2_simulation_output_analysis/2.1_KingsCollege_site/Figure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KingsCollege site figure\n",
    "- This script is used to visualize KingsCollege site flux;\n",
    "- Simulations: CNTL, WRF_LCZ, LI_LCZ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cftime\n",
    "import string\n",
    "import matplotlib.dates as mdates\n",
    "from scipy.stats import gaussian_kde"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('export_uk_kingscollege_df.csv')\n",
    "df['time'] = pd.to_datetime(df['time'])\n",
    "sequence = ['Rn', 'SWup', 'LWup', 'Qh', 'Qle', 'Qtau']\n",
    "sequence2 = ['$R_{n}$', '$SW_{up}$', '$LW_{up}$', '$Q_{h}$', '$Q_{le}$', '$Q_{tau}$']\n",
    "entity = ['obs', 'def', 'wrflcz', 'lilcz']\n",
    "label1 = ['Observation: mean', 'CNTL: mean', 'WRF_LCZ: mean', 'LI_LCZ: mean']\n",
    "label2 = ['Observation: 5-95% range', 'CNTL: 5-95% range', 'WRF_LCZ: 5-95% range', 'LI_LCZ: 5-95% range']\n",
    "#lc0 = '#083550'\n",
    "lc0 = '#1b9e77'\n",
    "lc1 = '#d95f02'\n",
    "lc2 = '#7570b3'\n",
    "lc3 = '#e7298a'\n",
    "#lc1 = '#006bac'\n",
    "#lc2 = '#3da4e3'\n",
    "#lc3 = '#6c64d4'\n",
    "lc = [lc0, lc1, lc2, lc3]\n",
    "alpha = [0.2, 0.15, 0.1, 0.1]\n",
    "labelfont = 6\n",
    "linewidth = 0.5\n",
    "labelcolor = '#6b6b6b'\n",
    "padding = 4\n",
    "metric = ['7-day mean', 'Diurnal mean']\n",
    "pad = 1\n",
    "xwidth = 0.5\n",
    "length = 2\n",
    "handletextpad = 0.5\n",
    "def rmse(y_true, y_pred):\n",
    "    return np.sqrt(np.mean((y_true - y_pred) ** 2))\n",
    "mid_date = ['2004-01-01 00:00:00', '2004-05-01 00:00:00', '2014-01-01 00:00:00', '2012-01-01 00:00:00', '2012-01-01 00:00:00',\n",
    "            '2004-09-01T00:00:00', '2020-01-01T00:00:00', '2018-01-01T00:00:00', '2018-01-01T00:00:00', '2016-01-01T00:00:00',\n",
    "            '2012-01-01T00:00:00', '2020-01-01T00:00:00', '2010-01-01T00:00:00', '2010-01-01T00:00:00', '2007-01-01T00:00:00',\n",
    "            '2013-01-01T00:00:00', '2012-01-01T00:00:00', '2004-01-01T00:00:00', '2008-01-01T00:00:00', '2009-05-29T11:30:00',\n",
    "            '2012-06-01T00:00:00']\n",
    "psites = [\"AU-Preston\",\"AU-SurreyHills\",\"CA-Sunset\",\"FI-Kumpula\",\"FI-Torni\",\n",
    "          \"FR-Capitole\",\"GR-HECKOR\",\"JP-Yoyogi\",\"KR-Jungnang\",\"KR-Ochang\",\n",
    "          \"MX-Escandon\",\"NL-Amsterdam\",\"PL-Lipowa\",\"PL-Narutowicza\",\"SG-TelokKurau06\",\n",
    "          \"UK-KingsCollege\",\"UK-Swindon\",\"US-Baltimore\",\"US-Minneapolis1\",\"US-Minneapolis2\",\n",
    "          \"US-WestPhoenix\"]\n",
    "sitesequence = 15\n",
    "mid_time = mid_date[sitesequence]\n",
    "GRIDNAME = psites[sitesequence]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8qyF0XcftduN2u0kmk/zgBz/giiuuSOcIFChQYP+h4KkvUKBAgQIF8vDwww+nS12Wl5dPuBb/TNPX15eu3mSaJscff3zBoC9QYD+l4KkvUKBAgQIFChQoUGAvp5AoW6BAgQIFChQoUKDAXk7BqC9QoECBAgUKFChQYC+nYNQXKFCgQIECBQoUKLCXUzDqCxQoUKBAgQIFChTYyykY9QUKFChQoECBAgUK7OUUjPoCBQoUKFCgQIECBfZyCkZ9gQIFChQoUKBAgQJ7OYXmUwUK7OO88847xGIxampqePvtt/nwhz+c3haJRHj66ac5//zzs/YJhULcdNNNNDU1YVkWCxYs4Oyzz6a3t5edO3dy/PHHT+sct2zZMmJue5Jf/epXxGIxdF3nwAMP5IMf/OCszKPAxBmU72OOOSbr83yyvWPHDv70pz8BEIvFOOigg0aMyaSzs5P169dz2WWXTf/kp5F33nmHp556Ctu2EUJwySWX8OSTT3L55ZejaRr/9m//xpe//GXq6+t5/PHHqamp4YgjjpjtaRfIQy65HpTFs88+u6C3GSnz119/PT09Pfu13BeM+gzyPRxgeh4Qe8vDAQpGzr7E888/zyc/+Ul27tw5YltlZSXxeBxZlikqKsratnTpUj796U8jhOCxxx7jscce47zzzqOlpWVc32vbNi7X3rEY2N/fz5e//GUA/uM//oNzzjkHSZJmeVYFxsOgfA8nn2wvXryY6667DoDf/e53rF69ek9NdcZQVZVHH32UL33pS3i9XmRZRtM0NE0D4LXXXqO8vBxd1xFC8Oabb/KlL31plmddYDTyyTUU9DbklnlJkvD7/fu13BeM+gwmcxPtiw8IKBg5+wqyLGPbNl6vF3BeLG+//Xbi8TiXXnopzc3NLFmyhE2bNrFmzZqcx5AkidNPP51vfetbrFq1Ku2ZyfTSDL6wrlu3jqeffhqXy8XBBx9MdXU1f/3rX/F6vYRCIa644gpKSkr49a9/jW3blJaW5r3ntmzZwpNPPonP5yMUCnHaaafxwgsvoCgKn//85ykuLua+++6jq6sLl8vFxz72Mdxu94hjb9u2bcQcmpqa0t9jmiZutxuXy0Vvby/FxcUFWd9LyJRvIQT33nsvnZ2duFwuPvnJT44q26Zpsnv3bi699NIR2yzL4je/+Q2yLFNVVQVAPB4fIVsPPPAAhx9+OIsXL2bTpk1s3bo1ywGyp2Q4Ho+j6zrRaJTa2lqKioqQJAlVVQF4/fXXWbduHaqqsmXLFhYtWpTWCQXmHplynUsWgf1eb+eSeYBAILBfy33BqB8g8ybKpbxdLtekHhDjfTi4XC7uv//+vA+Iydwogy8iBSNn/6W3t5fq6ur037quc/XVV9PT08NDDz3E5z73OWpqanJ68TPxeDxYljWu71QUheuuuw5JktiyZQuWZXH11Vfzzjvv8MILL3DuuedyzTXX4Ha7uf/++9m8eTNutzvnsYQQXHnllTz//PO8+uqrXHPNNTzzzDO89dZblJSUUFRUxBe/+EV27drFX//6Vz784Q/nPPbwOWSupvX09BAOh/n+979PJBLh6quvHtd5Fph9MuX7rbfeQpIkrr/+esDxOI4m25s3b2b58uU5vZJvvvkmtbW1fPCDH+Tvf/87u3btoqioaIRsrVu3jn/+858sXryYV155hdNOO23EsfaEDNfV1XHCCSfwy1/+Mq3/m5qa0DSNnp4eqqqqKC0tRVVVNm7cyPvf//7puPwFZohMuc4li8B+r7dzyXxzczOBQGC/lvspGfV/+MMfOPbYY1m4cCHPPPMMmzZt4qqrrkIIwf/+7//y9a9/fbrmOeNk3kS5lPfKlSsn9YAY78Nh5cqVYz4gJnqjXHjhhTm/q2DkjJ99ScYBWlpakCSJhoYG4vH4uPczDAOPJ1tdZL7oCSHS/54/f37WtsFl38rKSmRZJpVKcc899yDLMrFYjJaWliwPVCbNzc0AlJeXp/9dUVFBOBwmmUzyxhtvsG3btvTx8x17+Bwy6ezs5H3vex+nnHIKd955J7FYLOsld39gX5Dznp4eli1blv57rBCCjRs3cvTRR6f/Xr9+PW+99RYHHXQQtm0zb948ABYsWMCuXbtyytbKlSvp6upCURSi0SgNDQ0jvmdPyDDAiSeeyIknnshzzz3Hs88+m3Ywvfjiixx99NH09fURjUYJh8PjDsXYl9hbZbyvr2+ELI6XfVlvQ26ZHzyH/VXup2TUL1q0iF27drFw4ULa2trSSrS3t5f6+vopTSwcDvPWW2/R2trKhRdeiN/vn9LxJkIuARuLzAfEZB4OAPPmzRv1ATHRGyXfuRSMnPGzt8t4XV0doVAo/XdHRwdCCHp7eykrKwOcUKuxzuXJJ58cEVpWVFRENBpNH3eQsVZ1NmzYwEEHHcQxxxzDvffeO5HTSSOEoL6+nsMPP5wzzjgDcFbFnn322Qkfu6uri0WLFgGwZs0a3njjDVauXMm7777Lli1b8Pl8rF69ep9+IOytcp4p3/X19WzevJnDDjsMcDz1+WTbsixaW1uzVlbf//73pz15r732Gu3t7Rx22GG0trYC+eX2oIMO4u677+bggw+e0NynU4Z7enrS56koSvrfQgg6Ozs599xzSSQS/PWvf83KGXv11Vepq6vD7XbT0dHBtm3b8Pv9rFq1ihUrVkzofOY6e5OMZ8p1bW3tCFmEgt7OJ/OD35NP7ofLfFlZGRs3btxn5H5KRv3ChQv5y1/+wkknnYSu6zQ1NdHT08POnTtZuHBh1tgXX3yRt956C8uySCaTHH/88bz88ssIIbj66qtHLONUVVWxevVqIpEIPp9vKtMcF5k3UT7hHe8DYrIPB5jcAyLfjTLWd+Ujl5EjhNhvDJxM9nYZH4ytNQwDcOINb7/9dhKJRFpet2/fzkUXXTRi323btnHLLbdg2zYLFy7krLPOyvISNTU1oes6t91224Re+lasWMFvf/tb3nrrrSnFNx588MFs2bKFW265BUmSWLt27aSO3dnZmX4hX7RoEffffz8AbW1tnHLKKWzfvj19/fZV9lY5z5Tvgw8+mE2bNvHDH/4Qt9vNJz/5ybyy/d577+UNvQFYvXo1r776Krfeeit1dXVAfrk94ogjePTRR7ngggsmPP/pkuHHHnuMUCiE1+ulvr6ej3zkI4CTTHjUUUcB4Pf76e7uZu3aten9Ojs7OfTQQ3nhhRdIJBKcddZZpFIp2traJnwuc529ScYz5TqXLEJBb+eTeRhd7jNlftmyZbz++uv7lNxLInP9ZYLYts33vvc9rr76av785z+zcuVKVFVl586drFmzhqVLl6bHvvjii+zYsYNLLrmEP/3pTxiGwfnnn88f//hHDjroIMrLy9NGbyAQoK6ujjvuuIM1a9Zw9NFHj8jwngluv/12PvWpT9HT08Nvf/tbampq8Hq9HHjggRx11FH8+te/5qKLLhoxl3feeYd33303S6gGsSyLX//618iyTF1dHYZhcPLJJ+c8PjgVdL7xjW/w3//935SWlqaPk5ncMugROOuss3jttdcIhUK8//3v549//CMdHR3pG+WYY46hvb19xHdVVVWNSJTJrMhz++23c/7551NbW4uu69x0002sXbuWY489lu3bt1NWVpY2+vd19gUZf/vtt4nH43mrOq1fv35SBsm+zkMPPcSHPvQhHn/8cU455ZR9OsFqb5bzfPK9p2Q7Ho9z9913c+WVV87o98wEzzzzDOXl5bz00ktUVFRQXV2Npmmcfvrp+5y8720yXtDbM0OmzH/uc5/j7rvv3qfkfkpGPcBtt93G2rVrkSSJlStX8pe//IXW1lauvfbarDfWF198EVVVOemkk3juuefw+XwcffTRPPXUU1RXV6eXTGeTuXATzdUHxP5k4AxnX5LxAgXyUZDzibN9+3YeeughzjvvPBYvXjzb05kSDz/8MOeee+5sT2NGKch4geHsa3I/5WKkCxcu5Nlnn2XhwoWUl5fT398PkHMJKjNmay5WUxkMU8lFZWXljBv027dv5xe/+AWnnHLKjH7PZPjQhz4EwBlnnLFfGfSwb8l4gQL5KMj5xFmyZAk33HDDXm/QA/uUYZOPgowXGM6+JvfTYtSHQqF0rLnf72f+/PlTntj+yL70gNiXKMh4gf2BgpwX2NcpyHiBfZ0ph98UKFCgQIECBQoUKFBgdpn7vYALFChQoECBAgUKFCgwKrPWUfab3/xmVnmm/QnTNEc0hCgwNfr7++dk85C5KOdzVf4K8xqb6ZJzRVH48Y9/TFdXFzfeeCNNTU1s3LiRZ555Bq/Xy2WXXUZlZSXd3d3cdddd2LbN2WefnbOG81yU8T3FXJKNfYm5qM/nqpzPRRmci3OCuTWvmZLxWTu72tpaPve5z2H07aL1m+vwNa3C1lPUnPdfFK8+HXnTs7R/7xQWfW8r3tpFmNFudnxpPk3XPkDxwafT/ctPYoZaEbZJ+fGfoPzET9H27feBsEByasg2ffFB3EXl6e80Ih30/f5arGQYYZlUnPwZyo69nC0fd9P4+TspXXcRWvvbRB7/AYElR5J46T7MWDcIC09FMyWHnYPW9iaVZ9yAv+WgUc9P2fI8fXd/GVxugitOoPYj30pvM00TY8eLebcXmDi33377tB1ry5YtPPHEEwghOOmkkygpKeGhhx5CkiQuuugimpubicfj3HHHHei6znHHHceRRx6Z81i1tbV86vzT55SMy7vfIP7kzTMq4wDypmfov//rSG4P1ef/N0UrThj1eHNJ4WYyl+Y1XXLu8/n43Oc+x0MPPQQ4pXeffvpprrvuOnbv3s0TTzzBxRdfzCOPPMKll15KaWkpP/vZz3Ia9fu6Lo/89TYiT/yIwKI1NF1zX9Y20zSRN9xH9MnbkLwB6i7/Mf7mA6f46xSYTn0+XcxFXa61v03oz9+naOm6GZNxACsVpfeOqzDjvfjql1L/8dF/n7mkMzOZS/OaKRmftbPLbOYSXHECTdfchxFup/O28ylefToA/oVrSGx4gKozbiD56oP4FxwOQOqtv+Apb6Dxs3cAYKUi6WM1f+kxXIGSnN/Z/fPLqT7vGxStOAFh2yhb/g6At3YR0fU/o3TdUCOHivd9nor3fZ7Y33+DraWoPOVq5xi/+MS4zi/855to+Mxv8DWuoO27p2JGOvFUOo0ewuEwxijbC8weuq7z9NNPc9VVV6Vv/ptvvpnPf/7zqKrK3XffzVVXXcVf//pXTjnlFJYtW8aPfvQjDjvssJxVgQblfC7JeCwWQ2JmZRyg//6v0/ylx5A8Xjp+eA5FX3t69OOFw3PSEzZX5zUV3G53Vh+Mvr4+Ghoa8Hg8LFmyhAcffBBw+mYMnntRURHJZJKSkmzZ29d1eem6iyg+5Ez67/3qiG2h/j7Ux3/A/H9/ATPRS+8d19D8xQfHddwCexdzUZcDaJpG0wzKOEDoof+k8swvE1gwvlKec1VnztV5TSezZtTbtj3yMzkKGXm7weXHoWx5Hs64Afndpyhe5XRpdfmCaG2vY/TvxluzAHdx5ZjfZ4TaQNhpb6HkclF0wInO8Yqr8M9bTeqdp/CUN0z4XHp+87kRb66+pgOx5CjCMsG2kHxDzSh0Xcc/yvYCs8fOnTvxer3cfvvt+Hw+LrroIlwuF0VFRRQVFSHLMgC7d+/mvPPOw+VyMX/+fDo7O1mwYMGI4w2X87kg44YhMdGehxOVcQBhm7iLK9L/thL9uEtr8n6HrusTnNWeYa7OazqRZZlAIJD+e7B+QmYdhWAwSCqVoqSkhA0bNrBhwwbA6eHR3t6OHenCLQQ9PT1o7ZsxDR3DMOjr68NqPITEu8/iPe5KQq8+gjTvSKdbp2ahbH0Ja/Or1C49lK5ICiIpTMskmUoS748CUF9fTzQaRdM0XMkehG0RLl5MuL2d0tJSPPPW0t7ejuUtIdC8iq5//hHDX4GpKAgh6OjowIxE8EkmflkmHA6jyTLFuk4yFEJRFFwuF+4nv4l56r8jhKC4uJhAIEAormNHepEsm3A4jCzLSJJEc3Mzvbs2EwxUEYknKCqqRm57h/b29nQzm2QyCUBLSwtdXV1YlkUwGKS0tJTe3l7A6UZqGAaJRAJwOn729vZimiZ+v5+Kigp6enoAqKiowLZt4vE4AI2NjfT392MYBj6fj6qqKrq7uwEoL3e8vrFYDICGhgbC4TC6ruP1eqmpqaGrqwuAsrIyXC4X0ejI6+3xeKirq6OzsxOA0tJSvF4v4XAYcLqxJxIJFEXB7XbT2NhIe3s7ACUlJfj9/nS39pqaGmRZRpZlXC4XTU1NdHR0IIRI69jBMpNzjbmoyz3lDTltqdHIpcs9ZXUYmpx3H23360Q1GaNnG5WnXUvJmg+N+h1zVWfO1XlNJxM26qcrNCGz7quy+W+0/u8JaK2v0/SF+zPGuPBUNKLufg1XoAzcjilSdOD70Do30fXTi7H1FPUfv53gUqfNe8cPzwLJjeQL0HLDn9PHMqOdeCoa855X5elfoueOq6i96HsTvSQ5l6JK1p5H120XIHn8lK67MG3cAHi93lG3F5g9EokEfX193HjjjWzevJnHHnssy9hxuVyYpollWekW88FgMG3sA1kGTzwep6u7C/XdZ2n9n+NRW1/Hf/HP6evrw2+ZJJMp8JYRfvd5LHeQeEol1R+i+aTTEZtfZvfN54Op0PiJ/yNSvABN09j9vQ8guTyYkpvAFXekH8DyttexApXYtp31APZ4PBi6jnToxWh//ibqMddgyDJdXV00NDQQiUQQegopGsXn85GSZfSeHuprl5FMJh2D57T/AEg/gIuLi/EefCbtt3wY3D5K111ITLORI+1pg8ewJVrffomi0nK09rfp2L4JV82ivAZPLBabkwaPYRgkk8k5YfDMFMFgEFVV038P6ucsPa0oFBcXA7B27dp02/VbbrmFlpYWDL9J65a/4/3lR9K63Ov1UltbS6qzHNvvwRveRnFlPZ7KatxuN83HnEtE7iJxzxdoy9DlbW4PsZ9flNbl3hv+TG1trTOP7R1EK5tobGnJOofi4mJ2+3zUnPlleu64ivqLvkckGESSJFpaWojtrMTWUulr2V1UhM/no7S6euggOXT54Ln1uV1UVVVRVVWV3la7YAWa3E95wI0Z2o4I76K5oR7J4yUYDFJRUZEe29jYOOK4mQzKJDjyONrYsrKy9L8HyzMO0tTchEsaqoGRuSIz3Es5/LiZqzCD1zvf2Ex5rM68hjnGZv4dCASyrmFzc/Oo+84VBu+FuWavDD6HxstYoTO5UHe8RO1Hb8LXtJK2b59E0cGn4/IF8o73er0IIRAIJKQ5U+d/f+ixMyGjfjpDE/x+f/rfg8tZ8RfvQt70LMUHnZbeVrLmPHp+9Wmqz/131NY30p9XnnI1ladcjd75Ht2/vpL5//Y3IP9ylqeiCTPSmffcvLULcRdXoe3eOJFLkpe+O2+g5WvP4K1ZROdt56N1vJuOtaypqaHjFxfk3b6n0RIqLsBbmv8m3V8IBoMsXrwYj8fD8uXLRxj1tm3j8Xhwu93Yto3L5UJRlKwHXKbBc/vtt9PY0EjfgSelZVxre4va2kuQ+z2UlpZSdND59N/9xbSMB2qq8Xg8zDvvq3DeV7NkvM3vp/n6P42Q8draWgzXoXQ/E8blco14MHp9PuYffDRd/6imzu5DLSqiYcDIqKysxNZ8aQOkuKiIyvp6/H5/1n0K2Q/g1p/8Kwu+/re0DBcr3VS1DMlwyyd+Qt89X0UvriAw72CaVxyKK1iavs6D3yeEAK8XQ/LQFYoRjqUoLS6ioX7Iqz9dBs/wseMxeCzLwu12zwmDZ6aoq6uju7sb0zRpbW1Nf29ZWRl9fX2UlpYiy/KI0BuYuC4vOedf0VvfYPCumuu6fDRq6+pQP/ItOm85D2/NfAKL1yF5Zs9wSOgqbckIB1blNwgLTI5BOZ9r9kpgmI6eCTxV8wgsPgIAX8NyzGgHvrolOcdG1BS9LoOeUAcAbsnF/NIqSkd5CdhT1NTkXyneV5jQK15maML//d//EYvF0qEJVVVVWaEJy5cvx+12p0MThpPpFRqk7KiLkd9dj5UMpT8rWnky/nkHU3Tw6enPzGgXtpYCGHU5PxNv9TxwuZE3O3FpQgjkzX/LGlN15pcJP/6DcR1vTCQJd1ElksuFu6gCW4mnN3V1dY26fU+T0DVa23smvIy3L7JgwQJ6enrSS/YNDQ1YloUsy0QikbSxNn/+fLZu3YplWbS1tY3wwg0yXM7ngoz3PTrx1aicjCHDgYVrmPeVv1J/xc9wlzekDfpMkqkUm7bsomN3L7H+KEpcJRpO0tbWS2trF3Ohjcagx35f46c//SmbNm3izjvv5JVXXuHkk0/mlltu4dFHH+X00x1ZPOecc/j973/PT37yE84444ycx5moLrdXnEjK0IC9RJePQldXF8WrT2fe156m6px/HTMhcU9gCxuroMunnbmoy8OP/wBZUSZ1PhPBP281es82hG2h9+7AUz7yeWfaFrviIdqSERL94fTnlrDZFQ8RUpMzPs+x2Fd1eSYT8tRPR2jCWJQf93Fiz/6SwJJ1AEhuDw2f+U3Ww90It9F35w1Ibg/CMqk5/7/T2waXswAarvwN3uqhbnENn72D3t99gdAD/zGQTX4lZBRz8M8/BE/l2J6xnl9/FpffWYZu+MxvCD38zRFLWtXnft2Zi9uLr3EFgSXrUHe/jrr9BVh+Ts7ts4Wiq4RTKeaZNj7f/t26oKSkhNWrV3PzzTcjSRKXXnopsViMn/3sZ0iSxIUXXgjAqaeeyh133MGjjz7Kcccdl7PNeD7yyXgmMynjUtnYeSNTlfGK932e8KPfJfX2k7h8QWo/duuI7+ju6qejqw/bzm249/RGQAjmzW+cM8u3+xJXXXXViM/WrFmT9XdjYyPXX3/9pI6fT87bkxHsAX2+N+jy+It3E13/U4yerbR/7zSab3wC+e0nsQ0F6o+g9w/Xo7W/hbu4kvorfjapazVdCMAWAlPYuAttaGac2dblnspmrDHmOFUZL11zHjUX/C89v/4sQlcoP/FTuPxFKKZBTFcwLBPDtlAtAzPPy6RA0JGMolsWjcXlOccUmB4m1FH2nXfe4Z133uHCCy/EMAx+9KMfEQgEuPbaawH4/ve/z4033sgPf/hDrrvuOlwuF/feey/r1q1jwYIFWbHG/f39fPzjHweyY1+Hx8kOLrv3dPTg9bhpXjgvHfvq8/morq6edLKPx+MhEnEy0evq6ojH46iqitvtpqGhgY4OZ/mopKQEn8+XjpOtra0dijUeFvuaTq7KESc7GGu8bds2AoHAiMSg2Uiucns9pFIGHe2dNDZU0djoGHx7W3LVb37zGz73uc+NV5T3GD/+8Y+55pprZnsaWcTj8awQlT2NZVnsbuskHEpk5plhGBpe78il5NrqcpqaavH5J5reOz3M9vXK5Pbbb59zcj5RGd+dCJHQVQ6q3jOhRTPJbMiGLeysmPlMQmqSjmSU+aVVVPj33uIL+4Kc7yn2tAwqpkGvEiem5V8h0FMKvuJgzm1Ly+so8hZ0+UzJ+IQ89QsWLODpp5/OCk0IhULIsoymaSNCE5YuXUpbWxsf+tCHgOxY45/85CdZsaRjxb7GSmSKJV/aWBzEsK0pJfsMJn7ByHir0eJkR4s1Hr7v8DjZhoaGrDlmjt3TyVUhNYkS66XIH6S6pi4dY5wZa1xTWU0ymqKstizncfflWOOpMhc9zBNNrJpOFEVlZ2snqcTIkI30tbJMQIDbiU3uC8UIRxOUlRVTV1dJWWnuEnAzxWxer72BuSjje4rZkI22ZIQFpdU5txnjCLuJajLFXj9el3u6p7ZPM1flfE/KYK+coFuOjTlOcg1dK2GaSBm14bvkGEvKa3PtNuPsD7p8Qkb9dIYmZNY2Hg+mZdGrxakkuxyUklLxlhbn2WtuEo1GcyaczSTCFlk32iCGYiCEhZTsx7ZyVx3Qkxq7O7o5uHZuvOHuTUxUzvcEsyJ/QtDTF6Kzsx/LHGZ4WAboMkYqjtftwgkikBBF5eB35mlZNpFIglgsQV1LLS21NXvsITsb12tvYqIyLoQTIqKYBsFZTCqdDva0bBi2lTfEAcC2nGCM0ZbfO1Mx5pVU4vUVjPqJMBd1Oew5GexMRukfR1y8rWrIu9oISm7QnBKSnqXzcRU7Dr2UoRHTFMr9uT35M8n+oMsnXNLyxBNP5MQTT0z/XVtbyw033JA1pry8nC984QtTn10GiqKnFdYgtrCJxZOU7WVG/Z7Gsi0Su8OUtlTh9mYrclMx0E0VbBMlR8IbQG8ygWqaaIqGPzjzmfYF9i0Mw2Dnzg5i8WG5NZYJchTJHJA7YQGDhrpAkqMI04Sichgw4G0bejr60V02C6tqce8Hnpe5ji0mlpSpmI5xNBeSoPcmYpqC2+UiZWgkDY2SYaFqpm1hRGPou3Zjrckdt6yYxoR/rwL7N0II2pIRoqPUsQfHI2929yNCEYRuQIZ33tzZjmfpAlwBR2a75ThlvsCcXf3Ym5m15lOD4SvhsMxtP3ye+oYSdN3iA2esYMXKOrZv6+f/fvoiX/m391FVXUQqHOG++zu44lMlrDiglvvueoP+UApV1TnhRI0jj5rP7T/+J7YNg8/5Kz51BJaq4Qt4ePudfmIxlZPfv5TOjhg/ve2ffPNbp+NySfz2V69wxjkreeapbfR0J/B63ZSU+Ljk8sNxu11Zx503v4KzPjiy9OQ9d77OiScvpqFxyJvd05PgkQffxTAtEPC+U5YSiSi89mo7ktQKQGtrlM9dfTTzF+RvSLF7V4Sf3PIP/vs7p+P3D/1kti24587XicVUqqqKOP/Cg3G7s40cy7bZFe4jEopwQFUxJeXDGwTZWLJTicLWRxr1sqqSSjg3c3t7D4pl0NxUT2XZvv22O134/f5xy3girvK//7Weyz+5Ni3jkYiCbQuOWDcvr4wHg463c+OG9nHJeHdXHJ+vdUZl/PWNHekxu3dFOOmURqpqMkqaWQZSMgS2yfoXDFySY7CvPVhQXjqk6CU9yfrnokgeD0JIrD2qlrJyH9HOCNtdgsUVtXhmOIxgeGhggWy8Ph+WbROLquOT8+4If/zldj582UGsXT0/LeemZbPuqPmjyrktbF5/tXOv1OU//8kLdLTH+OjHDuPAVdkyNZYut4VNytTxDlwQzTJGGPWyaTiOGVVDGGbOOaiWkU6kLTAx5qIu7+lO4PG4KC3tnjEZf+mVVgzbkadQp8ypH1tKTXO2I9XqC2P19PPs8wqRmOCIQ9wUZYq4YWJubyUcqOXtF/sRAo45XuboNYum90cag/1Bl8+aUZ+5lLV4SRWXfWIt0ajCHf9vAytWOjHzzS3lvPVmFyeevISd2+NUVrixLZst7/VRUubng+cfyLvvbObAAxswbMeL/6nPHpll+L6+eSf11RXMX1DJnx/ZBEDr7ijNLeX0dCdobCqjv1+mttYR0gsvPoSGxjLuu/sNtrzXx8oB5Tv8uGNhWTZ33vEal15+OHX1JZimRXtbjKOOWcCSZUXU1tby7ts9bHilbVSDHuD5v+2kuWWk5+XtN7uoqiri4o8dxrPrt/H2m90cclhT1pjtPV3IKQWBIKUpFIlAOskqoavIho6mOTfscMeZaVskkgpJWQFTI5qQsG2BoepQ5rzBF960R2dQzscj42+92U1zi6NkB2X8ox9z2nLL8lAnvHyyOF4ZP/X0BRy4asGMyvhRxzjddf/5/FY0Xc026E0DKeUY9AAnrfPgdkl09Jls2WlxxOrsOZy0zo3bBT1RD1veibD2mHps3SbVE6fD42FB2czWHo5GoyNyRQoMYZhmWv+OR853v91PZaXzIpYp591ynDKG5GS4PIbVFLYQe60u/+jHDuOlF3bn3DaWLk/oGnFdGTVcSbdMrHTHzNyrIIOlLiOqTOVenEg7G8xFXX7hxYfg9mg8u75zRmT8oCMaKFru3KvtW2JsfzOcZdDbyRRmew8ojkNw3SEetrdaWDkqmpmKzjsvtnHsCeV4JHAZXajxagJ7MHF1f9Dls7Z2nasmuqoYWYblosVV7NrhVEvp7FKoq/FgmCZen5vOjjj9oSTCMLA8Nu09fekyaZmYpoVl2dTUFhMKObVi21qjHHvcQnbvihAJy1RUjlwGUlVz1LjEsWjdFaGpqYy6esej7fG4WbjISfTUNI1UUucvj2/m/I+sds6vI8YL/9g14jg7d4RpbCrD7x/pjQyFZJqanRuiuaWcnTtCWdst20LuS2LGHCWiaDqJ5NASmmYaWHIc2xr4LYZ5b5KKSkKRwVCREv3p0oOhSAxd14nKKUx7rIJa+zfD5Xw0Gd+2pZ+lyx2F4/W56eqIEwk7v1dR0djVAsYr44bhyMNMyjhAX1+cZ5/eyZojnXOKhjW2b+pDSvanDXoA90Cuh25ke+mHb7c0jfKACobzADFTJpHeKGE1NYWzGBtN02b0+Hs7wrJH6N58cm7YFp1tCvV1jsGRKedRTcYfyG+IdMsxVMsYl5xr1pDTaK7o8vLy/M13xtLl4DhZ4pqKFY3nfNYBWNaAg0bNLbOKomBsHTm3AmMzF3U5ODI4EzI+f2EF7UmnOqAqm7zxXBfrzpwHQLhbZtNTOzC37k4b9ADBwECYZI7ZhCICN4Lnn47w/LMRkv0JWt98Gz2x5+rX7w+6fNY89ZlCuWN7mJ/e+g86O+Jc/om1WWNKywJ0tMfweiVckvPwWLqshq6uOH+8521ScRnNKqa6IQACfvXzl3G5wOt1c/ln1iIQqAMGRHlFkGhEQVEMlq2o5dGH3yUY9LJg4ZB35d673kBRDCori1hxwNAb3eBx1x29gMPWjF15JRbXKCvPHX/u8Xh44L43+cAZKygucW7wpuZymppHeuOf/9tOPvLRQ9jyXu+IbfX1pWx+r5eDD2lk65Z+ZDk7kUdTdAx9wOjWZWQ1SED2Uj5QPcRMatiaCQOGuTD1rP3VpEJMlp1ERoQTA+32YJgWiqqjCwNFdVE6g+3r93YG5Xw8Mu4PeHC7nfFLl9XQ053gD3dsRNctzr9wdVpOM2X8U5/N7m8wHhl/6i9tPPFY24zKuG3Z3Hvna6xaXYk/4AZTo8IbpbLBGOFEVDTBPzeapBTB8WtGGvWD22VFcNwaD1IyhCitAY8fM6bTHuijuNGP3z0z6szjmTU1uVcguSR6lQRlBMeU8/b2GF4vSLaFIFvOE4rG6eeZHHaAk7CfS84HVwTGknPDtuacLh+NsXS5jcAWAmFZWLs7SBSXUTusmZtgKKxGUXOXGzSiMURSJhXqZweCpuIKAnt5svKeYi7q8nvveoNEQqW2tnTaZbwzFUMfeEl8+fE2DjmxkUCRB1vVKYn1UFKdOwcPQGKkHtd0QVIWvP8YD739gne2WBQdnCL07maqVx2Ar2TmcyP3B10+6zH1MLSc9drGDrZv62d5hnAetLqBe+98ncUtHiJRA2MgVvCwI5tYfkA5b2/YxIv/6OT0cxYggE9nLDslZQWEwBp4ECxYUMmWzX0EAl6CQS+KYtDaGs26GS68+BCqq4v55f+9hCIblJQ685zoclZ5mZ/33s0t9G2tOoGglwMPGj2+a/u2EE1NZQTyeK9Wrqpj+/YQP//JC9Q3lFJalu0JUga9NaaBlApjlhRjFg3Fwgsx+D+54y8Nw0QkDRg09k0FdIFuF2GaFrZhYYzZ+mL/ZlDOx5Lx++5+g1M+sJzOjqFyYccev4hjj19Eb0+S++95g6uuPRYYXRbHI+MXf2wNtbUlMyrjT69/D5cbmlqKQUsgjVIGLeiXeP/RXkIRi7c2W5xwpCv39qid3i4lw4iSGoTHi96n0OrvY2lNw4yEg2WW0C0wEq/PR9JQ8ZruMeX8wXveYskSH5FuJV3FZVDO//n2Zp758zYOXt6IILc8DnpGR5NzxdQRYm7p8rEYS5eH1RS2qoFlgS2wkyNXpyzLGvIc5wh/EEKQiDmeV9OySKQSKP6iglE/TuaiLr/w4kOorAry/37xyrTKeEJX0yugO94M4/W7aVlejp2SMbe3OXI4Ct4clfa8HomaSgm3S6KuGjZtFwgEYTmOd/M2Kg5cgSeYfzVrOtgfdPmshd/kai1+2OHNbN3STyrlGJFCCFoWlFFbX0xDDYDAtiziMRU5paEaGgFJB2GjG9qIrH5h20iJXtRIP0II5i+o4B9/38W8+Y4Xxe/3sG1LP/MXVGTt5/W5Ofa4hTz3zPZJn9/8hZV0dSbo7XGWlkzTZveuCNGIwjNPbeWcD60a8xhdnXG2bu3nlz9/ia6uBPfc+XrWdkmSOOfcA/ns1UdTVOxl1bAHi2lYoCVBdpYE9VQKy8424A3DRBrlBhWGhjRg1EtqCkmNIyyngkKvnCCupNLVLAqMZLicD5dxgCVLq2lsLMtS1vGYij6Q61BcPP5GHeOR8b6+3hmV8d7uGC+/2M4ha6qd0K1RDHrbFukqKJbLxu3Ov93rkYa2C8uJy7dMhCVIdMfokeOTPpfRGGygViA3uqphC4GVEYswQpcjqJkXpKGxlMZ6r7PwZxhpObeFjdvt6O+opuSsjGNGoqT6nbCU8ci5LcSc0eVjMZYuB7AjMUSGsT68GonZ0w+qPny3NLqhY/Q7Rr3VE8LY2YGZyt9AqEA2c1GXA/T19Uy7jHckovS1p0jFdTa93MfaU5sRhoG5s31Mgx5AzxFeXVUhEU86+jyaEBQXOYa/YhqkVIXE9l2Tnv942R90+ayuRQghspQUwNoj5/HyC63MX1iBbQtikTjnnn8gm1/4h+N1N02iUYUH738Lw9CxdI21h5djJ8II204vOwGc/aEVYJkYioxmGMxbUEF3V5x58ysAJzO8vS1GIDDSU7Hq4AaeenIrp54+fk/0/fe8ic/nXNKLLj2USy4/jD89+A6GaSFseP9pS9m4oR1ds/jNL19O73fKB5ZTVORl964IRx+7MP35cScs4rgTnOzw23/8Ty665FAA/vTgO5xx1gGoqsEffvcaLslZ4lu8JLshSTwlIymJgVKBIAyDUDzMApyGVkJNYSoyUp5KCLawndCbgf3TcdDCRjFNbNsmkVLxB5MEy0dP9i0wRKaMA7jdLi669NCsMdGowiMPvYvLLWFbgtPPGuoPninjF116GJWVQ/V+54KMP7t+O7pu849nusByHngHLnXj8zlxlUsXDFnuigYvvW4iSY4RtvYgZ56btlvMa3QhSUPbJQkOOzBDZdkmyCEoqcPSbLo7+ylbGJy1boUFssmUc8O2SVga51+8mrdefAEQ2IaZlnPJBYlEgqPPXErKdFYYh8u52dGHq8QJ9RtNzuMDVbz0AX01F3Q5OKESO7aFeOetHnq6Epx8ytJx63LbthGWjQg7L8i6ZaAPM64EIGTHSLdzxNTLeobzZSAO2pJTUD16onm/kqQ6UFwoipCDfVGXq7qBYhocfGw97/wjjK5aPHPvDoTirBTl0+UAL79p0heycXtAlS1WLnHz2rsmB69w4/dJNNe7eOZFR58fcfCQLo+oMkG3l1RbJ8Xzsot9FJgYkpilYsG33XIrn7ngCtzlAdwBDy6PIxy6aeLzDIXP9PdGqa4pd4x6BFWLlrN46UL6+iPs3r0bwr34y8tRUzJ1y5ezoLkZSzdx+zzEEym2vPhPhDfAgeuOoHiGl3bGSywWy+oEOxPYts3rGzdhR4ZKCwpPAMnn54A1qykJBOnYuZ3OXa1IA572qpYWFq8cKn+1c9s2Qrtbs5IaneP4qF2yjHAiirBd1FRUsmBedjfbPc1cbCsOc7O1+EzKXzKW4r2trQjbREr0jZCd0TBsO12ybyIITxBKq5EkKG4uY0V9U7rC03SwJ+7X8TIX5fzmW2/hfZecz8KyGtySRLF3ZPz5zng/EhKNxeW89fe/gWFSddBKFjc6iXeqofP2P56n/KCVuL0eGosrRlR62fjM00hFAQ5bd8yo89kR60e1dBaV1RD0zOwL3p6SjU07t5LasQtsAIG/qYH6RYuoK3Li6mVDp2PnDmK7nfKalU2NLFl1cHp/IQSRaJQdG17JOm7LihU0zF8w6ne/2d/O8or6PRqmM5NyvmPHDv70pz8Bzu930EEH0dbWlq7mdswxx3DkkUeO2G8u6nKYfhncGu1FGZZfZ3Z0Y/eGx32MyejyIo+f2mAZZSuX4i8vHXuHSbA/6PJZ9NRLqLKOO+DCIwmCA0Z9d1+Y+Y1O3JOmGZiWhciosCIGqxoIG/SBTH9LDHzk/P/O3n7mtTQgDA0QSJZBXFMIBnxISFP2ONx71+uEQ0NLn4evbeHIo+aPe3+vd+aVoy1sLEPNSleRLANh+zB1i8HKcdIor3SqKuc0yiTLwtZU7LgCkgdRPnSQ7niUhrKK6TmJfYC56N0aj/xNRsaFEHR09CIQIMcmZNADOVKrxrmfqSDkGKKoHLk3SWdxlJbSqrF3HCd74n7dm5EyXqDakhEOqBz5gi9Eun/YULy36ciHZdvElRQYJqP3QnXCsVTTGNXAVC0D07bRLYvgGE+4vUGXgxO+lBknbygyWjIFA0a9ZpsYVv77LaLJpFKJEZ8rqaHY/FwdfhMDqx4xXdlnYu8XL17MddddB8Dvfvc7Vq9eTVtbG1dddVVWrt9w5qIuh7FlcCIyHtfVEQa9FYlNyKCHyely2dSIqEncO3fjOegA3DOQ1Lo/6PJZM+o1Q2NHIkR9URXFbggOWJlKaihura8vhKSlwC4mrewHFxYMHWEaSDgxnZJlY2iOwa8YBj39IQKDN6FtEQvHKQ0GkSSJYt/UuqJeePGhU9o/HA5TtCcqxgwPqxGWc/0yL2VG6bfh44WR5wErLFRTd1623O6s8nG93f2U+4P4XG7c3n0/03ws5mJr8fHI32RkPBqOEx/oaSAZo3cfzIVu23gm2SFW0hIIjxebIvq6w5T7iyj1Tc/K3B67X/dSzAEZ16yxu5VmLgzbA/HfqmUQGYwPz1eq0R4omzmBknSmGDsUYW/R5Wp3X9bfwrQxk0kYZ96fLQQhZWTpQCWZQFVVVNPA6x+5qjFo1Bv7YOli0zTZvXs3l156KS6Xi5/+9KcEg0EuvPBCqqpGOgXmoi6HsWVwIjLeOywvSRgGVnvPhOc0WV0eNxS8CTfe3W2ULpn+xlT7gy6fNatL4Chq0ZVA/cI9hJbXIhQD93lLYel8tJdbKf/E/SRuPgbmNeOKGzR+4120f/Egli7F+s7z1O3sA0uQOrYO+chyAl99mlAgSFDTMCUX9k2npb/N0nRSl9yN+MW5FFc5Rr32civas9sp+5eTR8wv8eN/4D2ogcBJS9KfWT0J4v+7HjuqgGVTdOEhSKUBUr/dkB5jbu6l7BunETzjgPwn35Oi+6wfUn3/ZXiXDSXUmO1RYv/2hDPjmIJ7XgWVt503uesrSHvCMpEMJa8vTBkwzi3bxu1yMZrXTNFkMAywbNS44wGKKTJ60mRnaycuIbF4SQu+GSozuDdhdsQIXfg7PEtrEIpB6ReOw3/8IrSXW4l84h5q/vIZPC0VWH1J+t53OxW3nYf/uEXE/u1xrM44WDbB8w+m6PzVhK64GywbBrpNVv74PFylQy+p/R+5g5r7Lk//vadkXAhBZ1c/ANW3bMCdNJBMQfSDjehLs7sPu6M6Ffd1IKkW+pIS4mdOT+iWJMcQHj9WXKczEmJF/dil3ApMH6nd/VR84hFCy+tGyHnwE/eg//EjmC1+XBGVxm+8i/qvfsSiZSj/+heCrSECCRnjFBX9rKXE/usJZJc7LeeBH5zhOB1sCUF+OQ/ccHy6wZI2EHM+U7o8+tXHcD+3ndQ1x1F86eEjtse/+zTG2z24qoKUf+tMXBNIkhzO8NwzYVmIjJh6rT+EHsmfkG7qOnaO4hS2YWGmZFLJKEXFpRTXZDfm0ezBxoSzEqU7o2zevJnly5fjcrn45Cc/SUlJCVu3buXee+/NGxaxL+vyuK4iD/PSl379Ofxvhomf3kDqhJG5F55OhYqHO8EUKIdVkDpu6o0Aw2oKb3cvvqoq/JVzI1Rmb2LWLC6vxwtCYCaTuFdWU/mLCxF9KdRP3wkXHw2AsaAM9z974IgDCb4Rw2gJAoLE37ZBeYC+a5c6B9MlwEm6rfz5BbRveY/isiq8gaFFINu0QUAsFMPWLZoaJi58sa88Rsk1x+JbOw9hC4xX2/EdMY/A+5x5GO/2EP/WegKnLh/1OMWP7EIcPtLg8LRUUP3bjwLOTerJ0UV2vAghkLSRy63YJiJPtRrDtLBsm6guUx0oyes1A0A3kAwFJBdGUkI1DcK9EYRtk4o5yVrh/igN9TPb7XOuM7ic6ztiHpU3n4vVnSBy7UP4j3e8EJ5VDWhPbsHzySPRntqK90Cn6oX2j524aoqp+O5ZANixoQdy5c8vmJKBMFpZr8nKeDySRFY10JKEL58HHhfukE7l3W30DzPqyx/uIvKRFuyK7KVQ//DSNxNFWCBHESXVpMIpohUyFdPQNXN/KIMGk481HvTI2Qj0Q+uo+fEFuPuULDm3V1TjemYXfKw6Q5c7ck5VkNCVR2Dt6iToCmBoKrawqfz5RWk5T8YTTviJe0inDzkfhrCFUyYPnA6r+ZrjTYcuL/3SibjXNIM+cnXCeLsbO6xQ/buLUR5/D/nu1yj51LocRxkfYvgKiGFg2za6ZaYdJ6aSv5KN2R/G7u4fuUHTUTQNy7TTzQUHiWnKiGTcfYmNGzdy9NGOrVFS4uioZcuW8cADD6THbNiwgQ0bHCM4lUrR3dWFa1UNNT/7CH2bdhP++tN4DvggxaaJWFZFz32vUPbpozAefRuxtJJQfz+Vf7NQiyTMbx+Pz+fD6ymmvb0dt6ZRdPPZuEv8RKNRlFgf9YF6otEomqbhGfiNByu2FKVSCMOgvb0dcPRSPB7HNE2SyQSVoj69raSkBOPGP6NdfACsrqOmuobEP7ahLQ/g+vYJNDU10fHsW7h+oqCsrQVFYUvbTnTLJFBeiqnp6L0hEqdWUrkogKmYyKaJW5LwuFzpF+aWR7roumI+hs+5L4sAxbSwhfNSnTnW53IhcOLtAYJuN6ptI4TALUl4XS7UgbH9ahJ781ZEQy1uj5vGxkb6+/sxDAOfz0dVVRXd3d0A6Rj5WMx5qW1oaCAcDqPrOl6vl5qaGrq6upzvDAZJJpNEo1EA6uszrrfHQ11dXfp6l5aW4vV6CYfD6eudSCRQFAW325lT5vX2+/2EQk51rpqaGmRZRpZlXC6Xc707OhBCUFRUNKOrBbNm1FumjhTrwtRKcVsWlmZAQkXYNrKu4wa0ZWV4N0cBCGxJoi4vxYvAkgTWtjDug6qxqnwQcDvxuwNGqKmpWMLGNIY81baadOr0xmUMw0LBoDKljPsCWF1xhC3wrXUSuySXhO+IeentQjeJ/eeTVHz3LCSPC6sviXz365R+4bis45jtUQzTwNc4eiKI9sw2in/z0XHObmAOA+cvSdKoBrluKMDIajXCsLFsC0sxnJh7K395NKEJQIBwaiPrio5liqzgWVnJv//+gjlstcROqFm/jW9NM/rGDoo/CdoLu/Ed5SStSX4P5nu9WB0x3M3luEbpRjlREokE1dXVIz6fiox39fSDbTnVljyOoSVpFkbjsHlbAndYp+KhTlxJk/hZDeiLnKYjpm3jnqJhLxkKwlCwUkG6Y1Eq6qauPPNdr32NycYam6aZ5TU2bAtpmJwbh9TifrkNPrYmrcs9mobk92Bt6sHVVY4F2MWeAcM8m7SnWAhM3dErPXKc2qJSvK4hmTEzjF9L2DnDRqZLl7vrSlBVlSLXyBdssy2K9wDnZdB7YD3qn96BKRj1I7AFcVVGGzTqhYDMMoLD9H++p4GdcV2tjJcC3TLpTMUwJpgXs7dgWRatra1ceumlgNNpNxgM0tXVlWVwrV27lrVrnQZTt912Gw2NjSSKNuN2u6n2lxLzeqmprUXbqVB89CKs7TFKS0uJvNZDyQnL8NbUIAU8+FqT1EqluOscA7SlqpiQ309paSmuYl/6pQKgttZZLen3enG5XLS0OM3YtM5WNK+X6oG/wTEeJUnCV1IKkpQea3XFiUoSLWcOrSAFTjko/W+hm/h/+joV3zsXT20V4fY+Kh/aTeqTTnK1y7ZxJWWoDsI2Ba/LRVFGjHuRx4O7X8NlQ8Pv25Esm+iHmzHrPQQ9bjTLSjtpiobFxmcm0AaH6fvBsZZt4xM2RaZF2ULnmVhfn13mtSXjOoBjhA8y3BEzODYUClFSUpLzeuc7bqY8DH8ODB+b+XcgEMgK42pu3jMrx7Nm1Nu2cEojWhrBTSGin7wPe2s/8lUrSLV3sQgJLB273Iu+qRc74AKPBBZIa5qx1jVQ9ZttuHSbyEUt6IuKEcIm/Nn7qUnGkQJexE0fSH+focqYto0wdFQktF6DYstKXwDbsBCaibsk98PL6k3irivJuQ0g8aO/Ezx3FZ6B9uHu2pIRDwGA1C9fRv/QUnwP7Mh7LHNbP+760qyluPFgmTbYAs8YTScUPXcjFWE48famNrZ3RoiMZBjDQLMHvP+pMBRVjvpCsD9hDRg7+itthD52J+amXipuPXdogCThqi3GeLcHqcQHXkfJ+Y9agLk9RPSGRxCKQdl/nYbvUEcpRD57P7hdSH4PVf93wYTnpOTx6E1WxuPRFImUAmoyXf609tZteHo1wh/LTshyJU18HQrdVyxAeCRq/m8nvTc63lBrmpb4JTmGKPUhR1NEymUqp+itz3e99lUmGmts2zbCcF7ufa/3Er/8HlxbwllybhsmUokba3N/WpcLHDl3vbGLyu+/hpTSiV2xDPuAMsCRc9sF7oAP72AopS2QFRk3Tix+SE3RUFSW/p7MZFHZ0InpKsP7VE6XLgen9ntRjlh0z5Jqko+8S9HH16K/sBs7nr/75rgYHkppWQhZwZwmT7phW0iyQt/OndQuWoSV8XJk7G7HWrZsWr5nrvDee++lQ28Abr31Vnw+53e88MILc+4zF3U5OPppuARORMZtYdNTZGF8cqhaktnZk/2SmAN3wsTbqdD9rwfgjuiUP9hJ6HOLne+foi4XCGKahrs/ilYdxV9ZMaXjDbI/6PJZM+oHDULDstCWlVJ+y7m4X2on+czrpI4yYEBM1cOqKfrW31FOKsfX4fwgpmUhn1xPcpWEp0el8q42+q5bBkJQ/rPz2LXxZXz+INUZQilME9uyHI++AbbwYVtDgid3xcHtorjYlzPL3V1XgtUzMtEInJvc3NZP6b+cNOo5m61O4w/3GC3E1b9sJvCB0UN4cmEJG8MwKPF7Ri0ioSu5vS+2sJANI90aelQyPDiSbWBqFkIYSKaGMFKg6yjKFB9k+wCDsjQYfqM8tgn9pTb8xw4lAQVOXU7s609QctUxGO/1pj8vvvRwii89HHNHiNh//IXq318CTD38Jp83fLIy3tPTD5aBpA3t23ftUtxhnepf7qT3gKG6zHbQjVnjd1bYAOGWwHLCKnK1Fp8UtglKAtvjpjsRpcIXnFLliqmuHuxtTDTW2Lm2NuBCP7QOz00foOK5bpIv7EzLuZ2SMdfUov73MygnDOlyAPeHV9J7IHh2x6i8ZyfavzrGReXPL6BXUjGFwGsOKbSUqTFoxid1FYrKnJ4nCOSM0EKBQLfMEUb9dOjyQfKVTvUur8W7ppnwx+/Bd3ADrprhs5ggxkjjXSRlQpEQlcHiHJ758XnqBY7ejyUS+F0uwmqCWpzfzEqlMHpDiHCcZH8/lO07q1WrVq1i1aqhpmFf+cpXxtxnLupyyK2fJiLj/Uoqa0XGVhSI5d43EzvoRp9XhAi6MYNB3MkMm2AadHnS1KggiNzWNW1G/f6gy2cvpn6wtJClgbAxDJOSs1bi+8mzyAkNBpY09aXFsLgCdWXJkFHf3oedcpS3XTx0CkIILMtGDBjzIsNbLDmZuaAlwBUAjw9rIJtd2ILWaJigx0ewtgS3L8dN0liG5JbQN7TjW9uCEE4cpueAOuLffprKn354TMPB3NyHua0f/zeeR9/Sj9UapepXFyIN86yrz2yn6tcXTeBqDhHpieKywe3L/9PaZp43cF0jqcpYOaokDEcyMt54bZNYKIzbkEHY6Q6iRiSEEIvmbCmw0diwYQP3338/3/nOd9i4cSPPPPMMXq+Xyy67jMrKSrq7u7nrrruwbZuzzz6bFStW5DzO8LCF4Fkr6f/tBoqjQ9fPd+R8PCtq8R+/KP0gsPqSSMU+XEU+XBkNSaaDxsbGnJ9PRsblpEwsIYOSwAnHEo5955aw/S6Ef9i95HNhF7uRZAvcIJlDcdKDZW2nA0lPIYxi1JhCtESmMjB5oyrf9dpXmWissSzLJHsjmElBjRDE+sNwSCn8/HlCl6xG7evD0HWSS4KULiwnssxPVVsKly3o2bSLiBJHVTWCRW5sIUjFk1iGQSqVoi/Wi4XAX1WLZllYQiBCIcoEhNs6MD1u3JqFPxlHJJK0t7fhKy9FlxUs3SDlilIhbEL9/Yh2PyUlJfjKfWimTvvjG6l93yoSiQTqy624llXj+/azaP9xDHJHB8XFxQQCgZxxspIk0dzcjEA4MbkDVTX6+weSxaurMc9fgfKBZtQnd1C1bCldXV1YlkUwGKS0tJTeXuder6qqwjAMEgknB6qpqYne3l5M08Tv91NRUZF+WRkel1yiqfT09NAfjqBaFr6BuGQpkUwfLxaL0R+JYAuBPlBFyCVJ+F0u4rqKu7sbLRRG93jQ3RK721rB7SK2aRuWbTvmWSzO7rZW3JJrxmONB6/hXGMu6nJw9FOC7JX/8epy07boU7Jz78yu7EpL+TBr/bhSphNSmTCwg0P6ezp0uRA2sqHjkl1okdi0JM3uD7p81ox6fcCglkwnFj6lJCi2SlGPrSP45HY4/VDHQHBL2F8+Cja9m97X6k5S+uPXKLZNJ7bwLOeHkoDk1Q9Rl4gjuVzw1aOHvtAykFST2lveA0nCKgsgn7WEoGli6SaKoaNbFpas4/Y5N17ylr8j3+E8uEpvPIny755F/H+eInHr351s8osORX+jE7s/RfQrj6W/qugjq/Gtmz8iDjNw6nICpy6nvb2dkp++SfEnjkDye0j+4iUCZ6zA01KBuT2Eu75kwqE34LzU9EVimG5obBjFq5LPEy9sjJSG0CceOiOnFLyu7ARcwzCwbAvPXlYBx7ZtXnvtNSoqKrAsi6effprrrruO3bt388QTT3DxxRfzyCOPcOmll1JaWsrPfvazvEb98NbiAEXnHYR835t4DxmQW4+Lim+fmTXG6kqQ+O7TTmUEy6bki8entw0u2QJUfPtM3E1D4QciqRP+5D0AuKqKCF54yIjvb29vT8f+TVXG+3/2d8RpTUMlLE1B7e0DDxgbYmc7lW2KXgpj1vnRFxUTO7uRml/sRLIE8TOGKt/Ipjki/nLyCFASWHE//VXJKRn1mddrX2cyscY3fe97BIVFoLISYZiUVVfSUlrFjhMb0O/dSPMxB6C43Hh9Hoq/+X6CL7+M1+VCkiQqDT/i2xsJqrKjyz+8gCKXC5cQ6F96nEpNweX34fr6yekYXY/Xg53SqPvvV50uqnUluD5yCMGSEoI1Tq6QTwjctYOhQlH8f9iE68+7sAHvjSdRf/OHif/PUyTv2oxk2VRfdCjW37qR+1MEfvQqMNDn6SOraRzQ5YEvtGTFySZ++Dekv27C6/LgSUDgq++j4vEOR5cHg8ife5gil4RnRS2BKw6k0Z3t1R8uU5lNcerr6xGI9EpAvrhkSZKcsbqKOvCSUOTxUOoPUFJSgiRJlJaWoqTixCIRAsM8lUG3m6qSUtRwGCwLj9tLRW0NoZ278EsSDIyXLJPm5mY8GfkLMxVrPFfvtbmoy8HRT+VMTpfrlonnrIWIw+sJPryNxMVLs7z0ZY90EXw7BjZ4+jViH26m9K89yIdXYFX7SbyvltrbtoGA6IeHfsfp0uUxXaXEG0Du6JoWo35/0OWzbm1Z1T7Cn1xIlalh2BbKyQ2AwH/kfGKxZjyQrtYSP6OBivpaREUl/V9ZhZXROAOg7wtLqD/+RHb9/W94AgGqaosg4yW0599XDdRld6rlUFqCfUgLrmgSWwhsyySWSFEd9FB6zbGUXnPsiPnmKjGZr6pBvjhMgIpvDd34JZ8Z2t+zpJrKn3w4736jIWwLy9RIpmR6e/OP09TBm3bkomw4maR0EuFwtmWl61VnzGigYsXEjzebbNiwgcMOO4z169fT19dHQ0MDHo+HJUuW8OCDDwKO92swGaeoqIhkMpmVfJOJp7mcypuHYi+LLj4s/W//kdkx55kyV/2HS0cca7A6Uj5qH//0iM+Gf0fmd01FxlVFo/fMhZDI8Kx5XfR9YemIsfK6oYe4vqiYvi+OHDPdSIaM0FSSsRRyiU6Rd2a7i+4LTCbWGHASWGt8hD65kKpIBEqrUE+oxyovxb98PnG5BUkMlZmMn9FAaW0NvtWNWLecQt87A04bnw90ndDXDuWQtUew6e03cJkWJQ0l0OYMMSMxSh+5gtaNG5CQcDfWUFtSiXTUQoj3YydT2H1h3BWOgVR89TGU5dDFU9XlpV86gdiFi2nMMBIydflY9+pY9ClJgh4fpTk69I5gmM62ZQU7I/HcHiUUUs108ugGdjI1Mpxn9PDq/YZ9SZdbts2mSFc6WTr1yYMxd7RmjY2f00j8nGzvduLUoYRV9ZAK1EMqRj2PqWDaJoqlE0yCFovjLy8be6f9nAkb9dMVljC8FBmmianli+UeUjC6bhKwDCcpNAdOtQOBaZroxjCPc7pJ0kAHWkvHtm3Un79I7UZHmFWPj+5182j6l1NGuwxTIp/xN1WEEGBpaIqPPjl/kxZhONfOHp7MYtvp/1TDmFBtYmFqWMMrTVg6yaRKReXe08XNtm02btzIlVdeyfr165FlmUBgqILL4DXJvDbBYJBUKpX+XTNDExKJRHopOrMs1+Cyek+P09hjtLJcPp+P6urqdFmusrIyXC7XuMtyeTweYt9/BmlTCK/Xi0vX6VjTiOuy1TQ0NNDR0QE4cunz+dLL6rW1tSSTSRRFGbFUXlxcTF9vlFQihKQm8LvdWLZIVx8p8nhQTAuBGFEGzedyYeNUu8kcawoxpTJoOccmQmDr7JQEK5oXTKoMmiRJ+3wZtEEmE2vsdrsRtsCOxEFWsJODjccEVk8/pNODcuuTrPjvgVXCwS7iQjMQkF2aUjfRBhoOCk3H6g1hufxQXELxz1/Hs7HTiUEPbMY4qgVx3cwtuc+ULrcsi2g8CmUVoxr1Qsu9qmpaVpaOEtYoVvkwPR9X5ZElNAvgmYEOp5Mh8cO/ob/Rmf47cFg9XDdx73NEk7NsgPHG0o+HyTYRzEVSVwkGfSgdXVM26mfqfp1LTEhKpzMsQXINi7MWFpo+YHSn5WzkQ8C0haOs7NwPiI54KL2rPkb2tiTHEX4P8iUHED3GAwhEaS3V1bWj7jdVRisPN1UkNYWwbIS/OG+qymDOgZajXr0diiLKgySSKYwcyVn5v1dGuH1Z3ylZJvFUiorK0ct3ziVefvllDj/88LSnMhgMZi27DsaUZ8aWK4pCcfFQeEdmaMJPf/rTrOW+6SjLNchEynIVf+PsrPkGg8G8YzONx+GyOrhUrqo6iWQ3RZhIAw87t1vCR0a5smFxlcOXY32u7LFeW0o/DCZTBi3nWCyEy4vl8SBllIcbZDzXe/B67ctl0KaCJEmgGViyo3tFpt4d1NMTXP0zFcVx0NgWhgAjs2qFppOMRxGyAjaIpAKmiRCC1GcPRX8zCJaFq64aT3P9SOfFNDKTulyOxrElqA3mN0TseJ5kSNsckSybD2uYFz+cjDPCdErKmJaVFX6zv+GaRkN1KpR+6YSsvydb0SWkZkc6jDeWfjxM55VSTAMLC+Ip9HgCX9nk7YmZvF/nChO69oNhCZIkjQhLGPT2DYYlBIPBdFhCLkwz22BULQNLSZFL++tZxqUYyMfLbXDKsaH29HJiLGEXYGokwomh79VklOEe/mlm0DM33ZimU91HMlWQw/kHDmS6W0aOlx7bwrYtDM3E0CdQo1gIsEeG3+xtnQi7u7t5+eWX+clPfkJfXx/PPfcc3d3dmKbJjh070kZWWVkZfX19qKqKLMt5PQD6JPITZprpkL+u7n5sXUEyp+/8xnoJnzRKAiOmEdHkscfmYKbu130F0zTBGPgPnGpGORA5YpJHOSim6qw22kJgZ3rqbZvoQAy+470xSCUSKJaBreowsHJja87+XXL+TqtTZaZkQyCwOntQw1ESev5VV3OUe0bYgu5YZOB4+VGGhbGi5WhOaFmje/v3A+aiLofJyWDK0NCsod95Or30ML26XCCQBxy+Smf3lI61P+jycXvqpzssIZlMIg/U3g263STiKRStC1M38Ls9tLe3I5smAbdJKpVKj/WYBrFEEkXTcWHjd7lQBpS41+WCnhCGaYJp4Xd5MQcqJgxm+2eOlYCIrGDbGkEhMGwbS0li9XUjxKJJhyWMVTEhEnEU7fBs/+rqajRNS78ItbS0TKhiQndXt9P1zbLxuqT0uQ4PSyj2eOjp6SESi2FlVEwA8NoWUcVG6+1GNjSCjKyYMPwaOjewRcDjdZKNBzpQBlwuIqF+3JK114QlfOhDH0r/+7vf/S4f/ehHefXVV7nlllvSYWYA55xzDr///e+xbZszzzwzz9H2TTRVJxyKgTJzxtJ0IpkqdkKhPxGnZhSvZ4EpkBWukd+EtJRxvljZQw4BkSOxX4tnV+yQTYMiayAePP2hitUXJrUXdrUWgvTLyWjkM+oHP9b7Q8hFoyeJ68NeGoQQaMPr4gOyrhIMTF8TvAKzx0x66WeChKlR6gugRxMYKRlv8cyHIu6tjNuon+6whJt/+MOsZfOgL0BRcTGyoYJwDNrugYo3Eu70WBc2ZaXFqP1uEAOtiTOX33UZ78DfQhgjWs8PX6r3eLwIvEja0Fiv26nMMJmwhEFGW1JftmxZ1gtR5thgMEhFRUX67+ElmEarmFBVWUV8WNe3TAbDEoRwwkB6OzrR5NTIsaZEiduL6faAbY6omDDiGqaXJe0R17uiyJc1570pLGEwnnjNmjWsWbMma1tjYyPXX3/9mMcYTDKcS9TUTM3I6e7px9aSSFYOj94UGC4704qaQo4WkazUKBlP4mEGU71e+zperzdvOORoWAOWp62PLkfCMEe8J4hofMQ4NRpDyBmrAYaJLctYk5jbeJkp2UiHzggLeZKrYU6Dx8FzH/81sGNxRMYzaJCUIlNdNvLz/YW5qMth4jJo2hYxbSiKYbq99DD9utywDHTbxOfyoHT14F26aOydcrA/6PJxh99Md1iCNdzDYKoII8cyowBzWDiMapr50/EzPpdyxIyPGG5ZTrhKBroxuRi18SLLkwsDmDYGFL2dZzlR2KBqalaDqclijOM32JexxuFt29NMRf4Mw6Q/FEUaVtt4OhgtlGCqSIaMndDoT0183rN+v85x7En+bqZto8kKtponvGS00D1jmG5SNaxECpHINk6EMrMhEzMtG1ZXiJ7enkns6TTjsmwL3TZz5k/lxbRyro5Yiek1/PY25qIuh4nLYFiVs/ItrJ7p7wswE7o8qTl2mh6OYuZJEB+L/UGXj9tTP91hCSMeBCKPL0EI9JScTsC0bRt7IrHe42GYx1EaWPqdqaZJsiznbLc+VTKbbY2FaVsIM4+SEjb2NFU/MNRp/q32Mubig2Aq8tfTG8aWE0jT8MI3nKm2Fh8VYSPUFJFwjLrisgmVt5yp+3VfYXQZz/AUi5H6VNPVUV8KnKRbgTKGt1okUlBSAsO9/pqOyBFKMl3MlGzYg/Hrtp0u6ZwLMbAKkSspVjd0DMvCFmJi95ZtY+V40RJjrKjs68xFXQ4Tk0EhRFboja2qiMj0v6zNhC5PWTqVOM0N1a5uShbmLu85GvuDLp9UjabpCEsYod4FuctoCcv5bwDTspFHSRyaKDm9+baZVeN3upn1LHohUFUDkccwk0wN9nMP+3QxF7vpTlb+LNuirzeMpM6Mx246WouPenxNxoobdKeiLK6oG3uHAWb9fp3j5JJxU9Wyq+DkQBjZJYZzjtFNLCHQx2VQ5TjOQPnT3YkQC0pHacg3SWZKNvSsAhOjXJ88LyySDUo0gtA0zHzNBkfByHm9966iB9PNXNTlMDEZjOkKRsZz3/HST//vOhO63BY2KVOnxONH7Q8TbGnCPcEyo/uDLp+1M/R6h9UuF453fDAsJzPhVspQMMISCG36jPrZoKmpaXYnIASRSGzUjiL2tFUA2r8rJszFElqTlb++vgimHMt6yZ5OpqO1+KhYBkJViEeSJPTxV2KZ9ft1jjNcl5u2nTe0L3ugha1q+cNvGFrRFcPDbXKNVfN/Z6bjcDorcs2UbNiZ4RG9kfwDB4x6e4QRLkhFos62aDw712BcExipt/e2SmbTzVzU5TAxGcz20uuI6PSHUcLM6fKU4cixMG3U7okn9+4PunzWjHp9WPdR07SwTIFp2iBAy/QuZBgRkhDY2swbivokvBvjZbCqzmyi68aodYwn493JySjJlPoMLovPFXK1Fp9tJiN/Qgh6+yJIw6omTCfynpAHJY4Z1+lOjb9yz1y4X+cyxjBdblgWar6VvmHGojBNRJ5wSqfplAmWhZUjMXYEeeUnW8+lprEM6x6RDTl/jpc0EH6T6M+OixYIkv0hhGWjKdq4KulksZ+H2uRiLupyGL8MKqZOKiNv0eoLTSrBfTzMlC5XTSNdilPp6cWcYCj2/qDL58xahFOL2EgLmZ4vwck22RPe3/aO3hnzTMy+x0Og6sqo8xB5ak1PFFk3UDIe8BE5hTzgmVMm6j0qMC1MRv5C4ShaPDYtydOziWRqCEUhGUuNu2797N+vc5sR10eM7E/hVJQXAx2/hz7UhIWSJ5zStiznHcAW6Spdo85jHAaKEIK4pmQZN1NhrsiGniNx0BhwzKiTSA7PhTlDK3QFpsZ4ZbBPGQrpEoaJCO8dJYmHE9edl1xhWCgDZbDHy1y5X2eSWTPq3cNjmywDTTOQxuEhHs+YqSJHZeTemYkd3hPt3sdCj0dGNdyl6cpeN2xMbej36mjrwRpIApvpJl9zgZnKy5gKk5G/nt4IaDOzVDuIR9pD6kiJYsd0euRxeH+ZG/frXGa4jAtNx7SzDUBhD1bVGlkTXc+jBxxDfmT/k3wYo+Ra2cLGsm361ST9apLWxCghLRNgtmVjrK6xVkpBmqbCEnaOl6aZXNGea8xFXQ7jk0FjWBlLqz+cM8RquphJXS6bejovQO2NoCfHv3o82/frnmDWjHopR8KC07FuwKcz2nLhHvAY6MkwkYFQA1M1UPunz8CfC4JlaSNrP2cxTddYDD7NcR7MakIhlXQ8pPEZyLqfa8zFB8FE5S8eTyHHYuMqETsV3K49k4gmmQZ2KokSl4mPI7Z+LtyvcxnXsARCYYm0lzgbgZLKsTqSx7gwJyhv9ihx90lDI64r6aocAjt71WCSzAXZGO2FR9VUtGlynljDygFatj2jZWjnGnNRl8P4ZLBfSaZfAIVlYYeiMzqnmdblQy8oArl1/N76uXC/zjSzZtSbOWKuMj3wEynPOBNIahI52odlmChRmfbe0LS1ye4fFv84EfRwCkuZBuPKNNkT1QwkU0cMxLDGEimnIsZATJwmz6zndy4wF1uLT1T+evrCMAN16Yej7cGScZISx45phMZRyWcq9+v+gDFCl4usRNl0eVzbxsjwqo3pfJ8m9WT1D3nlB+NxTdsmOQ0hOHNBNkYrtSjG9OVPnm45PoNHn3vMRV0OY8ugEIKIlpEgG4qO7PMwzcy0LpdNHQvnO4x4CrV3fPfhXLhfZ5pJlbScMaapNvr0IIhFYkTiSTq7ujASUWJV5VQ0VMzqrNr7IxQXBahvmWJnNFPfY9db2IJEUiYRT0GqH80oA8DYz5uZ7A1oqk6sPzyiQdtej21iJ+LE4kG04gr87rmlCvd2rOTQUr9TjnLAS5hR6UYgRo2Dj+xum5YQAbs3BI1OCVNtHwwXSSTGeuGeHsN7+OMiOpCTUjzBDs2zTSgU4qabbqKhoQGAT33qU2zdupVnnnkm3XOnsrJylmc5fSQMLWtFxeoLz+JspgeBIK6qVAaKAZDbuwjU7fvdYsfDrHnqPaOUPJo77/4CUzPQw90IXaarr98JW5ki1dWTr5esWDoJbRoMrD2YMKLIKrKsYGgmkmmgDy7B2/t+hYW52Fp8IvLX2xdB7AEvPYDftWeXtyUliZXQCSmjv1xO5X7dH/DmqBWd2XPEMs2MKhsZpRqFNWrHaXk69ByALbB6+7ARWfXXJ1LWNB9TkY22RJj+MWRvPPS17pqVh6ZApJNnlTFfLOYWS5cu5brrruO6666jqKiIp59+mi9+8YucddZZPPHEEzn3mYu6HMaWwcHEUsCpIrUHKhvtCV2eMDWsgaIplm6ghsZ+WdkfdPmsGfXjqVQw20imTkpOOBU/hIUS7SM5DRVbplIaS9j2NC157rnrn5I1LMtEGUxOM3UM28K2bUx13zbs52IXwvHKn23bhHpDSEb+knrTyYx2lM2FsBCRGGE5OWoH5blaym66CYVCfPWrX+Xmm2/m5ptvJpFIsHHjRn7wgx9w6623EonkTi61c/xuti2cKjgIbM0gp76xLLR4fmMwZzPCSWIlFTTLwhJ2ujymOkq53fEyUdmQM+LbjWkKARI5qg3NBJnPHcXUMfoiGAO/nzZdL2B7iB07dvCjH/2IP/3pT/T19dHQ0IDH42HJkiV5yx7ORV0OY8tgplFv90dneDYOe0KXC2FnOTjHE4KzP+jyWVtztsZYVh2tdfgeQ1goyRiDDyTLUFFiCcorS6Z02FQqNfnlvUQvWMXsaPcQ9PlorMt485yj70myKiMn4rhs54EmxEDtZEOnNxqjqWHfXTabiw+C8cpfKBTFSMWQ9pBgmcLGt6f9DGoSI6ERKZOpDuS+r6d0v+5lLF26lE9/+tOAI7tPP/001113Hbt37+aJJ57g4osvHrHPcBl3SleaGY6bPPJjC+Q99JCVbEAIrN4QVncf7oqyaTnuRGWjW47RWFxB0OPFkhUITE/oyqg21Aw40BTTwOrpQ5Q64Q9xXaFi2r9lZigrK+Mb3/gGPp+PO++8k9dff51AIJDenvmCtGHDBjZs2ABAOBymfaCEYmNjI/39/RiGgd/vp6Kigp6eHgDKy8sBiMWckpENDQ2Ew2F0Xcfn81FdXU1XV1d6Li6Xi2g0CkB9fT3RaBRN0/B4PNTV1dHZ2QlAaWkpHo8n/XJdV1dHPB6ns7MTVVVpaGhIv5CUlJTg8/no6ushKicIVpSixZLokSgSEkGPO11L3iO5cEsS2kDiuN/txrIF5sBLdZHHg2JaCARuScLjcqVj5n0uFzakw3sGx2qWhcA9YqzAeZkFCLrdqAMdn92ShNflQh1lrGbb2ELgkiR8A2MVK4nX7UbCRaq7h5jfS/P8eenr7fV6qampSV9vRVHwer3jvt5er5dwOJy+3olEAkVRcLvdNDY2puWhpKQEv99PKBQCoKamBlmWkWUZl8tFU1MTHR0dCCEoKiqa0YTdORtI2pOcGzVUbT2j8ZVlEI+FaaBxSseccrtpYZNKyHhL52bb6uGokTC4PAS9jrK0B25kADEN3rICE2O88tfXE0bSZ67Z1JzAMrDCMULVJXmN+rnaHn4mGPRgLlmyhCOPPDLLg/nggw/m3mnE9RFYpjmu1Vgztafka1Df2GkLWAgnidc1hfJ7E5UNx4PpfL/R3oXt9WCVO06NEWWeJ8TMv3gbikY8FiNYWuKszphm+lrGw2FE47y94l7J7IB86KGH8uKLL2Z1i808h7Vr17J27VoAbr75ZlpaWtLb6uvrs46buQ0co3CQurq6UceWlAzpntra2lHHFhcXp/9dU1ODpmk0NjbmHBusqaJEcc7XqzqG6yBFw8LmilxDf7vdUpaDZXiH2OH7+lzZYwUC/0C1oOFjvZlj3aMfN3NsIM9Yy7Yp8/kp8ngJutxp4zyTwevS0dFBSUnJhK53pgE+PHxn+NjMvwOBAFVVVem/m5ub2RPMWviNb1hr8SyEQAvvmSX/sTCVbE+SoU09BnKiP272w9H5t5lMYBsjaz7PSWwbSVfSqzOaaiBsC4SNGGcDoL2VTA/QXGE88pdMyqSi/Xs0eX24Qt9TiGSMVELO25BoTynj2WbQg3ndddeRSCRG9WBmMlyXW6aJnEw6la7mCEJW0aMxxEB8v7F1F6ZtZTXGmwwTkQ1b2CM77QrokmP0zmDeynRVbRO2hWU4XUkTxtBzUbNM1NjeE1OfGYKxbds2Vq1aRXd3N6ZpsmPHjry/6VzU5TC6DA6WfhSWtUebTe1JXR7T1XRomBqKjLo6vj/o8lnz1A9vLZ6FIF0GcRDFNnFJEn5pzybT2caw5eFpMHI6Oztpamoa9/hUd5xgbQlur3PuAkBLYqlzM3FnOJLpGEu6KiEBtmkj9pM25Jo2PZ0rp5PxyF9fKIqkzpwXVQhBXypFeEsE1y6Vyg6TIkVCSAACwyPRe2QRi49qxjvD9aEl08AOJwlVpHJW8pjo/bq3MhEPZlZYQiSSXsoPut1ohoWtG3hdHnx+L909Pcimmfa6DS6pB9xu9Iwldb/LhTLwQM411rBtrDxjJUDPMVZCIuB2oWias6yeknHZNno0jrKzlcYDSlHjSRRFGbFMXlxcTCAQyLmkLkkSzc3NvP3221RUVKSX1AdL5lVXV6NpGsmk4wRqaWmhraODRCJCjyFoqq4jkkrhdbsRySQeXLRHHMO4qamJUDiMbJrjCkuIRKPoqo4LkQ5LyLyGiiIjhBjzeue9hpJEYGBsV08PZaIWxVCRDRO7rYv6qnq0lMKu1lYCPl9WWEKwKEhRsGhCYQkzXXZw+/btPProo+lQmLPPPhuv18stt9ySrn6Ti7moyyG/flIymjRZkRjswVBQxbRGePdnCltYpAyNEm8AYVjooTDButqcY/cHXT5rRv3oIYBiRMWNvzz4FtUpN8dfvnpmJzacGfBUTjRfYFe4n8UVfoq8bizTeScVloE2jYbxoKLPxWYtxv+LbuW79Wun9B2DXWotTaG7a9+vFwtzc/VkLPkTQhDt6Z6RJm9bNncT/FuUmpCzTBusC2DOq0SsLUGtchN0e7Bt0CM6gfU9iGe3sPkgH82nNVMSnDlPmR2OEGsoxyquGBEGMSfye/YAqqqmvZGDHsznn38e0zRpbW3N8nJlhiXc9L3vZXnmBpfJ3ZIbJIn6+npiu3eltz+SbOOO+HYeanlf3iX1QTKX3/1jjPWMMdZdEsBKpMDlwuNy4Sopxuf3UVJSmjV2uDdvtCX1ioqKrO2Z/w4Gg1RUVKT/rq2vJxaQKCmuwOfzURTwE/T6sCyTYl8RLQ1D4RzVVVVEd+/Oe66Z18VrWnjcrvxjM/491vUe6xqWV1bg8/txhfspcrspCQTw+Xz4PW6Ka6uoKyrNug59SgJ/wD+hsIThIQ3TzapVq1i1alXWZ2vWrGHNmjWj7jcXdTnk10+xjOpOeypBdpA93b8gqikUe/1ISKi9obxG/f6gy2fNqHeN0nHMsqwRVv81f3MBgvbLZ3ZeYyEG4sGnEjsYDAbHPdYyLfRUEkMzoQgMw2TQtDGn6c27w5C5+c3XuGnNsTm3u59L8IfHvLTenN/wHxeDRqKwiMciSAC2QFg2ktvlhBlJ+1YM81zsQjiW/EXiScxUnOn8FaKySvSe3dRH3CRPbUFa4qdc76Aq9B6u6C5cOzqwtw/FN9cKG/OoNcTq34/7uQS+H+5iywEeGs5ooaxo+o17yVQxoikiZTI1wezY+oncr3szk/Vg5osFt4SFG/eILM4nUrmri8w0tmZm173Xpt5MaCKyYQ/G0g92shVgIVC7ewgWlUHtkFFvTyC5dU8amzaCULgPa6AruGaYGLoBlp13Hopp7HW17HMxF3U55JfBwao3tiKDsmervrj38DPcEhYpU6fE48dMyuiJJL7SkTlS+4MunzWj3j1aHVNBznT+lG/2bypbF1iaiScwSk7AGGQmaYyFqRpYcgRFlSmnBITA0Exsw8YYR53lZ1JdnFw8emLvS3/bxV1/KqE9j6NCCjmrJm/GQ6wqq8I7hcSyNANlEuOJFOUJhdKKYkJ9UYrLiggGZ+8BsGvXLu6//37cbjcVFRVcfvnlvPHGGyMak3R3d3PXXXdh2zZnn302K1asyHm8ufggGEv++nr7kMzp6Z4ohODdDW0sfzJF+Ihqqk58h4bW30O/wC6fj1W9DGPpB7BLm7EGqjAAYJt42l+g+vWbqGqsJXHsubhe9BD80S62nFbK8iOmPzbSDkUJ11eNMOoncr/uzUzWg+kaU8azdXmV20+vNful5ex4csoG8URkQzUNrEgMwz+UeGdnJM7uCToNme+E3uLWhnWT2t8wDWRVxT+46iosLF0HBLamw0AOpy1sErrT9Ei3zIJRP4PkkkHNMtP5G1b/ni864plS0vfkiOsKJR5HzrS+/pxG/f6gy2ctUXZka/EMhMgZ9mJ6Zz8O2zA1UlNsGNLX1zfusbFECsmykBNObWUsA0VWcVqxjywll4ktBJf9a++Y33FYh/NbmHlCjRq6BPGAG/fd3fz1uc3jnvtoSAO/v6WbpHSVZEKmq7MHQ5ndVtyVlZVce+21XH/99VRVVfHmm2/mbEzyyCOPcOmll3LVVVfx2GOP5T3eXGwtPpr8GZZJonf88jkakZRC+/9tpv4lE/1SP6u8t+HWwijH/xvKyf+FdvinMBecgF0+H1zu7NbiLg/m/ONRTvov9GVnUbr1Pg6u+A3Wx6BlfYotG2bA2yvLpGKpEQmzE7lf90dGy4/KZa5e8Q+Dx/5v7pWxVUxjZCLrGExUNuxYHHswbNJ2DHoxUG5zTxALRzj/b5MvTqAkUohIDDFwrwoh0k28jPBQHwNLCFKGRkSTCakpTNua8LWda8xFXQ65ZTCaUYBCRPd8ErM2C6WcDctEsZzfSIvEcibM7g+6fEKe+un2YuYlRyxvZ7UJwoUwbSTP+N9FhBDowp62DmfCtqaldflYaAkFf2kQVZOdJjmZ32mbzkuPmX2d9GEP1x07w8wHkrZBiSv/ykKdsh1YTErWKC8euTwlu1OEF/+TM949ia4OF9ZJUzixNM5DTEvGQZORdRMt0kWyopTikiLcs7QqM1hjGMDj8dDT05OzrF8sFkuXzSoqKiKZTO4TXoD+SBymocvlu693sOyxBLGjy2kqfQhXl4x61PWIYNXYOw/DrlyEevSXkFJ9VLxzD8mTamh56nC2SJ0sXzONSU/Cwg7HCFdV7BOexTlBDmP1+OeLCJiC3cLGPR2rfuOdSnx0ue6R4wgEi8qm94VDNQ0CHi8CgYgl00a90DXwe5CmqToNOM+7r/S9yvfqcuc/lb7Zz6n/qOLt83Uq3BMvtCBUFaHpCJ9zfwhNJz6w6mraQ8+j5IBBbwkbn8uNbBp493DH6P2Z6EDVGysSc0qP7ifEdYVg0Ddmwuy+zISM+kEvps/n4+GHH057MYc3Jxn0YpaWlvKzn/0sp1HvmUBmtBAC0+XG8AokWcVXNv7C/Q1fepO7j3Zx8oUHj3ufMeeT4X+yLRuXe2IPpswkoXx09fbS5GuCweo71pD3UErHZJokdJVSXyA9l6x59jj73P2XN/jgqauocPvw5ageFPPWY0mgtoYpXzkyrOGwHT7aPnMIvAuNMYOdwk6H4NwR28bl5UtH7POb6DY+XjHy8xHYBnpSw7ZTYBkkolEq6qopmuVQq3A4zKZNmzj33HPTFSxgKH41c9k+GAySSqXSRn1mZZBkMjnnGpZIkkR7eztut3tEw5Lu9jbkAU+13+3GzKgiMt6GJZtf6+SA9SryOTFW9PwapfmjiMZDHe+NaeZtWGIJgWZZ+RuW+Cuxj7ga76YHsI/7J/P+ejTv6m0sPKJx5FjyNyyB3NVVdNvG6uvDLPJSv7iU3oHfxufzkUwm9+mGJVPBM8HydamARCApUIRFyQwY9RFL4+9yLx8snZe9YXhhgYy4dVvYA/8J1AEDdDx14/PpcsXUCbgdQz6sydRIxVi6U07Tlge9qML5v4HwlengFTWE0huButzbNdMEPPyhaxOXNx1I6SjOnpz7R6LOi5pv4IXAsjEGPPCqaRBWUwQ9PnrkOFbGyu9cTTKdCN7RynDPIsNlUDF1tIH+L1Zkdvr9+GYh/AYcGdQsA7/bmzNhdjy2197OhLTxdHoxhS3oeLGbdXf1sP3mg0YtValbJkv6JN5a4CHVH6ZlwKjv0JM8FevkitrlufczTbw2XPYPm/YLJ3KmYzCgoEzVQI+rFNWVjrHDsHnp+pgPaFOOoaYqsDO8H2nFOKgsbUFrVw+rFiwY+Dv7jTzwZi8g8Z9PeOGJLfzxGC/rLjpw5Hw0iZ5KgZ6nEUxHtYVr8SpiR1xB+Svf5vWOXo5oaUAIwQOJVs4umUeVe8izuVWP86Gfaxj/Yo8r/j4a7sccOLWUpqIkZYqKZ68msKIo/Pa3v+Wyyy7Dtu2susaDSbyZybyKomQ1BMmsDPLjH/94zjUsKS4uzqrKMTg2pWuY8XhWRYzhcaRjNSzZ9EYnBz6l4Dv6GSooQXvff+Ma8AiO1bBEt6X0Z6NV+xAHnk/xtsfRj3mOxpfeR9vOHSy4cHE6jnM8DUtyHXdwrEu4SVha+rpEo9F9vmHJVBjdYBu5TRIQKnWhz1APhJ1Gkv8X2zrSqM8xt8HZdcsJkgMvs9tivbSUVFLhH/slKp8ut4WgW45T4vWDEE5FN1UZ3Oh8uz1QjcO0sOSxcwwu6XiOO5tPHHWM1RXiwV/UsfPm3Lo3ELaIB3xcdL/Cs6v+yTmnjn684Qz+1mbGyrEYmPvQLy0whoU+2MJmZ7yflpJKynxzs977WMzVF5PhMhhRnZdGYZiQ2DPN3TTbYreZ4l0tymY9hmZb6VU4CTgmWMexRXXTk483BnFdoTboxUzKGCkZb/HQtRmP7bW3M6lE2cl6MTM9mLF4nLIXHENw0XVvs+OHB+X1qCWSGn2lHoTXxOjXUBdY2ELQ93KIGx5Uaf2uyQ4jwXJfGZIkpb1vakJD9ropMixUyxq1Lm8qrBBLqTTNqxyqbTww1uNy4coYqxsGoVCIvq4+lHiSg4oOpjfWP+7axm1tbSSTyVFrG/eHY8hWG4aeQjZNfIpCSpHTntJBr6S2q512t5umpiZi8XhWbePylI/tKzayZLOzSnH+Pw22nG+O8GA2J1VsV5DungQNA+eY6cHE5UL1VaJ89E/EWjeiPNtD60dKqPb4eeKnddx64y4+VrYkXds49UKEkztNXvv+OxRfvzLn9c70AstJx5CUTROi/XR3ewmWBtLezj3pwbQsi1//+teceeaZ1NfXY1lWujFJZlm/srIy+vr6KC0tRZblvKE35hxc+kwmk1lG/SD9fSEkbfJN3954p4OVj6XwHvEk3qVHoLUcNaH9Tdset4fHWHoGXs+zNFh3keq9HOXWbXR/ooGFlRWTmHk2djhKuKE63WE23/Uq4DBasxcArTe7fG1Vyqa7QkKVFSgdf5iTEI53P/NFMve4cR7QNDFicewaH1FtyPixxfgL8uWTDWfFyCKuq5gD4aTCGKkLDMMA20Yfo7O2JQQfet3Ebhq9AtmBTyUAL7KuU+4faTy7Ddi51uDQ5/2sbAvQd+qoX5t/PoN6zbKwBpoaDXqHhchezVZMg/ZkFIHA3oPN7KabuajLIVsGhRBEB6veRKJZq1HTiRCC9XIXLypOjLpPctPiKWKVv4LTAvXYfQkCkgvhdmF4XPxdJPjv/jdY5a/g7JJ5FI9xD08F2dSxhIlb8qD29uFdtCC9bX/Q5RO+slPxYg6vbeyznaQGF6N71CxVI1UiY/oN+hIWiwbGLng1QcAUFHk8PP/0dqpPXkmjtzjtfdsUaqOyKkFRTxF+l4uUMCnxZC+hDXr3+p7s45B3ZFLfHvLA5avhK0yobqwm3N2HZJvImjah2saVlZWj1jYuLS4l3NaGnggRLClHeDwUB4NYlhg5J6z0/mUlJegDhrCzLY55YA1shs1L21mxrSW9f+b1NuxiTL/N8l3u9LXL9GBaGdciMU/hklf8fLt6Kx94/wGsSFmU/d0ieZZBkeTB73ZT0aNgA4e127SPUgc5ywtsm0Nzw4VXuGfFg/nqq6+ya9cuHn/8cR5//HGOP/54Tj755BFl/c455xx+//vfY9s2Z5555ozMZU9iC5twR/uk99+8q48DH0rgXvtXPKuOwWwavWLKdGAuPAm7YiHLX72d1rrLaf5pNy9fmOTIZVOscy3LJGNJlJIKgp69o8HbXMaMZ3sLW2vB8FmYcRlKK8Z9nCu7/0mfpfFQy/tGHaeNt7+CLbBsx0FkDsuT6pHjFHt8+NyTMz4s2yZl6Pjcbid8VNhOdZjhjPMNpMdU+NKTNdxy/G5+F9/Bg80n5yz9K1nOZ0oimdOo92hgLQsABtYoZaUzOb/9Ge5tPim7TGHmC4qRmfgLMV3BVjWwLFzFRXu8Zvn+TNLQ0rkNVjg+I9/RZqT4TWwbh/ir+GrZAVjvdiE2x/B0xfENOHlNn41wgUtIeA04HTjhIA+bTgjyvdBbHBWs5bTiphnLqYnrOpV+D1o4StH8ljlbuWgmmJDGmk4vps/rBZcM5H9o9sgyd254h4uDjTT0+wmoQZZth9j7ne0NfY4i/o++1/h/j3lZv0yhcdFQmEHTBovm3iLaa00+0voMphs+W7GcM0pGPvSXbk5RKduMZ7FKN5wOfUY8gakkiPX3UF5dNo49HcYySEOhEFgWwrRREgMrIUJgm7m8HLkVpi0E1aFikgfUEV57I1UHnUN/zzxeUHo5Opgd1tHQF2DLCUmMfg/lGZ/fHdrOY3IHv2Ho3ErWemADXPWsl5sO3s7hwI1Pw73zeqk73Hkj9qox2la+grf36LEuRU40RSURjlJVXD/24GnmyCOP5Mgjjxzx+fCyfo2NjVx//fVjHm8uthbPJX/90RhWfHIPge5IgsV/CCEO/TveQ47Hajh0UscJTkLx2hULkU/8Bi2v/h+x4xZw2H0refbYLRx7wtLJL/UKC7s/Qri6iuYS314RAjOb+Hyjv/jII8qjCiyvwEpNrKzleS9pLO33wadHH5cSjsGp2daYBRJ0y8ypQXXLJKzJNBQ5ui9fb5J8smEjMGyni65h28R1lUHtPVj0QEDaOBZjeLCNLsdZ82rKWfW4NbKJL1aNDKVMldsoXhdGUoEc+b4eXcIs9bHtAg/e52TGLEYqBMWKyWtqiLXBPAnEA+dgR2LooQiqz4W5qwN3VSnC50XKiEXXrLnp7R4Pc1GXQ7YMDla9sRVl2mvT95gKjyTbCFs6n6WFyl+34YtHUCts7Hlu7DOqsJbUI3k8SMOaWRp9UaQndnHkrb2sqoE3TorzX7V9fKpiGQu8019gImVoVPqLEIaFEYrgrnNkd3/Q5RN66mV6MW+++WZef/31tBfz0Ucf5fTTTweGvJg/+clPOOOMM3IeyzANdP+QctvZHRkxRn8hzA/+6ENqSxIrchNfvIne+qESjbUJRx3/v/9xTkPb2ZV9AKmXHQe8S0ufhxd/WM2Gm6p587VOtulxfte7lavf/pszF9tisNPOuOLmBNimjarKgEBLKliaOe6Yu+7u7rzb+roj9Pf1Y1oWkmlARhkwXR9/KbI3Xu+gOmnjrm5E/tgf0Q77BDUJwUe+2kXEyi7ZFzQEUlUZLj37oXXl92Se+X45C/qGPpdWOCEV5apFf1uYXU3OS8fBT8ZJ2SatRhJibhJLj8ec5BKbnAgTjvRjqgbmLJe4nCpzsbV4Lvnra2udVPfkpK5T8osO1APeIXj44ZM26AHUyVYB8QRQ111LyfxS/Ec+yNrXXLxx/xb0KXTEFbE44WQCW9ij3q8FRi9pCYwoEQpgeQRCnti9ceUz1bzvrbHzl1zdUb693o86jt9/NI2tmDqGbSEbOjvj/VnVXQYZSzZMYWPYZrrsI4CdsbrNYGlIc/Rnx7YOJxH7sw/pbLipmvc/Ec45zmUIFB/YidxhdF7dhbekiMDxy1nQJ2GN8dt1mDJP/7iOH3a+Puo4AAwTyzQQpg2ahq1o2MPKKRq2NWdj08diLupyGJJBWzgvjwBWaPoSZHcbSX4Ufoc74zs4saiBa1+Epp90YK7yY399Nb5rDiV47sH4VjQjDay2D9flntoKApcdivFvqxAHFbP2UYP/+pWLf772Ov3m9PessISVLm+p9ofSn+8PunxCVtd0ejGFAKPI+eEl4KBbOkh9uzJrTPkm58cQ3iSR5l7U5QciJRUGozA7qy2aQkO+Bnfcuemu7X6Jg/wVnG34iTUvZVciwsJ259i33e/nn292clzQxdfeKKf9FuiJyRwVs4kUu+jvTFLbPPqDQwC6rmGbzvclknHibRGK60rxlY39Nj9aDGooGsaSZezB5c2MWEtDHalU8i1t1uyIs3VBhKB3qETlziaZRZ1F/OXRTXz03EPTn3fWGHhLipAM1VkqRuCVXJSpNpYEfR98AjjEGexy03P4zdRvvI7b7y4nEXSz44CXWLj9SH707Dtc/pyHlqiXnae6AXPS3Xd1LUWiP4FlWNQs2nvLUs3FB9hw+UsqKkpfKM/oUY5jWyR+sRNfSxf1a6oxG6cWcjPVZXpj0ftwVS6hxr6d5Vs+wYY/b+OoM5dPrguyZWCEosQqq8eMGd/fGZTxyi+/RaxcYH999dBGS2APS+CvTLhJVJvYyvi9tkIIZJ+LIt3JwxmtY2X9ewlO3VjC+o8qlI9VtjHj/rQiUVzFRUgDKw+qabI12ktDURlJQ0MxDUqHVeXKJRumbaWPq1smViIFFRUYdv7zFcMr8wyjudN5Vh77nrMS/b4NZfReMnKcW5fQ/AI7ldsZ4jFcSJXOMbY32cRe3krdsSM9/oOktjmVsf52ay3tt4w6RQDimoLb63I6hSdkhM+HlUjhLnW+M6ym8Lk81BVNrLjEXGAu6nIYksGYrqYrDono9ITe7NQT/Da2nc9XrqCqNYb3/3ailwu0Gw8gOEoxi3y6XHK7CZywHE4AfVsXl/1B4iFlAycff9S0x9kndZVg0IcRT2EqKp5gYL/Q5bPWfMrlkrB8gl31FtubDCrl7IudUnUO3Or827TdJEtqcJf4cRuOMv9HtJumkJsdR70LOAZ+oC+FLiwe+HaAb/6nikcVUOLBxInF3rrcGXvMFo3j3xjyZOg7EiheF34din/bRqp79CAcYdpYGcuItq7QFenh/7N33nGSVNXi/1bo3D09Oe9szhmW3SWJwIIgCoJPUMlBBCOYXvo9fYan75lIBgQVWJUMAoqghCXKEpdl2Zwn5865qu7vj5rpmZ7pSTszO71Lf9+H505X9a1bt0+dOvfcc88JdodJBkeedQ61jBfxR9C6O0mEAoNz9RsQjI/eU68mokRqMx8s+1VmcM03nu97IR5IhUlJKjaPAzUlszHSwed2v5w+rghInXxDRjvJi25L/7tt6V6sHz+BlAL/8TjU+jWSioR1Ri11HRLv7x25+FU2jKRBc+NBmtoa0eJHbtESeYpSew3HQPlrb2oYlDlpNOx8ZD82NULlsUG0GR8ed78morS4UTid+Kn/RemiBzh2G7z42t5Dbkvv7KY7GsrZZfdcoVfGXUmD6o5MnSOEMDO89MMTNzAsQD+jvkOLc3XLq0NeI6Gl8LvNtuMjVDtuiZv6+786No36Hox4Er2pHa2tb3KbMkynRKrHQ98ZD5McED6STTZ8iSixfiushj+IEY6g9/Nemu30/T0wpn8gc3fa8bnMcW4uA1XPbjTJKYjbdIhmf54tuoTdbSYW6KrT0NuH3xivNoSprxy9MZvUNSIdPRujh7intujkxHpPNrmoy6FPBrt66ovovkDmnodDpDkV5feBPdzgmo/3jzux3dtO4pxCrNevQBkhO91odLl1ThXx66dx7osFvP3MRlITvIk6pqXQMZ/dRI9MfhB0+ZRJqSIr6NgwSt4iUt23RLcvGTLzVYf6jGPXQQVZlrF5HHj9VnYlgyy6ywzXsV74SfbMbSQyM84p7xfy66Zt6e+pcVA8NsSCVgwJnFedhe/kH6ePa7JE2EgR6WynccYBQsf8hnltKeb/aM+wfU8kksRi/b3mgqi/mY62Jjo7u9CTw88GCwqyx9+H4jFiiUTW4lsCQdw3/GTjwK4WQj/eRu1XN7P0bStRe2asml62iNZPvEBClXrKk0Poj/VM75CQCxxYUhJlfw/y7C8LAWjvCbCXBmwWlPq1a//02VCzgIK4+UD6nBZ0WULYC9k7zU/hprF7gAFIpdBifrRIN7EcreQ3GnIxt3F/+dMNHX9zyzBnZ2fTqweZfjBBxXFbSM0/b0L6ZZmol6bFSfLkr+Nc9ggf2qDx0raDh9SMlEwQ7OjC5jq6U6CNl95NaLurs+gtYWD002eGENSXCfxKih2hvhCScGsX//fA0M9KLBgmbtM5UCmIRoc2RHUhqIia7XzEUclrsRGcCqLHm97eZRpCAzaz6sJIG+ihZJzGsC8jnCibLh/k0NU1Uh3dGSeYWW/6f2RkeBEHxtj7C1M0fMLUw7GPF9JYlt3QVjSJpMNAjmQ36iQhIffoJL1QoXhv3zO39/7X0Ad0Xo7qdE/XqB/iegNJdfnRwz3OJ91ARPpiu9NVaBEZk56BE6VcJRd1OZgyGE0l03tXJiI3fZee4Nf+HXxVnk7xz3cjnBL6vy/GtnT6yF9m9LpcLS8i+ZXZnPyWh10PvZauezIRCASRZG8Ijvn8DWV7HU1MmVHfdaALJSnwzV5BwZK+h2XZtw/wp/pd7NlpxhCGbTJL9wpEVRxHoYtqn8EvG7dS25Fi7/Q2UKw4rjkJy7oFAPzHL80Z4u4qGa9PxVnownnaCtov3oRwFBH5lz8BsKsuQsAJTbfv5uy/ScSdNaQ++aN0P8QwnhM9GUfPEicaDXUQ6GolNoJHvb09+4tGpCJIw6U2GyHdYOzurSxsMr9v1wQlWfafaB/6AjZNsK2pk8t2v8Bp75sKVS5wMadZxhk2Z7SGECSsgoaV6yHLrLtp6cN0rLkZFGvGce3TbxE9/fcAhKYJLNqhlaiOp2KktBTC0ElOQIXTqSIX4zD7y19HSxtGYmwxjVv3tLFsQwTP6hdIrbxswvoVn8ilUYsT48NfQln+JCc/EuWfB5oOqRm9y8eBcWQF+iDQG1OfVAzaC7J76BJC5zsdm9JedqssM7uzz1mwv76BFfvNwMrXYu2DvHZJf5iU1SBlMUgMU/Y+IXTK4ma7b7c18H9d7w/bdz0SNeO8+yUkGEh/Iz6mpdKeexisyxO6lj6u+4PoHd2IeAoRjRHr95zpicEFp7Rwv7SasUxdb41LWIvdNN6yHNvy6UhkH2c1JREpETi6sr+/VK3ve3qdh9kNMqmeLCmnvOZE/d57HOgppAagxgSGcwxmQiwO/SZdIhRB7w6g+/wYGbn4zXs3hMFufzuRVILd/naCyTitOerJz0VdDqYMdsTNZ0KkUuPOTe/TE9zcvY0vihoqftVI9MMOHBcuRxpDIoOx6HK10IXxtQXM6LCj/+xdgt2D91ceKuGeEGkjqRHv6h7S9jqamDKjvrQtiZqQwWlDXbaasE3BEILCmMGcLQ3M2Woam91eUzgcFVVIBWYM94/u91AUEaTOrANA2L2olUXsn3mQoqjOvtkHmNVqML1TwjGtBOEqRzvuivS1O079De7LSygJC07fab6Qila3gc1D/dIniFhl2hqGVixCGD1KefDnqWiMxAge9WEazuqlNw8JxAjLzot3ZRpn7hPmDj5JsZBUZLzvdvH8L4qwaYID899Attpo9wrKGkyv5N2tO9FkgXTFz7L355rvk/jsXem/9y7exZ75DSSWX0bs7JsASDrcaInxi5gWTqCnjv5YuKmgq6V55JP60eQPseC+Llj5AsaJ12ad8OUKwlaAdNrl6Es2sOaPQd46lE1S4SihSCjrJsk8mZSErBhDPO6hYIjPPRinJegjqRjU7Xdwxnt9XrPlW/oMhmNvamL/ALnUgnE0iyBu0fH7TANGFwaRnhCZjp7NdjGhU9ZppdsjM61HhY8UC61rGvTkIBfRGFpTG0Y/A7t/ZVR9hBCB1mgAX0/Oe707kM7hDphpHnsZ0I5AkOxnzPSfOCSETlm3FXtJX24yTR4ie5AGWomKksh8LgN6kr1tLTjjfZ/PWGy+H2Z+d3/6s5pusDxZn/5bjQsk1/hjnfV2H/r+Rox45jsslEyg92zwNDcmHxle+1xCM3SCPRPG8eam9+kJft69jeu0Kmp+00bkDCf2k7LYEROM4rDh+uKxhFZZKbytge5Xtk9IuyldI96zYTZxCPvGjkSmNEhsyW4FYbGAYsWd0En+jxk6U9fdQqjAx87FuzAUUwkVTPOCJFEY1SmJmArRWtFvs40kkVxgGvna8bPwu3py5tsGu6sTn/gVRul8tP55ehedCYB8+ddNH8jf24bsdzKepLMj+2wypSVIdXUMiiPtT1FR0aDPwtHYsN9JjSJGrjfv8M5aQf2KBxH2wqzn7VreDIkIvp4xCletBkkiZoXygERroeDb/5ugtmP0M3P7JR/GcfmajM+sqszaTePP893d6SMZ6Xsh6vEU0fZDWwE43OTikm2v/IXDEWK+0XtFUsLAdncD8bmbUc68HJSJvbfJKC0unKVYzvgkxoLXWP47H1u6O0f+UmYLyNEYHUfwatFko6oqmi9EpV9gyNkNimRHgLW7nNh3djGzTcFXNcBBISBqlflJ1/vMapOhJdOpYoQT6DZBYcSKb1c7Qgj++pcNbPzzy2x/fwdl/7MTgFgqyYxWmbhd8O9/N1P37kwO4/kVAmGIPkPIEBjBMHprVzpcZCBd8Ujae59NlwNo4bAZzpNKDQrpGYpEt7/vfvsNY1dDK6UhgVLQl/QgbtP4+/7Bhs/8ZhW5zIE6IJNZ/f1vUH1XCw2zh/Y2N/ZkrCzvtpl7CYSBCAtwqghgV2IcYR1CgK6nJ0u9oSL+ARndAuMofjfZ5KIuBzActvTGVL3r0Fc5eg36z1ND3W/biZztwr52ziG1dai6vOLUpTR/vgLXqzEiv30HMQEFv3ozTyX9YQrcR94G7bEyZUZ9wgLbl+2lZJq5caGtKMWcDvMH9MQklr1biMZc5JPqaVj5R3CbGifgkCkJmwJsKchUqI6TZwNgXzKL0EUp9q7aMWwfOr9mKpi9p2yC3nzGFietS57FEx764UilUqSGWCIUhk6Tv5NUSicVSmBkSdM3sDKdruk0tXQgUkMb9clkfMTcIP6CGLvn7sH72RTyJf8x5HmJkmKUqCBm68lacYrpAZreIeFKGgQrTYXb8tFdI1yxD+EsQ7gys9SUnVxJc2lmONG2P+9j5/1j27wYCbbR1NAXPtHZFaCl88hYRsvFjAm98tfZ3MTwSf0y2fLkPhxyAPfHTgOLa+QvjJHJGinhrsB61kfRZ73D7Ds72DfMs531+8EInf5u9BE2M35QEUKgP2TuQ6ruhsZ/DN6gqvl7Stcbgq0Lk+glmQ4DW9TMbHPL93qqeHdneqFFMEXKBu1zUnzm1SKm3fAeX3yulMtfKqZwa5janpD1eDLJwUpB/MJShAT/9boF6wj1CgaFWsYTiHgcrTm7jomkEoR7jPqhqowa8YQZhpLSwDBI6Rp6xl6AwdI+VMin4Y9yoMrICH+Y3WTl2jsH31d9mUApcqGmMo36E7Z4mduikDpmcExxxCoT7/Kjy4K3rnJhiys0vr4N17ff59i9DgrKzLTG6rvjCEMb8B4MJXvCInqr0B96y2PmwIED/PSnP+Wmm27irrvuQtd1vvvd73LzzTdz8803s317di9xLupyQxj4exwORiQKWTLkjYaQkeLn3du41jGLaXe2EDnFjn3V7EPu13hGqrKygsANC2ixxJF+vBWteXwe9piWQhcaIIi2dYyrrSOBKTPqhSwoPS6Gpdj0pmhqn8JavG0pbeVBxIJu5JM+jnTFT9LH/KWmwdl2zM1IVkdGm7LLQ9P/zUB2uFCXHYvt0ouG7YM27XgaLtiP9ewLMvt2yr9Q5LOjJTQCWTLhSHoqI9XkoHvTU2ixBJGWAPHuwd8PhTK9zM1+HzF/B/FhYva0hJYR7ymEIDigD1VdNkKuOrSa44Y1ugy3hZJ6i/kCPu1VRPGMjOPWEy00lCeQ112QvYFRIjzVVHdaSCR7C6wIFr0XYfG20Wfx6SXQ1UqiJ691sKudeLA7J5XsQHKxtHgoFEIIga916NWogWzd38HKd2O4z1UQ7skpCpaaRKNZuCtxfOw0qN6B57dNtCdH7xFMGTrJlja64uOLVc11DtXY0XUd0S/ueu2TWV4rgR4jTtMxZFCqM3W3fUCYntWf6SW3BnVSbhCewaEgK1+3pf8d7/BjTcio00uZ1gXnvVTAr307h7xngSCaZX8UKQ3R6RvSU9iredp93aPL5qIb0C+kRo8PDp0ZSp8ZoQShwsx+7F1p/h02Br+HnEVuLMnM8SyImc+WrSBz0/eqr3XiShrM+d5BCiIK7mllVHcLqp9PUR4UtJSAbV4NzSUSBYegt9MMSHaQGhDOdjjDboqKivjKV77CjTfeSHFxMe+99x52u50bbriBG264gYULF2b9Xi7q8u54lHjE/F30Tv8htZESBrd0b+My90yq79hPdKFkpp0cB+PV5eU2J8WXHMtjJ4ew39lE6sDo31UDEQgCPbaVv739iLAbxsOUGfW6J0py2cVgNcNjose0ELEq7FlkKmA15sZSMXimmOoJ70pdflfWeF5h9w76bDikUz6B5Mj0XlhrqykPGsz41lbm/Xgvrdu74Wd7MeI6WsfollJ9jW00N23H19KIFk0OK0iRTh8kEgh9mLa1VEYc5gFfkNbbB3vSZe/I/SupcDC71wl13hf62ly2kYbL2lCXrkD++jyYgBLOrUU6UV8Q4wfbSfxoB7XdBqWhsT9UIhXH3xPylExESMbC6AmNWEeE1BiL2OSB7s5O9FEudR+IhZh3bxupE/cgZgyuU3GkIDxVuM87DqejBd8fDxAdgyEhAkE6Ap0YE5x2LZc4VGMnoqcweqLsHj0xQFPx4HO6Gk3DV4RSCEVgrS1JH2vTYsiaxI5jTEOv2yVjC2bqCDUGosAKxaYB314g8doXTV1vMQQRq0yXnuD5pn20F8eRbX1hf5WJofd9GEJkbGAdiBjC8xnvyd6iC4Oufqkuh1Tzup4RU5OuFN6PVL/9VMl4grZNPTmdIyk0W+a5nkuPIWqV2X/Pa4PasXlcqPqAmPqeSZfqzTTq/zDtQ+l/F0UM3MVegnaZ6W3m92t7otWajtHQDjW0vl/4kdHlx4gnBqXwTKUz40w+Xq83XQVZVVUkSSKZTHLzzTdz1113EYkcOZP3XkeD0PVDyk0vhOA3vp2c4aqm7o/70YoEjvOXjfzFw0CxYuPDJ6zmnnOCOO5pI9Vy6B77sJZAx8DQ9KM+tn5is/2PAWdNZqiG46yPEDixGaPrBNjWxYwOaPLaBj3ktlNncKD7SdTeYkiTgKQqNJbp1HYoWHTBqtsbzAP/amZSaLxl5Gt3dzSR1BJovm6s9QU4vR7spS4Ui0J1dXXGuUY4iNBSgCN7YwAIhBDsfXAXcy6az/4X9nHZLpn+yQgbyuN4lo+cfs86fzawddDn6tWf77vaEPH4Y6XSp9BybyN1Pfmr99aGcEQ9hBIJFn5rB698tIgZH6kbRUuCttYGKqZXIhIpDE0j6gsRbW3DXTlE+fIcIBfz4lZXV7P7vc3DnhM1NF7tbKHk+TArdxvEZu7Hecq5k9ovxxiyKxwqRkEt7k/GmP4HH5ue2M1x581HHWHy6lAUMCDe2oqvqIwS++B9OkcDXm+fQ2SgseP1ernwwgtxuQavADZ0thE3qnh4dYiZ1aXUvKrTP1AjqaX4+Dtm22rAQCgS1govYG6GDT63jWM7VRr+fRm+nduJ2Q3s4T5ZEEKYGz/dKu7KIkL2DuLfW8I0SQI201okITC4uvEVPhuScQzY2Ll6WwyGUDFJPYUYJmXuUEZ6b9hIaUU5zdEAmmFgHYP4htoHGxaxftVdG157l2PvjrLDehAlqmPYB09MnEmDs9/1MjAoRrVYMASkGjpR7m9C/uZy4lYBUbAWZ8YUe+S+OPG3fjyHSqAgbhCzyLx/nsRxD/dMNKwKHGrqyf4GfDRmZgSabr6nptJp2t3dzfbt2znrrLOYM2cObreb119/nSeffJILL7wQgLfeeou33noLgEgkQmOjOdpVVVV0dnaSSqWw2WwUFhbS1mZ6k3ufo0DA3INQWVlJd3c3yWQSq9VKSUkJLS3mm7ugoABZlvH7/QBUVFTg9/tJJBKoqkp5eTnNPdmIPB4Pqqri69kH5S7y4u82V6zDB5ux6jqxnsmRKsvIQLJn7G2KgmaYhdskJByqQlTTeDxcT7XiZOlTLchhieQXFqIKgW4ItB4HhlNViWk6ArPomyrLJHquY5VlDPrqLKTPFZDQ9UHnCvq8+A5FIW4Y5vMtSVhkOZ01p/dcq5A5fslyHolt5oI7IXC9glrswdrv3N70mb3t2hWFpGFgCIEsSdhkmaiWpDkcpMjmpLupCaMnzn6k8bZYLHR3m7F95eXlhEIhYrEYiqJQVVWVlge3243NZqOry3yuS0tLiUajRKNRZFmmurqapqYmhBA4nU6czslLkzxlRr02MDxEVjC807DbwkBPJdmSwZsapIIS1CsnLo3eUEiXRWjZcD8pw4tFClO16WoA2gpH9/1kTzaGZDJCY+MOqiKVWL2zUSwK7e3teFQXrlLz/lKJOLqup4W/P7GWKJaHW1C/PJuu7igf/mecjQVNfPZlBb9TysjxqylWjJJZI3dOVmkuNjCumHyv454le1j5ft9mm9iSBKUvean+lrnfwe5vZ8g37kBSPWOaSiAQBH0d+P1tJG25m4ElF9OgNTU3EerKXmYeoFtP0HzvTs44KBGra0C5UMU5a13fvpNJImEY2A+DYS9K5+K4aDOr1yd43rOLD582D8swhn1vv4zuMO2BDoptrkOqknykMBpjpz/LGm2oUUFXnU7ZnGIgM2411pOnfOc0DbtPQiuXMjzpnvaeyuKqSuvCFAkXrHhRZV8qyYxvbkcWEJwLss2Ce3olgf+rTC8xH/zf+SgOO67/2MJrN5dhMQT1Neax/f85HfXugyzYO/RmfSMSQ7IP7UwxugMoHhfJHftQp1chOzLP9Xd1gyNTZoUAbQRrtX+e9l6C/VYMLI82AjIL7vCzaZ6BVjVYPg/+cD7T/2OnWeBL12n98ybWdFjTRr5vfyvHNEIjkLQIQnYZVR282fOmMzv41NtlVNrMd3LYJuNOGFSdvJyGE3QzcYQqk9ImJgOU6PRhVJhOvbieIrWnnuS8meaxw2Tlx2Ix7rnnHi699FIURcHtNifqK1eu5J///Gf6vFWrVrFq1SoAbrnlFmpra9PHKioywxD7HwPTKOylvLx82HN7rw9QVlY27Lm9E+v6UDcOr4dodwA1mQJJwqlmmnRqvw2rygDdukcL0i0SXP2+gmOHQP/6Ylw2S8+5EtZ+gRwONfO7A6/Tf2OsQ1WI6zq2nusNPLd/DvuBjpxs585SPURXL+bZ1HZO/009yS/NRi50DdvuwPeIU1WRJUHS0HEndAo8Xmxe8/cZabz7G+AlJSXDntv/b7vdTnFx37JlTU0Nh4MpM+qHenZlu5uwTSbi0CZlM95oEXUno19+MjKgA/WpH6LppczavoZ9gRRW7+h3wutCp8XXijNSicVpR9M09u/bznRlERZZJhWPg9S3pNufwGP1rNqToBEI+btpKRYUbe1ENUASsGlbA2tOM8+d2SKzRxldyIzxb3PANvk7wROnr4L3/eyb2UBStlJ06mqa9mzFu8fNvtog3oOF6XNTLQnkEhVlGJeXntQRPS+XSCBIQk+RjObucmkuxu/5Ojsy4nv705qMwp27mUMIvjIHR8FSDlfAiXEYx0pUL0e56E3W3SvxlLqDMz60YEjDvrdfkiGINbfhL6ygyHZ0FqQarbHT34MJYIsqBG2CmeWVQAdRTUOWJKyyTDAYZn+lQaREp7zBRsNsnWhPfHJc14lrGhvPUqkRAuUzy3AIAS9uI3jvZmRhGuT2qEzcpqbbtcmy6ZW0qFgMg6JIn5QaX5hHStfRi90kVlup3CDRnozjkNW0B9OnJfmx731ucZ9CKholqmkZHkwAVZJRuv1EkwlEKIzTF8DQDLR4Ak21grcUX1cXKZtKawqqi0rpamwmGggQczvRDGOwBxOBEktgGH1j0OuV1PQkjY2NVFdX4wgogKCjQGLlLhtb/2UaUU3L9HbazNf3wVff56SHDKZjjlVU07ADCX8csBBJJHAK8P3vEpK6nuHBjOk6x52+Bv9HbLgNg6Rh0HpcEm+9hKzrpmdX11GtCsdtcbJL07DIMhJwMBnGbyRZ4SjJ6gWGoT3GqY4umrGiJ8OmHm/rQLXbUJ1mIoTJRNd17rrrLj760Y9SUVGBpplpUS0WC3v27Blk5PWSS7pcM3SCPfuC9GgUNTa2WiMxQ+Oh0AH+tbsM9/M+4l+ehWqbuOw+E63Ll9iKeG7NTN7U6znuF3tJfmkWauHYVkw1QyeiJSm2OUi0daSN+qONKTPq5WE8XR0LtiO5LKisPIw9Gh758q9iM1K0/t8ewns6KD62euQv9UMXOpEuHwVlJaiSQjwaon7vQWJa0lyelCREJDmopMjCfebScOOOdqR3wlR0SzhjMgEHeGMGdc+0w5f6zleUUf6kh8GgB/DOnAb4sX7xNKyGjrB58Fy1kJYNPyNUeAaznwCfZhDZHmLBbw/QUGqB/1qUtS1dF3S0tyN6zMxQ2FyGjKTGptAOJ7lWWjyeTBDu7CKb77I+EqTo1/tRSoPYLzse5MOrHkZTWnxCmXkc0mfe4Zx74TF1G2edsBCbNHhC2b9fRneIdn87RRUzDmNHDw9jMXYyPJgbvsnMFpnAGgNZlmksBockMe1r77PnUw6c3VFmtsr4qiVqu6C5xJH2skXveoeVO93sXWhB6udp3Fups+IdU0oPlgvKuywo86ah9vPO9ffU7Z8umHnQ/J1UtyP9YtOPmU7ktR28m+rmdFc1mjDYmQxwIBVhdyqILCRsmo4Yol0Ap8OBCMegw4cqJKxeNwVFpseuuKyUiE2mwFWI3W7HnTAdM7IiIcvyIA8mgMNqIZ5MZvF2KmlPn68kRbVPpaxnb4G3InOjQn+vpGtXDL/TQWHUYMvCJEWqigHY/D2T0a4guiJQJSmrB3OG2vcuUGUZ56eOZSC24+YSfXxHRp/3NNTzfqCDY1edOsgLPJLH2CqbYajt77yBU1VQnXb0lg4SFVAxwKs90bz99tscOHCAp556iqeeeoqTTz6ZZ599FqvViqqqXHLJJVm/l0u63J+I9RnO2eoVjMD6wF4+o1dQeH8X0UvLsBZNrD0wGbr8dFcVf16bRJdbOf62fcS/OANL8dgqxCZ6HKdJfwAtnkC120b4xpHHlBn1A5VAfyxXDV7inXKsLgQgDBvFT/rg2Gr0tgRymQVplA97MOqnClCSSRA6MX9rX7EpIZC0vlCNg8Eg0wsKEJLE9gUHUTeCTY6ye/FB5m9dyLb5YUJdLo7Z3XftAyueQ7VmN4inDEmm6YdVYOnzbEquYvSP/Q/FqRjuh3bh/vqW9LGwt4uh1IueStHROLgyaOoQ03gdDnItt/HBXTtQeyaR/dnd1caMO1pJLEjiPP/kKenbaEuLTyizjsH47GY+8SeDDV3biZ7i5YyCmgyvfUa/DEG4sYlQUSUea+7tlxgPh2rsgJlxoShmjpNmEcg9q2mdXSFUh8rbyxOkEjpgpWjpDMA01ivrbbiSBkpR5srH/rPtVNyrs7MmSo3PgSduEBjmWbJ8bQV8dTPvnwaF/T5XCpyomkKTFuXewD6W2Ar5due76eNGT/rK4UjH3BsCo70LYRgYBeZV3N4CIvEwMS1JSySAQCBiCQzr0CE/YhSFzJJVCq/UJjnpheHrfLxxQhIUJR0zr/TLT195wMrOWo26n7fiSEmDYu/Hgmyz0Ok1MjJrHPdMjAsaS4msGnt7vUW5REozx9UfRIQiUOAk2XwIReLGwOrVq1m9OnPD/7HHDp7IDCSXdHn/DbKWMXrpN8e7kZCY/8cOwh92YJ9VNeH9myxdfr5nOhvWWnlO2s/pvzhA4sszUccwIVEkQTgVx22xk2jvQK2rHflLRxhTZtSncjA91Gg48JE2Sl/x4gCm/9CMC/eV2Aj961xk2/DxwImYhq8tQFNHT+qZIarHxl/s5sRHG3jt+7NwqhBZVkXBa9BRIBOeNRs13IlysotkIgh/8PLMzx/hjK99kopv30RXQ8NE3u6EIFzZPS+SxcH+6S3MPFhFm1el7ZSNLHtiFXx1M423LCexK4qsgGV2zwtf6CTC/sEN5bAs5VJMfUdXG6G2NuK6nuFJ29q8n4W/DxJbq+JeN3kb0EdiYL8OF9Ks5YgrtnP8U+04bhFsntNB5Owa1hZVZO2X4Y/S1t2Kp3LGYe/rZHKoxk6ix+5csdTcO1PVJXPgfVMPWWMCQ9ExLLB5tY26ZnA4zbj0mENnenuPd70ocyndVVaIO9FBaK5M8bOj639TBThmFA76vLoTXmzbR5dT5cF+2YQ/nPKiGUa6cM+QJDP1i+j0YRQXQXEFvo5O8NhJ9SztG4YYMrY0buh8tvlFnrCePeK9VGyTaZ0r8eLlViyqOuSuI90lY/MZpCzmNVsWShQAsiFhSck0zo4zv1Hl9XME44/oFaS0FJaeuHxZA7kn6imop7BIEo6e1b3nIs2okswpzsoh28r4H81syAhGoSg3J8u5ossjqQSJnnTWRpefeCo1ar3ZpsV4OHSQf3tFQXcJ7KfMn5Q+TqYuP9VVxTsnWPmH2MG6X+5Du3E+imt0MhPXdQLJGC6LjVhHF/aaqmEdzEciubOedITgmbWIuU1WxP+YOZt3LnqXoq4Edd96H5Ea3gOTjIbYt+0dQv6hq3hufWofcx41X4jltx7AmRCULJvOvAbwJBSKSjzYr1uBa8karKs+RNCu4DDMTY9H4uY9y41n0rDiQRqvaKXolEvTn8de8DH7l7up/eWezC8cxSkFJ5OUlqJx1w4GBshv3vMei38XJH56Aa51i6emc7nAtIXYrj0F/UtlzBMJ1t7WxnP7shdfkwxBoKHRDJ3LQ7TUfI2ESk3DfNvsOPPXmykblZjA0Wxgj8mcs+QYjP/umzQKpc/4dQxYRq8oNzNaJWZ5ef06N8+sGLmCtPiP5diWTx/0uQR86yUn//6iwoxunT/c52VVk85Pb1YxhI4YSadkqQg7VHx1cpgMMa/HOzAwjXuB4N/a3x7y3Oouga0LZh+zkLplc4c8Ty62YQ1KTOsw03zOP8OchNV2QVnQQK0wJ1A1Z64Yso3RYigQjJo50SORCEv2WkmqZla2yxpf4k/BfelztyT8PB7qczClhEFqwDhHU0lEj6HcmzpUZCnWmCeT/vUy9I6hEx70khA6z0da+EHnZh4I7ud6XxEF74N61ZGr74+xl1B10mLeWBZGvm0nRmrwfsSh0AydSCqBSOkkjsJiVFNm1B+ps6PCCnOpalp7km3rXsF1zWfZvfg19tTGmPaN90k+2DLMtwVCS6EO8RJpTkb4yNPmy2v/imeZ3WHQWhbB4jFjWRfttWAvL0A4S9LhE/vmxBBH8qY9SUa68n+omHU6kmqlefVP2Tezibl/rgfAogvijeMoejLF5MqS7cH929HD5ouzd2n0/Z1vs/xeiH2sHOfxMw9vhyQQkoRQJGRVwWq14HU5cLsdeAqceLxOZPXw6wipuBLbxceT/JiTM/4Q5x+b38oaHyqCURrbB4eCfRCRraZHrjdkyRPs89Atf9eKrkA0y2Jd83TTAN750wXIA+PLLVaeODZAwewaZs6bxYIrTjzk/u1ZJTh9i4dPvlHIw78rZ2Gjyu33mh3q1kfhfc1SSMeImiEP7gJzMqLrOildRxsmPWZJQuKRu4tpi4cwUhp//B+VaCqBHo6h/fTdjHO31aVoOHXkRBGqy86SXaaOSX5/cH5x1/JaHjjFP2I7o0FTDfRAlBff3cz2l82QybKQIJ5I8NbPS/nYo33XsUsKBoJYTy2Ih4IHuLl7W0Z7AtGXu/8IqNacC7q8/wZZrdMHydSwoS5BPcmPOrcgEHy9eDFfcs2l9sEg0U8VZ2SgmmgORyjlIlsh4ox57JwZwbhtKyJL9sCh+uVLxDAwiGdJLXukM2XhN4e3MPQEIknUVyQQSoKCj10PkozjqisIde1i3x87mPUqtO4MEvvCLCwlo1cCjakI0Tv3ArB3wTvYrvw6B/72CJbT1qVHqtsjY6/KfDu6P1rA4lOP3IJAAzEu/gOxcBf8ZyPd3hgtZQGc91sxPC7ExdUo7ikU2Sw89thj7N+/n+LiYi655JJBk9VcyJhQ37gXf0NmRb4duzay+EEn8fOqcCzPnu1hIpFUGXeBg6LSclwFBciyubIkI6EoFpBlwpGYmQauxzg0EhH8XZ0EOrpIYYAsEJJELJ5Ej+lIxuSNrfWYOSQK2jj7T238PfQyp5z4oczsOAYEDxykzVNIhado0vpxJCAXufnvc9u52mYalVtXacz5S99z4OpWCM0c/JJ3La+BDd24LNk3q/39o4V8SzU9zeNZhZRPrIC3zJDHgFPGGzUNyMZS8OtJihXz+p9ofJ4zXdV8oWjBiG0mejzWyWCI5L59iNISUv6AWTl2CEpeaGR6h8rL3a3UlJuT6MadB/A0xDiuQaL/FFGRZSrsI8cKuxdOA3YMepv+xxVBFIvK58vKqbxgYjaeahaB0R3hw49DSdDJ5qUJnD6FnZt2MBcJe78qtvZIgmQiymdSL/FY7Wl06Qk69czY74Su9W327N0XlQP6cihyQZf33yBrjOCl79Di3OrbztWFc6na60fasAVrh0L4WBn7gqMjlny1o4znPpbC81A95b/agvqFpUijcBgbQiecTFCATLyzC3tpyYjfOVKYNAtpJGNHP4KX2eR/P878R++LRrXjqVhG/Zq/QsGzzHp/HXzP9Eoc+PIc1DmZHpeUYUBTktCmborPrWa/P8DrL+7hG7tUdi5oxnX9lWazH/1kWlnvPKYZqa4A54CMJK6ZSymaPvTy7JGI113C3Zc/QkHNauo0K4t/nASS8J8+Dvz3Qgp/vpfCYJJ9X56Ldc7UrVI0NjYSCAS48cYbefrpp9m0aVM6I0gvU11avKF5P+279oHe90Las+tl5j1SSPyj1TiWT2LhLhlsbjtlVRWUlFSiOguGNc5CXUG8pX2/p2y1U+opoaTOwHQCSCBJaMkYfn8b3R3thLtCiOTk6BLLnAoSn3fxkTvgre6nmX/OGTiVvom6iCRp2r4F1/JVuI/k1bJxokgyf52vcE3Pb3vHYo31lX4+vF/my8+W4YwoSFkm40U15fxtxX6Gql/576UTU9nSOquK/f9mRS0vRFIUvF/tKbwmQTQcgWIPGyLmCutL0TbO99RRpQ7/exopjdZokHAgAIYgFY6MaJBGe+LenTs6ibsrEIBtk49j3nbQ7c6c9MjCzKAzEi6rOSF54owI/Xc/fGHlxG92L/RbqLw/hiHJ2DWDpKaj2iWWbjD77vdo6U3Kl91jcJFSwGcu8QPQqcfZmQzSkIowzeICBPF4nCPJuTfVuhz6Qm90nz89EUoZxiDPeEMqwh3+nXzRPpvSX+xB0iG11k3q+FmHpRZItj5NFqe7qnnskxon3t+K9873sF43dNbE/v3yJ6O4LBZirR1HlVE/KaPe39ipqKhg06ZNk3GZqUOSBmUPAag78Rys13yNv63bm/5sxm170O5rQrqnEdv/7ib+bBfa423M/OlOlj3XQe1XN3Pydw7wjedVkoqE67qPZL2k6/KzcZ566EvQRxrrjrmW1RUrqKw5hldu6GTfF5qIqzIz/ns7hcEkO+vizLptN7Vf3YyRmpoJ4v79+1mwwPTqLVq0iH379o3wjcNLU/tB2nbtyTTo332COY8WkVpXi+PYSTLoFQlPTSmzj1nBktUnU1k3H4vLe8jeVkmWkWTF/F9JwmJzUlYxk/lL1rBg1So808qQLJOzn0StdBP8ylxWbK2j40/P4EtklmI3AnH2b91MKkuNiQ8KqiTxWO1p6b9/XXk8C2um89xK08te5RMolsFGvctqo+zSQ0idcghYqkrSHrzGW5bT8PMlhAs1LLvNmNpbfNtZYPUSFzrvxTP3POlC8Dv/7kFtJnWtb4+KpkFKA12nv6H6aOggn2h8HiEEclBj3zSDabsMop1+2r0SlU02UqqMJEAP9au3ISSkUdYcWf+dQpafs3b0g3GINFxThj0pKO6pCVD++dUYqmB6m8TW5RpF7eZvvCMRwJqUsaXM8RZCpKs2x3qSQwggEQwMtumTH9znaCT6b5DVW4cOG9kc7+Z3/t18TZtG9S0HSc1VUb6xAvtJc0flxT4S+YSnjpcuKicsNBJ3js7eFELQFY+ghaMkAiPv2TlSmBSjfjTGjjWLkj/i6TH2l3zsfJ7/fhG/O/8hDix7nBkbO6l5p4uyliiWN/cw59UuXv6XgzR928Wus19h76cPcPArgpYbnOnQgzx9zJh5Otb5H+XtK3x0uVX2LHqdg5d7qF/2ZxrLUtR9Y8vIjUwC0WgUu93cde9wOIhEBhfB6j1+uEhqKVo669my9Q1at++GlPnWFLrG3hd+x8wnZ2Gsq8W+ZuI9E5IqU1hbwcLjj2f+omMoKiofkyFfVTX21GouVyHzF6xk7nHH4akrR7ZP/EvL6bXDNxdR7Z+J9vt3aPHtzzie6Aqxd9smwtkyM30AsA5I3yhLEtcVzecXtSeyeUZPWMXC7HU9RvKITxaSopB0CySf2b9jbMV8Z7uLgpg+qFZBUuj8JdzAdzo20Zwyw256M+YUens2+A4yRgW7k0HWB0wHT8BI4YjIxKsEjoiCvrMTCbBoEu2FOi0lCaJtfZMJb0BBGmUF59OKp6eN5snEWVaIXTPve0tdEkWS0c6soL5MYPtQFZ6wha0JHzc1vkOlX2DRJL7+qsRLsTZWvR/ix2+52JXsKSwlBFooPDiWfhRx0VPF4dblA+lOe+kDfeFKZFZlfSbSzLORFr7VUUzZHS3EznLh+NiSw97XgZViDwefLJjBhk+XEk1q8L+biT36Hpov01gf2K+YliKsJYi3TG4a1cPJpFjW0WiUgp4NRP2Nnf5VCLv9gXTVOYeikDCMdKU7qywT73m4e5dKUj0Pv11RSPY7N11ZcIhzUz2V7rKdK9FX6a7/uRISdqXv3KGq4mWtoCfJKJJErbOa2pP+G+kUhX/sfIw3o614y5ZzTs1qtiZTVKiLiEsSto98gYSuo2NWFkwOVYVQklBlmURPn3orC8aTfVUIg+EwUU1DkSQs/cYwowrhBI/3kGMoSdiHOXfYMRxivMsWnkDnv3Vjt1/CDMlK9LL/5I3unVz2w6l5ETgcjp4lZLMSZ2/57v5y3t7eTmOjmSG6qqqKzs5OUqkUNpuNwsJC2trMWHev1wtAIGC+9CorK+nu7iaZTGK1WikpKaGlxQwRUFSZoL+dWDQOkoTbbiMYiZBIJNETKawC4j1p+CyyjB730/rcn5j17umkPjmN5Fw7ek8FzfHKeaIn57bL46Jy1kyEIePvDuCqcdPc3IxhGDidTpxOJ52dnYBZajuRSBAOmxlSamtraWlpwefzUVFRgcfjob3djIEuLi4mlUoRCpnKubq6mvb2djRNGzSGNTVziJfX0dJUT7CzG2syNSFybghhyvb1c3Dea0W/u4M9n2iietratJy3N3XS3tqFw+OkqKIcTReoskpleTmxaIx4PI4sK1RUlHOwoQFD6Hi9RXjcBXR1mV630tJSotEo0WgUWZaprq6mqakJIUR6DHOR4cISgsU6e+M6bsvkbco7VIRLJuGPoguDO34gAQkeclfy2cvf48Pz+lIxBgzTYN+c8PGN9re4t+ZD5vcRdAWGqn4q+Gb7W5SGDf77ORevXNbGiVEJsdCJoyFGKqKxd6HGrB12VE2iukvmwNOt8Hnz2+UBQfswlbWngt66DO+dBc6PHANA2ew6+H91WBNJ6jo7eOi5t7n3pQrAoMov+Mw/i1l14jZ+v9lDXaud353Qxu5kkBvLlhGJxRHCnB4NV4wyV0gOswl6stENg0BvBdm2TC99wjCwyTJ/7Mk+9IUdVjzP+YleUYlt+uQW8hqKhGEcljCfgXyyaCaPX6bQ3tTKee/IlP96HymXQL52IYrDlrVfvngUhz+APRDE1jtJP4KZFKN+KGOnfxXCn/z4xxl5TLNVuutP//issZxrG0Olu7GcO1IFvf6x74sWnE//klAuNfP8wZUFB1chHOpcu9WarkJY4HaT7O4e8txDHcORzp2sMcx6rsdUUr3mzWkVS/jbj9qhJczhZtasWTz//POsWbOG7du3M2vWLCBTzm+++eb07wNQUVGR0Ub/Y4C5UbSH8gGVFXvPbW1qINTQnv7c3/O/lp7/oGcshcC37x8E3mxn5p51aJfPwFrnQNO0YeVvTHKuqDgripmzcBlWNdNwq67O9M72v1eHw0FhYWH676qqKnRdp6SkZNC50DfpAXPCM1S7bqC0qJhYMsHOTW8iBTMzJx2KnEc1DVWWzXG5fDaxJ53UPNROy7qHmbXyIuhXCZVwAn+kARQJSZYItDRkrIe2NezuWdGDAo8bh8OR0X+73U5xcV/10Jqa8WcXn2yMYTKXPPbxAt6L+7hLyT2j3uqxsuJ1lX/Guyi3yDSdpTLnL0n+4wUXn3A+zx2Vx1OuOujU43zSM51HQgfRMLipeys3ykuI7q8nnowPqs58R9dOVsdKmZdQufN3RbiSBltafDjjCsr8SuQXDiLFNDQ7OOMSTRVJXlsWoy7S5wm+6N8T/LC08LCOx0jIkkRSkXCeMgf7gFWE3kwqX/97GWCw95pCZv/WD0C15EASUBg1KN7bzYvTVK5NJXAFDB4IHeD+4H4eqvlw5kb0HGQ4OZ9sfIkohhCml35AsSndMPhdaC8VioMzXgrg2KwT//KsCa8SOxaMKdxUfJ6njvC8Kl6ubeONWCcXv5pk3s92krx2Gkbx4DExhI4vFsHW0HRUGPWT8hTNmjWLnTt3AmQYO/2Z1JzqWUq9TyQWlwN5HOmter0SimUU/ZRz34Mx1SxzTo03ora2Fo/Hw0033URLSwsrVqwYdM5UlRYXgQZan/0fYs9UMqPtBIwvz0GpM2OcJ8wrJksUzZnOgiXHDDLoD4WBYRzjwWG1MXvFscgex7jbGjhejnOqkE+qo+7ppTQ9fTMinhlnjwA0gUgaiJiOiPT9Z0R0jLCGEdIQU7QXZKIZTsa/WrSI31Qdfxh7M3rkE6YTt+p4dnbgSBnY1y0E4ORtZr79a1tfY28yRNzQqQkLHq05laQweDnaBoYgGo1hhDJD7pJC52+RRv67411u/b1p0AMsejmCNaWgFHuQADkmMNwqkoCkqjMjaGfZZgtdm8z6J3884dODwoBygfafL8M+xIrR7nPMd2KHV8a2dDq7Tzafmyd+7KQ4aKWhCs7ocHNxwSz2JUPmGPakvPTruV/zYap0OfTbIDvAS28IwT2hvVSrTs54ogvLdg3txgVjqrI6GUz1yotbtnC2u5bvlK1g/xm1/O1DYey/bkBsz56GOKIl8Pt8xNs7D3NPJ55JkdLRGDvqMDmoy4sr8QyTJs45jAGhWmx4K6twFQ5VyW782F0unO5Df2h6PfFWR79MH6pCWVk5FqXPk2ix23H0hDEhKUg9P5esWpF6vBrqBBpCk4aiYpaAOfo4//zzufHGG7niiitQs1TQm4zcxt17/gnd+yBLuXnRsQ3fhu+w7dm3KXvzU1TMr4GvzELy9vXDOgEvJ8lqoXrZEmbPmI88QR62/h7qicBjczBj2QqkcRr22cbLdkIR4lMzqHn7LFoeewi9dfO4rnEkow6zzC5LEtYcNE4BbB4Xsi7h2RGmucTUT/u/WoEs4K2flLCyWePlaBuJYITLfhzB8oMt3KYuYqG1kHYthojHsQ5Qa7Esz+SBf62lqs0OwozllwQoMZDdVt7+13KUzy9j01mmnj9w6yjL5+YgjjMXse+6EhLfW2r+/S/L2HulucJm0SQSq6ys2Oni4p8F2Rc3w5aCqRiXUckb8U7u8e9hXzLE3Q25+SxNVZ767niEhJ4a5KXXhcFv/DuZZfVw2oMdKN0g37gUxZE9RezhZCLeMRPFGa5q5q1dyq8/GcbzWIDYk1uzntcdj9B9sAE9h/d1jIZJ2616/vnnD3s8lRochykhI2SoXryQlKGx9dWXB52jKhbchcVEO7NvbCgsr6K4qhKn08l7G/0YyXjW88ZDQUkpsqIS9g3OE6vYrOiJJFhUMxtCPyRVQVIVouEoTquakd1AcToprKwg4guQwvxeWVEpcVkQ8wcoqiqnyO1l364deMtKiIejxKNhKmf3FQ3qfb+o3iK0YGDs1VcVC+gaE51mzFFTTrKjGz02zt+id7iOIAfnZJQWf+/FdgLd2ylS1lOshJEkBQ2DpKHQGj2J+XsuYUmhDXFtBZQOVvDjLeGteAuYsXgJRS73eG5jEK2trYPCbsZLsdNDcskSmrZsQYQPTf6GGi/LXBfGl+dQeYfKwaebqar7Dq65Z6PXHAej3OR4NJAcQzXHXEKVZAzA0QXdayQKAcusSup/WoL42Vbu/FMF11yyj0RI4iLKqeoUSHe00/zpLq5NvsZjtacR7xfKJoTg8pZXKJAtXPB2gt6CuWp1CbVtjbQWSmhAzK5T4LMQLnFTU2KG482prOGf8zfjneXN1tUjBuvCzOfXtmIG7YVbiLsMbKfOY9pf3gcE1k2NcOoMzv1bmA+94+Lcz2+nuUDhz+F6pju8XEru1X6YDF0+EoYwaI2aK4H9vfRRQ+PW7u2c4Cxj9R9akA1GnaP9cDDed8xEM8vqoXzRcdxcuI2rH4yTaNiE9ZqlSP36KBC0BrpxNrVQWHfk5vHPnVGXwFVciJ7UUe0WFKGiWm1oyYR5sMfQdDg9WIu80M+olxUVoRsIDNxFXlxuF4pFweMtJNAx8buabVYbjgIvktWC6M16IAMGyIqCDjjdbqI+f8b3rA47rqpKwjt2Y3F7KJ01m/ot70MyiUWWsducKC4nBEzl4SwtwSpLdDc1ododSL2rGxKU1k6no7mBwuJ+aQl7lrwqqsrp1HUS4QFhAX0nks1wL6msItjdSSpdCGRiZqwVhWU0dwVItybJptFjGKguF9qQ/czEWlCAFo1hfMDTnh07cyXB7U6UrqXEJDAks/KuDNQUWRCXVSLKR+etEaodEEiGDsbIeZjV4mLmLVuGMwc3Pg5FpbsIfckS2t7fghGe2Bez7LUgbpxN9YNOpNcrea+tk2lF/4XX7cHiqUG4qxAWB5IWBy0O8QDJYD3JRJAde5ZTef3vJ7Q/ecZGXYeEPWEn+tk+54hssRA/yQkPxfju0xVsnOVn9yqB7cJF1H1rOw/eXcnnvxpLnx8yUnhkC8FUkuvfU1hyxjHM2biPoohB4y3LAUjJEl3zUngBRZco88tos/r2S0yzuHh+nmDFrKMnX3YvgdUycqULm6Jw4LtzSLx9kKXbFJpOijCtycHBWsFZe2ReXuNhdyrEv80+gaa3t091t3OCtmgIzdAzvPRtWoxf+HbwWed0Zt9dj2ERWD9/zBT3NPdxyxauqVzMlut9FD68i3k/3op2TR1qed8E0hA6jfv24iwtxpqjiQlGYsqM+t4NeJJiRehJrHYnVbNm4+oJa5EkCcVqRUsmcBSWEPObsU52bwFOpwu7w008Zm6OVG1WtHgCSbZgsVrTseoFZRUTYtRLkoyQZDA0ZJsNd0kJRkrDZrMS7zEwJVVFJDXkHu97tpAE1eagvLyCqD+IqhmUF5eSnD+H1i3bAAlkCdVuhQBYFAt2pxO5J42WKkumwQ8oVisVtZW4i72o/cJ1rDbzXKfHi9XZRSLL3lHZYkN1utGSSYxYCEm1gjAQuobD7UBYarEYFhJaHF9HM1IqNriRjMFRRjT+pf5LcZKCo7QKxSaTCPoomVZN6/aQaeSPUNyjdt4C6t99D4PMiVQuMxlLtp7TphOojKIB1p6YXWHtqcI6mj7JCsLqwuoppLyyHMkqo+kGWkonFg2RDAZIRWOQTGSs9qhlpcxfsgTHBMTPZ6P/ZtiJpsZTjLFoER1bt2JExha/O1IRFUmVsX22BhHVmPcPL+o7V9BaINNRmCTk9aPZwwjDDoYLJTWNktBaSv0yjsCRvykLyBp2dqTQXAzesESyKHPVyXLsdPZYGphzb4RPdhayZ7VAtlnZf2MlZb9q51uPClLXGbwUa+Ouju3cXHcCex99natfKybw8j6iTsH+r89Mb1xvu2kZvdLtL0zSVpFgmpqpGyo+tIRpxx99tUgc5yxO/1stdCFW1rL2iRRfePtV/jteSfBEG1c9UcYXnhPsLy9Csb9B0yemNiY8G4c7/Capa3TFwwhdR2817Z9tCT/3B/fzRWkaFbc1kKiTUD694rD2azQcrsJTY8Uiy6xxlBG+uJBXXnmbD/+6ge7TW/GetDB9TjyVYPeW91i4evWEhZYeTqZcG9tdDmLBJM6CAtyegoxsGxaLhQRQUllOo78LENhdThzuAgpLy+jqkEglY9icHixWB1o8jtov/31RaTENitoTUtJTMKrXSJEUZMWKoSeREIihQlUkGW9JGTZvAW319ThKi7AqKvGUjtSrlCWwlBWTbGpPC4HFkZnTVrJZsBW6cDvcOOx26Il78xYU0dqzGVbqSSko2SxYdBlFUbBYLaCqKP08o2qPcnE5M2OFrQ4Hks2OxWrDYncDgzd9eIoLKauejiZ0Dr7/HgXuAhKpFPFIANlqY0ZtObIso2kasqzR3dIMeqZnXKhWQEFWLbgtNkJRvznGkoTsKsKI+PsMfacHh6cApdALiQQWdyHT580ipaXoEOAtKaWj1oe9sIjY3gaM3lzvVktG7mdbYSHF3kJaSgvRWs2NalhtGfl6P4j0GvMjIskI1QZWO6piZVp1FWWVxVmKApl7UWKpJL5QiM7WVlL+ALLLwdxFiybNoD8cTPOWIRYvpqt+H3p70JShCURyqlg+UQnnVlDZkaDsYBzRWIzs0xCqDKqEcCpIC+0otVZqTpqYiql5Dp2NZ2oEiqycMeBzxWFDWTOHveo+Zq8PYTlvPgCWGRW0nt7JiqcdPJ8Msu/9Xbx6fxndrr0cFylm7xKDadslrCmJruLsk7aCa1ehZtlIuMRezMLiydsLlitYigvYtDLJr+6roLlUYD1hFvZHtxGyy8xsh/rPFAJHdlzzRNAaDWIIgdbYAvEET4ebeD/h41ttRRQ+0kz4NAf2k+el0/Iedhx2JJfDXPQXwrSvZAlJkpA1Dbk3LM0wENE4xOMTrnMPFbds4eQPrWXX/BZq72qla8ebeC9fjtpjZ8WCQXZt38b8hYsnN6nLJDBlRn3vZgSb000sGMDudg1Kn4cEKBZKSstpdzWTjPixOpxYnFZUh5WCkmJUxY2zwIGWTJGIx7H323xqtVmwOD2kIiEcxdUUWF10BjvQ9RR2dxEFThfd/g5kITBSCbRYEGQVDB3hdGMp8eJW7VRWVKDaLBiqgq3HoLbZrLjKS4kFQjgqSykuLKappRPZZsc6uwx1gPC6ayopqZoGgGGzM6t2OgAuhwNLgRfJakO22SisriQpDCyJvkmDzWlHUVQsdifICu7C7PGGsizhLS7B6XRQUFxMd/0BZFchJCIYWgpZteMuqaKwJ1Wav6ySkuISOtqaiUdMD3/vDn9VVXEWFJKK64Q6zTzrQrWAxYnd4aXA46GkzEs4Ekdrt6ILGWSJ6dNrOLhnN4lIACwOXOUlOGxWSqprCZWWUur24HbaATuF8+eBLFFaPY1Kl5cOTaF521ZUtxtvZQXdrS2IlAaaTnFPar9ps+awx+9DAIWzZhFoacIYUGAil0gd9nhjCRQVISnmHglVRVasWG0OChxOvIUeglE/ldOGzxjksFhxFJdQWVSEPxHDIiu4LJO7ASsQCGSk9JwM6rxllCwooKWsmeCBeoxY0nzRCMlcdZJlUBQkiwJ2G5LNjhYKYREKIhZD0hKMuOdElhAVduQKO6w2Pxr4WhAAo6wYmusMl6c+11l9/LHDHreumM7BRVrG5kP7RxbT/NZmNj//Fv/1XAV7VmhYmwTBb87GVuSh/sn3mfHs0IbWwHSQH0REnZ3oVkhcU43FYkmHKXXcvonyj6+Cja9PcQ8Hc7h0ecrQaY0E8Cei6O2dxDt93BXYQxEqX3xOx7UrSOyKKuzTy3rONw6fZ1yWkbwunBUVeIqKsCsWZKnHkO+n5dqaW6isqTaPYa46RBNxYoGAWQvEZkW2WEglEsRDYbRoFBGOTrqTbuBYza6oQvtGGfqfNiH9eBtbPu1myazZSJJEuKmZvV43M6vqUHJ05SEbU+ypl/AUFhJobUFWBndFtjlxeGQsDivF5VW0tUu4nGbOe3dhGe4CA4fTbhaw0AyEZgxKE1lYXIVPsjF//iwkRaaow0MsmaS4phhJliiJFaELg662drrqIzhKq9E0jaoZNZQWe5GQ0jO1qqqqdKYHSZIoKCsn4AtQM3MODpuN7kCY4roaKlwFdHa202HeIordQXlpFQU207PuUC1pIZElGe+MaaRSKRSLireoFKxOiCVQbaZRXz5nAQUFHmwWC67yMjwF2cMUZMWCs9CMyXQXFCC5Cpkxcw6Ne7eT1FI4C9xUVpWlzy+fOR27zYI/FABJwT5gebGwwIsFC8FYFCkWxOKpwONwUVVTgt3lQLYo2O1WbKqCq8CJxWpBUmWq6mbS2tZBSZEXT7EbSZKoLCxkoA+qd6PwNE9xT58LkQq9FJWUUlpbi+60YUXGqtoo6skfW+BwUrF8OYosU+5wEygqprW1BXbvzS5iRyF2uwdHYRUJLYmup0CSkGQrimKGn9kVKw6bFYfThs1uxWq1IFsUZLtqKqvG0U+CZEmm2O6axLs5/LgsNuZUziRSUk0wGSehayS0FLIk4bTYcKoWrD36SEKiqakJb3kp4VCUkD9MyudHxKOmgT/WzehHII899hj79++nuLiYSy65ZLDz5ShGUhQUx+D7TVTofPPpMg5W69ivXAn07eO3rptH4zHhqX655jTlpy2l+7S+uhq9WK9biTpglftwMdVyLoSgMx6mPRpCFwYRf4C/7Xybt2OdfNrvYdFDYRIVBto3l2CxTX4okCIpOO12CgoLsRV4UDweVLcTq6JiGWFiGrJYKbD2/Y4ui40iuwu82feMJHWNmJYiFA4S6ugk2uVDRKNZz51oVFWl/PLjiGzaz/I/hnh9xatIZy5gpb0E/+59bJMFxZ4iSuwu7OrUZEAaC1Omd2w2G4Vl03AVFFJYVkNZ5eAy4tXltSR7XprlFWW4JQcWh7k84vaObhNDVUUJbocD1dnzvdpC+kdPuixmO4YQhKIas+ZMw2qzoGSJE7UOMHqLHW6sixbh7vFgLl7YF5flLS7Bs2QuqmLFo9op8BSkJwcDCxDNKKvCEAaSJCEpCkUFHijo81hWlPSl+pu1aNGQs0aHx4Wnx/i126zMWbAQb7GH1vYikokEloISpH557wt6YvRdBcX4inVU64DCVh47FpuFmkgNvqiX6dUV2N329FgCqA4rRY7MkIyyyiLsbhse99g2mrjdDuYuX44qyTgtVuZUZN+BXuMuTP+7yO6ioG4WL47pSocPm23ivdtup5PpFbUIQ5grXpKpmGRVRrGqWNxWJJs65LLhQPnLFQ53v1wW26hWH2bU1GKxWCh3eqCigkgiQaArSMAXIhaLomuaadwL3Sx7L4y+/+0twiKZKwBm4SnJ/NsytWXnR0NjYyOBQIAbb7yRp59+mk2bNqULq/UyUC/mArIkY0zihMt+5XIaFQUpS5Ed2WZFrprY9KxTiYSEIssIAfoEJU/INUYj55OhyxO6RkcsRExLEddSaIbOdl8rr9fvZHdnC+eH3Jz1DDjbokQ/UYht6fRBbUxE5VZJkrArKhZJxV5QgLu0GE9JCRbXoW0WHasutyoqVkXFa3NASQVCCCKxGGFfF9EuH1F/gETSrEA8HoYbK9fKmaTmxlj2+x2oNzXy3Ood+FfXsOq9INPnz6fL7cGqqCiSbP4nSxhCIASU2F1m33OAqXMmSBKzl5txitMXzEl7pfvjLu+LSVSdVgqqCzKM0tFg9Too9oz88vS4nMyvq8XmGtsP4x7CKLDICvOrZmY95vf7KSsry/hstBsybFlWNHqxDphFenuqp5VXVlGfEpSWZA/bKfZ6cGrZr69YFSrqSnG32HCWuNMbgUdirAY9mGPQf3Y/WnJ5aWwylmxVhxV3nTmRErrR46kf/XORTf5ygSOlXy6bDVd1GdXVZRhJjUQsSTQUIxFPkkpqpHQ94+UjSaZRpEgSiqygSBKqpOCx5/5G2f3797NgwQIAFi1axMaNGwcZO9ok53WWJAkhBKqsYJFlLLIFm6qi6bqZvxuB0rP432vMFzmc+GOxdLikBCiSRDSVIqIlsSgyhhCkdDN0yKpYsEgyUT2Z1XBwqTZiuobRY9T2pg5MZik7n/0eZIQwkMwdXACosoIhBIYwsKmWQbHRiqRgUWQSmoZDtWCVLSiKKUexpLnZ20AQ1ZLm6q+koAsdCYmIljAn9QKQJByKSkLXcVlsFPQYp3FNI6FpeG024rqOIkkY9OVGM4TArigokoKEhI5BdyyMJgSyBEndvJaBwKYo2GQLumEQ1ZPYFQsO1UJS17GpKook0RELI0syuuh7PiRJNtvoN1lQpiCGeTRyPhm6/OWm3TzbuIOuWJjuRAQtkeTEJoOP7Eny2e2gpCLEjrOhXT1/ULX2XkYrg/2RJAmLrGKTFBwOOx6vF5u3AGtxMap9/JOX8epySZJwO524nU6oMcOWE8Eg4e5uwn4/0UCQhKahG3r6eRoNI42V4nagfGUlyYPtnPpEE9aNQQ7ObOdvszezZVEhOGym01G1mrZYT3jRwqJKvrri9EO+34lkyox6wzDSnkTVPrKnR1ZkcBzaJr3RGDyKRUEpPjwpjA53vtuSskKcFgvO4ux5xVWnFbdz6LFV7BY80woz8urnGR2TXVr8UH6Tqci3PBqOxH7JVhWHVcXRs3IodAMjqSM0A8MQYBjpPWRIgCQjyxKSIiHbcj9AIxqNUtBTAM/hcBDp3cjeH0misKICSZYQQ2yEkyQZWZLQDdMQVBUlbVBLvbG4kpQ+pxenxYrbYiepp7Aoat+5w9Dbj2zavH/gYtLQ8MejWBSVQpsj3XZMTxJJpdIvf0MI3FYbQph5wzVhoOk6iizTGQxS5HaTNAxsioJVUUnqWnoiIkkSVllJG/ACQdLQMQwDh2rtM/AlBSH1jV1ZUQleYV5fFwbKAKdPYb9/pwwNi5wpS0lDQ+kZ84wx7odzwL+H/f3SYzp0yk1JMhemBAJZlge15RKlqJJCytBIGjqqJGNRFHTDICUMhBBYJBlX0eFP6zkaOZ8MXW7//j/41J6eDHqAZEDKDUaVBfvF0yk4Zl7PMQlVNp8PWZZRkFB7HARtAT/l3kKAtDz16hyp5/8EAl0Y6IaBJMk4nC4UqwXZYcfqmdh6IzA5utxWUICtoIASQNc0tFAYLRYjkUySTCYxdK1nz6753CmShCLJfSvWQtARDFDWL3xZpP8Tphwa5rMtKipg9VLijR1UP7eD6rdjnPdcCiStb4NUz1YsgFRpGzz4ATfqj7QdxRPJ4U6NJcnSkAb9qNtQ8wb9oZCLcj5VlRFH4mjol6TIKI6j51lxOBzE42Z+7Fgshstl7q946623eOuttwCIRCLYi01DrKqqis7OTlKplBliWVhIW1sb0JeyNBAwq4nWVFbS3d1NMpnEarVSUlJCS0sLFqCgoABZlvH7/QSBiopa/H4/iUTCjIEtL6e5uRkAj8eDqqr4fD4AysvLCQaDxONxFEWhsrKSpiazPLzb7cZqtdLd3Q1YmDa9jnA4TCgWQ5ZlqqurCTU1YRUCh8uF3W6nq6uLMFBaWko0GiUVjZr7hGpq6N62DVFQgNfpxOl00tlpZhwrKSkhkUgQDoeJYVZZb2lpQdd1HA4HHo+H9vZ2wKyknEqlCIVChBobqa6uxp9MoNktg8awsLAQwzAIBoMZ4x1LpbBarRQXF9Pa2gpYBo13Zb/xtlgslJaW0tLSAj3jLckyAb8fMMMnhhtvi8XSM4bmeIdCIWKxGIqiUFVVRWNjY3q8bTYbXV1dgHlNLRpFj0YRskxZdTVNTU0IIXAOGMPDyWjkPBgMpu9rLHJeOYScAyz72ZVpOYeRx71XzjXA2yPnyUSMqMsxjJxDWVkZ4XCYRI+cl1ZXmeOejOPSUmk5hz45j/bIeU1NDc3NzRiGMeg36i/n0CfngUBgWDkHqK6upr29HU3TRi3nqX5y3hExr+ktKcYyxHhLA+TcbrVgLysbNN7JnvGeNlDO58yle5m5gjOSnMdisaxj2KtXBsr5pCGmiF//+tdTdekpR9O0qe7CUUeuylMu9itX5S/fr5E53PLU0NAg7rnnHiGEEE8//bR48803p7xPuUQuycbRRF7OR08uymAu9kmI3OrXZMnTlLmUemfFH0R6Z415jn5yUc5zVf7y/co9amtr8Xg83HTTTbS0tLBixYpB5+SijB8uPsiycTRxJMt5LspgLvYJcrdfE0nuB3XmyZMnT54p4/zzz5/qLuTJM+nk5TzP0cCUGfUdHR3cfvvtU3X5KSUQCKTj7vJMDA0NDVPdhazkopznqvzl+zUyuSjnuSjjh4tcko2jibycj55clMFc7BPkVr8mS8anzKifNm0a11133VRdfkq5/fbbP7D3PlnkorKF3JTzXJW/fL9GJhflPBdl/HCRS7JxNJGX89GTizKYi32C3OrXZMn40ZOmIU+ePHny5MmTJ0+eDyhTZtQPLOzwQeKDfO+TRa6OaS72Kxf7BPl+jYZc6ksvudinw8UH+d4nk1wc11zsE+Rmv3KxT5Bb/ZqsvkhCjL3u7oEDB3j44YdRFIXCwkIuu+wyNm/ezIYNG7BYLFx66aUUFRXxwgsv8Pzzz1NXV8c111wDwKuvvso///lPJElixYoVrFu3blD7jz32GPv376e4uJhLLrkEpacISHd3N9/73vf41re+RXV19Thv/dAZz/1v3bqVv//974CZO/biiy9GHlARdcOGDWzatAmn08nll1+Ow+Hg8ccf5/XXX2fVqlVccMEFh/2eJ5vJHtNbb72VhoYGLrvsMpYuXQqYy1/RaBSAT33qU0ybNm1UfR0on9n62Z933nln0PHW1lbuu+8+DMPgYx/7GPPnzx/X+GXrVzAY5IEHHiCRSDBnzhzOOeecjPP37t3LY489hiRJXHTRRdTU1BAMBlm/fj3JZJKTTjqJ1atXj6tPsViMX/ziF7S0tPCNb3yD4uJi7rjjDnRdR1EULrnkEoqLizO+czjGa2C/qqurefHFF3njjTcAWLduHStXrsz4zmSNVzbZ//3vf08kEiGVSnH++eczZ86cjO88+OCDNDc3k0wmOeOMM1i5ciWJRIL169cTCoVYunQpZ5xxxiFdO6/L87p8POR1eV6X95LX5YdPl6c5lDyYfr9fJBIJIYQQjz32mHjnnXfET37yE5FKpcSePXvEvffeK4QQIhgMivb2dnHnnXemv9vR0SEMwxCGYYif/exnIhgMZrTd0NAg7r77biGEEE899VRGvtj7779f3HLLLaKpqelQuj1hjOf+U6lU+t/r168Xe/bsyWg7FAqJW265RRiGId544w3x9NNPCyGECAQCYufOneKRRx6Z7NubEiZzTIUQwufzib/+9a/ivffeS3/W0dEhhBCitbVV/OpXvxpVPwfK5+uvv561n71ompb1+B133CHa2tpENBoVP/vZz0Z17bH068033xS///3vhc/nG/I7N910k4hEIqKrq0v88pe/FEII8fDDD4vt27en+51MJsfVL03TRDAYFOvXrxdNTU0imUym+7R161bxwAMPDDr/cIzXwH4JIcT3v/99oWmaSCQS4n//938HfWeyxiub7PfKdGdnp7j11lsHfaf3eCwWEz/84Q+FEEI8//zz4tVXXxVCCPGLX/xi2N9+uGvndXlel4+HvC4fH3ldPr5+CfHB1OW9HFL4jdfrxWq1AqCqKm1tbVRWVqKqKrNnz05XNfN4PINm2aWlpUiSZJbxVZRBFTf379/PggVmBa9Fixaxb98+ADo7O5EkadDMeSoYz/2rqrk3WfQskAyc2R48eJC5c+ciSRILFy5M339vCeujlckcUzAr1A2ktLQUIKscDsVA+dy4cWPWfm7cuJF9+/bR0dGR9XggEKC8vByHw4HT6UxX5DtUBvZr7969dHV18eijj3Lrrbem5Wjbtm1s3ryZZDKJLMs4nU6Ki4vTXq6DBw8yb948FEWhrq4uXV3vUFEUBY/Hk/7bYrGkfwtVVdPjfrjHa2C/wJSHVCpFPB7H4XAAh2e8Bsq+JElpmY7H41RVVWX0pfc8gGQySWVlJZApAwsWLGD//v1jvnZel+d1+XjJ6/K8Ls/r8sOvy3sZV/ab7u5utm/fznnnnZfxw4hRRPRs3ryZ0tJS3G53xufRaDSt9BwOB5FIBIBnnnmGM888k7/97W/j6fKEcqj3v3HjRp555hnKysqy3r/dbgfM++8VuA8KkzGmI/HnP/85a+hANgbKJ5D+vfr3c+3atQDs27cv6/H+99Mr52Pt93D9am1tpampiauuugpVVbn99tv51re+xaJFiwDw+/0Z/ZJlGU3T0HU9/aKdTPnTNI2//e1vfPaznwUO/3hlY/HixfzgBz/AMAwuvvhigMM6Xr2yf9ZZZwFw00030d7ezmWXXZbRl15+//vfs3v3bs477zxgsO7o1Z1juXZel+d1+USR1+WHRl6Xj58Psi4/ZKM+Fotxzz33cOmll2IYRka1tZFmyo2Njbz44ovp1ELPPfccW7ZsYcmSJTgcjnRbsVgMl8tFR0cHACUlJYfa3QlnPPe/du1a1q5dywMPPMDmzZsRQvDqq69SV1fH/Pnz6ezsTF/D6XRO6n3kEpM1psPFrT755JPMnDlzUJzbUAyUT2DYfvY/v//x/uf1yvl4GNiv0tJSwuFw2tOlKEo69hHA6XRm9MswDFRVRVEUDMNAluVJlb/77ruPk08+mfLy8iHvAyZvvAYSi8V45ZVX+Pa3v42u69x6660sWrQofd3JHq/+st/7G9144410d3dzxx13sHDhwkHfueqqq4hGo/zkJz9h9erV6bFzOp3EYrGsXs6Rrp3X5XldPhHkdfmhk9fl4+ODrMvhELPf6LrOXXfdxUc/+lEqKiooLy+ntbUVTdPYt28fNTU1Q37X7/dz//33c9lll6WXKU4//XRuuOEG1q1bx6xZs9i5cycA27dvZ9asWTQ1NdHa2sovf/lLduzYwf33308qlTqUrk8I47n//v222+1YrVaOO+44brjhBi644ALq6urYs2cP0Hf/HwQmc0yHYuPGjfj9/lF7doBB8rl27dph+znUfRQUFNDR0UE8HicajY7bUzGwX/Pnz8flchGNRkkkEmiallYwAFarFV3XiUaj+Hy+tAKrq6tj9+7d6LpOQ0NDerlwIvnb3/5GaWkpxx577KBjh2u8BiLLMhaLBYvFgtVqRdO0DI/SZI7XQNkXQqDrOgA2mw2bzTboO70yb7FYsNvtyLKcIQM7d+5kxowZY752Xpfndfl4yevyvC6HvC4/3Lq8l0PKfvPGG2/w8MMPp7MWnHzyyQC88MILGbuc33rrLV566SU6Ojqorq7mi1/8Ivfddx+7d+9Ox2J95jOfoaKiIqP9P//5zxw4cICioiIuueSSdMwRwB/+8AdOP/30Kc2YMJ77f/XVV3n77bcBU+g//elPD4orfP7553n33XdxOBxcccUVOBwONmzYwBtvvEE4HGbWrFlceeWVh/emJ5nJHtM//vGP7N69G7vdzrHHHsu6deu48cYbmT59OrIsU1JSwqWXXjqqvg6Uz82bNw/q58aNGykvL2fWrFm8/fbbg463tLRw//33YxgGH/3oR7PO3sfKwH7V19fz+OOPo2kaZ511FkuXLmXbtm2kUimWL1/Onj17ePzxx5EkiQsvvJDa2loCgUBGBoA1a9aMu1+/+tWvaGxspLi4mMWLF/PUU0+lDZyZM2dy3nnnTcl49e/XSSedRCgUSnsG165dy8knn3xYxmug7K9du5aNGzcC5lL1xz/+cebMmZPRlzvuuINoNIqu65x++umsWLGCRCLBPffcQzgcZsmSJZx55pljvnZel+d1+XjJ6/K8Ls/r8sOvy3s5JKM+T548efLkyZMnT548uUO+omyePHny5MmTJ0+ePEc4eaM+T548efLkyZMnT54jnLxRnydPnjx58uTJkyfPEU7eqM+TJ0+ePHny5MmT5wgnb9TnyZMnT548efLkyXOEkzfq8+TJkydPnjx58uQ5wskb9Xny5MmTJ0+ePHnyHOHkjfo8efLkyZMnT548eY5w8kZ9njx58uTJkydPnjxHOHmjPk+ePHny5MmTJ0+eI5wPrFG/detW/vnPf2Y95vP5eOSRRwZ9vm/fPm6++WZuvvlmvvvd72Y9p5fm5mb+8Ic/TFh/J5M777yTtrY2AFpbW1m/fj0AiUSCb3zjG+ljTz31FG+++eaU9TPP6OiVbSEEv/nNb7jllluGlOmuri7+7d/+jVtvvZWbbrqJRx99lGQySWNjIy+//PKE923Xrl08+uijE97uaOkv65CX9zx58hw59Or2bHo0r+NNotEo69ev56abbuKnP/0pmzdvnrK+TAXqVHdgqnjllVe46qqrsh4rKioiGAwSjUZxOp3pz2fNmsUNN9wAwB/+8AeWLVt2OLo66XR2dlJWVgaAzWYjkUgAsGnTJrxeL8lkEiEE7733Hl/72temsqt5RkGvbAeDQQC++tWvAmSVaYA5c+ZwzTXXIITgySef5Mknn+T888+ntrZ2VNczDANZPjL8A/1lHfLyfrSydetWAoEAJ5xwwqBjPp+P559/nk9+8pMZn+/bt48nnngCgEAgwJIlSwad00tzczPPPfccl1566cR3foKJRqM8/PDDdHV1oes6Z5xxBsuXL5/qbuU5BHp1+/79+wcdG8pugQ+Wjv/973/PaaedxqJFi4jH4/z0pz9l3rx5OByOqe7aYeEDadRHo1EMw8BisSCE4KGHHqKlpQVZlrnkkksoKipi9uzZbN++nWOPPXbQ9zVN4+DBg1x88cUZn+u6zt133000GqW4uDj9eTAY5K677sIwDDweD1dddRWPPvooxxxzDLNmzWL79u3s3r2bc889FzBnuv/4xz+wWq10dXVx5pln8tprrxGLxbj++utxuVxZ+5ztOnv27OGZZ57BYrHQ1dXF5ZdfTnV1dbpvqVQKWZbTD6zdbicejwPw7rvvsmbNGuLxOLt27WLmzJlYLJYJ/z3yTBz9Zfvhhx9m//793HnnnXzuc58bVqYBJEnirLPO4oc//CGLFy/m/fffZ8mSJbz//vtccMEFGYbMrl27eP7555FlmaVLl/LOO+9kyJjb7R4ki0O9FA5F3hVFGbesQ17ej1byTps+PuhGztFCf90O0NLSwh133JGh6w5Fx19wwQXs2rVrkJ5fs2ZNho4vKSkZpF+z6flsHC4dv2PHDrxeL4sWLQJM/V5dXU17ezvTp0+f4F8kNxnX1OtPf/oTBw4cAGDDhg386le/AkAIwQ9+8INxd26yaG9vp6SkBID3338fp9PJV7/6VT7+8Y/zzDPPAFBaWkpra2vW7+/cuZN58+YNMlLee+89ysrK+PKXv0xdXV36c6fTyZe+9CVuvPFGCgsL2blzJ2vWrEkv7b/55pusXr06oy0hBNdeey0nn3wyb7/9Nl/60pdYtWoVW7ZsGbLP2a4D5mTj2muv5dxzz+W1117LuE5bWxsVFRXpv+12O4lEgra2NoqLi/F4PMTjcTZu3JjV63W0c6TJeH/Z/sQnPsGcOXP43Oc+Bwwv072oqoqu66O6ViwW43Of+xwlJSWDZGwoWRyKscr7RMg65OW9lyNNzoejv/Gj6zq//e1vue2223jggQfSIZG9xk82ep02s2fPzvhc13V+97vfcdttt7Fhw4b058FgkFtuuYWbbrqJ3/72txiGwcMPP8y+ffsA2L59e3oFAEwD5xe/+AV33HEHP/rRj3j77bf5xS9+wU9+8hPC4TBgjvuDDz7ILbfcwm233YbP58t6nV27dvHLX/4y3VZzc3NGn4czcj5oHOky3l+3Q3ZdN1k6/vjjj896zbHo+cOh47ds2TJoMh6JRLDb7aO636OBcXnqZ86cyYEDB5gxYwYNDQ1pI7e9vX3Qy3OsdHd3s2XLFurr67nwwgux2Wzjam8oWltb2bx5M3v27AFML85IvPPOO2khf+6559iyZQtLlizBMAymTZsGwPTp09MKJBKJ8MADDxCNRgkEAtTW1rJw4UJaWlqIxWL4/X4qKyszrlFTUwOA1+tN/7uwsJDu7m7C4XDWPme7TnFxcXqJraioiGg0mnGd5ubmjGtLkgTAxo0bOf744+no6MDv99Pd3T3qpbqjiaNBxsdCKpVCVfvUQq88gKmU+1NXV5c+PlDGssnicIxV3idC1vvf3wdd3o8mOe9v/Lz33nuUl5dz7rnn8sorr6TDFkpLS7OGMMDITptzzz2Xl19+Oa3fe40PRVF4+OGH006bf/7zn8yaNYs333yTM888M6OtXgPnlVdeSRs4GzZsYMuWLRx//PEZRs6BAwd45plnuOCCCwZdR1EUdF3ni1/8Ilu3buW1117LCBnKGzl9HE0yDoN17mgZqONhaD3fX8dnu+ZQejgbh0PHJxKJjBWoWCxGIBCgrKyMRCLBhg0bOOuss0Y9Vkci4zLqZ8yYwd///nc+/OEPk0wmqa6upq2tjf379zNjxoyMczdu3MiWLVvQdZ1wOMzJJ5/MG2+8gRCCL37xiyiKknF+cXExy5Ytw+fzYbVax9PNQZSXl9PV1QVARUUFxxxzDGeffTZAegbb2dmZ9UHXdZ36+vp06M3pp5/O6aefDpgxuY2NjaxcuZL6+vr0d9566y2WLFnCCSecwIMPPpj+fMmSJdx///0sXbp01H0XQgzZ56GuMxwtLS2DfishBM3NzZx33nmEQiGeeeaZDK/l22+/TXl5OYqi0NTUxJ49e7DZbCxevJj58+eP+l6OBI40Ge8v2wMZSqb7849//CPDCHA6nfj9fgCampoyzu2v7AdyKLKYjaHk/YUXXpgQWe+9xlDyPlDWCwoKeOedd446eT/S5Hy0dHR0pB0tdXV1Qxry/Zlqpw1kdzZNpJFTWFjI008/fdQbOP050mV8ON3ey6HoeBhazw+n42Fi9PxE6vi6ujq2b9/OvHnz0HWdBx98kLPOOgtZljl48CChUIg///nPHHfccUet02ZcRn1lZSVtbW2EQiE8Hg8zZszg4MGDHDx4MGtMl8vl4rOf/SxPPPEEjY2NfPnLX+aRRx5hz549eL3etCFst9spLy9n/fr1HHvsscRisUEbP8aD0+lEkiRSqRRLly5l165d3HLLLUiSxKpVqzjhhBPYu3cvF1100aDv7tixI6sXB2DZsmW8/fbb3HrrrZSXl6c/nz9/Pvfccw9btmzJiNE97rjj+Otf/8q//Mu/jKn/Q/V5qOsMR0tLC7t27eKll14C4OqrryYej7N27VrA3EjY2trKqlWr0t9pbm5mxYoVvPbaa4RCIc455xwikQgNDQ1juo8jgSNNxvvL9kCGkuk9e/Zwyy23YBgGM2bM4JxzzkkbLNXV1SSTSW677baM2MWROBRZHIps8j5Rsu50OoeV9/6yPnfuXN59992jUt6PNDkfjv7GT1lZWdrR0tjYmD4nl502kN3ZNJFGzgfFwOnPkS7jw+n2Xg5Fx8PU6/mJ0vEnnHAC9913HzfddBOBQICzzz47rcsPHjzIWWedxf79+4cdwyMdSQxcUx8jt912G6tWrUKSJBYuXMjf//536uvr+cpXvpIxY924cSPxeJwPf/jDvPjii1itVo4//nieffZZSkpKWLly5bhvZiy8//77BIPBIbMjPPfcc2M2tsdKMBjk/vvv59prr53U60w0GzZswOv18vrrr1NYWEhJSQmJRIKzzjrrqNxYeKTJeDbZPlwyfbTRX9avu+467r///qNW3o80OR+O22+/nauvvhpZlrnrrruIRqOUlpYC8NnPfpa77rqLiy66aJDxtXXrVrZt28anPvWpQW3qup5uq7y8nFQqxaWXXkpjYyP33HMPpaWlWCwWFi1axNq1awkEAnznO9/h+9//Ph6PJ91O/02JvSEb55xzDps2baKrq4t169YhhOCRRx6hqakpbeTU1dUNuk5xcXHWjey9aJrGfffdR2dnZ9rIWbNmDc888wxr165l//79eDweZs6cOUm/RG5xpMt4LtgtRwrr169nxYoV6VWJxx9/nPPOO4+nnnqKdevWHVW6uz/jzn4zY8YMXnjhBa688kq8Xi+dnZ0AWZeg+i/ljLSsM9ksWbJkyGNFRUWT/mDs3buXxx57jPPPP39SrzMZnHrqqQAcc8wxPP7444PiRY82jjQZzybbh0Omj0b6yzqY3rKjVd6PNDkfjpNOOok333yTE044gSuvvBJFUXjllVeIxWL4fD48Hk9Wb+rixYtZvHhx1jYVReGaa64Z9HltbS3/+Z//OehzSZJYtGhRhkEPMG/ePObNmweYHspeT35/Q1GSpKzPa7br9LZVXV09KMWmqqrpz9avX58OxYlGo3g8Hpqamli3bl3W+z0aOdJlfKrtliOJyy67LOPv8847DyC9+nW0MiFG/YsvvpheyrTZbGmPSJ6hmT17Nl//+tenuhvjpvdBOZrJy3ieXo5meT+a5Ly/8fOb3/yGRCKBqqpcddVVuFyuD6TTpr+R80ExcAZyNMl4njzZGHf4TZ48efLkyZMnT548eaaWI6NEWJ48efLkyZMnT548eYYkb9TnyZMnT548efLkyXOEM+6Y+kPle9/7Xkbaxw8SmqYNKv6QZ3x0dnby//7f/5vqbgwiF+U8V+Uv36+RyUU5z0UZP1zkkmwcTeTlfPTkogzmYp8gt/o1WTI+ZXdXVlbGddddR6rjAPXfW4O1ejFGMkLp+d/FtewsottfoPHH65j5491Yymai+VvZ97U6qr/yKK6lZ9H626vQuuoRhob35CvxnnI1DT86DYQOklkYovqrf0ZxetPXTPma6PjjV9DD3Qhdo/DUz1Fw4mXsukKh6vp78ay5iETj+/ie+hn22asJvf4QWqAVhI5aWIN75cdJNLxH0dlfx1Y79C50gNY7ryTRvA3Z6sK1/KMUf/Qb6WOaptHykzPTffV+6EoKTrx0mNbyjMTtt98+1V3ISllZGVd/8qyckvHowc0E/3HzuGU8tusVOu7/JsgKjvkfouxTP8w4nmzeQdvd1yEMjdILvotz0enDtpdLCrc/udSvXJTzo12Xdz3xP0TffwYjFaPghEspOuNLGMkYLb+8CD0WRFIsVF33R1RvZt774Mb7Cbz4WzAMSj/1PzjmHD/xg3+Ukqtynmu6PNH4Pl1/+ynOOWsmXMZheBlOtu6i5ZefJtmyg9m/aEe2u9PHckln9ieX+jVZMj5ld9c/+b9j/oeo/tJDpLobab7tk7iWmVXubDOOJfTWoxSf/XXCb/8Z23QzrVxky99RvZVUfX49AHrEl26r5mtPZghXf1p/cxkl538H5/wPIQyD2K6XAbCUzcT/3K/xrOkr2lB42vUUnnY9gZfvxkhEKFr3RbONO68c9T1WXv27rA9Tb9XA4fqa5+igV85zScYDgQAS45fx7r/9hMrP3Y21aj4N/3cGmq8ZtaivcEnnI/9JxVV3ongraPrZOdSNYNR3d3fnpCcsV/uVKxzturz4o9+k5Nz/ROgaB/9rBYWnX0/kvaew1i5BnHID9t1/J/DS7yn5+L+nv6P5molseoLabz2TM+kQ84yPXNTlYFYNrp4EGdcDbcPKsFpUS+2/b6D5lsEZnnJVZ+ZqvyaSKYupNwxj8GdRP/RLxuOYdxKxXa8AEN32LK7FplEgWx0kGt4l1XkQAMVVNOL1Ul0NIAyc8z8EgCTLOBecYrbnKsY2bRmRrc8e0r203X3d4A8liba7Pk/jT84kUb8541AymQRZpunnH6Pp5vPS95Hn6GOgnOeCjB9KNb1sMm6tXoQe9SN0DQwdyZqZ91vztWCtnIviKEBxFaGHOoe9RjKZHHO/Dge52q9c4WjX5ZJq5jAXWgJL2SwkWcFaMQeRiJBMJtGjfhRPScZ3IlueRlJtNP3kI7T85jKMePiQ+pMnd8hFXZ6tXyMxWhkfSYZlmzNjZaE/uaozc7VfE8mUeer7z/xiO1+i/n8+RKL+Xaq//HC/c2TUwiriBzch2wtAMQXPueg0Es3bafnVZzCSESquuD29LNT083NAUpCsdmq//rd0W5q/GbWwasj+FJ31NdrWf4Gyi3485nupuGLwMkrZp3+C4i4h2byD1t9eSd23X0sfs1gslH7xARR3CdEdL9L+x69Qc8PjY77u4cZIJZAUFUlWprorRwy9cp5LMq6u+9cx30c2GXevOp+W2/4FSbXhWXMhiqsw47gQfS8b2elFj3SjeIbOCd1b4U8IgUhEkCx2JGXql0qP1sqDE8XRrssB2v90I6E3H6LwtC8AYKmYS7J5O/Fbz0BTFeq+vTHjfD3Yjh7upOabfyfw/O34n/0lxR8b+3N3KBipBLLFdliu9UEiF3V52UU/RpbH5psdrYyPR4b760yhJdOThqnmg6DLp8xTb7P1KR3H/A9R958vUXHlb4hufyHjPPex59P2u2twH5NZ9KVo3Rep+/Y/qf7CA3Q80CdoNV97kmn//nzGAwKgFlaj+ZqH7I+lbAaKq5jEwXfGcVd9KG7Tc2OtXgBICENPHystLU0fdy44Bc3fMiHXnGyMcBear2mqu3FE0SvnuSTjzuD+cd6VSce9X6f23zcw4/92kmjaRqJpW8ZxSepTL0Y0gOIqHra90tJSjHgYrfMgmr+ZVMc+Up0H0cNdTGU5jXxxmuEZqy63LPso/lgo/Xmu63KA8otvYuaP9xB+5zE0fwvBV+7BPvdEZv7ofUrO/2+6nvhBxvmy04tjwalIkmQadU1bJ6wvRiIy5DEhBHqwbcKulaePXNTliYPvYLdNzARuoIyPR4Z7daaRSpg6POKfkD6Olw+CLp8yoz4ejw/6rGDtZ4huew493JX+zLnwVGzTluJcelb6M83fklZsw3n++mMpmQayQnSnGZcmhCC686WMc4o/+k26n/rZmO8lG3osaPY12I7Qkxne7ZaWlvTxRNO2QR7OXEUYOkJLIsa43PdBZqCc54KMd/x17B7MrEgSirMISZZRnIUYPTLdi1JYSbJ9L0YshB7xDXsfwtBp3L0Vzd+M0PuWSIWWQA93oXU3ILSxhw1NBC0tR8ake6oYqy63zVmDSEaBI0OXG6kEAJLFhmx1IlnsgEDxlNLS0oLiLsWIBjK+45h7Ion6dwFI1L+LpXzWhPQFMMPdhjxoIPSpeU6OdnJRl3c/9TOisdgh3U9/ssn4eGS4paUFYRjo/mZAoIfaMfpN5KeKD4Iun/q17QF4T7qCwAu/xT57DQCSolL5ubszzkl1N9Bx79eRFBWha5R+8vvpY73LWQCV196NpaQufazy8+tp/8OX6Xr02z27ya+F+X3t2uqWoxbVjNjHtrs+j2xzmW1+7m66Hv/eoCWt1t9cam6IMfT0MnDkvacxUjGoOI7G/1uHbHUAUH7pbaMcnalF6EkQhvlfvsTBITPVMi4VVI7Yx9HIeMl5/8/si2LBWjUf++w1xA++S3zvaxSedj2ln/wBbb+9CmHolHziO0NeS+gaWncjQksA9uznpOKkuutRvZXpfuXJbYaS80igz5N8JOjyjj/dQLJlJ0JPUnD8xSiuIjxrP0vLrz5N/JV76bKqVFx1p9ne3ddRccXt2KYtw1IyjYYfnYZksVL1+T+OcfQODZFKgKEjdC0nQteOdqZal6tFNfTFAGTnUGVccRVlleFeGdcjPlp+eRGJhs003XwexR/9Bq5lZ6fb1AItGRNMLdCKKssgq4hUHJEyJ0mSakVSLEg2V35T+QQgiSla1/7FL37Bl770pUm/jhAi5wQlGAxSUFAw1d0YM8nWXYC5NJhrWXtuv/12rrsuy4blKeZwyflYyCX5E1qSVHcjGBqhaByPM7tRn4kEsowkycgOL4p7+LCe8ZJL45WLcj5WGe9o308o3MWsWasmsVeHh6mQDT3iR1LUrDrYSETRfI1YSmfkTBzzoXA0yPnhIpf0E4AwDPyt9bjksW1KlZ2FqAWTm5kml8ZqsmQ8JzbKTiaarwlJtU66sIyFsW5smWqMRDTzgymMbz7SyLUJJUy9/BmxEEJLILQkRjLas/Izln4J0xuJjh7uxEhEUL2VSOrkbIKa6vHKdcYq49FkDI6SEL6pkQ0xZAiOudpleuyPZKN+IggGg9x5550oioIsy1x++eV0dnby2GOPIUkSF110ETU1NQSDQdavX08ymeSkk05i9erVWdvLRV0OuaOfjEQEIxowQ42SCbCPTf6MqB/D5prUldhcGavJJCfy1E86xkgLVIcXv9+P251bnu7hMGJBjHhfvLSRioNqRZKV/BLvCBxWOR8lUyl/esSHHurIeiwQjuEa44sAQKRipLoOorhLJ2V/ypH2vB5uDkXGjxa/QM7JRs8EWXCUDPA4cLvd3HjjjciyzMaNG3nttdfYsWMH119/PfF4nPvvv58vfOELPPPMM6xbt465c+dy0003sXLlyqxZUnJRl0NuyKC5QtSXRONQdbkWaMVSMn3S7IpcGKvJ5uiftuSZcEQqbnpKc2yylCe3EVoqY1PZxDZuoIfaSXXVYyQHb9zMkzuInv8/UH/kN+CPj9GO37AbbY8iZFlOe2bj8TilpaXIsozT6aS4uJho1FyBPnjwIPPmzUNRFOrq6mhuHjrrTJ7BCEM3K9lOBBPZ1geUKXOz9qaH6u6OctvPX6Gi0k0yqfORs+czf2E5e/d0csevNvKv/3kaxSVOAp0+fvSjV7nsquOYv6CMh+7bjM8XwzAEx62Zxuq1ddz+i39iGNC7wnL51cehGjpCknjnrUYCgTinnj6H5qYAv7rtn3zvh2chyxL3/O5Nzv74QjY8u4e21hAWi4LbbeWzlx2DosgZ7U6rK+SccxcNup8H7n2XU06dRWVVX7xWW1uIv/x5GylNBwGnrZuDzxdj09uNSFI9APX1fq774vHUTR+6IMXBAz5+ecurfP9/z8Jm6/vJ6g/6eOLPW1EUmQKvnU9fvAJFmfh5Wu+Sbr9PJvwaRys2m23UMh4Kxvmf7z7HZVetGpOMOxymV2m0Mt7aEsRqrZ9UGX/3nT6vTa+MVxX0hdr00u1P8ciTrUgSWFSJi85zY7dl1kHYVx/lhX92IQQcf2whi+Z5hhxvkYqjddcjO4tQC8rG/Htlo6KiYkLaOVoZqy6PRjTu+U0Hl13ewoIl1Wk513Wd1WunT5icHwm6vH9/jltTx7HH1Wacf/+fNrFzewfrPjKXE0+eOfwPYWh9E6VhMuBo/hYzu8oHgMbGRu677z5isRhf+tKXeOedvjSnsiyjaRq6rqeNf4fDkTb2B5KLurytNYSqyng8rYdVl/eXcS3QCkbfRLG+KcYdf2zjv270YLPK/P2FDt7dGmTZwgLOPm2wTg4EU/zlmXYSSYMZtQ5OP7kULdgxYfq7Px8EXZ4T4TezZhdz6ZWr8PtjrP/9W8xfaMa/19R62fJeC6ecOpst77VSXWUum+za0YG7wManL1kJQDTatyHj6s+vzjB8k8EUkjCom17O3/6yHYD6g35qar20tYaoqi6gszNKWZkZx3XhZ5ZTWVXAQ/dvZteODhYursja7kjousG96zdx8WXHUF7hRtN0GhsCrD1hOrPnOikrK2Pb+2289WbDsC8BgFde2k9N7eDKbYWFDj7/heOxWBWe+ut2tm5pZdmK6lH3cTQIIQalSBOaOd4iFYd8kZNh6ZXz0cp4Ta2pZMci473UTS8alYyfcdZ0Fi2ePqkyvvaE6QBpGa8pV9ADg/NrO+wyl3yyGoddYcNr7bz1boCT1vRtfE2lDF59w8dln6pFVUYf02pEfaS0BGph1biLpfn9fsrKJv4FM5VMZLzxWHX5vt0RSstMOesv50YyTlzrc0qMV86PFF0+XH/O/thC5swtJZEY6F3PdKwIQzerO/ekThxqFdVIJTCSMYQQ+OIRimxOpKM4zri2tpZvfvObvPPOOzz99NMZaSkNw0BVVRRFwTAMZFkmFovhdPZVxn7rrbd46623AOjo6KC1pYXKagfXfH4N+/c189gjWyguXYamGZSVO3jlpR2cum4ub73ZRFm5g67OTrZv05FVnY+fX4fVasXpKKCxsZFEIsHFl63A7rDg9/vp6mqjoqICv9+P1ZZg965WTjl1Fpvf3U9pmZ09u1uo2cl3BAABAABJREFUqfXS0hIgkfBhGAZnnDUdQZi3NgbYuaOdAq9GIpHgM5cuw+120N3dTWNjI2VlZYTDYWKxWHoC09bWjqYHcblcWCxW7vnd65x1zgzmza8lGAxzcH8X06a7WXvCCTQ3N7Nntw/VAuUVDhobGwEodKjE/J1EYub7qbrUy4bX2ikukvGFohQX2Jk9S6G4uIDWNp1gJE44ZjoJvQV2OoMRNmwIceoJRVRXuOjwh2nuDOB1JyGWINKjWqqqqujs7CSVSmG1WikuLqa11fToe73m8xQImGllKysr6e7uJplMmoU+S0vTqSw1TaO0tBS/3w+QHu9EIoGqqpSXl6dXajweDxaLhe7ubgDKy8sJhULEYjEURaGqqio9Dm63G5vNRleXuRJdWlpKNBolGo0iyzLV1dU0NTUhhMDpdGbI2EQzZUZ9ttLG8VgqI9Zy5qxiDuzr5pRTZ7NnVwezp5uZMSxWhZamIL7uKEXFTpzO4WO3hBCUlrvo6jIVXkO9nxNPmsHBAz7sdpXCIvugTTDxuDYuf3T9AR/V1QWUV5gTEVVVmDHTNFYSiQSRcJK/P7WTa69fC0BzU4CDB3wcf+KMjHb27+umqrqAUDBLLmhvX6YQRZGR5EnYyJNOYTngM8TRExg7iQyU8+FlvJM580zjcawyDlBaNjoZT6VMBTyZMg6kZfxznz8OPdRMS1uc+uY4a1YWps9x2PsMbkMYg57D+uY4FlXijw83YbFInHtmBR736NSWSEbRuupRCqvHVWEzkRi4UnXkM5HxxmPV5Q0HY9TUmm30l3OvW55QOU/3JYd1uSRJ/P6ON7A7LHzigsUUFWe+7L3eobNBdccjeK0OFFk2wyET0RFDa0QySlxLYdGSdDdtw101F6tr+InIkYqmaaiqqSvsdjs2mw1d14lGoyQSibRhVVdXx+7du5kzZw4NDQ184hOfSLexatUqVq0yszTdfPPNVFZV4XT4UBQFt9uLxWKlrKyMYKCT+Qsq6e6K4vF4aDgYZtGSakpKi7BaFQI+DZezOP37uj212Gz1eAo82GxqRpx3WVkZZWVlPPN0I7IsEw5JnHb6fLq7UpSVSZSVe5g2bRqy3EVRUSGaLiPJMUCittZs1+v1YrOpGcajLaNIVTsVFeVpT/3+vV1Mn17KsuVz0uNVXt6XU99bUMrbb+zh2uvXYrdbkSUPB/Z1UT43htXloMBpJ5iM8daudtwl4AsKYlISQ7Izp66YfUTp7IxQ4LJT4LIT11I0hH1okk4wovPyGwES8QBnnFxKXa2Z6hsRxVtShewwV2YHetlrazNXtTyevhXc8vLyrOc2NjbidrsHjfdw7fYfw5KSkmHP7f+33W6nuLjvfVhTM3KK3YkgJ7Lf7Nvbza9ufZXmpiCXXbkq4xxPgZ2mxgA2C8iSAbrGnLkVtLWG+NP6d0gmdT554TKmzzAV0+9+8wayDBaLwtWfX5NxTW+hA78vRiyWYu78Mv76+DYcDkv6uwAP3reZWCxFUZGT+Qv6fuzedtccP52Vx4784wSCCQq82Q0JVVV59KH3+MjZ83G5zZdYdY2X6prB3vhXXtrPpz69nF072oe8lq87yq6dHZx+5twR+zUeYnENRZGwWsbn+cxlurq6+MlPfkJlpZnL/eqrr2b37t1s2LABi8XCpZdeSlFREa2trdx3330YhsHHPvYx5s+fn7W9XjkflYzbVZQeb/ScuaWTJuPP/r2Bp59smFQZB9Iybjf8GIZOVYWdqorsRkosrrNtR4yrP5OpYMMRjS5/is9fWsfeAxGef7WL8z4y+iVUoadMw95TdsibaHsNg6OJ/lkgBsYbO53OjHjj888/H1mW0/HG06dPz2hrNLpcpGK43SpNjQGsNhkhmS64/nKeiCW44IL5zJhXA0JklfPeFMVHiy6/5Ipjcbms7N3TxWOPbuXKa44bsT+9RFIJHKoFh2xF6FpG0bZsCC2F0FMkjBRuPUUsFSeYjFN6lJZ9aGxs5M9//jOyLGOxWLj44ovp6Ojg17/+NZIkceGFFwJwxhlnsH79ev76179y0kknYbVmn1jmoi5/8L7NhEJxyso8h0WX95fxck8SI+ojrmm0RQPE9RTvvBvlhFMcNBxIEtESHAx14rE6SOp9OiKhaTSGfRjCIB4XdHVqnHS6nVKHmyefbOf6y/v0i5nbXkG2TYxn+2jU5QOZ8ph66Fuy3fROE3v3dDKvn3AuWVbJQ/dt5sNr3LS093k4Tjx5JieePJP2tjAPP7CZL3zlRGD4pczp04vYtbMDu92Cw2EhFktRX+/PeBgu/MxySkpc/PaO14lFU7g9thHbzYa3wMaObdk37DXUJ7E7LCxaMrxxsndPF9XVBdjtQ183Hk9x/5/e5cLPTk48fX93W1t3AE0SzKkqPaq99HPmzOGaa64BQNd1nn/+eW644QYOHjzI008/zWc+8xn+8pe/cPHFF+PxePj1r389pFHfK+cjyvj9m1n3kXk0N/VVppwsGf/MJcdSVuaeVBl/+81G7A4LC+a40PzDbzzTdcFDf2nlY2dU4HRkThgdNoXpNQ5URWL2dCcvbfSNun999FQ0TPakvhxjOM5Ar8/RwkTFG49KlxsGC+YV8tD9m1m20kZzS4reEJLj11ZzwvE1tOzez5//uovrv1SMYLA86rEgkqwi2ZxHjS53uUwjafacEv76+LZR9wnM0dN79XC/cJuUrjHQPItpKcLRALZUglAyTlHP6op2FCc7mDFjBjfeeGPGZ16vl69//euDPvvyl788Ynu5qMsv/Mxyiood/P7ONyddl/eXcWHoGFE/nbEw3fEIAkFbs0ZRiYLFKiH3s0VCyRhtUY1QUhBIROmMRdB7Vv+tVglPgYzLLRMnBhLohkBJRx0INH8zalEtsnU0NUyG52jV5f2ZsmC6bKXFVx5Tw+5dnUQifR6H2XNKqKxyMXdW30wtGIiT7Ikx7FWKQ9LP+KybXsirLx9gWp3pRbHZVPbs6qRuemHGVyxWhRNPmsGLG/aO9bb6rjWjiJbmEO1tYQA0zeDgAR9+X4wNz+7m459YPGIbLc1Bdu/u5Le/eZ2WlhAP3PtuxnFdN/jT+ndY95F5lJdPfpqmSChGMhDHMI7u0Jt9+/Zx00038cQTT9DR0UFlZSWqqjJ79myamsxNQ4FAgPLychwOB06nk3A4nLWtgXI+lIxXVRVkKOsxyXg/RiPjHR3tky7jL7+4j4+fuxAtOPQKUy+PPd3GkgVubI7Bm/tqquy0dyUQQtDSnqCo8NBz0YtEBK2rfsxZVo7WbBi98cYf+9jHRow3Bv4/e+8dJ9dd3vu/T5s+23elVbOKe8PdBmzAITRjAiF0G7ipNyEFyIWQ3HCTSwoJuSGYQBKSXwIphBYIhBJKMAaMibHl3m1ZtqRdbZ+detq3/f44M7MzW6SVtZJWNp/XS9rdmTNnznzne57v832ez/N5uvjGu3fv5mMf+xgf+9jHmJqaYmxsjMmJCYwxTE1NMTxieOjBSSrlBjMzM9SDgN4hGBrOki9GxJFEac3evWM8+eQBpqamSKchlpLx8XGUkjTqdcbGxhgbG0MIwezsLGMH9jM5OcmWbb189zuPUigaKpUKjgMPPXAQx/WJ45goCpmammJ2bprnPHc7X/3yPYyNjSGlxPf99nmjKGJubo6xsbH29+z7PlNTU8zPzyccZNdn/745Duyfo1QqsW/ffnbf/ijl+YBvf/MhLr6sj1KpRBiG7fMGQUC5XG7/PXGwygP3j/PRD3+Pg+MVPv3JOxkbG+Pxx/fh+z57HjuIbSvGxsbQWjM5OcnY2BgzMzMopdrnqtfrVOsNxienKU1OI6RkamqK8YlJZit1pNLsm5lnfGKSarVKrVZLPtv4GOWgxtjEQaZLdaZnZtDaMDuTfPZqtUq9Xmffgf0cOHAAIQQzMzPJ9zo5ida6/VkqlUrXGMZx3B7DFne59Vy5XCYIgvbfYRi2ed6t8R4fH2dsbKxrDNcj1qMtB5iZmTr2tnyRv6Ib89RFyFxYb8unlkqKyXHJjf/ZYH5O8cObgq7XSKOY9KtIs7CRdF2LdMYijgyx0IRCYlhK95XlcYw8eknRp6st78S6y0VcctlWbvvv/Wzb3gckXPHXv+E8xOyT7WPK5YCvfOlBbMdCK8NLX74QJW2lnQBef92F5MyCA7r1lD4mJ6ps3daHFhFbtvQwdqBCJrPUUTjnvI18+1uP8aKXrj6S8fnP3ksq5Tbf+wLe9JYL+fIXH0BIhdHwwhefyp27x4gjxT/+/W3t1/3kS04nl/OW8DCvfN4OrnxeonjwsY/+kNe/6QIAvvzFB3jZy8/k3nsmOLCvzI3fepQbv/UoVzx3OxdcuMaFsh3KN0ooLCziSOK19JBl/LRqctLT08Pv//7vk0ql+NSnPsXdd99NJrMQIWg1YO5sxJzNZmk0Gm2eXmdxVaVSYXJiAj9I1D1mZ2c59fQiN337IU47YyO1Wo2JiYNc81O7CEOfarXK3Kwkm3X4j3+/HywDxuLlrzi77YT83V//ENu2kFLy4pedwqmnbW0X+6QyFpMTVdKZmLGxMYaG0+zfp5idnQISZ63RaDA2NsbgsM23vznD2eftJ4oiKpVKu7gKWLa46lP/shvPs3Ech595/Xm88MWb+Nyn78CyHZRUXHzZMN//bpUgEPztX30foxW2bfGC5wwSy4jJacFVlw0RS0kjiDk4GXP/IzWm50LE7YqzTw+58rJBvviNCS5+Vp7+YpYtoyk++o9PYlvw2ldsYnq+hlSatOfSk88wU04Wot5CFq01NT+ZsxsGipSqPkIqUp5DXyHH9Ows1nyDgdFT2t8PHLq4KggC6vX606q46mj5xou5xlu2bKGU87Gs+Tb39YrnSO7cPcG27cNM76/hphyuf+ulPPzYj0ilBI5t4zp5vvLlJ7BtCxVHXPOTW9m0cQTHmeYz//pghy0fZKi/D6MVbs8wfX2CudmQ85+1nd7eXnbsHGJyosGuXcn3mk5n2LBhAxtHexgZ0dz4X48xMjKK6+4nl8vR37+g+tHNNU44tDd/d4pUaq753hfw1p/vayuDtGz5TTfuQUmLb/3ngjpIy5bffecUz37udvr6+gDYsoUuW/7G6y8inXb58AdvxvOS17/m9RcxuqmH3bcdYHikwCnbN/L1rz7Eg/dPoY1ByRQ/9dNbkMT0FPJoKRFGs2HDBnSQQ1YSm5TOO2wa6MctFhNKSLFIza8yPbWHdBoKTor+nE3Jt+jv72HzhgUesJNJMRvU8TzvhHGNF792PWNZf+W6C7qOORJ/pb8/236u01+BRMnmRPgrQSC6/JUXvuhUUtE0d+2ZYddZCy7kWeemOevc5F76+hdrPOfq5LM8dF/E3scEUWjwG5qrXpjj8Udievpshje4XHBpmpu+6aOU4fyL00w0qmwtLqrz0Ao5P4Y7uO2ohQ+e7rCMOTEh1yNpuaxFhJzbB4Dbt2nZ9tgrYc99t2G7HjvPurD9mNEKMb33qHi2R4NKpdKu2F7v0GGdeH4cKQ0PP5xIt23dPMzgli1YjovleEf0fRwrHIuWyw888AC33nor6XSa66+/HoA///M/513velf7Z+u9r7/++mWbWqzH1uLHY/7pqNHVjGQ1qDZCMqkUUaSTbJBq/tMGUjaWY2NZiVSbbVvYloXjgOseecLR7V0owDoc1tP9ulbz/Mknn1yWb/wf//Efbb7xli1bqFQqXeo3l19++ZJzrWaOi9l9zMURGzedzsOP3YqUgrN3XoydzhEENdKArExgeRnsdAGj5ZIu4PH0XizHxR3Yum66e56IuSHqc9iWzZONClY6z87eYXRQQ1Ym0EaztzLLqUPb8Aa2tJVt5spTlKb3EjaVzDbkepjyqwz0b2bzxl0LnycKGG/Mc/bA2gaHjhTHwp4fLdajLYfjPwdVbZbS/DiTjcqKx0SRPCL6z2Lk3BSDmQI5rztgaHnZo5JjfTra8sVYF4WyRwKzWInlMBCNCNtelLYxhkS95ak1O/ncp++mNLfALb3oki1cdsW2Vb9+uW516xmhrxDBQgQgFAKtDY7DErnLkx1hGLYj83v27OGcc87hBz/4AVJK9u/f344q9fT0MDMzQ7FYxPf9FbvUrRfnoxOrmX9HM8eN1sjK1BFdkzEGKSHUGhNKCCRdWVgfYkcTuhrLdcimU7hWErGxbY3rWti2hdGgjcGyIJWysVcYf1mbxktlV9W58GS7X1eDteQbr2aOqyZ322i1YHebjymtaIT1Dh64wYhuxSGjExWuMKhTUAJWkR3URtMQMcVDcHFPRluujcG2QIoIL91d5RorjTIaaRSdV6aUQHTw52eDpvTlojVQaIXST19q5dFgPdpyOPwcPNo53gmjFbIxz1ywPN20Bfsolfh8GePXS+TcFMPZHjLNrKIRAaoxj/MUFZuejrZ8MY7IqV9LZZC1brkcTz6GN7xjySJtjsBAaRGtSvrudW+84EgvrwulUumY6pSuKYxBNyRxfYEfF4SCoFomV8hhuSuPl478NataP154/PHH+epXv0oqlWJwcJBrr70Wz/P48Ic/3J7jAK94xSv45Cc/idaaa665ZsXzrcfW4quZf0czx1V1uqsZyeGgjSEIFKVyg2E3ixGKShQgtWp3Hw2VQpvu1LLnuaRtj5TrkrJdUq6LlRwOtkWcd0nnXFKetXRB1gpZnsAd2HLYxfqkul9PAFYzxyMZY7BAa7TSxLEmkhFZihi6aX5ahEuVXJqbAV/GrDYvqIyhJqJDOvUnoy1XxuBHfpvL3P2cQsqlNU8G0y5ObB2XPLFw3I87MR8a69GWw+Hn4NHO8U6o+hyVqNG1QVwOUaDw1kAlz5cxY/USpxQH8Jq+narNYqfzT4n2+0yw5UccqV9LZRAAOV5h7nX/gnvqECYQFH/9StJX7SC6bT/zP/tZhr75i9gbssjZgOob/oueD76E7AvPofK7X0cdrILSZH/mPNznZij97OeS6F6z8rr/oz+NMYm6BsDsa/+ZoX97S9uQxXdMIH50Dz2/dXXSZCn2282Uah+9Be/cjWResJCaVFM1qn98I7ocgNLkXvcsrGKGxj/tXvg8j0zT8/svJvuyM1cexKkGky//CwY//2a807p5i3PX/Wty/UrT+76X4J46tMJJjh9Kfp2av+DUy1gQhT6WJcj1dF+fjhpYqRyWZSErk3hD20+q5ibnnHMO55zTXRR08cUXc/HFF3c9Njo6uiTSuRJWM8fdLX2omTozP/Ex+j7y06Sv3LFkjud+5nzm3voZULprjtvFhY1Ve443Ed22n+i7j9PzW1cvua5jMcdlaZ7yr309oc04FvnfuhBn44IRVRMNGn92FwCmJrBGc9i/fTEqUBBIRCpmJqwjVtHKXgiJQELTH3Rth950joKbBmUw5ZgwUMR5Fy9t47l2W2YOmlGfyhRu38bDvtePcXgsnuf5X7mM7NVnEN22n+Bnv4L55xfCCOjZmNFfuYPa+9NkrhnA/z83Yo3NE2tIX7uLzLU7qb79e1jOreAkjkHfDdcCoIwmkoL6Gz+zqnkutTqutrz8v/8TuWcOK+uRed5O8j/f3ayr+kffRjw6gwklhZ+/jMxLVl4bV0KsJHGrAWDzsU4H3xiItSbb8ZqVSLYGk9QHdWZQfowV8Uyy5e3zjFeYueYfGPj0GxC9Zer//DC9d5exYk34giHCa5axn9rQ//Z7CF6yYfnnjwDKaMYbZbYWBpKeDJjEtxh8atmGpzuO2KlvKYPs2rWLyy67rEsZ5Itf/CKwoAwCtJVBFtMTOouTUpdupf+GV6Ima8z/xpdIN9thu+dsJPrWo2Tf/CzCmw5i7+oBDNEtT2AP5en7wMsB0JUQGSR87/6/fQ12s8LcaI3RBt1hrIxW6KDFBeswhCJE1WYOuQOsvOdrFH7tuaQu2YrRBnHHGKlLt5L5iaRhg3hwiur7byTzotMPOYb5rzyJuWh57diBT7wBK+UQ3bafxj/tpvcPX3rIc60FjEmoSIsLULSI0CIkEmIhskMyagao+SG5nu5zycokdqaIU0gKp4wSWPYzt+tsa54fbo67P3cZ0bcfwzs7KTJcbo630DnHnwoOJet1NHPcaIVqzFL43xdhD2WJb58m/Owe8m8/v32MM5qn50NXIqXG/6eHMUM5VF1gAolnWUz4FZ5qmY/UirmgRs0J6U1nyTgudgQ6UkSeTZR2cLI2uazbTg/rsIqqe+35eqTj9WNAKtXUSY8a3fP8Vz9P5vnN3hmn9aK+NwbPOo/Uj0rE2/OkSea5GciQfucVOJZNSnjtjtV9f/0qnJ6EXmKkQDU3b1qurhmYMQZfxvQtM5+OpS3v/eOXLgnWtFD8rauxUg66EVO6/lOHdOpXEiGIlQQVL8lCG2PQ2qCV6VIYSZ5b3mE3SoDRGBmhwzqh5S7JANTikLTjkloFVe3pjPVoyyGxTxH7ljy+VnO88fHbcC4Y5eDMk9QthXnlRnjNJlCG/t+8l/AlG2BRt+/+u6voobVb9yMlmfArbM73YVlW4q/VSziFgcO/uAPPBFt+RCHUljLIO97xDmq12hEpgyyGlEsjcboWdoUUUhdvJr4zKbaLd09hzk6+QCvtIh+eRjV1YO0Vuu4ZJTB6UdqxXlqWB95aSFaCmqhitCF1SVKkYdkWqUsXCjZMLKn832/R+4cvxXJt1Eyd2kd+sPRzj5URUmCPLl+kZ6USx9o0YtzTjlOU3mhUZWqJM2WiBqpRQqruBaK1lhihkVHU/TpjMHGArEwmFIzDjOvTHYvn+aHmePTf+0hdkah3rHaOPxXUarVlHz/aOa7qc1iewR5KYoSWay2xMFJqanWJH2jkD6cIzigwWypzoD7PdFB7yg59J2IlmPGrjNXmmQoqVOMAJQTUBWouolGO29m71nWroLri+VYarx8jgZQimdOdVI5amER+mzQseVYR+4F5jIpJ3VsmOr2AlAIr7WIensFMBQlXvLiSg2Pa/xsRrUpSN1SSWEnkIof2WNpyC4vq732T0s9/DrFMw8C2fQ/EYbOwRomVZfyMWUJx0wb8SKLCZqF5B+KVMl9Go+pz6KCGiQP0MrSK2eoM8ghlYJ+OWI+2HJa3T2s5x30pqPdZ+GE12fB5TaMuNGpDZolDjzKkbpkjes6ROdyHQ0NEzHRw+VWjdNgOyovxTLDlR7T17iwyuOCCC9rKIC20uKmdHNUgCMjnk2hLp9RfS6uWyTre7QeYfN0/YvbM4/zR1Ykm8cwMVqNBqi9F5Y4n8W2NiAJyUlHfmUVcNET8G/+OIwzRr16I3CzJScHsL3wWZTQm5TD4sZ+mHkZo22JychLXwPjBcYyU5NIebiAI63WqY2MM5j0qVR8RHcRNpSlgmJudxYylKRQK2AdKRAWbsbGxJVJ/mzZtYuIPv455/mZqfRaZIGAuKsNPb8cLw7ZMnWVZFP7+AWauHqb/yweQlQqFsMjs7CyQyIKFB+cJ3/NNmPYZ+thrmJiYQClFNpulWCwyPZ0sFAMDAwgh2pN006ZNTE9PI6UknU7T19fH1FRSrNjX14fWmmo1cVxGR0eZnZ1FCEEqlaIvn+bg+DhOXbUl2CqVCjqsM5Ax+LFAKIVtWaRtm7If4JQd0o5DtTxPUCrjFAYY7i1QKteIhMR1bIb6CoyNj2NnCieN1N9aQzU3RPHtB5i7/lPIh6bp+8tXLhxgWdjDecSDU1iFFDR5iOkrTkE+Pkf5f30FEwh63vdiUhckEcH5//l5cGystMvA373miK8pCIJlH1fTdZxD9Duofehmsq88B3dHYqyd4QLFX78SAKMk2l9QQzBCE/zTI+TfdUH7MSk1vq8wgUTtrRDnLaZFh5FeYyEugyGUglAKyrFPzknRk86SqkIjUmQH03hNJ0tVprC9zLKR0ZXG68dIoFXi8GkRdc3zwh9fiRZJVFKgsfs9xIPTmLSNsS20kaSvOAXuGCF+/10QKpx3X0rqrESdovwrXwLHwsqk6P/rn1p4PxGsiioyHyXBpMUNlo5mnnei8fe3Eb/qVFL/vrf9WPG3XoDdl0XunaPyO19n8LPXL3ld+X99hfi2/RT/1/MPef3aT9RtHHcZtQ6jQURtp0YrjVIaaTSWWo5Tv8J4yRjtl7G8LEZLiH2wu90C1ZhnLp0n562tk3ayYT3ackjs02KrtVZzvPr/3crcz+wk/7c/6no8//F9pH84R/jSpU3X0jfPUr+kj4xlYQFam6MunG1hPmqQcV16Utn2htTtXX2H8WeCLT8ip/5olUE6tY0//OEPs2XLFqRVodZMZwVfewj58AzeNR5Dw8NEhSrpK3dQ+8BN6BcMYO+rgTZJCuVXfwJ+lcR4/p9vUvzgJdRsh/6/fV07naVFRMp1sGyLDSMjzFmwaXgAI0JqVYFjGQqFAj1btqDqJQZ6cri9g9jZIjX2Mjg0RKapmau22oiaZrD5d+dmJr79AJnJkP4/uLa9oVlOl1fun6cBDJy9jeyNc+R7e/Eyma5js7uy8PmfJb53gtoN32f0717b9R0s1vDtlGdqFTCvdGxPzwJXpqUlDYls5aahXryRTW0KTrFYRNVLRPPTZGwXr2OjlncchnpySKFJaZu+vjTe0Ag6Dhjs7VZj2LxxqIvacKy0jdcrWvMh1THH4x8dIP3cHe1jMi86ncp7v0Hhbc/piu7lr7uI/HUXJXP8977J4CffBBx9ytZxli9gckYKqKnlVQ3i2w8g98xS/K0XLPu8qs/RSWdr/MXdpF+5A2dLcu9LqfEbClONieKY4Af7Ec/q5m4lS8CxgTGGhowSOkY6Rw9ZgqkAM5QhlXUBg6xO4w0s1cheabx+jCZa1cla4l24gYGPvpbgaw8R3bmXzFUJx9cA/hVDVN73HaIX9WM9Xm9Pl+jazfT91Db0/jr1G+5m4CPPA6Dvb14FjugSPzDNTKBREmPMIYuc46YTphZtAI5mnrcg9yedjZ3N3Q633Zdkqdydg2CBURprUafvvg++Al0JmXvDJ8n81DlYyzg8xpikeNh2gO730NoQiihx7Js0G2PASEMcSyxhEGqRE3+oiD8J/RQMCB8yyX2pIx8rlcVEDYRfgeIz26lfj7YclrdPazHHxb556iJC9S+tt2j83Ck0rt9K33sfJHzhMLq/+RmUIf3DEqVf205mdwWlIAwMlm1wXYumiA1KJf+MNu0kn+tZpFKHXwOm/Bpp2yPtuuiggsn3r7po9plgy4+IfvP444/zgQ98gA996ENUKhUuvfRSrr76aj784Q/z1a9+lZe+NOF/t5RB/uqv/oqXvexly55rccMPgOzLzyK6dV9S2NFE6rJtOKcOUjsjn6RelUTN1NF+Quuw+7PtCbccb9Bgmjnb5jFN6o2pxJhoGRrOMooCAM5oD5ZjEe8ea76XId59AF2PqP7Jd5I01mFUNOQjM8g9s6R//wfEP9xH9X3/hYkW0kdGqDZP0i6msZZpMnFM0DLsy1BlYtnNp29Ba4OQhnqUfFdG6y4Fix8jweJ5vtIcd88YbnMzgaVzfA0xOjq67ONPdY4bJdEd9JXgnx7GGc2TvjrZeCll8BsSU40J4ogpv0r6vjLBed3OStY99gbXYJiPGswEVaTSBDMhkZ/cgyb20eHShXCl8foxEmSac7wzFZ59+VmI3ZOoUr1ZswPivF7Mzj7Cc3rb2zc5XUMHTZtc9BbRP5JaHx0upMyV0VSjoPncU8vsHEtbruuJDVRzDYxQSxx6EydjZGVdrHxqWYe+fewK1AIpNZGUiaJNkxKipUILjfAlltLEQtFVM9YxVCLuoEnRossmj7UyK6EUaL+MESGxVqsqXH+6Yz3acljePq3FHJ+/fz88XqLvt3+Id2+F4t8+AbEG0fSzPAuTsjHewhy3ywK7LBi94Qky/zFJ7ltTpB6vJ4ml2BAEhsA3xJFBSYPWC3NTCpMoNx0G2mgONsrtWklZm1nNMAHPDFt+RJH6tVQGWdxyuYXcT5+L/2/34j0rGXzLtcn+/gvQt/4IMCitURM1ah/4TlI1LgS5X7kEKZMvuJXOAuj5gxd2ndvUY6q/+X0wUHc03rWnssCGXDqZ6h++Gf+fE7pQ8V0voPcDL6f6R9+m9pc3J9Xkr7+A+J6D6NkG5fd8beEzvPZ8Updvw//M3V0prcyLTifzotMZGxuj8Nf3kv/ZS7HSLvX/70dkXnYGlm1Tfs/XsBwLLIue333hkms6Fmg744s3RUYnKibLQClQocLOtG7oFRbZE9PbbN1guXm+3Bzv+5NuWcyuOa40hbdf1X6uc473/ck1OJsWIt6mHlP6uc8CYA/kyL7uWUvef2xsrJ0FWYs5nn3LabTuHzUdEPzLo7jnDSDumsE5ux/9+tNQ//oo8uIBZvISZzJA9XmYbLcT70tJzj0+xXi+jIkbFYazRZgN0QNpsgUPVZvBSue7FrzO8foxliIMkjkuRdDWowfIvHwn4Rfux7miqaxhGZzfvQwe3pMcrzTiYIX0H9xB5DnIWBG8aVsSpQbKv/IfYGmM0fT+8UsxaUNDROS8NClfUvrZT2M57orzvBPHy5aXf/PLSSGk0hTf/QIAopufwESCzE+eTvk3v4KuRSAUhf95xSGvWWmFtUKgRIgQ23YQcVNvXhuUMk09f9BygRKVyDQ370+VBGNcL4k8x0qijG73fDDNOiitBFqEBHHQJTTxTMZ6tOWQ2Kde1naOR0oyfcUQ+vzLseqzFD/yOP4rRyFlU/jbJ3DGAyxpCJ83hCm4OE808B6p4794hKk/PId6NWLojip2qIh3ddCADuMOxFHSY8RZzNNffJyWTPk1RvO9mKixavnsZ4ItP2EdZW+44Qbe8Y53rOrYWnmeR26/HYBTzzydvq3b28+JmSewvAzzUyV6RwZJ9S9QUHQccufNN2NbNhc+73lgWYiZvSiheeiRcQZHhzjlnORGEfPjmKiB07sRJ7tI0mWNsd4mlqzOoP153P7N2B3NTFRtltrkOHueXNpIaLC3h6gRk+pJsWXzAE7vxqTQKqh0HefkB3CKx77gdz12IIQjm+fHC2s5/4wUiNknWclaN3yJLEUoITjYqC3Rmu/E8XTqW7Asm8FMnryXJjWQIltM4RQGuyhj6+l+XY/z/Ia/+At+41d+kdrsfkpxwPadF2NZVmJTRQSD23jksYSTOzqwibHHnkD6kuzOEc7Yeib79t3DYCbPE3PzpD2HbT0DOLZFZsMOdFjHDyq42T6s2hR7yjMUUhk25Xuxc/04uT4sd/mM5qPlKUIpGMoW2ZQ/Nl0kj9XcMMZQH3+ITDqHN7y9+z3Hn2B6bj+plI1xM5x36qUE09M0piaZqMwTBRF9W4fZdfr56KCKk+9n7/77qdfnEAJkQ5Htc5AS8mmXrcUB3CbtcrxRppbpY2cuTypqMO+kmZ7dh1cc5swtZ63551wJ63Ker0NbDms7B5XWzIR15oI6ymisuf2r7jki4mTDiIHAj8nmUiA1ljQJRc+yMHbyk9ZPZbBijRXrJOqfdcCCTMZaFQ//lOIQGdfF8jKrkrh8JtjyE6ZR5T7Fxdss4goaozFhDeYjzODiiEKTemM16SXNFKLUuouqU/uL7xPd/jhgsNwU6efuovDLz35K17carNR99FjCaI2JGtjZ5VR3Vt7XrRCoR0aKugzp183vUcbP+Kj8cniq83ytUfuL7xPfcxAAT0rqV63NHFeNEivNnyjWyJpAScVUUD+kQw/gnoB+BsZoZoMaUml6S4CGLCXsTE/bWTwR9+vJBMdxMEYTKoExKpFi7GjiZ0gicO2O783popRAKNlFedRNjq2QmpZGyFzYIO+mKbRUt1pKOCJAVgLcga0YEWF5aWof+j7i7nGwbLIiwr5kI/IXLz1mn/1Yzg1hFMvqpEQtHr2BFi3GGLRYyKzqSHcpSWmdcJsd18KOFFo7iNjgW90OmzEGqz4LuaaUaItW9zTrHP5UsB5tOUDmwg3wjqN3VOfCOpON6kINSlhfvUMvDEK0blCDJwxOOT5kZN5YFlbHHLVChXIsSNmEoSGdAsc9tGM/F9bZXOjDiBAdB9ipQ9Obngm2/ITNUvsIFvDOeRGtMMk0eol+se7aACS8wVhohEgWkpbsV+E3nk16diNhLMgPbz3mkfrl6gmOFWRlCjvXB4CO/WWd+jaXfrFigowRK4x3PepOReqwBrbTVeleb0iKWcPTvzRlZRzJPD+WKP7m89q/B0FANnv03E4toiWZmRakMoRN/fnZoLYqTu6JHKly3EBoyaApYCmD5R7EHdyGZVnH9X49GWHbFhiN1GpF6URjNFpbSbOj5mNKJsXLoRIILVGhwHE9wiDCsZyESkISPYyVwpdxm5/fPGmirx5UwbKwXI/Cr12OEQFWrpfxmTHAYB+m++XR4FjODW308jVeHQ9Zzc9Wj3yCMMRqrmkmXgiy6OZ3Y5I/QIFeIVoTSdlNw2za/x/Ha9anLYe1UXTxRczBeqV7vvnlVb1W6w6HXhvsusSRBg4TabeWmVS2r9CuBbZFFBk8A5638nnqIiSUgozroRvzh3Xqnwm2/ITN0jhevX55ZwGnWcEYHaiVCBt1TIcBV3HcCtInhl8ronpE3GgaquYmwJgkqjFZr7Vlq44lWlKNxwMm9hPDfKgdd6uIeFGhrNR6xQBNq3i2vb5qBVpTr4qEE2sMMzPzzFWe/rqwh8KRzPPjhbWaf6o+u/zjOimMpS6YC+qEKylvLEJ8grWwGzJiJqgSVCP8yTKiPAkc3/v1ZIQQEoyhGodIo1fMyGidaNq3pUuNQujE9tZFDKFChDGVRgNdl4lOu9FJw0BjmG34qA799UrkJ+cNKm1pxpYtC2TSVOlY98k4lnMjFIJqvJTHLe749BIvO/SrXc2oWgW0GIPQujkeoH2FZQxKGOyqSAofl2Bpga0yCrXM/SmO4YZpvWE92nI4+jmojeZAfb7boY8aWOrwn9cYQxQ2N9rKYNckljJP2ZZbJtkUtCaeiJPzq0MU0M42xQ10VF+5r0MTzwRbvj62nkeArnbYTUeynWZcvPNrReoNyPocsnyQ+bpP1W8qtkidOLJG04gElfkGUXOheLrANPWiVHV65XBL6wZsLoi6uZBIqYnloW9OJTSxSErYhRCUpucJI0W9IfEbIWHwY0WcpyN01MBES5vKaWPwfYmpCcqBT+MkU0QKpGDKrxHUY+pPThFX50/0JZ0kMCgSW1xulJc8a/nJQl0P6+3udUZIaGZMy0GQmB+taYikW6rRhkhJgjhInIfFzX/aNJyFBnhSRJSjBvVGGTAQLS/rt+5hDL4S+M1NSReV5rHvYUXNQAzJGNVFt/OvpGpnOlrnQ5t2UEwLk+jZa9PeKCUFyh2bpjho/71c1sAX8ZIeAD/GyYdJv0a0OHq3yih93OoDp5sOvV7Z+V4tLGWwGx3BWWWIIkPg64WMQAcaIsIXyX2iVnndT2ecMPpNKnUE2qwdBs2IztSgJlHEaf655AtvGiljoVXCJxSRxAjVehqjEw6okhpijRLH3kgNDR2nTrFNGK0wSmB5S1NTOg7RMkJpcGgadhFCKoPxBfiHpk1ooQkqAqffIg4ENRHRV46xRMKNfabnbY9onh8nrMX8U7WlUXpjDEGg0FVBIwioxEe2QU6vEw3hWAkmG1U2mCL6kX307Nh6+BedZHjyySf5/Oc/j+M49PX18Za3vIV77rmHm266Cc/zePOb30x/fz+Tk5N8+tOfRmvNtddeyxlnnLHkXKmUl9TstKRxm86f1KpNvTMKTKwJpeyijxgZJxx6Ydpy98YYwihCSUE1qILRhFqiSaTwdKr5PrpptDocz7l6CSUiHNeFhZjOMcOxs+ULgSodh5jYxykspxNviJUklDFZ3HYWRBmFihsLNj1W2FWJaVIZrI7IpzCKNC7K6AU+tdbU44gF7VGxZBxba+t6xVrOcVifthyObg42RMRssCibHgcrqi51QohEcYlmdL2TTnO0ttwSGrsu0YXOHhVJ5N6ywF3EtZ8N62x1+xPd+sIg1gpUqePte50InLBI/ZHRXDoLftSSx1tOula6y4nUnV30mg/Xo5CwowtfS5c6FnEi0RQd+8ii7x/nbMAhqDcm9pPGMHXRjuob2exUqOThV0RtoC7QQtOIQ4zWCD8iiKMmj/aZ7dQfDzrXkeJo55/yK8v2JAgjjawKVCwphUf+HuupFb3UiolGlUhEzO15Ahk+vQoF+/v7+Y3f+A3e+c53MjAwwL333st3vvMd3v72t/Pyl7+cb3zjGwB85Stf4brrruNtb3sbX/va15Y9l1SKuGM+tJz7ehwRtyKA2mDJZpClaRM6O5+2adwmicCX44BISUwcgDGEcYCOJUYsiBzozlS7lhhtqIZ1/NgnlocJRqyRTOOR3kt1Ea2KjtaiQsZaJjZaLC8B3fUaFrrnKq3Rzeil0RqUwMK0I6lWKzimk+9Lm+7CWqMVkRJtyo2lRBe19WTAWs5xWJ+2HI7Ono8vk1VbTZS+k0dvN1RyL3dgLWy5JTR2Y+l9HMdmiV8RyJixehmtu3umLMZx971OAE4Kp74z2BvFTR68FOjIRypDJJpfvDZdzWNE+z0SjrdSCkubhaYFScVQ8ntTUYDD0E3WAsdrYhljks/XWjhXMMxKGkw5bt8oOqgkjUcWLT7uvu+Tuv/TK75XK4sy5dcphY0VG3mdDNi9eze//du/DcD73vc+brjhBm644QYeeughACYnJ/nQhz7EBz/4QR555JEVz7MeF4KjmX9GyWW59HGsiWsCQkU58A+rdLMc1DrL6mijmfSrVPwatSfGUdHqlCBOBvT29rYjj67rMjU1xcaNG3Fdl127djE+Pg5ApVJhZGSEbDZLLpejXl9KZ9FKo/yFhTRsOtS+jBOuPCRR80ULsVEdFqKjANZoQyQFkWpxaw0YjQo1dqgRTTsTqLiddjdKIo0mjoPktVokr2sV1y5CQ6wNP3o191InTaUSBeyvlw77GmMMollLoPx5ZCfVrdlDQbWoNCvcNzr2AZPY/GaApuV8tQtq2/WNTZ37VnFt8/10526ribqICGRMIMVS2sY6wlrOcVifthyeuj2vxuHSDaaMsMShC2+NSegwGLACtbBB7MBa2XIr1thVgRWoBd/MQBSZJfe1LyP21+aJGivz5p8JTv0Jo98crptZJ2Tc0XWVVmfYGB03CBpyoSOkAVWbxs71gtHtwiFjTJIKtrpTQslm0iCVxBcdMmHHGMetil4nXQVbUp5GBBgRYnVo0QMoIanHIXmhaCk+GyUoRw0isXDTN24WODPbcM9deK1EU5cRaZludSxHG7V4/T6poLXmrrvuoq+vD4BMJrNEn7gV3SkWi/zN3/zNiinbI5nnxwtPdf4ZY5Dlg+2NcAtKGcKGhIYkUoK6PHxUcTlYrL+xMsZQi0Pq81PYYzkKWwZw0utD2m4tUCqVeOihh3jlK1/Z5cy0qTQd9jCbzdJoNCgUCuzevZvdu5NGN5VqlYm5CvUwJl/wqJQqjMVjVKoN8mmbYGwcP4pJWS4mkvhCgjbktEdlvkyjEqFDQ0aBrzRoC1eDH4TMl6oEIibtKcJYYiKFqlo08pL5sk/oSkZ6ioTGpxFDveqTzbtUy1Vi42OriMygZmws6ayZz+cxrs3jE2OM5noZHh6mXq8TBAG2bbNp0ybGx8cxxpDP58lkMu3iuqGhIXzfx/d9LMti8+bNlMtlAHK5HLlcjtnZZMM7ODhIFEVUalVmgzrn7DyNmalppusVtGsT5/qZnp4GoLevDykEjUbiSG/atImp6RmqlZBMBkRWMTlXJi3H6OvrQ2lN3RcgFD19KeZmZmk0IpxYo7QmVArLWNRrNWJpmAtm8OsxKQOx0mhjsC2LtG3TCARTpRp9+RxhrKjVYmzLIswIGo2YWCq0gmKvx8Hxg7iOC2kP13UZn5nCsWzyp+ykVqsRBAGO4zA6Otoe70KhQDqdXnYMF4/34jE80XMc6J7nlUr7c42OjjI7O4sQgnQ6TV9fH1NTST+X3t7e9vEAGzdupFQqEccxqVSKwcFBJiYmAOjp6cG27fY82rBhA+VymSiKcF2XkZERDh5M5CuLxSKu6zI/n9T5jIyMUK1WmZ+fx3EcNm7c2N6oFAoFUqkUpVKygVxunu/Z9wShFHiZNE7KI6zWIaiQ9TRSJP+woFBMUa8l8pSuZ6ONRdAQoAyZ2CBZiMznXJdAKmKlcSyFa9tEzc1QyrYxgGgem3UcwiZtz7EsPNsmXO5YCVnpEDc0ygIr5+KlXcolgedZpDKJXxeHijoxQk7QH1poO4XneQwNDbXHOwxD6vX6qsfb87z2GI6MjKzpPD9WOGGr05FIC61YjKM1VGNU0/FUYiGFqMM6xiwqrFqkcd8KU0SxpF5vYEFTbUGvyMlaC2zatOmYnbsTrai8OUwVu4g0c2GdIZkUrkEiUak1XQ6aNddP/7RHJwMvkoJICjJ+mlCs36jNkWD37t1ceOGF3HjjjUCienDDDTfQ29vL6173OvL5fDu6A7SjO8tp4K5HCa2nOv9UZWoJDcCYROnG1GIMhrllimdXi6y7Pjj1i5F1HKb9KtbsQSzbJTeSx82vv+/1SBEEAf/0T//Em9/8ZrTWXR0zW5vRzk1pEATk80lA4JJLLuGSSy4B4K//6qMM9OeIGsn939tfZMuGLURqnjj22b5hmLk9LpZj4zqQdV0sbbC0pidlCLMe2hdgQdZz0a6NrRTZlEdUTOFIULZF2nOxpYXluUip6e3JUExlyadSTFZmGCwOEpNEZh0H0o4NysUY0244I7SiFDbIDw0w3DtM2nGX3KObN2/u+ruzWU0mk2FgYIHbfu655654bDabJZXP0WjWn4yOjhJVUwRSkEql2scerJdx0g5b+hdeu2FkmLn6PtKui+c69PSkGW4e79g2ubSLSjtYFvQP9lGZSVPEJVYROdfF8iyyGRcv41HMZqiWPFQsySziOueyLsViGstO6tUKxRS2bWHZkMulSBmF72u0gk2bRkl5acpNMYnMYF/StK3pqK40Docbw0ON99HiaOY4dM/zj33sY13XtmHDhkNed7G4IB/dWitWOrZz7RgeHj7ksZ3XNzQ01MUTX3xsp/PYOc99EeP2F+lcsQoDRaxSGbBxXRs6SvAKxeS+ErFBC0M262FXJZad+AupDn8p6zpdtnxxQ0Gv89jF8/EQx7bmrhGg05DOeLieRSqVfH+pVPK8RiNUhW1bL2h/t0cz3p1juJbz/FjhhNFvlmu5vBjmULysZlp2KqhRa55LRSppXhI1UNVpdLzA8wwbErFIvku3+IStrmeAiQNU/djKHrV208ccrdRoZ/ZhyZgapGw6/yLqotws5q3ZsYVwNURLZSpFINDLFBlHa5TmPl7QWnPnnXdy0UUXtR975zvfyTve8Q7OPvvsNudyuejOcljNPD/eeCrzTzXK6HApVzGMNLomQEM1ClelR78S/MPwoE8UfCkxxjBVm6FemaUxWSOeP7nTuEopPvGJT3DNNdewYcMGRkZGmJycRErJ3r172wtQT08PMzMzhGGI7/vLblyDKOymgLQpGcuk6zppWQaEDJccZrUbKPkLmUIl2/QdFQjiWOJHCoNBNZVZ9OITNa+jUztfad3mngdrIHd5uHvJYJJmh636L7oVZrTR1ES0IoUGoxMp5kU0CQPQpD0YrZIHOjnxTUWSMKwThvUV9QqM0szW/WQdrEtElBxYjgJk67tq0aTWGT3ucFjLOQ7r05bDU7PnM+EyUtP+8j1HWuhsMGUF6pBKN8fSlltqQfZSCoNcRu6yGtZoVGeWPH7cfK8TiHWdR5aVCexFii2dWrxaxsRKYDUr8EWT/9fqcqk7bkJZFWh70URTmiiIUVGHcV+jAqpD4XhQfJpvRBAqUplEmzgKFYXepe8dNxfOQAjyIkQqg+tYmEU3ixPZ1NMu9t4nsM86v+u5IIyIraVjZ9apo7YSbrvtNi666KIuikrLyF944YX88Ic/BA4d3TkZUrbGmFWnbP16DdOYY+NAkYm55LW5TAqMxex0DWJNxrWZDmpI052GNSSp1cVpWM0xTtk2j430At0g1XFsKwLUOjbjOMS6m5oQqFbRYeKYxVrz+ORedrppSvvLMG7IDBbYsGHDSZeyveOOO3jyySf5+te/zte//nWuuuoqrr76aj784Q+3lUEAXvGKV/DJT34SrTXXXHPN8iczCc964W/d8dRyjv3CT601Sps28cpo2oorJvJRKjmHTYQVNx2K2CDrESKjEh54cxVb2awuPKGMbvPw1wKrteWhkmTdVHd8xSRNtVZq2JWcP/kZd2yGpqIGqVhhWTYq56B1kmHtlPxsHS61QliHGBud0OcSJRONaUZeO7nyVqggYxH7FVKpDKGSpG1nyXfbEBF5b/1ksNZ0jq9jHKk/ESlJdVHzSIzBOoT8q5QG0bz/EBo7OrGiBi3HXhdc4jgpM3GcbvrmwZl9nNYz3LVWHzff6wTihDn1zmokj7Re0hDJ6CRqZpRAS4lRIFvqLq1iK91SR1iYeEorzCL5LW1AlmtoFgymMaxKaeBocCz5VJ0wGGQpwhmwMLZirlSlMLxh0UGmXdhmRCJ9GZQiMr1e1w1ghRU0Fg+eUueMR13SZ3Wfpi5jHGuZxM/i4jitsOz1SbOApAB2bGyM22+/nZmZGT772c/y6le/Gs/z2LNnTztV14ruFIvFJdGdzpTtRz7ykXWXst2wYUNXWvBwKdteO0Knk+saHUy6LQehIp6PGXayqKziYL1CyrFJ0Z2G7TrvotTq4pStY1mkHHvZY482ZXs0x8ZK49o2bvPYmdn9bNl0Oq7t4CkXS5mTLmV72WWXcdllly15/OKLL+76e3R0lHe+852HPJft2DREhNaGzhKSJNPX7foZZbA6e40A1uIEn2op4nQo4wjRlsyzjMH3Q4yW1GxBTzpzyOszzaik0AqhFQ2xdgpnq7blXc5E8vvD81Nszve1JYe7Dl/kMHc2fpqOA7YohYWFEhohYtCKoINeajWbTtV1hG95rATLgBQKqQzaLJWFNsYkDlykiWWIL2LqcYhyu+Ud27SmdeTUr+Uch1X6LCcAR+pPzAb1pZvtqLFiQFMpQ9xy6LXB9g9fMOwu5wusMSyZ6NnrgkscGdIZ2t3sASIRMFs6wPDgtvZjx8v3OpFY3059EwZAhmA5IBMZNKNkoqjQMTdDKQhDTc6JsCyrXVQLSVQwteg9pZIYqRHOgkE1zYXoWOK4TSytmg1HFKGUycKwaKcqpUbHTUlQkcha6oYk9BI5tRbUwf3EHty/UXDWvWXc8duQmzsMpjHdEqLLwGiN9svY2V4sZ30miV71qle1f//ABz7Ay172Mv7iL/6CVCqF67pcf/31wOqjO+txITiS+acjH70oguMHElEW0KQ/TPv1NZEIdA7TVvxEYfF1SREwURpj6+A2RChpHKySG8ljZ9enjvWxRiurZUw3u69LIhGwaPWuWDgmkhIjFyL1ll5QwUmUDxKVC71oagijQRn8KAKKC8cvB5E0rwqkIFSygwpz9FjtvdQ5Ei2qTbvZU20GY/qhZ4EbrUxTtq/pG3XSc7RlY5TGcpKPHNaTzLRapDhVChtYrouxdbJuLgOtEidNpRVKKuxmFsyyLKQ0tM2XSTK5meZnMc3vsnVdsZLUREisJKl1atuPFuvRlsOR2fNAxpTCZaiiy9FxSDbmcVPpBqkT+cpVqGAcL1tuCY3VkJi8SxQudeynZw/Q17sRr7kJ/bFTfwyxqpbLHY5C8bOvxsIgfu5GTOwnkot0G+ZACeJqRC6fQymDVAq7tAe7MY0+9UVLbL7BUAobZPILkQy1uJj2GGB2dnZNi4FWgokDhJY4wkUh0WbRQisFotogaqoLGS0xWjEb1MjYGVSTs+nuvxn57ZsZH76W8PQ+dny1yN4valK/1vFeLJWYWnI9MkbV57BSuXXr1HfiPe95T9fPTqw2urMeW4sfyfxTtW5eYhBKRClu83lLQWNBi/woESm1JJK+HrDcdUW1OQ6mcowWh5BCUZ+skRvM4/YcOmr8dIQQC+pjnXs7JVsO7Mp2QWrVnkvJSej+PVZJ5D7bHflLIvwG46uEU2/MQuOkRbBin7qIkFox7S+tCzkaPBVbbhbZYUvFS4MtWqMUyHZjrQ67bdnJ7skBlKG+gkMWKYFn2XCozKgBNIhIJh3YtUEIsKyEr2xnrIVr7qgHaF1P2HHvS61piOhp69SvR1sOq5+DUiuerJaWRumVXFbGsiVdaQwQaxx/FX1rmjiettyONdpWmKyzxLHXWnFgboydG3YCx8/3OpE4YYWyh4Jq6vgm1JskMt9725/Qc9ufoprUGzBLCmm10ZhYorQh9kXSIOHW+/C/fpBYScQyKjrS6K6JKtXSaPbJCGMMKqwx2agSxDFj83WkVIgO3qWOfXTHrl1pgxIRDRnhN3xks/A1enAv3vg1PLw9zUUjmxC2RWoZbdoVrmTpI2tQoPZjHHuooNrVZCqMFPHcgkNfjYOnLF/5dIA/P8H+2iyxVmhlaMw2EKXGM4K32Y1mH5DYdNHttDEImTj1Fgaju+tk24X4hxgurZdRLSOhXBoNltQILdGa9uZyuYZ3lThgbrkI5THG4itpRbmlWSiexZgljpY2GiUNUmkCIbo2LNqyQSuMIaHGrKAOFwlN/TCOqKVMUm8VhEilsXRSeNjsQ4hWC99R0Owqq5sa+qYjrDbfVMR5ps38kwXGGPbX5hHLNaJcgUsfR8km3YoUTmP1Dv2JgB0qrCC5J6KwO8BYr80xG6xcL/B0wwlz6lOpVELHiLudAi2iJel+Ywz7t2oqObtlaVDaEAayi2LTPl4aTNxsUvLoNvqfPJOGjLrSTu7d/wHN5h6dk1Ubs2y3zLXEYo7tMYExaGmS9uzGYMIYP4yoLVZpMQv8TalVu8FGqGQ7nase3cmGssXB8w1bU3le+wuzPDW/fGERe6ZgPbYWX838M8agagt60XGsiGajtkPfkBHzRyFfuRzS67TWYsXr0pK4MsWBaomGjDHa4M8HiOkGJhTPGOfecZ3ELEuNiTo6k+rEkY+b6loYuoQOLANG6CXdKDthtMaStHn27ceNaZdQNcLEqW9JGy837KX6/BK1m0MVqK4Wq7XliQqOageWWmNUiZMIqRBxV9ApqRtLPqAv4y6nX5DBassVL0qPdECp7k3WoaCFWqDvNFXirEgtKItoQCYqR5FamNutsa4vqlMIZIw2elXdc08WrEdbDqubg5N+lfoKtYLWMh1YRZw0I7NivSoO/WKcCFve6djHUYedEQET1TkCGR8f3+sE48R2lNVqiUEyIuyyysYkKUE0RCmSqIaMUbFCNpYunKWgQRxpRKSJGwInsoib7JqWYVQzk2z8xHb0R7+BjjRxh9SlWaURPBocD2ks03S4kgy2wpImSVdHEbplgJurYkuFRAuD9FsL48I4eFGOr757ExvSOfrsFOMFgyMcMBp37NancHEntnL+eGI9diFczfxTtRloFqWHgSKYWXDoIyWYC9c+8rHeOsq2cKjrsmSMbpQ4WC8zE9QTx74WUp+sER6sIirBcbEpJxItyqIxoJoSc3NhHSU1WmsCEbf3810OvOoOqCxGrJIMyKEcU2M0vpAYbQjb3WWXtpFnGXpBZyt7qRVzYf2IndDF95I2ust2thz1ahwi9ILSjW5uSlrXUAtrNES04CxrnXwOtTSK/0T4Kmh2R2+Z0uUaOFuRxgiFFQdHFEixA4UVKmxfoVsFkiahUrUyBiudbSFApImUXJESdTJiPdpyOLw9b4iImWB5ihYihEXReyWb0pWxxm48tY3vibLldqiwfIlStOU3AUxUZ39tHj84uaWIV4MT6tQbo9HBIm3UrlSiSdp8K4GOLaoZiZirIKMQGWp0vNRg+DJGNiLmq7VEukw4xF7HOZVA372f8cE0bnVbsiB17ERVpAlD1aXXvtZYSdN8LaH8+YXfOxYvI2JUdbr9d6xlsnEiceTjYKmBcCKXb6UnOTvdh2NZ/N3m55KOwf7KDch/OsyFdGZBZMR4pUIY+xituyLBT1esx4XgcPNPxwHaL6OUoTEfEc0EbYc+VDHTQe2YRKHlOnUADnddVtyAqEY58jnQmCfWCiU1USBozDSo7y8ja+HTNnKvtU7S9E17LFSihhRHAqMMUoiFKL1JZCX/o7YfacwhA8mxUliqxUFf+rylkidioUAadKwT6ogyGN2kBLU2EfFSp74FpTUNEVOOAqrx8g7SctRNWHovxUoRdmQAWo0TIyWJO2zBbJgokHRy5ctR0H6f2EjsQGF8SaRk9ybFSrp2A+2ixeUoR1asMUKjlexaVsXh5rNKgmjt30nMeKRkm8ZgMCBCTJN+t/j7qYuoWeds1lRt6ERiPdpyOLQ9N8Yw3iiv/OJFUXqtDVFsQDY59E8RJ9KW25HGClU72wBAVCdSgrG5pdr1TzecOE69Vsi5fctKVi6G0QIbi3peIsZLxPUGQTmgEi5vqKNQoo1Ba9HmMLasjrj1Znq/m+Yb58xg0AnlpMMgCqUI5nx0fOx2dJ26qccKRsZIrdAmcTDaj2uNEQE6DjAy6jK4Rmrk4pR0WEUCP4xm2OUlKhP9ToqRisWmG1/IlunuPgKHQiRjZsfLiFAkDcIa84d/0Y+x5jjU/DPGoCqTRIGkMeGjqrK9wNdFyLRfWxOlm6cbLL8CQZVQSg7USsyFjfZmWklFfapOcLCKbkSY41CMf1zRdNjtWGOHCh01FWakxsiOplAGtNB8qvoEn6jsoSHlIZN2QquFLMchnH+jDcQGmnrrWiT8fq1Y0NZeBi06Tmd0WWjVFQRpYbnHYOm9NBVUGa8v2LUFpZvujUFr89DJcY6UbH9MqXV7A1QXIZ3+5Jn7FDSmuseg4/d/qz7Zfk+jQEvTpi+VVMRrx7+77GfpRMsZajVlbK2frTELpYBGCd2Yb1539zg3REQ58pn2q1TiluP/9NzUnmgcyp7PhY2Vs09KYnVQKFuFsSiDXVfrmkN/ONhBUoAfR8nm3pIRKEFjJfWfpxFOmFOfySR6tkZ1U2iMEm2KTaMSE/oCdDIp63mFnvSJI0UoBGGHQbRnH2lH+SMhkCjwq2gLlA3KT75I+WiGgTrcss2gMGijKccLX7JQgulGDXMMnfrj0i7YLKSgWx1jYUG7X4e1JeEVISWR6HbqzeMHqOfhgyOXto3HYg1aZ/fnl759UIXdN9JpGUQgsJRBK92smzBP+6LZTGb9qaEcav6p2gxBxSecDtvNxwyGUlBnLlxG33gNsR6Vb2D112WFVajNolWi2f1EdY4pv0rU3CjHgaA6UaNxoEw4WUPWIkwsT3pnx0t5rQpQYIHCaJE4hS1HsKVP/4XaPnpsD1/H6EM0sYlkEuG3zCEoOAasepw0oZIGRHMjoZMoXStivxw6O9CGShApQSUOqDalGTuxUqR+8b0klGp/31IrGh2Nrjp5560IvmzRUKFbktKYdhReKdNWIgPYNOtgomr78xtjuiQr/7Wyl71xDWOSQmKjFyL60x2F7dFynJ3W27fGu+ncK80SYQqr+f34IqLTzs9HPr6M2z8XNgRixXE8GbAebTmsbM+FVkwdSu0pqND5vcVxQl2zG7LdE+KpYj3YcruhMDJRcwIgalAYHuBgo7wm3aTXK06YUx+2uriaRdX7nRq+gUT5cVv8uFZUUIqJpSSScqHpFBD9y2PIsaSrYzUOCaVAzZQJ0po4pZAzSdrQncny3XMyvOis02m3LlwEI4+ts9nqPnlssdBhsFPxpn2rao2Qqot3FkqJEt1GVz5eZXxIsdPrbp39nHfM8z/epnlwS4rRfzkNyy+BinGm7sWZuBPzj59ky78MdddHNN9dRCJpbc7TP3oTResv9bzS/NORjz85QzQXtSeKRjMTVKkdB5WbQK7PBf9IrsuSIVZtOqEmYKjGIftrJSYalTYtQ0pNWI+oT9WojVcIxyqI6TrxVJ1oskYwUUWUA0x8cnRjFrHoqhvo4tNqQ+h3OKDNh0Oj+KE/fchIve7SrF/5OBlLLJFw2e0gqR8iMhihITx8VsRAu0Ykkb2s0Vhk/w/U5/FFTKxkV3az815qaeFrk9QUzAR1pFlw2JctVJQLDrFavHFp/imEWSJwI2XQjsjDQkRdGcPtfz7IwWZQyigwHTZ+WgWgYW9c4/Xj3+PnJ25Zdkza3XtbEXsDlhLL0JgWNRczC1mNFp++9TNaTnnlJMJ6tOWwsj2fbFRWrmnQCqtDClVrg5IGy1cL2ZmjQCAVdS24Oyzx5dp+7grnCBZ9/5FWx3T9t0yyQZGxTmxJWKcxN482hn210orZt5MdJ2w71RWdjwOs7EInTak0TlO9xXYUxijAEPZa6DGJHwkanQo1MmLHk1vZc+887rat7YiHnvHxsxrXSMxsg9SmBl7s8K5rxvlIegvKmcQKJFbWxapP4k7dh9j1oiTodAwNkD5Ok0m29KM7itMWujQmDnxn191WQ5FO6EmXPTslZy1K8cWe5n5vnre8AXb/+SDipluxZg/S8+AVTG8+yKmPP5epfhtXaYySWI6bdD4ERFgHk2su2k9vp349blqWm39GCmpPPkk8v3BfKRRTfh2xRjr0h8OxzAIcDY74urTEqs9i3BRke8FNUxcRdRGRcz0KqQxFN4NtW2hliJQkihYteI0Y17VJZVzcbAo8Gyt14qNfy8IscLABjFJYrei8NNCR9VSx5gJ3gHMfzvIvp+7l1f07VjytFmA3dwKHY3xZymDJlvOb2BVjLCxh0Bps3STnL0NVCKXoKpqNlGC8Pk9vKoPdzkomNKJQSSIl2p1TtdbMBnX60tmk+LU5V3wREyrZpj7o5ezAIie5c561Dm9dvxUsePXSaP67Osa/uobX9mxvftYkRFVRIRYwW65iFTZgbAtLG5RJGnzVZkN2f3CQv/vjpJZtTi3vqC4XqTXGYHV9EUuPUUYv+azxIlWfkxXr9dqXs+cNEbVlRpdFUOm6qURssCKFvUyd4mowryIeiavsE3XGRIOalhRtl9NSPWz18uwXDb7VOEhkdDuS7Fo20mh+rvc0NnnHpimUpZKus7FrkbFjjEoUjGIlebI2xynFAdx1qrr2VHECO8ouJAkSCcnEqY9iRdyQpAdgPmjg4aLDBtoGerOYPRqU7jKAat9+qlkHa6o7EqLmIxo5TVobzLyPu3cvpDTnpfsYdbP4aUFuqoG7vZfw+/vo++9BxB8mGshaykROzV77ZEY2u3oe+lOBkTFxrIgbyQ0qO4yqUImMpx37EFrt4raF1y6Mq7vvZtR8msaOIovxD6PPTaI8Ftz08jTnfn87w7XNgKLn8a18702jbP+vgxS0bG/a4iBZwGIhCOsNqnM+o0NLTv20go2FCQU4Nrj2camnOBwWzz+tNbU9jxOXF+4faSSTjdqSLpXHEo5lYYVl7NLjOJV9C6oMlo1xPHAzGDeLGjgNUxw9rtf1VGDJOOkW6qYg0wNeBl8KfCmYoU7O8yh4aQpNB38xpNTIegz1GMexcdwTP3eWg2XbXV0mlTG4rbB8hyN4Y/0gl+c3cPnekF/8osUn380h5SwtuRCpV9ocNq3cJZcpE2UYSyXBIceJMFEdMt22rC4inqzOLTlXS52mM5lbjQJCJcm4C80Ks9ksDRljW1ZXkW1NRMtHAiMfvAzYNlZlEpNa3plpq+AA+Aq7g35jLCgEFWAzwmhcsTCOjTvvBnLkSwK2NClPyvCBufvY7hXYMFYGHO6PyryqsI39olvFqqxi8rabNK3qhGgGYBZt8C0Zdznxc2GDbMf4dH4WjGEubDCa7132M693rNeOskvsudEc6KjrWAKtu2QslUpqUJzgyGz9uPD5Sv0AkzKg30lxZqqXC9IDvKKwFcdYpJ2k5bE9/zgXpXswA1uXbKqnZMDHy49xRqqXVxa34iyed2sAS2h0XSFdC9deCJ40RMSeygzbi4Nd9/TJjhPo1C/cIDoOaP2lajGmkUR5IilQEvTUPFFKk+7LY4f1Jao3ctxnbqODN78oQlARxAUbbWy8cowqC57Y6vKHw5cAEGYF6ZnEqWdK0191CGgWC2qdUHBcD2uNd3KFQuHwBx0FtAgR8zEmWnqTqlgTB5JMbvlJ3FnEV/uC4bSDaQZGepYcN+ik+dKWn+CesMS7z76b/7xlhFIRXvJLc7z7nq381ZaH+bhbQMyVMTpREzJNOSkRKyrVBtPzFTbKGFLrk6u4JlBQn6zhOBaOZeOkHeyUmzj4ng2ec9wd/c75p5Wm/the4vJCVOe4OvTGYJcexXviO2Sr45jsALp/F6p/J9geragrOk6oLSIg/eC/YTemUAOnIzdegBrYBaljd0+5R7mxt2QM9VlwvMSJczMY16MhYhoiZrrp4Oe8NHk3hbeMvVFKs07FN3BsG2Pgw6UHcS2LXxu4pBlqbn13CmE0H5l/mAsyg+w8GABZtuscymgCo0hbzhJH0urIIq6GEmA0XXSdtnJL6zRt/fzkQG1cKlGwYiZGY3BIikONgVLUwFpE2SwUCtSiOrFWCX+8CbkCd9yqTWP6RsFOJ7RTJZHSYCmx4qbFEt0NElOux/Meu4n3bz6b145/l68MvqT9nDObzJ1MpZs+tDuc40fhLNeXLGAAnqgzcEYPX2o2emzh7VM/4lXFbfx08ZTua1jUzwXZ7ExEsplpUYekVhizsoPUGe0OpCBlOzjHIHB2LLBenfrF/sSkXzt0D4aw2h2lFwlN5UgSkvdH8/xb9Ul+qe90Nrm5JWuY0hpn4g7SD30J1bsNS4VYjVnAYPIbUENnoIbOZENxM789eB7f9Sf5g9l7eEvvqexKLQ0iHi3sUCFr4PV0Z6ZiJXm8MsPW4gA9TxM/5IQ59XHcoYfesftXkWQubNAPxEpjK0MwWSFOGXL9PbiisZSxMVnhrkGHFz1QoKJVuy22qRvkJo9GFkbvKVMo9fDQq1zObb6sXlAUpqvAJqw5j1LBxogYy0shQ4n2ywC4fRvX9LPPzMwck1bFxpjk5tIJh2w5fWwdK6qzPkG/R97OLrmPYxSZWz9C9KzryPoDgGJ7p8Nk0XXzPyszwMsLW/jyRVU2z9i8uLiJP7vwANvdAqV+gXPvQfSVVex0ri2dWRcRQUWgtSIIAgq5ZNNglAT7+Du5xxJCCpTUJDZWQSiwrCRT5Tg2tmPjeDa25yTRfNvCciywbXAsLGftF7yZmRk2b9xEXAkJpiYR1Y6ozXFy6K36FN4T38Gdvg81cCri1JdRL2xZVYGV2PmTYDTO3KM40/eTevwbEDcwmV7k5iuQmy4Gd+0M9Jq1PFcCK6gAFbBdjJuBdBbjptsO/gzgOS55N0XBS5F1vGUpI0eLIAj46Ec/ysTEBO9617vYtGkTd955JzfddBOe5/HmN7+Z/v5+Jicn+fSnP43WmmuvvZYzzjhjybnqcYTWhpv8yWSBF3EHrSThzvzAT9RaAi3pK3uEnsUlUS9VLfi9mbu5Or+RVy9yJDvR6eCvCLM8bcSKNGQ77iMZQVBF9G5EOCvP81AKvJRDIDs7uhriDmrmzMwM9GQxxqzoyLehJF36nE15GhEbHFd1OfWqXXO29DR2OsNYegQXi+1eoYuaFEtDOQ+Zqmy/XinD6akeHoorDPoOk4OaKx7TmLNTXJIZRBnTzkZVtFhCyWmtK1qTBN+MoXOHGWuZCFw4rXuk+6JDJZgPG4RKEjd51Mpo9lZmGMwW2JhbGjRaj4gP0533RKHTn/BFzNyhuqcakyh1NaGkwdQV9iEyZotxU2OC28M5fmfwfDLLBCDs2UdI3ftJrMHT8J/7W5DucNKNwapP4sw9gvfoV7BrB4nPu46rh87koswg/1jZQ6+d4o09O9a8gZXlK2oyoK/fh/RChkwZzf7aHLt6R5ZkmU5GnFCSpjEGKTSeJdu866TINUaKRMdeSkkw1yDKaFI9BWzZaZxDUnu+Tu6eXcxfVgPjEN/xJKlLdwFg+xZWf4bK5jSnfwEO9qfp3bUwwfweGLytRPBySAUeE0MBxTvvxL38Cvw4Jhsmzo6O+7BPhl2c0YjyJAA1P2A+Xka6yYARivJ8TJwW1ESAt+ebZG77S2pv+hpBvcS2Tz+Phyf3k7Mt3vTuiPd5+YWXuwlPtRM/13sqv3zZrYw4Gf64/0x+qe90HMvm0b7/pmeigREROg4ImrxhHWmM1dS07igGFHP78Qa2gLs+O/etFYxpUisWOSq2bWHZFpaVyJTZloXtWDiekzj9ro3l2onz79hYy1A2Dvm+SqMCQVwOqMbzqHoZ1WngjWTSP7YOvTt+G95jX8OkexA7foL43NdDK0Irj6COxbJRQ2eihs5ceMifxR27lewtf4Zxs8RnvwZ9CM72CYWWWHEd4nozgp8FNw1OCqEkZSUpRz62ZZF1kyh+bg3vi1QqxS//8i/zpS99CUg0uL/zne/wjne8g3379vGNb3yDN77xjXzlK1/huuuuo1gs8jd/8zfLOvX31KY4q1HnvHQf90XlJBK+iAQ/KRMnPzSK3noGR8POH5W5ZeM0sVHUDle30TQ5n68+yauK25YocAFdFCCAHwUz/MncfXzp9Bd1HyhCLBklNUSHCE+u9IwxMBvUGcoWmscZait06+yClslZlQSnOT5SQKgg53TXF8kFp9xadCW24zKb2cqL85v5z8ZYlzCQV4tpFNIUq2LhQyh4KK7wK31nMBg8STgk2D7vMmt79Nop6lrQ6yRz64xUD3eG3dH7nx6/ic9tfj5pDY4IIZVtK9JBQulEJwEZrMVXm1CZZsI6GcejGgdJDUmzMdXxqi97JsAYw1hj/tA1QGGNVqcyYwyxr7DD1dn7uhZ8uvoEDhb/a+CcJbREKyyTvvufwILyZW8nU1iGW2tZmOIosjiK3P6C5DX3fQrvkS9jnX89bx84mzuCOd4/dy+npnp4cX4TG9w1oisbML5CVsq4I920t6R4do7TekdOmszRSjhhTn3K81DaYKSGlNN26uOWcW9GApTUhGUfkYGc5wFW2/jpyTHiL2xipG7wLxzlRnc/F92k4bQM9G0m7dvYGwcZGnS4d1fId17pcGFqISrw6PO3cuGdNZjdi20UE0MOAzd6mMuhXPEZHEg2AEaEa0oRGRgYWLNzLYYRIZbjMR/6yxb2aAxCaYxjiKJETi+alEQH3o2tFerJSWZ7HUZ2C2oZeJQ6WTuZJsaysLIORqsuLqxj2fxi32lMNRVSWrw40+cixwVBGJFL1fAbyfOdi28UJtEPFVTbHUyfPnF6wLIJpSDtuIfNQGhtVpDuS+4Jx7UTGo9tg2Vh2WDbNnbKAdtKnP3mhqCVUTHaoGOFiiRxmGQNcnYOVZtPxpzEKanHEfOx386mrDXsygHS9/wjumcbwXN/Czo2ii2kjtKYmtwQ4vRrEadfi1WfJP3AZ7FEQHTO64/KuT/a6zoslMAKWk6ShXFdsFPgOGjHoyFcGnG0phF7x3EoFhcCHDMzM2zcuBHXddm1axdf/OIXAahUKoyMjACQy+Wo1+vL0gerKiZjuVyUGezKEP7S2C28N3cRA05SWPqu6d18sdHD+KYpXn7vCO99WZWs7RJqibv/FqpbLsdraJysi+0uHfdPVveStV1eXjh8prMVcZaxJg0QNZKAQewnTmjUwHgr2/WW/QwW6XwroyhHPkPZAgMDA0wKf9WdaEtxxOT8JGcNbwMMSIHtq2U36MoYlotVOimbQG8lZzucl+5DC81nK3t5bm6EgbmAqJAmEyyMnW4qmr2ksJlIzKJ2arbfluI57xnj/j/IU+1w6ou2R9FeiFa2+PIfKT3MuwYvSDZrSoKSKGlQ2pCC5DE33TVunUjU6lpKQLqtCrQ+S0+Xh+etzyhuy5+YaFQOOw+tJvvAGEPka6z64R16bQw3+RN8z5/itcXtnJfp7z5AxaQe+TLu5N2E578ZPXQG7io3aybTR3jp27ArB8jc+y8YN8Ml57yBi4Yu4JG4ymeqT6CM4X/0ndq2IUeDtGWhpqpYfcM4qe7vM1aSA/V5tvcMHvX7nEicMKdea42IE9WHVM5LdOFTmXbTDdNUEtDGoGoSkXfob6ZjWvrCcn+NndMZahmb0eEif3WuzX99q5/HfzhF5rKATGAzsDFPxnH59dfcjzKGV3untq/hnGw/kVdB/OedpDmTu17ocdY/BlgkmtKttKNqzAEGJ9+/zCc5csRxTC53DKq9daJ5bLBWbBAUaYlE48QOwk3GMd6b59SD/RyozKHGfB7eFnPu4zk8Lfj46HPbr7Vs8AoOKuOgZuKuVPdl2eEl72X3Z3AejZir1kmlvGULx6RW6KjRpjodVubiJEOsRLtoyXMcUrZL2kn+ZR3viKICXTSeRbDsJLJvWTSj/YlXL6VGL0qtRvU5nCaFIFQxpSg4dgo3IiB9379i+7OEF/0iprAyle1Q37zSid6wlJBKQco7tINrChsJL3/7mjj3x3dGmkT/m8Xfh9VBb1h7+L7fpcPdLtTsuMez2SyNRmNZp/7/HrwDA7wwN0pVCQabr5tWISURUW9Gdi1jkKaI2HUv+QMjnJYqcm9Uwpg607d+m597TsRX/26E6lk2uTee3fUe0XyNt96ZJfvC1aXl51TEGake5kRAVqewRYBRxbYyz+GUt7TRaKOXdJnVxiz0HohjxGozW0ryndlxPntwL5970VsPeWioJD8zfhPX9exMFG46UFSannKeKRnQY6eII8lna08yrULeZA3jbUiRmVtQ1alrwQtzSWG5V5pHXLaRwXIyBmc/WqF03gBbvXwzoNJ9X/1XI5FLvDmY4t2RwVIykSOWMareWm9MsmFKZcFylnXUDQbZUl4DKlEypvFJJHO5XtVv4jjGtzSz4SFoNwBhvS0+EIcGanJJdqsT2hhuC2f5en2MSzJD/N7Qs7ozZEbjPfldvMe/RXzqy/Cv/oN21vVIbabu3Urw3Pdgzz9B5u6PozP9nHn+mzlz4GwOiAZ/WXqIq/MbeV52w1HRczXgSo0cn8PevvRc1Thg2q8xnC2ctDTgI14ldu/ezec//3n+9E//9ClzMIFEI11q4jhxno2WGClo7d3DVhTRaOyGRm3KkLEcDIaouePXk4JHLi7y56dVeaOT5vR8Hx+4tsLrHgiJH5xnx9wQwkvjWDYG+J2h87CbX5RxLM5I99LISEYfPJO/eZFmR18/qBCMQSpFGAqy2RRoharNYqVyWI571IWz9Xqdvr6+ozrHcmhpvwe1xooGSGqF1ArPGFwr+RyWbzHd6yJ/dAdxeYgnCiHO6BAX7q+hO3fHdkIRsVOgHeAw9jg1UsQLBfPlBj2F7LJRaINBVacJIh8/ihgoBKRSx1YdaDVYq3luRJSoXNgOwvYQrkvD8ZIiUNvGc1zStoNrO7i2jWvbeLZDynKXVURZCUYnzdQOCa2R9VkajQrGc6mJMGnwcyxgDO7YD0k98hWic16LGr34MIcbKnWFXzOU5hTeREgmVKQjSTpSpIUiLTSZWBK7DpWsh5/3iHMeZG2srI3V65EbcukpWuQyScZiwbmfajr3PuGz3oIpblr1R5FaH/to/WGxVHlkLZHNZgnDBee1taB1LmxBEJDPJxmW3bt3s3v37uQYFiKuw06afUGdzPQsYT1xHOt+TFnF9Noe76KHjK4SXXYV4Q9CakpQ0YJyxef8W14Fz5ljY0VhHjWEWhMoyYfKD/J/hp5FcNNt/Pr3hvh/z0uykEEzm+vZdhKIaQYNMo6D0Jq7wxJDdpr/Of5DPpV/IZ5r4eoKrlKEgcSICpl0D2GtjoxiLNsiP9hPfSahn5QEqLxgfmo6OW9vERnFyDBK5tbAKAcOHMD3wEp5eJk0QSXR/s70FFBCIIIkW1AYHqAxN89U3acuY0qNOulqjNEaW2tMLJidmGRudo7nn3E+Y6VEkedfq3v5meIp/MifYd89P+Qlu85n/pIRGndfxc3BfTw7M8y+RpVe26PPTpERDfRoD5nxENEcj+83ptgvGonqmepHbD+FtHqUyLF51Sdd/uYPG5yd6iPWCa/fwsJXEqf584W5UWZUSD2I8coaL5RYnovfiMGyKAiJqNSQtTiRph7sa4+hl03jeB5hNXE4s71FSnMlJudnsCwLd2SQJ/bvw7MdcrkcuVyO2dnZo57PnXN2rWpH5JHQA48jpudLhMVVUPOChGoZRwZTk4csPr81mOE/62NckBngtwfPa2fr21CCzK03oPt3JM680/3+T9Vm6v4dBFf+Ds7UveS+/4fEp72crduu5L1D5/PvtX38eekBfr7vtKcctW9fV72GmO3FG8oscd4n/QpTfpVUM/jWk8rQl852yNuubxyRU6+15q677qKvr++oOJiQREEOVipYvqa3t0A2D0Z1qAeEC5EGO7IwxTSuZaMsEKGPlUrh7pvgW6dWGN+e5/RUD7/afxbv3XUb7/lqki7+hbdJ/m/zi/jH0Su7HHqn6KLLApUKyYUeD5wneEN2mCg9RvrAOGzbQmm+QZ8w5IopLItEEs3LYKWXUgfWBZpR7ng5mYyoRvbG3yO45kMACLXQydLy8zy2XbHxwRRWxpC/cIgvnjbL1GiWSzpO4Vo2/akcZRGAZ3V1MVwO+Y3DpKIyJlaUawGyWkV+4btkfvanFi5ZKWqx5EBplngmJlscPZZCJqvCWs7z5IQSS0sggs5aK8drOvpewke1HbDddqF3Enm32j9TlkPacfEcB9eym88lDo3VjLG1jl0MKULq5YOEYdjs9HjsDJQVlsns/ht0z9ZlDX4LxhjmK4bxCYX3WINtUzW2NCKilOLABs1UXjHXq6jnoJGFIANeziMVQbos6S0big3IzlkUQ5vBhkOP7+BoGz/tUdpcwDq3wOD2FOnCBsLLfwOrdpDMXR9H92wlOvf1a1pQezJjZGSEyclJpJTs37+/3aWyp6eHmZkZisUivu+3o/SXXHIJl1ySWIfKn32AbcVernn069h7vsd9V/0ahZ4UM7UGG9ws98XzfLMxxidGn0tu8kksz6I4MEip9wn+rT7DBfP7+d933g78Ejd+7/8Bf0pUCPFsmx8Ec9wVlXj1+E18+7ZRQFCdr2P1WXy08jDvGDirrZrTUikyxpCybZ4QNX539Hx+NDFLLpcinbGACGybgpfCZNLYnkump9vgFIYTOkM+14PruO2/AdyUB8XE/sdaUejpwSq4bZpK17HpFOlCnlLY4NHyFKdnLWZMyKmFfm6d2cdfP/Tf/PP5L+D6g9/lS6f/JKW0YSqY42rbpmIrrslvZlqF2JZFHUm+r5dCoUCuP8flex/n449+m3+54HreM3sHAL6RuMImtX0Y64c13KYt8JFcnd+Y1OhoKPRkmfkJg7wgR/ZjDWZ1MtazOmTYzWBhUTExo24Ox7Z4fnoDX64fIO+6pHqa97KlMa6L5Vg4KsTNOph8EXJ9S8ZhybhkcxTc5G+pNSOjG8m6HpN+jYFMZk1FJNaydmQ9whcxc1GdfPEwlN44wJIRIk6YDyvp0Rtj+EJtH/M65ncHz1++WFWGZG/5M8SuFyO3XLEGn2Ip1Ibz8YfOJPXg58ne8gGCK97B63p2sE/U+WjpIS7NDvGS/Oa2T3eksKTAVHwkBm8ou4TaaDBEzS7T1ThgolGhP5NnOFtYVp1sPeGInPrdu3dz4YUXcuONNx41B9NzXaJqiK3AbwRk+w1GRAmn12iixkIqyQ1trIGErhKmNZnJCs7WQRAbmT+zh7cPJCn9HsfjPVsv5J4d4wzNS+7OL+gPd375zqBHwUlRLQs+dd0gN1Yn+NTw83Esi33Dks137ye9bQvzpRpz81U2bxpksL+AqpeSSH3/ZqyjKFpbqa3zWmE5OSux5wm2fPN/MHbNwmMtjqMbunz87IB/+PQQ+zZqUmcM8UA0z94tpsuptyybTVvOIl06yHh0EKFFoiyxAvrSibqOZaA6W6d69wHOv/sUnrz1HqzTT8FEEWzeRCnwESWBpQ0iOvEKA2s5zw9pdJRIujQuYVo4GMdBWy5gmh06DbFlU7eaKjkJzwasDgNjJfUmtpXo41todByhZdQV5U0dYYHtkcCZuIP0A/9GeNEvogd2LXleG8NsyTAxLsnvqbNzqsZIHHHfDsG/PlcQbPA4Ld3LqakeTnUyXGh7q9KJD7RkQgbcJeo8EdcISmXOfrzKxd9Oka7Z1Hoz+KcXyV4wgnnee3HHfkTuu/+XeNdLkNufv1Csuwyy61TK7mjx13/914yNjTE1NcWVV17J1VdfzYc//OF2BBPgFa94BZ/85CfRWnPNNdcse56U6/Cm3p30PPpVAG5s0lWmZchOr8CjjSRCOPzIV6joc1AZMNkBsPbiKs1f3PtZyuoyAPZM/R278rNs35/iQKPGh+cfar9PWgr2jzaYmwxhB/wwmOY6uYPNHfUZDS257uD3+duNz+b89AADTppLM0MgNSzDUD9UWMJ00GyWw5RfpWdkkFqjfIizwPX/9QlyrscXLn0pxsCrt57Oj+bG0Bj+fO+9QNKf5V8e+RG9TVWOX7/n6zwr3d/esERGtQul+wbTOI0Yb+IuLrvs17jJn+TdA+fy9+VH+UVZwN6YxxMwqyKG3Qyfrj7BnxTPIpgr0e+nCYHolRcAkIvuY/Pe78FFp/FoXEUbw5CbpqQiRt0cH6/s4QPDF5OyHCIlSdFc94xOMq9Wk3pzBOz42iI6kwGE1l2detcKa1k70klPWw9oNHss5AZXQQv2y4hIo8qyq+dBJ7QxfKKyhz7b4xd6T1s2OGRFVbK3/BnRuW9AjZy7zFkSrInNdFLE572pGbX/Y4LnvptT0j28d+hZfKMxzh/N3sOv9J/J8BEEZrquK/bRDQ9FgLOMY98JZTSzQY35sMGGXA+Dmfyy42OMaUvfphyXlO2QOoa0yeWw6nfTWnPnnXfyS7/0S9x4441PiYPZmbItzZcJosTRqPghanYeIQV+EOBhMV2u4kuJZ9t4sY3pydGwNX5WwlSFVP1ueiob2DHaxw6n2D52yE7z+tccAOALm15ApBTKGGzLIm3b+FqTCiUDg0NIXeW0VA+PZqs4WERK8cD5GS79jGH2Wk29yT8bmynhpR3qfuJwbkj14MsktWfbNps2bWJ8fBxjDPl8nkwmw9xcsqEYGhrC931838eyLDZv3syDDz5Ib2/vknTj4OAgURRRrycbmi1btjAxMYFSimw2S7FYZOrgGBjD4IZRhBDUakm6d9OmTUxNzxBVKgRBhDaGUCmQEalUlnBvCKTxD+wju2UbkU46/zlxnYwyPLw1oJrJk4oVo8UCf1K4GElyTNw8Np3yMG6GMHKTWqmMBYFekvZWzVqEjJ3QnlrfTTTmc8sZkk3fh8F/H2O+XzJ3+i4C0SAlDb6UTExN4xZ7SafTy47h4vFujeFaYa3neT0I8Jtp26zjtMfdtixStp18RyQUAqCZMpdktEOsg/axadvuohssHNs97q1jG4egJtSFxLWT2H7GWTiva9vYHcemHQfZ+j6xyLpO+7O4lo1jWW0qXBpN+u5/xKiI2SvfSy5TIJAJ39axLNAWe/ZLnLtrnD1RZTOC23fGfPgnBZs39HFRaiOvtTJk3YSGFCnVrq0RxrQ/a9ZxCLXGNGX4vI4x3Obm2ermuTw9DEVwdsCDV1a4LywRjc9x2SMNzvq4TWy5zJ17GpnLf5/8+NdI3/i7iHNehz9y/rLjrbQh6zqHnOcrUUEOOYZHOt5rvBl729vetuSxiy/upkmNjo7yzne+85DnETL57HLjhQgVM91UupkWAedk+nmg9gTvTQ3g3Pav6B2nogoONiAyKTbWBY2zX0fpjj62ARfvKzE2CDO9Bu+Bh2ETXJYZ4i5/ktleRWMozXNqdvv+G5c+m708rxr7Dv88ehUTMum38KnqXkabqhlpyyb2BenCIkdDxYhDcLoN0BArBxm0MZRmZiG3sIw+WZ1jKqiyKdfH1mJ/21H1O2hufbbLzdP7k8eVpNf2uD8scc/cOKf3bVg4zknRaIoHPBRVuKYpt2cu2sDEsE0euOrG9/KBi6/nkswgH43rODqP5Tk4WByUPo/GCY1111+X2TmZfJaxjs+QUpqz//tczIWG635nih++bp77nnUqd4clHojKjGrYqTQDToqSiiiYbNuZuduf4//O3M3XR17eHLDVOfbRIhrZquRA1whHUzsSRWu/6XiqqMUh+2pzaGPwSxXyg30rHywjVLmGKqtlJV8BhNH81fzDnJvu4yfzy1MTrdoE2R99eMWATSdCpcm6axMMURvOJ0wVyN78foJn/yZ2foRrClu4KDPIX84/xHU9Ozkz3buqc3VelxXVMZkCsgG2FWANZBPBiUNdi9EcbJSZj3xGskXyXqrdkbYahxxslJcEVW3LalN5sk6KvJc6ptKZq3bqb7vtNi666CLspkNxpBxM6E7Z/r8/+7O29nO9FrJtm4dtedRnPKSUyQLXfF4Lh4GRAsW+NJNFSXHPHsz8NK4cJeN4SzSkPz76XCZlgGPbS2IzhZ402zYOkBvaysyBSV5ob+KFTX5t2nEonDnMXE8JyjPkBpoGNtbMRQE7hpIUl6VDBge7o+2Lo++dKcRMJtOleNPb29v1fOfv2Wy2i28/OtrdOXPTYA86rOM1Hdne3oXJPDI8hPJipmcrNMo+ORNR/PxrqL3pq1hzin1DFvb9Zaytp5Bp7lit736O0dnL+dne08hFc/SEDtLt5pllXAdjWWR6sjiOw9btO3BTMfPVWRoCMtpghxqTtknhdEXvhQM5AWRtsiXwrxxhxyfqTPa4KEswkMlgRSHSSHKuy0Bvsf35DzWGxyrbsdbz/P1/8Add8zOzKIKxeO56HTzEIzk2fZhjOxsoubbV9fyhjl3ccGXxsTnbxfJnyN76l8SnvgS17UpaW6ys6xCEhj2PxQzeXeay6SqPngF/dm2dob4iV+e3805vYeH0m/f94T7r4ijQoY69MDfIhblBGIDK2TG3hHM8MjPLhXfGXPR3NnHqYmpnXUXvk/9F/+PfSIp5c0Nd5/WlbNc7tHAk4324MTySY9clWo6R7ZGdvIs5mTg/t/uzvL5nB9V5wWlP3s7wXb9BKV9C9Z+CDag0bK3CvnAXV9zXyxOXDbPjthks1+aJ4Sob94SwyeF/D52Pv6eGIovVE3LhvgmCr76fwUtewKeqT3BeOolUvmXiZn6tP5E4/Z4/xV9tSKgBRdujImMKJo1lWTzRqDInIi4e3IzUmmoc4NrOEslQpRWHikAro4lETOcy+rbvfRqA1592CW898woemRvnTdvP5VNP3k85DrAs2J7OoIF3bj2fDx24l+t6djIRJ5uR3754oZHUiyOf8dmHCQfO4ZZgmnd3FLFKAw+Z6zmr/kkAeu7/NH+0//s45n+StISC7/qT3ORP8qrCNlJ6Fsiy7429XWvi2FW9XHEz/Hl1L+8CnvO5fqQ8yAfP0IxLn199+CF2fG2Qgff1MCsjtijT7mx8UHb0IgAeL0+y6ymISQitiJREG4M2+phyl4+mdqRcLjM2lmyJRkdHmZ2dRQhBOp2mr6+PqamkF0NrTa5UkgzVxo0bKZVKxHFMKpVicHCQiYkJIKG32bZNuVwGYMOGDZTLZaIownVdRkZGOHgwKVYuFou4rsvY9CSlsEGmr4fYDwjKycYtN9BLY7YpypDN4HguYbWOmZvDDQRSLR8wiIzio+WHuTq7kYvSSd8CpQ2ySefNuS5i6kEK9/4TlcvfgV3cSNR8bcq20SQ0qtaxgVRESmFbLARomse2sjJw6ADNkmP7dlC+9Ncp/vCD1C95G07fNnrweHvvWfx99TEuEgNcmUn8tYzjtAORywVopLYWAilBFeGm8X1FqtEgt3WQxlzyXXi5DLbjENUazWvoQfgBMhY0bJtgMKY+U8KzHbL5HLFlumpHRBgltTqWRX6on9nJKTDgZtJ46WMn273qFWNycpKxsTFuv/12ZmZm+N73vndEHMzF6CwCNErh130K+XS7PXcUiqRJiJvGMtCbS+F4FjMjDpd+ezM5McqvX+fzm5mlXLIBJ90upDCWBfZCd8F0xmFww6kox8PJ2+hKdyrqiuww+wbGGX34IKnnNJ16A/WJBlGhh7lGg2JBMtC78SkXzB5NGs+0OjUuA6kVtSBCNPXPreokvbf/KfGm9xNUXsJ/7/S5eGyRjNPjlyMdi5cWNnPtL+/l6ol+rlvML0vZmIyDk1+YLhtHT8dx08RyHybWqFDjZmyMSnToW/CzEnuygbujl3zNwzttC9/8ie+y4YXPZvCGJxBCIDtqAE60bvFaz/P1WEC/GjrLqs81eRfpBz5HeOmvonsWNmHVumH/fQHb7ytxaRhyy8Wav3tRgxf0jPKO7K5ldcbX8rqWQ6+T4ur8KFfnR4lOUdwZlnhgYoaz74658N7LiZ3LsQ58m/SFBeQZ17QpOcf6uk52WE3a1+PTV6Dtl9LQgpko4HZ/lnf0Jwo2w99P7ht7eg593rMAUNYc7xkvIfduATTpa4aY3leHnMvBDTX6n8gDiQNmDvawZT5mT5/NObfshPt38i7nC9x41lWMS5+fyG7gO8EUH51/mBs2XMa3GwfZ7CXbyx7boypjNioIELztvh8A8OVnb8Qxhvfd9jWGs8UuhxogUGKJGkwnGiIiQNEq65+PFroyf/ax3bx65wV88fG7uKpviJ2FPn7jvh9gDPS6KX7/9Is53U6cv+fnNvBv9X1cMbyNK0cTdbYLbJtnf+f3AHjsrNckY9exZvaFiq23n0vlEpc/Hb4Y+9v/wHlunbLd1yLIsF80yKiY5+ZGiDNJRtds6270pK/dBDdXuGvvY4wNjrJlDnZ936N0auIonvrEhcnP8ZAvb9jPBaPDba9hToVsdnOESnFvZY7ff3Q3/7n5rBXHayUEUrQ5zA/PT7Eht/YdRVs4mtqRv/zLv+wKNG3YsKHr3ItrATppPy1qz0rHdq4fw8PDyx5rjGHCrxIV0+SLiX+T7S12/eyqZTCGvJNGhhKDtWzAYFoGfKT8MG/t3cWpHXLfjmORarZDc8d+RHbP1wme/3t4qXz7tZ3oLIrNug62xZoHaFK9m4mvfA+9t/wZ4cX/k1z/DnK4vHvwXD5TfYK/rjzMdb07yVm5FQNikVLdARoRYGd7IO0ktWnVmMJQf9fC7WUWinKd3u652RpvTXJbdI7/YsnMwtCxkzLvxKqd+le96lXt3z/wgQ/whje8gTvuuOMpcTABnA6H2GAo130sA1qZJl1Pkf70/yB6cxL5SNkulmtR2l4kJwQPn5plz+ZGm3NoLAvjWThpG1NbSH9YNtiulZzXscgUCtiZAloKLG9Bz7uFrO3ijzhEB0I691JGKR7bM4lCUe7xUakcIyPbVzt8XejpOTYd9LQyPHlwtj0m8VQZcIkefj7pRprdL4l5zr8nDrRdehw9sAsVBPzGazTvBbYP9fCDvhrXdZ40bZMa9LAscL2FG8yyLAYKfVQrk9R1U3/escADrRysWGMpQ5ATuAfnKezoxVUWXj7NOa9MFk9Nog6jjeHr1X1c+O0bGfnV38ZojXWC1EbWep4fyik4UfDWYmyNIfXQF7Ar+/Cf//vtgtO6bzhwe4PT75nl4rzhK8+OuH005uWFLfyf9PI8zTW9rlUibTlcnh3m8p3DRDsUd4ZzPLB/lqv++xLO+rcYPfA9nBeNYk4/87he18kIx7Zx5h7hnN2bSUt49LmTvOWumwDI42AbzchU4jx5/gB6IFnsqn1bcGbnce1J9r71WaT6U6jXbcL2LC6dm6d4f40bZvdhF/dDVbD/sjSp7T1Aoqzyom/+DBffnOIr75jjz94vueRdgAXbvQI/n97Zvr6i41EVMUjNhEyiblszefbX58mm8jw0P0lvS3HLmPaCLrXCwuaB0gTvvuULfPGaX8a2rK5CuVRuQanr8coMv3D6JfzM0CZe9sMv8+D8BDtyPZxV7CebyvInD9+GBahQc3n/CNFEzJe2/ATGGP6rfpCz7MG2xO2p+28D4MDwWcyqiC1+iU35vvZ7HdhY4Ow9DQ6Kazi7PkX+/vdQzjnsrNQZAywjsWb38v/uidn0xiJ27DC1BZzh7rXHziSr3Ef+I4dvw773n4Fzw8MERvFWK8tgCvwsXPN3Mb/37jlqsaAv7aC14Y5gjjPSvZRFxDcmE8qriho4nUISIoRD9AKAhLbQoiNIrdrR2bXCWtWOxGhmgzp96WybcnG8ECvJ/loJXy6lg3XOwTaMgUqEnC6tqIS3N67xicoefq3/zBUbPDnT9+Pt/RbBVb8LzuopI56dUOTmyoaJGU3KtSjkLQo5yGUsvMNIEq8Ekx0guOp/k/3BnxI+663ooTNwLIvrencyLnz+tbKXfifN63u2U7CXXu8SW240RHXI9mIMiJrAAyimktq1kxBPKbf7nve8B0j4l0+FgwkgFslDyVhSVQFCCixsqE0yvPu3OfDK6eYu06Yvk0dvKgIl3vmTE/zJhsvbr7czFm6/h+dk8Wvl9uPGs2i1M7J7XfoHkl2wbTv09g8yH85i6t3XIrbksO9cyp/TUmIBshYzdXCc/v5NeF5iFFvNs1aD6enpp1zhX/bLBKHP1qWy8ElKzBfI5udV+0PuObefodkiloZTt2zEUSWsygGiL34e7+fejRum2bojMcLvHXpW9wktsAtOO0LkLVIxcXN9DBYGcKwycwOQy6VxXZtaKkRWJDrQVPIpBm6uIS+oYFndpWpRypCanKGUtvmJTzzIaXsvpv5zPqI6g9c7csJ1YtdinutV8kyPJ0Kljo7WIUMyP/oIevA0wit+EyyLIDIc+FGDXXfN8qxe+MzLQx7pjXlVcRsvTvcdn+t6ikhbDs/OjvDsM0YonRrxmcpBCruHuPo/YgribqKLBkk/fxSyJwEV5gRAKo1Vn2Rm2KJvvp9/2P0J3nLZL3J1YSM9n7qGP9j1k8BPsnv7ANtKHmI0hQFyo2nc+8EYDzOQ2CB1ahKxzA2eA1+/n/Mf+Bw88Dnq1h9i+j1SuzZx43UG7hrnhQ/mGPBjhm6eA4psLbn80hnnET3xCLtuCDnwwfOwXJui7VFTMSjDdBTw9u3nMR9G3Do/xTmdLexlBHHQVnBJKAWad9/yBQB++j8/BsB/vuLX2i8JytV2dO6x8jTP6hkEKfijS17GgUaVUEuytsNVg5v4E+CLW34CK1CYtNXmN7fs3EPVOYzW4MDbVBL1t22Xx+Ma/3z7P5C/9EVw2WsByPRmgQZn3fMcpl5wgNDbwMaK5PGrUqSBTbMu7/vWCGc8OcD4ix7FEgLxOxevGGIYmO+ltkHj5DNY2sFRhrf+4A+Ym/ldpv/nTrbfsBeAfX6dR2WFc3NDaAyjbo55EeHHgucUR6iFdfq6nPpoRaf++wcf4zkbd2JbFo0VijfXAmtWOxLHHGyUmWhUko1ss67IacoQO5ZNxnHJeWnSq/AF6iJiJqjhWDZFL00hlVmirBLImPnQZz7yUSv0cOmcgwBECqsRE9ciTLhMV3ng3rDEl2r7effAufSs4Kzb5X2k7/80/lXvXbVDb4yhXDM8fkDSaFgM9llsGrGRMgn4zM0b/ACEMmAgm7HYOmozPGCtWsLZpHvwr/pdsrf8KfE5r28X7G72crxr8Fzuj+b5wNx9vKFnJ+csWnuWW2OsqIFJF8B20Mog6oKU1JDzMFl3fabbD4ETuEp1OztCKUzzizZogkcSbWDvMx/Bd15DTyrH0IZTGUg9wvuvTxH12gx1VD1bno1tW/QOjNA4WEnoNp5NdiCNLktiW5HKOeAlqRTbthkd2U5lrrTQyqcZtc+ePkzx27WVGygIQzTTIPYDvN7E0dVhnbVsUNUJVS/hFJKb1o9iKkHA1uUuS7a69Bm8Pd9EPTrH97e5vHoyjbYtLs4MYpgl88XfYdO972Zsdj+OtLh2aFv3iTwLhMHybDI5jx4vw3zs4y2j+NPbN4p2POriAAN9Q+S8NP7sPnTRxWhJ76tOp+f9Y4w//ASpgqYzTlTt0Yj7n2Bmk+GU6c0cGDYUpaBam2ZOKbb1Dh/TgpIf48hh1Q6Sve2jROe8HrXxWQhpOHBng63/PcO5PfDvrxDc0xPwM8VTePUqi5fWEwacNK8b2IF+keGB55V5dM99XHFrmY0fquL1ZDHPHcSc3wPOyWXojyXsg3eTmnuQzPzPEqcNmxsJ1eORmUS55sL8ZsY32NibbEaeFIwNpDC2hbOlF3nnFnaNp3my0H2fe4UMtrIY1y9Aio3kaiXYOYzlOJxx2Tb0yCAT9jSV8FEueHCYgwPwxY/3cu+fpwk/lwZC5BM1vNN6SVk2fzf/KBeNjPDHj93FH+24hDMyPXxo+n56i8P8/mUv5z8fv5vfueWL3FOd7XLaATble/m9S1/OL3/3U8t+/tmgjmPbPFae5qcKW4mn5hkaznBno4wvBVnHxQjBl858EdQVKjaY2LSXQGfyLt7RexrfUDNJA0FAP3k7/x38Mz0PxfzjGQ/xsr0fxrvnbopNp95+2TB7T8uz8zP7QMdEGQ0+qC1JfVjo2uRrw4Bi8x+FgNtVINuJfc/zOOX7Auc95wAQFRp85ru38/ncv/Gi+DHYkmO2D15TPIXffiLJIOzK9lBXgpzl8GClTKAlpzo9zMcBfQAyBkzSvdd2IFMgaJSRlkUx18tsUOdP7/gm159xGW86/TJOpt6ySSMthVymCWALru2Q6XCELYt240HHtpkPG9Q71H7KTepWQg1xcC0HadSqOxUDIDVWQ2AiRRwpdFDHWmYj8AN/ih8G07xn8LzlJSsBy58hs/tvCK78bfAO3zem1jCMT2qm5jS9RYvNoxabUpB9qEr69jo65yAHU6iBNGrEQ/Z5mJxDzYexCc3DexW5jMXGYZsNQ9ZhmwuSyhNcmUTsY+hS4jk33c+uwSJ/X36Me8ISr+/Z3u5yvyw6ovUAWhrCusSJNF4gIetiXBs8+6Rw8E+YU+86DlZQwtvzTeLz3kgUCaKO+1ofqAM5Nj7wGvZslliuRzqTZ8hN85FN0/xC7+ld57OaxTv5bBE8C8u18HpTuJ4LrkWMpD+dpz+zEJlJpbMM9w4wWZrEZJ2Ee99QbO3vxZYp7Lu+jL71YXjp89E7FrIC9Tv/P3LnvxUZL6TCpkpjYDSbVuHU9/ev3vE3WqFF0I5wS18gyyvc6LKDy/71MjseP4fK1Skyu2Om+w0bnCzGiVF73gtEqM/9FxaX0u92N3KwXAsjDTgWI/2j9Pdvwt93D9nU0oYPdrZITkmc0jhetki2Z4QtjsvY1F6MpxjNF5nuyTL4X5NMbuntcur9IRv3QB27GjK+Y4TsnGBq/wQib2GiAJUrwknu1J/obMNyeKqNlLwnvoP75HcJrngHKjvMwXsDBr87zZlZw9ev1fygp8qritu4dpk6l2N5XccCtmVxXqaf8859HuUzQu7ddxO99+7Du/1KtnwzjTOSgSv70bvyz3gH34pq2JWHqOQ95gbTbIgu4X3bLuaqf3kDAHp2P9jnUtiUZ2rETxZG28LblGfneGJTnN6lzsWmOZdNcy8F4PFNMd6OBQ6zvT2P+sUdTP0oxZmfmuXBF1fY9P+z995xcl3l4f5zzq3TZ3vRarVqliVLrrItd1wxxRgDMRgbAw4Q0wKmpJEfSYAQCCEGB4y/kGAwJC4UG7DBYFzBttzkbllWl1ba3man33J+f9zZ2aq6K2ktzaOPPrs7c+fec8+888573vOWPySI9naxYGeKbUflmf+dLXQ0d9L6mTMZ8Iu05wKvZaMWobqg0Z5P05vPsLR2Do6T5/lUUG1rfV8733n5MT647HRWVDezMzNEa6ya317yCf75ybvLncYBrFiEy+/7ARJBlR3GbnbwPEWt59Ob6qWIwhYafsHFL6ogStlTeBkviOEd3knk/n/gohVXcd4bP1i+v7xfz5teDBIk37ttNfN769h+bz3zPxw8r9eZUGey5cFexI6tmNQDOlqzhaEZ7PiLKFW/ztJXo1PTN6HCz4SQU+2dy2i/NF/uJ+HPzbPoT29iPhsBSbuhUbQUZ+v1/JytwfuRS3FFfD6teoS7UzuYo0fYVBhm87qn+EpVM309W7FjtUSKWR7s28m581bw/odvx0Xxiws/wEce/F9Oqmud1K13NiP3YRfR9T3S+1HRx/X90g7RXhrzSmFZFmKwgHA9XEfhFDxQCjEmxwOCCje3pTaT9V0+W33Mrg1dJ0vo8evJnfrXKDu5y0sPDfts2u6TSitikcAjv7TKJ/JiCvORYbA08stiDL21CVnw0foK6H1FzK0ZtAEHmXGpAZoWRckvi5NKmHT0Kp5+0cf3Fc0NkpZGuWsD3wiRO/PvAsNeKbyGFeWnQlLnk9VLeTjbyZd6n+eKUoWcXX3HiHwaZUbGde32HB/P8dHyHoYZ9IVRloayNDBnb5njQ2bUK6WQwzuxX7iF4oor8IvBByD08JdQRpjBzrP5tzfm+PvfhxBeGCkEmmZQbYTIKY/5I1UzSl7l6up65rYsxheCSH0EU5o0xRPoUmOzM4jCoal+PtIaLX+oaQbRWA3QiW4JlBD4GQ9bajx8VJaL72glkZ1H7w7I/3OQtJvpXUfjz05lk/UqC48/AeW5+PlhhvNplO9RN7ATPVa72zr2u+tMp3wP5eSRpS1MP5dCOWMUn6uguIvXj1HU0e7lgMvCtipMp4eCVSAmBAW/lrnDab51rsGnHzyFrioPZ4LhaVsRcoVhdKlTUzMXoZssmrscYU5dOtIybRYl69HsKEJKklXNDA52kDI8irgMXV3FMV8bYvupVrB4KvpBDuKcKMazWeJpBXNj5HK9FLf3Ea6rwazz8XdTI7rC/rPPPjEni/30TahIHdmzv0jPBpfoH7azQPN47M0av0r08aZIC18MzZ/WIma2+uoimskJi94ECzyGN97LwIaHeUVdTOvDC5h7ZwdmRMdfFkOtiOLXWq8Lb85M0++exIKeLLnTw7gvHs0Kowo/VMXmwjuo2/gy+YQkfFaS4hnxIATEFBix4Itx04cWYBq7/5JcuNOkvX6yU2HhKc10DD1P/PwL2PHs86h7fk37otNQHz6JZ+9Yy/I1TeR6BgH4u61PcUaikRrXQCifnO+xebiftwuT50oG/acXrOBf1/yR7nyaDYNdNIXjnN5Yis938gwXcnRkh8rx7crzWJyoZ/1QN/Ni1VAqgRkSPlnfpTOfw3MFmlLjGv6IfPC73PEa8Se/Rr71EeQYp2qHdz5a1MIJu7xzQ1DyWJ8ilC9ieQw+P5d5/cFXudlsYlsamUSc6kyKbdcdzUDGQUidkdlTUpQLR5QZs+utnbmMwTUdJDMwtCpYoJuuouH5Aa4/5RQSIZtrNj+CqxRLrDg3Da1jlV3HSeEa/qPrJSiked/Tv2dBNMmmdDD3ZzcvYlEkznOpPr74+K+oC0X5l1Mv4V+evHs37/osY7aEUro+OD7C9RFFD3IFlCkoFj38kYaQhTSMKde6uRQ//6boHE4L1e/ixIDvEVp9PYUVV+6y63Y2p3h5vYdScHQr1PblsdZnMP+UxY/p5I5NMPiBVnRr1Lz0AbfeYlJQs+tjbcwQebyPZFeBOQmD/NFRMoujtGfg6RddNCk45iiNaHgKvWqEyJ3194T+9G8UlY/XOD6E+JxwIydYNdyW2sTvMzu4IjafejnVzoOCXAqikx1SZePekOi+QhZclBRgaIyNZ1OaDBw8mig7LoInFHgq+KngQKfZHTKj3vM8fE8w5C5B+F65i+bA028AY4BwXuItiXHhwh6Wh5J8qfS62pJRXlOqbiPCOkooIslqpBHkay+YswSlFHakCiElZu4lwsIqh7CMRZglZSaCIYzo1bZ3rcBas4k7r4mx6IEhqn/4DdT7P4Xzp4cJF07DfSWFM9yDa4MqZCgMFCh6LlvldmqEpCrZOOla7T3byBYyhImNK0U5lv6dr+JrBnVNwU6EKuZAqXLyqALwfZTv7bL6jsoMUpdyufijw3w1dBQ9sQF6q0xqgS9/aIi3/66GR1Z18ukHbRoGtEnbslrYROR0DMssL06kves2r0K30KtbEcbol+6cZCN6sZPOvgKtTQm2VdtUHd+KnTDwJZhKI7KgltAjA+h5UOfU4a3fTNWfX0Mctxg3r+jqbUcpn0SsZvI9+j7Kc5DG5C/62cSukpQOJY7v73Xyp77jKcy1vyC//L30dC0i8t0dzBE+r74xxNeqOlgVquOfIsdPWc3mQI7rYFIel9SILX4LLLyYc7Y8hGN8h7XHzucp4wyO2iJZcvswdVkPWWXgzw8j20K4zTYqfHjH4rvefJziWWy+uBljQYz888cQ9hRuLs8JTy5mc2sSZ04IG8oJ8Jou8EuLH3Px1M6Cre+dhzwxhnxlGOPeHVMvloTAuyhIbEwOhYj0vIGtx0XQTJ26q1bwqv4Mxq+38rO/egN/seMhPle9HFGK3z42Us2TvdtJ9PTxV41LWNnQQMZz6c6n+fLRJ/NMNkV7eoBl1U2QG4JChjfUNvPva/7AhXOX8cbWpRSzearsMHec9mEiTg5/e1DWUHnwWnqQlOughh18e2pdnd8RzEfm5RWE3jGqK2pzMWSVhLyPcoOdXX+KZMbUqnoW3xE4fbIRHaFLpKZhH1XHlreBXmdh103YidUAr2Tc+5P1k9FYT/qr9aTHPFbbJ6m9fQDr9FZw4C8SbVwRnY8Ugh1OllBI46xIA48kuxH5NIsicTaUDPp6K8wXnv49fcU8t590Ae9+5o98YP6xSCFIFfPk3CKhaTRzPFj4RReRdVAjBSN8AAU+QX6EP/pfjBhxZQSqbOxNOLEUQXiHPnLe0mt9gvdn4nknuD/yuQLK00cv57mIfBAC5yqfXw5vZauT4TPVx5DcRXfvEaznfoQ751S8+mMmPZfOKtZv8cjmFCckHeY80Yd8wqXYFqZwdIzhixrKu5ZF190741KXFJbEKCwJIii0wSLWq2lq79xJneOzdGmMnkVxnl2riMcESxdo6PoEPaDbgWH/2Ddw3NykLrdxzeAjVUvYVBzmxsF1HGtVcUlsbrmgyAjCyaLcCOhT2xQjxr2UAqEJZMEf6fcY5AtOHFdwVg62u+oQeuohvVWxZM0H6W/+O7IXfgOUoq6nikQuQVdS8Z55izjDn1My4EtfAIbFf9SvJG5Y4CusqI6PT7iU3ARgTzDeNSmxQ1Mn64jSGxvSLDRTMFTa9ppjRdjxH8tYieTJWDvv/PYlvPbnp4htbOPFk0OEejM4joNXiqX3ci44HkN6FtNOU5WcfK1iLkUhPUA4HkO5Rbx0H3pyfB36jtQQmtKoaxqZJ7/Uvc8DKXEcFwV4+TRSalMa287mnbw436cQ96nTbN59ZTfvTs7jaAANvnLhRn7ZcC6rPvMwp1LD50Wg4FEgTEG0KoHSCth2ZNK5p5xDKUcXRyX0ZDNhx4VtgQdM/tMSYkBdOIbQBHmlqGmIIx0do6jjz7PZXF/PiufqyAJqyCFj9JILRaY06gupbvr6d5CoayvttlSYSUR+COvZ/8bRq+jgs8RvSVFT3cfWt4X5VqyTObrP38ZWEJGHt8E6JVLDW3A+cv55rOh+iRM3/QG3OsUrS1dwa2QhoZ44y3cWaH6kQP2QQ9j1kZbEa7IQrSFoMvHqbdQuDL3XG3bKZslLktdOCxFqMiBnIgoFthifoBaQbggm2u22RGUl2//tGMQu5kE7NQmAOi5J8bjkHsfRcdVOjMgCtAWjjXES7zwO/m09UkjuajkPxiRknpts5oVMP06myFtirRghg6GSp31FvJrf9XfxQt8Ozmk+Cop5hJPn1GiS7214ltcGuzmmuomY52FpOlEhUJlhvHypHnbGI+O5XF2/OMhPMlXgpEt3IVPbEV6R8CNfptP7O15cniA2XGRs+PMW72QSiwQ1LwlShVU8f6JF42WTq6bpdvD9teVry9FDwTxatkHWLKKfP7VeFAgUChXWEOm92w1tf2eCll8Mlf9+b3xhOdE3InSqNAvlQWchS94p0GCF2ZBJ8cWjTuKZ4QHu6QgSbeOGySnJOs6pCZxec2NVPNO9jTObF+3VOA4pSiGyzn46WtXk3ZEx7Os5lQLPVXiuj+v4jKuemEuB8tlUHObHQxs4P9LEX8Ta9riLamz8AwiBs+CCcY/3Dfq8ttlHCsVxWp7ml/pRumD4/HrcxpntsuslTbKrqsmuqkYUfay1w7T8didNVQbrT6jjz88o2uZI5s2R4+9Ht8md8XeEVv8nOFnc+edNOvcCM8bnk8ewxunnS73Pc0l0LifbNePPkxuC2G52MgC/tMCaGFwlBGiGRCstzpSvyk69kUsIEVRcPJCbuYcupl5KhvNZIIzq2BI86Obpi4Yx3CyJtMLRDRYHBYYQpfJCQkoWWXGELVBZRV11I5YdI2ztOpmjNhRDmbtP9oiF42imxtAY/4QlAiV5yvwWfvs+l+N+P0zVYA2Zqxuxbh6iN58m4lh09KeCChAK1KDDgNZF69zJSqo4WCQ/UMBoMsgMdqC7BTS/YVz5RreniD82C1z5pIt5kr6LwMB3HHzfo5gZwJByvFGvAjHLbh0kVRXiJ81nA7AwEiVuBiv0L9eeQFH5SCF4c2IO25wMaAJZbSKUQihBNBInIhSasf/eEyEE4WgSpY1u9QohSCab0CiigEFzAOUI8iEd2zRZ8M6jybz2QhCahcBzwc1Nnb2fGuijY9sASqua1Ua9nIWhGLtt4e17GBt+h/vydoZ6LiXcB9Yyn6ffH+F3WheteoS/jC7ep9bcMzKuQ8guxyUEXsOKIJazmOGY9tUct+UefKBzwWLWHt/ML+w4aUdSn45wTJekaXuRxItZ4sNFTNdH0wXCFPg1JqrGRKs1UNUmXpWBH3v9VF7Y3BIjvDzQRSE3je85eOl6Ohttans1ttWMOlqkJqkOhxnK5co9NWYC+7iLJj0mDR0pnaAK1YS5PCPWwLd5KfC0lTzWCcPktpMuwJIarlNgZ2aIlmgS0r2gFA2WzdJEPWuHulk32MXieB1Zpxi0nE/lylu9vqP4v6PPx3YFKufhl8Jt7DU/xNjyBGhBIILd24d76tGY9wtUVy/eUR5YsGpTN51HNWPqsHxdI11ve5jGTBNwxbh70JdG6frLtrJBDxCyLQaZWm8Gk1KqZhaSeFnYdUWIUfzTmmGMUS+Uwu0YwO9Oc/OxZ2AgQcGqqno68hkc36fRCnFadQMppWgLx/nW8tMA+JejTwapo4DT6uexoW8HZzYvwvE8jFmqAyCoROR5KvDUjomsGGkdo8Z4ZAWjxttMfIRVqdy37yl8P/g5Qig0xgZwC+QLKX42vIVet8B1e+GdB9B3PInesYbcGX9TfsxxFS+u89DTDm8YSBHbmKbYGmborY14NbvfIZ8JXa5MSf64BPnjEpgb0yz9fTsL5oZ5IVnNI0/6LFmg0Vg3xtuuGeRO+xz2U99FeEWcRRdPOmdY1znLaOBku5Zfpbdxb7qdS2JzOd6qRgiBcIuoYhZ2EWq82/EqcIs+bnHmdNr+cMiMetfz0DKBEVrzzD/SfjWYv/oUPn/F83MtmlJ6eXBKE1g1JcEtrXRkyMbLZYnG6wmHdl/3PVw1dWwYAFKikjp6TRWmL2GKGgFCCBYeN4edf/LYcH6YBXURcDTcrEffYIZcf37cKtx3HLxiHm2C9xrXQxR9Nq9/gXDERjqwJFKFERtTn7K07TbC0OAw7X0DJOoclGeA54APXj6DbhjjSmkqP/AyqW6fQvPotf+17sSycSmEKC9WrkkuDh6zJKYpAEEikiQRSeL6DmIvlMHuMHQTTZP4peZSQpNY1Q3BPSAQ+hZqhwQvLdaI6ALXlyjh07X5eZoWHI8acHCNHH4xjzRt/HwaYdj4uSFSmRQ4PrlsllR2eFrjPJAo1w+2TveyXNfBoOD7k5pzAMhXXsJ7uAO/fwH52uNInRbjrnn9vFTcycl2DZ+LHEP4AHrmdzWuQ81ejcuM4Cw4H2fB+VBMU9fzCo29r3L+wCZQPrlIHdvq5vBSWw2vmDHymoblGTR5UeqyNvV9GvFBCL1WJJTOEsk4WAUPXVNoUgQGfo0BK3Y/jEOF/tkF2LqBRJCXOqKYJpH2KZxdT+Mdm5ExrewhrrGjNEYT6HlB5/Dk0sG727FWukC4e7+dbWg6virAtpdh3vJxz5me4O6T3ojbFezOjiTAJkrOjCcHuliSVJjFDMLJ47sKr9ehSbcxa1vYmRkklUqxIFaFyAygPEVC6rj3fBS36STESR9EiVLTQ6VRLAySe2EeNevfTOc5d9Mz78NUd2xHa0oA7ZjP/i+cERgi2xpsak6ZQ3ZdYJw7536I+gsunDxVERPn2NFQTik0QqGpiwvopc9uPByiPz8c5ASGdLysu8cIAWmYbK93UIPDyGQMlGLu17ehKWj/9tzS/EGjGeZjL/6ZKsPi/046H6eoOK+mlYsa2xBjc8N8F4Y6WWyHeSqX4uEd6/n6mt9z+xs/RH34wPRxmS6e61LM7XvyK5TMFjlq6CsF+CWZkwIhBVopdCVYKARe3mCjXu02nL9QyGLbEXyleLjnZR4c3sFlsVZOTOyds0vregFjw71BpZtS5EJHt8/G14qc09lPcqBA+swaei+q3+vvsZnW5cWFUfoWRLDXDnPKg+2smBPiaauKjds0lh+lkYiVjHupkT/lE9hPfheEhrNw/GdmZFy21Hh3fD5p3+E3w9v5TbqdN0XmcJJdg8ylULpVDgl/vXHowm8AlYXfrnA5c72FTLVT/6ePs7NK5/Z3+OC7fK50rDQl4VAQClL22IdMRNIhZIT2uK20u86vmi6xIhq6JtHMCMqUQSzbBA9SxDCJXBd436vnNNHHC6jBFFklwRt/rJdxGEh1UzuhVOTIB7OY8tCG8wil2G5uZ8GyutLzwQfZ9b1yGcvBoRR+JtgiVZ5TPofyFBjBYxPr45tDOvqqMZ6x3c2PAFHawjU0nWS8HqGbGFVzdv2avUTqJnrEpODnEa7CMI3gvRqJnxSStQujmKfUocct8sMeW5dVseC2bXR9bBt1yVY8N0hELrpFOna+SmNVI34hSzYbLGBSA11kin3THusBY3secf1mNAIPj5RBiJOK6hDVEVENYhoqZuBFdfyohh/VAzk8QF5av+Re0nqLqPUZxHOdiD6HdEQnc8KxvHo8POp3IxniQquZv4jPPyg7DrOxpj/sx7jMKO6cU3DnnBL8rRQi3cGCvvUs7t3EO4d3gOfgC0jbCfpC1exMxHi5PspWM0LKjKCEpAqbRjdClROiasggPqCA3Izf33TZMSdC1LRQKEKGRcF3CN/1l+SHv0XstGq4YzNaWBLSDSQSQ9MQQuzaK6uJUmLZ5KeEIcHdO8NKlxpR3SKHjvfKq4FR7+aJ/O6vyV7wNXy9BuUHcu37inzaxwhJhKdwsx5nVzXyyEAn5IbBcVFu4CG9rvlY/Na5XHrP9zindj5XLzkBP5dBFX3MdX8idu+H2LbwBeTyLJTi4COdL2Ldex396X8CoPHht9JIB4OhEHprhPo+nf5XH8Tf+jSsOJeFOwt4yRCZa+ay01MgtVE9X/os1oSCOOS+3KhTQ5MSTUqELpkYH1BrRxBCoiclw0LHNAx0WzCQy5RDaXYVZw+QrzYw122DU4/B7WhHU7CzLrBQZcpF2ZL6ZHC/mVIpRn/QQUQkxA280nsqUEhdItwiNUpxf/d2NmSDHfJbX3uKLxx/7l69vweb6WgnpUrf2VM95wWJlN4+VK8ci+/7vJDu4M6u5znJSPBPtcftdY6T1vsq5is/J3fWP4Bm4rqK519xmb9+gEs7hsmcV0ff0qZ9/i46ILpcCPLL4uSXxrBeS3PmQzvJ1tk8XqzGi+ksW6wRsgQISf6Uj2M/8W2U1MaF4kwcV1QaXJFYQNp3uDe9g9+kt3NOuJFVeITjza+bndKxHLrwm22PE+9s5LG5DbT16dT+8p+Av+bOq0O8s6FuXGt2OxKmJhmUMxOmAQhqEgkGySOnESICgec6FIphGKFAIVYZUPDxB3exhSKhtmUO20NQ2LyZ6mUrUG6RxO2X0rXiveQWv4WUm6VmeA61tRNeWwqa1FyF0BSh+/+B9FuvLz/t51Io5eMrxfDAThLhJLlcAd/3KTgONqKsWYZSOaTUkSXv9UirdnyXcNqgpm1yIq7S5ehipfTFqdkGsWiYgu/QUr+A6BQJvvuNkCxsaWDr5k6y6Tzx+IQYfSFIfjKIf41FYxScAs0Xt9L8UIp1a7ZQd14rg7kcVel+HMNiMJPFcdopCg83W/KuZTzc7P55Tw4G21p8/veNgohvEXdtop5JOKdjpUEMe5Dx0Ha4WIUcVt7DynuEii6mr9DkaBK90AUqrIEtIawhohoqrOGHdFRI4tsaytZQY5N1PIXMe8ich0i7qK4idBWxBos4RUXK8shGh1BLMmw4fh5PizxDficrjRquDS0hPs2dmn1Fm6UKdNrjEgIVa8aNNeO2nTP6uO9h5vqZk+5kbqaTVekeZOZVRH4AlI+DYtiM0Wcn6LBjrGmMMp/lu77OIaKpKUJNKMpgIUdUt+g1XIp+LW29GVKmRtf727DnmCSsEKY0kCWP5MRuy76tIfMewtZQWa/sBTE1A0NI0qoYdAHPgRKjzZuAcWF+I4Q0E13TGDqlHm/zFup++wm0gY0AxH55JdlzvoiKnhUc7CrkkIPv60gdyHh8ds4KPnrMGQg3R3HIQxYgpBsUfb/cJOjh3s38zbKTgkIjLgw+7GJHTRq2Hk9//8Zy/Wz33qCxkSnq4KaVDPzjE0hDkuzI4MQjdDSHyRVOI/zTjxH7+lqeWFzH6TETMPBRZS/7WKQQWFKW8sICgzle7sMy+X3SpMDQNUK2je7kqAvFQFcMpXOogocyJDKk42ecKeO/vaSP9vTDcOoxOJt72XyMgdXr42WGafniZjae4tP4oeB+f3zMuUHssacQjgOuj5f1EXkPFdII2ZJw2GC4mGNuKMqG4T5uvegvueIP/8OZNU2Trj0bmI3liV/L9nJH57O02Uk+l1wyZRfVXSH71mO98BOyZ/4D6Db9gz7tj6d5w2s9+Ccm6Hv7gv0u13tAdbkQ5eRac0Oa8x/qIB02eKyvGrPRZMkCDdOQ5E/5a+zV14PQynp3V+OKSoN3xdt4m5rLw9kuvtvzAvmeF5gfbWBpuJ6F4RqSu+i6O9s4dEZ9/xIWbajnqHMbeS7TxXuf/Gs2NgguXbBg0rGhMbXllRnUCY2EE2jF6XutTMNmXssxmIZFoZAlYhk4eORxRrNXxug3KTXC4RjpevBeS8FSGB7YiNzwNQqFR6lb84+EqCXftgK3OYemByEy/f3t5PMZtmW6qDITWENb6H/uYmJvGjVIXc/FUwq3Zz2bYhrHNi2hkHfBV7gFF1+5QddYINOfw3Z0pN5HKFodbDW5HnLnc0hXJ25PUVM+qqFKixVD13A8l7AdprVhPsr3sGbSoKcU6hOuwjQHyZJHC0UmPT9CNBylkE7jhhxeeEeM2KspOs/K0kiYjvZOklVxvJ4i6aRCWBLfV7jKDzwSs9PBC0Bbqp3znnsQRwjSRohBI0yPEWKrbpGtDZFvDJE1QuSMCJoVJ6bZxDCJeia2a2J6OrqroxUEZlah5UFkA0NdH/QwiwUMxw/+Fz3kGC+bLwWOoeGYkoKp4SYkYv4ghvs8YTbzfNNRPFqzEFuv53jb5kp7Trmq1KFgNla+gQM4LqmhInV4kTq8qeJqlE84P0g000VbupszMt08NvOpDHvkrrvuYvPmzVRXV3PVVVehTfCwG5qOQJC0QiBgbmeIrtxn6awT1JoW+ZOqQXlou5jHkG6Q91z0uAZKIkPg5gKjP6ybVIXCpAtFMF0sUydkWmRVgaLjIqXE930sQ8fxXBCBh76IV+7wWXtmG8NPdSBjm8gefTmDhbcRcbeRePgfGG6+nfit7yZz4Tfw6pfjZzx8SyIA6QmSbg5NSIQK5EAXOvmiiwZ8bMU5/GHrK/g5D78vi7nhIfQtSax/Ppm+69dAx0tQvxxj4x/ozbwZJ3UJS3YOoZkatf9+On7eJbuuD0tIhpclWfLH8+lbsgyA864/l3x3H0JIlPLQx4Q96BEDrc5E5gUaOnOiCTxf4fgeEd0K+q3I0XDLcrJe6W9D05kbrcbSNDzdpzYZo6drkFDUprW2hp0dvaTTo9+tIwumwlGLSf7KRO18Bl56Fa/xDJxcCuex9WyvlZhbHaqExf8370TsYSg6LsJTpVhjhZ9xEa7CUhp1WoSobpJP5/hc7TG064qEDJwId2x5kYuYff1J5AxU+JoJUm6eRwY388xwO3OtJNc2nkxdMQu76Dg7FVrvWqwX/pfsmX9PgTAbnsyx6JkezoxB+ppW/Nj05v9g6fLioih9i6IYW7Nc8GA3uQ2S1VurCC0Ks6hNkl91HfYT30L4Ls6C8/c4LlNoXBhp5sJIM55SbBY+64ppHh3aSsrLowlJtR6i3oxSpYexpY4tdTQhSbl5Btwcaa9ArRGhxUrSYscJySAs8WAtCg9p6YqzPjXAv7UspLsxBk+meOaMOKePeX5kK1Ab04DIjBioWpN4rI6q+O6zlPcGXdcZmQZTM1hQ3cygk2drroOoaZNzi7jDxXLZKil1dN3AmxfFejXYMsy89iILU0ej7byAxp4zyBqS9oEuYv1JahMNZAtpdvbvYMvOF2i98ye8cPRxHPPKZhZvezPb0v3lsaSH0gjPJ/y7f6Z41U/J5TKMBNjnnQK2JvEzKeznf4x72qfoGBokGiowt87FkBrFoktmXZ7FAzHapxAg3RbllhbSNKEQNPUyE9Ofx13Ob7yOcLKPwf5B9NAEz2/JNrBMC7u6ESs1DAwTO6EF4889PPfoN2ha+VfkZROu5YCv8NMuDCqU5yN/9i62vO0HtIVmb6KsV70Ecdr7MX2PmkKK2vwgi/MDiMIwophGFDOI4R4opPAKQ7jKK/0PFi1FqZPVLbKaSVbqDGkGhYRBsdqiqJsUNAtHGnhSQ9csNKWw/AKGWyBUTBNPd1OT68PwiqStBFtqjmKgdimt8Qs52oxzjmbPGg/UVC28ZwOHbFxCokLVeKFqqF0aPLZx00EdQnt7O0NDQ1x33XXce++9PPvss6xcuXLcMUXXAQlSD+o2D37xFBq+9CTdeghL07A1HR8ZhN6EJHape6xl6MSMEDHTouB7xJNhXDtoNtOV7ke4UJOIowo+IcMgHXEISYOEZREWJv0qjWmZpDIZYpYNKEKGQd516TNyaG5wTbPWJpyPkHjy30g8CY20A5LuE1owfv5V4k9+DTdyJ9k3zSPx88sZuvzn6DufwW9ahorPQTqCmLSxTI2846AcD4o53tq2gjeEm5HpYezff57CxjnUDV+EVm3T0xLD3eBhHKuwH7uBhpf/FRgi9bkVVJs2ynWQNkSPa0ChCL+5FueBDjhxfK9wU5PkXY+IMbqai4VCkEwg+nxwQENDk0HHUiFB0yQxw0LoikQ8Qi5bCJxBQiJL5VXtkjzrQqM6HMEPedRURbFtjdpEDFkUmJqGh8KI6nT3DDLv5Gpy96wj/MA/khn6F4yLGvF7hll0j87GM3TsVyVkCpwYqg7q8Jdy5vB81LCPcBW2bpDY/BD5fApxwvvQHEmzH6JF6fg7O7nhhPP5Xec29rr50kEk5eQYcvPENeug6UylFENeni25AV7KdLIlP0BIGpyZaOMf5p2HAeT6d+6TR13rfgnz5TtIn/H37HxVUv3Idk7RPZw31ZNq27uKd3viYOtMZ16YgQ+0oXflecODPeTv7eWpeTVUr4zSfMqnCD19I/hFhtou3OtxaUKwCI2FoQaoWRIURlA+/U6WbidDv5Ml7RXoczK4yieh27RYCaKaSY+T4cVMB/f2ryPvO+Wwn7Fl6t94gMzvQ/oN+uP5Z2EKjXk1Ef78jQynGeMFSuqgioF3fAShW2BG0A9AQp3QDfTaeSSyg0TT/SysayWdHmBbqB/lK9yuAiNvSXLFHMxH+3B6X6X6+QJrz6ym9rIW1r2YZuEPNxG56d9JffnfiGg6uWIBJ+tTePAFWp5/D85AD/O2LUUBA9u2l69fcBwGHvsRK576POsv68V3/bIX2svlyTouxXVrEL9bhTo5UHoDfT5zFpUExleoTQPcfG6YySlVYOk6DkWUEGi2CcPZgxIzFo7HUDGdWCw57nEjaeHjsHT5SvRQCL3kyTfiJi3dNt7qCxGbv47/wetxXZfIrZfQseRtxE/6CGs6n+Itq/+ZQvqHqGs+N8VVZxlSQ4WqUKEqYP5uDy0vM5UC36HGyYKTRbh5hFsAN1f6PR887jn4Tg7fG8QXEl+3UUYEIk3I1vMQsSbQLZqBpUDWdWel8Vxh9rF582aOPvpoAJYtW8bq1asnGfVIgdQCT5TQoKYtRkdDhPquIJm/ltJOqwBhachIYNTrukbSDgXGvtSJGBaYgT6qjcTABavKotCXx3R0YqEQVTKEl3axTINaFYWQTiqTQeiSiDCDkELhoZkCPWwilEY4b9J/egMdrw0hFkSoensbqcf6GP7T36KH/sTTi2tofvUNxDOXA5C4410A5I9/P379VfgZRdKOQv9m8r/9NLzzR4hUN6p2HvguYufLKH8x9evPZPDLp1EDGIuqKLxQjT28gwGuIgEMfnQJyWU1SE1H2DHcVE9pWgRzE1WoH5zNUccuLk+rFtUxCwZ5HMwxn1dbNzHsCJmEgztQDPIPSghNIAybWDiO7RnY1SEsoeMXfaQhiFRP3okTQFS3yh1GQ1GLqhzg+OhhDT2m46VddCFJ9unQ9zUSQOfCKIa+nOGtawlftpzid55HW/MYnDQaYqZLDdcLvsdiP/sLRN1Scl0vgZujsPYu7MtvZQjwHReVyTA/HuIL80/n3i0P74OUHhyGvSK3dD5Dyi1MtYmPJTUimokhNHQhMYSGKYPfdaGVX+ErcJRH0XcpKI+sV2TYK+JP8LT7pbMndJt5dhWnJ+ZxRcPxo51g3QJkB0tV7/ZOn+s7nkJ/7W46YtcRvrGH+Tb4l9aTmbvv1V5mI26DzeB75qINFDnj/m5y/9fLS4urqTvjYzRv+D7hwq9g2Tv2ye4RxQzKdyBcjabp1JlR6sxd9+0BWMTE2OvJPNP7yF6PYV84ZN/sw7aOWRJ0IQRtZnQ0QWqk+oE2uQyCtKO77dY6XYQQ6GaEOfE6jKo5JMJVLDUs8gpe695cXmZFasPEB0P03nkPC9ddwJYrAm93ZEWUDcdVc/Szl9DlOmRT/eQzRfz+Im2vzeely9tYfge88qY6iu1dFLeMlglTvsJ8KvDIpdq78N1i+fbzvos/lGf4WZcFfRrbijmkGULlvXJ5K9W/Fbu3jrqzmyfFnAJUVzUy3L8VE0m0OkK2Z/BANzcDIB5PclxbC7o9PiZtflUthUi0bMyPLaH58jlNtK7uItbxAdqVz+ALf0Rt/DIZp4/4SRB/rB/db2bhixfxo54XqCbGbGRaHh0hQDNRmgl2cq+jjPa08XnEhblMk9k6roNBNpslHg+qkYRCITKZoBrL008/zdNPPw1AJpulz88jlKDGCDFQyKF/YTmDfQ41mkZPKkjkjFaF8aXPQG+g82riCVKFIo7voUtJlDBdfSkALEtH6YrhdJpioUhCWoR9k4FMDrfgUpeI0Z8tIgpFHN/Hlz59Th4/51KVDBYKOa+IMhTRkEHobXOAOYR0I8idOr2Khbe+xpb6s4i/vw5xSxrn/K8SaTqDnWsfRatdQOS1H5AfcLF/9QEGjrsa8fi96C9/EO+EP5GOnE1E34mXzuNteQp3/YWsXVVDU7WiJ5/Fbo2SeSyO9uJttD19IX0fXo5YEaE/lycaDpFMxOnoyYAPMTuE5/pkXYdiZwdz57cx4OXxKJDzAi/fgF9AtbeTTCZxsmmGeodwPUUiYjHQk8YtxfknDYudPf0Ucy6m6yE8jT4vh5d3qTPC9A6lKToehq5RHQ/T1T+MW/AwPRCORk9vFgVU14bp2TqErxQh3SbZFKejfYChfzme9LODxM+Kg+ch22z6v7SUiK6Tswroz/4RdeLZFAa3YykPq+d58ke9DbH1z3iDTUhpkVnxX7h2I4mHPoD+6DcpLL0CT7fRpY8x6DLgzL7QG4BmK87VLWfs8vm875D1HBzl4fh+8FMFP91SuWlR+mcIDUtqWFInrBlEpFnO09gjSkE+VW4wtdeNBF/6Dc5zOv6O9xFtyKJd2YQzRZfmmeBQ60yvyiT1rhbksMMJ9/fg/Hcf645+Dw3NfyL51HfIn3QtaHsvZ8ItQroXFU6AMbtj6w+ZUf/y3AITq4jq4TBuIQ+ahnJdNFvHyxXHHXMwjFBpmETrg9h+aYUx6hdi+B5CbMEMlTrZCkkqZjP/pQt4dqlGXdWoQRr9QAvb2/ug32UgloWix3M7n+J4N0Ti9DivLT2aWMKg47eDaJ3Z8us81yOUsXlhqcLZkcYrlkqAuXmU65PK5gh3x9laB8UN7YSWjXp1ALK/+XdaO9/CzrYooYRJfqhQXhQoQxKPJDGi7YT1JNXVzXSLHQdlG1EYNlqsDmGMDwi2audhuqPvr2mNzmHiHfX0b04jurKkd2wg83Qfy1KtFLqSbOpdS9sWiw0fXcii723k/P/dwrNvm5laf6lUih/84AdomoaUkve///3cfPPN5XJ3p59+OqeccgqpVIpbbrmFYrHImWeeySmnnDIj169QYTYRCoXI5wM9lMvliESCBfjKlSvLHvvv3PBt5i9qRRUy5LMuVekQWEAUTMtmTss8in292NUhDENSpUXRwkl8YVLVlQEUepWFJiXNtQmEGUY5eRASoRmk/QE8zyeeCFMVD5MLO0TiFguTFr6nSNsh4nURio5Pob+IFdGoiY2WRcwNFKibkM9TF4kycM0xJO7ciJqfpGDb1PxbAXicZDxEdWoLm5e3kqy+k+Ir70V0PILf9R6SaY30s3djHXchTj6L5TtoW7ZStfVomv9+MZqmYVUlYI5G5ofPIl57jc62dzHntKDQAxIijTWEqmto7OvCyytCc+spdHUTKZgkG5uQUtLSOhd3qBN/sIhSiubaONUtLQB4mk/EHy2EYEmNYt5DOT6ahOb6WtwqhTM4hBUxiIRtsh1ZhJQkEiHKXjMhaWmZS36gHylAszTiYtRgSTQkMC1JyNZQStEQi1JnRWBuDZ5y0YSOh0d/TlFth8m0LmRo85upe+EnGH+sw9c60eruoMGK4fz5P7Be/Xcs1yf5SC/QC3yOlPV31K27m9R7fgWA8AVV8dlZ0nJP2NLA3odE1X3GLYKTQxRzQUnQvXqNj7EljfjdC7ipBQwuqyf80Sqs+MEx/QxNJ6SZaEKUE+N9pfAJKv3lfRdnf8v+7AV+zCD79mZE1mPR/b1w3zF01izH6LydyIVvhVByH07mItJ9oBkoOwpGeFZWxzlkRv3x0cCzPbaEVjyRwGpZRrEwTDbVi1106R92EOPixQ7OJI4tgymkBCkRUhttFCVA/eNiNnx9A+6KKSrNAM5wGko1lePPbiWfOJ6QEBDXEJrEXFiNfv9w2WB0fRehBPk6gdbl4gz3I3rXo9/zdfy/+w3FgoOd19nc5lDz2gCRpk3BQAgqOAyuPYawKZljRqib08r21AaUCBqraFEN3Qpz1LwleJ5LOBRHT1qY4QOfeSekhhapmvq5MbsummmhSm20hafQPruAdTe/iHh2O/b2BC9f0UDLr3ew8Df/w5zt72P74jDrP7uIxd+EZ2dorNFolOuuuw4pJatXr+bxxx8H4GMf+xiWNerVuO+++7jgggtYvHgx119/PSeccAKGMVmhqwNR2muaOL5/yD0pU1EZ1+xjwYIFPPDAA5x66qmsXbuWBVMUMnB9hR6rx6MHSxRwkh5q2EN5PsIKYycTGIaDbnrIcBVarBYhBNL3kKaOcj3CURNhhRFSR4vWlL4sBUJKtLSDroKFhRSCSNxEWhH8QhpNFyTqoyB1LOEia61yve8R5Ni/BUhbwy/6VJ3ZyLaTY8TtKN0Jm+KxFlXnzyHx7efIfXkV+v9IeOLL1Hd9AbouA2DbGU2w7k2Ev/MG8hd8lXz1MvzOE/GPrmGOJjGbWoi01FDsGwJp0JV7F7XHjOb86LaGFQ0jdAM7ESXvF4k0NqGrYfLDzmjJZjOEUTef2JZBYrqNpY/9PtIQRgjlBMmsdtJEph2Kw0X0kIUeb0DmhtBUDqGZCM9BGLKcPKvXBAswoZsgdYxCGjFFkmUspo/rhBlU2Qk8zprQ0UwBrkZdKI4wBJHmCPPuC/OSrTO3Zy6JXDNsPpEUf0e65+/pPrmRurfPJ1ljIqWk65N/omh8H03/NbG7/wrv7TdT8GZvJTPfd0ud3acZ+ju2Y9U4RBBKo/xSP5pSJ3nfQ/juLg35Ef0kch56dwG9J/gvtubIpx067SLZJXXUn3sUVcbM2U9CSMKaga0bmJqOJiWO5+L6Pn35HE2RGOZe9DUp+g7DhQJF5aEJkEgUipzrjGvoNR1UWMO5pIHsG6tJvpaHh09F/sc2Mgt7MS6Yi74vOxaeg8gMAAMgtKC6mZTB75o+Kh/j3mMxxnQ9sC1lD5lRP3JL0pZB+TIhSFRXUROrQkWTUDOXbHYItHXUVI+u3IPeU4dmdWRVh5Aj3uTSEOy/XcTEAlxh02bAVKS6u6lraQOgZnMTrEyOO65u2VyKv+rAVz6a0Mh1dOJqPsypIrRmmKF0lqGnX2XZmk+SKuRwsykUkG4L0fjCMIX/3o7mmqi3lqob5Ot4dOkQy6TADsfB0gIFkVfBvGkm4WQTyvcRUrJs2fHos2grSeo6WtygNhqnr3MAz/Pwz5pL3S8ySMcmvKyauls60Abex6Zjk5iaJNQa4bX5M7fSl2OMt3w+T1NTE6+99ho33ngjoVCIyy+/nOrqarZu3cpll12GlJLW1lZ27tzJvHnzZmwcFSrMBlpaWojFYlx//fVUVVVx/vnnT3mc0A30qmZkPk1ck6SLOdxsFmmGMKujGHETv5BFhuLl3UEhNaRhoIREr2lFGlN/sYYTIfy8h17dAsrHywygJRoRhTTecA/4Hloojp9PY1AMkrFQgTFkRZAhG+hFi+hoEY1IbR25/jz5zl4ak1VE6ptJfhpGvoDVTW9AFwJ7yGI48Y9UoWi/qA17TpialQmGvtTPkPoqyXX3wNEW3mAT8b88CrNuDtGWGjQtaBWvCYOj1zfivqsaoRtB/49EBFlqlhiauwC73kEIgVnXhlmrEKWiECNzFKpOku8dDOrOl5BWBGlF8LJD+PlhtEgVWrSAFurDqmkKzlEKLZChGH4hQ6TOB6lh1LYF9e7HzLWRbA7mUSlkpArl5PFzqdGymFIH30VGNTzXL8fwWzU2btalOORgxgwaz2tmx587WP7SMYwtkh9/8muYuqTx84sxQjpC01CeT+31Z8BHHgTOJHNZP0mh6BegcqMhqbMJ4bmIoY6yITelYTauxawqL05LT+5ThZop8RRy2EUfKKL1F9H7iiTas+hFRV6XDEYsuk2DwVAYtXSQNvcRqk+5lOpYw35fUoigGtRIzL8uNeJGiIhpIicEe2qaCRrkXG+vDHoAUxrUTNEwzcMjUyyScYs4vjszDjIpYHkMlsdw02nE3X+GWwpovo27KIJ2Ygy/Nbz3zSKVF2x6jbytsyC/+9CVtCx5HnRdoxiBkG1T0xA0PBpRaGE7SlvjQmRo1BOuHcKyUo0NtciSoIopIpcNy8IpFGhunUNnWJHvGIRgx5RoOoI4LvBWj7RQtuM2vifxPRdNavS/tJVczKPu6Dmoh3tJ96cwXg3hSIE72I//xB9AtCKW1BN5OMec7sAgf+VLn2Hl125iUXuELcfEQNOJxqswq00818WzfaJhu5xwPLLbYIYn7zAcSkzdYG5TDXaskcGBDF42R9OiJI4fIVRQFMIGO64/luLOIkbVqLfEW7UQGJixcbS3t3PrrbeSy+X4xCc+wTXXXEM0GmX9+vXccccdXHvttXieV14AhEIhstnslOc6GE2b9pWZaOF9IKiMa3Zy2WWX7fZ52x7d7ZN2NOib0bN1/EGagQwnJoX76QkL5Xu7NOgBtHh9EL5Xar4krVIOTigOSuGluoLFghnGS/eiRWsQJaNW6CauEySkWhENI2Khx+sx/Qz5zl7i8RhWJMywHcHPp9FiNfiFDNIIUT2UR3te4X7zHOZVBdf28HCzisRTkk3uGdRu/yz+wH9Sc3wb8QX15fuTkRB6zEDfqTDmRpGmjhGvw0yExixqBKJUEWxX9y9HdgenUCNaOIG0Y4E+tyLYY/LNpB3Fzwwg7RgIDeW56PH6SY0Kg2uEEVrQaCd4PoGK1+MNdSF0AxlOooo5bNGJ7yi8lIPQwEjUgurFyzjotoVb9Em8ZzH861MM3LCS2miC7Q91YPxhG85RSeaGgmtrlonnFNFQ8MPz2fztl5CPvBtvxwVQrCGvdDjni7uUh0OF3ZGn6qfbEG5QyWePTuSxpU5GjtUESo4/RiiCruMjSIHSAI/JjcCkwI1o5GMmgyGDvnCYbcsSOJpGMi6oTQoac2tYvPFOnKbTcRZ/CPbTZjI1g5hhETbMsvfcL9Wx2pNjtdqevrNQQyNuhoibwbk8PIqeR951KfoOBW/fDf2xulxGo9jvfiPG5j9S3PQcffJK9PsGqRnoxDAFqtZEzLWgxsKPangRHWWNltAWI30YXBXIRFEhin7wvyQjwlGIvIeWcRGlfjrKkihLgyXTnqIpOWRGvVvqwhqrqmfY6aG+evJKUmg6Wmx8FrGl6QxPOvLgUNO4eJfPNTRWk3UUruMSnzcft8rA6wo8DgUnj+lJ5jQ1YUZMXt2+DVsLQnkUMNS1neq6ZgqbB8hUQ/OcBLmixfCz99Pc1cD6uT76i69hPhNiQ5vG3KoE8ZTJ0wsUKzdBz7oguXZHjY84JYYe0dCkRNeCLVPdkNQ0zZ81pQt3hWaY1DYtQSmFFTIpZoPt5e2nJqh6IUtTIoqfc5BzStvIUsP1PWKrqmDjzBn1LS0tfP7zn2fNmjX8/ve/54orrgBg8eLF/PKXvwzGqmn4vo+UklwuRzg8Wj1gbBJhOpcjW+otENI0Cn7QXEwKgSkl+dJ280h4h+MHnwtb0yiOOdaSktxujnV8H28XxwqgOObYjOuWYxxtbfRYXQZ+l5FjLU3DLZ1XIAjpWvledCHRhKDge+VjvVLvAICwrpNzPRQKTQh0Kctb66aU+IDrjz+26HvYmjbpWDXmXkOaRt73USo4rzFmDqc6dibm21eq/PzE+RZCYO9mvnc7h/s633vrPTrIFIsT8p40fUyYYimcZBe6xwoZ4O3+vnbXEVza0aBjr24idBNptU4+qOQs1auaMRKBI8NMhIm01GAkk+hhCz0SRUWShOpi5HqDZOD0tcdj/fBFYo0R/EJQ+UxDx7l4Hj0hk/BdG8ld+RXkZh09Eht3j1LXqf/s8XT/+3M0mBpWXYhwdRyMfVwg7rFb+qjBNjaUUUgNo64NAE03kaHYbudR6OM9pUII9DF9S4QdxdLn4fudZFI9mEkbI16D0A20WBYrWY8M9+MOO3D9mVSXypbOfUMT6pzG4HzRCNKuQgtZeN3bGPHmt31iBf6H/0jX0V+i4cnSPZzDQWdP/Ri6bZN7G+vxpMDXdh1CMeLEF0IEjQNF8DGQKHRAE2rUgR80D0CVfgqC+iAaCh8o+gLHhUIRXE+VTelwSBCPChIxwYqoT0QX6NsfxXzpXtyG48ie9QUw97085UhviLhlY07IDxAINPZOflPFAlXWzEYBaGiENI1QqSmiQuH4bsnQdyj4Hu4ecg0Kvo899n0VAmfBhcj6Y5n77P/gn9LC0OJ3M5DSye8ows481o48VsElVPAwfX/0fZcEsiAFniZxNYFT+ulJiacJPCFwTJNCOEyx5IjUHQ/T9YGpHYHT5dDVtSutsHRLR5dh7Nje1RpPWmHCB7D6zd5S9siUmp/ULV5O785tKBkYevqcGPamHrI/u46+lVfRpJtUL12MHYtidXeB56JpOjlbsXH1k1S98SJEd4Fci4nUdbYdL2l+yGRzvUauziW8vp/IUB3DVzeyRDO481ST7qMk6po5nPvGiwCY0yfJJZKEkkkAQrpFxLKRSmHps7OiwFiEEKCbCCAWTZDuS6FQLDivFc6DloWL2L52Xfl4PWThZmb2g+G6bql3QeCBNE2TXC5HKBSio6OjbLy3trayfv16Fi1axPbt23n7299ePsfYJMKvfulL48pH2hO+KCaWlhwbu70vx1p7OFYfc6wmxLjnd3vsHs4bHrPFqmkCc8wOVkjf/WtNOf5Y5aryfYw7VkgMORqfONFzPlNzuKtjs66LLuW4edmn+Z7BY2cjvj85pCDSNhffBcPc/fhlKB7EDe8nQb5OcrfH6CETvc5GWkbZCBZCEG4ZDZez43F8pbCSIfL9WZSvqD2zBX9lNUJKjOo5gMDPpWh4Uyt6vJauuzeR/J5O9xsbkeZ4b6iQAqFrNPzDiUg7jBmvQVj7/l4KoYHU0CPT68WxO4N+r8+hG+ixGFpsALO6FiEluh1GM4xgIWeGAx2esMb5cYUQaLEaQk21CAXS1ECrotDRi1/wMRJJti+uouHRwDHT94Fjgc3THu++sDf9GOJVklNP230OmlKqHIEz8t8f+emD5wc/RzzM4xzNIojO8f3gNUKAoYNhCEwDDH2KRYSTg/W/I7zzSdyWVWTP+SLo+54nZ2gGYc0gZll7bbjvDncKnTDTCASmNDClQbRUhMPDI+e6KF+NW3O5vo/n+7gqjxQavhqvc1S0gdxZ/4Devprqx/6JWOtZOMeeCyeN5gJ6nqLojH9vRxZwmgBDQkQyupAbk48yEd9XrNncO/OTwqGMqZflO2Zuw0LC5t7VSbU0HWuKLcSDjiaRUhKrjpHJZtE0SXNTE6o+UL5Vx84j8Ug/c/o+wHPZdQy2noAdC2qbNi9oI2prpIdzpJM+uefaKZyZxhyQ+GdUUd1QhXZsPa33u/zpbIHfGKXpkTxZU7AyESSZPX52kTdGmmkKRdCkxC0GK1RT10lEkwC0Ni0OtqGVf0DLgB4Iapoa8J0MXYNDqGxwb+FIYty2n2kY5AEpZi48or29nTvvvBMpJYZhcOWVV3LDDTdgmsH8XX55UMv6wgsv5JZbbuHuu+/mzDPPLD8/iVnoXJ11IUFSR2kGQvNR5kgYggjigqUeJB8BeG4Qw+jk960CxHSHN9vma5YxNg9lBN3S0WNJjMju9Y4WOvCVTjRNEgnraPqu9YTVMFp3OtIUw8+7ZAoWfj4bhKRIDalLhKfj5UCzLeJfWMX2m55j4QePw06M90oKIRCGgXKKSDteDhna57GHY1i1OjIyOyrC6KZNuNrCiJbmS2plFTf2YxI0+tbKCzbdMtAsoywPRqwFlQW3KLGiBg2fO47UlgxFXzHvDYv40+8OrlG/V/0Y9kINBAnFe3PFaegUJ4O+42n0HU8gnAzZeeeRPe9fywmaAoGu6bi+h5oQwz/ynCEEutAwNI2QYcyIIT9yBWmHsDQTzbTx3TzKcRgfqyQQhonULZTy8YvZYCUzA2hoRHfzOdekTtKy8ZRL3vPKnek936OoPNyW08g2nYi+/TFCf/46KlKH0/YGvNqlaJpGaIamSR7AXddDZh2PeKSi0SrisepDNYz9xjRNiq7HwqXH4DgFdN1AjKmzHlvaQLRPMhTSOG7NEnZ+/ejycw1zWtF1nTw7KdRohHZmKfansLMayQWNNLQspGnuZjS1E7Gihrq51bTdnuXuCwXHl5aFX6g9FgBhlr5sNMln35nnOiEIlxSuNsti5vcFK1ZF86Kjkdu3snNzO1LX0HU98IAhqIvFCcUSUHCxI2HYMDPXbWtr47rrrhv32N/+7d9OOi6RSPDJT35yj+eTs9CqN6cwwg4uAqVbYNqBV6lktJu+D7sbm1Zqy6VbqFAiaL7iFkfdJl4xqCc8QxUTRjj08zW7marqE4AVNhBTJMAdbGQohmFFxoWq7A49ZELIpJCK4g4PBoa1aSDjFsWBHMUh0EyD0PwmWv9BQ4tVIaYIqxG6iXKKCGnsd7ELMxnCTIQmuHQPHdK0MWvbyk4iIbVJ1WCEpqNZCt/TUb6GcopYdYlJCzwZTSBSBYSlIw2N5OJg4XIowkT3ph9DLpc/uKGUysPO9+OmOtCHd2INbkTL9+NLk0LTSrwT/4qCFcNXAktoVJk2Gc9BFxph3URK6MtlybkOtq5hCB0pA2O+xg7Tm8/iuh4KgSkVqWIBgIRpU/Bd8q6LEIJaO0xfPhvsZGk6tqYz5BSQhkkiFKfoOuScPCBprJtDb3oQF0EKQSRSw0B6AJQibkdwPJecUwQEdeEEA+kBPM3C0CEM9A31AYqoYeKjyDpB5mmNHWaomC/3Y4gZJv2FIDw3UrK7Mk4QBlhthRh2RvtfJEybvnywo2/rOjnXIV06tsoKkXGLuChsTSceMunOpVELL0Q/6k3oQ9uQGx9AvvIzdCuGV7MUJ9YEsTmEq+bSV3BwUAgUupA4KtghMIWk4Lm4ykcgiBgmRd9DQSl0dOZkdyKHLqbe9VC6RN/LDOnZRqguiuXZSDuKZU/uLiY0yeaPLSKVFAxtHmZZ/ehORGdnJy0tLUSjMcScMKEnsmzt6AYlaIonAqWo6/z3h+Oct6AOQze4+V9rWWolAdB1A9croqTANG2sUpLVO954Kqoo0I3Xl1d+V0grghGtAtqJJxNlj2A0FKZ+2VHohkm8PoGrGfDEE4d2sLvAnyVfxmM52C28lW4H1TZGDABpjBrvUiB0gZCCfCYT7MaIIFZYCIGmB94kTyhcfJCgXIVf9FAFDb9oj0syU74XbEkXc0H33RngYM/X641CYep5NqojsyaPZ28N+nFoBjIUIVSXRLcM0CXFoeB+jKiFW/AZ0m1Cu0hEFFIgNC1Y3OynZ04E5d5mi00P7DqpV2g6WiSJNDQ0kUaPJCmkPdzhwSnnXyCQukQaFlqsBm+470APfZfsTT+Gf//8R2h8+f+CLQnfBTcPXhHleyghiBPEeYuRMpTKL1W7GamII0rV+yA5kixNqZqfGPM7lLY9JDLagIw1Iuvmoy99MyJSN+kz1ZPLlPswTHTjhaO7tgVq7fHREXWhUR1nahqxMe9zjR1GaBrCsJFGiIgZKm/NhCZctzZeQ/dQD8lI8Gh9sr78nA3j2kTWTHDoNkdr8PNpvPww+B6RMREGE2P0J/aeCI8JMU5a9pTHjsxVaMyxCXP8sc1jnaH1S4P/gJ8fxOt5FT/VjrflFfwXu6j1dvEdI0RgwOs2UreQ0iztPGuADHIo7FVTv3aaHLrwG02g1xrjSnW9npjfdswejzGWRKgBahrCyCli2iORGPUntmH/fpjhpx9B06IkS1njAsHFy+cH54kYXKjmgCkQER1Ds3ELRTRNotuj5z2xtYXh4T6s11moze6QmoYSglh9UDh0TnM9uhTY8SDWTQ9FODA98SpMF6UbQXMP3ULKMYaJFGhhHT1mEY2GCekmIc2gv7ObOS1zSocI9CnigB3fw/V9fOWX4zaV66McHyfvkM4XyOZy5AtFVNGBYjZo2OJO3AKucKDZX0N2tiCkQI/WYMTGfOmP5FLZBtIMvIhiF243zY4gdBujdvqLm9myONod0jAINTUghYcRrUNaYfyOARR6uf7+WKywgV0VwskUgzKdho2XHdz9bt0BYm/6MchIA/FlxwEKNBOhWaBbCFFKay0pOCH1IGxQalCuSS5eF+/hKEH4mNAtpG4hRpwyBxopkeF4UIo1P4yXS81YaM50kXYSOXfvDXGlfHByKK8InoPyi6WECj8II912YBaxh8yoN3SdqG2jTfFhPxwQE7w3Y+8zkRhdCbYsW8LGyEtU/TrOb89y+cvFgTIRMljpxRa2YvuSnvQWYrU11CZDpAuCXG8KITWqGkarAzXVt1IXS6Ltpjzc6w3TNgg1hbBKHodoMo60Ju+MzFZmoyI/kI2UlGYEITWmjTAtZMRAi5vBl7qvCGkGUTtE3A4R1s3xFUOqqzH3kC9jSG1yO/Ux4j7iE/J8n1Qxx3A2x1A2h5Mr4BfyqFzwXzqFvU7QPFIbT+0tuwq/eb0jNclECdEiCYwqAZqGGTaI2BGM0NQyG0rE8RXjHC+HM5qhEaqN4Hs+svR9p0dCOBlvSj0owyaIMc3BpBYYkfvS5XOG2Jt+DJoZQauebOzDoU2diszYzrxAWDbSDCONfajVPvLq0uFKQcTevxySsSeToTjSiuEXhvFyw9NKqB9h5uZqzwghwYwg2MVcHHZGvWGxaNEpwXb5EUxVbSOr3xrmsh/nyZ5cTWNpO8rQdDzdYFHrUeQLWQqGYk79XCJWGKdjBwC6bdLQMFrBwbIiWPuZkDVbiUbiLFtwHKJUq1aP1+/hFRUOBUq3IJJEhGyEIZGmhhE1CVsWtmYQNkyihjWl9/1AoElJlR2hyo5AdRDHWPQ9ip5LwXXJOw7FTIZCNk0hm8HJ56DogOMh3Ameodm3LqtwELCT9qQ64UbYhOooUgqUrSMAaUz9NWpUh8fXHz9CkGMcWJouMUwNOVXOwYjROCaxUe5lwYwDwZ76McxWpq2epIZmR4PysLsreyoArZQlViq7KzWBrkk0W0fqGvh+sHOadrF1naLr4XvT+AzIknFvx1DFHF5uCOXuf4enI0GVHzKj3nGCTnpTNcM4HJATdiB0bdRbMzQ0RCwWRJbZpsXKhW1c+NFH+VniuPIxLY31aJaJJiWRUJSj5o52KjBKoTxWyEKOKYE3G73C00VIDRGK7fnAWcqMdMGbYUbais8IQoNkEq0miRm1iFohYqZF1LD3uUrV2M/FTCOEKFfOio04axKj5cqUGq2x77oevuPh+y5K+OzcuZNwIkI6n6dQLIDrgFMMknRdD+X6+L4KmsiUak3jK5SnUL4CnyAuuvSVImahTEwHx5kFbRQPANIysBvHy7Bm6Wh1wU6hMDSKqoBu7iKm/nUaWjqTaJZOuD6K2E1FknGVQGbxrpg33W6wB4i0UxwXI753BF55zYwEDrNd2A66oWFFTTTbQJhaOSFcKQUl58dUSeLp/ADJ+lpMx8MruPhOSU8qAFUOxfQ8f++MfiEQVhjdCuMXMniZwf3y3O/fXL2+ODwt6lmAYVtIU0NJUHkPMzx1IwZD16m2wtzaei7RuaNGRrypFmFMXW9W0zUQEJklZc4qHFkoISASRotHidXWkYzHiBqBR/71urAUQmCUSqMapgZjdmlT4WFaGoLW0G7pi2Qkoa3gueTcIvliHscr4nkuvuegAE+Vukcq0NwCmuchPAfP8/Bcl6Lj4pW8TsoDPBV4uVxGPbw+iJFcgMNrLfC6YHfyLEwdLWYjjpDwmv1BaBL2EGIrDY1owqaQc/Fcb78rBR1yNLHrkJWRgvV7uy6w9OB8nh/Mh69QHkEne90ItE8pVl96Cq2qDmmaQV6uM5Kc66FcF3yv1MoqqByHpgc7ImMbl2kSPV6KY3R9UGBGTfSwOWVujBBit43UhBAISw8qG0VH4yOVr4Ji/Z6P8hQ4Hm7ewXN8HCcoMbknpBVBmmH8/DDKc0v36qN8P7hX35tdmeUHmUNm1FvW4RP3PRVNjY14TgorFGPH9q3oY+IuGxpGu+cKITBtC+FrzJ03mnw7sZPuWMxIGGNuAjN5eIXaHI7olo5RF0aooGuggsDQ8/1A4XqqlDfjgxd4QILtTZhky410ICxVUVBjda0bfGHsjRd4fEe9kge5fG7KuV1CC2KHlamDZSItC2HamJEI1eE4NXZkjzHw+8LYz8VsYuy4JoYPhaVJ2DAhtH95Ho7vkS3mg0VBMYtTyOI6uaChkwKlxHhbfuOm/brOgeRw1+W7o7G58XW7kJ0tCCnQaiOE8g7FjINpz05foyG1so4UlokeDqPHokhDR2geQgUVb4ICh4HBrHw3qI6jFFIzELqB1ExwfZTjobygKzVSIoREWgaELDRdG13clAxXDw3lg+t4oILFvhCCsFTYdpCfpJQKaq/nnMBDnnfxioGRKzUZ5C8Ige/6+J4PAsxECCsZmtHa6bvS5UKKIMTHGO1vYCgbo+hhOR5u3qWYLeI53u7t8lLM/S7xFcp3wHNRyi+VXpXUxxW6HlRf03WB57j4rjO6+FFjFgUjb4DvBYuH3Q1oZIeWKZqKIdBMAzNhIzWjlCt74BYdhzT85nBGWmFaWpeQc10WxBKEw6MCODg4SF1dXfnv5sXzSOWGSVp7F08YNkMsW7xyVtZArzAeOxTluOPPnPS45/u4vofnFvFKTUJ83w863alSJQXl43kujufiey4uCg+BR8nnIgMJ8H0f1yviFB083yt5eVUp+76k0IVE6jqabpDPF4kn4xi6gSZ1dBlUZpBIpKYHIV2aHpT0E4F3J6hGI5FCYkrtgBgyEz8Xs4UDOS5DaiTsCAk7Auy5a+jDf5x9pVsPd12+O2arzL7eEEIgQiYmIEOzs3qbiISoPeUEfF8d3AIfpXCREWPNMMc7Fnp6egiFAhkUQqBpAi1qQclDrpQKFgETjPaR0NBDrcuFEFDy6ptRC5MIylcox0U5frAAKXr4RRffD0IafRX83KWdLQWaYSO0kTKiQV3Y/uFBGhMJNEsPdpCkKHcB9kuhQMr3S5VIS6VINYmmS9CDpVU5jFIKNF1HaFppESbKc+oVPbyCg+946GETYyqZ/uN+TOxesE9GfSqV4gc/+AGapiGl5P3vfz+9vb3cddddCCF497vfzZw5c0ilUtxyyy0Ui0XOPPNMTjnllEnnmqq1+OGGNEME/TbGxwlPrOtcWzuXuLdv3TEnVQCpMCvZlZxrUqJJWVbYM3a9UqlHT/koBbqUGBOM8Pb2dlpaWmb0ujPBruqdH2pm67j2l1wux3e+8x06Ojr43Oc+R3NzMwBr1qzhwQcfxDAM3ve+91FVVUVnZye33norvu/z1re+lSVLlkw635Ggy3fF4SYbh5rZHMrk+37ZaJ5N7EkGR3odTPn4AWK6nwshBcIywGJcr1vl+ZPCEH3PH12gSInQxKScxhH6t6cxGg5cjt7InOqWjm4dGp/5Pl01Go1y3XXXIaVk9erVPP7447z66qt89KMfJZ/Pc9ttt/Gxj32M++67jwsuuIDFixdz/fXXc8IJJ0wqe3Ykb1lOVQJuJsMYKsweDracSyEx9+BFmq0lCCvjOjiYpsm1117LXXfdVX7M8zweeOABPv3pT7N161buvfderrjiCn7zm99w5ZVXEovF+N73vjelUV/R5RVmitksS7N1bLNRBg/UmKbqd6DtQ0K6ac7OXaCZZJ/2kKSU5a6e+Xye2tpapJSEw2Gqq6vJZoNWvFu3buWoo45C0zRaW1vZuXPnpHMdyXGYtbW7jpevcHgxG+V8tspfZVwHB03TJlUZ6unpobGxEV3XWbhwITt2BGVzh4aGqK+vJxQKEQ6HSafTk843G2X8YHG4yUaFXTNb5Xw2yuBsHBPM3nHNJPvsHm5vb+fWW28ll8vxiU98gjVr1pSfk1Liui6e55WN/1AoVDb2n376aZ5++mkAurq6aG9vB6CpqYne3l4cx8GyLJLJJF1dXcBoo6ahoSEAGhsb6e/vp1gsYpomNTU1dHR0ABCPx5FSMjg4CATJGoODgxQKBXRdp76+vrzAiMVi6LrOwMAAAPX19aRSKfL5PJqm0djYWP5ii0ajmKZJf38/AHV1daTTaXK5HFJKmpub2bFjB0opIpEItm3T1xc0FqitrSWbzZLNZhFCMGfOHF555RWqqqoIh8OEw2F6e3sBqKmpoVAolL84W1pa6OjowPM8QqEQsViM7u5uAKqrq3Ech+HhYQCam5vp7u7Gdd1Jc5hMJvF9n1QqNWm+TdOkurqazs7OPc63YRjU1tbu93wbhlGew/r6eoaHh8nlcmiaRlNTU1keotEolmVNOYcT53tkDmcrI63HZxMdHR2zMvymMq5DRzabxbZHq22NbGePLckaCoXIZDJEo+OTgmejjB8sjgTZqBAwW+V8NsrgbBwTzN5xzST7bNS3tLTw+c9/njVr1nDvvfeOE3Tf99F1HU3T8H0fKSW5XK5sdK1cuZKVK1cC8K1vfWvc5E7Mlp448WM9S/X19bs9duyXzsRkjYnHRiKjFWQmruImHjvWeJy4ap8zZ84uX2vbNtXV1eW/q6qqxj0/9vdQKEQymSz/3dTUtNsxje1O29jYuNtj4/HRZN1DNd9j57Cmpma3x+5uDifOd4UKFcaTSqX44Q9/OOnxa665ZpwugEDvjNXlI6EGY0MOcrlcWV+OddAMDQ0dsQ6akWtWHDQz56AZmcMKFSrsO/tk1Luui64HL7FtG8uy8DyPbDZLoVAoG2ytra2sX7+eRYsWsX37dt7+9rdPOldPTw833XTT9O/gdcjQ0NA4Y7zC9Nm+ffuhHsKUzEY5n63yVxnXntkXOY/H43z605/eq2Pr6+vp7OzEdV22bdtWXjTH43F6enqIxWJks9nyAn6sg+YLX/gCd999977dyGHCbJKNw4nZqM9noy6H2SmDs3FMMLvGdaBkfJ+M+vb2du68806klBiGwZVXXklPTw/f+973EEJw+eWXA3DhhRdyyy23cPfdd3PmmWdOmZwwd+5crr322pm5i9cZN9100xF77weK2ahsYXbK+WyVv8q49sxMyfmNN95Ie3s7XV1dnHnmmaxatYpzzz2Xb3/72+XqNwCXXHIJP/3pT/F9nze/+c1Tnms2yvjBYjbJxuHEbNTns1XOZ6MMzsYxwewa14GS8X0y6tva2rjuuuvGPZZIJPjsZz876bFPfvKT0x9dhQoVKlSYcT72sY9Neuykk07ipJNOGvdYU1PTJJ1foUKFChVmJwexg8J4RrZuj0SO5Hs/UMzWOZ2N45qNY4LKuPaG2TSWEWbjmA4WR/K9H0hm47zOxjHB7BzXbBwTzK5xHaixCKX2oq/8BLZs2cLPf/5zNE0jmUxy9dVX8/zzz09qXPLQQw/xwAMP0Nrayoc+9CEAHn30UR577DGEEBx//PFccMEFk85/1113sXnzZqqrq7nqqqvQSm3t+/v7+dKXvsTf/M3flBumHAqmc/8vv/wyv//974EgSevKK68sVwoa4cEHH+TZZ58lHA7z/ve/n1AoxK9+9SueeOIJVq5cyTve8Y6Dfs8HmgM9pzfccAPbt2/n6quvZsWKFUCw/TVSmekv/uIvmDt37l6NdaJ8TjXOsexvU599ZeK4UqkUt99+O4VCgUWLFvGWt7xl3PEbN27cr8Zx+8LERkfV1dV8//vfx/M8NE3jqquuGpcADQdnvqZqwPTwww/z5JNPAnDBBRdwwgknjHvNgZqvqWT/hz/8IZlMBsdxuOyyy1i0aNG419xxxx3s3LmTYrHIhRdeyAknnEChUOCWW25heHiYFStWcOGFF+7XtSu6vKLLp0NFl1d0+QgVXX7wdHkZtR8MDg6qQqGglFLqrrvuUmvWrFHf+MY3lOM4asOGDer//u//lFJKpVIp1d3drX7wgx+UX9vT06N831e+76tvfvObKpVKjTv39u3b1Y9+9COllFK/+93v1FNPPVV+7rbbblPf/va31Y4dO/Zn2DPGdO7fcZzy77fccovasGHDuHMPDw+rb3/728r3ffXkk0+qe++9Vyml1NDQkFq3bp36xS9+caBv75BwIOdUKaUGBgbU3XffrV544YXyYz09PUoppTo7O9WNN964V+OcKJ9PPPHElOMcwXXdKZ///ve/r7q6ulQ2m1Xf/OY39+ra+zKup556Sv3whz9UAwMDu3zN9ddfrzKZjOrr61Pf/e53lVJK/fznP1dr164tj7tYLE5rXK7rqlQqpW655Ra1Y8cOVSwWy2N6+eWX1e233z7p+IMxXxPHpZRSX/7yl5XruqpQKKivfe1rk15zoOZrKtkfkene3l51ww03THrNyPO5XE599atfVUop9cADD6hHH31UKaXUd77znd2+97u7dkWXV3T5dKjo8ulR0eXTG5dSR6YuH2G/wm8SiUQ5+VXXdbq6uqZsXBKLxSatsmtraxFClNota5O6tG3evJmjjz4agGXLlrFp0yYAent7EUJMWjkfCqZz/yPVg1Rpg2Tiynbr1q0sXrwYIQRLly4t3//EEnSHGwdyToFxZUJHGClhOpUc7oqJ8rl69eopx7l69Wo2bdo0raY++8LEcW3cuJG+vj5++ctfcsMNN5Tl6JVXXuH555+nWCzud+O4fWFioyPDMMrvha7r5Xk/2PM1VQOm2tpaHMchn88TCoWAgzNfE2VfCFGW6Xw+Xy5rOzKWkeMAisViuZTtWBk4+uij2bx58z5fu6LLK7p8ulR0eUWXV3T5wdflI+xznfqx9Pf3s3btWi699NJxb4zai4ie559/ntra2kmNTLLZbFnpjTQ7Abjvvvu46KKL+O1vfzudIc8o+3v/q1ev5r777qOurm7K+x9pAjO2cdeRwoGY0z1x5513Thk6MBUT5ROYsmnPqlWrANi0adN+N/XZFyaOq7Ozkx07dnDNNdeg6zo33XQTf/M3f8OyZcsAGBwcHDeuPTWOm2lc1+W3v/0t733ve4GDP19Tccwxx/CVr3wF3/e58sorAQ7qfI3I/sUXXwzA9ddfT3d3N1dfffW4sYzwwx/+kPXr13PppZcCk3XHiO7cl2tXdHlFl88UFV2+f1R0+fQ5knX5fhv1uVyOH//4x7zvfe/D9/0pG5fsivb2dh5++OFyaaH777+fF198keXLl49rgjLS7KSnpweY3KzoUDKd+1+1ahWrVq3i9ttv5/nnn0cpxaOPPkpraytLliwpN98Y27jrSOBAzenu4lbvuece5s+fPynObVdMlE9gt+Pc16Y++8vEcdXW1pJOp8ueLk3TyrGPEDTL2ZfGcTPNrbfeyllnnTWpsdnBmq+J5HI5/vznP/PFL34Rz/O44YYbWLZsWfm6B3q+xsr+yHt03XXX0d/fz/e//32WLl066TXXXHMN2WyWb3zjG5xyyinluQuHw+RyuSm9nHu6dkWXV3T5TFDR5ftPRZdPjyNZl8N+Vr/xPI+bb76ZN7/5zTQ0NIxrXLJp06bddvscHBzktttu4+qrry5vU5x//vl8+tOf5oILLmDBggWsW7cOgLVr17JgwQJ27NhBZ2cn3/3ud3n11Ve57bbbcBxnf4Y+I0zn/seO27ZtTNPk5JNP5tOf/jTveMc7aG1tZcOGDcDo/R8JHMg53RWrV69mcHBwrz07wCT5XLVq1W7Huav7GGnqk8/nxzX12V8mjmvJkiVEIpFyYzjXdcsKBsA0zXLjuIGBgUmN4zzPY/v27ZM6Gs8Ev/3tb6mtrZ1UPhEO3nxNZKT3hmEYmKaJ67rjPEoHcr4myr5SCs/zgKBz9cTu1TAq84ZhYNs2UspxMrBu3Tra2tr2+doVXV7R5dOlossruhwquvxg6/IR9qv6zZNPPsnPf/7zctWCs846C4CHHnpoXJbz008/zSOPPEJPTw/Nzc18/OMf59Zbb2X9+vXlWKwrrriChoaGcee/88472bJlC1VVVVx11VXlmCOAn/zkJ5x//vmHtGLCdO7/0Ucf5ZlnngECoX/Pe94zKa7wgQce4LnnniMUCvGBD3yAUCjEgw8+yJNPPkk6nWbBggV88IMfPLg3fYA50HP605/+lPXr12PbNieddBIXXHAB1113HfPmzUNKSU1NTbnhzp6YKJ/PP//8pHGuXr2a+vp6FixYwDPPPDPp+Y6ODm677bZyU5+pVu/7ysRxbdu2jV/96le4rsvFF1/MihUreOWVV3Ach+OOO44NGzbwq1/9qtw4rqWlhaGhoXEVAE499dRpj2uk0VF1dTXHHHMMv/vd78oGzvz587n00ksPyXyNHdeZZ57J8PBw2TO4atUqzjrrrIMyXxNlf9WqVaxevRoItqovueQSFi1aNG4s3//+98lms3iex/nnn8/xxx9PoVDgxz/+Mel0muXLl3PRRRft87Ururyiy6dLRZdXdHlFlx98XT7Cfhn1FSpUqFChQoUKFSpUmD0csuZTFSpUqFChQoUKFSpUmBkqRn2FChUqVKhQoUKFCq9zKkZ9hQoVKlSoUKFChQqvcypGfYUKFSpUqFChQoUKr3MqRn2FChUqVKhQoUKFCq9zKkZ9hQoVKlSoUKFChQqvcypGfYUKFSpUqFChQoUKr3MqRn2FChUqVKhQoUKFCq9zKkZ9hQoVKlSoUKFChQqvcypGfYUKFSpUmBZbt27l+9//Pu3t7fv82rVr13Lfffdx66234nneARhdhQrTpyLjFV4P6Id6ALOJl19+maGhIVpbW9m8eTPLly/ngQce4J3vfOe44zZt2sSvf/1rAIaGhli+fPmkY0bYuXMn999/P+973/sO+PinSzab5ec//zl9fX14nseFF17Icccdd6iHVWEG2FvZ7uvr4xvf+AbNzc14nse8efN461vfSnd3N5s3b+ass86a0XG99tprvPTSS7zjHe+Y0fPuLUeKzB/oeZ43bx7HHnvsfr126dKlLF26lF/84he4roumaft1nhEZr62tnXSvAwMDR4wuP1JkeiKHs4xPlO1zzz23or85cmV9d1SM+jH8+c9/5pprrsEwDFpaWgBIpVJks1nC4XD5uAULFvDpT38agJ/85Cf7/UGfbfzwhz/kvPPOY9myZeTzef7jP/6Do446ilAodKiHVmGa7K1sAyxatIgPfehDKKW45557uOeee7jsssvKr9sTvu8j5etjE7Ai8/vOa6+9xiOPPFL++6KLLqK1tXVar/3Tn/7E0qVLsSxrv8c1IuObN2+e9FxVVdURo8srMj19ZpuMT5TtXckzVPT3kS7rFaO+RDabxfd9DMMYt/pcuHAha9eu5aSTTpr0Gtd12bp1K1deeeW4xz3P40c/+hHZbJbq6ury46lUiptvvhnf94nFYlxzzTX88pe/5MQTT2TBggWsXbuW9evX87a3vQ0IlMMf/vAHTNOkr6+Piy66iMcff5xcLsdHP/pRotEoSil+9rOf0dHRgZSSq666Ck3TJl1nw4YN3HfffRiGQV9fH+9///tpbm4uj+3VV18lkUiwbNkyAGzbprm5me7ububNm3cgprzCQWJ/ZBtACMHFF1/MV7/6VY455pjy68aeY8R7eeqpp/LAAw8gpWTFihXU1NRMkrdoNDpJLqdiT3IfiUQqMr+fOI7DLbfcwumnn86jjz5KLpejvr6eYrFY9kDvaf6POuoojjrqqHHn7enpYe3atXR0dFBTU1P+Ut28eTM///nPMU2TRYsW8Za3vGXSa//0pz+xbt06Fi5cSFtb2yQjZW8YK+NT3evSpUtnTJfvqx7fmzmtyPTMcbjJ+FSyDVT0d0XWp2Ray7H//d//ZcuWLQA8+OCD3HjjjQAopfjKV74y7cEdTLq7u6mpqZn0eG1tLZ2dnVO+Zt26dRx11FGTVrUvvPACdXV1fPKTnxy3ug+Hw3ziE5/guuuuI5lMsm7dOk499VSeeuopAJ566ilOOeWUcedSSvGRj3yEs846i2eeeYZPfOITrFy5khdffBGAl156iXA4zKc+9SkuueQS7rvvvimvA8EX1Ec+8hHe9ra38fjjj4+7zosvvjjJS5XJZLBte2+m77DlcJDx/ZHtEXRd3+sY0Fwux4c//GFOO+00YLK87Uoup2J3cl+R+f3DcRxuvvlmzj77bPL5PPX19Xzyk59kzpw5k47dk96ZSF1dHR/84Ae57LLLxnnJXn75Zd785jfzqU99ije96U1Tvvass87iQx/6EOeee+5+GfQwWcbH3uvSpUuBmdPl+6PHoSLTB4PDUcYr+rsi6/vCtDz18+fPZ8uWLbS1tbF9+/ayQuzu7qahoWFaA+vv7+fFF19k27ZtXH755dPalj1QrFmzpvwBuP/++3nxxRdZvnw5vu8zd+5cIIjDGzEKM5kMt99+O9lslqGhIVpaWli6dCkdHR3kcjkGBwdpbGwcd40RZZRIJMq/J5NJ+vv7Aejs7OT5559nw4YNQLAtN9V1qqury9tvVVVVZLPZcdcpFArjFFUul2NoaIhkMsm9997LxRdfPJNT97rhSJdxx3HQ9fFqQghR/l0pVf69tbV13HMT5W1XcjkVu5P7dDp9QGW+rq6OQqHAgw8+eFjJ/QsvvMCKFStYvHgxf/jDH8o6aiTPYix70jt7y9lnn829997LU089xcknn8wxxxwzA3eyZ8be696wL7p8f/Q4HHqZ9jyPxx57bMbjqmcTR5KM7w1Hqv4+EmR9V0zLqG9ra+P3v/89b3jDGygWizQ3N9PV1cXmzZtpa2sbd+zq1at58cUX8TyPdDrNWWedxZNPPolSio9//OOTEkeqq6s59thjGRgYwDTN6Qxzr6ivr6evr2/S4729vVMab57nsW3btvJ27fnnn8/5558PwLPPPkt7ezsnnHAC27ZtK7/m6aefZvny5Zx++unccccd5ceXL1/ObbfdxooVK/Z6vCMfxoaGBk488cSyh8DzPB566KEpr7M7WltbWbt2LUcddRSe53HHHXdw8cUXs3XrVlpbW+ns7OTpp5+mUChwxhlnTPmldThyOMj4vsr2WP7whz9M8oaEw2EGBwcB2LFjR/nxsV8IU7Er+d8XlFIHXOallIel3J900kkopXjooYeoq6sr66h9qeYx1gjYG2zb5vLLL8d1Xb7+9a8fMINnooyPvdc3vOENwMzp8pnU43DwZPqJJ56gu7ubHTt2sHnzZjZv3swll1zCxo0bqa+vR9M0duzYwcknn7xP459NHI4yXtHfU3Oky/qumJZR39jYSFdXF8PDw8RiMdra2ti6dStbt26dMs4rEonw3ve+l1//+te0t7fzyU9+kl/84hds2LCBRCJRVpq2bVNfX88tt9zCSSedRC6X2+9t2b0lHA4jhMBxnHGPb9y4kXe/+92Tjn/11Ven3K4FOPbYY3nmmWe44YYbqK+vLz++ZMkSfvzjH/Piiy+Oi487+eSTufvuu3nXu961z+NesWIFr732Gt/+9rcRQrBy5cpdXmd3nH766dx6661cf/31DA0N8aY3vYmVK1dy3333ccYZZ/Cb3/yGuXPnUiwWJ62YD2cOBxnfV9nesGED3/72t/F9n7a2Nt7ylreUd5sAmpubKRaL/Nd//de4GMc9sT9yORUHWuYhKF93OMr9O9/5Tm699VZ0Xaerq4sbbriB2tra/a44syceffRRnnvuOXzfZ9WqVQfkGjC1jI/c6+OPP85pp502Y7r89ajHAYrFIueeey4DAwPle/V9n507d3L88cfz+OOP7/XOxmzmcJPxiv6emoqsT41Q+7osncB//dd/sXLlSoQQLF26lN///vds27aNv/7rvx7nfVy9ejX5fJ43vOENPPzww5imyWmnncYf//hHampqOOGEE6Z9M9PlpZdeIpVKcfrppwNBGbT7779/v5T0vpBKpbjtttv4yEc+ckCvs7fccsstHH/88Rx77LH86le/4tJLL+Xhhx/GdV2i0SinnnrqoR7iQeVwkPER2U4mk2zatIkzzjjjoMj264WxMg8cEXLveR6apvHnP/+ZXC7HhRdeeKiHNC0m6u+xHAxdPpv1OAQVVDRNo6+vj6amJp555hk+8pGP8OCDD5JIJHjiiSe49tpr9+ixfT1xuMj4obJNXi9UZH2UaRv1v/nNb3jppZf44Ac/SGNjIzfeeCPZbJbPfe5z445bvXo1hUKBc845h4cffhjLsli1atWsMHgOJRs3buSuu+7isssuY8GCBYd6OBWm4HCR8d7eXm655Rbe9a537XV5tgqHLzfeeCOFQgFd17nmmmuIRCKHekivWyp6fHZSkfEKRxrTLmnZ1tbGww8/XI7tsiyL2traaQ/sSGHhwoV89rOfPdTDqLAbDhcZr62t5TOf+cyhHkaFWcLHPvaxQz2Ew4aKHp+dVGS8wpHGtD31FSpUqFChQoUKFSpUOLS8PtqGVahQoUKFChUqVKhQYZdUjPoKFSpUqFChQoUKFV7nTDumfn/5yle+8rqMS64wO+nt7eUf//EfD/UwJlGR8wozyWyU84qMV5hpKnJe4XDnQMn4ITPq4/E41157LU7PFrZ96VTM5mPwixlqL/sXIsdeTHbtQ7T/+wXM//f1GHXzcQc72fSZVpr/+pdEVlxM539fg9u3DeW7JM76IIlz/pLt/3YeKA9EUI+2+VN3ooUT5Ws6Azvo+elf46X7UZ5L8twPEz/jal77gEbTR/+P2KnvptD+EgO/+yb2wlMYfuJnuEOdoDz05ByiJ1xCYfsLVL3ps1gty3d7f50/+CCFna8gzQiR495M9ZtHK6V0d3dTEzHpvuVjuKluzIZFNHzgpgMz0UcIN900O+cvHo/zl++8eFbJeMcLjyCeuHnaMg6gfJ+t/3gsifM+StUFHx/33NixJs7+IPEz3rfbc3V3d4/r6zBbmE3jmo1yfrjr8uLOV+n60bUo36X2Hf9CeNn55ee6u7sRD11P6rGfEF91BXXv+caBmeQjjNkq57NNlxfaX2Lnnf9K1TFnHzAZB+i54+/3ScZnk84cy2wa14GS8UNm1Pu+X/49tORsmj/xM5z+dnb+1zuJHBu0ZrfaTmL46V9S/abPkn7mTqx5JwKQefH36IlGmv7qFgC8zED5XHM+cw/Sjk55zc7/dzU1l/0T4SVno3yf3Gt/AsCom8/g/d8jdupoI4fkeR8led5HGfrTj/ALmbLB0vmDD+71PTb+5f9M+WEqFov03fdVqt78eex5R2YpzyOFETmfTTLuOA4mMyPjw6tvRa+eu8vndzfWiRSLxb2+7sFkto5rtnC46/LeX3yBhmt+gJZoYMc330LrGIOnWCzSeNGniKy4iMzzv92r81V4fTIbdfnIuA6kjANU7aOMz1adOVvHNZMcspj6qYr++9lBGFOMJ3TUmeRe+zMA2Vf+SOSYQNCkGaKw/Tmc3q0AaJGqPV7P6dsOyie85Ozg+lISPvqc4HyRaqy5x5J5+Y/7dS9dP7p28oNC0HXzX9H+jYsobHt+3FOGYVDY+hyD93+P7f92Huln7tqv61aY/UyU89kg47q+72v5qWRc+R7DT/2c2Cl/MfWLpGTHf76VHd+6tHwfu2M6XQoPJLN1XLOFw12XuwMdmI2L0UJxtEgV3nBv+TnDMNCTjcDh18Smwnhmoy4HpuyEvDv2VcaBfZbx2aozZ+u4ZpJD5qm3LKv8e27dI2z717MpbHuO5k/+vPy4EBI92UR+67NIOw5a0L0zvOw8CjvX0nHjFfjFDA0fuInQotMAaP/mmxFSR5g2LZ8dXVW6gzvRk027HE/VxZ+h65aPUffuf9/ne5kqdKbuPd9Ai9ZQ3Pkqnf/9QVq/+Hj5udraWjZteoK693wDs3kp2//tDYRXXIw07X2+doXZzYicz6SM7/jPt4DQ9lvGq9/1b6T28T6mkvHhx/+X2MnvQnnOFK+A5o/fjhatIf3sb+j+6SeZ8+lf7/YaszVedbaOa7YwXV0+uO35AyLnM6XLlRrdiZDhBF6mHy0WyERFNo4cZqMur3v3v2OP+fztDfsq4/tDKBGjP5/BUz6+Uri+j6s8fKVojVaj7eNCZKY4Ej6vh8yoz+fz5d9HtrNSq28lu/YhIssvKj8XPekyuv7nQ9Rc+v+RH+Pxrrrg41Rd8PHAaL75I7R+4REAGj/5a8x4ctL19GQz7sDOXY7HqGtDi1RT2LpmBu4OtGgNAGbz0YBA+R5CBrFzHR0d6NVzsRecHBzTeBTu4A7M+oUzcu0Ks4cROZ9JGd/Vlu3eynj3Cw8x3eWj8j2Gn/wZzZ+6i9SjP5nyGBlK4PS3YzYtCTxPE8/hFlFOARmKAcHnoqWlZZojm3lm67hmC9PR5a7vUTj9fbS98dM4HetmVM5nSpcLMWqA+NkhtEh1+e+KbBw5zEZdXti6hmwuN+17252M7yuO7/Hylg1Eaqc+x/b0AG3xmnGP9ebS2LpB1Ni3Bcq+ciR8XmdVScv4qivIvnI/Xrqv/Fh46blYc1cQXnFx+TF3sAO/kAGYtJpU3tS9tIyauSA1suuCuDSlFNl1j4w7pvrNn6f/d9+ckXvxcoEv1E11o7xi2aAfwZp7LMWuDSjfo9i9CT2x61V5hcOHmZDxXbG3Mu78+f9N9zZwhzpxh7rY8Z9vZeD31zP0wPfIbXqy/LxfyFLY8TKqmKXYtQFpRVBuccI5uvDzw9MeS4XZx97Keb5/B05uGIWacTmfKV2uJRspdm/Ezw3jZQam5cGscPgwG3T5bJTx7uwwu2tpmirm6MmN6v2BfIadmcFxj1XYf/bJU5/L5fjOd75DR0cHn/vc52hubuZf/uVfSCSCjO03vvGNLF26lM7OTm699VZ83+etb30rS5YsmXzhXcT1Js78AEMP/Tf2wlMBEJpO44d/NO4Yp387Pf/3WYSmozyX2nd+ufxc53fehtSDuKnGj/wIo6a1/FzjX91C908+Sd8vv1jKJv8IjBma1XocetWcPc5D181/hbQiwTk//CP6fvWlSVtanf/vfUFCjO+Vt4EzL9yL7+SILz4f+13/StfNf4Uq5kic85dIK7zH61Z4/TGVnE9Xxke2bGH/ZNyo3rOnYk8yblTNYd4/B0b8SHJWaMEpZF64F3e4h9DCU+n8f+9DGMGeQM2l/x/ucA9G6fPlZQZRTg6FQPk+Qkri8fgex3UomK3jmi1MR5cX+rbh3/oZ2q0weN6MyvlM6fLad36Frv++BuV71Lz9n4DxunzgDzeQevQneOle3IEdNH30/0bvz3OxtOlviHu+f8hCFioEzEZdrlfNQe4hTny6Mh476bJdyrjre+hjHJYFz6U/n8EMh3Y7ps5Miohu4Sqf9vQgAMPFPHnXwdYPXNz7kaDLhVK7W1ONx/M8stksd911F+effz7Nzc18/etf52//9m/HHfeDH/yASy+9lFgsxve+9z0+85nPTDrXd7/7XT7+8Y9Peny6FIaGsRKxGT/vTJJOp4lG964iSIW946abbuLaa6dIWN4Pplq8rlmzhgcffBDDMHjf+95HVVXVXi1eD5ScT4cDJX/K93CHOlElr9RU6FUtCN3E6d0CpThOPdGEDMVm7ediNo1rJuV8ppiOjPfnM7SnB1he04wUrz+jdU+yMVTIkbB2b+DsDalinvgRlHN1uMn5geRQ6adUMc+O9CDz4zVlQ3zrcB9DhRxOLo8R2r286lLDL8Xcj1BjR5kTTZb/Lnoum1N91IYiVFuRKZPy94UjQZfvkxbVNI1YbLzBXCwW+da3vsXNN99MJhN8mQ8NDVFfX08oFCIcDpNOpyedy3GmTq6bNr6H78zuskWDg4OHeggVdoNpmlx77bWccEJQbtTzPB544AE+9alP8Za3vIV7770XgN/85jdceeWVfOxjH+Oee+6Z8lwHTM6nwYGSPy/dv1uDHsAb7sFNdZUNeqAcgjNbPxezdVyzhenIuGKvfUqzkj3JRsFz2Qe/2S7xxnxeKhwaZqMuh0Onn3pywzi+y6ZUD1mnSNYpMlQI4vsL6eweX+/63jiDHmCgkMH1PSAIOdo23E/Bc9iRHmT9UDepYn6qU+01R4Iun/a+4HXXXUc0GuWJJ57gnnvu4fLLLx+nxEKhEJlMhmg0ytNPP83TTz8NBIZ/e3s7AE1NTfT29uI4DpZlkUwm6erqAiiH9gwNDQHQ2NhIf38/xWIR0zSpqamho6MDCLZWisPDpHu60cNhGhoaGBwcpFAooOs69fX17NwZJJ/EYjF0XWdgIKgZW19fTyqVIp/Po2kajY2N7NixA4BoNIppmvT39wNQV1dHOp0ml8shpaS5uZkdO3aglCISiWDbNn19QZxdbW0t2WyWbDaLEII5c+aUrxkOhwmHw/T2BuWjampqKBQK5UVQS0sLHR0deJ5HKBQiFovR3d0NQHV1NY7jMDwcGETNzc10d3fjuu6kOUwmk/i+TyqVmjTfpmlSXV1NZ2fnHufbMAxqa2vHzbeUsvxB2dN8G4ZRnsP6+nqGh4fJ5XJomkZTU1NZHqLRKJZlTTmHE+d7ZA5nkomL156eHhobG9F1nYULF3LnnXeW52ikkcXI4nW2eAEOBaq4Z0Wu3AK4hXGP+YXMjBg+FV5/HAlvu0IhplnycsTQqVBhNpBxCmScQI+7vs+mVA/mDISZ+UrRl8/QEI7TkRkiOyYPK+86bEn1EjEsGsLxA55U+3pl2u/CiBFzwgkn8NhjjwHj67nmcjkikSCea+XKlaxcuRKA733ve+OykBsaGsadd2KG8lgja2JHsLHHZrM5YtEQVnWQ6FFXV7fb846MDSaXO5p47Fjj0ZpQRmrOnPHxm2Nfa9s21dWjmeBHH330uHqpY48NhUIkk8ny301N4xNoJ45pxAiHwADf3bFj48lmar6BcYbsnuZ77BzW1NTs9tjdzeHE+T6QZLNZbHt0K3HEAN3V4nUsE+VkNjDxvZ8JlO8FBvv+vRpVyOzVuJRS096C3VcOxHwdTsxGGT9YHCzZcPyKp/5QM1vl/FDop+4JSa2+UuTd0Z2McFVi4kv2mr58BlPT6c1PjvCAYEGxaaiHmGkzL1a9T2F7R4Iun5ZR77rB1qJhGGzYsKFs0MXjcXp6eojFYmSz2Sm9lyNbWf39Wf7rP/9MQ2OUYtHjjW9awpKl9Wzc0Mv3b1zN337hPKprwgyn8vzrv9zP1desZMnRdfzs1ucZGMjh+4qTT53LKatauek7j+EWikgpkIbB+//yZEKhwHhe83Q7Q0N5zj1/ETt3DHHjfz3Gl756MVIKfvw/T/GmS5by4B830NU5jGFoRKMm7736RDRNctN3HsP3QUqY25rkLW9bNul+bv+/5zjn3AU0No0azl1dw/zmzldwXA8UnHfBIgYGcjz1xJayUb9t2yDXfvw0WudN3ZDiH//ud7S0BB+QS9+xnKbm0fP7vuJntz1PX28WULzr3cdR33DkeooPFKFQaFzZvhGjcleL17E7Uj09Pbzy8kZu+9/XaGpKkM3mOfX0RpYcXUd/n8///L+neP+HltLaWk96uMi3/uNx3nrpfE5ZtZhbf/IMg4N5QLDq9DZaWnV+cft6pNSQUuC6Lm99+wJaW5sZHBzk+Wc7yGY93vq243j+uQ38/LYNfO7vT8c0DX5y8zOccXYzL72Qon17P4ahEY4YXPPh0+js7OAXt69HSA1Nk9TUmJx5TvOkHak/PdTN0cui1NTa5R2p9a/t4JEHdyAAr1jk+GNDpDM+W7Y6FF0XFHT3OFx9eTN2OPA2VsXCFF2XTC7wwtx97xCu2obrKS55+9HMX9Awbkfqq//yMHX1waLq4ncsI2b7+J530HakXNeltrb2sNuRmimmo8urW0M8+qut/Dm7FeUzTpeP6FzgdanL/VLIjFKAgD8/vInnn+sgHDG54qrjse1Rx04u5/DfN62mqzPNJz59xrhrA6SdPLD/hlKF6eM4zozbKzMh4+BTVRU5YDL+3Jod5WO2bRvkg9eeTC4+tQPn1Sd72Lp2EF1XnPWuhZjWaBJtMe9x//9tZKg3z8UfWEyyfupcE9f32D7cv8f3Y7iYZ0uqj7Z4zV4b9oODg5Mcj4cb+2zU33jjjbS3t9PV1cVxxx3HmjVrME0TXde56qqrALjkkkv46Q3fKYUAAQAASURBVE9/iv//s/fecZKd1Zn/995buatzmBw1ygJJSEhCARBgMMlkWBkBy9pee1mMwcb2rndt/7zJ64TAZo3XNl4DBmHARgZsBLYkk5NgJCSUZ0Yz0z0dqyve+KbfH7did3Xu1rSEHj5ouqpuqlvvPe95z3nOc7TmZS97WdfjtLcWP3zOEG95+5WUSj4f/au7Of/CODK8Z28/9/1wkufdeA73/XCKPXvjAfjIQ7Pk+9L8m5tjzrPntVI0b37NYTI9abILVmT7DwzyT59/EIBTJ0vs2dvP9FSVXbv7mJvzGB2NHbI33nQpO3f18elP3ssjD81y4cXxcX7m568inV797VJK84mPHuXNb30WYzvySKkYP13mmmsPsHe/w969e3ng/mnu/t7pJR16gNHRPL/wzmu7fnZmooKUmne861pOHCvw1X89zuvf9MxVX+PTWB3GxsaYmppCSsmpU6eaWYKlFq/tGan3v//97Ny1i3PPLXaM8etvuIhKeY69+/qZm4GLL+7lvnsfZ+++foZHRjj+WJGRsX7+7c/GTUo8LyKXS5FOn+LtP7d4LI6OjnLZ5Tn+6fMPYts2YZBm7/4BAt9mcLAH1zVc8oxzeOD+e3nxSw9w6WXn8ulP3sujD89x4cV7lzxuZ3Rqhh07xpoTgVKaO758hje/9QqGsj5RpcCZqZD9l2XhuniPhx6r8YP7Khze37nYzKaT9GbT+FLw727qI5Fw+P4xwQ/vmef8C/Z1ZGlGx/L80q/cAMAZt0w+me4oGtzqjNT4+Dj5fP4pn5FaLzZiy489WiCTT/DWtz8bx7Y7bPlSNvfJYsvLoY8rQ/p0hsCTPPCjad7xrms5+oMJvvn1k7zgRUea26ZSDm//uav4x889uPj+Gt0RBX0aZweNcb6Z/spmjHGpKnzr64UtG+PXXHsAoDnGM6MOfhefPvAk44+WefFbj/DQt8Z55O45LrmuZX8TSZsb33SYH9w5sXjndaImQk5W5znYO7yqDG4Yrjeb/OTBmp36d7zjHR2vf+InfmLRNrt27eI973nPssfp9gMEvujgWB46PMTjx+d53o3n8Ngjcxw5L55IkymHyYkKxXmPwaEcuVyquY9RGtMlVTky2kOhEBfxnT5V4rrrD3Ly8SKZTIKBwcyi6wkCuaESrlOPF9m9u68ZOU8kHA4eGqr/ncCtRXzpiw/z7//DNQCcmShz8vEiz7nuYMdx5gseH/qTb7JjZy+vfPVFJJOtle/AQOzYGGPwfEFPPsXT2By0L16vv/56brzxRj7wgQ801W9gdYvXReNqE8b4UljtGHfq3MfNHONirkAiYbN/byv64nqKf/lagZtft5sw1BSKEacnA66+fACtDdMVF18KDo+kwWhUELBj5+JoZPszcPGNY2DME6oEspRk49OIsRFbnkjYFKd9ivM+IyM9mzrOm9dylmz5+VeMIrRCGc3p02UOnRM7HudfMMbffuKejnM4jk0+353eIZ+m3mwLbEdbDvEY3MoxDjTH+Jt+5pkUQpf5KY+5CY/zrmhRlgtnPMb257Esi50Hc9zztXLHOWzHItOz+ba0GgWcrM6zv3dwxYj9j4MtP2vfsD0CePzYPH/6x9/gzESFt779yub7lmXR25dhYrxMOpPAceKBfOTcEaanqnz8oz8gihSve+MzOXAwjpB8/Lbj2LZFJn+Kn/n5qzvO2T+QpVT08X3BueeP8oV/eIBsNtncF+BTt96L7wsGB3Ocf0ErGvfh//tdbBuufs4BLr9i5ehZuRLS19/dSI+NjfHxj/yAl7z0/KYjvntPP7v3LHZofu2/3EhPT4p//tIjfPPrj/O8G1tdZ3M9KRzb4g9+91+bEfunsTlYuHgFuOKKKzper2bx2hjnmznGG2MxmXTWNcbv/Odx/vFzJzZtjC/Fp//cl6Z5wXVD2NoQuIL+vgQ7RvuRyuB5kmLRRWhJIZnj7z4/Q6kiufl1CURxAic30NRWbj4Dtz/CPd+Z4PLrntjo9cII/tPoxEZs+b5zBtg30c+tHzuKEJs7zs+2LZ8P3GaE3fcEmUw83WYyCXxv9ZH3UMmu72ujn5QyoE9WbEdb/qlb78X3BINDWzfGAT71qXt45vN2MqdjnvvQzhxDOzvpgFGgSKbj8di3Y5DQLyw6zlahEvk8Vpbsyw+RXUbn/sfBlp81p76do9xIZx39wQTHHpvjvLbBeckzd/LpT97Li15yHmcmWiu/6244xHU3HGJmusZn/vZe3vGuON//06/YTzqXJn9g36JzHjgwyCMPz5LJJMlmk/i+4NSpUsfD8MabLmV4uIe//PPv4HuCfG880NeazurvS/PQA93ll+745/vJZJNcdMnKRRs9PfFE8cxLd3HXHY91fPbow7PYjsWv/caNnD5V4gv/8AA3v+2Kbod5GmcJjXG+mWN8ubG4mjH+vBfs5KKLDm/aGDfR4jblR++vkMnYHNiTRc6HoAzSV9SyDpYFftlH1gKwLEIl+Pc37+fog3P881dmedsb08jIJzl8ACuRbD4D5z1jlH/60iyBFAitSC7o0rxVOHPmzFO+tfhGsFFbfv6zR3nNS57J/Ky/qeP8bNvydrm+TDbB3FwceQ0CSTa3+gY7/hLUG08Kconk0479E4TtaMvfeNOlBEGJL/7j+JaM8UhJ/vWbjxHZktHDy9f0pDIO1WIc3CmfKZDOPrHuZSAFx8oz7O4ZoC+VQWiF0JpsItmcK34cbPm2sgaXP2sPjz4yh+u2OGfnHBlm166+joFcKQdEYRy9aEz4DRgDWhpMFwmw/QcG+MbXHmff/jgink4neOyROfYfGOjYLplyuO76g3zlrmPr/i77Dw4yeabKzHS8spVSc/LxIqWiz9Hvz/LKV1+84jGiUKJ1PDGcOD7P8EhPx+cGmqm8nnyKIOge0Xka2webMcaXw9kY41MTcVGTVIZTEz6liuCb3yvyoueOIEsRqLpzIzRUBKYsqPqx/KVRmtJ8FaUMqaRFMlk3SUYjqzMdz8Bjj83SOxhPWjXx1OdGPpmx2nHuViNkpDBsz3FujFm3LY/a5qB9+wc4cSx+Th55aLaD2rBeVEKfQD5t888WtostTyTtTfdXIiE5+tA4R0+e5vvfmODKn1g52j+8K8fMqXj/6fGA0b2d/gpagVuELaSTaWMYrxV5YH6SR0szPF6Z42Sl8GMlmXzWIvVLcZuuvGof3/3WKfYfHABiruGb3nxZxzalks/nb3sA27HQyvCTL2918rz1CyexACdzkn/zlisZHGxxfPcdGGBqssK+/fGx9+0fYPx0uUOFoIGLn7GTf/nyo/zET65eH/gzf/tDUqn4e73pzZfx02+9nM999kcIqTAaXvjiI/zg7nGE0Pz1X363ud+LXnIeuVxyEad+bs7lU7feSzqdIJtL8qafju/D5z77I1768gs497wR7v7uaT70wW+ipOYVr1pc5f40zi66jfONjvFGahXgTW++fM1jPJeLje1mjfF/+NT3kUJhDDz/OUN89Ufz+KHiE5+ZaDr0z7m4l0zK5kwh4pnnpJvRRz+Ef/2Oy9cyp7BsePVPxrKsX/n2PM+4QCCSFn/32cdIpxNYKbjy5fHkUosCBtNPjBrMwoZ7T6MTG7Hl1XLIHV84zrdT4xjNpo7zBjY2zg1vevPl67Ll+y+Nr01qTX8+ywUXjfF/PvANsrkkN9WLJhu2PJly+PCff4czExVmZ2pcc+0Brrxqcba5HQYoRz655NO1VO247bbbOHHiBENDQ9x88804Thylvffee7nrrrsAKBQKvOAFL+DGG2/kd37nd5pqWC95yUu48MILux53O9pyiO3Trl29m+qvhFJywXNGGH+kTBQo7vrU8eZ+z7xhJ6mMs4hTn+lJsOdIH1/6yKMkknDDa3cDcPeXx7nsmh6SUYU7/qlKsaCoFALOvWKMcy7tFBHYCngy4oxbZk9+4MfCllvmLC1htqrlcuXBR1FC0XtgkET/9tQk9Txv28rTPVmxHduKw/ZsLb6Z489ohZhpRYiMMUTCEFYFptqdNlAMXCqik7LTl+1heFcfvT0LeJ22Q3LkIJbt8GhpBr/ejCRpJ7hwqLMvw1ZhOz2v23Gcb2SMn3HLzPlVzh/cSXoTmtdsNpQUOMtwdJcbGxO1EoWgxs5cPyPZnnXTZGa8KlNemWeOdNIGxmtFbMtmd89TT+pyveN8fHycO+64g7e97W3cfvvtjIyMNJXI2vGBD3yAt7zlLQwNDfF7v/d7/Pqv//qKx96Othy2xj49XJwmVOtXXBJBSDJTt+XVWaygumgbk+qB/DCs5bnXCtZJu9yXHyStrae8LT9rVnSrWi4roTBSbqARzsr41K33MF9odc981pV7ueqa/avef35+ftsMrKextdiOrcVXM/5WO8bb+fRSGYJAoWoC/O4RI42m1uXZdIMAOWXh7HLIZGId/ngHhSpPY/WNdcj6CS0JpCCzjMO1WXj6eV0emzHGG5ruTzRWHOcrxLxWMzYqkc9QJoe9suJeV5guuibGGLQx2BvSPNl6lEOfpO08YdmEEydOcMEFFwBw0UUX8e1vf3uRU1+pVJBSNqVjoyji/e9/P/39/bzxjW/saEjZju1oy2HlMbhWfyVUckMOPUBYdWOnXmus0G2+L0KNk7SwbQsrcqHoY3KDkFvFwlQEWJUZzNA+WEcDwgm3RK4mOXzgwJr3fTJh+4VGNgCjDUZptAG/FpHcoszOG2+6bGsO/ATgbHTkfBpPPqx2jOuoNVmEoUKVopg7vwTcKOrqwCmjiMIQMRMgUzbJngSJnEMiYaPDGv5UGaMNZFsa9K4MnxCn/mk8dbHSONdS4mzQIfWlQBmzqZOtrjv1bGNTboyhJkKyieQT5tR7ntfsU9Ho8r0Q99xzD5dddlnz9Xve8x7y+Tzf+c53+Md//Efe+MY3Nj9rbyRYLpebjePaG9stbIK3XGO7VCrF8PBws7FdX18ftm2vurFdIpGgWCwCsZJLpVJhfn4ex3HYuXMnExOxBnw+nyeVSjE/P8+1N4wsaiQINBvbNRoJNprgWfkMQdVFBiFYkB8Zwp0rYowhkU6RzKTxy3HkPdOXRwmBqAvX50eHcAslgkqNRCpJ0hYE5bgA13FsZFEgbEOix6G3L4VXCzDlSexUgfTIDrxafJx0PofRhsiLg0a5gTzhmXG0lNhqhvTwCN58fH9TPfGCJnLjuSg31E9YdVFCYiccMv29eIX4/pYiwdDQILVyZVX3+8nYSPCsS1rKiTKFN36MxJERjC/o/cXrSd9wiPC7pyi+/W8Z+dLPkdg7gJqtMfuCP2PgT15D+vpDlP/LF1FnKqA02dc9g9zrnsn8v/0ksuJiLINOOOT/ah92byudP/eGjzLy6bc2X4ffPUX4r8fo+7UbF11f9YPfIHnJTjLPb0lIqukqlf95B7rkg9Lk3ngpVm8G9yN3N7eRD8/Q99svJvvSC7p+79mf/Asyoz0UbJv8z19D+tqDnee95atE3zkFQO+vPp/UFZtXqa1DFyuRXlu662lsCOl0elPHeOFtnwSlwYkN8+AHX7PmMd6Q9droGM/8xGG0FxtWYwyqKkBo1P/5IdT/tt94BOv8WILNYChHPkMffhzHlVjSUPqpXURHYm1kbXQclYw0URQRVSysrIOTS+CbECuqYvwKDO4By6IahQxntr6D8o+DDNpGsBFbzuVD9P2Pb1Gbi/A0mzrOG9jIOO/59etJvuZZXb93cPvDZP76e8xnk/T/7stwdnbyda3/8AVGHp6j8l+fg3nZ5o8hbbrF8LcPtDEIrciytoX3RhjB7d2/27t8t+Oee+5pNsoEmk3lLr/8cr75zW92bNveSPBDH/oQO61eCm/8GKUjIzi+YOAXryd9w17C754i0Rjjvb2o2RruSz/JwJ+8BmenTeqPvodTt+Xh657B3voYF/UxnqU+xpPJZmO7uTd8FPvTb22qtTTG+N62MT4yMkJfXx/hn3+P8BKPvQvGeM/v340u+dTqY7ynPsYLQIZ4jGfq/krjPMfKs2R6e6C3h/Rdp8h96vsMpB0qv3ENui92RvOjcZZj4JfuIPFokcp/fQ7RtXG9U8/wAJm+PE4yAfPj5PtSCGGQZUkKi5S2MJGF0aazt05tmnxuAHKDzUh8qicbZ8tKZ8jlbCCFSUhwnOY1NJDKtXqXZAc6OzI3tlVCMqsCztmzu0mHe6o1Ejxr3p1sq9pPPXsfg+9/FWqqSvFdt5G+4VB8cRfvJPzyIyT+3VWE//IoyYtijnz4jRPYIz0M/N7LAYjm4oprDNi/cjkkLVL92Y5JYDNQ/vV/JP/O60hduQ+jDeL746SevY/MC+LOgOKBaSr/6w4yP3HeksewetNY73/JosEBoEs+0XdPMfzJm1GTFcq/eTtDf/nGLkdZH3ToYdsJrKed+icMjXG+0TGuyy25scH/+3rsNagoLES1Wu06/mBtY1yWx6HuVihlMFEcgbd//hKshI2Z89H/70GcX42deleEKKOYf9t+SNg4hYjBT55mru7U+yKiEgb0p+vFYspgahLpK4p4RElDMhWCV4Kewfh4WuPYWyvitdz9ehobs+V8exw1lCH/h68im0ht6jhfDqsd58nndU/VG6lxP/I9eP+LyZ+JqH3om/T/zks6ttH/7fmEn75vS64fINKSSG9f9RuDWRetaiNLlcOHD3PnnXdy9dVX8+CDD3L48OGOzxdSb6SUGGNIJpM89thjixy8dmxHWw6xfep2hPX4K1IrPBE1XpD71EMU//iFJB+ap+cj91P91as6zlH5zeeQ/dxi1R3h+Ti5JJaKUNIgXIUTtCiZVqgRcxJnMEEi1bLflleC0MVk++pTi4lpNzJEa4MINOlciBEBJNfegFB4Pn4ywcnqPIf6Rlbe4UmIsyZpqdRizq2uBh0cxtQVe4h+EKeTwm+dJHVNbGCtdAL50AyqrgNr99Y7q7IiBXL91ztZwWhD6spYkcCyLVLPbqkTmEhS/v++TP9//0mshI2arVH9k68vOo7xBNE7Pk/pV78QR4naYPWksPuzGKHQlQB7cGMpGr1IOsqgRXe95aexNVg4ztc9xvs3r4Oq7y/WlYe1jXEdVlDTJby/fggAEemm0o2VqJuVQGHtaUXSy1F97NU/t0KF2NX6XsoYysJDLHRUlEFUAnQxIqhpVKUISqKM5nhlDtFFvrYdU15lhTuyPJa6X08jxkZsOWmH5GNF9JkKkZJYfZsbiOl6vWux5ZbqasvVySKJw8MEMiL1rL3IR2YXnUePtOz3RqalpfaVWiG63PsGKlHQctDOEgxrc9IjJTfUQXfv3r309vZyyy23MDk5yWWXXcatt97a/Pzee+/l0ksvbb72PI/3ve993HLLLdxxxx28/OUvX/LY29GWQ3f7tF5/pRoFzd/LGa8iD/RB0kE8Y5TE8dKi87SP8XbISIAf290o1Nhel3EqNHJOENZassXxxQusWgHLLWC581iRh9aGqCwxJYmSGvzObrUouSrnT0ZxrUA1CpjxFhfvPhVw1kK27bzu6HunKdz8CeSDMwz88avaN8Ie7UE8MI2VT0EyrnpOX3MAeaxA6Vc+j/EFPb/xAhJXxw+Qft8PwIIwm4RPdJemWg/UTA1nbOlUf/WWr5F91cUk6vrDzmie3l+8ftF2wx//aab9EunvFqh98Bv0/dcXtb5u0iFx/ihzL/swJpQM/tnrNnTNWukmfw5ACI0ja6srSnkam4LGON/oGO/7nReTuixO3xV//jPg2FjpBEN//vo1X1ND4m0hVjvGnf19iLnHsYcyJN9yBF8IatUAqQQJyyZh25j/fRSmPOyfjWVWXRki25z10T9+jMRMyPzNrYItCwtjDIXQZWe2NUYVGqkVlgarLBCBQtkzJMd24cuIx0qzHOob7sqvD6RgxqvQn8qQTawvIrbU/XqqwPd9PvjBDzI5Ocl73/tedu/e3fxMa80nPvEJZmdn2bdvH69//eLxthFbzrP3ED48hferX8T4guxvv5DeK2JbvtFxvhTWYsv9qXFSO8cW2XJdCbDy6dbY0FsUTarjntnTVKKA63cfaV2DkssqgSijcc5i/VQcZFvbfXFFuOGar9e85jUdr2+66abm3zfccEPHZ319fatSvoHtacuhu31ar7/yeKXVBdaqRpj2Jmlq9b+lZYEVukhpsFyFtcTzYWkDZUkowck6OAmanXgb0NoQVRSWGy8MlKtxEh5GyZhK7Few3HmwbExuoKPuatH52vyh+dBlLPfUk7g865x6aKWz/H98kPBbJ0hfd6j5WeYnzqP8X28n/45rEQ/NNN/vefOz6Hnzs5DHC5T+yxfJ3hpPBPYvX46Vskn1tvReNwPOWB5Vb8ywENH3TiMfm6P3156/4nHsgSy7BrKYF4/gfaYzNSuPFxD3TTJy+8+iZ2qU/tM/MfyRf7Ou61VKY5SCZOsnloHA2qRstpER1jqdpB8nNMZ5+xiPvnN6zWO8/FtfYvhvfhrYeMp2165dXd9f7RhXpUmop9UnvQpuFBIVBVZbgazzzsNky4q+P3+UxCVDVKLODNHsu47gzEcM/+UJZi6IdZuziXhyCqWgGLikEglsy0KqzsidHWrMZJmIHKmxfoSWHK/McrhvdJFjPx/EhXKzfo39vetr+LPU/XqqIJVK8Qu/8Avcdtttiz67//776e/v5+abb+YTn/gEx48fX0RpWMqWr3ac+687jz3/7nr040VKv/lFMp94C7B19Ju12HIVdI+E271pTC1sjY31StusAsYY5kOPgaBV9GkwGCWwLHtp8YOz3HBHaY0vBYNr2MdXkuw2LX7fjrYcYvtU5XjHe+vxV7TR1Noy+aY3heW1qeA4qx/jPTkHXIPwFHa0QubFgF2TaF+hEhYkbeykTTPHIwxWrRUQ0p5C9zpYXhG0wmqINRiNVZsDv4zpG4PE4qxfz/BA8+9ISTwRPeX6PJw1+k17a/EGsi+/kPBbpzpoKamr9pM4f7TJWwNQszW0F6cVVU+q03jV/1ZycyXSnF19WI5FdPd4/TSG6O7T6FpI5XfvrKdqlx/0JlKYSDI+Pk70/XES9aYSrQ3A6stgOTZWXwbjrT91qqIQIxZMSIHctM5q5umOnqvCwnGeffmFhN8+uaYxbg9u7gK1UbG/EKsZ4yby0WE8WWijCaRAK4PVeN6MAWVQRuE6EpGM9bSjhkRa/XMAnbYx6VaUyWvjZleEz5xfZcarMB8unpwsY2BmFlmN75HUmjNuqWMbYwylusEvhz6hWh//eKn79VSB4zhLNmU5fvx4sxnPhRdeyPHjxxdts6QtX8U4Z84DP/5drIEMxrAspWQzsBZbrpfo2OocGEQeLzB+4iTR0QkS5y3NxQY2JBFoAKNkR9YVWGzfu+x3NhEX8pq1rS22cefP7WjLobt9Ws0YB5j2KlSioO7QR7GiUuO69/aSOFkBoUjcN4s8PLC6C/LLuJOTSKGb0fXVwFIGO9TYNQnFCIoCqyg6HPrGdtLTWEEVK/IwxhB6mqAkCWuKyAuhOAlysf9Um53veF0MvUXbPNmx7SomUy85hPfpH5K8NI6AWAmbgd99Wcc26kyF6u/fBY6NiSTZX2gVb+j33QM2KMtCfXAfzu5WKsbUIub/3d8CYA/lyL7xUpZD7QNfw/torIbQ+97n0/97L6fyP/6F6h9/LVZMeNNlRPeeQc+5lH79H5v75d7wTFJX78f75D0daVtdCSj+wt/h2Bq3N0f//4gfrNpffIfMS88ncc4wzmiewps/DlKT//nnrOMOxpBVt7sU2yakiY0x6MjDzj71UldPBHKvuWTlMT5Zpfp7d8bqH0qT/6VW2riRsgUY+N2XPaFj3IgAjCb9ioOIS/rI/v1pglfsankQ0jD6Z3XHT0P5FTvRRpP7zjxyLE20N7vo8/XCkgI1MYXTsw/bsamJkHLoNwttS5Hf5OcaDHN+jT35gXWf78cRnueRycQc4Gw2i+fFk2BXqb+pGjljmJ6eRghB8kUHqX3yKNV9aaxaDct3cX7jBiZmp7ArFQbUKLVHztD3pz+glE6RsGwKbzhIdOYMvVIy97N/i6pnhAZ//xVUs7opPefUIs68+aMAJEfypF53CW6tRmV8vCn1FwQBjuOQx1D8wzsxf/ENEo5D9t3X4737WQQf/BqpQKMiiXzZYayvPURizmXm3X8PgO3YiOtGqXga+wuPMfTLNzZl6izLYuitV+K958vMZVOkf+tGgiCg8MGvYp63n+FLDmB+6w5SR6dJ/OtJxETE5KuPoJQim83S29vLzEwcyR0aGkIIQbUac3x3797NzMwMUspYPStl41VrFJmnNlJDa02xXKI2V6ZnV4qZmRmEEKRSKYaGhpiamgLAZJJIJ0HRjfn+7dKKyWSSkZGRdUsrrkbqL1SS0IQETprxuqTgSlJ/oYkoz8xtwUjeGjyZbblQkkgrSq88B3nFTnL/cAzefnHrgAkb7/XnM/iuOzEpm8pvXBN/5795gOAF+9FD0Pv7d5O6v0zq6+MkjpXwXjXS1KWXVYW1BsrOWqA9hemJ71vkKkxZxYEe4qko6lGkOAODu2EZRkE58tht+pcMyAZS4Ng2yXU2vDobOGsdZT/4wQ/yzne+c9H7tfET5Pce6rJHC0ZJLCcRt6aPQrRUpPt7kaGkeM99RCrCyWTY+azlH4KzhVKpxMDAwJYd38gId/wMdjpPblerwrt67ARO1iK3++AGjy9QtQKJgSemo+dqsB07bcLS4/xsYr3jT/kVVHmq+XrGq1IMXYKCwFqi2dRaEGlNah1KNtboIMm98aSachKcNzCGbdkcL89Sa8so2ZbFBYM7SazRQG/187oWbOU4/9jHPsYLX/jCDk79bbfdxiWXXMKRI0c4evQohUKBF73oRR37bWSMNzrKHumPJR9PVOY41Dey5SlxqRUGVpys77v7OzzjyquX/Hy5sXGsPItbH3/78oMMZro3NVoJZ2ol/ubBb3Dp2EFecuDi5vXfd/xh7N4enjm2v6tT8lBxir35QfLJrS8+7gZPRDxWnmE028uuVXa9Ha8VSdgOt330E9vOnm9HWw7rs0+NWqR1KQ0ZA7W5zi6xlh3/v1435VUETlVtabrIGk6hpYZKy6Hv+DzvkBxIQ/8OSMWBnrDmks53PocH+0boS7WKl42JpZfnA5eaCLEti6F0D6O53k117rdFR9luBVU/+MEPuOuuu0gmk7zlLW9hcHCQqakpbr31VrTWvOIVr+D8889fdKxFqcQ6L9CEq6Cc1KM3qln13fmDRlqTWUNEuvq+rxLde6b5Ov2cA+R/oRUlN1phbeKP2c5B3QoYY9Chi1aJxe9vAivJKIHZxlJq2wkLx/nZQvsY11pRu+5wxxhfCbEOfWcEzZMRSsW68puB9d4pM1dE51PYA8NESjLr1xhI5zoceojpADN+jd2rdDAa2OrndTvj0KFDPPTQQxw5coQHH3yQa665ZtE2mz3GNyJpuJItb6AmQgwwmF5aYUwbjViCftM8/irHxkbVb7RU2GvkzZ+tLr0NNH7H1X53o1Vc/LhNo6Lb0ZYD2M/eA++8YZk9OmGM4XStuL7nTCsoTWKpBX6a0U2/TEYa40owW1ukrcsSS+klB5ipKYQVkWISk8pBzxBOcnG9Rin0mk69MYbHqwWqbTVg2hjmghrzocvungGG1rk4f6KwJqd+YUGVUoo777yTd7/73Zw8eZLbb7+dm266ic9//vO8+c1vpre3lw996ENdnfooWjwoDDZKrOwsGq2xACMlBqvVUr75uenghq2E3l9+7vLni3ysTWxyUygUFjUt2FSYupBYF06ZlptAv9ESNthG+scFi8b5WUL7GB8fH2d0jeNPu/PNKAzEkcJQCZQwm5ZijbQmsZ6J04CamsVOAPlhZv3qkvz5Ob+KLyN29/SvWg1ny5/XbYA//dM/ZXx8nOnpaa6//npOnDjBTTfdxCWXXMIPf/hDbrnlFvbu3buoSBY2OMYbNVCRj53auNTfSrZ87Vh+bC83NjYzCa61ajbLWXCWFfeVWq05O7VRrOe7a7cYzytnKbOwErajLYfYnq+lGHnSqxDIdc7fbrHp0EeBRglNIu00+1kKT2EqiihSJBJby+62VlE3aaqSQBmcnhqJ0CUQGfILmkBVIh+lNbZlLXLo26GNYbxWRGjFjtzSCjtnG2u66wsLqmZnZ9m5cyeJRIJzzjmHz372s0DMsWx0YczlctRqtWbXtiVhDGHVQwar+KFEAKkMRim00Ni5dP0Q9QnC6MVa1xuAkSHG9GxYauuJglIarQ3GLL4HehMKiI0I4qhKFwUcrfW2iWg8jc2B0QrlFjve86VARAblqbNXbd8G42vERAErU8MeGKWYFVip7ubNFSGPlmYYzuSf5tjX8Y53vKPjdSMi7zgOb3nLWzZ07CXVWdogai7poc3V714JxhgiJUk5CYqh1z1q/wSxU1fKBhulF0fqjcGouBjVovv9NfWurk+0Uy/XkSUISwVM5okdAz9uiJSk4HdXxVkRMsIKqvFzU9OYqsTWBuUoZMoGC2xfYW2zWmfLUyhfIzM22qnGC0enFbHXdbpNJQqWdOjbMe1VkFpv27ljQ0up9iIqaDnV7av0bDaL67rk8/mO4qparcb4+DjGaHbv3sPs9AzufAVTq9EvBNPT0wD098ep8nI5bjawc+dOZqam0OkauubRn0ozUyuRrFXIpTL4UlAKA2wl2CPEssU+iUSCYjF2VhYWV+3cuZOJibiRRNYEZEySYjlupjA6OkqtVsP3/UXFPj09PWQyGQqFWO+1vTDIsiz27NmDlLECTi6XI5fLMTcX0xqGh4cJw5BaLX7o9u7dy+Tk5JqLqxylsWsRRenjjo8zMDCA1prpchGjNb1KMTc317W4qtv9XlhcdXpiCh2FDFlZkrn+juKq2TPjKDtBMpls3m+jJH0Dg6sqroK4bXc6ne56D7sVV7W3cd5uSKW2n1zWyMjaOulptxhr0NcdA2MM89UAEWjsVSzCV4v0BvXgjScxnkSXQqzhUZJ7BprtxruhENQYyuRWjNiv9X79uGGlMS60Imk7XR37WNlFg2zFnJ8on0AZjarPVb4U9CW7dCdeJgtljCFtC7QIsbtEl03H392Po5VCFuawe7IkerpH/4wxKNPFqY8/XPL6Gtewlqz1ZkGu0BBuIYwxeK6PIgHblN2wHW05rM0+zQXu+ult7jxaKaKSxPJ1cxlpKbOopmqjtnyzYZn4GhMJC12Zwx7slCmeqJXWdF8KQQ3Httm5DSP2G3Lqs9lsh8xTw2i3G2/f9+npiZ/SK6+8kiuvvBKAP/mTP2Hv3r0It4alIsaGB3B9Qeh5JCwWpTQbGQKjNfmkJr9nN+74NIiI3WM7SA/0IbwAlUiSSTokLIdkMrmo7fPC4zauDRY/HI1txfw4Tsoht3sXaI2VSC7iUu5ZkNJpP08mk2m2pYZYaaD9dfu22Wy2o+hloUb2wutvOOEQO+AAUaVKZabAWG+OofbtewfQIsRxHHbs2LHscdszMo2sS/N1vh9RLtOby+Dk8x1ZmIF0lnR/DqfeAGLv3r0dEf12B3x4eHjZa1juHi6839sV3bptnm0sXIwvB6MVpdIUpcBlX+9gPPn6mqobYFdl1wKl9UJqvTmNnrTEzM3UefbLewir0a9fy/36ccRKY1zXndKE1f231ZUqOpXdVLrKamAabeiJndBCsLgZjTGx3voiZx9AK06eOoFOOuwY3b/481VAC0n5+Bl6D+8nsdRQNQatNE4b/aa5AApWlhbWxqwqW7KZkLptEbLK37VaCVDL1DicbWxHWw6rt09Ka4ptvQ7WhMjH+DVEQWKtooZq02z5JkOFElWoYGfzkGk96+tZ6Mx6VfpT2W3XV2FDmfOxsTGmpqaQUnL8+PGmo9XX18fs7CxBEOB5XlfqTeMBUUGICj20V0J5ZYwGGS7N9zJKUChVCKTERIKOeEjdeERKEhmFXkVVqKiuIhVlNEYKjJLocJ0PRRsasnBbBSNDjDaYhcXCxmCUxKwxirIQKpKoUHU31tog/E7uoalPKj+O2I4TwVrGn/ZKFIMangyZD1zCSBN6YdxRc5N/U7WZxzMKOX4G06VGR0uNqoREsx4l3yVaQb9+q5/XJztWM8aXihZHJR9ddjEiesKjyu0TeaQk5cjvspEiWKJ+yBhD6EpCbwP1RfX6p+XyEyby0FJ2OOXGGDAKXV251f2MX8XtUl+1lVjrr6i1hlBjtglvvRu2oy2H1dunYug1JWLXClOZRRTEqkURNtWWbyKUMZiaQhXm4qLfOsw6FEQMMcd+u/k2a47ULyyouvHGG/nABz7QVL8BeOUrX8nf/M3foLXmZS97WdfjNJt7hBEWFioIWvSdFW5w4ImYNx4G2Ekb36uQHqinQUydRygF1SigP7P8yl+HIaYn19E+ePFGOj6wEuiwhtMzsOwxV8KWc86Nrt+HzvuojY4nEa03pDBghG7e526nVl4EC4Kf2q/g5NamOvJUwHasw1jt+DNaUyvP4NcdgulalRFliKrhlnAkluIFrxtCIE9PkDwUR1FVtYqaq8ZN3epjVyZ3MdfjLlLE0UY3CxOfrhFZHqsZ40s567JcxvgBuieP0qrerGjrJ0lDHLlsf70wWhc7zqbZ62AhtFAYYZBR90XhaqJ/pr5gWG5bE/hopXAW3GcjBFawfFDKGIMrwjXTYTYKY8yy1LcuO6C0ARluOyepge1oy2F19snUFVzWDBFgakXktAvR6n+XTbflmwQLCwzIYoCdnkDbWZRvY6RDam8flrM2W+/LiLmgxmhbz55ACpK208zuxR2hXWb9Gjtyfcsqbm0G1uzULyyoArjiiis6Xu/atYv3vOc9yx6nQV8xUsVt4NewUjLCYKSOC/hCidQeEFM1NC3j5Ybhsk59NQqQMsKRikRq6R/T1J1ho5eITq8R7TrQW4lFV9poxGP0Jj1yXe7FEvfHyB/PDrTbUQ5xteNPeyXm/TgSaIwhqkhm/SKpLSq6yyY2/7im4iLPnETXorhzqaFj7KvpOeYHcuzM9WJbNsYYTtXmiZTiUN8wCdt5wp7XJytWGuOGZdRQQg+U3PQO4KtBtJKjazQGkKb7dlIrso69pNMfhn6s3W3bS04bWtT3VaLZf2XReYKwLmnZSb+xtELX4zTdDHpHo/Xup98yrIfOoDFxBnibKqttR1sOq7Pn5chfMSPZARFArYCqeaiiXLa2pBu2wpZvBhrXZQlDNOVhyVo8J1gOMpsgObZ2lcNpr0JPIo0rQ+YDr9k9OpNIknWSVNsW1RO1IhknsWrltfXgrIWgGlx8o3VMwZF61f6y0jqmlhgwIkK3pcXaoy/t7xutEb7fQclxZUSwTFqykTFQWqEjL+Y2yrCrVORa0CjA3Wpo1TnZGCHr2d5NkLVc9hCtD41uyw78GGJha/HtgNWMP2MMXmWGmoivX1QVlquQWuFtUSrfW0ETfF0woGc88GRXz8byakSFMvOBh9KaE5U5yqEfN2cpzxIq+YQ9r09WrDzGDTURdpXRa9jYdju87CJgE9FOq4kzBAsvzoBeOlIvtMJdQhrQGIMRPgQr02Mglqxcal7RkUQr2RGpN8agpUH7S9tVU/8fsKTE61ZhPTQqpRUIjelGg9oGWIstd0VIZRVKKpuB1dinuTUq3uhqgWimhpoTa3boYYts+Sag/bos0aZxbxR6ZhYdrP26tTE8Vp5h0i03HXqII/bF0OvIkmljeLwyv6WZs60VEl0ltC8gCWoBX8tovYgWI6VCCoWWEqMNKlKEQT2FaXRH9KVjYtAKUXKxBhPYmfiYWsfSj0tfmAQ7ReQJbMtGGwfpRSQHNxbpXm7C2oyCJikbTT8WppMhkgI2pSnJah31emJbRU94sdbT6I7VOEwm8ijWo/Qq0pjK9jTSG4dBTc8yMzLIfOh2OJ6Rkhwrz5J6gvnITzUYA/Ohi2NbZBYWldUpLkppZL3oUxvNrF9bVLS62RclO+aKLps0gzqL7ZwxhplGFmuJqLRRCrRc1VyhgzCWA+72oYjro9ptpzYGdKzqoZcq5GVrIvRLFg63od2pXw2PW7nlujQnsIbGkU80jDEUQ4+qCAiVJFKSjJNkJJunP5XFYJhyK8wFNbKJVEen0vZjbOY82M2eV6OAUugRaUWo5NqcSBmiZl3wtmcNwVbBEj5ycp7UobGVN94AhJacrM5v2fHPWqS+URkddzmN6TSNFaFprgzNIn696wdoNDLw0fXoSsyOiR+2RmQRWkbZGINUErlgxenJiEjKpSkjjSiSL4gijY4CtDTIcGMOzrISjJvhcKulj1GNQvQS6eS1YxXGt/4DGRHG/QWW2kwKdBvnzzzBkaWtwnZUAFhJAtQYQ6kyRyWK61zEE+TQJ7o219l6WGGINzXTNZIstWJeRxwrz65Kw/jHEasZ4wsdY9HuZBiNNhJZl/b1RMS0V+ncZgugTdykcNItd+1romXc5r6blQuUpBoFJCy7qyCDIe7hoOcqK6Q1489U4KGWmFcaBcR22/LAYFacKzYrK9t5TLOqolvTttTpWoC8AOF8rb6fXizwsE1gLItHSjOM14qUQ59ACrQxeDLiVHWeh4rTPFKaaXLXfRkt6mptjOFYeRZXbB4dtd2em/p4PlGZoxh666qnMLUi2t/Ys3e2bPlKWPG6ykVUaeszRZv5+y/EWXfqIS44aqeKdDp0nQ+4MjrW7Y0iYhOg0VoTKkkp9DrSjEpE+FJQjnxmKhWIOgdqoARhmzE3xixKmUmtqFRq6FCBVjFNaINUkuWcKiU2YSJrLI66GEehNWIVXXtXgyVVdDrInLq+nVl2gjEqQlXn2l5vT17lWvFkcuqFVkzUSjwwP8lE8QwGgwwN1iZq0S8Hxz57WRwzXSCcLRJVI2Qt6njGE+kUrgg5UZlj2quctWvcrliVU7/AAV3oaIgoJKz4zW0NpmuEfLNhiCfYbnSR5Xjhuv59lhuzlomDFatxrI1cYvXQPJ/pImlZ/2+X/jBbBV1v2LXWfVaVHSTOPjTrDLYZIlQHvWIhhJaL7s2s30m/mg9dPBnxeKXQFCDYKBr2XGjFicrconOuCVqhSrUNK5udTVu+HFa8LqOQE5PIgrthP+9s4azRbxotl6Wv4ge+ThlRqDhy41cxqUxHdKIdQkXxdtoQSYnQqt5FtTUYgyCgHPlUo4BMl6iGMfFqumGg44mkNdmESqCVQqFRkYJMAuEpshv87nNzc0u3nd/EgRR1iT4ZqeJMwwYy29oYAiHoX8Kpb/8N2rMly8GIELNpGYTtg+3SWrwdS42/Ob9GIaiBElhKxBmuqnzCVv6hUuS2uLX4UrCUhPFJTCKFyvZjBvMkh+MnPShXyY/Gck7lyN/WLcLPBtYzxjvMgTZEkUQ4cUDFj0JkqYru31gavF3BaF1YyXk1hlCppX1xLTBycba54xD1z5ZzZI0xaOhoPhXLBDfOU3fqIx9rCWWNzXL4QyWXlPhsh9B6zfOk0Iak6R6MWituu+02Tpw4wdDQEDfffHNz4fnII4/w0Y9+lJGREWzb5l3vehcAd911F0ePHiWXy/G2t72NbHbx1et1BMOqUYAvBdlEEqV1MyigTFy/c7hvdDElrQ1Ka6b9Ko5lLWl35ubmGN65g8crha4ZpzUhqKLdjQfUzqYtXw6ruS4rCtCnTqFme7BHh3D6c9iJ7Zl56IazftejKMSuO4e+ioikol8ZZGUa0TNEKpUl2U0RwGiMliitqMqAiVqRwF2QHjeGQApCJUjLCB0qjBoAOh8ioTUp4oYJUYdTLykEtTjSEGmEVnGGYAupIZu5OpRddHWNMahlGpYENR87myTV5Z43ILQiMmrJSL0UbUahnlmZd312DC0TqdcKEUpSQFCZIeEksVMbXT5tHgqFAn/wB3/QbPD1Mz/zMzz66KPcddddTTnXwcHBs3yVG0MprKcd6+lyERjs8MkZrVgvLBlBdRYNqHwSJ935HARSEEixaCIOpCDtJJ6uGekC04woL7kBWmvKoUcKiAIPPTWL3H9oQ+eNI8SL+d/GdI/BL3xXrdiox3T8s/hjgwk0qGhFNRgtDXqZ4kKDWaR+0zy91hglUUENe4FTv9mxe40hWoVe+3oWEcYYQi3p3eBFj4+PUy6Xec973sPtt9/O0aNHm00vAZ71rGfx2te+tvm6Vqtx33338Z73vIe7776br371q7zkJS/Z2EW0Yc6vsq93iGmv0lF0LbXmRKXA4f4R0gvm24YE5YxXRRmNhUU+maanS+diXwqOl2fXrUHfdlJ0ubQm6cqnLgyWX8OcqiGSOey+AZyeNHY+Cemz7jYvi7O2/Gi0XA7r6gBaG3whiLRAq5jiEnhlqmGno64bZkopjDYEIo4SRUqi3c60kxYh2ujYuGtNFPkIFTW5saZOCWlwIuf8WsdDV3CLhCIgCCVogy8jhNYdqjrrwcJOqguxGU0uNHqxU1+PgKi2e7qQD1p1A8rF5SkGpu6oT5SLlMLFjS90m3FRUuAFAZX57hOk0XGmxqgIGcTXW6gVqa23890W4siRI7z73e/m3e9+N7lcjjvvvJNf+qVf4uUvfzm333571322Y2vxbuPPFWEryhP5aG1Q1Se2riG9RVKZ64JfQs3FNQWZvk6Zs26qFpNemckfU2rOasd4u2O7yMlti87KmgtSddiR9WA+8JBdjtHg0reupTuUlst8Gu+53JjVdZlOLU1Xes/iwy1VcKvRmEWR+gZkFMVBEa+TC9yufrNZiBdEKx9zrb9dLDSkN6X52IkTJ7jgggsAuOiiizh+/HjH5/feey+33HILd911FwAnT57k3HPPxbIsLrzwwkXbN2CvM/JcCmO2QKHLnCa05Hh5roM2LLXieGWOSbfcdNQNhlPV4iJK2pxfo5ZcXTHyighdVHVzuN7bypa3YT3XZQkPU5hEzMwhplxMJdz0WpXNxFlz6huOa60+QYZK4MqISCt06CNVXDCzsLFHo6GSKwRKx5x6qEe4F9A3pFa4dadfBZJaFDHnu0ijWw9R5DYfCL3ACPp+FSlCtI7pPUrGSwqzAadbaPWEyBwqrZcsNGvn7auo8yGuuSXC2emu+5m6Vn+kNUpqSq5PLVpsBNopTDoICKvz+EGI6lbAawxGhEihUF78m0itENvQqT9+/Di33HILn/vc55idnWXnzp0kEgnOOeecJWXFtmMXwm7jrxmlNwZL+IjQYD/B3Nbt1IXQkgLjuahKhIo609GVBYV/Yb1ocs6vUg63pxzfVmKtY9wo2XRKG/Y7/qAedChWaXemldbrcvDLkd8hnNA8zVJqNQveXijd2u54NTZV9WxA12M13pZ6VfUBSw3/mCJKZ/OpdqdeCpRUiHIrqNUoxNPaYPzqpmaAu9YfGNMx33RbTK2E1u+yMTvgeR6ZTKw6k81mcd3WXHLgwAF+8zd/k1/8xV/kwQcf5NSpU4u2X6pD63ppQQbDyWphyXEndKyyFUiBJyIeLXUvpBVaMu4W4+8oIh4tzXDGLSE3SvHUCmoFqMxgNlgg28B2suXtWP91Gaygiq4UiOYD1IyHkdtvboezSL9RKo7Q+kqgUARSxpx2Ywg9D+1IaqmApKliBoew6iusdt1dJYLmg2a6aKlqo7Dq20dK4QYuWRHQY3SzUMvSqmmMPBk1q6ONMZjIwyQy1IWTmxH6budaLR4tzZB35ZJUDW00RCFOdmNdx+J72d2wGtXiuYtyjWSmRXMRVY9GQGKh9FYwOxEXH4c+0ihkTceyn7QiM1XpUQsDRtvOpUSIlAolZJP41Di20bHsm/IlWtSLoAMPZS3NMzwb6Ovr47d/+7dJpVJ84hOf4J577mlOBNAZObv77ru5++67AZifn2d8fByIm7LNzc0hhCCdTjMwMMD0dLyA6u/vB6BcLgOwc+dO5ufniaKIVCrF8PAwk5OTzWuxbZtSqQTAjh07KJVKhGFIIpFgbGyMM2fOANDb20sikaBYjCeDsbExpqamcF0Xx3HYuXMn4+PjnHFL2Jk0DhK/5COrioyxkFqjjMHCIptwmjq/CcvGsSzC+rOTdhyUNs3JPJdI4Mu4Q6hjWSRsm7D+/KRsGw3NrFhj25if7Cza1hBT5ACyjkOg43HiWBZJ2yZYZttQx9E/27JItW2brFMyGttmHIeobdu0beNVCiAl5CzshENYiyd7PdjP1Mw0MhIkEgl0T5rabCxRdswNODQ0hluuNO93tVrF930cx2HXrl3N8ZDP50mn0xQKBQBGRkbwPA/P87Btm927dzMxMYExhlwut6Jq0dnCWp16VSugQhfGDnd3drtMvFJrUit0ewyV7KAxGAyeiBhuUxWMlMSyrAWR+u72vOZ2LtCiBcdHr+C8Ns4RVDF66fqASEtcEdHT7RD152thpF43u07V51Kp0aL1PRpzmlYKdXoSneiB3s2hB0ZKLpK1LEU+s36V8wZ2NM+/FgrOZtZTZbPZZuDC9316elp3tr2B1CWXXMLExAR9fX3M1ZWXfN/veM7abblbLjef857hAfxSNe70m0yQzvfgFWPbnerJAYaoPn5yQwN4lRpaSuyEQ6avF2++VN82i2VZ1Goe987MkRnoJ6jVUEJiOw7ZgT7cQmy7U7kMc0FIYXKaQEmyA31Eno87V0RGgtxQP+5cvG0ym8FJJggqsRJPdqAX4YcI18eKJD2DedxyGYwiaUIcG7yKQEfqKWnL/fq2ShtsC6K2bUVjjrMsMm3bJu24orOxbdpxkKGLFAIr20uPhtAJwLJIZtM4yWTrfvf3IoIQGUZYlkXPyCC1uXkwkMikSaS3LoN/VslBvoyL8ZRuRDLiQldpFMaNqIY+NoY9SmHZDmHV61jEC62wOqTRljYiofAxWhNKSTUKSWYaniuIegQmlBJTN9qBFBgZoUnQkDULAxU7pev06ZWOFxPLpcqMgapbJoMihSGRXU9RXpzuVUtE6ht1AzKQyFoAO1qfuUFIuicecEZprISDDEMS6TTBbJUoCilXqiSxsAOJqUcxY8cvLnZqP69REeg4w6GbkqUSIwJEMkOpOMlI3wgyrO+jBGI+xE/HnH1rm6TxksnWIuOyyy7j29/+dscE0b74ufLKK5sczve///0dRak7drTdbFhUsNrb26pgHhsbW3bbfL5FCxkdHV122/aJra+vjz179rRej41QrNav350nk0zEjo61WNlkYZFRzm69dhyLVFvyb2FXwYX7puzObQ2GdP18C7dNtm+7wjW1b5vZwLY9FhjHwfcVTgj5oQGoO5bJfA87M3mMMTxYnGwW0gLMKp/dO0bJ1/mvC+lOe/fujaNyMiKbyXb8VplMhqGh1rHaf6enCvxKgB/4rLYONlRxwGe5Oh+IM3y2ZZFssxn+gqLOQuAykM52pUIs4tT7suN9Xwp623XHV+m0qlIFPbZENHUFZRgjRRxTgsVFv/X9pJRgNCpob6YVw40CtBt37O1WC7JWGGKqhzQap+1ZN226/1KrdTfXsbTasFjE4cOHufPOO7n66qt58MEHOXz4cPMz3/ebRbDHjh3jhhtuYMeOHdx5550Ai7Zvt+W/+4e/3/Gc54b6O87b/hlAKtcKluUG+5bdNpltjavswPLbkknTsPzZ/l5UJOgZHui6beO1jkKccg2nWMEoiZgvkrLiRsSGmP2WwaIR0Xuq2fLGtp6UJGybRNu26RWO276t4zikURij0KEi25fDGWr9zu3330l1Pmv5kQW/4xbhrNFvMplMs4o+0rrOb48/C4VEhQrpRQSBjIvT5mtEs/MtySitMUIvy+9r2koRoL0yjZ7aqi1Sj2lFNdo5iJ6MEKHBePGKXxtNuVJddLa1yHs1jj2yc8eS28T0HB9Zmemqf7yq8yiBUEs31qrW6QM6Eou6GMp6FAZA1av9RaVK5cTDsZZ8KLC0QmmFMVAqnokXKlrHhcaqM0Kj63zQpuUAGg1nAiWp1ObjSUnFk5sOakilEJFEFk6t6/tvBdopK4899hgXX3wxU1NTSCk5fvz4ks5XezR/u2DhtZaiVrrZBC76CebSN7Ad1RKsoEo204soRahpD+qOXoNmU4r8RR1Hg3rh2kPFKaa9yiL9+2Lg8lh5holaCU9sP3WktWK1Y7xhFowv0TXZ+Wbnlm3/jQUUZtpk+nToIotnup5DtGUNpF7cpbwYulSiYFUqIY19ZZ3+EyqxyN4vO2aN5qGwjFuLlqVsNuaFJecSbZiUXkekHgxWff7URsXFsl2mCxHWa860XJVe/GqxMMMSKNnMfniyTo1dS6S+cTijutKZ1oK9e/fS29vLLbfcwuTkJJdddhm33norAEePHuUP/uAP+KM/+iMGBgY4cuQIvb29XHzxxbzvfe/j7rvv5rnPfW7X4zrbsD4Kujj9C6BqNcRDxzFz851y4aY+xreAKbMdbTls3nVZYRXCKrIiMLXtJb991u58GNY5Y00BgZZDHciInImbUVmBxPdDMm6ADDxkQ2pK6ViGroFuBqRBk2kYoPr2QqvWRKxNU/seYsPqS0GkFZanMFadqqJUTL9ZcMe6cbRK1Xmyub5FFe0NTE1O0ntgaWWHsBYQ5SRjPYOsx4wYo5Y0qJFSJBqpKK/aoWBj6k+4rE94RkmiwEa6LuG0W1/WNybc+F8RCgIlCZUgaTlUI590u1OvDUiDpU3svHdcp8H3fWp+gK3iugUjQyIhMJHGKLFtovXHjh3jC1/4QpMK84pXvIJkMskHPvCBpvpNNzTHeRs2LLW3QZw5c4bdu3cD8eTcbKqkFaoSYIktsPKrgC/VoojQWYcS+MVpsv2jyAh0wSe5I4dLFDemWqb2I1KSaa/CtFchaSfIJ1NoTAfv/lRtnvMGxjqVTcyTq/NytzG+HKRWKzcNbdNeLwZeHGTRiqTtEFarSM9dxCZpRJEhXlhJrTBW3Huk0dnTmMU+TMtcdb8oZTSirozmipCUk2jOF77sLnFsMBil+E+z3+eXBi/kYJfGZgsvopsGuggFqIi3/w1M/ehO9v+P17cuun2hoPXiy6/OInW6fp0RoV9lIJ1bcl5aCxYF0+rFx9poSqGHwTBeKYDwIZEi7ha79JiuuYUNX1M7XvOa13S8vummmwC49tprufbaaxdt/4IXvIAXvOAFyx5Tie3lvDXgFor0DHenVhmlUSensDZAGV4PtqUtZ3Ovy/IqGCeNKEIyZWGlFjxXUqOLAfZQppnhfSJw1pz6VjQ3Xi7qNiPRcLhN1IqgA8gggLoJNSKm67QdcRUnjf8RSjULqOJus6LDCQ6VoOhXY9UBGfuyuj4buFFAf3vB1ALn2WjF3NQjDI7sJz24u+tlrBSBl56HawSjy0hANs+nJNZCI73Ebo00tglbUbJ2JRyDARESyXgAykCiAhfTKFbuclztSeYDF20MuUSyrmLQ9luGkjCqR/7rlWNam1jhyDJoJYiCgKSIpUer9a69WCZWj9gmBTcXX3wxF198ccd7V1xxBVdcccWy+7WPD6U1c0GsBd+TTLMvP7ilzn3DCVqI9vE3W5dsBTBu6axF6WFpbvNZh/SxqjOYXD+aLHo+wB7LMePXVt0ZUGhJsUvH0EhJJt0Ke/IDAMwHLlNehV09/QymOztFnqptXWvxjWAl7rRaoBZUCD3CyCUmHS3e16iW5y20wq87xI3zBJ6PLgSwv3M/HXpoJw6DNJx7VXcyG079ktFjr4zM9XddcIdK4Mso7iCaNs3riq/e0C1EHijZ/A6apeUqRV27vDeR6R6h1nGg5eoTMBOcbr6tfB8vEqRNvQZsgQiB0RoxW8b0DtTfoOl0d4OO/KaE8HzgMpTpxvBvO/4yrxvPsdISqzKN6R1lORitCOv3R2u2bUfZ7TIXLcRy90udKcBZ6JWyXW355l6XwXLnMfYYcsrH6U9i51Pg2JhQImd9tO+RsC2c4QVL/26iIZuEs9pRttFp1JNhh7KAbhTNtAsjaE3Fb6tKX/iAdXvgjEJMziB92XksTIeygfLDDn16Xef5G0VzhRtEAnS8n6pfX61WQi5IaRoT6/jK+iTQjkYhRyqzWGu2HVHoY0lFFK3MS1Ru94m+2+BtpF/bo/M6kMgwREUBoVJYKiJUgvnAxS2W0LUKwq0teX7jR5RCD18KAiUwGKRqqVXU/ABVl6psTKg6ChCuF/NAiwITRWgRU3im3WLzHnqbJK91NuE4DsYYZrxqk44htaYc+jxaml00RjYL47UiD9fPt3Aiz2azyHr3wZmGDKOS6LliK7t1FtCp7rF99PEdywItsWoF8MoIX2EqEXMb6dzYhkJQY86vcaw8y3itiNSK09X5ZmdIpeNGNdtVWWeljrImklC3DRBnqqSuq1y1T27aQOhhtVFjVJuEYmNkztdqoGXHYiJSkkBEaOl3bAsNGk7cCdXQncOuyrVYBaQL4mJx3VELJer0Q2eJjIqOoxvx37TqiRbdm0bEX4vu7oYxGAXzPUmq/a3gjQ4V7zzzbb7mTzevR5m2YJMSmGrUbOrYWDAs5ZcaGWFkRCUKmAuWsfeN4twFgSnddnxdp1fqSKI8gRFqWX9Ya4306sIVqnXfthusOrdaKb3iQvaJxFJFl7rko+eLT/DVxFjquTjb2PTr0hLcIlobRDFCTLroUoA8XcDMT2HVCqhirRVIhfiZLm6dAuJZi9Q7jtO0MEJ3PiQLn2kDCN+LoygNAy9FZzJPLeYUGikxBRedyeIsMJntERuhZIfBng/cRVGTyLQvOiShkkzPTzDQ0wc9/c3PwsDDK4X09EU4IsQA2XpxUoPH76zg1CslY1GZcGWn3nTTiV+Cv9n4xro+ITZ4nsoP8CfOkDz3UJw4UZp5t0KqWoor9vUyD0I9mi60RGin/lZMb0rYUA48dNCoS6hPcqGLqtZQyQQ6MvjVAr06TaQksu64aKOZL7gM1Atwn7SwbR4pzXRNrYdKcKw8SzaRitWEtKYnmWJ/fmhD9IsztVKTFjLtVSiGHiOZPI5tk7BsVMrhkdJMRzGbqRVQ1Sc2vexMHiUx8R1Mfgc6vwvLSZOauQ97/lEsozHJHOLgjcg9V4GdiB3rsILJDMATSF9qL5SywiomkUSUIJm2sTKbo9J0xi0tem/SLSOUwpXRprWU3wqs6NQbExfHt95Bq6hOXey0y6o8X4/UtxzxdrgipBaG9OlGNtEnkcniyYhiuYJtSYZ6Rzrmk4YSWIfEr9YQedA/isGgfYFTnUWP7KXZSb7t0hoNlxZG+hO23dVh1TqmhyawEMsJI9T/XbKwVCuM0kzummPv9a2It9CSggoJtAJjcGVEWJ9HLctCR6q+mjCNw+CwdKRytlBgqL+Xip2IRSLWSAFrp6FqKaBWwJBFFQROXi1f+6bMlkYuNwuWsYimPXSosByLRD6Jk0tA8uzSTJLZLv5ELULNzDfV6Z5otNvM7YStuC5L+phaAbDQRmPmFbTN98Yvo4s5nB0OWBamHCLKWxeg2bBTv95Om1EUYerh84UGzWjTYSSUVriBR0X4pOu66NYCCSwTytjBbIcXgFZYXhWSdQMlF/DwpSEKJZW2KFgcxe8kX8q2lUYURURSIIRPpDsb0yivCjWJ70aonviYDae+cYRyoYjpHwLL7mo4pdDomiGScRR8YKnW3zruqmu0bkYRAEwkukYSmhEvZVBBgBARgYpInBxHRRFWzW3eN3d+FjuMsLQi47QiAQsXXFZdvaFDJs7Ev59SKqb81O95R8MULyDqiR2VRpTJVxFOEBOxLCAQIVoZ7BVGqdmiaPdmoOa7XR36BpTRHVra5dDnhC5wsG9oXdScSbe8KNIWKdnhNNZm5zuLq2SInis/YVF6uzJB+t6/RvfsRJz7MiyvgF2bhKBKdfQ5TFuvI/GQh95pk5+9j7FH/zvGcnAZxGUHw5xAX/PvMdknpoPvwtbillfCOEnkjIWVDLHSCcjYOLnNL6RbLmq6XRCtlN7XBu36mH7TfG3q0U6tFcJokvWxruf8eg2UAa2Qdmc0PlQyLsyXGq01olzGSWcIlUQb8N1qq0eHCCDZKuJtzClSq3omqM3xVxojBcqYtkmx9XmDAtSkqtXfD5XqKkWJ0aAVPXYCV3dmijs2W0Fas9HoL3QypPdd1Hxfth2wYS+1ju+J4zho0Uldbcy1Sz3hM/NzKOFjRncBUJ6dJmMnyYws3yixgfZsoNESXatAT72OoT6fLzTj2iwuZN7OkKHA+DIOMmmDLIboSkRiOIOVO3sSzH6p2rLn2mDKIbLsY7zNySSuBwtt5nbBVl2XJZZ20i0ZIWsedj6JBchyCEswLDYDm/Ltjhw5ws/+7M8CsWbunXfeybvf/W5OnjzJ7bff3ixSWYiluWAaQ2v1K7SKo/laEwX1yPQCa6D9ACPiB06HCithQdtco+tpSNqKuow2sbZvpGIOZMe16c4uh20rXl9G5FRE5LkUnAzDQ7IptxYU3Xg9EGpcEZFNpJo8TdnkYTa48Eno4tRrqUCBFBHWMqoJOqjWo/Kd90IoSU1GtIe4jRQIEWLPH0cPHkaFIUEUZxKkH9+TsK5xi9Go+RJa2rhSk8m2O/VdZicZQiqLjkKMiBdExdBr4wQ3FhN1+UtpUJFk1o2pH1qbWAHJEFOe2n/bFaz+1OSjZO3tqUqwXtREwIlKgQO9Q0gdN0qTRtGXynblyENcGDjplVtFr2uAqRaQrtr6jIjwSN93K7Y3Q3DZ2ynpHRR+5GO5/djeAdLFkL0zHucMFzDPyGNOBdh37ydSb8UCcjb0JC1C/3IqxS8xePW56D3L1zRsCYwGbx6dHwNtQRhBBUxekRjOdH2mf+xRLFDqH2BXTz/KxIEFL/TRkeQNE//KbXtfQD1NGP+r6xrrbYcwtLjynpBxhrDs4owoCkENbRTSlWhMXJdVd+rjLqgt2xXWm1/pUKLr6kOW0Uhfd2jdtzvZjYV3KfTZkVsoM9zNGTf84cz9lLWInfolYJbw9rUIsSyLIIrtqa0t7OQCqT0smvkPYzpUmHSja/oilmq3GgaJDAI8KUgPxdmA2tQcqR2LVdr0EssC0/zXEJYKyKLAybauZ2EPAYBASqoiYNhePnO9naGVQRYCkikHEqsMwoQS0lvg8AqFng+RVRfc4raiMP7Ywy8jS3HPI10rN5WrtgKbMrIanTbPOeccrrrqqo5Om5/97Ge77pNMJmN5pS7jTimFNG2RZ0ComNOuS/XI1YKoovE8rHqHr/2/dj+nDvdj/dJBOg5Cp5Ezqq4YoDr5kkCcr1zCn4yieFKQFQlWxKRXZiSTpyeZxjS7tcZc0GLokk+m6U1lmnUD+YHelYtuTDwJibpcpNMtbaQVQTkitaOTolIM/Jg+1BbpDebnUZ7HzGfuZfhNPUih8ESIpTWN9ZPwg+ZCxkSCSghpx24qF2g0s/5itQ91ZpLE/gNooTCFClY+V1/A1NPrjXtfpwopN0LLVkvwSEqsqL5tW5FcpNSKnRC9yEdv4QOyUay3tbgrQh6Yn+x47wxl8qk0g+lc07m3sCiGXkwZW2URUKavLbsU+aj52taqIxhD4uRXST32RcKL34A/8iwmvl7l3O+eYMeBDAwkYSSBPDeLPG9Ha3K8qr6/MhgbdN1ZtmdChm91KJ7xSJ7/F+QP7EHtfTYmt3xB3nqR6vLsWVJg3CLk+qH+W6iaAKVJjOZocTie+mjv4bAUtDBEQbUuf1vXM1eKYnuhsdFoaVEXHIuj523Unvb+F6ESGBE07YPUGhX6WHWakvZKqJqLkxuoH1o1KTQARhpMtYoerWcRJVCN5ZMbRbXdbLQh1mM3dd54yra70h2NNnzNjfnurpEsOZksMQ8YGYJlx1RMZbA12O3qGsaQtZ1m8zfT9n6341qNt7ucS/oehAKl7CbtVCzR9bzJnV/i+0RKxWpIbd2orSWCd4ESuCJiOP3kcOptraA2Hy/abTt+7u0Emgxyzicx1vbcB3XmQH5BwCmQWNUIegxmQXTfiJhKtUhFZQVk+vIQKeScjyqXsILKRr7mpqCbzdwOOFvXZckI7cfRfCva2uzrhp36jXTanJmaxZNycbcwBfNCECpJ0rbxPZeq5xNKSabiEikVc73buoUZ1yPVZlz3Hy9zyphmt7DGtm7VxUnMk8plMJ7EDwWYCgnfJ3RdRBDhJBNkU6rVPdO2sWl1FrODiFMzU7iBwLI8epVifHycnJMg8j2UNlSqNZRjkenvZV4UKQtFIXCxBnqYn5olWamR6xukJ59vdrMbHh6mUq02z2u0ZmZqCj9ZZDDfR29vLzMzMwAMDQ0RVqtMzZQpZyfYt38fMzMzSCkpui6W0QQivq6BgQHmC/NUizUuu/8QJ64PmC7MMl0sko0MWSfFfOgTCbfJO41cVY+YpzhVLZJLpOMuvlou7rg2V8M2p+jv7ydUCuX6cOYMw4eO4PohyPrvGArGx8fxS0UyWuK7HkJKrKqi107hCYlV0zia+v2WPHDqOOcfPKdrp003CqlVquSd7TspbGZBlcFQjYJ1RePboYRsFlfp8iyqtgVReqOxC4+SnPgOztxDyB3PxH3+f2PymGboU6e4YNBG/MeDBPmWCYq07m50nc6rM2Np5LsO0fO9Mqmv30j0A0V57CSpsc/Td9Fh1P7rwNm8dPhSy0pLeFAJMJk8pPJg2yhfwbSL05/GyiZ+LKL2qxnjxpMoL6oXkMb2oxz5vH3y662NNM0O4I3XHbVWbWSSUCuk52La3EtjVCxRrDWiWkWXfZyx2BENZk4gsgP1vev/DQJ0JEB6MeVnQW2A7CITaQz1OUU3LhHTLfUuIn6iZxf/7E7iarnkcnupYKqWIehYStM1EsfYnU691mQsB98otNFUo6hD9aKhhtPMrDYLXBcvQCK3EgdZ6p1pwcSUqo46tzjbvGShbf2DUEu0u0CZzpjOxVIdoZJEWq4c4NouMDp+5he+ne5F0Y9VDHD6UjH9pRZhIpfk2DDWQP17C4Uph4S1gJQyWEm7ycc3kUTMBKA0TsaJufrZ5Kqi/8oNwLdR5WKsn74NsFKOQBlDSUfk7QRp64mrSTiruQu/3MlC2CJs2KnfSKfN4b5BgsQE0NkBzGiLlOM053LHdujNZXHq3M2u3cIsB+zOG2Zb1qJuYVnbJjnUj+U4KBnE+6YS2EmHdDZDQgqSwwOY6vySncVsO24FnXESOD0ZIiXpHx0iqSTBjMGxLbL5HKrOc+vrjeXpKvOTSK1w0ilGB4ZI9vXjJBIdHSX7enub51WRILVzhP5cL8PZuNNo+7aR75BPJNi1eze2bTfrGuaOHUNailyydez5ZIJkPXpiRES+N0+lnCCj4izAaLaHqpZIO56gGr9qY1LoS6XwBFSEWHxfsLF8AVkd328bUj0ZAi3JpdPNIiglFYf27qUaCubcSTI9PSTrx5I65rpZSbs5ISVsm+xAL8lUiqEunTanyrPUCieJgu07KSzXdOZsQfgB6XwOQhc17y0ZSVsv7PljZH7wF6iRC5B7ryZ85s1UKhDcOsPhWRfx+l2E+xfXicilnPpusCzsqwaQVw1ge5KBoxUSdw8R/SiisvNr9D7TJfHMlzWj6BvBstdlNJZfgcDFpHsg3YMKQc342AkLuyeJ3ZvE2oaazZuFhf0nFuJ9D3yVA4HgcjECxNQbMARttSRdCzMXygV31DhJ3FIVp02JxEQGI+OO2aIcYeqqW0Ho4oYekZOGBgVESZSr0NogT09iSYlxnOYSQWpFKLtE4OvqOUprkGGcIdAaX4pm7RSAUgJhDB/bfQMfmH9gyXujlyiQrfoBaSnQ0lBSEefPpLEWZEQyVoLAKCIZZ13z1J35RNvNagg8NCPsizFfqcYZ0kQsLa0jgRu2JJ8nvQpZJ8Fgpie+P12Ktpu0HGPi4syOE6nYeYdYstgY+tPZ1n3ENOWOtzOWWrzGxfMJZLUH7cpYvrQ2h6UE0SmPZDSKPdyHni4jivNYMiISfaTsQayRHEYo5Ixffy5A+grpKxIpQSKbgLSDySQWZ/+MwfIlwWyNjJPGCrdP/c1Cm1lSEd8L5jgaFIiMxsZiwElRUQKJ5txUH3sTPSQti4RlM+pk2JdcXlZ1M67ricRWUm7asWGnPgiCZmS+0Wnz61//OlJKTp06tWyb83CJAkdLGyStiUJqiRYrPPS6Fd0GmOlfYruqi3b9OPqn6gVT7SlL14eROF28FJQxaKmwhAYtUCbmz4dBFVmfqKTWi6KfDRmwQEaoKGKlWKKWsSqPWMIxrHkRxdBnVCsSKx4NCOvFXhWXUEaoUCCII+JJ20EtU3AqtEIuw9HTQkG9iBllUL6HH3RGNSLRKJhd4iAx8bXjLT/wCJSgpwvvUhuD8TXC2r7KINsZ8vQUxtvEydQYko98jsT0ffjX/yd0up9iUeN+sczhHxbQ1w0SvPlwc3L6rj/Lt/1Zfqp3PweT+RUODqFRfHD+IQacFG/uO0ym4bDnEljXDaGuG8KpCPq/Okj6CwXmHvoS/S+7DPq694vYVBgVp72DKibVA5k8mgS6HGFVI5y+NE5/altG7m+77TZOnDjB0NAQN998c1PN5pFHHuGjH/0oIyMj2LbNu971rnUd/wsTD3JjcoTLG492o14pbFH57g9LPCOzsJvUAu31hhxlncZYrVXpb6u9iu1Hw8k3TUNjjKFY8LDTeXASzWyBpesOeluwuHGJypi6Q7zYWAmtUCpqXr/Ri2UytTZERpG2bO4OCl2P07i2bvArAVYUYdIpjkVVLpYWvfs6qRwZyyYwstXXpS0L0rx3DfGIZWgzntuWaWg65Y1Iv6EmApJ2vAhXSoJfQfd3Ut0aX0MZg1WX+6TRqbr+mTaaGb9KynZip76+r9CqQ2L6yQjLLWHsBNpOgjvXdOAsJZATZ7AK85ggaPoEll8hmoeUZSED1Sl5KiMQPjJKoFWepNDYnsSkHUw2AY6F8SXUBCKq7xuVWJLi9QTCGMMPwyJfcacI0AijURjyVoKrsiO8c/BCcguUL6TRPBZVmZQenjEIo/l+UGBcuFyaHuLSzBBlHTEjA6pacEVmmEPJ/JOqQd8TiQ079evttJnJZDoUZ5aDVgZWqXCifMPEaICll/5qaqoAfoi9a7hj4lCnTkK9uHPFhiqN9GbdIArdqcUstW652XWFmMZnuZFByq5LygyzbPyufg2B6i4xJoKISHXqGyutuxpuIC7QAfh+muCtCqMNoZQUjIttdSr8LESjYBMgmvMwn5wg/c5zm59b2jRVbgDU3Bwy2dlwof3oS6ZxF8qSCp9pr8Lh/sV8aaWiWH3nLHVAXQ2cRBIi1VywWCrulIwFJGyMY0PS3nwOdqSwfBkf17EwjhV3tUvY9AwPIMZnMZvZB0BLst/6I9TweXjX/wbjPwrp/dYZdpV81CW9yHcdxOTqBYjG8JnqSQoq5HW9B/iH2mlcLbk2O8rjrssJUcXTkht7dvGi3C4syyLUij+a/xEvz+9FY/ifhXt5fe9BLs0saJHel8R+xQ7EC4cZ+Gga98OzpF5wP4kLngPp3nV9tewKko2dMDFnUngxLSfdi9EWshSiagInF8uaWQAW2OlE/Pt3S7MrHXOTbSveZgsmsfHxccrlMu95z3u4/fbbOXr0aDObCvCsZz2L1772tcseo51u2Q0p20HEXJq67nwM4bXGX7qL0lNcWN9azJuWFwiAH0bkSSLLU4DVqt2h08k2kSYqh5jBACeVXdyAqv7aotXV1choyTmnEvmxbKMIyTpOLIogQ3LJltNtpEYYQ3KlVjBLROojISjNz9E3tgOBptor6Dn0jObnSigytkOgVT1KbqjJkCgKSWSz6HpW2zSys6qxwFn6UgwGFYUdHAVDPEeY+lczIsAKKh29TtpRDmrNRVfjuzUWPcrEXWflggXGdtJ8Xw72ss+fwarNY2y7a0TWdKlRsPwK0bSERDKe+LTGUlGHHKKOAqKeQeyUgwkkFIO6IlJrXGUdG2uNXZ03G2UV8TVvmu8Gc1ycHuBNfQcZTKRJ0l3hrx0Jy+aCdD8XpDsjscpofhgW+ZY/w5CTrkfvc/yrN8VHRI3LMkNcmRlhbyK3agd/bbb8yYkNO/Xr7bQZhmGHhqr4YRWGkhz4w4c58/5LOzc2uqOyPzRqSR6WLEXYiRCtUthCYye7GFU3jrDoWmfRp3ElVqpuYFbIDDQNUqhAa7RtQ+R1Gk0l48gQpslV15USfqiYjXxGdu9fdFz/3m9CpQJ9g82odU2EjNeK7OvtdGCCqNN4aBO3Ml9U9Nu45iC+3zsnBW5b6ruhyrNUt0FoyE7WDfnULOc86jG+8PiirfmJJ4nKi9OB2mgKQV3msZsxX1gArTU1ETalPX0p8GTIcCZPFAX16NT2XbFLP0TMLOZhtslFgA1OysHKOFhJpykTCsQqCatVVWjAjWInUpimH2hZFthg2xaeWyPdaDq1GTCGzHf+GLH/eirDz6H28RmOFD3Uy8cID+/m4ajCSTFNn5+i305yuzvB+al+Xt97AMuyeMfgBczKgG95s1yVHeHf9B0ELD5fO83vzd/PW/vO4a/Kj/Kq/P5mNPeCVD8frxzn87W4y2avneRQspfn53bS5yQhm8D8/Dmk7y6S/XySyh2PEwwXSByR5C9/FuSGlv4+CxAovfbW4g1aTlh37lM9GKmRlYXPWITtWNgJC8u2MI5dV+5SaNGQiwXLtrZEMePEiRNccMEFAFx00UV8+9vf7nDq7733Xk6ePMlll13GjTfe2PUY4QoORdKykfXoedih31zv24FF1IV+oZWO9flDFxIpGnrzjUdDK0lVaaIzBdg1EkvbmlglTUOzmLUZVBENNr2pc8fr1yFbNqcWhcs2hNNGUxUhMggJq4Kv1KZ44dA+KlHAaJ0iabRuOraWZXFVZgRZfz1fqzA/P82R/efWt20r3m3TajdG44URqcin9x6XPbNWh+NihCRtOYRGt1TVjCYSgkxQixXU4jfb/0EZ3aVrbj0QZUBGIdgxDckXIU49GNXugAtPtShPCz53RdhUtVNuy6kXWuOJqL7gatzLeD/5JNCoh8U9ChbBqGXV6rrBEh4sE6+0hA8VgUpmQYVYUgAGkjnI9oLlENaKZM8Co6SmBV/3Zvh+UCBj2VybG+O3Ri4lYdn4UpFajiuvBHZ1AiusgBJYWqCzg+jBI03KpGPZXJ4Z5vJMp6zqxelBlNHcGxb5kjvBuPAYdFLckNvBZemhZRdf67LlTzKcNSHRhavzQx8+3vxb+RIn29Y9T8ea54393jTxlboE2mIc+f1HuOewzaDvo8sR9sgyUSS/Td6yHtnQqu5QLff8GpohZQsDKoLSHNrYHftpIbFsh1ApIIj1wCdnUKk0ota92PHxP38EcYFD+upBojbH2++y+hcLZDgjpai0dbh9MCzRW5zm/MFYmsyEivv3SXpsi7RpFYEt58w34MnWvcp/OXZSo2PTpM5pyZ51SJRqEHU5tubn9UjNfG0eawnlo4X3XUUaxy0S5eJVfDFwCZRkOJMHGS2WwNxmiLPZK1yfjqlWeBLL6lzr2I6Fk7KxswmsRmS/UWyiTTxe2/oUGDdCuXKRCEa8+Il/a+lVSW9iU5L0PX+NGj6fSfcKRm89Qe8V/YQ3HwLL4g53kh8EBZ6X20FZCyaEy4t7dnNxupNqMZrI8KLcro56jVf37ueUqPFX5Ud5be8BLkoPND/L2gl+duC85nerasmDUYk/LT5E1nZ4ZnqQY6LKmYM+3s9Jfk7s4+ADO0h+r4D+yhnKex8i+8LdpPYcXPH7bai1uJZYXgn8Kiadi3XTtYyjmJYFqTxa0ZZ+X8opMLF05ibD8zz6+mKJxmw2i+u2Ah0HDhzgN3/zNwH48z//c8455xz2718ciFgp0pqwnTgL6Mca8k07EUZcmRnmssxQ1wZNURQRZBxUuYSd78f00tl5XAp8CY4XjxlLxJlSreIspGUMFCcgNRg7sob4vttOK3KtW/TLWE89Fmyw6v83ENOF6s+XNgYLg5KaohfwJ+UHef7g3mYgxWiF8AJ00PqteuwEXj0rXZiYoDo1BXWnXrfZzPa/MXUaaig4/KNpoDPLpIzif7/P4k+fO4+8XDXrYgwgai5qCTpLIagxmM6RcurNu9oW91JJrEhiZ+Iu2L6MaJDiGkE15QtUWaLqi4aoPI1K9bT0+5WGqJ750HWmvYl/twm3CEDC6fRAF3anfRoLoOWiAth4MeBjEgmMlqzYzGUTMSsDPl87zRnpcWNuF78ydHGLClnHQptpuTMkpu7Fmb43duTtBLpvDyYziLGT4CRJFh7B/uHHIZFGjj0DteOZ6P4DXTOUjmXzrMwwz6o7/AUVcqc7yWerp7giM8xzczsY6iKgsSFb/iTBWXPqbdtecuWrpqs4B1uTvi5Xmk50ZDTv+6cU4c8tHa13EgY/bZEqVmE5p74e6TZhiJyM5cesWoBewuFuQXekyJAhVl2Ord2bMqFAV10qO5MkbQcTxN3obBlAlyIsACvjYLx44gp9gW0MtDd2apxSK+SCBlyVKKDgV5vXkPvsac488nXOf/fr4quONMV8SN98Lp7w1ukMV/M1xkih/2kKfrFTy9j7foXcFbGTEMgolvYiTs9liCP+SIU2aqXENACWH2IlgqZspSujJi9Rh2H9K2zfB3WtvL+FP4lWBu0r8BW2bWE5FrZtobVBy1U2bzEGhAdRgCVDElIsUvpYL5IP/wPYDhPzz2XPV8aR/3YPakdcBPeJ8nGE0fzy0MWras/dbZv9yTz/deTSLlu3YFkWfU6Sq7OjXJ0dZVr6PBSVeWnPXnYnsoRG88fFB7jqulGe/+ILwZP03nGa9F9VmB/9AZnXHyC5TJOdTWktbhRWUIWgc3I2oQe5QUicnV4L2WyWoE4N8H2fnp5WcVq74MEll1zCxMRE06lvVzKrVquMj8d5u127djE3N4cQgnQ6zcDAALY2BFoSegKvXMKNIpSUZDw/DqRr8Ora8e3KWn4QUgk8oqkqPQMhZTtL2a0ShSFpYyj5IQksiByE5+P5PkZoRCTwfB9PSMKCR3bnAK4U2JUqaVtieodwKzWMlASBjy+jWIULSAHjE+NYQuKHcUfY2pkzkM6R6e9FhhE6jJB+gF8vQv3ruYf596X9uOleZicnY4deCbQxeFKSwqYaBIxPTFAslZCRIIoiZmZmKJXKyLrzWyiVscbH2b17NxXXxZMSUS2jLJvHLvwBsq5kprVmrlLisGu47mQSt1aLZTWVimvZJqepFKtxb5dIIKXEcmOVtmqpwpmaIJvOMJhNcvrUOKVqLe6AC4hKDcsPSRlDuVpDTEzgBVWSIw7Fh8epVsogFEHN43TlJP78GYb3HMYrFAmqHnbOIQV4UmIFAY7WOFJRmZlDGU22v5cw8hivhhRDF2cgz8TUJJ6UJCwbx7YoVs6+JGM3bD8Kt8GSYnPs0ypQURG3Vk5Q0YJX5fdx3gK6TDucenQqcerrpB77IrpnB3LnpYSX/8zKTQOFS2L6fpLH/xmndApj2bT4iolYgGHnZejBw03Z7mEnzRv6DvJas5+7gwIfKx+jpCJ2JXIcSPbgWBYOFv1WisudjXVs3+44ey2/HLurBvlkfwZ1ugJtTn37dp4UPPdHvbxi6rv82a7n4H2tSM83C5hfPxK3xgb2nMlwYo+FnPZInbvoFItgaYMp1idbLcGtLtMYCywRtpx6DSaKC2C02+boR3Ha2DIOkZJEIkKHApQhoSUsISKoMhbai7+HkrLp+MqwLUIVBbj19uHtiLRERGG8YDCGnrJHwqlTjQonUKFCpBS2tjCBx3LOsNEaHRqcbMv5c0/UsO4uk5vLAZIjj8HxDx5Dv2KUzMHYkT/voyd4/MJLSOQcEFEcGZMBf/3DD/POG97L7MwUJmjcp5U9UkuFQDJOwfcOdSxuwkqw6cotm41FXWGNgcgFEYKRWEpjbBtSufj/y6i1aG1iyb7VnlyreAyGLrQtAJObVP2feOQLnCk8zD3uG7j26Di//+YI13kE5sDXiutzY7w0v3flA23yde1IZNmRaNVzZCyHXxm6hP9XfpSJsstzsmMMvWwPfS9PkLvjOJk/nWDuonF6X3URdhfN9c26rm6wlIDqLCaVI+aF1+UAE6k4qr+J0pzdcPjwYe68806uvvpqHnzwQQ4fPtz8zPd9stn4Ph47dowbbrih+Vm7ktmHPvShDlWuHQuaFmVTaQg1SRWXdeRSyTiybWKect6JpT8ty+rI1KQTNvneDMFcClON6Dsnh8ql8CYnQVvkLIu0k8CRhkTSIWsnIKFJJJNk0ymkn4DIwiI+rpNQoDTJTJqevh50uQqWJm07sQpXKr7XwzvHSAaS8uOPgw3pXBK7rryVSCWxevPIag1vIn6mPl87xbv6ekml0/QPDpL2Q6aLc1hWrMy2J5njh9VZ9uYvIJNKEQEJx2Hv3r0kPI+gGHeXHBrob97Hvp4eZLmMhUUySmKuuqbjHmezGWppm7S0yaYdCH2U4+A4DmOZJMZJUlECsEglElg9OWzbJjvQx67BnaSdBJVjDzOWyVNNu8ggzsSmenNYmSxqrkhPTw87du+mWprGAP15BxHaKNvGTqUY6e3hkceP4+wxpImz1onBfsSZ2VjJLJPGSI3lGLJjw02akJNOs3d4N7JSoBoF9A71E4y3fvfBvoXNvbYHrK1vz7cubKV9gpiydbt7hu/6s9zUd3gR970bMrM/IvvAp5A7LsV73m9DohUgUMpQcQ2VqiEIQUiDDA2ptMXoiM3QQA72Xo3ce3W8Qz2wCYAMcWYfIHnya9j3fgQsG923DzV4GLnnapxUTzO4Y4xhSvmcFl6s6mQM90dFvuyd4S39h9mzBeo62wFnzamPeZidDtlsn0Xt0By2NwCA/MIkaiSN9YKelg6uF0eVEkGsxnLeZ04BMA7IYuz43nb1KS6qjmIX1qDn3c4ckRpdrGIBygsY/v8epfT7z+jcvo0aowKJUDougtQqZuf4EaQNxko1tzfV2MEOxNKtiqcsj6yvyFHnKyqFkQotYlWHcuSTnJygnLJBLKZQGAWWN0vv78xgepOYHfEDOPE796J3SEjWv6rrIwsV8n90jOC/L46EBl95nHNvq3Ly9y/BStpIX5P/+Cn2zMYO+akXzrL/jlEOP1rj9Kc9+NVn4NUpHeKbj5N40TmY+iJLffhD/MEDL+D7V1boDcM1BdZNpAlmI6KeWszrD110IoUvo+ZY2M5QMoSwCk4KRLjIwQZiHqZfBr+CSaQgmY6NYGIZ/X0lQfqx4pCdgHS2tb0MIahhiYBuNzvYhFbZqYc+y0PzjzBf+Wle/GCE+aXzeE96Y9H/zbiupeBYFj87cB7f9mf5lj/LvAopqpDhK9O88drDDH3qUZK/+yClwzaplx8hPdjK8G3ldcUwWNGCpm7CB7+McZKQyEAyFY+hTcbevXvp7e3llltuYXBwkBe+8IXceuut3HTTTRw9epRvfOMb2LbN4cOHOXLkSNdjrMSpf+dHZ5nYHcGrLqLkVjvoOhYx574b/UZriY4MlpZoaZC+DyaEtsJ4Q1xjZbRpZnMxSyTZjWwLKDTeigsUmwdjQbZMg3Il9kC9Q20610zhf6b6ONm6Vrwh5rZXSkV6Sy5hG1H6vFQ/94dVXBEXsgKEUUQaiW5TAOqWdTNCk/MdErs6M0k1EdFjEQdopES7dc5+ZZaBWtgKhLXPa3MFrP7eVtO/eZ9Uf7IzCywFkG3bt1X8I1UbdQpDUPMQoUAKiaqWmva+ee11KuucX2X/Uuo/mIVlVNsWK3LqzxI22z7Nq5C/LD3afCY1hmuyo/z2yGXLFwsbgzP5fVIPf46wbx/edb8O6V4iYZiZ0hSmJZmTLsOlgBE3Yk8kY9lfCyzHQgtDoGCqP8vMWA61P8vgaIL+vBXT6jRAit4dl6F2XR6fUyvs6jhO4VFyX/ufyN1XEp33CnBSWJbFrkSOXYmWfPJlqSGqCD5WPsaIk+FNfQfjYMBTCGePUw9gDGO/fB+PvyqWnHvsZ+YZ/vYgVMC97Qecf5fDiZ0ac21L+eS+mXGuBP7iLwf5T6/8Ph8mnuiM1oQfPoabsimMPIIV7OXyOwMevSqC75ZI3jhEom91X9dUavWCFLj7+KO8LtSUmh8adCiw2pRDTMnDYMU88brxsspnoGc3uq5vY1VnQays4KMH0yQfbiswFQEEGiPioqIZv8poWRDZQVP/vfMAEvfjR9kT7KAvgNnd8b0bmbHpfzzBzLX1utL5OeSjZUYqcFop3G+VyV8/RHC8FBfrzcSZiwO/dj/H9+Q4POERJuIHupK1sX/qRRyb+wEjjzrsG9eMA3//1b/nvRwi+aMIXtS8FRx4IK5/qE1PE+4/2LxUK+qufmRXxpHZEexkBhPFHNawEsXSnjJEB1XO2AlMJLDnHkaPnL/ifT1bsLTG8sqr3NpgyTB2yuO9MU4CrESsr61VTOPQqqPIHICo1uJVbiJffvElGlIPfJrHKtPox97Es6sK/c6DWMknR/HRNdlRrsm27MmJqMr/q55g6DU9vDq5i4EvP0rm/zxEmEri70qhzx/BnJ+D/rNjKi0l4qzgFopbvOY1r+l4fdNNNwFw7bXXcu211274+Oc8PoCl4wV4KGSHc2SA3ocjZs5bPH6qno+2U/UxbzCBQLgLxA1MXFvkLNDK73DrVUPumHghLCOsOsddhmGbN72E06Zi6WKrNoexx+IMCpC2HH6qZx/HVGyvlTGUA5d0EGBSdjOym7Zswvoz2eDpa6WQ3ixohT72OHguHDjQ9fTpyMYaG+h4LxQBjo4XRWEgSdZ57KLsUfEFopHPa7vXcrKAk0q3FlXGUI38BeIOpm2RYzp+q2jOQ/kxTQljkPVuwDW3ggojWEiolIJHogq/PvF9/un8ixbcUt2s5WpE8Esqovcp5mA92fBQWObjleP8wsD57Eku7iXSFTIgefIrJB//CnLsEvxrfxXPyWJJh8eOhuz64TznlnwuzlioIznEBXnErgxeb5csZKTZdcrjwLEa1jfmkZ7GT8TiEY6Oax3n+tLMjWSRB3NkhpPksvvI7dhPz8EbSZ76Grm7fgux71rEoRdCanE0fkciy3uHL+FoUOB/FX7IK/P7uSo7ssE7t31w1p6gRCJ2UlJKc97fx3zMX0xo/upMyDlTNvN1cQo/KUm2KQQ8MDWFtMcYrUk+fGvs0IcJi6mTZZ5RUMwMSn7qWb9A2X8EgHP/94MA3ON7DL5mH86CaOLgf76PE9cPMvDyNppAvSDtI6XHSI1PAWNEj0yROm8n1r3/gDX5GPLlv9pMxpmorklvG4wGa/Zh+LMK+nd3QaJ9woj/Xq75QfiMXWS/cxz5+BTO300gf3cAY9KgFeUoIJSSqgyohX5ds1m0lAi0xhRnyc3tgbrOf+/BXQA4dTtd3F3EP91L+nQBMxFPtOL/PswFDwuOH0lx6I9PMjtko5wkpy66neTU8zk84XH6/K9jv+knUYFhZmiQd43/K9GLNc6LFN/5ozH0/z7KeycPxeeclTTc9T8q/Ihb6n/L8SI8u60wrBaADMBOdtBOok9+mij/KNmb/k+T+yaFohDUiFVFyoTJFMarYv3dJ+Fn//OS9/NsY2PcPVOXRxOwGj99lc78Wptv3O3PcU9Q4MbiaS56/F85ln0Wg19/CdkLc5i3jG4aP/FsNAU5lOrl14afwSNhmY+5p4iem+TGl+zk2bNFsvedwXz/DKl/HkBkM/jX7CB3VR/2WtWInuJIdqEstcMQ+5aPRRXOlztod54vfbjGyz9l+NgvDcHh2DFucISl0tihaqrWaCU71UUMLeUVrZuF96qeLR0XLo622efWm/m5ChwLHfqYIF4cRKFgYVxft6tPAeNukYNhHiMFloyaTv3ORJarMyMcb6ipGYPSCl9KdNsiN2XZseSxbAkHeFFANhRIoUl+uoSJEuiXtb7bG+/5LH82FtMPLG3h9HTKA4dC0BNpEsqiKkMaWk5KKlwVohoR9XpW0AQBKIUulTgt7uXc82N1OleGHY4/gUc9TobGEDUWRFJCFGLX+f9CBCQi3byW2D9fEGRSml+f/T4LYTBUIp+gTmFtNK76t5Nf5+LUAP93icXN2cZ25WFvht0MtOJOb5L7wxL/efgZi7Tku8EunyL18D9gubPIA8/Fe95vQSKD5xuOPRCy7+gM17k+0Y2j+BfswF+N3UzZREfyREfqJdrGYAUaHAuZsEAbRicCdp9wsb4/jXEVcTmJYWogi3ftNex+3nVkJr9N9lt/iEn3Iw48DzV0BNK9Hffq8swwF6UH+Ezlce7yJnlGepA9iRx7kz0M2alt+3uvhLPm1GshUHMVpNXm9wKffPYMP/+9MSyZ5k9+rsArbxvCGIOMDHx4nCuP9HDyfJdzHoxXkVOX/C1+4acIvjBBRmoy+/4EO/NXqCMB0EqhX/atMtM/Cjnzit2MXh0rCXzLn+G1vmZq4gwD7OVnJ7/BnAqbyjqTlRof/OIYAL0fm+Wbz36AV91xiEL+HPyXtisV1JUP6tGSnveFDHgpxoXGIl4giLBV1LhcEk/uHWT/uM3MR8fZVUgyXq5BSoBRzPk1DIaS7yGJ9c7zX/h5/N7fJ3fdT2NkhCp4RJmQx//DHg5+aIq+4TgqmQ80hbzD/zqU59ZvOsgTPvasYq4PDj8sELaFumMCx8DOggY0D/67m0h94RRBJYH1jv/YUF/kbwr3NyXolO3wqWsLvPGbcXr4d153P//hXy5BTJewx/p41V3/wFzvK5k6J8Ke6Wy7bgJB/pNvILrgp4iu/DkeCEv8Y/UkH/jRC4AX8KJrvsBfH35lfP+UjNuKl4K4a1/Sw7rvG+x64NUU/vI34EXvWGnIPY06VptENsbwudpp5OwD/NyxOzluXcvRybdy3kmNfP1uEhes3DBqK65rK3Beup/z0v1UVMSd3hS/2xOgrxkg95wEozLi4h99h3N+sBvrjp2UD/aSfekuUiNnp7h1u2El9Zu+QJMMLN47czefP3i447Nf+FQcSXvLB+YZ/8A+3nzmq/zWyKVNpSMThXypOkHOTvCqcA/aa6OrYJrOq6m2iitLXgWhJL86cze+UfxD/kUAWNKANMhAoquyeYwWw8TUqTutAAzAfzz+VT6/+yaINI7wIRtzvmta0GMnsQxNyWKjY714P9A4lkXmW+8je/4riRyDnjqJFcULjjm3wuhMgAoVxliLFhZTHXSsNk5xHZHRzPXapKTDlPAZquv5R0qhpVjM/26IQlRdqmUID4XM+NU4E9J2buUKbFWpn1U3HW5tDKpN5jkQkkxd7MFvZDsW1Mg9HnUWhTe/jYFpv9pS1GmjI44sRzlcA5ZqqHbfffdx++234zgO+/fv5/Wvfz0Av/Irv8K+ffsAeMMb3rBs08zthvXazZoWfKk2wYNRmQQ2l2eGeO/QxSvo8ded+fv/FmyH6MLXoQfiRVixrDlzr8uhB+d5jhCIF45QuWDXxiqMLQvTVteHbSEO5BAHcvD8tu20YdeDVey7JqndDifPv4Tkpdcw2jNDevLbpI5/GaIaKj2IPP8V6OFYOS1tOby5/xwKKuREVOVxUePr/jQzMmB/Ms9VmREOpvLkrMQTVpC8UZw1p15JhSoHVLM25UN38NjOCeCl/HCkBxNYJLTho32GX5y1+FatxuA/17jg4SJWMY/cFRvkxw8d559vegfP/5d7ueGuLGeGJfxULMHWu/+5BMn7mL/gb+nJ3EP/9/4XOyoBOz5xnPGrYw75vxw9zhtMlnzdFs2pVo67qiI++Efxj3jX5acZLI3hlOJVXvtva4zBMlaz7tWU5hnw6o+ZMLFKiVtDF2Kllpnx7zCQHaPUv5sJt8Sh1FjHfbl8/zk45ii7CvXol9Z12oVp6idHSsQSa1oyNflirMc8hq8D7XmgDGkvQXRkGJgin4oXNmduTFCuzfPW/nM4cXCGAzVIlVPUfmEPtb//Mjo3xrnfHeWR3Ybzzlh899kTvEMV+MRLdjN35VTz+r7nz/F1f6b5+tbdz+Xt13+DN34zfn3XOfv47b+Dwp+cZuq3z+P5X3s5oDkzkCBZDDoKkE8HVZ7zvf/BiaIieSUc+Is7+C89X0Za/5GEMfzpnY8QHIybrERaktAKLU08IecV7qn4YR9+4C0x3WcbYjs2VhFar1hcJYzmL0qP8Oyph3jmt3PYUz/H/qE06ln9OP8mT2IL6Darua6tRp+T4tW9+3l1b6zy4mrJVOSx+/nPIPt8C+/k1/G/cQ/5v3wmKtNDeMkAycv7SA5vbUHrdoaUy2eIpm8eQN03x7XZsTgSvAS+Up0kMIq/LD3C73/rAKm3xFSRb/oz9NgJXu7WUG6nVKOuO4S62nKCZRChpWF/soeHo8qirslayqYnJOrN+l49fief2X8jqchD5/pQ9axXk34iNbKmsDNBk37yJfcMN+UPxZKVImo+61JpalFA1nJIfOG5DD/wGcRrX4OcbdEvpaz36dAGWxnkgsdpNJlDGk3CsolLNDufi0gpopQhI2ymIpfz6/J9TT15DH9VepQ39x0mbTutTt0yDqzMV8v4IsQKOp1xSxuM3zhGC0pKLKkxVus7qCi+R7JR26V1s6zh4+VjfLp6EoBn94x0FDsaTEeXdKk1UzLO7X7Fm2ajWK6h2p49e/jlX/5lHMfh//2//8fJkyc5cOAAY2NjvPvd7172uNvRlsPa7aYxhm/4M3zZPcOre/fzU737SXZp/rYQVlAifc9HwCiiZ/w0qnc3pYph5qhP7qEaB6eq7BtJEr58mJmdqS2uQ1oA20Jc3AcX95EpRez/Xhnn78aJQs3swDWo7HWYHgfVGzL88J3kw48jzn0pcs/VYFkMO2mGs2kaHTqMMZySLt/z57jTm8TVsllsuzOR46J0P+em+hhxMtvO2T+rBLbCV++nJ2Fx80suIHCeyaf3PJ9/rkxwcHqeUyNgbIvjB08THM2RPSkJHYuDM5pj52pOXHA/1mVHeGl+L/qSMtwlcZSD6I8nY8uyiC7/db47egHqmf+dg+dOcsUnYirKq0/dyXtHLub//m2c0uwJHSqqNVm8evxODhUMH03uwDaGva+8CvvPx+kJEpwZtFCJmG7zvanv8z9lmdv2v7C5rzhRYD6fYKgmmRAeYyaDUyzGaSQNzt9rRqYLTO4/zve/dJxD7/+ljnsykomjn6eGciSNz9HZx7l8T12Pu6PqSaNLs1zw4IWcStbgbaDceFFiGXjD1Fd59WvH+aAT/8Qj73kFdz94NxnpEwwlMadCEgHMjfYw/M6bMErCrzxAeVjyxSt+wG+fF0fUfto+w2cvu7EZ9/mDwv0AHX0CAqN4YF/ERadTvK7vAL/16gf4b7ftZvi9D3Fsp03yVy4i+Z3HyDwUxKpAdYxXYgM+byT9j57h/EePAEdoTCe7H3kx342qnJcZABNzL5uTViBxTqb4by8L+K1/Wr6j5dNYGcYY/rL8KNPSx6rH+d7+yGPs+uoR/AOjiPfuwEnay3dAfgqix06wK5FrFlPlDj2P1EHNlx//Fy5+8Ef0nzwf+5592FGS2liWxIV9ZC7txck/zQ1uIDU2RI8oxYV39QBFN3zx0YdgDI6LGgfvmuL068awbLvZvMrUwiX3NWHLSVRKo7Wm105yTWaUyHQ2wrHanFhRl9IEqGlJTkaxVn098h426CtorECjXEEiX8GtF7ymLIes7VATIUpFIEK00fhaksVmoJZEHH/1okJg7dXA2NTufZCdsxYnduiOgMdHbv9zCm+5gl7Lhrhiq3XNIiSKBDKhSSibyciFbEzAMW1SyZ+rneYFuV0cTOVBmbhpY/0J1n4ABnQkFkdS65ehwhqqVoxf+xVYkNVYpBCnTZPu8+nqSUadDLMq6Fwd1J37hZmJggraNtmY87xcQ7WhoVbTOcdxsOvO8NzcHLfccgu7du3ida973YqUsicrJqXHR0rHOCfVy2+OXLoqZx6jST52O8lTXye47G24+fOYfiAgf3SS3fM+e4YS6Ev7CF914P9n77zjJKvKhP2cmytXdZ5OkwMTyQw5ZxWzgIBhUTCt8hlWXd11zWHVVVERd3FFFgEREBRUJAsSRmCAGRgmz/TMdE/HyuGG8/1xq6u7untyarCe3w+drnvuve89derc97znDWSC5d/ZLhb6BxI3bsDZjbhnN4LtUd9TwEu7uCmX0lYH4/kTyYiTyHX3k2j+Fu4xVyAjrVXXEEIwVQ8zVa/ejR7OqLOymOR36U30uUU8JKZQOTnYzNFWfXkhfug4ZG8eXdPY2jPAisWSgmpwbfNx6ELhglgHkgE6++CnLUsZnP0CgTVFtDy8evSvWPTUZWinTkONz/GrqgHKtJnAKj814Cjf7NS776usvGhy2fbEzYS7F/H5BxPktV4Ath7/f5gbLuH25Eb+99YpBKIB3nX+Oi5YrbJmbpaO4JcJ1P+B9u5tzOyO8IFLh/i3+2P8d//LfP7bGne8N4nX5lW2rLzuAl3zHNIbBR/peRKAu6IXguf7lB61JUyk4BFb2cCL88dPHEFV585FRdTLpxH72SqeeWEFR7TOHrG0DOM42M9txhUQu+gU/zPXg8xAZWKNHzVSAXJl2s/4ERAq+bYQ1rOStl64rG8Zn69fxGFmnIyp4Na5fHGen+Xin2KzeSjXzRd6n+NrTUfSZWcp4XFz6ylVovx7wxK+eO6zfOGBOs4PtXPx7LVccYrDrEc1HKuAaahYLTHcv2cgn6Hwp2dgU5G/zc9xbGMzoZLLrGt7K9db+y/zMFtN2j++nJXb1jBn+tGM22R0QC9FeHxqhqfne4wtzjJZ2NVW5qFgolLZt6TW06YF+UB8DjKXxbv1GfQt80i/YxqhubsZMHUA5JoMjJVLEwrnTz+HNW1LWWfniPe+TKLr77jbAmReXkz88XoMV6UY1XCmhgguiOJ1GhTLzgyWUDHFrsunv1awrJ0vqvVEEKVUdpfJjyi37hjl7ZqHm/j8JUNMX+f3d7YvRzgRYlmhn0XDxcp2pO+NSqHium7Fwh5RNXKeizHqO5RjEhYUyvNl1nMqqYor2dbKyvi1rz7N5swQ34oeB55LVy7JEjNBQNoYQqXoOmwb6CvvGEjy0uWqh75MQfscqYaN2LI6tatMJZFKDCfrW6iFlFVuKE3PfpzNlxQJaypj0x9nsoPIdAFXlSieoLuYrSSswfaNJnnP4T/ubSbztiKpuEFPqcCnty/jjrbTUYTATmfLrkdy4iKA+NVl3XQvUg3hDmRR8V8twzsVVfHFw3+MygrXpFpMHU4bWMqBEfTjEtwiWKPna8mqYoojzTqeLQ6Q38dA/50VVBtm48aNZDKZisvNv//7vxMOh7nvvvt49NFHOfPMM8edMxnncti9eTPj2dyW2sCAW+Q98ZlVGWF2hkhvxVr2U5zWY+g+4ktkH0jSvmEd8Xod74Q4pTlTKCnj+2XSzOW6gtPuP6sADBnDFQIlZWP+NQJ/eyvZNesJHflX5JI3gh7Y6eVGZ9Q5MzSl8nnas3k4281X+pYzXY9Qp5roQmAIlZlGhKla6KDN94dMqc+VisxJBgnOg7vaj6k6JoA1rSViioGcGqO0poT04F9PPJNr9ZtQm75VKToAgGYxePSXMGKNOFw/8Q0VFe/qNzB4+828ddlJgMuWBhv5zm8w55qX+Px/DG8Ll/h5wxyOeKyflRdJMmf8oeoy711yBOL+dRz3tySGG+BX/zODd3z+Gb7ffCwAs/7oceNJKU4lxvvDM3mq1F8pEvl8chNLHcm5H0rzp59GiMwc77cnhCDynvl06mGeSZRYst3gzWvu464Z5/kNhn0XPYnzUh8/PyPIvCMc/PwvktKqrRQN/2t9c3Ra5bpXPn83AJ9tXozSEWPa1hxPzcmS9mw+1/ssv207ncvfu54PTz8R+pfTqYV4Y6SDxVaCj/c8zTf6XuAwM857YjPHBdEcYdUzb8HpbF1UorFsCbr4uCTLHq1nSk+AFKC1JQjmu/GyJWbda5A1LE4KlnA6VerX+r7Ja1ocZnVrmK3+VvKri8Fb0c8n9Uf43rzzqu4pXQdshf6gw4pYnF2UszhkeDvUQg4dRc/DGjXpPpztJiddLtGn4P5mFebKIgML2gl+ZgYh4+BZHcbKNVnYkVyzjCgYUQi1wLTTQUqU5EaULU9T6nmJUqaBofQi8vc2U5dS8QKCzW2CwYjLQMRjMOYwGJNEQhbTzQiLzURVfv3XCrtKaaknwtRtVQG3Kg2vg8emRonhurQMaBy10eGK6AwK3esoqYJsf5JwIkSDahIUKt5wUOyukB55p0hQaASFRtaziZfTgRali1UsIqWkKD0C0iNTViKzTgk3X8RzHTzPt7Z/o/8FAJ5ObmPILeFkHDT8miBH6VFit70V800/8WuR4EA5eLTguRQ2vxvL8dAcfZyl3sm6OCGXfDrP+oYgqsyOS5loZ/pw9BZQPUabfwqFEm66gKd5KLbKtlKWT/Y8w1cbjyBQfqVnfrOFC1c43HHiAB8svFRZuKTKfeENW/TLYqU9m4hSbWTKZfPomoI7NIQUqm9kl8NByy6VTG9OEQlstrNMl5JfJdcCsMnJ8OWGI/h1Zj2eXUAp5enLpng1O8iJM48a1Rl+AaXTQi08WxzglUz/rr/jnbCzgmoAg4OD/Pa3v+UDH/hA5bNw2LfIHn744dx///2Vz0cXWcvk8+TKFuiAqlL0/OrDihAYikJhVOE08N1iACxVpTSqrakoVUXWxra1PQ93B20FUBrTtuC6GIqKpY601RQFBegqZflrYTur7RRvCnVwmBGv7PoMP4smFFQhKJZ3p0xVxXU99NW/x+h+lt7ZHyX/sKS1ayORY2PYb5pGWi0n/cB3MR6OjwhqGnnHpeS5WKqKpigUyzIZiuIHWZfbBlSVguen6laFQB/VhxO13R/97UnpHw8KOCdB8Zw65KN1iD/XYT+0EndWBve0RRCJjutvU1Vxyt+NQBDQ1EofmorK+aE2zrBa2OhkKeJS9Fzy0uX+9Fa2ujkiis5sPcpMPcJ0c//Go43mkCn1z2V6OCczheZ54xXbrgaHp2Zt5xShwrQOOrrWUzQEX2w7BeUd501wNci+88aK5X5HyEACceZ5bFubJXXhWsKHnQCKyrp5zzLjlSPpOfwnZN1TOeLhBfRFFaJnjOSm39Dp4gQLJHSTolHksBctVp2u0/yUS1t51Ts8KW8PWkztFZxz22ZuuKDE9nyaJjTmvZBma30dl7XP5MenbeMjZyydUM7O8pbPnM4GxOoMkOLe5EbezCxkpp/CfXejdbmEt3dy61lJpr/6FBfPO5be//w6kZVH8+zcNN9sPGrcdecYUY6aOoeurVuAbTghmKaH2WBneNuWhyAaZpaVqHKtmaqH+XbjUXyt/wVsJJ+tXzihzAFFY64VxCmUuL7leD7Y/TfWNYbJNm4hAYigieZCqVAkZfkV4ub2BFGWRmj8u/+DWzMnAI1WJbxZtBucvzrPRVteYW0G5s3+EH8b2sKxMoDq+IVVft56An88fwuJvp1+9YeOMTpIwXPZ7uZJqOa4l+jBYnicelKycqgf+cwAH3hFh9QrpDqTeJ9cTDR84CadXck12dhtuYTAi0/Di09DWfBO6lybxv5X0bqfReldherOYHpxCdMyEURfACWnYORctJKDK7PYIk13QJKNqyhhDS2oYYR06uoDmI0WXnxyugTsyl3CbIpg5lyWLjNgVIKBguOgAz0XurT8yn8VLe2NkFhWx2CkRHEwhyclM/QIbjlsE3zlsWOCwjG29FAQqFLS7+TL1jKFrU6eNj3EZjvLx3qe4nehc3my0MtPBl/hpvDZDJZjqe7LbGFevsEvdOdq/Ca9gVUlP2h02Hqc8RysfIqcYxO1i6S2fILmZBdF53BG+6blpcPM9b51Xi+Gxi9FpKQ3n8XLu3R1DDGja/x36wzmKCpJhOESGGWtd3MlZDqPq0k0oNvOkZQ23U6e6YZvAT/iiSEABvoGKMRGdgAG3CJx1aBUqE4nfPnWx6rmfQBR8EjZWX8H2BCVZ1hfSjN3VLpLWZI40uVjPU9xz9Q2flv2pU97DoZQsIRGvlQk6Hn8ffsmfrjxJe6deZS/+6z4BdfM/ld5w+PfZXp0AR8DvsC0cf2xu+ysoFqhUOAXv/gFF198MZGI31fFYhFd11EUhbVr19LYOJLydnSRtfd/8dN8f2glcpQ7lJ9nXzLDiHCEVcdcI1ZxaRnt5z7WKDDW33x0W3MXbbUxbV0pCWhqpW3Gs3k4t42nCn3EFYNTg81cGpsxbqdh7HUrxjqnQPhv3yMTOorerqtpeDpN+NR67EubEYr/5GM1rdFZZQKainRk5Tl29qxjLfo7a7snfbijtjnHQVOUqj4MndGAd0YDXlcB7+GNhH/cBQqUWkzkvEYCh0cRQf98dTe+m/l6vOqz0/Et+gNukVdLKZaXBrg7t5mzODDFrw6p46eUCkqsHCiqisoWqvjkVE41Fvq+fpEwQdtjfVtu51tG5u65X4gpM/A+uY1QZHFlkjLf91Y2XH8v+iXfwrDieJ9YTnJJP6Pj8NWPzkHTDAxFZ3u0n6NfbmXD+xvIrNzGts3b+ZidZVshzS2NDWw/PgJ/tpm1IsK/Tc3xIR7jt+2nM+fvksFjXZZajciLGqlrHm9fFko56FZCcG4LDfdu5ob3nciPX7yZMxYfSaB7EPOZDjp6JANhwQ9mLOWkU/0o0WlPHgl4PDoLPmPGGOVGyu+Pu5RC11ZMM8i0SANHf/pv/KD5WP5LDyOl5NKtj5KX7oQ+dnPMGEnP5tlCP7qqjXcFGkOTFuC6luN553v/xm/aTqt8HslLfnvfwyzqjJE2Hc56Mcz6y+u5Y2k/9UmPhW9bUn2h+iiz750OTKfL3I573lb+c93jHBeawqfDrSCgUbe4PDaTZX3rdirToUKuy5L568t4wo91UBA0IXCRpIVEFwqGrqKFDGRIA0sBTfj/GQpKTEOJa8iwhhfS8AIqUimnV/MkwvaQmgKqIOPZ5D2XUBHC/S6kS6RKDimnRKZYopRx8DIuVsajeUBglAQzFGirT5OZt57QkUcSbdj3vOR7y2QLOBpmr+VSddymBbhNCwDQ8wNYfa+gZDcjcttRsr1QViilYlEIduB505C5OLkSZIrgpj0GV+eIZjziBQlvnXw7GcougvTM5lZeWvwwb3n4DJ4/I83MsuL5tS3P8lXFQl+8kAePuYMznlnEzO/7Vt5XWzKQ1vhm/4s8XejjaKuefDlRwMd6nuLW1lP9ANAynpR8qfd5OvQQH2puYsAp+sFvqskrpSTHBBq4L7MF8P3v+50iac8B16so7g/lunl7eiZ1ThGJynR95J0y7IaTdIrU2Q5bcklmDmVo39LCmx/5LH9bfCJ4I/2QL1s917ZGmLmVqlgAr5BCMUKUgGzOpk9zmIGON8rX/+U5ktKTK8jMXYxqusRGd6gEkS/haeCoHuRKEBDckFzNVxqPpPTXQXrikoJVhGQ5zgpoUC022VlmGJGq4GYpJcu+U0/WfInBby/k2UI/R1rlYlfDtVAcB1cqvFJM8tnev/Objg7CXll9kB7bbH+nu5CvDoQ2hEoQlXQ6h6Xo5NIjMVVeLo8SDiI9yHUrRJ/+JscDHLFvlvqdFVR76KGH6O/v57bbbgPgwgsvJBAIcNNNN2GaJsFgkCuuuGLC6842IpzdsHjc556UrLczPFvo5570ZlwkhlCYqoeZa8SYa0QrMTl5zyHl2TSq1n5z51GFIOc5PF8Y4In8dhwkJwQa+UzdwqrfyG5RymI+9m36tlxOdCuETg9gX9ayxxlsXotzudFuwWVz8QC7rxt72St4L/VgPNyA1xiCc5sQnXu/k1qnmlV1UpYNHRid5ZAp9aesMWgaUukvd7JQK67gyGB1IYChsMuK9h72i7ohxLigCGnF0D72LmRZod32xQhmpLp6oihvlyjAv56u80VzO9Oal1CYvo1/XV/Hv5q9/OQvAbDgn+vms/Zf0sz81jpOfTjEDUsE8q6fMWPLUtb/0ww/QGcHg0s1DYShI4s2xvR6Bus3kvjGq/ys90h+J+7he5Febvba+PMijaaESYNqIoTALW8v3fn1Vj4TKlsaRv2g680gPUIBVUXRVP6v9RSCmo7QgBL8evYZ2NkxxbEMAeU0nTe3n0q/XUDVBV5Rgi5GKjsGFMh7VcFcLVqAm1pPriwShBCkw3DSK/W8errGbXo3p62IoNUH4c0d/D7fS3XIMOhzm+mLZ+g/p5mZf1Z4YvM6bv/jM9xwynxKQwaO4RHUFLB34BQ6CRBTSmgnFLEJIFQFRRUIAYpi4AiTla7H2nyJVLpEJOvQXNKJSYOopxAoKrCuhMjmMAoOSsFGK7kooypmOpqC6nr+gkFIIlJQ0BTWhDUyln8/VVHQdJOAZWFGSuiJIbTO5URZj9Iwk9LMc5Gh4w65o9ChznyzI/aXXDJQh9Oxg1nMKaBkeohltpHIrEHkBxCFQUQxDfX+N5OyosBbJj7/ELKroEIhBIF3XULme1v45PZnaFED/GvDYoJFF0eVRI0g4TecxNq+Fcxc304yCNl6P8vV04V+DjfrWFboJ+XahBSVu37eQqGjC64cyWe+2cmyojTEitIQHyoV6HcKnHK7De9u4plCH0/n+7g369dDQUq2Or7yKR2XlGfz3aZj+OT2Z9iez2IXilhGAGMCA8eQU8Itedyy+SV+vG4NcBQD2z9NvpDBc0eMTnnp0NUI5r/MoO9fX8EqjqR39H78BFtPiNB+/IlQlDjWBO8C1+OMp+ewrCGDDArGiiJyDp4mKRguU4ckifoW6hTfDDXjN5sAwUsnadTnbD4cn8tPhlZxSrCZ7W6+3AUjc+ayQj8dQKjock+uh/8cWMGpwWauqVswckNP4uHyw0G/7svXV/+V73aeXO5Pjy47iykUNmX9QnufqVvItwdeQhcKAaGSy+Xw9ADbS3laDb+fZKGAFwqAlMQ3jfTBzGBin4ut7aig2vnnn8/5558/rv1nP/vZXV5zXJrQMooQzDQilcUq+DuyG+wMq0pJ7s9urQRcB4RGWNHY7hbQhcI8I0aTahFRdMKKRkjRCCoaAaGR8kqsKqV4pZhkyCvRqYeYrodpUgMMeUX63CLdTp5XiylMRWWxmeDK+JyKq9meIooplPt/TumlS1GOiONeXo+2l8r5a30u1xta0M9r8V0qNzxK5tnnUO88hnBBwWkLIBZHYF4YeRDdU3eXQ2qp77syU/mZKEEdd1RmFP9DQMKXLnkJYlP3TKkfZfnfLUbNmrJ+xk4awn/PORMxt7wYOayOxb8Y4J6H/S2W9bP965idCVZfahF4qMApz/fT8ZDvaqPXW+QcZ4fpnoSi+lVEsVEslUKzzcxndV5YKgkt7+Kn9mb6gk18/ry+qu1SVVV56kvzOSY0+gU78oNUTAUlpCJUgRINEhpMIUIB0HUYSKK3t8C6bij7WoqAit4Uwu7OIB1JrDlKXE/g5AT0JhGGibQLSEWg10XwCjZCquCVKguB8LB7iQBD1xiKu3SsswgunM2RgSV0H+5LeGygkWNHVflEU8Fx0SIahf+YSwgoPbgB855/p33FlbzFW8PQlI1kG+uZYQWQ9vhAqMmCgkvE7kLYOX/VKgEkwi0h7BzNTp4T7TxIFykleemQky456VBSXWRCx24OkzXDJMwYUTOOolq+4cR1KHolND0ARpyiGkelRKTQQ3N2OyLXhyiOylphBPEibeTDbaitb8K24oeya8axv8ud7y8OilyahRefWsn5PA7pYeT7YdvEub8PJbvyqQcg1IirbAOg283T9X/r8BYVsFXf2U6JTyN4WZ70dxxyQQ8vpJBK+37Rxwcaeb44wMrSEDP1CEcM2TA0RBdTUb+/jsK727lVXc9X/tDMi1OKyI4MfU6eBc+nWX+ex1Y1x3rb77cjzTrwJBFFJyQ08DwGXd+qf7RVz51DGzl2sJ1wcwwltYX3r3+MG6b7yusV0ZkMeSXcrIuuKFh53xVx2oZ6/pLNIwMj75u8O2IJT9cXmd6dhHKs7LRNU+id4b/rRFHQnAgx1k8vkncAlYX3a2zuHBzXnYEujbS00Kwh2lNw4qYYX4q9ynvis7BVge5KiGjENivIpP8eODXYzANZ/ztwSiPfWUl6JAMqsbzLLWtfhYSfWrJKqa+0LSdhUAy/VgqAB/elNlOUHv+z9WXOC7Vx5M82M+fdUSyhssZOU5/upc1qZcgtUaf6qUr/0LWCG59eyS1Hv41QcOT926jun1z1+5s9cQ+0FJV5Zox5ZmyHbfKew6ulFP1ukT67QNqzyXr+3J/3HEKKxjwjxhmhFuKKyUY7wwY7w3OFARKqQb1qssSs4/xAG1F972tmiPQ29PVPUXouC11vIHdJJ4EZ+xbb87qZy4XAnX4qoY4ixit3YW9bwwDnwwuCxJ/6MDSw54RRjojgtVi+O9kh5oD1+o6KPwxTnJ5FzDsbFBC6gqirh6FtSEUg8CPy1YiOl3P41MKJt8N2SrlvRVAFVyKLe2HNVfDdIFyqFgiVKGYBxpJ21h2VxFUE1jtnoQ//uAQEjptLPrGV//dj+MOROWKXLGKqKpCuGFdRu0p0pey/GG+EMw7jlbYhrDlhTviRimAm33wX3Np2+rjzFh+/kP61a8B2AeG78pTRggG0xjAYKhhhZEBFBE1QNWgKIgIRRDQJqSzSkSjRKCRaULweNMVGhhvACIDXB5pAqQ8jFQ9MA6WhBUVRMT0Hr387TteQ/z1KidBVCChEwkEyp7UQeqWfwcDOJ20RsJDprP8DKbv6GJ7BSY9dzfqFERq2zSGd307fsTEIhSC949zXhxov0IA9Z+LYiYkQQKj8HwBuCVFMI0pphJP3K/A6RSQCqWh+MI9TRBTThHLdIDS8UBN23SxksAFpRhlr5ivsZFF5SBAKUtWQUkUali+v8D9HUfxtPCHKn5e39DwX3BK4LngOwit/dsj3Gw4QQkEGG4HJp9TvFkLQ3gen99Tx1ZtVTMdj+lCcBZt1yvZz3IbD6D/nD6BrRNKNtKz0Pz/79hyHxVrZfK5kVSnJzKBLKuzgZEq0b0izZlOap1v6+PS2FuqzAVzPY+OA71Ljbs2wqjlFSzkAWRMKnpRkpENWOpQ8l1WlFI0rb+e98y7i5tR6POmRLZUIDqzhsk1PEmk9hh+YFtONML9OrefkYDOHBROIfI6XTw+QeCFPfzaHCIzsoMnCIA4WGqC4Bm+6Pw5HA/kMAGq6bDyxQQY0/x+lkZ1S6/KlpH/wPJGC5KVpreMMWvFcH3psCFFspzmrcO7d/VhzA3S9O4Ood+ia+yjhyDks2RCh/tu9LDxmNs4lJimvnN3HGXkf/qj7JY6PxBgKWZz5apjT37yAj/U8VTkupay88zr1MOeG2igoUBol7wvFQT5bv4i7spvw+tPM2qDzzfhCFEXl3FAreSTC9bMCJTSDgl3kus3L/ZM9F6sk2BazmJIs8JXO43ikb/mej7HXGAFFY4lVt+uGZRapCRZZ4112c3uYPlIUXbRX+uCZDSg9ApsABf0wslMiWNc0EwhOPhe/Q45mUlr4LjisRNOmx9G13+HNqmN7w4UUNuiE7u4jmiqhGQK3M4AyL4QzLYQ8BH15QN7sOyv+MIxS51uk1PoIakMCqVnANrSYgXQkXrqEDMdQ9DxuX9kSqwlwJFIVCFciAiqyVK5S6kmUkIaXdVBMX4mRuoJRZ+DZLvb2UtX7XhgCJaDhpuzK58IQgECWPERIRQmoYHtIW+LlRpUnL1uSRcSCVAHjigmCR8vbPIE5rWyaMcCiS49D0RWEqWNYYRAKqrbzL1wJBjA7QpgdIRAKA/9ZB9LlAwHLV8izGQhbCLUcmJOIos2YhnRdhFfA68+MPK8VhkQbhqpRch20eXNH3cm3kutTO5GlPPRthXgIhEBtaKlSk7R4FIK6X1UxFvN9RYfdfHQLpWEKmmEiVAGFDA3xJqRTJOhB32HtbP5eq7/eUvAzL+haVUYMEGAGkFJB4EE2D1KSvmQqufowRoNC8o5XmfdwA4FPz0MJRfESLrB2p315qNjnNFaqgQzWI4P1+0cgDsHWqFCRqgqKAZoG6vBuUllRL9dSMOwS6AZCEwhF8QMPVQUUUdbzy7tjo4u/ebKSXk86EuwSsmgjHRukV86dXc6Pjosot2dU+sBdMVm3kicLu5vTW5HwnRsFwylXFnQZ9Earfx/amRcCUHr+WfIvCU4INGGtK9AeKvHy2RHy0iUVUkiZIAf8Bc5zG1fhtEjCdoHWjEqvW0RsytIfjqFsTvHu2TO4J7OZrzUeyV+yW8lKh0xZuf3x4Ctsz6ew7kjQ8EWLJ/Lb8RyPvGOzeoPCac9+i/NKP+K0916Pg6RO3cr1g6/yVLYHYUcQjSquCr8ZWs9ldSMum9GeIexAExqQOyuE/ViGOoDn1/Jqi0lgyLeUqyUFghqKzJP/6Tfhf+4AoH5pJ088/HdOfErhiDfMG9eXA56BaJhCW1KjPqeQthTiRZX/efF53h8WfGTpYm5KBojm/D6ytqSQSruftrOMV3IRv97GX5/155a1i/7M/P7jCC7L8du7EvR/zSGgaLxly0N8p+loZhtRNASnBpu5K7cFxy6R85yKn3JE0XilkOTyrXEA3J4sSkeUiKIzaGdB9wNLI4rO0PoN/vctBHnXRi8Jiu05SCqIdHUQ72Th0NtgJ2a3XUrWZVDv20oxWWRbAuyZCaw3Tyea0FBVsV+TQk/WOXOf5VINnOmn40w/HSXVRdO6B1DFKzgnLqbYcTLbSy0UX81j/D1L4o/9BD0PJajgtZiINgviuh8XZx04Zf+AKPU7K/4wjNBUtI44IhwFK4wAtM44IpJAuhKRSaHEogglhpdch/TAaDaQnkSaMTzHQxEewsnj5RzcpIMSNhCWQATDSMdBE0VktBlRyqLJJF7Wj+L3Bm2UsIU6pQVvwxZkuUqh2hBGSgW3N4U6ZQqKZSD7tuGpLkLiBygFdAiGkANDKPUJpNePl7erLPlqTEOELShJnEEH5f8tQhgKiiZQmhK40sQ0Q6gTBLGoioKhqhSBcChCqjUBuTTEGiEQhsEeJApaSwsU0hCMVe2CKMFhK3gQNTA+utpSdVShkHdK444BCCMArTN3/OVqhv8fgGaiKUolnZVAgBAosbICGq4jFmskrJtk83lYtQWhK6jNMbBLeNkimBYymUGYOiJqolhhRF2ClmCMvkKGQncaHJvQNAuhKXhFG/2YHO4jryBMAzVqoEabYdmORT6Q7GpH6h8HAYqKVHTfpUs1fKu6qoHw4wjQFIQm/P9Xy4q7Vt5RUgQUVczIvmYEKPs1ux6e44EjwS0nFpV+3grpSHA8pOMiHaecItbzrf7S9QunjF4EePZO7ldjd4sFvXr4AzS+eh6eWqI+LQjYki3vD1WyXY3GmL8A/vAKLZpFUzLL5qBC1nPYXEwjEHgWFPqSOEJgpUG3IZFRyJsOH+h+gmd+W88rM5Mc9pDDlDOaSXk2840Yf8/0MuSWyHsuX208gqfzfVxSCBMbCJIuz8cb8mmme3W0DcQwHUlw5ccoCoGO4Mn8SD0NUQIsDYGfRUwOB7pKSWybixtz0RQVqy6MYm8n5zmIzQXWTfOYs85XoRRHIMImDYNFrL8dX7m2qmmQUAFZlaljmLiroYYs1LzgxJVRCqYg7ukc12WzLZ4HFLTGKLqXYsPiJxH9xzP1Ey/w9Cf7cT0XVVHJPZdi3rN+UOr6w/7GTXOmctEyA+XxfupyLo/mhlgY9C3Dd6Q38i/1fja4gKJR8Pzfza8feI7li7xK8B9A3YBH2tKhe4Doo+8ieuFP+J/Mek40/aQYYUUjmc5wjNVAoxkg49jM6d2I3TKVrkQzgXSByYiyPo/+t7WoFSMD/v/U6XiHhSnNCuOFNZS0gzZQQhQ9nEYDN2FUu2WMqq57oFEHShgvp1CeGWKb6pGetp6WE+bTUN9xUO7/esaLtlM8/D3guajbXyKw+m46U1249XNxzj8WJzGb3pxCut/B21JEbC5hvppFL3notgtnHxi5DohSv6PiD6NzviZTGXLSgnSJkOGRH0rjeRZqzsYMh8hjQDKHEQoiO1opZQuUpEKwLkohW8DDRVFUrIYmcn39YHrIcBhFSIr5Iig6wVgjxXwJt6SgGDECdRbZZB7ZJDHCATxPoRCJQAisaIii6+GWSoiWRvRQmEz/EGgx9KCK2qBQzJaQQhCIhnB0g2LJQ4RDhNqCpAfzSFeiB3SEJikWPRACq9PCGezD0UKgG4Sj9WTXbSZUrzIwMEAwGKSvz8/HWF9fT7FYpBgKUTQNpkbq+Lu6FVcz0QBdE+TLaTvNko0ndezeAbbmPTra2hnq6yeTz6HqOmY4SCGVo8vrIh6Pk8tlyPQPoBgBrESM3GASz3FRdQ0zEiI34Ac4GSH/+qWs79ISrItRTGdxbQdFUwknYqR6BwBIxOJ0ROOs2dZFzikRbGrCSecoFAooqkIgEaNn6zaGVA0rFIT2eorZPEINELB0XOngKAIlphBsayeTzKMLFT1fIhLS2D6UoagUCTRGsAtFnEIJIQShpc2s+7Ef5OulsxjW3vsT7gu7syM1GUuL72lZ8XEIFakaYBigmwjTQGgaaAqKBqhlpV1VKu8uoSm7fJHZyeJ+UOrLqAqKqsBeuudKz0M6Eul5FPoGscIRpGP7FTulv8KX0l8w4El/EeCUFwWuvzMgHQ9RiaN4/eLsYvtfsQIIBMH3fgL5ryvoT9gMzHqM2c+diXXYxPFLmmHiSkm7EmRrQ5aObQqn/aLI7ec6SNPDDng4gwUyAYUFXWEuycRZ39aP6oY5hUYEHolFz9CVPBXR74AOzqZBPv+9HH/4VhHFLVGX6mFQ2Lz9xUeB88AtsciM8/ENj3Nnczt10lfyQyXJsFf72aEp3F/2Sxe2wLP84lBNQsNzJQrg/PHPLFoeoTDHtwzKhiiuLbhk66Pc3B+iuFDHWG2jrL4f1a5HjVgYjj//Ss9DKAqKoaO2hIDM2K7h0b6NNNkKethATbhMeUijp8Fl6iYFrxggc0qMm1rnojn+wCsdtZSWW/yUan+8roWG7Ets/v4i8s9uIW0ppEMlrLeexLs9k8hjBZq3Z+iOw1NPrqdwgstMPYJSSeHoF08reC7Sdvn33xv8yzwF1dRZYCb4TdtpuH9+Dilg+k1JsuY3mBX6Fhz9TtaV0mx3Ciymjkw561MIlXQhzZI1i9gYq8NJ9FJ89EaYc9ruDb6DiD0tQN8bZ1AoShwHXE/iulDaWiS0KsOUv27Bclyypk46qOFoCtHMELGCjaaWNw3LxkFdkYgWE3dGEKfexI1oeBGtOvBSsFvKv+156AhE0UPvKaBvKaBuzuFtKzFoaKytV9HmvEjn3DjRGecclAXFPr9jDhAHRC5FxW1ZgtuyBKSH2v8q2tZnMF+4mYAZpaFpAe78eXjHT61KXsLa11D2mx0Vfxid8/U73/su4cYRf7JgXXVAyehjYGHGRo4HDRNd0bDLW4mRlqaqc/XIyEZSwKy2A4UD1ZtM+pTmkX+PeY5qGaCtMUpPzvfVVM1qbSESrJZ/9LW0aPk65R9TMBrhsBkzUcq+zu3tI9UGA4EADYpLxDDRFJWm1jbSpRHLxViZzFCQ1ropKIpCvKEerzDyvHUtTbTH/efLKZKw4jI12sBAMUswsbP+BiM4cp1APDpyP82gvqWZkuvQGqkjbAZoa29jWzZJPBgmHgyzNTvkP7eiMKOzHSEEjudiuM0YwwYdKVFjjb5ftF0EzSDcGKApGKU5EEEIQXtbG5sz/utUNUb1qB0gZmq+C5BuEdYPTWDV7uxIIQDVj5EQ5X9X8llLEF55ovfKFRo9/DiT4XNHKYSSUX+L8vWUUQeHXxqj/vablv+hCIQuELaCMFT//tLD8/z7Sll2X9cEQvfTZApNA8NAaAZoJujlOAzVt7C/XqqijkUoCsIAUFFMDTUeYKRs554h7RLStpElB5wSuA64LsJz8Dz/XtID4TrIYfcID39nwQPh+QVedlT5c7KjGjpCU/xFkioxpIF26QfpOeEREEt2eu5JN2XJB3xfvcXr8jgPCOJpSXeHYMvWIVoiOjN6DN7wfAA3JinmNMJ9HlvrHeQJ78N9+lWOeFXl5ilLaPz1VsDPc7/4qe0c+4DOr64ZILGqnMUlM8C/1C/i58nV5LODdPYPwqiydiI/QJsWIq4Y/HTaiQj77xAykUJQ55QouA5BRUN9oYWObbDu5AiWaiAjEt1RmalHMFIK6rQY7f0DFK9rptDoYUQCvPi+Vurv3kaD66ApvpEiNr2ZjFmuODvK+cO453MI+y2YkTDa1BY07yXc+gHYXsf0HpX1C9vQFR3Ktg6ztZFovpeNLXmmdvuf5V/IEOvX6I9lMW0Nt2khgcIQzdv9HPOZxtV8457Z/PdSh0ui0/ljdgu/GFrNM4U+dKHgSA97my/baQ9oPP8Wf37WhYLoz9GfMIjmIVQUhB76EBzdz1fLhbwiQiPl2gggInQy5febfFcryo+3IV5ohjl7Ps4ONFpuO/VDTyDNONIMIjXTd49sS8BxQQpFSaYkCQYE9Zr/fdmOpDsjyeYlqiLQNN9oP5j0yG8uEV+fI7QiRch2CBZddNerzP0qvhevqoAwFP8dMTwvMOJZaHkSpMDWFHqDBn0xi0xrHdHDupnWfwtzE02U5r8Nz4zu+OFq7B+EgtswD7fB1wtEfhC172X0DY+gJDdQ5cTVsRexorvBAVHqd1b8YRjDrLau+uGxOzZpKUJURZ9bmoZwoeTuWZDIvhDWTVKasUPXlR0yRvHpaGurKPQTETVGFNWQZlYp9ZamU3Cq3QF2pFapE9xDEaJidQlqBiXPRSCwPb8oQ4MVoTuXRCBoC8fpylRnXghpBhJII4mZI8FnALqiEtB0tpZDICzVqCh9wyk8NUVFV9RKHxpmiJJmls/XsFStco6l7cBXV7f8/8oED5FSvzs7UgXPo9TkL5hCdVHyqezIDkk4QG7Q93k1QgEEklLW/66DiSiFtN9W0VSsaIjcQAqkRLd031slkwckwWiAYq6E67goqkogFiQzkAEh/F0MVaGYLSCEQiAWRs/mKTouQlUIxsIUBtMgBJploOoahXQOEAQSMeyijVMsIaQgFI2SKe/S6JaJaugUUr4l0YpFcIolnEIRBIQb6sj2DSKlRDMNdMsknyz790bDuLaNnfetdeHGOrL9Q75VPJlGDwbID/mLZzMSwnNd7JzfL6GGBPnBJJ7rVXakcoO+ldMMB5GepJTzlY1gfZxCMr1XO1JWLEKufwgAzTKx8wWKmXLbRIxiJodr25UdqWyf/zvRgxaKqlJM+2MhEI9i54o4JRuhKIQaGv0+VEAPmKi6Tr7ch4FYBCdfwCnk/R2puhiZgSSU+1AzdHh18sWOWNZEDjQjKIpSmaPysRKqo4ERxp5z4U7P0xSY+kqONdNh9XkhZv8xy5GvwsY3vYySncfbHrDY0OSxbu4yZiw7mldPDBHfpnDpvQq2JlHNKINtKoffu51URCGc81dFLxQHeUfBN2o09QwwpW8Wm9o06O0nFG0m6znknAINyZlkDYVQyd+dsW++jrq3vIMhr0QAFdcW/vhUBS05m5Rn8+1ND/I5pRFQsJZ0EDUCFF0bTQjW2Gmw62huruf293qceE8SzRWYAYO6xRG832+hWLLRdANhGkTiYfq/NR8VkKMmeWvlG2kesKA+TrGc/1ye3c6aEwPM+u9+9Fi80nZdi4ceTwC9yCP6WPf8FLTgSnpXa3RQJJwxqcu6dAHSjLFq7hoMfQOR4zvYOuCS9myml4shPlMYyR8vkQysH0C05piSzLPOnYLxvxspXdHOtK4oaz+ch5+YvHJyjHmPJSvn/bzlBP6S3cqt6Q2cHGgiomoM2kU2NUk0U4WSxZRtI0auyYSiGYhSBiXZ5SctcIsIp4TI9YIeRJ9yJKFgA8rWLtRUF6KUxoh1EohPxwtP8ZMeOHlwbRqbpyKnNwBBPE9StKG/vAMgy8aZkg35giSX93AKsrz7CUp5caBpAk31w9IMXaDrEJM9HLb1T2g9L+AoCymd+GGKu1nDZ38ytqjUZOFgyyUDCZyOE8anMpYS1q0/IPc8IEr9RMUfxuKOCo4UCDRFrVjeRxPWLUAS1i16cqmK4i8QhDRjQqU+qBnkHXv3SorvAYaq0RyMsiG1e+VLxy5Ehskn0xAZH8U+TNwMVJT+OitIdy6JpqhIKUmYQbY5I5OkKpSKFcdSdQKage05OF513nhVGVGuh33528IJtmWTaIrCUNEhUrZ6CwRR06LOCtGdS+F4LnEzSMl1aAiEsT2XjD2SEs0oBzoqQpSrtak4nltlQVeEgqnqxMwAEd1kbbK3InPJdRAIpkfrqxR5VSiEdJOS62J7DoaqkTCDld0S8MdHVN+5UnGg2J0dqW989zuEW0Z2g4IN1dbecEt1QTUjMmpHyhzbtvrv0YXrAmOMMJFgvOpvY/SfJY9w08h9wlOqXV7CoZGXwNgdqbE7OqP/1gwdRrnPhBoSO25rGpjhUW3r4+QGk1ixyIT3MUMj/RSsi1cdG7fLFBrpp73dkRrdNjeYxIqE0AOj20YmbDuMbo30mxrbedvRf6uGDqPah5sameyUxqYiHoMiBEJRARd51cyqLC87o2W7P2d5LQMEj5+K8+ccmifRTriQ0N+6AI/+xs3EDz8aVkH08O1kStswNx2DrbmoQMfU+yg9+0aiaV+h31bnsSw3xNX9/ng69VWHl2dKTJnGeuROvI4vgIQNuSEO75esOirB3L/3oaQ2M+2FC9k2I8kpi5sRmQGUgoMTUSEEc7Ym2dCW4b+/P/JbN2LDhiuBZjt8rVysaYoRpO+wKO4fhkBKAprm79YAslzsSS27CKhCAQW0wIgy0jRg0pBxyHTWU0iVA25nzEIUiqzt2IQ5yogUfHcGxwiwtd7FOHIG4ijBlpemEnxJxVFL9Db3M+Q5GCwBIUicYuNFjsVuX4onXuLJwR6u+V6SB97osEgzueiEY8taJ7A5xVBrgcC2KPWP5Wl6rsimt0TorROYc48FlqMttNiyKsmVsdn8d3I1TYUk7972Ag/HmjCESgSdxzO9nFB+TdoBGAxPToXQseLYMyfOZCaKKdRtz6ImN+FGO3DajkMaIdTkJpShDejbV4BmILUAKDr6pr8i8n2gBfDCUzBDjUSCjUgj5McmCQ0ZDiGbGvz4pNFICXYWJdeHkt2ON7gBI7UJpZDECzZgTzuF0vx3Vrt6HGSKnjeuqutkYNLIdQB3uA9YXruxxR/GIr0R5VxVFAKajmd7uKOKYqhCoS0cRxECXVERArZlk6jCL/Mb1k0GiyPpDIet/aaqUXQd3H30Z46ZAZLFfOXfqhCYanWXqUKpknk0UyP1bM+nydrVeZwde+cvtdFWfE1R6QgnCBsWeccmqOn05tNICQkrREDVKlbwuBkgZgbozaXpK2Sqxo2haGiKiiL8fkuXCliqxvRoPZ6UuNKjIRDGVDUMVSNm+IpRQNNJl1w6womKBV1TFCLGiIIT0k3CuokqFBShEDUsBgrZykJimNnxEQWlMRChN5+uLAhgvGXeUDVmxhqxPZfBQo5UKU9jIIzjefQXfOtmwgwQ3IccvfvC7uxIMQl96j1n9zO/HExqcr02GV0JdSLUUX5iYifGjLFs/0QGdf1DhJZ+GGlG0TzJhnl/QwsswWzZyrrpm/jD8YO8p+N4tke348w5n0jPD9BflvQ0elhA/uTPo286n1fkeSTECyRWfJg5fYJEyqA/bNC5aQ6ZRkHdphjxdZeyuXcrWFDIptnULNAuaqZ39QDy5juA05ny0mo+edzZaJv+hii2kgsI7IDN4pXP8YYZQc4JNBLNe9z/7akcMWoCbh7UmHtbCcuR2KpOhx7GFmC4+Pnyd4pACY+0sTUJCIQRAoqsP24zuliCErAwP1VtEXTKBaLM0+4l3/h5EAqh6ZIZv7dZO8Mjffk8LKEynFurtPDiyrmdfQq3fs+f579+T/n+d74IgPZZsAYdCgsF4Y0aFz9YpKvRofhyLyI8snssIv75b4h08GJxEHHvK7Q+Ph3lczlMoRJVdB4a2sI/C7/gZOr4aeQHAHbPcHZQ2clULs0ozrTTxn3uNs7HbZy/4xPtHEqmByXXi8j2oiQ3gfSDkEUpjZLrA9cetx0v9TAy2IAXaqRYNxs561ykteOc+AebPcnpfzCZrHLtTw5ZsmqlnIZRVQQCX1luDITpK2RosML05NIU3JKfh7s8oqNGgL58lpgZIKybRHSzkqIR/MVPSDPL1upqa/LuMNoFyFR1WkNxCo5D0bVpDyVQhIIxSlFtDERQhUJ3LjnuOvVWiKBm0ByMsi7ZW3Vc280UcMMkLN+iqRtqRTZNUagzg+XFzohrC4xYeUZb6iOGRdwIoAkFVdOrlHRVCKZHR6r4BjW9srCot8Jk7WKV77QiFOqtautuYyBSWfAY5S1hZcxMVL1YUcptfZkNdcdDUVdU4maAiGGhCIUpoShF18b2PEKHyPUGdm9HalwpyEmAMply1I+iJtdrE2UXgWeqUPbqZ+BOOxF32omVv/uP/BbPnnwlxwLa3NnI9gSXhJtQhYq84hlgAfaJHyV210qMUVVaUxffTZ2iglDo+cl9/L8nHTq2JljzoQbqfrWVIUvQ856pxK9djXjoNsQFFzE4lMRTVcyYRmPKg5RfF2Tamvls3LaFQpfFzCSkVJV8cwNyfT0XbltOMnwaH317lq+b8cr9NaHw3AmNHPGE//cWSyeuhuiJuczu0kntymonBEpgZKeqeUADPNRoDPoG0S99Q3X74XTBo8if8oXKv8Nt0zGdV8knXNqiO86Csm7BY8xYcTLblvyKKcsvrzr2/js8jJyC0hInnFXpD6kU4zlYn8YNmRXFQo2PvCc+17CY4uBfcYX/brJxiag6pudU3hT1x0Zp6Ozg1Yce2XmfHAoOhHFVD+IlpuMlpu/1JVzX9VMGTyKUA2iJ3hcmq1z7k0P2tjINk/ZwgqFijqLrYqoaId30Uy4qCmG9hCe9KkXQVDXawnECmo4mFIQQqEKpWMsVoTAj5luDS3uh1OuqWlkgTI/WoysqqvAXHcODQQj/34aqMSUUw/U80nah4iICvgLdGo4DoHrj32axut23Vk3ElGAMV3o79DmPl11UIka1wjssE4wsACYiYYUq81dA0wlq4y3hY3csQrpR+a6GFfadKdzD35taabtza/topV8RCh2ROvoL2Z0uBg4Gu9qR2lUtgkOBFQ0fkOuOTm+6N+yOXAkzSMSw6M6lKr9VXdFoDkYIagZpu0CqVCBnl3bqfqeWx1BfPr3LeWK0XCHdHLfz9o/OrvLUC7F/AqrPPfNK/qW+XBNENRDR1krosj37Av8fikomAEZmlEueOiKffdxhHHljkr6QgdoUJZLbBmGV0Owga44yMQomQRTUV58AtdrqvelL8+j80iuIv/wFaS5k/ZFhDEWhP17HlDUnclV6gNZehU8dcUzVebqq0viuVnpXZRh6VxthRcENBUm1W0RX26RGtX2mdxOnRxf49RyGEQKhjcyl2beZpB5NUq9pZUPUWMZE2I89qvu9Vmrb+UoreOGxJEOfhTO/wcuZXg5b679bX52iE3DS6CWLYEsLtjKEHRhCGgWMzSWcjiAasPobiwgMV4otpNC3PoM3EGAgovPNulk03/tntr9pFgv6k0ilGWUXcXWHmrFGqsmCMQmzzExGmWDyyrU/OWRPWCoWqbNCTAnF6YgkKorjsJKXsII0BMa/5CO6WWWdVoVCzPRTpo0ODB1OW7Qz5XUslqqXdw30Kj9xU9OqXkqCkWBTVVFoDcWZEorSYIVRhKgEkPptx5PtH1/2e08I6kaV+8tYTFXz/deNvcvWEdKMyvehCYUpoV1v641efAkhsDQdfSd9b5YXRcOW+sCOgmJ3gK6oNAcOfgDQnuLuwtXqUJAbGNrjcyYKuh57fFasacIF4O5SGEzusk2dFSJuBpkbb2ZKKMaUUIy5iSbqrBCWptMYiDAz1si8uhZagrEJF32aojIj1kDUsJgWrd+lzMP91VC+dnQvf1evV4rF3VzkDFfo3t22Y3hTuIM2LTj+wBjyhz2AN3viIDRrtp8pbftbDLSYju5JQhF/7nHq62ldcR6fWvY/NPQ0IOP+vVb+i5/JQkmYvPLmJmSxFZG2cWcFkQg6FvrtWnvqWDUtQp3qK+CVJy2/O4r/NofA3BCmphMMh2g6ZRqPvnkkniJcgA0P31M+d9T7RkBjZKRd/cXn4X3qGD/joTq+o/wiiuMZ/S7sOv67NC5prTou1erznLZjSV/8O9yWw4nEfsffZ8GjR+Yx39uAWTDRbQPCjeSCJVQ3iNfURNP2Otw6//mHFXrVc9Bu/BbyFo/O7hC2WaDx/i9R/9AMwoM5/rmvk+IUY9Jn0pqsrhsFd/K5B05GmWDyyrU/OeT7ytYOLK26ohKZIABy7A+/IRAmpBkUXQdLrQ6yFAhCukGymK9yrbE0PzjTk7IqmLU5GKHoOlVWY0szcL3qgSDEyOIDfIU0oOmYqk7EsMZZnQX+wqDg2AQ0A1XsYfacvWB2vLHqxbAnVC1ghCCwh4qaoai0hxI7naRDuklIN5FSEtSMXSqNu5KzxoEjbgaJm8GdBohPCfkKdEekjjVD23cYZzIRAsGUUIxcXuIaVlW2p9EY5d088L/7xp0s6nRFpSkYoSkYIWsXyTs2BdfG9lzaQvFRi3aF6dEG1qZ6x2WVGk3EsGgtL247IwnWJt0Js2DpikbMsEjZhYOamWvSowgURSWcCJHqTe26uaLileddXdVxPIf3x2ePbzjaKF3+t37aCSAlrlAwVbXqe9WiQV44uZ66Y0ZlWIn5LiJiRhBNSqzedSxMXoJ3vG9UiraarP7yAgKAOi2O8fCROJFuRH0IVahEmyzWzQtQv8kmdM34uJqxs5QQAlXViNUFiZ0eRFM0HM+hLi2Yf4sJ/1R9jkBgWuPHuqnoaJpOaYzhQDUNnFKhKq0tDO+kld9l7/ol1pj5U1EmKLRcDtLsP/s9ND/1HXjLTSA99FI3tl5EVVS8aZvIpTWIzCRg59Dn+IsFRSh40kMoIZpXvBOAtKXh6imyW+b5dcz/+jBWrgPZpKAqaiXWrsbrB6mZCM+FcYlQRHUw79hK36ru10NRNb8woOcgPGeC69QYzSFT6oe3bHemmKm7sVUSLVusg5pRlW3FUP3A0KZAhEypSFA3yNklXOkRMwKkSwVyTsnPuuO6ZcXcL/g0+r71Vgh7rFKPMqFvlqGoVSkZwbdINwYjeJ6HV85eY6oH3tK3s5SZBxqz3Pe7gyj783uTeNt1X1AOoq+jKhQkcpcWpWg0OmHK89ZQnP5ClqI7oiQIBC3BKIaq0RCI0JdPjzsvpJvUlWMsTFWjNRSr1BcQCOJmgJLnTui2oikqUyN1hHSTdFwSiUTYmk1OeJ86c+8KUw0vIHeEqviK/eb0IBl7/IIiEo0ydZSlVBEK06L1rBnqReIRKO9sRQ2rsgBu8lw2pgcqz6wIQWsojq6obEj1T2o3gz1lV+43AGgKqlSJBSKkhh1OJvD9rjQPGJTSeRD++BksZHHKL3w/r3/5REWMVPPWhF8QuO1YwN/RHK3Ua4qKEAp1b69OmajUl1POHhZi9fFNlFKfJpxzcVtGdigDMf9VaXWYTBkq0OvVk0mYRA0TXVPJd9YxlEsz0a997HtMANFAsOJ2Y4zaeXZbygsMVfWfzZMEDXPCnUxFKChjrOtSEQRiURxVAdclnxz5zVW5j4x5fwWtAHlRAntiS2Z969Hw5hvL5yq09km2tLhIwLxgAaKUptQzBViLPt1/vxmqSsHxyE8Pw3ZfjuQ3F6D9YAOzV0TJGSrNj0xl85xB5JQoimCvDVEHg8lqRJoURZ7KBQmFWwLp+kXXVB2CcdBMFEVBkxLddUARSEVDKhpCgIJSDqYHR0pc18aRHs6owiyqIghrFkHdIJ1PkcsOIUv5cuXv3Z9LJ0VfHWAOuaV+fzElFMOrypzjvwz8IMsgdVaQzZlBPEdiqTquLsk5JVShEDQNGsuuPmP91H1XlupuMlR1hzngx/7whRA0BcIUXZeAbvi7Ck5+fz32pGRPre6qokz4Mnx9sP9fBAkzSNYpVVmCtbJimnNKbMkM7fDcxkCEsKnQL0ukSiPjMGH67m4h3WTN0PaK0lkfCFes2lOCUfJOqUo5FwjaQvFq+awQmXJw9egA6qFiju5cCtt1CekGUSNA3AyMWwC2lt2yhouYDd8nYe3a9WJv0csuOUPFHFuzSRzPxVR14mYAw5DjFsm6ojI30bTDxbOmqMyINtCVGaTkubSHE5V+6Igk2JwerFLsTVWvWky93jAbLGRJwQhYKIaCV/IQuo4s+pk9DF2nVLIrir5VF8Z2SsSDQayShlFOkwsQCwcYSvl1ABRN4JWVeiNh4uQcvIz/u9ACKqFwkKEtOSSynHRBYXQvd/3XYiyhENQMsk4R6+3NzP7kdvojgkzd+B1KoSmsf3M70+/qwmm00BUFBYheUI88r258e8NCL4EQCrL8fhJCoI7ymTdCAdxcllWfmcOi8/1sNYoQCFNF5h10Q59YoRRgBA0KmSy6qmG7DoomiMQjNDa3MNTXzcbk9krzgGWSy1TvLglDQZY8IjELy9UZyO9kF2XUWC9pkmhGkoRKkGe4WbD5e4sqM56OSgEb8YYWeKqflz88m6iioF8+A778HD3TIkx/dQhjIEihnMb1UBqjauwNAmmFUawopq77HhKujVsqEi2nqA4FDJACx/F2uBMjxASJ4qSk4DlIKQmoemUhGtYb8cINpJ0CQggMRcMQAtspkc+nyBcyuKU8CtWu0ghByfMIGwamoqIpKh4ejuthSw/bdbE9m6LrVumSrzUOmVJvHwBf49ETglpOragKhdZQDDEqHWXMDGCqGn35NIoQVZlgdoewbk6YLnNH11CEQkDz03ZKKdmeTBKJTH5/8L1lslo0DgXSc2kKRomVM/d4UiKlxCtb1D3psT2f3qnrx2jCukVHpA7X89iSHWKomENXtEqO/4BmkLGLlVSso2kIRJgSitHV1UVnWyvrUx5Zu4il6bSVg6gDmk5LKFpJHTs6bkGUfysb0v2VGJaYaU0YsN0RGa/gxM0gMSOAJ+WEu3DJUb+LhkCYkuvQV05dGjbMncZo7C/iZpCIblHy3Ip1tKuri0R0fFzJrhQQIcQO+8GTkq7MIFEjQHMwiqmqvDq0/TXpsrM7c3lDQyeWZmAAwWwPRbuE6pkUem0MVacuFqKnL4ViKEhFooYCqE1hQnoQIw+GVyTnlPyiXGGLoUIOShJFU/BsD1RBXShA2imSxe9DRQhCIRM9aFDKFQlqJkVnTP+W63bErQDZTBGhKaw5rpFZT/WSt4Yzc+mUXBtd1bFdG7EwAndBIhQmZztEDd/SLsZYzYWmIgwVXeiECyZpu1wdVlWqxk6so4PihnWE2gIYgfIONiDCFjKfwdpBDIcQgnAgQIpBQoZBsuBSF43QHIyhhBLoqcGqtpHGIP05//eEJ0GBQNQg11dAUcAo72YZIQNXk7hJ/3uVikCMUsZUodL7uShSM5GKQPH8YlQBTSc3aqFaiXuLamz87iIimu8SG2i1WPvPcxBBhTV/yDHrRdg0s4GwZpJ1ipU4q8mGnKQ+9bbnHTALtK5qmMJPcy0UgV7eDXYAqQcQVpSAaWEp/vFh7Xx7so+mRAwjYiIMDZDgSVzbRXoSIcthM0KApiAU4b8Xiy6y5OC5/m87pPsldd28g1t0cF2/WKaqQb0RwnW9ymLANAKYRoB4rBlcGyc7iBzjytmbzxIeFXSuoqKqKiZUacO251B0HfJOCVt6SOlXfPfFVVDxx7KHhy0lrjd5FgKvG0v9WIxyvvXRBFSjsmqzNH9VGdD2LkDH3Eu3iprC+4+Fpeq0BHdenttU9Srr+I7QFY3Ocp5vVVHojNQRKbuWjB7r7aEEOduuZGMSCOoD4YpPOPgK6dRIHRtS/XRE6qqUjMZAhHSpSMQwxynfhqoxJ97M3uJnrNq938CUUIyC65CxCyTMA2elH4uqKAQO8DZtnRUiPPZ7CyfGpb99vRC1QqiKhkDS1NROVyZJ1NFQBovEEyGisSCKqeCoOkk3SVt9O3bjNHQpUZLd5ITLUDaLEAph08SKmRR6CwTMIDlySCGIBsIYJY2cyCOlRAgwTQ3NENh5Qci0KCk5cEARKl7ZncdQFFThz+eaoqGf3czqBZFKZh1DNXA8D0uo2NiEWoJs/tgc2hSVHA6aqlRXRC/79seDYTTLxMMjqOvknBKudH23mVGpkeN1DQx2bSbPSKpnRVexGurIDWRRzepFsyifq6kKbiyOFRsgUR8jkA4QbY6ihOsRQqDFW4DNxCJh6kNBrKBFqDWInXQopYsolkZdOEQ+VUQPh7BtD2EK2pvrKakOW8QQwpMITeAOjCzcLFUj1zgTpCQaClDK2xQdG03VwClhaTqulERMk6hl0ZvPUCzvjwhA6CrmzABBzSA5qw5e7CZcFyBiBQh7JsGD+Fv/R0UI4Y91IfCQuGVjk64o6IqOrqoENA11zB660A0UI4Rihn33MEDRFKxEAD0wvLMlSak5rCnj33uatWNXPQEo5a/e//2O/Eb0aHlRZY9yu1MEsuRQypSwM35F9QqqjhZtQhZzuIU0eG55UbZ77x5d0dAVrVz8dPcoeQ4Fx6bo2rjDRjtveFYY9f8HeHF46FJamgc/v7iuqlX+8fWBMHV7MYEIxB592WNpbt57pajGa4vdGecBTac5GB1X72A0AsHUSN04V5WENd7PXFV8hb0rO0iiHOQ62so9PP40RWVWvGnC+3VEEmgHeSt87O9CCP+Z16f69jqT0/7gQP1exxodwuXYhIFCdlxbS9NpDkR54gDIcdddd7F+/Xrq6uq47LLLUMsGC8/zuPnmm+nt7aWjo4O3v/3tE56/O2NcK1vHhKIQi7dihRuQ6SyBaJFIayN6JIKR7AHdpNHswLLCWPg7XTR00mT0sj03iK5rGGGTRi/KZgro4TANMQ1X1bHCCQwxRGDIJFsookZ0UFQ6WurpL6VQ4wZaqgQ5aAqEyZSKZJwC8UAYgaA5GEVXNfIdJbJTLHRU0k6BgGGgSA9D0/y/VZPGIyIo+LVCVAR1wTAD+SxSeiiKiq6qxNraCTbW4bkuxlCa/MqXSdl5FEXDskxUTUV4vuU+GgiRSWXQyzEZiqLQHKmjvz1NrKWhqi9V00A1VSKRIJajYU2pJ6DrREIWSjBeUYaCgRDtMxqIWhaaqqEEE8xUBH1Gmh7DIxIyCdc3E5MlYg3NeK5LyCqnMUaHRsg5JShJBrD9yu+qRtS0cIoeRcemoT5C99YhhCIIxy3S2wtYikHYNFBVDVxJRLcolnciVU0l1tRMKpcjYlh4S1rZ/kQKU9UJdLZR3NKzV2N4LHs6ph966CGee+45gsEg73nPewgExs83lqaTMEM4nlepnq4o/u6r7TjY0sGVZX0TMSbdqKTkebieW1HvhlNye5LKAtP/3K/KrvohFX68s/TKivd4a7ClqihCRVUUdKGgKypa+Xkd18XxfOu2rvptjLLryW7FLygqWiiIaoUQugXlIqBCFQhFQbM09Ig5zljZ0jZl19feCRMZP4UQYIxZZFg6lqVj1gfxSi5uwcEp2LglF8/1gCCaGfRN7AKa6hxUx8Yr5pF2Aek47IlP/s4wFA3D0IBdv6s8PJaxbr/cdyyvK/ebXRFQdfKj3ByaA5G9spzrqrpPW4RDQ0M0Nk7+8u819p3dHedNwQhpuzBhMGlQM2gMRPaocm5QN3ZoUd+d8XcwXF3GMpFcqqIwM9Z4SHe4DubvdUowRrpUxPYc31VB06kPhA/YTkVXVxfJZJJrrrmGP/7xjzz33HMcffTRALz00kvEYjEuu+wybr75ZtatWzdh1eTdGeNi1M6HqmoEVQ3HKSFaAuixOlQzgJcdROgWRrhu1HllC3q4jlDEQlN19Lp2onqS0ECKeDRKpFzoSQs34GkmbR0uG5N9xOqb0EJ1CNFP41QL3dKwHQ+ZdTEiFpGCQti1CDV0YCRUxKatyKJDWLcIG5a//e45qKZBQtMQARVb1wkgUKXArE+QUgSRQAAjVyDc1+8HeGsq0xfMxqprqjxvqMmkMdmM2TtAQyKOFQgSCfiKiKapxBujULTRy1Z5TSjEzCBW+zwCwWpXzUgkiD7N/00YmoFqBVGCCbxSFjUYr+rzutBIWmhFt/CAhnAcVVWJWEGsUILOUh5NM5HCRtUNUDSE51BnhagjRLZUYiCQIexZxAwTFV8pLGkuscZmskUHXRaJmBZqOO7vNgR1FKng5W1CmBghlXSpSCAcwzJMWkJRBBDpiNL7+dlE4hFCzU2UevrHBfHuKXs6ppuamnjxxRe55pprWLZsGY8++ijnnnvu+AsLiIeDvruHEAwbfoXwd09EZffFd9WQHmPTGCEBV7qAqBhopOcr7Z7ngqKg6xqq4V9PetI/7ko8x8NzpN9ulLU3XbSJBwOj7j/KR92TSFf6OySaQKiK//+aALUss1BRVAOh6AhRzkAkFLRwCD0cQp0gbequONg6jhAC1dRQTQ0jNmJwlWVruaL48Y69vb0kGtt9a7kncUsOTi6Hmy8gPXekDyW+dd8rp5Aq96f0JNK1kY7rGxyk5w9XIcvftRj5aqS/IpPS/w5klQvbgVO9D5lSv6vS4geC4Sqsw+ytorCvL9jdzutc4zXPnozzYX91BYGqKJiKRsIK7XEO/10xWcffjuQ61C5rB7O/VEVhWrQOV8qqys4HivXr1zNvnp+Hff78+Tz55JMVBWjdunUsXOgXezrssMN2qNTv7VyuBmOYgFIe33rD1B22FZrBjJaZgEAxAphRhVmzp6GG6/0ATtdBqBpKME5YSg4LRlCCcZRABCUQQfM8f5dA9BMMpQg2NuDZDp4jMBJRFEVBDYaQrg0eCCGRUpBIptGiUfSggVA1GhTVr+DpuGimQaqri1BrM06phBYyacvlQIBVN35BHZ8+nUBdFDPRhFAUpnR0MJyQ32xsJaqO7HgIVUNVVEITuIEFAxEs0Y7QTP+ZLV9xV8cErKOoaPFWUHWEoiJUDU3VwXOot8KooQQoKnrDVIRmIJ0SKCqKHgAkXimP0ExiQjDPMHEcBdU10IImIdsBWUKNNtI+M4R0inhqEKn0oIUsFEtDjyQQioKdzuDmC4QzWbRwBC0UIJgvYNTFCbsO2hadSIOvAEYXzKlY1feWPR3T+Xye2bNnI4TgsMMO41e/+tWE1xWGRmxma1lb9q3m0vN8e3xFiwc/VaMfpulTVuakrJw7zLArxvAhoYytejq8ahj5bGx2s0LfEInmRv/kclsx/O+KPCpC1RGa4aeR9FcS/tEDUMBxsrxj/PSxI303LJcQ/qJGCxhogb2vrTIa6XnlfvXHgZSjxoX0KosAf7HgwtP75bbjOGRK/aF4UWvlbadDzW6lgKvxumBPxvm++qvvLpN1/NXk8tnTuhD7Qi6XIxr1fV8DgQDZbLbqmGVZlWO5XK5ybNmyZSxbtgyAVCpFV1cXAFOmTKGvrw/btjFNk3g8Tk+P71IRi/kxHcmk72bW0tLCQLZEabAbwzCor69n27ZtgJ92VVEUhoaGAN8FaqgoKBaLaLlumpqa6MkrkB8kEomgaRqDZRmamppIFTwK/SlUNUtLSwtbtm4FIBwOYzQ20z0wAEBjYyODg4Pk83kURaG1tZUtW7YgpSQUCmE11NHb3w9paGhoIJdLksvlEELQ1tZWefZgMEgwEWdwuMpxPk+xWCST8QNT29vb6e7tw3VdAvgyD0oAD5HLYds26UKJga4uWltb2b59O47jjOvDeDyO53mkUjkYylX1t2EY1NXV0d3dXd3ffb0j/T2UolQqoes6DWGVbVvH9ncKSPn9nS1RLKbRNI2mppls3boVVIgYJnoozMDAAGzdRlNTE+lciXx+ADVkMWXKFH88ZLsJh8OYpslAOgWWTkM8TCaXIxc0UEoFWltbGXJsUp6LMzBAMBisjIG9ZU/H9O6O83Q2T3fa35WaMmUK/ZVxbu1inE9hYGCAUqm0e+N8aMgf55pGU1OT3+8wMs4H/QDopqYmUqkUWTdDb0HQ0tLMli1bgPI4Nwz/O8If55lMhnx+YOJxbln09/cDw+M8VzXOt27diud5/jgPBunr82uW1NfXjxvn27ZtI5lMEggEiEQibN/uZ2Cqq6vzx3naT1e8e+M8VenvXY7z0fNKub91XaehoaHS37Ztk8lkdru/dV2v9GFTUxPpdJp8Po+qqiPjvNzfpmlO2Idj+9vvwwNT1R3+wXzqJwsNDQ27blTjdcFkHOeTdfzV5Dr4BAIBCgU/Q0Q+nycUGonRCAaDVceCwZEdyqOPPrpi/bzuuutobx/J/T42BmH0MaAq81dTU9NO24bDIy+/sdv5Y9uOln3sdza27ehnGfsbbWtr2+G5lmVRVzfiHjR37twqy/LotoFAgHg8Xvl7ypRqP+OxMg0rJ+ArJjtrO6y0wqHr79F9WF9fv9O2O+vDnfX33rCnY3q0kvpaHOeJRKIyBg/UOG9tbd1h24nGeVNT0w5lOpTj3HVdVFWdFOP8QHHIksIO/7D+EdlXS0SN1w6TcZxP1vFXk+vgM2PGDFatWgXAyy+/XOVeM336dF555ZUJj41mMo7xg8XreWy8VtnTMd3Z2cmaNWsmbD+ayTrOJ+MYnIwyweSVa39Sq/RQo0aNGv+gtLe3E4lE+P73v8+2bds4/PDD+fWvfw3AwoULGRwc5Pvf/z66ru9Q2alRYzKxp2M6EomwYMECvve977Fs2TJOOeWUQ/wENWrsPUIeoooK//qv/0pHR8ehuPUhJ5lMVm1B1dh3Nm/ezNe+9rVDLcY4JuM4n6zjrybXrpmM43wyjvGDxWQaG68nauN895mMY3AyygSTS64DNcYPmU99R0cHV1999aG6/SHluuuu+4d99gPFddddd6hFmJDJOM4n6/irybVrJuM4n4xj/GAxmcbG64naON99JuMYnIwyweSS60CN8Zr7TY0aNWrUqFGjRo0ar3EOmVI/HFH+j8g/8rMfKCZrn05GuSajTFCTa3eYTLIMMxllOlj8Iz/7gWQy9utklAkmp1yTUSaYXHIdKFkOmU99jRo1atSoUaNGjRo19g975VO/YcMGbr/9dlRVJR6Pc8UVV7B8+XIeeughdF3n8ssvJ5FI8PDDD/Pggw/S2dnJlVdeCcDjjz/OE088gRCCww8/nLPOOmvc9e+66y7Wr19PXV0dl112WSXf6cDAAF/+8pf5zGc+My5v6sFkX55/xYoV/OlPfwL8/Kjvfve7UcZUDXzooYd47rnnCAaDvOc97yEQCPC73/2Op556iqOPPpq3vvWtB/2ZDzQHuk9/+MMfsnnzZq644goWLVoE+D5tw4VG3vGOd+x2ENTY8TmRnKN59tlnxx3v7u7m17/+NZ7n8YY3vIG5c+fuU/9NJFcqleLWW2+lWCwya9YsLrzwwqr2a9eu5a677kIIwbve9a5KMZ0bb7yRUqnESSedxLHHHrtPMuXzea699lq2bdvGpz71Kerq6rj++usr+YIvu+yyqly+cHD6a6xcra2tPPLIIzz9tF/m76yzzuKII46oOudA9ddEY/+GG24gm81i2zZvectbmDVrVtU5t912G1u3bqVUKnH22WdzxBFHUCwWufHGG0mn0yxatIizzz57r+5dm8trc/m+UJvLa3P5MLW5/ODN5RXkXjA0NCSLxaKUUsq77rpLPvvss/I73/mOtG1brlmzRt58881SSilTqZTcvn27/PnPf145t7e3V3qeJz3Pk9/97ndlKpWquvbmzZvl//7v/0oppbzvvvvkM888Uzl2yy23yB/84Adyy5YteyP2fmNfnt+27cq/b7zxRrlmzZqqa6fTafmDH/xAep4nn376afnHP/5RSillMpmUq1atkr/97W8P9OMdEg5kn0op5eDgoPz9738vX3jhhcpnvb29Ukopu7u75U9+8pPdknPs+HzqqacmlHMYx3EmPH799dfLnp4emcvl5He/+93duveeyPXMM8/IG264QQ4ODu7wnO9///sym83K/v5++eMf/1hKKeXtt98uX3755YrcpVJpn+RyHEemUil54403yi1btshSqVSRacWKFfLWW28d1/5g9NdYuaSU8itf+Yp0HEcWi0X5zW9+c9w5B6q/Jhr7w2O6r69P/vCHPxx3zvDxfD4vv/71r0sppXzwwQfl448/LqWU8tprr93pd7+ze9fm8tpcvi/U5vJ9ozaX75tcUv5jzuXD7JVPfSwWwzD8UuaaptHT00NLSwuapjFz5sxKqeJIJDJuld3Q0IAQAiEEqqoihKg6vn79eubNmwfA/PnzWbduHQB9fX0IIcatnA8F+/L8muZvjsiy19PYle3GjRuZPXs2QggOO+ywyvOPrqz2euRA9ilQVfFumOGqkxONwx0xdnw++eSTE8r55JNPsm7dOnp7eyc8nkwmaWpqIhAIEAwGK2W295axcq1du5b+/n7uuOMOfvjDH1bG0cqVK1m+fDmlUglFUQgGg9TV1VWsXBs3bmTOnDmoqkpnZ2elZPbeoqpqVaU/Xdcr34WmaZV+P9j9NVYu8MeDbdsUCgUCgQBwcPpr7NgXQlTGdKFQqFQiHZZluB1AqVSqVGUcPQbmzZvH+vXr9/jetbm8NpfvK7W5vDaX1+bygz+XD7NPKS0HBgZ4+eWXueiii6q+GLkbbvrLly+noaGhqlwvQC6Xq0x6gUCAbDYLwP33388555zDvffeuy8i71f29vmffPJJ7r//fhobGyd8fsuyAP/5hwfcPwoHok93xZ133jmh68BEjB2fQOX7Gi3n0qVLAVi3bt2Ex0c/z/A431O5dyZXd3c3W7Zs4f3vfz+apnHdddfxmc98hvnz5wMwNDRUJZeiKDiOg+u6lRftgRx/juNw7733cumllwIHv78mYsGCBXz1q1/F8zze/e53AxzU/hoe++eddx4A3//+99m+fTtXXHFFlSzD3HDDDaxevZqLLroIGD93DM+de3Lv2lxem8v3F7W5fO+ozeX7zj/yXL7XSn0+n+eXv/wll19+OZ7nVZVQ3tVKuauri0ceeaSSL/SBBx7gxRdfZOHChQQCgcq18vk8oVCI3t5eAOrr6/dW3P3Ovjz/0qVLWbp0KbfeeivLly9HSsnjjz9OZ2cnc+fOpa+vr3KPYDB4QJ9jMnGg+nRnfqt/+MMfmD59+jg/tx0xdnwCO5VzdPvRx0e3Gx7n+8JYuRoaGshkMhVLl6qqFd9HgGAwWCWX53lomoaqqnieh6IoB3T8/frXv+bkk0+mqalph88BB66/xpLP5/nrX//Kv/3bv+G6Lj/84Q+ZP39+5b4Hur9Gj/3h7+iaa65hYGCA66+/nsMOO2zcOe9///vJ5XJ85zvf4dhjj630XTAYJJ/PT2jl3NW9a3N5bS7fH9Tm8r2nNpfvG//IcznsZUpL13X5xS9+wQUXXEBzczNNTU10d3fjOA7r1q2jra1th+cODQ1xyy23cMUVV1S2Kc4880w+8YlPcNZZZzFjxgxWrVoFwMsvv8yMGTPYsmUL3d3d/PjHP+aVV17hlltuwbbtvRF9v7Avzz9absuyMAyDY445hk984hO89a1vpbOzkzVr1gAjz/+PwIHs0x3x5JNPMjQ0tNuWHWDc+Fy6dOlO5dzRc0SjUXp7eykUCuRyuX22VIyVa+7cuYRCIXK5HMViEcdxKhMMgGEYuK5LLpdjcHCwMoF1dnayevVqXNdl8+bNle3C/cm9995LQ0MDRx111LhjB6u/xqIoCrquo+s6hmHgOE6VRelA9tfYsS+lxHVdAEzTxDTNcecMj3ld17EsC0VRqsbAqlWrmDZt2h7fuzaX1+byfaU2l9fmcqjN5Qd7Lh9mr1JaPv3009x+++2VrAUnn3wyAA8//HBVlPOyZct49NFH6e3tpbW1lY985CP8+te/ZvXq1RVfrEsuuYTm5uaq6995551s2LCBRCLBZZddVvE5AvjVr37FmWeeeUgzJuzL8z/++OP8/e9/B/xBf/HFF4/zK3zwwQd5/vnnCQQCvPe97yUQCPDQQw/x9NNPk8lkmDFjBu973/sO7kMfYA50n950002sXr0ay7I46qijOOuss7jmmmuYOnUqiqJQX1/P5Zdfvluyjh2fy5cvHyfnk08+SVNTEzNmzODvf//7uOPbtm3jlltuwfM8LrjggglX73vKWLk2bdrE7373OxzH4bzzzmPRokWsXLkS27ZZsmQJa9as4Xe/+x1CCN75znfS3t5OMpmsygBw3HHH7bNcP/nJT+jq6qKuro4FCxZw3333VRSc6dOnc9FFFx2S/hot10knnUQ6na5YBpcuXcrJJ598UPpr7NhfunQpTz75JOBvVb/xjW9k1qxZVbJcf/315HI5XNflzDPP5PDDD6dYLPLLX/6STCbDwoULOeecc/b43rW5vDaX7yu1ubw2l9fm8oM/lw9Ty1Nfo0aNGjVq1KhRo8ZrnENWUbZGjRo1atSoUaNGjRr7h5pSX6NGjRo1atSoUaPGa5yaUl+jRo0aNWrUqFGjxmucmlJfo0aNGjVq1KhRo8ZrnJpSX6NGjRo1atSoUaPGa5yaUl+jRo0aNWrUqFGjxmucmlJfo0aNGjVq1KhRo8ZrnJpSX6NGjRo1atSoUaPGa5yaUl+jRo0aNWrUqFGjxmucmlJfo0aNGjVq1KhRo8ZrHO1QC1DjtccT29byn8/dj0DQHIzw9ePfTNwMcs1jt3HVwlOYl2g5aLLctOopLpt7HAD//tQ9fPao8who+kG7f439Q371E/Td/gVAgqLS+M5v4hUydP3neUz/zhr0unYG//JjFDNEYe2TlLa9SmnbK2j1HShGiPqLvkDvbZ9l6peePtSPUqPMb25ZznHHd9I5NcFjj6zj1Vd6+aerjkNKyXe/9Qif+uxpfOtrDxKLWdi2y4KFLZxx9mwAvvDZ+2hvjwFw3PFTOeKotnHX/8F3H+Pjnzy56rMVL3XzyINrEUJgGCpvfttCHn14HT3daQAcx2Pb1hRf+/YFB/jpa+wOzpYk6e88TOK/Lqp8lr72cfSFLVinzRzXvv89t5D4yVtRQkbls9JzW0j/12MgJUJViHzyVIqPrqP41Ca/gZTYz22h8cGrURvDB/yZatQ4lNSU+hp7xGAxx789dQ+3nncl9VaYu9Y9zxefvJsfnXrxfrm+lBIAIcRutb951dMVpf4/jnvjfpGhxsHFzQzQc+NHaP/kvWjxKbi5JPb2tQAYLXMY+tN/0XjJf1baN7/3OgC6f/4+Eud/ErN94SGRu8bO6ZyWYNPGITqnJtjSlURR/N90b2+WpiZfubIsnas/egKeJ/nutx7mxFOmY5oajY1hrv7oCXt0v97tGR76yxquvPo4LEsnmSxQLDq85e2LKm3u+d1Kjjhy/AKhxmsTbyhP6sv3k7j+7aiNYbx0EXfzEOEPn0D4w/74yd70LPqClppCX+MfgppSX2OPeGDzK5w3dQH1lj9BvnnG4Xzn2fvxpAfAz1f8lS2ZIRJmkGtPvZjn+7r496fuIaDpLG2ZziePOJuHulbxoxcewvU83jf/BN4843Cueew2gprBulQfp7TOpi0U500zlrAh1c93nv0zPzr1XVz6pxuwPRdDVbn+9Mu4c93zrE328vb7fsY1S87k+8sf4JdnvRdPSv750VtJlQo0ByP818nvZNn2jfz4xUewVI1N6QF+dOrFzEu0cO0LD/Om6YvpjNRVnvG7z93PhlQ/g8UcAOd0zufu9ctptCL89PRLKTg2n378t/TkUoR0kx+e8i5CujFOvohhcdod3+Xwhg5WDm7j6oWn8NaZRxz8L22Sk13+B8JHXoQWnwKAGoyhTjuS3MsPE1p0Lvk1T+JmB/f4us5QN0MPXUfDW75U9fmGzy3AmnEMhY3PUXf+J8k8ezelntU0v+cnBGafSPaFPzLw+28gPZf4WR8huvQSUo//iuSjv8ArpEic83GiJ15O353/gb19LW62H1nM0fbJe1GMwP7oktcFU6fGefAva4Dp2CWXltYovdszbNowSOfUeFVbx/FwHA/Pk3t9v+XPb+W446diWf5OXSxmVR1ft7afnm1p3vCmwwBY9vRmGpvCTJ2WqLRZ9vRmVr7Ug+t6ZLMljj9xGn9/pgspJVdefRyKIvjdHSvo7k6jCME7L11CPB7g5l89SzJZQHqSSy4/kkQiwH/956NMm55g86YkCxe3cPqZs/b62WpMTPHRdZhnzKoo7ErERJnfXDnubBggf/cK6n91id/+sfXIoo111pyRazy9iezPn0JYGm5XktAHl5K/40VkqkDiZ29HiQfI/OxJio+vBwnRL56FPqeR1LcexF7RA0WH6JfOQT+smf733II+vwn7pR70uY1Ev3DWwe2QGv/w1Hzqa+wRPbkULYFo1Wf1Voj+QhaAIxo7uOW8K+mIJPjTppU82PUK1xx+Jr85/4Ncc/iZSCn5wfIHueXcK7njgqv535f/huv5C4KF9W38+twruWj6Eu7Z8AIA96x/gTdOX4wiFH5x1hX89oKrOKN9Lnevf4Er5i1lZqyR28+/ihNbR16Y/7fqac5on8tvL7iKOfFm7l6/HADHc/mfM6/gc0efz62rlwHw0cWnVSn0w8yON3HTOe8nZgYouQ63n38VJc9hY7qfX7/6DCdOmclt53+Qt8w4nP9b9fSE8gH05tN8Zemb+O35V3HDyif287fx+sAZ2lZR6CcifsZVDD3w0z2+rhZvGafQAzjJbpouv5bWj/6G3t98nparbmLKB29k6KHrkVLSf/fXaP/M/XR8/hGG/vITpOcSPvptdHzuQTr+9TEG//yDyrWMltm0/78/YM08jtyK+/dYxtczTc0RtvdkyKSLhCMmnZ1xNm0cYvOmoYoiXSjYXHftE3z9y3/hyKPbCQR8hby3N8N11z7Bddc+UXGd2RWpZJFozJzwWKHgcPedK3j7xYsru4BHH9tRpdAPEwzpvO8DxzJzdgNbupJ88MNLmdIaZf26AV5euZ1AUOfqjxzPeRfO5eEH1gDw9nct4UMfPYFTTpvBU09s9O+Zdzj19Jl85OMn8uyyLXvWeTV2C3d7BrVpYgu8dDySX/gjsS+dgzB9+6V58vQqhb6CJ0n86C0ELzmCwr0vU/fzd2C9YT6FB9Zgr+7F2TBA/Y2XEP/uG8n84K8ARP75ZOpvvITov59D9oZnKpeyzpxN/a8uwV7RjZcu7v+HrlFjJ9Qs9a9jPvfEnXTnUvt0jZZglG+c8JbK383BCBvSA1Vt+gsZ6swQAIvr/a3tJQ3trE/18Z55x/OD5Q9y57rnecuMw1nS0M66ZB+X/vl/AEiV8pUFwZKGdgBaw3HSpSLpUoGHt7zKBxeeTNYu8i9P3Mm2bJKhYo4Lpy1iR2xI93PpnGMq11y2fSOtoTgL6nzFsTUUI1nM7/S5DyvHBbQEohxWPq8l6J+3Ormd5X1d3L72WRzP49jmaTuUrzNST8TwLYbDuxmvZXp++WGcwb1XULREG83v+Un1Z/EplHrW7PCcyHEXs+mrJxE59h1QHmf7gt44A8UKo8VbMZpnoxgWWqINLzuIm+7F7nmVru+cB4CXG8JN9ZJf+zeG/vwjQGKPktXsPNx/hrqOvdpNmCzc8ZsXSSYL+3SNWMzire8Y+V0qiiAUMnh5ZQ8dnXE6OuM8+JfVbOlK8oY3zQdG3G/6+7L89rYXK+fujftNNGaS2sEz3H3nCk45bQbx+K53UqZM8Y0W0aiJYfivyFjcIp+z6e/LsuLFbtav7UcC8XgAz5Pce89Ktm1NY9suLVMiAASCOom6IACa9tq3nyX/48942zN7fb7SFCb27+fsR4lAbQrjbJz4d5e9/knMk6ajj7Lc7whtTmNFxuF/q01h3K0pnDX92M9tof89twAgym5k2Ruepvg3fwEnRn2/2mH+/ZTmCDJVgMjEC80aNQ4ENaX+dcxoZXx/cWb7PN5+3/W877DjKz71RzR2oir+pPbSwFYWN7TzQt8WFje0ETEsvnb8RZRch/Pv/hH3v/njzIo3cvM5/4Shatiei66oACij/OjP7ZzPT158hKmROkxV4y+bX6YznODaUy/mZy89Ssb2LSAT+d5Pi9TzfF8XixvaWd7XxfRovd+WkbaSnW/zj77u6DtIYGaskSMbO3n7rCMBsD2XP29aObF8u9mvrxXGKuT7g9CSCxn41pnEz7ja96nPp6oUZ6FqRE94N8lH/pvEudfs+w2rxkz1t6uGGzCmzKP9039EaAbSsRGazsDdX6fj8w8DgvWfnjXxteTeu44cakYr4/uTjqlx/vroBi694giiMYv+Pt+lTTfUqnb1DSEam0K8vLKHw3ZDCZuIJYe3cuvNz7NoyRQsSyeVLFAoOvRuz1AsOhx5dPvuXWjUV1r99Uoam8MsPnwKZ53jW3td12PrliT5vMOHPnYCLy7fxsoVPXsl/2Rnfyvk+wPzlBlk33crwYsP933qM0XcTUMgJcUnNlD3y92M9aqaEqp/09qMOoxjOoh9xV/oS9vFG8pT/NtG6m+6FHtFN6lvPzzhZV+7M0KN1yo1pb7GHpGwQnz5uDfygQdvQgCNgQjfOP7NleMv9m3hXet+TsIM8ukjz+Z/X/4b9218CcfzeOfso1CEwj8vOYNL/vQ/KEJQb4W47vR3j7vPG6Yv4rjbvsn/nHkFAEc2dnLtCw/z0sBWGgNhWkNxAGZEG/jAg7/igwtGsmBcOvdYPvbILfxu3XIaA2E+vOhUlm3fOOHzTORTvyvePedY/uWJO7it7MJz1cJTdihfjV2jhutovuLHbPvpu6lkv3nXt6raxE75J/p/95WdXsfLp+j6tq94qJEGGi/53oQ+9TtDKAp1b/w8Xd85F4SCGmmg9SO3Ej7qLWz++qmYU49ADY132agxMZ1TEzzx2IZKYKxhatQ3BCdse8JJ0/j971butlJfLDpc/5MnAQiFDd59xZGcftYsbrj+6arsN/f9/hU0TeG6a0fc365439GsXNEzzqd+V8xf0Mza1f1c9+O/IYAjjmrj8CNaGRzI8/OfPknjDlxBauwY++9dDLz/VgCM46ftsv3QR+6AsrU88d/vJPpvZzP0qd+PZL/51Kmkf/hXvKE8A++/rXJe7Itn4W5Lj/Op3xX63CbUqQn6r/g1QhEYx08j9IHjUGIW/e+5BWPJjl0Ha9Q42AgpX8PmpRo1atSoUaNGjRo1atQs9TVq1KhRY/KSz9v88n+eqfrsDRfNp70jfmgEqnFQGPjg7ciiU/k79P5jsE4dn7u+Ro0aI9Qs9TVq1KhRo0aNGjVqvMZ57Yfk16hRo0aNGjVq1KjxD05Nqa9Ro0aNGjVq1KhR4zVOTamvUaNGjRo1atSoUeM1Ti1QtsYe88S2tfznc/cjEDQHI3z9+DcTN4Nc89htXLXwFOaVCzcdDG5a9RSXzT0OgH9/6h4+e9R5BDR9n6552+pl/HD5Q7SEorQEY1x7anWuY9fz+MRjt9GdS9EZSfCtE96KpqicdPt3aAn5hWv+efEZnNI2m2/+/Y/8detaPnH4GZzVcRgvD3Tz0JZVfHjRqfsk4+uN/Oon6Lv9C1RSWr7zm3iFDF3/eR7Tv7MGva6dwb/8GMUMUVj7JKVtr1La9gpafQeKEaL+oi/Qe9tnmfqlp3d5r/X/Mhct7hdJq3vj5wgtPLvqeO7lh+i7/QsIVaP+bV8hOPcUko/9LwO//wZavA0t0cqUq2/C7l3Ptp9dgRqpp/VjdyAUhZ4bP0rDO76OOqbq8j8iv7llOccd30nn1ASPPbKOV1/p5Z+uOg4pJd/91iN86rOn8a2vPUgsZmHbLgsWtnDG2bMB+MJn76O9PQbAccdP5Yij2sZd/wfffYyPf/Lkqs9WvNTNIw+urUpp+ejD6ypVaR3HY9vWFF/79gUTyrx2TR+3/N/z1NcHURTBBz98/Lg299y1gq7NSUJhg3decjiWpXHdtU/geaAocMxxnRx1TDtP/HUDzy7r4qhjOjj+xKmk00UeeXAtb7ho/j716+sJZ0uS9HceJvFfF1U+S1/7OPrCFqzTxgfF9r/nFhI/eStKyKh8VnpuC+n/emwkpeUnT6X46DqKT23yG0iJ/dwWGh+8GrVxfMpRZ0uS/nf+Cm1WAwCJ778Jpa467Wr2F89Q+MuriKBB7OvnozaGGfr8vThr+hEBHeuUGYT+6VgKD64h+7MnMU6cRuSfT0IWHVJfe4DYl8/dH91Vo8YuqSn1r2Ne6Ova52ssbqgu2DJYzPFvT93DreddWSk+9cUn7+ZHp+5mkY9dMBy3PVFRqYm4edXTFaX+P457436RAeCf5p/I++ZPXNHyvk0r6IzU8aNTL+YnLz7CfRtX8Mbpi4kaFreff1VV27XJXu688Go+/uitnNVxGDe8/DhfOe5NO723Jz0UMTk30Urdr+7T+UbL+PzQbmaAnhs/Qvsn7/WLT+WS2NvXVtoP/em/aLzkPyvtm997HQDdP38fifM/idm+cI9kUAIxOj734A6P993+Bdr+3x8Qms6W772RYLlt/Ox/JnHWRyrt0svupPFd3yL3yiMUNz2HdEqYbQt2qtDv6fg+WOxrNVnwK8qOpnNagk0bh+icmmBLVxKlnFu8tzdbyVs/XFHW8yTf/dbDnHjKdExT26uKsr3bMzz0lzVcefVxWJZOMlmgWHR4y9tHCmvd87uVHHHk+AXCaJYc3rpDxXvzpiGymRIf+tgJLH9uK08+voHTzvSLkf3TVcdimiOv1DWr+/joJ07ixhuWcfyJU3nkwbWcesbOs7d4nqz002TD3YdqsuBXaN3feEN5Ul++n8T1b/eLT6WLuJuHCH/4BMIf9sdP9qZn0Re0TKjQD2Mc01G1sBiN25uh+Og66m66FPvFbjI//Ruxf/MNAbGvnYc+u7HStvCHl6m76RKGPnkPALmbniX47iN2+gzSk5UqtTVq7Cs1pb7GHvHA5lc4b+oC6i1/gnzzjMP5zrP340kPgJ+v+CtbMkMkzCDXnnoxz/d18e9P3UNA01naMp1PHnE2D3Wt4kcvPITrebxv/gm8ecbhXPPYbQQ1g3WpPk5pnU1bKM6bZixhQ6qf7zz7Z3506ru49E83YHsuhqpy/emXcee651mb7OXt9/2Ma5acyfeXP8Avz3ovnpT886O3kioVaA5G+K+T38my7Rv58YuPYKkam9ID/OjUi5mXaNlh8akbX3mSeza8wHvmHc9FM5ZUHduY6md+nV9wZFF9K3/cuJI3Tl9M1inxtnt/RksoyleXXkTCDCIQ2K6LoWg8umU1xzVPw5pgJ2FzeoBPPHYbTYEoC+qn0BKMccvqZ8iUily54CTePutIvvvc/WxMDzBYyJFzStx0zvsJaDqf/9tdrBrs5vDGTpb3beb2869ieV8XX33mXlzpcU7HfK5edMqBGA77hezyPxA+8iK0uN+najCGOu1Ici8/TGjRueTXPImbnbgU/M5whronLD7lFTJs/sbpaIlWmi77EWq4+ruXnoNaLh4mPQc33QdA8sGfknn6NmJnfIjo0otRzADSLiCLWRQjxMADP6H5vT+bUJbun78PYYawe16l5YO/ovtnlyNdv1rtlI/ejhqIsuFzC7BmHENx0wskzv8k0RPeTWHjc/T84iq/b6Qkcd7/IzDvVHpv+jjFLSsQikrzlTeg1+1mtdSDyNSpcR78yxpgOnbJpaU1Su/2DJs2DNI5NV7V1nE8HMfD8/Y+Gdvy57dy3PFTsSz/9zV2kbFubT8929K84U2HAbDs6c0TFp968YVtbNo4xOIlLZx06oyqY/39Oaa0+Yu2tvYYf1/WxWln+ou0G65/Giug8+a3LiBRF0QRAtf1EIqgpydNMKgTiZgTyv6f33yYzqlxLEtn3vwmHrh/NXbJZdHiKZx+1iyWPb2ZlSt6cB2PdLrIe//pGKIxiwfvX83KFT10dMZZv26AT3zqFPr7stx5+0s4jktrW4w3vWXBXvfpZKf46DrMM2ZVFHYlYqKMKl7mbBggf/cK6n91id/+sfUTFp+yn91C/2U3YxzVTvgTJ1ctut2tKbSZ9Qgh0Oc3k/riHwG/Qnnq3/6ECBpEPn0a+rwm0BVwPRDgDeZwezPoc5smlL3vbb/EOKodbzBP6APHkfrqX8B20Re0EP3CWRSf3kT2v59CmBpuV5LYty9En91I/q6XyP7fs2izGrBf7Kbx9+/HG8yR/OKfkNkSSkOI2DcvQKiT0zBU48BS+9Zr7BE9uRQtYyyR9VaI/kIWgCMaO7jlvCvpiCT406aVPNj1Ctccfia/Of+DXHP4mUgp+cHyB7nl3Cu544Kr+d+X/4br+QuChfVt/PrcK7lo+hLu2fACAPesf4E3Tl+MIhR+cdYV/PaCqzijfS53r3+BK+YtZWaskdvPv4oTW2dV5Pm/VU9zRvtcfnvBVcyJN3P3+uUAOJ7L/5x5BZ87+nxuLVeD/eji08Yp9Od2LuCBt3yCG89+H9eveIyeXKrq+Jx4E09s8y3Jj21dQ7KUB+CuC67mtxdcxeltc/juc/f715q6gP/3199w5YITuWvd88yvm8LnnriTu9ctH9e33bkUPzjlnXx08elcOG0ht59/FXde+CH+e8VfK22mR+v51Tnv48jGTh7buprlfV0kS3l+e8HVnDKqD76x7I/8/IzLuOOCq3myZx29+fRufb+HAmdoW0Whn4j4GVcx9MBP9/i6WrxlwmqynV94jI7PPURo0bn03zn+uNBM7P5NOMkeSl0v4WYHCR/5ZqZ+9QXarvk9g3/6L5yhbUSOu4TU478CIShuXUno8Dcw8Idvsf2mj+PmU+Oua009gvZP/xkt1kzrx+/yZVh8Aemn/GqaTrKbxst+SPvnH2boL9cC0H/HvzHl6pto/fhduLkhwF8EKaEEHZ99gPq3fYWBP3xr3L0mA03NEbb3ZMiki4QjJp2dcTZtHGLzpqGKIl0o2Fx37RN8/ct/4cij2wkEfIW8tzfDddc+wXXXPlFxndkVqWSRaGxipblQcLj7zhW8/eLFFYXt6GM7xin07R1xPv250/ngh5ey6pVeujYPVR1vbg6zbk0/8v+3d+fRUZfnAse/s08ymclkJkMYCElIaAwQCPsSkS0UrzWCbMpWWkQQi21vj/Vye6713va2x6VHvS5URClSVFwoiJeW9SJUZBG47KSAQmJWQjJkkkwms/7uHzMMiUmwlvZC5Pn8k3Mm72975zfJ8z7v8/5GUTh3tgZvUwCAud8fzMM/zGf02Ew+WH8KgGEj0nh7zRFG5KezZ/cFcvOcbFh3gr17itucn7uumcLJfZg0pS8ZPW08/Eg+j/zzKE4cryTgDwGRWY35C4cxdHgPjh+rpKG+mTNnLrHkx7eTPyoDrzdyLps3/YUp03NZ/Eg+wWCY0i/q2hzvmyJU3djhDIASDON+fAuJ/zERVXQGxXBHzzYBvcZhInnLg9jWzCLsasK3vfVspDbNSuBUFYo/iH9fCeHorJb5X8ZiXzsXy78VUP/v2wAwzRuC+2ebMU68Dc+qg8RP70/9r/+HxuX72p5fvY/4OYOw/qYQbZoV2+qZ2NfOJVTZQLA4msQIhEl6aQrmn4zGu/4ESiiMZ/Uh7G/NxrJ0HOGqyN+ZxtcOED93ELZV96O9zYFvx7m/vVNFpyaZevG1pMSbKW5wtXqttrkRm8EEQH97ZGo7LzmVC/U1fC9nJC8c28mG80eZkjmAvORUzrtrmL1tJQD1fm9sQJAXLfXplmClwe+jwd/MrvKzLMq9A0/Ax9K9G6j0uKnzNXF3Rj86UtxQy+zsobF9HqouoZvJSt9odr2bKRG3z9vh9omGOAASdAZGds3ks7pqNp4/xrbS0xSk5rA4dzT7qs5z3+YVZCel0CUu8k8lyRjpg7sz+rH2bOTLcqZlDWRa1kDeP3eYKVkDWF20n1+NnMzST9Yz6UszAL2TnOg1kY/krvKzrDz9CYoSuZ4rcm3dWl2DNxiI9fmVnwBFlyt5cOcaANw+LxUeN444c4fXfCNprU78Fz/r8Pfm4TP54lejMA+bAdH77HpoEuwAJAyZjnv37wg1uqh4aToAziXv0mXO81S9vgCNyYqhRz+0iV1RR/tOFWcmPmcM/ooi4vuMp+vCVSjBABdXL8Z212MEqs5hHjmbhv1rsY5rXYplzIzck+HmRi6+sZjg5XJCHhfmIdMA0DkyY6U7SjgSyAXrq2MlS8a0AQD4K4po/N8P8J75GFDQ3oRZegC1WoXJpKfodCST3CPNys4d5ygvc1M4KVLecqX8prbGwx/eOxHb9m8pv7EkGqjvoIzoww2nGD02E6s17pr7aFk+07tvCpUV9Vyq9nBgXwnde1i5Z3IfembaeHXZPnqkWTFbIoMIU7TGO6uXnU0bTwOQneMgO8fB55/Vkppm5dCBUibcmc3O7efw+YKtjmVPjic+PrKP8tI6tm89Rygc5rLLS2OjD4Du0RmCRGsc5aVuXC4vTqcFlUqFo0tCbH/V1Y28/04kaeDzhcjOcdDja/Vk56HpkkCwpP1ZPM+K/RhG9UTXInPfHpVeiypaom+ckI3/eAUqi5HGV/ah6ZKA9TeFxM8cgOvB99HldEHbM5IEUkfvJW2mHVSghMLo+qRgfX4SwZLLhIpd+A+VYSzsjf9gKcELrti2ACqLAW16ZFAZKnNT/8wulOYAoTI34UuRUidtTiTLr3aaUdw+wq4mNE5L9Jy1aLpH1p0EP68lcLySxlf2QXMQ1SRZt3GrkqBefC0FqTlM37yC+b1HxmrqBzrS0Kgjkz4nXRX0T07leE05/ZO7Y9Yb+fXIyfhDQe768CW23/tjelkdvD1xAXqNlkA4hE6tAUDdYsrzzrQ+/PbEbtLNNgwaLTtKi0hLSOLlMTN59eSfaQxE/tG1V5ucYbZztKaM/smpHKspo6clEsSpuNpWoeNp/gZ/M2a9kVA4zJFLpXwvZwS3d+vFotyri/KeGFYIwLNHtnO7Mwt/KIgCGDRaDlwsJiN6TABvMMDB6hKeuX0qb52JLOS8kt1vqeX1v3jsI/5w10OoVJC/7pnY6y2vV0Ehw2JnR2lRrO+v6GNz8uq4uVii16G+yWq4WzLl3Y3r6QKs4xdHauq99QRaBPkqjRZL/hzcu18n6c6fXNexlKAfRVFQ6wx4z36MLiULTYKtVY291uKgx9LthBpquPj7JajjzIS89WjiLCjhEM3nP8U6/uFYe/eu10gcs5Cw34MSCkAoQDg6UG0luk7Cc2IrOkdPnIvfxLX5OcLN0Ux0O++R1tIFf9U5dCm98JUeI2HIVPTO2zAPnYF98uPRawpcV5/8I/VIt7Lnz8XMnjcQS6KR2pomAHR6Tat29mQTji4mik5fpPdXBGEdyRvQjXffPkq/PCdGo456dzPNviCXqhvx+YIMGvLVg5/m5kCsfKf4gosR+elkZtlbLdQdW9CLsQWRkpiuTkur7S5WRcpsrlAUhcMHS5l+fx4fbjhFKBTG7w8RDIYxtJhUaPm53rXzc6bO6IfNHs8Lz3589S9Vi9tDQcFmi6OqqgEo50h2AAAFTElEQVRFUaip8eCLfvuqo0sChZN6k2SLR1GU6ypputkZRmfimf8u8TMHRGrqG32EvqgDRcG3txjb6q9e6xX2+GMLb/2Hy9Bm2TCMSMcwIj3WJm5yLnGTc/F9+gXqpEgwH270oU4wEKr1oARCrcpdPKsPYX50DN73jkEwDMEwSpO/9YFb1NE3vXMU0/eHYMjP4PKS9bG1N19+z9W2eEJVDSj+EIo3QKjcDYA204axIBt99B5XAqG/ug/FN4sE9eJrSTKa+OXwe1i4801UgCPOzJMj7439/kRNOfeff40kQzyPDfo2bxTtY3PJSYLhMPd9azBqlZof5Y1n1taVqFUq7EYTy8fNaXOcwp79GP7eU6wsmAfAIEcaLx/fxUlXBY64BLpFa54zLcks3LmGRX2vBtyzbxvGD3e/w8bzx3DEJfCDfmM4VF3S7vW0V1P/2qk9fFR+BkWBezPz6PGl8pzqpgaW7F6LWqVilLMXI7pmcsnbwLztbxCv1aHXaHn29umx9quK9jK/dyTrODVrIFP+uJw7WpTKtOeu9L5M/dNycu3dSNR3nF3MS07FrDcy7U/L6WvrFhsg/WzwP7Fw5xrCioJeo+X18d+97qcC/aNoEmykzFtG5StziD395v7WJSWJoxdQu/E/r7mfsLeesmcmRvZpTsYx67k2NfUhz2XKnytEbTCh0ulJeWBlm/24Nj2N5+Q21Po4HHNfBKBu63/hOb4FUDAPn4nOkRHZn7ceX8VprBN+gBIOUbvhF3iOb8b50Jsdnqex1whcm57CV3IETWIKWltah23tU39J5fI5aBNTUOnjUWl1mAbcQ9Ppjyh9qgBUKiwjZpE4ZsE1++ZGSUtPYu/HxbGFsXqDFntyfLtt80dlsGnj6b86qPf5gqz47X4ATAl65swbxLgJvfjdik9bPf1m86a/oNWqWf7y3ti28+YP4fSpi21q6o8dreTTfSWo1WrSeyaRmWVvc9zlL+9FrVbR1WmJLah9ddl+dLrIZ2/K9KsLt48eqaBfnhO1WsXgoamsWXWYlK7mWGa/Pf3ynPx+1SG6Os2tsvlfZrYYyc5OZtkLn9A9NTGW6f9OYQ7r3z9BIBgZzM+YlUdS0rVnKG6kwOEyXA9EStD0IzO+sn3dkvWxgDjp9fuwPPFt6n666erTb346hoYX9xCu8+J64L3Ydok/n0CosqFNTX3gcBkNL+5BZdSiSU0k4Uej2h7z0f+OZcktP58AgHvpHyOlOKEw5sfGxtr6j5Sjy05GbdJjmJiN+7FNqK1xmBYO7/CaDOOyaHhyJ02ZNrjGIEylUWOaN5jaOW+hzbSjiQ4qEx4aifuJrTQu+wQA86Nj0OX+/z2FTtw8VEpsSCiE6IyuzHbsLj/LlpJTPJk/5Uafkvg7UYKRxbRKOEzZ0wU4H377musPxK0lFAqj0ai5VN3Ihx+cYsGijgNH8c2gBEKodBrCdV4uP7QO+7vfvdGnJG4ikqkXopP7173rKa53oaDw/B0zbvTpiL+j5vMHqFn3OOGAl4SBk27JgN7rDbB65cFWrxVO7kNqD+uNOaGbyLYtZym+4CIYCHHvtI7XGXVGrkXrUKIlRQCmB4ZiHHPtR4LeCpreOUrzjnMoHj/mdmYVxK1NMvVCCCGEEEJ0cvJISyGEEEIIITo5CeqFEEIIIYTo5CSoF0IIIYQQopOToF4IIYQQQohOToJ6IYQQQgghOjkJ6oUQQgghhOjkJKgXQgghhBCik5OgXgghhBBCiE5OgnohhBBCCCE6OQnqhRBCCCGE6OQkqBdCCCGEEKKTk6BeCCGEEEKITu7/AJ7RtnoLK+dvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 750x450 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diurnal_mean = df.groupby('diurnal').mean().reset_index()\n",
    "diurnal_5 = df.groupby('diurnal').quantile(0.05).reset_index()\n",
    "diurnal_95 = df.groupby('diurnal').quantile(0.95).reset_index()\n",
    "numeric_cols = df.columns\n",
    "cols = numeric_cols.drop(['time','hour', 'minute','diurnal'])\n",
    "seven_day_mean = df[cols].rolling(window=336, min_periods=48).mean().reset_index()\n",
    "seven_day_5 = df[cols].rolling(window=336, min_periods=48).quantile(0.05).reset_index()\n",
    "seven_day_95 = df[cols].rolling(window=336, min_periods=48).quantile(0.95).reset_index()\n",
    "    \n",
    "fig, axs = plt.subplots(3, 4, figsize=(7.5, 4.5))\n",
    "axs = axs.flatten()\n",
    "for i in range(12):\n",
    "    col_name_obs = sequence[i//2]+'_' + entity[0]\n",
    "    col_name_def = sequence[i//2]+'_' + entity[1]\n",
    "    col_name_wrflcz = sequence[i//2]+'_' + entity[2]\n",
    "    col_name_lilcz = sequence[i//2]+'_' + entity[3]\n",
    "    if i%2==0: # analyse period\n",
    "        for j in range(4):\n",
    "            col_name = sequence[i//2]+'_' + entity[j]\n",
    "            axs[i].plot(df.time, seven_day_mean[col_name], label=label1[j], color=lc[j], linewidth=linewidth)\n",
    "            axs[i].fill_between(df.time, seven_day_5[col_name], seven_day_95[col_name], alpha=alpha[j], color=lc[j],  edgecolor='none', label=label2[j])\n",
    "        xticks = [df['time'].iloc[0]] + [pd.to_datetime(mid_time)] + [df['time'].iloc[-1]]\n",
    "        axs[i].set_xticks(xticks)\n",
    "                #axs[i].set_xticks([df.time.iloc[0], mid_time, df.time.iloc[-1]])\n",
    "        axs[i].set_xlim([df.time.iloc[0], df.time.iloc[-1]])\n",
    "        axs[i].xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "\n",
    "        axs[i].text(0.01,0.9,'RMSE CNTL: {:.1f}'.format(rmse(seven_day_mean[col_name_def] , seven_day_mean[col_name_obs])),\n",
    "                    fontsize=labelfont, color=lc[1], horizontalalignment='left',\n",
    "                           verticalalignment='bottom', transform=axs[i].transAxes)\n",
    "        axs[i].text(0.01,0.8,'RMSE WRF_LCZ: {:.1f}'.format(rmse(seven_day_mean[col_name_wrflcz] , seven_day_mean[col_name_obs])),\n",
    "                    fontsize=labelfont, color=lc[2], horizontalalignment='left',\n",
    "                           verticalalignment='bottom', transform=axs[i].transAxes)\n",
    "        axs[i].text(0.01,0.7,'RMSE LI_LCZ: {:.1f}'.format(rmse(seven_day_mean[col_name_lilcz] , seven_day_mean[col_name_obs])), \n",
    "                           fontsize=labelfont, color=lc[3], horizontalalignment='left',\n",
    "                           verticalalignment='bottom', transform=axs[i].transAxes)\n",
    "            \n",
    "            \n",
    "    if i%2==1: # dinural time variation\n",
    "        for j in range(4):\n",
    "            col_name = sequence[i//2]+'_' + entity[j]\n",
    "            axs[i].plot(diurnal_mean.diurnal, diurnal_mean[col_name], label=entity[j], color=lc[j], linewidth=linewidth)\n",
    "            axs[i].fill_between(diurnal_mean.diurnal, diurnal_5[col_name], diurnal_95[col_name], alpha=alpha[j], color=lc[j],  edgecolor='none')\n",
    "        axs[i].set_xticks([diurnal_mean.diurnal.iloc[0], '06:00', '12:00', '18:00', diurnal_mean.diurnal.iloc[-1]])\n",
    "        axs[i].set_xlim([diurnal_mean.diurnal.iloc[0], diurnal_mean.diurnal.iloc[-1]])\n",
    "             \n",
    "        axs[i].text(0.01,0.9,'RMSE CNTL: {:.1f}'.format(rmse(diurnal_mean[col_name_def], diurnal_mean[col_name_obs])),\n",
    "                    fontsize=labelfont, color=lc[1], horizontalalignment='left',\n",
    "                           verticalalignment='bottom', transform=axs[i].transAxes)\n",
    "        axs[i].text(0.01,0.8,'RMSE WRF_LCZ: {:.1f}'.format(rmse(diurnal_mean[col_name_wrflcz] , diurnal_mean[col_name_obs])),\n",
    "                    fontsize=labelfont, color=lc[2], horizontalalignment='left',\n",
    "                           verticalalignment='bottom', transform=axs[i].transAxes)\n",
    "        axs[i].text(0.01,0.7,'RMSE LI_LCZ: {:.1f}'.format(rmse(diurnal_mean[col_name_lilcz], diurnal_mean[col_name_obs])), \n",
    "                           fontsize=labelfont, color=lc[3], horizontalalignment='left',\n",
    "                           verticalalignment='bottom', transform=axs[i].transAxes)\n",
    "        \n",
    "    axs[i].set_title(f'({string.ascii_lowercase[i]}) {metric[i%2]} {sequence2[i//2]}',fontsize=labelfont,color=labelcolor, pad = padding)   \n",
    "    axs[i].set_ylabel('W m⁻²',labelpad=5, rotation=0, ha='center', fontsize=labelfont,color=labelcolor)\n",
    "    axs[i].yaxis.set_label_coords(-0.05, 1.025)\n",
    "    axs[i].tick_params(axis='y', which='both', left=True, right=False, labelleft=True, labelcolor=labelcolor, color=labelcolor,labelsize=labelfont,pad=pad, width = xwidth, length = length)\n",
    "    axs[i].tick_params(axis='x', which='both', top=False, bottom=True, labelbottom=True, labelcolor=labelcolor,color=labelcolor,labelsize=labelfont,pad=pad, width = xwidth, length = length)\n",
    "    axs[i].spines['top'].set_color(labelcolor)\n",
    "    axs[i].spines['right'].set_color(labelcolor)\n",
    "    axs[i].spines['bottom'].set_color(labelcolor)\n",
    "    axs[i].spines['left'].set_color(labelcolor)\n",
    "    axs[i].spines['top'].set_linewidth(xwidth)\n",
    "    axs[i].spines['right'].set_linewidth(xwidth)\n",
    "    axs[i].spines['bottom'].set_linewidth(xwidth)\n",
    "    axs[i].spines['left'].set_linewidth(xwidth)\n",
    "    axs[i].grid(True,linestyle='--',linewidth=0.5, alpha=0.5)\n",
    "    if i >=10:\n",
    "        axs[i].set_ylabel(r'$\\mathrm{kg \\, m^{-1} \\, s^{-2}}$', labelpad=5, rotation=0, ha='center', fontsize=labelfont,color=labelcolor)\n",
    "    else:    \n",
    "        axs[i].set_ylabel('W m⁻²', labelpad=5, rotation=0, ha='center', fontsize=labelfont,color=labelcolor)\n",
    "    axs[i].yaxis.set_label_coords(-0.015, 1.01)\n",
    "plt.subplots_adjust(left=0.04, right=0.98, bottom=0.1, top=0.92, wspace=0.25, hspace= 0.35)\n",
    "lines, labels = axs[0].get_legend_handles_labels()\n",
    "legend = fig.legend(lines, labels, bbox_to_anchor=(0.5, -0.015), loc='lower center', frameon=False, \n",
    "                        handletextpad=handletextpad, fontsize=labelfont, ncol=4)\n",
    "llc = [lc0, lc0, lc1, lc1, lc2, lc2, lc3, lc3]\n",
    "for i, text in enumerate(legend.get_texts()):\n",
    "    text.set_color(llc[i])\n",
    "plt.suptitle('Timeseries and diurnal plots for ' + GRIDNAME, fontsize=labelfont+2, color=labelcolor,x=0.5, y=0.99, ha='center', va='top')\n",
    "output_dir = '/mnt/iusers01/fatpou01/sees01/a16404ys/scratch/output_analysis/project2/sp/uk_kingscollege/'\n",
    "fig.savefig(output_dir + '4lines_'+ GRIDNAME +'.png', dpi=300)    \n",
    "fig.savefig(output_dir + '4lines_'+ GRIDNAME +'.pdf', dpi=600)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>30571 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index    LWup_obs  SWup_obs    Qh_obs    Qle_obs  Qtau_obs  lndgrid  \\\n",
       "0          0         NaN       NaN       NaN        NaN       NaN      NaN   \n",
       "1          1         NaN       NaN       NaN        NaN       NaN      NaN   \n",
       "2          2         NaN       NaN       NaN        NaN       NaN      NaN   \n",
       "3          3         NaN       NaN       NaN        NaN       NaN      NaN   \n",
       "4          4         NaN       NaN       NaN        NaN       NaN      NaN   \n",
       "...      ...         ...       ...       ...        ...       ...      ...   \n",
       "30566  30566  344.601101   9.31594  9.251287  12.525817  0.339585      0.0   \n",
       "30567  30567  344.605029   9.31594  9.262772  12.512814  0.339056      0.0   \n",
       "30568  30568  344.590535   9.31594  9.250594  12.482662  0.338551      0.0   \n",
       "30569  30569  344.569255   9.31594  9.251749  12.411749  0.338362      0.0   \n",
       "30570  30570  344.548868   9.31594  9.226931  12.337452  0.338371      0.0   \n",
       "\n",
       "         LWup_def  SWup_def     Qh_def  ...  LWup_wrflcz  SWup_wrflcz  \\\n",
       "0             NaN       NaN        NaN  ...          NaN          NaN   \n",
       "1             NaN       NaN        NaN  ...          NaN          NaN   \n",
       "2             NaN       NaN        NaN  ...          NaN          NaN   \n",
       "3             NaN       NaN        NaN  ...          NaN          NaN   \n",
       "4             NaN       NaN        NaN  ...          NaN          NaN   \n",
       "...           ...       ...        ...  ...          ...          ...   \n",
       "30566  343.890129  6.299344 -19.702116  ...   342.116594     6.299344   \n",
       "30567  343.896334  6.299344 -19.690651  ...   342.120366     6.299344   \n",
       "30568  343.877550  6.299344 -19.733804  ...   342.100740     6.299344   \n",
       "30569  343.853234  6.299344 -19.742930  ...   342.078220     6.299344   \n",
       "30570  343.831665  6.299344 -19.764195  ...   342.058965     6.299344   \n",
       "\n",
       "       Qh_wrflcz  Qle_wrflcz  Qtau_wrflcz  LWup_lilcz  SWup_lilcz   Qh_lilcz  \\\n",
       "0            NaN         NaN          NaN         NaN         NaN        NaN   \n",
       "1            NaN         NaN          NaN         NaN         NaN        NaN   \n",
       "2            NaN         NaN          NaN         NaN         NaN        NaN   \n",
       "3            NaN         NaN          NaN         NaN         NaN        NaN   \n",
       "4            NaN         NaN          NaN         NaN         NaN        NaN   \n",
       "...          ...         ...          ...         ...         ...        ...   \n",
       "30566 -27.391989   16.013724     0.201557  342.223333    6.299344 -27.330794   \n",
       "30567 -27.377631   16.038593     0.202082  342.226093    6.299344 -27.314653   \n",
       "30568 -27.414052   16.044508     0.202245  342.206908    6.299344 -27.342250   \n",
       "30569 -27.412554   16.034586     0.202234  342.185116    6.299344 -27.340344   \n",
       "30570 -27.421220   16.035942     0.202686  342.165882    6.299344 -27.346964   \n",
       "\n",
       "       Qle_lilcz  Qtau_lilcz  \n",
       "0            NaN         NaN  \n",
       "1            NaN         NaN  \n",
       "2            NaN         NaN  \n",
       "3            NaN         NaN  \n",
       "4            NaN         NaN  \n",
       "...          ...         ...  \n",
       "30566  16.096704    0.202551  \n",
       "30567  16.118565    0.203043  \n",
       "30568  16.121047    0.203168  \n",
       "30569  16.111025    0.203126  \n",
       "30570  16.111502    0.203560  \n",
       "\n",
       "[30571 rows x 22 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seven_day_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
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       "      <th>LWup_obs</th>\n",
       "      <th>SWup_obs</th>\n",
       "      <th>Qh_obs</th>\n",
       "      <th>Qle_obs</th>\n",
       "      <th>Qtau_obs</th>\n",
       "      <th>lndgrid</th>\n",
       "      <th>LWup_def</th>\n",
       "      <th>SWup_def</th>\n",
       "      <th>Qh_def</th>\n",
       "      <th>...</th>\n",
       "      <th>Qle_wrflcz</th>\n",
       "      <th>Qtau_wrflcz</th>\n",
       "      <th>LWup_lilcz</th>\n",
       "      <th>SWup_lilcz</th>\n",
       "      <th>Qh_lilcz</th>\n",
       "      <th>Qle_lilcz</th>\n",
       "      <th>Qtau_lilcz</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "      <th>diurnal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-04-04 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-04-04 00:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>00:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-04-04 01:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-04 01:30:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>01:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-04-04 02:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30566</th>\n",
       "      <td>2013-12-31 19:00:00</td>\n",
       "      <td>344.410004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.209999</td>\n",
       "      <td>13.84</td>\n",
       "      <td>0.234095</td>\n",
       "      <td>0</td>\n",
       "      <td>343.213928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.395554</td>\n",
       "      <td>...</td>\n",
       "      <td>29.614624</td>\n",
       "      <td>0.221264</td>\n",
       "      <td>344.087799</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.597271</td>\n",
       "      <td>29.792339</td>\n",
       "      <td>0.226079</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30567</th>\n",
       "      <td>2013-12-31 19:30:00</td>\n",
       "      <td>343.339996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.410000</td>\n",
       "      <td>17.99</td>\n",
       "      <td>0.155406</td>\n",
       "      <td>0</td>\n",
       "      <td>340.834473</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.912714</td>\n",
       "      <td>...</td>\n",
       "      <td>27.424082</td>\n",
       "      <td>0.253514</td>\n",
       "      <td>341.808960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.406370</td>\n",
       "      <td>27.755402</td>\n",
       "      <td>0.258587</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "      <td>19:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30568</th>\n",
       "      <td>2013-12-31 20:00:00</td>\n",
       "      <td>340.529999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.430000</td>\n",
       "      <td>15.04</td>\n",
       "      <td>0.162532</td>\n",
       "      <td>0</td>\n",
       "      <td>337.237793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-28.519697</td>\n",
       "      <td>...</td>\n",
       "      <td>21.114172</td>\n",
       "      <td>0.106125</td>\n",
       "      <td>338.649902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.087069</td>\n",
       "      <td>21.863586</td>\n",
       "      <td>0.113614</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30569</th>\n",
       "      <td>2013-12-31 20:30:00</td>\n",
       "      <td>339.470001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>4.84</td>\n",
       "      <td>0.200483</td>\n",
       "      <td>0</td>\n",
       "      <td>336.669281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-32.902271</td>\n",
       "      <td>...</td>\n",
       "      <td>24.705360</td>\n",
       "      <td>0.205438</td>\n",
       "      <td>338.185303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-25.940220</td>\n",
       "      <td>25.216858</td>\n",
       "      <td>0.213636</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>20:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30570</th>\n",
       "      <td>2013-12-31 21:00:00</td>\n",
       "      <td>340.549988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.090000</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0.242485</td>\n",
       "      <td>0</td>\n",
       "      <td>338.404785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-31.011383</td>\n",
       "      <td>...</td>\n",
       "      <td>27.538219</td>\n",
       "      <td>0.283998</td>\n",
       "      <td>339.687317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-24.331919</td>\n",
       "      <td>27.849283</td>\n",
       "      <td>0.291926</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>21:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30571 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time    LWup_obs  SWup_obs     Qh_obs  Qle_obs  Qtau_obs  \\\n",
       "0     2012-04-04 00:00:00         NaN       NaN        NaN      NaN       NaN   \n",
       "1     2012-04-04 00:30:00         NaN       NaN        NaN      NaN       NaN   \n",
       "2     2012-04-04 01:00:00         NaN       NaN        NaN      NaN       NaN   \n",
       "3     2012-04-04 01:30:00         NaN       NaN        NaN      NaN       NaN   \n",
       "4     2012-04-04 02:00:00         NaN       NaN        NaN      NaN       NaN   \n",
       "...                   ...         ...       ...        ...      ...       ...   \n",
       "30566 2013-12-31 19:00:00  344.410004       NaN  18.209999    13.84  0.234095   \n",
       "30567 2013-12-31 19:30:00  343.339996       NaN  11.410000    17.99  0.155406   \n",
       "30568 2013-12-31 20:00:00  340.529999       NaN   5.430000    15.04  0.162532   \n",
       "30569 2013-12-31 20:30:00  339.470001       NaN   8.250000     4.84  0.200483   \n",
       "30570 2013-12-31 21:00:00  340.549988       NaN   2.090000     1.97  0.242485   \n",
       "\n",
       "       lndgrid    LWup_def  SWup_def     Qh_def  ...  Qle_wrflcz  Qtau_wrflcz  \\\n",
       "0            0         NaN       NaN        NaN  ...         NaN          NaN   \n",
       "1            0         NaN       NaN        NaN  ...         NaN          NaN   \n",
       "2            0         NaN       NaN        NaN  ...         NaN          NaN   \n",
       "3            0         NaN       NaN        NaN  ...         NaN          NaN   \n",
       "4            0         NaN       NaN        NaN  ...         NaN          NaN   \n",
       "...        ...         ...       ...        ...  ...         ...          ...   \n",
       "30566        0  343.213928       NaN -16.395554  ...   29.614624     0.221264   \n",
       "30567        0  340.834473       NaN -23.912714  ...   27.424082     0.253514   \n",
       "30568        0  337.237793       NaN -28.519697  ...   21.114172     0.106125   \n",
       "30569        0  336.669281       NaN -32.902271  ...   24.705360     0.205438   \n",
       "30570        0  338.404785       NaN -31.011383  ...   27.538219     0.283998   \n",
       "\n",
       "       LWup_lilcz  SWup_lilcz   Qh_lilcz  Qle_lilcz  Qtau_lilcz  hour  minute  \\\n",
       "0             NaN         NaN        NaN        NaN         NaN     0       0   \n",
       "1             NaN         NaN        NaN        NaN         NaN     0      30   \n",
       "2             NaN         NaN        NaN        NaN         NaN     1       0   \n",
       "3             NaN         NaN        NaN        NaN         NaN     1      30   \n",
       "4             NaN         NaN        NaN        NaN         NaN     2       0   \n",
       "...           ...         ...        ...        ...         ...   ...     ...   \n",
       "30566  344.087799         NaN -13.597271  29.792339    0.226079    19       0   \n",
       "30567  341.808960         NaN -19.406370  27.755402    0.258587    19      30   \n",
       "30568  338.649902         NaN -23.087069  21.863586    0.113614    20       0   \n",
       "30569  338.185303         NaN -25.940220  25.216858    0.213636    20      30   \n",
       "30570  339.687317         NaN -24.331919  27.849283    0.291926    21       0   \n",
       "\n",
       "       diurnal  \n",
       "0        00:00  \n",
       "1        00:30  \n",
       "2        01:00  \n",
       "3        01:30  \n",
       "4        02:00  \n",
       "...        ...  \n",
       "30566    19:00  \n",
       "30567    19:30  \n",
       "30568    20:00  \n",
       "30569    20:30  \n",
       "30570    21:00  \n",
       "\n",
       "[30571 rows x 25 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv('export_uk_df.csv')\n",
    "seven_day_mean['time'] = df['time']\n",
    "seven_day_mean.to_csv('export_uk_7day.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "380.5247266484339 384.8382155661288 383.024761340147 383.09949043052586\n",
      "4.31 2.5 2.57\n",
      "3.45 3.41 3.45\n"
     ]
    }
   ],
   "source": [
    "print(seven_day_mean['LWup_obs'].mean(), seven_day_mean['LWup_def'].mean(), seven_day_mean['LWup_wrflcz'].mean(), seven_day_mean['LWup_lilcz'].mean())\n",
    "print( round((seven_day_mean['LWup_def']- seven_day_mean['LWup_obs']).mean(),2), \n",
    "      round((seven_day_mean['LWup_wrflcz']-seven_day_mean['LWup_obs']).mean(),2), \n",
    "      round((seven_day_mean['LWup_lilcz']-seven_day_mean['LWup_obs']).mean(),2))\n",
    "print( round((seven_day_mean['LWup_def']- seven_day_mean['LWup_obs']).std(),2), \n",
    "      round((seven_day_mean['LWup_wrflcz']-seven_day_mean['LWup_obs']).std(),2), \n",
    "      round((seven_day_mean['LWup_lilcz']-seven_day_mean['LWup_obs']).std(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.49 2.57\n",
      "0.73 0.86\n"
     ]
    }
   ],
   "source": [
    "print(round((diurnal_mean['LWup_wrflcz']-diurnal_mean['LWup_obs']).mean(),2), \n",
    "      round((diurnal_mean['LWup_lilcz']-diurnal_mean['LWup_obs']).mean(),2))\n",
    "print(round((diurnal_mean['LWup_wrflcz']-diurnal_mean['LWup_obs']).std(),2), \n",
    "      round((diurnal_mean['LWup_lilcz']-diurnal_mean['LWup_obs']).std(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diurnal</th>\n",
       "      <th>time</th>\n",
       "      <th>LWup_obs</th>\n",
       "      <th>SWup_obs</th>\n",
       "      <th>Qh_obs</th>\n",
       "      <th>Qle_obs</th>\n",
       "      <th>Qtau_obs</th>\n",
       "      <th>lndgrid</th>\n",
       "      <th>LWup_def</th>\n",
       "      <th>SWup_def</th>\n",
       "      <th>...</th>\n",
       "      <th>Qh_wrflcz</th>\n",
       "      <th>Qle_wrflcz</th>\n",
       "      <th>Qtau_wrflcz</th>\n",
       "      <th>LWup_lilcz</th>\n",
       "      <th>SWup_lilcz</th>\n",
       "      <th>Qh_lilcz</th>\n",
       "      <th>Qle_lilcz</th>\n",
       "      <th>Qtau_lilcz</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00:00</td>\n",
       "      <td>2013-02-16 00:00:00</td>\n",
       "      <td>368.978149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.107864</td>\n",
       "      <td>10.687767</td>\n",
       "      <td>0.230667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>367.217926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.969138</td>\n",
       "      <td>10.755898</td>\n",
       "      <td>0.169343</td>\n",
       "      <td>372.287628</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.186077</td>\n",
       "      <td>10.933187</td>\n",
       "      <td>0.172633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00:30</td>\n",
       "      <td>2013-02-16 00:30:00</td>\n",
       "      <td>368.460175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.386906</td>\n",
       "      <td>10.966841</td>\n",
       "      <td>0.230804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366.740448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.132294</td>\n",
       "      <td>10.546378</td>\n",
       "      <td>0.166101</td>\n",
       "      <td>371.789429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.548819</td>\n",
       "      <td>10.738297</td>\n",
       "      <td>0.169551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01:00</td>\n",
       "      <td>2013-02-16 01:00:00</td>\n",
       "      <td>367.603729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.267212</td>\n",
       "      <td>11.555216</td>\n",
       "      <td>0.232187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366.095062</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.286006</td>\n",
       "      <td>10.680665</td>\n",
       "      <td>0.165146</td>\n",
       "      <td>371.122467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.894144</td>\n",
       "      <td>10.897151</td>\n",
       "      <td>0.168860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01:30</td>\n",
       "      <td>2013-02-16 01:30:00</td>\n",
       "      <td>367.055878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.145683</td>\n",
       "      <td>10.246067</td>\n",
       "      <td>0.223178</td>\n",
       "      <td>0.0</td>\n",
       "      <td>365.619843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.001425</td>\n",
       "      <td>10.514145</td>\n",
       "      <td>0.156723</td>\n",
       "      <td>370.602997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.788540</td>\n",
       "      <td>10.764087</td>\n",
       "      <td>0.160739</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02:00</td>\n",
       "      <td>2013-02-16 02:00:00</td>\n",
       "      <td>366.471497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.792473</td>\n",
       "      <td>9.176048</td>\n",
       "      <td>0.217918</td>\n",
       "      <td>0.0</td>\n",
       "      <td>365.078033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.825698</td>\n",
       "      <td>10.191563</td>\n",
       "      <td>0.160731</td>\n",
       "      <td>370.004089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.758117</td>\n",
       "      <td>10.447473</td>\n",
       "      <td>0.164756</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>02:30</td>\n",
       "      <td>2013-02-16 02:30:00</td>\n",
       "      <td>365.897095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.412251</td>\n",
       "      <td>8.360415</td>\n",
       "      <td>0.210671</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.549652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.629966</td>\n",
       "      <td>10.077915</td>\n",
       "      <td>0.155398</td>\n",
       "      <td>369.400452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.727299</td>\n",
       "      <td>10.341196</td>\n",
       "      <td>0.159789</td>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>03:00</td>\n",
       "      <td>2013-02-16 03:00:00</td>\n",
       "      <td>365.428680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.463573</td>\n",
       "      <td>8.634274</td>\n",
       "      <td>0.214073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.151978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.300602</td>\n",
       "      <td>9.658520</td>\n",
       "      <td>0.156984</td>\n",
       "      <td>368.898560</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.468583</td>\n",
       "      <td>9.969198</td>\n",
       "      <td>0.161580</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>03:30</td>\n",
       "      <td>2013-02-16 03:30:00</td>\n",
       "      <td>365.022614</td>\n",
       "      <td>0.680986</td>\n",
       "      <td>33.952492</td>\n",
       "      <td>9.834349</td>\n",
       "      <td>0.206201</td>\n",
       "      <td>0.0</td>\n",
       "      <td>363.840393</td>\n",
       "      <td>0.005461</td>\n",
       "      <td>...</td>\n",
       "      <td>9.183133</td>\n",
       "      <td>9.363967</td>\n",
       "      <td>0.149580</td>\n",
       "      <td>368.490417</td>\n",
       "      <td>0.005461</td>\n",
       "      <td>13.507803</td>\n",
       "      <td>9.642113</td>\n",
       "      <td>0.154430</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>04:00</td>\n",
       "      <td>2013-02-16 04:00:00</td>\n",
       "      <td>364.617554</td>\n",
       "      <td>1.043147</td>\n",
       "      <td>34.810764</td>\n",
       "      <td>9.197115</td>\n",
       "      <td>0.203933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>363.500336</td>\n",
       "      <td>0.078480</td>\n",
       "      <td>...</td>\n",
       "      <td>9.228721</td>\n",
       "      <td>9.509408</td>\n",
       "      <td>0.146639</td>\n",
       "      <td>368.017059</td>\n",
       "      <td>0.078480</td>\n",
       "      <td>13.672853</td>\n",
       "      <td>9.835212</td>\n",
       "      <td>0.151541</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>04:30</td>\n",
       "      <td>2013-02-16 04:30:00</td>\n",
       "      <td>364.303192</td>\n",
       "      <td>1.989947</td>\n",
       "      <td>34.592896</td>\n",
       "      <td>10.006502</td>\n",
       "      <td>0.202137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>363.294373</td>\n",
       "      <td>0.671070</td>\n",
       "      <td>...</td>\n",
       "      <td>8.888434</td>\n",
       "      <td>9.201855</td>\n",
       "      <td>0.146095</td>\n",
       "      <td>367.624451</td>\n",
       "      <td>0.671070</td>\n",
       "      <td>13.426234</td>\n",
       "      <td>9.531624</td>\n",
       "      <td>0.151435</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>05:00</td>\n",
       "      <td>2013-02-16 05:00:00</td>\n",
       "      <td>364.312408</td>\n",
       "      <td>3.616215</td>\n",
       "      <td>36.517780</td>\n",
       "      <td>9.784019</td>\n",
       "      <td>0.209330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>363.647003</td>\n",
       "      <td>2.082330</td>\n",
       "      <td>...</td>\n",
       "      <td>9.657184</td>\n",
       "      <td>9.793872</td>\n",
       "      <td>0.142599</td>\n",
       "      <td>367.629059</td>\n",
       "      <td>2.082330</td>\n",
       "      <td>14.117352</td>\n",
       "      <td>10.155578</td>\n",
       "      <td>0.147928</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>05:30</td>\n",
       "      <td>2013-02-16 05:30:00</td>\n",
       "      <td>364.642639</td>\n",
       "      <td>6.237055</td>\n",
       "      <td>39.760674</td>\n",
       "      <td>10.307780</td>\n",
       "      <td>0.203489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>364.693115</td>\n",
       "      <td>4.256886</td>\n",
       "      <td>...</td>\n",
       "      <td>11.016133</td>\n",
       "      <td>9.921067</td>\n",
       "      <td>0.143217</td>\n",
       "      <td>368.074097</td>\n",
       "      <td>4.256886</td>\n",
       "      <td>15.252253</td>\n",
       "      <td>10.208029</td>\n",
       "      <td>0.148449</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>06:00</td>\n",
       "      <td>2013-02-16 06:00:00</td>\n",
       "      <td>365.520172</td>\n",
       "      <td>8.981276</td>\n",
       "      <td>44.584625</td>\n",
       "      <td>10.887638</td>\n",
       "      <td>0.207058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>366.599548</td>\n",
       "      <td>6.847970</td>\n",
       "      <td>...</td>\n",
       "      <td>13.087701</td>\n",
       "      <td>10.222957</td>\n",
       "      <td>0.144736</td>\n",
       "      <td>369.067688</td>\n",
       "      <td>6.847970</td>\n",
       "      <td>16.908476</td>\n",
       "      <td>10.521984</td>\n",
       "      <td>0.150296</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>06:30</td>\n",
       "      <td>2013-02-16 06:30:00</td>\n",
       "      <td>367.038086</td>\n",
       "      <td>12.165964</td>\n",
       "      <td>51.435818</td>\n",
       "      <td>11.401515</td>\n",
       "      <td>0.216000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>369.380249</td>\n",
       "      <td>9.982753</td>\n",
       "      <td>...</td>\n",
       "      <td>16.282021</td>\n",
       "      <td>11.082158</td>\n",
       "      <td>0.146711</td>\n",
       "      <td>370.576843</td>\n",
       "      <td>9.982753</td>\n",
       "      <td>19.524809</td>\n",
       "      <td>11.306367</td>\n",
       "      <td>0.152022</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>07:00</td>\n",
       "      <td>2013-02-16 07:00:00</td>\n",
       "      <td>369.070831</td>\n",
       "      <td>14.934931</td>\n",
       "      <td>58.895370</td>\n",
       "      <td>16.721815</td>\n",
       "      <td>0.227488</td>\n",
       "      <td>0.0</td>\n",
       "      <td>372.865845</td>\n",
       "      <td>12.856707</td>\n",
       "      <td>...</td>\n",
       "      <td>19.101406</td>\n",
       "      <td>12.543097</td>\n",
       "      <td>0.153000</td>\n",
       "      <td>372.547211</td>\n",
       "      <td>12.856707</td>\n",
       "      <td>21.497515</td>\n",
       "      <td>12.732733</td>\n",
       "      <td>0.157947</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>07:30</td>\n",
       "      <td>2013-02-16 07:30:00</td>\n",
       "      <td>371.705994</td>\n",
       "      <td>17.906923</td>\n",
       "      <td>67.381058</td>\n",
       "      <td>17.026798</td>\n",
       "      <td>0.239098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>377.219299</td>\n",
       "      <td>15.599544</td>\n",
       "      <td>...</td>\n",
       "      <td>23.512949</td>\n",
       "      <td>14.846460</td>\n",
       "      <td>0.162816</td>\n",
       "      <td>375.148163</td>\n",
       "      <td>15.599544</td>\n",
       "      <td>24.930838</td>\n",
       "      <td>14.990194</td>\n",
       "      <td>0.166929</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>08:00</td>\n",
       "      <td>2013-02-16 08:00:00</td>\n",
       "      <td>374.675873</td>\n",
       "      <td>19.114040</td>\n",
       "      <td>76.197388</td>\n",
       "      <td>20.837414</td>\n",
       "      <td>0.257103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>381.932678</td>\n",
       "      <td>16.841387</td>\n",
       "      <td>...</td>\n",
       "      <td>29.733999</td>\n",
       "      <td>18.009895</td>\n",
       "      <td>0.178179</td>\n",
       "      <td>378.023956</td>\n",
       "      <td>16.841387</td>\n",
       "      <td>30.166506</td>\n",
       "      <td>18.053703</td>\n",
       "      <td>0.181272</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>08:30</td>\n",
       "      <td>2013-02-16 08:30:00</td>\n",
       "      <td>377.940552</td>\n",
       "      <td>23.962999</td>\n",
       "      <td>87.786781</td>\n",
       "      <td>19.583595</td>\n",
       "      <td>0.277092</td>\n",
       "      <td>0.0</td>\n",
       "      <td>387.268890</td>\n",
       "      <td>21.396444</td>\n",
       "      <td>...</td>\n",
       "      <td>36.740524</td>\n",
       "      <td>19.499098</td>\n",
       "      <td>0.186997</td>\n",
       "      <td>381.316498</td>\n",
       "      <td>21.396444</td>\n",
       "      <td>35.997036</td>\n",
       "      <td>19.472580</td>\n",
       "      <td>0.189223</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>09:00</td>\n",
       "      <td>2013-02-16 09:00:00</td>\n",
       "      <td>382.017334</td>\n",
       "      <td>27.766520</td>\n",
       "      <td>99.092987</td>\n",
       "      <td>24.737637</td>\n",
       "      <td>0.285688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>393.121613</td>\n",
       "      <td>25.195759</td>\n",
       "      <td>...</td>\n",
       "      <td>44.268829</td>\n",
       "      <td>23.030441</td>\n",
       "      <td>0.192974</td>\n",
       "      <td>385.087280</td>\n",
       "      <td>25.195759</td>\n",
       "      <td>42.394009</td>\n",
       "      <td>22.924789</td>\n",
       "      <td>0.194129</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>09:30</td>\n",
       "      <td>2013-02-16 09:30:00</td>\n",
       "      <td>385.965302</td>\n",
       "      <td>31.005306</td>\n",
       "      <td>110.820412</td>\n",
       "      <td>25.692566</td>\n",
       "      <td>0.311313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>398.743408</td>\n",
       "      <td>28.890066</td>\n",
       "      <td>...</td>\n",
       "      <td>53.350819</td>\n",
       "      <td>27.484110</td>\n",
       "      <td>0.217984</td>\n",
       "      <td>388.903717</td>\n",
       "      <td>28.890066</td>\n",
       "      <td>50.387611</td>\n",
       "      <td>27.256721</td>\n",
       "      <td>0.217801</td>\n",
       "      <td>9.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10:00</td>\n",
       "      <td>2013-02-16 10:00:00</td>\n",
       "      <td>389.776947</td>\n",
       "      <td>34.497181</td>\n",
       "      <td>117.053284</td>\n",
       "      <td>25.233829</td>\n",
       "      <td>0.312732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>403.967987</td>\n",
       "      <td>32.063293</td>\n",
       "      <td>...</td>\n",
       "      <td>61.617607</td>\n",
       "      <td>30.674995</td>\n",
       "      <td>0.235041</td>\n",
       "      <td>392.575012</td>\n",
       "      <td>32.063293</td>\n",
       "      <td>57.666412</td>\n",
       "      <td>30.354588</td>\n",
       "      <td>0.233696</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10:30</td>\n",
       "      <td>2013-02-16 10:30:00</td>\n",
       "      <td>393.332703</td>\n",
       "      <td>35.956627</td>\n",
       "      <td>124.727760</td>\n",
       "      <td>29.129009</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>408.450256</td>\n",
       "      <td>34.083298</td>\n",
       "      <td>...</td>\n",
       "      <td>67.310318</td>\n",
       "      <td>32.358280</td>\n",
       "      <td>0.251133</td>\n",
       "      <td>395.847626</td>\n",
       "      <td>34.083298</td>\n",
       "      <td>62.612377</td>\n",
       "      <td>32.056030</td>\n",
       "      <td>0.248708</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11:00</td>\n",
       "      <td>2013-02-16 11:00:00</td>\n",
       "      <td>396.691010</td>\n",
       "      <td>37.982395</td>\n",
       "      <td>135.801727</td>\n",
       "      <td>28.752262</td>\n",
       "      <td>0.346127</td>\n",
       "      <td>0.0</td>\n",
       "      <td>412.685303</td>\n",
       "      <td>36.114418</td>\n",
       "      <td>...</td>\n",
       "      <td>74.319794</td>\n",
       "      <td>35.145542</td>\n",
       "      <td>0.263387</td>\n",
       "      <td>399.038483</td>\n",
       "      <td>36.114418</td>\n",
       "      <td>68.882065</td>\n",
       "      <td>34.790443</td>\n",
       "      <td>0.260072</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11:30</td>\n",
       "      <td>2013-02-16 11:30:00</td>\n",
       "      <td>399.408691</td>\n",
       "      <td>38.441444</td>\n",
       "      <td>139.036438</td>\n",
       "      <td>32.470287</td>\n",
       "      <td>0.356622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>415.385803</td>\n",
       "      <td>36.785889</td>\n",
       "      <td>...</td>\n",
       "      <td>78.057457</td>\n",
       "      <td>37.855965</td>\n",
       "      <td>0.273499</td>\n",
       "      <td>401.366882</td>\n",
       "      <td>36.785889</td>\n",
       "      <td>72.194031</td>\n",
       "      <td>37.444229</td>\n",
       "      <td>0.269478</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12:00</td>\n",
       "      <td>2013-02-16 12:00:00</td>\n",
       "      <td>401.783081</td>\n",
       "      <td>39.226788</td>\n",
       "      <td>141.258728</td>\n",
       "      <td>30.248692</td>\n",
       "      <td>0.369895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>418.018463</td>\n",
       "      <td>37.358585</td>\n",
       "      <td>...</td>\n",
       "      <td>82.743782</td>\n",
       "      <td>38.132927</td>\n",
       "      <td>0.287662</td>\n",
       "      <td>403.708160</td>\n",
       "      <td>37.358585</td>\n",
       "      <td>76.437370</td>\n",
       "      <td>37.730888</td>\n",
       "      <td>0.282938</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12:30</td>\n",
       "      <td>2013-02-16 12:30:00</td>\n",
       "      <td>403.359283</td>\n",
       "      <td>39.057743</td>\n",
       "      <td>144.044891</td>\n",
       "      <td>31.346935</td>\n",
       "      <td>0.371397</td>\n",
       "      <td>0.0</td>\n",
       "      <td>419.434570</td>\n",
       "      <td>37.340157</td>\n",
       "      <td>...</td>\n",
       "      <td>87.121727</td>\n",
       "      <td>39.582298</td>\n",
       "      <td>0.303878</td>\n",
       "      <td>405.289978</td>\n",
       "      <td>37.340157</td>\n",
       "      <td>80.405113</td>\n",
       "      <td>39.243320</td>\n",
       "      <td>0.298816</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13:00</td>\n",
       "      <td>2013-02-16 13:00:00</td>\n",
       "      <td>404.047852</td>\n",
       "      <td>37.642460</td>\n",
       "      <td>140.867615</td>\n",
       "      <td>28.256861</td>\n",
       "      <td>0.381470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>419.411163</td>\n",
       "      <td>36.053856</td>\n",
       "      <td>...</td>\n",
       "      <td>85.375305</td>\n",
       "      <td>38.423313</td>\n",
       "      <td>0.312121</td>\n",
       "      <td>405.810577</td>\n",
       "      <td>36.053856</td>\n",
       "      <td>78.651703</td>\n",
       "      <td>38.087757</td>\n",
       "      <td>0.307083</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13:30</td>\n",
       "      <td>2013-02-16 13:30:00</td>\n",
       "      <td>404.772736</td>\n",
       "      <td>35.813457</td>\n",
       "      <td>142.373398</td>\n",
       "      <td>32.699226</td>\n",
       "      <td>0.385448</td>\n",
       "      <td>0.0</td>\n",
       "      <td>419.097290</td>\n",
       "      <td>34.699013</td>\n",
       "      <td>...</td>\n",
       "      <td>83.894341</td>\n",
       "      <td>38.996822</td>\n",
       "      <td>0.317455</td>\n",
       "      <td>406.273102</td>\n",
       "      <td>34.699013</td>\n",
       "      <td>77.163750</td>\n",
       "      <td>38.703648</td>\n",
       "      <td>0.312417</td>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14:00</td>\n",
       "      <td>2013-02-16 14:00:00</td>\n",
       "      <td>404.313354</td>\n",
       "      <td>32.140118</td>\n",
       "      <td>134.061722</td>\n",
       "      <td>29.743917</td>\n",
       "      <td>0.377285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>416.842163</td>\n",
       "      <td>31.701096</td>\n",
       "      <td>...</td>\n",
       "      <td>79.627373</td>\n",
       "      <td>36.141705</td>\n",
       "      <td>0.319248</td>\n",
       "      <td>405.353973</td>\n",
       "      <td>31.701096</td>\n",
       "      <td>73.273804</td>\n",
       "      <td>35.897285</td>\n",
       "      <td>0.314371</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14:30</td>\n",
       "      <td>2013-02-16 14:30:00</td>\n",
       "      <td>403.507172</td>\n",
       "      <td>29.199314</td>\n",
       "      <td>125.818207</td>\n",
       "      <td>29.299221</td>\n",
       "      <td>0.376423</td>\n",
       "      <td>0.0</td>\n",
       "      <td>415.409973</td>\n",
       "      <td>29.400772</td>\n",
       "      <td>...</td>\n",
       "      <td>75.929413</td>\n",
       "      <td>34.734394</td>\n",
       "      <td>0.311197</td>\n",
       "      <td>404.942719</td>\n",
       "      <td>29.400772</td>\n",
       "      <td>69.691185</td>\n",
       "      <td>34.476532</td>\n",
       "      <td>0.306631</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>15:00</td>\n",
       "      <td>2013-02-16 15:00:00</td>\n",
       "      <td>402.177460</td>\n",
       "      <td>26.043524</td>\n",
       "      <td>120.492455</td>\n",
       "      <td>27.457294</td>\n",
       "      <td>0.365096</td>\n",
       "      <td>0.0</td>\n",
       "      <td>413.103577</td>\n",
       "      <td>27.005392</td>\n",
       "      <td>...</td>\n",
       "      <td>71.380539</td>\n",
       "      <td>33.009598</td>\n",
       "      <td>0.304381</td>\n",
       "      <td>403.835419</td>\n",
       "      <td>27.005392</td>\n",
       "      <td>65.421524</td>\n",
       "      <td>32.770771</td>\n",
       "      <td>0.299842</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15:30</td>\n",
       "      <td>2013-02-16 15:30:00</td>\n",
       "      <td>400.380371</td>\n",
       "      <td>22.205402</td>\n",
       "      <td>111.787369</td>\n",
       "      <td>28.488848</td>\n",
       "      <td>0.369061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>409.448059</td>\n",
       "      <td>23.530514</td>\n",
       "      <td>...</td>\n",
       "      <td>63.801655</td>\n",
       "      <td>29.902882</td>\n",
       "      <td>0.305130</td>\n",
       "      <td>401.846558</td>\n",
       "      <td>23.530514</td>\n",
       "      <td>58.416821</td>\n",
       "      <td>29.676310</td>\n",
       "      <td>0.300701</td>\n",
       "      <td>15.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16:00</td>\n",
       "      <td>2013-02-16 16:00:00</td>\n",
       "      <td>398.268982</td>\n",
       "      <td>19.294044</td>\n",
       "      <td>103.631966</td>\n",
       "      <td>24.583515</td>\n",
       "      <td>0.357327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>405.821014</td>\n",
       "      <td>20.351397</td>\n",
       "      <td>...</td>\n",
       "      <td>56.703140</td>\n",
       "      <td>28.749935</td>\n",
       "      <td>0.297765</td>\n",
       "      <td>399.812195</td>\n",
       "      <td>20.351397</td>\n",
       "      <td>51.977360</td>\n",
       "      <td>28.542410</td>\n",
       "      <td>0.293648</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16:30</td>\n",
       "      <td>2013-02-16 16:30:00</td>\n",
       "      <td>395.971527</td>\n",
       "      <td>15.731369</td>\n",
       "      <td>92.973663</td>\n",
       "      <td>24.098232</td>\n",
       "      <td>0.348876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>401.463501</td>\n",
       "      <td>16.805141</td>\n",
       "      <td>...</td>\n",
       "      <td>49.468678</td>\n",
       "      <td>26.981606</td>\n",
       "      <td>0.294109</td>\n",
       "      <td>397.240997</td>\n",
       "      <td>16.805141</td>\n",
       "      <td>45.609200</td>\n",
       "      <td>26.791174</td>\n",
       "      <td>0.290554</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17:00</td>\n",
       "      <td>2013-02-16 17:00:00</td>\n",
       "      <td>393.501587</td>\n",
       "      <td>12.497808</td>\n",
       "      <td>85.765167</td>\n",
       "      <td>21.730272</td>\n",
       "      <td>0.335636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>397.476562</td>\n",
       "      <td>13.444634</td>\n",
       "      <td>...</td>\n",
       "      <td>42.646706</td>\n",
       "      <td>24.346956</td>\n",
       "      <td>0.280146</td>\n",
       "      <td>394.868927</td>\n",
       "      <td>13.444634</td>\n",
       "      <td>39.487282</td>\n",
       "      <td>24.193169</td>\n",
       "      <td>0.277375</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17:30</td>\n",
       "      <td>2013-02-16 17:30:00</td>\n",
       "      <td>390.645721</td>\n",
       "      <td>11.241938</td>\n",
       "      <td>76.480659</td>\n",
       "      <td>19.921059</td>\n",
       "      <td>0.327406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>392.858673</td>\n",
       "      <td>12.395447</td>\n",
       "      <td>...</td>\n",
       "      <td>35.281277</td>\n",
       "      <td>22.432106</td>\n",
       "      <td>0.270609</td>\n",
       "      <td>391.956055</td>\n",
       "      <td>12.395447</td>\n",
       "      <td>33.130550</td>\n",
       "      <td>22.309542</td>\n",
       "      <td>0.268288</td>\n",
       "      <td>17.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>18:00</td>\n",
       "      <td>2013-02-16 18:00:00</td>\n",
       "      <td>387.800018</td>\n",
       "      <td>8.745860</td>\n",
       "      <td>68.881714</td>\n",
       "      <td>19.175411</td>\n",
       "      <td>0.327013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>388.599274</td>\n",
       "      <td>9.676536</td>\n",
       "      <td>...</td>\n",
       "      <td>28.218279</td>\n",
       "      <td>21.487835</td>\n",
       "      <td>0.267576</td>\n",
       "      <td>389.211151</td>\n",
       "      <td>9.676536</td>\n",
       "      <td>26.876156</td>\n",
       "      <td>21.394999</td>\n",
       "      <td>0.266027</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>18:30</td>\n",
       "      <td>2013-02-16 18:30:00</td>\n",
       "      <td>385.055817</td>\n",
       "      <td>6.616109</td>\n",
       "      <td>61.354805</td>\n",
       "      <td>19.646238</td>\n",
       "      <td>0.308403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>384.635437</td>\n",
       "      <td>7.101329</td>\n",
       "      <td>...</td>\n",
       "      <td>22.758224</td>\n",
       "      <td>19.756466</td>\n",
       "      <td>0.257172</td>\n",
       "      <td>386.545319</td>\n",
       "      <td>7.101329</td>\n",
       "      <td>22.259462</td>\n",
       "      <td>19.708750</td>\n",
       "      <td>0.256189</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>19:00</td>\n",
       "      <td>2013-02-16 19:00:00</td>\n",
       "      <td>382.374695</td>\n",
       "      <td>4.670651</td>\n",
       "      <td>54.832306</td>\n",
       "      <td>16.157976</td>\n",
       "      <td>0.288530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>381.006317</td>\n",
       "      <td>4.669284</td>\n",
       "      <td>...</td>\n",
       "      <td>17.692375</td>\n",
       "      <td>17.841995</td>\n",
       "      <td>0.241696</td>\n",
       "      <td>383.959259</td>\n",
       "      <td>4.669284</td>\n",
       "      <td>17.854975</td>\n",
       "      <td>17.811300</td>\n",
       "      <td>0.241148</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>19:30</td>\n",
       "      <td>2013-02-16 19:30:00</td>\n",
       "      <td>380.349213</td>\n",
       "      <td>2.898779</td>\n",
       "      <td>48.870079</td>\n",
       "      <td>16.142771</td>\n",
       "      <td>0.283656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>378.148315</td>\n",
       "      <td>2.334485</td>\n",
       "      <td>...</td>\n",
       "      <td>13.481588</td>\n",
       "      <td>16.596684</td>\n",
       "      <td>0.230798</td>\n",
       "      <td>381.947876</td>\n",
       "      <td>2.334485</td>\n",
       "      <td>14.279750</td>\n",
       "      <td>16.581781</td>\n",
       "      <td>0.230861</td>\n",
       "      <td>19.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20:00</td>\n",
       "      <td>2013-02-16 20:00:00</td>\n",
       "      <td>378.182159</td>\n",
       "      <td>1.583521</td>\n",
       "      <td>45.550079</td>\n",
       "      <td>15.059552</td>\n",
       "      <td>0.274030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.694550</td>\n",
       "      <td>0.780130</td>\n",
       "      <td>...</td>\n",
       "      <td>11.044538</td>\n",
       "      <td>15.402448</td>\n",
       "      <td>0.223681</td>\n",
       "      <td>380.067017</td>\n",
       "      <td>0.780130</td>\n",
       "      <td>12.232304</td>\n",
       "      <td>15.434050</td>\n",
       "      <td>0.224316</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20:30</td>\n",
       "      <td>2013-02-16 20:30:00</td>\n",
       "      <td>376.598816</td>\n",
       "      <td>1.049953</td>\n",
       "      <td>42.925087</td>\n",
       "      <td>14.093812</td>\n",
       "      <td>0.271848</td>\n",
       "      <td>0.0</td>\n",
       "      <td>374.108612</td>\n",
       "      <td>0.071046</td>\n",
       "      <td>...</td>\n",
       "      <td>9.624560</td>\n",
       "      <td>14.438796</td>\n",
       "      <td>0.209918</td>\n",
       "      <td>378.804626</td>\n",
       "      <td>0.071046</td>\n",
       "      <td>11.200467</td>\n",
       "      <td>14.479424</td>\n",
       "      <td>0.210960</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>21:00</td>\n",
       "      <td>2013-02-16 21:00:00</td>\n",
       "      <td>374.953491</td>\n",
       "      <td>0.871818</td>\n",
       "      <td>41.333794</td>\n",
       "      <td>14.272805</td>\n",
       "      <td>0.264440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>372.629974</td>\n",
       "      <td>0.002129</td>\n",
       "      <td>...</td>\n",
       "      <td>9.420352</td>\n",
       "      <td>13.214850</td>\n",
       "      <td>0.201006</td>\n",
       "      <td>377.458801</td>\n",
       "      <td>0.002129</td>\n",
       "      <td>11.258385</td>\n",
       "      <td>13.285855</td>\n",
       "      <td>0.202599</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>21:30</td>\n",
       "      <td>2013-02-16 09:30:00</td>\n",
       "      <td>373.700745</td>\n",
       "      <td>0.861446</td>\n",
       "      <td>40.783138</td>\n",
       "      <td>14.037176</td>\n",
       "      <td>0.260063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>371.498077</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.565265</td>\n",
       "      <td>12.787423</td>\n",
       "      <td>0.191897</td>\n",
       "      <td>376.417847</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.602067</td>\n",
       "      <td>12.887271</td>\n",
       "      <td>0.193559</td>\n",
       "      <td>21.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22:00</td>\n",
       "      <td>2013-02-16 10:00:00</td>\n",
       "      <td>372.666779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.991543</td>\n",
       "      <td>13.240686</td>\n",
       "      <td>0.247374</td>\n",
       "      <td>0.0</td>\n",
       "      <td>370.560303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.877815</td>\n",
       "      <td>12.326810</td>\n",
       "      <td>0.184373</td>\n",
       "      <td>375.515900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.143968</td>\n",
       "      <td>12.446898</td>\n",
       "      <td>0.186531</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>22:30</td>\n",
       "      <td>2013-02-16 10:30:00</td>\n",
       "      <td>371.767731</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.923145</td>\n",
       "      <td>11.971110</td>\n",
       "      <td>0.246489</td>\n",
       "      <td>0.0</td>\n",
       "      <td>369.693390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.980227</td>\n",
       "      <td>11.766883</td>\n",
       "      <td>0.176933</td>\n",
       "      <td>374.712311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.495615</td>\n",
       "      <td>11.879762</td>\n",
       "      <td>0.179512</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>23:00</td>\n",
       "      <td>2013-02-16 11:00:00</td>\n",
       "      <td>370.763855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.805416</td>\n",
       "      <td>11.472060</td>\n",
       "      <td>0.237756</td>\n",
       "      <td>0.0</td>\n",
       "      <td>368.792358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.617725</td>\n",
       "      <td>11.479810</td>\n",
       "      <td>0.171750</td>\n",
       "      <td>373.834351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.421941</td>\n",
       "      <td>11.638631</td>\n",
       "      <td>0.174648</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>23:30</td>\n",
       "      <td>2013-02-16 11:30:00</td>\n",
       "      <td>369.741211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.832653</td>\n",
       "      <td>10.389789</td>\n",
       "      <td>0.231675</td>\n",
       "      <td>0.0</td>\n",
       "      <td>367.907440</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>9.807671</td>\n",
       "      <td>10.980524</td>\n",
       "      <td>0.169643</td>\n",
       "      <td>372.990845</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.782582</td>\n",
       "      <td>11.147270</td>\n",
       "      <td>0.172781</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diurnal                time    LWup_obs   SWup_obs      Qh_obs    Qle_obs  \\\n",
       "0    00:00 2013-02-16 00:00:00  368.978149        NaN   37.107864  10.687767   \n",
       "1    00:30 2013-02-16 00:30:00  368.460175        NaN   35.386906  10.966841   \n",
       "2    01:00 2013-02-16 01:00:00  367.603729        NaN   35.267212  11.555216   \n",
       "3    01:30 2013-02-16 01:30:00  367.055878        NaN   34.145683  10.246067   \n",
       "4    02:00 2013-02-16 02:00:00  366.471497        NaN   33.792473   9.176048   \n",
       "5    02:30 2013-02-16 02:30:00  365.897095        NaN   33.412251   8.360415   \n",
       "6    03:00 2013-02-16 03:00:00  365.428680        NaN   33.463573   8.634274   \n",
       "7    03:30 2013-02-16 03:30:00  365.022614   0.680986   33.952492   9.834349   \n",
       "8    04:00 2013-02-16 04:00:00  364.617554   1.043147   34.810764   9.197115   \n",
       "9    04:30 2013-02-16 04:30:00  364.303192   1.989947   34.592896  10.006502   \n",
       "10   05:00 2013-02-16 05:00:00  364.312408   3.616215   36.517780   9.784019   \n",
       "11   05:30 2013-02-16 05:30:00  364.642639   6.237055   39.760674  10.307780   \n",
       "12   06:00 2013-02-16 06:00:00  365.520172   8.981276   44.584625  10.887638   \n",
       "13   06:30 2013-02-16 06:30:00  367.038086  12.165964   51.435818  11.401515   \n",
       "14   07:00 2013-02-16 07:00:00  369.070831  14.934931   58.895370  16.721815   \n",
       "15   07:30 2013-02-16 07:30:00  371.705994  17.906923   67.381058  17.026798   \n",
       "16   08:00 2013-02-16 08:00:00  374.675873  19.114040   76.197388  20.837414   \n",
       "17   08:30 2013-02-16 08:30:00  377.940552  23.962999   87.786781  19.583595   \n",
       "18   09:00 2013-02-16 09:00:00  382.017334  27.766520   99.092987  24.737637   \n",
       "19   09:30 2013-02-16 09:30:00  385.965302  31.005306  110.820412  25.692566   \n",
       "20   10:00 2013-02-16 10:00:00  389.776947  34.497181  117.053284  25.233829   \n",
       "21   10:30 2013-02-16 10:30:00  393.332703  35.956627  124.727760  29.129009   \n",
       "22   11:00 2013-02-16 11:00:00  396.691010  37.982395  135.801727  28.752262   \n",
       "23   11:30 2013-02-16 11:30:00  399.408691  38.441444  139.036438  32.470287   \n",
       "24   12:00 2013-02-16 12:00:00  401.783081  39.226788  141.258728  30.248692   \n",
       "25   12:30 2013-02-16 12:30:00  403.359283  39.057743  144.044891  31.346935   \n",
       "26   13:00 2013-02-16 13:00:00  404.047852  37.642460  140.867615  28.256861   \n",
       "27   13:30 2013-02-16 13:30:00  404.772736  35.813457  142.373398  32.699226   \n",
       "28   14:00 2013-02-16 14:00:00  404.313354  32.140118  134.061722  29.743917   \n",
       "29   14:30 2013-02-16 14:30:00  403.507172  29.199314  125.818207  29.299221   \n",
       "30   15:00 2013-02-16 15:00:00  402.177460  26.043524  120.492455  27.457294   \n",
       "31   15:30 2013-02-16 15:30:00  400.380371  22.205402  111.787369  28.488848   \n",
       "32   16:00 2013-02-16 16:00:00  398.268982  19.294044  103.631966  24.583515   \n",
       "33   16:30 2013-02-16 16:30:00  395.971527  15.731369   92.973663  24.098232   \n",
       "34   17:00 2013-02-16 17:00:00  393.501587  12.497808   85.765167  21.730272   \n",
       "35   17:30 2013-02-16 17:30:00  390.645721  11.241938   76.480659  19.921059   \n",
       "36   18:00 2013-02-16 18:00:00  387.800018   8.745860   68.881714  19.175411   \n",
       "37   18:30 2013-02-16 18:30:00  385.055817   6.616109   61.354805  19.646238   \n",
       "38   19:00 2013-02-16 19:00:00  382.374695   4.670651   54.832306  16.157976   \n",
       "39   19:30 2013-02-16 19:30:00  380.349213   2.898779   48.870079  16.142771   \n",
       "40   20:00 2013-02-16 20:00:00  378.182159   1.583521   45.550079  15.059552   \n",
       "41   20:30 2013-02-16 20:30:00  376.598816   1.049953   42.925087  14.093812   \n",
       "42   21:00 2013-02-16 21:00:00  374.953491   0.871818   41.333794  14.272805   \n",
       "43   21:30 2013-02-16 09:30:00  373.700745   0.861446   40.783138  14.037176   \n",
       "44   22:00 2013-02-16 10:00:00  372.666779        NaN   41.991543  13.240686   \n",
       "45   22:30 2013-02-16 10:30:00  371.767731        NaN   39.923145  11.971110   \n",
       "46   23:00 2013-02-16 11:00:00  370.763855        NaN   39.805416  11.472060   \n",
       "47   23:30 2013-02-16 11:30:00  369.741211        NaN   38.832653  10.389789   \n",
       "\n",
       "    Qtau_obs  lndgrid    LWup_def   SWup_def  ...  Qh_wrflcz  Qle_wrflcz  \\\n",
       "0   0.230667      0.0  367.217926        NaN  ...   9.969138   10.755898   \n",
       "1   0.230804      0.0  366.740448        NaN  ...  10.132294   10.546378   \n",
       "2   0.232187      0.0  366.095062        NaN  ...  10.286006   10.680665   \n",
       "3   0.223178      0.0  365.619843        NaN  ...  10.001425   10.514145   \n",
       "4   0.217918      0.0  365.078033        NaN  ...   9.825698   10.191563   \n",
       "5   0.210671      0.0  364.549652        NaN  ...   9.629966   10.077915   \n",
       "6   0.214073      0.0  364.151978        NaN  ...   9.300602    9.658520   \n",
       "7   0.206201      0.0  363.840393   0.005461  ...   9.183133    9.363967   \n",
       "8   0.203933      0.0  363.500336   0.078480  ...   9.228721    9.509408   \n",
       "9   0.202137      0.0  363.294373   0.671070  ...   8.888434    9.201855   \n",
       "10  0.209330      0.0  363.647003   2.082330  ...   9.657184    9.793872   \n",
       "11  0.203489      0.0  364.693115   4.256886  ...  11.016133    9.921067   \n",
       "12  0.207058      0.0  366.599548   6.847970  ...  13.087701   10.222957   \n",
       "13  0.216000      0.0  369.380249   9.982753  ...  16.282021   11.082158   \n",
       "14  0.227488      0.0  372.865845  12.856707  ...  19.101406   12.543097   \n",
       "15  0.239098      0.0  377.219299  15.599544  ...  23.512949   14.846460   \n",
       "16  0.257103      0.0  381.932678  16.841387  ...  29.733999   18.009895   \n",
       "17  0.277092      0.0  387.268890  21.396444  ...  36.740524   19.499098   \n",
       "18  0.285688      0.0  393.121613  25.195759  ...  44.268829   23.030441   \n",
       "19  0.311313      0.0  398.743408  28.890066  ...  53.350819   27.484110   \n",
       "20  0.312732      0.0  403.967987  32.063293  ...  61.617607   30.674995   \n",
       "21  0.331357      0.0  408.450256  34.083298  ...  67.310318   32.358280   \n",
       "22  0.346127      0.0  412.685303  36.114418  ...  74.319794   35.145542   \n",
       "23  0.356622      0.0  415.385803  36.785889  ...  78.057457   37.855965   \n",
       "24  0.369895      0.0  418.018463  37.358585  ...  82.743782   38.132927   \n",
       "25  0.371397      0.0  419.434570  37.340157  ...  87.121727   39.582298   \n",
       "26  0.381470      0.0  419.411163  36.053856  ...  85.375305   38.423313   \n",
       "27  0.385448      0.0  419.097290  34.699013  ...  83.894341   38.996822   \n",
       "28  0.377285      0.0  416.842163  31.701096  ...  79.627373   36.141705   \n",
       "29  0.376423      0.0  415.409973  29.400772  ...  75.929413   34.734394   \n",
       "30  0.365096      0.0  413.103577  27.005392  ...  71.380539   33.009598   \n",
       "31  0.369061      0.0  409.448059  23.530514  ...  63.801655   29.902882   \n",
       "32  0.357327      0.0  405.821014  20.351397  ...  56.703140   28.749935   \n",
       "33  0.348876      0.0  401.463501  16.805141  ...  49.468678   26.981606   \n",
       "34  0.335636      0.0  397.476562  13.444634  ...  42.646706   24.346956   \n",
       "35  0.327406      0.0  392.858673  12.395447  ...  35.281277   22.432106   \n",
       "36  0.327013      0.0  388.599274   9.676536  ...  28.218279   21.487835   \n",
       "37  0.308403      0.0  384.635437   7.101329  ...  22.758224   19.756466   \n",
       "38  0.288530      0.0  381.006317   4.669284  ...  17.692375   17.841995   \n",
       "39  0.283656      0.0  378.148315   2.334485  ...  13.481588   16.596684   \n",
       "40  0.274030      0.0  375.694550   0.780130  ...  11.044538   15.402448   \n",
       "41  0.271848      0.0  374.108612   0.071046  ...   9.624560   14.438796   \n",
       "42  0.264440      0.0  372.629974   0.002129  ...   9.420352   13.214850   \n",
       "43  0.260063      0.0  371.498077   0.000000  ...   9.565265   12.787423   \n",
       "44  0.247374      0.0  370.560303        NaN  ...   9.877815   12.326810   \n",
       "45  0.246489      0.0  369.693390        NaN  ...   9.980227   11.766883   \n",
       "46  0.237756      0.0  368.792358        NaN  ...  10.617725   11.479810   \n",
       "47  0.231675      0.0  367.907440        NaN  ...   9.807671   10.980524   \n",
       "\n",
       "    Qtau_wrflcz  LWup_lilcz  SWup_lilcz   Qh_lilcz  Qle_lilcz  Qtau_lilcz  \\\n",
       "0      0.169343  372.287628         NaN  13.186077  10.933187    0.172633   \n",
       "1      0.166101  371.789429         NaN  13.548819  10.738297    0.169551   \n",
       "2      0.165146  371.122467         NaN  13.894144  10.897151    0.168860   \n",
       "3      0.156723  370.602997         NaN  13.788540  10.764087    0.160739   \n",
       "4      0.160731  370.004089         NaN  13.758117  10.447473    0.164756   \n",
       "5      0.155398  369.400452         NaN  13.727299  10.341196    0.159789   \n",
       "6      0.156984  368.898560         NaN  13.468583   9.969198    0.161580   \n",
       "7      0.149580  368.490417    0.005461  13.507803   9.642113    0.154430   \n",
       "8      0.146639  368.017059    0.078480  13.672853   9.835212    0.151541   \n",
       "9      0.146095  367.624451    0.671070  13.426234   9.531624    0.151435   \n",
       "10     0.142599  367.629059    2.082330  14.117352  10.155578    0.147928   \n",
       "11     0.143217  368.074097    4.256886  15.252253  10.208029    0.148449   \n",
       "12     0.144736  369.067688    6.847970  16.908476  10.521984    0.150296   \n",
       "13     0.146711  370.576843    9.982753  19.524809  11.306367    0.152022   \n",
       "14     0.153000  372.547211   12.856707  21.497515  12.732733    0.157947   \n",
       "15     0.162816  375.148163   15.599544  24.930838  14.990194    0.166929   \n",
       "16     0.178179  378.023956   16.841387  30.166506  18.053703    0.181272   \n",
       "17     0.186997  381.316498   21.396444  35.997036  19.472580    0.189223   \n",
       "18     0.192974  385.087280   25.195759  42.394009  22.924789    0.194129   \n",
       "19     0.217984  388.903717   28.890066  50.387611  27.256721    0.217801   \n",
       "20     0.235041  392.575012   32.063293  57.666412  30.354588    0.233696   \n",
       "21     0.251133  395.847626   34.083298  62.612377  32.056030    0.248708   \n",
       "22     0.263387  399.038483   36.114418  68.882065  34.790443    0.260072   \n",
       "23     0.273499  401.366882   36.785889  72.194031  37.444229    0.269478   \n",
       "24     0.287662  403.708160   37.358585  76.437370  37.730888    0.282938   \n",
       "25     0.303878  405.289978   37.340157  80.405113  39.243320    0.298816   \n",
       "26     0.312121  405.810577   36.053856  78.651703  38.087757    0.307083   \n",
       "27     0.317455  406.273102   34.699013  77.163750  38.703648    0.312417   \n",
       "28     0.319248  405.353973   31.701096  73.273804  35.897285    0.314371   \n",
       "29     0.311197  404.942719   29.400772  69.691185  34.476532    0.306631   \n",
       "30     0.304381  403.835419   27.005392  65.421524  32.770771    0.299842   \n",
       "31     0.305130  401.846558   23.530514  58.416821  29.676310    0.300701   \n",
       "32     0.297765  399.812195   20.351397  51.977360  28.542410    0.293648   \n",
       "33     0.294109  397.240997   16.805141  45.609200  26.791174    0.290554   \n",
       "34     0.280146  394.868927   13.444634  39.487282  24.193169    0.277375   \n",
       "35     0.270609  391.956055   12.395447  33.130550  22.309542    0.268288   \n",
       "36     0.267576  389.211151    9.676536  26.876156  21.394999    0.266027   \n",
       "37     0.257172  386.545319    7.101329  22.259462  19.708750    0.256189   \n",
       "38     0.241696  383.959259    4.669284  17.854975  17.811300    0.241148   \n",
       "39     0.230798  381.947876    2.334485  14.279750  16.581781    0.230861   \n",
       "40     0.223681  380.067017    0.780130  12.232304  15.434050    0.224316   \n",
       "41     0.209918  378.804626    0.071046  11.200467  14.479424    0.210960   \n",
       "42     0.201006  377.458801    0.002129  11.258385  13.285855    0.202599   \n",
       "43     0.191897  376.417847    0.000000  11.602067  12.887271    0.193559   \n",
       "44     0.184373  375.515900         NaN  12.143968  12.446898    0.186531   \n",
       "45     0.176933  374.712311         NaN  12.495615  11.879762    0.179512   \n",
       "46     0.171750  373.834351         NaN  13.421941  11.638631    0.174648   \n",
       "47     0.169643  372.990845         NaN  12.782582  11.147270    0.172781   \n",
       "\n",
       "    hour  minute  \n",
       "0    0.0     0.0  \n",
       "1    0.0    30.0  \n",
       "2    1.0     0.0  \n",
       "3    1.0    30.0  \n",
       "4    2.0     0.0  \n",
       "5    2.0    30.0  \n",
       "6    3.0     0.0  \n",
       "7    3.0    30.0  \n",
       "8    4.0     0.0  \n",
       "9    4.0    30.0  \n",
       "10   5.0     0.0  \n",
       "11   5.0    30.0  \n",
       "12   6.0     0.0  \n",
       "13   6.0    30.0  \n",
       "14   7.0     0.0  \n",
       "15   7.0    30.0  \n",
       "16   8.0     0.0  \n",
       "17   8.0    30.0  \n",
       "18   9.0     0.0  \n",
       "19   9.0    30.0  \n",
       "20  10.0     0.0  \n",
       "21  10.0    30.0  \n",
       "22  11.0     0.0  \n",
       "23  11.0    30.0  \n",
       "24  12.0     0.0  \n",
       "25  12.0    30.0  \n",
       "26  13.0     0.0  \n",
       "27  13.0    30.0  \n",
       "28  14.0     0.0  \n",
       "29  14.0    30.0  \n",
       "30  15.0     0.0  \n",
       "31  15.0    30.0  \n",
       "32  16.0     0.0  \n",
       "33  16.0    30.0  \n",
       "34  17.0     0.0  \n",
       "35  17.0    30.0  \n",
       "36  18.0     0.0  \n",
       "37  18.0    30.0  \n",
       "38  19.0     0.0  \n",
       "39  19.0    30.0  \n",
       "40  20.0     0.0  \n",
       "41  20.0    30.0  \n",
       "42  21.0     0.0  \n",
       "43  21.0    30.0  \n",
       "44  22.0     0.0  \n",
       "45  22.0    30.0  \n",
       "46  23.0     0.0  \n",
       "47  23.0    30.0  \n",
       "\n",
       "[48 rows x 25 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diurnal_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# examine sensible heat flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index                        30254\n",
      "LWup_obs                353.201816\n",
      "SWup_obs                  9.317955\n",
      "Qh_obs                   21.250729\n",
      "Qle_obs                   20.47482\n",
      "Qtau_obs                  0.424914\n",
      "lndgrid                        0.0\n",
      "LWup_def                351.677559\n",
      "SWup_def                  6.450469\n",
      "Qh_def                    -25.9346\n",
      "Qle_def                  25.999079\n",
      "Qtau_def                  0.322551\n",
      "LWup_wrflcz               350.2308\n",
      "SWup_wrflcz               6.450469\n",
      "Qh_wrflcz               -33.089481\n",
      "Qle_wrflcz                24.44082\n",
      "Qtau_wrflcz               0.310642\n",
      "LWup_lilcz              350.354978\n",
      "SWup_lilcz                6.450469\n",
      "Qh_lilcz                -32.637698\n",
      "Qle_lilcz                24.553864\n",
      "Qtau_lilcz                 0.31482\n",
      "time           2013-12-25 07:00:00\n",
      "Name: 30254, dtype: object\n"
     ]
    }
   ],
   "source": [
    "var = 'Qh_def'\n",
    "min_index = seven_day_mean[var].idxmin()\n",
    "min_row = seven_day_mean.loc[min_index]\n",
    "print(min_row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-12-25 00:00:00', '2012-12-25 00:30:00',\n",
       "               '2012-12-25 01:00:00', '2012-12-25 01:30:00',\n",
       "               '2012-12-25 02:00:00', '2012-12-25 02:30:00',\n",
       "               '2012-12-25 03:00:00', '2012-12-25 03:30:00',\n",
       "               '2012-12-25 04:00:00', '2012-12-25 04:30:00',\n",
       "               '2012-12-25 05:00:00', '2012-12-25 05:30:00',\n",
       "               '2012-12-25 06:00:00', '2012-12-25 06:30:00',\n",
       "               '2012-12-25 07:00:00', '2012-12-25 07:30:00',\n",
       "               '2012-12-25 08:00:00', '2012-12-25 08:30:00',\n",
       "               '2012-12-25 09:00:00', '2012-12-25 09:30:00',\n",
       "               '2012-12-25 10:00:00', '2012-12-25 10:30:00',\n",
       "               '2012-12-25 11:00:00', '2012-12-25 11:30:00',\n",
       "               '2012-12-25 12:00:00', '2012-12-25 12:30:00',\n",
       "               '2012-12-25 13:00:00', '2012-12-25 13:30:00',\n",
       "               '2012-12-25 14:00:00', '2012-12-25 14:30:00',\n",
       "               '2012-12-25 15:00:00', '2012-12-25 15:30:00',\n",
       "               '2012-12-25 16:00:00', '2012-12-25 16:30:00',\n",
       "               '2012-12-25 17:00:00', '2012-12-25 17:30:00',\n",
       "               '2012-12-25 18:00:00', '2012-12-25 18:30:00',\n",
       "               '2012-12-25 19:00:00', '2012-12-25 19:30:00',\n",
       "               '2012-12-25 20:00:00', '2012-12-25 20:30:00',\n",
       "               '2012-12-25 21:00:00', '2012-12-25 21:30:00',\n",
       "               '2012-12-25 22:00:00', '2012-12-25 22:30:00',\n",
       "               '2012-12-25 23:00:00', '2012-12-25 23:30:00',\n",
       "               '2012-12-26 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq='30T')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = '2012-12-25 00:00:00'\n",
    "end_time = '2012-12-26 00:00:00'\n",
    "times_of_interest = pd.date_range(start=start_time, end=end_time, freq='30min')\n",
    "times_of_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'URBAN_HEAT' ()>\n",
      "array(12.557889, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ko['URBAN_HEAT']-sliced_ds_wrf['URBAN_HEAT']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'WASTEHEAT' ()>\n",
      "array(2.522545, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ko['WASTEHEAT']-sliced_ds_wrf['WASTEHEAT']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'FSH_U' ()>\n",
      "array(6.270673, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ko['FSH_U']-sliced_ds_wrf['FSH_U']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'Qh' ()>\n",
      "array(21.719582, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ob['Qh']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'FSH_U' ()>\n",
      "array(-8.914458, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ko['FSH_U']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'FSH_U' ()>\n",
      "array(-15.185131, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_wrf['FSH_U']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'FSH_U' ()>\n",
      "array(-13.390544, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_li['FSH_U']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'Qh' ()>\n",
      "array(11.789287, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ob['Qh']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# examine momentum flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2012-12-25 00:00:00', '2012-12-25 00:30:00',\n",
       "               '2012-12-25 01:00:00', '2012-12-25 01:30:00',\n",
       "               '2012-12-25 02:00:00', '2012-12-25 02:30:00',\n",
       "               '2012-12-25 03:00:00', '2012-12-25 03:30:00',\n",
       "               '2012-12-25 04:00:00', '2012-12-25 04:30:00',\n",
       "               '2012-12-25 05:00:00', '2012-12-25 05:30:00',\n",
       "               '2012-12-25 06:00:00', '2012-12-25 06:30:00',\n",
       "               '2012-12-25 07:00:00', '2012-12-25 07:30:00',\n",
       "               '2012-12-25 08:00:00', '2012-12-25 08:30:00',\n",
       "               '2012-12-25 09:00:00', '2012-12-25 09:30:00',\n",
       "               '2012-12-25 10:00:00', '2012-12-25 10:30:00',\n",
       "               '2012-12-25 11:00:00', '2012-12-25 11:30:00',\n",
       "               '2012-12-25 12:00:00', '2012-12-25 12:30:00',\n",
       "               '2012-12-25 13:00:00', '2012-12-25 13:30:00',\n",
       "               '2012-12-25 14:00:00', '2012-12-25 14:30:00',\n",
       "               '2012-12-25 15:00:00', '2012-12-25 15:30:00',\n",
       "               '2012-12-25 16:00:00', '2012-12-25 16:30:00',\n",
       "               '2012-12-25 17:00:00', '2012-12-25 17:30:00',\n",
       "               '2012-12-25 18:00:00', '2012-12-25 18:30:00',\n",
       "               '2012-12-25 19:00:00', '2012-12-25 19:30:00',\n",
       "               '2012-12-25 20:00:00', '2012-12-25 20:30:00',\n",
       "               '2012-12-25 21:00:00', '2012-12-25 21:30:00',\n",
       "               '2012-12-25 22:00:00', '2012-12-25 22:30:00',\n",
       "               '2012-12-25 23:00:00', '2012-12-25 23:30:00',\n",
       "               '2012-12-26 00:00:00'],\n",
       "              dtype='datetime64[ns]', freq='30T')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_time = '2012-12-25 00:00:00'\n",
    "end_time = '2012-12-26 00:00:00'\n",
    "times_of_interest = pd.date_range(start=start_time, end=end_time, freq='30min')\n",
    "times_of_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'TAUX' ()>\n",
      "array(-0.28040463, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ko['TAUX']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'TAUX' ()>\n",
      "array(-0.2652955, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_wrf['TAUX']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'TAUX' ()>\n",
      "array(-0.2698817, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_li['TAUX']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'Qtau' ()>\n",
      "array(0.37533382, dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "delta = sliced_ds_ob['Qtau']\n",
    "select_date = delta[delta['time'].isin(times_of_interest)]\n",
    "print(select_date.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# latent heat flux"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.376408 19.77735 19.972143 19.971203\n"
     ]
    }
   ],
   "source": [
    "print(df['Qle_obs'].mean(), df['Qle_def'].mean(), df['Qle_wrflcz'].mean(), df['Qle_lilcz'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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