PFOptimization.ipynb

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   "name": "PFOptimization1.ipynb",
   "provenance": [],
   "collapsed_sections": []
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
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  "language_info": {
   "name": "python"
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 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fBiU8DdlsevD"
   },
   "source": [
    "<p><img alt=\"Colaboratory logo\" height=\"45px\" src=\"https://www.quantreo.com/wp-content/uploads/2021/04/cropped-Logo_Quantreo_transparent.png\" align=\"left\" hspace=\"10px\" vspace=\"0px\"></p>\n",
    "\n",
    "# Portfolio Optimization\n",
    "In this section you will learn the concept of portfolio management.\n",
    "The idea is to combine our strategies to reduce the risk of your investment. The notions we will study apply to strategies as well as to assets. You will be able to create your own portfolio using the algorithms that we will present.\n",
    "\n",
    "<br>\n",
    "\n",
    "### Content\n",
    "* Sortino Optimization\n",
    "* Min Variance Optimization\n",
    "* Mean Variance Skewness Kurtosis Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0nzOYQ4xsYZ0"
   },
   "source": [
    "### environnement"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FgQ_rx97sJeD",
    "outputId": "40d0763d-e950-499b-9403-fc6410bea89b"
   },
   "source": [
    "!pip install ta\n",
    "!pip install yfinance"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Collecting ta\n",
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      "Building wheels for collected packages: ta\n",
      "  Building wheel for ta (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Created wheel for ta: filename=ta-0.7.0-py3-none-any.whl size=28718 sha256=2145b324be66eefd34fc13f661c25cd31de88640fdbaa9d958faef5705b51f8c\n",
      "  Stored in directory: /root/.cache/pip/wheels/5e/74/e0/72395003bd1d3c8f3f5860c2d180ff15699e47a2733d8ebd38\n",
      "Successfully built ta\n",
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      "Collecting yfinance\n",
      "  Downloading yfinance-0.1.63.tar.gz (26 kB)\n",
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      "Building wheels for collected packages: yfinance\n",
      "  Building wheel for yfinance (setup.py) ... \u001B[?25l\u001B[?25hdone\n",
      "  Created wheel for yfinance: filename=yfinance-0.1.63-py2.py3-none-any.whl size=23918 sha256=c7e8704f88b61cf357345c5d29658b66d044d39dcc78f14007f8aad8baf75947\n",
      "  Stored in directory: /root/.cache/pip/wheels/fe/87/8b/7ec24486e001d3926537f5f7801f57a74d181be25b11157983\n",
      "Successfully built yfinance\n",
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     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "AEYJf9IAsSvS"
   },
   "source": [
    "import pandas as pd\n",
    "import ta\n",
    "import yfinance as yf\n",
    "\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Trm5QWowsWAy"
   },
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib import cycler\n",
    "colors = cycler('color',\n",
    "                ['#669FEE', '#66EE91', '#9988DD',\n",
    "                 '#EECC55', '#88BB44', '#FFBBBB'])\n",
    "plt.rc('figure', facecolor='#313233')\n",
    "plt.rc('axes', facecolor=\"#313233\", edgecolor='none',\n",
    "       axisbelow=True, grid=True, prop_cycle=colors,\n",
    "       labelcolor='gray')\n",
    "plt.rc('grid', color='474A4A', linestyle='solid')\n",
    "plt.rc('xtick', color='gray')\n",
    "plt.rc('ytick', direction='out', color='gray')\n",
    "plt.rc('legend', facecolor=\"#313233\", edgecolor=\"#313233\")\n",
    "plt.rc(\"text\", color=\"#C9C9C9\")\n",
    "plt.rc('figure', facecolor='#313233')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lHScxsr6sgyC"
   },
   "source": [
    "### Functions"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "2YxMqnAxshp-"
   },
   "source": [
    "def RSI(val,neutral, window):\n",
    "  \"\"\" \n",
    "        ------------------------------------------------------------------------------\n",
    "        | Output: The function gives the returns of RSI strategy                     |\n",
    "        ------------------------------------------------------------------------------\n",
    "        | Inputs: -val (type dataframe pandas): Entry values of the stock            |\n",
    "        |         -neutral (float): Value of neutrality, i.e. no action zone         |\n",
    "        |         -window (float): rolling period for RSI                            |\n",
    "        ------------------------------------------------------------------------------\n",
    "  \"\"\"\n",
    "\n",
    "  # Print Error if there is no column Adj Close in the dataframe\n",
    "  if \"Adj Close\" not in val.columns:\n",
    "    ValueError(\"We need have a columns name Adj Close because all computation are about this column\")\n",
    "  \n",
    "  val[\"rsi\"] = ta.momentum.RSIIndicator(f[\"Adj Close\"], window=window).rsi()\n",
    "\n",
    "\n",
    "  \n",
    "  \"\"\" Long buy Signal \"\"\"\n",
    "  # We put threshold\n",
    "  overbuy = 70\n",
    "  neutral_buy = 50 + neutral\n",
    "\n",
    "  # Put nan values for the signal long columns\n",
    "  val[\"signal_long\"] = np.nan\n",
    "  val[\"yersteday_rsi\"] = f[\"rsi\"].shift(1)\n",
    "  # We need define the Open Long signal (RSI yersteday<55 and RSI today>55)\n",
    "  val.loc[(val[\"rsi\"]>neutral_buy) & (val[\"yersteday_rsi\"]<neutral_buy), \"signal_long\"] = 1\n",
    "\n",
    "  # We need define the Close Long signal (RSI yersteday>55 and RSI today<55) False signal\n",
    "  val.loc[(val[\"rsi\"]<neutral_buy)&(val[\"yersteday_rsi\"]>neutral_buy), \"signal_long\"] = 0\n",
    "\n",
    "  # We need define the Close Long signal (RSI yersteday>70 and RSI today<70) Over buy signal\n",
    "  val.loc[(val[\"rsi\"]<overbuy)&(val[\"yersteday_rsi\"]>overbuy), \"signal_long\"] = 0\n",
    "\n",
    "\n",
    "\n",
    "  \"\"\"Short sell signal \"\"\"\n",
    "  # We put threshold\n",
    "  oversell = 30\n",
    "  neutral_buy = 50 - neutral\n",
    "\n",
    "  # Put nan values for the signal short columns\n",
    "  val[\"signal_short\"] = np.nan\n",
    "  val[\"yersteday_rsi\"] = val[\"rsi\"].shift(1)\n",
    "  # We need define the Open Short signal (RSI yersteday>45 and RSI today<45)\n",
    "  val.loc[(val[\"rsi\"]<neutral_buy) & (val[\"yersteday_rsi\"]>neutral_buy), \"signal_short\"] = -1\n",
    "\n",
    "  # We need define the Close Short signal (RSI yersteday<45 and RSI today>45) False signal\n",
    "  val.loc[(val[\"rsi\"]>neutral_buy)&(val[\"yersteday_rsi\"]<neutral_buy), \"signal_short\"] = 0\n",
    "\n",
    "  # We need define the Close Short signal (RSI yersteday<30 and RSI today>30) Over sell signal\n",
    "  val.loc[(val[\"rsi\"]>oversell)&(val[\"yersteday_rsi\"]<oversell), \"signal_short\"] = 0\n",
    "\n",
    "\n",
    "  \"\"\" Compute the returns \"\"\"\n",
    "  # Compute the percentage of variation of the asset\n",
    "  val[\"pct\"] = val[\"Adj Close\"].pct_change(1)\n",
    "\n",
    "  # Compute the positions\n",
    "  val[\"Position\"] = (val[\"signal_short\"].fillna(method=\"ffill\")+ val[\"signal_long\"].fillna(method=\"ffill\"))\n",
    "\n",
    "  # Compute the return of the strategy\n",
    "  val[\"return\"] = val[\"pct\"]*(val[\"Position\"].shift(1))\n",
    "\n",
    "  return val[\"return\"]"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "6nSEtxeCvazD"
   },
   "source": [
    "def drawdown_function(serie):\n",
    "  \n",
    "  # We compute Cumsum of the returns\n",
    "  cum = serie.dropna().cumsum()+1\n",
    "\n",
    "  # We compute max of the cumsum on the period (accumulate max)\n",
    "  running_max = np.maximum.accumulate(cum)\n",
    "\n",
    "  # We compute drawdown\n",
    "  drawdown  = cum/running_max - 1 \n",
    "  return drawdown"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "5AF_uwtvsncZ"
   },
   "source": [
    "def BackTest(serie):\n",
    "\n",
    "  # Import the benchmarcl\n",
    "  sp500 = yf.download(\"^GSPC\")[\"Adj Close\"].pct_change(1)\n",
    "  sp500.columns = [\"SP500\"]\n",
    "\n",
    "  val = pd.concat((serie, sp500), axis=1).dropna()\n",
    "  \n",
    "  # Compute the drawdown\n",
    "  drawdown = drawdown_function(serie)\n",
    "  max_drawdown = -np.min(drawdown)\n",
    "\n",
    "  # Put a subplots\n",
    "  fig, (cum, dra) = plt.subplots(1,2, figsize=(15,6))\n",
    "  \n",
    "  # Put a Suptitle\n",
    "  fig.suptitle(\"Backtesting\", size=20)\n",
    "\n",
    "  # Put the cumsum\n",
    "  cum.plot(serie.cumsum(), color=\"#39B3C7\")\n",
    "  cum.plot(val[\"Adj Close\"].cumsum(), color=\"#B85A0F\")\n",
    "  cum.legend([\"Portfolio\", \"SP500\"])\n",
    "  # Set individual title\n",
    "  cum.set_title(\"Cumulative Return\", size=13)\n",
    "\n",
    "  # Put the drawdown\n",
    "  dra.fill_between(drawdown.index,0,drawdown, color=\"#C73954\", alpha=0.65)\n",
    "\n",
    "  # Set individual title\n",
    "  dra.set_title(\"Drawdown\", size=13)\n",
    "\n",
    "  # Plot the graph\n",
    "  plt.show()\n",
    "\n",
    "  # Compute the sortino\n",
    "  sortino = np.sqrt(252)*serie.mean()/serie.loc[serie<0].std()\n",
    "\n",
    "  # Compute the beta\n",
    "  beta = np.cov(val,rowvar=False)[0][1] /np.var(val[\"Adj Close\"].dropna())\n",
    "\n",
    "  # Compute the alpha\n",
    "  alpha = 252*serie.mean() - 252*beta*serie.mean()\n",
    "\n",
    "  # Print the statistics\n",
    "  print(f\"Sortino: {np.round(sortino,3)}\")\n",
    "  print(f\"Beta: {np.round(beta,3)}\")\n",
    "  print(f\"Alpha: {np.round(alpha,3)}\")\n",
    "  print(f\"MaxDrawdown: {np.round(max_drawdown*100,3)} %\")\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "pEvBP0B1swFG"
   },
   "source": [
    "def opt(f):\n",
    "  # We set lists for the possible values of neutral and window\n",
    "  neutral_values = [i*2 for i in range(10)]\n",
    "  window_values = [i*2 for i in range(1,11)]\n",
    "\n",
    "  # Set some Border of the datasets\n",
    "  start_train, end_train = \"2017-01-01\", \"2019-01-01\"\n",
    "  start_test, end_test = \"2019-01-01\", \"2020-01-01\"\n",
    "  start_valid, end_valid = \"2020-01-01\", \"2021-01-01\"\n",
    "\n",
    "\n",
    "  # Initialize the list\n",
    "  resume = []\n",
    "\n",
    "  # Loop to add the values in the list\n",
    "  for i in range(len(neutral_values)):\n",
    "      for j in range(len(window_values)):\n",
    "        # Compute the returns\n",
    "        return_train = RSI(f.loc[start_train:end_train], neutral_values[i], window_values[j])\n",
    "        return_test = RSI(f.loc[start_test:end_test], neutral_values[i], window_values[j])\n",
    "\n",
    "        # Compute the sortino\n",
    "        sortino_train = np.sqrt(252) * return_train.mean() / return_train[return_train<0].std()\n",
    "        sortino_test = np.sqrt(252) * return_test.mean() / return_test[return_test<0].std()\n",
    "\n",
    "        # We do list of list to do a dataframe\n",
    "        values = [neutral_values[i], window_values[j], sortino_train, sortino_test]\n",
    "        resume.append(values)\n",
    "\n",
    "  resume = pd.DataFrame(resume, columns=[\"Neutral\", \"Window\", \"Sortino Train\", \"Sortino test\"])\n",
    "\n",
    "  # Order by sortino\n",
    "  ordered_resume = resume.sort_values(by=\"Sortino Train\", ascending=False)\n",
    "\n",
    "\n",
    "\n",
    "  for i in range(len(resume)):\n",
    "    # Take the best\n",
    "    best = ordered_resume.iloc[0+i:1+i,:]\n",
    "\n",
    "    # Compute the sortino\n",
    "    Strain = best[\"Sortino Train\"].values[0]\n",
    "    Stest = best[\"Sortino test\"].values[0]\n",
    "\n",
    "    # Take best neutral and best window\n",
    "    best_neutral = best[\"Neutral\"].values[0]\n",
    "    best_window = best[\"Window\"].values[0]\n",
    "\n",
    "    # If the Sortino of the train and the test are good we stop the loop\n",
    "    if Stest>0.5 and Strain>0.5:\n",
    "      break\n",
    "\n",
    "    # If there is no values enought good the put 0 in all values\n",
    "    else:\n",
    "      best_neutral = 0\n",
    "      best_window = 0\n",
    "      Strain = 0\n",
    "      Stest = 0\n",
    "  return [best_neutral, best_window, Strain, Stest]"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yXGhnTQysyuE"
   },
   "source": [
    "# Section1:  Sortino Optimization"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "2Ne3r48msz1F",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "52550b98-e4f9-4a33-d148-1e71bf9637be"
   },
   "source": [
    "# Import Yahoo name\n",
    "assets = pd.read_csv(\"/content/Names.csv\")[\"Symbol\"]\n",
    "\n",
    "# Initialize the lists\n",
    "resume = []\n",
    "col = []\n",
    "\n",
    "# Compute best parameters for each Asset\n",
    "for fin in tqdm(assets):\n",
    "\n",
    "  # Import the asset\n",
    "  arr = yf.download(fin)\n",
    "\n",
    "  # Put the values\n",
    "  resume.append(opt(arr))\n",
    "  col.append(fin)\n",
    "\n"
   ],
   "execution_count": null,
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   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "zEvAde_Is5_v",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "outputId": "af339fc5-89b9-4e47-c091-027b419b6912"
   },
   "source": [
    "# Add asset columns to each list of resume\n",
    "for i in range(len(resume)):\n",
    "  resume[i].append(col[i])\n",
    "\n",
    "# Create a dataframe\n",
    "res = pd.DataFrame(resume, columns=[\"Neutral\", \"Window\", \"Train\", \"Test\", \"Asset\"])\n",
    "\n",
    "# Indexig by asset\n",
    "res = res.set_index(\"Asset\")\n",
    "res"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Window</th>\n",
       "      <th>Train</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EURUSD=X</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURGBP=X</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURCHF=X</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURCAD=X</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EURCNY=X</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCOM</th>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1.348638</td>\n",
       "      <td>1.762285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INCY</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHKP</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOXA</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOX</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Neutral  Window     Train      Test\n",
       "Asset                                        \n",
       "EURUSD=X        0       0  0.000000  0.000000\n",
       "EURGBP=X        0       0  0.000000  0.000000\n",
       "EURCHF=X        0       0  0.000000  0.000000\n",
       "EURCAD=X        0       0  0.000000  0.000000\n",
       "EURCNY=X        0       0  0.000000  0.000000\n",
       "...           ...     ...       ...       ...\n",
       "TCOM           14       4  1.348638  1.762285\n",
       "INCY            0       0  0.000000  0.000000\n",
       "CHKP            0       0  0.000000  0.000000\n",
       "FOXA            0       0  0.000000  0.000000\n",
       "FOX             0       0  0.000000  0.000000\n",
       "\n",
       "[152 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "NyfRKxylt7su",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 700
    },
    "outputId": "978bf76f-8d66-472d-a9da-d0ba1482a408"
   },
   "source": [
    "# Order the dataframe using the Train sortino\n",
    "values = res.sort_values(by=\"Train\", ascending=False)\n",
    "values.head(20)"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Window</th>\n",
       "      <th>Train</th>\n",
       "      <th>Test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MRNA</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>67.114532</td>\n",
       "      <td>0.519912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BSV-USD</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4.165631</td>\n",
       "      <td>2.001604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BNB-USD</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>4.164220</td>\n",
       "      <td>1.113779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BCH-USD</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>3.524647</td>\n",
       "      <td>0.739881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADA-USD</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>3.299248</td>\n",
       "      <td>0.707319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XRP-USD</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>3.156333</td>\n",
       "      <td>0.509452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIOTA-USD</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.867645</td>\n",
       "      <td>0.663595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XLM-USD</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2.799206</td>\n",
       "      <td>1.379741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALGN</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2.664059</td>\n",
       "      <td>2.348276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH-USD</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2.650708</td>\n",
       "      <td>0.810355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOGE-USD</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>2.646644</td>\n",
       "      <td>1.035733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTES</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.638077</td>\n",
       "      <td>1.054469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEO-USD</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2.619008</td>\n",
       "      <td>1.173869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USDMYR=X</th>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>2.591273</td>\n",
       "      <td>1.696166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINK-USD</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2.518708</td>\n",
       "      <td>0.803128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIDU</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2.458010</td>\n",
       "      <td>1.459894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRX-USD</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2.386607</td>\n",
       "      <td>0.652456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC-USD</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2.366589</td>\n",
       "      <td>0.582449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MNST</th>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>2.296366</td>\n",
       "      <td>1.033525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QCOM</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1.962014</td>\n",
       "      <td>1.872762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Neutral  Window      Train      Test\n",
       "Asset                                          \n",
       "MRNA            10       4  67.114532  0.519912\n",
       "BSV-USD          4       2   4.165631  2.001604\n",
       "BNB-USD         12       8   4.164220  1.113779\n",
       "BCH-USD         16       6   3.524647  0.739881\n",
       "ADA-USD          8       6   3.299248  0.707319\n",
       "XRP-USD         10       2   3.156333  0.509452\n",
       "MIOTA-USD        6       2   2.867645  0.663595\n",
       "XLM-USD         10       2   2.799206  1.379741\n",
       "ALGN             2       6   2.664059  2.348276\n",
       "ETH-USD          8       6   2.650708  0.810355\n",
       "DOGE-USD         6       8   2.646644  1.035733\n",
       "NTES             0       2   2.638077  1.054469\n",
       "NEO-USD         16       2   2.619008  1.173869\n",
       "USDMYR=X         6      16   2.591273  1.696166\n",
       "LINK-USD         2       4   2.518708  0.803128\n",
       "BIDU            18       2   2.458010  1.459894\n",
       "TRX-USD         10      10   2.386607  0.652456\n",
       "BTC-USD         16       2   2.366589  0.582449\n",
       "MNST             4      18   2.296366  1.033525\n",
       "QCOM             8       4   1.962014  1.872762"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "i82rHZQ2t_u5",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "c86768d2-5dd4-47f2-d4ad-f363b2b2b919"
   },
   "source": [
    "# Border of sets\n",
    "start_train, end_train = \"2017-01-01\", \"2019-01-01\"\n",
    "start_test, end_test = \"2019-01-01\", \"2020-01-01\"\n",
    "start_valid, end_valid = \"2020-01-01\", \"2021-01-01\"\n",
    "\n",
    "# Create a dataframe to put the strategies (The assets of the portfolio)\n",
    "strategies = pd.DataFrame()\n",
    "for col in values.index[0:15]:\n",
    "  \n",
    "  # Import the asset\n",
    "  l = yf.download(col)\n",
    "\n",
    "  # Extract opptimal neutral\n",
    "  best_neutral = values.loc[col][\"Neutral\"]\n",
    "\n",
    "  # Exctract optimal window\n",
    "  best_window = values.loc[col][\"Window\"]\n",
    "  \n",
    "  strategies[f\"{col}\"] = RSI(l.loc[start_train:],best_neutral,int(best_window))"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "YduTyi3w4Lqh",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 657
    },
    "outputId": "7681ba8f-8278-43bd-89fe-251b115d79ec"
   },
   "source": [
    "strategies"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MRNA</th>\n",
       "      <th>BSV-USD</th>\n",
       "      <th>BNB-USD</th>\n",
       "      <th>BCH-USD</th>\n",
       "      <th>ADA-USD</th>\n",
       "      <th>XRP-USD</th>\n",
       "      <th>MIOTA-USD</th>\n",
       "      <th>XLM-USD</th>\n",
       "      <th>ALGN</th>\n",
       "      <th>ETH-USD</th>\n",
       "      <th>DOGE-USD</th>\n",
       "      <th>NTES</th>\n",
       "      <th>NEO-USD</th>\n",
       "      <th>USDMYR=X</th>\n",
       "      <th>LINK-USD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001796</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>0.006880</td>\n",
       "      <td>0.030327</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.036214</td>\n",
       "      <td>0.041306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.003338</td>\n",
       "      <td>-0.007800</td>\n",
       "      <td>-0.004871</td>\n",
       "      <td>-0.002481</td>\n",
       "      <td>0.028123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.021510</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.021016</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.050029</td>\n",
       "      <td>-0.048863</td>\n",
       "      <td>-0.030341</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.045196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053242</td>\n",
       "      <td>-0.048477</td>\n",
       "      <td>0.048840</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.007970</td>\n",
       "      <td>0.028955</td>\n",
       "      <td>0.045817</td>\n",
       "      <td>-0.016662</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.005714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044149</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015527</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.005087</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.019838</td>\n",
       "      <td>-0.033595</td>\n",
       "      <td>-0.007577</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.011015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.034849</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117870</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.119794</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.023744</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.069399</td>\n",
       "      <td>0.009394</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.021791</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017789</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.040371</td>\n",
       "      <td>0.002093</td>\n",
       "      <td>-0.029958</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.036990</td>\n",
       "      <td>0.005952</td>\n",
       "      <td>-0.043725</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041506</td>\n",
       "      <td>-0.069959</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.142195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.018069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.044246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035228</td>\n",
       "      <td>0.031369</td>\n",
       "      <td>-0.005464</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.005206</td>\n",
       "      <td>0.014751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006535</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.026624</td>\n",
       "      <td>-0.053906</td>\n",
       "      <td>-0.031812</td>\n",
       "      <td>-0.025313</td>\n",
       "      <td>-0.012167</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.007298</td>\n",
       "      <td>-0.036932</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.030964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>-0.024055</td>\n",
       "      <td>-0.017707</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.030793</td>\n",
       "      <td>0.023729</td>\n",
       "      <td>-0.143095</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.012234</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.017311</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.071478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-20</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.122836</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.050054</td>\n",
       "      <td>0.100833</td>\n",
       "      <td>0.101607</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.011440</td>\n",
       "      <td>0.072298</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.026182</td>\n",
       "      <td>0.104334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>701 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MRNA   BSV-USD   BNB-USD  ...   NEO-USD  USDMYR=X  LINK-USD\n",
       "Date                                      ...                              \n",
       "2018-12-07       NaN  0.001796  0.085433  ... -0.004871 -0.002481  0.028123\n",
       "2018-12-10       NaN  0.021510 -0.000000  ... -0.045196  0.000000 -0.000000\n",
       "2018-12-11       NaN  0.053242 -0.048477  ...  0.044149  0.000000  0.000000\n",
       "2018-12-12       NaN  0.015527  0.000000  ... -0.034849  0.000000  0.000000\n",
       "2018-12-13       NaN  0.117870 -0.000000  ... -0.000000 -0.000000 -0.000000\n",
       "...              ...       ...       ...  ...       ...       ...       ...\n",
       "2021-09-14  0.000000  0.017789  0.000000  ... -0.069959  0.000000 -0.142195\n",
       "2021-09-15  0.000000 -0.018069  0.000000  ...  0.014751  0.000000  0.001087\n",
       "2021-09-16  0.000000  0.006535 -0.000000  ... -0.036932 -0.000000 -0.030964\n",
       "2021-09-17 -0.024055 -0.017707 -0.000000  ... -0.000000 -0.000000 -0.071478\n",
       "2021-09-20  0.000000 -0.122836 -0.000000  ...  0.104334  0.000000  0.099083\n",
       "\n",
       "[701 rows x 15 columns]"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "n_A6TGp0GxyE"
   },
   "source": [
    "strategies = strategies.iloc[:,1:]"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "_6XDz28CuQMu"
   },
   "source": [
    "def SR_criterion(weight, returns):\n",
    "  \"\"\" \n",
    "  ------------------------------------------------------------------------------\n",
    "  | Output: Opposite Sortino ratio to do a m imization                          |\n",
    "  ------------------------------------------------------------------------------\n",
    "  | Inputs: -Weight (type ndarray numpy): Wheight for portfolio                |\n",
    "  |         -returns (type dataframe pandas): Returns of stocks                |\n",
    "  ------------------------------------------------------------------------------\n",
    "  \"\"\"\n",
    "  pf_return = returns.values.dot(weight)\n",
    "  mu = np.mean(pf_return) \n",
    "  sigma = np.std(pf_return[pf_return<0])\n",
    "  Sortino = -mu/sigma\n",
    "  return Sortino\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "n=len(strategies.transpose())\n",
    "\n",
    "# Initialisation weight value\n",
    "x0 = np.zeros(n)+(1/n)\n",
    "\n",
    "# Optimization constraints problem\n",
    "cons=({'type':'eq', 'fun': lambda x:sum(abs(x))-1})\n",
    "\n",
    "Bounds= [(0 , 1) for i in range(0,n)]\n",
    "\n",
    "\n",
    "# Optimization problem solving\n",
    "res_SR = minimize(SR_criterion, x0, method=\"SLSQP\", args=(strategies.loc[start_train:end_test].dropna()),bounds=Bounds,constraints=cons,options={'disp': False})\n",
    "\n",
    "# Result for visualization\n",
    "\n",
    "# Result for computations\n",
    "X = res_SR.x\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "f3cyTrMJuTuG",
    "outputId": "9c9e1756-beff-41f2-d41e-37a683b1d810"
   },
   "source": [
    "# BackTest Sortino Optimization\n",
    "sr = np.multiply(strategies.loc[start_valid:end_valid], X).sum(axis=1)\n",
    "BackTest(sr)"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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wmksuuZim5iZcThdPPvE4H3/yCW63O3id/Hzu/PKXQu/Foz9+BIvFwuWXXc4Na6+ntq6WG9auZeKkiSxffi633Hwz55yzjIb6BqqrqyPwt0QIIcRw6/ydAuByuSgsLCI1JZWVK89n3br1YT//Nc3Al7/0Ja677louvuRicnJmc+TIUZxOJ3a7nV889SQff/IJHo+HOXNyePTHj9DQ0ED5sWMYDAaeePwxCgsKaWpuJiM9nbvuupO1a6/njDNOp7W1lby8PN54800AzGYzn7vuOm666SbWrLmEGTNmcPjIEdxuN5MnT+bhh37Ev957D6UUy5Yu5cEHv0txSTH19fXEx8fz1JNPsGHjRuLj4njyicepra3lS1+8g2uuvpqcnBwOFRTgab8XEWKskjF3YszKyMggIyOD7du3D/lcs2fPpqa2hm/f/x3+8Mc/ct2113LLLTfz4osvcd83vklVZRVrr//8oM/vcrn43e//wH3f+CaPPf4Es2fN4tI1awB48aWXKSkp4a233+aee+/jRz96qMfx27dvJzMzk+zs7NC2pUvzKSoqor6hAZPJxL33/CeVlVV87/s/4KGHHyY5OYnrB9hmo9HIeStWAFBdUwMEs6K33HwTr7zyCt/45rd49tlnWXvDWmbNmkVzczP/+7//RyAQ4J577+Oee+9jy5atA34/zj3nXP76179yz733sXfvvmB/8vN5//113HvfN/jww4+47bZbMZvNAz6nEEKIsWfHjh0kJyczYcIEIPzn/xtvvsl3HvguDz74PTxuD3fc/m8AOBwOjh07Rm5uLgBzc+dSU1ND7tzg79OnTUNXitKyMgwGA3d/7W6OV1byrW/fz69//RtWLF/epS2f//znmD59Gj/72c/47oPfo83Rxtfu/iqaplFeXo7f72fGjBnBa83NDV4rdO1cjh+vpLW1NXS+0884nccef4LvPPBdYmNjuPKKy4frbRQiYiS4E2NWfFwcAE1NzUM+V3V1NZ98sgmlFAcOHKTN4eDgwYNUVVWh6zrbt29nytSpgz7/gQMHqaysRClFbW0tH320gdzcOQM+3ul0sXfvPpYtXRratjQ/n02bNgOwcMECNE3j9TfewOfz4XS6+Ofrr3PWmWeiaVqv511zySU88fhj/L//+QVXXnkFf3rueSoqKgBYuWol6z/4gJKSwyil+Ky0lG1bt5Gff/Yg34UTPv7kY8qPHQPA5/MBsGPnTo4cOYJSio0ff4zNZiMjI2PI1xJCCDF6GpuaAIiz24Gen/8VFRUUFRXh9/txu928+dabzJgxI/Rwr6CgkLntAVZubi7/fP11cucEvz9z5+ZSVFiEUorp06aRmpLCq6++hs/no6a2lvffXxdqh6ZpLM3P55//fJ2m5ma8Xi+vvPJXMjMzmTZtGgCFhSeuNWfOHP7xz9eZmzs3dO2CgoIufXvzzbdwOBy43W62DfE+QYiRImPuxJjV2l4imZSUSFV7GeFgNXebeMXn9dLcfGKb1+vFEhs76PPPzc3l0ssuJXPCBEwmEwaDocvTv4HYtHkzt//bbfz11VeZPXs2VquV3Xv2AJCalkpKSgpPPP5Yj+MSExJoag4fAL/9zju8/fY72GxWbrn5Fubk5LBp0yYA0lJTmZOTwwWrV4f2NxgMXUpRB6u+vqHHtuZObfR6vQBYLJYhX0sIIcToSU5KAqDN4QB6fv6npaVx3bXXMn36NGI7fc/Gx8fT0NDAoYICbrv1Fmw2GxMmZLBr124uXbOG7OxscnNz2bF9BwBJycm0traFHhgC1NXXhX6Oi4vDbDZTV18f2ubxeGhtbSUlOZmjR49yqKCApflL2bV7N06Xi127dnHjF27AbreTmzuHv/zlhS5t7/K95RnafYIQI0WCOzFm1dTUUFNTw5lnnElBQWGv+7ndHmJjTnzgJiYmDvnaJ3NOo9HIV75yF6/97W9s2rQZn8/H+eedx4UXXhDaR1eq32seOnQIv9/PwoULWbx4ETt27Ax9iXWMT3v4kR8Pqj9Op4vnnn+eRx55mEULF7J33z4aGhrYvGUL7733fthjwrW5Y9xjbGxMaFtiUs/3Rg2gv0IIIU59p59xOo2NjaEx1N0//2+68Qs0NTfzyI8fxeFwMHFiFj/4/vfpqDkpKSnBbrdz/nnnUVxSgq7rFBQUsnjxIqZPm8Zzzz0PQFNTE/HxwQCu47sxNTU1dJ22tmDgl5qaEpo0LDY2NhhENjYCUFBQwBduuIElSxZTcOgQSilKSkpYsWI5CQkJFBUXD+dbJcSIkLJMMaa98MKLnHXWmVx11ZWhACs+Pp6LL76YM844HYCysjIWLVpEXFwcsbGxXHXllUO+7smc02QyYTKZcDqd+Hw+sjIzOf/887rs09LSQkZ6ep/XVEqxZetWVq08nyWLF4cybAD7Pv0Uk8nEJZdcHHrymZSYyOJFiwbcJ6fTybr313HVVVeiaRrr1n/A6lWrmDVrJpqmYTQamTJlMlOmTAm12Wg0dvnydDgc1NfXs2zpMjRNY+LEiZx7zjkDboMQQojxITk5mcsvv4yl+fm8/Morve5nsVjweryhCVSuuPyKLq/7fD4OHznChRdeQMGhYFlkQWEBq1etoqWlhZr2ceJHjx6loaGBa6+5BrPZTFpaWpfKE6UUW7Zs5corriAxMRGz2cx1111LVVU1n332GQB1dfU0NDayetUqDoWuVchFF17I0aNHQ1UlQpzKJHMnxrRDBQX8/LHHuXTNGr7/vQdD69x9+umnbN4cHI/2/rp1TJo0iUcefoi2tjb+9re/s2zZ0n7O3LeTOafH4+GFF17k2muu4aYbb6S0tIxt27dzzrJloX3WrVvPrbfewhOPP0ZTU1OvGbjNmzaz5pJLqDh+nM9KS0PbfT4fTz71C66+6ioe+tEPiY2Npbm5mR07d4adfro36z/4gNWrV5F/9tls3rKF5//8F6699lomTJiA0nUqKyt5/fU3gGDm9KOPNvCd+7+N0WjkpZdeZuu2bfzx2T/xhRvWct55Kzh69CifbNrE0vz8AbdBCCHEqenSNWu4+KKLUErhcDg4cuQIP//5Y12+r7p75a+vctONX+CpJ5+goaGR9957jyVLFnfZp6CggNw5czh06BAARUXFxMTEsGv3iXVZdV3n6Wd+yU033sjPf/bf1NXVsfHjj7n+85/vdK2/cs3VV/PAd+7HZDJx+MgRnnnmmS7ZxIJDBSxbtpTCoqLQ72uvv55D3cbbCXGq0pacdrrUTwkhhBBCCCHEKU7KMoUQQgghhBBiHJDgTgghhBBCCCHGAQnuhBBCCCGEEGIckOBOCCGEEEIIIcYBCe6EEEIIIYQQYhwY18FdYnLSaDdh2EVDH0H6OR5FS1+ln2IsioY/r2joI0g/x6No6av0c3iM6+DOYBjX3QOio48g/RyPoqWv0k8xFkXDn1c09BGkn+NRtPRV+jlM1xvRqwkhhBBCCCGEGBYS3AkhhBBCCCHEOCDBnRBCCCGEEEKMA+M6uGtubBrtJgy7aOijEEIIIYQQon/jOrjTNG20mzDsoqGPQgghhBBCiP6N6+BOCCGEEEIIIaKFBHdCCCGEEEIIMQ6YRrsBp5qn/+9/qaiowGA0UlVZxR+ffRafzzegY7Ozs0lKTGT/gQMAmEwmvva1u4mz23nn3X+xc+fOsMfdd+89/PXV1ygrK+PrX7ub3/3+D7hcroj1SQghhBBCCHHqk8zdSfJ6vTz6k5/yyCM/JhDws2LF8gEdZzAYmJydzfz5eaFtkydnA/DoT37aa2DX3f/+39MS2AkhhBBCCCF6kMzdEBSXHCZ70iRsNhu33nILaWlpeH1e/vznv1BRUcHll11GWnoa6WlpNDQ0MHPmTMxmMzNnzuSdd//FVVdeQXx8PA9+9wF+9evfkJqaynXXXYvRYOCz0lJeeOFF/H5/l2s++uNH+MlP/wuHw8Hq1as4Z9kyFPDJJ5+wfv0Ho/NGCCGEICM9nQUL5oOmUVZaRnFJSZfXDQYDpy1ZTGJSEj6vl+07doYe1s2eNYspU6eAUuz7dD+1tbWj0QUhhBCnuFM2uDNdch2GzOy+d9LAqAZ+Tr3qGP53Xh3QvgaDgfl58zhw8CBXXH455cfK+eWvfsWcOTn822238uhPfgpAVlYWjz32OD6fj6X5+UydOoUXX3oZgNaWFi648AKefvoZTCYT9917L0/94hfU1NTwb7fdxooVy3sN2KZMmcyypUv5+WOPo+s693/7WxQXFVN+7NjAOyyEECJiFi5cwKbNW3C5XJy3YjlVVVW0trWFXp8yZTJen49169YzaeJE8ubNZcfOXcTHxTFp0kQ++OBDLJZYli1dyvvr1o9iT4QQQpyqpCzzJMXExPDgdx/gge/cT0NDI598somZs2aydetWAAoLi7Db47BYLADs27dvQGPyJkyYQH19HTU1NQBs3rKF2bNm97r/rJmz2LNnL16vF4/Hw+49e5g1e1YEeghaagYY5K+GEEIMVHJyMg6HA6fTiVKKiorjZGZmdtknKzOT8vLgA7jjlZWkpaUDkJmZSUXFcXRdx+l04XA4SE5OHvE+CCGEOPWdspm7gWTYjEYjgUAgotftGHM34P093ohef9jFJxL7Hz8gULgf45z5eH77OKr86Gi3SgghxjSLxdJlPLTL7SY5OanXfZRS+P0+YmJisFgtNDY2njjW5Q49IOxs6tQpTJs6FYDq2lqq2x8GDobRaCQvMY0MZRz0OcaaBF3DhIYGJCgDVX4dV8KEPo8xKpgZMOHWFGXGgd0vTAoYqTYEMKLh0U6iPGiYGAIG9H76OR5ESz8hevoaDf0MoCg0KJJTUwZ9jsb6hpPa/5QN7saSkpISzjrzLN56+21yZs+mzdGG2+3usZ/b4yY2zBc2QHV1NampqaSnp1NbW0v+2WdRXFzc6zWLS0q47dZbee/99zEajSxevJg//uGPQ+6LZrUBYJwzP/j/vCX4JbgTQohRV1paRmlpWUTOlZyawsw2Ra7BjFcNT4Bi0jT8w3TucJI1EzoKBVQqH3l6LLF+HQ86Fgw40Ql0a49FM9Ci/GRpRpKVNqDrJGoGpgAWDLQQGNE+hmM2G/H5Rj/IHG7R0k+Inr5GQz+NGPjM6KW68eQCtKGQ4C4C3njjTW695Ra+9+CDeH1enn32T2H3Kyws4uKLLubB7z7AO+/+i9aWltBrfr+fZ//0HF/+8pdCE6ps2Lix12uWl5ezectmvv2tb4YmVInIeDuLtcuvytHWy45CCCE6uN1urNYTn59WiwW3yx12H7fbjaZpmExmvF4vbpcba6fPXqvVEvYBYaT5UTiVjgN9WM4/lVjK8QzT2XsyKNA1QMHL/nrMCYmc1hxgrmbFpCncSsfZrTUBpahTPsxotDCwzF0MGm6lMGoaLSqAj9G9OY0xGPEOsO2nsmjpJ0RPX6OhnwmMfHWEBHcn6Z577+uxzel08stf/arH9jfefLPHfv/13//dZVtRp+xcYWEhPwlT8vnEk0+Ffn7we98P/bxu3Xo+/PCjiJaeahZb1w2+U6ysVAgxYjQUK1Ka2N9qH7Eb+LGqqakJu92OzWbF5XIzadJEdu7a1WWfqqpqJk/OprGxkYlZWdTV1QW3V1dx+mmncfjIESyWWOx2e5cyzeHiVzrawJJVJ81AMHg0oKGPVPCjaWgo/JqihQBJGhxXXhYYbHjQMdOzsyY06vCTRUyvpzWjEUChAxrBflk0DR8qzBmFEGJ0SXAnurJ2C+7MvX/hCSGiW7bFwyUZ9TT4TET7PL2qfQmDpfn5aJpGWVk5ra1t5M6ZQ1NTE1XV1ZSWlXHaaUtYvXoVPq+XHTuDwV9raxvHj1eyauX5ofOMBB+0j1CLPCMaXnSMgL/fvSNDofAoRQP+UDjZpAKY0WhVOmZNo3ucqTRo1P309TbYNUPoXEY0fEph0Qx4JLgTQoxBEtxFA5sdQ8ZE9M96H8PXQesW3GkxscPVKiHEKW623Ymu4LDDRqw8B6KmpoZ167tOclJQWBj6Wdd1duzYGfbYouLiLpUcI8E/jMFJMLhTGGmvkxwBCnCjU6GfqDhpUn50FI3KTxpmLGh42sflAehK0awCwQxmmGZa0GhSARI0IwaCN00BaA/ydAwj2KDNZf4AACAASURBVD8hhBgIme8+CsTcfDcxt//ngJY36FGWKZk7IUQ3OXYHcUY/s+1OKtyxOPXxM+NiNBnOskIT4FNqxG8y9upOdumO0O9t6DiVTonyYNY0YjUDCRiJa2+ZhhYM7no5n0UzsFt3cDDgJA4jRjQcBPCjpCxTCDEmRSRzl5GezoIF80HTKCsto7ikpMvrM2fMYOrUKehK4fV42L1nb5cpo8Xw0tKzgj+YzOD19L1z97LMGAnuhBAnLElo4fqJNdR5zSSbfXxUL+uxnaqGO3PnRsc+guGdUtCiAj0miKlUPiqVlzYVYIvexnHdy0pjApMMwe+3ZnoP7hTgUYo9OJmv2UBpVCsf6ZoZPZSZFEKIsSMin7oLFy5g85atrF//AZMmTSQ+Lq7L683NzXy0YSMffvgRxysryZs3NxKXFQMVaB/xYDaHfdl0xQ0YF58NBMsylaMNvexw8HcpyxQiKiSafOQnNdNXiVm2xc01mbVUumNIMfuo8cSwpTFx5BopIso/jOWEmgYudLThmrGlF4EwfXov0Mwx5eXNQBMHdCe1+Hk50MB7gWYqdR/+TmWavZ2zVvmpVj7iNCPVyodb6XglcyeEGIOGnLlLTk7G4XDgdDoBqKg4TmZmJq2dsnd19fWhnxsbGsnOzh7qZcXJ6JhN09QzuDPMmofpjHNR85YQOLQXLDZUcyPe3z1JzFfuB7MEd0JEgxWpTSxLbqbJb6Kgzd7j9Xijn5snVdLqN/Lb8knEGnRa/Ub8Sqr7T1UBFMM1ZExXwfFuadrIDu0PN2trxxIHx1TX2Z/36y72E6wi6shidn8rlDoRMG4POJhmisWhdFoI4FQ6CZqUJAshxpYhf+paLJYuJZYut5vk5KRe958ydQo11TVhX5s6dQrTpk4FoLq2luqarvs1Nzad1FNATdMwGiP/wXvxxRdx5hlnoOs6SileeOFFrrr6KhITEvD5/Hg8Hp7/85+pqanhlptvZtasWbjdwffoueee51hFBQCf/9x15OXl4fV6ee6550Pr1J199llccvHFALzz7rts3bpt0H1USqG1f90lpqVhMHV9/zyrLkV3OdBsduLOvwQ9PgECPuypKXiUDnY7cakpg3+zIsRoNJI8Btox3KKlnxA9fT01+qmYnxBcHHtNZhNNTVl41ImvByM6NyQVYDUp/tw4l9ikYPl2fKczDLWfjfUjt8CrCAqo4ZsKRBGcqGQk1/dWhM/cDUSgjzi3I2A8qtxUKx9OdLYE2pigmcnWZOiCEGJsGdFHatnZk0hKTOKTA5vCvl5aWkZpaVnErmc0GiO6BhzA9OnTmZ+Xx6M/+Sl+vx+73Y7JZAKl+N3v/0BZWRnnnnsOV199Fc8880uU0nnttdfYtXt3l/PMz8sjLT2d7//gh0yfPo21a6/nv3/2c2w2G2vWrOGnP/0vQPHAAw+wZ88enM7wYxQH0kfd68UAtDicqG43UDG2OFTh/mDGbvFS8HrQKz7DWd+A2elEs1hxjoGbruTUlKi4+YuWfkL09HW0+qm136Z25CPOSGzhqNNKva/nzWhWrIeEdC/7W+3MjXNwa+I+XqyYQJk7uLD2lRNqmWRu488VmRS1uoGeC2xHy5/neDLYQGggNE2jcVgLP8NcEwa9pl6glxJL1emcfuD1QBMNyo8fRbwmI+6EEGPPkOtp3G43Vqs19LvVYsHt6vnFn56WRs7s2Wzdtg1dP3WXu01MTKStzYHfHxzH5nA4aG5u7rJPcXEJGenpfZ5n4aKFbNmyFYCjRz/DarORkJDAvHnzOHToEE6nE6fTxaFDh5g3L29ojQ6VZfa8qdNMZpTfj//Dt9BsdrSkFFRHJtbrkdkyhThF3ZZdyYOzjnLlhFpWpDRxXVYtV0yo67GfxRBg7cRqPLrG36sy+FVpNkrBnVMrOCe5iTSzl7OTmtnUkMj+1rgwVxKnqgAMX2ZNBdeFG8khd8HM3eAEg7vw4V3nc9YoXyhk9aMY0Q4KIcQADDlz19TUhN1ux2az4nK5mTRpIjt37eqyT2JCAosWLWTzlq14vd5eznRyLs+oJcvS98yPGlo/w6S7qnTH8kZN30HZoUOHuOzSNTz0ox9SUFDIjp07Ke62NtHChQuoqDge+v3Kq67k0ksvpbCwgL/9/R/4/X6SkpJobGwM7dPU2EhSUhLJPbY3kZzUe5nrgLQHd5rZ3PPdMJnB70NVlhMo+BRj7gJwB8dP4vXIbJlCnIIMKGbYXLT6jZyR2ILZEPyXn2TuuZz0/HgHE2K9/KE8C0fAiCNg5P99Npnrsmq4fEId+clm/EpjvcyKOe4MZ+bOoEGj8o/sEnDaEMoygXBTjmlovZ4zuF3WuBNCjC1DDu6UUuz7dD9L8/PRNI2ysnJaW9vInTOHpqYmqqqrycubh9Fk4swzTgfA6XKxbdv2ITd+NHg8Hn7y0/9i9qxZ5MzJ4UtfvIO///0fANxxx+34vF7q6xt46eWXAfjb3/9BS0sLJpOJm266kYsuupC33np7ZBvdx4QqmEzg9wHg//AtjLkLUG2tACifF4NMqCLEKScj1ovZoPhXXSoFbXbm2B3kxDlZktBKjEHHq58o2kiN8RJQUOI4sQyKWzfyQkUmt2ZXkhXr4e9V6TgCIzsxhhh++kk9/hw4A8HlA1zo6CiSMNI06JzawGkEJ3IZDB+KmHCZOy38JC0QXMdPQjshxFgTkW/rmpoa1q3vOvlJQWFh6OdNm7dE4jJd9Jdhg+EZcwfBgLaouJii4mKOVxwnPz8fgN+3j7nrrKWlBQC/38/mTZu54MILgGDGMzn5xJPwpORkmpqaaGxqIidndqftSRQVdc0MnrRAMHgLF9xp5hhoLzFVleV4fvs4qqYy+KLXC7IUghCnnEntVQ0V7lg8uoF9rfG4dAOnJ7Yy2eLmsPNEIJdi9tPoM6N3u7HV0fjjsfY1MmVk0bgU/HaMfHhiQsOBjg78yV/HreY0tGGcvKWz4RhzF+ildnVYy1qFEGKQZA7rkzRhQkaX8XTZk7NpaKjvdf+EhITQz4sWL+L48WC55r59n5KfH1xbbvr0abhdLlpaWjh48CDz5s7FZrNis1mZN3cuBw8eHFKbVUeA232dO2MwtlftmTsAVX4UPO1jJr0etJgYGVMgxCnkjMRmPpdVg19BvffEv/lylwWfrnHVhFomxp4YF50a46PBG34NzGBQJ//+x6tgWWHk/3yNaLSp4PdOMwFaVADzSPw9UoMfc+dX4YO74CQtvRyDGqbcpxBCDJ7U2Zyk2NhY1l6/FpvNSiCgU1tby/N//jN33fnlsPvfccftwUXdNY1j5cf4ywsvALB//37mz8/jkYcfwuv18uyfngPA6XTy1ltv85377wfgzbfeCq0hOGidxtx1YWr/4/f3HIcDoHztYxrN5mAWTwgxpp2W2MJ1WbUAFDtsXXIRbt3IH45lsTarmq9OO8br1elsa0ogxeyjzGUZrSaLUTRc69wZgVZ1IsxqUAGmaEa8wxwIaQx+zJ0/NKFKz+P7GnMXfhIWIYQYPRLcnaSysnJ+/thjPbY/8eRTYfd/6qlf9HquF198Kez2TZs3s2nz5sE1MJxAe/DWvSyz4/dOmbsuOgI6c6wEd0KcAhYntFLtieGZ0klhFxc/6rTxP0encP3Eaq7JrGWq1Y3VqHfJ8Ino0bG2W6QZ0UILhwPUKx8ztZEp8R/sXNx+enkvVO/BnV/ydkKIMUjKMqNBR2au18xd+OBOtQd0moy7E+KUkB7jax9nZySgwt+2O3Ujzx7L4qP6JE5LDE6e1OCT4C4aDddsmZoGjk7BXZMKjEx17xBmy/T3Muaur7LMUOZTCCHGEMncRYP2dQW1buvcae2ZO9VLWSbe9rJMWQ5BiDFKEWvQ8ehGYgw6SWY/NZ7+AzWFxju1aVR7Ylia3EyZSx7gRKMAXWc5ydLMVCvfoLNfHXQFrk4zjbQSGPQslidjKIuY+5Uefnh5HwGjX2I7IcQYJMFdNOj49jF1++PurywzNOZObvyEGCvijH4uSq9nT0s8q9MamGJx82rVhFBQV+sd+MOY3S0J7G5J6H9HMS51nnzE1L6eWwwa7ghk9FydQsTWjsXMhznAC65JNzj+9uN7nrPvCVWkLlMIMdZIcBcNOh5HnmxZpjs4o55mtcn3lxAjKNXsxRkw4tKNPV6bZXdxZlIrZya14g5oVHliWTuxmiPO4KQoJxPcieimo0JBlwWNVqVjxoB7iGvSKQ1c6kRI5Oqx0MbwGMrclb5eyzL7W8RcCCHGFgnuooHWPrSyW1lmKHPn6yW4a24IHp6UMlwtE0J0E2sI8PVpxyhy2HjheGaP1xNMwTLqbU0JrK9Lps1v4tqsGk5LbCXQbfkDIfrSeRIRk6ZRrXxM0sxDzkYZVNfMnQ+FYZjDu74ybAPR25g7UH1m7gwjkJEUQoiTMa6DOxUFq4sOqI+GYHDXfSmEfsfctbWgfD605NQhtVEIMXBnJbVgMerMi2vDagj0yN4lmv24Agb+VpUR2vZKZQbVnhjsxsAI5UjEeKB3mspfBw7rbjINQ384oGng7hQS6ZxYJHy4vpU12sskBylc5q6/gFEyd0KIsWhcz5aZmJw02k0YdgPqYyhzd3JlmSiFam6QzJ0QI8SI4pzkJuq8ZkwGWJjQ1mOfRJOfZn/353IaGxqSebs2bWQaKsYFnWApo4Fg0HVEdw/50YARcKue05r4elkkPFI0QB/CA93BlF72unyCEEKMonEd3Il2vY6562dCFUA11qMlyQ2jECNhUUIrieYAr1en0eQzMdXq7rFPoslPi29cF12IERQALBio0r00EZz4pLeAZSA3DEY0HGHyXR70YS3NDC5gPnjhljXQCK4F2PdxEuAJIcYWCe6igaG3zF1HcNdLWSagmuolcyfEiFAsT2mi0h1DkcNGrddMWoy3x14J5nCZOyEGJ6AUFs1AqfKgCK5JZw4TrtgxkKr1//fOiIZD9QyzPO0ZwuHT+8QnAxEIM/FlMGDs+5wupdNz2iMhhBg9EtxFg/bMndYtuNPMwS9q1VfmrqkezR4HspC5EMMqx+4k0+JlY0MSoFHnjSE9xkfnW04jijhjgGa/3E6KyOgYC1epgt8D1cpLTJjgzoyGRylM/eSpjEBLmODOq/RhveGISOauxzn7X1qhlQBGyd0JIcYQCe6iQUfmbpBlmQBakkyqIsRwOjuphWafkX0t8QDUes1YjDrXZNaS3p7Bizf5MWjQLGWZIkI6gpea9uCuUvkwhVnNWwdq8WEPc9tgxYCJYABoQqM1TEjkHuYZM4c6oUoA6D5kbyCZuxYV6DfgFUKIkSTBXRTQOr6oe51Qpa+yzPblEGTGTCGGhYbCgGJCrIfPXFYC7TeKdZ7g0iVnJbWwNLkZCK5xB0hZpogYP4pG5Q8tXN6o/Ojh4hkNCvXwy5vbNANWjCRpRuI0I21hxtyNTOZu8MFdzylgBnbOJuWX4E4IMabIHUI00DqWQuhlnbtAH8FdR+ZOgjshhsXlE+rItrhJMvvZ13LiAUxNp/XqZtqczLE7uDqzhqNOC0ecttFoqhiHAiiO6SfGdjaq4OMFG4b2yVEC6ASfBNcoX49Qx0gwQDQCXqUwaqrLAuYdRiJzFxjibJmDGXPX0j4JjayKIIQYKyRzFw16mVBFM5mD4+36+kJ0tqG8HplURUSUEcUqeylxxt4fLESLXLuDKVYPRg0aOpVbtvhNlLtiOe6OISPWx02TqqjyxPLssSwCSjIFIjJcSqdcnQjuWglQqFyUKy8lyk2cZiRFM9GmdJxKp3vFphUDDqWjtODw7lYV6LKAeeg6oZz08NCGOqEK0D1CC5Z69s2h9PCZTiGEGCWSuYsGHd/GFgtaUkqo1BKTqc+SzA7B5RAkcyciJy++jTNs1ejpCV0W44428SY/KTEn/g02+k48gFFoPF06mcxYD/85vZwGn4nfl0/Eo8tkKiJyDrUHch0U8HagOfS7LWBgvsGGAfCi98hQGdEoU27ysOJBsVd30tTLbJnDvc5d76PH++dHBYcwdOqfhtZvNtAZtqBTCCFGj2TuooFmQG+oBZ8P861fh7jghA2YzH1OptIhuByCBHcicuym4M1fNGegZtuc3DO9rMu2Bp+5x35Vnhier8jkt2WTcAYksBORtV93hV2XroMTnW16G1v0NryoHpk7DSjR3Zg0A050PtZbcYYbc4diOP+5D3XMXa3yUat8xHda2GAgk7Q41PBmJIUQ4mRJcBcNDBqqphLv80+jxSUSc+u/g9XWHtwNIHPX1CBj7kREWQ3Bmz93IDo/gjQU12bVYDMG3wefrqGr3mbB1DjQGkdbQAotxOjydsu+xaDRiJ865UcD2sJk7Dr4VM8xbZE09DF38Lq/ET+K2PZeDmR5BSc6BonuhBBjiNwtRAPNAEqhjn2G74VfYb75q5ivvhl83j7XuOugGuvRLFawWMHtGoEGi/HObgw3wiV65MY5SDL7+bA+iRKHjTUZ9diMAXTJAZySzGYzZ5xxOjarFafLxY4dO/H5en62Tp6cTc7s2QAUFRdTXn4MgHOWLcVisRAIBP9dbNq8Ba+35wL2o03RXr7Y/rMVA/t1Jy50Aiha+wruCLNKeIQNZSkEgFZ0/hlo5HPGFPztYZ2/j6wmBIM/jwou0N73nkIIMTIkuIsGBgPowa8d/WgRgZ2bMC4+G/1o0YDLMiE4Y6aqPDasTRXRIcEczBjHGsbe7VB6jJc6r3lYRwgtT2mi0WfivdpUdDQ2NgSwGoayBLMYTbNnz6Kuto7ikhJmz5rF7FmzOHjoUJd9zGYzc3Jy+GjDRgDOW7GcqqrqUBC4c+cumpqbe5x7rHEr1T5DZjBWK1VevCh8KJr7yHP5hjn00dD6nfxkII4pL5v0VpYZ4wkohW8A2cBWAtgw4I3ax1VCiLEkOmuioo2mQaepqVVlOVqsBS0ja8ATqgBoSWnD1kQRXZJMwb93MWMsuMuK9XDfjDIWJbQN2zUmW9xMt7n5uCEplKnb2xLPlqakYbumGF5ZmZmUlZcDUFZeTlZWZo99MjLSqa2tw+fz4fP5qK2tIyMjfaSbOmQedAxowb+5GlS1L37uUAHa+gzuhn/MXX9ZtoEqVV50Ombg7F+rLGQuhBhDJHMXDTQDnedq1quC2TdDSjp6S1O/h5/I3MlyCCIyEkOZu7H1pHtBfDCoy41zsKclfliusTylEVfAwI7mhGE5vxh5sbGxeDweADweD7GxsT32sVgsuFwnytpdbhcWiyX0+5Ili1FKcbyykqKi4rDXmTp1CtOmTgWguraW6pqaQbfZaDSSnHryn+nKE0OsrmFFo9agE5+QDIDbbcZkjiPZaA17nFXXiHWb8WhGhiMOitE1zGY7yZ3Wcx1sHzUFsS4zOooYk5XkmL5vlTxeMxa/Gf8ofZ5pmkZMbEz/O57ioqWfED19jYZ+mnQNo1Ef1GdRh8b6hpO75qCvJE4dhm6Zu5qqEz8PIHOH24VyOWXGTBERJk0nvn22zLFVlqmY3x7czbI50VARLc2MM/pZldZIXryDDQ1JeHUpnDiVLFuaHzZoO1RQ0GObOsmJPXbu2o3b7cZkNHLmmWcyOdtF+bGeJfClpWWUlpaFOcPJS05NOekbBoAmYzKJWgxGTWN/oIXGNicAhwx2Sh0eGnspjmwEDhkDzDJYaO5jbN5gBTDS7GmlUXeGtg22jwAOkxmrZqDF46Cxte9MfpXBRq4hAe+A8nyRFxMbg9cz9sZoRlq09BOip6/R0E8/RgKBAI2Ng/ssGgwJ7qKAphm63mwE/MFgzWpDP7h7QOeQ5RBEpKTFnBjnOZaCuwkxXtJjfRx1Wphuc5MV6+G4x9L/gQOUG+dgaXJwTNWmRinBPNVs2ryl19c6snUd/w83GYrb7SYt9URpu9Vipa6+LvQagD8Q4FjFMZKSk8IGd2OBSwWCzwuBY/qJfu7UHf0e+36gmUlaDBY03BEen6a0yIy561CPnxnEDmiSlo5F3GXInRBiLJBHx9FA01B615to759/ife5pwns3DSgU8hyCCJSplmDN7I1fuuYCu7mxzvQFfyzOjgOarbd2c8RJ6cjW/n9whm0+uW52nhSWVXFlMmTAZgyeTKVVVU99qmpqSU9PR2z2YzZbCY9PZ2amtpgWVJMsCxJ0zQyJ0ygtaV1RNt/MhzoxGgGPEqn/iTDKTeKNwNNWDQDdgzYInoLooa0zl13NboPE1rYNfu6c6AzhFUYhBAiouQOIxoYDF3KMgFU+ZGT+hpUjfUYZuZGtl0iaixOaMWAYl9rHFOtLpp9RmoDNjINY2d2wLz4NkpdFqo8sRx3xzDb7uKjCFZRxJkCuAIG/EqeqY03xcUlnHnG6UyZMhmXy8X2HTsBSEpMZNq0qezZuw+fz0dRURErViwHoKioCJ/Ph9FoZGn+2WiG4DQltXW1fFZaOprd6ZMLHTtG9umDe/hxTHnZEXCwxGCPaLsUQ1vnrrty5WWb3sZBvf/lf5wqAJK5E0KMERLcRYNuE6oMhmqqR4uJBXscOIZvJkEx/sRoOp/LqsaowWWBOgwoCh12dLNxzEyokmr2kmXx8kZ1sGyu2GHjnJQmYjQdb4SCsThjgDa/MSLnEmOLz+cLW7bZ1NzMnr37Qr+XlZeHZtXsEAgEQssjnAo8SlHVHvgM1sd6K8eVl8uMyRFsWf8Ljp+MEuWmJOAe0L4OdCmDEkKMGfJ5FA26TagyGKq1BQDN3j6DoCbTPouBmW5zYdTgnZpUShw2zAZFQZsdrzKMmbLMvPjgeKH9rcFswqE2OyYNbsmuZHVaPYYIPJKPM/lpC0hwJ05txcrNc/46moYQSimgpSPbFSFKEdGyzJPhRRFgWCYBFUKIkyaZu2igGRjygABn8CmtZovDkH8+xrPOw/s/D0WgcWK8m2V34tM1PmlMxK8MGFDowMREN2aDav99dG+L5se3Ue6KpdlvBqDUZeUfVelclVnLLLuLYy4LhY6hlZHFGwNUenrOtijEqcSPGtAkI/3xRnQu2qDRCu4A2trXuvNJbaYQYpRJ5i4aaBroQ8zctQd32OMwZE3GkJoOCTLjn+hbnNFPXryDUpclNNYsGMhpeFUwizXaC5lbDQEmWz0causavG1pSuR7hTNxBQyhJRKGIs4UkMydEO18KLQIxkGqPXs2WlpkIXMhxBghwV00CDOhyslSnTJ3xCcGT5uRNeSmifFt7cRq7MYA6+p6Lt7ZEdyNdmlmdvvsnaWunsseBJRGQZudefGOHqWZy1MauSCtfkDXMGk6VqMuY+6EaOdDRbi6XxvVzF2z8iP/uoUQY4EEd9EgImWZ7WsY2ePQ2oM7LWPiEBsmxjMNxRSrm21NCXzmsvZ43deeyRvt4G6yxYOuoMIdvmRyf6sdm1Fnuu3ErHlJJh8XpdezPKUJk9Z/++3GYE6hVYI7IQAiXr6oaaNbltmEZO6EEGODBHfRIAJlmeg6yulAs8WhtZdjapK5E31IMfuIMSiqehlndiJzN7pjVLKtbmq9Zjx6+MCryGHDo2ss6FSaeV5qIyYNYgyqS9DXm4417toCMsxZiA7+iI67UwRG8aOkrWMhcyGEGGUS3EUDQwQydxAM7pJS0SzBLIyUZYq+TIj1AlDtiQn7ekdwZxnVzJ0i2+LhmLtnSWYHvzJQ2F6aGWsIMDHWzcKENva32vHpGnMGsNh5XHvmTsoyhTjBq1QEb0K0iEz0MlhOAkNdcUgIISJCHiNHg0hk7gCcrRgmBEsxlduFlpw29HOKcSsz1ouueg/umgLBjF56rJdip20kmxaSZPITbwpwLMx4u872t9pZmNDG3VOPkRHrA2BXcwJmTZEzgODO3p65c8iEKkKEeFGYIzVWbhSXQgBwKF0WMhdCjAmSuYsCWkQzd8GJMVRtFVh6jqMSosOEWC+NPnNobF13bXoMTT4TU6wDWyh4OGRbPQCU9zLerkNhWzBL1xHYQbBcs8hhIz3WR4rZ18fRJ8bcOSRzJ0SIJ8KLf4/mbJlO9IjO/imEEIMlwd14Zwj+EashzpYJoBwnxhzptZXBoDG274yHiE52o585cQ4+6ycjVu6KZbJlFIM7ixu/DlX9BHdeZaDIEcwu/rZsIj87PJWA0ihsC26bY3f0ebzNGMCna3hlUI4QIR6lMERo1J2mgT6KaTMPOkb55y2EGAMkuBvvOuaajkRZpuvEDayqrQr+INk7Ecb5qY2YNMWHdcl97lfutpAS4yfO6B+hlnU12ermuCeWwABuMD+oT2Z9XTKHnVYafcHFzut9MdR7zeTE9V2aaTcG2ksy5e5PiA7uCGbuNEa3LDPQ/p8GGIFEWRhBCDFKJLgb77T2P+JIZO4a6gAI7NmKamoInl6CO9FNgsnP2Ukt7GqOp84Xfrxdh7L2zN7UUSrNzIz1cryfrF2HCreF9+pS6R6gFTpszLC5+lwSwW4KyHg7IbrxqN6DuzgMJ3WDEgzuRpe7vT+xGLBocnslhBgdMqHKeGdovxGNwJi7wO7N6KUlqLpqDNNzghsluBPdrEptQNMU68MsXN7dMZcFr64xw+7iQFvcCLQuaF5cG36lYTPqNPuH9jFY1GZjWXIz020uih32sPvYjQGcAbnZE6IzNzpaL9lsm2ZEKXAwsAeTOqM/l4kbhQUN4ygvqC6EiG4S3I13HU8PIzFHs66j6qqB4GyZAJrFJl9hIiTF7OOMpBa2NiXS5Df3u38Ajc+cFmYMYK24SDGguC6rBo8e/LfRMsTg7ojTik/XmBfn6DO4a/D2/34IEU3c9L4Ugg+FqT306+87ZrRLMju4VQCbZsJIcDyhaZSXZxBCRCd5lDzedYy5i0BZZhftwZ1k7kRnF6Q1EFBav2PtOjvitJEZ6w2Nu7MYAmSHmWTl8oxazktpHHIbp1jd2Iw6yebg9Vp9QwvufMrA3pY4zkhqIdXsDbuP3ahLWaYQ3XjQsWsGEsKMT9NR9AN0bwAAIABJREFUoEGmZiamn7GqGuCPQHXKUDkJThCjNPCiY5YxtkKIURCR4C4jPZ3Vq1ayevUqZs+a1eP11JQUzluxgisuv4ysLFn4ekQZOjJ3kQ3uTmTuJLgTQRkxHhYltLK5MZHWwMADpsPO4N+h6e3Zu3NTmrhzSgWGTk+87cYA+cnNnJ/agLmPsW0DkRvXdWbLlggsT/BubSq60lia3NzjNSM6FgnuhOjBqXRKdE/YEMgAGBS40LH1M37NAIzOlExduVQAI4TKSSW4E0KMhogEdwsXLmDzlq2sX/8BkyZNJD6u69gZp8vF7j27qaioiMTlxMkITagS4aeaHsnciaCOIOyi9Aa8usaG+oFn7QCOu2NxBzRmtgd3iSY/ZoMiznRieoS8+DaMGliMivnxbb2dqlcaCq29nXPinF2qlIc65g6gLWCixW8KrWfXmdUQvO2U4E6Irg4rDy8G6qnHT2ynQMhAcAxdjGagWQX6/frSxsgYNwd6MHMHVCpfqHBGCCFG0pCDu+TkZBwOB06nE6UUFRXHyczM7LKPy+WipaU14vGFGABD5GbL7ELXUR43msUW2fOKU8p5KQ08MOsz8uLayIt3sLEhGad+ckGMjsZnLisz7cHgriOoSzCdeBa/OKGVGo+ZBq+JvP/P3p1Hx3Vnh53//t57taKwEyAA7hQpUdRGUUtrV7fU3eq2252xE/fiOePYTrwej5eZM048iXM8J4njJMfJZOw4jp3YcXuP1/bSdm9St6SW1GqK2iUuIiksJHagCrW+9Td/vKoCQBRILFVAsXA/5+gIqHr16j0QqHq37v3d237tmXK1fGpwkn9+9BLdEZeBmFOdWecGilJQn+p0OzCImSv/zhJKgjshruXtoLCsu6SBwtEaD82FoMT8VcHf1RQ0xdq2IgGGUigF09qVax4hxLbY9FVNPB6nWFxshlAslYgnZLB106jnnLurlYqSudshFJoP9s7RHXGBcF3cR3bN8tG+OVKWz2eGJsh6Js/PdW1o/xfyCXZFXTosr5r9ai8Hd7ujNoeSJU5lOhguJtY99NxAc6IzR9IMONGRBeCF+U6gUpJZn4/XnUARM1b+nSWN8GeWr0P5pxCtaDhwloVmJrCAT14HTGiXN5cEfyawVy0fsdIswZ2tNQEapSGjfRlrKYTYFk3VLfPAgf0cPHAAgMnpaSanpja1P9M06e69fjv2G9n1zjFo78QGkskkVp1/FiXXwezopL1BP+PSZ34Ec+wSkee/uCP+LaF5f2d7zQJP9Vzgqb45XioMciI+RdzwOVPqoc1w2RfN8mJ+L209u6jdL3Klpec6bcWAWe7oN+iIhhdpA50xJmI9PJ56H08rLhj7iRqz3B3Jsq8vRS649gy9igORxXVwj/QuMOfFmYvvIdDjFFS8bj/vwJymzXBX7K/DCmdCGu29dCdb94Ovzf7uzs/O1fFoxI1kFg9bB1iEa+cMFEUdkMNnVntMas1j5Y+iIyjccpfNykcpzdJQxSZA63ACUVZv99Q9IcROtengrlQqkUgsZm8S8Til4sYGEg8PjzA8PLLZQ6rq7u1p+QuG652jChQxIJ/NEtT5ZxHNZ9GGSaFBP+N4/xBe/xC5z//Bjvi3hOb9nd3dlofydfsDyXHeybbxlZkexu0YBxJF7u2Er09YBKz92JeeaxpNocNgIJgmqcJMl2UvUJg3Od4zzesLKa7MZDHjAR9uh/biBKNrnIv3SP909euk4XE63cbsbJr5TovZYv2CilzMpyPmrthfYm/4ejg2ncPRhbo8VzNq1t9dcWM4H5S43UiygI9BWOL4JT9dbZQyh0cSgwgGRR02K7HL2TrVJCMHHDSmgqIOcAi2f/CeEGJH2nRwl06naWtrI5lMUCyW2LNniFdOn67HsYl6aFRDFcKOmap9Y2V44sag0BxMlNgdswH4hfcO4gRGdUYcwHAxwXBxc+W5GsXFQoJbUgUiRvi72m553N2xQMzUvFQuoxy3o3ga9iVKvHOd4C5m+OyN2+xLlLiQT7A75pCyfN7NhbnF37k8SKmOg8XtwKhZlpkyXEq+wtEyeUaI1VzUNncQroU1UBR0sKwD5ltBgcfNDtCQIyCBgU2YHQvLMrefrQOiGMzg4aLDVRES4AkhttimgzutNW+8+RYPPvAASilGRkbJZnMcu+UW0uk0E5OTdHV1cv999xGJRBgY2M2xW27hma99rQ6HL66r0Wvu+mS0RWvS3JrK85FdcwzGHbwgbD6SreMatatdLCS4fUmzlA7LZ393hrFijLFSWM7oaYNpO8ruWO15cks90TvPY71pAg3PznURAPuUzXB59MKkHavr8a+25i5luJselC5Eq7usHVCgdLiursDyssaLgV0tzZzVHgeMaDVwUoRDz7dbnoAr2uGrfganPPNOCCG2Wl2uOKampvjq08vXx505e7b6dTqd4Utf/ko9nkqsV6O6ZQK6WMDYioYqpjSi2ErHUnk+umuWwbjDjBNh3rXojnhM2RaN7BBwMb/4u+QGiltSYQnjH4/3L9tu2okyFLevszfNHR3hyARDwWgxzpsLKdotH79B5xB2ywxHLuglz9FmOmQdCe6EuBYHzZXAYZeKgILiVcFaGp9ZPPaoKGOBzT69uObWQpFrgjVuBQI+581Uvw/KrwTbH3YKIXYSqRNqdZWyzKABby+VbpmNGOZjLPnVTHXUf/+iJoXmHwxMEjU0fzLez3+8uJ93smEZ47wbaehzTzpRcuWOkuN2eOGW80zeWFhefjntROiJuFjXGGa+N27THfGwg/B3c7QU54od52x+re1e1q9SqlopK61IGW454ymEuJazukRMhXPinBofSL4VFHDR1SxfVXn0QLNxtJbcnRBiy0lw1+qM8ltLIzJ3pSLKMCBa3/I2AKzFQEJ1yLq+rbInbtNmBXxlpodXMh0EKEaKYUnkvNvo7FO47g7gi9O9fGGql18d3ot31Vq1KTuKoaA3svrF3O3tOTwNvzM2yJene8huQVlkJbiLLQs6tZRlCrFGo+WRCFpTbZay1MXAZk57zGkfteRuTZjZazZSmimE2A5yxdHqKlm1RrSJLpU7/8UTYG+sQ+qqrMVfTdXRBYXMNTYW9XKkLfw3Pb+kRLIS3M06jc3cAbySaSdlebxfSHCxkKy5zbQTZvX6Yi6TTq0PFjR3tOe4kE9yoRD+txWcSnBnBGTL15lxIyCiArIywFyI65rDo6gDLBRTNTJxGXz+0JvFJljWrMQA5nUztFRZzik3fhFCiK0krzqtrlqW2ZjMHYCKN+Di2bwquBNb4ua2ApdLMfL+4s8/7UX4jZEhXs50Nvz5z+Xb+I2RvQTX+LR72okQaBiMLa67G4zZqPKV3lDMpifq8WZ2baMS6qWSuft4/0z12DrKg9izDc96CtEaXvCzfN6fp0Dt96wiAQHhjDtF2HylpIOamb7tZpfn8QkhxFaS151W18CGKpSDOxrQVEUtzdx1rX0wsnnvw0Q+/Y/rfjw7QcwI2J8oLcvaVVwsJKuZqe3maYNLhQQnO7MYaA4kivzEoVEe6g6zu3d05PA11bWCW6Wyvu94e4F7uxYAuLMjh9bhmj8hxPW9qYuM6et3w3V0gAFEMZhuwqwdhKMRpCxTCLHVmuNqTTROQ+fchSV8qhEdM5esuTMOH6u9jWliHLtzWTdN8/7HMY+fgLatzdq0gsPJAqaCc/mtKWPcjOfmuuiKeNzVkWVPuXNmX9QhLMnMcyGfoBhsbSnk0tl/uyIuptLc37XABaeLuQY3oxFip7HL69miKCaasJkKEJaPbvdBCCF2HKkVanWNnnMH0MCyTP/iWczDtxB0dsPsXHg+5UDVPPkQkU98muDKKO6f/TZ4HsbuIQCMvYcIzr5Z/+NqYTe3FbADxUhhC8ZbbNLZfJKJUpRHe9LVYLTgmwzEHHqjLl+f3fpS3qXBXW/U4c72LO2Wzxdyu6EJmz2I+olEItx77z0kEwkKxSKnTr2C664MOB544AP0dHczOzvHN19+uXp7Mpng3nvuIRKNkkmneeX0q+hGrJNuISUCOjCbtlMmQElrjEZ0kxZCiGuQ4K7FKaORmbvymrtEIzJ34a9m8O7rmIdvwf7sjxJTBhgm7u/9F4ILZzBuvg2dW0B1dhH94Z9Bjw2HxxUEGPsPS3C3DnHD50RHjrO5tobNgasvxXNzXXz30BQpKwycYkZQXet2cRsC1KVlq90Rj0d60kzaEYbdDmB+y49HbJ2jR48wMz3D+ffe4+iRIxw9coR33n13xXbvvXcB0zQ5eODAstuP33qcCxcucvnKFe688w4OHNjP++8Pb9Xh35BKOqBLWU3bKRPC9YGNLI+6EV6phRBbT8oyW135U0PdiMxdpUNmI8oyy5k7PTWO+5W/xDzzBv43vw6ug3H8BFgRjEM347/1Cvav/gLBxXOovQfwnv8y+vIwxt5D9T+mFvZIT5q4GfC12e7tPpQ1e32hnYxr0l4O7pKmT2ckXHuT2YbRA0szd4aCobjDC/NdyCVY6xscGGBkdBSAkdFRBgcHam43MzOD561cH7Zr1y6ujI8DMDo6xuBA7ceLRcVysxIDSDfpmrtSg4O7bmXRjnTiFUIsJ5m7VtfAzB1BEM66a0hDlXCNkvY8gue+RLS3h/zsHKq3H+PwMazHP4aKRAnOvQ25LO7v/1r1sda3fTfmXffX/Zha1b54iQ/2zvP6QopxuwEzCxvER/GN+S6+rX8WKAd3lkfOM1bMxtsKTrA8iCv6Bq9m2kmtvR+QuEHFYjFsO8wa27ZNLLb2v6NoNIrrudUyzGKxSDxeuwHPgQP7q1m/yelpJqemNnzMpmnS3Xvj/nIqJ0rSi1BQmmRHN7UWB2z3OcY9C9OJEDUaE4CpQBFDYStNNBZtyHM0E6XUjjhP2DnnuhPO0woUphls6rVofnZufc+54WcSNwbVuCHmQLjubumau7YUODbUWG+yLpVumf7y/QQXzxK59S6Mx57CO/0CwcWzKx6qp8fDgLOjCxbSmzuOFhc3fD4zNMGCZ/EXE33bfTjr9nK6k0PJInviNkkzIMDbtoHhupyhGy3GGIrbvJzuwN2GIFM0xkMPPlAzaHv3zJkVtzVqvdzw8AjDwyN12Vd3b8+6LxiayazRhmV2ciUoMl+oXfa83ec4p+K4VheObkzZqIsZLkM3TBz7+h1Gb3TRWHRHnCfsnHPdCefpYeL7PvPzW/daJMFdq2vgnDsIO2YuzdxFv/+n0FdGcP/sc5vbcWXO3VUlTP6ZN7Ae/jD+qy/hff1va2Ykg6kJAIy+AYIdG9xpTnZkOZtPLptZd/U23zUwRWfE478O76W0xd0l68EODD43NsSnBic5kChiKE16G2fK/cvzh7ADg76oUx22LlrDCy++tOp9lWxd5f+Os/aLFcdxiFgRlFJorUkkEpRKpXoccksroYlhNG2nTAAX3ZCimQqNBi1l30KI5eRj5VbXyDl3UM7clYM7pVA9fRi3nYTkJkcRVDN3V62lWEhj/8d/gfe1L6xaaqpnwuBO9e3cdSt74jbfPTTFd+yeWXWbuzuy3NGR54vTvTf8HLaCb1TLMrcrcxceh4mvFRN2DF8uunaM8YkJ9u/bB8D+ffsYn5hY1+NnZmcYGhwEYN++vet+/E7kaI2LZqZJ19tBObhr6HB1JUt6hRArSHDX6qplmY15g1m25i6eRJkmyrIwT3xgU/utDDHXNZoPXFc+h87ndnRwdyyVB8IOkqu5rT3PnGPx/NzWjw2ot4JvEjc1bVawLc1UxM52/vx79PXt4sknPkRf3y7On38PgK7OTk7cdWd1u0cefoj77r2Hvr5dfPQjH6avLyyFfuedd7nppsM8+eQTRKNRRkZGt+U8biQOATYBaZo3uHPQi+/BDaFpaOwohLghyVVQq6uWZTboHaBURO3eEz5Vqh0A7fuY9zyE/8JXQSnMx57CP/0iZDNr329liPlGgjtAT09g7NjgTnO8HNzFawR3u2M2j3SnuSlZ5J1cW3Wt2I2s4C+WlGa2sSxT7Eyu69Ys20xnMrz2+hvV75//xgs1H18oFHj2uecbdnytyEZT0gHzDVrPVg+ebvSrq8JQSIAnhFhGMnetzmhsQxVdKlTLMlVbGNz5b3wLY9du1IEjqMF9RJ74BNF/9NPr27G5SlnmGgWzk6je/g099kb38b5ZhuLhmp/e6Mr1KI/3pLm3K0vcDHi/cGOXY1bk/cWXsnk3so1HIoTYCg6aOTzcJo5sHIKGBl5Khbu/8T+eE0LUkwR3ra7BDVUoFSEWD99lUh0A+C8/iy4WsO59uFoaaXTvQg3sWft+rdoNVdZKz02jUh3hse0gt7Tleaw3zYvznfzdVC/tlr+iNFMtudoYLrbGz6e0ZMZcq5yTEGJ1c9rlWT+73YdxTS66oVWZGvC1lkl3QohlJLhrdQ1uqKJLRZRhQCy+WJaZng2zd7eewNh/uLqt6h9a837Vag1V1npcs9Phfrp3bejxN6KoCvh7A9NM2lH+ZnIXM06YweqNhFm8pOnzaM88fTEXJ1C8nO5omY6O46UY46Uovza8pyXKTIUQ1+YBo7q5W6i7aIwGvx45SHAnhFhOgrtW1+CGKhSL4dPEE6i2drTvQ7GAf+obqEgE8+4H0dmFcJtk29r3a1roTczK03Pl4K73xpvdtlFP7pqjO+Lx5xN9+ChmyuWJfVEXheZTg5N8W/8se+I2pzPt/PlEf8sEQjnf4v97fz/DxcT1NxZCiC0QAH4DX2UVkNU+5jqewQDiLfK6L4SoTYK7FqfKZZm6gXPugHDdXaod8jnQGj11hWD0Eso0CcYuoYMAlVhHcGdZG87aAei5cASA6mnt4K7D8uiyXAZjNg/3pPnmfEc1wJm2o5R8xeG2Ig92Z7glVag+blbWpQkhRMO5jWyqomFcu1jrGLuSwiSpJNcnRCuTtnKtzmhw5q5UydwlUW0d6PxC9S7/lW9g7DuEnhqHA0dgPZk7K7Lh9XYAuA56Id2Q4K4/alPwTXKrDgffOj92YJTOiM9YMUbBN/m76d7qfQGK9wpJbm/PcbIj4N1ckgOJEkkzYLZFyjGFEKKZOQQYKIIGdVYZ1Q73XOP+bmWR0R5LP95t1LEIIZqDZO6akFHPF16jsQ1VdDm4I55ApdrRucUF7v5bp/HPvoV/5g10Ib++zJ1pgbfxskwISzONOpdlxoyAHz1wmU9cYzj4VlFoOiNhG/C9CZu/mtxFKVj+iezZXJKkGVAITP50fDdvZ8N/gzln+wNTIYRodTa6YRdaCpjQDlrVzg7GUBR1QKcyiZa3MBTkdVD9XgjReiS422ZhJ8PFYO7O9iw/d/QindbmApuqRq+5K5dlqngizMwVcov3uQ7u7/8a+soIFPOQSK55t8qy0JsoywQI5qbrnrm7t3OBuBlwMFFku4cL9UQWf0dOZ9p5M5tasc27uTaulKL80ZXd5H2Tv5zs43+MDjLpxLbyUIUQYkdydNC4Cy0VNm25Yvh0YtKpTPpUhEg5cItj8HqQ5/PePDEMepTFjPZ4P7AluBOihcnH99sgogJ+7OAYM06EW1N5puwoL6U7eS3TzoPdGeKm5tGeNH89VYfARDW+WyYQZu4SbQTFfO3tivlwNMFamdbmyjIJ192p9k6IRsHZfFc1heah7jS+hs6IT6flkfG2b+3aUNwG4Jcv7eWKXbv9f963+OX391e/97TB2fw6MqhCCCE2zK52zKz/h4EK8IGnozb5XBoHzWNGO8eNJC4+GrisHYa1wx/4MxxTCd4ICuw3otymEtv9+aQQokEkc7cNDieLDMQcbm/PM16KoYHvHJjmZ49c4mCyRNE3uK9rAUvVISBrcFkmdilslpJMoeIJdLFQe7v1lmVakU01VAHQs1NA/ZqqHE/l6Yl6fH22G4CDiVJd9rtRQ3EbT8OkLVk4IYRoRqUGZu5UeS2foyBHgINmRDsYinA8goJJHVZ4TGuP54IsGXzmtY/fqGoeIcS2k8zdNjjaVsANFL97eYBLhQSuVuyPl/hA9wJ74iW+me7kk7tn6I86q2Zk1swor8Fq1Au51mCXUF094ferBHd6nWWZWPVZcwdhcKcnLm9qXwAP96SZcyyenu3h4e40h5JFXs+2b3q/GzUQc5i2o/hSXiOEEE2phEY17DVac/XHttPaRQEdyuLrfoZSjfTcgvYbeExCiO0mwd0WU2iOthW4VIhzbkl53Egpwch42MK+PxqWEO6O1Q7uzA88juofQo9eJFiYhdm51Z+vswttl8BuYJapVKwOC9eFVcoyC/lwXZ5pgu9fd5fKtNB1KMuE+mTu9sRLHEqW+OvJXfhacTbfxvH2PJ+f1Fs2K85SAZ2Wx6wbdrrsjrgtM4RcCCFaUYnlmbsYCh/wNlkTWSnJvNo8Pgva552gyCtB7Q9bc/jhcnxJ3gnRkiS421Ka7xqYoj/m8uxc96pbzTgRPA39sdrrxKyHngwzZfc+jA2YX/0rrJMP4f71HxK89+6ybVVPXzWD1Sh6SXC3WuauensiCUs6aq7KsqBob+7AHBudzdRlkPkDXRlKvuJUJlw3+GY2xZ0dOQ4li1wsrCMjuS6awZjDeLns8qHuDB/tm+WXL+1jyonSE/E4m5P1c0II0axKBMs+/othYChI6+t/yHk9q400+F1vBvsakVtA2DHTQm06yBRCNB9Zc9dgt6ZyHGsLs1n3dy1wb1eWp2e6eSWzejlfgGLGibJ7leCOeALvpWewf+VfodKzWA9/GNXdi3HLHSs23ZrgroBqD4MevVpDlXJGb83r7qzNN1SB8jiEOmTu+mMOo6U4dhD+yZzNJfE13JQsbnrfqznRkeMnDo2yLx5mXffEbUwF39Y/Q7vlEzE0czKMXAghmpaLZml0V69QSgH+Kju7VmBXMae9aldNIURrkeCugVKmx6eHJvlfBqbYHbX5RP8M53JJvjLTA9d5UZ20o+yO1gjuDCNsXFLIo6cnMC6/H5Y7AsbeQ8u3VQrV1VstT2yYzPzi16VVgp1y0Kfa1rZGTSVTdSklDeZm6lKWGTcCiv7iDDlXG7iBImo0qFENmkd7wp/r8fYcn+ifZihu4wSKm1NFHujKADDnSvJdCCGalauvWhdXnjOX2OTll2Jzw8hnJbgTomVJcNdAj/WmiRnhoOl/tP8KpcDgj8f717RGa8qO0hP1iFzdMTMWBnKVIMqYGK3eZQztI/bPfonIZ34Q46ZjqK6ecF5cgzN3wfxi8Lha5i6YGEP7PsZNxxZvNM2a29KWQnV0EdShCYqem0Z1dEFkc2vTEmZA0V/+5+JphaUaU9JyOFlkKB4G94/2pHm4J8OuqMtL851M2xEe7w0Dv3lHMndCCNGsHDR6SUMzpeGtoEBMLV4HbCTEWm3N3VrN4mJIbCdES5KP/Ws42bFAf8zh1Uz7poY9H04WGS7EGYrbtFs+vzU6SM5f2498qtwoY1fUra65AqpZusrIAWM8DO6CcvmhisYw9h/GvPWu6mOC2QaXZS7NDK6WuSvkCd4/j3H7SUzPxbzrflQyhf1L/wzc5V0xjYF94X4nxjZ/bNVxCLvQk1c2uheSpk8xuDq4MzAbFNw92pMm65nkPJPB+GIGd9yOcamY4B/uHSfQML+Nc/aEEEJc29Vr2pSCM0GJD5ip6m39KkJW+xRW9L68FlWeZLcxWR0QyHI7IVqSBHdXSRo+f29gmqihebw3zVgxxqsL7ZhK83jPPH8+0c/budR196PQ9EUdXk53cC6fxNVqWXfM65m2w4v2/qizLLirjhMoB1Fqbgbv+S/jn3kD64lP4D39N+grIxjHTxD5tu9GJdvQMxNr/wFsgJ6fXfzmGvP0grdPE/nk92A88QmCuWlUIonq7EHPTC7bTg3uDbevR3C3dBzCBoO7qNKYimVlmRBm7iINCO76og7HUgW+PN1DT9RdFtxN2lHG7Sjn8wm6LA9fy0evQgjRrByCMKIrv1UoYA6Pce3Sg0WRAJuAqFIU1vF2YgCbGRaU1z4Nmq0uhNhmEtxd5YHuDFFD8+sjQwzGHE52ZvmO3YuZqWOp/JqCuw7LI2popp0oL6c7130cM26UQENfzIElzSWrmbtSmLlTaLwvfx4A97d/ubpd8OYp7LdPQ1s75HPrfv710PNrW9Pnv/ZNtOeFGbzuXUS//ydR7Z0rgjtjYG8YMK7WeXM9x1aHcQgJMyx+KdQqyzTq/874cHcaN1B8M93JPZ0LALw030GH5TNlRwHF74wNEm/Yej8hhBD14OrFhRgm4VBzCEszP2J24mpFVvtEUJisr9RyM50ucwQ0qPBECLHNJLi7yq2pPBcLcS4VklwqJHlhvotdEQcfxXcOTDEUX1t7/v5o+JnatL2xtV6+Vsy5kerMu6r48szdNQUBZDMbev51yS2sbTvfJ3j9ZQC0Wf7V6+havk2qHePm2wjOvFGfY7NL6NzCpsYhJMzwzbgYXJW5C+q/5i6hXE52Znl1oZ28b3KhkCDjmjwz28OCt/jn6moD15cls0II0cxcdDU7ZqAolr+5FNhgQhTFhHZpUyYJjDWXWm62oUqRAK3CNYAS4wnRWuTqcAlLBQzGbYYLiWW3z7hR5t0Il0sxdsecNa2z6iuPMZjeRMOLKTtCX3R54YVKLF9zd6PS2TRA2OxkCeuJ7wDTwvva39bvueam65K524qGKicSU0QMzfNz4c/lcinOL144tCywE0IIcWNw0dXGJSZhUAWQJ2Bcu7QrkxntYetgXRdkCvD05t5/strHko6ZQrQcuWJcYigWzhEbLdVuonK5FMdUsDtqc8WOX3NffVGHom+Q81fpCLkG006Um9sKGOjFMajrydxtIfvX//2KxijX5LroYgHVvliyqgb2Yt79AP5Lz9S1w6eem8E4dPOGH58olz/WWnO3nuBuV9Shw/JWHXpuKs3JxCRnckmmnc119xRCCLH9HDSq/P5toijoxXL6t4KtFyTYAAAgAElEQVQCN5tx5vDoxMJYxyI4hdpUt0yAjPbYraJhdlEI0TJaN3NnGHgnHkRdPfvtGvYmwpLLsWLtwO1yOeg7kLz+/LWeiMusE2FjTY5D004Uy4DuSBg0qcG9GIdvQXseuKsMON8m+vIwemp9DUv0QnpZ5i7ysb8PxTze1/+urscWzE2jOrshsrEsarUscw2ZO4Vmf7xIrTfo//PwCD+4f/Wf0bG2PG2Gx4vz61+jKYQQovlowC8PQDJQ5PRiSHYpsMlon6wOKGl/3Rdkm+mWCTAnmTshWlLrBnda4z72ccwjt15300d75vnRA6Oc7MySdi2yq4wrmHctJu0It7fnwDAw738s/L/S7IsvD/i6Ih7pTZbSVTpm9sXC4C72I/80PJ8my9ptlM5mqpk749a7MA4dxXv6b+p+fro8CkJ179rQ45OVssyr19zVCO5uaSvwowcv80h3etntQ7HrfyBwsjNLzo/wXr52Zk8IIcSNxymXXJpAfkm+LU/Aq0GeGe1SQJczd2uz2Tl3AGk8TCXBnRCtpqWDO1wHotcubzvaludjfbPsjdsMxGz+avJaAYDizYV2DiZKdB45ROTbP0Xs0E18/94r/NjBMVKmV3nyMLhzNxfcVWbd9V3dVCV27ZLQG8XSzJ154gGCuRn80y/U/3mWjEMwDh6Ftut3O604mCjyWE8aX4MdLH8T9IKV3TJTVvg78O27Z/nOgSksFWCg+Vj/4rgI6+rB9ECb6XNLKs87du9iCa4QQogbXrU0Uy2uuat4NsiSI6DIBtbcbTJzF866k5JMIVpNa6+581yIrAzuuiMuH+qd5/m5Lj49OMmkHeX3rwwQNwLGStcOnN7Ipvhw3xy3JzJMTb/ND0T+FsrVfl0Rj5xvkTQDooYm7W5uwHQpMMl65oqOmWqD5YVNZyENqQ6wIhi7hwhGL11zTt5GVYI74+bbMO9+kODcW7h/8Otreuz/tnecpFk5pquCuxqZu5S1+Fnq/V0LnMm1cShZ5GhbkbFijL0Jm4QRkL2qxPOujiymgrdKu4DrZ/mEEELcGGw0bSg04KwSTNkE65o7pwBfb+79Mrfpwk4hRDNq3cwdoFwXVSO4e7g7zX1dC/zkoREspfn9KwPMONHrBnYQroMbL0W5XY+xf/otAILyq2N7OWvTZYVllJvN3AFM2VH6y503gzXOk7tRBKMXUYaBcexOVHcvweTlxjxRqYjOLWDd8zDKMDCP3Ynaf3jFZrXKNp1g9T+RWsFdu+lT9A1+/txhir7Bt/XP8GhPmhfmO3m23AGz0n1zqbs7slwuxZjxpSRTCCFaSaUsU+swi1d7G816QrV6NFTJaV/qRIRoQS0d3OE5qMjKAGtveVadoeBPJ/qZWWdnwjezKQ7aYxy5coq01ckvXjgIQEc5a9MVCYO8za65g3CUQjgOQaOsKMHEZexf/YVN77cZBO+fR9s21qMfAUBPrq8hy3q4f/WH6FIR75VvoDPzRL/3xzHveah6v3HsTqI/8S9Qu3YveZQmbvikXYs/Ge9fsc8wuFv+dpyyfLKeiR0YnMkl2RV1eS+f4G8md1W7bSbM5Y+xVMDehM27OQnshBCi1ZTK6+k0etXOlA7rGzgXlmVuToGgOqZBCNE6WrYsM274/MTXf5pv9D3K80tujxk+exMlvjbbxWsL7UzatcceXMubCyk+2jfHwek3Odt5K3nPIdDQUcncVYK7TZZlQpgpTJgBKdPHjUQILp1raBC0pTyP4OIZzFvvAmhc5g4IzryB/e9+FgIfL5ki8vf/IZFPfg/GoZtx/+oPMQ4dDbOIew/iz0wCkDQC4qbmKzNdvJLpWHn4euWau3bLq46/eH6uC1PB5yf6CFAUyqWYyauCu87y7828E6mW+AohhGgNtg6DKKUUzir5OWcDI8n9deX6VgqAgg6w2HygKIRoHi2buSsFJhrF4dzFZbcfaStiKjiXa9tQYAfhUPPxznDEwlTXIQIUOc+kvZy56424uIEi72/+xztll5uqxJxw/aC3jllyNwDvuS8BoEvFcA1eI/leWBeTz+L+zn/G/cpfYtx2kugP/wzmsTDAVIP7qpv3lAfIz61SXusFBpaivJIi1G75ZMsZ2yt2nD+4MkCh3GWz0m3zcLLA3iXdVTvrmOkVQgjRXEpLOmGutubO0Zr1NK5UUJf5dBlkHIIQraYuV5P9fX3cccftoBQjwyOcf++9ZfcbhsHJu0/Q2dWF6zh869QrFIuNb+c/3H4TN8+/ickgj/bO80h3moIfNikZXmWW3ZokU7x98G4GX7/E1K6bgXNkfYsOy2N3zOa+rgXO5ZNsZsZdxbQTpnL6Yx7jpolusvl2m6UvD1P6xZ9BbXUHUK3xn/sSwcgFYj/w09WbjcG91a8r8wXnV8nAero8mFbp6tcp0yfn1R5cX5mT90hPhkd6MvzsmSMAdJUzdxnXgsRmTkoIsdUikQj33nsPyUSCQrHIqVOv4LorP4R74IEP0NPdzezsHN98+eXq7XefOEFvby9e+YO706++xsLCwpYdv2i8EuW1bddac7cNZZkA6cCjz7BY15MLIZpaXTJ3d955By++9E2efvoZ9uwZoj21vNX8/v37cFyXr371aS5cuMhtx68/e64ehtsOkfQK/NThEZ7qm8NSmr6Yy+sLqU21m1fdvbx26KO81XuSCwcehEiUBc+kJ+Ly2aFJioHBn0/01eUcMp6FHSj64uWX8RoXDTe8YgGdntuWp9bDF/DPvxN+nVtADeyl8vFpdzmjNned4K7SVCWiAuJmUM3cXc0OjGrznaUqmbuMZO6EuOEcPXqEmekZvvr0M8xMz3D0yJGa27333gVeOf1qzfvefucdvvb1Z/na15+VwK4FlcpDzJW6VnC3vquScM7d5gOyOTzJ3AnRYjYd3HV3d5PP5ykUCmituXz5CgMDA8u2GRwYYHR0DIAr4+Ps2lWfwOd6hpMH0CjiRsDvXR7gN8eGyLgm30p3bmq/qquXfKKbP0p+iGKsk+j3/QTZIEpfzKUv6vA/r+wmv8og9A08G9N2lP7yIHNaLHPXDNw//xzei0/jPfM3qFgc4877AdgVdch65qodM92rgrvKGITKmruraVTNxetdEY+sZ+Lplq2SFqJlDQ4MMDI6CsDI6CiDgwM1t5uZmcHzZGXTTuRoXS3kWW02nYPGWEddplZscsVdaEH7aInthGgpm45A4vH4shLLYqlEd3fXqttorfE8l2g0iuMsD1QOHNjPwQMHAJicnmZyampTx5Y1UvzmY/+awp/9McVoBKLwX9N7oB26N7Ffd+8+PKDt0rs4d92Hsfcgdvce8NN8szjIXGIv3XUsr8uqOQai4c8vGYti9fZU7zNNk+4l37eqhp/nt76GVgbO2CWi3/FpYoUMB/VlpvzUqs8bi/nANL09nUSCGMdi4aDyINFNt3X9DxB6e7sIMNiVnCavY3T39uyYf0+Q391Ws9nznJ/dnuz9ZsViMWw77MBs2zax2PrXch+/9Ri33HIzM9MzvPPuuwSrzPus53vkTvi9bJZzNH2TiG3hKOjuuMbxFE1iGGsKtsxAEUm20W3FN3Wehm9g2hZRo/aHks1EKUU0tr7u5jeqnXKuO+E8rUBhmsGWvj82VR3Y8PAIw8MjddtfyrEZ7bsfe+Y367ZPACuaxMxlybx3Dv7LvyX+M/+GV9qO4128wldmkgTU9yIlY3nsLzf3yKfTBEv+kbt7e27Yi6L12LLz/IPfIPYj/5TgyW+n95m/5rX5xKrPu9BegA4oZOYpeBaPHxpmvBTl9Qlv9d+BJUnrUnqGvG/R1lFk2o4wPzu3Y/49QX53W00rn+dDDz5QM2h798yZFbfpVRpmrOadd9/Ftm0Mw+Cuu+7kyJGbOHfufM1t6/ke2cr/XhXNco4GFkT6yGmf+cLqxzNiQr+KUFxDTs5VJmOlNPPa3eR5mniRPhy92al5jReNRXHsnVG9tFPOdSecp4eJ7/vMz2/da9Gmg7tSqUQisZimSsTjlIqlmtuUSiWUUlhWZEXWriE8FxWNhoXu63zDvRbV3YtOh1ka8ln0Qoa5PbfzpZdrr6fYrFJgEFd+eA5SltlYuQXcP/ktDn3sUQwFo6XVP4V3lzRUeapvlnbL53cvD6555UTSDPB0QE/E5azMuBOiab3w4kur3lfJ1lX+v973tkrWLwgCRkZGOXLTTZs6VtF88uXVcfZ1grZzusReI0pRQxIDC8XCKqPKFZDWmy/zzRG0btt0IXaoTf9Np9Np2traSCYTKKXYs2eIicmJZdtMTEyyb1/YhXBocJCZmZnNPu3aVJqPWBGIRLA+/EmIb75eUnX3oudnq98HE2OogT2b3u9qSr6JqTSWb7dmQ5UmYwyf5e5vfQ6AUX/lfLsKvxzcHUkWeLA7wwvznYyVrt318wtTvWTcsPwlafrc3p4jYmjeyqau+TghRHMan5hg/75whMr+ffsYn5i4ziOWW5oRHBwYIJuVhiqtpoTGQWPrawd3Y4GDiaJLmWQqHTZrMAln59l1aKjionG0lgBPiBay6cyd1po33nyLBx94AKUUIyOjZLM5jt1yC+l0monJSYZHRjh58m6efPIJXMfh1Cun63Hs16W88ieo0SjGkduwHv0oOj2Lf+obm9ipQnX2ELy1eA56YgzjpmNgWuEstTorlRt6xN08ecncNdRAzObTQ5MM5B2eO/5p7IVpuFK7DKqSufv23bPMuxZfnu697v6fm+vmYiHBjx8cI2n6nOjIMuNErpkhFEI0r/Pn3+O+e+9h//59FItFvnXqFQC6Ojs5ePAAr73+BgCPPPwQqVQKy7L46Ec+zKuvvc709DT3nDxJrLzmJLOwwOvl7UVryWmf0nWCsTk8vu4vMKYdJrXLT0UGak4oiGIwo+v3QW8GnzaMVTt5CiFuLHVZczc1NcVXn16+sPvM2bPVr4Mg4FT5DW9LlQMhFYlhHg+HVBv7Dm8uuOvoQpnmYlkmEMxMYZkmqqMLPV//rGQluIu5BfAkuGuUA4ki/3jfZYqByf9IH2P4ru/HuPi7+KsEd96SVe/PzHTjrLHbZaHcTXMw5nA4WeTpmR7qMRNRCLH1XNetWbaZzmSqgR3A8994oebjX3jxxYYdm2geC9qnuIZ1baeCfPXrkg4wYUVhZgTFZD2DO+3TpUwJ7oRoES2diVeVEsa2FMZN4Ww9tf/wpvZpdO8CWFaWSbH8Ypxs29S+V1MqD7+OO3kpy2wAE83t7Tke7UlTCgz+06X9nJ300Y6D2j246uO8YDEgm3LW3u2pEtzd35XBUPDqQvvGD14IIUTTS2vvupm7qxUIMGt88GcoeF/b9To05rXMuhOilbR0cIcXBkLm8ROoSAT/zJsYPX2QWn0d1fWo7rD0bmmGThdy4X2JBgV31cxdHi1lmXV3c6rA/7pngtva87yx0E7eN0Fr9NwU1kNPEvnsD9V83NLM3ZxTe9B5LXag8DV0RnzeL8RXHZIuhBCiNczhkV+lOcpqcnplcJfAYFZ7DOv6XQuk8aR4RIgW0trBXTkQMu+8D53L4j3/ZQCMGtk7856HiXzqB667S9Xdiw4CdGZ+8cZCgzN3S9bcVQJWUT/dkcWf6anMYuBfyc6ax+5E9faveNzSoePZVQaX16aq2bvXJGsnhBAt742gyJtB8fobLpHTPle/s8RQfMPP1u/ACIPIOjYUF0Jss5YO7lRlzV1HF/7ZN9BXhtGuUzO4i3zys5i3ncQ4evza++zeBZl5WDJkVpfLMlWDyzJjbkFGITRAp+XhBIp/ee4Q4/ZiYxPv7/4U9/O/B4Bx/MSKx3nLJs2u72PPgm/gaXhzQbpkCiFEq3PRuOssy1zAX5a5i6PI4HOxjiWZAHntr2lwuhDixtDSwd3SLFfw7uvg++jLwxj7rgruTAtdDtbMh55cfp9hEP2Rf4J5/2NEPvtDmHfeR3B105RSER0EjQvulpRlypq7+uuOuKRdi0Kw/DNSnZ7DP/0iweglzFvuWPE4bxPvhiPFOKczHSueUwghhADIE2AseZuJY/C8n61725McAYZk7oRoGXXpltm0yoGQLhUJLp4DIBi5iPXYU5iPPQVKEVw4AyiUYaDzOYz+5Q00jKO3YQzug7t8jL0Hw/0t6ZQZ3qDDpirJxmRhnMAgAGJ2rq7D2EWoK+KR8Vb/UwhGLmLe/ygYxrKM7WaCuz+b2L3hxwohhGh9RR1Ux55HUeQIeE+X6v88BGgFStecvCCEuMG0dHBXmXMXnHu7On/Of+8drMeeIvLkd4QbPfGJapMS/8zrmHc/uOwi3jz5ULivof1Ldrwy4akL+YY1VNEoHBUlXpLhto3QFfF4N7f6v10wMYYViaJ6+tAzk9XbnUBxOtPOqfTGG/QIIYQQtRQJCMrRVgKDU0Gea49B37hwfZ9Co9fZ9kUI0WxaOrijWMR/9SW8l5+t3qSHL1D6N/9X9Xvj5tsxb70LXAc9Poa6xwgbo+Sy0N6JcfNtaN9HmWH5XDA9gb9kf1WFfMMaqgCUzDgxJ7fq/T3l0sJAWl6ti6UC2i2fjLv6n4KevAKA2r1nWXAHij8elwycEEKI+ivqAKUI02kKZrXXsOfKaJ9+FSGpTDytyUiIJ8QNq6XX3Ck07l/8LvrqIdSlYvW/4I1v4f7Rf8P9s8+hc2EHKlUelWCe+ADKMPBffxkA7bk4//lfr9wfYVOVRq25A7DNODEnX/O+uOHz04eHuaNj9eBP1NZlhW+W89cK7mYm0b6PMbBnqw5LCCHEDjePhya8UNMaFmhccDevPTqUSXoNg9aFEM2tpYO79dK5sOxRtZWDu7sfJLh0vrwur9waf5U1b40sywQomYlwFEINbaaPpaDDatwLf6u6vRwQT9rXGELue+iZSYybb6s5EkEIIYSotwC4HNjEMVCE2bVGORXkmdQuL/pZLmmbLrWy2Vevau1iLyFahQR3S+XLa9pS7RgHj2L09uGdfqE6sLwy96ymQq5hDVUAipE2uorTqBrLnSPlNlcxo1HV+K2pw/L4YO88b2XbuGLHr7mt/8JXUT19RH/8nxP5ru8l8tkfxrj1ri06UiGEEDvRBW0TU4pAhV0tGyWDz29707yti3zBT3MxsOleMoghiiKKsWLunhCi+Uhwt0Q1c5fqwDz5ILpYIHjntWp3TH31CISrHqsikevOyduot/rvo6c0wy2pwor7ouWgLi7B3bo81TeLieYLU7uuu63/2jex/9+fx3/pGYxbT2AeuwPz5INbcJRCCCF2qtHAARTpBq63q6hcQbho/sqf57WgQJeyMAiDu7z2ici6fiGangR3SzkO2rFRu/oxjp/Af/NUOCsvn8M7/QLBO6+t+lD/1ZcIrowQ+cwPNiTAe3vwQdLRLj7QlVlxX0SFmbuoBHfX9PG+GW5pC0tb98VLnOzM8vx8F/NuZG07yGfxvvjn2P/un+C/dRpjt6zBE0II0TizePyRN8O3/K1dUx8ATwcLPOcv0KUsIkqRJyAql41CND35K72Kzi1gnXwIFYnin36herv3+d8neP/86g8sFXE+9yvoqXEin/lBIt/741if/GzdjiuIJZiO9tFmrqy5rwR1MZlCuiqF5rHeNN+3bxyF5hO7p1nwTJ6Z7Vn/zlyX4PIwqrO7oR1ShRBCiMva5d0GzLdbi28Fed4JCiQwGNV22L1TCNHUJLhbRXBlBD0+tr4HFQs4v/3L6MlxzJuOYd3zcN2OR0VjOFjV9XVLRcu3SVnm6lLWYlB8oiPL/oTNF6d7cYKN/Qno8VEAjIG9dTk+IYQQohm9GhRIa59Lgb1aTzkhRBOR4O4q/qsv4V86h/s///vGdlAq4vz3/4D3yjfQfh07W0WiuBhE1fIA7qHkZQ4mioCUZa5mf6LI0eTiWsVPDU0xVozxaqZ9w/sMJi4DoAYluBNCCNG6JrTLX/vzjGkHpZBVd0I0OelrexX/2S/iP/vFTe7EQ89MhYPPY3Gw61BOEY3hBMayzF3M8Hmk7TJuQpW/l+Culs8MTdJ+1ZiIv5jsQ2/mLaqYD8dfdG6grFMIIYS4gQxrB4DX/QJ3mcmGjmUQQmyOZO4apRAufq7LYHPTQpkmrl6euasM4K4EfHFTgrurxYyA7oiHVY7jxoox/nS8n8ula48+WAudz6LaNp79E0IIIW4kLwRZijogJvk7IZqWBHcNogvlgeP1GGweDQdsO4EqB3JhMNcVWZ6NkszdSv1Rp/q1p+FXh/dyKtNRn51LcCeEEGIHsdF80c+QVHL5KESzkr/OBqkEd3XJ3EVjALgBGArM8gdmnTWDu9ZZ7fxD+8d4qm/12YJrsTtmV7/OetbmSjGvovNZaGvc4HohhBCi2byvbc4GJTpkpLkQTUnW3DVKsZy5S27+4l9Fypk7PwxMIirA12a1LLPCVGApjadbo1ziULLEoWSJL07XHjKeNH0+1DtHzAiwFMy5Fl+Z6V22ze6YQ6DDoDjj1vfXXedzGIfqlAUUQgghbhAv+TmOROKt9HmyEC1DgrsG0fVcc1fJ3HkajHD0QSlYmbmDcNad1wLrnNWSdwy1yrvHsbY8j/RkWPBMLKVJmgHPzXVhB4ufJu6OOYzbMfqiDgtenYO73EL472sYEEhJrBBCiJ0hgyelX0I0KfnbbJRSER0EdQnuVDm4c/wwyKk0VemKuCu2bZV1d0tn9vXWOE+AjnJw++8vHOCvJ8PsXuqqIe/9UYdJO8pfTvbxwnxnfQ8yHwbwyLo7IYQQO4gH5HUgGQIhmpAEd42iNRQLtRuqWBbx/+dXMO97ZG37qpRlemFwV+mO2WnVyty1RnCXWNL5cyhu19ym0/Io+AaeNsj5Ybaubcmw8rjh0xnxmbSjvJLpYLiYqOsx6nwWACXr7oQQQuwwc9ojIpeRQjQd+atsIF3IoTq7q8FZhWoPM0jWx/4BatduUNdZI1fulum6YeASMQIUms6Ih19eXxeUKxdbJ7hbDNIGVwnuOiy/uo4uVy65XJq52x0LO2VO2tGVD64DnasEd5K5E0IIsbPMaI+IjEQQoulIcNdAyopg3nIH0e/98eV3pDrK91vE/vefw/zA49feT2XNnRsGblGlaTN9LAWzfjivrZK5apngbsl5tJm1FxF2WF51HV2+krnbwuCOcuZOyjKFEELsNDO4pJRBp5KumUI0EwnuGsg/8wY6CDD2H8Y4fEv1dpVa3mHReuhJMK9RuV5Zc+dUhpYH1Rl3M14SgJwXvrjGWyW4KwdpgV5eornUsuCufP6pJWWZu6MOtq9I17mRSkW1LDMlHTOFEELsLPPaY0w7199QCLGlJLhrIO/v/hT7X/0f6IUM5sMfrt5eKcsE0I6D6uzGvOu+8IZoDOOWO5bvqLLmzgkDl6jS1U6ZU164jmzOjQDLg5sbWSWgm3etmgGrgSZl+SyUgzofRdE3lpVl9sccppwoNKpsxC6hfR8Vr+9aPiGEEKLZXdYuv+PNMKM9YlKeKUTTkOCu0XwP79RzmEduRfWEHR1VarGMz3/jWwSXhzEf+SgYBuY9DxP9nh8O1+KVqWgMHQRL1tzp6oy7aT/M3GVcCzdQdNRosnIjqpRlzruRmpm7lOVjKJaNN8h55vLMXcxpXElmhV0CCe6EEELsQAFwLiiRkMtJIZqGdLHdAv7pF7Ee/zjmvY/gfekvINWBLhXR0xP4r79McP5top/9IYzjd2MM7gXA2HcIf2Yy3EE0Co6NG4QvnlEjoCvi4gSKjF8u2QwMFjyzdYI708cNFFnPojtSXHF/pVPowpLB5DnfrK65S5o+7ZbPpNPY4E6XipK5E2KHi0Qi3HvvPSQTCQrFIqdOvYLrLh/h0tHRwV133oFlRdBozp07z5UrVwBIJhPce889RKJRMuk0r5x+Fa1lOrS4MUxoh9ZYECJEa5CPWrZCNkNw5g3MEw+AZaFSnei5GZz/9kvokQsEZ98kmBrHevQjqIEwuFP7Di0+PhoD18Epd8aMKE2n5ZF2LUpBGNw4WrHgWdXZbzeyQ8kCN7cVKAYGxcCombmrzPhbup4u7y9m7nZHw3UAUw3P3BUhJsGdEDvZ0aNHmJme4atPP8PM9AxHjxxZsY3v+5x+9TWe+drXeOnFl7jj9tuwrPD16/itx7lw4SJf/erTOK7LgQP7t/oUhNiwjPalKFOIJiLB3RbxTz2PakthHL8blepA5xYW79Qa77kvYQzsxdg9BICxdzG4U/Ek2i7haUWgFxuqZDyLorZ4N5fk/UIiDO6afM1dm3nt4NNA8z1DkwzGHYq+Qck3ymvu9LJt+qIugYZZJ1K9PeeZ1f03vFNmRakkmTshdrjBgQFGRkcBGBkdZXBwYMU2+XyefD4PQMm2sW2bWCysvNi1axdXxscBGB0dY3Bg5eOFaFZZ/IYtbRdCrJ8Ed1skuHSOYGYK675HUO0d6Fxm+f1vvUIwPxN+PT2B6huASBi4GHsPosfHAIWrVZi5i3ik3QgaxefGhni/WAnuPJYGQs1kX7zE/33kffbES6tuc7StUM2+dVkexcDAUBBVYfauO+Ly8zdf5GRnloxn4erFX+GcZ5GyAgw0/TGHkm+QaVCnzApdKkA83tDnINmG+eCHrj8PUQixLWKxGLYdzuNcGrStpqurC8MwyOfzRKNRXM+tlmEWi0XijX5NEaKOAmBB+zLzTogmIWvutorW+K88T+Sp7wq/X1ge3BEE+M99CfWJzxC8/SrWBz8ettjXoDq7CUYuAOAGBgnTXzbAu2LBNYkamrgRUAqab+5Mb9TFUHBbKs/lUu2Ll7s7sxT9sBQzZmpKlfl9KszIHUwUiRia3qjLuVxy2WMrnTM7Ix63pvKMlmI0/ONEu4RqcFmm9dCTWI9+FF3IE7z+ckOfSwhR20MPPlAzaHv3zJkVt11rvVwsFuOek3dz+tVX130MBw7s5+CBAwBMTk8zOTW17n1UmKZJd2/Phh9/I9gJ5wjNcZ5522IwMMIOgmgAACAASURBVCmqxn24rJQiGmtwNU6T2CnnuhPO0woUphls6m90fnZufc+54WcS6+a/+k0iT30XulTEO/X8yvtfeQH//DvV0kza2lG9/QAEw+8B4GrFruiS9WZL/gUrWaoOy6PkNF9wlzDCjNyxVJ4vzfSuuD9mBBxP5TmV6aDgG8w6EZxyZi6uwsfuidvV7aeXlGTCYufMh7vTdEU8Pj/R15DzWCrM3DU2uKuU8Jq33iXBXTNJtIFpQC673UcitsALL7606n2VbF3l/45Te/aXZVk88IEP8M67Z5ifTwPgOA4RK4JSCq01iUSCUql2dcPw8AjDwyObPxmgu7dn3RcMN5qdcI7QHOc5YqQYNFI4NG5pSDQWxbF3xly9nXKuO+E8PUx832d+fuv+RqUscysV89j/6eex/8PPQTZTe5uFNDqfA0C1tWMcuAldLKCnwvUYTmDQVwnursrcZcvBTWeTrrurNEYZjDt0We6K+29rzxExNK9lUnxlppdXFzoo+uGvaMwIM3dDS4K7mas6YVaCu/u7Fph3Lc7ml2f2GqJUgli8sSWT5SyAcfPtUprZRCKf/CzR7/vJ7T4M0QTGJybYv28fAPv37WN8YmLFNkop7r/vXkbHRhkvr6+rmJmdYWhwEIB9+/bWfLwQzex9beMrTQcmClmCJ8R2kuBui+m5mXA22rW2yYeZANWWwth/E8HIxeoFftE3qmvSri7LzJVLGJNmcwZ3cTMgKFdsHEsVVtx/d0eWWSfCyJKSzZK/mLlTaIZiNu8X4vgaRorLS6Sy5bLMiKEZL8XQW/D2ou0iyjDCjqaNYobnpUwT2jsb9zxiXdSu3Rh9AxgHj679QW0pzPselSC9xZw//x59fbt48okP0de3i/Pnw0qLrs5OTtx1JwB79gzR29vL/n37+ODjj/HBxx+jo6MDgHfeeZebbjrMk08+QTQaZWRkdNvORYiNGNcuv+VOc14X6VQW3coiJZeYQmwLKctsRpXgrn8Io28A99XFcqArpRgHk2FwmPEsUkseViwHd4mmCu40JzuyvJVNkTB8sp6Jqw2OpfK8lF4MVDosj8PJIk/PdrP0M79iee1gwvD4RP8cMVPzzXQnvzU2hBMsf+PI+ya+BlOtLNlsmFJ5Bl88cd2gfcPMxT9TlUiiF9KNeR6xLqorrJ83Tz5I8P75NT3GvO0kkW//FLpUJHjzVCMPT2wh13Vrlm2mMxlee/0NAMbGLjM2drnm4wuFAs8+t7JUX4gbSY6AL/gZTgcFjqg495ltzdrfTYiWJh+rNCPPQ5eKmLfeBUAwfKF619Ks1tJOkUC1hDFZYy7cdumNuHz30BS3d+RImAFF3+RMLsnhZJGIWjzOuzqyGApey7Qve3wlc/dE2wgP9WR4fq6TNxZSKwI7AI0iV87eTTd4eHn1OcvBXUObqpiL6ydVoq1xzyPWLtmGisbQpSLG8RNrXnepUmGmxvrQt4MhL79CiNYzoV2GtV2t1BFCbC25umhSOp/9/9m77yi57uvA89/fC1WdIxqJRBAJkBRzAAiQlEiJkqhkyfLIaW2PPN5Z7/Hxase7njlnj3fn7E5Ye3bHx+vxzNjjbI9la2RTsoKVA8UkghITAJIASGQ0gG6gc6r03vv99o/3qrpSx6quqq66n3/QqHpV9V5Xdfe7797fvai+AUwmgxlZXEA/nFy6RbZGkQqshsrcZQPNLjug3Q5IaouT8524lmFfZzK33d72FNfSLhNeYVCW0hbagKU0f3t1G1+7PoReptwyu+5uvGaZuyhbt4Gty1Ve5g4J7hqC6gsbAgUvfh/lxrDvOrC6B3Z2Y7TGGhzCvv8hrDsfwPnIT23gngohRO3JYHMh6keCu0YVlWbqKxcgWAzWJr3lK2kTgdVQmbtwADl02gHtVpi5u5hoJxWEpZlZ/a5XMJA8y6D4/MhW/nrqDo7OdpfcXywb3NUucxeuHVRtG9i8JT9z11GDJjFiRao3LMkMTh5DXx3Gvv/h1T2uswszNoq+dBbnsQ/jPP5RnEOPQW//Ru6uEELU1BwBRkljFSHqQYK7BmWi9uomryQzpPiTSzv5D+d2lX1cUtsNlbmLR4FmpxOEZZnaIkDx9kIHt3UuEBbkG/pdj2mvfLbttdkexoLVBTVjGZcpzyER1GgURHadXXwDhw7bNsYPu4VK5q4xZNfbmelJgteOYO3chdpx48qP6+zGzM/iffcfUD19WNGoE/u2u8s/QEo3hRCbkAGmjC+DzYWoAzlzaFDZcQi6JLiDc4kOrmXKd2dMBhbtViNl7sJAM8zcBbl1gReTbfS4QS6j12YbJpcI7tbie+MD/OcL5QPfjWASYfZxTR0T18p2IJ3EeB6qXTJ3jUD1DYTrLVNJguMvYbwM7o//PGr7CgFeZzcszGMuniE4fQLj++ipcezb7y19jR03Ev8Xv4X92Ic26CiEEGLjTEhwJ0RdSHDXoMz0OCaTRl8+t6bHJQK7oUYhZMsyu52AuG1yHT2zQV6bpel3w5l3UyuUnK6Gb6zaZe0Akgv4R57COfAI9uH3bMxr2Db4PiQXoEMyd40gzMBFw8tTSbwvfxY1uHXFz4Dq7MqNOvG++Fdk/uL3CF56Dmvvfqx33FKwbexnfhnV2YXz6AdR/YMbcRhCCLFhFoyOpt4JIWpJgrsGFbz4DJk/+C3IZNb0uGRg5YaFN4K2aF8G3fA4klGXy1Q04iBuafpjYcnhVBUyd/Xgf+uLBCeO4nzwH2G9szQDUyllO5ggwCQT0i2zUVgW6MWLKPr1lzET11FLBd+WhfXOe1Bt7bngjoV5zOXzBD98Bj01jvPEJxa3j7eh+gfxjzwFxmA/8oENPBghhKi+BQI5yRSiDir6uXNdl4ceOsz7Hn8vDz10GNctf3J++PAhPvLhD3HowQcrebnW4nuYqYk1P2wxc9cYPYizmbs2OzuEPQzqsiMOhuIe7xmYAqqTuasLY/C+8F8xl8/jfvIXsW65c33PY9ukP/6PUbveUXI7gQ+JBQnuGoVlgSn8GTPLvD/2fQ8R+9lfDv+TDe6yfJ/g2W9h7dyNddNtAKiowYoevoB+41Xsuw9ArHwpthBCNKIkGiOJOyFqrqLgbv/+fYyPjfO9p77P+Ng4+/ftK7vdmTNneeXV1yp5KbFKSW1hK4hZjRHcxYvW/2XLMVNRBu/9Wya5sT1NIrBy2bxNyffI/Lc/xlwfwf3v/kfU0PY1P4Ua2o7eux97/x2Fd1g2BAEmuQCy5q4xKAW6KEOeWICOrvLbdy12es2up80XHHsJMzuN89gHw6ePgjszM4X/8vOoeNuGZIWFEGKjpI0pvgYmhKiBioK7Hdu3c2l4GIBLw8Ps2FH+hHZ8fBw/2+1PbKjserN2qzHW3bUVlYhej0YUZIO77Hq7P750Q213bCMkFsj87Z+gLKtk/RSA2roTqzhwy79/aEf4b9SJMcd2csHdkmV/oraUBabws20S80u+P6pncdSBSSZKNwh8/Ge/Ga69u+3uxW6cM5OYyxcwyQTWrr1V230hhNhoKXSD1BAJ0VoqqoOLx+Ok02kA0uk08XhlZUN79uxm7549AFwbG+Pa9esVPZ9t2/QPDqy84SZWfIxWTAHX2T7QhRXUPhDYF5tiNohxPXrtrnjRe9izjX6gTXnARWwFo14Hma4d9C+R9IDN814aIDU/S9vNtxA7+0bBfcn/6X8HoP0//p9lH+vteQc+4A5tpyvvWNPtbWCBZTR+Ryd9gwNNsUR9s7yn5aTjcYxlFey/h8Zvb6dvyyAq73K1bdvEtu0gGwr2BmlUmeM250+RnrhG/CM/iXXuFEHg0xdzUIMDpMdHsXbfRHcDf78qfT+nJiaruDdCiHpLo1GKRlklIkTLWDG4e/ihw2WDtpOnTpXcZirMv1+8eImLFy9V9Bz5+gcHmv6EofgYx9uT0Ave/BR+ZoEDvbN8f6KfWo0S/dD+c/ha8Xvnd5PQNk5XGm3AUnApGc/tq4WBLeFjZtMrn9htpvfSHT6PHtrJQtH+ZifhLXUcblcfNhB09RZsEwsMBGmCK5dwD7yb2Z03oV9/eYP2vnY203tazPV9lOcV7L89MY6rLKYTqbBEM9I/OEDQ1Ys+9iO8v/8rUss8r/W1J4l96tP4dzyAmZlmeiJcd+tcPIt9+D1MTc9A0BhZ+WKb+f0UQlRf2hgJ7ISogxWDuxeOvLjkfdlsXfbfzBo7O4rqS0Tljh225vauBZ4YmuS1mW6m/Y3vRBm3NB22Bhs+sf06n726nTZb8/ZCB9rAV68P5bbVKDJaEbMMC0Fz9dPSly/gvvOecP1VonR9Fa4Lnldwk3XHfdjvvAcA1dMLjgt+tI1tYzJpgqM/xL73EO6P/QyZyxcwU+O5xzs/9jOQSuJ/9ysbdlwij2WVrLnLzjxUHV25r1GK4KbbUP2DmGM/XPFp9dlTBG+9gX3rnZiZxQtd+uowjuOitu7ATFzHfuhxgue+XbruTwghGoRk7oSoj4rOqkdGR9m9KxwYvXvXLkZGR6uyU2L9knlr7tyomYlbo+YqfdH6uYuJNu7qWeD+njnilmbGc/jMlZ0low6y6+5qOpeuBvTpNwFwDj26eGO8bfHrtqKmKLZN7Kf/KQAqCtgK1t3ZTth2X2syX/ivoDXuT/6TsItmdpNb78J59xOoG/ZU9VhEearMmrtcIJ/XMdN538fI/NjPAaAnV9f91v/2F8PRF9OL25urYaBn7dyNtf8O3Md/DHXD3vUfgBBCbLA0pimWEAix2VQU3J0+fYahoS287/H3MjS0hdOnzwDQ19vLvffcndvuXY88zMEDDzA0tIUnPvB+hoaGlnpKUaFEsJi5c1UY1DmqNsFdvxM2zfn69UHOJ9r42LYxOuwgF8QVy45DaLbgzly7SvDmq9gPvw/70Q+CbRcMoS5pl9/VA0Bw/CXcZ78ebtOXN7TathdL8Wam8L7yWawb9+K896PRE6rcc7gf/enw/2JjWQqzZOYufH+t2+8LA+6xkfD+kdWVnJvxa3if+X38Z76xeNvUeDjncOduVHdv+Dob0DlV3bgX58OfrPrzCiFajwEyxsisOyFqrKKGKp7nlS3bnJ6Z4eix47n/P/+DFyp5GbEGvrHwtKLdDtDRgJmYVZvSrWzmbtJz+buRbfza3mFsZZYM7tLR7QtNFtwBeN/8e9xYG+77PoZ98zsJjr+0eGfRSXm2k2Jw7EdYmbCTYn4wmJtzF9EnjxGcPIZ998GwDLOzG2VZ6AtnsPbuw77vMMGrRzbu4ETULbOwA7CJMneqoxO1dQfuJ34BfeksHV/563Dt3BpKKPX5t0tvu3oJa8dudCoZ3rABwZ1932GcA+8iOPojzMhw1Z9fCNFaUmgslPTNFKKG5IJKE0oEVpi5i4K6mmXuXB9PKxYCm2nP5SvXtkT7Uz54SzVxcMfsNN5f/wGZJ/8CdcPucE1cpDjjonr6ADCz07Awj/G80rLMoiYaZmYK3HCsRDaT4x95Cn3xDM77f1zm4W20MkPMSUaZu8GtuD/7y5BJkfm7P0dFJbWVMiPDqG07Ub3hZ2MjMnfWtnAkiX27zNQTQlQuiaYJ/8IL0dAkuGtCycCm3Q5yZZm1W3PnM+M7uSr712a7+bNLOzk62112+8WyzOb9GOo3XiHzZ78LczO524pnoaneKLibmUJhMDOThWWcto0JiuZEepmS4M7MTuN97Ulo78B+8FHEBio3xDyTwWQy2I+8H9XTT+Zv/7Tgfa+UvnIJ5ThYN4UzFKse3CmF2roTACtq7iOEEJVImjBzJ4SoneY9q25hCW3RYQe5oM5VtSnL7Hc9prz8Sl/FmUQHmaXW3DVpQ5ViZmSY9B/8Funf/83whrbSzJ1JpyAdNsk3UxOovi2LG5TL3HkZlOuGJ+Q9UXA3N4O5dgUzNY61dSfE4ri/+D/j/PjPQVv7xh1gKyrXUAXwPv8X6Ldex3vyzzHD56v6ktk1e6ozulhS3JinQqp/EBWPh+WfQ9tRW7ZV9fmFEK0ng6y5E6LW5GeuCSUDm3ZL58oxa5W567QD5v3VL+Ns5jV3JVJJzPURTBCULcs0s9O5/5vpiWXX3AGLoxRcF9XdGzb3WJgLHz8xhhocwrr5NuybbsW5/2Gch9+3IYfVsqwymTsIA7vP/Qn6rder/pJmamJxxALVz9ypbTcC4D8dNnKR7J0QolIpydwJUXMS3DWhZGDRnrfmzq3Rmru4pUnr1f8Sn/MdfN38mbsCyYWCVvkQNlQpCO6mJsLSzez4hPxumVleNFPSjUFXbxjYRcGGmRxDDQxh7b8dk0wQnH4T+/6HC0YniAqpMmvuakDnNzkpvkiw6yasOx9Y93OroTBTp8+dQg+fx36nrLsTQlQmjZYTTSFqTH7mmlAisMOyTFXbssy4pZfsjFnOD6d7+cNLNxKY1rmqZ5KJgoyL2rk7LH8rytxB3jiEMmWZ2eBOubEwczc3u/j4yTFUvA37nkPoc28R/PAZVHcP1q13I6qkzBDzWsjOu4PCkRr2/Q8R+6VfC0dhrJPq6ccszIHnEZw8hnXDbujtr2h/hRCtLSmz7oSouYpGIYjGlNQWMcvQbkfdMmtQlmkrg2MtllquRlpbXEm1rbxhM0mnsO+8HywLk0rg3P8wZm4G/+Xnc5uYqSi46x/EXL9atqGKycvcqcEhzPWRxfsmroePdxyCt95AnzmJmZrAPvgu9InXNvgAW8QSa+42mo6CO5NKhusolcJ538dw3v0EJpPO3baerKLq7Q+7sBKO2+CJT2C/8x6CF5+u5iEIIVpIBgMKZBKCELUjmbsmlC1z7ImGisdqUJYZj0pAl2qeIiKxOADWLXdi33MI/wffJf2f/i3m8oXcJouZuwGwolLKksxduOZOdfVgbdlWUK5nJsZyX+s3XgFj8F/5AfZNt6IGt27AQbWgJdbcbTT99ht43/4SwcljqPZO7Hd9AOfdT+C/9Dz+099AWdZiOe8a5Qd3ZnIMPXoFW9bdCSEq4BsjcZ0QNSaZuyaUjEYLdNphQODWYIh5NrhbS+auFXn/8N9QvQOYKxfBGMzUeOlGiQVMOhU2VbGXCu6issw9NwNgriyW65mZSfT4dYKXns01YglePYLz3o9iH3gE/1tfrP6BtRilLEwd1tzh+wQ/+C7O+z4G7R047/4gwYmj+F/9HPa9h8J9a+8IM3trpHr60BdO5/6vT7+J/dDj4Ljge1U7BCFE68iga7I8eUA5TBp/5Q2FaAFyJt6Espk7Kyp0r8UQ82xwt5Y1d63IXDqHfv1lzORY+cAuu112HIIdXX9ZoizT2rMPAD2yGNyhNZn/9G8Ky+kW5tAnj2Lfezg8WS/W2Y2KBliLVbCssENpnZhkAmXbYNv43/lS7jZgfSMS4m1hUDgzmbtJXzqHchzin/6XOB/72WrsthCixfhsfObOBtqxcGR1nxCABHdNKVk0FLwWoxByZZlNPJC8lnLjEFbI3Fm7bw7X6OW1yF9KcOwlVEcn6oY9izcqhf3go8R/7f8i/qu/gfuPPlWtQ2hu61zXVjWpMJALfvQsZjK8SJAN7lRRN9bVUD194XPMLDb20dGcPtU/iHPgXRXtrhCiNXk1aqiSIKBdTmmFACS4a0oJXdjyvhajEBbLMuXKWTWEmbtBlFM+c0cmKst0HPTkGKuhRy8DYG3dHt7Q20/sl/4X3I/+NHr4PPrSWdSOXVXZ/6ZXp4YqWfr82wSvv4z/zDcWb0xGAX772gfWq6grZn7mjuQCOq9RD0p+toUQa+MZw0Y3xFYo/Khvi2gcnVj0KRnBVA8S3DWhZNHcuFquuZOyzOow0xOoeBy6e8P/F2Xuct0yYfGkfiWz05hUEjW0AwD3E7+A2raTzN//Fd5nfh99fWRdWZ+WVKeGKllmagLv838JeWvrsuvs1pW56x0InyNqqJLlP/8ddJQZzH4WhRBitWqRuVNAICMXGo4r70jdyJl4E0prRZCXrKtt5k4+UtWQHYdgDYaDpUsyd3nBnVlFSWZu27FR1NB2rH23Y990K/73/gF97EfhnclEyWBssYQ6DTFfVnbN3TreQ9XbF64hnJ8tuF0f+xH+Vz8XbpOduyiEEKvkYWoyBiGNkeKCBqMVpI1ZU7msBWxXZfoCiDWRbplNSZEMLLqcaM5dTYK78DUkuKuO3DiELdHogiXW3AGrWm+XpcdGsfffAQ+E8/WC/Pl6yURYBurGCp9flKrTEPNl+R7Gy6DWFdwNwOx02WMy02GppuofxFw6C5aFddNt6CsXV581bmKu63LgwAN0tLeTSCZ5+eVX8LzC7qI9PT3cc/ddOI6LwfD226e5evUqAPfdey+Dg4P4UUfSV187yuzsbMnrCLEZeZiwudsGnoYowq6ckq9oLMYYzps0t1ltJFf5/jsoMhi6sJinwf7GbiIS3DWpZGDngrtYDcsyZc5ddeQGmWfn0hVn7vJOwk1ifvXPOzaKuv8h7NvvxX/x6cKT+dyarQ4J7lZS5zV3S1pv9rWnDzM7Vfau7Do8a2AI7j6I89iHsbZsxX/u2/jf/Uole9sU9u/fx/jYOKfPnGH/vn3s37ePEydPFmwTBAGvvnaUhYUF2uJxHnvsUa5fv47vhz/Xb544wcjISLmnF2JTC8syNzalli3LzBiDBRISNAilFCeCBLeq1c9etYB5E9CuLBl8XwE5E29Sybwgq1ajEDJaoaXGujoyaczCfF5wFyy56VrKMvXJo7mvgzdfLXyeCrottpw6r7lbikkmUOsYhRAOMJ8uf6fvY2ansR/9ILFP/iJ4GczcDGrLtgr3tjns2L6dS8PDAFwaHmbHju0l2ywsLLCwEP6cptJp0uk08Xi8pvspRD0YarMeTgMZjJzUNhAFXDMeY/i0rfITYKO4ajxmTEAchYuiC2nKslaSuWtS2Vl3Ga1qNgpBSjKry0xPoLbfGH69THC3lrJMMzVB+j/+G6ybbsVEre5zcsFdh1wwW0kjrrkDSCbWOQqhH33y2JL3B2dPYW3biffst9CnjuP+zP+weOGhxcXjcdLpNMCqgra+vj4sy8oFewC3v/M2br31FsbHxjlx8iR6iQsHe/bsZu+ecJTJtbExrl2/vu79tm2b/sGBdT9+M2iFY4TGP06TdIhjU2kzbaUUsXis5PaYAUc5GBRtZnHG72a21LFuGgYcY9He3c9F3+IhL44uU0VWfJwxrdCO4k2lecwLf5cqIKPsTdsO1dEK29YV/YxOTUyuvFH+a677lURDy3bMTAYWrqpNWaYEd9VlpiawsjPplsvcrXHdk5m4TjBRelKYfR7r9nsx6SRm5PKanrelNOKaO8CkEti33Y3787+CvnCa4AffK93ItnHe8xH0ubfQ59+Gzi6U62Jml8jcAf6X/rrwdSbHsPbdHo5HUI2Zxaymhx86XDZoO3nqVMltZpmgPx6P88D99/Hqa6/lbjtx8iTpdBrLsrjnnrvZt+9m3n77dNnHX7x4iYsXL63jCEr1Dw6s+YRhs2mFY4TGP84Fx8ZC4Vd42TAWj5FJly4ZsLFIGI804CqHTBNcnlzqWDcLB8UMMJWc5Dg2B5whMpSexxQfZzs24+k5XtUJDrpbacMiZTQGEzbn2YR8bIIgYGqqdj+jEtw1qUQ0TDwR2AzEvBW2rkyf47GjLSPBXZVlm6oApWvu8q1hzd2yrxdl7pxDj2Ft3UnmL3+vKs/bjJTVmJk7feo4attO7FvuxNq6s2xwZ+3dj/PoB+HRDxKcOk7w+ivAYuOU1TATYyjXRe3cjftTv4T/jS/A+JWqHUejeeHIi0vel83WZf/NZMqfkDmOw+FDhzhx8hRTU9MFjwfQWnPp0jD7br65ujsvRJ1lMHRscNpFY8gAFhvcvUWsigNMR8HcLAET+HRhkVrhvVEKEhh8DK8EC9xjdXBGp7jT6sArExy2RQ1Ymvvy4trJ2XiTymbuEoEdjULYuF92P3fDKNviGbrsZQIQsWb5J9vLNU1Zy5q7ZeVlANWOG2Vo9VKi74tpwIYqwWsvkvkP/wrvqa+i+gbCzqdFrL37MUGA99RXsd5xC7Gf+iWgdMbdckyU+Y39019HdfXk/t+KRkZH2b1rFwC7d+1iZHS0ZBulFA8ePMDw5eGSxin5GcEd27czNyedMkVzeVun6NzA082woQokTSAntQ3CRjFrFoOxF4N5Yspace2dAdLR39bX9AJfC6Y5Z9JlHxVH0aYsumVQegnJ3DWpbEOVpLawFNhQ5ppHdQxGmcET810b9AqtSV8PTwL9574Ny514Z9LVecG89u2qrR01MNTSJ+1Lyga9DVyKaKLPjhrajrlaWMZnveMWzJWLBM98k+CVH2Df8yCgMNdWn3nTk2Ph89s23jeexIxfgwZe87ORTp8+w8EDD7B79y6SySQvvRxmQvt6e9m7dw9Hjx3nhht2Mjg4SCwWywWC2ZEHD9x/P/FozcnM7CzHjh2v27EIsRFe0vPsVTG2qRhzG3Amku2WmUJHmTtRbwrI5F0APW1SvBTM86DVRWqZz4AxkI7ycGkMl00m7KBJ2GAlnZeoaMPiqWCWx+zuDT3H3YwkuGtSs75T8G+brdEGkrr6Vzh8o3hpuoevXNtS9eduZebiGdK/968wk+N1eX21Y1dJcKdufAfmyoXyJYmdXVg37EWPDMPcTG12sh6s6NpwA5ZlZmWDO2vrDoL84M6NoXbuJvjBd8P/z8+VX5e3ktlpvO98GTN6GX3m5MrbNzHP88qWbU7PzHA0CtQuX77C5cvlg+cXjhzZ0P0Tot408PVgmk85QyUn6NUSGEMCLX0VG0jxu5xYoZ+6Ez2m+POhgSPBPB+we0nnhXBawYjJ8EaQ5C6rg1kJ73Ikg92kTsx18kcXb2A84wLw6MAU/+wdwxvwSoYOO2AhsNi0rYwa2HKBnb58YeNe1/Owb71rMZABrFvuJP7L/xz7gUfKPsb5wCeI/fyv0PYvfpP4r/9b7Iff+KcQ7QAAIABJREFUt2H7V1cq+p40cuZuahzjeaitOwtuV1t3oGy7Kp+d4PnvtHxgJ4RYnTk0XwumaVd2RSee3ZQ+PjfnDlNxR05RHQpVEtxljMEs8f4oYKeK4SqVK8vM97ZJEajCoEUZSBjNWyYpp59FJLhrUhrFhWQ7OvpJ6nN9+ly/zI9bZeKWwVGLoxdE7WT+9HdI/et/VtXn1Neuoq9dJXj1Bey7D4RrqobCuV3Oez8CgH3vodIHKoW9/3aCMyfxvv4kJpPGfuDhqu5bw8hl7ho3uENrzOhl7Acexn7w0VwpqbUtDPaymT0hhKiVCybNc8EsvWr9RWMdyiJedOqqUOGcO2OklUoDKf4LmUYvWfDioJiLMq/lMrsehhNBomDmnaXCbOBV45ExWkoR88j3oskF0c+IE41DcJTBW+rSyTp02GEaXIK7OjCm6qWBmT/4rTAQMAZ98QzuR3+a2K/8b+gTx7B27kZfvoC16x1Ye/ejLyy2a1fbb0R19RAc+yL6+EuovgHsA++u6r41jE2w5g7A+8Jf4vzYz+J+9Kex73kQ7wt/iRragclkCjuxCiFEjbysF7jX6sBFrb21vQkzdE6ZNE1A2JVTorvGUZK5Y+ng20GRNJoYKrfmrthRneBuqwMIM1Nps9gl87Lx2KNi+NI3E5DMXdPLZu5i0SDzeJkhkpWQ4K4JRQGjfvM10v/5N9EXz2LffQB9+QKZv/lD9MQY7qc+jf3Qe3MPcQ49Fj7mbFimZ2anUbEYtHfUfv832iZYcwfhnETvM79P5sm/QA1uxfnwT6G27sCMjTb8vgshmteo8YiVCdDiKPqWWTXnAClTuq5usSxTTuwbhSIcT5Evg1myCbeDYsRkmMZfcuXcBD7nTJpubBwUC3lbjpgMrtRm5kjmrskF0YfdUeEPmWuZqrYU6oyCu4VArhM0pcQ83mf/CP2u96OPvwyJeTJ//O9xP/ELuB/6JNaNe9GnT2Dfdzjs6rkQjmzIttVXPf25+XlNYxOsucun33iFYMs27Mc+BDpAv/FqvXdJCNHCrpoM+632ktL2OBZqmRN0F0UKg6VUQVpIAT6GjDHImLvGUfwXMrPMUgYbuGTSnAxSyz7ni3qem5w4jlHM5Y1amDSSs8snZ+RNTke/5GJRxi5W9cxd+HySuWtivkfw9DcwUft7Ukm8z/0J/nPfxr7zAexDj2ESC/jf/UruIbngrrcf6+6DqN031WPPN8ZmWHNXxH/lB2AMynHRRaMRhBCiliaMjy5TPWApCNTSkZljYMoEmDKP1Rim8Zk3Ae1yatsgCt+nNIal3l6jYN5okiuEaNeMx7DO0KPsgjl60yaQpip55CegyQVRWaYb/UTF1epOSONWwC/vvky/G84+G3QzfGLbdayiH1Ypy2xdwWth+3dr5+6w1C+PmZ0GQPX24X74J3Eeerzm+7dhsnUlm6m0cW6GzB//Nuk/+/8IXnqu3nsjhGhhk8Yvex6ul/mVahMOxr5iMqii2r7sEHMf+HYwQ5sqzf91YNEhp7w1k31P8mXQZcsyFeGf04VV5t6O6HkUMJP3CtMUfqbiLR7pySe9yWXX3LmWKfh3JXd0L3BTR4r3b5kE4K6eeQ71z7IllinYrsMOwvl5UpbZcrLt9gH0+LXCO+dnMUGA2r4L1dGJ6uqpwx5ukE3SUKWYGb2MuXRu0+23EKK5zKGZjYZS5zOwZGanGxufsKSz+LQ9W5YJMGwyvKVTdBetzHOVIrZs0aeotuK30i9zm2NgSLnElWLBrG7N0BWT4YxOM59XPaOBGRPk3uMdKlbBnm9+ckbe5LI/Kq5aW1lmNuNnR79pt0ZBXa/rF2zXYWtS2sLIr8zWozVmPMzYZf/NMQbmZrDecUv4/+7mCe5UVJZplrvMLIQQYkkvBvMlmTQFSzbcCBR8PZ7knEkTFJ9xqMIhT88Es2hV2FVTmzDT0yVjzmumXG/MtDEF73qnUTwXzHJFZ5hfw6q5rwVTnDGF6/OuGw8XhU0Y7G9UgLMZznYluGtyQVHmLr7KzF1xcDcUCzM0vU7hlZUOO5CSzBaWnZdWXJYJYKYnsAaHAJosc7f51twJIUQjedukuGAy9Csn19nPWuas2TIwE52/LBhdELgZTEFnxnk0zwVzdKrFU1yl4Ed6HpvNcXK+2YXdMkul0djRO9CNzRVLc0TP8zfBRC77uhoL6JJRGqNRx0w7GrNhbcA7HUexXblVf95qk+CuyRWPQnBXueYuG9w5yqAwDMWzmTuvYLtOO2DBl+CuVelrVwEwY9dK7gvOnsp9rdwYtLXXbL82lLW5umUKIUSj8TH8fTDJt4NpupVNHEXahB0vy52YWkrlhlvPmqBk1l3xb+Nx4xcuizYwrDOcMSnJ3tVIuVAthcYiHH1glOHZWLmR5eszaQI0YOWCu+qLYZFaYtZiI5HgrskVV47FVpm5s6KMna0MPY6fy/iVzdxp+Ri1quCVH5D52z8rOxRbnzxW8P+myd7lGqpIcCeEEJV4Qyf5uh8GePMEpMtkXMKB1Rod3Txt/ILwzFB6rhMUhQxKhYHFkUCyd7VSPOcOsnMKFd3K5il/lvlVnpOuxpTxQYXNd7wlLhJUylYwtwk6sjb23omKBUW/wlY7xNzNC+62RiWZgYFep3TNnZRltrBUEn3itbJ3FZdqNk1wl8vcyZo7IYSo1CmT4kv+FG/rFOkos5MvHFi9eO4yQ7nMXeHv4wCDKYrg0hjG8blo0tI5c4MtNW5wwgR0KIvTOsmbJlnV15yNznidDSzL1AbOmVTDf3oaff9EhYKi327uKoO77Fo7Ry2WZA4n28o0VJGyTLG01G//Buk//Z3wP9299d2ZapE1d0IIUVWnTYoX9TxpUxrc2Sjm8zopzhtdELgZSssyA7OYnbOBZN7v66vGywWHXVi0yalw1RnKB3fP6lk+50/w7WCm6q+pCbN3cWVtXEMVBad0suFTv/KJbnLrLct0ouDOAoZiGRKBxdVUvCBz5yhNzDIkZAyCWMr8XC6Dp8p1zNyM6/BkzZ0QQmyIVJmMiw2FwR1ByXq64sxdfnMOG0UiL/ybzRt47SiFwbT8XLRqU0UdTLMM4biK6q20K3Qt6pjpb1DmTgEjxguDyAb+zMhZeZPTRZm72Cobqjh5mbut8Qxj6RgzvkObrXPP0WGH/0pZplhWKonxMth3H8R+9xNY+24Hy0Jvu4G23/htrHfeU+89XJvNOMRcCCE2gaQJymbu8gdWJ4wuyZwU/zYOMLkbbRSJMsFhHMW48fmRnqddyelwNZmygxA23qgJe2hOG7/qAY6DYt5oNHBapxo649u4eyaqonjN3Vozd7YyDMU8rmdcUlHjlOysvA47/GUpwZ1YSfDDZ6CtHff9Hyf2j38V+/6H0Fu2A2DfelflL+DGcN73MYi3Vf5cK5GyTCGE2BDJMhkXpSiYgZZAlww7LynLzPvaJmyCkTUfBYdtWFzQac7qdPkaQlGReoR3U8YniWaWoOQCQCcW3RWEPQ4wa8LqtUsm09AfGWflTcRmVrzmbrVDzLPBXZcT0G5rxjIxPJ03ViEIxyCABHdiZf53vgzf+TK0tdP2G7+N6u6FdCK8Mxav+Pmd934U55H3YWanCV56ruLnW1Z2GJM0VBFCiKpKFjVUcVEoVEFwliQMztRicq6kO2aACYssTLRmLy/8WyDMDmrgiskwQ8B1fHqiNveiOurxnZwyASmjmTOFn4jO3Kdq/aWUDios6SXMEBZ/BhuJZO6aXOmau7U1VGmPSi/H0jE8E35c3JLMnXyMxCqlkphkAto6wIkGgcZiFT+tfcd90Rc1uNAgmTshhNgQGTQdyqZX2XRjE0PxVX+KCyZdsN2C0blxCOVKAH3M4mm8Ck/6s9KYcE2eCoM7gON6gTYpzayapRqqbLQ5AqYJmDGL6zI7sQiAbwUzuVUV62GjmI5ywj6GqzrTsKWZkrlrcqVr7lZZlllUvjmWcRmMRiJkn0PW3Il1SSVRbe0YP/xjrSrM3Kn+Lai+gfDrGoxbUFFDFSMNVYQQoqrSxnDNeIya8MT5W8E0cyVFl2Hb+wHsqHGKKmmoolnM0WgD0xR2+p4xASN6sbHHeZ2WdEeV1SO4M8Df+uNsVzEMBpewscvf+RNk0JXtlArXa2adNilusGIkGzB1J8Fdkytdc7e2skwATysmPZeeaIB5NnMnZZliPUwqAe0d4KXCG9o6Kno+1b9l8etajFuQzJ0QQmyIcybNFX+8YK5dObPGZ0g5ZM/Wy20dEAZ4tooGXOd5Uye5oBezgfNoRvAYwAnLPkXF6tVzzAcyGFCKDhRPBTNM4NOGypXqrocxUTOfSLZ5SyOqKLhzXZcDBx6go72dRDLJyy+/gud5Bdv09PRwz9134TguBsPbb5/m6tWrFe20WL2gwlEIAOMZF4PCi7KAi5m7gFSgSgJIIZZjkokwc5cKm59UGpCpnr7weednazNLT9bcCSHEhvCzJZMrmDIBTrbGTpWOQgDwTZi5SRldspbuFb1Qsv3rQYInnN6GzMSItfGMJoYiieakDi8kZyocj2CgYKTGhPGjoelANH6hUVQU3O3fv4/xsXFOnznD/n372L9vHydOnizYJggCXn3tKAsLC7TF4zz22KNcv34d3/eXeFZRTSVlmevI3I1lwjVRGV285k5L1k6sXSoJg1shHv5xVR2d4LpQdGFo1XrD4E5fvYTqHajWXi5NMndCCFFXcwQFWZhy19p8DDEUI2Z1f1vOFa3ryxdHEcdinkDyeqtULuCulUy0DvNHwTxeXnY3iNZirmfPFIWZOw/DrAkYUi5pNDMmWPrBNVZRhfGO7du5NDwMwKXhYXbs2F6yzcLCAgsL4UlcKp0mnU4Tj1feHU+sTvEvodXOubMLgruw8UUmChRdazFzJ8GdWCuTijJ3eWMLVE//up9P9fRh5mcx05O1KcuUIeZCCFFXC0YT5NX9lS/LNMSVxfVVBndJNMm8Ri1ZPVG+55xJ063knGe16pnHSmMYNx6v60TB7Rmj1x34WAoSFAZwV02GANNwhTwVBXfxeJx0OrzSsZqgra+vD8uycsGeqAWVK80MDDgWWKv4kXPygsCxdJi5y41CUBLciQqkkuGau3hb2DkTcD/xC7mmKGulevows9OYuZkwC+hs8FLi3BBzCe6EEKIeEgSo6HdxmIkpU5ZJNHia1WdUpghwWXzefmxGjMdf+eO8qRN1W0e22dT72+Rj+Iw/XlKOm15naaZFmKkrrjm8YjLhxYMGW5204lnQww8dLhu0nTx1quQ2s8ynPh6P88D99/Hqa68tuc2ePbvZu2cPANfGxrh2/fpKu7cs27bpH6xBmVYdreYYNRY2mrRx6FA+W7f0kjbLv/Xt8etM+G2M+J1cj++kP+biEgAX6O1qo98eoCs2zJzXXpPvcSu8l9Aax+kp8GNxTHsn9pUL2KffwHv848R/9TeIfe8r2GfeLP+4B94N7R1YVy/i3/EA1pWLOK8+T7p/C2puirgJ8IDeG3djzU1v2P4HPT1kCNcTW15yxe1b4T2Fyo9zamKyinsjhGhmCyZvkLmhbPiWLcdLryHUmNAe2yyXFAHd2LxlUnwjmEYTNekQq9MA89/KXX5NR6W6a2WjWChzQfekTnFFe3zK2VLmUfWzYnD3wpEXl7wvm63L/pvJZMq/iONw+NAhTpw8xdTU0iddFy9e4uLFS6vY7dXpHxxo+hOG1RxjMACuDQlf0RGDxPQks/7yb73pzDAD/M2lfmAOIGwoOwQ7mOAeFmjHYyrh1+R73ArvJbTGcdqTE7iA6enDO38a/8VnUW+9ifvJf0LmIz9DcPIYZnY6zIyZ8C+E6ujEvvcQRmvU/Y9gfA/9jltJD2zF6uohOPcWeuQqMWBWg9nA76G1fSF8nampVb1OK7yn0DrHKYSovyS6cM1dmVAiu74qs4Z02zg+tlKocAweR4LF8edeNBuv7lHLJtGI36a00fQqm7XunQ1Ml1lT52OYxG+4geYV1S+NjI6ye9cuTp85w+5duxgZHS3ZRinFgwcPMHx5mJGRkUpeTqxTtiwzFQ0bX01TFUcZ0rqwateg8LTi1q4Et3aF5XRSlinWKluKie2EJZqAmZog8+e/i/Pej2Lf/xBYdlj+mOuGZhEcfxnvG09ibd2Jvj6Cfc9BnA98AmXbmNlp9OhlAKwb9xJcOrdxB5BrqNIov8aFEKK1aChYH1fut7FnDJYKB6Ov1qwJ0MbQjsUVkwlP3CMZKcVfk3KlsvWWZH1r7hwUc8uU9y4Yjd1AHTMrCu5Onz7DwQMPsHv3LpLJJC+9/AoAfb297N27h6PHjnPDDTsZHBwkFouxe9cuAF597Sizs7OV771YFR2loJNRsLaapiqOMszr0tS1ZxRu3oc3EcjUT7FGqcUFzibva7TG/94/4H/vH5Z9uL5wGoDgyPcxI5dxPvgT6HNvwfwsemIMa/fNBC88tSG7DkhDFSGEaABzaLqjFVTlfht7GAxqTWWZsyYABTEUp3Rh2X0Gg2qMc/dNoRG/VSmjsdT6yjJnl+mGOUvAAHa0zhO6lcOCCepWyltRcOd5XtmyzemZGY4eOw7A5ctXuHz5SiUvIyoURF0uk1GWbTWz7mxlco/Ll9EWHfbir9EFydyJNTKpvD+Y6VRFz6UvnCbzR/9+8f+XzmLfcmeY8Ysya9ZdB1A9vQQ/+F5Fr5UjoxCEEKLuZk1Av7JRS825w6AxayrLnCHAx9CGxWVduNQog6loCHaracRv03ozd5YKLyYsZdb4bFUOvVgEyjCiMwwpl8wamvlU0wa3lRONINuiNaXXVpbplw3uCm+TskyxZsm8zF1y5YYka2EunEHdd5j4//E74esYg+oNxywER75flWybioaYG8nciYjruhw48AAd7e0kkklefvkVvKK5je3t7Tx48GBUbWxx/vx5Lly8CEBvby/333cvlm1z/do1Xn+jfFMhIcSiKeNzkwob/pXN3BkdBndrKMv0MHzFn+Kw1cVU0Ym515DhSuNqxO9Wap3BXbYMeCnTJqDTsjmuF3jWn+Nmq43HrVjdvgkS3LWAbAZucc3dakYhlA/uPFP4YyHBnVgrMzOJHr2CbSnMcHXXxgVvvgodnaiubmjrQNk2ausOrJ27w5EJ01Vo+GHJmjtRaP/+fYyPjXP6zBn279vH/n37OHHyZME2qVSK555/Hq01tm3z+Hvfw+joKKl0mnvuvoujx44xNTXN4UOH2Lp1K9cr7BYtRLObJSC61lY2c+cRnpSvpSwTYNhkGA7K/63wMFiUDybFIkNjrrnLYNDryL4aE2b9lnJKJ7lk0lyJZirOR2s360UWTLWAoMyau5/Yfp1Pbr+25GPC4K704+GVZO7kIyTWyPPI/Jd/R9vn/hAzOVbl584QvPA9/G9/Cf8rn8X74mfwv/NlAFTf4MqPt23sdz8BHZ1Lb6NkzZ0otGP7di4NDwNwaXiYHTu2l2xjjEFHnxnLssgORorH4ziOm+skPXx5mB3bSx8vhCi0YAK0AYUqe9rtowkwVc24pY3BbrShZg2q8UK7sHOqjaILi/gy76OLKrxXLZ+5myLIBXYA8+i6zr6TzF0LyJVl5tbcabbGMtjLrAx2lMl12cxX/FmVzJ1odGZ6Ivyit3AGm+obxGRSkFjI3WY/8Aju+z8Ogb90UxZL1tyJQtmRQLA4IqictrY2Dh8+RGdHJydOnCCVTtPX20sqbx1qMpmira2t7OOrOQu2FeYvtsIxQusep60tnLSDbRR93QMlJyiu52J8h/6eKn5vUi5btEUGw9wqqqDWSylFLB7bsOffaK5WdHf10G8v/3ey1p/dtFZc9iBubLZqC1PuPTTQbyw0kFAaT4XHE+/upX+VHXUco3CTLjHLxtEK29Y1nQMrwV0L0LmGKotlma5lsJa5rmJb5csy3bz1ep5WJWWaQjQaMxNmRFRf/+KNlkX8f/3X6NHLZP7L/xPe5jg4734ivHvv/qWDu2ynLSnLbCkPP3S4bNB28tSpktvMEp+NVCrF008/Q1s8zoMPHuTqyNU17UM1Z8G2wlzCVjhGaN3jDLAxzhApBVOTpcc/bXUwb3Uylaje92bWGWRIxfGM3tBmGbF4jEy6/OzozcDDZiY1y5RZ/hhq/dmdAi4C25TLzzqDZMp0wHRQTAJPBbM8YffSoRQBitHJiVWX404DvutijCalFAvaL/sZ3SgS3LWA3Jq7vIYqtjI4S1yBUBgcRdngLvuYN+c6S5qrCNGQAh8zO1NQlmntvyP8d/uNudvsBx5B9fShR69g7b65oONmgWxwJ2WZLaVcZ+isbLYu+28ms/wJTSqdZnZujoGBQSYnJ2lra8/d197eRipVWRdZIVpBggAfOBLMlb3fZ/l1UuuRNpogO+Fcru8to7G/ObMmYKnEaxuKsybNGZPiqp/hgNW55LiNpRjC2Xf9yuabwTSeal/xMdUkaZcWkL0uERhFWqswc7dMcJct1/R16ccj24zluck+/m5E1oWIzcFMT6D6BlBD27EPPYbz/o+Ht2c7GjouzrufQJ8/jf/8d1DtHai8wK+ANFQRRUZGR3NzXHfv2sXI6GjJNm1tbdFau7C75uDAAPPz86TTaXzfo7+/D4BdN5Z/vBCikA98LZjiZb1Q9v4AQ6LK5fMJo0ljUNR1SdUmoBqyoUpWEo2vTNkgyFGKYROW2SfQPKvneEaXv4CwnGGT5oVgntd1dbuCr4Zk7lpAtixTE86pi6kwc+cuMcw8G/SVz9yFj5H5dmIzMdOT2HcfwP70vwRAT46hL57F2nMzdPdi33EfqruXzJN/gRkbxfg+9j0H8UeGS59MGqqIIqdPn+HggQfYvXsXyWSSl15+BYC+3l727t3D0WPH6e7u4o477ggvCijFmbNnmZsLTxiOH3+d++67F9u2uXb9unTKFGKVzkYn4eUExrCwxHnOeqXQTEelfHFUyZDqlRJ6LqolRiqsNdNVD1MmoAurpJuqBsaMX/HzfzOYqfg51kuCuxaQDe4Co/CizJ2jNO4SOelscFduiHk2c5fwJbgTm4f//HcwU2OY6Un0ubcx0xNYN99G7FOfxr7rAM67PkBw7i3MxTMA6JPHcB56HPueQ2T+/HcxY6PguhCLS0MVUcLzvLJlm9MzMxw9dhyAsbFxnn76mbKPn56Z4ftL3CeEWJ9J41c9wkiiGTNheHazihcEdwrYrlwmjV92/EInFrZSdGSb3KHXPKZhszA0emEmTBiffhUveQ8UMF2F4K6eJLhrAdmyTG0UaW0Rs3ReWaahuLhguczdU+MDfGjrRG6sghCbgbl2Bf/alcLbJsLsiPvBn8BMTeB//cncff6L38e67W5wY7gf/znM7BTWLXdCEBD8KDoJl8ydEEI0rDH8qmRg8k0an3mjmcXnZjtOJxYLUQTZhc248elUFulyjTqU4rvBDOd1mjutDg5bXaQ3sCmLWN41k+EOq50Fszgl0QJ8DKmGD02XJ2foLSDIZe7AMypsqGIZLAXl8m/xqHVtuYYpz0z28xun9kUV50JsXmZmKvd1Opudy953+QLp3/x1/Ke+irX7Jqy9+9HD58O1eDt2RRtt7l/+Qggh1uasSXPSJLliPP7aH2ceTa8KJ9/ZCo7o+SXDAgNc0x4LaC6v0EVys1Oq8TN3x3WCl4N5+pRDLDqndVFMlQnMNxvJ3LWAXFkmioy2iFsaJ4rNHEsT6MIQb4sbNpmY8Nya7qcQNWUM3jf/HnP9KsxOl70/OPIU+sxJzNhIuDbvn//fWDe+I3e/EEKI1jRFwN/443zY7uMOq53TOsXrOsF9Vgf92Mzm1YRagMYwTphJTBrd9B03G7mhCoQNeZ7Wc1w0GT5q92ERjkGY0N5KD214krlrAdkh5tmyzI68oZJumY6ZW2LhFaWxzOYdoCnEagRHnkKfLZ1TlmNMGPwZA7PTmLkZVEcnJtj8V/aEEEJUxsPw9WCaN3WC56KOip/3J5kgoFctXjjvVjZvBYvFfkk0q5yHvWltluuf502a7wQzxLFwUIyxudfbgQR3LSGgsKFKu7V4YlquqcpQ3GPGs8nIujohCugrF8MvNstfLSGEEBvKx/CVYJprJsz4JNA86U9wWWfoVTYuiqTRfF/P5h6TQudGpjajzdBQJd8Fk8aoMJk60wRlmXL23gKya+60gYyxaM/L3JWbdTcUyzAuWTshSpiRywAoRyrahRBClJfG8KUgXNcdQ3HVeAUjEAxhaaZNuM6r2fqPb7aK0zSGN4ME4/iMmc1flilnKC0gW5YZrrlTWHlXi0pn3RmGYh7HZrtqtn9CbBZ6/Fq9d0EIIcQm4GOYNQGDymG8zDqueTSdWHQoC2Ngpok6Z262zB3Ad/Vs4w/nWyXJ3LWA3BBzo0pKLYvLMtstTbutJXMnRBlGgjshhBCrNGl84li5wef55k2AgyIwNF2JZpi522zhXfOQ4K4FLK65g3RRcFdclpkN9tJlxiAI0eqys/GEEEKIlWRL/ObKZOXmTICNQrFYYSVENUhw1wKCbFlm1FAlX3G3TDv6vy4zwFyIluc192wiIYQQ1TNpAtJo5spk7mYIcABrE8yEW48mqXDclCS4awEFZZmmKHNXVJZpR79iAhlSLoQQQgixbrMEJIxmvkzmbsr42Cosy2y6Uy7TnAHrZiHBXQuY8hxmPRtNaVlmcUMVK8rcBZK5E6Is/4fPoMdG670bQgghGty08blmvLKtUkaiks1ZgqaceSdr7upHumW2gFdmejg6242htCyzeM2dHd0t9d9ClOd//cl674IQQohNYAHN54PJsvcl0Iwbn2smQ7/V3nSpriY7nE1FMnctwKDwo3LMlbplWrLmTgghhBBiw53WKUaNh6YpKzNFnUhw12KKu2CWNFTJrbkTQgghhBAb5UU9x3GdyA00F6IaJLhrMZ4pztwVr7kL/5U1d0IIIYQQG8cnvJieQGM1We6bKGocAAAVH0lEQVROumXWjwR3LSaz4po7KcsUQgghhKiVRDTzrthmPUmXM8j62qyfG7FO+d0yA7P0nDvJ3AkhhBBCbLw5E9CpLLqwcPJCoxtUbNOWa0q3zPqR4K7FeHnBXSqweHhghru653K3ZcsyJZ0uhBBCCLHxXtDzfCOY5oxJ06EsYlFcpKFsRm894jXMp6kmHcy+WUhw12ICFH70ExeP1tv93A3XcvdbSOZOCCGEEKJWFtC8oZN8I5jmok5jo1CAj6lKcGcBQ8qt2fwzgwR39STBXQvytIWnFU707k9k3Nx9suZOCCGEEKI+UkajCNeteZiqlGV2YjNH+XV9G0EhwV09SXDXgjJa4RuVa64y6y/+6rBz3TLrsWdCCCGEEK0rhcEyYKHwTfUyd0lT246cchpZPxLctaC0tvCN4v89u5fziTZieYPMs0PMA+l1JIQQQghRUynCzJ0FBJiqtZ68ajI1bc4iDVXqR4K7FuSZMLhLBDZzvoOrFtunZIeYS1mmEEIIIURt5Qd3GjBViJGUgnHj17AsU0loV0cS3LWgjFb4UUlmRquCzF22LFPLT6UQQgghRE1lMIThkcKvQohkE67jW0CjanjdXk4j60eCuxaUicoyIczi5WfuLJlzJ4QQQghRF54xGAwWMGuCissybRTzaJJG1zDgkqLMeqpVV1TRQJ6Z6KfNDgO64sydrLkTQgghhKgPD4NBYaGYIWC7cVd+0DJsFPMmIIWWqqwWIcFdCzqfbM997WkL1zKo6JdJdrGt/AIQQgghhKgtL1xphwXMGB8scAz463w+G5iLgjtVoxkFcgpZX1KW2eIyUfmlG2XsbCnLFEIIIYSoi4wJ19xZQBLNt/0ZurCJr7OiykYxR0DK6JpFXRLc1ZcEdy3O0+FHwLXCMs1sWab8YAohhBBC1FbYUMVgVFiiecIk+XwwiYuic5nT9i4sepVNOxZ9OHRH2yoF8+hwfl4NrtvLAPP6k7LMFpfN3MUsw0IQZu58A1UbrCKEEE3OdV0OHHiAjvZ2EskkL7/8Cp7nFWzT3t7OgwcPohQoZXH+/HkuXLwIwCMPP0RbWxtBEADwwpEXyWQyNT8OIUT9eZhcgORFcxCGTYbP+hN80hmgXzkEZeYj+Bh+GMxxs9XGWZ3iFqudLcpBm3CAuYchoDbBlwR39SXBXYvLZDN3UcdMC5lxJ4QQa7F//z7Gx8Y5feYM+/ftY/++fZw4ebJgm1QqxXPPP4/WGtu2efy972F0dJRUOg3AK6+8yvTMTD12XwjRQMLgTmFM+HXWBD5/449zt9WBE82RswArKuE8bVIMmww/0gsAHNMJfsIeYI8VJ0l4jjduPHqxSW1w+CW9MutLgrsW5+nFzB2EmTtZbyeEEKu3Y/t2nv/BCwBcGh7mXY88XBLcGWMw0dV2y7KQ6gghRDnZzB1kSzQXLaA5oudX9TxpDF8IJnnIdDFlwnYswzrDVquTFEE1d7mAlGXWnwR3LS7XUCW35g4C+akUQohVi8fjpKMMXDqdJh6Pl92ura2Nw4cP0dnRyYkTJ3JZO4D77rsXYwxXR0Z4++3TZR+/Z89u9u7ZA8C1sTGuXb++7n22bZv+wYF1P34zaIVjBDnOZuQnIea6dHT30G/plR+wjNeBdvppBxZ8GyfjELPslR62bpYBB0V/z8rvVau8p5Ue59TE5Jq2l+CuxWUbqsTyumVKWaYQQhR6+KHDZYO2k6dOldxmyqyHgbA08+mnn6EtHufBBw9ydeQq6XSGV159jVQqhWPbHDx4kF03Jhm+fLnk8RcvXuLixUuVHwzQPziw5hOGzaYVjhHkOJtRpqOdNl8zMTXN1LqHIJRSOPjuFrTRBFF+rUfZKGDaVCebF9YlWEwlVn6vWuU9rfVxSnDX4rJlmW5Ulmlh0FIuJIQQBV448uKS92Wzddl/V2qGkkqnmZ2bY2BgkJGREVKpFAB+EHD5ymX6+vvKBndCiNaQVOBiSFNZ1q7YFD4XdZotyqVTWdgoruoMO6xY1V4jLMuUErB6qii4q7RDmKi/jMlm7sJfIOGau3rukRBCbC4jo6Ps3rWL02fOsHvXLkZGR0u2aWtrI5PJoLXGdV0GBwY4e/YcSilc1yWTyaCUYvu2bYyNjdfhKIQQjeL7sRRTC9PMVzm4M8AXg6mC21wUn7a2Vf11RP1UFNxVo0OYqK/ihirhmjvJ3AkhxGqdPn2GgwceYPfuXSSTSV56+RUA+np72bt3D0ePHae7u4s77rgDjAGlOHP2LHNzc9i2zUOHD6EsC4VibHxMLoAK0eKmLMPUBjY9yedh8PPGL6zEQeGvsKUEd/VVUXAnHcI2v0zREHNZcyeEEGvjeV7Zss3pmRmOHjsOwNjYOE8//UzJNkEQ8Myzz234PgohxFIWjMZBFYxeKMcCtiqXCeORXmJbBVXON4q1qii4q0aHsHzV7AQGrdGFp9JjDCelnKOns41+NUBbbBxlmYb7vrXCewmtc5zQOscqx7k6rbCoXgghGtGcCdii3ILgrgubAJObkZe9bcJ4xLFI1yizKNZuxeBuIzuEFatmJzBojS481ThGb1ARpBaYmpgkaM+QsYOG+761wnsJrXOc0DrHKscphBCikc0Yn+3KLbjNVmEJZjLv1N5ScDxIcNDqWubZlDRUqbMVg7uN7BAmGoNnlAwxF0IIIYRoQTME2HnLplwU2hQupOrA4qrxOKVTHLS7llxYp0ByenVmVfLgbIcwYNkOYeFaO3Idwubn5yt5WVFlicCmyw7nqFjKSK20EEIIIUSLmEODCoOCLix6sBk2afKv9cdQvBTMM4WPxlQWQIgNVdGau0o6hInGMZqOsT0eZl0twkyeEEIIIYRofgsmQAGdyua8TvGWTjFqPP57NQSEmbwEmgsmbKNyVqfZp+JhUFiGJAnqq6LgrpIOYaJxjKZj3N61gKs0tjKktVyPEUIIIYRoBZdMhj/zxkgQ4OfdbhQoA93K4vvBbC5oO6GT3Oa0049iyhQWYa52pILYOBUFd6I5jKTiWAq2xTOy5k4IIYQQooUYYLbMSrk5EzCoHC7qNG/oZO72yybNmPawFPRgk0DTjY0G0mhpqFJnkqIRjKbDbqjb4+loiHmdd0gIIYQQQtTVtPFZMJovB1Nk8gI2H/hMMM73gllcZdGhbN7USewoNyBlmfUlmTvBlOeQDhQ74hlsDFoGzQshhBBCtLRzJs1xnVhyYPllk+FJf4JR4+Fh/v/27v2riTOP4/h7SEImwkoQwQQIF1e7ttXKtV73nL39YftP7XYvp9vLulttRQW1glyTgCQkIQpIJiQw+0MkxQNsiyZmMvm8zvGI8UkynwnhyzfzzDNcaPLjfXMFZakdNXeCjUEi7ydk5vEYNnualikiIiLS0B7tbf/smLj902XQ1uwC/YZf0zJrTNMyBSgtqhL272hapoiIiIic2Au7QLPRpNauxtTcCVA67y7g2eNX3qIWVBERERGRE0nbRa2W6QBq7gSA1XwzAB4DnXMnIiIiIify0i5dSMG21d7Vkpo7AX5aMRM0LVNERERETmaTXYrYWi2zxtTcCQD5vSbWd0rr62hBFRERERE5CQsbiz1Ny6wxNXdStvrm6J3OuRMRERGRk9qwd7VaZo2puZOyxJvz7nQ4XUREREROKmvv6vfIGlNzJ2U6ciciIiIi7ypjF2q9CQ1PzZ2UJazSkTs1dyIiIiJyUll22dG0zJry1noDxDnWCz6+zQSZ2TpV600RERERkTqzbhcJ4qn1ZjQ0NXdSZmPwRepsrTdDREREROpQwi6Q0NTMmtK0TBERERERERdQcyciIiIiIuICau5ERERERERcQM2diIiIiIiIC6i5ExERERERcQE1dyIiIiIiIi6g5k5ERERERMQF1NyJiIiIiIi4gJo7ERERERERF/DWegNERETqmc/nY2xslFOBANu5HPfvT1AoFI4c6/V6+cPvf8dqIsHjx08AaGtrY2R4iCaPh7VkksdPnn7IzRcRERfRkTsREZH3cPHiBdKpNF/+6yvSqTQXL1w4duylS78hk8m8ddvVz67waHKSL7/8Fy0trXR1dVV7k0VExKXU3ImIiLyHcChELB4HIBaPEw6HjhzX1taG3+9nLZUq3+b3+/F6fWSzLwGIL8cJh46+v4iIyM/RtEwREZH34Pf7yefzAOTzefx+/5HjLn/6CRMPHtLZebZ8W8A0saxc+d+5nIVpmkfev7+/j4H+fgCSqRTJtbV33maPx0N7x5l3vn89aISMoJxu1ChZlfOXyWbWTzTe1c3d6dbWE++QetMIGUE53ahRsiqnO9y8cf3Ipu3Z9PSh22zbPnTb4MAAybU1LMt6522IRmNEo7F3vv9B/f19FXssp2qEjKCcbtQoWZWzOlzd3A3097v+m6YRMoJyulGjZFVOd/jvd3eP/b/9o3X7f+/s7Bwa036mnY4zHQwODODxeGlqMtgtFplfWMQ0A+VxgYD5Xg3gL+X21wsaIyMopxs1SlblrA5XN3ciIiLVtppI0BeJMDs3R18kwmoicWjMgwcPy19HIr0Eg0F+fFY66lcsFmhvD5LNviTSG2FhcfGDbbuIiLiLmjsREZH3MDs7x/jYKH19EXK5HD/cnwAg2NbGwEA/jyan/u/9p6YeMzw8hMfjIbm2xtp7nEsnIiKNzRMOd/+51htRTa9evar1JlRdI2QE5XSjRsmqnO62t7dHfHmZxcUl4svL7O3tAWDl8ySSyUPjNzY23mrgrHyepaUoC4uLH7Sxa4TXqxEygnK6UaNkVc7KM4ZHRg+f+S0iIiIiIiJ1Rde5ExERERERcQE1dyIiIiIiIi7gqAVVTNNkZGQY0+/HtiEaLZ2D4PP5GBsb5VQgwHYux/37ExQKBXp7erhw8QIGUCwWmZx6zMbGBgBdnZ1cuXIZDINYNMbs3NyRzxmJ9PLRxYsAPJ+dJR5fBuDjS5eIRHrx+Xz85a9fuDLj9evXMP1+DKOJzHqGqanHrsx56+YNTNNkd3cXKC1pftRS5fWc0+vxcPv2rQPbFWB5eZknT59WJKeTsgJ0d3fz0UcXMTBIJpP8+OxZXee8fv0aZ9rbyWTWuff99+XbBwcGOP/r87S2tPDF3/7uyO/boaGrhM6dI5/P89XX3xz7nMfti2pldBsnvf9UH92TU/WxMpySFVQfnZazFjXSUQuqeDwestks09MzLMfjDA1dJZ1KM3h+kK3NLe5PPCBgmnR2dpJKp/F4vczPzzO/sIBl5bn86SdEY6XrSNy4cZ3v7t5jdnaWK1cuk8lkDu0Qn8/H6MgI3/77DtFYjNGRkfLJ8Lu7Rebm5jl/fpDZ2aO/4eo9YyKRZGFxkaWlJfoifRjA5uam63L2RSJMTk4xPfOcpWi0XMTclLNYLLIUjZb/9PR0s7CwQC6Xc11Wj8fD5+Pj3LnzH+bn5+mN9FIsFtne3q7LnACWVVp4IxgMsrKyUr7d3ttjKRolHA4RjcYq9r1byYyFnQKxeJxwOMTSUvTY5zxuX1Qro9s45f2n+uiunKqP7sqq+lgZ9V4jHTUtM5/Pl1eTKe7usrm5hRkwCYdCxOJxgPIOAshmsxQKhfLXpmkC0N7ezuvXr9ne3sa2bVZWXhAKhQ49X1dXJ6lUmkKhQKFQIJVK09XV+ebxXpLP512dsVgsAmAYBk1NBpVcWcdJOavJiTlbWlrw+/1k1tddmbWl5RSvX2+Vi0AqlaY7HK7bnADpdLr8fjzo1cZGRX8BqXRGgMz6+s9+kvj/9kW1MrqNU95/pcdTfXRLzmpyYk7Vx/rKCR++PkL910hHTcs8KBAI0NbWRjb7Er/fXy4k+Xwev99/aHxfX6S8hLRpmm/tiJxl0d4ePHSfw+Nyb70g1eaEjDeuXyMYDLK2tsaLFy8qlu0gJ+QcHh7Ctm1erK7y/PlsxbId5IScAD093aysVOe13FfLrGtrKVpbWwkEAliWRTgUoqnJqHRE4MPkrLX3yfhL1cu+qBdO+VlTTU7IqPpYOU7ICaqPldQI9RHqs0Y66sjdvtJh5TGePH1yZLdu229/hna2o4P+vj6e/li5ecXV5pSM3929x9//8U+amjx0dp6t6GODM3JOPHjIV19/w507/6HjTAeR3t6KPfY+J+Tc19vTw/KBqQuVVuushUKByanHjI+NcvvWTbZz29hVuKBLrXN+CI2Q0W0a4TVzSkbVx8pwQs59qo+VUeucH0q95nTckTvDMPh8fIzl5RVWVxPAT93x/t8HD2+ePv0rhoau8t3de+VDopZlEQgEymMCpomVs2gPBrl69TMApqdnsCyLsx1nD4wLkM6kGy5j6fyCBKFQiFSqcvmdktOyLKB0aH15ZZlge5D48rLrcpYe+zSGYVTtYplOyZpMJkm+uTh0f3/foR+w9ZTzqItcfwiVyHgc0zS5fu1zAJaWorza2DhyX8jJOOX910gZVR/dkbP02KqP9ZazVvUR6rtGOq65Gx66yubmFvMLC+XbVhMJ+iIRZufm6ItEWE2UdnIgEGB8fJyJBw95/fp1efzLly9paWnh1KkAuZxFT083Ew8esLm5xdfffFse5/P5+PjSx/h8PgA6OzsruqqQkzN6PB68Xi/5fB7DMDh3rotMprJz0J2Q0zAMfD4fOzs7GIZB6Ny5ihZop+Tc19vTXdVPJZ2Stbm5mZ2dHXw+H4MDA/xwf6Juc9ZKJTIex7KstzIahnHkvpCTccr7z+0ZVR/dlXOf6mP95ayleq6RxvDIaBUO2L6bM2fO8Nvbt3i1scH+ceQfn02TzWYZHxslEAiQy+X44c3So0NXPyMcDpfnqdq2zTff/huArq4urlz+FMMwiMXiPJ89eh55XyTCxY9KS8nOPp8tnyj5yScf09vTg2maWJZFNBZjZua5azL6/c1c+/waTZ4mDCCdzvDk6dOKfcLjlJwej4fbt25iNDVhYJBKp3jypHLLHzsl574//fEP3L33PVtbWxXL6MSsoyMjtLWdBmBm5jkrFTwfphY5b9+6SWtrK16vl52dHR4+miSVSnF+cJALF35d/oQwmUzyaHLKURlHR0Y4e7aD5uZm8vk80zMzxGLxQ8953L6oVka3cdL7T/XRHTlVHyvHSVlVH52VsxY10lHNnYiIiIiIiLwbRy6oIiIiIiIiIiej5k5ERERERMQF1NyJiIiIiIi4gJo7ERERERERF1BzJyIiIiIi4gJq7kRERERERFxAzZ2IiIiIiIgL/A+MJT0R24Wr8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sortino: -0.754\n",
      "Beta: -0.171\n",
      "Alpha: -0.173\n",
      "MaxDrawdown: 38.215 %\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2QehlheC4R9W"
   },
   "source": [
    "# Section 2: Min Variance Optimization"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "vwn2aKYu4RJ_"
   },
   "source": [
    "def MV_criterion(weight,Returns_data):\n",
    "  \"\"\" \n",
    "  ------------------------------------------------------------------------------\n",
    "  | Output: optimization porfolio criterion                                    |\n",
    "  ------------------------------------------------------------------------------\n",
    "  | Inputs: -weight (type ndarray numpy): Wheight for portfolio                |\n",
    "  |         -Returns_data (type ndarray numpy): Returns of stocks              |\n",
    "  ------------------------------------------------------------------------------\n",
    "  \"\"\"\n",
    "  portfolio_return=np.multiply(Returns_data,np.transpose(weight));\n",
    "  portfolio_return=np.sum(portfolio_return,1);\n",
    "  mean_ret=np.mean(portfolio_return,0)\n",
    "  sd_ret=np.std(portfolio_return,0)\n",
    "  criterion = sd_ret\n",
    "  return criterion\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "n=len(strategies.transpose())\n",
    "# Initialisation weight value\n",
    "x0 = np.zeros(n)+(1/n)\n",
    "\n",
    "# Optimization constraints problem\n",
    "cons=({'type':'eq', 'fun': lambda x:sum(abs(x))-1})\n",
    "\n",
    "Bounds= [(0 , 1) for i in range(0,n)]\n",
    "\n",
    "\n",
    "# Optimization problem solving\n",
    "res_SR = minimize(MV_criterion, x0, method=\"SLSQP\", args=(strategies.loc[start_train:end_test].dropna()),bounds=Bounds,constraints=cons,options={'disp': False})\n",
    "\n",
    "# Result for visualization\n",
    "\n",
    "# Result for computations\n",
    "X = res_SR.x"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DAyQIvzduVpV",
    "outputId": "57469a91-31af-43cc-928b-d1e71b204e8b"
   },
   "source": [
    "# BackTest MinVar Optimization\n",
    "mv = np.multiply(strategies.loc[start_valid:end_valid], X).sum(axis=1)\n",
    "BackTest(mv)"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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FBQUFeNxuysrLSU9P53t3fxebzcZ5553LRRddSENjAx63h8cefYQPPvwQr9cbPk9BAbd+5cvR5+KhHz+I3W7n0ksu5dprrqa6ppprr7mGyVMmc+aZZ3DDF77A6acvp662jsrKyn54lQghhBhobb9TADweD0VF+8gal8U555zNmjVrY37+K2XwlS9/mc985iouvOhC8vPzOHjwEG63m+TkZH7x+GN88OGH+Hw+5s7N56EfP0hdXR2lR49iGAaPPvIwRYVFNDQ2kj1hArfddivXXHM1p5xyMs3NzSxYsIBXV68GwGaz8dnPfIbrr7+eVasuYtasWRw4eBCv18vUqVN54P77+Pdbb6G1ZvmyZdx77/fZX7yf2tpaUlNTefyxR3l/3TpSU1J47NFHqK6u5stfuoUrr7iC/Px89hYW4otciwgxXMmcOzFsZWdnk52dzZYtW477WHl5eVRVV/Hdu7/HH//0Jz5z1VXccMMXePbZ57jrW9+m4lgF11z9uT4f3+Px8Ps//JG7vvVtHn7kUfLmzOHiVasAePa55ykuLua111/njjvv4r777u/0+C1btjBp0iRyc3Oj25YtK2Dfvn3U1tVhtVq5847/5NixCn7wwx9x/wMPkJmZwdVxttlisXDWihUAVFZVAeFR0Ru+cD0vvPAC3/r2d3jqqae45tprmDNnDo2Njfzyl78iFApxx513ccedd7Fx46a4n48zTj+DF198kTvuvIudO3eF+1NQwNtvr+HOu77Fu+++x0033YjNZov7mEIIIYafrVu3kpmZycSJE4HYn/+vrl7N9+75Pvfe+wN8Xh+33PxFAFwuF0ePHmXevHkAzJ83n6qqKubND/975owZmFpzpKQEwzC4/eu3U37sGN/57t389re/Y8WZZ7Zry+c+91lmzpzB//zP//D9e3+A0+Xk67d/DaUUpaWlBINBZs2aFT7X/Hnhc0XPPY/y8mM0NzdHj3fyKSfz8COP8r17vk9iYgKXffrSgXoaheg3EtyJYSs1JQWAhobG4z5WZWUlH364Hq01u3fvwelysWfPHioqKjBNky1btjBt+vQ+H3/37j0cO3YMrTXV1dW89977zJs3N+7Hu90edu7cxfJly6LblhUUsH79BgAWnXgiSin+9eqrBAIB3G4P//zXvzj1U59CKdXlcVdddBGPPvIw//e/v+Cyyz7Nn5/+C2VlZQCcs/Ic1r7zDsXFB9Bac/jIETZv2kxBwWl9fBZaffDhB5QePQpAIBAAYOu2bRw8eBCtNes++ICkpCSys7OP+1xCCCGGTn1DAwApyclA58//srIy9u3bRzAYxOv1svq11cyaNSt6c6+wsIj5kQBr3rx5/PNf/2Le3PD357z589hXtA+tNTNnzCBr3DheeullAoEAVdXVvP32mmg7lFIsKyjgn//8Fw2Njfj9fl544UUmTZrEjBkzACgqaj3X3LlzeeWf/2L+vPnRcxcWFrbr2+rVr+FyufB6vWw+zusEIQaLzLkTw1ZzJEUyIyOdikgaYV81dii8EvD7aWxs3eb3+7EnJvb5+PPnzePiSy5m0sSJWK1WDMNod/cvHus3bODmL97Eiy+9RF5eHg6Hgx0ffQRA1vgsxo0bx6OPPNzpcelpaTQ0xg6AX3/jDV5//Q2Skhzc8IUbmJufz/r16wEYn5XF3Px8zjv33Oj+hmG0S0Xtq9rauk7bGtu00e/3A2C324/7XEIIIYZOZkYGAE6XC+j8+T9+/Hg+c9VVzJw5g8Q237OpqanU1dWxt7CQm268gaSkJCZOzGb79h1cvGoVubm5zJs3j61btgKQkZlJc7MzesMQoKa2JvpzSkoKNpuNmtra6Dafz0dzczPjMjM5dOgQewsLWVawjO07duD2eNi+fTvXff5akpOTmTdvLn/72zPt2t7ue8t3fNcJQgwWCe7EsFVVVUVVVRWfOuVTFBYWdbmf1+sjMaH1Azc9Pf24z92bY1osFr761dt4+e9/Z/36DQQCAc4+6yzOP/+86D6m1j2ec+/evQSDQRYtWsSSJYvZunVb9EusZX7aAw/+uE/9cbs9PP2Xv/Dggw+weNEidu7aRV1dHRs2buStt96O+ZhYbW6Z95iYmBDdlp7R+bnRcfRXCCHEyHfyKSdTX18fnUPd8fP/+us+T0NjIw/++CFcLheTJ+fwox/+kJack+LiYpKTkzn7rLPYX1yMaZoUFhaxZMliZs6YwdNP/wWAhoYGUlPDAVzLd2NWVlb0PE5nOPDLyhoXLRqWmJgYDiLr6wEoLCzk89dey9KlSyjcuxetNcXFxaxYcSZpaWns279/IJ8qIQaFpGWKYe2ZZ57l1FM/xeWXXxYNsFJTU7nwwgs55ZSTASgpKWHx4sWkpKSQmJjI5Zdddtzn7c0xrVYrVqsVt9tNIBAgZ9Ikzj77rHb7NDU1kT1hQrfn1FqzcdMmVp5zNkuXLImOsAHs+vhjrFYrF110YfTOZ0Z6OksWL467T263mzVvr+Hyyy9DKcWate9w7sqVzJkzG6UUFouFadOmMm3atGibLRZLuy9Pl8tFbW0ty5ctRynF5MmTOeP00+NugxBCiNEhMzOTSy+9hGUFBTz/wgtd7me32/H7/NECKp++9NPtfh8IBDhw8CDnn38ehXvDaZGFRYWcu3IlTU1NVEXmiR86dIi6ujquuvJKbDYb48ePb5d5orVm48ZNXPbpT5Oeno7NZuMzn7mKiopKDh8+DEBNTS119fWcu3Ile6PnKuKC88/n0KFD0awSIUYyGbkTw9rewkJ+/vAjXLxqFT/8wb3Rde4+/vhjNmwIz0d7e80apkyZwoMP3I/T6eTvf/8Hy5cv6+HI3evNMX0+H8888yxXXXkl1193HUeOlLB5yxZOX748us+aNWu58cYbePSRh2loaOhyBG7D+g2suugiysrLOXzkSHR7IBDgscd/wRWXX8799/0XiYmJNDY2snXbtpjlp7uy9p13OPfclRScdhobNm7kL3/9G1dddRUTJ05EmybHjh3jX/96FQiPnL733vt87+7vYrFYeO6559m0eTN/eurPfP7aazjrrBUcOnSID9evZ1lBQdxtEEIIMTJdvGoVF15wAVprXC4XBw8e5Oc/f7jd91VHL7z4Etdf93kef+xR6urqeeutt1i6dEm7fQoLC5k3dy579+4FYN++/SQkJLB9R+u6rKZp8sSTv+b6667j5//z39TU1LDugw+4+nOfa3OuF7nyiiu453t3Y7VaOXDwIE8++WS70cTCvYUsX76Mon37ov++5uqr2dthvp0QI5VaetLJkj8lhBBCCCGEECOcpGUKIYQQQgghxCggwZ0QQgghhBBCjAIS3AkhhBBCCCHEKCDBnRBCCCGEEEKMAhLcCSGEEEIIIcQoMKqDu/TMjKFuwoAbC30E6edoNFb6Kv0Uw9FY+HuNhT6C9HM0Git9lX4OjFEd3BnGqO4eMDb6CNLP0Wis9FX6KYajsfD3Ggt9BOnnaDRW+ir9HKDzDerZhBBCCCGEEEIMCAnuhBBCCCGEEGIUkOBOCCGEEEIIIUaBUR3cNdY3DHUTBtxY6KMQQgghhBCiZ6M6uFNKDXUTBtxY6KMQQgghhBCiZ6M6uBNCCCGEEEKIsUKCOyGEEEIIIYQYBaxD3YCR5olf/ZKysjIMi4WKYxX86amnCAQCcT02NzeXjPR0Ptm9GwCr1crXv347KcnJvPHmv9m2bVvMx9115x28+NLLlJSU8I2v387v//BHPB5Pv/VJCCGEEEIIMfLJyF0v+f1+HvrJT3nwwR8TCgVZseLMuB5nGAZTc3NZuHBBdNvUqbkAPPSTn3YZ2HX0y189IYGdEEIIIYQQohMZuTsO+4sPkDtlCklJSdx4ww2MHz8ef8DPX//6N8rKyrj0kksYP2E8E8aPp66ujtmzZ2Oz2Zg9ezZvvPlvLr/s06SmpnLv9+/hN7/9HVlZWXzmM1dhMQwOHznCM888SzAYbHfOh378ID/56c9wuVyce+5KTl++HA18+OGHrF37ztA8EUIIMUZkT5jAiScuBKUoOVLC/uLidr83DIOTli4hPSODgN/Plq3bojfk8ubMYdr0aaA1uz7+hOrq6riOKYQQQsRrxAZ31os+gzEpt/udFFh0/Mc0K44SfOOluPY1DIOFC05g9549fPrSSyk9Wsqvf/Mb5s7N54s33chDP/kpADk5OTz88CMEAgGWFRQwffo0nn3ueQCam5o47/zzeOKJJ7Fardx15508/otfUFVVxRdvuokVK87sMmCbNm0qy5ct4+cPP4Jpmtz93e+wf99+So8ejb/DQgghemXRohNZv2EjHo+Hs1acSUVFBc1OZ/T306ZNxR8IsGbNWqZMnsyCE+azddt2UlNSmDJlMu+88y52eyLLly3j7TVr4zqmEEIIES9Jy+ylhIQE7v3+Pdzzvbupq6vnww/XM3vObDZt2gRAUdE+kpNTsNvtAOzatSuuOXkTJ06ktraGqqoqADZs3EjenLwu958zew4ffbQTv9+Pz+djx0cfMSdvTj/0UAgh4qVZmOokyQgNdUMGRWZmJi6XC7fbjdaasrJyJk2a1G6fnEmTKC0N32QrP3aM8eMnADBp0iTKysoxTRO324PL5SIzMzOuYwohhBDxGrEjd/GMsFksFkKh/r3oaJlzF/f+Pn+/nl8IIYaLSYl+rp9SwYvHsjnIhKFuzoCz2+3t5jx7vF4yMzO63EdrTTAYICEhAbvDTn19fetjPd7oTcCejtli+vRpzJg+HYDK6moqIzcD+8JisXBu2kTmBK0ctgTxD5MlUyeaBummQYklRJZpkKwV9KJttcqk1jABMEIGZtrEHh9j1TArZO36PBqOWUI0q3Aq0JSQhUojRHCYPGfx9nOkGyv9hHBfpydPwaZhvzVIL5LQRpT++JtODlmoNkIEYrwfbRqmmVYOWgbuOUw3FVZU9HOnoxCaIkOTmTWuz+eor63r1f4jNrgbToqLizn1U6fy2uuvk5+Xh9PlxOv1dtrP6/OSGPky76iyspKsrCwmTJhAdXU1Baedyv79+7s85/7iYm668UbeevttLBYLS5Ys4U9//FN/dUkIIXo0J9kNwH5XEpb0IW7MGHDkSAlHjpT0y7Eys8ZR63SRZ6RwirLi0rEvTAZbijKo1gFOUjZCaCq1H1+cl2WJKE5UidTr8P42m4VAoOfH2pVBkw7SQOybwQ4UJ6lEGnR4Dvw4ZZALeIfJcxZvP0e6sdJPCPeVYAivNjklYODXo7Pf/fE3zVQG0wF3h/ejAtKUhUod4HRlw4NJcACex1RlwYKioYtjWzA4bPFTWd+7AO14SHDXD159dTU33nADP7j3XvwBP0899eeY+xUV7ePCCy7k3u/fwxtv/pvmpqbo74LBIE/9+Wm+8pUvRwuqvL9uXZfnLC0tZcPGDXz3O9+OFlSR+XZCiMGQYQvQHLCSl+Sm0mejKWglc6gbNQi8Xi8OhyP6b4fdjtfjjbmP1+tFKYXVasPv9+P1eHHY2zzWYY/eBOzpmAPlI9NNFlYSjCSauwhsBpsGmnSIBBQGiheDtTQTXxA1V9lJt1pojPQlwbDgj6Nffq2p1AFeCsW++DrZSCbdaD1uEgaNOoQ3znYNtHj7OdKNlX5CuK9JaKoJMk5b8QyT11p/64+/aSIKlzZxdXiOUrFQaHp4IVTHIiOJs400fJh4+3EMz0r4MwsNTV30Iw1Lv50vXv0S3PVU6Wv2rFlMnz4NU2v8Ph87Pto5Ysv533HnXZ22ud1ufv2b33Ta/urq1Z32+9l//3e7bfvajM4VFRXxkxgpn48+9nj053t/8MPoz2vWrOXdd9/r99RTIYToygyHhy9PK+Ow28FUh5ctDWlD3aRB09DQQHJyMklJDjweL1OmTGbb9u3t9qmoqGTq1Fzq6+uZnJNDTU1NeHtlBSefdBIHDh7Ebk8kOTmZ+vp6lFI9HnNA+0RoWN3lVZGLpAnYUECwx0e0CqLpy415Bfi7ueDzYaIVtOxiIgULxACLvNZqzQDZxnB6hw4/ms4Z1QbhjauDDZiEb2Q16hCXWzPx6v67Zk7CQo0OkIWt347ZH/rlFdNTpa/Gxkbee38doVCIGTOmR6uHCSGEGB5symRSop9Sb+zUcRq9VXIAACAASURBVIA0a5DrplTgMw1mJ3twBQ22NI6d4E5HljBYVlCAUoqSklKam53MmzuXhoYGKiorOVJSwkknLeXcc1cS8Puj33XNzU7Ky4+x8pyzo8fp7piDxa1NzDaBy1AzlKLJDGEoUCocsMWrr5dsBuDvJsXSr3W7VsS6mBSiP4Vfk5p6Qljk1dat8Pux/XPkwOCw6cPZZjTvqPb3+zOpFJSbAbJU7OAuETUkN4KOO7hrW+kLiFb6am4zeldTWxv9ub6untzcHpYwEEIIMahWZNVz3vh6fnk4l7IYAZ5VmXxhyjFsyuSJI1MZn+CnzGunKTi27ipXVVWxZm37QiaFRUXRn03TZOvWbTEfu2///nbZGt0dc7C4CWEOl8CO8CiZGxMFkZG7+BsXRPcpRlWobkfuAh1GBE20XG6LAWPQOprcrEPtRo1FewbhgiUdAyibUhw026e3B9A06RA2FIFePqEJXXxGmBqqdABlEPNvlKKGJjQ/7m/leKqHtTVt+jSqKmN/ifVUCayxvgGl4n+alFJYLIOf6zqYeuqj1pr0bv4eI4XFYjmuSkMjxVjpJ4ydvo6Ufp6YUQ7AqpxmXmrKof3YhOailENMdfh4uTGPYGomFYAlkehcu+PtZ2+rgYn+4dZm+E89DC4eLSg82iSIRilFqJfBWqiPnWgJKrviR7drSayRAiH6y0Rlw6cVTsLzyMxRWkylPxgogppOs9o04RG1jo7pALNVYszgzh4J4Dp+EiSiGK9slOnO1e8tCqrMQMw87QRUtAhTQA3u33BQb7nm5k4hIz2DD3evj/n7/qwEBgOzFMJwE08fR8NFU2bWuFHRj56MlX7C2OnrcOlndoKPhoANv+78LZRhDTBxgpsqn43ZiY1c5tjNC+UTcZvhr8zTMhpZ5KhhTU0mW2o00Lk/w6WfonfcmBjD5NrRAjToUDi4g15Xtgui+xSoKui2ImcAM5x/FdlF0jLFQAoBNhQ+NC4dCg9qDJP36HBjIXxTx6KM6HPUMppXF2PGbpn2s9hIwhK5PePVrQVWUpQFn9adiks5lIFThyLnamUAPq1pIoSK8bljx2CX6eYds4lMNbg3eI87FTSe6mEAE8aPJz8vj02bN2Oao7PqjxBCDBfnja/lC1OOsTDVyVS7lztnlbIqu6bTfgaaVdnh1Pmnj+bwSsV45iS5+ebMUqY7PDiMEBdNqGGfM4k1NcN/BFL0jgcToxcZMQPJgqIZk4AOB3e9TZ0K9fECWKnuz+XXusM4tqRlioFhQ+HTJlbAp02cmFK8pxsGCrPDyLoNRV0XRVMOmT5Whxr4TaCKl4N1aMJVNcPPu8Zo88a2oUjHQhBoJoQ18q5PQOHAIA0LtQTxx0gLhfANoCPa12997Y3jHrmLp3pYeloaixcvYsPGTfj9sqi3EEIMLM2yzEbshsmCVFd06wxH5xtvJ6S6WJTm5PWqLGoCCdQ0JFDisXPdlEpumVrOPlcSdovmteosuaQdhUzCd6+NyM9DyYKiWYcItYzc9TK4C6LpS/aTSfejhP4Ox9VItUwxMBJQeDDJ0AqvNgmg8WndadRIhBmAn/aDZjYU1WbsWKOJELvMcI2QI9rP08EaLrNmMlMlcsD0MpEEADKw4MRkk+mkyPRyuiWVaSoBR+Q78Jj240dTZHrQhG8OtR28sxJOxSyNkco5GI47uIunetiCBSdgsVr51CknA+D2eNi8ectxN36orLroIj71qVMwTY3WJn/92zNcdeUVpKWnEwwE8Pl8/Pnpp6msrOKmG28gLy8vOi/xqT8/zdHIenRXX/05Fi5YgN8f4Kk//5nS0lIACgpO4+JVqwB47fXX2bhx09B0VAgxImXagiRZTF6pmECN38aJaU6mO7yMSwhgoCPlKsKyE8JfPuvrW1chL/fZ+fWRKXx9xlEWprrYWJ9GpS9x0PshBoeTEA6MbouKDAZDKZoJp2UaPRQ5iSXUx9sPmu4DST+6XdqVQqGVllQ50e9sSlGng0yBaLrgUe1nmkrotI6bCN8Q8mHiaPNetCpFVZyLqLgweSFYy2lGKiXax2et40jWBscI8GywtRhkow5iVYkoBe+HmtgeCRBb+LWOpIOGpSgLH4acvc4+6C/9Mueup+ph6zds7I/TDAszZ87kxBMX8pOf/oxgMEhycjJWa/hp/MMf/khJSQlnnHE6V111FU8++WsAXn7572zfsaPdcRYuWEB2djY/+q/7mDlzBtd9/lr++39+TlJSEpdccgk//enPAM0999zDrl27cLtH5rqAQojBN9keTgUp9SZS5rVT7E7ixNRmrptSyaREH+W+1mqY4xICNAYsBDvMxXOGrDxxJBeHEaLKL4HdaNasQ6QoC0MdrWh0tKCKQR/SMvs45w7d/bk6B359q8opRCzJGIQALyamhnIdIB/wREKFI9rHbCNxqN+ew5IBNOsgGaq1pIqpw3N34xUEPjSbAfBokyRl4YNgc7t9GghhUeFR/jLduVCLD00C4SJQNhR+rdllujrtN1gks6CX0tPTcTpdBIPhuwIul4vGxsZ2++zfX0z2hAndHmfR4kXREblDhw7jSEoiLS2NE044gb179+J2u3G7Pezdu5cTTlgwMJ0RQow64xP8rBhXj6mh0pcQ3V7qCQd0Z45rwKpa7wCPswWoC8Reo6c5aJXAbgxo0q3zSYaSqcNzAAOEY7RAN2vPxRKkb4VOehq5g9Y78y37C9FfrEphb/PKLTf9hFTryF2VDsiYXVcU7daya9nWqOMbuetop+nmX8F6jnZIp3TpEEqHbyBVxwzuWudGJmPwrtkU/fsNhRG7QNGl2dXk2LufqKhQvbq/dsybyKtV3Qdle/fu5ZKLV3H/ff9FYWERW7dtY3+HdYsWLTqRsrLy6L8vu/wyLr74YoqKCvn7P14hGAySkZFBfX19dJ+G+noyMjLI7LS9gcyMkb+UgRBi4E1K9HHrtDIcFpMKb0K70biGoI21NZmsHF/PJLufv5ZNosafwDhbgP2upCFstRhqjYQ6lRIfEio8/88kHEANVlomxBHcRUYTW/aS6vSiP7Ss56gVGBpMpanRQYJo/JEXWbUOtu43hG3tLYNw2uRApiaagEub6Mibs+X939THGYobTGfM7W5MEpXBR6FOoSQQ/tzKVFaSUVQTZI85tNl2Iza4Gyo+n4+f/PRn5M2ZQ/7cfL78pVv4xz9eAeCWW24m4PdTW1vHc88/D8Df//EKTU1NWK1Wrr/+Oi644Hxee+31oeyCEGKUWplVR0jDc+UT243atXirJovDHjtX51Ry+/SjvHBsIum2EPVdjNyJscGlzWGxULIRGbkLV8ADfy9H7jThi71ed0X1vOyCDxMHRqQynxD9IzFSpdGKIgFFrQ7iwSRIa6pwkHDAl4LR7ZIdw00aFrQKj3p7BmrsUYObUDSoc2BQavr6vfiMS5s06BDbu0i19KKxAhaleDvYOOR/pREb3PU0wgYDt86d1pp9+/ezb/9+ysvKKSgoAFrn3LXV1NQEQDAYZMP6DZx3/nlAuMpoZmZmdL+MzEwaGhqob2ggPz+vzfYM9u1rPzIohBCxZCf6OeJx8FFTapf77Hcl86sjU7lpyjFuzD0GQK0Ed2Oam9CwGIkyVDi4s2Og0fSlzlzLGnm96U48lTl92iRZhUfCh8NzJUYHOwYHtY/pRiIJ2qDc9OLFJKg0bZMxj5g+TjaS6f+wZeCYwIehZk4xkknB6Jw+2Q804NZm9A2foBT7zc5VoY9XMyFWh+qp7+L592qTdMPKZtNJRYy0zcEmc+56aeLE7Hbz6XKn5lJXV9vl/mlpadGfFy9ZTHl5OF1z166PKSg4DYCZM2fg9Xhoampiz549nDB/PklJDpKSHJwwfz579uwZkL4IIUYPA01WQoBqf8+BWkPAxpMluexpDqdjxhrlE2OHB3PIV+UOlzTXhAgHWibg78PFYFD3ITWzh4IqAJ5IBU/V+hAheq3jRbdWcEwHUDp8c6NKh1+JbnS7tORyPdS1bHtPAYWmh78Fa3FhkjYgyd8aX5t0bK2htItlEI7vLOGlE7riwaSJEB+EmrvcZzCN2JG7oZKYmMg1V19DUpKDUMikurqav/z1r9x261di7n/LLTeTmpICSnG09Ch/e+YZAD755BMWLlzAgw/cj9/v56k/Pw2A2+3mtdde53t33w3A6tdew+12xzy2EGLsMdBMc3g54rFzcnozecluXq0aT4IysSqo9scXqPlNg7+U5ZBpC3ZZUEWMDW3vfA8VCyrcDsKBloY+paCFi6r0buxOqZ5H7rzaxFCtKZ9qGKSxipElBQO7MqhpU+xDa6jXQYxIJca6SAl/p2qdcwdQqQNDff+lVyyE38cto3XPBGu5wprJeKxdjuBlKSu1vS2EosJLISgFNq1wYdIwBKOb5drPG8GGYbNchQR3vVRSUsrPH3640/ZHH3s85v6PP/6LLo/17LPPxdy+fsMG1m/Y0LcGCiFGtbxkN1+cegxfSJFo0Zgapjq8bIysU1fdi1E4jZLATuAmHLgMZbBiARoj5ctNwsVReruIOYSDtIReXgbHk5bpjcwFJFqobSRdaovhwILq/DpT4eIfZuT39ZHg5oMEH7Wu1kDHiYkXEyvEuYLb0ErAoKLNSJcHk50hNxda02N+zlhR2CLzDnszRqkAX2S0PgmDHUO0/EDJEC1W3hVJyxRCiGFFc8OUcpZnNsT87biEcD5/okXzr8rx/OpwLhalWZUdTg+PJy1TiLbCxUv0kF4QWFA0t7nr7de6T1X2AvStHz1dMHsipc5bQjoJ7URvWICA0ni1bp+cqMNrq7m1iVOHoqPVTUbn1/5R7SdxhFy2t71Z06KOYJfjWgkomnQIR2/7pyGASYjwe/Kw7r6K/lgxMl4lQggxRuQluzkh1c2KcfWoGBe3adYgQQ3fL5zN+voMyn12njycS6XPRo3fhtccFkXtxQjjxBzSte4skYu7Fn50jxUsYwnSuzl38YzaQThFVEXSMgMgKZmiV2woqs0A1TpAQptL75YiQi5CMddPa+uI9mFTI+O2gkJ1umFSr4NdBh02FLUx3olW6HGuXpDwXFutwgvACwnuhBBiWDlzXAOmhnRbiFlJndfKSbeGaA5a213CNgRt/N+haTxxOHcwmypGEacOYhnK4E4pmtvMlfFrs29pmdpsU/akZwY9L4PQ0p6WUYcApozciV5RkfXejml/NG1YER41D6BxapPyHlL7qnTXI1/xtWHwxLpp4kPj0mb7+WA6vHwBCopMT6egpGWkMrWLcEUpMAmP8h81/QO6pt5IIsGdEEIME5MSfeQle3inNhO/qZif0nn+QJo1SGOg83TpEAqPjNqJPmrUQ7uQuYnG02ZdOy8moT5cqPnp3UVsy0V3z8fVaN0ychcexRMiXi3VYGt0MPoCtUC0iNBB7aW0h+CuJjIq1ZuXXgKKTCykY2GSsg1aoY2uRsQrdICkyCeNHUW6NiI/G+ElBGJ0zkPXN2xazuPFZJ/u/yUQRioJ7oQQYpg4c1wDflPxYV0GVb4EJiR0/rJPtwVpDEotLNG/GgkNaVqmjixg3sLX54IqZq8ubMIXhz1rCQCjwZ2M3YleCBf+MKlvc8vCgsIdec1/Ynp6TCkMAdU6QGIcrz1FOJ3RiuJds4lmQpRpf7uU0IHUVXD3QagZDybpykIAzRsJXv4SrKFc+6nTwU7vK0W4uFJ3nwQh4JDp47Ap8+1ajOorBD0GVhodC30UYixItQZZlNbM5vp0PKaFGr+NaY6OdyI1adYge4PJQ9JGMXq5tIke5IqZbU+nVXi5gRb7TC91uvclzf1ao3oxrBbvnDs/Otrg8Pwe+e4V8TMiVSAb2sw7s6Bw9vI1ftj0caqRgjdGuX87BnbVmvK51/TwXqgZNybbTTenGsksN1KPsyfx6ep9VUeQp4LVZCgrtTpAqnUcTYR4OliDhk5hXHcj6y1prQDrzOGxvtxwMaqDu/TMDOpr64a6GQNqLPRRiLFgeWYDBvBBfQYQXq9uUZqTNGuQ5qAFjcJhmCQYOmZaphDHw43JYN4rTMEgURnRda2MDiN3e3Xn+abxCPShoIoZR3AX0C0jdwozMqooS92JeCnCNwj8bead9SW4OxZZ786Kwhp5LbYsHZCoFBtDTkq1n7IYi57X6L6MhfeRosvV5nxoKjuMUra0y6T9TZ/WILFzyxXxzZcdiyQtUwghhoG8ZA8H3Q7qI+vO1fhtGArumXOYgoxGIJySCUhapuh3biIjd4PEqlS7RZqVah/c9ZW/D8FdKJ6CKoQXeg+niUFQy3IIIn4G4Iu8vmsiFTMthNe4641y7edIpNx/rQ4QQJOuLGQqK04dYqPp5GiMwA7C1SoH60Wrie+mSUexHhPQsdOg4x11H4vkCkEIIYbQDIeHNGuQTFuAUk9KdHu1v3Ux8rkpbrY2pnHZxGqCGo5541+oXIh4eHSIwcw0NHU4oENHKlaie3mZG1vvR+5UXOf1txRR0ZHgLrKe3vGHo2Is0Ko1EDmmA0xTiWhFu+U/4uFD83Kovt22ScrGAuXoMqhr0UiIkNbYUXgHOCjShN/jvWXqziN3gS7m3KlYi8ILQII7IcQQSVRBwh/hY/v+9/kTapli95FoaOoCrQuQ17ZZjHxGkofrp1Qw3eHl2fKJ1AYkuBP9y4OJMUB5hopw1T5fm4MbimgaqAWFS/dPmBQkEjX2oh/xVOVsWRy9JY0zhI4UoJGLS9EzTXi0F9qkR+rw+pLHq0IHwpUme2ACL4XquNSSSboKL3zi0Wa792V/6ku121CHmzMt6awqxlstXIFUxCJpmUKIQTcxwcd/jt/O0rSxPQnaQDM1EtgB0ZRMAL82eOpoDq9UjCfR0MxNcfOPigl83Dw4E+LF2BIkXJY9U1lIw0K66p+FEQwgU1kJARlYSMHAhooUPgnvYwFc/TQG1tX8nK7EWy1T0xLgKUJay4id6BWtW0fuGtqkR/Z2zt3xOqYD/C5YxdOBGtaGmgBFKpZ+Dwa07mtwR6fgrqvROUnL7JqM3AkhBt3USBXIWUkedjSlDXFrhoaBJj/Zjc1o/XKq87f/SC50JmM3QizPbGRzYxpbGtMHu5liDHk+WEuukYBTm5xiJDNJ2Y57ZCFVWdgWcvKe2cwMlcgJhoM8w84R00euCi/n3JfCEl0J6u7LpnfUmwtEv9YkoQihMWU5BNFLLa+zekIowgPMrn5JRu69WoLUmkGOmn7OtaaRqxKo7+dAsy+fHGbkBkrLDRpND8FdP434jzYS3AkhBp3DEv5A9ppjN3ng0xOrKchsaretoc3IXQuvaeHRQ9MHq1liDKsnRL0ZrlLpMkNcaxlPOhZchOIa3eooXVnwa80W04UGDmkfh0I+bKFwpb/rVRYWFBZUr+cedSUYWWw8XuGCKvFdIPrQpCqFiSRjit5rKekfbFMxsy/vq/5US5Dng3V82TqBhMhyDf1B0/eRu3Y5AwqCXUze626ZhLFu7F5ZCSGGjMMIX0z5xmhwl2IJRgO7w247TQEL3pDCPUafDzH8VOkgTwer2Wo6sWGQgYVULNjiHK1KwcJR088fg9WdUi4DaDyYuDGxEJ5/19hPIxi9PUq8BVUgXO3QiJzD7GXhFjG2dRyBqtKBfnvN94edppsMZcXRb2FB31KXO76vos9bjBguXGxFxCIjd0KIQZdqDd+vtIzRhYALMsNLGzxycBo1fhs3TDlGqrXjbAMhhlY9IT4wnWwwncxSduYZdmYre48XpeFUS3g31NTt8gbhOX5WTMIXu/0h2EVlva60VOOLh1ebWJTq9TmEQLVfk61C+xlH50yNofKx6cYEFhtJZCgLjbrrcbd4ygipSPpyb3UsqAI9zbmTtMxYJLgTQgy6lvXaEo3hdYlkVSYXTajl/bpMmgZoLTmrMjkto5E9zUnURJY7+HtF9pgNdMXwFwL2ay+VoQCzrPYe90/Dwm7TQ00PSWdOHcKqFJr+De56K96LUC8aS3TOnRDxC6+P2Po6O2D6KFfDZ9zJi2ab6WKX6WaFkcpiSxJObXa68eHAIEUZOLWJRne9pEI3i5h3J9ZjgsS+7WkQXgNPdCY5QEKIQZdubQnuhtcl0rwUN6ePa+SU9Kaed+6jpWnNpFhNPqjLjG5zhqw0BofPXVwhYmkmBKr78WULYCrYYPZcCdeJSbIyKDf9/RYshdpU4YxHbwqqeLQZWdtOY2pJyxS9oNuPENcQpFQPv0L+ATRrzCbeCDWS3KFibjIGGlgTaiJVWUhT1i6DiPA6d3240aK7SMuMQfXjHMHRRoI7IcQg02TYhmdwtyDFCcCcZPeAHF+hOWNcA2XeRA55eh4BEWI40UC9DnY77y5FWdgccsZVZdOFiSacEtZfejtyF+9SCABeTCwoWlbolDRqEa+RVrZ/r+mhTgdIwsBCeBkTP5pngzVsM128HWqkUQe7DCLC60H2XqhjFVodSWeN8VbrTUr1WCPBnRBiUDkMk4RIOuZwCu4sSjMvxUVIwzSHl4R+bts0h4f78g+SnRhgXV0GcmEoRqJqHSShi9duS7GRHaYrrmNVmH5Wh+op0t5+a18Iel3KMt60TE+0oEo4LVPewSJeSg19Zcze0MDroUY04FAGu0w3fwvWUB9JnNxmunBhRpYtiK0/1rmDcFAc6ywtC5yLzmTOnRBiUE22+6I/93cAdTzmJLmxWzTv12awIquBWUkeCp3J/Xb8KXYfCYZme2MqHzel9NtxhRhMFdrPYiMJQ4cvtzThoM6DiR2DXSE3vjgvuKoJUm327yVvVxeCXerF3KCA1oSIpJzJRaXohZE2cgfhebB/ClZjdjG3zqtNUlQXY0R9XsQ8dlpmzOAuUtxIdCbBnRBiUM1weDE1HAmkkWT03x3747Ug1YU3pFhbO46CzEbyktz9GtylWEKYGl48li1F1MWIVWx6CVCPV4cX8g6hmahsnGGkYgK7+jHFsi9C6PjK+UX0JlDzR/prysid6IXepP4ON+5ukitbCgzFerMdT1pmR10FcDryfhSdSXAnhBhw1085hlVpNtanMyPJQ4UvAScJZFqG9kKwhYFmfoqTQmcyPtPgkNvR7/PuUqwhXCGLBHZiRGvG5JPIQuctynWAAksqB0wvtUN8GRtA93q+SRdrJMc4thlZ465vF65ibFK0XwZhtPDoUNfFi1QfR+46FURqHS3vSAOh0fe09gsJ7oQQAyrdGmBhqougGa5GCbChPp0EuzFs5txNd3hJsZrsdobTJYtdDi6Z6GZCgp/qyHIFxyvFEsQZtPS8oxAjTADNm8EGjvXTcgbHw6TXU+7ivgj1t4zaadBKbtOI+BiMzsIfnkiBoVj6PnJH+4IqqHYj5W2fxe4qaY51UlBFCDGgZieF7/I/eSSXv5VN5JPmZLY2pOLXlsicu6H/cF6Q6iRgKvY5kwD4pDkFT8jg9umlfHNGCUlGX1bsaS/VGsIZkuBOjE5F2ktTn1a26n+9mXendfwXoX7dPi1TiHgo1KgN7rqrltmXkbtO4/6qZaQ8xrH6OK9vLJDgTggxoGYne3AGLRzzJfJxcyp/Lcuh3GfHb1qwKLAO+eLdmgWpLva7kvDr8EdiQ9DGr49ModyXyGS7nxNS46v+150UawjnAC2MLoRoFYpdOb1L8c+5M6MpmbLOnYiXQfi1M9r40Ohu3gR9Xwqhw3F013NcpVpmbBLcCSEGzMwkNwtTnRS7HJ0+sv06PIqVaAzth3OmLUiGLcg+V1K77VX+RH5XMoVav5UFqc5OjzPQkSVd46FJsYRolrRMIQZcb+bdaeK/+x9ARu5E7xko/KNwzp1f6y7nq/b1/dExuGtJ74y11J1Gj8oR0f4gwZ0QYsBcNrGGxoCV1VXjO/0uEBklG+rlEHLt4YqdpZ7EGL9V7G5OYU6ym8QOqZnXTang5qnlcZ0j0dDYDC1pmUIMgmDHhZB7EHdaJppQZBRBlkIQ8VIQ9/IgI4m/i3dBX1MyoXNKdUs121jHU0oRGIVBc3+QHCEhxICwKpMJCX7erc3EGer8UdM6cjfUwZ2PoAmVvljBHexuTmZFVgPzUtzsbEoFYLrDw4JUF6aGJEsIdw9BW4olPJNACqqMXDabjVNOOZkkhwO3x8PWrdsIBDoXEJk6NZf8vDwA9u3fT2npUQDS09M5aekSDIuFqspKPv5kNwBz5+Yzfdo0/H4/AHv2FlJVVTVIvRqdAuguF1qPJd5AzQQChC84NR0LPwgRmwH49Ogb6+0qJVLR9yqWnUbuNNEKtZ3ebXp0prv2BwnuhBADYnxCAIuCSl/sapPDJrhzeCn3JRLq4kKt1GunKWBhQYozGtydOa6BgKmwGZq8JDc7m1O7PUeKNTzqJyN3I1de3hxqqmvYX1xM3pw55M2Zw569e9vtY7PZmJufz3vvrwPgrBVnUlFRSSAQYPGiE/lo507q6xsoOO00srOzo0HcgYMHOXDg4KD3abRqufsf7/Vlb8rA+DAJRcqzi7FNAalYsCqFS4e6HJ0zUKNybpi/ZdmCDl0Lp1L2rb8hAKXaHdOMpEJ3XFNPqdFZhbQ/SFqmEGJATEoMj0R0Fdz5IsGdYwiDO4Vmit3HUa+9y300it3OFOamuMlJ9HF6ZgNzk11saUjDFTTIT+l5PbxkSyS4k4IqI1bOpEmUlJYCUFJaSk7OpE77ZGdPoLq6hkAgQCAQoLq6huzsCSQmJmK12qivbwCg9GgpOZM6P170j4A2MeIdVVO9uxD1ajMycicFVcayJAxSlYVC7aHE9GHt5tWgAO8oHGHyY8a8g3I8i7abaHTbAI6u15UcrUFzf5ArDSHEgJiY6COoobaLdeLqQuGAKsfuo9CVPJhNi5qQ4CfR0ByNOd+u1SfNySzLbOQ/ZpZGt+1sTiHJEiIv2Y3q4VKvJbhzycjdiJWYmIjP5wPA5/ORmNj5NWO32/F4Whf49ng92O12HHY7Xm+b7R4v0V2mxAAAIABJREFUdnvrDYVZM2cydepUGhoa2L17T8x0T4Dp06cxY/p0ACqrq6k8jvRNi8VCZta4Pj9+OLP4EkkwLXgVJCR2v06l1VSkpKQTiLOwk/YlkGJNIzFkwRq0kmAM/XtaKdVjP0eD4dRPu6l4N8HHQauFM/wJJAStXb6GEkyF1ZZEpi3+to+E92eCBpvH1voeiBQ9sQBBBZnpPbe/Yz+Tglas/tZjWk1FWloGFl8CCZroAudKA1qRkTa8n6MWx/v3rK+t69X+EtwJIQZETqKfGl9Cl+mOPm2lymdjqsM7yC1rNdURvljvbuQO4LDbgTtkkGQJ3z+s8CZQ6rGTZQuwJN3JpEQ/x7qYsweQHEnLdIckWWI4W76sIGbQtrewsNM23U8T+Q8fPkxR0T4A5s+bx4IFJ/DRRztj7nvkSAlHjpT0y3kzs8b1+oJhpGi2pBNUdnSiFb/P3+2+dmWhvr6B+jgTLXcbDkpNH1lGEiEjBf8wSNBMSEzosZ+jwXDqZxALld5G6rWfeiOVkJHc5WshoCw0+JqpNz0xfx/LSHh/KsBltZKEEV1h3FAKpw7hwaTe1XP7O/azSTkIWA38OvxcBpWFurp6PFYDK0Z0pM4CuFHD/jlqMdh/TwnuhBD9bkKCn7xkN+vqMrrdr9RrJz/ZTUt5gsGWa/fiDRnU+G3d7mei2NOczCkZzfz8wHQaAlY0iv2R5RPyk13dB3eWEL6QIqgluBvO1m/Y2OXvWkbrWv7fUgClLa/Xy/is1sqwDruDmtoaPF4vdrujdbvDjtfrjRy39TiHjxyh4LRT+6MrY5pfa4y4VzH//+zdeXRc93Xg+e/vLbVXYSNWEiAlkRQlUgspydosyZZkeYvtOI6TTPbt5HSfpDun+8xkTs+ZmfzTfabP6Z50preZTk88ccfpOHbsxEsU25Esy5K1WNRKmrtIAiBB7Fvtb/vNH6+wEQCJHVWo+zlHFFB49eo9oIB6t+793bu6NXcnKhfoO6/ITqyKmmuScqNh3hC+uu3Ero4a+LI3xgNGiivaoV+X+VWrFQu15m6Z199vrixz4e1SknljcqUhhNhwT+0ax9WKl8abbrhdfzFG2vJptNdaob8+e2JlrpaiK1o98w+jLXzpSgfjrk1Q2T7nW1wtRbn9JuvukmYgJZk17trgID3d3QD0dHdzbXBw0TbDwyO0trZi2za2bdPa2srw8AjlchnPc2lqCt/s6N4zd//5mcLOzk6ms9ktOJudzVn1KITVXyTKKIQ6p+fW0bkz9YjLbarDJj870QQ+3wumOKWLZAno1w5xZaw5nz0z026GQoUNVfTCV2mDsLmRWJpk7oQQG6ojWuaeTI4XRptuGtD0FsNyyFviRd52b5w92wytEYfjU5kVbTvtWfwkl1p0+7lcgsdbJogaPuVg6fNdybgEUd3On7/AA/ffR09PN8VikTeOvwlAY0MD+/bt5Z1338N1Xc6dO8fjjz8GwLlz52bXz7333gmOHr0X0zQZGh6e7ZR5+M47aWjIoIFCocC77763Lee3k+TxZ9fm3MxMZmC1lmzNLuqGoaBUCdhcrW8Yumnqp6vjRV3iLhL4a8xULn7TJJwr6TMzeiT8uoGitAOzoRtFgjshxIZ6etc4Jd+4aUkmhJ00C77BLYkib0+vLMjaCI82TeJqRdTUTK+zg+W5fIIP75pgf6K4ZPAHYVmmZO5qm+u6S5ZtTk5N8c68gKyvv3+2q+b1273wgxcX3f7W229v7IEKruhVFGytslvmjLWWnYmdYX7AtlzXyPnqJbjrCxyKpr/mTGUAC7plznx0/e+bwc7sQLpRJLgTQmyYrmiJw+k8z400U1wmizWfRnGpEOe2xMoXmq+XpQKeaR2jHIRV6esN7vqKMUq+wZF0btngLmH6DC/TNVQIsbFGtIenNeYKri8Vq1tzN0Myd/XLJByJMcOrZJeWpcCrkyzTND7f9ibZpdb2uurPW38//3fTh0VlmaUdOBh+o8iaOyHEhvlI6zgF3+DliYYV3+diIU5zxKPRCsvXMpbHnancou1+afc1PtU+su5jvCVRJGJo0pUOltPe+jJqAYrXJzPcncmxO7Z058+k5UunTCG2iAYu6BKxFYZfa8nCaVY+JF3sLCaK4ryf/s3yxIr6ydwBnNMlXgkWv4avhJ737/xh6AHXr7lTkrm7gQ252mhrbeWpJz/MU089yYH9+xd9vaW5mScef5xP/dQn6ezs3IiHFEJUmZ5YkUOpAj8ca1x27dlSLhbCLoK3VrJ3DzdN8ku7BzHV3Ithg+VyJJ3ngYZpYsb6Wo8fSi5sfpLdgMHiL4w14wSK+xumF33NIiBqaHLrDCKFECt3MShj6JsHd3OreFZHAyte2Cd2FAMo6LnXIU8vbKhiAHEMGjBpwETp+gru1mN+5g4g0DO3L2QQdikVS9uQ4O7uu+/i1dde5/vff4Hdu7tIpxaWJhWKRd5+522uXr26EQ8nhKgauhJsaT7SOk7OM3l14uZr7eYbKkfIe+G6O4C05WMoSJtzHTSPpPMA2Ibmnszq3xE01dxx3p7K4897nV1v5g6gHBjkfIu4ufjFJmaE5yENVYTYOlf0yuahrTUDpyV3V7dMFIV5gYVzXVapSVmMapfjQR5faUyldmy3zI02/xU0LMsMv2/+dd0ytbp5xrSerTu4a2pqIp/PUygU0Fpz9eoAHR0dC7YpFotMT2epk5JjIerGJ9tG+Rf7L/NAwzT7k0VeGGvCWeUsN43iUjHObckwuEuZ4Xt0M2WTAPdmsgyUIgyXbQ6l8qs+zt/svsofHrzErohLS8TjJ9nwDSgnULNr79arHBhEjcXBXUKF5abSUEWIrVMkYMLwic67JDSBKAobhUl4AbTWOgC5nKlfJorcvPVeLpqZQpM4Ble0w1f8cV4OsgwF7uw24uaCeVnQ+Z1sry/L1EBZgoplrfuqJhaLUSzONUMolkrE4rH17lYIUUUMNJ9sG6EtUgYgbXl8un2EDzZPETE0P90xwoRr8frkytfazXexEKfJ9mi0XZKV4C5jhRmvrmiJPfEyx6cy9BVj7ImVWc2llYnm1kS4Fu5YJpwh9lplTWDYTGVjSqvKgSKyVHBXydxJcCfE1nrf9IlVLnOiKJLKJEuAg8ZA0aSsdbRslwCvXhlAbt7bAi56tkI3qhRngrlr4j7t3Lzhipi18Ps0Nwzdr3w+QwOTenvm49aCquqWuXdvD/v27gVgaGSEococoLUyTZOmluaNOLSqVQ/nCHKe222XWeCDzVN8sHmKd4qtHI6NYgBvF9toNEvcEpnm1WI3meaWFe9z/rmOmjFglLtaDTKR8I95e0OUgWgzT6Qu4WiDi2YPMXOMlJVlb2uK6SB6g73PuTUyOfvxI83TjHhxphJdBPoqRWIb9v3W5ghJw120v7Q1BoCRaqEpsXPf+Frvc3dibHwDj0YIuGr63KMs0BBXBl/zxumbV655txHnfmPpDrc3I0mDOnZdSaBXySopwudFXzD3HBvSLnm9vnXi9WR+hm5+5s6/LnOngAkkuFvOuoO7UqlEPB6f/Twei1EqLt0x7mZ6e/vo7e1b7yHNampp3vEXDPVwjiDnud3aU3moXLffFRvhnek0z482M+HadEUjHGtQvDxsoFn5sc8/10k0uYxBhx4lXiljNMvTFCcMDrWM8s5UmqHRaeyYzzNpSJeG6M2u7KJs97wOm1HD59RElPGxCcYyNiOljQsqclGfhpi7aH+JPWG288poDlcXlrrrjlCtz11Rv8ZUgI8mgqKs9YLADuBEUORqpWxutdYyG0/sHNfPYnPRNCiTM0GR8XlBx7B2GZYM04r5MJsSX7Dmbl5wF0ExoT0J7W5g3cHd5OQkyWSSRCJOsVhi9+4u3nzrrY04NiHENlNo9icLdEbDAOVfnt9HwTcXvIc2UI4xMLy+jNTMvLvbk3lsI/xjnrE8jjVkiRh6tozyWjmKF8CeWImTNwnu4oZPT7xEd6zEhXyctqhDxvI5k0sC8MUrnZQ2aL0dhGvuImpx8U3KcCn5Bu4q1yIKIdZHKzjnl3jATPF2sHitrgbG1niJKKFd/dJcXz4IrtZElMFLfnbB7XkCvulPbNmx1br5b5rMH4UwP/cZxeBisLYkUr1Yd3Cntea9Eyd5+KGHUErR19dPNpvj0O23Mzk5yeDQEI2NDXzggQewbZuOjnYO3X47L/zgBxtw+EKIzaDQHE7neXrXGO1RF1+HzUfCdWOb0/77YiHOXZm5C7CM5dMTn6KvGOVaOSzB9LVixInQHr15J7wnd43zweYpAg0vjjURaIUdL9FXDAPRMXdjh4o7gVqyoUrSdDakI6cQYvVeD3IcNuNcCsobul/plVnfri/LddCMB2VysrpuXXzCCSMZbeKj5w0xn3tL2VRwjbVl3OvFhqy5Gx4e5vnvL1wfd+bs2dmPJyen+N4/PLcRDyWE2GR3pHI80zpOR9RhqGwz7lg0RzxGnY1rPrKUi4XE7MdOoDiYCksYvzLQtmC7ESfC7tjNLtT07PgEQ0F/Kcp72RQp0yfYpHMoBwZRU6PQCzKbKcMl61TV8mYh6sYUPt/yJrmiNz64k+iufl1flpvXASfnNVIRa1Mk4KveGIdUnENGnEKlK6k371U1AMak1PWG5IpDCDFLofnZzmEKvslXBtp4ZzrNJ9tGebR5iknX3tTHHnZssp5J2vIZLEfoiZfJeSYnriu/HHFsjqRzWCrAW6bUcU+sTKPtUQ4UUUNzpRgj62/un7uZkQq20jjzhicnDZdRb3O/d0KI5V3QG1/CpSWyq1tLlWW+EmQZ1JJN2ghXtctV7fJCMI1dCekCCFN6OuxWOi7B3Q1JcCeEmNUVK5MwA7451Mq702kA+osxYIoJd7P/XITr7u7O5PjuSAu7Y2VOZlOLAriRcgRDQYvtMuQs3THzSDqHr+HPr3SyN17a9MAO5oK7qBHg+DPHrEkZLtNefPk7CiFqjoR2dUwv/vn365svFRCrEwDlync6HD2isVFktT97u1iaBHdCiFn7E2Ep5IX8XDDSW1mjNr7JmTuAt6fSNFgelwvxBWWa8w074Vq51uhywV1Ykvl+PsH7hfC/reDMC+6ylYUCUSPAVgHZGp5xp9p3g6HQ165c9wWFatqF9j3ITW/PwQmxTWYuNkV9km6pW0tXftsiKC5LIH1TEtwJIWYdSBYZKEXIz8t0TXo2/19/52yQt5nO5JOcySdvuM2oEwaZ7dHybMfMFtth3LXRKDqjDi0RlxfHGjf9eOebydw93TrOC6NNDDlRMlYY5WW9TfpTaxioPbdAEIBTRmcnobhx4xZUYzOR3/h9sGz8H/8QEklUugGVaUQ1NKOiYXDt/s2fQ//5DXtcIapdeKm5eWuQRfXSMpR8ywWVWNpWiiuBBHc3I8GdEAIAWwXsTRT50fjioOjcTQKureRqg0uFGMcyWV4YbaYrVuZ3913hG4OtvDbZwJF0jkDDqdzahhOvVTkIL/TuyeTIeSbfHm7lcDoHwNXiygaur5b9uV/DPHLf7Oc6CNDjo8wWDfl+GPS5DirThB4fhlIRYnFULAHxBCoWBw3+yTdRqQyqsQlUGKiq1o5wvyODmA9/GLJT6OlJ9OgQwcUz6KEBAIK+9+U6V9QVubivZ0qytltsdt6d1tJMZQUkuBNCAHBrooil4Hx+a8oY1+Pl8UZ+Zc8gd6bzpK3wD31HZRbf4XSOS4V4ZWzD1nHmzcxribgYaB5qnOKSk2F0g8cuAJgPfQjzyH14P/wuQe8FsKMYHV2oXe1zrfxMCyJRVCSKHrmGau0M24eWiuhiAbKTBMUiqrEZ65En0ZPjBOMjYSYQCE69jf/6D9GDV8AwZm9fUkvzhp+jENVK8nb1TYL7rTWBhwYMlAR3KyDBnRACgAPJAm6gtqT8cr1O55KMOjaPN09woRKMFnyT1ohDe9SdHXq+lcrzgrtdEZfD6RwNts9zU+2suf1CNEbkV38P/42X8N95HWwbY+9+jMPHMO/5AP7pd/Ge/9bs5sHpd9Z8/K5phpm+5dwosBOizoRz7iR7U5/kJ7/VRrRHgMZDU5DQ+qYkuBNCYKuAezJZzucTy44X2Gh+RzfWY58gOHeS4OxJ8Ff+bpxG8dJ4I5/tGCFphgFJxAhm599tVROV+eYHd022y6NNU4w7FhedRmBiTfs073sEY88+VHsX5kMfQrV1oUwTXSwQnHob99t/tUFHz40DOyHEArLmrp4paaiyxTTQGzjUbmuyrSXBnRCCB5umSFkBLy6x3m4zqJY2nJ/9TUwN1tGH0I5D0HuB4OIZgv7L4LmggzBbFASgNSRS4fowpUAp3jXgGf0szZEwoEu276JhKnxHb9JIgRmsKmBcDeO2O7Ce/jQq04gul8ApofMT8MYfAGAq2Jso8ffRB3BufRSzWMTo2kvQ9z7+6y/OfR/auzDvvJeg932Ci2cXPohlYz30YYIrl0GpMKB7+XsE/ZfDbTfp3IQQNyeX9vVNckdb72RQIMbWvPlc6yS4E4slUlDIbfdRiC3SGnH4yK5xzuUS9BW3Zh6bceQYKEX5j/8Qo303xm2HMG47hP3Rn1nVft44keBDJ/8CgOSuZpr3dVDsHcH8F3+ESdhgBN+Hcgk9NQ6ui/bcMHgsFdH5HAR+uDbNNMG0UKYJloV2XbwfPAtT12XdDAP70/8DOgjwz7yLikQhGsOJLvzeOWaU937qD/AiaWxAZ6cxjxxDj40QXDgFgPWxz2Heeju6WKD8b/7FguyZ+fCHUQ1NuF//bwSXpROlENVEgrt6JmWZ2+GiLm/3IdQMCe7EQvEksf/5X+O9/iLes1/d7qMRm8xSAb/QNYgbKL422La+naUbwqYb84Ih1dKGcfsR9LUr6Nw05pH7MG49iNFzG8a1PpiaIJiaIDh3MrxDphGjfXe4H8MIuzYaRpitKxXRhVxl7ZcG3+dHfpFDjXFazBKx3lOkogNMKRv3O18Hy0LZdhi4xZOoTANYNioag2Qa1R6HVDp8DN8LAyvfA89D+z5GpgHzwJ0E167MHY/rQiGHamzG/e//heDsiYXfg0OQ8y1Spsfb0xkm//hf0diQYXI6B8UC0f/xX2He84EwuEs3YOw7QHC1F2P3XoxbDhJcOA2Ase8A1hMfwz/1jgR2QlShAB1WZcpVfv1RUpYpqpsEd2IB1dYJgPXgE3gvfQ+yU9t8RGKteuJFrpWiuDdYQ/fx1jG6Yg5f7O9keh2z2KynPoX1+EcBCK5cxj9xHJVMYz7yFMqa268OAvTEKADm+Z8s3tH0JMH05IofNwf8X2O7+fnOQbrjZVRhlCnfxD/1/TWfywzVuQfr6c+gkqkwoPR9aMqgDtxJMHxtLiCd599d7Gbas9gXL3G5GIMgh4pFZod8+yffxLzvUSLd+1CmjTIMnL/9CyK/9c8wH/oQRvctGIfuxujYQzAyiPutL6/7PIQQG0/LtX1dC+TnL6qYBHdiAWNX++zH5uGj+K/9YPsORqxZa8ThH++9yqsTDXxzqHXJbQ4k8zzSPMWPxhsWDQ5XrR1hoG9aoAyCcyeWHY5tHDyC9fhH8d87TjB4BfOu+7A//rMA+O/+GO/7f4dx6C4IAvxTb0Mui9q9l1g5v2HnWwhMEqZPxAgYKG/MTDl97Qrun/+nxV8wjPDKbomru2EnfOzlBrH7P3oODBNl22jDJBgdRA8P4L/xEtZjz6BvuwPd9z7ud76G/+6PobBx3yMhxMaRdir1TWI7Uc0kuBPYP/sb6IlRvOe/hdrVhnYcKBUw9uxD+ufVptuTYVDQYrvLbnM0kyXrmfz9yK4Ft5v3PYr1yZ8L155VBAN9+O+8jtrVjmpuDbNxSqE9D6Ozm2CgH/cbXwLPw//Rc+GstSBAj48ALHqTQF/tRW3gXLSCbxA3wyXuU+4m/1lbx0gAPTmO962/XHS799w38V57Idy3BHRCVD25uK9vsupOVDMJ7updJIpx+CiqsqZItbSjx4fR46Oo3Xu3++jEGt2RDgMEQy1+AUpbHkfSOfYlSlwuxPD1vPeflcJ6+tPo/os4z34VXAfV0Y39s7+O/YnPo4sF9Ngw2nUgCFCJJPge7le/AN5c90Y9OrTp5zhfYd7A8ql1lJduq1x2u49ACLFCsuaqfikt3TJFdavRqyCxUVTnHpRhEFy5jPXBj6B9n+D0uwQDfdh33iudM2vQ0cw0++IlAFoiizN3T7WM82BTuAbslfGFw77V7r2oRBL3jZfRQwMA6PFRyn/0PqCrNgCZH9xNb3bmTghR9yS0q28S3ItqJgMj6pzR2Q2A85d/gvfmK+GA5NEhgv5LANgf+xmMO+5F7dkHyVR4J9tGtXZANLZNRy2W0x4t87nOYS4W4rw41kiD5WGphe8x2sbci1LvdaMPjP13oIOA4P0zC3ecm67awA6g5M/9KbtclOelEGJz6dl/RD2SzJ2oZvIWd50zunrQ05OQm8b71l+iB/rwz52E6Um8l76H+ejTmPd8YHb7YGwElcqgotGw8+GVywQXz0BDM/rqZfw3Xl7dAVh2GDReP0tMrJpC89n2EYq+wV9e7eBgqoChoNn2GHYiGGj2xEs0VtbhXStFGCjNNR+xPvozWI88GQb2xdpa9zXq2AB8Y7D1ht1BhRBiI0jmpn7JBAxR7SS4q3Oq+xaCgf7wE63xj88FZ95z38T70XOohiZUuhHV3hU2z8hnCfovYbS2Y9x6CPPxj6EMA334KP6br6yq4YT1kc9g3vsg5X/7v4QzxMSa3d8wzd5Eia8MtFEIzNmApyXiMOxE+GjrGI+3hGMGjk+m+drgXGdU1dqB+dCH8E++hfvdr2/L8a/HmBvhfz97qwR2QogtIRf39U2Ce1HNJLirY6qrB6O5Ffel7y2/UbEQNtEYvArnf7Kge2YA8P2/g1gc4+ARIp/7NcwPPE5wpXduKDQa1dIOirDUr1ya24FpYd79ACoWx7j10OKB0GIFNApImj4fbxvj/Xyct6fTAAw7EXwNPfESTmDMBnYAY66N2r0X8/5HMdp3h2MPnDLut/+q5rJ2MySwE0JsFRmFUMeUBPeiuklwV8fMex5Aey7+qXfWt6NSkeDU2+hP/+LsfLOlaMdBDw+gx4YJRodQiRQqkUQHAcahuxcFd8YtB9FOGX21d33Ht07GvgOQSBJUvk8GulJvrzAffhKUwn/71WXnwG2mn+scpjNa5lo5gm0EfGOolZlLDicw6C3GOJzKczSTZbhs0xJxMRVMdN5B5Gf+OThlgoE+gjdexv/JWzUb2AkhxFaSi/v6pZA1d6K6SXBXx4z9d4bZtFJx/TvzPNy//C+ohqZwDZ9hhgOwDQM9MQqWhXn4GKq1A2Pfgdl1fP75n0CpiHnnvQQn3wxLOjONmEeOYR48AoDOTlHOTmGPDBKMDOK/9sLWlXAaBvZnfwUSKcqXzqOKOf7JrVe40PkA343ej/XExwAw734A54v/fssDvKMNYZOTjpjD86NNjDiRBV8/Z+zmY9H38TR88XIXn+4cY1+sQO7xnybovYD75f+6MJsqhBDipqQsr77JnDtRzSS4qzKd0TIfbhnnq9faN7fMzI6gmlvxTxzfsF0u6rB4Ha/v4oLHJxoNOzA2NBHdu5/Ir/7e7Jd1Pof7D98Ap4zq3IPZ1omxdz/mPR9AWRbeC89u2HHfiHHnUVRjOGzb/uwvc2j8PTou/zmG18/zz/yvaN/H/Zs/x/7MLxH51d/D+eJ/2JhgeSl2BNWxBz02DIUcSXNurtyoE+EHY02zn6uuHoxbb+f9D9yLfvYf8Xz7RxhOd/K1fXt4tHySK5Ma9+v/rwR2QgixBhqks0adUijJ3ImqJsHdNlBoPrJrnBHH5lhDlr5ijNcnG5j2LJ5omeCuTJ5LxWlenWjcvGNo6wyboAxe3bTHuCHXCf8DmJqg/Cf/BqPnVnQuC7lp9ORYZc1eKN3SzMTYOPav/C7mPQ/ivfidMECMRFGRKEQqGSvXhWgMnZ0K2/evornLUszDR9GT4wQj1zAOHObh7/0pALumeok6OQr9VwhOHMctFbB/4XeI/Mrv4vy3/7gwaIrFMY89gkpl8F7/wZo6g6q2Tuxf+B2MllYgzGb2/OS7cOkL/NXdv8el7gdRI2NEIlFIpjGadwEwdPEs//bQH5Dffx92OkMO+Pazg/jf+pN1fV+EEKKeSUxXz7QEd6KqSXC3DbpiZT68K7zAdwLFrYkiT7RMcDqX5PZkWNb3waZJXptoqLTL2HhGx24A9NA2BXfXy04R/OTtm27mv/0a5ud/g9gf/vubbqtdBz00QDDQh77Wj3/mxKoHshud3QT9l3C//kU64y63dF/iXC7BwVSBzpe/wrnesCwyOH8K9yt/iv3zv03k1/8pwftnCC5fwDh4GPPeh8LREb6Ped8jeN/9Ojo7jS6XQCmUYYQNTWJxnJZW7ECDZYVjIgA9NYF51/3g+zh//WeoZArVsYeuaJghvHTLoxR6+1CpTLjP6QncH/0DenyMoO8C454HfA3VvAvV0Exw6dyqvgdCCCEWkoYq9UsCe1HtJLjbBvsTYQD37nSKF8eaKAUGDzZO8UDjNLaheX0iw4NN0zTbLmNu5CZ7WxvVvhtdKqInxzdl/5slOPU27nMtYJrglMEpo8vh/zGMMJtXLqHSGVRLG0ZnT9iR8wOPYz05hX/iTfTUeJghdB1w3bBpy9jQ4vVy8SSqqYXgjZcgCHgkPYoTKL4+2Mof3NbLnp/8A2dGW+aO7ewJ3K9+Afunfh7zkaewHnsmbFhz4s1wnWCphP3L/xj7M7+07Pn55SJGsYj2XPA8MAyMWw+hhwdwv/5F9Pjo7LatXUOMZxJMf/nP0L3v3/R7p8dHF9xfCCHE2siaq/okzVRELZDgbhscSBa5Vorw5YGO2du+M7KL50ababY9okbAg03TtEU3L7gzdu8Ns3a6xl6gggD/RqMblqIUqqsH+9PaGxN3AAAgAElEQVS/iHnfo6hodNEm2nVxv/SfCS6fn71tNrt5rZ+k6XFPJsebU2mmPJv+YoxDqQLPzQvuAILT71I+/S4kkhi79xJcubwgaHT+n3+Nam6DSARlR0BrtNZh05ncNE1NjUyMrSzgbrEdRnMm+srNAzshhBAbJyBceyXqjzTTEdVOgrstdiiZZ1+iyMvji9fTedpg2IkQNcL3hdoiZU6T3PiDSCRRXT14L/79xu+7GmmNvtqL83//H+HnyTQqngDbDpuURONYH/8c9md/Be/1F8O35iwbY88tAATXrvBgJav6SmUd5Mlskk+2j9Fsu4y79uLHLOQJzp9afLvnoYcHwsNa5WkcSub58K5x/rR/N05g0GS7XCktDlSFEEJsLrm8r0+SuRO1QIK7TRYzwqYgpcCkPVLmF3cPMlCKLuhseL1yYDDpWrRHN6fdv3HbHSjDWDr4qAf5LDqfnf1UA24hR+Tnfxv7o5+du9338S+dg2Ke21oL9Bejs6MGTmZTfLJ9jDtTOV6eWP5nuR7tkTI/3zXEn13pYtqzOJTK0xMv80TzBD8cbyJpBUwsFVgKITacbdvcf/99JOJxCsUix4+/ibvESJbu7j0cPHAAgHPnz9PffwWAOw4dort7D7Zt83fPzr2xZhgGx47eS0NjI67j8MbxNykWN6njrtgwet6/or7UWsGTqD8S3G0iheZ3eq5SCgy+0N/FL3QNUQoMvnilk1Jg3vC+w+UIbVFn4w4m04j1yFOoXe0YXd3oXBY90Ldx+69x+mov5T/63yAWD7t0+t6CTptxI1iQoZv0bJxAkbb8pXa3IZ5omaAz5nBroshgOUJ75fnwWPMk/cUYwNJZQyHEhjtwYD+jI6Ocv3CBA/v3c2D/fk6dPr1gG9u2uf3gQV784UsAPPH4YwwODuG6LoNDg1y8dImnn3pywX16erpxXJfnn/8+u7u6OHznHRx/860tOy+xdnKNX3/CzJ385EV128RBauLeTJbOmMMtiRKf7xymI+bw19fayPs3j6mHHZu2iIPaoD8i9ic+j/nAY6hkiuD9s7jf+kt5+2kppWLYaOW6EQpxM6DoL/x18bTCMjbne5ixPO7OhJ09n2kd4/dv6WdfosTZXAKAz3SMADDuyPszQmyFzo4O+vr7Aejr76ezs2PRNm1trYyMjOK6Lq7rMjIySltbOL5kYmKScrm85H5nsnsD166xa1frJp6F2EjSMbM+SVmmqHZyZbiElOlhGZrJdWZFjjVkyXomacvn7kyOl8cbOJdf2Rq6oXIE29A02N66j4N4EuPAYfwfv4j33b9Z377qVNz0KQXXBXeBwlKbE9w91DiFAvKeQZM9N6z8bD7BQCk6O0pDMndCbI1oNDobnJXLZaJLNGaKxWILSiqLpSKxWOyG+51/H601nucSiURwnA2s3BCbQt4erT+y5k7UAgnurqPQ/Fb3AB0xh4uFGG9OZTg5ncJUmgebpni70i1xJVojDufzCdqjYQbuuyMtN79TxczarraIs+7gzjx8FGVZ+O/+eF37qVcGmqihKfoLS2k9vTnBna0CHmya4lQuiQHcmc7Pfm2oHOGtUoz7G6exlL5pea8QYuUeefihJYO202fOLLpNb0Plw969PezbuxeAoZERhoaH17wv0zRpamneqEOrSpt9jmbRIoKJ3ub0nVKKSHRzOmtXk2o4T0uDDzQ1bO7vTj38foKc50qttIv6DAnurnMwWaAj5vDOVIo98TKf7xzmM+1hCVzE0MSMgO+M7LrpfiJGQIPtM1y2+fbQLnwUnl55FexMcNcacTlXubZXnd2g1KrXypl3P0AwNIAerJKB5TUmbobr6pYsy9yE4O5oQ5aEGfCj8UZuT+W5c97XhsoRyoHBVwfaaYlsTsMdIerVK6++tuzXZrJ1M/9fKrNWKpXY1TL3+hCPxRkdu/FsyVKpRDwep1QqoZTCsuxls3a9vX309m7MWummluZVXzDUms0+x7Jt4+pg2zM5kWgEp7zzM73VcJ4ahYtmorC5vzv18PsJcp6bRYK763ygcZop1+Sr19oJgL3xEvdkcgQaHm6aojO6eM3EUloj4R+gESdCcQ3ZlYJvkveM2f0QixP51d8Dw6D8H/8lZKdWtB/V1IKx9zbcf/jGqo9BhOJm+NJ9/c9xc4I7zaNNk1wpRrlcjNFYKcn8k74upl1rdr3m+UKC84Ub7UcIsZGuDQ7S093N+QsX6Onu5trg4KJthodHuOPQHdh2WG3R2tq6qOnK9QYHh+ju3sPExARdnZ2Mjt44GBTVQ5at1x8F+FKQK6qcBHcLaHriJc7kEgSVZdK9xTi9xTgQZuPuSBVYyTLq1kpWZSYDtxYjToTWSodE65EnUYkk2nWxP/6zuF/50xXtw7z3QXQQ4J84vubjqHfxytzBpTN3G/ue7S2RKdqiLn810A4oTmRTeFcVlwpxZOm+ENvn/PkLPHD/ffT0dFMsFnnj+JsANDY0sG/fXt559z1c1+XcuXM8/vhjAJw7d252XMKdd97Bnt27MU2TZz7yNL19fZw9e47evj6OHTvKU089ies40imzhmi0/FWuQ9udqRXiZiS4m6fR9khZPldKSy+Av1aK8kBjlozlM+3d+FvXGnHwNYw7a18vN+JEOJQKazKNA0fwL54luHgW++lPEzz+UYL3z6C9UrixUuFQbtOGwA/b+QPmfR8M59lNTaz5OOrdDcsyN7hb5v3xQaZdkxPTKQB8rTiZTW3oYwghVs913SXLNienpnjn3fdmP+/r75/tqjnfqVOnOXVqcRYvCAKOVwJFUVvkIr/+yCgEUQskuJtnTywsuewvLl5QDzBQDm/fHSsznbvxt25XxGXCtfHX8b7eiGPzgOUTs0G3dRC89gP8Hz2HefAI9lOfgqc+RUkHRCbHUekGlLV0IOn/+MU1H4O4QVlmoIiZK395/+n2YZojLl/o373k11tsh1si03xvpHldzxshhBCbr0hAHEPK9OqM/LRFtZPgjnCmWFvEYW+8iBfAUHmZ4K4UxQkUB5N5TuduPNKg0XaZcNf37R0pVzpmtqYYtmyCa1cgCHC+8O9QjS2o9i4St+ynnGwgmJ5A57Lh8G3DBLPyX6lI8P7iTm9i5eLGcpk7A0stHGKesTw+1zHE1wfbFnRVjaiAB5umb/g4RxuyaA1vTmU26MiFEEJslrwOSCkTudyvHwrwZbGlqHJ1H9zZKuA3u6/SHnUJNJzKJZfNmrja4EwuyeF0nm8OzVXb740X+VDLBF+60jl730bL42w+sa5jG6mUdLY1xxgG9GA46Bat0ROj6IlR7JEr5Oqg09B2UWhS1kxwd/OGKgeSBQ6mivzi7kH+4mrnbPnusYbsgn0uXqmhOZrJ0utmblryK4QQYvvltY+pZNZofVFSjiuq3sp78+8ompTpAZqf7hihNeLy7nSK8/kEf32t/Yb3PJFNkbZ89iWKRI2Ap3eN8Y/2XuVQqkBzpYmKWQkIptZ5kT7h2nha0dYcQztl9NjaZxqJtflU+yhP7ZrA0ywK+pcK7qKV5is98TJ/cNtlmm2X9kiZj7WNLtpmvr3xEs0Rj5Olm4/ZEEIIsf2y+JhSQl9XpFumqAV1mSK4J53jF3YP8cZkhmMNWZ4baeb5sZUNFzybS+AEirvTOZpsj6d2zTUqSVseI06EjO1hKNY3fNyyUA9+iHHn72i1S/jHX5a+y9vg4aZw5IS1xOv3UsFdqtJ85WQ2yZF0nvsbprk7k6UcGPxgrIGPto4TN4NFw8ePNWQpB4rz5SZgZWMuhBBCbJ8cAUopqcqsIwr5cYvqV5eZu8PpHAAPNE5zIR/n+2NNK76vqw3O5hIcTufpqsy8++ZQmG3JVMr3Gq1wNtnkOtbcWR/5aexnfpoRs4mWvvfwvvs3a96XWLuZdZNZb/GswqW6ZaYsjynX5C+udjJQivDhXRM0WB5/caWDocoaypk1fHM0R9I5TmVTuKx+JqIQQoitV9QBWi716871r+BCVJsdG9yZSrM/MkFb5Pqh4+Esu5xncj4f568G2lc9qWamNPNoQ5arpShvVRpgpCtBXaMdlmdOrTW4S6Ux73sU761XGLrQT7NRwJQXkC1noGmwPF4eb+CPLvYs+roXLM7cpS2fXGXQ+E8qIwy+MdRKXyk+u2Yvfl2HzYQZkDADrpaWbuQjhBCi+pQJCOSlua7IKARRC3ZsWaaB5tOZC/zIa+A7I3MXza0Rlwbb5+vXWnljqmFN+z6TS+IEioQZcCYXoRwonECRqQR3DXYlc7eWNXe2jf25XwfDwH/pe4x4EUwFzRF3XQPRxeo1VMprB8vRRWWUsHxZZq6S5XtxrIkL+Th9pTgw123z+uCuYX6mV+I7IYSoCUUClNTp1R3plimq3Y7N3Lna4JqX5NZEEQBLBRxJ57i7UpJ5vrD2TpYzpZkAg+UIoMh6FmnLBzS3JYpMeyaeXv231/rE5zH2HcD92y+hx0dnO2a2Rpw1H69Ym6ZKBna5kRaeVhgqfCNhRtryZks4fdRsYAdzc/I+1zHMJ+Y1WGmsvBmw3gY8Qgghtk5Jawns6oxk7kQt2JCrybbWVu666wgoRV9vH+cvXFjwdcMwOHb0XhoaG3EdhzeOv0mxWNyIh76hfifDg4kBbksU+HT7CG2VcQeXC7H1NTsB3sumuSuTZ6BSSjftmaQtj0eaptifLM6uw1vEtDA/8BjB+2cwDx/D2H8HxBIo2wY7gkok8X74XYL33gCYzdZJcLf1mitB1/gyzxVPh+W8ltI4WqHQJC2fnL/0urlCJXMXMwMea57k2eHwOdJghUHkpGshTbWFEKI2FAkwJHNXd2QUgqh2GxLc3X33Xbzy6msUi0WeePwxBgcHyeZys1/v6enGcV2ef/777O7q4vCdd3D8zbc24qGXpxS9yb08wgC/3TPApGtxLpfgYKrAu9Ppde/+ZDbJf+3r4mIhzMxkPYv9yQI9sVFO5xK8OrFEyWdDE5Gf+y2MPfvQrouybYLL59HX+ghcFzwXPTWO/+oLs3dxAoMp16Qt6q77mMXqNNkuvobpG2TuACxD4/gQNwIsFT4Xlt7eINCEFwPzNNgenoa8b9K4oWcghBBis7hoAsISKLngrw8KJQ1VRNVbd3DX1NREPp+nUCgAcPXqAB0dHWTnZe86Ozo4c/YcAAPXrnHXXXet92FvzrK4+Ln/ieHn/hn9TQd5Nn8LwY9/wAdLI7w1tf7gDhQX55V2Zj2ThBkw7Zp87Vo7XNekxbjtULiWzjRxv/9trCc+jvfWK3jf+O83faQRJyKZu23QEXUYc2yCZRrueMFc5g6YHXaeW6Kz5oyFgZ0GFI2Wx7RrrbqxjxBCiO11wi9wt5lgSsslfz2QOXeiFqw7uIvFYgtKLIulEk1Njctuo7XG81wikQiOszBg2bu3h3179wIwNDLC0PD6hnbr7/0tf1q4H+/AwwQPH4GHP8IrUxNYgY8V+OAHEHgo34cgAN9HZScxBq/M7aRyva2KBbRpQTKFTqTQyTQ6kUYnU+hYguyV59Hnv8w37/sn6CdvJ97QBK6L9d6PCXa14x8+hhobIfLslzEmx9CXTkM+h2q5+Xy9ojlJmz1N0xLbmqa55O07zdafp6Yn2ctlJ7Ps40ajATBCS1MGM4jxSLIPgGK8labIzdd0trY04mHSEh8iT5ymlua6+XmCPHd3mvWe58TY+AYejRBb47Ugx2EzjgV4230wYktIllZUu6rq4NDb20dvb9+G7a+JM0yPjcNP3kX13Ir10Ifwo3GUaYFpQiQCpgGVz5VpQboBZdy8EYr2fchn0dlpdHaaH6fu5vzhPYzEdqM8D91/GdWxG/2hT6IdB/+t1/D+/q8pupWAdhUXMtOmS6TBW/Lip6mluS4uirb6PBssl1Sry/uTionJpR93Kp2HDOSnJokpzf27BnljMsP54RJQWnrHrXMfOlOjTHk2qcYilwsxJsbG6+bnCfLc3Wnq5TyFmK9AwKt+lg+aGcne1QHJ3IlasO7grlQqEY/PdQSMx2KUiqUltymVSiilsCx7UdZus+m+i7h9F2++YSqNamoF5nXBUqCS6XBNXHYanZuCQh7mtcN1gUUtYgwDkmnIZ8PM4BqVAoOIEaDQUrq3Rbrj4XzE/lJs2W1m1txFjIDPtI9Q8E3+frhlxY8RNwMs5dBke7xalhkIQghRi94JihwzUkRRlNHYKDLKZExLLm8nkm6ZotqtO7ibnJwkmUySSMQpFkvs3t3Fm28tbJYyODhEd/ceJiYm6OrsZHR0dJm9VYFcFp3LLrp5Tb/KQQDZqXUfUikwMBREDE05kOBus3VGyzyza4yyrxi8wWDxmeDug82T7ImX+fLV9tlxB8v5T5f3cDid40MtkyRMn1tSJQLNhjT5EUIIsfU8NC/403zSbKSMTxyDgg5IY5KV9hs7ilJQluBOVLl1B3daa947cZKHH3oIpRR9ff1kszkO3X47k5OTDA4N0dvXx7FjR3nqqSdxHWfzO2XuMKWZFvqGTznYsaMJt51C88HmSZ5pHSPvmXzxaif+DTKlM8HdPZkcZ3MJ3s2mbvoYV0oxPK0qwV3AvQ1ZLhXiTMuMOyGEqFkXdAmtQFU6Ip8KitxjJGRMwg4TAINaupeL6rYhV5TDw8M8//2FzU/OnD07+3EQBBw//uZGPFRdKlWyQTEjYP15QLGc+xqm+UTbGCezSf7mWhuFm2ThvHmB9vGpDNd3SF1OoTIH70CywK6Iy4tjTWs+ZiGEENtPA6PaI0048uZCUOJuI4GMwds5wpEXmmEJ7kSVk3RBDZgpxYyZ0qNpMyRNj4+2jrE/WWSwHOEvrnawkkBtJnMHMO6s/FdpZpj50UwWLwhnJgohhKhtVwOHeyoB3Zj2GNIuLVgUpb/ijhDDoD9w5Kcpqp7U+NWAkj+XuRMbb3+yyAONWZpsj7en0qw0Azc/uBtz7RU/nqcNnEBhG5rTueRsZlYIIUTtGtQOtlK4aIoE9AZlItIEbcewUZK1EzVBgrsaUKqU/0UluNsUGSvsaDZUjlSCu5WZH9yVVxmgFSvZu3ekkYoQQuwIY3j4aE4GBQCuaUdKMncQBbjyExU1QMoya8BMcCdlmZujyfYo+gZ/fKlnVfebH9ytVsE3iRias3kpyRRCiJ1gRHt8yR1lpDLOfER7GJVFdw2EbwBOSffMmqUIO6MKUe0kuKsBc90yJbjbDA2Wy6S7+l8Fbx1jKd6YzOCj8NcRIAohhKguM4EdhAPOczrAQqEBU7qr1DStkNBc1AQJ7mqAqxW+luBuszTaHpNrGEUwk7mb9la/Zu7VycZV30cIIURtuaId9qsoKInrap0GfPkpihogwV1NUJQDg6iUZW6KRtujtxhf9f1cbfCNwVbO5hObcFRCCCFqXZ8uc8iIEegw8yNqm6cluBPVTxqq1IiSb9wwc3comSeiJPhbrYgRkDCDNZVlArw22cDEKjplCiGEqB8z3RUNCexqntbIGARREyS4qxGlYPngLmV6/Fr3Ne7JZLf4qGpfkxW+8K41uBNCCCGWM6bDNXie1lLQtwNIWaaoBRLc1YhyYBAzl17KOxP0xaVsc9U+0DRNoOFqKbrdhyKEEGKHCYBB7YZtViQuqGmy5k7UCgnuakTOM9lluxhL/GGxjfA2mYO3Om0Rhwcbp/jxZIYxN7LdhyOEEGIH6g3KlKSgbwfQ0i1T1AQJ7mrEm1MZMra/ZOllpBLUSTfN1flE2yhOYPDcaMt2H4oQQogd6r2gwDtBfs33lwu1aqEkcydqgvzNqBFn8wmGyzZHG5YI7lT4xyYiwd0N/fLua3ygcQqAg8k8t6cKfH+0iby/+lEGQgghxErkCBjVaxt/HUXRrqRpV1VQUpYpaoMEdzVDMe7axJcI4OzKbVKWuTwDzeF0ns92jGCg+UTbKKOOzasTMm9OCCHE5tLz/l0NozIAXWw/hcaXH4aoARLc1RA3ULPr6+aLyJq7m0pb3uzHDzVN0R51eXa4BR/pTy2EEGJzBWhYw+uNQvqwVA8pyxS1Qfq/1xBXG9gLZtlpnk5dBqcIQHSJwE/A7ck8zRF39vNPtY/yfj7O6VxyG49KCCFEvVjrW69hcCev7VVBS1mmqA0S3NUQ57rMXcwIOBYfxqt08Y/KKIQlfbZjmLS1sMfVN4ZaWcu7qEIIIcRqBeg1p+EknKgOCqRbpqgJEtzVEFcbROZl7hrtsNTQqhTXSlnmYjHDp8Ge+3N8Npfg+FSGEUdGHwghhNgawRojNCnLrCLSUEXUCAnuasjcmruwdr9h3joykOBuKe1RZ/ZjT8OfXelEMnZCCCG2UsDaXnmUNFSpKpK5E7VAGqrUECdQGAqsyuiDBnup4G7nvAz8/r4+Pt0+sq59tEXmgrusZyGBnRBCiK0WrOO1eee8qtc2A8ncidogmbsa4uowFreVxtPQeF3mzqwEfp7eGQFMR8yhI+bwzaHWJb+eNH2e3DVOzAgwlWbCtfjuSAvzA7j2qEOgwVAw5crTXQghxNYLYE1R2s54Nd8ZZuqmhKh2crVbQ5wg/DNvG5piMLfmbr6oEeD5tZ+QnV+IslxRyu2pPI80TTHpWlhKk7J8fjDWRDmYG0reFnUYKEVpizpMe/J0F0IIsfXWmrmTNXfVQQFrG0MvxNaTq90aMpO5m2mq0mC7i7aJGpr8DigKj81bP9hiu0vWuWcqmcv/82IPd6Vz/FzXMCnTXxjcRRwuFBK8NtnAiGNv9mELIXYo27a5//77SMTjFIpFjh9/E9dd/De4u3sPBw8cAODc+fP0918B4I5Dh+ju3oNt2/zds3+/YPvDd95JqVQC4OKly/T19W3BGYmttPY1dyDh3fZTIAPMRc2Q4K6GuPMyd8Cihiqwc5qqxOeNdeiMlbmyxDYZy6fgG3jaIOeHAV3S8hmrXG/NdMocLkd4cyqzBUcthNipDhzYz+jIKOcvXODA/v0c2L+fU6dPL9jGtm1uP3iQF3/4EgBPPP4Yg4NDuK7L4NAgFy9d4umnnly076sDA5w4cXJLzkNsj/Vk7sT2C8cgSHQnakPt1+/VETeorLkzAhSaBsvDr6yvm2mzvHOCu7lcXee8jpfzZSxvdh1d3guDu9S8+7VV7jdUlrEHQoj16ezooK+/H4C+/n46OzsWbdPW1srIyCiu6+K6LiMjo7S1hWuGJyYmKZfLW3rMonoEsMbUnVrjHcVGUigpyxQ1QzJ3NcSpBHK20iRNH8uAES9Gq1Uk75ukLX/HBHfzyzJTlg+Lq5/IWF6lAybk/PD/yXnBXXulU+awzLQTQqxTNBqdDc7K5TLRaHTRNrFYjGKxOPt5sVQkFovddN9dnZ20tLSQz+U4cfInsyWa19u7t4d9e/cCMDQywtDw8FpOBQDTNGlqaV7z/WtBNZ2jpcEu2kQM8+Ybz2MGBgqIKGPZGE8pRSS681/ntvM8bQ2OgqaGrXk+VdNzdzPJea7MxNj4qraX4K6GzJRlRgw9OwZh1IvTahXJejsruJtflhkzll5EmLG82axcfl5Z5oy2qIMTKCalS6YQYgUeefihJYO202fOLLpN6415F39wcIirVwcIgoC9e/dy7OhRXnn11SW37e3to7d3Y9bjNbU0r/qCodZU0zkagG9HcfTi5RQ34qnwtc3Ryy+mj0QjOOWlK1x2ku09T0WegIn81jyfqum5u5nkPDeHXPXWkLlRCAENVnhhMeInuINxJlybrpgTZrl2gHgloBt3rAWB3gyFJm35sx0wfa0o+saCssz2qMNwOYKWkhYhxAq88upry35tJls383/HWXyRWSqV2NWya/bzeCzO6NjoDR9zflOW3t5eDt95xxqOXFS78FVs9W8IaMKEnXTN3F6y5k7UEllzV0Pmj0KYGYMw4sUBmPIsvGCug2StmwnoJlx7QYnmjJTlYygWjDfI++aC4LYt4khJphBiQ1wbHKSnuxuAnu5urg0OLtpmeHiE1tZWbNvGtm1aW1sZHh654X7nZwo7OzrIZnMbe+Ciaqw1NJgJ8MT2MVB4G5StF2KzSeauhsyOQjACGiwPN1BM+OF6DidQTHsWmZ2SuTN9PA1Zz6QxvjhgnQli5wd3Oc+cXXM30ylTmqkIITbC+fMXeOD+++jp6aZYLPLG8TcBaGxoYN++vbzz7nu4rsu5c+d4/PHHADh37txsZu7OO+9gz+7dmKbJMx95mt6+Ps6ePcett95CR3sHWgc4rsvb77yzbecoNtfMOITVhAgST1QPaagiaoUEdzVkdhSCCjN3U541O9PNCYxKcFf7mbu2SJk9sTJF36QYmEuuuWusnOeUO7c4Pe+bNFdm/810yhyW4E4IsQFc112ybHNyaop33n1v9vO+/v7ZrprznTp1mlOnTi+6/fTpM5w+vXhNn9h5gsoigbWECJK5214G4EpwJ2qElGXWEE/PNFQJM3eTrkVRWwyUIgyUokx7FumqD+5u/MdRofn17mvsTxYp+gYl3yBmBovut6vSCXPUnQvect5cWeZMp8whKcsUQghRBdYyBFujJaSoAgqkLFPUDAnuaohG4QQKW4XdMqc8iwCD/3C5h7P5JFnPrOrMXXu0zB8euDQbmC1lX7xEU2U9YcIMKAYGpgK7shw9ZXr881t6uSuTZ8o1cYK5p3DeD8syFVo6ZQohhKgqwZrae6nKv5K7204Wikl2xrIXsfNJcFdjnEARNYIFA7xnTHsWMVMTqdJxCO0Rh5gZcCS9fMOAexuys+WnKcun5IdP0WilNPOWRJHWqMvuWJnR67Jy056FocL7HUgWGChFpVOmEEKIquCzlvLKMHcnr2TbSyu4GCw9f1KIaiPBXY1xtUFLxMVQLMpKzTQXyZjVmb2b6YB5Ryq/5NdNpbkrneNkNsm5XJzvDDdTrKwpjKnwnLpi5dntRxx7wf2nvXDb+xqmaY+6/Hgys+HnIIQQQqxFsKa+l0rKMreZjSKnfUaozmsrIRuDsJAAACAASURBVK4nNWs1xg0UuyJh05Apz1rwE5wJbjK2t2AtWrWIVzpZ7omVSZr+7ODxGbcn88TNgHem05zLJwHYnygAEFXhfbuic8Hd9Zm7bCW4fax5kpxn8t50enNORAghhFilADBvutXSJHO3fZIYvBbIiBJROyRzV2PKgTE74+76ssxcJbhJmtVZFz4zr85QcDC5OHt3byZL1jO5kE/M3laqrKmLGR6g6YqVGatk7AbLi8syIVyrN1CK4svLoRBCiCrhr6W8Usmcu+2mFVyQkkxRQyS4qzGD5bmBt5PewrLEYmV92kz5Y7Voj5ZRaOJmQNYzyXomt6cKC7aJGj6HUgVOTKcI5r2MzZxTVPkcy2RJWQEvjzfyny/v4f1CfME+cp5JUKlfub5kUwghhNhOa33bNZDCzG1jo8hLSaaoMRLc1Zj+4lxwVw4W/viKlc8TVZS5y1ge/3RfP4fTeeKGT8E3OJtLcDBZwJj3gnUkncc2NO9cV0pZqqy5eyhxjc93DfN+Ps7b02n6SzGufy8zQM2Wel5fsimEEEJsp7V0y1SAh2TutksCg5NBcbsPQ4hVkeCuxoRBzdI8beAEingVdctMWx6GgkbLJW4GlHyTM/kkcTOgJz5X5nAgWWDStegvRRfcf6Zb5i6ryCvjDXyhv2tRUDtftrLuUDJ3Qgghqkmg11CWqcFDyyiE7SIlmaIGSUOVGjNcvnFGqugbs41LqkG0EmgmLX+2LPNCPoGn4VAqz+ViWFrZbLsMl22uf3/SR/HaRIZxs4WXhm++FD1squIwKsGdEEKIKhKOQlCwyjJLT8oyt0VYkhlISaaoOZK5qzEBimeHW/jSlY4lv17wTRJVtOZupolKwgyIGz5F36AcGFwuxBesu2u0PSbcpQOybwy1cbLcuqLHm3Qtyr6aba4ihBBCVAN/jUGat5aMn1g3C8Wodrf7MIRYNbkCrkEvjTct+7ViUF2Zu1gl0EyaYeZuZm7dhXycj7WNEzN8fK1IWz4T7vqfjj8Yb+K9bEpGvgohhKgqaxliHq65k1e07WACWV0911NCrJQEdztM0TdptqvnnaaZzF3S9IkawewaupwfPvViZkBEhe9mTi6TuVuNSdfekP0IIYQQG2m5UQhJDAwgyxJVNwpcLWWZ28FEkVvqZyJElZOyzB2m4BtV1S1zJrhrjrgYam60wUyQFzMCmirB6EZk7oQQQohqtNRIAwVYSuESrvFa6uuuZO62hVKQX/MACyG2z7qupm37/2/vzp7jONN8v3/fzFoBkAQIggQkAaDUJKWWWq2FpESpu9Uz3XN8MVcOO+JcOHznK9/5TzgRjrD/Cy9xIhyOiRNhz4yP+4ztntbWWrpJiaQWUgA3ACSxVIEg1lqyMl9fZFUBqCqQWKuyqn6fiG4KQC35YqnMp57nfZ44ly5dpCedZiOX4+rVa3hefdboypX3OTkwwOLiE77+y18O8pTyHDnfjdScu0pDleOx8AWyUpa5OZw84HSyCLDjnjsREZF251tLynGwFjbKGaEUDrPWo2ADXjQJvJoA0GDCzJ2iu6YLLOSVNZU2dKDM3fnz58hmsvzxX/9ENpPl/LlzDW93585drn3z7UGeSnYpFzgkHEvMRCPAS9UEmpXMXWWcwd+desLfn14EYM1/fjdMERGRduQTBnOr+PTjcsK49BiHySBH1paINYzgbLmcU9FdK+RUlilt6EDB3cjwMNMzMwBMz8wwMtK4g2M2m6VUUivZZtgoB0hRmXWXqjmOShfLSuZutDzr7p/nT6nwREREOpYP5An4h9Ii/1Mpw5f+Glnr8SAoskQJ0+AUGKAh5q1iDRRsNK6lRPbiQGWZyWSSQqEAQKFQIJlMPucezzY+PsbZ8XEA5jMZ5hcWDvR4rusyMHjyQI8RdbVrdJMWyHBm8BgxP93040mYEiXrEJTfN+hLZbZ9faN3hIFeQ8oUgWkSjmWhlOaW8woDgzs/bjf8LKF71gnds1atc3eWFp8c4tGIRE8JS9aWyGPJ4/N1sMbXwRoAvdYhqKkANITB3X5HKMjBGKvMnbSn5wZ3H35wpWHQduv27brP2QPWJk9NTTM1NX2gx9hqYPBkx18w1K4x05OD4+CtPmE5l2Ig7rHoPXvw+WH6716eZsmL8b8+HAEMbm8Byk+/WIzxZHEJgFUTwKnw86uF51/YdcPPErpnndA9a9U6RQTCIG0qKDT82pr1w8zdlssoU77PTl025Wg5BvIKrKUNPTe4++LLr3b8WiVbV/m3WCwe6sHJ3m2U97T1uAG/OLbGv31hnv9h8mU2gqPfz+ZgGUoUOZMs8n7/Cl8/PUHSDZgvJDiTLPLx4uZ8vpI1+BZcs3nMIiIineqeLVDa4U3wVfyG+2QCaxt22ZSjVcmalvS9lzZ0oKvq2bk5xkZHARgbHWV2bu5QDkr2r9KNMu34HIv5uIamDTU/ES+F73T5hr8/nWUwXiTlBMzkkvz3Ey9zdfnEllub6jiEDTVSERGRDjdvPRZp3H+gBDyxJZJbcnSbAYb23DWbC2xov520qQMFd5OTdxgaOsXvf/e3DA2dYnLyDgD9J07w9lu/rN7u17/6kMuXLjI0dIr/7N/8HUNDQwc7atlRpRtl2g2qHTPjTnPeeeovz6v7p/khStbwb1+YJ+345AOnYeaw0jFzXcGdiIh0uW+CddJm87IsLMvcefi5HB0Ho/120rYO1FDF87yGZZtPl5e5fuNm9ePP//zFQZ5G9qAQhJuye9zNLdhx05zgbiAWviM5nUvxf84N8V+9OF89pkYqHTOVuRMRkW43GeT5nXsChzBjZzD42PBcbgyqEGweBygqcydtSpudOozFkA8c0m5QDepiTRqL0B8Pg7vlUozvVo9xfbkPoFp+WWszuNOvoYiIdLc8lttBjj7CNzwrmbsAWwnxpEkcDEV9z6VNHShzJ9G04bukHR/KJ4hmZe764yVWSi4lGwZr/zg/RMKx3NtoPJJBe+5EREQ23Qw2+HksDTYM7jwCFQe2gAMU9J2XNqXgrgPlfIceN8CzYZV+84I7j6fe5q9UPnD5949Gdrx9obwPT8GdiIgIzFqPFeuTxJDAYT7IE9hoV2TGMcR22KN2DJcc/g5tZKLLYMgfcLyXSKuoHq4D5XyXtOtXg7pmNVQ5ESux7O3+/YK8GqqIiIhs802wTso4OAbm8SI/CiGFQ69pfDnpAL203zneAfLK3EmbUnDXgTb88p67clAXN815gUo6Afk9zNPbLMvUr6GIiAjAT0EeCMsyl2w4aa1ReBcjGhdxlZENjQSGtpzjoLJMaWdReF2QQ5YLXHpakLlLOQGFYPev4pMbaa4v91UzeCIiIt0uR8BkkCeG4Yn1wxCjwWn8mIlxLAJZMUM4bH0ngYVYm0V41oAX8YypyE60564D5XyHlBMQL3fJjDVhz53BknTtjmMPGrm/0cP9jZ4jPCoREZH2cyPYYMjEyRHsWJZZshYnAjHTszJ3BlgnIIGh1EbBkgWK2nMnbUopkw604bs4Bo7FfIBqkHeUKtnBvQR3IiIiUu+hLfJ/lJ4AYeDUcBSCCbNirfa8+HLD+m13sWktbRWMimzVbn9vsgu58h624+Wh4s3olpksB5AK7kRERA5umfAN2jCwqw+hjA1nm7eafdZ8dQsbBDhtVpYJaM6dtC2VZXagXLmpSaLaUEXBnYiISDvyaZwdq5ZkRiEGMex4HGFw114sytxJ+1Jw14Fqu0/GmtBQRcGdiIjI4Ss1KMo0Wz7f6rNu47xiyADrNohEhnEvjAFPe+6kTbX6NUGOQK5mblyiCaMQNoO7NnsFFxERiTDP2rrgyMWQswEr1m95v8znhUB5Amy7XRpYdcuU9qXgrgPlWpm508w6ERGRQ+Nh6yIol7AL5ZL1Wz9mYKdBfIQZsAIB7ZYEMyi4k/alK/EOlKspjWzOnrvwOYoqyxQRETk0RYK68M3FsG4D7gR54i1Oi1nsM8PLnN1pmEN0Gc25kzamK/EOVLIOxS3lkc0YhaA9dyIiIofPo3FZ5qr1mbQ5CsYSb2H2zjznuQttGCRV9jSKtCNdiXeoraWZ6pYpIiLSnnzqqx5dYBWfEnA9VqTHtO7caxsEn4MmRi8Oprw3sNWVo3sRBnbRaEIqsh+6Eu9QG1uaqsSaFNwFFoptt2taREQk2orWbr9gM2EXSoCJWImAVl7QNT7vuxgslgIBTbgMOTQO4Nmjr3gSOSoahdChtu67izepoUq4307BnYh0nng8zqVLF+lJp9nI5bh69Rqe59XdbnT0JS6cPw/AxOQkMzMPcV2XS5cu0tvTi7WW+fk5frx1GwDHcXj3nbc50d+PVyzy16vXyOVyTV2bRF+RMLirhByBhZXykPOCgRv+Ou84vdXB501l6s/8gQ334QVsKSttkwAvhmEFBXfSvpS561CVcQg53yHepFEIGoMgIp3q/PlzZDNZ/vivfyKbyXL+3Lm628TjcV69cIFPP/ucTz/7nFcvXCAejwNw985d/vVPf+LjTz7h5MmTnD59GoCxsVGKnscf//iv3L17jzde/3lT1yXtoUiAUxNCLdtS9b+vBxtA695erY3brKEcjNpqYNoukjg8CoqtPgyRfWunvzfZg8og85zvNC1zp/12ItKpRoaHmZ6ZAWB6ZoaRkeG625w+PUQmk8XzPDzPI5PJcvr0EL7vk11cBMBay9PlZdKpVPVxZ2YeAvB4dpZTp4aatCJpJwUbVC/YTPn/1rdkl5bxuWPz9LVg6l0lKbc1sLQWjDEE5SkO3nM6akaJMTBrFdxJ+1JZZoeqZO42fJdjsaMt03ijb403j68zk0se6fOIiLRKMpmkUCgAUCgUSCbrX+9SqdS2kspcPkeqHMRVxGIxhs+c4d69+3X3sdZSKnkkEgmKxfqLy/HxMc6OjwMwn8kwv7Cw7/W4rsvA4Ml9378ddNIaTSFFMnAIjCVuIWeg/3i4tso6JwKHn+djFIzb1BRevPzGbtK4VAp44oFDDPCMZeDESYJcjBTgH+C4jDEkkokDH+/zuIHB6+tlwOk58ufa8Rg66Hf3WbTO3VlafLKn2yu461CVPXcb1cxd7ftqh+e/GAkvMI46iBQROUoffnClYdB26/btus/ZfUxlNsZw6eJF7t2/z8bGxp7vPzU1zdTU9J7v18jA4Mk9XzC0m05a41PnBENOiiIBcRxmbZGl9SVgc51LwJPYEC6mqTPaSrjl7Jxf3fFXMi5gKFifpY0nrMWGSGEoHuC4EskExcLRZtQM4bE/WFps6a67TvrdfRat82gouOtQlW6ZuSD8N2YspSPqZJnzXXrcgH/JDB7J44uINMMXX36149cq2brKv40ya/l8nlODp6ofp1NpsovZ6sdvvfVL1tfXqlm7yn3S6TT5fB5jDLFYvOFjS3fLY6vz5OLGYSGob+YDYeOVnhYUQPpbyi4dwoYvxlAN9vIE9LSgZHSvHCBvA7VTkbamTVIdar6QoOAbnhTDzfyDcY9zPXt/p3g30q7Pl0snuL5y7EgeX0Sk1Wbn5hgbHQVgbHSU2bm5utssLGQYGhoiHo8Tj8cZGhpiYSEDwGuvvUo8Fue773/Ydp+5uXlGR18C4IWREbLZbN3jiuTwcQg7OZYI+CFofD73W7G3zVT21IXP7GAIyhm6yr95W98QJgoSNcfkNDnrKXIUFNx1qKlcmn83+TNWS+E7Zb86+ZT/+qXZQ38egyXlBNUGLiIinWhy8g5DQ6f4/e/+lqGhU0xO3gGg/8QJ3n7rlwB4nsfExAQfffQbPvroN0xMTOB5HqlUilcvXODYsT7+5rcf8Te//YixsTEApqanSSQS/P73v+NnP3uFH2/datkaJbryWBxjOIbLJ/4KazvkllqRcTJsb5iyObLBVI8nt6UhTJQMmTjJLQGegQOVjopEgcoyO5xfftFKOQFJx5ZHih7eu2dpJ8Ax24emi4h0Gs/zGpZtPl1e5vqNm9WPp2dmql01K/L5PP/4T//c8HGDIODq1WuHe7DScTwsPTjcI893wc5zEH1bninXRKa8l66SBXPLQZ0hzCRCWJYZxcydR0AMQ6F8nA4ocydtL4pvpMghCsr77CrjEBKHPBahxw0r6hXciYiIHA3PWtbx+ZfS8jNDj9KW8shmsdhqEAfhhWWlPLRSlrlBNDN3JcJS1wqDoWi1407aWxT/1uQQ+eXX27gpB3eHPNB8M7jTr5KIiMhRmLcefyg9ZZHSM2/n09xB5qb8nCW7fc9dnqAc3IUKBK2bsP4MFrvtuBxUlintT2WZHW4zcxe+xCacYLN91SHoccPHVeZORETkaKzgs2Kff/JudkOVSullqWbP3ZoNwm6Z5ZEhRWsjFzJVgs/Abv9cQZk7aXNKt3S4yp67auZOZZkiIiIdyd/H/MWDMEBg7baMoWMMG+XPVMLRIpYmH9pzOYAtj2zY/NzBZvGJRIGCuw5XeUdqc8/d7t6RGoh7/I+v3eHl8viE0VSe/3Z8hnhNWafKMkVERKKh1ILMXYntGUOLZcMG2/biFbFELXcXNoIJtn2/DOUSUpE2pivyDudXyjLLQVnC7O7F9eV02I3r8olVAF7tW2csXeBUYvvg1B43wLeQD/SrJCIi0kphvqx54Z3B4GPx7JbgzsIjW6weD0DRBphmt/F8DgdYK7d8qRxZGNxFKwgV2StdkXe42m6Z8V1m7ipBoVsOBofKQd2J2PbN3D2uT853ieROaRERkS7Smj13lec1JDE8pcSiLWHZ7JZZxBK1mMkhDDqf2FJ1jIM1YfZTpJ0puOtwlXfNKnvukrsM7kqVrlfV4C58F+54vD64U0mmiIhI6/ktCExKWEqEgV7KOPwQ5PDLZZjVhipNDjp3wynPt7sX5EltuRz2orY5UGSPdFXe4eozd7t70arcL2bCweendszcBWqmIiIiEgGt2HMXYClt2bt2Lwh3rVk2s2AeQdOHqz9PZX/dtN1soWKtMnfS/hTcdbhKeWXFbhuqVMoxXWPpj5eqQeHxBmWZCu5ERERar5JBaxZDOOOuhCVhDGvWZ5FSOXO3OeeuBNv2tkWBgyFvLXPWw5jw2LYGpCLtSsFdh/NrXqN221AltiW4O10uySwFcEJlmSIiIpHU/D13plqW2YPLD0GufBxhoLR1Ml/BBpG66HQIM3celgXrkSx/5xTcSbuL0t+ZHIGA/WXuKsFdzNjqfrupXLqmLNOqLFNERCQiAsA2sf6x0lClhKVAwF1bAKhm7rbuASxgcSKUu3OAfDm3eKey786Ap+BO2pyCuw4X1JVl7jVzB0NJj7WSy2whUc7clffvGUvcsawruBMREWm5sDtl84KTMLizBFgWbYkF65WPg21z7iDMkkXpotNuCeQe2iJB+XKppNhO2lyU/s7kCNSVZe51zx1h5i5TjLNSipF0bDVA1ABzERGR6AiaPHHAAL4Nh5bfCNa3HwvbSxzzNohU5s4CxXJnzEpQ6tqw+YtIO9NVeYerK8s0eyvLdI1lKOGRKSYolAeVJ8uP0eOG/6osU0REpPVaMQohAO7aAl/XBHd+TeYujz3yi87ELoPHFAazpTNmCXgUFIkZQ+nZdxWJPAV3Ha6+W+beyjKPxXz6Yj6ZQhwv2D5WYTNzp+BORESk1SojCJr7nI2fsWTZlgPbOOLMXRLDkInv6hlSxqHHuNv2192xeXysGqpI21Nw1+GCmteo+B7LMlPl7FymmKBonW2PobJMERGR6Ahobl2mwez4dOFevE25I95z52JYwydd8ywxIFYT8lkLy7a0Lbh7bIts2EDBnbQ9XZV3uLrM3S5HIdQGgQvFRDVzV3mMXpVlioiIREYrdovt9JylcqOVihwBzhF28nSBVesTr3mOY7j0mc3LXUPYTOXjYIWndnNYQ8aW+CZYV2gnbU/BXYfb7ygEd8vdvMDw1IvhBY0zdzkFdyIiIi0XlEcQNEtl8HcjHnZb9ZBn7Y4lnIfBwXAvKFSPq8Ka7VVMMQwr1uf7IEduS2hqgb/U7BsUaUexg9w5Ho9z6dJFetJpNnI5rl69hud5225z/Phx3vrlm8RicSyWiYlJHj9+fKCDlt2r75a5tz13ANliHIvBK2cB42Zzz13eN/gR6n4lIiLSrfzn3+TQ7bznbnswVyDAHmHkaQxk8XgYFDlj4myUAzdLGOBVDiWOIWO9HR9HpN0dKLg7f/4c2UyWyTt3OH/uHOfPnePHW7e23cb3fb759jrr6+ukkkl++9uPWFhYoFRSP6JmqJ9zt7dumRDutwMoVsoync1umSrJFBERiYajzIzt/JyNlWr23BWPOKsYWMhbyw82x0uxRDWYs3Z7NVIMw6LVNah0rgOVZY4MDzM9MwPA9MwMIyPDdbdZX19nfT1Mc+cLBQqFAslk8iBPK3tQ+6K721EI7rbgLg6wpaHKZuZOwZ2IiEg0NHvPXZgQaxyyFWuCO68JgWeOgAd2e2lmZax75WPHwJIGHkgHO1Bwl0wmKRTCP6LdBG39/f04jlMN9qQZTLU007cQc8DZxQtso8xdbUOVMLjTtk0REZEoCLA0e6fETgGlV9NQpWgtR9hPBWvCQek5AmaC4raumSVriWFwCDt8LttWFLCKNMdzyzI//OBKw6Dt1u3bdZ+zzyimTiaTXHz3Hb759tsdbzM+PsbZ8XEA5jMZ5hcWnnd4z+S6LgODJw/0GFG3mzUGOLgEFGyMHlPi9KkTFOyzf/Tp5AJLpSQZv4ds4gUGBuPE8YEHHO9LMeCepC8xw6qXbsr3uBt+ltA964TuWavWuTtLi08O8WhEulPtPvtm2KkU1KsrywyOdEyDsZAvP+OPQY7RSmmmgZwN6MPFNfBnf5VpWzy6AxFpsecGd198+dWOX6tk6yr/FouN/1hisRhX3n+fH2/dZmnp6Y6PNzU1zdTU9C4Oe3cGBk92/AXDbtbon4S4C7mSoScBG0+fsFJ69o/e9hZ5iuF/nh4EVgHCaTZDcCp4wnk/TwqPpzm/Kd/jbvhZQvesE7pnrVqniDRLgG1q4s6wc+ZuIsiR3bK3rYDFeUZ7TecZj7UbjoF8+cEzdrMI1FjI2hKDToz/WHrKDzZ3gGcRib4DNVSZnZtjbHSUyTt3GBsdZXZuru42xhjeu3yJmYczzM7OHuTpZJ8qL5b58iiD3ey7ixlLIdhecmkxeIHhzePrvHFsHcdogLmIiEhUBNDUIebV52xgqiY79qzh4D049BmXhX12sTSEnUIrz7GMXy3KNMB3wQbfBOvMKGMnXeBAwd3k5B0uX7rI2NgouVyOv169BkD/iROcPTvO9Rs3efHFFxgcHCSRSDA2OgrAN99eZ2Vl5eBHL7tSGWSe87c3RHkW10DJ1r//51lDvPLuGxpgLiIiEhV+kyO7MHO3u+e0hKWajTJ0cQzeAfJ2LmHpZYWHZcMGuIQjEmZssSkNXUSi4EDBned5Dcs2ny4vc/3GTQAePnzEw4ePDvI0ckC1wd1uxiHEjKUUNAjuAhO+ipYpuBMREYmGVoQve3nOom0c3IWNVvZfUOpgtg0kB3hiSwyZOIZnZw1FOo1q6rpAUH5NywdhILbb4M5vkLkr1pRqqixTREQkGvwm77nDmL0Fd1icBkfoW4tvLft9u9gh7JS51YL1SGAo0ZqgV6RVdGXeBQJqM3e7GYUQ7FiWudV6SZk7ERGRKAigyaMQ9jaYvFC9ItmuknmL7fPgHUy1mUpFlhIxY/Bss6f/ibSWgrsuUMnAFbY0VPnd4BP+7tTijvdxjW0Y3NVl7gIFdyIiIlGw2/1vhyUcEL775yzYoGHmDgOztniA4A5yNbPrlq2PIcwWinQTBXddoDL3JhdsZu7O925wvndjx/vEdgjuamnPnYiISDT40Dh4OiKWvZU8FsoNVbaKAes2IGNLB8rc5WqOZMWGHTMLCu6kyyi46wJBtaHK5p67mLHEzM4veDsFd1s7bZYsFBs0XREREZHms1v+v8IB+nA4hksSQ7853Ddl91L0mG+QuYvjsGRLrFh/3yWlDrDB9szdKj4FrMoypesouOsC9XPuwsAuvmNwZ4k5NGyoEi/PyHucTzBfSNLk4n4RERF5hso53wD9uPQYl7u2QAnLceNSsJb0IV7+2T0kxvIN9tzFMCzaEhsE1QZwe2bqyy8tYWmmMnfSbQ40CkHaQyVI84NwCHnCCYg5O2fu3PIr77Myd//XwikebKSP5oBFRERkXyodM1M4zOLxH7xFSsBlp5f3nD7+k/+U/zw2QO6QYp697LnLNyjLdA0sUqJwgDl3lnDMQq2sLVE6wOOKtCMFd12gUpbpA4VKcPeMssxYOTvXMLgr32e95Da74bKIiIg8R2DDrF3CGH7wNyiVP38j2GDWejy0RSaCPD8zKVZqShn3Y2+jEILw4LbcKbCwav0wOKv52q6PwTZunLJgi/Tse8CCSHtSWWYXqDRUCazBsw4Jx+IaWy2xrFUJ+vwG++kq91EjFRERkegJsAyY8L37B0Gh+vkiloe2CMBfg/VD2VWx14YqRdsgz2fC5icFAp7RCuC5x+E1yNA9sEXu20L9HUQ6mDJ3XaBS4e5bQzFwiJuAuLHEdph3VwnuamfaAUys9/Dm8XUFdyIiIhH0Q5DjJ5snsJbVHUoSF6yHXy6RPFDRot1bcOdh6/boOYTNTwDMPjN3GCg0KMuct94+HkykvSm46wLVPXfl7paVzJ1rwMHWbW+ulF6WbH1i9x9mz/CHjL/DGFIRERFppU+D1efexhJm9V42SdYOuCdtL7P1vJodeg5hNq9Y3uix3ysLs0NZpkg3UnDXBSrdpwLCzF3SCaqNUWKOrRtnMJAI3+l66tX/epSsw5Knal4R6S7xeJxLly7Sk06zkctx9eo1PK8+KzA6+hIXzp8HYGJykpmZh7iuy6VLF+nt6cVay/z8HD/eul29/Ruvv04+nwfg3v0HTE9PN29h0rUmgzznYqn9Zcq22Gvmbqs4hqflnJ41rQAAGAhJREFUXYGWMEDbTzYxHFauxikioOCuK1Qyd4E1FK3hZGxzA3XcWIo1tx8qB3fZYrxZhygiEmnnz58jm8kyeecO58+d4/y5c/x469a228TjcV69cIFPPv0MgN9+9Bvm5uYJgoC7d+6SXVzEGMOvPvyA06dPs7CwAMCjx4/57rvvm74m6W6PbfHAgR3sMbizdlvpZRzDYlCqfr1o9xfcOQ1GIYh0K6VgusDmnjsoBg5pdzO4izVoqnIq4ZHzHda0r05EBICR4WGmZ2YAmJ6ZYWRkuO42p08Pkclk8TwPz/PIZLKcPj2E7/tkFxcBsNbydHmZdCrV1OMXqbVKwAo+iQNus9hz5m7LHWIYsmwGdznqh5w34gAncDllYjiE3cCVtxMJKXPXBSrdMn0MxcDQsyW4izdoqnIqUSxn7bSvTkQEIJlMUiiEXfcKhQLJZLLuNqlUilwuV/04l8+RqgniYrEYw2fOcO/e/ernXhgZYXBwkPW1Nb77/odqiWat8fExzo6PAzCfyTBfzvzth+u6DAye3Pf920E3rBEOts5HRYc3S3FWd2iw9jyxwKGv7zgD7u5GKiQtxHMxEk745rETGIJEDwOx8O/JFhIkA4dGbTONMaQSCXqtgwVuxYoMBg4jQZJlYxk43jk/a/3udpaDrnNp8cmebq/grgsEW8oyvcAhtiVmazTrbijhcV8DykWky3z4wZWGQdut27frPmcbdOZ7HmMMly5e5N79+2xsbAAwNzfPo0ePCYKA8fFx3n3nHb748suG95+ammZq6nD24w0MntzzBUO76YY1wsHW+aNJ8Jp7kuI+592VcFnOL7Nkazd4NOYCQTxJH5Yn1sfD5XH+KUvlrpbLbj8DJtlw/1wikaDXC/jaX+GbYJ11Al530vyX7kmu+ksslf+mOoF+dztLs9ep4K4L+FvLMm3jzpibHwf0x0tktN9ORLrMF19+tePXKtm6yr/FYv3FbD6f59TgqerH6VSa7GK2+vFbb/2S9fW1bVm7rU1ZpqameOP1nx90GSK7Nmc9MGGibL871vZyPx94GBQZceI4hOWVK3YzsMzZAMfUVw25QJ81TAUFPtvSDfRhUCTrlJgIGme7RbqR9tx1gWq3TGsoBNt/5DFn+7tjaTf8eK2k/XYiIhWzc3OMjY4CMDY6yuzcXN1tFhYyDA0NEY/HicfjDA0NsbCQAeC1114lHovz3fc/bLvP1kzhyPAwq6trR7gKke08LI+DIql9Xg42GEn+XP/gL7JoSyQwWAMbW7J06wQNj+QYLnkDX9SMeVjB538rZXec5yfSjZS56wLVOXeEZZlb1WbunPILtebYiYhsmpy8w+VLFxkbGyWXy/HXq9cA6D9xgrNnx7l+4yae5zExMcFHH/0GgImJCTzPI5VK8eqFC6yurvI3v/0I2Bx58MorLzN8ZhhrA4qex7fXr7dsjdKdbtkcLzkJcvtK3Zl9BXhLtsQZJ02mZsh4jnLmbstDGsAa+IfUBpmN+vEjy/ssKRXpVAruusDmnjvqZtrVBndu+WPfKrgTEanwPK9h2ebT5WWu37hZ/Xh6ZqbaVbMin8/zj//0zw0f99at29y6Vb+nT6RZJoM8v3NP7GsEAfu8T9Z69NLL9WB7eXPRWhIY+nCqw9WTGBasR0mXJSK7orLMLlCyhsCGAVvR1pZl1mTuyi+e/n6L70VERKRtFLDc9nP0sr/tGPu5XHhqfSzwuCZzN2eL/H/+MhhIYEhi6DMud4PCvo5NpBspc9cFri4fZ76YwJZHIWy1U+YuUOZORESkK2TxiLH3Ltk1FZS7tkbAGj5Ztgd3S/hcDdbxsVxx+lghYDoocjvI4ZDYxzOJdB8Fd11gpRTjh9U+IBxivlW8Zoi59tyJiIh0lzyWwLCvSG0/e+5Wrc+6DViypYZf/zbY4Ntg+2iDgb0fmkhXUllml6kN7mrLMl2VZYqIiHSVgg3Yx+hGLOzrfiv4/MF/SuPQTkQOQsFdl3nenDtHDVVERES6SgG7rwwc7K+hCsDMLgefi8jeKLjrMnWZO+25ExER6Wp5G4Qb6PZIVwoi0aPgrsvUNVSpGWJe6ZWlcaAiIiLdoUCA2ed2DO3iEIkWBXddZmvmrhAYTsY9Us7mAFCVZYqIiHSXPLY6Cmmv9lvOKSJHQ8FdlyltCdqKgcPrxzb4b0YfVz+n4E5ERKS7eAcI0VTpIxItCu66TGXWnW/hWCzM2L2U3hwOWumWGeiNOBERka5RsMHex5hblWWKRI2Cuy5UDJxtGbwHG6nqf7uacyciItJ1clicfZz7VZYpEi0K7rpQMTCUAsM/z58CNksxt/63yjJFRES6R876e87c7XPuuYgcIQV3XaiSuftiqZ/vV3u3zbpzNMRcRESk62xgOWFiJPeQvbMouBOJGgV3XahoTbUs0wsMCWfzpVlz7kRERLrPjWCdq8EaKbO3S0M1VBGJFgV3XWjrnjvPOsTN5kuz9tyJiIh0n2lb5Jq/vqdUXFiWqdydSJTEWn0A0nxLXqz6UlysydypLFNERKQ7LeGzhk8ah9xucnJGZZkiUaPgrgv90/xQNS/nBQ5xZ/MFXA1VREREutcf/GX+3u2n37isWP+5IZ6CO5FoUVlmFypZB8+GP/qiNbgGnPLLs/bciYiIdK9Htsj/UsrwF3+NY7j0PeNSUd0yRaJHwV2X84LwVyBRzt5V2iBrg7SIiEh38rD8OVjj35eyFLEknrEPX3vuRKJFwV2XKwbhC3a8vO/OMZbAglVDFRERka62SIlvgw3SO3TQNOjNYJGoUXDX5SrlmYlyx0zHWO23ExEREQAmg9wzai+N8nYiEaPgrst5NZk710CgV2oREREh7KBpDTvU86goUyRqFNx1uWI5S1fJ3LlYfJVkioiISJlntVlDpF0ouOtylYYq2/fc6SVcREREQiVswwtGZe1EokfBXZerNFRJVIM7DTAXERGRTSUspiZ3p2YqItGk4K7LVRqqxCtlmcYSqPhCREREyoo79NDWe8Ei0RM7yJ3j8TiXLl2kJ51mI5fj6tVreJ637TbpdJr3Ll/GGDDG4f79+zyYmjrQQcvhqR2F4KJumSIiIrLJ27EsU+GdSNQcKLg7f/4c2UyWyTt3OH/uHOfPnePHW7e23Safz/PZ558TBAGu6/K7v/0b5ubmyBcKBzpwORzVUQhOZRSCumWKiIjIJs8GGBNja65OZZki0XSgssyR4WGmZ2YAmJ6ZYWRkuO421lqCoBw4OA47NdOV1qhm7sxmQxVl7kRERKTCo/EFo9WbwSKRc6DMXTKZpFDOwBUKBZLJZMPbpVIprlx5n96eXn788ccds3bj42OcHR8HYD6TYX5h4SCHh+u6DAyePNBjRN3B12iBexzvTTJgTpJKZDGOjdz3rRt+ltA964TuWavWuTtLi08O8WhE5DAVbYCped/XoLJMkSh6bnD34QdXGgZtt27frvuc3eEtnHw+z8cff0IqmeS99y7zePYxhUKx7nZTU9NMTU3v5rh3ZWDwZMdfMBzGGouDBj+/wdLiE4J0kaLrR+771g0/S+iedUL3rFXrFJF2V2zQLRPUUEUkip4b3H3x5Vc7fq2Srav8WyzWB2xb5QsFVlZXOXlykNnZ2b0frRyJYmC27LlTWaaIiIhsKu7QR1t77kSi50B77mbn5hgbHQVgbHSU2bm5utukUqnyXruwu+bgyZOsra0d5GnlkK2VYpyIl4DyKAQFdyIiIlJWbNAtMyzLFJGoOdCeu8nJO1y+dJGxsVFyuRx/vXoNgP4TJzh7dpzrN25y7Fgfb7zxRrjr1hju3L3L6urqoRy8HI65QoLxnjwQRvul1h6OiIiIREgRizGmJpoz2nMnEkEHCu48z2tYtvl0eZnrN24CkMlk+fjjTw7yNHLEZgtJ3j6xRsrxcY3FDzTbXkREREI+lqAmkDOA35rDEZFn0FW8MFdIADCcLGrPnYiIiGzj7dAwT3k7kehRcCfMFcJuqMPJAq6GmIuIiMgWJWzdTDvtuROJJgV3wkrJZcN3wswddoeeWCIiItKNdiq/rC3VFJHWU3AngGGukGA4WQz33KksU0RERMpK2IbNUzQKQSR6FNwJAHP5ZLks0+LrjTgREREpK2GhpqpHZZki0XSgbpnSOeYKCZKuJe74mnMnIlIjHo9z6dJFetJpNnI5rl69hud5dbcbHX2JC+fPAzAxOcnMzEMArlx5n1QyiTEOi08WuXnzuz09rkgrlaxtGM1pFIJI9Ci4E2CzqYpj0J47EZEa58+fI5vJMnnnDufPneP8uXP8eOvWttvE43FevXCBTz79DIDffvQb5ubm8TyPq1evUSqFU0QvX7rEiy+8wKPHj3f1uCKt5mMbXhkotBOJHpVlCgDzhUS1S6bKMkVEthsZHmZ6ZgaA6ZkZRkaG625z+vQQmUwWz/PwPI9MJsvp00MA1cDOGIPjmOpF8W4eV6TVPGxdJGfQnjuRKFLmTgAoWocnXpxTCU8NVUREaiSTSQqFAgCFQoFkMll3m1QqRS6Xq36cy+dIpVLVjz+48j79/f0sLCzw+PHjXT9uxfj4GGfHxwGYz2SYX1jY93pc12Vg8OS+798OumGN0Jx1Ji3EczESjlv9XNxCwokxkGzO97hbfp7QPWvVOndnafHJnm6v4E6q5goJTiU87bkTka704QdXGgZXt27frvuc3WGo87N8+dXXOI7DxXffZWjoFJlMdk+POzU1zdTU9J6ft5GBwZN7vmBoN92wRmjOOmMY/HiCot0ciuDikLMeS2vN+R53y88TumetWufRUHAnVXOFBL84tq4yCxHpSl98+dWOX6tk1Sr/FovFutvk83lODZ6qfpxOpckubg/ggiBgbm6O4eFhMpnsrh5XpNVKWFatTxyDhyWGoc84zO/jTQ4ROVracydVc/nwHWuVZYqIbDc7N8fY6CgAY6OjzM7N1d1mYSHD0NAQ8XiceDzO0NAQCwsZXNetZgSNMZw5c5q11bVdP65IFNwNCqRxSGLow+H7IMe8VWdXkahR5k6q5goJQMGdiEityck7XL50kbGxUXK5HH+9eg2A/hMnOHt2nOs3buJ5HhMTE3z00W8AmJiYwPM8kskE77/3Ho7rYIBsdpEHU1PPfFyRqJmyBS6bPgJr+Q/+E2assswiUaTgTqqeeHE+Xeznp7WeVh+KiEikeJ7XsGzz6fIy12/crH48PTNT7X5ZUSgU+fSzz/b0uCJRM2s9poMCfwyWydhSqw9HRHag4E6qLIY/ZE49/4YiIiLSVXIE/O/+YqsPQ0SeQ3vuREREREREOoCCOxERERERkQ6g4E5ERERERKQDKLgTERERERHpAAruREREREREOoCCOxERERERkQ6g4E5ERERERKQDKLgTERERERHpAAruREREREREOoCCOxERERERkQ6g4E5ERERERKQDKLgTERERERHpAAruREREREREOkBHB3fH+/pafQhHrhvWCFpnJ+qWtWqdEkXd8PPqhjWC1tmJumWtWufR6Ojg7uz4eKsP4ch1wxpB6+xE3bJWrVOiqBt+Xt2wRtA6O1G3rFXrPBodHdyJiIiIiIh0CwV3IiIiIiIiHcAdGXnh37X6II7S8vJyqw/hyHXDGkHr7ETdslatU6KoG35e3bBG0Do7UbesVes8fOaddy/apj2biIiIiIiIHAmVZYqIiIiIiHQABXciIiIiIiIdINbqA9gqlUrx7rvvkEomsRampqa4d/8+8XicS5cu0pNOs5HLcfXqNTzP46UXX+Tc+XMYoFQqcePmd6ysrABwemiIN9/8BRjD9NQ0k3fuNHzO0dGXuHD+PAATk5PMzDwE4Oevvcbo6EvE43H+4//9h45c45Ur75NKJjHGYfHJIjdvfteR6/zVhx+QSqXwfR+AL778imKx2FHrjLkuv/71r7YcV5qHDx/y/Q8/HMo6o7RWgBdeeIELF85jMMzPz/PjrVttvc4rV97n5MAAi4tP+Povf6l+/uWzZ3nlZ6/Q19vLH/7Tv0Ty9/btt99i+MwZCoUCf/r4kx2fc6fvxVGtsdNE6e9P58fOWafOj4cjKmsFnR+jts5WnCMj1VDFdV2Wlpa4ffsnHs7M8Pbbb5HNZHn5lZdZW13j6rVvSKdSDA0NkclmcWMx7t69y91798jnC/zijdeZmp4G4IMPrvDlV18zOTnJm2/+gsXFxbpvSDwe5+K77/LpZ58zNT3NxXffZebhQ4IgwPdL3Llzl1deeZnJyca/cO2+xrm5ee7dv8+DBw8YGx3DAKurqx23zrHRUW7cuMntnyZ4MDVVPYl10jpLpRIPpqaq/3vxxRe4d+8euVyu49bqui7vXb7M55//mbt37/LS6EuUSiU2Njbacp0A+XyBufl5+vv7efToUfXzNgh4MDXFyMgwU1PTh/a7e5hr9Ioe0zMzjIwM8+DB1I7PudP34qjW2Gmi8ven82NnrVPnx85aq86Ph6Pdz5GRKsssFArVbjIl32d1dY1UOsXI8DDTMzMA1W8QwNLSEp7nVf87lUoBMDAwwPr6OhsbG1hrefToMcPDw3XPd/r0EJlMFs/z8DyPTCbL6dND5cd7SqFQ6Og1lkolAIwxOI7hMDvrRGmdRymK6+zt7SWZTLL45ElHrrW3t4f19bXqSSCTyfLCyEjbrhMgm81W/x63Wl5ZOdQLkMNeI8DikyfPfSfxWd+Lo1pjp4nK31/4eDo/dso6j1IU16nzY3utE5p/foT2P0dGqixzq3Q6zYkTJ1haekoymayeSAqFAslksu72Y2OjLCwsAGE6des3IpfPMzDQX3ef+tvltv1AjloU1vjBlffp7+9nYWGBx48fH9ratorCOt95522stTyenWViYvLQ1rZVFNYJ8OKLL/Do0dH8LCtaudaFhQx9fX2k02ny+Twjw8M4jjnsJQLNWWerHWSNu9Uu34t2EZXXmqMUhTXq/Hh4orBO0PnxMHXD+RHa8xwZqcxdRZhWvsT3P3zfMFq3dvt7aKcGBxkfG+OHHw+vrvioRWWNX371Nf/y//y/OI7L0NCpQ31siMY6r33zLX/6+BM+//zPDJ4cZPSllw7tsSuisM6Kl158kYdbShcOW6vX6nkeN25+x+VLF/n1rz5kI7eBPYKBLq1eZzN0wxo7TTf8zKKyRp0fD0cU1lmh8+PhaPU6m6Vd1xm5zJ0xhvcuX+Lhw0fMzs4Bm9Fx5d+t6c3jx4/x9ttv8eVXX1dTovl8nnQ6Xb1NOpUin8sz0N/PW2/9EoDbt38in89zavDUltulyS5mu26N4f6COYaHh8lkDm/9UVlnPp8HwtT6w0cP6R/oZ+bhw45bZ/jYxzHGHNmwzKisdX5+nvn5eQDGx8fqXmDbaZ1z5XU022GscSepVIor778HwIMHUyyvrDT8XsjeROXvr5vWqPNjZ6wzfGydH9ttna06P0J7nyMjF9y98/ZbrK6ucffevernZufmGBsdZfLOHcZGR5mdC7/J6XSay5cvc+2bb1lfX6/e/unTp/T29tLTkyaXy/Piiy9w7ZtvWF1d4+NPPq3eLh6P8/PXfk48HgdgaGjoULsKRXmNrusSi8UoFAoYYzhz5jSLi4dbgx6FdRpjiMfjFItFjDEMnzlzqCfoqKyz4qUXXzjSdyWjstZEIkGxWCQej/Py2bP89eq1tl1nqxzGGneSz+e3rdEY0/B7IXsTlb+/Tl+jzo+dtc4KnR/bb52t1M7nSPPOuxePIGG7PydPnuQ3v/4VyysrVPLIP966zdLSEpcvXSSdTpPL5fhrufXo22/9kpGRkWqdqrWWTz79DIDTp0/z5i/ewBjD9PQME5ON68jHRkc5fyFsJTs5MVndKPn66z/npRdfJJVKkc/nmZqe5qefJjpmjclkgvffex/HdTBANrvI9z/8cGjv8ERlna7r8utffYhxHAyGTDbD998fXvvjqKyz4u9+/zu++vovrK2tHdoao7jWi+++y4kTxwH46acJHh3ifphWrPPXv/qQvr4+YrEYxWKRb6/fIJPJ8MrLL3Pu3M+q7xDOz89z/cbNSK3x4rvvcurUIIlEgkKhwO2ffmJ6eqbuOXf6XhzVGjtNlP7+dH7sjHXq/Hh4orRWnR+jtc5WnCMjFdyJiIiIiIjI/kSyoYqIiIiIiIjsjYI7ERERERGRDqDgTkREREREpAMouBMREREREekACu5EREREREQ6gII7ERERERGRDqDgTkREREREpAP8/wVC9IG4C8YEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sortino: 3.444\n",
      "Beta: -0.006\n",
      "Alpha: 0.084\n",
      "MaxDrawdown: 3.051 %\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ta2I0twL5VXN"
   },
   "source": [
    "# Section3: Mean Variance Skewness Kurtosis Optimization"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "cJtXpqmr5JtN"
   },
   "source": [
    "def SK_criterion(weight,Returns_data):\n",
    "  \"\"\" \n",
    "  ------------------------------------------------------------------------------\n",
    "  | Output: optimization porfolio criterion                                    |\n",
    "  ------------------------------------------------------------------------------\n",
    "  | Inputs: -weight (type ndarray numpy): Wheight for portfolio                |\n",
    "  |         -Returns_data (type ndarray numpy): Returns of stocks              |\n",
    "  ------------------------------------------------------------------------------\n",
    "  \"\"\"\n",
    "  from scipy.stats import skew, kurtosis\n",
    "\n",
    "  Lambda_RA = 3\n",
    "  portfolio_return=np.multiply(Returns_data,np.transpose(weight));\n",
    "  portfolio_return=np.sum(portfolio_return,1);\n",
    "  mean_ret=np.mean(portfolio_return,0)\n",
    "  sd_ret=np.std(portfolio_return,0)\n",
    "  skew_ret=skew(portfolio_return,0)\n",
    "  kurt_ret=kurtosis(portfolio_return,0)\n",
    "  W=1;\n",
    "  Wbar=1*(1+0.25/100);\n",
    "  criterion=np.power(Wbar,1-Lambda_RA)/(1+Lambda_RA)+np.power(Wbar,-Lambda_RA)*W*mean_ret-Lambda_RA/2*np.power(Wbar,-1-Lambda_RA)*np.power(W,2)*np.power(sd_ret,2)+Lambda_RA*(Lambda_RA+1)/(6)*np.power(Wbar,-2-Lambda_RA)*np.power(W,3)*skew_ret-Lambda_RA*(Lambda_RA+1)*(Lambda_RA+2)/(24)*np.power(Wbar,-3-Lambda_RA)*np.power(W,4)*kurt_ret\n",
    "  criterion=-criterion;\n",
    "  return criterion\n",
    "\n",
    "\n",
    "\n",
    "n=len(strategies.transpose())\n",
    "# Initialisation weight value\n",
    "x0 = np.zeros(n)+(1/n)\n",
    "\n",
    "# Optimization constraints problem\n",
    "cons=({'type':'eq', 'fun': lambda x:sum(abs(x))-1})\n",
    "\n",
    "Bounds= [(0 , 1) for i in range(0,n)]\n",
    "\n",
    "\n",
    "# Optimization problem solving\n",
    "res_SR = minimize(SK_criterion, x0, method=\"SLSQP\", args=(strategies.loc[start_train:end_test].dropna()),bounds=Bounds,constraints=cons,options={'disp': False})\n",
    "\n",
    "# Result for visualization\n",
    "\n",
    "# Result for computations\n",
    "X = res_SR.x\n"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "mugX0_-J5oIK",
    "outputId": "1d80efb8-19ab-42e5-d187-d59bfaf90046"
   },
   "source": [
    "# Plot the performances\n",
    "s = np.multiply(strategies.loc[start_valid:end_valid], X).sum(axis=1)\n",
    "BackTest(s)"
   ],
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\r[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sortino: 2.496\n",
      "Beta: -0.009\n",
      "Alpha: 0.072\n",
      "MaxDrawdown: 5.497 %\n"
     ]
    }
   ]
  }
 ]
}