V1298Tau_Paper_Calculations_and_Plots.ipynb 1.46 MB
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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Laura Ketzer committed
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    "With this notebook you can reproduce the results from the paper <br> **X-ray irradiation and evaporation of the four young planets around V1298 Tau - Poppenhaeger, Ketzer, Mallonn (2020)**\n",
    "."
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 4,
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   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
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    "import os\n",
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    "\n",
    "# Planet Class\n",
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    "sys.path.append('../platypos_package/')\n",
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    "from Planet_class_LoFo14_PAPER import planet_LoFo14_PAPER # this is the code with fixed step size\n",
    "from Planet_class_LoFo14 import planet_LoFo14 # this is the code with variable step size\n",
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    "from Planet_class_Ot20_PAPER import planet_Ot20_PAPER # this is the code with fixed step size\n",
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    "from Planet_class_Ot20 import planet_Ot20  # this is the code with variable step size\n",
    "import Planet_models_LoFo14 as plmo14\n",
    "import Planet_model_Ot20 as plmoOt20\n",
    "from Lx_evo_and_flux import Lx_evo, flux_at_planet_earth\n",
    "\n",
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    "# for evolving more than one planet\n",
    "sys.path.append(os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/population_evolution/')\n",
    "from evolve_planet import evolve_one_planet, evolve_ensamble\n",
    "from create_planet_chunks import create_planet_chunks\n",
    "from create_summary_files import create_summary_files_with_final_planet_parameters\n",
    "from read_in_PLATYPOS_population_results import read_results_file, read_in_PLATYPOS_results, read_in_PLATYPOS_results_dataframe\n",
    "\n",
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 18, 'legend.fontsize': 14})\n",
    "mpl.rcParams['axes.linewidth'] = 1.1 #set the value globally\n",
    "from matplotlib.ticker import ScalarFormatter, FormatStrFormatter\n",
    "import matplotlib.ticker as ticker\n",
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    "\n",
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    "from astropy import constants as const\n",
    "from astroquery.nasa_exoplanet_archive import NasaExoplanetArchive\n",
    "from PyAstronomy import pyasl\n",
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    "from sklearn.neighbors import KernelDensity\n",
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    "\n",
    "p = \"../supplementary_files/\"\n",
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    "# Tu et al. (2015) - model tracks\n",
    "blueTu15 = pd.read_csv(p+'Lx_blue_track.csv')\n",
    "redTu15 = pd.read_csv(p+'Lx_red_track.csv')\n",
    "greenTu15 = pd.read_csv(p+'Lx_green_track.csv')\n",
    "                    \n",
    "# Jackson et al. (2012) - Lx sample\n",
    "jack12 = pd.read_csv(p+\"Jackson2012_Lx_clean.csv\")\n",
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    "\n",
    "def read_results_file(path, filename):\n",
    "    # read in results files\n",
    "    df = pd.read_csv(path+filename)\n",
    "    t, M, R, Lx = df[\"Time\"].values, df[\"Mass\"].values, df[\"Radius\"].values, df[\"Lx\"].values\n",
    "    return t, M, R, Lx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "# Present V1298 Tau parameters, $L_x$ evolutionary tracks, and planet models\n",
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    "First we need to define all the necessary system parameters. <br>\n",
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    "This includes the host star parameters, parameters to set the shape of the assumed future $L_x$ evolutionary tracks, and the planets themselves. <br>\n",
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    "To model the radius evolution of the planets we use the results from *Lopez & Fortney (2014)* and *Otegi et al. (2020)*."
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 5,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "# Stellar Parameters (David et al. 2019, Chandra observation):\n",
    "#L_sun = const.L_sun\n",
    "L_bol = 0.934 # in L_sun;\n",
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    "mass_star, radius_star = 1.101, 1.345 # solar units\n",
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    "age_star = 23. # Myr\n",
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    "Lx_age = Lx_chandra = 1.3e30  # erg/s in energy band: (0.1-2.4 keV), error +- 1.4e29\n",
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    "Lx_age_error = 1.4e29\n",
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    "# use dictionary to store star-parameters\n",
    "star_V1298Tau = {'star_id': 'V1298Tau', 'mass': mass_star, 'radius': radius_star, 'age': age_star, 'L_bol': L_bol, 'Lx_age': Lx_age}\n",
    "age = star_V1298Tau[\"age\"]\n",
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    "\n",
    "# Lx evolutionary tracks:\n",
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    "# create dictionaries with all the values necessary to define the evolutionary paths\n",
    "# this includes: starting age, saturation age, two fixed ages at 1 & 5 Gyr which are set by the Tu et al. (2015) model tracks, \n",
    "# and (if wanted) a time interval in which and factor by which Lx drops.\n",
    "Lx_1Gyr, Lx_5Gyr = 2.10*10**28, 1.65*10**27  # Lx value at 1 and 5 Gyr from Tu et al. (2015) model tracks\n",
    "\n",
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    "track1 = {\"t_start\": age, \"t_sat\": 240., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\n",
    "track2 = {\"t_start\": age, \"t_sat\": age, \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}\n",
    "track3 = {\"t_start\": age, \"t_sat\": age, \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 20., \"Lx_drop_factor\": 16.}\n",
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    "list_tracks = [track1, track2, track3]\n",
    "\n",
    "# these are the tracks with upper and lower value of the current Lx\n",
    "track1_lower = track1.copy()\n",
    "track1_lower[\"Lx_max\"] = Lx_age-Lx_age_error\n",
    "track2_lower = track2.copy()\n",
    "track2_lower[\"Lx_max\"] = Lx_age-Lx_age_error\n",
    "track3_lower = track3.copy()\n",
    "track3_lower[\"Lx_max\"] = Lx_age-Lx_age_error\n",
    "list_tracks_lower = [track1_lower, track2_lower, track3_lower]\n",
    "\n",
    "track1_upper = track1.copy()\n",
    "track1_upper[\"Lx_max\"] = Lx_age+Lx_age_error\n",
    "track2_upper = track2.copy()\n",
    "track2_upper[\"Lx_max\"] = Lx_age+Lx_age_error\n",
    "track3_upper = track3.copy()\n",
    "track3_upper[\"Lx_max\"] = Lx_age+Lx_age_error\n",
    "list_tracks_upper = [track1_upper, track2_upper, track3_upper]\n",
    "\n",
    "# additional tracks could look like this (different t_sat)\n",
    "#track2_2 = {\"t_start\": age, \"t_sat\": 70., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 20., \"Lx_drop_factor\": 5.}\n",
    "#track2_3 = {\"t_start\": age, \"t_sat\": 100., \"t_curr\": 1000., \"t_5Gyr\": 5000., \"Lx_max\": Lx_age, \"Lx_curr\": Lx_1Gyr, \"Lx_5Gyr\": Lx_5Gyr, \"dt_drop\": 0., \"Lx_drop_factor\": 0.}"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "## Plot current V1298 Tau $L_x$ & evolutionary tracks"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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SpRk0aJClQxJJJDEUQggh8kmpUqU4fPgwX3zxBVFRUVSrVo1PP/2UcePGWTo0i/rll1/o1KkT1apVY/ny5Tg4OFg6JJFEEkMhhBAin9SoUYN9+/ZZOoxCp2PHjshQtsJJJp8IIYQQQghAEkMhhBBCCJFEEkMhhBBCCAFIYphrso6hEEIIIYoLmXySS4sWLZJ1DIUQQghRLEiLoRBCCCGEACQxFEIIIQqln376CU3TCAkJASAkJARN0/jpp58sG1ge2LlzJwsWLEhz3NfXl+bNm2errpkzZ5psXxccHMzMmTN58OBBruME+Oabb9i4cWOe1JVVlvxaS2IohBBCFAGVK1fm6NGjtG3b1tKh5Jq5xHDatGn4+/tnq66RI0eyY8cO4+vg4GBmzZpVpBNDS5IxhkIIIUQRUKJECVq1amXpMPJVrVq1sn2Nu7s77u7u+RBN9sTExGBvb2/pMHJNWgyFEEKITCR3cW7ZsoX69evj4ODASy+9RHh4OBcvXqRTp06UKlWK5s2b88cff5hcazAY+OSTT6hduzYlSpSgbt26LFu2zKSMUoqZM2fi6upK6dKlGTp0KBERESZl0ute1DSNL774wqRc6q5Vf39/NE3j119/pWPHjjg4ONC4cWN+/fVXoqKi8PPzw9nZmZo1a7J69epM34t58+bh5eWFs7MzFStWpHfv3ly8eDFNuQ0bNtCiRQvs7e1xcXHhxRdfJDQ0lJkzZzJv3jxCQ0PRNA1N0/D19TV5nwEuX76Mpmls3brVpN7ExEQqVarEtGnT0jzv/v376d27N6DvOqNpGtWrVyc8PJySJUum+77XqFGDsWPHpvusHTt25JdffmHZsmXGWJNbNKtXr864ceN4//33cXd3x8nJCYCjR4/Sp08fqlSpQqlSpWjcuDErV65MU3doaCiDBg2ifPnyODg40KhRI1atWmX2fd+/fz+lS5fmvffeM1smL0hiKIQQQmTBlStXmD59Oh988AHffPMNP//8M6NGjcLHxwcfHx/WrVtHQkICPj4+Jtu9jR49mg8++IBRo0axZcsW+vXrx/Dhw00SvIULFzJ79mxGjRrFunXrsLe3Z+LEiXka/7Bhwxg0aBCBgYEopfD29mbEiBFUqVKFdevW0bJlS4YOHcq1a9cyrOfatWu8/fbbbNq0iW+//ZbExETatGnDw4cPjWVWrFjBK6+8Qq1atVizZg1Lly6lbt263Llzh5EjRzJ48GAqVarE0aNHOXr0qDHJS6lGjRq0aNGCgIAAk+MHDhzg9u3bDBw4MM01TZs2Ze7cuQCsX7+eo0ePsmHDBsqVK0e/fv1YunSpSfn9+/cTEhKCn59fus+6ePFiPD09efHFF42xvvTSS8bzq1at4sCBAyxevNgYZ2hoKG3atOG7775j8+bN9O/fHz8/P5OkOywsjOeff54TJ04wd+5cNm/ezIgRI7h69Wq6cezYsYMXX3yRCRMm8NFHH6VbJs8opeQjBx9AaUBFREQoIYQQ5p09e9bkdWJioop8FGnRj8TExGw9w7Bhw5S1tbW6ePGi8diECRMUoJYtW2Y8tmXLFgUYn/nChQtK0zTl7+9vUt/rr7+umjdvrpRSKiEhQVWuXFm98cYbJmW6du2qAHX58mWllFKXL19WgNq8ebOxDKAWLVpkct2MGTOUi4uL8fXSpUsVYBJDcpx+fn7GYw8ePFA2NjZq8eLFWX5fEhISVHR0tHJ0dDS+D4mJiapKlSqqX79+Zq8bN26cqlatWprjw4YNU82aNTO+nj9/vnJyclKxsbHGY6NGjVL169c3+7ybN282ed+S7dq1S2mapi5dumQ89vrrr5vcLz3NmjVTw4YNS3O8WrVqqlKlSiomJsbstQaDQcXHx6tRo0apTp06GY9PmjRJOTg4qBs3bqR7Xcqv9aZNm1SJEiXUZ599lmGcSqX9XkspIiJCAQoorTLIb2SMoRB56ORJ+PlnSEyExo2hUyfT8+fPQ926oGmWiU+IwiA6Jpqly/5n0Rj8hg3HsZRjtq6pXr26yRi42rVrA9C5c+c0x65fv069evXYs2cPVlZW9OvXj4SEBGO5Ll26sHr1ahITE7l69So3b97k5ZdfNrnfK6+8wu7du7P9bOZ06dIlw9idnZ2pUKEC169fz7CeY8eOMW3aNH799VfCw8ONx4ODgwE4f/48N27cMNsKlx0DBgxg3LhxbN++nZdffpmEhATWr1/Pv/71r2zX1aVLF6pVq8ayZcuYNWsWkZGRrF+/nk8//TTH8XXp0oWSJUuaHLt//z4zZsxg06ZNXL9+ncTERADc3NyMZfbu3UuPHj2oXLlyhvUHBgayatUq5s+fz1tvvZXjOLNDupJzSXY+ESnVrg23b8O5c3Drlum5U6dg/nz46iuIjrZMfEKInCtTpozJazs7uzTHk4/FxsYCcPfuXRITE3F2dsbW1tb44evrS0JCAjdv3uRW0g8LV1dXk/pTv87L+NOLPfl4cuzpuXLlCt27d0cpxddff82RI0c4ceIErq6uxuvu3bsHkGnSkxVubm60bdvW2E27Z88e7t69i4+PT7br0jQNPz8/li1bhlKKNWvWkJCQwODBg3McX8WKFdMc8/X1JSAggAkTJrBz505OnDjB8OHDTd7Xe/fuZen9+fHHH43d4AVFWgxzSXY+ESmVKQNDhsBPP0GKsd8oBcnjp4ODISoKHBwsE6MQluZg74DfsOEWj6EglCtXDhsbG44cOYKVVdq2GFdXV2NLYlhYmMm51K/TU6JECR4/fmxyLGUrXl7bvn070dHRbNq0iVKlSgGQkJBgck8XFxcAbt68mSf3HDhwIJMmTSImJoaAgACaNGlCnTp1clSXn58fs2bNYt++ffj7+9O3b1/Kli2b49i0VN0/sbGxbNmyhS+++II33njDeNxgMJiUc3FxydL7s2jRIubPn0+3bt04ePCg8b3NT5IYCpELsbFw/TqkXGGhfn39IyVNg3fegWXLoHVrqFChYOMUojCxsrLKdjduUdW5c2cSExN5+PAh3bp1S7dM1apVqVSpEps2baJHjx7G4+vXr8+0fnd3d/766y/ja4PBwN69e3MfuBkxMTFYWVlhY/MkfUhueUvm4eGBm5sby5YtM84QTi2zlsmUXn31Vf7973+zYcMGNmzYwOTJkzMsn7rVNqWqVavSvXt3ZsyYweHDh9m+fXum989OrHFxcSQmJlKiRAnjscjISH788UeTJLJLly4sXLiQ27dvp9vqmMzJyYkdO3bQoUMHXnjhBfbu3Wuc/ZxfJDEUIoeiomDhQj0xHDNG70bOiIMDvPFG2vGFBgOk05AghCgGPDw8eOONN/Dx8WHixIk0b96c2NhYzpw5Q3BwMN999x3W1tZMnDiR8ePHU758edq1a0dgYKBJwmdOv379+PLLL2nSpAk1a9bku+++S7PMTV5KTnT9/PwYMWIEZ86cYe7cuSZd0lZWVsyZM4chQ4YwZMgQBg0ahKZp7N27l0GDBtG8eXM8PT25ffs2/v7+NGjQgPLly1O9evV07+nq6krHjh0ZP348Dx48YMCAARnG6OHhAcDXX3+Nj48PDg4ONGzY0Hh+xIgRvPrqq7i7u5tN1lPy9PRkx44d7NixAxcXF2rUqGG25c7Z2RkvLy9mz56Nk5MTVlZWfPLJJzg7O5t8Xd555x2WL19Ou3btmDJlClWrVuWvv/4iKioqzWx0FxcXdu3aRbt27ejVqxfbt2/HIR+7nOTXkRA5FBICoaEQHw8HDmTtmtRJ4c2b8OGHkGrZMyFEMfLll18ybdo0li9fzosvvoivry9btmyhffv2xjJjxozhvffeY8mSJfTv359Hjx4xZ86cTOueMWMGr776KlOnTsXX15fGjRszfHj+ddM3bNiQpUuXcvz4cXr16sWqVatYu3Ytzs7OJuUGDx5MYGAg586dw9vbm6FDh3Lu3DkqJHWXDBgwAF9fXyZOnIiXlxczZ87M8L4+Pj7cvHmTVq1amU0gk1WrVo25c+eyfv162rRpk6bVslevXtjY2DBs2LB0u/dTmzp1KvXq1WPAgAF4eXllOq9g1apV1KhRg6FDh/Lvf/+b/v37M3ToUJMyFSpU4MiRIzRp0oQxY8bQq1cvvvnmG5555pl066xcuTJ79uwhJCSEV155Jc3wgbykqRRrLYms0zStNBAREREhYwyfEvfv661+KXoIOHoUzp4FX1+wts5effHxMGUKPHwIpUrBjBmQ6merEMXCX3/9Rb169SwdhhAAbN26lV69ehEcHGycnV1cZPS9FhkZmdwN7aSUijRXh7QYCpGJqCj4+mt47z04ftz03PPPw/Dh2U8KAWxt4dVX9VbEDh1A/r4QQoj8c+PGDQ4cOMCkSZN48cUXi11SmFckMRQiE/b2epexwQD79ukzjFPKzZqEXl56S+HLLz8ZZ2gw6F3T9+/nvF4hhBCmvvnmG+O6g4sWLbJ0OIWWdCXnkHQlF1/x8XoLYMqhJ3v2wLVr0LEjVKuWv/e/cAGSdnTijTegSZP8vZ/IWwkJcPeunuCXKWO6LNH9+08S/po1Ta8LCdGvLV0aUk5SjI7W/+8BuLub1nfjhj4Uwc7OdGY8QFgYODrqf9hYekF16UoWomDkRVeyzEoWIoULF2DFCmjbFrp3f3I8xYYB+S55IqKmpZ3pvGuX3gVdr55p8pCRhAS9O1zTIOUqB7duwcGD8OgR9OoFKdfS3boVfvtNT1JGjzat7+uv9boaN4YWLZ4cv3dPf/9AP5dyM4AzZ/QExskJGjR4cjw2Fk6c0D+vW9f0mUJD4dIlPf6UXwuAlSshMhI8PEx3lwkJgeTtSAcPNk3iN2/WJ/k4O8Pbb5vW9/77+r/t2+vd+slOnYJNm/Qkb+xYPdFLtnAhXLyof41SbsIQHq63AgP4+UGrVk/OHToEW7bo79+SJaYxLFmiJ42tW8OwYU+OX7kC//mP/vm4cfr7lGzHDjh2DFxcIOX2qUrB7Nn6HznduoG3N0IIkSWSGAqRxGCA77/Xdy758Udo2tR0keqC0rs3PPecnhCkbIxWCrZt05M8Ly8YOfLJuePH9e7nx49h8mTTMY8zZ8KdO2kTjshIvSUU9LGSKRPDu3f1xCzVpggopSeMBoNpedCTuOT96T/80DQx3L5dX9i7Th3TxDAqSn/PAYYONU0M//xTT+Y0TU9uUrZ6nT6tJ2CpdqLi8WM9OQSIizM9d++e/p6mt8pEcotc6lU+YmL0meOgJ6gpxcfr90g9OTBlS3OqNW2N8rujJi5Ojw9k7KoQInskMRQiiZUVvPaa3hLUuzeUK2eZODRNb+lK3WV9//6TpCNpmS6jiAg9MYMnXeHJktZ65dEj02scHfXEytExbQLj5qYncI6p1iBOTIRnntFb+lInjRmt/2quKzMrXZxK6UmZre2TY5Ur67GnjsHREZ59Vv889TJfVavqiWh6idJzz+n/Vqpkerx8eWjWTI8z+X1M1qyZ/jVKnSCXLq0n7VZWkHpVjVat0nb5JhsxQn/O1DPTn3lGbykEvSs5pRdf1Fu3U743oH/9R4zQ/1+k3CAiMhL279cT7dRJtRBCgIwxzDEZY1j0XbsGf/+tdx+mFBWlLx9TGCmlt2A5O5vGeOqU3mJoZ6cvnZMyKTpyRE8WK1UCT8/8iy02Vk88NA3KljVNTiMi9BhsbU27sw0GvYtZ0/SxcCmXAoqN1RNROzuwsbH8OLmi7to1+OQT/evg7a0nhwVFxhgKUTBkjKEQObRvHwQE6MlGnTp6C1SywpoUgh5vlSppjzdpYn6SSps2+RtTspIlzbdCmdvBycpKTyLN1Sfyjpub3o1+65beulyQiaEQouiQxDCXRo8eTf/+/c3uBykKp5TdaxcvmiaGQhRHmgY+PnrrqyzfJoQwRxLDXFq0aJF0JRdyCQn6RIbGjZ90R7q7w6BB+gxPSQrF00J6c4UQmZEFrkWxduGCvmPJkiVPllJJ1qGDJIVCiKzx9fWlefPmlg4jx9asWYO/v3+a4x07dsQ7m+sZpX4vgoKCMt3rODvmzJnD/v3786y+rNi/fz+apnH69OkCvW9hJIlhHggO1tdHi4mxdCQitUqV9AWCQZ+EkR1Xr17N+4CEKCRu39aXP5L5h08Hc4nIiWpnAAAgAElEQVTh4sWL+fjjj7NV17Rp00zqCgoKYtasWbmM8AlLJIbiCelKzgM7dujrqrm6PlkoVxSsuDj4/Xd9du7w4U+W7yhdWl96xslJX/svqy5evIiHhwfBwcHUMre+iBBF1PHj+pqTSulrRzZtaumIhKXUr18/29cUlp+JMTEx2NvbWzqMYkdaDHNJKX3JE0g7oPvuXfjgA1i7Vt+eCqXSrqAr8sTp0/Df/8Kvv+qfp/TCC/oCzjbZ+DMoMDAQg8FAYGBg3gYqRCHQoIG+pJGm6dvqiZz57bff6NKlCw4ODpQtW5YhQ4Zw+/Zt4/n27dszatQo4+sdO3agaRpjx441HgsMDMTOzo7o5K6NdEyaNImGDRvi6OiIu7s7Q4YM4datW2nKffvttzRs2JCSJUtSsWJFvL29efjwIb6+vgQGBnLgwAE0TUPTNGPXb8qu5H379qFpGmfOnDGp9/79+9jZ2fHf//4XMO1K9vf3Z3TS9kjJdXfs2JEzZ86gaRoHDhwwqevRo0c4OjqycOHCdJ+1evXq3Lt3j1mzZhnrS2491DSN+fPnM2bMGCpUqEDDhg0B2LJlC926dcPV1RUnJydatWrFzp0709T9xx9/0Lt3b8qUKYOjoyMtWrRg165dZt/3H374ATs7O5ak3qaomJMWw1zSNH1tsIsX0y4GHBwMV6/qHy1bAkePwg8/QJ8+BLt1ooSDNe7upuu9iYzFxcG5c/ouFp07PzneqJG+Bp61ddqFnHMiMHA1XbrUJDDwByZOnJj7CoUoREqV0te7LFtWX/i7oKnERAwpEihLsKpYES0XP3zv3LlDx44dqVevHqtWreLRo0dMmjSJbt26cfLkSezs7Gjfvr3JH5cHDx6kZMmSHDp0yORY06ZNcUi9InsKYWFhvPfee1SpUoU7d+4wb948OnfuzJ9//ol10jN88MEHTJ8+nTfffJPPPvuM6OhotmzZwqNHj5g2bRpXrlzhwYMHLF68GAD31KulAx06dKBy5cqsWbPGpGt4w4YNAPTr1y/NNS+99BLjxo1j3rx5HD16FAAnJyfq169Pq1atWLp0KR1S7DO5du1a4uPjGTx4cLrPumHDBjp16oS3tzcjk7Z3Stmq+dlnn9G+fXtWrFiBIWll/suXL9O7d2/Gjx+PlZUV27Zto2fPnhw8eJA2Set1nTt3jjZt2uDh4cGSJUtwcXHh5MmTZocM+fv7M2rUKL755ht8fX3TLVNsKaXkIwcfQGlARUREKHNOnlTqk0+UeucdpRKjY5UaO1apUaOUmjhRfTgtRo0apdR//mN6TWKiUgaD2SqfeitW6G/hW28pFRdneu7KFaXi43N/j5CQEGVra6MuXRqrbG1tVGhoaO4rFeIpdvbsWZPXCdevq+tg0Y+E69ez9QzDhg1TzZo1M75+9913lbOzs3r48KHx2PHjxxWgVq1apZRSavv27QpQYWFhSiml2rVrp9566y1lbW2tIiMjlVJKNWnSRI0fPz7LcSQkJKhr164pQB04cEAppdT9+/eVvb29euedd8xe179/f9WhQ4c0xzt06KD69+9vfP2vf/1LeXh4mJTp3r27eumll4yvU78XixYtUno6Yerbb79VpUqVMj6rUvp7kPJ+6XFxcVEzZsxIcxxQjRs3zvDaxMREFR8fr7p37678/PyMx318fJSbm5uKjo5O97p9+/YpQP3555/qq6++UnZ2dmr16tUZ3qswSv29llJERIQCFFBaZZDfSFdyPmrWDN59F+bMASv7Evo+Wa6uPH75Va6E6av3pt72LDgY/v1vmDv3yR6tTxul9Mk8a9fq+/KmlNRzAMD166bnqlbNXnexOevXr6dz5zrUrFmOTp3qsH79+txXKkQhFxUlE1GyIygoiO7duyfvJAFAixYtqF69OocPHwagdevWWFtbc/jwYeLi4ggKCmLkyJGUL1+eo0ePEhERwR9//EG7du0yvNe2bdto3bo1zs7O2NjYGFv7goODATh69CgxMTH4+fnl+rkGDhzI+fPn+f333wG4e/cue/fuZeDAgdmuy8fHB9BbCQEuXbrE4cOHcxXnSy+9lObYtWvXGDZsGG5ubtjY2GBra8vOnTuN7w9gfIbMxiQuXLiQMWPG8MMPPxjjf9pIV3IBMCYr9erBjBnYWlkzuzZcvqzvRsDBg/r5du0IDdWIi9OXVkm9A0dgIDx+rG9rZm6Xi6IoISFtQvf99/pWaXfv6usPJvP0hLfe0v9NvXdtXgkM/IFhw+oC0L9/XVas+IExY8bkz82EKARu3IAvvoDmzeGVV/L/flYVK1Ix9V92BcyqYsVcXX/z5k2eTd6YO4WKFSsSHh4OQOnSpWncuDGHDh2ifPny2Nvb06hRI9q2bcuhQ4dISEjAYDAYuzvTc+LECfr06UO/fv2YNGkSrq6uaJpGq1atiE3aoPzevXsAVM6D9beef/55nnnmGQICAnjuuecIDAzExsaGvn37ZrsuR0dHBgwYwNKlS/Hz88Pf359KlSrRo0ePHMdXMdXXzWAw0KdPHyIjI5k9eza1a9emVKlSTJ8+nbCwMGO5e/fuZen9CQwMpHbt2nTt2jXHMRZ1khgWNBsbNPQZzK6uwP37sG6dPnguJIRqLYbSrp1+OPU2YseO6XNXoqNNE8MbN2DLFr2+du2gXLmCfKCsU8p0v9tz52DVKrhzBz7+GMqU0Y9rmp74nTihJ8IprytRQh9PmF9u3rzJ8eO/sGGDPq6wb996vPXWT9y6dYtKlSrl342FsBClYMUKfdzujh36xJS6dfP3npq1Ndbp7e1YhFSuXNkk8Uh2+/ZtmjVrZnzdrl07Dh06hIuLC23atMHKyop27dqxceNG4uPjqV+/Pi4uLmbvs2HDBipUqEBAQABa0g/C0NBQkzLJ19+8eZPy5cvn6rk0TWPAgAEEBATw0UcfERAQQM+ePXO8kcPIkSNp06YNFy5cYPny5QwdOtQ4LjKn8aV08eJFTp06xbZt20wSzphU68e5uLhwMwvdcCtXruSf//wnvXv3Ztu2bU/lrGdJDC3t4UN9emBcHDRrhqennhSlFhcH5cvrayU+84zpuWvX4ORJ/fPUS7L4++vna9fWt8NKZjDoe6aWKQP29qYJW048fqw/SlSU3j2esr7Fi/XW0UaN4PXXnxwvUUJfSw3g/PmkCTpJ+vWDwYPzfr/cffv2Zbg+1tmzZ2nXrjYVKujNta6ujrRtW4vRo0dnuKxDx44d6dSpU94GK0QB0DQYMUKfRNeli+l2kcK8li1b8tVXXxEZGWlMmk6cOEFISAht27Y1lmvXrh2LFi3Czs6Ol19+GdBnK0+ePJmoqKhMu5FjYmKwtbU1SYhWrlxpUub555/H3t6eZcuWMXfu3HTrsbOzM7YwZsbHx4e5c+fy008/ceDAAVavXp1hebuk7pvY2FhKpvqh3bp1azw9PRk+fDhXrlzJ0kSO7MSanACWKFHCeCw0NJQjR47QKEUrQpcuXVizZg0ffvhhmhhTcnd3Z8+ePbRr1w5vb282btyIbfL6Z08JSQwtrXp1mD5d37MtdbdEiqayEiX08YoGAyQmmhYzGPSkMTxc/zela9f0WdGpWx/v3YPkSWe+vvpyLsn27tXXAyxbVl8TMKWFCyE+Htq2NU3kjh/Xu39BHx+Z8o/LqCi9pTP1H2uVK+vdxJUr62uppVS2LPni2rWrfPTRh5QubYe3d0OsrEwzYhcXePvttibHZs5sx+rVp7l923T2msGgWLv2Tx49ekzt2oVjXS8hcqJ8eZg9W/8bVWTN2LFj+eqrr3jhhRd49913jbOSGzZsSP/+/Y3l2rVrR2JiIj///DPz5s0D4LnnnsPW1pYTJ05kOkylW7duLFiwgDFjxtC7d29+/vlnvk/+YZukTJkyTJs2jSlTpvD48WNefPFF4uLi2LJlCzNmzMDNzQ1PT082bdrExo0bcXd3p0qVKlQx02rbrFkzateuzahRo7C3t6dXr14ZxuiZ1Jrx+eef07lzZ5ycnPDw8DCeHzFiBBMmTOD55583ls2svi1bttCjRw8cHR3x8PAw22Lp6emJu7s748aN4/333ycyMtL4zCnNmDEDLy8v2rdvz7hx43BxceHUqVO4uLgwPNUvupo1a7J7927at2/Pa6+9xurVq7GyeoqmZGQ0M0U+cjcrOVdu31Zq9myl/v47y5ekNyN33TqlFi5UavNm0+PBwfrs3lGjlDpzxvTc99/rx8eNS1vfW2/p59atMz3+yy9P6rt82fTctm1KLVum1K5dWX6UfBUUFKRq1aqmXnjBU926NUkp9UG2P27efFd17+6patWqpoKCgizyHELkJ4Mh7cz/nMpopmRRkXomrlJK/frrr6pTp07K3t5eOTs7q0GDBqlbt26ludbT01M5ODiox48fG4/16NFDAVla9eDTTz9V7u7uysHBQXXp0kUFBwcrQC1atMik3JIlS1S9evWUnZ2dqlixonr11VeNs6bv3Lmj+vbtq8qWLasA46zf1LOSk02ZMkUBysfHJ9P3wmAwqAkTJqjKlSsrTdPSzH6+cOGCAtS3336b6bMqpdTJkydVy5YtlYODgwLUvn37lFIq3WdWSv+Z7uXlpUqWLKlq166tli5dmu7X6/fff1c9e/ZUjo6OytHRUbVo0ULt3r1bKWU6KznZr7/+qpydndXw4cOVoYgsF5IXs5I1JdPQckTTtNJARERERI7HXphlMOhTmS9f1rfwmDYtbZNaLkVE6GP8HjyAFi2ejO8D2LlTnxVsbQ3vvGN63YIF+mSR556Dbt2eHL9/X6+vVCmoVSvtxJnCJiIigjff/Ae7dm1j+fK+vPBC1vvPduy4wKBB63F2akm9euOxsZFmFlF0NPcqx7Rp9dOM1UopPl4fhhITA2+/DbltLPnrr7+oV69e7ioRRdbixYuZOHEiN27cMJnFLfJeRt9rkZGRye+/k1Iq0lwd0pVcGGmavtZNSAh06JA0SyVvOTnpCWF6unfXP9JjrtejbFnT7ujCzsnJie+/X82KFSt49dV/8o9/NOPDDztjZ2f+W+Lx4wTefXc3X355EkPiSB5Ftuf69QhAdrMRRcf27bco42zLv/5tfobJTz89Gbe8dStk0pMoRLpCQkIIDg7mo48+wtfXV5LCIuKpazHUNG0x0BtwBiKBtcBEpdTjpPM2wDzgdfQtA9cBbyml4lLVk38thsmuXdPXs8ntzBCRoQsXLuDj442DQxSHDplfX6tVq++4dcuOfv0WUL58NbPlhCisTp26z4+bbmBjo7FvXydatkp/NmxcnN5p4eAAb7yR+x4AaTF8Ovn6+rJq1So6dOjAmjVrKJtfg8eFUV60GD6NiWF9IFQpFaVpWgVgDbBXKfV+0vnpwCvAi0A88CNwVCk1NlU9+Z8YpqaU3rcjI8Tz3IoVK1i4cDonTow0W+a5576icdMu+Pn60qxp84L7uguRRx4/NtCp4z5OnAinalV7gk50w8WlRLplIyP1FQvyYtF4SQyFKBh5kRg+RdNsdEqps0qpqJSHgNopXo8EPlBK3VBK3QFmAn6apln2vTIY9MXGPvtMX8hQ5KnAwAD69894nOHAgfX5849T/HnmT5avXMb+A/uIjDT7vSVEoWNnZ8XKVa0oW9aWq1djGO4XhMGQfuNA6dJpk8KnrB1BiKdSoU0MNU2brGnaWk3T/tY0TWmaFpJBWStN097RNO2cpmmxmqZd1TRtnqZp6XaAaJo2SdO0SCAMaAQsSjpeBqgKnEpR/Fcg+bjl/PwzHDmir2adtL2QyBuPHj1ix47d9O//ZLmgoKBrvNx7JUFB14zHvL2f5a+z57Gzs8NgMEiCKIqk6tVL8b+l+gDjbdtu8dln57J03bFjMH++PvlMCFF8FdrEEPgI6AxcAu5nUvY/wHzgLDAafdzgv4DN6bX0KaU+UUqVBuoD3wC3kk4l9w0+TFH8QapzltG6tb4lQc2akM5ekSLntm7dSp06FahTpzwGg4E5cw7RqZM/zmWb0andd3z6yQEMBgN165andm1XnB3L0LVzV5ycnCRBFEXSSy9VYfwEfZ25GdNPc/DgnQzLnzwJS5fqe7mvWZOzez5tw5aEKGh59T1WmGcl11JK/Q2gadppwDG9QpqmPYueDK5XSvVPcfwysBDwAVald61S6i9N034D/IGu6JNRQJ+Ycjfp8+SFXCz7G9/KCkaO1DcIzsV2QiKtdet+wNvbg5s3Ixk6dCMhIXEcPHiYZs2a8c8OHRny9hvs3hrMijU+eHvXZcOGtfj4+FC3jgfBF85z4uQJHkY85M8zf3LmrzPUr/cszWUMoijkZs9uwLGj9zh8+C6vDTnGiZPdqFgx/R0hGjeGGjX07SubNs3+vWxtbYmJicFBxkcLkW+Sd8nJrULbYpicFGbBIEADFqQ6/i0QDbyWyfXWQJ2kez4ArgKNU5xvgt5qeDXtpQXM3l6SwjwWExPD1q3bcXCw5bnnvsLNzYtff/3DuNfp8yNGsLdVW8oeu0qjegspVcqOLVu2ERMTg7W1NfU86/Pa4Nfp2rkrzk7OGAwGTie1IO6TFkRRiNnYWLHi+1ZUqFCCW7diGfr6cRIT029xsLGBf/wDpk5Nf8vOzLi6unL9+nWio6Ol5VCIPKaUIjo6muvXr+OaB8vbFYlZyckthkqp6umc24He2ueQzpIyR4C6SqkKSa8dgVeBDejdxQ2AAOCwUmpUUpnpQF+gF/qs5E3AsUIxKzm1P/+E0FBZZCwXNm7cSL9+/XByKsXixV8zZMiQNGXiDh3iXvv2rAemONoT8SiGjRs3Gvc9TWYwGDgffJ4TJ4N4GKGPRrCyspIWRFGo7dlzmxd7HkQpeG9KPWbObJAv94mIiCAsLIz4+Ph8qV+Ip5mtrS2urq4ZrhVZrJarySQx/BNwVUql2RpE07Q16IlgCaXU46TJKBuBZoAd+uSTQGCGUio66Rob9PGKyesYrgXeVkrFpqrbsonhnj1PBvu8/DK8+GLBx1AMTJr0Lnv37mL16rXUqmV+v+O7nTvzeN8+brRvz5sxkXTu3I1PPvk03bKSIIqi5v33z/D+7LNoGmz+qR3du1fK9JqwMDhwALy9ZalVIYqCpykxvATYKqWeSefccvQEr2xSN3FexlQaiGjcuDHWSd27o0aNYtSoUXl5G/PCwvQVaOPiYPJkMLMZushYfHw8NjY2GW4PBhC3fz/3OnUCwOXYMayaNs10LIfZBNGzPs2aNceptOwCIAqHxERFr5cOsmdPGOXL2xF0ohvu7ubHA/79N3z+OcTGQp8+Mh9OiKLgaUoMs9ximMcxWb4r+f59ffmaZ5/NvKzItbsdOvD44EFK9OqFy+bNWb7OmCD+EsTDh5IgisIpLCwWr+a7uHkzltatXdi1uyO2tukPQ4+JgY8+0v8+7dkT+vYt4GCFENn2NCWGWR5jmMcxWT4xTM+lS3D3LrRsaelIip24vXu516ULAOVPnsQuaYJKVkmCKAq7w4fv0K3rARITFWPH1uWTT58zW/bGDT0xbNzYbBEhRCHyNO18cgL9OVqkPKhpWkn02cUn8/Pmo0ePZnM2Wo/yVUQEfP01+PvD6dOWjqbYsevUCbs2bQB49P772b7eysqKep71eG3Q63Tt3A1n56RZzGdPs2Llcvbu30tEZERehy1ElrVtW4H3P9Ann8yfH8yPP143W7ZKFUkKhSiOikOLYUPgd2BDqnUMR6OvY/i6Uur7fIip8LUY/v03zJunb03Qu7fMVs4Hsbt2Ed69OwAVTp3CNhe/Gc21INbzrE9zaUEUFmIwKF555Qhbt9ykTBlbjgd1o0aNdDeRMpGQAKdOgZdXAQQphMi2It+VrGna60C1pJej0WcRz0t6HaqUWpGi7CLgbfRlaLYC9dB3PjkCdFZKGfIhvsKXGAL89hvcuwdJXZ4ibymluNu6NfHHjlGyXz/KrV+f6zoNBoNxoewHD/U5UpIgCksKD39MC69dXLkSTbNmZdl/oBMlSphfQ/XBA72z4u+/YehQSGpYF0IUIsUhMdwPdDBz+oBSqmOKstbAGGAUUB1915IAYLpS6lE+xVc4E0OR72K3bye8Z08AKvz+O7aNGuVJvZIgisLkRFA4HTvuJT5e8c9/1uLzhea3PHnwAD74ACIjoU4dGDdOlrARorAp8olhYVdkEsP4ePjvf/Wpg9WqZV5eZEopxd1WrYgPCqKktzfl1q7N0/rNJ4j1aN7UK8MFTIXIS19+cYF33vkNgO9XtmLAgKpmywYH63sqDxig75QihChcJDHMZ8mJ4bBhw+jfvz+9e/e2dEjp+/57OHRI/0k9dixksIizyLrYLVsITxrDWeHPP7FtkPe7RUiCKCxNKcWgQcdYH3gNR0cbjh3vSt26hfgPYSGEWZIY5rMi0WKolL5DSmAg1Kih9+/IXst5QinFXS8v4n/5hZIDB1Luhx/y7V56ghjMiZNBkiCKAvfwYTzPt9rNxYuPaNDAmSM/d8HePms/R8LCIA+2bhVC5AFJDPNZkUgMk126BOXKQdmylo6kWIn98UfCX34ZNI0Kp09jW79+vt7PmCD+EsSDB5IgioLz228PaNd2D3FxBnx9q/PNtxlPPTYY4McfYft2ePttyIcGdSFENj1N6xiKzNSqlTYpDAqCO3csE08xUaJ3b2ybNAGlePThh/l+PysrKzw9PBni8xrdunSnTJkyGAwGzpw9w4pVy9mzb49x6z0h8lLjxmX4/PMmAPj7h7B8WUiG5cPDYe9evdNi1SpITCyAIIUQeUJaDHOoyIwxTM+ePbBmDZQvDxMmQJkylo6oyIrZuJH7/fqBlRWuZ89i4+FRYPc2GAxcuBhM0EnTFkRPj3o0b9YcZyfnAotFFH9KKYb7nWDlylDs7a058nMXGjQw/3/s5EnYsgXeeAMqptmwVAhR0KQrOZ8Vqa7k1DZuhG3bwN4exoyB6tUtHVGRpQwG7jRpQsIff2D/2muUXbEi84vymCSIoqBERSXQuvUe/jobQV2P0hw71hVHR/NTkBMTZVizEIWFJIb5rEgnhkrpA4CaNQN3d0tHU+TFBAZy39tbbzU8dw6bOnUsEof5BNGT5s28JEEUeeLs2QhaP7+b6OhEBvpUZfnylmiyaKEQhZ4khvmsSCeG5iglq9LmgDIYuPPccyScPo39sGGU9fe3aDySIIr8tnJlKH6+QQB88UVTRv0j82Wwzp/XOyrefBPs7PI7QiFEapIY5rNilxjeuAErVsDIkeDiYuloipyYNWu4P3AgWFvjev48NoVgvUg9QbyQlCDeB1IkiE29cHaWBFHk3Jv//IXvvvsbOzsrDh3qTJOm5lc9OHcO/vMf/fNOncDHp4CCFEIYyaxkkXVRUfDFF/pGp598AtHRlo6oyCnp7Y1N/fqQmMijjz6ydDiAngR61PVgiM8Qund9gTJlymIwGDj711m+X72CPft28/ChzGIWOTP/P4157rkyPH5sYNCgozx48Nhs2bp19cUR7OygcuUCDFIIkW3SYphDRXpWcmoGg74I9u7d0Levvn2eyLbo1at5MHgw2NjgGhyMTY0alg7JRHotiJqmUc+jnt7FLC2IIpsuXnxEyxa7iIxMoM/LVVi7trXZ8YZ37+qTUWSGshCWIV3J+azYdSWDPgiobl3TcYYRESALJ2eJSkzkzrPPknD+PA7/93+U+eYbS4eULoPBwMVLFwg6EcR9SRBFLq1ffw2fgUcB+Oyz5/j3mLoWjkgIkR5JDPNZsUwMU7t/HyZPhtq14ZVXoGZNS0dU6EV//z0PXn9dbzW8eBGbatUsHZJZkiCKvDJu7G8sWnQBGxuNvXs70er5zMcpR0XB9ev636JCiPwnYwxF7h07ps9UvnBBFiPLInsfH6zr1IGEBB598omlw8mQlZUVdet4MNhnCC90e4GyZcqilOLsubP6Tip7ZQyiyJqPP2mEl1c5EhIUgwcf5e7duAzLBwfD7Nnw5Zdw714BBSmEyBJJDIV5TZpA9+5Qrx4884zpudOn4bH5weZPK83GhtJTpgAQ/d//knj1qoUjylxmCeLuvbt5+PCBpcMUhZidnRWrVreibFlbrl2LYbhfEAaD+d6ouDh48ABiY+HIkQIMVAiRKelKzqGnoivZnCtX4OOPoVw5GDFCuphTUQkJhHl6knjpEg5vvkmZL7+0dEjZoncxXyTo5HHu33/SxezpUQ+vZs1xdpYtFEX6tm69Sd+XDwMw+/0GTJpUz2zZdev0iSht28ryqUIUBBljmM+K1azk7Fq+XP8z38oKpkyR3VPSEb10KQ+GDwc7Oyr+/TfWbm6WDinbJEEUOTFlyp98NuccVlawY2cHOnRwtXRIQggkMcx3T3WLocEA+/ZBfDz06GF6TnZPAUDFxxPm4UHi5cuUGj0a54ULLR1SjkmCKLIjIcFA924HOHz4LpUqlSToRDcqVSqZ6XVK6R9WMsBJiHwhiWE+e6oTQ3POnIGdO8HbG6pWtXQ0Fhf13Xc8/L//gxIlqHj5MtZFfGVf8wmivtVeGUkQRZIbN2Lwar6LO3fi6NixAtu2d8Da2vwfjFFR+sZLZcrIrihC5BeZlSwKlsEAa9fqe1/Nm6ePLn/KOQwdivUzz0BcHI/mzLF0OLmmT1Kpy+CBQ3ihWw/KltUnqfx17i++X7WC3Xt38UAmqQigShV7lq9oiabB/v13mD37TIblV6yAU6f0joizZwsoSCFEuiQxFHkjPl6fvWxlpe+cUqKEpSOyOM3ODsf33gMgaskSEm/dsnBEeSN1gliubDlJEEUaXbpUZNr0ZwH4+KO/2LHD/P///v31HxlNmkhngxCWJl3JOSRdyWbcvq3PVra1fXLs11/hzh19+mGpUpaLzQJUXBxhdeqQePUqpcaNw3nuXEuHlOeSu5hPnAwi/H44oHcxe9T1xKu5dDE/zRITFb17HWL37tu4uNgRdKIbVas6pFv2zh0oX4vvCqMAACAASURBVF6GKAuRX2SMYT6TxDCLHj+GadP0Rctq1IBJkywdUYGLWryYh2+9hWZvj2tICNauxXOWplLKuJOKJIgiWVhYLC28dnHjRizPP+/C7j0dsbWVziohCpqMMRSFQ1gY2Nvrn7drZ3ru8WN9GmIx5zBiBFZubqiYGKLmzbN0OPlG0zTq1K7LYJ8h9Oj+pIv53Hm9i3nXHulifhq5upZk5crnsbbWOHr0HlOn/JnpNUrpGy9FRBRAgEIIE5m2GGqatjeP7qWUUl3yqC6Le6rXMcwupfRJKbVrm3Yxr10Lv/wCXl76IKNi7NGiRUT8619opUrprYbly1s6pHwnLYgipXnzzjN50h8ArAtsTZ8+6a/tmZAA33wDv/8OjRrBm29K97IQeSHPupI1TTPkUUxKKVVsNtyVruRciouDd9+FmBj9p/9bbz05l5Cgf5TMfO2zokLFxnK7Zk0MN2/iOGkSTh9/bOmQCoz5BNEDr2YtKFNGEsSngcGg6N//CFt+uomzsy3Hg7pSs6ZjumW/+w5OnABnZ5g8GcqWLeBghSiG8jox3A58mot4JgHdJTEURnFx8PPP8Ntv0KoVPP/8k3N//KE3GfTrB507F5vmgkeff07EmDFojo5UDAnBysXF0iEVKD1B1NdBDA+XBPFpFB7+mJYtdhEaGk3TpmXZf6ATJUum/bUQFQUbN0Lfvk/dfDUh8k1eJ4b+SqnhOQ1G07SlwFBJDEWWJG+5p2n6lnvFZP0KFRPD7Ro1MNy+jeOUKTh98IGlQ7IISRCfbieCwunYcS/x8Yo33qjFwkVNLR2SEE+FvJx88jtwJZfxXAX+yGUd4mnRtKned/Tyy8UmKQTQ7O1xnDgRgKiFCzEk7R7ytNEnqdRh8MAh9Ojek3LlkiepnOP71SvYtWcnDx7IJJXiyqtFOT77rDEAS5ZcIiAga79eYmLyMyohRDJZriaHpMUwn0VFgYODaTeywVDkN1I1REURVqMGhjt3cJw+HadZsywdksWZa0GsW8cDr+ZelC0jA8yKG6UUgwcfI3DdNRwdbTh6rCseHun/HFUKDh+Gdetg7FioVq2AgxWimJB1DPOZJIYF7PFjmDsXWrSALl2K9LjDR599RsTEiWjOzvpYQ+k6BfRk4dLfFwk6EcS98HuAJIjFWUREPK1a7ubixUc8+6wTR37ugoODTZpyd+7AjBmQmKh3IEyZUqS//YWwGFnHUBQvq1dDaKi+xM3vv1s6mlxx+Oc/sSpfHvXwIVELF1o6nEJD0zRq16rDoIGD6flCT1zKuaCU4nzwOVau/p6du3dy/8HT2f1eHDk52bL6h+cpWdKKM2ci+Pe/TqVbrkIF6NEDXFz0+WiSFAqRv/K0xVDTtDpAIyBUKXUyzyouhKTFsICFhsJXX+nb7U2YUOR/O0R+8gmRkyejlSmjtxo6O1s6pEJHWhCfDkv/d5l//EP/dfHtt80Z5lsjTZmEBL3FULZgFyLn8q0rWdO0V4CRwCyl1PEUx6cCM4Hk39irlVKvZTPuIkMSQwuIioLYWL3pIFl0NISHg7u75eLKAUNkJLerV0eFh1P6/fcpPXWqpUP6f/buOzzKKu3j+PdkZpIQIARIQm+h99ACAkoXG74uCOqKsK6oa0HXuqsi6trALrr2tQBiQRHEgggKokAIVVroPSEhkJCeTDnvHydtIKEMmcwkuT/XlYvJ85yZuWeFzS+n+i0TEPewJi72lIDYjj69Yqgrm9xValprbvl7HLNnH6BGDQu//zGMrl3lFyUhyps3g+E84FIgUmudXXCtC2bVsQNYDXQGwoCxWut5Hn0CPycnn/iJWbPMfogjR8JVV4H19DlK/irj2WfJmDIFVbcuDQ4cIEB+wTgjCYhVV1aWg/79l7J9Wzrt2tdm1aph1K5tK7N9UpL56tatAosUopLzZjDcByRorQeUuDYNeAi4WWs9UykVBWwDftVaX+7JB/B30mPoB44fhylTzGrlNm3MkkVL5dkq05WebnoNU1Op/dxz1H7kEV+XVClIQKyatm9Pp/9FS8jKcjLuumbMmtUXVcqUkZUrYc4c80996lT3AQQhRNm8ufikPnDklGuDgExgDoDWei/wO9DRg9cX4tzUrw+PPgpdusCtt1aqUAgQEBpKrfvuAyDr5ZdxZWb6uKLKwSxSaVOwSOWKEotUdvDp57NZ/PNPpFbTPSIrs44dQ3nr7d4AfPnFId57d2+p7UJDwW43hydt316RFQpRPXjSY5gHfKu1HlvwfSBwEliutb6sRLvZwBitdY1yrNdvSI+hn9uyBTp18vt9D11paabX8ORJak+fTu2CDbDFuSvqQVy7huPHU4CCHsQ27ejTW3oQK5u77lzH++/vJTAwgN9+G0rPXqf/91u4EDp0gLZtfVCgEJWUN4eS9wNZWuvOBd8PBxYDT2itny7Rbj5wkda6wfmX7/8kGPqxdevMWcsdO8Itt4Cf//dJf/JJMp96ioCICCL37SNADof1yJkCYu/efahXt56PKxTnIjfXycUDf2HTpjRatarJ6tjh1K0b6OuyhKj0vDmUvBzooJR6WCnVDXga0MCiU9p1AQ578PpCeE5rWLzYPD5wwIw3+bla996LCg3FdewY2e+84+tyKq2iIeZxN5gh5vrhZoh51w7mfP4pi3/+iROpJ3xdpjiL4GALn39xEaGhVvbty2LSpDjO1oHhcpkvIcSF86THsB0QB9QqvAQs0VpfekqbeOAdrfWd5VSrX5EeQz+Wl2dWK/ftC127+rqac5L++ONkPvMMAZGRptcwJMTXJVV6Wmv27ttLbFxsUQ8iULQPovQg+rdvvjnMdeNWAfDCC935533tSm13+DDMnGn+uQ8bVpEVClG5ePVIvILtae4HIoE1wIta65wS9+8AbgMe01r/cN5vUAlIMKyE/PisZdeJEyS1aIHOzCT01Vep9c9/+rqkKkMCYuX14AMbmTFjF1arYunSwVzUP9ztvtbw1FOQmAiBgebovPDwMl5MiGqu3IKhUmog8IeWQ5XdSDCsZFJS4PXX4frroXNnX1dTqvRHHyXz+ecJaNiQBnv3ompUyXVbPlMYENfExZIiAbFSyM93MXTIr6xZc4KmTWuwJm4E4eHux5/s3AmvvgqXXmq2MrWVvf2hENVaeQZDF5AMzC/4WqK1dpRjrZWSBMNKxOEw3QrJycWbnzVs6OuqTuNMSSG5ZUt0Vhahr79OrXvu8XVJVZIExMrl4MFsYvr8zIkT+Vx6aQO+XXgxAQHu+xumpoIsPhfizMpz8ckbQC5maPh74JhSapZSaoxSSiZCCf9ntcLo0ebP4cOhgX8ulLeEh1PzrrsAyJw+HZ2b6+OKqialFK2jWnP9uBu44rIrCa9vxh537trBp5/N5qefF3HihCxS8RfNm4fw0ccxACxenMT06fGntZFQKET5Oec5hkqp3sAY4BqgPWYlci5mq5p5wEKtdZqX6vQ70mNYCSUmmp7CUk5T8BfOY8dMr2F2NnXefLMoKArvKbsH0ZykUq+e9CD6gylTNvPC9HgCAmDRT4MYPDiy1HYOB8TGQv/+fv1PXYgK5+3FJx0xIXE0EI0JiQ7MVjbzgAVa60QP6q40JBhWAU4n/PorDB7sV2csn3zoIbJeeomApk1psHs3Kijo7E8SF6woIK6NJSVFAqK/cThcjLx0OStWpNCgQRBr4kbQqJH7PNykJHjnHUhIgAkTYMCAMl5MiGrIq8HQ7QWUakFxSOyHGZ52AbGYkDhfa73ngt7ED0kwrOS0NntcrFwJvXvDpEl+073gTEoiuVUrdE4Odd5+m5r/+IevS6pWygqIbdu0I6Z3H+rVk8N5fSUxMYc+vX8mOTmPSy6JYNFPl2C1Fs+Iys4204nT0qB9e7jvPr/5Zy2Ez1VYMHR7MaUigb9gQuJgwIbpTXxIa/1Kub2RH5BgWMnl5ZmljPv2QWQkPPywX52QcvL++8l69VUszZsTuWsXKlBOfqhoWmv27Tfb3EhA9B+//prMZSOXozX8+5GO/Oc/Xdzub9kCBw/CyJGV7vh0IbzKJ8HQ7YWVqgNcjQmKK7XWL3nljXykMBhOnDiRMWPGMGrUKF+XJM5XZibMn28WpvjZhtLOxESSoqIgN5c6771HzVtv9XVJ1ZYERP/z7LPbeOrJrQB8u3Agl13WyMcVCeH/fB4MqzrpMRTedvLee8maMQNLy5ZE7tyJkg3afEoCov9wuTSjrlrBzz8nUb9+IGviRtCsWdm/3DkcfjWNWAifkGDoZRIMq6gDB6BxY7/YJdeZkGB6DfPyCPvf/wj5+999XZKgMCDuY01cLMdSjhVdb9umLX16x1BfAmKFOHYsj5g+P3PkSA59+9Zj6S9DCAx034HNbod582DvXjNbRIaWRXVWnvsYulFKOc/xK1cpdVgptUApdc2FfBghvE5rWLYMpk2D2bPN9z5madyYkEmTAMh49lm03e7jigSYfRCjWkVx3djrufLyq4gIjwBg1+5dzPn8UxYt/pHjJ477uMqqLyIiiNmz+2GxKGJjTzDlsc2ntfnlF/O1fz/8UCUPZxWi/HlycKw6x69AoDEwCvhaKfVReRQshFdoDXFx5jzltWvNnod+oPa//w2BgTj37iXn0099XY4oQQKi7w0YGM6zz3UF4LXXdjJ//hG3+8OGQdOmEBUFffv6okIhKh9P9zF8AfgH8BYwBziA2aKmJfBX4E7gfeA1YAjwIhAB3KS1nlMehfuaDCVXQRkZ8N//wrhx5ieJn0i74w6y33kHS5s2RG7fjpLJUn5Jhph9Q2vN6NF/8P13idSpYyN2zXCiomoV3U9Lg9BQCPCkG0SIKsRrcwyVUjcD7wKXaK1Xl9GmL7ACuFNr/YFSqh+wEnPO8qXn9YZ+SoJhFaW132185jh4kOQ2bcBuJ2zmTEJuusnXJYkz0Fqz/8A+YuNiOXasOCC2ad2WmD4SEL0hNTWfvjE/s39/Nj16hLH8t6EEB8uEQiFK8mYwXAuc1FoPO0u7pUCY1rpXwffrgOZa64jzekM/JcGwmrDbzQkpwcE+LSPt9tvJfu89LO3aEbltG0pm0fs9CYgVa23cCQYP/pX8fBe3396aN97sWWq7XbvM1qV16lRwgUL4mNcWnwAdgKPn0O4o5kzlQnuBUA/eTwjf2LcPnn0WPvvM15VQ65FHwGrFuXMnOV984etyxDlQStGqZRTXXXs9V11xFRER5nfi3XvMHMQff/qR48dlDmJ56d2nHi++2B2Ad9/dw+efH3S773TCRx/BSy/5zfoyIfySJ8EwD3M+8tlEF7QtFAiUmVCF8Ds//GAWoaxeDfHxPi3F2rIlIRMnApD59NNop9On9Yhzd8aA+IUExPL0jztac+3YpgDc8Y+1xMenF92zWMwAAMCePVCiE1cIUYInQ8kLgKuAp7TW/ymjzRTgP8C3WutrCq79WfB+XS+sZP8gQ8nVQFoaPPOMWdo4cqTPZ6879u4luV07cDqp+/nn1LjuOp/WIzxjhpj3syYuluRjyUXX27RuQ0zvvtSvL0PMFyI93U6/fkvYvSuTTp1DWblyGCEhZsFWdjbMmgVjxkB4uI8LFaKCeXOOYXdgFRAE7AS+wKxK1kALYBxmuDkP6K+13qiUag7sB97SWt993p/GD0kwrCby8iAoyNdVFEm9+WZyPv4Ya+fORPz5J0qWWlZaEhC9588/0xg4YCm5uS4mTGjJB//r4+uShPA5r558opQaCswGGmICodttIAmzNc2SgvYRQDcgXmt9hCpAgqHwBcfu3SS3bw8uF3W//JIaY8f6uiRxgSQgesfHH+3jttvWAvD++72Z+LdWpbZLSTHb2QQGVmR1QlQ8rx+Jp5SqAVwLDAKaFFxOAH4D5mqtsz164UpCgmE1lJcHCxfCpZeanyQ+kjphAjmzZmHt2pWIjRul17CKOFNA7NM7hvD6MvZ5PrTWTLoljlmzDhAcHMDvfwyjW7cwtzZbtsD//gddusDf/+53O1UJUa68OZQcCugzvWh1IMGwmsnKguefNzPWu3eHO+7w2U8Rx44dJHfqZHoNv/6aGqNH+6QO4R0SEMtPVpaDAQOWsm1rOm3a1mL16uGEhppz0LWG11+H7dtN23vvhU6dfFisEF7mze1q0oAlnhbmS0qpIKXU+0qpvUqpTKXULqXUP09p01op9b1S6oRSKkkp9bxSSrpkqruaNaFFC/M4O9v0HvqItX17alx/PQAZ//kPnvb6C/9kVjG3Yty113HVFaOIjIgEYPee3Xz2xRx+/OkHUo6n+LjKyqFmTSuffXYRNWta2L0rkzvuWFf070UpuOUWaNYMbr0VOnb0cbFC+AlPegxPAgu11uO9U5L3KKVqAv8GPsHsq9gN+Am4R2v9hVLKAvwJfAdMBSILHs/RWk8/5bWkx7C6ycqCNWtg8GCfjznZt2/nWOfOoDV158+nxv/9n0/rEd4jPYgX7rPPDjJxQiwAM2b04B93tCm654eHHQnhFd4cSl4N5GqtB19QhX5CKfUhkKm1vkcp1QkTDGtqrfMK7v8NeEJr3eqU50kwFD6VesMN5Hz+ObYePQhftw4lP92qNK01Bw4eIDYuluTkpKLrraNaE9O7L+Gy/8oZ3X3XOt57by+BgQEsXz6EXr3r+bokISqUN4eS3wcGKqV6eVrcuVBKPaKUmlsw7KuVUvvP0DZAKXWfUipeKZWrlDqklHq5oIfwTO9hBQZiwiCYFdWnNQNaFsytFMJv1JoyBZTCvmEDed9/7+tyhJcppWjZoiXjxoxj1JVXExnZAIA9e/fw2Zdz+GHR96SkyBBzWV56OZro6DDy813ccMMqUlPzT2uTlgaLFsmpKKJ683S7mhnAeGA68A1woLCHrdwKU0oDJ4D1QC8gXWvdsoy2rwP3FNTyI9ARmAysAIZrrV1lPO8toA8wQGudXxAUtwILgCeABsB8oDvQTGt9uMRzpcewOktLMyejhIXBFVf4rIwT48aRO3cutt69CV+zRnoNqxHpQTx/e/Zk0q/vEk6etHPVqMZ8/XX/on8z27fD22+b6cMTJsCAAT4uVohy5rUeQ6WUE7gLqAM8B2wHspVSzlK+HB7WD9Baa11faz0Csw1OWfV0xoTAeVrr0Vrr97XW9wP3A0OA68t43suY3sLLtdb5AFprBzAK6ILZtHsR8Clmr8bUC/gsoqr54ANYvtx0L6T67q9G7ccfB8C+di15ixb5rA5R8U7tQWwgPYhn1bp1Ld7/wGx2/d3CBF57bWfRvebNi/eyP3rUF9UJ4R88mWNYau9bWbTWF7yiVym1BahVWo+hUuoZ4DHgEq31ihLXg4HjwHKt9RWnPOc1YBgwVGt9xhMzlVJ3ARO01n1PuS49htVZfDy8+ip07Qp//SvU8918pRNjxpA7bx62vn0JX7VKeg2rqcIexDVxsSSd0oPYp3cMEeERPqzOvzz04EZef30XFoti6dLB9B9geld37oTMTOjZ08cFCuEFXt/guiKdJRj+BAwHQk4dzlZK/QG001pHlLg2AxgKDCktFCqlumFWLOdiehxnYoLhz6e0k2BY3R04ULyFjQ/ZN27kWI8eANRbtIjgkSN9XJHwJQmIZ5ef72LY0F+JjT1BkyY1WBM3gogI/zn6UghvqE7BcDMQqbVuUMq9L4GxQFDBHMIWmDOb84CSw9wrtNaXFzznaeBOIBiIB6ZqrU+b2S/BUPiTE9dcQ+6CBdguuojwP/6QXkMhAfEsDh7MJqbPz5w4kc+IEQ1Y+N3FBAS4/7txuSAhAZo29VGRQpSj6hQM9wA2rXXzUu7NBG4C6mqt08q5ptpAenR0NBaLBYDbbruN2267rTzfRlQmJ09CnTo+eev89etJ6WU2Cqj/888EDR/ukzqE/9Fac/CQWaSSlFQcEKNatSamT/UOiIsWJXL1qN8BePKpzjz6aPHRJ8ePw0cfwcGDMHUqyFoeUdlVSDBUStXBrOqNwKxMXunxi535fcqlx7Cca5IeQ2EcOwZz58Lu3fD00+aUFB84PmoUed99R+DAgdT/7TfpNRRuJCCW7vHHNzN9WjwBAfDjokEMGWJOmtm6FWbMMG369YObb/ZhkUKUA2/uY4hSqk7BxtDJmJNDZgOTSty/UymVoJTq58nrn6cEIFwpVdoEkSZASnmHQiHcHD4MmzaZk1F8uJ9g7SeeACD/99/JX7bMZ3UI/6SUokXzlowdPY6rr7qaBg3M79J79+3h8y8/4/sfv+dYyhnX4lVJTzzRmUsuicDlgpvGryYxMQeAzp3NIUeDBpn1ZUJUF55sV1MTWAb8DbOFy4+cvjH0IqAhcM2FlXdO4jCfI6bkxYJVydHA2gqoQVRn0dHQoQP07w8XuvDDbodt20wPpN3ufm/bNli5EhITS31qYO/eBBXsqZjx1FMXVoeosiQgurNaA5g1uy8NGgSRnJzHTeNjcTjM5hvXX29CYZCsSxHViCc9hg9iNnyeDURpra86tYHWei+wE7P619u+wOwz+M9Trt8KhGD2IfSayZMns3DhQm++hfB3SsE998DEiRc+x3DzZnj9dViyxOydUdJvv8Enn5j7Zag9dSoA+cuXk7d8+YXVIqo094D4f6UExO+qTUBs1KgGs2b3IyAAfvvtGE89tRUo/QzlSjAtX4gLYvXgOWMxw7e3nuW0k4NAZ4+qApRSNwGFe4FEAIFKqSkF3x/QWs8C0FpvVkr9F7hbKTUP+AFz8sk9wHJgjqc1nIs33nhD5hgKKFiAdME6doSAgt/Xjh4141mFEgr2eW/Vyv05mZkwfTp07kzgoEEEjRxJ3k8/kfGf/xC0dGn51CWqLBMQW9C8WXMOHjpIbNxqkpKS2LtvL3v37SWqVRQxffpW+TmIgwdHMvWJzjz5xFamT4unf/9wLr+8UdF9l6t4P/sbb/RhoUJ4mScbXGcDP2mt/1Limgv4WGv99xLX5gCjtdbBHhWm1DJgUBm3l2utB5doa8H0GN4GtARSMD2JU7XWmZ68/znUJ4tPROm0Nj9B6tWDvn3P3HbPHhP0Akp03u/YYY5hqFHDvW3h3hngvn/G6tVm+STAAw+Qn5JCSsF5XqHTphF05ZVYO3ZElVd4FVWaWaRykDVxsRxNKj4CJKpVFDG9Y4iIiPRhdd7lcmmuHrWCxYuTqFcvkDVxI2jePASAr76Cnwt2s739dtkEW1Q+XluVrJQ6Caws3Pev4FppwfA3oJPWukou8pdgKMo0ezasWAE2G/zrX9CsWentdu2CV16B9u3h1ls9X828ZYv5iXXkiOk5tFhIGTGC/CVLipqooCCsPXsS2KsXtp49sfXsibVTJ5TN5tl7iipPa82hQweJrWYB8dixPGL6/MyRIzn07VuPpb8MITAwgNRU+M9/IDQUJk0q+5+1EP7Km8FwDdAcaKm1zi245hYMlVJ1MRtJr9daD/HoE/i5wmA4ceJExowZw6hRo3xdkvAX27ebeYD168Odd0KTJqW3e+0109ZqhYcegpYtL+x9HQ7zWoBj1y7SH34Y+5IlODPL6DS3WrFFRWEbOBBbv34mMHbpgpKZ9qKEsgJiq1ZR9K2iAXHVyhSGDVuGw6G59962vPhSNGAOO2rUCAIDfVygEB7wZjB8GJgGzNBa/7Pg2qnB8G3MsO7dWuu3PfsI/k16DMUZbdgA7dq59wLa7aYXsVBeHsycaeYVDhxY/jXk5cHatTjXrsUeEoI9JQX7+vXY16/HuXdv6c+x2bA1boytSRNsffpgu+EGbN26oWrUMMHT4YBgj2aHiEquugXEV1/dwb8e/hOAL768iL/8RY4/EZWbN4NhCGaLmA7AKmAe8BJmC5u5mMUpg4DNQExV3UNQgqE4by+8YELVyJFm+BjMfEQfbETtmjcP+5dfYk9MxN64Mfnr1+M8dRV0IYsFa8eO2Nq2xZaUZILjY48REB1d3GbrVnPyS926JuiKKqu6BEStNddeu5KF3yYQGmolds0IWreu5dZm1y7IyYFu3XxUpBDnwasnnyilmmBCYD/MVjGq4E8KHq8DrtFaHznvF68kJBiK87JvH0ybZh5ffjlcUxFbfJ6DvLyiTdpc6enYN2zA/vrr2PfswZ6WhuPwYbPo5VRKYW3fvmi+ou3QIWwnTxLQqpU5P6yQ3Q6PP24mZl16KfTuXXwvIcF81axpeldlcUylorXm0OFDxK5ZXWUDYmpqPn1jfmb//myio8P4bcVQgoPN39Ply+Hzz82w8qOPQoPTzt4Swr+cazD0ZLsaCgJff6XUZcAVQBRgAQ5hNryeryvDIcxCVJQ6dWDIEIiNNccp+IsS8wkDQkMJGjSIoEHFmwG4srJw/Pkn9nXryP/9d+xxcTgOHACnE0d8PI74eHLmFO8IZalfH9vWrUWBMbBlSwJSU80eH3mn7G61cSMsWGAev/mmezB85RVzkkx0NJScv5uVZfZ3DA01czdlWNtnlFI0b9acZk2buQXEffv2sm/fXlq1bEVMn75EVuKAWLduIJ99dhGDBv3Kxo1pPPjARt78rzmTPCzM/M7kcJh1XxIMRVVxQWclV2ey+ER45NR5hpWQzs3Fvnmzma+4bp35c/NmyC991oilfn0zZ3HYMGwjRmDr2RNLgwbmdJclS0yXyxtvuD/pwQchIwMuvhjGjy++Hh8Pr75qHj/wgOlpLLR0qVkd0LAhFJwAYwrWkJICISFmC6AAj04CFWdR2IO4Ji6WxKPFp/NUhYD4ztu7ueeeDQB8MrMvN9zQHDB/fdu2hRYtzvRsIfyDV4eShQwlC1GSzs/HsXUr+QWLW+zr12PfuBFyc0ttH9C4MbboaGwdOhDYvj22K68koHFjVOF8y9mzIT0dunSBSy4pfuKaNfC//5nHTz5plogWevNNc3JM06Zm+LpQdjbcd595PHYsDB9efO/oUXPEYIMGJlBKaLxgVTEgaq0ZPz6WuV8eomZNCytXDadjx1BflyXEeSm3YKiUt81cCgAAIABJREFUuh/YorVe7GkxSqlLgS5a61c8fQ1/I8FQiDPTDgeO+Hj3nsUNG9BZWaW2D4iMNEPQJfZatLRoURwWwQTNY8dMaGzXzr339ZNPTI9is2Zmm6BCKSnw2GPm8cSJ5kzrQj/+CPPnm8evv+4+NF24F2XTpu4biotzcsaA2LsvkZGVKyBmZNjp128Ju3Zm0qlzKH/8MYyaNd1nY2VlwYkTsseh8E/lGQxP27z6fCmlPgImaK2rzOxyCYZCnD/tdOLYtau4V7EgMOr09FLbq3r1zFzFEoHREhWFOp+evZwc2LTJ/NTu1Mm9l/Hjj2HVKjNn8cUX3Z93770miA4eDDfcUHx9715YuNCswL7ySrNfZdEH9M0qc39WlQLi5s0nGdB/Cbm5LsaPb8H/PuxT9IvLwYPwzjtmzuGUKeavlBD+xKuLT4QQwhPKYsHWoQO2Dh3gr38FQLtcOPftK+5VXL+e/HXr0CdOoE+cIH/JEvdTXEJDi1dDFwRGa9u2ZR/5V6MG9OtX+r1x40zwO7UXMy+veBg8LMz9XmIibNtmHpecywgmGRw+bLbsKTk3UmvzVQ2Hqk9dpFIYEPft38e+/fsqVUDs2rUOb7zRk1tvXcvs2Qe4+OIIbv67Obt8/344fty0W7HC/M4gRGV0rj2GmZjzhz0VDtSsij2GsvhEiPKntcZ58KB7z+K6dbiSk0ttr2rWNHMWSx7517EjynoBv/vm5UFamgmWJbt/4uLMEYSpqfD880WnzQBm3mNiInTvfvpw9hNPmLmMY8dW670etdYcPnKY2DWrK20P4qRb4pg5cz/BwQGs+H0Y3buHobWZGtuwoZnGKh3Hwt+U91ByedBVMRjKULIQFUNrjSsx8bSeRdeRMrZLDQ7G1r178dY5vXph7dwZ5c3zzL7/3uzNGBUFw4YVX//zT/jvf83jBx80S1kLzZtntuCJijI9mNVEWQGxZYuWxPTpS4NI/93/JTvbwYD+S9m6NZ02bWuxevVwQkNtMpNA+LXyDIblthBfa32gvF7L1yQYCuEfnElJZmPuEoHRuX9/6Y1tNmxdu7r1LNq6dUN5ez/Ew4fNXMbERLjlFvejEl96yRyh0aKF2Sm5kN0OH35oVjL06OE+N7IKKQqIcbEkJiYUXff3gBgfn07/i5aSmelgzLVNmTOnn/tCKczsgexs9//cQviKbFfjZRIMhfBfruPHsW/YYLbPKQiMzt27S29ssWDt3LmoV9HWsyfW7t0JqKif5vPnFwfDkj2GBw7Ac8+Zx5MmQZ8+xfe2bzcT2po0gZYtq0Q3VWUMiJ9/fpAJN8UC8NprPbjzrjZF9wqPQj98GB55RPZiF74nwdDLJBgKUbm4Tp40PYsl5i06duww3TqnCgjA2qGD2wIXW3Q0ARW51HT7dpg1ywTAU/ds/OADM9cxLAymT3d/Xny82V6nlvu5vpVFZQuIk+9ez7vv7sFmUyxfPpTefeoB8Msv8MUXps2QIXD99T4sUggkGHqdBEMhKj9XZib2TZvcts5xbNsGTmep7S1t2xb1Ktp69sTWowcB9ep5t8jsbNPdVHJF87PPmv1ROneGe+4pvp6RYeYwgtlix5+OXzxPZwyIvfvSwE/OoMvNdTLokl/YsCGNFi1CWBM3grp1A3G5zJ7rWpvZA5U0p4sqRIKhl0kwFKJq0jk52P/8031F9JYtZs5fKSytWrn3LPbsiSUiwrtFulxmo2+HwwwnFyq5yOW++6BDh+J7331nToZp0wauvbbSDD8XBsQ1cbEk+GlA3Ls3k74xSzh50s6VVzVi3rwBKKXIzTUnPlbDXYqEH5Jg6GWyXY0Q1YfOy8O+davbAhf7pk1mIlkpApo2de9Z7NULS0UsHsnNNRtw798PQ4e6T2x75RXYscMEyalTi69rDcuWmVXRzZr5bYrRWnMk4TCxa/wzIC5YcISx164E4Plp3XjggfaltnO5/PZ/YlHFSTD0MukxFKJ603Y7ju3bi3oV89evx7FxIzo7u9T2AQ0bnn7kX7Nmp61k9ZoFC8z8wxYt3Ce8JSTAU0+Zx+PHw8UXV0w9HiorILZo3pKYPjE0bNDQZ7U9/NAmXnttJxaLYsmSwQwYGO52/48/4Lff4P77ISjIR0WKakuCoZdJMBRCnEo7nTh27HAfht6wAZ1R+v8HB4SHn3aKi6VVq4oLi2DSysyZ5vGpi1zmzjXLart0gREjKq6mc1AUEONiSUjwj4Bot7sYNnQZq1cfp3HjYOLWXkpEhEmAGzfC22+bdr17w623Vnh5opqTYOhlEgyFEOdCu1w49+wp2pC7MDTq1NRS26uwMGw9ehT1LAb27ImlbdvzOx/6vArU5mSWPXugb1/3uYdTp0JSkpmreN99xdcdDjh61AxL+3iuor8FxEOHsonp8zPHj+czYkQDvl14MRaLwuWCN94wa4buuMNM9RSiIkkw9DIJhkIIT2mtce7ff/qRfymlnzyqatUyYbHEULS1ffsLO/LvbFwu+OwzMy+xXz/3c6F37DBzFuvUMSmnVSvv1XEezCrm1T4PiD/9dJRRV60AYOoTnZkypRNgFphnZ0N4+JmeLYR3VHgwVEq11lrvOcP9oVrrX8rlzfyABEMhRHnSWuM6csStV9G+bh2uxMRS26saNbBGRxf1Ktp69cLaqRPKZiv/4k5dMfHNN7BokektfPll96M91q+HunV9uvF26QGxBTF9+lZYQJw6dQvTnt+OUvDjoksYOrT0xTFyjJ6oKL4IhhnA/Vrr90+5HgRMB+7WWnvx19uKJcFQCFERnImJpx/5d/Bg6Y0DA7F16+Z+5F/XrqjyXumQkgJbtpgtc8aOLb7ucsFDD0FmpullvPnm8n3f81S4zc2RhOLztCsqIDocLi6/7DeWLz9GZGQQa+JG0LhxDbc2iYnwySfmYBvpRRTe5otg+AJwP/ADMElrnayU6gnMApoA92qtPymXN/MDEgyFEL7iTEkp7lUs6Fl07t1bemOrFWuXLqZXsbBnsVs3AkJCyr+wQ4fgmWfM4wkTYMCA4nu7d5v7nTtDZGT5v/cZ+CogJibmENPnZ5KS8rj44nB+WjwIq9X0vJ48CU88ATk5Zr3PI4/ISmXhXT6ZY6iUugT4BAgB5gK3AiuBiVrrMn7FrZxkH0MhhD9xpaZi37jRrWfRsXNn2Uf+dezo3rMYHU1AefySm5ZmNtKOjoaSrzdzplkBHRgIr74K3pwfWQZfBMRly5K5bORy05n6cAeefbZr0b2vv4bFi2HUKLjyShlSFt7ls8UnSqloYBUQBKwH+mmtHeX6Jn5AegyFEP7OlZFhwmLJI/+2bzdDvqdSCmu7du57LfboQUBYWPkU8/zzZuPtrl3h7ruLr9vt8OGH5np0NHijJ/MUpQXE5s1b0Ld3DA0blv9G5M8/v50npm4BYP6CgVxxhXkPl8t0pLZrV+5vKcRpfNVjeAvwCrAX+Ap4FNgM3KS13lVub+QHJBgKISojV3Y2jsLzoQu20HFs3Wq2oCmFpXVr9yP/evTA4smEOK3NZtpOJzRvXny95DF+t90GvXp58Kk8U1EB0eXSXD1qBYsXJ1G3ro01cSNo0aLm2Z8oRDnyxRzDb4ErgJeAx7XWdqVUB2A20AF4SGv9drm8mR+QYCiEqCp0bi72LVvcj/z780/Izy+1vaV5c/dh6F69sHh6JN3KlWZMNS/PrHAuOdHuxx8hLAx69vTqBLwjRw6zZu0aDh85XHStvANiSkoeMX1+5vDhHGJi6vHLr0MIDHTfm9JuN4tRevSo0HwsqglfBMN9wASt9YpTrluBJ4GHtdaB5fJmfkCCoRCiKtP5+Ti2bTO9igVD0Y5Nm9A5OaW2D2jcuCgoFp4THdCkybmd4uJymQ2zGzcuvma3wwMPmMBYQUeFeDsgrlqZwrBhy3A4NJMnt+XlV6Ld7r/5ppmeabWajx4VdcFvKUQRXwTD2iXfSCnVAAgqXHSilOqntV5dLm/mByQYCiGqG+1wmCP/SvYsbtiAzswstX1ARMTpPYstWpxbWDxyxCxSyciAO++E7t2L7x09ChYLRESU0yc79a1LCYjNmhPTpy+NLjAgvvbqTh5+eBMAn39xEaNHNy26t22bOR0lKsp85Joy2izKkc9PPlFKrQL6VKW9C0uSYCiEEAVH/u3aVdSrWHTk38mTpbZXdeu69SraevbE0rp16Uf+OZ2wdSt06uS+ivm992DdOrP1zeTJXlvO642AqLXm2mtXsvDbBEJDrayOHUGbNrWK7sfHQ+vW4I19ykX15i/BMEZrbfHKG/iYBEMhhCid1hrn3r3uR/6tX4/r+PFS26vQ0OIj/wr3WmzXDmUp5cdHbq4ZZ3U4Tt9Eu3C1dTmfK30k4Qhr4mLLLSCmpeXTN2YJ+/Zl0b17GCt+H0pwcNk/KuV0FFEeJBh6mQRDIYQ4d1prnIcOuQVF+7p1uJKSSm2vQkKwRke79SxaO3Uy50MnJsKqVWZ4uXXr4ietXQtffQWDB8OQIeW+YKU8A+L6dalccskv5Oe7mDQpirfeLn21yerVpnP0H/8wo+dCeEqCoZdJMBRCiAvnTEhw61nMX78e1+HDpTcODjZH/pXca7Fz5+Ij/954wxzVFxwM06ebP72gvALiu+/sYfLk9QB89HEMN97Ywu3+unVm1BzgkkvgxhsvvHZRfUkw9DI5+UQIIbzDmZx8+pF/+/eX3thmw9a1qwmJoaHY0tOxDR6MKpmi7HbYtw/ati3XMdkyA2LvvjRqdPaAqLXmppti+fKLQ4SEWFi5ajidOoUW3S/cwSc52ewJ3qZNuZUuqiEJhl4mPYZCCFFxXCdOYN+wgfwSC1ycu8o4N8FiwdqpU3HPotWKLS6OgIYN4aGHoG7dcq3tSMIRs0jl8KGia+caEDMy7Fx00VJ27sigY6dQVq4cRs2a1hL3IT0dmjQp15JFNSTB0MskGAohhG+50tOxb9jg1rPoiI8v/XxowFq/PrbLLisOjNHRBKSkQKtW5bJgpbSA2KxpM/r26XfGgLh580kGDlhKTo6TG29swYcf9Tnjlj4Oh0+OmhaVXLkFQ6XUJR7W8DbQQYKhEEKIiuLKzMTx559uPYuOrVvN1jelsISGYmvWDNvllxM4cqTZmLtevQuqwZOAOPOT/UyaFAfAO+/04u+3lL67dXIyzJgBo0ebA2GEOFflGQxdgCfdigrQEgyFEEL4ks7Jwb55s/uK6M2bzdzDUlhatnRf4NKzJ5bIyPN+34TEBNbExXLolIAY06cvjRs1Pq39rZPi+OST/QQFBbDi92FER4e53Xe54IknTDi0WGDKFPfDYoQ4k/IMhvvxLBgCoLVu5elz/ZkEQyGEqLx0fj72tWuxf/kl9o0bsefkYN+0yaz4KEVA06YElthn0dazJwGNGp3TKS7nGhCzsx0MHPALW7acpE2bWqyOHU5oqPtO19u3m6PzLr0Urr5a9jcU587ncwyrOgmGQghRtWi7HUd8PPZffsH+wQfYs7KwHz1a9vnQDRqc3rPYvHmZYfFcAuKOHRlc1G8JmZkORo9pymef9Tvt9ZKTwYMOTFHNSTD0MgmGQghRRX3wAcTFgVLop5/GkZLiforLhg3o9PRSnxpQv77bCS62nj2xREW5hbuzBcQvvzzE+BtXA/Dqq9HcdXfbM5arNZw8CWFhZ2wmqjkJhl4mwVAIIaqo1FRYtMjsE3P77cXXtYZ330W3bYszIgL79u3Y168vOidap6aW+nKqTp3isFhwTrSlbVsSk46WGRCnPXeUd97Zg82mWLZsKH1iSl8Q43LBxx/Djh1mJ57w8PL8H0JUJRIMvUyCoRBCVDO7dsFLL5nH48bBsGFFt7TWOA8cOP3Iv2PHSn0pVasWtuhobL16caJ7NzYG2TicVhwsmzRuykf/s/LTojxatAghds0I6tULPO11Sp6O0r073Hln+X1cUbVIMPQyCYZCCFHNbNoEX3wBaWnwwgtQq1bxPbsdbO4LRbTWuI4ccetVtK9fjyshodSXP96xA9tHX0NS4+ItbfbtCeT772rTsWML5s0bQECA+3xDrWH+fNi6Fe67D2rWLL+PK6oWCYZeJsFQCCGqIa0hMdF9nxinE5580myUfcUV0LDhGV/CefSo+5F/69fjPHCg6H5KVCu2X3EZyR07FF1zxR8jVDXj5tdvK7Wk/HwoPDJaiNJIMPQyCYZCCCEAWLXKTPQDuOEGGDz4vF/CmZJSfIpLQc9issvJtisuI7lTx6J2tbck8JepD1On4Zk34d6yBRo1gvr1z7sUUUVJMPQyCYZCCCEA04P43XewZw88/bT7kHJmpvuQ83lwpaVh37CBg+vWs+JoCllRZog5+GgK3ToNpO/1l5b6vI0b4d13zZHQDzwg4VAYEgy9rDAYTpw4kTFjxjBq1ChflySEEMKX8vLcx3Nzc+HRR80Q8zXXQLNmHr+00+nkq4deIqW5BVdgIAF2O/X25TLu5X9jsbgfMPbNN2ZRtdUKd98NHTuW8aKiWpFg6GXSYyiEEOKMfvoJ5s0zj2+/vVwON1779TI2blhKTuMIAGruPMLwm2+jeffWRW20hgULoG1b6Nz5gt9SVBESDL1MgqEQQogzOn7cdN3t3296DkueYJKS4vGmgxnHT/L149PI6GKGlgNPZtDC1pzLHrypHIoWVZUEQy+TYCiEEOKcuFwQEFD8fVoaPPYYtG4N114LzZt79LLfP/chhyxHsdc2cxhDtxxl7POPEVLn9D1r0tNh5kyzNkbmHFZP5xoMA8q6IYQQQohyEHDKj9qffwaHwxxXkp/v8cte+ejfGTHkGkJ2m30R07s0ZM6059m48A+3drm58MorsHkzvPiiCYlClEWCoRBCCFGRRo6E4cOhWzdo08b93smT5/VSrWM6MXH6M9TbnoZyOMhpFsnK3SuZ+/DLOJ1OwKyH6drVtO/cGWSQS5yJDCV7SIaShRBCXBCt3ecdJiaa7W5iYswq5rCw83q5Pz75gW371pAbafY4rLX9CFfcO5kGbZqitdnGpnv30zswRfUgQ8lCCCGEP1Pux9uxYIE5RWX1asjOPu+XGzDxCsZMuotaW44AkNmxCQs++5Bf3/oKpaBHD/dQqDUcPXohH0BURRIMhRBCCH9wzTUmvfXv737kHpg5ieegXtMIbn5rOk0TFNbsHPLqh7HFdZhZd00lLzu3qJ3W8NVX8MwzZu6hEIVkKNlDMpQshBDCK05dxbxrF3zwAYwaZULjOY4Fb/tlHasWfU12S3N2c8i+owy4ahwdBkVz5IgZtdbaZNDHH5ch5qpOhpKFEEKIyujU8d5588wWN59/fl5LijsN7cX4J6cStuUYuFxkt2rIr3E/Mf+Jt2jSBO64AyIjYfJkCYWimPxVEEIIIfzZxRebg4+HDnVfkHIOI35BIcHc9NbTdFZNCDqRhiOkBocaOPjorn/TPCKFJ5+EevW8V7qofGQo2UMylCyEEKLC2O1miLnkWcwbNsDvv8OYMafPSSxF4s5DLHrjTTI7NgEgOPkEnaNi6D/hiqI28fEQGws33mjOWhZVhwwlCyGEEFWFzeYeCh0OM8S8ZYvZvdpuP+tLNGrXjAmvPUfDfXlY8vLJjazH+rR45tzzDA67g0OH4K23YOVKeO+9c+qQFFWQBEMhhBCisnE4oEMHs+XN5Zeb4HgOLBYLY198iIui+lLjcDLaauV4h1A+eXgKGQfiadTIvGRMzOm76YjqQYaSPSRDyUIIIXwuMREiItzHfWNjzVF7AwaccVVJVmoGXz32POldzKplW3omTVyNaH7lzXTv7u3CRUWToeRSKKWClFLvK6X2KqUylVK7lFL/PKVNY6XU10qpFKXUcaXUAqVUU1/VLIQQQpSpUSP3UJibC3PnwuzZ8PbbZ3xqzbq1mfjWc7TJCCUwPQN7aC32h2Ww8Z1HyEotzg0ZGSZ/iuqhWgVDwAocBS4FQoGxwCNKqetKtHkLsAGtgGZAFvB+BdcphBBCnL9jx4qHlWNizukpl//rb1x26Vhq7koAIL1rIz576QXWfbOcvDx480144QXYvdtbRQt/Uu2HkpVSHwKZWut7Cr7/E5iutf604PsrgXe11k1PeZ4MJQshhPA/drsZTh4wwH2i4Pbt0KIFhISU+jSnw8GXD0zjRFQILpuVgPx8asS7WO18ABVg4eKLYfz4CvoMotxV+qFkpdQjSqm5BcO+Wim1/wxtA5RS9yml4pVSuUqpQ0qpl5VSNc/yHlZgIPBnicuvAOOUUmEF4e8m4Pty+EhCCCGE99lsMHCgeyg8edIMLU+ZAuvWlfo0i9XKDa9PoVd4Z4KTjuMKDCSrWzAxQY/SsfEBbrihguoXPuW3wRB4DhgK7AFSz9L2VUyg2wZMBuYC9wALlVJn+owzgJPAzBLX/gDqASeANKAd8G8P6hdCCCH8w8qVkJcHWVlQq9YZm/a7cSRjb7+H2lvM0HJOpybUsM7k1/9+UdRGa9nOpqry52DYWmtdX2s9Akgoq5FSqjMmDM7TWo/WWr+vtb4fuB8YAlxfxvNexvQWXq61zi+4FgD8DKwCagG1gSXAj+X3sYQQQogKdtllcPvtMHgwtG/vfu/48dOahzWuz9/emkbzJBvWrGzy69Zhe0AiM+96nNzMHBYsgA8/NLvmiKqlUswxVEptAWpprVuWcu8Z4DHgEq31ihLXg4HjwHKt9RWnPOc1YBgwVGt9rMT1cOAY0ExrfbjgWjPgIBChtU4p0VbmGAohhKjcjh6Fp56C6Ghzgkp4+GlNdqzYxO8LPic7qhEANfYfZeuB0WSH9WbAAJgwoaKLFp6o9HMMz0MfwAWsKXlRa50LbCy4X0QpNQMYzimhsOA5KcBu4C6lVHBBuJwMHC4ZCoUQQogqYcECc9Tehg1mmLkU7S/uzk3PPEXdrcdRTic5LRvSLuYXWh6fweWXV3C9wuuqQjBsDKRorfNKuXcECFdKBQIopVpggl4bYF/BXoaZSqmSQ8X/B/QseG4C0Bu42psfQAghhPCJa6+Fvn1h0CCzYrkMgcGBjP/vU3QJbEnQ8TQcNYKpNRS+e/JfHD+QVIEFC2+rCkPJewCb1rp5KfdmYlYV19Vap5VzTbWB9OjoaCwWCwC33XYbt912W3m+jRBCCOF9WruvYt6/HxYtgrFjoX59t6bJexL4/tXXyezUBICgYyfo1KwXYTGjyMyEfv0qsG5xzs51KLkqBMPNQKTWukEp977EbGIdVLjApBxrkjmGQgghqh6XC6ZNgwMHzJ6Hzz8PwcFuTZxOJ9889jrJkU6cwUEopxP77+nEhz7GyMutjB7to9pFmarTHMMEzHBxUCn3mmCGmcs1FAohhBBVlt1ujtoDs4r5lFAIYLFYuHba/QzseDE1DiahLRasg+rSq+YUQrK3Vmy9olxVhWAYh/kcbmf/FCwciQbWevPNJ0+ezMKFC735FkIIIUTFCQqCm2+GBx/ktNUlBw9CWvHMrG6X9+OvjzxKnS1J4HKR164x+xO+5btn5STZyqoqDCV3BTYB32itx5S4PhmzgfVNWuvZXqhJhpKFEEJUHw6H2drm5EkYPdr0Jpaw+JVP2Ze3n/w65mdi7S2JjHjwXxxKDpN5h37gXIeSrRVX0vlRSt0EFC6RigAClVJTCr4/oLWeBaC13qyU+i9wt1JqHvAD0BFz8slyYE7FVi6EEEJUQZs3Q3KyeVzKztaX3n8jhzfvZfEH75LVvgkZXRrxw4evsCN+EAfHDePaayGgKoxTVnF+22OolFoGDCrj9nKt9eASbS3AP4HbgJZACvAFMFVrneml+qTHUAghRPWyfTv89htMmgQFO3IAZsFKQepzOp3MffAFjrcIxBVoIyDfDuvt3P7hQ1itljJeWHhblVqV7I8kGAohhBBAbi489xwMHAhDh4LVDEbGfbmUjZuXk9vInKZSc8cRLp10O027Rvmy2mqrOq1K9ilZfCKEEKJa+/57SEqCr7+GTZuKLvcZN4zrJt9P7S2JAGS1b8L3C+ew+NU5HD7sq2LF2UiPoYekx1AIIYTADC9/9hnUqgUPPeS+UXaBhU+/z5GgFOy1aoLLhV51jC4TH2PopSE+KLh6kqFkL5NgKIQQQhRwOMxZy3XqFF/LyIA1a8zqZYuF3Su3sPzrT8lubfZIDD6QRO+LR9HjKlmyXBEkGHqZBEMhhBDiDD7+GFatgqZN4YEHICQER76dWfc8R1bnULTFgiU3j8hkC3959t6i42WFd8gcwwoicwyFEEKIU+Tlwd695nG9elCjBgDWQBs3v/ME3UJaE3QsFWdwEInNrcy891EObEnwYcGikPQYekh6DIUQQogzcDhg8WLo2xfq1y++npcHgYEcP5jMty+8QmbnJgAEpaRSy9mdvz51jY8KrtpkKNnLJBgKIYQQ50lrePttEw5vugnCw/nqkRkcC8/FUSMY5XQSui2N6199jMDgQF9XW6XIULIQQggh/MumTeYrPh6++w6Aa5+/hzbhQwjefxRtsXCya31mTXmCHSs2neXFhDdIMBRCCCFExWjXDi65BGrWhDFjii6PmNiHMQ9OIWxLMrhcZEc14pfVPzJv6js+LLZ6kqFkD8lQshBCCOGhrCwTDgtpDStWQL9+LHnnG/Zk7CS/rtn6JmhDAtc++TD1mtTzUbFVgwwlVxBZlSyEEEKcp5KhEMy2Np9+Ck89xfAxAxk1egLBW44AkNejMV+/9zqr5/zkg0KrH+kx9JD0GAohhBDlZMYM2LrVbJD91FNQowZHjzqZ88+XCeoXgCswkAC7g3p7cxj3yr9lz0MPyKpkL5NgKIQQQpQTlwuWLTPb2nTvXnTZnudi3fzf2PLnUnKaRAAQsiuBIeNvIapXWx8VWzlJMPQyCYZCCCGEly1dChs3knH5/zHvjf+R3tUcp2dLz6ChvSnXPD7RxwVWHjLHUAghhBCV14kTsGAB7NxJ7bmfMvGt5wjdUwuX88mHAAAYFElEQVRbRib20Nocqn+ST+54lKzUMjOO8IAEQyGEEEL4n5AQ6N8flILrrgOl+Ou0v2NlNME7zPF56V0b8tmL09mw4HcfF1t1yFCyhwqHkidOnMiYMWMYNWqUr0sSQgghqp5jxyAiwu1SRuxmFs7+hhNta6GtVix5+YQd1Fz30v2yMKUMMsfQy2SOoRBCCOEDR4/C009DWBgrIzqyNXUXuQ3MWcw1thzhynvvplH7Zj4u0v/IHEMhhBBCVD3LloHDAceP0/+qflz217uxrjVDyzldmrBw7sf88tZXvq2xEpMeQw9Jj6EQQgjhA4Vb22RlQcE0rvh4mD/1TerEZOEIqQEuF2HbTzDuxUcJCgn2bb1+QoaSvUyCoRBCCOE/HPsPs2vuD6w8tp/slg0BqLHvKP0uu5Yuw3r6uDrfk6FkIYQQQlQPDgfWWR/Rcfc6xteqSdjWFHC5yGnVkBUbfubLf7/p6worDQmGQgghhKjcMjLMEDMQ1L0LN/33P+htTQk8cRJHSA2Smrv46K5/ceLwMR8X6v9kKNlDMpQshBBC+BGHw8w9HDwYrFbsdpjz7mHyt75BbtcmAAQnn6BzVAz9J1zh01J9QYaSK8jkyZNZuHChr8sQQgghqjerFYYPN38CNhtMvL0BN4dZabjqIJa8PHIj67E+LZ6P//E0DrvDxwX7J+kx9JD0GAohhBB+7vvv4dtvAdjYJoa1qfvIaRoJQI2dCQwb/zda9e7gyworjPQYCiGEEKJ6u/hiiImBZs2IfuBm+o55BFYeBSCnXWN+WjqP75/70MdF+hfpMfSQ9BgKIYQQlUReHgQFAXDoEMx/8iNsbROwh5qf37W3JDLm6X9Tu34dX1bpVdJjKIQQQggBRaEQoFkzmDw8mMv/PEDN+EMAZHRpxOevvMSauct8VKD/kGAohBBCiOojKwvWraNFbcXEJhGE78wgwG4nt0kEcUfWMuef03E6nb6u0mdkKNlDMpQshBBCVFLp6fDll3DNNdjrhPPiPxYT3uQP8hrWB6Bm/BFG3v4PmnRu5eNCy48ciedlEgyFEEKIqsHhgLkzT5D7xzNk9moJQGBaOq1rtmb4vTf4trhyInMMhRBCCCHOgdUKN/Q7ys3WLFos3ootK5v8sFC2W5L4361TyMnM9nWJFUaCoRBCCCEEQFgYV7cIZljXSwjZkwgBAWT3iOTTp59ly+I1vq6uQshQsocKh5InTpzImDFjGDVqlK9LEkIIIcSFyM2FgwehXTviVufz+6vPYRtYB22xYMnJpcHxIMY8d6+vq/SIzDH0MpljKIQQQlRtqamw/Jl3Sa5zlLzwugDU2nqEKx+4j8iohj6u7vzIHEMhhBBCiAtQV6Vxjd7BNbsPU2vDPgAyOzdh/qx3WTTjWx9X5x0SDIUQQgghShMaCoMHExli4ebbbyRynwtLbh55EXXZrfYw6+4nyc/N93WV5UqGkj0kQ8lCCCFENZGUBA0aYLfDR8/GonK+JbdFAwBC9iYyaPSNtOnfxcdFnpnMMfQyCYZCCCFE9bRrWzar3nyOk53CISAAa1Y2jbLqcc2Tt/m6tDLJHEMhhBBCCC9o2ymECW89Qwd7JIFp6ThqhnAoMpf3Jv2btKNpvi7vgkiPoYekx1AIIYQQhzfv5cd33yW3UxMAgo+m0L3TJcRcP9zHlbmTHkMhhBBCCC9r2jWKkQ89h/23bALy7eQ2DCcuaROf3fc8TqfT1+WdN+kx9JD0GAohhBCikN0Oa75YyrYdy8ltFA5AyI4j9Bt7G50HtvZxdbL4xOskGAohhBDiVOnJacx7cjoZXRoBEHgyg5onWzB+2nif1iVDyUIIIYQQFSw0Moy/vfU84QdqYMvMIr9ObVKbn+CTOx8j+2SWr8s7KwmGQgghhBDl7Ibpt9KqydUE70wAIL1LA+ZMe45N3688p+e7XC7mzp2Ly+XyZpmnkaFkD8lQshBCCCHOxp7v4MsHniO1XW20xYIlLw/rdgs3vHwvtWtbynzesmXLGDJkCMuWLWPQoEEXXIcMJQshhBBC+Jgt0MqNb0ylR622BB07gTMoiLxoK3MefJQ//0go83lff/21258VRXoMPVTYYzhx4kTGjBnDqFGjfF2SEEIIIfzY8QNJfP3MK+T1MHseBh1Po11kVwbf/he3di6Xi2bNmtGpUye2b9/OwYMHCQi4sL48WZXsZTKULIQQQojzpTV8dPcb5EXl4KgRjHI6qbMjjXEvPkZQjUAAVq1axaBBg9i5cydt27ZlxYoV9OvX74LeV4aShRBCCCH8jFLw9/9OZlCPYYQcOIq2WEjrVJ9Zjz/BjEc3kp5uho+HDRtGy5YtGTZsWIUOJ0swFEIIIYSoYJ2G9mL8E1Ops/UYuFzkRDXCVu8nZj/wFl999RVjxowBYMyYMXz11VdU1AivDCV7SIaShRBCCOGp9u3bs2vXrtOua62xWq0kJCQQERHBsWPHaNy4MQ6HA6XUae3btm3Ljh07zvp+5zqUbD2vTyGEEEIIIS7YjBkzmDBhAjabjTfeeIMmTZoU3atbty4REREAREREsG3bNlJTU4vuHzlyhMmTJ2O325nx/+3dfZQU1ZnH8e+DEVDZCMYEA0lAzQmoGyUBjBKPDgZl1agEBZQT1CC6GxZcknX3RI/muOTlD3cVVzF7RF1edDXGHVBZQdREWNFEJa4OShIVARUEX0IUhAGEZ/+4t6Fsqme6h67unp7f55w6PX3vrVu3qHuGZ6rq3nvzzWVtl+4YtpHuGIqIiMi+WL9+PRdffDHLli1j5syZnHPOOa3u8+CDDzJ+/HgGDx7M7Nmz6dmzZ1HH0uATERERkRp22GGHsXDhQq666irOP/98Jk+eTHNzc2rZrVu3MmnSJEaNGsXVV1/NggULig4KS6FHySIiIiJV0qlTJ6688kpOOeUUzjvvPLZv385tt922V7kpU6awcOFCnn76aQYNGpRdezKrWURERESKMnjwYLp3707//v1T8/v160ePHj0yDQpBgaGIiIhI1b3yyissX76ckSNHArBx40amTZu2e9DJyJEjaWpqSh3JXE4KDEVERESqrLGxkUGDBtGnTx+WLl3KcccdxzXXXMOAAQN46qmn6Nu3LwMHDsx8smsFhiIiIiJV1tjYyIgRI5g6dSpDhw5lzJgxbNiwgdGjR9PQ0MDUqVMZMWJE5oGhpqtpI01XIyIiIuWwatUqjjjiCPr06UNzczOzZ89m+PDhu/MXLVrERRddxAEHHMCaNWtYtWoVffv2LekYmq5GREREpB2YN28eAP379+fFF1/8RFAIMHz4cJqamnYPTJk7d25mbelwgaGZdTGz283sdTPbbGavmtmUvDKb87YdZtZUrTaLiIhI/erfvz/Tp09vcW7Cnj17smDBAqZPn15w5HI5dLhHyWZ2EPAjYDbwOnAssAi4wt3vK7BPE/BLd/95Ik2PkkVERKRd0KPkAtz9I3e/1t1fc/dd7v4C8DDwzbTyZnY8cDQwq4LNFBEREam4mg4MzewqM7s/PvZ1M1vdQtlOZvYDM/ujmTWb2ZtmdkO8Q9jSMT4FnAQUelR8KbDQ3de1+URERERE2oGaDgyBnwOnAiuBja2UnQbcCKwAJgP3A1cA882spfO8GfgAmJOfYWYHAhcAd5TcchEREZF2ptbXSj7S3V8HMLOXgG5phczsGEIwONfdz0ukryIEfhcA96TsdwPhbuGp7r49perRwBbCo2YRERGRulbTdwxzQWERLgQMuCkv/XZCYPfd/B3M7CbgdOBb7v5egXonALPd/eMi21E3ZsyYUe0miJSF+rLUC/Xljqca17ymA8MSDAZ2Ac8mE929GXgh5u9mZjcDwwh3Ct9Nq9DM+gFDgP9s7eDz589vU6NL3a/Y8sWUa61Mvf4Cauu1qvXjl6PeWuvHxZZVX66vY9djXy5XOfXl9nPsctV7/fXXV/z4tf4ouVi9gPfcfVtK3lpgiJl1dvftZtaH8Nh5G7DKzHLlnnT3MxL7XRrTXmnpwJs2baKxsZGGhoaSG13qfsWWL6Zca2V27tzJpk0FR7O3W229VrV+/HLUW2v9uNiy6sv1dex67MvlKqe+3H6OXa56N27c2KZrnnb8YutpN/MY5t4xdPe+KXkrgf3d/UspeXOAcUAPd/9LGdvzeUAjlUVERKQ96eXubxfKrJc7hluAzxXI65ooU07rCXcqN5e5XhEREZEsdCPELwXVS2C4DjjazLqkPE7uTXjMnDbquM083GotGHGLiIiI1JhWnyfXy+CT5wjncnwy0cy6AgOAZdVolIiIiEh7Ui+B4X2AA1Py0i8DDgT+q+ItEhEREWlnajowNLNxZnaNmV0DfBY4OPfdzMblyrn7cuBWYKSZzTWzCXHy6huBJaRMbt2RlbLUYCz/DTN73Mw2mdmHZvaImQ2oUHNFMu2zZtbLzOaY2btmttXMlpnZqExORDqMWumzZtbFzKaa2Soz22ZmK+P/ofuX4TQlIV7ntG2vsQhm1s/MHjCzjWb2kZk9aWanFqj3YDO7xczWxiV/Xzaz71tiWpVE2TYtD/yJOmp5VLKZLQZOKZC9xN0bEmX3I9wxvBzoC7xHuJP4Y3fXAJEEM3Pgz8DzwEDgw7TR3rHsCcBiwrQ/02PyJMJgnyExKBfJVFZ91swOIbxq8jnCH5JvAWMJv3fGu/vMDE5HOoBa6bNm9gBwLmFO3t8CJwLjCYs3XLLvZyo58Zo/CeRPOLnD3e9LlDuSMO/yx4SFOT4gPOH8a+AMd388UbYzsBT4GnAL8AfgDOA7wL+4+3V5bfh3wnLA84CFwFGEKfqeBIa5+65WT8TdtXWwDTgi8fNLwOoWyj4LfAj0TqT1jmmPVvtctHWMLas+C1xPeA3l7ETafrGO9wlTZFX9/LW1v60W+ixwZix7Q14dN8T0IdX+d6qnLf6bziqi3K+AncCARFo3YA3wJ+JNu5g+MdY7Oa+ORmA70CeRdgxhsY/GvLKTYx1jizmPmn6ULNnwIpcaNLMvE1aNud/d1yb2XwvcDwwzs8OyaaXIHhn22bHASnefnyi7k/CX+SGE/1hFSlYjfXZs/MxfLjb3fa/lYmXfmVlnM+tWIO8g4Bxgsbu/kEv38GTzDuArfHK1trGE6fZuz6vqJmB/YEwireTlgdMoMJSW5Drnb1PyfkfogAMr1xyRVhXdZ+Mk9b1jelrZZH0iWcmyzw4G1rr7m8mC8fs61L+zcD4hCNtkZu/EdwMPTuQfC3Sh8PWGeF3MrBPwdeD/PCzxm/Qs4e5g/vUuenngQuplHkPJRq/4uTYlL5fWu0JtESlGKX1W/VtqQZZ9thewosBx1wJfKLKNUpxnCXd5XwM+Tbh7Owk4xcyGxLuCpVzDHsABaWXdfZuZvc/e17uo5YFbOgkFhtKSA+NnWidrzisjUgtK6bPq31ILsuyzBxYomyuv/l1G7v6NvKQ5ZtYE/Az4h/hZruudK1/K9c6VaTEw1KNkaUluGcEuKXlZLTUosi9K6bPq31ILsuyzWwqUzZVX/87evxICsbPi93Jd71z5Uq53ss6CFBhKS9bFz7THabm0tNvhItVSSp9V/5ZakGWfXVegbK68+nfG3H0H4TocGpNKuYYbga1pZc2sC/AZ9r7eh8a8tLqLWh5YgaG05Ln4eWJK3gmE4e+/r1xzRFpVdJ9197cJv1RPKFAWtJymZC/LPvsc0NvMvpgsGL/3Qv07c3Fp3i8AG2LScsLj3kLXG+J18TDn4PPA11KCveMJMVz+9d7n5YEVGEpB7v4aoSONMrPcC7PEn0cBv3H39dVqn0i+NvTZe4EjzezsRNn9CPN+/QVYUJGGS4eVcZ+9N37mLxeb+67lYsvEzD5TIOsnhPEc82H3tDTzgQYzOy6xfzdgAvAqnxxVfC/hvcDL8+qdQpgg+1eJtLIsD1zTK59INiwsJ9gnfp0MdCZMeAqwxt3vSpQdAjxBmF3/lsQ+PYFvuvuLFWm0dGhZ9dn4y/z3hEcyNxLuxlwINAAT3P3OjE5J6lyt9Fkzmw98G7iTPSufXArc7e7jkLIws2mEO35PAG8QJqw+ExgKPAMMdfetseyXCcHfDmAaYSLzy4CvAme5+6JEvZ2Bp4HjgJsJK5+cSVj55Kfufm1eO24hjISeR/gj4SjCSihPAae6Vj7RlrYRll7yAtvilPInAr8GNgObgEXA16t9Hto6zpZlnyW8e3MXYRnNZsKjmzHVPmdt7XurlT5LGHTwU2A14RHm68C1wP7V/jeqp42w7OAiQqDeDHxEmDvwaqBrSvmjgAcJd3m3EJa9G1ag7u6EpRLXxWu4ghD8WUrZ/YB/JKygsi2250ZKWMVJdwxFREREBNA7hiIiIiISKTAUEREREUCBoYiIiIhECgxFREREBFBgKCIiIiKRAkMRERERARQYioiIiEikwFBEap6ZeRu2WXHfhvh9cXXPom3M7JKUcxuUV+a6RN5bcYm0QvWdlldX36zPIR53Ssp5VOTYIlK8T1W7ASIiRZidknYYMJywwsB/p+QvzbRFlbeSPef0XgvlegOnAY8UyL+kjG0qxQr2XMfzgYOq1A4RaYECQxGpee5+SX6amTUQAsP30vITniUsP7Uli7ZV0NJWzhNgGTCIEPztFRia2acJa6z+AfgSFQzO3P1R4NHYjoZKHltEiqdHySJS19x9i7v/0d3fqHZbKuAZQtB3rpl1T8m/ADgAmFXJRolI+6HAUETqWqF3DM2sb0xfbWadzOyHZvaymW2N7+ndaGYHxrI9zOymWHabmb1qZj9s4ZhmZheY2aNm9l7c5w0zu70C79XNAroSgsB83wN2AnflZ8Q2vxr/TU4oVLmZzY1lJibSFse0BjM72cwejue9y8xG7PspiUilKDAUEYF7gKnAKsLjzoOAHwCNZnYI4U7cGOA5wnt+fYEbzOzq/IrMbH/CO4/3AicR3q17iPAu5ATg+fzBI2V2FyH4+15eu/oBJwCPuPvb+Tu5uwO3xq8T8/NjHb2Bs4FNpASXwCjgCcJj6seAXwM72nQWIlIVCgxFpKPrAwwAvuLu33b3c4FjgfeBvwGWAC8Ch7v7KHf/FpC7C/aj3F3FhJ8AI4H/jXWeHPc7CpgM9AB+aWaZvOMdg75FwPFmdlQiKxcozmph95nAZmC0mR2akv+3hHfT57j7ppT8icD33f2r7n6hu5/m7g+XfBIiUjUKDEVE4Ap3X5f74u5vAnfHr30IwU5zIv9hoAn4K8JgDwDi3cUrCMHVKHd/K3kQd58OPAwcCZyRzakAIcCDOAI5Tl8zDvgz4e5lKnf/gHAnsAswPpkX74ReFr/+okAVj7n7jDa3WkSqToGhiHR0O4DfpKS/Fj+XuXva9DCvxs9eibShhMEdS9z9nQLHWxI/Tyy1oSV4iBAEjotB4emEdt7j7ttb2Xd6/Pw7M0v+HzGSMEXQYndfUWDfufvQZhGpAQoMRaSjW+/uH6ekb46fb6XkJfO7JtKOiJ9nFZp4G7g+lvnsvjW7sBj83QN8njClT+4x8syCO+3ZdwXwOHA44VF6Tu69w1v32mmPNSU3VkRqiuYxFJGObtc+5iflVhz5E/C7Vso+U0K9bTETmARMAU4Gmtz9+SL3vQUYRggGF5jZMbGOdcADLey3te3NFZFaoMBQRKR83oyfy4uYjDpT7v68mTURVkGB0uYu/B/CCO0z4vQ6fx/TZxS4uyoidUKPkkVEyudxwjuLwwpMMF1pMwijq99hz2CaVrn7LsIAk07APwHfBT6O9YlIHVNgKCJSJu6+gfAOXnfgITPrn18mTpY9wcx6VqA9t7r7oe7e093fLXH3OwnLCE4kjL6elzb/oYjUFz1KFhEpr38mjAAeDbxkZi8QHst2Bb5IWLe5c/zcUK1GtsbdN5rZ3cDlMamlQSciUid0x1BEpIzcfYe7jwHOJbyr1yv+PITwx/g9wHeAlVVrZPEei58vu/uSFkuKSF2wsAqSiIjUIjO7hDDCeHalB7SY2TzCKi8T3f0/yljvasLE4Ye7++py1Ssi+06PkkVE2oeTzGxW/Pm6rAMqMxsInEMYvDKnDPWdDoyNX9OW2xORGqDAUESkfTgybhBWJ1mdxUHM7A6gG3Am4XWjH7v7R2Wo+mjg4jLUIyIZ0qNkERHZLa7Osouwiskv3P3fqtwkEakgBYYiIiIiAmhUsoiIiIhECgxFREREBFBgKCIiIiKRAkMRERERARQYioiIiEikwFBEREREAPh/1gbj4DkbmIkAAAAASUVORK5CYII=\n",
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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "#ax.set_title('$L_x$ evolution for different \"evolutionary tracks\"')\n",
    "\n",
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    "# plot Tu15 tracks (for a Sun-like star!)\n",
    "ax.plot(blueTu15[\"time\"], blueTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"blue\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"fast rot. (solar model)\")\n",
    "ax.plot(redTu15[\"time\"], redTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"red\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"slow rot. (solar model)\")\n",
    "#ax.plot(greenTu15[\"time\"], greenTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"lime\", linewidth=2.5, alpha=0.5, label=\"__nolabel__\")#, label=\"interm. rot. (solar model)\")\n",
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    "\n",
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    "# plot approximated tracks\n",
    "step_size, t_track_start, t_track_end = 1., age, 5000. # Myr\n",
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    "t_arr = np.arange(t_track_start, t_track_end+step_size, step_size)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\"-\", zorder=2, label=\"high activity track\", lw=2.2)\n",
    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:grey\", zorder=3, lw=2.2, alpha=1., label=\"medium activity track\")\n",
    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", zorder=2, label=\"low activity track\", alpha=1, ls=\"-\", lw=2.2)\n",
    "\n",
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    "# plot current X-ray luminosity of V1298 Tau as measured with Chandra & the assumed X-ray luminosity at 5 Gyr\n",
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    "ax.scatter(age, Lx_chandra, marker='*', c='xkcd:pale yellow', edgecolors='black', linewidths=1.1, s=500, alpha=1, zorder=4, label=\"__nolabel__\")#, label=\"today\"\n",
    "ax.scatter(5000., Lx_5Gyr, marker='*', c='white', edgecolors='black', linewidths=1.2, s=350, zorder=4, label=\"__nolabel__\")#,  label=\"at 5 Gyr\"\n",
    "\n",
    "ax.loglog()\n",
    "ax.set_xlabel(\"Time [Myr]\", fontsize=22)\n",
    "ax.set_ylabel(\"L$_\\mathrm{x}$ [erg/s]\", fontsize=22)\n",
    "ax.set_xticks([10, 100, 1000, 5000])\n",
    "ax.set_yticks([10**27., 10**28., 10**29., 10**30., 10**31.])\n",
    "ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))\n",
    "ax.set_xlim(left=4.9, right=6500)\n",
    "ylim = ax.get_ylim()\n",
    "ax.set_ylim(abs(ylim[0]), ylim[1])\n",
    "ax.set_ylim(10.**27, ylim[1])\n",
    "ax.tick_params(direction=\"in\", which=\"both\", labelsize=18)\n",
    "ax.legend(loc=\"best\", fontsize=15)\n",
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    "#plt.savefig(\"./Plots_PAPER/Activity_tracks_v1298Tau_largelabels.jpg\", dpi=300)\n",
    "#plt.savefig(\"./Plots_PAPER/Fig8_largelabels.jpg\", dpi=300)\n",
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    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 35,
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   "metadata": {},
   "outputs": [
    {
     "data": {
210
      "image/png": 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viW6vnj5UvTFJnKUup26eZFnenz8u3tqPPifvfsuyJdjvNL5qSctCn393Bfp45trqczr2msqzpEXh5Ie2tNUfbVXX7eVsW6H/xF1RRyr6DX82oHcutqK/hi/6sKutLhvrNuOPOb+P4sLUb5eyLy91e9Hf1yqOzTPoN9v5AP3mOxdw4EZXluUvW9KSsdwUB30oo4Y+JPFj9M7ClkrH22In99ND/PH6WF8tzQ+9I6VZjiOTM6/52trA2ePPwe1JseTrV0fHrcNt6Uw7utKWNMBrvFLaEPR58iseFcfBvkrLllfLc3elY2k71W5wVulx0XSUduJzdv+4+nqtaR/dz6V18F+3xPsN+udWqmV5JtDWideSy5+DjeHh9gDkUQ+N+sdsJ7U9nq2W70H0N9ef0TtOrnQA/NB/ev7e8oK5gD4e7gf0q/S72MkXwR9vqKm11Y3+AbECvcNyAf0imC/RLzqytX5NbzZ/srxxFqGfGdiE/qYfg/1Oox+w1PImcKF6+bXU51TsDpQXhWsf2tJW9dBerrQV+ofoesv+P4v+K8kC9J/Lt1AHHXzL+mssef7qzLFSV/uyDrY3EH0o4T7LMVCIPi3hu1S7qVYt7fBCpeO3HTAU+Cd65ycb/cZBKejXIcW6sJ/aVtQP/MVG+lZL2o81lGGvU3Ip7z02jz8HtuekJV/3umhHZ9rSmXa0LHO6LWmA17gl7f5Kx4W9xxYX8tjtLNuIwZX943Q717KP7q8p5trKRB+l8Dh65/wc+oiD/wDhLrSXS5+DjeFRcRMbcYWzjKXbgH4h3o/oY/dHa5q23q2BCSHqhFLKgH6Gyxt9WENjvLBTNDClVAD6FKsKaKlpWqOcUUQ0fkopvbeuabXNfiSQi2wFoJTqiH6zh6Wapv1L07R96N/m59u58EgI0fjMRL9R2Vrp3AsnzEDv3O+Rzr0QjYecwb/CKaVaoY97/AkYqVkOCKVUd/Qz+WM0TVvnxhCFEC5SSl0HPAOEo880dBb9gr7f3RqYuKxZZqpaBnQAuqFfaDpM07Qv3RqYuKLJGXznSAdfCCGaKKVUDPrsQ+fQ543+m6Zpm90alLjsKaWi0GfryUM/0fOSpmmb3BmTENLBd4508IUQQgghhGhC3H2jjEbNcme5UPSr/4UQQgghhKhPBuCEVssZeungX5pQ9FkphBBCCCGEaAhhQI13M5YO/qUpBDh27BhGo9HdsQghhBBCiCaqoKCANm3agAMjR6SD7yKl1DBgBIDRaJQOvhBCCCGEuCzIHOcu0jQtHv1OakIIIYQQQlw2pIMvhBBCCCFEEyIdfCGEEEIIIZoQGYMvhBBCiCanvLyc7OxscnNzKSsrc3c4QjjM19eX8PBwvLy8XC5DOvhCCCGEaHIyMjJQShEVFYWXlxf6rWuEuLxpmsbp06fJyMigbdu2LpcjQ3RcZJlF53V3xyGEEEKIixUVFdGmTRu8vb2lcy8aDaUUQUFBnDt37pLKkQ6+i2QWHSGEEOLy5uEh3RzR+NTFF1I58oUQQgghhGhCpIMvhBBCCHGZWLlyJX379nV3GJe1jz/+mIiICAwGA3v27HF3OJcl6eALIYQQQjQxSimSkpIapK7777+fefPmNUhdAE8//TRvvvkmhYWFREdHN1i9jYl08IUQQgghhFVpaellXV9qaiqdOnWqk7KaKungCyGEEEI0sPT0dEaMGEFwcDBBQUFMnz79onVSUlJQSlXptMbExLBixQoAkpKS6N+/P4GBgbRs2ZLRo0cD0K9fPwC6du2KwWBg3bp1AHz++efceOONmEwmbrnlFvbv328tNyoqipdffpkuXbrQvHnzizrKmqbx5JNP0qpVKwIDA+nSpQsHDhzgnXfe4f3332fx4sUYDAaGDRsGwEsvvUS7du0wGo107NiRjz/+2FrWypUr6dOnD08++SQtWrRgwYIFdrelsvPnz2MwGCgrK6Nr1660a9fObuyHDx8mJiYGk8lEp06d+Oyzz6zl3H///UybNo3Y2FgMBgN9+vThxIkTzJgxA7PZzPXXX9/oh/5IB99FMk2mEEIIIVxRVlbG0KFDiYyMJCUlhWPHjjFmzBiny3n22WcZPHgwOTk5ZGRk8NhjjwGwbds2APbt20dhYSGjR4/m559/5oEHHuCf//wnp0+fZurUqdx1112cP3/eWt7atWv54osvyM3NpVmzqrdK2rhxI9u2bePXX38lNzeXdevWERQUxJQpUxg/fjx//etfKSwsJD4+HoB27dqxfft28vLymD9/PhMmTOD48ePW8hISErj66qvJyspi7ty5drelMh8fHwoLC63bdvToUZuxa5rGsGHDGDx4MFlZWbzxxhuMHz+eI0eOWNf/4IMPWLhwIdnZ2fj4+NC7d2+6detGdnY29913HzNnznS6PS4n0sF3kUyTKYQQQghX7Nq1i8zMTJYsWULz5s3x9fV16cJaLy8vUlNTyczMrLWMd999l6lTp9KrVy88PT2ZPHkyPj4+7Ny507rO448/TkREBH5+fjbrKigo4JdffkHTNDp06EDr1q3t1jdy5EjCwsLw8PBg9OjRXHPNNezatcuaHhYWxmOPPUazZs3w8/NzaltsqRz7zp07KSwsZNasWXh7ezNgwACGDh3K2rVrrevfc889dO/eHV9fX+655x58fX2ZNGkSnp6ejB49Ws7gC9syMjLYsGEDcXFxbNiwgYyMDHeHJIQQQojLQHp6OpGRkRedJXfW4sWL0TSNnj170qlTJ9577z2766amprJ06VJMJpP1kZ6eTmZmpnWdiIgIu/kHDBjA9OnTefTRRwkJCWHKlCnk5+fbXX/VqlXW4UAmk4kDBw6QnZ1tty5ntsWWyuVlZmYSERFR5T4IkZGRHDt2zPo8JCTE+r+fn99Fzyt+KWispINfDzIyMtixYwdms5no6GjMZjM7duyQTr4QQgghiIiIIC0trdYLQps3bw5AcXGxddmJEyes/4eGhvLuu++SmZnJP//5T6ZNm2Z35pyIiAjmzp1Lbm6u9VFcXMzYsWOt69R2g6XHH3+cn376iYMHD/Lrr7+yZMkSm/lSU1N5+OGHefPNNzl9+jS5ubnccMMNaJpmty5ntsWWyuWFhYWRnp5OeXm5dVlaWhpt2rRxuLzGTjr49SAxMZHIyEjMZjMeHh6YzWYiIyNJTEx0d2hCCCGEcLOePXvSunVrZs2aRVFREefOnWPHjh0XrRccHEybNm1Ys2YNZWVlvPfee1XGna9fv9568tBsNqOUwtPTE9DPUCcnJ1vXffjhh3n77bdJSEhA0zSKior44osvKCgocCjm3bt3k5CQQElJiXVYkb26ioqKUEoRHBwMwL///W8OHDhQY/k1bYuzevXqRfPmzVm8eDElJSVs2bKF+Ph4l65zaKykg18PcnJyCAwMrLIsMDCQnJwcN0UkhBBCiMuFp6cn8fHxJCUlcdVVVxEeHm6d6aa6d999lyVLlhAUFMTBgwe55ZZbrGm7d++mV69eGAwG7rrrLl577TXatm0LwIIFC5g8eTImk4kPPviAHj168O677zJ9+nTMZjPt27dn5cqVDsecn5/Pww8/bD1pGRQUxNNPPw3Agw8+yKFDhzCZTAwfPpyOHTvy1FNP0bt3b0JCQkhMTKRPnz41ll/TtjjL29ubzz77jA0bNtCyZUumTZvGqlWruP76610qrzFSlX8uEc5RShmB/Pz8fIxGo3X5hg0bMJvNmM1m67KcnBxycnKIjY11Q6RCCCHEleXw4cN06NDB3WEI4RJbx29BQQEBAQEAAZqm1fjTi5zBrwedO3cmNTWVnJwcysvLycnJITU1lc6dO7s7NCGEEEII0cRd2uXbwqbw8HD69OlDYmIiycnJmM1m+vTpQ3h4uLtDE0IIIUQtMjIySExMJCcnB7PZTOfOneUzXDQq0sF3keVGVyPspYeHh8ubgRBCCNHIVMyEFxkZSVRUFHl5eezYsUNO1IlGRYbouEhudCWEEEI0PTITnmgKpIMvhBBCCGHRWGbC27JlS5P+ReH+++9n3rx5Dq+/ceNGhg8fXo8ROWbmzJm8/fbb7g5DOvhCCCGEEBXMZjN5eXlVluXl5VWZGa8uxMTEsGLFijot83LS0Ns3Z84cZs2aBUBWVhZjx44lLCyMwMBA+vTpQ0JCQpX14+LiiIyMpHnz5gwfPpwzZ85Y095880169OiBj48P999/f5V8O3fuZNCgQbRo0YLg4GBGjhzJ8ePHrenPPPMML774IhcuXKi/jXWAdPCFEEIIISxkJrzGZ/fu3eTl5XHzzTcDUFhYyE033cRPP/3EmTNnmDx5MkOGDKGwsBCAgwcPMnXqVFavXs3Jkyfx9/dn2rRp1vLCwsKYN28eDzzwwEV15eTkMGXKFFJSUkhNTcVoNPLnP//Zmt66dWuuv/56Pvvss3re6ppJB18IIYQQwqJiJrycnBz27NlDTk5OvV5gm5OTw9ChQwkODsZsNjN06FDrHV0Bzpw5w5///GfCwsIwm812h6G8/vrrdOzYkYyMDLKzsxk6dCgmk4kWLVpw6623Ul5eDujzq8fExGAymejUqVOVjuj999/Po48+ypAhQzAajfTq1avKnXOr27lzJ7fccgsmk4muXbuyZcsWAObOncv27duZPn06BoOB6dOn28w/cuRIQkNDCQwMpF+/fhw8eNDZ3Qfo9x/q37+/9fnVV1/NzJkzad26NZ6enkyZMoULFy5w5MgRAN5//32GDRtGv379MBgMvPDCC/zf//2f9a6+I0aMYPjw4QQFBV1UV2xsLCNHjiQgIAB/f3+mT59+0V2IY2Ji+OKLL1zalroiHXwhhBBCiErCw8OJjY1l3LhxxMbG1utY9/Lycv785z+TmppKWloafn5+VTrEEydOpLi4mIMHD5KVlcWTTz55URkvvPACK1euZOvWrYSHh7N06VLCw8M5deoUJ0+eZNGiRSilKCkpYdiwYQwePJisrCzeeOMNxo8fb+34Aqxdu5b58+eTk5ND+/btmTt3rs24jx07xpAhQ5g3bx5nzpzhlVde4d577+XUqVO8+OKL3Hrrrbz55psUFhby5ptv2iwjNjaW3377jaysLLp168b48eNd2oeJiYlcd911dtP37t3LhQsXaN++PaCfwe/atas1vV27dnh7e/Prr786Xfe2bdvo1KlTlWUdOnRg3759TpdVl6SDL4QQQgjhJkFBQdx77734+/tjNBqZO3cuW7duBeD48eNs2LCBt99+G7PZjJeXV5Uz1ZqmMXPmTL7++ms2b95McHAwAF5eXhw/fpzU1FS8vLy49dZbUUqxc+dOCgsLmTVrFt7e3gwYMIChQ4eydu1aa5kjRoygZ8+eNGvWjPHjx7N3716bca9Zs4Y777yTO++8Ew8PDwYNGkSPHj348ssvHd72Bx54AKPRiI+PDwsWLGDfvn0XXf/giNzcXIxGo820/Px8Jk6cyPz5860XTxcWFtq8kLriDL6j9u/fz9///neWLFlSZbnRaCQ3N9epsuqadPCFEEIIIdykuLiYqVOnEhkZSUBAAP369SM3N5eysjLS09Np0aKF3Qt8c3Nzeeedd5g9e3aVDuszzzxD+/btGTx4MFdffTUvvfQSAJmZmURERODh8Uf3LzIykmPHjlmfh4aGWv/39/e3jluvLjU1lfXr12MymayP77//vsoFpzUpKytj1qxZtGvXjoCAAKKiogDIzs52KH9lZrPZZuf87NmzDBs2jJtvvpnZs2dblxsMBvLz86usm5+fb/dLgi1JSUnExsby2muvceutt1ZJKygowGQyObkVdUtudFVP5C54QgghhKjN0qVLOXLkCAkJCYSGhrJ3716io6PRNI2IiAjOnDlDbm6uzQ6j2WxmzZo1jBo1io8//pg+ffoA+hnkpUuXsnTpUg4ePMhtt93GTTfdRFhYGOnp6ZSXl1s7+WlpaVx77bVOxx0REcHEiRN59913baYrpWrMHxcXx6effsqmTZusNxQzm81omuZ0LF26dLloeM358+cZPnw4bdq04Z///GeVtE6dOlUZQpOcnMz58+cd3g+pqakMHDiQZ599lokTJ16Ufvjw4SpDgNxBzuDXg4q74JnNZqKjozGbzezYsaPKRTNCCCGEEAUDjFe4AAAgAElEQVQFBfj5+WEymThz5gzPP/+8Na1169bExsYybdo0cnJyKCkpYdu2bVXyx8TE8P7773PPPfdYp4L8/PPPSUpKQtM0AgIC8PT0xNPTk169etG8eXMWL15MSUkJW7ZsIT4+njFjxjgd94QJE4iPj+frr7+mrKyMc+fOsWXLFmtfJyQkhOTk5Bq328fHh6CgIIqLi5kzZ47TMVS48847rcOaAEpKSrjvvvvw8/Nj1apVVX6xABg/fjzx8fFs376doqIinnvuOUaMGGE9g19aWsq5c+coKyuzbltpaSmgX3swYMAAHn30UR555BGb8WzdupXY2FiXt6cuSAffRUqpYcDrttLkLnhCCCGEqI1SihkzZnD27FlatmzJzTffzB133FFlndWrV+Pl5cX1119Pq1atWLZs2UXlDBo0iH//+9/cdddd/PTTT/z2228MHDgQg8FA7969mTZtGjExMXh7e/PZZ5+xYcMGWrZsybRp01i1ahXXX3+907FHRETw6aefsmjRIoKDg4mIiGDJkiXW2XqeeOIJPvzwQ8xmM48//vhF+SdNmkRkZCRt2rShY8eO1ikubUlLS8NgMJCWlmYzvVu3bgQGBlq/4Pz3v//l888/Z+PGjZhMJgwGAwaDge3btwP6Gfy3336b8ePH06pVKwoKCli+fLm1vIULF+Ln58dLL73EmjVr8PPzY+HChQCsWLGC5ORknn/+eWu5BoPBmvf48eMcOnTI7TfdUq78FCJ0SikjkF993FZcXBzR0dFVvjGWl5ezZ88exo0b54ZIhRBCiCvL4cOH6dChg7vDsKtbt24899xzbu8INhUbN25k+fLlfPLJJ26N46mnnqJdu3ZV5tV3ha3jt6CggICAAIAATdNqvCJYxuDXg4q74FW+KKY+7oInhBBCiMbn4MGDHD58mOjoaHeH0mQMHjyYwYMHuzsMli5d6u4QABmiUy/kLnhCCCGEsOVvf/sbgwcP5uWXXyYyMtLd4YgmSoboXAJ7Q3RAZtERQggh3OlyH6IjRE1kiM5lKjw8XDr0QgghhBCiwUkHv57IGXwhhBBCCOEOMga/Hsg8+EIIIYQQwl3kDH49SExMxM/PjwMHDlBQUIDRaKRVq1YkJibKWXwhhBBCCFGvpINfD5KSkkhPT6e4uNh6O2h/f38iIiLcHZoQQgghhGjiZIhOPTh8+DDHjx/HaDTSsmVLjEYjx48f5/Dhw+4OTQghhBCXgSNHjhAdHY3RaOT11193dzgOef/99x2ea75Tp05s2bKlfgOqI0opkpKS3B1GnZIOfj1IT08nMDAQs9lMcHAwZrOZwMBA0tPT3R2aEEIIIS4DixcvJiYmhoKCAh5//HGXy4mJiWHFihV1GJkuJSUFpRSlpaXWZePHj2fjxo0O5T948CAxMTEALFiwgAkTJrgcS31tY1MmHfx6UFRURKtWrfDw8KCoqAgPDw9atWpFUVGRu0MTQgghxGUgNTWVTp06uTuMJqHylxChkw5+PTCZTJw4cQIvLy9atmyJl5cXJ06cwGQyuTs0IYQQQrjZgAED2Lx5M9OnT8dgMPDrr7/yxRdfEB0dTUBAABERESxYsMC6/rlz55gwYQJBQUGYTCZuuukmTp48ydy5c9m+fbu1nOnTp9usb+TIkYSGhhIYGEi/fv04ePCgNe3s2bM89dRTREZGEhgYSN++fTl79iz9+vUD9D6NwWDghx9+YOXKlfTt2xeARx55hKeffrpKPXfffTevvvoqAFFRUWzatImvvvqKRYsWsW7dOgwGA127dmX9+vV07969St6lS5cyfPjwi2K3t41KKf7nf/6Ha665hmuuuQaAJ554goiICAICAujevTvbt2+3llNWVsaiRYto164dRqOR7t272xxZ8f333xMREcHmzZttN14jIRfZ1oP+/fuze/dukpOT8fT0pKysjPz8fPr37+/u0IQQQogrTlmZxsmT5xqkrpAQXzw9VY3rfPfdd8TExDBhwgQeeughADIzM1m1ahWdOnXiwIEDDBo0iBtvvJHhw4fzn//8h7y8PNLT0/Hx8WHv3r34+fnx4osvsmPHjirl2BIbG8t7772Ht7c3f/vb3xg/fjx79+4F4Omnn+bgwYP897//JTQ0lISEBDw8PNi2bRtt27YlNzeXZs307uKRI0esZY4bN47x48ezZMkSlFLk5OSwceNG3nrrrSp133HHHcyZM4ekpCTWrFkDwPnz55k6dWqVu7WuWbOGefPmXRR7Tdv4ySefkJCQgJ+fHwA33XQTzz33HIGBgbz22muMHDmSlJQUfH19efXVV1m7di1ffvkl1157Lfv378ff379KeV9//TUPPfQQH330ET179qyxDS930sGvB/feey/FxcWcPHmS4uJifH196dSpE/fee6+7QxNCCCGuOCdPniMq8vMGqSsldShhYX5O56sYrw7QpUsXxo4dy9atWxk+fDheXl6cPn2apKQkunTpctHZ79o88MAD1v8XLFiA2WwmLy8Po9HIe++9x86dO2nTpg0At9xyi0Nl3nrrrSil2L59O/369ePDDz+kd+/ehIWF1ZrXx8eH0aNHs2bNGl588UUOHjxISkoKQ4cOdWq7Zs+eTYsWLazPK4/zf+qpp1i4cCFHjhyha9eurFixgsWLF3PdddcB0LVr1yplrV+/nrfffpsvv/ySzp07OxXH5UiG6LhIKTUMsHnZe3h4OBMnTuT222+nX79+3H777UycOFHmwBdCCCGETQkJCdx2220EBwcTGBjI22+/TXZ2NoC1TzFmzBjCwsL461//SklJiUPllpWVMWvWLNq1a0dAQABRUVEAZGdnk52dzblz52jXrp3T8SqlGDNmDGvXrgUgLi6O8ePHO5x/8uTJxMXFoWkaq1evZtSoUfj4+DgVQ/Xpx5cuXUqHDh0IDAzEZDKRl5dn3Yfp6ek1bueyZcsYNWpUk+jcg5zBd5mmafFKqS3A/bbSw8PDpUMvhBBCXAZCQnxJSXXu7PCl1OWKcePGMX36dDZs2ICvry8zZsywdk69vLyYP38+8+fPJyUlhTvvvJPrrruOBx98EKVqHg4UFxfHp59+yqZNm4iKiiIvLw+z2YymabRs2RJfX1+OHj160Rnt2soFGDt2LIMHD2bWrFkkJCTw8ccf21zPVlk333wz3t7ebN++nbi4OOLi4uzWYy+Wysu3b9/Oyy+/zLfffkunTp3w8PCwbifoXwaOHj3KDTfcYLOs9evX8+CDD9KmTRtmzJhhN5bGQjr4QgghhGjSPD2VS8NmGlJBQQEtWrTA19eXXbt2ERcXZ51zfvPmzbRs2ZKOHTsSEBCAl5cXnp6eAISEhJCcnFxjuT4+PgQFBVFcXMycOXOsaR4eHjzwwAPMnDmT1atXExISwq5du+jWrRvBwcF4eHiQnJzMtddea7Ps6OhogoODeeihh7j99tvtTiYSEhLCN998Y735Z4VJkyYxffp0mjVrZr14117+mraxYjubNWtGcHAwpaWlvPTSS+Tn51vTH3roIZ599lk6duxI+/btSUxMpE2bNgQFBQEQFhbGt99+S0xMDN7e3kybNq3G+i53MkRHCCGEEMLNli9fznPPPYfRaOTvf/87o0aNsqadOHGC++67j4CAADp06ED//v2t482feOIJPvzwQ8xms8359CdNmkRkZCRt2rShY8eO3HzzzVXSX3nlFTp37sxNN91EixYt+Nvf/kZ5eTn+/v7MnTuXPn36YDKZ2Llzp824x44dy6ZNmxg3bpzdbRs5ciQAQUFBdOvWzbp84sSJHDhwgIkTJ9a4b2rbRoDbb7+d2NhYrr32WiIjI/H19a0yhGfmzJmMGjWKwYMHExAQwIMPPsjZs2erlHHVVVfx7bff8vLLLzf6efdVxU8XwnlKKSOQn5+fj9FodHc4QgghhLCoPEOLuDydPXuWVq1a8fPPP1unuhQ6W8dvQUEBAQEBAAGaphXUlF/O4AshhBBCiAb31ltvcdNNN0nnvh7IGHwhhBBCCNGgoqKi0DSNTz75xN2hNEnSwa8nGRkZJCYmkpOTg9lspnPnzjKrjhBCCCEEkJKS4u4QmjQZolMPMjIy2LFjB2azmejoaMxmMzt27CAjI8PdoQkhhBBCiCZOzuDXg8TERIxGI8nJyRQWFmIwGDCZTCQmJspZfCGEEEIIUa+kg18PkpOT8fLyIjQ0lJCQEIqKisjIyHD4rnNCCCGEEEK4Sobo1IPc3Fz8/f0xGAwopTAYDPj7+5Obm+vu0IQQQgghRBMnHfx6EBAQwOnTp8nLy6O8vJy8vDxOnz5dMXepEEIIIYQQ9UY6+PWgffv2tG3bltOnT5OYmMjp06dp27Yt7du3d3doQgghhGjiFixYYL3TbVpaGgaDgbKyMjdH5ZzY2Fj+85//1Lre9u3bue666xogoku3cuVK+vbt2yB1SQe/HnTu3JmzZ89yww03cMcdd3DDDTdw9uxZOnfu7O7QhBBCCHEFueqqqygsLMTT09PdodhV+QtJhQ0bNjB58uRa8956660cOXLE+jwqKopNmza5FEdKSgpKKUpLS13KfzmRDn49CA8Pp0+fPuTk5LBnzx5ycnLo06ePzKAjhBBCCNGINZbOv3Tw60l4eDixsbGMGzeO2NhY6dwLIYQQwioqKoolS5bQpUsXmjdvzoMPPsjJkyeJjY3FaDQycOBAcnJyrOvv3LmTW265BZPJRNeuXdmyZYs17ffff6d///4YjUYGDRpEdna2Na36WenqZ7grnz2vWPff//43ERERmM1m3n77bXbv3k2XLl0wmUxMnz7d7jbt2rWL3r17YzKZaN26NdOnT+fChQvW9IMHDzJo0CBatGhBSEgIixYt4quvvmLRokWsW7cOg8FA165dAYiJiWHFihWcP38ek8nEgQMHrOWcOnUKPz8/srKy2LJli7WPNXHiRNLS0hg2bBgGg4HFixczZMgQ3njjjSpxdunSxeYddPv16weAyWTCYDDwww8/sHLlSvr06cOTTz5JixYtWLBgAUePHmXAgAEEBQXRsmVLxo8fX2UilfT0dEaMGEFwcDBBQUF299kzzzxD3759ycvLs7tPXSXTZNaT3bt3s3HjRk6dOkVwcDCDBw/mpptucndYQgghxBWnTCvj1LlTDVJXsG8wnsqx4TAfffQR33zzDaWlpURHR7Nnzx7+9a9/0bFjR2JjY3n99deZP38+x44dY8iQIaxevZo77riDb7/9lnvvvZdffvmF4OBgxo0bR+/evdm4cSMJCQkMGTKEu+++2+VtSEhI4LfffmPbtm3cdddd3HHHHWzatImSkhKio6MZOXIk/fv3vyifp6cn//jHP+jRowcZGRnExsayfPlyZsyYQUFBAQMHDuTpp58mPj6ekpISDh06RK9evZgzZw5JSUmsWbPmojJ9fHwYMWIEa9eu5cUXXwTggw8+oH///rRq1YpDhw5Z1129ejXbt29nxYoVDBw4ENC/0CxdupTHHnsMgH379nHs2DHuvPPOi+ratm0bbdu2JTc3l2bN9C7ykSNHSEhIYMyYMWRlZVFSUsKxY8eYPXs2/fr1Iz8/n3vvvZcFCxawbNkyysrKGDp0KAMGDGD16tV4enry448/VqmnvLycqVOnkpaWxsaNG/H393expey7ojr4SqnlwDAgECgA1gN/1TTtgiX9UWAy0AXYqWlajCv17N69m/Xr19OlSxd69uzJyZMnWb9+PYB08oUQQogGdurcKbps7N4gde0f/BOhfqEOrfvYY48REhIC6GPJW7VqRXR0NAD33HMP3377LQBr1qzhzjvvtHZKBw0aRI8ePfjyyy+57bbb2L17N5s2bcLHx4d+/foxbNiwS9qGZ599Fl9fXwYPHkzz5s0ZO3YsrVq1ssa5Z88emx387t3/2MdRUVFMnTqVrVu3MmPGDD7//HNCQ0N56qmnAPD19aVXr14OxTNu3DimTJli7eDHxcUxdepUh/LefffdPPLII/z2229cc801rF69mtGjR+Pt7e1QfoCwsDDrF4RmzZrRvn1768QpwcHBzJw5k+effx7Qf8XIzMxkyZIl1i8JlS+sLSkpYezYsZSWlhIfH+9UHM640obovAlcr2laAHAj0BX4W6X048BLwD8upZKNGzfSpUsXwsLC8PT0JCwsjC5durBx48ZLKVYIIYQQTUhF5x7Az8/voueFhYUApKamsn79ekwmk/Xx/fffc/z4cTIzMzGbzTRv3tyaNzIyskHiqu7XX39l6NChhIaGEhAQwJw5c6zDhdLT02nXrp1L8QwYMICzZ8+SkJBAamoqe/fu5Z577nEor4+PD6NGjWLNmjWUl5ezdu1aJk6c6FT9ERERVZ5nZWUxZswY2rRpQ0BAABMmTKiynZGRkdbOfXVJSUl8+umnzJ8/v94693CFncHXNO1Q9UVA+0rp/weglLrqUuo5deoUPXv2rLIsJCTkop9ohBBCCFH/gn2D2T/4pwarq65FREQwceJE3n333YvSUlNTycnJoaioyNrJT0tLQylls6zmzZtTXFxsfX7ixIk6i/Mvf/kL0dHRrF27FqPRyLJly/jwww+t27B27Vqb+ezFWsHDw4NRo0axdu1aQkJCGDp0KEaj0eGyJk+ezMSJE+nbty/+/v707t3bqTiqL589ezZKKfbv309QUBCffPKJdZx9REQEaWlplJaW2uzkd+jQgUcffZTY2Fi+++67epvi87I8g6+Umq2UWq+USlZKaUqplBrW9VBKPamU+kUpdU4pla6UWqqUam5n/VlKqQIgC30ozhu21rsUwcHBJCcnk5SUxIEDB0hKSiI5OZng4Lp/0QshhBCiZp7Kk1C/0AZ5ODr+3hkTJkwgPj6er7/+mrKyMs6dO8eWLVvIyMggMjKSHj16MH/+fC5cuMD3339PfHy83bJuvPFG/vd//5eSkhJ+/PFHawe8LhQUFBAQEIDBYOCXX37hrbfesqYNHTqUEydOsGzZMs6fP09BQQEJCQmAfhI0JSWF8vJyu2WPGzeOdevW8f777zNu3Di764WEhJCcnFxlWe/evfHw8OCpp56q8ex9cHAwHh4eF+W3tZ0GgwGTycSxY8dYsmSJNa1nz560bt2aWbNmUVRUxLlz59ixY0eV/GPHjmXRokUMHDiQo0eP1liXqy7LDj6wCBgAHAVyaln3H8CrwCHgMfRx9Y8D8Uqpi7ZP07SXNE0zAh2Bd4C6++pq0bVrVzZv3kxBQQHBwcEUFBSwefNm65XhQgghhBCOioiI4NNPP2XRokUEBwcTERHBkiVLrB3iuLg4EhISaNGiBc8//zyTJk2yW9YLL7zA0aNHMZvNzJ8/v8bOsrNeeeUV4uLiMBqNPPzww4wePdqaZjQa+eabb4iPjyc0NJRrrrmGzZs3AzBy5EgAgoKC6Natm82ye/XqRfPmzcnMzCQ2NtZuDLNnz2bhwoWYTCZeeeUV6/JJkyaRmJh40Xz7lfn7+zN37lz69OmDyWRi586dNtebP38+P//8M4GBgQwZMoQRI0ZY0zw9PYmPjycpKYmrrrqK8PBw1q1bd1EZkydP5rnnnmPAgAGkpKTYjclVStO0Oi/0UimlrtY0Ldny/wHAoGlalI31OgGJwMeapt1bafljwOvAeE3T4mqoZxQwRdO0gdWWzwCG13aRrVLKCOTn5+dX+alow4YNHDx4kF27dpGXl0dgYCA9e/akU6dONR6UQgghhKgbhw8fpkOHDu4OQ1wmVq1axTvvvMP333/v7lAcYuv4rfiFBAjQNK2gpvyX5Rj8is69A8YCClhWbfm76BfLTgDsdvABT+AapwOsRVJSEl5eXowdOxaj0UhBQQFpaWkkJSXVdVVCCCGEEKIGxcXFLF++nGnTprk7lAZzuQ7RcdRNQDmwq/JCTdPOAXst6QAopQxKqT8rpUxK1xl4Fvi60jrNlFK+6F98PJRSvkoppy9xzs/Px8vLi4yMDH7++WcyMjLw8vIiPz/fta0UQgghhBBO+/rrrwkODiYkJKROhyNd7i7LM/hOCAOyNU07byPtGHCLUsrbMs+9BowDlgLe6BfZfgTMr5RnXrXnZ4GtQIwzQXl4eJCWlkbbtm1p1aoVubm5/P777wQGBjpTjBBCCCGEuAS33347RUVF7g6jwTX2Dr4/YKtzD3Cu0joXNE0rAgbVVJimaQuABbVVqpSaAkzBzi8g5eXltG3bFqUUJ0+exNfXl7Zt21a5dbQQQgghhBD1obF38IuBVnbSfCutU6c0TXsHeKfiItvq6QEBAZw+fRpfX188PDwoLCzk3LlzFRdGCCGEEEIIUW8aewc/E+iolPKxMUynDfrwnQsNHZTZbCY9PZ2ioiLOnz+Pj48PzZs356qrLun+WUIIIYQQQtSqsV9kuxt9G6rcNtZyoeyNgFtuHXv69GnS0tIoKyvDx8eHsrIy0tLSOH36tDvCEUIIIYQQV5DGfgZ/HTAHmAFsr7T8YfSx9+/XV8VKqWHACFtp+/fvp6CggDNnzlBWVoanpydeXl7s37+/vsIRQgghhBACuEzP4CulJiql5iml5gHBQGDFc6WU9R7DmqYlAv8DjFBK/Z9S6iGl1FL0O9tupeY58C+Jpmnx6HfMvcihQ4fw9fWlW7du9OvXj27duuHr68uhQ4fqKxwhhBBCNCJRUVFs2rTJ3WE4JDY2lv/85z+1rrd9+3auu+66Bojo0q1cuZK+ffu6O4x6c7mewX8Q6F9t2QuWv1uB1ZWWzwBS0Ge1GQJkA28Az2maVl6/YdpWXFyMp6cnGRkZlJaW0qxZMzw9PSkurvPrfYUQQggh6syCBQtISkpizZo11mUbNmxwKO+tt97KkSNHrM+joqJYsWIFAwcOdDqOlJQU2rZtS0lJCc2aXa7d1cvXZbnHNE2LcWLdMvS57ZfWW0BOKi0t5cyZMxiNRjw8PDh//jwFBQWUlpa6OzQhhBBCiCah4iSquNhlOUSnMbCMwX/dVpqmaZSWluLh4WF9lJaWomlaA0cphBBCiMvd+fPnmTFjBmFhYYSFhTFjxgzOn9cnB+zfvz8fffQRAN9//z1KKb788ksANm3axI033mizzF27dtG7d29MJhOtW7dm+vTpXLjwx8SCBw8eZNCgQbRo0YKQkBAWLVrEV199xaJFi1i3bh0Gg4GuXbsCEBMTw4oVKzh//jwmk4kDBw5Yyzl16hR+fn5kZWWxZcsWwsPDAZg4cSJpaWkMGzYMg8HA4sWLGTJkCG+88UaVOLt06cInn3xyUfz9+vUDwGQyYTAY+OGHH1i5ciV9+vThySefpEWLFixYsICjR48yYMAAgoKCaNmyJePHjyc3N9daTnp6OiNGjCA4OJigoCCmT59uc38988wz9O3bl7y8vBpaqvGQDr6LahqD7+fnR7NmzfD39ycwMBB/f3+aNWuGn59fA0cphBBCCK2sjLKTJxvkoZWVOR3fiy++yM6dO9m7dy/79u1j165dLFy4ENA7+Fu2bAFg27ZtXH311WzdutX6vH//6iOadZ6envzjH/8gOzubH374gW+//Zbly5cDUFBQwMCBA7njjjvIzMwkKSmJP/3pT9xxxx3MmTOH0aNHU1hYyL59+6qU6ePjw4gRI1i7dq112QcffED//v1p1arqbYlWr17NVVddRXx8PIWFhfz1r39l8uTJVYb+7Nu3j2PHjnHnnXdeFP+2bdsAyM3NpbCwkN69ewOQkJDA1VdfTVZWFnPnzkXTNGbPnk1mZiaHDx8mPT2dBQsWAFBWVsbQoUOJjIwkJSWFY8eOMWbMmCr1lJeX8/DDD7N//342btxIYGCg/YZqROR3jXoSGhpKaGgonp6elJWVoWkaOTk57g5LCCGEuOKUZ2dzKjq6QeoK3rMHz5AQp/K8//77vPHGG9ZO8vz585k6dSovvPAC/fv358knnwT0Tu/s2bNZsWIFAFu3buWJJ56wWWb37t2t/0dFRTF16lS2bt3KjBkz+PzzzwkNDeWpp54CwNfXl169ejkU67hx45gyZQovvvgiAHFxcUydOtWhvHfffTePPPIIv/32G9dccw2rV69m9OjReHt7O5QfICwsjMceewyAZs2a0b59e9q3bw9AcHAwM2fO5Pnnnwf0XzEyMzNZsmSJdShP5QtrS0pKGDt2LKWlpcTHxzsVx+VOzuDXA03T8PX1xdfXFz8/P+v/MkRHCCGEENVlZmYSGRlpfR4ZGUlmZiYAvXv35tdff+XkyZPs3buXSZMmkZ6eTnZ2Nrt27bIOZanu119/ZejQoYSGhhIQEMCcOXPIzs4G9GEr7dq1cynWAQMGcPbsWRISEkhNTWXv3r3cc889DuX18fFh1KhRrFmzhvLyctauXcvEiRNrz1hJREREledZWVmMGTOGNm3aEBAQwIQJE6psZ2RkpN1x+klJSXz66afMnz+/SXXuQc7g1wsfHx9KS0vJzs5GKWUdk+/j4+Pu0IQQQogrjkfLlgTv2dNgdTkrLCyM1NRUOnXqBEBaWhphYWEA+Pv70717d1577TVuuOEGvL29ueWWW3j11Vdp164dLe3U95e//IXo6GjWrl2L0Whk2bJlfPjhh4DeSa48zKYypVTN2+fhwahRo1i7di0hISEMHToUo9HocFmTJ09m4sSJ9O3bF39/f+vQG0fjqL589uzZKKXYv38/QUFBfPLJJ9Zx9hEREaSlpdm9GLdDhw48+uijxMbG8t133zWaKT4dIWfwXVTTRbYhISHk5ubi4eGBt7c3Hh4e5ObmEuLkT3ZCCCGEuHTK0xPPkJAGeShPT6fjGzt2LAsXLuTUqVNkZ2fz97//nQkTJljT+/fvz5tvvmkdbx8TE1PluS0FBQUEBARgMBj45ZdfeOutt6xpQ4cO5cSJEyxbtsw6019CQgKg92FSUlIoL7c/0/i4ceNYt24d77//PuPGjbO7XkhICMnJyVWW9e7dGw8PD5566qkaz94HB6g/lk4AACAASURBVAfj4eFxUX5b22kwGDCZTBw7dowlS5ZY03r27Enr1q2ZNWsWRUVFnDt3jh07dlTJP3bsWBYtWsTAgQM5evRojXU1JtLBd1FNF9l27dqV3NxcTp06RVZWFqdOnSI3N9d6NboQQgghRIV58+bRo0cPunTpQufOnenWrRvz5s2zpvfv35+CggLrcJzqz2155ZVXiIuLw2g08vDDDzN69GhrmtFo5JtvviE+Pp7Q0FCuueYaNm/eDMDIkSMBCAoKolu3bjbL7tWrF82bNyczM5PY2Fi7McyePZuFCxdiMpl45ZVXrMsnTZpEYmJilS8x1fn7+zN37lz69OmDyWRi586dNtebP38+P//8M4GBgQwZMoQRI0ZY0zw9PYmPjycpKYmrrrqK8PBw1q1bd1EZkydP5rnnnmPAgAGkpKTYjakxUTIu3HVKKSOQn5+fX+XnqTfeeIPExETS09M5d+4cvr6+RERE0LlzZ+uFIUIIIYSoP4cPH6ZDhw7uDkPYsGrVKt555x2+//57d4dy2bJ1/Fb8KgMEaJpWUFN+GYNfD/Lz87nxxhvp3LkzZ8+exc/PD09PT5lFRwghhBBXtOLiYpYvX860adPcHUqTJh38euDh4cFXX31FVlaW9cKOVq1a2b2QRAghhBCiqfv6668ZMWIEAwcOrHHsvrh00sGvBz/99BMnTpzg6quvxs/Pj7Nnz5KcnMxPP/3k7tCEEEIIIdzi9ttvp6ioyN1hXBGkg+8iyyw6I2ylHTp0iBtuuIF27drh7+9PcXEx5eXlVW7tLIQQQgghRH2QWXRcVNMsOuXl5YSGhpKdnc3Ro0fJzs4mNDS0ximnhBBCCFG3ZCIR0RjVxXErHfx6UFZWxsmTJwkICKB169YEBARw8uRJysrK3B2aEEIIcUXw8vLi7Nmz7g5DCKeVlJTYvfuuo6SDXw+8vb3Jysri/PnzeHh4cP78ebKysprcbZCFEEKIy1WrVq04duwYxcXFciZfNBrl5eWcPHmSwMDASypHxuDXg4CAAJo1a8b+/fvRNA2lFF5eXhVzlwrRYDIyMkhMTCQnJwez2Uznzp0JDw93d1hCCFHvKj5zMzMzKSkpcXM0QjiuefPmtGzZ8pLKkA5+PfD09CQ0NBRvb2/Kysrw9PTkwoUL/P777+4OTVxBMjIy2LFjB5GRkURFRZGXl8eOHTvo06ePdPKFEFeEgIAAObkmrkjSwa8HQUFBnD9/nuuuu46AgADy8/PZv38/QUFB7g5NXEESExOJjIzEbDYDWP8mJiZKB18IIYRowqSDXw9uvvlmDh06xI8//khJSQleXl4YjUaio6PdHZq4guTk5BAVFVVlWWBgIMnJye4JSAghhBANQi6ydZFlHvzXbaVFRERgNBrx8fGxPoxGIxEREQ0cpbiSmc1m8vLyqiz7/+zdeXxU5b348c+ZJTOTzGQy2UN2EggBImFXcEVFFEutS6tVq629rdeq1VptvfZW26v1tv25W3vbSuu+onUFRURWFULYBghkT0jIOplMMpl95vz+OGaDBEjIJAGe9+s1r0zOOTPnORlIvs9zvs/3cTgcPSP5giAIgiCcmkSAP0xHq4NfVlZGWVkZFouF3NxcLBZLzzZBGC0FBQXU1NRgt9sJhULY7XZqamooKCgY66YJgiAIghBGkigdNXySJJmAjo6ODkwmU8/2pUuXEgqFcDqdBAIBNBoNRqMRlUrFxx9/PHYNFk47ooqOIAiCIJwaOjs7uyeNR8uy3Hm0Y0UOfhjU1NSQkZHB7NmzMZlMdHZ2UlpaSk1NzVg3TTjNpKWliYBeEARBEE4zIsAPA7/fT0pKCkajEQCj0UhKSgoVFRVj3DJBEPoqKipi9erVtLS0kJCQwOLFi5k7d+5YN0sQBEEQTogI8MNAo9HQ2tpKS0tLz0JXkiSd8LLDgiCMnKKiIt5++23OOOMM5s2bR1NTE2+//TaACPIFQRCEk5qIOMPA5/Ph8XiYOHEiUVFRdHV1UVlZic/nG+umCcKoGs9zAFavXs0ZZ5zBhAkTAHq+rl69WgT4giAIwklNVNEJg2AwiM/no729nZaWFtrb2/H5fASDwbFumiCMmu6VdC0WCzNnzsRisbB582bq6urGumkAtLS0kJSU1G9bUlISLS0tY9QiQRAEQRgZYgR/mL6pg3/lQPtUKhURERF4PB68Xi+yLBMREYFKJfpTwuljvK+km5CQQFNTU8/IPUBTUxMJCQlj2CpBEARBOHEi4hymo9XB12g0pKSkkJeXx6RJk8jLyyMlJUXk4AunFbvdjtls7rfNbDZjt9vHqEX9LV68mN27d3Po0CGCwSCHDh1i9+7dLF68eKybJgiCIAgnREScYRAXF4fNZkOr1aLX6/F4PNhsNuLi4sa6aYIwarpX0u27cu54Wkm3O89+9erVbNu2jYSEBK655hqRfy8IgiCc9ESAHwYJCQn4fD6ampp6tgWDQXHrXzitFBQUsHnzZkAZuXc4HNTU1LBw4cIxblmvuXPnioBeEARBOOWIAD8MtFotfr+fSZMmYTQacTqd7N+/H61WO9ZNE4RRk5aWxsKFC7FarVRWVmKxWFi4cOG4yL8XBEEQhFOZCPDDQK/Xk5iYyN69e/F6veh0OnJycujo6BjrpgkCMHrlK8VKuoIgCIIw+sQk2zAIBAI0NjaSkpJCTk4OKSkpNDY2EggExrppgjDuy1cKgiAIgnBiRIAfBl1dXXR1dWEwGIiJicFgMPRsE4Sx1rd8pUqlwmKxkJmZidVqHeumCYIgCIIwAkSKThi43W5iY2Npbm4mGAyiVqsxm820tbWNddOE08xAqTh2u52srKx+x5nNZiorK8emkYIgCIIgjCgR4IeB3+9HkiSSkpKIiIjA5/Nhs9nw+/1j3TThNNKdipOZmUlWVhYOh4PNmzcTCATGdflKQRAEQRBOjAjww0CWZQBMJhM6nQ6v14vNZuvZLgjHy+UChwNkGZKTYSiLIQ+2kmx5eTk1NTXA+C1fKQxstCZHC4IgCCc3kYM/TJIkfQt4eqB9oVAIlUqFw+GgtbUVh8OBSqUiFAqNciuFk91XX8FDD8Hvfgdud/99x/rnNNhKsiqVioULF2K329mxYwd2u12UrzwJiMnRgiAIwvESI/jDJMvyh5IkrQNuPnyf3+/H4/EQCAR6gv1AICBSdISj8nqhsxPi43u39R2x7xvQB4Pw5JMwbRpccglI0pHvd7SVZEX5SsXJNCI+2B0Zq9U6btssCIIgjA0R4IeB3++nvr4ei8WCRqMhEAhgt9tFmUxhUF1d8PTT0NEB990H3TH51Knw4x8rAbxe33v8hx9Caany0OnggguOfM+TYSXZsTTYHIUTuZsRzg6DmBwtCIIgHC8R4IeBz+fDYDCQkJBAZGQkLpcLp9OJy+Ua66YJ41RREVRXK88//RSuvVZ5npSkPA43ezYUFyvB/dlnD/yeaWlpTJw4kdWrV9PS0kJCQgKLFy8mLS3tpBq5DpeRHhEPR4ehr6PdkREEQRCEvkSAHwZarZb8/Hy0Wi3BYBCTyUR+fj7FxcVj3TRhnDrvPGhsVEbyr7nm2Menp8MDDyiTcLXagY+pq6ujsrKSCy+8sGcEv3u0t7KyMmyB6MlipEfEw51CI+7ICIIgCMdLBPhhYDKZiIiIwOVy4fP5iIiIIDIyEpPJNNZNE8YJjwdaW6E77pMk+N73ep8fD72+f9oOwN69yoh/fPzgAefq1auZOXMmlZWVOJ1OjEYjMTExp10u90iPiIc7hSYtLY2FCxditVqprKzEYrGcdp0yQRAE4fiIAD8MsrKyKC0tZcKECURFReH1eiktLT3ij79welq1SnmYzfD73/cG9Mcb2A9EluH995X3zcqCe+9VAk6TyURxcXFPIJ+RkUFNTQ0JCQkkJyeTlJREV1cXdXV1p90k8JEeER+NFBoxOVoQBEE4HqJMZhgkJiYiSRI+nw+3243P50OSJBITE8e6acI4EBGhVMxpboYDB0bmPSVJuSsA0NAAdXVKudb169fT0tKC2+2mpaWF9evX09HRQWRkJEajEUmSMBqNREZG0t7ePjKNOUl0j4iPVLnQgoICampqsNvthEIh7HY7NTU1FBQUjHDLBUEQBOHoxAh+GJSUlGAwGPB6vXg8HiRJwmAwUFJSMtZNE0aRzweffKKk0Sxe3Lv9rLOgrEyZHDt58sid7+qrlVr5l16qLIr1/vs2amtrmTx5DhMmmLDb26itrSUYDGKz2YiKisJkMtHZ2YnNZiM6OnrkGnMaEik0giAIwnghAvwwqKysJD8/n5SUlJ4ymQ0NDZSVlY1104RRIstKnfqKCjCZYNEi0Hzzvy0yEm69deTPqdHAD3/Y+31zczNz5szl1VcLCAY9TJ8eYu7cuaxfv57s7Gyam5uprq7GZDKRnZ2NdrDZuqeocFS9ESk0giAIwnggAvwwcLlcdHR0EB8fj1qtxu/309HRIcpknkYkCS68UAnwY2OVBaxGu5phKBSitTUSt1tHIKAmFJIIhUJYLBbq6uqw2dQEgwbAic/nY9myZWFph90OTU3K85yc/lV/qquVSkBRUZCZ2bftSkUhvV7puJzI/ITBHKvqjSwrqVQ+n5JW1XdCc0eHMkk6GISJE0Gt7n9NbjcYjUq1o25eL5SXK8/T0pQ5GN3lSisr3UhSIpMn53LRRcn92rl7t9KGhIT+P6PmZti1C/x+5W5Q3xswa9Yo//YsFvjud3u3+3zwl78oz885B+bM6d23YwesXas8v+02MBh69738slLlaeJEuOqq3u2trbB8ufL8iisgL6933+efw9atyuf9y1/2/9l/9ZWyiNuECf1/RoIgCMLIEAF+GKhUKkwmEw6HA7vd3vO9SiWmPJyqZFkJYpP7xGazZimBUkFB/xVpR0tSUhLFxZuZOzeK9vYMcnKcbNmyhfT0dFQqFVVVkygrS8Zo9LBs2dae14VCSoDq8ylBat+AvKZGCSzVauX6unm98Oyz4HTCRRdB33mq27fDW28pz//4R4iJ6d331ltKIJqfD3fd1bu9vR3uv195/oMf9H+/L76AL79UAv9f/ar/NT/xhNKGWbNg6dLe7eXl8OqryvNbblEC7O6qN598YqS0VEdcXJDrrgv1VL1xu+Huu5XXXHVV/zSr4mJ44w3l+WOPKT+nbq+/rgT506fDHXf0bm9rUxYzA/iP/4Dk5N47CIFAAV98oeX99zuYMiXQ7y7ACy8onZ3zz+8f4Dc2wooVyvNp0/oH+BUVys89JaX/z0eWYf/+3tf01d6uLJwGcPiafAcPKp/94VWbfD7oLhLkdPbfZ7crP4eICI7wzjtKp/fss+HGG4/cLwiCIJwYEeCHgSzLhEKhnq+SJPV8L5x6mprglVegthYeeqh3pF6SYMaMsWtXfHw8kyd3oteXkpFxAEmSyMrKwul0UlhYiNWay4QJGnJyfMyc6esZuS4tVQJlgHvu6T9PYO1a+PpriIvrH+Brtcq8AlkGm61/O/oGv8ere8IwKIt59WW3Kz9rzQC/vQ4dUkbXJ07sv93rVfaBEpRCb9Wbjg4Lra3Km/WtetM3MA0G+79f3xH7w/cdr753ECRJQqfTodGYsFp39AvwuzuHoVD/1/fteHVfUzeLRQnuExKObHdOjvK8b0er+/tJk3qP6yszUwnuDx9t1+mUzhkoqWh9paQonZzDP6dQqLczIKZ9CIIghIcI8MMgFArR1tZGbm4uRqMRp9NJQ0MDocP/QgunhNbW3pHPDz9URpzHA5VKxQUXXEBtbW2/MpkrVqzAbDbzve+1U1UVgckU6levvW9ge3jlzO59hweUKpUyIqzRHLny7tSpSkdBko4M9q+/Xgnm+6aDgBIsXnutsu/woDI5Gc4448ggFJSA0uXqXV+gW3R0b4ckKkr5WlBQwEcffURbWytarQmfz8/OneVcfvnlgPL+V12lXHN3UNz3PHfcoRwTGdl/3w03KB2Kw7fHxfXecUhKgtLS3jKmwaCPBQsspKZOwG6393vdPfcoHafudnebNEmZ5xERceTPom9aTl8aDdx338D7Zs5UHgO5/vqBt8fF9b/z0tfChf3vvHSTJHj8caUjdvgdAUEQBGFkiAA/DHQ6HRaLhUAggMPhQJZlLBYLjY2NY900YQTIcv+c8GnTYP58JUi94oqxa9fhLBYLGo2G2bNn92yz2+0kJCTgcDhISrKQlOT+ZnvvyHVCgrLoVkSEkiPd12WXKROGDx9Vh/7pKH2ZTEeO7nZLTR38NRdcMPC+BQuUx0Buumng7enp8NOfHrm9srKSqqpPcLvdGAwGKit7b1dIUv+0nL5iY5XHYOcaSERE/zsLoVCIoqIicnJySEqKoquri4qKTSQcNuzeN82mO2ffbrdjsVgoKCg46Sb1SpLS+Tm8A3TwoNIxC8d8C0EQhNONCPDDIDIyEr/fj9/vR5IkZFnG7/cTefhfNOGkU1qq5D3ffHP/4PeHPxx/gclgCzktXryYoqIifD4foVAIlUpFREREzyTb7qo/AxnticLhtGLFCmw2GxdddBHx8fG0trayY8cOVqxYwV2DDUuPoFAohNfrJRgMIssywWAQr9c76J2+uro6Pvroo57fKZ2dndTU1HD55ZefdEF+X83NSorbgQPK3YDulB9BEARh+ESAP0ySJH0LuHKgfVqtlmAwSDAYRKVSEQqFCAaDp10ZwlNNW5uSmx4Kwdtvw513jswqtOGSlpbGxIkTWb16NS0tLSQkJLB48WJSUlJQqVQYDAZkWe4JGMNhPI84b926lZkzZxIKhairqyMiIoIpU6awdevWY794BGg0GubPn09FRUVPudL58+fT0NAw4PHr1q3D7XaTk5NDVFT3iH8F69at44YbbhiVNodDVBRUVSnPV68WAb4gCMJIEAH+MMmy/KEkSeuAmwc7Rpk0p9TBF05+sbFK1Y+vv4bc3CNTdcaburo6KisrufDCC3tG8CsrKykpKaGwsLAnJQeU1J3uSbYjef6RrjM/krq6umhra0OSJILBIGq1GlmW6erqGpXzWywWDAYD55xzTs+27o7QQEpKSpg7dy7GbyYyGI1GsrOzKSoqGpX2hktUlFJ5yeeDiy8e69YIgiCcGkSAHwY6nY5gMEhZWRmBQACNRoPRaEQ3UOKyMO7IslIR5rPPlFVn+1aLueIKpfzi4RVIxqPB6ry///77zJ07t9+xfSfZhvv8I92RGC5Jkqirq2Pq1Kk9k+H37duHNEq9toKCAj744INBU6UOFwqFcDgcNDc34/F40Ov16HS6U2Ly/re/PdYtEARBOLWIAH8E1NZCfb2yyEt6Ovj9frRaLXPnzsVisWC329mzZw/+w0uSCOOS3w//939K7fHumurdDq9k0peMTDPNaNAQR1z4G3oM3XXe+zKbzahUqn7lIIEjvg/n+Ue6IzFcMTExOJ1O6uvr0ev1eDwe3G43MaPYextKqlRycjJffvklM2bMIDExkba2NoqLi5k6deqotVcQBEE4OYgAfwTs2gXr1/eWfwsEAqSkpKDRaHC53Gg0GlJSUjhw4MBYN1UYQHOzUtGju4RjRAScd54ygp+aqiz6M1DN9b5ChPgrf6WFFuYwh8u5PPwNP4buOu+HB/L5+fnU1NQA/SffLhyopmEYzj/SHYnhio6O7hm5b21tRa/Xk5ycPGoL0lmt1iGlSlksFiwWC83NzTQ2NqJSqXq2nUpCIWX13hkzxncKnCAIwngmAvwR0NSkfM3IUALFiIgItFottbV1WK3XYTC0k5kZRURE1dg2VOinq0upI15bC1df3T//96KLlEoyg5V3PJwKFWbMtNDCAQ6wlKVIjG100l3nvXtkuPtrd513q9VKZWUlFoslLHnxg1XxGemOxHBFRkZis9nw+Xz4fD5UKhVer5e4uNG5+zLUOxwajYZLLrmEiooKOjs7MZlM5OTkDDop92TU0ADLlyslM2+7bWwXihMEQTiZiQB/BPz4x8rCNn2Xam9ubkajmUgwGI3TGU1z855+o1HVW5sJrtuI5YalWFL0YqRqFDid/RdaiozsXTG1qKh/gH+0VJzBzGMeOeQwhSljHtx3C4VCR+R4j5a0tDQWLlwY9o7EcPn9fhoaGsjPz+/pgJSUlBA9SsurDvUOx1An5Z6MjMbelZDXrhUBviAIwnCJAH+ExMcrD1BSdDo7O8nNjaagwEF9vQFZLiUY/KaajizT+NSb+LbvofqtrSza+Dsw9C7p6PGIFR5HUkUFvPmmMlL/8MO9n5MkKSP1HR0wZ86Jn2cyk4990CiyWq3MmjXriBSQdevWodVqR6W6TVpa2rgJ6A/X3t7O/PnziYyMxOfzkZycTHR0NNXV1aNy/qHe4TjWHZlTgcmk3E1ra4MlS8a6NYIgCCcvEeCHgUqlYuLEibS17SUQ2EVKioaYmFgOHGhRDvB66Wx2owOCU6Yh9QnunU745S+V1USvugoKC8fmGk5WXV1KcNB3NdGoKPgm5ZyiIrj00t595503uu0bTYOlgJSUlDB//nz27NnTk+qRmJgYluo2RUVFR9ThP7yCz1hRq9VotVpkWe73Va1Wj8r5B1un4GifwVjekRkt4ySDSxAE4aQmAvww8Pl8eL1eJk2ahMFgwO12U1dXh8/nUw7Q6zn/43tp+nAr/kn9K2DU7WhB64ukuTmKw/92f/65MhqdmQmXXDJKF3MSeeMNWLcOkpLgd7/r3Z6UBGecAWlpUFAwOm2RkQkRQs3oBIsDGSwFxOFwUFVVRUZGBllZWXR2dlJVVTXik0uLiop44YUXSEhIIDk5GY/HwwsvvAAwLoL82NhY7HZ7v6o57e3txMbGjsr5B1unICUlZcAgf7A7MuOl7Gi4yLLyGKW5z4IgCKcEEeCHQSAQoKuri+bm5p7b6F1dXf0WvNJGSKRdNb//C2WZ1E+Xc1dbM9tzv0dGRv/9e/cqj4aG/gF+KAQffAApKTB5MpxCKblHCIWgtFRZ1n7GDOg7QG02K4FAYyO0tvZPxfnZz0apfYRYwxpKKGEGMzif80fnxAMYLAUkMjKSuLg4zGZzz764uLieyjojZcWKFZjNZrKzs3s6uj6fjxUrVoyLAD85ORmr1Up6ejrx8fG0trZSXl5OwSj1Aoe6ToDdbkev1/e785KTk4Pdbh+V9sLor0wcDMLrr4Pbrcx1EnOVBEEQjo8I8MMgEAjQ2tracwvd5/PR2tp67BVt9+7F1FKFKQly5lSBsX+AbzYrwXvf9BNQqvisWqU8/9GPYH6fl23fDtXVyij2yXTru7NTWVvA4eh/PbIMTz2lBPqy3D/ALygAlwumTRu7hahUqKikEjt29rN/TAP8wSa5tra24nK5cDqdREVF0dXVhcvlGvH675WVlVx66aVERkYCStWa3NxcVnX/Yx1jiYmJzJ49m3Xr1uFwODCbzZx//vmjVgd/qFV0AoEAa9euxWKxoFKp6OjoYO3atUyaNGkUWjs2KxN/8AFs3Kg8z8gQdy4FQRCOl7jpGQZutxu9Xo9KpSIYDKJSqdDr9bjd7qO/cNo0+OlPYdIkuPLKI3bfdBP87/8qX/tqbu4d2UpM7L9v92749FPlD2VfsqxMOH3ySSUvvX/7ob1dCaLDyeuFL7+Ejz5SOiF9ffopPPEEvPhi/3ao1b3XePjAZVqaMm9hypRj160Pp6lMxffXZtY/WIqMzH/91242bmzB4wmy+OJ1AHz00SH++McSAO6+awfF29poa/Nxxbc3AfDWWwd55ukyAH76k23s29dBfb2ba6/9CoAXX6hi+fNKIHjjjV9TXd1FWVknP/rhVgD++lw5r71WQ1paGsuXR3PhhVcyYcJZ/O+jTUycOJGirYmsX9dFRUUFDz3oJi42FZ8vk/vu3QXAww/v49NPGwG44PwvCAZl1q5t4sEH9wAc1zV1dSXR0uLmz39Wfi5ffQWfrJLR6XQndE0AV1+9maYmD7t2tXP7z4oBeOyxA7z3Xj0ASy/bQGenn6+/sg16TT5fgB07/Kik73Lvvfcih77L7t0+3O7AqHxOFouFp5/ez2OPfc3KlSu54fpiiopKaW+PHvCa/vQnFa2tXTQ3mfli7QSioqJY+7mZLV+7T+hzOt5ruuH6rWRmZrJuncy77yoj+C++YGTNml0n/Dn5/QP/slm8WPn/npHRv6MvCIIgHJ36oYceGus2nLR+97vf6YD777//fnQ6Xc/2p556iuzsbGJiYtDr9ej1eiRJoqmpibvuumvwN5QkJc/mrLP6R6ihEKxcqQzda7VH5KImJysjW3PnKi/vO0ewqEhJWUlJgbPP7t3ucsHbbyupLDk5MHFi774vv1SC65Ur4Zxz+lf0ef11JVBzOCA7u3d7UxO8/76y6Fdycv9ylCtWwKuvKncT+t5F8HrhT39SUm6Sk5V2dLPblfeSZeUPe9/3y86G73xn/P7BzyCD5A83kJ4Sy+Q551AavYGzJ03FZNSRNzmatPRIzGYtEycaiYvTkZYWSVZWFFFRGiZPNpGaasBiiWDixCgslggyMiLJyookKkpD3mQTKRMMxMXpyJ4YRUxMBNlZUWRmRmI0apk02URysp6EBB3Z2VFER2uZmG3s3T/JRGZmDG1tVcyalU5h4RRSUiQkVRsLFsxk5qwJJCToSE7Wk539TZsmmcjIiMRk0pKbayI+XseECYaeNg92TV5vHeXle5k61UJSUgQ+n53Wln3Mmz+Na767aESvKSlJT1KS0maTSUtujpGMzEiMJi25k0wDXlO7o4JDhypxe6rYu3cLwVArRqOHjIwkrr56Ydg/J73eQ3HxF0yfnsi0aROJiuqiunobCxfO4dxzJx9xTUVFH3LWWZMJhbrQG9oxmWQm5sThcteyZMkFw/6cjveaWpp3UTgzi1iLhrR0HSaTmvQ0HR5vPfPnF57Q53TzzVuJjY0gO7t/fdqICCUV7/zz+/8OEARBOB35zjETiwAAIABJREFUfD4effRRgEcfeugh39GOlY62NLpwdJIkmYCOjo4OTH1WRMrNzWXmzJnEx8ej0Wh6UnZ27NhBeXn50E+0cqUSPcfGws9/rkTDQxAKKQF93z+QDocSrNvtsHSpMgm12/vvK6eUJHjuuf6T237zG2hpgXnz4JZbereXlsJjjynP775bGUXv9uKLSqfBYlHuQHSTZeVyvF6lss0VV/Tua2+HQ4eUjklMzMmbe9vpsvNQ6aP8acaj1LsPkaRLRKfWHfuFYTYaudR1dXU899xzVFdX4/F40Ov1ZGVlcdttt42LSaEPP/wwBw8eZMKECT1zBA4dOkR6ejq/+c1vwn7+VatW0dzczP79+2lvbycmJoYpU6aQmJjIpX1LPX3j/vvvZ+7cuUdMCi4qKur+hR/29h6+cq7dbsdutw/Y3qGw2bzExipVBaTj+M8eColJt4IgnH46Ozu712qJlmW582jHihz8MJBlmVAoRHR0NDqdDq/XS3NzM8PqTMkydOfk6nQwjFU2VaojR7/MZrj11oGPnzlT2e92H/lHNDFRuUNweKERrVapYa1S9b+DAMrdAb//yLx4SYIHH4ToaOX1fcXEjF0e/Yn66qtSntr8M67Ymkxqm5bJVz/A819Xs8r0LFm+GeR5FxIigIaxLnGo1O3v7IDaGi9QMaLvbm+3s2/fBNrbIwn4A2i0GlyuGF55pRFLjHdEzzUcn3wSICVlFsi9/5jb7Mns3dtAQvzI/iwGsm5dPSFZJjZ2DnqdnvZ2DytXtqGS6qmtOfL8jY3TeeWVRpKSZPQ6PR6vh6amJszm6fzt/8LfXnt7AhUVFcTFOjEY9LjdHmxtNnJycgZs71BMmx5NTo6RH9y4hU8+PQ+1evAgf9cueO89+MUvjn+laUEQhNONGME/AYON4BcUFJCbm0t0dDQqlYpQKERHRwfl5eVYrdahn0iWYcMGZUZpZuaItV8Ij7vuPY/Xzi5HCoJ/00xc715CqL73rotmWhkRl23E9ecfjWErBWF8efbZWZx3fiJ5eYNH7VVV8Mc/Kr8Sc3OVNUNO1rt7giAIQyVG8MeY2WwmMjISSZLwer1EREQQGRnZU5ZwyCRp4BWZ3G4wGE6sscKIs+RcTubBx6lJV6E5bwfR5+wg6sBcLJuXoWvJAOYR2jgDaY6WQ9f+iaT3b0Pjig5LW3x+H26Xm2AwgFqtwRBpIEI7OncO6urq0Ol0BINBgqEgapUatVqN1+sdFyk6dXV1BENBtBptTzlbf8CPWqUelfY1NBzC7w+g1+t7Uvk8Hg9arYaUlAkDvmYsP0+f34ez04lKpcLr9aLRaAiFQkRERBAMBjGajMNqi8Php7zcyS9+sZMvvriAl16qJhSUufmH2Uccm5WlzL3ZsUNZ6VYE94IgCAM7bUbwJUl6DvgWYAY6gbeB+2RZ9n2zXwM8BtyIUl1oBfAzWZYHzSUYbAT/xhtvpLy8nPT09J4yhAcPHiQ3N5eXX355ZC5o7154/nml6k7fhHdhXGi79ae8d/Ad/vYdPVUJvf/HLk1ewi/yfs6MGGXSQ0nHfvKjp/Bu3XuYtdFcmLRoxNrQt6xh3zr44Sxr2Nedd94JcMSdLICnn3467Oc/lmeeeYadO3diMpl62tfZ2UlhYSF33HFH2M//yCOPYDAYcDqduFwuIiMjMRqNuN1uHnjggbCff6i6c/D37NnTs46C0+nE6XQyceLEYefiezxBzjt3LTt2tJOebuCNN88iOdlAenrkgMcHAso8oJSUE70iQRCEk8tQRvBPp2lKzwJTZFmOBgqBGcCv+uz/L+A8YDowCZgGDHvmWlNTE7t37+brr79m9+7dNDU1Db/lh3M64W9/U2bO/t//KV+FcSX6F/ewZGcE//5tkOWa25mhVWqVr2r8hIvXX8r3v76RbW3F5EcrnbOJUdlMMEygK+DitZo3RqQNVqsVg8HAnj17+OSTT9izZw8GgwGr1UpdXR2rVq3itddeY9WqVdTV1Y3IOfsKBoO43W7i4uLIyMggLi4Ot9tNMBgc8XMNV3JyMjqdDq1Wi06nI3mIE9hPhEqlory8HLvdjt/vx263U15ePuIrCo8Uu92O2WzuWWQLICoqCqfTidlsHvaCW3q9mjfeXIDFouXgQTcP/nYviYk67v3lThwO/xHHazRHBvenyTiVIAjCcRuff0nCQJblfbIsd/XdBOT2+f7HwMOyLB+SZbkFeAj4oSRJQ/4Z7dq1i8zMTM477zyWLFnCeeedR2ZmJrt27Tqha+hhNCrF8LVa5WvkwCNdwtjZHdTTOPNsVEjk3f8h/3xU4tWZ/yTLr6ySuqZpLZdtXMbVX17LEx99TIJrMpmaXP711h5ava1YrQ5e3bARX8jH++/X09zsoaPDz5tv1gKwY7ud4m1tAKxYcRC73YfN5uXdd5VAfesWGxvWV7Nnzx42rA/g86moq3PyzjtVFBcX8z+/f5OXXvqcNWvW8NhjRSxf/hJbtlT01Ilft66ZsjJlcOCfyysJhWQqKpysXat0VD/7rJHq6i4CgRAv/KsKgP37O9i4sQWAlSsb8HgiiY1NZsOGIJWVlezf30UgkI1Goxn2Ne3a1Q7A66/X4nQGaGhw89FHhwDYvKmVffuUOwQvvVSN1xukttY16DU52juIi8snMXERl1xyCfHxF5CQkE+7vWPQa6qvd+NyBXj1VaXOu9Xq4OuvbABDvqa6ujpqarS0tZlQq9WUlyXS3u6htLR52Nc0nM/peK/JajXicDiw283s3Nnxzft3IElGamvbKCkxDftzys6O4sWX5iNJsGZNE48+WsLsObFERBz716/dDo8/DgcPHvNQQRCE08a4C/AlSbpfkqS3JUmqlCRJliSp+ijHqiRJuluSpP2SJHkkSTooSdJjkiRFDXL8ryVJ6gSagTOAZ77ZHgOkAzv6HL4d6N4+JG63mylTppCUlERMTAxJSUlMmTLl2AtdDcXs2fCHPyglb4Rxx/nft/KluQlUKtS1ZXRccyvnJF7I/PWP8O6Ct5iumQPAhpaNPBr8CTfvvZb1zRup3q7lzsm309zs4dXWf3LQdZBdJU20d7rxeIJYrQ4ADjW4qatX/j1ZrQ48niBdXcGewOlgnZvKKjter5f2djMJCSkYjfE0NKjYtGkTe/Y0AWZSU1PpcMSyZ88+3n13FaUHlGCxqrILW6tSYreoSAlQ7W0+KsqdAJSXObHbfQSDMtu3KyO3tlYfVZVKH/rAgQ58PhWgps1mxmQyEQwa6ezUIcsy+/Z10NUVHPI1NTZ6ANi1qx2fL4TTGaCkRNlfXdNFS4uyf8d2O8GgjMPhH/SazDEx2GxeqqpcyLJMZaUbm81LtDlm0Gvq6PATCMjs3KHsb272UFOr7B/qNTU0NNDRoaGpyUtbWxsH61R4vQHq623DvqbhfE7He00uVzw1NTWAheLiKlavXs3az8soL69hW5EVrzd+2J/TM0+XkZ4eyX89MBWAPzxSQnS0lvp6d09naSBeLzz6qFKm969/ha6uQQ8VBEE4rYy7HHxJkmSgDSXAng10yLKcNcixTwF3Av8GVgH5wB3ARuAiWZYHXB5RkqR8lFz752RZrpMkKR2oBRJkWW795hgt4AMKZFneM8j7DJiDP2XKFC677DKio6MJhUI9y8qvXLmS/fv3D/VHcvxkWcw6GydC7e1IZjNtd9xGw+vLMRfMIkLSEff359FMUtJ1vrZt5fEDT7KuZX3P62ZbZnFP3l1cmLiopx74qzWvU+Gs5LfTHkCW5eOqEw5KDnxCQgIZGRk9dd5ra2t5++23Oeecc0hJSUGtVhMMBmloaODQoUP8+9//HrGfwX333UcwGGTq1KkYjUacTif79u1DrVbzpz/9acTOM1yrVq3CarWya9eunhz4GTNmUFBQcMJ13Y/HxRdfjF6vJyoqqmcOQFdXFx6Ph88++yzs5x+Ouro61q5dy+eff45GoyEyMpKoqChiY2P5/ve/P+y5HStXNnDGGWZSUgx8e9lGVq9uIiZGy4oVCykv7+RHt0wc9LWrV8M77yiLYV1zzdiuYi0IghBOJ3sVnRxZlisBJEnaAwy4fqEkSdNQgvl3ZVm+qs/2KuBp4FrgtYFeK8tyiSRJO4EXgItQJt2CMgG39Zvn3VXYj/oDHIjBYKCyspLk5GS0Wi1+v5/GxkYM4ax4Y7fD8uWwaBHMmhW+8wjHRRUTg3f9egK5qegMBjyle/noJ7NJ1u/lOygB/plx83hrwWtst+/g8QNPsrppDcX27Xz/6x8ww3wGd+f9nCXJi7k+8zr8IT92n51rvryOT879CI3q2P91o6KilBH6jg6ampowGAykpqYSCASIiYnBZDL1BPhut5s9ewbsxw5beno6NpuNgwcP9nR0TSYTccNYyyEcmpqa+Pzzz5k/fz6pqanU19fz+eefk5iYOCrn9/l8TJgwgdTU1J7Pob6+nurq6lE5/3CkpaWhUqkoLCwkJyenp4hARUUF69at44YbbhjW+152WQouV4AnnjjAv16Yx1lnfk5trYtf/nIn6zcs4rPPGpk9O7ZnMay+Lr4YMjJErQFBEIS+xl2KTndwfxyuAyTgycO2/wNwAcf6S6NGmUyLLMvtwEGUybfdZgLd24ckKiqKiooKmpqaaG9vp6mpiYqKCqKiBswcOnGhkJKEWlYGL70ENlt4ziMMiSo+nuiZC6i5ejoA533RzHkbHJRvWcl/FvdWaZllmckrZ77I5+d9ytKUywDY5djNzVtvYdG6xXxQ/yFqSY0lwsKL85ejUWn48/7H2W7fMeB5u+Xn59Pc3ExaWhqzZs0iLS2N5uZmNBoNbre7Z+E1WZZxu90jPrkzNze357wWi6WnHbm5ucd+8SjYuHEj8+bNQ5ZlSktLkWWZefPmsXHjxlE5fygUwuv10tHRgdPppKOjA6/XSyg04I3HcaOkpITs7GyMRiOSJGE0GsnOzqakpOSE3lerVSFJEmZzBG+8cRYRESp27mzn53duZ9s2OwcPDlxMQJJEcC8IgnC4cRfgD8FcIARs7btRlmUPsPOb/QBIkmSUJOmHkiTFSIoC4L+BT/u89HngAUmSJkiSlIAyyfZfg6X5HI3b7Uav11NSUkJRURElJSXo9fqRzcHvS6WCq765iTF3rrI0rDDmtNOmoV9wDrGXXo1eHYWlzI569z4m6JL4Wa6yjPAj+x6lpkvJMS6Imc6/5v2D9Rd8zndSv42ExL6OEn687VbOXbuIFQffJUmXBMD5ieeSFZVJhbOCFQffHfD8ixYtIjo6mpqaGnbv3k1NTQ3R0dHk5eXR0NBAY2MjNpuNxsZGGhoayBzhRdQKCgpwu91Mnz6dJUuWMH36dNxuNwUFBSN6nuGy2WwkJyej0WjQ6XRoNBqSk5OxjVIH2eVyEQgEqKysZM+ePVRWVhIIBHCN86pYoVDoiE7IQNuGSqtVcc89edTUdGFv9/Hkk8r8ohdeqCY5SU9BgZmtW4792YRCykq3YSgMJQiCcNI4mQP8CUDrIHXq64F4SZK67+fKwPeBSpSUm/eBj4G7+rzmD8AmYC9QDuxDKZ05ZHa7HY1GQ2FhIQsWLKCwsBCNRjPsMnLHpbAQHngArr9eqa4jjAuB/fvJfmU70Vdfh4SE/8sv0U/OJ9+jTEg8I+YMEvUJ7LTv4rPGNQDkR0/hb3OeY/OidXw3/WrUkppSZxm3bb+DhWvP4/WaNymMmUFsRCyBUBC1pAZgXfMG/KHesoJpaWksW7aMvLw8kpOTycvLY9myZXzrW98iISGB1tZWKioqaG1tJSEhgUWLRq4Gf/f5Fy5ciN1uZ8eOHdjt9lGrwX88JEli9+7dREVFkZKSQlRUFLt37z7uOQ4nyu1209jYyKRJkzjnnHOYNGkSjY2NRx0IGI3ypseSn59PaWkpDoeDUCiEw+GgtLSU/Pz8EXl/R7uf+jo3t/w4mxtvVDqdd965nc/XNPHn/3eAYHDweWOyDM8+C6tWiUm3giCc3sbdJNu+unPwB5pkK0lSBaCVZTljgH0voUyitXyTfjPS7foJ8BOUDtLMwyfZZmZmMmfOHJKSknomzzU1NbFt27ZvqlCMEo8HmppghEdmhaHzV1fTdu65yIEA+ssvR52ZianPYkbb7Tto9bZycdJFfNTwMUuSL0GrUjpqVV3VPFP2F96ofYuAHAAgIzKdOyb9jGvTv4tOrcMX8nHH9rt4ovAxnAEnloiYntcfrq6ujg8++ACfz9eTGx8REcGyZcvGTfA9Gm688Ubq6+uZNm0aMTExtLe3s3fvXlJTU0duQbqjmD17NtnZ2RgMBoLBIGq1GrfbTVWVUsr0cGO9cFnfdrz44ovU19fj8XjQ6/WkpqZy0003jWg71qxpYtq0aL51+UZ273aQlRXJ11suRqOR8PtDxMXpBnzdqlXKCP6ECXD77TBOpnwIgiCcsJN9ku3xcgGDzYbT9zlmxMmy/Hfg791VdA7fbzAYMBqNmM1m9Ho9Ho+Hrq6u8E6yPbKR8PLLypru11yjlJgQFXbGRKizk/arrkJ35ZW433oTz55dJD33XL9jZlmUdASn38m65g1clnIpFc4KknRJZEdl8Xjhn/nF5Lt4puwvvFr7OrWug9y769c8ceApbp90G9dnXsff5ijv+XTZM2REZvD9zGsJhAJHTMjtHtm3Wq3Y7XYsFgsFBQWnVXAPoNPpSElJYd++ffh8PiIiIkhJSUGnGzhwHGnx8fHExcVhMBiQJKlnLkRn58C/s61WK5mZmVgsFoCer1arddQ/u7i4OOLj43uqOoVjoOjLL1tJStLz5lsLOHP+GqqrXfzw5i2cc04CUVEa/vO2gedyLFkCERFw9tkwSh+lIAjCuHMyB/iHgKmSJOkGSNNJRUnf8Y1Bu4iOju7Jre0OHDQaTXeva3TU18P27UpCqtWqBPjCmFCZTMS8s4LN6q+Y8O8Xiaguw/n44wQPHCBm+fJ+xxq1Rh4rVEpIvlP3HgXm6SxOvgiH30FaZCp/nPEH7pp8B38p/ysvVb/KIU8D/2X9b54sfYbbcn/KTVk/4Nf59yHLMlbHHn6392FWLDhyZdy0tLTTLqA/nCzLzJo1i4yM3puAtbW17Nu3b1TOX1hYiNPpRKvV9gTKfr+fwsLCAY+32+1kZWX122Y2m6msPN66BCeue/TeYDCQnJxMVlYWcXFx2O32Ee9o/Pa30wiFZDZtauVfL8zjyu9sZtWqRubOi+WX907B4fBjNh95l0qS4MILR6wZgiAIJ6WTOQe/CKX98/pulCRJj1INZ9tYNAqUkbWqqirUajVxcXGo1Wqqqqp6RtxGRVoa3HsvTJwIP/qRGL0fY5rMLNQrPqHyO3n4CeB8920ib7/9qK+5b8o9XJpyCdb2Pfys+OcA+EN+UgwpPFzwe7Zd/DU/y/1PItWRNHubeWjv/zDns/k8VfoMzoCTAvN0/jHnr8iyzH8U3Uqta/SX+hwPOeODyc3NpaWlhba2NkKhEG1tbbS0tIxalZ+rr74ap9NJS0sLzc3NtLS04HQ6ufrqqwc83mKx4HA4+m1zOByj9nulO0UIlAnURqORffv2YbPZMJvNYZljZLf7+Otfy7nkkmR+9WulVM7//H4fq1Y1sOiCL2hq8hzX++zfD4cOjXjzBEEQxq2TOcB/E2Xy7F2Hbf8PIBJ4NZwnlyTpWyj19o9gMBiwWCwEg0E6OzsJBoNYLJbRTdEBJbi/7z4wDriUgDCKJEkiO2IynT+4BIPWhFRbT8BqxfXKK8d8baFlBq+c+QKyLLN047ep7lLmcSTqE3hw2m8ovngLd0++E5PGhM3XxiMl/8vsz87kz/sfp+FQA5988gnZFZlY1+3mrb0r+LRxdbgvF+gNCC0WCzNnzsRisbB58+ZxE+QXFhZSUFBAc3Mz27Zto7m5mYKCgkFH0EdaSkoK6enpqNVqZFlGrVaTnp5OSkrKgMcXFBRQU1OD3W4nFApht9upqakZtapE3SlCSUlJOJ1OjEYjiYmJVFdXh62jERen4/XXz8LtDnLzTdksWpSILMMPb97K62+cSVKSnkDg6NV71qyBJ59UJt2O8wJFY2Y8d8QFQRiecTfJVpKkG4HuWaF3ABHAY998XyPL8st9jn0GuB1lJduVKCvZ3glsBhYNp8TlENs64Eq2ixcvJj09ncTExJ6Frpqbmzl48CCrV49OcDWo7duhvFzJyxej+qMuZLfT+cgjuF97DVVaGhHz52N+/HGk46x85PA7MGvNvFT9Cl2BLv4z96f99v2j8p/8veJ52v3K3HK9rGeZcSnXJ38fyQkbazcx9YypXDJ5MR8c+ogrUpehksLTz1+1ahUWi6Vf4Ge327Hb7aOyUuyxhGPSal1d3XHPbXjllVeoqanB2KcD7nQ6yczMHHTBqKG8/0h77bXXmDlzJs3NzezatYv09HSMRiNWq5XU1NSwTvZ9881aqqq6uOWWicyf9xl1dW5mz7bw4EPTeO3VWl56ef6gr/34Y/jgA6W42O23i5r5hxsvk7cFQTi2oUyyHY8j+LcA//PNIxFlRdnu72857Ni7gF8C04C/oKxe+wxwebiD+6ORJImUlBRiYmLQ6XTExMSQkpIyauX3BlVcDH//O3z+OXz00di25TTV8cADaKZPR9JqCdXVEXHWWchDGFY0a80AfCf12yxLvRxXwM1txXcQCAUwa838Mu9uii/+mt/k348JIx7Jw1td73Bt5fW87n+L6anTUVVJtPns7GzfhYREhbOiX3nNkWK32zGbzf3bH6ZUjuEY6TKeQ71jUVRURCAQwO/39zwCgQBFRUUncllhY7FYKCsro6ysDJvNxsaNG1mzZg1utzvsweD3vpfBr3+dj8sV4NVXz0SrlSgutvP+e/U88WThUSf5XnaZMgXpV78Swf1A+k7eVqlUWCwWMjMzsVqtY900QRBOwLgL8GVZPl+WZWmQx/mHHRuUZfkxWZbzZFnWybKcKsvyL2RZdo5R8wElcGhoaECtVpOYmIharaahoWHsR0NyciA2ViktMU5WEz3dmP/yF6JuvhnDjTciI9P5+GO0XnABgaqqIb2PSWsi1ZCKWlJxVdp30Kg0vHVwBXsd+zBpTdw5+XZ+03U/tyXcSqw6Frfs4fW2N/mJ7TaWd71AUA7w++kPIkkST5U+y6723fhCvhEN9Mc6Z3y0DTVQqq+vx+v1YjQasVgsGI1GvF4v9fX1Ax4/1ilP8fHxbNq0Cb1ez9lnn82sWbOQZZnFixeP2u+2e+7ZiSFSw//7f0oa1fLlVXz8cQO3/rSYzZtaB3yNJMF110F6+qg08aQz3jvigiAMz8lcRWdMfZODf+VA+y644AK2bt3Kjh078Hq96HQ64uLimDdv3kCHj56YGLjrLmX1l+zssW3LaUqSJDrefo0DUinROg+GQzXEPfJnNMP8PHRqHRcmKQtUGdQGDGoDrV4bRW3bSLYkEdcaS1JzPJukL9lq2YZD3cEG7SbmrlnA9zOu5Y5JP+PpWU8A8H79B2xo2dRTxedEFRQU9EzKPPzW/3jQNzUhKysLh8PB5s2bhz0abbfb0ev17Nmzh87OTkwmEzk5OYMGSqFQiPb2dpKSktBoNHi9Xtrb2wddEdZqtWIwGPq9f2Ji4qiVyWxtbWXGjBlUVlaya9cuoqOjmTFjBq2tAwfW4fD22wuQJIkJE/R89ZWNN96o5fafFfPKq2cyb37scb9PZye43ZA4WKHl00h3R7xvx/tU7ogLwuli3I3gnyxkWf4QJd//CHl5eQSDQSZNmsScOXOYNGkSwWCQvLy8UW7lABITjwzuq6rAduwl4IWREbngXHb/tIDSH5yBHz9dzz5L5x/+gHfTphN6329NWMpEYzat3laquqqIj4/n3zvfI1Ifyc0ZP+AB1684q3weSepEfCEfL1S/xPw1C7l7xy+p6qrm26nLeLjgd7gCbi7bsAxXYPAVVY/HeF/JdqRTEwKBAFu2bCEuLo6CggLi4uLYsmULgUBgwOMnTJiASqXqKc1ZW1uLSqViwoQJAx5fXl5OVVVVv/evqqqivLx8WO0dqsrKSrq6upgxYwZLlixhxowZdHV1jWqZTkmSqK938+1lm/jLc7OYNi0ajyfEr+7bRWWFkx/evPWoK90C1NTAI48oK956jq8IzyltrCdvC4IQHmIEPwxaW1uZN28e+/fvp6mpiZiYGObNmzeqI13HraMDnnsOAgG4+WaYMWOsW3TK06SmMbvpTNontaLXVxNqaEB2u9GMUNrUlOg8pkTnsXLlSmry69hh3cH6rRuIM8Vy5eQriFXF4ixw8WTZ01R1VfNq7eu8cfAtrky9grsn30muKZenZj5OpMbA8soXmGaeyplxw7v7NJ7r7dvtdkwmE8XFxT1VYTIyMoadmqBSqdDpdKjVaiRJQq1Wo9PpUKkGHkeZN28eW7ZswWaz9awIGxUVxfz5A08Y7ejoQK1Ws2HDhp4R/KSkJILB4LDaO1Tt7e1kZmb2TAo2Go1ERkaO7urcQGqqgQ0bF6FSSbzw4nwWXfAFFRVd/Pr+3dx2Wy5q9dHnOlmt0P0Rf/klLFo0Co0ex7o74larlcrKSiwWy7jqiAuCMDzHDPAlSfrtCZ7jJVmWq0/wPU4qlZWV1NTU9OTYdnR0EAwGcTrHdGrAwHbuVIJ8gPHYvlPUdFcu7opYuOlHdP3tb3g//hjDjTcSstnQTps2IueoqKjgos7z0cfo2ZG5iyqpBku9hbaONu6+7G6uSb+K9+o/4MnSpyl1lvF23TusqHuXb6d+i7snK3X3p5unMcGQQou3lfXNG7g6fcCstEGNZdWXYwmFQhQVFZGTk0NSUhJdXV0UFRWRkJAwrPdTqVTMnTuX2tpaGhsbMRqNPd8PJD8/ny1btpCVldWz4nVLSwv5+fkDHt/R0UFtbS3Tp08nPz+flpYWdu3a1W+hrnCKjo7GZrMRFRWFyWSis7MTm802ugskZwt+AAAgAElEQVT4fUOrVfHcX8qxtXl5fvlcvvfdr/j4owbOOisetzuIWi2xdOnAd0KWLoWDB2HyZLjgglFu+Dg1njvigiAMz/GM4D+EUm9+OCVgZGATUD2M1560ioqKOHToELGxseh0OoLBIDt37qS5uXmsm3akc89V6uQfOAALFox1a04bmuxsTA8+iL+qCtdLLxFsbKTriSeImDdvxAL8+vp6dDod6enpfEu3FI/XgzW4hxfiX+Gqg1exx7oHX7uH+2PupXliKy+2vsy+jhLeq/+A9+o/YGnKZfxi8s9Jj0yjwllBR0DpCG5rK6YwZgYa1dF/fYx0jvtIC4VCeL1egsEgsiwTDAbxer2D5sAfi8ViQaPRMHv27J5t3R2bgQz1Tl9rays5OTm43W7KysrQ6XTk5OTQ0NAwrPYOVW5ubk/J3507d9LZ2YlWqyU6Opq6urpR/0xv+XE2KpXyZ+kX9+Tx+GMH+O1/W3niiULOPCt+0NdJEtx6q6gSLAjCqe14c/DvBrKH+JjN8DoFJ729e/ei1WrJzMxk2rRpZGZmotVq2bt371g3bWCzZillJvr+xQuFYJRu/Z+uQu3ttC1ZguEHP0BGxvf11xiuuw7ZPzKVbFwuFxqNpidglUMy0ZhYsG4eX335FS+qX4U8ibjYOCL36Xkp95+8OO+fzDCfAcDHDSu5cP0l3PD1TTj8Hfwo+2ZCcohnyp7D7m/H4XcQCA2cXw7jv/yeRqNh/vz52Gw2rFYrNpuN+fPno9EML3OxoKCAnTt3sm7dOr744gvWrVvHzp07B81lLi8vp729nXnz5nHVVVcxb9482tvbB82p93q9uFwu4uLiyMvLIy4uDpfLhdfrHVZ7h6qgoIC6ujoOHjxIQ0MD0dHRJCYmMnPmzDFZwEynU6PRSFy6ZAM33JDBuecmEArBww+XEBsbwV+eLcPnG7izdnhwL8siH18QhFPL8Qb4rbIs1wzlAQx8X/oUcbSVbF0uF3l5eajVaux2O2q1mry8PFwnyzKKgQD84x/wz38qgb4QFqqYGAK7V/Hx7Qk4In0EmxrpePBB2q66akTePyoqitTUVDo6Oqiurqajo4PU1FQCXQEyMzP5edrtTIucyiF9AxviNrF3z14uTbmE1eet5PUzX2aORRmJXt20hiUbLue7X36frW3beHH+chJ08bxY/Qp/r3wegNAAy06M9/J7FouF4uJiVq9ezSeffMLq1aspLi4+oeohoVAIt9uNy+XC7XYf9W5AR0cHcXFxmM1mVCoVZrOZuLg4OrpT5g6j0+kwm814PB4OHjyIx+PBbDaj0+mG3d6hUqlUOByOno6bRqMhJiZmzDpukiTxxptnMX16DC+/Mp+UFD0tLV6u//5X2Nv9uFyDd0C7uVzw/PPw1FPi150gCKeO4wnwzwI+HcZ7t3/z2vG5assJOloVHZ1Oh81m49ChQz0Pm802qn+IT8gHHygr3m7bBitXjnVrTmn+CInEXz9P+Xen4sePZ9UqYv7xjxF57/z8fCoqKnA6nYRCIZxOJxUVFcTExCjBpCYWvUpPRkQ6F8VdiN1u59GSP2L327kwaREfn/M+Kxa8wYK4swBY17KeZZu+w3c2X83Glk3ckXsbP5n4Y6q7ali26cojFhsa73Xwm5qaeOedd3C5XERFReFyuXjnnXdoamoa1vtZrVZmzZrFpZdeytKlS7n00kuZNWvWoIFvTEwMLpcLp9OJLMs4nU5cLhcxMTEDHp+ZmUlFRUW/aicVFRVkZmYOePxIs1qtFBYWkpmZycSJE/F6vRw6dIgPP/wQt9s9Zh23+HgdFRVObr5pK6++eiYajcTWrXZaW7wEAjIbNrQc9fVr1ii/6iorYawXGhcEQRgpx7wXLcvyluG8sSzLQWBYrz3Zpaamsn//flJTU4mIiMDtdlNfX09qaupYN+34LFkCe/dCRIQoMRFmmVIWu6+5AMPUGWjf/h1yayvuV/4/e+cd3uR9/e370bCGLVle8sYTD8DMmBVmCCQkJC1ts3eaNOvXZrVpOpLu7E2zmiZpGpqkISkNIS8JCeA4LGPA7GG8t2XZsmXJGpb0vH8IqTbYgIWxDej25cu2nqEj2ZbOc77nfD7L8bS2ovnLX07L/Tg3N5ctW7bgdruRy+V0d3fT0dFBdnZ2r0RbI9UQ74ijVddKgsqNVqZlW1sxMkHGnJjZzImZzRbjVp4vfYnClu/YZNzCJuMW8iMv4OGsB5ivn8ffL3gDQRD49Z7HuDXtZrI0o0e8Dv5HH31ETEwMOTk5hIaGYrVaOXToEB999BG33nrrgM83UB389PR0RFGkvb3dP5SblJTU7+88NTWVAwcOUFRURHd3N3K5nPj4eFJTUwccayCYTCZSU1Pp7u5mw4YNSKVSRFHEZrOxatUqJk2aNCRx9EV6eih/e+sCUlJCeeqp8fz857t57bUy9LFKQGTOnP4HpxcvhpISiI6GEfKnGSRIkCCnTVAm8wyh1WqJjY1FrVbT1dVFZ2fncId06qjVXkOskBCv622QM4aAwBWLnsNVsouua6/H9vbbWN99l9B77vH2C0ilAZ/baDRyxRVX0N7e7peB1Ol0tLW1+aUNj028fYOSHc4OpBIZHtHDty3fMS9mDp/M/IjtbTt4ofQlvmleT3Hbdq7deiOTdBN5MOt+LlHGsiThMkapkykx7aJR2jSi5ffq6+u55JJLkMlkWCwW5HI5WVlZfPVVIAuW/9PBz87OJjU1lc7OToqKivrVtc/Ly+P999+nubnZL5MZGxvLTTfd1Of+VVVVuFwuFi1aRExMDC0tLZSUlFBVVRVQvAPFtyLjW5FMTk5GLpcjk8mor68nMvLUjaYGG0EQjib326mtrWfqVA/btkl4+qkDbNp8Mfv2dZCZGYZSefz/k1wODz8MoaHBwdsgQYKcOwyK0ZUgCDJBEH4oCMKdgiDEDcY5z2aam5sZM2aMfxnd4/EwZsyYgJf+hwWN5vjkvrjY64IbZFARELCvXo1i+nQEjQaxrQ1BEHDt3Xtc28tAMJlMqFQqurq6sFqtdHV1oVKpkEgkJzWgWhh3MRfp52GwG/hP3UoAqq01TNRN4IPp7/P13DUsjrsUgJL2Xdy87TYuKlhEq7OVEEkIEkFCiEROYmIikklSrrnuGhYvXjxiknvwJuRdXV2o1Wqio6P9F+P9GVOdjIHq4Dc2NlJXV4dOpyM1NRWdTkddXV2/qjilpaXMmDEDjUaD2WxGo9EwY8YMSktLA4p3oPgMkfbv309YWBhms5nW1lZsNhtarZbdu3cPSRz94X0uy7niimheenkso1Jk2GwefviDQpa9coQ9u9v7PTYsLJjcBwkS5NxiwAm+IAjPCIJQ3ONnAfgG+Bh4E9grCELG4IV49uF0Ounu7kYikfg/u7u7cTqdwx1a4JSUeCfRnnoKzqYLlbMEzWOPobz8clS334aIiOXVVzH/6ld46usDPqfL5eKrr76irq4Ok8lEXV0dX331FS6Xi6SkJBYvXsz1119/wsQ7ThXHsskvIQgCzx5+nu2mnTg9TnI02bw37W02zPua7yVcgYDAfvMBflx8F/M2XEy5pYL5+nl0ujpZ3eCd42iwNZxQdWeo0el0NDU10dXVhcfjoauri6ampn574E+GRCIhJSWFLVu28Mknn7BlyxZSUlL6TfDXrl3L2LFjiYqKQiKREBUVxdixY1nbTyO4w+FAqVT2uk2pVA6Zik5SUhLp6elUVlbicrnQ6/WkpqaSkJBAd3f3kDnq9od3RiCF1NQo/vznRh5/LBmVSqCy0o6p3Un+1Ehqa08udCCKXgOsnTuHIOggQYIEOUME0qJzKd6E3scVwBzgGWAXsAx4FLjztKMbwRxV0enT9UcikbBv3z5yc3OJiIjAbDZz8ODBft/ozwr27PF+NZuD8plniIrXf09t22bitSJakwnlPfcgSUxEFMWAevFNJhPNzc2kpqYSFhaGzWajubmZ+Ph4iouLWbt2LS0tLcTExLBo0SLy8/NPeL6/Tn4ZURT5uvkbPqv/nFenvMIYbS5v5b/BLzqP8FLpK/yn7r8c6jzM3Tvu49lDz3N/1k95YeKzSAUpL5UuY2HsAhbEXoRH9JxUR/9MM2fOHHbv3k1paSkymcxf0Z8zZ05A53O5XJSWljJ16lS/EdThw4f7bdGpqqrC6XQiCIL/AkMURRoaGvrcX6/Xs23bNhISEvwzFQ0NDej1+oDiHSh1dXVUVFTgdruxH9WU9Hg8uN1urFbrsLch+mYEBEHgmmuiGD9ezR/+mMQjv6jlvyvr+f3v97NhvYGCb+f79fP74oMPoLDQ26mYlgYjZCb8lBjJxnJBggQZWgLJOJOBIz1+vgKoFEXxUVEUPwLeABYMRnAjmROp6KjVaqRSKa2trdTW1tLa2opUKkWtVg9xlIPIzTfDkiVw553QM2E5jRaSIL2pvXEq3/5pGnt/MgEXbqyvvUb7nXfi+PLLgM5XXl6OXq/HZrNhMBiw2Wzo9XqKiopYsWIFKSkpXH755aSkpLBixQqKi08ueCUIAoviFvLCxGdxeVzML1hIu7OdLM1oXpuyjM0LCrl+1LXIBBnl1gp+VvIgM9bN4f2qf/Gncb/n4tgFFLQU8n877w/oMQ0m8+bN47LLLkOj0dDd3Y1Go+Gyyy5j3rx5AZ1voC06nZ2dtLW1ER0dzahRo4iOjqatra3fRDk6OpqmpiY6Ojqw2Wx0dHTQ1NREdHT/pk6Dic/XQC6X43Q6qa2t5ciRI1RWVuJ0OoddJaynatPkyaE0NjjZvt3Ekiu8F5LPPH2IP/5pHG63iM3Wf5FizBjvV5kMRoii6ynhM5aLiIhg0qRJREREDIs/QZAgQUYGgST4IUDPV8f59K7oVwDxpxPU2Y5SqWT06NFYrVZaWlqwWq2MHj36uOX1swpBgCuugHHjet/+73/Dm2/CCDEvOpuZqplPUoWbTHc6Mq0OT3s70qQkFAsXBnS+xsZGFAoFqamp5ObmkpqaikKh4MiRIyQmJtLQ0MDGjRtpaGggMTGx39aQvlBIFcgkMj6avhxdiI43yv/Gf+s/Iz0sjZcmPc/WizdyS+pNhEhCqOmq4eHdjzB93SzeqXyPGZHTeGbCk7g8Lq7dciMm5/BkUXl5eRgMBiQSCWq1GolEgsFg6NeY6mRIJBLy8/P9cqQWi4X8/Px+E3y1Wo3FYsFutyOKIna7HYvF0m8hwOl0snDhQn+cEomEhQsXDlnrn8/XICbGq0jjq9673W4EQSA+fnhf9vPy8ti5cydr1qzhiy++oGTXenTh9Tz//BRmzozC7Ra56cat/P53+/jbm+X9nmfSJLj+enj8cUhPH8IHcJqMdGO5IEGCDC2BrJHXAtOBvwmCMBZIBx7vsV0PWAYhtrMWhUKBwWAgISEBQRAQRRGDwTDsFa5Bp7sbtmzxWkC6XBBgYhTEiwoVN0feT/eoAtz35GB5+mns//43ITNmIISEoJg/f0Dn83g81NfXYzKZ/G0+voFbk8lEcnIyKSkpWCwWamtrA6r0xam8M/VXJixBRKTN2caLh1/mj+N+z7MTnuLBrJ/x1yOvs7z6A+ptDfxq7294qfQV7ht9NzeNupFHc35BREgEK+s+I0YRzayYodMp3LVrF3v27CEhIQGtVovNZmPPnj3s2rUroLYGXzteT8xmc7+6//Hx8YSFhbFv3z6sViuhoaFkZ2djsfT98imRSMjMzGTy5Mm9zj9U+vO+CvmSJUtYvXo1oaGhKJVKLBYL7e3t3HbbbUMSx4mwWCy0tbXhcrmQyWSMSonk67Um7r0vk/JyC83NDjZuNLL267m43SJSad+tOnPnDnHgg4CvRakn4eHhVFRUDE9AQYIEGVYCqeB/BNwiCMJqYDVgBnq6IU0C+i+PnAdER0fjcrmIiooiJSWFqKgoXC7XkC2lDxk2m7fcpVDAjBm9t1ks0HJig5kgxyPTx6K8+mrk06Yh0enwdHRg/+wzxACUXSQSCZWVldhsNqRSKTabjcrKSjweDxqNBq1Wi0QiQavVotFoTkuxJ0GVQKIqEakgZUb0DARB4OPaT/CIIk+M/xPFC7dwT8ZdqKUqmh3NPL7vD+R/M51C40Ys3RbiVfFEKiKxurpYWfdZwHEMhJUrVzJ37lwWLFjAzJkzWbBgAXPnzmXlypUBnS86OpqCggI8Hg9paWl4PB4KCgr6/b+Pi4ujqamJGTNm8KMf/YgZM2bQ1NREXFzfQmS5ublUVlb2MsaqrKwkNzc3oHgHik9FZ/78+Vx22WU0Njaye/duOjo6uO222wLyDhhMCgoKkMvlzJw5k8WLFzNz5kzkcjl19fvIzQ3nXx/MQCoV2LKllV//ag8zZ3yDwWA/pXMbDN46xkhmpBvLBQkSZGgJJMF/EvgHXpdaEbhZFMV2AEEQwoErgXWDFeDZSHh4OGPHjvXbukskEsaOHUt4ePhwhza4aLVw663w7LMwfnzvbR9/DH/4g9caMtinPyDEtjY6f/1r3D+5nm66ca5fjzw/H/cAFXVkMhlJSUl0dXVRX19PV1cXSUlJhIeH09LSQltbGx6Ph7a2NlpaWsjMzDzt2MPl4VwW75XP7HB2IJfIqLfVU9dVxx/GPc72hUXcP/r/CJOFYXS28ucDTzDl62lsMm4iUZWA0WGk3OqtOB40H8ItnrmB7tbWVrq6uli1ahUffvghq1atoquri9bW1oDOZzQaycnJYefOnXz44Yfs3LmTnJwcjEZjn/uHh4cTFhZGW1sblZWVtLW1ERYW1u/rxLx587Db7axfv54VK1awfv167HZ7wDMDAyUpKckvr5qWlsajjz7K559/ztdffz3syT3AwYMHiYyMpLq6mh07dlBdXU1kZCRu9wHGjNFSXNzG478bC8CyZWXc+ZN09PoTt036FHX+/Gf48MOheBSB47sA6+l0XF1dHXDLWZAgQc5uBtyiI4qiA/jx0c9j6cTbf39yLbJzGI1GQ25uLl1dXX4DG7VazcGDB4c7tDPDsa1HjY1QdNTEuKwMAuwhP1+RREUhWf8JH3e+zoK/C2jbjHQ++iii1UrE+++f9PgqqnDholvXTbQsGp1OR0hICE6nk/b2dnQ6HXl5eVRUVFBWVoZWqyUvL2/QjYruzPC+RGwxbmVPx16mRE7G4DDw69xHuTfzbt6qeIe3Kt7G1N3O04ee4/Wyv3FH+u3clXEHAE8dfJbfj3uMWEUsCmkIUiFw06++8Hg8bN++ncmTJ6PT6Whvb2f79u0Br2SUlZXh8XiYPXu2X0WnpqYGaz/eETKZjIsvvpjy8nK/8+306dP71cEHkEqlKJVKZDIZMpkM6WkYoQVCUlISSUlJfrWWwsLCEaPW0tHRQVVVFampqcTGxmKz2aiqqjpaZAGZVOD2u9MpKTHx35X1PPKLPSgUUoq3tfHKssn9nnfbNnA4YOtWuPhiSE4ewgc1AHwXYCPVWC5IkCBDyykl+IIg1AL/PfpZIIp9l9VEUfQAHX1tO9c4kUxmbm4u1dXVhIWFAWCz2WhpaRmypfRhJz4efv5z+OQTuO66oINMANhFO3OWvE3ZdeOY/Ooe7OvXE+O7aOqBBQthhPW6bT3rqaEGQ76BSY2T0Gg0OJ1OwsPD6UzsJKI9gs7OTvR6PTExMQiCQGdnJ3PPQONxcXEx69euo6WlhT/o/8i3kzay6uL/IBEk/Dz7Qe7OuJN3Kv/BG+V/o81p4oXSl3iz/C1uT7uV5yY+TYwimrcr/kGLw8CjuY8ELBnaF1FRUXg8HuRyORKJBLlcjk6nC1jO1mw2k5KS4q/Ah4eHExUV5XcNPpaIiAhUKhWzZ8/23+aTN+yLni0ooaGhWK1WysvLKSgo4MYbbwwo5kCoq6tj1apVOJ1OPB4PBoOByspKrrzyymFNJtVqNWazGZfLhSiKuFwuzGYzarUaQRC4/4EsDAY7V1yRwL59HZQdsfD00wf5179m9HtOQfAuUr7yClx11chN7n34LsCCBAkS5FQr+KuA7wP3Ae2CIHwB/Af4ShTF87JaL4ri54IgFAC3HrstOzubLVu2EBMTg1KpxG6309LSwqJFi4Y8zmFj9Gh49NHeyb0oelV3srNh4sRg4n8CEoQEpO98RFdcB8qPHsLT2orlhRdw7d5N5MqVCFIpVqy8yqtkkMEiFqFFC4AVb8U4ITyBik0VREVFoVAoMCqNFI0rIuKqCCqqKrAcsvhXmBITEwf9MRQXF7NixQrGjx/P1KlTaW5uxrrBwsHog3wV+g0p6lHclHoDPx19H3ek/5j3qt7ntbI3aHG0sKzsVf5e+TY3p97EfRl3ExESQYvDyK3bfsyqWf8ZlGp+eHg4WVlZlJWVUVNTg1qtZtq0aQE7w/qcaMvLy3G73X6ZzP6Ms/Ly8ti0aZM/lo6ODqqrq7nwwr4HjQ8ePEhWVhbV1dXYbDZUKhWRkZFDvjK4fv166uvrCQsL84sItLS0sH79em6++eYhjaUnKSkpdHZ2UldX53/+Y2Ji0Gg0/n1sNjcNDTY+/ngmsy5cR+lhC888c4jUFDU/uiqZSZOOv7jS6eCxx4IvV0GCBDm7OKVSlSiK94mimIxXPedN4ALgU8AoCMJngiDcKghC1BmM86zCaDQydepUHA4H1dXVOBwOpk6d2m8v7jnLse+IJSWwYQO88QasXz88MZ1FxGZMZfLqNlRXXw2A7cMPUf30PsSjJj0b2IANG/vYRwP/M0e6gRu4nduZ6Z5JWFgYnZ2dNDQ0UBlViTxEjgULCo+ChQsXcv3117Nw4UKkUinrB/l3snbtWlJTUzGZTJSUlPhVPtauXctvch/lulHXcNhcytJNVxEmC+W+zLvZvnALfxn3R+KUcdjcdt4sf4v8b2byu/1/xOF2sGzSS0gFKS8cfom9HftOK76YmBi0Wi1Llizh6quvZsmSJWi1Wr8M5EDRaDQ0NzfT3t5OR0cH7e3tNDc390owe9Kzp933/JyopcLXgqLVaklNTUWr1fpbUIaSbdu2YTabqa+vp6qqivr6esxmM9u2bRvSOI4lPT2d+Ph4f0HFbrcTHx9Peg+ty5SUUH75y1wcdjd//JNX8nfFx7V0dbnJyAjr79THvZQFx4qCBAky0hlQD74oituAbcCvBEHIAZbirey/DXgEQdgIrAQ+E0Wx73Xp84CKigrkcjkzZszwL6U3NTUF5cqcTggN9b47Tp8+3NGcFYhOJ4rLLsP2ySd4WlrYte1d3M1rmHnds8yRzsGGjW66ySbbf0yk78MZiTvVTUZGBqGhoXRaOyloL2Dn7p1MmjqpVyvJ6KzRbC/ePqixn8ipVRAEZIKMbG0W/5j6dwB+sv0eHsp6gDszfsxNqTfwYc2/WXbkVeps9bxT+Q/er/oX1466mp+N/j8uiJxCoiqBcks5h8ylXJ6weMDxLVq0iBUrVgAQGxtLc3Mze/bs4aqrrgro8XZ0dNDV1UVUVJTfGbe1tXXQEnC1Wo3RaEQul/vPbzQah9xAr6qqCo1GQ2hoKIIg4HK5RoSTbXR0NBs2bGD8+PG9fp9jfM5VPSgubmPs2HDuuSeD118v5803y1m4MJbtO0w89tiYE7aBGQzw7rvwox9BRsaZfEQDJ+hkGyRIEB+BNZsCoigeEkXxSVEUpwGjgAfxGmA9B1QIgrBTEIRLBynOs4r29nbUarV/CTssLAy1Wk17e/twhza8TJ/ulaO4915vou/D44HVq+EYDfEgoL7pJuTjxqG69VYcOIl45ytsRZvZ3LoaLVqu4iqu5moEjk9IjjVe6rJ08aPkHxFXEderz7xeVc+6tHV0hQ9ut92pOrVGhniHex/KeoDMsAy+NRTyUc3H3JZ2C1sv3siLE58jRZ1Ct9jN+9X/Yvq6Wayo/RST00SX24bNbQNgs3HLgFR38vPzmTNnDhs2bODVV19lw4YNzJkzh/z8/IAe75EjR9Dr9eh0OjQaDTqdDr1ez5EjR/rcv66ujtWrV1NTU4PZbKampobVq1f360cQERHh73lvamrCYDDg8XiGXAaxra0Nt9tNcnIy2dnZJCcn43a7aWtrG9I4jsVoNDJv3jy/PKxEImHevHl9rpzefU8mCxbEcuX3EsnPj8DlErnnnu1oNbITVucdDnjqKaiogHfeGVnSmUEn2yBBgvQk4AS/J6Io1oui+FdRFC/Ga3R1G1AFjDvhgecoWq3WX7nzeDx0dHTQ2tqKVqsd7tCGH7Xa25/fk82b4fPP4be/hYaGvo87j7EuWwZOJ4rYRBQ2EVlsPBdo5vm18WX9LMRFREQgk8mYMmUKc+fOZcqUKchkMtLT0/166t10UxhWSLm9nOZFzTgZPFfUgTq15mizkUlkpIelM16Xh0f08Ncjr/GDpO+zZUEhf538MplhGbhFN/+uXcGF6+bx6pHXGRc+Frvbzhvlb+FwO2l1tJ1Sol9XV4fVauXOO+/kySef5M4778RqtQacEBmNRvR6PWlpaaSmppKWloZer++3Na+goACbzUZ8fDyjR48mPj4em81GQUFBn/t7PB60Wi1NTU0cOXKEpqYmtFotHo8noHgDxeeSCmA/muH63FOHE598Z8+/97S0tH6NwJxOD2+9VcHrb1xAdHQIjY0OvviikU0bWyje1vfFikIBl13m/X7iRBhiEaMTEnSyDRIkSE8CcbI9IUc18f959PO8JDMzk+7ubgwGg385Oy0tDblcPtyhjUx8FU61Gvox+TmfCX3oIQSJBGlMDOJvf8OYd/fSte0WJPffj2LBgn6Py8vL45VXXqG0tNQ/lJmVlcVVV13Fjh07aGxsxCW4CO0KJSQ2hBuSbiCEEP/xVqyIiMep9Jwq8fHx6PV6qqqqOHToEBqNhokTJ2IwGE54XLI6iWR1Eja3DZVUhVKipKitmPzIC/juog183rCaFw+/wsHOQ/yn/r+srP+MJQmX8UjOw6hlKp4+9Cxjw8dwdfKP8IgeJELfiefevUnRzn4AACAASURBVHtRqVTs27fPL1Op1+vZu3dvQG0N0dHRHDlyhObmZiQSCR6PB7PZ3K/R1cGDB8nPz/erbYWFhZGWlkZxcXGf+7e1tbF9+3aio6PRaDQ4HA42b97MBRdcMOBYT4e4uDjsdjslJSU4HA4UCgXh4eH9GnQNFT6jp54rGicyegoJkfDRRzNwOj088eR47r5rO99+28Kyv5bxk5/033uzYAFkZsIxprHDjslkQqlU9vp7zsjIGDKn4yBBgowsBpzgC4Jwskk8EbAB1cDXePvxz6uRpLy8PL+MnM9x0ul0cuWVVw53aCOT226DadO87rc9q4AWi1efbtYs7/Zj9fbPEwSJBGdREc6SEmRx8bibmpBPmHDC5B5g165dlJWVkZOT49d5Ly0tpbm5mSVLlvh7dS+RXkJiYiLj9L0X3IooYiMbySSTq7m635WC/sjNzaWlpYX58+f3knU8VblYlVTFPZl3AVBmKUMUPSSqEkgPTWfD/K9Z0/gVL5R6h20/b/iCzxu+4NK4RTyYdT8TdOPZaSrhhcMvsXz6e32ev6ysjM7OTpRKJRKJBLPZjMFg6Hco9mRkZGRQXFyMIAhIpVJ/20p/LT8ej4etW7dy4MABLBYLYWFhjBnTf/93SUkJarUajUaDTCYjJCSErq4uSkpKAoo3EOrq6hAEgerqasaMGUNUVBQWi4UDBw4Mu6HSQFWJfOzY3kbR1lZ+/4dxPP7YPv67sp7rrhvF6tUNXHZZPBJJ79+HIIy85B7A5XJRVFREdnY2qampdHZ2UlRUREJCwnCHFiRIkGEgkAp+OqACfFITvsZynxZcC97Wn8uAu4FNgiAsFkWxb7eXs5QT6eCDdxlbpVL5dbvPs2ucgdPHIBwbN0J1tfczNtYrr3meIhszhtCf/ITuKVMw//rX2P71L6TJyUi0WlTXXtvnMStXruTCCy9k1KhR/ttiYmJYuXIlS5YsOWGVWkRkD3vw4MGKtVdyL+L9W+6r778n8+bNY/Xq1TQ2Nvr/D1QqVUDOqzemXA/AIfNh3q58l5cnvcAFkZP5es4a1hnW80LpS+wwlfBl01q+bFrLRfr5PJR1Py9MfA5RFLlv58/47ZhfkaD6X7JTX19/nK5+R0cH5gBnQaKiosjNzfVX5AH0ej1RUX0LjNntdrZv305eXh5RUVG0trayadOmfivyjY2NTJ061a/V7/F4EEVxyNRrfD3earWazMxMpFIpRqMRpVLp/3k4CdToacbMaGbMjKahwcbmzUa+XNPEj2/fxqJL4pk+PYro6BMXFtxurxnW9OnDK6UpkUiw2Wzs2bPHLxPq8XhOqXUqOJwbJMi5RyBNk/PwOtU+C8SKohgpimIkEIt3wNaKV0YzGngemAU8PijRjiBEUfwc+Flf2/bu3UtCQgJSqRSbzYZUKiUhISHYCzlQwsIgMhISEiArq/e2jz/2Wkv24xJ6riHRaJCmp4NKhTQhAdFux1lSgnzSpH6PaW1tPa56l5CQQGtr6ynd5w/5IRdwAfn0rkA308xzPMe7vEvHMb52vuQfvAnXkiVLGDVqFFqtllGjRp30wuJk5GizeXnSCwDctf0+KqwVzNXP4YtZq/h4xodMj5oGwHrDBpZs/D737LiPTcbNXD/qWuKUcRQYCikwFALQ0tKCxWJBoVAQExODQqHAYrHQ0tISUGwSiYS8vDzMZjN1dXWYzWby8vL6TbCqqqrIyclBr9cjl8vR6/Xk5ORQVVXV5/6+WQan04nNZvOvENqHaNLT1+MtlUrJz88nISGBiIgI1Go1c+fOxeFwDEkcJyIpKYnFixdz/fXXs3jx4gH9rT3z9CFuuCGF9PRQLBY3hw+ZEQQ4eLD/C77WVnj6afjHP+DbbwfhAZwGJpMJhUKBRqMhMjISjUaDQqE4aYtOcDg3SJBzk0Aq+C8Cm0RR/GXPG0VRbAEeEQQhAXhRFMUfHP05F/gh8MvjT3VuUlZWRnt7OzKZDEEQsNvtNDY29mt4E6QfZs2CmTPBZOpdGjMaYd067/ff+97/pt7OdUQRZ2Eh6p/+lM5f/Qrnl18i3n8/zuJiQvpoA4mKiqKhoaFXBb+hoaHfinJPBASSj34cy252Yz36oULVa9u7vIsVK2MZy0Vc5HfWPNWq/0D4z4UfIxEkvFf1PjXWWh4b+2vm6eew2biF5w+/yHfGTf7PaZFTeSj7AZQSpVfa0eOiOrKWmerpNDY2UlFRgUql8vfRB4LH46G8vJwJEyb0aknqT1ffZDIxbdo0HA4HTqcTuVxORkZGv/fvcrmorKwkPj6ekJAQnE6nd47i6LD1mcbnY6DT6fyKOTKZDJvNhtFoDNg/YKTw4ksTEQSB2FglV17xHfv3m7nxhq3kjtHywgt9X0irVOAThSoqgrlzh6+K73NS7nlR39DQ0K+Tso+ew7mA/2ugsyhBggQZGQRSwZ8PfHeC7RuBi3r8/A1wXr1K1NXVYbPZSEhIID09nYSEBGw2W7AiEggSCRybkDY3ewdyAcaPH/qYhglJWBi6V19FeeWVSJOSEB0OLE89hf3zz/vcf+nSpXzzzTd8+eWXFBYW8uWXX/LNN9+wdOnS04ojjjgmMpEssnoN5YqIGDDQSitd9JbcrKGGp3ma13iNZpp7bWukkXrq6WRgOuq+4dmbU27kwayf0dndyeLCJVwQOYVPL/yY1bP/y0X6+QAUtW3jmi3X88cDf8Lc3UGLvQVzQielpaWYpO2o1Cq6u7spLS1FpVKd6G77xePx4HA4cLvdiKKI2+3G4XCcUOWmubkZnU5HXFwcOp2O5ubmfveVSCR0dXXhcDj8n11dXUOmXuMbYs3KyqKoqIju7m5iYmKQSqUUFhYyYcKEIYnjRNTV1bFmzRo++OAD1qxZM6DXXEEQsNncPPhACc8+NxGAdesMZI3W0NRkx+0+vs1SrYbbb4eFC+Hhh4e3RUen09HV1YXFYvHPfnV1dZ20sGQymfyeGD7Cw8ODw7lBgpzlBFLBF4CcE2zPObqPDzfeodvzBp+Dou+NVyKRoNVqaWxsHObIzhHGjoXnn/eKUScm/u92UYR//QumTj2+peccQfR4aJ09G/Xdd2N54gmcBQVEb9mC6HIhyI7/d25tbaW5udnfay7rY5+BMuHox7F48HABF2DEeFzlv5VW7Ec/5PRWk1rHOsooI5547uIu/+1OnKxlLeGEk0UWscT2GY8gCITJvX3vr05+hRBJCK+U/pULIqfw0YzllJh28WLpy3zZtJYdphJuLLqVvPBxxHfE0tbWxq6p+4jbrSfMFIrT7ghYV14mkzFt2jTKy8v96lnTpk3r9/8+OTmZffv2oVKp0Ov1GAwG9u3bR3Ly8asmACqViuzsbJRKJR6Px282ZR2iNjXfEKvdbmfWrFkcOXKE3bt3k5yczOLFi4e9B9/XapKSkkJqaiodHR1s2rTplPrwfahUUrYVL0QmE9he3MZ771Xx8MO7WLmynt/9biwzLzxeEWn06OOVf4eD9PR0RFGkvb2dpqYmwsLCSEpKOqFpFwxcfShIkCBnB4G8238D3CMIQpEoih/13CAIwnV4B2tX97j5Arya+OcNarXar2ntky6Lj48fckv5cxqJxKtV15Mvv4TvvoNNm+Cuu7xC1ecYgkRCzPbtIJViW74cd00NnU88gfO774jZuROhRzV3+fLlTJw4kbFjx/oTwf3797N8+XKWLFky6LFJkXIxF/e5LYoo8smnk87jZDd9lXsNvdVrzJjZznb/tp4JfgklHOYwEUSwkIVIji5GpoelAzBfPw+9Moa6rnq2tG7ln9PeZV/Hfl4sfZnVDf+PvR37YBIoRylIKIklrE1NS7qRzkgLCRWBqY5EREQcZ+LV2dnZb6I0depUioqKKCwsxOl0EhISQmpqKlOnTu1zf5VK5R+k9A1PSqXSgFccBopviPX1118nOTmZuXPnkpqaSlRUFB6PZ0jVfPpisFpN5HIJa9Y04nB6mDIlgh07TJSWmhmdpcFqdREaevK3TbsdlMrAHkeg+C7A0tPTB6QiFKj6UJAgQUY2gST4DwFTgX8JgvAcUHb09kwgHmgEHgYQBEEJpHCeaeLn5uZy+PBhv5Ot1WrFYrGcsjxgkADRakEmg4iIc7aCDyAolXT++c8ov/99rK+8gmPVKiK/+KJXcg9e1ZUxY8bQ2tpKQ0MDCoWCpKQk1vnmF4aQlKMffbGUpXTQgYLeaiV27KhQYcNGBL2T5DrqOMQh1Ki5hEt6bXuN11DqlExiErG2WJJU3lWew52lPD/hGR7J/jkvHnmF/9SsxB7loOLiGprMLaTsT0a7I4wqRzVLN/2ID6cvRyk99SwtOjqad955h/DwcORyOc3NzezYsYPbb7+9z/3Dw8NJSkoiMzPTn7Db7fbj2iV8hIaG0tLSQmpqKmq1mq6uLqqqqgjt6Qo9BKjVan9vd3l5ud/vYLgrvr4ZgZ6Eh4dTUVEx4HNdemkcc+bE0Nho48KZ66ivt3P1VZuwWt1sLbr4OOlMHy6X17Nv61b43e/+10k4FCQlJZGens7atWtpaWkhJiaGRYsWnfTiJtDjggQJMrIZcIIvimK1IAgTgEeBJcC0o5uqgA+Ap0VRbD26rx1vz/55RW5uLlu2bMHtdiOXy+nu7qajoyOY4J9pLrwQkpK89pJD+c46DITMno00IwP7qlW4q6roeu89BIkEzR/+gHC0dCiRSKisrEShUPhl8xwOx7A7jh5L3NGPY0kiiV/ySxw4jtPgDyWUGGJQ0/v3bMeOAa+RVhZZJKgSuDIxAVEUKej4lkOxB1BKlfws5z42PlKA5HI5zektdGltHJxRinKcEsV6GS+MeQGlVMk/Kv/JeF0ekyP6VyvycfDgQaKjo/1Ooh6PB7lc7je0OhaJREJsbCwZGRm9hnL7+/30HGj1DdnabLZBabs6FXwtMPHx8Wzbto3Ro0f79f43btzIVVddNSRx9MdgtpoIgkBoqIwnnzjI/Q9k8Yff72fTplYeejgbQQCPR+wzyd+717uQCPDJJ3DzzQE/nAFTV1dHcXExoaGh/tWe4uJi4uPjT5is19XVUVFRwYIFC/wV/IqKipMeFyRIkJFNQO/0oii2iaL4iCiKY0RRVB39zD1626lp8J3DGI1Gpk+fjiAIGI1GBEFg+vTp/VrWBxlEUlK8SX5P1qzxvuu63cMT0xlAMXcuuN1+DXz7xx8j0esRu7v9+8TExGAwGIiIiCArK4uIiAgMBsNZp3aiQIGU3v3dF3ER93Eft3Fbr9vduJnIREYxqldLjyAI/GDs9+iWd7OrfQ9bDFuRtcqYVD+eH+78HpEeHQJg19jp+J6FO4rv4t3K90hUJRKjiMFgb+GLhjUnjPPgwYPEx8djsVhoamrCYrEQHx/PwYMH+9xfIpGQn5+PxWKhvLwci8VCfn5+vwm+w+FAp9Mhk8lwu93IZDJ0Ot2QyVP6WmA8Hg/Tpk0jJCSEzs5ODAYD8+bNG/bXt7y8PHbt2kVBQQEbNmygoKCAXbt2nZYB1zPPTuBXv8rlN7/1FmdeeP4wd9xRzKt/Letz/4kTvSNCKSlw0UV97nLGWL9+vV9JZ/z48aSkpGA2m1m//sTelD1bmyQSCREREaSkpARlnYMEOcsZUOlHEAQVcBVwWBTFojMT0tlPWVkZHo+HqVOnotFo6OzspKamJmADnSCnQWOjd83c7Yb6evjxj4c7okHD8fXXSHQ6pGlpuCsrcTc14WluRpDJEI4ObgLYbDZqa2uRSCSkpaUNaYxDbaATSijf5/t9bosnnolMJCcxh2u5lrei3mTTuCJ+UHolcZoYwj0aOho7MYW1U2er55d7fk2MMppJmROYEDkeuS0EWMye9r2MCx/rV/HxYTQacTqdjBo1ioSEBH9Fvr//+4iICGQyGVOmTPHf5nue+qK7u5v09HTUajUulwuZTEZXVxcNDQ2BPVkDxNcC09nZSVJSkt9DoKOjA61WS01NzZDEcSI8Hg9Op9Pf8hQSEnLyg05AVJQCs7mbL9c0sXBhLF9/3cx/V9bz8MN9m+4JgvclRqXqbco9FPhWinwtXuHh4WRlZVFcXHzC4waztSlIkCAjh4Gu7TqAt4D7gWCC3w9ms5no6GhaWlqora1FqVSiVCqHvcJ1XuJ0gl4PTU1wcd8DoGcroXfc4f1GqcT8wAPY//1v3DU1hD34ICHTpxMeHk5cXBy1tbVYrVaUSiUZGRk0NTWxevVqVq5cSWtrK1FRUSxdunTQB28HQ9VkMMk5+uH/OTkH82ozlkQLRoMJbXcYSTvimeDII+unOfyz6n1a7EbW7lvHd4rN/DTjXszdZv5y8Clem7yMjZLvcMlcpAvpTGISnZ2dKJVKv5qMVCrF6XQeN3jrIy8vj9WrV/udrn1f+/s9RERE0NDQQFZWFjExMXR0dNDQ0DBkve++Fhi3282BAwdITU1Fq9WiUCgoLi4e9pWhvXv3Mnny5F7Ph8lkOm09d61WzgcfzkCrlTNt6tdUV3dx801FxMQo+Of704mJ6T07MsQjEX4kEslxqz993XYsQRWdIEHOTQaU4Iui6BEEoRbQnqF4zhoEQbgC+EFf2yQSCYcOHSIyMtJf4Wpra/NXVIMMISkp8NvfQmmp9/ue1NRAcvLwilefJo5vvsG+ciWyjAxc5eVIExMJmT4dURSJi4ujrKyMMWPGoNPpaG9v58CBAzidTpYvX+53I21oaGD58uUAg5rkj3QDnSlTprBt2zaqy6tJrkj0avtmSWiZ3sa/x/2en43+P54oe5JPKldic9h45sDz/L3sHe5KvxO5RMY/y5cjykWuybiKieJEwsLCUCgU1LTVYEgwEOoIJUQdQlhYWL8xVFZWUl5ejs1mQ6VSkZGR0e++2dnZtLe3U19f71+RCQkJITu772ryYONTW+ns7KS1tZW2tjasVivR0V7pyFMxUDuT+BTL9u3bR2dnJxqNhoyMjEHRc09NDWXNmkZmz46hqamWPXs6uPzyeKKi5Cc9tqzMW81PTz/tME5Ibm4ulZWVvWY6KisrTzr7FVTRCRLk3CSQRcT3gJsEQVCcdM9zGFEUPwd+1te2jo4OnE4nDQ0NlJaW0tDQgNPpDMpkDhcyGYwZ0/u2qip44gl44w3o6urzsLOBkNmz0b3xBqEPPwyAbcUKzI8/jnXZMsLDw9FqtVitVhobG7FarWi1WkpLS8nPz2fUqFHIZDJGjRpFfn4+K1euHNTYRrqBjsfjQRRFMjMzyc3KYXTGaCIM4cxvm4Moivxqz294NOMRdi/azkNZD6CVaWlzmnjy0DNM+Xo6SlHF+MRxiBa4ast1pKWlERYWRqWrku267WzN2ooYJ/Zqi+ppAvbpp5/S0NBAeno6kydPJj09nYaGBj799NM+483Ozkav1/tXBJVKJXq9fsgSfJ9MpslkwmQyYTabCQkJweFwIAjCsL++uVwuioqKiIqKIi8vj6ioKIqKigbN6XfWrGgee3wsL7/sHbj+4otGHn9sP088caDfYz79FJ57Dv7+d6905plk3rx5qFQqGhsbOXLkCI2NjahUKubNm3fC43r+XktKSjCZTMO2yhYkSJDBIxD5hc14K9e7BEF4DTgCHJchiaJYeJqxnbUYDAbsdjvjx48nMjKStrY29uzZg812Xvl9jWw++8xrjLVnj9cZd4h70wcLQaHAYzbTvWcPstGjcR05gruxkbBHHkG2ahXjxo2jqKiI9vZ2dDod06ZNo6CgoJedPUBCQgLffXcig+qBExERQWVlJe3t7VgsFsLCwtDpdCNm6b+hoYG4uDj0er2/PUYul2Op9bbU3JF+O3qFnq+avmaibgI7Fm3l7Yp3ebP8LUzd7Xxduo4tFUXclnYLj435NbVtNfxu/x8Zl5pLdnY2lk4L9R/Vc/F1/2sN+4zPqKGGbLIpLCwkLi6O0NBQJBIJMpkMrVZLYWEh999//3HxRkREsH37dmpqavy6+aNGjWLy5MlD9pwlJSWhVquJiIggMzMThUKBw+GgrKyM+vr6IYujLyQSCQqFAqlUiiAISKVSFArFoKlGaTRyNBo5rx40c/nl8XzxRSMvvniYZcv6f/4jIrwvM2azt6aQcyKLyNMkKSmJJUuWBDTzkpSUFEzogwQ5xwgkwf+6x/cvA8f6dwtHbxteW8NhxG63k5OTg9VqpaWlBbVaTU5ODocOHRru0IL4uOMOeO89r17+WZrc+5CEhyONjib0wQfpuPdeHGvW4Lz2WiK2bmV3QgKzZ8/2D3sfPnyYkJAQSktLCQkJwW63o1QqcTqdg95iER0dzYoVKxg/fjxpaWk0NzdTUFAw7HKKPkwmE3FxcTQ2NtLd3Y1cLkej0dDU1ORVvoryKgAnqhJwi27UUjVhsjCKF27hvarlvF72BkZnK8uOvMrbFe8wR5zNVP0FsEnEWNyKJE7GRbMu8vfki4g00QR4jbsMBgOjR48mPDycLm0Xe1P2ItaI1K/oO1HesWMHpaWlpKWl+XXwS0tLiYyM5MYbbxyaJw3o6uqis7OTqqoq/7BvWFjYsDvZ+lSJampq/E6uvp8Hk2uvGUVqWiiLLy1k9+52nnzyIIYWB9dcM4q0tN4N+PPnQ2srzJkDsX0bMQ8qjY2N7Ny5069nHx0dHUzcgwQ5Twkkwb/t5Luc30RHRyMIApmZmahUKmw2GzU1Nf5e1SAjgNBQuOee4283GECj8cpgnCUIISGE3nsvzn37kOXk4Dp0CNs77yAPDe2zopmVlcXmzZuZNm0aiYmJ1NfXU1RUxHXXXTeocRmNRmbNmoXBYGDfvn1oNBpmzZo1YobNLRYLra2thIeHExISglQqpbW1FYvF0mu/PN04ANqcbdjcNrRyLVMj81k693t80fj/+OuR12l2NPMla5HppIyfnEeyNRGJIMXaZeWb+nVcIl4CAlzN1TTSSCKJSKVS6urqqKyspDm5mRZNC9JwKRp5b0ffgxxEhoxNJZtITU5Fr9cjl8sJCwvD5XKxdevWIXvOAJqbm3G5XCQnJ6NUKrHb7dTW1g6ZHn9/DFSVKFAuyI+krc1JTo6GqioL1dVdfPpJHdddN+q4fQUBhup6tri42H9BPXXqVJqbm1mxYgVAnz4MQYIEObcJxOjqvTMRyLnE1KlTqaysxGAw+NUxfLcHGUEcO1xrt8OyZd419Z/8BEYd/4Y9UhHdbsw//SnqO+/E/POf4/j2WzofeojpaWlUHtVl91U0jUYjubm5lJSUsH//frRaLZdffjmxg1xiNJlMTJo0qVePuMfjoaSkZFDvJ1Dcbjfl5eVMnjyZ+Ph4DAYD5eXl/TrJRoZEcn/WTwEoaPmWxXGXcEPK9SyMXUBBSyF/Kv4LVkUXOyN2sUe3lwucUxAPe9gdvhcREaO9lXRFOplCJgAajYaqqipyc3MJjQ9FtIjUWGoY5en9d7eOdRgx0jijkWmWaVitVn+LTmxsbL86+2cKo9FIQkIC8fHx/pUEn1vycDKUw6I6nZwf/DCJa65N5gdLN7N7dztvvVVOdraWm25KPeGxdjsoFIM/27927VqkUimrV6/GarUSGhpKWloaa9euDSb4QYKch5xWyeXooG000CKKonNwQjr7mTdvHhaLpZf8nUajOemwU5BhprDQW8EH2L//rErwBamU6A0bED0eut5+G9fBg4xevZpQg4HITz7xZxMmkwmPx8PChQu55JJL/MeficR7pMvvSaVSJkyYgMFgoLKyktDQUCZMmEBtbe1Jj/1lzs8B2GTczH/q/svzE59h8ysbaR1l4kDKYUxSE1sV2xDGCaQ0JlNtreGF0pf4QdJS5sXMAbzOtElJSbhcLjx7PKRL0omzxeGU/e+l1IGDVrzegZIGCdWmanQ6nddET2OkLLuMdlc7Zsxoh0jcTKFQkJSU1Ku1KSkpidbW4fU4TEpKIj09nbVr1/pbVBYtWnRGWlQkEoGlS5PYuLGFK65M4PNVDTzz9GGuujqZG25I6dPlFrwvK++/D5dfDrNnD25MxcXFWCwW1Go1UqmUrq4uiouLT6jiFCRIkHOXgKaPBEGYLAjCeqATqAFmHb1dLwjCOkEQzi3B8QGSlJTElClTMBqNHDp0CKPRyJQpU4K9kCOdhQvhBz+A8ePh0kuHO5qAaL/jDhRHY484cIC9t92Gqb0dj8eDyWSiurqa3Nzc4xRPzkTinZeXR3V1tf+iwnf/p+MsOphER0cjk8lIS0tj3LhxpKWlIZPJBtRKd2H0TJ6f+Axu0U3htM3M087h5rLrmF81hwinDlEiUpVYw8x1c3B73CQqE/i6+Rse3PVzbDYbUqkUk8lEa2srJpMJmVTWaxhfgYJHeISbuAl1uZqWlhYEQSAsLAyr3opJasKR50B2TK2mllqsWAftuepJfHw8YWFhpKenk5aWRnp6OmFhYcTHx5+R+ztV6urqqKioYMGCBdx1110sWLCAiooK6urqzth9RkcruPeeTC6+2Lv6te6bZr791kBZmeW4fbu7vcm9yQSffAKDLTpUVVWFKIrodDqioqLQ6XSIokhVVdXg3lGQIEHOCgac4AuCMBH4DsgA/tlzmyiKBkAF3DIo0Z2lDMcbTZBBQBDgkkvg3nt7r5+Lx86Rj1w0v/kNoQ88gHzcOKQSCVN27iT8/vvZs2mTX/7uoosuGpLE21dRXbduHW+++Sbr1q0jPT19xFzojh49GqPRiNVqxeFwYLVaMRqNjB49esDnkgpSfuP6JS2VLXwXugmbx8bSkiu4qHwusW49Hjx8Wr+SORsuYkXtp1yTfBUms4nt00pIzExkwoQJpKWl0dnZSXt7e69zq1CRQQZqm5rIyEjsdrs30W8TiDJHoWpVoUbt319E5AM+4FmeZS1rT/t5OpalS5eyd+9euru7SUxMpLu7m71797J06dJBv6+B0NN3QSKREBERQUpKCnv37j1j95mTo2XBxbEsuFhPQoKStjYnd96xneLi41czZginfwAAIABJREFU5HK46SZQKuGHPwTtIC+4+LwUXC4XNpsNl8vlnwELEiTI+UcgLTp/BBqASYASuP2Y7euAq08zrrOakW7wE+QkHJvcf/SRd+j2e98b8aZYsowMHIWFKJYsoXvfPli3DtMtt+BWKv37+HSv9+7dS0VFBREREWdE97rnha6vJ7qiooL4+PgR8X8QHh6OXq/3J4QejwepVNpvD/7JiNJF8e+d/yYqJgKpUkq5p4JdcXt5TvYUkgtkvHD4JfabD7CqYTWrGlaj+r6S2OoYnGYne5P2IeuW4fF48Hg8fZ5fJpMx5qifg8vlIkVMYXzjeL4t/BYe+99+BgzY8CZ1UfRWRmqiiTbayCSTEEICepw+M7SVK1fy3XffERUVxY033jjoTsgDxWQykZqa2uu28PBwKioqzvh9R+hCWLZsMtdeu4Wami4Kv20hL0/HuHG9/5bGjvXab5wJt1u3241KpSIuLs4vX9rd3Y3b7R78OwsSJMiIJ5AEfzbwpCiKln7MrmqAhD5uP28YzjeaIIPMV19BQYH3e4UCFi8e1nBOBdFiQTZ2LO6sLJx79hB9+DAxKSm0xcayadMmfzJ/ppPskX6hK5PJmDRpEsXFxX6fgPz8fOwBOhIVFhYSERGBy+6iy9yFXCEn9/BoNtVtIjY7nr9Ofpk6Wx0vHH6ZkvZd2LLtVGXX0l5rRlevJbxFi1wuw5rZd8VVq9XS0NBAZGQkgiDgcrloa2sjXNs7iYwiilu5lUoqyaC3M24xxexgB0qU/JyfH9fac6osWbJk2BP6YxnOmY/bf5yO1erimmtGsXx5NW+/XcnmzUbWrZ9PdHTvt8kzkdyDdzYiNDQUjUZDSEgIISEhhB5V0goSJMj5RyCv7krgRN2DQzPpNYIZ6cOFQQbAlCnw3XfeSv6MGcMdzSmhvOwyRI+H6nXrSNq/H/nmzbjCw4lMSUE82rIwFAn2SL/Q9Xg8VFdXM2PGDEJDQ7FarZSXlxMTExPQ+fbv309GRgYZGRm9zrd//34u0V1KoioBuUTOXel3olPouOmjW+hOcdGe3EF7cgdxbbHoDmjpcnkT/HJLOemh6X4VLp1OR01NDaIo+lsvamtriYuL6xWHDBmpRz96IiJSSSUAyST3Su5duNjFLrLJRkNvmc6+qKurC8hQ6UwylCo6fdHd7SE+Xsl1143iww9rqKy0UlPThdstEhur7PMYq9U723/ppae/OJiWlobL5cJoNCKVSnG73bhcrl5OykGCBDl/CCTBLwemnGD7RUD/3t3nAcP9RhNkEImJgV/8wjshp9MNdzSnjGP1akK2bEEcMwYOHgSrFU9aGuEOBxUm05DEMNIvdD0eDw6HA7fbjSiKuN1uHA5Hvy0yJ8PtdqPX69FovAmyRqNBr9dTWlrKgtiLAGi0NyGTSLlIPw/1ZiX6fTF0zLRgiGihKbKZplnNhFTJKDAU8uqR13lu4tPEKvUoJAoaGxtxuVyYTCbMZjNut5vu7m4aGxtPKT4BgXu4hwoqUNI74aykktWs5gu+4AZuIJPMfs9TV1fHe++9R319vd8obefOndxyyy3DmuQPVetZf+h0IfzlifEc2N/Btm1tlJdb+P73vmPe/Fj++c9px+1fU+NV5TWbvR2Apyuydskll1BQUHBcz31PtawgQYKcPwSS4P9/9s48Pqrq/MPPnX0ymUz2fQ+EQEiQJSDKDuKGuC8Vf9altXXXttbWpda2Vm2rdanWLmpbwN2CKGhZBNlEQbaEEMi+75kks2eW+/vjJkMCCUvIBtwnn/nMcO+55547JDPvPef7ft93gCcFQfgA6PLVEwEEQfgpcAlwbJ31c4jh/qKRGWB6C+xzcyE8HIbZOaQvtIsW0ahSoSwtJeLpp1Ft3gxPP401MZGQefOGZAwj/UZXpVIRFBTEu+++S1tbGyaTiTlz5vS7YFNCQgJmsxmj0eifwTebzSQkJPjbjA3KYGxQBm6fG88YL7GFsRjXtxNsNNKQ1URrXDsdyR5u+Pp7TA6ZRKG1kNU1a3D6nJjNZkJDQ6mpqcHn86FQKAgKCqKlpeWkx6hGzRjGHLO9BGlVRYGCBBJ67CuiiAgiMCFJgT766CO+/vpr1Gq13wq4oqICg8HAQw891J+3bsAYCunZiSgssnLHnSk8/9xB6upctLV1YLN5CAhQ+ldjACIjoetXrajo9AP8rroKwcHBqNVq3G43ra2t/hvO4zESV2RkZGROD0E8RYcQQRA0wP+AWUABkAHkAhFANLAOuEwUxf5Ng51BCIJgBNrb29tP6kNU5iyhtBReeEGyxXjgARihS+BV335L08MPYwS0hYXYJ01iz223ceGMGUP25T2SA4eXXnqJ3NxcJk6cSHh4OE1NTezZs4esrKx+BarLli2joKAAn8+H1+tFqVSiUCjIyMjglltuOab9eeedR2JiIp7ZPoxKI7EV0Xzn2E1pSgWuMUe88CeYsrln1N3cs/BuND/SsajwEsLDwmlubmb37t2Ul5dTXV19Wu+FiEg11TTSyEQm+rd78PBH/ogLFzOZyXzmM3/+fAwGA+np6f4bmcOHD2Oz2diwYcNpjeNs4g/PH+SJJ/IAGDvWyOuvT+HCGT0tWA8fhqYmSf13uhKdxYsX09raiqGbyN9msxEcHMyqVav6PK6qqopt27aRlJR0zI34SPlblZGRkbBYLARJFlxBoihajte2P5VsOwRBuAi4H1gCOIF0oBB4EXj5XAjuT8RIDmxkTpPiYkmy4/FAZeWIDfDjpkyBn/yEkqIiEp57Du3OnVx48cUEf/QRDNFM60iYUe2Luro64uPjMZlMKBQKTCYT8fHx1NXV9au/3grciaLYZ4E7o9GIIAiE7JZmxssCy6ieXEvS0jhe/eGrvFL4Oqtr17CvbT8/+u5uFPcLJDUmEBQUxHcRe0hQxTF69Gjq6+v7+xb4ERCI7/zpTimluHABEIXk9V5XV8dFF11EQGwAatSE6cJITU1l3bp1pz2OswVRFCkutnHHnSm89WYpBQUWnC4vPp/YowhWerr0GAhKS0vJyMggIiLCr8FvbGykoKDguMeN9GR4GRmZ/tGvtWhRFD3Anzsf5ySCIFwBXNPbvu4zIsnJybS1tfVwL5E5w1mwQBLNhoRAp23hSERQKIi76ipCP/oIx8yZuHftQrN1K+qf/GS4hzYicDqd5OTk+ItM6XQ6srOz2bJlS7/66ypwd7KVVNPT0/F4PAQEBEhyG18Qxm1G1Olqnjn4PE+Oe4yfZ/yUlw6/wsrqVfhiRQpiC6my1BJZH05InQl3sAftpN4TOAeCUYziLu7iAAdIR4pERVHE7XazL3wfTVFNhDaEklCVwKmuBp/NCILA3/4+Bbfby/59rezaZebm731Namog27bP77PSrShKibf9KT7r9XoxGAxERUX5bTLtdvsJbTJHejK8jIxM/+hXJVsZEEXxU+CB3vYNR8EVmSHmwgtHdHDfRXV1NUVLl7IzIwOXy4Vj61YEpZKO7duHe2jDTkREBEVFRRQVFXHo0CH/6/666Jxqgbu5c+cSGBhIY2MjFRUVNDY2Ygw0MnfuXN7O+QdZpvEUWgpJMSSzbd4mAnMN4AOr0UrJqDK2zfiW4ogSCJGCxe9adg94kC0gEEssF3GR3zdfo9FQVF5EY2gjSrUSn8dHaUkpGo20340bO/YBHceZSnW1k+BgDTExWsxmN263D7e79wVuqxX++ldJ/ed2n/q5TCYTOp0OhUKBIAgoFAp0Ot0J6zp0JcN3ZyQlw8vIyPSPEwb4giBcIAhC2Ina9XKcsvPYc06cbjabj/lQNZlMmIfIvURmGHC5pMTbEURVVRWrPvuMLxcs4FBoKPWxsThdLhp//Wtsr79+zs+4xsXF8eWXX/pn7nU6HV9++SVxcXH96u9Ub+zHjBmDzWbDZDIRERGByWTCZrMxZswYjGojCkHBRdELuDnpeyQbkhGUkLw0npSqJASfgDXASnlWFa0XWvhH8Vs8X/AnnD4nbe62Qf2/jYqKoqGmAcebDhrWNFCzpob6+nqioiQJzyEO8Uf+yNu8TdtxHZXPfpKTDaz5fBZvvTUVpRL272/j0ks28/Zbpce03bYN9u2DmhpYvfrUz5WZmUlLSwu7d+9m27Zt7N69m5aWFjIzM497XFZW1pBUtpaRkRlaTmYGfwvQH5+t4M5jc/px7BmNPCNyjtHUBM8/D6+/DifQuw4lX375Je3t7SQlJXHJihV0zJiB6PPR8c03GB5+GPppB3m2UF1dzdy5c7FYLOzcuROLxcLcuXP7nbB6qjf2Bw8eJCEhgczMTLKzs8nMzCQhIYGDBw/62+iVehIDJFeb8OJQzgufAAdEYj+NJDwvFIVXgSfYw+N5T3LYcpjl5e/ym7xnWFm9ClEUBy3QV6vURDujGVM4hmhrdA/noUMcQkSkjjoC6ak1aaABH+fe711ubjtXXCHdOG7d2oTTdaxs5qKLIC0NJk2SXp8q8fHxtLW1ERUVRXp6OlFRUbS1tZ1QFtrl+mY2m9mzZw9ms1mWk8rInAWcjAZfAMIEQUg8xb5DO4895xjp9oAyA4zNBg0NUsC8fTtkZAz3iAApgExJSaGwsJC8+fPRBgczb/Rogg4fxvLcc3jz8wnfvh3FCZbwz1YaGxu5/PLLUSqV/m1er5fV/Zk+5dR9/w8ePEhGp3TK6XQSEBBAWFhYjwC/C5VCRWxzDPpEPZmJ49B16AhxB/Nd0V5qvbWYx7dR46zlsdwnidRGkhqYwubGrSwrX84/ct7o1/X0RZf1Z2RkpD+ZMyAggMrKSgByyPEH9kqOvLcuXPyNv6FDx0IWMoEJAzqukcwDD47mwYdGc8MN2/lkZQ2P/nwfGzc28Pe/TyE4WJI2KRSSKZdW2z9HHYfDwdixYwkODsbn8xEYGIhOpzvGF783RnIyvIyMTP842STblzofp8o5qQGQffDPMZKS4NZboawMrrtuuEfjx2azUVZWRlpaGoFJSQStW0dJcDATRBH3tm0Ev/32ORvcg6TBr6+vJzY21r+tvr6+3xr8U72xt9vtFBcX+4Pkrme7vXf9enZ2Nps3bya+JR6VXkmBcIiq4Equ1V3NrKmz2dSwmaXly2lwNfB0/u8I1YSyJPEm2jva+dWBp/nl2EeJ0kX269q6Y7PZMBqNBAcHo9PpcDqdOBwObDYbAImdP0dTRBFevNiwYcDQY18jjQQRhBbtaY9vJKJQSI5KNdVO0kYFUlxkZceOZjyenl+RutPIl3a5XIwbN46ioiJsNhsGg4Fx48aRn39O152UkTlnOZkA/+nTPMc5mYovz4icY0ydKj1GEAEBAajVajQaDYIg4EpMpEqrJbKmBlNbG/Y338Szfz/6JUtQdgtyzxUWLlzIW2+9hclk8hcGamtr44477uhXf/Hx8aSmpp60i47b7aa0tJT4+HjUajVOp5Oqqqo+a2oolUrcbjclJSV+G85gIQjlQiUrq1fxkzEPcWfq7fyr9D8sLV9OS0cLrxa9ztLy5cyPmodaULG5cQtqQc308PP7dY0gBZI6nQ6v10t7eztKpRKdTofL5TrucSmkcCVXUkghyST32LeWtdRQwyxmMZWpCGfh4q8gCGz6ai7FxVamn7+eulonS5bsYMnNidz6/WOtdkURduyQZvQnTTpx/1qtlr1796JQKPw3inv37pVrtMjInKOcMMAXRfF0A3wZmXOTggIYM+b0K9j0k4SEBDo6Oo5UOjWZ0CiVtE6bBmvX0rF1K+rsbCmSOAeJiYkhJSWFlpYWHA4HKpWKlJQUYvpZnbi7i07XDH5JSQkxMTG9Bvl2ux2Hw4HFYukxE95dMtSdvXv3kpaWRmhoqH+2v6Wlhb179/LfX/0XgBVVnyAisnvhN/y16G+8VfpvWt2tfFy1grV167koaj5Xx12Jx+dhc9NW5kbM7lFd9WQQBIGysjJSU1MxGAxYrVbKyspO2E8AAUzs/OmOCxcllODFSyWVTGPaKY3nTEKtVuDzQUJCAAUFFjZ+2UBUlLbXAP/NN2HnTjAaJdVfQMDx+/Z4PBQUFJCVlUVYWBitra3k5+czceLE4x8oIyNzVtK/muwyMjJ94/PBBx/Axo1w7bWwcOGwDCM1NRVRFGltbcVqtRIYGEiCxUKA14t2zhxcmzbh3rsXNBq8jY0o+ylNOVPJzc1l1qxZPTTyZrO53wV+TrVgkNVqRa/XU1ZWhsvlQqvVEhISgtVq7bX/xsZG0tLSiIuLQ6VS4fF4UKlUPfzKr46/kqviFmP3Ovhv9Sdsm7eJdyre443iv9Pc0cJ/q1fyRd1arou/BofPwbzIOdQ4aojRxZx0oB8VFUVzczMOh8O/kmC32/0uOqeKBg3XcA2b2MQ85vXY58SJFu1ZNaM/blwQ279ewNO/zuPllwv54P1K0kcbufueUYSFHZEonXeeFOArFFKKz1FW9cdQXl5OTEwMhw4d8v8+xcTEUF5ePrgXJCMjMyKRA3wZmYHGZoO9e6XXn38ueeYbDMc/ZhDo0oSnpqYe0YQrlVx4220E1NXh2rSJju3baX/0UXQXX4z+xhuHfIzDyUAX+OkqlpWXl4fFYsFoNJKWltani05rayuiKDJx4kSCgoJob28nPz//GAeuLlQqFV6vF5/PhyiK+Hw+vF5vDwcbkGbYDaoANs5Zh0EVQIOrkWeznqHWWctrRW/Q4GrgP+XL0Ct1hKiDOdhewK8zf8XYoAyUgvKEgf748ePZv38/oaGh/oJKbreb8ePH9+t9ExDIJJNxjOsRyIuIvM/7ePCwgAUkkdSv/kcigYEqjEY1qakGSkpsvPjiIWbNimTW7CM32ZMnS97406ZJdfVORFVVFUlJSWRmZvpXhKqqqvqswyAjI3N2Iwf4MjIDjdEI990HK1fCzTcPS3APfSd7R7tcmB94AM3cuXRs3IhoNqO74YZhGeNwcqquNyfC4/HwzTffMGbMGJKTk7FYLHzzzTc9kniPbm8wGGhra/MH9QaDgebm5l7bp6SkUF5eTk1NDQqFAp/Ph9vtJiXlWHkHgEElaTp+NuZhlIKSOmc9hZYiMk3jeLXwNWqctfy95E00Cg1Ly5eTGJBAS4eZX2U+ftzr1Gg0pKen09jYiM1mQ61Wk56e7i901V+OnqUvoYRSJL/4gxw8qwJ8gAcfSuf7tyUz48IN1Ne7ePzx/fz66fHMny+thAgCzJlz8v11ycyCgoLQarVoNBpUKtVJuejIyMicfcgBvozMYBAfLwX5IxBlaiqhK1bgq62leeNGOr75Buvzz+NrbMT0wgvDPbwhY6DtbBUKBVqtFqVSmgVXKpVotVoUit7LjWg0GpxOZ48ZfpfL1WegbDKZOHDgAMnJyWi1Wux2O1VVVSQmHt/BOEQj3bAoBCVLkr7H5NBJNLua0Sl1/Kd8GRX2Sv5V9h9Ugoqr467kQGs+vzv4LP+eJgX/R1NdXU10dDQTJ07EYDBgs9k4fPhwv+sH9EUsscxgBnvYw0xm9tjnxdvDgvNMxGRSExioIiJCS2Oji2++aeGhB/ewb//FKBS9r6JUVEBMDKjVx+7T6/X4fD7q6ur8ORo+nw/9yUz/y8jInHWcTKErGRmZgWCIk1mrqqrYtm0bISEhTJw4kZCQELZt20Z1dTWKkBAcH3+MZv58AFxffYXhkUeGdHzDTZfrzYYNG/jb3/7Ghg0bSE1N7bf7lUKhICcnB6vVSnFxMVarlZycnD4DfIVCgcfjQa/XExAQgF6vx+Px9Nm+rKyM0NBQamtrKSgooLa2ltDQUMrKyk5qfIEqA5NDJTuWhIAEfph2J/+Y/Ab3jbqHFEMyHtHDh1Ufs2DzJYBIha2CZeXvkNuW16OfLolQdXU1hw4dorq62i8ZGkj06FnAAh7m4R62mj58/JN/spKVNNI4oOccapRKgY//O4Nnn8sG4NAhC2+/XUpj47GORFu3wnPPwTvv9P5REhYWhlKppKOjA6vVSkdHB0qlkrCwUy5ELyMjcxYgz+APElVVVeTm5mI2mwkJCSErK0u2zTyX2b0b1qyBn/zkxHYYA8Txkj7jLrkEZXQ0ukWLaNmwAc++fbh37KCjvZ2AW28dkvENN6fqenMiQkJCUKlUTJ482b+t6++/N+x2O0qlEo1G458Jd7lcffrgV1RUEBERQUpKit/Ws76+noqKilMe6/eSpHwLh8/BpJCJ/DLj5zxb8Dz/q1tHobWIDQ0b+fLLTUwNzSFWH0u9s4ED7fnMi5yDTqejvr6ejo4Of7JvV/7BYKA66msqjzxqO39iiCGCMzs5PCXFwLy5kSQk6KmsdPDA/bupr3fx2GNj/W26auh5vfDNN1Kl26OVX8nJybS0tBAVFYVer8fhcFBaWnpMnomMjMy5wYDN4AuC8JwgCMqjtpkEQfhwoM5xptDXzKmc7HSOUlAAf/sbVFZK029DhNlsxnRUISuTyYTZbGbXrl281NrK7//6Vw5FReF2u7G9/jqec8hxIzc3F71eT15eHl988QV5eXno9Xpyc3P71V9WVhbl5eWYzWZ8Ph9ms5ny8nKysrJ6be90OomMjKS8vJzdu3dTXl5OZGQkTqez1/Z2u53w8HBSU1MZNWoUqamphIeH93lDcDJcED6dRbGXYffZqXHUsmnuep7LfoaxxgxERL5p+Zabvl7CPd/dx8b6jQC06FpQqVUYDAZ0Oh0Gg8Hv4z8URBLJWMb6bTe700gjPgZ2JWEoyBxv4t33ppM+xojbLfLuu+WUlh5xU1Io4Ec/gsREaY6gt7SOjIwMEhMTMZvNlJWVYTabSUxMJGOEVNaWkZEZWgZyBn8GsE0QhJtEUSwTBOEC4B3g6wE8xxnBqdrlyZzljBkDWVlQXCx53w0RfSWRNjc3s3fvXrKzs5m8di0VM2fieOst2LMH4y9+gbehAWXk6Vc8HekUFRXh8/lITEz0J8WWlpb2KZE5Eadawdrn89HU1ER2djbBwcG0trayf//+PqUuRqMRjUaD2Wz2J9lqNJoBKWRkUpt4Y8prAJTayvh5xs8AePHwS+S25bGlaRtbmrZRbCvl0IVFXPjtdNyCG42gQfSJ6PV6LBbLaY/jZIgmmhu5ERcuNBzJEXDj5m3eRoeOi7iIsYw9Ti8jC5VKwdSpYVyxKJZXSws5VGBhas568g9eQkSEtDJiMsFjj/VdVmPUqFEkJSXR0NDgd3GKjIxE3ZtgX0ZG5qxnIAP82cBvgO8EQfgYuB74mSiKbw7gOc4IBtp+T+YMRxDg+98Hjwf66dDSH/pKIq2srCQ7O5vY2Fhqn3oKNdC+fz+K3buxPPccvqoqwr/9FkVg4JCNdThob28nKSnJv8phMpkICws7Ld/wU6lgHR8fj0ajwWKxYLPZ/AmRfR0fGRmJ1+v16/S7bDIjB/hm7Lfjfw3Aty07idJG8tj5S3nh0EvsMn/H+voNEAH7puWh79AS1hrG6Jo0dDrdoEl0+kKLtse/97EPe+ePF++QjmWguOX/kkhJMXDffbtpa3PzzvIKfvTjNHQ6aXH86ODe55O2CYL0975q1So6OjoQRdGvw1+8ePEwXImMjMxwM2ABviiKXkEQXgWuAH4A/Ad4e6D6P5MYaPs9mbOAYSgX39eM8tdff43ZbGbz5s1YLBYuKS4mJiqKIJ8PT14eprfeOuuDe4Dg4GDsdjtWq9Wvgbfb7QQHBw/J+adNm0ZeXh6VlZX4fD4UCgUBAQF9Vh4NDw+nqakJk8nkH29zczPh4eGDMr6poTm8PfWfaBQa/nDwBf466S8sLV/O9uavaQiXklttOgcKn4Ly0ArmHpg1KOM4WbLJxoePgxxkHON67Msjj1hiCSV0mEZ3cmRmmkhPN/L660Xk57fz6KP7WL++jk8/O/a9LSuD5culOno5OdI2q9VKS0uLvwhaaOjJXe/OnTtZu3YtjY2NREREsHDhQnK6OpWRkTkjGbAAXxCES4B/AR8BNyEF95sFQfieKIqVA3Wefo5NC/wFmA9EArXAa6IovtStTRrwCjAdcANvAY+LonjKgs6Btt+TOQux26GpSRLVDiK9zSi7XC62bNnCpEmTCA0NpT0piXWHD3NjYiI5ra3Y//xnnB9+iPHRR1Glpw/q+IaT7pV+6+rqCAwMJD4+/qQrup4ugYGBfi/8LtxuN4F93FwpFApUKhUNDQ3+bSqVqt+SopNBq5Rmyf9z/ltE6aJw+pzYD9qo8FXSEm2mKbSZptBmjC2BONKc/CbvGW5LvZXEgIRBG1NfaNAwtfOnOzZsrGQlXrzMYQ6zmT3kYzsV1GoFT/5qHC/9+TDffNPC3r2t1NY6iIk5Ynfp8cBrr0F7u1Q0e/x42LRpE01NTf5kba1Wi8/nY9OmTdxyyy19nm/nzp18+OGHZGdnM3XqVOrr6/nwQyl1Tg7yZWTOXAbym2Ep8CNRFO8TRTEfuBBJf793AM/RX1RAHbAQCEKSD/1SEIQbATqTg1cBeUAMMAW4DOiXb2DXzKnZbGbPnj2YzebjanFlzjE2b4bHH5cSbz2eIT+9UqkkPDwcvV4vST1iYkjVaKiLigJBwJ2bizo7G2XC0AdpQ0lWVhYWi4XU1FRmzpxJamoqFoulz6TYgWbnzp00NzejVCrR6XQolUqam5vZuXNnr+2dTidjxowhMDAQQRAIDAxkzJgxQ5LcGqWTii8tjL6I88xZ/Nj3Q8bY0kmxSsWnLKFWPk39nM9qV7PPvJ+95n1sbxoZ6VdFFOHBg4hIAmfG7/S11ybwu2eyMBiU1Ne7mDJ5Ldu2HrEEVang2mul55kzpeeNGzdSWVmJWq0mNDQUtVpNZWUlGzduPO651q5d65fsKZVKYmNjyc7OZu3atYN9mTIyMoPIQGrwJ4qi6LeJEUXRAzwiCMK6ATxHvxBF0QY82W3TXkEQViPdhLwPjOl8TBJF0QVUCoLwZ+Ap4Pn+nPNUtLgy5xg+nzSDb7fDnj1H1teHCJVKxcSJEyl7+HCIAAAgAElEQVQvL6e2tlaShWRk0Njaim7xYpyffILz009RT5uGMiICVVrakI5vqDjVpNiBpqCggJiYGARBwOv1IggCBoOBgoKCXtvrdDqKioqIjY0lJCQEp9NJUVHRMU5Jg0mENpwkQxIt7S0kuxJJaUlib8x+aoLqqAispMxezp277iI5IIlFsZczLXQqO827mBY6dchWRo5mAhOIJppCCkkltce+SiqJIw7FCCwJM3ZsEA89PIZnf59PY2MHq1ZVc+GMI5ag06bB6NHQZXNfXFxMamoqgYGBKBQKNBoN7e3tFBcXH/c8jY2NTJ3ac9UjKiqKXbt2Dfg1ycjIDB0DqcGvAhAE4UfAg6IojuvcfkrTAIIg/BKYBEwGUoByURST+2irAB4EfgQkA43AB8CvOoP6vs6hQnL9+UPXpt6aAcmCIASJoth+KtcgI3NcZsyAAwekqbchmi3uTkREBEqlkhkzZvi31dTU0KhWE7BgAc5Vq/Dk5+NYvhz9VVedtQE+DO+NuMPh8Af4LpfLn6jaVzK+Xq+nra0Nt9vtr1LatX0oMRqN1NXVMVE9AUWoglBVKEmliVyecgn7kvL4unkHZfZy/lL0Op/XfUGgysjqmSuxdFgJ1YQMS6Af1fnTnSaaeJu3CSecxSwmnpE1IRMZqeOppzIpLrLy3nsV/PnPhXz9dQubvpqLQiEgCEeCe5Ckdw6Hg7KyMn9Oh9frxeU6tmhWdyIiIqivrye2m/dmfX09ERFndn0BGZlzncGYtghHmg3vL78H5gHFgPkEbf8MvAjkA/cDHwIPAJ92Bv998QrQhpQIDHCo83y/FQRBLwhCMtKNA0iSHhmZgUOlgnvvhezsvj3vBoiqqio+//xz3nnnHT7//HOqqqpYuHAh+/fvp6amBq/XS01NDfv37+ey9HTaH3kE3dVXA+DJz0c9Zw6+0/BZl+kbjUZDTU0NQUFBjBo1iqCgIGpqatBoNL22VyqVREdHY7PZ/Frr6OholEplr+0HC4VC4ZcUCYLARPsEooUo9IKe2REz2TJvIxdFzUeBgmJrCfta9zHry3ncvOP/2NTwFaIoIg5xVefe2M52fPhopPGYYlojiSuvjGX2bCnY3r+/lcJCa6/tOjoUlJVFotFoCAoKQqPR0NraiucEMsC+Pg8WLlw44NciIyMzdIzET7U0URRLAARByAN6zTgTBCETKaj/ryiK13bbXooUwN+E5MN/9HEvIM3ezxNFsQMkOZEgCFcALwHlQAvwJpDNiW8yZGRGJF0F15KSkkhOTqatrY1t27Zx4YUXcv3117N27Vp27dpFREQE119/PRNzchCvuQZveTnOlSvxHDyI5ZFHwOvF9PLLw305Zx0JCQkoFAqqqqqorKxEEARUKhUJfeQ+2Gw23G43CQkJ/kqlVVVVQ+5z3tbWhs/no6qqio6ODn9AGWwO5N4x9+ITfXT43Hw2YyVvlf2LldWrKLGVAvDI/l8yN2I2IiJ/Oq9f6scB41IuJZRQrFiJJrrHPheuY2w4h4trr0tgztxIJmT/j4YGF5dc/BV/+EM2199wJEG/thaqq7+PVptIQsIe4uIaUCqVqNXqEwb4XYm0R38eyAm2MjJnNiMuwO8K7k+C7yHJaF46avs/gOeAWzgqwBcE4SUkJ515oig2HXXew0iJtV1t7wV2Hk/qIyMzIDQ1wc6dcOmlA9rt8QquXXrppb1/gbvdtP/sZ5IWf+VKOnbtImzdsKfRDBpVVVXk5uZiNpsJCQkhKytryCQ7oaGhNDQ0EBUVRWBgIFarlba2tj597e12O0FBQXg8Htrb2xEEAb1eT3v70CoIKysr0Wq1ZGdno9VqcblcFBUVUVkpmaUpBAUfXvAuAN+YdxKtjcLitfJuxftU2Cv4d/lSonRRpBeP5uvmHbw68SUC1UNvy6pGzQxmHLO9iiqWspSZzOR8zh8Rs/tBQWouuTSGD96voLrawfLl5Vx3fYJf7qRSgcejR6uFkpIwFIp9/pWSEwX4IElySkpKaG5uxmKxUF9fP9iXJCMjM8gM/ydX/8kBfMC33TeKougUBGFv534/giC8giT9mSuKYiNHIQhCNlACOIG5wBPArYMzdBmZTvbtg7//XXLTiYuTZDsDRH8Krgk6HYYHHkARH4/z00/xFhVhf/ttfFVVBP3udwM2tpHA8VY4hiLIr6+vJzU1FYVCgcViQavVkpKS0uf/j8vlor29Hb1ej06nw+l00t7efkKN9UBjt9tRKBT+2fuOjg7cbjf2XqRc9476MV7RS6Ozke1NXzMrYibLy9+l3lnPE3lPEaIOZlnFO0RoIkgLTOW8kAlDei1HIyLyBV/gwsVGNpJJJiEMf/0StVrBP/+Zw+TJITz4wB7WrKnjmqu38e5709HplEREgNH4MdHROWRmlqNSBeP1enG73ScM8D/77DOWLVtGTk4OsbGx1NTUsGzZMgAWLVo0FJcnIyMzCIw864CTJxZo6nS9OZpqIFwQBA2AIAhJSHKeUUCpIAjWzsfn3Y65Hkme04a0AvADURTP3qlLmZFBSgp0SSxKSwe0666Ca905mYJr2jlz8BYUoLvySgAcS5ei7qP40plM9xUOhUJBSEgISUlJ5ObmDsn5PR4PXq8XtVpNUFAQarXaX6m2N1wuF0qlkpKSEr/zj1KpHPIA32AwkJycTHNzM/n5+TQ3N5OcnNzDz787SkFJtD6aT2eu4Nns33F57KVcHXcVAUo9Zncrv8p7mp/v/yUfVX1MnbOerxo2D+n1dEdAYC5ziSSS6UwfEcF9d3KmhDJlijSmzz+vZfu2IwvRJlMZ4eE78HikG66uh1Z7fKnRihUryMnJITExEZVKRWJiIjk5OaxYsWJQr0VGRmZwOakZfEEQfnIKfQ5VNacAoK9vNme3Nh2iKJbTu1OOH1EUn6SnlWafCIJwF3AXZ/YNksxIICgI/u//IDgYBtit5nQKrnVs347uuutwfvIJ3tJSRMC1dSvaGcdKGs5U+rPCMZCEhITQ2NiIXq/H6/WiVCpxOBx93oC1tbXR1tbGeeedR0REBI2NjezdO/RlRsaOHUtNTQ1jxozBaDRisVg4dOgQY8eOPe5xoRqpquojY35KlC6KHU3X8WrRa+xvy8XisfD3kjd5r+JDxgWN5byQCbS520nQD13hsS7SSOPH/Bgv3h7bD3OYfPKZz3yMDH1laoApOaFs3DSXaVPXkZ9v4bbbvuXFP5/HddclEBwcjMfjwW63IwgCoijidGpOWJm5ubm5h4MOQGxsLFu2bBnMS5GRkRlkTlai86dT7HcoLBLsSFVpe0PXrc2AI4ri34G/C4JgBGQLTZnTY/LkQen2dHzeg37/e0SvF9211+L84ANsf/oTivBwNJMnIwyxLeNg0bXC0T2gPpkVjoEiISGB3Nxc9Ho9er2ejo6O4xbaslqtTJgwAYfDweHDh9HpdKSlpbFv374hGW8X8+bNY9WqVZSXl/vtGIOCgpg3b95JHZ8amALA+OBMfjn2UdKNo/jBzh+zr3U/7Z52drR8w6S10zCqjCyb9i/Sg0ajFtRDGugrOn+68OLlf/yPZpopoYQHeRAlQ+te1IVWq2TW7EiqqhzU1Tn51ZN5XHVVHCEhIahUKsLDwxEEHQUFE6isDGP06HeP219YWBg1NTUkdquqXVNTQ1h3D04ZGZkzjpMN8OcO6ij6Rw0wThAEbS8ynTgk+U7HMIxLRub0EMUBs888HZ/39p/9DFViIoJKhbe8nMCf/hROsNx/JnE6Kxx9cSpJuy6XC61WS1tbG2q12i+n6Etyo1Kp8Pl8+Hw+NBqN/7VKNbSpVPHx8eTk5LB27VoaGxuJiIjoV95ClC6SKF0kHp+H+VFz+ceUv/JcwR9ZUb2SNnc7Fo+FK7ZeTXZwFheETefRsT8bpCs6MS5cRBBBM81cwAXDFtx38cork7jiilguv2wLRUVW7rprF16vEq/XhdvtxmKJp6YmGZfLQXX1zOP2dfXVV/s1910a/J07d3LLLbcMxaXIyMgMEsJI8CPuiy6bzN4KXQmC8DvgcWCWKIpbum3XAc3AZlEUB9aW5NgxGIH29vZ2jMbhWbKVOcuorIT33oNFi+AEkoeTQhSlPqOiegbnlZVQXw/JyVK1nF5uKHw2G0JAAO2PPopj2TKUyckIWi2m115DlZGBoDjzFWoD6aJTVVXFZ5995pdHdD0vWrSo1z5nz55NdHQ0o0ePxmAwYLPZKCwspK6ujq+++uqY9vPnzycoKIjw8HCUSiVer5empiba29vZsGFDv8bcH7onJx99Y3S6ycmfVK9CgYJyewWvFr6G2d0KgE6h47r4q6mwV/HhBe8OW1XcMspIIKFHgH+YwxgwEEfckI7FYnEzedJaysqkheq0UV8zY4YFq9WKy+WipmYBEE5AwPt8+eXHx+3rs88+Y8WKFTQ3NxMWFsbVV18tJ9jKyIxALBYLQUFBAEGiKFqO1/ZMdtF5H3gMeAjoLhb8IZL2fvlgnrzTN/+awTyHzDmGywUvvAAOhxTkP/HEkQTc/lBVBS+/DO3tcM89MKGbQ8mOHbB+PSiV8Morks9eF50rCAqDAdemTSgiIhDUarxlZRifegr1uHG0P/EEiogIAh98EPvbb6O74QYUfSRZjmQGspLtpk2bcDgcpKWl+QP24uJiNm3a1OtsaHNzM6mpqYiiiMvlQhRFAgICaG5u7rX/9PR0ysvLCQ0NJSwsjObmZqqrq0lPTx+Q8Z8sx7NfPd338sq4xQBU2avZbd7D+WHTeKXwLzS4GllW8S5qQc0v9j+OSW3ixsTrSAsc2irLyST3+LcLF6tYhRUr05nOxVw8ZGMxGtUUHLqMq67cyhdf1FFaMomAgPWkpXnRaDTEx2+hra0FUfSesK9FixbJAb2MzFnGiAvwBUH4PyCp858RgEYQhCc6/10uiuJSAFEUcwVBeA24TxCE/wJrgLFIlWy/opciVwOJKIqfCoKwCbhtMM8jcw6h1Upe+CtWwPnn9wy6+0NEBHRZF+bl9QzwKyqk5/j4nufx+eCxxyAxEebORRkfj1ajQWxpwf7vf2P/178IuOMOjE8/jdgZlLr37UO/ZAmOFStwbdhA8F/+guvLL1FlZqKMijq9aziDOHjwIDk5OQQGSp7ugYGBpKSksHPnzl7bi6LoLxjVpWXv2t4bXYH9oUOHyM/PR6fTMWnSpCGvCjsUycnxAXG8NfUfAGxt2k6qIYVPaj6l2lHN22X/RikoqXHWcGPC9SgFJReETx+wc58KlVTiwAFwTLGsoUChELjxpkS2bWvCYoEDeReRkPAper2AIHjweDr6rIwsIyNzdjPiAnzgTmD2Udt+2/n8FbC02/aHgDIkR5vLgSbgVeBXoij6BneYMjKDwIIFkJoKo0ef+rFWKwR2Kxik1cIll4DJBJmZPds+8IA0w99xVJpKaSmYzdJj4kRU06cjxsfja2/HsWwZ3vJyHB99RMD3vocQEACA6SWp1pzu8svRXHABAB1btqCIjkZ0uWi7+27CVq/GXVCAEBCAqlsy39lElyb+RNu6UKvV1NfXk5GR4Z/xLygo6LMyrc/nIycnp0cya2tra583EIPFUCcn/3vqmwBMCM7i7dL/UOuso9xezgeVH/FR5X+ZEjKZlye9gN3rYHxQ5pDKd0Yxinu5l33sI5ueNSzcuFEz+FWGlyxJImOMkenT/4fPp6G4eDaLFjXg80luOmazVIz9u++goABuvvlYRZ4s0ZGROfsYcQG+KIpzTqGtF3ih8yEjc+ajVPYvuG9thWeegZwcuO466NLHX3FF7+3VasmD/2i0Wpg+HQ4c8N8UeCsrcbz7LvoJE7Bv3IjlgQdwrliBevx4VOPHo87KQpmaiqDR+GfsjU89BYDochH0J8mEq+Orr1CEhqJMSKBl8WJC3n0X0elEtNlQJSUdO5YzjLFjx3L48OEe9pGHDx/u0z4yMTGRtrY26urq/Mm1Doejh5vJ6fQ/WAxGcvLx6ArYr4q7kouiFqAQFMzeOB+loKTEVsq35p1cuGEOodoQ3j1/GWmGVAwqw5AF+qGEMvcoHworVt7gDSYzmZnMHPRquJOnhBIY2ILVGsmhgnDa2yxMv+Ag0dHRNDc3s3kzLO8UrSYlQXe3W7nQlYzM2cmITrId6chJtjKDTkcHfPABXHSRlCjbF6++Kslw4Fi9fX842slHFPHcdx8ty5fjA+icve/aJ4giqtGjUU+bhmriRNRZWVIibi/yANHno+Orr9DOnYvz88/p2LKFoN//nvYnn0R/882oRo3C19CAMm5okxZPl6qqKlatWkVHR4dfcqPRaFi8eHGv2vTHH38cr9fLgQMHsNvtBAQEkJmZiVKp5Jlnnjnt/geTgUxO7g8l1lKSDIn8ZM8jbGnaSpWj2r8vQZ/AD1Jv58dpdwEMS0Lux3xMLlLBtCUsYTT9uGk/RebNW0hZ6RyqqkajUPi4+uoD2Ox7sdvtrFixnt/+FtxuuO22nh8Pd955J+PGjetxY1lRUUF+fj5vvvnmoI9bRkbm5DmVJFs5wO8n3ZJsb5MDfJlBweOBP/wBysshLg6efLJv+8zmZvjrXyV5z803D/hQfDYbTRMmEPbTn+J2u/F0dODOzcWTm4u3svKI1j8wUFqFAASVCmVMDOq0NFRTpqC+8EJU48ah6ONvxbV+PepJk/A1NdF6zz2Er1+P4733UERHo50zB197Owrpg23EciqB77Jly/j2229pbm72W2aGhYUxderUPi0KhzuwHmkUWopQK9R82/wtv8n/PQ2uBv++nJApBKuDWT7930M+rmqq+ZRPCSGEG7lxSM55ySWXIAihbNq4GK9XiULhY9as1SiUDXzxxReUlkpqvdDQnsddddVV3HjjjT3sVj0eD++//z4rV64ckrHLyMicHOeKi86wIifZygw6KpUkkykvl6rcHm8mMiwMHnnk9BNz+0BhMBC2eTPK2NhjHMB977+Pe9kyPGYz7rFjcefnS9VvPR48Bw/iycuD1auh02VHmZyMOj0dtdWKatQoVDfeiHLiRLQLFkjnCgoifN066XVcHIqQEESPh4YxY4gqLcV94ADesjL0116L6PEgDLEP/PE4FVcej8fDjh07MJlMKJVK2traKCwsZNKkSQPS/7nAaOMoAAJVgSgEJSZ1EE/m/ZoSWyk7zbsAuHH7zWiVWl4870+Ea4emeFMccdzFXXTQM8ellVbKKGMCExCOX1z9lNFoNISEqJkzdwObv5qN262loGAa2RPWAr0r8kAudCUjc7Yiz+CfBrJER2bQEUX48EOYORNiYo5s379fCvznzfMHzkOB5fnnMfzwhyiOngZ0uaChARISAPBZrXgOHMDzxBO46+pw+3x4LRZEj0dq7/VKScEABgOKuDjU2dmSrr+5GXVtLYrRoxGeeMJ/ClEUETZtwl1ZiRfQ3XorTTNmYHr1VRQJCbhWrybg+98fgndhYFi8eDEOh4OwsDBUKhUej4fm5mb0ej2rVq0a7uGdsbxQ8GdSA1P4W/E/2d26x7/9wvALuDjqInJCpzA5tO+bqMFCROQd3qGQQlJI4RZuGdCCWZdffjkmk4nw8HB2fJ3C3r1xgEBQUBNNzXcf076pCQ4fhpaWYzX4XYWuZA2+jMzIQp7Bl5E5WxAEuOGGY7evWSM53iQmnr7e/hRQRkYidnTg+OAD0GjQX3UV7oMHUY0ahdAZ3AMoAgPRTJuG5tNPoaYGtFrEiAg8hw7hzsvDs3kz7o0b8dTXIyoU+Orrca1bh2vdOknu43ajCAhAtX//kWTejAyU77yDWqFAfeWVAIRt3AgKBd41a/AtXQo1NbRVVKCeOZOAm2/Gvnw5+jFjpLnSiAgpw3CEUFRUxOTJk0lNTUWn0+F0OikpKeG7774b7qGd0fw042EATJpgVtesocxWxpambWxr2s62pu2MDcrgybGPE6DUc0HE0NlrWrFSRx0AwQQPeDVctVqNz+cjKiqKG29SEmAws31bKO3t4Xz+eS2XXnpkguDQIXjjDankxv33L+KWW2DFihVs2bKFsLAwObiXkTkLkGfwTwN5Bl9mWLDbpYJYVVWSW87TT0Nk5JAOwb1vHygUqLOyaJo1i9CVK/EcPoxz9WqCfvtb3Pv2oYiLQxkeftx+RK8Xb2mppOfPy5Oed+3C19oq3dx0r74rigg2G6qQENQzZqC67LIjybybNsHHUrVO37PPglqNoNHQdu+9mBITcWzahNdgwPjpp7g2b0Y9YQKKrvcxMBBuugkyMo6cq7hYmuI0mXpuH0DGjh3LokWLiIiI8G9rbGzks88+4+DBg4NyznMRh9fBvI0LiQ+I56vGzf7tYZpQXp74IucFn0ekLuI4PQwcTpxsZjMzmEEARxLVffhQcHqVoX/4wx+i1Wqpq6ujo6MDtVrL+nVX4XBo0GoVvPf+dC6/PBaQPjqee05Kur3iCqlwtoyMzMhHTrIdAuQkW5lhRRShrEzS3HebOR9OvI2NeCsq0EyeTPuTT6K9+GLUWVm0LF5M2KZNeA4eBLcb9QlWHERRxFdT0zPoz8vDW1srXXeXw09nToI/mTcoCFV4OOoHH0SVmXkkmfcXv8BXX4+YnY3y4YdpveceAn/+c8S8PGyPPELwrFl4rroKxdSpKLp0x8uWwZYtYDRCp82nn+eeA51OKkZ2/vlHtjscYLNJDkN6/fFzJoCcnBzGjx9PcnKy3yazrKyMvLy8Ife2P9sRRRFBELhr590U20rIbcvz7zMoDfxi7CPcmXI7KsXQL2qLiPybfxNDDHOZi4b+FaZ69NFHsVgsKBQKPB4PKpWKhoYA1q/LxmrVMmGCiS1b56PTSSsHu3ZJf0o5OQN5NTIyMoOJHOAPEfIMvozM8RHdbjwFBaizsnB88gmi1UrAkiU0zZhB8LJlCFotHdu2ob/uOkSfD0HR9yymr7lZkvd0C/o9x6meqkxOlmb409JQp6aiyspCOWbMkf4OH8b74Yeow8KwlJWhnjED7cUXY775ZkLmzEH89luIjkbx/PNHOu3ogPvvl15fdZVUebiL7dvh352OLc89B90LP33wgXQDkJoq5VMA99xzD1VVVURGRqJWq3G73TQ0NBAfH8/rr79+8m+yzEnjFb14RS+f1/6Px3KfpMnVhIj0HRiti2JyyGT+mfMGSmFg5TPHYyc7Wc1qAGYz+xhP/ZPl17/+NXv27GHKlClERUVRX1/Prl27MBpn8OEHyYgiZGWZ2PXdRcNiHSojI3P6yBp8GRmZEYGgVqPOygJA36mbBwj58EMUERF4S0rwVkse5q133onu0kvRXXcd1t/+lsAnn5SSd9VqBJUKRVgY2tmz0c4+UujaZ7Xiyc/3B/3u3Fy8hw8jejx4y8rwlpX1GI8iKgp1VpYU+I8fj/rWWxHj4zF2BjxiRwcBd9yBMGcODlHEW1qKEWh/4gkMP/4xioAAxIwMFE7nsXUJWluPvO5eJwDg4EEpF8Fi8Qf4t99+O2//6U/M2rePVq2WQzExuENCuP3220/jHZc5HkpBiVJQsjh2ETmhk7G4rSz55vtU2auoc9azunYNF26YTaDKyGczVqBT6QZ9TMkkE088FixcSP+LhblcLqZOnUphYSH79+/HYDAwdepULJYWHn30Ep57roDc3Db+9XYpt9+ReszxXi8UFg6aIk1GRmaIkQN8GRmZIUfZ6QikSk8nMD0dgOB//EOKMjo6QKdDUCiwvfMOnv37Mb38MpZnn0V/880oo6PxtbSgjImRknmnTkUzdaq/b7Gj40gyb14e7v378eTnIzocUjJvfT2u9ev97RUmE6qsrCPJvOPHI2o0BNx3n7+NOjsbITgYd14elnXrCFuzBvvy5agFAfXEiYgdHQgTJkBwsJQjcXSBr4gIyTWo201BTk4Ouquvxv3CCzjb2hBzcphy883kdGkmli+H/HyIj4e7j3JBKS6WzhUc7K87IHPyCIJArD4W9PDG5L8QqArkqbyn+apxCyW2UgBmbpzH9LBp3JX6A8YHZw7aWCKI4A7uoI22HvIcHz5KKSWNtJPqx263YzKZmD9/vl/yVV1djcNh59lnx7N9exObNzdx3327USoFbv3+Ed/MjRu/4w9/MFNVpWf+/FyWLJl85PdQRkbmjESW6JwGskRHRmbw6dJP2999F93FF+OtqaH95z8nbM0abP/4B8qkJHQLF+LeswdVdjZCLwGvP5k3Lw9P50y/Jy9PSubtBUGnQ5WZ2SPoV2VkIGi1/vE4V61COXo0yrg4yaO/pgb3t98i2u1o5871t+uBzyclRndx8CB88gnU18NTT0kBexevvAIHDkjOP489dmS70wkPPii9vuYauPjiI/sqKiR3pYgISE8ftLoIZyOFliLy2wvY3LiZdyrewyt6AYjWRXNnyu2cF5zN7MhZQzaebWxjHevIJJNFLEKP/rjtH330Uex2e4/vIovFQkBAAM8//zyHD7dzzdXbOHzYSnS0lr37LiE0VMPOnTt58821FBdfh0ZjIDS0lpiYD7n++uvlIF9GZoQhS3RkZGTOGrqC5IDvfQ8ARWgoYWvWAKCePBmFyYTo9dJ6772Ef/UVznXrcO/Zg/GXv6Rjxw5U6ekoQkOlolqjRknaebol8x4V9HtraxGdTtzffYe7m2WloFKhHD36iLxn/HiU8fEojEaiKioQBAHR6UR0OABoPO88QletQlCrce/Zg+7yy3sG9wBjx0oPaUA9940bJ9U4OLrgUEvLkdfddf4g1Uf49FPp9V/+0nPf2rWSK1Fq6ohJzB5JjDaOYrRxFPOj5hKgDMDt62Bp+TvUOet45uCzGFVGHs34GTPCL2ScaeygjsWDhx3sAKCeetSoT+q46upqYmJiCAgIwG63U1tby+jRowFITw9i5SczOW/C/6irc3Hxwk18vWMBa9euZcaMJLKyLJSX+1iwwEpDQzZr166VA3wZmTMYeQa/n8guOmeGIrkAACAASURBVDIyIxNvfT2+5mbU48bR9tOfEnD77QiBgbTdcw9ha9bQ8d13CDod6szeZRe9JvOWlh4bgHfiT+YdP97/rAwPx9fWhmA04jl4ENfatQQ+/DCtP/oRumuvRbtgAc5Vq2iePJncvDzMZjMhISFkZWWduFKt0ylJdFpapJuA7jcA//oXfP21tBLQPTlYFOGhh6RjZ82CJUuO7GtokFYK4uKkcqfqkwsmzwX+WPACBe2HWFe/AafPCYBKUPHTMQ9xe/L3CdWGnqCH/mPFyud8zjSmkUjiCdvfeeedxMZKNph2u52AzjyQmpoa3nzzTX+7p58+wDO/ywfg0V9kYLG8yeWXX46y28qX1+tl9erVvPTSSwN5STIyMqeJ7KIzRMgSHRmZMwOxowNvWRmq9HQc772HYDSivewyGjIyiNixQwrqDxxAf+WViB4PwlHSlmOSefPypGRet7vX83Ul83YF/erx41HEx+NrbETQ6cDjofbuu9l6zTWM27uXAK2WxksuoWX9erJvuon4UaP6d6E+n5Tsa7VKRdC6sFgkmU9HB9x4o1QBuYutW2HpUun1b37TM3m4pERKGI6MPHb14Rxic8NWHst9gmpHDTavDQCdQsv8qHm8PPFFgtRBQzaWb/kWPXqyyOqx/Qc/+AHx8fEkJCQQGBiI1WqlsrKSqqoq/vnPf/Zo+/DDe3jtL0UAXH55PddeF+y/OQDppqC8vJy7736co4tWy8jIDB9ygD9EyAG+jMyZjbe+HkVkpCTR2b2bgNtuo3nxYgz33Ydmxgxsf/wjxqeewme1Imi1CN1mt49J5s3NxXPggF+iczQKk6nHLP/25mYM48YRKorgdiPGxqK86y5K7rmHOcnJUtGw3/wGT2kpishIFAbD6V2sKEqz/lqtVNyri48/luQ7arWk++8eyD/1FNTVSdWS77nnyHa3W7pZON0xnWE0u1qYu3EBVq8Nq8cKQKAqkHh9HJ/M+JgQTcgJejg9qqnmTd7Eh49ZzGIeR27UnnnmGZRKJfX19VgsFoxGI1FRUXi9Xh5//PEe/VitHi6Yvp6CAgtaLSy5ZR8zZmT47TX37dtPTMwPKCpK56GHpHQOGRmZ4UcO8IcIOcCXkTn7EL1eEEVEmw3H8uUY7rkH64svIlqtGH/1K9p/8QsMP/kJgsGA2NqKMi6ux7H+ZN4uB58+knkdDgfqwEB8aWl409PxpafjSUtjd3s7N1x2Gd7ycjRTptB6333or70W9ZQptD/yCMFvvIHPbEbQ66XVgNO+4M7Av6UFOvXagBTE33+/tP/SS/25CwDk5cGrr0o5APffL8l7uvd3Fvusu31u7F47d393P1sbt/mlO4GqQMYaM/jDhGfJNI0blHP/P3v3HR9Vlf5x/HNm0qkBAtKMShMhgCBNFEUQVqxIsWFZdO3YV7HxW2WxLoiKXVkrFuwgiIqKSA29CxJ62ZAQ0jOZcn5/3BASSDAhPfm+X6+8Eu6ce++5k7nkmTPPec4WtvAFX5BFFjdxE804POo+Y8YMPvzwQ7p3706zZs3Ys2cPsbGxjBw5kosKWKo2Pj6LLp1nk5CQTatWwYy4Yj3JyfFERUXRrdtgpk8/HZ8PGjWCceNq9Ac4IpWGAvxyogBfpGrbtWsXa9asKVIOfG41nw8/JHzYMLzLl5P++utEvv8+qc89R8iZZxLSpw/ehQsJ7t07d3Jwvsm8eYL+jG3bMC4XrjzBcMBaAsZQO2+t/o4dCerQAYzB8+OPhA8ZQtqECVi/nzoPPkjK2LHUvu8+TEQEuFxHpRcdt0DAqcqza5czKTc6+vBj338PX33l/DxxYv6R/EmTnDcLXbo4VX6qKV/Ax96sfXyy4zMmbXoJr3XStcLd4VzY9AKGNL+U808YUOrnTSGFneykA/nnkMyaNYt169axZMkSkpOTqVevHj169KBDhw5ckHdBtjxef+1P7r13BX4/XHZZMz6bdrgO/4IFMGuW88FNTlVbEalgCvDLiQJ8kapr165dzJ8/n+joaOrVq0dycjLbt2+nT58+fz3R9QjZS5fibtoUU7cuScOG0XD2bDI+/phAQgK1R4/G8/PPuRV/cs+/Zg1rvviClmlp1Nq5E7txI2bnTkKCg/NNeDwk32TenPKdrkaNyHj1VSJuvhnP7NlkvP8+DT77jIyPPiKkZ0/crVqBtcdcIfi47NjhTMxNTISRI/M/9sADTs5/795www2Ht8fHO28KTjoJevbMXxK0CgvYAMsOLGf5wRU8t/E/pOak7gSZIK47aSQDmpzHgCb9y7QPe9jDI98/wmlbTqNNszbUqVOH1NRUduzYgcvlYvSh1ZcL8Mrkzdx770oAXp58Orfccnj+h9erOdcilYkC/HKiAF+k6po1axaRkZFE5ik1mZSURFJSUqEjnsXh37cPm5FB0CmncPD226nzyCP44+NJe/ppGkybhue339hvLWsyMkg6cIDIBg2IOeUUGqek5JbtPHIyr9/vx+fzEQgEcLlcBDVtSkTXrodz+9u3xx0dTeZ77xHSty+43RwYPJjG69aRvWABhIUR0rVria+tUIEAfPuts2pv587QJ8/KrIsWwX//6/z82GP5S3X++SeEhUGzZlU6F2Ru/G+sSFrJlG3vsi/rfwAYDNdGX8Ook28ok/KaHjy8wRt8/dvXNHA14KxVZ5Gdnk14eDhut5ukpKSjcvDzCgQCdOv6I+vWpWAMLIkdQOfOBc8l8Pm0tIJIRVId/HKQp0ymiFRBSUlJnHTSSfm21atXj7i4uFI5vvuEE3J/rv/qqwC4oqKo+5//AOCPiyOqVSv+1r8//4uOpsmOHfjWrcMfH0/EqFFYj8dZWCtnMu//5s5l15w5NEpIIHT7dmxmJtl79sD+/biPXJm3Y0f827cTFBND/Q8+cOYGxMdjIiKw1rK/Y0caLVhAIDkZ/86dhOYNxEvC5cqfq5+X1+uM2qenO4F8Xp98Ajt3OmsC3HNP6fSlApzTuC/nNO7L4GYX8Oqfr/N7wgJ2ZOzg/e0f8sH2jzivcT/Gd3yCU+qcUqrnbUYzjDGErQkjPCicptFNOXjwIFu3bqVenk+NCuJyufh9fn969/6JjRtSuWLEAn6f359GjfLP7/jzT3j7bbjxxvxTNUSkctIIfgloBF+k6irrEfziCCQl4YqMJHvJEvw7dxI+dCgJfftSd8IE3NHRZLzzDr936UIDt5v6TZuC243ZtYvMFSvwrVvHyR6PM5k3KanA45uwMIJOO43gmBjcHTrgbtyY0HPOwbtsGdmxsdS++26SRo2i1u23E9yxI9lLlhDat4xWbU1Pz5+zn53trMwbCDir8ubN29+yBd5/H1q3hsGDj170q5LL9mdz5aKR7M7czdb0bYAzot+6dmv+2+Mt2tYpnUjZYrnvtfuovbc2O3fsJD09nVq1atHyxJaEhYYdcwT/kF9/jefCwb/h9VpiYuoRu/R8XC5nfkh6Ojz8MHg8zq9u3LgaV0BJpFJQik45UYAvUnWVZg5+WbCBAACBhAQ8s2fztdtNr/nzoVEjvFdfTej//R+Z//wna5YvZ+gFF+Bq3jz/ZN41a/CtWYN/794Cj3/kyryuevUIPuss8HpJe+IJ6r/zDumTJ+Nq2pTwoUPxrlpFUIcOpTeJN/dCrVOKc+tWaNEif/3+2bPhyy+dn599Nn/e/oYNUK+eMwO0klft8Vs/Y1Y9yqx93xPv2Q84gX7z8Ga8dcYbdGtweonP8c9//pO4uDi6dOlCkyZN2L1/N9MbTKdTRifevf/dIh3jq692ccWIhQD8e3xHHnzwcErRggXOcgkjR+bPvBKR8qMAv5wowBep2opTRaei5fvEwVqCpk9n/1ln4fvpJ2KSkqg3cSIpY8YQNmIEwTExZC9eTOhZZxE4cOCooL+oK/O6IiOdkf8uXUg4+2wazZlD9qJF+Naupdadd+LfvRtXkyalH/QfEhsL8+dDSgqMHXt4u7UwZoyzqFfPnjBqVNmcv5QFbICPd3zGmNWP4Al4crd3rX86t7T+B0OaX3rcx77xxhs58cQTCQkJISsri7iYOHY22klycjKfXvopbSlaMftBA3/ll1+cNyHTPj+TSy89XAI1IcEpmykiFUMBfjlRgC8i5aUonzhkL1lCUKtWWI+HlPvvJ/Ljj0mfPBkTEUHEqFFkzZhBSL9+AIdX5l29ulgr87oaN8bdoAGhF19M0rBh1H7oIdwtW5L+xhvU/de/CKSmYmrVKt3KPUfW1o+Ph8cfd34eMQL656lSs3GjszrvaadBt27Owl6VjD/g56f4OYxefg8Hvcm528+J6stFTQdz/cnXFvuY//jHP2jTpg1NmjQhPDycrcFbmRs5l/S4dH4Z9Qsuivb78PkCDBo4l3nzEmjUKIQlsefTokVEwdfhd6ZdVPIPUESqDQX45UQBvoiUp+P5xMG/dy8EAribNydp1Cjqvfgi2YsWkfnxx0ROmULWrFkEtWuHu0ULvH/8gS/vaP/69diMjAKPm7syb8eOuE48EZuaSq077iDl4YcJOuUUIm65hbRx46j90EPgdjvzBkorEjyU1rNpkxPIR0Udfuyzz2DOHCfyfOEFpzrPIZWsDEy6N4MFiQsYt/4pNqb+kbv97EZn8feTruOi5hcW+Vjjx48nMjISv99PZmYm4eHheMO8HDx4kCf/+WRuOx8+3LgxFP672LMnk3ZtZ+LxBGjePJw/Ng0mJCT/G4SMDHjjDWfCbQHraIlIGVCAX04U4ItIVWSzswkkJuJu2pT0118npHdv3C1bEt+5M0127CB7/nxsZiah553njPRv2XI46C/CZN6gDh0IOu00fMuXU+fZZ8maOpXs5cupP3kyGVOnEtqvH+6yWj3p88+dtJ7mzZ2a/If4fPDPfzrbBw6ETp3K5vzHweP38O2eGXy0/WMWJC7M3d4tsisPnno/50ad85dvjmJjY3n11VcJCQnB7Xbj9/vJzs7m9ttvp3v37rntvuVb0kjjUi6lFoXPlJ3/ewLnnfcL1sIdd7TmhUn55wm88gqsXu38fMcdlerpFKm2VCZTREQKZUJCcgPsWrfemru98YYN+QJJ43aTdM01NPjyS4JjYjAREUR+8gm+1avx7diBf9Om3KDfv2cPNisL7/LleJcvzz1G1ldf4WrdmuD27Ul/+228K1cSfMYZBPbtI+Whh2j4ww9kL1iAKyqKoNKovzhsmFOJJy0t//a4OGfYefNmOLJCUEoKhIdX2KpOoe5QhrccytAWQ3j1z9f5bf/v/Lp/LsuSlnPFwmtoGd6SZzuPp3/j844Z6Ftr8fl8eL1ejDEcOYC3gQ0sx/ndzGQmwxle6LH6nNWIu+9uw6RJm3nllT856eRa3H334Tz+YcOc0pnt2sGpp5bwCRCRUqcR/BLQCL6IVHeH/kYEdu8mOzaW8CFDSL7vPkJ69SJs6FAOXncd9d9/H9/mzXjXrsXGxxdpMq8rOprgNm0I7t4d/44dhPTpQ9jgwezv2ZOo2Fj8u3Zh09II7tixdC5k3z747TdYuxYeeih/nccpU2DFCujaFf7+99I5Xwl9sO0jPt05jSUHYnO31Quux6QuE7ig6SBcJn/KzPjx4/F4POzYsSO3TOaJJ55IaGhobpnMLLKYznS2spVbuZW61D1mH6y1XH31Qr74fDdhYYaFi86nQ4fDdfXj453sKOXgi5QPpeiUgzwLXd2gAF9EaiKbnU3WjBmEX345mZ9/ju+PP6jz6KMcvOUWat9/P9Sti2fGDIzb7azMu2bNMSfzmsaNCYqOJqRPHyf33+2mzmOPcfD666kzdiyuE07Av3UrwTExpXcRgYCTypOe7gT4t9xy+LHMTCfPv127/Ln85Whu/DweXvMof6Ztyd3WJLQJd7e9k7+ffD1u4wZg2LBh+Hw+6tevT1BQED6fj4MHDxIUFMTnn3+eu6/FkkIK9ci/AJYHD6EcPSE5NdVLm9YzOXAgm6jGoWzYcAF16xb+SUcgUKUXIxap1BTglxON4IuIHC178WKCOnQgsHcvaZMmUf+VV0h94gmCTjuNsMsuI+3FF3GfcAK+DRv+ejJv3bq4mjcnpHdvXA0akL1okVMd6KWXCO7ShdB+/fBt3oy7devjm8Tr98PKlbBmDXTsCGeccfixZcvgzTedScIPPghHrHxcnjakbGTUkn8Ql74Vi/N3OzoimhEth3Fv27vo1aMXrVq1okOHDkRERJCRkcG6devYsmULS5cuPeax17Oe7/iOIQyhNa2Penz16oOcfdZPZGZaLhvSjE8/PfOo59pa+OEH52m8++4Ky3YSqdYU4JcTBfgiIkXj37sXgoJwNWjAwRtvpP4775D52Wd4Fy2i7sSJpL/6KiY8HP/evc5I/7p1x5zM62renOCYGIK7dSPtueeIWrmS7HnzsAcOEH7llQQSEzENGpSscs8HHzglN4ODnYo8eaPW5cudCbtNmhz/8Y/Dn6l/MmbNY8zb/3tuoH9yrZNImp5In/TeNGvSjJCQELKzs9m3bx+rV68mNja20OOlk87LvEwWWYQTzj3cU+BI/tSp27nh+iUAPPBAW556unO+x+fPdxYdBjjzTLj++lK6YBHJpQC/nCjAFxE5ftbrxaak4GrYkLQXXyTskkvA7+fAkCFErV5N5tSp+PfuxRhD9rJl+Ddtwr9nT4HHMkFBmKZNCYqOJnTgQNJfeIH6H3yAKzISz8yZ1LrjDmx2NiYkpOgd9HicSbmJiXDOOfm3338/eL3wt7/BkCElfCaKb2vaVl7cPJmpOz7J3RaWEUb0xhY0+/MEgggiODiYHTt28PPPPx/zWKtZzQxmcDmXcyqFz5gdfedy3nhjC8bAT3PO5eyzD5cn9XrhpZdg/3646y5o1qzk1ygi+SnALycK8EVESp/NysKEheH5+WdMeDjBvXoRf/LJRC1fjnf1arK++YagNm3wLFyIPy4O/7ZtBU7mtdbibtYMV/36hF12GVnTpxN+5ZVEXH896RMnUiunjGaxR/nXroWXX3Z+PrJGZHy8U5GnnP4mbE7dwttx7/DfuPc4tJZVqDeU0+JOJfCrl/CgcL799tu/PE4GGUSQf0GrdNLzldL0egP07PEja9em0KBBCCtWDqRp0/DDx8hw3vtERpbOtYlIfgrwy4kCfBGR8mGtxRiDb+tWfJs3EzZwIAdvvpnwK67A3b49yTfdRPjw4XgWLMD/xx/4t28/ajLvob93rqgojMtF+NVX4/vjD1yRkdR9/nmypk0jdNAgXPXqFdSF/PbudXL3BwzIn7rz+uuwapWziu5NN5XmU3BM515xLvu7HCCx3QEC7pzr9Bqaxjbh9/HzqBVU8Gq0hUkmmdd5nRhiGMhAgnKqau/alUH3M34gMdFLjx4N+OXXfgQHFz6rNjsbivOhiYgUTgF+OVGALyJS8QIZGWTPm0fYoEFkvPsuNjOTiJtuIvHCCwm/7DJ8mzeTHRtLYN++oybzWmvBWtz162OtJXzIEAgPx7dqFZGffYY3NhZ3dDTuouScpKc7i2n5/UcnogcCTj3JMqopef7559O7d2/iffuJbbmcvU324XP7AAhzhXF761u4o/Vt1Aku2t+qqUxlE5sAuIZraMPhNQrmzo1n4PlzsRZuuulkXn3tjAKPsX27syDWyJFaCEukNCjALycK8EVEKq/sxYsJ7toV39q1ZH75JXX+9S8OjhqFOzoagoPJmj4dm5ZGICkpX5qOtRYCAVy1akFoKCHduxNy1llkTJlCw19/JbB3L7hcBEVH5z+htc6CWgsXQq9e0DpPRZrFi+Gbb6BnTxg0qNTLbg4dOpRGjRoRCATwer0EIiyrOq8lvkE8/pAAALWDajO0xRAebT+G+iH1j3m8gxzkcz7nBE7gIi466vGxY9fyzNMbAPj4k94MHdoi3+MeDzz6KKSmOh9wjBun1B2RklKAX04U4IuIVC3+ffsw4eEQFETqmDHUfekl0saPx7thAyFdu5L55ZfYtDT88fHA4fx8ay34fJiwMEydOribNiX8yivJ+vJL6r7yCu5GjQgcPEjQiScWfOIXX4T1650Ftp57DoJKdyH5e++9l2XLltGlSxcaNmxIYmIiK1euJKZnDOHX1ObzXV+S4Xc+vagTVJuLml7I2A6P0TC0QeHPFX4sNjc9B5xc/RBCcFs3Q4cuYMb0PUREuFi4aADt2+dPbVq1Ct54A664Iv8cZRE5Pgrwy4kCfBGRqs/6fNiMDFx165L2/POE33ADvlWrSL7/fmrdcguZX31FID6eQEIC1ucDl+tw4O/xQEgIrgYNMIEAEbffjm/tWsKuuILQfv0IxMfjbtIE5s6FBQucWvpXXXX45B4PTJsGfftCYW8OimDMmDGkpKSQnZ1NdnY2ISEhhISEULduXZ555hlSvam8FfcOEze9SHYgG4AIdwRXnXgF97a9m8ZhUX9xBggQ4H3eJ5tshjMcV3Jtunadzc4dmUSfFMGKFYOoXTv/G5cDB6BB4e8hRKQYFOCXEwX4IiLVl/V6McHBZM2e7VTjadmS/aeeSp1x4/D8+CO+deucFJ/MTHC78wf9QUG4oqII7NlD7TFjsBkZuFu1IuLqq7EeD66InEmvc+fC1KnOz/feC6cWXqbyWO655x7OPvtsEhMTycrKIiwsjIYNGzJv3jwmTZqU2y7dl8H72z7guY0TSPenAxDiCuG66JGMbnM7TcObFnqOWGL5ju8A6E53LuRC1qxJplfPH/F6LcOGt+Cjj3odsyqRz+dMvI0o3pxfEUEBfrkwxlwMXA7coABfRKRm8W3ejD8+npAePThw6aWE9O2Lf/duPNOnQ1AQgZQUMAbjdgNgs51Rc1fDhvh37qT26NG4mjeHFSuoFRwMDRtixo8HV56KND5fkVN5xo8fT3R0NM3yTAbes2cP27dv59FHHz2qfaY/k6nbP2HiphfZ79kPgNu4uTb6Gka3uYOWES2O2sePnx/5kTjiuImbCMEpj/POO3HcdusyACZM6Mzou9oW2EePB157zSmned99pT4NQaTaU4BfTjSCLyIihwRSUvCuWkXImWeSOm4cNj0dU7cuGZMnYyIjnZV5/X4IDsYY45Tx9Plw1auHTU0lZNAgQs87j8Du3dS+4w7MuHHORN1LLoEjJ/QeITY2lmnTptGpUyeaNGnC//73P1avXs3w4cPp3r17oft5/B4+2v4xr/z5GjszdwFgMFx14hXc3XY0J9c66ah9ssnODe4BLJbbRi9hyus7cLthzpxzObPP0Sk/M2c684zBqS46fHgRnlQRyaUAv5wowBcRkWOx1uJdtoygrl3xTJ9O1syZBLdtS/rkyRAaSiA5GTIzITzcqfVvLWRkOJN5PR7cdeoQftddBKwl4sYbcUdFQVZWgcPfsbGx/PDDD+zfv5+oqCgGDhx4zOA+L2/Ay5St7/JO3H/ZlrEdABcuBp1wPo+d9ght6rQudN95zOM3zwI+OKM9cRs91K8fxJq1F9CkSf4++v3OpFufD269VfXxRYpLAX45UYAvIiLHwx8fj6ldm8CuXaSOH09Iz56kT5mCTUzEZmdjU1Kwxjj182vXhpQUTL16uOrXxxw8SESPHpguXQgZOZLgNm3++oRF5Av4mLrjEyZvfjU30DcY+kadzZMd/4/2dfPPETjAASYzmQABXDujmNy1GQcP+ujbtxHfzz6HoKD8i2B5vc5SAKVcREikRlCAX04U4IuISGmxfr8zQddaUh58kOBOnfD88APZ8+ZhwsMJJCdjXS5nhN8YbCCAqVsXV+PGkJlJxK234mrcmODTTye4U6djTnb9KwEb4Ovd3/Dv9U+zK3N37vbzGp/Lo+0fJqZ+x9xt61jHTGZyLdey5ke4cPA8AO4c3ZqJE0//y3Nt2gRt2pTZGmAi1YYC/HKiAF9ERMqaDQTA7yf9zTchLQ3/77+TMXs2pnlzbHKyE+gHBYExWI8H43LhatIEm5VFxMiRBLVrR9CppxLctavTrhgCNsDsfT8ydu0TbM8Z0Qfo3/g8Hmh3L90adAXAi5dgggF45pkNjH18LQDTPj+TSy9tXujxf/0VPv7YqRJ69dUK8kWORQF+OVGALyIiFcLrxbrdeH78Ee/69ZCWRvrzzzsj/T4fNjMTExqKdbudpHev16nHHxpKaN++BJ95JsFt2zpBf2joX57OWsvP8b8wfv0zrE1Zl7u9b6OzeeDUe+nVsGfutkDAcu7AH1g0N4WQMFi+fBBt29Qt6BIYPx727oXwcHjsMWjUqHSeHpHqSAF+OVGALyIilcKKFfD661hr8fXpgyc1FTweMj7+GJuSQiA1FZuY6Kykeyjoz8jA1aQJrshIgtq3J3TAAGekv3NnXHWPDsjBCfTnJfzOfza+wKIDi3O3927Ykwfa3cdZjfpgjeX5pFd5vlskKTtDaH1qBLGLBlGr1tGfHhw86Ey8veqqEq3zJVIjKMAvJwrwRUSkUjhwwKlDuWQJPP20E8gfsmAB/sxMsnbsgMxMsr7/Ht+GDQTS052gPyzMmfXq80FaGkRFEdSkCe7mzQn9298I7tiRoJgYp4JPHgsTFvH8HxP5PWF+7rbO9TrxcPsHad/4VCat+oqXekfh98Hllzfn4096FzgvwFql5ogUhQL8cqIAX0REKotdu3axPjaWhMxMIiMjiYmJoUXTpvDQQ5CaCp07w+2357YPpKeTvXAh/q1byV6yhOy5c7FZWQQSE51FuoKDwedzFumqUwdXkyYERUUResEFBMfEENSxI+6WLVmWtJwJf7zAnPhfco/drk5bHmr/AAk/tOfmm5xFsF5+uSvX3dqMMMIwFB7Rx8fDxo1OXr6IHKYAv5wowBcRkcpg165dzJ8/n+joaOrVq0dycjLbt2+n7ymn0PTDD53a+TfcAL17H97J63VG7vMMn1uvF+/GjfhWrcK7bh2eb77Bn5GBPXgQsrKcoN8YrM8HoaG4GzTA1bQpof37s7FTfSbVlWBuAgAAIABJREFUncdPGYdTd1rXbkX9X4by03Mn4DIu7pyXwFk9mnIhF+ZOys1r71544QVITobLL4dBg8ryWROpWhTglxMF+CIiUhnMmjWLyMhIIiMjc7clJSWRlJTEBQMGwMqV0KkT5J1Q+9lnTo3KwYPh9NMLzZOxgQD+bdvIXr0a/9q1ZH3/Pf69e51FulJSnJz+kBDw+zFuN5vb1uLNoeH8eGoWNueQwftP4OBH5xP8ZwduXrGNEY0u4AzOOOpcu3c7AX5qKlx8MVx0Uak+TSJVmgL8cqIAX0REKoOpU6dy+umn43IdXlgqEAiwYsUKrr766qN3SE2Fhx92RvHbt4d77inW+ay1BPbtw7tmDb41a/DMm4dv40b8iYlO0G8McSeH886Fbmb1CSHgciL9yD3BtJ7fiWlvfkxERK0Cj713L6xerdF7kSMpwC8nCvBFRKQyOOYI/gUXHL1DVhbMmeN83XILtGt3+LGUFKduZfDRKTR/JZCUhHftWnxr1pC9dCnZy5axzbubdwYZpp8VjD/ICfRP2eLm2S5vc855A/HixYULN+7CjxtwPmDQZFypyRTglxMF+CIiUhkUloPfp08fWrRoUfiO2dlOIJ83cn77bfjjD2cIfcCAEvctkJ6Ob8MGtq77jSe3T2VOxz0E3IawLMs1mwdzxsN/I8l9kOEMpx71jt4/4HSpYUMnL19BvtRUCvDLgTHmYuBy4AYF+CIiUtF27drFmjVrSEpKOlxF51jBfUHi42HsWKd2Zc+eMGpUqffzow/f5On0J4g/wfl3h5URnHHlxXRv14sRjDiq/bRp8NNPzs8jRkD//qXeJZEqQQF+OdEIvoiIVCuBAMTGwvffwz/+Ac2aHX7s0JKz9euX+DT79uzhltcuZOEZ8QBEHrDclnUr99w29qi2iYkwcSJERMC99zrfRWoiBfjlRAG+iIhUSwWtPjVxImzZAuedB0OHlsppnp04hrei3ielrnOufktO5PX7ZxLZIBIPHkJxqv4kJTmFemoVPC9XpEYoToDvOtaDIiIiUgMdGdzv3u3k5ft8zih/KXnovmf4/LRvOG2dMyz/S48dDPioE298PYnJTGY72wGIjDw6uI+LK9WuiFQrCvBFRETk2Jo3hwcfdFbDHTgw/2NbtsC+fcd96C7duvPjP9dz5bI+BGdbdp4Y4Enfcyx8/Es+8X6CF+9R+yxfDs8/D1OmgN9/3KcWqbYU4IuIiMhfa9UKbr8d6uWpdGMtfPAB/Otf8NFHx33o4OBgXnryM94IfZkTdxi8wYbfztjGdxNeZ23sinxtAwGYNcv5vnKlMzVARPJTgC8iIiLHZ+tWZ/TeWqeOZQlddMlQ5ly3lgFLTgFgY/tshm66jKf+8wAAHjy4XM66XK1bw223QXELBYnUBJpkWwKaZCsiIjXevn3wyy/OxNuQkMPb4+KgcWOoXfu4Dvv6G8/xYvAkEhs68wF6Lm3IGTdfysCWg+lFL7BGNfGlRlEVnXKiAF9ERKQAXi889pizYu6QIXDuucd1mC2bN3PH1EtY3iUFgKh46BNyDo9f/ywtaZmv7cGDTpbQNdeUSiVPkUpHVXRERESk4ixf7kTcWVn5R/WLqVWbNnz32FpuWXcx4RmW/Y3h2zq/8vijN5KelpbbLiMDJk2C1avhmWcg9Zihj0j1pwBfRERESlePHnDXXXDGGdCrV/7Hdu4sVn1Lt9vNuEdeZ2qzj2i9OYiA2zCzxzr6v3kav875gSyyCA2ztGvntO/c+bizgkSqDaXolIBSdERERIohLQ0efRTq1oUrr4QOHYq1e2ZGBqPHD+W7bqvwuw1hmZZ+mzoz6NFrucw1hI0rwunSBVwavpRqSCk6IiIiUvn8+quTthMff1y7h0dE8Pb4WbyQOY4mew1Z4YZZnVfz1vjH+WrjNLp2zR/cBwKwYoVT5EekJtEIfgloBF9ERKQYvF747Tf480+4+eb8K+YmJECjRkU+1L49e7j11QtZ0N15sxCZZBmdcQd33v4o4AT1n37qFPjp3x+GDz96gV6RqkQj+CIiIlL5BAc70fYttxwd3I8dC6+8Av/7X5EOdUKzZnz97xU8EHcddVMsSZGGJ5u/yohHepGYkEBSmpcVOWtkbdgAHk8ZXI9IJaUAX0RERCrW9Ong9ztlcLKyirXrg/c+zRcdp9NxbTgAv/bcyfmfdObZH+/jljFJdOjgzPcNCyuLjotUTkrRKQGl6IiIiJSCgwdhxgzIzoZRo/I/lp1dpFKbfr+f+58cyecd55IdYgj2WvquasN7Y38kJDjkiLbgdpfmBYiUPS10VU4U4IuIiJQia/On7mzbBi++CBdcAOedB0FBf3mI72Z8ydi4u9gZ7cQ37TaEManfh3Tr2RuAdeuc3Pw77oAmTcriIkTKhnLwRUREpOrJG9xbC19+6axi9fXXcOBAkQ5x4UWX8/P1azlv8cmYgOWP9lkM/3Mo45+7j/0JljfecNL8J0505vyKVEc1IsA3xoQaY94yxsQZY9KMMZuNMfcc0aaZMeYLY0yCMSbRGPONMaZFRfVZRESkxhswAE44Ac49Fxo3LvJu9erV55OnfufJ/ffRMNGSVtvwYrtPufal04jpsQFjnDL8wcFl13WRilQjUnSMMbWAMcB7QBzQCZgN3GWt/TSnzdc4b3iuAfzA20CktfaCYxxXKToiIiJlye8Hnw9CQw9vW7cOfv8dLr8coqKOufvWLVu49YOLWXF6MgBR8XCbdwx33j66LHstUuqUonMEa226tfZxa+2f1tqAtXYl8B3QJ0+zU4BPrbWp1toM4CMgpiL6KyIiIjnc7vzBfSAAn38Oy5fDs886wf8xnNyqFd8+voLL1/cmPMOyvzH8+4Snuf7R/qSnpQGQkgLffOMcWqQ6qHQBvjHmYWPMtJx0GmuM2XaMti5jzL3GmI3GmCxjzE5jzIScEftjnSMIOAtYnWfzRGCEMaZ+zsj8tThvAkRERKSy8HigZUvn5/79izTxNtQdymsPT+PFFi/SZlMQAbdhVo+N9H/zNH6c9T2TJ8PMmTB5svLypXqodCk6xhgLHACWA92AFGvtSYW0fRG4C/gKmAW0B0YD84AB1toC34sbY14FugN9rLXZOdvaAFNwRvUtsArob61NOkZflaIjIiJSEbZvh6ZN85fQXLrUmZTbp0+hdTA9Hg93jhvKjNOX4w8yhGZZzpzTn7r2Xfr2dTNypFa8lcqpqqfotLLWNrTWng/sKayRMaYDTjD/pbX2cmvtW9ba+4D7gH7AlYXsNwFn9P6CPMG9C/gRWAjUBuoAP+G8aRAREZHKJjo6f3Dv9cK0afDRR/DSS4XuFhoaylv/nsEL2U/SdA94wgy/XPgzW1q0p1uXdQrupVqodAG+tTauiE2vAgww6YjtbwEZwMgjdzDGTAIG4ozMJ+R5qAEQDbxkrc3IycF/GehpjGlUzEsQERGR8rZ//+FR+x49/rL5sKtu4N4h/6ZrbD0A1nZJZ8iy83lp8jjAqdK5fXuZ9VakTFW6FJ28jDFrgdoFpegYY2YDA4AIa63niMfmA22ttVF5tr0EnAf0s9buL+B4m4HPgSdyNj0JXGWtbXmM/ilFR0REpLLw+WDxYujdG1x5xjA3bYLmzaFW/il6FstKVvLDpM94q+F7pNR1hu/PWXIil7SbwYKFDbn6ajj77PK8CJGCVfUUnaJqBiQcGdzn2A00MsaEABhjonHSeVoDW3Nq4acZY/Km4FwKdM3Zdw9wBnBJQSc2xtxsjFkKzC21qxEREZGSCQpy8u/zBvepqfDaa/DYYxAbm6+5wXA6p/PQPU/zVcx3dFwbDsDcHjuYkNKZlOSp/PSTJt5K1fPXU88rrwigoOAeICtPm2xr7XacdJ5CWWvXA4OKcmJr7ZvAm4dG8IvWXRERESl3S5Y4E28hf7nNI8R0OZ0fY/7g1icvY1bHZexpCfGXP0CtZdOAzwCtiiVVR1Uewc8ACrtTw/K0ERERkZrqvPPgttugb1+IOWJ5myOG5l1uFxc+cSOX1hpMy20ufMGGr3stof+EU4ldtACAhAREKr0ak4NfRv1TDr6IiEhVlJgITz8NAwc6bwJy6ulbLItZTKPUhox9+kbmdP8T6zLUTrMM/2MEKfsnMXgwXHhh/kwgkbJWU3LwY3H6n2+qvDEmDOgCLC3LkxtjLgYKr8MlIiIilde0aU5+/hdfwI4duZsNhl70onWdNkx96jfGJz5AowRLWm3Df7tN4886Mcz4dptG8qVSq8oB/qc4C1Ldc8T2f+Dk3n9Ulie31k7HWWRLREREqppzzoETToCePeGUUwptdtNN9/H+uV/TaXltAFb1OcDvPfow87vJ5dVTkWKrdCk6xphrcWrSg1P5JgSYkPPv7dbaD/K0fRm4E2cl25k4K9neBcwHzitsJdtS7KtSdERERKoqnw+ysyEi4vC2Xbtg5UoYNAiCnYm129jGZ4FprH52Dr9HryajlsHlt5y/9FRef+Rb3EG1CQsr5BwipaQ4KTqVMcD/FTinkIfnWmvPzdPWjTOCfzNwEpCAM7I/1lqbVqYdRQG+iIhItWItTJgAmzdD48YwdmxukJ9OOnHEkfp7Eg8uvI5NbZ0JuifFuem64XUuvHgwF12EVsKVMlOcAL/SlcnMG8AXoa0fZ3R/wl+1FRERETmmtDTIzHR+jonJDe4BalGLGGLgLJjT/Q9GjxvG9NOXse0UP/ua3UT8tPNp3mwKXbu5K6jzIodVuhH8qiJnku3lwA0awRcREakmAgGYPx+6dcufurN3L9Stm7sarg8fD306mh8PfMu+Zk6TTqtrMfnyrzi1Q4cK6LhUd1U6RacqUYqOiIhIDRAIwLhxkJwMQ4dCnz5YLItYxMx901n58ncs7uGU1al30HJnys3cdee/lK4jpaqmlMkUERERKXsrV8KePZCeDinOAvYGQ296c/cJ9/H1+OWM2TaKusmW5PqG8Se+xcUP9ODrr/ajcVSpCBrBLwGN4IuIiNQA1jpB/q+/wp135svNx9rcmbXrVq3kzhnDWNfRyeNvtguuTnmWBx8eWQGdlupGKTrlRAG+iIhIDZadDc89B336OHX1XS5W+lcw7v/uYXHnzWSHGIK8lktWdufFxz8jNDS0onssVZhSdMqBVrIVERGp4b7/HnbuhE8+gaVLAejs7sL4f7/B30P+TvRWN75gw5fdlzJg0qksWjivgjssNYVG8EtAI/giIiI12Nq18PHHTrWdhx8GV/5x09TUVG575mJ+PGMT1mWolW7pu2oAbz/9X4JdKqcpxaMUnXKiAF9ERKSG83qdibcNGx7elpkJq1dDjx5gDG+/M4mJ9jkSopxc/U5L6vPff3xHy5NOqpg+S5WkFB0RERGR8hAcnD+4B/jqK5gyBV54ATIzuenGe3jm9I+JWVIbgNU9DjLopzP575QXK6DDUhMowBcREREpLVlZsGrV4X+HhQFwSbdzmPF/67km9hIiMiwJUYYxkc9yzSPn8H3q93jxVlCHpTpSik4JKEVHREREjpKZCd98A/36QZMmh7cHAuBysWj+b9w//3o2t8sGoGWcm75thjD+oqeJIKKQg0pNpxz8cpBTRedy4AYF+CIiIvKXpkwBtxuGDsUTHMydTwxlRrfl+IMMoR7L8HX9eX7su7jdmoArR1MOfjmw1k4H7qrofoiIiEgVsH49LF4MCxbAF18QGhrKK/+awd+XPkPT3eAJNXzY9WcGPn0q69esxo+/onssVZgCfBEREZGy1rgxdOwIoaFwySUAhITAU09fx2udV3B2bAsA1sRkcOnyv3H/i6P4iq/IIqsiey1VlFJ0SkA5+CIiIlJk1kJiIjRqlH/b779Djx688PpTvFr/LZLrO+U0T19cn1H3PMYVja+qoA5LZaIc/HKiAF9ERERKZOlSeOstiIyE225jfUoyd3w5lHVdMgBothseb/QMQ0dcW8EdlYqmHHwRERGRqmDZMue71wuNGnFaTCdeuGQjfb/uR6jHsqc5jHY9xC2PX0KWJ4skkiq2v1IlaAS/BDSCLyIiIiVyKEUnPBzOOCN3c/JBy68/z2D8jtvZdnIAgLYbg+hx1iUM63MVveiFS+O0NYpG8MtBTpnMlyq6HyIiIlKFGQNnn50vuAeot+Z3Lt28njkX/MSgxW0xAcumU318tf0LXh3/FNlkV1CHpSpQgH+cVCZTREREykRKCnzxBcTFUef99/ng33O4f/MYGsVDei3DDx1XcPn/9WB7XFxF91QqKQX4IiIiIpVJaKgzqu9ywYgR4HJx9913cXv6fDoviARgaddELvj5LN555wW2sIUMMiq401KZKAe/BJSDLyIiImUmISF/SU0gZdVWJs18gf9Gf056LYMJWHouOYFejwzjyjpXcgqnVFBnpawpB19ERESkqjsiuCchgbrvvMBYXxSf1n6NthvDsC7Dol7/48u3X2PhzF8rpJtS+SjAFxEREakKZs8Gjwd27aJH23bMvnM9fWZ0I8hr2XFKgDFpj3H/v67B7/dXdE+lgilFpwSUoiMiIiLlxueDWbMgIwOuuAKAPXvg309+wIJOY9jT3GkWsyaCcRe/g6ezn770xY27AjstpUUr2ZYTBfgiIiJS7qx1ymvmCOzYxYHp33Db7veY23M3APWSLb0SutP3vosYxSgF+dWAcvBFREREqqs8wT3W4vpkKo1Wr2aa52we2Xoj9Q9akusZZrdaytcPTyYpPrHi+ioVQgH+cdJCVyIiIlLhPB6oVcv5uVcv7rnnSR72fs9pyyMAiO2VQP8vuvL5p+8DYFHmRk2gFJ0SUIqOiIiIVApr10Lr1hAWBsCihX7e+uBafjjnVzxhBrfPcvHyrvQYexk9QnsQQwwG8xcHlcpEOfjlRAG+iIiIVEo+Hzz9NHN2buWR9j+y9ZQAAK3+cNPjzIv4x1m305GOFdxJKQ7l4IuIiIjUZIsWwa5d9DfB/NzsP/xt8am4/JYt7fx8s+NrPh3/akX3UMqQAnwRERGR6qZPH7jySmjVilpDh/LiP+cweMbDNIqHjFqGNzp+w4X/14HtcXH48OHBU9E9llKkFJ0SUIqOiIiIVGp5SmpmZcHH727mix2XsKRnCgCNEizD7HAibzqJy7iMkzm5Insrx6AUHRERERHJV1IzLAz+HrWaGYl9Gf1NJ2qnWRIaGd5oOI05Y95jZupMVdmpJhTgi4iIiNQEPh9s3AjA4+3O5dNWn9NufSjWZVjSez/vTnmaH2ZOr+BOSmlQik4JKEVHREREqhS/H+bMgfbtoWVLfpjt5dVPR7Bk8GJ8wYaQbMvwtefwn7Ef8qf7T1rRiiCCKrrXgspklhsF+CIiIlLV7dkDP//nQSa0+ojdLZxtp62NoNuFg2nd5VSGMYwooiq2k6IcfBEREREpmmYH1zMyPZk5SwdxztyGAKzvmMG3q6ex4D8/4MZdwT2U4lKAf5yMMRcDL1V0P0RERERK5JRToFcvGtStw7RHfuaejbdQ/6AluZ5hdpsl3Pjo+cTv21fRvZRiUIpOCShFR0RERKqNAwegQQO8XnjztXV8ljCUDd2cTJAT9hgeixxHlyv7sI999KGPRvbLmXLwy4kCfBEREamuDvy2imfevoOPL4zDE2Zw+yy9lkXT8f/60zq0DddxHQbz1weSUqEcfBERERE5fj4fDb6bynPhHXh/Zj9O3uLCH2SY33MHP734PoE5AQX3lZgCfBERERHJLygIbr0VGjem322j+fnmDfT58TRcfsuWdn6e+N+jjH3qdvx+PwBevBXcYclLKToloBQdERERqda8XifYN4aUFHj8sVeZ03E88U2ch7utqMfEaz9lZqsf6EY3zuZs5eaXEeXglxMF+CIiIlKTWAu7d+zgtncuYnG3RAAaJlp6ZPbh5Ds6cBVX0Y52FdzL6kk5+CIiIiJS6oyBFtEnMv3J1dz1xwhqp1kSGxq+bzafpQ/OpEly44ruoqAAX0RERESOw2MPvsCz7i9oszYE6zIs6bObAe/G8P13XwOQSip72VvBvayZlKJTAkrRERERkZruj41e/vXyCH7rtxhvsCHEYxm2ri9njL2IOHccZ3M253Kuqu6UkFJ0RERERKRctDs1mI9f+YpXXRNpvtNFdqhhatd5vP70WOIXJZJEkoL7cqYAX0RERERK7NLLr2TOlSvpt7glAJtiPHy/eRqrnlpcwT2reRTgi4iIiEipaNCoIZ+MX8TVc28nMsmSUtfwWocPGfJYV+L37cNimclM9rGvortarSkHvwSUgy8iIiJSsB9mbeC5JZezuksKAE33wshat5I20o/buLiCK1RSsxiUgy8iIiIiFWrgBe2Z/dha/r5qEGGZlr1NYWKt11h8/ywCieGcxEkV3cVqSwG+iIiIiJQJt9vNs2On8F6jKZyyxY3fbVh+zi5mT5nM0rkLKrp71ZYC/ONkjLkYeKmi+yEiIiJS2fUb8De+u2495/zcAZffsqWtj2v2XMPjT92K3++v6O5VO8rBLwHl4IuIiIgUzzvvvM4k/5P8r4lTOrPrinrc3PEbLr60DcHBFdy5Skw5+CIiIiJSKd14463MHryEXkujAFh+ejKPpJzLLbc+z549Fdy5akIBvoiIiIiUq2YtWvDtuJXcs+kqaqdaDjSEGZdM4v6XzyI5+WBFd6/KU4AvIiIiIhXikX/+h2ltv6bdulAA5vTaSv/3OjJz+pcV3LOqTQG+iIiIiFSYbj168PM//2DEst4Eey07oi03Z97J3WOv0ATc46QAX0REREQqVHBwMJOf/JzXgl6gxQ4X2aGGj7v9Tv9n2xO/T6veFpcCfBERERGpFC657ArmXLOafotPBCDEF0TDqKgK7lXVowBfRERERCqNyAaRfPrUQp7YexeTh3+B2+2u6C5VOaqDXwKqgy8iIiIi5UF18EVEREREaigF+CIiIiIi1YgCfBERERGRakQBvoiIiIhINaIAX0RERESkGlGALyIiIiJSjSjAFxERERGpRhTgi4iIiIhUIzUmwDfGhBpj3jLGxBlj0owxm40x9xzRJu2IL68xZnVF9VlEREREpLiCKroD5SgI2AcMBOKATsBsY8xea+2nANba2nl3yAnuPynvjoqIiIiIHK8aM4JvrU231j5urf3TWhuw1q4EvgP6FNTeGNMDOA14txy7KSIiIiJSIpUywDfGPGyMmZaTTmONMduO0dZljLnXGLPRGJNljNlpjJlgjKn1F+cIAs4CCkvBuRGYZa3dc9wXIiIiIiJSziplgA88BZwHbAGS/qLtC8BEYD0wGpgG3AVMN8Yc6/peApKB9498wBgTAVwJvF3snouIiIiIVKDKmoPfylobB2CMWQvULqiRMaYDTlD/pbV2aJ7tW3EC+CuBqQXsNwFn9P48a212AYceAWTgpPCIiIiIiFQZlXIE/1BwXwRXAQaYdMT2t3AC9JFH7mCMmYQz0ba/tTahkOPeBLxnrfUVsR8iIiIiIpVCpQzwi6E7EACW5N1orc0CVuY8nssY8xIwAGfkfn9BBzTGtAPOBKaURYdFRERERMpSZU3RKapmQIK11lPAY7uBM40xIdbabGNMNE46jwfYaow51G6etfaCPPvdmLNtU2EnNcbcDNxMzhuk1NTUkl+JiIiIiEghihNvGmttGXal5A7l4FtrTyrgsS1AsLX2xAIeex+4Foi01h4so741BVRlR0RERETKSzNr7d5jNajqI/gZQONCHgvL06as7MP5FCEN2AqcXIJjvYRT/acs9ylq+6K0mwucU4xzVwcl/R2XpuN5vVTkOXR/VG/l8XosjrK+V0v7ekt6PN0flVtluj+q2t8OKNn9fLx9Kc5+ZdW2MLVx4s9jquoB/h7gNGNMaAFpOs1x0ncKqpJTKqzz8cdeAGNMkrX2uHN1jDFfFnf/4u5T1PZFaWeMCZTkequikv6OS9PxvF4q8hy6P6q38ng9FkdZ36ulfb0lPZ7uj8qtMt0fVe1vR87xjvt+Pt6+FGe/smp7DEU7VxVP0fk38CjQ11o7L8/2MCAR+O2I/Pqy7OdSa+0Z5XGuyqCmXS/UzGsuLTXtuatp11vZ6Pmv3PT7keLQ6+X4VPUqOp8CFrjniO3/ACKAj8qxL2+W47kqg5p2vVAzr7m01LTnrqZdb2Wj579y0+9HikOvl+NQKUfwjTHXAtE5/xwNhAATcv693Vr7QZ62LwN3Al8BM4H2OPlN83HKYQbKq98iIiIiIhWtsgb4v1L4BJy51tpz87R144zg3wycBCTgjOyPtdamlWlHRUREREQqmUoZ4IuIiIiIyPGp6jn4FcoYYwv5qtKfHBhjHjbGTDPGxOVcz7a/aN/TGPOTMSbVGJNijPneGNOlnLpbYsaYtsaYJ40xi4wx+3OuY6Ux5lFjTK0C2rczxnxtjEkyxqQbY+YZY86riL5XBsW5D6rSc1eW94Exppkx5v2c11umMWapMWZ4mVxIFZTzOvnIGLPBGJNsjMkwxmw0xkzMWX+koPZFel0ZY+oZY142xuw2xmQZY9YZY24z5vDqh3JYZbkPjDGhOf9PbzXGeIwxW4wxjxljgkvhMqWUlNXfg+Lct8YYlzHm3pz/M7KMMTuNMRMK+ntenWkEvwSMMRaYx9ETQLzW2k8roEulIue6DgDLgW5ASkFVjHLa9gJ+xVk5eHLO5jtx1ic401q7pqz7W1LGmGeAO4BvgUWAF+gHjABWA72stZk5bVsBSwAfMAlIxpnU3RG4wFr7U7lfQAUr6n1Q1Z67sroPjDENgKU5j00EdgFX46QljrLW/rcMLqdKMcb0x6mQtgjn+fEBMcDfgRSgi7U2PqdtkV9XxpgQ4HfgdOBlYANwATAEeMJa+69yuLwqpbLcB8aYr4FLgSnAQqA3MAp4z1p7Q8mvVEpDWfw9KO59a4x5EWcu5lfALJy5maNz+jWgxszNtNbq6zi/cCr4vFvR/SiD6zolz89rgW1SIAffAAAL5UlEQVTHaLsE5w9u8zzbmuds+6Gir6WI13sGUK+A7f/O+R3fmWfbZ4AfJ8A4tK02sB34g5w3zTXpq6j3QVV77srqPgCey3nOLs6zzZ1zjEScssAVfv2V8QsYnvPcPXg8ryvg9pz9Rx9x3C+AbCC6oq+xsn1VhvsAGJzTdsIRx5iQs/3Min6e9JX7Oyn1vwfFuW+BDkAA+OKItqNzjnF1RT9H5fWlFJ1SYIwJMcbUruh+lBZrbVxR2hljWgPdgWnW2t159t8NTAMGGGNOKJtelh5r7VJrbXIBDx0abegIkPPx3iXAr9balXn2TwPeBtriPB810rHug6r43JXhfXA1sMVaOz1PWz/OyFQDnGBGCrY953skHNfr6mqc1c3fOuK4k4Bg4Iqy6XbVVUnug6tzvk864rSH/j2yKH2U8lPKfw+Kc99eBRiOfq28lXOMGvNaUYBfcsNwXjSpxpj4nByxehXdqXJy6AZcWMBji3Busm7l151S1yLn+/9yvncCQin8eqGSBanl6K/ug+r83BX5PsjJH2/O4Ws+sm3e49V4xpgwY0wjY0wLY8xA4I2ch2bmfC/y68oY4wK6AiustVlHtF2CM+qn5/74leV90B3Yba3dmbdhzr/3oN9bZVNqfw+O477tnrNtSd6GOfuupAa9VoIqugNV3BKckYk/gbo4Iw53AucYY8601b9MZ7Oc77sLeOzQtubl1JdSZZzyq2Nx8gOn5myuttdbQkW5D6rzc1eca6vOz0NZuAlnRPeQbcBIe3jl8uI8n5FAeEFtrbUeY0wieu5Loizvg2bA+kLOu5vDgzFS8Ur770Fx79tmQIK11lPIsc80xoRYa7OLd1lVjwL8ErDW9jxi0/vGmNXAeODunO/VWUTO94JupKwj2lQ1k4D/b+/uY+WoyjiOf3/hrShGSFsJINCICWI0WF9IwZgU4kvkJRoiCNJCS4VoCAn4EgMRW0MTQlAwNCoWW5AWKmikUsUgLxIRRQsUhRjQhpS2FBsQCIW+UOHxj2eWbpa5l7t7Z+7unfv7JJu9O3Nm9zln59w5M3vmnBnAxRHxRLGsyfnt2QjrQZPLrpu8Nbkc6rASeJzsmzud/Fl/atv6qsq+ld5l37s668HbhkjbSu/vbUDUcDzott6+1b7SStP4Br676FTvCnLHOaHfgYyBrcXzXiXrJnWkGTckXUpecVgcEZe1rWpkfmvSWQ+aXHbd5K3J5VC5iNgYEXdFxMqImA+cBVwu6aIiSVVl30rvsu9dnfVg6xBpW+n9vQ220RwPuq23b7WvtL9no7mBX7GI2En2CZzS71jGwKbiuexn7daysp/gBpakBcC3geuAr3Ssblx+61JSD5pcdt3krcnlULuI+AewhhxVA7orzxeAbWVpJe0FTMZlPxp11oNNQ6Rtpff3NsBGeTzott5uAqYU68re+7mJ0D0H3MCvnKRJZH/AzW+VtgFWF89Hl6ybQQ5J9dDYhTM6kuYD84EbgC9HMbZWm0fJn/6Gyi/kuM4TXkk9aHLZjbgeRMQz5MFoxhBpYfyWw1jZmxxlBbrYryLHvn4YmF5y8D+KPB667HtXZz1YDRwk6eD2hMXrA/H3NtBGczzood6uLpYdVRLDh5hA+4ob+D2SNHmIVZeS9zasGmJ9Y0TEWrKynCKpddMMxd+nAPdExH/6FV83JH0HWAAsA+ZGyUQYxc1Bq4CZko5s23Yf8mbAf9Nx537TjbQeNLnseqgHK4DDJJ3UlnY3cpzmF9k1QsyENdTwupKOJYetfQB62q9WkP1vz+146wvIG+pvqSgLE07N9WBF8XxBx8e2Xt9YSSZsVGo8HnRTb28mTyY795VziveYMPuKZ7LtkaSryDPNPwDryZvAjidnQP0rcGwUs5+ON5JmA4cWL88H9iQnFAF4KiKWtaU9hiyDjewa7eJ8YH/g4xHx9zEJehQknUfOurgeuIQcYqvd5oi4s0j7XvIfz07gKnICl3PIWTZPiIg7xiruQdBNPRhvZVdXPSgOgg+RPy1fSV7JPB2YSf5ytKSmLI0bkm4FDgDuIce+n0QOsXga2X92Zmv87G72q2JGzD8DRwJXkzNiHk/OiLkwIi4Zi/yNJ4NSDyStAk4ElrBrJtt5wPKImF1Rdm0U6joedFtvJS0i76O7lTxRPIKc2fZ+4LiyC3iN1O+Ztsbrg5wy+w7yn9J24BVyjNWLgUn9jm+UebuXPAMue9xbkv5o4G7gZWBLUS4f7nc+usjv9cPk9015Jv9Z/Jq8yrSVnEL7k/3OR5/Krqt6MJ7Krs56QPYFXQY8V5Tbw8AX+53nQXkApwK/BTYU5bONHE1nEXDIaPYrYF/yhH4T2U3gn2RjYKBmUh6Ux6DUA/IkbyE5VOoO4Enygswe/S4jP974jmo7HnRTb8kZkb9Ozoi7o4jnSibYLOG+gm9mZmZm1iDug29mZmZm1iBu4JuZmZmZNYgb+GZmZmZmDeIGvpmZmZlZg7iBb2ZmZmbWIG7gm5mZmZk1iBv4ZmZmZmYN4ga+mVnDSZomKSQt6HcsZSStK+ILSWv7HU8ZSdvbYry33/GYmQ3HDXwzs3GmraE5kse0fsc7Qo8Ds8kZKN8g6fq2vHy0bENJF7almVNTfHOL+J6r6f3NzCqze78DMDOzrs3ueP0J4FxgMXBfx7pnyWng9wb+V39oPdscEcuHWb+dbGQ/WLJubrF+Uh2BAUTECgBJC+v6DDOzqriBb2Y2znQ2hCXtTjbw/zJMI3l77YHV61bgdElfi4gdrYWSPgZ8ELgJ+FKVHyhpb2BnRAzyiZGZ2Zu4i46ZWcOV9cFvXybpVEmPSNomaa2kuUWaQyT9UtLzkrZIWi7pHSXvf4CkH0taL+lVSZskLZb0rgqzsRTYD/h8x/K55K8Uv+mIaXqRv9Ir7pJul/SSpLcXr1tdgaZKWippM/AK8O4K82BmNibcwDczm9hOBK4GfgV8E9gCLJV0BtndZwtwMXALcAZwVfvGkg4hu818gbyKfh6wDDgNuF/SOyuK85HicXbbZ08CTgeWAzvbE0fEGuAhYI6k3TpiPgj4NPDziHil43PuBA4ELgUuAl6uKH4zszHjLjpmZhPbEcD7I+IpAEk3AxvIRvo3IuLKIt01kvYDzpR0QUS0Gr6LgD2A6RGxsfWmkn4BPABcCCyoKNalwA8kHRwRG4CTgX2L5e8rSb8Y+AnwGeD2tuVzgN2An5Zs81hEzKooXjOzvvAVfDOziW1lq3EPEBHPAk8ArwM/7Eh7H9mYnwZQXJ0/EbgN2C5pSusBrAPWklfKq3IjeaX+zOL1XGB1RDw2RPqbyCvw81oLJKnY7tGI+FvJNt+rLlwzs/5wA9/MbGJ7smTZC8Az7Tezti0HmFw8H04eR+aR/eA7H4cD+1cVaEQ8T55MzJF0KHAccN0w6V8GVgAntd0PMBM4DFgyxGb/qipeM7N+cRcdM7OJ7bUulwOo43k58LMh0m7rJahhLAV+B1wLvEo24IezGDiHHFr0++TJyA6yC9KbRMTWyiI1M+sTN/DNzKxXa4EA9oyIu8boM38PbAQ+BdwUES8OlzgiHpS0BpgnaQnZb39l8WuAmVkjuYuOmZn1JCL+S968erKkGZ3rlaZW/JmvkyP1fBe4fISbXUveTLyInPCr7OZaM7PG8BV8MzMbja8CfwL+KOkGYA158eg9wOeAG6huFB0AIuI2si/+SN0IXAHMIm/+vbvKeMzMBo0b+GZm1rOI2CDpI8C3yAb9LHLW3A3AKnL8/L6KiJeK4T/PBpZGRPQ7JjOzOsn/58zMrJ8krQOeJk8QXouIF4bfoqfP+BFwLjCtfbz+LrafTN5U/DDwZETMrDZCM7PquA++mZkNgmPIoTVXV/3GxXj9s4Dbe2ncF54m4zu4ssDMzGriLjpmZtZvZ5A3vwJUNkylpA8A04GzgH2Ay0bxdp8lZ7+FXfMBmJkNJHfRMTOzRpK0AJhPXn1fGBHX9DciM7Ox4Qa+mZmZmVmDuA++mZmZmVmDuIFvZmZmZtYgbuCbmZmZmTWIG/hmZmZmZg3iBr6ZmZmZWYO4gW9mZmZm1iD/B2QDVlPZ4dbyAAAAAElFTkSuQmCC\n",
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      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
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    "ax.set_title('$L_x$ evolution evolutionary tracks with Jackson12 sample')\n",
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    "\n",
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    "# plot Tu15 tracks (for a Sun-like star!)\n",
    "ax.plot(blueTu15[\"time\"], blueTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"blue\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"fast rot. (solar model)\")\n",
    "ax.plot(redTu15[\"time\"], redTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"red\", linewidth=2.5, alpha=0.6, label=\"__nolabel__\")#, label=\"slow rot. (solar model)\")\n",
    "ax.plot(greenTu15[\"time\"], greenTu15[\"Lx\"], marker=\"None\", linestyle=\":\", color=\"lime\", linewidth=2.5, alpha=0.5, label=\"__nolabel__\")#, label=\"interm. rot. (solar model)\")\n",
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    "ax.plot(jack12[\"age\"]/1e6, 10**jack12[\"logLx_cgs\"], ls=\"None\", marker=\"o\", color=\"grey\", mec=\"k\", alpha=0.3, zorder=1, label=\"cluster stars from \\nJackson et al. (2012)\")\n",
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    "\n",
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    "# plot approximated tracks\n",
    "step_size, t_track_start, t_track_end = 1., age, 5000. # Myr\n",
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    "t_arr = np.arange(t_track_start, t_track_end+step_size, step_size)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\"-\", zorder=2, label=\"fast activity track\", lw=2.2)\n",
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    "# 1 sigma errorbars on Lx at 23 Myr\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1_lower) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track1_upper) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:royal blue\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
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    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:green\", zorder=3, lw=2.2, alpha=1., label=\"medium activity track\")\n",
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    "# 1 sigma errorbars on Lx at 23 Myr\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2_lower) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:green\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track2_upper) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:green\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
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    "#####\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", zorder=2, label=\"low activity track\", alpha=0.9, ls=\"-\", lw=2.2)\n",
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    "# 1 sigma errorbars on Lx at 23 Myr\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3_lower) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
    "Lx_arr = np.array([Lx_evo(t=t_i, track_dict=track3_upper) for t_i in t_arr])\n",
    "ax.plot(t_arr, Lx_arr, color=\"xkcd:red\", ls=\":\", zorder=2, label=\"__nolabel__\", lw=1)\n",
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    "\n",
    "ax.loglog()\n",
    "ax.set_xlabel(\"Time [Myr]\")\n",
    "ax.set_ylabel(\"L$_\\mathrm{x}$ [erg/s]\")\n",
    "ax.set_xticks([5, 10, 20, 50, 100, 300, 1000, 5000])\n",
    "ax.set_yticks([10**27., 10**28., 10**29., 10**30., 10**31.])\n",
    "ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))\n",
    "ax.set_xlim(left=4.9, right=11000)\n",
    "ylim = ax.get_ylim()\n",
    "ax.set_ylim(abs(ylim[0]), ylim[1])\n",
    "ax.legend(loc=\"best\", fontsize=12)\n",
    "#plt.savefig(\"./tracks_v1298Tau.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "# Create planets"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
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   "metadata": {},
   "outputs": [],
   "source": [
    "# Create planet objects using either LoFO14 or Ot20 results:\n",
    "R_c, R_d, R_b, R_e = 5.59, 6.41, 10.27, 8.74\n",
    "a_c, a_d, a_b, a_e = 0.0825, 0.1083, 0.1688, 0.308\n",
    "Mcore5, Mcore10, metallicity = 5., 10., \"solarZ\"\n",
    "\n",
    "###############################################################################################\n",
    "# 'fluffy' LoFo14 planets with 5 M_earth core\n",
    "planet_c = {\"core_mass\": 5, \"radius\": R_c, \"distance\": a_c, \"metallicity\": metallicity}\n",
    "planet_d = {\"core_mass\": Mcore5, \"radius\": R_d, \"distance\": a_d, \"metallicity\": metallicity}\n",
    "planet_b = {\"core_mass\": Mcore5, \"radius\": R_b, \"distance\": a_b, \"metallicity\": metallicity}\n",
    "planet_e = {\"core_mass\": Mcore5, \"radius\": R_e, \"distance\": a_e, \"metallicity\": metallicity}\n",
    "\n",
    "# fixed step size\n",
    "pl_c_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_5_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore5_PAPER = [pl_c_5_PAPER, pl_d_5_PAPER, pl_b_5_PAPER, pl_e_5_PAPER]\n",
    "\n",
    "# variable step size\n",
    "pl_c_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_5 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore5 = [pl_c_5, pl_d_5, pl_b_5, pl_e_5]\n",
    "\n",
    "###############################################################################################\n",
    "# 'fluffy' LoFo14 planets with 10 M_earth core\n",
    "planet_c = {\"core_mass\": Mcore10, \"radius\": R_c, \"distance\": a_c, \"metallicity\": metallicity}\n",
    "planet_d = {\"core_mass\": Mcore10, \"radius\": R_d, \"distance\": a_d, \"metallicity\": metallicity}\n",
    "planet_b = {\"core_mass\": Mcore10, \"radius\": R_b, \"distance\": a_b, \"metallicity\": metallicity}\n",
    "planet_e = {\"core_mass\": Mcore10, \"radius\": R_e, \"distance\": a_e, \"metallicity\": metallicity}\n",
    "\n",
    "# fixed step size\n",
    "pl_c_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_10_PAPER = planet_LoFo14_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore10_PAPER = [pl_c_10_PAPER, pl_d_10_PAPER, pl_b_10_PAPER, pl_e_10_PAPER]\n",
    "\n",
    "# variable step size\n",
    "pl_c_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_10 = planet_LoFo14(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Mcore10 = [pl_c_10, pl_d_10, pl_b_10, pl_e_10]\n",
    "\n",
    "###############################################################################################\n",
    "# 'high-density' Ot20 planets\n",
    "planet_c = {\"radius\": R_c, \"distance\": a_c}\n",
    "planet_d = {\"radius\": R_d, \"distance\": a_d}\n",
    "planet_b = {\"radius\": R_b, \"distance\": a_b}\n",
    "planet_e = {\"radius\": R_e, \"distance\": a_e}\n",
    "\n",
    "# fixed step size\n",
    "pl_c_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_Ot_PAPER = planet_Ot20_PAPER(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Ot_PAPER = [pl_c_Ot_PAPER, pl_d_Ot_PAPER, pl_b_Ot_PAPER, pl_e_Ot_PAPER]\n",
    "\n",
    "# variable step size\n",
    "pl_c_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_c)\n",
    "pl_d_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_d)\n",
    "pl_b_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_b)\n",
    "pl_e_Ot = planet_Ot20(star_dictionary=star_V1298Tau, planet_dict=planet_e)\n",
    "planet_list_Ot = [pl_c_Ot, pl_d_Ot, pl_b_Ot, pl_e_Ot]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create file structure for saving the results\n",
    "\n",
    "- check if directroy for saving the results for one planet existst, if not create\n",
    "- create folders, one for each planet in the population (what this means: two planets with the exact same parametes will get two different folders) (len(list_planets) folders; folder name: planet+number)\n",
    "- then I have folder-planet pairs, this way I can keep better track of what I am evolving\n",
    "- divide the list of folder-planet pairs into chunks -> multiprocessing issue\n",
    "- initiate the multiprocessing\n",
    "- wait and have fun with the results!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evolve planets using a fixed step size"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "## LoFo14 planets with M$_{core}\\,=\\,5\\,$M$_\\oplus$"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 16,
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   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder results_LoFo14_Mcore5_PAPER/ exists.\n",
      "That took 0.0005047361056009929 minutes\n",
      "CPU times: user 6.34 ms, sys: 19.7 ms, total: 26 ms\n",
      "Wall time: 32 ms\n"
     ]
    }
   ],
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   "source": [
    "%%time\n",
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    "folder_name = \"results_LoFo14_Mcore5_PAPER/\"\n",
    "curr_path = os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/example_V1298Tau/'\n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore5_PAPER, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    # evolve the ensamble (multi-threading)\n",
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    "    evolve_ensamble(planet_chunks, t_final, init_step=init_step, eps=eps, \n",
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    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
417
    "## LoFo14 planets with M$_{core}\\,=\\,10\\,$M$_\\oplus$"
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  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Planet:  planet_3_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_2_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_1_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-23:\n",
      "Process Process-24:\n",
      "Process Process-22:\n",
      "Process Process-21:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"../platypos_package/Planet_class_LoFo14_PAPER.py\", line 201, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"../platypos_package/Planet_class_LoFo14_PAPER.py\", line 201, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\u001b[0m in \u001b[0;36mevolve_ensamble\u001b[0;34m(planets_chunks, t_final, init_step, eps, K_on, beta_on, evo_track_dict_list, path_save)\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0;31m# \"join() says that the code in __main__ must wait until all our tasks are complete before continuing!\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0;31m# Make sure Python waits for the process to terminate and then exits the completed processes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstarttime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/process.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_pid\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a child process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a started process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0m_children\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiscard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m     46\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;31m# This shouldn't block if wait() returned successfully.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWNOHANG\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, flag)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                 \u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m                 \u001b[0;31m# Child process not yet created. See #1731717\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 425, in mass_planet_RK4_forward_LO14_PAPER\n",
      "    planet_object.distance), planet_object.metallicity)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"../platypos_package/Lx_evo_and_flux.py\", line 144, in flux_at_planet_earth\n",
      "    flux_earth = 1373*1e7*(u.erg/u.s)/(100*u.cm*100*u.cm)\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 956, in __truediv__\n",
      "    return super().__truediv__(other)\n",
      "  File \"../platypos_package/Planet_class_LoFo14_PAPER.py\", line 201, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 394, in mass_planet_RK4_forward_LO14_PAPER\n",
      "    Mdot2 = mass_loss_rate_forward_LO14(times[i]+0.5*dt, epsilon, K_on, beta_on, planet_object, f_env_05k1, track_dict)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 444, in __array_ufunc__\n",
      "    converters, unit = converters_and_unit(function, method, *inputs)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity_helper/converters.py\", line 166, in converters_and_unit\n",
      "    converters, result_unit = ufunc_helper(function, *units)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity_helper/helpers.py\", line 218, in helper_division\n",
      "    return [None, None], _d(unit1) / _d(unit2)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 652, in __div__\n",
      "    if m.is_unity():\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2152, in is_unity\n",
      "    unit = self.decompose()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2146, in decompose\n",
      "    decompose_bases=bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2119, in _expand_and_gather\n",
      "    new_parts = [x for x in new_parts.items() if x[1] != 0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2119, in <listcomp>\n",
      "    new_parts = [x for x in new_parts.items() if x[1] != 0]\n",
      "KeyboardInterrupt\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 71, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"../platypos_package/Planet_class_LoFo14_PAPER.py\", line 201, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 400, in mass_planet_RK4_forward_LO14_PAPER\n",
      "    Mdot3 = mass_loss_rate_forward_LO14(times[i]+0.5*dt, epsilon, K_on, beta_on, planet_object, f_env_05k2, track_dict)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 388, in mass_planet_RK4_forward_LO14_PAPER\n",
      "    Mdot1 = mass_loss_rate_forward_LO14(times[i], epsilon, K_on, beta_on, planet_object, f_env, track_dict)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 32, in mass_loss_rate_forward_LO14\n",
      "    Fxuv = flux_at_planet(L_xuv_all(Lx), planet_object.distance) # get flux at orbital separation a_p\n",
      "  File \"../platypos_package/Lx_evo_and_flux.py\", line 153, in flux_at_planet\n",
      "    A = (4.*np.pi*(a_p*const.au.cgs)**2)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 32, in mass_loss_rate_forward_LO14\n",
      "    Fxuv = flux_at_planet(L_xuv_all(Lx), planet_object.distance) # get flux at orbital separation a_p\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 945, in __rmul__\n",
      "    return self.__mul__(other)\n",
      "  File \"../platypos_package/Lx_evo_and_flux.py\", line 153, in flux_at_planet\n",
      "    A = (4.*np.pi*(a_p*const.au.cgs)**2)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 929, in __mul__\n",
      "    return super().__mul__(other)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 993, in __pow__\n",
      "    return super().__pow__(other)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 473, in __array_ufunc__\n",
      "    return self._result_as_quantity(result, unit, out)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 473, in __array_ufunc__\n",
      "    return self._result_as_quantity(result, unit, out)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 39, in mass_loss_rate_forward_LO14\n",
      "    rho_p = rho = plmoLoFo14.density_planet(M_p, R_p) # initial approx. density\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 507, in _result_as_quantity\n",
      "    return result if unit is None else self._new_view(result, unit)\n",
      "KeyboardInterrupt\n",
      "  File \"../platypos_package/Planet_models_LoFo14.py\", line 39, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 586, in _new_view\n",
      "    if obj is None:\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 956, in __truediv__\n",
      "    return super().__truediv__(other)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 459, in __array_ufunc__\n",
      "    for input_, converter in zip(inputs, converters):\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
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   "source": [
    "%%time\n",
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    "folder_name = \"results_LoFo14_Mcore10_PAPER/\"\n",
    "curr_path = os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/example_V1298Tau/'\n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore10_PAPER, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    # evolve the ensamble (multi-threading)\n",
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    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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    "## Ot20 planets "
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  },
  {
   "cell_type": "code",
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   "execution_count": 45,
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   "metadata": {},
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Folder -> results_Ot20_PAPER/ <- exists.\n",
      "start\n",
      "Planet:  planet_1_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Planet:  planet_4_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Planet:  planet_2_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Start evolving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-52:\n",
      "Process Process-49:\n",
      "Process Process-51:\n",
      "Process Process-50:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 30, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 678, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot3 = mass_loss_rate_forward_Ot20(times[i]+0.5*dt, epsilon, K_on, beta_on, planet_object, R_05k2, track_dict)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 88, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\u001b[0m in \u001b[0;36mevolve_ensamble\u001b[0;34m(planets_chunks, t_final, initial_step_size, epsilon, K_on, beta_on, evo_track_dict_list, path_save)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mprocess\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprocesses\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m             \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstarttime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/process.py\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    138\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_pid\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a child process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'can only join a started process'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0m_children\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiscard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m     46\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;31m# This shouldn't block if wait() returned successfully.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWNOHANG\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0.0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/multiprocessing/popen_fork.py\u001b[0m in \u001b[0;36mpoll\u001b[0;34m(self, flag)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m                 \u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwaitpid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m                 \u001b[0;31m# Child process not yet created. See #1731717\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "Traceback (most recent call last):\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 297, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 30, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 30, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 678, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot3 = mass_loss_rate_forward_Ot20(times[i]+0.5*dt, epsilon, K_on, beta_on, planet_object, R_05k2, track_dict)\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 88, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/multiprocessing/process.py\", line 99, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 684, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot4 = mass_loss_rate_forward_Ot20(times[i]+dt, epsilon, K_on, beta_on, planet_object, R_05k3, track_dict)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 88, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"/media/laura/SSD2lin/PhD/work/gitlab/platypos/population_evolution/evolve_planet.py\", line 30, in evolve_one_planet\n",
      "    path_for_saving=path_for_saving, planet_folder_id=folder)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "  File \"../platypos_package/Planet_class_Ot20_PAPER.py\", line 155, in evolve_forward\n",
      "    track_dict=evo_track_dict)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"../platypos_package/Mass_evolution_function.py\", line 684, in mass_planet_RK4_forward_Ot14_PAPER\n",
      "    Mdot4 = mass_loss_rate_forward_Ot20(times[i]+dt, epsilon, K_on, beta_on, planet_object, R_05k3, track_dict)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"../platypos_package/Mass_loss_rate_function.py\", line 88, in mass_loss_rate_forward_Ot20\n",
      "    rho_p = rho = plmoOt20.density_planet(M_p, radius_at_t) # initial approx. density\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1028, in _compose\n",
      "    unit = self.decompose()\n",
      "  File \"../platypos_package/Planet_model_Ot20.py\", line 74, in density_planet\n",
      "    rho = (M_p*const.M_earth.cgs/(4./3*np.pi*(R_p*const.R_earth.cgs)**3)).cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1081, in _compose\n",
      "    composed = (u / tunit_decomposed).decompose()\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n",
      "KeyboardInterrupt\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/quantity.py\", line 768, in cgs\n",
      "    cgs_unit = self.unit.cgs\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 654, in __div__\n",
      "    return CompositeUnit(1, [self, m], [1, -1], _error_check=False)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1082, in _compose\n",
      "    factored = composed * tunit\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 694, in __mul__\n",
      "    return CompositeUnit(1, [self, m], [1, 1], _error_check=False)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2119, in _expand_and_gather\n",
      "    new_parts = [x for x in new_parts.items() if x[1] != 0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/utils/decorators.py\", line 744, in __get__\n",
      "    val = self.fget(obj)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2115, in _expand_and_gather\n",
      "    scale = add_unit(b_sub, a * b, scale)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2119, in <listcomp>\n",
      "    new_parts = [x for x in new_parts.items() if x[1] != 0]\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1331, in cgs\n",
      "    return self.to_system(cgs)[0]\n",
      "KeyboardInterrupt\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2099, in add_unit\n",
      "    new_parts[unit] = a + b\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1312, in to_system\n",
      "    composed = x.compose(units=system)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1269, in compose\n",
      "    max_depth=max_depth, depth=0, cached_results={}))\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 734, in __hash__\n",
      "    def __hash__(self):\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1107, in _compose\n",
      "    cached_results=cached_results)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 1082, in _compose\n",
      "    factored = composed * tunit\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 694, in __mul__\n",
      "    return CompositeUnit(1, [self, m], [1, 1], _error_check=False)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2052, in __init__\n",
      "    bases=decompose_bases)\n",
      "  File \"/home/laura/anaconda3/lib/python3.7/site-packages/astropy/units/core.py\", line 2111, in _expand_and_gather\n",
      "    if isinstance(b, CompositeUnit):\n",
      "KeyboardInterrupt\n"
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    }
   ],
   "source": [
    "%%time\n",
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    "folder_name = \"results_Ot20_PAPER/\"\n",
    "curr_path = os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/example_V1298Tau/'\n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Ot_PAPER, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 1.0, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    # evolve the ensamble (multi-threading)\n",
803
    "    evolve_ensamble(planet_chunks, t_final, init_step=init_step, eps=eps, \n",
804
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)"
805
   ]
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
811
    "# Evolve planets using a variable step size (faster & recommended)"
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   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# with code update from 18.6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder results_LoFo14_Mcore5_varstep_18_June_2020/ exists.\n",
      "Planet:  planet_3_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Planet:  planet_4_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Planet:  planet_2_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Planet:  planet_1_track_23.0_240.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "Planet:  planet_3_track_23.0_23.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "Planet:  planet_4_track_23.0_23.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "Planet:  planet_3_track_23.0_23.0_5000.0_1.3e+30_20.0_16.0.txt\n",
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "Planet:  planet_2_track_23.0_23.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Done!\n",
      "Planet:  planet_1_track_23.0_23.0_5000.0_1.3e+30_0.0_0.0.txt\n",
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "Planet:  planet_4_track_23.0_23.0_5000.0_1.3e+30_20.0_16.0.txt\n",
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "Done!\n",
      "Planet:  planet_1_track_23.0_23.0_5000.0_1.3e+30_20.0_16.0.txt\n",
      "Done!\n",
      "Done!\n",
      "Planet:  planet_2_track_23.0_23.0_5000.0_1.3e+30_20.0_16.0.txt\n",
      "Done!\n",
      "Done!\n",
      "That took 1.2606542468070985 minutes\n",
      "Total # of planet folders =  4\n",
      "Non-empty folders:  4\n",
      "\n",
      "Total number of planets to analyze:  4\n",
      "CPU times: user 113 ms, sys: 29.3 ms, total: 142 ms\n",
      "Wall time: 1min 15s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "folder_name = \"results_LoFo14_Mcore5_varstep_18_June_2020/\"\n",
    "curr_path = os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/example_V1298Tau/'\n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore5, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
    "    evolve_ensamble(planet_chunks, t_final, init_step=init_step, eps=eps, \n",
    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "    \n",
    "# read in the results as a dataframe\n",
    "planets_LoFo14_Mcore5_dict_new, planets_LoFo14_Mcore5_init_df_new, tracks_LoFo14_Mcore5_dict_new = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore5_varstep/\", N_tracks=3)"
   ]
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
886
    "## LoFo14 planets with M$_{core}\\,=\\,5\\,$M$_\\oplus$"
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   ]
  },
  {
   "cell_type": "code",
891
   "execution_count": 12,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Folder results_LoFo14_Mcore5_varstep/ exists.\n",
      "That took 0.0004577438036600749 minutes\n",
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      "Total # of planet folders =  4\n",
      "Non-empty folders:  4\n",
      "\n",
      "Total number of planets to analyze:  4\n",
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      "CPU times: user 113 ms, sys: 20.1 ms, total: 133 ms\n",
      "Wall time: 138 ms\n"
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     ]
    }
   ],
   "source": [
    "%%time\n",
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    "folder_name = \"results_LoFo14_Mcore5_varstep/\"\n",
    "curr_path = os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/example_V1298Tau/'\n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore5, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
919
    "    evolve_ensamble(planet_chunks, t_final, init_step=init_step, eps=eps, \n",
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    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
921
    "    \n",
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    "# read in the results as a dataframe\n",
    "planets_LoFo14_Mcore5_dict, planets_LoFo14_Mcore5_init_df, tracks_LoFo14_Mcore5_dict = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore5_varstep/\", N_tracks=3)"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
930
    "## LoFo14 planets with M$_{core}\\,=\\,10\\,$M$_\\oplus$"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 13,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Folder results_LoFo14_Mcore10_varstep/ exists.\n",
      "That took 0.0004350026448567708 minutes\n",
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      "Total # of planet folders =  4\n",
      "Non-empty folders:  4\n",
      "\n",
      "Total number of planets to analyze:  4\n",
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      "CPU times: user 52.2 ms, sys: 28.6 ms, total: 80.8 ms\n",
      "Wall time: 94.3 ms\n"
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     ]
    }
   ],
   "source": [
    "%%time\n",
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    "folder_name = \"results_LoFo14_Mcore10_varstep/\"\n",
    "curr_path = os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/example_V1298Tau/'\n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Mcore10, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 0.1, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
963
    "    evolve_ensamble(planet_chunks, t_final, init_step=init_step, eps=eps, \n",
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    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "\n",
    "# read in the results as a dataframe\n",
    "planets_LoFo14_Mcore10_dict, planets_LoFo14_Mcore10_init_df, tracks_LoFo14_Mcore10_dict = read_in_PLATYPOS_results(path_to_results=\"./results_LoFo14_Mcore10_varstep/\", N_tracks=3)"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
974
    "## Ot20 planets "
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   ]
  },
  {
   "cell_type": "code",
979
   "execution_count": 14,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Folder results_Ot20_varstep/ exists.\n",
      "That took 0.0006747603416442871 minutes\n",
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      "Total # of planet folders =  4\n",
      "Non-empty folders:  4\n",
      "\n",
      "Total number of planets to analyze:  4\n",
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      "CPU times: user 56.9 ms, sys: 23.3 ms, total: 80.2 ms\n",
      "Wall time: 106 ms\n"
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     ]
    }
   ],
   "source": [
    "%%time\n",
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    "folder_name = \"results_Ot20_varstep/\"\n",
    "curr_path = os.getcwd().split(\"gitlab\")[0]+'gitlab/platypos/example_V1298Tau/'\n",
    "# chunk_size...\n",
    "path_save, planet_chunks = create_planet_chunks(curr_path, folder_name=folder_name, list_planets=planet_list_Ot, chunk_size=4)\n",
    "eps, init_step, t_final = 0.1, 1.0, 5000.\n",
    "\n",
    "# this is where the mulit-processing happens\n",
    "if __name__ == \"__main__\":\n",
1007
    "    evolve_ensamble(planet_chunks, t_final, init_step=init_step, eps=eps, \n",
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    "                    K_on=\"yes\", beta_on=\"yes\", evo_track_dict_list=list_tracks, path_save=path_save)\n",
    "\n",
    "# read in the results as a dataframe\n",
    "planets_Ot_dict, planets_Ot_init_df, tracks_Ot_dict = read_in_PLATYPOS_results(path_to_results=\"./results_Ot20_varstep/\", N_tracks=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0       23.0\n",
       " 1       23.1\n",
       " 2       23.2\n",
       " 3       23.3\n",
       " 4       23.4\n",
       "         ... \n",
       " 3265     NaN\n",
       " 3266     NaN\n",
       " 3267     NaN\n",
       " 3268     NaN\n",
       " 3269     NaN\n",
       " Name: t1, Length: 3270, dtype: float64, 0       6.728688\n",
       " 1       6.727284\n",
       " 2       6.725889\n",
       " 3       6.724502\n",
       " 4       6.723124\n",
       "           ...   \n",
       " 3265         NaN\n",
       " 3266         NaN\n",
       " 3267         NaN\n",
       " 3268         NaN\n",
       " 3269         NaN\n",
       " Name: M1, Length: 3270, dtype: float64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets_LoFo14_Mcore5_dict[\"planet_1\"][\"t1\"], planets_LoFo14_Mcore5_dict[\"planet_1\"][\"M1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evolve LoFo14_PAPER planets (fixed step size)\n",
    "\n",
    "Run the cells below to start the evolution calculation (or read in the results if already available).  <br>\n",
    "Once this is done, the data will be read in & can be plotted. <br>\n",
    "**The calculation results are already available. If you want to redo them, delete the results-folders. But remember that this will take some time** <br>\n",
    "For each planet scenario (\"fluffy\" and \"dense\"), we evolve the planets along three tracks. <br>\n",
    "The output will be four arrays for track: t1_XXX, M1_XXX, R1_XXX, Lx1_XXX (time, mass, radius, Lx evolution). <br>\n",
    "The high activity track is labeled with a \"1\", the medium one with \"2\", and the low activity track with a \"3\"."
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   ]
  },
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t1_c_OtP, M1_c_OtP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots from the Paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot mass & radius evolution planet c"
   ]
  },
  {
   "cell_type": "code",
1102
   "execution_count": 18,
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   "metadata": {},
   "outputs": [
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    {
     "ename": "NameError",
     "evalue": "name 't1_c_5P' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-1b506c732ce0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# 5Mcore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt1_c_5P\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM1_c_5P\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr\"fast, step = 0.1 Myr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"xkcd:royal blue\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt2_c_5P\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM2_c_5P\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr\"med, step = 0.1 Myr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"xkcd:grey\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt3_c_5P\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mM3_c_5P\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mr\"slow, step = 0.1 Myr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"-\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"xkcd:red\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 't1_c_5P' is not defined"
     ]
    },
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    {
     "data": {
1118
      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 936x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,2, figsize=(13,10), sharex=True, sharey=False)\n",
    "\n",
    "fig.suptitle(\"Planet c\")\n",
    "#ax1.set_title(\"Planet c\")\n",
    "fig.subplots_adjust(top=0.93)\n",
    "\n",
    "# Otegi/heavy\n",
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    "axs[0, 0].plot(t1_c_OtP, M1_c_OtP, label=\"high activity track\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[0, 0].plot(t2_c_OtP, M2_c_OtP, label=\"medium activity track\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[0, 0].plot(t3_c_OtP, M3_c_OtP, label=\"low activity track\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
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    "\n",
    "# 10Mcore\n",
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    "axs[1, 0].plot(t1_c_10P, M1_c_10P, label=r\"fast, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[1, 0].plot(t2_c_10P, M2_c_10P, label=r\"med, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[1, 0].plot(t3_c_10P, M3_c_10P, label=r\"slow, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
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    "axs[1, 0].hlines(pl_c_10_PAPER.core_mass, t1_c_10P[0], 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
    "\n",
    "# 5Mcore\n",
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    "axs[2, 0].plot(t1_c_5P, M1_c_5P, label=r\"fast, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[2, 0].plot(t2_c_5P, M2_c_5P, label=r\"med, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[2, 0].plot(t3_c_5P, M3_c_5P, label=r\"slow, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
1151
    "axs[2, 0].hlines(pl_c_5_PAPER.core_mass, t1_c_5P[0], 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
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    "axs[2, 0].text(1600, 5.006, \"core mass\", fontsize=11.5)\n",
    "\n",
    "#radii\n",
    "# Otegi/heavy\n",
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    "axs[0, 1].plot(t1_c_OtP, R1_c_OtP, label=r\"fast, step = 1 Myr\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[0, 1].plot(t2_c_OtP, R2_c_OtP, label=r\"med, step = 1 Myr\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[0, 1].plot(t3_c_OtP, R3_c_OtP, label=r\"slow, step = 1 Myr\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
1159
1160
    "\n",
    "# 10Mcore\n",
1161
1162
1163
    "axs[1, 1].plot(t1_c_10P, R1_c_10P, label=r\"fast, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[1, 1].plot(t2_c_10P, R2_c_10P, label=r\"med, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[1, 1].plot(t3_c_10P, R3_c_10P, label=r\"slow, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
    "age_arr = np.linspace(23., 5000., 1000)\n",
    "axs[1, 1].plot(age_arr, plmo14.calculate_planet_radius(pl_c_10_PAPER.core_mass, pl_c_10_PAPER.fenv, age_arr, pl_c_10_PAPER.flux, pl_c_10_PAPER.metallicity), color=\"k\", ls=\":\", lw=1)\n",
    "axs[1, 1].hlines(plmo14.calculate_core_radius(pl_c_10_PAPER.core_mass), pl_c_10_PAPER.age, 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
    "\n",
    "dy = 1.25\n",
    "axs[2, 1].text(575, 3.19+dy, \"thermal contraction\", fontsize=11.5, rotation=-12.5)\n",
    "axs[2, 1].text(570, 2.82+dy, \"without mass loss\", fontsize=11.5, rotation=-12.5)\n",
    "#xkcd:goldenrod\n",
    "\n",
    "# 5 mcore\n",
1174
1175
1176
    "axs[2, 1].plot(t1_c_5P, R1_c_5P, label=r\"fast track\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[2, 1].plot(t2_c_5P, R2_c_5P, label=r\"medim track\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[2, 1].plot(t3_c_5P, R3_c_5P, label=r\"slow track\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
    "axs[2, 1].plot(age_arr, plmo14.calculate_planet_radius(pl_c_5_PAPER.core_mass, pl_c_5_PAPER.fenv, age_arr, pl_c_5_PAPER.flux, pl_c_5_PAPER.metallicity), color=\"k\", ls=\":\", lw=1)\n",
    "axs[2, 1].hlines(plmo14.calculate_core_radius(pl_c_5_PAPER.core_mass), pl_c_5_PAPER.age, 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
    "axs[2, 1].text(1475, 1.55, \"core radius\", fontsize=11.5)\n",
    "\n",
    "axs[0, 0].legend(fontsize=10, loc=3)\n",
    "for ax in [axs[0, 0], axs[1, 0], axs[2, 0]]:\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xticks([23, 100, 300, 1000, 5000])\n",
    "    ax.set_xlim(right= 6000)\n",
    "    ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))\n",
    "    ax.tick_params(direction=\"in\", which=\"both\", labelsize=16)\n",
    "    ax.set_xlabel(\"Time [Myr]\", fontsize=16, labelpad=8)\n",
    "    ax.set_ylabel('M [M$_\\oplus$]', fontsize=16, labelpad=8)\n",
    "    ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f')) # No decimal places\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f')) # No decimal places\n",
    "\n",
    "ylim = axs[2, 0].get_ylim()\n",
    "axs[2, 0].set_ylim(top=ylim[1]+0.02)\n",
    "ylim = axs[1, 0].get_ylim()\n",
    "axs[1, 0].set_ylim(top=11.02)\n",
    "    \n",
    "for ax in [axs[0, 1], axs[1, 1], axs[2, 1]]:\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xticks([23, 100, 300, 1000, 5000])\n",
    "    ax.set_xlim(right= 6000)\n",
    "    ax.tick_params(direction=\"in\", which=\"both\", labelsize=16)\n",
    "    ax.set_xlabel(\"Time [Myr]\", fontsize=16, labelpad=8)\n",
    "    ax.set_ylabel('R [R$_\\oplus$]', fontsize=16, labelpad=8)\n",
    "    ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f')) # No decimal places\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f')) # No decimal places\n",
    "\n",
    "plt.subplots_adjust(hspace=0, wspace=0.25)\n",
    "fig.align_ylabels(axs[:, 0])\n",
    "fig.align_ylabels(axs[:, 1])\n",
    "#plt.tight_layout()\n",
    "#plt.savefig(\"./planet_c_EVO_eps01_Zsun.jpg\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Planet c - larger labels for in-text plot"
   ]
  },
  {
   "cell_type": "code",
1225
   "execution_count": 39,
1226
1227
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1229
   "metadata": {},
   "outputs": [
    {
     "data": {
1230
      "image/png": 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gEWHpZ5U899wWnn12C2lphiOOzGTuPj4ynBWUlZUS6OGX/LS0tHaFyuv14s5yx2T4GSgRsYpYNyGoq68DlZVIRQWB8nICFRXdByVjMDk5XQehggKSvF6SvF4cXi9Jo0ZZU766XHH156sShwadwYvVWgKwfXsj/3xhG3//+1a++KKSgw9xc+BBDoqK6qiuLaW2tue/cqfTGaolwbridDqHqfXDRwKB9jWlp1BUVRWqIwG7pkhVVfcnD68p4SEovJZ0fMRwwFSqKxp0YlAsF6cgEeHTTyp4/vmtPPfcFpyuZE7+f+M48mgXOTn1lJTupKS0hPLy8h7DT3paut3dzb7z4/HiSuBfziUQsIpWMPgEi1bwdXl5WygKe09qaro+YXo6Djv0JHm97V63+3rsWOsKX4L+uavYo0Fn8EZCLQEoKWkKhZ5PP63g6GNG873ve9l77yTq6sspLS2hpLSEmu5+ztmcmc7QmB+v1wpALqdrmL6L2CR+f9vFtLALauGvwy+0BcrKCJSWdtv9zjid7WuIx9P+67FjcYwda9WUGP43pxKHBp0YNFKKU1AgYIWe557bwvPPb8WdncIpp0zgRz+aQGFhBuXl5VbwKSlhZ2kJFRXl9PTvJCMjo13w8Xq1WPVGWloIlJYSKCnBX1JCYOdO67X9HLC3+e3XXU7wkJoaKlCOceM6vXaMHUvSuHHWpAxKDTENOoM30moJQFlZMy/+0wo9H31UzhFHjuJHP5rAD384jkCghZLSEjv4lFJaWkJ1TXWP58vMzAy76+Ox7/wk7sW0vgrU14dqSvDhD3vd7uvSUvD5Op3DuFwkjRnTLvy0ez1mjBWI4vBOnIodGnRi0EgsTkGBgPDxR+U899xWnn12CzNnurnwoiK+972xOBxWYWltbaWsvIySEusqXUnpTioqKno8rzPT2enOTyz30Y5lImJ1cQgWqh078G/fTmDbNvzFxfiLi0OvpYvuIyYrKxR6HOPG4Zg40VoUMOxZuzeowdKgM3gjuZYAVFS08K+XinnyyU2sWFHNL86eynnnTWXChLafL01NTZSWlYbu+pSUllBd3XP4CV1MC+v2lsg9CQYrWFP8O3YQ2L7dqiH2s7+4mID97C8uhqamTscbt7utloQ/gnVl3DhMag+DuJTqgQadGDTSi1NQS0uA55/fygP3r2HHjibmnV/I2WdPIT8/rfO+rS2UlZVRUtJ256eqqrLH87ucrtAsb8EQlJGRMVTfTkIK1NZaBcsOPoHi4vavt2zBv20b+P3tjksqKOgyADkmTcIxebJ2k1O90qAzePFSSwBWrqxh8cPreOqpzRx8SAEXXljEEUd4u/w50tzcTGlZ212fktISqnoaq4IVfjwFnrbw4/WS5crSn1MRJCJIdXWn8BPYvt2qJVu24N+82ep1EM4Y685QsI5MnEhyMAxNnoxj6lTtaaC6pUEnBsVTcQpa+lkFDzywlpdeKuaHPxzHRRcXseeeuT0e09LSTElpqXWVrsS689PblTp3ljvUNzt4xS49PT2S34rqQHw+Atu349u0Cf+mTfg3b27/vGlTp/7exu0mubAQR2Fh++eiIuvqnc4ElPA06AxePNaSujoff31qEw8+uI7mFj8XnF/Ez34+iZycnq/4t7Q0UxqsJ/ZzbxfT0tPT29318Xg8cTeBTiySxsZQ6PFv3ozPfg6vLbS0tDsmyettqyNTp7arK0mjRunfWQLToBOD4rE4BZWWNrNkyQYefmgt48dnctHFRZx00nhSU5P6dHywm0JJyc5QAKqp7XmAarY7O+zOjwdPgZe0tM53ldTQEBGkshL/pk1WGNq4Ef+6dfjWrsW3bh3+jRvbjxlKTSV5ypR24Sd5xgySZ8zAMX48Jqlv/1bUyKZBZ/DiuZaICB+8X85DD6/l1Vd2cPLJ47ngwiLmzMnp8zlaWlo6dXurrOwl/KSl4/F42l1Mc7s1/AwnCQQIlJZawWfDBnzr11s1Zd06/OvW4d+ypd2U3MbptMLP1Klt9WSXXUieMYOkggL9u4tzGnRiUDwXpyCfL8C//rWdB+5fw6pVtZxz7lQuuKAQr7f/d18aGxspLbW6uwXv/NTV1fV4THZ2drs+2p4Cj975iRLx+62rd2GFyhf2OnyckMnIwLHLLqTYwSf0mD4do90W44oGncFLhFoCsGNHE0uWbOCRxeuYMCGTy341nR/+cBxJSf3/BTbYjbpj+Onpd5u0tLT23d48XrKzY3PduEQgzc34N25sF4DCawvNzaF9TW5uu+ATel1YiElJieJ3oSJFg04MSpTiFLR8eTUP3L+W55/fytnnTOGKK3bpchxPfzQ0NNh3fNru/NQ39Lxatdvt7jRAVcNPdIkIgdJSfKtW4Vu5st3Dv2FD21U7Y3BMmmQVqZkzSdltN5J3242UmTM1AI1QGnQGL9Fqic8X4KWXirn1lm/x+4UF18/iBz8YO+jA0draao0htYNPaWkJFZUVPYaf1NRUPAUe6+5PgTXjW05OLkl6RzqqxO+3usMFa0rYc2D79rYdk5NxTJ1KyqxZVi2xH46iIu1aPcJo0IlBiVacgrZsaeDWW77l73/fyvkXFHLZZdN77XfdH3X1ddZVupJgsSrtc/jRMT+xR5qa8K1Z0ykAta5Y0TazT1ISjqKiUJEKFizH1KlarGKcBp3BS9RaIiL861/bWbRwOSKw4PqZfP/7gw884VpbWykvL2s35qe3pROSk5MpyC8ITXXt8XjJy83DoT+LYkKguhrf6tXt68k33+Bfu7ZtIe/0dFJmzrRqyezZoZqSNGaM3sGLURp0YlCiFqegDRvq+f3NK/j3v4uZf8k0LrlkGllZQ3MLub6+PmxdButR381CaUHuLHfbonQeLx6vl4x0vWsQK8Tvx79uHa3LltG6bBk++9m/dm3oDpDJyCB51ixSdt+d5DlzSLEfSTl979+vhpYGncFL9FoiIrz4YjGLFi7H4TAsuH4WJ5wwdL+Q+nw+ysrLKC0tpbTMuphWVl7W46LZSUlJdvix7vx4PB4K8gtITk4ekjaq/pPGRlpXrAjVEt8339C6bFm7O0AmL4+U3Xe3HnPmkLz77qTMmqU9CmKABp0YlOjFKWjNmlpuWrSC//53J5f9ajoXXliE0zn0P/xD3d6C3RRKSqir73nMT1ZWVqc7PzrVdWwJ1NfjCytWrcuW4fvqKwJlZaF9HBMntgs+KXPm4Cgs1AkQokCDzuBpLbEEAm2BJyUliQXXz+T444fnCrzf76eisqLdIqdl5WX4ulhgM8gYQ15evnXXJxh+CgpITdG1ZGJJoLy80wU137JlbbOMJiWRPH26VVPsAJSy++4kjR+vd3+GUcIFHWPMdOAM4BigEEgH1gHPAveISKfL+caYE4BfAXsDacBW4DURmd/HzxwL3AocB7iA5cBtIvJsN/trcQqzYkUNixYt5/33yrjyyl244MKiPs/SFinB8BN+56e3CQ9cLlenMT+6yGlsEREC27fT+tVXtH71Fb6vv7aeV60KrQ1kMjOt7gl28AkWrST9vzmkNOgMntaS9gIB4YUXtrFo4XIyM5O58845HHBgQRTaEaCqqtIKPmVtXalbw2ef7EJuTq41eY7HEwpBOoNobJFAwOpREKwl9rN/48bQPiY3NxR69O7P0EvEoHMrcDHwIvAR0AocDvwY+BrYX0Qaw/a/AbgReBX4D9AATAR2F5ET+/B5ecBngBe4CysknQ58BzhbRJZ0cYwWpy58/XUV116zjI2b6rn33j054ohRUW1PY2NwwoPSUAiqrev5/5DL6cLjbbvro+EnNklTE63Ll+OzA1DwIWGLDjqmTm0LP3bBckyZolfqIkSDzuBpLelaICD87W+bufaaZRx+uJebf78bY8ZE95dMEaG6utqqJfaU16WlpTQ1N/V4XGgcaYEnNPYnI0NrSqwJVFdbd3zs4NP69dd692eYJGLQmQusEZHqDttvAq4FLhGR++xtRwGvA9eLyKIBft7twK+BH4jIS/Y2B/Ah1h2lSSJS1+EYLU7dEBFeeqmYK6/4kr3n5nHHHXMYPz52fqgHp7pum52ntNd1fpxOZ/s7P14vzkznMLVY9ZWI4N+ypa1Q2Q//mjVtY3+ysjqN+0mePZskp/599pcGncHTWtKz2tpWbrnlW5b8ZQO//s0M5s+fNuy9BXoiItTW1VpjfsK6vjU0NvR4nMvlsmqJHX48Hg/OTKf+whxj+nz3J3jnZ7fdSN51V1JmzCApPz96DR9hEi7odMcYsxvWHZ2HReQCe9sbwCxgnIj4jDEuoEFEuh9Z2Pm8W4EmESnqsP1nwOPAqSLyTIf3tDj1oqHBx+23r+Thh9Zx+RW7cOml02OqQIVrbGoMrcgdDEE1NX0LP+Fjfpz6y3JMCtTXW4NTg93f7Kt1ofV/jMExbZpVrGbP1rV/+kiDzuBpLemb1atrufKKL1m/oZ67796Do48eHe0m9Sg0iU5ZW13prSt1ZkZmaKHT4Jo/WVlZGn5iUK93f4CkggKrjuy6a7s15RyTJumMoh1o0LEZY47D6pq2UERuMMY4gWp727+B64GxQCNWt7dLRWRnL+ccAxQDT4rIGR3emwasBu4UkV93eE+LUx+tXVvHFZd/wbr19dxzz54cdVR0u7P1VVNTU6cxP72Fn8zMzC7Djxaq2COBAP5Nm9qCT/DuT3dr/3R4JHm9Cf/3qkFn8LSW9J2I8O9/b+fKK75k1uxs7rxzD6ZMGTkXlxobG6wLamHd3qprqns8Ji0trd2dH13oNHZJIGAtghq+nMK33+JbubLdhDqkp1td4MJryrRpJBcVJeysohp0CHUlew+YC8wWkVXGmDnAl0Ap4MaaTOAr4BDgUmAVMFdEur2HbIzZG2t8zu0i8tsO72UC9cBfReT0Du9pceqH4JoJV1z+BXvtncc99+zJ6NEjb62bpqamdlfoSkpLqK7uuVBZV+ms7m5ee8prp9OlhSpGSWNjl2v/+FatQhrafpSYnJz2q3QXFZFcVISjsDBhJkHQoDN4Wkv6r6nJz913r+YP967m17+ZwaWXTsfhGJk/T5ubm9uN9ykpK6GysrLHY1JSUqzJDuzZ3jweL7m60GlM85eVWQuf2sGn3aLaYVObJ+Xn47BDT3JREQ77ObmoCJOXF7e/N2jQAYwxfwTmA9eIyC32toOBd+1dzhORP4XtfyNwA3CRiDzYw3kPAd4BFonI9R3eSwL8wD87TmoQLE577LFHaCGxefPmMW/evEF9n/GusdHPzTev4NElG7jn3j055ZQJ0W7SoFmFqv2EB1XVVT0ek5GR0Wm2N5dLw08sk0AA/9atnQPQypXtV+sGkrzedgUq9LqwkKS8vCh9B5GnQWfwNOgM3Pr1dZx37qf4/cIjf9qHadPi48+vpbWF8rJySux1fkpLS6iorOhxrZ+2hU6tmd68Hg95efm60GmMk6YmfGvX4lu7Fr/9HHzt37y5rXcB9sW1YE2ZNo3kwkIcU6eSPHkySWPHjujucAkfdIwxi4DrgMUicn7Y9uDdmACQKSLNYe9NAdYDz4jIqT2cW+/oRMHHH5Xzi7M/Ye7eedz7hz3JzY2vtQeCV+nC7/xUVfUefoKhx1Pgwev1kuXS/tkjQaC2Fv+6de0Llv11YOvWdvua3Ny2YjV1Ko4pU0ieNAnH5Mk4Jk7EpI6c/wsadAZPa8ngBALC/fet5fe/X8F1183kwouKSEqKv5+ZPp+P8vJye6rrtrV+elvoND8/PxR8PB6vLnQ6gkhTE74NGzoFIN/atdZkCOF/98nJOCZOxDF5MsmTJ1v1xH6MhCCU0EEn7M7MEuAcCWu8MWYUsAMoF5GCDselY43VeV1Ejunh/DpGJ0oaGnxcc/Uy/vnPbTy8eC7HHBPbg0sHq6WlOTThQYn9XFXVcxeF9PT0TmN+dHDqyCKNjfjWr28XgELFatOm9sXKGJLGju2yUDkmT8YxYQImhtbk0KAzeFpLImP16lrOPedT0tOTWPzIPkyePHLG7gxU20Kn1lo/1nNp7wud5uaFprkOhp/UEXSBRYG0tODftAnfxo34g48NG0Jfd+xl0C4ITZmCY9IkHBMm4Bg/HseECSSNHx/VmUcTNuiErY/zOPCLrmZSM8ZsAiYArvBjhDjQAAAgAElEQVSxOMaYImANXQSYLs7R26xrPxGRpzu8p8UpQv77353MO+9TjjtuDLfeNgeXK3GuNrW0tIT6ZwcnPOitf3Z6enpoRh6v14unwIvb7dbwMwJJayv+bdtChapd0dq4Ef/WraFFUQErCI0ZYxWrSZNIGj/eKlR2sXKMH29NkjBMV+406Aye1pLI8fuFe+5Zzf/cuZKFC3fjnHMTb80sa6HTqnZ3fkrLSmlpaenxuJyc3NACp1b3Nw/p6SNvHK2ySFMT/s2brZqyYUOnGhPYsaPTMSY3t10t6fg8lGEoIYOOMeZ64HfAE8BZ3U0XHba2zuUicnfY9v8BLgd+KiJP2dsysRYSrRaR7WH73gFcSdfr6EwDJnb8g9fiFFlVVS1c/qsv+fDDMp54Yn/m7hM/4xj6q6W1hbKyMkpKSkJd3yqrKunp/21aWlq7xeg8BV5ycnISrsjHG/H52gWhUKHasAH/li1WEOr4C0xyMo6xY60QFCxUHcPQ6NERCUMadAZPa0nkrVhRwy/O+phJk5088sg+ZGenRLtJUSUi1NRUh3oSBMNPU1PvC512rCu6eHZ8kKYmq7bYdcS/dSv+LVsI2M/+rVsJlJZ2Oi4UhsaPxzFmDEljx7Z7dowda9WXlP79n0u4oGOMuRi4D9gMLMAagxNup4i8bu/rBj4GpgOLsWZdOxj4KfAGcIyI+O19DwPeBB4TkbPCPi8fWArkA3cB24DTgMOAc0Xkz120UYvTEHj++a1cMn8pN944m/PmTdVf1G2tra1d3vnp6f9ySkoKBQWedtOS5ubqzDzxREQIlJW1K07tHsEw1PEXGoeDpNGj2wpTV8Vq7Fgco3qeCl6DzuBpLRkazc1+fn3lV7z++g6e+usB7LlnbrSbFFNEhLq6utBaP8FZ3+ob6ns8zuV0hWZ6C4YfXUIhPnUXhvxbthAoLsZfXExg58723a9tSR5PWz2ZNImchx7q8bMSMeg8CpzZwy5vi8hhYfsXAIuAHwIFwFbgr1gzqTWF7XcYXQQd+71xWNNTHwe4gBXAbR27rIXtr8VpiKxeXcupP/6APfbI5f4H9iIzM3G6svVHa2sr5eVl9hU6a1rSioqeZ+ZxOBwUFBSEpiX1erw6M0+cExGkoqJzACoutorV9u0Eiovbr/MAmOxsxvQygYYGncHTWjK0nn56M7+67IuE7crWX/X19WEX1ayxP7W1Pf/XDk2kU+AJdXtzu3Wtn0Qgfj+BkpJQHQl/9hcXh8YKeT79tMfzJFzQGQm0OA2t+nofF1+0lK++quLpZw5k+nT9M+4Lv99HeXmFNS1pSQklZaWUl5fhDx/n0YHOzKPAHty6Y0eoSEl9PZln9Di8UYNOBGgtGXqrVtXyk1Oti2f33b8XTqf+bOuPxqbG0EQHJSUllJb1vn5camqq1e2twEOBHYDycvO0R4HqkgadGKTFaeiJCI8sXs+NN37D/Q/szUknjY92k0Ykv99PZWVlp8Gp/Z6Zp6CA1BSdmUe10aAzeFpLhkdDg49L5n/OZ0srefrpA5gxwx3tJo1ozc3NlJWVUhLW7a23saQOh4P8/IJQAPJ4PHpRTQEadGKSFqfh89mnFZx22of8+NSJLFo0Oy7XSBhuA52ZJzcnF48n2O3N6p+dFkPTHavhpUFn8LSWDB8RYclfNrBgwTc89vh+HHVUz2PQVP8Eu1Nba8iVUlZW2utaP8YYcnNzQ5MeBEOQ1pXEokEnBmlxGl5lZc2ccvL7jB6TwZIl+5KRoWNKIk1EqK6pbuubbV+la2rufWae0Fo/9lW6jAydmScRaNAZPK0lw+/dd0s5/bQPWbBgFvPOL4x2c+JaeI+CYPe30rJSWltbezwuNONb2HTXziiu86KGlgadGKTFafg1NfmZd95nrF9fx/N/P4hRo3SO/6HWbmaesADU0NjQ43Eulyss+FgzvmmRij/xFnSMMd0Vx3oRcfXh+LeA73Tz9j4i8lkXx2gtiYK1a+s48cT3+O6xo7nt9jk4HNpTYLiICNXV1Z3CT2NjY4/HZWZmtuv2pmvIxQ8NOjFIi1N0iAgLF67gyf/dyAv/PISZM7WfdTTU19e3m5a0pLSEurq6Ho+xilTbmB+Px0OWK0uL1AgWp0HnXaylCsK1djcDZ4fj3wJmAb/q4u3/iEhFF8doLYmSysoWfnLqh2RkOnjiif3Iykrs9XaiSUTaZnwra+tO3duMb+GTHgTDjy6jMPJo0IlBWpyi64knNnLVb7/m8Sf248gjtZ91LGhsbLCnuW4LPzU1NT0ek56e3n5BOo+XbJ2WdMSI06DTafmBfhz/FjBZRCb34xitJVHU2hrgl5d8ziefVPDiS4cwblxGtJukwjQ1NdnBJ3jnx1pDrifBSQ+CXakLCnTSg1inQScGaXGKvnfesfpZ337HHE4/fVK0m6O60NTURFl5qd3lzQpAlVU9F6nQFbqwBelycnL0Cl0MitegA8wDUkWk59uUnY9/C5gMTMVaj61Weim4WkuiT0S4445V/PlP6/nPy4dSWNhrL0UVRa2trZTZkx6U2mv9lJeX9zrpQV5uXmiqa530ILZo0IlBWpxiwzffVPP9773LNdfsynnzdFDpSNDS2kJZWVm7MT8VlRU9TkuanJzcKfzk5ubqQqdRFqdBpx5IBxxAKfA0cJ2I9LxwCKGgcxDQCmQADcCrwDUisrKbY7SWxIhHFq/j5ptX8OJLh7D77jnRbo7qB7/fT0VlBWV2+Ckps2Z96/ekBx4PzkwdTzrcNOjEIC1OsWPdujqO++7bzDu/kCuvnBHt5qgB8Pl81hW60lJ77E/vV+jad0+wJjzIz8/D4dDuCcMlDoPOx8CzwFrADRwPnAosAw7s7Q6PMWYJUAx8DfiB/YD5QAtwsIgs6+KYLKBmjz32CAX3efPmMW/evEh9W6ofnnlmC5f/6guee+4g9j8gP9rNUYMQPulBcLrrfk16EDbdtU56MLQ06MQgDTqxZevWBo477h1OPHEcCxfO1h9IccDv91NRUW7d9SmzxvyUlZXh9/u7PSYpKYm8vDwK8j3k5+WTn289nJlO/TcxBOIt6HTFGHMNcDPWXZ2bB3D8IcBbwBsicnQX72stiTGvvLKdc87+VNfaiUPWpAd17cf9lJZSW9f7pAcFBQXk5+aTl5dHXl4++Xl5upRChGjQiUFanGJPaWkzJxz/DgceWMBdd++hC4vGoUAgQGVlZbsZ3/qyJkN6Wnoo9OTnFdjP+aSmpg5Ty+NTggSdFKAOWCoiBw7wHG8ChwBZItLY4T2tJTHo/ffKOPXUD3jwobl8//tjo90cNcQamxrtLtWloWmvextPCpCRkUF+nh1+7BCUn5dPerouf9EfGnRikBan2FRV1cIPf/Aes2dnc9/9e+lV/AQgIlRVVYW6J5SWllBeUd5r9wQAd5Y7VJhyc/PIy80jNzeH1FQdoNoXiRB0AIwxG7CmmJ4+wOOXAGcB40SkuMN7Wkti1NLPKvjBD97jkT/tw/HHj4l2c9QwC016YF9QK68op6KiotcLawDOTCd5eXnk5uaRm5NDTk4uuTm5uFwu/b2kCxp0YpAWp9hVU9PK8ce9wz775HHX3XvoD5UE1dDQQFl5GeUV5ZSXl9tFqhyfz9frsU6n0y5QueTm5loBKCcXp1O7wIVLhKBjjEkHaoGPROSQAZ7jPazxOlki0tThPa0lMeyTj8s56aT3WfLovhxzzOhoN0dFmYhQW1dLRUUFFRXlofBTUVnRp9qSnJxMTrYdfHJzrRCUnZvwF9g06MQgLU6xraqqhe8e+w6HHebhllt3119OFWB1fauprbGCjx2CysrLqa6u6nHWt6CUlJR2ASg3J4fs7Byy3dkJ2Q0unoKOMSZfRMq72H4HcCXwWxG53d42BsgGNotIg70tG6gTEX+H408A/gW8LCLHd3F+rSUx7sMPyjjllA903TbVLRGhpqaGispyyisqqCgvp7yynKqqqj4FILAmQcjNySUnx6op2dnZuN3ZZLuz434abA06MUiLU+yrqGjhmKPf4vgTxrJw4exoN0fFML/fR3V1NRWVlVRWVlJZVWE9V1bS6uu9mwJAZkYm2Tk55GRnk+3OITs7mxz763i9UhdnQeduYH/gTWAz1jo4xwOHAx8DhwfH1xhjHgXOtLe9ZW87EbgLeAlYD/iAfYEzgArgIBFZ3cXnai0ZAd57r5RTf/whTz61P4cd5o12c9QIISLU1dVRWVVJVVUllVVVVFVVUlVVRU1tzwtqh0tPTyfbbQef7OxQEMp2u3E6R353uEjXEp17VSWEvLxUXn7lOxx99FukpSVx7bUzo90kFaMcjmTy8vLJy2s/nWx4kbKCT0XodX1Dfbt9GxobaGhsYPv2dkMwAGvAqlWYrODjdrvJynKT7XaTmenUxVBjw1vATKwAk481PfQa4Frgro5dzrqwClgKfA8YBaQAW4GHgN+LyLahabYaDgcf7OGpv+7PaT/5kBdeOJh999Opp1XvjDFkZWWRlZXFxAkT273n8/moqraCT2VlFVXVlXYgqqK5ubndvk1NTTQ1NbGzZGenz3A4HLjdbrLd2WRluXHbn5eV5SbLlUVmZuaID0L9pXd0IkCvwo0cO3Y0ccQRb/LLS6ZxwYVF0W6OihPNzc1U11RTXV1FVVU11TVVVFVbXzc0NPT5PElJSWS5sshyu3Fn2Q+3VaTcbndMT40dT3d0okVrycjy738Xc8H5n/H6/x3GjBnuaDdHxanm5maqq626Ul1TQ3V1NTU11VTXVFNXV9enrtZBDocDl8tl1RRXWAiyA5HL6Yr64tvD2nXNGPPGYD/AJiJyZITOFXO0OI0s69fXccThb/I/d+3JySePj3ZzVJxraWlpC0HVwecqqqurqa+v7/0EYZKSknCHipKbLJcLlysLl8sVeqSmRGd8kAadwdNaMvI8/thGFi1azltvH8G4cRnRbo5KMH6/n5ra9uHHel1DTW1Nn2aFC2eMwZnpxOVy4XQ6cTldOF0uXE4XLqcz9Do5eeg6hA130Ol+KfL+ERGJbkQcQlqcRp4vvqjkeye8y1//dgCHHuqJdnNUgmptbaW2rpba2hq7MNWGClRtbU2fpsXuKC0tzSpKofCTRZbLhdPpIssORSkpKRH/XjToDJ7WkpHpjjtW8tSTm3jjzcPJzU28iUhUbBIRmpub7RoTfNRQW1dLjf16IDUGrPXonM5gIGofglxOF05nJunpGQPqgRCNoPMKcNsgPuMq4BgNOirWvPHGTn52xse88up32G237Gg3R6lOWltbqa2tobrGCj5WEKqmtraWmtoampp6GyrStWAYcjqdZGY6cTqdOIPPodeZOBx9v2qnQWfwtJaMTCLCr6/8is8+q+DlV75DRkbc/rqj4ozP52sXgKzaUkt9fR119fXU19X1eRKejpKSksjMyCTT6cSZmYkz00mm08mkCRMZPbr7taiiEXQeFZGzB/wB1uJoP9ego2LR009v5uqrvubtd45gwoTMaDdHqX7x+XzU1ddRV1dHXV2t/VxHXX0dtfbXAw1D0HbVzul0MnPXWUwrmtbtvhp0Bk9rycgVCAhnnvkxPp/w5JP7k5QUm2PplOoPEaGlpcUKPnV2+KlvqzP19fXU1df1687QQQcczF577tXt+8M969pXWFNrDsYW4OtBnkOpIXHqqRMpLm7kpBPf4623j8Dl0okI1cgRWnAuO6fbfXw+n12UaqmtrQsFo3q7SNU31NPQ0NDlgNam5iaampsoryhn8qTJQ/idtKfjQ9VIk5RkeOSRfTjm6LdZuHA5N96oyxiokc8YQ1paGmlpaZ1mIg3n9/uor28I1ZeGhnrqGxqs53qrxtQ31NPU1ITTObwXlXXWtQjQq3Ajm4hwwflLKStv5tlnD9QrcSrhBAIBmpqaQt0VgsWpvqHBCkQNDey3z349hp1IXoVL1PGhWktGvp07mzjk4P9y4+9mc/rpk6LdHKViit9vLZjaU7doXTA0BmlxGvlaWgIc99232f+AAm6+ebdoN0epEWcIgk7CjQ/VWhIfvvmmmmOPeZvnnjuQAw4siHZzlBpRdMFQpYZAamoSTz9zIAcf9F9mzMjiZz+bHO0mKZXodojI2wM92BhzVgTbolSfzZ6dzZ/+vA+nnvoh77x7BJMnO6PdJKUSli7BrZStoCCNv//jYK767dd89GF5tJujVCLT8aFqRDvuuDH86vLpnPrjD2hs9Ee7OUolLA06SoWZOdPN4kfmcvrpH7Jz58Bnq1JKDZyI7CkiNw7yHNeLyJ4RapJS/XbZZdMpmpbFJfM/79fq9UqpyNGgo1QHJ5wwlp+fOZmfnv4RPl+kxkQrpZRKJMYYHn54Lp8treCRxeuj3RylEpIGHaW6sGDBLNLTk7ju2mXRbopSSqkRyuVK5tlnD+R3v1vOxx9pl2ilhltEg44x5kBjzGXGmCuMMUcZY3SeXjUiORyGxx7fj7//fSvPP7812s1RSik1Qk2blsXDi+dy2mkfUlraHO3mKJVQIhJ0jDHjjTHvAy8C3wH2Ax4FvjHGzIrEZyg13PLz0/jb3w7kkvlLWb1aF3pXSik1MN/73lh+ctpEzjn7EwIBHa+j1HAZdNAxxuQAbwIrgInApUABMBl4HnjLGKOrZqkRaa+9c7n++lmc8dOPaG7WmXOUUkoNzO9+N5vKyhbuvXd1tJuiVMKIxB2dq4GNInKeiDQAGcB3RMQnItdj3eUZzKJvSkXV+RcUMmmyk2uu1vE6SsUi7TatRoKUlCSe+N/9ufOOVXz6SUW0m6NUQohE0DkZuMcYk2eMyQNyAIwxufbX9wM/MMbo4qRqRDLGsHjxXF58cRsvvVQc7eYopWzabVqNNJMnO7nv/r0444yPqK5ujXZzlIp7kQg647Hu4pQBpcAH9vbg18cAaVjd2ZQakXJzU3nssf248ILP2Lq1IdrNUSrhabdpNVKddNJ4jj12NPPnL412U5SKe5EIOqVAPbAPsC/wY3v7vvbjLcAH6H1aNaIdeFABF8+fxtm/0MGkSsUA7TatRqxbb9udL7+s4m9/2xztpigV1yIRdP4FnCkiS0VkKfANQNjXPwTeEJGWCHyWUlH1m9/MoLk5wH1/XBPtpiiV6LTbtBqxMjOTeXTJvlxx+ZfaS0CpIRSJoLMIONwYc439dSuwGcAYcxYwH/hNBD5HqahzOAx/WbIvt976LStW1ES7OUolMu02rUa0vefmcfH8Is4951PtJaDUEBl00BGRYuBY4FxjzDLgIuAZY8yHWN0GThIRna5KxY3CQhcLF+7GL876mJaWQLSbo1Si0m7TasT7zW9mUF/v4/771ka7KUrFpYgsGCoiXwIzgFuBFMAJ/AWYJiL/F4nPUCqWnHPuFMaMyeCmm1ZEuylKJSrtNq1GvOTkJJY8uh+///0K1qzRhamVirSIBB0AEWkRkSdF5FIRmS8ij4iI9u1RcckYw0MPz+Uvf17P50sro90cpRKRdptWcaGoyMXVV+/KBed/pl3YlIqwiAUdpRLN6NHp3HrbHM4771PtwqbUMNNu0yqeXDx/Gq2twsMPrYt2U5SKKxp0lBqEn/50IuPGZXDHHSuj3RSlEo52m1bxwuEwLH5kLgsXLmfjxvpoN0epuGFE9DbpYBljsoCampoasrKyot0cNcy2bGlgv31f57XXD2P27OxoN0epqKitrcXtdgO4RUQHGwyA1hJ1++0refONnfzn5UMxxkS7OUoNu0jXEr2jo9QgTZiQycKFuzHvvE/x+/XCgVJKqYG5/PLplJW18NRTupCoUpGgQUepCDjn3ClkZDi0f7VSSqkBS05O4oEH9ubqq76mokInDFRqsHrsumaMeWMQ5xYROXIQx48Y2t1AAXz7bQ1HHP4mny09hnHjMqLdHKWGlXZdGzytJSro0l9+TnNzgIcenhvtpig1rCJdS5J7ef8wQICBdBTVPjwqoey6q5vz5hVyxRVf8re/HRDt5iillBqhFi7ajT3mvMr775Vx0MEF0W6OUiNWX7uufQJcAvyoH48fd3mmfjLGTDfGLDTGfGSMKTXG1BpjvjTGXGuMcXbY90ZjjHTzuLIfn5ltjPmjMWabMabJGLPcGHOh0ZGBqhdXX70rX31Zycsvb492U5RKOMaYJGPMLyJ0ru5qSd0Az/eMffw3kWifim/Z2SncceccLp6/lNZWXb5AqYHq7Y7OX4ETgX2BvYBXgEeBF0XEN7RNCzkbuBh4EXgSa1G4w4GbgB8bY/YXkcYOx/wKKOuwbWlfPswYkwq8DuwJ/BH4FjgOeAAYBdw4oO9CJYSMDAd/+ONezL/4cw4/3Et6uiPaTVIq7hljkoCfAdcBU4ElETr1u8DiDtta+3sSY8z3gJOBjrVKqW6dfPJ4/vLnDTz4wFp+een0aDdHqRGp1+ml7T7DpwFnAftjdUmrwAodj9rrGAxdA42ZC6wRkeoO228CrgUuEZH77G03AjcAU0Rk4wA/7yLgfuCXIvLHsO3PA9/HWp9hU4djtF+1audHP/qAvffO5aqrdo12U5QaFkMxRscYMxY4Busi007gNXuh0PB9Tse6AFWI1c16p4iMicBnC/CYiJw1yPO4gBXAC8APgDoRmd3NvlpLVDsrV1pjP7/48lhGjUqPdnOUGnLDPr20iNSKyGIRORBrYbbbgWbgl8BSY8wXxphfGmOGpBOpiHzWMeTYnrafuysYbmNMb3esunI60AA80mH7PVgL0p06gHOqBHPbbbtzz92r2bZNL+AqNRDGmEuBdcCfgd/bz+uNMfPs96caYz4EngCKgDqsC11FEW5Hqh1WBupmrN4T10WoSSqBzJjh5uc/n8y11yyLdlOUGpH6Nb20iKwWkauBicAJwHNY4eduYJsx5onIN7Fb4+3nnV289zVQDTQZYz4wxhzXlxPa3R/2Ar4QkaYOb38CBIB9BthelUCmTnVx7nlTue5aLU5K9Zcx5lCsupKGFWA+xwo9DuABY8zRwPvAfoAPuBcoFJFFIhLJZeVPwbrwVWuMKbHHbvZ5VWBjzL7AfOAyEamJYLtUArnm2pn83//t4KMPy6PdFKVGnAGtoyMiARF5WUROxQoc/8a623FsJBvXHWOMA7geq8A9FfZWFVZ/6kuAHwJXA5OAfxtjzurDqXOBDGBbxzdEpBkoB8YNpu0qcfz2tzN4882dfPyRFiel+uli+/kBYJSI7CMi04E9gLXAP7G6sy0DdheRX4lIx3GZg/UJVpe4U4AzgTewQsu7fbnDY/coeASru90zEW6bSiBudwo3/353Lr/8CwIBndBWqf4Y8IKhxphdjDG3AF9h3d0Ba+D+cLgHa7zQ9SKyKrhRRO4RkfNF5DEReVFE7gB2x7rrc3cfilOm/dzczftNYft0cuihhzJ37lzmzp3L4sUdx6+qRJOVlcLCRbtx+eVfanFSqn/2BzYDl4bfXReR5ViTzaRjDew/JrwGRJKI7Ccid4rICyLyuIj8BGtc6G7ApX04xa+BabSFtj7TWqI6Ou20iQQC8OyzW6LdFKVGlF4nI2i3szFu2iYm2Bdr4Gc51l2VR0XkiyFoY8c2LMLq67xYRM7v4zE3YF2ZO1ZEXuthv3ys2dqese9WdXy/BFhrj1cK364DSFWXAgHh4IP+y0UXT+OMMyZFuzlKDZlIDiA1xjQCr4jISV28l4M1Ic6rItKnbsmRYoxJwepKt7RjHeiwXxHW3aabROTmsO0b0ckI1AC9/XYJ8877jK+XHUtams7oqeLTsE9GYCzHGGOeArYDDwJzgf9grZczVkQuHaaQcyNWyFkCXNCPQzfaz71NmFCJdZWwU/c0Y0wakE8X3dqU6k5SkuGuu/bkumu/pq5uuGZkV2rES8P6edyJiFTZL3cMX3NCn90KFNN7LfkfrDD2D2NMUfCBNSlBqv31oGeGU4nlO9/xsutMNw8+sC7aTVFqxOgx6BhjbsbqPvAy8BNgA/AbYLyIfF9Enrd/8A85+67MDcDjwLnSn1tRVvcB6HrighARCWANet3TDjbh9sX68/qsH5+rFPsfkM9hh3m57bbh6tmpVEIY9v6gxph0rHGpPdYSrLGhY4HlwJqwxziserSGzjN7KtWrW27Zndtv/5aKipZoN0WpEaG36Zevxiomn2EtFPqxvX2cMabXQfki8vmgWmczxlyP1fXsCeAXdiDpuE8y4OxivZ0JwIVYXew+CNuegrXuQoOIbA475K/AQcA8rAVDgy7DmvxAB5Wqfrv597uz916vcfbZU5kyxRnt5ig1Eoy2Z1/r9/si8s5gPtgYky8iXc0isgirbr4Utu8YIBvYLCIN9uYrgZwujn8Aa6zn5Vg9JJTql113dXPSSeO59ZZvuf2OOdFujlIxr8cxOsaYAAO/aiYiMpB1bDq24WLgPqw7SwuwpngOt1NEXrf7bW/AWpTtW6xuD7sA5wIu4DQReTbsvJPt/d8WkcPCtqdiBaI5wB/scx0PnITV33pBF23UftWqV4sWLWftmjoee3y/aDdFqYiL8BidqNYeY8zdWBMivIlVe1xYdeBwrAt+h4tIo73vo1izsh0uIm/1ct6N6BgdNUg7djSxx5xX+fCjo/TCmYo7kR6j01sx2EwUugd0EFy3ZiLwWBfvvw28jjW25nmsdRVOxCpMZcD/AbeLyCd9+TARaTHGHAXchDXxQj7W+g2XAPcP/NtQie6yy6Yzc9eXWbasmt126/NSHEolomjXnreAmVgBJh/wY3U3uxa4q4t11pQaNqNHpzP/kmksWLCM//3f/aPdHKViWr9mXVNd06twqq/+cO9q3nyrhH/84+BoN0WpiIr0VbhEpLVE9VV9vY9ZM1/m2WcPYp9986LdHKUiZthnXVNKRc688wtZ9nU1H7wf6bUNlVJKJQqnM5kbbpjNVVd9hV6wVqp7GnSUGkbp6Q4WLJjJggXLtDgppZQasJ+fOZmS0mZef723SQCVStfZRJMAACAASURBVFwadJQaZj89YxIlpc289poWJ6W6Yoy53BhzzCDPcYwx5vJItUmpWONwGG68cTbXL/hGL5wp1Y3e1tHRYqNUhCUnJ7Fw4Wyuu3YZgYAWJ6W6cCfW2m2DcRpwRwTaolTMOumkcQQCwgsv6FrmSnWltzs6WmyUGgInnjiOlBTDc89tjXZTlFJKjVBJSYaFi2Zzww3f4PfrhTOlOtKua0pFgTGGRTftxo03fkNra6f1b5VScIoxZv1AH8DJ0f4GlBoOxx47mrzcVJ56alO0m6JUzOnLomqnGGMOG8RnFAziWKXi1pFHjmLC+Ewef2wj55w7NdrNUSrWuOzHYOglbhX3ghfOzj3nU049dSKpqXoNW6mgvgQdLTZKDZHf/W42Z5zxEWf8bBJpaY5oN0epWDEl2g1QaiQ55BAPRUUulvxlA+dfUBjt5igVM3oLOlpslBpC+x+Qz8yZbpb8ZQMXXFgU7eYoFRNERPvgKNVPCxfO5uST3+fnZ04mI0MvnCkFvQQdLTZKDb0bbpjFKad8wJlnTdHipJRSakD2npvHPvvm8+CDa7n88l2i3RylYoJ25FQqyvaem8eee+Xyp0fWR7spSimlRrAbb5zFXf+zipqa1mg3RamYoEFHqRhwww2zuOOOlTQ0+KLdFKWUUiPUrFnZHHXUKO69d3W0m6JUTNCgo1QMmDMnhwMPKuDBB9dFuylKKaVGsAXXz+KB+9dSXt4c7aYoFXUadJSKEQsWzOTuu1ZRW6tdDpRSSg1MYaGLk08ezx13rIp2U5SKOg06SsWIWbOyOeKIUdx//9poN0UppdQIdvU1M3l0yQaKixuj3RSlokqDjlIx5LoFM/nDvaspK9MuB0oppQZm3LgMzjxzMrfe8m20m6JUVGnQUSqGTJ+exY9+NIGbb1oR7aYoNeIYY5KMMb+IdjuUigW//s0MnnlmCxs21Ee7KUpFjQYdpWLMdQtm8fTTm1m1qjbaTVFqRLADzpnAKuBP0W6PUrGgoCCNCy8q4qZFy6PdFKWipscFQ40xbwzi3CIiRw7ieKUSkseTxhVXzuCqq77iH/84ONrNUSpqjDFjgWOAUcBO4DURKe6wz+nAjUAhYOz9lFLAZZdNZ/asl/l8aSV77Z0b7eYoNex6DDrAYYBgFY/+kgEco5QC5s8v4s9/Ws9//rOd448fE+3mKDXsjDGXArcCqWGbW40xvxSRxcaYqcCTwL5YNaoWuBO4a9gbq1SMys5O4eabd+fii5fy3vtH4nAM5Nc5pUYuI9J9HjHGBLACy6fAE8CO/pxcRJ4fVOtGCGNMFlBTU1NDVlZWtJuj4sTrr+/g4ouW8uVXx5KZ2ds1CaWiq7a2FrfbDeAWkUH1uzTGHAq8FTw1sBrIBqZghZrjgMex7vS0Ag8AN4tI2WA+N9q0lqihICIcecRb/PjHE7jgwqJoN0epHkWylkDvQedJ4EQgA/ABrwCPAi+KiC7hbtPipIbKT3/6EVOnOlm0aLdoN0WpHkU46DwN/AgrwFwpIk329lnA88BEIB1YBvxYROJiwRCtJWqoLF9ezdH/n737Do+q2ho4/NvpPfRUSgglSIfQu9KrYEHFBgJiw3a9inr1u1ZEr41rA1GuCoIo0jsBQofQkV5CSYGEkJ5Jm/39MQPGmJA+M0nW+zzznHDOPuesSSCLNXufvftvZv+BQfj6ulg7HCEKVd6Fzi0nI9BajwN8gSlABDAcWATEKKU+VUq1K2sAQojCffhhW+Z8e45jx5KtHYoQltQVuAg8e6PIAdBa/wE8j6nIyQAGVpUiR4iK1LKlN488GsTL/zxk7VCEsKgiZ13TWqdorWdprbsDIcAMIBOYCuxTSh1QSk1VStWp4FiFqHb8/V15/fWWPPP0Pm7V+ypEFVMPOKC1zi3g2E7zNlxrLRMPCFFMr712G9u3x7N581VrhyKExZRoemmt9Smt9TRMwwaGAb9iKn4+AaKUUj+Wf4hCVG+PTwkmLS2XH3+8YO1QhLAUZ+B6QQe01onmL0v0zKgQ1Z2HhwP/+bgdTz+9H4OhoM8QhKh6SrWOjtbaqLVerbUeCwQCKwFHYFB5BieEAHt7xZdfduS1Vw9z9aqh6BOEqB6ki1OIEho50p+WLb14842j1g5FCIso9VROSqnmwKPAQ8CN+W+Pl0NMQoh8OnSsyUMPN2LKlH389lt3lJIpQkWV52uefa3Ex7XW4RUUkxCVmlKKL77oSGjHdQwb7k/v3nWtHZIQFeqWs679rbFSXsD9mAqcG2sXXAPmA3O11gcqIEabJzPlCEvIyjLSvdsGnnqqKeMnBFk7HCH+opxnXbuxtEFpaK11pZyPXXKJsJRVq2J4dup+9u0fiJeXo7XDEeKm8p51rchkoEwfHQ/AVNyMwjTVdC6wij+nms4uayBCiFtzcrLjfz90YUD/zfTuU5fgYA9rhyRERbmIDE0TosIMHerH8mU+vPD8Qb6d08na4QhRYW5Z6Cil3gUeBvwx9d4cw1Tc/Ciz3QhheS1bevPKKy0Y/+gewjb1xcGhVI/ZCWHTtNaNrB2DEFXdhx+1I7TjOpYujWLUqABrhyNEhShqwdAbwwciMBU4u0tyca31/rIEV1nIcANhSUajZuiQcHr3qcurr95m7XCEAMp/uEF1JLlEWNqO7fGMHbuD7Tv606CBm7XDEcLyQ9fMQs2vktAluL4Qopjs7BTfzulEt64b6N69Dn371rN2SEIIISqh7j3q8Nzzzbn/vp2EbeqLs7O9tUMSolwVNe7lYhlelyomZCFEYKAb38/twsMP7SYqKsPa4QhR5SildCGv1GKc66iU+loptU8pFa+UylRKnVdKLVRKtbdE/EIU1wsvNMPP34WX/nHI2qEIUe5u2eMi46SFsF39+/vw5FNNuP++nWzY2BcnJ3leR4hythWYlW9fcSbfccI0CmI78COQgmmh7fHAbqXUYK11WHkGKkRpKaWYM6cz3bpuYP78CzzwQENrhyREuSnR9NKiYDKuWliL0ai5++4dNKjvxqefyQfFwnqq2jM6SikN/E9r/Wg5XtMP04iH9VrroQUcl1wirObw4UQGD9rCmrV9aNOmhrXDEdVUeecS+QhYiErMzk7x3XedWLculh/+F2ntcISocpRSTkqp8prL/SpgAGqW0/WEKDdt2tTgk0/bM2b0dmJiZEi0qBqk0BGikqtRw4nfFvfg1VcPExYms74LUY7uBtKBFKXUVaXUTKWUd3FPVkrZK6XqKKV8lVKdMC2u7YFpHTohbM7YsQ2Y8FgQY0ZvJy0tx9rhCFFmMnStHMhwA2ELtmy5ygP372Ltuj60alXs/4sJUS6q4NC13cAi4AzgBQwFxgJHgO5a6+JMStDK3P6GJOBL4A2t9d/+F3kjl7Rr1w57e9PsV5MnT2by5MllfDdCFJ/WmomP7SUxKZtffumOvb2ydkiiGinvXCKFTjmQQkfYinnzLvB/bx4lfOvt+Pm5WjscUY1UtUKnIEqpV4F3gde11u8Wo7070A3T5ARNgAeBvcA/tdZpBbSXXCJsQlaWkWFDw2nd2pv/fNwOpaTYEZYhz+gIIQo1blxDxk8IYsTwrVy/nmXtcISoaj4EsoBhxWmstU7TWm/QWq/SWn8O3A4MABZXYIxClJmTkx2/LOpOeHgcb799zNrhCFFqUugIUcVMm9aCfv3qMWL4VlJSijMTrhCiOLTW2UA0UKeU56diKnIGKqWCyzM2IcpbzZpOrFrdm19+ucQnn5y0djhClIoUOkJUMUopZnzYlrZta3DnqG2kp8sDpUKUB6WUCxAIlGXWjxtjSmuVPSIhKla9ei6sXt2br748w+xZZ60djhAlJoWOEFWQUoqZ/+1Agwbu3DVmuxQ7QpSAUqp2IYfexrTQ9vI8bf2UUiFKKbc8++oqpf6WX5VSvsA9QCrwR/lGLUTFqF/fjdVr+jB9+nG++vKMtcMRokSk0BGiirKzU8z+NhQ/P1eGD9tKcrIMYxOimF5XSu1USr2nlJqilPqHUioM+AewG5iZp+37wHGgc55944BzSqlPlFJTzdf4GFNx4ws8q7VOt9B7EaLMgoM92LCxH59+eoqPP5ZhbKLycLB2AEKIiuPgYMe3czrx7NQDDBq4hRUre1G7trO1wxLC1m0GbgMeAWoDucBp4DXgY621oYjztwKdgBGYChsnTMPdNgCfaa13VEzYQlScoCB3Nob1ZdCgLaSm5vCvf90ms7EJmyfTS5cDmRJU2DqtNdOmHWH16hiWLetJw4bu1g5JVDHVYXrpiia5RFQGV64YGDliK+3b12Tmfzvg6CiDg0T5kemlhRAlppTi/fdbM2F8EH16h3Fg/3VrhySEEKIS8vFxYcPGvkRFZTBm9HZSU+UZUGG7pNARoppQSvHsc8345NP2DBsWzooV0dYOSQghRCXk6enI4t974O/vSu9eYZw7l2rtkIQokBQ6QlQzo0cHsmRJT555ej///vcf5ObK8FUhhBAl4+hox9ffdOSxx4Lo3SuMdetirR2SEH9j84WOUqqZUuotpdQupVScUipFKXVQKfWaUuqWDxoopZ5USmnzq9gLvCmlvJVSM5VSUUopg1LqD6XUE0qeuhNVROcutdm5qz/hW65y56htJCRkWTskIYQQlYxSiqeebsrPC7oxaeJeZsw4gTz7LWyJzRc6wATgeeAs8BbwEnASeAfYoZRyLegkpZQ/pmk/S9SfqpRyAtYDU4CFwDPm+30JvFm6tyCE7fH1dWHN2j6EhHjSresGeW5HCCFEqfTqVZftO+5g6ZIo7rprB3FxmdYOSQigchQ6vwKBWutxWuuZWuuvtdZjgXeBNsBjhZz3BXAOWFLC+03ENC3oC1rrF7TWs7XWY4DFwKtKqYalextC2B5HRzs+/Kgdb7/TmmHDwpkx44QMZRNCCFFigYFuhG3qS7NmnnQKXSdD2YRNsPlCR2sdobVOKuDQQvO2Vf4DSqnRwEjgcUzrH5TEA0A6MDvf/k8BR2BsCa8nhM2799767No9gPXrYul/x2bOn0+zdkhCCCEqGWdne6ZPb8Pc/3XhiSkRvPD8ATIySvrfMCHKj80XOrcQaN5eybtTKeUF/Bf4Rmu9pyQXVErZAR2AAwUsCLcHMGLq7RGiymnQwI216/owYqQ/Pbpv4Juvz2I0Su+OEEKIkunbtx57IwZyNS6TDu3XEhZ2peiThKgAlbLQUUrZA28AOcD8fIc/wPS+ppXi0jUBVyAq/wGtdSZwDQgo7OTevXsTGhpKaGgos2bNKsXthbAuOzvFCy80Z/2Gvsyff4E+vcM4cqSgDlUhhBCicLVqOfHTT1356D/tmDwpgscm7OHaNXl2R1hWpSx0MA0j6wq8obU+eWOnUqo7puFqLxQy3K0obuZtYf8SDXna/E14eDgRERFEREQwefLkUtxeCNvQsqU3mzb345FHghg0cDMv//MQiYkyM5sQQoiSGTbMn4OHBlGjhhNtWq/li/+eJjvbaO2wRDVR6QodpdTbwNPALK31+3n2O2F6rmaD1vrnUl4+3bx1LuS4S542QlRpdnaKiZMas//AIK5fz6JVyzV8+cUZSVBCCCFKxMPDgf983I616/qwalUM7duvY+XKaJmKWlS4SlXoKKX+D3gd+B7T9M95PQWEAB8rpZrceAGe5uNBSqnGRdziOpBBAcPTlFLOQG0KGNYmRFXm6+vCrNmdWLW6NytWRNO27VqWLo2SBCWEEKJEWrXyZsXKXnz0UVumTTvMkMHh7Np5zdphiSqs0hQ6Sqk3Ma1j8wMwUf/9f1kNMb2f1cDpPK8x5uN7gMO3uofW2gjsB9qbC5u8OpuvH1GGtyFEpdWmTQ1Wre7Np5+2561//0HXLhtYujRKJiwQQghRbEopBg/2Y9++gdx9d30efHAXw4ZKwSMqhqoMn8oqpd4A/g38CDxqLkjyt2kLNC3g9KeAvpgWHr2utV5ibu8IBAPpWuuLea7zFKZZ26ZqrWfm2f8bpimrm2mtz+e7tyeQnJycjKenJ0JUdUajZvnyaN579xg5OZppr7Zg9OhA7O2VtUMTVpKSkoKXlxeAl9Y6xdrxVEaSS0R1lJVl5If/RTJ9+nGaNfPk+Rea0b+/D0pJPqmOyjuX2Hyhk6fwuAj8C9MUz3ld0Vqvv8X5c4FHgLpa6/g8+xsB54EtWuu+efY7ATuAtsDnwHFgKDAaeEdr/a8C7iHJSVRLWmtWr47lg+nHiY7OYMoTTZgwIYiaNZ2sHZqwMCl0yk5yiajOsrKMzJ93gc8/P02uUTP1maY8MK4hrq721g5NWFB1LHTmYipUCvOXQuUW5xer0DEfqwG8g2nYW23gLPAl8EUBQ+YkOQkBROxN4IsvzrByZTT33FOfKU80oXVrb2uHJSxECp2yk1wihOkDtM2b4/j881Ps2Z3Aw4804tFHg2jeXP5NVAfVrtCpDCQ5CfGn2FgDs2ef5fvvzlOvngsPP9yIsffVp3btwiYzFFWBFDplJ7lEiL86fTqFOXPOM++nSIKDPXj00SDuujsQT09Ha4cmKogUOjZIkpMQf5ebqwkLu8IPP0SyZnUst99Rj7vvrs+QIX54eDhYOzxRzqTQKTvJJUIULDvbyJo1scyde57wLXH0H+Bjzie+uLlJPqlKpNCxQZKchLi1xMQsFv92mcWLL7Nz5zVuv8OHMWMCGTbMDy8v+WSuKpBCp+wklwhRtPj4TJYuieLXXy+xd28CAwf5cuedgQwc6EONGvJ8aGUnhY4NkuQkRPElJGSxYnk0v/12ia1b4+nQoSaDBvsyaJAvrVt7y0w7lZQUOmUnuUSIkomLMxU9y5dHsW1bPO3a1WDwED8GD/ajVSsvySeVkBQ6NkiSkxClk5aWw5YtcaxdE8PatbEYDLnccYcPvXvXpWevujRu7C6JqpKQQqfsJJcIUXoZGbls2XKVNatjWb06BoMhl16969K7V1169a5LSIin5JNKQAodGyTJSYiy01pz+nQqYRuvsG17PNu2xgHQs1ddevaoQ9dudWjZ0gtHx0qzznG1IoVO2UkuEaJ8aK05fz6NreFxhIfHsXVrHGlpufTsVYfu3eoQGlqLdu1r4O4uz/fYGil0bJAkJyHKn9aac+fS2L4tnvDwOPbsvcbFC+m0auVNx4416dixFh061iQkxBMHByl+rE0KnbKTXCJExbl4MZ3w8Dh277pGREQCx44lExzsQWhoTVPh064GLVt5S/FjZVLo2CBJTkJYRkpKNgcPJrIv4jr79iewb991Ll9Kp1kzT1q18qZVa2/TtpU3AQGuMkzBgqTQKTvJJUJYTmZmLkcOJxERkUBExHUOH07k5MkUAgJcadnKm9bmfNK6tTeNG3tgby/5xBKk0LFBkpyEsJ60tByO/ZHMH38kcfTon6+sLCMtW/5Z+LRq5U3LVl4yK08FkUKn7CSXCGFdOTlGTp9O5ciRJI4cSTTlkyNJxMdn0qKFF61b1zAVQOYiqE4dWR+uvEmhY4MkOQlhe65eNdxMUn/8kczRo0kcO5ZEzZpOhIR40by5p2kb4knz5p74+rpID1AZSKFTdpJLhLBNSUnZ/HE06a8F0NEknJzsaNHCi5AQ86uFJy1aeMmIgjKQQscGSXISonIwGjWRkWmcPJnCyRMpnDyZzAnzNjtb07y5qegJaeFl/tqL4GB3eQaoGKTQKTvJJUJUHlprYmIMnDiRzInjKRw/nmz6+kQy6em5Nz9MC2nhZS6GPAkKknxSFCl0bJAkJyEqv/j4TE6dTOHEiWRTIWT+Oioqg6DG7jRp4knTJh4EB3sQbN7Wr+8m47bNpNApO8klQlQN169nceK46YO0EyeSbxZBMTEGGjZyp0kTD5o28aBJE0+aNPWgSRNTPrGzk3wihY4NkuQkRNVlMORy6lQKZ8+mml5nUm9+HReXScNG7gQHe9Ak+K9FUMOGbtXqkzspdMpOcokQVVtGRi5nz6Zy5kwqZ86kcOb0n18nJGQR1NjjL0VQcBPTnwMCXKtNESSFjg2S5CRE9ZSRkcu5c6kFFkExMQbq13ejUSN3GjZ0o2Ejdxo1cqdRQ3caNnLDx8elSiUuKXTKTnKJENVXWloOZ8w55MyZVM6cTjEXQakkJmbRoKE7QTfySJBpG2T+umZNxyrzTJAUOjZIkpMQIr/MzFwiI9OJjEzjQmQakRfSuHAh3fR1ZBrJydnUb+BGUCN3GjZ0p6G5IGpk/trHx7lSJS4pdMpOcokQoiCpqTlEmnNH5HnT9vyN3BKZBnCzAMpfDDVs6I6HR+VZG0gKHRskyUkIUVLp6TmmwudCGhci001J64IpcV24kE5KSjYBAa4EBrqZtvXdCDRvAwJcqV/fjdq1nWymGKpqhY5SqrDkmKa19iji3JrAw8AwoAVQB7gIbAHe1lpfKuQ8ySVCiBLRWnP9ejaR503FT/5i6NLFdJyd7QgMdKNBAzcCA835JM+fAwJccXa2t/ZbAaTQsUmSnIQQ5S09PYfLlzO4fDn95jYq358zMnL/LIQC3QgIdKW+eRvg74qvnwv16rlYZMKEKlrobAVm5TuUrbVeWMS5g4EVwEYgDIgHWgGPA1lAd631sQLOk1wihChXWmsSErK4eNGcOy6lc+lyOpcvZXDpUjqXL6cTE2OgZk1HAgPdqN/AjfqBbgTWN+UVfz9TLvHzc8HNreJ7hqTQsUGSnIQQ1pCWlvOXIuhSnmIoOjqD2FgDCQlZ1KnjjK+vKVH5+pqTlq8Lvn6u5q0Lvr4uZfpEr4oWOv/TWj9ainMbAfZa67P59vcH1gO/aa3vLuA8ySVCCIvLzdVcuWLg8qV0Ll4yFUGXL5sKotgYA7GxBqKjM3B1tcfXzwV/P1f8zAWQv58rfv4u+Pmat36uuLraTi6RQqccSHISQtiq7GwjV69mEhuTQUysgdgYA9ExGcTGGIiJzbiZxK5eNeDp6fhnEeTrio+vCz4+zrRtW4Pbb/e55X2qaqEDTAactNap5XTda0Cc1jqkgGOSS4QQNunGELkbH6JFR2cQE5NBTLQpl8REG0x/jjHg5maPn78r/n4u+PiYPkjz8XWxSi6pPE8nCSGEKDFHRzsCAkxjsG/FaNTEx2cSYy58YmMyuHI1k6jLGbi62BeZnKqou4EHAXulVBywEHhda51UmosppbwBT+Bo+YUohBAVTylFrVpO1KrlRKtW3oW2uzFULtpc+MTGGrhyxUDU5QxcnC2fS6TQEUIIgZ2dol490zM9bdtaOxqbsAdYBJwBvIChwNNAH6VU91L28LwOOGLqKRJCiCpHKUXt2s7Uru1M69aFF0SWIoWOEEIIkY/Wuku+XT8opQ4D7wLPmrfFppS6G3gRWAt8f6u2vXv3xt7eNMZ98uTJTJ48uSS3EkIIYSbP6JQDGVcthKjuqtozOgVRSjkCqcA+rXX3Epw3FPgdOAzcobVOLqSd5BIhRLVW3rnEruwhCSGEEFWf1jobiMa0Lk6xmKeaXgz8AQwsrMgRQghR/qTQEUIIIYpBKeUCBAJXitl+EKaenBNAf6319QoMTwghRD5S6NiAWbPyr0cnSqo6fg+r43suiHwfKl51+x4rpWoXcuhtTM+2Ls/T1k8pFaKUcst3jYHAEuAUpuFqCRUV7w3V7edUEarj97A6vueCyPeh4lnje1wpCh2l1DSl1CKl1DmllFZKRd6i7QCl1NdKqb1KKYO5fd9S3LO5UmqJUuq6UipNKbVVKXV7aeJfvnz5LY+X9gdf1HUr4vzinlNUu9IeL2y/rf6CKuvP6FZmzJhRpvNLGlt5/eyLalNVfvZQcT9/S//bL+vftUrodaXUTqXUe0qpKUqpfyilwoB/ALuBmXnavg8cBzrf2KGUCgWWAgrTxANDlFIP5n2VJijJJeV3vLL9PpFcUvI2VeVnD5JLyqKyzLr2HpAA7AdqFNF2HPAApnUKjgPtSnozpVQwsAPIAWYAScAkYK1SaojWekNB50VHRxf4AOmCBQvo2LFjoffLysoiOjq6pGEWed2KOL+45xTVrrTHC9tf2u9hRSvrz+hWEhISyvSeSxpbef3si2pTVX72UHE/f0v/2y/O37WUlCo1/8Bm4DbgEaA2kAucBl4DPtZaG4o4vxXgYv76k0La/FTYyZJLit9OcknZSS4xsdWfPUguKYtKMeuaUqqx1vqc+eujgIfWulEhbQOAeK11plLqH8CHQD+t9eYS3O8X4C6go9b6oHmfB6aHSQ1AiM7zjVNKtQUOlua9CSFEFdNOa33I2kFURpJLhBDipnLJJZWiR+dGkVPMtlFluZdSyh0YCWy+UeSYr5uqlPoWeAvohGkxuRvOAURFRcmUoEKIaiklJYWAgAAw/z4UpSK5RAhRrZV3LqkUhY6FtQGcgZ0FHNtl3uYvdADw9PSU5CSEEKJMJJcIIUT5qBSTEViYv3lbUM/QjX0BFopFCCGEEEIIUQpS6PzdjSlCMws4ZsjX5i969+5NaGgooaGhNj17hxBCCCGEEFWdDF37u3Tz1rmAYy752vxFeHi4DDcQQgghhBDCBkiPzt/dmPeuoOFpN/aVacIDIYQQojxprakMs6gKIYQlSaHzd0cwDVvrVsCxruZthOXCEUIIIQqXnp7Oit8WcejgfmuHIoQQNqVaFzpKKW+lVIhSqs6NfVrrVGA50Ne8psGNth7AREyLxv1txjUhhBDC0rTWLF/yGy+/eZ1nn53J9evXrR2SEELYjEpR6CilHlJKva6Ueh2oC3jf+LNS6qF8bdvkaXuHefdDedp752k+GjgOPJ3vltOAJGCdUuoVpdSTwFZMQ9ee0TI+QAghhA1QStGzTz/atMngxMm+rPx1MWPHjuX48eMYjUYZziaEqNYqy2QEjwF98u1727zdAvyYZ3+HPMdumJDn658wFTGF0lqfUUr1AKYDrwBOwH5gsNZ6Q8lCF0IIISpOQEAgT0xsycmTscxfYuTRcSMIDg5mw4YNlbA8zQAAIABJREFUfPfddyxYsID4+Hhq166NUsra4QohhMVUih4drXVfrbUq5NU3X9u5t2irtNaRBbT9vwLueVxrPUprXUNr7aa17ilFjhBCCFvUo08/xo/LYNcONw4eSSIpOYkBAwbw1VdfATBu3Dj2799PcnIyJ06csHK0QghhGZWi0BFCCCFE4RwcHLj3wZEMH5HMzwtqsvrnBRiNRmrWrAnAmjVr6NChA8ePH+fzzz8HYN26dcTFxVkzbCGEqFBS6AghhBBVgK+PL49PuQ13DyOLw9zYtWTxzWNKKZRSdOnShS+//BIwrf2WkpLCxYsXmTNnjrXCFkKICiOFjhBCCFFFdO3SlcmTNJs3ebDm4BWiDx0stO0777xD48aNyc7OxsnJCYC33nqLY8eOWSpcIYSoUFLo2ACj0WjtEIQQQlQB9vYO3P/AYIYMTWHBgpqsXr4Mw5UrtzwnODiYhx4yTWDauXNnfH19iYiIYNKkSQDk5ORUeNxCCFERpNCxopiYGBbPn8em9WusHYoQQogqom6dujz/Qlvs7TVrDvuzccb76IyMYp07ePBgatWqRcuWLXnhhRcAGD16NOHh4WRlZREdHV2RoQshRLmSQseK9uzYyhvvaVauvUBqaqq1wxFCCFFFhHboyNTnXFm/zpM93k058NQTaIOh2Oe7urrSokULABYtWkS3bt04ceIEkydPBmD37t1cvXq1QmIXQojyIoWOFXXu0Yu27TJYv96DfTu3WTscIYQQVYSdnR0PPziIQYMNLFrgza6QFlwaO7bYPTt5ubi44OjoSJs2bVixYgVgmsUtMjKSxMREvvjii/IOXwghyoUUOlbk5+vHyIGuxMY4smbDOdLT060dkhBCiCrCw8ODt9/uQVaWYtuh2mwLbkTciBEYy2EEwZtvvknnzp1JS0u7+Zzpp59+yq5du8p8bSGEKC9S6FhZzzv60qdfGuvDPNi/Q3p1hBBClJ9mTZvw2r/qsXqlB2dqN+WIsyPX+vQhNza2XK4fEBDAM888A0BoaCiBgYFcvHiRQYMGAZCZmVku9xFCiNKQQsfK/P0DGDXYkbOnndiw+RQZhpIPKxBCCCEK8+ADfRk4WPPrQm+ODR1CTFYm8V27kl3O00j37NmTwMBA/P39by5K+swzz7BgwQK01pw6dapc7yeEEEWRQsfKlFL0GdCHnr3TWb/ZkwPr11k7JCGEEFWIo6Mjn35yB8nJ9kTsdWXv1KfIcHAgvls3MpYsKff7OTg40Lx5cwC++uorRo8eTUxMDBMnTkRrze7du6XoEUJYhBQ6NqB+/QbcOdKZo0dc2BxxHkMJZsYRQgghiuLnV4+P/tOU5Us9ib2Wy75338IuJITro0eT/Mor6ApaK8fe3h5nZ2f8/f0JDw9HKcXx48e5cOEC2dnZvPTSS+Tm5lbIvYUQQgodG6CU4vb+3ejSNZ0N22oQMe9Ha4ckhBCiirlrTChDh7mxeJEX0dfiOfvxR7hNmULqBx9wrX9/ci5csEgcjz76KAMGDCArK4vmzZtjb2/PzJkzmTdvHiALlAohyo8UOjaiUcMgxoxxZl+EKzuikkmzUMIRQghRPSil+OqrgcTHu3DooAv7Dh3g+sv/pMYPP5C9bx9xrVuTPncuWmuLxOPu7s7EiRMB06KkvXr1wmAwEBQURGZmJleuXCExMdEisQghqiYpdGyEUorBg7vTrn0Gm3bVYtd/P7dYshFCCFE9eHq6MHt2J5b85k1qqmL9xnXkjBpF3cOHcWzfnsTx47k+ejS5ly9bNK7AwEAaNGiAi4sLx48fx9nZmeXLl/PNN98ApkVLM0qxBpAQonqTQseGNGzQkHvHurNzuxsHa/kTP3u2tUMSQghRxQwcGMSoO+uxdLE3mZmZrF67CurXp/amTXh99BGGtWu52qIFqR9/jM7Otnh8Hh4eAEycOJGXX36Z3Nxc1q5di729PeHh4SxatMjiMQkhKicpdGyIUoo7R3UnpEUmW/fUYM/mjWQfOGDtsIQQQlQxn3/ei5hod44ddeZq3FU2bdkESuHx4ovUO3YMpz59SH7xReI6diRz82arxmpvb8+3336Lk5MTNWrUoG7dumitGTZsGLGxsWRlZcmEBkKIAkmhY2MC/AMY96AnW7e4c7JjNy5NGE9ufLy1wxJCCFGFuLs78P33Pfh9cS0yMhQnTh7n0OGDADgEBVFr+XJqLlmCTkriWr9+XBs+nOyjR60cNbRp04a+ffsC8O9//xsfHx+WLl3KpEmTANi/f78McRNC3CSFjg26997uNGiYze69Hhxt25qEUaPQ8otbCCEsRimlC3mlluAaQ5VSO5RSaUqpBKXUIqVUUEXGXRK33+HLqJENWLOqFgDbdmzj4qWLgGmEgeuoUdQ7cQLPDz4ga9s24tq25fqECRabne1WlFKEhoailOKee+7h66+/BuCjjz4iNjaWyMhIFixYYOUohRDWJoWODfKp58PDj3izOcyd8x07ER95nusPPYSWrnkhhLCkrcBD+V6PFedEpdQYYAXgCrwEfAj0BrYrpfwrJNpS+PCjDpw748mZ0y5orVmzbjVJSX/OdKZcXfH85z/xOXsW9+eeI2PePK42aWIqeE6ftmLkf+Xk5ATA/PnzCQoKwmAw3OzZefnllzly5Ahaa5nkR4hqRgodG/XIwz2oUzeXfftcOT7tZQy//UbiI49U2KJuQggh/uac1vqnfK+FRZ2klHIEZgKXgF5a6y+11u8DgwAf4P8qNOoS8PJy5OtvOrHs93pkZioyMzNZsXoFWVlZf2lnV7s23v/5j6ngefJJMn7+mashIVwfN47sI0esFH3hQkJCGD9+PAAjRoygfv367Nu3j0GDBgFw8eJFMjMzrRmiEMICpNCxUbVq1WbCY3UI2+DBJRTJH0wnY948Eh96SIodIYSwEKWUk1LKo4Sn9QH8gW+11jeHummtDwKbgbHmYsgmDB7sR79+/kTsbgxAQkIC6zauw2g0/q2tfWAg3p99hs/587i/+CKGpUuJa9OG+P79MSxfji7gHGvr2bMnNWrUoGPHjjeHs82YMYONGzdiMBiYP3++lSMUQlQUKXRs2JTHe+Hiojl6xIX9DQJx/+ADMhYsIGHYMIxJSdYOTwghqrq7gXQgRSl1VSk1UynlXYzzOpm3Ows4tgvwApqVU4zl4qP/tGPnDiPpafUBOH/+HDt2bi+0vb2vL94zZuBz4QKe779PzsmTJIwcydVmzUj97DOMycmWCr3YlFLUqmV6Hum///0vQ4cO5dq1axw1T7IwZ84ctmzZAiBD3ISoIqTQsWHu7u488aQ/G9e7E3/tGpeHD8X722/JDAsjvnt3cs6etXaIQghRVe3BNMTsbuARIAx4GthajB6eG8/gRBVw7Ma+gHKIsdzUquXEp5+153/fO+PhbioGDhw6wJGjh295nl3t2ni+8go+585Rc+FC7OrVI/m557gSEEDik0/a5LC2vAICAnjvvfcAaNGiBf7+/ly/fp1WrVphNBq5evUqaWlpVo5SCFFaUujYuGee7kFurj2nTjqxa/cuHB96iNrr1pEbE0Nchw5kLCxyuLgQQogS0lp30Vp/pLVeorX+QWt9H/Aa0Bp4tojT3czbgh4CMeRr8ze9e/cmNDSU0NBQZs2aVeLYS2v06EDatq3JH4dDcHM1hbdl6xYiL0QWea5ydMT13nupu2MHdfbsweWuu0j//nvTsLZevUifPx9t48/EdO/enaZNm1KjRg3WrVuHnZ0d8+fPv/kz+PHHHzEYDEVcRQhhS6TQsXFOTo4880xDNq73ID0jnX0H9uHcrx919+3D4bbbuH7ffVwfPx5jQoK1QxVCiKruQyALGFZEu3Tz1rmAYy752vxNeHg4ERERREREMHny5JJHWQafftqeBQuiCQzog4ODw82Z2OLi44p9DadOnag5dy6+ly/j9dFH5MbEkDhuHFfq1yf51VfJiYysuDdQDpRSBASYOtyee+45nn/+ebKzs9m1axeOjo6sWrWKuXPnAjLETQhbJ4VOJTB1ameSkpw4f96RAwf3k5SUhENQEHXCw/F4/XUyfvyRqyEhpP/wg/zSFUKICqK1zgaigTpFNI02bwsannZjX0HD2qzOx8eFGR+2ZdrL5+nbewAA2dnZLF+5jNTUYi8hBJiGtXm8+CL1Tp2i1tq1OHXvTuoHH3C1cWOujRiBYdWqSrNsgqOjI1988QX29vY0bdqUNm3aoLWmbdu2REdHk5iYSExMjLXDFELkI4VOJeDkZM9zzzUhbL0Hubm5hG8zPSypHB3xevtt6u7bh31wMImPPEJ8t25kbtxo5YiFEKLqUUq5AIHAlSKa7jVvuxVwrCuQDJwqx9DK1f33N6BBQzd+XZRFz+49AUhLS2P5ymWlmpJZ2dnhMnAgtZYswScyEo/XXiN7714Shg3jatOmpMyYQW58fHm/jQrTtGlTOnTogFKKDRs24Ofnx65du3j//fcB+OWXX7hypai/IkIIS5BCp5KYOrUtsTGuRF12IPJCJOcjz9085ti2LXW2b8f722/JjYriWv/+xN9+O4bVq21yqk8hhLBlSqnahRx6G3AAludp66eUClFK5X3mZgsQA0zMO3GBUqot0BdYZO4dsklKKb74oiNff3UWB7vGtG7ZGoD4a/GsXL2CnDIscWBfvz5eb7+Nz8WL1Fy4EPsGDUh5+WWuBARw/aGHyNqxo1KNTKhXrx5KKQYPHsznn38OwKlTpzAajRw9epRp06YBlOl7JoQoPSl0KgkXF3teeDGEsA2mmU3Dt4X/5RensrPD/bHH8Dl9Gq/PPiPnxAkShg4l7rbbSPviC4zXr1srdCGEqGxeV0rtVEq9p5SaopT6h1IqDPgHsBvTYqA3vA8cBzrf2GEuYp4F6mOape1JpdQrwDogDnjTUm+ktAICXHnnndZMmhRB9269aBxkWmMnKjqKtevXFLjGTkkoJydc772XOps3U/foUdwnT8awbBnxPXoQ1749abNmYSzhUDlb8frrr+Pn54efnx/Dhw8H4N5772X9+vVkZ2ezbds2K0coRPUhhU4l8tRTIVy66EpMtAPJycns2x/xtzbKxQWPqVPxiYykxk8/oby8SHr6aWJ9fEi4804yFi3CKFNlCiHErWzGNLzsEeBT4N9ALUyzrvXVWmcUdQGt9SJgJKaZ1z4CXga2Aj201jb5fE5+j45vRM2ajnz22RkGDRhMgL/p8aJz58+xacumcut5cWzZEu+ZM/GJisL7m28ASHr8ca4EBJD0zDNkHztWLvextNq1a9OjRw8AFi5cSJ8+fYiOjmb27NkALFq0iF27dlkzRCGqPFUZuoiVUtOADkBHIAi4oLVudIv2XYB3gS6ABnYAr5hXpS7uPYdg+vSuJeAJXMY0XOFDrfWVfG09geTk5GQ8PT1L8M5KbsaM46xYcZh77ruCvb094+57EG/vwtev01qTvX8/GfPmkfHzzxhjY8HZGec+fXAeMgTnwYNxaN4cpVSFxi2EqNpSUlLw8vIC8NJap1g7nsrIkrmkuCIj0+jebQNhm/oRFOTM4iW/EX/N9DxNaIdQunXtXu731FqTvWsXaV9+ScYvv0BWFk59+uD+5JO43Hknysmp3O9pDWvXrsXPz4/GjRvTr18/du3aRWJiIm5ubri6ulo7PCGsorxzSWUpdDSQAOzHVOwkF1boKKW6Yvo0Lgr4r3n300A9oLvWusjVy5RSk4BZwD5gHpCGaaXr8cBFoLXWOi1Pe4slp5SUbJo1XcnEx6Oo55NLg/oNGDl8VLEKFZ2bS9aWLRhWrCBz9WpyTpwATDPjOHbvjpP55Rgaip1boUs8CCHE30ihU3a2WOgAfP3VGebPv8imzf0wZKbz6+JFJCcnA9CrRy/atW1fYffOjYsj/fvvSf/6a3LPn8fOxwe3SZNwnzwZ+/r1K+y+lmQ0Gjl9+jTNmzdn5syZpKamMm3aNH766SfGjBmDm+RjUY1U10Knsdb6nPnro4DHLQqdPUAI0OLG8AClVACmMdS7tNYDi3G/k5h6cRprrQ159r+DaejCaK31kjz7LZqc3nvvGFu3nmboiAsADLhjICHNQ0p8nZzz58ncuJGsHTvI3rGDnJMnTQccHHBs2xanbt1w7NoVp27dsA8Kkl4fIUShpNApO1stdIxGzYD+mxk5MoBnn2tGUlIivy7+lfQM01JA/freTqvbWlVoDNpoJHPtWtK+/JLMlStBKVyGD8dtyhScBw5E2dtX6P0tzWg0MnXqVD755BPCwsI4c+YMTz31FDk5OTg4OFg7PCEqTLUsdPK6VaGjlGoCnAa+01o/lu/YHEw9Mv5a69gi7hEDJGitW+bb/yTwBTBQa70+z36LJqekpGyaN1vJcy8m4O6RiouLCw/e/yCurmX71Cc3Pp7snTvJ2rmTrF27yN6zB21+nseuXr2/FD6OoaHYubuXx9sRQlQBUuiUna0WOgBnzqTSq+dGtm67gyZNPIiLj2Pxkt/IysoCoP/tA2gR0sIiseRERpI+axbpc+ZgvHoV+4YNcZs0CbcJE7D387NIDJZ08eJFrl27Rvv27Wnfvj2//PILtWrV4ty5c3Tq1Mna4QlRrso7l1S1yQhu/IvfWcCxXYDCNPStKGuB25RS/1FKtVBK1VdKjQH+hWna0LByibaUvL0dmfJEEw4daASAwWAgfFt4ma9rX6cOLiNG4PXee9QJC8M3MZG6Bw7g/dVXOA8eTPaxY6RMm8a1vn2J9fYmrkMHEp96ivSffiLnzJlKNSWoEEKI4mvSxINXXmnBlMcjMBo1devUZdSIO3F0dARg46YNnDptmaWBHBo1wuu99/C5dImav/yCfZMmpLz+OlcaNCDh7rsxrF9fpZZWaNCgAe3bm4YH7tixg+DgYM6fP8/vv/8OwGeffcbRo0fRWkseFiKfqtaj8yKm2W2Gaq1X5zs2FFgJPK61nlXEPbyB2cAYIG9/+Pfm87Pztbf4p3AJCVnc1mI1789QpKZFAjB86AiCGgVV6H2N166RtWuX6bVzJ9m7d6PNU4Da1a2LU48eOPXqhVPPnji2b48yJ0EhRNUmPTplZ8s9OgC5uZq+fcIYN64hU55oAkB0TDRLly8hJycHpRRDBg0luHGwxWPLOX2atFmzyPj+e4zXrmEfHIzb5Mm4jR+Pfd26Fo/HklasWEGHDh1ISUlhwoQJbN++ndOnT1OrVi1q1y5sSSghbJMMXbt1ofMv4C3gDq11WL5jtwMbgee11p8WcQ8383UaYpppLR0YBEzANCxuUr72nkByu3btsDePE548eTKTJ08u1XssrtdeO8K1a+m0Dz1AVlYWHh4ejLvvQZwsOCONzs0l59gx03C3HTvI2raN3LNnAVBubjh264ZTz5449+qFY9euMtxNiCpKCp2ys/VCB+D48WRu77eJnbv606iR6ff5pcuXWL5yGbm5udjZ2TFsyHAaNWxklfh0ZiYZixeT/vXXZIWHg6Mjrnfdhdvjj+PUp0+VftZUa018fDx169Zl+vTpNGvWjFGjRvHGG2/w1ltv3fz/iRC2TAqdCu7RUUrZYZq1zQHTegc6z7HpmNZCGKC13pBnv1WSU1xcJq1arubnhQ354/hWAFrd1op+fW+3WAwFyY2OJmv7drK2biVz61ZyDh0CrcHeHscOHUw9Pr164dSjR5X/pE2I6kIKnbKrDIUOwIwZJ9i86SorV/W6WThcuBjJilUrMBqN2NvbM3TwMKsVOzdkHz9uepZn7lx0YiIOzZvj9vjjuD3yCHa1alk1NktJS0vju+++45lnnuGzzz5DKcXUqVM5c+YMjRs3xs6uqj3BICo7KXRuXejcD8wHJmmtv8137MaU0cO11itvcf3emJ7D+YfW+j/5jnUEIoB3tNb/yrPfasnpny8dIjvHSO++57kcdRmAEcNGWj3B5GVMSjL1+GzdSta2bWTt3g2ZmQA4hITg1LMnTr1749yvH/aBgVaOVghRGlLolF1lKXRycoz07BHG448HM37Cn8Olz0eeY9WaVRiNRuzs7BgyaAiNgyw/jC0/nZFBxqJFpH3zDdk7doCzM6733GPq5enRo0r38uSVnJxMRkYG9erVo0uXLqxbt46oqCjOnj3LyJEjrR2eEIBMRlCUveZttwKOdcW0eOi+Iq4RYN4W1MfrkG9rdc+/0Iz58y7QqmWvvzwUmpFR5MLdFmPn7Y3L4MF4vfsudbZswS8pidrbtuE5fTr2wcFk/PoriQ8/zJX69bnSpAmJkyaRPn8+udHR1g5dCCFEPg4Odsz+thOvvXaYqKg/c01Qo8YMGTQEOzs7jEYjq9eu5vSZ01aM1ES5uuL28MPU3b6duocP4zZxIoZly7jWqxdxrVuTOnMmxsREa4dZ4by8vPDx8UEpxZ49e6hRowYGgwGDwbSKxtixYzl+/DgZGRlcvnzZytEKUT6qVI+O+fheoDkQorWONu/zB04Ae7TW/fO0rQPUAWK01knmfW2Bg8BhIDTvxANKqa+AKcBdWuvFefZb9VO45587gJOTHY9OcGRDmGlEXZPgJgweOKRSfFKljUZyDh8mc/NmsjZtInPLFnRSEgD2zZrh3Lcvzv364dS3L/a+vlaOVghREOnRKTtr55KSevvtP9gXcZ3fl/y1VyTyQiSr1qwkNzcXpRQD7hhA82YlX+utIhnT0jAsXEja11+TvXcvytUVl7FjcZ8yBcfOnStF7ixvJ06coFGjRhw7doz333+fRYsW8euvv3Lbbbdx2223WTs8UU1Uy6FrSqmHME0MAPAM4ATcGFZ2QWv9Y5623YFNwGVgZp5zfDA9c3MoT9v/A94Exmut5+bZ/ytwF6Zi5yf+nIxgBKZpqntqrXPztLdqcrp8OZ2OHdZx5Ohg9u7bwLnz5wAY2H+gzSWX4tC5uWQfOmQqejZtIis8HJ1i+rvuEBKCU79+psKnTx/s69WzcrRCCJBCpzxYO5eUVFaWkW5dN/DCi80ZN67hX45dvHSRlatXkJOTA8Ad/fpzWwvb/M9y9oEDpH3zDRnz5qFTU3Fo2xb3xx/Hddw47Ex/p6ut+fPn07ZtW+rVq8ddd93Fli1biI2Nxc3NDW9vb2uHJ6qg6lrobAb6FHJ4i9a6b7723YB3gC6YhqvtAKZprffna/d/FFzoOAHPAeOAZpiG+F0AfgXe1Vqn5buO1ZPT00/to0ZNJ157LZh5C+aRkZGBk5MTD4wdVykS5q3onByyDxwwFT2bN5O1devNKa0dWrbEuX9/nAcMwKlPH+w8PKwcrRDVkxQ6ZWcLuaSk9u+7zsiRW4nYNxBfX5e/HIuKuszylcvJzjENjOjTuy9tWrWxRpjFYkxJIWP+fNK/+YbsAwdQ7u64PvCA6VmejsVZgq/qysnJ4ezZszRv3pxPPvkEFxcXnnjiCaZPn87UqVNxcyvbguVC3FAtCx1bZwvJ6cKFNLp03sCx40NITLrMytUrAPD382f0qDFVamYVnZ1N9r59pqFuYWFkbt0KBgM4OuLUrRvOAwbgPGAAjqGhKJlOUwiLkEKn7Gwhl5TG668f4dSpFBYu7Pa3IV8xMTEsW7mUrKwsADp36kLnUNseGqa1JnvvXtK/+YaMBQvQ6ek4hobiNmkSrvffj10l+tlUpJycHKZPn86rr77K/PnzOXv2LG+++SanTp0iKCjo5nPDQpSEFDo2yFaS05THI6jn48Jbb7UibPNG/jj2BwCdQjvTtXNXq8VV0XRGBlnbt5O5fj2Z69eTfeAAAKpGDZz79btZ+NgHB9t0chWiMpNCp+xsJZeUlMGQS+fO63njjZbcfXf9vx2/cvUKy1YsvfnQe5vWbejds3KsaWNMSiLjp59I++Ybco4cMfXy3HcfbpMn49ipU6V4D5aQlpZGYmIiAQEBDB8+nM8//xytNevXr2fKlCloreV7JYrFooWOUiqs0IMlo7XWd5TTtWyOrSSnyMg0unbZwNE/BuPtbccvvy0kISEBgDtHjqZ+4N8TUFWUGxdn6ukxFz65Fy8CYN+o0c2ix/n227GTFaOFKDdS6JSdreSS0ti96xr33LOD/QcGUqeO89+OJ1xPYNnypaSkmv5qNG3SjAF3DKg0i1hqrcnes4f02bNNvTxpaTi0aYP7pEm4PvggdjVqWDtEm3P+/Hn27t3Lvffey/33389jjz1G37592b59O336FPY0gqjuLF3oGMt6AzOtta4cv81KwZaS01NPmp7Veffd1lxLuMYvvy4kJycHNzc37r/3gWo3jlZrTe7p0zeLnsxNm9DJyaAUjp074zJkCM5Dh+LYsSOqCg3vE8LSpNApO1vKJaXxz5cOERObwY8/FjyCIDU1haXLl5Jw3fQBXIP6DRgyeChOjk6WDLPMjMnJZPz8M+mzZ5O9b59pxrZ77sFt0qRqtS5PSSQnJ+Pg4EBiYiIvvfQS8+bNY/78+dSoUYOhQ4eSlZWFk1Pl+nsgKoY1Cp01wAdluMcrwEApdCzj4sV0Ondaz5Gjg6lb15k/jh0lbLOpY65B/QaMHD6qWv8S1jk5ZO/dS+a6dRhWryZ7zx7QGru6dXEeNAjnIUNwHjgQ+zp1rB2qEJWKFDplZ0u5pDTS03MI7bie6R+0YeTIgALbGAwGlq9cRuyVWAB86vkwfOiISvshXNb+/aZennnz0CkpOLRogdvEibg+/LDkkSJERETg6upK8+bNCQwM5Ny5c0RFRZGQkECXLl2sHZ6wEmsUOnO11hNKfQOlvgcelkLHcqY+sx83dwemT2+D1pq169dy+swpALp26Uanjp2sHKHtyI2PJ3PdOjJXrSJz7VqM8fF/7e0ZMsQ0qYH09ghxS1LolJ2t5ZLS2Lo1joce3MWBg4OoWbPgT+izs7NZtXYVFy9eAEwLWY4YNpJaNWtZMtRyZUxLw/DLL6TNnk32zp3g5ITrmDGmXp6+fSWHFMFgMODi4sLGjRs5f/48EyfSfSTRAAAgAElEQVROZNKkSbz66qv4+/uTmZl54/eLqOIsXegcAJZqrf+v1DdQ6i1ghNa6fWmvYetsLTldvpxOaMf1HDo8CB8fF7KyMlmwaAFJ5kU4Rw0fRYMGDYu4SvWjjUayIyLIXL36r709deqYenuGDpXeHiEKUZ7Jqbo+H2pruaS0np26n7S0XL6dU/iHarm5uYRt3siJkycAcHZ2ZtjgYQQEBFoqzAqTffQo6bNnk/7jj+jr17EPDsZt4kTcHn1UFr0ugfDwcDp37sz+/fuZPn06y5Yt4/fff6dly5Y0a9bM2uGJCiKzrtkgW0xOzz93AEdHO2Z82BaA+Ph4Fi3+hZycHJydnRl7z314e8liX7dys7dn9Woy16wx9fbY2ZmmsB45EpcRI3AICanWQwGFuKGcC51q+XyoLeaS0khNzaFD+7XM/G9HBg0q/D/2Wmv2Ruxh997dANjZ2XFHvzsIad7CUqFWKJ2RQcZvv5E+ezZZ4eHg4IDLyJG4TZqE84ABsvxBCdyYtW3u3Ll07tyZ2rVrc+edd7Jz507OnTuHs7MzAQEFD5cUlYsUOjbIFpNTdHQG7dut5eChQfj5uQJw6vRJ1q5fC0CdOnW4e/Q9Ms99MWmjkex9+zCsXEnm8uVk7zetPWsfHIyLuehx6tkTJd9PUU1VQKFT7Z4PtcVcUlobN15h8qS9HDg4CC+vW/9ePHHyBBs3bcBoNNW3nUM707lTlyr1IVL2iROkf/stGf/7H8b4eOwbNsTtscdwGz8e+8DK34tlabm5uVy8eJGgoCBmz56No6Mjjz76KE8++STTp0/H2dkZBweHSjOrn/iTFDo2yFaT00v/OEhurubjT/4cNbh1ezgHDx0EoHmz5gy4Y2CVSiaWknv5MoYVKzAsW0ZmWBhkZqJq1MBlyBBcRo7EefBgmW5UVCsVUOhUu+dDbTWXlNaUxyOwt1d88WXHIttGRV1m5ZqVZGZmAqb8dHvfO3BwcKjoMC1KZ2ZiWLqUtNmzydqwAezscB46FPdJk3AeOhRVxd6vJRmNRubPn8+4ceNYuHAh69evZ86cOSxevJgePXrg4+Nj7RBFMUihY4NsNTlduWKgbZu17Ns/kIAAU6+O0WhkybLfiYqOAqBXj160a1tlH5+yCGNqKpnr12NYvpzMFSswxsWBgwNOvXrd7O1xCA62dphCVKhyLnSq5fOhtppLSispKZv27dYy57vO9OtXr8j2169fZ9nKpSQnJwNQr54PwwYPw8PDo6JDtYqcs2dJnzOH9O+/xxgbi52/P24TJuD22GM4NGpk7fAqvZycHBwcHHj33Xd5+OGHSUpKYvr06fz000+cP3+e2rVrywQHNkgKHRtky8nplZcPkZGRy2efd7i5Lz09nYWLFpCalopSimFDhhPUKMiKUVYdOjeX7D17MCxbhmH5cnL++AMAh5YtcRkzBtf/Z+/N46Oszv7/9z3JTDLZCNkXAkkghCVAgLAjsu9oRbFWigtYWlufqtXW2se2Ll9bbdXaR6v+hCgu4IooBWTf17CEJexbCJAFQjaSmWS28/vjTsIWQsJMMtt5v168iPc5931fKsxnPudc57qmTsW3Vy+5iybxOGTVNftxZS25XZYtK+Dpp7LZvWcsQUG33q0wGg0sW76M/IJ8AAICApg4fhKxMbEtHarTEGYz1UuWYJgzh5rlywHwGzOGgNmz8Z8yBUX2l3EIRqOR3Nxcunbtyp///Gf69u3LpEmT+P3vf89bb72F1WrF19dX6rOTkUbHBXFlcbpwoZqePVawc9cYEhICrrp+gYXff4vFYkGr1XLfPdOIkNXEHI7l1CnV9Hz/PaZNm8Bmwycpqd70aAcOlGVHJR6BNDr248paYg+PPpJF27baa9KoG8NqtbJx80ZyDh4A1CIFI+4cSbeu3VoyTJfAkpeH4aOPMGRmYjt3Dk1UFAGPPKLu8shKYw7HYDDw5ZdfMnPmTN544w0qKip4+eWXWbBgAZMmTaJNG1m0qbWRRscFcXVx+tOf9lNRbubd/1ybJ33y1EmWLV8KQFBQEPff91MCAwKdEaJXYL1wQTU9331HzerVYDajiYnB/5570E+diu7OO2UxA4nbIo2O/bi6ltwuJSUmeqevYMGCQQwZ2vQFtQM5B9i4eUN9kYKePXoxdPBQrzhgLqxWapYvxzBnDtVLloDViu6OOwh47DH877sPjZs2WHVlhBCYTCb8/Px49tln+etf/8rOnTtZtGgR77zzDjk5OSQkJEjz08K4tNFRFGUw0B/wAfYBa4QXOClXF6fi4hrSui9n+47RJCZea2R2Z+9m67YtgNqheupP7vW4w5+uiK28nOply1TTs2wZwmBAadsW/ylT8J86Ff+xY1H0emeHKZE0GWl07MfVtcQefvjhPM//cT+7do8hIKDpGnM+/zw/rliG0WgEID4unnFjx3vVopy1oADDJ59gyMzEeuIESkgI+gcfJGDWLLR9+8pUqxakqqqKoqIikpOTeeaZZ7jvvvtITU3lhRde4L333qOiooLAwECvMN+thUsaHUVR2gFfAanAJsAMDAbKgfuFEAftfokL4w7i9Je/5FBYYOTDOdc2cBNCsHbdGg4dOQRAp46dGDdmPBqZTtVqCKOR6pUrqf7uO6oXL0aUlaEEBuI3YQL6++/Hb+JENIHeI+oS90QaHftxBy2xh4cf2kFEhB9vvpXerPsqLlew9MclFBcXA+q5nfFjxntEc9HmIITAtHEjhsxMjN98A9XV+PbqReBjj6GfPh1N27bODtErqKioYOPGjUyePJmXXnqJkJAQnn76ad566y1+9atfESB32+zC5YyOoiihwE5gPfAkEAHMA8YCfwEeBzKEEGfsepEL4w7iVFZmolvXH1m7bgRdulxbZcRqtbJ4yQ+cO38OgB5pPbnzjjvlKpETEGYzNevXq6Zn0SJsRUUoAQH4TZmC/v778Z8wQe70SFwSaXTsxx20xB5KSkz06b2CTz8byLBhkc2612w2s37jOo4cPQKAoigMGjCIPr29c0fDVlaG8YsvMMydq/Z18/NDf++9BMyahW74cHn2sxURQmCz2XjppZd48cUXWbBgAdnZ2bz55pusX7+eXr160Vaa0CbjikbndaCPEGJM7T+nAofqehcoipIJBAohHrA3WFfFXcTpn/88wu7dpXz55aAbxqqrq1n4/beUlJQAMLD/QPpl9G/tECVXIaxWTBs3Yvz6a6oXLsR28SJKUJBasvr++/EfNw7F39/ZYUokgHONjqekTbuLltjDkiX5PPO7vU2uwnY1QggOHT7Ihk0bsFqtACQlJjF65Bj8vfiz0JydTVVmJsbPP0eUl+OTnKyWqX7kEXzi450dntdhNpspLy8nIiKCp59+mieeeAKLxcI//vEPMjMzOX36NG3btiVU9tprEFc0OidQd3K21V5KAbai7uwoQCKwGTVgi10vc1HcRZwMBgtdu/zIokVD6dP3xtWFysrLfPPdN1RWVgIwYvhI0rqltXaYkgYQFgum9esxfv01xoULESUlKCEh+N99t5reNnasLEEqcSrOMDqeljbtLlpiL7NmZhEY6Mv/vdPn1pMb4OLFCyxbsay+305IcAjjx00gOsq7G0IKoxHjd99hmDsX0/r1ajPSCRPUAgaTJsliN06ksrKSY8eO0adPH15++WW6devGPffcwy9+8Qs+/PBDqqur0el06KSOu6TRqQZ+DnwNNPSw/wVeBeKFEIV2vcxFcSdx+uD9EyxZks+SpcMaHC8pLWHhd99SXVONoihMGDeRjsmy2aUrIcxmatauxfjVV1QvWqSe6QkNxf8nP1FNz+jRUtAkrU5rGx1PTJt2Jy2xh7IyE316r2RuZj9Gjrw9c1JTU8Pqtas4dfoUoJagHjRwML179fbKVLbrsZw4oZapnjcPW0EBmuhoAh5+WJapdiFqamr4/vvv+elPf8rcuXM5fPgwb775Ju+88w7Tpk0jKioKRVG87s+zKxqds8Bs4ELtpURU01OX96QFNgDBQgiTXS9zUdxJnEwmGz3SljNnbr+b5kgXFhWy6IfvsFgs+Pj4MGXSXSS0S2jlSCVNQZhM1KxerZqe779HVFSgiYjA//77CZg+He2gQV73ISlxDk4wOq2aNq0oSgBwEFXj/iOEeKKJ900EXgB6ATXAGuAPQojTDcx1Gy2xlxUrCnniN7vZvWcsISG3tzAjhCB7bzZbt2+h7rtMQrsExowaS6As4AKo2QA1P/6IITNTlql2cYQQKIrC//3f//HQQw+xf/9+/vGPf7BkyRLWrl1LYmIiycnJzg6zxXFFo/M+0LZOTBoQm78D6UKICfYG66q4mzh9/vkZ5nx4kvUbRtz0S/CZvFyWLFuCzWbD19eXuybfTXyczPV1ZURNDTUrVmCYP5/qxYuhuhqfxET0Dz6Ifvp0tN08v9mexHk4wei0atq0oihvAL8Egmii0VEUZSrwLeq5oTlAG+ApwIq625R/3Xy30hJ7+dUvd6HRKLz3ft9bT26EwqJCVqxaXp/K5u/vz+iRY0hKTHJEmB6DtaAAw6efYpg7V5apdgOEEFRWVhIcHMwHH3xARkYGnTt3ZuzYsWzbto1Tp04hhKBTp07ODtWhuKLRiQOygX8LIf6mKEoy6kHQJEVRHgHeAQYLIQ7YG6yr4m7iZLUK+vZdyauv9mDSpLibzjt+4hgrVq1ACIFWq+XuKT8hNia2FSOV3C62y5epXrQI4/z5anNSmw3f9HQCpk9H/7OfyQOqEofjBKPTamnTiqL0AbKAPwBv0gSjoyiKFsgFLEB3IURl7fV0YDeQKYSYfd09bqUl9lJRYaZP75W8/0FfxoyJsetZJlMN6zdu4OixI/XXevboxZBBQ2RvuOsQQmDatAnD3LnXlKkOmDWLgOnT0YSFOTtEyU2wWq2cOHGC1NRUvvzyS6qqqpg1axYPPPAAb7/9Nnq9ngsXLpCSkuLsUG8blzM6UP/B/R1QBaxAXa0aBiQD04UQq+1+iQvjjuK0ePF5XnrxIDt3jUGjufkqzpGjR1i1ZiUAOp2On9x1j9cf+HQ3rIWFGL/6CuP8+Zh37gRFQTd8OPrp09Hfey8aWflF4gCcYHRaJW1aURQfVJNTADwBnKZpRmc0sAr4ixDilevG1gAZQIQQwnzVdbfTEntZs6aI2b/Yye49YwkNtf8g9tFjR1i3YR1ms/qfNTwsnDGjxxIZ0bxy1t5CfZnqzEzMu3erZaqnTiXgscdkmWo3YuPGjQwePJisrCwWLFjAu+++y2uvvcaoUaPIyMggLy+PDh06ODvMJuGSRgdAURQdMI1rS3x+JYSocMgLXBh3FCchBHcMXcsT/5PCAw+0b3TuocMHWbNuDQB+fn7cc/dUKRpuiuX4cYwLFmCYPx/r8eOg0+E/aRL6GTPUqjyy4ovkNnGC0WmVtGlFUZ4FXgK6115qqtF5HvgbMOb6xT5FUV4F/gSkXV0Zzh21xBE88Zvd1NTYmDO3360nN4Hy8nJWrF5OUVERoBYq6J8xgL59+spm2I1QX6Z6/nxEWZksU+3mbNmyheTkZDQaDXfddRc7duxg8eLFaDQaJk+eTHl5OW3atHF2mDfgskbHm3FXcVq37gK/fnwX+w+MR6tt/MP/QM5+1m9cD4C/nz93Tblb7uy4MUIIzLt2YZw/H+OXX2IrKkITHq6e53n4YbR9+sh8bUmzcILRafG0aUVRkoAc4GUhxOuKoiTSdKPzDuoOUDchxOHrxn4N/AcYJ4RYedV1t9QSe7l82UzfPit5+999mDjRMenRVquVnbt3smv3zvpCBVFR0YwZOYYwmZrVKPVlqjMzMa1bJ8tUexC7du0CICMjg06dOrF161YuXbrEzp07eeihh7BYLE5P9XS0lsilDS9mxIgoEhMDmffxDcV/bqBHWk/uGKKWpK6uqeb7xYvIL8i/xV0SV0VRFHT9+tHm7beJPneOsGXL0I0aRdWHH1KckcHFnj2pfOMNrIUeWRFe4gHUHuQfBzymKMoB4NfA14qibANeB+5xwNnQ91GNzVu3cW9dOauaBsaqr5tzDcOGDSMjI4OMjAw+/PDD23i1exEcrOXDOf34za93U1rqmOKsPj4+DOw/kGlT76/vSn/hQhFffvMFe/buwWazOeQ9noii1xMwfToRa9cSdeIEQX/8I+Y9eyi95x6KEhKoeO45LMeOOTtMyW1Q97kCcPz4caKiotBoNATUVt+75557WLNmDQaDgXfffRcAd98QkTs6DsCdV+F27Sxh2rStHDw0noCAW7v4/Tn72VC7s+Pr68vkiVNk6WkPwlZaivHrrzHMm4d5+3bw8cFv3DgCHnkE/ylTULy4+7ikcZzRMBRaLm1aUZSfA58Cw4QQm2uvJSJ3dFqMp57MprzczMfz+t96cjOwWCxsz9pO9t499ddiY+MYPXI0oW3kGcWmICwWapYvxzB37pUy1UOHEjBzJv7TpqEJCnJ2iBIHYLVasdlslJeXk5mZyXPPPcef//xnwsPDeeqpp8jMzOSnP/0pgYGBLZb1IVPXXBB3F6cHHthGenoof/xj1ybNP3zkEGvWrUEIgY+PDxPHTyKxQ2LLBilpdSxHj2L45BMMn32G7dw5lNBQ9D/7GQEPP4y2f3+Z2ia5BmcZnZZAURQ/4CxqEYKnrhqKR21Q+jnquZ1iIUTZTZ4hz+g0k6oqCxl9V/L6P3px112OPxOSX5DP6jWrKK8oB9RdnwH9BpDeqzc+Pj4Of5+nYi0sVLXho4+wHjuGEhSE/qc/JWDmTNm7zQOxWCzU1NQQEBDA73//e1599VV+/PFH/vvf/5KZmcnSpUvp2bMnCQmOWfSWRscFcXdxOn78MsPuWMv+A+OJjPRr0j3Hjh9j1ZqV2Gw2NBoNY0ePJaWT7LbsiQirlZq1azF+8gnV332HMBrx7dIF/SOPEPDQQ/jEypLjEo8zOqFAaROm/l4I8cZNniGrrt0GmzdfZPqD29mTPZbw8KbpUXMwm81s2baFAzn7669FREQwavhooqKiHP4+T0YIgWnrVowffYTxq68QVVX4dulCwMyZ6B96CJ9oeY7XU7FarZSXlxMWFsa//vUvxo8fT1BQEPfeey9ZWVns3r0brVZLz549m/1saXRcEE8Qp6efygbgX2/3bvI9p06f4scVy+pznYcNHUavnuktEp/ENbBVVGD85huM8+Zh2rwZfHzwnzyZgMcew2/8eBTZr8Jr8TCjowXubmAoEngPWA5kAvuFEMcURYlFbQaaJ4QwXPWMM4CZa/vo9AL2AB8LIR677r1uryWO4PfP7qWgsJrPPx/YYu84n3+edevXUlqm+llFUejVM52B/QeilYftm43t8mWqv/mGqsxMzFu3XtGGmTPxmzBBFjDwAmw2G0VFRcTGxvLtt9/i5+fH5MmTycjIYNWqVRgMBrRaLdG3MMCtanQURVlrx7OFEGKUHfe7DZ4gThcv1tCzx3I2bhpJSkrT/x3yzuaxbPnS+p4FfdL7MHjQELl17QVYjh3D8NFHGObNU6u2xcUR8OijBMyciW9ysrPDk7QynmR0bsbNzugoijIPeBgYIYRYf9X1acBXqOeG5gAhwNOoDU77CiHOX/d8t9cSR2AwWOjXbxUvv9yDe+9t12LvsVot7Ny9i917dtUv2IUEhzD8zhF0aO8ePUdcEfORIxg//hjDJ5+o2hATQ8BDD6F/9FG0Xbo4OzxJKyKE4PTp0yQlJZGVlUViYqLLGR0b6gfy7XxrFXX9DDwdTxGn118/THZ2GV9+OahZ9128eIHFSxZjMBoASE1JZdTI0TLn2UsQZjPVS5dimDuXmh9/BJsN3ahRBD72GP4/+YksYOAlSKNzo9GpHZsMvAD0RK3AtgZ4TghxsoHne4SWOILt2y4xbdoWdu0eS3R0y36GXCq5xNp1aygsulJlslPHFO4YMpSgIO/+/2APwmym5scfMXz00ZUCBkOGXClg4OV/xiUN4yyjsxP4DGhWrVkhxEK7oqP+QGcfoC+QBJwRQiQ2Mn8A8CowADX2rcAfhRB7m/i+eaiC1RDThBDfNnCPR4iTwWAhrftyvvhiEAMGhjfr3vKKchb/9wfKytVzuQntEpgwbiJ+fo7PsZa4LtZz5zDMm4chMxNrbi5KWBgBM2YQ8NhjaNPSnB2epAVxBaOjKIoGeFgI8bEz3m8vnqIljuKFFw5w8GA5333X8lkCQggO5Oxn6/at9RkKWl8t/fv1p1fPdLlwZyfWwkKMn32GITMTy9GjKIGB+N9/PwGzZqEbPFhmgUjqaW2jMx/4CaAHLKh5yfOAxUIIi70vb1KAiiKAEtSc5r5Axc2MjqIoA1Er4pwH3q29/AQQRRMbx11ldGY0MLxRCJHXwD0eI06fzDvNxx+fZt36Ec3+4DEajfx32eL6btRhbcOYPHGKS3belbQswmbDtHYtVXPnUr1oEZhMaAcMIOCxx9A/8IAsReqBONPo1BqcGag7J8numk3gSVriCEwmG4MHreaJJ1J45NGkVnlnZeVlNm/dzPETx+uvtQ1ty53DhstWCg5ACIF52zYMdQUMKivxTU1FP3MmATNmyOI2ktYvRlD7wfsz4BFgIOouSQkwH5jX1J2S2w5QUZKFEKdqf84BghoxOllAF6BrXe6zoijxwGFguxBibBPeNw91RbDJ3/I9SZysVkG/jFX89cXu3H1388t7ms1mVqxazulctQmpv78/E8dPIj7O8aVCJe6BtbgY4+efY5gzB8uhQyjBwehnzCDw8cflLo8H0RJGR1GUOGAsEA0UAStrG4VePedB4EWgI2qadZEQwi2/LXmSljiKnJxyxo5Zz5ato0lKCmy19549d5YNG9fXFysASOmUwtDBdxAkF2ocgq2ykupvvsHw0Uf1xW38Jk4kYNYs/CdOlAUMvBSnVl1TFKUz8CjqylkcqunZD3wMLBBCFNsb0C3ef1OjoyhKJ+A48JEQYtZ1Y5m1cccJIRpNv7tqR0cDBAOVQohGWyh7mjitWFHI736Xzd6949BqNc2+32azsW3HNvZk7wZAo9Ew4s4RdOva3dGhStyIupW8qg8+wPj111BTo+ZrP/44+vvuQ5Fpjm6Nw8VJUZ4EXgN0V102A78VQnyoKEoy6oJbf1SDcxl4A3hLCFFl7/udgadpiaN4662jLPlvPqtWD8fHp/VSnKxWK/v27yVrZxZmy5V0tr59Muid3htfWWXSYViOHcNQV8CgoABNVBT6hx5Se/N0bVqPP4ln4Ggtada3WCHEMSHE80B7YBLwLeoOyr+A84qifGZvQHbQr/b3bQ2MbUcVwr7NeF557S+joiiras/+eAVjx0bTPiGAjzJP39b9Go2GIYOGMHrkaDQaDTabjTXr1rB5y6b6yjYS70NRFHSDB9P200+JPneOkDfewFpURNnPf05Ru3aU/+EPWE7ecD5b4oUoijIMVVf8gErU1OWTgA/wnqIoY4AtqGcxLcC/gY5CiFfc1eRIbs6TT6o92v7972Ot+l4fHx/69O7Lzx/8OSmdUgAwW8xsz9rGZws+5eixo8gWHY7Bt3NnQv7+d6Lz8gj773/RDR5M1dtvc7FbNy4OHkzV3LnYLntkjRNJC2N3Hx1FUcJRd3Qmo3aJbrGOW7fY0XkGdTVvohDix+vGJgJLgV8KIT68xTvqVhB3A1VAL9TO2IG1z17dwD3BQEV6+pUDi7Nnz2b27NnN/nd0FbKzS7n7rs3kHBxPSMjtbx/nF+Sz9MclVFdXA9CuXQLjx4xDrw9wVKgSN6b+LM/771P9ww9gteI3bhwBv/oV/pMny748boQjV+EURfkKmIbas+ZZIUR17fXuwELUxTZ/4ABwvxDiqD3vcxXkjs7NOX26iiGDV7Ny1XDS0pxz7vPsubNs2rKJS5euJK9ER0UzdMgdxMXGOSUmT8ZaVKSmPWdmYjl8GCUgQC1gMHMmuqFDZQEDD8VlGoYqipKKem5nBhCLumOySQhxp71BNfLOxozOn4GXgVFCiLXXjY1ELen5tBDi7dt4bwqwF8gXQqQ0MO6R4vTYrJ1ERfvxt781v7Pt1ZRXlLN02RIulVwCICgwiAnjJxITHeOIMCUegjU/H8PcuVR9+CG28+fRxMcT+ItfEPDYY/jEyzNero6Djc4Z1NTojkII63VjE1AXroyohQeK7HmXK+GpWuIoPv7oNP/5z3G2bhuNTtf8tGpHYLPZOHzkENt3bK9vqQBqOerBgwbTJkQW33E0QgjMO3aoBQy+/BJx+TI+KSkEzJxJwEMP4RMnTaYn4dTUNUVRQhRF+aWiKNuAQ8BzqKkF76I2P2sxk9ME6j5xGkr0979uTrMQQhwHvgY61Z5T8gpefiWNjz86zcmTlXY9p01IG6bdez+dU9T/dJVVlSxc9C0HcvbLbX9JPT5xcQT/5S9E5+bS9vvv0fboweWXXqKoQwdKpk6letUqhEx99BaigOzrTU4tdenJGz3J5EhuzSOPJpLQPoBXXjnotBg0Gg3du6UxY/pDZPTJqM/iOHHyOJ8v+IzNWzZhrDY6LT5PRFEUdAMHEvrhh0QXFBA6bx4+sbFcfv55ihISuDRlCsZFixAmk7NDlbggtzQ6ispYRVEWAAXA+0AGsAw1tSBOCPGkECK7ZUO9JXWVeBpa+q27dr6BsaaSW/t7hB3PcCvi4vQ8+VRnnn9+v93P0mq1jB09jmFD76w/t7N+43pWrVlV37NAIgFQfH3R33034T/+SNSJEwQ98wymTZsoGTuWC6mpVL75JrZLl5wdpqRl8QNKGxoQQpTV/tisvm4S90dRFD74IIN5H59m+zbnfgbodDoGDRzMzx+cUb+IZ7PZyN6Xzaeff0LWrixMZvnF29FoAgMJePhhIjZsIOrYMYKeew7z7t2UTp2qnvV89lnMhw45O0yJC9Go0VEU5VUgD/gReAC1I/QfgHZCiClCiIVCCFf5lrqz9vdBDRhKi7MAACAASURBVIzVlcXebcfz61LWvGoF8cknO7M3u5QNGy7Y/SxFUejVsxf33D2VwEC1TOjRY0f46psvuVh80e7nSzwP3+RkQl5/nehz5widPx+fmBgqnn2Wwvh4Sh9+GNO2bXJX0HuR/+O9kOhof955ty8zZ2ZRVdUq7fwaJSQ4hHFjxjNt6rT6czomk4kdWdv59PNP2Ld/H1ar8+P0RHxTUgj529/UAgZLl6K74w6q/v1vLnbvzsWBA6maMwdbRYWzw5Q4mVs1DLWhisku1EahO5rzcCHEHnuCayCeW/XR2QmkAl3qei3U9mE4AmQJIUZfNTcCdXemQAhRXnstELDWHXy9am5v1MptJ4UQ3Rp4r0fnVX/77Vle+/thdmSNcVhpT4PBwPKVP3I+X91k8/HxYcigofTs0VMeMJQ0ivnAAbVE9WefIS5fxrdXLwIffxz99OmyEakTcfAZHRtqg+rXbjJlfWPjQoiN9rzfWXi6ljiSWTOzCAjw5Z13+zg7lHqEEJzJO8O27VspvqpgQXBwMAP6DSS1cyoajXPOFnkL1osXrxQwOHgQRa/Hf9o0AmbNQnfHHfL7hRvQqsUIrjI6t4MQQthdMklRlBlAh9p//B/Uimhv1v7zGSHEZ1fNHQysA84B71x1TzQwRAix76q5LwJ/BR4VQsyrvZaOunv1PWpPnrqqazMBGzBWCLG5gRg9WpyEEIwauZ7p0zsw67Fkhz3XZrOxa88usnbuqF+VT0pMYtSI0ej1eoe9R+KZ2C5fxrhgAVXvv49l3z61EenPf642Iu3Rw9nheR0tYHScqj3OwNO1xJGUl5vJ6LuSd//Tl3HjXKuwjRCC4yeOsX3Hdsoryuuvh7UNo3+//nTqmCK/cLcwQgjMO3eqBQy++AJRUYFPp04EPPooAQ8/LAvcuDCtbXRysSM9QAiRdLv3XhXDeuBmRQ42CCGGXzd/EPD/UPsrCGAr8Pz1u0s3MToxwD9Re/LEAXrUc0nrgL8LIY7cJEaPF6fsPaXcffdmDuSMp00bx3Yrzi/IZ+WqFVyuVP88BwYGMmbUWBLaJTj0PRLPpK4iT9X772P86ivZiNRJONjo5OJk7XEG3qAljmT9+gs8+kgWu/eMJSxMd+sbWhmr1cqhw4fI2rUDg+FKLaSwtmH0y+hPp46d5A5PK2AzGKheuBBDZiamDRtAo8Fv/HgCZs7Ef8oUFJ3r/dnxZlymvLTkCt4iTrN/sZOwMB2vvd7L4c+urq5m3Ya1nDh5ov5aj7SeDBk0BK3WscZK4rnYLl3CMG8eVR98gPXECTQREegffZSARx+V3bVbGEeLkzfiLVriSJ59Zi8FhdXMnz/Q2aHcFLPZzP6c/ezJ3l3fUw6gbdu29M8YIA1PK2I5cQLDvHkY5s1T2xhERKCfMYOAmTPRpqU5OzwJ0ui4JN4iToWF1fROX8HGTSNJSXH8v6cQgoOHD7Jp80YsFvXwZpuQNoweNUY2Y5M0C2GzUbNmDYb336d68WKwWtH27o1++nT0Dzwg0xZaAGl07MdbtMSRGI1WBgxYxfPPd+NnP2vv7HAaxWQ2cSDnwI2GJ7RtfUqbNDytg7BaqVm5EsNHH6nNqs1mtP37EzBzJvoHHkDTRvZDchbS6Lgg3iRO//jHEbZtK2bRoqEt9o7y8jJWr11NfkF+/bX0Xr0ZNGAQvr5umXovcSLWwkKMX36Jcf58zLt2gaKgGzGCgOnT8b/3XiloDkIaHfvxJi1xJNnZpUyetImt20bRoUOgs8O5JSaziZycA+zZuwej8UrPndDQUPqk96VLaio+PlLrWgtrcfGVAgY5OWoBg3vvRf/QQ/iNHIlS2ytJ0jpIo+OCeJM41dRY6Z2+kjfeTGfixNgWe48Qgr3797Jt+1asVrVnYNvQtowcMUru7khuG8uxYxjmz8c4fz7WkydBp8Nv9Gj8p07F/6678ImMdHaIbouDz+j8DsgRQqy04xljgTQhxFv2xNKaeJOWOJo33zzK0iX5rFo93GHVQVsas9ms7vDs3X2N4QkMDCS9Z2/SundHp5NnDFsLIQTm3bvVAgYLFiDKy9HExKB/4AH0Dz6INiNDFpFoBVq7GIFXik1z8TZxWrGikCd/u4e9+8bh79+yKx2lpaWsWruSoqIr7Yu6d0tj8MDB+Pv7t+i7JZ6LEAJzVhbGb76heuFCrLm5oNGgGzYM/6lT0d9zDz7t2jk7TLeiBaquzRNCzLTjGR8DDwkh3GY51tu0xJHYbILx4zYwanQ0zz3nXufxzGYzOYdy2Ls3m8qqyvrrfn5+9Ojeg1490wkICHBihN6HqK6meulSjPPnU710KZhM+HTuTMCDD6J/8EF8U1Ju/RDJbeGM8tJeJzbNxRvFadq0raSnh/K//3tDWyGHY7PZyN67hx07d9Tv7gToA7hj6DBSOskynRL7EEJg2bcP43ffUf3dd1gOHgRA268ffhMn4j9xorqSJ3PnG0UaHfvxRi1xJGfPGhg0cDU//DCUvhlhzg6n2VitVo4dP8ruPbspLSutv+7j40O3Lt3ond6HNjLVttWxlZZiXLgQ4/z5atU2IdD264f+/vvxv/defJPcssijyyKNjgvijeJ05kwVA/qvZtv20SQltU5OdHl5Ges2rOPsubP11zq078DwYSPq/lJIJHZjOXpUNT1LlmDevh1sNjSRkfiNH4//xIn4jR2LJsz9vkS1NC1gdCqB4lvNbYQIINCdtMcbtcTRfPvtWf7ylxx27hxDYKB7nnMRQnA69xS79+ymsKiw/rqiKCQnJdOrZzpxsXFykc8JWM+dU898LliAOTsbAG2fPvjfdx/6e+/Ft3NnJ0fo/jjD6Hid2DQXbxWn1147TFZWCd99N6TV3imE4Oixo2zasrG+ao2vry99+2TQJ72PLFYgcSi2S5eoXrmSmmXLqFm+HFtxMWg0aAcOxG/0aPxGjkQ3cKDs1UOLGB1HINxJe7xVSxzNrJlZ6HQa3v8gw9mh2IUQgvyCfHbv2cWZvDPXjEVGRpLeM52UTp3xkYflnYLl1CmqFy7EuHAh5h07APDt0QP9ffepOz3dukkzehs4w+g4ArcSm+bireJUU2Old++V/POfvZg0qXULBBirjWzZuoXDRw7VXwsJDmHokDtITkqWHy4ShyOsVsy7dlFda3rMu3aBzYai16MbOhTdyJH4jRyJtm9fr6zS42Cj08ExUYEQ4sytZ7kG3qoljqaiwkz/fqt4/R+9uPtuzyglX1xczL79ezl6/Gh9CjdAQEAAPdN6ktY9Db1enuNxFtazZ9VMgIULMW3eDELgk5KC/+TJ+E+Zgm7oUBTZE7BJtLbR8UqxaS7eLE4rVxby2//ZQ/becej1rf/l7nz+eTZs2sClS1c2HRPaJTBs6DDCwsJbPR6J92ArK8O0cSM1a9dSs2YNlpwcAJQ2bdANG6aanyFD0GVkeMWOjywvbT/erCWOZvu2S0ybtoUdWWOIi9M7OxyHYTAYyDl4gAM5BzAYDfXXfXx8SO2cSs8evYiMkNUjnYm1sJDqRYuoXryYmrVrwWRCCQ3Ff/x4/KZMwX/8eJn+3AiyvLQL4u3i9NOfbiUtrQ1//nN3p7zfZrNx8FAO23dsp7pGTWdTFIWePXrRL6Mfen/PETmJ62ItKsK0fj01a9ZQs3491uPH1QGdDm1GBn61xkc7eDA+ERHODbYFkEbHfrxdSxzNyy8fZMf2S/x3yR1oNJ61y2+1Wjh2/Dh792dTXHzt6YKY6BjSuvcgpVOKTOd2MrbKSmpWraJmyRKqlyzBduEC+PigGzIE/ylT8JswQaa4XYc0Oi6It4tTXp6BAf1XsXnLKDp2DHJaHMZqIzuytpNzMIe6P9c6nY6Mvv3o1aOX/MCXtCrWCxcwbd2KacsWTFu2qKluZjMAvqmpaAcMQNevH9qMDLTp6ShuXi5dGh378XYtcTQWi40Rw9cxbVoCv33SMw+J153j2btvL6dOn7xmzN/Pn65dupKW1oPQNqFOilBSh7DZMO/cSfV//0v1kiVY9u0DQBMfj9/YsfiPHYtu9GiPXAhrDtLouCBSnNRmbWtWF7F02R1OX5m4WHyRTZs3cj7/fP21oMAgBg4YSGrnLmhkmWCJExBGI6Zdu1Tjs3Ur5qwsbHX9oXx98U1LU41Pv37oMjLwTUtzq5xuaXTsR2qJ4zl1qpKhQ9awfMWd9Ozp2V/2Ky5XcPBgDgcPH7ymASlA+4T2pHXvQVJiktRAF8F69qxa7GbFCmpWr0aUloKioO3bF7+xY/EbN04tdqPTOTvUVkUaHRdEipO6cjZo4Bqeeroz06c77GjXbSOEIPdMLlu3b6GkpKT+enhYOIMHDaFD+w5ON2QS70YIge38eUw7d2LetQvzzp2Ydu1SxQ7Azw9tWhq+PXui7dULbc+eaHv2RBPummfPpNGxH6klLcOnn+byr7eOsm376BZvcu0KWK1WTp46Sc7BA9cs+AEEBgbSrWt3uqZ2lT15XIi6Yjc1K1dSs3Ilpm3bwGpFCQpCN3w4fiNG4DdiBL69enl8TzdpdFwQKU4qu3aWcM89m8neO46ICNc4fG2z2Thy9DDbs7ZTVVVVfz02No6B/QfQLj7BidFJJNcihMB66lS96THv24dl3z5sFy/Wz9HEx9cbH99a8+ObkuL0VT9pdOxHaknLIITgwQe3Exer58230p0dTqtyqeQSOQcPcOToEUwm0zVj7eLb0a1rNzomd5Kp3S6GrbycmnXrVOOzenX9mU+lbVv8hg1DN2IEfsOH49ujh8cZH2l0XBApTld45nd7KSszkflRf2eHcg1ms5l9+/eya88uzLXnJADi4+IZ0G8A8fHtnBidRNI41sJCzPv3Y9m3D/P+/aoBOnwYLBZ1go8PPh07ou3aFd8uXfDt2lX91aULmlZqpiuNjv1ILWk5SkpM9MtYyXvvZzBuXIyzw2l1TGYTx44f4+Chg1y4UHTNmE6nIzUllW5duxMZGSmzHVwQ67lz1GzYgGndOrXYzUn1PJYSFobfnXdeMT7du7u98ZFGxwWR4nSFy5fN9E5fyZy5/RgxIsrZ4dyA0Whkz9497D+wD0vdl0TUla0B/QcSF9u6/YAkkttFmExYDh/GnJOD5fDhK7+OH79igFB3gHy7dkXbtSs+KSn4duyIb6dO+CQmOnQXSBod+5Fa0rJs2HCBh2bsYEfWGGJi3Lv4hz0UXyrm8OFDHDl2pL7xdh3h4RF079qNzimp6PWyYqmrYj17lpr166lZtw7T+vVYT58GatsbDBqktjYYMgRt//5oAgOdHG3zkEbHBZHidC1LluTzh9/vY/eesU7prdMUDAYDe7J3c+DggWsMT0K7BPr2yaBdfDu5qiVxS4TZrKa/XW1+Dh/GcuQIorLyykSNBp/27fHp1AnfTp3w7dix/mef5GQ0Ac1rPugKRkdRFA3wsBDiY2e8316klrQ8f/1rDrt2lnhkyenmYrVaOJ17mkOHD5F3No+rvw9qNBo6tO9AaudUkhKTZWqbi2PJzcW0cWN9lU/LwYPqgK8v2vT0euOjGzIEnzjXXtCVRscFkeJ0Iw88sI2UlCBeeaWHs0NplCpDFXv2qIbn6m7T0VHR9O2TQXJSsjQ8Eo9ACIHtwgWsJ09iOXECy4kT9T9bT57EdunSNfM1MTH4JCbim5iIT8eOBL/ySqN/F5xpdGoNzgzgBSBZCOGaKyy3QGpJy2Ox2Bg1cj133R3PM8+kOjscl6Gy8jKHjxzh0JGDVFRUXDOm1Wrp1DGF1M6pxMfFy6ptboCttBTTtm1X2htkZSFqK/H5JCbW7/bo+vd3ufYGrWp0FEVZa8ezhRBilB33uw1SnG6koMBI3z4rWbFyOD16uH5ll8qqSvbs2c3Bwwev2eEJDW1L3959Se2cio+PW353kkiahK20FMvJk1fMz+nTWHJz1ZQIm43o2tSIm9ESRkdRlDhgLBANFAErhRD51815EHgR6AgoQJEQItYR729tpJa0DmfOVDF40Bp++GEoGf1kh/qrEUKQn3+eo8ePcvzE8RsKGAQGBpLauQupnVOJCPfufi/uhDCZMO/dW298TFu3YisoUAe1WrWqZ53x6d8f3y5dnHbWp7WNjg0QqOLRXIS7rqo1FylODZM59xRz5pxi85aR+Pq6xwqQ0Whk34F97D+wj5qamvrrQYFBpPdKp1vX7vj5uUZFOYmktRA22y1Fz+HipChPAq8BVx8kMgO/FUJ8qChKMjAf6I+qUZeBN4C3hBBV1z/PHZBa0nosXHiOF/53PzuyxhAS4j79qloTi8VC7plcjh47Qu6ZXGw22zXj4WHhpKR0JqVTimxI6oZYz5/HlJWFOStL/X3nTsRl9aNbCQ5Gm5FRb3y0GRn4JCS0SoaLs4zOTuAzoLA5DxdCLLQrOjdBilPDCCGYNHETw+6M5I9/7OrscJqFyWTi4KEcsvdlX1OWWqvV0rVLN3r16EVoqPxgl0jqcKQ4KYoyDFhf92jgGNAGSEI1NROAT1F3eszAe8CrQohie9571ftTgb8AfYA4QAvkAcuAfwohCpr4nImo6XS9gBpgDfAHIUSD22NSS1qXXz++m8pKC5982l+mKN+C6upqTpw8zpFjRykoyL9hPDIykpSOKXTqmCL787gpwmbDcvToFeOTlYV53z6orVSrCQ/Ht3dvtL17o+vTB23v3vikpDh856e1jc584CeAHrAAy4F5wGIhhOWmN3oZUpxuTl6egYEDVrFy1XDS0tzvw89qtXDk6FH27N1DWVnpNWOJHRJJ79VbFi6QSHC40fkKmIZqYJ4VQlTXXu8OLATaA/7AAeB+IcRRe97XwPtHAf8LbAfOoepfD+BRoAJIF0JcuMUzpgLfAvuAOahG7SnACmRcn4JXe4/UklbEYLAwaNAann02lRkzEp0djttQXlHOsWNHOXbi2DUNueuIiopWTU+nToQEt055e0nLIKqrMe/di3nPHvVXdjbmnByoTWlUgoLUvm69e6OtNT++3brZVdGz1YsR1H7w/gx4BBiIusNTgpoyME8IsdfeINwdKU6NM3fOKTIzT7Fps/uksF2PEIIzeWfYt38veWfzrhkLDwunV89edE5JRauVKRAS78TBRucMqtZ0FEJYrxubACwFjKiFB4oaeESLoCjKNOBr4DkhxD8amacFclENUnchRGXt9XRgN5AphJjdwH1SS1qZAwfKGTd2Pes3jKRzZ/nfvLlcKrnEiRPHOX7iOKXXLQYCREerpqdjcqe6zweJmyNMJiyHDqmmJztbNUB79yLqsl90OrRpafjWNraua3CtCQ9v0vOdWnVNUZTOqCtaM1C38wWwH/gYWOCotAF3Q4pT49SlsN05PJLnnnOvFLaGKCm5xL79+zhy7Mg1hQt0Oh2pnbuQ1j1NHtKUeB0ONjpGYLkQ4p4GxkJRF9tWCCEm2POe24irP7ADeE0I8Xwj80YDq4C/CCFeuW5sDZABRAghzNeNSS1xAh+8f4KPPjrNps0j8fPziqPFDkcIccX0nDxOWVnZDXMiIiLomNSR5KSOhIeHy0wID0LYbFhPnLiy65OdjXn/fmxFV9ahNHFxaHv3Jmzx4kbT3VyivHRtKc9xqLs8d6EeFrUAXwshZtgblLshxenW1KWwrVo9nO7d3S+FrSGM1UYOHjrIgQP7qayqvGYsNiaW7t3SSOmUIvsPSLwCBxsdG2rGwMxGxj8RQjxqz3uaEIc/EISaJtcNeB1IB4YJITY1ct/zwN+AMUKI1deNvQr8CUgTQhy8bkxqiRMQQjBt2laSEgP55xvpzg7H7RFCUHypuN70lJeX3zAnJCSE5KSOJCclExsTK0tWeyjWoiLM+/dj2b8f8/79iIoKwhYtavQelzA61zxAUcJRd3QmA8VCiCh7g3I3pDg1DU9IYWsIm81G7pnTHDiYQ17emWvG/Pz86JralW7duhMe1rRtW4nEHXGC0bnpuKNQFOUJ4J2rLuUCLwgh5t/ivneAJ4BuQojD1439GvgPME4IsfK6sWCgIj09vb6c/ezZs5k9+4YsN4mDKSkx0S9jJe/+py8TJrhldXKXpM70nDp9ilOnTlJ86cbEH71eT1JiEslJHUlolyAXB70clzE6tVVpHkFNY4tFrYSzSQhxp71BuRvS6DSNuhS2O4ZF8vzz7p/C1hDlFeUcOnSQQ4cPYTAarhmLioyia5eupKR0Ru+vd1KEEknL0AJGZzlqeemGWN/YuBBioz3vvyqOdkAX1F2d3qgZDJ8IId6+xX2ZwEzUM0anrhubCWQC9wghvr9uTGqJE9m06SLTH9zOjqzRxMbKz+iWoLyiXDU9p09SUFDA9d9Btb5a2iUkkNghkcT2iQQFBTkpUomzcPYZnRCuFCao611wCViAurqWbW9A7ogUp6Zz9qyBgQNWs3jxUPpmeG6jNqvVyuncUxw4mMO5c2evGdNoNCQlJtG1SzfaJ7SXjUglHkELGJ3bTTcQQogWWRJWFKUnaruFF4UQf29knl07OlJLnMfLLx9k29Zili4bhkYjz5C0JEajgdO5pzl56iRnz53FarXeMCciIoLEDkkkdkgkOipaprh5Ac6ouqYAY1DNzd2opaatXFtq2nyz+70BKU7N4+uvz/LSSzlkZY0hMNDzt6jLy8s4fPQIR44e5vLla//O6vV6Ujt3oUtqFyLCI+ThTInb4mCjk8vtGx2EEEn2vL8xFEXZDsQLIRIamSPP6LgpFouNsWM2MH5CLH/4Qxdnh+M1mMwm8vLOcDo3lzN5uRiNxhvm+Pv706F9BxI7JNE+oT3+/v5OiFTS0rR2H51XgYdQK6wpwCFUc/NZa5b0dHWkODWfRx/JIijIl3fe7ePsUFoNIQTn889z+MhhTpw8fk3FNoC2oW1JSelM55TOtA1t66QoJZLbw9Hi5KooirIP6CSECGxkjqy65sacPWtg0MDVfPPNYAYNlhU0WxshBEUXisg9k0vumdNcvHjxhjmKohAbE0v7hA60T2hPZGSk3O3xEFrb6NSlD+xCNTg7mvNwIcQee4JzF6Q4NZ/ycjP9Mlbyr7d7M2lSnLPDaXVMJhMnT53g8JHDnM8/f8N4ZEQknVM6k9Kps/wzJXELPMnoKIoSI4QobOD6CGA1sF4IMar2WixqM9A8IYSh9poWOAOYubaPTi9gD/CxEOKxBp4vtcRFWLIkn6eezCZr5xjCwm6/+aHEfqqqqmpNTy5nz+VhNt+YROTv50+7dgm0T2hP+4T28u+PG+Mso3M7OCRPujYFoA/QF0gCzgghEhuZPwB4FRiAGvtW4I9NaWxaW0p0BmoFuV5ANFCAavBevj7X+qr7pDjdBps3X+TBn21n564xREd77xZ0RUUFx08c49iJYxQX31iRJjYmlpSUznRM7khQoDyYKXFNPMzoLEItsrMW1bD4o2rQA4ABGF6nKYqizAMeBkYIIdZf9YxpwFfAPmAOEAI8japLfYUQN6xwSC1xLX7/7F5Onqpi4cLBMq3YRbBaLZzPz+fMmVzO5J1psEkpqBkS7RPak5DQnvj4eHRaaVbdhdY2Ork4OU9aURSB2hxuD6rQVNzM6CiKMhC1Gs954N3ay08AUcBgIcSBW7yrC3AY2AysBPKBZOBxIBAYL4RY18B9UpxukxdeOEBOTjmLFg2RQgKUlJZw/LhqehpquBYTHUPH5I4kJ3cktE2oEyKUSBrGw4zO/ajmpScQiaqDZ1DT0f4phMi7au48GjA6tWOTgRdqn1MDrAGeE0KcvMl7pZa4ECaTjeF3ruWBB9rz2yc7OzscSQNcvnyZvLN55J3N49y5s1TXVN8wR6PREBsTS0JCe9rFtyMqMkoWAXJhXKa8dGuhKEpyXXlORVFygKBGjE4WainQrnWrZYqixKOal+1CiLG3eFc4kHD97o+iKN2AbOCAECKjgfukON0mJpONYXes5eGHE3n8152cHY7LIISguPgix04c5/jxY1yuvPHvenhYOB2TO9IxuZPsMi1xOp5kdJyF1BLX4/TpKoYOWcMPPwwlo5/nVgr1BGw2GxcvXiTvXB5nz+ZRUFiAzWa7YZ5WqyUuNo528e2Ij29HZIQ83+NKeJ3RuZrGjI6iKJ2A48BHQohZ141lAo8CcQ3lXTfx3btRc61vyLGS4mQfx49f5s5ha1m6dBi9+8hD+NcjhODChQucPHWCk6dOUlZ+405PSEgIHZM6kpSYRExMrFytkrQ60ujYj9QS1+S7787x/B/3syNrNKGhMgXKXTCZTJzPP1+743OmwSwJAJ1OR3xcPPHx7WgX305WQHUy0ujc3Oj8DLWfzy+EEHOvG/sF8CEwWQix9DbeqwHOAWYhRIcGxqU42cnXX5/lxb/msH3HaEJCtM4Ox2URQlBSWsKpUyc5eeokF4tvrEaj0+lqS3Am0qF9Inq9bHwnaXmk0bEfqSWuy5O/3UPRhRq++GKg/BLsplRWVnLu/DnOnz/HufxzVFRUNDjP38+fuLh44uPiiY2NJSI8Qi4etiLS6Nzc6DwDvAFMFEL8eN3YRGAp8EshxIe38d7foJ75eUUI8ZcGxoOBivT09Pq/DLNnz2b27NnNfZVX85tf76as3Mznnw+QQtJEyivK601PQWFBg3NiomPUhmuJiXKlStJiSKNjP9LouC7V1VbuGLqWWbOS+NXjMs3aE6i4XKGantpflZWVDc7z9fUlOiqa2Ng4YmNiiYmOkT18WhBpdG5udP4MvAyMEkKsvW5sJOoh0KeFEG83852DUSvvHAUGCCFuOOkmxckxGI1W7hi6htmzOzL7lx2dHY7bYTQaOZN3htwzp8k7m0dNTc0Nc4ICg2jfvgPtExJo1y4Bvb/c7ZE4Bml07EdqiWtTl2a97Mc7SU+XxWA8CSEE5RXlV3Z8zp/DYDDcdH5YWBixMarxiY6Opm1oW7mI6CAcrSWe1Ja+7k+kXwNj/tfNaRKKovRF3QnKR90purGch8Rh4rrBZQAAIABJREFU6PU+LPhiEMPvXEv/AeFSSJqJXq+nS2oXuqR2wWq1UlhYUN97oKS0BIDKqkoOHT7IocNqQ/aoyCgSavsOxMbE4OPjSR8JEolE4jhSUoJ561+9efDBbezYMZrgYJlm7SkoikJom1BC24SS1i0NIQQVFRUUFOZTUFhIQUE+l0ou1c8vKSmhpKSEg4dyADVlPCoyiqioaKKj1N+Dg4Kl+XEBPGlHx6FndBRF6YPaGK4CuFMIcaaRuXIVzoF88UUer7xykO3b5XkdR1FeUU7umVzOnMnlfP55LBbLDXN8fX2Jj4uvNz5hbcPkh7SkycgdHfuRWuIePP6rXVRWWfj0U5lm7U3U1NRQWFRQb3yKioowW25sXlqHXq8nKvKK8YmKiiIwILAVI3ZPZOpaK1RdUxSlN6rJqURtDHf6FvOlODmYJ36zm8Kiar7+ejAajRQSR2K1WigoLCAvL4+z585y4eKFBucF6AOIj48nPq4d8fHxcmte0ijS6NiP1BL3wGCwMGTwGn772848OtPudoESN8Vms3HpUjFFFy5QdKGICxeKuFRyica+VwfoA4iIiCAiIpLIiAgiwiMJDQ2V5a2vQhqdxvvo7ARSgS5CiPzaa3HAESBLCDH6qrkRQARQIIQov+p6ncmpQjU5p5oQlxQnB2My2Rg9aj3jJ8Twpz91c3Y4Ho3RaOTsubOcPZtH3rm8mx7IlMZH0hjS6NiP1BL34fDhCkaNXMfKVcNJS2vj7HAkLoLZbKa4uJiiC0X15qehlhBX4+vrS1hYuGp8IiKJDI8kPDwMna6hkxiej9cZHUVRZgB1JZ3/B9ABb9b+8xkhxGdXzR0MrEMtBf3OVfdEA0OEEPuumvsi8FfgUSHEvNprHYDdQBjwEtBQ9+pFQoiq62KU4tQC5OcbGTxoNR/8fxmMHx/r7HC8AiEEpWWlnD17lvP55ziff57q6oaPpgXoA4iLi6ddfDyxsXGEh8mmpd6MNDr2I7XEvfj001zeeOMI27aNJjBQnm+UNExNTQ0XLl7gYvFFiosvUlxcTElpSaM7PwBBQUGEhYUT1jaM8LCw+p91Os/u5eSNRmc9cOdNhjcIIYZfN38Q8P+AAYAAtgLPCyH2XDfvRW40OsNRjVJjJAkhcq97lhSnFmLL5mLuv38rGzaOpFOnIGeH43UIISgpKeFcvlqJpjHjo9PpiImJJbb2V0x0DFqtPGPlLUijYz9SS9yPmY9modEozM3s5+xQJG6ExWKhpLSE4uKLXCwupviSaoBMJtMt7w0OCias1viEh4XRtm2YRxkgrzM67oAUp5bl/fdOMGfOKTZtHilXzZxMc4yPoihERkTWm5+42FiCguTfD09FGh37kVriflRWWhg0aDV/+EMXZsxIdHY4EjdGCEHF5Qp1x6fkEpdKSigpuURpWSk2m+2W9wcGBBIaGkpoaFtCQ0NpW/tzSHCIWzU8lUbHBZHi1LIIIXhs1k4MRivz5w+UxQlcCCEEpaUl5BcWUFBQQGFhQaP5yMFBwcTExBAdFU10dAyREZFy18dDkEbHfqSWuCf795cxftwG1q4bQZcuIc4OR+Jh2Gw2ysrLakta1xqg0kuUlZU1yQApikKbkDb1JqjOAIW2aUNgYJDLpZxLo+OCSHFqeaqrrYwds4GRo6J48cU0Z4cjaQSDwUBBoWp6CgoLKLpQdNMPY0VRCAsLrzU+0URHRRMeFi4r0Lgh0ujYj9QS92XunFO8994JtmwdhV7vPqvnEvfFarVSXl5OSeklSkvLKCsvpaysjNKy0gYbhjeERqMhJKQNbUJCaNOmTf3PISFtCAkJQadt/XQ4aXRcEClOrUNRUTV3DF3DX19MY/r0Dre+QeISWCwWLly8QGFhAfmFBRQVFTbacdrX15fIiMha4xNDdFQUISFtXG7VSXIt0ujYj9QS90UIwYwZO2gTouU/7/V1djgSL0YIQXV1db3pKSsrpay8jLKyMsrKy7BarU1+ll6vp01IG9qEtCGkjWqCgoNDCA4OJigwqEVS4qTRcUGkOLUeOTnljBu7ga+/HsyQoRHODkdyGwghqKqqpLDoSvnNogtFmM03b7ym0+mIiIggMiKKyIhIIiMjaRva1q3yjj0daXTsR2qJe1NRYWbggNW8+FIa99+f4OxwJJIbsNlsVFZWUlpWSkVFOeUVFerv5eWUV5Q3qsMNERgYSHBQMMHBwaoBCgpSfw5SzZCfX/NLZEuj44JIcWpdVqwo5BeP7WT9hhEkJ8tKbJ6AetantLb3QCFFRUUUXypuNP/Yx8eH8LBwIiMjVfMTEUl4eIQ88+MkpNGxH6kl7k/2nlImTdrIxk2jZKVQiVtRtxNUXlFea35qTVDtr5v12GsMnU5HcFAwQUHBhAQHExQcTEJ8AtHR0Te9RxodF0SKU+vz/nsneO/9E2zcOJK2bT2jpKLkWiwWC8WXitX+Axdr+w/cwvwoikJoaCjhYeH1pTfD2obTpk0bufvTwkijYz9SSzyD/7x7nM8+O8PGTSPR6eR5Q4lnYLVaqKi4zOXKy1y+XMHlykr198vqtcrKyiYVRxg8aAh9e988vVMaHRdEipNzePaZvezZU8rSZcPk4U8vwWq1UlpaysXii+qv2iZst9pu12g0hIaGEtY2/KrGa21rDZAsWe4IpNGxH6klnoEQgqlTt5DaOZjXXu/l7HAkklbBZrNhMBpU4/P/s3ff4VFU7cPHvye9JyQEEgiSEGpEBEkAQSMdQQRRukoXVCz4UGx0kOdVEVGwgL+HKihdpEgRpEeqCtKRDqGEQHrPef/YZGRJQhJI2JT7c117bebszJl7N8nO3jtn7hMTQ2xsDNF33CclJdGm1dNUr1Y9x34k0SmC5OBkGenpmt69d5OQkMaPPz6OjY18c1Yaaa2Jio7i+vXrGTNPRxB58wYxMbm/PyqlcHVxxd3dPaPcpkdGCU4P3FzdpPpbPkiic//kWFJyREQk0SBkI9/OCKZ1ax9LhyNEkZCcnISVlTU2Njl/wSiJThEkByfLSU5O57mOO6hc2Ymvv6kvlbmEITklmZuRN4m8+e/Ea5GRkcTE5u1908rKCjdXN9zdPXBzc8PN1Q03N1fcXN1xdXPFwd5B/t5uI4nO/ZNjScmydes1er28mz17W1G+vIOlwxGiWJBEpwiSg5NlxcSk0LrVVto87SNz7IhcJScnE3kz0lRyM6PcZmbpzfxUnLG1tcUto8ymm5s7bkbVGVdcXFxwdHQsVWeEJNG5f3IsKXnGjPmb/fsi+XnVkzLZtRB5IIlOESQHJ8u7di2Rpk1/443B1Xh9cFVLhyOKIa018QnxpuQnIwGKykiAomOi8112UymFs7MzLs4uuLi44OzskvGzs9nPJeUaIUl07p8cS0qe1NR0mjfbQqfnK/LOOzUsHY4QRV5BH0tKxhFWlHrlyjmwdm0oLZr/hoODFf36V7F0SKKYUUrh7OSMs5MzFStUNHtMa01SUhIxMdFEx8QQHR1FdIyp8kx0dHS2iZDWmtjYWFNJzqs579fBwQEnJyecnJxxcnTK+Nkpy8+l7QyRECWBjY0V8+Y3pEnjTYQ+6U39YE9LhyREqSKJjigx/P2dWbf+KVq32oqtnRUvv+xv6ZBECaGUwsHBAQcHB7y9y2V5PDMRio6OJjYuhti4OGJjY4mLMyU6sRn3qampWbZNTEwkMTGRyMjIXONwdHT8NwFydDLF5OiIg4MDjg4OODhk/my6v9sFn0KIB8Pf35kvvqzHyy/vZveelri6ylxfQjwoMnStAMhwg6Ll6NFonm6zlU8n15XZqUWRobUmOTnZSHr+TYLiiE+IJyE+nvj4eOIT4rNNiO6FrY1tRjLkgIO9A44ZSZGDgyMO9vbY29tjb++QcW+6Odg7YG1tne9CCzJ07f7JsaRke3XQPpKT05k1u4GlQxGiyJKha0LkolYtN9asDaVd223Y2io6dfKzdEhCoJQykgkvT68c19Nak5KSYiQ98fFxGT8n3PZzPAnxCSQkJtz12qGU1BRSYlPyXGkuk5WVFQ53JEAPB9UmsEpgvvoRQvzrsyl1adToVxYsOMeLL1a2dDhClAqS6IgSqXZtd1b+/ATPtt9OaqqmSxc5syOKB6UUdnZ22NnZ4eHhkev6aWlpJCYmkpCYkDEMznSfkJBIYlKC6T6zLeM+OTn5rn1mTvwWnxBvtFV+yP9+n5oQpZqzsw3z5zeiXdutPPmkNw895GTpkIQo8STRESVWvXplWPtLKM+2305CfBq9evtbOiQhCpy1tTXOzs44OzvneZu0tDSSk5NITEoiKcstkaQk02P/rpOIi4tLIT4LIUqHunU9eOvt6gwauJc1a0Ol5LQQhUwSHVGi1anjwYaNTWnXditxcam89rqUnhbC2toaR0cnHB3lG2UhHrRhw2qwetVlZnz7jxyThChkUqtUlHg1arjy66ZmfPHFCSZPPmbpcIQQQpRiNjZW/G9WAyZMOMzJk1K3Q4jCJImOKBUCApzZtLkZc+ee5d0Rf5GeLtUGhRBCWEaNGq588EEQA/rvJS1NjkdCFBZJdESpUbGiI1u3NmfPnkheeul3EhPTLB2SEEKIUur1wVWxt7fi88+PWzoUIUosSXREqeLpaccv60LRGtq13UZk5N2rTwkhSh+lVHWl1Hil1O9KqetKqRil1J9KqQ+VUnmu+qCUaqeU2qWUilNKRSqlliilAgozdlF8WFkpZswM4bPJxzl9OtbS4QhRIkmiI0odBwdrFixoRHCwJ089tVkOMEKIO/UD3gH+AcYDw4HjwERgl1LKMbcOlFLPA6sBx4ztPwVCgZ1KqQqFFLcoZgICnBk6rAZvvXkAmcBdiIIniY4olaysFJ98+iivv1aV0Cc3s2nTVUuHJIQoOpYCflrrF7XW07TW32qtuwEfAXWA/nfbWCllC0wDLgBPaq2/1lr/F2gDlAfGFmr0olh5++3qXL6cyJIlFy0dihAljiQ6olR77fWqfL+gEX167+aLqSfkGzUhBFrrfVrrqGweWpRxXzuXLp4CKgD/p7U2Thlrrf8EtgDdMpIhIbC1teKrrx5j+LA/uXVLhlMLUZAk0RGlXtOm5di+owXff3+Ofn33kpAgRQqEENnyy7jP7RRwSMZ9WDaP/Q64AdULKihR/D3euCxt2/ry30lHLR2KECWKJDpCAP7+zmzd1ozUtHSaNN7E0aPRlg5JCFGEKKWsgdFAKrAwl9Uzr8G5lM1jmW0Vc9o4NDSU4OBggoODmTlzZr5jFcXT2HG1mTv3LGfOxFk6FCFKDBtLByBEUeHkZMO8eQ2ZPesMLZr/xn//W4devf1RSlk6NCGE5U0FGgEfaK1zqwfslHGflM1jiXesk8W2bdtwdXXNf4SiWPPxceD1wVUZM/pv5s1vaOlwhCgR5IyOELdRStGvfxU2/tqUz6eeoHevPURFpVg6LCGEBSmlJgBvADMzigrkJj7j3j6bxxzuWEcIw3/+U4OtW6/x55+3LB2KECWCJDpCZOPhh93ZtasFbm42PFZvPRs2XLF0SEIIC1BKjQVGArOBV/O42eWM++yGp2W2ZTesTZRyLi42vPV2daZ8JpOIClEQJNERIgdOTjZM/6o+M2aGMPj1/QwauE/O7ghRiiilxgBjgHnAAJ33sox7M+4fz+axRkA0cOL+IxQl0YABVdiw4Qpnz8q1OkLcr2KR6Cil3s+YUfq0Ukorpc7msn5DpdSvGbNZRyul1iml6uZzn/fdhygZWrYsz/4DrbGxUTxWbz3Lll2UMtRClHBKqdGY5ruZD/TVWqfnsJ6vUqqmUur2a262AuHAAKWUy23rPgo0BZZoreVbE5Etd3dbevXy57vvTls6FCGKPVUcPrAppTQQCRwA6gPRWmv/HNZthGmegkvA9IzmN4ByQGOt9aE87C9ffSilXIHo6OhouYC0hNu27Tpvv3WA8uUdmPJ5PYKC3CwdkhBFQkxMDG5ubgBuWusYS8dzP5RSgzG9958HRgF3JjlXtdYbM9adA/QGmmmtt9zWRxdM8+78BXyHqaT0O4AG6mutswxdk2OJyHTw4C2e77STEyfbYWUlBXFE6VHQx5LiUnUtUGt9GkAp9Tfgcpd1vwSSgdDMA4lSajFwFPgMaJ2H/RVEH6IECg31Zu++Vnz7zT+0bPEbPXpU5t33alKunEPuGwshiovMeXAeAuZm8/hWYOPdOtBaL1FKJWC6vmcypgpsm4B3s0tyhLhdnToeOLvYsHdPJA0beVk6HCGKrWIxdC0zycmNUqoqpgPUktsPJBk/LwFaKqV8CrsPUbLZ2FjxxpvV+POvNqSna+o8sp5Row4RGSkzWgtREmit+2it1V1uTbNZd0s2/azWWjfSWjtprctorTtrrf95kM9FFF/Nm5Vjx84IS4chRLFWLBKdfMhtNmqFaehbYfchSoFy5Rz4fGo99uxtRcT1JB4O+oURw//i9OlYS4cmhBCimKsf7Mkff9y0dBhCFGslLdG5r9moC7APUYo89JAT33wbzO+7W2Jto3iiySbeevOApcMSQpQyn332GRUqVKBChQo0a9YMgG7duhltw4cPB6BKlSpG2+LFizl06JCxXKFCBc6cOcOsWbOM5Tp16gDw+uuvG239+/cHICQkxGj79ttvuXz5sllf+/fvZ+XKlcayn58fACNHjjTannvuOQDatGljtH300UckJiaa9bVhwwa2b99u1nbz5k0+//xzYzk0NBSAnj17Gm1Dhw4FoFq1akbbjz/+yJEjR8z6+ueff5g7d66x/PDDDwPwxhtvGG19+vQBoGHDhkbbV199xZUrV8z62rNnD6tXrzZrAxgzZoyx3LFjRwDatWtntE2cOJGUlBQqVKjAO+90Z+XKRaxfv55du3aZ9RUZGckXX3xhLD/xxBMAvPTSS0bbO++8A0CNGjWMtoULF3Ls2DGzvk6ePMn3339vLAcFBQHw9ttvG229e/cGoHHjxkbb9OnTuX79ullfu3fvZu3atWZtaWlpjBs3zlhu3749AO3btzfaxo0bR1pamtl2a9euZffu3WZt169fZ/r06cZy48aNAejdu7fR9vbbbwMQFBRktH3//fecPHnSrK9jx46xcOFCY7lGjRoAvPPOO0bbSy+9BMATTzxhtH3xxRdERkaa9bVr1y7Wr19v1paSksLEiRON5Xbt2gHQsWNHo23MmDEAZtutXr2aPXv2mLVduXKFr776ylhu2NA0oWyfPn2MtjfeeAOAhx9+2GibO3cu//zzj1lfR44c4ccffzSWq1WrBsDQoUONtp49ewIQGhpqtH3++efcvHnTrK/t27ezYcMGs7bExEQ++ugjY7lNmzYAPPfcc3z22Wf5ek8rEFrrYnUD/gbO5vDYKEwXejbP5rHmGY8NyaX/fPcBuAK6bt26un79+rp+/fp6xowZOq8mT56sfX19ta+vr27atKnWWuuuXbsabcOGDdNaax0QEGC0LVq0SB88eNBY9vX11adPn9b/+9//jOVHHnlEa631a6+9ZrT169dPa611cHCw0fbNN9/oS5cumfW1b98+/dNPPxnLFStW1Fpr/eGHHxptHTt21Fpr3bp1a6Nt4sSJOiEhwayv9evX623btpm1RUZG6ilTphjLTz75pNZa6x49ehht//nPf7TWWletWtVo++GHH/Thw4fN+jp16pSeM2eOsRwUFKS11nrw4MFGW+/evbXWWjdo0MBomz59ug4PDzfra/fu3XrVqlVmbVprPXr0aGO5Q4cOWmut27Zta7RNmDBBJycna19fX+3j85D29q6v161bp3fu3GnW140bN/TUqVON5SZNmmittX7xxReNtiFDhmitta5evbrRtmDBAn306FGzvk6cOKHnz59vLNeqVUtrrfVbb71ltPXq1UtrrfXjjz9utE2bNk1fu3bNrK/ff/9dr1mzxqwtNTVVjx071lh+5plntNZaP/PMM0bb2LFjdWpqqtl2a9as0b///rtZ27Vr1/S0adOM5ccff1xrrXWvXr2MtrfeektrrXWtWrWMtvnz5+sTJ06Y9XX06FG9YMECY7l69epaa62HDBlitL344otaa62bNGlitE2dOlXfuHHDrK+dO3fqdevWmbUlJyfrCRMmGMtt27bVWmvdoUMHo2306NFaa2223apVq/Tu3bvN2sLDw/X06dON5QYNGmitte7du7fRNnjwYK211kFBQUbbnDlz9KlTp8z6Onz4sP7hhx+M5apVq2qttf7Pf/5jtPXo0UNrrfWTTz5ptE2ZMkVHRkaa9bVt2za9fv16s7aEhAQ9ceJEY7l169Zaa607duyoJ0+enOv7WHR0tM54f3TVReBYURxvmceS6OjoXF9vIYQoiQr6WFIsqq7dLrMYgc6m6ppSaiimiz7baa1/ueOxdsAaYJDWeuZd+s93H1IpRwhR2pWkqmuWIscSIURpV9DHkpI2dK0gZqOWGa2FEEIIIYQo5kpaopPbbNQa2P8A+hBCCCGEEEJYUIlKdLTWp4B9QBelVGZRATJ+7gJs1lpfua29bMaM1u732ocQQgghhBCi6CkWiY5S6mWl1Eil1EjAG3DPXFZKvXzH6m8D9sB2pdQQpdQQYDum5zr0jnXfwDQJaKf76OO+zZyZ4yVDIo9K42tYGp9zduR1KHzyGhcP8nu6f6XxNSyNzzk78joUPku8xsUi0QH6AxMybuUAj9uW+9++otZ6F9AUOAtMzFjnFBCqtf4rLzsriD5ut2rVqrs+fq+/+Nz6LYzt87pNbuvd6+M5tRfVN6j7/R3dzSeffHJf2+c3toL63ee2Tkn53UPh/f4f9P/+/f6tiYIhx5KCe7y4vZ/IsST/65SU3z3IseR+2DzwPd4Dfdss1HlcPwxokYf1xgJj76eP212+fDnbSjk//vgj9evnPMdocnIyly9fzvHxnOTWb2Fsn9dtclvvXh/Pqf1eX8PCdr+/o7uJjIy8r+ec39gK6nef2zol5XcPhff7f9D/+3n5W4uJkUJrBUWOJXlfT44l90+OJSZF9XcPciy5H8WuvHRRpJR6FPjT0nEIIUQRUPdeznwLOZYIIcRtCuRYIolOAcic++DSpUsy94EQolSKiYmhYsWKIPPo3DM5lgghSruCPpYUi6FrxYWrq6scnIQQQtwXOZYIIUTBKC7FCIQQQgghhBAizyTREUIIIYQQQpQ4kugIIYQQQgghShxJdIQQQgghhBAljhQjEEIIIYqxyMhIrkdcJyU1hdpBtS0djhBCFBmS6IgSIzo6mmvXrpGSkmLpUIQocWxtbSlXrhxubm6WDkXc4c/9e5j08Sm8vY7yw6KvLB2OEEIUGZLoiBIhOjqaq1evUrFiRRwdHVFKWTokIUoMrTUJCQlcunQJQJKdIsbdy5uLFy+Qlm5PYmIiU6ZMoUePHgQEBFg6NCGEsCi5RkeUCNeuXaNixYo4OTlJkiNEAVNK4eTkRMWKFbl27ZqlwxF3cHd3x7eCLWXLBhEdE02VKlXw9PTk999/59133wVMyaoQQpQ2kuiIEiElJQVHR0dLhyFEiebo6ChDQ4sgdzc3vLzSuHHDhqioKLp37467uzs1a9akR48eALRr146//vqLlJQUkpKSLByxEEI8GCUu0VFK6RxusffY3+KM7f8u6FhFwZIzOUIULvkfK5rc3NzxKpvKjQhroqJuGe0eHh7UrVsXgDlz5hAUFMSOHTvo0qULAKdPnyY1NdUiMQshxINQUq/R2Q7MvKMt319DKqXaAy8ACQURlBBCCFHQ7O3t8fFMIzLSmqgrV7Jdp3z58gA0a9aMJk2aAPD+++8zfPhwAgMDOXLkiNEuhBAlRYk7o5PhtNb6+ztui/LTgVLKBfga+AqQQemi0PXp04fg4OBc11NKMX369Hz1ffbsWZRSrF69+l7Duy8nTpxg7Nix3Lp1y6x9zpw5KKWIjc37CdctW7aglOLvv00nWZOTkxk7dix//vlngcS6YcMGpk6dWiB95Ye/vz/Dhg174PsVJUPlCjZE3rDhVkREruva2dkBsGjRIoKDgzl37hxr1qwBYMmSJVy4cKFQYxVCiAelpCY6KKXsMpKVe/URpjNeIwsopCwiIiI4fuIYR44eLqxdiBIoLCzMGHpSXJw4cYJx48ZlSXSeeeYZwsLCcHJyynNfjz32GGFhYQQGBgKmRGfcuHHFPtER4n74+JTB3j6dC+Hx+d62bt26TJo0CYCLFy+Snp7OP//8w8cff1zQYQohxANVUhOdzkA8EKOUuqaUmqaUcs/rxkqpBsAbwBCtdXRhBXn40J8MH76LZUu2FNYuRAnUqFEjYxhKceft7U2jRo2wssr7W5GbmxuNGjWyePGJtLQ0kpOTLRqDEJncfXzw9ErjQoQiPT39nvt55513qFy5Mo6OjgQFBQHw5ptvsnfvXrTW99W3EEI8aCUx0dkDjMWU7PQGNmNKWrbn5QyPUsoG+A7YoLVenJ8dh4aGEhwcTHBwMDNn3nmJUFaOrm78c8qOqxHWpKXJBaHCZOPGjdSpUwdnZ2eeeOIJDh82P+N359A1rTWjRo0yJnPs168fP/74I0opzp49a7ZtfHw8gwYNwt3dHT8/P8aMGZPrB5c1a9bQqlUro/9GjRqxYcOGLOsdPHiQZ599Fg8PD1xcXGjQoAEbN25ky5YtPPvsswAEBASglMLf3x/IOnQtICCAESNGZOm7c+fOPPnkk0DWoWuurq4A9O3bF6WU8bxDQkLo27dvlr569+7NY489lu1zHTt2LJ999hnnzp0z+urTpw/w79DCn376iYcffhgHBwd2795NeHg4/fr1o0qVKjg6OlK9enVGjhyZJQlKSEhgxIgRVK5cGXt7ewICAnj//fdzfN0vXbpEjRo1aNmyJfHx+f+WXpQu7mXK4OmVxo0btsTExtx3fxUqVDD+bwcNGkSNGjU4cOAArVu3BuDq1auS9AghirwSl+horRtqrSfjpg7rAAAgAElEQVRrrX/SWs/TWncHPgQeAd7OQxfDgWrA4Pzue9u2bezbt499+/YxcODAXNd3dHDE2TmduFgrEhIT87s7UQKdP3+e4cOH8+GHH/LDDz9w7do1unbtetc5MKZOncqkSZN49dVXWbp0KY6OjtkmCwAjRozAxcWFpUuX8tJLLzF+/HiWLl1615jOnDnDs88+y/z581m2bBmNGzembdu27Ny501jn2LFjNGnShPDwcL799ltWrFhBp06duHDhAo899hiTJ08GYPny5YSFhbFixYps99W1a1cWL15s9nxjY2NZu3Yt3bp1y3abzZs3AzBy5EjCwsIICwvD19eXAQMGsGTJErPrf2JjY1m2bFm2CRDAgAED6NmzJz4+PkZfo0aNMh4/e/YsI0aM4P3332ft2rUEBAQQERGBp6cnU6ZMYd26dQwfPpzZs2fz5ptvGttprenYsSPffPMNgwcPZu3atYwbN46IHK6nOHv2LKGhoVStWpXVq1fna2ifKJ3c3dwzSkxbEx1dsAMRateujZubG/Xr12fxYtP3f++++y7r1q0jKSmJ7du3F+j+hBCiwGitS/wNsAWSgF25rFcVU4W1D+9oPwv8fZftXAEdHR2t8+PkqRO6abPZukvX7/T169fzta0wd+TIEUuHcN969+6tra2t9YkTJ4y2FStWaEAfPXrUaAP0tGnTtNZap6amah8fH/3666+b9dW2bVsN6DNnzmittT5z5owG9Msvv2y23qOPPqq7deuW5xjT0tJ0SkqKbt26te7bt6/R3r17d12xYkUdHx+f7XarVq0yiyfT7NmzNaBjYmK01lofOHBAAzosLMxYZ+HChdrKykpfuXJFa631b7/9pgF96NAhrbXWMTExGtCzZ8826zsqKko7OTnpWbNmGW3/+9//tJ2dnY6IiMjxOQ4dOlRXrlw5S3vv3r01oP/4448ct9Va65SUFL1gwQJtb2+vk5KStNZar1u3TgN65cqVOW5XuXJlPXToUH3y5EldqVIl/fzzzxvbFyU5/a9FR0drQAOuugi87xfH270eS7TW+lbULf3iSzP1443n6EN/H8z39vciPT1dnzp1Sr/yyitaa9P/+bFjxx7IvoUQJVNBH0tKanlpM1rrFKXUZaBsLqt+BkQCK5RSVW9rtwHsMtritNbhBRGXQ+YZnTgrEhKlgnVhsLNdYrF9J6fkv2CAv78/1apVM5Yzx8hfvHiRmjVrZln/woULXLlyhQ4dOpi1d+jQgV9++SXL+pnDTm7v//z583eN6eLFi3z44Yf8+uuvhIeHZ34gMytFu3nzZl566aX7vm6mXr16VK9enUWLFtGoUSPAVBmqadOm+b4uyc3Njc6dOzNnzhzjDM6cOXPo0KEDXl5e9xRfxYoVjXlJMmmt+eKLL5g5cyZnzpwh8bazs+fPn6dq1aps3rwZT0/PLL+nOx0/fpzQ0FCaNm3KvHnzsLEpFW/RogC4urji5ZnKvr2O3LpyBR5+pND3qZQiMDDQGKodERHBQw89RGRkJOPHj5eiHkIIiysVR1GllAPgB/yey6qVgQpATmXQTgJrgPYFEVfm0LWrV2xIlESnUNxLsmFJHh4eZsuZZWATcxjaeCVjzgxvb2+z9juX79Z/Tn0DpKen06FDB2JiYhg/fjxVq1bF2dmZ0aNHc+3av1XXb9y4ga+vb4795Ee3bt2YNWsWU6ZMISYmhnXr1jFt2rR76qt///40bdqUf/75B4Dt27ezdu3ae44tu2Rr6tSpDBs2jPfee4+nnnqKMmXKsHfvXgYPHmy8tnl9fXbt2kVkZCQDBgyQJEfki5WVFZV8rLhxw4ao8MsWiSHzerZbt27x1FNPAfDhhx/SpEkT2rVrR2pqqvxdCyEeqBL1jqOU8tJa38jmoQmYnuuq29b1BdyB81rrzCt9hwEeWTfnayAR+A9QIGdzABwcHXB2yTyjI9foiPzz8fEB4Pr162btdy7fq1OnTvHHH3/wyy+/8PTTTxvtCQnmibmXlxfh4QXzr9G9e3cmTJjAjh07OHPmDGlpaTz//PP31FdoaCjVqlVj7ty5aK2pUKFClrNa+aGUytK2ZMkSunTpwkcffWS0HTlyxGydvL4+ffv2JTo6mueee45ff/2VBg0a3HOsovTx93cjNsaKiMhCKxaaJx4eHnTq1AkwFTJwcnLi8uXLtGjRgiNHjnDp0iXKlCmDs7OzReMUQpR8Ja0YwUilVJhSapJS6lWl1DCl1GZMCcxu4Pavhf8LHAWMTxJa61+11kvvvGEqVR2dsbyTAuJg7/Dv0DWpqiTuQaVKlfDx8WHlypVm7T///HOB9J+Z0Njb2xtt586dMytEANCiRQsWL16c49mh3M5M3S4oKIjatWuzaNEiFi1aRKtWre461Cy3vvv168fcuXOZN28evXr1wtra+q77z+0s150SEhLMXh+ABQsWmC23aNGCyMjIPE3Y+u2339K+fXvatm3LoUOH8hyHEJ6+5fAok8a56xhDTC3toYceomzZslSoUIG9e/eilGLOnDksXboUrTULFy4kLS3N0mEKIUqoEnVGB9gCBGEqK+0FpGEabvYhMEVrXaROm1hbW+PhlFF17Y6JFIXIC2tra4YPH87w4cPx9vamSZMm/Pzzz8YH5PzMT5OdmjVr4ufnx9ChQ5kwYQIxMTGMGTOGihUrmq03ZswYQkJCCA0NZejQoXh5efHHH3/g5eVFv379qFGjBgAzZsyge/fuODk58cgjOV9D0K1bN7744guioqL47rvv7hqjnZ0dAQEBLF68mNq1a+Pg4ECdOnWMBKh3796MHDmS1NRUY2hNbs/56tWrzJkzh9q1a1O2bFmjHHZ2WrVqxZdffknDhg0JDAxkwYIFnDp1Kss6bdq0oWfPnowePZrHHnuM8PBwtm3bxowZM8zWtbKyYt68eXTu3JnWrVuzbds2s+u2hMhJGY8yeHqGc+WmHQkJCUWuWp+Li2mGh5EjTfNwR0VFsXPnTnr06MHPP/+Mo6MjrVq1smSIQogSpkSd0dFar9Rat9FaV9RaO2itnbXWdbXWk+5McrTWfbTWSmu9JQ/9+mutaxdGzF7uynRGp4DLgYrS45133uGDDz7g66+/5oUXXuDmzZt88MEHgOmC/Pthb2/P8uXLsbGxoXPnzowaNYr333/fGH+fqUaNGuzYsYOyZcsyYMAAOnXqxNKlS6lcuTIAlStXZvLkySxfvpwmTZoY83PkpHv37kRERGBlZcVzzz2Xa5zffvstERERtGzZkpCQEC5f/vcaBR8fHxo2bEiTJk2MhOtuunbtSp8+fRgxYgQhISGMHTv2ruuPHj2aHj16MHLkSHr06IGdnR1ffvml2TpKKVasWMHAgQOZOnUqbdu2ZeTIkZQtm319FBsbGxYtWsQjjzxCy5YtuXDhQq5xC+HhUYay3qnciLDm5q2blg4nV+7u7nz11VcopfDy8qJMmTIkJyfTokULkpKSSE2V+eWEEPdHFZXT28WZUsoViI6OjjYmL8yr+TO+Y+Db7nz/bRwv9Ml+bg+Ru6NHj1KrVi1Lh1FkDBgwgI0bN3Lu3DlLh2JxkZGRVKxYkenTp9O/f39Lh1Ps5fS/FhMTk5lYu2mt73/GylLofo4lALeibjFgwDIiI635cnIdatfLfmLcoiwtLY39+/fToEEDPv30U5KSkhg5ciTHjx+nevXq2V4nJ4QoOQr6WFLShq4VO25ujtjYaG5FJVk6FFFM/f333yxatIjGjRtjZWXFL7/8wuzZs/n4448tHZpFxcTEcOTIEb744gtcXV3p0aOHpUMSolC5ubpRzjuVE8ftiDxzGophomNtbW0U4Rg2bBjx8fGkpaXx8ssvs2nTJi5dukR0dLQU6hBC5EmJGrpWHDm6uOLsnE5ElFyMKe6Ns7MzO3bsoGfPnjz77LOsWbOGjz/+mKFDh1o6NIvav38/jRo1YteuXcybN6/IXa8gREGzsrIi4CE7rl+z4eZt5d+LK6UUzs7OWFtbs2fPHlxdXblw4QJHjx4FTMNGIyIiLBylEKIokzM6Fubo7o6zcyyRsXI6XtybgIAAfvvtN0uHUeQ0bdq0yFSeEsWPUiqnP544rbVLHvtoB4wEHgWSgE3ACK31mYKJMqtqtTy5dSuJGzEls5JnZrECrTV+fn64u7uzcuVKdu/ezaRJk4iJibmnYX9CiJJJEh0Lc3RxwdklnZg4a1JSUrC1tbV0SEIIIUy2AzPvaEvJy4ZKqeeBpcBfwHBM87YNAXYqpYK11oUyq6e3txdlylzgbJQD6enp9115sahSSjFw4EDAVL49s4pjq1atmDVrFj4+Ppw7d4569epZMkwhhIVJomNhjg7/zqWTmJgoiY4QQhQdp7XW3+d3I6WULaZ52y4AT2qtYzPafwH2A2OBgQUYp8HDwwPvcme4esOWqOgoyniUKYzdFCkuLi5G6eqdO3diZWXF3r17WbZsGfXq1WPOnDk89dRTBAQEWDhSIcSDVjK/6ilGHBwc/500NDEh9w2EEEI8MEopO6VUnoaq3eYpoALwf5lJDoDW+k9M8711y0iGCpyHRxm8vVO5ft2GyOPHC2MXRZq1tTVKKRo0aGAUZElJScHGxobjx4/z+uuvA8gkpUKUEpLoWJijoyPOLpq4WCsSEyTREUKIIqQzEA/EKKWuKaWmKaXc87BdSMZ9WDaP/Q64AdULKEYzZTzKUNY7jYjr1tw4caIwdlHsvPLKK1SqVAlfX1969+4NwIsvvsgvv/xCWlqaUdxACFHySKJjYQ63DV2TMzpCCFFk7ME0xKwz0BvYDLwBbM/DGZ4KGfeXsnkss61iThuHhoYSHBxMcHAwM2feeYnQ3Tk6OuJbQZkqr129kq9tSzo3NzcaNmwIwNy5c2nevDkXLlwwKlRu2rSJw4cPWzJEIUQBk2t0LMzR8d+ha/ExMseeEEIUBVrrhnc0zVNKHQQ+At7OuM9JZi3z7CZIS7xjnSy2bdt2z5XDlFJUq+bC/OuaW/LlWY7s7e0B8Pf3Z+3atQBcuXIFR0dHEhIS6Nu3LwsXLgQosQUdhCgN5L/XwhzsHXBxSScuThEn8wEIIURR9imQDDyTy3qZtZ3ts3nM4Y51ClzVqp4kxFtxVTlIifV8ePHFF2ncuDFKKfr06YOVlRXjxo3jiy++AODgwYNybY8QxYwkOhamlMLLDeJirYi7ddPS4YhSZvXq1SilOHv2LABnz55FKcXq1astG1gB2LBhA1OnTs3S3qdPH4KDg/PV19ixYylbtqyxfOLECcaOHcutW7fuO06AmTNn8tNPPxVIX3lVkn7XD4rWOgW4DJTNZdXM0tHZDU/LbMtuWFuBKFvWC6+yqVyOcyX29OnC2k2J5eDgwNNPPw3AqFGj6NevHykpKbz22mukpKRw4MABNm3aZOEohRB5IYlOEeDtaUdMrDXxsbG5ryxEIfL19SUsLIwnnnjC0qHct5wSnVGjRjFnzpx89TVgwADWr19vLJ84cYJx48YV60RH5J9SygHwA67msurejPvHs3msERANFFqlAC9PL6Py2rU9uwtrN6WCjY0Nrq6u2NrasnPnThwcHIiLiyMmY6h5r169uHLlCqmpqXL2TIgiSBKdIsDb14XEBEVMfHbDuYV4cOzt7WnUqBEeHh6WDqXQBAYGUrt27Xxt4+fnR/369QsporxLkMqMD4RSyiuHhyZgurZ11W3r+iqlaiqlbr/mZisQDgy4vXCBUupRoCmwJOPsUKHw8vTMqLxmw/WTUnmtoD355JM899xzaK156aWX8Pb25qeffqJfv36AaYhbfHyhjUy0qJSUFK5fvy5D+ESxIYlOEeBWxgNnl3Su3ZJvg0qzzCFVa9asISgoCCcnJ5555hkiIyM5deoUzZo1w9nZmeDgYA4ePGi2bXp6Ov/v//0/qlatir29PdWrV2fu3Llm62itGTt2LOXKlcPV1ZVevXoRHR1ttk52w5mUUkyfPt1svTuHcs2ZMwelFAcOHKBp06Y4OTlRt25dDhw4QFxcHH379sXd3Z0qVarwww8/5PpafPbZZ4SEhODu7k758uV59tlnOXXqVJb1VqxYQYMGDXB0dMTLy4t27dpx7tw5xo4dy2effca5c+dQShlj7m9/nQHOnDmDUsq4GDlTWloaPj4+jBo1Ksvz3bJlC88++ywAAQEBKKXw9/cnMjISBweHbF/3gIAA/vOf/2T7XJs2bcr+/fuZO3euEWvmGSd/f3+GDh3KhAkT8PPzw83NDYCwsDA6dOhAhQoVcHZ2pm7duixYsCBL3+fOnaNHjx6ULVsWJycn6tSpY1xgnZ0tW7bg6urKBx98kOM6pcRIpVSYUmqSUupVpdQwpdRmYBiwG9NkoJn+CxwFGmQ2ZCQxbwOVMFVpe10p9R6wAbgOjCnM4J2dXfCtANeuWhN5U4ZEFxalFK1bt8ba2poXXniBadNMfxaffvopFy5c4MKFC8yePdvCURas/fv3M378eLZt2waYjj0ASUnyRa0omiTRKQKcXFxwdU0nIs5GTn2XcufPn2f06NFMnDiRmTNnsmvXLgYOHEj37t3p3r07S5cuJTU1le7du5v9rbz55ptMnDiRgQMHsmbNGjp16kS/fv3MEpYvv/yS8ePHM3DgQJYuXYqjoyMjRowo0Ph79+5Njx49WLZsGVprOnfuTP/+/alQoQJLly6lYcOG9OrVi4sXL961n4sXL/LGG2+wcuVKvvvuO9LS0mjSpAlRUVHGOvPnz+f5558nMDCQxYsXM3v2bKpXr87169cZMGAAPXv2xMfHh7CwMMLCwoyk5XYBAQE0aNCARYsWmbVv3bqVq1ev0q1btyzbPPbYY0yePBmA5cuXExYWxooVK/D09KRTp05ZPths2bKFs2fP0rdv32yf69dff03NmjVp166dEeszz/x7rfvChQvZunUrX3/9tRHnuXPnaNKkCf/3f//HqlWreOGFF+jbt69ZEnnt2jUef/xx9u7dy+TJk1m1ahX9+/fnwoUL2caxfv162rVrx/Dhw5k0aVK265QiWzANL+sNTAXGAZ7Ah0BTrXWup9a01kuADpgqr00G3gW2A0201oV2fQ6YPoDXqO7C1as23LKxQicnF+buBKbX3MXFdPJu/vz51KhRg+TkZJRSAAwZMoSdO3eitTZ7Hytujhw5wurVq0lNTQUgMdFURHDOnDkEBQWxceNGS4YnRBZSXroIcHJ0wtU1jehYaxITE3B0zLHqqMiHbTu2ERFx3WL7L1vWm9AnQvO1TWRkJGFhYQQGBgKmIRCffvopc+fOpVevXoDpDMEzzzzDsWPHqFWrFqdOneKbb75h9uzZxmR4LVu2JDw8nHHjxtG+fXvS0tL4+OOPGTRoEBMnTgSgTZs2tGrVikuXCu4z17Bhw4wYMuNs2rQpH31kqsTboEEDli5dyqpVq3jttddy7Ofzzz83fk5LS6NVq1aUK1eOlStX0qtXL9LT03nvvffo1KmT2Yf7Dh06GD/7+voaQ/Hupnv37owdO5akpCSj5OyiRYsICgrKdoibm5sbNWrUAKBevXr4+/sbj/Xv35/WrVtz+vRpqlSpAsDs2bOpX78+jzzySLb7DwoKwtnZGW9v7xxjXb16NQ4ODsZy9+7djZ+11oSGhnLx4kW+++47evToAZhew6ioKPbv34+vry8ALVq0yLb/n3/+ma5duzJx4kSGDRuW7TqlidZ6JbAyj+v2Afrk8NhqwCLVHmo/4sG1q5Hc8i5P8p9/Yt+gQe4biQIVGBhovJe/9tprlC9fnjNnztClSxf279/Pnj17KFu2rPFeUdTNnj2bmTNnYmtrazwvJyfT55WTJ09ib29vnPlOSUnB1tbWYrEKkUkSnSLAyckJV9d0YmKsiI+XRKegRERc59LlQv3itMD5+/sbBxCAqlWrAtC8efMsbZcuXaJWrVps2rQJKysrOnXqZHzLBqYPtT/88ANpaWlcuHCB8PBwOnbsaLa/559/nl9//bXA4r/9g3R2sbu7u+Pt7Z1rcvX7778zatQoDhw4QGRkpNF+ImOm9+PHj3P58uUcz5LkR9euXRk6dCjr1q2jY8eOpKamsnz5ct56661899WiRQsqV67M3LlzGTduHDExMSxfvpyPP/74nuNr0aKFWZIDcPPmTcaMGcPKlSu5dOmSMV6+YsV/i3xt3ryZp59+2khycrJs2TIWLlzIlClTGDx48D3HKYqWSn7eODre4HqSK7d27aK8JDoWlfnliIeHB3v3mmpV/PXXXwQEBODn58frr7/OjBkzsLKyMs4CFTUpKSkcO3aMhIQEqlatip+fH5MnT6Zr164cOXIEX19f48shSXJEUSGJThHg5OSEi2s6MdFWxCfE40VO18GK/Chb1rvY7f/OIgB2dnZZ2jPbMocMREREkJaWhru7e7Z9hoeHc+WKaYb0cuXKmT125/L9yi7O7J5TZuzZOX/+PK1bt6ZBgwbMmDGDChUqYGdnxzPPPGNsd+PGDYBcP8TnRcWKFXniiSdYtGgRHTt2ZNOmTURERJidNckrpRR9+/Zl1qxZjB07lsWLF5OamkrPnj3vOb7y5ctnaevTp4+RDAYFBeHm5sY333zDypX/noS4ceMGISEhufb/888/G8PuRMnh5eVFeZ9Url614fqZfWT9KxKWkjkB6SuvvAKYioy0adMGa2trPvroIxwcHBg6dCgHDx4kKCgIG5ui8VHt8ccfN4b7vvXWW2zduhV/f39u3LhBeHg4jRo14q+//mLjxo2EhobSqFEjrK2ts/SjtSY9PR2llEzGKgpd0fjvKeWcHJ1wc0vn1k0r4qKiwK+SpUMqEfI7bKy48vT0xMbGhp07d2Z70ChXrpxxpufatWtmj925nB17e3uS7xjjf/tZloK2bt064uPjWblyJc7OzgCkpqaa7dPLy/RlQHh4eIHss1u3brz33nskJCSwaNEi6tWrR7Vq1e6pr759+zJu3Dh+++035syZw3PPPUeZMmXuObY7v91NTExkzZo1TJ8+nVdffdVoz7woOJOXl1eeXp9p06YxZcoUWrVqxbZt24zXVhRvnp6epkTnig03Eq6i09JQ2XzoFJbn6OhIly5dABgxYgRxcXGkp6czePBgfvnlF06dOsWFCxeMIiiWcvToURISEmjSpAkPP/ww1atXx9bWls2bNxMREcHGjRu5du0aN2/e5OOPP6Z3797GZKu3U0plmwAJURgklS4CTGd00oiJsSYu45t3IfKqefPmpKWlERUVRXBwcJabnZ0dlSpVwsfHx+wbfzBdTJ8bPz8/jh49aiynp6ezefPmAn8emRISErCysjL7FjPzzEimGjVqULFixSwVzm6X25mj23Xp0oWEhARWrFjBihUrcj2bc+dZtdtVqlSJ1q1bM2bMGHbs2JGn4XX5iTUpKYm0tDTjeiKAmJgYfv75Z7P1WrRowfr167l69e5Tvri5ubF+/XqUUrRp0yZLJT5RPDk5OuHnB1ev2BDl6UHKH39YOiSRB7a2tnh4eGBlZcX27dtxcXEhNTXVeP/r1q0bZ86cITEx0ZjL50E5fPgwSqks1xseOnSIS5cu0bNnT6ZMmcKGDRt44403mDdvXpbiBGfPnuW///0v48aNY8uWLWbv67fTWhtnfoS4H5LoFAH29va4uWliYqyIteDF86J4qlGjBq+++irdu3fn448/ZtOmTaxZs4ZPPvmEAQMGAGBtbc2IESOYOXMmo0aNYsOGDQwaNMgsgclJp06dWLBgAV9//TXr1q2ja9euhfphODNx69u3L5s2beLLL7/kvffeMxsCZ2VlxSeffMKyZct48cUXWb16NWvWrGHo0KHs27cPgJo1a3L16lXmzJnDvn37OHv2bI77LFeuHE2bNmXYsGHcunWLrl273jXGzPH2M2bMYPfu3Rw6dMjs8f79+7Njxw78/Pxo1apVrs+5Zs2abN++nfXr17Nv3z5jaF523N3dCQkJYfz48SxbtowVK1bQsmXLLEMX33nnHTw8PHjyySeZO3cumzdv5quvvuKTTz7J0qeXlxcbN27k1q1btG/fvsTOAVKaKKWoUcNUeS2qYkWSf/vN0iGJexQcHGwMLR0xYgR+fn7s3r3bKJm/du3aHKspFqSTJ0/i6elJzZo1AYyzMvv376dOnTq88sorVK5cGRsbG7p27UpSUpLZpMpTpkyhcePGLFmyhF9//ZVOnTrx5ptvEhcXZ6yTOVdYZqn920cppKWlSeIj8k0SnSJAKUU5L2tTMYKogplpXZQuX331FaNGjWLevHm0a9eOPn36sGbNGkJD/x2+N2TIED744AO+/fZbXnjhBWJjY7P90HunMWPG0KVLF0aOHEmfPn2oW7euMTFeYXjkkUeYPXs2u3fvpn379ixcuJAlS5Zk+SDfs2dPli1bxrFjx+jcuTO9evXi2LFjeHubro3q2rUrffr0YcSIEYSEhDB27Ni77rd79+7GOPPbK6llp3LlykyePJnly5fTpEmTLENK2rdvj42NDb17987TGPSRI0dSq1YtunbtSkhICKtWrbrr+gsXLiQgIIBevXrx9ttv88ILLxhV+TJ5e3uzc+dO6tWrx5AhQ2jfvj0zZ87koYceyrZPX19fNm3axNmzZ3n++eezDFcUxU+dOmVNZ3QqVCBhyxZLhyMKQP369bG1teWpp55i6dKlABw7dozY2FguXbpkvDfHx8cX6HQV4eHhXLx4kcDAQBwdHUlPT8fKyorExEROnz5NrVq1qFTp32H3iYmJJCUlGW3btm1j9OjR9O3bl++//55Vq1Yxa9Ys/ve//5mNLBgyZAhPP/00mzdvZtmyZaxcudJIfqytrbMkPjIlh8hV5ulBud37DXAFdHR0tL5XM79boN3df9RLp352z32UZkeOHLF0CEIY1qxZo5VS+uTJk5YOpcDl9L8WHR2tAQ246iLwvlwcbwVxLLndkaNHtGeZH4q6wuAAACAASURBVPSEj6bpI4FVdHpCQoH0K4qmmJgYvW3bNq211m+88YaeNWuWTk9P18uWLdOpqan31fdff/2la9asqYcMGaK11jo5Odlor1Wrlh41apTZ+nPnztWOjo46PDxcJycn60GDBmk/P78s/bZt21a//PLLOj4+XmutdZs2bbSXl5du2LChbtu2rVZK6b179+p169bp9957T1+6dElHRkaa9ZGenm7cp6SkGMuieCroY4kUIygiypV3JjEhieik7MerCiGKvsuXL3Py5Enee+892rVrZ5TYFsISvL29Ke+TypUrNtwq60XSli04PP20pcMShcTFxYUnn3wSME0QnZaWRmxsLKtWraJTp04sX76c1NRUunbtSlpaWr4KAlSpUgV/f3/++usvtm7dSvXq1fH19eXgwYNorXn44YeNddPS0vj999+pUqUKPj4+nDt3jmPHjnHp0iUqVqzIo48+ytNPP02jRo2oVq0a+/btw9HRkcTERA4fPoyVlRXjx4+nTp06XL16lUcffZQhQ4Ywd+5c4uPj+fvvv9m7dy8vvfQSn376Kc7OzqSmpmJjY5NjhTqtdZEt2y0KlwxdKyLcXF1xcUnnWoLUnheiuJo5c6Yx7820adMsHY4o5cp4lMHHN42rV2y4FRBA0po1lg5JPCBKKWxsbHB1dWX27NkZ12zVoEaNGmitqVq1KpGRkYSHh3P48OFc+3NxcWHo0KFYWVnRrFkzRowYAUBYWBhKKWrVqmWsGx8fz759+2jYsCFgKqBy8+ZNXnnlFaZOnYqnpydff/01zZs3Z9q0aUZysn//fuLi4njttddo3bo1Pj4+PProo8THx3Pu3DnS0tKIjo5m3LhxjBs3jvnz57N27VpjDrSqVavywQcfZHuNYWaSk56ebsw7Bhg/79q1i/Xr15OUlHSPr7goquSMThHh4uKCi+sVIhPtjW8mhBDFy9ixY3O9FkiIB8Xa2prAQAeOH9dEtwoi8X9zcPvyS/lmu5S6/azLwYMHcXV15ddff2Xv3r08/PDDTJgwgRdffBF/f3+jGMDtWrZsScuWLQGMudn8/f0JDg6mSpUqxnpXr17lyJEjxgTEVatWJTk5mWrVqtGlSxe6dOlCSkoKV69e5Y8//qBs2bKAKWkqU6aMcW1pcnIydnZ2nD17llOnTtG0aVNmz54NQGBgIIsWLeKDDz4gICCA/v37c/z4cb788ktq1qxpds3in3/+SWpqKrVq1TKmLMiU+RxnzJjBli1bWLt2rdnrJIo/OaNTRDg7u+DqmkZMtDWxsbGWDkcIIUQJEBTkzpVwW256liH13DlSjxyxdEiiCHB1dQVMycv7778PQK1atfD09GT37t20bdsWgAMHDmRbot7HxweA4cOHM2/ePFxcXIzHbt68idaaOnXqAKYqmZ06dWLKlCmsWLGChIQElFL4+Pjw7LPP8vjjjwOwb98+/Pz8CAgIMLYDOH78OImJiWZFXzLPWHl6erJw4UK6devG4MGDqVOnDrNmzTLWCw8PZ9KkSfTr1w9fX18ee+wxI1m6fR/Xr1/nscceIzAw8H5eVlEElbhERymlc7jlmj0opWyVUt8qpfYrpSKUUklKqTNKqUVKqXqFGbeLszOubunExFgRc1Xm0hFCCHH/6geX50q4Dcka4jw9Scylop8ovTp37oyHhwePP/44ixcvBkylq48fP05sbCyvvfYaWmuzoV/ZCQkJIS4ujrp16xptH330ER06dGDo0KE888wzvPvuu3z44YdMmjQpsxAHhw8fpmrVqvj6+gL/JiHHjh1Da01ISIjR340bN/jnn3/o2rWrcUbIy8sLd3d3I4kD07xEgwYNYtWqVWzcuJHGjRvzySefsGnTJmOds2fPcvHiRSpXroyDg8P9vISiCCpxiU6G7cDLd9z652E7OyAY2AlMBF4H5gGPA7uVUs0LJVpMQ9dcXTMSnfPnC2s3QgghSpFqVX2wt9fcjLQmttlTJGaUJBbibtzc3ABT6fvQ0FC01rRs2RKlFKNHjzauQVy/fn2OpehvH/qmlGLq1Kl888031KlTh6NHjxIZGUlwcDBKKc6cOcP58+epXr06jo6OwL+JzsmTJ3FzcyMoKMjo7/z581y9epVmzZoB/1YQPnToELVq1TImIi1btiyBgYEkJycTEhLC9OnTCQwMZM6cOUZfhw4dIjEx0bjOKC9z9WQmfDKvT9FXUi8EOa21/j6/G2mt4zAlOmaUUt8C54FhQKFMCe/s7IKbWxoRETbEXAkvjF0IIYQoZcp6lcW3QgqXL9sQ88QTpCxdTuqpU9hIRUCRD66urrzwwgsAjB8/nqSkJFJTU5k5cybNmzc35uDq379/jtcZOzg40KZNG9q0aWO0ZSYKERERBAUFGcPWMvu4dOkSFy5coFq1atjZ2RnbHD16FHt7e2N4nFKKy5cvc+7cOR599FFsbGxITU1l0KBB7Ny5kxs3bhAdHU3t2rW5ePEizZs358aNG3h5efHHH39gb29vlkjl5Nq1azg7O+Ps7Jxj1brMOYZE0VBifxNKKTullEvua+bJNSARKFNA/WVhZ2eHpydER1kTc5dZ0YUQQoi8srW1pUqgDZcv2XKzgmlIUMKiRRaOShRn1tbWODk5YWNjw7Jly7C1taVy5crGRfwNGjTgxIkTXL9+nd9+++2ufWUmBCEhIYSFhdG5c2ez9rNnz3Ls2DGziY7j4uL466+/eOSRR7CxsTGG0v35558opQgKCkJrzTfffMPs2bN55ZVXWLVqFatXr6ZLly4kJCTg7e1tXFd0+PBhPD09qV69utm+AWNY3c2bN5k0aRKdO3fGz88Pf39/3nrrLU6dOpXjc9L/zo8lLOiuZ3SUUgV19kJrrVsUUF950Rl4CbBWSl0HFgEjtdZRedlYKWWNKamxASphOpPjAqwtnHBN30b4VHAgKkoRFx9XWLsRQghRyjzyiBu/rE3hWnQUtk2akLBoEa4ffmjpsEQxk56enm01NjBVVsucN2zXrl3Y2dnx999/s2PHDpo1a8a4ceN4+umnadCgAVFRUXh4eGS7j8wkIfO+SZMmbN++HVvbf6feiIyMZN26dcaZocxkYteuXQQGBlKpUiWio6NZu3YtrVq1YujQocY8OlWqVGHcuHH4+/tjb29PTEwM586do1q1apQvXz7H5z548GCWL1/Oyy+/zNtvv82VK1f44Ycf2L9/P4GBgSilSExMZNeuXaSnpxMSEoK7u7sR3+3lrW9/fqLw5TZ0rWkB7edBprR7gCXAKcANaAe8ATyllGqstc5LSbNawKHblqOA/2bcchQaGmqcyhw4cCADBw7MV+B+FRyJikohLpcL/UTJ1KdPH/7++2/27dtn6VDuyeLFi4mPj6dPnz5m7U2bNqVs2bIszce1AXe+Fnv27GHt2rUFVrr5k08+oUGDBjRt2rRA+suLLVu20KxZMw4dOkTt2rUf2H6FCK5fnv99F01S0i2Su3VFvfX/2Tvv+JzO//8/r9zZewoRGjtqxSZq1kqNj1liFDWq5WO36IdaLbpQqz5Vo+mHL1VCi1/tEWqlCDWSGBGRkD1l3vf1++POfZoQhCRinOfjcR7JfZ1zrut9DrnPeV/X+/16jyf74kVM6tQpbdNUXiIefDmXUqLT6R4K4TIk9NetW1cJLfPx8eGNN94gIiICHx8f/v77bwICArCysqJBgwaPHdfDwyPf5woVKrBlyxbKlCmTz65t27ZRsWJFLCwssLKyIjs7m7S0NLKzszExMSEjI4OVK1cCUK1aNQAuX75MUlKSsprzYNiZEIJTp06xadMmFi1axIQJEwB9/Z2mTZtibW2NEIKEhAT69+/PlStXuH//PllZWQwZMoRFixblc9IKuodSynztd+/eJSIigmrVqinOksqzU5gcnT+AL4swxjSgYxHOfyqklE0faPITQlwAvgDG5/58EjeBDujFCaqiXx2yA8yAnEeddPTo0XxqH09LeXdrUlOSSNW8qqlTKq8yv/zyC7GxsQ85OitXrsz3RV8YZs6cSXp6uvL59OnTzJkzp1gdnbFjxz5XR0dFpbRo3KQCycnXycwUJLVshr2pKffXrMFuyZLSNk3lJeHGjRts3bqVMWPGYGlpCeidgAednLzOT1hYGJ9++inDhw/n7bf/Ceq5eFE/jxwbG0tWVhZarZY2bdqwZ88e0tPT0el0uLi4PNIWIyMjpdaO4TPAZ599hp2dneJo9e/fn5kzZzJmzBi8vb3ZsmULhw4dolGjRkooXFBQEFLKx+bn/Pbbb3h4eDBw4EClTaPR0KiRPqU7OTmZESNGcOrUKX788Ufq16/P4cOHmTRpEm+88QZTpkwB9CFyY8eO5X//+x+ZmZmKSpxhtUer1aLRaNixYwc7duxg7ty5yhgqz05h3qjvSimPPOsAQoihz3puMfI1MAvoQiEcnVxRgv2Gz0KItcBZYBvQ6VHnFRV7exssLROIlTZo09LQPFDYSkXlZaQwCZ4P8qLUMkhPT1cUgFRUXlZcyzjj6qrlbpQx0ffTKNuzJ/d//hnbhQsRqpyuSiGIiYlh6tSpDBw4UHF0li5diqurK/369VOOy+v8WFlZYWtrqzgeOTk5/PHHH9SuXRsPDw969uwJ6FdRvvvuOywtLdm1axfnzp1j/vz5LFu2jD59+ihy00+if//++T77+vqSmZmJn58fgYGBzJgxg5MnT2JnZ6fUAfr777+xt7dXnlMFheVduHCBOnXq4OCQP03bsFIUEBDAqVOnWLhwoZJjVLFiRQICAli7di1TpkxBp9Nx9epVjhw5woIFCwgNDeX48eN4eXmxaNEimjRpoty34OBg7ty5o+Q8GVaZDCF6asHfp+NJQYJB6NXGisJt4EIR+ygSUspsIBJwfsbzU9E7OR2FECX2BmZtZYWdnY7EFFNSQkJKahiVl4jz58/z9ttvY2lpiYODAwMHDsxXvK1Vq1b5QiT37NmDEIJJkyYpbVu3bsXU1JT79+8/cpxp06ZRp04drK2tcXd3Z+DAgUrl67ysXr2aOnXqYG5ujqurK3369CEpKYmhQ4eydetWjhw5osRwG1Zf2rRpo3z5Hzp0CCEEly5dytdvQkICpqamrFmzBtCHrhlmstavX8+///1vAKXvNm3acOnSJYQQHDmSfx4mNTUVa2trli5dWuC1enh4EBcXx5w5c5T+Dh8+rPRvCE9wcXGhTm5oz65du+jQoQNlypTB1taWZs2asXfv3of6vnDhAt26dcPe3h5ra2uaNGnCvn37HnnfN23ahKmpKatWrXrkMSoqRUWj0VC5igmRd0y4d+8uViNGIOPjSd+2rbRNU3lJSE9Px93dnbCwMADOnTvHhAkTGDZsmHJMWloaa9eu5fbt2wC4uLiwatUqWrRoAUBkZCQffvghFy9ezCfLHB8fT2BgIHFxcfTt25f58+cD+pUaCwsLjh49qjgxQUFBxMbGFspmKysrxowZw6lTpzh79iy9evXihx9+YNiwYUo+zokTJzAyMlLC4/I6EQYbExISFLlqA3lFBnbt2oWzs7MSIZCZmYmJiQk1atQgOTmZqKgojIyMOH/+PAD3799nxowZ+Pv7k5yczNy5cwFISUmhd+/e/PDDD6SkpLB//37i4+OVFau8+VE6ne4hm1QK5rGOjpSyvpRydlEGkFJ+JqUs0WKbT0IIYQ64Aw+X9y08hmldx6JbVDBWVtbY2mlJSjIi5frDSh4qrxcxMTG0adOG+/fvs3HjRpYtW8aRI0fo0KGDUregVatWBAQEKOccPXoUc3Pzh9oaNGigzMIVRHR0NJ9++im7du1iyZIl3Lhxg3bt2uUrDPf555/zwQcf0Lp1a7Zv387333+PnZ0dqampzJw5k7Zt21K/fn1OnDjBiRMnGDFixEPjtG7dmnLlyinF6Az4+/sDKDN8eenSpQuTJ08GUPpeuXIltWrVolmzZvmqXANs2bKF7OxsBgwYUOC1+vv7Y2dnx/Dhw5X+8saIf/3110RFRfHzzz8rztLNmzfp1q0bP//8M1u3bsXb2xsfHx+OHz+unHf16lVatGhBVFQUq1atwt/fn549eyoP/QdZv3497733Hj/88AOjR48u8JjXFSHEwWLaDjx5tNeDWrVtiLxjTGxcLEatWqGpVIn7q1eXtlkqLwmJiYk4OjoqE2aGOjQ+Pj6EhoYCsH37dkaPHq3kVs6bN4//5IpeZGVlcf36de7cuUPDhg3z5aQEBQUxevRogoOD8405ZswY7O3tadGiBUtywyy3bt3KlStXSE5OZuTIkYDewSpsPZvevXsrMtkAq1atYtasWQWu3Bts7NGjB4cPH86XPyuEUEKyw8LCqFq1Kk5OTso+gJCQEFxdXcnMzESr1XL06FHat2/Pt99+S8uWLenQoQN9+vTh/Pnz3LhxAxsbG3r16oWVlRVCCIYOHUq1atXYsWMHaWlpbNu2jZiYGMW2ByW8tVotWq1WVXp7gFcqGUQI4SSlLEibeR76a/09z7Hl0OfdhEsp7+e2uQBxUsp8fzFCiLJAXyAVyD8VXYxYW1ljZ6clKVFD8iNejlQKT9KECWTnzqA8b0y8vIoc//7tt98C+lUaQ/G26tWr07RpU7Zu3Yqvry8tW7bkiy++ICYmBhcXFwICAhg+fDirVq1SVjYCAgLyxUcXxNq1a5XftVotzZs3x93dnePHj9OqVSsSExOZP38+EyZMYNGiRcqxvXr1Un53dHREp9PRrFmzR45jZGRE37592bx5M3PmzFHaN2/eTMeOHXF0fHgewcXFRZlte7Dv4cOHM2HCBJYvX65Iha5bt45u3bop1bIfpH79+hgbG+Pu7l6grWXLlmXzA/K7Y8eOVX7X6XS0bduWS5cusWbNGmW2cs6cOdjZ2REQEKA8NDt06FCgDatWrWL8+PH4+fk9FG6hArycQjgvNI0alWH3rjh0Oh1x8XFYffABKdOmkR0UhEm9eqVtnsoLjpOTE3fv3sXFxYWDBw9y5coVhg0bhlarJS0tDa1Wy6pVq+jVqxc9e/YkNjaWrVu3KoIB3333HVOnTsXa2pqFCxfSpUsX2rRpg4mJCVeuXMHFxUUp2JmTk5MvBE6j0SihZobVj9TUVLp37w7on5VmZmZMnTqVH3/8kQEDBjx2Yi8vDRs2fOIxAwYMYOfOnUyePJmxY8dSoUIFIiIiaN++Pc7Ozjg5OXH16lUlr8hQ7ycgIIBmzZrh7OxMdHQ0f//9N3PnzsXBwUHJx8nKysLR0ZH4+HgqV66MkZER9vb2TJs2jaFDhyoFUA8dOsTw4cPp2bMnnp6erFu3jqFDhzJ16lQltO1RdX1ed141fbsZQogTQoj5QojRQogpuRLZU4BTwLI8xy4ArgBN8rQNBG4IIRYLIcbl9rEIvXNTFhhvcIpKAhsbG+zsdSQlaUiOiS6pYVReEk6fPk3Hjh0VJwf09Qk8PDw4duwYAN7e3mg0Go4dO0ZmZianT59mxIgRODs7c+LECZKTk7lw4QItW7Z87Fj/7//9P7y9vbGzs1OcANDPSIF+JSU9PT1fmMKz0q9fP4KDgwkKCgL0CakHDx7MF+ddWAxOwpYtWwC4fv06x44dK5KdXbp0eagtIiKCIUOGUL58eYyNjTExMWHv3r3K/QGUa3hSTs/SpUuZMGECmzZtUp2cx/MH0LYI257nb/KLS8u3KhIVaYxOB3fv3cNq1CiElRWp33xT2qapvARUqFCB6OhonJ2dWb16NZ6engwcOJDAwEDKli2Lv78/oaGhfPDBB4D+OzMyMlJZPRkwYAA1atSgfPnyBAUF4ePjw7x58zAyMuLw4cPUrVtXmawyNjZ+4ku7tbU13bp1A/QiBIY8mCtXrmBqaoq/vz8zZswA9CIAjwvdfhJubm4sXrwYZ2dnRo8eTZ8+fdizZ48SWdGtWzf++usv9uzZg1arJT09nY0bN3Ljxg169eqFtbU1oaGhJCQk0KSJ/pXTcH3Xrl3D2dmZ8uXLA3oVOI1GQ8OGDRFCULNmTUxMTAgODiYnJ4dDhw4RFRVF+/btsbKyIisrizVr1vDOO+/Qp08fli1bRkREBECBKzs6na7AVR+dTkda2qtZ2uSVWtEBDgNvAkMAJ0ALhAL/ARZJKTOecH4A0Bjoht6xMUUf7rYf+E5K+WfJmK3HwsICewfJtVANKSkpJTnUa8HLrigUFRWlJCPmxdXVlfj4eEDvHHt5eREQEICzszMWFhbUrVuXt956i4CAAHJyctDpdMqqQ0GcOXOG7t2707NnT6ZNm0aZMmUQQtCsWTMyMvR/MnG5RWwLmxT6OJo3b07FihXZvHkz9erVY+vWrRgbG9OjR4+n7sva2pp3332XdevWMWzYMNavX0/ZsmXp3LnzM9v3YC0FnU5H9+7dSUlJYe7cuVStWhUrKys+++wzoqP/mZCIi4sr1P3ZunUrVatWpX379s9s42vCqyCE88JQsaITlpYQH6fh7r0ovOp5YTliBGkrVmA7fz6aChVK20SVF5j79+/j4ODAH3/8wZ49e9i3bx/Ozs5cu3YNBwcHVq1axVtvvcVbb70FQHh4OLGxscpnKysr4uPjmTJlCuPHjyc+Ph5ra2uSk5O5fv06FStWZMmSJaxevRo3NzemT59Ohw4dHllvxhBWnXfVB/6JhGjfvr0Skvzll18ydepUbGxs+Omnn5g5c6YiJFBY6tevz44dO5Rry8nJwc3NTRnL19eXjz76iF69ehEZGcnWrVvp27evEvVw9uxZ7O3t8wntpKWlcevWLapUqaI8Owz30yB/bbius2fPkp6ezq+//krDhg2V/Jzw8HBSUlLw8vIiKiqK1atXs3PnTlasWKHUNcrLg/fTsBp08uRJNm3axLRp05Tryrv/Zebltv4BpJQ7pJSdpJTlpZTmUkorKaWXlHL+g06OlHKolFJIKQ/naftLSjlQSllVSmktpTSVUlaQUvYraScH9HGd5cqZk5SoIUWnxlm+7pQrVy7fi7SBe/fu5QvxatmyJQEBARw9epQWLVpgZGSUr+3NN99UYocLwt/fHxcXFzZv3kz37t1p1qyZEiZgwHB+VFRUka9LCMG7776rhIdt3rwZHx+fZ5ZmHzFiBAEBAYSGhuLn58d7771XpCX8BxVtrl27xrlz51i2bBnDhw+ndevWNGrUKJ/8NejvUWHuz4YNG0hLS6Nbt24P9aGi8EoI4bxICCGoXsOY27dNiIyKREqJ1YQJICWp331X2uapvODExsZSpkwZ5s6dS9OmTWnYsCERERF4enqyYcMGAgMDGTx4MCYmJmRnZ3Pp0iUsLS2Vybpbt24RGxtLw4YNMTU1pWzZslhbWxMeHk54eDinTp0iLS2NOXPmYGxszOTJk7lx4wbwjyiATqcjISEB0DsABX3PG461sbHhjTfeAMDPz49atWphZmamCMyMHj2aTZs2IaXEz8+v0Dk+oFdUq1y5svLZ0dGRhQsXMmTIEI4fP05MTAyrV69WRGbS09M5deoUXl5eODo6Ku92V65cITExUXFqwsPDiYiIoFKlSlhYWChORkZGBiEhIbzzzjtKqJ2xsTHGxsZUrlyZSZMmMX/+fNatW0dAQAB3797lm2++USYqQZ8j9fvvvzN58mQWLVqkSHwb+P3339m9e7dyjsGRWrRoEXXr1i3wXeRl4ZVydF4F3MtbkpxkxH0bG3SRkaVtjkop0rRpU/bs2ZNvde/MmTOEhYUps2Sgd3TOnTvH7t27ldoCrVq14tSpUxw4cOCJYWvp6emYmJjke8HfsGFDvmOaN2+OhYUFP/300yP7MTU1zffF+jj69+/PjRs32LlzJ0eOHHliCJch5rmg/r29vfH09OT9998nPDz8oTo+RbXV4IyYmZkpbbdu3conRADw9ttv88svvzyxX3d3dw4cOEBoaCh9+vQhOzu7UHa8TrwqQjgvGg0a2BNx24S0tDSSU5Ix9vDAol8/7n//Pdp7RdHqUXnVMTU1JTo6mpSUFEU8JTk5GTs7Oz7++GPatGnDO++8A+jzZ86ePZuvMPKZM2ewtbV9qPhncHAwiYmJLFmyhNmzZ+Pr68uyZcu4fPky586dA/SrECkpKcyePZu2bdvi6OhIhw4dClS+fNzqg6urKz169CAhIYGvvvqKXr16kZKSwunTpzEyMmLNmjWsWLEC0CtoGkLTCkOFChX47LPP+PPPP9m7dy+DBw9W5KizsrI4c+YMnp6ewD/O2IULF9BoNIozeP36dRISEvDy8gL+WbUKDg4mISGB+vXr52uXUnLp0iU+/vhj2rdvz/vvv8/u3bvp2bMnhw8fVhzBpKQk2rZti6+vL2fPnuWnn36ic+fOTJkyBSMjI5YvX86mTZuoU6eO4sAZhA569erF559/ni+E/mVDdXReMN54w4akJA1pTo5kXyox3QOVlwCDRHSnTp3YsWMHGzZsoFevXtSpUyefakzLli3RarX8+eefiqNTr149TExMOHPmzBMdnQ4dOhAeHs6ECRM4cOAA8+bNe8ihsbe3Z+bMmSxatIhx48bxxx9/sGPHDkaNGsWdO3cA8PT05OLFi2zfvp3AwEAiH+OoN2zYkKpVqzJq1CgsLCzo2rXrY200PCC+++47zpw585A6z/Dhwzl27BjNmzdXjn1Sf7t27VKUdB4XKurp6Ym7uzuTJ09m165dbNq0iY4dOyox1QZmzZpFUlISrVq1YvPmzezfv5+vv/46n9CDgcqVK7N//35Onz7NoEGDnmo2UUXlWfFu4cbtcH24juHv02bWLGRmJqkLFpSmaSovOBUrVqRx48b07t1bmWhzdXXl2rVrJCQk8MEHHyihYAkJCQQFBeWbkDt+/Dg1atTIJxKj0+k4deoUlSpVUpwk0D9vypUrx/Xr1wF9iNeECRP4/PPP8fHxYenSpdjY2DBx4kSuXftHofbIkSNs2rQpn+xy3sgYg4OwfPlyli1bRmpqKra2Q3BK6wAAIABJREFUtixfvhzQ59oYnkWffPIJiYmJBAUFsXjxYsWOZ8HOzo5Lly7xxRf6Mo4GZ+zPP/8kOTlZWdHJzs7m3r17St0hA0FBQWi1WqXWj+E6/vzzT3r37o2/vz916tQhLi6O6dOnM3fuXDQaDSYmJiQnJzNjxgyCgoLw9/dn8+bNHD58mFWrVinPnUOHDnHr1i1+++03nJ2dad68Obdu3QL0z6ru3bs/ZNNLpfAmpSy2DfAGJgCTgfaAKM7+X9QNsAFkcnKyLCqnA09LK8tN8qtvl8q4RYuK3N/rwuXLl0vbhCIzZMgQ2bBhw3xtZ8+elW3btpUWFhbSzs5O+vr6yrt37z50rqenp7S0tJRZWVlKW+fOnSUgb9269cSxv/zyS+nu7i4tLS3l22+/LUNCQiQgly1blu+4VatWyZo1a0pTU1Pp6uoq+/btK5OSkqSUUsbExMgePXpIBwcHCchZs2ZJKaVs3bq17N2790Nj/uc//5GA7N+//xPvhU6nkx9//LEsV66cFELI1q1b5zs+NDRUAnL16tVPvFYppQwMDJRNmzaVlpaWEpCHDh2SUsoCr1lKKU+fPi0bN24szc3NZdWqVeW6desK/PcKCgqSPj4+0traWlpbW8smTZrI/fv3SymlPHTokATkxYsXlePPnj0r7ezs5Pvvvy91Ol2hbC9tHvW3lpycLNGrnNnIF+B7+WXcivNZUhD37t2XNtab5JJl38n9B/cp7QnDh8s7pqYyuxDfFSoqBu7evStr1qwp27dvrzwHpJTy6NGj0sLCQv7xxx9KW506deS4cePynZ+WliZbt24t+/XrJ6WUUqvVSiml3Llzp6xRo4b8/vvvpZRS+vv7SysrK7ly5Url3PPnz0t3d3c5fPhwKaWUGRkZcsSIEVKj0Txk57Fjx2R2drbSf40aNeTgwYMVmw3tBREeHi737dP/rfTs2VPu2bNHpqamyhUrVhTyLj2a6OhoeeTIEeW5ff/+fdmxY0fp5eUlP/nkE3nmzBkppZRTpkyRjRs3lhcuXJBSSpmZmSmllHLUqFGyQYMG8tSpU0qfISEhsl69erJPnz5SSikPHz4sa9asKcePH//Q+BkZGVJK/XNICCGXLVsm/fz85NixY2VsbKwMDQ2VZmZmyvPxeVHcz5Li+nJ2B44DsYA/8AsQgV6trFZxjPEib8X5cAoOCZbly2+Un85YIW+MHVvk/l4XXgVHR+XZWbFihbSyssr3sFUpGVRH5+V4ljwKN7df5MxZy6Xfhp+Utuxbt+QdU1MZP2RIiY2r8upieGE24OfnJ4UQMiYmRmlzdXWVgwcPlikpKUpbeHi4dHFxkUuXLpVS/uNwfPHFF/LNN9+UBw8elFJK6evrK5s3by4jIiLyjTNw4EDZqFEjKaWUt2/fll26dJE+Pj5SSqk4DxcvXpRCCLl9+3aZlpYm+/btK62treWkSZOe+jp1Op3UarXy3r17csGCBVJKKWfPni3XrFkjpZTywIEDihPyrISGhso5c+bIRo0ayblz50oppWzatKls2rSpcu8M96ldu3bSx8cn3/fFr7/+Kk1NTZV7umzZMlmtWjX522+/SSmlzM7OfmjMlStXSmdnZ8WRMvT/66+/SjMzM3n27Fnl2Pj4ePntt9/KQYMGya+++koGBwc/9nq0Wu1TT+IV97OkyKFrQgh74BBwGagIjAecAQ9gK3BYCPFGUcd5XbC1scHBIYeEeA1Jd9UcHRWVxxEWFsbevXuZP38+Q4cOfanjiFVebIQQlkKIm0IIKYRY/hTnvSOE+FMIkSaEiBdCbBFCVCpJWx9H7VqW3A43ITExUZHcNa5YEeuJE0n/6Sey/ixx3R2VV4y8+YsAgwcPJjQ0VAlT0+l0fPzxx+zZs4fevXuzcOFCQJ/rmJycrNQzM4R0Xbx4EScnJyWk66+//qJRo0aKKI4hrzExMVGpSRMdHc3Vq1dp2rQp8E9417Fjx3BxcaFChQoYGxsTGxtLZmYmK1aswMjICB8fH6UI5+PQ6XT6l2YjI8qUKcO0adMAmDhxIv/617/QarVKUdN9+/Ypv0dFReUrvP0kqlatymeffcaZM2eYOXMmUkrmzJnD+PHjFfltw33q0KEDf/75JwcPHiQiIoLff/+d2bNnk52drdTOy8zMJDU1VVF7y5vDZLBr//791KxZU6l5ZMjXPXLkCDVq1FAU4c6ePUv37t2ZP38+aWlprFu3jr59++bLV9VqtaSmpiqfjYyMHhL4ed4UR47OdCBMSjlS6mvMWACtpZQ5UsrPgN+AL4thnNcCG1tbHBy1JCRoSElKQj7FH4iKyuvG7Nmz6dq1KzVr1mTevHmlbY7Kq81c9JN4hUYI0QvYif65+DHwNdAKOC6EcHvcuSVFkybO3L6dm6cTdUdpt54xAyN3dxLHjEHmyXFQUXkW8sooGxkZMW7cODZs2ED58uUVxa+jR4+SlZVFpUr/+P1paWlERETg4eGh1HMz5NwY8kQMSfbnzp3D09MTjUZDWFgYsbGxSp6q4eV63759VKlSBScnJ0xNTWnevDlvvPEGv/zyC7t27aJ///4P5Z8YMCi8Ga7hQaEDKSU2NjY4OTmh0Wj47bffMDU1pWbNmoodI0aMICgoiMjISL766iuApxKgEULQqVMnfH19H9o3YsQIWrRoga+vL127duW7XPVEW1tbJVe1fv363L17V7E97zUYfg8MDKRBgwbY2dnl6//EiRN4eXlhb28P6Iu1JiYmsnHjRjZv3szu3bsxMTFh6tSpSsmL8+fP89ZbbzFt2jQuXrzIkiVLFFGJ0qI4HJ3ewBIhhKMQwhGwBxBCOOR+XgF0F0K8ajV7SgRLC0ucnCTx8RrSbGzIuXq1tE1SUXlhWb9+PVlZWezbt09RuFF5/RBCeAshJgghJgsh2otinkIUQjRAn3866ynOMUFfpPo20FJKuVJKuQDoBLgCs4vTxsLSslUF7kToVQzzCoYYWVtjt2gROefPk5arPKWiUlyYmJjQvn171q5dq6h6Dhs2jJ07d+Ls7GwI3eTvv//m8uXLithLRkYGHTt2zCd9bGRkxPbt24mKisLHxwdAES5o3Lgx8I9q2MmTJ6lfv75SkuHkyZNUr16dJk2a4OPjw5AhQwosbXD//n169+5N//79OXfuHAsWLGDv3r35lNiEEAWuVri7uys1fHbt2kWDBg0QQiiO27///W/8/PyQUrJ8+fJ84glPg7OzM7t27eLSpUssXLiQxYsX07dvXypXrqw4MXXr1qVatWq8//77XLlyhYyMDJKTk9FqtQghSExM5Pbt2zRo0EBx+IQQJCUlceXKFZo0aYK5uTkXL17k4MGDTJ48mY4dO2JiYoKHhwcfffQRly9f5u+//wb0q3SZmZn4+fnxzTff8H//93+0bNmyVCcii8PRcUc/WxULxACGdW/D546AGU85E/a6IoTAzc2UhAQNaY6OZP/1V2mbpKKiovJCIoRwF0IcRx850BpoCqwH/hZCPFxt99nG0ACrgT+AbU9xamvADfhRSqnEckgpz6Mvbt0v1xl6rjRu7EzkHVN0OrgTeSffPvM+fTDz8SFl+nRyHlA2VFEpbsqVK6eorRkchooVK/LZZ5/RqVMnQL+K07dvX7Kyspg4cSLHjh1j3rx5fPDBB7Rv3x4fHx8yMjK4ffs2Tk5OWFtbo9Pp0Gg0xMfHExUVRb169RRn5urVq3h6ej62thzow+JSU1PZvn07S5Ys4ejRo3Tt2pVPPvkEgDt37uDv78/Vx0xG51UmK1euHAMGDADg+++/p3///mRkZBAeHo5Go+GHH35QnIGDBw+SnJxc6PtYqVIlOnfuTJ06dfjss88IDAxUxnd2dubnn38mJSWFLl26MGDAACZPnszq1asBvaIb8ND9uHLlCunp6UrdoStXrpCWlqb8exkcUw8PDxITE7GysgIgJCSE4OBgxo8fz8yZMzly5AjDhw/nq6++Uux63hSHoxMDpAGNgSbAu7ntTXK3w0AOEF8MY70WVKhoQUK8hvsuzqqjo6KiolIAzzE/dCLgCYx9yvMa5/48UcC+k4AtUL0Idj0TDg6mODpqiIk2JjYuVsnTAf3Lpv2PP4K5OQnvvaeGsKk8d8qVK8f48eNp06aN0tamTRvmzZvH/v376dChAxs3bsTX11epeWNubo5GoyEjI4O0tDRlNWPjxo2YmZkpoXFXr14lJSWFGjVqKFLYjyIqKoqLFy/Sr18/pk2bxrZt25g9ezY//PAD06dPZ968eSxcuBAvLy/+/e9/Fyg9bShq+uCqjxACU1NTLCws+OqrrxBCMGjQID744ANAX0Q7NTWVc+fOMW7cOABu3LhRaOfHcP0ajQYpJU2aNGH//v3MmjULJycnnJ2dlbITJiYmVKlShT/++IPbt28rdeNOnjxJmTJllFWopKQk7O3tsba2VlaDdDodUVFRGBsbU65cOaSUBAcHU6lSJSZPnkzVqlUxNzdn5MiRpKenl1qB7OJwdHYCQ6SUf0kp/wL+Bsjz+V/AQSll4SsvveZU8rAhIV5DWhkXslRHp9AYZhhUVFRKhhfsb6zE80NzRQPmAHOllGFPebohB+dOAfsMbeUL2EerVq1o1KgRjRo14ocffnjKYZ9Mgwb2hOfW04m4E5Fvn8bNDfvvvyf79GlS1Lw3lRcAIyMjhgwZQmhoKNHR0ezcuZMlS5ZQrVo1pRZM27Zt0Wq1TJs2jcOHDzN9+nRmz55NvXr1lGT648ePY2trq4gcPK5+mSEUa/LkydSsWRMLCwuGDh1KRkYGR48epV+/fmzfvp3FixezcuVK5XjQF/icMWMGo0aNYvny5YSEhDxyHMOqj4WFhSIG8N///hc3NzcqV67M+++/r7QdP36cjIwMRo4ciZSS5OTkJxY1NThZrq6uDBkyhNWrV7NgwQKlSGnjxo0ZO3Ys27dv54033lDqvv3xxx/Url07X0i4sbExwcHBSo5Uamoqv//+O9WqVcPNzY07d+4QFhZGgwYNMDY2VsQOUlJSkFJiaWn5WFtLiuJwdOYBbYUQn+Z+zgbCAYQQQ9HPgn1SDOO8NlSu7EhamhHpRqakhYSoggSFwMTEpNRmC1RUXhfS09OfOBP6HHke+aHfAzeBRc9wruGpnlnAvowHjsnH0aNHCQwMJDAwkFGjRj3D0I+nVevy3L6lV8q6fTv8of0W/fph8d57pM6bR8auXcU+vorKs2JjY0OVKlWUSRfD6oWPjw+ffPIJO3fuZOjQodSrV4/4+Hjc3NwU9berV69iamqqJN0/KC5gQKvVcurUKapXr07NmjWVF3ZDcdJhw4bRtm1bypUrh6+vL5aWlvkcnaCgIFJSUkhLS2PlypV07twZPz+/Asd61KoP6AuNenl5AfDll1/i4+ODTqejY8eOCCFYv3498+fPB2DBggXExcWRk5PzVCpvJiYmjB8/ntu3b3P9+nV69eoF6Ce1GjdurCi9tW7dGhsbGxYuXEhUVBSxsbF8+eWX7N27l8GDBwP6sLXo6GjFZsO/UWBgIM7OzqWWR1tkgQApZaQQohOwTQjhC+wBfhFCnAAqAz2llBeLOs7rhJOTPXZ2WhITNKRYW5ETHIxJbkVclYIpU6YMd+7coXz58lhYWJS6nKGKyquElJL09HTu3LmDq6traZtjIG9+aN6lptjcn//hn/zQu0/buRBiEPoc01ZSysLLJP2DISbMrIB95g8c81zxbu7C8uWWQCLhEeFIKR/6zrT7/nuyL1wgYdAgXM6cwbhq1dIwVUWlQB78/2piYsKkSZOYNGkS6enpWFhYcPPmTcqXL684Oi1btmTz5s389ttvGBsbU7169QIV19LS0ggMDKRJkyaYmJgojsPhw4cpX748zZs3V469cOECrq6uilACwLvvvsu7776rfP72228ZN24cDRo0oHbt2gDExMSwcuVKwsLC8PLyonPnztSoUeMhW6SUSqiYRqPB0tKSvn37AjBu3DjFmbCyssLS0pKDBw+yYsUKduzYwdatW6lTpw7Vq1cv8G/8QfKq3+3ZsyffvurVqzN37lw+/fRTmjVrhpOTEzdu3GDMmDGMHDkSgNDQUDIzM2nUqFG+f6OjR49Sq1YtRRDieVMsSmhSyvNCCE+gL/q8HHNgLbBZSln4jCoVAOztHHBw1BIfryG1jAvZp06pjs4TMNRPiYyMfCrpRhUVlcJhYmKCq6vri1SrKG9+KOhzc35B/wwCMOEZ80OFEGboV3F2A3eFEIa3fEOomV1uW6yUMvER3UTmOefKA/sM/RQU1lbi1POyJ/oeZGQIIJXEpEQc7PPPthpZWuK4bRsxDRsS360bzseOYfSEBG4VlRcBCwsLAKZPn56vvUOHDowaNYq1a9cyd+5cDh48mC8XyEBsbCyhoaGMGTMmX/uhQ4eoVauWEmIG+hA3ExMTKlSoAOjV2s6cOcP27dvRarW0b9+e5s2b4+XlpYSDBQcHM3r0aGJiYqhfvz4//vgjixcvZvDgwfnUyTIyMjA3N1cU5ArCkCszbtw4fv31Vzw9Pfn1118BfciYVqvl7t27tGnThqtXrxIYGIgQgoYNGz7dTQUGDBhAu3btOHDgADdu3KBdu3a0aNFC2X/u3DmklNSrVw/Qr5hJKfnrr7/o06ePIljwvCk2yefcHJwNuZtKEbCzs8XRUUdCgobUihXJOn4cy2HDStusFx5bW9sX6SVMRUWlZDHkh/YHEEKkgj4/NPfzAp49P9QCcAG65G4PMih3+xj45hF9nMn92RzY/8C+ZkAy8Ojg/RLE1NSI2nVsCb8VS/UaWdy+Hf6QowNgXKkSjv7+xHXqRFzXrjjt349RKb2sqKgUFQsLC2bMmMGMGTNIS0t75ArH5cuXSUpKUiSiDTkp58+f58MPP8xXb+bChQvY2NgoeT9z5szhxx9/pG7dupiYmLBt2zbu3buHVqulT58+ABw4cIDMzEzWrl1LkyZNiImJYevWrUrdnpiYGHbs2MGPP/5IbGwstWvX5t1336VLly4P1bqBf0LwRo0aRadOnVi/fj0AQ4cOVY45cUKviWKQlG/YsCFNmjRh586d5OTkEBYWhre39xPvYdmyZRk4cOBD7Tk5OVSsWJG6detStmxZQO+EhYeHc+vWLRo3blxqYc/FkaOjUsxoNMa4ltXolddqVCczIKC0TVJRUVF50SjJ/NA09BEKD24f5e7/I/fzb7njlRNCeAoh8ubcHAGigBFCCGtDoxCiHtAG2PKMIXHFwltvuRJ5R++03I64/cjjzFq3xmHjRrJPnSKhb19knhAdFZWXFUOoV0EIIZTCogZu3bpFUlISderUwdRUX4dKSklISAjly5dXCqQuX76ckSNH4ufnx7Zt27hx4wZffPEFpqamSkiXk5MT4eHhBOS+27m4uDB69GgmTJgAoEhNt2rViunTp2NjY8PSpUvZvXu3Mq4Bw+83btzA0tKSpk2bYmb2cLSsIT+me/fudO/eHYBNmzbh4uLC7du3OXr0KKCv8XPy5Emys7OVtsJgbGzMp59+ir+/f772mzdvYmZmhoeHR6H7Km7UIp4vKO7u5gSeySG1TBm0ISFoo6PR5FkuVVFRUXmdKcn80FwH5NcH24UQHrm/XpdS5t2/ABgCtEVfUgEpZbYQYjywGQgQQqxGLyk9EX3YXaGLj5YEzZo5ERBgDSQQcScCnU73yORsi1690P33vySNGkX8v/6Fg78/RqWkoKSiUtJ06dKFLl3yL+Reu3aNsmXLKnLLoM9JiYmJoVGjRpiamiq1Z9q1a6eEsoF+hcjKyor69esD0K9fP4KCgli0aBH79+9n7NixdOzYUXFQjhw5Qrt27Zg3bx5mZmb07NmTAwcOKDk0efNtDDWDTpw4gRCCypUrK+15/56llEqb4VzDsU2bNqVp06aA3tFxdXUlNjaWVatW0apVK3744QesrKwYOHAgZ86coV69eoqz9yRat25NWlraUwkkFDfqis4LSqVK1iQkaEgxMUYCWceOlbZJKioqKi8UucU3PYGF6HNyrNDnh1aTUj4YLvbckVJuAbqjV177BpgKBAAtpJSlkp9joFkzJ0KCJTodZGVlEXU36rHHW40cif3atWTu30+8jw+6pyhoqKLyMiOl5O233yYyMjJfeNetW7dIS0tTnBqdTkf58uVZt24diYmJhIWFsX79evz8/HjzzTfzrbTMmTOH//3vf5iZmTF06FD8/PwUx+Sdd97h4MGDrFmzBp1Oh6OjI3379lVWhAqakDhx4gRlypRRnCEjIyN0Oh03b94kISFBETMoKFwv7wpR9erVsbOzo1y5cmzcuBHQ5zZ5e3sjpeQ///kPmZmZBAQEKPlEwcHBpKSkPPL+CSEem2dU0qiOzgtK9eqOxMUak63TkenirDo6KioqKgUgpcySUm6QUo6XUo6VUq4uKREcKWWYlFJIKcc+0D40t/1wAefslFI2k1JaSikdpJR9pJTXS8K+p6FcOQvsHUyJidHnH4SF3XziOZbDhuGwYQNZf/5JrLc3OTeffI6KystOXufAkK8DegfgypUrDMvNoa5Vqxaffvopu3fvxtvbmw8//JDvvvsOMzMzmjTRa6REREQQHx+PiYkJbdu2xd/fn86dOzNnzhylRMaoUaPo1asXU6dOpUqVKqxYsSKfqlteDE7PuXPnqFy5Mm5u+vJdycnJfPLJJ3Tv3h03NzcqVarE8uXLC1xZMVyfQeHtQSpVqkSlSpUQQrB3715sbGyoUaMGXbt2BWDZsmVcvHiRuLg4JS8oMjKSuLi4wt/kEkR1dF5QatRwJjXViOwsyGjdmqzDh0vbJBUVFRWVV4jmzZ1JjHcB4GYhHB0Ai/79cdqzB21kJLFNmpD5FHH8KiqvGmZmZkqtGYAPP/yQoKAgpkyZwsiRI/niiy9wcHCgevXqAKxevZp+/fpx+PBh4uLiSExMVGr7GMQILC0tWbx4MTdv3sTHx4eFCxey6xH1rIQQpKWlER4eTrVq1XDKVUacPHkyy5cvp1evXuzatQtfX1/mzZv3UJ7P+fPnOXToEHFxccqqz4NotdqHiquWKVNGCcVbvnw53t7emJmZMWjQIAC2bt3Kli1bABg5ciSJiYkkJyeTmpr6bDe6CDx2LUkIcbAIfUsp5dtFOP+1xtlJLzEdF29MetMmZP+6Fe29e2henBoWKioqKiovMc2aOnHwUAI1at4lITGBxKRE7O3sn3ieWbt2uJw8SVy3bsS1a4fNnDlYT5uGKOAlSUXldcPDw4P3339f+Xwzz8pnixYtOH78OL1796Zs2bJYWloSHh7OrFmzcHNzY9u2bXh4eFCnTh2cnZ35+uuvOX78OJs3b6Z3794P5ecYGRlx9uxZsrKyFEGEoKAg/Pz8mD9/PpMnTwagfv36HD9+nIULF9KtWzeEEHz33XesXbuW1NRU7ty5Q5UqVRg+fDhjx47Nl4NTkPNTENbW1rRv3x7Q5/qA3qHq1KkTtra2HDhwgCpVquRzDJ8HTwqaa4O+ENuzVF+UTz5E5VHY2Njg7JxDbIyG+/X1/3kz9+3DMtdbVlFRUVFRKQot3nJh2fJg2uROSYaF3cSrXv1CnWtcvToup0+T9OGHpMyYQeaBAzj89BOaPEnYKioq5MvN6dixIx07duTmzZscPXqUyMhIunTpwpu5tRLXrFmDVqtl4MCB1K9fn8DAQG7evEm/fv2A/GF0hlWZEydOYGdnpzg6W7Zswd3dnR49egB6h8jBwYFOnTqxePFiAEJCQvj+++/p27cvI0eO5N69e+zcuZOAgAAmTZoEQHR0NKtXryYxMZHmzZvTunVrZcWosAghFFntDh06PPW9Kw4Kmx10GviZZ6gurfJsaDQa3MobExdrTJKZKUbOzmTu2aM6OioqKioqxUKdOnYkxOegzbFFY5zMzVthhXZ0AIzs7LDfsAGzjh1JGjuW6DffxOaLL7AaM0Zd3VFReQyGvJcHmTx5MmvWrGHixImAXnr6X//6Fx9++OEj+zp16hTu7u6KItzZs2d58803FafEsPITGxuLs7MziYmJxMTEkJmZScWKFZWtevXq3LhxA4ArV64waNAgtFotbm5ubN68GXNzc6ZMmcKoUaOK+3aUKE9ydP4P6IG+0nQD9LUD1gO/SSlzStY0lUqVLLh8KZP4+HjMOnQgc+9epE6HeIQEqIqKisrrjBDCCH0R0XWlbcvLgEYj8G7hTGKCOU4uyURG3iEzM7PAOhyPQgiB5dChmLZuTdJHH5E8fjzpGzZgt3Ilps9QfV1F5XWmXbt2tGvXDoDw8HDi4uKUXJgHMYSUhYSE0LJlS8qXLw9ARkYGFhYW2NjYAP8IFhw7doxatWqh0+lo0aIF77zzDosXL+b+/fsMHz4cOzs7Zazff/8dnU7Hli1bqFq1KmFhYfj7+yuiCI+To3/ReKyVUsqBQFlgNBAIdAW2AFFCiCVCCK+SN/H1pXo1W2JjjUlKTkLTqRO66Giyz54tbbNUVFRUXiiEEEZCiCFAMPBjadvzMtGqlQu3wiwA/cvLrfCwZ+rHuFIlHHfvxn7jRrRhYcQ2bkziqFFoY2KK0VoVldeHihUrPtLJMXD9+nUuXbqEq6srFhYWSCnp3bs3Bw4c4OrVq4De0dm3bx+BgYH06NFDKR46adIkunbtyueff07lypXZvHmzIjqg0WiIjo7m/PnzCCGoVKkSkyZNYuTIkUqfLwtPtFRKmSKl/EFK6Y2+XsFX6GsCjAP+EkKcE0KME0I4l7Ctrx21arkQG6v32NO9vUGjIWPr1lK2SkVFReX5IIRwE0IMFUJMzf3pVsAxA4Cr6OvnVAGin7edLzOtWrpw7my6knx87fq1Z+5LCIGlry9lQkKwmjiR++vWEV2tGqlLlyJz1CAQFZXixs3Njd9++03JxxFC0KVLF+rXr8+4ceP43//+x5w5c+jXrx/e3t4MGjRIyfOpUqV906EtAAAgAElEQVQKX331FSEhIbRv356pU6cSHa3/+vzoo4/o2LEjkydPZuTIkZw7dw4ACwuL0rnQIvBULpmUMkRKOR2oCHRBXznaE1gM3BFC/Fz8Jr6+1K5dlqREDTk5kKDLwbRtW9K3bs1X3ElFRUXlVUQIMR64DqwB5uf+vCGEGJW7v7IQ4gT6/NGqQCowK/d3lUJSz8ueiIh0HB08ALgVfovs7Owi9WlkZ4fdt9/icuECpk2akDx+PDFeXmQeLIqQq4qKyoNYWFjQtWtX6tWrp7R5eHiwaNEizMzMGDduHL/++iujRo1i3Tp9RG9AQACDBw/m6tWrZGRk4ODgwPDhwwkPD1ccHQsLCxYtWsS8efM4d+4c3bt35+BL+vcrivrSLIRwAtahD2uLlVKWKQ7DXiaEEDZAcnJyshITWRxotVrc3X9h5AfxdOrkhVfQRZI+/BCXixcxqV272MZRUVFRKSopKSnY2toC2EopH10muxAIIVoBhw1dAyGAHVAJvQqoD+AHuALZwErgCyllbFHGLW1K6lnyJLp1DaBrN0uytMcB6NyxM9WqVi+WvqWUZOzYQfLEiWjDwjDv0wfbb77B+I03iqV/FRWVx5OQkICdnZ0Sbnbw4EEmTZqElZUVzZo1w9bWlp9//hkHBweOHDmCVqvFzMxMWeWNiYmhd+/epKSkKCs7JUlxPkugCAVDhRA1hBALgCD0qzsAV4pqkMo/aDQaypYVxMYYEx8fh3mPHiCEGr6moqLyqjMm9+dKwFVK2VhKWR3wAq4BO9A7OReBulLKiS+7k1OatGzlwpUrAhMTE6Bo4WsPIoTAokcPyly+jM28eWTu2kW0pycpc+YgcyvBq6iolBwODg75cmratWvHxo0b6datG2fPnuX3339n4MCBrF+/HktLSz7//HN8fX25cOECaWlp6HQ6ypcvj6Wl5UOFQ18GnmpFRwhhC/gCQ9ErsQkgDtgIrJdSlryr9wSEEI+6oDQp5WOrFAkhHID30DtuNQFnIBw4AsyTUt5+xHklNgv3rx7+mJrG0bWbMUMGDyW2TRu0UVGUuXo1n566ioqKSmlSzCs6t9DXYqsipdQ+sM8H2AWkA5WllPeKMtaLRGmt6Jw8EcfYsX8xf6EkODQYY2NjRgwbqTg+xUlOeDjJH39Mxi+/oHnjDWy//RbzXr3U55mKSimSV0Vt48aNfP3110RGRlK9enW0Wi3BwcF8++23DB06tMRtee4rOkJPRyHERiAK+B5oBOwG+gJuUsrxL4KTk4cAYPAD2/BCnNcU+Bb9A3Y5MBb9dQ4CLgoh3iwRax9D9WrWxEQbk5ySTFZ2FpZDhqANCSH75MnnbYqKiorK86IMcO5BJyeXE7k/j75KTk5p0rCRAzdvpuHk5AFATk4Ot8JvlchYxhUr4rh5M06HDiFsbUno04e4Dh3IvnSpRMZTUVF5MnlXfAYMGEBgYCD+/v688847tGvXjsOHD/Pee++VooXPzmPr6AghvkC/wuGGfvXmMvo6Oj+/4A+YG1LK/z3DeVeBGlLK63kbhRC7gH3AXKBPMdhXaGrXdmLnTv2tjo+Pp0yfPoixY7m/bh2mzZs/T1NUVFRUnhdmQEJBO6SUibmz/2oB62LCxMSIt95y5nqoOSYmJmRnZxMSGkzVKiWn62DWpg0uZ89y/7//JXnmTGLq1cNqzBhsZs3CyNGxxMZVUVF5MhqNBm9vb7y9vUvblCLzpBWd6eidnED0MdPvAQeB8kKIBk/aStj2xyKEMBVCPDZU7UGklGEPOjm57fuBeOC5KwA0alyOe3f1/mhsbCxGNjaY9+1L+qZN6O7ff97mqKioqLwoqPKTxcjbb7ty8GAMVSrrnZubYTeV4oAlhTA2xmrMGMqEhGA5YgRpy5dzr2pVvRx1EZXfVFRUVKDwYgSN0IdynXmK7XRxG/sU9AHuAylCiGghxDIhhN2zdpZ7rg3w3Fexanq6kp0juJ8miInRy/5ZDh+OTEkhfePG522OioqKyvOirBCiVUFbIferPAVvt3flwIF71KheA9DH7F+7HvpcxtY4O2O/ahUu585h0qCBXo66Th0ydu1SyymoqKgUiceGrqFPxH/ZvmVOA1vQK/PYAu+gz7VpLYTwllKmPkOfMwAT4KfHHdSqVSs0Gn2Bz1GjRjFq1KhnGCo/RkZGuLsbce+eMTGx+grTpm+9hbGXF2lLlmA5fLiaxKmiovIq0il3Kwj5mP2SJz/bVB7gzTdtyc7WkXHfDmsra1LTUrkafJXateo8NxtM6tbFad8+MnfuJGnKFOK7dsWsQwdsFy1SSyqoqKg8E499GEgpPZ6THcWGlLLpA01+QogLwBfA+NyfhUYI0QeYDOxBXy/okRw9erRElHKqVDHn3j1jYuNi0Wq1aDQarCdOJHHIEDL37cO8Y8diH1NFRUWlFHkZJ9leaoQQSvhaHa8anD33F1F3o0hKSsTOzv652mHerRtmnTqRtnIlKXPmEFOvHpajRmEzdy4aF5fnZouKisrLzzPX0XnJ+BrI4p96P4VCCPEOsAH4C3hXltIaes2atty7a4xWqyUhUZ+fa9G/P0Zly5K2aFFpmKSioqJSYkgpPaSUlZ51K237X1bebu/K/gP38KzhqbRdDQkuFVuEqSnWEybgeu0aVmPGcH/1aqKrViX1m2+QmZmlYpOKisrLx2vh6Egps4FI9HVxCoUQojOwDbgEdJRSJpeQeU/Ey8uV6Hv6xbeYGH34mjA1xWr8eDL37CHr1KnSMk1FRUVF5RXh7bddOXokBjtbB1xyV06uBl8p1TwZIycn7JYuxeXvvzFt2ZLkjz8mulYt0v391fwdFRWVJ/JaODpCCHPAnUKKCQghOgH+6OWm20spC5Q5fV40alSOe/f0hdsMeToAVmPHYuTsTMrMmaVlmoqKiorKK4KrqzkVKlgSeCaBmjVqApCcnMztiAJrZT9XTDw9cdq5E8c9exDm5iT06kVc27Zkn3uRSvipqKi8aDzW0RFCTBJCFCkBJLfY6KSi9PEUYzk9Ytc89PlIv+c5tpwQwlMIYflAHx2B7UAI8LaUMr6k7C0sVarYkJpiRGamIDpXeQ3AyNoa62nTyNy3j8yjR0vRQhUVFZXi42V79rxKdOjgyr79d6lR3VMR1/n70sVStuofzDt2xOX8eey+/56cS5eIadiQhKFD0UZElLZpKioqLyBPWtH5BuhfxDF80efIPA9mCCFOCCHmCyFGCyGmCCEOAlOAU8CyPMcuAK4ATQwNQohGwA70xVHXAT5CiEF5t+d0HfkwNjaiQgVj7t01JiYmGp1Op+yz+ugjjMqXJ3niRKS2oCLiKioqKi8dL9uz55Whs085/t/uKMzNzalWtTqgr6mTdj+tlC37B2FsjNXo0ZQJDcVq8mTS/+//uFetGsmffoouKam0zVNRUXmBeNVC1w4DycAQYAkwB3AE/gO0kVKmP+H82oA5+qrci4GfC9hKhdq1bYi8Y0xOTg5xcbFKu7CwwO6bb8g+e5b7P/5YWuapqKioqLwCtGjhzLVrqdy9m0HtWnpJZ51Ox+Url0vZsocxsrfH7uuvKRMcjEXv3qQuWKAXLFi2DJmVVdrmqaiovACIxyXzCSF0QCoQ+8iDnowzYCWl1BShjxcaIYQNkJycnFwi8tIACxaeZ8+e8/Tum0zrVm2oW7uusk9KqY9VvniRMlevqvKbKioqz52UlBRsbW0BbKWUKUXp63V99jyPZ0lh8PU9QedOZXlviAf/t3kjcfFx2NrY8t6gIS903basv/4i+eOPyTp0CE3VqtguWIB5794vtM0qKir5Kc5nCRRuRcca8CjCZl1UI1WgaZNy3I0yA+Du3ah8+4QQ2K1YgUxN/f/s3Xd4VMX6wPHvbHoPqUAChF4CQiCNFkIT6f2i4hX0eoGfooiIXVGRol71csGGKCJFBKQXQaT33gk9oYT0QNqmz++P3awkJCSBJJts5vM8+4SdM+fsu7thJ7Nnzvtyd9w4lYlGURRToMYeI+nTpxYbNt5GCGE4q5OUnMT1GxFGjuzBLNu1w/Wvv3DZsAFhZUXi8OHEdexIxt69xg5NURQjKa56tKpHUEm0bu3M7UhzcnMhKirqvu0Wvr44fvIJSW+8gXbhQmyffdYIUSqKopQJNfYY0RNP1OK1iSfIyMihaZNm7N2/l+zsbE6eOkm9uj7GDu+BhBBY9+mD1eOPk7ZgAcnvv098p05YDx6M48yZmDdpYuwQFUWpQA+c6EgpK/fXN9WIq6sV9vZmJCaYodHcRatNw8YmX8I47F57jfT167k7fjyWQUGYN21qpGgVRVEenhp7jMvd3YpmzRzYsyeO7t09ad6sBafPnCLiegQJCfG4uBSV4LTyEObm2P3rX9g8+SSpX35JymefEbN2LbZjx+IwZQpmHh7GDlFRlApgaskITJqvPiEBQFT0/Wd1hJkZzgsXIqysSBg4UGWfURRFUR5Kn7612bhBt0y6zWNtDO0nTp0wVkgPRWNnh8P77+Nx+TK2Y8aQ9v33xDRqRPK0aeSmVp5McoqilA810alC/Nu5ExlpCcDt27cL7WNety41Vqwg+8oVEp9+GpmdXZEhKoqiVHlCiKZCiMVCiPNCiLtCiDQhRJgQ4kshRK1SHKePEGKfECJVCJEghFguhKgSy/L69KnFxo2RSClxdnamvo8u7LALYWi1aUaOrvTMPD1x/uYbPM6exap7d5Lfe4+YRo1I/fZbZFaWscNTFKWcqIlOFdLGz5X4ON1ytVuRt4rsZ9WlC05z5pCxcSN3nnsOeU/dHUVRFKVY3kAtYBXwNvAq8CcwBjgqhCh23ZMQYgiwHrABJqOr6RMC7BVC1C6nuMvMY485kZUlOXcuCQC/1n4A5OTkcPrsGWOG9kjMmzbFZdUqXHfvxrxhQ+6++CIxzZuT9uuvaqxUFBOkJjpVSOvWzty6qVu6FhMbQ2ZW0XUC7MaOxWHaNLSLFnH3//5PfYAriqKUkJTyLyllNynlO1LKb6SUc6WULwPPoZsAjX7Q/kIIC3QFqm8AnfXHmAH0AjyBD8v1CZQBIQSDBnmxapXuS7Xatb1w15cuOHX6JNlVfLWAVadOuO7ejcv69QhbW+48/TSxbduSvmmTylyqKCZETXSqkMaN7UlJgZQUDbm5udy+HfnA/g7vvIP922+TNncud555BpmRUUGRKoqimKS8JAk1iunXBagNzJNSpuQ1SilPoCtsPUI/GarUBg32YtXKm4Bu4pN3Vker1XLu/FljhlYmhBBY9+2L+4kTOC9ahExKIqFPH+JDQ8nct8/Y4SmKUgbURKcK0WgE7dq5cPOGbny8eavo5Wt5HKZNw2HmTLS//kp8nz7kJiSUd5iKoigmQQhhLYRwE0J4CyEeB77Xb9pYzK4B+p/7C9l2AHAEKn2e4/bt3YiJSefyZd1crXGjJnmF/Dh6/Cg5OTnGDK/MCI0G25Ej8QgLw2nOHLIvXCCuY0cSBg4k60zVXaanKIqa6FQ5gUFuxMU6A3Dz1s1i+wshcHjzTZx/+YXM3buJbduWzMOHyztMRVEUU/ACEItuCdpmwBl4Rkq5u5j98q7BKezbqLw2rzKJsByZmQkGDvRi1SrdWKPRaPBv6w9ASkoK5y+cN2Z4ZU5YWmL30kt4XL6MwyefkLFjB7GPPUbi6NFkh4cbOzxFUR6CmuhUMQEBLkTesgEgNjaGjBIuR7P95z9x27MHgLiOHUmePl1lmlEUxaQIITRCiOfK8JCrgZ7AYOBj4A7gXoL98oqcFfYBnV6gz31CQkLw9/fH39+fuXPnliLcsjd4iDerV/09X2vWtDkO9g4AHD12xGTO6txLY2+Pw7vv4nn1KnaTJqFdupSYpk25++qr5MTEGDs8RVFK4YEFQ4UQ2x7h2FJK2f0R9lcKERDgwqWLOeiulZTcirxJg/oNS7SvZWAg7seOcWfsWJLffRftsmU4z52LZWBgucasKIpSnoQQGuCfwHtAA2B+WRxXSnkTyDt1vloI8TtwWAhho08uUJS8/MtWhWyzLtDnPrt27cLBwaHU8ZaHkBB3rl5N4caNNOrUscXMzIy2bduxc9cOkpKSuHjpIs2bNTd2mOVC4+qK0+efY//KKyR//DGps2eT9uOP2E2ciP1rr6FxdjZ2iIqiFKO4Mzqh6C6qDH3Im1LGvLxssLIy5+4d3Rw1IqJ0BcQ1Li64LF9OjZUryY2JIS4oiIQnnyT7ypXyCFdRFOWhCSFqCyFGCyHe1P+8Ly2zEOJpIAz4CWgIlNtX7lLKU8Bx4MViuuZliilseVpeW/EXWVYCFhYa+vatne+sTotmLbCztQPg0JGDJnlW515mderg/MMPuJ89i9UTT5AydSrR9euTPH06uSkpxR9AURSjKenStUPAy8DwUtz+UdbBKjoBgS6kJNcEIPx6+EOlwrQZPBiPsDDs33uPjHXriGnWjMTnnycrLKysw1UURSk1IcQE4ArwIzBd//OqEGKMfnsDIcR+YCHQCEgBpuj/XZ5sAJdi+uRdCNm+kG3BQBJwsSyDKk+Dh3ixcuXf14Sam5vTrp3uWp2kpCTOmkAGtpKwaNYMl+XLcT92DMtOnUh+911i6tcn5YsvkFqtscNTFKUQxU10fkW3njgQ+AoYBUhgjZTy9+Ju5Rx7tRUQ4EJ0lG5ZQ0pKCgkPmUlN4+iI49SpeFy+jN24cWh//ZXYFi1IGDpUJSxQFMVohBAh6MYcK3QTmGPoJj1mwDdCiJ7AXiAIyAZmAQ2llFOllKll8Pg1i2jvCrRElzktr62WEKKZEOLea252AreBF4QQ9vf0bY1utcNyKWWVuUiyZ8+ahIUlcf3636vtWrZoacjAdvjwoQfWdTM1Fn5+uK5bh9v+/Vj4+ZH0+utEN2xI6pw5qoyDolQyD5zoSClHAjWBccARoB+wHLgthPivEKJN+YeoFNS+vRsXwv4+ixN+PfyRjmdWqxZOs2fjGRGB/bvvkrFtG3GBgcR17076li2qeJqiKBXtJf3PbwBPKWWAlLIJ0Aa4DKxBV3jzNPCYlHKilDKuDB//WyHEASHEdCHEWCHEBCHEL+gyryUDk+7pOwM4j+4LQQD0k5gJQB1gtxDiRSHEW8AWdFncppRhrOXO0lLD4MHeLFt23dBmZmZGcGAwAGnaNE6ePGms8IzGMjgY1y1bcN2xA/NGjbj78svENG5M6rx5KtmPolQSxS5dk1Im66tCdwCaAZ+hyyTzCnBUCHFcCPGKEMKtnGNV9AICXAgLS8Ha2gmAiIjwMjmumYcHjlOn4nn9Oo7/+Q/Z58+T0KsXsX5+pC1apD64FUWpKMHAdWCClDIvSxlSyrPARHQX9GuBx6WUF8rh8X8F4tElOJgFzEQ3kfke3cTqRHEHkFIuBwagGy//A7wJ7AY6SimrxPU593ryqbos/fV6vrYmjZvi6qob+o+dOIo2vXou37Lq0gXXnTtx2bIFTa1a3P33v4lp1oy0hQuRJn79kqJUdqVKLy2lvCilfBuoC/QFVqCb/HwF3BJCLCz7EJWCbGzMeOwxZ1KTddflRt6OJD09vZi9Sk7j4ID9pEl4XruG808/QVYWd/75T2IaNiTlyy/JTU4us8dSFEUphAdwXEpZ2F+JeUU4d0kpo8vjwaWUy6SUfaWUdaSU1lJKGyllMynly1LK6wX6jpZSCinljkKOs15KGSyltJVS1pBSDpNSVsnML506uREfn8nZs3cNbUIIOgR3ACAzM5PDhw8ZKzyjE0Jg3bMnbgcO4LJuHcLRkTvPPktsy5Zoly1D5uYaO0RFqZYeqo6OlDJXSrlJSjkC8AY2ABZAr7IMTilap87u3Liuy3ojpeRa+LUyfwxhZYXtc8/hfvo0LuvXY9agAUmTJhFdpw5Jb71FTmRk8QdRFEUpPSsgsbANUso7+n9GVVw4ikYjGDGiDkuX5j+rU69uPby9vAE4deYU8Qnxxgiv0hBCYN2vH+5Hj1JjxQrQaEgcMYJYPz+0a9aopeCKUsEeumCoEKKpEGIGcBLd2R3QrVNWKkCnTm6cOpWJtbWuJMPlK5fK7bGERoN137647diB26FDWPXqRcrnnxPt40Pic8+RdbZ6ZNxRFKVSUX8xVrAnn6rHb0uv5/tjXQhB544hCCGQUrJrzy71xzy6cdNm6FDcT53CedEiZFoaiYMGEefvT/rateo1UpQKUqqJjhDCUX9h5n7gHLo1x1bAHKCdlLJLOcSoFKJ9e1eOHkmkjncDAK7fuE5GBWR7sQwIwOW33/C4dAm7sWNJX7aM2JYtie/bl4wdO9SHt6IoZaWmECKksFsJtytlrHVrJ6xtzDh4IH+mTzc3N1r6tgTg5s0bXL121RjhVUrCzAzbkSPxOH8e5x9/JPfOHRIGDiSuXTt1hkdRKkCxEx2h87gQYgm6dJnfAv7ARnT1cmpLKSdIKY+Xb6jKvZydLWnY0J50bS0AcnNzy2X5WlHMGzTQZWq7fh2Hjz8m6/Bh4rt2JS4wULceOTu7wmJRFMUk9QK2F3GTD9i+zRjBVgdCCJ56qh6LF99fqDooMBgrKysA9uzdTbYaA/IR5ubYPv88HmFhOM+fT25Sku4MT7t2aFevVhMeRSknD5zoCCGmoct8swl4ErgGvAF4Syn76+vlqFRcRtK5sxth5wXWVvrla1cvV3gMGldXHN5/H8+ICJy++47cu3dJHDGCmCZNSJk1SyUuUBTlYVx/hNsNI8RbbTzzTD2WL7+BVps/T4SNtQ3BQbr6qEnJSRw5dsQY4VV6wsIC29GjdROen38mNzmZxMGDiW3bFu2qVSppgaKUseLO6LwN1EZXQ+cl4Fl035Z5CSHaFncr59irvW7dPdmxPZYG9fXL165HVMjytcIIGxvsxo7FIyyMGqtWYVa7Nkmvvkq0tzd3X3uN7PBwo8SlKErVI6X0kVLWf9ibseM3ZXXq2NKuXQ3WrLk/Q3bLFi1xc9Olmz567Ei1T0zwIMLcHNtRo3RL2hYsQKamkjhkiG7Cs3KlmvAoShkp6TU6/uiuwzlcilv1zTNZQbp0cefQoQS8vBoCkJOTw6XL5ZeUoCSERoPNoEG47dmD28GDWPftS+rs2cQ0bEjCsGFk7NmjTtEriqJUYaNG12fBz/cvldZoNHQL7Y4QgtzcXLbv2KY+74shzM2xffZZPM6dw3nhQqRWS+LQobosbb//riY8ivKIipvoqOUDlZiDgwV+fs5cu2KFvZ09AOfDzhk5qr9ZBgZSY8kSPK9dw/6NN8jYto34zp2JCwwkbfFiZGamsUNUFEVRSmnAgNqcOHGH8PDU+7Z5enjS+rHWANyOus2Zs6crOrwqSZibY/vMM7oJz6JFkJFB4rBhxLZpg3bFCjXhUZSH9MCJjlo+UPn16FmTv/6KoVnTZgBERUeRkJhQzF4Vy8zbG8cZM/C8cQOnb79FJidz55lniK5fn+QZM8iNV8sbFEVRqgprazNGjKjLwoXhhW4PCgzGwcEBgL3795KSoq7VLKm8LG3uZ8/ivHgxZGWROHw4sa1b6xL95BRWQ1dRlKI8dB2dykoIIYu4pZRw/38IIeYLIU4KIbL0+/qUb9QPr2cPT/7cGk3zZi0MbWFhlbOckcbODrtx43A/dw6XDRuw8PUl+Z13iK5ThzvjxpF1vnLGrSiKouQ3arQPvywIJzf3/qVplhaWdO3SFYCsrCz+2v6XWsJWSsLMDNunn8b9zBmclyyBnBxd4VFfX9J++QWZpfJAKUpJmNxER2838M8Ct3+VcN8X0WWY0wJXyiW6MuTXtgYx0emkpFhSq6Yu1XTYhTByKvG3PkKjwbpPH1y3bMH99Glsnn6atJ9/JrZFC+J79yZ982Z1ml5RFKUS8/OrgbOzJTt2xBS6vV5dH5o1bQ7o6rydVkvYHoowM8P2qadwP32aGsuWgbU1d0aNIqZpU1K//x5ppAREilJVmOpE56qUclGB228l3PdZwEFKGQxsLccYy4SZmaBHT082bbxNi+a+AKSmpXLlaqWfowFg0bIlzvPm4Xnjhq4ez/HjJDzxBDHNm5Pyv/+Re/eusUNUFEVRCvHcc/WZN6/o4qAhnUJwsNcvYdu3h8TExIoKzeQIMzNshg/H/fhxXNatQ+Phwd1x44hu0ICU//6X3LQ0Y4eoKJWSqU50EEJYCiHsS7uflPK6lLJKVTrr39+LdesiadK4CdbWupo6p06fNHJUpWPm7m6ox+O8eDEaFxeSJkwg2suLOy++SNbZs8YOUVEURbnHyGfqsfXPaKKi0gvdbmVlRY/uPQHIzs7mz7+2kKvO1j8SIQTW/frhtn8/rlu3Yt6kCUkTJxLj46O75jUpydghKkqlYqoTnWFAGpAshIgRQswWQjgZO6jy8sQTNdm7Nw6tVuKrP6tzO+o2sbGFLymozISVlW5d8v79uB0+jPXw4aT99BOxLVsS162brqCaqritKIpidE5OFgwd6s1PPxV9Vsfby5s2rf0AiI6J5tARVXmiLAghsOreHbft23HdswcLf3/dNa/16pH0wQcqyY+i6JniROcQ8CG6yc4odAVOxwO7H+YMT1Xg6GhBhw5ubN4cRcuWrRBCAHDy9CkjR/ZoLP39qTF/Pp43b+IwcyY5V66QOGQIMQ0akDxjBjmxscYOUVEUpVobO64R8364SnZ20Wdq2ge1x9XFFYDDRw5x/cb1igqvWrDq2BHXjRtxO3IEq27dSJk6leh69bg7eTI5UVHGDk9RjMrkJjpSyiAp5X+klKullL9IKZ8E3gVaARPK87FDQkLw9/fH39+fuXPnludD3ad//9qsXROJo4Mj9X0aAHDhYhgpKSVKNlepmbm54fDmm3hcvUqN1asxa9JE982VtzeJo0aReeSIsUNUFEWpltq0ccbb25YNG24X2cfc3Jxejz+Bubk5AFu2biY19f4aPMqjsWzXDpfff8f9zBmsBw0i9csvifbx4c748WRfV1mRNTkAACAASURBVJNLpXoyuYlOET4HMoG+5fkgu3bt4siRIxw5coQxY8aU50Pdp1//2mzefJusrFza+rUFIDc3l+MnjlVoHOVJmJlhM3Agblu34n7uHLb//jfpK1cSFxBAbHCwLuWmVmvsMBVFUaqVseMa8v13D06A4+riSmiILuW0Vqtl859/qOt1yomFry81Fi3C48IFbJ95hrS5c4lp2JA7//oX2ZcuGTs8RalQ1WKiI6XMAiIBN2PHUl5q17ahSRMH/vormlo1a+Ht5Q3AmXNn0GpNLxuLRfPmOM+Zg+etWzjNno28c4c7o0YR5eXF3ddeIysszNghKoqiVAtDh3pz8uQdLl16cGHQ5s2aG2q+3Yq8xcHDBysivGrLvFEjnOfNw+PyZezGjSNt8WJimjUjYcQIMo+ZzpegivIg1WKiI4SwBryBaGPHUp6efLIuS3/VnZ4O8A8AdJlujp88YcywypXG0RG78eNxP38e1+3bsXr8cVLnzCG2eXPiunZF+9tvyMxMY4epKIpisqytzRg12oe53xdf1qBL5y64uLgAcOTo4SpTCqEqM69bF6fZs/EMD8f+jTfI+OMP4tq1I75XLzJ27FDFXBWTZlITHSGEaxGbpgLmwLp7+tYSQjQTQthWSHAVYNjwOmzYcJu0tGy8ansbCoieOn2SNBPPsS+EwCo0FJelS/9OXhARQeKTTxLt7U3SW2+RfbXozECKoijKwxszpiELF0aQnJz1wH4WFhb07tUHCwsLAP7cuoW4+LiKCLHaM6tZE8cZM/C8fh2HGTPIOnGC+K5diWvfHu2aNapQt2KSTGqiA7wnhNgvhJguhBgnhHhdCLENeB04CMy+p+8M4DwQeO8BhBAhQoj3hBDvAf765vH3tFVanp7WBAW5sG5dJEIIAgOCAMjKyuLw0eqT0tPMw0OXvODyZVz++APLTp1I+c9/iGnYkPhevXQpqrMePBgriqIoJefjY0fXbh4s+Dm82L4uNVzo1fMJALKys9iwaT3adHV9ZUXRODnh8NZbeIaH4/Ttt+TGxJA4aBCxLVuStmCBGh8Vk2JqE50dQBK6tNL/BT4CXNBlXQuVUpbkk7QbujNAU4Egfduke9oqtRFP1mXpUt3ytbp16lLHuw4AZ86e4c7dO8YMrcIJjQbrXr1wWbkSz4gIHD76iKxz50gcMsRQa0BlolEURSkbE15pwuzZl8jJKX4pVH2f+rQPag9AUlISf2zepJITVDBhY4PduHF4XLyI85IlYGHBndGjiWnYkJT//Y9cE18JolQPJjXRkVKukVL2klJ6SSmtpZR2Uso2UsrpUsr0An1HSymFlHJHgfYP9e2F3ir0CT2EgQO92LM7jpgY3dPt2L4joMvAtv/APmOGZlRmXl44fPABnteu4bJ2LRZ+fqR88gkxPj7E9+6NdsUKdS2PoijKIwhu74qHhxXr1kWWqH+7tv40btQYgJu3brJz9051vYgRCHNzbJ96CvcTJ3DZsAEzHx+SJkwgpl49kqdOJTchwdghKspDM6mJjqIrHjpwoBcLF0YA4O7uQdMmTQG4fOUyN2/dMGZ4RifMzbHu3x/XDRvwuHoV+/feI+vMGRKHDyc6L2Pb2bPGDlNRFKVKemVCE2b992KJ+goh6N61B+5u7gCcOXuao8ePlmd4ygMIIbDu0we3Xbtw3bMHi+Bgkj/4QFd89PXXybl1y9ghKkqpqYmOCXrhhQb8OO8qubm6b8baB3UwFGrbvnMHOTnZxgyv0jD38cHx44/xDA/HZdMmLENDdRnbWrYkNiiI1LlzyU1KMnaYiqIoVcbgwV7cuJHGkcMlOwtgYWFBv779sbe3B2D/gX1cuKjKAxibVceOuK5bh/upU1gPHEjqf/9LdP363HnhBbIvlmwiqyiVgZromKCgYBesrc3YuTMWAAcHB4L0iQnu3Enk2HGVP/9ewswM6yeewGX5cjxv3cLxyy+RqancHTuW6Fq1SBw9mozdu9WSCkVRlGKYm2t4aXwjZv2v5H8M29vZM6DfQCwtLQHYum0rN25W79UHlYVFq1a64qOXLmE7ZszftXiGDyfzqDr7plR+aqJjgoQQvPBCA3744e/6BK0fa4Oriy779uGjh0m8k2is8Co1M3d37CdOxP30adwOHMBm5EjSV64kPiSEmGbNSP70U3KioowdpqIoSqX1/PMN2LI5ihs3Sn4xu6uLK31790Oj0ZCbm8vGPzYQGxdbjlEqpWFev76uSHdEBPZvv03Gn38S5+9P/OOPk7Ftm/oiUKm01ETHRD09si5/bY3m1i1dojkzMzO6dukGQE5ODn9u3aIy3DyAEALLoCCc587F8/ZtnOfPx8zDg+S33iLa25v4vn3RLluGTE8v/mCKoijViJOTBc8+68Ps/10q1X7eXt707N4TgMzMTNasW01iovpSrjIx8/DAcdo0PK9fx/Gzz8g6fZr47t2JCw7WlW5Qf1colYya6JgoZ2dLRo6sx5w5fw80tWrVok3rNgBEx0Rz+OhhY4VXpWjs7LAdPRq33bvxuHAB+8mTyTp1isQRI4iqWZM7Y8aQsWeP+kZLURRFb8KrTViw4BoJCaXLZtmkcVM6d+wMgFarZfXaVSSpayUrHY2jI/aTJ+N57RpO339Pbnw8iUOGEOvrS9rPP6sspkqloSY6JuzlV5ow/6drJCX9XfyrfVAHXFxcADh85BBRUbeNFV6VZN6kia6ydHg4rlu3Yj1wINolS4jv3JmYRo1I/ugjsq9eNXaYiqI8AiFEEyHEx0KIA0KIWCFEshDihBDiXSGEXSmO00cIsU8IkSqESBBCLBdC1C/P2CsLb29bBgzw4ttvL5d63zat/QzXlaakprB67SpSU1PLOkSlDAhra+zGjMEjLIwaS5eClRV3nnuOmEaNSJk1i1z1vilGpiY6Jqx+fTu6dfdg/k/XDG3m5uY83qMXGo0GKSWbNm8iTRUFKzVhZoZV9+7UWLAAz6gonH/5BbMGDUj+6CNiGjYkrnNnUn/4gdw71atIq6KYiOeBicAV4GNgMnAB+ATYJ4SwKe4AQoghwHrARr//50AIsFcIUbuc4q5UXpvUlG++vkxqaukzfQb4B+LXpi0Ad5PusnrtKrTaktT8VoxBmJtjM2IE7seP47JpE2b165P06qu6Wjwff6xq8ShGI9Rym0cnhHAAkpKSknBwcDB2OPkcPZLAP/6xj/NhfbC0/Htee/LUCXbt2QXo1kUP7D8IjUbNex9Vzs2bpC1ejHbBArLPnwcrK6wHDsT2n//E6vHHEfqsQopiapKTk3F0dARwlFImGzueRyGE8AcuSSnvFmj/BHgXeFlKOecB+1sA4UA24CulTNG3twGOAj9KKccUsl+lHUse1vDh+wjt4s5L4xuXel8pJTt2bufMuTMAuLq6MXjAIGxsbMs6TKUcZO7bR/LMmWSsW4ews8N27FjsJ07EzNvb2KEplVhZjyXqL1sT187fhebNHfl5/rV87Y+1ak3TxrpCojdv3WTf/r3GCM/kmHl74/Dmm7ifPYvb4cPYjRlD5rZtJPTvT1StWtwZO5aMHTvUBZuKUolJKY8UnOTo/ab/2bKYQ3QBagPz8iY5+uOeAHYAI/STIZP3xuRmfPXVRbKySv+ZJ4QgtEtXQ9Hr+Pg4Vq1ZpVYhVBGWHTrgunatrhbP4MGkzppFdIMGulo8Fy4YOzylmlATnWrgww9bMmPGebTaHEObEIKuod1wc3UD4PjJ45w6c8pYIZocIQSW/v44/e9/eN66hcv69Vj37o128WLiu3Ylum5d7k6aROaRIyqJgaJUHXlfRUcX0y9A/3N/IdsOAI5Ak7IKqjILCHShYUN7fvvt4eriCCHo0a0nTZs0AyA+IZ5Va1aqyU4VYtGqFTUWLsxfi6d5cxKGDVO1eJRypyY61YB/gAtt29Xgh7lX8rVbWFjQp3dfbGx0y8137trBlatXCjuE8giEpSXWfftSY9EiPKOjqbF0KZb+/qTOnk1cQAAxTZuSNGUKWWGqGriiVFZCCDPgA3TL0ZYU0z3vGpxbhWzLa/Mqo9AqvclvNOPzz8PIzX24L3U0Gg09uvWgebPmACQkJrBy9e8qQUEVc18tnq1bifP3J65nT1WLRyk3aqJTTUyZ4svnn4dx925WvnYnRyf69x2AhbluFcXmP//g5i1Vkbq8aOzssBkxApfVq6kZHY3TvHmY1a1LyiefENu8OTF+fiR/9hnZ168bO1RFUfL7LxAMfCClLG7dTd5FJBmFbEsv0Oc+ISEh+Pv74+/vz9y5c0sfaSXTvbsH1lYaNmx4+CyfGo2G7l170KJ5CwAS7ySycs3vpKRU6cvBqqV7a/E4fPop2WfOqFo8SrlRyQjKQFW5gHTMvw9To4Yln37W+r5t4RHhrN+4Dikl5ubm9OvTnzredYwQZfWUExWFdtkytEuWkHXwIAAWwcHYDBuG9dChmPv4GDdARSmGKSUjKEgIMRV4D5grpRxbgv6zgfFACynl+QLbXgS+BnpJKbcU2FYlxpKHsWLFDWb99yK7dndDCPHQx5FSsn3nds7qExQ4ODgwaMBgnJ2cyypUpYLJ9HTSFiwg5bPPyLl6FfNmzbB/801snn5aJfCphlQyAuWhTf2kFQsXhhMWdn/xNZ96PjzeoxdCCLKzs1m/cR03bqozOxXFrGZN7F95BfcDB/C4cgWH6dMhM5Ok118npn59YgMDdWd6rqilhYpSkYQQH6Kb5MwHxpVwt0j9z8KWp+W1FbaszWQNHuxNfHwmu3fHPdJxhBB07dKVx1o9Buj+KPp95Qri4h7tuIrxCGtr7MaOxePCBWr8+quqxaOUKTXRqUY8Pa15443mvD7pRKFrYZs0bpJvsrNuw1ouXb5ohEirN/MGDXB4+23cjx7F48oVHD/7DIDkN98kplEjYtu2JXn6dLIvqvdGUcqTEGIKMAX4BXhBlnwJxGH9z/aFbAsGkoBq9R/YzEzw2mtN+fKLR8+2JYQgpFMXAtrpcj6kadNYueZ3bqsC2FWaMDfH5skndbV4Nm7EzMdH1eJRHplaulYGqtJyg6ysXAID/uSNN5vz1FN1C+1z6fIltmzdTK5+nWzH9p3wa+P3SMsNlEeXHRFB+u+/o12xgqz9umRO5o899vfytubN1XukGI2pLV0TQnwAfAQsBEZLKQu9cEAIUQtwAq5LKdP0bRZABJBF/jo6rYFjwHwp5QuFHKvKjCUPIz09hyaNN7LpjxB8fZ3K5JjHTxxjz749gK4gdt/e/ahbp/CxTal6MvbuJWXmTDLWr0fY2WH38svYTZqEmZubsUNTyklZjyVqolMGqtrgdPRIAgMH7uHI0cepWdO60D7Xb1xn4x8byMrSJS/wbeFLSKcumJubV2SoShFybtxAu3Il6StWkLl3L0iJWePGWA8ciPWgQVgGByPMzIwdplKNmNJERwjxEjAHuA68DxSc5ERLKf/U9/0ZGAV0lVLuuOcYw9HV3TkJ/IAupfREQALtpJT3LV2ramPJw5gx4zxXLqcw78eA4juX0LnzZ9m2Q5e1S6PR0KvnEzRq2KjMjq8YX9bp06RMn472t98QtrZqwmPC1ESnEqqKg9N7753mwoVkli1rX+RZgLi4ONZuWGNI4enh7kHvXn3yfgGVSiInMpL01atJX7OGjO3bISsLjbs71v37Yz1wIFY9eyL0KcQVpbyY2ETnZ3STl6LslFKGFuibb6Kj39YP3fU9j6HLwPYX8KaUstCL7ariWFJaCQmZNG+2kWPHe+HlVXafS5evXGLzn7qVCEIIuoV2N2RoU0xH1rlzpEydqiY8JkxNdCqhqjg4ZWTkEBy0lZdfbszz/2pQZL+UlBQ2bd5IVHQUAFZWVnTv2oOGDRpWVKhKKeTevUvGpk2kr1lD+saNyKQkhI0NVr166SY9/fqpAUEpF6Y00TGWqjiWPIxJr53AwlLDzJmPlelxr1+PYMMfG8jOzgagc8cQ2rRuU6aPoVQOasJjutREpxKqqoPT+fNJdO+2nU1/dKF166JTc+bk5LB3/15OnjphaGvapBkhnUKwti586ZtifDIzk4ydO3WTnjVryL15EzQaLDt2xLp/f6z69MG8RQt1XY9SJtRE59FV1bGktCIiUgkK3MqFi31wcrIo02Pfvn2btRvWkJmZCUBQQBAB/oHqc85E3TfheeUV7N94A42zSjdeWm5ubowfP54PP/wQgNDQUNzc3FixYkWFxqEmOpVQVR6cliyJ4JNPznHgQA8cHR884Fy6fJHtO7eTkaGrgWdnZ0doSCj1fRqoQaSSk1KSdeyYYdKTfeoUAGZ162LVuzfWffpg2a0bGnt7I0eqVFVqovPoqvJYUlrP/vMgrds4M2lS0zI/dmxcLGvWrUar1QLg19qPjh06qXHKhBkmPEuXImrUwOHdd7F76SWE+jK2xApOdM6dO4eFhQWNGzeu0DjURKcSquqD0ysvH+Pq1RRWr+mEufmDM46npKSwbcdfRFyPMLTVq1uPkE5dcFbfoFQZOTdvkr5pExkbN5KxdSsyJQUsLbHq0sUw8TFr0kT9YVBCWq0Wm2p+HZSa6Dy6qj6WlMaJE3cYNHA3Fy/1xdKy7CtdJCYmsnrtKlJSUwBdQp3QkK5oNKqqhinLOn6cpLffJmPzZszq1sVh6lRsRo6sdMl5ynrMKIvjFZzoGIsqGKqUuS+/aoMQggmvHC+0vs697O3t6d93AN1Cu2NlZQVAxPUIFi9dxJ59e9CmaysiZOURmXl7Y/fvf+OyahU14+Nx/esv7F5+mZxbt0h67TVimjUjplEj7owfT/r69eQmm87frbt27aJr167Y29vj5OREaGgox48fN2w/ceIE3bt3x9bWlho1ajBy5Eiio6MN28PDwxFCsHjxYp599lmcnZ3p37+/Yfu8efPw9fXFysqKevXq8Zm+DtKD+Pj48PrrrzNz5kxq1aqFk5MTkyZNQkrJxo0b8fX11VWAHzSIxMREw36pqamMHz+epk2bYmtrS/369XnppZdISspfFPjHH3/E19cXGxsb3Nzc6NKlC2fPnjVsnzFjBo0aNcLa2hpPT0+eeOIJoqKiHur1VZSSaNPGmWbNHPn995vlcvwaNWowdMgwnJx0aazPnjvLlq2bycnJKZfHUyoHCz8/XP/4A9etW9G4u3Nn1Chi/fxI37z5oY9ZWceMSZMmMXXqVLy9vQ1Jovbv38+AAQOoXbs2dnZ2tGnThsWLFxf6nFq3bo21tTXt2rVj37599/UJDQ1l2LBhhvujR4/G398/X5+857Z+/XpDW3HjTUVTuYIVzM01LF4STNfQ7Uyffp53331wphohBL4tfGlQvz77Duzn3Pmz5ObmcvzEMc6cPY1faz/82vhhaWlVQc9AeRTC0hKrbt2w6tYN/vMfssPDdQkNNm1CO38+aV9/DebmWAYHY9WzJ1Y9emAREICwKNu19RVhx44d9OzZk65du7JgwQLs7OzYu3cvt27dws/Pj9jYWEJDQ2nevDlLliwhJSWFt956i549e3LkyBEsLS0Nx3r99dcZMmQIy5cvx0z/beHnn3/OO++8wxtvvEFoaChHjx7l/fffx9bWlvHjxz8wtqVLlxIYGMj8+fM5evQo7733Hrm5uezatYupU6ei1WoZP348b7/9Nt999x0AaWlp5OTkMG3aNNzd3blx4wbTpk1j+PDhbNYP7Lt27WLcuHF8/PHHtG/fnqSkJPbv38/du3cB+OWXX5g+fTqffvopvr6+xMfHs23bNkO2RUUpL/8e05Bvv7lcZE23R+Xo4MjQwcNYs3Y18QnxXLp8iaysLHr36qNKJZg4q+7dcTt0iPTly0l65x0SnngC68GDcfzqK8zr1SvxcSrzmLFkyRJ8fX355ptvDAk4IiIi6NixI+PGjcPa2pq9e/fy3HPPodFoeOqppwCIjIykd+/eBAYGsmLFCiIjIxk5ciRpaWmlfZnvU9x4YxRSSnV7xBvgAMikpCRZlUVGpklf301y5sxzpdrvdtRtufz3ZfJ/X88y3L6f953ct3+vTElJKadolYqQq9XK9L/+knffflvG+PvLW0LIWyAjHRxkXP/+MnnWLJl57pzMzc01dqglEhwcLNu1a1dkvG+++aZ0cnKSd+/eNbQdPHhQAnLJkiVSSimvXbsmATlo0KB8+969e1fa2dnJDz/8MF/7+++/Lz09PWV2dnaRcdWrV082bNgwX5+AgABpZmYmr169amibPHmy9PDwKPI4WVlZcs+ePRKQERERUkopP//8c9m2bdsi93nppZfkkCFDitxeUklJSRJdjRgHWQk+l6vizVTGkpLKyMiR3l5r5Pnzd4vv/Ai0Wq38bflSw/i0buO6B/5/VExLbnq6TJo+XUba2spIGxuZNHWqzNVqS7RvZR4zatasKbUPeB65ubkyKytLjhkzRnbt2tXQPnnyZOni4iJTU1MNbYsWLZKAnDJliqGtS5cucujQoYb7o0aNku3atcv3GHnPbd26dVLK4sebkijrsUQtXVMMatWyYcuWLixYEM6MGefzBt5i1fSsydDBwxjQbwAe7h4AZGRkcOTYEX5eOJ+t27YSGxdbnqEr5URYW2PVrRuO06fjfvgwNePiqLFiBTYjR5J97hxJEyYQ26IF0d7eJI4aRdr8+WRfvVri352KlJqaysGDBxk1alSR1x4dOnSIxx9/PF+tqMDAQHx8fNizZ0++vn379s13f//+/aSmpjJ8+HCys7MNt27duhEdHc3Nmw9eohMaGmr4lg+gUaNG+Pj4UL9+/XxtsbGxhoxSAAsXLsTPzw97e3ssLCzo1KkTABcvXgSgTZs2HD9+nIkTJ7Jr1658++Zt37hxI1OmTOHQoUNqaY9SYSwtNTw7qj4/zrtaro9jbW3NoAGDqV2rNgDXrl3lr+1bK+XnlFL2hJUVDm+/jfv581j16UPy++8T26YNmYcOPXC/yj5mdO/e/b7Mt4mJibzyyivUq1cPCwsLLCwsmDt3rmE8yIu5Z8+e2NraGtqGDBnywMcqqeLGG2NQEx0ln9q1bdi6NZTly27wysvHyc4uWBC8cEII6tX14R/DRtDnib7UqlkLgNzcXM6HnWPpsl/5bflSTp85ZcjaplQ9GhcXbIYOxfnbb/G8fBmPq1dx+uEHLDt3JmPjRu48/zwxDRsSXacOiSNHkvr992SFhVWKPygSExORUlKrVq0i+9y+fRtPT8/72j09PUlISLiv7V5xcXEA+Pr6GgYYCwsLunbtCsCNGzceGF/BZB6WlpaFtkkpDYPHqlWrePbZZ2nfvj3Lly/nwIEDrFq1CoD09HQAevTowfz589m1a5chXeiLL75oWJr2/PPPM336dJYtW0ZQUBCenp68//77asKjVIjRo31YuvQ6OTnl+xlhaWlJ/779DV/GXbh4gd17dpXrYyqVi3ndurisWIHLH38g09KI69CBpPffRxbxx3hlHzMKe9zRo0fz22+/MXnyZLZs2cLhw4d5/vnnDeMBQFRUFB4eHvn2s7Gxwb4Msq4WN94Yg8ktUhVCFPVpmSqlLNG7KITog66adWv+rmb9hpTyWtlEWbnVrm3D9h1deXLEPoYN3cfPCwJxdrYsfkd0E56GDRrSsEFDbkfd5viJY1y5qisCHhMbQ0xsDHv27cGnXn0aN2pEvbo+WFTBaz0UHfP69TF/4QXsXngBmZtL9vnzZO7aRcbOnWRs24Z2yRIANB4eWIaEYBkSglWXLpj7+lZ4FpwaNWqg0Wi4fft2kX1q1apFTEzMfe3R0dG0a9cuX1vBb/hcXFwAWL9+faEDUNOmZZ9Gd/ny5QQFBfHNN98Y2nbu3Hlfv1GjRjFq1ChiY2NZuXIlEydOxNHRkZkzZ6LRaJg4cSITJ07kxo0bLF68mHfffRcvLy/GjRtX5jEryr0aN3bAw8OaA/vj6dipfIs9WlpaMaDfQFau/p2ExAROnj6Ju7s7zZs9+LpUxbRY9+qF5enT3J0wgZRPPiHjzz9xWbkSs9q18/Wr7GNGweOlp6ezYcMG5syZk++zOzc3/xfWNWvWvC9mrVZLSkrKAx/P2tr6vjM0BSdz8ODxxhhMbqKjtxuYW6AtqyQ7CiGGACuAk8BkwAl4FdgrhPCXUkaWZaCVlZOTBWvXdWby6ycJCtzK4sXB+Ae4lOoYtWrWotYTfUlOTuZc2DnOnz9Hckoy2dnZXL5yictXLmFubo5PPR8a1G9I3Tp1sLGxLf7ASqUkNBosfH2x8PXF7v/+DyklOZcvk7FrF5k7d5K5cyfp+sJjwsEBi8BALNu3xzI4GIugoHKvaG1nZ0dQUBC//PIL48ePL3QpQlBQEN9++y3JycmG9L6HDx8mPDzcsCSsKO3bt8fGxobIyMj7liiUF61Wa8h+mKewDDt53N3dGTt2LCtXruTcuXP3ba9Tpw5vvfUW8+fPL3S7opSHQYO9WLXqZrlPdED3zfWAfgP5bcVStFot23dux93dAzfX8n9spfLQODlR4+efse7XjzujRxMbEIDL2rVY3jM5qWpjRkZGBjk5OfnGhOTkZNauXZsv9oCAAH766SfS0tIMy9dWrlxZ7PG9vb0JDw8nPT3dsGTuzz//LLJ/ceNNRTHVic5VKeWi0u4khLAAZgM3gM5SyhR9+ybgKPAhMKYM46zULCw0/HeWH51D3BkwYDfjX27M6683K3XNAwcHB1116nYB3Lx5g/MXwrgWfpWsrCz9pOcyl69cBsDD3YO6depSt05dPD1rqsw4VZgQAvPGjTFv3Bi7f/0LgOyICDJ37SJz/36yDhwgZcYM0C+RMmvUCMvgYCzbt8ciOBiLVq3KPLPbzJkz6dGjB71792bMmDHY2dmxf/9+/P396devH6+99hrffvstvXr14s033zRk0GnVqhVDhw594LGdnZ358MMPmTBhAhEREYSEhJCbm8vFixfZvn27YUlZWerZsycvvfQS06ZNIygo+nh67wAAGXJJREFUiI0bN/LXX3/l6zNlyhQSEhIMywiOHz/Ozp07Dd+ujR07FhcXF4KDg3FycmL79u1cunSJTz/9tMzjVZTC9OldixdeOFxhj+fg4MATj/dm9dpV5OTksH3HNoYNGa7qhlVDNsOGYd6kCQkDBhDfvTuu27Zh2batYXtVGjOcnJwICAjg448/xtHREY1Gw8yZM3FycspXcuDVV1/l66+/NsQfGRnJjBkziq3DM2jQID744ANeeOEFRo8ezfHjx5k/f36+PsWNN0ZRFhkNKtMNXaaGnwFLwL6U+/bQ7/9+Idv+Au4CFoVsM/lMOeHhKXJA/12ydes/5K5dMY98vKysLHnl6hW5+c8/5Hc/fJsvY1vebc63s+WyFb/JXXt2yUuXL8nk5OQqk91LKZmclBSZvmOHTJo5U8YPGiRve3rKW6C7WVnJGH9/mfjvf8uUb76RGQcOyJx7ssQ8rB07dsjOnTtLGxsb6eTkJENDQ+Xx48cN248dOya7du1q2P7UU0/JqKgow/aCWWYKWrhwoWzbtq20traWzs7OMjAwUH7xxRcPjKlevXpy0qRJ+doKy3Azf/58Ccjk5GQppZTZ2dly0qRJ0t3dXTo4OMghQ4bIAwcO5Itv3bp1slu3btLNzU1aWVnJJk2ayBkzZhj+L82fP1926NBB1qhRQ9rY2MhWrVrJefPmlfDV/JvKulYm45fJjyWFyczMkTWcV8rExIwKfdzde3cbxpsrV69U6GMrlUtWeLiMqltX3q5ZU2bHxubbVlXGDCmlvHTpkuzatau0tbWVderUkZ9++qmcMmWKdHV1zddv+/btslWrVtLS0lK2bt1a7tmzR7q6uj4w65qUuvGiQYMG0sbGRvbt21fu3bu3VONNSZT1WCKkNP5FwmVJf41OKmANmAGxwG/Ae1LKBybyFkK8DUwHekoptxbYNg14B2gppTxbYFu1qGYtpWTFipu8+84pmjRx4MMPW5Z6OVthcnJyiIq6TcSN61y/EUFsbNEZ2qytrHF1dcXN1Q0XV1cc7R2oU6eu+ibOREgpyYmI0J3xOXqUrOPHyTp+HJlXJFOjwbxZMyzatsWidWvMW7TAvEULzOrWRaiK50ZV1tWsq6PqMpYUxr/dFn74IQC/tjUq7DEzMzP5acGPZGVl0aB+Q/r2rpglp0rllHXyJLEBAdiOGYPznDnGDqfaKuuxxBTXBR0ClgOXAUegDzAe6CKE6CD1y9GKkHcl2q1CtuW1eQHGK/FqREIIhg+vw8CBXvz041VGjNiHl5cts/7nh5/fww9OZmZmeHl54+XlTYfgDqSlpXEr8hZRUbeJjLpNXFys4WK69Ix0bkXe4lak7u2wsrJizL/GlsnzU4xPCIG5jw/mPj6gL24mpSTn+nWyjh0zTHwytm1Du+jv1anC1haL4GDcCizbUhSlavCuY8utSG2FTnQsLS2p71Ofi5cuEnn7lu7bX/WlWbVl0bo11v37k7Fpk7FDUcqQyX0FKqUMklL+R0q5Wkr5i5TySeBdoBUwoZjd866ELyz/cXqBPvcJCQnB398ff39/5s4tmAuhaF988QW1a9emdu3ahrSCI0aMMLRNnjwZgAYNGhjali1bxunTpw33a9euzbVr1/jpp58M9x977DEAXnzxRUPbv/TXSgQEBBjavvvuOyIjI/Md6+jRo6xZs8Zw39vbG4D33nsPHx9vPp4agm/LH3l9clNeffV5Q79p06aRnp6e71hbtmxh9+7d+doSExP56quvDPdDQkIAePrpp2nUqBFdQrqwetUaRgwbwZxZX7Pol8X8uXkr1pbWODg4kpWlyy1x8+ZNrly5woIFCwzH8vX1BWD8+PGGttGjRwO6Cwfz2r7++muioqLyxXXo0CHWr1+frw10607z7g8cOBCAPn36GNo++eQTsrKy8u23efNm9u3bl68tISGBWbNmGe7nXaz4zDPPGNomTpwI6DKu5LUtWbKEsLCwfMe6dOkSixYtMtxv0UKXOWjChAmGtlGjRgHQoUMHQ9ucOXOIjY3Nd6yDBw+ycePGfG05OTl89NFHhvv9+vUDoF+/foa2jz76iJycnHz7bdy4kYMHD+Zri42NZc6cOYb7HTp0AHTZWfLaJkzQ/fds0aKFoW3RokVcvnyZuu3b0/Cll2g2bx6xn3/Ots8/x9/TE39XV0I9PbEdM4YPEhIM+z3zzDMAdOrUydA2a9YsEu7pU7t2bfbt28fmzZvztWVlZfHJJ58Y7vfp0weAgQMHGtqmTJkCkG+/9evXc+jQoXxtUVFRfP3114b7QUFBgC4FaF5bXvVrX19fQ9uCBQu4cuVKvmOdO3eOpUuXGu43btwYgEmTJhnann76acNnUV7bV199RWJiYr5j7d69my1btuRrS09PZ9q0aYb7vXr1AnTrsr/44osSf54pxlPVxpK8tkGDBrFsWQdmz37O0FYWY0le26RJkwBo3LixoW3p0qWcO3eO2f+bQ2xMLAf2H+DSpUtqLKltumPJpUuX8h0rLCyMJUuWGO43bdoUhylTmBYYaGhTY0nVH0tMbulaYfRJBlKAo1LKDg/oNxvd2Z8WUsrzBba9CHwN9JJSbimwrdouNzCW3NxckpOTyMrKxq2cs3UpilI8tXTt0amxpOKpsziKUrmopWsPQUqZJYSIBIr7izgvdbQXcL7ANi/9z8KWtSkVTKPR4OTkXHxHRVEURSmCmuQoimkzuaVrhRFCWAPeQHQxXfPyW7YvZFswkARcLMPQFEVRFEVRFEUpByY10RFCuBaxaSq6s1fr7ulbSwjRTAhx7zU3O4HbwAtCCPt7+rYGQoHlUsoSFR5VFEVRFEVRFMV4TGqiA7wnhNgvhJguhBgnhHhdCLENeB04iK4YaJ4Z6JanBeY16CcxE4A6wG4hxItCiLeALejSVE8pj6BLk7hAKVx1fA2r43MujHodyp96jasG9T49uur4GlbH51wY9TqUP2O8xqY20dmBbnnZKOC/wEeAC7qsa6FSSm1xB5BSLgcGoMu89h/gTWA30FFK+VDX56xbt+6B2x/2jS/uuOWxf0n3Ka7fw24vqr2yfkA96nv0IJ999tkj7V/a2MrqvS+uj6m891B+739F/99/1N81pWyosaTstle1zxM1lpS+j6m896DGkkdhUskIpJRrgDUl7DsaGF3EtvXA+tI+fnJy4ckhfv/9d0JDQ4vcLycnp8h9H6S445bH/iXdp7h+D7u9qPaHfQ3L26O+Rw+SmJj4SM+5tLGV1XtfXB9Tee+h/N7/iv6/X5Lftcr6HlRFaiwpeT81ljw6NZboVNb3HtRY8iiqRXrp8iaEqMXfGdsURVGqs9pSytvGDqIqUmOJoiiKQZmMJWqiUwaELj9lTXS1ehRFUaoreyBKqoHloaixRFEUBSjDsURNdBRFURRFURRFMTmmloxAURRFURRFURRFTXQURVEURVEURTE9aqJTjoQQTYQQHwshDgghYoUQyUKIE0KId4UQdgX6ThJC7BBC3BZCZOh/bhdCDDZW/BVNCPG2EGK5EOKqEEIKIcKL6R8khNiqf12ThBB/CCHaFNG3thDiF/37oBVCHBFCDC+XJ1JCQoimQojFQojzQoi7Qog0IUSYEOJL/UXJhfVfLYRIFEKkCiF2CyG6FXFsJyHEbCHELSFEuhDirBDi//TXABhVZXmfhRBW+v+f1/T/564IId4TQliUwdM0Kv3rWtjtvms/yuv3SgihEUJM1P9Opwshbgghvij42acUT40lpVNZPmMqihpL1FhSXkxiLJFSqls53YCZQDKwGHgZGAf8BkjgJGBzT9/fgJ+BN4Dn+bvIqQTeN/ZzqaDXSwLxwJ9AAhD+gL7BQDpwBZiov13Rv96tCvR1Aa6iu8D3Y2AMuppLEnjOiM+3O7ANmA68qI9rtj7OSMDjnr4N9a9NNPC2vv9xIAvoUeC4lsAh/bYvgX8DK/XP90P1Phv6r9Zv+xF4Qf9TAj8b+zUqo9d4F/BMgduIAv3K7fcKmKXftlLf90v9vtsAjbFfo6p0Q40lpX29KsVnTAU+XzWWqLGkPF/jKj2WGP1FNOUb4A84FdL+if5NG1/M/uboBrFkwMzYz6cCXq8G9/z7TDEfWofQFYf1uqfNS9+2pUDfz/Svd/972sz0x4gH7I393AvEO1wf7xv3tC0DcoA297TZAxHABfSJRfTtL+r3f7nAcX8HMoF61f19Bvro+35R4Bhf6Ns7GPv34BFf4xINsuX1ewX4ArnA7wX6vqw/xtPGfo2q0k2NJaV+vYz+GVMZbmosKf/3WY0l5ft7VRZjidFfxOp4A1rp36DvStB3o/5NtjZ23BX8GhX5oQU00r9+Pxay7Uf961XznrabwOVC+v5Tf5x/GPv5FogrUB/X/7d350FzFGUcx78PVxIugQSBNwSIWGKw5CaGCJgoIAgSoTiVlEGDRzhELC2LkqMEiyqRowzBMkrlrGAVRxANBSQxCUaBBCMECbcGcmAgEC1ykpjHP7rXnWxm3/fdfXd2dsffp2pr3p3pme3unXef7dme7lvi890IV6Jmp6S9LqYdnFg3H1hXec4AJ1UGvbwfeb3PwNS4bkBF2gFx/d15100P69UJV/V3ocqXryzPK8pfwE+qSNs7HuORvOuoCA/Fkm7VkWKJYoliSf312vaxRPfo5OPAuFxVucHM9jGzfc1skJldD5wOzHH3jU3NYWs7Pi6fTNn2FGDAsfC/Cfj6x/VpaZPHy4WZ9TazfmZ2oJmdBvwybnokLo8AelG9vBDLYGY7AMcAf005ZxYQPtBzLW8NsnyfjwdWuPuyZML4fCXtU0edOQ9YD7xvZm/H/tAfSmzP8rw6Pq5bkEwY932WYtRvK1As6RnFkjLFEhRLqmjrWLJTVwmkscxsR+B6YAswLSXJK0Df+PcWwk95Y5qTu7bREZcrUraV1vWvI21eRhP6U5csBS5x9z/G57WUYW+gT1pad99kZu+Sf3m7K8v3uQNYUuV1V1D+AtmuFgD3Aa8BexK6V1wBfMbMhrr7WrI9rzqA1e6+qcqxh5rZLu7+QW3FkhLFkoZQLClTLKk9bSm9YknQkrFEDZ3mu5NwU9y17v5yyvZzCT/J9Sf0r+1DOLneaVoOW9+ucZl24m+sSFNL2rw8BLxE6M96NHA2sG9ie6PKW0qfd3m7K8v3edcqaUvp26WOUrn7pypWTTazxcBPgO/EZZbnVVf1W0qjhk79FEt6TrGkTLGk9rSlv4tQR6mKEEvU0GkiM7uJ0BIe7+63pKVx9ycSTyeY2b3AfDM73N3XNCOfbWB9XPZK2da7Ik0taXPh7ssJfYIBHjKzB4CFZtYnnieNKm8pfa7lrUGW7/P6KmlL6duljmpxK3ADcCYhOGV5Xq0HPtxJ2uQxpUaKJQ2jWFKmWFJ72tLfRaijWrRVLNE9Ok1iZjcCPwImEIYG7a5JwP6Eq3MSrIzLtJ/NS+tW1JG2Jbj7YsKwjKVuJrWUYQ2wIS2tmfUidGVpqfJ2Isv3eWWVtKX07VJH3ebumwnl7hdXZXlerQT6xW1px16tbmv1USxpKMWSMsWS2tOW0iuWBC0ZS9TQaQIzu4HQ+p0MjPY4ZEQ39YnLfRqesfa1MC5PSNk2hDBCx18A3P0twj/NkCppAZ5pdAYboA/l9/x5wk+31coLsQzuvhVYBByd8sEwmPA/34rlTZPl+7wQ6G9mA5IJ4/MO2qeOus3MehP6i5duXM/yvFoY1w1OycNRFLB+m0GxpOEUS8oUS1As6Y62iyV5D11X9AfhZlEnBKbUiY0IQ/NtN2wfYdz2WaQMrVf0B12Pib+QMP59R2JdR1w3qyLtrVQfE38NsEdOZdy/yvrhhPHoZyfW3RfXHZlYVxqj/hW2HaP+cqqPUb8ZGJj3+5v3+0z4yd2pPvfBiXnXTQ/qtG+V9aX6SQ7dmcl5RRj2uLO5Dy7Ju57a7aFYUne9KZYoliiW1FenhYglFneQDJjZ5cBdwJuEscS3ViRZ5e4zzewoYB5wP2FipfcIP8ldDBwGTHL3Uc3Kd17MbCRwcHx6JWHc9tvi8zfcfUoi7VBgDqE/8tjEPvsBn3b35xJp+xKu1vQlzKi7glC3wwhXRe/JqEidMrPpwAGE2X3fIPQ3PRa4iNDndJi7PxvTfpTyTMJ3ED6cLyN8CJzp7o8ljrsL8GfgSODnwIuEkVLOAW529+uaUb5qWuV9NrPfAWcR5lF4knA16uvAVHcf2aDiNp2Z3UG4ijaH8NmzO+H9Hw48DQx39w0xbWbnlZmNJdxHMp0wvO0g4CrgT8BnPVzZk25QLKlNq3zGNItiCaBY0nCFiSV5txiL/CBMsuSdPObGdP2AcYSZq9+LJ8pqYCbwFRIt4SI/gLld1VVF+hOA2cBawozfjwHHVDl2f2BKrNeNhJ9OL8y5vBcAM4BlMU8bCCPmjAUOSkk/CPgt8C9C8JoPnFLl2HsRvhitJPykvCR+UOR+LrXK+0z4MnAzYQjWTcDfCV8id867jnpYvyNiHa2IdbCOMN/AtaRMFpnVeUW4Avo9whfuTTE/t9Nis8e3wwPFklrrqyU+Y5pYXsUSxZIs6rcQsUS/6IiIiIiISOFoMAIRERERESkcNXRERERERKRw1NAREREREZHCUUNHREREREQKRw0dEREREREpHDV0RERERESkcNTQERERERGRwlFDR0RERERECkcNHRERERERKRw1dEREREREpHDU0BERERERkcJRQ0cKy8y8jsfEuO+w+HxuvqWoj5mNSinbcRVpbkxsW25mO3ZyvFMrjnVI1mWIr3t1Sjma8toiIqBYolgi7WynvDMgkqFJKev2Bz4PrAPuT9k+P9McNd/rlMu0upN0/YFTgUerbB/VwDzVYgnl9/E8YLec8iEi/78USxRLpE2poSOF5e6jKteZ2TBCcFqdtj1hATAIWJ9F3ppofhflBHgGOI4QgLYLTma2J3AO8CJwEE0MEO7+OPB4zMewZr62iAgolkSKJdKW1HVNJIW7r3f3l9z9zbzz0gRPEwLPCDPbK2X7RUAfYGIzMyUi0u4US7ahWCJNp4aOSIpq/arN7JC4fqmZ7WBm15jZC2a2IfZNvt3Mdo1p9zazO2PaTWb2qpld08lrmpldZGaPm9nquM+bZvarJvQlngj0JgSiSpcC/wGmVG6IeX411smQagc3swdjmjGJdXPjumFmdrKZzYjl3mpmX+p5kURE8qVYsg3FEmk6NXRE6jcN+DHwD8JP4rsB3wUeMLN9CFe3LgQWEvo2HwLcZmbXVh7IzHYm9PO+FziR0J/4YUL/79HAosobQBtsCiEAXVqRr8OAIcCj7v5W5U7u7sC4+HRM5fZ4jP7AF4H3SQlwwPnAHEJXhpnAbGBzXaUQEWk/iiWKJZIRNXRE6nMwcBTwMXc/y91HAEcA7wKnA/OA54CB7n6+u38OKF1Z+mHpSl3CTcC5wBPxmCfH/QYBVwJ7A78xs0zuq4uB5zFgsJkNSmwqBauJnew+AVgLXGBm/VK2f5NwP+Bkd38/ZfsY4Nvu/kl3v9jdT3X3GTUXQkSk/SiWlCmWSMOpoSNSv6vcfWXpibsvA6bGpwcTPnA3JrbPABYDexBu2AQgXrG7ivABf767L0++iLvfBcwADgXOyKYoQAgyEEfFsTBE6EjgPcIVwVTu/m/C1bVewNeS2+LVxcvi07urHGKmu4+vO9ciIu1NsQTFEsmGGjoi9dkM/CFl/Wtx+Yy7pw3B+WpcdiTWDSfcoDnP3d+u8nrz4vKEWjNag4cJgWhkDEynEfI5zd0/6GLfu+LyW2aW/Fw5lzAM61x3X1Jl3wd7kGcRkXamWLItxRJpKDV0ROrzT3ffkrJ+bVwuT9mW3N47se4jcXmmVZl8DvhpTLNvz7JdXQxA04ADCMOmlroaTKi6U3nfJcAsYCChu0VJqa/1uO12Knuj5syKiBSDYsm2+yqWSENpHh2R+mzt4fak0izSLwNPdZH26RqOW48JwBXA1cDJwGJ3X9TNfccCpxAC0iNm9ol4jJXAQ53st6H+7IqItDXFku0plkjDqKEjkr9lcfl8NyZky5S7LzKzxYSZraG2+Q5+Txg16Iw4hOnlcf34KlcsRUSkcRRLRCqo65pI/mYR+mmfYumTrDXbeMKIP29TviG2S+6+lXCT6A7A94FLgC3xeCIiki3FEpEKauiI5MzdVxH6He8FPGxmH69MEyeMG21m+zUhP+PcvZ+77+fu79S4+z3AekKXgz2A6WlzJoiISGMplohsT13XRFrDDwij0lwA/M3MniX8dN8bGAAMAnaJy1V5ZbIr7r7GzKYC34irOrtxVEREGkuxRCRBv+iItAB33+zuFwIjCP2TO+LfQwkXJKYB5wCv55bJ7psZly+4+7xOU4qISMMolohsy9w97zyISIOZ2SjCqDeTmn1TqplNJ8zcPcbdf9HA4y4lTJ430N2XNuq4IiKSTrFE2p26rokU24lmNjH+fWPWH+pmdixwNuEG1MkNON5pwJfj0349PZ6IiNRFsUTakho6IsV2aHxAmHF6aRYvYma/BnYHvkDoEnu9u69rwKEPB77agOOIiEj9FEukLanrmoj0WJxxeythZuq73f1nOWdJRETajGKJNJoaOiIiIiIiUjgadU1ERERERApHDR0RERERESkcNXRERERERKRw1NAREREREZHCUUNHREREREQKRw0dEREREREpnP8CESM1e+f1p0wAAAAASUVORK5CYII=\n",
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      "text/plain": [
       "<Figure size 936x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,2, figsize=(13,10), sharex=True, sharey=False)\n",
    "fig.suptitle(\"Planet c\")\n",
    "fig.subplots_adjust(top=0.93)\n",
    "\n",
    "# Otegi/heavy\n",
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    "axs[0, 0].plot(t1_c_OtP, (M1_c_OtP), label=\"high activity track\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[0, 0].plot(t2_c_OtP, (M2_c_OtP), label=\"medium activity track\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[0, 0].plot(t3_c_OtP, (M3_c_OtP), label=\"low activity track\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
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    "\n",
    "# 10Mcore\n",
    "axs[1, 0].plot(t1_c_10P, (M1_c_10P), label=r\"fast, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[1, 0].plot(t2_c_10P, (M2_c_10P), label=r\"med, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[1, 0].plot(t3_c_10P, (M3_c_10P), label=r\"slow, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
    "axs[1, 0].hlines(pl_c_10_PAPER.core_mass, t1_c_10P[0], 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
    "\n",
    "# 5Mcore\n",
    "axs[2, 0].plot(t1_c_5P, (M1_c_5P), label=r\"high activity track\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[2, 0].plot(t2_c_5P, (M2_c_5P), label=r\"medium activity track\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[2, 0].plot(t3_c_5P, (M3_c_5P), label=r\"low activity track\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
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    "axs[2, 0].hlines(pl_c_5_PAPER.core_mass, t1_c_5P[0], 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
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    "axs[2, 0].text(1200, 5.006, \"core mass\", fontsize=15)\n",
    "\n",
    "#radii\n",
    "# Otegi/heavy\n",
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    "axs[0, 1].plot(t1_c_OtP, (R1_c_OtP), label=r\"fast, step = 1 Myr\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[0, 1].plot(t2_c_OtP, (R2_c_OtP), label=r\"med, step = 1 Myr\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[0, 1].plot(t3_c_OtP, (R3_c_OtP), label=r\"slow, step = 1 Myr\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
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    "\n",
    "# 10Mcore\n",
    "axs[1, 1].plot(t1_c_10P, (R1_c_10P), label=r\"fast, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[1, 1].plot(t2_c_10P, (R2_c_10P), label=r\"med, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[1, 1].plot(t3_c_10P, (R3_c_10P), label=r\"slow, step = 0.1 Myr\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
    "age_arr = np.linspace(23., 5000., 1000)\n",
    "axs[1, 1].plot(age_arr, plmo14.calculate_planet_radius(pl_c_10_PAPER.core_mass, pl_c_10_PAPER.fenv, age_arr, pl_c_10_PAPER.flux, pl_c_10_PAPER.metallicity), color=\"k\", ls=\":\", lw=1)\n",
    "axs[1, 1].hlines(plmo14.calculate_core_radius(pl_c_10_PAPER.core_mass), pl_c_10_PAPER.age, 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
    "\n",
    "dy = 1.3\n",
    "axs[2, 1].text(345, 3.31+dy, \"thermal contraction\", fontsize=15, rotation=-12.5)\n",
    "axs[2, 1].text(340, 2.9+dy, \"without mass loss\", fontsize=15, rotation=-12.5)\n",
    "#xkcd:goldenrod\n",
    "\n",
    "# 5 mcore\n",
    "axs[2, 1].plot(t1_c_5P, (R1_c_5P), label=r\"fast track\", ls=\"-\", color=\"xkcd:royal blue\", lw=1, zorder=3)\n",
    "axs[2, 1].plot(t2_c_5P, (R2_c_5P), label=r\"medim track\", ls=\"-\", color=\"xkcd:grey\", lw=2.5, zorder=2)\n",
    "axs[2, 1].plot(t3_c_5P, (R3_c_5P), label=r\"slow track\", ls=\"-\", color=\"xkcd:red\", lw=1.5, zorder=1)\n",
    "axs[2, 1].plot(age_arr, plmo14.calculate_planet_radius(pl_c_5_PAPER.core_mass, pl_c_5_PAPER.fenv, age_arr, pl_c_5_PAPER.flux, pl_c_5_PAPER.metallicity), color=\"k\", ls=\":\", lw=1)\n",
    "axs[2, 1].hlines(plmo14.calculate_core_radius(pl_c_5_PAPER.core_mass), pl_c_5_PAPER.age, 5000., linestyle=\"--\", color=\"k\", lw=0.9)\n",
    "axs[2, 1].text(1075, 1.55, \"core radius\", fontsize=15)\n",
    "\n",
    "axs[2, 0].legend(fontsize=15)#, loc=3)\n",
    "for ax in [axs[0, 0], axs[1, 0], axs[2, 0]]:\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xticks([23, 100, 300, 1000, 5000])\n",
    "    ax.set_xlim(right= 6000)\n",
    "    ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.0f}'))\n",
    "    ax.tick_params(direction=\"in\", which=\"both\", labelsize=18)\n",
    "    ax.set_xlabel(\"Time [Myr]\", fontsize=22, labelpad=8)\n",
    "    ax.set_ylabel('M [M$_\\oplus$]', fontsize=22, labelpad=8)\n",
    "    ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f')) # No decimal places\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f')) # No decimal places\n",
    "\n",
    "ylim = axs[2, 0].get_ylim()\n",
    "axs[2, 0].set_ylim(top=ylim[1]+0.02)\n",
    "ylim = axs[1, 0].get_ylim()\n",
    "axs[1, 0].set_ylim(top=11.02)\n",
    "    \n",
    "for ax in [axs[0, 1], axs[1, 1], axs[2, 1]]:\n",
    "    ax.set_xscale(\"log\")\n",
    "    ax.set_xticks([23, 100, 300, 1000, 5000])\n",
    "    ax.set_xlim(right= 6000)\n",
    "    ax.tick_params(direction=\"in\", which=\"both\", labelsize=18)\n",
    "    ax.set_xlabel(\"Time [Myr]\", fontsize=22, labelpad=8)\n",
    "    ax.set_ylabel('R [R$_\\oplus$]', fontsize=22, labelpad=8)\n",
    "    ax.xaxis.set_major_formatter(FormatStrFormatter('%.0f')) # No decimal places\n",
    "    ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f')) # No decimal places\n",
    "\n",
    "plt.subplots_adjust(hspace=0, wspace=0.25)\n",
    "fig.align_ylabels(axs[:, 0])\n",
    "fig.align_ylabels(axs[:, 1])\n",
    "#plt.tight_layout()\n",
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    "#plt.savefig(\"./Plots_PAPER/planet_c_EVO_eps01_Zsun_largelabels.jpg\", dpi=300)\n",
    "#plt.savefig(\"./Plots_PAPER/Fig9_largelabels.jpg\", dpi=300)\n",
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    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot mass & radius evolution planet d"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 42,
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   "metadata": {},
   "outputs": [
    {
     "data": {
1341
      "image/png": "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