2Bib_data_analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/GParolini/colabs_am_bib/blob/master/2Bib_data_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "#Content of the notebook"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OBHrBhUSr7kF"
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      "source": [
        "This notebook has been created to analyse the bibliographic data extracted from the *Second Bibliography of Literature on Agricultural Meteorology* published by the British Ministry of Agriculture and Fisheries in 1936. The data in the bibliography only refer to publications printed in the 1920s-early 1930s. Manual ETL with checks done as part of the project \"Environmental Factors in Agriculture: Observation and Experiment in Agricultural Meteorology (ca 1900-1950)\" (https://agriculturalmeteorology.wordpress.com).  The data analysis has been used in the preparation of an advanced review for the journal *WIREs Climate Change*. The data analysis work has been made possible with funding provided by the German Research Foundation (DFG), project No. 321660352."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRdv0xE5v7T5"
      },
      "source": [
        "#Module imports and styling of the notebook\n"
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css():\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
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        "  </style>\n",
        "  '''))\n",
        "get_ipython().events.register('pre_run_cell', set_css)"
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      "execution_count": 7,
      "outputs": [
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              "\n",
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              "  "
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      "cell_type": "code",
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        "outputId": "d8f924cd-5cb1-4967-99d5-855397e8566a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "from google.colab import drive #to access data stored in Google drive\n",
        "from google.colab import files #to save data in Google drive\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mt\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as mcolors\n",
        "import json\n",
        "from collections import Counter\n",
        "from collections import defaultdict\n",
        "import glob\n",
        "import re\n",
        "from textwrap import wrap\n",
        "\n",
        "import spacy.cli\n",
        "from spacy.lang.en.stop_words import STOP_WORDS\n",
        "\n",
        "from gensim import corpora\n",
        "from gensim import models\n",
        "\n",
        "from wordcloud import WordCloud\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
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              "  "
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FVUOX1-jlQc"
      },
      "source": [
        "#Reading and cleaning data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_z_96CqrwE0",
        "outputId": "417d3e5b-a67e-497c-b4bc-6cdd94eef4e9",
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      },
      "source": [
        "\"\"\"\n",
        "Data are stored in Google drive as csv file.\n",
        "\"\"\"\n",
        "\n",
        "drive.mount(\"/content/drive\", force_remount=True) "
      ],
      "execution_count": 9,
      "outputs": [
        {
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              "  "
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              "  "
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          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
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    {
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        }
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/My Drive/2_bib_data.csv\")"
      ],
      "execution_count": 10,
      "outputs": [
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        }
      },
      "source": [
        "#Avoid that the year of publication is displayed as a float\n",
        "df[\"Publication Year\"] = (df[\"Publication Year\"].fillna(0)\n",
        "    .astype(int)\n",
        "    .astype(object)\n",
        "    .where(df[\"Publication Year\"].notnull())\n",
        "    )\n",
        "\n",
        "#Remove columns with scarce information\n",
        "df = df.loc[:, df.isnull().mean() <= .97]\n",
        "\n",
        "#Remove column with uniteresting information\n",
        "df =df.drop([\"Date Added\", \"Date Modified\"], axis=1)"
      ],
      "execution_count": 11,
      "outputs": [
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      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": 12,
      "outputs": [
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            "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>Key</th>\n",
              "      <th>Item Type</th>\n",
              "      <th>Publication Year</th>\n",
              "      <th>Author</th>\n",
              "      <th>Title</th>\n",
              "      <th>Publication Title</th>\n",
              "      <th>DOI</th>\n",
              "      <th>Url</th>\n",
              "      <th>Date</th>\n",
              "      <th>Pages</th>\n",
              "      <th>Issue</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>Place</th>\n",
              "      <th>Language</th>\n",
              "      <th>Type</th>\n",
              "      <th>Archive</th>\n",
              "      <th>Extra</th>\n",
              "      <th>Manual Tags</th>\n",
              "      <th>Number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>88Z3WKI8</td>\n",
              "      <td>journalArticle</td>\n",
              "      <td>1930</td>\n",
              "      <td>Kincer, J. B.</td>\n",
              "      <td>Weather and cotton production</td>\n",
              "      <td>Monthly Weather Review</td>\n",
              "      <td>10.1175/1520-0493(1930)58&lt;190:WACP&gt;2.0.CO;2</td>\n",
              "      <td>https://journals.ametsoc.org/doi/abs/10.1175/1...</td>\n",
              "      <td>1930</td>\n",
              "      <td>190-196</td>\n",
              "      <td>5</td>\n",
              "      <td>58</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>English</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WB 00824</td>\n",
              "      <td>2B_p39_4</td>\n",
              "      <td>clean</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>H2HWQY4H</td>\n",
              "      <td>journalArticle</td>\n",
              "      <td>1932</td>\n",
              "      <td>Keen, B. A.</td>\n",
              "      <td>Soil physics in relation to meteorology</td>\n",
              "      <td>Quarterly Journal of the Royal Meteorological ...</td>\n",
              "      <td>10.1002/qj.49705824504</td>\n",
              "      <td>https://rmets.onlinelibrary.wiley.com/doi/abs/...</td>\n",
              "      <td>1932</td>\n",
              "      <td>229-250</td>\n",
              "      <td>245</td>\n",
              "      <td>58</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>English</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2B_p52_9</td>\n",
              "      <td>Quarterly Journal of the Royal Meteorological ...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ZZXJVDB8</td>\n",
              "      <td>bookSection</td>\n",
              "      <td>1932</td>\n",
              "      <td>Enquist, F.</td>\n",
              "      <td>Om klimatets inverkan på skördevariationer [Th...</td>\n",
              "      <td>Svensk Geografisk Årsbok [Swedish Geografical ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1932</td>\n",
              "      <td>122-146</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Swedish</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2B_p56_12</td>\n",
              "      <td>Institution; Sweden; Enquist, F.; Swedish Geog...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ZDF2SDR2</td>\n",
              "      <td>journalArticle</td>\n",
              "      <td>1932</td>\n",
              "      <td>Koloskov, P. I.</td>\n",
              "      <td>The climate of soy-beans and the possible regi...</td>\n",
              "      <td>Izv. Daljn. Vost. Geofizin'a [Records of the F...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1932</td>\n",
              "      <td>5-74</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Russian</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2B_p57_8</td>\n",
              "      <td>Koloskov, P. I.; Geophysics Observatory of the...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BZT555AP</td>\n",
              "      <td>journalArticle</td>\n",
              "      <td>1932</td>\n",
              "      <td>Hecht, W.</td>\n",
              "      <td>Bioklimatische Versuche zur Erforschung der Ur...</td>\n",
              "      <td>Zeitschrift für Heil- und Gewürzpflanzen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1932</td>\n",
              "      <td>15–50</td>\n",
              "      <td>1-2</td>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>German</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2B_p55_4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Key  ... Number\n",
              "0  88Z3WKI8  ...    NaN\n",
              "1  H2HWQY4H  ...    NaN\n",
              "2  ZZXJVDB8  ...    NaN\n",
              "3  ZDF2SDR2  ...    NaN\n",
              "4  BZT555AP  ...    NaN\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lLE8_pLEE_g"
      },
      "source": [
        "#Data inspection and visualisation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOZW1e7VDH31"
      },
      "source": [
        "##Items"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aUpckoovEa9X"
      },
      "source": [
        "###Item types and count"
      ]
    },
    {
      "cell_type": "code",
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        "id": "Kh79wbnC3t1r",
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          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "item_types =list(set(df[\"Item Type\"]))\n",
        "print(item_types)"
      ],
      "execution_count": 13,
      "outputs": [
        {
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        {
          "output_type": "stream",
          "text": [
            "['thesis', 'book', 'report', 'journalArticle', 'bookSection']\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        }
      },
      "source": [
        "count_item_types = df[\"Item Type\"].value_counts()\n",
        "print(count_item_types)"
      ],
      "execution_count": 14,
      "outputs": [
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          "text": [
            "journalArticle    514\n",
            "report             35\n",
            "bookSection        27\n",
            "book               16\n",
            "thesis              1\n",
            "Name: Item Type, dtype: int64\n"
          ],
          "name": "stdout"
        }
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      "metadata": {
        "id": "h2ikxriAEyAc"
      },
      "source": [
        "###Items listed per type"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DIVuIbXAvoyw"
      },
      "source": [
        "###Journal articles\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DnC47AKc6m5u",
        "outputId": "6aa1b713-18e7-452f-ce24-67fb2be38e33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "#Listing items per type helps in cleaning further the data (e.g. eliminate typos and inconsistencies in journal titles)\n",
        "\n",
        "articles = df.loc[df[\"Item Type\"] == \"journalArticle\"]\n",
        "journ_titles = articles[\"Publication Title\"].dropna()\n",
        "journ_titles_unique = list(set(journ_titles))\n",
        "sorted_journ_titles = sorted(journ_titles_unique)\n",
        "\n",
        "\n",
        "with open('2Bib_journal_titles.txt', mode='wt', encoding='utf-8') as f:\n",
        "  f.write('\\n'.join(sorted_journ_titles))\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3M2fOx8iuE1V"
      },
      "source": [
        "####Popular publication outlets (journals)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPdamlhkzCtY",
        "outputId": "ef4c8f45-77a0-4243-d26c-04aab03ce0da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "journals = sorted_journ_titles\n",
        "\n",
        "my_list = []\n",
        "no_art = df.loc[df[\"Item Type\"] == \"journalArticle\"].groupby([\"Publication Title\"]).size()\n",
        "my_dict = {k: v for k, v in zip(journals,no_art)}\n",
        "my_dict1 = sorted(my_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "one_art_journ = Counter(my_dict.values())[1]\n",
        "two_art_journ = Counter(my_dict.values())[2]\n",
        "\n",
        "print(\"In\", one_art_journ, \"journals only one article was published\")\n",
        "print(\"In\", two_art_journ, \"journals only two articles were published\")\n",
        "\n",
        "with open('2Bib_journal_art_dict.json', 'w', encoding='utf-8') as f:\n",
        "    json.dump(my_dict1, f, ensure_ascii=False, indent=2)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "In 119 journals only one article was published\n",
            "In 33 journals only two articles were published\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i72_xaU-vt9X"
      },
      "source": [
        "###Reports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZ9yay-XnH1r",
        "outputId": "b3fa59c0-e28b-4849-9935-4badda2d4ecc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "reports = df.loc[df[\"Item Type\"] == \"report\"]\n",
        "rep_titles = reports[\"Type\"].dropna()\n",
        "rep_titles_unique = list(set(rep_titles))\n",
        "sorted_rep_titles = sorted(rep_titles_unique)\n",
        "\n",
        "with open('2Bib_report_titles.txt', mode='wt', encoding='utf-8') as f:\n",
        "  f.write('\\n'.join(sorted_rep_titles))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0J0gsvzvyFm"
      },
      "source": [
        "###Books"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rV6h57WWwrEI",
        "outputId": "056dd05a-4fda-4838-ce38-02050521df88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "books = df.loc[df[\"Item Type\"] == \"book\"]\n",
        "book_titles = books[\"Title\"].dropna()\n",
        "book_titles_unique = list(set(book_titles))\n",
        "sorted_book_titles = sorted(book_titles_unique)\n",
        "\n",
        "with open('2Bib_book_titles.txt', mode='wt', encoding='utf-8') as f:\n",
        "  f.write('\\n'.join(sorted_book_titles))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-CBuPYvv1ub"
      },
      "source": [
        "###Book chapters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVYBrVYyzC9t",
        "outputId": "8016563e-0465-4c34-837a-3dbf8ff5635f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "book_sections = df.loc[df[\"Item Type\"] == \"bookSection\"]\n",
        "book_sec_titles = book_sections[\"Publication Title\"].dropna()\n",
        "book_sec_titles_unique = list(set(book_sec_titles))\n",
        "sorted_book_sec_titles = sorted(book_sec_titles_unique)\n",
        "\n",
        "with open('2Bib_book_sec_titles.txt', mode='wt', encoding='utf-8') as f:\n",
        "  f.write('\\n'.join(sorted_book_sec_titles))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkEfj6QSv5pV"
      },
      "source": [
        "###Theses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blq9TZ4g0mlw",
        "outputId": "6e8faeb4-03ee-4190-d248-63b9d52b0bb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "theses = df.loc[df[\"Item Type\"] == \"thesis\"]\n",
        "thesis_titles = theses[\"Title\"].dropna()\n",
        "thesis_titles_unique = list(set(thesis_titles))\n",
        "sorted_thesis_titles = sorted(thesis_titles_unique)\n",
        "\n",
        "with open('2Bib_thesis_titles.txt', mode='wt', encoding='utf-8') as f:\n",
        "  f.write('\\n'.join(sorted_thesis_titles))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-xr4nMOEiIG"
      },
      "source": [
        "##Languages included in the data set and their popularity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVOALKYs5K7U",
        "outputId": "4a22999a-7f6c-4a9d-a46a-4eece9b18202",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        }
      },
      "source": [
        "lang_counts = df[\"Language\"].value_counts()\n",
        "print(lang_counts)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "English         345\n",
            "German           92\n",
            "French           68\n",
            "Russian          38\n",
            "Italian          22\n",
            "Swedish           6\n",
            "Japanese          5\n",
            "Czechoslovak      4\n",
            "Norwegian         4\n",
            "Hungarian         2\n",
            "Dutch             2\n",
            "Spanish           2\n",
            "Polish            1\n",
            "Chinese           1\n",
            "Finnish           1\n",
            "Name: Language, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xHWBm52_vAm"
      },
      "source": [
        "##Distribution of the publications over time"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6h52fpQNYA0X",
        "outputId": "cb43030d-36d8-4731-bceb-b5224117fa82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "df[\"Publication Year\"]=pd.to_numeric(df[\"Publication Year\"])\n",
        "years = [x for x in range (1900, 1951)]\n",
        "no_publications = df.groupby([\"Publication Year\"]).size().tolist()\n",
        "my_dict = {k: v for k, v in zip(years,no_publications)}\n",
        "my_dict1 = sorted(my_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "with open('2Bib_years_dict.json', 'w', encoding='utf-8') as f:\n",
        "    json.dump(my_dict1, f, indent=2)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FWyYcvS0kaM",
        "outputId": "93de7a2e-8d4d-482b-c733-c3a6e9b4fb5e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        }
      },
      "source": [
        "#histogram\n",
        "ax = df.hist(column=\"Publication Year\", bins=50, grid=False, figsize=(30,15), \n",
        "             color='#86bf91', zorder=2, rwidth=0.9)\n",
        "#title\n",
        "plt.title(\"Publications in Second Bibliography of AM bibliography distributed according to year\",\n",
        "          color=\"#bf86b4\", fontdict = {'fontsize':30, \"fontweight\":\"bold\"})\n",
        "#ticks\n",
        "plt.xlabel('Publication year', fontsize=18, color =\"#bf86b4\")\n",
        "plt.xticks(list(range(1925, 1935, 1))) \n",
        "plt.rc('xtick',labelsize=20, color =\"#bf86b4\")\n",
        "plt.ylabel('Number of publications printed',\n",
        "           fontsize=18, color =\"#bf86b4\" )\n",
        "plt.yticks(list(range(0, 200, 10)))\n",
        "plt.rc('ytick',labelsize=20, color =\"#bf86b4\")\n",
        "\n",
        "#grid\n",
        "plt.grid(True,color=\"#D3D3D3\")\n",
        "\n",
        "#save to file\n",
        "plt.savefig(\"2Bib_publications_per_year\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioA3IwFHG85U"
      },
      "source": [
        "##Distribution of the publications per language"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hx4OqvUu2BiB",
        "outputId": "149db982-9903-4667-ae6b-281a81ddaffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "lang_list = sorted(list(set(df[\"Language\"].tolist())))\n",
        "lang_counts = df.groupby([\"Language\"]).size()\n",
        "list_lang_counts = df.groupby([\"Language\"]).size().tolist()\n",
        "my_dict = {k: v for k, v in zip(lang_list,list_lang_counts)}\n",
        "my_dict1 = sorted(my_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "#my_dict1 is the dictionary that contains all the languages in the data set and the count of the publications in each language\n",
        "with open('2Bib_lang_dict.json', 'w', encoding='utf-8') as f:\n",
        "    json.dump(my_dict1, f, ensure_ascii=False, indent=2)\n",
        "\n",
        "\"\"\"\n",
        "There are over twenty languages in the dictionary, but only 5-6 can be displayed clearly in a pie chart.\n",
        "For clarity's sake, therefore, I kept only the five main languages and grouped all the others\n",
        "\"\"\"\n",
        "\n",
        "list_main_lang = my_dict1[0:5]\n",
        "list_other = my_dict1[5:]\n",
        "other_count = []\n",
        "for item in list_other:\n",
        "  other_count.append(item[1])\n",
        "other_sum = sum(other_count)\n",
        "my_list_other = [(\"Other\", other_sum)]\n",
        "lang_list_pie_plot = list_main_lang + my_list_other\n",
        "\n",
        "#generation of the pie chart\n",
        "languages = [x[0] for x in lang_list_pie_plot]\n",
        "counts = [x[1] for x in lang_list_pie_plot]\n",
        "plt.figure(figsize=(30,20))      \n",
        "explode = (0.1,0,0,0,0,0)\n",
        "plt.pie(counts, explode, \n",
        "            autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "cmap = mcolors.ListedColormap([\"#86bf91\", \"#9186bf\", \"#b4bf86\", \"#a35694\", \"#bf9186\",\"#86b4bf\"])\n",
        "plt.cycler(\"color\", cmap.colors)\n",
        "plt.rcParams['axes.prop_cycle'] = plt.cycler('color',[\"#86bf91\", \"#9186bf\", \"#b4bf86\", \"#a35694\", \"#bf9186\",\"#86b4bf\"])\n",
        "plt.rcParams['font.size'] = 20\n",
        "plt.title(\"Publications in Second Bibliography of AM distributed according to language\", \n",
        "          color=\"#bf86b4\", fontdict = {'fontsize':30, \"fontweight\":\"bold\"})\n",
        "plt.legend(loc=\"best\", labels=languages )\n",
        "plt.axis('equal')\n",
        "\n",
        "#save to file\n",
        "plt.savefig(\"2Bib_publications_per_language\")"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "    pre {\n",
              "        white-space: pre-wrap;\n",
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              "  "
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            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
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        {
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        {
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\n",
            "text/plain": [
              "<Figure size 2160x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0zwgMHouUeI"
      },
      "source": [
        "##Complete list of authors cited in the bibliography"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7mF7jYRw-kQ"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmTzNLf7jPEq",
        "outputId": "9cec686e-2604-4a60-9246-853531559617",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "def get_all_authors(df):\n",
        "    reshaped = \\\n",
        "    (df.set_index(df.columns.drop(\"Author\",1).tolist())\n",
        "    .Author.str.split('; ', expand=True)\n",
        "    .stack()\n",
        "    .reset_index()\n",
        "    .rename(columns={0:'Author'})\n",
        "    .loc[:, df.columns]\n",
        "    )\n",
        "    authors = reshaped[\"Author\"].dropna()\n",
        "    authors1 = authors.str.strip(\" \")\n",
        "    authors_noduplicates = list(set(authors1))\n",
        "    return sorted(authors_noduplicates)\n",
        "\n",
        "my_authors = get_all_authors(df)\n",
        "\n",
        "with open('2Bib_authors.txt', mode='wt', encoding='utf-8') as f:\n",
        "  f.write('\\n'.join(my_authors))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2vZhuIquyue"
      },
      "source": [
        "###Number of publications for each author"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6RLbrVfhSLX",
        "outputId": "0228932f-bfb5-4374-9c42-567de9fdb1d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "def author_counts(df):\n",
        "    reshaped = \\\n",
        "    (df.set_index(df.columns.drop(\"Author\",1).tolist())\n",
        "    .Author.str.split('; ', expand=True)\n",
        "    .stack()\n",
        "    .reset_index()\n",
        "    .rename(columns={0:'Author'})\n",
        "    .loc[:, df.columns])\n",
        "    authors = reshaped[\"Author\"].dropna()\n",
        "    authors1 = authors.str.strip(\" \")\n",
        "    count = Counter(authors1)\n",
        "    my_dict1 = sorted(count.items(), key=lambda x: x[1], reverse=True)\n",
        "    \n",
        "    return my_dict1\n",
        "\n",
        "publications_per_author = author_counts(df)\n",
        "\n",
        "with open('2Bib_publications_per_author.json', 'w', encoding='utf-8') as f:\n",
        "    json.dump(publications_per_author, f, ensure_ascii=False, indent=2)\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ra0xZIzCXDVD"
      },
      "source": [
        "###Histogram of the publications per author"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHPmnPulXBso",
        "outputId": "545b3e9d-664e-4a84-daf2-e0a60dec474d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        }
      },
      "source": [
        "\n",
        "#No. of authors who published 8 papers\n",
        "my_list_8 = list(filter(lambda x: (x[1] == 8), publications_per_author))\n",
        "counts_8 = []\n",
        "for tup in my_list_8:\n",
        "  counts_8.append(tup[1])\n",
        "authors_8_papers =len(counts_8)\n",
        "print(\"No. of authors who published 8 papers: \" + str(authors_8_papers))\n",
        "\n",
        "#No. of authors who published 6 papers\n",
        "my_list_6 = list(filter(lambda x: (x[1] == 6), publications_per_author))\n",
        "counts_6 = []\n",
        "for tup in my_list_6:\n",
        "  counts_6.append(tup[1])\n",
        "authors_6_papers =len(counts_6)\n",
        "print(\"No. of authors who published 6 papers: \" + str(authors_6_papers))\n",
        "\n",
        "#No. of authors who published 5 papers\n",
        "my_list_5 = list(filter(lambda x: (x[1] == 5), publications_per_author))\n",
        "counts_5 = []\n",
        "for tup in my_list_5:\n",
        "  counts_5.append(tup[1])\n",
        "authors_5_papers =len(counts_5)\n",
        "print(\"No. of authors who published 5 papers: \" + str(authors_5_papers))\n",
        "\n",
        "#No. of authors who published 4 papers\n",
        "my_list_4 = list(filter(lambda x: (x[1] == 4), publications_per_author))\n",
        "counts_4 = []\n",
        "for tup in my_list_4:\n",
        "  counts_4.append(tup[1])\n",
        "authors_4_papers =len(counts_4)\n",
        "print(\"No. of authors who published 4 papers: \" + str(authors_4_papers))\n",
        "\n",
        "#No. of authors who published 2 papers\n",
        "my_list_2 = list(filter(lambda x: (x[1] == 2), publications_per_author))\n",
        "counts_2 = []\n",
        "for tup in my_list_2:\n",
        "  counts_2.append(tup[1])\n",
        "authors_2_papers =len(counts_2)\n",
        "print(\"No. of authors who published 2 papers: \" + str(authors_2_papers))\n",
        "\n",
        "#No. of authors who published 1 paper\n",
        "my_list_1 = list(filter(lambda x: (x[1] == 1), publications_per_author))\n",
        "counts_1 = []\n",
        "for tup in my_list_1:\n",
        "  counts_1.append(tup[1])\n",
        "authors_1_paper =len(counts_1)\n",
        "print(\"No. of authors who published 1 paper: \" + str(authors_1_paper))\n",
        "\n",
        "counts = []\n",
        "for tup in publications_per_author:\n",
        "  counts.append(tup[1])\n",
        "\n",
        "#histogram\n",
        "plt.figure(figsize=(30,15))\n",
        "ax = plt.hist(counts, bins='auto', color='#86bf91', edgecolor = 'white', label='hst')\n",
        "\n",
        "#title\n",
        "plt.title(\"Publications per author in Second Bibliography of AM\",\n",
        "          color=\"#bf86b4\", fontdict = {'fontsize':30, \"fontweight\":\"bold\"})\n",
        "\n",
        "#ticks\n",
        "plt.xlabel('Number of publications', fontsize=18, color =\"#bf86b4\")\n",
        "plt.xticks(list(range(1, 10, 1))) \n",
        "plt.rc('xtick',labelsize=20, color =\"#bf86b4\")\n",
        "plt.ylabel('Number of authors',\n",
        "           fontsize=18, color =\"#bf86b4\")\n",
        "plt.yticks(list(range(0, 510, 20))) \n",
        "plt.rc('ytick',labelsize=20, color =\"#bf86b4\")\n",
        "\n",
        "#grid\n",
        "plt.grid(True,color=\"#D3D3D3\")\n",
        "\n",
        "#save to file\n",
        "plt.savefig(\"2Bib_publications_per_author\")\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
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            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "No. of authors who published 8 papers: 1\n",
            "No. of authors who published 6 papers: 1\n",
            "No. of authors who published 5 papers: 2\n",
            "No. of authors who published 4 papers: 3\n",
            "No. of authors who published 2 papers: 63\n",
            "No. of authors who published 1 paper: 506\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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+cE+nApQkSZIkSZIkSdLw6Ldk238BC4G3xlC2in3jqr23aq+p2u1b9N0SWAm4KSvyeR2NUpIkSZIkSZIkSUOhr5JtWZHfD1wBvAHYr/FcDOV2wIdIVW9XVYd/ADwC7BZDOaWh7wrAMdXN07sctiRJkiRJkiRJkgbUxLoDaMPngcnAiTGUOXAbaTnIjwMLgM9kRT4HICvyJ2Mo9yYl3WIM5UXAY8COwIbV8Yt7/xAkSZIkSZIkSZI0CPqqsg0gK/IHgfcAM4E3kyrcMlLF2+ZZkf+wqf/lwFbAdcDOwBeAF4AvArtlRb6wZ8FLkiRJkiRJkiRpoPRjZRtZkT9MSpp9YQn73wjs0NWgJEmSJEmSJEmSNHT6rrJNkiRJkiRJkiRJGi9MtkmSJEmSJEmSJEltMtkmSZIkSZIkSZIktclkmyRJkiRJkiRJktQmk22SJEmSJEmSJElSm0y2SZIkSZIkSZIkSW0y2SZJkiRJkiRJkiS1yWSbJEmSJEmSJEmS1CaTbZIkSZIkSZIkSVKbTLZJkiRJkiRJkiRJbTLZJkmSJEmSJEmSJLXJZJskSZIkSZIkSZLUJpNtkiRJkiRJkiRJUptMtkmSJEmSJEmSJEltMtkmSZIkSZIkSZIktclkmyRJkiRJkiRJktSmiXUH0AkxlLsD51c3986K/OwWfT4CHARMBpYF/gf4dlbk5/UsUEmSJEmSJEmSJA2Uvq9si6H8R2Am8PQi+uwLXAFsDFwAnAX8A3BuDOXxvYhTkiRJkiRJkiRJg6evk20xlBOAc4BHgTPG6LMOcDzwGDAlK/LPZ0V+APAO4P8CB8ZQbtqbiCVJkiRJkiRJkjRI+jrZBkwHtgE+DTwzRp//BSwPzMyK/L7Rg1mRPw58rbr52S7GKEmSJEmSJEmSpAHVt8m2GMqNgOOAU7Iiv24RXbep2qtanPvPpj6SJEmSJEmSJEnSEptYdwDtiKGcCJwP/AE4bDHdN6za2c0nsiL/UwzlM8DrYyhXyop8bov7mgZMA3j2ybmMjIy8otiHybx585g6aUrdYWicGxkZGah5ssqyKw/U4xkv/N07vs2bN8+fkYaO817DyrmvYeS81zBy3msYOe81rJz7ndGXyTbgK8BkYIusyJ9dTN9JVTtnjPNzgJWrfi9LtmVFPguYBbDiD49cuP7667cV8DAaGRnh+odvrTsMjXPF5OlcGq+sO4yOmTppCtfPcd53WjF5et0haBFGRkbw/0cNG+e9hpVzX8PIea9h5LzXMHLea1g59zuj75aRjKF8P6ma7YSsyG+uOx5JkiRJkiRJkiQNr75KtlXLR36PtCTkEUt42WhF26Qxzi+u8k2SJEmSJEmSJElqqd+WkVwF2KD6/rkYylZ9zoqhPAs4JSvy/YG7gDWr6/6mEi6G8rWkJSQfbLVfmyRJkiRJkiRJkrQo/ZZsmwd8Z4xz7ybt43YDKcE2mli7Btgc2J6mZBvw4YY+kiRJkiRJkiRJ0lLpq2RbVuTPAp9pdS6GcgYp2XZeVuRnN5w6BzgE2DeG8pysyO+r+q9O2vsN4IxuxSxJkiRJkiRJkqTB1VfJtnZkRX5vDOXBwKnArTGUFwPPA7sArwdOyIq8ueJNkiRJkiRJkiRJWqxl6g6gF7IiPw3YEfgf4FPANOAhYK+syA+qMzZJkiRJkiRJkiT1r4GpbMuKfAYwYxHnrwCu6FU8kiRJkiRJkiRJGnxDUdkmSZIkSZIkSZIkdYPJNkmSJEmSJEmSJKlNJtskSZIkSZIkSZKkNplskyRJkiRJkiRJktpksk2SJEmSJEmSJElqk8k2SZIkSZIkSZIkqU0m2yRJkiRJkiRJkqQ2mWyTJEmSJEmSJEmS2mSyTZIkSZIkSZIkSWqTyTZJkiRJkiRJkiSpTSbbJEmSJEmSJEmSpDaZbJMkSZIkSZIkSZLaZLJNkiRJkiRJkiRJapPJNkmSJEmSJEmSJKlNJtskSZIkSZIkSZKkNk2sO4ClFUO5BrATkANvB14HPA/cAZwDnJMV+YstrtsMOBzYBFgRuBv4LnBaVuQLehO9JEmSJEmSJEmSBkk/VrbtCpwFvB/4JXAy8ENgY+Bs4JIYygmNF8RQfgy4DtgSuAyYCSwHnARc1LPIJUmSJEmSJEmSNFD6rrINmA3sCJSNFWwxlIcBtwA7A58gJeCIoVyVlJxbAGRZkd9aHT8CuAbYJYZyt6zITbpJkiRJkiRJkiRpqfRdZVtW5NdkRX5F81KRWZE/BJwxerPh1C7AWsBFo4m2qv9zpGUlAT7XvYglSa/U/AXz6w5BfcK5IkmSJEmSpF7rx8q2RXmhahvfadumaq9q0f86YC6wWQzl8lmRz+tmcJKk9kxcdiIhnlp3GBrD1ElTuDReWXcYABTZ9LpDkCRJkiRJ0pDpu8q2scRQTgQ+Vd1sTKxtWLWzm6/Jinw+cC8p6bheVwOUJEmSJEmSJEnSwBmkyrbjgI2BK7Mi/0nD8UlVO2eM60aPr9bqZAzlNGAawLNPzmVkZKQDoQ6HefPmMXXSlLrD0Dg3MjIyUPNklWVXHqjHM14M2jwZNONp3vv/tHpl3rx5zjcNJee+hpHzXsPIea9h5LzXsHLud8ZAJNtiKKcDBwK/B/bo5NhZkc8CZgGs+MMjF66//vqdHH6gjYyMcP3Dty6+o4ZaMXn6uFl+rhOmTprC9XOc9502aPNk0IyneV9MdhlJ9cbIyAj+Xahh5NzXMHLeaxg57zWMnPcaVs79zuj7ZSRjKPcFTgHuBLbOivyxpi6jlWuTaG30+BNdCE+SJEmSJEmSJEkDrK+TbTGU+wOnAb8lJdoeatHtrqrdoMX1E4F1gfnAPd2KU5IkSZIkSZIkSYOpb5NtMZRfAk4Cbicl2v4yRtdrqnb7Fue2BFYCbsqKfF7no5QkSZIkSZIkSdIg68tkWwzlEcBxwK+AD2RF/sgiuv8AeATYLYZySsMYKwDHVDdP71askiRJkiRJkiRJGlwT6w5gacVQ7gkcDSwArgemx1A2d7svK/JzAbIifzKGcm9S0i3GUF4EPAbsCGxYHb+4N9FLkiRJkiRJkiRpkPRdso20xxrAssD+Y/T5OXDu6I2syC+PodwK+DKwM7ACMAJ8ETg1K/KFXYtWkiRJkiRJkiRJA6vvkm1Zkc8AZrRx3Y3ADp2OR5IkSZIkSZIkScOrL/dskyRJkiRJkiRJksYDk22SJEmSJEmSJElSm0y2SZIkSZIkSZIkSW0y2SZJkiRJkiRJkiS1yWSbJEmSJEmSJEmS1CaTbZIkSZIkSZIkSVKbTLZJkiRJkiRJkiRJbTLZJkmSJEmSJEmSJLXJZJskSZIkSZIkSZLUJpNtkiRJkiRJkiRJUptMtkmSJEmSJEmSJEltMtkmSZIkSZIkSZIktclkmyRJkiRJkiRJktQmk22SJEmSJEmSJElSm0y2SZIkSZIkSZIkSW0y2SZJkiRJkiRJkiS1aWLdAfRKDOXrgaOB7YE1gD8BlwNHZUX+eJ2xSZIkSZIkSZIkqT8NRWVbDOWbgF8BnwZuAU4C7gH2A26OoVyjxvAkSZLUQ/MXzK87BPUJ54okSZIkaUkMS2Xbt4FXA9OzIj9t9GAM5YnAAcCxwGdrik2SJEk9NHHZiYR46isaY+qkKVwar+xQRBqvimx63SFIkiRJkvrAwFe2VVVt2wH3Ad9qOn0k8AywRwzlyj0OTZIkSdI4ZmWbloTzRJIkSdIwVLZtXbU/zYr8xcYTWZE/FUN5IykZtwnwX70OTpIkSdL41IkqyEFjVefLHTx1n7pDUB+Yv2A+E5cdhrdgJPWCv1O0JF5YMJ+/c55IPTMM/9o2rNrZY5y/m5Rs2wCTbZIkSZKkpWBSdvB1IsnssrSSOsn/e7pj0D5UVGTTnSdaIru+foe6QxgIw5Bsm1S1c8Y4P3p8tVYnYyinAdMA7vjv3zw9YcKEuzob3uBafZXV1nz86SceqTsOjW+HsV/dIXSU8747Bm2eDJrxNO+dK+qV8TTv1T3+Tnk55/7LOU8GXyfmvfNE/cbf9xpGgzbv/b9HS+qbqxw5UHO/B97Y6uAwJNtekazIZwGzAOYVz9ccTX+Jobw1K/Ipdcch9ZLzXsPIea9h5LzXsHLuaxg57zWMnPcaRs57DSvnfmcsU3cAPTBauTZpjPOjx5/oQSySJEmSJEmSJEkaIMOQbBtd9nGDMc6/uWrH2tNNkiRJkiRJkiRJamkYkm3XVu12MZR/83hjKF8FbA7MBX7R68CGwKy6A5Bq4LzXMHLeaxg57zWsnPsaRs57DSPnvYaR817DyrnfARMWLlxYdwxdF0P5E2A7YHpW5Kc1HD8ROAA4Myvyz9YVnyRJkiRJkiRJkvrTxLoD6JF9gJuAU2MoPwD8Dng/sDVp+cgv1xibJEmSJEmSJEmS+tRQVLYBxFD+I3A0sD2wBvAn4DLgqKzIH68zNkmSJEmSJEmSJPWnoUm2SZIkSZIkSZIkSZ02LMtIqgdiKHcBtgLeBbwTeBVwYVbku9camNQlMZRrADsBOfB24HXA88AdwDnAOVmRv1hfhFJ3xFB+HZgCbACsCTwL3A9cDszMivzRGsOTeiaGcnfg/Orm3lmRn11nPFI3xFDeB7xxjNN/zop87R6GI/VUtQ3FvsCmwOrAo6S/9U/JivzKOmOTOimGci/Sa9hFeTEr8mV7EI7UUzGUObAf8FZeWg3tV8CJWZHfXGdsUrfEUE4APlN9vQ2YQNp662xglu9ntsdkmzrpcFKS7WngQeAt9YYjdd2uwOmkP8SuBf4AvAb4BOk/pw/HUO6aFbklxBo0BwC/Bn4G/AVYGdgEmAFMi6HcJCvyB+oLT+q+aonymaS/e1apORyp2+YAJ7c4/nSvA5F6JYbyG8DBpNe2PwIeAdYC3gNkgMk2DZLbgaPGODcV2Ab4z96FI/VG9UHSQ0gfpric9Lt+feBjwM4xlJ/KivyCGkOUuuUC4F9I7+l8H5gLbEt6n3Mz4FP1hda/TLapkw4gvRAZIVW4XVtvOFLXzQZ2BMrGT3zEUB4G3ALsTEq8/bCe8KSuWTUr8ueaD8ZQHgscBhTAPj2PSuqR6lOA55BelP87cFC9EUld90RW5DPqDkLqlRjKvUmJtvOAaVmRP990/u9qCUzqkqzIbycl3F4mhnK0smdW7yKSui+Gcm3S3/F/Bt6RFflfGs5tDVwDHE1KSkgDI4ZyJ1Ki7V7gfVmRP1IdX470HuYeMZSXZ0X+7zWG2ZeWqTsADY6syK/Nivxuq3g0LLIivyYr8iuaS6uzIn8IOGP0Zs8Dk7qsVaKtcknVvrlXsUg1mU76hPengWdqjkWS1EExlMsDx5JWrXhZog0gK/IXeh6YVIMYyreTVrD4I1DWHI7UaW8kvTf+y8ZEG6T3OIGnSBXN0qDZqWpPGE20AVR/8xxR3dy351ENACvbJKk7Rl+Az681Cqm3Plq1v6k1CqmLYig3Ao4j7ddzXQzlNnXHJPXA8tUehW8gJZh/A1yXFfmCesOSumJb0purJwMvVnv5bAw8B9zi/j0aMtOq9jv+ztcAuht4HnhfDOWajUmHGMotgVeRlpaUBs3onsv3tDg3emxqDOVyrT50pLGZbJOkDouhnMhLaxtfVWcsUjfFUB5E2qtqEjAF2IL0BuxxdcYldUv1+/18UrXDYTWHI/XS2qS53+jeGMpPZ0X+8zoCkrrovVX7HHAbKdH2VzGU1wG7ZEX+cK8Dk3ophnJFYHdgAWlPcmmgZEX+WAzll4ATgTtjKC8nLRP/JtKWIT8D/q3GEKVuGU0sr9vi3HpVO7H6/vc9iWhAuIykJHXecaQX5VdmRf6TuoORuugg4Ehgf1Ki7SpgO9980gD7CjAZ2Csr8mfrDkbqkXOAD5ASbisDbwfOBNYB/jOG8p31hSZ1xaur9mBgITCVVN3wDuCnwJbApfWEJvXUPwGrAVdlRf5A3cFI3ZAV+cnAJ0iJhb2BQ4FdgQeAc5uXl5QGxOiywF+Mofz70YPVnrRHNfRbvadRDQAr2ySpg2IopwMHkj75sUfN4UhdlRX52gAxlK8BNiMlmm+LofxIVuS/rjU4qcNiKN9PqmY7wSXENEyyIj+q6dBvgc/GUD5N+ptnBi/t+yANgtEPJc8HdsyK/L7q9h0xlDsBdwFbxVBu6v8HGnCjS0ieWWsUUhfFUB4CfA04FZgJPAS8BQjAhTGU78qK/JAaQ5S64SLSe5YfIlV1/gepov+DwGtJK7m8AXixtgj7lJVtktQhMZT7AqcAdwJbZ0X+WM0hST2RFfmfsyK/DNgOWAP4Xs0hSR1VLR/5PWA2L20YLQ27M6p2y1qjkDrviaq9rSHRBkBW5HOB0ZUr3tfLoKReiqF8G+nDdA8CV9YcjtQVMZQZ8HXgR1mRfzEr8nuyIp9bfXB0J+CPwIExlOstahyp31R7cH6UVMn5MLBn9XU36Xf/U1VXKzuXkpVtktQBMZT7AyeRPu39AZca0DDKivz+GMo7gXc1bzAt9blVgA2q75+LoWzV56wYyrOAU7Ii379nkUn1GV0yeOVao5A6766qfWKM849X7Yo9iEWqy2hV23eqN2WlQfSRqr22+URW5HNjKG8hJd0mA/f0MjCp27Iif4GUbP564/EYyhWANwOPZEV+bx2x9TOTbZL0ClUb6h4H3A5sa4JBQ+4fqtYX5Rok84DvjHHu3aQX4DeQ3qB1STENi02q1jefNGj+i7RX21tjKJfJirx5CaWNq9Y3oDSQqjda9yD9PT/W3z/SIFi+atca4/zo8ed7EIs0XuwGLAd8v+5A+pHJNkl6BWIojwCOBn4FbOfSkRp0MZQbAH/OinxO0/FlgK8CrwZuyor88VbXS/0oK/Jngc+0OhdDOYOUbDsvK/KzexmX1G0xlBsBf8iK/Jmm4+uQ9jUBuKDXcUndVFXqXwHsCOxHWr0CgBjK7Uj7mzwBXFVPhFLX7QqsDvw4K/IH6g5G6qLrgX2BaTGUZ2ZF/sfREzGUHwY2J+1jdVNN8UldE0O5albkTzYdexfwTVIV/3G1BNbnTLapY2IoPw58vLq5dtVuGkN5bvX9I1mRH9TzwKQuiaHck5RoW0D6I216i6XF7suK/NwehyZ10w5AiKG8gfSJ7keB1wBbAeuRNpTeu77wJEkd9M+kvUquA+4n7d/wJiAHViDt43N8feFJXfN50gcpToyhzIHbgHVJr3cXAJ9p/uCRNEBGl5CcVWsUUvf9ALga+CDwuxjKy0ivZzciLTE5ATg0K/JH6wtR6pqfxVA+S9oO5ynSvM+BZ4GPZkX+/+oMrl+ZbFMnvYu0mWKj9aovSC/QTbZpkKxbtcsCY+3P83Pg3J5EI/XG1cD6wBakN6FWA54BZgPnA6da4SlJA+NaYEPS7/vNSfuzPUFaNvV84PysyBfWF57UHVmRPxhD+R7gK6QKty2BJ4ErgJAV+S11xid1S1XRvAXwIOkDFdLAyor8xRjKHUgfsNiNtD/bSsBjpPl/albkP60xRKmbfkCa97uT9qH9I+lDFiEr8gfrDKyfTVi40NdGkiRJkiRJkiRJUjuWqTsASZIkSZIkSZIkqV+ZbJMkSZIkSZIkSZLaZLJNkiRJkiRJkiRJapPJNkmSJEmSJEmSJKlNJtskSZIkSZIkSZKkNplskyRJkiRJkiRJktpksk2SJEmSJEmSJElq08S6A5AkSZKkfhNDuRA4LyvyveqOZWnFUK4EHAd8HHgd8EBW5OvUEEcGXAt8Oivyc5eg/7nAnlmRT2g4NgM4Elg3K/L7uhHnEsS1F3AOsHVW5LGOGCRJkiTVy2SbJEmSpHGhIfkCMC0r8rNa9FkIlFmRf6SXsQ2YLwFfAI4HfgM8VW844181NzPg5KzIn6g3GkmSJEnjjck2SZIkSePRjBjKC7Iif7buQAbQtsAdWZEfXHcgHXAMqUpvXpfvJyNV0J0LNCfbzgcuAp7vcgySJEmSximTbZIkSZLGm1uBKcD+QKg5ltrFUC4LLJ8V+dwODbk28IcOjVWrrMjnA/NrjmEBsKDOGCRJkiTVy2SbJEmSpPHmEmAC8KUYyllZkT+6qM5j7Z/Wai+thj2+3gZMA/4ZmATcDOyTFfldMZSfAA4HNgL+DHwtK/JZY9z3B0nVVe8AngQuBr6cFfnTTf0mAYcBOwP/WPW9uup7T4uYtwU2BfYC3gDsTaqqGus5mAgcCOwJrAc8A1wHfCUr8juaxgZYt3reAI7KinzGGOOuA9wLHAXcBRTABsBfgO8Cx1QJr9H+EVineQ+4xnFa3VcM5RdIS1u+gZQIPC0r8tPGerwN182gxZ5tMZSrkpbL/ASwLun5+B0wMyvyi6o+bwGmA1tV97ts1ef0rMjPbhjrXNLzCnBvDOXoqaOyIp8x1p5tMZRrkp63HYHXkObSj0g/k0cb+o1e/wHg3cDngNcD9wPHZkV+XtNjzoFDSHN4ZeBhUoL60KzIZy/uOZMkSZLUecvUHYAkSZIkNVkIHEpKgn25S/dxHvBO4Gukvcs2AX4SQ7kH8C3gcuBg4HHgzBjKLVqM8e6q383AQcD1pOTNf8RQ/vW1VpVouwnYByhJSaWZwDbAL2Mo39hi7OOB3YCzgP1Iia5FuZC0nOKDVdxnAFsDN8dQTq76XAfsATwC/L76fg/g3xczNqSE0enAFdX4d5GSXC/bV68NXyD9vC8gJfOeAE6NoTyyncFiKFcjPd+HAb8lJaaOAe4BGvf6y4AtgR+THtMRwAvAWTGURUO/M4HLqu8PYAmet4af+eeAn5CqNK+qbt8QQ/mqFpd9rRr3zCrmF4FzYyg3bxh3K1LCbjVS1ee+pJ/BGsD6Yz4pkiRJkrrKyjZJkiRJ405W5FfHUP4M2CeG8pSsyO/v8F08BOyYFflCgBjKR4BTSIm2t2VF/kB1/GLgAeDzwA1NY7wd2Ckr8sur29+OoTyFlHD7J9I+XgBHk6rNNsmK/L9HL64qpu4gVT/t1TT2isDkJVk6MoZy2+r+LgF2a3hMlwC/Ak4FplYVdPfEUB4D/Dkr8gsWN3aDdwLvzYr819XYM0nJpr1iKM/MivwXSzFWsw2AjbIif7Aa+1uk5/rwGMrvjB5fCl8jVX39W3NFYmMSFDg/K/Izms6fBFwDHBpDeXxW5C9kRX5zDOVvgJ2Ayxsr6BbhEODNwOezIv92w/i3kxKth5CSe42WJz3Hz1d9f0BKEO4L3Fj1+RjpQ7PbZkX+l4Zrv7oEMUmSJEnqEivbJEmSJI1XXwKWozuJhFNHk1KV66v2R6OJNoCsyB8mVXG9ucUYdzUk2kYdV7U7AcRQTgA+Saoq+2MM5ZqjX6SlDX8BbNdi7NOXYo+2nar22MbHVCX2rgC2iKFcawnHGsvPRhNt1dgLgW803X+7LmxMqFXJppNIHw796NIMVCXTdgN+12rpz6zIX2z4/pmG61aIoVwD+Hvgp8CqwFuW8nE02om0vGNzDGdWx1s9Z98eTbRV8f0RmM3fzr05VbtztXSoJEmSpHHAZJskSZKkcSkr8tuA7wOfjKF8R4eHv6fp9uNVe2+Lvo+Tlulr9rvmA1mR/4m0DOJ61aG1qmu3IyVZmr+2Je3n1Wxp9t5al7Tk4MviAf6noc8r0WrsO6t2vRbn6hp7TWB14PbFdYyhXCWG8vgYyj8Az5KW13wYOLbqsvpS3nejdUnJ2PmNB6vbs2n9uJrnJMCj/O3cmwncBnwbeCyG8soYyukdSKZKkiRJegVMtkmSJEkazw4H5gNfb+PaRVX+LFjK4xPauP/G664mJdZafX2oxXVLWtU23iwc4/h4rML638AXgStJ1Yfbk34eJ1Xne/16ebFzLyvyR4H3kvbjOw14FSne2TGUm3Y9QkmSJEktjccXPJIkSZIEQFbk98ZQng7sF0OZjdHtMdLyf81eacXV4mzUfCCG8rXAarxUpfQwqdJt1azIr+5SHPeQEkMbAb9pOvfWqm1Vsbc0XvZYG8ZurMh6DHhPi76L+lks6dhL4hFSJeI7F9UphnI14COkfds+23Tugy0uGSuJOJZ7gA1jKCc2VrdVSz9uwNI/rr/KinwBEKsvqqrPX5ES03m740qSJElqn5VtkiRJksa7Y4AneWmPsGazgU1jKFcaPRBDuTrw6S7HtWEM5cebjn2pai+Hv+4RdiHwvhjKXVoNEkP56lcYx+i+cUW1R9zouBsDOwI3VHvPvRLbxlC+u2HsCcAhTfcP6WfxqhjK9zX0XQY4YBFjfzKG8vUN/Zer+i8Afrw0QVbP9/eBt8ZQ/mvz+YbnZ7SKbELT+dcCn2kx9NNV2yqp28rlpCVEm8fauzp+2RKO8zeqvf6a/Z60DOaSxiZJkiSpw6xskyRJkjSuZUX+SAzlN4GvjtFlJnABcE0M5fmkyrK9gfuBtbsY2h3ABTGUZwF3k5b22wX4OXBxQ78vA5sDl8RQXgL8AngeeCOwA6kqaa92g8iK/GfVuLsBq8dQ/pj0uD8PPAdMb3fsBv9Nen6/BfwJ+BjwQVJl2M0N/WYBBwKXxVCeQnqcu7Do156zgV/GUJ4BPAX8C2mpxK9mRf5AG7EeDmwDnB1DuR1wAympNrmKY4+syJ+KofwpsHsM5bPA/yH9PP6NVAXYvEffL6r26zGUF5Ke199mRf7bMWL4BrAr8K0qSXlbdf//CtzF2InjxTmrSkz+lDS/VwT+mbSc5PfaHFOSJEnSK2RlmyRJkqR+cCIpyfMyWZFfSKqyem3Vb3fgaOCMLsf0a+DjwGbACcCWpMTfR6sKq9H45pCSbUcCbwMCaQ+6HUlJnNM7EMsngUNJCaMTgH1ISb9NsyK/rQPj/wj4HPDRavy3kJKff1M9lhX5vaSonqjKAAABGklEQVTn5OHq/CHAjcCeixj7NNLz8amqXR3YPyvyr7QTaFbkjwObAt8kLWl5Aum5Xx+4oqHr7sB3q8c0s4r7y8C3Wox5I6lq8U3AWaTquZaVilX/0Z/5maSE6qlVewawRVbkT7Xz2IDzSf8O9iQ9b4cDLwC7ZEXeiXkkSZIkqQ0TFi5c2qXnJUmSJEnDIIZyHVKl11FZkc+oNxpJkiRJGp+sbJMkSZIkSZIkSZLaZLJNkiRJkiRJkiRJapPJNkmSJEmSJEmSJKlN7tkmSZIkSZIkSZIktcnKNkmSJEmSJEmSJKlNJtskSZIkSZIkSZKkNplskyRJkiRJkiRJktpksk2SJEmSJEmSJElqk8k2SZIkSZIkSZIkqU3/H/oPHZARe5YDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "dark"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3LGBp0tD8xS"
      },
      "source": [
        "##Themes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sU3Txe-RD8dZ",
        "outputId": "ac0e1309-aa6f-4a4e-d76c-d46403df914d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#loading Spacy and it English model\n",
        "spacy.cli.download(\"en_core_web_sm\")\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "nlp.Defaults.stop_words |= {\"=\",\"inch\",\"+\",\"°\",\"p.\",\"possible\",\"cent\",\"c\"}"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlT3Ld2YI-9U",
        "outputId": "4f51b12d-c9f8-4468-aa70-bf5df492cdc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "#Get all the titles of the publications in English\n",
        "\n",
        "eng_pub = df.loc[df['Language'] == 'English']\n",
        "\n",
        "tit_eng_pub = list(eng_pub[\"Title\"])\n",
        "\n",
        "token_list = []\n",
        "for item in tit_eng_pub:\n",
        "  my_doc = nlp(item)\n",
        "  for token in my_doc:\n",
        "    token_list.append(token)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EqjhsfJEXv0c",
        "outputId": "84cb8d81-7d9e-4c7f-faea-d87674634d1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "#Clean the tokens\n",
        "\n",
        "for token in token_list:\n",
        "    re.sub(r\"[^a-zA-Z.!?]\", \"\", str(token))\n",
        "    re.sub(r\"\\d+\", \"\", str(token))\n",
        "    re.sub(r'\\b\\w{1,2}\\b', '', str(token))\n",
        "\n",
        "my_tokens_no_space = [token for token in token_list if token]\n",
        "clean_tokens = [token for token in my_tokens_no_space if len(token)>2]\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5gOm0F5XvQC",
        "outputId": "e54d3f61-c62e-46c0-9a0a-5dd922c57705",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "#Generate the lemmas from the tokens\n",
        "\n",
        "lemmas = [token.lemma_ for token in clean_tokens]\n",
        "\n",
        "stopwords = spacy.lang.en.stop_words.STOP_WORDS\n",
        "\n",
        "#delete stopwords\n",
        "my_lemmas_no_stopwords = [lemma for lemma in lemmas if lemma not in stopwords]\n",
        "\n",
        "#delete pronouns\n",
        "my_lemmas_no_pron = [lemma for lemma in my_lemmas_no_stopwords if lemma != '-PRON-']"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6dQAUeanB4F",
        "outputId": "c4b790d4-6466-41dc-8696-fbd56e212e81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        }
      },
      "source": [
        "#Counting the lemmas\n",
        "\n",
        "lemma_counts = Counter(my_lemmas_no_pron)\n",
        "lemma_dict = sorted(lemma_counts.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "with open('2Bib_lemmas.json', 'w', encoding='utf-8') as f:\n",
        "    json.dump(lemma_dict, f, ensure_ascii=False, indent=2)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_MgmwX6rzPK",
        "outputId": "78fa916e-4a56-437a-ff83-5989174ef122",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "source": [
        "#Visualising the most popular lemmas\n",
        "\n",
        "pop_lemma_dict = lemma_dict[0:31]\n",
        "\n",
        "lemmas = [x[0] for x in pop_lemma_dict]\n",
        "counts = [x[1] for x in pop_lemma_dict]\n",
        "\n",
        "my_lemmas_uniq = list(set(my_lemmas_no_pron))\n",
        "\n",
        "#bar plot\n",
        "plt.figure(figsize=(30,20))\n",
        "plt.barh(lemmas, counts, align='center', alpha=0.5, color='#86bf91')\n",
        "plt.gca().invert_yaxis()\n",
        "\n",
        "#title\n",
        "plt.title(\"Most popular lemmas in Second Bibliography of AM (English publications)\",\n",
        "          color=\"#bf86b4\", fontdict = {'fontsize':30, \"fontweight\":\"bold\"})\n",
        "\n",
        "#grid\n",
        "plt.grid(True,color=\"#D3D3D3\")\n",
        "\n",
        "#ticks\n",
        "plt.rc('xtick',labelsize=20, color =\"#bf86b4\")\n",
        "plt.rc('ytick',labelsize=20, color =\"#bf86b4\")\n",
        "plt.xticks(list(range(0, 105, 5)))\n",
        "\n",
        "#save to file\n",
        "plt.savefig(\"2Bib_popular_lemmas\")\n",
        "\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
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              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 2160x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "dark"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSFqw5MMfd_x",
        "outputId": "75a20818-f1f1-4de1-94fd-777083f7545e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155
        }
      },
      "source": [
        "\"\"\"\n",
        "Tentative topic modelling with Gensim.\n",
        "Data are too few to consider the results more than a rough approssimation\n",
        "\"\"\"\n",
        "tokens_list = [lemma for lemma in lemmas if lemma not in stopwords]\n",
        "texts = [token.split() for token in tokens_list]\n",
        "dictionary = corpora.Dictionary(texts)\n",
        "corpus = [dictionary.doc2bow(text) for text in texts]\n",
        "\n",
        "\n",
        "\n",
        "model = models.ldamodel.LdaModel(corpus, num_topics=4, id2word=dictionary, passes=30)\n",
        " \n",
        "topics = model.print_topics(num_words=10)\n",
        "for topic in topics:\n",
        "    print(topic)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "(0, '0.070*\"temperature\" + 0.070*\"disease\" + 0.070*\"climate\" + 0.070*\"weather\" + 0.070*\"development\" + 0.070*\"factor\" + 0.070*\"resistance\" + 0.070*\"moisture\" + 0.070*\"germination\" + 0.070*\"relation\"')\n",
            "(1, '0.079*\"crop\" + 0.079*\"frost\" + 0.079*\"wheat\" + 0.079*\"yield\" + 0.079*\"growth\" + 0.079*\"winter\" + 0.079*\"effect\" + 0.079*\"insect\" + 0.016*\"factor\" + 0.016*\"relation\"')\n",
            "(2, '0.085*\"condition\" + 0.085*\"cotton\" + 0.085*\"humidity\" + 0.085*\"influence\" + 0.085*\"length\" + 0.085*\"forest\" + 0.085*\"soil\" + 0.017*\"relation\" + 0.017*\"weather\" + 0.017*\"resistance\"')\n",
            "(3, '0.091*\"study\" + 0.091*\"climatic\" + 0.091*\"plant\" + 0.091*\"light\" + 0.091*\"day\" + 0.091*\"apple\" + 0.018*\"relation\" + 0.018*\"germination\" + 0.018*\"moisture\" + 0.018*\"development\"')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YQXQ9fTkVXG",
        "outputId": "2940ecd6-d0bf-445a-d462-f9a13fe43801",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        }
      },
      "source": [
        "#Generation of wordclouds for each topic\n",
        "\n",
        "cols = [color for name, color in mcolors.XKCD_COLORS.items()]\n",
        "\n",
        "cloud = WordCloud(\n",
        "                  background_color='white',\n",
        "                  width=2500,\n",
        "                  height=1800,\n",
        "                  max_words=7,\n",
        "                  colormap='tab10',\n",
        "                  color_func=lambda *args, **kwargs: cols[i],\n",
        "                  prefer_horizontal=1.0)\n",
        "\n",
        "topics = model.show_topics(formatted=False)\n",
        "\n",
        "fig, axes = plt.subplots(2, 2, figsize=(10,10), sharex=True, sharey=True)\n",
        "\n",
        "for i, ax in enumerate(axes.flatten()):\n",
        "    fig.add_subplot(ax)\n",
        "    topic_words = dict(topics[i][1])\n",
        "    cloud.generate_from_frequencies(topic_words, max_font_size=300)\n",
        "    plt.gca().imshow(cloud)\n",
        "    plt.gca().set_title('Topic ' + str(i), fontdict=dict(size=16), color=\"#bf86b4\")\n",
        "    plt.gca().axis('off')\n",
        "\n",
        "\n",
        "plt.subplots_adjust(wspace=0, hspace=0)\n",
        "plt.axis('off')\n",
        "plt.margins(x=0, y=0)\n",
        "plt.tight_layout()\n",
        "\n",
        "#save to file\n",
        "plt.savefig(\"2Bib_word_clouds_topics\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rhAI+4hE/yUiQoH/AEA6FQgGArul878nv5btPfDea0BDsnUB60EgpuZa7humY+LSPvKKlQnHoOPBPZSoaZL4VS1msNoFY571StdlRG0hGAvgNvRNjm680ugzScs3sxKEmWrG4bepNi8IWKavxochAOqnRoEHAr3cS0HKlOpLt+gj7x5O48nWOBQnOIfH2DcWChFuxpf1oe1DbiVtS0lcVYjVX7TxERIIGiWhgzy8u0RpH25htWg6VujmwMRsK+BhLRe7ZkF3L17g6v0Gx2tw18e7kRILnT43uuw+F4qOOEAKfOPCvn4GRSL69fvWgh6FQKPpwoLNJ2wvXNupKtU2Ds60vC16iVjTsx9A1/D6dpuV0Zam3k7vabJdZKtXMTnyqAKJBf8vw3d2AlHgSXe28/rpp47oSrUdsa8/9t1lCUrZ6bMlU7ejsENBL8qwXuia6ZAh6Lb27UnY9RISDPmjp5e7FVs+6lOwIQdmNRCSA/x6SuqSUZAo1Xr+20tc430o73lqhUDzeVM0qt/O3D3oYCoWiDwf+aBwJGh0DtVq3sB2J4RNI6BisQb9O0PCha4JoyKBpOZSqnoHalrQqtoxZTQjiW5K/2rqybSTw1s21gTRXJeyQBXNdCbvkKrRlqEo1L1GtXDdpNG0sx8VxXVxX4kiJbbuHshxt+B4KIfTDdmRXLG22VOcPvjU/0L7b9W0HMS7b9Cq+MQiOK3l/PtvpSwgYS0WYTEcJtTzplu3FZpfrJsOJwQtRHGXan6G66YWSPMh7RPHocKVLzap5D54CIkakp4JA13Z4YQEhX+973XKsTiKXT/MR9HUnajquQ82u7fqw7tf9BHyBgY9DSknDbmC73twSNsLomt55r2SWWamskKvnsF2boC/IcHiY8chY3+Po149EYjom13LXyDfyrTegatUoN/vHzRq6QUDfexVqa181q8ZadY2N+gYNu4GhGQyFhhiPjBMLxPYVjiGlpGbXcF0XIQRhI9y51lJKbNdmvb7BWmWVillBExrxQJzRyCipYApDu7cQLYXioDhwY9ZLAvMM1Ibl0LRsDJ8fy3I66gSxkL+T6Z+IBMiWGjRMm1rTJuHTsR1Jpe5tGzB0IsHuw9qeLFbfh5evC9l/TpZSYtoOd1ZLLGRKVOvWkcyU9+kPLifQcTy91za2I6k41i579Efuw3U9iC5tL4rVZpcneXYswXOnRtC2JfJ9FBPA3r29zmquyhPHhnjyWPqgh6O4B+p2nX/8Jz9Nrp7D0A3+/qs/xUx8Zsd2a9U1XvvaP6Fpe5+FmfgMP/XqT+HXd8bEf3Xxq/z61d8A4FMzn+LHnvmrXdusVld57Wv/BGuXz/33nPoefujcD+7rWH75vV/hrdW30ITGf/3y3+KJ9BNk61n+483LvL78OoVGAUduzvuGZjAcHuYzM5/mcyc/R8ToH4aUq+e4lr3GfGmBheICq9VVsvVspz1b2vyfr/9T9F3CJD57/LP86FM/sudxSCnJ1rP87p3f4xtL3yBbz2K5m+dKFzqJQILnxp7jz5/6c8zEZwYyMr0x/jMWigsEfUH+h0/994yER3Cly3uZ9/jtG7/NrfxtGnajM7dqQiOoB5mMTfD585/nXPrcnv0oFIeFAzdmtZaMVr7cwGkpEkRDfsp1i2bLq9dWMQAYioW4vVLEcSXFapN42E/TsmmY3kQTacl9bWW78bFbxa3d8Bt6z1hSKSWlmsmb11e7jCHR2icU8BE0fBg+DZ+uoWsC03ZYyBy+jNgH+TDuym4TVNfEPRvL92qgDoqU3v3UfgDRhGB2PLHDkIWPnr5k03IoVJqd+GjF0SSoBwn5ghSaBQSCpfLSDmNWSslcYY5MNdMxcuyCTalZ7ClFdTN3k0LTUxsZi+yMH3ekS6FR6DLQttOw6n3f60fVqnb6nS/OE/KF+Bdv/ksWy4s9t7dci5XKCr/xwW9ydeMqP/nST5IM9Nb2/pPFr/Gr3/7VXfsvm/3LhwPUrdqexyCl5Ftrb/OL7/4ia9W1nts40iHXyPGV+a/wzZVv8qNP/yifPfaZjie6f+NQbpYpNAv4LB+lZolUMMXlm/8vv/Xhb9F0dhYpcqVLza6xULqLX/fvOX6F4jBx4MYsQCoaYA7P61mqmYwmwxSqDVqrYQzFNpeGEi3ZK8f1YmpnRmJUGxaOs5kotn1+0reURNU1watPeQUQ9osQ9BTYNy2HN6+tduJ2Bd7y9MmJBMloEL+h7ahGtlGqczdTPiyhsg8FTxJr8+/pkRjPzN6bNuOD9Bj3Y6vH3vBpajm9Ralm9pVeUxwdNKFxPDHL1Y33kUjulu7y8cmP77jHr+dudK2E1OwaS+WlHcasIx3uljzjURc6xxPHd7Q1FBziC89+gWKzSLlZpmyWKTaLfJj9sBMmcL+8m3mXr8xfYbG8iCY0JqMTnEieJBlI0HRMFkoL3Mrf6hjU316/yr+9+uv8xAs/3lPfdiiY4sn0E12vlcwyS+UlAASC2cQswV1CI8aj47uOWUrJW6tv8TPf/FmqlpfoamgGxxPHOZ44RtiIULdqzBcXmCvOYbkWJbPEL7z7CwgBF45d2EchGYd8o8DN/E1+84Pf7IRejEXGSAVT6EKnbJbIVDOUzDITkXEmohMDta1QHBYO3JjdngTWlnHKlz3D0G/oxEKbT4mRoI+g30e14SkUtPdpT729pLO2ZsG7UiIEA2fG74WUkoX1clcC2smJJM+cGO5ozfbe8YF0f6jx6RqGrnViUG3HHbwa2QGwtdKc1tIb/qgjpWS9UFMe2ceEk6mTnf8vlO4ikV0ar450uJW/BcB4ZLyz7H0zf4tnR5/t+uzWrBrrNa8eedQfZSwytqO/qD/Cd5/4rq7VsbJZ5qf+4O9vxqDeJ2+tfguJJO6P81ee/lE+PvVxQr7N5EzbtXlv/T1+/lv/V6fPbyx9gz936ns5kTyxo71Xp7+DT0x9ouu1byx/g3/2xj8HvBjiv/nC32A2Mdt3THtpV69WV/k37/xCx5Cdic/whfNf4Fz6LIa26WixXZurG1f5N+/8AmvVNUzH5Neu/ltOJU8NHHIgkby+/DpvrryJhsbnTnyOz538bsYiYx2ZMVe6VMwKN3I3aDhNAvrgMcwKxWHgUNQPbCeBAVTqnsRWW9kgGjIIGJtPzz5dIx72jNtqw8S0HcqteFlNE8TD3SL4QghiIX/HQyilJ/n1oOIeJXQkpACChs65mVRXdateWLb72NuzPk0jvKV4RfvaHia2FkHoCooQ3e/fT+GE9vaO4+kJN02bpuVgO+4DLcLQbsvu0Y97j+N2JawXdy4D73Ze7uXcuK4Xc95s6UU77r2fm93GsvUcNVsx+pbt4LqPf0EMIQQzsWn8mjd/rlRWdsSylpolViorALw8+RLJYBKAW/lbO+LWs/VsZ7l9LDJK1B+lH9uLljxIJBK/7udvvPA3uHD8AmEj3NWXoRu8MPYCP/LUj3SSoBpOg3fW3u15vTUh0DW962e7MrYmtB3bbP3RRP/5XyL5D9d/m436BgDpUJr/5uW/zTMjT+PX/TvG/tzoc/zECz9B0Oc5akrNEv/x5uV95RH80cIfYTomn3/283zx2S8wHZvGr/vRhIYmNHyaj2QwyUsTL/Gp6U8O3K5CcVg4cM8sbFbsaloOddOm3rQ7UkxeGdPu7VOxICu5KqblUmtYVOrehBww9B3C/QCxsJ+g4euUsl3JVzk+nnggNWdcV3baBS9mN2Dsflrllspm98pR8BkK4RWo2GgZQ5W6RWlbYYxHjZSShulQa1o0Te9+a5g2ddPujBO80JF3bq339c7OjMQYTYZ7vtfup10IJFOokS01qNRNLNttrQ4IDF0jHPQxHA8xmY4SC+9ejWy3vqoNi5VclfVijWrd6urHp3u6xvGwn+FEiHQ81FJnEDvacVxJw7SpNrxrla80KGy5V1dzVRrbEiq3EvBpPHksjb6HfJ1slUIuVBssrlfIluo0TBvHlWhCEPDrpKJBpkdipOPBnrHL/VjNV1nOeg+Yx0djDCfCnf42SnUWMiXy5Qam7SCl94AcCvgYigWZTEcZig1WOvkoMhweJh6Is1HfIF/PUzbLXUoCS+UlKlYFgeCJ9JMsFBdYr61zt3yXul0nYkS6tm0v3R9PHD/QkrQvjr/IS+Mf63vdhBC8OP4CQ8EUG/UsAHPFO49yiB3Wqmu8sfxG5+/vPfk9TMWmdh37E+lznB85zxsr3n5vr71NvpEnHRosGVMi+eTMJ/mzx//MrvG2j+t9r3j8ORTGrBBeha9cuYFpORSrTSzb+8Ic2qblKYTwvmxo6ZhWm51Yx1jIj69HTGvA0BkfinBntQjAer5GtlRnOH7/0kqC/RuWpu2ynN09gWA3vC/2zb+btoNsGS6Hjal0lJtLeWzHM5RuLef52JnxB5potl+uzm+wkCntunTuuJKFTKnv+4mIv68xK6VkaaPCreUC+Uqjrze6AZTrJmv5GteX8pyeTHFuJjVwspuUkqblcO1ujoVMCXMX+bJK3WKjWOf2SpHRZJjveHqK7fZmrWnzp+8vd2LQe426UG12hdRsJxzwcW5maDf1utYDhc3VuSyLG+We56du2hQqTebXSowPRTh/Ynjgim6FSpO51mc9EvCRjoewbJf35tZZWCvvUBlpWl6lwY1iHct2Gdqlyt9RJ+QLMRGdYKO+Qd2uk6mtd2JhpZTcyN/ElS4hX4ip2BQnkid4J/MuhUaBjdoGkUSks+18cVNm72TyZM/+HgUCwadnPtVTZmwrESPCSGS0Y8wWm0Vc6T5SI1xKybuZ96hY3vwf8oV4aeKlPe9rTWicH3mmY8yWmiXmi/MDG7N+3c/nWhXXFIrHkUNxZwshOmL9tiPJluotj4loJXR1f9BjIX8nEStXbtBsGb7JaKCvYXlqMtkR0rddyds3M16s7R7Lip63yqVS772t0ERX/G21YdG0nJ7bttv68G62Izt2Lxg+Df+W0Iv1Qv3QLd+Dd13jkQBTw5tV3RbXy9xczuO4uy/ptpd8683NMsUPemyatuVH7Cyxq4nu97duv1ct+bV8lY1S93XRW/dKJGgQ8vu6vL6W7fLhQpYbi/mBlrqllFTqFl+7usTN5cIOQ7atHKFvS8IDGEuF6eVwdl1JvWnhuhLRPubtSg5bz8v2c6iJPQ3xthf5T99fZj5T6pwfIdqyegbBLefGlZLlbIU/ubpE4R7Cg+qmje24fPPGWqc09fbzs/3cPM5oQmM2OQt48bFL5cWuMJsbuRsApIIpksEEJ5MnEQhMx2SuON+17UJpAfD0ZY8ljh3Yw3TYCPdMPtuOEKIrltZy7X0t1T8oPsx+2Pl/OpQmHUoPFLYzEhnpzDsSyXJ5eeDPw3BomMldvL8KxVHnUHhmwVMp0ITAlZLVvLdEGAoYPQXwA35Pm9asNFkv1rA7xmxvj4oQXiztuZk0355bb1UbM/nq1SVOTyYZH4oS8utowivW4ErZEcfPluqs5qpomuDTz0zvMAwEMJoKkyl4UiwN0+H9hSzPzA53VaGSEioNk2t3c9xdL2+Wsr0HdE2Qjoc64RW5cp1rd3Ocnkrt6LNtNBr3KEd2vwjgyWNpcqU65bqFK+Hbcxvkyg1OjieIRwJefDFe/LHtuDRMm2K1yVq+xkaxzsfOjjGWiuzR0+A8dTzNmanUjtevL+Y73tiAofPqU5N9VRT2SiA8MZFgcaOC4dMYS4YZS0W8ymSG3rnPaw2LO6tF5tdKHRmzm8sFZkbjRIK7q200LYc3rq2Q3yIFFzB0podjjA2FiQQMdF3DbYUNFCpNMoUalYbJZDra814IB3189tmZrq9303b4+vvLHWN5djzBqclk33FpYnf5NatlWLbHLQRMpqOcmkgSC/vRNYErodawWMiUmFsrtnSkLd68tsonn5nqGSLRj1rD5oOFHMvZCj5dYyodZXI4SjTonR/Hcak0LNYLNfKVRpdyyuOIEIKTW5KeFooLnf/XrBp3S3cBmIxNEtADTMe92Mqm0+Rm7iafmfk0AA27wWplFYCYP8Zo+ODKOkf9UaJG/3jdNjseQQ/g+d927c55A6iYFX75vV8e6J7L1fNdxnfJHFzacTg8TEDJbSkeYw6NMRsJ+PAbGg3T6RhpyVuAFXYAACAASURBVJYM13Y0IUhFgxQqTWoNL8RA75H8tRUhBKcmEzQtm5tLeVzpSTG9d2eDDxayBFoVxsAzqCzb9RJ0WvtvL5G7td1jIzHmVopUWpXG5laLbBRrDMVCBAwd2/E8u4VKE8tx0TTBU8fSzGdKHfWG/XJyIsHyRgXL8SqJfdhaao6G/B3pMst2MW2HVDTAK08cjNSKEIJwwMdL58Z5/cNVqg0LKWFpo8JytoLfp3f0e9sPEZbjVUpr8yBzcrzxGLAtWVdK2ZVoqGmCWNjfVVZ3P30kI0E+8eQEiUig0+72ezNg6CQiAQyfxvVFL8u6aTlkCjVmx+L9E0ik5MO7uS5Ddjge4oUzox3lj637xlqxsqemkpiW03WcW9E1jXik+8Q0TLurrYCh7/o52w0pJbeWC53YZAE8MZPm3ExqR0ys36eRjI6Qjof45o1VbMfTcv5gPssLZ8YGDu3ZKNXIFGpEggYfOzPGcMLzzG3tKx4JMDEUwXFlz/nmcaOd/GM6Jovlxc5Se6aaodDwtFtnE7MIBKngEKlgitXqKncKt7FdG0M3KDQKFJpeKMd4ZJywcXAe7aAvuLfu6iHBcq1OiAFAoVngy3O/f09tOfuQNgv5gnuuJikUR5lDEWYAYPh0osHuJ8etxRK2s12CK+j37VnGVNc0nj4+zPOnxwhv2dZ2ZCfhpVQzqbXKz7ZtKIFXUrcfQb+PF890t1mpe56lG0t57qwWWS/WsRwXv0/juZMjnJlKkb7H2DzRMuafOTHc5QWrNW0yhRoruSqZlqep2rB2jaV8FLTH+8mnp5gYinSWuKX0jLdy67xX6l6IxlZD1t8qNHHUEAJGk2GCLS9iP+NP0wSnJpJdBmZhj+TA9r3VJhby8/K58ZZqR+++RCtkILgPr+aDpmk5nbh1gJFkmLPTXoxwr8IUQgimhqOcGN/0BC9ulDulqwfBdiSaBi+2DNndzs9eCiSPC+lQmmTAO6eZ6jp1u46UktuF21iuhUBwKnUSIQRBX4CZ+DQAq9U1yi1v4Ep1tVMhbDZ5sMlfuykHHDZc6eK4DypsavBjVoas4nHn0HhmhfDi1WzXbf0t+iZotZPAhmLBTgzcUCyIMYDRo2mC2bE4Y6kwi+tllrMVKnWr5eHcrP6k6xphv49ULMjEUIR0ItQ3aUkIwXAixKfOT3NzKc9qvkbTtLuqSQUMnZFkmFMTSZJRz/s1PhShUG12vkj3d74EJ8YTxMMBbi3nyZa87OytfbZjNNPbkui24tO9eOX2fsaAnkifrpGMBr3EM3oXk9g+3mjI4ONPTrBe8DLKc+UGTcvejJvEi7v0GzrxkJ/RVJjxVGTPAheiVUWubRDu9VDzKNjPl2s7jrZddnl7+eWtyFYMqbXlAeXsTIrQIS/w0NarbSdrCrzVhb09od59PrdaxHJcbEeyuF7uGUvfj4mhaMeQVUDI8JLAMrUMZbNMoVEgYkS4kbsJeDGo7cpgnmF7mjdW3qRqVVmuLJMKprhbvNtZ8j7I5K+jhtgW7HA6dYrvP/v93Is+zXh0p66vQvFR5eC/9bdwdmaIs9NDnb93++6Jh/189tktpRj3MRe0l5rPTKU4PZlq6U162pYC0HXNW/72aZ1ElEGSC6IhP8+fGsWyvbhPy3Fbhp5OwK9j6JonR1RpommCiXSUiaHonse6W5/peJB0fAKz1afdqoTmax2D4duZ5LKVWKj7PA46jnjYz4V97ieEQBeCsVTYe3BxXBqW42nuSommeXJVfkPvPJgMYoD4dI1Xn5zc0tFgx3BYEKK7wtn2bPutSGC9uFkqM+j3MZ7qX2f+MNGOKwevGMog8alCeDrU8UiAbMkLT1gv1HGlRB/wmKeGo0ftlnioCAQnkrO8k3kH0zHJ1DKMRcaYL3nqBGORMRKBhLet8Ly0mtBwpcudwh2eHn6axbIXW2toxsDi/Qrw6T5CRghaKoABPcjHxj92ZMIkFIrDyqExZkU7m3zAOVGInVna99Sn8Dx5D8Kb543f8yz6+8QlruYrfOODFXRN8JlnZ0hE7q/SSvtLJGDofWMh99r/Xg3pez3/7TEbPn1gT/DDGsujoO3xd6XEcTxFC8f1dGhd1/tpP4TsheO4nZhy8B4q+t1rh4l20mWbcMA38Lg9z7u/Y8zWml7oTMi/92qG3op9VsZWNyeSJxAIJJKVygqziVk2ap6I/8nkia4qVFPRKSJGhLJZ5nb+diuJaQ2AeCDOcHjkQI7hKGJoBiPh4U6iXbaepek0CWuPt4qGQvGwOTTG7EeFpul0DJm2lu6jQEpJsdrEp2sD63Uq7p22AVtvxTGvF2uUayYNy8HpVOTyjDyJHDjJzUtO3LxvIkHjSHgdHVd2hU8E/L6e8mD9CPm3xrh7Fc5CA5Sk1lvefsUmQohOEljTabJSWWWtukbN8jznZ4bOdm0fD8QZjYxSNssslpcom2VyjRwAE9EJwr7+YUyPDQ/oQyYQnB06y1ur3wI8Y3a5vMyp1Ck1JysU94Ga5R8xE+kI56ZTPDM73Fch4WFg2i5f/2CFm0uFR9bnRxUpJabt8u25Df7g7QW+eWONhUyZfMUr8GHabqeIxG4hBb3Yvo9/j1jlw0K7ZG0b34CFIWAzOauN26pUNui+ykjYyVBoqFOqNlPNsFC6iyMd/LqfE8nZrnPm03ycSHhyXrlGjuXKMlXLk0+cTcx+JIT4fWLzwUlKibmtDPCgCCF4fux5Arq3Ime5Fl9ZuHIgercKxeOE8sw+Yvw+nadnvYo7j/JLtlRrUmtaatJ8yLTL5b5+baWrPK6uCRKRAMlogGjQT8Cvd0T7NU3w7q31XStrHXl2lMob/D6UsvuuVabp/RP0BZmKTbJWXSPfyHE7fwuAoWCKkR5hA2eGTvPluS9Tt+pcz93AdLyQkZOpEzu2fRyJB+LoQseRDo50mC/OcXbozD3N4TPxGV4Ye56vL38DgD9e+GOeHTnPK5Ov7Nme91mQuNLFp6mvb4WizZH+NPSsyDXAZLDffXbbd79t3KsBez99SynZKNbvWa/1QRx3vzYexbm/n2u+XyReudythuz4UISnjqWJR/ydilpb+3el7FmGuRea1vY0esdkDRhre9BoQuDbWvFsn+PeGlPseWqVSXs/CASziRO8tfotCs0iN1vG7LH4MYK+7hUjIQSziVkMzcByLd7LvIcrXfy6n5nY4Mlfe1X829rfYcNLiouTa3h60L9z+3c4P3qe8cg44I15+/H1Ow5d6PzFJ/4i13LXyDcKNJ0mP/etn2etmuEzxz5NPBDfIaVluzYls8Tt/G1eX36dF8df5NXpVx/CkSoUR5MjYcxKKVnNVSlUm0wPx4iGDBqWw+J6mWyxjum4+HWNRDTAzEiMaGhnpZN2HfvlbIWNYp2m5XS8ZVPDURK7SP3I1rLmeqHGWqHWqV3v0zXCQYOhWJB0PES4hzySlJKljcqO4giaJjgxkRhIlN91JblKg9VclXKtVXhBCEJ+H8lokOFEiFjY31V61G2Vgq02LErVZkeXNF9u8uFCrqt9IbzKTturWrW9jBulOtlS3Ttu18XQNaIhP+NDEdIxT7Js63G35aNKNZOZkRjhgMF6scZytkK1boGAcMBgYijCaCq8QzB/xxgsh7V8lWyxTt20W6WONaIhg3QsxHAy1PM8tsvhLmUr5Ep1TNttSYp517yty/ogqdYtljc2RdHT8SAvnxvH2E3DVO7+Rb8Vn65h6FpHmqvasJAcfm+l1pKJK7eS1xqmsy9Fglpjc1m3rdShuHfalcAEglKzRLnp6ceeGTrTU5N0JDxCMphkvbbeKceaDCRIh9I925fSSyxbqaxQt+vUrBo1q07NrlFqFjthCgBvZ97Bci1CRpiwL7Tld4hTyVPEArGefTxK4oE4L028zO/e+V0AFstL/OM/+WlemXyFyegkQggadp1Ss0y+kefF8Rf4xNQnerYlhGAmPsNfe+6v83Nv/RxVq0rVqvJrV3+NL93+ErOJWUbCIxi6gek0KTaKZGrrrNfWqZgVJJJz6Sce5eErFIeeI2HMAixnK8ytlQj5fdiOy5vXV7uyowGWshXiYf8OY1ZKyVqhxju3Mp1McE14yTcruSo3lwucnU51BNy379swbb55Y41MoYaUnvHnVazytrmFV63sM89O98zOX8lVWFyvdC2X+nSNqeHonl/KtuPy7TsbzK0VcVxP01WIduIQsFbCb+h89vx0V/WmSt3ij9+9S9N2ujyy+UqD/DZRfk0IxociXcZs2yB9+1aGhukl7rT79o67ys2lAicmEpw/MbzDKFnaqHB3vYxP16g3bW4tF3ClRBOb521urcjxsQTPnRzp6WlzpeRupsz78xvUmr2r3WiiwKeemWIk2Z0N3H6IePfOekfbtN33crbCzaU8Tx5Pc3Ii2fUQcL8Uqs0ur+OJ8cTuhizece6mLbsVX+tBon0+SlUT03L2LK970Ai8ctPrLY91vWnRNB3Cwb090q6UFKubn/VI0HggKhgfdaZiUwR8ARp2A4lEFzqnU6d73qshI8R0bJr12jqu9O7vydikJzPVhy/d/h1+9/bv7hnaNF+cZ744v+N1TWj8vY//XT428bF9HtmDRyD4gXPfz438De4U7gCwXlvn8s3LPbefjO5ecVEIwSsTLxN42c8vvvtLrFRWkEiy9SzZenbP8XwU4pQViv1wuL8Be5ArN7ixlMeVkqeOp0mEAyA82Z9cqc7QtqpaUkpy5QZvXlvFtBwm01GOjcYJBXxYttMxkj9YyOLTNU5PJndM5u/PZ1nLeyUxz0ylSEQDaEJgWg75SoO1fI10PNi38MH5E17FL8txyRbrXJ3fe7Jqj30hU+L2SgGfrnH2WIqRRBifrmE5LqVq0zOwgci2wgIhv87zp0c7Ru+1u1mKVZOxZJjZ8UTXtkJAOGhse83TzdWEYGIowlgqQjzsR9c16k2L2ytFMoUat1cKjCRCTKajPb8EbyzmadoO40MRjo/GCQZ8NC2budUiy9kq86tFhuMhjo3Gdnh3FzIl3r6ZwXEl0aDB9EiMZDSApgkaTZtsqUHTcnZUg5NSspqv8s0ba7iuy7HRGNMjMQKGD9NyuLte4u56mffubOD36cyMPDjPj7nFKG1rpO5myEopqdTNjsG9FwIYSYY6mq1102YtX+XYaP/yt/eD53XfMt57bsfTF765nEdKLyFxvVjjWGD3cUspqdQsyvXNeOKRZHhfSgiK3qRa8bFrW2S2JmOTPbf1svDPcHX9aue1U8lTu1aW0oXWJfG1X7zkvd5zqk/z4dc8p4WhGQNXuPJpxuZ++uBff0IIhoJD/N2P/x3+3Qf/njdX3qRmVXd8HgQCv+4nOIDCgxCC50af4x9+6h/we3e+zNcWv8ZGbQNb7pwLNKER8oWYiU/z8sTLfGzixT3bN/TNY1XxtYrHnSN3h8+vlRiKB3nl3HhXac6JIYkktWNKc13J1bkNmpbD7Fic50+NduIOpZSMJMMYus61xRzXF/NMDUcJBzYnYNtxO96kp46nmRnpNrrGUl5JTs9j27taWdDv63jOpNzfkvBa3jNWj43FefJYuquPkUSIk5NJHEfu8C4aPp2p4VirT8ncahEwiYQMT0R+AMMnHvZz4bljBPx6yyvr7SOjAYYTYf7o3buUaibL2SqT6WjPNuqmzfGxOC9sO+/D8RBf/fYSuXKDxfUyx0a7Dcp60+b9uSyOKxlNhnnp7FjnHLbbmB1P4LqyU9iijeW4XJ3LYjsuZ6dTPH18uBMK0b7mmhDMrZX4cCHHeCqyZwWzQdH1rQa5V051N6SE2yvFfWXnT6ajXF/Md0INrt3NMZwI9wxzuV+8SnIa4BnpW5f798tQLEQiHOgkut1aLjKR3n11QgK3Vgqd8+jTNaYHvH8VuxP0BfmpT/x3nWQun+YjHoj33FYIwfec/B5emXyl81oyuPPBfyvfd/b7+M7Z77yvMfYLY/irz/wVfujcDwKe0TZoOd3Pn//P+OEn/xIAft2/rzK8QgiGQ8P8xAs/zg+d+0HuFOfIVNeo2w10oRPzRxkODzMeGWckMpj2rhCCodAQP/zkX+IvnL7IYnmJpfIi+UYey7UxNINESxptPDJBKpTEJ/b+nPs0Hz/50n+F1VJd2M2DrlA8Dhw5Y1YIOD87vKPGfKfowjYK1SbZUgOfrnFmKrUlgWZzn+NjcW6tFGiYNuuFOsfHtnoTNtttmjuXgttVrR4W7aZNy8GVoCF3jF/zPZz+hRA9i0kIIfD7NEaTYUo1k3qzf9ymT9c4O73zvPt0jYmhCLlyg0rDxHFlJ9SgHeJQN20MXeP8iZGe1xu6jcf2vhvFOqVqk4Chc2oy2WXsetfLixFeyJSp1E3ylQajyQcjWh4L+btCKZazlU5c8PZxeoZsgfm10r77ODbi3bMA5brFG9dWeP7UGImI54npFbsN3n1k2i7R0GBaw3pLl7jaMmLXizUqDYvoPWgV+3TB2Zkh3ry2giu9kJdv39ng/IlhfNtCMdrn585qkfnM5vmZGYl1hdMo7h0hBKOR0YG3j/gjRPyRgdtOBVOkgql7Hd6ubadDadinfSaEYDg8fN9960JnPDrOeHT8vtra3m7EH+Fc+izn0mf33mGA9sYiqtyt4qPDkTNmoyGvtOUgX6Rtw8aVkrDfh0/XMHsUKhACDF3Dbi3dS7lpMPp0wWgqTGWlyPsLWeqmzex4nGjI3+WtfFiMD0VY3qiwuOElaJyaTHpL7Y9QP1MCWwNv26ELbW+mK1sb9RhONGT0XGoXQhBoeVrdHnqrbW94Mhogvs8KTuvt0IuggSYETWvnsp1P19B1gWVLSlXzgRmz8bCfWDhAseV9XMh4cd7HxuKdCm2O41KsmdxZKbCcrSDxqtANHGogBE8cGyJXrpOveP1kSw3++L27TAxFGU2FvWPXBG6rWEG5ZpIre/HSY6kIL54ezIgReNrI7bCGhunwxocrPHEsTTLihXy4UrYKGXhliYcTvcvUCiGYSkdZH0twZ7UIeMZquWZyciJBMhrE59NwXS/0Yn6txNJGpXNvJCJ+njw2dOiT3RQKhULxaDlyxmzQ70PfR8Bcpe4toVUbFn/w9kLf7dqxjmYPyaAnjqWpN21W81VuLOWZWy0ynAgxMxpnLBnG8O2e4HOvCCGYGY5Rqja5vVLk7nqZpWyFVDTA9EiMyXSUkP/BLy23aas45MoNNop1ynUv2ahdjnUQ4yvo9/VNsOo3ald6JUsB4uHAvkvVVlpexEKlyZff2plY0qa9TG86D64Sm0/XePLYEG9cW8VxvfP3/kKWm8t5AoYPBFi2Q9NyOsmEpydTJKMB3ri2OnA/AUPn5XMTvHl9lVzZS+gzbZf5TIn5TKmVpCh6VhcbVDkBWvfgSJyFtVLHcM5Xmnz9/WV8rfu+XRDBlZJkNMiFZ2f6XjMh4PyJYWzXZTFTRgIbpTobpTo+XaAJDdkyjreOMh728/LZiR0eeoVCoVAojpwxu98vsnasnRBiVyO4vZy+vaKSEIKgofPKExOs5qrcWimQLzdYyVVZyVWJhf2cnUoxMxrboYTwINB1jWdOjDCRjnJruUAmXyNbapAtNbh2N8+J8QSnp5J7ZszvFykl+UqTd29nyJUbSOmdG7/RFvvXBnqo2M+Dx9a+2zGkg+qvbqWtSSrE7v2HW9f8QZY7bce0Pn9qlKtzGzTaD0m2i2l3q2/4DZ1z0ylOTSapN72QikH1V70EPYPveHqS64t55tdKXYoI7TK529E1MVAZ2K5x+jReOjfOt25kyJbqtB3x7YeBLvawk9shJi+eHiMZCXBjKd9Ry/A+q90PFj5NMDkc4+njaUIPISZYoVAoFEefI2fM7pd2TGUyEuA7np7a08vXy4vYFmmfGo4ykY5QrDa5myl39GPfurlGpW7x1Gz6gco8bR3TcDxEOh6iWrdYypa5mylTqpl8sJClUGnw8rkJjAcYO1s3bd74cIVKwyIe9vPksTRD8SB+n96J1X1/Icu1u7k929ovQmxeB+ceigK0DVgvcWx8z4y7zvapcCdu16fr92SIg3e/HB+LM5wIsbheZr1Yp2Hanq6qphEK+EjHQ0ylo53Y1VDA4KnjaUzbIdZDJ7lfPwHDxzOzw5yaSLJWqLFRrFOpm1iOt+SvCYHh04kEPU3ikZYm8X6PJxby8x1PT7GWr3p6x3UTy3aRSHy6RsDQiYX8jCTDe37G2p+nM1MppodjLOcqrBc2dYy1Vqz2UCzERDriaUAz+IPscCLEk8eGANA1ra/KiEKhUCgeDx57YzbSkpxqWg6a4L70KdvJXqlokFQ0yJmpFFfnsyxkStxaKXBs9OElp7QNyFjYz7nQECfHk9xeLfDBfI6VXJXVfIXp4dgD8VxJKVnJVqk0LAxd4+Vz4zuKSrSXlh8GbWOmWG1Srpv7LgrQvuYN00bXxcAe8/FUhPHUYAkue9GWNjs3M8TZGZCu5yfdaqhvPZ+6Jjg9dW/JMkIIwkGD2bE4s2NxpKQTZ9o2Attd3ev9IYTA8HkPdFPD0e4+RPdD4H6q4YWDBqcmkpyaSOK2kr4EdJL27mW8I4kwI4kHEwOtUCgUisPPY+2yEC2PpiY8kfZ8ubGveMHd2m1n+j8zmyZg6NiO24nVfNgIIfAbOmemhkjFPOO5WGnusY/3e9DDb8cahwK+npWypKST5PQwGI57qcr5ctOrcrWP69Y2ZMp1k3LNfCDX/F4RQnjyVrrWCc9o3z8Poy8hBJrmLeV7SW5aR0niQfTZs48tx3QvfbT3a3tRdf3hnSOFQqFQPH481sYsQCoWZCgewpVwdT7bKofabdxI6SWvNHq857gupuX0NYhM20uGaisiPEi8Erx2S6Zo55gd1+3ELfqN3T3ObS3Pct3coRzQi7ZSgeW42G73Ur+UkpVchWypPvCx7Id23Knf0DFth6t3NjBtt/d126aEIIRgNBkmHvZjO5Krc1lMe+f1a+/b7HHNFQqFQqFQHB0e+zADXROcPzHMn76/TK7c4KvvLXF8LN6p4mXZLpW6yUapjuO4fPKZqS7d2HLN5I1rq6TjIYYTIaIhA13z5IOK1Sa3lgvYjksiEiAR7Q4xkFK2En8cHEdiuy6FSsNLoJGyU8HK82553tatiVy24/K1q8uEAwajyRCxcADD52V7VxsWc2slSjUTv09jLBXZ1ZM1mgpzd71MtlTn/bks0yMxNE1gO55BnE6EuozxkUSYayJPvWlzdS7Lqckkfp9G03JZyXnlYP2GPrCc1H6JhgyemB7ivbl1lrIVqk2LYyNx4hHPS2xaDsVak2yxzvkTI11VwAyfp037+rUVVvNVvvreEsfG4h2JL9P2pKo2inV8usYnnty99KRCoVAoFIrDy2NvzIpWjOvHn5jknVsZCtUm357b8N6jO/l6JBFie3SmEIJa06a0WuTOarETH+h5c71t4mE/L54Z6+mZfX9+g4W1Em4ru7ztBHRcyZvXVzsSSpoGpyaSPD3bLept2S6L5TKLG+VOFSu2xCsG/TrPnhghvktSj6fvGWMlXWU5W+H6Up6by3loHYeuaXznC8cwQpuC++l4iJOTCW4tF7izWmQhU+rolrpSMjUc48R4gq9dXRr8YuwDIQSnJpMgvApXhUqTQmXde4/N69a+Ftv3HUuFefncOO/d3qBQbVK4vXNfgKnh3pXLFAqFQqFQHA3EHkush2L9VUrJUrZCvtwgFvZz/B7q0EspsWyX1XyVTKHmxWG6Ep9PIxr0k06EGEmECBh6V9tuS2c1U6hRqjVpWg6uKz2Jo4DBSDLExFCkk+W/Y9wbFfKVxkBjTMe9tjplY6WkXDNZy9coVBvUTQfH8bK9A36ddCvbu1dRgl7HbzuSpY0yq/kqDdP26ogbOolIgDNTya7kuLaxvpqrsrRRpta0EAjCQR8TQ1HGh7xEqbaH9sR4omvci+tlCtUmiUhgRwngNvlyg8WNMoZP58xkEr3Hw0DbC72crZArN1oyThJD14mGDUYTYUaT4b77Ni2H1VyVTLFGvemFFLT3HYmHSSdC+B+STrDisedh3DSHYs5VKBSKQ0jfOffQGbO9vGwPo33Z+mdr87v1tX0/xOZZfdiG0IPsu6st9m5nv9s/LPpdt0ErwfXad7/772cfxUcCZcwqFArFo+PoGLOVusm1xRx+n85Tx9MPpRCBQrEfHNflg/kcTcvm7MzQwDqwisceZcwq7hnHdfjK/BXyjTwAU7EpXp36hHpYVij60/fDcahiZqWUrOVrzK2WiAQNnphJo/TOFQdNrWFza6WA47gcH0sQCx30iBQKxVHHlS6/P/f73C7cBuCVyVd4deoTBzwqheJocuhMxfVi7aCHoFB0kNKLmbbvoRKZQqFQKBSKh8+h8sxatku+PFiylELxqMgU1AOWQqFQHDSudHkv8x5Vq8pTw0+RDCYPekiKQ8Kh8syWak3q5sPRLVUo7gXbccmVH05xCIVCoVAMTqlZ4l+99a/5F2/+S+6W7h70cBSHiAP1zG7NMrcdl9VctaPD6hUccHDdPsu7gk4pzf301TAdKg2TRtPGkRKfrhEJGERCRlfBgn44rttachb4W1WyXAmFSoNK3cLv00jFgl0SX1JKak2bfLmBKyWxkJ94xI/Wp2Sn7bg4jrvjGNtFGMq1JrWWzJTf0ImF/IQDxqYO7T7xKoxBw7Sp1E0arYpnhk8nEjSIBA10bX/lRV1XYjkOSNA0DZ8uus6HZbuU6ya1po3juvg0jVDARyRoEGhVMxtEbkxKMG2HasOiYdpYjotAYPg0Qn4f4aDRuU77VS5wWrJstcbmA5ZlOzR3eeDafqxb6dw7EhDeGLUBxuQ47maYgxC7SolJKbEcF+nKHdu2z1e1aVGpm5iWA3gyb5GAQSjg29d19qrQSWoNi2rDwrS99oJ+nWjQIHQf96RCoVBsZ6G0QLFZVFUbFTs4EGNWSkmpapKrNChWTQNrIwAAIABJREFUmpRqzZYx4nS2qTdtvvL2wq7tfOzMGBPpvUXvpZTkyw1uLhfIFGqt8qab72vC00+dHo5xciJJ0L9TM7bN0kaFd2+vo2uCTz49RSjg4+1b6yxulHFbVRQiQYPnT40ylgojgTsrRT5YyNK0vOPTNMF4KsLzp0cJGjv7urVc4MaSl+H6zOwwx8fimLbLzaU8C5lSqyTv5vaGrjGcCHFuZoihWHBfxoiUXpzyzeUCuVIdy3a70ql1TRANGRwfTXB8PD6QwQ+QrzT4+gfLSAkTQxFePDPWMWJvLReYz5SoNa2u4xACAobOaDLMC6fH8Ok7+2lPYrWmzUq2wkquSrHa3HFNwbuu7fZOTyVJRAK7jr1h2qwX65SqTYrVJpW6Rd20OwUqJPDG9dVdDdCp4SjPnxrt+V773gHvvH76/DTRAZQR7q6XO4U+fLrGp89PEwkaPbeVwJvXVsmVG+ia4FPPTBML+5FSsl6sc+1urmcMsE8XRIJ+zp8YZjQZ3lOmznZcFjJl5taKlGsmjtt98g3de6g7NZlkPBVRRq1CobgvpJRcXb+KKz2HhUKxlQPzzH57foPVXLXv+xI6xl8/tn+B9tvm5lKea3dzWH2SeFwpqdQtPrybYylb4cXTY6TjvY1Cx5WdcZVqJvOZEguZUtc21YbFWzfW+Myz0xSrTd67s941VteVLGcr+HTBx86Ms70b23E7feQrDUaTYV6/tkq21Hu523JcVnJVNkp1nj05MlBRibZB8v58lturxY4h3ut4i1WTd++ss5Qt8+KZcWKhvYs0uK6kaTpIoFK3kHgPKG9eX2Wj2Ps4PO+wQ8N00LT+7S9tVHj7VmbP+8OVkrppM58psZKr8sLpUaaGo33Hvpyt8K2bmV3btOzdE8F2SxTbeu/omthhfA+yn+PKPb0Spu3dPwLPQI+EDG4tF3h/fgPb6b2v7XgFOvQ9HlZk67PyrZtrrPe5juDdk5lCjY1indnxOM/MDuMb8EFIoVAotmO79v/P3nuFyXGm972/r0LnNDkDg0EmAkESJJfLzVpqpQ32yrKcpVWwpQvbsn2eE57nnAtf2T62L45lWyt5FWxZwdJa2l3J2pU2SNrACBJLgkQOA2By6umcKn3norprpjHdPT0ACAy49eODh9MzVV9Vf13d/a/3e9//y6W1Sw/7NHx2KQ9NzCYiAXcpfROFiuFFZxVF0BsLbRF6mwkG1PZ/xBUzV2bTXJ5d94RDIw0gHg6gKIKaaZMpVCmUDSRQKBucubzI+x4bpSfWOZI3u1pgLVemJxaiPxmmUDFYzripEhXD4vpClrW6O8NoX4xoSGclWyZXqgGwkC5xeNwgEQ22PcZ6ocrZa8uk8xVURZCMBklGgwQ0hZpls56vki8bgCu0zt1YJaRrDPV0jq7ZjuTcjVVubxLioYBKTzxELBRACFd8rheqlKomAOl8lTOXF3jusTEiQa1rYVIzbaqGxdlNQjYU0IiFdQKaiuNIyjWTcs3Csh2GeqId77vjkUDTzYGbaqETCwcI6qq7lF41SOerngg0LJtzN1ZIRoPE27T+DQW0ekvjDQzLJlcyvMepaBBda5/a8tA9aGXzjxXDYmY5z/lbaziO9CKw4aCGItzrv1Q1qRkWkZBGskNb5EY3tlcvLXjXnABi4QA98SChgObeGJZN1gsVDMvBkZLpxRy2I3niwCCqL2Z9djEbqW/u/xsRwC3dHZEgN7ZrbNnqM3HzmO3Ga7WPIx1KZpmKVXZTytQAET1KUA10NUansQ3HoGSUMGwDIQRhLUxEj6CK9quS3YwrkVStKhWrgmm73xsBNUBYCxPUggh2lq7WGBcgXUkzV9hony5x56gd3c61z3uDhyZmj0/2Nz2WwFvXV7i5lAMgHNB47tgo+l0azUopWUwXuTKX8YTsaF+MY5N9xMKBJrHUWDI9f2sNy3aoGBbnbqzwgeNjTS1e72QhXWSoJ8IzR0bQVQVHSr5/bZmZlQLgphcAPL5/gMnhJAJXIH7n7VlPuKXrLXrbveGyRVf4xsJu6kJ/Mty0zG3ZDtP1NAbbcaOt79xcpSc+4eWetpqb6cWsF1EWAvYNJzk83ks42HxJGJbN9fksV+cyONKN0p6/ucrTh0c63mhspmbanL+5xmquQiykc2RPL0M9UQK66r0OtiOp1CyWs6X6snTrwYUQxCMBxvtjVE2biYE4/ckwIV3bcj6lqsnb06ss1lcAqqbN7eU8xyb7Wo4/0htlpN6mt8FSpsTLFxbcYwMn9w/Qn9jFRrN3PK2ldbd9MxImh5McGEkRC+te5FtKNw84U6xRNS20Du8325Gcm171hGxAUzg22c/EQLxpPzcab/DO9BpLGXfuZ1by9MVD7vvA/3Lx2cW8OPsiL829DMCpoVN8YuqHt24k4c+nv8655XOA+7n0N4/8OPt79m/Z1LAN/uvb/41sNUtA1fncyc/RF+7bsp0i3PdQxazw2sJrvDj3EvP5eSpWpd6GWycVTHK0/ygfnfwok8lJb59ucKTDrewtvjv7PS6uXmS9uo5pmwghCGlBhqLDnB45zfPj76cn1NN1jYEtbeYL85xbfpuLaxdZKC5QMkpYjgUCdEUjqseYSEzw9MhpTo+cJqJ3DrZYjkW2mmWlvMJcfo7budtMZ29SMt3PE4nkDy5+ka8F/qztGIf7DvPZQ3+96/nxebR5KGK25UUs5RYxcjd3cQ1My11CbyyfD/VEOH1oqOVSp66pTI0ksR2Hd266uYnrhSrza0X2DrVfshcCDo71eHmkihBMDieZXS0gpRsZ7okF2TOY8ARoOKjRn4x4QjJfj9J2QlMVTh8abpkPq2sqB8d7MCybq3Nunm2+bLCwVmgrHEpVk6tzGS+msHcoycmpgZYFaUFd4+iePgzLZrouzhfSJdL5Cv3JcFevj2HZzK4WSEYDPHt0lFhoa5qCproiNRZunQu6GQE8vn+wfr7t77yjIZ0nDgyRf3vWiy6v5so4so8W6bgtx7kzN+terskHwh1ZBLOrBRQBJ/YNMDWSQijNz0gICAY0hno6r3JI6abGLNdvDBQhOLV/kPGB+Jb5ELgR6qcODfHi+TlyJQMp4ep8htH+eNubLB+f3YCiqLy5/CYAlmPywr6Po4rma9aSFi/PvczV9ave7470HWEqNbXl/ZCtZXlt/lXKVoWByABBNdTyuGEtxEp5hS+8+WtcWL2wKerrUrWrFIwCs4U5Xpx7ib955G/yI1OfQFW2f++WrTJfuvwl/uLWX1KxtqYHVawKmWqWy+nLfOPmN/j7x/4ez4w+01EsSym5kZ3mixe/yJX0Fap2a1vNKlAwiiyVlnhj8Q2mUlP8/BP/iL3JvW0/S99YPMuvvflrlK1y2+jr9cz1js87oPqdGn+Q2FXWXPcLt5NYyYsgqYrg6J6+jjl7Qgj2DiWIbIpMzqzkO+Y16ppKIrKRitD4Eg9siub2JyOod+R/xjcJtmoXVmTDPZGOhV2KEBwYTREKbJx7Q1DfiZSS2dWCt/we1FWOTPSiKu3nRlEE+0dTXvTNkZKZ1cK2570ZVRGcnBpsKWQ3I9o4PNy5jaYqKNtU3gvhVtYP9US831Vq1pb0lvc6o/XCRkVpXzbRmPd289lIF2hcUgOpMGP9W4Xs5vGCusq+4Q0fyGLFZC1X8SuRfXYtQggm4uOeEFourbQUaflansXiYtPvrmeubxGgAEvFJaq2G7QYjY0Q1lqLWcux+PzZz3N+9TwSSVgLMxYf40DPfibi44S1jRWhslnmf1z4H7y68Nq276eyWeZXz/4qX73+NU/IBtQAw9Fh9vfsZzI5STKY9G7cV0orfP7sr/Dt29/ZPj/frnFx7aI3RwJBRI+4Y6f2M5XaR1+4zxPFEsmN7A1++eznydVybce1Hfd7MayFiepRonqU0B3zFtJC3t9a/btze5/3NruqacL9ZD5d9H6ORwKktqlkBwhobs5ouebumysZ1Ex7y9J7g6CmbsmfVBUFTVU8sZi4IwdRCEFgU2SqXUHOZoZ7t3dsaOR7ztZFZq5kUDUtIsHmSKdTT79o0J8MNwn4dkRDOrGw7qU9rOcr2PUczG7oS4TpT3QXyb2fCCGa8lhtKT13gvckd0yvImBqJNl1Skg7ihWTbHHjS92NyG5zKkLQnwyjKsLLcV7LlRnti3be0cfnIdIX7iMZTLJaXiVfy5Or5ojqzdfsfGGekllCEQoRLULRLDKbn6Vm1QjrG6JTSslMftaLLk6m9rWNdp5ZeJ2aXSOoBnlh3wt8dO9H6I/0oykatmOzVlnjT69/lW/f/jaOdDAdky9f+TKnhh7fcn4NbMfm9y/9AWcWXwdAExofmHieT+z/BCPREXRVd3NzjRJvLr/JH17+I9KVNDW7xu+c/x3GE2Mc7DnYetVKCA72HORo/1Fm8zOcHDzJk8NPsS81SSKQQFM1kFC1qlxMX+L3L/w+C0U3ZWsmP8N3Z77LZw5+puXYp0dOc7T/aNPvrqSv8B/f+E+em8EvPPHzHOk70u5l9COzP2C8J8WsZTue6AK3aEdRRFcRoc3iz7RtKobVVsy28gkVgqbfhQJbC6U2P97unBQhSHTIqd1MbzzkiVnTtilXzS1itmrYFCtm0z7dnkc4qHnzWjUsDMvumGO5mYFU5J4FVbfc+VyanBEkXbsIPJLc8dxCAW1bS7Jth5SSTLHqCVJFEaTqRYvbXTdBXUVTFWzHvbkrlI2O2/v4PGxCeojR2Cir5VVqdo2l0hKj8VHv71JKrq1fx5EOyWCSJ4ef4K9uf5v1yjrpSppxfbxpvNu5W4AbtZxK7Wv7XqzZNXRF52dO/jQf3vvhJtGrKRpj8TF+5uRPY1gGL869CMBCYYEbmWlODp7YMp6UkvOrF/j2rW8DoKDw2cOf5ccOf3ZLoVcgHOCjez/KWHyMf/fqv6doFCmZJf7w8h/xfzz7v6OrrdO/NEXj5x7/WXRVd/NsW6Rh6arOMyNPMxQZ5F+99K/IG+531Nmls3zywCfRxNbv16AWJKg1F0bHg4nmx4E4veHelufl84PHe1LM1ky7ybYpU6xta7m0se1G9KlRHNMOVVW2RMI2IwC1ReRyJ7JCVURXOYZCiCbvUSndJfU7KddMrE2NKBbXS03ith0SyG+q6m8Um3VLN3ZeO6UhpEzL8dwQqoZVbzohsR0H25Fku8hLfq8SDmpoXTYW6cRmESql5OpcpqsbmTuvE8NykG4/Bx+fXYmCwmRqknMr55C4kdUnhp7YaD6C5Nr6NQAGIwOcGDjBt29/h5pd43Z+hrH4mLet6VheBX5ADTAeH2990DpPjz7NB/d8sG30Vld0fmT/J3ht4TVMx8SWNjcy1zkxcHzL56stbf7sxp9hOO57d3/Pfj598FNoSuuvfSEEh3oP8aGJD/G1G18D4NLaJWbyMy0L2xr7DMeGOz6nxnZ7knt4YvhJvjPzHQBWSqtUrArxQHzb/X18tuM9KWZNy2nqHJarm+DfDZ2WpBWxvTC9V3NnRREdPVc3o2sqQmxEHo0Wnqg1o7m5wFqu0tb3dTt2EuHsNoLb3XHdVIGVbJmZlQLr+QpV027rlfsDxR2XipsLfe/Dbm5oIiXeCsBOkfX/dnZL5+PzYJlK7UMgkEhmc83Ne8pm2WulOpGYYDI1SVANUrWrXF+/xvvHnvO2LRlF0uU0AD2hVMdIoipUPrb3Y1uKzTYjhGAkNkIymGSt4hYrr5bXWm67UlrhSvqK9/iDez5IqE3x2WaeGnmKP5/+cxzpYNgG51cvtCxs2yluZHrKE7M1u4Zh+ys1PveH96SYtZ1Wafg751H7ur1T87YS4t00mtjNNFoDn7uxwlKm1CSoG5FwVVFQ6zcBqqJgWHbLKPV7kjte3vsVAbXbtZX28XmPIYRgvF4EVrNrzBcXMB2LQH2pfbm07BUvTab20RvqpSfcw2JxkensTWxpe0vnK+VVz05qND7WsSgpFUp2rPBvEFADbl5uPQZRs7YWqEkpuZ65Qdlyfc51RedQ76GuimsHIwOE1JC3byNN4l4RQjTnE2/jE+vjsxPek2L2zvfr5FCiq7a3reiJPdyKyJ1EP+/Uqa3art75q6N7ekndxXMUgq4Kx+43NdPmzOVF1gsbH+DxsM5Yv+s3Gwnp6HWng4Z1142FrGe59ihyf27N7o3N15KuKZzaP3hX0fZWeeY+PruN3nAvqVCK5dIy6UqaklkkoPa4Ht3ZaUzHRBUqexITBLUgY/ExFouLLBYXKRpFUqEUUkrmCnPY0l3VaER72x+zj4i+vYe1QKBsGqfd58NM7rb3s6qoTGdusFJa3nb8kllqGjNbzeFIp2PEGDaaJpi2SdksU7bK1KwapmNi2iaGY3Ize3Pb4/v43A3vSTHb+MK060owHNQZ6W1vxL+bcaTs2krKtJpTCFp1qgrckX8bDwcYvUuh/6CRUnJjIdskZPcOJjixr99twNDBJupRRUqJtU0L3QdB4I7mIa4LxvaewD4+jyIhLcRobITl0jJls8xaOU1PqAeAq2k3XzaiRxiKDnmFXW8svkHRKLJUXCIVci3pNhd/7etQ/AUQD8R21ARhO9KVtPdz1aryhbd+7a7GsRyz4w21lJJsLcu55XOcW3mbmdwMuVoOwzawpe1FXxti18fn3eA9KWaDukZAU6nUPVwLlUc3L8d2HGqmTXSbG3YpJaXaRiGXEG4XtTuJBLUmq6RCxUBK+UgIPtuRTZZrsbDOial+Alp7ISvlzgrVdiOVLryI321ikQ3hatkOldpW2zcfn/cKDfH55vJbWI7FQmGeAz37MWyDmzk3ujgUHSQecL2Wp1JTCAS2tJnOTnO47zCOdLzc2kb0thOaot9zjUUDiaTSIv3g7sZqj2EbfOvmX/DV618lXUlvEasCgSLqTYUUBSklprN9wbGPz055T4pZXVNIRAOeCMgUqpi2syW69CggJeTKBj0dmiY0yGyKWOqq0uRu0CAc0ImEdK863TWxfzSqyw3Tprop97U/Ee4oZBsUd8nNzObldSm7y0OVkrsuXrxfCCHoiYVQhMCREind66ZTIw8fn0cZIeqR1HoR2ExdlK5X11mrF1xNpaY8Z4Cx+DgRPULJLHEtc50fxe2qtVxyXXR6Q71eZPfBPYeNn8NamPePP9fWyaATg5GhlhFj0zb5nfO/wzdvfsuLvgaUAHtTeznce5jxxBg9oR5CWpigGkBXdF5bOMMXL33xrp+Tj087dpWY3fxl735p3t2ShABG+2IsZ9wE9nLVZGm9xESL1puPAkvrJbetbodtaqbd5EqQiAQJtojMaqpguCfqidn1QpX1QoW+h9DQYKc4dyxT6V3cnNw5Lzul4Vgh6//upRBqs8WaIyXFitnRA1ZKSbFiNFmiPSyS0SDxSMAT1jMrefYNJ7ekrfj4vFcYi495LgXzhXkkkvnCPNV6xPNg7yFv21QoyUBkgFKuxExuhppdI1PNUjSK3lgP0sRfIAhrG50PQ1qIv/vY3yUWuD8pZVJKXls4w7du/oUnZCcSE/z0yc9xqPcQutLaivFy+vJ9Ob6Pz53sqna2m9uxGqZNqWbelaAVQjDaFyNWbxsrgYu30xTKRlfjSSlxnLsX0/eblWyJ9Xy17flIKbm1nKO8KWo51h/b4m4A7txMDiUI1PNpbUfyzs01qoa96+dGUxXUTZ6ppWpnkec4kmvzmaZ52SkBXUVsmshssXbXzz0a1puKpubWWrccbiAlXJvPYO4kTeJduh9RFcHUSNJ7nC8bXJ5NYztO19dNt9v6+OwGekO9Xu7rankV03YLmCSSoBpkMjXpCTZd0dmXmgRgvbJOrppjtbziWU810hAeJIORAe/nilWhYBSa2lbv9N9mbGnzl7f+witui+kx/vFT/5hj/ccIqO2b/JjOw0+Z8nlvsmvErBCivpTuPrYdyZXZdUy7+QtQ1iO2cpvIbVBXObqnz4v2lqomr1xcYH6tWC+Uklv+WbZDvmxwcynHq5cWumom8CCwbMn3ry2TzlebItauQJDMrBS4Mpvxto+F9Hq70dYfKPFIgANjG0te64Uqr1xcYCVbxqrP953/TMsmW6xybT7La5cXMB9CUVJAU5vaA69mK/U0iebrQEqJYdpcmklzfT57T8eMhPSm3OObSzny9Zuiza9DN9dlJKh5nbMAFtMlbixmsTdd4439a6bFhdtr3F7J7+yE3yWtKIRgYiDBYGoj2nN9Icv3r61QKBveDc7mf44jqRkWq9ky79xc48KtdIcj+PjsLjbnueZqOYpmkVt1h4DecC8D4f6m7Q/0HABc4bhYcp0NJLKefzv5QFe+hBBM9WwI6JpV41bu1n27mSyZJeYLC97jw32H2Zvc0/E5Sim9FA0fn/vNrkoz6ImFSEVDXheu+bUiparJSG+UUEBzO3LZDjXDombaHJnoJRENthxLCMF4f5xCxeDK7DpSQrFqcubKItGQTjIadEWKAMtyqBo2papJxTCxbImiCI7tgihSOKChKIJCxeClC/MMJMP0JkIENBXDtFmtNz1oeMqqiuCxyf6OXcOEEBwc66FUMT2xlClWefnCAvGI7qYo1Pc368U+papJ1bCwHUlIVx9KVaoQsHcoyVq+4l0Lr11eZO9Qgr5EGE1V6qK7xkK6SL5sIATsGYwzt1a8q6YKuqow3h/nytw64N4UvXh+ntG+WL3NsFsQZZg2FcNmMBVmz2Ci5ViKEOwfTbFeqOBIN9XgnZurLKwVGUhFCAVUbEdSKBusZssUqyZCuFH2hbXiQ68D1lTBEwcGefXSArmSgZRuusFiukgyGiQW1tE1Fad+M1GumZSqFjXTQkoeGdcMHx/YKAI7u3SWqlVltbTKQl3A7U3saWq32six1RQNy7G4nZthtbwKuPmqm9vhPigO9BwgGUqSrWaRSF6ae5lnRp9p2T52p7hWWxsrY33hvm0jz4ZtcDl96a6PeaelXyMq7OMDu0zMaqrgxL5+Xru86LWjzRZrZItbC2CEgKmRVMfxFEVwdKKPgKZyeSbttdIsVsxto66C3dE0oeHpefbaMqWqyeJ6icX1UsttVUXw2N4+xvpj20YBVEVw6sAgwYDKjYUstuN21cqVDHLb5Gi6Yz/42RFCMD4QYy2X4NayK8Jrps3VuQyQ2bK9oggOj/fWBWT1riLtQggOjKVYy1dI593c26phMb3YOuLbykFi81ijfTH2j/ZwfSGDlG4qwVq+wlp+a16vELB/JMX+0RSr2XLLjm4PkkbL5PcdHeWtGyusZMpI3JuKds+hef8Hc54+PveDzUVglmNxM3eTbM193x/sPbRFvA1GBkkGk6QraWZytykYbpe8vkgfqWDn76p3g1QoxbOjz/D16W8AcG75HGcWXue5sfd1FSXeHMW9c3tN0dCVjQLjxnPtNNbZpbPczN7awTNoJqJFUIWKIx0kkqXiMnLw0XDi8Xn32TVpBuC+YfqTYZ57bJTBVAS1TRtXgbvk3O7vm1EUwYHRFB88McHkUIJQQG0rw4Rw83ZH+2I8dWiYaHirG4AQrhBs/GuF93e1dSvRzWNs16rWsh2SsSDPHxtjYiCO3sKoXqmnaDx7ZIQDYz1dmdILIdBUhWOT/Xzg+Bhj/bGObg+KEESCGnsG4zx5cKilh23zAUBV3U5cmro15+puUYTg8f2DHJvsJ9ymaYMiBD2xIM8cHubInl4CmkJfPFx/TbroQXwHQV3l2SMjTI10LnhSFbHtvAgBxyb7eOLAkBfZbbVNIhLgiQNDHN/XTyigEQ3p3jXTcS7vuD67bYXcLZsF7VOHhuiNhzq+DzVFkIwGODTew2N7+u7rufj4vNuMxUcJaSEkkktrl6laVVShsr9n/5b3YUSPeGkJs/lZVuqR2UY3sQeNQPDJ/Z+iv54OYTomv3nuN/ne7Pcw7K31I156kHTI1/J8f+n7fHf2uy3HjupRBjbl5F5JX2a5tNJyTEc6vLPyDv/9nd++p2hqb7iXeDDuPf7e7PcoGkU/D98H2GWRWXC/LHvjId5/bJRC2SRXqlExTBxHoigKIV0lEtKJhvS2YqbVmMlogCcPDlEzbfJlg0LZwDBtJBJNVTzBEA3p3hJ7K9Ew2hejL+6avqrq1r+riuB9j416S9qtumSN9MXo7TDGZmxHIh1JLKxz+vAwpYpJplilXDVxpCu0UrEgyWhwe6HTAkUI+hJhehNhqjWLfNmgWDG8KKCmKoSDGrH63DTE2nbH6YmF+NipPd7jbl+r7XBFuODweA+TQwkyxSqFsoFlOyhCEApoJKJBEpGANx9SSk7s6+fwRC9C0DEFo90xQwGVU/sHOTTeS65Yo1h1jymEIKCpRIIa0bC+rfeqEAK1XoQ33h8jVzLIl2vUTLfhRVBXSUQDpKJBNFXxzv99R0c9b+CO0V/gqYND3rbbXV93Q+M12DOYYHwgTqliki8blGtmizkJeN7GjX19fB4VUqEUPaEeKsUK51bO4UiHVDDFSGx4y7aKUNjfM8XbK28zX1zwRNZUaupBnzbgvteGooN87uTn+JWzn6dcLwL71e//F75581ucGnqcsXqLXcuxKRoFlkvL3Mrd5nbuNplqhufHn+dDEx/aMramaDw79izXM9cBWK9m+E9v/Cd+/MiPM5nci67qGLbBUnGZV+Zf5sXZl6hYFSYSEywUFu5K1MYCMU4OnOTbM98G4HrmOv/fmf/AJ6Z+mJHYCIpQMByTslEiW8vSG+7laN9R/zPnB4RdJ2Zh4ws/FQuSirXOib2bMcGNvIYCWlMhy04IaGrHCGYjcnUvY9yJrI8rcIu34pH7e5ffGDsScj1oIXrPY2qqct/PczOiLlxHemOM9G6/bTCgEbyH02lcP40bnntFCIGuqfQnw/QnO3fEEELUX5fuxu1223ul8T5NRINtc9d9fB5lgmqQ8fgYC8UFz5JrND5KPBDfsq0QggM9B7y0BHAF7oMu/rrznE6PPMUvPPkEjP1HAAAgAElEQVQL/Le3f4tMNYMtba6uX+Xq+lV3m/pS1U7qIIQQfHTvRzi7+AaX01cAV1z++1f/PRE9gq7omLZJxap4wvXk4El++uTn+H9f/reslFd2/lwQ/LVDn+H86nnWKm4h2YW1C1xcu4imaAghcKSD4zg4OHxy/yc52nd0x8fxeTTZVWkGPj4+Pj4+u4VGEdhmDvTsb9t2djw+TkgLeY8jWoTh2Mi7eo7boQiFZ0ef5f95/v/m+fHniWjNgRzJ1jazITXIod5DPDP6TNtxY3qMf3L6n3B65LTXjMGRDkWjSKaaoWgWsaVNWAvziakf5hdP/1NGYiNM1i3MdooQgtHYKL/49C8ylZryXgOJ21XMsA0sx8LB8bb3+cFBbJNv4iejPAQu3Frj8qxbPR/UVT7+5N4mD14fH59dwbvxbel/5u4ylopLXFi74D0+0neE0dhoS7FkOiZn5s9Qtd0oblgLuw4CLTpvOdLh7OJZ8oZbzDoQGeTEwPFtRZgjHV5feJ2i6TZkGIoOcaz/2Lb7NfJXl0pLXFi9wI3sNOlyGsOuoSiqlwc7mdzL/p79DEWH2jY/2Dym6ZhcSV/hzeW3mC/MUzErqIpKMphkX2qSU0OnGI+Pe+JzOjvNrdwtNEXjmdFnCW8S/90gpaRslbmweoHzqxdYKi1RNasoQiGiR+iP9DORGOf4wHGGo8O+qH1v0fbF9MXsLsQXsz4+jwQPXcxKKXFwKBklMtUM+Voe0zFRhUpYj5AMJkgEEgS1IILOOfVSSizH8paFA2pgI/pV775XNIqsllcpGG7Dj2ggSm+ol2Qw4S31dnvepmOSqWZYr6xTsapoQiUZStIf7ieiu9HDbsazHMtb1tcUzROOUkoM22C1vMp6NYPtWET0KIPRAZLB5Lbz8V7G87VmwxfbTTe7u9z2VuM1rp13a47v93PweSRo+6L6CsnHx8fnEUNKSdWu8tbSW7w4+yLT2ZsUjAK2Y3tLxopQCKgBUqEU+1L7OD38FM+OPdsyStjgi5e+yJmF1xFC8AtP/DxH+48ipWSptMRXr3+N7y99n1wth+3Y3jHCepjR2CifOvApnh19ZlvBXDSLvDj7Ii/OvsRCYYGqvdHdUFd0esM9PDn8JC/se4GR2Mi2ouSluZf40uUvA/CpA5/khX0vYNgGL869xLdufov5wrzXiUsRCvFAjOMDx/n0wU8zmXx4+awPk8ZzFuzc3eVBjLdbj+mze/HFrM8jS82wvAYKQkBfIrzjCLaUkqphUzUswkGNoK7+QH65+Tw6SCmZzk7z2+/8NlfWr+LI1v7DtrSpWBUqxQqLxUVWy6s8Pfp0x7GztRxLpSUAlksrHOk7wjur7/CFN7/AaovuTba0KRpFrq5f5WPWR7c97+uZ6/zGud/kVvZWy4IjwzFYKi3ztRt/xstzr/B3HvvbfGjPh1CV9gWzZbPsnfOV9BXeP/5+fvPcf+WV+Ve2zI0tbbK1HC/OvcS5lbf53Mmf4vnx59vmwPr4+Dwa+GLW55ElXzZ47dKi6/YAPH98bEdiVkrJeqHqNukwLCIhneceGyUR8SvzfXYnUkrOr57nP5/9ZbLVjcYdCgrxYJyeUA/hutVSzsiRq+ao2W7TmefHn28yut+O9Uqaa+vX+M9v/DK5Wg5VuHmQPeEeAopO2aqwXlmnaBQJ62GO9B1peyMopeRK+gr/4fVfIlN1G5wIBH3hPvYk95AIxKnZBovFReYL85iOSbaW5TfO/SYls8wnD/xoV4Lzdn6G33r7t3hp7iUUoTASG2E8PkZUj1E2S9zK3Wa1vIpEUjAK/MZbv0lUj/HE0Cn/JtbH5xHGF7M+7wnuNrl7fq1Ipebm2xUrJsuZMvFwwP9i89l1SClZLC7xX978gidkBYKDvQf55IFPcqT3MNFAFFW47aYN2yBdSXMlfYULaxd5euT0jq7r6exNXls4Q76W57H+x/jMwU9zoOcAYT2MgoItbQpGgevr15nNzzaZ6N9JpprhC2/9midkI3qEv3H4b/ChPR8kHoh7eY6GbXBt/Rq/e+H3mM5OYzomX7z0RUZiwzw5/OS25z+Xn2MuP0cimODvH/t7PD36NGEtjEB4Ob/fvPlNvnLlKxiOax31u+d/l/09UySDya7nxsfHZ3fhi9ldSF8izIFRt/2hpiloLbp++fj4/GDhSIcvXfkjVuudpQSCj+z9MD95/CeJ6JGWLUcjeoTx+Dgfm/zYlvar23F26SxSSj6y9yP81ImfdEXhpmOoqPSF++gd7e1o4SSl5GvXv8Z8Yd47r585+dN8YOIDW6KtQS3IsYFj/G/P/gv+7Sv/jtn8LDW7xh9c/AMO9x0mFoh1PGeJRFM0fvbkz/C+O9q2CgSJYILPHvospm3y5atfAWC+MMfLc6/wI1Of8G9ifXweUXyVtAsZ7o3y+P5Bt23r3n5fzL6LjPRG3TxZIBbWGe6J+l9oPruS+eICbyye9R4f6NnPPzj+D1oK2c0IIVCEsuPr2pEOe5N7+fvH/94WIXvn+I1/rchUM7w495L3+ImhU7x//P1t0waEEPSH+/mJoz+BKtxc2Zn8LGcXz3bVuvRQ70FOd4hCq4rKj+z/ES+SLIGX5172HBF8fHwePXyV5PMDixCC/mSYjzw+wQdPjPPhkxPEwg+me5aPz06QUvLW0ptUrArg5sh+6sCniOrv7s3Xx/d9nJgeu+tjSCm5tHbJS4tQUPjwng97IrUdQghODBxnuN42ViJ5Zf7VtsVum3li6ImOjg0AyWCSk4Mnvcdz+VnSlfS2Y/v4+OxOfDHr8wONEIJYOMBAKkIo0L1Ppo/Pg0QivbahAMlQkqP9727f+ZAW4rH7cIzL6Suec0E0EGEqNdXVmGEtzMGeA97jW7lblMxSx30UoTDZZfvYw72HvJ8rVpXF4uK2+/j4+OxOfDHr4+Pjs8sxbZPl0rL3eCg6RCwQf1ePGdNjpIKpexrDkQ6LxQXvcTKY2jbvdTNjiXHvZ7dNarbD1qAJratzFkIwGB1saonayEX28fF59PALwHx2JY3cONtxu7sIIVCVR6ezS+P8HSlxHABZz10UNE7/Xp5Hq/m5H2N7XXWke+7uY4GigCLuz/x7c+NInE2de+7n3Mj6+N7cKG7506Nw7bTCcAzKZtl73BvuRX2XvVGDaqCjv2s3ONKhYBS8x7FAdNsUgAZCCFKbHAYa7gmdUBWVoNadtV5Ud50fGqkLBaPY1X4+Pj67D1/M+uwqpJSYtsNiusRCukixYmDZDqqiEA3pDPdGGeuPbTQ3EHTlyyWlZDVXYWm9/TKlELB/NEUkeHd5sw2Rli8bLK2XSOcrlGsWlu0gpURVBLqmEg3r9MZC9CXDJMIBV2h1IbIa4xcrJgvpIqu5MuWahW07KIogqGv0xkOM9sXojYcQojvx1hi3UrNI5yus5SvkSwY108Z2HIQQaKpCJKjRlwgz3BslEQmA6L4+XkqJlJAtVVlMl8gUq1RqlifGVVUhqKnEwjq98RB9iTCxcGBHz0EC+VKN+bUi6XyFimHhOBJFEYQD7rmP9cdIRl2x8ygJW9uxseRGgVJQffe9kIVQduyAcCeOdJoKq7Qd+NwCTb647meDse0+3Z6zqjQ3SLEcc0fn5uPjs3vwxazPrkFKSaZY460bK2QK1S1/L1QMljIlbixkODk1SCigev6R3bCer3BtPtP270LAWH/8rsSslBLDcrg0k2ZmOY9ptytUMVkvVJldKaAqglQsyNOHR4iGOh9TSollO1ydyzC9mMWwto5frJik8xVuLGYZ7Y1xfF8/kWDnPGApJZlClesLWVayZWqm3XbbXKnG4nqJK7PrTA4nObqnD03dXohLKSnXLM7fWmMxXcR2Wr9eBWAtX+HWch5NFQwkI5w+PExA6xwdlFJSNW0u3U4zu5rHsreOX6yYrOYqXF/IsncwwdG9fQS0nVf4Pyw295wHcGT712k34UbcN14/e4fnfef220WKZf2/bnCk0+SOoGxTlObj47N78cWsz65ASkm2WOPVSwteEwNwBaauqSiA5biCrlAxOXNlkcf29KEoAqeFeGlFKKARDwewbAfbcbDry9xduP1si2U7vHFliaXMRuRXAJqqoKoCpHv+tu14X7V2/fkEtM7LxQ2hfPbqEoubIstC1MdXBI7jRrSldJfX59YK5Ms1nj06sm0TiJVsmdnV5uXbzWNLCableCkBpu1wbT6D40hOTg3QSQ9KKakYFq9eWiBbrLUd33acJhFq2a4k0ZTt56ZUNTlzZanpBkgI0FUFRRHuPFvuvFu2w43FLPlKjWcOjzwy7Yt1RW+KxuZrBSTyniOn7zaqUIkGot7jslnGljYq2wtHKWXT0r+qqET1aIc93Ah2zap13KZBxaw0uSNsN7aPj8/uxRezPrsC03Z468ZKk5Ad6Y1yYKyHeDiAooBhOaRzbnQ1XzY4f2sNp02UrxV7hhKMD8RxHOmJp8X1Iu/c3NpzfidIKZlZKXhCVgCj/TH2DSeJhQNodTFr2g7lmsV6vsJytky2WGNiILGtj7AjJW9Pr3hCVggY7YsxOZwkEQ6gqgqO41CsmNxcyjG/VsCRbrrD968t8/7HxgjorcWDEIKJwQTXF7I4jqQ3EWKoJ0pvPEQ4oKGqihdZnV3Jc3Mp50VWby3nmBiM05cIdzz/a3MZT8gqQrB3KMGewTiRkI6qKF5qSalispavsJItUyjX2DuY6CiUwRXZZ68te0JWVQR7Bt3xoyE3hcO2HXLlGjcWsixn3LzT1WyFc9OrnD40hPoIiNmAGiAVSrFSXgFgpbyCYRuEtNBDPrPOKEJhODrEpbVLAORqOSpWhYAa6Gr/peKS93NIDW1b3GVLm1wt19XY6Uq6KfLbF+7taj8fH5/dhy9mfR46UkpmVwqsb4qs7R1KcGr/IOqmfNKgDrGQzlBPhNcuL5LOb01FaIeo53cqqsANCrnirlDu7ku14/kD8+mNCNJQT4TTh4abzh0gCK4NWDLMofFeilWDoL59GsD8WtGLnAoBx/b2c2CsB6Upn1QlVM8LjUcCXLqdRgLpfJVbyzkOjvW0PU4kqPHUwSGiYZ1YqHWealBX6YkNEA3pvD29isSNLC+ki/X83NZjW7bTFK3eN5zk5P6BLcVYISAeDjDUE+HoRC+FikksrG87NzcWsqzlXO9VVRE8eXCIiYF4fa7q++oq4aDGQDLCuekVbi3lAZhfKzDeH2O07+59VB8UqlCZTE5ydf0qAKvlVeYLC+zvmXrIZ7Y9h3oP8Ve3vw1AoVZgobBAIpDYds4tx+JG9ob3eCg6RDyY6LiPIx1m8rMcHzi+7bUznb3pPQ6oAUZiI108Gx8fn92Ib83l89CxHcmt5Y1oSiSocWxv3xYxCK5ACQU0jk8OeO4GDxvHkVSNjYhybyLc8twbNKrrE5EgwTYR0waWI7k2n/FSIYZ7ohwYS7WdG0URHBjrIRXbWJK+tdQph7c+bm+URCTYthit0eFp71CCeGTjBiBbrHXMULRsiWFuHLsvGe7oKiCEQFUVUrHgthHrqmExvbRx3ewbTjIxEG/ZjarhhvHYnj7CQfceXkqYXszdlzSTB8GpoVOelZRhG/zlrb/ccQ7qg0YIwbGBY54dly1tXpl/ddu8Viklt/MzzORmvN+dGnocvQsnhLdX3t52Xmp2jbdX3vYeD0YGvI5gPj4+jx6+mPV56BQrBoXyRpXyaF+sYwMDIQQ98WCTYHuYNKr9G2SLNXaQ/dCRTKFKrlSrHwemRlKeRVY7NEUw2rfh5VmsGuRL7fMIO7Ui3TK2qpDcNO810+7YYlRRRNNNR6vCvrtBSslSpuzdRGiqYHI4uW1b11BAYzAZ2TifYpVybfdXsQshONp/hInEhPe7781+jzMLr3fV4lXWbda62fZ+MxAZ4OmRp73HL82+xHT2ZttzkVJi2AZ/fOUr1Gz3uo0FYjw/8XxX1+mltUtcTV/tOP7ri28wm5/1fvfk8FO7PmXDx8enPb6Y9XnoZIu1pgr3wVSkw9YuihD0xjvnaj4oFAH9m/JGF9eLnL+5Srlm3pN4kFKymi17kcOAppKKBbf9QhdCNAlO1xKrdt+EzGZ3ASllR2s0XVOabjqmF7NcmctQM617Pp+VTekL0VCA2DaOEA02z41pubnGjwJhLcyPHfqsZ1dVs2t84c0v8L+u/S9ytVxTdX5DuNqOTaFW4OLaRV6ce+mhnLdA8NcOfobeek5q0SzyK2d/hWuZ61scBaSUFI0iv3vh93hj6ay3/wuTH+86DaBm1/j1c7/Bjez0ljlxpMPFtYv83vnf86K3iUCCj+z9yH18xj4+7z0c6fDdme/yJ1f/hD+5+ie83uWN9IPCz5n1eahIKSlUNqKyqiLq/qLbC7ZE5N7zXe8X+4aTzK0WqBgWUsL1hSxza0XG+2PsGUyQiAbqTQF2lhqx2QFA1xTKNYuqsf3SsmHaTRa8larVaXOPzR9OtiM9H9hGgwNHSkyr+6VtARwc62E1V6m7SEgu3Frj9lKOicEE4wOxtnm6nbAdSX5TND+gKfXraPsx7DtSLipGd3PzsBFC8PTo0/xo9kf40+tfxZEOZbPM7134H3x9+hsc6D3AaGyEkBbGckyy1SyLxSUWigtkq1lODp7k+fH3P3AHBCEEI7ERfvrE5/iV7/8qFavCXGGOf/PSv+Hxocd5rP8xUqEkhm0wk5vhjaWzzBfmvf0fH3qczxz8jJdi0YmYHiWkhZkvzPOvX/rXPDn8BEf6jpIIximbZS6uXeT1hTcoW24hoCIU/tqhzzAaG9n1edM+Pg8TW9r8+Y2ve3nsz4w8zemR0w/5rDbwxazPQ2dzvqmmKujbWFU12C7f9EEhhCAW1jl9aIg3b6x4kb6qYXF9IcvNpRypWIiJgTgjfVHCHVIoNuPUba0aFCsm3zk322GPDe5027SczjmzDUeB9XyV1VyZXLFG2bCwLFeASlwLs4aw7RYhBP3JME8cGOSdm6ueEC9WTS7NpLk2n6EvEWJiIMFwT4RAl1ZZlu00eeKu5ir81Vvdz81m7hS3uxlN0fiJoz9BQA3yp9f/lKpVRSJZq6yxNn9vrhzvJg0hbkmb33r7t8jVcpStMq/Mv8Ir86+03EcRCk8NP8U/PPVzRPTtV2sAInqUnzv1s/z6W7/BanmV782+yPdmX2w7/gv7XuATU5/whayPzyOOL2Z9Hjqb/UWVelvTblC3KRB6kAghGEhF+OCJcW7MZ7m9kvfElu1I0vkK6XyFy7Mq4/1x9o+miIY6V+s7zlah5dznZR1Zj7bOrBS4Pp+hUDHue0GUEIKJgTipWIirc+ssrBW9gjTLdljOlFnOlImGdPYOJZgcSroNMTrOzVZRfbdzs3sWyrpDV3V+/Mjf4PjAcb52/atcWLtI2Sy3LarSFZ3B6CBPDD+xbVRWEYoXAe0mEroTFKHw/rHnGI+P8aUrX+bt5be9COlmVKEyEhvhE1M/zIf2fIigun1qTYOaXWUyOcn/9dz/ye9f/APeWXnHy7ttIBAMRAb49MFP8dG9H23qMubj814kW81yPXOdqB7lcN/h+/7e3g34YtbnkabLbrYPBCEEkaDO8X39TI2mmF0tMLuap1DeEIhVw66nIBQ4MtHHvuEkSgdXhs3PLaApXivWnRILt07JsB3J29Or3FreqOoXAqJBnWQ0SCwcIBRQ0TQFTXEbQEwv5JrstrpBCEE8rPPUwSEOjfVweyXP/FqRctX0nmOpanLxtttB7di+fsY6WGbd+ZqHAxqx8N2Jkkjw0foYFLjpKkf6DnOo9yBrlTS3sjeZLyyQq2WxHAtN0YkH4wxGBplIjDMcHSaktbdQa/Bjhz7LD+39GODaVenq/RV6Qgj2JPbwi6f/KculZa6uX2O+ME/ZLKOrOv3hPqZSU0ymJglr4R1HTE3HwnRMxuPj/Itn/jmz+VmupK+wVFrGckzigQT7Uvs43HeoK3swH59HHSklL829xG+/8zsc7D3Av/zgv/TFrI/Pu8HmavdGXmY3OLLbxpUPFiEE0ZDO4fEe9o+kWC9U3KYK6yWMer5p1bA5N72CaTscHm/tAauI5rlJRIM8f3yc++VIJqXk5lKOW0s5bx4TkQDHJvvpT4bRN0W+G+cnpWQxvTMhe+cYiWiQ45P9HB7vZTVXZmalwEq2jFWP1harJm9cWcKpe8a2mhtVEQhFQD3TYKgnwhMHh3Z5P6z7ixACVagMRQcZig62LcboVrAJIRiODTMcG76fp9nyOKpQGY2PtizquieBWS98E0KgCY19qX1MJifv7zF8fB4hJJLzqxd20Oj50cQXsz4PnVBg4zJ0O3N1l8NomLvfY1PXBIOpCIOpCMWqyY2FLLfqXbSkhKuz64z0RltGXIXiWkk1Cp0aNlhimxav3WLaDtOLWe8DLhzUeN/R0W2bFdyPVAchBAFdZbQvxkhfjFypxrW5DHNrhXp7W8mF22kGU5Gm66OBpioENMW7BiqG1dG/9geBR/G5P4hzfhTnxcfnflEwCtzK3nrYp/Gu896LNfs8UjSWnxvYtqRY3d4qSUr5yFgqNXxc4+EAj08NcGr/oNem1bQdltZLLaNqAppEbqVmNRXL3Svlqkl5k8vBeH98WyELUOvCTaFbRD1HOhUNcvrQMIcnNlqKlqsm6Xyl5X6q4s5ng2LFxLQenUIuHx8fnwfBTG6GbC37sE/jXccXsz4PnVQs5BV9Sah7q27TIQhYL7QWOrsZIQTjA/GmHNbiJmuyOxnsiXjC17IdFtsI37vBsJymKGs8sr0lmmHZ5MvtGzDcLY3uZVPDySYf21KHG5uhnqj3c7nmCt/d5Hvo4+Pj0vD4rZgV1ivrrJZXWa+sU7Eqnhdwp3/txmy3XeNxxdo4XqaaoWpVt3gbd3v+lmNRMAqslddYLa+Rr+WxnO39su/l+dzNc79zmwtrF3Bk/UZfdj7W3TZWkVJiS5uSUWKtnGa1vEqumsOwjQfWrMVPM/B56MQjAeIRnVzJFXXza0UOjvUQbGPTJKWkUDZYz9+fblL3SuONupPlzM1bKm3SBoQQ9MVDJCJBrwvYjYUso30xIsHu7L06nZvre4tX+GXZjpdv2G6sudUi5Vr30eGdz41g86btnC2EEIz0Rrk8q1I1bKSEK3Pr9CXC6Jpyz3Pj4+Nz70gpqdk1vr/0fV6Zf5WZ3Awls4Tt2KiKSjwQZzg2jNamTfGh3kN8+sCnWv7tVu4WX77yFSSSEwMneGHfxwGwHZur61f5q9vf5tr6NfJGHsdx0BSNeDDOnsQeTo88xfvGnkNTOts7SilZKa/yytzLvLV8jpXyMlWrhkQSVIMMRgc5NXSKD4w/z0BkoOVnyXplnd+98HuYjklMj/KTJ36ypdXcQnGB/3npf2LXhefh3sN86sAnW57Ti7MvcmbxdQCeG3uO58beh5SSqlUlW8uyVFxiJj/LTO42b6++4+27WFzkP5z5pbafeYpQ+FtHf4Kx+FjHeWlsC1C1qpxdPMtLcy8xk5/1nFV0Rac31MNjA8f4yJ4PM5GYeFc/a30x6/PQURXBnsEE79x0fTJLVZNLt9OcnBpAUZrFRsMP9cKtNc/e6WFTNWxypRq9iZBXNNVJEC5nSk0pEp3a8mqqwuHxHt64uoQj3bl54+oSTx4YapsS0BBptiPJlWooiqAntrVVZziooamKtzy/nCkxNZJEbTPmSrbMpZl0h5nYSqFiUDNtemIht2hrm1zcudWCZ2kmBB3dG8JBjf0jKS7cds8pna/y5o0VTu4baGvt1Zgby3bIFKpEQnpbpwcfH5+7R0rJYnGRX3/rN7iUvrQRHdxEwSiwUFxoO4bSoaQzW81yZuEMEjfq+/F9P4RhGfzh5T/k69Pf2GLJBpA38swX5slU13l27FmgtZhtRGK/detbfOXqH5Otbl2mL5tlMtUMV9JX+Mb0N/ixw5/lY3s/tsUBJKAGuJy+TLqSJqAG+OSBT24Rs1JKLq1d4pX5V73frZRW+OGpFwiozZ9PEsmZxdc5s3AGcMUsgCUtfun1/8il9CVqdcF9J0WzyOt1EdwKVaj86P4fbfv3zYS0EOlKml9/69c5t/J2y9c3V8txM3eL78x8h7999G/xQ5M/hLrNDcTd4otZn4eOEIK9Q0lmVgpeBPLmUo6aaXNgLEUs7HbPsmyHTLHK9fksa/kKuqbUo4mdx3eXOTacEhoepbYjtyxjFysGuqqgKu6yd0OANaKYrQRSxTB59dIC4aDGQDLCQDJMPBIkqKueG4HtOJRrFgvpIjeXct7yfiSkM9wTbSvyhBCM9cdZy1eYXswBsJar8N13ZhkfiDOUihIOaihC4EiJYdmUKiaZYpV0vkqhYnB8sr+1mA1o9MXDns3WSrbMhVtpDoymCAZUBO6Y5arJzGqB6cUspuUQCqhddSEDyJVqvHFlmXgkwGAqQn8iTCysE9BVFCGQuF66xarpWpmt5L19e2IheuJbz3vz3OwfTZEuVFlad5/D3GqBbKHKxGCc/mSEUD26bzvu3BQrBuuFKul8hXLV4n2Pjfhi1sfnPiOlJFPN8Euv/0du5W4Brt/x4b5DHOw9RFSPkKvluLh6iZu5m54QEgj6wn30hnvpj/TzxNCpro6XrWap2TV+/8Lv8/XpbyCRKEIhqAYJqAFsaVO1qliOu6p0YuAEmmgvfyzHqnfW+7rX9lgRCvFAnEQwAUDRKJCr5XGkQ6aa4bfe/u+slFb5O4/97SZBG9EjDEeHSVfSmLbJYnGRicTElmNeXb/a9Hi1vEq2mmUwOtj0e8M2WSi4NwBBNchEYtz9/nCgYlUQCELaxudmza5589uYk3aoQkXt0rbLkQ6fP/srXFi74J1LKpQipIUw7BrrlYx3Q1E0ivz2+d8hEUzy7Ogz70qE1hezPruCgKZw6sAgr11acJeNgfl0kcX1IrrmCh/LcbAsBwkEdJUnDwxybnqVyjbL3pybELgAACAASURBVLYjOXN5kWLFxHbcjlab27M2kBLOXl3y8jcV4YpZVVGIhnSeOTKMrrW+q3TqBWnFSo6bSzkURbi+rJvErGU7bPb511WFk/v6CQU636kKAccnB5ASzw+2athcn89yfT7bbG3mdG+/IgQcnuglXahgWu5NwbX5DLdX8kQCGooiMCybqmF5jS2GeiIcGO3hlYsLO7JQy5VqrmPBfAZVEWiq4vrrSrc72Z03JeGAxsmpgabn1gpNVXjq4BBnry17gtbtLraOYN3z8JWA3MHc+Pj43Bt/ev2rnpANaSF+7vGf5bmx59AUN0WqkYLw1etf448u/xG2dFMPfurkT/HU8JOowv1c7Eb45Gt5vjn9Tb5581sEVJ2nR5/h+fH3MxYfI6iGcKRNpprh6vpV3lx6k1NDpzqunn395jeahOze5F5+7NBnOdp/lLAeRiComBWuZa7xlat/zLX1a9jS5s9u/Bk9oRSfOvApb3xFKEym9nJhzbXHms3P8vTI003HN2yDm3XHgVggRtEoUjbLzBfmt4jZfC1PppoBIBlM0ht2i2Y1ReOfPf2LnmAHN4r7hTd/jfOr5wHYk9jDP3/mn3lzeycCQSqU2na+Ac4svE7NrhFQA3x070f42N6PMRgdRFM0bMdmpbzCn177Ki/OvYgjHQzb4EtXvsTJwRNdd/TbCb6Y9dkVNPJDnz0yyls3VrwIrSNpalsKuK4A+wcYSEa4vpDdVsxKKcmXjY7FRA0cCUg3arsZy2kfAdZVlWhQb3JhcByJ4bSOXgrhFr0d39vPQGp7Y3ghBJoKj+8foCfudtHanKZw57luPk4sFCARaR15FELQlwjx1MGhppsCw7S32J6pimBiMM7xyQGEcJf4u5nPoK5tieTajsRuMzdKvf3tiX39JKPbd34SQhDUVZ45PML0YpYbC1mvBbCk/dwoQpCMBogE/e5PjzojsVGeH38egKAaaIpK+TwcCkaBVzctmX9o4kN8YOIDTWb9QrgRxM8c/DQX1y5yfvU8lmPxndvf4fTwUzuK3qUraf7n5T8kokf4+Sd+nqeGn0QRzbnzveFeplJTvLDvhbZNA6SUzBfm+eMrX/GE7KHeQ/yLZ/45PaFmP/CAGuCp4ac41HuIXz77ed5afgtb2nz56lc4MXiCPYk93vZTqSlvv5n81rbb61W3SA3gA+PP85e3/wrDNrieubFFeC+XlqlYbvHzWHyUsBb25rMhbBs40mlKU9BVnYHIQNsc5Z1QtatoisZPHv8HW9MHVFc4/6Mn/iGmY3otq+fyc9zI3ODE4Il7Pv6d+GLWZ9fQEFcfPDHOwlqBhfUSxYqBbUsUxW1EMNIbZXwgTlB33zj7hpOu6IG2wkRRBHuHEltE8U4IaErbTl2xsM5HHp9gLV9hLV+hUDKomm4003Ek1JsfhHSNZDTIUG+E/kQETe2cQ7oZ12heMDmUYLQvykq9BWy+XKNmOUhHIhRXWEeCGqlYkP5khJ5YEK1D218hBKN9MVKxEDMreVayZSo1C8dx5zyoq6RiQcYH4vTFw15x1sGxHgoVg3CgcyHaQDLMR0/tYS3ntvMtlN0cWi8SK0BTFEJBlZ5YiOGe/5+9Nw+SI7vvOz8vM+uu6vu+cGMADIC5L86QQ1IcWQdpm7IpWdRBOVaWLVuWN+x1rI+N2LUc9jpClzcsybvLtSmLlMwhl7a0kiiRw2uGc3IwA8zgHFzdjb6v6rqPvN7+kVXZVd1d3dVAA90A3mcCMZ1VmS9fvszK/Obv/Y4YHS3hiltH82MTMASHh9oZ7kkwlywwn8qTLVpYthccpmmCgOFZ2NvjYbpaI7TGQptafhW7n4d6TvJQz8md7oaihpncrO9nqgmNJweeaFhKOaAFeLz/Md96eG35Glkz27SFEPCEpyv57Mm/zRP9j2/otrWRewHAt8a+TcbMAp5F+edP/NwaIVvbXiKY4OdP/BzXl6+TMTPkzBx/ee0b/J1HftGv1jfUMkxQC2K6JjPZaSzX8kWmlJIb6RuU7BKGMHi49xHemX2XhcIC15avIpH+2Ekpmcjc8N0G9rXt27RE9e3ksb7H+Pjej6/rByuEIKAF+LEDP8qpmVNYroUjHa4uX+N49/FtdzVQYlaxq6ha2vb2tbK3r9VzCZDej1nXxZrE+CM9LYz0tGzYpq5pHB3pvL19DhoMdiUY6IwD+G4MVcGm1fjdVre56X0FDIa6Ewx1J3wfYCk9S2zVPWIr+6hWLDsy3MEDQx2Vgg4SIbyxW6/PBwaae9BUS/wOdxsMdyeQVFwhVo+NtvbcbpXqvvb2tbC3r2Vbxkax+1HncvexXFr2LZsBLUBHpHNDgdkT7UEgkEgKdoGcmduSmAXY27qXDw09c0vXQ97K8+7su/7y0c6j7G/bv2GbQgj64/081v8o3x3/HgCn506TLqdpD7cD0BXppCXUwmJxkWRpmayZo7PGino5eRmJJBaMMdI6wkB8gIXCApOVUs/xYNxfdzw97u0X4YnZHbr+daHzQ3s/3tBlASrGksQAraFWFotegHfVAr3dqDyzil1JtdCAV+lJJ2BoW7LW7RTVfuu6RsDQCQZ0gobu+4iKbToGfz/ayn4Chl4Rnze3j6qvcMDQ/Pa2q8/VNrTKOV0zNtt4bm/H2CgUiuZxZf0s2FZ/cTfj3f5w3yMbBjc1w0xuhqXiSsaWh3pPNnRJqEUgPHeAypGmy2lfdAJEjIhfurlgFVisEXRVayVAd7SblmCCfW37AC+wrVb82a7NZHYSgJARYigxdLOHesu0hlqaEtNBPUg0EPGXS/btSampxKxCoVAoFIptoz3c7lvsLNciWUpuWABgLj/vC9iwHiYWiK277kbsqfFRvVmmc9N+AJUmtKZzowohGIgP+FkMXOlyI33DP2ZNaOxr2wt44nUyO+V/ly1nmcnNADDcMoyhGeyvuA9YrsVYesxfN2/lWSh4Fs72cLtv+d0JOiKdTQVyea4WK1LzdoXhKjGrUCgUCoVi2xhMDNIe8YSWK13enHoTl/XzgpuOydszP/CXR1pH/PRXzaIJjXgwcfMdxhPVyeKyv6wLnZZg8/2IB+MEtZVgq1oLrxDCt7aCV2K2ylRummzFR7cqeIdahghVfGqvJq/66y4WFslbXtaWwcQgIePWLNG3QiIYb8pqfafYPT1RKBQKhUJx15MIJvjI8If95ZdvvMI3r79E0SrWlU3NlDN85eJXuLT0AeCll3ph3wsb+mE2otn8qBtRrpkC14W+pgDChvvX9LosAcVV0+lDLcN+0NdkdtIvq3t1+SqudNGFzt7WvV5WgnAH7RWf2tHUmF82dzI76VuO9+9w8JehBXZ0/6tRAWAKhUKhUCi2DSEEP3rgR7mweJFLS5cwHZM/OPsHfGv0W+xt3UMkECVnZrmeGmW+4mKgCY0f3vcCj/dvLS3X9va7fjq8kWvEusj6KfTVpbg7wx20hdqYL8wzn5+n7JQJG2GuJK8A0BJqoS/eB3j+sCMtw8zkZpgrzJEup+mMdDJesejudPCX14fdhRKzCoVCoVAotpVEMMGvPv4P+eK5L/KD6bcrvqKTfgBTLW2hNn784I/xIwd+5KasstuCgHhwxVfXkc66JXEbYbomlruSezu6yu83EogwkOhnvjBPxsyQLqcBwWTGG4+BxADxQLzSFcHB9oO8Nf0DClaBmdwMHZEOf+zCRpiB+MDNHuk9iRKzCoVCoVAothUhvGpSD/c+wpm59yjaRQYTg0QDURzXJqiH6Ip2caTjAR7ue5iuSNcOWxoF3TUpwhzXIVlM1vm6bkS6nMZ0TH+5J9q9pv29rfs4M/cepmOyWFjEla6fj/dg+0HfB1UIwf72A2hCw5Uuo6lRDnYc9DMbdEY6t5y67F5HiVmFQqFQKBTbipSSl2+8whfe/wKmY/Lc0LN87uTn/Ah4gagTb7uBocQgYSNM0S4ikVxLXefRvkc37Z+UkrHUuO/PqgudPa171mxX9XN1pctsfo6SXaLslBEIDnUcqlt/IN5PPBAnY2YYTY+RM3Nkyhmvny1Dt5yG7F5DBYApFAqFQqHYVtLlNF+79DVMx6Qt1MZnH/wsiWACQzMwNANd03dd3ufuaDcjrSP+8pnZ03XW1kY40uHUzCl/uSvaxXDLcN06QggGE4N+ENhcfo7xzA0kkkggwsiq9ROhBL3xXgCmslPM5+f9HK37m7QWA3UZB1zXvU2JsXYeJWYVCoVCoVBsK3P5OZZLXqqrSCBCpCZx/m7F0AyeH3nej9IfS4/z9szbGwaCSSm5uHiRC4sX/M+eGnhq3Vy5HZEO2ivuAXP5Ob+wQk+0e03OWEMYvmhNFpOMpcdxpIMmNPa2Nhf8JRB1/UiX05Ts4qbb3Y0oMatQKBQKhWJbCRthdM0L5prLz/HFs1/k/OIFJjOTTGen6/7N5+fJmTkc19laBoFtRgjBM4NPc6jjIOBZXP/w3B9xYfECrlybJ1dKyVh6jP/83hf8YLGuSBcv7Hth3fbDRpiBxCDgjclUdgqAfW37CGj1acCEEBxsPwRA0S5yaekS4FUTG0j0N31Me2oszclSkrenT+3oGN8ulM+sQqFQKBSKbaU/3s/xrgd5d+40rnT5zvh3+d6NlzE0Y01+Ul3oRANRRlqGeW7kwzze9xhBPbgjLggRI8LnTnyOX3/zN0iVUyRLSX7zrd/iueHneLz/cboinQghWC4tc2buPb43/r1KZgII6SE+e/yzdEfXD2YTCPa17uXd2XeZzk77ovJwx+F1+7KndQ9BPYjpmJydPwt4YrnZohJCCE72nCQaiFKwCrjS5UvnvsRyKckjvY8QC8T8rA1ZM0u2nOXh3oeJBbdegW2nUWJWoVAoFArFtiGlJKAF+KljP8VcYd63QLrSbeiDWrALLBYXOTP/Hk8OPMEvPvyLxAPxOy5ohRAcaD/A33/sl/k/T/9fJItJ8laeb1z/Bi+NvkSgUizAci0c6fjbRYwIn33wp3lm8OmGfa5WAqtuDxDQAg1zxnZFu2gPtzOXn6NYcQ8YbhmuqzS2GYOJQV7Y9wn+v8t/ikSSt/J85eJX+e8f/DG6piOlxJEOrusSNIL8u4/970rMKhQKhUKhuH+R0hNMX7/6db5342WSxSSa0IgaUUJGqM4qK5G40qFklyk7ZVzpVsrfvkVrqI1fOPm5HakyVbVo/osP/XO+fOHLvD93FtM1caW7JvesLnQOth/kJ49+hmPdxzYt8VotQ1sN5moPt9MT7Vl33YgRZjgxxFx+zv9sf3vzwV/gBYD9xAM/gZTw0uhLvii2XKsuL+7djtjEd+Lec6xQKBSK7eF2PGXVPVdxV5Mzc/zOqd/h9NwZwJtC/6uHPsW+tn2EjfA6YtalaJeYzEzw3y//sV8RKx6I828/9m/ojfWuu5+F/AKnZt8BJAKNJweepCPSvu66N4uUEtu1GU2NcnruNKOpMbJmBolXFGK4ZZiHek5yqOMQIT3UlBXZdExem3iNkrMiZp8ceHJdESyl5NLSJcbSY5VPBI/2PdJwTDY6DolkIjPBOzPvcC11nXQ5jeM6BPUgLaEEfbE+9rXt57G+RwkZa9N+eS8Zb/ouFb2xXh7pfWTTY3akwxuTb5I1vbRi/fF+Hup56GYt7g03UmJ2F1K2HGaWchsOfns8RFs8vG37lFJSKNsUShbRkEE0HNhVKVMUil2IErMKRQ1SSv7k8p/w5QsvIpEMtwzzL5/9F7SF2prK1TqeHud/feV/o+SUEAj+yVP/mCcGnrhDvd+YqiCsBoJpQkOwu1KLNcPq49iN+X43oGEHlZvBLqRQsjh9dQ53g8fasZHObRWzqVyZNy5MUzJtggGdZ44N0Nmy+1OpKBSKnUNKScEuMJ2dZiY3S6acwZE2AS1Ia7iVnmg3PbEeEsHEptOv67UtkSSLy0xlJ1koLFCwCgBEAlE6wu30xnrpinY1bRVT3F4s1+LNqbeQlXeyDw8/15SQBU9I9cR6SIQSlAolJJKsmb3dXW4aIUSd8LtbuVeOYzVKzO5CNCEIBQwsx8F1qzf128vccp6i6VUvKVsOM8k8HYmwekAoFIo1SCkxHZNvj32bl0a/xXxh3q9+VIuhGXRFuvhfnv2XdMe612mpcfuz+Tn++wf/jdOzZ8iZOVzqUyMJBEE9yIeGnuGXHvmlHfGtVNRTskt+blmAvljflp4htmvXXUdBVeVK0SRKzO5CErEgH39kBNtxsRwXy3bJlyzevzaPvZG59hZYfcPR1HPBC2QoWRRNm2goQDRkKHGvUOCJji+e+xLfHvv2uvk3a9cLG+GmUwmB97ubyc/yG2/8OlO56cbrISk7Zfrj/UrI7hrqp91zVg4pZVP3zap/aLVka0AL0B/vu209VdxbKDG7C9GEIBysPzXFss05TbCh78Et0NcRY3Q2TbFsEQsHGepKKOEGnB1dZGYpxwPDHRzb07nT3VEodhwpJe/Nv8d3x7/rC9moEeVEzwn2tu4hbIQpOybLpWWms9Mc6XzAL+HZDK50+erFr9YJ2f54Pye6T9AT60YTGnmrwEJ+gdn8LCd6Tt4X9yopJUW7SMEqEDbCxAKxXXfcESNMd7TLt86+cuP7PDnwFPEN+lp1J7mcvMKXzv2hn+5quGWYwUqBAYViM5SYVQDQEg3y0YeGKZZtoiGDYEDf6S7tOKbtsJwtqYgchaIGieTlG6/408FRI8r/+OQ/4kTPCT8gphpY7EqXrTpJLRQWeW/uPX/5WNcxfvWJX6Ut1ApQ177t2n6VqfuBr1z8Ki/feJkf2vtD/MyDn93p7qzB0AyeG36OK8mrSDxL62+++Zv82MEfZX/bfqKBKLrQkUgsxyJn5ZjITHJq5m3enjnl+0QH9SCffuDThJSbgaJJlJhVAN4DIhw01liE72fSeZOStdYPUKG4nynZJcZSY/7yyZ4TnOg5URdQUrXC6WLrQvNG5oYvajQ0fvzgj9EWaq2z7FX/DuiBddu4Fyk7Zc4vnKdgFRoWHthphBA8P/I8FxYv8lYlEOzi0kU+WPqAaCBKNBDF0IxK8YQyBbtI2S7XvfBEA1F+6uhP8Vj/o7vO8qzYvSjlolCsg5SSxXSBe7CEtUJxSxSsAnkr7y/vb9+/rT6ri4VFX9yEjBBDiSElaoD5/Hxd8vzdSkgP8Xcf+SUGE4N8e/TbpMopXFxyVo6clWu4XTQQ5Xj3cT516JMcbD94z0XbK24vSsxuE9VpLynBcV3cyrIQAl0TaJWb8W64KW+SW9in2b6ubq86DSglWI6LJsDQ6/PY+dOEjosrwdC9MdrqPiXgONIbbykR2q2Pd7XvC6nipse6mq3sb2WqVOK6LuD1Xdd2z7WiUKxmdRnP7fbdrCaTB2/aer0E7vcbUkouJy+vqT61GxFCEA1E+cyRv8nH93yMcwvn+GDpMnP5OfJWDtt10IQgqIdoCSXojfWyv20/hzoO0RPt8fK3qnufYosoMXuLSClxXMlSpshsMs9yrkTJdHAcFwnomiBg6MTCAdrjIbpao7TFQ+jaziVbXswUuTHfOH+fAA4OttESbe4hki1aXJ3yHP4PDraRiARZypS4eGOJTKGMpml0t0Q4MtJBLOxNC5ZMh0sTS8wtF3Bcl0jQYKS3hX29reh64zdyKSWW7ZLMlphPFUjlShTLNrbrIqU33kFDJxEN0tseo68jRtDY+OZYPYcl0yZfssgUTFK5Esu5lYfqTDJPyXIathEyNI6MdGLozUXtlkybqaUcc8kCuZKJbbtQEf2JaJD+jjgDnTGChq5u7Iots9pnddsTo9e9023v9dnsy/attL9e8nvYvhfI9XyGb2U/Esm5hXPb0rc7hRCCrmgXz488z/Mjz/ulamvH43aMveL+RInZW0BKyXyqwIXxJZZzpcZT0mWbdL7M9FIOTUvy5AN9DHTG72hfa8kWTMZm0xuuM9gVb1rMlkybsdk0EmiPh7EdlzcvTlP2xZ/DeMkinS/z7PFBhIAfXJphMVOsacMhlVugVLZ5cG/Xujc203K4MrXMxEKWQslqGFZSKNuk8mUmFrK0RIM8dKCH7tZIw5tloWzzxoVp8iXLfwlZTTpfJp1vbBWJhgweGO5o+H0V15WMz2e4dGOJQnk9f1yHXNFiZinPlckgx/d10d+x+6KWFbuPqkibzk5zZu4MV5avslxMYrsOYSNMX7yPB7sf5ET3cRLBzbOVONIhb+YpWAVyZo50OU2qlGIiO1lX0/38wnnsBjXeY8E4Hxp8Zk2QlpQS0zUpmJ7LQsbMki6lWC4t14k20zH57vj3iBrrF3A52HGQg+0HmxobRzqMp8c5M3eG0dQoqXIaV7rEAlEG4oOc7DnB0a6jRIzG94rN9lGyS1xLXePC4kUmMhOky2ksx0LXdGKBGF2RTkZaRzjQfpDBxABhfW0u7+p5NB2TdDnDfH6OsfQ45xcv+OvcSI/zl9e/0fA1IqiHeHb4Q7sigKrWf1rn/gnWU9xZlJi9SVwpuTad4sL4IrazepoddE3z13Nr0mmFDJ2OxM3dLLeLcNCgPR7GchxsR+K4Lo5b38+bJZkrMrmYxbQdIiEDx5GYtidqU/ky12fSuK7LYqaIoXtW1LLl4Lje+/r1mTTDPS20xtbehB3XZXwu4xd3AG+sDV0joHvWV8txsSzHF6SZgsnbH8zw7IODtMbWrxLkupJi2cKt5EOsruHWvJ0IKjdlgWeVqv0/oDWRmNd1JRduLHFlMulnWBNAwNAIGLr3gLddbMezGGWLXt9P7u9hb2+LErSKhkgpWS4t87VL/43Xp173A6hqOb94nu+MfYe+eB+fPvzXeXb4WQyt8SNgIjPJr7/x6+StPJbjuRasl5ng9anXeX3q9XXbGEwM8mT/E+tmHPiTy3/CN6+/RNkpY7v2uvlqy06ZFy+82LCPnzn6mU3FrJSS6dw0L174Cmfmzqw7Vf/+/Fm+OfpN9rTu4aeO/iQP9T7UtM+mlBLbtXlj6k3+/OqfM5GZqHPDWI+AFuBo11H+yVP/mLBRX8kxZ+b4T+/9Z26kx1kupyjZpTVjc3HpEheXLjVsvzXUymP9j+4KMatQ3AmUmL0JpJTcmM9wbmzRF4ACaE+EGe5O0J4IEwp408OW7ZArWSyli8ynCnS3RgkHd/bttL8jRl97DLcyve64LgupIqcuz95y25MLOXRN8MThPnrbY5i2w+mr88ynvIfr6GwKx5V0toR5+EAPsXCAVK7MW5dmKFsOluMyv1ygJRpcI97CQYOh7gTXplO0xoL0dcToao0SDwcwDA0B2I5kOVfi0o2k7yZQMh0+mFjmiSN961oyomGD508O1z2mTdvhzQszvhDf29fKgYG2hsetCeH7Ba+HlJLR2TSXJ5O+Bb+rJcLBwTbaE2ECuobEsz7PJPNcnkxSMr2XjbPXF4iHA3RtYF1W3L9IKZnKTvEfTv0OY+kx/3OBIKAH0NC8ykrSRiKZyc3wf5/5PFO5aT5z5G82zAhguxapcmrdyl7bQc7M33K50s1+DVJ60fS/985/ZKGw4H+uCY2A5h237do40sGVLqOpUf792/8Hn33wp3lh3wubClopJXkrzx+c/SKvTry6RsR6L8Gab22tYrkWfbG+dcVmySnx/vz7dUF2W0UVkVDcbygxu0WqVaHOjy35QlbXBMf2dLK/v20dX9gAbfEwg51xz/q4C8LjhRAIARoCQwfQiYYs39h4K9iOy76+doa6vWnMgKFxdKSTxXQRV0pKpoOhazx0oMe3lHa1RhjsinN9xnN9qPVVXd3vQ4NtDHTGaIuH1/U7DhgQDsZoj4d59dwkmYKXwmYhXaBsetbi1eiaRssqS3DZtNFqnmOhgL6uwG4GKSXZosXFG0u+kB3qTvDIwR7folwlaOgcHAjQkQjzxoVpX+BfGF/i2eODTfnkKu4fpJSkSil+553f9YVsSA/x9ODTPDXwFL2xHnRNJ2fmuLh4kW+NfZu5/By2a/NnV/6MtlAbP3rgR9a9rvtiffyjJ351zT1rubTMl879oe9q8Im9n+Bkz4l1+xcJRDD09R8zH9/zMR7sOrbm8zen3+L1Sc/SGzEi/PyJnyMWiK3bxlDL0PoDgzc2E9lJfvfU77FYXAQgHozz4eEP81jfo3REOhBopMsp3pt/n++MfYd0OU3JLvGH5/6Ijkgnj/c9tuFv3nRMvvDeF3h18jX/M0MzONR+iId6TzKYGCIaiGA5NkvFRa6nrvPB0mWSxSQfHn5u3TZbgi38/cf+Pk7NS0TZMfnSuS+SrlTHerj3YT6+52MN+xXQA0SNaMPvFYp7DSVmb4LR2TSlmqnuw0MdHBxs9yPo10MIcV8IESGo8/EUQtASCxIO6r6PaFssRGu0fsq/PR4GPDFbKNu1M/h1VHPhbvSA8XLm6hwYaOP01XnAs7QWyta6YvZOcH0m5fsQR0MGJ/Z1rRGyVYQQdCTC7O9v5eKNJADJbJFktkRPm3pAKVZwkXztg//GaGoU8DIL/L1H/x6P9T+KRv31dbD9IE8NPsVv/+DfM5oaxZEOf3z5j3m49yEGEgNr2o4H4zw58OSaz2dyM/zX81/GwhOz+9r28dTgU1vqtxCCvW172du2d813k9kp/29DM3ik7xHaw+1bah886+cfnvuSL2S7o938w8f/IYc7DtWNy0CinyOdR3ii/3F+663fZrG4SNkp8+XzX+ZIxwMkQol125dS8r0bL/P61Bv+Z12RLj538ud5uPdhAlqgbj9SSn6IH6Jkl5jNzzLcMrzu7z9khHi8/7G6zwpWga9e/CrgidneWC9PDjypZmoUigoqkdsWsRyX6cWVXHmJSJCDA20bCtn7CUPTiIYDaz6rLcbQGg9RO1xCCEI1rhe24za0YIsm03cJIWiPh/3zIiU1AWl3FtN2mVlauWYGuuJEmhDkA51x3xfXlTCbzO8Ky75i9zCRmeDViVcBb2r5rx/+azzR/zi6WJsFQwhBT7SHe4PVQQAAIABJREFUn33wZ/wp9nQ5zfduvHzPXVdSSs7On+XsvBdMpgudnz3+M2uEbBUhBPvb9vM3jvyEP0U/lZ3i1OyphmOTNbN8/eqf+/6s8WCcX3n8V3ii/wmC+tpZnOq9KxKIsK9t34b+ygqFYmsoMbtFcgWzLgp9oCtOwFDDWEXXtTW+o0JQl24r2mCqv4pbyfN6qxi6Viead+qBnSmU64LWelqjTQnySChAsObaSufLqrSuwkdKyRuTr1O0vawg7eF2Pjzy4U1fkg51HKqreX969vSurSh1s0gkr9x4xfdh3du6h0d6H9l0bB7te5S2cKvfxtvTp9YNepNScnbhHHP5ef+zF/Z+giOdDyhrqUKxA6hXwy0gpSRTNOui3LtaVFBOLbUFC2qp8yJukDvV99ltUrFVxanjShynkpGhkj3CkZJ8sXH6rjtJJm/64lwTnqW2Ni1ZIxzHrQvkKJk20pVwH7irKDbHci3f8ghwoP0AraHWTbcL6kFGWoZ9H9uFwgKpcopeo/d2dfWOkzNzXE5e8Zcf7D5OUA9uul08GKcn2styKQV4lu+SXSIaWOve897ce77QjRgRntvkRUKhUNw+lJjdIsUaq6yuiR3zwdytaJpgs/v5rbhkSClxpWelnFvOk8yWyJcsrEp6r+qLhpSeZWWnZ0+llBTKKzk4XQnvbCFrRG33XbmejUhxv5Izc8wXViyDHZEOUhURthm1ws50TNKlNL2xe0fMLhQWyJgZf7k93MZyaXnT7SSyruJY3vLy7K4Ws7ZrM5G54S/3xnrpiXZvQ88VCsXNoJTYFrHslXx/YpN0TPcjgs3T5dyslpXSS7t1YXyJxXQRZ4O8uLvJPlJ7zcCtZ4xQKADS5QwleyXzx3fHv8trFf/ZzSjXuBVI5F1RJnUrLBWTdSnFXrzwFb526WtNbVusGVPHdeoKRFQpO2U/swBAT6zH90NWKBR3HiVmFXcFUkpmknneuTKHWRPIFQ7qtMXDtEZDRMMGAUPH0AW6plEs27x7ZZZtqAVxS9TuXgjoaYvW+Qg3Szio3/SLgOLeo2gX6vKamo55U76vq3Og3gvkrVzdcskpwU3Ef3qzO2vHxnKsurFOBNfPeKBQKO4MSsxukVpLrJSer6bi9lMo25y5Nu8LWUMXHB7qYG9vq1+EYrW/mld+djuy594agZprRtc0v1iEQnErrK4K1R5ub5iPdSM0IQjr4c1XvItw3fqx6Y5231Q1rKAeXDfrgKT+BaDZamEKheL2oMTsFqn1kXVcSaFsr0m4r9hepJRMLGTr/JWPjnRyaLB9w4CL3ZBuSAhRl73BdV1MyyEe2TwYRaHYiKAerAsQ/NShT/KJfZ+4qbbutSnyWr9XgeBvn/wFjvcc33I7ArHu2OiajiFW0gmW7M0DOhUKxe1DidktIISgJRpEE/hT10uZIr3tzaVaUtw8S+mVh0U4qDPS07LpmJdMpy7zxE7hVTrzgtJcCcu5Mu2JsLpmFLdEIpggoAdwKiWXM+UMQe3mqtTda7SF29CEhitdJJKcmdvWsQnpIaKBGKmyV+hlqbiEK110sbOlyhWK+xU1N7JFEpEgkdDKm/rUUm5NgI9ie5HSq+BVJRQwNs3tK6VsKv1VQ1Y99G7FytsaCxGtvWYWs7tCZCvublpDrbSF2vzlsfT4GteD+5WeaA8RI+IvX69USNsugnqQ3liPvzyTmyVrZrd1HwqFonmUmN0iAUNjoDPuL2cLJlenU0qc3E6ElwatiuO4uBs8s6WUFMs2E/OZxittgiZEXZBWbaGMrRIwNIa6Vq6ZpUyRqcXclgSylOsHoijuXyJGhMOdh/3la8vX/NKt9zvt4XaGW4b95bMLZylYhW1rXyA42nXUX06VUpxdOHfHfqPqXqBQ1KPE7E2wv38l6Ajg8mSSyxNJLNtZ9yZTFSKO65IpmFj2zpRVvVsRQCK64mNaKNssZ0sNx7pkOZy+On9LAlTXRV2Q1kK6QK5k3fRDZH9/m9+eK+G96wtMLuZw3PVFqn/NOC6pXImpxdyadRSK54ae8wOUsmaWv7j2lziuur8YmsFzw8/5PsXT2Wlemfj+tonAarWwqOHln5VI/vTKn5Iup2+L0NSFXpcbOF1O33MZKBSKW0GJ2S0ihCdyju/tQqtYCx1Xcn58iVfen+TSRJLZZJ7lbInlbImFVIEb81neH13glfcnefn9CfKltXkLoZIdwZVYtkPZsimULbJFk1SuxHKuVJdiqlC2SGZLZPJl8iWLomljWg624za8mVbFkWk7lEybQskiWzBZzpVIF+pLpWYKJsvZEtmCSb5kUTJtTHvj9m8nA51xv9iCKyWnr80xm8xTthwcx8V2XPIli/G5DK+enWR2OU8woN90HmBR2WeVkunwg0szzCTz/ngUyhaZQpnFdJGFVKHhuAjhFdc4ub/bz2xgWg6nPpjljQtTjM6mWUgXWM6VSGZLzC3nGZvL8N61BV5+f4LvvTfB6Gz6po5Dce8ihGcdfKjnpP/Zt0a/xTeufxPLafziVX1RKttlri1f21aL5W5BCMEzg0+zp3UP4InNr178Km9Ov4Xjrm90gJWxKVgFLievYDnr36sBBuIDPDX4lL88nh7n86c/T7KU3PAeKaXEdm2y5WzT99KAHqA72uUvX0leIVnceD8Kxf2ECgC7CYQQDPe0YNku58eXsCvpuVL5Mqm8l3zc07miksNxhc2qX10YX2RqMeeVZ62UZl2vmtXYXIYb8xmEEAgh0DSBLgS6Lnj8cB+dLZE1bbsS3ro0Qzpfriv9KuXaaauz1xcqbXt91jTvXyRo8MyxAcLBO3fpCCHoao0y2B1nYt7zS8sVLd64OE244j/rSknZcnz/5VBA5/HDfVyfSTGTzN/UPoe6E4zPZ1jOeknUU7kyb16YxjA0hPDOrVsZx7ZYmOcfGm5YaVYIQX9HjEcO9fLetXnKlhecNrdcYG654BWbEF4asZ3Oi6u4ewhoBp89/jNMZCaZL8xjuRZfOvcl3p9/n+f3PM/e1j2e9VCA5djkrCxzuTkuJ69wfvE8i4VFfu0j/2rdcq13O7FAjJ8/8XP81lu/Tc7Kkbfy/N47v8ep/id4dvhZBhODhI0wSDBdk2w5y3Rumg+WPuD84gVs1+LffvTfEtDXz/SgCY2/ceQnuJz8gKnsNACnZt9h8vtTfHTPRznRfZz2SDsBLYAjHQpWkYXCAteWr3Fu4Rzd0S5++dFfbupYBIKHeh/i9NwZAJKlJJ8/83l++sGfpi/Why50XFwc16FklzAdk55Yj0oZprhvUGL2JtGE4MBAG62xEBdvLLGUKdX5zXqCZK0qCQd19A2shSXTbmi5XY0r8aKjkHUJwe2GuW+90qrNTL97TctK0yvtO467IyViNQEP7e9BIJhc8AKopISiaVNclSe+PR7ioQM9dCTCZArlmxKzAEFD44nDfbx7dY6ldJHKSDcI+Nt8UIQQDHXFSUQCXBhfYj5V8KuY+eO9ehu8dHC97fee2FDcOkIIBuMD/Mrj/4Dffef3mMvP4UiH03OnOTN3hpAe8tNU2a6N6ZjYru1PUd9M7tW7BSEEx7qO8UuP/B3+nzP/iYyZwXRMXp18jden3iCkh/ype8u1/LGp0hnp3LT9rkgX/+Cxf8B/OPU7zORmAJjNz/LlC1/m/9UMwnoYQzNwpOMXtaiO/RP9T2zpWJ4ZfIZvjX6byewkAGfm3uPS4gd0RDoI6kEc16bklCnaRboiXfzaR/5VXYoyheJeRonZW8CzGEZ49sFBlnNl5pbzpHJliqaN47oIQNc1QgGdRDRIV0uEjpYI4UDj9C0DnfFbTKgvGm4vhGBfXxtl6xZ8STVtzdR9LBTgyEgnIAkFjHXT34z0tNDZ4iVmb4muvcHGwrVt6Gss2EIIgobGY4d62dPTwuRilnS+XPE/FgQMjXgkQG97jL6OGAHds54OdMaxHU+G1vrdNoMQgngkwIeODTK3nGc2mSdbNLFsL92PUT23kSDdbVG0JrL+CCFojYV4+ugAqbx3zSSzJYrl6jXjlUiOhAxaYt410xYPE6xYgxWK1QghONxxmH/+oX/Gl89/mXdnT2O6nmgqOSWv+tU66EJnID7gWSfvUYQQPDnwJJ2RTv7own/l0uIlHOngSpeiXaTYID+soRkMJYbWLZiwuv39bfv5Z8/8z7x44UVOzb7jVwazXZucu76vu0BsedxbQ638vUf/Lr/3zn9kOudZgktOyf979boKxf2E2MTnRk14Nomss8quuARUp+mrKEFy61TH2svb6v2tVVwi4PaM8Xr73I5zW2239pqpPRao/Vuxy7gdZ+am77lSShzpcHX5Km9OvckHS5dJlpKYtidsDc0gYkToinZxoP0AJ3tOcLD9IBEjsqVrN1lc5kvnvohZ8Sf94X0vcLL35CZbNc8bU2/y2sRrAEQCEX7hxOeIBbde2awWKSWmY3Jx6SJvTb3F1eVrpMtpX3gGtADRQJTeWA+HOg5xsucke1v3eoUpmhib6thfT43y1tRbfJD8gKXCEmWnjCtdNKER0kO0hloYbhnhZM8JTvacpCW0eb7s1ftZLi3z8o1XeHf2XRYKC5Ttsn9+o4EoXdEujncf568e+tSmYlyhuMto+GNRYlahUChujl0lZv0GKvd027Up2kXKThmkZ20MGSFCeghN3Lylf113mG1847qd7VfbtlyLolXEdFfEbMjw3A40bm0WpCpsi3aRsl32iykEjaDvdgC3dkzVfRSswsr51T23hqAevKXzq1DsYpSYVSgUim1mV4pZhUKhuEdpeM9VoY4KhUKhUCgUirsWJWYVCoVCoVAoFHctSswqFAqFQqFQKO5alJhVKBQKhUKhUNy1KDGrUCgUCoVCobhrUWJWoVAoFAqFQnHXosSsQqFQKBQKheKuRZUHUSgUCsUdw5Uus7lZ8laB/ngfsUBs1yX4l1JyLXWd2dxMw3V0ofNw38NEjMht74/lWJyeO4PplAEYTAyyr23fbd+vQnG3cNeL2WS2yMR8FolXBvTwUDvh4F1/WHccx3W5PLlM2XIarpOIBNnf37rrHjwKheLuQErJ9yde5fff/33KdpkD7fv5p0//U1pCLTvdtTW8PP49vjn6UsPvI0aEf/exf0ckfvvFbNkp84X3v0CymATgU4c+qcSsQlHDXa/6sgWTq9MpAHRNsLe3RYnZm8B1JeNzGfIlq+E63a0R9ve33sFeKRSKewmJ5LWJ1yhYBQCuJq8xlhrjZO/JHe7ZWkJGmIgRwXZtHOngSnfVGne4WFvN7jYu3KlQ3H8o1acAvDrhrbEgQoDtSBzXxXUljqvumgqF4jYh2LUzPT/xwKd5Yd8nKNolClaenJnnrem3eHXi1Z3umkKhWIUSswrAs2o/dWQAV3oC1nFdLNvl1OVZUrnyTndPcZdg2Q6TizmEgKGuBIauYkwVKwgEHxr6EFeSlyk7Jse6jrG/bf9Od2sNQgiigSjRQLTu86XiohKzCsUuRIlZBeDdvIUADYGhA+i4QYmhKTGiaJ5ktsTpK3MEAzp97TElZhV1CCH4yMiHOdRxkIJVYDAxeEcCqBQKxb2NErMKhWJbkFKykC7eaU9CxV2GJjQGE4M73Q2FQnEPocwmCoViW3AlLKYLO90NhUKhUNxnKMusQqHYFkqmTaZg7nQ3FPcpUkokEsuxsF0bBAS0AAEtAOzeQDNZSU1guRaWYyGEIKAHMISxa/usUOw2dq2Yrf7ATdulbNm4rkTXNEJBnUDFD+9Wf+hSSlwpMS0X03aQsrKPgE7A2Hwf1T7WTquKLfbLuwHXb7/Zfv3tJJi2g2k7uK5EE4JgQCdo6IhdHCVc13fLwZUSrWbcmx3D1eNfu1313JZNB9N2EQIChrcPTYjmxndVu1JKypZDyXQwdEEkZNS1JSvBc4WyhZQQDlbPxdavB8t2KVsOruve1HltamwsB8t2kVJi6BrBwM39tqr7WsoUsWy35nNwN8ghtNXfiuLWcVyHv7z+DRYK8wB0Rbr50QM/gq7pTbcxnZ3mpdFvIXEBwbNDz3Ko4+Ca9aSUvHzjFcbSow3bChth/tqhv0YkcPN+s1JKksUkb8+8zfvzZ5nLz1K0S2hCEAvEGWkd4Yn+xznRfYKwEd4115yUEtu1Ob94ntcmX2csNUbByiOERluojSNdR/jw8HMMtwwjhEBX8QsKRUN2pZiVUpLMlrg2nWIxU8S0HKQEISAcNOhpi3JgoI2WaBDtJn7gUkqKps2N+SzTSznyRRPblVDZRzCg05EIs6e3hZ626IbiZ3w+w9hsGgBd03j0UC+xcKDpvpi2yzuXZzFtr1hBX0ecB4baN+y77bhMLeaYWMiSKZR9QSKEwNA1WmIhhrsTDHXFMXRtV928y5bDxEKW6cUc2aKJ7az0PWDotMdDjPS00NcRQ9c2F51nRxdJZosIIXjkYC+JSADbcbkxn2F8PkuuaOI4nqAydEE8EmRPbwvD3YkNx2ZuOc+liSSaEDx6qJdoKMC1mRRXppYpmw6aJuhIhDm5v5uWaBCAxUyRc6OLpPNlJBAydPb0tvDAcMemgVBVITyzlOPGfJZUvoxlr1z3hq6RiAQZ6k4w1J0gaGx8XqWEd6/OkSuaGLrGY4f7CAd0TNtlfC7N5EKOfMnEroyNpkHQ0Ols8XIJd7ZEGrYvpcRyXAoli2zBJJ0vky6YpHIlfx3TdnjjwjQb/TwPDbYz2JXYcFwU24smNEp2kb+49peAl/j/od6TDLcMN7V9VaB+/drXAYgGovzwvhcarn9m7jRvTL3Z8PuWYAs/sv9HblrMWo7Fd8a/w59c/hOWKgUFallgkbH0GK9OvMrB9gP87PGf5XDH4R2/J0opSZVS/Jezf8DbM297luQaFgoLXFm+wnfGvsOnDn2SF/a9QEgP71BvFYrdz64Ts64ruTq9zMUbSWxnVZJqCfmSxehsmumlHMf3dREObO0QpJRMLeY4O7ZIYb0CARKKZZupco6ZpRwDXQlO7usmHFzfwtYaDZHKlf18rNNLOQ4OtDVtPVtIFZhJ5gHPUnV4qGPD9ZdzZd67Nk8yW1pvBRzXYSFVYCFVYGw2zcMHe2iLhXbFzXs2mefs6ALZ4nrjLnFMm5mkzexynp62KA/t7yEeCWzY90yhzFLGG4t0voyuCd65MstCqrhmXdP2XpKS2RJTi7mKSF1/Kq9kOX67mYLJfKrAudEFqml3XUcynyrw9gezPHd8kGLZ5geXZiiZKxXUiqbNpYkkUkoe3Nu1oTjMFEzeuzbP4noBVNKzqJWtIouZIqOzaR4+0ENnS2Mrk0SSypVJ58sIAfmiiWnpvHN5luV1Uq25DtiOTWEhy8xSjgeGOzg81IGmrd/+hbElRmfTDS2vUsJybp1rtIahbnvD7xXbjxBeaqyvX/0LclaOol3k9cnX+cmjP9nUPaJoF3l75m1/+WjnUXpjvQ3X74v1MZQYomgXMR0T0zGxXGudAgRbx3RM/vDcH/HS6Es40vvdaWhEAhFCRgjXdSnYBUzHxJUul5NX+I23fpNffvSXeaT34R29J2bNLP/h1O9wfvG8/5kmNKKBKCE9hOmYFKwCeSvPVy5+lZyZI2SEdqy/CsVuZ1eJWSkl12ZSnB9bpDZXfyRkEAsHEEDJdMiXLcqWw5mr8+zra0WI5iqiSCkZnU3z/vUFX3wKAdFQgEjQQNMEZcshX7KwHRdXwuRClpJp89SRfkKBekErhKAlFqQ9HmYx44mnyYUs+/tb0ZsRs5X1q8QiAboaWMSklCxmivzg0iwlc0UEhIM60VAAQ9ewbJd82cKslKRNZku8eWGap48O0BbfOUErpWRiIcvpq/N1Lyi1fTdth0LJwrRdpIS55QJvXJjmqaP9tESDTfV9MV1gdCbFQrqIwLtuIqGAd15Nm3zJ8s+7J0RneObYwKauAHPLeWaTeQKGTiIapGTa5CqCPJ0vMzaXYTFVoGQ6xCPetZQpmH5p4NHZDPv62ohF1lrspZSk8mXeujhTV30tFNCJhb2xsR2XfMny20vny7x5cZqnjvTT1drYgrqyD5hLFZhZypPOe0I2GNCJhQIEDK/9XGnlurFdycUbSSKhACM9iXXb92Yw6s2utiPrzq/3e2ncL5X2bWfojfVysvckr0++DsAbU2/y4wd/nHgwvuF2UkquJK8wm5sFvJyxzw0/hyYan8fPHP0Mn37g01iuhemYlOwSX7/2F7y0QZnYZnCly59d+TO+OfpNXOkiEDzYfYy/sv+vsK9tHxEjgiNdlotJ3ph6k2+OfpOCVSBTzvD5M5/nX37oXzCYGNyRe6IjXb526Wt1QnZP6x5+4oFPc6jjkC9mJ7NTvHT9m5yafYe/uPaXG46zQnG/s2vErJSeJenijSVfyAYNjWN7uhjqTvg+rK4rSefLXBhfYj5V4Np0qqlUQJ4VtMjZ0UVf0LTHQxzb00VHS9ibcq60nytZXJ5MMjGfRQKL6SLnxhZ59GDvmoezJgTDPQlfzKbzZdJ5k/YmxGOxbLNQE/090Bn3j3N13wtlm3evzPlCNhIyODrSSX9HzPellNILwhmby3B5Mlnx37Q5fXWOZ48PEQo07xe3XVRdRt67tiJkw0GdoyOdDHTG/b67UlIyHcbn0lyZSmE7LtmiyTuX53j2wQFCTZQoHpvN4EpJPBzgwb1d9LRFK64E4FSum7OjiyxVztVSpsQHE8uc2Ne1abuJaIBnj/QTjwSxLIcffDDLfMo7d1cmk9iO5OBAG0f3dGLoGqlcmdfOT2Fank/zUqZINLzWCly2HN69POcL2VBA58hIB4OdCV8MSknFPSPDpRtJLMfzpz19dZ4PnxgiEtp8bC5PLuO6koChcXiog5HuBKGggVZpv2jaXJ1a5tpMyvd1/WAySX+nd32t5sG9XRwd6Vy1jyQfTC4DnsvC8yeHN7zmdH13uL/cbwgEH9vzUX4w9QNsaTOXn+PcwjmeGnhqY9cVJK9Nvu5bQbuj3RzvfrDhNkIIdKGjazohVqyK7eG2W+q/lJLry9f50yt/5lt4P7734/zciZ8lrNfPVrSGWhhp3cOB9gP87qnfpeSUSBaTfO3S1/iVx38FXdzZe6KUkvHUGC/feMX/bF/bPv6np/4JnZHOur63h9s50vkAL154kT+/+nV/3BXbj2xgDdvpGU1F8+yqV70rU8t+AIkmBA8d6GF/f6sftKNVfEI7EmGeOtJPd2uk6ZyWtuNybnzRF1TtiTDPHBuktz1K0FgJCtJ1jZZokEcP9jLc0+JvP7mQZSlTXHPRCyHoa4/5D23HlUwtZtkMKSUzyRxm5Xh1TTC0gf/gBxNJ3xoYCug8fbSfvb0thCsWZSEEmiaIhgMcHenwprUr2y7nyozPZRr+YG8nris5P7boH2cooPPUkQH29bXW9V3XNGLhAEdHOnn4QI8/vb2cK3FlOtVU310pCQV0njzaz2BXnGBA99uvvW5aYysP1vG5tD+uG7V7eKiDRCToB2MdGmz3x9e0XaIhgyMjnf611B4P0dO6Uj0olV87tS+l5Op0yv8uYGg8+UAfB/rbvOCymvMaCRkcGmznoQPd/gtVtmhWxGcTY+NKdM3z/31gqJ1oOOD7JGuaIBYOcHxfNyPdK9d8rmiyvI47S3U8gwG97l9tgEo14G71Oo3WV9w5hBAc7jjMntYRwLNyvnzj5U2n/tOlNO/Pv+8vP97/OIngnfd5lki+fu0vKNjey+RQYoi/deyn1gjZKpoQPN7/GE8PPe1/dnruDNO56TvW51q+P/F9irb3Qm1oBn/r2N9aI2TBO09BPcinH/i0yst7G5BSYjom11PX+c74d3nxwot84f3f5/ff/y985eJX+c74d7i0eIlsObsjz07F1tg1ltlC2WZuOe8v97RFGepqNMUpCBgaR/d0snR2asOIafAu2rnlAqnKg1kTguN7uxr6wXoPeHhguJ2ZpRyW4+K4kvH5DF2tawMVIiEvKG2i4jIwXfE5XM+iVcWVkqmFnL/cHg/TElt/Oj1XtJisEciHBttpjzf2lxRCsLe3lfG5jD+tfGM+w/7+Vow7aA2rukZULaEABwfaNvT1FEIw3J1gPpXnxrx3zONzafb3tRJtIrBuT29LQx9hIQThoGf5/MHFGSSeEJ1ayvFApL1hn6pCuPq9EILWWJCAofuBe11tUYKrrOotsSAsen8Xy2sFc8m0GZ9L+8v7+lrpbotuODZD3QnGZjN1bi2HBtubsroP9yQY6Iw3bF8TsH+glYmFLG4l40QqV6Zngz4p7k5CeoiPjHyEa6nrAFxcvMRUdoqRisBdjZSS9xfOkiqlAAjqQT409MyOXBdLxaU6Uf2RkQ+TCK7/rKgiEDwz+DSv3HgFV7oU7SLnFs4xlBi6o8dQdsq8P3/WXx5pGeZo55EN+xALxHhq8CkmL03eiS7eF0gpubR0ia9c/CpXl69iOuunFNSFzgv7PsEvnPyFO9tBxZbZNaaRpUzRt96B9+Dd6B4jhKA9HiZRiSTfjMnFrG/FbY0F6UxsnKJFCEE8HPQESYXFdHFtUFqFkZ4Wv7/5krWuFbeKlF5QUW2AzHBPAq2Br+xsMu9brAOGxlD3xjdu8CL3e9pWLIO5ormpBfJ2MLWYq3Eb0Rnuadm075om2NvXSjX2qGQ6zC0XNn071gQbijXwzmtPa7ROGM8v5zf0uQ4F9DVi0dD1OpeQtlh9cIYQom4ba9V1I6UXQFYNGNM1wXB3E2MjBL0dMX+5ULbIFNZafdfbbm9v67rXWG2f4+EgoeBKvwvriHDF3Y8QgicGnqA97GVOqQaCNfqNOdLxvq/cRfe17WNP65471t9ari1fI2d6hgBDMzjWdWzT340Qgr5YHyF95Xd6ffn6be3neiSLSRaLi/7yA51HCOobP8OEEBzpfED5zG4TVSH7W2/9NhcWLzQUsuBd9wfaD9zB3ilull1hmfWi9FeEnaGJDS048Ej0AAAgAElEQVSPVXRN0BoL+dbHRliOWzdd2p4IN4zSrkUIiIeDflR72bIpWQ6BVRZXIQSdLWHikSDZgomsBI71tcfWaxbwMipUfXdDlTr2jY631q82Hg4SbsJ/VAhRJ/QdV5IrmrTF71xErOvKuqwLLbEgkSb6Dl6WiHDQoFD2fITn0wX29rVsuE2wEjS1GQFDozUW8v1Us0ULy3EJNci1GTC0NSnghPCuvyrrWflrv19PIyykVyzWkZCxboDYaoQQJGrWkxIyebNh4KDfv5De1Iufpom6wKxqWjPFvUdHuIPH+x/3g7HemHqTTx765LqBYHP5OS4vXfaXnx161i9GcCeRUjKWHvdFdUALkDNzTQnTrJmtE4TJ0jISieDOWWYXi4uU7ZXn1UiTKdG6o90EtSAlZ+MMIYrNMV2TFy+8SMbMAF4WiSOdD/BI36N0RToRCPJWnvn8PLP5WY50HlUzU9uA4zpM56ZxpMtAvH/Tl7itsivELEC+xmoYCOh11qGNiDchAEqm40eCgxdMMzaXaar9WsuU40osy4F1UiIausZgZ5xLBS/X4fxygZJpEwmt7Z/tuEwvrbgY9LZHGwbxeCJ0pQ+ulNyYb67vq/0dS9adTYVk2i7F8so+E5Hghtb2WgxDIxoO+GI2WzBxpdwwS0TQWEn6vxm1141VKd7QaKpe17Q1/V7di/X3WyNmV30jpXdMtStMzmdoZoCyqyyxzZzXaDCwaa5bqBYyqO2WErP3Ms+PfISXb7yM6ZjM5+c5O3+Wpwefrnt4Syk5NXPK91FtDbXyaN+jO/aAXygs+H8X7SK//uZvNCVIJbIuiKqasutOWjzT5Yz/mxII2sKN3ZtqiRgRgroSs9vBZGaSq8vX/OVnh57lFx/+HwjpoTXX/Z1+2bmXWSgs8Gvf/9c40uFfP/9r2+4HvivEbLUaVJWArjWV2mr1VG4jzEoFsSpTizmmFnMbbNGYRv65QggGuxJcnV7GdiQly5sa39PbsuYHksyWyBXNynYw3N3Y4mg7bt3YpPNl3r0yd3N9d++sMLEcB8ddmV5v1ioLnqiqPbem7eC4ko30WLMFIoQQdfmJXSmxnMaRwpq28e1MAKIJS38tjivrXrByJYt3r85vqY0qzZzXYEBXt2RFHUII9rbu5VDHIc4vnMfFCwR7YuAJDLHy+zAdkzen3vKXT/acoDPSOB/27UQiKVqFus/upij/sr0iRoUQdW4PG6Fr+paqtCnWR0rJaGoMy/UMRIZm8Ff2//AaIQve+VFCdnuQUnJ1+SoZM0NQD94WE8muELOAP+UO+FHczdCMu4Dt3Bn7Uks0SEci4qdsmljI1vnSVplcyPrTzvFIcMOAKLdSevRuxHVl3fS6rjd/XoG6aHfXlchNRJsQYq3JtAF1143cWBDejtuZK+UdfbnYotZW3CcYmsFHRz7KhYULSCQXlyqBYC0jVEs4j6bHmMhMAF5AzHPDz+3oQ772bh7UghzuPHxTKbaGWgZ3XKw0u3eBElbbxVKNz3IsEKM72q3cCO4A5xbOb77SLbBrxOzqH+p2PuZXX6f9HbG69ExbaWc9t4Ha771IfE/MJrMlskWzbl/lisW2ymBnfNPp39rut8VDG/jibkxH4ubrn98MvrisnMytavLaYBSxev670fqSpp4Qda83TbS93azeWyISZLBr46T1jVgvw4ZC0QxCCB7ufYieWA9z+TlKdonXJl9n5NhKVoM3p97wLVkDiYEdLQcrEIRryrrGg3H+0RO/umnBh43au5PUWmKllJQ3CD6qxZHuXWWB3s3UjrmhGTvi+32/UXJKXE5e3nzFW2B3iFlBXcoox5EVIbP5jaaZAJVq4vyqNuptj7G/v/Vme9sQIQS97THCQZ2S6fi+sdUKVtUI9mKl8IGhi00zE+iaVmdFTESCHNvT2XD93YSha2hC4FaEo2k7SCmbfhDWZgAwNG3DSHzwrPuyCTUrpfSzQ4D3QDPusOlS00Rd0YBo2Lil86osC4qbJRFM8MzgM/zx5T8G4K2pt/jUoU+SCCbIWTnenX3XX/fpgaeIGDv78tQZXSlyUrSLFKwiLaGNg0N3Cy2hVgQCWfkvVU41dU8s2yWsJoWvYn2qxpE7mTO26ndruTaOa6MJDUMz/JmErdy3q/12peuXhNaFTkAPeHb7W3wGVNt3pIPt2l5lPaFhVAqf3Ow+pJTM5ebqfN1vB7tCzAqoi9A3bU8IbmaxlFI2FfwSDhoYmuaLo2oU++0QAOGgl5mgGmA2vZjj0GC7L9ZrfXU7EhESkY0j+gzd8++spnAqlG2kbM69YqepJs23V417M7hS1gWPhUNGXXaA9TBtB8fZ2K+2SqGmbV0XG+YEvh1omiASNPzgvmLZxnFlU0FaCsV2IoTgueFn+cb1b1C0i8wX5rmweJEn+5/g8tJlFgretGzEiPDU4MZVwv5/9u47Pq60PvT/5znTR6MZSaNRly13y91rb2/axi6YToBAICSBACGkJ797/Uq4uYSbOJCbcH8hgZvyC5BAaKHt4mVhF/BWe9frdVn3KsvqVhmNNP2U3x9nNJZsjTRyk8b+vl8v2ZrRmWeeaWe+5znf5/tcD4tCLfnfU3qKztFz1JbVzHm/ilHtD+NxuEkZ9iTO8fSNmQwlh4oexRV2bDCaGSWaihLL/T+cGmY4NTSpRnEim+DbR76DyzH16Oyt9ZtZEV5R8H7SeponTz1JPJsg4A7wpiVvtHNCLYt4Ns7unld5rfc1esd6SOlpNKUR9ARpDjaxJrKGDbUb8Lv8Bdsffyy6qXNy+BSv9uzm1PBpoukouqnjcbip9kdYHVnNbfW3UltWO+vPgWXZB1X7+/Zz8PwhesZ6iGfj6KaOQznwu/yEfWFaQgtZEV7JoooWylyFqy+ZlklaTzOSHqEv3se52Dn29x/Il0AzTIMfnfjRtGdTVlWvYmPthlk9lnkRzIKdbzouq5sk0tmiSlBNmhFegMflwO91MhK3tx0aTWJacK3WD2iOlHO2P5avJxsdS1Md8pHM6JMWEGiOTF9LF+z6oKGAJ79K1FgyQyqr458m3WG+cDk0Qn43iVwQG4tn0A0Ll3PmJz6VMSZVkqgIeGZ8rjJZg1RGxz3DpEDTgtiEcm5et3PG21xtCqgIePPluRJpnURan/Q5EOJ6aQg0sCaymt09r2JaJrs6d7K5bhOvdO/Orwy2IryC+kD9nPZTKcWyqmUE3AHGMmNYWOzqetmurlACOaVVvirC/mq6RrsAODpwlIyZmXYimGVZHB86IWkGs/TNw9/k+Y4X0C294Op2ST3JU6efKthGta962mA2a2Z5+swzDCYHCXqCtC24H5fm4vjQCb584Mu0R9svqQjTG+/l+NBxXjj3Ip+5/y+mrddsWRbdY91849A32Ne3P5/uM1HnaBf7+vbxxIkneGzxo7xp6ZsKroZ3Md3U2XF2Bz84/kMGEgMFq9ecjp5md89uHMpBxB/hg2s/wKa6TVPex46zz/L4iceJpqKk9TQmk597wzL4xdlfzNi3jbUbZtxmonkxDKSUoiroywcrZm7FrplOB2R0k+jYzAXjHZqipuJCnml0LE10LHVNTjeMP5ag3945mZZFz+CYvRrWSDI/g93rdlI7TW3ZieqryvK76XTWoGcwXjLL69VNKPAfT9kLRczUd3vFtgsLRShFUXnChmmvODZd+5ZlEU9liE04CKoKeGcc9b0W6qrK8hOzdMOka6C0l02cVNLLmjz5T8xvmtJoW9iWL1N1ZPAovfFeDg8cBuxUnHub77msiVZXW7W/etIX3e6e3RweODKrz479/rz+b1Cvw8vayNr85bOxsxwdODptX1J6il1du65H924oGSOLbtln4DSl5X8uPuiZ+LeJPw5VXHWccWk9TTwb5+jgMT7/yuc5Ez2DhYWGwqW5cGmuSWXgastqqS2rLdiefRBznL9+6a/Z3fNqPpB1KAfl7nIqPBX4Xf7844mlY3znyH/xpT3/l3h25hjBtEyeOPEE/7b/y5xPnM+XIfM6vIQ8ISq8FZS5ynBqFwYVDctgODWcX2xlKoPJAfrifaSNNChm9ZxP/JmteTMyWxHwEPC6GM2ddu3oj7GoLoTHNfWSs5Zl0TM0lh/1m45SioW1Qdp7R/JL0x4+O8jtrfW4iijnNHki0sxvboemaKwO5Bdz6IvGWWmE6ZlQW7au0o+3iFq6SikiFX7Ky9zEciPLxzuHqKnwE/C5iurPeP+v92k4pRT14QDHzg2RSOuYlt33qvKGgsvqWpZdsupkdzR/XajMQ1X59IsCjGvvi9EcKb9kYYuJzvSO5FMfFNBwmROvroRSiqpyL5Xlvvxo/anuKHWVZblR6Pn7uhYycXRbNyyS6WzBJaPF/KKUYlX1KprKm+iIdTCSHuGlzp0MJe262WFfmLWRtfPitVQo3rz0zezr289oZpSUnuKf9/4TH9v4MVqrW6fM7Rv/rGTNLN1j3URTI6yvWTcX3ee+Bfeyo2MHKT2Fbup849A3aChvoNpXfUkZR8MyePLUjzkzcmZO+lrK3rXynTyy6JFLrn/q1I95qWsnYNdM/u1Nn8BbIA+8pqym6PvLmllOD5/me8e+z3BqmNqyWu5quovW8EoqvBWAYiQd5dTwafb17WN51fKCI/KWZdEb7+Mf93yR/lyuqdvh5p6me7hvwb3UltXi1Jyk9BTtI+389PTTHDx/EAuLl7tfxuv08JsbfrNg+gTYKS6Pn3giP+LfElrIlqVbWFq5FL+rDE0pMkaG4dQw7dF2Xj//OscGj9McbM5XO5nKAwsfYF3N+knX7e17je8fs3PyXZqLj9/ycSL+SMG+VfkKB8uFzJtg1uXQWFgb4mC7nZ81lsxy4PR5NiypweXULvmQD42mOHx2sOiqB0G/m0X1IY53DgPQH03w6rFe1i6K5AvoX3wfYI+YjcQz9A3HWdJQUfTqW43VAY53DqMbJmNJe3nb8ZXENAXNNcVPWHA5NFqbw+w+1otpWSTSOi8f7Wb9khrC5faI9lR9N0yLeCpLz1CcmpCPquD1n7jhdTlY3lTF/lP9WEDfcIKDZwZY1RK+5EDCsixSGYN9p/ry6SOaUqxoqioY/F5seDTFofZBVreEJ9WdtRPx7bJo7T0j+e0rAh4iIf+cfEk7NEXrgip2He5Gz9WdfeVoD+uX1BCp8KMVeF1N0yKeztI3lCBU5qbmMqtbXG2hMg+astM4TMvidO8IGwMeNOZPwC0K8zl93NN8D/956D8xLZNfnP1FfmRrU/0ts5pkZVkWWTOLYdqTSbJmloyRIW2k8wEy2CM9HbEOxrJxPA53fgTLoTnyE2Wmqv+5MLSQ9656D1898O9kzSx98X7+ZtffcEvdJjbV3UJdoA63w4NpGcSzcc4nBmiPnuHY0HE6Y53c1nBrwWB2Ut8tnayRJWtmSetpBhIXyjqZlsW5WAe6mcXtcONyuHFpzny/nZpzyr63hFq4b8F9/PT0TwE4M9LO53Z+jrctfxvLq5bjdXrJmll6xnr4xdkd7OrahUtzoikHST2JmJlSivpA/SVpMZZlTRrldmpOllYupcx95ftQ0zL5+qH/JJqKcnfT3Xxgza/kRzAvvA8WsDayljcv3YI+TdqIYRl8+/C36IvbNeU9Dg8f3vBh7mm62x7pzLUX9ASJ+COsq1nHNw9/k6dO/QQLixfOvciayBrubb634GDg/r4DJHI1mys8Ffz+bX9A3RQ5t9X+apZWLuWhRQ8xnIqSMdKTRmsvVu2vpnrCJE2AnrHu/O9KKVoqWmi6ERdNAPsBLqoL0TU4ll+56tz5UcaSGRbWhgiV2RUBUhmdvuEEXQOjZHSTUJmHWDxdRFCrWNFcRSyRoXcoDkDPUJzBWIpIhY+qcm8+UNUNe+WqWCLDSDxNMq2jaYqWuuIrIAS8bqqDPnqH4ximxZneKKlcFYNyv4eq8uJyWsafm4ZwgGWNFRzvHMYCRuIZXjzYRTjoozrkw+9xoimFblqkMjpjSbvvY8kshmkRXNVQsH3dMEll7AlIumHmfzK6me8zQDKjc7Yvhsup4XTkfjQNh8NevGKqRQuUUrTUBhkaTdLRPwrAqZ4og6NJmiPlVAS8ODVFxjAZiiU51z/K2ITR9kX1IRrCgaKeK6/bQSZrcqonytBYigWRckJlHhyaIpkx6B4co2tgNF/T2KEpVi2sLjpQvtqUUtRU+Fm5IMzhswOYlr14ws4j3VSVe4mEfPi9LhxKYZj2ZMexRNZ+XVNZdMNk07LCp6mut1CZh2CZJ5/609EfI6ubLKgpx+d2YnFhEZBU2qC2yp9PxxFzTynFHY138KMTPyKWiTGYHATskZS7m+6e1QHJQHKQz7/yecYyY2SMDFkzi27q+eB2XDwb57MvfRZNc+BUdiDocrhwO9ysCK/goxt+c8rUBqUUDy58kKyR5dtHvkNST5LUU7zY+SIvdr6IU3OiKS0/ulkoZ3IqI+kR/u6VzxNNRS/p+8S81bSR5vOv/B97QQPlwKW5cDqceBxuFgYX8snNn5xyZMyhOXj3yl+iK9bJoVwaR0fsHP/w6j/ic9krfemGTlJPYlgGTs3Ju1vfw8nhk5JuMM8Np4ZZE1nDRzZ8GJ9z6rOJSilcDhcuph41tSyL09HTvNq7J3/dgy0Pcm/zPVOefldK4XV6eU/rezg1fJrjQ8cxLIMnTvyIzfWbC04wG5hQb7e2rJaIv7rgZ3x8AYm5WiylGPMmmAV79vstS2t4+UhPPqAZHkszPNafz8ebmAYSCflYuyjCCwc7yejT76yUskc4Ny+rZd/p87n8RHsGfDErgmmznFygFDTXlNM7nAucB+P5gLupOjDrHE1NU7QuDKNpGsc7hzBMC8O0S32N17W9XN2DY+w92Y+ZyyMrlGozlsyyZ8LqY3YZWYWmwfKmKlY2T/1G1zTF+sU1WFZuwQjG85bT9rOquOQ+lYJFdSHWtFQXvbpWTYUfv8fFsc4hhkdTDI+mCravaYrVC6uprZybUdlxSimWNVaiKcWRjkGyholp2vnVAyOlNQpjHxyEeeVoL7phYln2e6t7cGzKz++d3gYJZueZiL+ajXUbebbj2fx1LaEWFlUsmlU7upmlM9Zp583NtK1lgGGQYfJk3gpPxbS3c2gOHlvyGM3BBXzn6Hc4OXQyH2xODJgn0pRG2BeedlKPbhp0xjqJZ+Mz9t2wDIzc6oFJLnxe3Zp72qWgy93l/M7m3+HfD/4Hr3S/gm7qWFgkson8aBlA0B3kXSvfxcOLHuKJE09IMDvPuTQX71zxzoKBbLF2du7Kz/73O/08sujhGfNIfU4fb1j8Bk4MncDConO0k6ODx7ilbuOU208McgeS54mmooR94ZI9izavglmlFKEyD3eubuD1MwP0Dyfyq19N/BJ0OhTNkSCrFtqnqn0eFxl95p2mUgq3y8HmZbXUVfo50RUllkhPO1FFU4oyn4um6kBRS+dOvC87uHLa5bRy17scGg3VxY00XsyhabQuqKI65OPYuSEGY8lJK6dd2gd7CdmayjIqplkkwsyNyM6WvUaBhWnMsIKWUricGpuW1RIO+jjRNZwv02Xl/7nQ53Kfm+VNVTRFyu1T7UX2J6ubrFxWhc/jzOfpXtw+QMDnonVBeMYav0UroonpNtE0xdLGCqqCXo6dG+L8SAJ9mvrJCvC4ndRU+AhPkzqiprlUvOJvp5SirrKMzctrOdg+kC87BlMcrFxJl8Q1o7BHZ5/veD4/C/mupjtnXVi+zFXGm5a+sWBQWYwa/8xlhjSlsSaymuVVyzg+dJy9vXs5M9JONBUla2bRlIbP6aXSW8XC0EJWhleypHIx5e7ygm36XF4eW/IYmSIC8UIqvZXTTpZTSlHhreATt/wWDyxo46WunbRH24ln42hKUeGtZFX1Ku5pvpuGQANKKTbWbiSeTQAWq6pXXXbfxLVTF6hjSeXiK/peyZpZjgweyV9uDjZPO1FsnFKK1vBKAu4Ao5lRTMvkQP+BgiWuVoZX4FAODMtgMDnEl177v7x/9ftpCS2clMpQKuZVMAv2C1Luc3NHaz3Do2n6o3FiiQyGYeFyaoTKPNRW+gmWefLfhRuWREhljNwKXdM/JKXsYvULaoI0VJcTHUsxGEsxlsjYRf0Bp6bwup2U+91UBrwEfG6cs1yKFeySYLeuqMvXiAV79DkwQ23ZmfofCfkIBxsZTaQZjKUYiadJZXUs0x4d87gdBHxuKnKnfd3O6Y/oIhV+bm+tv6Jl18pnKCk1/rwvrg/RFClnYCTBYCxFPJXFzNVXDfhchEM+wuXeKVMWZpI1zHy6Sn1VgP6onUYynirh8zipDvqoqfTjdk4/MSkSuvCcTDVRT9MU6xZHyOomSjFlSa1IyJdvwzPDZL/xCWF3tDYwmswwGEvar2vGwDQtNM1O5Qj4XPnXdbqDK00p1iyqzleE8M/wuZjqcQH4vbMLYsZTYqpDfs6PJBiMJUmm7BSW8cdQ5nURLLPTcMT80xHryAey5e5yNtdvnvVnMegJ8surfvladO8SSik8Tg9ra9ayJrJmUlF5hcrn3xZb9L3MVcZ7Wt99XfrtcrhYW7OWtTVr0U3dDv6VPcJ3cb5wS0ULLRUt17xf4vI1lTdNW2atGGOZsUm52QtCzUVXEQl6goR9YUYzdkrf2ZH2/OIKE9mBbytra9ayr28fAAfPH+QzL3yGdTXraFt4PyvDK694hPl6mnfBLOQCH6UIB72Eg94p/z5RdWj6osOF7sPlUERC/mm/VK/khVRKXVbfimnXoew6pdMty1ts38u8LspmGbRcLqXsgKYhHKAhPHUVgct9zsdH/5RS+DxOFtQEWTDFRLuivtBmeE7GV3ubjt/rmlUwqJRCqVzu6TQHB8X0v5j+Xc3bXdyGx+WgMRyg8Sq/xuLaShtpdnbuzF9eE1lzyWSO+czeN9orFpWK8c+Cy+Gadva5mP8qvFe+suhYZoz0hDPNYV/xnz+n5rQrJ+TmOI+foZjq8+B2uPnIhg/zL3v/ldfPv45pmST1JC93v8yrPa9SF6jj1vpbuavpTprKm+b9aO28DGbHXa8nbj6/QDMp1b5fj36X6nMDpd33cTfCY7iZjBfn74x1AnY9y3ub7y2JxQiEmA+c6spDqrSRmTTR0OecxWRx7Mlgk9oyp66aoJSi2lfNH97+B7xw7gV+fOrHdI12Y2FPmuwa7aJrtIunTj/Fusha3rT0TSyvWj5vg9p5HcwKIYS4PnRT5yenf5Ivx7Ug2Myq6tZ5+cUlxI3qen7axishPNTyEHc03sFrvXt5tmMHJ4ZO5idvpvQUr/TsZn//Ae5bcC/vaX0v5e7Lm/dzLUkwK4QQNznTMnmx88V8/pymNB5b8tikUR4hxLXncXpwaI4LVTJ0e1XLohbSwSKlp/KX3Q53USk3SikC7gD3Nt/DnU130hXrZGfXLnZ27aQ/3o+FRdpI55buHeJ3N/8OPtf8mvMgwawQQtwkLMtiNDOKhYVLc2FZFrFMjF1dL/P48cfz1Qdaq1u5o/GOeTf6IsSNLuAO4HV686W5BhKDRd9WN3WiqQurZ1Z4QrOqRKKUwqWctFS0sDC0kC1L38RLnS/x+Ikn8nWn9/bu5flzz/PIokfm1f5BglkhhLiJ/OD4D3nh3Au4HW4syyKejU9aWarKV8Wvrv3gFc/KFkLMXsAVoMZfQywdA+zqIrql41IzB6Wx9IXFTgCagwtmrE9biFKKoCfIo4sfZUV4JZ/b+TmGUkNY2KuoPdTyUNFVFq6Hy3uUQgghSlLWyDCSHuF84jwDyYFJgWxdWR2f3PTbLAwunFejLkLcLJyak7U1a/OXz8XO0TfWN80tbJZlcXTwKGMZewEoDW1SO5fLXoJ5Ibc2bM5fN5gcImNmprnVRW1clAlszWJFvmJJMCuEEDeR5mAzLaEWIv5qKr0VRPwRVoRX8N7W9/Cpez7FqupVEsgKMUeUUtzZeAd+p52TmtSTPNP+zIxLMqf0FD8983R+5bnaQC2t4ZVTfpat3GqfszFxhNepOdBmET76Jqw2pps6I6mRWd//TCTNQJS0oN+DkVsta7qau0II2yOLHuGBhQ+QMTOYpommaXgcnkuK9Ash5kZzsJm7m+/h6TNPA/Dz9l+wuGIJdzffdcmpfcuyyJgZvnvsuxwfPA7YI6GPLX6UgHvqOt+GZbC/bz+LKhZR4a2YdkERy7LoT/TzWu/e/HWN5U2zqolcU1aDW3Pb+xzLZGfXTlqrW69qmoIEs6KkrV1UnV+4TL6GhZieFOgXYv5TKN614p2cHDrJmZEzpI00/7rvXzk2dIz7m++jpqwGp+YkbaTpGDnHT8/8hH19+/OjsrfU3ULbwraCAapu6nzt4NdJ6AlWVq1gTc0aWkItVHor8Tg9aGhkTZ1YeoQjg0f5yemf0Be3Ux1cmou2hffPqv50fVkdTcEmTkdPA/Bsx3OUucu4f0EbIU8w36eUnmI0M0alt2LWi7VIMCtK1vgHVYJYIYQQNwqlFBXeCn578yf4+91foCPWQdpI88yZZ9hxdgdlrjKcmpOMkSaeTUxKQVgdWc1HNnx4xgmclmUSTUXZ1f0yu7pfxqmceJwePA4PmlJkTZ2knsxXVQA71eDRxW9gXc26WZ3FcTvcvHXZW/jHPV8ka2bJmll+ePxxfnL6p5S5/IAia2bJGBl0Q+eD6z7IY4sfndVzJsGsEEIIIcQ8opSiqbyJ/+fOP+Ebh77J7p7ddrBn6oykRy7Z3u/y07agjXeueAeBGRY10JRGfXk95xMD+UVSdEtHz+rEs/EpbxP2hXnz0jfz8KLZVzFQSnFbw20Mp4b57tHvMZa1J6ml9NSkurh5l5FPq2ZIwr26GbpCCHHjuBYnBWSfK8Q1YlkWu3t2c+j8YQDK3GW8bflbL7sMXVpP88MTjxPP2AHgmshqNtdvvqq555ZlLy97avgUu7p2cWL4JCOpKPH1lygAACAASURBVLpl4HF4CPvCtFa3cnvDbTSWN06b/zqxzbSR5nT0DIfPH6J9pJ3B5CCJbIKsqWNZFk7NScAdoD5Qz5rIGjbUrqfSW3lFj820TDpjnbzY+SJHB48xnBoma2RxaBpep48qbxVNwSbaFtzPgtCCqZooeOcSzAohxOWRYFYIcd2MB7YZw55I5dAcuDU3mtIuO8i0LAsLC8M00E0dw7JXHtOUhktz4dTsE/jXIkDPGlkMy0AphVNz4dKcMwXjEswKIcRVJsGsEEJcPwX3uVJnVgghhBBClCwJZoUQQgghRMmSYFYIIYQQQpQsCWaFEEIIIUTJkmBWCCGEEEKULAlmhRBCCCFEyZJgVgghhBBClKyZlrOVZe+FEOL6kX2uEELMkozMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFkSzAohhBBCiJIlwawQQgghhChZEswKIYQQQoiSJcGsEEIIIYQoWRLMCiGEEEKIkiXBrBBCCCGEKFnOue6AKF07tm23itjsbNvWLS1X6f5+DfgysKht65b2K2inHvhd4BFgKZAFDgCfbtu65bkr76kQQlx9pbrPzbX1ZeAOoBF7IO0U8K/AF9u2bjGurKfiZifBrLgSd150+fvAfuB/TrgufRXvb3vuPnuusJ1NwHuxd9K7ADfwCWDHjm3b39q2dcuPrrB9IYS4Fkp1nwvgA76AHcRawKPA/4s9oPB7V6F9cRNTllXMgZ4QM9uxbXs78ELb1i0fmOu+TGfHtu0VwFjb1i36hOucwCGgr23rlvvmrHNCCFGkUtnnFrJj2/ZvAG9u27qlfK77IkqbjMyKa2rHtu23AX+FfXpJYY+Ebm3buuWVCdt8BXgYeA/2kfpaoBf427atW74wYbtfY4pTXju2bf9N4LeAViCFnTKwtW3rlpem6lPb1i3RKa7Td2zbvg/YfPmPVggh5tZ83OdOYxDQZ9xKiBnIBDBxzezYtn0d8CxQCfwa8KtAEHh2x7bt6y/aPAh8C/gq8HZgB/D3uZ3pdPfxv4F/Bl7D3jF/AHgOWDDLvrqxT6cdmc3thBBivpjv+9wd27arHdu2O3ds216xY9v2dwEfAv6uyIcnREEyMiuupf+Bnb/10Pho6I5t258G2oE/B945Ydty4KNtW7d8M3f5qR3btjcCn96xbftX27ZuuSQfZse27UuBPwA+37Z1yx9O+NP2y+jr/wSagF+5jNsKIcR8MN/3uVuAJ3K/W8Bft23d8pkibytEQTIyK66l+4AfTTyt37Z1Swx4HLj/om0N4LsXXfdN7KP9xgLtP4z9Hv7nK+nkjm3b3w/8d+AzbVu3PH8lbQkhxBya7/vc54Fbc+38NfDHO7Zt/8vLbEuIPBmZFddSFVPPgu3FPg020XDb1i3Zi67ry/3fCHRO0U449/9UfyvKjm3b3wJ8Bfj/2rZu+fPLbUcIIeaBeb3Pbdu6ZQR4NXfxZzu2bc8An9qxbfsX27Zu6bqcNoUAGZkV19YQUDfF9XXA8EXXVe7Ytt110XW1uf8L7eQGcv8XGkWY1o5t2x8CvoNd3uZjl9OGEELMI/N6nzuFV7HjkEVXqT1xk5JgVlxLzwJv2rFte77sSu73t2BPNpjIAbzrout+Geig8I71GcAEPjrbju3Ytv1O4IfAz4APtG3dYs62DSGEmGfm7T63gPuxc2dPX6X2xE1K0gzEtfQZ4M3Yp5M+i73T+m+AH/iLi7YdBT63Y9v2auAE8D7svKpfm2oiAkDb1i2ndmzb/nngD3M77Mex88BuA462bd3yralut2Pb9pXYExYGgL8BNu3YdmH+QtvWLbsu7+EKIcScmq/73C3Ar2NP/urAnnz2Ruyg+J/atm7pvvyHLISMzIprqG3rlgNAGxDDLv/yH8AYcH/b1i37L9o8hj0q8CHsEdMHgN9r27rlqzPcxx9jr951B/Zkhq/nbtsxzc3uwM4fWwj8Ath50Y8QQpScebzPPYUdb/wv4CngX7Br2/4q8NtFP0AhCpAVwMScGy/g3bZ1S9Nc90UIIW50ss8VNxoZmRVCCCGEECVLglkhhBBCCFGyJM1ACCGEEEKULBmZFUIIIYQQJUuCWSGEEEIIUbJmqjMrOQhCCDE1dQ3alH2uEEJMreA+V0ZmhRBCCCFEyZJgVgghhBBClCwJZoUQQgghRMmaKWdWiJJkl5yzSCVixAa7iY+cJ5Maw7IsHE433rIgZcEIZaEIHl85Sl2L9EchhBBCXGsSzIobjmVZZJKjnNj3NN2n9pJOjcEU9ZSV0iivauCuN38Sl8c3Bz0VQgghxJWSYFbccLKZJK/9/D8Y6D4+7XaWZeIvr8Lp9lynngkhhBDiapNgVtxQLMui48jOSYGst6yCuoVrKK+sR3M6MbIZkvEoo8M91C9az7WpsCSuJtM0SI4OAeArr0LTHHPcIyGEEPOFBLPihmIaWbpO7clf9peHue2xjxGoqAFAKcWFJZyt/HVifhsb7mPnj/4BzeHknrf/Ib5AxVx3SQghxDwhway4oaQSMRKxwfzlxqWbCFTUTApYL/wuQWwpsCyLob7TZNJxXG4fsq6AEEKIiaQ0l7ihZJJjGEY2fzlU3SQjryXPYqDrxFx3QgghxDwlway4oRhGFiwzd0nhdHnntD/iymXTKaLnO+a6G0IIIeYpSTMQNwzLssCyLpyEVlzVTILxXFvLMrFME7BQmgOl7GPCqz0CnL8/08CyTEDZE59y9zPb+5uuvSvt+1TPDSiUpl3R82NZFmPRXlKJkSvqnxBCiBuXBLOiJBlGlnRilGw6QSY1RioRI5WIMXK+40JNWcui68Qehvvap2yjLFhNw5JbZgyyLMvC0DMM9pyi/9xhRod6yKTjYFm43H7Kq+qoaV5FuGEZTpen6KBNz6Q4e3Qnhp7B4yunecXtaJoDy7LQM0n6Og7Tf+4w8ZHz6NkMmsOBx1dOMNxIdcNywvVLcDhdM96PZVkkx4bp7zjMQM8JkqND6Nk0msOJ1x+ksqaF2oVrKK+qRyltVkGnZVlkUmMMdB1noOck8Wg/2XQC0zTQHE5cbh/+8jCh6iYqa1sIVNThcLqmvI/xgNg0smRScRKjQ4wO99Db/jqWaQD2637m4HM43YVH3Ksbl1NVu6joxyCEEKK0KWuKYvIT3JQzLca//MeGe/H4gwSrGlDa7DMyLNOk+/Re0smxgtu4vWU0LLkF7TLav5n1nj3Ivh1fx9AzmKYx5aIIM4k0reT2N34sP3I4FcsyGeg+wdHdTzJyviM3onkppTRC1U2suHULkcbl07Y5LhmP8ux/fZZsOoG/PMx97/oTnC4vw31nOPjS9xgZ7Cz4uFweP/e+448oC1ZP03c7CD9z6DnOHHyOdCJWcFuny0PDkltYsemNePzBogJa09A5d/wVTu57hsToEDPtLjSHi/LKWlbf+U7C9Uum7G/74Rc4e+QlUvEoeiZV8PmeTuttb2HphodnfbvLcC2SsW/Kfa4QQhSh4D5XRmankIgNsOvJL5EYHcLhcrP+vl+mYfHGyzita3LqwC8YGThXcJtgVYNd61SC2VkxjSzZTJLJOQUwdSxweTGHZZl0HN3F4V0/RM+mLrSmOXA63QAYehbT1LEsk+j5DvY8/W+03v5WFq68a1YHQNlMEj2TZmSgk9d+9lXSydF838fbGR+dBAiEavCWhabpu0U2k+T1579N95l9E4JihcPpRnM4sEwDXc+AZaFn03Qc3UlssJtbHvoQ/vKqad/vlmlyYt/TnNj79KR+aQ4XDocTlMI0dEwjO2nENT4ygMvjL9ju2HAfo0M9E65RXPqazvB6yoQ/IYS4qUgwO4XhvnYSo3Z5JyObpq/9IA2LN15WW15/kKQ3gGFksQwDyzKYYTRcFCFct4Q73vjxS64fGeziyMtPMJ6z2XrbmwlVN03ZhstTRqHAyLIses8e5NDOH2DoaQDc3gALVt5J7cLVeP0hlFKkk6Oc7zzK2SMvkRwbRs+mObzrh7g8/lkdABl6luj5Dg6//EPSyVH85VXULVpPVd1ivL4gFhap+AjD/e0MdB6jprkVTSv88bVMgyMv/5Du03sBe+Q40rSS5hW3E6xqwOFyY+pZRqN9dBzdSX/H4VxAfpYDz3+LzY/8Bq5pTuVHB85x6sAv8oFsMNxIy+p7qYgswO3xg1IY2TTJsWGi5zsY6DpOdKCTcP2SfM3fqbSsvpe6ljWTrus+vZ+Ooy8B4HB6WHfve/D4AgXbKAtFCv5NCCFKiYXFAC8xwIsFt1E4aOEDeKm9jj2bXySYnYJyTF5dSHNe3tOkNAe3PPir6HoGI5smm02RTSc59uqTDPeduRpdvWl5/EEi/uAl1yulXRjMUxCKNBNpXDHr9tPJGEdefjwfyPrKq9j00K9REVkwKUD1BSoJVTdTv2g9e37278QGOzH0DEdefpzKyEL8wXBR92caOod2fo9kPErj0s203v4WvP5Q7jFdGHWuX7QeY1MGy7IKBsqWZdHb/jrnju/OPyfLbnkDS9c/jOZwTrqdP1hNpHEFx/b8mFP7fw5YDHQfp/P4K7Ssvrdgbmvf2dcxsun8c3DrGz6ML3DpaG6gopbqxhUsWfcQidGBfH+mopSivLKW8srJO+TYUHf+d03TCNcvwReoLPBMCiHEjaWPn3GIzxT8u4aHWh68qYNZObc9hXD9EiprW3A4XZQFq1mw8s7LakcphdPtxesPUhaKUFHdTHXDsnyQIuYny7LoPL6b+Mh5wD4oWX3H2y8JZMcppSgL1bD2nl/KlwJLjg3Tfvj5WYzC23naNU2trLvn3fmR38n3Z192ujzTjpoaeoZTB36eHzWNNK1k6fqHp5x4pZTC4XSxbMPDBKvqx58Azh59KR+sTiWRW1oWoLyqHl+gsmBwrZRCczgIVNQSqKiVur9iWhYWaQaIcbzgzygnMCj8/hTiRuKmCj9NuKnCQRkaLiR8m0xGZqfg8QW5/Y0fJxUfwe0N4PaWyRfwTcTQs3Sdei1/uaK6mZrm1mnfA0opKiMLiTSvpOf0PsA+Pb50w8O4vYVPiU/kcLpZvulRHLOoiHApi+j5DkYGu3L90mhZfQ+aY/qPutPto65lbX4UNB7tZ3S4l8ralqm3d3nyvydGh8imU7g8PvmciKviBF/iKP+74N8deHmAZ6hg7XXslRBzYykfpYX3o5NEZ4wsMbp4nCP89Vx3bd6Q0H4KSilcbh/llXV4fAH5gr7JJEYH86OyAJHmlWiOmUtgoRR1Cy98uabiUWKD3dPcYLLyqnqC4cYrer9ZFgx0Hc+Pyrq9ZVRUN8/YplKK4ITcYtM0GBnsKjiyXFW3mPF847HhPl5/8TskYgNYliU54eKKWegYJDFITPmjkwBmX+lCiFKjUGi4c6OzjQRZQZhbCbFqrrs2r8jIrBAXGYv2YeiZ3CVFqIhgEHI5n1X1aA5Xbha/ychgJ+GGZUXdPhRunHZSV3EsRgY685dcHj+ZdBx9mpSBcaaeRSktXw4rOTY05XZKKWoWrKYi0pxbmcui+9RrDHQdp37RepqW30oo3HRJfq4QxVrEr1LJRtIMkmGQNAMMsYfzPDfXXZt37KA/hYYHBz7UNakYJ8T8dtWD2fGRGUPPYOgZLMtEKQ2Hw4nD6UZp9uSqy10NCOzJMno2jWnqKKXhdHlw5EolXe6qSDORL+Wbg2VZpOLR/GVN0/BOMdGsELe3DIfTjWlkgcm5pTPxXIVcatMwJvV/bKSf57//d0Xd1v7sXhjtMrKZgtu63D7W3/c+9j33n4yct0vPZVJjnD3yIueOv0Io3Ej94g3UtazDX17JeL6vEDNRKAIsIYBdi3h8Tb+zfF2C2YtYWBzkM5zlGyzmN1jNn811l4SYE1ctmLUsi0RsgJ4z+xnoOk58dDBf9NyeZOLG4wtSXllHuH4JVfVL8Qcqi1pKc/xLNtp/lq5TrzHcd4ZUIoZp2MGs2+snGG6iYclGIo0rC64wNJXYYJc9WaZAUKuABa13Ud2wbLZPiShR2Uwy/7vSHPkDpWI4HE60CdUwJrY1E01zXHHAZ47Xjh2XO7C8HNY09fvHR6HveOPHaT/8Yn6hA7DryQ73tzPc387Jfc9Q17KWRavvy60wJgGtmJ0LI43y3rmYQZJefkqSTrJEZ76BEDeoqxLMGnqWMwef5dSBX5BJFVrtKp6rOXmWc8dfxuXxs+qOt9G8/PZp27ZHykY4uvsJuk/vz494TZRJjTEW7afn9D7C9UtYdcfbi849TCVidJ18jekW3qluXAESzIoiTTrNN5v00avyXW1NWjXM6fYRCEUuq3F/oGravyulcHsDLNv4BppX3E7P6X10nniV0aFue1U27M9mx9Gd9JzZz6LV97Fk/YP2GRoJaoW4YnHaGePUXHfjqrIPoi1M0mQZxSAJWGi4cRLAgR+Fo6h0iqkOyMdvZ2FhYZBlBJ0xQOEiiItyQLsq7ZukyBDFII0DNy4q5n0qiP286GQZzT0vJg58uAih4Zm3fb/iYNay7JWATu59On+KUikNl7cMl9uHpmkYeoZMOmHn7eW+aA09S3ll/QxtWyRGB9nzzFcmraKlOZy4vQGcLi+mmSWTHEPPpvNLj77yk39h4wMfJFy/ZMYvTY8vQHXDUjLpJHo2hZFNY+iZonIMxY1pvLwW2CtdGfqlB1CFmIaBaeoX2nJ7ptn66lPKMalyQUV1E7c99rHLCh6LWZLX3k7hK6tg0Zr7WbDyTqLnO+g6uYe+swfzK5ll0wmOv/YTUvEoa+75JRzFTKgTQhRkYTHIrlzAUdrGA9gk3fSzgz5+RpTXSdGHQRIrF8y6CVHOMup4lCbegY+GGYIrk9f5NDEO48DDej6Lj0YMEnTxOO18jREOozMKKNxUUMkGWvgQdTyChnva9uOc5QBbMclSwXpW86dYQIKznObLdPMkSbowSKPhxkc9dTzEYj5MOSvmVWBoYZGij3N8l26eYJTjZBllPJj10UgND9DCrxBiNXbYPn/6f8XB7OhwH2cOPpsPZKtqF7Fs4xsIVjfhzJUYMk2DTCrBWLSXga7jnO88iscfmnH0VM+k2P/cN/OBrNIcNCzeQMuqewlU1OBwujBNk1Q8SvfpvZw5+BzZdIJUPMr+Z7/O7W/8OGWhmmnvI1TdzO1v+gSWZdiBiJFldKiHl5/65ylHgcWNTSk1qSC/aRqkk7Gib28ftF04re8ru77F/TWHA4+vnLFoHwDpVDx3/bWf6zleAzdcv5Rw/VKSG99Ax7FdtB96nmw6AVicO7GbmuZW6hatl9HZG9CFkarcqiUwr77wpjK5z+Ouft8vvZ8rvQ+LPn5+pd2aF1L0cZBP080TpOin0CmtDAOMcYoefsJxvsBG/pYG3oQqUJjJXj3rec7zAgqNFj4EaOzhk/TwJBbGpO2zRInTTjdPsoD3sIG/wU244GuUZYQunsAkTYyjrOD3Oc/zvMbvEad9yv6P8Dpn+Qbr+F8s5INoOC5t+DozMeji+xzgU7mR/snPf5YYKfoY5jVO828s55Os5I9yo+Tz4/N9xd9wg90n0DP2uvUeXzkbHvjgJeu6O7AnjPjLq6hpXoWRTaNn02ha4RfRyhVuH+w+CdhflMs2PMyyjW9ATcgttNuuY/ktj1FVu4g9P/sq2XSCxOgQR175EZse+hBqmi/yC4XptdxokZdsWUK+aG9i5RW1OJzuXK6pRWywi5rmVUXldo8N9144CFKKYLjhur6XlNIor6pnsMf+3KTiw6STMfzlxa1EdnX6YD9ef3kVKza9kXD9EvY8/WWymSSWadB1ai91i9YhOZClJ8Mw/ezAwsRHPWHsBWUsdKLsp5efMsIhsoziJECQVup4hEo2zjjKdT3ZJ5gTjHKcIV5lhEMk6cEgiYYLN2HKWU41t1PBRpyUzdj3JL0M8BJgEWAxFWwAwCRLlH308TNGOEKWETRc+Gigis3U0IafpoIB2Xh/7f+zZImRoJMRDnGe5/PbjHKSTr5XsA0HXmp5GAfX92xRMSwMeniKFH356xz4cBPGTQUKBxmipOjBxN4vxznNbn6Te3mcKjbP+PpYmAzwAsf4PH08A2i4qcZLDRou0gySohcLHZMM7XwNnTFu48u4mLlWeIpeOvke+/hvZBhEw42XWtxUYZIlRQ8ZhnPb9rGH3wM0WvjgnH4uLAxO8iUO8KcYJAB7eVwPETxEUCjSDJGiL/f+G+YQf0mSLjbyeZz456zvE11xMJtKXBi18viDeP3BaVcCAnC6vTinWcEIIJOKc/bwi4wfIVTVLWbJ+ocKjjAppXLLZj7A0d3bAeg/d5jhvnaqikg3EGKcLxgmUFGTL3F1vvMoi9c9iGPG0U2L/nOH85c83gDBcOM17OnUIo0rOHv4RSzLJJtOcr7zKAtW3jUnnwGlFNUNy4g0raT79F7AruNrmiYOR7Flri/026L4CiTi6ovTzk4+iEmaKm7jQX5OhkEO8Gec43sYxC+5zVH+hnoeYx1/RYAlc/zFbRGnnQ6+yTm+yyjHczmZU9NwE2Itq/kzGngjappRtGH2spP3Y2HQyNu4m28xRjsH2EoPTxW4H4WXOpbzOyznk2h4p3x+sgxzkE8zzD7inCXNICZpJo6g9fAkPTxZsH9e6nmM1+ZlMOujgYW8n5N8iTB30MhbqOYu/DTlgiWFQZIoBzjEZ+jnWQDSDHCUv+VOvoYqIpw5zhfQieOnmVVspZ7HciOvGjpj9PMcB/lzYhwFoJPHCfPPrOAPZnzfZonxGr+PTpw6HqWVPyHEWpz4c9mzvZzhKxzj7zGIY5DgAH9KmFspZ+WcfC4sLLr5Ma/zZ/lAtpJbaOW/E+EuXAQBhU6cIV7lMH+VO2AzOcNXKWcZK/jDaQ/Erpcr7oHHd+GIJTk2zNhI/xV/2ViWxVDvKRL5OpeKBa13zzirXClF07Jb8fjKAbuEV9epPVfUF3HzcThcNC7ZlL883H+WoZ6T076v7VHZPvo6DuWvizStnFVZr6tBKUVV/RLKQjX5604ffI50IjaHQaCadBCqNG1Wu+2JB76mkc2lLIi5YTEeQCXoIMZRXuJ9tPMfuUBWoXBNCiwMknTyfV7gncQ4Om2VjGvP5BB/wev8D6LsnxRg2pOKXJMCVpMMw+xhFx/kHP81Y9/H/z7KcaIc5EXeRSffz9+Pwpl7blT+Fil6eJ1P8TqfxkKfst0MI7TzNQZ4iSRdmKSY3ezSCz2cjxSKFfwuD/Es9/EDlvJxKlmPhzAOfDjw4qaSCPdxJ1+ngo35257nedL0F3U/OmN4qOYuvsFiPoKfZpz4ceDFQzVNvIO7+DZltORuYXKCfyBFTxGtW+iM0cAW7uLrRLgPD1U48OLET4DFrOHPWc9f5j8fKXo5zheYq9clwyCv82fouYPQCPdyLz+gibfjpTb/3HsIU8cbuJvvTDgbY3CM/0OcM3PS94td8chsVd3i/CnZbDrB3p//Oytu3TLrElkXG+g+kZ8s5vL4qKpdVFRbXn+IUHUT/eeOADDYcxpDz0xaflOI6dgHRZvpOLaLsWgfpqFzaOcP2PzIb1AWilzyPrQsi3RylIM7v0cml6PqdPtYtOY+5uJUusvtY8m6Bzjw/LewLJOx4V72P/9N1t3zHrxlFQU/R5ZlYZo6Y9F+tFy6wlQMPctQ32lC1c243D6gcB1my7KIj5zPpz0AlFfW5etNF6MsWJ1fzME0dHrbD+TSN+Z+NOBmlmaQV/gIUfbhIcIC3k0ND+KlNndqfT9n+ApRDgAWMY6wh09wDz/AzZXXVL48Ggt5Px18GwuTMhYR4W7C3E6AxTgJ5EYAX+csX2eIPYwHKQf4FBHuw8f0E5fBnhj0Cr/BCIdw4KWOR2nkbQRYjMJBgg46+X4+39I+1ftF6nmEGh68ZJTOQ5j1fDZ3it1mkOQIn82fuq7hfpp4Z8E+OSnDWcTp8rnipRYvtdNuo1B4qGEJH2EPvw3YqS9xzuGjoaj7WcyvU8WtU46EKhRBVrKSP2YPvwNYJOikmydZzIdnHD11EmA1n8JJsED7Dhbx65zje/mayV08wSr+FD/X9yyehcU5vscIh3N9L2c9n8VLXcHnxkOE1fwpz/MOLLKk6KOD79DKf5vzFKIrDmaD4UYal26i4+hOAEaHe9nzzFcIVTfRvPw2ahesxlsWYjZF0y3LYnS4N3/Z6w9NGgGellKUVzXkg9lUIkomOSbBrJgVt6+c1tvfyt6fQGy4PwAAIABJREFU/zt6Ns3ocA8vP/VPLFn3IJGmlbi9ZSggm0kx1HeaU/t/lk9LUEpjyboHCVU3zdmp/calmxjsOUnnid0A9Hcc5qUnvkDT8tuoblyG1x9C0xyYpkE2nSQxOsjIwDkGe04SG+qh9dY3TxPMZtj/7DfQNAfVjcsJNywjWFmP21dup2KMT/pMjjHYe4rTB35Bcsz+wtUcLpqWbp7V4wlWNeArryIRGwDg9OvP4nL7qF+8EZfHDqZNQ88dUCfx+MvzZ2fEtWPlckEDLOFOvk4lGyedboxwDwt5L6/yCTr5AQDneYmzfI2lfGJOvvwUigj3sJI/IcztVHNn/lTqxP5EuI8W3s9uPkEn3wXsFItenqGFD8zYd4MEUQ7gooLN/ANNvDM3Kjtetul2mng7J/gi+9maX773FP9GDW1wUTqDiyBL+Mik6zJEOcEX88FsBetZym/NeVBxrSkUlWxAw4VJNldea7io22p4aeId054WVygaeQuH+Ewuh9eih5+wmF/n4tflYhWsI8TqaV8DBz4W8r58MJuinyFenYNgNss5vs34qHA1d1DJhmn7rlCEuR0/TfkR2V6eZiV/hGJuK9RccTCrlEbr7W8FFJ0nXsE0dCzTINp/lmj/WY75niLSsJymZZupql9SVI1Jy9TJ5Er6ALh9gaKX+RwvEzTO0LOkU2P4g9dvAowofUopahesYtUdb+fwrh+gZ9MkYgO8/sK3cbl9uDx+lFJkM0ky6UT+LIJSGgtX3c2SdW1zOnKoOZysvuudmKZh56rmytwd3/NjTuz9CQ6HG6UpLNMe7RyvC1ssyzSJjw0Tjw1w9shLOJxuOxfe6baDWT1LNpOcVOJOKY3Fa++fdQ67y+Nn8Zr7OLTzB1iWiZ5NcWjXDzmx72lcHj9YYBjZ3KqDWTbc/z4al26auWFxxRRO1vGXVHLLJV+CCoWbajbwtwyxlwRnAZPT/BstfKioSTXXgoaXNfx5vo9TsZMlKlnLp+nj57lgyWKQnbTwgaLvaTV/RjPvviR4UrlnZwkfpZMfMMCLAAzxMlliuLm+VVBKjT0hzwlksad2TZ2ecTEvEcpYPON2HmoIsjo/IW2EQ+gkcjVoCxuf6Did8YDQgS+XfmIyxKs08tbreiCSoo8oB/OXI9xbVN6xkwBlLMwHs2OcIksMD3MbY12FYFbhcvtYe88vUb9oHacO/Jyh3tOYhv3myiRH6Tq1h+4zewmFm1i05j7qWtZPm4JgWSaGceHN6XC6Z3W21uGa8GayzMteAUnc3JTSWLDyDrxlFRx5+fHc2QKLbCY55cpe3rIQS9c/zILWO4s++LpWxj+X6+97HxWRBZx+fUd+hS7LNNHNVKEb4g9UTnvwpzQHvkAlqWRsQt1oe/nqQtWZvWUhlqx7iIWr7pp1kK+UYsHKu0glYpw5+Fy+ykQmFc+ndUw0tzmZNxe75ucj0waFfppZyHs5wucAiHGUEQ5SzR3Xs6uT+lTsdgEWEWQlg9hnHhN0MbHs2HTKWZKbqV74/e7ARz2P5oPZNIOkGbipg9nxz69JFp1RssTQiWOSxiCNSYpRTl5SVqsYHmpwUjbjdgoH5SyjP1f6LM0AGYZnDGYDRQTKAD7qcVNJMpdLPcZJin1fXS1x2skykr+cIUo324u4pYXOhXkL469RyQezMF7eykGkaSXhhqVE+89y7tgr9HceIZ2rdmCZJtHzHezb8Z9Emvaw5q534Q9WFwhoL9rdzHbiyqTNZU34m4nHH6R5+W1YloVC4fVfWW6eUho1za1U1iyk9+zr9La/zthwH9lsEix7UYSyYDWRplbqF63HF6gs+v3mcLppXLopP3oZrCou56v4viucLjeL17bRsGQjfWcPcb7zKGPRvnyZLKU5cLo8eMsqCIUbCTcso7KmBbe38A7f6fJw22MfZaj3NAPdx4kN9ZBOjKBn05iG/QWjaQ5cHh9loRoijcupXbhm2nzdmTicLlZsfhM1zavoPPEq0fNnySTHME17SWuny4PHH6K8qo5QuOmy7kPMXhW34pzhC16hqONRjvL/s/fe8XGc953/+5mZbQAWvTf2TrGLVLeKLcm2bDlu5yLHTnNyTrn7XRIn/iW+2JdyiXOxneScS5y4nhM7jhM7kiJZsnqhRIoUewEbSPRedhdbZ+a5P2YxwBJY7KKRAP289QK1u3jmeZ59ZrD7me/zLV900x4NcpAK9i35LXGBgZ+JYEqbOBKZ16yruSenKHUE84QAskmmE9X/9OHUnRpjgP108wRDHCZKByYhbJJIbCQ247W15hI05aEYLU/ZM9l/1yaOSe58417K87qmdQIZfzcJBpDY1zQrQIzujIDDFr5IC1/K82g56ZGFzfXPyb+g5iMhBLruobx2DeW1q4lHQ/S1nab93EFG+tuQtoWUNn3tZzj87DfZ++AvTxvtLTQNfZKPq5mKO+Ikj08QKWWG1Wz8i07x00FRaQ3b7/rwgvbplG0tpGn9PprW7XWskFYKJGiGgW74JuUrzh+PN8DW2963oHOdjnHXmxWbbmPFpluxzBSWmUJKGyE0dMODphuuxTTX+xhfj5oVW6lZsdUNzJrsrqBputvvQt1QappOee1qymtXp31kE9h2+j3oBprhUUFh15hiNubVrojVeCgmiZOhJsTpHEdcW8YrUE2IJSstnsz0a+PtJv7NRTn5ubpo+HEscuNzmL3FcbkjsejhGU7yPxjmzWmyOoz7NGtp32PSqclmh5anX6dAoBPImJ9F7h3efLbpnXY62qQUadacs1PMndS04nz57motyl6o88XlfIE2b7yVxnV7GOg8R8vhJ90gmdGBDi6fepkNe94x5YtOS29jhgY7AUjEwthmCs2bXwR0LDzkPtY9Xrz+pRvBqVhYFtMKL4QAIRzfUGbOk5x3f9eQ8b9Lw+NbkBu88fkLoTsFUK7BTaNbLMXwoBuqJO71xk9NXpYop+Z9iStmY3QBNrkCahYL6doBLzPEYUY4xhiXSTCASRSbBDZJbJLE8krLdDUaARryWpupLZavoJgLEpvLfIc3+f/SZWUdsVfEGiq4hVK2UUAjXkrR02m0IrTyGo8gZ2kRzPdGwbm5sTNey+dcyquOmanl5P6vT55WkfG4jgfzzggxGQ3fdcxOMsGiO/YJIdANL9XNWyiuaODAk39HeNj5cOhtO8W6nfdP86UkKK1soveK45wcj4aIjY0Q9NbmHE/aFqNpEQwQKCqfcctUoVAoFHNjsvVqJgRGRluTSN7b9QuNjUkfL3KOv2SA/WkL1cIKSIHIyzdT4fhQH+MzrpD1UsE2/pAmPoAnLZKypYqa7VmziKW383PfRE22XAo8eV3rJpG85mGns1eM4wS0XVtB62TxGEewll+hjrdf0zksJNcsSkUIgb+wlLpV21wxm4yPYZnJKWJWCEFl4wbOH/2Js52YStDfcZai0poZrVlSSsZCA4SGJsRsZf06NF1ZcBQKhWLhyU9OiKtSX+VvwVpYLBKc5n/Swpfcikfg5NgsoJECGvFRhUEwnUy/kHa+T5hzsxzJ2RJXzIxEcoXvkqAfcCyy2/kTVvFzM1pCnZRcs78BcSqnJfNyN4jS4T42KMBD6QytHZxyuDKnFdckTHJS8JWfOq719VJAAwJP2rptE6UNyD9AcqkxLzE7uaJQvlumk7MUaLqRNXl6SUUjZdUr3WTrV87sp37NLnyB4IxZEC6fehkz6URq64aXhjW78pqXQqFQKGaHlaff4tU+h3qWsq2LiURyia9zhi+429MBGljPr1PPQxTQiI6fTFEhGebQHMSsIh8kFoMccJ8HaKCBh2e8NiRySvBSviToI8FATqu5TcItaevMqz6vrfQQZ8gnK0GE1oxMAsVsytn3QlPIKnyUu+nHBnidNXyS61HoZyGY162AlDbtLa8TGurCslK5y32O9NJ96aj7WnF5nZOXcrqJ6QZrd7zVtdpGRno5tf/fSCXGpowjpcS2TNrOvkZby8QfRv3qnRRXNqhsBgqFQrEIJBnIy0JmEnW3kQF8VHKtLVFJBjnHlycJ2Xru4N/YwH+jmA3prV7dtSKLdFCWSvW2eDgFDya28x2reK4YF0kfzzMX15AkowxxOOc5jXAp4wamnD3pQL2ZGeJNkozM2EYi6eVZ9zoUeKhg7zW/ufNRRQX73Oe9PEeUjmV7vc/PMmvbtJ56mbHRforL66moX0tpVTMFwQoMrx8hNGzLJB4dZbDrPO3nDk6qBGTQvPFWsqUoEEJQ2bCeVVvfwoVjz4KUdF06QmS0nxWbbqOsZiUebwDbMomM9tFx/g16L5/Etp27tWB5HRv2vD1rdLOU0onqtpJOFLbpJF03U0kiI71IObENFh7uZrD7IobHi2540XQPuu5Eahte37Rj2LaVTlVkYlupdAR5EjMZJxGb+OM1U3EGu87j8RegG07/4307YxlKjCsUiiVJmIt5tYvTk2GJKmLNYk0pK6OcZCy9lQqwik+kq5bNZAW0MuatWFgEeoaV1CSMTRKd6YNJJZIQLbTzgzmOaNPKN6nnHWkr/HRjWLTyzUnBaAYNvCuv3qNcoYv/yFolTiJJ0Ecb33NfC7KGUrbP4b3MD6e07ifo4sl0adpuzvLn7OAv0HMUfhhnXPguBdeEBfGZtcwkw32XGe67DDhCVdOcspbStpyqYJPEodB0tyzoTEJN03TW73oAK5Xk8plXkbZFaLCDE6983xlDN5C2nU6TNHE3ESyrY+c9H8uR11Jy8tV/oa/jrJtSSNoW0rbTlt+J/i6deIHWky856Zc0HaHpaLqBx+vn5gd+iWDp1HrSQz2XOPrCP2GZSWzbqYpm2zZImbEW0fAQB576KkJoCKGhaRpCM9B1g8Z1e9i4N78/IoVCobjWDHMEm0RWYQDOF94gBycFvGjTVgxbbKJ0TtqaFpRzc845JBhwKx0tTSb7IS8/i5pAp4TNbmnXMa4wwH5quX/KuZFIxrjEIX7F3RqfCz38hHP8Fev5L2h43XHG07N18hgX+Qe3fRk7qOKuPLMZWJzkcxSzkXL2XOUnLjGJcIL/TpgL7gqs5GevCsa6Njj5n99GPW+nk0cBuMTXMChkI7+FN10E4er3AE4gXZhz9PMKq/m5JRHsOC8xKzSNsuqVRMODrp8q4OacnNJeaBSWVLF2x1tpWLPLSeeTA93wsvmWhymuaOD80Z8QDQ0CctoxDI+futU72LD7wbwStMdjYbeoQy6ktB29PKnsp5mMIad5n+CU0XWs0Hl8wEiJlJaTfNgCSJACEtNUN7oRSMbHGBt1HP4RgmBZHYYnvzvBcaSUmMk4ZiqO11+0rCzY47sCqcQYhjeA4fEtm7krFJMZ5SQjnJjyxT0ZmwRtfN997qeKcvZcqym6TI1gn/mzWSLp5FHi9C3epOaBhpFhQUsywLWuIrUQNPIzXOIb6XRoCQ7xq9zE56nmLgyC2GmrYTdPcYG/I8oVCmgiRWjWVnMvlaQY5gSfY4DXWclHCbIBHS9xeungR1yaZJXVKWALv5+H6wM4SarKidLGy7yHVXycOh7ATy0SixBnuMhX6eUFxq+9MnayeoZgN4lNilEsYljEMYliMUaKMMO8OamdRR8vk2QYgyIMCtEJoONHJ4CH0mnH0PCxgy8Q5gIhTmOT4ixfpJPHqechytmFjwokkhRhYrQzwkmGeZMw5/FRwUo+CstezAqNrbe/j9U33c1wbyujAx1Ew4MkE2NYpmMt1XQDr7+IorIaKurWUl67Go83MKsvb003aNqwj5oVW+hrP8tAZwtjoQGsVAJNN/AXllBavZKaps0UldXmmcBe0LzhFirr183r/fumKfoAUFRazeZ9757X3fJCV4RaKvR3tnDk+e+418cdD/9Xiisa8j5eSslwbyvHXvoeiViYsuoV7LznY8smBVs8Osqbz32b8GAXgaIydtzzCMXl9UrQKpYdFlFO8UfcyrcxKJ7GmmZzhe/Rzyvua3W8gwC50ywuNAU0TorelgzyOvU8lHU7eJjDnOZPWap5XzX8BGggzHkABjlIggF8VC2Jbd98EAiquIOVfJRLfAOQRLnCAX4eL6UYFGJjkmLEtewXsopb+DZn+NM8y69O0MjD2KS4zHfo4jG6+A90fAg0LJIZeWs1vGzmd6nlgbzWU8NgG3/CGb5AhAuc5c9p4Uvp4ggyXRhhYle2gBXs4SuuBXQ6UoR4gbczxuV0FTQTGxOwMrSFxOQov5VO76WhYSAw0PARZA1v4clpS/EKBIWs5jb+iYP8EkMcAiRhWmihJd1CY8J3/Kp4Jcpzrsu1Yp5i1iljW1RaTWFJFY3r9wISaY9XUkmnZNE0xu8W5/qFLYTAFwjSuG4Pjev2pC2ldrp/fdZ9CyGoX71jTnPJh8LiStZsv3fR+l/WSIlMW7iltOf0VXHl7GtERpytpr72swx2X6Bu1bX3O5oLfW2nGep2fA1TQzE6zx+ieN+7r/OsFIq5oNHNk7zOx9nM71HC5nSgjCTJMG18l5P8oSsSfFSynl8nW/BX5s2/U77UKZdpZqTSAkmKCCZj6YpQevpLd+I74GoBUswmCmkmkvbzvcTXqeR2anjrpFRNkiQjdPIoJ/k8MTrR8GMTZ6kh0KnhXvp4AXCClo7yaW7i8wSoR6ClJYiFRRybBD4qr1OC/uwIPGznC2j4aOXbWIzhFPYdcotsjLer5i528hcUs5kq7py1mDWJsoevUEAD5/lbUgxn5HsdJ0ADW/g9VvHxvMvf2pgU0Mgd/IAj/CZ9vIQkhXVV1gWBQSW3s5P/RSnbc/psx+khyWAeMxivHmdhuaI8TJyiGVPhCQTFbOZO/p1z/BWX+Xa6UEj2inQCgwKaaOYDeeeaXmwWLM/shJAUCB0Wq7LL5KpD16t6jOL6Y6UmlxZ0tu0XAiltErEwUkp8gWBerjCzxTKTMz5XKJYDGj5W8wtc4ut08R/08jyFrMJPDU51rVaidDJujdLwsZXPUcKWrF/gFmOc5H8QpZ0UYUzCmIxhEs0QNjYJ9vNhPBSnt1GL0turQYpYzRZ+f4ofr5dy1vIpjvFpJBYJBtjPhyhjN8VsQKATp4cRThClDYlFBbfSyMMc4zMsNQutQLCCD3ORrxHlCiC5wj/Sw08IshaDQiwS6e34EQLU8xZ+jLFExMc4AoGHYnbyRVbyMTp5lBGOkmAIgcBLGcVsopa3Ucnt6AQQCJp4nyukStia11gpRjEoZCt/QDMfopNHGeQACfoAjQIaqOJO6nkXBTTN0sItSTFKDW/lDv6Nfl6iiycIcRaTCAYFBNlAHW+nhnvQKcjZv0EhN/F5TObucughOKNPOzjnwE8VN/F51vIr9PMi/ewnwkU324RBIQHqKWEz5dxMKdvw3iiWWYXielHdtJHetpPYlklBcSVlNSsXpN9YZJj9j/1vpLS47aFfp7CkakH6nUx57Wo8vsK0z6yfmhVbFnwMhWKxkVg08jMUs54TfJ4Uw4Q4RYhTU9p6KGUrn2U1Pz+jZdAizhW+RzyPErJxuqdtV8hqNvHpKV/gAsEafokxWrnIV7FJYhFjgFcYmOQG4bTVqONBdvMVLOKc4o8zUostFQpYwW7+mkP8SrpE8Hgu1al+vjqFLDVBPo6TCM2ggpvT/tR22iI4vs2tTRF+haxkHZ+a1ThOAKB0A8+c/K72pJ1k3Z3N7JHYWOl3UkAtD1DLA+n3Yaffg+6+33zQ8bOKj89hLnNDpAV9Mx+mmQ8zcR5gLvO/ligxq1h2CCFoXLeXguJK4mMjlNWsoiCY3e8oX6SUjPS1EYsMoxueGfMmz4eSyiZufeenCA11ESyrpaSyUfnLKpYdEguTMGv5z1RwC+f5G/p4kQT9SEwEBj4qqeYu1vIpytmdc4tb4KGcPSQYmPO8CqifJtjLwSDAdv6MKu7iIl9lhOOYhBgvcWoQpJhNrOQjNPHBtM9mnHoeYozLlLA56xe5lzIquRWJjYY+rY/i9MeVp4+TaHgw8jwOHFFRx4PczY85z9/SyzPE6MEmgSPaDAwK8VNLDfflvWV+PXHWV896DpfLWOPXiVgGa341E9f4tTkPC8HyW2WFAtB0fV7Be9kY6DrHYlsvhBCUVDZSUtm4qOMoFIuLs1kv0qm29vL3pBghTh8WMXQC+KjGSyn5Wrs8FHP7pMwHc2WmL2AdL428hwYeIsEgSQawSaHhw0cFXsoQeNz5avjZlw5Ocl6bXpBXsJd7eCavOUymktu5h2dnfdxEe0GQjeziS5iMkaCfFGFApl0wSvBQks7dqm6aFTcmSswqFGksM8lQz1LOKalQLDUmJ00XeCmflx/d+HbzYiPSnpoBanNmVnDa5haYAm1OwVVzPS6zD2f9PQTztggrFDcSSszOk/Gt6IyiEML5YJl4PP9+x/tcyO3oxRjD3ZqXThytQLhV3pb6Vno0NEg0nE/U6NJkwi1Cuo/Hq9Mt3NpLJ9+ynCjzufBjKBQKhUKRP0rMzoHxpPehwQ4Guy8QGuoiEQ1jWSmE0PB4AwSKSgmW11Na1UywtAY9j8T4Mp2yanSwg/72s4wOdpKIhUFKPL4AwbI6qho3UlazEt3w5uzPMlN0XTqClUpgeAPUr96J0DSklISHuui9cpKRgQ6ScSewweMrIFhWR3XjRspqVs2qEIGzJkkGuy/Q13aayEgvppnE8PgIltVS07yF8to1Tp9a/lYIKSVDPRcJD2UPCBGaQf2aHXi8+UfpynQlNjMZJz42SmS0l57LJ9zMAtK26LxwGF8ge7LsksomSqtXzLhGkZE+Bjpncl0QVDVtpLC4Mu+5T/dezGScwZ6LDHSeIzLSh5mMgRD4C4oprWqmumlTOgezlvOcxiLD9LadAikpKqujos4pPWqZKYZ6LtLXfobISA+pZBxN0/EF0mM0b3aq4eWV51mhUCgUioVBidlZYlsWvW0nuXjsOUYHOrDt6SuAjaPpBqVVK9j7wC/i8RVkbSelJDTYydlDTzDQeQ7bmppqqq/9DJdOvkhZ9Qo27HkHFXVrXKvYdFhmkrMHHyceHcXrL6Syfi2a7qHl0BN0nH8DM5WYOkbbaS6deIGqhvVsufW9FJZU5SXCw0NdnHzthwx1X8ywUgMMdJ7j8ulXqWrYwJZb34PhCTjW2jwDrDovvsmV069m/b3h8VHZsG5WYrbr0hEun3qZaHiIZHxsynrbtsW5N388Yx9rd7yN0uoVM7YZ6W/jxKv/MmObPW/7+TmLWdu26Gk9zrkjTxMZ7pmy9gDdrcc4f+RpaldtZ8PuBwkUlc94TsPDPZx49QcgJdVNm6moXU1ouJtTr/2QoZ6LSHu6MY5y/uhPWLHxFtbtelBVNVMoFArFNUOJ2TwZtzyePfg4V87sn17ECpE2wE2INNsyCRSVYnhmqF0uJb1XTnL85X92LLGT+tM0xzpqW6ZTYMC2GOq5xBtP/wOb9z1M84Zb8rJ0ppJxQkPdXDnzKj2XT7ivO9ZXDdu2JgoZ2BZ97WeIj32DvQ9+csbSwFJKRgc6OPzM14mGhyb9RriWXdsy032eJhoeZO2OtyKEhpRTkzFPh8dbgNdfiGWaSNvElnbeQjgbocFOhnouzauPfNANL75AEMtMYdsm0ranFZxzwbJSnH/zKS4efz6jtLMQGppuIKVMvy4xUwk6zh1kpO8KO+95hJLKprzE5lion+G+Kxx5/v9OuGBMc12CU9754vEXSCXj3HT7+xG6+nhRKBQKxeKjvm3yRNoWpw88mrYQpn0FNY2SyiaqGzcSLK/D4w1g2xbxyAgjAx0M97YSjQzRuO5m1290Sr+TSrMm4xHAsTTWr95J7aptFBSVg9BIxEL0d7TQce4g8egoZjLOqdd+iOHxU79mZ27rqW1x6rUfERnpRQiNivp1NK2/mWBZHbrhJZWMMdh9gcunXiEWcURpaKiLC0efYevt7yNbFGwqEeX4K9+fJGQFFfVrWbnpdndbOxEN0dd+mvZzB4mM9HLmwKOzEnTrdt7Pqq13YqWSpJJxzGSMrktHuHJmf959XE3D2t2UVGRmE+jvPEfbWadPTTfYtPfd+LOUKwYoKqvJOU5N82bK3/c7mGYSM+XMfaS/fdZrcDVS2rSeeJELR591+wkUldO8YR8V9evwBYqwbZtoaICuS0fouXwcy0wRGenlzee+zd4HP0lhcW6reywyzJEXvkM0POhcl2t2UbvyJue61DQSY6N0tx6j/dzBtIuGpP3cQWqat1CzYquyzioUiutKIatIpAtuFLKChczooOOnmE3YJCFd/EFxfVBiNg+klHS1HqXt7GuMC1lfIMjmfQ9Tu2o7uuHJ+NKWUtKMs80fGekjWFaT9Ut9XJSOC1mvv4gdd3+E6sZNGb6HwbIaKurW0rh2N28+/38JDXZimUlOH/h3Squb89qmjoz0IDSN9TsfYM32e9H0zHmP+1Ye/PHfu4K2u/U463bej7+wZNp1aWt5ndH+Nve1pg172XrrezN8hINlNVTUr6V21TbefPZbxCLDOec6jhACw+PF8Hivei9Tk4LPhuLyeorL6zNeSyVjtJ0dH1dz/ExLq+c1jqYb+AqC+Ca9phveSVb82SOlZKS/jfNHf+IK2bLqley85xEKiiszzmmwrJbq5s30tN7E8Zf/mVQyxthoP6dff5Td930c3fBkGwZwdhaioQG8gSJ23v0IVY0bmBwkWFRSTUXdWkqrmjn+8vfT1meLtpbXqWnekvUmTqFQKBYbgc4e/g+45VwzSx7PlyDreCsvTRrPO0NrxWKytIo0L1HMVJyLx55zt+ENj4/tb/kIDev2YHimBmKJtAg1PD5Kq5oc8TINUkq6Lx9jZKB9/EA27Hk71U2bEdrUQB0hBEVltWy784MYaf/Q+NgIrSdfyjvBf+3KbazZcd+0AWRCCIJldazaeqf7WiIeJjTUlXVd2lsOuM8LS6rYePND0wa7CSEoq17Jht1vVwJnnkhpc/H485hjpuKKAAAgAElEQVRJp16811/Itjs/OEXIgrPumqZTt3oHa3e+jfEP8r720wx0ncvruhFCsHHPO6lq3DglgEwIgdA0GtbupjwdKAYwOtBBKhldgHerWFo4qbMEevr/6m9ZsXRxkqr50Amkf3wLes0KtEl9B9CWSYGBGxElZnMgpWS4pzUjmr5x3c1UN22c9xaqtC06zr/h+n8WlVTTsGb3jP0KISitaqZ2UgnU7tZjmb62WdB0g9Vb34KmZTfICyGobNiApqctdlIyFuqftm1osJOx0ESlnoa1u/EFglnnL4SgZsVWZ4taMWdi4SEGOlvc53WrdhAsr8953TSt30dB0Fl7aVvOjUgeYrawpMbJhDFT/5pOdeNG93kqESURm3s9ccXSJMg67uZJ7uEZ7uEZqrn7ek9JoVAolJjNh/7OFnc7V9M9NK7fy0JsVcSjIUKDE1bPqsYNGN7sgWITCGpXbnPnEI+OMtrfntPKVhCsoLiiIacI9/mLMqzJqURsShspJcN9V1xrtdB0qho25Ozb4yugpLJpxjaK7Djrftk9J0Jo1K68Ka9jvf5CKurXus+Hey+TTOQWnJUN63Jel0IIAsGJmxTbtrCmyZahWN4YFFHJbVRxB1Xcjo+5p5RTKBSKhUKJ2RxIaTM62Ok+DxSWUlSa3Qd2NoyF+p18oGlKq5rzOm7cHcAVnFJOuCrMQFFpTVaXh4z+r3JxmC4VEzgpnMbxeP0UBCty9g2OD61i7owOdLiPDY+PotLqvK/HyddYIh4hGspdJOLqQLlsaFrmFttCZW1QKBQKhWImlJjNgW2ZJMZG3eeBYBlGHoIwH2KRkUmVmsSMKbCuxuMrwPBOhBXlU7lquiCurOSahpQkoiH3qeENYHh8MxyQ7lYIfAWzmIdiCpNToBneAB5v9vzFkxFCECgqZ/zkStsiNjaS6yj8hSX5XZfKfVKhUCgU1wElZnNg2xZmuioUOCJyoQKYJltlEVpeYnAcTTfQ9YlI9OlcAa4mH6tsvsh07tKJvj0ILT/n99m8T8VVSImZirtPZ7PuwJRiBrmuGyFEzowHCoVCoVBcT5SYzYWUGUEyM1Xcmg9i0r/5HzS5fX5R6QvLpHWZxdxV7tF5MvlUCzGry2bq2ueTBUOdL4VCoVAsXZSYzYHQnGpK4zhBLfOrPjWOPslCKaXEspIztM5E2lZG1SfDuNbWTjGR8QCc/KJ5pgez7fwqfymmQQj0STl3bTOV1ad5OiwziZx0/erKSq5QKBSKZY4SsznQdI/jWpAmHh3FthZGjAUKS1zrqpQ2iWju9FrjmMl4xnbzrPxhFwAhBF5/4aT5JLDzEONSSlIJlX90PgSKytzHZirzOpgJKSXxsVF3p0EIbcYKZwqFQqFQLAeUmM2BpukUllS5z6PhIRKx0AxH5E9hcVWGRTU02JmXdVNKyVhoACs1IR6LK+pnOGJxKCyeWJdUMko8TzE+OTetYvYUVzS4j1PJOLFJAWG5mFwAw/D43LyzCoVCcaMgkYzRToizGT8JcgdKX08kEpMIKcIZO2iK3CgxmwcVdRO5OVOJKH1tZ/LeUp8Jf2EpwbJa9/lA57kM14GZ6O8466Y+Mrx+SqtWXFNfVKd4QxPj/pSWmWK473LOdbFti9E80ohdF+bkT3ptEUJQXrPKDaKTtpW+FnLP1TKTDHSdd58Hy2pVZgmFQnEDYvMmv8HT7M34aeVb13tiMxKnh+d5gOe4lyhtuQ9QuCgxmwMhBFWNG/H6i9zXLp18kfjYyLwFraYb1K/d5T4fHexgsOv8jP06W8UjdF064r5WXrM67xyvC0lp9Qp8gYl16Tx/CNtKZW0vpSQ81JVRKGIpMdlKbtsWyfjSdIcoLK6kvHaidGzHhcMkYqGc101/x9mM3MB1a3ZOyQ2rUCgUyx+NTfw2u/grtvIHFLEWixiS/IxF88HGpJ/9xOnJ3fgq4vQwwnFGOUWMpfk9uVRRYjYPCoJlNK7b4z4fG+3j2IvfJRYZyikgHFE0Nm07IQQNa3ZRlLbO2pbJ6QOPEg0NTNteSicd1pkDjxGLDAOOT+/qm96C0K79qfQXFFOzYqv7fKjnEu0tB6ZNli/TKaXOHX4Ky8w/0O1aUhAsd9NcSduit+3kgljgFxqh6azedo+bai0aGuDswced4K4s101kpI8zBx93K7YVllTTsHqXyiyhUChuOASCSm5jNZ9gA/+NUvKrkrgQRGnjFd5LHy/N+tggG9jC/89WPksp2xZhdjcuRu4mChCs2X4vA13nCaWrgfV3trD/8f9N84ZbqGrciL+wBF03kLYj2mKRYUYG2hnoPIfh8bH7vk9Mm5/W6y9i8953cfi5b2OlEoSHuzn446+ybtf9VDasdxLiC2cbPzTQwYVjz9Lf0eIe37zxFirq110nUSJYfdPd9F45SSIWRkqb0wceIxGL0LRhL75AEITANlOEhrq4cPQZ+trPohvevAStK8ykREobKW3sdBaH1KQcvVJKErEwHm8ATdPTFcx0Z03S65LP+hSV1lBYXEFkpA+Ay6deIVBURt2qHW45V2nb2FYKMxXH8PgzguCyz10ipYVt20jbJBGLZLRNJaIkYhE0XXfmLzQnBVyWuQshqKhbw4rNt3Pp+AuApP38GyTiEdZsu4/iinpH6KaD7fo7znLuyNNE077KuuFl080P4Z1kVVcoFArF/JBIhjhEkuE5+bzqBNjE7wKzS3epUGI2L4QQ+ALF7Lz7Ed587tuEh7sBiIWHaDn0BOfffArd40PTDaS0scyUkzIpbaGsrF83Y9/VzZvZvO9hTr/+IywzSWS0jyMv/CNefxG+QBAhNFKJMeLRkGtZA0Hd6u1s3PPORct9mwshBEWlNWzc+xAnXvkBtpXCMhOce/PHtJ56KV05SieVGCMRDWPbJl5/IZv2votTr/0oZxR+KhHl5P5/JR4dxUwlsVJJLDOZFpMTBRssM8nBJ/8O3fA6xSQML7rHi+HxESyrY/O+dyP03Je64fWzcstdnNr/b0hpY6binHj1B5w/8jQ+f9C9qXAyCCTYvO9hVmy6bfrOpOTMG48zOtCOmUq4c7eslGNBnZSe7ORrP+TsG0+gGQa6np674cUXCLL1tvfimybjgBAaG3Y9SDIWoePCIZCSvrbTDHS04CsoxuMrTGfICJGMjzHu/6vpHjbe/E5qV96krLIKheKaILGxiCPQ0fCmX7MwCWOTQieAQQGgTSviJHKa9oU4km9hP8fGRahNEpMIEgsNLwaFCIys8xs/poefADY2CUwyXdU0vGhXya7xtbkaHT8ix+b5xLhxTMYAMChCw5dlnlPPg02cFBEEYsZjlzpKzOaJEIJgeR17H/wkLYeeoLv1mGtdtG0Le4Z0U7lyeQqhsWLTrfgCQc4cfIyx0T6QkmQsTDI2NUOAxxtgxeY7WLvjrVMqOl1rhBA0rtuLlJKzBx8nGXesjqlEdEoKrkCwnJtufz8VtWu4cOw5zNGZxaxtmfS1n8krlVcqGcuw1o6TjEXydhUQQtC84RZi4UFaT73sBOOl01nFJ5U0Hmc6dwr3d0iGui8y3Hc557hWKpHOX5yJ4fFjmkmmu3qEEOgeHzfd8QEKiitpPfkiqUQU27aIRYZdN5TJFBRXsvHmd1K/asd1cUtRKBQ/ncTo4hXeTyW3sJO/YJADnOUvGOYoFnE8lFDJLeziy3jIvHm3SdLJ41zia4Q4i0UcL6VUcQfr+Q2K2bxg4svGZIg3aOdfGeBVYnRjk8IgQBFrWMEjNPMBdPzuMRKLy3yHPl4ixBlGOQXACT7LGb4wqXfBTfwBTbw/Y8wIl9jPhzIErYcgt/MvFNCYda4SSYI+LvBVunicGI6RrYBGmvgAq/k5PJRkrE2MTl7h/VSwj238ERf5Gm18L+2fKyigkRV8lNX8HDqBZSVqlZidBU5t+zK2v+XDrNpyJ12txxjqvkgsMoxpJpHSRggNXffgDRQRLKuhsmED1Y2bcpbAFUKjduVNlNWspPvSEbovn2BstA8z6RRp0A0v/sJSKhvW07h2N8HyupwWWU3TqWrcQDItBotKa/J6n5puUN24yRWHRaXVOcbRaN5wC+U1K7ly5jUGOluIR0PYtoWuG/gLS6lp3kLzxlvTOVIl9Wt2EhrsRNN0PF7/9P3qBtVNmzKssLOlIFg+K7Gv6QYb976LqsaNtLUcYLS/nWRiDNu20qVdvXj9RRSVVFFSkf2DBgTldWvmtZWvG94ZSxALITA8Ptbvup+6VdvoOPcGA13niEVGnEA8ITA8fopKq6ldsZW61TvTlv6Z18MbKKK2eSsSOSWf8Ez4AhM+1EJoGfmZFQrFTy82KcKcR8dPF0/wBp9EIglQh0ERMboIc8G1Fo5jkeAEf8B5/hoNH8VsxKCIKG1c4pt082P28U2quXtBhJdJmIN8kgjn8VFFISvQKSRGB328SB8vEaWdzfyuazWVWPTzKmO0ouFDw4MFGBTjpyqjf53AlDE9BKnmLcTpIUYPA+zHoACbGYKpkUS4yGs8wjBv4qeGIOuQ2IQ5xzF+lx6eZh/fwE+Nuzbj58FkjMP8Gu38kCJWU8wmEvQzzFGGeZMY7Wzjj4HlEyAsclitll70yxJCSom0LcxUAjOVmBCzhhfD43UrZM3WcirTPqJuYQSJs/Xs9aNpRt79ZTu3uY7PFqyW75iWmSSVjGFbJrruweMLoOket498+1/I4Ku5nAOQWKkkqWQc2zZndW6vx9yllK4/sWWmXLFreP1pP9y5Xzf5HDuf62aZshhvTn3mKm44IrTyFHvQMPBQTBMfYC2/jJ9qJJI4PSQZoYydrvCSSC7zLd7gUxSzkb18lRJuQqBjEqaFL3OGPyPIeu7h2SnCcRyJ5ACf4Ar/xDb+mE18Ous8JZI2vodOgEpuwUs5oGExRgt/ySn+mAB13M8B/NS4x0is9GOTl3iIPl7kFr5FEx/M6F+gTXEdmOxbG6WDp9mDxOR+DlHEqmnnaRLlVT5ID0/TxAfYzv8kQF26j3aO8Ft08Tgr+Ah7+Xs0POnzcImn2INJBA8l7OJLNPIeNPzYxLnI1znG7+AhyFt5jSBrph3/OpL1M1dZZueBEAKhG3h1I2/rVd79Ch2vv3Be/c5VSMxHgLgCagbXinz7v97uEyAwvH43+Gv2x19bHMuxB93w5G6co59reZxCofjpIMkQDTzMTXzeFVgARaye0tYkwjn+GoFgO39KGbtdoeuljI38Fp08xign6ebHrOSReVtnBYJmPuQ+HkejhHV8ila+SZxeorS7YlakvU0B7PQrDvoU/9hsY0481sh1jyyRDPAqvTxHISvYyZ/jp87tp5BV7OKLDHGIDn7IOj5FBXun9LKaX2AFH3HFtUYRq/l5WvkGo5xilBNLUcxmRTnOKRQKhUKhWHQEHlbxs674m4kwLYRoIUA95exGYmFjuj86gXT6Kskgry3gHJ3/ZMZ/NjoFeKlAYk0bsHUt6eZpJCmquSdDyIIz/wKaqeIOLKL08PSUzAoaHhp5eIqV2KCAApoASZL8K0suBZRlVqFQKBQKxaJjUEgBTXlZUMNcwCZBgn5e4X3TRvaHuQBAnH4cD5357w7ZaSeGfl5mlNMk6MckikWMSHq86+sNZBPGSc9ZzMYsbQTFbAJglNNTfqtTSICGKa87R6ZzrZM9wHkposSsQqFQKBSKRcfxGc1PdqQIAU7QUoRLWdv5qcPL/MtySyQphjnG79HGP2MRx08NfmrxEETHlzNV1rVAIjPScGW7MTBwgo9NIlwt9J3UXPNzR1tqKDGrUCgUCoViSeHknYUStnAn/z6j+HKyIMzXKis5yR9xia8RZB07+AIV7MMgiEDHJsEz3EloGkvntcTx0HViaSzGnKwz07x3yxW82bLK3FgxDkrMKhQKhUKhWFIUsgqBhzj9aHjwUbmo4yUZoZMfIdDYxh9TxzsyRKJFFIvcOc8XH40g6+nmSUKcy9JGEk7/Lsh6bjThOh3X32auUCgUCoVCMYkSNlPEauJ0081TWcvDjodozReLGCZjCDxpIT0hACWSUU6liwtkRyBcC7JFbEHmNR11PICGhz5eIMFAxjjjqc76eRUNLzXctyhzWGooMatQKBQKhWJJ4aGU9fwaIDjO79HFY255WRsTkzHCnOMK3037hTpcnYVAYrliz3lspV/PFMEegviowiZBPy+7xzmFCM5zlN/JI4uBRhFrAejicUzCbh82ppuPNvtcTXDnak47V4Ggkjuo5l4iXOQE/50kg+l2zuyP83tEaaeGe6lg77Kq5DVXlJuBQqFQKBSKJYVAsIpPEOES5/kK+/kwRazBTy0SiwR9ROnEoIBa7sND0D32Cv9ID0+TIkyKMKF0idmL/AO9PI9BER6KaeQ9NPIeAAyCrOLjnOCznOCz9PMSBTQSpYsBXqGQldRyP708M+O8V/ARrvBdOnmM53krQdZjkyTJEJv4DLWTLKUmEU7yeWJ0kiJMkqF04JvkNT6GjwoMgvioYAu/7xZG0PGziy+znw9zia/Tx/OUsg2JZIRjjHGZUraxky+iMfs86csRJWYVCoVCoVAsGhpeStmKwJhVFL2Gj238EZXcxkW+yggniNKOkyk1SDm7aeRn8FCacdwYlxlNC1iAAPUEqAcgySBJBgEoZ7fbRiBYz68hEFziG/TwLCDxUk4D72Ezn2GQ10nQ72YKuBqBoIK97OPrnOHPCdNCmPNpn98qN+3VODYpRjjuzgcc9woASYo4PUAPUdqxiGWMU8Qa7uRHtPBluniMHp5FAD5q2MB/ZT2/QYCGq4o/eClhKxqerAUdCllFKdvS1c+WD6qcrUKhUMwNVc5WocgDZxM9CTiCarbb3jKdkCrJMCZhxsWshxIExhT/VmeLPneeVDFNlS7n6DAJBnHEbGlaLAtIuwtoeGZM0yXTG/4JBrCIo+PDQykGhRnHTV6XXGQbU2KTIkSSYUDgoyydgWG6tjOfh8lrp2FMEd9LgKwXjhKzCoVCMTeUmFUoFIprR9bPXBUAplAoFAqFQqFYtigxq1AoFAqFQqFYtigxq1AoFAqFQqFYtqhsBgqFQqFQLHHyTcD/05BTVKG4GiVmFQqFQqFY4sTp5TR/MmP0ew330cT7F0zQSiz62c8QhwiyllruR8e3IH0rsuNU8eqli8exSVHP2ylghbpRmQElZhUKhUKhWOKkGKWVb2MxlrWNQSFNvH/BxhzkIC/zHkxCCDzczN+xkkeUqFpkJCkO8Sm6eAyAK9zK3TyJQeF1ntnSRfnMKhQKhUKxxDEooIK9FLOZAprxUoG+yOJmiDcwCQGOwBrglUUdT+GQIsQwh93no5wiTt91nNHSR1lmFQqFQqFY4gRo5C38BzYpbBJYxIjRxUs8TGKRhI5TBUownv7YR/WijLOckEgGeY1BDlDKdqq5Z8Et1RrejCpjHoowKFjQMW40lGVWoVAoFIoljkCg4cGgAC9lBKiniNWzKg87W+p4kAYeJkAjtTzAGn5x0cZaPtic5Ysc5dN08KNFGcEgyEZ+myLWUcQ6tvBZfFQtylg3Csoyq1AoFAqFYgpeKriNfyRFBIPCOZWivdFIMswwby7qGALBKj5OE+8FSJen/ele91woMatQKBQKhWIKAoHAi4/y6z2VJcMop4jRs+jjCAQeihd9nBsF5WagUCgUCoVCkQOJpI+XkKSu91QUV6Ess0sU27awUgn3ue7xoWn6dZzRckEiJUhpI4QARPr/izyqlFipBFLaAAhNRze8sx5bSunMHQHi2sxdoVAsDhKJxCTJMAkGMAkjkej48VCKj7J0oI923beRJTYSM2tpBufT1EDkaQMb749Jx0kkSQYZ4zICD0WsxKDYfe8SiU2cCK2YRPBTTYCG9PG512d8vVOMkmCAFCEkFjp+vJTjowKdAsZtzvkyXrDCYow+np/0uoU9g7B11swz41jjc56pKIYz2/zWIBfOODYpwuk1GsEmhYYHD8V4KcdDcc55LzWUmF2iDHZf4NiL30VKidA09rz15ymtarre01ryWGaK80d+wkDnOQqKK9h480MEisoWXRRK2+Lwc98iNNgFQHXTJrbd+Z9m14e0aT/3Bu0tB9A9XtbvvJ+ymlVK0CoUywyJJEYH7fwrXTxOiBZSjGJjAhKBjo4fHxUUs4lq7qGR91BA83UTEL08y3E+CzPI2e38CTXcm1d/Ic7yBp/ExmQbf0gN99HG9znJ54jSDmgEWcs2/og63g4IRjnFUX6TAQ5gk8RDEbXcz3b+NC1qp67NuBgM00IXT9LLM4RoIclIusDE+HoHCFBHFXeyik9Qzi4E2Q1EEosIrYQ5zyinGOU4Ic4ywkm3TQc/ZIhDWfvwUs6t/OOMbho2CV7nE4zRmrVNKTexh79FzEOyOasUoYenaeNfGOZNEvRjk0RiI9DQ8OKhhEJWUcltNPAuKtib9w3M9USJ2SWKbZrEIiOARAgN2zav95SWBYPdF7lw9BmktBnuu4yvoJjN+x6+JmMnYhHiYyMAJOORWR8fGxvlzIFH3WOtVJJb3/kphK7+TBWK5YJFgla+xRn+NC3apiIxsUmQYpQIl+jiSfxU00zzNZ7tBE5g0+GcbfLFIsowR7BJMsDrgOAQv+rmrQUY5SQH+UXu4nH8VPMajxDiVMZ4bfwzKUa5jX+eNj1Vgn4O8+v08BNMwtPOZWK9Rwhxhit8j818hg38l6zZIGL08DxvI0YXYE/bJkE/CfqzroGP6pwuCRKbEKcIcTZrGw0v2W8yciOxGeA1jvEZhjiIxJqmDdgkMYkQo5MBXiFKGxXcPOdxryXqW1JxQxEfG3G3+gFikfw/fK83qUQUMxV3nydiIWzbQlsAMSul7Vj5lfuCQrFoWMQ5zu9znr/JEDECDz4q8FKGwMAkTIJBTCKApIAGarj3um7rBtnAGn6ZJIMkGSLJCAkGiNI2774HeZ1efoJAp4b7MIkwxGEkJgkGaOHLeCklxGmKWE2QDUS4QJjzAPTwLP28TB0PTOlbp4AxrkwSshpeyiigET+1aHhIMEiYcyQZBMAkxEk+RyErspb/FQh8VKBNkkkSmxhdrvuEQRAfFVnft4+KGa2/zmwNVvIIIc6SYIgkw6QYZYzLWERnPDYfJDYd/JBD/Kr7/sdH9lKCl0p0/NgkSDBIilEkJgKDFXyI5RJapcSs4oaiqLQGTfdgWykQgrLqFdd7SnnjDxTjCxQTiwwBUFLRiKbPP4eklJK2lgO0n32d6qZNrNs19QtBoVDMD4nNeb7Ceb7iih2dApp4P6v4GMVswqAIgYZFkgR9DHOUbp6kgKbrnke0lG3s5q8BZ4tdYjHEIV7g/vR2/dzp5Tn8VHMXP6KcvdikOMFnOcdfAtDJowA08G728BV8VBKnl5d4mBGOIknRw9PUcv8U4WlQyFo+yQn+gBruo5GfoZxd+KhMWzQFEpMoHZzlL7jE111LbQtfop53Tmvx9VPLvTzPZItoijDPcTdjXAagmQ+ygz+DrDchImcJWoGHjXw6/Uym/XATvMqH6OGpHCs7MxLJEG9wmF+bJGQ1KrmNdfwKFdyCl3I0DGwsUowQpoUeniVMC1XcsWz8ZpWYVdxQlFavYPtdH2Kg6zzFFfU0b7hl2VghvYEidt7zUTovvInXX8jKzXcsyNyltOm+dJThvssUlqjE2wrFQiORhDjDGf7cFbJeytjD39DAexDoGaJAJ4CXEopYSxPvS2/7Xt/Pqcnzc3wzDXQCC9K3xGQlH6OCW9PFHwzW8Z9p5dukGMYiiocSbuLz+KhGIPBTRzMfZISjgOOSILGm+I0KBM38J2q5nwD1TBfcJfBSyCp28r+I00dnutjBKKeIcIlStk6zHhoegtO8G23SI29GANtcyDzWWR3nepl/wLdFjOP8PgkG0r3rrOfX2cJ/T99YTb4mnUpjARqo5l4kKcQiFuRYaJSYVdxQaJpOw9rdNKzdDXBNhex8RxJCUF67hvLaNRmvzZdkPEJosHPe/SgUiuxc5Guu9Uugs5XP0ch7ZwyeEel49+UQYDMfBDrV3J0hngI0UMRKhtN+uCVsIcg6t41AUMJmHPFoE6cvHXU/VbYYFOZhARVo+FnDL9DFY0gsLGKMZRGzyx2JpJ9X6OdV97U63sFWPj9jadyJ9fcu+hwXkhv7L0jxU4lI+4Rea4vs3N3zJ5g894WxykpCg51zCkhTKBT5kWKUbp50nxezmZV89IYXqfmi46eAzGw8Gl781LjPi9k4xRLopdxdQ5PovPO7CgRFrEun5wKQJBmZV59LmQ7+zV0znQCb+O0Fs7YvNdRfmkJxgzPQdR4pF0JqKxSK6YhwMSNzQT3vwFDVm1x0AtNWs9InWQgDNEz5vTYp16mTi3X6rAKzm4vvqqCuqZH9NwIWMQY56D4vZiOlbF82PrCzRbkZLDOklMTHRhjuu0JkpJdUIoqmG/gLSiipbKS4vB7d45vRqielJBYeIpWMAaAbHgpLqnNaAqWURMODmMl4+jgvhSVVGccl42NOBgEhKCqpRtMNzGSc3raThAa78PgKqGrcSEllA0I491K2bTHce5mBznPYVopgWR1VTRvx+otmnFMqGSMaGsz6e4BAURle/8zbT7nes22lGB3sZKSvzU295S8qpaxqBcUV9W6Q1mw+ImzLJDLSO6PI9PgKZp0jd7w/aVukkjFi4WH62ydSviQTY4QGO7LOVgiRDqLL/GhIxiPpVHEOhSVVGB7frOYVDQ1gpguBaLpBUWm1ew0oFMuZCBcmBUkJytl7w4qGuaDhSwdjXc3EGnmm9T0VzOaTdbwggEmEOAMkGSDJKBax9E+cOD1YJHL2tdxJMpxOK+ZQyvYb1ioLSswuD4RA0wyS8QgXjj5Lx/k3SMSclC4ZzTSd4rI61u58G7Urt6Fp2YXCmTcep7v1GAAllY3c/q7fyCuf6enX/53eNicPYFn1Sm59568i9AlH9b72Mxx78WkjE7gAACAASURBVLsITePWd/4qgWAZR577DgNd5935Xjj6DBtufgerttyJtG1aDj/JpRMvOhkI0gTL69h1z8cIltdnFXNDPa0c+snXsgpCAWy760M0rd+b831Nh5SSwe6LtBx+gpG+K9hWZq5fTTcoq17Jhj1vp6xmFfosxF0iFua1//gb94ZiOpo37OOmOz6Yd5+WmaL93EFG+q8QGe4lGhkilYhmzLuv7Qz9HS1Z+/D4CrjrZ36TQFFZxutjowO8/uT/wTJTCCHYcut7WbHptryFdioZ4+BTf89YyAlEqFu5jV33/uz1jnlRKBaEGD2Mf75peCig/vpOaInhVK+aOaBpPsFG45XFuniSLh5jhOPE6cMinra8ShbGEWz5kGI0I7VXIcsns89cUGJ2GaBpGmYqzuFnvpkWhdMjbYvRwQ6OPP8dNu97Nyu33JHV8iVtC2lb7uN8mXpc5geElNIp8GBDaKiLtrOvMdB1LqONmYrT8sYTlFWtIDzczcXjz0+ZQ3iom5Ov/ZB9D34S3cjuiC6ljbSn33qS6d/PBSklnRcOc/LVH2QVnLZlMth9gTee+ge23v5+PN7873ol0ildO8Pa21neVzbMZJxzh58kEZs+cbg78gxjSmv6sorFFQ0Ulzcw1HMRCXScf4Om9XvRjdxfQFJKhnouMTba75wPIahbvQOWSZYJhSIXJmPuY6faVPYAm59GRB4W1rl+GtiYdPAjTvK5dF7ayZ+bThlYx11BTxdI0NKBeje2uJ0Q8g7GtJkZbhyUmF0GSAkth55kqOciCEFJRQNVjZsoKqlCaBpjoUF6204yOtAB6W3xlkNPUFazkpLKpuuWmqr93EEiwz3Urd5BddNmIiO9XD71CpaZwEzFuXDsWUJDXXi8fpo33kZRaTW9V07S3XockAz3tDI60El57app+y+vWckt7/gUqUSUZHyMZHyMsVA/HefemLOIhbT46m3l5P5/dYWsEBrltaupbt6Mv6AEM5VgdKCDvrZTxKOjnNz/r/j8RXmP4Q8Us/fBXyIZi5BMREnFx4hHQ7S3vD6jtXYmdMND04Z9rhuI815sui4dJZVw7tALS6qpalg/Qx9eDI9/yuuabtC0YR9DPZcAyehAOyP9bZTXrs7j+pJ0XTjsnpOCYAWV9euWTco0hSIXkwO9nPKq8/ftVORGYtPKNzjCb7lWSKdkwi5quJdStlNAAwbF6PjR8BKnm+d5W7pgxY3L1TcQN6pv8DhKzC4DbCvFUM9FNN3Dht0PsnLzHRl+sVL+P/beO0yO67zTfU917sk5Z2QQABFIECTBAIqSSCvRoiRKVrAsa71y2PXu42zvXq+vw9X1elf2PmtdryUrUMESrUyRFMUoRgAkCCLnyTn19Eznqjr3j+qumcZ0msEgDHBePENOVVedOudUT/evvvMFSeeWezj9xhN0H/sFIEnEI/SefI2te1tyN34ZmR7tpqlrJ9vu/mjSB9MqzXvu8M8BGOk5iuZwsGPfp6hv3wJAfdsWQsEJgpODmKbO9Gg3FXXtGYWPy+OnunGtvS2lZC4wxtD5Qxj68r9MDD3BqYOP2wJQczjZcMt7aN90J5rDiRDCdm0Iz05y9OXHGB84ZR9fCJrDSWXdvEiXUmLoccb6jy9fzLo8bLjlPWn7rPfOBbtvFbVt3HTHw0tuWwhBXesmfCUVRGanMA2dgbMHqazvzHtuNDTD+IIVhfr2Lbg8ynKluH5wU27/LtFJXMcR8tcSs5zlKP+XLWQ91LKDL9DEe9DwZvRbNolxI/g3OSlCw5UcL3au2esVFX2xiujccjedW/fhdHvTxJ0QAqfLw/qd76a0ssHePzl8DiNx9RzdhabRunGPLQCF0Gjs2o7DrmolKatuobZ1o52Kyun2UtWwxm5jNjBa+PVWKJXV5PA5pkd77H3Na2+h46a7cDhd9jVS/fWXVLHtrkfwl2QvaVgIK9H3i9N65Woz07H5znN7i2nsuNneHu07bgfEZUNKyVj/SeJJ1weH00VT1w5llVVcV1hpp6yvU4nOLGczuusoVg6JZIAfEGM8uUdjC39BCw/jwJc1AM8kcUNYzt1UpGWQmOU0XMfjVmJ2leArrqDjpruyBnVZQtBHbetme18sHCQWDWU8/krgcvspvihLgq+oHNeC7AKV9Z1oWvoCgb90XhhaIujKfikMdx+2/UqdLk9y3jMHLwgh8BaV07zMILPVRtPaXXYWg1g4yEjvsZwZGaRpMHT+kL1dVtNKyYIHLoXieqCE9biYdzMa5TmuZ+FwbSCZZv6zxUM1jTyYM4uERBJhEJNo1mOuF1yUU8x8AZ5p3iJG7uw/qxklZlcJVY1r8fhy5y1MpVVKYZo6+jKXrFcCl8eP053uf+lwutJSOhVflNpLCJEWSGXoiSuqZQ09nmaVLalooKisNuc5Qghqmtcjsgje6wUhBCUV9WmW84GzB9OyUFzMXGCM6fE+e7upa+eihxeFYrXjp4WyBVWkRnhGWWcvOzLN79VFCU7yxy2M8PNL8B9Nr9Y2n47t2kPgpJ777e0wgwzy4+v2PanE7CqhorawtBpO13zkv5TykgKhLhWn25vBoinS9rkzBE0tzMBwpfsfj84RDQft7dKqpqxW2YX4S6qWlM1gtSKERsv63fY9Ck5Y+XczWWellAz3HLFdXTy+EupaNykXA8V1h4abdj5B6is1zgRH+FN0ggWJB5n8p1gKGq4FvsoJZtHJnslFIglykh6+eQlXdOOmzN62HlgurSrZ5UIgaOaXcVOZ3GNygr9hhmPX5XtSidlVgcBXXLnqRIDD4cqTfknkTLt1NYhHQxj6/NN2ob6wTpcHl+dGELOC6sZ1FJVb1mrT1Ok/e5BM5nNDjzOSzGUMUNO8AW9R2aLjFIrVjkDQwsNUcYu9b5Cf8Dq/ygzHMUlPeZcSCiYJIgwzyI/TKogpCqOSHfbvMSbo5/uL/GFTMz3DMfbza0QZXvb1NNyUsdXenuKNpKV3sdFFLvh3tShhLV38OqmAtzB9vMpHGOJxdCJp/ZML3pVxAkzwCuO8tGoErVrvWwWkArxWG0LTclfBESsT+LSS6PFomjXY5fEX1EehOa45YX65cLq9NK/ZyamDPwWw0pOFZtIKLUgpCYz12QF8QnPQtHYXN0IUseLGxEUZO/gCL/MwEQYByRCPM87LVLOHSm7BRwMCjQQzhOhlhuMEOUmcae7hKYpoXdSuRBJjghij6ITRmSWR/IkykmaNnOYtzvPPuCi1l92tHz9+WjIuw6f8SONMoRNCZy7ZfpAgJ9OW5Id4gjiBi9ovwkkxRbQn87heGQSCRt7LSf6OOBOAyVH+K1FGaOJ9eKjGJEGYfoZ5kl7+lRhjlLAWgxhh+vJeIxMtPEwv38IkhkGY/XyGTn6NGvbiohiDGAkCRBnFRGcN/x4Hmb+/deYI0Y9BCJ1Q0rocJM4MIbrt4yIMc45/SgZ1LbyvRXipw01Vxu9agcYGfo9pDjPC04BlTX6VRyhnGzXcQRGdOPBhECbCEEFOMsNxwvSzjv9ADXuXNU9XGiVmVwkiRzWva5XVKFukNNOMjLmqqC1EsDrv0XIQQtDYuZ3zR14gEQsRi8wy0nOU9s1704T/0IVDdiBdcXkdlbWZU6wpFNcDAkEFO9nDN3mDzxHkJAAJAgzzJMM8mePc3K5Mp/jvnOOLmOiAmbQELraYjfMS47y0oEcaAgcCF3v4Ok28b9E5EoMDfJZxXkaiL2h7cfs9fJ0evp7c0uz23ZRzH7+gmPyp+laSEtayiT/kCH+KSRydWU7yeU7zP9HwIDExFxQPKGEDe3iUC3yZc/x/S76eQFDL3bTxUbr5GiQrj53ibznF3yUFZcraKSlhfdIymlnMjvI8r/EJJAkkRtb7GqaXt/hPC3qhARoaTtby22zlr7L22UU5t/JlDvEfkj6zBiZxpjjIFAfzjHh1WGVBuRkoloTMGbl+PSA0R5rgMgusjiYBzOt7bhbiL6mitmWDvT1w9qAVrJckHp1jrP+kvd3YefOSyv0qFKsRgaCa27mHp9jIH+CnNY9QFTgppoo9eMme5cMknqzolBKb1rm5f0iKuQQGkaQQzoTEIIpJbEHp10Lal7YwspasM8U3zB+fyW4oMvQ5WxvZLI9r+U228z+T6dGEPV86sxiEkBg4KKKFD3EXP0xaJO9K3pelP1xruNnOf2c9/3mBP6p11YsFqUbu1TqJkbw3iSXfV4mevG+5g9AEAi917Oar3MI/UcEOtCzieuEYS1hHxQI3jmsdZZlVFIyUEj1xfac0cbm9CKHZrgbxaAgpZV6LojQNdP3ajWxdcYSgZf1uhi5YacyCk4MExnvtTAcTg2eJJHPQOt1eGjq2Kaus4obAEg8NbOH/Zh3/kSneZJpDhOghQRAQuCjBRwMlbKCCmymmM6fA6OKzaZHpyyGbMBE42MZfE2d62W0LnPguEuPFrOFO/g2JiQN/xhK/G/j9ZOAclLFp0evpbfhwUrToGA03XXyWRn6JMZ5nijeIYrk3uamglA3UsJcyNiNwIRDUcz97+SESk3K2LHGsAhdlbOOv6OIzjPECAY4SYwKJiYtifDQlxeA2HGSPpajiNvbygyVd/2KKyFwh8+I+O/HTzidp4YMEOMYUB5nlDDEmk4Lfi4caSlhDOdsoZSMuynK7Cl5DKDF7w7LgDSoLW0ww9QSxSPZo0esBt7cYh8uNGbOsGKFgYVVT9ESMRLzwCmCrHSEEFbUdlFU1ExjvxTQNBs++YYlZKRm6cMiqw4yVS7iorOYq91ihuHKkLI5eamnkARp4d47j8rdVxqaMYm8lEGhUs2fF23VTnnXc1nVFWgBX5jbKcraxsC0/TbTxK7TxKxlfX0rfCkHgoIS1FLMmy+v5762Penw8eEn9WAoiuRJQzW1UsTvLMfP/XU0oN4MbFM05/xyj63HbtzEXkVCAaGjmcnbrquP2FuMrnl86mpnoz5lHNUU4OEEidvVy+hbOyrlCOJwuWtbNR2+PDZwiFpklEgowNXLB2ikEzWt2paVbUyhuNESWf4qV5WrM82q9t9n7fe33PRPqG+YGxeufT5EUCwfzilQpJaO9x9PSVl2PaA4nVQ3zVVPmAmPMTAzkrnIlJaN9Jwp6ILjiCJFWpCARj7FSglYIQV37Fvu9FA3PMDVygcnh88STled8RRVUN61TLgYKhUKhuGwoMXuDUlbdTOoJTE9EGTz/ZlbBJqVkbmaMnhMvZXz9eqOx82Y0h5VixjQSnH3r5xh6PGthgNDMOANn80WFXh00zYGnaEF97qkh4tGVc4fw+suob0/6nEnJWP9JxvpP2K/Xt92UsTCGQqFQKBQrhRKzNyBCCCrrO/H650XOhSMv0Hf6dfRELFk5TCYDvuKMD5zizWe+QmRuOk8RhCvPwr5KaS6qGCalRJrmRcdlt0wKISivaaWudbO9b2zgJMde+R7RUCB5DasNw9CZHu3m0PNfJxoKsNTlmUx9T+uavb+wvmcZEVX18z5d4dlJzhx6ingsvKhd0zQw9MSSr9G87lY7x+7U8HmmR638iJrDReOa1RMNq1AoFIrViQoAu0Hx+sto23QHp994ErCyFBx96bv0HH+J0spGnC4viXiY2ekR5gKjmIaOv7Saqvou+s/sv6p9l1Iy0nOE2alhEvEoejxCIhFFj0eTFbzmU9CcO/wMQ+cP4XR5cbo8ON0+XG4vLo+flnW3ZC6nqznYcMsvMTPRT3h2EqSk/8x+xgdOUV7bhtdfiqEnmJsZJTg5hKHHKa1qwldcyWjv0bx9D4z1MjF0Zr7v8Sh6Iooei1oPDElG+47z+hNftPrt8uJ0e3G5rf/Xt22hpKI+71wJIWjovJnu4y/ariQ9x19mYvAM5TWtON1eTEMnEYsQj84hNAe77v81XG5vQfdCCEFZVRMVde1MDJ4hFBy3XyurbqKsqlm5GCgUCoXisqLE7A2KEILOLfcQCk4wePYN2zIYnBwkODm46Hh/SRU33/0xEvEI/WcOcHWTKUt6T77K+MCpvEeGgxOEM2Qk0BwuaprWZxazQlBUVsP2ez/O4Re/RWjGEmjR8AwjPUcWHV9a1cT2ez/O1MiFvGIWrECpM29mT6CeIh6dY3LobMbXPL7SgsQsgL+kkk23fYCjL32XRDwCSOYCo8wlq3MtxFdcCTJTvsjsCM1By7rdTAydZaFpualrJ5pDfcQoFAqF4vKivmlykG259UpYmjSnyyoPKk2E5kgL4smFw+lOnicRDiealj1ht8PpZuudH6Kqvovek68yGxixAryS4xaaA4+vhLq2zXRt3Ye/pIpQcJyishpMPY7HV7LI7cDpmr++21ey+KICPP5SfLEwCM32Tc02Bk+mNgCPrxhfUXlBc5IJzelC5JgbIQQVdR3c9uDnOH/keUZ6jhKLzNpBXkJz4PWX0ti5nc4t9+Dxl6InYvhLKpGmmdNP1OX2XlLfEQKnq/DSuamKXb6ics69/SxTI93o8cgClwyBpmk43V5KKupzzku29mtbNuIvqbIfHNzeYuraNiurrEKhUCguOyKPf9ySzG9SSkwjQXByCFMalFU24XB5VtUXmpSSeGSWqbEeZiYGiM4FMPQ4QtNwe4rwl1ZRUtlAWVULLo8v49is8p7HFvlvLqS8tpXy6pasr5umgZGYzxzgcHkKKq2adp4Ap9OTt8yqdd90InPTROam0eNR0AReXyn+0ipbmAkhkNJET8RAWuVbHU53esUsQ7crQWV6XUqJkfTLtcblXiS4C2pDjyPNpVkQ01jC3IBlJQ3NTBCLWEnPPf4SikprcHuL7L5J00TXU3PjwOF0LXp/pObaNLJV4ykMh9O1ZKtnyjc3Fg4Snp0kHg0jpYnT5cbtLcHrL8Xl9aNpziX/zeqJKC//8AvMTg8DllV2+76PX+8puS7HB9uNU0ZOoVAolkbWz9yVtcxKyYn9P6bv5KtIJA3tW9l+7ycQq2Sp0TR0ek++wvkjLxAJTactmS5ECI1td3+U5rW3ZHw9MjvF0Vcey5mqaf2uB3OKWU1zoHmyVw5ZyfOEEDicLorLaykur81zrIbLnb19zeHMKbKEEDjz+GMW1MYVKo2aEnUeX0lWK7F9rJZ7blLtOZwuHM7FFunLjRACIRz4iissy/cKIaVkeqyXuZmx5HU0mtbuZLXmK1QoFArF6mJFVWYiHmWk56hdz3584DTRcBB/SWWeM68+UkouHH2RU2/8dLEITVmpkuJWc7oor2nNar0SDiceXwl6PIpp6slo+kuwIioU1zTS8rtO/t0UlddSWd+5qlZkFAqFQrF6WVExKwTpy4pCy7uMe60QDk5w/shz9hey0+Whac1OqhrX4fEWIaVJPBoiFJwgEQvjL6nK2lZJRT13/fLvYSTiySj7CKHgJMde+R6GHrtSQ1IorgCScHCS0b753LJNXTtwugrLhqBQKBQKxaWyomLW6fbStGYn3cd/AUDL+lvzLs1eC8hksvd4dA6wBPmm2z5A64bbALHIXzMfWjJwigUrzkWlNWgOB5foKqlQXFOYpsn5I8/ZfzsefynNa29RVlmFQqFQXDFW2JlVsH7nA0lfUklRac2qCQAJjPfav3uLy2no2Jax7+pLWnEjkukhzkjE6DnxMv2nD9j72jfduaL+uAqFQqFQ5GOF3QwEwuGgpKJuJZu9/EhJNBy0N/3FFXmDlBSKG4lIKMCZN5/C5faiOZzEoyECY73MTo/Y/uCVdR20b96rHvgUCoVCcUVZHWkGLjMSibmgapR2USooheJGx0hEGTz3JqaRyPh6cXk9W+96JG82B4VCoVAoVppli9ml1G9fijC8uF07h2dyvzQNdD2ONA2E0JL5Nl1Lvk7ataRELkjvKC5+PQNXQ+xm6k++flxq4QfrfImhJ5K5XyWaw5nM/aotsZ3M15dSIk0TQ49hmkYyfZV72fc103WlNDH0uJ3fdf694+Riv+iltJ3KeZsSeQ6nC4fDDWJ5bV67aDgczuT8Je+lELg9fmpbN7Nux7vwl1RdZ2NWKBQKxWpg+WLWNDhz6CmioWDWY/yl1ay9+R2LqkTlYnLoLANn3wCgoXMbtS2bAAjNjDFw9iATg2eJhAKYho6mOXB5iyivbqaxawfVjWsRmiPrF6qUktDMGNHQDLHILNHQDNHwDNHQDHPTI/Zxwakh3n7x22TKkyk0wZqb76eotLrgMa0EUkoGzx9iYuA0AG5vEet3PYDDmb8S1MDZg0wOnQOsfKnrdr47b55TKSXxaIjR3qOM9B5jLjCWrBolcbo8+EurqGneYFWWKq7IK2IC4330nnwFJNZ5XdsBCM9OMnj2DcYHTxOZm8bQE8kCFX5Kq5po7NxOTfMGNMfSEvmnCnhMjfYw2nuMmfE+ouEgerLCmeZwWpW4iispq2mhqmEN5TWtOAso8iGlJBoKMNz9NmP9JwkFJ9ATUQQCp8tLUXkt9a2bqe/YittbfF0IvKLSKva857eJhALosQgAbl8xxeV1VjWz6068KxQKhWK1sGwxa0qT4e4jGeu7p6iobWfNzfctKXX6XGCM/jP7rQ1hCZ++U69z+s0niEfmFh0fDc8wOzXE4LlDNHZtZ/Oeh3B5/Bm/WE0jwZvPfo3ZqaGcVtdoaIb+MwcyviY0jdYNe664mAUIjPXYc+MrrmTtjndSSOHR6dFu+7yi0mrWbr8fyC5mpWky0nuUUwd/ylxgjIuLEsWjc4RnJ5kYPEP3sRdZu/1dtK7fnbPQQWR2iv7TVh/isRANndsYvnCYE/t/RDQ0s+j4WDjI7PQIQxcOU9e6mS13PozHV1qQYJJSMjVyntNvPMnUaHfW4hXREMxOjzDWf4Jz2jNsvfPDtKzfnbNt09DpO72fs289TTQUWNzvyCyh4Dhjfcc5f/QFNt76Xurbb1o1gZDZ0BxOyqqbKatuvtpdUSgUCoUijWWLWSEE5TWtaA4neiKGkYhhGIlkqdOVqcg4Oz1C36nXOf7aD5LLuAKXx2f75cVjIav0KmCaOgNnD4IQbNv7kRxVxwRCcy4S2Bcvn2pa5vM1zYFgdQuTXEjTpOfky5zc/xMMPVVON33eE/EIiVgEkERDMxx79XtEQwHW7Xz3otK0mQjNjDN47hBHX3kMI2Hl3XW6fbg9/gXth5P9MRjpOYKUkh33fRJnHku0NE36Tr/Oif0/Ro9H0l4TmgOHwwVCLCgpa91zp8tLVeOanGLZMHROv/EEF46+YAtkITRcHr8VMChN4rGw/Z4MzYzx1vOPsnnPQ7Ru2KMslwqFQqFQXAaWLWY1zcnNd38MUxpIw7CF7NsvfpvJ4XMr0rng5CAn9v8I00hQWtlI17b7qGrowpkSs9E5hi+8zbm3n7HFz9C5QzSt2UlN0/pF4kFzuNh1/6dtv8kU0jR564VvEpwcAKyo7C13fjir+FgNFc2Wg5SSkb5jaUK2tKqJrq37qGzoxOW2xKYejzA5fI6zb/2cucAo0jQ4//ZzFJXV0rx2V17RFgpOcOzVf8NIxCgqq2XNtn1UN63D5SkCIBELM9p3jLOHniYWmQVgrO84o73HaOzcntONZLjnCMdf+8G8EBeC8uoWmtbspKK2HbevGCE0jESM8OwkU6PdjA+cpqSiHn9x9vsqpaT3xMtcOPo80jQBQVXjGjq33EN5dQtOlweJ5Zox1neCc28/QzQ0g6HHObH/RxSV1VDVkFssKxQKhUKhWDqXZJlFCBxo4HDhxIvbW4zD5VmxzpmGDoZOZUMXO/d9Co8/fZnZ5fbStW0f3qIyDr/4LaRpYJo6Q+fepKZpfcY+Z3IPME0jzYfU6fJQUlF/wwmPeHSOUwcet4VgVcMaduz7ZMZ5b1qzi4radg787J+ZC4ximjpnDz1FbfMGPP7chTKkaaDHDcqqmtl5/6cXBQ653F7aN+3FX1zFm89+BUNPIKXJ4Nk3aOy4OasPdjQ8w8kD80Jc05ys2X4/nVvuyegLW1LZYAcvmYaetV0pJXOBEc6+9fOkkIWmNTvYcueHcLq8F/XdR/vmvZTXtHLw6S8Ri8yix6OcfuNJbnvw3xfk46xQKBQKhaJwrvn1cqfbx017fnmRoEohhKChYxvl1S32vsB4fzLyXlEoUkoGzx2yfaCdbh+b9zyUc979Kf/b5Ouh4ASjfccKynThcLrYtOcDWSPghRDUtGygsn6NvS84NUQiEc3a/4EzBwgHJ+x97Zv3snb7/bjc3qwPJqnMCdn8rFP0HH/ZrnLlK6lk4+73LRKyC9ssr22jbeMd9r7psR6mR3uytq9QKBQKhWJ5XPNitqZpPaWVjTmFhuZwUlHXbm/Ho3MYeuwK9O76wTR0hs4fsrerG9fmnXchBNWN6/B4i+19o30nuDhgLBMVtR1U1nXkaV+jqqHT3k7E5n1pL8bQEwxdOGxv+4or6dp6b0E+vPmIR0OM9h23txs7bsbrL8s7N/XtW+z0YtI0GBs4uaSUdgqFQqFYHgYxQvQRopcQvehk/u64lpFIdCIkmEn+zKalEVXMc80XTahpXp83tZcQAo+/zN6eD+5RFEo0FGB2QXqy6sY1BaVUc3mL8BaV276tc9MjGHoCZx53k+qmdYg8QvPi+ypNAzOLxT0yN0VoZtzerm3ZiMdfmrf/hTA7PTyfcUEIqhrXFuSC4i0qx+XxEQtbfQ5ODFrBkTeY+4pCoVBcaQIc4Re8F0kC0NjDN2jgXVe7W0vmOH9BH48BUEwne/khTvxXuVfXHte0mBVCK9h3VdPmjcySpRV1UEAoOI6+wJpt6AmmRi7kPU9K0y5nChCPRzAS8TxiVlBS2bC8+5rlqTQ0M57mWlLZ0LViPs+zU8P2GDWhEY/OMTl8Pu95hh5HLMibEYvMWv7Z2jW/IKJQKBSrGolOghkkOiCS/199xJgkTC8ADjwUsvJ5I3JNi1nN4cTlUU8gV4LIXCAtpdrJg48XnB944YODFYSXOa9rCqFpuL1Fy+lmxuHdwQAAIABJREFUViKhAKk/cqFpK5ZxQkpJZG7K3jZNg8MvfquguZFWAwvO1dOEv0KhUFxNTHQMIggcOPClPXwrFKuJa9tEJMSK+Dwq8pPKjWqTLNVayM9icj85CsCRo8DCcljYfyG0vG4OSyGxzLm5ON+yWixQKBTXChLJID/maW7lAL+OJLcRQqG4lrmmLbMClH/hFSJ9+V5Q3bgWl8e35HYcLk+B6adW032dnxuhOahpXm8VX1gi3qKyVV8JTKFQXD8M8yRznMNLLWr5WrGauabFrGJlKCRNmcvltX8XQrBu1wNU1nVczm6tKAstsVKaViW6FWt7fm4cThdbbn8Y33VaOEOhUNwY6MwxyetXuxsKxYqgzESrmYIepK2qVPnwFlfYVnApTaKhAEKIZf9cabxF5fbv0jQJz06uSLtCiDThaugJYtG5VTU3CoVCcTFznCeUDCxSKFY7SsyuIhYuUVtZBPKrWUNPFCTsistq0qybgbHeVZURorisJs29YWr4wor1v7SyyZ57aRrMTPSvqrlRKBSrF5nl36W2OcErGERWqJdXnpWeE8XqRrkZrCKc7nkfVj0RJREP4/Zmz/YgpSQ0M05kbjpv297iCkorm5gasVJOjfadYO32d6541oHLha+kkqKyGoKTgwCM9Z8gGp7Bt8Biu1xKKxvwFVfYDwXDFw7Tsm53WglkhUKhWCmsZPmzBDjCFAeZ5TwJAkgMnBThpZ4S1lHOFopZg5PirJkI5kWeiUGEKOPMccHOXQoQJ8AITyPIFnAtqGI3bhZ/nkpMJnmdBEEAvNRTzra8mREkBhO8hk6ysiKNlLEl53nWWExmOccozxHgCAkCOPBTygbquJdytiJwIdDIFZthEGOCVzCxyp/7aaGUTQVndJBIIgwyw7HkHkElu/BQVdC51h0+xxQHmKMbkwReaqhgOxVsx0lJstWl2Ryttg1ijBHkNLOcIcIIJlE0PHippYS1lLEZL3WAluO9YzLHBfuhx4GfYjqXlPXCIMIc5+33oZc6PNSseOYMJWZXCUIIikqr7W09HmN6tCdrOVgApKT/zH4MPZ63fU1z0LLuVqZGL0BSBPeceJm1N9+PKDAvaspaeTWW0h1ON42d220xG5mb5tzhZ9l82/vRLjFzgsvjp7FrO+cOPwPA5MgFhrsP07RmV8FjvZpzo1AoVgcSiUmcfr7Lab5AkFO22MqEAy9FdNLIe9jI7+GmIkObBmf534zxArOcI8owOnNp2QuCnOAlHsp6HQ0X+3iOKnYves0kwSH+E9O8BUALD7OHb+Ydq0mcN/kdZrCqK7bxMXbzlazHSyRxpjjB39DD14mz2EjjwE8DD7CVv8RBERpODLLHjJzg/2GMFwCo5Bbu5edLKEggOcnnOcc/AeCllnfwal4xK5HMcZZj/AVDPImefAhIIXBRxiY28kc0835b1ObvjTU/I/yMPr7LJAeJM5kxS4XAgYcaGnmQDfxBToF6mi/QzVcB64HjPl7ER0PBfRrkpxzg15CYCJzczrdp4N0Fnb8UlJvBKqK8pmVBMJLk/JHniIZmFi15SykxTYPB84foO1WYg78QgobObVTUttvtnzv8DBeOvYieiGVcVk+loDIMndnpEQbOHkTmyTF7uRBC0LLuVopKa+x9vSdf4dQbTxCPhrK6BaT6PxcYzelb3L7pTvzJhwlpGhx/7QcMnT+Eaei550aPExjvZ+jCW5c4QoVCcb1jEucIf8oBfoMARxYIWYHAschKZxAlyAkG+B6SzDmsJQm6+RpDPM4sp5KFBDJ9TsusP/mW8a1rLzynUC6+VrajJDEmeJ1PcoZ/uEjIarZF2SDMAN/jZR4iTB8a2VM0arhp5xNY1ltJgMNMcqBgd4U4kwzxpN33OvbhpynPaCVTHOQXvI8+vrNIyFrHJAjwNvv5NCf5f3FTlqGlxZjEOcBneJ1PM8RPiTGWNd2axCDKCBf4F17ifcxwLMu4BW08AghMYoToYZgnC54jiUEf/4pBBJMYfpqo5rbLks94WSYraZroehxp6hiGjqknMPQ4eiJGYoEgSMSjTI9243T7cDhdOBwuNIcLTXPgcLoQmuO6slRZIlLH1HVMU8fQE5hGAl2PE5mdThN64dkppkd7cLjcOJzu5Nw4k3PjzmgN9ZdWU9O8nuHutwEITg5y8Ol/pnPrPsprWnE63RhGgtDMOIPnDjHc/RaGruMrrijI1cDp8nLT7R/k4NNfIhoKYOhxTu7/McMXDlPfsZWyqiZcHj9SSvR4lEgowOzUENNjPcxOjeAtKqWhY9slW0KXi8dfyqbb3s9bzz9qCXDT4PzbzzLae4yGjm2U17bh8RUjhIaeiBGZmyY4OcT0WDdzgTFuuf8zVDWuWdSuEAJvUTmb9zxktR2PEo+GOPzCt+g/s5+6ti2UVNTjdHmQpkkiHiEyN8XM5CCB0V7mZkaprOukseNmlWpOoVBkxLJi/Yiz/GOyBCuUsJ4WPkg523BTgcQkxhgznGCS/QR4mzjTtPBB3GTOsCJwsY7fIcZY2rV6eJRZzgDgp40uPpNjSdtBEa0rOt6lIElwhD9hhKftfX5aaefjVHErDnxEGWGU5xjgRwQ5xSH+IybRrG0KBA28myLaCNGNSZweHqWWu8iXOlIiGeMXhOlPtuWkjY+Ryz6Ycks4wGeZI1VBUlDKRpp4D8WswSRBkJMM8xRznOcEf0M5WwuaIw03tdzLEE8AAjfllHETFWynmC6cFJMgyBRvMMzPiDMBwCxnOMwfsJfv4yA9HadAUMlOKtnJBK8Akh6+QRsfw4F3cScuIkwv47xkbzfzAVwZXFVWgmWpjomhMxx5+TEMPY5p6JimgTQNpJle2nQuMMJrj/9vhNAQmoamOSzB5nDRtvF21tz8jhUbyLXC6YNPMHThLUvkG3pyXoykpW5+bvpPv87A2YOIZGEIoTnQHC6cLjfb7/k4FXXti9oWQmPdzgcIjPfZ4nRmYoC3nnsUh8uNpjmQpoGhx21rYV3bTTR17eDQc4+St5iBEJRVN7Nj3yd5+8VvEwqOI6XJ9FgP02M9IMR8IJQ0F1cBkKXLnreVQAhBXdtmbrrjQ5x47QfEY9aD1VxglLNvPQ2I+YeEiwLohNByzo4QgrrWzWy76xGOvfK9ZGlanfGB04wPnLbmJZUNwkxZKRQKhaIwJAbdfM0WspXcyp08hpeGNEtWyiom0YkwyCgvUMverNYuDSedfHrRtSZ4ZYGYbWIDv4fGtRcHIJGM8gK9/Ku9r5Jd3MajFNNlj1siaeUROvg0+/lVQvTkbdtDNS08zCn+FoBhniJMP0W05emTQR/fgaQ1vIR1VHN7HoujySn+liAnktuCTj7NVv560YPIJv6YY/w55/kyUxzMOw6rNUErH2acl6jnHdTzLvw0I5IyTyCS7x1JgKO8ziftvozzEtO8RTW3L2pXw0sHn2KCVyFpWZ7mLaryWFglkiGeII5VQdNJES186LJVmVuWm4GeiBEOThALB0nEwhiJWHK5dfEyh5Rm0koZJxGPEIvMEpmbIh6du+TOX4tEw0Eic9PEI7Po8Ygl+E0j89yYBqahWxbtWJhYeIZQcAI9i4+rEIKSinp23verlNW0LrDySYxkGymXAIfTRcv629h290cpq27G4SqkkIF1jcr6Tm578HO0rL8tLegMKW1xniZkhcBbVEZd201XvWKbEBrNa3dx6wO/QW3rpouCtGTaw8WCs/D4S3G5cz9pCiFo6LiZ3Q9+jvr2rWnZE6Q05+eGdJHsL62mpmWjssoqFIqsWIEyF+ztZh5aJGTBEiUCgYaLItrp4FMU0Xmlu3vFkBic559sK6uTUnbwhTQhC9izUs3tbOPziAKEuUDQxkdtv9QYYwzwo7zL6GH6GOMX9nYLH8zr2xqilz6+a29XsZttfB43lfY9Tf3zUMM2Pk8td+cdw0K81HM736aL36CYDjRcdpup8Qo0ytnKNv7aniOTGBO8lnHcAkEjD+JPWuYNovTyTfIZbExi9PNv9nYlt1LKpiWNZyksyzJbXF7P+l0PXJLxqdz2zbx4fyvrdz4AgOZw4vIU5oxdUdc+f57ThctdePUqIQStG/ZQ27wBAH9ZTZ4zstPQsY3iSzgfISgqye5ALoSgvLaNPQ/+JmMDJxnvP8lcYIxEPIoQ4PYWU1bdbC+rC6HhdLrZsPNB9EQUl8ef1w0glVt1296P0LX1XsYHTjE12k1kdipZjEDgdLnx+Espqainoq6D8ppWPL7sf8wllfX2/RGahsdfmBW3tLJpwXmOnNdIm6OaVm65/zMEp4YYHziVtGYH7GA4h9ONx1dMUVktlfUdVNR14PXn900SQlBa2cjO+z5FcGqYsf6TBMZ7k24ZCYQQOF1evEVllFQ2UlnfQVlVMy6P/7pyqVEoFCuLSPrFpogxWvB51zMRBhnnFXu7nvuoYGfWcVvuA++ijM0EOJy3/VI2UMvdDPE4AL18iy4+g5PMmXwkkmGeJI6V3cZJCc38cl4r5QhPE0su7YPGOn4bF2UZzxMInBSzlt9inF9k9X3NdF6hIr6a2/HTZFuw5+jOeryHWlp4iNN8AYBBHmcTf4KPxqznzHDMDgok+dBwOS3/yxKzJRV1lFSsfDQaQHlNK+U1S/fNqahtXxC8tDSE0GhdvzhKc+ntCBo6ttLQUZiPy6Vcx+Xx0di5ncbO7Un3DuvNbvkha/ZxYAm3zq33LPkaJC3BxeV1dNx0d9KiaSZf1xALrLD5hFpJRQMlOwuLgFxIaVUjpVXZ/2CyIYRAOJyU17RSVt0CkKH/86lbliI059tuoay6GZCYpgl22w7bnUEJWIVCUQgOfJSykVlOA9DNo1RzB408mEw1dWN+lgQ4mhbw1cCDOVKIWTjwU8vegsSswEkHv8owTyHRmeEok7xOLfsyzrlJLC2tWTW3U8r6PFeRjPGiveWlhhruynlPRTIdmodqogU+2CwFJ0V4qLHFrE7u4kpt/Arn+RI6c0QYYoif0smvZxyDRNLP9+2UXj4aaOBdl/U9rLIZrGJSFaU0hxU05nBafrMrXWlq/jrO+es4nKumqlX2/muX3H+R9CN2pLW98vdAoVBc72h08Rk7sCbOJK/xCV7hIwzzJAmCBUeRXy9IZNKv0zIUaLgp56aCRFEZWwq6hkBQxz22IDWJ08037GtezAwnFlgcNdr4qO2Xmg2DGMHkQwpAER0F5aN1U2Ev718Ki8tLmEjkRf3OPF6w5qiMzdSw126xh29mDbBLMMMgP7a363k3XuoveRy5UHlmFQqFQqG4ylii6j428kec5PPJdEZRhnicYZ6ilPU08xDNPEQpG24Ya22YAft3Bz485HfjE4hkLlSNXCIthZNS2vgoR/gzAEb4GSH6KaY97TiJZIDvYxAGrMC5et6R9z4YhGy3BOu85rwCGKz8voXmdF3Yx1SCr1nOEeQUIXqJMY5OCIMIBlEMIguC0fKTsmCP8DQSgyneZIq3qGbPogDFSV63MzZouO30XpcTJWYVCoVCobgG0HCxkT+kgu0c56+Y5k0kRnL5+zgzHOc0X6CGO+niN6jnPjS817WoTTBj/67hWZQ+KhupqmiF2LIFghY+xGm+QIwJYowzyA9Yx++mzW2CIIP8yN5u5JcKEtcp8ZjCKm5RyD0TuCg8S5DEZIZjnOP/MMLTRBjMWXRjKVgPW/soYW2ymEeUXr5BNbeRPhZJH99BogNQxmaquOWyv0eVmFVct0hp0nvyNSJzU1mPcXuL6di896rlxlUUjpSSgbMHmQtY/mO+4graNtxecIU6hWI1oOGkgQeoYS/D/IwLfIkJXrOtgTpzDPMUozxLLfeylb+knJuvW0G7MPhJoFFoeVfLr7bwOSmijXreTS/fAKCXb9PFZ3FSnOyHZXGc5RxgCetWHimoP9YDybyFeCmBUIUEdIFVie0s/8gJ/tpOh2Wd78RFKW6qcFOGAz9O/Gh4GefFjJXUsuGijFYe4Rh/DsAQP2UTf5pWLCLCMCM8a2+38DCOLMF0K4n6Bldct0gpGTx7kKnR7FGaxWW1tG28XYnZVYFk+MLbjPZZtdDLa9to3bDnOv0KV9zIWDHpJbTwQZp4LzMcp5/vMcAPksu3JiYJRniaAEfYzVeo475VJWgtz838UfoLq3hJdMykxS9/++lpEvOj0cEn6ee7mMSZ4RgTvL5gXiV9fNe2OJazlQq2F9SyVb1tPmjNzFFiN/M48h0j6earHOFPbEusmwpa+QhNfIBS1uOiPJmqy6okZxLnWe5ekphN5bK1qrBNEWGYIR6ni39HKo/tKM8QZQQAF+U08YEr8r5UJg3FdY2/rAZ/SRUeXwlOlzcpWlfPB75CobhxsZJ1eahkB1v5S97By+zmX6hgB6nPsSgjHOJ3iTF+dTu7RExiGHki6MEqbJDCIIpOYTnqrbK9+f1lU6SyB6QEqkmCHh4l5XNrVRh7xj6+lQ8X7PLgwJd2bIwpChPaMmPJ24uJMsoJPm8LWS/13MG/sYO/T5bZbcZFMQ48aDiT1mRRYB/SKaaTet5p929hIJhETxaTsNqt5S6Kr1AOZGWOUly3CKGxbe9HktXYEuiJOHoiyqkDP2Gs/+TV7t4NRTwaYqz/BA6n+5oorqFQrDasZPqVtPExGnmQt/kjLvAVQDLLGUZ4hnY+dtX6lsISkJJ8RoM408QX+MNmbpc0MWQQIUw/JSwuO74QiSREH0sVaw58tPNJJjkASEb4OWEG8dPCKM8RYRgAN1U08b6CLY6pNFgRBgGr6IJJAscCq3Pmcej2NXMxyX67tC7AWn6LmhxV4VJt60nXlaWh0cGnGOD7mMSZ5i2mOUw1e5jjHJPsByxrdBsfy5tGbaVQllnFdUsqHZfL7cXjK6GotIrSykbc3uKr3bUbCikl4wOneOv5b3Ji/48x9cKX2BQKRTqWC0I5N/HnC9IdSQK8fVVSdwk0NOarJ8aZzmsRlcmSqvmtrIJytqDhTp5nZK1UdfEVpnmzgN5ffDVBE+/Bl/QBjTHBCD9DYjDAD0iJ4zruzVvydiEabsoWVL+a40JBuWNjTBGiN+cxEskc50hZkAUuark7r9COMrqs/LVWwYU9lGPl0zeJ0s+/JYtJPEUiaUkuop1a7rliri9KzCoUisvOxOAZLqlkoEKhsLEEbRlu5qsWFlolKtXCwmXvBMEl+XEubmk+fdQc5/L6YUoM+vkuhaTNKmVjWq7VQX6IzmzOc2JMpBUpWApe6mnmA3ZPB/kxEQaZ4HVg3uK4NPlkpV1LWavjTCVTXGX/TJRIJnitIPeRhQ8PVsHa3OXrJZIhnkjLFLEUHPhp5+OkxjPMz4gzyRA/tY9p4n24qVxW+8tBiVmFQnFZMfQ4U2M9V7sbCsU1TYIZIgzbCe1zIZHMcDQtB2sJa5dwNUERHfZWiB5mOb1My66gkl32VpjBpKUus1CVmAzxOIPJ8rH5cFFOMw/Z2wGOcIF/ySreTXTO8cW8Fs1sCDTa+bgdgT/FIYZ50i5FW0wXNdy5JItjKofwfPlXyRn+gQhDGedcIkkwwxn+Pu9DipVTd75KpkmCICez3kurEMVxTvM/WK6BwbJgv99eGQjRwyjPEuAIYIndVj58RQMSlZhVKBSXlVBwgkhwMv+BCsUNzDRv8wx3cpB/xwA/ZI5udEKY6EmBa2UwiDHFID/mAJ+1l+k91Cw5m4G1FG2FzejMcZjfZ5bTmCQWXE9HJ0KMqaxZBASCBh7AZVuJTY7x53TzVeIE7LRUJgkiDHGGv+cgv4FBpKDCAQBdfBYfzYBl1T3Gf+MU/4Mo42nth+hPvvZ3S07NtZBytlDD7YBlRb3AV0lZkZt5CBflS27TRwOd/JrdpyAnOcBnksLTWFCbS2eO87zB55jg1YLGUMmOBfloJaf5e0J0221aeyUGUUb4Ga/wSNLHdvkS0EcjTbwPsIL5LvAV4gSS/dlJGTctu+3loALAFIoVQErrA8PQ4yRiYfREDITA5fLi8vjt8r+FtjP/xDxfFldKCVKSiEdIxMKYpoGmOXF5fLjcPlhmCV0pJVKaJGIR9Hgk2a4Dl8eP0+3N0aa1P9vr1lgkU8PnMYz5JUyJdb3sLH0c82MIk4hFkNLE4XAlx+BZVpsKxZVFEmGQbr5GN4/iohQfDXipx0VJ0lo3TYi+pEXPEpcCFxv4vSVFjQsENeylit1M8AoAY7zIs9xNGTfhpgKJToIg8aSQvYvHF1XESlHGRjr4FGf4X4AkzhRv8Juc5G8ppgsHHmJMMsc5oowBkgbejYMiBvhe3r4W0c42/oqDfA6DMDpzHOHPOMcXKWE9TvzEmGSWM8SYQOBkI79PN18jwlDB8zJ/TRcdfIpRnkOi2/63Topo4eFlWRwFGmv5bcZ4nnFeBmCUZ3mOe6niNorpQGIwxwWmOEScCTzU0sR7ucCXc7ZdzFqaeH8y+wIEOMzzvJMmfokS1gOCMP1M8ApTvIlJjHJupoKb6earSx5LakQdfIIevo5BhFGeJxX418ojaSnVrgRKzCoUl4g0TQIT/QycOcDk8Dmi4SCGnkAIgcPpxl9SSW3rZlrW3oKvpDKvqOo9+SrD3YcBWHvz/VQ1rkWaBuODp+k79TozE/3EY2GkaaJpGi63j9KqJprX3Upd6+YlCedELMzQ+UMMdb9NKDBGIhGdb9fjx1dSmTXzQE3Terq23ZfWnh6PEpmbZm5mjODUELNTQ0yP9tjHREMzHPzZl3IWOli3411UNeSOVgYrWwWAnogz0nOEgbMHCU4NocejSCnRHA7cnqJkPtrbqGpYo7IoKK5ZNFxouJOVokwSBEgQIEj2zCseqtnIH7KG3yy4mEAKJ8Xs5B94jY/b14gzxTi/yHBsCeRc7tbYzH8hyljSxUBPCrNzyeCk9GPruZ9dfJEBfpBXzEKqQteH0QlxhD9N+uSahOlPi+IHcFDERn6f9fwuY7y0TDErqON+iuliljP2/kp2U7ogkGupuKngVr7Efj6d9MG1hP8wTyw61ks9u/hH3FTRzdeROXyaBQ628BfMcT5pzYUwvZzlHzMeXcVubuXLRBigh2/YD0ZLQSAo52aquI0xnidlufZSSyMPXPGcx0rMKhTLJCXezhz6GX2nXrWssRdh6HHi0TkC4330nXqNDbsepGntrpyiKjQzngyYgsq6TspqWjm5/0f0nz6AaaZ/6JgG6IkYkVCAsYGTNHZu56Y7Hsbl9uUUtFJKZib6OfLSd5iZGFj0ut3uXPZADq+/7KI9kqOvPMZw9xFMI/MHr2kkmBy++MstnbaNd+R8PYXT5SEWmeXoy48x1ndikbXXNBLo8Sjh2UlGeo7SsXkv63c9iMNZePUdheJKUcEu9vJDBvhBMtXSIDpzyOSyvyUfnDjxU0Q79byDdj5OCeuXLGRJtlbGFu7mp5zliwzxE8IMYBADTAQONDy4qaScm+xKWNnaclHGLfwfGniAbr7CDMdIMIvESObLLaKUdbTzCdr4GE5KqGQXFexAYubNDqDhpJPPUMEOzvC/GOMFYkwi0RFouCijit2s43eo4S4EDurYh85csn+Fl4UFS3i28EFO8Df2KNt4ZEnVuy7GsjJ3ciff5yxfpJdvEqY/GXwnEThwUU4972Ajf0AZW4gyQinriDONlzoyuR1YfrNN3MFjnOYL9PGdpP+1TspaquHBTwttPMJafjNZEaycUjYRZyJZYndpaLjp4JPJYDvr87eed9rZIK4kSswqFMtEj0c5/OK3GOk5Yu9zON34iitwe4swTYNoaIZYOIiUJtFQgCMvf5dEPELHTXfZlsVcBCb6OXngJ/SefA2QaA6X3b6UJtG5ANFwEJBI02Tw3Js4XV5uuuODCJFZMEspCQcnePPZrxEOWkENDpeH2uYNVNR14HR7iYWDTAyeZmq0G2kmU74IjaLyWvzFFfhLqqhp2ZixbafLA675JSY9EZsXt0Lg9vjJ6gcmWEI1NsmRl77DWN8JwBK3vuIKXB4/hp4gMjdNPGr5FJpGggtHX8DtLaZr2z7lcqC45nDgppZ7qeVeTGLEk5bZBLOYxBFoOPHjpgoPVWh4L9n6JRD4aWErf8Um/pgoI8SZxkTHgQc3FbipxElx3nyhltT20cZHaeVhYkwQZQyDMAIXXmrwUo+Gx+53Fbu5L2kJLiQfqUCjgh3s5st2BSqdOTS8+KjHQy1WhSur/U38MRv5A2BpJWRTLAy+8tFAA+9akTn3UM1m/ox1/DaznCXMABIdN1WUsBYfjfY4vNSxjxewxKIja6EG69hatvKXbOA/M8tZIgxjksBJEX6aKaIdF2X2GDzUso/nAGPZbgFuKrGqf1nuGW18lKtRmEiJ2atAPBoiFJzA5fZRVFazol+sUkpCM2Mk4lGKSqtxey9/TeQbEdM0OP3mE7aQFZqD5rW30HHTXRSVVluCTEoS8SgTg6c59cYThIMTmIbO6TeeoKSykerGtXnv/cTAKaSUCCGo79hG19Z9lFTUoTlcVvuxMEPdhzn9xpMkYlYC7IGzB2lZv5uK2iyWDik5e/jntpB1efxsv/fj1DZvtP1upZR0bd1H97EXOfXGT5GmidA0Nt36XmpbN5HZX1aw9c4PYRoLlyMlJw78hIEzBwDwFVew55d+C6cz+wen0+3N+tpCJofPYxoGmsNJ6/rbaN90J76SKjSHAykl8cgsvade4/zbz2EaCaQ0uXDsBRrX7MBfvHQrhEJxuUmJDEdSnPnsPLKX/7ouSnBRsiJtCdz4aEyLss98rJa3cECm9sGBhxo81OQ8VsPJcmVOghkG+JG93cADC/L6XjrWLFVQxa1UcWuO4zTcSwg4s2yw1WmV03L34eIVtqVg0sd3bNFfygaquO2KuxiAErNXHCklPSde4cyhp6ht3sAt7/p1VvIpxtDjvPns15idHmHHvk/Q2FlY7WhF4UgpmRq5QN+p16wdQrDm5newbvs7EZojTeB5fMX5WFRxAAAgAElEQVQ0du2guLyO/U/9E7FwED0R48yhp6isa8fhzJ0P0DStD4n2TXey6bb3ozlc6e37S2nftBdNc3L05ceQ0sTQLR/S8prWjGI5Gg4y2nfc3m7beAe1LZvSjrX8fV103HQ3YwOnmBw6i2no9J1+3TpWy7DUJQQuj3/RXDkcrgXHaHi8xQUL1lyYho4QGut3PUjnlnsWuW74iitYt+PdSNPg3GGrDGXK4tyybreyzioUioxIJOP8wvb31XDTxiOoUujphBlgZEGJ3xYezumOcjlRqbmuMFKaTA6dQZpGnoju5REOThCaGU+2r5LUXw6kNOk+9iJGspJVWXULXVv3ZQ28EkJQWtVE6/o99r7AaC+B8f5Fx2aiuLyWdTsfwOF0Z22/sXM7/tL5J/HAWG/W99dcYJRE1KqJLjQHdW2bswo7zeGktmU+4CEw3kcivpwSiJeHqsY1dGzem9UHWdM0WjfcniayFwakKRQKxcWYxDnPl2yLYwU7qOTWq2JxvFaRSPp4zC7qYPkYLy/Tw0qgxOwVJhaZJTiVv9bycpBSMjXajaHHL0v7CovI3DSTw+ft7eY1Oy0/0RwIIahr3WSLLtPUmRg8U9ADR2PXjrzuIk63l9LK+So80fCsbdW9mGh4xr6uw+HC688eGCGEwF8yvySfiEdJxCJ5+3xlELSs3225XOTAW1SKv6TK3o6GAqhqZAqFIhMyWfVrlOcAy5d3DZ/L6qt6IyKRhOjmHF8k9VnaxAcooeuq9Um5GVxhZiYGiMdCl6l1ycTg2cvUtgLmswCk/FM1zUFFXUdBS9be4nIcLi9m8v4HpwbzniOERlVDft9aALd3fnnHNBN24FaGQVy0nbvdhYL7WrJLON0e/v/27jw4jvs68Pj318dcwOA+CYAACRAUbxISaUkUJVkyrdhKUrHjdbzykTjnJnEllVQlf2S9lXJtbTbZlDeJt7JZx9lsokqceO2s4zhxHMmWrfvmJVK8AOIg7hsYYAZzdPf+0Y3BDDEDgORQHJDvo4KqBuj5dU9jiHn9+vd7r7Kubd1zoyk9KzNrpZLePORbfYRCiGLleG0E3EoQCtJNBZ7hJL+JjVudpoajNPPjd2VWNncXMTeQfZNfIcoA4Dbt2Mmvczvzo5s6mF0uIm9ZKRw7hQNoSnNv93oZsJspIm9bSWzbRikN3RvzZubZOY7D1PDl1cFEAbg1Q2PMTgwUfOxr9+PYNpa3oEZTGpphopRWsDmI7j4st9C+46A03buFX7h93IzMzLpSGjNjfSzOrd8/O5WMQ8at/3hsAcex81YdALfKQGZmdC1ZtVud9P9WCZRWoJTmzq+1ksQWZ/PWv3UXFK68NsMXwPQXR4bCFyjNCuDzUtl/B2T6jRACbE7x20Tpx6AUmzgRupnnPDbu3U0/tRzg99Otbe9G/XyVKV7HRwUONgv0MsELLDEK4DWo+G3K2HVbA/5NF8w6joNtp5ibHGR84F1mJwaILcxipeLeYhMD0x8iVFZDRW0LVQ3tlFU1rlo4k2tcx3GITA8z0nua6bFelhZnsVJJNE3HHyqjsq6NLdsPUl7bsm5gtRyQJeJRlhZmiMyOMT81zFDPifQ2kZkxzr3yzfxVijSd9v3vz1HP0x3fSiVIxBZYnJ8kMjPCzFifdwvVNXjpDWbGevMeY0l5HW27j65bImr5tcyM9zPad4aZ8X7isQi2lULXDQIlFVQ3dtC4/QDhyoYNjTd4+U3mJq+ilEbbnmOUlNXg2DZzU0MM9bzNzFgvce9WuW6Y6X00tR+ipLzutga1Sxm1Vy0ryblX/98NjWNbqXUvbHTDRF9j5f+NKKvcgj9UxtLiLI5tMdRzImd22a2jG8sqPVZRuxXTF7p2yNvC9AWuo4yXEEKscHCY46xX8H81H1V08SWquO+uzMoum+UdbzrBagqddn6ODn7phmodF9Km+iRwHIfZ8X4uvv2vTI10u8FAHrMTAwz3nEDTTfYf+zjNOw6vOW48FuHiW//KcM/bOYvfRyNTzIz10n/+ZRq3HeCew08SKKnIG1RZqSQnnvtrZsf7061HrxVbmKb33OpuK8s03aSl80jOYHZmrJfTL3yNpegcVjKec7HP+NXzcDV/95iaLTto23V0zXvHy5m58298m/Gr7+Y854vzk0yNdNN79nla7rmfHQePY/pDawac4wPvMnzlJADlNc34g2VcPvkMfedeJJVcWr2PuQmmhi/Td+5FOrueoHX30dvWzSnX++NGrZcjVEpbs1vWjfAFS2npPMLlk88AcPXi64TCVWzdeb+XdVXgOMSis1x861+Zm3SnQ+iGj7bdxyiW+/M3e6dECHH3Wm70oBPAJomTblpQRi1HuYffoloWfaFhotCzau4qTEpoYwe/zHZ+Ho2br05zszZNMOs4NoOX3+Lcq99Mz1dcppTmBjZKYdsWTkbgaBg+qhq2r9k/PhqZ4sRzTzM73p81pukLohkmtpUimYi5t75TCbdt5tQQXY//NKUV9bnHdmwW5yZIeMe6PO3BuSaoVWsEZG6wlvu4k/EY0cike8tUKfdWtZPd814pbc3AY619g3fxMDHAyeeeZtGrSbr8PNMXRNMNrFSSVCKG49gkEzGunPkB81PDHHr0k/hDZRsKNmYnrjI3OciVs8+7mUqlMM0AuunHsS2S8Vi681ViaYF3X/sWmmGydef9tymYWdmnbvpp2XEY7Qa6SgXXuBjKvbfCUEqxfd+jTI9e8S4Kk25jhndfJlzZgG76SSwtEpkeJh6LAO57sePg8Q3VxhVCiOKncZj/RYxhr1FEwmsu0EKAhqzmC3ezTj5HPY+xxCgpougEKaGNcnZhUlE052hTBLOO4zDaf5azL38jIyumCFc1smX7Iarq2/AHwyhNI5lYYnF+wv2gHu6mvKaFUGlV3rFTiRinn/+7dCCrlEZ96x5adz1EuKoB3fBhWykW5yYYvPQGg91vY1tJ5qeHOfXDv+XwE7/g7vuaD3jd8HHvB34mK5Pp2DZnXvq/zE+5ma7K+m3seeAjawQHipKy6pw/qWrYztEf//Ws7y3MjnPq+a+mA+ad932Y2uadeV+7YQbyBruO4xBbmOHUD/82HchquklTx7207DxCSVmtF8wmiEyP0H/+Fcb6z+I4NpNDFznz0tfpeuzT667yB7fIv5VMgAPVjR1s2/sw5TUtGKYfx7FZnJ+k79xLDPeccOcy2ykun3iG+pbdBEpupuDzjTH9K1ehum6yo+sJ/MGbLzb+XjL9Iboe+wzn3/g2Qz0ncGyLaGSKaGTqmi0VobJqdhw6TnPH4YJniYUQ4nZwO3G53dREbssdyNw2usVtUwSz8eg851/7VjqQXZ5L2n7g8Zw96CvrWmlqv5dUcsntRrRGwNb37kvpXvFKaXQc/AA7Dn1wVc1QfzBMZX0blfXbeOflb2BbSWYnBug+9Sx7HvgI1+bPlKZRVpXd/cS2razgzvQFqPDm314v0x+konZr9j69ebzLt65DZdWrttkox7G5dOK7LMyOAW4gu/fBj7qlkDIzuv4QgVA51Vs66D71PS6feAbHsRkbOMvAhde8tq1rX7ktZ9pbdt7Pngc+gmH6s57jC5RSXtOMYfroP/8K4E7RmBi6SPOOw+95pjCzzFMqGScRW1izvFUxUkrhC5RQXt3MSO8pLMemomYrpj/ovk8NH6FwNVUN26ne0oEvUCoZWSGEEEWp6INZx3G4eumNrNvcbbsfYue9H0LT89+GV0ph+tZedR2PRdLBEbgF2DsOHkfPcctYebfymzuPMDt5lf53XwJg8NKbtO56kHBl46rnbGaRmVFGrpxKP27ece/qQNajlELXTToOPM7MWD8Tg+fBceg9+zxNHV0bylqGKxvYdeTHVgWyy+NrmsH2/e9nuOckyYRb53R6tGfNudC3glKK8pqWdDUA20oyOzlAuKpxUwV7juNw5ezzXHjTbVW7bc8x7jn8JLrpw2uyvfy/TfW6hBBC3H2KPpi1Uon0QiFwW1R2HHj8plcxO47D5NBFYgvu6n+lFG27j+UMZDO52x1l6PKbpJJxkokYw1dO0dnVcMd86DuOw0jv6XQm3F3489C6GWRNN9m29xiTQxdxHJtoZJqJwYs0ddy77rlp6XwfvkBJ3u2UUoRKqygpr02XH4tGptNzbN9L5TUtBEoqiC1MAzDUfYKm9nvXfe8Uk9jCDD2nn8OxLYLhKnZ0PbHSYrbApzNzaoLj2HlqFwohcrFJMsmrWKxeGLvMTw2VHCrY/EW3AmuUKIMYlBKk8bavVs+UYpFJXksvSipnNyGab/NRidup6IPZaGQ6q85l/dY9+At0S3dy6BLL68l9AXcawXpBl1KKkvJaSisamJ3oT4+z4+Bx1B1SJsixbbcersd9vXkWumVQSlFR25ou+wQOE4MXaOq4d83nabpJTVPn+uNrWlaWN+VVcXiv/8j6AiU0dXTRfcrtST010s1Qz9u0dL5vQxc0WU0IbtMF0OL8RLp5h2H40HXDayRQ+OPJ7F6WjEdJxBbWvWsihHClWOB1fjZdoD6XRn6EY3wTKEyFlyTzvManGecFTEo5xH+nhX9XNIt9YgzzMh8nhbtA9T7+J+38/G0+KnE7Fc+lVh6Lc+NZ7VmrGgvTLs2xLSIzo+nHoXAVPv/G6mdqmkG4qmHlGOcn07e+7wSp5FLWQqBwZcO6LUOXmf4goYxFa5GZkTVLqLnPCREoqdjA6OqaxgC3J8O3nMUvKa91D8O2OPfqP9J//mUvwM4+ruUaxo5tE49GGO1/h4ELr+Qa+j1j+oLpKSMLs+OceenrjA2cY3a8n9nJq+mvuclBIjOjLEXnsa3UdTccUEp5c8fdD8FUYomrl153q45I8wIhNkCh40fDYLlT1bVfDnm6/d2gWU4xyrNYLLLEGFf4q4Lv4+bZZJ8HcTcr+lRiLKNAvdJ0QuHqgmSPLCtJYmmlraw/VLZuqapMwZKVrkwpr1/9ZlvRnk8yESOVWLmltbFA06WUllUXNx5bwEol1rwFb/oC6IZvo3vY0Fa2lXL3bSWwUkmsZJxUMk4qsZQ1/zqZWGLkyil8gVIM049u+tAN98v0BTByLDAECJSUs+/ox3j7+0+TjC+SSsQ4+/I/MHDhVWqb7yFc2YBhBrBti8TSItHIFPNTQ0SmR1iKzdPYdoCt9zy4wddceOHKBmqbdjI2cA7HsRnqfouh7rdzVivQlIbuCxCuqKd5x31sae9CN3wb/ndYVb+dULjSnRYC9Jz5AfHYAlu2H8QXDIPjuAvplhaJx+ap37qHYOnGup4JcaczCfMw/0ScaZLMkmCaJSa4yB+tma29GTZJMgNEhxQSMEKSCDGG0QkRorloMtViEwSzyYygStO0DZV62oh0u1SPYW40mPK2960ch2PbWdnjzc62Utj2ylV4rkVZa8n8HbljrW4YkenayhGFMDMxwFvP/G+sVBLHsXBs28sEZv9BjsfmOfX8VwHlLfJzs79KM2hqP8S+hz6ec3ylFDVNOzn0/k/xzktfJ7YwjePYzE0OMjc5WNDXUmiO46DpBjsOfZCFufGMaTzOqjrIABYWVizJVCzC1GgPo/1nOfDIv8fnzz/HOZMvWErHoQ9y9uVvuO8HK8XAhVe5evF1N3h2vLm0jp2uAiLBrBAuhU4p7ZSyclfSJskAf3/LgtkK9lHOHmY5i0GIVj6BKtAUhs3KwaGXv+YM/5E6HinotA5x84o+mF19NXhrroSu+5oz8wlycXbzCnwOHStFMh7N2RktzzO86QCAbQFJUom1O30ppahr2cUDT/4K3ae/z0jvGZLxxTWfY5h+yqqbaOro2uBxFZbjOMSj81w++QzDV06SWFpEaTr+QCn6tRctjoNtW+mFjo5tgeMw1n+WS29/l70PfpSN/OKUUrR0HsGxLS6feIal6Jw3vI1jZf9+FGrtBX3qmm2FEAXnp46H+TYznCJAAxXsl39v2IzxPSyiWBSuC6QojKIPZrOyfLaNlSrMm0hpRtat7fUCl2slM1quKqVdx23y4qcbJpquY3uZ61Ri6boWB2VOUdB046YrT9yI0soGDj761E1Nqw2F8zfbWKaUIlRWw/5jH6fj4AeYHulhdmKA2MIMqVQCTWkY/iCh0irKqpsor20hFK5GW6MVa1NHF2XVTYB7x2Cj763mHYepqG31nudH11c/Lx6L8Nb3/pKZsT4A6lp203Hw8Yx50ZnH5Ab4VirOwswYF9/+LtOjPQAM95ygff9jGzpH4HYQa911lPqte5gYvMDMWB+x6By2lULXDXyBUkrKaiirbqKsKl+ZO7dzWeO2gwD4AyUbrNGsaD/wGE0d97nPC5beUG1nIe4WCkWQLQTZsv7Gd4k4U8xwav0NxW1R9MFsILQyX9OxLWILMzfcCCCTrpv4g2Gi3vzJeHQOx7Y2XJEgcy6v4QtgbnDx2GZg+oKYvmA6KM18retxHDudeQM3cLgdJasCobL3rAatG5QqSspqCIWrae48gnvvnKx6rSvbrj1WRe3W636PK6WorGulsq417zaO43DlnR+kA9nK+jYOPfbpnI1HMpm+AP5gGXv8QV75py9hpRIk4lEWZkY3HMwuH2OwtJKWnffTsvN+cp2jtY5DKUXNlh0b3l/m82qb8nfCE0KI9cxxliXGbvdhiDyKPj1RWlGbldmbHr1SkFXQStMo97JfANGF6awFYWuxrRSR6ZH041C4GtMXWOMZm4tuBigtr0s/jsyObnhOcDIeJTqfWQmhEU0r+mumgknPu1WaO/fW68q2/HU7Wak4Y/3n0o+bOw6vG8guW67za/q9klqOQyIRvaHjWOscCSHuXI5XecEiTooYFks4WN73C7PAzEnvJentI4ZN6qb24eAwzvPeQjhRjIo+ygiV1RIKV6fbqo71n6Pj4HH8wdKbGlcpRW3zPfSffwXHsUksLTI9eoXG7QfX/FB1HIeFufH08QBujdQNV0JYGfuWlya6wfGVUtS27GJi6CIAi3OTRGZGqajduu65mRnrIx6LLI8kGbEikkrEs343oXDVdQWQlpXMKLOmMIzCLMYUQtw+MUbo5a/TDQhyqeNhajl2w/twsFngCsP8C5O8xCJXsYiiMAh6c3INclcDClDHNj7rlSbLR/MaPcQY53mG+TZznCPBLAqFjyrK2csWfpRajqETyDsHeCXgXmCJMSJ0M8dZ+vn79DaL9PIu/5W8HUgx2MZPE6Qh589F4RV9MGuYfhq37efyyWcBt9h779kf0nnvh3K2Vr0e1Y3tlFbUufVmHYfecy9S17JrpRNSDo5j03fuxXSm0jADbNl2YMPZrcyx47GIO2ewQPNtddPvBtVewBFbmLmhQvhKKRra9tF96nsklhawrSR9517gwMNPofT859xKJek992J60VWwtIKa5p2ScSsWXjbU5RBfimz4/eE4DhNXL5CIu9lY3TApKa+5hQcrhHgvxBjmHP8Zm/x33/byuzcUzC4HmJf4Ey7xP4gzsWqbOd5hlGfzjlHOXtr4NGuFKwYhFujhJL/BKN/HIblqmwle5Ap/QT3H6eKPKGFbzoDWJs5b/CqTvMwSY6SIwjU1dhfp5SxfyHs8Gn4aeUKC2fdQ0U8zANi684GsWqc9Z35Iz+nvpxcm5eJ4K7Gjkam8DQ0MX5Dt+9+f/oCfHrvCpRP/hpVK5Cx8b9sWVy++zuDlt9Lf39J+iHBlvgUr11KEK+rTjxZmx5ke6y1YhtYfKMEfWMlYj/SdWfMcrSUUrqZ11wPpx8M9p+g7/1LOYvfuIqEk3aeezegcpmjb/dAdU3v3TmD6gllzXAcuvEZiaXHN98fy+3584BwX3vqXdLa/oq6VUJkEs0JsdjoBSthOgHpMytAIFKwMl0OSd/g8Z/lCOpANUE89H2Arn6CRD1NCK9dmODX8+KmljF1Uc2SdLo+KBLO8wicY4bs4JFEYmJThowqdlW6DNklG+A6v8RniTOYczcFimjdZoNcLZN0jWk3L+1VMrX/vFkWfmVVKEQxXsevIj3H6hb/HtpLYVpILb32Hkd4zbNl+iIq6rV7LTIWVirO0MMf89BDTo73Mz4xw+PjPUtWwPefYTe33Mjl0maHut8BbIDM/M0LbrqOUVTehGz5sO8Xi3ARXL73BcM+J9K3WcFUjnV0/snYpoWv2V7d1N71nX8C2U9hWkjMvfI2dhz9MdUM7uuHDcWysVJJkIkYyHqOyrnXDC6gMX5Capk4GLrwKwOz4AKdf+Dtv1Xk1SlPYlkUqlSC5tIhmmJRVbcmZmVNKsX3f+5ke7WVqpBvbTnH+tX9idryflp33U1peh2YYWKkEkekR+s+/wvjAu+msbG1zJ627j0pWtohoukFz52FmJ6+C4zA9eoU3/u3P2bbnGBW1rZj+EJqmeRcnCeKxBeanhhjtO8PE0KV0dQvDF6Sz64m7ai60EHeqMnZxnFewWMJikSQRFunjdT5Lkrn1B8jDwWGEZ+jmz70pDIpWnmIfXyBIkxcw2ySY5hJ/ygX+0MsOK/bwebbxM5iUoeNn7bybw3n+GzEG0QmxlZ9iKz9FKdvR8JFghjGe4xJ/TJSrAEzxBt38GXv4T6uysxoBDvNlUixm7MHmDL/DLKcBqOAgB/gveY9LoWXVBRa33qb4NHKDzi6SiRgX3vhnUsklcBzmJq8yN3nVW0jiXkk6jp1V+H29EjyabrD3wY9ipRKM9r3j3U49z+TgBQwzgGaYbgCYXMoat7SinkOPfopASfl1BWyV9dtoaNvH8JWTAEQjU5z8wd9g+oLouukGs97cRMMX5JGP/ha6Ub7OqCu27X2EsYFzxKPzgMNI72nGBs5h+oKglNcswi1c39TexcFHP5l3LNMf4sAjT3HyB08zM9aHbacY6n6b4Z6TGL4Amm5gWykv+7tyG6aqoZ39xz6BYd45i+LuBG691/cxM9bHUPcJwGF2vJ+T4wPops9rjqGB42BZSaxUYlUrYn8wzJ4HPkJ1Y4dcqAhxB1BomIQxCQNui24/tWjc3PQ3B4sevoLt1WSt4ABd/BEmFRkBpI6fWnbzO0S4yFW+ATgM88908msYbKxKUIxBDMIc5s9o5idR6Ol9hGiinD3U8AAv8hPEGQccBvganfw6PrI/XzV0angg63s2KXysNHLxUUU9j9/1jSSKyabJhStNo233UQ5/8OeoamjPWnDlOHY6Y3ttByNfoGTNOalKKUx/iIOPfpLOrg96GV739moyESMenScZX0yPqxsmW9q7OPIjv0hZddN1f6Brms6eBz9Kw7b9K6/BcUjGoyxF54jHIqQSS24QseGC/yuvJVzZwMFHPklJxi1gt7VrhHh0nsTSIlYynrPTU67xQuEqDh//OVp3HU3X/HUcm2Q86p2blcYEhi/Itj0Pc9/xzxIsrZRgpwjpho/9D32cew4/6XXZcnu9W8k48eg8S4uzLEXnSMajK4GsUviDZWy9534eePJX2dLeJb9bIcSaEkwzw4n04xY+ek0gu0LDpJWnWA5JZnmHRa5c1/46+RwtfAwNY9U+FIoq7qWNT6W/t8gAi/Re1z5E8doUmdllSmlUb9nB/fVtzIz3M371PHOTV1mKzmOlEihAM3z4AiWUltdRVb+N6i0d67bGVEphmH467/0QTTvuY6T3DFPDl4ktzGClkmi6QSBURmVdGw3b9lFe05IuJ3T9r0HhD4bpev9nmBy+xEjvGSLTwyTiURzbRjcMTH8JJWU1VNS1rpRCuo7xa5t38uCP/RojvaeZGLxANDJNKhVHodBNP4FQmNKKehpa921oPF8wzL6jH6N114OM9J5mevQKS9E5bMtCN0yCpZVUNWynoW2/Oyd4A2WW6lv3pn8vbnZ7Y9dVDW370oG6GzBvmuuxorC8CLHj4Ado2fk+pkevMDPWx+L8hBvA2hZKaRimD18gTEl5LRU1zZTXtOAPhVlu+yuEEGuJM5kxTUFRxu68FQQUilLa0Ql4HbZiLDJAOXs3tC8/NWznZ9fJlCrqeZxL/AkOFjZxolylkoPX98JEUdpUwSy4H8a64aNmyw6qGzvcBSpWCsdx5+QopaHpejrI2egH7/J2peV1dBx4nI79j2FZSbdfvNLQdOOGA9jcr8GkrmU3dS27cWzLfQ04Wfu6nuO/dvxASTlte47RtuchbCuFbbvnR9N0twqEWr9IfeZ4KEV5TTNl1U1eJjzlnRsdXTc2FMBmjte84z7Ycd91v66WziPX9RyRm1KKQKiMLdsP0LjtAEDWVJHMtrISvAohrpdNAiddBUB5c1/zU9dkVO3raBlbwSGCNK8zviJIAwrTm8Pr3NScYFFcNl0wm2m50LqmFbaV7HLwZmi3to7mcpCgblHL1+XOVLrhK9jMHvec6zddFk0UC5Vev6iU/E6FEIWxvHjLDUptYoy4CZs82dk4E1heeTCFjo/qDe+rgr0bmr+qMLP2v1ZtXbG5yD1aIYQQQhRUgIasFf1u2azcHbQcbEb5t3R9WD81hNl46+ogW27uYMWmJ8GsEEIIIQpKJ0grn2K5huww3+EK/weLeLqt7HKvrVGepZuvpJ/bzEcIUJ9r2Dz7CuXN+Iq7w6aeZiCEEEKI4qNQbOMzDPEtJngBmyVO8hsM8g/U8Sh+akkRYZLXGeUZUrittis4yC5+m3ytYvPtS9zdJJgVQgghRMGZlPM+/pK3+RyjfA+bBGM8xxjPrdpWYVDPY3Txx15TBQlQxcZJMCuEEEKIglMoQmxlP79HhG4W6MagBJNybBIoTPxUUcF+mvlJGviATBkQN0SCWSGEEEIUnIPDPBd4ladYoJtS2jnMlylnDzYpNAx0QugEcENfCWLFjZFgVgghhBAF55DiHT7PPBdQmBzii9Ty8CYNWjOP2fbKjIliIdUMhBBCCFFwcSaZ5FUADEooZ++mDGQVGibh9OM4k9heTVxRHCSYFUIIIUTBKTSWM5pJ5rnEl4hwmQSzJIlkfaVYxE2h0EwAAATuSURBVCaJ4/1XXBRhOtOPFuhhmjeL8DjvXjLNQAghhChCDjYOFg4WNklsEtjESbKAxVJ6O4tFFujFIISGHw0fGgYK3WsTuzpv5QZiDg4pbCwcb3yLJaIMZnXHijPFIn1oBNDxo2F64y6Pnzvb6qOKOh7hKl8HbC7xJXr5K0wqVnXs0vDhp4ZKDtLMT1DDg6s6dt0uCkUDx7nEl7xzFOMtPsdB/oBqjqAT9H5HCS84n6OMe7y5wOK9IMGsEEIIUYSG+Q7n+QNSLGAR877i2CRIsZjebpJXeYYjaPjQ8aMTRCeIQQkd/DKtPJUzKLzAFxnkH7PGt4ljkcDKGL+bL9PH016QHPDGDmEQ5hBfpIqunMevMDjA7xFlkClvukGSeZLM59w+wkUmeZke/oKtfJyD/CE+qooioK3hQRr5EEN8C4AIF3iJjxGgFoMSbCwsolhE0QlynFcJ0XKbj/ruIcGsEEIIUYRiDDPFa+tu52Clmw5cq5EP531ehItM88YGxk+S9FrNZlJoJJjOe0zjvMgl/oR53k1v72ZbtawtHS9DvJwNtonTx9+g0LiPP0NhrnuMt5qGny7+GIsYY3zfy5gniTGcY2s9x/fErSTBrBBCCFGEwnTSxmduaowK9uf9WQ1Hb2rep0IjyJZV33ewuMyf8g6/S4oFfFTSya/RwBMEqF81xcAhSYJZ5jhHL3/FLKcBhwG+Tju/RDWHs7Y3CdPKU+mpFpnzWdfio5xWPplevFVKx3W8VkWQJh7kawzzba7yD8xzniRzOFho+DCpIEQL1RzBpGLDY4ubpxxnzTeyzG4WQojcbsW9T/mbK9IKtcAo1236Qi5eyhzfwWGSV3iBHyXFAgalPMBXaeQJ1qsl6+CwQA/P8ShLjAFwgN9nJ7+5ah9rHcNa49/I83KPY2MRI0UMsFEY3vQOP8tr64thesQdJu8JlcysEEIIUYRuZTB0K8fu429IsQBAPY/RwPGci9ByHVMJbYTZmQ5mowzl3O5GFOo1u+PoGJRiUFqQMcXNkdJcQgghhCgIhxRznEs/LmffqmkF642QSZOcm9gACWaFEEIIUSDuYq5lNvHremaMYSJcTH8vzI6CHp24M0kwK4QQQoiCUBiU0JZ+PMqzJJhed46ug0OSGc7w+fQUA5NyanhI5p6KdUkwK4QQQogCUTTzEZQ3PWCWM7zOZ5nmDZIseA0abBxsbFKkWGSBK/TxNM/zJAN8LT1OK09tuFKBuLtJNQMhhLgxUs1AiBxSRHmDX/A6f7lvaZ0gIbYSogmdEsAmSYQ448QY8RopLL/9NbbwJEf4StE0TRBFIe8bQYJZIYS4MRLMCpGDO2VglrN8gV6eztvQYTWNEC108It08B8wCEsgKzJJMCuEEAUmwawQebhdvSzmOMsg32SCl4gyQJIFHJK4LRdMDEoJ0EAFe6njUep4BD91EsSKXCSYFUKIApNgVogNWG5XmyKKRRSbpNc+wcQghE4g3bJWglixBglmhRCiwCSYFUKI907ev7lSzUAIIYQQQmxaEswKIYQQQohNS4JZIYQQQgixaUkwK4QQQgghNi0JZoUQQgghxKZlrPNzqZEhhBDvHfmbK4QQ10kys0IIIYQQYtOSYFYIIYQQQmxaEswKIYQQQohNS4JZIYQQQgixaUkwK4QQQgghNi0JZoUQQgghxKb1/wEMt2kzOorCmAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "dark"
          }
        }
      ]
    }
  ]
}