Simple_Moving_Average.ipynb

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    "colab": {
      "name": "Simple Moving Average.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOfbO2WCYZ4vv58o5NJlWnh",
      "include_colab_link": true
    },
    "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": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ishika-mittal/Stock-Prices-Analytics-Dashboard/blob/main/Simple_Moving_Average.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pKfi7PPyUdsB"
      },
      "source": [
        "### Simple Moving Average\n",
        "\n",
        "1. There simple objective to smoothen the fluctuation by determining an avergae price within the given time period.\n",
        "\n",
        "2. They help analyse and predict the trend of the stock. A rising SMA-> uptrend, while a declining SMA-> downtrend.\n",
        "\n",
        "3. You can also plot SMAs with two different time periods to identify crossover. An upward momentum is confirmed with a bullish crossover, which occurs when a short-term moving average crosses above a longer-term moving average. Conversely, downward momentum is confirmed with a bearish crossover, which occurs when a short-term moving average crosses below a longer-term moving average.\n",
        "\n",
        "4. There is no fixed time period for calculating SMA. Shorter moving averages are typically used for short-term trading, while longer-term moving averages are more suited for long-term investors.\n",
        "\n",
        "Detailed Explanation - https://www.investopedia.com/terms/m/movingaverage.asp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8JmzxBRUKAP"
      },
      "source": [
        "#Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pandas_datareader import data\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "from datetime import timedelta\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9sRID36URIM",
        "outputId": "96923fa1-c458-42bb-9854-d91a21cab560"
      },
      "source": [
        "#Installing yfinance library for fetching historical stock data\n",
        "!pip install yfinance\n",
        "import yfinance as yf"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/a7/ee/315752b9ef281ba83c62aa7ec2e2074f85223da6e7e74efb4d3e11c0f510/yfinance-0.1.59.tar.gz\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.19.5)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/c0/d0526314971fc661b083ab135747dc68446a3022686da8c16d25fcf6ef07/lxml-4.6.3-cp37-cp37m-manylinux2014_x86_64.whl (6.3MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3MB 10.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.20->yfinance) (2020.12.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Building wheels for collected packages: yfinance\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.59-py2.py3-none-any.whl size=23455 sha256=2b1d06797b3a23c8f1385b66e4af2e99a753a7dc9bfef84f41e51b9c5673e558\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/2a/0f/4b5a86e1d52e451757eb6bc17fd899629f0925c777741b6d04\n",
            "Successfully built yfinance\n",
            "Installing collected packages: lxml, yfinance\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed lxml-4.6.3 yfinance-0.1.59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FCKODqSUStx"
      },
      "source": [
        "#Function to get the stock data\n",
        "def stock_data(stock, start_date, end_date):\n",
        "\n",
        "    tickerSymbol = stock\n",
        "\n",
        "    #get data on this ticker\n",
        "    tickerData = yf.Ticker(tickerSymbol)\n",
        "\n",
        "    #get the historical prices for this ticker\n",
        "    tickerDf = tickerData.history(period='1d', start=start_date, end=end_date)\n",
        "\n",
        "    return tickerDf"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "qPiHTsEPUUXD",
        "outputId": "d6177120-acee-4953-ea58-340da7eec0e8"
      },
      "source": [
        "#Fetching a 1Y dataset for better representation\n",
        "\n",
        "df = stock_data(\"ITC.BO\", \"2020-06-08\", \"2021-06-08\")\n",
        "df.reset_index(level=\"Date\", inplace=True)\n",
        "df.drop(columns = ['Dividends', 'Stock Splits'], inplace=True)\n",
        "\n",
        "print(df.shape)\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>182.945035</td>\n",
              "      <td>184.074883</td>\n",
              "      <td>178.199672</td>\n",
              "      <td>178.742004</td>\n",
              "      <td>744734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-06-09</td>\n",
              "      <td>179.826649</td>\n",
              "      <td>186.289389</td>\n",
              "      <td>178.696801</td>\n",
              "      <td>179.510300</td>\n",
              "      <td>1341970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-06-10</td>\n",
              "      <td>180.052627</td>\n",
              "      <td>183.668142</td>\n",
              "      <td>179.284339</td>\n",
              "      <td>180.097824</td>\n",
              "      <td>1024793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-06-11</td>\n",
              "      <td>180.775730</td>\n",
              "      <td>181.905578</td>\n",
              "      <td>174.990912</td>\n",
              "      <td>175.894791</td>\n",
              "      <td>855456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-12</td>\n",
              "      <td>171.375387</td>\n",
              "      <td>176.482296</td>\n",
              "      <td>169.974373</td>\n",
              "      <td>175.352448</td>\n",
              "      <td>980250</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date        Open        High         Low       Close   Volume\n",
              "0 2020-06-08  182.945035  184.074883  178.199672  178.742004   744734\n",
              "1 2020-06-09  179.826649  186.289389  178.696801  179.510300  1341970\n",
              "2 2020-06-10  180.052627  183.668142  179.284339  180.097824  1024793\n",
              "3 2020-06-11  180.775730  181.905578  174.990912  175.894791   855456\n",
              "4 2020-06-12  171.375387  176.482296  169.974373  175.352448   980250"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "_5LP10iAUY1s",
        "outputId": "cb52de8f-15e2-474f-c6f4-69f8f814db56"
      },
      "source": [
        "#Creating a dataset with only Closing Price column and Date as index\n",
        "\n",
        "data = df.sort_index(ascending=True,axis=0)\n",
        "df1 = pd.DataFrame(index=range(0,len(df)),columns=['Date','Close'])\n",
        "for i in range(0,len(data)):\n",
        "    df1[\"Date\"][i]=data['Date'][i]\n",
        "    df1[\"Close\"][i]=data[\"Close\"][i]\n",
        "\n",
        "df1.index=df1.Date\n",
        "df1.drop(\"Date\",axis=1,inplace=True)\n",
        "print(df1.shape)\n",
        "df1.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(250, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "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>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-06-08</th>\n",
              "      <td>178.742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-09</th>\n",
              "      <td>179.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-10</th>\n",
              "      <td>180.098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-11</th>\n",
              "      <td>175.895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-12</th>\n",
              "      <td>175.352</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Close\n",
              "Date               \n",
              "2020-06-08  178.742\n",
              "2020-06-09   179.51\n",
              "2020-06-10  180.098\n",
              "2020-06-11  175.895\n",
              "2020-06-12  175.352"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeuGTcsiUaeO"
      },
      "source": [
        "#Defining the function\n",
        "\n",
        "def MA(data, window_size):\n",
        "\n",
        "  data1 = data\n",
        "  data1 = pd.Series(data1[\"Close\"])\n",
        "\n",
        "  #Calculating Moving average for a given time period\n",
        "  moving_avg = data1.rolling(window = window_size).mean()\n",
        "  #Parsing the results as a new column in the existing data frame\n",
        "  data[\"Moving_Avg\"] = moving_avg\n",
        "\n",
        "  return data"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "OoVySwAEUo4R",
        "outputId": "439c3fe9-ba27-4d53-91c4-dc8544b33176"
      },
      "source": [
        "#Implementing the function\n",
        "MA(df1,30)\n",
        "\n",
        "#Note: First window_size values under Moving_avg as empty as they are used in inital calculation\n",
        "df1.iloc[20:30,:]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Moving_Avg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-06</th>\n",
              "      <td>189.843</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-07</th>\n",
              "      <td>184.663</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-08</th>\n",
              "      <td>186.516</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-09</th>\n",
              "      <td>185.756</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-10</th>\n",
              "      <td>184.663</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-13</th>\n",
              "      <td>187.657</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-14</th>\n",
              "      <td>186.279</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-15</th>\n",
              "      <td>188.655</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-16</th>\n",
              "      <td>184.235</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-17</th>\n",
              "      <td>184.378</td>\n",
              "      <td>178.87983</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Close  Moving_Avg\n",
              "Date                           \n",
              "2020-07-06  189.843         NaN\n",
              "2020-07-07  184.663         NaN\n",
              "2020-07-08  186.516         NaN\n",
              "2020-07-09  185.756         NaN\n",
              "2020-07-10  184.663         NaN\n",
              "2020-07-13  187.657         NaN\n",
              "2020-07-14  186.279         NaN\n",
              "2020-07-15  188.655         NaN\n",
              "2020-07-16  184.235         NaN\n",
              "2020-07-17  184.378   178.87983"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "ElvK5StTUqkt",
        "outputId": "8dad2452-5089-4274-ce31-f5ba33303873"
      },
      "source": [
        "#Plotting Moving Average wrt to Closing Price\n",
        "\n",
        "#Setting labels\n",
        "plt.figure(figsize=(17,8))\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Closing Price in INR\")\n",
        "plt.title(\"MOVING AVERAGE INDICATOR\")\n",
        "\n",
        "#Let's go\n",
        "plt.plot(df1[['Close','Moving_Avg']])\n",
        "plt.legend([\"Closing Price\", \"MA\"], loc =\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z61GWwYPKr6fCoxsbaev1c9PZdQBUF2arwZxIClOwLiIiIiIJaQ0H6xVRBevOMS1qMjfpfvPKfuZU5HHevHIAqouylVkXSWEK1kVEREQkIf0BpydwTkbauMcezqxrfNtkWn+wi7cOdnHTWXWDvQKUWRdJbQrWRURERCQhvuAAWekunMm9Y6soyMIYlNGdZHe+so/8rHTed+qMweeqi7Lp7g/Q79cAJpFUpGBdRERERBLiC4TISo/u18qMNBdleVkqg59ELT1eHt7QwPtPnUF+1uFhUNWRKgfdOBFJSQrWRURERCQhvmCIrChK4COqCrPUYG4S3bvmIIEBy41nzTri+eoiNfsTSWUK1kVEREQkIZEy+GhVF2bT7Nae9cngD4a4e81+zl9QwZyK/CNeiwTr2pIgkpoUrIuIiIhIQmIpgweYU5HHrtZe+nzBCVyVADy+uYmWHh83nz3rqNciZfBqMieSmhSsi4iIiEhCfMEBsmMog794cRX+YIgXdrRO4KoEnMZys8pyeduCyqNey8tKpyArXZl1kRSlYF1EREREEuILxpZZXzWrhJLcDJ7c0jyBq5JN9d38bX8nNw4Z1zZcdVE2jd39k7wyEYmGgnURERERSYhTBh99Zj09zcXFi6t4emszgYHQBK7sxPantYfIznBx9aoZox5TXZRNk/oHiKQkBesiIiIikhBfcICsjNh+rbx0SRVub5DX93ZM0Kqk2e1lenEOhdkZox5TVZhNs/asi6QkBesiIiIikpBYy+ABzptfQXaGiyc3N03QqqSzL0BJbuaYx9QUZdPS4yWoCgeRlKNgXUREREQS4gTr0ZfBA+RkpnH+/Aqe3NKMtXaCVnZi6+oPUJw7elYdnMx6yEJbr3+SViUi0VKwLiIiIiIJ8QYGyI6xDB7gkiVVNHZ72XCoewJWJV0eP8XjZNYPj29TkzmRVKNgXUREREQSEk9mHeDSJdVkpBkeeqthAlYlXZ4AxTljZ9ari5xgXePbRFKPgnURERERSYgvMBDznnWAotwM3rawkoc2NDAQUil8MnkDA/QHBijJGzuzXlmQBUCryuBFUo6CdRERERFJiC8YirkbfMRVp0yj2e1jzd72JK/qxNbdHwAYd896YTjz7g4fLyKpQ8G6iIiIiMQtOBAiGLJxlcEDXLyoirzMNB5cr1L4ZOr0OJny4pyxM+vZGWlkpbsGg3sRSR0K1kVEREQkbv7wyK94yuDB6Qp/2dJqHt3YiC84kMylndC6PE7wXTJOZh2gKCeDbo+CdZFUo2BdREREROLmDSQWrAO865RpuL1Bnt/emqxlnfC6wpn1oiiC9eLcDLr6tWddJNUoWBcRERGRuEWy4dkZ8ZXBA5wzr5yC7HSeVbCeNIcz62OXwUM4s64yeJGUo2BdREREROLmi2TW42wwB5CR5qKiIAu3VwFjsnR6omswB5FgPTjRSxKRGClYFxEREZG4+YKRMvj4M+sAeZnpeHwKGJOlq99PZrqLnCgqHopyMun2qAxeJNUoWBcRERGRuEXK4BPZsw6Qm5lGn18N5pKlqy9AcU4Gxphxj1UZvEhqUrAuIiIiInFLWmY9Kx2PX5n1ZOnq90e1Xx2cYL3PP0Ag3NlfRFKDgnURERERiZs3EM6sJ7BnHZzMusenzHqydHoCUXWCh8P72t3KroukFAXrIiIiIhI3XxJGt4GzZ71PmfWk6fYEopqxDk5mHaAricH645saeWlnW9LOJ3IiUrAuIiIiInGLlMEnMroNIDdLmfVk6vT4Kc6JvgweSNq+9VDI8qUHNvGtx7cl5XwiJ6r0qV6AiIiIiBy7ktVgLpJZt9ZG1RRNRmetpas/QHFelJn13OQG61ub3HT0+XH3B+j3D5CTmdiNHJETlTLrIiIiIhK3ZDWYy81KI2QPn0/i1x8YwB8MxZ5Z9yQnWH95l1P+HgxZNhzqSso5RU5ECtZFREREJG6+QPIy6wB9mrWesK5w0B3rnvVkZdZf2tVOTVE2AOsOKlgXiZeCdRERERGJmzeSWU9CN3gAj2atJ6zT4wcOd3kfTzKDdW9ggNf3trN6WTV1Zbms3d+Z8DlFTlTasy4iIiIicYt0g89MSzCznhXOrKsjfMIi5ezFUc5Zz0hzkZeZNpiRT8TaA514AyHOnVdOlyfAizvb1IdAJE7KrIuIiIhI3HzBAdJdhvQEg/VIZr1PHeET1jkYrEeXWQcnu56MzPrLu9pIcxnOmFPGytpi2np9HOrsT/i8IiciBesiIiIiEjdfMJTw2DY4nFn3KLOesK5+pwy+JMrMOkBhkoL1l3a1s2JmMflZ6ayoLQGcbLuIxE7BuoiIiIjEzRccSLi5HCiznkyRcvbIXvRoFOdm0B0O8uPV3R9g46EuzplXDsCi6gJyMtJYd0BN5kTioWBdREREROLmC4SSEqxHusErs564Lo+fnIy0mCoeklEGv6ull5CFk2cWAZCe5uKkGUWsU2ZdJC4K1kVEREQkbt5giKwklMHnZoUz6+oGn7BOTyCm/eqQnGC92e0FoKYoZ/C5lbNK2NzgxhvQz1UkVgrWRURERCRuvkByyuAHM+uas56wLk8g6k7wEcW5mQkH603dTrBeXZg9+NyKmcUEQ5ZN9d0JnVvkRKRgXURERETi5gsmpww+J0OZ9WTp8vgpjmG/OjiZdW8glFAGvNntJTPddURWf+UsNZkTiZeCdRERERGJm9NgLvEyeJfLkJuZpsx6EnT1ByjJiy1YLwwH9+4EsuuN3V6qC7OPmKlenp9FbWkua/eryZxIrBSsi4iIiEjcfMEQWRnJ+ZUyNzNdmfUk6PL4KcqJsQw+HKwnUgrf5PYeUQIfsaK2mLUHOrHWxn1ukRORgnURERERiZvTDT7xzDpAXlaausEnyFpLd398DebAycrHq9ntparo6GB9ZW0JLT0+GsJ72kUkOgrWRURERCRu3uBAcjPrmrOeEG8gRGDAUpgdX7De7YkvWLfW0tTtpbow66jXVtQWA2iEm0iMFKyLiIiISNySNWcdIC9TmfVE9XidYLsgOz2m90Uy8fGWwXf3B/AFQ1SNUAa/uKaQ7AyX9q2LxEjBuoiIiIjEzekGn5wy+Nws7VlPlDscrBfG0Q0e4i+DbwrPWK8eoQw+I83FSdOL1RFeJEYK1kVEREQkbk43+CRm1tUNPiFur/P9izWzXpCdWGZ9pBnrQ62oLWZLgxtfUDdjZHQPrDvED/+6c6qXkTIUrIuIiIhI3HzBENkZScqsZ6bjUWY9IZHRa4UxButpLkNhdnrco9uaw5n1kcrgAVbUluAfCLGp3h3X+eX4Z63l+0/t4H+f2Tm4neNEN2HBujFmpjHmWWPMFmPMZmPMZ8LPXx3+PGSMWTXsPbcaY3YZY7YbYy6bqLWJiIiISOKstfiDSdyznpVGn/asJ6QnnFmPtcEcQFFuBl0ef1zXber2AaMH6yvVZE7GsbnBzcGOfoIhy4s726Z6OSlhIjPrQeDz1tolwJnAJ4wxS4BNwHuBF4YeHH7tWmApsBr4qTEmObdpRURERCTpfMEQQFK7wXvUDT4hPYNl8HEE6zkZ8ZfBu72U5WWSOcqNm8rCbKYX57DugJrMycge2dhImstQkJXOM9tapno5KSG2+pgYWGsbgcbwn3uMMVuB6dbapwCMMcPfchXwe2utD9hrjNkFnA68OlFrFBEREZH4+QLhYD1Zc9Yz0/APhPAHQ6MGfTK2ww3mYv81vyQ3k444R7c1u72jZtUjVs4q4c19HXGdX45v1loe29jI2XPLKMnN5LntLYRCFpfrqJjxhDIp/xU0xtQBK4A1Yxw2HTg45PND4eeGn+sWY8ybxpg3W1tbk7lMEREREYlBpFlYssrgc7OcALNf+9bj1uMNkOYy5MTRR6AsL5OOPl9c123s9o7YCX6olbXFNHZ7aezuj+sacvza0uhmX7uHK5bXcNGiStp6/Wyo757qZU25CQ/WjTH5wJ+Az1prE+4oYa39ubV2lbV2VUVFReILFBEREZG4DJbBJ7EbPIAnoH3r8XL3BynMTh+pinVcZflZtPfGt2c9msz6itoSAJXCy1Ee29hEmstw6ZIqLlhQgcvAM1ubp3pZU25Cg3VjTAZOoH63tfbP4xxeD8wc8vmM8HMiIiIikoIGM+vJ6gYfzqz3ad963Hq8gbj2qwOU5Wfi8Q/gibHJny84QEeff9SxbRFLagrJSnexdr+azMlh1loe3djImXNKKcvPoiQvk5W1JTyzXfvWJ7IbvAF+CWy11n4/irc8CFxrjMkyxswG5gOvT9T6RERERCQx3vCe9exkZ9bVET5uPd5gzDPWI8rzsgBizq63uJ3S+eqirDGPy0x3sXx6EesOKrMuh21v7mFPWx+XL6sZfO7CRZVsqncPjgQ8UU1kZv0c4AbgImPM+vDjCmPMe4wxh4CzgEeMMU8AWGs3A/cDW4DHgU9Ya3VbVURERCRFHe4Gn7w566DMeiLc3kBcY9vAyawDtPfFFqw3jTNjfagVtcVsrO/GH/67I/LoxiZcBi5bWj343CkznVF/e1r7pmpZKWEiu8G/BIy2WeaBUd7zdeDrE7UmEREREUkeXyC5DebyspRZT1SPN0htaW5c7y3Lj2TWY2sy19TtBOs1RTnjHruytoQ7XtzL5obuwT3scmJ7dGMjp88upaLgcGVGUY5zwyneUYLHC83EEBEREZG4JLvB3GBmXd3g4+buD1CYE2dmPS++zHqkVHm8PevgjG8DNZkTx87mHna19HLF8pojno8E624F6yIiIiKSyt7c18EbKTif+vDotiTNWY9k1n3KrMcrkT3rg2XwMe5Z31TfTUFWelSz3asKs5lWlM3aA2oyJ04JvDGwekgJPEBRrjLroGBdREREJKVtaXBz/S/WcPOvXudQp2eql3OEw3vWk1UGr8x6IkIhS68/GHc3+NzMdHIz02Iqg29xe3lkYyPvXTk96nFxK2aVKLMugFMCf9qsUiqHVWXkZ6bjMgrWFayLiIiIpCi3N8A/3v03inIysMC/PbAJa+1UL2uQL5DkMvgMZdYT0eMLYi0UxplZBye7HksZ/F2v7icYsnzonNlRv2fFzGLqu/ppOcE7fZ/odrX0sr25hyuWVx/1mstlKMzJULA+1QsQERERkaNZa/nCHzZwsLOfn16/kn9dvYjnd7TywLr6qV7aoEgZfHaSusGnp7nISncpsx6nHq8T2MTbDR6gLC+Ltigz6/3+AX63Zj+XLK6irjwv6mtE9q2rFP7E9tjGRgBWL6sZ8fUiBesK1kVERERS0S9f2svjm5u49fJFrKor5YYzZ7FqVglffXgLbm9q/ALrTXJmHZxS+Hi6we9u7eUDt79KZ4zN0Y4n7n7n+xbN3vHRlOdnRr1n/U9rD9HlCfDR8+bEdI2l0wrJTHONWAr/yu42fvzMzpSqIJGJ8eimJk6dVUJ10ciNCRWsK1gXERERSTlv7OvgG49tY/XSaj5yrlNe7HIZbr1iEV2eAE9vbZ7iFTqS3WAOIDczbdQ56229Ph58q4FQ6OhA7p41B1izt4P1B0/cvdCRzHq8e9bByay3942fWQ+FLL96aS8nzSjitLrYRrBlpaexdHrhiJn17zyxne8+uYPHNzXFdE45tuxt62Nro/uoLvBDKVhXsC4iIiKSUlp7fHzi7rXMLMnh21efdETTrhUzS6guzOaxjakRyPiCIYyBjLToGotFIy9z5Mz6oU4P77/tFT597zr++Q9vERwIDb4WClke2eCU1O5t60vaWo41Pd5wZj2RYD2cWR8vs/3s9hb2tPXxkXNnR91YbqiVtSVsONSNP3j453iww8O6A12kuwxffnDzCR+oHc8eDZfAX77s6P3qEYU5GRrdNtULEBERERHHQMjymd+vo7s/wE+vP/WooMvlMqxeVs3zO1rpS4EmbL5giKx0V1zB2mhys9KO2rO+p7WXa372Kh19fj54Zi1/XlfPJ+5ZO5jZX3ugk6Zws7L97SdusO4ezKwn0mAui2DIDpbUj+aOF/dQU5Q9ZmZ0LCtrS/AFQ2xrcg8+90g4gPvf61bQ1uvj249vi+vckvoe29TIitpiphXnjHqMMusK1kVERERSxv88tYNXdrfztXcvY8m0whGPWb2sGl8wxAAuxwEAACAASURBVLPbWyZ5dY5mt5fVP3iBnc09+AIDSS2Bh3BmfciNiK2Nbq65/TV8wRD33nImX3v3cr78ziU8sbmZrzy4BYCHNzSSme5iTnkee9tTa7zdZIpk1hMJ1svDs9bbxiiF31TfzWt7Orj57Doy0uILJ1bUFgOwdv/hUviH3mpgRW0xly+v4cPnzObuNQe4/hev8cO/7ky5sYUSvwPtHjbVu7lilMZyEZFg/UTuX6BgXURERCQFPLOtmR8/u4sPrJrJ1atmjnrcaXWllOdn8tgU7el9bU8725p6+L/19YOZ9WTKzTycWV9/sItrf/4a6S7DfR87i6XTigD40Dmz+dgFc7j39QM8sbmJRzc2cuHCCpZMK2TfCVwGHykZTmTPemmeE6yP1WTuly/tJTczjWtPr437OtOKc6guzGZduMfA7tZeNje4eedJ0wD4/KUL+dj5c+jsC/CDp3fw9Ue2xn0tSS2Pbop0gR+9BB6cYD0YsnhO4OkQCtZFREREUsA3Ht3GwqoC/vOqpWMel+YyXLKkmme3teANTP4vsVsanLLlZ7e14g0MJG1sW0SkG/xre9q5/o7XKMrJ4A8fP4t5lflHHPe5SxawpKaQT9+7jpYeH1eeNI3Z5Xkc6vQcsQ86akEfHHwDmrck6SuZfD2+INkZLjITuIFSlpcFQPso49uaur089FYD16yaSVFO/DcFAFbOKh5sMvfwW40YA1ee5GRbczLTuPWKxTz6mfO4dEkV25t7ErqWpI4nNzdx0owiZpbmjnlc5O/XiVwKH3+NjIiIiIgkhbWW/R0ePnR2XVTB7+XLqrn39QO8sKOVS5eOnZ1Kti2N7sGPWRmuCcmsN3Z7uelXr1NbmsvvPnoGVYVHj3bKSk/jh9eewjv+9yWyM1xcvKgSfzBEyDrN6OZU5I9w9hHsexme+wYcXAMD4WzyjNNg5Y0wbSWUzYOMkUdLTYm+dmjeCK3boWgmzDwd8soBpxt8TVYA3vwVhAageBYUToPcMsgthfSscU9/uAx+5Mz6na/uI2QtHz5ndsJfyoqZJTy6sYmrfvIye1p7OWN26Yg/63mV+fx1awv+YCihGxFyNGstjd1edrb0sqe1l9Nnlw5WsEyEPl+Qtw518/ELxh/3NzRYH2tv+/FMwbqIiIjIFOvo8+MPhqgZZd7wcGfNLSMjzbD2QNekBuvWWrY0uDl5ZjFvHexi3YEulk0feW99vPKy0vEHQyybXshdHz5jsCx7JPOrCvjhtSvo8vjJy0pndrmTqdvX3jd+sO5uhMe+AFsfhMLpcMbHYMbp4K6HN34JD37KOc64nKC3YiGUL3A+Vi6GmhXgmsDAcSAAnfudwLxpIzRtcj72NBx9bMlsmHk6lx4I8G/BJ+DhUbYCZBZAbokTvOeUQulsWPVhqDpczVEyWAZ/dGa9zxfk7tf2c9nSamrLxs6KRuOKk2p4fV8HvmCIlbUlfOz8kQO4eZX5DIQs+9v7mF9VkPB1T2RvHezi5d1t7GrpZVdLL7tbeo9o6HjuvHJ+99EzJuz6b+7vZCBkOXNO2bjHKrOuYF1ERERkyjV2O53Mq4uiyx5lpLmoLMimOdwBfbK09Pho7/PzyYvm0er20tDtTXqDubctrKC1x8d/XrU0qhFkQ/e91pXlAbCvbZxmZE0b4e5rwNsFF/47nPUJyBwSfJ7xcWjZAq3boHWH87FtB+x+5nD2vagWTrkOll8D5fNi/joB8HTA3uehvxN8PdB9CDr2QPtu6DoANhxEmTTnJsHs86BqGVQvdz7v3AcHX4dDb8DuZ7mgr41Xs87mnBu+CkXTnWC/p8G5jqcD+jvA0x7+vB3Wvwpv/ALmXAhv/zJMW0FGmovi3IwR96z/ae0h3N4gHz0v8aw6wPTiHO64cdW4x80rzwcsu1p64w/WQ6GJvblyjLj516/T6QlQVZjFvMp8rl41k7mV+cyvzOeuV/fxtyEN/ybCa3vaSXcZTp1VMu6xkWC9y6NgXURERESmSCRYn1Ycfbl1dVE2Td2TG6xH9qsvnVbEhYsquXvNgaSXwZ89t5yz55bH9d7SvEwKstLZN9r4Nl8vbH8MHv4sZBfBR56C6mVHH2eMk22uGtY/YCAIXfvh0Juw4ffw/Lfh+W9BxSJYeDnUng0zT3My2FgIesHbHX64h/y5Gw68AtseORz8g/O+sjkw7RRY9j4oneOsoWLRyKX4hdNg1tnOn63lAz95lpzcfM6ZcarzXME4VReeDvjbr+G12+COi+D0W+DCf6MsL5P2Yd3gB0KWX720l1NmFrOydvxAKyka1sNb97Js4x/ZmdWJ76FSeLkG8iogv9L5mFcBOSWQkeP83HpboacRepudjz1NzsPbBbnlzk2Mwhnhj9OhaAYU18L0U8GV3BtPqcYbGKDTE+DzlyzgUxfPP+r19Qe7eHRjE939gYT7EYzmtT3tnDyzmNzM8cPQyBpO5FnrCtZFREREplhTdz/gBODRqi7MHtw/Plki11tUU4C7f2KC9UQYY6grz2NvpCN8KOSUke962smKH3gNQgGoWg7X3+8Eu7FIS4eyuc7j5A9Adz1sexi2PgQv/whe+p/oz5VT6pSgL7/aCRqz8iEz3wk442EM7T4Xi0tj+PU+txTO+zyc9lF4+r9gze2w8ykWZf8Hrb1H7m9/emsz+9o9/PiyhZh41xgNa2HfS85NkH0vQlomZuEV3Ls9jQU5/ZxZMAB9Lc6e/b6WI292RLgynBsV+VVOz4G6c52Avq/V+Zl17nWu4es+/J7yhXDhrbD4quM2A9/a49yAGakvAMD8cBPHXS09nDqrNOnX7/MF2RDlfnWAQpXBK1gXERERmWoN3V4y0gzleeM3AIuoLsrmmW0tWGsnNngaYkuDm9rSXAqzMzh7XhmZ6a6kl8Enqq48j5x9f4U//QL2POsEaOAE6Gf+A8y72MmAp4++Fz5qReG97md8DPx9UP83qF8bDiCNc43sYieLn10Y/lgMWYVO8JiW3F/Fe7zBqLYOHCW7CK78Lix7L9x7Hf8d+Bz/kvUfwFmDh/zipb1ML85hdTQ9Ejwd8MqPnIB6PMYVzpJXOkH0gVedLQD5VXDp12HF9ZBTwtO/ep3f9/h49PrzDr/XWqdKweeGgNfZNpBX6Xxvowm4fT1O8N60AV78HvzhZufnU74g/JjvfBzSxO9Y1hruQ1BeMPLf/fmVzhaDnc29ExKsx7JfHaAgKx1jFKyLiIiIyBRq6vZSVZiNyxV90F1dmE1/YAC3NzhhJavDbWl0s6TGaSiXm5nOly5flJRGY8n0juBTXOb7OnZ3OWbuRTD3Iph74fgl4YnKzIPZ5zuPKeLuD1CYncCv97POho88if35O/m+50uwcybMv4QNh7p4fW8H/37lYtLTxgiCvW5Yeye88B3nz1VLx68UGAg6Abqn3QnQZ54B534OTr7WKW0Pm1eZz5q97YRC9vC/E2Mgp9h5xCOrACoXOY9l74PNDzgZ9/ZdsOspWP+78IEGak52SuUjnfVzSpzqiMif86uc6ogU1hbOrJfnj3xTcHpJDtkZLna29MZ1/sBAiKe3tnDhoooRb+LFsl8dwOUyFGZnxBysrz3QyckzikmL4b+nqUrBuoiIiMgUa+jqj7oTfERV+Pimbu+kBOu9viD72vt4z4rpg8/dnITxXUm18Y9cuvu/eXbgZGpv/D/mVic/O5iqfMEBfMEQBYkE6wAVC/njKb/kzDWfZOk912Au/Rp37zmJ2Vlurp3rh8YNThVBoA/8Hgh4wN8LDetg05+dz+deDJd8deR+AKMZCIArfdTgfl5lPt5AiPqu/nHnc8fFlQbL3+88Ivq7nOaCe1+E3U87wXx/J2BHPkdxrdNfIK/S6bofCegLapxAP78i+euOQVu4aWBFwcjBeprLMLciP+5g/a5X9/NfD2/hYxfM4dbLFx/1eiz71SOKcmIL1g92eLj6Z6/ymYvn8+kR9uUfaxSsi4iISNz++LdDrD3QyapZJZxWV8qMkpxJK8k+njS5vZw8I7bsYCS4b3J7WVg98eOstje5sZbBzHrK2fYo/PkWeqtP5+P7/oHbugLMndwR9FOqxxsEDu/zTUROeS1X+/8fz865l+onvsS3AAzw8zHelJHnZKdPvRlmjN/h/ShpY6973uB+6t6JCdZHklMMtWc6jwv+xXkuFHKa1fV3Dumw3wHuQ9CyDdq2O2P2+jsh2H/k+UrqoOYUqDkJqk92PuZXTs7XArSFy+DLxthus6CqgDV72mM+d79/gNue2026y/CLF/fyrpOnHTGvvbs/ENN+9YhYg/U7XtyDy8A1q2bGdJ1UpWBdRERE4nbbc7vY3drHPWsOAE4AuaqulNPrSlhVV8rCqoKYSrtPRNZaGru9rF4aW2a9OtwkqnmSOsJHOsEvmZaCwfruZ+EPN8G0U/C/7158336NveONbzvORIL1hDPrwDuWT+O+N6o4e+9NfKpsOZ6uZv7hkpMoLS5xRtxl5jnBeWYuZOQ6jfFyiiE9+p4LsZpXcThYv3DR5AW4R3G5nNL33FKn0eBYAv1O0N653xmvd+gNpwJhy/8dPia/2gnaa06GFTdAyawJW3prj4+inAwyx2gKOa8ynwfW1dPjDVAQQ/+Du9fsp63Xx89vOJUvPbCRW/+8kQf+8ZzBUvTvP7mdkLVcvqwmpjXHEqy39fq4742DvGfF9JiadaYyBesiIiISF2stDV1ebj67jg+cNpM39nXw+t4OXt/bzkNvNQBwwYIK7vzw6VO80tTW0efHHwzFXAZfWegERo2TFKzvaO6lMDs95nVOuJ1/hftvcBqBXf9HSnNKKM7NYHND9/jvPY70eJ2AJq4Gc8MU5WZw3y1n8U/3reeHm11cubyG0gtWJnzeRJTkZVKWl8muOEu0p0RGjvMonAazDjfro78LmjY6je0aNzgfdz0Nb/7amVIw/dQJWU5br2/UEviI+UMqGFZEOaLP4w/ys+d3c/bcMi5dWo03GOLT967je09u558vXci6g13c9dp+bjqrjmXTi8Y/4RBFORk0dPWPfyBw5yv78A+EuOX8cW6iHEMUrIuIiEhcOj0B+gMDzCzNZXFNIYtrCrnxrDqstRzs6Oebj2/l6a0tDITscdHoZ6JEgu3qopxxjjxSVnoaZXmZNLknJ1hvcnuZVpwi2xxCIdjzDLzwPWdeedl8uOEByC3FABcurOSZbS0EB0JjN0Q7jrj7I5n15PQvyMlM46fXr+ShDQ2cNTe67t0TbW5lPrtaj6FgfTQ5xTD7POcR0bYTfvde+M074Jq7YP4lSb9sW6+P8vyxpyDMrwp3hI8hWP/da/tp6/Vz2wcXAPDOk2p4bnsLP31uN5sa3DR191NdmM0/X7Yw5jUXRplZ7/UFuevV/Vy6pGpwy8Tx4MT4r5eIiIgkXX2nk+2YXnxkkGmMobYsl7ctqMQXDHGg48QqR45VJFiPJ2NdVZhN8yQF6y1uL5WjzGeecAGvE8zs+is890340cnwu/dB5z5Y/S342AtH7P29bGk1XZ4Aa/Z2TM16p4A7nFlPRhl8hMtluOqU6VQWpEY1xbzKfHa19GLtKA3ejmXl8+Ejf3Xmwt/3QWh8K+mXaO3xjdoJPmJmSQ6Z6a6oKxg8/iC3P7+H8+aXc1qd09DRGMP3rj6Zr717GWv2tLOjuZf/umoZ+Vmx/92MlMGP9zP//esH6O4P8PELjp+sOiizLiIiInGq73KC8BklI2eE51c52Y0dzT3MLs+btHUda5q6nZseNcWxB0Q1Rdk0TFIZfEuPbzDrNrEX2gYb73f2+XYdgK790Nt85DFz3gYXfxkWv3PEfdIXLKggO8PF45uaOGfesT8fOxr72vuA0f89Hg/mVeTT3R+grdc/bjn3MamgCj74Z7j9fLjvBrjlOWdvfJJE831LT3MxpzyPnc09UZ3zrlf3097n57NvX3DE88YYPnjmLM6YXcrWph7evqQqrjUX5WQQDFk8/gHyRgn2/cEQv3hxL2fOKY26GuBYoWBdRERE4nJolMx6xGA5ZXMPly09gdpyx6ih20tGmqF8jA7No6kqymbdwa4JWNWRQiFLa4+PqsIJDJAGgvDyD+D5b0FoAIpmOKOw5l8CxbPCj1qnqdc4HbRzMtN424JKntjcxH++a+kJ0eRwV0sv1YXZSSuDT0VDO8Ifl8E6OOPdrrkLfn05/PkW+Lv7naZ2Cer3D9DrC46bWQenI/zaA53jHtfrC3L787s5f0HFqLPT51cVJHSTLzKWsrs/MGqw/pf19TS5vXzzfcvjvk6qUrAuIiIicanv6ic3M43i3JGDg/ysdKYX57Cj+TjYYzqBmrq9VBVmxxVQVhdm09HnxxsYIDsjbQJW5+jw+AmG7MSVQwe88Nv3OPvPl74Hrvgu5CWWEV+9rJrHNzex7mDXqIFEsjR1e+n1BZhXOQmVB6PY1dI7WM1yvDocrPekzD76CTHzNLj8m/DI5+Fvv4LTPprwKSNj2yqiCNbnV+bz4FsN9PmCowbI4DR06/QE+Ke3T9w886HB+rQRbgyHQpafPb+bxTWFXLBgaufYTwTtWRcREZG4NHT1j9twbEFVPjuiLKc8UTV09cfdYT0ynqjF7Uvmko4S2Rc/IZl1a+Hhf3IC9XffBlf/JuFAHeCixZVkpBme2NyU+BpHcdtzu7ngO89y5jee5u3ff4Hrf/Ear+6OfUZ1oqy17GrpZW7F8R2s1xRlk5eZdmx1hI/Xqo9A3Xnw9Fehry3h07VGgvUoKhIWVjs3ncb6b3ePN8AdL+7hwoUVE1p6PjRYH8lftzazu7WPj18wJzWaXyaZgnURERGJS31X/6gl8BELqgrY09pHcCA06jGRkVMnqia3N+ZO8BGRWesT3RG+pSfyi/4EZNZf/zm8dQ9c8EU45e+SdtrC7AzOmVfO45uaJqQhmbWWnzy7i5yMNP79ysV88fJFbG/q5bo7XhscXThZGrq9ePwDx31m3RjjNJk7HjrCj8cYuPJ74O+Dv3454dO1hf8NR1MGv7imEIBtTaMH63e+so8uT+CoverJNlawbq3ltud3M7M0hyuXxza//VihYF1ERETiUt/Zz/RxmlnNryrAPxBif7gj/J7W3iN+6drZ3MOKrz7FG/tOnK7dQ1lraez2Mi3BzPqEB+sTlVnf+yI8fissvAIu+NfknhtYvbSaAx0etjS6k37utl4/vb4gHzhtJh89bw4fv2AuL/3rhcyvzOf2F3ZPasfySKZ53nGeWYfw+LYTIbMOULEQzvoErPsdHHw9oVNFMuvlBWOPbgOnD0leZhrbRvl34/YGuOPFvVy8qJKTZxYntK7xjBWsv763g3UHurjlvDnH7YjG4/OrEhERkQnl8Qfp9ATGzawvDDcW2tHUQ5fHzzv+9yW+9vCWwdef3NJMMGTHzOAczzr6/PiDocGgO1aDwXq4o/xEiZTZJ7WpV9cB+MNNTsO499yelCZaw12ypAqXgSc2Jb8Ufn+4+3rdkEkH2Rlp3HxOHZvq3by5f/wGXckyGKwfR/OlRzOvMp9mt29wVN1x7/wvQOF0eORzThPGOLX1+AEoi6KRpctlWFhdwNZR/rv8m5f30d0/8Vl1OBysu0cI1n/2/G7K8jK5etXMCV/HVFGwLiIiIjGLzFgfb0zUvMp8jIEdzb3cveYAHv8AT21tHiyLf35HKzDxwWaqSmTGOkBBVjq5mWk0dU/wnvUeLyW5GWSlJ6mJnd8Dv78eBgJw7b2QXZic8w5Tlp/F6bNLeTyKfeuhkOVz963n//1lU1Tn3tfuVIvUlR05lvC9K2ZQlJPBr17aG/uC47SrpYfSvEzKoihxPtZFqgd2nyjZ9ax8uOy/oWkjvPmruE/T1uujODeDzPTowr9FNYVsa3QfVSHS3R/gFy/u4ZIlVSyfURT3eqJVkJ2OMbD2QCfffWI7331iO32+IFsb3Ty7vZUPnVM3oc01p5q6wYuIiEjMDnWNPbYtIiczjZkluWxp7Gb9wS6KcjLo8gR4Y18nS6cXsjacfWycpFnho3lkQyP52emcM7dsUssp94cDvtrS+ObQG2OoLswebAA3UVrcvuR2gn/sC07w8Xf3Qfm85J13BKuXVvOVh7awu3XsBmzfe2o7f15Xz9yK6H4W+9r6SHOZo25Y5WSmcd3ptfz8hd0c6vQwoyQ3ofVHY1dL7wlRAg9Hjm873mZqj2rJVTDnQnjma7D03eOOLhxJa48vqv3qEYurC7hnTdDZpjPkv/NPbGrC7Q3yyQsn9t9thMtlKMvL5NGNTaS5DCFreWRj42CzwRvOrJuUdUwVZdZFREQkZpHM+nh71sHpCP/Ulmaa3T6++d7lZKW7eHJLE6/saiMYsuRkpNE0hcF6ny/IJ+5Zy02/ep0zv/E0X3lwM+sPdk3KnuO9bU52sK48/oCuuiibxgmuTGju8VGZrP3qu5+Bdb+Fcz4DCy5LzjnHcOnSaoAxu8L/ZX09P3l2N9kZLjo90ZVX72vvY0ZJDhkj3Ny58axZGGP4/pM78AYG4lt4lKy17GzpZe4JUAIPUFuaS2aa66gmc77gAG8er70vjIErvgMBj3Ojy++J+RRtvT7K88ffrx6xaLDJ3JH71jfUd1GQlc7y6ROfVY+4/YZV3Pnh03nry5dyz0fPpN8/wCu72/m7M2opGmV06PFCwbqIiIjErKGrn3SXiSrbuqCqgJB1ZveuXlbNefPLeXJzM89tb6UgK53zF5RPabAemT987WkzWTWrlHvWHODdP3mZC7/7HP/z1A72tvVN2LX3tPVRXZhNbmb8xY41RTk0dE3s96/V7U1OZt3XCw99Bsrmwdu+mPj5ojCtOIeTZxYftW89FLI8vKGBD//mDT53/1ucPruUD50zm06Pn4HQ+Ddq9rX3Mats5Cz8tOIcbj67jj+vq+ft33+ehzc0TNjNn/Y+P12eAPNPkGA9Pc1FXXnuUWXwD73VyPt/9urx26yyfD6c/8+w+QH4n6XwzNehtyXqt7f1+mKa5hAZ37a18ch96xsPdbNsehEu1+SNSTt1VgkXLKggPyuds+aW8dhnzuPWyxfxyQsnbr57qlCwLiIiIjGr7+qnuiibtCh+YYv80veRc2djjOHSJdXUd/Xzl/UNnDOvnJkluTR2eye1e/ZQbb1O46XVy6r52Q2n8sa/v51vvW85NUU5/OiZnVz43ee469V9E3LtvW19zC6PrwQ+YlZZLk1u74RlcEMhS0uPL/FO8NbC0//pNJZ7148hI75xdfG4fFk1bx3qpr7rcAXCr17eyyfvWceWBjcfPW82d9ywiqqCLKyFLo9/zPNZa9nf5mF22egVEf/xjiXc89EzyM9K55P3rOP9P3uV9Qe7kvY1RZxIzeUi5lXms3NYsL4vfFPtt6/un4olTY4L/hVufhRqz4QXvuME7X/5BLRsHfetThl89Jn1wuwMphfnHNH80x8MsbWpZ1L2qo+lJC+Tj10w97jPqoOCdREREYlDfef4M9YjLl1SzX9dtZT3rpwBwMWLK3EZ6A8McMHCCqqLsukPDIw4mmcytPceOX+4KCeDD5xWy723nMkrX7yIRdUFEzY3e19bH7Oj3CM9mlnhgPFgR+ylsdHo9PgJhiyViXSC766He69zZqqffgvMOit5C4zCZZFS+CHZ9Uc3NrJseiEvf/Eibr18MUW5GZSG/w50jhOsd/T56fEFR82sR5w9r5xHPn0e33zvcva3e3j3T17ms79fR0NX8rYtRILW433G+lDzKvI52OE54gZV5EbMY5saB6tljjvGQN05cN298Mk3YeWNsPFP8NMz4Xfvgz3Pj/i2fv8Aff6BmPasAyyuKWD7kDL4Hc09+IOhSS2BP9EpWBcREZGY1XeNP2M9IiczjRvOqhvsQlyWn8WqWaUAnL+ggpoi5zxT1WQuklkvGyHrVFOUw7nzytlwqBt/MJTU63b2+en0BJiTYGY90o080p082ZrDY9uqCuMogw+F4I1fwk/OgD3PwaVfg8u+kdwFRmF2eR6LqgsGu8K39/pYd7CLty+uOqI6pCwvM/z62MH6vsGxbeP3GkhzGa49vZbn/uVt/OPb5vLopiYu+t5z/OjpnYNTERKxu6WX/Kx0quP5+Ryj5oW31gzdonKo08P04hwCA5b73jh41HumqnJnwpTPgyu/B5/bAhf9h9Ow8a53was/OerQyM2LWEcvLqwuYHdrH76gc1NkU303gIL1SaRgXURERGISGAjR7PYyI8rM+kj+4cK5fOTc2UwvzhkyK3xqgvVIZr00b+QS0ZWzSvAFQ2xtdI/4erz2hgO+RMvgI8F6ZO53srX0OD+XmBvMte2CO9/hzIeevgL+8RU4+1OQNjXDiC5bWs0b+zpo7fHx/I5WrIWLFh3ZVbsk1/k70NE3TrDeNvLYtrHkZ6XzhdWLePpzF3Dxoiq+/9QOrrvjtSNK8+Oxs6WHuRV5GDN5e4inWqTz/a4hpfD1nf2cOaeMs+aUcc+aA0f0HdhwqItlX36Cv4WnTxxXckudveyf3eh0jX/iS/Di9484pDUSrMeYWV9UXchAyA5+nzfWd1OQnT5YzSMTT8G6iIiIxKTZ7SVkOWKcT6wuXFjJf7xjCXB4xvhUZdbb+/wUZqePOkN8RW0x4Mz5Taa9rckJ1otyMyjOzRjM9iZbSzizHnWDuYGAEyzcdjY0bXL2p9/4IJTOmZD1RWv1smqshb9ubebpbS1UFGSxbNqRGcJIdUXHOGXw+9r7cBniGss2szSXn1y/kh984BS2NvZwxQ9fTKgsfldLL/MqC+J+/7FoTkUexhwO1gMDIZrcXqaX5HDDWbOo7+rnue2Hm6/9eW09ff4BvvXYtuMvwx6RngXv+xUsv9rpDfGXT0K/0yOhtefIrT7RWlzj/L1666CTUd9Y383y6UWpdWPI64ZDb8K638GT/w53Xw0/WA7r7p7qlSWF5qyLiIhITLrCo61KRslEx6qigcQWIAAAIABJREFUIAuXgaYJHj82Gmek0ei/xNYU5VBTlM3aA1186JzkXXdveE73zNLEs1SzyvIGZ7YnW2SGe1QltP4++M2V0LAOFr8TrvguFFRPyLpitai6gLqyXB7e0MCGQ91cvqz6qI7Wg5n1ccvgPUwvyRnc2hGPd6+YzqKaAlb/4EUe3tDALefPjfkcbm+AZrfvhGouB5CdkcbMktzB8W1N3c4NxBnFOVyypIrKgix+99p+Ll5cRShkeWxTIwXZ6by+r4Pnd7TytoWxzyk/JqSlw3tuh6IZ8PKPYOeTcOqHmHGwha+l72fei3+AkAdc6ZBVANmFzsfBR6HTvK64FoA55fksrCrg9hd2c9Up09jW2MOHzqmbuq8v6IeDr8H+V+HQ605jPXf94dfTsqB8Acw4HQqnTd06k0jBuoiIiMSkxxsEoCA7Ob9GZKS5qCjImsI9674R96sPtbK2hLVJLqHd29ZHbWnuiHO6Y1VXlpv0zH9ES4+P4twMsjNGrjw4wtq7nED9PbfDyddOyHriZYzhsmXV3P78HgAuWlR11DGZ6S4KstJpH6cMfn97X0wl8KNZVF3IkppCntjcHFewfiJ2go+YV5k/OL7tYKdzoyoy9/7a02v532d2crDDQ7PbS7Pbx3fefxI/fHon33liO+fPr5jU0WOTypUGb/8KLHk3PPgpeP6bLDLpVKflkN1S7gTlNgQ+N/h6nMy0HTJJIiMXLvkqnPZRXC7DF69YxId+/QZffnAz/oHQ1HSC7++EN3/tNKjsaQQMVC6GunOhYiFULHIeJXXO138cUbAuIiIiMenxOpn1gqzkjc2pLsqhyT1Ve9b9zK0YO9hZUVvMIxsbaXF7qUxSI6+9bX3UJWnv56yyPB56qwF/MJRQtnckzW4vVdGUwAf9TjZv1jkpF6hHrF7qBOsZaYZz55ePeExpfuaYe9attext6+Pdp0xPypouW1rND57eQUtP7LPsI8H6iTJjfah5lfm8tKuNgZClvtOpyok0vbzu9Jn85Nld3L3mwOC/idXLqnEZw+f/8BaPbWriypNqpnL5E2/aKfCxFyDo48uP7OThDY2s/8ylRx9nLQT6ncC9rwWe+jI8+s+w/f+zd9/xbZfX4sc/j6zhvWccZ9nO3oSQwQgkhELZ0JZx2S20pZfSFvjdrltub9dtS++lmz1KoZRRCHsmkISEEDLIIMN2Eo8klrcl25Is6fn98ZUcJ/GQbcmSk/N+vfSyrfH9Ps6wdb7nPOe8AV96nCUTc1hcksXzn1YDw9hcrr0RPn0M9r5rZNH9Xhh/Fpz/axh/BiRkDM86okz2rAshhBBiQMKdWQcoSI2P6p717JR+MutjjTeG4cpeBwO+8dnhCbLGZSXi10ZH7HCzO9yhNZf77B/gOAhnfDfsawiXWaPTKUxPYGFxNsm2nv/9ZiZZ+xzd1tTeicPlDVuTreBe+nd21g74tWV2J1azKSxbKUaakpxkPF4/VY3t1DR3oBRdkyUK0hJYNiWXf26s4o3thzizNIeUeAuXzilkYl4y972zOyyd+GOeUmCJp97h6X2rj1JgTYSUPMifAf/2gtFlft8H8NQVKLeD758/BYDUeDNjIv1vze+DTx6GP8yF934KHqfRmPK21XDDCph68UkTqIME60IIIYQYIKc7AsF6enxUusF7fX6a2j1kJfUdjE4blYo1zsSmyuawnLe21U1Hp2/IM9aDxnZ1hI9AsN4aQsbX74M1/wcFs6B4adjXEC4mk+Lpr53Gb780s9fnZCZa+xzdtiswd3qojQGDJuYlMy4rkTe7zYAPVZndyYTspKPGz50sigPVBHvtTqqbOshLiT+qquS6BeNobPNwqMXFBTOMvglxJsX3lk+ioq6NFzZVR2Xd0VDvdIfeCV4pOPWrcOVjcHATPHU509Pc3HrmBK48pSgyzeWaK40tNM/dBL8thde+B3nT4RsfwddXG2X9Bb3/nz2RSRm8EEIIIQYkWAafHM5gPS0ep9uLw9VJSnz4yuuDfH7NXz8o5+JZo47KQja2e9AasvvZs24zxzGtMJXNYcqsV9Qb5ctDnbEeFCynD3dHeL9fU+cMIbO+82VoLIcvP2m82Y9hY/vZa56ZZGXHwd7H9P1jQxUpNjOnTcgKy3qUUpw3LZ9H1uyjpaOTtITQ//2X2Z3MjMYe4hgQ3KdfZndS09TB6Iyjp1MsKs5ifHYS1U3tLJ1ypD/B8ql5zCpK5/5393LJ7MLQejGMcHVONzNHpw/sRVMvhi89Ds/dCP87nR/Mvhrm3WKUzQ/1/7irBSo+gIpVxq2x3Lg/OR9KzjXOPemCmP9ZMhwkWBdCCCHEgDhcXqxmU6+jzgYjP1C+erjFFZFgfWt1M795azfPflLFc19fSF5g33kwgxrKSKNZo9P558aqsKxnX314xrYFZSZZSbGZw55Zb2r30OnT5PXVCV5rY1Rb9kSYfFFYzx8NmcnWwEUcfVwW8XCLi9e3HeL6heN6LaMfjOXT8nngwwpW7rJz6ZzQ9sK7On1UNbVz+dzw7J0fadISLOSk2CizO6lubmfumKNLo00mxc8vnU5lY/tRF0CUUtxz3iSuffhj/v5xJbecPn64lz7s6h3ufi9I9mjKRfDN9bDuj7DlGfj0cUgZBRPOguQ8o5t8fBrEpwc+phkl6hnjjc703Xk9xkW97S9A+Xvg84AlyWgSN/9rMGGJ0SROAvSjSLAuhBBCiAFxuL2khjGrDkfPWi/NC//M6G3Vxpxgu8PFdY98zLO3LiQj6Ui5c1YIwXpOio12jw9Xp2/I2bh9dW3EW0zkh6NZXXsjqmk/kzPDn1m3B+Yz99lUb+87ULsNLv0LmEb+DsvMRCser582j++4gPyp9Qfwac2Ni8aF9Zxzioy99H9aWcYXpueH9O+rvM6J1lB6ks1Y7640N5k9tQ4ONbsYPSvhuMcXlWSzqIfXLS7JZnFJFn9eWcZXTi0K64WXWNPu8dLm8YU2erEn2aVw0f1wzo+NpnNl70D5+0aHdl8v20XibJA3zdgWUzDLGOm4/s/GmLXUQph/K0z+IhTOA3N4RoCeqE7cf5lCCCGEiAiHyxv2N7fBoDVS+9a3VjeTnWzj91fN5sbHP+FHL23nT9fOpaHNCEb7G90GdGXnWjs6hxys1zrcFKQlDG18lNbw2T/hzf8HHU08B9ibs+AfC6HwFBg9D0bNMUY1DXadgQ79eX2Vwa++D9KKYMaXBn2eWJKZdGTWevd/565OH09vqGTp5DzGhKm5XJDJpPj5ZdO58bFPuO/t3fzwi1P7fc3JPLYtqCQ3mafWH8CvoTB9YH8ndy2fxGV//ohH1+zjjqWlEVph9NU7Qq8e6lNSNsy9zriB8fPH6zJGv7lajtza6qB2OxzaCttfNDq6gzEl4qLfQ/E5J8RFveESsWBdKVUEPAnkARp4UGt9v1IqE3gWGAfsB76stW5SRp3R/cAFQDtwo9Z6U6TWJ4QQQojBicS+8mBZeqQ6wm+rbmHW6DQWlWRz6exRvPu5Ha01dYHMcXY/DeYA0hON77m5o3PI49sanG6ykoaQUfK64fmbYderMHo+LPg6H2zYSPO+rVxs/xy169XAE5VRWjr6FCOAL5wHuVOPL1HtRVdmvbcGcwc+gqr1cP5vIC782xeiIXjhprHdc1RQvmLLQRrbPNy8eFxEzrtkUi7XnjaGh9fsY+mUPBb0sye+zO7EpGBc9snXCT6oJDcZvzY+P3bPen/mjMlg+dQ8HvqwgusWjCVjKP8fY1id0/g/HHKDuVApBZYE45aS1/NztIbmA8ZouNwp4T3/SSKSlzW8wPe01lOBBcDtSqmpwH8A72mtS4H3Al8DnA+UBm63An+J4NqEEEIIMUhOlzesneABrGYT2ck2Drd2hPW4YHSvL6tzdjVYmlGYRmObh4MtLhraPFjiFKkJ/X8/6QnGm/nm9s4hr6ne6Q4pm98jrY1uybtehXN/Cje/CdOvoHbmN/l25+1UXbsG7tlnjGBa8n1ILzLKV1/9DjxwBvxyNDz6BezP38VfH/wDXq+311PZA5n1HktofV54/2eQlHMk23YCyEgMBOuBqgswRu09unYfk/JSWFgcnsZyPfnBBVMYk5nIXc9t7Zq60Jsyu5NxWUlh7R0x0pTkHKkqKBxgsA5w13mTcHq8/PWD8h4fb3N7uegPa3hy3f5BrjD66oPB+mDL4IdCKcgYJ4H6EEQsWNdaHwpmxrXWDuBzoBC4BHgi8LQngEsDn18CPKkN64F0pVRBpNYnhBBCiMFxRCBYB2PfeiQy69trWtCarq7Z0wuNj9uqmwMZbltI44i6Mut9zOAOVYOzj7nH/dnwIGz+G5xxFyz+NpiMYG3aqFQA3vm8FhIzoWQZLPl/cO1zcHc53LEFrngE5t0Efh8ZO57g6wd/hP+vZ0LZu8ZFgGPYHW7SEizHl/37/bDi3+HAWlj6EyO7doIIjvHrPr5tfUUjuw47uGnxuMiMrgpIspm570uzONjcwc9e3dnnc/fanV3jy05W3bcAFKYP/N/gxLwULptdyOMf7e/a8tHd2zsPs62mhXtX7GDlbnu/x9tS1cy1D6/n7ue28tzGqq7JGZHm92uqGtu7tkZ011U9FO7MuhgWw7JnXSk1DpgDfAzkaa0PBR46jFEmD0Yg373FanXgvkPd7kMpdStG5p0xY8ZEbM1CCCGE6JnD1UmyLfwlz/lp8VRGYE54sLlcMFifUpCK2aTYVtNCg9MTcoY7uGe9uWNob8B9fk1juyekpnbHqVgFb37fGGt09g+PemjaqDTmj8vk0TX7uH7hWCxx3XIySkHmeOM240p8fs2i/36DhZ61/Nr1Mjx1Bcy9Ab74u6NK5GtbXcfvV/d1wrv3wtanYckPTqisOhjd4AEa244E64+t3UdGoiXkTu1DMW9cJredVcxfVpVz7tS8rrFjbq+PffVt7K11UmZ3sr++jeVTeyk/PknkpNhIiTdjM8cNuo/E7eeU8OLmGt7acZjrF4476rGXNh+kMD2BtAQLdzy9mae/toAJOUkkWOKO6zfx2meH+O4/t5CaYGHnwVae+7Sat3fW8tD18wb77R1Ha015XRtldgdldid77ca/hfI6J65OPyYFH95zNqMzjmyNCGbWB13JI6Iq4sG6UioZeAG4U2vd2v1qpNZaK6WOv4zbB631g8CDAPPmzRvQa4UQQggxdA535DLrH1c0hP24W6ubKUxP6AqO4y1xlOal8Fl1C60dnSEHzWmJRxrMDUVTiLPdj9NYAf+8wRiRdtkDPTZpuvXMCXz1yY289tmhPgPLTZVN1HVoVrCILyz9Ghc0PA5r/hdaD8LlDxp74tsbyGv4mGlx7fDJXmhvNBpHla8Ed6vR0fmsewb43ce+JGsc1jgTjYEKiqrGdt75vJZvnFU8bDO571xWyspddu55/jPmjMmgvM7JgYa2rv3ZSsG4rKSj5oefjJRSTM5P6akoJGQTspPITLJ2XdQLqnO4Wb23jm8sKeaa08ZyyR/XcNEf13Q9bjObSLDGkWgxLhRU1LdxytgMHrzuFDISrfz3azv527oDNLV5wrYf/pE1+/jZa593fV2YnkBxbjKnjc8iK9nKb97azYd76rnmtCMJzXqnm/REy9EX78SIEdFgXSllwQjU/661fjFwd61SqkBrfShQ5h6sKakBirq9fHTgPiGEEELECL9f44zA6DYwMuutLi9tbi9JYew2/1l1S1dWPWhmYRpv7zxMotUccilxis1MnEkNec96V6YrhKZ2XVyt8MzVRpR29dPGfOMenDM5l5LcZP76QTmXzB7Va8n2u5/XopRR+X64TcOye429pa9+F359ZO70T4OfvBb4mDIKpl1qZPZLzzshZyIrpchMstIYKIN/4qP9xCnFdQvHDtsabOY4/vcrs7n+0Q0caGhjSkEKF80soCQvhZKcZCbkJA3bhYNY95srZ+EfQrSulGJGYRrbao4O1l/ZehC/hktnF1KYnsAL31jEB3vq6PD46Og0bi6Pj/bA18un5XPnstKuv5cr5o7msbX7eXPHYa6eH55q4M1VzRSkxfPAdadQnJN81M9JrTVPrT/A6r11RwfrDk/4m8uJYRPJbvAKeAT4XGv9u24PrQBuAH4V+Phyt/u/pZT6B3Aa0NKtXF4IIYQQMaDN40VrSI5QZh3gcKuL4pzw7MVtbvdQ2dh+3Jvl6aPTeHZjFU3tnSHv5VRKkZZgobljaHvWg3uhQ86sH9wCr98F9Xvhuhchc0KvTzWZFLeeOYF7nv+M1XvrOXNiTo/Pe+9zO4uKs/hkfxO1jsBe3VNuhOxJcGANJGahEzL5t2fKOHvOFL66fB4kZJw0M5Ezk6w0tnloc3t5dmMV588ooCBtePflTylI5ZMfLhvWc45E47KThnyMGYVprCmrx9Xp6wq2X95Sw/TCVErzjNGHY7OSuH5h6OeaNiqVCdlJvLL1YNiC9XK7kykFqV3NMrtTSnFmaQ6vbz+E1+fHHMik1zndsl99BItkPcRi4DrgHKXUlsDtAowg/Vyl1F5gWeBrgNeBCqAMeAj4ZgTXJoQQQohBCHaoDvfoNoD8VCMYCues9c+O2a8eNLPwyNcDGaGWnmAJX2a9vzfQrQfhX9+AB5cYJfCXPwgTlvR7/EtmjyIv1cYDH/bc4fpAQxtldidLJ+eRm2LD3nqk6zljF8KZd8O8m2kadwFrvVOIy59qjGY6SQJ1CATr7R5e2FSNw+XlpgiNaxOxYcboNHx+zc5DrQCU1znZWt3CpbMH36NAKcWFs0axrqKha6rCUPj8mor6Nopzer9gcMbEbBwuL1u7lfTXO91kR6MTvAiLSHaDX6O1VlrrmVrr2YHb61rrBq31Uq11qdZ6mda6MfB8rbW+XWtdrLWeobXeGKm1CSGEEGJwHK5gsB65zHq4OsJ/XNHAr9/ahUkd6QAfNCk/BXOgQdRAsk5piRZahrhnvb6/zLqnDVb9Cv5wCmx/HhbfAXdshhlXhnR8mzmOmxePZ21Zw3H7cAHe/dzYgbhsSh55qfE9dsEGsAcy7r3OWD+BZSZZaXB6eHztfmYVpTN3TEa0lyQiaEbXhAjj/8vLm2swKbho1qghHfeimQVoDa9vG3qxcHVTOx6v/6gO+MdaXJyNUrB6b13XffUOt5TBj2DSaUAIIYQQIQuOIopIZj1YBt8ytFnrOw+2cuNjG/jKg+upd3j4v6vmdHVyD4q3xDEp3yhvHUiX5HBk1hucbswmddya8PthyzPwh3mw6pdQuhxu32DMUo9P6/lgvbj6tDGk2Mw9Ztff+7yWiXnJjMlKNDLrDncPR4DaQMb9uG7wJ4HMJCuVje1U1Ldxs2TVT3gFafFkJ1vZVtOC1pqXthxkcUk2ealDu1BVmpfC5PwUXv1s6MF6eZ0xlq2vYD0jycrM0ems3lsPQLvHS5vHR3bKyVMVc6KRYF0IIYQQIQtm1pPD2AAuKN4SR0aiZdCZ9cqGdr79j81c8PvVbK5s5vvnT2bV3Uu4uJfsWDCbNpDMenqiNSx71rOSrUc3f9Manv4SvPR1SMmHm9+CLz9hjFobhNR4C9ecNobXtx06ahxeS0cnG/Y1dnUR7zOz3npyZ9YBclNsnD+9IMqrEZGmlGJ6YRrbqlvYVNlMZWP7kErgu7twZgEbDzR1zTsfrOAM9f76eZxZms2WqmZaOjqpdwSreE6+C24nCgnWhRBCCBGyYLAeiW7wAPlpCQPes17ncPOfL2/nnPtW8daOw3xzSTEf3nM2t/Uzamv++Ewscaqr/D4UaQkWWoaaWW9zH98J/rN/Qtm7Rlf2r74HYxYM6RwANy0eT5xJ8fCaiq77PthTh9evWTYlF4DcVBsOl5d2j/e41wcz7rknaWYd4LoFY7Ga5e3yyWBmYRp77Q6e/riSeIuJ86bnh+W4i0uyAdiwr3FIxym3t5GdbCU9se8s+ZkTc/D5NWvL6qkL9MfIkT3rI1bE56wLIYQQ4sRxZM96+MvgAUalxYecWW91dfLQhxU8smYfbq+fq04t4o6lpSGXrl46u5DTJmSFPGcdjGC91eXF59fEmQY3tqwukFnv4nbAO/8JhafAom/3OD99MPLT4rl0diH/3FjFncsmkplk5b3Pa8lMsjK7yNiDHcya21vdjMs++m2hvdVFarz5pBwRduq4TBZMyOTaBcM3rk1E1/TCNPwaXtxczYUzR4Wtemh6YRpJ1jjWVzTwxZmDr9Ioq3MyIYQpGbOL0slLtfHQ6gpuO9OYHCF71kcuuVQohBBCiJAF96xHYnQbGAHm4RA6J7s6fVxw/2r+8H4Z50zO5d3vnsXPL5sxoD2mJpOiMH1g47jSE42LFK1DaDLX4Dym4dOHvwXnYTj/12EL1INuPXMCrk4/T67bj9fnZ9XuOs6elNt1oSG4H72nfeu1re4h79kdqSblp/CPWxd2ZdjFiS84Dk1ruGzO0BrLdWeJMzFvXCbrKxoGfQytNWV2Z5/71buf77vnTmRzZTNPra8EpAx+JJNgXQghhBAhc7q9mBQkWSOTbS1Ii6exzYOr09fn857bWEV1UweP3jiPP14zl/FhmLUcimCw3jykYL1bZr1uD6z/M8y+FkbPC8cSj1Kal8KyKbk88dF+VpfV09LR2VUCD3QF4z3tW691uE7KEnhxcspLtZGdbCMzycoZpTlhPfaCCVnstTu7xjYOVEObh5aOTkpCyKwDXHlKERPzkllTZjSaG0gTTRFbJFgXQgghRMgcLi/JNvPRzdHCKD/NyHT31vQMwOvz88CHFZwyNoOzJ+X2+rxISE8w3vQ2tw+uyVy7x0tHp88ovfd1wotfA2uysVc9Qm47q5im9k5+8OI2rHEmzph4JBDJS+k9WLe3urseF+JEp5TijqUl/Mf5k7HEhTdEWjAhE4CPKwa3b7082FwuhMw6QJxJ8f3zpwCQkWgJ+/cjho/8zQkhhBAiZK2uzojtV4fQZq2/+tkhqps6+MZZxRG7aNCbtCFm1oPdmbOSrPDBr+HQFrjofkiO3EWHeWMzmDMmnUMtLhYUZx21Fzc1wYzVbDquU7XWmjqHmxzJrIuTyPULx/HleUVhP273feuDURbC2LZjLZmUwxml2cNWdSQiQ4J1IYQQQoTM6fKSEqH96tB91nrPwbrWmr+sKqc0N5lzJg9vVh3omo0+2I7w9W1GUDzBvRNW/xZmXQNTLw7b+nqilOK2M4sBOHdK7nGP5aXajsusN7d34vH5JbMuRBgMdd96ub2NBEscBQPoIaGU4qHr5/HkLacN6pwiNkiwLoQQQoiQOSIdrKf2nVlfV97A7loHt51VjGmQ3diHIj0YrA8ys97g9JCKkxnrvgupo+H8X4Vzeb06b1oeD18/jy+fenzWMC8lntrWozPrtY7AjHXJrAsRFkPZt15W56Q4N2nAP/PiLXFh62ovokOCdSGEEEKEzOGObBl8ks1MaryZQy0dPT6+N7B386yJ4W0AFapgZr15sJl1h4vfWh7A0l4LX3oM4tPCubxeKaVYNjUPm/n4xoC5qbau4DzIHgjeT9Zu8EKEW3Df+vu77AN6nd+v+fxQK6W5KZFYlohxAw7WlVLpSqkfRmIxQgghhIhtzkCDuUgqSEvoNbNe53ATZ1LGnu8oMMeZSLGZae4YXIO50bsfY3ncp3iX3huR7u+DkZsST92xmfVAWXxuimTWhQiHmaPTmTYqlV+9sQu7o//xlEGfVjZR53CzZFJ0LlCK6Oo1WFdKFSmlHlRKvaqU+qpSKkkpdR+wBxj+TWJCCCGEiLpIl8FDYNZ6L8G63eEiO9kalRL4oLREy+D2rG97nsUV9/Mu87Esuj38CxukvNR4HG4vbW5v133Bueu5smddiLCIMynuv2o2bW4vdz/3GVrrkF73ytaDxFtMLJuSF+EViljUV2b9SeAg8AdgGrARGAXM1Fp/exjWJoQQQogYYwTrkSuDB6MjfG+ZdbvDTU6Us73piZaBd4Pf9jy8+DXKE2ZwX9L3YJi72PclmD23d+sIb291kRpvJsF6fNm8EGJwSnJT+OEXp/DBnjqeXHeg3+d7fX5e33aIpZPzSJK95yelvv7WM7XW9wY+f0sp9SXgWq21P/LLEkIIIUSscXt9eHz+Ycms1zvdeLx+rOaj8wr2VndXx/hoSU+w9j1n3e+DhjI4uAUObjZu1RtgzEJ+7r6HJB2dEv7eBPel17a6usY82R1ucmW/uhBhd92CsazcZecXr3/OouIsSvN634u+vqKReqeHi2YVDOMKRSzpc8+6UipDKZWplMoEGoC0bl8LIYQQ4iTicBll0pEO1oOz1o8dJwaBIDLKmfW0hB4y63W74c0fwKPnw6/GwJ/mw79uhU8fBzQs/BZc+xwHOxRZybEWrB+fWa9tdXXdL4QIH6UUv75yFsk2M3f8Ywtur6/X576y9SDJNjNLJskO5JNVX79t04BPge51WpsCHzUwIVKLEkIIIUTsGa5gPT8tAYDDrS6KMhO77vf6/DS0xUCwnmihNRisux3wwf/A+r+AyQz5M2D2NTBqDhTMhuyJEHfkz6vB6eHUcbGV8whm0A9368Bvd7hjbp1CnChyUmz8+sqZ3PLERu57ew8/uGDKcc/xeP28sf0Qy6fmEW+R7Sgnq15/22qtxw3jOoQQQggR45yBYD3ZFvk963D8rPXGNg9aQ06Uy7PTEyw0t3vQ255Hvf0jcByCOdfBsnshKbvX13m8fhrbPWQnx1bGOjXeTEq8meomI1jXWmNvdcuMdSEiaOmUPK49bQwPra5gycQcFpUc/bPjk/2NtLq8XDBDSuBPZr0G60qpMX29UGtdGf7lCCGEECJWOVxGNnk49qzD0ZleOFKmnRPlYDfP0s4TcT9HvbDDyJ5/5amQxrDZHS60PnIxIlYopSjKSKSqsR2Alo5OPD6/dIIXIsJ++MUprCtv4HvPbeXNb59JWuKRC6FbqpoBOHW8VLiSXI5WAAAgAElEQVSczPr6bfsaRrl79zJ4DeRgjG6TegwhhBDiJNI6TGXwKTYzSda44zLrwdnEUc34et2cv+Nu0ky7aTr7V2SccSuYQntLFNyDnxdjwTpAUWYC5XVtANQGZq7LnnUhIivRaub+q+Zw2Z/X8oOXtvHHq+egApMitlY1Mz47ibSEyFYyidjWa4M5rfUMrfXMwMcZwEXAWsAJ3DlcCxRCCCFEbAhm1lMjPLpNKdXjrHV7a3D2d5SCSK1hxb+T27iRuzu/Tk3JNSEH6gCHW4z1x1pmHejKrGutj1wUkcy6EBE3Y3Qa3zl3Iq99dojXth3qun9bTQszCtOiuDIRC/rsBg+glCpVSj0OvIHRcG6q1voPkV6YEEIIIWKL0x3csx75eb+j0hN6yKwHyuCjFayvvR8+e5aq2d9jhX8RLQOctX4oUNafH4Mj0cZkJeL2+qlzuCWzLsQw+/pZxYzJTOSFT6sBo4roUIuLmaMlWD/Z9RqsK6WmK6WeAV4A3gWma60f1loP7DeTEEIIIU4IwW7wyREugwcjoD0us+5wkZ5owWaOwk68+jJY+QuYchGuBUaBYb3T3c+Ljlbb6sJmNsVkWWtRhtF1v6qpXTLrQgyzOJPinMm5fFTeQIfHx2dVLQDMKkqP8spEtPX123YrUIWxd30+MD+4hwJAa31HZJcmhBBCiFhyqMVFZpIVS1y/hXlDVpAWj93hwuvzYw6cz97qjk5zOb8fXvk2mOPhgt9SFJ+EUlAR2OMdqsOtbgrS4un+fipWFGUa4/KqGjuwt7pJiTeTYJX2REIMl6VTcnn8o/18VF7PZzUtmBRMG5Ua7WWJKOsrWL8Fo6GcEEIIIQT769sYl5XY/xPDID8tAb+GOqebgsDc9TpnlMaJbXkKDqyBi+6HlHziMTLR5XXOAR3mcEsHeTFYAg8wOphZbzQy69GeZS/EyWb++EwSrXG8v8tOTXMHpbkpJFojX8UkYltfc9YfH8Z1CCGEECLG7W9oY2Fx1rCcq/us9WCwbm91M384xxj5fbDuT/D+z2DsYphzfddDxTlJXd3TQ3W41cXcMRnhXmVYxFviyE2xUdnYTm2rO2YvKghxorKZ4zi9JJv3d9nxeP2cMzk32ksSMaCvOeuv0EdmXWt9cURWJIQQQoiY0+HxcajFxfispGE5X3DW+qFmF4wBrTV1DvfwZXzry+Clb0D1Bph8oZFVNx0p/y/OSeaj8gb8fo3J1H9Zu9aa2hZ31/cVi4oyE7v2rJ8SoxcVhDiRLZ2Sy9s7awGYKfvVBX2Xwf922FYhhBBCiJi2v8HIIo/PGZ5g/Uhm3eig3tLRicfnj3wneL8fPv4rvPdfxh71yx+CGV+CY/aZF+cm4/b6qWnuoCiz/60BjW0ePD5/THaCDyrKSOCT/U3UOSWzLkQ0nD3pSDZ9lnSCF/RdBv/BcC5ECDF0WuuYbFwkhBj59tcbwfq4YcqspyVYiLeYujrCB8e25UYyiGysgJduh8qPoPQ8I5ueWtDjU4tzkgEor3OGFKwfbjW+j5gO1jMTeWnLQSCK4/GEOInlpsYzvTCV3YcdTMpPifZyRAyIfDtXIcSw+POqMhb+8n1cnb5oL0UIcQKqCAbr2cMTrCulKEhL4FAgyLUHZn9HrBv8p4/DXxZD7Xa45M9wzbO9Bupg7FkHQt63XhsM1mO8DD5IMutCRMcd55Ry57KJ0RlRKWKOtBgU4gTw6mcH+fWbuwEoszuZXiilU0KI8Npf30ZOio1k2/C9deg+a73OGZj9HYlu8PZdxmi28WfBpX+BtMJ+X5KZZCU90RJyR/hDLSMgWM84EqxLN3ghomP5tHyWT4v2KkSskMy6ECPclqpmvvfPrYzPDmZ5BjZKSAghQrG/oa3r58xwKUg7EqwHM+sRCSI/eQjibHDloyEF6mBk/otzkim3h/Yzt7bFhUlFsDIgDIKz1kEy60IIEQv6DdaVUhOVUg8ppd5WSr0fvA3H4oQQ/fvpKzvITrbxzNcWEGdSlIX4xlEIIQZiX337sHWCD8pPi6e21YXPr7E73CRY4sKf2Xe1wJZnYPoVkJQ9oJcOZHzb4VYXOSk2zHGxmycpSEvAHOhsH5V59kIIIY4Sym+854C/Ag8BshlWiBhT1dTBOZNyyU+LZ0xmogTrQoiwc7g6qXe6h22/elBBWjxev6bB6eZwq4vcVFv4m2hueRo62+C0Wwf80uKcZP65sZqW9k7SEi19PvdQiyumm8sBxJkUhRkJNDo9JFplp6QQQkRbKD+JvVrrv0R8JUKIAfP7NY1tHrJTrIDxxlGCdSFEuDjdXpJtZvbXtwMMexl8fppRlv2nlWW8se0QF84cFd4T+P2w4UEYfSqMmjPgl3d1hK93MrefueS1ra5h//MbjDGZiVhiOPsvhBAnk1B+Gr+ilPqmUqpAKZUZvEV8ZUKIfjV3dOLza7IDeyBLcpPZ39CG1+eP8sqEECPd1qpmZt77Fm/tOMy+4Iz1KGTWAZ5Yd4BFxdn86ooZ4T3B3reNcW3zbxvUy4tzA8F6CBdJD4+AzDrA//vCZH526fRoL0MIIQShZdZvCHy8u9t9GpgQ/uUIIQai3mk0XOoerHf6NJWN7UwIZHyEEGIw3ttlx6/hRy9t5+JZRkZ7bFb/88TDqSgjEUucYsmkXP5w9RziLWEcZeTrhHf+EzLGw9RLBrm+BKxxpn73rbd7vLS6vOTFcCf4IJkmIoQQsaPfYF1rPX44FiKEGLh6hxGsZyUbZfAlgSxPmd0pwboQYkjWlzdQkBaP3eHmsbX7GJUWH95gOQRpiRZW33MOuSk2TKYw71Xf8BDU74arngGzdVCHMMeZGJ+dxI6DLX0+L9jRvmAEBOtCCCFiR6/BulLqHK31+0qpy3t6XGv9YuSWJYQIRX2bBzgyCmhCjlGiWlbnZHnUViWEGOk6PD42VzVx8+njMSnFX1aVMz4nOvutIzKXvK0eVv0KipfCpPOHdKizJ+fy0OoKGpxusnoZy1bT3AHIODQhhBAD09ee9bMCHy/q4XZhhNclhAhBMLMeLINPjbeQl2qTJnNCiCHZeKCRTp9mUXE2315ayozCNBZOyIr2ssLn/f82OsB/4VcwxO7yl8wehc+veX374Z5PtauWO/+xhQRLHKW5KUM6lxBCiJNLr5l1rfVPAh9vGr7lCCEGot7pJs6kSEs4MjKoJDc55Lm/QgjRk4/KGzCbFPPGZhBviWPFtxaHf2RatNTtgU1PwvxbIWfikA83OT+FiXnJrNhSw3ULxnbd7+r08YvXP+fJdQeYnJ/C76+eQ06KzC4XQggROpnNIcQIVu90k5VkPWovZ0lOMuV2J1rrKK5MCDGSrStvYHZROkk245r+CROoA6z8GVgS4cy7+39uCJRSXDK7kE/2N1HdZIy423GwhQv/sIYn1x3gq6eP5+VvLWZinmTVhRBCDIwE60KMYA1OT1cJfFBJbjJOt5faVneUViWEGMkcrk621bSwsPgEKnsPqvkUdr4MC78FSdlhO2ywW/7LWw7y0IcVXPanj2jt6ORvt8znRxdOxWYe3sZ8QgghTgyhjG4TQsSoeqeb7GPKKotzjnSEj0hjJiHECe2T/Y34/PrE2qMe9N5PITELFt4e1sMWZSYyd0w69729G7+Gc6fm8T9XzCQzaXBd5oUQQggIIbOulEpUSv1YKfVQ4OtSpZQ0mBMiBtQ7PWQf82awODC+raJemswJIQZuXXkDVrOJuWMzor2U8CpfCRWr4Iy7ID417Ie/buFYEq1mfnn5DB687hQJ1IUQQgxZKJn1x4BPgYWBr2uA54BXI7UoIUT/tNY9Ztazk22Y1JFO8UIIMRAflTcwd0z6sM9Ujyit4b3/grQimHdzRE5x2ZzRXDKrMPzz4IUQQpy0QtmzXqy1/jXQCaC1bgfkN5EQUeZ0e3F7/WQnH529iTMpMhKtXTPYhRAiVM3tHnYeamVRcfj2c8eEz1fAwc2w5Ptgidz2IAnUhRBChFMowbpHKZUAaAClVDEgKTshoqzeaQTjxzaYA8hKttLolGBdCDEw6ysa0ZoTq7mczwvv/TfkTIZZV0V7NUIIIUTIQimD/wnwJlCklPo7sBi4MZKLEiJayuwOEqxmCtMTor2UftU7jWtmWT0E65lJVhra5JqaEGJg1lc0kGCJY9bo9GgvJTxaqmHlL6FhL3zlKTCdQKX9QgghTnj9Buta63eUUpuABRjl79/WWtdHfGVCRMG3nt6MOU7xyrdOj/m5wg2BYP3YMngwAvjPD7UO95KEECPcR+X1zBuXgdU8wie7NpTDmt/B1mcBDfNvhcnSG1cIIcTIEko3+MsAr9b6Na31q4BXKXVpCK97VCllV0pt73bfLKXUOqXUNqXUK0qp1G6PfV8pVaaU2q2UOm+w35AQQ1HT3MH2mlY+3tcY7aX0qy5Q5p7TQ2Y9O8lKg5TBCyEGoM7hZk+tc2TvV6/dAc/fDH+cB9ueh3k3wR1b4ILfQIxfgBVCCCGOFcql859orVuCX2itmzFK4/vzOPCFY+57GPgPrfUM4F/A3QBKqanAVcC0wGv+rJSSWjUxrNo9XhwuLwAPr94X5dX0L9jtvafxQJlJNlo6Oun0+Yd7WUKIEWp9RQMwQverV2+Ep6+CvyyCPW/Bojvgzm1GkJ5eFO3VCSGEEIMSyp71ngL6UMrnP1RKjTvm7onAh4HP3wHeAn4MXAL8Q2vtBvYppcqA+cC6ENYnRFjYW43gd1xWIu/tqmVffRvjs5OivKre1TvdZCRaMMcd/180K1Aa39TmITc1cp2PhRAnjnUVDSTbzEwfFf4Z5BFT+TGs/Dns+wASMmDJD+C0W43PhRBCiBEulMz6RqXU75RSxYHb7zDmrg/GDozAHOBLQPBydyFQ1e151YH7jqOUulUptVEptbGurm6QyxDRsr2mBbfXF+1l9Ki21QXAt5eVYjGZeGxtbGfXG5yeHjvBA2QFsu31UgovhAjR+vIGThuf2eMFwJjktMMTF0HdLlj+M7hzOyz5fxKoCyGEOGGE8hv53wEP8Gzg5gZuH+T5bga+qZT6FEgJHHdAtNYPaq3naa3n5eTkDHIZIho+PdDIhX9Yw8tbDkZ7KT2yB8rKp49K4+LZo3huYzWtrs4or6p39U5378F64P5GmbUuhAjB4RYXFfVtI6sE/tMnwOeGG1+DRf8OtuRor0gIIYQIq36Dda11m9b6P4IBstb6+1rrtsGcTGu9S2u9XGt9CvAMUB54qIYjWXaA0YH7xAnk/97dCxhvCmNRMLOemxLPtaeNoaPTx5vbDkd5Vb2rd7q7yt2PFdzHLuPbhBChWFdhDHkZMcG6zwufPgYTzobs0mivRgghhIiIXoN1pdT/BT6+opRacextMCdTSuUGPpqAHwF/DTy0ArhKKWVTSo0HSoENgzmHiE2bKptYvdd4Mxir2V67w43NbCI1wczsonQmZCfxwqbqaC+rV/V9lMEHx7lJR3ghRCg+KmsgPdHClPwRsl999+vQWgPzvxbtlQghhBAR01ejuL8FPv52MAdWSj0DLAGylVLVGB3kk5VSwRL6F4HHALTWO5RS/wR2Al7gdq11bG5sFoNy/7t7yUi0YIkz0dQemwFkbauLvNT4rvnql88t5Ldv76GqsZ2izMQor+5ork4fTreXnJSeg/XUeAtmk5LMuhAiJOsqGlgwPguTaYSMN/vkIUgrgonHDp0RQgghThy9Zta11p8GxqfdqrX+4NhbfwfWWl+ttS7QWlu01qO11o9ore/XWk8M3P5Da627Pf/nWutirfUkrfUbYfr+RAzYUtXMB3vq+OoZEyhIT6CpPTb3gdtb3eSlHgl+L51j9Dh8aXPs7ciodxpBeHYvZfAmkyJDZq0LIUJQ1dhOdVPHyCmBr9sN+z40ZqibZMqrEEKIE1efe9YD2e2xSqmeIwIhQnD/u3tIT7Rww6JxZCZaaIrRMvhah4vclCNjzkZnJLJgQiYvbq6h23WlmPBRmTEPeUJO7w2VspKsNPTxZ93p81Pb6sLnj63vTQgxvNaVGz9PFo2UYH3zU2Ayw5zro70SIYQQIqJCmbNeAawN7FPvaiyntf5dxFYlThhbq5pZubuOu5ZPJNlmJiPJyp5aZ1jPYW91oZTqtSQ89OO4OWvi0ce4fO5o7nn+MzZXNTN3TGyMA9Ja8/CaCibnpzBvbO9rykq20uDsvQz+7ue28tKWg5hNilHpCZwzOZdL5xQya3Ra11YAIcSJ76PyerKTbZTkjoBu6n4fbHseSpZBskyEEUIIcWILZXRbOfBq4Lkp3W5C9Ov37+0lLcHIqgNkJlrDvmf9m3/fxPdf/GxIx2hze3G6veSlxh91//nT84m3mHgxhhrNrSmrZ0+tk6+eMaHPoDorydZrM7/1FQ28tOUgF88axW1nTWBiXgpPf1zJpX9ay/3v7Y3U0oUQMUZrbexXn5A5Mi7S7V8NjoMw8yvRXokQQggRcX1m1pVSs4EdwA6t9efDsyRxothW3cJ7u+x879yJpMRbAMhIstLu8eHq9BFvGfpeQ79fs+NgK2OG2AAuOGM995jsfEq8hfOm5fPK1kP8+MKp2MzR3x/58Op9ZCfbuGhWQZ/Py+xlz7rX5+feFTsoTE/gf66YSYLV+J5aOjq57pGPWbW7jjuXTYzI2oUQsWVffRu1rW4WFWdHeymh2fos2FJh0vnRXokQQggRcX2NbvtP4J/AFcBrSimZjyIG5P739pIab+aGxeO67stINNofNIepyVx1Uwcdnb6uhmuDFZyxfmxmHYxS+JaOTlbusg/pHOGwt9bBB3vquGHh2H4vHGQnW3G4vbi9Rw9WeHpDJbsOO/jRF6d0BeoAaQkWZhelU253xtwefSFEZHy8rxGABRMyo7ySEHja4fMVMPVisCREezVCCCFExPVVBv8VYLbW+mrgVODW4VmSOBFsr2nh3c9rueX0CaQGsuoAmUnG5+Gatb6n1mEcr92D1+cf9HGOBOvH73tfXJxFboqNFzZFvyv8o2v3YTObuHbB2H6fmxWYwd79z7qxzcN9b+9hcUkWX5ief9xrSnKTcbi91LbKyDchTgafHmgiM8nK+OykaC+lf7tfB48TZl4V7ZUIIYQQw6KvYN2ttW4H0Fo39PNcIY7y+/f2khJv5sZuWXU4klkP1771PXYjWNfaCNgHqy5QBp+Tcnxm3Rxn4tI5hazcZQ/bRYbBaHC6eWFTDZfPHU1mUv8DGoLP6V4K/5u3duN0e7n3omk97k8NNpgqs4e3CaAQIjZtrmxi7pj0kbFffcvfIXU0jF0c7ZUIIYQQw6KvAHyCUmpF4PYKUNzt6xXDtUAx8uw82MrbO2u5efF40hIsRz2WEQggBxv0NrZ52BAo2wTYc9jR9Xkw4B6M2lYX8RYTqfE9t3G4fG4hXr/mla0HB32OoXpqfSUer59bTh8X0vODM9iD49u2Vbfwj08quWHhOErzeu4RGQzW99odPT4uhDhxNLd7KK9rY06MTLroU90eKH8f5t0IJskdCCGEODn01WDukmO+/m0kFyJOHL9/by8pNjM3Lx5/3GNDzaz/7NWdvLz1IJ/8cBmZgTFwqfFmWl1e6ntophaq2lY3eanxvWaXJuenMrUglRc3VXd1th9Ork4ff1u/n7Mn5VCSG9owhswkowy+welGa81PVmwnK8nKneeW9vqanGQbaQkWyawLcRLYXNUMwJwx6VFeSQg2PABxNjjlpmivRAghhBg2vV6e1lp/0NdtOBcpRo7yOidv7jjMTYvHkZZoOe7x9MB9TW0DbzDX3O7h1W2H8Pk1H+6pw+fXlNc5WTAhC4D6IWTW7Q4XeT2UwHd3+dxCtla3UBaFrPOKrQepd3q45fQJIb8mK/lIGfy/NtewqbKZe74w+ageAsdSSlGSm8xeCdaFOOFtPtCEScGs0TEerHc0w5ZnYMaVkDRCutYLIYQQYSC1ZCKsXt16CKXg33ppgGaJM0rNB5NZf2FTDR6vnwRLHCt326lsbMft9bOoOBCsD6EjvL3VTU4PzeW6u3j2KEwKXhzmRnNaax5ds4/J+SksLskK+XUpNjOWOMWBxjZ++cYuZhWlc+Xc0f2+rjQ3mfJuwfqmyibK7A58fukQL8SJZFNlM5PzU0my9TnFNfq2/B062+C026K9EiGEEGJYxfhvaDHSvLH9EKeOzSS3hxFoQZlJ1gHvWdda8/THB5hdlM6EnCTe32XnC9OMbuazx2SQYIkb8p71JZNy+3xObko8Z07M4V+ba7hr+SRMpiMl8+/srGXnwVa+vaz3EvPBWlvWwK7DDn5z5cwBNYFSSpGVZOPZT6ro9Gkeun7eUWvuTUluMv/4pIoGp5uD9Y3c+8BzxOHHZLEyJied8XmZlBRkUjoqg/F5mZgtNoizQpwFRkKTKiEEPr9mS1Uzl84ZFe2l9M3VAh8/AGMWQcGsaK9GCCGEGFYSrIuwKa9zsuuwg59cNLXP56UnWgecWd+wr5HyujZ+feVMEq1xvLiphmc3VgFGJjg7xTrozLrT7aXN4+txbNuxLp87mjue2cz6igYWlRwpx7z/vT2U29u4Y2lJ2LsqP7ymguxkGxfPHvib6swkK4dbXXx53mhmF/VS6up1w4GPoHY72D/nMnsNkyz1JDx8L9Oay1hh6zYSryFw29nDcRKzYMLZMP4MMFnA5wanHZqrwNUM5nhjNrIlEayJxkdLAthSIasYsidCct8XTIQQ4VFmd+J0e5kby83laj6F526C1hq48HfRXo0QQggx7PoN1gOd4I+tf20BNgIPaK1dkViYGHne3H4YoMf53d1lJlm75pqH6ukNlaTEm7lo5ig8Pj9xJsWq3XWMzkggyWYmO9k26AZz9sBackMI1pdPzSPFZubFzTVdwXpNcwfba1oBo1t9cL55OJTZHazaXcd3z52IzRzX/wt8nbDjJdjyFHg9/MLl43C8iSX+IliRYATMZqvxMc4GjRWw6zVwtxivT84jJTGXBOWhLi6X1/QMksbO5YYzJoPPAz4Pfq+bumYnhxpb+PDzGpLNfm5eMArqy6D8Pdj+fLcFKUjJh4RM8Lqgs8MoZ+3sMI53rFFzYd5NMP0KsI6Auc9CjFCbKpsAYjNY1xrW/xne+Ynx8+OmN6BofrRXJYQQQgy7UDLrFUAO8Ezg668ADmAi8BBwXWSWJkaa17cdYu6YdArSEvp8XkailV2HjOD2UEsHj6zex13nTSLe0nMw2tjm4Y1th7l6fhEJ1jgSiOOUsRls2NfIxMAIsuxkG1WN7YNad22rkZHvr8EcQLwljgtmFPDqZwf5r4unkWQz886Ow12P1zR3hDVYf2TNfmxmE9eeNqb/J+9+E179DjgOQuYESC1kcrKDKdY2bDU1Rqbb6wKvx/iINrLaky+EaZdC4TxIysLs11x/71vEN8fR6Pbw0rmLoVtW3gTkBW6bc/fxX6/sZEHJGUw9MxX8fmjeDygw24xsu7mXPw+fF7wd0N4IDWVweBtsfQZW/Dus/CVc/gCMP3PIf4ZCiKNprVm9t47MJCtjsxKjvZyjtTfCS9+APW8aP5su+SMkxOAFBSGEEGIYhBKsL9Jan9rt61eUUp9orU9VSu2I1MLEyHKgoY0dB1v50Ren9PvczCQLTe1GN/h/ba7h4TX7yEmxcdtZxT0+/8VN1Xh8fq457UjTurMn5bJhXyOlecZc8JwUG5sDmaKBsjuCmfX+g3WAq+YX8ezGKh7/aD+3n13C2ztrSbDE0dHpo6apg5lh6qzc4HTz4qZqLp9b2P8FgE+fgFfvhLxpcNH9ULIMTCZ6/Y60Br8XlAlMR18kMZkUxTnJbKtpYWpBKrNGp/V62ktnF/LL13fx3KdV/GTUNGP+cWaIHevjzBCXArYUyBgLJUth8bdh/xrje3niYlh8Byz9yXFrFEIMjqvTxw/+tY3Xtx3mltPHh33bzpAcWAcv3AJtdXD+r2H+rdIHQwghxEktlG7wyUqprrRe4PPkwJeDH2wtTiihlsADZCRZ6ej00eHxsaXSmPP751XltHQcP85Na83TGyo5ZWwGk/KPzBc/d2ruUSOHspNtNLR58Pr8xx2jP8GS/FD2rAPMGZPB0sm5PPBBOQca2vh4XyNXnFIIQHVTx4DP35vfvbOHTp+fW04/fl79UdbeD6/cAcVL4aY3YeJyI2jui1JGQ7heguDSXOO/+DWnjenzzXxGkpVzp+Xx0uYa3F5f3+cMhVLGnvfbPoRTbjC+t5dvNzL2Qogh6fT5ue6Rj3lxUw3fPXdiSBdXh4XWsPo+ePyLRrPKW942Or9LoC6EEOIkF0qw/j1gjVJqpVJqFbAauEsplQQ8EcnFiZFjW00LYzITGZ3Rf0llRqIx/7ux3cPmqmamF6bS0tHJQx9WHPfc9RWNVNS1cc38o8vAS3JT+ODus7s6wuckW9HaOOZA2VvdJFjiSB7A+KK7zpuEw+3lq09sxOfXXHlKESk2MzXN4QnWP6tu5ukNldywaBwluSm9P/HAOmNf57TL4OpnwJbc+3MHYP74THJSbFwSQlO7L88roqm9k3d32sNybsDYr37R/XD2D43S+FfvNN7QCyEGbcO+Rj7Z38R/XzqdO5aWxk5WvXIdvPdTmHKRcaFu1Jxor0gIIYSICf1GJ1rr15VSpcDkwF27uzWV+7+IrUyMKFWN7SHvfQwG6ztqWqhzuPnW2SWMy2rk0bX7uGHROHJSjmS4n95QSWq8mS/OLDjuOEWZR86XHSgTr3d4yA1h73l3tQ43eam2Ab1xnVKQysWzRvHyloPkp8YzszCNwoyEsGTWfX7Nj1/aTnayje+cO7H3J7qd8NLXIX0MXPxHI1MeJlfNH8OX5hURF8Kot9NLsilIi+fpDQe4YEb+cX+O68obWLXHzvfPH0QW78y7jf31q++DilVQMBPyZkD+dMibbnzvsRJwCBEhLe2dpMSbQxq92JeVu+xYzSaumDd6nJkAACAASURBVFsYppWFyZ43jQkSF/8B4lOjvRohhBAiZoSSWQc4BZgGzAK+rJS6PnJLEiNRZWM7YzJDC9Yzk4xgfeVuIxM7uyid7547EbfXz6Nr93U9r8Hp5s3th7h87uhem88FZQcC/MGMb6ttdYW8X7277547EbNJcd60PEwmRWF6AtVNg2ty191zG6vYWt3CDy+YQmp8LwG41vDOj6HpAFz217Bl1LsLJVAPPu+W08eztqyBFzfVHPf4n1eV8cAHFeyrbxv4IpSCc35sZNkLZkHtDlj1S/jHNXD/TPjVWHj0fFj3Z6NxnhAnmA6Pj8X/8z5Pb6gc8rHe321nwYQsEq0xNrV17zswdqEE6kIIIcQxQhnd9jegGNgCBDelauDJCK5LjCCtrk6a2jsHEKwbAejKXXVYzSamFKRiNZs4a2IO/9pUw13LJxFnUjz/aTWdPh1SJ/ScQGa9zjHwYL3O4WbaqIG/SRyblcSrd5zOqHSj+/3ojAQ27G8c8HGO9dq2Q5TmJh9fgu71wIG1sOct2PMGNO2Hhd+CsYuGfM6humnxeN7eUctPVuxg/vjMrqqHlo5O1pU3APD+Lnv/++97ohSccqNxA6OiwP451G6Dw9uNWcxvfR82PgJf+B8oXRaeb0qIGFDb6sLp9rJqdx3/tmBs/y/oxYGGNirq2rhuCMeIiOYqsO+E5T+L9kqEEEKImBPK5fV5wFStZcOo6Fllg5FNDrUMPj1QBn+41cXcMelYzUaBx+VzC3l/l5115Q0sLsnimQ2VnDoug9K8PvZsBww2s661prbVxTmTcwf0uqDJ+UeC/MKMBBwuLy0dnaQlDL4kvczuZOGELKOcvK0e9r5tlImWvQ8ehzEjffxZsPhOmPNvgz5POMWZFPd9eRYX3L+a7zy7hWdvW0icSbFqtx2vX5NojWPlYIP1Y9mSoehU4xa052146wfw9yuMUtq5UvwjTgzBn2mf7G/E79eDLoVftbsOMCZpxJSyd4yPpcujuw4hhBAiBoVSBr8d6L/FtzhpVQbmmxeFmFlP7xbIzhlzZH7usil5pMSbeXFTNevKG9jf0M41ocwXB5KsccRbTAMO1p1uL+0eX8id4PtSmG58/zVD2LfucHVyqMVFcU4SfPRH+O1EY+Zw5ccw/XK46hm4pwKu/SfMuyms+9SHqigzkf+6ZBobDzTxTKBk9+2dtWQn27hm/hg+3teA0+2NzMknLoevr4GSc2HFHbD575E5jxDDLPgzraWjkz12B2D83Hpy3X46BzD94v1ddiZkJzEuOykSyxy8ve8YvSey++jPIYQQQpykQgnWs4GdSqm3lFIrgrdIL0yMHMFgPdQyeHOcqSvzPLvoyEzyeEscF84s4I3th3l4zT7SEy2cP/34xnI9UUqRnWyj3jmwfcu1rcYb4YE2pevJ6AyjHH4oHeHL69qw0smllb+At38Ik86HW1fBdz+Hi38Pky8wOqXHqMvmFDJ/XCb/+84eGpxuVu2yc+7UXJZNzaPTp1mzty5yJ7fEw1eegglLjHFvH/wGfMePAxRiJOm+tWfDPmObzRMf7ec/X97B69sOhXSMDo+PdRUNnD3ICqKI8bqNxpGly6VRpBBCCNGDUIL1e4FLgV8A93W7CQEYwXpmkpWU3pqh9SDYZK57sA5w+dzRdHT6eH+XnStCaCzXnRGsDyyzbncYgw1yw5FZDwTrg2oy5/fBhofIfeU6PrF9g8L9L8JZ/wFf/psxxqi/uekxQinFjy6cQkObh68+uZE2j4/lU/M5ZWwGKfFm3t8VxvFuPbHEGyPspl8OK38GDy81mtIJMULVOT0oBXmpNjbsa0RrzXMbqwB4bmN1v6/XWvP8p1V4vP7YK4E/sBY6242KGCGEEEIcJ5TRbR8Mx0LEyFXZ0B5yCXxQRqIFR7KtKxsdNG9sBkWZCVQ1dnD1/KIBHTMnxUZV48ACZXsgs543iG7wx8pKshJvMQ28DN7VAi98Dfa+hS1+LG/pBVxx7e3ETRyZjdJmjk7n8rmFvLiphiRrHAuLs7DEmThzYg4rd9cNad9tSCwJcOWjMPUSeO178NBSuOSPMOPKyJ1TiAipd7rJSLSyYEIW68ob2LCvkf0N7UzKS2FteT1VjT3//HV1+vjX5hoeXbOPvXYnxTlJnDo+o4czRNHedyHOBuPPiPZKhBBCiJjUa7pOKbUm8NGhlGrtdnMopVqHb4ki1lU2tjN2gMH6l+cV8c0lxcfN5FZKcfd5k7ntzAmU5PbfWK677pn1UPsh1rYamfVwBOtKGePbgmXwL2+p4eOKhr5f1FwJDy+D8vfgi/dxT/4jPJLxnREbqAfdfd4k4i0mlvx/9u47PKo6e/z4+05LMkkmvYeeAELoIB0BC3YR+669rXV1V92v29ffNnctu+7a+6prF1HXXkCQrtJ76CEhvUwymX5/f9yZSElCEjK5M5Pzep48wM2UgyZhzpzzOWdoZkt3xMlDM6m0u9hYWt8zQQw7D25eBrmj4Z3r4JNfQsVWbe2dEBGiyu4iPcHCiQNSqbC7eOiz7STEmHjsx2MAeOf7w6vrlXYXD3++nSn3f8Uv52/AbDTw8MWj+PiOGcSYOt6p1CN2fAb9p4X10R4hhBBCT21W1lVVnRb4tXMZk+hVPD4/B+qaOXdU7rFvfIhLT2x7cNy5o3I7/XgAGQkWqpvc/PS1NXyxpZwfT+zLr8484ag3BA5VYXdhtRhJiOmevcN5KVZKapvZVFrPnW+sxWI08PJ1EzlxQOrRN/a64I0rwH4QrnwP+k+jeNFCivKSuiUWPeUkxfHerdNIT7C0XDtpcAaKog26Gpmf3M69u1FCJlz5PnxyL6x4XPuwpmvr7vpPg35TIXNYxBwzEL1PVaOLjMQYTuyv/QxZtaeGy07sQ0FmItMK0nnr2xJ+OruQHRWNPPfNLhasKcXj93Py0EyumzaQSQNT2/0ZqJuaXVC9AyZcr3ckQgghRNjqyJ71QUCJqqouRVFmAiOBl1RVrQt1cCL8ldU58flV+nZwbVso5adaUVVYvKOSUfnJPLNkNx6fyu/PGdbmi9XyBme3VNWD8pLj2Hignj9/uIXkODMp8Rau/89q3rppCkOyj3jf67PfQtlabSha/2k4PT721TiYOzqv2+LR05F/37SEGEb3SWbh1gruPKUHJz+bLHD2wzDldtjzjXZOds9S2BKYkxmXAv2nw5y/QHLnjl4IEWqVjS7G9k2hIDOB1HgLNU1uLhqvfZ1eNL4PP31tDec9tpQNB+qJNRu4ZEIfrpnan4EZCTpHfgw7vtB+LZTz6kIIIURbOlJOfAcYryhKAfA08B7wKnBmKAMTkaGzk+BDae7oPAoyEyjKTcJsVPjLR1t4Zslu/KrKfecObzVhr2hwkZl4/MPlgvJT4qhpcrNsZzX3nTuck0/I5IInlnHl8yt55+Yp5KdYtTbsNa/AqqeoKrqO332fywMDveytdqCqUJgV5i+yj8PJQzN58LPtVNid3TKBv1NSB2gfY6/Q/ly3T0va934Dm9+H50+Hq96HtEE9G5cQ7aiyu0lPiEFRFGYOzmB7hZ0xgcGcpw3LIjMxhgq7k3vmDOHHE/uSbLUc4xHDxI7PIHWQfL8JIYQQ7ehIsu5XVdWrKMr5wL9VVf23oihrQh2YiAx7a5qA8EjWLSYDYw/Z2/6rM0/AYFB46utd+Pwqfzyv6KjBZuV2Z7e2ZAcH5g3MiOdHE/tiNhr4z7UnctGTy7n+uSW8M6WE+DVPQ+VWyD+Ru+vmsaj4ICPzk8lJ0pLXgszoTdZnBZL1RdsquXi8zlXs5L4wui+MvgxOvBFePl9L2K94F7KL9I1NCKDJ5aXZ4yM9QXtD8f4LRuLzqy1vPMaajXx190xiTAbMxgg6yuF2wJ4lMO4avSMRQgghwlpH/nX3KIpyGXAV8L/AtY7v6BJRbV+NA4vRQHY3tpJ3F0VRuPf0odwycxD/XbmPXy/YgN//w3AxVVWpaHCR1Y2V9WE5NkwGhd+eNazlxfPQeAefjFjMq/briP/s5/gVE8x9gk2n/ZdFxfXEmY08vXgXG0rqMSgwID16hy0Ny7GRbYvlqy0hXuHWWTmj4JpPwGCCF8+Ckm/1jkiIloGZGYGfURaTgTjL4UPiEmJMkZWog3YcxeuEwsgepCmEEEKEWkf+hb8GmAz8WVXV3YqiDABeDm1YIlLsq3aQnxoX2lVcx0GbLj+E22cX8Nqq/dw7f31Lwm4PVK2688x6YVYiG++bw6yhmdrk8fk/gX8UkbfhMTy5E7jM/Ruui30Yz4hLefybEhJjTDxx+Vhqmty8tGIv/dLiw29iczdSFIVZQzP5prgKt9evdziHyxgM134Cccnw0nmwe4neEYlertKuJeuHDmqMCjs+A1Mc9JumdyRCCCFEWDtmsq6q6mbgbmCDoihFaMPm/hbyyERE6Mratp6mKAo/P3Uwd5xcyJvflnDP2+vx+VUqAmvbMm3dV1kHrTWV+gPw3Gmw5QMYfy3c/h1ZP3mXs8+7mIXbq7j5le/4eEMZl0/ux8whmUwrSMft9Ud1C3zQ7KGZNLq8rN5To3coR0vpp1XYk/rAfy+Cii16RyR6sWBlPdgGHzV2fAYDTwJz+HVkCSGEEOHkmMl6YAL8DuAx4HFgu6IoM0Icl4gQJbXN2tC0MKcoCj87dTA/O2Uw73xfwm8WbKS8QXsh3O2DzlQVPrgDfG64aQmc+feWIUo/ntiPO08p5IstFZiMBq6Z2h+AO04pBKCwFyTrUwvSsJgMfBlurfBBthy4cgHEJMKbV4GrUe+IRC9V2egGfmiDjwo1u6FuLwyarXckQgghRNjryIC5h4DTVFXdBqAoymDgNWBcKAMT4U9VVRqcHlKskTPC4I5TCnF6fTyxaCfFFXYAsrq5ss7a/0Lx53D631qddHzHyYWYjQZssaaWNwom9E/lsR+NZUL/lKNuH22sFhOTB6axcFsFvztnmN7htC4xGy54Bl6aCx/eBec/CeG4q1pEtSq7C0WB1PgoaoPftUj7deAsXcMQQgghIkFHzqybg4k6gKqq25EBc1HF5fVx15vrKK1r7tT9HG4fqgoJsR15zyd83HPaEM4oymb1nloAMrtzOF7dPvjkV9B3ijZhvBWKonDrrAKumNz/sOtnjczp3ljC2OyhmeyuamJXZRhXrQfOhJn3wvrXtVV7QvSwqkYXKVZL5A2Qa8+uRZCYA+mFekcihBBChL2OvAL4VlGUZxVFmRn4eAaQUclRZGdFE+98X8LS4qpO3a/R5QUgISay3rsxGBQevng0I/KSSI23kBDTTW82eJrhjcsBFc57FAxR9AK7m80emgnAV1vDtBU+aMY9MOAk+OhuKN+kdzSil6m0u6JruJzfD7sXa2+ESaeKEEIIcUwdySZuBjYDPw18bA5cE1Gi2aMl3Q1Ob6fuZw/cPtIq6wBxFiOv3TiJt26a3D0PqKrwv59B2TqY93Sr7e/iB31SrRRmJrBwW5gn6wYjXPAsxCbBW1fL+XXRo6oaXdE1XK58AzTXaMm6EEIIIY6pI9PgXaqqPqyq6rzAxz9UVXX1RHCiZzjcPgAamj2dup/dqd0+sbsq0z0sIcbEoIxuGuj23Quw7jWY+UsYckb3PGaUmz00k1W7a1q+jsJWQqaWsFcXw+uXQe0evSMSvURVozu6hsvt+lr7dcBJ+sYhhBBCRIg2k3VFUTYoirK+rY+eDFKEVnMwWe9k0tTSBh+BlfVu5WyAL/8I/afDjF/oHU3EmD00E49P5ZsdRx+/qG/28NjCYq56flV4JPMDZsA5j0DJd/DYJFj8IHjdekclIty+agevrNiL0+Nr9fNRV1nftQjSh2gbF4QQQghxTO1lWWf3WBRCV82eYGW9c23wjcE2+AitrHebFU9orZ2n3ifn1DthXL8UbLEmvtpawRkjfnjxvmDNAX67YCP2wJtBWw/amdA/Va8wfzD2Sm3d1Cf3wld/hPVvwJkPavuiheiCF5bt5oWle3h2yS7+cv4IphSkt3yuyeXF4fZFT7LudcHeZdr3kRBCCCE6pL3Mwgzkq6q699APIJ+OrXwTEcLRxcq63SXJOo4aWP4oDD0b8mSbYWeYjAZmDM5g4bZK/H4VAJ9f5S8fbaFfupUHLhwJQHVjGFWwk/LhklfgR2+Bzw0vnQvv3AD2cr0jExGotK6Z9IQYVOBHz67k7rfWUdukfb1XNWqnzaJmwFzJavA2y5tbQgghRCe0l6z/E2ho5XpD4HMiSnT1zHqwsp7Ym9vglz4CLjvM+rXekUSk04uyqWp0tQyaW1pcRYXdxa0zC5hemAFAdVMYjsgYfBrcsgJO+j/YvAAeHQ/r39Q7KhFhDtY7GZZr49M7Z3DLzEEsWHOAkx/+mnfXlLQk61FzZn3XIlAM0H+a3pEIIYQQEaO9ZD1LVdUNR14MXOsfsohEj2t2a0m3vZPT4INn1uN7a2W9ZjesfApGXAhZw/SOJiLNGZ5NTlIsz32zG4D535dgizUx+4RMUuO1imJYVdYPZY6DWb/SkvaMIfDBHdBcq3dUIoKU1jvJscUSazbyi9OH8sHt0+ibauVnb6zjzjfWAkRPG/yuRVr3UWyS3pEIIYQQEaO9ZD25nc/FdXcgQj9dbYNvdHmJNRswG3vhOW1VhQ9+CgYTnHKf3tFELLPRwFVT+rNsZzWrdtfwyaaDnDMqlxiTEYvJgC3WRE1TmCbrQWmD4KyHweOANa/oHY2IEG6vn6pGFznJsS3XTsix8c7NU7jv3OHUNmk/j7NssW09RORw1sOB72UKvBBCCNFJ7WVZ3yqKcsORFxVFuR74LnQhiZ7W9dVtXhJjzaEIKfyteQV2L9aGyiXl6R1NRLtsQl+sFiO3vvo9To+feWPzWz6XlhDT0g4c1nJGQr+psOpp8Lc+2VuIQ5U3OFFVyEk6PBk3GhSumtKfz38+g5euPTE62uD3LAXVJ/vVhRBCiE5qL1m/E7hGUZRFiqI8FPj4GrgOuKNnwhM9Ibg2yO7ytgz66ohGlzdid6wfl9I18OmvteRs3DV6RxPxkqxmLhqXT6XdRf80K2P7/tDUkxZvCf/KetCJN0LdPtj+id6RiAhQVu8EICep9Ua1nKQ4ZgzO6MmQQmf312CKgz4n6h2JEEIIEVHaTNZVVS1XVXUKcB+wJ/Bxn6qqk1VVPdgz4YmeEKysqyo0ujt+br3R6eldO9b3LodXLoCnZ2or2s75l6xq6ybXTB2AyaBw0fg+KIrScj013hK+Z9aPNPRssOVpcwyEOIay+mYAcpOjoM39WHYtgn6TwRQFXQJCCCFEDzpmpqWq6kJgYQ/EInQSTNZBa4W3dbC1vdHljf61baoKO7+CJQ/B3qVgTYPZv4UTb5BBSd2of3o8X90187Dzu6C1wX+/L0KGthlNMOF6+PI+qNgCmSfoHZEIY8HKenYblfWo0VAGlVth9I/0jkQIIYSIOCErCyqK8ryiKBWKomw85NpoRVFWKIqyVlGUbxVFOTFwXVEU5V+KohQrirJeUZSxoYpLHK3Z80M1vaG545V1uzOKk3W/H7Z8AM/MglfmaZPfT78f7twIM+6WRD0E+qZZjxpWmJ6gtcF35niGrsZeCUYLfPei3pGIMFdW10xirCl6f4YG7V6s/Tpwpp5RCCGEEBEplD28LwKnH3Ht72it9KOB3wX+DHAGUBj4uBF4IoRxiSM43D5MBq312N6JifCNLm/0tsF/8n/wxuXaKq5zHoE71sKkm8Fi1TuyXiU13oJfhbpODj/UTXw6nHAOrHsNPM16RyPCWFm986jhclFHVWHTfIhLhawRekcjhBBCRJyQJeuqqi4Gao68DNgCv08CSgO/Pw94SdWsAJIVRckJVWzh4PVV+3hh6W69wwCg2e0jMzBxuKETu9ajdsDct89rU70n3gy3fQfjrpazljpJC+yYrmmKgInwQeOu1lZVbVqgdyQijGnJepS3wK95RRu4OOU2me8hhBBCdEFP/+t5J/CAoij7gQeBXwau5wH7D7ldSeBaVFq3v45fL9jIf5bt0TsUQKusZwUqPB1d36aqKo3OKKys7/kGProHCk6FOX/WziEL3aTFWwCoamfI3Bur97F4e2VPhXRs/adD6iBphRftKqt3Rvdwucpt8PEvYMAMmHqn3tEIIYQQEamnk/WbgZ+pqtoH+BnwXGcfQFGUGwPn3b+trAyjF+gd5PT4+Pmba/H5VSrs4VEtbPb4yEoMJOsdbIN3ef14/SoJMVG0Z712D7xxBaQMgAufA4NR74h6vbQELVlvayL8vmoHv3p3I394fxOqGibn2hVFq67vX6ENmhPiCC6vj6pGF9m2KK2se5zw9rVgjoPzn5afpUIIIUQX9XSyfhUwP/D7t4Dg0tUDQJ9DbpcfuHYUVVWfVlV1vKqq4zMyIm8H7QOfbmNnZRPTC9NxuH00uTredh4qzW4fWbZAG3wHB8zZA+3yUVNZd9nhtctA9cGP3pABcmEiLb79NvinFu/E51fZVdXE2v11PRla+0b/ODBo7j96RyLCUHm99vV85PaDqPH576B8I8x9AmxRfaJNCCGECKmeTtZLgZMCv58N7Aj8/n3gysBU+ElAvaqqZT0cW8htKWvg+aW7uXxSX+aO1rr89a6uq6qKw+0lMdZMvMXY4cp6Y+BNhqg4s+51wfyfaOuFLnoR0gbpHZEISLFqnRuttcFXNDh569sSzhmVS4zJwPzvW31/Tx/xaTB4Dmx8G3z6vyEnwkvLjvVoPLO+9SNY9RRMukX7HhBCCCFEl4VyddtrwHJgiKIoJYqiXAfcADykKMo64C9ok98BPgJ2AcXAM8AtoYpLTw99tp2EGBP3nDaUzEAlu1LnZN3l9eNXIc5ixBZn7vCZ9cZgZT2Sk3VVhY3z4dEJsO1DmPNXGDRb76jEIUxGAylWMzVNRyfrz36zG6/fzz2nDWHO8Gw+WF+K2+vXIco2jLwUmiph10K9IxFh5ocd61FWWa8/AO/dAtkj4ZQ/6B2NEEIIEfFClmmpqnpZG58a18ptVeDWUMUSDr7fV8sXW8q5+7TBJFnNZCSGR7Le7PYBYLUYscWaO1xZt7u020VMG7zXBfUlULcP6vbCvhWwaxHYyyBzOFw+HwpO1jtK0YrUeAvVR7TB1zncvLJiL+eOyqVvmpV5Y/N4f10pC7dVMGd4tk6RHqHwNIhLgXWvQ+GpekcjwkhpoLIeVavb/D6YfyN43XDhC7JBQwghhOgGEZJpRb4HP91GWryFa6YOACAzMNCtwu7UMywcnkOS9ThTy1n0Ywnryrq7CRY/qCXm9fu1X+0H0TYHBljTtCnFQ86EogtkAFIYS0uIOaoN/oWle3C4fdw8swCAaQXpZCTGMP/7kvBJ1k0WGD4P1r6qzUSISdQ7IhEmDtY7scWaiA/Hn59dteQh2PuNdk49vUDvaIQQQoioEEWvFMLX0uIqlu2s5ndnD2t5cZYcZ8ZkUMKgsq4l3bFmrbJe3sE3D1rOrIdjZd1ogeWPaYONkvrAoJMhuQ8k99U+kvpoH7L3NyKkxVvYUdHY8udGl5cXl+3h1GFZDMnWEmCT0cA5I3N5ecUeXF4fMaYwefNl1KXw7XOw+X0Y82O9oxFhorTOSW5yFJ1X37scFv0VRlwMo9pqqhNCCCFEZ4VhphV96hweRuQl8aOJfVuuGQwK6Qkxug+Ya3ZrZ3ytFhOJsSZ2VHSwsu4K48q60Qy/PijJeJRIS7CwYtcP3yevrtxLfbOHW2cdXr0bmp2Ix6dSaXeRn2Lt6TBblz8BUgfC+tclWY8S3+2t4fVV+/nrvBGYjF37GXOwoTm6WuA/+7X2BuhZD2mrC4UQQgjRLSSb6QFnjczh/dumEms+vNqXaYvRvbLuCFTWrcEBcx09sx7uq9skUY8aafEx1DV78Pr8OD0+nlmym2kF6Yzuk3zY7YJDG8sb9P2eOoyiaGvcdi+GsvV6RyO6wftrS3nruxIWrC3t8mOU1TnJjpZJ8M11ULpGq6jH2vSORgghhIgqktH0EKWVakNGQhgk64Ez63HBAXPNHrR5f0f7z7I9/PtLbdteo8uLxWgIn3ZjEbXSEiyoKtQ6PLz1XQmVdhe3zDp6vV7LHIgGfedAHGXC9RBjg8UP6B2J6AbFldqRjEe/2oHX1/ntA06Pj+omN7nRUlnftwJUP/SfpnckQgghRNSRZF1HmbZwaIM/fMCcX4WmwLVDbS5t4P/9bzPPL92Nqqo0Or3hW1UXUSUtPlgxd/LU1zsZ0zeZyQPTjrpdlu2H24WVuGSYeBNseR/KN+sdjThOxRWN5CTFsqfawfvr2q6uOz0+th5s4OMNZazbX9dyPfj1GTVr2/YsAWOMduRDCCGEEN1Ksi0dZSTEUNPkwudXMRr0OefnCCbrZhO2WDMADc2ew86i+/wqv5y/Hp9fpdbhobLRRaPLG57n1UXUSY23APDisj2U1DZz37nDW+1USbFaMBsV3d8Aa9Wkm2HF41p1/aIX9I5GdFGD00N5g4t75gzhg3WlPPpVMaP7JLO32sGuqiZ2VzWyu6qJ3ZVNlNb/8KZRXnIcS++dDWjD5YDoGTC35xstUTdHyZsPQgghRBiRbEtHGbZY/CpUN7rItOnzQic4DT4ucGYdOGp920vL97CupJ5LJ/Th9dX72VHeiN0pybroGekJWrL+zvclDM1OZPbQzFZvZzAoZCTEhNeZ9SBrKpx4I3zzDxhxEQw9U++IRBfsqmwCoCAzgTtOLuTm/37P7Ie+bvl8YqyJgRkJTByYxoD0eAakx7NsZzWvrdqH0+Mj1mykLJp2rDfXwcH1MOMXekcihBBCRCXJtnSUkaC17VbY9UvWHYe2wQcr64cMmTtQ18wDn27jpMEZ3HXaEF5fvZ9tB+00ujzSBi96RFrg+0RV4ZZZBa1W1YMybbFUdHD9YI+bcjts/xRevwxG/QhO/wvEpegdleiE4sAKwYLMS2OSugAAIABJREFUBAakxfOnuUVYTAYGBhLz1HjLUV+fHp+f11bt40BdM4MyEigLVNxzomHAnJxXF0IIIUJKsi0dZSRqSUhlo36VwGCyHmvWzqyD1gYPoKoqv12wEVWFP80tIj3BQmq8he3lduxOL9k6vcEgepfkODMGBfqmWjlrRE67t82yxbC7qqmHIuskayrcuBC+/rtWYd+1EM55BAbP0Tsy0UHFFY2YjQr9Uq0YDAqXT+p3zPv0TdXWCO6vcQSS9WaSrWbiLFEwnFPOqwshhBAhJQPmdJQZTNZ1bNt1enzEmAwYDcpRlfWPNhzkq60V3HXaYPqkWlEUhcFZCWwvt2tn1qWyLnqAwaBw00mD+OPcomPOdsiyxYbnmfUgUwyc/Fu4/gutqv7qxfDuzdBcq3dkogOKKxrpnxbfqf3qfYLJeq3W/n6w3hk9b3TKeXUhhBAipCRZ11G4VNatgQpPYmywsu6l3uHh9+9voijPxtVT+rfcfnBWItvlzLroYb84fSjTCzOOebvMxBjqHB6cnqM3GoSVvLFw4yKYfjesfwMen6y1yIuwtquykYLMhE7dJyMhBovJwP4aB6ANmIuK4XLOeu28urTACyGEECEjybqOYs1GbLEmXfdCa8m6lnQnHjIN/v5PtlDrcHP/vJGHVZEGZyXS6PJS0+SWyroIO8HZD5XhXF0Paq3KvuFtvaMSbXB7/ewNtLJ3hsGgkJ8S15Ksl9U3R8dwud2LtfPqA2boHYkQQggRtSRZ11lGYoyulfVmj7fl7KTFZCDObOSrbRW8tmo/100bQFFe0mG3H5Kd2PL7RKmsizCTFUjWw27XenuCVfbMYdpZdlXVOyLRij3VTfj8aqcr6wB9Uqzsr3Xg9PiodXiiI1kv/hIsidDnRL0jEUIIIaKWJOs6y0yM1bUKeGgbPIAtzsSafXXkp8Rx5ymFR91+cOYPybq0wYtwE5wDEdbn1ltjioGJN0H5Ru0csAg7h06C76y+qVb21zRHzyR4VYWdX2pVdaNZ72iEEEKIqCXJus4yEmN0TSwcbh9x5kOS9UAr/J/mFrW0xx8qyWomy6YlRAmx8iJNhJeIrKwHjbwY4lJh5ZN6RyJaEUzWB2bEd/q+fVLjqG/2sO2gHYCc5AivrFcXQ90+KDhZ70iEEEKIqCbJus4yEmN0raw3u32HrRCaXpjB1VP6M3NIZpv3GZylVdelsi7CTYrVjNmoUK7jhoUuM8fBuKth64dQu0fvaMQRdlY2kpcc1+qbmMfSJ0WbCL9qdw0QBZX14i+1XyVZF0IIIUJKknWdZSbG4HD7aHR5dXn+Zs/hbfC/O2cYfzh3eLv3CSbriTJgToQZRVHITIzVdWjjcZlwPSgGWPWM3pGII+ysbGRQF1rg4Yf1bav3BJP1CK+s7/wSUgdBSn+9IxFCCCGimiTrOguub9sZaLHsac1uH3HmziXdQ6SyLsJYpu2HoyV2p4cGp0fniDohKQ+Gz4Xv/gPNdXpHIw5RZXeTHTgC1FnByvqm0npSrGZiDzl6FHG8Lm2uglTVhRBCiJCTZF1n0wszSI238H/vrNdlN7TD7T2sst4RZ47M4Z45Q46aFC9EOMhKjG05s37NC6u54tmVqJE0YX3qneC2w2qproeTBqenZaZHZyVZzdhiTfjVKGiB37ccPA4oOEXvSIQQQoioJ8m6zjISY3joolFsPWjnzx9u6fHnP3IafEckxJi4dVYBRoMSoqiE6LosWwzlDU7W7a/j2721rCupZ11Jvd5hdVzOSCg4FVY8AW6H3tEIwOPz43D7SDyOoZrBVvjcSB8ut/1TMFqg/zS9IxFCCCGiniTrYWDW0ExumD6Al1fs5YvN5T32vD6/isvrP2zAnBCRLtMWS4PTy9NLdmG1GLFajLy6cq/eYXXO9LvAUQ3fv6R3JAJodGozRWxxXT/6E2yFz47k8+p+P2x6V3szydL5qfhCCCGE6BxJ1sPEPXOGkpkYwwfrS3vsOZsDbfedrawLEc6Cu9Y/XF/GBWPzOXdULh+sK4uss+v9JkPfybDs3+B16x1Nrxf82ulqGzxo69sgwtvg9y0HexkUzdM7EiGEEKJXkGQ9TFhMBkb1SWbDgZ5r13W4tWpRXBdWEQkRroK71gGunNyPH03sS7PHx4I1B3SMqgum3wUNJbDuVb0j6fUamrWflcezAaNvoA0+oifBb3wHzFYYcobekQghhBC9giTrYaQoN4ndVU09tsbN6fYDEBfJk4mFOEIwWZ8yKI3CrERG5idTlGfj1ZX78Pr8OkfXCQWnQN54WPygVNd1Zg9W1uO6XlkflKGtfeuXFqHt4z4vbF4Ag0+XFnghhBCih0iyHkaK8myoKmwpa+iR53N4tDcFpA1eRJN+aVbG9k3mpycXtly7anJ/th60M+PvC3li0U7qHBGQ/CoKzPol1O+HNS/rHU2v1h1t8JMHpfHOzZMZ2ze5u8LqWbsXaXMUii7QOxIhhBCi15BkPYwEV6Ft7KFWeIdbO7MuA+ZENIk1G5l/y1QmDUxruXbhuHyeuXI8/dPj+dsnW5n01y/51bsb2FFu1zHSDhh0MvSZCEse0vZbC110Rxu8oiiM65eKokToFo2N8yHGJivbhBBCiB4kyXoYyUyMIT0hho0Heqay3hxI1q3SBi+inKIonDosi1dvmMTHd0znvFF5vP1dCaf+YzFXPLeSr7dX6h1i6xQFZv4SGg7A+7dDY4XeEfVKDd3QBh/R3E2w+X0YejaYI/jMvRBCCBFhJFkPI4qiMCLP1uOVdasMmBO9yAk5Nv524UhW/PJk7pkzhO3ldq56fhWr99ToHVrrBs6EqXdow70eGQ2L7gdXo95R9SoNTi+KAokxvfRn5cb54LbD2Cv1jkQIIYToVSRZDzNFeUnsqLC3VL1D6Ydp8FJZF71ParyFW2cV8OVdMzEbFb7cEqZVa0WBU/8f3LoKCk+BRX+Ff42B1c+BL4LW0UUwu9NDgsWEwRChLezH67sXIX0I9J2kdyRCCCFEryLJepgZnpuEX4WtB0PfCt/SBi/JuujFEmJMjO2bwpIdYdoKH5Q2CC5+Ca77Qvv9hz+HxyfDlg9AVfWOLqo1NHt7bwv8wQ1w4FsYd7X2xpEQQggheowk62GmKM8GwMbSHkjWPZKsCwEwY3AGm0obqGqMgCFufSbANR/Dpa9pydMbl8Pzc2DfSr0ji1oNTs9xDZeLaN/9B4wxMOpSvSMRQggheh1J1sNMXnIcKVYzm3rg3HrwzHqsDJgTvdz0wnQAlhZX6RxJBykKDD0Tbl4O5zwCtXvg+dNg/o1SZQ8Bu9NzXGvbIpa7Cda/CcPngjVV72iEEEKIXkeS9TCjKApFeUlsLA19st7s9mFQIMYkXwaidxuem0Sy1cySHRGSrAcZTVp78k/XwKRbYf0bsOMzvaOKOlobfC+rrDsb4NVLwFUPE67XOxohhBCiV5IsLQwNz01i20E7Lm9oh8zVN3tIijNH7t5fIbqJ0aAwtSCdJTsqUSOxMm2Jh1Pvg6S+2k72SPw7hLGG3lZZt5fDi2fCvuUw7xnoc6LeEQkhhBC9kiTrYagoz4bHp7KjPLTrmWocblKslpA+hxCRYkZhOuUNLnZUROhaNKMZptwO+1fC3mV6RxNV7E5v7zmzXrNLO1JRvRMuewNGXqx3REIIIUSvJcl6GCrKTQII+b71OoebZGsvqhYJ0Y5phRkAkdcKf6gxl4M1Hb55WO9Ioobfr2pn1nvDNPiydfDcHK0F/qoPtFWBQgghhNCNJOthqF+alcRYU8jPrdc2eUiNl8q6EKANdxyclcAnG8v0DqXrLFaYfAsUf6ElXuK4Nbm9+FWivw1+92J44SwwWuDaTyF/vN4RCSGEEL2eJOthSFEUhufa2HggtOvbtMq6JOtCBJ03Oo/Ve2rZV+3QO5Sum3A9xNhgiVTXu4Pd6QWI7jb4ze/BKxdAUj5c9xlkDNY7IiGEEEIgyXrYKspNYktZA16fP2TPoZ1Zj/JqkRCdMHdMHooC7645oHcoXRebBBOu0xKwqmK9o4l4DU4PQPS2we/6Gt68CnLHwDUfQVKe3hEJIYQQIkCS9TBVlJeEy+unuDI0w66cHh9Oj18q60IcIi85jskD05i/piQyp8IHTboFTDGw9J96RxLxGpqjvLK+7N+QmA1XLJBd6kIIIUSYkWQ9TBXl2QBC1gpf63ADyJl1IY4wb2w+e6sdfL+vTu9Qui4hE8ZcAeteh/oI7hIIA/ZgZT0az6zX7NLmG4y7Wpt3IIQQQoiwIsl6mBqQnoDVYgzZRPjaJu0FqLTBC3G404uyiTMbmf99id6hHJ8pt4Pqh+WP6h1JRIvqNvhvXwDFAGOv0jsSIYQQQrRCkvUwZTQoDMuxsSlEE+GDlXVpgxficAkxJuYMz+J/68vw+yO4FT6lH4y4CL57EZqq9Y4mYkXtgDlPM6x5GU44G2w5ekcjhBBCiFZIsh7GivKS2FTagC8ECUMwWU+RZF2Io0wtSKe+2cOuqia9Qzk+034GHgesekrvSCJWQ7NWWY+6ZH3Tu9Bcq20PEEIIIURYkmQ9jA3PteFw+9gdgoSh1hFog4+PwtZOIY7TyPxkgJAdQ+kxmUNh6Nmw8klw2fWOJiI1OL3Emg3EmIx6h9J9VBVWPgXpg6H/dL2jEUIIIUQbJFkPY0V5SQAhaYWvawq0wcdJZV2IIw3KiCfWbGB9SYQn6wDTfg7Oeu18sug0u9NDYrQNl9u7FMrWwqSbQVH0jkYIIYQQbZBkPYwVZiYQYzKEpLpX43CTEGPCYpIvASGOZDIaGJZji/zKOkD+OBhwEix/DDxOvaOJOA3NXmzR1gK/7FGwpsGoy/SORAghhBDtkEwtjJmMBobm2EKyvq3O4SFZJsEL0aaR+clsLK0PycyIHjf959B4ENa9qnckEafB6YmuSfBVO2D7x9pZdXOc3tEIIYQQoh2SrIe5olwbG0vrUdXuTRhqHW7ZsS5EO4rykgIzIxr1DuX4DTgJcsfC0kfA59U7mojS4PRGVxv88sfAGAMTbtA7EiGEEEIcQ8iSdUVRnlcUpUJRlI2HXHtDUZS1gY89iqKsPeRzv1QUpVhRlG2KoswJVVyRpigvCbvTy74aR7c+bm2TW9a2CdGOkfnazIioOLeuKFp1vXYPbF6gdzQRxd7siZ42+KYqWPcajLoEEjL0jkYIIYQQxxDKyvqLwOmHXlBV9RJVVUerqjoaeAeYD6AoyjDgUmB44D6PK4oSRaN3u64oV0sYursVvtbhIUXa4IVo06CMBOLMRjYc0Dpbfv7mWu7/eKveYXXdkLMgfQgseVibBi46JKra4Fc/B14nTL5N70iEEEII0QEhS9ZVVV0M1LT2OUVRFOBi4LXApfOA11VVdamquhsoBk4MVWyRZHB2AmajwsZunghf63DLjnUh2mE0KAzLtbGhpJ4Faw8w//sDvLhsN42uCG0jNxi0vesVm7RVbpKwd4jWBh8FlXWPE1Y/A4WnQcYQvaMRQgghRAfodWZ9OlCuquqOwJ/zgP2HfL4kcK3XizEZGZyV2K1TqT0+P3anV5J1IY5hRF4Sm0obuO+DzeQlx+H0+Plk40G9w+q6ERdCv6nwyb3wn3OgcpveEYU1p8eH2+vHFg1n1je8CU2VUlUXQgghIoheyfpl/FBV7xRFUW5UFOVbRVG+rays7OawwlNRrpYwdNeQuTqHB4CU+Ch4ASpECI3IS6LZ48Ph8vHiNRPom2plwZoDeofVdUYzXPU/OPsfcHA9PDEVvrgP3N07EyNaNDi1n5UR3wavqtpguewRMGCG3tEIIYQQooN6PFlXFMUEzAPeOOTyAaDPIX/OD1w7iqqqT6uqOl5V1fEZGb1jQE5Rno2aJjel9d2zI7nO4QaQAXNCHMO4fikoCvz05AIKsxKZOyaPpTurONhN34u6MBhg/LVw23dapf2bh+HxibD9U70jCzt2p3bkIaIHzPn9sOppqNwKk2/Xhg0KIYQQIiLoUVk/BdiqqmrJIdfeBy5VFCVGUZQBQCGwSofYwtLwvOCQue5pha8NVtZlwJwQ7eqfHs/ie2Zx66wCAM4fk4eqwvvrIri6HpSQAec/qVXaTbHw6sXwxuVgL9c7srDR0Kz9rIzYM+v7VsJzp8DHv4A+k2D4+XpHJIQQQohOCOXqtteA5cAQRVFKFEW5LvCpSzmiBV5V1U3Am8Bm4BPgVlVVfaGKLdIMy7FhNChs6rZkXausy5l1IY6tT6oVJVCNHJAez+g+ycz/PgqS9aAB0+GmpXDy72DH5/DW1TJ8LqDlyFCk/ays3av9f3z+NGgohblPwDUfgynC/h5CCCFELxeycoGqqpe1cf3qNq7/GfhzqOKJZLFmIwUZCWws7Z71bbVNgWQ9Xl64CdFZ88bm8bv3NrGlrIETcmx6h9M9TBaYfhdY0+GDn8L6N7Vd3L1cTVOEvbHpbNCONSx/HBQDnPR/MPUOsMTrHZkQQgghukCvAXOik4bn2aQNXogwcPbIXEwGJbIHzbVlzBWQNw4++w04u3ddZCRq6UIK9zc2/T747kX491j45h8wfC7c/h3M+pUk6kIIIUQEk2Q9QhTlJlFhd1HRcPRgq+3ldrYe7HjVvc7hxmIyEGc2dmeIQvQKqfEWZg7JYMHaA/j8UdYubjDAWQ9pK74W/kXvaHRX63BjNCjhPWBu7zJ4agZ8cAekDoTrv4J5T0OSbD8VQgghIp0k6xFiRH5gyFzp0dWu3723kXveWt/hx6ppcpNqtbScwxVCdM7cMXmUN7hYsata71C6X+4YGH8NrHoGKrbqHY2uah0ekuPM4fuzsqkaXp6ntb9f+AJc+ynkj9M7KiGEEEJ0E0nWI8QJOTYUBTYeOLqCXtHgYlu5Ha/P36HHqnV4SJYWeCG67JQTskiMMUXXoLlDzfo1WBLg89/qHYmuapvc4d0C/+3z4G2GH78FRfNkLZsQQggRZSRZjxAJMSYGpMe3em69usmN2+tnb42jQ49V53BHzsAkIcJQrNnIGSOy+WRjGc3uKFxcEZ8OM+6GHZ9B8Zd6R6ObWoc7fGd7eF2w+hkoOAUyh+odjRBCCCFCQJL1CFKUm8SmIybCe3x+6gO7gLcdtHfocWodblLiw/QFqBAR4vwx+TS5fXy2+aDeoYTGxJ9Acj9t2Jw/Ct+Q6IA6hyd839jcOB8ay2HSLXpHIoQQQogQkWQ9ghTl2ThQ19yyTgh+mFYMHUvWS2od7Ktx0CfFGpIYhegtJg5IJTcpNjqnwgOYYuDU/wcVm2H9G3pHo4uapjDtQlJVWPEYZJwAg2brHY0QQgghQkSS9QhSlBcYMndIK/yhiXtHkvVHvtiBoihcNaV/t8cnRG9iMCicNyaPxTuqqLS79A4nNIadB9kjYMlDva66rqqqVlkPxzPre5bAwQ0w6WY5py6EEEJEMUnWI8jw3KMnwlc3asl6aryF7eXtJ+vFFY28830Jl0/sR25yXOgCFaKXmDcmD59f5YN1pXqHEhqKAtPvhupi2LxA72h6VJPbh9vnD88z69/8E+IzYeQlekcihBBCiBCSZD2CJMWZ6ZtqZdMhE+GrA5X1yQPT2FPdhNPTdvXrH19sJ9Zs5JZZg0IeqxC9QWFWIsNzbSxYG6Wt8AAnnAvpQ2Dxg+Dv2MaJaFAb+Nkadm3wZetg55cw+RYwx+odjRBCCCFCSJL1CFOUZzussl7TqLXfThqUhl/VqudHUlWVN7/dz4fry7h26gDSE2J6LF4hot35Y/JYX1Lf6vdeVDAYYPpd2tn17R/rHU2PCc4DCbs2+G/+ATE2GH+t3pEIIYQQIsQkWY8ww3Js7K124HB7Ae3MuqLApAGpAGw94tx6vcPDba+t4Rdvr2figFR+ctLAHo9ZiGh27qhcDArRO2gOoOgCSOkPix/Qhpv1ArUObctGWLXBV++Eze/BhOsgNknvaIQQQggRYpKsR5g+qdoU99K6ZgCqmtykWi0MSI/HYjIcdm595a5qznhkMZ9uPMg9c4bw6g2TSIwNoxeeQkSBTFssUwvS+d/6KD23DmA0wbSfQ+karQW7F2hpgw+nyvrSf4LBLOvahBBCiF5CkvUIkxcYDLe/VkvWaxrdpMZbMBkNFGYmsPWgHY/Pz4OfbuOyZ1ZgMRl45+Yp3DqrAKNBpgYLEQqzhmSyp9rB/hqH3qGEzqjLwJYHX/eO6npLG3y4nFmv3A5r/gvjr4GETL2jEUIIIUQPkGQ9wuSlaMn6gWCy3qQl6wBDshLZeKCeC59czqMLi7lwXD4f/nQ6o/ok6xavEL3B9MJ0AJYWV+kcSQiZLDD1Tti/AvYu1TuakKsNHDFKiguTbqQv7wOzFWbco3ckQgghhOghkqxHmMzEWEwGhQOBNvjqJhdpCYFkPTuRmiY3e6qaePzHY/n7haOIjzHpGa4QvUJBZgJZthiWRHOyDjD2Cm1l2KL7wRulu+UDah0ekuLM4dGRtG8lbP0fTLsD4tP1jkYIIYQQPUSS9QhjNCjkJMe2WlmfOyaP66YN4OM7pnPmiBw9wxSiV1EUhakF6SwrrsLvj+IWcXMczLgb9iyBR0bDiifBHVmt/+UNzg79P6pxaPNAdKeq8PnvICFbzqoLIYQQvYwk6xEoLzmOA3XNeH1+ah0e0uK1VWxZtlh+e/YwcgPn2oUQPWd6YTq1Dg+byxr0DiW0TrwRrngXUgfAJ/8H/xyhrRNzhv/fu8nlZcbfF/LowuJj3rbO4SY5HCbBb/tYO3ow816wxOsdjRBCCCF6kCTrESg/xcqB2uaW1ULBNnghhH6mDtLak5fsiPJWeEWBQbPhmo/gmo8hZxR88QctaV/4V3DU6B1hm8obnLi8fp5Zsov6Zk+7t61p8ug/XM7n1f7bphXCmCv0jUUIIYQQPU6S9QiUlxxHud1JeYMToKUNXgihn0xbLEOyEqN7yNyR+k2BK+bDDV9B/2nw9f3wr9HaPvAwVBNYx2Z3enlh6e52b1vncOu/tm3tf6FqG5zye219nhBCCCF6FUnWI1BeShyqCptK6wFJ1oUIF1ML0lm1pwanx6d3KD0rbxxc+l+46Rvw++HTX+kdUauqA8l6/zQrz3+zmwZn29X1miY3KXq2wbsdsOivkH8iDD1bvziEEEIIoRtJ1iNQfuBM+roSLVlPT4jRMxwhRMD0wnTcXj/f7qnVOxR9ZI+Ak+6B7Z/Aji/0juYowcr6b84aRoPTy/PftF5db3b7cHn9+lXWy9bBS+eCvQxOvU87eiCEEEKIXkeS9QgU3LW+oUQq60KEkxMHpGI2KiwprtQ7FP1MvAlSB8In94Kv/XPhPS2YrE8rTOeMomz+9eUOXl+17+jbObTb9fiZ9eY6+OgeeHom1O6Bec9qRw2EEEII0StJsh6BcpLiUBTYerABRdHhBaUQolXxMSbG9E3pXefWj2SKgTl/heodsPJJvaM5THWjm3iLkVizkYcuHsX0wgzunb+BxxYWo6o/rHOrberhZF1VYe1r8Oh4WP0sTLgebvsWRl7UM88vhBBCiLAkyXoEspgMZCbG4PGpJMeZMRqkRVKIcDG9IJ1NpQ0tVdyOiqr97IPnQOFp2nT4+hK9o2lR3eQiNbA9w2ox8exV4zl/TB4PfLqN+z7Y3PL/oLalst4DZ9bLN8ELZ8CCmyC5H9ywEM58AOKSQ//cQgghhAhrkqxHqLzAufU0Oa8uRFiZWpiOqtKp6nppXTNj//Q5n28uD2FkPUhRtIRT9Wnt8GGipslNWvwPPzPNRgMPXTSK66cN4MVle/jp62tweX0tazFDfsRo/Zvw5HSo3Abn/huu+xxyR4f2OYUQQggRMSRZj1B5KVZAzqsLEW5G5iWRGGvqVLL++KJi6hweNh6oD2FkPSylP5z0C9jyAWz7RO9oAK0NPu2In5kGg8KvzzqBe88Yyv/Wl3Hdi99SUusAIDmUbfBuB3z2G8gdA7d/B2OvBIP8kyyEEEKIH8grgwjVUlmXZF2IsGIyGpg8MI0lO6oOOwfdlrL6Zt5crbWKlzc4Qx1ez5p8O6QPgQ9/Do36D92raXK3+ganoijcdNIgHrhwJMt3VfPwZ9sBSA5lG/yqp6GxHE77E1hTQ/c8QgghhIhYkqxHqOBEeKmsCxF+phemc6Cumb3VjmPe9olFO/GrKjlJsRyMtmTdZIF5T4GjGt68ArydO8ffnVRV1ZL1hLZ/Zl40vg9PXzEOk1HBFmvCbAzRP5HOBlj6Tyg4BfpNDs1zCCGEECLiSbIeofLlzLoQYWtqQToAS47RCn+w3snrq/Zz4bh8hucmcbA+ypJ10Nq8z3sM9i2Hj+7SJp/rwO7y4vb5j9mNdPIJWbx90xT+dsHI0AWz4nForoXZvwndcwghhBAi4kmyHqHyU6QNXohwNSA9nmSrma1lDe3e7smvtar6rbMKyE6Kib42+KARF8L0u+D7l2DZv3UJoaZRq+ofOmCuLUV5SZwxIidEgeyCpf+CE87R3sgQQgghhGiDJOsRalBGAnefNpgzRmTrHYoQ4giKopBqtVDX7GnzNuUNTl5dtY95Y/Pok2ol2xZLrcOD0+PrwUgP9+bq/WwoCdGQu1m/geHnw+e/hQ1vh+Y52lEdWKXXXht8yPl98O5NYDDB6ffrF4cQQgghIoIk6xHKYFC4bXYhmYmxeocihGhFktVMvaPtZP3Jr3fi86vcNqsQgCyb9r1c0eDqkfiOVNvk5t7563ni6+LQPIHBAHOfhH7TtIR1/Zvga/u/T3cL7r3XtRtp6SOwfyWc9SAk5esXhxBCCCEigiTrQggRAilWC3XNrQ9Uq2hw8urKfZw/Jo++adoaxuwkLVk/dMicz99z57sX76jEr8K6/SFcH2eOhUv/C5lDYf4N8I8i+OrPULc/dM8ZUNOkvQmi21DO/ath4V9g2Hkw4iJ9YhBCCCFERJFkXQghQiA5zkxdG5X1pxbvwutXuW1WQcu1bNtKwuV+AAAgAElEQVThyfrS4ipG/OFTdlY2hj5YYOHWCgAO1DVTYQ/h2fm4ZLhhEVz2OuSMhMUPwCMj4dVLtH3s/tAcA6jqxJn17n/yHfDqxVo1/ex/gqL0fAxCCCGEiDiSrAshRAi01QZfaXfx35V7mTs6j/7p8S3XswKV9fLARPgVu6pxuH089fXOkMfq86t8vb2SAYF41oeyug5gNMGQM+DHb8Gd62Haz6F0Dbx2CTwyCta93u1PWdPkJs5sJM5i7PbHblf9AXh5HigGuGK+7FQXQgghRIdJsi6EECGQHGfB7vLi8fkPu/704p24vX5um11w2PXEGBNWi7Glsr71oB2Ad9ccoKy+OaSxrt1fR63Dw80zB2E0KKwrqQvp8x0muS+c/Fv42Sa4+CVIyIIFN8Puxd36NDVNbtJ6aric2wEb34FXL9XefHBUa29MpA7smecXQgghRFSQZF0IIUIg2WoGoP6QifBVjS5eXqFV1QccUlUHbYJ8ti22JVnfdtDO6D7J+FV4bsnukMa6aFsFRoPCnGHZDM5KZO3+HkzWg4xm7Tz3lQsgrQDevhYayrrt4aub3KEdLufzQvGX2vC8Bwu1+MvWwsSfwI2LIG9s6J5bCCGEEFHJpHcAQggRjYLJep3DQ3qCdk76mcW7Wq2qB2XZYimvd9Lk8rKvxsGF4/IZkB7Pq6v2cdvsApKtoUk2v9pawbi+KSRZzYzuk8SH68tQVRVFj7PVMYlw8cvwzGx462q4+n9aIn+cappcZCR083l1VdXa9ze8pVXSG8shxgbD58KIi6H/NDD0cNu9EEIIIaKGVNaFECIEgol1fWAifJ3DzUvL93LuqFwGZiS0ep/sJK2yvr1ca4Efkp3IT04aiMPt4/fvb8J7REt9dyhvcLKptIGZQzMAGJWfTIPTy55qR7c/V1B98zH2yWcOhXP/BftXwBd/6JbnrG50k9qdw+UayuDJafDMLFj9LORP0Nr4794B5z0GA0+SRF0IIYQQx0Uq60IIEQLJcT9U1gG2lNlp9vi4cFyfNu+TaYuhosHVcl59aHYi/dLiufu0wTz42XaaXD4e/dEYYs3dlwQu2qZNgZ89NBOAUX2SAVi3v+6oVv3ucvGTy8lJjuWFqye0Xb0fcaG2k3z5o1oiPHxul59PVVWtDb67zqx73fDWVVCzS5vuPnwuxKV0z2MLIYQQQgRIZV0IIULg0DZ4oGUdWnCfemuybbG4fX5W7KrGajHSJ0XbwX7b7EL+eN5wvtxazpXPr6LB2fpKuK74amsFOUmxDMlKBGBwViJWizFk59brHR62ldtZtK2SzzeXt3/j0/4MeePhvVu19Wdd1OT24fb6u+/M+me/1t5IOO9RGH+NJOpCCCGECAlJ1oUQIgSS47TEsC4wYK48MDguy9Z2K3Zw1/ri7ZUUZiViMPxQdb5icn/+dekY1uyr5ZKnVnTLLnS31883O6qYNTSzpcJtNCgU5SWFbCL8plJtLZzVYuSPH25uvx3eZIGL/wOmGHjjCnA3dek5awI71lO7I1lf9zqsehom3wZFFxz/4wkhhBBCtEGSdSGECIHEWBMGBeodWqJY3uDCajGSENP26aPgrvVah4ehgUr3oc4ZlctzV01gT1UTFz25nH3Hea589Z4amtw+Zg3JPOz6qPwkNpU2hOSM/MZAsv63C0ayv6aZpxfvav8OSflwwbNQuRU+uEMb6tZB//xiOw98upXKRhfA8bfBl63TYug/HU657/geSwghhBDiGCRZF0KIEDAYFJLizNS2tMG7yLLFtjthPVhZB224XGtmDM7gvzdMpL7ZwwVPLmNLWcNhn99d1cTshxbx5rf7jxnjwq0VWIwGphakHXb9hBwbbq+fPdVdq2S3Z8OBBnKTYjlnVC5njsjm8UXFHKg7xh75QbNh1q+1qeurn+3wc731bQmPLdzJr+ZvADi+AXOOGq26H5cKF74ARhn5IoQQQojQkmRdCCFCJNlqOawNPiOx/WQxIzGGYC4/tI1kHWBs3xTe+slkjIrCxU8tZ/WeGgD8fpV731nPrsom7n1nPR+ub39P+VfbKpg4MBWr5fDEc2i2DdCG4nW3TQfqKcpLAuBXZ54AwF8+3HLsO06/CwrnwMe/gM3vH/PmXp+fgw1O+qdZ2RaYrt/lM+t+H8y/AexlcMnLkJDRtccRQgghhOgESdaFECJEkuLM1AXa4CsanGTZ2h4uB2A2Glp2srdVWQ8qzErk7Zsnk5EQw+XPruSrreW8tnofK3fX8PtzhjGuXwp3vrGGr7dXtnr/vdVN7KpsapkCf6hBmfGYDApbDza0cs+uszs97KpqaknW81Os3HxSAR9uKGNZcVX7dzYY4MLntYFzb18LO75o9+Zl9U58fpWbZw7id2cPY0Re0jH/+7dp0f1Q/AWc8XfIH9+1xxBCCCGE6CRJ1oUQIkSSrWbqmz2oqkp5g4usY1TWQWuFT0+IIS3h2LfNT7Hy1k2TGZyVyA0vfcef/reFKYPSuHpKf567egL5KVb+8fn2Vu+7cKu2su3I8+oAMSYjgzIS2NrNlfVgpb4oz9Zy7ScnDSQ/JY4/fLAJz7HOyMckwI/f0vawv/4jmH8jbP0QPEcP2yup1Vrr81OsXDttAB/cPg2LqQv/5G39CBb/HcZcDuOu7vz9hRBCCCG6SJJ1IYQIkeQ4M3UOD40uL80eH5ntTIIPOmNENheNz+/wc6QlxPDajZOYNDAVgwL3zxuJoijYYs3MHJLBtoN2fP6jh7It3FbJwPR4+rexS31oTmLLvvfusuGANlyuKDep5Vqs2chvzx7G9vJGXlmx99gPEpcMVyyAkRfD9k+1pP2BQfD2dVp7vEdL0ktqteF7+SlxXQvWXg5LHoZ3fwK5Y+DMh6CdeQNCCCGEEN1NJuQIIUSIJFst1DnclDdo08g70oZ9y8yCTj9PQoyJl6+diN3pJSmw3x20QXHNHh97q5sYmJHQct3h9rJ8VzWXT+zX5mMOzbbx3tpS6ps9JMWZ27xdZ2w6UE9GYgyZR/x3OG1YFtML03n48+2cMyq35ShAm+LTtR3nZ/8Ddi+GzQtgy/9g49tgjofBp6FwKoqSRE5SK8m6qsKOz2DvUnDZwdWo/epuBFeD9ue6veD3apPf5z4B5i620AshhBBCdFHIKuuKojyvKEqFoigbj7h+u6IoWxVF2aQoyt8Puf5LRVGKFUXZpijKnFDFJYQQPSXZaqbB6aU0MO08MzF0CZ/BoByWqAOc0MaguOU7q3F7/a2eVw8amqOdmd/WjdX1jaX1jMhLOuq6oij8/pxhNLt9PPDJto4/oNEMBSfDuf+Gu3fAle/BqEtg92Iu3HQLn8b+GsvaF7VJ7qAl6Qc3wsvnw6sXw4ontGp8ySr+f3v3Hp1Vde57/PvkQgJJuIVwjUCCQCBcgiAEkRpxQylFsMO2goBWure4tRs3Y3jOsT2t11JLx972jFY93naLrY4op9pawe1WNqCIRQ2IgXAJIQQMIJcQguEeMs8f70qaYAgJeS8r5PcZY428mWuuteZ684wkzzvnmpOK0sBEcok9oHcWXLcQfpQHP1gOna8KzhsgIiIi0gyh7FlfCjwF/KGmwMxuBGYCI51zZ8ysu1c+FJgFZAK9gZVmNsg5dz6E7RMRCanOXo/0zkOVAPRowjD4YBrYI5HoKGPbgeN8e0Sv2vJV2w/RoV0016Z1ueixNYn+9i+PMzata4vbcurseYoOVTI1s2eD+6/unhR41n7dbm4f15eRV3Vu3gWiYyA9J7B98wmee+oJpp74CyxfBG//T+iRCeW74XQFxHeCqUvg2h8GEn4RERERHwpZz7pz7gPg6AXF/wz80jl3xqtzyCufCbzqnDvjnNsNFAFjQ9U2EZFw6NwhsFTYTm/psAuHf4dafGw06d0S6q3F7pxjzY7DTLi6G3Ex0Rc9tkfHODp3iA3a8m15e45S7SCzgZ71Gvf/w0CSE+L4t3eb0bvekNh4/nj2Bn494Pdw9/swbgHEJcGwW+Hb/w7/shGy71GiLiIiIr4W7mfWBwETzWwxcBp4wDn3KdAHWF+nXqlXJiLSatUMS99x8CsS2kWTGBf+aUKG9OpIXsnfPzctPFjJvmOn+NGkxp+NNzMyeiY1a/m2L46e5OnVRfx0+tCv3euLa3fTLbEdNwy6+BrlSfGxfH9MKs99UEzFyXNfG9bfVFXnqzlQcZrUrgnQe3BgWLuIiIhIKxPu2eBjgK5ANvA/gGVmzZte18zuNrM8M8s7fLjh9YNFRPygdhj8wcrLX+O7hYb06sj+itO1672v3hEY0JQz+OJJc42Mnh3Z8eVXVDcwm3xDfvVfO3j10y944YPieuUF+yt4v/Awd01IIz724r35AFMye3K+2rFqx8EmXbMhXx4PrLHe53JnghcRERHxgXAn66XAGy7gE6Aa6AbsA+rO4JPqlX2Nc+5559wY59yYlJRL/7MpIhIpNcPgK89UkdKENdZDYYg3UVzNcPZV2w8xpFfHhmdJb+DYk2fP84W3DFpjig59xfL8/XRoF82La4spqzxTu+/Z94tJjIthbvbFZ5+vMaJPJ3p0jOPdgstP1vfVrrGuZF1ERERar3An638BbgQws0FAO+AI8FdglpnFmVkaMBD4JMxtExEJqi51hnFHqmd9aK+aGeGPU3ToKzbsKWdSRtM+6Bx8kdnkG/LbVUW0j43mpfljOXXuPM+s2QXAnrITrMjfz5zsvk1aAi4qypg8tAfvFx7m9LnLm2O0tDZZ73BZx4uIiIj4QcgeoDSzXCAH6GZmpcDDwO+A33nLuZ0F7nTOOaDAzJYBW4Eq4D7NBC8irV1SfCxmgRXDwj0TfI2UpDiSE9pRsP84y/P3kxQfww+uS2vSsQNSEgDYfeREo/V2Ha7krc/380/fSOfa/l259ZpU/rh+D9FRxvLP9xMTFcUPJzTtmgBThvbk5fV7WVd0hJuG9GjycTVqkvXenbU2uoiIiLReIUvWnXOzL7Jr7kXqLwYWh6o9IiLhFh1ldIyPpeLUuYj1rJsZQ3p15M1N+6iqdvz6tpFNHpKfFB9LSlIcxYcrG6331Koi4mKiuXtiOgD/OnkQb36+nxfXFnP9wBSeuDWtWTPhZ6cnkxQfw7sFBy8zWT9Jj45xjc52LyIiIuJ34Z+aWESkDencIZCsR+qZdQg8e/5h0RFyBqdwS1bzFtpI75bQaM968eFK3ty0j3+cmE5yYuAe+3Ruzzv3T6xN9purXUwUkzK6s3LbQc5XO6KjmjUPKaXlp+jTWc+ri4iISOsW7mfWRUTalJoZ4SPVsw6QM7g76d0SWPyd4TRzAQ7SUxIobiRZf2pVEe1iovgnr1f978cltugDipuG9KDsxFm27Kto9rH7jp3S8+oiIiLS6qlnXUQkhGpmhI9ksj7h6m6seiDnso5N75bI0RNfcOzk2dp7qbH7yAn+smkf8yekBX3kwPj0ZADWF5cx8qrOTT7ufLVj/7FTTB/RK6jtERERaY3OnTtHaWkpp0+fjnRT2rz4+HhSU1OJjb30hLs1lKyLiIRQZ29G+O4RHAbfEmndApPMFR85wTV96yfrT60qIjY6irtvSG/o0BZJSYrj6u6J/K24jAU3DGjycXuPnqSq2qlnXUREBCgtLSUpKYn+/fs3e3SdBI9zjrKyMkpLS0lLa/qkuxoGLyISQj07xpOc0I6EuNb52Wh6zYzwh+sPhd9TFuhVn5vdj+5JoRk1MD49mU93H6XqfHWT6p88W8X9r35G+9hostO7hqRNIiIircnp06dJTk5Woh5hZkZycnKzRzgoWRcRCaF7c67mtQXjI92My3ZV1w7ERBnFR+rPCP/UqiJioowFIehVr5GdnsyJs+fZ3ITn1s9XOxbmfsaWfRX8dvYo0lMSQ9YuERGR1kSJuj9czs9BybqISAh16hDL1d1bb+IYGx1F364dKK7Ts7637CRvfLaP28f1DVmvOsA4r3d8ffHRRus553j0rQJWbjvEIzMy+YehzV/uTURERELjyy+/ZNasWQwYMIDRo0czbdo0CgsLKSkpYdiwYZd1zuuuuy4obVuzZg2dOnUiKyuLIUOG8OijjzZYLy8vj4ULFwblms2hZF1ERBqVdsHybU+vLiI6yrinGc+SX45uiXEM6hF4br0xL67dzR/+toe7v5HOHeP7h7RNIiIi0nTOOb7zne+Qk5PDrl272LBhA0888QQHDx5s0Xk/+uijILUQJk6cyKZNm8jLy+Pll19m48aN9fZXVVUxZswYfvOb3wTtmk2lZF1ERBqVnhJI1qurHV8cPcnrG0u5fWzfsMxwn52eTF7JUc5d5Ln1FfkHWPz2Nr49vBcPTs0IeXtERESk6VavXk1sbCz33HNPbdnIkSOZOHFivXqnT5/mrrvuYvjw4YwaNYrVq1cDUFBQwNixY8nKymLEiBHs3LkTgMTEwKjFNWvWkJOTw3e/+10yMjKYM2cOzjkA3n77bTIyMhg9ejQLFy5k+vTpjbY1ISGB0aNHU1RUxCOPPMK8efOYMGEC8+bNY82aNbXHV1ZW1rZ1xIgRvP766wC8++67jB8/nmuuuYbvfe97VFZWNna5JmmdMx6JiEjYpKckcqaqmv0Vp1jyznaiwtCrXmN8ejJ/+Nse8ksrGN2vS719eSVHWbRsE2P6deHfvz+SqCg9kyciInIxj75VwNb9x4N6zqG9O/LwzZkX3b9lyxZGjx59yfM8/fTTmBmbN29m+/btTJkyhcLCQp599lnuv/9+5syZw9mzZzl//vzXjv3ss88oKCigd+/eTJgwgXXr1jFmzBgWLFjABx98QFpaGrNnz75kG8rKyli/fj0/+9nP2Lp1K1u3buXDDz+kffv2rFmzprbe448/TqdOndi8eTMA5eXlHDlyhJ///OesXLmShIQElixZwpNPPslDDz10yes2Rj3rIiLSqJrl215ev5fl+Qe4N2cAPTuFZ934cXXWW6+r+HAl//iHPPp0bs8Ld4whPjY6LO0RERGR4Pvwww+ZO3cuABkZGfTr14/CwkLGjx/PL37xC5YsWcKePXto3779144dO3YsqampREVFkZWVRUlJCdu3byc9Pb12mbTGkvW1a9cyatQopkyZwoMPPkhmZuDDhxkzZjR4vZUrV3LffffVft+lSxfWr1/P1q1bmTBhAllZWbz00kvs2bOnRe8JqGddREQuoWb5tmff30Xfrh3C1qsO0DWhHRk9k1hfXMZ9N14NQMXJc/zg958SZcbSu66lS0K7S5xFREREGusBD5XMzEz+9Kc/Xfbxt99+O+PGjWPFihVMmzaN5557jkmTJtWrExcXV/s6OjqaqqqqZl1j4sSJLF++/GvlCQkJTT6Hc47JkyeTm5vbrGtfinrWRUSkUSmJcSR668Q/OiMz7L3YgefWyzlbFXhu/a+f72Pv0ZP83znX0C+56X9IRUREJLwmTZrEmTNneP7552vL8vPzWbt2bb16EydO5JVXXgGgsLCQvXv3MnjwYIqLi0lPT2fhwoXMnDmT/Pz8Jl235tiSkhIAXnvtteDcEDB58mSefvrp2u/Ly8vJzs5m3bp1FBUVAXDixAkKCwtbfC0l6yIi0igzIzs9mZlZvbkxo3vYr5+dnsypc+fJLz0GwMpth+if3IGxaV3D3hYRERFpOjPjz3/+MytXrmTAgAFkZmby4x//mJ49e9ard++991JdXc3w4cO57bbbWLp0KXFxcSxbtoxhw4aRlZXFli1buOOOO5p03fbt2/PMM88wdepURo8eTVJSEp06dQrKPf30pz+lvLycYcOGMXLkSFavXk1KSgpLly5l9uzZjBgxgvHjx7N9+/YWX8tqZstrjcaMGePy8vIi3QwRkStezd8Ks/BP4lZ+4iyjHn+PB6YM4q4JaYx67D3mje/Hz6YPDXtbREREWpNt27YxZMiQSDcjIiorK0lMTMQ5x3333cfAgQNZtGhRRNvU0M/DzDY458Y0VF896yIicklmFpFEHaCL99z634rLWLvzCGfPV3PTkPD38IuIiEjr8cILL5CVlUVmZiYVFRUsWLAg0k1qNk0wJyIivjd+QDK5n+ylW2IcHeNjuLa/hsCLiIjIxS1atCjiPektpZ51ERHxvez0ZE6fq+atz/eTM7g7sdH68yUiIiJXNv23IyIivjcurStmUO3QEHgRERFpE5Ssi4iI73Xu0I4hPTsSHWXkDFKyLiIiIlc+PbMuIiKtwoIb0ik+fIJOHWIj3RQRERGRkFPPuoiItAozs/qwaPKgSDdDREREmsHMmDt3bu33VVVVpKSkMH369Hr1brnlFrKzs8PdPF9Tsi4iIiIiIiIhkZCQwJYtWzh16hQA7733Hn369KlX59ixY2zYsIGKigqKi4sj0UxfUrIuIiIiIiIiITNt2jRWrFgBQG5uLrNnz663/4033uDmm29m1qxZvPrqq5Fooi/pmXUREREREZEr3X8+CF9uDu45ew6Hb/3yktVmzZrFY489xvTp08nPz2f+/PmsXbu2dn9ubi4PPfQQPXr04NZbb+UnP/lJcNvZSqlnXUREREREREJmxIgRlJSUkJuby7Rp0+rtO3jwIDt37uT6669n0KBBxMbGsmXLlgi11F/Usy4iIiIiInKla0IPeCjNmDGDBx54gDVr1lBWVlZbvmzZMsrLy0lLSwPg+PHj5Obmsnjx4kg11TfUsy4iIiIiIiIhNX/+fB5++GGGDx9erzw3N5d33nmHkpISSkpK2LBhg55b9yhZFxERERERkZBKTU1l4cKF9cpKSkrYs2dPvSXb0tLS6NSpEx9//HG4m+g7GgYvIiIiIiIiIVFZWfm1spycHHJycgDYt2/f1/Zv3Lgx1M1qFdSzLiIiIiIiIuIzStZFREREREREfEbJuoiIiIiIiIjPKFkXERERERG5QjnnIt0E4fJ+DkrWRURERERErkDx8fGUlZUpYY8w5xxlZWXEx8c36zjNBi8iIiIiInIFSk1NpbS0lMOHD0e6KW1efHw8qampzTpGybqIiIiIiMgVKDY2lrS0tEg3Qy6ThsGLiIiIiIiI+IySdRERERERERGfUbIuIiIiIiIi4jPWmmcGNLPDwJ5It0PoBhyJdCPkiqBYkpZQ/EhLKYakORQv0hKKH6nRzzmX0tCOVp2siz+YWZ5zbkyk2yGtn2JJWkLxIy2lGJLmULxISyh+pCk0DF5ERERERETEZ5Ssi4iIiIiIiPiMknUJhucj3QC5YiiWpCUUP9JSiiFpDsWLtITiRy5Jz6yLiIiIiIiI+Ix61kVERERERER8Rsl6G2RmV5nZajPbamYFZna/V97VzN4zs53e1y5e+RwzyzezzWb2kZmNrHOuqWa2w8yKzOzBRq55p3fenWZ2p1eWZGab6mxHzOz/hPr+JXj8Ekte+W3euQvMbEko71uCI0Lx846ZHTOz5ReU/8g71plZt1DdswRXkGPod2Z2yMy2XOKaDcaaYsj/fBYv/2Fmn3vn/5OZJYbqviU4fBY/S81st/39f+isUN23RJhzTlsb24BewDXe6ySgEBgK/Ap40Ct/EFjivb4O6OK9/hbwsfc6GtgFpAPtgM+BoQ1crytQ7H3t4r3u0kC9DcA3Iv3+aGt9sQQkA3uBFK/eS8BNkX5/tPkrfry6NwE3A8svKB8F9AdKgG6Rfm+0hTeGvO+/AVwDbGnkeheNNcWQ/zefxUvHOvWerLm+Nv9uPoufpcB3I/2eaAv9pp71Nsg5d8A5t9F7/RWwDegDzCSQ5OB9vcWr85FzrtwrXw+keq/HAkXOuWLn3FngVe8cF/om8J5z7qh3nveAqXUrmNkgoDuwNjh3KeHgo1hKB3Y65w579VYCtwbvTiUUIhA/OOf+G/iqgfLPnHMlwbgvCZ8gxhDOuQ+Ao5e45EVjTTHkfz6Ll+MAZmZAe0CTSPmcn+JH2g4l622cmfUn0BvwMdDDOXfA2/Ul0KOBQ34I/Kf3ug/wRZ19pV7ZhZpSbxbwmnNOf6xaqQjHUhEw2Mz6m1kMgT+UV13WjUhEhCl+5ArWwhhqKsXaFcIP8WJmv/eulwH8tpnnlgjyQ/wAi71h9r82s7hmnltaiZhIN0Aix3s+6nXgX51zxwMf7gY455yZuQvq30jgl831IWjOLGBeCM4rYRDpWHLOlZvZPwOvAdXAR8CAYJxbQi/S8SOtn2JImsMv8eKcu8vMogkk6rcBvw/m+SU0fBI/PybwwUA7AkvA/S/gsSCeX3xCPettlJnFEvhF84pz7g2v+KCZ9fL29wIO1ak/AngRmOmcK/OK91G/9zIV2Gdm4+pMeDHjYvXqnHskEOOc2xDUm5Sw8EssOefecs6Nc86NB3YQeJZMfC7M8SNXoCDF0MXOfVWdGLqHS/w9E//zW7w4584TGN6sR7daAb/Ejzck3znnzhD4kGdscO5Q/EY9622Q93zUfwDbnHNP1tn1V+BO4Jfe1ze9+n2BN4B5zrm6CdCnwEAzSyPwy2MWcLtzrgConZXSzLoCv6iZHROYQuATwRqzgdzg3aGEi59iycy6O+cOefvuBb4f7PuV4Ap3/MiVJ4gx1CDn3BfU/x0UQwOxFpy7kVDzS7x47RjgnCvyXs8AtgfhFiWE/BI/3r5ezrkDXptuARqdVV5aMeeDWe60hXcjMAzHAfnAJm+bRmBG7f8GdhKYoKurV/9FoLxO3bw655pGoAdzF/C/G7nmfALPFRcBd12wrxjIiPT7oq11xxKBD3y2etusSL832nwbP2uBw8ApAs//fdMrX+h9XwXsB16M9PujLewxlAscAM55sfDDi1yzwVhTDPl/80u8EBjZug7YTCDJeoU6s8Nr8+fml/jxylfViZ+XgcRIvz/aQrOZ9wMXEREREREREZ/QM+siIiIiIiIiPqNkXURERERERMRnlKyLiIiIiIiI+IySdRERERERERGfUbIuIiIiIiIi4jNK1kVERNoYC/jQzL5Vp4zE8wUAAAGjSURBVOx7ZvZOJNslIiIif6el20RERNogMxsG/D9gFBADfAZMdc7tuoxzxTjnqoLcRBERkTZNybqIiEgbZWa/Ak4ACd7XfsAwIBZ4xDn3ppn1B/7o1QH4kXPuIzPLAR4HyoEM59yg8LZeRETkyqZkXUREpI0yswRgI3AWWA4UOOdeNrPOwCcEet0dUO2cO21mA4Fc59wYL1lfAQxzzu2OzB2IiIhcuWIi3QARERGJDOfcCTN7DagEvg/cbGYPeLvjgb7AfuApM8sCzgN1e9A/UaIuIiISGkrWRURE2rZqbzPgVufcjro7zewR4CAwksDEtKfr7D4RpjaKiIi0OZoNXkRERAD+C/gXMzMAMxvllXcCDjjnqoF5QHSE2iciItKmKFkXERERCEwWFwvkm1mB9z3AM8CdZvY5kIF600VERMJCE8yJiIiIiIiI+Ix61kVERERERER8Rsm6iIiIiIiIiM8oWRcRERERERHxGSXrIiIiIiIiIj6jZF1ERERERETEZ5Ssi4iIiIiIiPiMknURERERERERn1GyLiIiIiIiIuIz/x987ctGNsCTcgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1224x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}