOEBFXSloPmASsBtRIOg0Y2kgBj9WAhcA+BccCyDXBqoox8aHGOgE2MzMza1O33QajR8NBB8EVV4D881ixllSlwYq6CVbPyp4rbjRcUQEbbNCWoVkjGhmxqz3/DjCwGRc8rrUx1ae1CdZzGquvABUaqyGkuvP/bX1YZmZmZtaghx+Gr34VPvtZGD8+jbZY0WqTqAanCFrnJp1OxAVIvyGNWK0o4tTWXL61fxtPAX5EmrN4E3A/8LNWXtPMzMzMGrJsWSpqMWgQ3H039O6dd0QdzuKqxUA9CVb3XsxdNDePkKxt1Ra2mFSOi7c4wdJYVQATYkzsRUqyzMzMzKzcxo+H11+Hv/wF1lgj72g6pAYTLI9gdQ0Rd2dfry/H5VucYMWYqNZYLdRYrR5j4sNSBmVmZmZm9aipgV/8Aj71KTjggLyj6bAaSrB6VvZcXuTCOi/pbuqbGlgrWlcRvbVTBBcDz2qsHgQWfBzTmNbNWzQzMzOzetxxB7z0UhrF6tYt72g6rMZGsGrPWad2Ufb1UGA94E/Z85HA6629eGsTrL9mj2aRNBy4FKggbXh2fp3zys7vTyqdeGxEPNVYX0nDgCuBnkAV8K2IeKJlb8vMzMysnYmAn/8chgyBL30p72g6tMbWYHmKYBcQ8TAA0rlE7Fpw5m6kR1p7+VYlWDGm+fMWJVUAvwX2BqYDEyVNiIgXCprtBwzJHjsCVwA7NtH3AmBsRPxN0v7Z891a/ObMzMzM2pO//Q2efhquvTaVD7cWq02wenXvtcJxTxHsctZG2piIaQBIg4G1W3vRViVYGqvXqGf+YoyJjRvptgMwNbI3IulmYARQmGCNAG6IiAAek9RP0vrAoEb6BmmzMIDVgRmteGtmZmZm7UcEnHcebLghHHVU3tF0eI1NEVxWs4zqmmoquv1/e3ceX1V19X/8s5Iwz0NQBhUHUJEqKoKzYmtVnH0eqzjUgUq10Gq1v1q1baRqi2PrWKuAyqNiHVuqVK2tYqlDRUQmRRBRAkgSZhKGhKzfH/vGXEJG7nCSm+/79Tqv3Humu+7hJNx1995rK4ltBn4KvIXZ4tjzvsAPEz1pol0EB8c9bg2cC9RVzqY3sDTueT6hlaqufXrXcew1wGtmdheQBRxZd/giIiIiTcC0afDOO3D//dCyZdTRNHm1dRGs2N6uZbu0xyVp5v4qZv2A/WJrPsV9S6KnTbSL4Koqq/5gY2068OtaDqtumvGqrWA17VPbsVcBP3X3F8zse8AE4Ds7vLjZKGBU7HEtYYqIiIg0ErfdBrvsAiNHRh1JRqitBQvCRMRKsLZnxlHAzcAehBzCAHentp5rTUE/YF9CY9FBmIH7pEROmGgXwUPinmYRWrQ61HFYPrBb3PM+7Nidr6Z9WtZy7CXA1bHHzwHjq3txd38EeAQgOzu75vKMIiIiIo3Bf/8Lb7wBt98ObdrUvb/UqbYy7YDGYVVvAqFL3YfAtohjSQ6zPELNhgHAVEIdiOlAdAkWcHfc4zJCWcPv1XHMB0A/C4PIlgHnAxdU2WcKMCY2xmoosM7dV5hZYS3HLgeOA94CTgAW7uR7EhEREWk8brsNunSBq66KOpKMUVcXQVUSrNY6d/4edRBJ9r/AQcBHuF+G2S7U0EjTEIl2ERzW4GPcy8xsDPAaodT6RHefZ2ZXxrY/TMgghwOLCGXaL6vt2NiprwDuNbMcwvxcoxJ5byIiIiKRmzMHpkyBvDzoUFcnIamvuroIai6sar1pxp3Ai8A345TcmRldSAnbhHs5ZmWYdQQKIPEuj4l2EbwaeAzYADwKHAL8wvP89dqOc/ephCQqft3DcY8dGF3fY2PrpwOHNvAtiIiIiDRev/0ttG8PP/lJ1JFklDpbsJpIF0EzmwicBhS4+8C49T8GxhB6mL3i7j+v5tha56WtRkVhufgid07oOdZUzcCsMyGP+RDYCCQ8j26iXQQv9zy/18baSUAPQkvTY0CtCZaIiIiI1GHhQnj2WbjuOuhaV5FmaYhNpZswjBZZLbZb/80YrKbTRfBx4AHixgyZ2TDCNEYHuvsWM+tR9aB6zku7HXca3HOt0XP/UezRw5i9CnTEfXaip000waoowzcceMzz/GMbq9J8dbnmGpg1K+ooREREpDG77rM7OclbcP70a1l9fNTRZJbP11yKrT+RYW9u/7F1/ZZDYcWb/GTqt+gaqycyaBD84Q9pD7Fe3P1tM+tbZfVVwDiPlRt394JqDq3PvLSEbVzkzpNmXFt9DNyTwFuIVigpfiGwF+6/wWx3zIbgnlArVlaCYX1oY+11QoL1mo21DkB5gucUERERadbalG3gOyuf5h+7XMjqlrtGHU7GKfdysmzHj8FZlv3N9kYix8xmxC31qTHQHzjGzN43s2lmdlg1+9Q052x1KurVd6hhacoeAo4ARsSebyC07CUk0RaskcAgYLHneYmNtW7EClJIzRrrtyAiIiLSSEx4Fv5TzKkvjOTUI6MOJvNcMeUOXln4Cm9dt/1MQQuKlrHfg8O48ZynuOBbVYtcR6LM3QfXvdt2coAuwOHAYcCzZrZXrMZBhfrMSxtWOn+K/RzbwDiagqG4H4LZRwC4r8Es4Zm8E60iWG5jrQ9wgY01gGme539LNCgRERGRZm3CBNhvPzjiiKgjyUibt23eocAFZMw8WPnAi7GE6r9mVg50Bwqr7FPXvLTbMaM1oXHlAMKkvAC4c3mS4o5CKWE8WkguzXJJQm+8hLoI2lgbR5jcd35s+YmNtd8lGpSIiIhIszV/Prz7LowcCRranhKbyzZ/UzEwXsW6Jl6m/S/EKvuZWX+gJVBUZZ9v5qW10GJzPmEe2tr8H7ArcBIwjZCUbUhe2JG4D3gJ6IHZbYRJhhPOZRLtIjgcGOR5oaOqjbUngI+AGxINTERERKRZmjABcnLg+9+POpKMtbms+hasti3aAnDTv27i9v/cDkCn1p2Yc9WctMZXX2Y2GTge6G5m+UAeMBGYaGZzga3AJe7uZtaLUI59eB1zy9ZkH3fONeNMd54w4+nY8U2X+1OYfQh8m9Bt8izgq0RPm2iCBdAZWB173CkJ5xMRERFpnrZuhUmT4IwzoMcO1bUlSWpKsNq3bM+tw27l8zWff7OuIulqjNx9RA2bLqpm3+WExpGK59XOLVuL0tjPtWYMBL4G+jbg+MbFrDfQE5iN+6eEcvbXAJcCvRI5daIJ1u+Aj2ysvUnI+o5FrVciIiIiO+dvf4OiotA9UFKmpgQL4KZjb0pzNE3GI2Z0AX5J6E7YHvhVtCHtJLNrgJuARUArzO4F7iHMJ3ZooqdPtMjFZBtrbxEqlABc73n+daJBiYiIiDRLEyZA795w0klRR5LRNpdtpmOrjlGH0dR0orJaeEUp8zIzBrkzK5qQdtooYF/cV2O2OyHROhb395Jx8kTnwYJQO/544LjYYxERERFpqPx8eO01uPRSyM6OOpqMVlsLltToUOBKwnxZvYArCDnAo2b8PMK4dsZm3MMQJ/evgM+SlVxBgi1YNtYeAvYBJsdW/dDG2nc8z0cnHJmIiIhIc/L441BeDpc35arXTYMSrJ3SDTjEnY0AZuQBzxOGCH0I3BFhbA3VB7P74p732O65+08SOXmiY7COAwZ6Xpi4LFZFsHGWWRERERFprMrLQ/fAE06AvfaKOpqMt7lsM62zlWA10O6EqoQVSoE93NlkxpaIYtpZ/6/K8w+TefJEE6wFhIv9Zez5bsDsBM8pIiIi0ry8+SYsWQK33RZ1JM2CWrB2ytPAe2b8Nfb8dGCyGe0I8+E2He5PpPL0iSZY3YBPbKz9N/b8MOA9G2tTADzPz0jw/CIiIiKZb8IE6NwZzj476kialNc/f513lr7T4OPWb1mvBKuB3LnFjKnA0YTq4Ve6MyO2+cLoImt8Ek2wfp2UKERERESaqzVr4MUX4Qc/gDZtoo6mSbnqlatYvGZxg48zjIE9BqYgoszmzockuTtdJko0wSr0PN+uSdDG2vGe528leF4RERGR5uGpp2DLFs19tRNWblzJtYdfy90n3R11KJIGZjYROA0ocPcdMmQzM+BewoTKJcCl7j4zvVEmXqb9WRtrP7exZjbW2thYu58w+bCIiIiI1MeECXDwwWGReiveWkxxaTE92vWIOhRJn8eBk2vZfgrQL7aMAv5Y7V5mV2DWL/bYMHsMs/WYzcbskESDTDTBGkoocvEO8AGwHDgq0aBEREREmoWZM2HWLLVe7YTCkkIAJVjNiLu/DayuZZczgUkevAd0NrOe1ex3NbAk9ngEcCCwJ3AtoQUsIYkmWKXAJqAN0Br4wvO8PNGgRERERJqFP/0JWreGCy6IOpImp6C4AFCClWG6m9mMuGVUA4/vDSyNe54fW1dVGe6lscenAZNwX4X7G0C7hoe9vUQTrA8ICdZgQkWRETbWnk80KBEREZGMt3IlPPEEXHQRdOkSdTRNjhKsjFTk7oPjlkcaeLxVs86rWVeOWU/MWgPfBt6I25ZwpZlEE6wrgIXAjZ7nXwM/BmYlGpSIiIhIxnvgAdi6FX72s6gjaZKUYEk18gnz8lboQxjCVNWvgRmEboJTcJ8HgNlxQMPLUlaRaIJ1GXA4oe8iwAZC30cRERERqcnGjfDgg3DWWbDvvlFH0yRVJFi57XIjjkQakSnA9y04HFjn7it22Mv9ZWAPYH/cr4jbMgP4baJBJFzkwvN8NLAZwPN8DdAi0aBEREREMtr48WH+q5//POpImqyC4gLat2xP2xZtow4lMmY20cwKzGxu3LqbzWyZmc2KLcNrOPanZjbPzOaa2WQL3eUaNTObDLwL7Gtm+WY20syuNLMrY7tMJbRALQIeBX5U48ncy3BfU2VdMTA50TgTnQer1MZaNrG+jTbWcqm+n6OIiIiIAJSWwj33wLHHwuGHRx1Nk1VQXKDugaFs+QPApCrrf+/ud9V0kJn1Bn4CDHD3TWb2LHB+7HyNlruPqGO7A6MTfJnqxnE1SKItWPcBLwE9bKzdBkwnCc1qIiIiIhnrmWdg6VK1XiVICVa9ypbXJgdoY2Y5QFuqH6vUHCXcWJRQC5bn+VM21j4kVN8w4CzP808SDUpEREQkI7nDHXfAwIEwvNqeW1JPBcUF7NF5j6jDSLUcM5sR9/yRelbWG2Nm3yeMKbrOq3SFc/dlZnYX8BWhIvjr7v560qJu7Mz+RvWJlAHdEj19ol0E8Tz/FPg00fOIiIiIZLx//hPmzoXHHgNLuCdSs1ZQXMBhvQ6LOoxUK3P3wQ085o/ALYQE4hbgbuDy+B3MrAuhMN2ewFrgOTO7yN2fTDjipqHG7pN1bKuXhBMsEREREamn++6D3FwYUetQEqlDuZdTWFLY7LsIVsfdV1Y8NrNHgZer2e07wBfuXhjb70XgSKB5JFju0755bJYbW1eYrNMnOgZLREREROpj8WJ4+WUYNQpatYo6miZt7ea1lJWXKcGqhpn1jHt6NjC3mt2+Ag43s7ZmZoThPs1rmI9ZHmZFhJ54n2FWiNmvk3FqJVgiIiIi6fDQQ5CVBVdeWfe+UitNMhxUV7YcuMPM5pjZbGAY8NPYvr3MbCqAu78PPA/MBOYQcoL6jO3KDGY/BY4GDsO9G+5dgKHAUbFtCVEXQREREZFUKy6GCRPgnHOgT5+oo2nylGAFNZQtn1DDvsuB4XHP84C8FIXW2H0fOBH3om/WuC/G7CLgdeD3iZxcLVgiIiIiqfbUU7B2Lfz4x1FHkhGUYEmCWmyXXFUI47BaJHpyJVgiIiIiqeQO998PBx0ERx8ddTQZQQmWJGjrTm6rF3URFBEREUmladNCafbx41WaPUkKigswjG5tE56ySJqngzBbX816A1onenIlWCIiIiKpUl4Ov/wldO8OF1wQdTQZo7C4kG5tu5GTpY+yshPcs1N5+ki6CJrZyWa2wMwWmdkvqtluZnZfbPtsMzukPsea2Y9j2+aZ2R3peC8iIiIiNZowAf7zH7jjDmjTJupoMkZBSYG6B0qjlfa038yygQeBE4F84AMzm+Lu8+N2OwXoF1uGEmakHlrbsWY2jDAj9YHuvsXM9FsnIiIi0fn6a/j5z+G44+DSS6OOJqMUFBeQ2zY36jBEqhVFC9YQYJG7L3b3rcAzhMQo3pnAJA/eAzrHJk2r7dirgHHuvgXA3QvS8WZEREREqnXttVBSAn/6k8ZeJVlBsVqwpPGKIsHqDSyNe54fW1effWo7tj9wjJm9b2bTzOyw6l7czEaZ2Qwzm+HuCbwNERERkRq8+ipMngw33gj77ht1NBlHCZY0ZlGMDKzuK5yqmU5N+9R2bA7QBTgcOAx41sz28ipZlLs/Qmym6uzsbGVYIiIiklybN8OPfhQSq1/sMNRcElS6rZTVm1YrwZJGK4oEKx/YLe55H2B5PfdpWcux+cCLsYTqv2ZWDnQHCpMXuoiIiKScO6xbFyrwucPWrfDZZzB7dli2boUjjoCjjoIDDoCsRjat5+9/D198AW+8Aa1aRR1NxikqCfPDKsGSxiqKBOsDoJ+Z7QksA84HqtYtnQKMMbNnCEUu1rn7CjMrrOXYvwAnAG+ZWX9CMrbjDM0iIiLSOBUXw5NPwn33wfz51e/TrRtkZ8OkSeF5585w4olw2mlwyimQG3HhgxUr4Le/hbPOgm9/O9pYMpQmGZbGLu0JlruXmdkY4DUgG5jo7vPM7MrY9oeBqcBwYBFQAlxW27GxU08EJprZXMIMzJdU7R4oIiIijdC2bTB2LDzwAKxZA4ccAuPGQevWoXUqOxv23hsOPBB23TUcs3hxKH8+bRr8/e/w3HOhkMQxx4SKfeeeC+3bp/+9/PKXsGUL3Hln+l+7mVCCJY2dNeccJDs727dt2xZ1GCIiIs2XO1x9Ndx/P5x9dqi8d9RRDau6V14OH30EL78MTz8duhO2axeSrHPPhRNOCMlaqs2cCYMHw3XXKcFKoadmP8VFL13EgjEL6N+tf9ThpJSZlbh7u6jjSJdYEbrBUceRqEbWaVlERESalbvuCsnVT38KL74IRx/d8JLmWVlw6KGQlweffhpatkaMgBdegFNPDd0GzzsvVPVbty4178M9vIfu3UMrlqSMWrCksVOCJSIiItF4+ukwEe/3vhcSrWQwgyOPhEcfhcJCmDoVLrggdCW84IKQbJ18Mjz8cBgvVdXq1fDBB6FVrKHv5e234ZZboFOn5LwXqVZBcQEtslrQqZWuszROSrBEREQk/f7+9zBW6rjjQsGKVFQCbNUqFL74059g2bLQsnX11bBoEVx1FfTqFaoR3n57SIyOPDIkYEOGwMCB8PjjoWJhXaZPh5Ejw/EjRyb/fch2KubAMk3ejJlNNLOCWA2CinU3m9kyM5sVW4bXcGxnM3vezD41s0/M7Ij0RZ7ZNAZLY7BERETSxz1UCbz22lC04s03QyXAdMcwbx785S/w0kth7JRZGD91yimw226h2+Ls2dCnT6gKePHF1Z/rk0/CmLHcXHjnnVDlUFLq9Mmns2z9Mmb+cGbUoaRcXWOwzOxYYCMwyd0HxtbdDGx091qbhc3sCeDf7j7ezFoCbd19bdKC3wmZMgYrijLtIiIi0hxt3QqjR8P48XDmmaEkexSV/sxCC9XAgWG81LJl0LLl9iXeR46E114L1Q2//314/XV46CHo0KFynxUrQkLWsiW8+qqSqzSpaMEScPe3zaxvQ48zs47AscClsfNsJVThliRQF0ERERFJvbKykIyMHw833RQKWkSRXFWnd+8d588yC2O1pk+H3/wmjLE65JAwpmviRPjhD+Hww6GoCF55BfbcM5rYmyElWPUyxsxmx7oQdqlm+15AIfCYmX1kZuPNrNlUK0w1JVgiIiKSevfeC//6Vyg+ceutqRlzlQrZ2fCrX8Fbb8HmzaEq4ciR8Oc/Q//+Ibk69NCoo2xWmlmClWNmM+KWUfU45o/A3sAgYAVwd3XnBQ4B/ujuBwPFwC+SFHOzpy6CIiIiklpffAG//jWccUbTLQJxzDHw8cehUuCAAbDPPk0nScwgxVuLKSktaU4JVllDxyS5+8qKx2b2KPByNbvlA/nu/n7s+fMowUoaJVgiIiKSOu7wox+FZOSBBxo+x1Vj0rUrnHVW1FE0a5oDq25m1tPdK+YgOBuYW3Ufd//azJaa2b7uvgD4NjA/nXFmMiVYIiIikjrPPBMKQNx3X6jOJxlv49aNpKpK9ZK1SwAlWBXMbDJwPNDdzPKBPOB4MxsEOLAE+GFs317AeHevKNv+Y+CpWAXBxcBlaQ0+g6lMu8q0i4iIpMbq1bDffqEAxDvvhPFMktFufftWfvXmr1L+Oh+O+pBDeh6S8teJWl1l2jNNfcq0m9nJwL1ANiFhHFdlexdgImEc2mbgcnffoRUvldSCJSIiIqlx660hyXrjDSVXzcTbX75N3859GXPYmJS9RufWnRm066CUnV8aLzPLBh4ETiSMI/vAzKa4e3z3xhuBWe5+tpntF9v/2+mMUwmWiIiIJF9BATz8MFx4YZhQWJqFBasWcPTuR3PdkddFHYpkpiHAIndfDGBmzwBnsv34sQHA7wDc/VMz62tmu8QX/0g1lb8RERGR5LvnnlDW/MYbo45E0qSktISv1n3Fvt32jToUabq611GWvjewNO55fmxdvI+BcwDMbAiwB9AnVQFXRy1YIiIiklyrVsGDD8J558G++rDdXCxctRBACZYkoqiOMVjVlSGtWlBiHHCvmc0C5gAfAWXJCa9+lGCJiIhIct17L2zcCDfdFHUkkkYLVi0AYL/u+0UciWSwfCC+HGkfYHn8Du6+nlhFRDMz4IvYkjbqIigiIiLJs25dKMl+zjkwcGDU0UgaLSgKCVa/bv0ijkQy2AdAPzPbM1Ze/nxgSvwOZtY5tg3gB8DbsaQrbdSCJSIiIslz//0hyfrlL6OORNJswaoF7N5pd9q2aBt1KJKh3L3MzMYArxHKtE9093lmdmVs+8PA/sAkM9tGKH4xMt1xah6s5jgP1sqV0KYNdOgAVl1XVhERkZ3w1Vdw0EFw1FHw8stRRyNpNviRwXRt05XXL3496lAyhubBaprURTAKZWVQU2JXVgbr10N84rtxI0yZAj/6Efzv/8Kzz4bKTPXhHhKqv/wlHL/PPrDrrtCpE7RuDX36wNVXb/96IiIiDbVtG1x0Ufh/7A9/iDoaSTN3Z8GqBSpwIYK6CEbjqafg0kuhY0fo0iUkO5s3Q2EhrFkT9mnRAnJzw/bPPoPSUmjXLhzzwgth/YgRMHw4DB0K3buH44qK4J//hDffhLlzYf78ynO2bw/DhoVEq7w8vN78+aGv/MCBcMUVkVwOERHJALfdBv/+N0yaFL7Mk2ZlxcYVbNy6kX27K8ESUYIVhQMPhLw8WLs2JD9r14bWpNzcsLRrF0rcFhSEhGn4cDjllNDlIjsb/vUveOwxmDgRHnoonLNfv3DcrFnheadOoZvGeefB/vvDoEFw+OHQsuX2sZSXw0knhVasY46B/VT5R0REGmj6dBg7NrRgXXxx1NFIBCoKXKgFS0RjsJr2GKziYvjwQ3jvPXj33dC1cNgwOPFEOPRQyKln/rxiRUj6+vQJ52rVKrVxi4hI5lizJnyJl5MDH30UelpIs/PwjIe56pWr+PKaL9m90+5Rh5MxNAaraVILVlPWrh0ce2xYEtGzZ2gNO+MMuPFGuPvu5MQnIiKZbeNGOO208EXd9OlKrpqxBUULaNuiLX069ok6FJHIqciFBKefDqNHwz33wGuvRR2NiIg0diUl4f+O99+HyZNhyJCoI5IILVi1gP7d+pNl+mgpot8CqXTnnXDAAXDJJWH8l4iISHW2bIGzz4Zp00JRi//5n6gjkoh9WvSpxl+JxCjBkkpt2oRvIdeuhcsuU+l2ERHZ0bZtcP758PrrMH48XHBB1BFJxDaXbWbJ2iVKsERilGDJ9r71LbjrLpg6FR54IOpoRESksbnuujC34r33wuWXRx2NNAKLVi/CcZVoj4CZTTSzAjObG7fuZjNbZmazYsvwWo7PNrOPzEwzgyeREizZ0ejRcOqp8P/+H8yeHXU0IiLSWDzwQEisrr4afvKTqKORRkIl2iP1OHByNet/7+6DYsvUWo6/GvgkJZE1Y0qwZEdmYZ6tLl3CPFobN0YdkYiIRO2VV0JidfrpqjYr21mwKiRY/bv1jziS5sfd3wZW78yxZtYHOBUYn9SgRAmW1CA3F55+Gj77DK64QuOxRESas7lzwxduBx0U/m/Izo46ImlEFqxaQO8OvenQqkPUoWSiHDObEbeMqudxY8xsdqwLYZca9vkD8HOgPCmRyjeUYEnNhg2DW2+FZ57ReCwRkeaqpAS+9z1o3x7+9rfwUyTOgqIFGn+VOmXuPjhueaQex/wR2BsYBKwAdmhyNrPTgAJ3/zCp0QqgiYalLtdfD++8EwY1H3YYHH541BGJiEg6XXMNfPJJqBrYu3fU0UiCPv76Y+YUzEnqOT8p+oQLv3VhUs8pO8/dV1Y8NrNHgeoKWBwFnBErgNEa6GhmT7r7RWkKM6MpwZLaZWWFOU4OPRTOPRfefRf6aJZ2EZFm4bnn4NFHw5dtJ54YdTSSBKdPPp2l65cm/byDew1O+jll55hZT3dfEXt6NjC36j7ufgNwQ2z/44GfKblKHiVYUrcuXeCFF+C44+Doo+Gf/4S99446KhERSaUlS8IY3KFD4ZZboo5GkmDNpjUsXb+UG46+gcsPTl6J/ZysHPbotEfSzif1Z2aTgeOB7maWD+QBx5vZIMCBJcAPY/v2Asa7e41l2yU5zCMoXmBmJwP3AtmEf+hxVbZbbPtwoAS41N1n1vPYnwF3ArnuXlRbHNnZ2b5t27bkvKnmYMYMOPlkaNkS/vEPOOCAqCMSEZFUKC2FY4+F+fNh1izYc8+oI5IkmP7VdI557BheueAVhvfTZ+ymwMxK3L1d1HGki5nNcPcm3xya9iIXZpYNPAicAgwARpjZgCq7nQL0iy2jCIP16jzWzHYDTgS+SvHbaJ4GD4a33w6Pjz0W3n8/2nhEpOHcYetW2LABVq2CFStg7VrQl00SLy8P3nsvdA9UcpUx5hXMA+CAXH1BKpJKUXQRHAIscvfFAGb2DHAmMD9unzOBSR6a194zs85m1hPoW8exvyeUm/xrOt5IszRgAEyfDt/5TuguOHZs6Juvkr0i0dm4MUwKvngxfPVVWJYtg9WrQxK1enWoBLd1a2iZqEnbttC5M/ToEZbc3FAxrnXrsOTkQHl5WCBs69wZOnWC7t2hV69QBKFbtzCfnjRNb7wB48bBD34QqgdKxphXOI/2Lduze6fdow5FJKNFkWD1BuJHV+YDQ+uxT+/ajjWzM4Bl7v6x1fIfe2z+gFGxxzv3Dpq7vfYK3QWvugpuugmmToX/+z99yymSLvn58Pe/w5tvwsyZYb66+O7eFclO9+5w4IHQtSu0axe691YsrVqFny1awKZNoUVrwwZYswYKC6GgABYuDInZ5s1hKS0NX6ZkZVW2hFWnZcvw+hVL166VSVrbtiEB6949JHAdO0KbNjsurVopSYtCQQFcfDHstx/ce2/U0UiSzSucx4DcAfr8I5JiUSRY1f1WVx0IVtM+1a43s7bATcB363rx2PwBj0AYg1XX/lKDrl3D/Finnw6jR4cPcePGhaQrS9Oriey0oiJYvjy0Oq1ZE5aKx6tWhUqes2eHfXv3Dl13R4yAgw+GffeF3XYLSUw6bN0K69eHLoaFhSHu5ctD61nFzzlzYN26kMRt3gxbttTv3GYhIWvTJryfli1Dy9m2beFnp06w6647Lj17Vj7u2lV/jxqivBwuuSTca6+/nr77SNJmfuF8TtnnlKjDEMl4USRY+cBucc/7AMvruU/LGtbvDewJVLRe9QFmmtkQd/86qdFLJTO46CI45hgYNQrGjIGnnw599gdUHVYnTU5FC4VZ+JCana0WhZ1RXBySj6KiytagTZtCYrJqVVi/ciUsWBDmGiqqoTZPdnbojvetb8Edd8Dw4eH3LMp/k5YtQ0tU9+6wzz71O6asLCSMRUXhumzYEK5HbUtF98aK1rOsrJC0ff11GCe0YkXYr6qcnJBw9e0blj32CIlZRWsahH+fkpJwfJs20KFDaFXr2HH7xz16hOufqb8DJSVw2WXw6qvw4IPhPpOMsnrTar7e+LXGX4mkQRQJ1gdAPzPbE1gGnA9cUGWfKcCY2BirocA6d19hZoXVHevu84AeFQeb2RJgcF1VBCVJ9tgj/Kf85JNhQspBg8LExNdfHz6QSOOxaRN8/nn4YFtcHMburF8fPuB//XXlsmJF+Fn1Q2v79pXduzp3Dh9IK1oZ9tkHDjkkLD17pv+DaHl5+NC9dm3lB+aqS1lZ6HrWunX4uWlT2H/NmvC4Ylvr1mHfiq5xtS0bNoTrWVgYEiYI3e5atAitNSUltcedlRWuZ//+cPbZoWvW7ruH1pcuXcLStWv4sJ8JH+5zcirHeCWLe7iX4+/hiiU/P5QbnzYtPK4YP7YzWraEXXYJvwPxydcuu1S2nO29d2jR79AhaW8v5fLz4cwz4aOPQvJ+1VVRRyQpUFHgYkCuvgAVSbW0J1juXmZmY4DXCKXWJ7r7PDO7Mrb9YWAqoUT7IkKZ9stqOzbd70GqYRb67Z90EvzsZ6G74COPwI03hi6EFd8WS/qUlIRv96dNCz8XLAjFD2qamqFr18quVUceWdnFCkK3rG3bQjJWVBSWtWtDS8TmzSFZe/LJynN37w79+oVln31CV6OsrHCftGkTxt5UFFGo2jKwdWtlV7OKAg2rV4ek76uvYOnSsK3ig7JZeP3VqxP78FyXnJzK5Ct+ad8+JESHHlpZ3KG0NCRoLVtWvsfu3cM4qPjjcnNDi4q6sSXGLCQ0HTqEe64mVZNm9/Bv0rZtSK63bAn3+IYN2/9cty6MTVq5MtyHa9aEbStWwKefhkSuuHj719prLxg4MPwbVyTKvXqFsap9+4aErLS0Mpbu3UMM6bRlS+gKOGpUiH/KFDjttPTGIGkzrzBWQbCHWrBEUi2SebAaC82DlUIffQQ33ACvvRbGhNxyS+hOqGqDqbVpE7z0Ejz+OLz1VvgAl5UVvlE/4IDQStKvX/hw165dWDp0CAlAoh/uNmyAjz8ORRfmzg0FEhYuDONw6pKTEz5gQvgQW93fpVatQiKz227hg2pOTtjPvbJwQrdu4YNs27bVF06oaFWq+FDbpk1I7rp0CY+3bq0cK9SixfatXTmal11qsXFjSLgWLAi/Bx9/HJKvii8JNm+u/fjs7PBlxIABYdl778qlZ8+ak/Bt28JrzpgRksCKhD6+EmTr1uH3ZMWK8Ds5b1742/zGG+GLmL32CsmV5jbMaD+e+mOe+PgJ1v1inYpcNCGaB6tpUoKlBCu1/vWv0FVwxozwbe7vfgennpoZXZ0aA/fQ/en998O1fvbZ8G17375w7rlw/PFw1FGhlSQqmzeHxMU9tDCVlFRWqavoWldQEBYICVSfPiGJys0NrWhdu4auWGrpkaZq06bwZcMXX4Rl5crtE/j8/DCp77x5sGjR9vOStWkTkqC99w6/ExVde1evDvtv3Fj7a3fsGM4X38rWt2/4Wzx8OAwbFl5DMtoJT5xASWkJ7/3gvahDkQZQgtU0KcFSgpV67vD886G74KJFIdG69FK48MLQDU3qr7w8tA699VZY/vOfysSkbVv4n/8JA9WPO07JiEhTVVoKX34ZxktWXYqKQsLUqVP4uf/+cNhhoZpknz6VRVMqvrSoWMwqu+327x/GzuqLrmZll7t24bR+pzHhzAlRhyINoASraVKCpQQrfUpLYdIkGD8+jAnKzoaTTw5lgU8/XeO0ICRQixaFMUYVrTvLloUJZBcvDvMdrV0b9t1zz1DB8fDDYejQUPWrRYtIwxcRkcanqKSI3Dtzufu7d3PtEddGHY40gBKspkmDCiR9WrSAkSPD8umnIdmaNAleeSWMgRkxIiRbhx3WvL5Z/eSTcA3+/W+YPj10+4mXkxO+bd5rLzj/fDjiiNBCtcce0cQrIiJNSkUFQZVoF0kPtWCpBSta27bBP/8JTzwBL74Yxuvsv39ItM45J4zHybSWrW3bwniLF18Mlfdmzgzr+/WDo48OY6b23DOMtcjNDcUfVGBBRER20kMfPMToqaP56pqv2K3TbnUfII2GWrCaJiVYSrAaj3Xr4LnnQgW8//yncn3nzmGemZycUGZ527bKct9t2oRKeD17hmSs6tK1a7StYdu2hVapP/8Z3nknlHMuLKwsJz54cKiueO65oaiDiIhIko1+ZTRPznmStdevVQXBJkYJVrX7nAzcS5iyaby7j6uyvRPwJLA7obfeXe7+WIpCrj5GJVhKsBqlhQtDYrJiRVhWrgxJSXZ2WMrLQ2vXpk2hgtby5WGsUmnp9udp3z4kMUceGZZDDw3JWir/gykvh3ffDUnV88+H+Nu2Dd36+vSpnGvqhBPCpLIiIiIpNOyJYWwu28y7I9+NOhRpICVYO2zPBj4DTgTygQ+AEe4+P26fG4FO7n69meUCC4Bd3X1raqOvpH5H0jhVVLtqiPLykIgtXVq5fP55KKhx++2VZY87dKicDyr+54ABISFrqJKSUJhi4cLQSvXss6ELYKtWoQTyeeeFyTvbNZu/jyIi0ojMK5jHGfueEXUYkgJmNhE4DShw94GxdTcDVwCFsd1udPepVY7bDZgE7AqUA4+4+73pijsBQ4BF7r4YwMyeAc4E5sft40AHC8217YHVQFk6g1SCJZkjKyt0FezZE4YM2X5bcTF88AHMmRMq8S1cGOaOevbZyu56WVlhos2hQyuXAQN2nBx527bQQvXXv4bJOT/7rHJbixahMuK4caEyYseOqX3PIiIitSgsLqSwpFAFLjLX48ADhGQp3u/d/a5ajisDrnP3mWbWAfjQzP4R3xIUke5mNiPu+SPu/kjc897A0rjn+cDQKud4AJgCLAc6AOe5e3kqgq2JEqwIvLXkLR6d+WjUYTRPvWPLsG7AEeSUDqHHyo30XL6BPb5YzT4Li9j7mUm0Hz8egE2tc/hi764Ut29Jm5JS2mwqJbegmI7rt1CWk8W8gbvw2fnf4uteHVnRswNf9+rIltY5wFR4Y2otgYiIiKTe6k2hMu0BPZRgZSJ3f9vM+u7EcSuAFbHHG8zsE8InpKgTrKI6xmBVN8aj6nink4BZwAnA3sA/zOzf7r4+OSHWTQlWBIpKivjvsv9GHYZUMCoTr6Pbgbdlj8JSDvpyC4OWbObAL9fRYY2zsXUWRa2z+Hi/lkwb0Jm392/LxjbZwKbYshJWRflGREREdnRYr8MY0ntI3TtKY5RTR4tOTcaY2feBGYSWqjU17RhL0A4G3k8o0vTIB+JLYfYhtFTFuwwY56HQxCIz+wLYD0jbh28VuVCRCxERERFphOpT5CKWIL0cNwZrF6CI0LJzC9DT3S+v4dj2wDTgNnd/MZmx74x6FLnIIRS5+DawjFDk4gJ3nxe3zx+Ble5+c+xazAQOcvei1EZfSS1YIiIiIiIZwt1XVjw2s0eBl6vbz8xaAC8ATzWG5Ko+3L3MzMYArxHKtE9093lmdmVs+8OEpPJxM5tD6Kd0fTqTK1CCJSIiIiKSMcysZ2yMFcDZwNxq9jFgAvCJu9+TzvgSFauIOLXKuofjHi8HvpvuuOJlRfniIiIiIiKyc8xsMvAusK+Z5ZvZSOAOM5tjZrOBYcBPY/v2MrOKxOQo4GLgBDObFVuGR/EeMpHGYGkMloiIiIg0QppouGlSC5aIiIiIiEiSKMESERERERFJEiVYIiIiIiIiSaIES0REREREJEmadZn28vJyzKw8opc3wgRwkny6tqmja5sauq6po2ubOrq2qaNrmxpN8bq2jTqANEvrfFWp0qyrCEYpU6qkNEa6tqmja5sauq6po2ubOrq2qaNrmxq6rpIu6iIoIiIiIiKSJEqwREREREREkkQJVnQeiTqADKZrmzq6tqmh65o6urapo2ubOrq2qaHrKmmhMVgiIiIiIiJJohYsERERERGRJFGCJSIiIiIikiRKsCJgZieb2QIzW2Rmv4g6nqbKzHYzszfN7BMzm2dmV8fW32xmy8xsVmwZHnWsTZGZLTGzObFrOCO2rquZ/cPMFsZ+dok6zqbGzPaNuzdnmdl6M7tG9+3OMbOJZlZgZnPj1tV4n5rZDbG/vQvM7KRoom78ariud5rZp2Y228xeMrPOsfV9zWxT3L37cGSBNwE1XNsaf/91z9ZfDdf2z3HXdYmZzYqt130rKaMxWGlmZtnAZ8CJQD7wATDC3edHGlgTZGY9gZ7uPtPMOgAfAmcB3wM2uvtdUcbX1JnZEmCwuxfFrbsDWO3u42JfDnRx9+ujirGpi/09WAYMBS5D922DmdmxwEZgkrsPjK2r9j41swHAZGAI0At4A+jv7tsiCr/RquG6fhf4l7uXmdntALHr2hd4uWI/qV0N1/Zmqvn91z3bMNVd2yrb7wbWuftvdN9KKqkFK/2GAIvcfbG7bwWeAc6MOKYmyd1XuPvM2OMNwCdA72ijynhnAk/EHj9BSGhl530b+Nzdv4w6kKbK3d8GVldZXdN9eibwjLtvcfcvgEWEv8lSRXXX1d1fd/ey2NP3gD5pDywD1HDP1kT3bAPUdm3NzAhfwE5Oa1DSLCnBSr/ewNK45/koKUhY7Juog4H3Y6vGxLqxTFQ3tp3mwOtm9qGZjYqt28XdV0BIcIEekUWXGc5n+//sdd8mR033qf7+Js/lwN/jnu9pZh+Z2TQzOyaqoJq46n7/dc8mzzHASndfGLdO962khBKs9LNq1qmfZgLMrD3wAnCNu68H/gjsDQwCVgB3Rxddk3aUux8CnAKMjnW9kCQxs5bAGcBzsVW6b1NPf3+TwMxuAsqAp2KrVgC7u/vBwLXA02bWMar4mqiafv91zybPCLb/Qkv3raSMEqz0ywd2i3veB1geUSxNnpm1ICRXT7n7iwDuvtLdt7l7OfAo6k6xU9x9eexnAfAS4TqujI19qxgDVxBdhE3eKcBMd18Jum+TrKb7VH9/E2RmlwCnARd6bBB3rPvaqtjjD4HPgf7RRdn01PL7r3s2CcwsBzgH+HPFOt23kkpKsNLvA6Cfme0Z+wb7fGBKxDE1SbH+1BOAT9z9nrj1PeN2OxuYW/VYqZ2ZtYsVDsHM2gHfJVzHKcAlsd0uAf4aTYQZYbtvU3XfJlVN9+kU4Hwza2VmewL9gP9GEF+TZGYnA9cDZ7h7Sdz63FjBFsxsL8J1XRxNlE1TLb//umeT4zvAp+6eX7FC962kUk7UATQ3sepLY4DXgGxgorvPizispuoo4GJgTkXZVeBGYISZDSJ0o1gC/DCK4Jq4XYCXQg5LDvC0u79qZh8Az5rZSOAr4NwIY2yyzKwtoZJo/L15h+7bhjOzycDxQHczywfygHFUc5+6+zwzexaYT+jiNlrV2KpXw3W9AWgF/CP2t+E9d78SOBb4jZmVAduAK929vkUcmp0aru3x1f3+655tmOqurbtPYMfxrqD7VlJIZdpFRERERESSRF0ERUREREREkkQJloiIiIiISJIowRIREREREUkSJVgiIiIiIiJJogRLREREREQkSZRgiYiIiIiIJIkSLBERERERkST5/54unOEFZQK2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x1296 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "band_names = ['blue', 'green', 'red', 'nir', 'red edge']\n",
    "\n",
    "fig, ax = plt.subplots(5,1, figsize=(12,18))\n",
    "\n",
    "for i in range(5):\n",
    "    ax[i].plot(exposures[100:280,i], color='green')\n",
    "#     ax[i].set_ylim(0,.002)\n",
    "    ax2 = ax[i].twinx()\n",
    "    ax2.plot(gains[100:280,i], color='blue')\n",
    "#     ax2.set_ylim(0,8.2)\n",
    "    ax3 = ax[i].twinx()\n",
    "    ax3.plot(dls_ed[100:280,i], color='red')\n",
    "#     ax3.set_ylim(.22,.5)\n",
    "    ax3.spines['right'].set_position(('outward', 60))\n",
    "    \n",
    "    ax[i].set_ylabel('exposure')\n",
    "    ax2.set_ylabel('gain')\n",
    "    ax3.set_ylabel('DLS Reading')\n",
    "    \n",
    "    ax[i].yaxis.label.set_color('green')\n",
    "    ax2.yaxis.label.set_color('blue')\n",
    "    ax3.yaxis.label.set_color('red')\n",
    "    \n",
    "    ax[i].set_title(band_names[i])\n",
    "    fig.tight_layout()\n",
    "#     fig.savefig('gain_exposure_dls.png',dpi=300)\n",
    "    fig.show()\n",
    "#     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0000SET  RedEdge  SYNLog-19-08-13_27-08-2021.csv\r\n"
     ]
    }
   ],
   "source": [
    "! ls data/210827_A2_frontcross2/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is the typical full workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/210828_A2_dyefrontcross2/panel/IMG_0003_3.tif',\n",
       " 'data/210828_A2_dyefrontcross2/panel/IMG_0003_5.tif',\n",
       " 'data/210828_A2_dyefrontcross2/panel/IMG_0003_2.tif',\n",
       " 'data/210828_A2_dyefrontcross2/panel/IMG_0003_1.tif',\n",
       " 'data/210828_A2_dyefrontcross2/panel/IMG_0003_6.tif',\n",
       " 'data/210828_A2_dyefrontcross2/panel/IMG_0003_4.tif']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(os.path.join('data', project_name, 'panel', 'IMG_0003*.tif'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First process the Altum images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From https://support.micasense.com/hc/en-us/articles/360025336894-Using-Panels-and-or-DLS-in-Post-Processing\n",
    "\n",
    "In the case of a clear, sunny day, we recommend using only the calibration panels. Light sensor data will not be as effective and so should generally not be used in this case.\n",
    "\n",
    "Light sensor data is most effective in overcast, completely cloudy conditions. In this case, we would recommend using both light sensor and panel data for calibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Panel irradiance calculated.\n",
      "used warp dir data/210828_A2_dyefrontcross2/sea/align_img\n",
      "Aligning images. Depending on settings this can take from a few seconds to many minutes\n",
      "Finished aligning band 0\n",
      "Finished aligning band 1\n",
      "Finished aligning band 2\n",
      "Finished aligning band 3\n",
      "Finished aligning band 4\n",
      "Saving time: 0:18:27.342645\n",
      "Alignment+Saving rate: 0.26 images per second\n",
      "Finished saving images.\n",
      "exiftool -csv=\"data/210828_A2_dyefrontcross2/sea/stacks/log.csv\" -overwrite_original data/210828_A2_dyefrontcross2/sea/stacks\n",
      "Finished saving image metadata.\n"
     ]
    }
   ],
   "source": [
    "# to use the DLS and not the panel set this to None\n",
    "# panelNames = None\n",
    "panelNames = glob.glob(os.path.join('data', project_name, 'panel', 'IMG_0003*.tif'))\n",
    "\n",
    "warp_img_dir = os.path.join('data', project_name, 'sea', 'align_img')\n",
    "surface_out = process_micasense_subset(surface_dir, panelNames, warp_img_dir, overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1 directories scanned\r\n",
      "  288 image files updated\r\n"
     ]
    }
   ],
   "source": [
    "! exiftool -csv=\"data/210828_A2_dyefrontcross2/sea/stacks/log.csv\" -overwrite_original data/210828_A2_dyefrontcross2/sea/stacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now process the RedEdge Images\n",
    "\n",
    "Need to ensure these are the correct images via time. They do seem correct.\n",
    "\n",
    "Also note that the first couple sea images were bad because of spray and glint and shadow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/210828_A2_dyefrontcross2/RedEdge/panel/IMG_0000_4.tif',\n",
       " 'data/210828_A2_dyefrontcross2/RedEdge/panel/IMG_0000_1.tif',\n",
       " 'data/210828_A2_dyefrontcross2/RedEdge/panel/IMG_0000_2.tif',\n",
       " 'data/210828_A2_dyefrontcross2/RedEdge/panel/IMG_0000_5.tif',\n",
       " 'data/210828_A2_dyefrontcross2/RedEdge/panel/IMG_0000_3.tif']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panelNames = glob.glob(os.path.join('data', project_name, 'RedEdge', 'panel', 'IMG_0000*.tif'))\n",
    "panelNames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Panel irradiance calculated.\n",
      "used warp dir data/210828_A2_dyefrontcross2/RedEdge/tube\n",
      "Aligning images. Depending on settings this can take from a few seconds to many minutes\n",
      "Finished aligning band 0\n",
      "Finished aligning band 3\n",
      "Finished aligning band 1\n",
      "Finished aligning band 2\n",
      "Finished aligning band 4\n",
      "Saving time: 0:00:16.405573\n",
      "Alignment+Saving rate: 0.73 images per second\n",
      "Finished saving images.\n",
      "exiftool -csv=\"data/210828_A2_dyefrontcross2/RedEdge/tube/stacks/log.csv\" -overwrite_original data/210828_A2_dyefrontcross2/RedEdge/tube/stacks\n",
      "Finished saving image metadata.\n",
      "Panel irradiance calculated.\n",
      "used warp dir data/210828_A2_dyefrontcross2/RedEdge/sky\n",
      "Aligning images. Depending on settings this can take from a few seconds to many minutes\n",
      "Finished aligning band 0\n",
      "Finished aligning band 4\n",
      "Finished aligning band 3\n",
      "Finished aligning band 1\n",
      "Finished aligning band 2\n",
      "Saving time: 0:00:03.419420\n",
      "Alignment+Saving rate: 0.88 images per second\n",
      "Finished saving images.\n",
      "exiftool -csv=\"data/210828_A2_dyefrontcross2/RedEdge/sky/stacks/log.csv\" -overwrite_original data/210828_A2_dyefrontcross2/RedEdge/sky/stacks\n",
      "Finished saving image metadata.\n",
      "Panel irradiance calculated.\n",
      "used warp dir data/210828_A2_dyefrontcross2/RedEdge/sea\n",
      "Aligning images. Depending on settings this can take from a few seconds to many minutes\n",
      "Finished aligning band 0\n"
     ]
    },
    {
     "ename": "error",
     "evalue": "OpenCV(4.1.2) ../modules/video/src/ecc.cpp:572: error: (-7:Iterations do not converge) The algorithm stopped before its convergence. The correlation is going to be minimized. Images may be uncorrelated or non-overlapped in function 'findTransformECC'\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRemoteTraceback\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n  File \"/home/clifgray/anaconda3/envs/micasense/lib/python3.7/multiprocessing/pool.py\", line 121, in worker\n    result = (True, func(*args, **kwds))\n  File \"/home/clifgray/Code/ocean_color/micasense/imageutils.py\", line 163, in align\n    cc, warp_matrix = cv2.findTransformECC(grad1, grad2, warp_matrix, warp_mode, criteria, inputMask=None, gaussFiltSize=1)\ncv2.error: OpenCV(4.1.2) ../modules/video/src/ecc.cpp:572: error: (-7:Iterations do not converge) The algorithm stopped before its convergence. The correlation is going to be minimized. Images may be uncorrelated or non-overlapped in function 'findTransformECC'\n\n\"\"\"",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-9baf94e0760d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mwarp_img_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproject_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'RedEdge'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sea'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0msurface_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess_micasense_subset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurface_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpanelNames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_img_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Code/ocean_color/utils.py\u001b[0m in \u001b[0;36mprocess_micasense_subset\u001b[0;34m(img_dir, panelNames, warp_img_dir, overwrite, panelCorners)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0mthumbnailPath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'thumbnails'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0msave_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputPath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthumbnailPath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpanel_irradiance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_img_capture\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Finished saving images.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0mfullCsvPath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwrite_exif_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimgset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Code/ocean_color/utils.py\u001b[0m in \u001b[0;36msave_images\u001b[0;34m(img_set, outputPath, thumbnailPath, panel_irradiance, warp_img_capture, generateThumbnails, overwrite)\u001b[0m\n\u001b[1;32m    142\u001b[0m     \u001b[0;31m# you need a relatively flat image and it should be at the same altitude\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[0;31m# see https://github.com/micasense/imageprocessing/blob/de96aad06fc32a35597a8264190f81cd35206383/Alignment.ipynb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m     \u001b[0mwarp_matrices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_warp_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwarp_img_capture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputPath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Code/ocean_color/utils.py\u001b[0m in \u001b[0;36mget_warp_matrix\u001b[0;34m(img_capture, max_alignment_iterations)\u001b[0m\n\u001b[1;32m     66\u001b[0m                                                               \u001b[0mmax_iterations\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_alignment_iterations\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m                                                               \u001b[0mwarp_mode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwarp_mode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m                                                               pyramid_levels = pyramid_levels)\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0;31m#print(\"Finished Aligning, warp matrices={}\".format(warp_matrices))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Code/ocean_color/micasense/imageutils.py\u001b[0m in \u001b[0;36malign_capture\u001b[0;34m(capture, ref_index, warp_mode, max_iterations, epsilon_threshold, multithreaded, debug, pyramid_levels)\u001b[0m\n\u001b[1;32m    231\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmultithreaded\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcpu_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimap_unordered\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malignment_pairs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m             \u001b[0mwarp_matrices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'match_index'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'warp_matrix'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Finished aligning band {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'match_index'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/micasense/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    746\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msuccess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    747\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 748\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m     \u001b[0m__next__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m                    \u001b[0;31m# XXX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/micasense/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mworker\u001b[0;34m()\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mjob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mwrap_exception\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_helper_reraises_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Code/ocean_color/micasense/imageutils.py\u001b[0m in \u001b[0;36malign\u001b[0;34m()\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m                 \u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindTransformECC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriteria\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputMask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgaussFiltSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m                 \u001b[0mcc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfindTransformECC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarp_mode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriteria\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31merror\u001b[0m: OpenCV(4.1.2) ../modules/video/src/ecc.cpp:572: error: (-7:Iterations do not converge) The algorithm stopped before its convergence. The correlation is going to be minimized. Images may be uncorrelated or non-overlapped in function 'findTransformECC'\n"
     ]
    }
   ],
   "source": [
    "# to use the DLS and not the panel set this to None\n",
    "#panelNames = None\n",
    "panelNames = glob.glob(os.path.join('data', project_name, 'RedEdge', 'panel', 'IMG_0000*.tif'))\n",
    "\n",
    "blocked_dir = os.path.join('data', project_name, 'RedEdge', 'tube')\n",
    "sky_dir = os.path.join('data', project_name, 'RedEdge', 'sky')\n",
    "surface_dir = os.path.join('data', project_name, 'RedEdge', 'sea')\n",
    "\n",
    "warp_img_dir = os.path.join('data', project_name, 'RedEdge', 'tube')\n",
    "sunlight_blocked_out = process_micasense_subset(blocked_dir, panelNames, warp_img_dir, overwrite=False)\n",
    "\n",
    "warp_img_dir = os.path.join('data', project_name, 'RedEdge', 'sky')\n",
    "sky_out = process_micasense_subset(sky_dir, panelNames, warp_img_dir, overwrite=False)\n",
    "\n",
    "warp_img_dir = os.path.join('data', project_name, 'RedEdge', 'sea')\n",
    "surface_out = process_micasense_subset(surface_dir, panelNames, warp_img_dir, overwrite=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1 directories scanned\r\n",
      "   12 image files updated\r\n"
     ]
    }
   ],
   "source": [
    "! exiftool -csv=\"data/210828_A2_dyefrontcross2/RedEdge/tube/stacks/log.csv\" -overwrite_original data/210828_A2_dyefrontcross2/RedEdge/tube/stacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1 directories scanned\r\n",
      "    3 image files updated\r\n"
     ]
    }
   ],
   "source": [
    "! exiftool -csv=\"data/210828_A2_dyefrontcross2/RedEdge/sky/stacks/log.csv\" -overwrite_original data/210828_A2_dyefrontcross2/RedEdge/sky/stacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1 directories scanned\r\n",
      "    3 image files updated\r\n"
     ]
    }
   ],
   "source": [
    "! exiftool -csv=\"data/210827_A1_frontcross1/RedEdge/sea/stacks/log.csv\" -overwrite_original data/210827_A1_frontcross1/RedEdge/sea/stacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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