Gaussian Mixture.ipynb

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      "C:\\Users\\40kmp\\anaconda\\envs\\damir\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.datasets.samples_generator module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.datasets. Anything that cannot be imported from sklearn.datasets is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Regions</th>\n",
       "      <th>Population вensity per km2</th>\n",
       "      <th>Total population</th>\n",
       "      <th>Total population per 1k</th>\n",
       "      <th>Regions, rus</th>\n",
       "      <th>Share of people with higher education. 2010. %</th>\n",
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       "      <th>Unemployment rate. 2017. %</th>\n",
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       "      <th>Terrorist_crimes_2017</th>\n",
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       "      <th>Criminal possesion of weapons. 2017</th>\n",
       "      <th>Fertility rate. 2017</th>\n",
       "      <th>Museum attendance. 2010. per pop</th>\n",
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       "      <th>0</th>\n",
       "      <td>Adygea Republic</td>\n",
       "      <td>59.43</td>\n",
       "      <td>463088</td>\n",
       "      <td>463.088</td>\n",
       "      <td>Адыгея</td>\n",
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       "      <td>0.40</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.197</td>\n",
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       "      <th>1</th>\n",
       "      <td>Altai Republic</td>\n",
       "      <td>2.37</td>\n",
       "      <td>220140</td>\n",
       "      <td>220.140</td>\n",
       "      <td>Республика Алтай</td>\n",
       "      <td>18</td>\n",
       "      <td>24872</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.1</td>\n",
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       "      <td>0.14</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5.39</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.174</td>\n",
       "      <td>39.2334</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Altai region</td>\n",
       "      <td>13.79</td>\n",
       "      <td>2317153</td>\n",
       "      <td>2317.153</td>\n",
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       "      <th>3</th>\n",
       "      <td>Amurskaya Oblast</td>\n",
       "      <td>2.18</td>\n",
       "      <td>790044</td>\n",
       "      <td>790.044</td>\n",
       "      <td>Амурская область</td>\n",
       "      <td>18</td>\n",
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       "      <td>5.9</td>\n",
       "      <td>14.9</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arkhangelsk region</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1092424</td>\n",
       "      <td>1092.424</td>\n",
       "      <td>Архангельская область</td>\n",
       "      <td>17</td>\n",
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       "              Regions  Population вensity per km2  Total population  \\\n",
       "0     Adygea Republic                       59.43            463088   \n",
       "1      Altai Republic                        2.37            220140   \n",
       "2        Altai region                       13.79           2317153   \n",
       "3    Amurskaya Oblast                        2.18            790044   \n",
       "4  Arkhangelsk region                        2.64           1092424   \n",
       "\n",
       "   Total population per 1k           Regions, rus  \\\n",
       "0                  463.088                 Адыгея   \n",
       "1                  220.140       Республика Алтай   \n",
       "2                 2317.153         Алтайский край   \n",
       "3                  790.044       Амурская область   \n",
       "4                 1092.424  Архангельская область   \n",
       "\n",
       "   Share of people with higher education. 2010. %  \\\n",
       "0                                              20   \n",
       "1                                              18   \n",
       "2                                              17   \n",
       "3                                              18   \n",
       "4                                              17   \n",
       "\n",
       "   Median salary. 2019. rubles  Unemployment rate. 2017. %  \\\n",
       "0                        23943                         8.8   \n",
       "1                        24872                        12.0   \n",
       "2                        23480                         6.9   \n",
       "3                        40376                         5.9   \n",
       "4                        41030                         6.5   \n",
       "\n",
       "   Share of poor people. 2017. %  \\\n",
       "0                           13.1   \n",
       "1                           25.1   \n",
       "2                           17.8   \n",
       "3                           14.9   \n",
       "4                           14.1   \n",
       "\n",
       "   Gross regional product per capita. 2016. rubles  Terrorist_crimes_2017  \\\n",
       "0                                         17450.16                   0.40   \n",
       "1                                         18489.39                   0.14   \n",
       "2                                         18098.68                   0.00   \n",
       "3                                         30784.41                   0.02   \n",
       "4                                         32647.68                   0.03   \n",
       "\n",
       "   Murders and attempted murders. 2017  Criminal possesion of weapons. 2017  \\\n",
       "0                                 0.31                                 1.26   \n",
       "1                                 1.75                                 5.39   \n",
       "2                                 0.68                                 2.40   \n",
       "3                                 1.42                                 5.00   \n",
       "4                                 1.54                                 1.60   \n",
       "\n",
       "   Fertility rate. 2017  Museum attendance. 2010. per pop  \\\n",
       "0                  1.52                             0.197   \n",
       "1                  2.36                             0.174   \n",
       "2                  1.64                             0.181   \n",
       "3                  1.71                             0.649   \n",
       "4                  1.65                             0.896   \n",
       "\n",
       "   Share of people using internet everyday, 2016, %  \n",
       "0                                           40.1610  \n",
       "1                                           39.2334  \n",
       "2                                           43.1501  \n",
       "3                                           44.0290  \n",
       "4                                           53.8225  "
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   "source": [
    "# Gaussian Mixture\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics.pairwise import euclidean_distances\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns; sns.set()\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.datasets.samples_generator import make_circles\n",
    "from sklearn.cluster import SpectralClustering, KMeans\n",
    "from sklearn.metrics import pairwise_distances\n",
    "import networkx as nx\n",
    "from sklearn.cluster import MeanShift\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from itertools import cycle\n",
    "from matplotlib import cm\n",
    "%matplotlib notebook\n",
    "#%matplotlib inline\n",
    "df = pd.read_csv('BUND2.csv',delimiter=\",\")\n",
    "df.head()"
   ]
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       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gaussian mixture\n",
    "plt.style.use(\"bmh\")\n",
    "X = np.zeros((85, 11))\n",
    "X[:, 0] = df['Share of people with higher education. 2010. %'].to_numpy()\n",
    "X[:, 1] = df['Median salary. 2019. rubles'].to_numpy()\n",
    "X[:, 2] = df['Unemployment rate. 2017. %'].to_numpy()\n",
    "X[:, 3] = df['Share of poor people. 2017. %'].to_numpy()\n",
    "X[:, 4] = df['Gross regional product per capita. 2016. rubles'].to_numpy()\n",
    "X[:, 5] = df['Terrorist_crimes_2017'].to_numpy()\n",
    "X[:, 6] = df['Murders and attempted murders. 2017'].to_numpy()\n",
    "X[:, 7] = df['Criminal possesion of weapons. 2017'].to_numpy()\n",
    "X[:, 8] = df['Fertility rate. 2017'].to_numpy()\n",
    "X[:, 9] = df['Museum attendance. 2010. per pop'].to_numpy()\n",
    "X[:, 10] = df['Share of people using internet everyday, 2016, %'].to_numpy()\n",
    "\n",
    "gmm = GaussianMixture(n_components=3).fit(X)\n",
    "labels = gmm.predict(X)\n",
    "\n",
    "plt.rcParams.update({\"figure.figsize\" : (10, 8),\n",
    "                     \"axes.facecolor\" : \"white\",\n",
    "                     \"axes.edgecolor\":  \"black\"})\n",
    "\n",
    "plt.scatter(X[:, 1], X[:, 8], c=labels, s=20, cmap='brg')\n",
    "#plt.scatter(X[:,1], X[:,8], c=clll, cmap='brg',s=20)\n",
    "\n",
    "plt.xlabel('Median Salary')\n",
    "plt.ylabel('Fertility Rate')\n",
    "plt.title('Gaussian mixture')\n",
    "plt.show()\n",
    "#plt.savefig('Gaussian_mixture.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to rearrange colors\n",
    "clll = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "clll_green = clll == 2\n",
    "clll_blue = clll == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "clll[clll_green] = 0\n",
    "clll[clll_blue] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population вensity per km2</th>\n",
       "      <th>Total population</th>\n",
       "      <th>Total population per 1k</th>\n",
       "      <th>Share of people with higher education. 2010. %</th>\n",
       "      <th>Median salary. 2019. rubles</th>\n",
       "      <th>Unemployment rate. 2017. %</th>\n",
       "      <th>Share of poor people. 2017. %</th>\n",
       "      <th>Gross regional product per capita. 2016. rubles</th>\n",
       "      <th>Terrorist_crimes_2017</th>\n",
       "      <th>Murders and attempted murders. 2017</th>\n",
       "      <th>Criminal possesion of weapons. 2017</th>\n",
       "      <th>Fertility rate. 2017</th>\n",
       "      <th>Museum attendance. 2010. per pop</th>\n",
       "      <th>Share of people using internet everyday, 2016, %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59.43</td>\n",
       "      <td>463088</td>\n",
       "      <td>463.088</td>\n",
       "      <td>20</td>\n",
       "      <td>23943</td>\n",
       "      <td>8.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>17450.16</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.197</td>\n",
       "      <td>40.1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.37</td>\n",
       "      <td>220140</td>\n",
       "      <td>220.140</td>\n",
       "      <td>18</td>\n",
       "      <td>24872</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>18489.39</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5.39</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.174</td>\n",
       "      <td>39.2334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.79</td>\n",
       "      <td>2317153</td>\n",
       "      <td>2317.153</td>\n",
       "      <td>17</td>\n",
       "      <td>23480</td>\n",
       "      <td>6.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>18098.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.181</td>\n",
       "      <td>43.1501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.18</td>\n",
       "      <td>790044</td>\n",
       "      <td>790.044</td>\n",
       "      <td>18</td>\n",
       "      <td>40376</td>\n",
       "      <td>5.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>30784.41</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.42</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.649</td>\n",
       "      <td>44.0290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.64</td>\n",
       "      <td>1092424</td>\n",
       "      <td>1092.424</td>\n",
       "      <td>17</td>\n",
       "      <td>41030</td>\n",
       "      <td>6.5</td>\n",
       "      <td>14.1</td>\n",
       "      <td>32647.68</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.896</td>\n",
       "      <td>53.8225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population вensity per km2  Total population  Total population per 1k  \\\n",
       "0                       59.43            463088                  463.088   \n",
       "1                        2.37            220140                  220.140   \n",
       "2                       13.79           2317153                 2317.153   \n",
       "3                        2.18            790044                  790.044   \n",
       "4                        2.64           1092424                 1092.424   \n",
       "\n",
       "   Share of people with higher education. 2010. %  \\\n",
       "0                                              20   \n",
       "1                                              18   \n",
       "2                                              17   \n",
       "3                                              18   \n",
       "4                                              17   \n",
       "\n",
       "   Median salary. 2019. rubles  Unemployment rate. 2017. %  \\\n",
       "0                        23943                         8.8   \n",
       "1                        24872                        12.0   \n",
       "2                        23480                         6.9   \n",
       "3                        40376                         5.9   \n",
       "4                        41030                         6.5   \n",
       "\n",
       "   Share of poor people. 2017. %  \\\n",
       "0                           13.1   \n",
       "1                           25.1   \n",
       "2                           17.8   \n",
       "3                           14.9   \n",
       "4                           14.1   \n",
       "\n",
       "   Gross regional product per capita. 2016. rubles  Terrorist_crimes_2017  \\\n",
       "0                                         17450.16                   0.40   \n",
       "1                                         18489.39                   0.14   \n",
       "2                                         18098.68                   0.00   \n",
       "3                                         30784.41                   0.02   \n",
       "4                                         32647.68                   0.03   \n",
       "\n",
       "   Murders and attempted murders. 2017  Criminal possesion of weapons. 2017  \\\n",
       "0                                 0.31                                 1.26   \n",
       "1                                 1.75                                 5.39   \n",
       "2                                 0.68                                 2.40   \n",
       "3                                 1.42                                 5.00   \n",
       "4                                 1.54                                 1.60   \n",
       "\n",
       "   Fertility rate. 2017  Museum attendance. 2010. per pop  \\\n",
       "0                  1.52                             0.197   \n",
       "1                  2.36                             0.174   \n",
       "2                  1.64                             0.181   \n",
       "3                  1.71                             0.649   \n",
       "4                  1.65                             0.896   \n",
       "\n",
       "   Share of people using internet everyday, 2016, %  \n",
       "0                                           40.1610  \n",
       "1                                           39.2334  \n",
       "2                                           43.1501  \n",
       "3                                           44.0290  \n",
       "4                                           53.8225  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numbers = df.drop('Regions', 1)\n",
    "df_numbers2 = df_numbers.drop('Regions, rus', 1)\n",
    "df_numbers2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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rUICEpCABCQgAQlIQAISkIAEJCABCUhAAgpxzgEJSEACEpCABCQgAQlIQAISkIAEJCABCVRAQCGuAshWIQEJSEACEpCABCQgAQlIQAISkIAEJCABhTjngAQkIAEJSEACEpCABCQgAQlIQAISkIAEKiCgEFcBZKuQgAQkIAEJSEACEpCABCQgAQlIQAISkIBCnHNAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIVEFCIqwCyVUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQCHOOSABCUhAAhKQgAQkIAEJSEACEpCABCQggQoIKMRVANkqJCABCUhAAhKQgAQkIAEJSEACEpCABCSgEOcckIAEJCABCUhAAhKQgAQkIAEJSEACEpBABQQU4iqAbBUSkIAEJCABCUhAAhKQgAQkIAEJSEACElCIcw5IQAISkIAEJCABCUhAAhKQgAQkIAEJSKACAgpxFUC2CglIQAISkIAEJCABCUhAAhKQgAQkIAEJKMQ5ByQgAQlIQAISkIAEJCABCUhAAhKQgAQkUAEBhbgKIFuFBCQgAQlIQAISkIAEJCABCUhAAhKQgAQU4pwDEpCABCQgAQlIQAISkIAEJCABCUhAAhKogIBCXAWQrUICEpCABCQgAQlIQAISkIAEJCABCUhAAgpxzgEJSEACEpCABCQgAQlIQAISkIAEJCABCVRAQCGuAshWIQEJSEACEpCABCQgAQlIQAISkIAEJCABhTjngAQkIAEJSEACEpCABCQgAQlIQAISkIAEKiCgEFcBZKuQgAQkIAEJSEACEpCABCQgAQlIQAISkIBCnHNAAhKQgAQkIAEJSEACEpCABCQgAQlIQAIVEFCIqwCyVUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQCHOOSABCUhAAhKQgAQkIAEJSEACEpCABCQggQoIKMRVANkqJCABCUhAAhKQgAQkIAEJSEACEpCABCSgEOcckIAEJCABCUhAAhKQgAQkIAEJSEACEpBABQQU4iqAbBUSkIAEJCABCUhAAhKQgAQkIAEJSEACElCIcw5IQAISkIAEJCABCUhAAhKQgAQkIAEJSKACAgpxFUC2CglIQAISkIAEJCABCUhAAhKQgAQkIAEJKMQ5ByQgAQlIQAISkIAEJCABCUhAAhKQgAQkUAEBhbgKIFuFBCQgAQlIQAISkIAEJCABCUhAAhKQgAQU4pwDEpCABCQgAQlIQAISkIAEJCABCUhAAhKogIBCXAWQrUICEpCABCTQRAI/+tGPsm9961vZf/zHf2TXX399dtttt2Xf//73syc+8YnZpEmTshUrVmTPe97zsrPPPruJ3X9Unw4ePJj9xE/8RPj///zP/8wuvvjiUdHvH//xH88OHTqUvfvd785+/ud/vu8+7969O5s8eXL2mMc8pu9rDfICa9euzf78z/8827x5c3bfffdl5513XnbllVdmv/IrvxL6114++tGPZh/72Mc6Nvmkk07KTjnllOzCCy/MLr/88sB54sSJg+yedUtAAhKQgAQk0CMBhbgewXmaBCQgAQlIYDQTQHj7vd/7vWznzp0Bw+Mf//hs3Lhx2amnnprdcccd2eHDh8P/82+OQ5BrelGI60+IQ6z68Ic/nH3pS1/KtmzZkp1wwgnJTplPfvKT2R//8R+H9iNMI6AdOHAgu/fee8O98v73vz972tOedlz/ohCH6DZnzpzjfofAfeedd4b76pFHHsk45s1vfnP24he/OFlGNlwCEpCABCQwWgkoxI3WkbffEpCABCQggR4JXHPNNdmrXvWq7MEHHwyWb6973euyJz3pSdkTnvCEY1fcu3dv9vGPfzz76le/Gv7vD//wD7PnPOc5PdaYxmmIJfv37w+Nveiii7ITTzwxjYb32Ur6TN+x+Dr99NN7vhoWZL/8y78czt+2bVuyQtyaNWuC1RvljW98Y/bSl740e9zjHpc9/PDD2R/90R9ln//854MYh9XkBRdccIxXFOIQtL/5zW925Hj06NEg8P3jP/5j+P073/nO7PnPf37PzD1RAhKQgAQkIIHqCSjEVc/cGiUgAQlIQALJEvjOd76TPf3pT8/uueeebOnSpRmWP1i9DVUQHj796U8Hq6BvfOMbfQk1yUKz4bkINEWI+7Vf+7Vs1apV2TOf+czsgx/84HF9x5qN/9+1a1cQsBG0Y8kjxMVj3/rWt2Z///d/HyzjEPTGjh2bi7EHSUACEpCABCQweAIKcYMfA1sgAQlIQAISSIbAO97xjuA6eMYZZ4TYcCPFf8MKiLhpWPK8613vyn7hF34hmb7a0GoJNEWI+9M//dPshhtuyH7pl34pxHNrLwhwX//618O9wD3RixD3ve99LyM231133ZW94AUvyLgvLRKQgAQkIAEJpEFAIS6NcbKVEpCABCQggYETuP/++7Mrrrgie+CBB7KXv/zl2Rve8IZcbfrbv/3bEKz+J3/yJ7MJEyYcdw7//zd/8zfZt7/97RBvjn/j4opr55Of/OTgqnjmmWced86MGTPCvwmE30noQABZt25d9spXvjL77d/+7WPn4kr7xS9+Mfvv//7vbN++faGuc889N1u4cGH2whe+MFu0aNGj+kMsPOq58cYbsyNHjgSXQtxxn/KUp4RzTjvttGPnjBQj7n/+53+yf/qnfwqJLYijh3UU9VMv/Zw7d+5x9f/u7/5u9pWvfCWILCS+QOBZvXp1iBXGeStXrgwWVd1YQ0U29Im2f+ITn8iuu+667KGHHgr9oh3Pfe5zQzuwuEJ0veWWW4Jr5fz587Pf+q3fyi699NLj2tmerIH2/czP/EyG9STz5XOf+9xxxzN/qGPPnj0hecFnPvOZINaS8KG9MFbjx48PolanMY3HR2syxpI2xxLbhov0P/zDPwRX6R/84Aehr7QLS00K8ds++9nPBr633nprGOfp06eHdv7sz/5s6H8Rhbqf8YxnZLhuv+lNb8p+9Vd/9dhlu7GI46T3vve9wc11zJgx2dVXX11E87yGBCQgAQlIQAIVEFCIqwCyVUhAAhKQgASaQABR5Dd+4zdCVxDPFixY0Fe3ECOIn0UAegLzI76dfPLJQZC5++67w7URTL785S8f5/7aixCHZR4i06ZNm4KoQkZT6kKA+e53vxsydGKd1Jr1E5c/hDzEk7POOisE3EeMRMRDRJs2bVrgEMW4oYQ4jkW0/Nd//dfQp/PPPz+IJ1gzIe6RfZb+E1Pvx37sx44xjUIcllP/9m//lmEFBSOOxbWRgiAHn9ZYY8MNShS0fvEXfzGch2sj2TcZg8j8//2//xc4/cu//Et2zjnnhGsjksKQ4//u7/4umzVr1rFqOmVNRXT89V//9XAMgtHP/dzPHTue6yPO0vZ//ud/DnUg8CHM7dixIxyHoEb5yEc+Elj1K8RxPQTHqVOnBiGZeHaMHeW//uu/wvgg1CLAwYO/M86Uyy67LIigw7lg57kRiKWHqzaWpNQP3ygEcn63Qhzz8zWveU2o+t///d/DvWKRgAQkIAEJSKD+BBTi6j9GtlACEpCABCRQCwJRKEC0wvWu36yWUVzBwoprI05QEK6wHHvLW94S/o5FGO53sfQixGElxXUQWbAGQ1SjYAlGIom/+qu/CvHrsIhCjEEcw+Ls9ttvPy7gPueQSIA4YFh+vf71r89e8YpXhGsNJcQRWJ++YOmHsNQqtu3evTuIKfyJxRkiVyxRiOPfiJ60EwGRsnnz5mBNhVXfS17ykpCZNk+JzDn2p37qp7L3vOc9od9wQGTFsuqxj31sSDSBMPnsZz87XPa2224LY4BIyv+R9TOWTkIcv3v7298exC7cmL/2ta+F8SVO4G/+5m8G4RMLNCzmYhnONbVfIY46YE+fKYwdbtU333xzEF8RGREOYRCTjmAFiRCLYPysZz0r+8AHPpAH8aOO+dCHPhSEVNgxrxhLxMnJkycfd2y3Qhzti9aLQ1mH9tRgT5KABCQgAQlIoFQCCnGl4vXiEpCABCQggeYQiJZMWIdde+21fXUMt0WC1mMVhmUQboDtBQs2xJkXvehFGXXH0osQF0Uhrvn7v//7x1WFCIOLJ0IR8buwWItulRy4fv36ICa1FgQz3GkR1WLcu6GEOMQ6LJao+81vfvOj+onoyP+fcsopwRItlijEIYphYYYFWWtBRPvCF76QzZkzJ1i35SlR0MIS61vf+laoMxZEuOgqiViGlVpr+djHPhYEU6zKENZiGUqIa3VBxS2ZMYhjjngJl9ZSphC3ZMmS7C//8i8fhejVr351sIh78YtfnL3tbW971O+3b98exC4ENCwa6Xu3BaEPd+RYcCUmq2rMrBr/v1shDos9uFLIpEoSFYsEJCABCUhAAvUnoBBX/zGyhRKQgAQkIIFaEECoQIAaTohD5CIWV6eCwHXVVVcd9ytcAKMFUusvcAd97WtfGyyoiNH1B3/wB8d+3YsQh8XQ+973vuBe+MY3vjFYRg2XaOKHP/xhcEkkO+yTnvSkEG8OizWsxYYqw8WI43r84NrZXnBVRPTi2jfddNOxX0chDguq6EbZeu5f//VfZ+985zuDlRxuinlKFOLoPxZirQXLr2gxxjjT39bC/zEHcFVFhIxlKCGO32/dujVY0n3/+9/PpkyZEiz/sIDEArHdorJMIQ5rt9/5nd85rj8IsMTn40/i4c2bN68jQuYfVpDMGywhuy24PzP3cUPGFfdTn/pU4IFbNmMcS7dCHO7JxJujKMR1OyoeLwEJSEACEhgcAYW4wbG3ZglIQAISkEBSBHCxQ0QYzjX1z/7sz4KlVWvBDRCRp5MQx3EIWFu2bAkxufg7yQGwRCImGgWLJES0WHoR4nDhJC5ajK1GH4hzRrIHrNoQZNoD8kfhKdZL0ojly5eHxAmcE11p4+9HStZAfzZu3Bj6hzhDzDD6SXKAWPh3LFGIe9rTnpb9yZ/8yaPmSnR5HTduXPbNb34z11yKQlwnd9bW9iOAtifWiPW1j+NwQhyNIiHEhz/84dA+3GCxACQBQ3spU4hDQMTqrbUQj46kEpRLLrkkuCR3KsSuw3KTOHe4lPZbops08w1LSeL+UboV4rCefP7znx/OJfFEq5tvv230fAlIQAISkIAEyiOgEFceW68sAQlIQAISaBQB4lzFLKRY9kRBbKRODiXgIEgR9wzrKmLBxYLghRUYAhUxvIoQ4rg2YhyCBS6GMRB/rBNRDYupGHMr/j/iENZ011xzTYijFgvWa2T6xGU2CnJDCXFYP+HWiQUbiSFiweUUl1xEKaziKJ2EOMQigvy3l36EuPaMsly7tf0xW2lrnb0Kca2CFzH6yFzayQqyTCHu3e9+93GJOOgXoiiZb/MW3EARy4ooy5YtC8kxsPTE4o7SrRCH5Wl0s+Yeypuwo4j2ew0JSEACEpCABHonoBDXOzvPlIAEJCABCYwqAggHV155ZXCrI6g9rqN5SicB54477gjWSPxJzCzcF7FKwn2Rf1OIIYZoNpQQN5QVEJZLJJPoJDbF9iLEIfysWbMmuMsi0lHIjvmUpzzlUd3ChXbDhg3ZunXrslWrVmUEyqfMnj07o39Y2A0lxJGogWMQ74gXhlUdGVdxKcVVtTU2W9OEOOYKMfTgRf+JtTZUPLZ+hDgEXeYD2VGxOItlOGs93ICf85znhEPJqNpvVtRYJwk+sHjEBXcoV2aYYAXamuyjWyGOBB3EBuR+IYagRQISkIAEJCCBNAgoxKUxTrZSAhKQgAQkUAsCUVQiThxWce3umZ0a2UmIw4X1gx/8YEbSgK9//esd47UhziGQtAtxiF/EW/vkJz+ZPfnJT35UlbiOHj169DghDsEPF0MyVbbHhkOEI5ECccAQ4RDjiBuGmMLv2mOlUSECYUw2gIXXzJkzOwpxZBvFjRWLv05WWVyL+GRvfetbQz+aJsRhyffpT386zBP6jzgKC/6PcWotwwlxJDZANH3Zy17WMeFFFG27EeIY26VLl4a5NFyMODLUYsGH5eJpp5027H1IHDhiCpeBbGgAACAASURBVMZxHSruXJyjCIhRDOxGiKPtXAN355e//OXZG97whlo8H2yEBCQgAQlIQAIjE1CIG5mRR0hAAhKQgAQk8L8EEJawZCOJweLFi4PLJaLcUAWh4E1velPITNkaWyxmYEVU+8pXvvKo03FJxWUPkaTdNZNYWGQ15boxy2e8ANkpsTqjtFrEIXZgAdXpHI5917veFbJqIuwh8BEjjcyhCIVYwLUnWSBmGJZtFIRGrPk6WcS1xvGKgl1rZ+kf7pGIPRTEwJjEIMaIS9U1lWyzCJxYwRFbEIEKMe6LX/xiNmbMmJAtt3XucHyM44ZFI667seASjWs01+BareX+++/PnvrUpwbrym6EOK4RBb5nPetZ2Qc+8IFHzUPE2J/+6Z8OVqDEh8PacqRCTD9E33YBOZ4XXbyZU1iynXPOOeFX3QhxMSkKWW+JM8e9ZZGABCQgAQlIIA0CCnFpjJOtlIAEJCABCdSGAIIJWSgRQBAAXvOa14Rsm2ecccaxNiLYfe1rXwvugrjqUXBr/exnPxv+HrOY4rpHIP+YrRNrKYQvguvHJAaILAh+sZBhlJhqiDmf//zngzsrhQydxHkjCQKlVYijHVgfIVyQ+GDlypXHrofLKcfee++92Tve8Y7gJotFHBZHuOM+/elPz97+9rcHUY5CvxGUEOBwCyRjKaJKJyGORBX0G8Htmc98Zjjv5JNPDtc5dOhQEHcQ/WIhblm0ukpZiMNiC3GLPiKCwp7ywAMPBGEVgat9XBEhY7w0mOK6GwsiKWIpCQ6YL8Rro2D5iIsmc4bSrRDH2CMWMj5Y2zGXmSMUYtsx1xDVSIiB4NUp6237jdlqLcn5r3jFK46JiohwiGgI1NT16le/+tjpeYQ42vLxj388WKNSWmPMtbfDf0tAAhKQgAQkUE8CCnH1HBdbJQEJSEACEqg1gZ07dwaxDIsvClZcBIvHwgnx6fDhw8cSMODS96pXvSpYExFLjYJQg+gSkyYgdHAurn1YNmENRcIGYrLNnTs3IzB9LFi2YUWGmIEwM3Xq1JBIgcysZPrE3ZDYWa1C3A9+8IMgHhKPjYKrJD9YtiEWUYgnhhgSLdJwhcTtD2soMmqS3RLhEBGJuhHUsJ6LlnFDxYjD0uozn/lMqIOsoVwHMY++IzwSuB8BjjYiXtIfSspC3Bvf+MYgFiGW0icScMRy7bXXBks0+v6e97wne97znhd+BRNES9giejJv+D1uv/yO+YMQRYEhgtnu3bvDHOB6jEW3QhzXYq4gtDLOjCnuy63jc+655wZryUmTJuW+JxF7EcxaxxzRMIrSiL3UGe8HjotCHGLfnDlzjqsLARMr0Hg+8xEBMmZNzd0wD5SABCQgAQlIYOAEFOIGPgQ2QAISkIAEJJAuAeJ6EeONWG4IUSQ1QGxClEMUwZUQy7JOQevJIEqssG9961vhXFwYOQ+X15e85CUhLheWTwgtZPGMSRyghQDziU98IkPUwWqN87CwIokEoheiTHuyBqzcyFyKZdOuXbuCdRZWfLjHYr3FT6swQj1Yaf3FX/xFEMoQUqLgiGCEBdWFF154bPCGEuI4gDq/8IUvBCGJfiNMkXUW90UsxBAWYYhFH4IhJVUhjvnwute9LvSBMULgbC/RNRkxDcEOAZVC4gxiB5JRl98hxMXkGbhD45aKBSFCLwwRQXEhxnrypS99aU9CXJxPWFeSHRdrTuYBbWL+Ms7RfbSbO5V7g2siVscxJ4HDi170ogz36vYShbhOdSBMc1+Rdfbyyy8P7tdmSe1mNDxWAhKQgAQkUB8CCnH1GQtbIgEJSEACEpCABCQgAQlIQAISkIAEJNBgAgpxDR5cuyYBCUhAAhKQgAQkIAEJSEACEpCABCRQHwIKcfUZC1siAQlIQAISkIAEJCABCUhAAhKQgAQk0GACCnENHly7JgEJSEACEpCABCQgAQlIQAISkIAEJFAfAgpx9RkLWyIBCUhAAhKQgAQkIAEJSEACEpCABCTQYAIKcQ0eXLsmAQlIQAISkIAEJCABCUhAAhKQgAQkUB8CCnH1GQtbIgEJSEACEpCABCQgAQlIQAISkIAEJNBgAgpxDR5cuyYBCUhAAhKQgAQkIAEJSEACEpCABCRQHwIKcfUZC1siAQlIQAISkIAEJCABCUhAAhKQgAQk0GACCnENHly7JgEJSEACEpCABCQgAQlIQAISkIAEJFAfAgpx9RkLWyIBCUhAAhKQgAQkIAEJSEACEpCABCTQYAIKcQ0eXLsmAQlIQAISkIAEJCABCUhAAhKQgAQkUB8CCnH1GQtbIgEJSEACEpCABCQgAQlIQAISkIAEJNBgAgpxDR5cuyYBCUhAAhKQgAQkIAEJSEACEpCABCRQHwIKcfUZC1siAQlIQAISkIAEJCABCUhAAhKQgAQk0GACCnENHly7JgEJSEACEpCABCQgAQlIQAISkIAEJFAfAgpx9RkLWyIBCUhAAhKQgAS6IvCjH/0o+8EPfhDOOeGEE7LHPOYx4cciAQlIQAISkIAEJFBPAgpx9RwXWyUBCUhAAhKQgAQeReCRRx7Jovj28MMPZz/84Q/DMSeeeGL22Mc+9pgI97jHPS78mx+K4pyTSQISkIAEJCABCdSDgEJcPcbBVkhAAhKQgAQkIIGOBBDfENy+//3vZ4hv/JvSKrxhDRdFt/h7juHviHBRlIvnKMw52SQgAQlIQAISkMBgCCjEDYa7tUpAAhKQgAQkIIEhCSCgIbzxg+tpJ/Gt9eRWIa79ovHcKMrx+yjOYTmnO6sTUQISkIAEJCABCVRHQCGuOtbWJAEJSEACEpCABIYkgMtpq/gWBbM8QtlwQtxwwlysgz91Z3VySkACEpCABCQggfIJKMSVz9gaJCABCUhAAhKQwKMItLqcIsAhxFF6ievWjRDXaSh0Z3WCSkACEpCABCQggWoIKMRVw9laJCABCUhAAhKQQHAx7cblNC+yfoW49nra3VmJTfe9730vO+ecc4LlXB4rvbxt9zgJSEACEpCABCQwmggoxI2m0bavEpCABCQgAQlUTqDV5fTBBx/Mvvvd7wZBq0gxq2ghrh3S3Xffne3duzebN29e+FVM9qA7a+XTyQolIAEJSEACEkicgEJc4gNo8yUgAQlIQAISqBcBrMmi+IYlWXQ5Rbx64IEHsl27dmXz588vtNEnnnjiMXGs0Av/78XuueeeIMS1tlt31jJIe00JSEACEpCABJpOQCGu6SNs/yQgAQlIQAISKJ0AohTZTaPb6VBZTnHv3LlzZ3JC3L333psdPHgwmzVr1pAszc5a+jSzAglIQAISkIAEGkBAIa4Bg2gXJCABCUhAAhKongCWbohvWL398Ic/DPHfortpdN1sbxWuqfv27ctmzJhRaIPLtojrpbGtwhzn687aC0XPkYAEJCABCUigaQQU4po2ovZHAhKQgAQkIIFSCAznclpkvLdeGl+2EHf//fdnR44cyaZOndpL846doztrX/g8WQISkIAEJCCBBhBQiGvAINoFCUhAAhKQgATKIYBwhLUbLqdYvg3lcpq39oceeihYxE2fPj3vKbmOK1uI6xQjLlfDRjhId9YiKHoNCUhAAhKQgARSIqAQl9Jo2VYJSEACEpCABEonEF1OEd9wPc3jcpq3Ubim3nTTTdmCBQvynpLruFSFuPbO6c6aa7g9SAISkIAEJCCBhAkoxCU8eDZdAhKQgAQkIIH+CUSX0xjvDSGuSPGttYWpCnFY8mEVd9555/UPvMsr6M7aJTAPl4AEJCABCUig1gQU4mo9PDZOAhKQgAQkIIEyCBTtcpq3jYh9d911VzZmzJi8p+Q6rmyLuOie+4QnPCFXe8o8SHfWMul6bQlIQAISkIAEyiagEFc2Ya8vAQlIQAISkEAtCCDg4G7a6nJKwx772Mcey+hZdkOxtsMq7pRTTim0qrKFuO9+97vZnj17svnz5xfa7iIupjtrERS9hgQkIAEJSEACVRFQiKuKtPVIQAISkIAEJFApgXaXU6y6KDHDKX9WXXDx3LZtW7Zw4cJCqx7NQlwnkO3urFGAPfPMM48Jr4MY/0IH3YtJQAISkIAEJJAkAYW4JIfNRktAAhKQgAQkMJQAU2SW06IpK8QVTTTf9Y4ePZp95zvfyWbOnHnM+hEhDmvIxz3uccfE2XxX8ygJSEACEpCABCTQOwGFuN7ZeaYEJCABCUhAAjUg0O5ySpP4vypdTvNiSFWIizH1TjjhhLxdrdVxt99+exDiZs2aFdqlO2uthsfGSEACEpCABEYVAYW4UTXcdlYCEpCABCTQDALEWmuN90avBulympcqAlAUCfOek+e4sl1TiWt3xx13ZOPGjcvTnNodgwhHkoxp06YN2Tazs9Zu2GyQBCQgAQlIoJEEFOIaOax2SgISkIAEJNAsAjHe28MPPxwEOIS4VMS31pEga+qhQ4eyiy++uNABKluIq3OyhkJBtlzM7KxlkfW6EpCABCQggdFNQCFudI+/vZeABCQgAQnUlgBCCMJVtHzj34cPHw4ZR88555zatnu4hiEkbt26NVu0aFFf7UeIvPvuu4O75T333JMtX748u+WWWwKrs846Kzv77LOzxz/+8X3V0Xpy6kIcrO67775s/PjxPTPRnbVndJ4oAQlIQAISkEALAYU4p4MEJCABCUhAArUhMJLL6Z49e7KTTz45u+CCC2rT5m4aglC2ZcuWbPHixd2cFo7l3DvvvDPEvnviE5+Y7dy5Mzv33HOD6AYTfo/gxA+/P/XUU7Pdu3eHvyPOnXbaaccSFXRb+fe+972MOGtFW/J1245ejydZA66pM2bM6PUSHc/TnbVQnF5MAhKQgAQkMCoIKMSNimG2kxKQgAQkIIF6Eogup4hIWIuN5HKauhBHRtf9+/dnkyZNyjUgDzzwQHbSSSdlt956a3bw4MFgCXjeeedlZ5xxxnHnd3JNjVZz0Rps7ty5GfwoiHNnnnlmyBg6Gsptt90WBMqihbh2drqzjobZZB8lIAEJSEAC/RFQiOuPn2dLQAISkIAEJNAlgZiBM8Z7i+JFniynuEgiHmHt1dQCj7179wa3U5jMnj07iHHD8ckbIw73TKzqEKUQ9E4//fQg8GExhzhHPZ0K3Pft25ch5qVY6Dei5pgxYyptvu6sleK2MglIQAISkEASBBTikhgmGykBCUhAAhJImwDWWcR7Q3zDKgyBopcsp5xPGUowqjslGGzevPk411R4xHhvWAbOmTMnuIFi9ZY3zlteIa6dD/Uh+FE/7qfErsNiD7ETcQ6XV8Yp9Rhxcc6dcMIJA58iurMOfAhsgAQkIAEJSGCgBBTiBorfyiUgAQlIQALNJNDqcorYgxBC6UV8ayWEVRai04UXXpgkOIS4TZs2ZfPnzw+WaVj2wQbXU9xOEb96EYt6FeI6QbzjjjuOWc1NmDDhmNXcvffemy1cuDBY5qVW4IuYOH369No1XXfW2g2JDZKABCQgAQmUSkAhrlS8XlwCEpCABCQweghEl9MY760bl9O8lKK11rhx4/KeUpvjHnzwwSCyXXvttcGiD+Ft7NixIQtsv6VIIa69LbSbbLUIdJQlS5aEf2OtR5y5XoTDfvvb7fl1FuLa+zKUOyv/D+vIG1HbIgEJSEACEpBAegQU4tIbM1ssAQlIQAISqA2B6HKK+Ia1V68up3k7dODAgWCRlYoQBxfEQ0QsxLJ58+ZlWJYRj63IUqYQRzvpB3HWsNijHDlyJPQpJkDAsg+LM36f1522yP6PdC3aC/c6WsSN1HZ+z311/fXXh6y1uCwjwnEfxJ9oaZrnWh4jAQlIQAISkMBgCSjEDZa/tUtAAhKQgASSIhBdTlvjvdGBfl1O80KI1kJ1tQbCzfSuu+4KcdcQpBBOyNiJ9RtWcAiXCFhFJw0oW4gbLkYcY0J8OURShDn6iQsrce6IMYdIN+jxYlxgD6dUC7EFJ0+eHIQ43VlTHUXbLQEJSEACEsgyhThngQQkIAEJSEACwxKowuU07xAgclGiZVbe88o8jgQSiGuIbYhPWI7FeG8kPWgtCEIbN27Mli5dWmiTBinEtXcEwQtLrT179gQuiHRkW33CE56QPfTQQ0FIqjrOHNZwjBPjkmpBiJsyZUqI2ddezM6a6qjabglIQAISGI0EFOJG46jbZwlIQAISkMAIBFjY447Y6nLKKQgog7RuOnjwYLAGIonAoEpkg/iFu2AUeMaPHz9iNtdUhTiELNjPmjWra+wIcxSusXfv3mMurGSHxYIOi7myLdVwTUUgnTZtWtftr8sJiJhwyitimp21LiNnOyQgAQlIQALHE1CIc0ZIQAISkIAEJBAIIJi0im/8X1Uup3mH4NChQyHL6EUXXZT3lMKOe+CBBzLqJ9spVkmIUohwuGLmLTDetm1bsBArspRtEVdkW6OQCbebb745WM0xzxDmcOeFES6tRRaSS2CZN3Xq1CIvW+m1SDhx9tlndzXfWhuoO2ulw2VlEpCABCQggSEJKMQ5OSQgAQlIQAKjlECryykCXLRcGrTV23DDgWsq7azCxZA4eDHeG/Uhvt1zzz2h7rItuLqdkmTSzGsp1e21Of7+++8PCRrKErIQNHHjxUJu165dQeC84IILggUbAhrCXD+WmIh9WJRdeOGFvXS/Fufg0jxz5sxgQVhE0Z21CIpeQwISkIAEJNA9AYW47pl5hgQkIAEJSCBZAu0up3SE/6uz+FYlbMQaxLfzzz8/CEKwOffcc0OW0yKELq6HRRzWX0WWsoW44ZI1FNmPeC2sHhkLBLgtW7YEd9bTTjstCFHRPbM9/l4Z7ajTNYsW4jr1TXfWOo24bZGABCQggaYSUIhr6sjaLwlIQAISkMD/EkjB5TTvYGGVhUAzceLEvKcMe1y0CoTR1q1bg/CGxRvZTosQ3tor5/pr167Nli9fXkj740WaJsR14kaMN4Q5MtLu3LkzuGiOHTs2uClj0TmclSJuncwbxjXVsn379tB+kl5UVXRnrYq09UhAAhKQwGgioBA3mkbbvkpAAhKQwKggwOIZYQn3vlaXUyyxZs+enSHapFoQVB588MG+hTgSB3At4r1h/YbAgVBTtsiRqhAHG9xyzzvvvNpMHWL2Mb9xGUbcxIoOy0UyiyLSxfiGNJjYfswbfmfpnYDurL2z80wJSEACEpBAJKAQ51yQgAQkIAEJNIAAC2RimsVkC3HB3Opyun79+mzBggXJC3EIMJMmTepq1OCC6EasMEQ3RMqY7bRKYZJxQQDEkqvIUrZFHCIXDMsWKvthQvuIMXfWWWeFeHZ79uzJzjjjjBBrjnsDMTFlIQ4X3RkzZtRuDHRn7WfWeq4EJCABCYxGAgpxo3HU7bMEJCABCTSCQLcupxs2bMjmz59fu0QD3QwG4hn9ziMIYQFFkgEyTSJiEOSeeG9nnnlmKW6neftBm4oKuB/rLFuIqzpGXF6Wwx3HPKHd/Ek2VmKsnXLKKcFqDjEWZv0kgCiijd1cY926ddm8efNyzf1urlv0sbqzFk3U60lAAhKQQNMIKMQ1bUTtjwQkIAEJNJZAdDnF8icKUnQ2uuCNJCpgDYYIkXKQ++iOiKVTe4EPPwhdxNOCB66UEyZMqNWcuPbaa40RV/GIMG8Q5JgfWM1hJXfgwIHstttuC/cE8wTBts4F99tLL700iIoplVZhDvZkwR03blx4DmGxG2MxjvT8SqnPtlUCEpCABCQwHAGFOOeHBCQgAQlIoMYEYjKBGO+tk8tp3uYTFw1LrDKSEORtQ7/HHT16NKMfrS6GLO75f1xPJ0+eHAQVXCnrKlgoxPU7C7o/H9ENAZv50VqwmmT+IArh0orYhcUk4hxiXZVuyyP1qglCOm7ZWClOmzbtWHd5piHCRVEuutMrzI00I/y9BCQgAQmkSkAhLtWRs90SkIAEJNBYAljuENMK8Q1BKS5UW4PP99L5TZs2ZbNmzaq9a9twfbv99tuzu+66K8T+4k9iZmHVhGCCkFIn4WSofqQoxDEn+UmBbyfuCHHcUyPFFuQYklIwtzh27969QThCmBszZkx22mmn9XLrFXIOgiHzPmUhndh9ZL9tFeKAoztrIVPEi0hAAhKQQCIEFOISGSibKQEJSEACzSUQXU5bxTd6m9flNC+ZzZs3B+Hq5JNPzntKbY7DtRA+WDDt2LEjZDqN8d5Ss5xB0CpaTCk7RhzcscjCpTDFQtZU5g+x4bopjBXCEcIcFpYx3iDCHD/nnHNO4WM5VPvWrFmTLV68OOkYj4iaWCbCbbhidtZuZqnHSkACEpBAagQU4lIbMdsrAQlIQAKNIBBdTmO8t35cTvMC2bp1azZ16tQkhLjI4zvf+U6wSsJ1EBEIAS71QjbPkSyzuu1j2UJciskaumWY53jmJaIwwhwWaliY7tu3L/wfFpn85Ekkkqeu9mMQ4pYsWZKsVSL9iRa+vVhWmp21l1njORKQgAQkUEcCCnF1HBXbJAEJSEACjSQQXU4R37DOKcrltEmwsLpCfEPomDNnThAdsB476aSTQjcRPxCFLrroomS7naJraupCHC7N3H9lCLm4kDMvmbO4rmKpSbIQLOYQ5vi/Iqw2yZq6cOHCpIW4gwcPBpf79lh9vdzMurP2Qs1zJCABCUigDgQU4uowCrZBAhKQgAQaSaAql9O88Pbv3x+yQ5ZlsZO3Ha3HsShHfMP1ESsxrMVOP/30IGB0yu7KsfxMnz69l+pqcU6KQhyZLhGzunXtrAXwLMuY+whxEydOLL1J1IMwxw/x5ubPn58hQCG+I86RDCLlzMX9AMwbq6+XOnRn7YWa50hAAhKQwCAIKMQNgrp1SkACEpBAYwm0u5zSUf4vZgIcZMdvuOGGIKQgdA2yIOrAI8Z7I15UjPc2UrvIjIrFXMpC3K5du4KLcJGlbNfUIts6iGvhPkoZlJB4//33BwEZcQ6RGUEcl2v+jjgXLT6HY3PzzTeHeV90fMEqx6NKQTQ+e2P/zM5a5UhblwQkIAEJDEdAIc75IQEJSEACEuiTAAs83E1bXU65ZB3Et9auIcTh0knmxSpLXABjFXT48OEQbB7rNyyDunXZQ7yL8biq7EPd6ypbiMM1FTFr7ty5dUfRsX0IYBRErzoUrONiBuB77703W7p0abg3KIhzJFRpvzeuvvrq7PLLL09aiIsu+TwDBlF0Zx0EdeuUgAQkIIF2AgpxzgkJSEACEpBADwRwP2sV37hE0VlOe2jWsKfceuutQYiowjUVPtGNFPc8sj1iCUfmyTzWP0N1hIU8P1X0oWj+8Xpr167Nli1bVujlqxDicBvGzTLFwpyh9JIkoKr+Yu0ZrebGjh0brERvu+22Y3HmVq9enV155ZVdi9dVtT9PPQiiPCcR4etQdGetwyjYBglIQAKjj4BC3Ogbc3ssAQlIQAI9EGh1OUWAQ2iiRDexbi27emhC36eQsZB2luXa9tBDDwUhgesjItxyyy0ZbqeIf0XFxGIhj6A4c+bMvnkM6gIpxohLPVkDbqDMy5SSfHA/YSXHnMcSlEQTp556ahATEbLqLCoOdW8h5tLuCRMmDOr2G7Fes7OOiMgDJCABCUigTwIKcX0C9HQJSEACEmgugVRcTvOOwE033ZRhaVOUex58iH2FGx3iGKIBAtyYMWNCpsgyCqLEkSNHslmzZpVx+UqumaIQh0UZLsGDji/Y6wClKMS19pV7jTFA6MalFSvTKVOmZGeffXbI1oo7K9amdS+I81jEjh8/vu5NPda+Tu6sbMTw/9GFOIWNmGSA21AJSEACo4CAQtwoGGS7KAEJSEAC+Qm0u5xi/Xb06NFgwZH6Yotg71jVsGjvt+zevTskTWDxTwD5TjGt+q2j0/kIEIcOHcpmz55dxuUruSYxwYoWtMp2TeU+uO+++wqZO5VAbqskdSGO7qxatSpbsWLFcQIRlnLE7kOgxuKPWHOIddyXWM/V7ZmFiE58OAT7VAsCHG7EbDxccsklxxhj9csYpGQlneoY2G4JSEACqRNQiEt9BG2/BCQgAQn0RYBFFeLbww8/HGK+RZfTGO+N/9+2bVu2cOHCvuqpw8nbt28P1mpY0XRTsMSJ2Ur5+7x584IVDlZvVQddZ7xiFtpu+lCnYxF2yZpZZClbiEvdNTWFGHEjzYd2Ia79eJ5diEAIc1jNEZMRy1GSs2DNyJ9luaWP1Pam/R6xEytghDiK7qxNG2H7IwEJSKBcAgpx5fL16hKQgAQkUEMC0c0rJluIi6hOWU4R4rZu3ZotWrSohj3prknEnMJqI09sKSxtWGxiVcM5LDpjvLdBLuZpF6LghRde2F3na3R0iq6pqQtxCMfM27okCehlOo4kxHUS5ni2IchhwcoY0n8SbvB3rFirFtKJEcczpWghuhee/ZyD0MnPUJa5Zmfth67nSkACEmg+AYW45o+xPZSABCQggSwLlm5YxSCsReuYPFlOSXCwf//+bNKkSclzRIzAZe2UU055VF/igp1F8o4dO8JCHfcxXFlZsNel4NbJeERLlLq0q5t2pCjEwf3gwYPJxuZLIUnASHMIq7Z+7kXucZ5/PAO4xxGSeAZiNYd1K8+6srMRUy+WeRdccMFI3a3173mf8JNnU4OOmJ211sNp4yQgAQlUTkAhrnLkVigBCUhAAlUQiC6nWL2x+Gx3Oa1b7KQqmOzatStYxOCeGguiJAHUEelYhM+ZM6fUzKr99hNBCNc72plq2bJlS7BKKrKU7ZpaZFsHca0UkwS0czpw4EDh2UZ5NvIsJOkKrus8L3k+zJgxI7iz8kwo8llJHSSLQeBPufAcwjq39VnabX90Z+2WmMdLQAISaA4BhbjmjKU9kYAEJDDqCbCwwaojim/DuZzmhYWAt2HDhhAEPfWCexrWcLjokWiBRfbkyZODZQxx4/JadwySA4tf2p5S1sUqeJUtxCHUEGh/6tSpVXSn8DpoO4z6EU4Kb1SXF7zqqquyst95vwAAIABJREFUlStXdnlWd4fz/ESAw0IOl/wYC3LatGnHErLgqt5rQcDCHbZsy7te25f3POYTyUvgUlTRnbUokl5HAhKQQP0JKMTVf4xsoQQkIAEJDEOg1eWURSSLmTwup3mhcr21a9dmy5cvz3tK7Y5jYU28N/hgAUefiPeGddwg473VDlRFDbrhhhsKt+grW4hLPUZcRUNbajVVCHHtHeBZgeCEOytz4KabbspOOumkYNE2ceLE8DzpRsBHiONaXCPlQsZUYu+VKUzrzpryDLHtEpCABIYnoBDnDJGABCQggaQIRJfTGO8NIa5o8a0dSBkxvcqEDg8SM2B1snnz5iDAEe9t7NixyS+AsczCso/MramWMuaTQtzwswF3ZsQf7oFUy/XXX1+LeY+wz/MFF9P169cH13/+ThzN6Mo6lDsrGaiJD8dGQMqFjQ0sr6uOdac7a8qzxrZLQAIS+D8CCnHOBglIQAISqD2BMlxOu+k0liAEGK97YYFMIgPcyVgYz5w58ziLlUOHDgUxImX3PKxQdu7cWXiMtSrHNkUhDuHlnnvuSTbbJeItItG4ceOqHOpRUReC1N133x0sbBGoiEXJ85LnDNmN40YJMBDiEENxhbf0T0B31v4ZegUJSEACgyCgEDcI6tYpAQlIQAIjEogupyzyojslJ+FKWWTw8BEbkmXZ0aNHaylAYIly5513hphpWLwhvuH6xSK3UxwnrIKIz8TiONXSBCGO+FJFW2aVbREXYy+mGturCULcunXrkohVybObzQue3TyX1qxZEzYAzjrrrBB/jviOPKtSLmQQRgSbMGFCrbqhO2uthsPGSEACEhiSgEKck0MCEpCABGpBoN3llIU/pch4b712tAwLpl7bghCF+IZlz549e8JikMUuFigjCZRYyyHQpWwVxLwgYcOpp57aK8KBn4d7bdHtL1uISz1GHHHOmPsnn3zywMe/1wasWrUqW7FiRa+nD+w8nlHMeazmsJJjYwPrXMQ4/p2im+revXvDptBFF100MK55K9adNS8pj5OABCRQHQGFuOpYW5MEJCABCbQRGLTLad4BGaQQByMsSxDZNm3aFCzaWLgipnWbvZDFMNchc2qqBetIXCRTXLxH5mXMJ4W44Wc0c5/7JVWLPp4DV199dZJCXOvIIGAhvjEWCHMUEj9wT5x++ulBnOPfPOfqXNgE4Z6rm0VcHmbt7qyI1BRcixmXuPmV51oeIwEJSEACvRFQiOuNm2dJQAISkECPBKKwVAeX07xd2LJlS+UxybBAIjMfi1XcGC+++OIgyPWzQMWSjJKqGEHb6QOZGxcsWJB3+Gp3nEJc9UNC3DIE6FTdsnlu4oKecnxHRp3kMZMnT35UzM0osBPfEnHrtttuC/HmcGdFdEekq1PBMhmLuJSfpZEnIQtwJ+YdQ4mW1Yhy9DFm1h7J4rpO42NbJCABCdSdgEJc3UfI9klAAhJoAAE+8hGRovgWP/bdef+/wSXeGwtPFtsTJ04MLqfwwkKkW8u3oaYM7mCMRYpWHLFPJA0g4PvChQuTvTNSFOKYN/xgBZRiIcHHaaedVnhsvqpY8DxAlEeYSrlg1Tt16tQRhTXmGvEu6TPPPwTU6667LlhtwYCfop6LvfCkbdwLKbs6x353crPVnbWXWeE5EpCABPITUIjLz8ojJSABCUggJ4FWl1PEJBZVlFR31m+88caQgTS2PyeGYQ+DEVYVuMydd9552datW0PcMOK9Yf1RhvUBFnZYnqQQ12goeAiWWDfNnj27iGEYyDW4H4qcS3SibNdULBGJTZiqRdktt9wS7i/cHlMsxEYk6cGVV16ZYvOPtZlnEBZuj3/847vqB89LEj1gMYc4N23atBBrDkGMzQqEuSqt01IXdlvhs0GDqHnBBRcMOSZmZ+1qunqwBCQggREJKMSNiMgDJCABCUggD4FowYXQg/gWP9wHkeU0T3u7OWb9+vXBFbJfayAEmCjA4V5JJkFczapKnkC2TsYmZSGum3Gr67HEl8LqsUixtWwhLvVkDXWdC3nbhRCHJeUVV1yR95RaHsfzh7laxNxHlEeUQ5xDhCMbK5smCHP8YAFZRD2dQG7fvv1YPLtagi65UWZnLRmwl5eABBpPQCGu8UNsByUgAQmUR2Aol1MsFVgkNUXw2bBhQ4gR12t8NiyJiHlEkgHiI2ERArter1feiNb/ygi9WKPMmjWr/o0dooUIKsuWLStUJFCIG346YImFG2Gqrp3M+40bN4Z5k3LhWYo1axkJY3imIsxFcW7evHnZ7bffHuJKIszFZARF8Nu9e3e4HhbMqReyaTMe/fZFd9bUZ4Ltl4AEqiSgEFclbeuSgAQkkDgBPrSj+IbQFl1OY6y3aH1w6623BjeiSZMmJd7j/7/5CIuIZ3ljEhHHjFhvCHAsOon9hlDCYrBol8RuALNAZdxwhU21YFFD8ozFixen2oVs7dq12ZIlSwqdC2ULcbhRcx9gyZdi2bFjR0gQMJz7XYr9Sq3NWBfPmTOnsthquP7z/OXZh8s/8TFxU45Wc1glj/ZSlnWf7qyjfWbZfwlIYDgCCnHODwlIQAISGJYAH9PR3TSvy2mM3TNlypRG0EVUJKbRUCIajFjwwYk4VAhFCHdYGJTpHtUtXKzyaCdWeakWGBPwHSEr1YI1DSJ1kaJs2UJcqqxjuxEbsGBKVYhj3iMgTZ8+PemhWLduXYalWpXx3FqB4eLL+wl3ViyUeY4g1PH/iHNYTeZxZyXBwdlnn/2o7K8pDg5hEnhXlZ2RV3fWFGeHbZaABMoioBBXFlmvKwEJSCBhAli6sfDDeooFCh/Q7VZvw3WP8zi/DPejQWDdvHlzNmPGjOOsOKI1IIs4FsgsLAlkX/Zipp/+N0EghTvuZqkG3e9n/IY7t2whjhhx+/bty+bOnVtWF0q9LnOGexSrqBQLz1Syhi5fvjzF5h9rM9bCuOQXKUL3CwRrOeYH4hzPb6zmSGCAGzMbKZ3aesMNN4TnPWJc6gXLbQTIQWSA1Z019dlj+yUggV4JKMT1Ss7zJCABCTSIQF6X07xdZrHFD65gTShYuJGlD2ERMQu3U4QJXKxYVOKy2m8ihyo43XfffSFeUr+xgKpo61B1xGQXWB6mWnDPW7hwYW5X5zz9rEKII8kEsRJTLGwMsJmQ1728bn3kecqGQOox4g4ePJiNHTu21uOA5TftRKDDgvjyyy8PYQaYP1hVcq9df/31IQYqVnSpF9zOcdGtwztMd9bUZ5Ptl4AE8hJQiMtLyuMkIAEJNIwAH7xYu7HowNqiyCynWBbgBjlz5szkqSFcsejCMoJ4bwcOHAh/Z0FWJ6uOPKBj1tZUxQj6SB8QslIWJHDPW7RoUaFihELc8HdAWXGw8tx3RRyDkEjCidQT4KxZsybEd0wlUU20BmcDJibcYQy433h/Em+TsAUplzqLirqzpjyzbLsEJDAcAYU454cEJCCBUUQgupwivrGw69blNC8qLAmOHDmSZGZLmNx7771hoQWnXbt2BfcjrMhSdWuL44YbLT8px5lSiOt8F5YtxPG8IFZiqvfAzTffHO7jVBOVxI2TOlgt5X0PdDrummuuyZYuXVoL66te+xHfEbiv8q7j35dddlmIOYfAiOV0njhzvdZf9HlYWhI3NBULdt1Zi54BXk8CEhgEAYW4QVC3TglIQAIVEYgupzHeW7SI6ibeWy9NxQUSIQ53zhQKXLBuw9qNxRULqVa3I/oSA3mn0J+h2oh7FbGQiHeXamFOk3U05VhZCL1FJ/EoW4hDlOa+Jm5WiiV1IQ4RlLhkKScpYd5s27YtbNCkZk3cPueJ18eGBvcxlnFYGePOSsZwXD35HQl7cG1F4Kpzf+kL7+pURfZ2d1bGgfh9MWxE/N5J8bllmyUggeYSUIhr7tjaMwlIYJQSKNPltElIWTwhShHvDYGBBS5xmDrFyrnxxhuzcePGBXfUlEsTXIaZ3/zUeWE70hxh3rFIL7IPZQtxxERMOUYcQiK8U3XLRtxBxEpdiBvp3kjl9xs3bgyhFzrFqmRjhx9CPuzYsSPEE0WwW7BgQRDmcGWtk2tuFLJSsuIbbp5Eq8v4fI394t7n/9r/P5U5ZzslIIFmEVCIa9Z42hsJSGCUEuBDmoVmq8spKPjgHMTHNXHVsCybMmVKrUYEqxKEN7hccMEF2d69e4MgMpLFwk033RSOT9UaKA4C4iPWkanHNEIQmjRpUq3mVjeNIcYdi/Ii3QwV4oYfAe57skKmmuQDIY4NAeKrpVyIj4iYOIj3UpHcuIdJ1pMn0yjvZzZ5yNq7e/fuYDXHOwgrNCyteSbzu0EV3oNsNNVJHOyHxdVXXx0SbLRudOjO2g9Rz5WABMogoBBXBlWvKQEJSKBkAtEqKCZaQGChlO1ymrdbCF4ER7/00kvznlLKcXDCGiFmOyXYNrHe+OH/8hZcUxHrUl3Ex36yGMT6D/Ex5XLttdcm7ZqaohCHOy0uX7gVplgQ07nvx4wZk2Lzj1lYDVKwKQLcqlWrshUrVhRxqYFeg3cv4nevgiLPYs7lTwRWrkcMQxICsZHFZkmv1+4WDK7+vKtT36CJ/d66dWs2d+7cYTGYnbXbWeLxEpBA0QQU4oom6vUkIAEJlESg1eWUj3ZcXyiDsnobrpssJFj4YvVTdYkJKHbu3BkSEyCeYZmH5UJVC5uq+5y3PkRJ4uBdcskleU+p5XGpC3HE+sKtLSWLuFpOhC4alboQh5sjMR6xzE25NEWI279/f4hDVtQ9zGYaVo/EaUOYw4ITd1aSKMTYbWW5VaeWybaM+W921jKoek0JSGA4Agpxzg8JSEACNSbQ7nJKU2N8rDqLSjH+WlWLRoRJFi78UDe7+4hOiHBFLF7InIoLERY1KResmvbt2xdcqlIujMfUqVNT7kLhbS/bNZXYVliGpsod4YQYj6nGecSSlXhjCxcuLHzuVHlB4iOmapXYyqls8SpmZiVmaUzUgcUa7yDEuZggooix43mKq38R78oi2tPvNbDwW7ZsWb+XCd9ascQNvhhjLm6A1vk7rG8AXkACEiiVgEJcqXi9uAQkIIHuCWDp1hrvjSvUxeU0b2/4aMUqLk/8nLzXbD8OYQCLN6wGWKDgdhrjvRX9cUxcH6wSzjvvvF6bW4vzsLggPhELOcvgCJDBE0GrKGsaelK2EIewfcsttwzc3XxwozbYmhHREUwGYWVcZM+x6sMFM/WyevXqIPYUeQ8PxyS+U3mG857btGlTsKBjg+jiiy8+Fjahl3dfFJlSH5PY/quuuipbuXJl4d3RnbVwpF5QAqOagELcqB5+Oy8BCdSBAB93McNaq8tpauJbK0t268kqt3Tp0sIQx3hvxGrD7RQhLsZ8KjtuEgIE1nXnn39+Yf3xQr0TKMriofcW9Hcm98a8efMKDY6uEDf8mGBNxvMiVREI0eXw4cPJWiTG0WmKayquzjNmzCg083G3TxW+F+6+++6wGYXFJAI/70fmOckX8gpsjMmVV17ZiNAN9JlkDVXEIdSdtdsZ6/ESkEArAYU454MEJCCBARDgA45MadHyLX7Q1THeWy946A9iyfLly3s5/dg5MS4ewts999wTFhwEi6+aE9Z91ImbUMoFFydcU4lPlnJJPUYcQhzBxIucT2ULcQSVZ9Gfqhi9bdu2EF8t9UQlKd+3tL0pQlwdx4ENPSxXuVe5T8lQi7spVnPEsxvKQr1JY8I3A5bygwojoTtrHe8M2ySBehJQiKvnuNgqCUiggQSGczklODPuJQhNTSm9iiUEJY/x3lg44MJ31113hdhOg4phg7UBQlw3mVbrOI5Y1SBqzp8/v47Ny92mXudW7gpKPvDo0aNBECpyPpctxGHlyuZBqpkVUxfiUs9aG28pYqtddtllJd9h5V8+hX4gCmE5joCOJSgi3d69e4Mwx/MnxuprkhDHdxabdmeddVb5kyBHDbqz5oDkIRIYpQQU4kbpwNttCUigfALR5RSrN8SlmOW0k8spi8QJEyYEt5KmFBbteeLnxMUCu9jEYEP04oeFAnHZeol5UzRDAr0jmuDuk3LBIm779u3Jx/liQZnyvYIgijs14m5RpWwhLvUYcbQfETFVIRExhecQLs2WwRNIVbzCupu5xPt5/Pjx2fr168N7GlGO92+RVrqDGCX6t2XLlkKSNZTRft1Zy6DqNSWQJgGFuDTHzVZLQAI1JRBdKRHeEODyupwSb2bs2LFhp7opBRdIrPw6FURJRCHirm3evDkcgisJ7jQnnnhi7RAcOHAgCIIsXFIuWDVhLZBqnKzIHouylBNnXHfdddkll1xSqCikEDf8nYlFGSJcqkJDE4Q43oe862bPnp3yYzS0PVUhrh08ghwxUNkUwH2V9wOJj/gW4T2R2oYHmxxsbC5ZsiSZOaY7azJDZUMlUCgBhbhCcXoxCUhgNBJAVOJjFvENoSMGSI6Wb3mY4D7CArGOIlSe9nc6ppP7IB/JuMZEMYhg1/Ar0jKo1/YOdx4LE8aVGFMpF1gz17A0TLmk7pqqEFf97LvhhhuC0JCqCM37JWbJrJ5eMTXy/LnmmmtCYoDUS1Oyv7YnN2CMEK0Rfvkdm2lslhE2A3dPfop0qS96HvB+I7twyuEX2t1Z8RCgxPAc3XxbFs3X60lAAsURUIgrjqVXkoAERgmB6HLaKr7R9X6ynPLhiwhXdvbPKocIseTSSy8NgZOJ+YYFE+6mfFSy21538a1KVlXVxWJ+69at2aJFi6qqspR6FOIejbVsizgW6PzkcTcvZdD7vGjqQhwW1gThTzmOKBtV3LtXXHFFn6M5+NNvv/32YzHWBt+a3lswkjjK9w4CMMIcP5MmTQqx5ojbynscYa5J3y29kyzvTLwLGIeLLrooVBLDdSCI8h0Vv6XqEMajPApeWQLNI6AQ17wxtUcSkEAJBKLLaYz3ltflNG9Tdu/eHayUUna3o69wQWjDWoBFI65g/B9up7ihpvqhiJDI2OM+nHKhD8TPWbx4ccrdCH0gVlaq84nFb9FWDWULccRe4r7GqizFkrr7P+wPHz6czZkzJ0X8oc3Me9z8hwpZkFLHrrrqqmzlypUpNbljW9lQRKRm0yxvYUMHUQ4xjufO5MmTMxJO4caKOIdYPKhnMxZxiKQTJ07M253aH4frMB4TraExdGet/bDZQAmMSEAhbkREHiABCYxWAtHlFPGCj9VeXE7zstuzZ09GhtAUXR/hxIc5H+QbN24Mu+MIb/Slzi4seceG42699dYQ0w5rgJQL8xgRK3WLuJTHgLYjRnB/FOGKzsKTmHlY1ZLwhaDrWB4Vce1Wzqkna0h9zmBZzHOI2IKpFt6hPIOKnptV82h356y6/rrVBw9EuSjOzZo161jSJSzmEOiq+hZogmDdPr68L9jUHG6j1uysdbsrbI8ERiagEDcyI4+QgARGCYEyXE7zojt06FBYnKRkEUect4MHDwY3FbKJ4jaBABA/uImDNXfu3OQXXYwhMeIQPNj5twyeAMG4Weyl6t68adOm0P5eXLp4TnHPYaWJZRF/ct/h9o0YjiUK9x5sWARPnz49DFi/rFIX4nDvglGqrp0IDAiuM2fOHPwN2GML2LBhbi5fvrzHK9TjNDaf2HRKKSHAUOSwXCemWtECL+6sPJsQ53jOTZs2LdRDnDOs5spKmkKd3CdNSAjSz2w3O2s/9DxXAtUQUIirhrO1SEACNSVQtstpTbvdU7OwCOMjF8sMBCkW9lgL8mHdaZHPQgUhrqwP7p460eNJLCpYRKae1ZYFJG4uU6dO7ZFEPU5bu3ZtWAT3Ky4NqjcEP0dQySvEMW7R1XvDhg3BehbLN36i8N3umsrxiOWI+7iNsSBGmMO1lHu228I9wAI3VZev66+/PmwWpH4PdztudTqeOcncX7ZsWZ2aNarbwnsd19SyRUWeYVh0xlhzWGVjxRvfqzzTinBnZcOMn5Q2NUeagHhMxHh8Ix073O91Z+2HnudKoHgCCnHFM/WKEpBAzQnwMYKA1OpySpNZ1BfxIdhL96NVy/nnn9/L6aWcAyc+lPmoJTYaMZZOOeWU4HbKnyOx6sfqp5QO9XFR3KmwOsI9JOXCmCJipW6Rsm7duuBeW5W7U9FjjnUW99RIIjX3Hm5JLF4R3aZMmTJkluGRYsTFuE5R/CNpB4s7sojiJjvS/Vw0g6qvl7oQh0UiP61xoqpm2G99xBlkHJYuXdrvpQZ6Pu+DHTt2NMLqimfMzTffPJBwBWwUIO7j1somAc83rOxjnLleNlpi5vpUk8p0mthYgPOMxqK3yKI7a5E0vZYEuiegENc9M8+QgAQSJMBubKv4Rhf6yXJaNIIjR44Ei5dBW5vwEQsrFkx8/JFgAeGtlyQFiHgIdqmKJa1jjBUgP9HNr+jxr/J6qWcchRWuwghTvSzUqmTdbV0s8HFBJNg4ojz3DxYrLFJH6utIQlx7W3geIvCRXIXYh1jFUKiLe75ddEYEQkDEyjXFwjM25QyPCBbMjZRdU+N7OPUNDd7VbDSlvqHBfRyfAzxPB114/rHxgDDHc+myyy4LfzJvsOLNI64R5oPvF0S9ppSqNhF0Z23KjLEfqRBQiEtlpGynBCTQFYFWl1M+NPmQowzS6m24Dgw6BhkWebiN8NGL2ykLcRj2I6LxMU08ptQDczNuURyZMWNGV/Owjgfv3bt34IJvv1xwkyzKlanftvRyPgsrYibRh2iphivV9u3bwzOK+28ol++h6utWiGu/DovgGGwdywvufYQ3xCus5hDpcWvuJrtiL2zKOof3AH0aSdAsq/5+r9sEIY4x4F3Ty8ZOv/yKPL8pln0w4b7nGYToX6cSk2OxKcG3CdZzxKIlLib/h9VcJ0EXIY8+pZ5YqXUsuGdOP/30gVjk685ap7vCtjSNgEJc00bU/khgFBNodzkFBf9XV/GtdaiqjkEWAykjMM2ZMyd85LJIZfFflIsaFjZY+KUaHL11fBAosKghwH7qhUVKHsuCOveTOGnz589PVuQlThYLRWL/sAhG+OLf/YhE/Qpx7eON8MZzAUEd4YGMrNEyA3GO+7qoZ0UVc41swTyPeomPV0X7RqoDC2OsI1OOfTVIN8iR+HbzewRFxCDiLaZeeLchXqVg6cqGKs+l3bt3h+cS/8YqkXnF9wti4v79+8OQINg1pdA/Nm36eT8UxWI4d9aYLKiouryOBJpOQCGu6SNs/yTQcAJ1dznNi5+PSwSSslx2YqZF6jnjjDOCGxpWLjHeW952dnMcrq0s3qkv9QK/KOqm3pcmuKamKMQhorB454eFPAtf5hTu30UIWkULce3znOcTVlm45SHgs9DleYXLNsIcFht1WCgOdX8ifmLtm+rziHddv1bKg352YXFNbLWFCxcOuil91d8UF1sgcC8fPnw4bMilVuKmEu3HJZVnLGI7bv38nXu9zs+kvLyJiTpv3rzcyX3yXreI41qFOd4HTeBdBBevIYE8BBTi8lDyGAlIoDYEeOnzEYwVSavLaZ3ivfUCi91d3FOLjP+D6AYX3BpwKcOChYC/iG9VlF27doUPYhboqRcsgliwNMECoglCXAoZeXlWYcmA8IYrXrRoIhYT9yYWDv24frffU2ULcTxvEVIQ3WJhsctzi+cXZcGCBWFRj2VK3RbBqQtxuOcxh3BpTrVgic38SD1rc1MEReYR3wcI7LNnz051Wh1rN9+G9IfnKlZ+xLXkOUtiH97hhMlIMVQG72zE65GS+wxyAHnf0T6FuEGOgnWnRkAhLrURs70SGIUEeMGz8xmTLcQduBRcTvMOFy5g7OgW8THMgo3FPyLAJZdcEnZRYVXkoj9vv5pyHAtg4mWlaDXQPgZNEOJYcEXxvU5zjGcTCz4WfwRzj/HecCdsXQAi6iJQF7koLFuIY1GbJ0YcC2Cs5DgeKw4WZzDBJXSQzyAs+eCd6kIR13gEoJSFuDrdq/20hbmNeyTCc+olfk8VYZVbBxY7d+4Mm45sfrQ+jwkDgAjM/Y9lLBsibOjGLNJ1aPtQbVi9enW2bNmyWoeUgDUWcU2ZR3WeD7atOQQU4pozlvZEAo0iwEI7BhHmTwoveF72LPQGnV20aNgIPcQ2QTjrpsAjxnvjT2KYsWDD3Y2F/iA/ihgnrGJSjcnUOg6Imiy8EBZSL8yTugXm7pYpcwvrziKFrG7b0Ho8lmG0CYGNIOJYtmL1NpTwRKw1svpxnxZVqKtMoSuvEBf7Exf4bDLwbONPYuGx2UBcKhbLVcYqZIMCa76y3P+LGsehroOIwL2bsjUZc+jgwYOFbDiVzXu46zOXSXpDnMrUC31BKMd6vQmFhDfc50PFUkSQj54VW7duDRu8fKOwycbfeacM8rup0xikIJbyzY6oWTd2TZjT9qG5BBTimju29kwCSRGIH0Z8CLFLGbOctruc8v/r168Pu4NNKrH/eRbSMGBBwyKWv2OlwgIXl1MscepScE3lA5ed59QLH++44F100UWpdyVYBaTuYou1GaLzoKwZENlwx8QNCuGNhR8LWuIu5hGXUhTi2CxAbOzVajdaNsPnxhtvDPxY9LIAjovfMoVV5gwiVqqu8mxI8byvs3vaSA9HBFhE2dQ3NBgHfvLc6yMxGfTvm2ZpyfziuyPvBiDPcixNOR4Rj/c8mwRs9vJsr0NcRqz86m4JqxA36DvZ+lMkoBCX4qjZZgk0hAAfOHwERfEtj8spx6xduzZkympSgQEfgOPHj+/YrWhdw0cmQgqLSY7N+7E5CFZYkNHOlLP8DYJb2XU2wTWVeF8zZsyoVHhGCMHiDWEZ8Zt7lr8jwnXr7kjAeu7fIi0Ty7aIK2NeYkmIsIQrPc8LOI4bNy4I3kVn901diEMI5T2AOJBqQXzFIi6FDJ3DMcYykc0wrHJTL4TEwCIOC11LFoQ37jVEVu433jVY0fKcR8gfRNKmVatWZStWrKj18OiaWuvhsXE1JaAQV9OBsVkSaCpEXbCUAAAgAElEQVSBVpdTPnJ4eXebaIEFW9M+GrG4wkqkNeYMfHCnIt4SC1YCDrM4TSUgLtYPWLgU6X43qPui0/gMqi391osQh0Vpyi4kCNIsgsu2DoqWqtybuCdjeTpp0qRaWsKULcTRf6xnynSN5D7DIppnBhsu8GcBzPO+X5cxLHSxBC1S/Oz3XuzmfAQs2BDfKtVC+3mX1XkDKQ9b3sm8m7sNJZHn2lUfw+YC3xpNsFyHHc9qNjmKyo4c48xhNQcj3FkRYXkuUU+0sC3zfaoQV/VdYX0SqIaAQlw1nK1FAqOWQFzIxnhvfPBRuhXfWgFG8a5JUFmg4K5GbCnc3eAzYcKE4MbD4h9XiTI/9MpgicUQpUx3szLa3emajA8f4IihqRcWKsyzbq24Uu933vYjBmGhxX2IqzeWd1hIFHkPsplAMPEiRaGyhbhuY8Tl5T3ccdx3WFHhdh+tgbEIQ4TluTiaCm7BvEcRglMtjCf3V6ruwZE7zwd+enXTrtP4pRB/rBteVWRH5tuGjUae38zpG264IQh/hCbgu63Iwvjgmjp9+vQiL1v4tWjnoEJFFN4ZLyiBiggoxFUE2mokMJoI9OJy2g2fJlj0xP5iIUhsKT5gcFejsOhkkZn6Rw0LZ8QBXM1SL3x4b9myJVu8eHHqXWlE+xGtscwqSsiKCU9YSLGwQnTiPsQyqwwBnIUbMYgQ94oqTRTi2tnEBTBjgiDHuyDG6EOcGy7GJkI61mSpWugePXo0WC4h4KZamiJg8c5GnB8qlERK40PSCTZlmhD/FO7XXXddEK2KfLaONJ7cl7wzsPbE6hY3eL7teDb1u+GSilCqEDfSLPH3Eng0AYU4Z4UEJFAIgehyykKJXfteXE7zNgSXpSVLliRr0QMrPnwJDIy1BzupLMqLEhXyciz7OFypmAdF7xCX3e5O12fMcM1L3ZKDviFIkOgg5UDj/SY7iO5GWLwhiiG+IbyRObCKrJrUefHFFxc6n8oW4rBkwgqkTtkVedfQJp6jiGz79u0LgdcRVPlpfaZu3LgxWIKmKsQN4rlXdJ0Icbh1Mg6WehAg3iUu/k0QFSHKs4D39CDfb7xf+F6gLYhxPDtxjcedledSN27AvJsQF+scFzmKhalvHtfjjrQVo4mAQtxoGm37KoECCZThcpq3eRs2bMjmz5+flMtjTMaAuxvsiAXHhxpCQHQRXLduXbZ06dK8GGp/HEGg2Sluwk47Y8YisgmJJ1K8f9onO2IiLnrdWj2wqMHFj/sQkYaMnYisebIVF3nDUT8CfJEx7soW4riXEb6qECp7ZU0bsVZiAcyzFXH1pptuCgtg4tsR06vbOdNrW4o+j+cpJWULY56hCKd1zwA50tiRWAnBtwmxahGIeBamnkk7jhn3PjEI6xZ6gYQYzH1EOTZhcKFFLESoQ5gb6rmKlR0bN2w+17UoxNV1ZGxX3QkoxNV9hGyfBGpEoGyX07xd5QOYD8e6fWi1tj9m3mL3n4UfP3y8D+fu1oRslq0MEDgodR6nvHOuSdl6sQwia2GRIlBejkUdh4jN7vtIAhpzkMVPFN6w5uKexPV7kLELWZRR/0jt74ZX2ULcIGLEddP/TsdG138W54hAbIDwHEawwzIFy5kyXI/7bXen87H2o7CItwyWAJnLcWcvM3FJVT3kWcQ7OuX3QSurNWvWhBASg3y+5xm7aM3L+4kNPgQ6xHaEOZ5NMdkE77qbb7651vFpowdMnTdp8oyJx0igagIKcVUTtz4JJEaAFyzWXK0up1FcGdQChoU0Hyl1+9CK1hhYX7DLzE5mjPeW5wOlaUIcMXSwQGpKUPWmjA/3Dx/7RYpAVT/WEFZwMey0eOQ+5Pfch7hHs5DhPmRxM0h3pVZG27ZtCy7bRWX249oKccPPQizlmDM8l7kHmCM8mxBnEVaYL4xHXTcOmhDLC+YxjlbVz4wi6+O5gnDSBIs4xoRNDSxIm1BWr14d3DhTe7+xacA3E+OBSIfIi+U3345sPBMDsy7vr/Z5ohDXhDvHPgyCgELcIKhbpwRqToAPglbxjeb2k+W06O726pZWdDu4XnTRxf0JSz1cInC74SOwW6ESi5MiF+Zl9LebaxJcnA/LJixW6DfJGnCJTr0wJnzYp7ZQaeWOqw4uz/F+ia4x3IfcRwiNxAyrq5WHQlz1dxEu2WS5bI/FybsOIY4FMFZOl112WbCg5P7geV6X+wRBmZJyjDvcg+lH6pZkWJHxndSEuK48M9mo6CZuWfV3b/4am5TMi3uFZxE/fFeysYllb0xQU5eYbLx/m2RVmX+2eaQE+iOgENcfP8+WQPIEeIG+7W1vCwuU5z73uUGAa3cp7FZQKhtKGYvYbtrMRzgfQ3wcsSjiA4nFP25O/VhTcE0+iOvGuxs2rcfCh8Vt6vGAeu1/Xc8jNs2MGTOStoDgGUA2OqxSuG+474j3hsjIPdnPfVjFuJXxDCvbIo73Aj91tcoYadyGEuJaz4uWHbfeemvGRgJWdIhGWM4xtxDmBtV/RELmdV0W3yPx7vR7XO+wiEtdiGuCKBrHh2cR1lZNsVzvZV7W+Rysd3keEd+S7/M777wzhFvgWUXSE9xWeechzhECZRDfjwpxdZ5Btq3OBBTi6jw6tk0CFRDAumzevHlhgRFdXwbxIu+mq2T5Yve2qgyWfGQgtPFBhBsTi3+s3/hwZUe8KF4ka1i0aFFtLDC6GZNOx7JwhROCSRMKVliIPakXLPsQR1O05kAER+DlfsTVlAVxfBYUdR9WMb4IWtHKuKj6yhbiYM8iMNWg7ps2bQoL125c8Hj2xwy7u3fvDsIc5/Oc5vnG36sS5pqQ3RJ+uN2lLvo0wU04PncIo0GMsqZY4yMsIlo1pcRNXzKdtxeeTWx28hOToCDWRatwxrQKi16FuKbMNvtRNQGFuKqJW58EakYgCnE0i4/klBazZaIkzhSLfD5ksCDiYwZrNX7Kik3XhGyWZY7JoK/dlBhxCIq4baYixCEAYQW0Y8eOsMDgHkQQ70ZQGfTcaa8fCwc2P4rsQ9lCXIrJGsoYd9zDcHlGwCDxA9ynT58e3hUsSMt6PyAEch+knDWV9yqlCnGgjLGP19yzZ08QYInzaKkXgVWrVmUrVqyoV6P6aA0bwGxo5k3SgsVpzE7MHMXbBRGfjWus5sp4PvHc454u49p9oPNUCdSegEJc7YfIBkqgXAK8oGMMLxaHKSzODxw4EEzwCa5dZOFjgo8YFjyIcOzaYzkU3ZWKrKvTtW688cbgslPXuFbd9h9hl5hAWKE0oTRFiEthLLCa4T6PsbouvfTScB/GBTwWKdyfVVnFFs2Mex1BBTGuqKIQNzzJnTt3hsVs0c/XmHUS11HGlYKVJu8O5nFRFnOIPwhxKVsY79+/P7g3T5w4sahpP5Dr0A9Eh5THIoLbvn17Nn78+KRjD7ZOgquuuipbuXLlQOZFHSvlvcl3GBZz/OABg9Uc37oIc2wG9bsBrxBXx5G3TSkQUIhLYZRsowRKJID4Fj8m+dBPwWUE8ZCYGFjF9FtYPMVguFwPFw3+D+uGfj9O+m1b6udjQYOY0hQ3ET5miaWT+rxgTLAqK9Iaq9+5Gt2/o/BGIgZ4085OGYeJi8P9WrQY328/8p5PgHRcPFMS4nguEjctVREF138WoWXHWMNiDlYscq+77rqw4GWeIgIWGcog71yr03H79u0Lzclr3VOntje1LcxRLDrZ3Ey98B5hjqX6jOrEn290LEmLtITl2wxrXlxaGXfcXonfyHcvm1vdfuPE2KFaxKV+B9n+qgkoxFVN3PokUDMCvJDjYjBmIqxZEx/VHD60sGroZTeaDzUstTD3nzRpUsiWx//FeG+D7DuWeCzO6ySQ9MMDdwrE3blz5/Zzmdqcy+K6iN3jQXeIGDoIXYO2JuPjnXsRUR1XGuKPYUnEvTjSB33qQhyLHsSZIi2Qy7aIG/S87bf+qoS49nZifcKCl0Uu8x3LPEQ6RGaE/bwFYRoRMVXxmX42JbbawYMHw71LnMrUS54kJqn0kW857reirV4H2f+yxesYB5N6eE7x3bZkyZLAEYtenlsjWfXyLuedPdJxg+Ro3RKoIwGFuDqOim2SQIUEeNHGRe/69etDNsW6F4Jkkz2OBXyeQh+xUsCyhj4iQLDYZ9Ffp1g1CKFYCgxaIMnDNM8x7OJiDdKEnXb625RkGmVk7MwzHzgmunkjRiCG85GPIN6t+MyCgXPKtm7K2686HFe2EMdzFyElVWEdS1CEr5FE3rLHkvcRLmK8k9h44bnCXEZgwyJ7KBGBe4ZnaS8bUGX3Ke/1WdxTBj0Geds71HG4c7KB2Y2Q2m+dZZ2PlSuCYhNEFO4pLPyWL19eFq7Kr1t1khbe0RQ2xtioZvMA63PCprBhxrxvt1JHiOO5Vafv6coHygol0AMBhbgeoHmKBJpGgJcqHzDf/va3swULFtS+e7SVj4VOLmutjWexQywXLJkw6yewMh8MiHh1LGXEjRpkPxHi+IhrgtUAHJuSTKOMRAHDzTPmQXT/5t7lGYOog5De671I5mI++lNdPLKQZ3GDZVRRpWwhLvVkDYhAzJdu3a6KGp+hrhOfk4jLiGw8MxFHEOZ4dsaNDJKVIFynLP7QR+Zp6hk6schlbBBOUy9YQGHd1+uzuE79J17j9ddfny1durROzeqrLTx3uWfybjz3VdkQJ/PdzDc3mwHcw/x78eLFwWqO5ykbYnyPK8SVQd9rNpmAQlyTR9e+SSAnAT74Ea2+/vWvZ1dccUXOswZ3GC46LMTb44DwQYmVDYt+du/YdWeRw0KmbouvTvRoO20dSWAcHPnuamaMEBdTEHfz9Gzjxo3BGih1txfuCe6HMhdeCG7MZ+YyH+m4YuKKx256EfUiSmDVmkJMy05zqwzXWoW44e/itWvXZiT9qPvzNbpss+DlPuU9h7U09w3iT8pxKpuQ+ZVZxjcGVoyDFEfyvLPyHLNmzZogqqRupUhfY8IU+tOUglcBGwh1Gp+4qUGcOSyNsfK98sork90Ya8pcsR/pEVCIS2/MbLEECidAxiwWyl/+8pezpz71qYVfv+gLYi2ARQ0ubVgPIMBh7cZuHR/HLM67dXUruo29XC91K5/2PiPGbN26NVu0aFEvOGp3DgtkFsYpiLrDwStDBKI+5i8iJe6LuLAgvGH1VcZilXudDQTqSLGUMQZlC3EstmIimxSZpyLEtbONiUwQttkwIws1Wb1573EPYF1WhLhdxZju2rUrWF/hkpty4VmHONIEC6DVq1cHV84m9IV3NM+p1DfLWu8NNjMJo8JPXQvc2eBI5TlUV462a/QRUIgbfWNujyXwKAJkzGJh+8UvfjF79rOfXWtCfGSxG82iBDGORQiL8Sa4iLBIiUG8az0IORvHWJEdMtWYUu3dJEA3wlKddqZzDsVxh+EWyUd9ES7DfIDDBeENwYCslCzo+CAvU7BMXYhD0GIeFTmXyhbisIKI4k8v827Q51x77bXBOrfuFnFDcWp1h2T+cM/FWIm8wxHAsTjlp66iCht+WMimaskaxwYLRcTEIp6hg74vrr766uAJUebzuqo+4pqKWJ2y+3Y7qxTmmkJcVTPceppGQCGuaSNqfyTQAwEWJ5s3b84+9alPZS94wQt6uEL5p7DoIHAsH1pYwkVLmCbtwOG2Q6KGJoiK5c+I6mvYsmVLNm3atEIzXVbfiyzrx60TsQ1rHO5HXFynTJkS7ksWpFVaoaZunYglb4yrU9QcKFuISz1GHO8ORLhUBQc2NXjvdbKM4X5A5EKYY24RIwuXNu5RNnfqKswVNfervg5xyMg8XWSMx6r7EOuLCXQGVX+R9SLC4SrZlM0/2KQw1xTiipzFXms0EVCIG02jbV8lMAQBdkOvueaa7MMf/nD2spe9bOCc+DDE8gLLNzI3YWXD4h9XENzcWFCx4EjdvaUd9J49e4KY0ZTdXMYRt4pLLrlk4HOqiAbwQYzwVIarZRHty3uNbl2go2scIgZWqAhvCAL8DMq6CCEQISvVDMNFWiXGcVeIG/4OwHKTZAipilI8S9mkGckdu1PWQ/o9efLkYEmOeDSoJCdsNmHFXmc3uzzPUTZliN2H9WHqBetiNpiaUJjfJCNqyjcHYxLv5zpvINBG3IGbtDHehPvBPtSfgEJc/cfIFkqgdALEhfvGN76Rvfe9781e/epXl15fpwrYUWM3kx18rGtYdPAnLiwsuFsLYsC+ffuyOXPmDKStZVUKA0qTPmZwByP+TBMKO+0sINvnY2p9Q8RGQCNW01AlWkng7sbCBvGRhWddhK/UrUdTFOJ47nIPzJ49O7UpH9qbelD6bgX01kHivcr7BWtY7n+EOALaE8ezaBfp4SZHU7KNEnaB93QT3tVXXXVVtnLlyiTv6fZGY7XLdyTWik0pvIMR0ge16ZWHI98LKVsb5+mjx0igDAIKcWVQ9ZoSSIzAc57znOyrX/1q9ta3vjV705veVFnriTnEhyy7mCys2aknBtdIcVewjuN4LOWaVPiA5IOGANxNKU0S4poyJkOJWCzUsUDFEhXRhYU69xrC46AsaIZifssttwRxkOdFioXFFc+7Il3byraIS5Fza5tTF+KwAiWrdhEu4Ih6LJwRVvfv3x/ewwjt3E+8l8ta9A/nXpvS/IpZoMviVCWLJglxVXKrqq5169aFb906bwDy7UD76my1V9V4WY8EuiGgENcNLY+VQEMJvPCFL8y+9KUvZa9//euzt7/97aX3ko9YFhUsBrCuYGHRze4yu/ikTSdTapMKXLBcaNJuLm6MTXEhZhFJX1J3R2oVsbDsQAhHxMFljN9h9YcYXGdrDzInswhOfSyKfH6VLcQhymIdiXt2igUra5Ia1E1Uzsty27ZtwTJmpI2qvNdrPY53cYy7uGnTpvB3ngFkaGVzqCh3XiyWuG9TF7A2btwY2KQepoA50CTXVL4LSWRCHOGmlBQ2EBTimjLb7EfVBBTiqiZufRKoIYGXv/zl2Wc+85nsla98Zfb+97+/0BbyEY91DVY2LOQIossimg9YXON63UFrUoDhCPzIkSNBnMQyoSmFj+LhXCBT6idCHAvhIq2YBtF/rGAYE2ItEjeLhT2WMAhxlmoIMAbwLlJILFuISz1ZQzUjW14tVWZPxCqOpA/Eo8OdlPc3z73x48eHOdvre5tvAUQ44kmlXNavXx/ikKX+bksh/lg384TNTN5rqW4WdOorFuwIi3XeGNM1tZtZ6rES+D8CCnHOBglIIHvta1+bfeQjH8le8pKXZB/96Ef7JoJVF26WxJ7hgx03LGK98VPETjhWPOzaL1mypO+21ukCLHwQ4pqUNbVJrqksSBGsUnQd5kOZRQoLaixKia/IYhjxptdF9SDvHZ4puMKkmtgEK5QYA7Mojgpxw5PEimn+/PnJWsRFa9VBxGnkncs7nWcGzwuSFSDI8SwcN25c7mdIlWJiUfdVp+tw/1588cXJC4p8q/GOJmFXEwobHMzVJlnEYW1WZxGOeaMQ14S7xz4MgoBC3CCoW6cEakbgLW95S/a+970ve97znpd97nOf66l17KAjvJFEAcsuds9x4yvDyoYPE3akly1b1lNbPak6AmvXrg2Cad0/JPMQwbqvysDmedo03DG0F+HtgQceyGbMmBGynbJ4jiJ5yoIvQhyiPhaKKRYW8ogYI2XA7KZvZQtxbBIwd1KNy7d69erwzkjVNbWbuVD2sXGzjezmCFJbt2495s7Kc2WoeFYch1VdipsZZTMdxPX5btuwYUN22WWXDaL6wuvknUdJ3VKxFUwKMfwQ4uocw67wieYFJVAQAYW4gkB6GQmkTODd73539ra3vS17xjOeEWLF5S18jGNlQ4wpPugWLFgQdsaiJVze6/RyXJMsrWL/CZQPy2nTpvWCpJbnEGh40aJFhcUYGmQncc3DIqSuH5zce7h/I8ggmkQLGsSe1gDvBGfnGCxZUi0I/jxnUo0/SOZK5lERgffjGJYtxLEBUmYg/7LnYupCHBa53LODsIgbaWyYG1h0M6/ZhGPjBbEcwY2fuCFHnLvo3jrSNev8ezYCL7300vAMSrlgHX3dddc1JrM57z/mXpHP1UGP76pVq7IVK1YMuhnD1q8QV+vhsXE1JqAQV+PBsWkSqIrAhz70oZCo4clPfnLInjpU+f/YuxNo26rqTviLxHwmfmUqKdOAFCoKEhCl7x6tQLBDwC400kQ6G5DmIUJQEJRGRQUREekEiSCoKEpPBJVOQDppVFBp7GJMqkaqki9VVkW+8Vu4Xs673PvuPufsfc5a+641xh2vuefsvdZc3Zz/+Z9zumyBEVg2wlPQ/wFxQqwm7YHkiVdBrk+NESPZsCTQfWnAWopxieGPM+fghz/8YWSUKWaQS0u5EuV6sxeBOxgqy8pjJ3zHnJQMxMkDZE21Eeo+jbkElALO2mRndQ3ElZ4jzr3lriqVnSscVP7QNvMKdrX2AXPWizvNXl199dVjUQDrPQFzpc4Dmd16661hww03bHX/djUXy3quecIi64su5Y6mi5bqoJltrnIH4lKewVwdlNPYV/WdVQJNJVCBuKaSqp+rEuixBM4888zw9re/PYbtXH/99UuNFJCCqcWjTalmzGDYMGimmXBZ9b5S80PNtZQwCoApKsn2pQGIzFObgMO0ZCNpMjbKtEM6GU9CBO1FexLjkLELlGrC0MAa0Equ+AeIB8SVOoYuQN0KxC37ZEhsrVKdAvfee2944Qtf2Em6h0mcqQo1AOMY7im/mjMMMAdcLAmYu/nmm8OiRYuK6vNsc4zh6i7Jybk0zlp8+OGH4/7ok25o3+TIgk3zVIG4cVZs/e5Cl0AF4hb6CqjjrxIIIVx44YVhzz33jFXA3vrWtwbl0l/84hfHUFVKGkX5ec97XlZ0f6GpgMNSjarZFp4kw5gypYILs41JUQ1siD54S4GKxtFmXq+mBxBAHJDgT+9ncCh+AhAfFuS0xkoPJcHqs/eFuZXYKhA3+VkrHTyx/zGXmoDtk5dusze6D1ZZZZUILDiHMMCNi3NBagtnrLBCjF7Acq6tdFA3yZVDw12y7rrr5irqofrVxbk6VAda/rB98Q//8A9ZA4uJlV8qO73lKauPqxIYSgIViBtKXPXDVQL9kgCjnmKMEXfjjTcuZdAz9r/yla9Elk2OYFefco+lVQWIE84DXOlLE04l592kQ5f7ID/rwd6TN1C+N8ZpG1VbsS7tfeB6qa0CcU+fua4ZcYxCP8MCv7mssdKBOMwYIFWp8rcO5CPj5JsrFNLZhIHPAZgqOys24+zLady/+tWvesEis6YwveW7qy0/CQCr6egbb7xxfp37bY8qEJft1NSOFSCBCsQVMEm1i1UCbUpAWJqk1TfccEME3yi8g423Wq647bffPuscUg899FBU6HNSzsedJwbHD37wg14pxQoDAI/64C0VDg3s6DqMx3t4weXuEaaMIQmQayt0S/VUIUnCw0ptZKSVGoIE0NLamlPP6hqIE/4MJCk1/xIWtbxebcp8kvtnPhBrkn0Z9V2ANudnk7QWDHxAkTuEngKMA85Zgxh102QGllDJsskcYcS5D+hSfWgKhGCK5xzKOYyc6YQPPPBArDyfa7NPnalN9nSuY6j9qhKYlgQqEDctydf3Fi+BI488Mnz5y1+edRyXXHLJEjCF8v+JT3wifO9734sXFZDrXe96V1QWBhuF6JOf/GS49tprY+4nuWD222+/GB46szV95szvAd4OPvjgGBKSGiALY+mb3/zmkkpnxU9OoQMwL6rK9SVMpNBpmLPbXVUbBXDY84xOhTqEa2EQMia6YKMCsay1koG40teW+QZOt2kwdg3ElV6sofQ1A4hbbbXVik5dwAFA5xjlXMMQtsZVjwXGMf5f9rKXLXneJIGAvgBxpe+Jmf3/7ne/G5neyypWVNKYAXEqhOdcwKsCcSWtqNrX3CRQgbjcZqT2pxgJvO51rwsuSUUOZjalxoUXqqwFTMMy83nJ+M8///zI4vjiF7+4RKEWJrb33nuHO++8M+y6664RGLviiiviv48//vjwpje9ackrmj5zNkGee+654cMf/nA0/rbccsuw9dZbhy222CKysHjcKLIMxBKavCYUrrZyjz0Zngx3/M4d4cHffTD80ZN/FF75f18ZnhWeNVFRMFKMS66+vjQAtOqcEiiX3uQvonSutNJKYw8F2y0lOSYjoeCYIgC4UYzUsTtU2AOEhjk3S2XEdVH4owJxy17EWNTyVZa6vxRroBuUnENUSgngWRv3NmcCUE/ovv2EZequUVn217/+dacMHePAriy9YRqS34te9KLShxL7X3pBkxInoQJxJc5a7XMuEqhAXC4zUftRlAQYgBILY6uddNJJs/bd5fTqV786KoSXX375kpwomGf7779/OOSQQ5aAeEC3ww47LBx99NFh9913j8/j/fV3uaGEkcqpMswzZ+uUfsutJLRo0HsMCEiVOnmaSwj3vP/++8PKK688Z66ZYRbUY8s9Fl73B68L/7TcP4V/We5fwu+H3w/PCM8IJ/yvE8Ie/3ePYR5VPztDAtbWCius0AsPNdaqPTgKiynlUcF2S8w6QPI0Cj9wCGDhCRkutZUeXutcd6a3WYG3ayAOeGz9OndLbDfddFPYbLPNigXiSpT5zD7ffvvtMVqgi1QFHFnAOc4MTkz/VmiKI1QYa6khyV3OO4eGn75Uav/FL34RneBdrK8u52GuZ2MhO3MB8Lk2uo27Z5qh4rnKpvarSmA+CVQgbj4J1d9XCcwiAZWZgHDCU9/ylrfMKiMUeUy2QcAtfRBAx3t7zTXXxP/CmvvOd74TKKmDANlVV10VDj300PCxj30svm+YZw4zcajvvMiaHC6jAA3DvK+Nz2I38H7/5//8n8d63P8O/zus9f+uFX6+3M9DWG7pR/3hk38YLvy3C8PL//3lY72j6ZcpNEJT5cHpS8O2xCMCg6UAACAASURBVPTqQwEKhp1921TJN58UaSxTP2uttVZkcQG6mz6ji3Ug/5ww2JJZEKUDcebAGhj3/BpcH10DcV2sxUk+ExCHrV5qw/pSJXiaZ8e4spNWQ+qFSYSRcmamqu/YzBwgQhaxaN1JyTkyypg4Al/60peO8tWsvuMc4nzNOfRxGIFxfAOESmW9zhwrtqJUEjlHSVQgbpgVWj9bJbC0BCoQV1dElcAIErjyyivD4sWLwznnnBMVe0wBoRaDHtfPfvaz4YQTTgif+cxnwqJFi5Z6yxFHHBErkgLfgF4bbbRRzAl38cUXL/U57LVtt9027LXXXuGoo44KwzxzmGHxiCZmxiOPPFIEU+a///f/Hj3f4xoln3/G58Pi318cmXCztXX+fZ3wzf/vm8OIc6zPMlRyrpA17ODsDYpxH7ylFGLh6MtiBAHqGH+YGQw++YywMuSEzEUG9jtWHKZIqU3RGaBmH0Ke25qDroE44K1k6KUCEI8++mixbD5rBMuLk0bl1FIbJu60KrE7L1JxKgD4bbfdFoFw4JwogaaRAIAHFXhLBnXT+nE/A6/6klOtS8blNPZcCUBpqqSdi34zjXmq76wSGFUCFYgbVXL1ewtaAhhqn/70p8Muu+wSvvGNb0SPFeX4L//yL8Pf/M3fRPaPXGxysl199dURZBtsH/3oR8NZZ50VQ1YlTBeqgfHmuYONgsToAcYp5ND0mcN6NyljKe/MfffdV4SxQqFvgw6/x+/vES7/vcvnXM/PfvLZ4af/8tOw3Ey6XEc7oG9AHOOdgtZGTqCORN74sUJEAEAz9zMDL1X4wwAEDgG2pxF22mQwQmwBil1Xf23Sl1E/g51I5pNg1ozax2V9DyjEkdBmeHDXQFzpxRqsmZKNxTbzq3Wxpps8EzMN6JVDmKhz25rm1KOHAZndVxwnzu65cvEBHuTqFeZceqNjGk8f7mdzAVxdf/31i97ng2vKPc2plzNQav04V5sC2aXvmdr/KoE2JVCBuDalWZ+1YCTw1re+NQJwQLI3v/nN0aCiAKiWKu+TQgwnn3xy/LeccDMTip9xxhnh4x//eGTACTXhWX3jG98YGXQzm9wdGFLnnXdeOOaYYxo9c9iqmy5SRlxSZHKmwSf5CA9OgMc4C2/fZ+4bLv1/Ll0mEPezf/nZOK8Y6rtynMip1pfWRS6saclmZkincFPgHOPtxS9+cWRXCInJwchclozs9xROMi1ZjvveJuzEcd/R5fcBcZw3bRabqEDcsmes9EqXQivdDSWDibfcckvUZ5K+0eUeG/bZzsUEzAGmsJgfeOCBCMxxrtI3nO8+JzSe7lZ6A4wC42Y6l0odV87raxSZAuHc1TkDpfYDh1iOe3oUmdfvVAlMUgIViJuktOu7spDAbHlBhs0V8tWvfjXmG1F0YZCRccEFF4QTTzwxvPOd74wsuS984QuB8j+T9YDddtppp0VQjWKtcql8ciqkzgbEbbLJJpFd9973vrfRMzHshm28v5hxX//612MF1dxbW4bsDb97Q9jrD/YK/7zcP886ZNVTL/23uYG6tuWEcVVyVbyZ8mhrntqW8yjPY7AA3zAoVF8U4inMioGWO/g2OF45gfR9tdVWG0UMWXxnLnZiFp1r0Iku9kXXQJycW+6IUsOBSwfiyN8cl5z/SkinVB0lnJeJ5ey8F9LK8Qq4wqxUaAWjuIRxLOs4kv7Euiq1AMvMsQGu6OQl75HBMZUwPxWIa3Dh149UCcwhgQrE1aXRWwkIv5KfiYdz6623fto4FT4QmgC0auvSZqirpvqyl70s/px//vnh2muvXVIIIXUihaYqxgCkW2+99cL2228f/P9gS6Gpr3rVq8Kpp54aK7Q2eeYoSdgplUAGFVwBg7k3BSYwA4S5jNN+E34Ttn7W1uGB33kg/Hq5Xy/1qD/6zR+Fa//t2rD6b1Yf5xVDfVeOE0Bo6Qp+GrTiH+apzeqQQwl0zA8LgWaoUO7vvvvuCJIKY5Lbsa1zY8wuDv11hiUga9gQ9qFf1OEXsBOd8aUyOawre7zN0NqugTgABEdBzmFSy1pypQNxkyx00NXWxTDDuC/17LT+nT2AdDk26W/AEow5jOjSWEH0KM2d1odmPH0Zi/kQLu2eEGmTawNY0/FKW/u5yrP2a2FJoAJxC2u+F8xoMT7OPvvsWChBY0ALH9WEyn3qU5+KFUh59ylSb3/728M222zTinyEXQAdEsNNgQXFGAabYg3ywynWwLPq90LbLrzwwqU+l4o17L333sF3/B5rrskzhx0M5QXLD0sP8Jd7G5bFuKzx/Gv413DQ7x8UrnvGdeEZTz4jAOf+/Mk/D2f+rzPDur9Zd6KikAcIMFuVmomKfamXWVvOEEqwvwtBEkLYByaZgWJ3CIHG6iu18cJrpQLWQER9T/dSG/PQNRBXeo44+ZZKLnTQt/xXbaz5aTwDiE5/3HDDDSM7jlODc0OKAtELgDp7EWBdQt6sNnWpaczH4DtLB9tnyo+9wlmTcxh0qiRf6l087TVb37+wJVCBuIU9/70d/ZFHHhmrkqowhl10yCGHxBAyzJYDDzww5ncDlmGtyeHGm4Np5t/zNeCdIg2o/PK8DTaGOkVM0YZ99tknfu7www8P++6771Kfe/WrXx09wqqvaj577733BmyoQcUNY+7QQw+NbDjg2D333NP4mfONY+bv5aL73ve+F+Xw+te/ftivT/zzwATz2WZC/H8O/xwe/Z1Hwx8/+cfh+U9Ox0MMnF1rrbWKzgM0uBiEQFLU2kxK38ViY0RhhPrBFEshYIOGex+YZGSHCWzvjFtxuIt5aPrM0gtOAHnJv818kBWIW/bq4WjKmVky39rvAxDH0QTAKrkBdDH75krhAYjzQ0ehJ4o28He55nLL76ef+qRvfWg33XRTL6rZDs5F7kBpBeL6sHPqGKYlgQrETUvy9b2dSYCncrfddosg3Ec+8pGlPEk33HBDOOigg2IIgTBQBrdcbgoubLrppjH0s0l+rje84Q1BdURFGQbDu44++uhw6aWXxgqnGHaqnfISAQXTcwF/cssddthh8U/tsssui9VWjz322LDrrrvG/2Ms65cQiBtvvDEyJ1zITZ85rIBVmrrrrruCQhK77777sF+f+OdLT9Y+l8AoNUDaUkN3Zo5LUmusgdzCRewlDB+Azoorrhj3M3YqYHcugAoIz6AaNxx64ptlxgvl0QE8tgliT3pMAN5//ud/juFhJbYSgThrBkub06bEVrqRDgCStL3ku6EPjCV3B0dGE1DNZzHo6HEcOdhNADx3j7lsMzR9lD2p6BXdsvQ7LY39wQcfjKHPfWmY62yHnPNyViCuL6utjmMaEqhA3DSkXt/ZiQSS1+jDH/5wDEkVYonhRmGiuGILLF68OGCZAZoUPkhNuKfcaIorrLTSSvP2jzcUYEaBAZbJDwIsk4h4p512Ch/60IfiMwB/BxxwQAw73XnnnSPbRt/QzD//+c8vAeeAbsBDz9U3Od6+9rWvBd5j4OAgQ63pM+cdxIwPbLnllrGwBPAyAYTDPmOSn59ZwXKS7+7yXZJBY2tOW0Fva4wUScDPC17wgrYeOfJzKIx+GLQU9pRwO3e23sgDnuOLABXsoJINllQ0Y9VVV21bPBN5Xhe5E7tmxE1EMB2+pHQgzt0AMCk5BKwPQJz7zPnDgTNso4+KehByyJlIb+XM5RymB06apfzwww9HkKfN6s3DyqR+fm4JPPTQQ1EfzNlpZg3TV0s+l+oarBKYlgQqEDctydf3ti4BSgxDBIiEhaPKKAPbJaEB4+Rtu//++2OOuJe//OURpKMUffnLXw7vec97IjjWJDzV8zDvTj/99MgiU1QB0ABsA6gNXkjCYDHkMG4oPAohAARnXqw8pKecckq47rrrYkJsSciN5ZWvfOXTZNX0mcMIWejrNddcEz7wgQ+Egw8+eJivTuWzmEwYSqUWAZhLaNYVILYJM3Mqgh/ypdayfcbQmFbDRGD0YFClUCHnwrB5+ABYkkFj25bc7B2MrJLHYS6srWpA/sdK7BqII2/7aJRiQDnsF3e/ypeltltuuSWmvijZ4MVkFapZcnP2YJIpzDVuwxbX6DJSg9Bj6YYqWvtdE9bdOH2wnzmUc2ZcNR0fPUP6lrlChps+J6fPcc4D3zn7c210KQByyUzdXGVb+9V/CVQgrv9zvGBGmIA4jDIGidBTCk1iylGchKXyRH7pS1+KbJAExH31q18NRx11VDjvvPOKz18y6oQLtxUiK78eWeTezKufko2S2WRM8UpVOXOfgyb9s8cm7en3zn/6p3+KrAUGDSDOOpEHZ5z1AsASYlSyMW/OMAI5K0oFVJqsu9w/wwDmBGqT6dA1EFd6sYbc18R8/cO4X7Ro0Vhn2Hzv6Pr38ujmDCo0GT+HDkeGXK5tN+AbUI7j6r777ospBPydAwlg1nbaikFHddtjmfTzOMQVZlMwrS8Nc5re0mZRn7ZlI8qg9JD5tmVSn1cl0FQCFYhrKqn6uewlkEA1hQ947hU4wJZI/y/UUwjqc57znMhkwziTu8MFctZZZ4Uzzzwz/ilX2kJse+65Z6zKig2HFZd7owxTUkrNVzSXfI2JoVJydb/BsU0qhJASLmQIk1DBC8or5gUjpi1PrXMFoN+FAZb7fsutf8BV7Bph/yU2zEpslzZzM1UgbtkrofRCAdjSnABtnWfT2DelhweTmXvA2TOJdAv0V7qOew0QLrLCnQbAb+PskKKBnkwvLr0NVrMtfSyp/yXkDK5AXF9WWx3HNCRQgbhpSL2+sxMJJEbciSeeGK6++uoYfir0KtH7Tz755Biu+rrXvS4cccQRsbR8UqpUNcV2UQUVQLcQ29ve9rbw6U9/Ouy3336RTZh760uYYO5yHrd/GBCMFsy0LhojBUsNEMcwkX+xqypjzhjKfulhw8aA0TFYaKaLuenymYA4eSK7Wldd9t2z5egDnI2SZ2quvnUNxAG6FSspNadiH0Cgrtdl18+vczCehN1B7jz3HQANuCw/F8eTfw+bY064tjuzD1VTyYZemHT78SSdx7c5FTmbc2bE1dDUPNZK7UWZEqhAXJnzVns9iwR4ZYSdCd845JBDwo477hgOP/zwyHj7/ve/H4szCPs75phjYh43wJtca6qcUmYOPPDA+LNQm7x1ctQpPgHEzL0J33jkkUd6x04yJoy4PniorSGAyS9/+cvWQB/zjmUH3EvhOhTwFLbT5br1HuNpM5ywy/4+Jf8QHnzwd8NvfhPCGmv8JvzJnzwZQ1MxK9Zee+2uX9/Z89teV511dI4HY7e4rxQMaat1DcS5Yzm2hjX22xrfuM8pHQS68847i89/ddttt8U8dyU3ziVOgBwcGfaks8R5CCAH0gl7B6zRIZ797GcvU9TCXzH7ppnDta214GzCVuwTEMc2kbeaHZNrA8Tl3L9c5Vb7VSVAAhWIq+uglxI44YQTYpjlVlttFRVXRQh4/oSUqaoqBxdwAGAnuSvQDkuu9Nwl40zm0UcfHUN3MQYvuOCCcR41ke9SQDF7cvYUjiIIoY+U4tITWqexm6dRCiOk7/su0JyBwXNPPoAAYNikw3cxgoTyrLvuuqNM7US/A3g766xnhCuv/L3w7/8egpo1v/u7IWy11b+Ht771f4Qf/vB7rSQbn+igBl7G6PJT6v7HaBFi2GYy9q6BuNJzxJVeKKAPFUendV60+V6OIEBcjmkx3LfuS2ChRtcV0gyQA87RLQbzpHJsYdTJV1l66zJ337RkA7iWLqfNe6LtsVQgrm2J1uctJAlUIG4hzfYCGGsKSaNwC7NUhIHxwHDnVTrggAOWJHLFCqHYysGx5ZZbtspMKFHUJ510UizS8IpXvCJ84QtfyH4IFE7zXGqY1FwCVkxE6GNfxgUs5a1fYYUVGq8p+zgVeeCx521NOXGmmR8J8KM/JeSRvOCCZ4TPf/73wp/+6ZNhueWeEj0w7le/Wi5st93/CXvt9Q9FOx4AWYzIUtkPP/nJTyIQB1xuq1UgbtmSLL1QQOlAnDsbE3f11Vdva8lP5TlAOGspB0bcfAJIjix3sJ9VV101AnX0Yo5nd5o/cwZ65htj+r3x/fSnPy2+mNLgeDH8JsH2byrj2T5XgbhxpFe/u9AlUIG4hb4Cejj+wfxQ2G4PPfRQNOQ33HDDsNJKK8UKjoON8aKlHHM9FEmjIZ122mmxUMPmm28errzyykbfmeaHzNddd93Vuyq3gCsea17qPrRhKo0yDoTVMHIAFPJnAeRyMRL0RZi73JM5t3/7txB23fUPwrOfrarw0j0Fxv23/7Zc+NSnfhVWWqm9sMhJy6P0Yi0ViJv0ignR8bbFFltM/sUtvREA9Gd/9mctPW3yj3Fnf/vb3w6bbrrp5F/e4hsxowFYbYaVt9i9eR/FCa2qeALn1ltvvXjvYstxbJTKjis9b+hsE6d4V5t5ROddHEN+IFXdraGpQwqufrxK4LcSqEBcXQpVAlUCUQLnnHNOLNSA7XPDDTdkLxUKwO23396rUvWEDrjClCm9IEBaQMvK5ZdyrqV8bz4L7OKhL9UYyGHjfP/7vxMOP/yZ4b/8lydn7c6vfvVk2Hnn+8Nee5VZcdSggLbYDzmGhzVZA20DcfYNQ1QImp8umKMYTX5K3ZulA3Hmt+Sk+taonFeLFi1qskWy/Qwgy1jmy7+W7QAGOmY+OJYSKKcYCx0wjREwl4sjrAR5tt3H3M+sCsS1PeP1eQtNAhWIW2gzvgDGS4H4+c9/HvNS+bsfYUzpB+OIwZ/+zbvp7/J+yCf3+te/fgFI6elDvOiii2KhBoYtr3UJTd6/l770pSV0tXEfgQvWLvZmH5q99Ytf/CLmqtEYMPYfI+buu++OrAJhpwzMwdw1OY7dvMgRlzsj7pFHlguHHfb74Y//eHYg7h//MYRddrk37LHHX+Qo5kZ9AlhjcayyyiqNPp/bhwBa2jhrHisHiI3VDbx+/PHHY95M+wvYwahmRNtjbQBzno2tqjpxie3WW28tGgQqvdgEx4v7Ld0FJa4hfaZf2mOlnj2DcqdDCbGdCbbZ587XVATiRS96UcyL557Ola2vr+YlZwbZsGu+BCDO3VJqAZ9h56N+vkqgbQlUIK5tidbnTU0CqWoqNtc73vGOSLFnODD8/Qw2Sd4BdDOb4g5nnnnm1MYwzRdffvnlYaeddooVtCT3rW06EqDkM7BLN1ZmSg9wIv+dfSf/nXU2GEY+HWkP99ZSWJii73fZ5feD6OaZObjhP//jf4Tw7nffGjbdNP+iE8PNUDmfBpJZT8MynADbGGnAbWFLQGz7CYM25YhL+8rvGdPyDL34xS+On3Un+uwowFzpxRrKWR2z97R0IC7l/iydYWVf2UfAqYXQEhNWRXdgl8Zpnc6iXEAYZ6Kzrg8AaVpX9957b9bVzdNdk8saWAj7sY6xXxKoQFy/5nNBjyYBccIVFy9eHBlFFD65CwBv/mSoCAn6zne+E17ykpeE3XffPSoT2DmYCb7j/xdiu/7668N2220Xc9CoTFlCkzhfJdw+NQwXa7J0ry5DJVWXMx6VRv1fV2Fzk1oD2KIbb7zxpF438nuuv/53w0c+8v+E5zznyfB7v/fUY/gj/umflgv77PN/wpvetLRzYuQXTemLmA8Mr1KN4WGZr8aKkYLB7Y5yn7mzBgG1uYo1JEPad7/3ve9FMByDznOA/oC9JsBc6UCcfLGlhjLbZqUz+qw/d7Z8vSU3e9G+ed7znlfyMGLf5VGmQw3DzDV2urUzjD7tfOFYU5DJHE+6mnmaBP2ha7zwhS8sfl5KGQAgrk85jUuRe+1nfyRQgbj+zGUdyW8lkLyujJKZygWF4Ze//GUQhnndddeFj33sYwsWeJu5YCj5kigDSnh8S2hAkY022qiREVnCeErvI3BEEmiFFh577LE4Lwx+BQ5KN77S3GAYLb/88kVM1Q03/G44++zfC//6r0+VTX3mM58Me+75f8JrXvN/w49+9MNYQa/UNkwRkBzHOB8Ql8JOMTzME6ass3lZYaZNq6YKEfTcP/zDPwwPPPBAZLlgkDOml/V8+9v9ufLKK+co0nn7VDqjbN4BZv4Bjhhs+77cBZmLu1H37InNNttsLB3KWZWKoAFa/VvhB0A/ndu51ATob9ThZXxIAR/vG5ZlPO57u/q+SB7n89prr93VK8Z+bgXixhZhfcACl0AF4hb4Aliow8c62meffaKB89GPfnTBV0y1DlDg11lnnag0yQ8yCcVp3PV35513xj6Xmjx8tvELWfuf//N/FpEjjhKWFHCedWvnT//0T2MOqVSNmGJsngCmfWgAjJIKaQhF/elPlwv+XGmlJ8Nvi0THPJAlMPvmWjPm4Uc/+lF42cteVuSycsbaP895znOW9B+bw35hyGJv2Ut+miaFbwrEzRSYPezcwWQBtgHOGbP6Vgro3GQRlA7EYTOuvvrqTYaa5WcwMQELwhpLbpwxnLwlV7BN8u9iTwCQsGc54R5++OEI3gPmOOgSSNaFfunc9Ny+6IPYhfTynHWnCsSVfJLVvucggQrE5TALtQ+tS8DlkKr5eHj6OyMHpR7Q8d73vjcqCVdffXUF4kKIslhttdXiXAAqS8j5IPSRsjdMWEXri63lBzLQyT/NRcuPb+VxQBDhOdhvgAKhIECE2ZI423v2G/ZNH1rpAFaag9LHwegCHll/pTfnGBYywwvbTC43bVhj1TnYhhGaKrDqD+NZUZWU4oGBXWo6gEcffbRYNp/1kHvi9vn2YQKZS9AtljUW68g+60NBJfe4kNIuG6AfW43u7UxzntAH6G5kOOw5N1df5aClgziz+tA4RRSHyhm4pt9xAJWe97EP66WOoUwJVCCuzHmrvR5TAoCON73pTVERuOyyyyILoU9gzijiYQgmBUb1vRLo/QChlPtvlDHn+B3gAo+7Sma5NPtDvwAGlHaKtTAjjJkmCpj91gfAxHyUDmClNSV0WChiqc2anGY+onHkBkSUhxOoJewIU4gxM25107aAuJlj01/hq84lgLtCMskJ4g5tcgaMI6+2vptyW7X1vEk/p3QgztrhvOka+Ol6XvoE+DiDJu0kc3Z7b2L+q9wK/KdzYhk+61nPGmkKnam+W2pV59nOXQBmcsyMJJSOv1SBuI4FXB/fewlUIK73U7zwBuhieOKJJ+LFzlAb/EkJ5OVD+/rXvx7e8pa3hCOOOGLhCWmWETO0GFWa0KQSlBn5SFTIKilUcL7FZr0CGNNczPf5rn4PbJOHS0gJOTO2gWn6lcJOm767L+BVAuL6kJfQ/A47j03nexKfA15h8ZbCzgICYTgwejFCMGqEgkqP0BYjpCsgLs1nKtYg9xPmrh9NuKQwYSG0jOlcgbkuwvAmsdbTOwBAJSeiB7yovKlwT8mNrmT/Ng0Zz3msOewJwBxdw3lC33AeOk+cJfQN+SubNPeB87Uv4fTJhslZvzV39kKuZ36TdVM/UyUwTQlUIG6a0q/v7kQCQAzJZ1HUeWAZQC60mU1hghNPPDH8+Z//eSf9KO2hACDsMu2uu+4qIpG7fDPYGX1QiNN6AZBYt9OqPAbgYPAxmjDeAAWpRP2oa7pPQBzFk6HQFngyqkzH/V7pc2Kd/uAHP8g6kbU5so/sJ04gTKBUaVHxBXeT86ut1jUQp7/kPpNB43zAlgNQ+AGOOsd8nhHdRrjsuDLSx5tvvjlsvvnm4z5qKt9P+ThzkOWoAii96m4at/u56702qoyH/V4OQNzMPlvrgDlnCeeF6ABnvbUPnAPWzeZEoue7l0t2MA3KwvgV9XnpS1867LRO7PMp3U/J59LEhFVfVCUwiwQqEFeXRe8kQPnfYYcdIksKGAfQSD9o6y5yxpCQoJK9y21PHOWHV4sBxWApIQm6hOYrrrhiVMz60iieQiwmJf8ULiR81N4AFiQmXFtgk4TDOVf+GmbtCOl0fpQeyl46EAfYkrxesZacWqp2aj/ZS84me2xm2CnHhzM3OT/aGEPX4ICxcXQ1YagwIqU7ENIu3Ayg7//mMqLbGP+ynlE6EMfgxeTnZCy1WTsA2xe96EWlDiH2GyhkHZfOvLInbrnlliLWlLWDMecMEa1Bt1cEAmPOXACC/JtTdtTQ1twWpTBu+wUDOdfmXDIXfQE/c5Vz7Vd/JVCBuP7O7YIe2VyJ4xe0UBoMHtMBg+O6664ruqJig6Fm+5FJMH1SiDagIDFz5CGhxLYFvmUr4DE71pdKvaUDcYxIRkAOBgCAitGEXS2kCiAmjHtZOd8A7vrvM221roG4URlNqbIyxwlgDviINZecP5MAtb3LuTctpvG4c9wHIG5cGeTyfak7AEB9qJo6Ltt9WnPCkSFPJWAOE46jz53mDJbruA+hkpw5wMecC3dVIG5aO6C+ty8SqEBcX2ayjuNpEmAcyRUH2FCZKbGmgHQMg1IV8i6nGjDDA/eVr3wlbL311l2+qpVn84DyfvYpNJWCqVJW23l0KKsMXuw0yhPgjcI6iZACBrjwkkkY3K0srGU85Dvf+U4EEUpX9AFBJTMHrGcV/6ZVuTCFCtqrAB7AmzDTpsCgvtvrbRbMyBWIG9xO5JbW3k9+8pOAYWodmkfAht93cU54rjCvaa2Xcc8lOgugQUqNUpvE89Z9ToWIRpElJq77s/QCRM5QhblKZyimObz99tsjO8v5Im+lv2PSYfrPVtF9lLmv31laAsmZ0vTeq/KrEqgSWFoCFYirK6KXEgDCfe5znwuXXnppZCpQXg888MAYiipR9nnnnRe22GKL8Fd/9Ve9HP+og6KQyWd08cUXh9e85jWjPmZi30sJwvvgmU5Co9i0ZYwCoQGrPMfYjjyrjP9nPvOZE5sjL+oLi8xYAAeTAjC7nCRMyBIKsswlAw4VOSLbBqyXJXP3ir30D//wD0sKLTD0pEEYlklaIhCHLQ08W2ONNVpbms46MvQnY5kxTZ4YR4DNtkC5lN38WwAAIABJREFU0hll5OM8Lxk8x+5x7qy55pqtrZ9pPCg5tdpam9MYg3fSBTjmFB/qQ6O7Che2R1KeOeA71hzmsXQf7AF/n7QONIp87RdrLWf91rlKlqXvhVHmp36nSqANCVQgrg0p1mdkJ4GrrroqLF68OF7KPOCAgK222iqcdtppMb/HfvvtF0Mw/XubbbbJrv/T6pCksIzbc845pwiQ8tFHH40Gcem5Wgbnm2LDS73yyisPvQwon8LHhDTw2PNSpqIL01Q8+8IiG3pCMv4Cdk3J1V8Bcffff39Yb731OpVyAt8wqjEtsHoYRssKO23SIXuUIQzUbat1zYhrq5/Les5gonZ3t/saAAWYw0AaFYhyrt52223FMsr0H3grJ2qprYScV01ky7nFsTXqWmzyjkl8xr6i722wwQaTeN1U35GKLKnaax36N2a7iABnTo4RMiJ6MGFH0QUnJewKxE1K0vU9fZVABeL6OrMLdFwuBcbIPvvsE+67777wiU98IhpqH//4x8O5554bvvjFL0bKujLnBx98cARwgHFNkk8vBJFuvPHGkZFAbnvttVf2Q+YxBDb1qVgDpdAcmIsmzZoHtpGBENAUdqriaS7hk0KIEzDYZEw5f8a58uIXvzhLxX0YuVljDLBSPdmYAthZXRgpjB/nilxQgDdrFwDSZmGFYeaq6We7BuKmkWw/ORec9cBP4AdjGjDnpykb0bnofCyVjQUQ5tDYZJNNmi6H7D4HePYzs+pudh2dp0McAAB0IY8lN84M4G6blZunKY977rkn6vdNzmn7yRkPlMOk828McVEzUg1wXA7Lcm577Nj3zvRUabvt57fxvArEtSHF+oyFLIEKxC3k2e/h2JMB9brXvS4AIk499dSovAMCXvva18bw1N133z1ebp/61KciOHfJJZfEHBmlJq1tcxrlhbvxxhvDBz/4wfCOd7yjzUd38qw2wzg76eCID22SSJ8SLTQXUMCwkXeH4jht5XG2IfO89yV8ARCnAmTpbAh5jgCKNbfLUysWsIelJuyUUcaBk4r+dLGnGIBAiTbDg7sG4kYt1jDiMTjr1xh++gGY44AQboY5YuyAOfuyi/lqcwyjPMtalFajqYNmlHd0/R3r3d5qs0BJ132e7fnuALkdS3cA0p+0vuwXziVFG0Zh/zv/7THMOIA3MM78uiM5NJ0vk5aTO8J7cwZ8U0qBUh160zg/6jurBAYlUIG4uh56JYEExO2xxx7xMv7Qhz4UATntFa94RQzFeve73x0VwSuuuCIcfvjhMZecPEMViAth++23D1deeWV43/veFw477LDs10aq+NkXj24SOIBtZgJliiLFTI4qyiJDQO4TSlruSlBfwCvzgw2BhVW6MZn95p6ng/aD/Ebrr7/+yENJYafASKxoDLs2wk6bdEh4G4C6TUbfQgDiZpOtSqzORuAcppK59HfAXAo5oxtYL12HMjeZ+1E+Y61iAraZn2+UfozzHSC3eSm9WIOUGKpzlu6MyQFYH2c9zfyu0HP3QRuRAM4LTk5g3M9+9rOYG9bfRdGY+0nYCymcdtIA4DBzQg45sAeH6XP9bJVAThKoQFxOs1H7MrYEEhB3wgknhGuvvTZ8+tOfjlR17e1vf3v0eJ144onxIgXAfeADHwgXXHBBBOhSWOvYnSj4ATvvvHMscAGgPProo7MfCaajcCnhBH1s1ivgTWicP41VniQKYc7K2cy56BN4ZQ6EvpTOJJMbiEE8iaq5XexNZ/1dd90VNtxww6Een0KShGRJ5G1vuQ8mDax2cXZ1DcRhNAH/c8/JCeBMSdqdk0KwsR3lm9tss82GWi/1w+1JwJoHmioaVNv0JWAusEmxSvvQgLxA+C4ck4mJ60/616233hrvDI7Qroo3SY3AmZB7sQb6UEn6aB/Weh1DfyRQgbj+zGUdSQhLvFTCrg466KCwzjrrhH333TfSy88666zwta99LRx11FERnMP4UsTh/PPPr4y4366et7zlLVEeBxxwQDjppJOyX1OMK17LVVZZJfu+Nu0gpo/QCLmPGJQpP9Uo4RZN39n154A+WIvPfvazu35V588HRJgbFR5LbkAsxVlKHcewQBzgDSNHyJFxp1DpaRkQAF2gYJv5SbsG4hih+lzSWZQcbD/84Q8jqwVD7iUveUncwym/UQn7GAjKMJdgvtRm/2FhlX5fY1bSKUtnxPWlim3aD8lpOYn9kXLz0gcUlXG2ADYBZ3S2NnQdOS2BcJ6XayOHCsTlOju1XyVIoAJxJcxS7ePQEqC0YnVdd911MZ8TQI6HHPCWcj5QplRMxZ5r0xgaurMZfUEOvU9+8pNh7733jvn1cm9AKwZ5SYbhbDJNSZMpkoAR1Rl5qfui4JijaeRY6WL9AvlXWGGF4s8M+aYAEqXuHSExcoTNlfg9hZ0CvBj+wot8dtxqp22tKWcXI6ZNILRrIK7kUDayBjwkNrGxYMhZRxiRUgGYk1wZogBkzOKFUOGyrT3W1XPokc7O0oE46905mWPF0FHm7qabbgqbb775KF8d+zuD1Z6d6QC57373u5Ex52cUpp79rkiQZ+XaamhqrjNT+1WKBCoQV8pM1X42kkBSpN/znveEL33pS1HppmgAOuSN4KVKxg/P8v77778kdLXRC3r+IfnzTj755LDLLrtEBmHuDeD6L//yL0vyAObe39S/pLQB3qxJCr0cRzyflGKKPvA4V6NwWDkLSUrjHPa7uX0eK4XhnnMC5SYyS6BEqSG29pD9I1Q7NWe98925ADC1nzAK2mAnNJHpMJ/BDgIkzswFOcwzZn62AnFzSy+Fls10utENgFxA2jvuuCM6duxtOThzyn2EHY1ZXDIQ58yxNzkySm7WSXKUlTwODj/zUfpdlubgW9/6Vthiiy2ymBL3EycQxhymHOBWDlJ3FHmzTebT75xLPjPf56Y5YONsUqV2mn2s764SyFkCFYjLeXZq34aWQMoRd/bZZ8f8QZtsskkEOYAbgDg//u5HSfCcPU1DD76FLxx77LHhuOOOixVm5dDLvWE1UG4oObm3xOABCgiTogQDEYAFM1kxwEXrtotcJ9OQE+aJsfZhvwlBSYr0NGTZ1jutMedgyUCcKnmqSALkFG4BVsgXmUJ5phV22mSOKhDXRErtfYYBLOSfTrCslvLg2eNSH2DS+/u0zy9ORmkYUvGp9iQzuSdhpQIX2gSfJ9f7/3gTYAVwW/r93Lecfe4D+Z5zbXQ+wFzKZcdJ9Pjjjy9hzM0sMmF+6ExtFJ/oSiYViOtKsvW5C0UCFYhbKDNdx1kl0EAC2HBYcdtuu2247LLLGnxjuh8BJqhgJudTrg04rAoq5UtYnAT5wI9lgQTYcU08prmOeWa/VPtjzOac66QUWbbVT5VshemXGJaUwk6tK0Ac5gHWQC5hp03mqEQgDqvMeZazYTiX7JsCcTO/DzhydjM4MbmEdDubGciTrFhN7tb5XKHYTdbctD8D1MRALL24EuDf/JcOxMmbSYeSvqW2yUsAuO4eAM5xKrvLgG/WFeau0FZVknMOga5A3OTXTX1jvyRQgbh+zWcdzW8lwKvNa+mSoICnXGL+nkJV/R8lGzOudMWwrYmXH06euEWLFoVrrrmmrcd29hzzhxGXkyLJYKJYYVMA3PQNsMZoa8o+ApL4Xs4K2DCT+vd///dxLCUbkWm8jEmJ3ksP56Hky51WyhpzbmO8AdvuueeeaKg449ddd90iK7YBtbQ2jfmuQ1MTKPXc5z53mO2fxWetH3lhxw3ttAYBc36c0c56ABlgZpQ8UE2F4x3C4tdbb72mX8nuc/ardV86M1rFTNWacw4ZbDL5dBR7WrGB0pt1JR1BCdERy5I1kNe8OFfokmuvvXY8X+gbueVzZV9pNTS19N1T+z9NCVQgbprSr+9uXQIuBkwjSVuBSgx/ijNgLoFw/mSw8OoLEdx1113D0UcfnXWi5tYFNccDVUxVOdXlL99Gbc0kANQFtllv8j5RorC/RmVMSNK78sorR9ChtrwkgN0ovFhYSclNvilrDKiYc7OfsF6d4xhJHCd9aJgoxjSY427ccXUNxJVcrGFc2c71fXPo7AfMAbU59YSvA5vaDF+0Xh5++OEIPJfaAAtaU4dUruO8+eabY4hz6ePIVb6j9IsOJrfufKHnozx7Wt9JeiDnH53S+Q4AFqIO/Jo2AFaBuGmtjPrePkmgAnF9ms06luhBohxdddVVYfHixeG//tf/GoE5oJs8XP7kOZOrhHdWrpL3vve98fJOIN5CFuOll14adt5557DaaqtFpSb3RvnCElhzzTUn3tW01uQ/S2Xrrbc2Qv0AxECH3EGSpkKXB4U3d/nll2/6lWw/9+Mf/zjOi4INtbUvgRR2KmTHHhAGyJEC/JwZzg1MnMbeb2PUWA8MKqzEtloF4uaWpHUF0BWO3WVzL9AvAHNAZLoFEM3atYZHZUB6BieAIlOlNveA9vznP7/UIcR+043WX3/9Ipm4g4IXmkrvLZHhOnMBuSMwXnPOETfuok86J6BflAH9V6qTVJSO7jnJvKjJZsqNqTeunOv3qwQmKYEKxE1S2vVdE5MA75H8QQxmwFyqPOTvcpSgel977bWR9XXIIYeEbbbZJgJ0oyrJExtYxy+64oorYqEGgNJDDz3U8dvGfzzFRFEOXsJJNMacHB6MaOsFO8F6AvJOUgGaxFjbfAcDDAjeB4UfeOI8KR0k5WWX+L0N4HjctWJfAS6wDIEN9pK/z5fz7dvf/nbMq1NiKxGIk2zc+YdJWVqbhqGeDFUyU0wEMEd2K664YgTnZgOXS5PrMP0FINCx+sJqHWbsOX7Wveys7cN8AKXsM3urL01xGaHoc+mWxuyMwcrlkHbGicRYffXV49+71ksrENeXlVbHMU0JVCBumtKv756qBIST7LffftHgO/PMM5ew6abaqSm//MYbbwxbb711NNCxB3JvFIFUObGrvjI+Gc28fsKNGFTCyYQhdQW+pUpafcipZl7k8SMrAG/pDfCqzax0W9q4HnzwwZgbaFprzN4FpAP8GRIMiBe84AVDhXuVDMQBZTiE2mSJds2IK22ND/Z3GkDcTHmlNc+Atu6Bcfbfy172sshu4ayY605xD2GJ2iOlNjoFJ0bpOcmEDOZcIKrp+ujLfBgvxyiHTp/YWYgCW2yxRdPpjPepcwLAb40iJGDLCZfnOHS2tKmzOs/cOaXrQo0FXD9YJdCBBCoQ14FQ6yPzkEDKX+CCHgw7dVm5OIAde+yxRwx/uuSSSyoQF8ISUAtLhnexhMY4aTNfl7XCQKK4PPHEE0vyvQHfJpWTQy4gYKifPjQJoSmAfVCSrQnM2tI974AAY6CoT6qlsFPAtjNGWKa9Nh/zba7+Ca1yftf2lAS6BuKAh1hNJYIQgC5OlJzYP3STBMZJceDOtR/1UeEHLRnOmLhkX3Joqv1vPKUXOZCDePPNNy/+2JHz0V1WOrvbRDibpPRYZ511ip+XNIBx15nzxRzTW8lHMQsgHV2WU3TcdDwViOvNUqsDmaIEKhA3ReHXV09XApTCK6+8MhovO+yww3Q7k8nbedF45zV5z0oI1aVotMHqoVQIi8OUpLjIvdE1tX+uaadQMsjaTOQ+zSXGS0sp7EPxib6w++SYsca6Dk1N4BtgAehgv1rXbYTlWVelVH2duf+cMxgLNUfcZE4m53tinU3mjcO/xRkJcHPvcBiqnm3fYGI7OzkP0/08/NOn/w3h51h/9n7JbVyAJJexYyID4vrAaOoDUD1zXQDm28xF63wByGEHc17fcccdERRXVIxDa1hHcwXictnJtR8lS6ACcSXPXu17IwlgsMifwJuckibzBi1atCgyQlxKfWDqNBLGPB+SiF4BC42hXoKRO2p4GqUE2KhcPANtjTXWiH8HTjAWptkolfpQgvybyMlaovSXmFtq5vjkVrN2cmLWNJmDmZ9h6GOndAG2p6TSzl1rWdips3ZYRX++cY269+d77iR+D4jz02bxgK4ZcSVXTcXK/e53vzuxfKJtrCH7yP6htwCv9R97CTBnT5UGoHAw6X/pLFY6Zennv/WJhQnobRPsaWPdj/IMTg13c4ls3dnGC+Ry3nbJWE/RHwBy54uzRqEZwJxojMTKnWs+fB+QO219eZT1Ur9TJZCLBCoQl8tM1H50IgGG2qmnnhqrKc1sPMtCUxUnGJei3Unnp/BQgElSkoFylP3cmxxxG2ywQSNAgWFDYQMIYOoIbaOAMGwoFLm0vuQhS/Lk2ZUPS66S0hsQru1cK9OQCSMsrf023g/MFnLqx/qVZLrrQiYViFt65roG4swxFmIbDOQ21twwzygRiBscX8qBhdGSDGe/F27r/vKTu0NRAS1suDbzIg6zBtr6LKdS10zitvq6rOdwlFg3bab2mES/Z3uH/UG/6wsoRD9VrEHV5Uk1dhA9jY6cnI1sJ3sWOAeYG9STfQajri8yn5Sc63uqBAYlUIG4uh56KwFhhrvssktkOwHbeP1OP/308JrXvCayvj772c/GS+WMM84oOtyjzQmk5Ccj64EHHijC66tqqrw5c+WdSewcwKJ8chQK7JycwyT7kocsrU2GI1ZHH1gEvMbOlNLz97WRhzCFndp7PPfCdtsKO21yrpUMxJGdnzZZr10DcfrLUJuPKdFk7ib9GeCJO43TpsTmDMVWGWT8OIcYzX4H9PY7CfiBRACW3BhzGEvWu76V2oAVN998cy9yxMkTCoQrweE633qhX9jjfUnnkUtxGTJ1vjhn2E1sBDoQPdqdz6ldgbj5Vmf9fZXA3BKoQFxdHb2UAPDluOOOC5dddlk4/vjjw0477RTHKe/XscceGwE6Rtw73vGOsOWWW4ZTTjmll3IYdlCDOXTuvPPOsNpqqw37iCw+b/6xsISaYkKsv/760VDBGGizalRXg2WwUPhLry7XlXym+dy+hNlip1CmhzXCgDEcGJg4gAHf5+SYBrCdQPZprodR383Qcja1GXrUNRBXcmgq9oY7oO3w6FHnf9jvMYbtuTXXXHOZX+Vs8lk/q6++enRQMabt9WowDyv1p3/eOrr11lvDZpttNv7DMnhCX6JB3MvOpzZD/ac5PfYsoBSzPKfm3kr5TZ0tUrqUXnwlJ/nWviw8CVQgbuHN+YIYMWVp2223jUDGBRdcEEENBqRw1He+853hgAMOiHI49NBDw2233RZBudqekgCwisHyjW98I6y77rrZi0UCa+G05hzwBmgT+uL/gQRtJIWftBAYXNbr85///Em/upP3UZB5VPswHoYur3DKpdiJwCbwUAo1QK2JEu38ZOgIO2UgYN44JwA/0wS25f18wQteMAFptf8KQAl5tunsqEDc3POU2Jul5idj/NqDL3nJS4ZajM5ejh3nLyaj7/u7e7HJ3h/qZfN8WI64lH+qzedO8lmcldJhbLrpppN8bSfvwmDG7G6TldtJRxs8lM4kbL7N4jcNXrugP0LnxrrNKa3Lgp6QOvgiJVCBuCKnrXa6iQTWXnvtsNtuu4XFixcvUTh5b/bff/9w0EEHxUd84hOfCOeee26455576mXyW6EKG6GoX3311dkrmxQvzB7htAwVYQl+SlcsAR/aNEGOJnus6WesJ0YkhkbpjWELjCo9wbVCJZToufZKAi7sMaAjY94+ywnY5kDZaKONitwnJQJxAGjGu3u0tGYdY5hgR5fYAEAYoOPkgfN9YO33vve9yJjDkAOqA+QY010b1OSf7ugS50CfS6i+21S28n8poNQmK7fpu9v+HP3PnVV6/sEkF+cVx3LO6TwqENf2Kq7PW4gSqEDcQpz1BTLml7/85dHwlwMuNbnEgHNHHHFE/K+99947MqeuuuqqsRTcPolURVkhZ1/84hfDdtttl+XQzBlmEgMCWKUIQIkJxOcSLoMX2NOHJMrGmKpxlWjAZ7kBWuiUnIkqGA4CigwZyjW2HGMdo5ThnOveKhmIsycYj20WMOmaEdfCspvaI0oH4vTfndBmbkrPA+xJ44Ct5u/yp/pxDrRdUfnBBx+MQEmbY5j0ggKIYl/lDJA0lQkHNAYZ50pteUmghCqwQGlgftcAfl4zU3tTJdCuBCoQ164869MyksDhhx8eATb53xYtWhRzGPGG77jjjmGfffYJ119/fTjppJNinjihqvUyeWry5NjAMrvwwgujrKbdUhl3YVyMEeHFDFjzyXDAtOLR7UMVsyRrnlCK2Kqrrjpt8bfyfsCOZMolG2BJEJgk5qf0XDQpqTvD2HgYlwxzwFDKG5c7IxN4oL9tAwatLPopPKRrIM75q9J0iWHZ8vEpglPqvnX/2addsYrds/Y/oAlALw+afHoY8kCnNvLLAZ89cxxW3xS21VKvLL367uBg7AeOmJLnI42nbwWu7HXO5pydl8D6lKJi2vuyvr9KoFQJVCCu1Jmr/Z5XAryv8sExMIFywLctttgiKpTPfe5zg2IE66yzTgTq+kJnn1coDT5AJkIWPv3pT4ddd921wTfa/4gLXuhcYrylfG+AnJkGgc9qfTLGuwhba3+Wmj8xsaz6AJYCSDFIFH4ptWG+qSqtAqbQNH9yROQUdlqqbJv2G+vVPm8zb2LXQFzJxRqazkuun0tFGCZ17gDmAK/WKH0JcJ+qJWJqj1KcxZovvcpi6dV3B9e3e5mzJXeHS5M9ieEt1YKIjj40jkv3cs4pMCoQ14eVVscwbQlUIG7aM1Df34kEUiUojDg54CTEPvHEE8MOO+wQHn744ahYbr311jFMtc3QoE4GM+GHSkLMGw6gBF5OqqUKiHIQCY0VDidEZr78JaNWf5zUuEZ5DwWM0VtqYvGZY065/ISGl94AxFhBXTFTupIP8I0RyYC+++67Y9J0Yaclg2/f+c53ojOlRDZzF4BuBeLm3j3OIMb6fFVHu9p/4z7XegFkTSskkk4FHADMYcFgyj3wwAPxTz9NzpHvfve7sf+KRpTasLvlyyyRFTpT5nfccUdwJ/eBEUcPdLf1RWcqwcFcgbhST7Ha75wkUIG4nGaj9qVVCaQcJ0AdCixP8jXXXBMrgqq0p3pYiQZcq0Ka5WHywgnbBVweeOCBnb7OHAm58UPJV6U15Z1oynCbLddVp52e0MMTmDyh13X6GuE8co4BTUpvQrecIaUUBHH2YbMA4RgpyZDHFHb+5exxn2+tJFbzpKs/ztevJr/vAtDtGogDQgCESmSQu1844UqoBN5k/Uz7M+6nxOq0JgA6zhQONcAcUGQm0+q+++6Lutd8zrVpj22hvF+OTfsBk6z0lljdGJd9aOwWelPOgK8zwNppqqv3YV7qGKoE2pZABeLalmh9XlYSmC/hcJ/AjrYEv9NOO4XLL788vOc971lS1KKtZ3sOEEP+C4DGSiutFEECijvlfJQQicceeyx6dPviCSUjRiO5CBvsQ2PACxXvgxGMWcYAtWZzbKnaKXDbnsBUsedmMlb6kFOnZCCOkcV4bDNvYtdAnPvU+iqRQVM6EJecVapc5tqci+52rDlsWyC/kFpnJccFBh0gruTiANYR559ctaU3kQ8bbrhhTAFSehNB4Fwq8WyaTfbuZ+et/ZJrYz+R9yh6e65jqv2qEpi0BCoQN2mJ1/dNRQIJcPNnAufq5TH7VAjXvfjii8PixYvDscce28p8MTqxb7BAAEypGmMbCjmQwVy2kUy6lcG28JA+hXISR9p3fWCg5giSAkewUhi/ChgAZOYLOxUCbt+UnFOH4S+xfIkeecwhzog2DceugbiSc8TlDqDPd20oSkT+JRWbSGGcgDny32STTaIOYM3LF1qiDlbyHpi5xtxlzs8S52HmWPpQkXdwTPRk+tK0QtHnO4/8ni2FgdiH9dNkvPUzVQJdSKACcV1ItT6zSqBgCey3337hnHPOCW9729vChz/84bFGgl7PgADEqAAq71vblzZlkkLg2X1pfQrlTAob4KfNxPTTmmssJoUOps2IsKcAOUJ+hZ0Ct7FlmoJSxqExxEptqifLN1UiwCvxvfOxzap4FYibeyXbKxwcpd4T8lK660qtpO2Oxl7XsOY40OTulasSSFdKIR9AIrbStM//Ns5socTyJfeh3X///dGpZD31oZXgYK5AXB9WWh3DtCVQgbhpz0B9f5VAZhI4+OCDw2mnnRb23HPPcPrppzfuXUrmTMmmrK699tqRpUPB7lLJZqDwvOdM4W8sxN9+kIIDXCkZJBkcs/EII9xoo42GFUV2nweSquLLiJx0Gww7tadWWWWVaJzPlo9pvr7ZM/ZsyTl17rrrrhi+XWKOowrEzbdC2/099iS2aKl5Kt0HwMQ2WOTtSrb50+xXuXrda8bi/DEuzgRnGxavc9Xvcg2X1E93QMnzkGbsW9/6Vthiiy2aT2DGn3zooYdiqpM+zAsxp6IoOeuANTQ14w1Ru1aMBCoQV8xU1Y6OKwFggItDSyXb22ZnjdvHHL5/1FFHhZNOOim88Y1vDOedd94yu0SeQjWSQf+DH/wgKtPyHrUZcrWsTvzyl7+MuZb6VP3WWgVo/tmf/VkOS2LsPlgft99+e9h4443HftZCe0AC3zC/gG7223xhp01khA3h2SWzFFV/VXRnUmdNE7k2/UyJQJxzSUhtiWkASgfiAOdkX0qRmNn2gSrHGKCzjQH45h6XK/bee++Nf8ducj75fC66mnkAxPWh4ESfgLim524pn/v+978fcyvmrAOW7sgrZS3UfvZbAhWI6/f81tHNI4FarOHpAjr++OPD0UcfHV7zmtfEXHEzG2OMUgwAE2qCjSO8YVrJ67HvABQl57qaTcZ9YZClsWGjYHCV3oSMSNaN2dFVS3ksKeMAG2GnK664YqvstT4wSbEgVJUrEYjrIm9i16GpAAhMjRLD2VL14FJDCkuoojjfefjTn/40FnBoAuSmvJcYTpwP0gHQMZyFfqbVOMgUoGgzpHxaY7nnnnuKZYjOlJmKyPKplczwHhyTuw0IN821Pt/56VB5AAAgAElEQVS6rEDcfBKqv68SmF8CFYibX0b1Ez2QgFxCjzzySDQihEIw3IRApHwrFZD7j0n+2Mc+Fg477LCw1VZbhYsuuijwYvNKA9wooYABxoy8TMJHSszPlPuSrgyyfGcIECcfzXrrrddqJzFCVEb0wwhVYRb7oquqZIB0Z2GfQrpbnZCOHwbU4kRYfvnlW3tT10BcnxLVtyb0CT0IiOXsKZn57VyjM4zCbjP2VPRBCCI2rLA94Bzm3KRCWYFw+tGlI2ZCS6pXr7njjjuiXtoXII6zDwiXc07LCsT1agvVwUxJAhWIm5Lg62snIwEXxbe//e3wt3/7t+Hmm2+Ohm1qlLlXvepVYffdd4+epwrGPSWZU045JRx33HEx3wZmDoCAsX7yySdHpYDS2zQh/CRmGdOBcowZ06f23e9+txcJodOcCE3tQ444xuR9990X1l9//bGXm2dxEgCz7S3Gdhthp2N3rJAHcK5gQZTIiANqKWAitLatVoG4uSWpUIN7olTgGSPOeVFq/82MO0Du2Db2K10OIOaHTISyArYBc8L4uwLmOCPt3ZIBUXNB38WI4/DpQ6PnG0uJ+UJLlH9K89MX4LPEOah97ocEKhDXj3mso5hDArfccktQfEDo4mabbRbDF+X2wEaQH0OJ8De/+c3hiCOOWNAXuHxR119/ffzBgEuXLLGSl0qqu+yyS5brrAuDNsuBFt4pinIfcsQBp7HWhFiN0hjTgDd7jnHN6+1ZGKeTbPYNRtyo45hkX+d6l1xSmM1dFoPpapxdnFtdA3HALExK1XlLa+Ttvl9rrbVK63pv+tslWAKY49QAzAHhnAvmm/5SamXlLifePXbrrbdGvbgPjRNrhRVW6E2ExoMPPhjP2VxzQlYgrg+7po4hBwlUIC6HWah96EQCPJcANpR1Oc9e8YpXLJVgV36zj3/84xF8+vCHPxxe/epXRwbYOGwvAN/rXve6yLy74YYblhoXJfQTn/hErBDGa/fyl788vOtd74qFDQYb0PCTn/xkuPbaa2MoKM8rIEzOtpmt6TNnEzAj/LOf/Wy47rrrwgMPPLDURxJYcPbZZ0fDpSvvchsTzxMuf0ypuX/mkoHwRxUh+9KsVYy4UcKScpIBBRQgMUw1M98BYGDk2Hep0idAblryACYKMy85bx9movQCuRory1q31oHzHfO4rdY1ENdWP6fxHGvdnV8qEGetOEcwZkttt912W2QSN8kRN+4Y6XKcHSq3Y8p5r/BW/w+cG1XPA/bR8+TsLLmJdKAbL1q0qORhLOm7OekqjcM0BCRHMLZ0rndbiiBqg906DfnWd1YJ5CKBCsTlMhO1H61JIF0QmF1//dd/HXbbbbegEqiWEmRTwhjAEqLuvffeYcsttwwf+tCHYlWycXKevf/97w+f+9znopI2CMTxPALTGL2AOkbB+eefH/MDffGLX1xi1Hu//riEd91112hkXnHFFfHfiii86U1vWiKnps+cS7Af/ehHw1lnnbXk18I7tttuu8jMedvb3hYTcksWn3ujUApPFTLbp9YX4CrNCUaKxNvTAp7aWhtN8/elsFOgHTBdGCIDMBcZMOwZqSlPZlvymeRzgInYLpMw7Cc5rlHf1TUQ55wFZpXoICi50IT18Pjjj8dlUXKV42mBJYm9gy2HOUX/Eh2BceTv0gI0Beb6UDTDOhqX2T3qGdXV90S4bL755sXrF0k+bYZxdyHzCsR1IdX6zIUogQrELcRZ7/mYE6sNULXPPvtEgG2HHXZYCmRLlwiWyiGHHBLBN3nkgDqjsr9uuummCLb5vpxzCYjzLmw73tjLL798SQjaN7/5zbD//vvH97/97W+PswJ0UygBg0/uOk2f/F3yVs8ElA3zzLmmWw6yM888M6y55poRgJNjDUhCoQFMAg2AB7k3840VB+DoU6OIbbDBBo0NhNzHDviRv6epwZPzeOYKswW+WY8YsZiv2K7OghwTLtszQMKSGTYMeyDcOM6Taa0zzGfgSs0RN5kZsC/9jHq/T6aXc7+lD0AcfYKTctp3AP2JXqUJAeQkoldh1VsjzpS5HEbG4JwvPUeccTr/J50Soat9RP8GxPWlcTLRH6a9V+aSpz2kbzUnX19WXB3HtCRQgbhpSb6+tzMJJJBN2BJW2ZFHHhn23HPPCIQlJRzzjLIlzOCNb3xjzCcClBqVESf04bWvfW3MtyEBrnclIA7ghck2CLilwadw2GuuuSb+FyAPkw8IM3jBXXXVVeHQQw8NKpoKUR3mmcMK+q677ophHN4PPMm9UajJHGjVpyaMUf6wXBWxYWVtXaWQzGG/m9vnZwJx9okwKOGGDDTzpuXM/kvs4BJBrLQehG9jtZRoTALi5LBqk13WNSOu5Kqp7ugnnnii2BQG2FzOk5KZ3wpmCYXM7U5zFtqPzhF7EmuOY0/YeEodks5yewCIhYlbcnNXSUnSF72pb0Cc84ozPFcdogJxJe/+2vecJFCBuJxmo/alVQkwjPfaa6+ouMp15lKb2U4//fQIwB100EGRnTZq5VTfp9R89atfjcDeIBAnD9sJJ5wQPvOZzzwtH4ccdl/5ylci+Ebxk0OLIX/xxRcv1VVe2G233TaOR5jtMM8cVqiYPGussUb8Wkp8POwzJvl5SrHQ3T5U5ByUG2+1ilS5GS2jzu3dd98d2T99yClif9sbvNZ/8Rd/EUOjgfy5hJ02mSNhqRjB+l9qA8QJqS+RDVsiEMfp4VzKkeE53xouHYhzz2kl3wfAEs7KXMGFtIbI2plO1hwV9DNrHqNbOhH9Lz0cHiNa6pH11ltvvq1TxO/dyX1iZ+UOLFYgrohtUTtZgAQqEFfAJNUujiYBRoNw0w9+8IMRAFP1k8FGyWKAAgYuu+yyaMh9/vOfH9m4AKRh3V1wwQURDHrlK1+5FBCnEMS5554brr766qeFM6Q8bUJW5X5Ze+21I+MN822wUTIwJ4BxCjk0feYoRrYQGDLReIZzN7ooBIC4DTfccLSFkum3sPxWX331CMb1oQF4gcylAnHCkYRCKtIg6bhqo8JOSwLfBtcRYOLv//7viwbiOD+cmyUCce4n66lNNl/XjDh7gAFfIhsI8Ow+a5OBOMlzGVML2N9mcY9J9t+7Si1ARGfEhJNPTtQEBrR7GTCH/VziHQ1o/OEPfxjWWWedSS+D1t9HB5S7ssRqznMJowQgDkhdOiDd+mKsD6wSGFICFYgbUmD142VJAIB1zDHHhCuvvDLm9eAxc3mg5WtCMBcvXhzWXXfdkQYmce+OO+4Y3vCGN4S/+Zu/ic+YCcR5/yWXXBLkhONNHWxnnHFGrNyKASd5sBwXGHUYdDMbltrGG28czjvvvDimJs8cZVyUzJQ36pFHHomAQ22Tl0DJFSEnL63u3shgYQQ7M1ZYYYXwvOc9L8yVI667XrT/ZEYl1jCwt9TGKE6Fd0obg/sIK67NUMOugbiSQ1OtFeBnqY4AOWLpL/SE2qYnAfMAEAWgY0UDpa0pQBBgzs8f/MEfTK+DC/DN9rY7edNNN+3N6G+55Zasx5PybVYgrjdLrg5kShKoQNyUBF9fO1kJYK3Jq8YrrlGWsMUAcaN60VxEQkXlmfvyl7+8RMGfCcS9973vDV/4whdiEYSZoBZ222mnnRZBNUb+FltsEfPJqZA6GxC3ySabRHZd02di2A3bhB5h/Wj33ntvEUmJAYYlV3+cbY5Kzn8123h+9KMfxTX+rGc9a9glOdHPp2qncvTpL7YVQH+Q+dYHII7xYqwlsjnSgsDok3KgRMMXs8yekCC+rVaBuLklCUgHfJZanAQABPBR7KDUdscddxTPXMcic94MzoOzFMMYMGdf07tEFgAp6Jo5nrH6SR/uA7DrHhM+TD+ubTISSEVNSi1+Mxkp1bdUCcwvgQrEzS+j+onCJTAz75uKhoNecdVVXeLyvPlsyn0w37ABYh/5yEfCWWedtVTlOyGwDHdhrxQxeejOP//8cO211y4J+UzPTqGpijEA6eTr2H777YP/H2wpNPVVr3pVOPXUU8NJJ53U6JkqoQ7bXLApgbsQvDar+g3bl6afB4wIC84990zT8ficdaj1ZUw5hxFS5BkljHSgLkBjWWGnABRhtiXPjTMFwykVlhhmbeby2YceeigaxLPl/8ylj3P1o0QgDjtUwYMS7oSZcgeSYLCXGppqv5aem4wzkrOx5AZ00+YrcsNBK7rAveKuwKBz3mLA5sDKtB8wotdcc82SpyP23f2Ntf7iF7+4+LEYAB2cHqKIXK6tAnG5zkztV2kSqEBcaTNW+9u6BD71qU8FIaIYSMO0PfbYI/DwLqvJW7bddttFhpsCCzMLCijWID8cIJCi5veUiQsvvHCpx6ZiDXvvvXfwHb9v+sxhxpQ+ixGHGff1r3+9iKpaqsyq/lVyIuuZ8/SLX/wiAgy5M8iarq/cQBNAp1A1ueuwZVTHYzD1aQ0ta26AKlgbJRti5g5rscScZdYc+Y+Sx3OueS0dqGl6lozyudKBB4AOx16bOQVHkeM43+kDEKdAD4bbsHkpAe/0OPPo+3Kz+TtdaxpFBoCEWN+pMNc481q/264ESmD4AeJSqp92R1+fViWwsCRQgbiFNd8LbrRf+9rXYqiApNiAJcaPH3/HjPN3oQa8/Awi3k4K1nHHHTdvqCOGDw/nzKaqqYv05JNPjoUO/B1L7vDDDw/77rvvUh9/9atfHb3ccthp++yzTwwHBSwNUr4x5g499NDIhsOKk8i/6TNHmXTMIIoa+W255ZajPGKi3zF/mDHzeakn2qkxX/aDH/wgMrSEtvShMYQZHdNkAwyGnQovWmWVVWJ1vGELLgDOhR+VHJbRRdXOSa9TDCcgXAqln/T7c3tf10Cce5ODYBSm9bRl5a7GUho1FcW0+48hA4QDPJfagFilhgYnmas06k7GmB610TPpKg8//HDUs+xbLNMUYj+JvFvuPXdAyespyZ+OT49fa621Rp2SrL7HXpFKJ+cCZBWIy2rJ1M4ULIEKxBU8ebXr80tgPrYB9osflwpj3AUIoJM7TujpKKDBzBxxmDeqnXqPXHXJaFS8Yf/99w+HHXZY/FMTzqrow7HHHht23XXX+H+Utje/+c2Ren/jjTdGhtQwz5xfSk//hKqp2Bpy1wH+cm8lJ22fS7YML2EsJYcODo7NvmJ8TBq8SuAbwIYx7mfcaqeAOEr/JAymrvaes04VSWBkbZOXQBfy7xqIK7lYw+RnuN039gGI44wp3bGEhetObhNQTHcjhhxwzj0JIFMRmm7aBUu7T6kv+lQB1qlBZ5H/NOcKydYP/adPzu92T+z6tCqBZhKoQFwzOdVPFSoB+duEnwGvgGpCAvyZfniYr7vuuliJ9G//9m9jaBomm+ILqhYJVxu2zQTifP+GG24IBxxwQAw73XnnnaMX9DOf+UxMlPv5z39+CTgHUNptt90Ctt3uu+8emQdYaUJg5YV7/etfv6Q7TZ85bP99XrgChVMfVYTNvWERyqdRYtL2uWQrbMR4hg2ByXWuJgksJuMFe0F1UPvYXmsrafZdd90Vc01NI6Qo1/mdRr8YrQziNiuPTmoc2GX2RJssjgrEzT17QA73bqlFfYQ1cuKVDGTddNNNsTJ8yc08SBkh2qGrlhzC3iH/LSDOvKvYPYpzeLZ+YrZixJW6HwbHpFCGqIg2C990NbdNnkt/AXTlDHLpo7XYBUjcREb1M1UCfZFABeL6MpN1HCNL4Prrrw9HH310BLwYdQorfPCDHwx/93d/N1JFqdmAOJ37xje+EVRJFXJIwZK0ePHixU9jPGEdnHLKKREgRLkHDmLMee7M1vSZwwoHIxDYIHceQDD31rcKo+TdJ4+18ShwAFQcJ6RnWesQiC30CYDJo7zuuutGditlse2iCgwY4H7JSijZqMS4+uqr57695+xfyeHb0iLo/yiVrecSSNdAnDUD0Fp++eWLWzPCUrFMSiw0UZywZ+mw++zmm28uHoibxlwA7VMxIQCaO47zgb46aqEaYf0AvxLDzGfOgfveWPritOQ8fOyxx1p10rS9bisQ17ZE6/MWqgQqELdQZ36BjDt5ltJwBw3y5HESLpHy3mDMYClgm73pTW8q2vs8zhTLCyexsqqwKWx2nOd1/d0HH3ww0vi79FJ3PYaZzxcajPr/3Oc+d9Kv7uR9QB+MDtWB22op7FS4KyUcW2HcsNMmfWPUM4AmHWbbpG9NP8NwwXqVNLzUhhGH7TgKc3naY06VetsEprsG4tyn+t0WK2eSc1B6cnrnpzOnxLVunq0dzP6NN954ktPe+rvoGvLRTqtADL0VMEdv5QgCiosIAMxhzdGBmjiIpCWwl0vNmTg4sRwEfvqi/5VQWKYCca0fLfWBC1QCFYhboBNfhz23BCg6FJSFHHYmL9w111wT3v/+94dDDjkk++VCCQOK5EzlH1aIQCWGtZDKPjSKm/GMy04Tam6eefT9MEwZI5OsJnjffffFkJ6SK9raM4xKzMFSG4MUWF1irj774de//nVr4dLmsGsgruQccdY78HlUBtG090gbRQKmPYY+vN/ZL4duLuuIvipyAnjjB+MT6GytJ2ButjvX2eO7JYLqM9cRhqCxz5cTupT1V4LTwNphIzUBfUuRe+1nlcA0JFCBuGlIvb4zOwm4VMYFCLIb1BgdeuMb3xi+9KUvhSOPPDKoApt7o4TJp9anHHFAJuGW8sL0oSUP/ihMAvvzl7/8ZQw9Bb7IBZMSBU9j36poJqSn5Gqd1pbcOqUybOwJwBBjoK3cf5PcZ0JTgSvjMhLtDaHS9pfk7or6pJxS9lqbzomSgTjAg59JAvZtrqcuigS02b/5nuW8EW1QOliCfSZdSM7sK8AcMMeZADB0LmDAAeasf3emvezPPoRzCjk3HjmY+9A4G+2XnEHSBOJOQ//qwxzXMVQJJAlUIK6uhSqBKoGnSWDPPfcMF154YTjooIPC8ccfn72ElK6ncLZZyWzag+6Tx5osh2X4pbBTwBvDJyXJZjhMW/ljUApPKpkRh5EFwMnZoJxvD5oHYFOJ+37c0GCGmh/AAINNiCtmKANJ6DQjXBi4NQrABriOu3dKBuLIADhRak5EeaNSwan59kWOv3eeqza9ySab5Ni9xn2yB+ypktISAHbcn/LMAekWLVoU81NaT+7Wad+njYU/xwc56Tg2MBX70FSBNWc5FyGiP3CAlb52+rBe6hjKlkAF4sqev9r7KoFOJPC2t70tfPrTnw777rtv+NjHPtbJO9p8aBf5x9rs3yjPolgC43IJgRllDIPfacLwo3xS8BJbCHgAYCgZLBpXbl19n6zvueeesMEGG3T1is6fWzIAPwoQJ7yS0SkUC7tFfqfBNAqzhab6DsYIcM7eUogHKIXNiUE8jCHl+8C/EkOBSwfiACjYnyXK3kHgLrv77ruLzxEH4HYflQTEDR7EKfrD2c/JZU+vssoq8Z51JgEZhzkTOj/kF+ALsBftFyBprq0CcbnOTO1XaRKoQFxpM1b720gCqeJk+tOX5vp7emCpilUjgQz5ocMOOywCcG9+85vDpz71qSG/PfmPMzQZKCWH2c2UGsMRC4KS3IfGG8+I/5M/+ZOnDQdI8POf/zwaBkI+0zzmahAI/8PCKjXMzQSYC5WRN9xww2KX1xNPPBHnAChVWnMfMWbmCx1lkDkLjBEYY48wmmcLx50vR1wywq1fz/Tsl770pRGQa5IviqFuH6+wwgqliTuy4Zwzq622WnF912H5HDEeS73jAP/O+NJTLWD1rbHGGkWzoa0nDDn73rniLgDoW2P2OCaWXHP+vwS9mHNCa7MQ1DQPCfeauyFnhl8NTZ3mCqnv7pMEKhDXp9msYxlbAjVX3FMiPProo2NI6k477RQ++9nPji3X+oDhJcBo9NOXvCeDEkhhp8AAOYOEYgAkMA1yBd8G+y9fEzBilHx3w6+Ebr5B0QfElcyI60Yyk3kqYIIDYbZiLOZGnjc55ITCAX1VT54vZ9B8QNzMkemD78gVKFTN361rBmDqw+B3Sg5NncysdvcWIIm5KRF0JhW6lfVWKqMvzazKr3KUlpiXssnqBL5JWYCJb80B3t1zANRxQ9ubvH+Uz3AsAAxXWmmlUb6e3XfcC87/Nitqtz1I+7mve6BtWdXnVQksSwIViKvro1cSENIGvODp59nDHsAi8P/+TcFg9Pu3kB1/+r2LXF40wBNFZD6WQq+ENstgTjrppFik4RWveEX4whe+kP1weUTNG2O1L43RKwwmZ6/oMLL+xS9+ERl+gEUAEK87gKEU8G1wrEALfS/VKB5m3nL+LIYNY6DEeZhZtdb55e4SduquWm+99eI9JhyxKTg9LBA3c25TZVF78rbbbougCdkycPWjZCDO2eP+l9uxxPbQQw9FIC7nvFHLkitd64EHHige+JeTUZ7B+UDx3NeYdB7W0nzrCXgKqMeecz5wQvkOZmYuDLQf/ehH8R4odW/nvlZm618F4kqctdrnHCVQgbgcZ6X2aWQJnHrqqeHMM88c6fuHHnpoeOtb31qBuBDCaaedFg4++OCw+eabhyuvvHIkeU7yS0AeSmJfQKtJyq7rdwG+H3vssQiAU+bHrRLZdX+bPB9LwFhK9wgrdrDqqqs2GXKWn+FAMQ9C9kprzivAhIqGqXqhfYIFgY3SFHwbHPe4QNzgsxhaHFmp6IMzFoOVkwqYXlrFYACnsZRetbO0dZ76ay0BE+UorG36Ehg11Bmb3f0HWHV2qSCeAHug/TQYj/a19/ahAqyV4V6TciHnIkQViJv+Hq496IcEKhDXj3mso/itBK666qpw+umnRw++i9llxrihdMiFBFjixUNjpxjefvvt4eqrrw7bbLNNOOaYY2pS+N/K8Zxzzgn77bdfZGXceOON2a8vRhagR36xvjRMTcZvaTnieNDlY2K0Y3Aw2DF7KPAU5j6E2gJRhI5Mw+hoc31/+9vfLjp5eolAXMpVam9jcsiZiMnbRlGWNoG4metMvxNL1/2pz4PsmNxBaaxpzJ5Sc8TJG4WFVBoAmtaR+0yoNZ2i5EaXBOaWHjXBCWAPj8smTve6u93zMHtTMRnMuUnckfQL81H6nKR9YZ+Yl1xDU9MdlvuZX/I5U/u+cCRQgbiFM9cLdqRHHnlk9DCpAjozpxOlAfsLgPee97wnbLXVVrPmxllowrv44ovDbrvtFpMSM9Zzb0K5KIR98YiStzAq61Yy9dwb8I2XnAdX2CaFeGbYqfnxoypb6a3kap2Dsi8diBNuCBQqAZwAYgGDhJ9KhC60rW3jsUsgzrrh7Hj88cfDmmuuGe9Je954GOEAFkAXI21SBvgw50hKSZGrcTvfWDCP5OkqNS+l9eL8Lz2k85ZbbonOi9JBH3vWudn2fKT8r55vzp0VieHlXOii+AOHAIdGzgyy+fb34O9HZSsO845xPluBuHGkV79bJbC0BCoQV1dE7yTg8gcMyGnzd3/3d0HI6bHHHhve8IY3xPDF5KEDwvm76lHYX4yjj370o/G7XSgLJQn68ssvj/nyhB7cf//92Xed8scrWoJB3lSYGJtCB9daa62mX5no51Lybd5bfcXWeOELXxiZYrM18+NzpRqSg2OSX8dayyVHzqgTjy1ccji3NdU1+DSqbBOYDiyUuwj7zZphMLpf9J3B1WaodteymC9HHONbonF/YqUrRGH89vxc58I48h3mu/QCZ1apAMp9990X92obzMlh5NbWZ+legNsSw8gHZXDTTTeFzTbbbKTQ8bZk2cZzMBSBcF3ruvZcOhPMv0IXzinnn3Ohjf2YO3A17HylnHxtg6TD9mOuz6eidrn2r61x1udUCUxCAhWIm4SU6zsmLoFU8e2GG24I73znOyPA9spXvjJe/gmISxW8hAjtu+++EXQ644wzao64EML1118ftttuu0iNx/7JvTH2fvazn0UGX19aSpLMcM+lDYadys0lFJhx3qTggs/99Kc/7cUclRgSOdsaKr0wDXaW8zynIi3unpT/Td+coUK0Z+Z8cxdhObWZM2vaQNzgGiMHY3z44YcjU06aCKDjsAUo2jr7gAHOoFJD491vnB2lhoPNB+K2Nc9dP0eIMGZi6e3uu++Oe8G+nGQD4tDXOMCdC85H4eL+LqJhFGCOsxjw3wcnn7mwV0QOdA2SjjrvFYgbVXL1e1UCT5dABeLqquilBNJFwVO21157xZDTj3zkI7OOVVXQ973vfWHvvfcO73rXu2poagjh1ltvDZtuumlUjBgAubfBkKnc+9q0fwxZCZGnzfJL4BsllwJN1qNUO+3THJkboMcoCfWbzv8kPld6aCqjmOE27Wp5gDd5kfwAzhmF84Xh+T2WU0lAnD47kwDvw7RU+MFZJnRdiC5mF+fXqIUphnm/z5YOxAHNsQpLPXPcHRi4uTK8m64n658TqvSmcrlcd9PUL5wLaV3LWWeNkK1w1hS234RJS0fxuSafLWHepgWSNpWNeSNrUUe1VQlUCYwngQrEjSe/+u3MJYB+D4CT82z33XcP22677RLFgwfuO9/5TrjooosiRd9nGFC1hXDvvfdG9oLLVjhB7sq/kEfhghS4vjTK5T333BM22GCDiQ8pMUqFndonwAVhZuOwMcwRRlypjJTBSRB6Z2+U7oGvQNzoWwsopSCJvWE9AOOwO5oa6faYPJDDglrL6nHXjDhjdqeOu+6NndHNkPNMRrj8UWTZVbh36UCcu0DhnlLzoFrrgGrpC0pu3/rWt8IWW2xR8hBi3++8886oLzU9ryYxYACP8wUbjAMY8xxQyNEipDk52Gf2hV7BOdiXUMkc52ZQ5hWIm8RuqO9YKBKoQNxCmekFPE7Kn0IMcntoLnUGiyTTqQT7UUcdFbbccssFLKWlhy43WQJMGJt9UXBKmmDGKoVso402mki3eabNtR/hY+uuu25QBAP4ljsQOxEBDbxEWA2ZACdLbqUDcfaINikmxCDTGvjRxtsAACAASURBVLDA+LMGRmEGpGIHwg3bal0DcV2FF6ZiL4xwedCEmrlzVA4E0I0SrjZTpt5B5qPMVVvzM85zcmfJjDO2kr7bFyCuBJk7b52z9i1w7rbbbouhtM4FTvN0LnCoS0vSh0JQ5sV4FOnKVe+uQFwJu6f2sRQJVCCulJmq/RxLAoAFjDc5eQBzLnbsBYljN9lkk0jRr+0/JMAbmUAG4STjlrjvWraMLPmi5CzrU+saKElhp3KRUHCTZ7lJzrdh5WwPWkt9YMSRE2W0dAYtlmLJxstglc5h12PTzwOoMd7cG9hba6+9dnTgjAtQd8F4LRWImzkX2IWpGmtynJkD95CzaRTglUHvDMr9LptrXQLi5NKaZihh0z0z2+fsVXuo9PO/L0Cc3L8rr7xyKyD3OOtimO+6c6W4cBbI06cADvDe/2H3Ae1r614CqehNyrfd/RvrG6oE+iuBCsT1d27ryAYkMJPSPvPfKRSvCu0pCQhHTQaL0vDTzsE037x0YdTO985J/H6uUIxx3k1WPMnA1pQAXNhH10mbGcFC0DDtSm/2hzOjTTbTNGSiUI25L5Xx2BUgmthqAGo/3sNxIySzLVl1cWZ1DcQxeOXlU2F8kg0zF4iTQsI50KxdoBwgucmc+DwwbtVVV51k11t7Fxlg840CQrbWiTEeBFgVHjzptTNGl2f9KjC+DwDELbfcEjbeeOOigLiZE+KcxtJVEIaO6j6Wg5Lu6qfp2dD2Ghn3eXRu5IAm59q47xrl++TuXuzDPhhl/PU7VQJtSqACcW1Ksz4rawlQ5F1wEkWnvGeMK8rIsvJPZD2ojjoHNEm0eEl9czdeKAZ33HFHnMs+NR7fNlh+AD3rXtgpD7KE2ZQogNyklL0uktP3aa6nMZbbb7895iAs1bgHJGOstVHF0B7RADbAJiAPVnCbOdwG51i/H3rooRiC1FbrGohrq5/jPsdcYdf+4z/+Ywyfl8+UYWj8c4Vz/fznPw8YoPKsldiAWICGUo3flPag5Mrmad1hkpXebr755rBo0aJiz/7Z5J9yTwLs5aB0LnCiOBcAc/LhTUrfGWd9SKOz+eabj/OITr9Lzs6hXKu6djr4+vAqgZYlUIG4lgVaH5enBCSdP/vss8OVV175tA4CJfbcc8/wmte8Js/OT6FXFE4XLWORUlBCpTOhqarw9amNE5qawk6F0TEcgAsqFHYRdtpE5pQ3xlhXydib9KGtzzCKk1zbeuY0ngO8Xm+99YplRTC4rCtJ/kdpKQcRkBpjB9MqgVlt5CUbpU/jfKdrIA6QBahswzkwzjgHv+uOMm6gHMeFOQWgAmedgclY9Hshr7mzu+eSS+l5sMjeT1fAdlvraVnPcdaoKL/ZZptN4nWdvkNoaqmg9EzByB3HCTsbyCYk2t53V9j77gpnPWBunOJTXU5O7uHPFYjrcvbrsxeaBCoQt9BmfAGOF/vtLW95SwDGqZqK8n3mmWfG3HC8s5/73Oeisn7GGWf0ImyurSmmMAtFuu6664pgmg0aXW3JYNrPAcQp1tDUi5sSkgOJrHcMCgnlAXDTbgxkhnypOY4G5Qe4wSwsvQIghrCcTSWCTuOsZ3MnTBEj2j4RcmqfTNLDz5gRRrX66quPM5Slvts1ENdVsYbWBBBCzAPnB/vF+WlOnYOcNKWyycgn90qK880hEM79VPL5D/S1pjbddNP5hpv97/uUjmUY4AqDVrElYBwm+IYbbhgrwzsvcimO8OMf/zhr3SIVvVloekP2m7p2sEgJVCCuyGmrnR5GAkC3U089NRx00EFhv/32i8q4cKC3vvWt4cADD4wFHPbee++w/vrrh9NPP32ixtgw45j0Z1dYYYWY0+UrX/lK2HrrrSf9+qHfNw57bOiXTegLjz76aDQg5wsdpFQKvQIw8HKn3GVNAbwJDScaMX0IHxbmDqTOiRk0qTnM6T3OJwZ+EyYsINj+ALwxIKQjcMZNqzHqhf0zBNtqFYh7uiSB/ym3HDZfClMDvJYECgHNAf+5gAXDrll7VbigghOlNnsWk6zkMSTZ5x7+OMwaGQaIG3xuAiMBX5xr/s0x8uxnPzsya6dRYdl7AdY5Ow0qEDfM6qyfrRJYtgQqEFdXSO8l8IY3vCGGkQlLTcCE3BGvf/3rwxFHHBEv26OPPjpcccUVwYXuEq4tRJCBgnLRRReF7bffPnuRDMsey35AIURlUJsJqFHUgG9CPRkFmCqYH9MKO20qy74AcfLdmQNe9JLbgw8+GBlxOSv9y5IvYAUQ94IXvGDWj/mdPQI4FRLtx7zlEJLEmMFywnhtq3UNxJEnhjkQs7QmnyA9AAsSMGcNqLKYkrtzXpS+n3Oek9KLZeQs21H61icgzpnURsVU97qGLQ341twZdGEg7CQYYCWwLlPqmknIY5S1Xb9TJVCSBCoQV9Js1b4OJYFUcXLLLbeMLJxjjjlmSRUl//7Lv/zLcNRRR0Xl+8ILLwwnnHBC+OY3v9mLHFZDCWqOD8uXdP/994dzzjkn/NVf/VUbj+z0GaXnu5pNOHfffXesMocFAUAQVqESGCYNxROrI3fwbXBcfQHizANQIoeQ33E21eD6Guc50/rubLn6GFNyAgFcgC8AL3+3b3Jq7if9b5OV1zUQR5bkWyIrS9J2e3aQxWo8wtIAc4x5+RIxYzCQ5ZDKCaC+7777IltnGiydNvaNtY6dWHI4v/Wj8rd1UnrrExDH2UIXaruZb+C91AVyNDr7nAvyT3YF2nuHd0mdk2tzbroD5ovUyLX/tV9VAjlJoAJxOc1G7UurEki08x133DGCFeeee+4SJRY4JxT1uOOOC//pP/2nCMhdf/314Wtf+1qR3v5WBffbhwErVVU87bTTwl//9V938YpWnwkcoRzkFo45ziDvvffemHgcm0BY3XOf+9yoBCaQeZxnT+O7k/Iqdz02hntiI3b9ri6ff88990TjPgeG2CjjlAvMmkr9x/CzTyTktk9yAlJmjs8edma1adB1DcSVkCNurnUEBKITuO+X1TCNMSjtcUxLBr4wdMb4NBkgnBjrrrtusUDcKPs7t+8AZQBxKk2X3rC+5tsLJYzROaoC7CSqjDqvE/vO+SDXHGDOfdMGI4+8vUO6nDZTFrQ9jxWIa1ui9XkLWQIViFvIs9/zsScg7gMf+EC45JJLwvHHHx9e9apXRbAGG27ttdeOLDlg0yGHHBJ/9773va8XykkbUysv3I033hg++MEPhne84x1tPLLTZ2AyCC+aZML1LgaUwk4ZgzyyKp6WEHbaRBZy3hlP6Y0ybr0p/FJyY0xwUpS4Z5zvWDYAUQDJmmuuGUOK5P0qAYwvMTS1ZCCOgUvmwzAjGfnAF9VYMeekraA3+D/g7yTXmcqQnIc5g8vzAZzutpKrZgNzH3rooTgPJTf7wP3VJht3WvKYZiincyBV7uYw5dhynwLnAPejMMbMDQdTzs6xCsRNa7XX9/ZRAhWI6+Os1jEtJQE5YA4++ODw2GOPxYINQKVddtklhi0p2iAclUdL1VRhgLU9JYHXvva1MW8ecPKwww7LXixCd1ZdddWhDK1cBsVAYehZh9YrBU44HYCEQjZNJkabMupLHj8MGQBQyWFW5hVwhZFVyvoCjNgnwAh7xpnu71h9oxg9ba7tYZ9lLJxAbRYvqYy4uWdBaCoDd9Q9a744RpzHwBjhz4xuz0t5ZbsE5pw3HE2lrfM0I9hD9mzJjhj9B8aZ95KbcUjlsWjRopKHEfsulFMKFWzRabYE2gPm/Cia5X6lKwDmpLFosnedMe64nAHrVMiiyXimOSf13VUCJUigAnElzFLt48gSSKy46667LgJtFNmzzjor7LXXXjEPAyNuq622iqGXfcj7MbKgZvnizjvvHC699NLwrne9KzIHc2+UMUp+KeEWqTqWKo4pnI5RN6jcUOgwfErMyTTbeuljHr/c98Wy+icEhsEwDEto0uNNYdiAFD9AD2Gn/iy5em2JQJz7FAOlRFZW20AQWWAIAuYY2xwoWDBCWbswolUcteZLNX6feOKJyEicq7DKpM+VUd4H9AGutBWGOEof2vgOQFp+0DadAG30q2/PwJgD2KfiMAojPf744/GcAObOtpeBcPaKHM25NndX39LA5Crr2q/+S6ACcf2f4wU/wgTGKTsvpEx+j1tvvTWGNTHoKIaYSLUtLYG3vOUt4fzzzw8HHHBAOOmkk7IXD3YMIwhwlWsbDDsFfgDeGHNzFVxQuUv4CMWtD00V3uc///nFMLDmkjlWhLx9g4nfS5wf4LU1mNueoegzeAFtcnapcs0Ilqh+MIyWgeNzzvHSmjECtdoMC+6aEZdyJJUY0oYB7/x1/nTREqsZo2XFFVeMBXXsK84/jJhxWae33HJLrLDb5nrpQg5zPRPAYM2XDGKVHJo9OC+AuEceeaQXESDOJOCW6vG5N+c9ZxI7xFqyn50XGpDd+e1O+/nPfx5TLeTa2FSTDs3PVRa1X1UC40qgAnHjSrB+vwgJDCaJF+JBaabQYk+lZNmlJsDvagIOPPDA8MlPfjIA5D7+8Y939ZrePzeBb8A0Chj2hLDTJtVOsSyAiwy52vKRAAajvFE5e62bSEtxA8BELixSACcWAWNd34AY1n+JDKwm8uc8aJMh1DUQ1xcgosncjPsZBnYKU7PHrGvyGyZMbbAPgDgMpnEBvXHHNer36WBaqf3X9xLYSqPOT6nf44h5+OGHpx6aOqz8kr3B2QScMw53Ab3Q/0mz0mWo+7D9Hfx8BeLGkV79bpXA0hKoQFxdEQtGAgzna665JsglxjvOK+jSc/nttttuNTR1xkp497vfHU4++eQgRPXss8/Ofp0oBADsysHjnliYwk6FFAEUJPMdNgGvEAVrtC+MOMmM11hjjeJDbQFGGAVrrbVW9vsi9w4ySAAUGMsMD5WBl19++Ubdtrd8d6WVVmr0+dw+JGdim+FhFYibe4aFiDmXOUGm0TB3OAGBc5idCr0wwJ3tgPD5jG7FGjBoSg1NdT9zfpa6V60Z5w0mdOlFetxfnADu4tJbX5wDKezfnyrzcvbJKyePdWJOz3dGTGoua2jqpCRd37MQJFCBuIUwy3WMQYjfkUceGfO4UHp5qDHhhKdShoEdJ5xwQqymWplxTy2Y4447Lhx77LGxaMPnPve57FcRQ948TsvQoiwx9ngzhdEJp2N8VQr/fyyde++9N4aQJBZq9otqjg5iOWI3YmuV3Jx9GDqTzhHHCQKUsFeE8QEj7J9h14WwVT9y75TYSgPiGPDuzFELHkxzjnLLUcbgFlIHmGN0b7jhhnEPYIwN67CZplybvltaAgAkh1Rt05UAVj59iY5SeqNjAeOmpfe1LT/VbLFnpVswLncjp59zFzAHyE6O2WkBc2ykPp5Rbc9lfV6VQBMJVCCuiZTqZ4qWAONh8eLF4Rvf+EbYY489wstf/vKYTFmyUeEjGHKYX/LEnXLKKTF5eQXjQpQJVtw222wTvvzlL2e/BnjcGfJN2TRtDCiFnfL0AzMAG03DTpu8X64QxktfchgqDiCvWm45yZrMxeBnGNEMgEkDWMP2c77Pq/4ICKPgd93SXrE/GOWpMvA4awGIAfwuFYhjYAlBaqt1zYhrq5/TeA7QS+sqR9w4YwLA2Q+Mbf30b0x97FD7Rmg2djUnxrSM73HG57siEhjvzptSG0ac8NQc19AwMjUOjLg+MLrtD/ulLwWt5qrubIzmLaVpYLe4tznSnBOTPBcqEDfMbqufrRJYtgQqEFdXSG8lkMIDb7jhhgjE7bjjjpHlNVu76KKLwoknnhgrhKqgOphTrrcCmmdg8sPJE6fEvZDe3BtWAWNmWFbNsOOi+GEtUJiEOAs7lby8izxbjDKKF0WrD80cMcZKzhNkHjC6FDoovdLyJIqBOEu9Rw4cgLLKxm3NP+aAn5pD8anToWsgDpPGmZRzIvG5zkkMVgZkCWsFo9o94z5T3d1+4VBU4b3U0FRjsj5LLTZhXWHxAv5XX331oq9jDmh3cQl7YT5BmxM/fQizNdamDgPnQ0rN4E6VU1Uzp3RSDtyuWgXiupJsfe5ClEAF4hbirC+QMScw7ZJLLgnHH398OO200yIbjhEN3KAUpqINkr2+7W1vC5tttll4//vfv+T/F4ioZh2miqkKNay99trhW9/6VvaiwFCiIHQBxHmuMDo/FFgyYRwxKrr0RP7kJz+Jz+9LOA9jGAOqSyVxEguVUckjvf7660/idZ29QxiMam1tMvs4QMyzvQI0wFZbVmXgcQbnjHeGl8qGuOOOO2JIYlutayCu5HxMJRcLcLfJEQeIw4wDyjG4Mfvb3LttrcPZnpNy43XhsOqy34PPdl5ixJVQoXNZMnF/+Sll7SxrLObEfVN63r40RvqlNixTPKXLsM84vNg4KfWEs6ItnYsurNXQ1EmdOvU9fZdABeL6PsMLeHwJiPvSl74UPvCBD8TKn1tuueVSIFsC4uTL2H///WMy5JNOOqkCcSGESy+9NBZqYEjzyufehHFSLtsKG0mhdBg3woR4KuXmaFLttC1ZYRFpJRsvg7LAjMLum0QoZFtzMNtzrA2FJzbYYIMuX9P5s4Fm2rgsG8o5AxUIjjWFNSIEVeGUcZ+9LCEwwBhipRphpeWIKxmIE4pnLcq9VGIT2ims315jrMuN6F4AQvtdYsLYg106h0aVHWenu3OSqSNG7etc3yN3oCjGUckt5bLtA4tM8Qx6Upsh/tOcW2uMg3dc5xIdJVVttmY5j+XJ9FxnxaiVyCsQN83VUd/dRwlUIK6Ps1rHFCWQQlN5kt/xjneEXXbZJRxxxBGzSueyyy4LRx11VAxhBcil7y5kUV555ZVh++23j2wsuaRyb/LrAM3Q9EdtlBdzj3GAeUDhBihMCzjC3kyl4kcdU07fI1Msjhwq244jF8qony5BpnH61/S7jGNrfBzDknIPBAcKAKwnCRoD/xhipYaKlQbEcXQ4YwEqpbXSgbjkNJwpd/eDEDVGNxBc3i9MaoY2g3tcg76teVYoiyPL+V9qMwfO/VFBjFzGja1svZTqwBiUo/VvTtpKdzDtOeoSsDbvQNjEIMRqo+vSx5qGjKf82bmcK9Oer/r+KoFxJVCBuHElWL+fvQQoHEC2m266KRxzzDExFMjFk8INGUNnnHFGpIKrDirfV20h3HjjjWHrrbeOyjwjJvcmTwjP6ChAHHYBMIE3UrEOY8YqmDazANtHn0qsUjjberGO7L1pAZttrWFnh7GMstba6kMbz1EswHwMUwzEerQunavrrrtuXJ9YOE0V+Tb6nZ4BgLBvSwXi9L/NvdB1aCogznynqn1tzmXXzyodiKO/bL755o3E5D5jcPsTiw5wmuZtGvtUpwH2+lHi2klClxMWu4hMS27Ob2dPqUVuBmXPGQOM60O+O+MSNeA+7roiOx2GvmxfAubcQy996Usjs50zba5zogJxJe/82vccJVCBuBxnpfapdQlIrP7Od74zViWjfADbeDf9W1iHS0foqgqhtT0lgdtvvz1svPHG0cimuPWppbBT4B2vMKXUGphk2GkTeeqfvgEHa8tLAm2zmaYxOiH5FPD5lH4sKOxMOYUkhfZ5P23lnRl17IwCRlipbAhgyTAg6Hxy6hqIKzk0FYiolcpmGgaIG1wnKZRVaoVBgzsB6KWzeufbE23+fq6Klm2+oz5rOAk0LW4w3FOn92lRA6IwJg0spiJkgMCUz/ElL3lJvOOdEemOTZEA0777pzdD9c1VAu1KoAJx7cqzPi1jCTzwwAPhggsuCIxPnuLkRZM7YYcddogVEKfNgMpJfOTFQ6bxkuWusDMsGBpzFTagaPBmAxbvuuuuyAQCJgAicp1369RPHzzX1hFGAflPWsnsYl/1AYhTPS8VHZlNRvbTj3/84/gr+yq3sDL7WR9LZTG3vYYqEDf3Tk93WIlhtUalYNIWW2wx1lHGiAZIMqLd7wxuxVrcLym3XFd3IfatEPiSz37sIXqjEPySm3mnDwF8Sm+PPvpodKKutNJKpQ8lm/47JzjfhJ9yBtu7wljl9/XjjKihqdlMV+1I4RKoQFzhE1i7P7wE5E+hlLtsULBXXHHFeqnMIkYGeArBkIdp2CpOw8/MeN/AHAP0zExALG8OrykaPiVCsu5Erx/vjd1/u5R+NpUEpRmrKjdAp2n/Bz+XkqeP8t1cvpOYoGlvM9Ip3nLJKHpC+bYGc62uh6HlPOe5L7GVBsQ5Q52lJcq7dINdLqe296G9bQ95LnYmJ2XK49Y2SINJq1DDOPkop73HS668Oyg7ZyYgrvTUCsYkWgMQV/K6GpwbejfHEmA8l+accP7QDwD3/mwzpUIu46z9qBKYhgQqEDcNqdd3TkQCqbpPSqyeEvEzLpPXN7GkeH8S5brNUKGJDLSjlwjbTUwTykHucmEkMrbki8IiAyjoPyNDWF1uYadNps26xPgBIPahydPEk1oqg6kPczA4BvvFfPhhyAhL8XdGOHCuK3ZMW3JMIPuaa67Z1iMn+px77703VrNrq3XNiGurn9N4jrUuLHUuxvQ0+jTMO4VFdt13QBMnJb0IW/yOO+6IBncq6DJOCDgGnnusZEYcJpnIgJLz3FlzfQvnHGYf5f5Z0RrSpeTq+E6FMUoN8c99/mv/Fp4EKhC38Oa89yMGulx++eWBUil0yQ9Ag3LpBwuEAef/eXYYLz4LuGGEfvWrXy2GMdXlZJJRCuORYw9DJtdmXnn0KQnml8GQe9hpE1mWDjTMHCP2BUNmkpU1m8h5lM+0zWYapQ/jfMc5CQhyBgLe5CHMHXibOV5nuHO7hiU9JZmugTisCOzoEpPV6zfmzHz5EMfZU11+d9QcceP0ydkAfEpFH/wpBQRgDhg1TLoK9zKWz//P3pmAWVUcfb9mQFwx+r4akU1QkUUQWWUX9zWPRnHDXdTEDfeoMfq5kWhMRBGNiUYNGFyiAlFcCahsr6AYAiiCIpoAKsZ9xTjzPb8mjZfLnbnn3Nvn3K4z3T7zgMy553ZXb1X/+leV5txSOCTpf9KAaDlzFuWzzCNnhWZQ1I6TsEnWotZ9nT9fc+bMERxLPjHicvtIaDbnaADiouy08EyQQHEJBCCuuIzCE8okgLFANb/cBjBjwwr4d8JRuehgUcH8ogE08blf/epXykacTHeRl62chGfcx1L3gAmUe2ceMQ6YQ82Kfv5MYvQQArnLLrskM8kpv5X5omVhjrQBcSjQsF0IO0X+5DmCJQTYnhUjJuXlXPbXEa7nMswzaSBOc7GGsierwi+oBBCXP2SAb5u3FKY51bzJmwagQ8hafUA+TiWiETQb8ITuojeiP2pu3AU+VIV3IcNXX33VOJJ8j9iIOlb2E8zRSlU3LtZP1g572Nf+Fet/+H2QgG8SCECcbzMS+lO2BGBFjRw5cq33FZYbBsQf/vAHU5DhkEMOMR59FCrLqBg/frzMmDFDbr75Zunfv3/ZfcjKC1CcSej+3HPPrQduVmKMudVOmT8MATzthM/gvYe5xxxnpQFcAZxo98Db+SCHHwAvefq0N4C43XbbzWsWmWWIEuYCcwwgzlZki8Nm8XWuAKoBE7WGproGcwMQV/dKxcAFgCZPmcY2b9486dq1q1dd5z4mZBbWHHuxb9++JpcURnp+PjvYt9zXWotlIHhYleiT2plkAIrMTxZSXmQh5Dl3U6PzAXT5yk4PQJxXR3DoTAYkEIC4DExiGEJxCfzud7+T++67Tx544IGCYUwokqeccopJIs+zWTBSi0ul+BMonOQoe+KJJ2TAgAHFP5DAExZ8I/yAvuBZLxR2CsBDfo3evXsn0IvKvNLmN8zKesSQQdH0Ocw56kyzDgmx9VVhJh8fiawxuHA8FEr0znzQf63gBEY/RqVWxqg2IA6nDGewxvUCsxjHknY2U9TzKe3nMNC5p3C28MM5z7mDTsUdDnOJ/4c5F1plJUAUAYCoxn2cLzmiIQBHNTMtc8cE8xVd21e9gn2OQ6OcfJGVXf3h24ME/JJAAOL8mo/QG0cSsIUauDS4MLjYYLrdcMMNJo9YbjVK+8ytt94qd9xxh5CjwXV1MkfDSv01MLFQqh9++GHZd999U/t+q9QvWrTIsHgIOyUPFIZUXY05ffHFF6VPnz6p9TPpL4LlBxOiZ8+eSX9VKu8nDJx8jVmo1gbDjHXpC0gKKAXwBkMU5gx/woSrz0Cheh4Kv1bGJWMmR5BvTKGom2nFihVOWSlJM+I4lwFVNIaWawbiuNtwMmm6B1gnOMf4gQ1H9AGgnK1q7ivQUN/ezUo+MvQqnKyuK+NGPfdcPocTHXA3S0DcwIEDXYrI6bsCEOdUnOFlQQISgLiwCDItAQu47b777qaaJkAbDeUQRdCCcoRSXHbZZTJp0iTBIxVKc69ZFu3btzc52MaOHWtCepNszAkFFwA48KaTrw9DmxDUqEo7yn59YF2S/U/i3Vlj+WWJ4Qdg361bt4rmSmG9Y/DibKDiKYYVP1GBEsLKmBOtxQ7YHwC7Wot/0HeXSbmTBuI054iDwco+0ehkw/idOXNmxVjpLu42zirWD+fTP/7xD5PyAkcGrCwtc8IZSy4y7Tk1mQv2QhbAq7lz58pOO+2k9g7I31s+5IKsb7+jL7BuAiPOxakY3hEkIAGIC4sg2xKwQNzPfvYzeeqpp+Q3v/lNQWbX888/LzwDWDd69OjMXOrlzi5AA95sAMyhQ4eW+7r1Pm/DTrnYMUgBBsqpdgp7L0uhRxhgAD7kIstCI4wEcDULnngYKl26dIkMermcPxKmv/3228L6IN9eqfJkLjgjYc5pbJwfGPdaczZpC03VDMSx1jEeCWPT1rIAxMFIhHmL/DlzmA/OMZyeAOqcZwBz/Ph6HmWlMAByR8ZZcFq+9NJLhmWpBcwtdvb47kzmLGIP+xIJUEye4fdBAr5LIDDifJ+h0D8nEoCKf9ZZZ5mkwkcffbS0a9fOXCQkGCbc0R+9jwAAIABJREFU4Omnnza5b+6++27p16+fk+/MwksI58UTT/GLYcOGORmSZfAAugGcoXhvu+22TsBP14atkwGX+RLWqK+GSdyh2Yp7eLC1N9gR5DyKyj4rZ7yEKMMWpXAHCc9txbty1wWsFIxirQYZRgvzgMNAY3N9XgVGXN2rQHOCevYoTPFSAXcf9gYOJYqqFGKAYtzDWOR+YE8DrADc8Sz6gS/gKY4kzvs0zvwk54yiVoCiW265ZZJfk8q7Sd3RoUMHb9ZIOYNmn5MuwudiVgGIK2eGw2eDBNaXQADiwqrIvAQsK27y5MkyZswYmT179npj5iI/44wzDFsuahhk5gUnYuTx7LPPyogRI+Scc84pecjMASACxgTK7K677mrYCYQEu5S3hkqWcYVIXrUsJFVm3IDdrANCnkOrXwKwRDBOKVJCKBegG4a4ywIRFGsAjGvTpo3K6aDvsFQCEPf99CUJEmCEsS41hrThcGPv4PTR1rg/YSNqTpmB3kVRlaigv3Xa8Cf3BesaFh3sV/SGSjTy1cK8SnKPpTEuwCvOfM3rKQ05pf0dnK3osDjAfW0BiPN1ZkK/tEogAHFaZy70O5YEYJRgPABqzJo1y4RB4HmlchQKCQydHXfc0byTi8aCQy5Bolgd9uThQw89VCZOnCiXX365XHLJJbF6ZcNOkTNVMqniCKiA8peUXF9//XUzj1nKX+GaNRNrEh0/DLuPIgLNmzd3/Ob0X0fuRDzXUQ3LqD1kvyxdutQY3uQjsudS1M/HeU578QxySQKw7LzzznGG7c2znJGuQYUkQQKAT1jlGsEsGFaA2RqdGqwTgCzNbH2YPsi+VBCX8xAWPesPMA7WHP8GuJpWmBwOEc589BjNjbuLFB7lMqp9kMGCBQvMWkhrDSQ5Zu4zct75XHAMpwB3TBbkneRchncHCUSVQADiokoqPBck0AAlcOyxx8q4cePkggsukKuuuqqoBDAYuKgBXAgHJqwEFk/wvBYVXZ0PZAmIK10K/n0So4zQ1HKNGfYLTAuYgrAtADkwMDH2klZ2+U72ahaq2Pq3Qor3CIcQTgqXLUkgTnOOOJcyTvtdOBLJhdW3b9+0v9rZ9zEGVwx4m96CEEvOTsA48vvaJPJJOfrIl0tqABy4ofkhAYobDBgwIDHnbpqjBIjDCUeEjq8NfYVQ8aT2mK/jDv0KEkhKAgGIS0qy4b1eSYCQSEIcoH7DOOHCQzHkT7z8/N3+v/03lD0MVYoU+HwxJino0047Te666y75yU9+IjfeeGOdX4VsCXMj7BQGDx5rm8cqyf7lv3vhwoUGHHHNUkpzDPnfhccXxk8WFB/WB8xIcgVpbxiBAFilVOy0oWZ8FpYHcgGwZt9kic2Z9BxzRpP7S+v5nATIHoC4wquO+4mcYxrZTOgmr7zyivTu3TvpLZXY+4lE6NmzZ8mMuLo6xllKCD8OkRUrVshbb71l5hiHhuvqpugXRFCU63xJTMgRX5wlPcn3KqMRp0TNY0QMoV9nQR9VI/TQ0UxLIABxmZ7eMDiANwxbQKSHHnpImjZtaoA3DDiUW37PD5cLP4Xar371K/nxj3/cIIV57rnnyqhRo+SEE04w1WRts2GnJJAnfwteabzdSYadRpkAVyylKN8VnokvAdhXhIiRK0h7A3zmPIkDfGA0AkQC8AMKEBJfSdAYhhN5lzSG67F+OMPJd4SBr7FpA+JYK4QzwwrS1gjHg8mkda1rk3d+fyn6BJDoOhQ7/3vQ49BHOGvRR1588UVTlAAnB86OpFnGGuYpbr4+n8dEyHOrVq187mLkvuGQgyXtc6qFAMRFns7wYJBAJAkEIC6SmMJDWiVggbjbb79dxo8fvzZHia18hTEMzZqQMAxi/s6/8Xv+jc+jPGbloo87jz//+c/lhhtukMGDB5sQKsJjDj/8cBMKgHKLx7nS4FvumGCP0U8Akqw0ktFT5bfU3Do+yeGrr74S8vhRrEN7g1mLUVnMsGTM7733ngG8yGUDeM2e8WE+6QsFNFhfGhsOAZhCvXr10th9k9/OtezjAMMqhVZipzUDcQDOGOhJ5ossUayRPzZjxgyT+yptxi+y44wjtxx7DQYyzljSZsCci9Mf7i4Ycb5UcY0s/LwHcQBQ4Eb7OBgW9yq6ehYaAPI777zjtaMyhKZmYaWFMfgkgQDE+TQboS+JSQBPEwYxShc/gG4YwnhH8fCgrEG15nc2pLIhU69hykyZMsWw4VBcc2UBEHfppZd6SU2nEABKmQ8gh6vFTPJePKRZUJoBttmHpYRzupKnq/e89tprJvypUKgbhp49VwBbYGLw49sc2sqEMPM0NmQMuzCwnL6fvSSBOEIAAYQ0hpZbFqpGJw2gP2xvzaGpjMGH3FL0A1COsw+HCGcHOg7AHIzJ+vS+rDDJ2AsUAsoCO/CFF16QQYMGaby+1usz65Lwap/PV4C4SrL4MzHRYRBBAjkSCEBcWA4NUgJceLBBYFSggG2zzTYNUg65g7bg21NPPSXTp0834KRtsH4OOeQQ2XvvvaV79+6xvMhpCpbQR0C4JI3RNMfDd5EgmvBfmJraG0Ac7ASMAO2NYiScGzBDbeNMIVSGc4X8cb6Pk/3CDyChxoZRgGGtdW/MmTPHhNW6dPokefZpLtbA2YOcNYIPWQDiYPoQWeByrbs4s9BzyB8IMAe7isq0MJNsVERufwlzhc3tm0MlrhzYxwDSvs1F3HHgiCHkmQiNLDQcePz4XAwkAHFZWGlhDD5JIABxPs1G6EviEli+fLk8/vjjJm8IipdVzGG2DBkyRPbYY4+y+4DCedNNN5kwToxcvFsUO8hXFggPuPXWWwVmDUof333RRRcZYDC3wea77bbb5OmnnzbgIfl5KKJw0EEHrdfXqO/M/+CkSZPkkksuWQd8I6dKy5Ythd8xhr/97W9lyybpF8A+AhjxHQCJIwfWKYpZFlh+2nN65c4bYUrsEQx8GAawylCiYdVqYfyhVGPMxAnPirN2k36WvnPOamUKcV7vtttuTg3iAMQVXnXsV9irGp1uFixq3bp10lsqsfdrSKrPeYhOCEMOcI7znHPdnvPoXzDotJ6XdnKzUmkUhxcsRcDTLDQAd/QJn4uBBCAuCystjMEnCQQgzqfZCH1JVAIAZOQ8w3ADeMOII3dT8+bNTQJqFDB+f+yxx5pEv6V4CwH6CN2kUeAAryO56ahSdcstt8j+++9vfocXDzCNnC8UgiCk8t577zVK3sMPP7z2IuZSPuWUUwTmxDHHHGNynAAk8v/XXXedHHHEEWtlFvWdhYQ8cuRIueOOO4zCue+++5p+knfp/vvvN+Po0qWLkOPF90YhAGSuleFTSL546TGui+Ui831u6B/r+eWXX1YLnNgxEF4LCwu2IuFNrDeNDAOYHxicHTt21LB81usjZzhnIWCWxoZDiHPWJUsrSSAOQIjziDWvrcFgtQn7tfVdO2COvDUAcfnrAqAH2XPeo8Nx3nDWo4fxd5f7Ns01mZVwTuYGnSILuhHzT1gq56uvuSCRNy2Epqa5W8N3ZV0CAYjL+gyH8RkJYEAAXD344IMGWDr66KNl8uTJhml27bXXGuYX1VEJK6M6KOFCpYBxF154oTz55JPyyCOPrDVuuVgPOOAAoyzAKuO9Bx54oKxevVomTpy4lj3z/PPPy+mnny7nnXeenHHGGabfgG6884orrpDjjjvO/BvKIX9funSpyeMG+ybOOwstCeSDF5jQkVyl5tFHHzXA4g477GCSovveAFtRErIExFGsoUWLFiqN3/z1wjqdP3++18mI61rj7GPyYwGaU6QEliJrTWPOKTtGxoLyrxWIYz0BZpEEXmND/sXyUsUdV9JAHCzvQnkR4/Yz7ec1A3GcPTDne/TokbbYnH0f5z4OPc2NlB0dOnQwjHscSoBARDDgzNUUHs84shDOmQWmaO5+QAfHLvC1KnUA4jSfXqHvvkogAHG+zkzolxMJWDANMGPYsGGGfUABAhpA3Lnnniv33HOPYejMmjXL/P/BBx8sV155pVGy4oYgEFpKOAMhp7lt+PDhJrQU5Q0ADSZbLuBmnwWgw9NKnjYarDkYfBibuQbWE088Ieeff74JgSVElUTOUd8ZR7D0AxARBiGhPaGlL4FCucjS70XD+0bODoASwk5hAAE+ECZO6DNMiCywL8kVhPJPNVeNTTsjglA3DPlS2Nd1zVeSQJzmHHE2bFwjewbwk3tAMxCn8XzJ7zOsvv79+5vz3zLlSB0B05K7gYgI9jM/WtITaJ4XAGp0exznWWjcxawrHOI+NmtPac+R6KNsQ58argQCENdw575BjNyGD5CL5+STT5ZrrrlmbTgnDDRyt8GAowgBYaqAYyjqY8eONcwzF0o77wEsI0k9gBrvHjFihAEA83NbkKdtwoQJBnyDaUPIFd4xQkRzG8w9+nziiSeacNoxY8ZEfmeciSeEYffddzdABN/pe0MpZs5hLGWlLV68eK1yn4UxLViwwNuqYCiaGFQYUQBt5GyBXYlhlQ/KA6iTy0Vjzim7jnA2wCrQHGqybNkyadOmjcqtQX4jwJW4Dp/6BhuAuMLS4W6AtURVbW2NM4l7gEJJWhtrXWsuRytz9KGBAwcWBM65O6gqzDrjXCW8EPAUPY77w6czFqcqBaC0tyzsC01zEIA4TbMV+qpFAgGI0zJToZ8lScBeHIRVkvuN8FOqf9Jgp/FvsMpgosFOOPvssw3zDGCrFEZcbidh07zxxhvy+9//XgD9bHjpr3/9a/njH/9oQljzKei//e1v5Q9/+IMJWd1uu+1MhS5APPqY26CvE+YBGEd4bdR3ElYRpyEjvI0UCkDB9L0RZgewgOyy0koJkfZ57IDivoUSAt4Sdrpq1SpTEQ+GGCB8fUwlnuWs0Jgvy64PzhFYTpqLm/i4nqLuP21AHEAD4f8777xz1CF68xyhnaxzjU4azicYfZrCH/MnPgt5ydAtohZN4t7G+YrexA97Bp2SeYRBF/U9SWwgjfn6CsmB3H040LU6YvLHhP4KYMv68LGxpmGDJuns8XHcoU9BAklKIABxSUo3vNsbCcCaOPLII2WvvfYyYBwNSvthhx22lhGHcnLmmWea/Gsw08oFQAD5YLbRAMwAy2DQEPZKrjrAOYoz5Lbbb7/dFHWAAUfeOryvVHOFQZffAAsANO6+++7I74zrUcd4sWFrKJOVVB6jLCaUMsJ4fM2xEWUM+c9kAfDJHZMvwAlKPLIFjIK9QMEWFOCGpGTCKHjrrbdU527yZT2VsrdJKYCBroURV8oYffmMZiAO8AbHnubcp1kA4jgr27ZtW/KStjk5P/zwQwMKU5EVsA5njsszoL4OoteSIw7dMjS/JABTkbWQbxf40ssAxPkyE6EfWZJAAOKyNJthLHVKAHAGAGzSpEmm+AEAHPkl9tlnH1OVFKWIqqUovLDRXFyE5IRDuQKMu++++wxLa9y4cXLjjTfKX/7yF0ExzQ9rg91GDjuAOvKyDRo0yITSUmiiEBDXt29fw677xS9+EemdMOziNNgPll1G/goSi/vcABaYQ0JBstIAkWFpsR6y0AC8XOyvUmTB2qBxHhBaimHLTylhQ4Rq4x2mkIbWhhwIwd1ll120DkGWLFliqhiGtkYCSQLJ3JlU2aV4j7YG49XmetTWd825+ayscXpoZCPmrhWXTDLYcTQcwlSvJmTansPF2NjlrF/YlQCKGvdw/rgBNHG+ai02lD8e350FAYgrZ+eFzwYJFJZAAOLCymgwEsDwJvSUP6+66irZb7/9BCALpYc8boBx119/fSLVpB566CETmnrOOeeYPCKAfgB1+ZR6G5pKMQZAOvIHUTyCf89tNjSVQgo333yzYflFeWdc5YtwXas8k6OmUgBK1EWKksmPi9x+Ub8z6ecAQwF0NQM+uTIC/MHocJmgvtgcYASSSNuy3yi4UO73+55YuZhM+D058BgHjMDQ0pcAOaQAEV2yYZIE4rIACKU/y+V/I0wqwJO4jrTyv9nNG2yYpq8hd1FH6RKIy/3O3NykON5w8gAac+dbp2K595X9Pr7LAipRx+3rc9zr6KhZAeIoDEXUDD8+NtYNd5XvkTE+yi70KUigLgkEIC6sjQYlAdgfsM0o3U5YJ5VUAZfInca/JZXAFiYBYaGDBw82VbdguJGHjmIMuY2QWBh5sOhIGM/vCV+gwENus8UaYPPxGX4f9Z1xJpx+W6Xg73//u/chn3iWYVzFzYUXRyZpP0voCg3wKAstibxY+XKBbYCCjqLOWsCQBaBgT7kyaAD2+J7WrVtnYVrUjkFzEvg5c+ZIt27dnDgOcEBw/sFaTqoggWYgDuYkzjaN5yhMb+41rYB5VsIhYa+lUWGavcydZfNxkauXdQsoB4Obfy+1wQpHl8vXPUt9XyU/BxuOMy8pvT3tsZGDEKCrnPlNss/WyR2AuCSlHN7d0CQQgLiGNuNhvEYCMGMwzAG0qGq1xRZblC0ZaPLHHHOMdO3a1eSDy22AAgBw+++/v5x00kly9NFHy8UXXyynnnrqOs9RNAKggBBaGkAhShPVVnNZXjDmzj//fMOGgxVHMYqo74wz0FzP6cyZM72tdmnHhKHInGpMJl7XvGTJg80YAZlhdrhmLaIkwvAijJfvwHCBVQow4Qp8y50jG+bK92ltjAHnRBrGZVIy0pwjzhUQB4sGoIa1juOGcxCmI4Y74BN3nIvGesEx4DszutBYFy5caPqdpbQFLuY0jXdwNqM/4OwMLb4EiNhg3wE6AcZSBAyZwjDknotzl3JHkptSewVbpMh5hDMsKcdD/Jkq7xPo8cyvq/O6vN6s/2nWHCBcnPXmug/hfUECWZNAAOKyNqNhPEUlwGVSyONUbnEGvhigjRw6sNpyw05hq8FaI8SUKqgUb6APEyZMWMs4o3jD6aefbnLY8Sft0Ucflcsuu8yE0gLy0VA8KARBqMrUqVONEkLfo76zqIDyHoARBzNu8uTJ3itvWUg+nz8/GNgUFignSXTcOU/yebz7VPx1FUJHqCvgK0YKhjb7zsVeLiYD9gR70VeluVj/rSEDQBG3iEuUd6f1jGYgjrsChkvc0FTOA8KYKKADqM25xz2AgcS+Yv2zL/g9a5SzgzBYwt0AokoFj7k7AQVc7d201gjfoxmIA1hlrWhl/rAGcY5oZmGx9nGIks6k0g0ACscvP+xnAHjWh/17fYwq7i2YfT179qz0MMr+fsbCvMB0z0JDN4LB72toagDisrDKwhh8k0AA4nybkdAf1RLAKITlBsNu6NChxvB57rnnTGEGQDoYbLBzpkyZImeddZZhLxx11FEmjO6ee+4xlVIfeOCBtRcxCizvWbBgganmSo63xx57TAjHIi8cRSdsi/rOuALGUCTEj+/dfffd43481ec1G4p1CQqDm7yCcfP7pSr4GF/GHLEHSmWpATJgmCIXCyqgkPP3NEM6slChF2bw/PnzTS5KrY0QLs5Zjc0yOKPsBcKWMMAB0WC02EIj+aBaIZCMPYPRDjDHD0nhAdT44a6KCgRqDk0FqN94441LBiErub5gQ8FwxIERWmUkgC6Gfkdkg2+Nc5z7iL3N32G7obOx3gF1cs8XyxzPAosMBxxnWFaclOj1nM2lFI9KY02ydrhfot4XafQpfEeQgHYJBCBO+wyG/nsnAYyk0aNHGw8wStH2229vKp/CYssFCgDoqJJKyXI8mlRIveCCC0woUW7D+Bk5cqQ888wzhuXA+2DMAezlt6jvjCM0GEZUnCO3HmGwPjeUMhTQrFQYRdaAtDYkxWfZR+0bBiUgQhxWDUACLCCMByqLMc+8A/CtUmESWQBIMS5ZW5rD9TRXY+SOIJVBfTl3OP8p2MLZj6OmWNGWqPsKYI48h4A8vBdjFkC7vkIqmoE4WIOAlhrzGzFXK1as8D41RF13AOc1IfBaGX2MCyCcUPJ+/fpFveoq8pxlg3NmcEdxb5KqA9aYdYKxj7VXsEW46KWAjFnJ04qjhfMpTYdinEUagLg40grPBglEk0AA4qLJKTzVQCSQtXxcLqaN/FGAHzD2Dj/8cBevTOwdKMvz5s3LRNiFFZJlrvjqJY07mQDVsPuihF8AFOH1xqAAYIARgpIahUEUt19xnwfAgKmo2QjgvGMMOAK0Ns2hqYXCtJkTWH4wXMhxiGHGuQbrL8q6jwrE2fnm+zhjaJydsPTIOwVoAvMh9zs1A3Gwyps3b27yamlrGOgAKi5y2VZi7Dgk586dawpkaW2AEKx/bXPAHUqj7zh9bUEAAEV+p5ndhFOP/mfF8Qp4ilPEVyCOu4L7SPOa0Xr+hH5nVwIBiMvu3IaRRZQAlwtKVrhcCgusV69eht13++23m/BYnxuKJcZtFhIRWzljhMGG0WhAFlorGMSwLOvK6wIQAMuJPQnIBRMElmipea18Xq+V7lsW9ktWgDjWPWA7ucy4kwDh2PNx76W4QFz+GgSUsyxJDHf+zv4j/yJsOdaMRlaZZiAO8AQwK4rzotJnSqHvz0Klziywh5kb7lYAH/KCEgpJ45xp1aqVtyGRPq7pJPo0bdo0U9AkisMlie8v9k50MvQwX4HCYv0Pvw8S8FECAYjzcVZCnxKXQF3gG1WLxo8fb/K3YQiFJiYvHDnubrzxRvnJT37itUiYV5KSd+zY0et+xukc7BhCyDRXtswdL2NB8Sd/jW0YmSh35LjBSLD5r3KfiSOzNJ6FYQArbrvttkvj6xL5DhRrwq00J1GnqjQFCzQ2AArYnrDfANwIUy0nfyIyKBeIy5cjTCz2JXuRvi1evNgwywDnNOWZIkE9zBnYftoa6QlYJ1rvAIBEig4B9mht7APAXByTmhv3FuuJFCc0u78JVQWk4yyy1ZZ9L4IAWx5wOitOSvRsUtT42gIQ5+vMhH5plkAA4jTPXuh7LAkA0tByvU02lBFWBQYplUhReCmYgHGXRvXFWIOowMPkhXvqqafkmmuukfPOO68CPWjYX0noILlQOnfunDlBsNcw0GADEQpHiKSv3uB84QPCYbRQ5Uxr43zj3MsSg9T3uYBxhiGMUQPLhv+HbeYKdHYNxOXKE6cAQByGL+cSwCHAOt/Jv1UqX6Pvc15u/wBIWDNaHUw2/Fkjk9LOXZaqjda1HpkncikCvOMYI0wShi7gNfvbN+Adpyv9wnGXhQbQ67OeF4C4LKyyMAbfJBCAON9mJPQncQmgaGBMkA8HIxRGBcmQycEDLZyqWPvtt5/aMBDXAhwyZIg88sgjcskll8jll1/u+vXO30cuGsIustLwWGPs7rjjjuqHBPBAlU7ANqqDAcQBQDRt2lQNAGcngZC9lStXqjWO1S+m/w4AphMAhc8ALgYMDQOXpPUwTgDflixZYhKpuwy7ThKIK5QjDpCIH+5QqoADpsP08w1Uh2mL3DWGd1r5ai12QGoFQBPN1ZkB4pYuXeo1UBLlTOcMYh9HqTQKMMc9Z6st45wGgMd5AACW5FkTZSyc/YBw+QXOonw2PBNfAqwH5jyEpsaXXfhEkEBdEghAXFgbDUYCsIoIPQWomT59usk9RYgNYVn8wGzBONKWjDfpCTzhhBNk7NixMnz4cLnuuuuS/rqy3685Z1TZg/fwBYAQME8BSth7KHGEfZP/zWfwpJgos5K774033lAN8r744osmXMxH4wCjFcYnhmwu49P2lf2gCYgDjGA8Nqwtd49YxjljBmzkT4xk7lX2f6XZUBSJ4cwJ93uxk839763zM0sOMvdSSueN7F+AOEDzUhrAHO8AeEdfJt8rf4c1FzefZSnfn/sZWKIA667YxOX2p5zPc36Ss8/nNBHocuQx1ay3lTNH4bNBAklIIABxSUg1vNMrCeC9I2Tm0ksvlQkTJpi+4dmjAij5z/Jp7Vw2Php1lRLqGWecIXfccYeceuqpctNNN1WqG5G/12fDPPIgch7EiIU5oy0/EMYXTD5COAG8MYLZW7AKYJ+Sk0Zzg91HbjvtRoB24BrjBaZN2kZgXWsXBisGImFd5DCCQQBjo1DYJqkQeM4lSFVploqVC3udkHPC2Sigw99hpGG4V6ICtGYgjpBgQFCt1SEBZQH8u3XrpvbIh9UHAEXFb80NBzRryQXDnj2Ofk2ECfc8+5qz2ALvSevRnClZqeKJHLnLqGbrawtAnK8zE/qlWQIBiNM8e6HvkSRgS7SPHDlSxo0bZ8AAEqzjqQeEI4EwNH3XzIRInVPw0IUXXmgAuKFDhxpAzveGQgjQk7QSmJYcbB7Dnj17pvWVJX0PHl0MLsJNYbyxzwCq8is/AlQASrgEH0rqcJkfAmgESOnSpUuZb6rsx7UDcYQHs94qud9Z+zbsmj/pT4sWLSrSpySBuHLyVXIWWOaMLVABMAcgn0ZSeAAIWDsagXMAIMA4raGplhXtMgQ77VMTJhjhzaRU0NwA4QB9CB132TgDudsB3gFd2W98Bw44WyDFNZOKtDKwc12PxaVcor6L8xGGdJ8+faJ+JPXnmGP2sOt5TH0g4QuDBDySQADiPJqM0JVkJUBidZKhEjaDAQ3LCGUBmj6GAIAcP3g8DzzwQAPOhWINIldccYUJST300ENlzJgxyU6Sg7djsDCfvjBkyh0Shj2MEl8T6uOVxhNO7jf2i83ZUlfidox55sa3xM9x5wmGBGeJdsNs2bJlhqWktWH8VSJcBnCBkFPuFUBlQr2sIRpVlrC0+Jwrhpg1lKJ+f9znCuWIi/sOnqef7B/kh1EHK5B8T4D2gHNJADaco3xXJQHbUmTFZ8oNJyz1e119jjB+gCzNlehx8P3rX/9S73jhvqa5OnPqWiOcj5wX6AH8nZzMAHI2P6YLMAe9CIe6xryP+XLjfEJ39bkCbADiXJ2I4T1BAt9LIABxYTU0WAmgkNhy7VDCx48fb0KJaBdffLEMGzbMsByyAuiUOtHXX3+9XHbZZbLvvvvKww8/XOprUvscXlKYAxqZD4XN5iSiAAAgAElEQVSEhPLDOvWpMhgedcJOYbbAwAGMop9RjFyAHwxtrWFWdo4AXV5//XUT5q65aT/jXnrpJVO9Mw2GJWscY4n1CyMShhfgAgzcUgxLcpZSaMKlUZwkI84VEJe/XzDUATpsUnjC25Av8ianm4s7GCAAwJm50taQO/qKT3dAHBlmgU3Gec+a1J5SgVzJNKJC0mzcM+xxZMg+pBor+xrgqdSKyxRbo8poFnQ9dCqcEz6fT5zHLu+qNNdf+K4gAV8lEIA4X2cm9CtxCeDdJF8VLDk8a4BxhAFyEQI8wQAL+eJERo0aJeeee64MHDhQJk2alPi8lPsFsExgNWbBS2plAYuMyqKVbLAaAAQB2zAICQnEKImrmBHegwJO6J7mhlIaFXz0eZzaQ1PTAuJw0sCg5lzBkHQRTpmE0yBJII77kdC2NIxFQDnkDYgDaABrju9G/qWAnsgalnsafXe9323V3SiODtff7eJ9WWCTAZQwD0nuLxeyLvYOokFgqRH9UclmQ9WtMwNHCo49GHNRU4sA7rEnSjkPKjn2Qt+dlJPD5TgDEOdSmuFdQQJrJBCAuLASGowE8GgCvKGIwESYNWuW+X8arIRBgwYJebhQUGA5ZMHL5mJy77rrLjnttNNMEt6pU6e6eGWi71i0aJEx2lwYyol2NMbLKwWWYHij6BIahVEM8Fbu3uBdKM6aw5SYOgwzZEIOHM2tUmvLlcxwopCnz7WBDPCMYUjhBRifsBW4E1wy7wDYCdF2wfiy8qR/SRmmnAfIIc3Kozb/HvsNpxn3OAwaitfEAcI155NymWDf1b6L8x7WOfsIIFRrwwnFGNAVNTd0Xhi9vjnCrKMPYA6AjcIeOPvQ4/gpdKbBrkdfd3l+VmpufWeN2qrYcR2vlZJn+N4gAS0SCECclpkK/SxZAjbP28yZMw3TjZw+GD+EWmLAUT2KMDmAgSRy05TccU8+eP/995tCDSigVCQNLX0JpA2WAEAAmGH0UsSE/YIinJSBn75Ey/9GPPrkxQOg1tzSXluuZeWSFQHYg0HIPQBwA/AM+zMppwxMIZiudeVTLEVWSQJxPrA2mCP6ARgHMEeoGywaKjPXx4LWXGGRNAD0X3vFzlLWsy+fQW8ELNFaMMMXOcbpB2G0gJ+A/6SAwNmCPm/P4xkzZpjiBlkA4rgLWGPkvPOxBSDOx1kJfcqCBAIQl4VZDGOoVwI2vPSZZ56R4cOHm2cxsA4++GDDgkOxyk+QGkJSvxfpxIkTTZgu4UEAD743jBaqaFU6lNOlnABAKdaQFBBmE8+T/wpgGhnihUaOrr8TxRoQC8NZcwMQgFnbq1cvzcMw4X6aC2eQYqBZs2ZlgVmAOYRMA/DAFEkrbAuwj2INLuWfdSAuf7PhLCCMlfMeYxHGO8DcVltttY5ccS4Q8qbR2cYa58ykQqTGBsuJOWrXrp3G7ps+ay+YYQUPy4zzRlOItg3NZh1RIAlmLuc0+wIgziVLWe0CTbjjAYhLWMDh9Q1WAgGIa7BT3/AGjrcJUAkvG/R8QhhR4gEE8DSjJEKHh+ECMBcqpq5ZI5MnT5Z99tnHMEMoS+97oxou1bkwxEKrWwIot7CJUGrJq0e4GaxQ/nQNvuX2gn0I+KM5TInxID9CY7Qax7mGGUBWknOe5D4speABZzvhcqxFW6UTJhxrP808XBQQ4N7RAsQhM+5PEqT72GwxDUAfGGSwedmjyJccf4CeGh00nNGMzXX4dVpzmIWwTs575kA7+wq9F/1Wa+EP1iz7gfOaPQ1LkbuLMZG7U+seAWTEHvEtZNieEdYe0ujISOucC98TJFCKBAIQV4rUwmfUS4CcKwA2AEsoJhhENok8IM7gwYPluuuuC1VTRYSQ3v79+xuGFHLzvQGyEqKkPQdZrpwZE4xEF0YA7B/mEcUP5g9AdJxcS+XOP8wUDPoQZlWuJN18niI1OB9crC03PYr3ljhAnA075czn75wRAPYuQ0Pj9P61114zxqPL0NckGXFxxubLszbPH0wgwr6432GXlVrpthLj4rwECNLEYsqVE3PAfeNr2F2UOYUtDgsa4F5ze/XVVw0IlyVHJaA764txocfzd+aJyBctuYJ9zwMZgDjNuz703WcJBCDO59kJfXMuARRwGA+5hhdeKCjuKOrQ3p999llDdf/Tn/4UgDgRkysJpiByI4+F78wZ+sj8amQ+1LXgy0lIjwIF+IYxBOgGGw2DAkW8EuAL+82yj5xv8BRfiFwJGSY0RnObM2eO2d+VAqPKlR2MJ9Z1XUwIAAzCymC/8XdyDXGGpcl8K3eMcT6fJBDH2YEsNbJAcbyxTmDTEL4KuEVuWBiJ6AU+M2kAF1i7gLYaG0AJMibVgdamPTzYyh09l3nIQlV5csThJM5t3MvklIMVy55Gz1m4cKEB5mDN2Xy3vq1D2H3oZb5GClhnrc/npG9zGvoTJBBFAgGIiyKl8EyDkgAKIz9aPGlJTw4GDCE9NMAc36smEW6J0qAVWCg0n3FYP/bz1rgESAV4wFuMh7jSckHZ5Mf3dRRlX2kvdMAYYYeRF7DS6yKKvKM+wxkAK4I9ANgCkMHa9834xPFDKJLLvZAkEOdDsYaoa6DYcwBbOASQPWA0AB3GOiCjb8amdiAOOXPm+7b/iq2R3N8zB5wrvgIlUcfCus9C4SV0vOnTp8vAgQPrHTrj5dwCmONO6N69uxAqzecB5nwJtSTElpZmReqoa4bnAhAXR1rh2SCB6BIIQFx0WYUnlUsAJQpQiT8t2MafKIn84LW1ObMIFTrssMOMcg5DDmP1ggsuUC6B0rpP4v6WLVuaD8M+yS9sUdpbk/sU/WUeW7dundyXpPxmvPEAacUMRNYrHm8AUxRMqgIji0ow3+oSke/VweJMbRaAuDjj9fFZCsgQ5syZjbGAQbN48WITgkjuO5/D+cjNSN9dAhQBiCu8SmHFAKLUlY8PXQBDHQYN7BQMd4A5zl2X81PKHoKFSGM9a2zcSbCyNRdrsOCN5txqrB3SsMCs9PlcjLLGAdhImzJgwIAoj6/zDDqIDZfGEYKzhnuDO6NSDil0N1jaPulquUILQFzsZRY+ECQQSQIBiIskpvBQFiTARbv//vsbg816aC07xyaut5WBYMO99NJL5rkRI0bI448/LnPnzs2CGGKPwSoofJCcRr4mk7UDQ+ln3rSG8RSaoPq82BiQGAmAdITjomBiLLhk2cReNPV8gPXEHHXs2NHlayvyLsLbtIdAkzMIRlwxkLciAo7wpbBFOdPJ4QSohTFF89WgyR2SNiCOcxUjViMgRHg/OcqigGq54W0Am7ApccYBzNniHhGWZnjkvxLwPf9VQ5oozkvOe+33FnsUplu5gCLv4VyDnczZxl0Caw7HPM7MtFIYkK8aJ4Gv1eRtoZJQobYhnRZhrGlIIABxaUg5fIcXEiA31VFHHWUuboxOgAp+uGy5fO2/4REDiDvxxBMNw+L11183wMEee+zhxTjS7gRAj6XvY8z47tUmFxQ5QjTmMaprbhcsWGCKNVjlGe8p6xTmBmwJDEWMY5dJ35NaZyjP9JuKhtqbBT01j6OUsOdKjxfDCfAZpwDFJgCeYe1qMxJYP4QiuQRBk2TE4RDAeeWyv2mtpThAXH6fGDdGug1v69Wrl2HPYaQzf0mDvjCi+Q6YOxob+hPAhuawTqIB2Fu+OyKLrY9y9kGxd6f5e5t+AN3HVbOgHHYBej+6JLYAuiR59ZIM6eX7bOV6V+Nx+R7OQHRObXesSxmEdwUJJCGBAMQlIdXwTm8lgCJtE3WjRPNj/9/+aY0My45jML4XKEhS4MiByxfFZ9q0adK1a9ckv67sd2cxRxysJTylgKIY7wCNu+yyizGI7RouW3ApvYD1lGaV1iSHlYXQVHIIwhTylUFp5481gzEAKG0rngK+4WCh75UKKSpnfTEOzlaXrIskgTjNOeK4+3HCuVonAME4QQDoYGJSgRewCcada32B4hL0myrXoVVGAjCmOGe0A3EUXGEcLs+cSswIew1Gce/evRP7eu4cWO+cqdwzhLcDlhG+7hoUJ9qE97oEFl0KhrsXObg6P132LbwrSECzBAIQp3n2Qt8TkcATTzwhd911lzz66KOJvF/jSzFgMMKeeeYZ76tEojABVKHUaG8oPzAv8MZj7BF+hyLIfLg29tKSlQUTbd7BtL43ie/JAhDH+sLb76OCbZlIMBNY74Qzk2sqF+wg1Jn/1+ipJ78dLCGXhYECEFd4p3N/sU5cs9csQAwwgDHN/cPdw1pl/boAPJYuXWqcLlrPzA8++MDIQnN+NfILs09dAzBJ3Ev1vRPwmPXp43kfRxYAiuy3Hj16xPlYWc/i5OW+YY+zF2GTA2qSN5mQ9XJk6nuOuADElbV0woeDBOqUQADiwuJocBKg+hVebBRnGAm5hRu4DJ9++mlTjenMM880suGiPf300xucnHIHjPKJAjd+/HjZa6+9vJaF9hxkGHYofLkeWOuFLUfR82XSGBdhGLvuuqsvXSq5H3jkKYihFRRl4ABbhDS7BihKFWouCwFjCwYobCP2QCFQIz9su9TvrcTnkuh7AOIKzyQ5Xzt16lRnsQZX80/oLnuK9QrbFD0DIx0mVV2FIop9N6wc1n6U/HbF3lWJ36Nzsa9Jr6C1cRZxRvpSZbNUOeI8IgeaxvDy3DHjbH377bfNnq5Uw17AkcUP+9v+P8Bc3JB19Gs+4yszHSCONeOLnlCpOQ/fGyTgWgIBiHMt0fA+ryWAMn7HHXeY6qkoyFycgB72x4ajctnwb7Zdf/31csghh6g2uMuZGNhYeOXHjRsnBx98cDmvSvyzWnOQYWyhjBFCRaEJ2AM2QS5MOAzsUg25xIUe4wsAvwnxwBgIrfISSKJgQKmjIqk7+bAwRtgDMPWKNdYSIXtRni32rrR/rw2Is0WNNLIP0wLi8tcQjgfOdIxswByqegPMwUqKep7zDoA4rSAQgAlNMxCHXmHzCKd9Trj8PiqNEs6ZBaeeS7m4eBe6DexPmz+SPLjkwyWCgfy+9TnsfC+iEYA4FyskvCNIYH0JBCAurIoGIQEADS7B4447zlRDhc5OdT0UK1usgT/xOMPWmTx5spx//vkmDIHPYeh17ty5wXqDyEdGGNWdd95pCl743DBaCGXTUDUV5gSMn9atWxtjjVYoxAEQlLUJM0h7gzEC2KJhforJmtx97du3V30uJBEeWUxu9vc4Qlj/5NvifAWo4ByOAzhQbY59obEKIHvB5iqNKrNizyXJiMPQxMjUGJ5H1XNyIUYFv4rJuZTf20qPnPWsfc4O1i9rFxZNXQCn7xUVi8kit7BFsWd9/b3vObyiyg3HC2eti5DpqN+ZxHM4Lrk3fC7KxX6HDcp+R9fr2bOnca5iU8BCzwXmfC+iYXNFB0ZcEqs5vLMhSyAAcQ159hvQ2G2ulgEDBhgAbvTo0YZxBOuNiwWlhIsGJsbYsWNlxIgRMnHiRKMou8rzolncffr0kRdffFFGjRolJ510kuahVLzvGN+sOQxD/mQdAiTUp+CQIw5wQqMBXHGBJ9gBQH2Kl2hkCFmxACbiaEgLyOLMhY3MWcseAHhm/WsNuytneSURjpQkEKe5WEM585TUZ9E5ABMsi4azBOOcPWKrNPLdAHHsD63nv40u0GzEc05yV2ch92xS6znN9wJucX5qqb6OHcHeZr+jz5ESBwcsP/ydf8M56WvIMP1HB9UO4Ka5RsN3BQlEkUAA4qJIKTyjXgIWTLv22muNB23o0KF10sQxNgiRateunWqmi8tJIy/clClThBBdmzvP5ftdvgulZtGiRaaqqC+NPsHSg/2DJ5S8JjAiogI4KJwYMb5W1IojZ/YiAFaS1c7i9KecZ/FikyPOV+W5nLG5/ixsN9gB5HBs1qyZM0YkYW+AeS4LHrgee13vSyKsNgBxhaVN1UsAZ5/3KsAc+WtZ0zB+SAiPvsJ9Rmgr+0Zj015sApkvXrzYAHHMg+bG3QszS3uzADZFUTQ2HLJWB5w9e7axR2DFkgYGXc+3vLMBiNO4ykKfNUggAHEaZin00ZkECFvEq8MlyA+Xi02Oz99t5SLyZ0AlB0DhOT4HMNcQWRsI/0c/+pE8/vjjcuWVV8pFF13kbD6SeBHzRb6NXr16JfH6yO9kLaEsEgrFesKDiyIPENeQG/sNdiUsS+2N+UV51sz0IGcVLOEkQvZIqA0ATY4c1j3/D2jm0qtOyBhMIY0GsjYgDjAVp4DP4WB1nSlz5swxoLmvydDz+231Ec4WwBNYZeydHXfc0UtDvb6zHEYf+5+E9qFVVgIvvPCCDBo0qLKdcPDtnEUwq7l/s9AoooGjoHnz5gb0hRDA2GCL++BkCkBcFlZZGIOPEghAnI+zEvrkXAI2L8ODDz5oKpmh3AKucZHb6qkWmOOCxxttwTqb1+Wee+6Rvn37Ou+bhheSF+6hhx4yIBxgnM8NhQGja7fddku9mzbU+a233jLsNxhsgATlgm8APgB7KGlZaCidWQDibE4zzUCcayAL5wWsI4wJgDcMCfZBVPZn3PUNW4jvAEzU1kjkDbjiEgRNkhGnTb65/YV1AktaCxCX2/eVK1ea/YNOwlqn2BQFfAiTxLnju4NQe2gtcwGjEkai77IutkezAsShD6Gb+8xwLTYXub+fNm2akDoHJhzjQrcgtyLnlc1dzV0BOMf/p82Ys7J26USLI5/wbJBAViUQgLiszmwY1zoSQIGFlfTTn/5UnnvuubXACP/GD4Y0ii2XO22nnXZam6OFSw9g7tJLLzUe9YbYTjnlFAGIJCyV8FTfGwpMWp5SgD/YbgBvrBMKgdBYV66UJVgoAMdt27b1XfSR+oec0spJFqlDJT40b948w5R1CaSU2JWSP+YCyGIPsP5hvwHE7brrrmtzb5bcsYgfZK8jf40AS8QhxnosSSCOfUvYJMnetTXConGKJAUIJykPCkjB+LTFejCKAbm5d3DycJYCdmGo8xOn2EmS/bbvRq/iLtRcqRMHLkxQjdWZc+eYMzoLRZ8o+MRdo5GdW2jPAcQNHDiw4Ha0ZADuOuwUckkyfuwW9Nw07j7ueL7HlU6bxrkTviNIQIMEAhCnYZZCH8uWgGXEEeLBBYYyhUKON40fFEQ8PXiaf/Ob38jee+8t55xzjrl4+HcuvKx43koR5tlnny233XabnHzyyXLLLbeU8opUP5M0EIdSQi4fqghi3OEtxwDCWEpCUbEgH/lDstAYD0wO7Y0KdBgCPoSOlCpLDDMM+bhgot0DMIphapJsGoYOskhiD9Q1PvYg57NGIx8QFNm5NO6TBOI0F2uwRXLSXJul7sn8z7FOMLjrOjPtXgSYw1AnBxj7mrXAnVRpxi79ssweVzJJ+z2kuyAsWLMDyQI6pArQ3gDWWfdZqL7OXLBH0CGjNvRPIiXQdbfbbjvDCOd85s8knA0BiIs6M+G5IIF4EghAXDx5hacbgASoDPrss8/K1VdfLd27d28AIy4+xJ/97Gdy4403CiGqd955Z/EPVPiJJEIfUWJRRlB2YChgPBOqkgbzDrAD769Lg72SU5TE/FRiPAsWLDBKsGbjjDVNixJywh6wxhzgAAYde6CSYaGEwFomUCXWQDnfSSVG8ma5NIwDEFd4RshLCVPTN7ZYlPUDqAZQHuecscWBKI5C9XcAOe6QSoS1FQMSo8ig0s/ASueu1+yQ5ayfOXOmCYHU3nD8cGdRdTQLjcrJpVbk5U4mYgKZAMyxx7t162byXHNuRLnbi8mQ7+Ds1OjIKDa28PsggUpKIABxlZR++O7UJWDBlPwv5nLBY46SRSXE4cOHG/bXqaeeav5dI9vCpXABJa+66io5+OCDZdy4cS5fnci7AHrIEedCaSAcC6MGjyXsJwu8pckyILSHn7ispUSE6+ClWQHiMGxYYy7WmQOxlvQKWMAAafUZASj51rAHeORZzlIfzkWAOPZkqUZMSUJz9CHX+fnoVpJAHGcQuYtcAoeORFn0NZqBuHLOGQueY5SzV9jLMOsA52xO06LCK/MB1jn7U3PVb5xh6IcuQI0yxVnyx9FlyZXYr1+/kt/hywfjOJB86XNd/XANkNpqrOw7WHMAc6TbsSB8KfpKAOJ8X0Whf1olEIA4rTMX+p2YBFDYhw0bZvKh8ZOWsprYgBy8GDYcrLi99tpLxo8f7+CNyb6CMBLyaJSqNJN/BwUGb+uKFStMaA9GRKWAB1gNJOzu2LFjsoJL6e2EdJJvsRSFMKUuRvoa5gRQQjNASm4pxpBvJNuqvzDeCOdnL/GMb4yiKEBipMmswEOAI8jTZShR0kAcZ6PGCrWA/7BEfFu/UZYdzEnAMxdgM2AMYCrsaooa2WqsVGxMKtcUeQVZMxoBXDs/yIrciOUWXooy30k9w5k+d+7cihSycj0m9DP2chaiBJIESAHQ7D2DHmmZ7OQJ5G6P2nhPUudD1D6E54IEsiiBAMRlcVbDmOqUAJeJLciAFyr3h7ANqtjdddddMmXKFLnmmmvkiCOOMM+UCuhkZSpuv/12Oeuss4wn9amnnsrKsNYZB/PM+gDEA3jD8EFRSZP5VpdgCYdlbe68886ZlL3WQbkodFDpsZPfkJA3m3+KfbBw4UJjrAO8AUZXCoCOIhuABABdjWc0+bww7F2GuyUJxGnOEQcTDMNd4zphP3IXxckhFWXv8Axyge0NyEdYG8xXvof/1+xgiDr+qM9pZlRGHaOm52B32vQgmvpdqK/YHqS5SCMVDvc79w7fyZ0PQ5L737LK67rrAxCnfZWF/vsqgQDE+TozoV9OJWCLNVD5itBKPDt49kn0TcgBf6KQ4mVDEaXy5ejRoyua+8ipAMp82b333mtCdWGZUd3J9wa4gIe/mIGLMkLhAH6g7ROuw78V+1za48ejCasgK0AcIRNUG/UZ4Ikyx+QKBKxKI09glP6U8gznH+uLHDUo6Z06dTLVf9MuulBK3/kM/eY811gwQ1toqmYgDmYzLBAfHCtx13qSQFxuX2z+R/YU4DZVugE8CF0vJwk85ySy18iktPKBUQlQ4ptuEGctodvAbs5CpdEs5B2MM3dJPQsbDwDeFn0ApOOcR6dhz3Neci7QAiMuqVkI723IEghAXEOe/QY0dhteOmHCBLn00kvXhhfYansAAlwyeIGpODZkyJDMVGNyMc0PPfSQKdRAngkqz/reAFwB1QqFkcCIxNDAC8jfUTzwDAbvf3qzSh5GQlM1GzVIiwTeAEAaQSDAN84/9gJKONU7YcJoYwzlM/rSW8Xlf1MSjMokGXEAtDgECNHT1mbNmmUcbBrPHMBydJS0HRcA85wP9oxAN8JpiWFOmGnU1ALz58+Xli1bqnZsoisg/6hj9nF/4HzmzGEfaG/Lly83jLg4BUx8HTMkAMZDVd5KN/Y3DFl0As5KHKZvvfWWAdI1OxwrLdfw/UECdUkgAHFhbTQoCZDTCYUczyxKFaEqXDYYLwBxADcYo8Hzs+6ymDRpkinUgDJNvhrfG4o/3nwLkNjwYvJh2TAcgAct+V4wAlDU2rRp47voI/WP8F/y3YV9FklcTh+C8UuYMyAce4S8McxDnHwxTjtU5suWLl1q2KwYCtoaICLOH5e5s5IE4rTJN7e/3PsASS7z8aUlD4Aw7jIfzkuiBriLYM1wH8E8Byis7y4lJygh7poZcYARFKrR5qjIXaMA6eTUTCMEMq29kYXv8ZlpDInBgp4a79gsrI8whmxLIABx2Z7fMLogAScSmDp1quy5557GI0aJdN8boaZ4SvEA83cU0F69ehkGHMCrNq82ISWAi1nwZLN2MCwtGO77WqqvfxrYWISeIG8AOMBPmyvLGvUwnNgT2267rcqpYH/jUHEJZqkUxH87nSQQR95AWKAaQ9sI74QlnTarzMVa4uwH8PIJyMK5xdli86raog+w5vMrSQP2wzjXyEa08zd9+nSTI1czEAdgyl2AQ1V7A9wFCM5CsYaPPvrIhAwTJeBjs6l9NBa68VGeoU9BArkSCEBcWA8NTgK27LkdeC4oow2gSWvySOi62267Ga83Br2vDYUBpQZ6PcxGvNjk8MKA0axAY/AQbgujIwsNMAijTGO+plz5+8rG4ozD6AJsIwyYXC94swvlfQNYAZxwUZExC2szzTHATORschlelSQQ5zNzI815S/u7NDDKcBYBuJHmAdCTM547mHOH+wsgTiMIauea3LgDBgxQ58TLXavoR/xo1oXseGDVE8rp8uxMe1/b72Ov4Cy2BZMq1Y+6vjcAcb7NSOhPliQQgLgszWYYS5BAQhKgopP11qFs+6TIWeWSnBaEnqKYAUTYnFcJiSTV1zIejHZCY7LQWE+MRbsSDdALOO1LWCcKPZ51wq8BoHfYYQdjeGXZwQCjD0+9L3MQZ38mUewjAHGFZ2Du3Lmyyy67qASDuNdY31pyUXLmwEbnHOKMp/+AcLbyq0ZmDYVVYBRrbuhIFC3RmOMxX+44mDp06GDSEmhvllnqa9i8BW81M1q1r5HQ/+xKIABx2Z3bMLIgAWcSgPmDUU8jz54Pyg/J5mHnEWpBOIxl+6Dwo/gTqgYYkZVmC45kYTwwJgi10h5WAusMULqSCirgG/uA9c++hLUEuy0q2xBGHE0jkEW/CZXHsNcYWqsNiINpjDGvUdYzZ86U3r17qwTitJ/5gCYw4wht5mfXXXc1DgOcGDBCo55V2uVQ6f4DjHLeZ6H6OgwyWJZZWDvMCfc2eqyPLQBxPs5K6FNWJBCAuKzMZBhHkECCEsDQt4Y6ebEqBXChxGMItmjRYq0BTl/yPYm+hgyWM0X/93//J3369CnnFd58FnYBxrxPOY9KEQ5gMGyztIFpW8GPdQ5DFSOXsJZSAEEMYhRtgFGNDUYc+x8GrLbGWQYY4bJoDGvgo4/Iw1gl1dW10qKFyEYbuZGMzQtWyjpz04PS3zJjxgyTXkFjeCTnJftTCyMuf2+EA2sAACAASURBVJZIqwDIkFuZHPCBwg+cXwBD/A5mEGepjwxegFxyxGluyBvHpXZmH3PAWMhZnAUgjmIIONR8qJpaaH0HIE7zrg99910CAYjzfYZC/5xLwOaI43Kpr1llMD/xsPMOKXgh3kfLXiJxdJohktb443uZCwAHgLj6lHUUen6fBSXNLo8sAXHMaRb2FeHCjCON5NecVxgfgOIA0t26dTPru9w1jhEA25KqhhobBgysRI3hbuTVcjGHdt6++UZkypQNZdmyKvNPXHHV1SL9+tVKz57133dR5p57AEcMjCZtjaIeMEV9SqsQVYZZyoeVP2abWuKTTz4xa4v9jJMGUIJ73Bfg9IUXXpBBgwZFnTIvn0OenPUaz8p8gWqugpw/FvQI5oYq5j429ij3lK+hsz7KLPQpSCCqBAIQF1VS4bkggQYsAZQ3qxBTuIHcHEk2DFRAB4wnAECq9BEGGJU5glJPy1IlxSwBccwrDAitDA+79pNmk6EAwxgBeIPxRU46EqCzJ1yxRng3wKjWuSAXFbLIZdskeTa5fPfixYvNfPLjoj3+eGN5910Kb3z/tpoa0glUyf7710jHjuWBcZqBOPYR94GrfeNivqK+g/x27dq1U5tTk1QRAP1RmJQAEtz1MONefPFFIyL2B5+vJICUBSAOBjfy1c5EZ01koYqt3f/o19z1voDO+ecS+gF9C0Bc1BM7PBckEF0CAYiLLqvwZEYkMHXqVFNVE89roR+bx4TwL/scf/L/jz32WEakEH8YVGBEDsivR48e8V9Q5BMoiIRNoCSSLwMDG/ZbKWF/2lk+hUTFutQINhQaS1Zy+AEookS7zpmF4ovRREga65+w8KSqmgJ6M4aoILfzjV/mC9NkJZbZ1fU+DhBHeJWLuf3wQ5Fx4zaQli0brfc9334r8tVXIsOGwUQtfRSagTjNhju5vQDftRrCOO8olIEOEbcByqEXEH5PoQHCuQHm2DNp3odUY6f6tObmewhkHNm+9NJLRg/VCKznj5P9zd72NWcu+gj98xUojLNuwrNBAr5JIABxvs1I6E/iEiiHzUWS+XJDwRIfYEJfgMGIMvrEE0/IgAEDnHyLreiIQYqCjXJNmF8pCntuh8g/A8jqK9W/FOExJoBJjaFV+ePNYg6/UuY09zOA3ISdAu4Res1cWwW43HfX93nte0UzELdkyRJj3LsA4hYvrpKnn24srVqtD8Qx/9TkOOUUmI+lrybNOeI0A3HaE9PDbCOcuVxGG/oCTjqAC0ABzkkKngDMoZ8kBVTyvYCBlcqNW/qOXfeTMLhxvBBhEJo/EuAegJHu2qHnaoQBiHMlyfCeIIH1JRCAuLAqGpwErrjiCqPEwTSBBcKfeFb5NxTeiRMnCgnAR48ebUAhwilQIPkzTQ+sbxNDsmgUuYcfflj23XffkrvHpQ6gB+AA6w2vJl5vwDdX3k3CkHin1kqQhYQ7Z84ckxcsC15Jwo7ZT9pDhzEIWWcYhKU0ywLFwIPtwd6A+VEuEB2nL4B/7EOtxhlAIoayr0ZMfXMBE5EzzwW4/sYbVfLkk/UDcaeeWiObbBJndaz7rPaqqRS7cSHr0iVY2idh/3Tq1Emt/vHOO++Y0HrXdxfnJQAZP+gUFOMAqGOOXYYhs09JDdG/f//SJtCTT3HWIzONZ2W+CCkAojFXZaGlAJhMJAh3v4+NNYO+1lBJCD7OSehTdiQQgLjszGUYiSMJYNidfPLJMnjwYLnkkkscvVX/a9q3by8w18aMGSOHHnporAHZXFd8CDATRhSMH9ggGg2jWIN39PDLL78sXbp0iZRnx9FXJvoay4ZM9EsSfnmpIBZjJ+wUoxE2FMVPkmJzFBMBoCgOCK1AXLHx+fx7AATrDCq3n19+KXL33RtIixaN1gs/JSx1ww1r5eijG26OuHLlW8nPawfibJVnV462+uYCBx8hmJxpO+ywg3HGwQIrJ/QehwnhtdqrplZyDbv+7izk7LMywcHdtGlTbx2TAYhzvXrD+4IEvpdAAOLCamhwEsC7md/yK6SOGjVK/vznPxt2HIpcFkCDcicaNhZeyDvuuEOGDh1a9HW2Ki2KMUnmod7DHEojzwqMOIxcX8vBFxVegQcAQdu0aZMJII5wQgDYUplkpcgvic8AYlEYpNg6Yy9gGALcwSrq3LmzAeFQvtMwTpMYuy/vZJ9zpvvKJqhPTq5zJc6eXS2zZzeRZs1EGjde882AcB9/LHLkkTBhyps1zTniFi1aJDiTNO43nDAw4soBk8qb+fI+PXPmTOndu7dzRlx9vQI84FwABPzHP/5h/o7TY6eddjIfi7MOeMeCBQsMI11z497F4aM9UoD7dNq0aeqr2GpZS8ibdRMYcVpmLPRTkwQCEKdptkJfnUgABc2CRPkvRFmDgn3//ffL1VdfLZMmTTJe1dDE5IWbMWOG3HTTTXLqqafWKRLC3AAcMJA7duy4ltLuOiylvjkBHMErjvESmn8SSLraaFojJg8hwFpdVej4PWcNhhzh7oBFGIM+KbSAK+xZrcYZIb3IF1ahtuYaiKutFXn99Sbyf/9XLatXi1AxdautamWPPQjdLV86FIyBMa6RPYnhzh0WB4ApX2LhDUgAvYGw4Eqee7DiLAN53rx5hiXHWcy5pxXgjLu6OG8Yq3YHGHcq5772cdj5I/c0qV98LtZAep4QvRJ3x4XngwSKSyAAccVlFJ5oYBIAqDvttNNk1qxZpkoqQFxgxInJC/fss8/KiBEj5JxzzllnVQA4AH6h1JLvAvYbea8ANSvRABcAPmAeZaXBiEPxLKWKrG8yQIkm3Kd169a+dS1WfxgDP/k53WDKwT7g3KBgCInEfW3kuePHMkV87Wdd/dIMxAFqYRi7zJUIc6G2tko+/1ykUSORTTfVNqPJ9FdzKNubb75pzspKha+XOyO+FcrgXMb5wLkHQ58zHOcQwByFH/KLSuBsgdVfTqGvcmXo4vO+FwWIOkZ0dHKzZiVn8yuvvGJY9TDkfWzIOwBxPs5M6FMWJBCAuCzMYhhDLAkQXglbChYFlzlefstsQTEDwCEny9577y3XXnttKqGUsQZQoYd//OMfy4QJE+SCCy4wCinhHkcccYRJ/ksj5xvFLXxgHKBYM6++ehhLmULfvaZxxgQbAWOo3Cp6cb4ziWep9AvohhLN2QETFECLcwWjWYOhYMdA2J7G9vnnn5uwM5dglkY52D6z7pI6g5H1smXLVDo4ALO0sttdVR2t1LpGv3JZjMn1ODg/SGfBGY5euPPOOxs9EIcibGeAuFdffVV69uzp+qtTfR+MQM4H7QxA5gM9nuIcWWi+54BEV8OpHhhxWVhtYQy+SSAAcb7NSOhPYhIAnCE8csiQISbfR32NC/7yyy9XyxJxLUTYboBusLJQqG1oL7nifvrTn3oHOKBYw4qrK2TQtXzSeB/J/al2loUxYZhh8GgGStkD5D/EeLOgL6GnjCkpICSJdUbf2Ssac6whD851m0w6Cfkk+U6bOxP2sKuWJBCnOUecvf9dyTnN91Cxs3v37hVjmJc7VqqmamM/r1y50qTXAKDDuQIwR6oLGOmazvfcuYMFCKCilVlpx4L+gA7fq1evcpemF59njcGGq2Todn2CCECcF8skdCKjEghAXEYnNgxrfQlgrOHRuffee413kzAEGCt4B/mTH4xoQhUAPLQapq7mHqVtypQpJk8eoSUAJ7Yhn/32209OOukk4zX2rcG4mj9/vvTo0cO3rpXcH8BQQFDtLDIEAIBlq9qVLJAKfRBvPGFMGGkYZZwp5ELUapyhZPOj1dtNeCeGGSHA2hosLQww2MSuWgDiCkuSHHEDBw50JeZU3wMLEbZ5mnlWXQ5Qs+w5G9EnABO5gzlrmAvOG3QiTaAWei9nDbqv5oZuSvGVrOh3NkTa1zsY28lnRqvmtRz6HiQQgLiwBhqUBNLO9Ua+tNGjR8vs2bNNThIArIMOOkjOPPPMdbzbeLxvvfVWgfWEx3KPPfaQiy66yOQryW2EBt12223y9NNPGyCApNnks+Od+S3qO/M/B0uDqrFTp041SqdtAEAYvSS8Bsz0GXiA/UDejax4TJkD5gKPaaXy7rk8KFi7H330kbRr187laxN7F6GmgIeA9gAn9B1jxhrGPu+FYkLBGw/7AzBRYyMcmLNVYwEBgDgcGS6dPkkCcQAPyFojK1czGATDGyNd6zmjWfacicgfnQIdiL/jjAGYQMdiXtDTqGjuOygHi6x58+Ze5yyNcgehxwMO+cogizKG3Gd8KGZSX58DEBd3RsPzQQLRJRCAuOiyCk9mRAIYE3g4aVzoKFa2kqr9O//PMyRZx/AGBFm6dKkxvqMaTXhQDz30UKOcEcKJJxLF7amnnpL+/fvLH//4R6NYz5w504Bp5JkiDxteV4AuCh88/PDDa5Pz07dTTjlF5syZI8ccc4wBMR5//HHz/9ddd50JHbUt6jsLTSmhpoBwNPIuUaQBoO+vf/2rXH/99XL44YfLPffc4/VqYP7IIUI4T1ZaEuyZSsmGNQ4A5HOlSwwvFH1CksgDx/5l7+cWZsAgA1TUXL1Ne4VhzUAcYcGsMZcs16SBOPqsMR+fZjCI+7x3795qGXGw07t06VKp66bs7+WuQp/bZZdd1nsXThruAAAudESY3gBzhJv7losNIA42n0YgPVfw6OPkNtV87+aOx/eKzjafr1ZHQNkHQHhBkECCEghAXILCDa/2SwKWDUe4JQAWxg9GBYoUBnXuD0AOVQ8vvvhiOfHEE00+ClhsRx55pJx99tmRBgagRUgnAFYuWwMwCyALZttee+0lBx54oAH9Jk6cuDbM8/nnn5fTTz9dzjvvPDnjjDPM99HnCy+8UK644go57rjjzL8BFvB3FEDGBbuCcUZ9Z6GBvPzyy4Zx17dvXwMYWgYWYB/fzbsfeOCBSDIID7mTAExFFHsA2tCSkQB7xxZdwKDaddddDZBeFxsFdhAAaSEDLZkeun8rCbw560hQrrFZJ4pGdgQMZ1iV+VV3y5mHJIE4zTniuOs1FE8pNPcwZshbqzU0tZz17MNnYUCTjqAYmMj9wXnKHcJdjSOX+wFgjr/7zpjzQdZR+hB1PqK8y4dniJgBaPe1BSDO15kJ/cqCBAIQl4VZDGOIJAGbrBkAjDBQ8jtxwaDcYsTxJ6ATPyhRMEUAw4466ihjqN55550mJ8UhhxxS9PtgrxEWiYEOuy23EX4KUw522wEHHGCYbLmAm30WwAsjEwYdDdYc1ZWooJYbnvjEE0/I+eefLzfddJNhrtlqplHeWXQgOQ+MHDnSVEzdfffd5bHHHovz0Yo8i5y1htsVEhgeeRR5wpu1NwAIQj19CCfkDABg4MeyGsgTyU8xDzDG/ZIlS6Rr165qp4SzCpawSzAoTWGwlnCmaMx7hAOFeygqyzqKXAMQV1hKACmwgTQ230PXisnUd6ChWP9hX61YsSJ2tWDOVkA5GHPok4Cp/D97FFZpsfulWL/i/h5nHvqD1rPejhcZkiZFq/Mo7rxV+nl0JO1rptIyDN8fJFCXBAIQF9ZGg5EAShGAGx7KhQsXmpBTwDdAN4whC27BfoEtx+8o3mAvIPv5KALj4kLx58/8amEo1YBwAGuEu40YMcIw5Pr167fOqy+55BKZMGGCAd/oK0ocwMX999+/znOAhHvvvbdh7v385z+XMWPGRH5nlLHYZ37/+9+bCql47iZPnhznoxV5ljDgPn36VOS7w5fWLwEfWFjsTRhtJLBmj8M0jJs0HyYrBoG2ioC5swMIR+iVy8qdaa5/woYxcgnt19ZcslwBhQELmE8A5STmE9CTwgGdO3fWJmrRHJrKOQWbL23gxtUkv/DCCzJo0CBXr0v9PThE+XHBSCQfJ+H03IHt27c3bDn2bBphrEQ7dOjQYW26k9QF6egLkR2Os6yEpvoeuh2AOEcLN7wmSKCABAIQF5ZFkICIMUQB2vKLIyQhHFh2hJ7eddddMmvWLJMr7sknn1yPHfTb3/5W/vCHP5iQVXJpESYH4w3mW24DDCBkAjAOtt+vf/3rSO9EIYvT7rvvPjn++OPNdwEm+t4A4gAvtRov+fIlHIM1qpH5kz8WDHpAiGKhPq7XGHsF4AZDCNANdgAyzUIBjFJlpT28Vlvhj9x5wokCCFwqaMZ6Zi1zb9kUCzhtaPyJE4ffcWZEYXiWuoY0fE4zELd8+XIDrmq9y7QDcdxXAN1xHTX17QsL7pHvDMcw9xDvJ/dvUkXFyCcMiJ4G6KfhTPChj6wDckBSBM3HxlqkBUacj7MT+pQFCQQgLguzGMZQVAJcJosXLzZ54KCzw4wj3ICwTsImYK/hlYQJh8JL6Oiee+7pXCGC+UaOOEJcx40bJ1deeaU8+OCDBpjLz/11++23yy233GIYcITUDBw4UIYMGWLYbvmtU6dOhv119913R35n3EIGjz76qCnUACuPQgi+N4wXgBZfS8LHlR+edNYv1dm0N/YaXu00gG8bHkSOHthrfDehgC7yRQGE4M1mP2tt2sNrYUZgKGchZDvKGrJGOhW5YQJivHNnWTA5NzTVFhPhruPegzFHA5grxbBirbCHfAgpjyKr3GfmzZunNoTc92TuxeYC50epYHOxd6fxe9IosIfiOi/j9I27BKcIgDkAOvucfeoylJTCQ5wV2nPVoQtxFu2www5xROzls6TMwQbJj4jxpbMBiPNlJkI/siqBAMRldWbDuNaRAHnTCAdFCaF4AiDNDTfcIM8884x5jgsdQwYjA+YRHsOTTjpJzj33XGdgHCGjv/zlL41y9Ze//MUoWL/4xS/M3/EY5+cJgt02atQoA9TxLKEd5JOjaEIhII7iCrDror4Thl2cRq46ctrRF4xA3xtGqE2073tfo/QP5gvKZ9u2baM87vUzeIFh8LgAw+oaqAXf2c+AcACYrplvgHoY+D179vRa3vV1jn1C/kGYGKGlKwHuG86oYoC0zWPIGQB7G+AXwx3HUT5Lqr4ccYDfgAq20i/OH4BMqihGcVhoLtaQ7sy6/TbNbD7WLmsWgElrY5+yd3baaadUhmDTJpALDX3R5lTlnOCn1CrLOKXqKjyUysAcfQmOc84/jQ6BfBEwJ6wvX8NsAxDnaNGG1wQJ1CGBAMSFpZF5CaAE/r//9/9MJdBrr73WgFn8+ec//9kwvACX8NZipHMpcsmPHj1a3njjDQPW7bfffmWBcVxkv/nNb0woKt9D8Qabz+hXv/qV+X/6ls90sqGpsPYA6TC+Dj74YOHfc5sNTWUcN998s0R9Z1xvIsYAYCB585CR7+2VV14xxRpKYX74ODaMYECTLISmMg7CceKyMovNCwaLBSwJe4UxRIheFJCh2LsL/R5vNuuMwiyhVUYCAK0ASxqBRPKtYVTXxeazlbw5wyg+A3DG/q+vQmzUYg3cS4TFcc9ZoIQ9Y4saFZpNzUAcLCOtgLlmNh86FWkiqMCutXHG4DiqFOsWxxV9AJhj3wIIUugFAJ2fqBWjCYEkx6+LXHeVnEscR8gkC9EBjMNG41RSpnV9t2Vglwr++jim0KcgAZ8kEIA4n2Yj9CURCWBoDB061ORXA5CDzXXccceZiqZUQi1kpKP4UpgAAxtWWpxCDfkg2WWXXSaPP/64SegOYy03sfvYsWMNww22HPnMchvFGsgPhwGx2Wabmd+jgPGZ3GaLNcD44zNx3hlH4CT6xZDB0EMh9L0xhxjnSbKu0pQBChFKW1SlO82+xf0ulyGdGEjIhT1KIRZAa8CKNMJvLHOB/am1MReckYS3a2yEjBH6RuJzba0uIA72Db9jbbdq1SoWABAViMuVFesYti1Fizg3CZGDecN3556fAIPIu1KARDnzq5lVVs64K/1Z30PvKi2fUr6f/Up4JnqYZchyB9LYw3XlEpw+fboJgUzKMVXKWEr5DOciLQtOVpyHpM1x7ZQsRa6FPsNaY724jiZw1b/wniAB7RIIQJz2GQz9LyoBgDdYcFdccYX5c9GiRSYHHAAZlUYxRK3RboEOLseLLrrIhLA+9thjJQFxAAPDhw83FUZhGlB4gRC53Aab5uijj5aLL75YTj311HV+d+CBBxqFatKkSebfhw0bZnKzvfjii+t4NGHMnX/++YYNBysuzjuLCi/nARgZ1lhHAUwD6IjTv/xnyd1FGKdmkCR3THjECSvTCDjkz40LJhlrEBAabzLzXCmmIAy8/LDyctZt2p91CYqm3Xe+j33BHCSZvympccHYBFjHeAZ8I0QJ1jQGJusa9nHcBP2lAHH542NNwDKEbUO/MPrZX9xfGGS+n/2F5kszEKeZzcc6RieBiaW1oQcyDh8ZWDZ0EGcETDFYrhT3AkSnz7kACilQyDUc90zxbd44k2D1ZcHJylhgN8ZNFZPWnAQgLi1Jh+9pqBIIQFxDnfkGNG6Uk3333Xct8AZr5kc/+pGcc845csYZZxggzlL1bYl6/g2AjLC2P/3pT4ZxE9eLCDD2u9/9zni6CEvF2MpvXHJUO+XdEyZMWPsMxRuornrhhReaP2kUSwA8vOqqq+SYY44x/wbYd+yxx5oKlFOnTjWKSZx3xlkGyBEFjwYA8oMf/CDOx1N/FrAHucadt9Q7GvEL8XxjEBNuq71Z4yGOQcAehI0D6EJuGPaoBTHivMe17Ai7olCK1qY9zx39Zy0UOl99nxNYaBjKS5YsMUYzoacw0cphvboA4nLlxl4FMASYg4liAUJAOvJ+aTlfNQPmmkFE3/dglP6h+6BracjPit5jdR+qpHKWcKZwZ7J/NZ6T+XPEeYmDVSMzN38sOJJI9ZJ2Bfko655nAhAXVVLhuSCB0iQQgLjS5BY+pUgCGO8//vGPDZMIVhoNMAtmGaGiFHDIbxRKoAIplUvzQ0ajDB3vJFVXMVoA0wqVvUepIzx2ypQpctZZZ5mw06OOOsoYPFRXpVLqAw88sFZxQhEkxHbBggUmtJYcb7D1qLhEXrjDDjtsbdeivjPKWOwz9MtWPoNKn1/lNc670niWOUDpzILXFHnhOQUApfphFhpe4GLJllECMR5gCMGqoIgK7DMAgEqCb7ny1w7EWYCzUozCctcyIBwsEN8dA3acyJuzCfYbIaCkKuAOcgVouQbicueHHHGEMXP2cx9gRJKuwOYR8vmsxZEBeKixaQbi0IEIs9aYw9GuFaqN0qwjUtMa4v604eSkF7HAHDqp1tBOolzYy5qZ6Ln3gSUA+LiuAhDn46yEPmVJAgGIy9JshrEUlADeQdhpsNJOPvlkU3AAVhyFGGCr7bHHHgZg4sJZsWKFCf+cO3euueiPPPJIU201brPhovV9DtDtmmuuMY8899xzAviHgkE4En284IIL1gu3A4wZOXKkqfaKEQeQAWNu//33X++ror4z6tgwdq2hBSgSt9hD1O9x9Rxgoa0y5uqdlXwPQCxGjVblOV929QFYgCuEAwFY4PkmJNoa+5Wcg0LfTUVmAHWtzVbk5NzR2GBrsVZ8znFnWWXkWOOugcHMnxjIMOIKOYNKnYukgTjuThtGZZmthMwDtnDXEr7HeHzLZ6kZzNLM5gMI4ozUHJqK7kPDEaS5EZratWtXk1uOaA/0OUBG9CTCzrWEnNtCFfmpXjTODc4N9oh1cvs2Bs54wFsta8M3+YX+BAkUk0AA4opJKPw+ExIAwDr77LMNeyxOg3VDbrTQvqeoIwuqb3Xu3NlrsWAwomwWYiN63fE6OoeRi+GelfHkA3GAbwBvKHywm/g7Yw1JgpNdrQAm5KDSaij7DMRhxHD34GDBiAegwui1jZA3jJwWLVo4m+QkgTjWCudQXXuSPczvGROMc0B0mJawu10x/koVlGYgjjWuhfGZPz+AWLD4NVeWJmcw69dnxmeUfQEQh5PXNqtTAMwxRtitgL44+3DMVHrPRhmT9mdINwIYR0SMj82y9QIQ5+PshD5lQQIBiMvCLIYxRJIARsK4ceNMPh6YCVwshC6idJAjjj8xHPg3SnVjbPAnyW1DWyMBZENeIwpQ+G64o1Ayf1rDkfLXHF5TQOFu3bplYjkClMKeQdknRwpsVIA3wt40MQ8oCuJrfpcoCwWwCAdFKSH4Ud6f9DMYkBgzvoS+YdwStknoKTmMYG0g40LgFSxXWjk54XLlS+HE1au/kVXLp8uni2dJdY3IVm17yzY7DZLGm5TPeIxTNdWCkDgP2Ofcuxh1AHOElrsac9T1NWPGDOnfv3/Ux716Lh9A8apzRTqD3gVjEjBWa+OuQj90CZinLQv2I8xEGHH1Ne5h5gsHAmkwcGZyTvl0JxMeT1gqfdPeYHOj2/kaYcKZja1k82hrl3fof5CAbxIIQJxvMxL6EyTgsQQASmAq/fWvf5XBgwd73NPsdU17dcvcGSG3FEAFTA/COgEpMMx9yfsWZ/VozxGnHYiLM1dJPWtDT2EtETbF/0cxFMlbxpovl+20erXIvHnVsnD+x9JowV3S5NtPZYsOG8r/tvlKaj7/QDaRTaTTQZfKRv+zJh/qZ5+JLFhQJW++CQgo0qmTSPv2tbLRRvVLCOZGbmhqHHkSVg/zBpASViAOEu4S/p6FBPJxZBH3Wc1AHIa8zzmwoswFIDJr1GUIeZTvreQzzJlNW7Bo0SIDxuFYIB1KKcXLXI5l3rx5Btwv99x02adS38V5iGx9zXcXgLhSZzZ8LkggmgQCEBdNTuGpjEggqlJon+NPLklYDhgS/ABGaQQMXEwhyg85RR588EE54IADXLwysXfg2UWR1OzFzhUO65C8d1qrpsJcwsuOMYMxDxBHtdG0mTGuFxxAHGyyhnomuJZn3PfB0GUtFSv8Efe9UZ7nfuA8hEGCoU5BoDghPBRfYd2UwhaC/bZoUbUsXQqoVi1bbimy6ce3SdNPV8l/Nvlf+fKzDaTplqtl++4fyzdffyRbfLe57HzENfL+qiqZOLHaAHB8hvd8+CH5r2rl0ENrZbPN6h55Ls7gSgAAIABJREFUOUBc/lth18EGwRBFBoQucj4gxyTOBMCEDh06RJlW756ZPn26DBgwwLt+RekQ5z73Fvl4tTZyOrIuNaeFQIeFvV0qox7dl7OWkFXCztmjsFvRr9JOH0GO4h133DETjDjf9wR3nHWU+t7X0L8gAY0SCECcxlkLfS5bAvfee6/xxtuKe+Qx4QfjgH/jT4oh8G/8P0oIigz/hlKstcJguYIjVOHVV181VV0PP/zwcl+X6OcB4pg3jZXOEhVMii8HCGUPLVy40AAUeH3xqtNQ5jG+teehIQRaOzhPqA+GjcaGoY+hnFZ4MPcAwBvrmnAi5r/UROfFgDiYbt98A0gm0rjx97OzcqXILbc0kVWrquWLL2rlvfeqZOONvpJ9N7tFWnXbUKqq1jz72UdNZMfuH8vmW30jX7/3jnQ78Gp5+OnWstFGVbLhhuvO9scfi2yzTa3sv39tncsAQ5wwYNdhVJZhA1jGvUwuLnImAX4A0rkAuTXniNO4L22fbaXdUgEgzWP3qe/osRQhw/lVbuNe5ywARCeVBHuWEHTYrejGpARJsnH+Et4ex+mRZH/KeTd3F/LylW0ZgLhyZjd8NkiguAQCEFdcRuGJDEnAKvwwWAiLq68BEFDJiMueH3JkYHyNGTPG2wpHSU8VwAmJ3anwevzxxyf9dWW9H5YM89W2bduy3uPTh6noaysW+tSv3L4AVKCYA1CgLLdq1cqA2fnVXufMmWNYEkmwX9KUDeA8wIELsCDNfud+l+bwWuRPOGiSQByGJ4xUvos8jdwLgMrkFC2nWTZYbgEH3vf55yKzZlXLsmXV8u67VbJ8ebVsu22NHHDAd7LDDjVyww1NBJAORtvixdWmC99984FssHSO9O2zXJq1+sz82+pvqmWTpt9J264fyzfv/1N+2OU8mTynq/wXC1+v6++9J3L88TUG+KtUs+FwnBeEssKeRT7Iu5wqiZqBOM1sPliO7E/f76361jusV8IgNeebTbJ6LWcjYfbsV3KJETnBvmW/IjPXdzwgIOeDdicea46wZ+4R66Cs1Llb1/dyHmP/uJ5D38YZ+hMkUCkJBCCuUpIP31sRCXCpYDCT4wyQxhZrsIUZuHC43J977jm588475aqrrpL99ttvbflulIwsXP6lCn/33XcX8tXceOON8pOf/KTU16TyOViNKIjaK51pAEwAuPGIY6zgrUbpx3C2jJZCEw6TibnRvp8ApkmArdk7rxmIg/VKzsEkwsZs2CvAMsxa+x1JrtmvvhJ55JHGJo/bjBmNDBC3wQZrmHHkcNt+e/ZalcnpRnvttWrz++9qPpdN3vybbLZ5Ixm4+xKpqhb5z7dV0niDWmnX6yP5+r23Zeuu18jUOdtJs2aFj+D33xcZMqTGAHx17dlly5alWjHb5pazOapIOA/AD/MmDgAOMOCayZfGRYbOAgtfa9Eo7gbmMGmWVJJzQRQAe19zJAQOMsCyNMbAnOMIBZjjO2HhcZYC5tSnE0SdQ+4rnHhph8RG7V+c5wDZASyTuL/i9KOuZ5lL9m6Sd56LfoZ3BAlolUAA4rTOXOh34hIYPny4USJ++9vfNlgGXL6QDzzwQHnyySfl6quvlvPPPz/xOSjnC2yIcRYqa1k5+AiYENYIswfPN57wfOZbXXPIZypRPbGcNVXosy+//LJhY2k2CgiP0cocxVAoxLgsdZ4BDQCTAZIB4ABCLDO61HfW9blCeSxffrlaXn65kbz4YrWsXEkhh+8/DTj36adrCiwMGFBjwlX/9a8q+frfX8rW1f+Wqn/PlM+/2FT2PHCpNN7wO/n0K5Ft234pW2+1Sjap3ViaDxohj45vVBCIq60VAYg78UQMr8I9dpkjrhRZMhcA+Jwd/LDvYCmyBjhL6qvsBxChsfIfY6biq9YccThlYMX5CjREWYekViAEM5+5GuWzvjzD+sf5nLY+ZJ3fAHNUR7ch/ciTPpVyb86cOVN69+6tcj/nrwfOM5x4vgLVAYjzZQeHfmRVAgGIy+rMhnHVKwFbESr3IRtaZpWDxx57TK644gr5/e9/b5KxV7pSlA9TOmTIEHnkkUfkkksukcsvv9yHLtXZB1gyhEdqTdBdaGAAA4AClWx4tpErRjkVTwnxJrQibugCCZcpPBEVuKvkmOv7bvLukDvRV0U6itxgG8WdvyjvTeMZ1iPhPbASy2kYjLATAA3YY4RUJ8VyhOX2979XyxtvfCyNGtVIs2b/I999VyVNm9bKkiVV8u23VfL0041k881FvvtuzQ/g2xqwrEq+/lqkX78a2XrLb6XF0ukii16XRo2qpKb2a2n84TJZ3fcDeW63DeTrDb+V5m1WyHabbChDu10vrTbvIg89VGW+a9NN15XWv/8t0rZtrQweXCvki6OIAyBgbm66SgNxheaXcx5AE0Yuc9a6dWvDvsln3mgNTWVdEtqpkc3HfDE/ADBJho6Xs++jfJZ7lzuO9CRamy+5+mzxM/4k1QZMKwBOcpRGTe8AQ7Rfv36ZYGnh7IfZWwogmcZa5Pyhb4ERl4a0w3c0RAkEIK4hznoYc1GQjktnwoQJcumllxogjpDMAMSJnHDCCTJ27FiBLXjdddd5vZIAiKjK16lTJ6/7GadzgAR4tKMqrHHeXd+zMI4AqC1YAVsIhkM5yuO8efOkXbt26kOHORdcJZR3NV9x3+Mj0zLqGGBYvP766yXloIKNAKgMqwpQmf93ETpVX9+nT6+WZ58lxUGtLFv2pbzxRhNp1GhD6dChRtq2rZGnntpAampqTfgpgFx19RpQDBBuww1rZdtta2XFiirp3btG9vhusjR5501Z/Mk28uGH1fLVlyJVzZdJu52flne2+x+p7bSBfLfZdlK1bXPp1PdbOabREGnyydamaiqhrk2brnk3TLuttqqVXXaplZkzq+SLLyiQsAaE69WrVrp2JaWDmBDDzz//Qrbccouo05PaczaPHwY+4aukJiDkyzpiYJVpDO9kXJwxWoFyAFIKfOCs0NpYS7Aptc4BckcfIqy8XIeF6zmEMQkYBUOOfcv+BZhDv6jLSZeFO9fKET2ISALSevjYAhDn46yEPmVJAgGIy9JshrE4kQDGBvlkRowYIbB2/vjHPwZG3H8le8YZZ8gdd9whw4YNk5EjRzqRd1IvwbjGE9++ffukviL1986ePVt69OiRmkEAm4HKjijL5MgCgHPVYDG1bNlSNcsAWbDGygUlXcm01PdoBuJYmzDZoiaDtxWwMeYWL15sDEAYcGmELU6bVi2jRjUR8sC9806VAduaNAFcW8N6A5zbaisxQNu//11lQDKKMgDGkQeO/99441rZdNNaabPVZ3LIJ/fJitpm8vnnVea5L7/+Thrv9zfZ5JtNpdkmq+XtEwbJZk2rDND2Py2/lF3bbyiH1x5uQLilS6vkzTfXgG0239xjj1XLD38o0qTJmpXE9737rkjv3rXSvDkhkjWyfPl/ZOONN5KOHWulR49aA+b52HJzYs2fP98ArrBuOMM0MZsYBxWm+/fv76OYi/YJ5xFgHGCD1gZza/vtt5fNoagqbYBd3FWdO3f2dgSAPqwXcssRao5DHGcqwFxuZWrNVb7zhQ+jHodk2iHDURcB9ySAaNrO36j9C88FCWiXQADitM9g6H8sCdh8FRRjIM8EHk4AG1gV9of/JxQBBeCAAw6Qa665xttLMtbgHTx84YUXyk033SRDhw41gFxo6Uog6cIA7A8UdoxWwrwwAmkYIEERKzzXePEJG4NJpbUBxBF+r3GOozKGYIQAKnO+Awq4BJXtvH/xhciCBdXy+uvVBjzbeeca6dixxoBoc+ZUy803b2DyvgFwffBBtaxeXSNssW+/rTaMNyLfCE+lEYJqQ1L5fwo1fPvtGmAO8sSPdl4i27zyrKyoaSZV1VVSJbUi27wvNbu+IvLxFtK20cfy0l4DZPueG/MbeXelSNN270nf10+Q7ZttIp061a7NP8d333cfRSEABtddxfxu/vwq2WyzWtl886/ks8/ek+22ayMffbTmuSOPrFknj52PewBjkpxyzD3ME+597ncS12PgpwHClioXzmAcMITihVYZCeCQBcT1FSypjFSS/1bWPiAuwBysRJwt7FuAuEGDBqm8r/KlhpOAteWrcyAAccmv8/ANDVsCAYhr2PPf4EZvEzaT++0vf/nLeuNHQcf7g5E2ePBgOfbYY6VFixYNTk51DfjKK6+Ua6+9Vg455BAToupzI5zy7bffNt7GrDQ884TalhMSWkgWKLnkOOP95MVi/WOgJpkXhLxHMMnIvaO5oUhT6EDzOMiphSGgEYjjTCcROOzK/EaoKb9r3ry5CavEqKCoSBLjhMH20EONDci25Za1Blj76KMq+c9/aqVx4yp55ZVqU+GUYgtffUXYKZVQKTQBCLcGnCMPHGBcVVWtAOoB4NkGQMY7aYB1p+zxhrRd9LS8tLy5YbWxjT7ZeKV8u/M/pPrTLeQHn3wss/ceJDv13lBWrhRZsqRaNmnzrrSfc5z8cOPNTMGGQw+tke23X1MA4s9/rpZChFeAwvHjq2TAAIC7rwxIb9lNn3wiss02tXLQQf/tmKcbGbCWPHL2LrcGPuAcZ1+3bt1M+CR7mJ8k1kepomHd4oDp27dvqa+o6Ods5UytOe4QHgV5YNZrPuNhmpEnTrs+C6sPBjQ6EGG26C3sb1+BrIpuPgdfjmyRsU9nooNhhVcECXgjgQDEeTMVoSNpSMAy4vBwLliwwBhlFnzjIudyB4iD3aIthCUN+V1//fVy2WWXyb777isPP/xwGl9Z8ndgwMBWIpQztPUlgHwwPjGsWfM2ZCUtheu1116Tbbfd1uxBzQ0PPaClZkMAsApQNEngNak5BkihqmH37t3NV9jCEwCk/N2GniaZ3wmAbOzYNSDcJpt8P1JCQJ94opHU1BBqCsut2jzzySeEktYaMI7P8nv+JA+bBdzWAHhrwkP5ofE7iJc826Hdh3JY1Sh57esfyAbfNJUtaraUT7/7Qr7ZbZps/FFTqfm2Vv7Wa6C0a1cjCxdWS6Mm/5EWHT6VDrNOli8+qzbfRUjqmWfWmH6MG1cYiHvvPZHnnqsyRRw23/xrWbXqfWnVqvXaQfL7YcPqrrTKg+/L+zK3aq6srFopm9ZuKt1ru8v2sr1US3VSy2Kd9wLAwoCvr/LoO++8Y6rlEupMvkDORPZDpdly6Cyw9UnorrFxv8Cy1pwiAuCW8yOtuzGJeWZtwy7TXryK/UBKAetgJX0GDDnmCJARJ4GmfM7cXeyNSp8zda25AMQlsRvDO4MEvpdAAOLCaggSqEcCFrgLQlojgVGjRsm5555rkl5PmjTJa7FghOPJpsx9VhrGIgBxqRU6UVYJu8aoAACzAEwlKpeSYJ/cXIBYoVVWAnPmzDGsIF+NgfqkA/MVwJ0chqxn/h9Qjv2f1nhWraqS++9vLM2arcsM+/vfq2TatMam+7DbCOeEOff112uKIXzzDYn41/wdcA1GHH+34BufgekGKw1Qz+aKa9JpsWx28HPS7p+vSddl78i//3dT2ViaSqOXd5OaNvNli+p/ydTWu8v7P9haqhvVCv9t1m6l7LRid9lqZRfTH/LU8d6zz66RLl1q5U9/qjYgX26VVJ5bvlxkxoxq+dGPyBW0/ky8/77I8cfXmH4WaguqFsjkqsmymWwmTaWpfCPfyMfysbSpbSMH1R4kjaRR4os/ChBnO8EZCQAHWw6Dn7MWAx9WZSUa6xgQBaeFxsaehI210047aey+6TP3LvJPqopyGoJhHgjP1wyI1icnHIuA6DjRZ82aZZzq5JZLsvq1i3nzvQIsNlAl9EMXsg3vCBLQIIEAxGmYpdBH5xKwrAmMtueff15ImEroCp5nyw4i/BLQQ5N3zbmg8l5I4YpTTz3VGLrk2fO55Ycj+dzXqH0rJQyS9UtDEcegAPyCJVTpnGawJNhr2pU8wmSQp2ZmX9K5B6Ou7zjPsb8JteIMhxVh2W/kAEubufL221UyceK6QBwg15gxjU1f+Ds51j74oEree69KPv54zb+RA86GmzJ2C8jBfCMXHKCYBb941jy/2adS1XOubLvJZqYIw441s6WfvCJNf/ClAeq+eHmwTOlfJau6fiXVUiVffS2y1da10u6T7tL8n32EbHK20ZdjjvlODj64Vl57rUomT64yhSN4j21LlqwpHjFoUK0J42Tf2vx69IfwVBhxuZ+xn/1MPpO7q++WZtJsPfbbSlkpe9buKZ1rk08ezxkIAx6mW5zGGiOvHEY+6woDn31Objl+kmRZ2n4ic1IGkMNRYwMcQX6a86tRLIP8ZKU6wHyYN8BQ1hL3v+bGWsLB2qdPnzqHYfctYdGA6OQG5a5gzwLO+TSP06ZNM0zdtO+sqGsgAHFRJRWeCxIoTQIBiCtNbuFTGZAAFzPA0r333ms8afmNXFzDhw83ueICGLdGOvfff78p1NCxY0dTyc33xrxqB3pyZUwYAx7eKNXb8H4TFkQ4CmGnyMGn8Brmhv5oZhkwNwBxABNUedPaGAMJo9NikJUrJ9Y0la1hPZAbDuMq6UYeNUI4qRJqCxoAogFmzZtXLc8+20jat/++cAFFGZ56ak04G5/bcccaefNNqgBWyZdfigHjALJoAHAAWZYNx580GHKAcfw/7zDPd3xNNmz9nrTcuonU1oj8a8mmslHz5dKq5XKp2eZ9qXrpcPnfj3aS6h+uki3avS8fvN9Ytm/SQjb6bt3QRvoF2+2002pkn33WfB9FGWbMWFOB9eOP1+Sy69y5xnwvzL3GjdfNEUfuuX79aqV798I54l6qeklmV82WrWV943+1rJZv5Vs5qeakpKfO2ftzc8vB8MK5ATCAgZ9UbjnOSRifWpnd9B/9SWtoLYuHYjY4H13nZnW2MCO8yDrkNKYfyB1eKcA0Y8dZAzBHCgkcZ+SoZd/a9DQRRJjII4CE6HQ+NkA4WpZ0aB/lHPrUsCUQgLiGPf8NevRjxoyRX/7yl8ZLfvrppwt54yhA8LOf/cyEptx4440mCTt/kmg4hKmKTJw4UQ499FATBgY7y/eGAl2f59T3/uf3D8MPZkZd+chIug9jhTAqgApYCIAUaTA34soSRdjmYoz7WZ+eJ3wNGacBBvk07jT7AtsNI4r1j9Fi1z8GAuAIQIXNEee6X4SdArLBCiOsFJCqR4/vZI89vpNJkxrLu+9Wmaqhc+dWm/DRli1rZdddawSW3Pz5AG/VstVWIq1aYQyK/POf1fLmm4Bxa94lUmvAug03XAPw8W9rqqXWytZbExYk8uGHVfL554BxVVK922zZaLMa2WQjkZovN5av/r2xbFDdWHYZ8prUtvyXtFzdRnp8vKesXl1l+jhuXCNZtapattjie7CMuwwb65//rJJbb11tijRw59GPN9+kcEMjMxZAx0aNCJ2tlSZNyGn3jXz22Spp3ryVAQY7dKiVPfesLciGY2SEpL5T9Y4JSS3UVskqObPmTNdTtt77WD/z5s1zvkY4bwn1J4yVEHvWJn/nLHDlYAB44CzHMaixkUMTME5zsQbO+O23316No6LQOgHw4axEp9XciFqB3dqrV6+Sh2HDvdmrMAUphILexJ5NClCvq7OMx9f8sgGIK3mJhQ8GCUSWQADiIosqPJgFCVgwjRC9s846ywzpd7/7nWFVkMx52LBhBozr2bOnTJgwQaiuyr+dd955a5OAZ0EOpY5h8uTJss8++5jwBowD31vWgDgUSIzlXK82a9omrOd3sLMA4nwNdbBr5q233lrrnfZ9HdXXPxRpmGSuDO//z955gElZXf//zOxS7BoJtgRRo1JEUQQEhNiNBXsUYrAbK/6jUYkx9vizxGjsvWOLxoI9KoqCBSwoIFgSFY0FUYOCBXZn/s/njgeGYXbn7XPfd+99nmGXnbfce24753u/55x6yOLNN980hrJNLjuMa4AOwFrqh4xhMgAul49t5gShBcIYZi3J/MsvScLQzoBg775b8tcEwHrvvbxhtK25ZkF+8pOiia227rrNMn16g2GSrb120WROnTSpwcRig9n2zTelhAwUnoub6oIFgFhFaWoqxUmD+dbYWLoIhlzv3s3ys58V5MUXG2X2bBIHiMzf7FlpbO4gxc9+Ks0LGqSxQ5P8dMVG6fjTb6Rhtdmy6UY56fP9QNlyy4J06VI0QNyLL+Zk3ry8cY/lGRDAARUB6g47rNkcMvEBKLzxxnZC2EZAwXweal7OgHK0a+utv5eZM+fKaqutLOusU5RaRMTJuckyITdBOkvnJURMrDjeOaI4IvYhDwAxceJEGThwYKzvYh3mgAHQGJCYREG4TsOksn09jkswZLlEBgBZrtRPAmSQp3CImubCes+eG1UGW7UJCE2j4Wlgu8KW411xsiB5NzHiiLlsY1HZ2KQX2CgnVycngTAScEBcGOm5e1MnAd1YOB0fPny4cT09/PDDTTsmTJhgQLeLL75Ytt9+e+PWd8QRR5gNH/acxpVLXaMjrDBg5aBBg4xMUFpsLwBxxNbJihH0zjvvGBdIPpzmMkYBJlCuYR2kyf0HwwDwql5B0KMau5yo0w5bT7W9tBM2MO7mNrigsM5ivJdn823NnSrKpCy4jM6YkTfupjDIYIyVmG0kNykBZf/5D6BWzjDkVlmlKL16lVw3Abf69WuWOXPy8vbbedlyyyZ55528LL10UcaNazBsNgXiuB7Qa+5ckjWQNCFvmHAlV1TYZ6Vrt9++WZZbLifjx+dk5sycLLtsTgrrTzcunfm5y0muXbMA262+cqN0G/y5fNn0P9liuU3kmAOWMXWl4FJ7110N8tFHJUYfoBpJIKj3AQc0y1JLLRohTz+dl9dfzxkGn7IhdO3k3k03LciAAU2eQed5Ms/EiOsknaRRSkkrtPw391/5VeFX0q3YzcsQDXVNUkCcVlIPR5hPuH3DmmPNZp32GyuNdZ2skJrVOpQg6nAzjDjkn2YAiNiA6BFpduvk4AtmfJcuizIe12E4hH4loC5hZeJioLOf4MrKvMPrgz7n4DkOrxg/SWRCCy7AAxwQF0Bo7hYnAZ8ScECcT4G5y9MtAd1YcGUaNmyYnHvuubLLLruYRvG3vffeWy688ELZcccdDdDxxz/+0VDW77nnHgfEiRj3HoIWY5whF9sBLuL3wKSxvZ5eZhVjF2aQxiKbO3euYb8RLy6N7as09L3IwMZr1AW4c+clWT821rdanQgGTza9eoGJGD+stxjsZCfEeEeeXk7iGUeAobhs+y24VwKWTZ1ait0GWIU76AorFE1m02efbTC/4zYKYAUz7j//yRvADCBumWWKsu66BQNcAeJxLwDXrFk52XvvJgPOnXxyB2nXrsSa4338DTYc1077aI7kV/3csOVIrNDh+xWk4etOsvaaRZOFtLkZ91XcRXMmKUJjY06W/+l38nHDfyW/9PfSoUiyk6J0aGyQnrvPkNUbVpHOH28kBx7YZIBDLYB8AIhvvQWbVqRHj6KsuWaJHVderruOmI0lQLC8lNiJTfLNN1/K9tt/Ir169VroylpL5v+Wf8tD+Yekg3QwmVOJDfd17mvpWegp2xS3WSx5RK1nBf0eg5fxVa85yvgmriEHJbB5ND4VrJta8T5hhbLuw9J3pT4SePbZZw1rKY377KI1YL6pf5qZ27QFvROPFr+JV4KOHAA5gD/AOJjXyBAQMEz2eq0L+imJknCNtbGw7gNExskKtLHdrk5OAklKwAFxSUrbvcsaCbz//vsGgNt3331l1KhRpl7EAeFv5513npAxlXhE/CSj0d/+9jeXsEEwWt8RaPuUWbNmWcGgaW1QYdCjMKUlCH1LbeE0G4YQShHueWk/1aadBE9G0SO2UppLFlxsAQkABJKcJ4vcIb8y6wrGjbqe+hkPPAdXQEANPwXg7K67Gk38tQ4dijJhQqMBqXDJ3GyzJll6aZEnnmiUd9/NSY8eBQOMAdbhkgrbjN8B6QDUunYtxVwDqNt222bDqPvtb3lGUS68sJ1hoX3xBQZNUdZYo2iSOxRW+0heLr4m7ZtFVmpYVgo/LCeFjvOlnbSXngs2kBOOaWfcW2Hk3XFHCRkjvhxg3gcfN0lx9Y9lwVJz5LsvO0qXjb6SbXr/RLoWu8qsz/Ky1164tFZPoNCajG65pcEw8zRTK7JV18rp0z+Q1VdfSg45ZNmFhhlGabmrfEtAxRyZI1NyU4RMqcvIMrJhcUNZQ9ZIBISjvQBxHFzUAr38jJ8w1wKuAQziyrruuuua8a/xPCuBErK2wqrDzTWNBTYgJc3ZOgHihgwZkkbxL6wz+63GQEtzQ9ir8MaoB0NUD33YbxjPzF/0YPYu9Bi/ICfP4/DJ731J9Z8D4pKStHtPW5aAA+Lacu+34bbDoCBG3FtvvWUSNmy99dbGHYr4Z6eddpo58T///PNNmvTrrrvO2hOrpLsQYIt4ehTATNtBFJtc7vz0FaewKHic/jIWyYCKO7Bm700SMPFTbz/XokxzIpxmlyXay5yAueUXCPIjq7ivxdjXrLpxvwtGEKAy45u4UWStA2AO6vaFsUAG51pJWQDKAN9gewE0PfRQg2G3rbxy0YBtMOPatSu5bi61VNHETyPG22uv5WX55Yvyi18UFwJxMOTefz9ngLLOnYsLQa9vvhEZMqTZuKoedFCTedZll7WXVVctSKGAG2rBiPepcSIzuz8phTnLS2M+L4Viswk+x7ye9eU82aJ/Uc7dfdOF8fAefzxv6ggI+OabJYYcDL1iriAdl2mWPXfNybLLlHxRyda6//5NNeO3VevnqVNz8uijDdKp03zD4IIJAngFkwwG4G67Nct661UmfSixBwE0YditskruxwQPP/rGxj2gqjwfuU+fjjuvGCZit24LZObMSdbu44ByfDDwWe9xIYRFBziHoY6+EpcrXtzdg57A3E7z4REgVpqzYtPHHDQznmAcp7mgEzFXyPJd76JbsB+YAAAgAElEQVRZlJm3rO09e/Y0bD3WTD619jQOOdgHVaeud3sq3++AONt6xNUnixJwQFwWe9W1yZMEiHdGfDg20AsuuMAEcu7Xr5/JfMaJNZvryJEjFyZ18PTQjF+EEqQKKe4ytioQ2g24GxPbg4DvtheUOhQzlDdik8DkwwAuj9uFYYxyZAuzI4xMP/nkExOHJe1Z3MLIwJZ7GW/0Q1QBsCvbxdhmPWU8ExsQoIHxHZXLS62kLG++mZcHH8zLO+80mPhuq6xSMMyv9dfHBbQoTz7ZIDNnlpIhANgB5Gy+ebNx3yRjKplTN9ywYBIvkAUVMA9Mbd48kfXWK8hKK4n5DgCPEEz77LPAgHPsLaNHN5pYcDDsSoBjTl789AN55csPpUO+vWHbmRhzP+CeKvL9/9rLOiMek71mbSsb9tzQgPFz5hTlscdWkYaGvMlk+tRTeePiyrN+8YuCyV5KgZEHyDhiRLPvoUUfffjhp/LIIyvL3LkdZKml5smKKy5P2gjT/tVWK8o++zQv5rba1CRCXDkyxpZczQEIi7LDDk2y7rrE1FuUWCYpt75PPxUZPRpmX9GAlTAIv/22IMsv/5Yce+z6C2Pn+RZQQjeUkmZ8ZQ66AMiJoalucLYyZ1oTDYxhAGb0qrQWwJJ6uTVHJTMOnTn0YCy5Eo8EmLsc6LPXAZ7DYlXWcLWwD7B0AUjjyvgdtpUOiAsrQXe/k0BtCTggrraM3BUZlsCYMWOED4y4PffcUzbaaCPDbCFeEjHkiAviJU5RhkW0WNMAilQesAVxq7G5cDqJC0G9Yl95kQ0K24cffmjYJ7DDWjuxhn0FUyLN7AKVCSw/xlOaXZZoC+wulO00G2pTp04NFEi+1vgGiMIApK9ZV7t27RqL+yvM14033rhqdV5+OS/XXdfOgEmARHPn5uT99/PyxRclEA3gCldSXE/VJRPmHNlQN9igYOLGvfBCg4kHR/w0wC68YLt0KQFwgHKzZwPK5WSXXZpkxx2bpVMnWHElMOzTT/Ny220dZOWVc7L00iWW2FvLTZLHXv5aVi6uLOtu8J3kG4oy/7sGmftFR+n2y49l2c2myRFNR0hH6WiAOMbYzJlz5OOP15Evv/ypfP31Ann77Y7y858XpWfPEghHJlbqMnx4s2c2nMZMBRzVuHydOq0hr766jLzxRs7EqCOr68YbF2XgwIJx2y0vY8bkZfr0vElaockhAL6Q529+Q4D+wsK4WkmAcrz70ksbjKtxeRIK+uLll0m+tJr07u3fZbfWOI/re+oNKMc+ptmZmUfsE2lhRQNKALwDAqW1PPfcc9ZmtvQqU+Y4B3hpZ/YxngCobdeB2PtY83DNBozmoIM6A0hr4jf2RQB37A4bC/sDczeNBwA2ytPVyUmgmgQcEOfGRZuVABsjyiwgCD9RcMmOhcKIUU28IorLlrr4EGFTRnbETSFxgyv+JKBxRjCuYTugsDHGGHe1WCOwyIhLAqCRhaJAQJrbwgk47Ugz40PZrVEwLWETM7YBWTnQAEggkUItN504xgDMtrPOaiczZjQYYI2EB8RZw7UUV0sKrqlgZt99l5PVVy+BNLDb1lqrKO+9V4ofB4YAuITrqXEJLYpsummzuQYACoDvV79aIKuv3rSwGRgwfGg3YN5jj+WN+ybXz+n0b/lyi/tklc97yszXOknzgrws2+l7+cXAz2Sltb6SuTJXjmw+crEYahpTb+7cH2TatBkyb94P8sUXa8g335TWgp49C9KrVynJQ60CqMNaQj+xhlNH1vXyPoLtBmsQWVUmb+D5AH/XXNNomHKVBfkB2h10UAmM1MQs+lPfoz9rrXu12qPf4476z3/mF/Zj+X20hTYdfXTJPTgthYMakjvQT8rUX2ONNYQkMTCK0VtwW42KXRq1XNjbykHYqJ+fxPOyAMQlIack3kFIC+aBDa6pftpLGA49yIbBzYE2+y3z1lavAHRTbCMHxPnpaXetk4A/CTggzp+83NVOAm1eAhjVGAePP/64tTF3tJMIgo2hYsMpsAY9x10WwwQXEQwojHWvhbhNGF8AeGkvxHkBpLGdVVlLzlliKdZqa0vfA8xrQHxO/xnbfOJS4HELBVQD1CJZ6muvzZClluph3BBhZ/GhEFft9NPbmyQMsN3IGor7JNcRbw3XUuK/MZ0++6wUTwwAiesB5V5+ucSU69695Fqq7qgATTvuuEA23LDZgDs//3mTrLpqKcMc87kl4OF//yuBQcut2CzXd7ha2kt7WUqWWkysn+Q+kV82/1L6Fvu22h3IHBYtaxtrCvJnPWkp2yyACNezfnM98T3DuAe//jrgYoNpd7UCcDlyZNNizDSuqwTlvCZ88DI2x47NySuvkNVw8as1a2qh0FlOPBHj0svT7LiGvRbWTGWWSPpT42UR+4v+ZC3STKxRgZthpfDuu++abLFp3rMmTZokffu2Ph/Dyinu+3GBZL4HyS4dd938PJ+DL3Qp4oumtZSyUJfC33CAh56KXsf6zfy1xQuHdZM9PC3s27SOB1fvti0BB8S17f5v062/++67DaDEhgjVHZYAMRv4CeuIv/EBLBg7dqy1J85JdyKuk2SUve+++4xLr80FBQdDtV6B9DGWkRXMEwx0XBA07obNckuiboCKuG3AmkpzYY1AYcUYTmuBZYCB5jeWIkAA97KOwlDAkIgbAJgyJS9jxxLrrSTtL77IySeffCbdu69igDnYariN/vrXxDzLy0kntTesN8C3115rMPHfuA5Q7euvcb0kmH/BxFzD/ZTEA7C8YL/BiNtooxIjrrx8+WVBZs5skJ13JqZjTopFgHWRX/+6IF6TAM+SWXJXw10yPzdfli0uK03SJN/mvpX1C+vLToWdpEG8A/SsKexdgNv0ASCN/o5bPm5pGqMvKuAbII7EDtUYccgKIO7oo0vZZ1sryvRj3JSvjUHYcgCn//pXTn4ksy98LevwBx98KMsvv7aceCIMrbTO1JbrDeOGvYZ+x6gneQmub4Bg9WTLsQcTezLtSQLSPmIA33GNTLOLMH2QpTi56D/socQxLgfV0VlZs1nL+d3PYW2U49QBcVFK0z3LSaC6BBwQ50ZGm5OAusN169atZtsJlM8p4r333uvbSK358JRegMGNW8xtt90mQ4cOtboV1BOAJMn4XWoEIxhYCpzgMoYwiMIWlDWAYVtdGfy0j7ZgOHqZh36em/S1GMAorLacYgdp/4wZM8wY9cIcBXgEWMaoYywCmHBfEq6nr7ySl4ceajQsLOK1/fe/OZk0KS/ffPM/2Wab5RYCMIBzP/1pwcRsGzmyg2G58TfimZVPQ8A33FJhxeFyuummBcNy4/cJE/Ly/PMN0rcvcXIA7koujSQBIOnDt9/m5PDDYXyVUB2Au0Kh9DeNNVerL76X7+Wd3DvyXu49Ew9ug+IGslpxtcVcUms9o9r39BHMRA6VGJf0LfseRnh58pcgz9Z7cE29+urGqm6gyBXW2cEH+0saEZYtRx9cfHHejAMAVi0Aca+//qnsttsasu226YkRR/05KARc8xMTS3UcxgAuyMxN1llc4ZSBGKbv/dwLE4v3aqgPP/facC3zHtf9DTbYwIbqBK7D5MmTDYssivADgSsRwY0ckjOG6wkuR9AM8wjmJgcolYcjrFeAdMx71vL+/fubgxTWbg7L4j7s0vYx9pFzvYDAqOTsnuMkYLMEHBBnc++4usUqgZtuuskwBzitxVgBKGGjY+MpKe6vy9VXX22yph588MGOnv1jb+AiQ5bFa6+9VvbZZ59Y+yjswzFiUCLiBkmU1aGBeTnJxPCImiUF8AEIknbwin5ljgFi2ZxIw8v4oz9QltPsKkNCBZJmABxXKwqQsCainAPsML6TVNBhtF14YTvDOAMYg/n29NPEYSOm27fSrt3SsuWWi9hOxIA7+OAFcu+9jYa5hSsqH9hQgDQa/4yEAsRwW2edUrZPYqIBzgHCTZuWk+7dAeAUvMnJZ581CJk5AdvIIloek+2TT3Ky/fbN0qdP8mCPAoUwFIl7CgOJPmJPo/+IM4YxR+nVq5dxOVI3Wi/jvNo199+fl7ffzkvnztWSNZRi6IUpuq7qM8pjy7VkjE6cCFMvb2IC4mpMX372GX34PznuuBVrMvTC1DeOe1nz6c9K11Q/7wKQpa9hrgIqVcbB9fMsv9dqJvC0urexT02cOFEGDhzot+lWXQ8DHX0krnABSTWWw1XWNOIkpr3gSg57FUZcrcKBLgeXzOUePXoYBrsCZbXuDfq9A+KCSs7d5yTgXQIOiPMuK3dlG5TAeeedJ4899phhf6U5xkmUXYfLy0svvSSXXHKJHHDAAVE+OvJnAcRRomCjVascbhIAMZxcdu/e3bxHA7RH3hghLtYcEwcIRSztBRAO+bUE/qSlfbNmzTLt8KJM29om2sBJe7lrKko4IADKPwcWAAGwEeIGtVuS0Ztv5uTyy9uZzKMrrFA0n2efbZDllwdsaZJ58xpl0KBmUQ9hXCO33bbJZD7FPfXJJxulsRGX05I7KkDaWmsBKoq8/XZOunaFDVdivTU0FGX99RfIDTdwMFMCdACBAPEA5xYsKCV22GmnZsP8ggFG7DfAQTKpjhjhjwkWZlzAqIBZwSEArB36sFTX6v6X9CHGOPcQewyDDtCOwwO/hTY/9VReJk8uyYb2d+xI4oqCrL9+OBCusi7V2HLl7Sxv73vviYwblzfusTAn+/QBHAU09e7u61cWcV3PHMQIBzyNoui8ZhzQ72QchiVF+AbGQtRsG/ZGDlv8ur1H0dYongGQSIb4AQMGRPG4uj0DEJY+SPLwJI7GZsnVGRCO+ehHP2Uv1lAEgOqsgRyM4SURdfIrnqcHNnH0pXumk4CTgIgD4twoaLMSYANU5R4hlCugmi3o1ltvlbPPPtvEQwNoiXqjS6PwiQtHzLxzzjnHsAVtLnFktER5wjjCiOF0FiUKNlESJ/4YBYCLaY/zwpgBQMB1KioDs17jEAAEgDRtWdzK5aVsKpR6+oXCWgcTB1YVrqdRG+h++uvll/Ny++2NMm1agwHgYDlRfviB2GxFA4S3b99JBg5sNsAcBXba0KFNstFGAIoip5zSwQAzuFTCriPuWwlgwzW1IMccM19WXJEMxiQ9KEr79nm58caO8sILJTdUxbVmzsTlnCylZMotyBtv5GX27NL3AFM9ehTkrLOaDAAUVwHEhuGCAYZhioGN+73fNYh+x0WcAjMcxqNm4YQ947XPOe/A9RdQkv4odwuNSwblbLlyd8tqICRrNjGy+vXrF1d1Ynsu6z19FNdBILJh/vBhDQOg02ysUYDu06dPN2OKPTKNBUYcBxVxyT8pmcDq4zAlKtf0pOpd+R7Y2+g/rH1pL+y1rFd+gLjKNjNXWSM40OSAHI8e9cgI676rITeSCDuR9r509XcSCCoBB8QFlZy7L9MSUMANNtwtt9wiZ555ZqoN7Sg7i7hwDz30kJx66qly/PHHR/noyJ+FmxZG65prrhnq2YwHQDCMChRzjF6yXXk1VEO9vOxmTkNRuqJ2eY2qfn6eQzsAEUhgkeaiYH7SYyFKmZHZkPpj8KN0M19sYSrOnJmT669vZ8CxceMaDRBHAUwjK+qaa5KBbrZ07NhJtt66xFCDmfXppzkDrikwRzwzYrgR/02zn5YYXAXBY3PkyO+lY0fApMaFWU+JOXb77Q3GdZUCwMRzwSo326xgGHm4xmpMODK5rr56Qfr3LxpWXNRJAVjLMERht7AGwcKMctzh9qTZjGHXaZIZxoJfkC/K8VnrWeVsOZVHOSAHuJRWIE4Bx6SMYdi9sCVxY8agJ34VBw2w5oLUAdYOY7VeCZNqjZ1a3wNGsPenHcACpOndu3fdGM215Oz1+yzst9rWqN1skQ17A3OXAzTmLYfGzGPWcL9uyQ6I8zoq3XVOAsEl4IC44LJzd7YBCXDahHGCEpp2Sn9U3TVs2DC56667DAgHGGdzga1EHwKa+S0oIbBOMEZRwokBRiwwmCP1KoxFDHEU6rQXjGNc47KQNRXjNW3ByHV843oKyKLMTtti9o0e3SizZuWMy+m0aXmTCVXBuM8/J5g9rqL/k0GDlpNf/KJoWGmAZZtv3myAOS0ffpiTm28mxhyJGQpSKACui3n2Djs0S//+uKyWRfj/8UbYd++/n5N//7vE+AL4e/DBBsFV9ssvcwtjxJHFtUOHogwcWDQx5A49tFl+/vPw7pnMeYARjCzWINYkjKooAbiW1hKMONhAvLNr164GTGHeJhkwPMg6V8mWYw/gEGXTTTdNRG5B6tzSPRjVzNGePXtG+VhPz2KN4DN16lRj4OO6Sj2YJ15BOdZ4xmtakwRwYASYyNhJcwGI22STTXyDMba1OWiGb9vaQX3idrPl0Ji1mzUE+wVQnfkIw90LsM46CqvO61y3UcauTk4CtkvAAXG295Crn5OAZRI46KCD5MYbb5QjjzxSzj33XMtqF646eqIIGIHywokiLhC4QiRh+NaqPUYwRsHGG29c61L3fUISSFsCDY3zRmwoxjnjWwN5J5ld2Gv3nHsurjYlQAtQ7M0384ahBtsM11RiuK29dpOsuCLgQAkMGzKk2WRALWekASh8+KHIE080GhfVfB5wLyfbbFM0bqZ+Csy3ww5rZ96vsdFwyezRo2iSPeD+ikvsjjuWYs75LRoHiDXogw8+WJh4wS+jwe97W7pegS1ACbJgAmwxVnBltDVcAzIERESGxNTEDYxDFHVjVePShnW9tX6CoUg76h0XlPnDfsheyBhgzQCYJURDPQ+nohrjLT2HwzeySvfp0yfuV7nne5AAicI4WM1CeA4S6Gh8Rg9ND30Jc5gYw6yJAOtkY4VlDUhX7QDOAXGhRe4e4CRQUwIOiKspIneBk4CTQLkEjj76aLn88svlwAMPlIsvvthq4aBsYDx4YcTNnDnTME9gBsE8sTG4NEoT7IQsAHGc1mLQ1dvADDuAcefklN7mdiBrxjbsTuI+4XZIURACcA5gol5AT2t98Pe/tzPgFmw0Ld99R+KSnOA62q9fs/Tt+x/p1GlNw4aDOVdObMP4AJTR9mJ0fPcdzLe8YbMFdR/9y18azbs0+yp11ELYtfXXL8huu/kD4livMJQAOYhJWu/YfC31C+MJYAtj+NVXXzVjB/cnQN2wcYnCzEcMR/qa+hFgH6CI2F7lrvyVbDkdF60luAhTp7D3wurWZEBhnxXl/YCxGPQY8KwbgAoaX7DcjRMQiz5IKyMOtjPMJdhkaS70A+xz24HnWjKePHmy0c/SOp5qtS+p79kXGQuwbdF9WTNJ+ACwzhoKS55rmMtpHzNJydS9x0kgiAQcEBdEau4eJ4E2LIFRo0bJ+eefL3vvvbdcd911VksCkAQAAqO2shD3Rd2uSBjAtYBv9TQkrRZmxJVD8YOV1bdv34ifnOzjcB0EOFljjTWSfXGNt6FEYyijRGO0ABYClFQ7+QasRvG2EXx+4YW8yXi66qqLs9aI80ZChkMPXSAffviCkM1ZSzn4xt8A36LOZjx6dIN5v8agK++Ojz8WGTasWbp3r820AwQFIP3Zz35mQDjWHwLbp8UdCGALsIKxBktOkwsAzAHUJdEODigwKFnreS+GOmOgVjiJaplYmS82seVYJ/nYHKNM2XIAhowFDoroC+pMwhdiTqaVwaTgrs0xEr1sPM8++6wMGTLEy6VWXwNDmH3M5vngVYCA16z1NsT8ZQ1lnWH9mzRpkpEvOg0hCRwQ57VH3XVOAv4l4IA4/zJzd2RQAnpKXh4INgkDIo2iPOOMM+T000+XnXfeWW6//XarmwDDBENA4+tw0gcAh4HGySqGY0vghI0NY3wSWJ9YH2kvGG8ofLhHpL3Y4p6n6xcAMxlpYVRVMoKqyZq4MTDlOA23rfzwg8gNN7QzWTl/+tOiictGrLiPPy79f9ddm2SllSbKdtuVkn6UZ4BVAC6ONvH+K69sEJL3lWdIhQ0HO+7oo5sWY/FV1oF64mYFcIXcYe3WAo7iaEfUz8SgA5QDlAG8gIUD0MhYjCILp9aXsY5bOMAy7DzWesZ6mBiHqgeo4alurOVZWaOWV63nMT5gn9mSPKVWffV7gDjWIRh9jAH2WWXYeH2GDddx0MIhnY1rox/5ZAWI89Nm269FB4WFRuxFmwrrIOsOazk6sgPibOodV5esScABcVnrUdceJ4GYJfDXv/5VTjzxRNlqq63k/vvvj/lt4R4P6Abbh9O9jz76yPwO84SPLeCJ3xa++OKLi7F//N5vy/XIH0MNAy3NhRhCnGzDqqxXUVYVgAdGL+PdDwgFywD3MoAMGwuuqM8/3yAPPtgokyeXkjV0716QNdYoCsDXp59+Jccf314gJdIOP8Hkw7R3xoyc3HtvgwAWipQyq8LcGz6cmHVLPhnASDM5czgAe0jjloWph833Ar4ACgPOUUg0g2Gn2WmD1J3DFdZzZBdXGAFb2HLMaYCgtCa1gS0M05Z1klAEyBVQC6ZNGvZgwF7GWj3X9yBzpPIeMnSSZTntZeLEibLhhhtmghGHW/96661nbYxF5ieHJw6IS/uscfW3WQIOiLO5d1zdEpMAjJCXX37ZuAZxAsoJFS5zac+UFYcAr7jiCjnqqKNkwIAB8vjjj8fxitDPRIHA6MVdCSYG7l6AcrAK0s50zAoQRydrAPXQHV7HB3ByTAyhjTYqMbKSKrA7AZYZ08T/0cQLQVx2MPaZF7jJJFm+/17kxRcbZMKEBhNrjYQH22zTJOutt6RLJ/Hfzj67vSy7LJncilIsLoq/9u23JGr4XoYMedm44QLuJpV4YsECkQ8+yAltWXllkdVWW7zusAoAoxgnALaAENQt7a5uQcYJzC4MO2ISAW6wz+L+xBrdWkF+MKtYz4lvCIBJLM8kwwhUY8sl4cJKm5nnaQXiYCrSVzreOTRQHeuFF14w3xFbjqzTNs4JgETAc42rGWTc23APboc2ytevbNB/iNeX5Nz3W0ev1zO3caW3MTYrbWDNC6JPeG2/u85JwElAxAFxbhS0aQkAzjz00ENy6623yvTp0xeTBRvQ1ltvLcccc4yJcZKG09skOvOmm24yiRoAHp577rkkXun5HRi9KDXTpk0ztHqUHFhXWcp4BnuJ8ZiFkgVQEaPyrbfeMkyfJArjGkAHpgYgHG7KaTyxhuV21VXtZPbskospyRjmzRPjgrrjjs2yxRalBAta3nlH5MYb28myyxbk++9zxv1zhRUWxfP69NOcHHccSSfmCMY+YNzrr79uwB4M/aSZZ7CYAJsAIuijpEHOJMZimHcAriEjmJus2WSDZr0u7yuNWQQLhhhjGvS/3uM9SbYcTELYZF26dFlC3N9+KzJzZilz789/XpSllw7TI/HcS9KMbt26VY0/yRhgfuDGjGs2P2krYyCp+IK1Wg3oyzj1kvCp1rPq+T262uDBg+tZhUje/fzzz0u/fv0yASqiO9icDMEBcZEMWfcQJ4FWJeCAODdA2rQEYHT9/ve/N0riIYccItD3H330URkxYoRRCh988EHZZptt5P/+7/9clqYfR8rdd99tEjVgXKJk17vADILRwwfFnkDRKBAYeIAWGONZYjZm5WSbcZMFII4xh0IdZ6IDmESMb4oarIAWUcUU07mTVBykr78Wue66djJ+fIOsvnpRfvazgslgSiEJA6Da8cfDYC3FfGOOv/RSg/zf/y0txWIJeMAVFHZcnz4Fk72Ue0aOXCCrrrpoRaJfWMf5wCgCoONZMLCikl35+sf7YM/QR7CYYEwBltYbOKr3Gu3l/TAGAZ0UYObAAZYnbmhJuRp7qWflNUmCcvpu5sgjj+TkmWfyZr5o5t+tty7IDjsUA2cCDtL+WvfgaUBGaZhvtQrzU+MLavZy5hPAnGPm1JJey98zRsePH58JIA7gluQGWVhTYYSim9rIiNN1zc274PPO3ekk4EUCDojzIiV3TSYlgPF53HHHCW6pF110kTlle/LJJw0wd+211xrXS9wwL7nkEjnllFNk3333NUZh2l0bw3bmww8/bBI1EGcNFkM9Cv2AywhGNYAErk64e8F6KS9ZSgig7QK8IsFBFhTRLABxjEHGYtSMJ55LAURifMPw4hNlwHsdU7jk875qrJuo5/f06XkZPbpRXnihwQBpNLOpKSfdujXLuuuW3Do/+URkq63my5AhTeb/8+c3yDnndDT3lHvP4s7KvUOGNMvcuTn5858XGKZcS0WzKAP24BJJezH+wxgbarAA+MOwBszkkwU3sKj7vqXnIUOAS8BR1mxYzAAxsJGYWxz64HLMIQR/t3ntUxdWbavqC0HdWJEFAG85C/rRR3Pyr3/lhZCOCsIByJGtd4cdCrL99rWz9SbVt8TBImt5kCQa9Df6GWsg44J9jznGGpiUHoZLNOtGmhMksbaz1w4aNCipbo/tPbhz4sachQI4OnDgwMTGsh+ZOSDOj7TctU4CwSXggLjgsnN3plQC6mL62muvGebbQQcdZAA5ytixY038MwC4Lbfc0iiBhx12mAlyy99QaOJgUqRJlE8//bRJ1ADTA/kkVdTA4cQc5iKuKxi8rWWc4h76LEtGMW5auNpmYRxmwd07atYlgAQZBzH+cOkCgIg7a6MmEIjb5XnOHJHzz28vP/lJUZ56qlGWW64EGBSLInzXvz/AekG++ionvXs3y267we7JyaRJjXLffQ0mFhtsunJyDQAczyNBws47L4ob19q6pOsCa8Mbb7xhjHwAfQL/ezHwFTiin2ByMR9hLYYB9JJaR217D4Y1+whADQH8K9dzXfeJJ0ksRoAYAGn6yvb1Iwq2HHOTMfaLX/zCdB2xCP/85wbp3Fkkn1+8NwHjPv9c5C9/aW4VkLZtDHipj4adwC0fl29YUYwX9IA4gVnGJywsguqntTAOAXO9sBJtb2OWsr/SFtyF4xy/QftT19Y4Dv6C1snd5ySQRQk4IIne8S4AACAASURBVC6Lvera1KoEdIPBZWK//faT8847T4YOHWruAeTgb7DgtttuO3Myf9JJJxlFDJdMB8SVZMTJNEYn8om7YITAXuRdmmEKY9krEJWlmGrImnFLfD4b3Rn8jgWyKQL+eAE//D47qethbQDqk9wlaMFIgnmBSyPsNJRfgKGk5MIcA4yqZJQGbU9L940d2yBPPdVgMos+80wjEJw0NBgozmQeBWAbMABX87zstVdBNt20BNRddVWDAee4dtKkvHz9Nb+TsKHEiAO8Gz16gYkzF6TAigP4BOAhVqgCc5WBtLkOQADDCVCImGW4zSXVT0HaZts9yJbDFAAm1nPmD4HXvRp8XM94BYiZNGmSuZc+oO9sXxNbY8u1ZIyzHgDyasbLf/9b5IorGmS11ar3LGzSo45qlrXXtqPnmScAZlH2DXJEJ+OAjfEwdepUMwb4MGejLIDtgMBpZsQhI/aXpEIPRCn/ymdlJdYd7bI5zIgD4uIcxe7ZTgKLJOCAODca2pwEdIMh0+Cee+5pXFEPPfRQIwcUur322suAc7vuuqsxGLimZ8+ecvnllzvX1B9l1KtXLyMvjNc4jFBYD8geFwTAGow0XP+CZMrKgvtj+STFKEMOccg96cUAQ5qYfmlmLAIs4KLtN6uesrKmTJliAH7GOsBOPfqV+UYdgriP+RkzN97YKJ99lpOllirKf/+bk8mTG2SFFUqsNz4kcPjlLwsyb15OTjppgXTsWHr61Vc3yJdf5gxQB+vnq69EZs0qAXMrr1yUZZYRGTWq5MYatmgSAdzhYP1y4AADhyDygHBrrbWWAX1c8ScBZMc8J64o44yxHpbNxBzCfZW9gufhxgpoAhhjS7D/lqRUjS3H3FdATn8COjI3AR4p//mPyOWXtw7EjRzZLF27+uufuK5OIsul6gsKmAH+MW8ZB2FZYBpXMuxz4pKvl+eiM8D8JfxK2gvrR1aSb8HuhNlrY2F9Yj0KonPb2B5XJycBWyXggDhbe8bVK3YJcEJI9k8UXmLE4fqBAgc77rLLLjNJGk4++WQZM2aMnHnmmbL77rvHXqc0vABgTJUHWA1Rsmgw1mCkoDgS840YTmGBiawBcTADCTYfJcOgXuMuS+w+LzJEuQW8xt2JdQfXUwCeOBM9eKkXjFPqEadRAMB1//2NMnkyCQxguuVkxgzcTWG3lmJdEfNt4MCiHHJIk3TpsijO1aRJObnnnkaT2KGyAOxttVWzbLONN7dUL/LgGgAe2DCwbADl6DfmHMAxbBz6zCsr1+s7s3YdABLrFfsE6ziZhdXAi6OtCsgAzAHEMJ6ZawAyXhl3cdTLyzPL2XLlrujsiRR1e2aOnHJKg0lkUmKTLirEWwSkPuusZmnXzstb47+mHgHpce/XJC3odcgO1nGQJC1JsYXj7AnawGFRlpJWxSmvJJ7NfIfdN2TIkCRe5/sdDojzLTJ3g5NAIAk4IC6Q2NxNWZAAhuE///lPk4gBtwNAN34S0BaDAcMUYA5ADmDOlZIEMNo1WC6x2sIEqWezJ3A6z4QtgdKMARxlViz6kOfaGIcjyJjiZBs3pXqDN0HqXnkPbsMEzU8zI442wWpTlmg1uWAIAUTA2NBg07CtwoLMUfQBzwAsYd6p+1tUz+U5mvWU3z/+OC9XXLGUrLEG8a1M6lMT8w123Cef5GTXXZtl+HCYeYvXALfViy5qNPGxVlpp0XfcWyjk5LjjFpjMqWGLhh5gjgGAwLLiQEABN03WA5sasIdDCPYMGF5ZWV/CylBZasqwZI4jR2V0hX2+n/s12D99Rf/ApFFXWJv7S9my7IuwMXFZZ53UOo8fn5e77y4la1B37KamUrKGffYpyKBB9iRroP9J7FRP0BrgnHoApjOfNYOyF3D2ww8/NGMGJmxaCyAkCX9od5oLa8q0adNMJuW0F9oCSL355ptb2RQHxFnZLa5SGZSAA+Iy2KmuSd4lgLGFy+l9990nw4YNM8kb9NSQIOm//e1vDWuuHkaE91YkeyUGO7KhAED4DfDOBg8gAeUdxRgjCVcvTqttASaSlai/t+E+jcyzMCbVPctmo9hL77TEugTgwpCmnYBcuMvZWGAHY6xGBcQBWNFmflIwwvkwvx94IC/PPdcgnToVDeAG4Qc307XWKjHhWmLykMzhrrsa5b33APBKjDoYcsOGNS2WSdWvfKknLo0aFJ9Yf9S7FjiscaoAxAFXuR/mFYZ+FuamXzlyPWv5u+++a9Z22MxRx+sKUie9BxdDgBfcwWA6MhcBiFpL9hPmfUHvZR4yTzDUAeIAMRljlWslGYQffbRBfvgBt24xyRl22qlgGKU2FXVJtmGNR4awJhmfhEWgbpqkpSUXPAA8il89x6Y+yEpd6C9Y9AMGDEh9k5jfHGTbmgTEAXGpH2KuASmRgAPiUtJRrprxSgBDmkC2KFsPP/ywMaZgrMD8ygLzKErplWchfemll6R79+6eH0/gaQxWlF7chpIwWCdPnmzq6OX023ND6nghGQZhIWZhXL7++uuGUZTm+DsMBQXiyhmetAujmnGXhb7yMuRZGxR804QqGODlADsJFqZNy8mTTzbI7Nk5WWaZookL17dvwZM7Ha53JGvgvk6dvNSq+jWwFAFnYLW99dZbZv1nzQ8KGOBOr8wrDhbefvttY+TzzFqgXvBW1PfOchATUIuxjvzijjUYttUaBxBXY36HNU1f4YacxJ5Urf6A9qztjBX2xpZA+/LYcriizppViiu32mqlmIlBx29YmbZ0//jx42XgwIFWHrIB7ODCyn4KY4yDQcYA/9cxzEEKMoWRmNbC4Sm6F6EQ0lyyFOvO9n5gneHwLAshUGyXtatf25aAA+Ladv+71sOtKJaChVMwpHBDwHCOMvZZ1gRNzBWM2KeffrrVwLkourBtUHYJZk/sFkCXJEExgDhcMmw3DrM2Rry0B0YlLj9pn2sYOQA5BJJmfAPgp4nhiaEGSzVIVr1y11MF3ZT95mUMJH0NaxCsLUBDWFvqZh9lPZAJLDvWPVzvYdkR6gCjJu2gM3JSVhHzlwJrKwyIGaXsgzwLUJa9nz4CrIAFxX4RJ4iK3sFY5GCKNRCZBhkflbHlaL/OQxtAOeJg4X5nQ11aGxvqUs2cRT/hMBbGkoLp9XStDTKmy+9hDQJQbC18Qth3JHE/url6UCTxvjjfwZpDiINNNtkkztcEfraywh0QF1iE7kYnAU8ScECcJzG5i7IugSeeeMKASiheKC0UwAE2ySOPPNIo5K4skgDyQE6wBwcPHryYaNjAAek42X/11VfN6TJKbb3AlizFVEPQJMuALQHQk/ZCvBfAkHqxUMLID6MA1zHcElFWiVujMcbCPDfsvSxf336bM9lIl13W29MAjQDMvcYQqnQ9hfHGfLfRtVwBD9wRlUlK3yU95hgnALYALsSsVLdNG2VWbdTQ54wRgCMOWAjhUJ5UwNtIS8dVGuifeQGbGlCOuQ2IGhZQ0jiD7I30PUwrxkIU46BaJtZyRmrYugfpvWeffdbagPSttYf+ZrzDlIOJBZgIcAqbP20MZ8Ar1p+ePXsG6UJr7qFP0C2zcJjB4RdMXJuBOPSarLK5rRnUriJtXgIOiGvzQ8AJ4OabbzbJGNgYca/BNQFDDdYEChhxk6699lpz6u9KSQIEj+aE9R//+IcBUvh9s802M7F3AOjUzbecbVgv2cH0gcGXFYWCcUlcozBJMurVF5XvJYg0hk1a2AYY0YxvFFQMZ9zJYFQB9jL+61k+/zwnd9zRaLKQ5nJFKRZzsvHGzbLHHk2y9NKt1wzAgbbUcl2qZL8xpypdT+spg/J3K2uLLMz8zprE2h4F4BGmjYBYyJE6MW6It0m9grARw9TD672sn8xTDl9Yexjv1LkeoI7XOkd1nQJbuCwSW44+Qw8ghIUCal7epS7rALHoFICY5SEevDwjyDXKltO+UuA0SQCVsZNmNjpjnvoDmNJ/CqZzaKEHtLbvX4BX9IOtMUq9jm1CPTAPSaaW9gKoiyv6RhttZGVTWN8cEGdl17hKZUwCDojLWIe65viTAIFfSdCAAXrooYcapoKeegPG/etf/5K//e1vsvPOO8vZZ5+dqEulv5YkezVKKKwBfnLaSjnttNOkf//+5nTfJsUUhQcgLkl32Dh7A0WUE3niUKW9AGph5NA/theYQLAjACEAossZVcSIY+zXC5wgkcF553WQfL4oK6xQkiSx2IjB1rlzQX7/+wULsytWkzNuMhxEVBtT5eAb95YnXrCtz6gr6xJ9pYAH9a1Xv9SSD/VlfQLsYkwBGjIXWEOjzBxdqx7VvsfoJXYesqNuWVhvgsih/B5AVGXkEB+VtQsQFXCy2kEP16rLKTEDAXPq5cJbL7YcWUcZP2kt9BtrfrkLO+MAeQJuAaajM8L6t7WdjENldKa1H6g3gDh7cBaypupaUi9PkVrjgL0pTYektdrjvncSsFUCDoiztWdcvWKVgDK1fv/73wvBhK+66qqF2VIrX/yHP/zBuK0++uijbdoYATR57LHHZMyYMcblVAuGGjGQzjrrLKuy5Gn9MCZhj2XFvRijHXAhzSyDNPQNBjQsMdxPcelRxbSawY1RzhyoF9tqzJgGee65RuncecmMiR9/nJODD14gG2xQymBarShzRuufJtdT2gOISHwvwCIYLBjFaWRtYdgDJHK4AesKRoKy0eI+3GAMYOjCamatxPVd46XGuhmn9OHlMcVghSM3wF9lXcJ2AeAm/lsccQjDiq0aW07nf5TANTHiKsNXhK17kvfDFm4t4Y5mgdcYm+hGCtAyj+Ket15kgVsqwLqtGTq9tIFrNLs18YbTXlgrAEhty9qscnVAXNpHmKt/WiTggLi09JSrZ6QSULeSoUOHGvDoiiuuMKea5TGeMEIwum+//XY588wzTTw03FTbUgGMAIR84IEHhFgvnOJp4TsU7FNOOcVKQ0PrSRwOjEoMpCwUFDhKGlhkteRtW9+wLgDocEpNRleUZIzoWqAn9yXp7lUp11NOaW/iweXzS0r8669F1lmnIAcc0NRid8DK0qx6rIEU2qNJF+oFMNYaPwBwsMioJ2EFssbaon24/XMIwmECmXgx3qJk9/I8mA/MRUA/XC+jillWq/+y9D1AB/E7mT+sH4AwyJZ9J8r+ikNmcbLl0g7E0YesL15DW6AjaaIWGHLokcxj5hRM9ihBTq9jAYYwoDAeH2kulQdGaW4LB3wcfHiNy5p0W5E1B0E2AMlJt929z0kgSQk4IC5Jabt3WSMBBdz23ntvw3S58cYbjSsQShQbD5sQ12CgAMI9/vjjMnr0aHO6HTbuGVk8hw8fLnfccccSsS7efPNNufDCCw0IQCHu1AknnGDisJUX6nn99dfLvffea4Lw4vKy7777mk+louf1mdU657DDDpNnnnlm4VcwTXDThR04btw44647cuRIa/q1WkVwZUAB1uDoVlfWQ+UwzBmDtrrBeGjCwktwW8JorXfiCYwt6gIbiXEC4OGnkGURIKheSuvJJ7c3Lqk/Jn9erOrffCPSpUtBDjmkOhCn7pEAMRhqxBGyNesp415ZEQAdrEcYurWAUj99aeO1tJs1XxPg0GbGadeuXY2x5LfwPHXhxUAnYDh7XT1AAr91t+l61QVgXWNUA2IC3NMnHFSx98BuZD716dNnIYhqs5yjBuVw3UyzKyH6E+tM0IM85hegC/ONw17Y1YwJDnm8gnthxzxAHHsc+muaC2A3hwVZOHCBQQuL0q+ukVT/oRdwgGDrIVxScnDvcRKIWwIOiItbwu75VkpAFegnn3xS/vKXvxgAixhxlWXs2LFy0kknyTHHHGOuCVtQzHkOLm933XXXYkAcyjwAHaDEsGHDDNgCQIjSft999y2mfPzxj380f9ttt92MEQUo9tRTT8nhhx8uxx577MJq+nlmtbYdf/zxhgm31VZbya677ir9+vUzRsXuu+8u999/v/zpT38S6uJKchJAqcYQrwRnk6tBNt6EoYyBBADBnAPgwdgKYhwRa5Kgy0FAkSikeeedjTJ5coOsvPKSrqmffJKT4cMXSN++i1xTK11PWWNoP/XnQIK4SMgCsAf51LtgzMIAhXGEIUZig3rF2qq3LHg/YxdjHoAAEBj5aH+1Nga5DvYjYBEZwulfgFebgSEb5F1ZB1izrMNk1WRPZK/WxCXV6quJQwC72fuReVqyRVcCc2qYx+HGamNfk9mb+RLFQZ4yumDxst5ygNCrV6+FcTfdPGx9BNgOXvkZv+gegKO26nEOiPPTm+5aJ4HgEnBAXHDZuTszIIEpU6bIGWecYVhlgwYNMq6nGJ6cvBFr6LXXXjNuBWuvvbb5O4YMTDkANTKp+smUiIsnoBUn55RKIO7ggw8W2HKPPPLIQtANIA3Qa4899jCAIYVr9tlnHznooINk1KhRC3uBeHcAi8Rxg51D8fpMv10JmIjLLqAf8rO5YPig4GYhyyhyRoHDPZUYUmkvGLMAB0n1jbqQ4obJuwEyMLLCuvkSFwimQ71c0GbNysl557WXFVcsSnneC5I4NDaKnHTSfOnQQQz7t5brqTLkYHCwVpHhsTxOUlLGIkYrhhcf6oTBGraf0j5fqtUf2dBPAHMwbphLjG2AOXWFA7yE2cO1sLZgULviTwLMG/Z/ZApjnbUDZo5f4F4zLwPC0C+AywqilieA8Ve7ZK4OwpabNGmSiZ+Z1oKOqEk2omyDxpaDEU48QfYkDheYn1FnNwUwZt0OyuqLst1hnqVZh9taiJgwMgt6L+MTm8cx4oJK0N3nJOBNAg6I8yYnd1XGJKDx34466ijDJPNTUGjYpC677DLZZpttPN1KwoeHHnrIAHrdu3c38ebKgTiMXoDAX//61wsBN30wTL1XXnlFyMzIxnj66acbt1ZYcOVBoHEB4X7e9bvf/c64Qnh9pqdGlF3E8wEi+XnBBRf4vT3R61HeMKJsPXlMVBiWvQw2D0AcxkecBWAdsB1AiUDPADrl2TQLBZF3383Lf/+bk2WWKUqvXgVZainvNaIdtKFejDhq+u9/5+Smm9rJvHk5cqYS5U1WW60gBx44X1ZYYZFbapCspwA7yI8TfGLa4FbFOhi1kq4JA2Bt4XaJuzBsRVszy3kfIcldiQsr/cUBBMARgD0GPrK1NTB4ctLx/yYOPZjfAJ3st+zhURb6iL0a1hyu4WTFBpBj3PsF+aKsl5dnKcNLry1ny6meRDKsNCdrQFekXVGvdZXy5T3sT6zPAOow8WDhAZ6x/oU5AAHsZSylPZwF8kGXS+rgzsscCHoNB0z0NYC+jcUBcTb2iqtTFiXggLgs9qprU00JaLIG3Ds58UTB5qQbAx1WC4oQyrD+n9Nr/Z3vMbj9KGa4dW677bbG/fWaa64xIF45EAcYeOSRRxoQDjCtvFxyySVy+eWXm4QJ3bp1Mww5jAJcRssLihyxWLbccktzvZ9n1hRYxQWw7y6++GIZMWKEeZfNBYUHAAHDPgsFUAnXKNzz0l4AWjAwlMEZZXvoc5hCmkmT+YxRUxnHDTbZhRe2l88+EykWAbFKLLL9918ggweXEhfUKsoUCWMs1XqHl+8BFN9/Pydz5xZlxRULstpqpfqzVinw6GfdqnynJmvRJAlkJlVmkJf6tXQN8oMdBNOItRdQMwvGVhiZBLmX/qF/OXxQ4IjxjyEOkIpBT5wo5kK9x2qQ9iV1D3spzGPNlsk6wjiPOwakxu6j7wAd2M/pT/7OvLC5z1piyz3//POy+eabW1331sYVcwlALEk2LrJkjwegJVFL7969DbiO3gmo7hegBdxlLKedCRs2PnNS64eX9xAagDFla59gI1E/m9ccL3J21zgJ2C4BB8TZ3kOufrFJIMlNXWPE0JhLL710CSDutttuM0khrrvuuiVOj++880457bTTDOAFA2/AgAGG3QWQV1lQeAEbiN/m55l+hUxsuHPOOUf23HNPE8fO5oJCi4EatbtHvdqMkcZnvfXWq1cVInsvwYopUTKeAOBw6YbJAsAOC6AlZXL+fJE//rGD/PCDyPLLL2oWiUM//TRnXDq7d18UW62lhsNGxV0GML+exYvraVT1UxdWlTMHGgBz5S6Rrb2L+3GZAijnPoBy5mm93Hujkks9ngNoA2AAQIoLbzUGDfsdID7y5TqMc/qKj2PKAcKX4isiR0JPAARjJAOA1aNofVjrYeShQ7CWcWihB4n1qJeXd7LnAmTCxtTYm7oGAyymybgnPAlMxXq7DTMOkCnypD6aldcLWw5ADxAv7fM8K8w+5hBxWNn3yr1avMytpK5xQFxSknbvaesScEBcWx8Bbbz9mj21mhjUqNWMdSgyUQQurwbEXX311SZbKuAZMZnKC0y4E0880biADh061LjWcc1NN920RLVh3aHokuXVzzP9DgOypf75z3+WHXbYoSog6Pd5cV5PH8NwyIqBzwk5CjnsyLQXjEvmV5i+UYABxg/zE+MPgM8L62fSpLxccUV7WXXVJZMcgBGuskpR/vzn+TXFDAgF0yhKQLHmS3+8oBx8409BXE+9vqu169TFDnCNscmcY+4BgJez8ACBOGlnDPM77DcMkjQZ51HIK+wzYBBiYAPMABwx9vwksACwxrhnDjJnSCSAoc4z6uliHVYufu8H/GXtgHFEnEfa7pfx7vedQa6nnnxY41566SXT3zC1YEbHzdTzWl/mPKxW1iRATEAGZfTxjHLmcDkgZ/PcJ/4nh171WNuryV3dgQE7OXDSLKLUsSWAlnU4jlACXsdFVNfZziLz087yuKt+7kvqWtXLbJ6bScnCvcdJIE4JOCAuTum6Z6dCAhjtJFJA4cIwwaBEuYHpgbGCcokL3ciRI01WUo0vF7Rx1YC4K6+8Uv7+97+bBAh9+vRZ7NGw20jKAFC30047GSCO4MfVmGgw5nBbIGGDn2f6bctFF10kxx13nPzyl7+UBx980O/tiV6PwgM4QGyrLBTGJswWXM7SXjCAaQ8glt/C/GSsEwsLQEIz2/kxSm+4oZ0Axq24YvW3f/65yHXX/VCzalOnTjVgRlKsicqsp1G5ntZsqI8LAIxhFxHvDSaPukwCcjAX680e9NEUqy4FjGG8AaABwgDEhXE3VoCEdZJ5pC6Ryhi33SUySOeo6x/gCsARPwGECUGRhkL9AbHRV1h3YMwBtgDMAaaGHQ9+ZKAZZHk/85r/1wKtymPLqaGvQJFthj/MUUDFMIdFfuTp91r0UXRWDjMmTpy4MCkD41nrPGPGDAOw2xqPzGubOSxgbGUhLAf6C3uhH33Fq5yiuM4BcVFI0T3DSaC2BBwQV1tG7ooMSwBjZvTo0XL++ecvbCVKLAY+myQ/MUSIEQMId9hhhxmFN8zmWQ2Iu+WWWwSW2Q033GASLJQXdU0lthzAV79+/UzAaP5eWXBNRUm5++67xc8z/XYxbDvkASDoN9mF33eFvZ6+w/2NJBlZKChIjEG/cWJsbDusKAw3PwHQAd7oT+YuzKsw4NfttzfK0083SqdOSzLiiLdGguOrr64NxGEIwVSJu08qXU8VgEvS8PY6jhinAAWA4PQvv5MdEDnh9qfgnNfnteXrAF1gbFFgvvB/L4zPIDKr5hIJ2MO+wnc2jjWv7QS0ABxm/QAI5lDLNuDHa1vKr2MtZH4BpAK44G7M/wHm4gCQdB3iQEiBKkCeIOtfS7HldJxloX+C9GmQe5ClMpPpD3QfgHUOuwDsbXWD9NpW2oHuHYVnitd3xnUdLHr6hKQsNhbGUpJxEW2UgauTk0ASEnBAXBJSdu+wTgJ62k/8DwAljBoyqKKolCdlYMPnAyiH4h6FUlsNiBs7dqwcccQRJu7aHnvssZi8NFkDzDOMML5HweKe8qLJGmDFcY+fZ/rtIMBLEjVgyBCM2eaC0QpjgLpmoQBc4aJBIO+0F0AaGFPEvGmpMFe5BiOTOGyAd8xFL7FxasnnP//JyZlndjCuqblSnoaFZfbsnAwc2CwHHrig1mNMQG0A+yjWh8qXVbLf6uV6WlMIP14ASEydJ02aZFgaMDMwNtSgxlDEOIRlAsOBAnBQ6cLq9X1ZvY71nLHP2IKRjRyTSBhQKU91iQRkoU816Ym6Hdouf2TIeKMdjLOPPvrIgIpxzFVbZAGTnzEDGIf+svHGGxswJmzwdeYuQCaHIeuuu64B1KMGymxjy7388ssm7mIaxwtrCOsH8cjYL9EZ0IUAa+MC8uOcA8xj9tksAESTJ082B1TskTYWB8TZ2CuuTlmUgAPistirrk01JaCxNHDhJAPoKaecIvvuu2/N+6K4oBoQB7gA22348OEmMUN5IdPqK6+8IiiEnBKfeuqpJi4bABgn3loIGE/G1RNOOEEOOeQQA1h4fabfdt17770mUQOKBAqFzUWBjDAsRpvah0FFbBgyqaW9lMcMqgZAAergMo7yjeGPIR2l4Ud89ksvbSevvtognTsXpaGBOEYic+aIAebOPHN+VbZcZV1x/cFVOMrTbQW0eJeNrqflMsDgwziHtYWBh4uaFxd+wBFl8mDUY5RwyEA/t6U4ZeWyBHQGLOIncvQT9y3u9UBBcQxi3MkJnk6hv2w07Kknc5O6wcDMSsIeP/3MHGMuTZ8+3QCSyIDEKF5dw9k/mdv0MToF629Q9pufenOtDWy5F198UTbZZJNUs7AAZtF/2EcAaDnUAlTt37+/Wac1LqLf/kn6elzyOZBgTUx7sS32YLk8dd5lAfBM+zhx9c++BBwQl/0+di2sIgEF4p588kkDXBGfDdAKFw8UlnJjvzyGSRTCrAbE8VwYZhgNDz/88MJYHgAuu+++uwHYzjjjDPN6AjXvt99+AkB3/PHHL6zSscceK0888YT5aAwNr8/02y6SQfzqV78y4AinrTYXDBEMCNwAslBQqqdNm2aMg7QX2H24N+l4xSiAJUfsOE7wYV2oIRlXW5uaRMaMaZDHH2+U5uac4JLaBKJhTwAAIABJREFUo0ez7LdfkwHnvBTmKUBSWAMhba6ngGYo6/QjzAv6sVZ8qNbkCcgMCAU4x3NgsTLeYfVECcB66dMkr6GNjHnWKNYqDGMOWWx3AyXOEUY9HwWimc/MgyBuimFlzvxh7MDagnGpWSKz4MoWVjbcD7jPnAWE4ydzTTPnVs5bjHGY14xHxiKHbvWWo7LlynUyftdPFDKqfMYLL7xgkmOl+WAAIJW5UM7q0xArjAENGQBAyz5GsXG95bC5S5cumQDUkb+tGYQdEBfHSuKe6SRQXQIOiHMjo01KQF1TAb6OPPJI2XrrreXkk09ORBYtAXGc9v3mN78xihBAG8YZCRkwaO65557FgvMfc8wxJjMqbqoAMuPGjTMAHAkljj766IXt8PNMP41/7rnnZMiQIYbBgiJnc0GOyKEyCYbNdW6tbhibAMZZOK3EGIRFBYCjgA5/A+ANA+gE6VsAuTlzctKxY1GWWcbfE3D3wdAJwriplniBOW8zCAPQQaZOmIoY6GHi9LUkaQVgCaYPewMwACMsC+Ne28w+RKwg2seYB4irB4Dlb7S3fDXtYFzAvGI+kIWUtSpuAAdAEJABQJz5BGuG99sIJkQl6yieo/HEkJlmzuW59Bl9p/1o45hMii3HfoSeY/N6XGssEIKF8A+trdMcpmiBrQW7G5AWgN2WecT8BmBPWjeoJd8g3wN+2rreq32URnfsIH3h7nESqKcEHBBXT+m7d1shARhoV111lWGkkXyATR6lC3YOp1Z8UEw5EcUQDFtaAuJ4Lm6eZEfl5A+Dk8QMsN4q30t9Lr/8cnnggQeMsozbDa61AHmVxesz/bQLV1k9JYaBYHOhH1FE6dssFJQkgo1jbKa9vPvuu6YtMGgwBLOgYHvtkzS5nlJXWE+wtjDQyTRNhsmkFHV1YWWcwO6AfadMnrQxVQA/AJ/5ScwmZAlDyRZj1+v4be061ij2KMYHQCpMR4BU9qmogFTeAVsLWbLGEwcNwChLcoyiL7w8AyCOAyvCXyBD5jv7O3JFFwJwt73ExZZjvfEaC2/+fJE33sjJV1+JQCzbYIOiNDbWX3LoayQ28uqOTP+zxgJCAuDh9YCnCGsuoKSbY+H7lNAy6Pe2gtz0cVL7e3hpuic4CaRXAg6IS2/fuZpHIAGMoAsuuEAeffRRE5OHzJooniginLJjTKg7B/F6/vGPf4TOmhpBtev+CJiEmoUUIM5mYxgjg8xurSUEqLtAfVQAgwP35M0228zHXXZcqrHEMG5wO0TRx0jHQE9zgRWKsVrLNbXc9ZT22px4gXEGAIahwHiDIQFzsd6GGDLU5B3UDQCXuEcYifSBjUYiewhjH7ADVgcgOkwTG42wOOahZvVkfrBf8KG/AOf8suXYs0nAA4MQ10nildka8DwOWUb1TI39BrsV+bE/8jfWJGWbESaAuUX/4baIzPWaqOoRx3OiZMvB/icbfa11ZcqUnFx6aV6+/TYn+TwhDXKy9NJFOf74ZmklF1EczZcFC0S+/14MszufF7PmsE4GBcGVOclhDIw0mHWswUHmb5gGv/nmm4aBHbQdYd4d9b3jx4+XgQMHWsm01OzYftfmqGXknuck0BYk4IC4ttDLro0tSgA3mu22284YRiigGiMOAwlwiY2IUyE2JhTR8847z/xeSynLusiRG4odBUVdY/Fkvd22tI8A0mkB4pgvgNrMJU7mUd4x6Didby1Zgy2y9lIPgF7aA7hSWdLmegr4BkuRDwZXjx49jPFtq2sWIBdAKEYi6zcsA9rAeKt3nQGMqBusZTL+2uTm5WVcx3FNOZDKnsu+yn7CutBaJmRc50g4QAEM0LiScdQxy89Ejug3HILAelJgpbU2M58AktF7WMPpJ/oLvanec6xWX4UF5QDiBg8e3OprPv1UZNSoRllppaJ06LDo0u++E5k3Lyd//WuTJJFfgCRDt92Wl2efzZtKtG8vsvPOBdl994K0a1dLUt6/ZwxpVl72CJi9/C3ug5CJEyead2UBiOMwG5aijcUBcTb2iqtTViXggLis9qxrlycJYLhxMsXGjnKK8aYAHD8B4cr/lgUFwJNgalyE0atBfTlttd0oQoHDQM9K4WQYgMTmAvgG2wJWBWMFIKISxOak/Z133pGNNtrI5qbUrBvZI1kbyudBZeIFdZuz0XClroBFrHnUjz6jLbifpqloplb6A5dFmD649Sd5UMCeggsvTC3YWwCFjH8b+92GvkU+9BV7CuzYciAV457vALkZj6wpbcl9Pcr+YU4DeDLX11tvvcBzgjWchBwAecwt1m/GNsAc88z2cV4JzGl99WflIeukSZNqhrW46SbAr5xxR60sn30mMnRoUfbcsxBldy7xrLlzRU44oUG+/DInK69cYsIR9/Tzz0VWXfUTufDCFaVDhwjRuB9roKxpwqkwLoiTCruS/TDqA+ssJM6IdRBE9HAHxEUkSPcYJwEPEnBAnAchuUucBJwEFpcABpPGjSH+GiCLzSVNDDKb5VirbijluJ1iOAM8oZgDQrTkuoxhDcuF+E5pLrQTdg/AFcCCFptdT7WOAB0EjsaIxrCOI/FCPfoWwAGwgIMUADrcGDWunNdYSX7rzViGZaSJF5xrjz8JMncwAgHwGZMUAprDmmMuueJPAhqPcK211jLuwKxPUc9v3K0B8QFSAZ/VbRE3ZNtjTIVly2lvHHtsgwG+qsWDw0V0+eVFzjpr0b7grxe9XX3PPXm58868rLrqkte/+eZXcuGFS0vv3vHOIQ3jwgGIsqoBaGECRzHu9IDDdrDXS49NmDBBBg0a5OXSxK9xQFziIncvbMMScEBcG+581/SSBDSNO4YaAVQ54cWYwogidtVWW21l6PDOJXXRiEEWGksGmRHvy+YCENe/f//IT2jr1WbAT1vAK50XZA5FUQZ488qmgj1ERlsy/6a1aKBz2E8YIACQCsDZ2CZAKYLcYyiReIH+Y63Lerwy2FWABYATxLfEtQpADgZHUMOOAwnkCBBBVmaeHbd7lo1jKoo6aQZlfrLfIkfmFH3DGIUFjFEPmBokO3EUdUzLM1iTSNKEboP7KMBwUkAmcwIgVYE/+pJ5AQgYNUMq6v7QhA/6XNZKGP8wtlur+x//2GBispW7peozSEZKXqU//SleIO7QQ9l3qoOB7747W7bddiX5wx+IXZdcYSyw5iI7dILyORzkkAJX/ygAveQkUP1NjDNcnocMGVLvqlR9v+r3Nsd+tlJwrlJOAgEk4IC4AEJzt2RPAk888YRcf/31RnmtLCj+Q4cOlVGjRmWv4SFahIsQxi2yA+SyucBYQoGz3RDwKkOC55MFNiiA4PU9rV2HkQzwhqKtQBqKmx8ZYzCirMfFUIqinS09o9z1FGYfscAIYE1SA9viqqFYI2eM4VdffdUAhhhGbdnNT8cufdarVy/D3mE+1TI+NIs215IRFDkCdNS6L86xmNZnaxIHZMhBGOMSsK3augbzCnAHY5zESTDmcH/jeid7MWAXABg/AY50vtdzbKirOIctzDMAVILtp8HlnRheHMjCyCSOHvsan2ourE88kZNbbmmQ1VdfEuj6+OOcHHVUs2y2Wbwg2IgRjYZ5l8st2eNffPG9dO/eXk49NV732NbGGvsl8kRfYB7jgo5ehjxhY3vRG7zE66vnePf6bvYQDodtZcTRVxon22ub3HVOAk4CwSTggLhgcnN3ZUgCBFrff//9jaK43377GSUWxYATZIzrq666yij9JGrYZZddMtTycE3BDYW4M2PGjJEtttgi3MNivhvFDyMgKVZAzM0R4tbAiEuaxYThDAAH2DRt2jRjBMOAC1oPFD7GULUkB3HLMMjzK+O+YUQwptQ443sMYQAuWIv8H/kA1ARhAASpY/k9uAqzhgE6AXIoA86L0RP23Wm5nz6isEa8++67pp8wvOmz8kK/wqLDkMQ412Q1aWmnTfVkXAJ0sJYw93Gd9Dsm1R2SfgOYg0EH8AOo7/dZNsnGT10Yu8iSdRl52pyNt9xtEUYuH90/bDiI0SQ1zG/21kp2azlbTkE5+oq1//vvc3LaaQ0ye7ZIp04lMIxlZdYskS5dRE49tTnSZAnVxsjZZ+dlxgxArSW//eijBXLQQQ2yyy71A+Kq1ZkwFoDHgOvElWPNpR9acmnOChDHvFUGvZ/5ntS11I/DjaB6XVL1dO9xEsiCBBwQl4VedG0IJYGzzjpL7rzzTgO07bzzzks8C4Vx+PDhBny47bbbMpGxKZTAfryZuD24IyK7HXfcMYpHxvYMWECAELbHrPEqAIzQMC51Xt+j12GUAFjDgIS9QiyxKAoKH6CizYzKallPvbieYrghN01sguwA+2HYemUABJExBi/gJgYiYwQQrl5AYJD61/Me7TOMQcBUxiZrBocOgAUwH/k9K4B+krKG0YYbL+MTVhTsGPbUsKCZxvlibrFG8R4AKYBSm7P9hpG9st8AMgAxWZPDyjFMfYLcq2sj9Qb4fvvttw0wx5hICgBg7ADeUpjrrJOMnVpJuarFlps3jxhtDfLCC2QshTknMmRIQfbZpyBJ5Lx5++2c4CLbufPi7qlkbv3ww8/lnntWMow5G4secAEowzJm72KfJPZw+bjOChDHmMMFnzbaWOgPDqTcPmdj77g6ZU0CDojLWo+69niWgMa2AnzD6Lr99tuNAoChoJu/nnyef/75csstt8gzzzxj7ebpueERXQiwRcyPG264Qfbaa6+InhrPY3A5hjWRBpcYLxLApQNgIC5Fibmhrqe8B4URwylqAIn34Ga72WabeWl2otdUst+U+RbUHRgjA1COD0ou4xE2D4Zn2H5Uw5BDA1z8MGgxbrMQTyfRThdZGAsUUAfgSN13iYMJwElx7pDeekVBbA4OYBtqvLI4D0QwcgFMYcixtvCT9QvmXVIAjzfp+LsKWQK8Aa7DIGI9SXubVAK0jbWQtRGAFrdF9hv6S5NC+ZNW61drnEzYWMiTLLJhYgCXs+Xmz88JceGWWy4nHTvmEwVIn3kmJ1de2SDkCwIIhJW37LIi22//ogwfvmmUIoz1WbhVo+MwvtHdNCsv8zjOtSPWRpU9nLGN7rzppnb2iQPikhoJ7j1OAiIOiHOjoM1KQE/Lt9xySxPj6owzzlgiZpImcrj55pvlwgsvlIcffti4JbkiJkbZyy+/LJdffrmMGDHCapEQcJnEG3Eo9fVoeFzAIsYdxs/MmTPNiS0MAVwrwwJFrcloypQpJkaXDaUcfKM+cWY9ZW2BBYLhCdgJsI3B4Qfoo79gvAEYdevWzQDNXth6NsjatjooYwswk8QL6iJV3h+wrvge0ABWKIYi99ngWmeTPDGkycaLrNZdd10DXJa78yVVVwASDHoAHhhyzBX6C4MeV+20sMjee+89M8fJRAoTvRZjKyn5xvEeBcToK9zqmYcwKNmHNIh80Pdq8g+AWuYwhxVR6wTlbDkdX6wh5Ye7Qevv5b5vvhGZODEnX3yRky5ditKnT1EmTrQ3Q2etNmn2a+Yw+x36Nwciyiz3s1/WeldS3wOkczBhS8KtynYzhnFNjVPvS0rW7j1OArZLwAFxtveQq19sElAgjrhwKHx33HFHi7GqDj74YGM033333UvEDoqtgpY/mLhw48aNE9iChx9+uOW1zVb13njjDeO2EQUAgNKFkovhgwENKIahlxYjNWzPtuR6Wh6YO+w7Wrsf+Ws2OJRzAFCMTlwgqxmJ1BdwASYHcfoA37xmqY2zHWl8tiYLQYavvPKKkTngcy3WhTJgmC/E5sJA5F7mZBhmTRplqHVGlqwjGMgA+RhxtiWxAIRTVirsXox6ABmAQpvYcsp+A8hkPWZ9YK23qY5JjVWAOI2Fh9siDFXGGOxKr3sUfU6cXwqxyJLMulsZW4466N7itf5Jydrm98BinDhxomGRwQzVbKzEdOb/zOda67Yt7WM+w8bk8MzG4oA4G3vF1SmrEnBAXFZ71rWrpgTUYILl9oc//EEGDx4se+yxh8lkxGk5BSPryiuvlMcff1yOPPJIAzjVI+h6zcbU4QLiwj366KOGSXjsscfWoQbeX0ksO9gEWXHVmz59ujEig7ZHwR8YPjAFkQ/GST1YIoCKG264offOjOjKWokXInqN78cA7nDij+ENIIrCjuGJAQrAoUZH9+7dfbHnfFckwzfAiCHxAnIGQMM4D1MACzCuWGNI0gGbgD7jk3XwhLUEAJlxSfuRZVqymALMwTbDdRYwtnfv3iY0Rb36jHcjTwAHZAnYpLpImPGZlXuV4chcY//DvQ99DCAVIKacHaVx9GCu8jtjst5ZoqvFlmspE2tUfcY+xyGyraCP13Zy2EGs38owFrr+AMxRAOaUzZVGtpxXecR5HWNGs4jH+R73bCcBJwHnmurGgJOAUbyvv/5643pKGT169MLYDWPHjjUgE+6rp556qlGOXSlJgLhw//znP2XUqFFy8sknWy0WDQSNwt6WC0orIMTUqVONAQNjpd4ZS1988cXEYsQl6XoaxTijr4hVhuEJwwjDHAUZo7NeYEEU7arHM2BUABYhP0AXXILjiLFV7koFU5H3wmCE5Ri1G1w95Mg72TORJe0i1iGAZpJB9qNut66LjAsOOWClslcA9sTdZ8p+w5UX4L1Hjx6ZTTIRdb8BpMJoVDdTABjcT2ESAtxo9mNbXexay8QaFVuOucoeywFzmguHHRye4KbcUmG/RK9hLrFvsk+yZ7L22lRYX/jgZm5jcUCcjb3i6pRVCThGXFZ71rXLlwRQiHBNAnj79a9/bTKRUdSNxZ1MLynO/fff3ySwGDlypJx99tm+5J30xTA2UMpsU8iCygGWFO3xcsKPIs44xjiBXQFDQONfBX1/lPdhJJA1NSrDo7Ju9XY99SsrNc4wLt955x0zZll/AASQEckY6EtAA+JvwZyLS3Z+627b9cpAYb7A+kSWSYAr5XLAOKS/mIP0IUxGmKiwWdPG2GAuwS7CRVDdA4Oycm0bK+X1YX1k/rG+AvZg2APM8YnK/Q05Ung+48PpGMFHBHOMOU6fwezmJ/sjoCbZqnXtDP6G+O+Miy3HWCaW74ABA+JvRIxvUPl43ev0QIT70HuQAWuWZub1+pw4msR8B4jjEMPGgswANNO2P9koS1cnJ4FaEnBAXC0Jue/bjARwT8HAAITTkyqUmLS42STdUUcccYRcddVVQvy8iy66KOnX+3ofSg+KRdzsBl+VCnFxLYafuvDQXpgrGCOw3zCa66mAVmsy9QMgibpetrqettTtGJMEZCdWHwAbDKOW4tTRvzA/YNDARoIpgJGBm2UWgRG/U6U8iQXZTmEPso7X27BQkJXYfrDJAAswxqhf1OPfr8xaul4zTAJGMU9JesDcspVlFFW79Tm0FeAUcAd9ACCVmLJ6EOK333gOoDAHJMQUdCz7YD3GXGKeU0hepBl5VV9Dvsx3kgHBKGY9Rd7oAX77LFgNw92la0V5kgdNeOKn/sxfQErmbZoL+x16D4nVghSND8n8IzES+ywyZt9MOvkJayk6Gfu8jQW5cODgZ5zZ2A5XJyeBNEjAAXFp6CVXx9glcP/99xv3VDZnFONDDz3UuF7y/zPPPNNsSieddJIxdNtqMO7KTiCuHu68w4cPl6uvvjr2PgrzAhR2lIqsgKqtMfwAZQB0UC6J12Q7+IiCTR2jAEkq2W9xZj0NMx71XmUrsq4QqwpABsDUbxxKNTJYmzBIYdLpyX9bAUwUnASI5ECFn8giaSPL67hQxgYAASxHwB4FU2E71rNUAvmwt5BlFMlh6tmuKN6NbAA2YDgiF+LKMceYuy3NtfJ4ZRjgXOtivwXrDfZygAz2ONZKDk5r6WSssxzGwZZjf9QEHS0lxAlWs/juCsOW4956xj2MSiocXOBuynyLorDeatIHWNKMJQBbxkgUukhrdUTnASBNMmmIH5mxN7WlhF1+ZOOudRKIWgIOiItaou55qZGAKm+wE0jCwEa/3XbbyYQJE8wmCduLjGX/7//9P+OyCuAE+OTFHTA1QghRUWLmnXXWWbLrrrvKrbfeGuJJ8d+K8g0Qh8KVhYIhATjMWETJhhUFoxO2Bj/5e1QuVHHLC5dw5plf8Km8XsgA5ZGCEo1BnFTW0yDy0QDaahACmEYV802zVwIUYHz27dvXuL8h3zAyDtLOJO6BpUQSC9x9MGyYA2k7yWcvAqyhzwDhABFhXQHMVQahj1umgEy8m7Gy3nrrOfCtFYGz7jDWGHsA6fQdewzMQdUvSEbD/FPGVlsBxqMcp8iSmI7sacgSwIT4i0HWM51rrL2aVAUgnIMLDmHT0D/V2HIKHlWufawrJLUg22iaC3sZehy6QtQFeZIkiQMsADoOR3gPdkBU+3J5nW0/GHZAXNQjzD3PSaBlCTggzo2ONisBlGiUrptuuknOPfdcOeGEE4yb5bhx4+RPf/qT7LzzzoYFx6b517/+VR588EFz3RZbbOGCKYsYWSAfwMt77rnH6nGEcUl/Ex8tCwUFkQ9KN1kaMSI40QWASxsIQSY0XEX8Aodpcz1Vd0kUftwRAV1Q+OM0/BQMYPxjxGBUwCDB4EzbOKkEXgGfaR+gh7JjssJ4ZW7TPsYI4wUwlYMimKNRtxEZYuTCDgF4470AgfVm5aVtnVZGJsAHoBFjElAO5hvMqzTPt3r2BXLElZexz9oVtes9egGHV8w1QJh+/fqZn+xHtrPJ6ZdKtpwCcvoTBmYYl8569n35u1kT2UPj7hONg4kuhcsz85kDkShjiyr7nYz1Nhbnmmpjr7g6ZVUCDojLas+6dtWUgAJxuFXefPPNcuWVV5rU5/ydTKko0/yNDRnWDmw43FWPPvpoc02cBnTNyltwwaWXXirHHHOMbL755vLII49YUKOWq4CLMQatJuGwurKtVI44YrQFYAWDZMMNN0w9KIyyCzDlxfCvlngBcCluV5Kg44X68sEVhWyMgKUwOerlLolRBiCAUQNLAgAXIMtWF5lKuWMgABSp6y1ZEaM2zIP2dVz3KZiKWxbMK2WqhQXd1WWNIObMPWTJeHCAkf+eZI7D1GIdggEH+w2wG4BTmcqs23yPjKMGU/3X2N471C2aPQ6mMEAI62WSayaur3xg2wJOc2BCsXWf0d6s5sLKmv/+++8bXSHNcxv9Df1H+yKpEayZedkjYWIyLgDm+HjRWarVk9AiuuYm1Q4/73FAnB9puWudBMJJwAFx4eTn7k6xBBRMu/32240bKgyvgQMHmhb9/e9/Nwy4a665xgT45USR7KCcluKOGRdlPU3iJKbeIYccYoLnPvPMM1ZXvdxt0eqKVqmcsmNwUUMRxTDRQNS2nqj6kXGt+D48q5L9psHtbTWMMH5Q2AGN1l57bVlllVWMSGwyhGAX4J4FSEjQaIw12AYACHG44/gZE+XXYgwDQCFPDHPNNtoWD0LUrQ4gh4MimBUAOzCuWB9qFe4HGALkoI/JKsk4COLiV+tdbeF7wAHceJnnMGZaY1xrTCrmHIHzMeQx8l3W49JIURAJhjTrOvJkbNdzjWftUbfFqVOnmkNZ5hqHKTYXdeVlXCqYyTgrZ8vZtBd5kaUNmUaRK/MY9iQ6CfsmazBrLwCh17GKPQFTloM5GwvtTBL4tlEGrk5OAklJwAFxSUnavcc6CbCRsnGOHz/esN1GjBhh2G6UBx54wGQCPf30040rKnHkcFvddtttHRD3Y0/ecccd8pvf/Ea6desmEydOtK5/yysEMMIHJToNpZwFg8uYukaocqSn9WnPhEZfaCysSsWvHHzjOtsTL2h2SUA3DCDqz+82gVqtjX0AAo01qK7CGGt+XYajmF/KisHAgUkI8ATLKG63pCjqnuQzFExl7AFczJgxwxiElawrAHwKoCuufrDfNCtvkvXNwruY18wTgA3kzr7id54zvrkPZidgHvsSgImXQ4ksyLC8DbCMAIaRR58+fQwLzUbGoK5JHIQBxMF8ZD1ibwZU8QrCxNl/1JE1gXAV1I01Qed5NbZceRxV24E51i09NIpThn6ejUypl8ZjJQ4ff2MstMaWs/1g2AFxfkaBu9ZJIJwEHBAXTn7u7gxIACMF0O3hhx+WE088UXbZZRdj/JGMAIYcbqrnnXee3HjjjcYV88gjj3SuqSIyZswYk6gBFgCnxTYX2COcqAIa2lxQ8qknih1GCWMTgKpSyccARFmy0WDxK19Abph9GDNpcz2lrfQDAAhjDIOa+ZBmhpEabDCncKHRJCcAYXEXmJ6a9RegA0CwLTLfgsi5nAVD38FUBjBiPVEXO9iOrgSXAGOTD+AGhyBRAMMK5gE6EwID4B4gFXAvC+t7NWkzzwGMGLPoWgDDrJ02gFleR4e6LCrDkb0LUJW+S3r9J34kYCbyw5UW2dY6QEH2utbTZtvZchxIInNbvQBUltQTlhxjvGvXruYAScE5HVscpDLfbcxCre1wjDivK4G7zkkgnAQcEBdOfu7uDEgA5emKK66Qa6+91oABG2ywwcIMSignnHgSn4dTUOLJoei4IvLkk08ahiAxpsg6ZnPBnQBFFVcs2wqGGGOQE1RO2THAMEpaM8IA7FBKk46XEofsiFXG3NJsxAA/gC/MPVsNM/qLk3AMoN69e5uf1N/W+obpN9qKYUf7Jk2aZMacxpWLor2a5RXQGSMLhiTjv5YhGaZNWb6XtQFDD6AId2OAAowr4mOytlQahVmWRdi2KfsNeQIKMw8wUONiudI3MMQ4iMFtDZYYa4uyrmxnLdWSN2sJQCbt48AiK1nMabeyTZlvAN7oifRl2FiOLckUoEddzZGpZusNsiZXY8vxHB1vaR93tcZlHN+zVnAAwlrx0ksvmZAKgLT0EwfXjH8bY7M6IC6O0eCe6STQsgQcEOdGR5uVgLqBEKz6t7/9rTnFBAAA4OAnCpTGoiKY/P77728SE7hSksDzzz8vgwYNMqd6gBI2FwwAFGSbFH+MLLKmWv2OAAAgAElEQVTBYWhRLz91w5CBgUWMkrQWdT1l7GC4oLgChNsKwGD4oFhT7ylTphjwEGM5afZDPfsb0Jh5xPjTzK/UJ0gAeo23AyMScA9WjJc4Z/Vsv63vVuMJIJ/9CxYG41PZhMwtNdxxW0POgDsAc87IXrJXGefIjpALyAp5sjYlXZR1xXxjnWHOwXJkvqSFLadgJvoU45C9mHGXZaYrc43CPsE+T38BzNHmsPNN4xJqlmPmcdhnVo7rcrYcz9bnl4NzSc8FwHDeb2tctdbkoW7NMJVhyU2YMMGsJ6wrcWdO99tPahfZqof5bY+73knAdgk4IM72HnL1i10CM2fONIy4wYMHG1cTFEY2IRRdfnICzt/qoYjH3vgQL3j99dcNGwgljexwUSuDIapm7a0o5WTPQ/liXKFUA0L5lR0KHQYZhlmaSjXXU439BqsS44I5R7uYi37lEocsYDrA0gKAUlcT3mND3eJor59nwjTF9RHZcMKPkYSx3VJ8HAAO+hijCiNEGVpxMYz8tCWN1zI2kSU/cUVVV/bW2sIcZL3mQyIR5h17HQZ9FK6WaZQjdVbACOY0axIZ1DWOrC1tAuDBXZz1H2COTJjKHLZxPUK3Yu2M0pXXlr7wWg/6jLnGPk/fKcMRVrHX+QY4whqLHJnv9DVrbRLrZjlbrhogl9S4wyuFMe/nwNJrHyV9na657J+suzDj0OdsWIMdEJf0aHDva+sScEBcWx8Brv2eJKBxoFCGYMXpZuXp5oxeRKBpddPFuLY5pgQGKnG8AA6TLpphF3nhqoICjRIehtHAc2DEtZalL+l21jL+lSXQmusp8wpQASAHgAejGOUUV0V1XU2iXTBRAEzpJ8YOTDjqkWUWRxi5KoMCYBlXYwAMZAfQo2wAXHNwlWQclLsih3lvW7y3PL4WslZAM+jYZL7BuOKDOyvzjDEPEyyIm1va+gRXXmXAARzBzLT90I05Rb0BcgAo2H8BehgLzLN6FerFOAIsIgwE6wF1TAIwqleb/bxX+w09kj2OuQYrnr2lGjOK6xmTyJN+hQFfb6ZSJVuO9mvShyCg3Jw5Iq+9lpMFC0TWXbcoXbtWlyggJmMpiVilfvo0imtZb9F1mDus4yR9QMejvUHX9aD10tAFbYnpH1RW7j4ngSgk4IC4KKTontEmJHDOOefIU089ZWKjUdo6GIdyiNFCITgt7mm2Flg6uImQACGJorFbAHOUrYJRm7RSlURbW3pHFFlPUVA5KUbBB7zB6MRoiQMk0PmsiRcATBnfTiH1P4oAMmF+YIjTZxji66yzjgFU29Ic8C+5lu+ATUssKGVm6tob5Tt4FgYggCn9B2ORbJ6Af1nrNw4y2LcA4QCMWVfSWjgoUJYcBzy6TrInJwHcsNazRuPKC7ikbuZBgJm09kGQejOvmM980BMAYPidv/N/xiUgKyCdjftQtdhyXjOxFosio0fn5Y478tLUlJNCgeRTIhtsUJRTT22WSjwZ9hj7iI0JDvz2PfOEQ+FqfaprLXoIfc98AoCNK9ZgZd0dEOe3N931TgLhJOCAuHDyc3dnRAIAJg899JDZ+DBEMCAxfPjJh9NnruF3NkS+gz3APW31tBd3C00WADPDZpcBjC1iI/Xt2zfWEYvyzFiBDQQDToPaR22QqFFuWxbY1lxPwwqeZzM/AeaYo/379zfxsDA0w4AEGmQbQ5bxwTNhd0bdZ2Hbn4b71YUKowngjfh/sIuYE/QbawT9yP8BPmw0Lm2SM+uWurIzLjlQYE0JM969to9+AgiHuYPhqO6rAOJh2Lxe3x/HdYw7mCcaZ4rxajv7za8cNPYi7BrWNlxsWTfpR1hVUa1rvAcwE9dTxgrAQhYBW7/yD3M9c5yEOMiRgzsShzE+y8GtMM+P+14/seUefDAnl1zSIJ07k7F1Uc2++EKkR4+iXHBBKc6eFnRvdO20rj3lbSG+cr9+/WraDsgTXQd9BIAWgB1wnQOtuDJgOyAu7lninu8ksLgEHBDnRkSblwCnyaeddprce++9rcpC3XQA4lAGOJk76KCDZNiwYW1ShhiFetpOwgubs8mqQhOX0YWy9NZbbxkDWV3F4hwUGJS4ahAjyIZSzn5LIuupxm7CrQfDmrkIMO41CxlzXuNpEeAecAHltq2C6mHHEPMLQ2ny5MkLg1C3ZCgAIgPQARRodkHGM0BBVCBB2PbYcD/zGwCFcUk22SSYTS21m/6l3+gz2E64jdPfcbFTo5S/Ml05LAKYov4w/drSXGe+0Wf0IdlfWS+Za0FkwNqpSWsABgDXOZBzczfYqKVPADORH8wnBb+RMX/jUAN2FCAqa0FakhW0xpYjl8Xw4e2koUEMC66yzJ4tctllTbLOOou+Yf7S/jQzV7U1zz33nAlx43fOMCaUMYnOgu7C3EMmUTEFHRAXbB67u5wEgkrAAXFBJefuy4wEYB306tXLnD4ecMABJi4DJ1B8MA41ccNNN90ko0ePNqnHNUByZoQQsCEAksjv2WefrUv8NT/VVqPWzz0tXQsQhEKEcQOYAEsFObQUpD6Kd5Y/o54x77QeUbieRiEXjUGGUYmiCoBBf1QLho0hDniHcQO4wceVYBKAtQHLDRd1ANC11lrLzAE/jAWNGwiAB7CPQcFzggAEwVphz120H/YbawqJF5SZaWOcNuqq2XM5mCJWJSAdbA1b+k7Zb9QTpitjra0zMHW+wWbn4Ij9CmAHo75W4bCJeGWsnbhMpgUQqtWuenyvzHHGJvuRgsMtMV11j2PfB4yC3c+hIuslP21cIyrlqmw5wKfPPhP53e86yEorcRVZWRe/+pNPRI49tiA77VRY+AV6N4eccTHBkhwHzKGwGcL1cFnje3Ioy5rHHEdGQVnTPJd7/ezjScrOvctJIGsScEBc1nrUtSeQBIjNAQh39NFHt3j/I488Ig8++KBceeWVgd6RxZtQJogl9Pjjj8uAAQOsbuKLL74om222WeA6aiIBTh6JoYQxijGCDPyebAauxI83omwhd3UNDvs8r/e35Hpqk+uMukHC2MF1lzhQ/E5fYYhjzKCopsF48dovSV6nrjLqxo9xFAXTVE/7YSuRVQ4gCjCVvrIF3IlazqwptBOjB1Yx7UWeNie+qSYD1kLmmQah14yjSfcb6xNzHfkB7jLn2xr7zesY1f2MvmOOMf4A1MtZjuWu0VzH/wFb3drpVcqLX8dch8EN4A77DVkH0R14jgLhPIc9j/6hb9IANn/xRVH237+D/OQnxR8FpGBcCZH79FORUaOaZdtt9Xsx7C8Af6+s92A9lMxd9H8cQDbhNTgYw2WcQ0bkpQlTvI4z1tCsuAAn05vuLU4C4STggLhw8nN3Z0QC48aNM0Y7SntrpTJBQ1uPiYLRiNsLbr3bbLON1aMhKBBHn+M6gvKEwdy9e/e6uokhZJQlgLiklNJK11NNPGGrQaauq2+//bZx7+OEl78BEGhMOVvrbuskQsFnHqCk44YeZxZb+gpjAsYdQDeAMyf/GK5pA6laAq6QJ2snbcKYzkLyH9qgrnXTp0837CnAARg/MM3jKryTRBaMFwxcGFuu+JNAOcuR+c3ayRxEnrj919M12l9L7LpaY+mxVzNGNfFQlIwjDkfQTwDnYMzBKuZvUcYEjFqqhx3WXr78UoRlgcQNIvyTM79/9VVObr11gZD/SwEkdXX1CihFXd8on4cHyZAhQ6J85GLPQlbYJnwAMAFuWYfXX399I8/WZMjey9hM+hAlNmG4BzsJWC4BB8RZ3kGueslIAHc2TpNwXWPT4gNDQ3/X02CUm2OPPda4dEyYMEHuvPNOIZtqnEZpMhII9hYy6sFeue2222To0KHBHpLQXa+//rqJqeZFkUMZ0WDXGHUYzIABtoAAKFivvPKKCfgbV6lkvwFm8bEVwFL3HT0Rxh1N49XxEzYHdcf1j9hGsLgAkm3O9htX33p5rhqQAJkY5swH1rk4AZWW6qVMEuqAkbDxxhsbwAdwwMt89tLeuK9hPrHHUH/GIb/jEpgGBktQ2Si4w7hhP1XgMQoXVuQJ8Ib8+OBu6dhvQXtKjNHO2kgfkXiBuQaQyjgFjINdQzyzpLI3Bm+JHXfqAR4MONZMQJAkwEzmBXsdMdXoL/QWDplrATBJS23KlJyMGtVell22KEstVXp7U5PI7Nk52X33Jvnd7xaYv2m9ATEZh7boYEHlxbgYP368DB48OOgjfN/H2sv6CON6ypQpC8NAsF5WypPxw3oa1LXVd+XcDU4CbVwCDohr4wOgrTdfWQjbb7+9cRngFAglBqVUT+AqZfTMM88YheCuu+4ySR5g09Vi0mVVzgBbbOzXXHNN6pNW0N/q2kQ8EhRYdT21rf+o60svvRTK1bZam9LgelpZb0AaAHT6a9q0aQsDOremSNJOFFPkCGuA+1BSs8K4CjNekQ0fsvfhhq1gpS2AF+sz6zR9powrQIKk4jMGkS3gBq6bsPuoK2OurRVlOQLw0IckDYDFAyDuF9wFbCc8AHMeF6y2ehAWxRjSAwr2E2UvVgZ+p+8w5gF3cHXDlRVgSYGSKOqRlWdwWAszrWvXrsZNEFnVCzxCj2WNpA4wVDVbNaw5G9bLN94gc2o7+ewzWFpFk7hh2LBm2WuvZhM3TpPEaFxXQsgAEulhoC17kp+xy1ziUJjDpHoUdUlnHWY/Qk/C9kH30ZAdDoirR8+4d7ZVCTggrq32vGu3kYACcaeeeqpxw8BAYhPi5JKPJmxAaVFlarvttjOGAxsZCgKJHrLMamhtqBAXDpfPiy++WA488ECrRxWZx9ZZZ50lgtAC5MAE4ANDBeBBA9ba3CBi+qCYRlHS5npKmznth8mKcUHmvqCJF1QxhV0D+4NA+awFzH/NLhiFjG1+hjJAMRxZy3r06GEM7yjdp+JoPyACRi8GBX3GmgygCqBQz7orWwt5AlgwxpBrPesUh/zDPBOZsH8qMMehjhrZlaxblScGI/Lke+c+FVz6yBOmK/JknSNRlbrz13oqc46wCBjtHFjxf4x45l0SjK9a9avX9+gRHEoCDmnyBZuAIuYbeyXzDT2X+aMADIB2vRhQuKJ+/rnIggU56dy5BMYBIDI+AdgZlxr/Dnmqzs5PXSfSDMzVa7zqe3UPZVwwhgEIGRv1Gg/1lod7v5NA0hJwQFzSEnfvs1ICnPIqGwjQrXITQjGwKSC9LULceuutZezYscY996ijjrKlWlXrQaYx4rsBqNKfKB4YE/wEdIDV2JYMibS5ntKpMN9gF6Gg03cYFsTeidrggakDiIJiilsmhoptrj1RTDbATIwY5AgQDQhdj+QjUbSF8Qz7g/lMXwG6E9MOUM4v4ypofTAYMWJwG1c2IYyv/8/efQdbV1Z54t/adk/o1pmqmZr5a0YFVCRJTgICrRIE2yajJCVHUcEIKphA6QYByUHJYYiCpCYHSZKDSDujdk2smrGnrKn+2Xbrrz4Pva7nve997z1h73P2Pmc9Vafufe+7zw7fJ+y1vs93rVX3+Bz2/tr6PbhROZrblG7mtE0R8xw599RTTxUFD4KjjsIgbcWh6fti53j3mSvWN3iOos7srdxojbSW2NCIgg/TPu7hiczSrDcRutt0P9ZxfgSMjadeAkbfmXOTVMsJQfWxbi+mdI6Ild7ccb02elvHHptCOhebXW1s8Ewiro09k/c0rQgkETetPZvPNTQCQtbs9DJQNDJ+1Tbbmhtr6Aet4Yvywt1yyy3V8ccfXx177LE1nLG5UwgHkBSdAxIkXJeTUEumjSQatIUj5ntRdKHNJDOSlJMutI/jgCwyJ8eRTNi1IzxGWBZn07U5ml1u1Aa/+MUvyiPI8ziuoh/jxMw459CZ636ndAwno04nLdQb1hXzSX4trc5rjBO3SV8LQYCMo3SM9YlTTnE1LkJ10hjUfX1q0aj0jTBqav2yXiJzzDlzTXEc/Yg8nRY1aKSw8C6SaxSZKYXFON5HdY+L+eejUNV/ETqOkDMHm7R9IxepTRNqV6S86w6Kp/P0ppPpVcu1aS1G1orOWG+99ZruzqHOj6S3Id1knw91Y/mlRGBKEUgibko7Nh9rcAQYkdddd1118803lx0r4ReaHUKO6hFHHFHtsMMOKdnugXaPPfYoufKOOeaYSnhvG5uk7tQ+DEzhT/6tT7tuaAgJ3mijjfpy+LsWehrGOXLDPBQuMWnjEOHCoaWCEAr7yiuvFAeTU9v20PTeQhaIN3mM3POs5Nbi3HleZG7kbRpVXQVD7wzKNyQHgngUdVEb185x3hNVprUZht6/gSfHFbGD+EByInaQOl1Vbo4LU1ga60gi4x8ZMU51ZpAi0gfoP/MvFOldVBeb6/D0CTKzi8/Rz/gzXkSF6Df9Z04aRzah6moxPqiHzWepJUJ5Puo1FlLLsfeCkJskMee9AdNJ5YhbCtsk4pZCKP8/EagXgSTi6sUzz9ZhBC699NLqa1/7WjFANttss0q1TM4/VcVdd91VjNmTTjqp+tCHPtThp6z31j/2sY9VF198cXXYYYcVbNrSkCahZqJujKILFBWD7rS25Znm34fk2iqDrohQXKjwgmdvMwGJ5KJIFZbGWEWAU8NM0nBeqP8Z+hEGyVmRJ1KD7STDeubfqzEAO/kEOTvIDcRhm8dA0/PN2oDUiXxkVC0w8eknNB0Za2zCkKK2KXVR0zi05fyIdnhS4cBzqcJH1JxCxzm0FMFC1M3HaVnX6+gXihvrqPluLZ20Gi3yk0kLIfyYTaXf9HWb1bixgYGIohbTYNrPOlFHP7bhHPGuMz8RZcLE9Z338jBkOGKd+s38pQqzHjc9d3vVcr3kaS85Ny6shaZa8yh829gin+ks2wht7Je8p+lFIIm46e3bfLIBEKBuQSoxCj/zmc+U/A2hmmKAcNaovrykzjjjjOIARNLYAS4zdYceeeSR1Zlnnlntt99+1emnnz7x55NDjJHHCZFfJAovML44Jwx/xuQ0NM4MVc9Cic0Zt5rnZuS2OfTUfVJvqILpvjmOFGdtI98WGzPWAoQcZRTyC6GA/J3EGuH6iCbKDXjaeY9Ko9Mw7ut8hnC09Z21Qygd9SyVWxTq6FUTWj+QCf42v7Jknfc17eeifjM+rcWIa+Nz0NxvUd2XY2/t0C/6x5o4a80mIYILSYnc4OzDo61raCSId3/GgLB/hDZyZ1IVRnvHjPkNT7YEEpM9OEvk22Lzx7s6cstRONqYMBeRcytShsPTGPVTQQvvd+q6OgsC/N3fVdW9976+evrp11dvetPvqm23/W31trf9brlH6VXLxfwYp1rO2PdpwzhfqJ/hY6y3de2YtbU9n3f6EUgibvr7OJ9wEQQ4qoyB8847r1T+PPnkk0v46ULtiiuuqL7+9a9XX/3qV4sqLr47ywAjLb/5zW9Wu+22W3XBBRdMBAoqKs6zHUYqOB8G/XyCSk41f/eZhha50xhMvaGnns2Y9mnrrmYvucEgRZpyzqchtI8THMSXsBsOirHpZ5PGrXnguq4RoZJ1hfpMw3zp9xk43wgNDqdqkghvTiSCZ9bVhP1iuNBxUZXz1VdfLc67TZK6cmtFHkekHDLumWeeKRtp01w0IMgN6zzFKyxtYnSNMOolw6NqtY00G0jjDKU1ZqOqqPdRFAzIvISLz3rqLvaXkHFz2jppHnqXex9FOK+x+Za3vGWUJWSF3/3pT19Xffzjf1T96ldV9YY3VNU//INNyKp63/v+sfrc5/6hev3rV3zZ+Wo5R8bGZRPv68ihiMRsY7NOt5nEbyNmeU+JwCgIJBE3Cnr53c4jEA4zgu3222+vzj333JLHpJdki98Zu0IwDzjggOqggw5KIq6qqhNOOKH68pe/XMhLROW4Wjh1L7/8cjH6OCEcsMVCcOQdYhzWmedkXM+70HVUgZXcOJ4Z6db20FMkFceR2oDhzjhHUrWVMBy1f0P5gXjQVyrs6S9kcF3hOJxXxJtrvfWtb21tyMuoWI7j+5wyJADnUegxdVEkZaeWts5MC5E/DjyD3IAncnP99dcvc72usb+iZ0CcUjn6mBPe4daeOufduPCbfx3z3PoJU2PSujIJ5W2Tz2+seEYqVQWW9FuT4cfGBlWezS1qLe+lJkiYJjFrw7nZZeaedCCRi1f4apPhvH//91W1yy5/VP3616+r/uW//D0Kv/tdVf3v/11VRx31D9Wuu74WIbBUWyi3XN2VWIXVe7eYt21sScS1sVfynqYZgSTiprl389mWRCBINuGVcsR95zvfKc5CJPd2gvj9/vvvrz7+8Y9XRx11VAljTUVcVZ1yyimlWurWW29d3XjjjUviPcoB4SQzZBjokV+k3/w3kS+r6wZ2qN8o/DgOku9Tf9QZ5jFKPy30XbvAHEeqrdVXX73cb9f7YRiMEHKcTCQkB5MjHcmRBzkfFQI8OY0xrqdBTTgIBnUeyxFH6OsP5D5Sn4IxCGIY6ztrPlJAeFVU8mxriFGd+Ax6LnhZo43J5557rmBVl/pt0HtxvHxU5ot55z6Qc4iCNuVzXOq5zHlrpveg9RS50aX7X+r5Fvr/qPDtvYHcMdds3EjsP2oz540JY1U+YGPEeJ3F99KoWMb3Iz0DDGHqpzXVehmRChHeX9c1haOecMIfVgtlHPnNb0QH/K668UbFSga/YhNqOWOO8pLd1saWoalt7JW8p2lGIIm4ae7dfLYlEQhl1Z133llyw+28887Vcccdt9z37EL7//vuu6/6i7/4i2rLLbcsBty0KnmWBO6fDjjrrLOqww8/vNpkk02qO+64o9+vDXQcowUZKicT8okjJeRuUOwpW3ynzcmhVwTMQqGnngV5wDlhADPwqP04Km1w0DjiHEbhKJRgyLdhkjsPNFg6cnAY+Jxr+Sn1ZeTGW9EjBPGP2LAeccT1dZsJ2LZ3h7VFoQDzCKnZj4Ov72JsI3ZU8vRv/TDrjrx1+he/+EUhm4WFyqXapqbvIn8mRbHf3Sdirq3zyBqq2i8VIXKji++vOsZAhB+ba2wA66B3n/6DST/2QKy75q15bw316Xczr47nmLZzILS9362d+mahatzRd3Bnu5l3wln1nX+PMvfOOusPqmuuecOCRByshavedNOvqz/+49GQr0stF2Own/E62h0P9+0k4obDLb+VCAyLQBJxwyKX35sqBDjEX/ziF0t4qnxnm2++eXFy7ZoKabz77rurhx9+uPrABz5QiLhsryHwve99rxRqWGuttaqHHnqoVljI92HPMGDkjRpSKvdTnKvWG23oZAtVPY3cbwtdEpnAadPkuOEQU6KMU30GXyQRJ9e9Ipc4TW01OhvquoFPS/HhwymRVw5haf2RIylCJfXvhhtuOJYqcwM/QIe+wHFEWsv9BG+4jppLD/EUSoeVV155Ljx4Fsa9dcq6E+oXWExS/TbIUPR+t0HjfuWuo9qJogFNh88udp8IDeSGdRy5iSjKXGXLIqavEDs2oeBkHCLWvfPm58nzTqKkh6mNIWtrb/XMQcZMHvt7BITzsp3lhfOu73fO6I8o+KDf5OLUP957g24iXnXVH1RnnfWGaqHUv8JTEXF33PHrkjuuzhaEWigoYzwtNa6MWcdQXLexea5UebexZ/KephWBJOKmtWfzufpGIPKrIH3kO3viiSeKQRE7dwwGjQpOoQaGerbXELj22msLcSnMDoEwStMPwr8YZMIXOB6MtLqqE3KUGe8qWra59arfGGxRdGEQp96Osxw7HGTEwwYbbFCw5NDVHXqj38wR1zNv5D7pWthXm8aDMRp9R1FEBUrxYdz26+i06XnacC8Rvouchm0UClhRlb9h75mD6VoIHoodRCqnH9EzjQ3pRlnkXUlR2OXwaGsm0huxY77pO7973w9KDgzT12GHGDdRHKTuypLD3FdXvoNQj9yjkTvP+ETMyaEJX4TROPqyK5gNep9BaJoXKkwjPtlpo9oU+kYfOa95aGOXrdJPwan/+T+rao89/ln1pjdVyxVl+OUvq2rrrX9bffGLvxn0UQc6fhC1XKhb+1FfD3QTNR2cRFxNQOZpEoE+EUgirk+g8rDZQIBc/p577inhYoyCMN6EXlKj1EUKTQuat956aynUwMCVW2mYRgnEOPZ9eEfo6ajG3fx74WQhOTjGbWtNVj2NgiT/+T//5+Ko2Imtw2lmhDOUKRfNG+RG05VB29Zvdd8PdYF+QmQi3qgMjFv9huyk/NB3+jTDqZZG3xiNQgEcR0oZY7butWWhOzGnzQ3qJk7Xj3/847LOWX+6qm4K9RtlkXBOzwHPaSSII4cYO8DzrbPOOoV40Id1jh/nhKdNKBsm3lF1E8RLz5TpOYINEcUskHP6zntJTi6/19l304Pa4k9i3nvnKFhm/fIOaorQRELrJxuySCvkPttisQ3wiy/+g+qii95Q/cmfVNU//+dV9dvfVtXf/m1V/at/9bvq/PP/vvr3/368PbWQWi42Um34Wzc9U9taEIqpiGtbz+T9TDMCScRNc+/msw2NQOS0cAIvpWl0NIYGp+eLcuZttdVWRRHx85//vO9TMuwYy4wthjEnmaEyS0byikJPe6t09Q1onwfabUboICSM8b/5m78phvUg1QSpDHzPuSi2ZjVnUZ+QL3kYoiYIUs4NR5waZ/5ciLBfDtEzzzxTnPVIXD5L82YpQIMAsyYJnYJlG0IlkQJIHSpV4av6T193IW8iUsqaIWE+4s3GC1xnpUV+RuMJYWbNk69tWDIiVJPsCnOZU04FnnbG8CPK5oWwP2OTLYJ8Q+LEOw+RQ71pvfW7tTMJhxXj7X0DTyk94LTqqquOPS+yeWJzyr1IsSHlBVvFZ/66ed99r68uvPAN1X/9r6IIqup97/vH6qMf/Ydq0vuuvWo5zwNTeHo/aW16dycRN/z6k99MBIZFIIm4YZHL700VAhwipJAXkd+j+d0OHdWWnxwSDgh1XLaqevzxx6uNNtqoGBYImsVaVB6EIaUPY1nozV2xRU4AACAASURBVLjUIQwgn0knEK8j9LSOsec+qK0ifBUZiigwvnsVGeYEZY/5IQSZI8op73IYWh34jXKOcOwpb1Xr44ibC/064rFRoP+oE6itKOQ4l7NaMMBaTUFhPEehgDY5OfPHizllDdT/yAO5q9pUACjUbxHSr0r1rG2WLDTHI4TVPDPWojBFPyGs3n2IDQpi70CqwmyjIcAuo6Zno5lH1sMVzXvrJiLOe47dgczRH0HsDJL+YbS7bu+3raNR9dgGETzbslkQZKvNRNXXzSN9rf9CIS4v3DAVUpvsEWSizUtjVXGfILwin1yMu0m/ryI8fn6OxSaxyXMnArOOQBJxsz4C8vkLAnIHHXLIIcWJjSqdXpqMEoZ3vDAROXJg3XTTTeXvo1R7mgboKSTWXHPN8igImoXwCCIHWcCg44BMQkXl/hhudnbH3ZoMPa3zWYRFciwZtcY5xYdwFIZZhIekszIc4jEPEBsINGFosKxjDeGghNpKGCQn09/6rSY43BNN/ltRfc/8sq4Yu13Lq+UZvGuQ34899lhJf4DUoeKrY2wM2kvef9R7nFsKIvcxS+q3QfDqJXbYCELHkcG9ip1QFllDQ61prGZo+SBIL3ts5NE0d4TwI7WtdYMSGdTFyFRrJyLPmow0sX7OWv8gKJFF7CSblV3IhRwbiexyc89GlPfeOAtUrWgUm+vWdWPUhpv3Mkx7x2iEsMY5wraaFDGXRNzwa1J+MxEYFoEk4oZFLr83VQjYFd11111LeIiXIKfIJ4wxxjUHeuONN64OOOCAarPNNpuq5x/2YeAiREeDD0WPlzkHklNMRcUA4Zho/Sp+hr2fxb7HWJeLZ7XVVmvi9MudcxKhp6M+mHvmlDDII7eOfmXgmhuZI3FwhKlgODlCpYS2wZJB3iShSWVFbWXMI1KEQrZJbTU4ist+wxpjnCpkAU8kMeer681z6TvkALWU9xJHzjOae4MSDYPgYYxG5VBrNrIo22AImGPeecamfkQSmYc2+PRnqogHw3P+0UFqmvfWNQq4utQ7QUKwaWzYef8hpILUaXLujYbK8N82XqOgRVTztNY0+W4a/m4X/6b+s4lgDfMzVNHjfu9537Mzw/bttyjDIAUfmsQw/J+mrpHnTQQSgWURSCIuR0QiUFVF+ca4E2IZyad7qzb5/3vvvbc6+eSTqzPPPLN697vfnbhVVTFYGcR2kPfee+/qkUceKSqq0047rRiwyJth8+jUDbDdbzuUTYfCzg89DdVTm41bTiNHg9OPeOOERziKHWbEgHlBbcRRYay3JVyl7nFS1/k4AxRF1EUcRjv2426Ri4qzKozc+hak6iRJ8WFwiIp9xidSQ46dqCo8zPm68B0qnVA6RvVVc7UupWMUs0Dy2Swx12c1tLmO8cCZptJBoCK/raf6T4if9yBiDiGfrX8EQvWK3LCGhqqo6fepuWGNtJmB3NFv3n/T0n+qlFpLraM2U+siNPvv2eaONGa8dyl55ZUzL61vbNWmnpOSks/guj42h0ZRVS6mlmuKFHbNJOKaG5d55kRgIQSSiMtxkQj0iQDD7OCDDy7G2cUXX9znt6b3MIbcNddcU51zzjnLGByM5csvv7w1BFz0AEeeUR3qvDp7Zr76DWnl07SzMOozINgQa+6VMR4JhFd0XgauHXTfY2zKDxiJ3Nv+rKNi1c/3g9hA9nIYqYyCMOrn+00e06u2Qggg0aOKcFuVju6Zg8Oh4gwbYxycpsn0JvthlHMj5lTds44hBN75zncOrHS0VnHkKLXMZXi2oZjFKLhM+ruB6VNPPVXsA3m1eitIRwir8etYyljvodjQmPT9t/H6UaHWePfegemk1im2g7xk1kvz5cUXXyxkuDW+KwUfenO9Sjvheayro5BFbRw38+8p8qlS/Okv48qzm3/ezaOG/7OFrKXmthy6TaheF1LL9ebrrIuYSyKuCyM673HaEEgibtp6NJ9naAQYW9pCLzUvPcbgN7/5zer6668vebNmsXGKb7vttuqGG24oO429TTLvPffcs6gF22jcUXtI6rzOOuvU1nXGBMdKC+Vbk1VPR71xhhYj1E74KqusUhwLjuMwxEaEfHCUhGFQyCF4mjBER33uJr9vDOh7eSaRW1EBsY1zoBcHpA4nIoobcHQRhwtVbG0Sv4XObWxRFRmnHF2JuduO5zgxgg8i3FyLdZiTSYHcW2il956CJEa+KcwS6u9x3vc0XStIYkotfbH++uuXd0E/jr3NC8QAdSNShCNPMYe865pStc4+jZBzc9+7aa211mod0RUbGpSOPnLkUtprbVg7F+oPti2S2NpgnUdC9dq5/9//J7rhddUf//Hvqn/7b+vs0fadCxZRuMt72/yjWEXK9Ru9ob/Ne2GnFLDW0nGmRuhVy0X+6rBBRyHlkohr33jNO5p+BJKIm/4+ziesEYFddtmlOIdCMCfZGICnnnpq9cADDxRjXgGCI488srGQWS/ok046qbryyiuLAioaB/nBBx8sCX7vuuuu4oy0tTHsn3322ZHvsYuhp0G4/ehHPypGo9DTXsXGqH0WFVidm8PPSI1caCsiBka95qS/j5S2FjDqERvIzK6qAs1vRLXwfGQcR42Doo1i2A/SR+7BOmI9o6rljBtDXVGcDPKsdR4big+EqtA5BCvFXOSVM045mVRYiIJUv42GPkfeOxDhZmNH2PmoOR+tIfrPJgmVI0JAv/ZLDIz2RJP/NmLDsxubCgZZf9pKai2Elv7zzou8gMaEcTLJzYN4D3v3y4trfZ+/luIPzzjjDdVNN/1BpdqoPcW3ve131XHH/aZaZZXfTX5gjOEO2C6IcDa1TTUbtd55CPH573NzUtEx7yl97DNp4rxXLRfv6mHVcpHfeZLjdgxdnpdIBFqFQBJxreqOvJlJIuAFG5VS/YycYn734kV6+WyzzTbVX/7lX47NQZ2Pid33PfbYoyhw9t1336KCECLKiT7//PMbKSQBg3XXXbfsUnM6PvjBD1Yf+tCHirPOaGZE33rrrdXmm28+yS5c9NqMLI7TGmusMfA9LlR4YSFDbeATN/iFCCNFFjHM1l577eLcNU0WhVKEY2m8cCxjx7nrpIrnsRYgPMw3ziLCox8VTINdXeupjZvIQ/bMM8+U8eIZfZpwOoJIev7550voGeemifDxWkFq8cmQRJSZCq4Yr94PxqtQumkap+PuAsSGdy7ShfKXsqjuFk61NYbS2HvHfHCtKCZQ9zUneT52hXXUekNJjIAfF/HfxHPrL3NOX1Gq2oRiL1FONf3e9TyuG+s3XI2bFVVfRrx9/ON/WD322B9Ub3oTu+A1RP7f/6Pur6pLL/376s1vng0yLsaC8YiEsglkrlMcB6nKnrHxbKxSv42jP4cZo/PVcs4RURpLzS3j1zs+ibhhkM/vJALDIZBE3HC45bemEAHkEvUAQwYJF58wrvxk1Fx00UVzlUInAcO5555biEC52bbaaqt/Mp7+XyHGGAe33357I8bsc889V/CRF6z3Rc3IZLhce+21haScptbF0FNqGGOVQosxOemKkgzDyEdlbAg3Mn56d23bPmbCYTT+ERvy6S1l1Lb9mfq5P+PIeKIW8Nz+zREZVanmPMh7KhIEEYfRettUIu1+nrXrx8A0yG9zHvmNgDPPXnnllUIgcywR45ytWRi/o/ZpbGZwvOFLbRiYjnrufr6PVPVx/ccee6yE/yN2ENVdJVWj6Aps2Q6Rr2vaxqP3HvLWvLO+mYOa/qNQrZPIQf6Z79ZTJHFUPF4M0+eff1110EF/VL3xjYiaZUfj//2/VfWe9/y2Ovnk3/QzTKfyGOPT2NRv5h2luLmv/7qi1lwot1xv6pT548MaxzZrYsNtKgdJPlQiUAMCScTVAGKeotsIxE7zoYceWuT7CAw7mdQ78ZNSg/O9ySabTFytsf322xen9c4771wGeGq4U045pbr66quL+mlcjSrO7uv3vve96s///M/HddmhrkN1I6fLYq039NRxXSm8gCxRpVM4EwenjdXdzC9zCkHIcYhKkOPMr9LvwHGv7lHj3HCo6gzn7fc+2nQcIkI+QIQPZ0RIPIWuNbMfR9r39T/inmODMELE9fPdNuHQpnuJkF6Kbg4+BYe+md8iWTun0joR4aucy1kJgRyk36xRVIUcb0qtSWOEGLAGWefl9zQH2QHub9L31g+uxh81IWLDmDNOh8lN2s+12ngMsgyxo/+897zz5BdDqg67AWFTRN9bk20WwbTfc51yyhuqa6/9g+pf/+vl0aKWo4x76KFfL0fStRHbOu8Jpua9d5JNC/2GnGIPGL/WTX/3DvN3/dcVBdlSajm2r/dzV0n+OsdBnisRGBcCScSNC+m8TusRYNh6UUXIYagGovplG3JdMRI22GCDaocddiikW2979NFHS6jq5z//+fJzXE3IqjCMs88+u/rIRz4yrssOdR0Ybbzxxst9t4uhp6GAQRa95S1vKQa4XfZ+DfGhAKzxSxwHTgkjllJHFV4OSt1qgUFuOcZBVEKjLKCCbcPcH+Q5xnFsFCmRdxHBxiFZeeWVF9xNVyCAWkOfCw2fVPXDceAyjmvE3Bd2boxG8vVBlAzUOsa5vkCI6B+kzqwSo72YyqmF5GpzQQtEtv4LtSoSpi3FVmIOBKZIi8ilh+BIR78qa6b5q//MPxuEUSl2qY0J6ymyiL2KFBpmPf3KV/6w+sEPXl8twNmX7vvVr14j4iJkdRzr2qSuYZxGnlvqfRuZS+V9RIqbf8g57z0bHN5vCNal+m9Sz9l73V61nOe48MILq0svvbRELHz/+99vwy3mPSQCM4FAEnEz0c35kNOCACXDBz7wgeqggw6qPvWpTy3zWJIcb7fddnNk3LieebPNNqsefvjh6i/+4i+qAw88cFyXHeo684m4+YUXgnytM2xkqBtd5EsMeLvgQoWRWFRFXQmVWNFjUXtEwnIKK2Qzp5KDMg6nzbU4RQxSjk2QEV0wqOseX8OcLyrRIVKtQ8LpjElqgre+9a1FPeDfXR+nw2BT53fMfeTQq6++WlIoIDeQ2KOOU/0XFXQ5k4ruUH5w8Ach9+p81nGdK6oeP/HEE3M5oRZSFI7rfoa5zvxiK5S71KraqGNjmPsx/21esFeMrRWpNIc59zR+B0becy+88MJcfk4RGL05VSP3I5LI5pt5P0qF8rvvfn113HF/uCAR93d/V1X/8T/+rrrssteqwU5rs9Z57/vAW/qFYeaL+ceGMN6t0c6DnIvK8m3FzybaWWedVXJMR+VfG+uKemVLBBKB8SCQRNx4cM6rJAK1IEB5plDDxz/+8eqwww5b5pzCE97znvdUu+22W/WVr3ylluv1cxJ54YTJfvWrX62OOuqofr4ysWNghCwINY8b6ULoKWeRscgA5xgLPe4tWz8xQBu4cISKS1Yu6TwygFFbd/hqkA8Ub3bBOY5CpqadeGigy5Y5JYfROsWwD5UckpXCoCtqzaYxGuT85gMFjPmP2FSht0kcY/5xKl0XGS4U0jwcxkkd5FnHdWyESSKI/a5SYtud5n6x8a5AClDocLQ9l7mHHGhS2Rsh0jCl7PKO6npxnn4xr/O4CEHWf5EGwLwP4r2uwg+/+U1V7bTTP6v+z/+pqj/5k98/wT/+42tquNNO+021ySa/rfPRWnMukSXWNWsqXG1m1jU3nM/4Z688+eSTxb40/yiX2xDC6v4UVvvOd75TPfTQQ3N9IqriyCOPrPbff/+ydmRLBBKB8SCQRNx4cM6rJAK1IGCn6sMf/nB19NFHV3La9TYkzZZbblntvvvu1YknnljL9fo5ibxwN954YwmJ/exnP9vPV8Z+TIQcSrbPSYm8OuNQWw37sBwbOWUQGggiZBFjrgv5gIZ95vnfiwqsHDqhIAznqOA5rJNnLMinJT+Pc9kJb/M4qAvLJs/DsdE32tvf/vaSNy6IU8Qc51wIFgKJgx55d5q8p66fmzNnjJrz/+W//JeyZtWhfhsEF/Mv8lBRzOnHmH91E+OD3NewxxqL1lPznXJTEn1O57QQjAutnxHCavzYfLCO1plw3lyOtZRSE/kuvcC0Yjrs2Bvke8gSm1CRR1MYalSUNm71n3E7qnL/v//311VHHvmH1X/7b6+rfv3rqnrDG6rqj/6oqj7xiX+odtrpHwe55U4cGyG9bpbyvekchVER3EaUDT/2HBs05sg47Q4KZ3mcFXljS0azec+f2HHHHdMO6sQozpucNgSSiJu2Hs3nmWoEEAh/9md/Vh1yyCHVJz7xiWWeNUJTDzjggOrYY48dGw7ywl1xxRXlZT5OArCfB5wfesoJY+AiBYQfCt/xt7p2Q/u5p6WO6U2+LkRKWA/HZtZbGLUIAf2KQOOgMGqXypHDAacuEDaizxnE+n9UR2aW+4RTH2NVP1AVIIwXcy5irimagqzjUCJDhiVVpxF/ScFffvnlQlaa+z5taeaPtVO/6zeqVfOv7XMJmSgHJWJDqDSneBab+Rd55Sh/V1999ZISAKkzqBLYufQ/Yg+ecE3ybbRRFSGO1sdYG3vJoqjiaw4a0yrYRwGcYW0YhRmee+511U9/+vrqjW/8XbXppr+t/viPR3uONn3bOEXAWUflwTTWRwnpHeXZomCHOae/2DA2sKyhTW2wUjYLP7388svL+qe5tg19kTXjLOw2Cnb53URgWhFIIm5aezafayoR8CJdb731SnXSk046aZlnjGINxx9/fLXXXnuN7fnlq1Ox1c/5BSTGdhM9F+q36imjCGkQ+TAoPTiXgzokdTwjUiPCz3ornrYhlKGO52viHPqZQodjqS/NCy3y/EUCZg4LAjsKLyTpM1pv2NHn0HDAVUwetpJsFOtAMjtnKHWcb9YIUuo3Dpn5bg1qW9L9hUZMJOI3/6gtNtxww7lNjTasW9YEKnEkoebfw47V0WZMO78dFRRVPLcxgQhQpELfrWj+BRHkp/U0KoDO2nyts0dD+e5dhiRSodfcWsoOiRBy3/NhyyB2olr6rJOixqix7b1ikwiubRun7D7VmRFz7lduTn9DlI1yr8bGvffeW8JP77jjjrJxoiHMRdIcfPDBJVQ9WyKQCEwegSTiJt8HeQeJwEAIyMnGSJPnobchwxBh1157bal8NK5GCfftb3+72nvvvcuLfxJtoaqnkfutn/uxq4wI4wQjGRhD1D1N5kWKsEuqNyE9GoOxiyFf/WDc5DER6iiET/9xJv1NbrlQv8y6YzIK/rCkKDQ+KUrNLcT1KM5C7/30FuuwtglvNR85lW0gdUbBbkXfjTyVcOVIw9ZYXcoBb+JeRj1n5FfzHNSRQpDf/OY3F6XHJFrkl4QnJUyTOfUm8Xx1XzNCkL17hK2Ze5FXLt5HyIJXXnml9ClM8z01Wi+wMbyTEPBCe9keo1QsZsNo1mpFH6yd+tBnVpp1CDkcyldjtisbO2HDUO6yYdiFNmVjI6GfPrShc/XVVxc7nKo6mk1K6jf5o3Mt7AfJPCYRGB8CScSND+u8UiJQCwJnnHFGdeaZZxYV2hZbbFHOKczrQx/6UHFaEXTjJB2+8IUvVF//+ternXbaqfrud79byzP2e5L5oacIAuTAKASBMEaOCKUHssHOP3zrqvjonu2CUmsIOwkVQr/PnMctj0AoNfQXXKngEKmMTlj7OwdynDlZpqGfOOgvvfRSUTzZQUeujIMo0p/IFI4UBeOaa6458rxuS3+E+s0aI09R5Cgb55rdJBYRQu55rHXGTxACTSUBR2ogNM19oVb+be6P8h5oEqO2n9s70PvPT3NfnyKKqVdTUTxa7yGJENZUiEKD67Ireu8qcgEGIUUZxtZoMgRyNFRG/7ZnVUkecYXU7Frl414ErJtU4t61kdNW31lHqfu9QyiQIyUH4u68886rLrzwwqIA1Kx9bHIb5ZtuuulYfYLRezPPkAjMDgJJxM1OX+eTTgkCDA6kGyd1v/32KySD8uNUVcg5L91xtq997WvVcccdV2233XZlN67p1m/oaR33wQGx08yQZfwgd1QQHLRFGJdzqE5lFzyrSA6K4sLH6xtOIzzlKerNlQN3hqn/14+qI3KA5IiZVqXVqKhGPj1k5hprrFEc8SacxX7uM0gdDof1LZLCIwWbTrTdz/31e4xxaNxxphCMyIyuqt/6feY4zngKlYpQZsS4vkPqjEqMC2/mrD7xxBNzSq2mci0N+txdPZ4yB6bmG3wRcKFYRQ5ssMEGZR7COUm5/nq5d/5TbRv/bIlxEcX6zRxkM7JfvCP1c9cLlXhHITWNReox43TYXHn99eRkjpI3VN9RG0eRNnNUX5qLjz/+eOUYDREpT/QRRxxRNs6yJQKJQLsRSCKu3f2Td5cILIiA8LBvfetb1QMPPFAMKgnoSc833njjsSN26qmnVp/85CeLOu+WW25p5PorCj2luhiXMWuXmeHHgXz66aeLE8KY9u8V3YP7RpxKvixEglPTlCqkEeBbeNIIk0SwUUohihBr/Tr1iBBqJIoZxACnaFrUSMN2F8LL+EaaGKtUoD5tC2PhbCBVEXMcrqiiW0cFwWGxW+x75r8iOsabdULIbb/jtIn7acM5EQL6MBRBnGjzr18HOipKUoH4LrI48mW14fm6eA+RJgGpYT1dd911y9xf0bqIEKBANL6toUHUz/o6Or/v4SPUN3IVmv/jUBQvNQb1sftiz7BJbGBZ/9twb0vde4T0W/uNQQRcnWkSlrr+JP9fH7H7b7755jnirfd+3vWudxUSji2eJPkkeyqvnQj0j0AScf1jlUcmAonAAgiQxEv+aqf87rvvrhWj+aGnCK8IP631QgOejDPIkeRgR3Jl6gFkm3uOMClGrg9HZ9Yd8AEhXubwIIoYokhQaiJE0bDql1BaMVajrxjziNUuKa1GwdR3EVu9Do2QHq0LDrWxEKQOchZ5wMGUu2qSc22++tX4Mra64OSOOp4G+X4UC0Coq+ynz+QeM68XatIDWHcdh4Rz7FLVkge5n1k8Fp4UNd5bkf+NarPfzS3EvWMproTMIZupVSeVG7ANfRikprUISRSbBf0SzeN8BmuVPnRvVKVU4pTl5lbb1itrPCx9bDy3dfOlqf7zrrv44ourc889txC7bB/rH1uI7dnb2DU25Xfeeefq/e9/f1O3lOdNBBKBGhBIIq4GEPMUicAsI3DZZZeVQg2UCY888sjIUES1y6j0FEUX+nUORr6BIU7A6KZ84SwiAigEON+UWtmGRyCIIgrQqHzWBFEkbJVKxxhDBEiYHP3XBVJqEIQjoTVHhhPOEWPMt9FR7Pe5QhElTJnDwkF529veVpyVcfVfJLyXKB0hwZntcp6ifrGv6zhh+xxKa+aTTz45l2wersL5jFtVIWeZ5KkLa6QGTK15kW5h1HmC1EOqWk+s1S+++GIJk0PszIo6xxj23EgsmxreIV1q7t/6ae2ietSX+k8/Tmpzg01lc8za7h3l3rr8rhp0PBhPZ511VnXVVVfNqeDgsc8++5QoGKQkYk50zH333Vf98Ic/LGk4ovl3VNId9Np5fCKQCDSPQBJxzWOcV0gEphqB66+/vuy8CW949tlnh3rWNoSeDnXjVVUIOKoXTrecHcgcTo0d5sgFg+hoM5E47LM38b1QFMGTEc45GCdRxKHUn5FXTlJkjivjv+t9iGCkKOBYma/DKgqb6Pe6zhmKFHMPgRuKNE5x3WrHGKswjWqSrt82NUld2I7rPBRW1lX4Gqc2OoxVGCe2w/VCEPCUpFRr5oafTRAsQUojdXxs0rmuv08q3+RwqC39LaQHhaZxSQFn82galJr6S14y/afPvINFAJiDTROrxonrIgONWyGXs5TT1TPffvvtpfopci2a6Au534Sf2uxZqNnMYHsi5rznDznkkLFtRi09W/KIRCARmI9AEnE5JhKBRGAkBO64445q2223LYaaXctBWltDTxd7BgYqg5SaSNVDxip10XxDsdcZ4UzKycLpQQg0bcgO0gdtOhZRxKmxg6uoxaSJogiFQwpEnh/GcFeUju4foWi8coYRHBSbs0RmIHM5dchxaoo61I6cb8QsR9HvFEDTRjCMe10IVaMQR8SbvoqCFtZaxJG11rqAQEKO5zraXy9JowDXSVaU1Hdy0MmZKom8/vX+b4II7A+V4Y+KsGpj0nud+m3ai68geKx3+lGfSQVAMbdYLsFBETanzXUqPLlcqTVnSVUsZ9+ll15anX322WUjItpmm21W1G+KtM3Su3vQ8ZPHJwJdRCCJuC72Wt5zItAiBB588MGSHJbBxNBeqs1Xv3Uh9JThjdRgjD7zzDOFTBPCiNTot3FAECKIEc6kHU3nnDTZ1O/9N3EcUpMz42PXG2lCTdBG5ywq33H+jQNj3TjgNLRtt964MmZVU4siIbPk0KxorC6kdtSXnMl+1I7mL6KYw4jYrFth18Qca/s5I/wctvKMWhOQnAuFShrT5qHvCNnSOOvW02y/RwBOCA1rFHwiNK0N65Q5aP5oTz31VJlD1lBkdj9zcJL9bGzakLEGCH1HbIYCfpL3Ne5r6z9jSV5BmxxsGRtUg9hDvfdsDUY8IYyNg1mbz8LEkW+XXHJJKaChwXePPfYoBNx666037i7O6yUCicCYEEgibkxA52USgWlFgDHNULBTx5BazAmOildRdGGcVU+HwZ/Dx/AW3hY5yjg5ozoMET5HQeh3pJ6wlllonpfhbbwgioLU7FI4j2eg2kGqepbVV1+9kIkcs0lVGo1qksIkOURyx5hvo47VaR2ToXbkBMEMUUmpM5+wjPyB5idiw/+n+m20UWH+WFsRbt4fFG7WwEGJIufguFpDpEUw9/y+WCXr0e683d9GFNnEePXVV4tSk1ILCTdq/remnjpU43LLIXIU7bBeIebaNMekR7Bh5n0NX7imGvO1URG5Ab2/bTZSzek/n8U2KiL81JhFFke+wll5X3l+4aPyv916663FDtSsX8JJDz304tkKmAAAIABJREFU0BUWrmlqPuZ5E4FEYPwIJBE3fszzionAVCEg5EWIpiZsodeZ6mLoKcPSLq9wU6F8HBphJ00RLJxJhj7n8Uc/+lH5OY2FAhBWSE0fzuHb3/72Yny21UkcZJJGQvko+EAt1a/KapDrLHSsMco5FCZk3CKMh1UmjHovXf2+dYqCCDFE7SF81RiFp/UApkLppmGsTrKPeisfwvcd73hHbbfTm9Mqqv9aV9uoWK3tof/pRMhIKi0bYUIG6wwXrPteFzufd2HkJfPORcYhd4yVcauke/OUWQMotmepSMAw/R7VyK2b5uPKK69cioLYoAr1oPMiXtmN+tcGxyyptY3xq6++uhBwCvtEW3vttYv6jQouSd5hRl9+JxHoJgJJxHWz3/KuE4HWIPCLX/yiKEk0vzOqIoTI3+xwUj91YaczcoFJSOyZxh02iqxC5vhQNCEBOf9IwS6SAAgOjlWQmBxFypemSM02TAp95vkoA5COSFWKn7rJschPZG5xeIyRbKMhEInXOUvmPhJgrbXWKv1oXaP66OI8HA2V0b4d6lG4IVOsbYjNptcAYe6UjkgBZJxQQv06LU6utZUqF8Hx8ssvF7KKsqgL79l+RxQiVQ42xI0NKht+ERbZ7zkGPc47GClv3ZanzM9cWwdF8bXjzX3vKXMw+hCWxixSs+k1YLi7buZb3iHnnXdeddFFFxU8NGuivG8IOOld8t3SDPZ51kSgzQgkEdfm3sl7SwQ6gACihaOjXXPNNdXWW289R761PfSU0R0VMtdZZ53ieLcpRxniKhJcK/hAJVBHaOw4hhWFBieK4Y3UrJuIGsczjHqNUKkhyziS1AEcu151QL/XCEIDuYB4i4IBmaesXwQXPg6h4cNR1D9Iot7E63A3BxFI+hMxx4FEKqXjtDj2MLO5AS95n4z7cbfI76mvnn766aLU8b5yP11MfI6Isq5GBUvK4mkfh/owCgMIZaZQs1lGTVWXSs35EW9IOOrX2Fwc93idputFuKU1gJ2ov/zNJhXCWDiyTappUcYv1HeiHFQ/vf766+dyI9rU2X///asjjzyyVIXOlggkArOLQBJxs9v3+eSJQC0IRLLzOBkHxy6fz/rrr986J4HT7Z450k8++eRcfqJxq98GAZ/jwYH0USwiwlc5lG1xwtwj5xBRJGeaf3PAu+jsDtI3/R4bFUyRE5QeG264YSlOYdwtFnZlvOpjBj0sOYmc0GlSvvSLYZ3HhfrNmKWWis2ExeYTEkRfUTcIXzUPKTz9zPYaAkKlkfDCQ0MF16a11dpPkYJsRbwiYJDjkwh/7HfMICpsyhh/xqnNo2mv0rkYNhHCqt/MX6G5cBm0D62tFFt+wjM2FXNt7XdkLnyccRpFLYT0xsZF79HWX+9EqUwiJUfklmuLTTMsCuy0m266qYSfPvroo3OnWWWVVYr6bd999+1M5fVhMcjvJQKJQH8IJBHXH055VCKQCCyCwPHHH1/UcJJExy6owzljH/zgB6s///M/rzbeeOOJkge9jjeje6WVVurkTmxU7+TwegbOJFUUcmbcpFevUgGpSf2WOcqWXipCAUCBwal+4xvfWHbG/dT8P4UWQgNxoBgK437c/bv0k3TrCHMnQhWp3DjcwxIakaQcAhQOch61tYpu070URUGee+65Mk6RxbBoO6ERfYgct3ZZQ2N8tCVsLvJpURRTFE1CVdj0+Bnl/PoQSanfQnlpMwips1gYMtJN1U/kHVy7VCxoFLya/G6oFm0WWhOtA/3Mo+hDY92miBQnUbSjTST+UtgZhxdffHEJQfV+ifbe9763Ovroo6vtttuu9WviUs+Y/58IJAL1IpBEXL145tkSgZlGwC7oDTfcUP2n//Sfqvvvv7/sNEcTgoCUo5TbbLPNxkIqcArteAfBgbwa1vFua8cyXqkCOBarrbZaUVgxYgetQDjI88GVs6O/kYBrrrlmJ0nNQZ65qWORbsKh9Js+hCtCwFjlILa56mFTmNR9XhgLj4rKtpQJdYW0BXEqXxcygDNGCezfiIAuOZKD4i6UH1lsDaLwtN53mSxGJHCgg9SRQN3fjJVxqnTMffeBTIr8ZP0QGoP23zQe792EyIk8XOZibGJYZ+HKBqBi1addHq9t6L8oagFXhJpNo1HninUFOa4PvQP1FRW5udBGcl+ORuq3K6+8sqSM0Kz9e++9d1HAiRDIlggkAonAQggkEZfjIhFIBBpBADlEno+Uu+eee4oxHA3RsOOOOxZSbsstt6yVNAq1EaVYJMunzJtmhzjIgAhhokxkCFKnIUDraHFu56IeQBgJy5t2XOvAbqlzIG2EOkaF3qg2jDBCdCDjMhfcUigu+/+hfkMSqc7JsePQjcvxNj/0nfug8qBk4qCO6qQOhkL9R3ueKEjy7LPPljVGkYBxV7Ws/8mWPWMUBVDZUOhj5CRrar1DYiAZrN2ICO8sRFwbiYemsa/r/JGCAglvLtqckvstKyCPjrD5gRxjC/zkJz8pmFLB1b2+RSVW9hyC2pywnpsvk1xzrIN33XVXyf929913zwEKhyOOOKI68MADy5qRLRFIBBKBxRBIIi7HRyKQCDSOAJXIzTffXF133XXVnXfeWcLtonGOt99++xK+qtDDsDv/8sYg3qheIpTPuWbRkWG8cuYYy4xjDrNQMZ9hHEmk6l//9V8XZaEEy1lFbvQpg8xA1iAxIqF8kDVxdsa+xNZIJI4HhQ7nIwsFrBh/WFoLJOaHJ8dokuOVoopzaj4KX+Wc6fOu5ZWDaYxFBPwsJRm3jlKrGk9UVZFLrI50AMgMJJE11nuL+m4W31mjr6jLnkH+TSotfbXuuusWAh4pb4PKpqCCF5GTbFoq6daN4ULnsw7YNGLTIYvZA+NqsQmA/JMKQ9OHNgPqVDgv9jzW8csvv7w6++yzC2keTeoV4ac77bRTrRvL48I2r5MIJAKTQSCJuMngnldNBGYWAeqfW2+9tSjlbrvttuI0R0P0yKOBlJNXYykVEHIidmU53ogmYSfjMsq60omckgj1UB3WzjKMVpQXh8HreEQRh5ujgvxpMty1K1iOcp+h1kTImAdRobMfXCNEjhOEcOaAIEQi7HqU++r6d0P9htBAPMudaG0Yl/qtX/wQAIgBax4HliMXucnadq+eKXC1Vrg/Y5bCto332m8fjHpcVNgVNocwo1q1TlKt9rvJ4RwcenOX4s53vbcmqfAZFZc2fN/6Sr0YuCJKjdf5uMZx+tBclNLB2uF7PnWrutqAzSj3AC/rFhxtbsY6MGnCmO2nD21sIFndl/eiTY665xLi9pxzzqm+973vleto1sFdd921hJ9utNFGo0Cc300EEoEZRSCJuBnt+HzsRKANCHBGkHFIOeQcwigaEm6bbbYp4avbbrttJVm13C/f//73i/Oy8847F8eHA7Pyyiu34XE6cw8KBHA8KD2EzXEENU4JTCVdRw4hipoIN+kMUDXdqHHNWec0yKUV1eKGdfgQOs6FIEGSIudCZVW3A1ITBI2chqqQMwQLjlhT4VFN3HwU5EB4c3KpHbVwdpu45iDnRPYKB6MCQxpaf7Mtj4C5HDnJ9KmNjnDS589v85aqELbIglVXXTVJnxoGFVy9z6yxiO13vvOdA+Pq+1HReq211ipra6iPa7jFzp4CJqGGF9bb1o0fYyCKdug7NmHkWx1W8Wg+P/LII9WZZ55Z3XLLLXM5jxG8Bx10UHX44YeXd062RCARSASGRSCJuGGRy+8lAolArQhwqu+4445CyiHbqLY0zgyiiDPYS9Qdeuih1f7771/7zmetD9Xyk1FZRfhj5NCRWHh+iGTLH6OVtxdqTYSyKnII4ybURFH5UWgbhYDk5MLnECfTqlxCvCE0hLhzupHFXW5RaVofIlUpTax3deV37AebyAEpnI/KS+PQ9qPW7Of8s3BMqFYRmDY7vLeoVq2xNj382zrrb6naHn1EsBFgjtS0Htg4Gja1RdyN9ZTNgYBS1dr6HVWAR7/jbpwhwnrhaS3yHhkV13E/eW/RDmubsG/z0Hq21AaYMcUOlf9NWo9oa6yxRgk//fCHP9y3+nXcz53XSwQSgW4hkERct/or7zYRmAkEKLPOP//86pprrik533oNJ4Yy9c9ee+1VqrCGmmsmgKnpIaNSJ4NTOC/nX0gHIgchKrSPM0ldgJSbdAhKTY89ltPALxQyHJlx5tKK0FcOpHlD3UjxKAR5KedjLOCMcBG4UhK95S1vKfm0PNu0qjU9a4TWCrk3N81T87Hufoyk567D4Zb3KYn4EQbqP321V/0WpBtCh5omSbjR8EXAKRBgLlBpmR9NtAhhNUcowcwR/ed60xrCKm1CVOyeliIsNmzMOUU7vEOsb+anYmLej1tssUUhW70zL7jggvLxjtGMsR122KEQcFtttVXt628T4zbPmQgkAt1BIIm47vRV3mkiMPUI2Im97LLLquuvv76QQ9HsStv1Viae0iAax5ERJaccY4nhmG1xBKil4AhTTvdClb04IAxRH0a5cCtY+yQptzy+jHpGvBAYBJEcMpN2YqJgh3uKfH/IHPfVby6rNswlKgaqBM4UZ4maaJbCbz230FWht9R/wq2oU0bJgxREPHUW8n2DDTYoBUNybo8+4qVboCq0riJr4GoOWhPMQ+84Ych+R5AvlQd19DuajjPYnIsckHC15o47XNo9UOOaj9RRkSfQZmBX1yQ4Iqd8qMZGTZvQ9tHm+dhAyLarrrpqGRsTBuwdGyHG1kc/+tHqqKOOmlMIt/3Z8v4SgUSgewgkEde9Pss7TgSmFoEvfelLc8YRp3CzzTYrVaj+9E//tOxoMhofeuih6tprr61uuOGGYjxGc/y73/3uQspRynHYs72WcJ3jwIEX6sZp4Bz2G2rCcIWt7yNHqXIQIl2r+lj3WAj1mZAoHyQXYrOtJBfChfOv/Yf/8B/mqgZyOOpWWY2KdVSU9ZPDi9yYBlXfqLj4vrmM9EXMveMd75irCNtPCKnwSJgihihfKDYRRm3r/zpwGuc5rLHWSLm0OPnWx6WIeGuGfvROUyzA+HaO7Ivf91yssd47NuCMV+/1fsb6OPpfTrKYi5TP+twcGzYn2TjuOa5h3BlvTzzxRFH4GbNduO+6MLJ+HnfccaX6qs2O3sbW3HLLLUvBMBu9s27r1IV5nicRSASWRyCJuBwViUAi0BoE/uqv/qq65JJLCqGmSMNiOZI4Pz/84Q8LKUdBx7GJxplRTv7P/uzPynkQD7PWEC9IIZUZOQecmFFDzmDO0YQvo925EVAM+bY4R033MyxDQbDmmmvOVZPtkpoIueoZEHNUHhRR/sYBmRQRYGxFXj0kEcdQXr1pzXM36jhFUvhQWsFL3yEpzPP5zVqAzDB35Z5DGGcbHYFQaSFj1l133bIGDqqMQgJYOxCsiDxOv3E/yykXrAOIN8pCayxcrQOTWpuWGinmofULyf3SSy+Vw5Fywujb1KL6KVxh+a53vausIW3FtQnsrIEqn6qAKoWEZnxRqbI3peTwjo9mbsLp2GOPLYrBbIlAIpAI1IlAEnF1opnnSgQSgYkgwJi0sxuknFwgvY0BhZDzkf9sWhscoiKq3xmQTeUjChIAmYMMgDEjl7KjqWtOqt88KzIjCi/ITYbwaKv6bRCcKCM4uZE/BwGAqBlXdTzOq5AzYdAc11CyzpJzOEh/LXSs8SnkETFE4fbUU0/N5ZPTnxxN4xdh3iXCeFRcmvh+YG2dU10avoizOjYiovCKn/rqxRdfnMtJ1q+CuYlnHtc5I5eXXGyUum1WGC+GiXlIxasPFerRd36fVAirNR62xq513sZkE/kmxzVOhrmOTcOzzjqruvzyy+eKfukXhRfkf1MpV9NvNnjvu+++6v77759TkW+77bbVt7/97WEund9JBBKBRGCFCCQRl4MjEUgEpgoBxibj97rrriuVr1555ZVlno/BJXyVWk5oV9eb55XX5Fe/+lVREf385z8vRv+48+fA0bUprTilb3/728s9dJlQ4cAgiTyTZ1FRtsvPs9RYRwAIt+KgGFcUA6F4rFOZRj0ivxKFputRICDg6rzGUs86zf/P2aZ6oc4yZkM9l4VXhu91a4HwfB8ktXDSJteCKBRgo4NaTp5OZKs+7fq62tsLvSotGIfqqElshx8Fg38z1lTrHWLRMyLFEeZNh4JG4SDjxybHQmrZwZ+oO98wtu6+++5CwN1xxx1zN444P/zww6uDDz64vN9W1LynkOEIdyGqXYysMO5OPfXU6oEHHig24qqrrlodeeSRJepkqUbd+Zd/+ZdzlWNFmVAGUlVnSwQSgXoQSCKuHhzzLIlAItBCBBhijAmEnM8LL7ywzF1Kfh6kXBdJFo6ZZ6JAi7xEbegG94WMQ7JE/jShOl1ITG7MuG8OlDAxRBySaBYUKb1jJ8KQEQGq6yKwGfVIiFGwECKJzEAKUac27Yy2YT40fQ/GKkWhsSqnnjFsTYgwSeMZ5pSr4ZBHVdam763r57fJQU1EQWQtQ2ZMasxGH1NcmTtBInRR5Yh8R0hRFtos8/6aBZWWZ7axgxzzvDYDESR1kavx/qLWNk58hFwOGjLd5XkL4yuvvLL6zne+s8xG7Prrr1/Ub7vuuuvUqfYX6i922B577FE21Pbdd98yDq655ppScfj8888vOZhX1MzJPffcs9hAzmFcXXzxxWXzQX7mxdLGdHns5L0nAuNGIIm4cSOe10sEEoGJIcC4oJTzEb7V25Sxj5xy8oW0cUc+jGxOtfBIO+ycxDaHSNqV50AidpAAVHPUAHU5HnUOJkQTIxXZZPcbWZTtNQSMPflz9CXlGkWQcbfUPAn1m+/5jvBlYzfVb6OPLGQGHH/0ox+VMYvMWEwJqy8ocpBwwv8Q48gc5NJS/Tj63XbrDEhLawECgwKEyrgtLfpQ/6sojNDRj11wjr0PkPGS5Vtju6gyqmscmI/W1eeff76QkkgPdsiwa6O0FLBFbiJq/ZylRgV87rnnFsLI+NLMXwW/EHCbbLLJTK1zsKBokw9vq622Kngg56RoQd7ffvvtK8Rj//33L5ElP/jBD+bWFfazjWt4fvWrX52loZXPmgg0hkAScY1BmydOBBKBNiMgfCtIuccee2yZW0UYBSlnF3XSigPOC4ULQ5NCg+Ms30zXnGeOBxIRKUNlBVttUom4gyRyTxzCIAenLcdd3fOQ00gVF2G7QQL0On5RfEHuRoQmkggB17UxWzd2o54PrlSKsNeELGqD4hqhj1HAAelk3OvLWXPgo0+Ma7giRGCAsJxEiP8gYySUq1RVb37zmwt5iCBHHLZlgybGLJU0NaF7Nc4m/V4dBOemj40QVv2maEfkdPTvxRTIMWbZLPKb6fNJKTabxmih81vH2G/UbzfddFOZs5o5fOCBB5YQ1FkNpdx+++3Lxsudd965DHTUcKecckp19dVXlyIV85sNSaGrlIPzCTeY2vh59NFHZ0JVOIkxndecLQSSiJut/s6nTQQSgQUQEHKk8qrw1YcffrjsUkdDIAQpJ0fGOEM8VONDElG9CTmbJgeZmoNjhhC1k08lt5Sip67BK1yHsxIV7hCbsxAWVRd+veeBJcPdvNCHL7/8chmvCAyh3xyjcc6ZJp6xDeeEM/LauDVngtis69449PoRycepp5bTj9NOnkbVyJ/+9Kfl+bterRcZow89yyqrrFJIHM44xeSgZG0dY8vabrwiRhAi0/QOqwOfFZFLNtz0oXXUHBTOipSLfjRupaWwSUcZP2vhp9YmNpv8b4ihaN45H//4x6u99tprpscasls19B122KGQbr0NiSZU9fOf/3z5Ob/Jq3fYYYcVEg4Z19tOP/30OdJTvrlsiUAiMBoCScSNhl9+OxFIBKYMAQavHBhIOVWz7OZHY+zuuOOORZ4vv8awISSLQcaAYoBzhqkzGOGTcqLG1bUcRc+MZEDaUP7JKcd5q0s1oR8p8RCbrrfuuuuWa03COR0XruO6DmdQM3aR2hrSCHEdpGdd/TiuZ2rLdTjkQoLhSPEk0fg4WlxXKJMciW9961vLWjgt/ciRtxZY76kKrQWTUuY22Z8Uj9IB6EckozHUZD9GNW1ruHeY8WLDpY6qsk3i1OZz27SyKYdgjbx6Qk+9u9qY4qFJLGFw0UUXVeedd17ZwIu23XbblfDT973vfflOr6qiqvzABz5QHXTQQdWnPvWpZboEMQ6vIOPm95fKsieeeGJ1wQUXVJtvvvky/33VVVdVX/rSlwoZ9973vrfJrs5zJwIzgUAScTPRzfmQiUAiMAwCjL4bb7yxhLDaJUTgRKP+QcrJt7HllluO5GhwXiLfD8eFQovDPYskUYToIM04y5xk+Zo4HMMoqyhEfJCojFM/kZvZRkdAwnGJoPWTKrmI02jh7FMacZgoDpEASOVsiyOAxEQQwQyBYW0Q3juJ9cB8RAIKeRMCZh4KK/QZZj5Osu+ts0EMy39kvFprZyEUXT8aVxRy0Y8UVnUpqUJZqMKkuU+l5R05iTE7yTHWxLXhaQ2g/DLvpFGw4eEdaT6uvPLKRf01zVjLo0f9JpzSONaodpFJFHCKXmT7PQJygCqyABvqtt7mffye97yn2m233aqvfOUry8EWueUQcpE+JA4S/vvpT3+6qOzYv9kSgURgNASSiBsNv/x2IpAIzAgCKg9+//vfL0o5OTeQD9E4yXYfKeW23nrrvqpKclyoFagyKDEQGZRFXagsOq4uD+dOkuCo2CmZdT+5j5BvwiQ5J5zCcSmJxoXNpK5jjBqzwswQpIiipcIXI48VxSNFB7VMkDmzQIL001e9IZIcbAQRJVqb8NGP+twGBUWO+0Tw6Ms256Vyj5xPCmP3ueaaa041abHUeAvVmjWV0tF81reIOe+fQQgd64H5jJTfcMMNS1qHrhG0S+E1if+PwkywRZ4KA5yvZHQM5ar3IfvEpog+NB+nYcPDvL3tttuK+uqBBx6Y6wYbOkcccUSloEDv5s8k+qmt10TafvjDHy4qwUMPPXSZ27TJY/N49913L8q3+e3ss8+uTjvttOqKK66o1ltvvWX+28b0Zz7zmVIEgs2bLRFIBEZDIIm40fDLbycCicAMIsD4veWWWwopx1CksojGACb7p5QTJhHEGmLorrvuKseqOoWE4xxyuCkHpiXkq6nhEMQlfDmOHA/hqxyPICv0i/+jEqD0sHPe9mTrTeFV93mjuh/H25j1GcbhDucRmUNhysHUZxyqfgjWup9r0uejdOEYRYgkfIznQciQST2DcEdknL5EdAt9RLS2JVzO/cUaIDzT/eVGx/KjRZ9FXjlrKgJYmDlF5kLvpVAWGqeqj4eCe5j1YFJjt63XtSYi3OJdFtW7+1kPIl+n7/veq6++Woo6WVu71Dee/ZJLLinVPoXlRxMmiVj64Ac/2EhakLaOiWHu68c//nHJbXzIIYdUn/jEJ5Y5RYSmHnDAAdWxxx673Olh/7Wvfa2EACva0NsiNFVoMFVdtkQgERgNgSTiRsMvv50IJAIzjgCCDRmHlLv11ltLLp5onD655CiGqLoipOLiiy9esFrVjEPZ9+NHxUckAEeR0/Hiiy8WYsfvWZGvbygXPTDUb1RQQoQjrK8fp7DfO9CXSBJEgN/XWmutorLTr3Vep9/7GcdxnjPCBCkXOMvIjDarypbCJcgZDqBxYw4qFjCJPuTIC0OnqJGnzL1k6w+B6EckDjLOJhGy3N/hiTSnLPR3Ku5s9SAQykLvNLiOOmajIjjV4y9/+cuSuJ+K39q6WBXWep5muLNIY0CNddlll5WxpyF7I8RSXtds/SHALqVmE6Vx0kknLfOlKNZw/PHHl6IW89s999xTVHTf+MY3yqZxb4tiDaJDcv731xd5VCKwGAJJxOX4SAQSgUSgJgQY03fccUd15ZVXVg8++GAxeHtDyxjBwnfk5ciQitFAj7BeP1WUDUWHnX8GO0ekiWIao911+78d+Yj8lO8p1C7jSLaO6DNnEHNRJZA6Z1pUjYgMz4XMQNIbt9PYqPw40gjGZ599tsxDxALypillDmITQSRNAEITKZ+5IEcbXcarjSWkeIT560sE67TMydEQGu3byE0kmXGKgPNvytK650iEvVt7hK8ix4UkU5NrkyDLAzn3dt9995X8bzY0o2I9ZSY1F0Io00oMN8622WabsvbaIO5t559/fsnxdu2115aNr/lNYRBqtz333LMUZuhtBx54YMlV+OSTT2YUx3Ddkt9KBJZBIIm4HBCJQCKQCNSAAMdPHhPKOIYlJyYaJ1EOHbuUGlJDLjnhq/JscFCzLY2AHX0hqQxzihehU/PDekNp9NJLLxUyIEKtuqw2WhqZ0Y9AgiEyGOFy6skBFw7c6Gcf/AzIHGoOc0Ui8p/97Ge1V9Id/K6G+4ZxaH0QeivnkxDJWQnDRegiGxDlSFUNudMbUj4cqtXc+BRqZdzC1TXGQRoPe89d+p5+g611AHEDV3PSmmqdpZRFsLYlFLlL2CLjFVywDvSb97Su57NhqE99EOUIc/NxnMU13IPCC/K/eVdHo7xWYIAKrq3Kvbr6oenznHHGGdWZZ55ZId622GKLubWX3WkuI+hWRMLuvffeFXWzY8x3TVQHhd2uu+5anXDCCbXfPptZ6KtrCkvOjZTaIc4TthCBJOJa2Cl5S4lAItA9BPbbb7/qhz/84dyNIzN23nnnIu1n4CLn7EBKdsspjWb32+4j40gVqjB6uodAc3eMgGMEIjQ42sLN+tnFRy7BGmHHyEOGwleeuX6+39wTtePMEb7EkeYYyRWF5GwbkRF9py+ROMKsjAWqsjarHhEZCGOKImM2CfeqjDHKHH1JFazaobE3CDGJpI3iC2uvvXbB15jNOT36uoJgQxJZJ5HHMF3IIXYcFRdizibHO9/5zkK4riiv3Oh31v0zGOfGrbUAcQyrQcZ9EwjoY5tbwrkpHeVko+JHzDVBhBl++R+nAAAgAElEQVRbqnLKP+a6mvnL/kHAyQOX87ienrbxC1d2D/sUaX7NNdeU3IHIuU033bRc6OGHHy7zuDen8QsvvFCKPfjOPvvsU4h3KVW8b202y8FbV7MBiHhjG1vbNdVZsxJuXQjnedqMQBJxbe6dvLdEIBHoDAIqUNnZff/731/tsssu1UYbbbSgdB+pIGwVKXfDDTcUpzQag1RyXMaTRLuhIOkMCDXeaDiETqlKGifPzv2wRjoDj/PPeWRMCgvkfMwqKUeN4SOUj2PYlST2vdVFObPUciuvvHJrQuUQhRwLDgyH1v1mGN/CC0MQPU8//XQhfTj/xuKKyFVrQhQIMG4p4FLpWt+iy0HnkEcxln4JeWMcqU9BI1UA4k5eOf047Hpd31NN/kze+YgNG0PGLHzbWpyJytEY8EHMWV+9O0dVPQplpM5i85jrGiJS5dMjjzyyzPts9SPgHfmtb32rRGvoR/MS4bnxxhvPXYz67fHHH6/uvvvuooaP9swzz5TqqFJUWGelVTnmmGOKWr6OJlcdAs5PNh6bl21snb/++uuXqxJcxzXzHIlA2xBIIq5tPZL3kwgkAp1EgLHt05sTbqkH4bxQ0dlhvO666wox0tsYS0g5H0UIpr3Bg1PHCbAzj4j0GQTTfjCKsBzOUSQkR+b063j2c422HRPqN8Tv6quvXpRJ1BhdfuaowMpJoK6gtkCA+YxTaRLkICLCeOJsUwzUneupbWOqzvuJ8H2KTHOf06gfOevWAyG9iJ13vetd5bJJ8IyOvvcVR92aQFmoD8ybUbC1ziBzEKXUoOYDgpXCbpZIU9hSvyHmJbWHCYJyFGxH7/HBzhDrq/Bkaj7vYiHK+rgfItEcpmwSforoiQaPo446qtp3331zk2KwLpmKo70nVWtF/LNxVcG1cY3s9bu8daeeemqr1e5T0RH5EK1AIIm4VnRD3kQikAjMOgKM3ieeeGKOlGP89jYVsIKUW2mllaYKrqjOySkUjiBkjcMyDqclwh45iwgAqgVOI/XdNBApng+OHCGOIJKoaw5hP4Pd/OH0RvVVDqPE5MJBqTqaGEsxbqkshcu6hy4Tm/3gPI5joqiFvjQP9SvFprGb+I7eA7EmPPbYY2V+UME0oYiNOYlINQc52shya+00rkF6xjMLCXz++ecLkQzbcW4KjD46Fj4DFZt3o36kYDJe9CP1L8IVkcIusc4KhRTGKARVf0cTLUCNte222/ZF5DX1LHneySJgU0WILJt2tdVWK+PIuPG+loOOQu/oo48uG9vTYINNFu28etsRSCKu7T2U95cIJAIzh0AkUaaU85EfrbfZMWSwkPJ3NY+GXXUGexBgHOxJ5ydzT5RVCAAhqwg5/3aPbc5FNn+ChPqNk8SQNV76VTFMy2TzvJ4fSSbH4Prrrz9HlI1CypmboSYUUkn5Ztx2aXy0uY8pqGxCcPqFq8HX75w0hI55ueaaa5bwtsS8/540bq1lnGDjfxJrgj4zH62v1FLmZOR67LLDHdVPYYtgjEI3XX6mxUZWKOXMxyOOOKIo/zTEozmpAIQQZQ0JiVhBwCFdsiUCEIiiWr1oCF/ea6+9qk9+8pPVQQcdlEAlAjOBQBJxM9HN+ZCJQCLQVQQYvXLPRfiqnfbeJkl2KOWEHI5CMowDIwTJT3/607lk7SrGtdWhRgD8/Oc/L4QhB0teuTYTWhRadpGNGWHO06p+G3SccvoRvQgeTiOVFWyQOv025+BoC+OjIuoqAd7v847zONhSzphjwtARp0Ib569lxjUHToij/EWO4/zLudVEYvlxYtDUtZBf8PWT4leKg4Wwber6KzpvhHMLT0PMRa7HptSrTTxfVEY3dpGcsA01dxPXa+M5b7/99kp1zl7lm/uMSrtIuO23376sudkSgcUQoNAVrnz66aeXXMtttrWyJxOBuhBIIq4uJPM8iUAikAiMAQHqOPnkfJ566qllrii5cpBycv60hZTjsFBCCNlBvPmdYd5WAm5+N3IqkFzCcaigNAQAtU4bQuUUskAYIinkuqPgy7YwAlElEHEjzxGllb5Ers1XsITSxZkoO4zbJnIWznJf/exnPytOPFwHJdTMSaoc413hFXm4Iq9cPzmsphl3qk1jG0EkTLvNhX9CYWWO2fSgZNWn+hIx17Zm3LlHqRRsfslj2JZ37biwomS8/PLLq7PPPruEpnqX6yuhuPozCjK4H3ORPbLNNtsUxVNX3vvjwnLar8P+MwbMkSDgF3pm+QQ/85nPVMcff3z1kY98ZNphyedLBAoCScTlQEgEEoG+EeD0SKKqAhNDTAUmFa9U+lyqUXWpwCRsQZOkVcLW+RWY7N5feOGFpWoS9Qrlipeyz6wZu0thSuEDJ2o5u4m9TRWqIOWEAE3CMbWjKb+TqlscK472ICqkpZ5/Ev8fOYCoOIQkcnh9xl0gIPKTuYdweqY171JT/Wx8IjH1JfUjZxERYIwidSg3OZfWKM52ttERiAIBiAwhphoV3Kjrk/eGvtOXzichvH9TKE1riOD83oiQdOPXmkQxaLNgVGxH7/XBzuDe2RrWWioz7zlEuTk4qWdxL9YE92BdoMqcxZB0xLlKl9/73vcK+a0h1lSNF34qV6Z3kyJU9957b/mYk9FOPvnkYpdkm34E5ud4mx+OGqRc/JQjmYLy29/+diFtsyUCs4BAEnGz0Mv5jIlADQggVPbYY4+yy04+zsCXcPUnP/lJdf7551ebbbbZCq9CxbXnnnsWp8g5vHgl80WsKWfvXNE++9nPlr8x1tZdd93q/vvvL2XVDznkkOoTn/hEDU8ynacQihik3MMPP1wwjobMVI1KXjkEaJOOKVKIUoDKRb/GDnmT15xkj3I6PC9nQ8gXVWKo5+q+rzBYkdqIcMQmpUsqDEZHOhxtFd2M4SBzZlHtMjqay58BUWac/uhHPyohqE0VCIgr60NhjxRhigRQLlmDpnUzR9i0d7M1SG69JoovNDEuljoncjHyyiF+NtxwwxJqa36OY92zLniXwRf5Js9ZG1TQS+FW5//D4KGHHirVT2+99dYSMqjZXDv44IOrww47rLyLFmqOffHFFwshJ6zf5mtXw1RH2Yj+q7/6q+qiiy6qXn755ZI+gp2wzz77dJqUtJly5plnVp/61KcWVeEbO3wFa7J1f9NNN6223HLL5YYLjOQc/PznP1+wydDUOmdxnqutCCQR19aeyftKBFqGgApYFG12Q7faaqtyd8g5hJldarlCVuTk7L///iWnzw9+8IM50g05hxjaaaedqq9+9avlfI6xs/qxj32sSNSjqaDkJe0aXuTZFkeAwYvMFL563333zRnOvqUAQZByyNM6nJkILaIecm3qIuTftDiD/Y43hiMjmwKQs0glJyxsVBVHqN84AhQH4YhOK6nQL951HBfG/gsvvFAcBeOWowhzjjdVLkc8QpFnbUyPgnFv0RBrxDrrrFNON85x67pIayoxm0bC4xEINgn0b5cbhZaxKdzfcxmbCKppbTFXg3QUykr53QSx4x1GLUv1Juzfe3PW5j4Mrr322kLA9eampWRlk9lcnYaKsP3Ml1E2otm9NpGpdBXYYnPdcsstBVMqQkRmF5tnOOaYY6rTTjutVMKd3yiSTzjhhLKZLpcxAhsRCcsvfOELxfY3p6LwjmgZ9j8SDhmXLRGYBQSSiJuFXs5nTARqQEDCXS/MO++8c5mzUcOdcsop1dVXX11Cu+Y35IHQ1V133XWOcItjDjzwwKKQePTRR4sD8eUvf7m68sory4ubARwNseH7dt6ymtJgnUmpJfeG8FXKwt7cLcJ9dtxxx2Ic2qEcxonjFNkZ9V0J7GfNWVlRbwjDoMYxjhEAyLjIX9UPEcHpdA7Gq7x0zjPpqrKDjbx2H01dY9yq7kdlo61ItYmUM4/0IQWipPf6MkOBF+5j4zbCkITxtaloCCfQO4lKTp5AalZ9iajrZ162YVQjF703jVcbU9bxUcn+NjzXIPcQKQIi7xQHH8HqY14O24xbm4TCY2E7ixt/Nh/YdRdccEGZKxqc2QoIOLZCV+bKsONg/veG3Yhmb7F/KVW///3vz9lYNuxEhxi37F1zuGsNiXjHHXdU73vf+6qvf/3ry6UdkWLGxr30NTbvhZl7j5544okldBmJFxEyxlNgtfXWW5c8cWlLdm1E5P0Og0ASccOglt9JBGYMAYoCSpwddtihkG69DYkmVNUOlp/zG/LHjh/VGzKtt6mOZLcVUSTfHIWcXTQ56HqbF/Raa61VXuaOzzYcAnb4b7755kLKIVTlEIrGUPzABz5QFI4MoYXUIpRYnD9KDAZVFF7g+MzKzvgwyMONQyPECpkm3JGjB/P5DjRHELmJIHDsSiutNMwl8zsLIMD5QcRQblIjINWQMIOQGJHjzPjXhxtttNEc6TTIeaaxg6iykJR+Co9sc4EA+JuXEfYYij3vOuOjbX1JnWRdgK0Nr/n5lqZxPA3yTPCxxuo/6hupGjjy0mEs1Zcxp/0UYomcp7KbNbLJhg/7in1gbmgIahEKyBSFgGa1DbsRHZvICynfvvvd71bf+MY3loky6Qq+8rmx963xiFupZmJDyzOw4z/84Q+XzVl2fjT+gBxwCnzA5KMf/egcOendfNRRRxUl6hVXXNF5xXJX+jLvc7IIJBE3Wfzz6olAJxDw0kTSUKNRpfU2iZS32267OTJu/gOprGUHzO7q5ptvvsx/X3XVVdWXvvSlYvy9973vrTbZZJOSGJ26bn4TRmm3+8Ybb+wEZm2/SYogoQXCV4VOcGSiMb6FGiBG4S7nnPxzQgd22223ilFK5WIXd9aclTr6lTMt6TVCU4gcso0TGZU8OYKpfqsD6dfOYWwLL4MxR1u4cB0tklFTfXFGEKvWr64XJBkEG5sknt0zI+kRRF0kMSL/IoUKJSuyHPEwyQ2GqD5pwwRxbM3NnJD9jU5EOZLVWio/IFLOu2p+fjeEAQUc26LpvIX93fl4jzJ/KbXYYFRK0Yx9pMh+++03ksJwvE/TzNVG2Yg2d5HC1pP51cwVPqMYoz7cYostmrn5Bs4KD6G2ctUaH+edd14priCiJeaXDRl+gZzPwk3Z9JdeemnZYPSsu+yyS9nIFfLN7tFghaRTCMTmvDG4WJXVBh4tT5kIjB2BJOLGDnleMBHoHgJ2SknIF9rV44S95z3vKQTNV77yleUeLiT9CDnVO3ubl+2nP/3porIT9rDGGmuUY+wUzm/k73a2SeGz1YsAYui2224rpBxyzs4k44jxiFzobZ/73OdKX2cbHQFEDoLIHKJAgDdHm8E+rcUtRketvzNEkneOASfcLjvSsylcowIrJSkyivPFydCX05rc3dgV3us5kRhdz7kWI0tfInCQi+YmApdyUl+O4xmRI3JtRsEbedC03PTob+73HhWFBZCrineYi95pws2N2yCN68iVOvjdTe4b1kN2FvvMWhWNGl74qc22ptbKyT31cFceZSN6RVdkcyGvkFqKGYwSTj3cUw3/LSo/NuAZZ5xRbP9PfvKT1eOPP15UbIpQaNYux1AXWz/NLxE18hMj2KjJFXBYb731ygZ8NDkJhaUqzmYcZksEph2BJOKmvYfz+RKBGhCQj4bM3Ivx0EMPXeaMHAY5Q+x6Ub7Nb2effXZJ5uol7aXb26jbFGVQBILiDhEnBJbMfX6jmPMyV7AhWzMIMAop3+xIcrB7G8Od86Kvha8y1BFH2QZDgGNIhQFfYRv+Lb8ep4fzT8VBIed3RN00EzmDIdf/0dYkO/KcbASRfGDjbFGBlSrHLj9FTijmxkHkNPWsQW4au9Zq/w4VZ1PXnPR5IxeZvjRH5TlSndS85GTWSY7ZAPGOM/etxRRwk1TkTRr7Oq9vLdVXqiLDWeg0NT9Vt76clerI1H9nnXVWsceQIZo5/JGPfKTYdwoxZFsWgVE2ohfC0likNlSADOFEXdalZqOATe/evVuRZ9RwfAMf6yQi7qSTTiopUBRrk5bGJmOo36gwVdC1sSvE1UYkkpxi7qmnniok5VIh5V3CLO81EVgRAknE5dhIBBKBJRFgvErov5DREKGpBxxwQHmxzm+XXHJJ9bWvfa2Ubpe0trdFaCppu501OSaQEP4+vwmR9CL30s9WLwIcv5NPPrmo4pBt0ZCiyCI7wpRyVAXRGE12z40LO51dTDZcL4qLn43xykBFalNoLZXAniMeahxkkuMZq2HIjvPe236tXnLTbjsyw/hsi8oFmSMUGZmDDEBidYnAirH75JNPFpWYsThL4be9458TjYjUl+ajNRI+yPRhSTnEPHLPeay3XVLHtH1tiNx6HPx11113mXmHJKfW8ZFXzntQQ+BPkxrM+oj0QcDdddddc10mTF/+XilHKD6zLYzAKBvR889ozRAFIh0Ie9emc1veU8P2v00vOaKtY+z9KLTm929+85uFiOMfxDuDGpCvgHATliv/c7ZEYFYRSCJuVns+nzsRGAABL05qNjnD7HL1tijWQE6+1157LXfWe+65p+ySSUqrXHlvi2INdseUdvf/jGHf6W1RrIEqrjfx6wCPkIcugsCZZ55Zwgw0hJp+toNJNRCNAXnfffcVIpSSkSMajdOCSKWUQ8oJAcxWFcVQqN848BxBpMwgO72OjwqqQkIQTBEKOKzjPy19Y13gxDzzzDMFF4Rl2yuZIrqRcMLBKPeocTgu41btLTUGjDvEuwIBcB5m7C51ja7/f4y/yBGoMADFXD8kJRJI/xuziCBKybaP3a70l7FL7RVFWYSi9pNbj+rb5odxL4+k95g+7urmB+Uf5RsCTuXuaAgg6redd955qErpXRkHdd3nKBvRvfdgc03RC7n4rKdIqLat+4Ngxi5hy5hvnkVki6Js7Hh/94771re+VVRxUs+IepG2wfMjIvfZZ5/qiCOOGOSSeWwiMHUIJBE3dV2aD5QINIMAqTin99Zbb13mAl7AcrwhaBba2bITjaTZc889S2GG3nbggQcWhRClhRf3F7/4xZLU9ZFHHllGYRWVp+yi2VnLVi8CnA/hqOuss06pTLtUTiuk0oMPPliqqwll5VBG04+bbrppIfOo5dpeObFeJF87G7KFo8255qQvpX4b5B44V4o8UDNILC90DTFHZTcLLQgiqiRqFzklEZJdJCWNEYS2+SY0jmqOMmWharrj6luEO1zdk5xaiCL300V8x4WZ61gTETnmPAw5oZFXrnc9NX459o41h+HbdUXMOHFe7FqIAe8y5DHyjD0yzLiNzQ/z4IUXXii2iTU2cvW15XlXdB/GniIAcsCFys9mmQT5CLiNN9647Y/QqvsbZSM6HoQdzN4VGsweVpRgGsLOo5gCG519biNXOppIW2L8CVulyNSshchHKjnh0EvZmq0aCHkziUADCCQR1wCoecpEYBoRoJiinOqt8IQUoILyMkXQrcjo3XvvvYvz4RgGrcYgCeXVCSecUP722GOPlV0yBssxxxwzB6McGkIqfGaR2GnzeOL8UEUiYhV76E387L4Z/cYIUk5FyWluKtEK1bbrS03YtDIQ4RfVAYVkcj4RAgzdYRzQNvcN0orKxbNZS5AYFEjT8pz6jTJKf1pXqVb85KyNI0zOtRRf4DgZu/DNNhwC83MECnukSDKG5eCyTlDNDaKMHe5OZuNbCDNrA+ff+mvjo86NCeusPrOeq15rnaVknSRhPr9njTlKI9VPRRhYTzRhtuypww8/vKg1sw2HwLAb0a5mXZdj2fqKDGXvThv5bg7KG4f8lWpGZdQg6fyfjTO2obnTW5xhuN7IbyUC04NAEnHT05f5JIlAowjYFUSoCLVTstxuv6pHVBPIOSoo7eGHHy6GhyqnYQzbVWaI+A6iDVERuTGoqnoJC0lsVUYlbyffv//++wsBR9KfMvZGu3jkkzO8qBuDlOMU9TbhzQg54whxNA2NkyYxMYcHIc0BmlSIGZWNfC3mF6UNxyuM4a5izcE2joT1eB7PNe0t+ixCHpE2cmfWHcZkrFJqxHk5TAiGJIjqGWH60Vyk+pZDLgoGIOMiR+C0EMn1IDbYWawN5gjbhFptHOQxtSMbiI1jLTJXvAMogSZBriB32VDCTxUViLbaaquVKvfShdRJSg7WQ9Nz9LAb0TYqFSNQVZTt+4UvfGF6QJn3JA888EDJFWfzVV7oroZ0T20H5YO1EoEk4lrZLXlTiUA7EeC0yfnghcsgXXXVVYux1xvqQP3G6Lj77ruXcZrlcZJDgoRd+A7FB9XbfJUUw9Ku7k033VRytXC+SdgRedm6gwAn9Nlnny1Ogg8FZG8TNoSQ85GgvEstiBIktDHKAZTnqy1hFhFeSE31xBNPFJKQErUrIYbuX4gZB9KHWoujO4ukRairrJlCkpG+EfI4SgVW6oSo/mkNnoYwqTasIfqLqhC++udtb3tbIeCCpEHMWSf0I/yNa2tHVqDur/dgqbI0DOWAm/TagASk9kHOmZf6Wx83TULA4MILLyyboOyyaPJwscnk053F9bK/UTT4UcNuRMuPZhPZu/ezn/3sgpsc0itQcXa1hT3EFpIrmu0vjzD/IFsikAgsjkAScTlCEoFEIBFIBBpFgKH20ksvldBVpJzwnt7GYBOmjJRbffXVW+tAhPqNE8R41jjYbXZ4JBvnJAp5FCJHySHUETnXRuUTAsOHSpZzMgrZ1OignsDJzSMOob6ktKAqlQ9LXyIlFmu+q++RBsaBcQHbcYS9TgCqsV8SnpG/EIlMLaVfFlsbYm6ah4g44avInEmpq8YO2oAXhCt1LHxs0LVJ6aXv9ae+VA3S/KQupdSrUylnc4v6TTSCTUvN3BelIJpA0atszSAwzEa0/GhXXnnlojdkgxqBOg1NQS8bRiIfsiUCicDSCCQRtzRGeUQikAh0HAEO6KmnnlqUfELcED92Kd/97ncv+WSSzF500UUlMb7d+FVWWaWEGCCNelvkt1vohLvvvnt14oknLnmtWTmAwxmkHKelt8E3lHJrr732xEkuDpUQKE4fVSdHuU3qt0HHjF1rxTWodqg3OLXapAgZc4qDjSBaY401yn1Qu7SRJBwU66aPNzYRcYjhqGyK+Oklhx3jb0LGjeEuVJZtGrc6z29t0AfeMZS9MZ8GvYZ+ElruPObmBhtsUN5VyNKm1VWD3uu4jkduyc2GmKfatC741ElsNfUsVHH6Uk5bRViQZog5eS0HXWutkapMytH70EMPzd2yXI7smI997GNFcZUtEUgEEoFEoFsIJBHXrf7Ku00EEoEBERC6sscee5QwILk6KG3sJiODhHVsttlmKzwj41ehCLvMdvg4ALfccktRdAn/OOyww+a+e8kll5S8GJ/73OeKsd3b3vrWty5YUXbAR5nKw+U0C1IOmdnbqAngTi1HgTZOcobjxAG0C86BmjalAcffx/yQw1EeMnOj6QIT0b8RviWvkRxldSdYn8rJsshDGa9ICk6/sEf9yYH3kZsRqTHO+TPN+MOSytQ6D2vEinkzKMGyFEbWH+fXb0jzCF9tswJ3qWfq5/8j1M26JEyd+q3LoelIchsgiDl9aDPH+PFMcgeuqFEWqWZ+7rnnlnkdTdVN9scHP/jB2sdcP/2TxyQCiUAikAjUg0AScfXgmGdJBBKBliLAiCX9P+ecc6qtttqq3GVUe+Xg3H777QuqrhjPFHN2mlUhC4OZY4vYo5BTSCIUEJLwymtHNdWFHfs2dhfH8/rrry/EnJ1/Dlk0ediClJOTsG6n13UQUxwk40K4jxDALqvf+u3jyGtF3QNnYzscxsUcxX7PH8fBl3qLggjG66yzTvmvaScWBsVp2OOtWQhO/fniiy8Wcsj6ZF5x/oVKNjFvhr3frn0PrrCk3kR0IsfGoVYTEm++CEu2gWRjQM7HacsrZ+zC1uaHHLLaNI5Xz2kcIeasiaeffnpRtCLYfKgg2S2XXXZZ6XPNOixPLgKOUjxbIpAIJAKJQPcRSCKu+32YT5AIJAKLILD99tsXI1fS3N5GDXfKKadUV1999YKGraISu+6663LKN+dQov0b3/jGMuSesvQMbKRdttEREK54ww03lJxy8o5wWKJxQqkBhLBuvvnmtRCfnD8V+BCvFBgc7VlsyAYhcQgzRKSiGogyRMCw+doQfCpywlSOJ2TfrOLbxJhC1FD8IpGF8PVWljVvqHH0pX6gjhMCSYXYluIiTWBS5znNB/hRNlsnKOHGQcAt9AyUj/rZu4ZiONasYUIe68Ro2HNZb0LNqbos8lg456yMTcTjbrvtVuZmNHPWmEPCmaeU9wcffPDY1MrD9mV+LxFIBBKBRGAwBJKIGwyvPDoRSAQ6hABjVq6dHXbYoZBuve3RRx8toarKrfs5v0VoItUBBUJvk2+Owg6Zt8UWWxSSaN1116223nrror5D/FHOTcpZ61AX9XWrnGBqQ6ScilzwjUblg5Sjlttyyy0XDfXpvVio3zhCHGx9FfnJ+rqpGTgoFInwF0KMjOP8I0L7aebfX//1X5fwMt8bV9hrP/fW9WOMX/0i71vkMdQv/SiIkDeIVsSqkGvnSFXi8iPCO8CGDGyQm/BtE05RgMM4QK56B2kU2W0nsoxZpCZ1rPGnSM8sNiQ6O8Lmns2K+TaDObrpppsW24Kif74tMouY5TMnAolAIjAtCCQRNy09mc+RCCQCyyGABFCN6qCDDqo+9alPLfP/lDnbbbfdHBnXL3x2qbfZZpuyYy188k1velMhKbbddtuSb07YqxxyyCLOxbHHHlttsskm/Z4+j1sCAQ7nzTffXMJX77jjjrnKcb5GzUYBKaccx6VXwaW/JKyXUJ0ii4ONtJCbDNnaJge7rYOAagPBzNGHn3BH5IQw3sCPM4nchKm/IzPMkcS3vl5FXlDAUUEhOIepHhkVWM0RpJxcZPpSnw5zvvqebrJnivDIKMxjPadKansL0pySWD4xhA5lr/5sU4OvNcHaawwjOLuAb90YWiPPO++8Uq1w6zIAACAASURBVAiKYlWz0WFTSUoMc1xxKeMvmjXUZtNpp502tDq57ufI8yUCiUAikAgMj0ASccNjl99MBBKBliMgEbx8bvMLK7htDot8LMJCvvKVr/T1JJyzo446qlJJ9ZBDDimFHDR55lxDuN3+++9fyB5OxgUXXFCM7LPOOqsY0NnqRUBFPcUzkHK33XZbRV0QTV8gTOUaUiVUn3EC5de58MILCzGU5NDw/YFwo8RR4RHh7HdjXYJx5OYs5NYbHr3BvolkQUBz3inYEBkItDpVT0hWfRjqL2QO9Y15NO3zBL6BsRxsxm+XwyM9CwKH6swYCdIcKYf0mkR/2sD6+c9/XqqgrrTSSjOrjn388cer73znO9WNN944p+y2gXTAAQdURxxxRCHWo5nnTzzxRFGB33PPPeU9ZhPE711VF49Swb531bTJutNOO5WNt5NOOmmwBTWPTgQSgUSgJQgkEdeSjsjbSAQSgfoRkHNGguOjjz66OvTQQ5e5AKMWObb77rtXJ5544pIXF1736U9/ulJJFblz8cUXz+Um+/GPf1zddddd1Z/+6Z9Wq6222ty5XIMij3KFMZ2tOQQ4nsg44au33nprUVxQZfWq4ii0PvvZz5ZQ5WyjI4DYNMZ9KFsQOchQxBwCh8JjEk7/6E/WjjOEysk6Jlk7jK0lTWNqw0Gf6k+Eq/WOwhepM02VVz2nDRnqI7n1kBvTSNDrQwQIMneNNdYoz0wxhwBqsj+jOAvyD9aIJeRu0+O3HbP393fBdpDv1IYcYi2aDTsbePvss09RDy/WrAWvvPJK6a+uVvAepYJ9LzbwtIH60ksvFfV7EnFtG/F5P4lAItAvAknE9YtUHpcIJAKdQwBBJndYr3otHiJCU+1ECx9drAlrPPLII6sf/vCHJQ+PnC79htMg7+Q3Uyyid7e7c2C2/IY5KkKCr7rqqkLEcT6jcQAotagyOKDve9/7yriwm84ZzdY/AnCmnhK+qEIwYkiob1RXjfx9nH7qIjn8qOMcl60/BKiGqN+MW8UVIhy4v2/Xe5RrI7Wtl8g5/dj1YibGL4KeQst4pYCbpXBcBCsyzjh75zvfWULHtTqrfUf4tPnvvfcv/sW/qHdgduBsyE8bdkJQhX5Ho9S2Ofj+97+/USK0bRANW8F+/nPIwwtXxG4ScW3r5byfRCARGASBJOIGQSuPTQQSgU4hgHjhyC5krEWxhuOPP77aa6+9VvhcEkofeOCBZTdaKOu3v/3tgZyKk08+ueSBsSPeq5brFJAtv1l9pLLcCy+8MHen/+bf/Jtq5513LoQbQohSDiEa+XgcSOEjAbbqq1RyvpNtYQQQFpxJHyQ01dtSyhYkjgqPvouMM4coYjjn/RQVmKW+CMWQZ6b0oH4TStikYmlQfN0jghWZrU+RLe5Rn9ZJ4gx6X/0ebyz+zd/8TXHgKcNmiXxbCCOKNcS6TQqbVvAwT32GaYg9+L7tbW8rGyHItzrDp4e5p0l858UXXyzqN5tCsSEEW8o3qS2Qn7PYhq1g34sVdTB77ZOf/GQpwJVE3CyOpHzmRGB6EEgibnr6Mp8kEUgEFkDA7jMnkUqqt1G1MeSuvfbaaq211loQOzvaQlspJ3bZZZfqhBNOWNDhRLYpHHDllVcul7vl4IMPLkmXH3vssTnlQXZUvQgIC5ZfR5PoWtiKYg2h0oqrCWm57777CimHGKUMiYYYUgGXYY+U62oOnjqR5aRzruEmzJeTTf3WG+7b7/U4/UjQyCUn3NF59VEXSJx+n3PQ4zjqCC1kMqWZEMkuNP2JwNGfQpTXXHPN8jt1VZuqRRtj1nE53+S9o4DN4izLjzBznfLbeESsPvvss4Uw93s/YZPUsc5hDM9i+Kn5IFes/G/eMdHgQU1PeT/LquBRKtgHljZWbZqtuuqq1Ze+9KVSHCuJuC68LfIeE4FEYEUIJBGXYyMRSASmGoEzzjijOvPMM0s4KaJFE/LFoLNbj6BbSNnDsN53330ryZXtZH/hC19YIU6XX355yTNHOXfMMcfMHadKp91bpJAd8mzNIMABVMFWCFS/RAZ1j+8gYq+//voSdheNCmnTTTctRr4qdsinWWuUT6+++mqZIzCtUy2ov8w5xJ4PR981ZsVR9fzUWQhNRJH1iAqpy4SkZ1I9GhmH1KaYRMhNStFHsWf8Gsewfctb3jKxe+ni2hHFO/Qn9SCCzpyNCsjIV+pY+K6zzjpFZdgmAnZcmCOYLr300uqcc86pfvrTn85d1oaQ/G/eIV2e13XhWEcF+8997nPV/fffXwo0eX8nEVdX7+R5EoFEYFIIJBE3KeTzuolAIjAWBGIXleO73377lVCqa665pjhpyDmEi/bwww8X5YT8YcJI5HSzk01BIcH/Qg7l+uuvX/ILcab33nvvkjNLcYYNNtigOKUUcsgFP2eRzBlLB9dwEaSrUOUg5VS87W0bb7xxCXFF3vZL9NVwW2M/BadSbjJzxBxArjQdvheqO+QN54oz++/+3b8r9zCNjj3ygsJWMQvkUL+5Jsc+GEa4IBJH3xlLCG4KKQrTpp/VPI6CIUh5v7v2pMjAESBs3Ve9Pyk3vU+pvPQtglMI9SyGn3q/n3322YWEoxrW4KD4EwKObZDt9wiMWsFe1fPDDz+8bGgqimVuJxGXIywRSAS6jkAScV3vwbz/RCARWBIBYV/f+ta3Sogo0kxoA2MZwRINkUb9prop5+LLX/5yIdAWa5IGI940DgrlnRDV//W//lch4FRllRMGsZCtGwgghigZg5TrVTl4AjkHg5RbeeWVu/FQi9wl8gJRIX+bMYxYNl4nQV7AnjrM/EHmUOKYu1Q4XU32HkQjEkPuLFVlKeGmkWRcaJhRTnGaqYKoKhEYSFYbHEvlGBxkcgmTlZvLumv9RnRmqweBqH5qDAeBTHFo48nc1J9RcbaeK7bzLObygw8+WMJPKemjqrHnl4JCnlIh0NmWR2CUCvY2SKWLkM/1G9/4Rjl5EnE5yhKBRGAaEEgibhp6MZ8hEUgEEoFEoHYEOFpyJV133XUlr5yE5r1NXiyhR4g55G6XGqKLY83Jod6gSGtjAQXqMcScvhDuiJCbBEk4aN+Gk/7UU0+V+0UOzWLurF7cKB450EG0brTRRoVwRUwOOvaiyIAxLOcbAsTfZlGdNejY7Pd44aZIOAS9fqOE61U1Rp5AJCh1p/BDBDNiapg8kv3e17iPE5ZLRY+A6y0I9K53vats6O25555T9bxN4DtKBXskJ9wVv4h8hfKN2gT1Oe6448p7oaubNU3gnedMBBKBbiCQRFw3+invMhFIBBKBRGDCCKhmiZDzef7555e5G0Sc0FUfSq461T51PXY41VQtzz333JySZVASpK77GeQ8nGEki9DOCJ8dR7jjIPfoWIq+COFbd911S3XRJIeWRzGUmBSn/+N//I+irELmLKVkQ+aZW8LH//Zv/7YQnJR2bZxvg46dthwvRF3uRgTb29/+9r7zQwZhh7SjFhbWbvwj77rYP8KqzzvvvFL13IaF5jlsvCDgVFHv4nNNYpyNUsH+He94x5K3rFiTVCLZEoFEIBHoEgJJxHWpt/JeE4FEYCYR4ASceuqpJbSWk4T0YXRKCL1UUygCcTG/qVY5n0wSinvZZZcVIoFzu9NOO5WQmyQSlkf5Jz/5SVHK+Qi76W2rrLJKcdao5dZee+2JO2uULRxrYZ5UWZzrLjfhjuYEpRn1TYQ7TqoaJlIJxhRZlBvCe5GEXSA42zAO4IdUo2jhsAfRaqxGCK+QXusSNZ0q110ld9qA90L3EPn1VEqlNkIwjZJfLxSL5iYSXY5UefuiUEtbcXBfVKzSTCjiI5WFhij+2Mc+Vt67K620Uptvv7X3NmwF+0ceeWS5Z0ISH3300SVP3P7771/Umj7ZEoFEIBHoEgJJxHWpt/JeE4FEYOYQoLDZY489igJEFVcOvjAZRJBiEwzRFbXYhX7ve99bvf/971/mMCSBvCvRorqsPCx2+immOCI77rhjdcopp8wc7oM8MGczSDlFH3ob5zNyyiniMa6wyqjM6SeCCgknufq0kaoUUsiZ3nBHRN04Kna6DtKIogv5pq+zjYaA8WqzgaoKCSLk1DqGqDOOu15ddjR0mvm28G/jWH49JFPd+Qv1IzJOWOFjjz02l1duFKKvbiQo926++eYSftq7httUkedVoael1Jp139O0nW/YCvYL4ZA54qZtdOTzJAKziUAScbPZ7/nUiUAi0BEEzj333EpRiHPOOackK9aQc0IgkTq33377ChVXig585CMfqU4++eRy/IqaEBzVYqnnTj/99LnDFLi44IILqiuuuKIUKci2NAKUZzfccEMJX33ooYfmEnr7JlVIkHKbbLJJY4opajFELeKCSoCaYxZahDvKVYV4RCyocltntU5EEVIIWYFcQBIhi6aN4JzkeNGPiCEf4Y36T9VOf6cwjXyGGRY4fC+p9IlAVkAEttaKcYxhfUhxZ41Ccvnpb0g5Ku1xN/fy3e9+t/KepbiMZvNK+On2228/ts2TcT/7uK83bAX7JOLG3VN5vUQgERgXAknEjQvpvE4ikAgkAkMgwBGwW3/nnXcu821qOEq1q6++ujinC7XLL7+8OvHEE4uyTaL7FbU413zCjZMk/HX33Xcv58k2GAIcXaQctdy9995bHM5oKpNSGwpf3XzzzUtVyWEbcuiXv/xlIS4os5AXSNpJOLbDPkPd3wuHHxYKAiCbYS7kelis9efPfvazEkIpB1adBF/dz9/F83HUqeGo3qi0/OxN+o/4RID6P/2JvHFMKpX6720K0qeffrps3iDphXZPktDU30hz7xprl80KSlPztsmmeMBZZ51VNpmEPWvG2l7/f3vnAmxldZ7hf9pp2iZpxqTGajTexSiKihRRAQW8YCW2gxdEo0aEoqDMoIAKijdQ8X5ro9Y7iFhaq5iIioD3GzFWJKaoic2tNTExiW3MdNqZdp6VrpP/bPY5+3L23mftfz9r5gx6zt7/XutZ/2Wvd73f933lK0GAI8+nrfEE6qlgX64XOuIaPzceUQISaD0BhbjWM/cTJSABCVRFgEUK4YyEkJaGhxI+Q6jq3Llzw7/l2vz587Ply5eHhReLDJx0sepY/vUkOl69enWoEFoq3uDCwy3x8MMPV9VnX1SeAAuHRx55JIhyTz31VBBXYyMvE6IcrsWDDz64agGNYyAqffvb385YYLOQ5Vj9ubBOcf5jDjLmgP/ebbfdQigrjrlKLiCuQQROhDeEIq4jq/M1dpa5LyGMICgjDhF+X6kR7oh4w/0K0QaBFFGJ879V4d+V+pjK38lfSJETBCfOfYTpZgtd9YydaxOxlaI43NsQzhuZj43jr1q1Kghw3INjQ/zjGThlypQg7tokIAEJSEACrSCgENcKyn6GBCQggToIEGJ35JFHZn/913+dnXPOOd2O8L3vfS874ogjusS4cocntxz5ywizIYQVIQFH0MSJE7Mzzjijyxl0zDHHhDAlQilLG244PmvdunV1jMC3lCOAe+3RRx8N4atPPPFESPQf22abbRbCoXDKESqcdwSxmCbHEtU4mUsSVg8dOjQID4oP1Z9rLMip1omQA1/coghyUcBEEKL98z//cxA4qcyJKCDj6hlXeiWuJ85nhDTYIhLVG0LNHBFiiNCKqDdkyJCM0Es2Hep1P1bqfzv8nfMcIRmhHrGJn3ZxyXJ+0HeEMTaIcEIitCKe13odcq/E+YYA984773RN3X777RcS/h999NEVBfl2mG/7KAEJSEAC7UVAIa695sveSkACHUQAJxtiGqEy06ZN6zZyhDOKKhx33HHZZZddtgkVxATyurEwRdhBtMMJgQCE4MbvqMRKo5pZdAuUHogk1eSaoxqkrfEEEAy+8Y1vBFFu5cqVXWFSfBKhj2PHjs1233334PhBhGNeyec3e/bsIKrWuiht/Aja94iwZJEOZ4Q5xByEG64Frh0E0kYnrm9fWn3veRQ4CSslvDSKQ5VcibV8cqzKyXwSBofzi1xknRJGHKufku8MEStWkmzn+wTuuCi0IoojmPMsY3y9Ca0UOCK3Kjng2LSg8Xo2nnimDhs2rJZTy9dKQAISkIAEGkpAIa6hOD2YBCQggcYReO2117ITTjgh7NrjYMs3FrOEMfaUvw0RYfHixSF/EmJdvk2fPj2E5vB3HFWxomppHjreQ9jrt771rezNN99s3MA8UlkCiKaIcVGUY9FYmkQdV8gVV1xh8YwGnUMIN4Q54qrCAYdwE0McEbtxa6UYxteg4bfkMLBFFEMcojgA9yQKLjQ7hDpWYMX1SEECHHjMLT9FCy/mfo/YxnMBJxkCXFFz5yGeM5c4WhHYKCpEwRQcxDwTYYD7jU2nmJeTTQuc5Tz7COG3SUACEpCABPqbgEJcf8+Any8BCUigBwLkTaLK5umnn57NnDmz26tiaOrkyZODO6qW9tJLL2U43eJxCYNkUfPcc89tchiEPiqBvvjii7V8hK+tgwCLRnL/LVu2LAilCBi06NwipDUmrCfcmJxyOBsJZ7XVRgDxjcU8Pyzit99++8A5ikP8nQU9ueQQOAgHJlyO0L5mC0i1jSTdV8MLRyGhkYjKuJnK5ahs1QgQuglfpW233XbBlYdAg1uuXecU0Y37M5V8KTBQb3hvq+agkZ/D9frCCy9kM2bM6FadmmsXLgh25Jjj2Un18KKJr41k2WnHihW2O23cjlcCEkiLgEJcWvNhbyQgAQl0EWAhQYgcQtmVV17ZjUws1nDhhReGSm+1NPLkUADipJNOyi644ILsrLPOCsUa1q9fv0moD8UayMtD5VVb8wiQDxC3BiGosSEWEJqM2IYrEaccBR8I04qNsD7mCFGOOUVYsPVMgFBgmCHKsFgnPLJS+GksikF4dsxbheMon79P5r8lECv4Ig4hGnP/aoX7rVb+iNyIsAhzCIYUxWFxns8VWOsxW/V6+onDj9BMxEQEJlx+7Rx+Wi87nJbXXntttmLFijB3paGqFO/AKTdmzJjsgAMO8JqtF7Tvk4AEJCCBhhNQiGs4Ug8oAQlIoHEEyN/G4oI8Yvn2d3/3d6GSKlVRBw0atMkHktcNkQ1H26mnntrt7xRuIEfOvHnzspNPPjm77bbbsuuuu26TY+GSO/DAA0N47EUXXdS4QXmkTQgQJrxgwYIgWuB2o6AGycRLF9e4PZ555pkwV1SyxbEVG+8dOXJkEOWowlpN9clOmQo4IXKyWCdnWL1he7FaJ+I0LiRC4xBBcCV2ohASzx/ESkRKQqk3btwYzj0YtYPTLAqtuIxxQSLe4N6r9xxp1jWFyInwhoDI+TZgwICOLUZBGDnhp9wHY7EbnI0878ipSSqFV199tVt1agRLUi2UusubNV8eNz0C0QmH45zrnXBl3XHpzZM9kkCnEFCI65SZdpwSkEBbErj55puzW265JUN4Q2Sh4eZBbEFUQKArt9hFJBg+fHgIu0OwiSFhVCfEZYUosWrVqpADi8UdxRwQ/WIBBz6H3Dt33HFH9vd///fZXnvt1Zb82qXTCDwU0WARWa2AhquH97AY/ad/+qewQI+NcwIRlfOE8GacX53WSOj+4x//OISd4oTDwdbI0EgEHJxJiHwcG4EPt2I9lR3bdW5wk3EvgQP3mh122KFdhxL6zTXFWHBJcg2R8B+hFadpf1VgReAkLH3bbbcN5zP9aZfqp408GZibr3/969nf/M3fhJDU2DjncHVPmjQpCMGxcc2zaYHo8uyzz4YCDzzvyqVgaGQ/m30sNsh4TjMmzo0vfelLYfzc7ys17k833nhjtmbNmvBeXNdsxlE5tmitJ4GN7084dXnWPvDAAxUd0UXj4ngkIIF0CCjEpTMX9kQCEpDAJgQIT0VMYXFIXjcWYQhjhJcizhFuQ2Nhwhd0KmrG5PJLliwJFVURCI499tiQlB7Bhp1gfs/vYlu0aFF21113BTcWCa+p2PqP//iP2fjx40NxAFvaBGJ+OcJXmTcEhHzDXcd5xA8L+iI3nEOEWSMS4WxC2MQt2OzG9cV1iWiCILDbbrsFl1w7uMJqYcO5xr2GhjOLexMCRysY19LPvr6WcbKhgdDKucRmBGPFKdcKIYzQXvLrcT4TCl2tQN/Xcaf2fubg3nvvDc5tNo1i4zlFISNC8iude7jmCO/fcsstgzDfrg0RiY007u+4+zgn+D7w9ttvh+8DbL711Lgv4RjkfCadBcL5Y489Fqqiz5kzJzvttNPaFUu3fvfmcONagiEcdtppp+ymm27qlhu0EAAchAQk0DYEFOLaZqrsqAQk0KkEyIODO40dcBb77IATWjps2LAuJOR7IxSHXG+ID7HhIGARQ7gYixUSehOOMWLEiG44+YJ69913h0IBhGexYCE33ZQpU4LzztY+BJhLFldRlPvud7/brfMUHoiiHIuRIjSEEhyBhIoimCBi4FLrDxGMhSD9wBn31ltvhfC4WK2zv1xVjZpjBDgW/QiNCLqphW82apw9HYdNDO7HCHEIrYQ7NvIci0VEuIbhy0ZMJxVgyHNH1Cb89P777w/iCQ2nImH7CHCd6NKOaSRuvfXWkBuUFh3yiP6knejpfJw/f3724IMPhg236J7DZXjMMccEV+srr7zSEoG5WdcoY8kLsuvWrQsOUgRbNgzi37l+2dTkXCJawCYBCUigvwgoxPUXeT9XAhKQgAQk0GQC0R2GKMcPlXjzbc899wyCK+GrCLzt1BgbiyvEC3JGEX6Ly6MVbqVqOdFHRDlcKOQeQ7jB4dNO4YWE+FF8AXGe0F4W/CkxrnYuGvW66KphLhF/2bhgPnEF1hv6zDERORGfOIe33nrrjmQMBzaTEOCeeOKJrinj3Js2bVo2derUwLlTG4V7EPaffPLJbghizliEtr333nsTPKSkwD2PMxoRL98I1X3jjTdCLljuUe3U2PTgnMkLcBQ0IkcgwiLXE5uJ55xzTrdhjR07NmwwIkpy7dokIAEJ9AcBhbj+oO5nSkACEpCABPqBAA6t6JQjfDPfEOKiUw7nZCOdPo0cKgvR999/vytf1o477tg24UUsiHFpILrgdCJPEUJiaq7TGN6FcEtuLUIjyZPWyQUpejqHcV9SfRUxAJEIdxGLe+a3t2soim+InAhvkW8nMuYcI18XAlx+s2DIkCHB/X3cccd1pDCZP+fI6UZ1X0JxKdSUb7GK+ty5c0PIamnDIX3iiSdmuOL4l4aTjjQWqd7na3lmcA2y4YG7jzx4iI7nnXdeEG5/9KMfhfQahx12WBDKCVMmpx4COo44xl8EBrXw8rUSkEAaBBTi0pgHeyEBCUigsATqSS790EMPZeeff36vTM4888zwhZrG7jdJp8s18sFceumlheVb78AIMSSfHD+vvfZat8MQshpFuX322SeJhQpheoR2siBl0YV4gSupHRsiTKzWCXv+HxEHV19/inIsaBEKCd9i0Y8oVCn/Vjvyb1afcWjCDmEOcQkHEmHThMFFgQ3hlfP43XffDaIAImenhp8ikhBuSVoEcpjRON/ITYoAh6CiSPLbs5Xz5cgjjwypJUodXoRMU3AJEQ4xrrSRR+7CCy8MhS5wi+Gcgzchm6S1wG3YjgIw99Brr702jAcRLgqObCoNHDgwFOqgOAX3NLidfvrp4XrkXzjgvswX+GjWfcHjSkACEihHQCHO80ICEpCABJpGoN7k0rhESK5d2hAsyJeHELN06dJs0KBB4SX33XdftnDhwiDekZsr36iqF1/XtIG2+YHfe++9DPETtxzuinwjV1UU5aI408rhIuSSpJ25HzBgQCFFC5xyhK8ixMXiADHUsdlCBFz5fERNirTg5iI8st3z2bXyHC33WdFVSI5GHJwxrBdBGXG7v3IY9jcXzjfymVINnDBCxEsa9+3JkydnbLAUvaBMPXPAtUmhBgRKhLN84/w66KCDgnOQQkylLYau8iyEN5zJ74gjjMqyhKVedNFF9XSr6e8pF37Kh3IecW9kk43NJAQ2ik5QPTc23kt4Kpt0fGe47rrrMsJ7Z8+eHSqOk38QR7VNAhKQQH8QUIjrD+p+pgQkIIEOIdCX5NLlEN15553ZVVddlV1wwQVhJz+2efPmhUUducIUEPp2cuFSQZRjcUP+IBY8sSEUHXXUUSGv3P777980txTOLPJvsSDHuYFo0SmFAVhQIsbhqiJnE44pBG3G30hRjgU54Vm4RT796U8HB4mtsQS4dsixx9yRh4v5JByQcxvBc/PNN++YkEvcf1TtxpWVd+DG4kPcz+vNsdfYWUvzaDBDMKNQxRlnnNGtk1zHFCXoyf1NyC/OMBxwK1eu7JYLjsIFbL584xvfCJVEW90QprnHc5/LF1yIz514z2Oz4KWXXgqVYtkoiPnsqCw8Y8aMEO7PZhyhu1Gki2I4+QYR4fiss88+O2w84KRbu3ZtYGKTgAQk0B8EFOL6g7qfKQEJSKBDCNSbXLocHlxyHI+8WlR3zYsShKUgYDz66KMdQrY1w8RpweIZUe7pp5/ucq/w6Ti2vvzlLwdRjiq8jRBAWUBt2LAhCE+EniL8dXJoJDw4r2OuNsSbnXfeuU+CHGwRRRCEYn6ydg3xbc1VUPunICggaCO4xVyA8X4VxTmEVuaB+xlCCo4wBOeiNcZJUvzbb789OANjI5QSd9ehhx7almGRrZ4n7gEU1SGscubMmd0+Poam4nTD7VXa7rnnnpAnDUfdJZdc0u3PCHCIU/n8ca0aG85I3GqIiKVFJGIfyLnIht6qVauCkMa9iucCr0e8o+GE5znFOAhBjS0vyJETFT48pyhoQTqLxYsXh0ry8XWtGrefIwEJSAACCnGeBxKQgAQk0BQCfUkuXa5DOAHY2SbfDdU+Y2PXe/Dgwdno0aPDrjd5Y1gIk5fJ1jgCOA5wHRK+Su4dOMeGOwFRjoXiqFGjanL5kDMLIYJqoswrQhHOmEa6vxpHyCcqCwAAIABJREFUof+OBG/Cq0jqz6KSXHKIoQg41eR3wplFmCS8CVGzWmDj5xKhgNxwzNEPfvCDIBhUug8hAiA2IFjRCKNnbpnTdr4GyMGFE4v8XYjJNMRf8pjhYGq3Ks2NP1tqOyLn1r777hs2Pq688spub47FGsgD95WvfGWTA+OC4/mZz6saXxTfS965vIhVW+/qezXPFJ4bbLbccccd4ZyILjaOyLnDOcTfCefmXse9jx9ExenTp4f7GOG15IslzJSiH6UbC/GYhOKSj3Djxo3ZNttsE9z1fHewSUACEugPAgpx/UHdz5SABCTQAQT6kly6FA+FBfjCjovihhtu6PZn8puNHTs2Gz58eBBxWAAiWhBqhzuAEEpbYwkQSoT7EFHuySef7Fpo8ykkvyapOHnlxowZs4nLh6p2iLQshHAEkftoyy23DKJFER1BjSX/26MhNP/85z8P4g3OQRaqhPMiiOYdhIhu0ZX1iU98IlwXlap5NqO/RT8mIgn3KOZlu+22CwJpPY2QOYQ7nIoxfJVrg9DhdmiM//HHHw/535599tmuLsMEEei0007bJIdnO4wrlT4efvjhwdGFiy3fYg645cuXl82HijuTezHvv+mmm7q9NxZyuP7664PjvNWNzTPckoiAONqiOw2xjBBcriWE29122y2cOxRDQXTj3kcoKgU9OO9wCpJKAeGOTbl8i8fkdYh4vJZNjRUrVoS8ozriWj3rfp4EJAABhTjPAwlIQAISaAqBviSXLu0QOeFYZBAiSShJvrHwI8SJPEws9HbdddfgRmGHHdGHL+aEvtiaQwCnFQtD5uaxxx4L+XdiQ0BAJEWUY0GFo27NmjXB9cDCb+TIkWERVI2jqzm9L8ZRETZZbHO+kz8JZwjhaoQC8v+IdQhxtsYRiE5OxGPEZ4TQRlZgRKTGMYQ7jnsbrjncP9U6IBs30spH4h5AmN/Xvva1jI2R2AhZ596MU7YRoeuVe1LsV9x8881B5ER4495JY/OJ+yvnCffhnlyUOOXIoYrwRjg0jXPs6KOPDvcORKz+EHy5T5F3lDBTQmjJAUdbtGhRcK8xVs4jGs5K3H3XXHNNuDamTp0aijNw3bEpxPcEnvWch+U4RMGNPIWwRJQ87LDDin3SODoJSCBZAgpxyU6NHZOABCTQ3gT6klw6P3LcJgceeGDY6WexV9rInUP+GHb84wKD1xDuiDOLhevq1avbG2ab9J5FIQslRLmvf/3rIUwPEYGE2PkQPUIjyRtlouzGTSziJm4RFtW4Qak6TIgvC1tcVYqdjWNNgQsq+cIVkbNS+GlfPxknD8IDLiCuKaoXc18k1LM/cygioiB6cF9GDKYhCE2cODEIcIb99XXmu7+fOUd0ww1LkQWEWYQ1KoMiWOEOo73wwgvhfCH/HucIjdeceOKJYeMDUY7nIqGaFDtYsGBBduyxxza2syVH43O5R5U7XwmLRURESKNvbObgfMfdzr9s4iBE8xrSUyC88f88P3DF8d2ATQhywL311lth0y6fviJ2BeGRDQlSK+DQ5BkUmTV18B5cAhKQQBkCCnGeFhKQgAQk0BQCfUkune8QLiuSU5NkmrwwtbQ5c+YEFxbhk4RH2VpDgLAiFuewZ/FDYyGGUEcuOISFQw45JCwqCYdSkKt/XmCJyPbNb34ziJ6E/OJsiUUBWJAjfCIe4RJh8d5s4aj+0aT5TljCEZETtyEuuJjHrdU9jq4e8v0hvCK2Uu2yVW4mPp+cXLiKEN1jdUvEEkL+CCdEoLQ1hwBzfvXVV4fQX1yZserssGHDuj6QCrQUQmADivtBbDjFEbYQ6nCXEZaJq4xNrGY1xDfOkdKQea6f2CicQO5AcuBRGZ3ri2uNPnJuUy2VMSOy8R2A6rH3339/ECHPPffc8DvegyhMdVgER/Ll9dSiI27JkiXZkCFDmjV0jysBCUigVwIKcZ4gEpCABCTQFAJ9SS6d79CsWbNCyOPzzz8fcmDV0ghvYdebimp5t1wtx/C11RMgHJkFE27I2EhcP378+JADDicC4hzuhdhYkFHgAVEOB+Pmm29e/Qd26CtZ2CJoxsUqC9hKIb5cjz/96U+Dq2rbbbcNnMkZZ8XUnk8iRE4EBPJKIXYSPpdSjj3mnFxXzCF5G2NeOcTWRudbxKWE8IGIgYsqNipQUggAMUSBt0NvSFUMm/MTpxrXEucnbklyvhKyjKCIEIeLN4aLcm/ib7yHiq5Dhw4NOV9jagpSTvBacsPyHQFBkry0hNpyPEJtefbkG/dLnIPkhsPJihCHu66di6JUgd6XSEACiRJQiEt0YuyWBCQggSIQqDe5dH7siDQIcIQ7lmuIbYSrUC0t5peJr2O3H+cAO+4soG3NJUBFP1wLNMQhnAvk4MnnJ2ORhKOGBRahUYhDsSF6kPsIUY7iHKXz2dzep390hCFykbFAxXGK24Vro9aFJAIOwhzH4L9xMCHO2X5LAOcmBRMQDxAA4Jt6aG90nHI98d849xgDYgSuuXobIf633XZbcCpFAR0WXKOEn5K/q9bzr96++L50CJALkNDk7bffPrgx4706X/WU3uLgu/XWW8P9nvOGv8eK27jcJ0yYEAbFBg3uNr4z4GqjkXuQIg5chwjAO++8cxcAKp6yycZzHfGN8FbuixyHwg64/Uob/WWzh+cRhSEQ/2wSkIAE+ouAQlx/kfdzJSABCSRAgGTufFHea6+9mtKbviSXpkN8ASfkBrcFX9rLNUJULr300mzKlClhZzw2QvXIN0MFNXbPbc0ngGtx3bp1wemAQ6FSQ1jiPSTafuihh4KjJzYW9+TvQdwj2TuOuk5tiG+ElREeiSuLn0Y1jo0oh2BDMncWtjhWCHPtJIEF8QrXF04yOFDkAnEhdQGup/MAwQMHEA5IxG+EcRpiRTXzyv0T8YPrMgonuIcoiEPVSgQYW+cRYNOLIgqIWty/Ea25TsgxR961fCMtwUUXXZQ9/fTTIQUBFcw5b8jvxmu5p1O8h4bohnOaZz6FlvbZZ58QknrqqadmBx10UBCDabFgAy45NulwX/M5bMQRxhpbaSXU+P88nxDqWhXK3XlniCOWgASqJaAQVy0pXycBCUigYARYqCFikeyYxTdfkvkizJfeRrW+JJemD7HyKrvd7IyXaywyyYnD4pndbhKZs1vPF3MSUvNvJ4s4jZrLZh+H8/Hll18OohzuR4SnfNtvv/2CC4efTnBvsXAkKTvhftHBxoK3mcn5WdAi3PCzyy67BAGGzy5y+BZjRAAmhx7uQqouF60xr4SAI4DgcEN0ZdyIIIT6sdlBeCv3UhxFbFzgIo6NcwH3Gw4iBYyinR3VjYdrBPc5ohpuM4QyNgQQznjG8u+VV14Z7s+xkVIC5xmiG8Ul8ucOjmcKSOB+wwVHK83xhsjHZgziGxtx3At5D7kJ+Wzey/2J53y+lYpw1Y3QV0lAAhJoLQGFuNby9tMkIAEJJEMAkYzdaiqRka+FXEMkAOfLLnlXcCKRUL+vrS/JpUk2PW3atIqFGhjLLbfcEkJUCc3ii/nBBx+czZgxI1Rcs7UXARZSOB0IZ0KU47zMN85XXHKco3kXRHuNsufe4lRFTMaVFkO/+mNshCLiqqIiJv1A0K6Ui64/+lnPZ7LIR4wiTA2RgZxRjc6rVk+/mv0exozAy/2SysY0GCDykmOLcFZEcRpVN8n/Nnbs2LZ1BjabZyccn/MBke2+++4Ljkg27CgAE92iPKf54fc4L3Fc8h7OHQolkYqA8wtBeMOGDeG+jtuN+xw5BpctWxYwcq9BoOP5TSg0n4E7jrxufD+J5+q4cePCxlzelRlzynXCfDhGCUigGAQU4ooxj45CAhKQQM0E2Fkm7IMv0+RwYdFPsmScZbghCD0hjIkdaXa1qwk1rLkTvkECFQgg/JDgG6ccP+Q1y7c999wzuDAQ5gg5ateG2MVClMUnC1YE8VSS3xOChluKhkDKQpnwVQo+VBPmmNKcEALHvQ3G5FHr1OIgiI8LFiwIoeQx9JR5ig4jQg1xGpunMaWzt3/6wvcCKpEed9xx3VJEcA0h4iK6cf+KhUxinjgEN4rK8N5PfvKTIcyZsNaPP/44nFs46b7//e+HzZaBAweGwRFySlEQXPCkm+C1uKO57/BdBXG4U6/Z/pl9P1UCEmgWAYW4ZpH1uBKQgAQSJsBii0qic+fOzcaMGRNyAcVGzijCtDZu3BiEOUSQmTNnhtBVQz4SntQO6RrFIFi4IcpxbuYbYYUxpxwCXeoiUbye3njjjSCGUHwBgSvlvGQssnHKEYpG8nQcVAiGhDsi3KfYYEtIZgyxRVgscrhtb3PA/OFS4p4f83MhkiC4IaSQJy/fBg0aFMQPhLnSEMAU59o+NY5AvD9RVOe8887LLr744pCvlRYrCpf7tPg+xHvuwYhxF154YbZ27drgVMcpx8be7bffnl133XXZUUcdlVF8gcY9HcGP9APc48sVWYqOzZTvk42bBY8kAQkUlYBCXFFn1nFJQAIS6IVAPiyVL8jsWMfd7fzbYs4oQtJSFzVaMeG4BSdOnBh28gmpqdRIrn/99deHyq04Blh8kOj8wAMP3OStjz/+eFiY4NYhlw7JrcnL1JeKh5X61+5/x9UZRTkcE/lGyGoMXyWfUUrnLzmPELsJERwyZEhwm+Ury7bTvCDIEQ7OWDi/cdbSyEmWQsNxA2uEJoTOVFyGrWbDPX/JkiUhDxchqLGxwYIwgoCNsIEovGrVquypp57qlqeR3KHcn9q91XJPLh0rhWXIn8dmAPcTRErYcX8pciPlAw41wkbZlEO45QeBDUcl/3IP4z5AFV02QfJh3qTAWLFiRXb++ecHkS02wk6vueaawBJxOBah4fsI90P+hsAfGwIfLaV7eZHn3bFJQALNJaAQ11y+Hl0CEpBAkgTIuUWuFwSBxYsXB2dLbOw2xzxQfuH93fQRHsMCgcXGgw8+WFGIIwcV7gHeR5JzhABCbt5+++2Q84Y8fLERssMihUITVBxFPKCQBgs88vK481/5MiIRPaIcP+QfyjfcFVGUg3F/8OSaiiGAiIaI21TmTEWwqky491cwPn5YlOOmZSHO+Lbccsu+Hrqm98ciF4T5EqoMc/rSzCIXNXWwxS/muiD1APeRmGcL5yKCCEL/0KFDy/YIjtyrEOVeffXVUAhnwoQJLe59Yz+ulnty6SdTpOCMM84I5zPVuOHDsxMB+t577+2qStvYHqdxNFxqV1xxRSiexHcFXLtcV4hwNEQzNu1i41wh7QWFQLgXcO/lPEN4y2844ITjWcf3EK5V8tDhasaVSfEQmwQkIIEiE1CIK/LsOjYJSEACZQiwgHj00UezOXPmhFwrkydPDl+YSXxcKfdKPjSVL+IsbjtBrCOkhtAcRAZaNULcbbfdFsJuWASPGjUqvI+FIPnMEIJwwMGOHDj8ndxgLOyiMLN06dKQj+fqq68OoTu26gkgwhB6TWjTc889FxbNsSGAwRMHEG6gZgs0hHCRlBxXFqF9VKAseoh3rPLKAptKpLiscLawgG/WAjvOMSInrjfcNZ0afgoL3FuEn1KMJ4bycX8nyf306dM7spJ0tffkcncaChGwgUXFzliFm7BsnMu4o7l3F7Vx/uAGxz0PA4TcuKnAdwfCR3mmca1TmAH3JQU+rr322rARBTsEtptvvjncA7lG77nnnpD6gvyz3Bu5VskbF+/HnMN8brPvz0WdM8clAQmkT0AhLv05socSkIAEGkoAMeiyyy7LyPvC4pgdfcLJWEzst99+QZQjiXlP1UZZXHdSeBchOVQXhAm79ixsqxHiWKCxWCHkJt9wwxFyE4/x2GOPhXCfUsGN9+IiwBVHBTlbfQQQwTjXEeVwtSCMxYYwhCiHODpy5MiG5jhDYKVxvRAOR1VOwrk6sXHP+eCDD8IP9xnYsJhvRNg1x0IQIfSbio3Mb6q56po99+T3JEE+4ZP5/ImECuJ+O+GEE5omhDZ7bI04frX35HKfxbnFZhWO23xDZEJs+uY3v9mILrb0GLVuCFABnbGyIcV1R1g91xobSvH7AmG7PDMR43BhIrzxfYPnJpsQm222WdiYwPWGw5Kq6IptLZ12P0wCEkiEgEJcIhNhNyQgAQm0isB7770XwlIJJUEMQlTDfcUXZcJEcKwQvjd16tRu4TYsdnkN7pb4JRwBI+Z1aVX/W/05hNWQrJwKbuRIuuWWWyoKcYgCMBw3blwQ3fLt5ZdfDqGqFMrgX0J+cAcg2G233XbdXksIFKE9MQSo1WMv2uchiD3yyCNBlFu9enVXJVDGiZuDHEjMNw7FenO2sUglHBABjoUnx7V1J8CCnvsJYjOhbjEPVLXuWgQEclLxL/cjcr8R+t2pC3qEDQR+BHvEThosOZ8R4Difq2Vb1HO1lntyOQYUq+CcxfEVN6J4hlJ8ALGJZ2O7NK67ZojVXJM4ukmzQI5BnPeE8nK9kycOpzJiMaIm30EGDBjQhQz3G+dop5+n7XIO2U8JSKDvBBTi+s7QI0hAAhJoGwIsXHFgsWNNwQAWb/l8WYSIIFSQY4sk1HxhpuEowrGFew5nDwsPdsb5Mo+bi0VKuVa6457/f3bMETvqFTxaBT1fxILQmmqEOJKhkyeHMDBY5xvFGMgDF8U4FsqEqb755pubsCBshwUebouYBL9V4y7651BkgIUiDheSkSOcxUaYFO4ZwlepKpxPPF6OCwtQqnIiBnFec03h/HBR2ftZBHPuCVxj3/72t0NoPM4a+Jdrsfop9yncjBTk6ORGzi7CTxGWOQdp3CfIzzVjxoyO55M/N2q5J5c7pwin5H5OODtsucZ5HnDvZrMF0TPlVs79xnMFtyobBjHcthaXXBTeSkU07ptcq+Q5zVc9RTDm/6MzOOaV7I+cnSnPlX2TgAQ6g4BCXGfMs6OUgAQkEAjgZFu4cGEQH2bPnh12pfkyjWDAl+FyX4j/5V/+JbjjWCzj3kLAw1nE71mI4LBD0CNMhcbxEDlKQ1sJGYufQ6gKi2l2zqMLjL6lHrpXrRDHAplCDYhshN7kG4sRKhCSLB0OzAHCJyE9pW3evHlhkU3VVUQeW3MIfPTRR0Hw5LpAqCZsKjYq2JLviMUlzkjCKXG9IU6ThwuhjnMY5wfu0KIUX2gO6Z6Pyn2D+wpCJsIA7lxEJQRNXDTcO5iXDz/8MDjgKomjre5/qz4PgYNQeTYE8kVJECWpyIwIlxc/WtWv1D+nlntyubHw/KPiLGG/+UbuUJin3PLiGv9N/kwEXJ7BNEQ4xEUc7j21eAwKDuH4JcdsaeP6JRwV5zh/nzRpUtl8mPnvAilzs28SkIAEmklAIa6ZdD22BCQggcQIsLilOAPCA2FM5INjNzsKcHzZ5ktyFOVYFLPgI3TymGOOCQULECZiI3Ez4Zc44igswGKZ38WwVv7lmHnnCn04/PDDg3iBEyk2KqaxwLzxxhuTrUBXrRCHe4J8TLgKqbSXbzinCGciP86ll14aFnFUJcQRVNoIX0UcQvDBAWRrPgEcIrhcEEA5H7kGYkPg+NKXvhR+F5OVkyMKwVRXR2PnhnA2Qtm4VyG6xRDWTuXM5gb3YQoOECIZ2+jRo4PgjwO3U0NzqznzarknlzseGybch9lEIa8k1z+CFqkGyt3nq+lTK1/DOYOLnQrSVP8mLJTcgVQBj4WDqGpKDke+A5Q7l/g9Yc68B7f2YYcdFvLm8cwnhQLh/mwa4fjmnthJuWRbOZd+lgQkUAwCCnHFmEdHIQEJSKAqArh+CJUcMWJE2LXuKXQu7n7j0kIsomIa4WIUcxg8eHC2++67hy/yhKYiROBmWbNmTTgeFUYRkMg1h3CB8Eb46cUXXxzypuEkIpSHkFaEOhY0fB45Zfgiz4KHogg0RDm+1OdzyVQ10Ca9qFohDrcgucZOP/30MM58i6GpCKK4Es8888xs1apV2YYNGzZxU8XQVPJgdaoDqElTWdVhcWLhkGOB+p3vfGeT0GFym7GoxS2Hc8vWdwLcD3CN4qhFhOOeAlsW/1w73Idw8HRK7j0EDlxY5PGMBUAQOKLQP2jQoL5D74Aj1HJPLsXxwgsvBHcXjlg2pmLjuUW4KpWZV6xY0e/PqXyoZ94FFytzc21ttdVWwb1LzraY3xWnH89a7mO43suFp0ZxjnB+Xs+1yHlIODmvx6nK9wFSLvCjK7MDLiqHKAEJ9ImAQlyf8PlmCUhAAu1DALHspptuCmGkCGAIRHk3XLmREPpEaCXVO/kCT9VJFskshglFxdXGAoSFMYUf+MKP046FI4tnig0QXkY4y0knnZRde+21oboh7YILLgh/z7df/epXQcBDuCMpdnTWkWuGxnv7M1datUIcjiny67GwwemXb7FYw4UXXhjGz9/vvvvuIEIScpdv/P2dd97JXnnllfY50QrSUxxZy5YtC+cuIVexce7jLuE6iI1wVFyOzDfOJBanttoJ4KyBOyIczp3S/JHcrwgLZuHPfQBxBacovIsUEhw3JggfRKSPjXvw9OnTg/ijQ7a286uWe3LpkXmmXXXVVdmtt94aHGH5xgYSc4ID7OSTT66tU3W+ulQo47rgd+VcbPG1l19+ebiXIZ6R75Uq0fFv5Gpkk4z38zxHoCv9bhBfy+8JaeV5T1oKBDqOOXz48Ozoo4/u5q43R2adE+zbJCCBjiCgENcR0+wgJSABCfyOAMIOi1h2xXsS4uKXbkImWVxE4Y6jsFimoAPuuphjhhCVRYsWhV1xwldx0OGKmzhxYvhgFtfskCM4IQayc87ufNyBjyEzvDbuvJM/jtAf8qgR+orId/311wdHHbv3BxxwQMuntVohjo4Rfss4S6vpIYTiCFy+fHmGm4XwR1yKN9xwQ3D/xQYfHIgIergXba0jwPmLCBoT4JO7kPxJuN8IkeT3CMWEr3KO4taKjcUsjlPObRK4m9uv53mLThruD4T84oDj3lRNiCXvRbiHPeIcGwMI9VHIb93Z0rhPIiz6gQceCPm73n777a4DI5IQ/kh6gNSL2zSORuOPVO09ufSTFy9enC1YsCDMyyGHHNLtzwiluJp5Xn31q19tfKezLJzfPB84x0kF0VNBBTYMHnrooRAmSjqIvfbaK+R0jc9thDKer7yGPIy0WEEVoY78bqRSINS5p/DU0s/mnCVvZv65xfWrCNeUU8GDSkACBSKgEFegyXQoEpCABBpNgKpqCBK4fHCzlX4JxxWEI4DFM6GYhOjwJZ7wVRYupWIfOY5wgPFaEuDzPhbgFDZgsYHzLRZvYNHBTj3HR8wgWTbhr9/61rdCOCzvy3/5R/RqdqtFiIuvRXjDfUBj0YKgg3sHgY7FCk4N/r7rrrtmS5Ys6RIhCEUj7x7iIxU8ba0jgGuRRTXnIuc/c9aTE5MFK6FrCKsscP/t3/6tq6PML4IxohznfKxM2LqRpPtJCADk7UI4w4HTiCqzCPrw5/pCMEX8bwdBAIclud/YqOA+SEPMQDhBgBs2bFhbjCPds+23Pav2nlw6Dpxf3IOZB8LUo1DMRhYpBl588cWwOcU9vBntySefDDnZZs2a1U3si89jznvCRXGzcT+KDfcuIiKiG9cBxyDUHvceDvV80QQ21Sg8wz2KzSE2HypVUM3/vSfhrhk8PKYEJCCBIhBQiCvCLDoGCUhAAk0gEAs3IESQKw4nG+F3sREWRjJ1wlZZ8CKaEbrDgiBWZI2vjc47BAlybVGAYODAgeHPLGJYILBYQOwjLI1QTPLMsJtPWFD+Cz9iFosEFhY4aBDv+G/cR1tvvXUTSPzukD0JcbDAhYAAGfPuILAh4Pz85z8PiydCyQgNwpGIOJd39DEGcvHhfCEROK5DRDn+n/FX4xBq6sA78OBU6CQvXy1CDuc5524U5ciPmG84HBHkOC+i4NxJaGHKPQNRneufe0YzErpz7SHM45CjwiPXHveVlKoyc09D8CXnGHm3ooDy2c9+NpsyZUpwWcV7SSedI80ca7X3ZIR13GXkhIvnDM8/HHEUOOAezbXOZsr69etDXtNyVUTrHQtiGc8KhFnuP1wvFPPhnkFuxPzzEOEWRzXPDJzpOKgRodn04j6Ei5fNMZ7RpJqgOBAFFijQUPp8Jqcp6SfYRMN9qbhW7wz6PglIQAKVCSjEVWbkKyQgAQl0NAEWAHyRZ9GMkLbjjjuGhQE77YhKONwQFthRJyyV3XnCVHEPsGCgsZgg/IyKcywmVq5c2ZVLhjDTKN6xaED8YMcesW7+/PkhKTmfzYJ6hx126BaaxYIFEQ/HHnlvml1RsSchLv6eRNe49WJjzPSLSnKEM+LigyVsShshjnfddVcYJ0UAcAyyGM9Xqe3oE7HNBs+5j+MrinLvvvtutxEg2kZRjoVzURscEC0QQRCsyYOIU7ZV4jLiH+GrXH+E6+E+Q8wgnK4WkbVR88O9k3sbos7rr7/edVgK1HBvwKmUkmDYqHGncpxq7snMAWkZSvN2Irzde++9YdOFxuZRdMw2cnx8/rp160LhBJ65eUGsVLzmWYtgxzMTZ3nMlYhAiLBGKDPnWtz44hnLfYnNIELoowDM9Rgd8IiN3LdsEpCABCTQPAIKcc1j65ElIAEJFIYAQhJf3PmijlAW3ULkgJsxY0YILyMslQIQuLhwd+FCia463CnknyEPDTnnyB9HI6ccu/nk5ML5hThBwznEwp2QHFxuhJshyBHOxgIBFw2iG+FcHJfPi/lvWrXAL8zkOpCmE+A6YGGMAENeOVyh+bbHHnt0ha/GisFN71STP4AF/r//+78HgZ5rOLpV+0P8ikNFEORegjCHoMH9hvtFK3Ja8ZkI7eR7RAyKjZBHwk/JPdafbJp8Onj4KgjEfG08S3FFEupKyCuN6wlhDicb4bFs6lDVGWGNf9nIQXTjv3me4qhGzCOPIs9oXNkIvLyOfHY4sjkfS8NPcaHj4k6hCmwVyHyJBCQggbYloBDXtlMS6Mz9AAAgAElEQVRnxyUgAQm0ngBf0HEKsOvODjtV/Gh8+ScUhjArvvRT1S+/+EU0o+DCm2++GYQzwllphAAhxOXDUtmtZ3GBGIf7gIUC4X4ck8UD7jNEO1x0LKzJgxMTWcfPrJTbpvXk/EQJ/I4Aod5RlEOgyzcW3zhM+cGZ0m7iDCI9wjvCEwVWcMAh1KfWcMjRT8KHEedwoZIfq9FVmZlfqkgTaoj4R8ONR+g998pm5RVLjbf9qUwg73wr97yk0jhCPk5pftis4rmLAMfzkbQNFDPCrc6GGc5r8r0R7sxzk5BUntWE1uJcx1GOw53nKD9cD7ESMdeDTQISkIAEmkdAIa55bD2yBCQggcIQyCd1LjcoXC/Tp0/PSGqNQIZglg9LRcBjp51FOWGpMXwG4Y4fnHQ45ViwX3zxxdmyZcvCrj2LVRb25J7D/YZoh1OAgg2E41CxDpGPRQQLDRa4OIpYeNjKE8AtgZOR6ox77713RUyEYTFHCLAs9BBfCQWeNm1atzBhhI199tmnq9Jo/sCEFOdzElX80A56AeHdiHL84DjNN0IpCV8lPA22KYtyv/jFL8LinoX+7rvv3nBBq5mnBE4kcoIhaCBgIM6RU477SD3h7twvETkICcTdFBth+WeddVbIKUZxCpsEIMCzkp/8uYagRqoH8rLy/KNRSTemh8DNhuucPKmkLyDkmlQOuODIbcd7uVdzrrHhRfVx8qhyfnM8Nq9iEaZy1dPND+e5KQEJSKC5BBTimsvXo0tAAhIoFIFyCwYGyJd2hBYWCoSQUuU05oYi7As3CIsE8umwq0+LSaZxwBFqQ5VUWgyNISx12223DWFc7P4T5hadb3weCaURk4477rgQpoPYQ4VRctrRl3KJzvOhsoWamCoHg1CC2xC30oMPPlhRiOP1scor84o4QYJ5+MZCGlEcwkmBaMR8xLmM3cJlNHr06Cp72bkvIz8glVdxvZBYPd+4FmJOORyl9QhEjSbLdci5RM43xHhC3xCvUhYMKzFgTITMMy42ATiXETsQ+SuFvRMGiOMXoYO5jI2qyOR/w4nUiurOlcbo39MkwDMOFzibGjxDcX5zTrHhFJ9nuCh5NlJRe8KECWEgvH7q1Kkh/J3wVYS42Ni8Ircdgh6bW4jBiHbkIB01alQowpTCvSTNGbFXEpCABJpHQCGueWw9sgQkIAEJ/D8B8iJdd911oZABjhB26hEaWCQQLoOAxsKDhQRCDpXfqCRKw4k1adKkIPxQVTXu/pOImlAvnHI4h2gLFy4MOeT4vBj+GiehUrhq/DvuAaoYEhZ4xBFHhMTuRWhr164NLgkEUFo1QhyMn3/++ZAviBCm2CjQgSMRxw+5rWjkMqJyYKz2WgRm/TkGqouSzwlRDldVdJjSJ66fKMrhPq0kEDV6HPSF4hMffPBBcOUgghdRYIr3BFyLCHMIyoQV48gl7A9xg/sDLHACc+4TLkjDXYfzFAEON6NNAr0RwAXOcxARm2sKAY6qv4jB5Hfj3k3jfjx58uRw/pG3FXc5LnNc5WyyXHjhhV0f8/7774fNLzazEOuoqsozkvOY45PWwSYBCUhAAv1DQCGuf7j7qRKQgAQKR6C3UBZCVgld/d73vhd24nG2sYhAWEOYQ2gjLJVFAvltZs+eHUJqCHPDLUdoJAnNEYZYHD/zzDPhvzkWVeMo3kCjgiohYUuXLg2J2GPya/6GYIf4xGfsv//+PfLHhUCl09hw2+FkGTduXHCEtWND8CRXEGIaobs4JCoJccwnhTcGDRoU8hDlG8UGcMoxb+eee27401VXXRUcHOTzM+yusWcJC2pC1QhfRVCNlQ75FBbthJwRsobzqlmCGNcd4acIuZxHCFPkkWq1CNhYstUfjfEzdu47OH9piCbct3C/IcBxv2FD4Ywzzgiv479tEoBAufDPSIbzB7GNsFEENzaiON/YECKklN/jROZfqu4iuOH8Jh3DmDFjwkYJmyDcB3h2cS6Sj5XchAj6FEd66qmnwr2c98a+8C+f0ynXsGeiBCQggZQIKMSlNBv2RQISkECBCfClf/Xq1WExS4VTwmpYLCDysLtPw2GC6IBQhMuNxT6hOITS4KijgAMOAYQ8QnjmzZuXnXTSSV1hsPye3DmEwlJMIi44cMmxQCEZOxVWWZDkF0bR+UIuO/6O0Ef/CLN78cUXQz/5XEQs+k5unXbKQ4d7inAl8uvhFkTYrCTEwYRFHP8SFplvuAYR4TjerFmzwp8QTsknh2OD95Con3A+W2MJkMsM9yGiHItrnC6x4c5CMEYkJewMV1YjGuc+blWEAELkiuISrYcN7jjE/A0bNnQTRDkW9xfuG1RCLb1m6vks39NeBMq5rhHNCf0sDdfOp3ngGYjLmI0pni154S7mTEWgQ6yj8TzD+RZzvHF98uzE5cYzko0p8hzyezbAcJm3c7h4e50F9lYCEpBAdQQU4qrj5KskIAEJSKAPBMq5AQhNJT8c1eHyYak42XC00Uhef+qpp4YwU4Q4QmnIoxMrxN14440hhBSXAKJDDIHltSyGad/97ndD3joWRIT69ZQ7joUKia5xDyA4INzhhuPzcPQhRDz99NPZG2+8Edx25LpDmGuHFvnQV1wU1QhxvY0L/oiVuBVHjBgRXjp8+PAgvFEF8tlnnw2LQIQJBDrCo2yNJ4CojWuG8NUnnniiqyonn8S1wjWAUw7XDOd0LY35I+8bIgLh44SBp1j9tJYx9eW1VFbFVYvQTw45GjxiEQ3CdPONewehgNy/yJ1nKyYBcmPyg/jdW2OTgmcI98Stt946XFOxxbxvPJ94piCuc93hVMN9zIYH4hobWTScqXwejnGeeXvttVdwxfF+XOc867gvc58mbDw2CzAU8xx0VBKQQHsSUIhrz3mz1xKQgATakkBv4TmElBJaSkgXYagsMnAK4HIjfJWdfVwEuLFYmJCbjHBIFsPxuLyWROnk2iG/GwskBDVyOpGoGmdAb33g86655prgNMCBkHe9RYcYTjxyQXF8Cka0m+urr0IcueFwbxA+FQVTFoa4Glk8wphFICIRrjsWn3nnXFueuG3QafJJ4SRFlONaQkiLjbyKhHGzeEccKnfO8noW/oS2IiohPlPlmEIMzQp3bQOsQZxHuCY0mNBTGiInebooIoOgwr0BoYUk+iTWj6GrvBYxhNDworVaqy/j5OS+jEjPuYpQyX2d3J/t2NjcoNI3HDbffPNwL8QFWfp84fxhY+j1118P+dwQwz7zmc+EqtOHH3542ITiOcIzhTQLuJdLnXVRqEMIPuiggwIuiiwQeorQS3qAmEOSc49+RNE8X728HTnbZwlIQAJFJaAQV9SZdVwSkIAEEifQ0+48ggCLCMJECcUhFIzcY3HBhguOxOiE5pDvJr9oiSE75J9DDGKxs2bNmuByQyDCFVS6yIn/j8uFBRGLaUS46OIqXciw0ELQYMFDeCAhsO3U+iLEURHy8ssvDwtPWFM0gAY7BCAWgAg+sSFcEKpHgnu4lnMjthO7dunrr3/961DZljkhN2AsIED/ubYQ4ziHEQJwjCIikHsOdynCNbnfcIh2ajgbjiSEN4qRIKTEhtsTUQQBBnGzp4aIiSBHHi+EuHa7R1Q6z2utvsz5ePzxx2e875RTTgniE+kDuIdyr8ZN204thpUyJjYaojDLhkNeiGMjiGIdnE+8ltyKCJKMnXsiaRVIrxBzvPH/hPrHnKe8D/Euhq6y2RHzdZJXjnstm0W4Yrkn5xvPV65fK6K205llXyUggU4ioBDXSbPtWCUgAQkkTCBfWCF2k0UOPxRLwEVAwy2HyMCChbBHGgsjFhw4gnCfINIhurHIITca4lNPLQpxhMHyekJceT1OFxZCLGZwBMXXxWTZ5I2LuewSxrpJ1+oR4hg7TkFCUSkOwGJw5513rmrYiEFwReQkDNnWWgI4SxFBEY3JLUceKc5pKieyeM+73XB5IrR2akP8J/SUcD9CUWNDsERQ4d9OFzbqqb6MkwtXGG5lchfSEOcQg+HJ/bzdRF8ENYoK4Yhj84gQZURFBFrEOJ4dbADxjGDciI3xWiPkm1yjuIa5JgldRrDlPktOOAS3fDoB7p1cv3xODF+FIZXFEc/JeWqTgAQkIIH2IqAQ117zZW8lIAEJdDwBnCoIbTgFCDcllCcmpcdpQLgqCxlcKbgFrrjiiuDE6i0kFai4vQht5VhUPZw4cWK3ZPdRKEQYRAzk83HnEWKVb3wOrdKCvVxi71ZMbq1CHAtCOOOswvGGO7GWRPTk1Zs6dWo4Rkw23opx+hmbEsCRxPWBmBLDLDkPEUUIFyTR+/jx40NodqnDpsg8ca4R5rds2bIQEk/DOYjzDQGuXXJBNnuO6qm+TJ/IVcj5hiCcbwhXCPyVCsc0e1w9HR/RGrdbT88OnhGIaxSm4RxiLBRLoVHohiqmVJ5G2KUR8k2OUX4I8YcJ7mtymOJKJeSfZxchvDy7+DvVUtl0wn1Mvjic3RQUsklAAhKQQHsTUIhr7/mz9xKQgAQKTaCnBRD5mMiDhWuNZPSxUQACZwGNhNiIDtW4BchxhmBHiBDJtKnWyiJo7733Di6ukSNHBtcC4h5iHZVBCV0lTC069cpNRKXwIBb9tSbR7+uE1yLE0X/GSHVOnBcsKMtVzGQu4DJz5syQOy/fEO6YB4QOigbYWk8A8RinF0IIc0rDEYdIgIhAoYef/OQnXR0jVxyh3TiWEBPIE1e0xr2FcZP/DbE4NkR7cr+RA66Tq8OWm+96qi8j8HKeIVAhVOUbIhOhqjjH+DeFxnmxePHi4ECj0AGbLaUtPpfoNyGm5C8l3x15SzmfaITdHnPMMSF9AoIdeUpJk/Dcc88FsRce3C953sRGaCt/32WXXcL9Fpcgz7Ttttsuu+iii0IhhtGjR3frTqUNphSY2gcJSEACEtiUgEKcZ4UEJCABCbQ9gShoES6KcMYCmqqmsXJqTwOMrjQSabOoQqRgcYS4Rm4owop++MMfhrezYCJcDSccuaLIH0WuIxquMUKUNmzYEMKNKGQQk2rnPzt+HotTRL/3338/JNtu5YK/FiEOcY18fFSJJSy1p8IULDoRbAjPIlwKIYdG7jjCihk3i9CY+6jtT7g2GgDnLDnhCJWjkaeKXFSIKnE+Oe8pgkLeP0LfCJ2LDTHggAMOCKIc70HgbufGtUdifM5r8uPFxhhxuiLsd3Jxit7mtp7qy++++25wWJYrWoGwhHAfxbgUziuK8bApExvnBJsuhJzmXcxcM7xu5cqV4bpBvGU8hOHiLEV4w1nNPY9nB8+HzTbbLBwLZzDH43nF53FdIXxToRtxGNGc65XNICqi4spEmIutv9zUKcyPfZCABCRQFAIKcUWZScchAQlIoIMI9BT+OX/+/JAIm4ULi6BqG2FBLKoQLAj7QaD4r//6r4yE2BwTBx4OBgSzAQMGBJENRxHto48+CmGXq1evDqGs5FBjQcUxyAOEIyIWKYgFKmKF2P333z+EKLWy9STEkViccSK60V/CqHBfsCAkJG2LLbbYpJs4RqLjkIp/LCAR42BFWBchZ4hxuOFwFdpaT4BCDVwPCAKciwhOveXj4tp65ZVXQqEHXEHf//73u3V66NChQZTjB6dOuzTcrOTqIgSda5aGqE4xEfgMGTKkXYaSRD+rFfTZ5KBQAYwpnpNvbERwLyXc8rLLLktiXAhm1157bUbOUDZkEB8REfPPk+hC455HoQSeH88880y2cOHCMFbyvCGiMS4cc4TyUxE8X8iGwfLMQRQmfx75B2PjMxH1KJrC88QmAQlIQALFI6AQV7w5dUQSkIAEOoYAeXhwaiHy4LhCPMBdwaIJ90E1zgEEIxZE5EAjF09pHjMWWrjryJ1FeFBscTHGQgs3HbnicEWQ8B2nDUm4ETRw6MVFZnSUEKqJaEVOIASNavrZqEntaQEdfw8LxopYiDuwt4aIEfMVMQbccIhviB6E3CLqwaSa8OBGjc/jNI4Ac/raa691iXK4m/IN0TWKctUW72hc7yofif4T6od7FXdrrIBM/rvTTz89CEOx8m/lo/mKPIFqhTjOH3Kg4Swr3Rxhw+Lggw8OYmgqec/YgOF8IX8dQhrhs4jR8V4Ng5gvlHs8Od5IVcDzgA0IcgsSws+GDcdA1MMRyL/5RjoERDvSIHCv7ek85Lict9Fl7FkoAQlIQALFIKAQV4x5dBQSkIAEOpIAucsQemi40QjvIZwSp0ulFsUvwoOo6klFOipGks8oLthxDvF7cpuxQHvggQfCAivf+D1iHguw6JLj7zjBWOhzfPIMkT8I1wSFC3DLIcrhOCo9XqV++3cJ9AcBrgnOZcRufkgcn2977LFHlyjX38UNCFXHGYsTkz7HhiCMIETOrlbnZuyPOWvmZ1YrxOG0JfQS4bNU2I+hqeTjowJpKo0wbQS2gQMHBscemwu493BH59MdcP/G/cZmEOGoVDfF4cYzKW7KcL6R5oDQffJt0gjlpzAIIuWcOXOCIGeTgAQkIIHOIqAQ11nz7WglIAEJFIYAwgBiFjl6CH8aPnx4RqgnuXdqSWB9zz33BLcDYak43ggHiiJdDCVFoCOkDRGNxWT8PTDpAw44qutRKRXnAoUdENsI6cSJQ0gczg8WnizWXn311dBHfmKib46Vd8bFv5uvqjCnbKEGghMUIYIfqkDmG+IyudZwy8Wk860YPA4rXEgUCPnZz34WPhIxnTyFCCKIKr2F5baij0X5jGqFOEKjyZnJ+cB9Nt9isQaqYJM7LZVGnk/CTnFJI74Rgs99n3s9mypUNqWRzw0h7eqrrw7PD0RHzvndd989uOJwXlJNlfc8++yz4T08JzgO7bTTTgsO7vwGTioM7IcEJCABCTSXgEJcc/l6dAlIQAISSJwAuYBYVOKmw5lRruFewPmAM44FGmIfjYUaSd8JkcUZx8KLZPjbbLNNENWoUvnrX/86JO5GnKNdcsklwVmHK4ccROTcOvDAA8PfCHkiDIp8bC7OEj9x7F4XAUJWEeRwynE95RvXQwxfJVS5GUIY1xfhhPQhFqXg+pk0aVJwIdEHW2MJVCvE8ankP2NDgU2JfEM0pZIqxQ5SC18nXBSBEIc0fSTNAKkLCCElfJ8iCoTgU2yCAg2xOA8bK+QLJR0BaQloCHtU5mUjhlBUwrgJe+2t4nZjZ8ujSUACEpBAagQU4lKbEfsjAQlIQAL9QqA3F91PfvKTsOBCbMPZgGtu/fr1wSVBTjiS4BOaR4XKl156KTjjKHZA+B7uCVx3cUFGDjocdKtWrQqCG8IEP7yGPHc/+MEPgni3/fbbh9xKfC5hdK3MI9cvE1DHh5JYHUETYXPvvfeueATCzagKWq4RfpY/Bm4dxAbmkLDnUaNGhYU4c2/rmQAFTh566KEgynEt5BtJ6wlTRJhDgOY6qbchWlN5mPDT/OfstNNOoTAA15lidr10K7+vFiEuvhbhLRZt4R7HeUAaAQS6Zgi0lUfR8yu4l7NpQlgpxRRwNMd8obvsskvYkOG+QD7NKVOmhIIONJ4P/Devv+WWW3qtwIuzmnH35Troyxh9rwQkIAEJ9B8Bhbj+Y+8nS0ACEpBAAgRYDLEQ6m0h+PHHHwd3zYYNGzIKLZAnCNcD7gecDeUSjSMGELqUd0aQI45cSYhzhC6xCCW/HKF0/D8iDyG2CG9UMCX8j0VdzIOXAK5kuoBgiQCKw6RUROupk4THkfOvXEVdHJFUxaW9+OKLYXGNc4X3MEcIpVtuuWUQmAg7tlUm8OMf/ziIcjjVEChi7kXeibOIkFH4Usm12mT05F5kLnAh4USNDbcq4adcmwobleemr6+otvoyn0N4KqIbmxYIpFQCJYffO++8E0KJmf8UG8VncL9xjpJmgAJAFNnhGYADjvsy92fuQ/EeTdjpMcccE/LA5Z1y+fFV88xJkYd9koAEJCCBxhFQiGscS48kAQlIQAIFJnDvvfeGRRniAQuxWCgCAQdXTr5ABGIBwgCiD26dz372s4EMOZIQEajCGnMiISKRm47jkIuORSqLOUQMQqIQ76i4R3iX7bcE1q5dG5KpE/JFq0aIY/FLlU8qGDKPPTXEIsQc5oCqiDEMmXlg0V2u+qPzUpkAye4ffvjhIMoxf8xHbJzzJLNH8OA6KFdshfxbXGe4HxHGaQjWiCCI3uSis7WOQLXVl2OPcBWTSw1BlvBhqkwzbzHfWut6Xv0nsRnCvZp8gzjgcHHSd3J+Ego9duzYkH4AEY7CPLGaKuc4TlrGpyuzet6+UgISkEAnEVCI66TZdqwSkIAEJNAnAogHuHC+8IUvBAfdueeem5FLaPTo0SF0EUcVC07CHwldJb/Q4sWLw2fiCjn55JOD040Qp5i3ilA9focDCyGCY+C8oBHCOm7cuOAmQTwyPDULzhRy88GP6pyEtVUjxJHHDBEOAe/UU0/t8Txg3sjtVE5wQ6AjhPnxxx/v03nU6W9G2CCsFHchgnbM6wYXRGtEOa4LwvsQQBHgeF1sXH+4URFGyctok0AzCCCsIboRYorQhisaYZ7zFVERUZj/ZtMFYc4mAQlIQAISqJaAQly1pHydBCQgAQl0NIHodshDIFk3izGcPghzuKgIi+O15JMj2TeOHdoLL7wQqq6Sh4zwJvIL/fCHP8wOPfTQkEeI9xCuh3g3ZMiQjMqThELinOP3OFCiQNfJE4FAAzOES8J5WSRXI8Qh2J199tnZHXfcEVxXuKpwVJWGMVIdd+HChdndd9+9ScgcwitzTUECnS6NOQtxNSJMI8oRys11Extzg/AZ23777RdcRoT+lXPNNaZHHkUCvyNAjrh58+aFcHQ2Q3Dy0chxx/2djRiqnyLc589JNk34MUzas0kCEpCABMoRUIjzvJCABCQgAQk0gACVG0nwjUuLMFZyIOGGQ1ijXX/99SFn0Pz580MRBhrOufPPPz876aSTQq45RCKq65GTDBFu9913DwUcEO+WLFnSa+LvBgyhLQ6B2ImISaslYTwJ1+F//PHHB8aESv7xH/9xEPWYg5gfjrBjcvatXLlyk2qbhAgj/hGyGhfkbQGtTTr5H//xH8HhSGgf4hyiHAL3hAkTkg9jbBPEdrNGAjiZSRHAOcl9gvs0Gyc03M+0P/uzP6vxqL5cAhKQgAQ6nYBCXKefAY5fAhKQgATqJoBbB9dDaaJ5nHIk+iZc9cADDwxuN/LDIaZReRNHD+42Fne4LfLiHJ2hGmhMcM9ijxA9QqRs3QnUIsThRkSAI5cYLsVPfvKTIX8fbjqqeeLIwuXGXPA7QiIRQ/ONEMkbb7wxuCAHDx7sdDSRAI4jhGzciwMHDmziJ3loCfROgLDoGTNmhE0W7jmERudbdG3qfvNMkoAEJCCBagkoxFVLytdJQAISkIAEeiHAYqy3hRhJyknqjaOLyqmEMpGAnnxk+++/f3BalWuIeuSmo4KnrX4hjpxkVFolr1h01HE03IsUyTjrrLPC/JCcffny5SGpfKnThTxQN910U1WhsM6VBCRQDAJshixdujQ4k9lcsUlAAhKQgAT6SkAhrq8Efb8EJCABCUighADCGaIcYXX5hvCGU46wRlxZH330UTZ37tyQiH7OnDmhWmd0YVGQgFxkCHU6gsqfYrU44no6SRFGqaY6aNCg4HQjDxSVbclXtv3223d7WwxNJS/UTjvt5HkvAQlIQAISkIAEJCCBmgkoxNWMzDdIQAISkIAEGkfgX//1X0PS79dffz0bPnx49vnPfz7kmqO4A6JcFOga94nFOVIjhDhoDBs2LNtiiy1CJU/CIRcsWJBRtIHiAPlGsQbywyGQUj3RJgEJdA6BSq7nziHhSCUgAQlIoK8EFOL6StD3S0ACEpCABKokUJpTLi7sCH0iHJICAT/72c+yP/3TPw15iE455ZRQubM0B12VH1f4l1UrxFEhlSINO+ywQ8jxlm8ffvhhCA2maAMVWBFEee3s2bOzyZMnd3stjkVcjhQUsElAAhKQgAQkIAEJSKAeAgpx9VDzPRKQgAQkIIEmEUCU+81vfrNJWGSTPq6tD1utEMcgjz766Gzjxo2hKEO+4iluRCrckv/tkEMOCcU3+JfQ4ocffjj71Kc+FRhRvIH8cuecc0741yaBWglQhGXixIkhBJp8Y5Ua5yvi8KuvvppRvGKrrbbKjjzyyGzatGnd8hxWOo5/l4AEJCABCUggLQIKcWnNh72RgAQkIIEOJID4Q145nG8xrxy/K80x14Foeh1yT0IcufgQMahs+sUvfjEcY8OGDUEEoVoq+fk+97nPZWvXrs2ef/757K/+6q+yRYsWdX3WmjVrsunTp2cDBgzIJkyYEFyKd999d7bNNttky5Yt6xLnnA8JVEuAQiGcdz/96U+rKvbB6zkv/+AP/iA74YQTQuj0yy+/nD3++OOhEvOdd97p/aFa+L5OAhKQgAQkkBgBhbjEJsTuSEACEpCABCRQHYGehLj4ewovjB8/vutg69evDw6j1157LVSvpRgDQhtCR2nF26effjq45BD0PvOZz2QjR47Mzj777GzzzTevrnO+SgL/TwDB97zzzst++ctfht88+OCDFR1xVFZGJCZv4Y477tjF8sorrwyicHRwClkCEpCABCQggfYjoBDXfnNmjyUgAQlIQAISkIAE2oAAocxUQEZM22233UJ+wUpCHO7YP//zPw+VfKngm2/f+c53glNu0qRJGcVDbBKQgAQkIAEJtB8Bhbj2mzN7LAEJSEACEpCABCTQBgT+8i//MhQCmTJlSnb77bcHR2YlIY6w9B/96EchX+G2227bbZRUU0aE43izZs1qAwJ2UQISkIAEJCCBUgIKcZ4TEpCABCQgAQlIQAISaAIBQqA/8YlPhCPXUlykp65QKITCIXfccUc2YsSIJvTYQ0pAAhKQgAQk0GwCCnHNJuzxJSABCUhAAhKQQAUC1VbUxCk1ZsyYXo82dMNx35oAABZvSURBVOjQbPHixeE1//3f/53ts88+4d/StsMOO4Tk/7bWEOirEEduOHLE7bvvvtnSpUtb02k/RQISkIAEJCCBhhNQiGs4Ug8oAQlIQAISkIAEqidQS0XNjz/+OFu1alXZgy9fvjxbt25dNnfu3OyUU04Jr6GCLOGRxx13XDZkyJBu7/uTP/mTbPTo0dV31Ff2iUBfhLj77rsvu/zyy0OxEOZ5q6226lNffLMEJCABCUhAAv1HQCGu/9j7yRKQgAQkIAEJdDiBeipqlkOGo+7EE08MwhqCT2yPPPJINmfOnGzJkiWhAICt/wjUI8SRJ+6aa64Joaif//znQ/GGnXfeuf8G4SdLQAISkIAEJNBnAgpxfUboASQgAQlIQAISkEDtBOqpqFnuU6iyedRRR2Xvv/9+9sQTTwTXVGxXXXVVduedd2avvPJKttlmm9XeSd/RMAK1CnHklzv//PND1VWKNjCPpcUbGtY5DyQBCUhAAhKQQMsIKMS1DLUfJAEJSEACEpCABH5HoJ6KmuX4kS/skksuCaLNV7/61W4vOe2007KNGzdmzz//fKjCSWjrpz71KaehHwjUIsQhrs6YMSN76qmnsj333DNUXP3c5z7XD732IyUgAQlIQAISaDQBhbhGE/V4EpCABCQgAQlIoAoCjaioSRGGUaNGZb/3e78XRJtYoTN+/PDhw4Pwtuuuu2bPPvts9pvf/Cb7whe+kCHQfeUrX6mil76kUQRqEeJuuOGG7Gtf+1o2ePDgEJaqeNqoWfA4EpCABCQggf4noBDX/3NgDyQgAQlIQAIS6HACtYg0eVQrVqzIZs+enZ177rnZpEmTulH8xS9+kQ0bNiyIdBRrGDFiRPbLX/4ye/DBB7P169dnU6ZMyWbNmtXh5Fs3/J7mmIIauBYR3b74xS9mH3zwQcj1h8hK+PIWW2yxSSepeDto0KDWdd5PkoAEJCABCUigYQQU4hqG0gNJQAISkIAEJCCB+gjUK8Qdf/zxQcR57rnnsk9/+tPdPvzDDz/M/uEf/iHkFRs7dmzX3/7nf/4nmzBhQvbWW29lTz75ZBB/bM0n0NMcx99fccUV2fjx47PHHnssmzlzZq8dYv4uvfTS5nfaT5CABCQgAQlIoOEEFOIajtQDSkACEpCABCQggdoI1CPE4ZzC5fYXf/EX2XXXXVfTByLQzZs3L1uwYEF27LHH1vReXywBCUhAAhKQgAQkUD8Bhbj62flOCUhAAhKQgAQk0BAC9Qhxy5cvzy644ILslltuyQ499NCa+vH0009nU6dOLVvgoaYD+WIJSEACEpCABCQggZoIKMTVhMsXS0ACEpCABCQggcYTqEeIIy8cOeLWrVu3SVgqPSTEkaT/hDkeccQR3Tp95513ZldddVX2t3/7t9mYMWMaPyCPKAEJSEACEpCABCRQloBCnCeGBCQgAQlIQAIS6GcC9QhxX/7ylzPyva1cubJs799+++2M1+yzzz7Z/fffn/3+7/9+eB2544466qjsf//3f7M1a9Zkf/iHf9jPo/fjJSABCUhAAhKQQOcQUIjrnLl2pBKQgAQkIAEJJEqg2oqa+e7vscceoSrqHXfc0eOoFi1alN11111BjBs3blz2q1/9KlRNRYzDDTdy5MhEidgtCUhAAhKQgAQkUEwCCnHFnFdHJQEJSEACEpBAGxGotqJmHNJ//ud/Zvvuu2925JFH9lqoAdcbbjjEt/feey/7oz/6o2zw4MHZmWeemQ0aNKiNCNlVCUhAAhKQgAQkUAwCCnHFmEdHIQEJSEACEpCABCQgAQlIQAISkIAEJJA4AYW4xCfI7klAAhKQgAQkIAEJSEACEpCABCQgAQkUg4BCXDHm0VFIQAISkIAEJCABCUhAAhKQgAQkIAEJJE5AIS7xCbJ7EpCABCQgAQlIQAISkIAEJCABCUhAAsUgoBBXjHl0FBKQgAQkIAEJSEACEpCABCQgAQlIQAKJE1CIS3yC7J4EJCABCUhAAhKQgAQkIAEJSEACEpBAMQgoxBVjHh2FBCQgAQlIQAISkIAEJCABCUhAAhKQQOIEFOISnyC7JwEJSEACEpCABCQgAQlIQAISkIAEJFAMAgpxxZhHRyEBCUhAAhKQgAQkIAEJSEACEpCABCSQOAGFuMQnyO5JQAISkIAEJCABCUhAAhKQgAQkIAEJFIOAQlwx5tFRSEACEpCABCQgAQlIQAISkIAEJCABCSROQCEu8QmyexKQgAQkIAEJSEACEpCABCQgAQlIQALFIKAQV4x5dBQSkIAEJCABCUhAAhKQgAQkIAEJSEACiRNQiEt8guyeBCQgAQlIQAISkIAEJCABCUhAAhKQQDEIKMQVYx4dhQQkIAEJSEACEpCABCQgAQlIQAISkEDiBBTiEp8guycBCUhAAhKQgAQkIAEJSEACEpCABCRQDAIKccWYR0chAQlIQAISkIAEJCABCUhAAhKQgAQkkDgBhbjEJ8juSUACEpCABCQgAQlIQAISkIAEJCABCRSDgEJcMebRUUhAAhKQgAQkIAEJSEACEpCABCQgAQkkTkAhLvEJsnsSkIAEJCABCUhAAhKQgAQkIAEJSEACxSCgEFeMeXQUEpCABCQgAQlIQAISkIAEJCABCUhAAokTUIhLfILsngQkIAEJSEACEpCABCQgAQlIQAISkEAxCCjEFWMeHYUEJCABCUhAAhKQgAQkIAEJSEACEpBA4gQU4hKfILsnAQlIQAISkIAEJCABCUhAAhKQgAQkUAwCCnHFmEdHIQEJSEACEpCABCQgAQlIQAISkIAEJJA4AYW4xCfI7klAAhKQgAQkIAEJSEACEpCABCQgAQkUg4BCXDHm0VFIQAISkIAEJCABCUhAAhKQgAQkIAEJJE5AIS7xCbJ7EpCABCQgAQlIQAISkIAEJCABCUhAAsUgoBBXjHl0FBKQgAQkIAEJSEACEpCABCQgAQlIQAKJE1CIS3yC7J4EJCABCUhAAhKQgAQkIAEJSEACEpBAMQgoxBVjHh2FBCQgAQlIQAISkIAEJCABCUhAAhKQQOIEFOISnyC7JwEJSEACEpCABCQgAQlIQAISkIAEJFAMAgpxxZhHRyEBCUhAAhKQgAQkIAEJSEACEpCABCSQOAGFuMQnyO5JQAISkIAEJCABCUhAAhKQgAQkIAEJFIOAQlwx5tFRSEACEpCABCQgAQlIQAISkIAEJCABCSROQCEu8QmyexKQgAQkIAEJSEACEpCABCQgAQlIQALFIKAQV4x5dBQSkIAEJCABCUhAAhKQgAQkIAEJSEACiRNQiEt8guyeBCQgAQlIQAISkIAEJCABCUhAAhKQQDEIKMQVYx4dhQQkIAEJSEACEpCABCQgAQlIQAISkEDiBBTiEp8guycBCUhAAhKQgAQkIAEJSEACEpCABCRQDAIKccWYR0chAQlIQAISkIAEJCABCUhAAhKQgAQkkDgBhbjEJ8juSUACEpCABCQgAQlIQAISkIAEJCABCRSDgEJcMebRUUhAAhKQgAQkIAEJSEACEpCABCQgAQkkTkAhLvEJsnsSkIAEJCABCUhAAhKQgAQkIAEJSEACxSCgEFeMeXQUEpCABCQgAQlIQAISkIAEJCABCUhAAokTUIhLfILsngQkIAEJSEACEpCABCQgAQlIQAISkEAxCCjEFWMeHYUEJCABCUhAAhKQgAQkIAEJSEACEpBA4gQU4hKfILsnAQlIQAISkIAEJCABCUhAAhKQgAQkUAwCCnHFmEdHIQEJSEACEpCABCQgAQlIQAISkIAEJJA4AYW4xCfI7klAAhKQgAQkIAEJSEACEpCABCQgAQkUg4BCXDHm0VFIQAISkIAEJCABCUhAAhKQgAQkIAEJJE5AIS7xCbJ7EpCABCQgAQlIQAISkIAEJCABCUhAAsUgoBBXjHl0FBKQgAQkIAEJSEACEpCABCQgAQlIQAKJE1CIS3yC7J4EJCABCUhAAhKQgAQkIAEJSEACEpBAMQgoxBVjHh2FBCQgAQlIQAISkIAEJCABCUhAAhKQQOIEFOISnyC7JwEJSEACEpCABCQgAQlIQAISkIAEJFAMAgpxxZhHRyEBCUhAAhKQgAQkIAEJSEACEpCABCSQOAGFuMQnyO5JQAISkIAEJCABCUhAAhKQgAQkIAEJFIOAQlwx5tFRSEACEpCABCQgAQlIQAISkIAEJCABCSROQCEu8QmyexKQgAQkIAEJSEACEpCABCQgAQlIQALFIKAQV4x5dBQSkIAEJCABCUhAAhKQgAQkIAEJSEACiRNQiEt8guyeBCQgAQlIQAISkIAEJCABCUhAAhKQQDEIKMQVYx4dhQQkIAEJSEACEpCABCQgAQlIQAISkEDiBBTiEp8guycBCUhAAhKQgAQkIAEJSEACEpCABCRQDAIKccWYR0chAQlIQAISkIAEJCABCUhAAhKQgAQkkDgBhbjEJ8juSUACEpCABCQgAQlIQAISkIAEJCABCRSDgEJcMebRUUhAAhKQgAQkIAEJSEACEpCABCQgAQkkTkAhLvEJsnsSkIAEJCABCUhAAhKQgAQkIAEJSEACxSCgEFeMeXQUEpCABCQgAQlIQAISkIAEJCABCUhAAokTUIhLfILsngQkIAEJSEACEpCABCQgAQlIQAISkEAxCCjEFWMeHYUEJCABCUhAAhKQgAQkIAEJSEACEpBA4gQU4hKfILsnAQlIQAISkIAEJCABCUhAAhKQgAQkUAwCCnHFmEdHIQEJSEACEpCABCQgAQlIQAISkIAEJJA4AYW4xCfI7klAAhKQgAQkIAEJSEACEpCABCQgAQkUg4BCXDHm0VFIQAISkIAEJCABCUhAAhKQgAQkIAEJJE5AIS7xCbJ7EpCABCQgAQlIQAISkIAEJCABCUhAAsUgoBBXjHl0FBKQgAQkIAEJSEACEpCABCQgAQlIQAKJE1CIS3yC7J4EJCABCUhAAhKQgAQkIAEJSEACEpBAMQgoxBVjHh2FBCQgAQlIQAISkIAEJCABCUhAAhKQQOIEFOISnyC7JwEJSEACEpCABCQgAQlIQAISkIAEJFAMAgpxxZhHRyEBCUhAAhKQgAQkIAEJSEACEpCABCSQOAGFuMQnyO5JQAISkIAEJCABCUhAAhKQgAQkIAEJFIOAQlwx5tFRSEACEpCABCQgAQlIQAISkIAEJCABCSROQCEu8QmyexKQgAQkIAEJSEACEpCABCQgAQlIQALFIKAQV4x5dBQSkIAEJCABCUhAAhKQgAQkIAEJSEACiRNQiEt8guyeBCQgAQlIQAISkIAEJCABCUhAAhKQQDEIKMQVYx4dhQQkIAEJSEACEpCABCQgAQlIQAISkEDiBBTiEp8guycBCUhAAhKQgAQkIAEJSEACEpCABCRQDAIKccWYR0chAQlIQAISkIAEJCABCUhAAhKQgAQkkDgBhbjEJ8juSUACEpCABCQgAQlIQAISkIAEJCABCRSDgEJcMebRUUhAAhKQgAQkIAEJSEACEpCABCQgAQkkTkAhLvEJsnsSkIAEJCABCUhAAhKQgAQkIAEJSEACxSCgEFeMeXQUEpCABCQgAQlIQAISkIAEJCABCUhAAokTUIhLfILsngQkIAEJSEACEpCABCQgAQlIQAISkEAxCCjEFWMeHYUEJCABCUhAAhKQgAQkIAEJSEACEpBA4gQU4hKfILsnAQlIQAISkIAEJCABCUhAAhKQgAQkUAwCCnHFmEdHIQEJSEACEpCABCQgAQlIQAISkIAEJJA4AYW4xCfI7klAAhKQgAQkIAEJSEACEpCABCQgAQkUg4BCXDHm0VFIQAISkIAEJCABCUhAAhKQgAQkIAEJJE5AIS7xCbJ7EpCABCQgAQlIQAISkIAEJCABCUhAAsUgoBBXjHl0FBKQgAQkIAEJSEACEpCABCQgAQlIQAKJE1CIS3yC7J4EJCABCUhAAhKQgAQkIAEJSEACEpBAMQgoxBVjHh2FBCQgAQlIQAISkIAEJCABCUhAAhKQQOIEFOISnyC7JwEJSEACEpCABCQgAQlIQAISkIAEJFAMAgpxxZhHRyEBCUhAAhKQgAQkIAEJSEACEpCABCSQOAGFuMQnyO5JQAISkIAEJCABCUhAAhKQgAQkIAEJFIOAQlwx5tFRSEACEpCABCQgAQlIQAISkIAEJCABCSROQCEu8QmyexKQgAQkIAEJSEACEpCABCQgAQlIQALFIKAQV4x5dBQSkIAEJCABCUhAAhKQgAQkIAEJSEACiRNQiEt8guyeBCQgAQlIQAISkIAEJCABCUhAAhKQQDEIKMQVYx4dhQQkIAEJSEACEpCABCQgAQlIQAISkEDiBBTiEp8guycBCUhAAhKQgAQkIAEJSEACEpCABCRQDAIKccWYR0chAQlIQAISkIAEJCABCUhAAhKQgAQkkDgBhbjEJ8juSUACEpCABCQgAQlIQAISkIAEJCABCRSDgEJcMebRUUhAAhKQgAQkIAEJSEACEpCABCQgAQkkTkAhLvEJsnsSkIAEJCABCUhAAhKQgAQkIAEJSEACxSCgEFeMeXQUEpCABCQgAQlIQAISkIAEJCABCUhAAokTUIhLfILsngQkIAEJSEACEpCABCQgAQlIQAISkEAxCCjEFWMeHYUEJCABCUhAAhKQgAQkIAEJSEACEpBA4gQU4hKfILsnAQlIQAISkIAEJCABCUhAAhKQgAQkUAwCCnHFmEdHIQEJSEACEpCABCQgAQlIQAISkIAEJJA4AYW4xCfI7klAAhKQgAQkIAEJSEACEpCABCQgAQkUg4BCXDHm0VFIQAISkIAEJCABCUhAAhKQgAQkIAEJJE5AIS7xCbJ7EpCABCQgAQlIQAISkIAEJCABCUhAAsUgoBBXjHl0FBKQgAQkIAEJSEACEpCABCQgAQlIQAKJE1CIS3yC7J4EJCABCUhAAhKQgAQkIAEJSEACEpBAMQgoxBVjHh2FBCQgAQlIQAISkIAEJCABCUhAAhKQQOIEFOISnyC7JwEJSEACEpCABCQgAQlIQAISkIAEJFAMAgpxxZhHRyEBCUhAAhKQgAQkIAEJSEACEpCABCSQOAGFuMQnyO5JQAISkIAEJCABCUhAAhKQgAQkIAEJFIOAQlwx5tFRSEACEpCABCQgAQlIQAISkIAEJCABCSROQCEu8QmyexKQgAQkIAEJSEACEpCABCQgAQlIQALFIKAQV4x5dBQSkIAEJCABCUhAAhKQgAQkIAEJSEACiRNQiEt8guyeBCQgAQlIQAISkIAEJCABCUhAAhKQQDEIKMQVYx4dhQQkIAEJSEACEpCABCQgAQlIQAISkEDiBBTiEp8guycBCUhAAhKQgAQkIAEJSEACEpCABCRQDAIKccWYR0chAQlIQAISkIAEJCABCUhAAhKQgAQkkDgBhbjEJ8juSUACEpCABCQgAQlIQAISkIAEJCABCRSDgEJcMebRUUhAAhKQgAQkIAEJSEACEpCABCQgAQkkTkAhLvEJsnsSkIAEJCABCUhAAhKQgAQkIAEJSEACxSCgEFeMeXQUEpCABCQgAQlIQAISkIAEJCABCUhAAokTUIhLfILsngQkIAEJSEACEpCABCQgAQlIQAISkEAxCCjEFWMeHYUEJCABCUhAAhKQgAQkIAEJSEACEpBA4gT+DxJC+iOqSIlhAAAAAElFTkSuQmCC\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'Gaussian mixture 3D')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian mixture 3d\n",
    "\n",
    "f = plt.figure()\n",
    "ax = f.add_subplot(111, projection = '3d')\n",
    "ax.scatter(df_numbers2.iloc[:, 1].to_numpy(), df_numbers2.iloc[:, 8].to_numpy(), \n",
    "           df_numbers2.iloc[:, 0].to_numpy(), c = labels, s=20, cmap='brg')\n",
    "\n",
    "ax.set_xlabel('Median Salary')\n",
    "ax.set_ylabel('Fertility Rate')\n",
    "ax.set_zlabel('Share of people with higher education')\n",
    "ax.set_title('Gaussian mixture 3D')\n",
    "#plt.savefig('Gaussian_mixture_3d.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0\n",
      " 0 0 0 2 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 1 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Regions</th>\n",
       "      <th>Population вensity per km2</th>\n",
       "      <th>Total population</th>\n",
       "      <th>Total population per 1k</th>\n",
       "      <th>Regions, rus</th>\n",
       "      <th>Share of people with higher education. 2010. %</th>\n",
       "      <th>Median salary. 2019. rubles</th>\n",
       "      <th>Unemployment rate. 2017. %</th>\n",
       "      <th>Share of poor people. 2017. %</th>\n",
       "      <th>Gross regional product per capita. 2016. rubles</th>\n",
       "      <th>Terrorist_crimes_2017</th>\n",
       "      <th>Murders and attempted murders. 2017</th>\n",
       "      <th>Criminal possesion of weapons. 2017</th>\n",
       "      <th>Fertility rate. 2017</th>\n",
       "      <th>Museum attendance. 2010. per pop</th>\n",
       "      <th>Share of people using internet everyday, 2016, %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adygea Republic</td>\n",
       "      <td>59.43</td>\n",
       "      <td>463088</td>\n",
       "      <td>463.088</td>\n",
       "      <td>Адыгея</td>\n",
       "      <td>20</td>\n",
       "      <td>23943</td>\n",
       "      <td>8.8</td>\n",
       "      <td>13.1</td>\n",
       "      <td>17450.16</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.197</td>\n",
       "      <td>40.1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Altai Republic</td>\n",
       "      <td>2.37</td>\n",
       "      <td>220140</td>\n",
       "      <td>220.140</td>\n",
       "      <td>Республика Алтай</td>\n",
       "      <td>18</td>\n",
       "      <td>24872</td>\n",
       "      <td>12.0</td>\n",
       "      <td>25.1</td>\n",
       "      <td>18489.39</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.75</td>\n",
       "      <td>5.39</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.174</td>\n",
       "      <td>39.2334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Altai region</td>\n",
       "      <td>13.79</td>\n",
       "      <td>2317153</td>\n",
       "      <td>2317.153</td>\n",
       "      <td>Алтайский край</td>\n",
       "      <td>17</td>\n",
       "      <td>23480</td>\n",
       "      <td>6.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>18098.68</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.181</td>\n",
       "      <td>43.1501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Amurskaya Oblast</td>\n",
       "      <td>2.18</td>\n",
       "      <td>790044</td>\n",
       "      <td>790.044</td>\n",
       "      <td>Амурская область</td>\n",
       "      <td>18</td>\n",
       "      <td>40376</td>\n",
       "      <td>5.9</td>\n",
       "      <td>14.9</td>\n",
       "      <td>30784.41</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.42</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.649</td>\n",
       "      <td>44.0290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arkhangelsk region</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1092424</td>\n",
       "      <td>1092.424</td>\n",
       "      <td>Архангельская область</td>\n",
       "      <td>17</td>\n",
       "      <td>41030</td>\n",
       "      <td>6.5</td>\n",
       "      <td>14.1</td>\n",
       "      <td>32647.68</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1.60</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.896</td>\n",
       "      <td>53.8225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Astrakhan region</td>\n",
       "      <td>20.52</td>\n",
       "      <td>1005782</td>\n",
       "      <td>1005.782</td>\n",
       "      <td>Астраханская область</td>\n",
       "      <td>18</td>\n",
       "      <td>26001</td>\n",
       "      <td>7.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>28674.24</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.302</td>\n",
       "      <td>44.5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bashkortostan Republic</td>\n",
       "      <td>28.25</td>\n",
       "      <td>4038151</td>\n",
       "      <td>4038.151</td>\n",
       "      <td>Башкортостан</td>\n",
       "      <td>17</td>\n",
       "      <td>30176</td>\n",
       "      <td>5.6</td>\n",
       "      <td>12.3</td>\n",
       "      <td>28479.05</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.207</td>\n",
       "      <td>43.4766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Belgorod region</td>\n",
       "      <td>57.09</td>\n",
       "      <td>1549151</td>\n",
       "      <td>1549.151</td>\n",
       "      <td>Белгородская область</td>\n",
       "      <td>21</td>\n",
       "      <td>29766</td>\n",
       "      <td>3.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>40644.79</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.654</td>\n",
       "      <td>37.3161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bryansk region</td>\n",
       "      <td>34.21</td>\n",
       "      <td>1192491</td>\n",
       "      <td>1192.491</td>\n",
       "      <td>Брянская область</td>\n",
       "      <td>18</td>\n",
       "      <td>24571</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>20111.95</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.780</td>\n",
       "      <td>33.8959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Buryatia Republic</td>\n",
       "      <td>2.81</td>\n",
       "      <td>985937</td>\n",
       "      <td>985.937</td>\n",
       "      <td>Бурятия</td>\n",
       "      <td>21</td>\n",
       "      <td>31260</td>\n",
       "      <td>9.6</td>\n",
       "      <td>18.5</td>\n",
       "      <td>17491.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.97</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.06</td>\n",
       "      <td>0.412</td>\n",
       "      <td>42.6082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chechen Republic</td>\n",
       "      <td>91.48</td>\n",
       "      <td>1478726</td>\n",
       "      <td>1478.726</td>\n",
       "      <td>Чечня</td>\n",
       "      <td>10</td>\n",
       "      <td>22501</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.6</td>\n",
       "      <td>10312.56</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.195</td>\n",
       "      <td>67.4520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Chelyabinsk region</td>\n",
       "      <td>39.15</td>\n",
       "      <td>3466369</td>\n",
       "      <td>3466.369</td>\n",
       "      <td>Челябинская область</td>\n",
       "      <td>19</td>\n",
       "      <td>31245</td>\n",
       "      <td>6.6</td>\n",
       "      <td>14.2</td>\n",
       "      <td>31058.32</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.185</td>\n",
       "      <td>49.2983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chukotka Autonomous Okrug</td>\n",
       "      <td>0.07</td>\n",
       "      <td>50288</td>\n",
       "      <td>50.288</td>\n",
       "      <td>Чукотский автономный округ</td>\n",
       "      <td>21</td>\n",
       "      <td>76865</td>\n",
       "      <td>2.9</td>\n",
       "      <td>10.2</td>\n",
       "      <td>113735.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.61</td>\n",
       "      <td>8.63</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.891</td>\n",
       "      <td>53.8740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chuvash Republic</td>\n",
       "      <td>66.39</td>\n",
       "      <td>1217818</td>\n",
       "      <td>1217.818</td>\n",
       "      <td>Чувашия</td>\n",
       "      <td>19</td>\n",
       "      <td>24069</td>\n",
       "      <td>5.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18242.06</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.308</td>\n",
       "      <td>42.8083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Crimea Republic</td>\n",
       "      <td>73.33</td>\n",
       "      <td>1912622</td>\n",
       "      <td>1912.622</td>\n",
       "      <td>Крым</td>\n",
       "      <td>20</td>\n",
       "      <td>25901</td>\n",
       "      <td>6.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>14286.24</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.801</td>\n",
       "      <td>43.8115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dagestan Republic</td>\n",
       "      <td>61.88</td>\n",
       "      <td>3110858</td>\n",
       "      <td>3110.858</td>\n",
       "      <td>Дагестан</td>\n",
       "      <td>17</td>\n",
       "      <td>20015</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>17075.76</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.23</td>\n",
       "      <td>7.47</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.169</td>\n",
       "      <td>45.4031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ingushetia Republic</td>\n",
       "      <td>162.36</td>\n",
       "      <td>507061</td>\n",
       "      <td>507.061</td>\n",
       "      <td>Ингушетия</td>\n",
       "      <td>16</td>\n",
       "      <td>19954</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9281.84</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.52</td>\n",
       "      <td>5.02</td>\n",
       "      <td>1.77</td>\n",
       "      <td>0.317</td>\n",
       "      <td>44.9688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Irkutsk region</td>\n",
       "      <td>3.09</td>\n",
       "      <td>2391193</td>\n",
       "      <td>2391.193</td>\n",
       "      <td>Иркутская область</td>\n",
       "      <td>20</td>\n",
       "      <td>37921</td>\n",
       "      <td>8.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>38199.91</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.86</td>\n",
       "      <td>1.86</td>\n",
       "      <td>0.355</td>\n",
       "      <td>45.0068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ivanovo region</td>\n",
       "      <td>46.51</td>\n",
       "      <td>997135</td>\n",
       "      <td>997.135</td>\n",
       "      <td>Ивановская область</td>\n",
       "      <td>19</td>\n",
       "      <td>23117</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14.3</td>\n",
       "      <td>15043.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.393</td>\n",
       "      <td>43.0924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jewish Autonomous Region</td>\n",
       "      <td>4.36</td>\n",
       "      <td>158305</td>\n",
       "      <td>158.305</td>\n",
       "      <td>Еврейская автономная область</td>\n",
       "      <td>15</td>\n",
       "      <td>34538</td>\n",
       "      <td>8.3</td>\n",
       "      <td>24.9</td>\n",
       "      <td>24333.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.58</td>\n",
       "      <td>5.48</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.288</td>\n",
       "      <td>45.8042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kabardino-Balkar Republic</td>\n",
       "      <td>69.64</td>\n",
       "      <td>868350</td>\n",
       "      <td>868.350</td>\n",
       "      <td>Кабардино-Балкария</td>\n",
       "      <td>20</td>\n",
       "      <td>19796</td>\n",
       "      <td>10.5</td>\n",
       "      <td>24.1</td>\n",
       "      <td>13273.22</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.251</td>\n",
       "      <td>43.4017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Kaliningrad region</td>\n",
       "      <td>66.94</td>\n",
       "      <td>1012512</td>\n",
       "      <td>1012.512</td>\n",
       "      <td>Калининградская область</td>\n",
       "      <td>24</td>\n",
       "      <td>31681</td>\n",
       "      <td>5.2</td>\n",
       "      <td>14.1</td>\n",
       "      <td>33834.62</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.60</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.158</td>\n",
       "      <td>51.7943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Kalmykia Republic</td>\n",
       "      <td>3.63</td>\n",
       "      <td>271135</td>\n",
       "      <td>271.135</td>\n",
       "      <td>Калмыкия</td>\n",
       "      <td>21</td>\n",
       "      <td>21984</td>\n",
       "      <td>10.0</td>\n",
       "      <td>30.6</td>\n",
       "      <td>17340.71</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>5.65</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.162</td>\n",
       "      <td>43.8996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Kaluga region</td>\n",
       "      <td>33.67</td>\n",
       "      <td>1002575</td>\n",
       "      <td>1002.575</td>\n",
       "      <td>Калужская область</td>\n",
       "      <td>22</td>\n",
       "      <td>33385</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>31891.35</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.662</td>\n",
       "      <td>35.6553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Kamchatka Krai</td>\n",
       "      <td>0.67</td>\n",
       "      <td>313016</td>\n",
       "      <td>313.016</td>\n",
       "      <td>Камчатский край</td>\n",
       "      <td>24</td>\n",
       "      <td>62444</td>\n",
       "      <td>4.3</td>\n",
       "      <td>18.4</td>\n",
       "      <td>54050.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.330</td>\n",
       "      <td>62.7655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Karachay-Cherkess Republic</td>\n",
       "      <td>32.61</td>\n",
       "      <td>465528</td>\n",
       "      <td>465.528</td>\n",
       "      <td>Карачаево-Черкесия</td>\n",
       "      <td>23</td>\n",
       "      <td>20788</td>\n",
       "      <td>13.5</td>\n",
       "      <td>24.6</td>\n",
       "      <td>13485.99</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.64</td>\n",
       "      <td>3.49</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.056</td>\n",
       "      <td>46.1661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Karelia Republic</td>\n",
       "      <td>3.40</td>\n",
       "      <td>614064</td>\n",
       "      <td>614.064</td>\n",
       "      <td>Карелия</td>\n",
       "      <td>19</td>\n",
       "      <td>34922</td>\n",
       "      <td>8.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>31963.69</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.85</td>\n",
       "      <td>2.44</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.723</td>\n",
       "      <td>52.3734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Kemerovo region</td>\n",
       "      <td>27.77</td>\n",
       "      <td>2657854</td>\n",
       "      <td>2657.854</td>\n",
       "      <td>Кемеровская область</td>\n",
       "      <td>18</td>\n",
       "      <td>34256</td>\n",
       "      <td>7.1</td>\n",
       "      <td>15.9</td>\n",
       "      <td>27231.09</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.11</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.485</td>\n",
       "      <td>41.6581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Khabarovsk region</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1315643</td>\n",
       "      <td>1315.643</td>\n",
       "      <td>Хабаровский край</td>\n",
       "      <td>24</td>\n",
       "      <td>43216</td>\n",
       "      <td>4.9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>41206.81</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.48</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.345</td>\n",
       "      <td>52.0147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Khakassia Republic</td>\n",
       "      <td>8.68</td>\n",
       "      <td>534262</td>\n",
       "      <td>534.262</td>\n",
       "      <td>Хакасия</td>\n",
       "      <td>18</td>\n",
       "      <td>29787</td>\n",
       "      <td>4.9</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29310.74</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.39</td>\n",
       "      <td>5.24</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.625</td>\n",
       "      <td>44.4349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Khanty-Mansiysk</td>\n",
       "      <td>3.13</td>\n",
       "      <td>1674676</td>\n",
       "      <td>1674.676</td>\n",
       "      <td>Ханты-Мансийский автономный округ</td>\n",
       "      <td>24</td>\n",
       "      <td>60570</td>\n",
       "      <td>3.3</td>\n",
       "      <td>12.6</td>\n",
       "      <td>160695.99</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2.47</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.281</td>\n",
       "      <td>63.5470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Kirov region</td>\n",
       "      <td>10.49</td>\n",
       "      <td>1262402</td>\n",
       "      <td>1262.402</td>\n",
       "      <td>Кировская область</td>\n",
       "      <td>17</td>\n",
       "      <td>25074</td>\n",
       "      <td>5.3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19341.84</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.719</td>\n",
       "      <td>44.2305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Komi Republic</td>\n",
       "      <td>1.97</td>\n",
       "      <td>633385</td>\n",
       "      <td>633.385</td>\n",
       "      <td>Республика Коми</td>\n",
       "      <td>18</td>\n",
       "      <td>42940</td>\n",
       "      <td>7.8</td>\n",
       "      <td>17.1</td>\n",
       "      <td>55046.14</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.362</td>\n",
       "      <td>50.6579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Kostroma region</td>\n",
       "      <td>10.52</td>\n",
       "      <td>5675462</td>\n",
       "      <td>5675.462</td>\n",
       "      <td>Костромская область</td>\n",
       "      <td>18</td>\n",
       "      <td>24172</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21274.89</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.629</td>\n",
       "      <td>41.7605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Krasnodar region</td>\n",
       "      <td>75.19</td>\n",
       "      <td>2866255</td>\n",
       "      <td>2866.255</td>\n",
       "      <td>Краснодарский край</td>\n",
       "      <td>20</td>\n",
       "      <td>29887</td>\n",
       "      <td>5.7</td>\n",
       "      <td>11.6</td>\n",
       "      <td>31531.38</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.364</td>\n",
       "      <td>42.8266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Krasnoyarsk region</td>\n",
       "      <td>1.21</td>\n",
       "      <td>827166</td>\n",
       "      <td>827.166</td>\n",
       "      <td>Красноярский край</td>\n",
       "      <td>20</td>\n",
       "      <td>38351</td>\n",
       "      <td>5.7</td>\n",
       "      <td>18.7</td>\n",
       "      <td>53189.65</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.606</td>\n",
       "      <td>44.7190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Kurgan region</td>\n",
       "      <td>11.57</td>\n",
       "      <td>1104008</td>\n",
       "      <td>1104.008</td>\n",
       "      <td>Курганская область</td>\n",
       "      <td>15</td>\n",
       "      <td>24717</td>\n",
       "      <td>9.1</td>\n",
       "      <td>19.3</td>\n",
       "      <td>19401.25</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.22</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.378</td>\n",
       "      <td>40.2268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Kursk region</td>\n",
       "      <td>36.80</td>\n",
       "      <td>1875872</td>\n",
       "      <td>1875.872</td>\n",
       "      <td>Курская область</td>\n",
       "      <td>19</td>\n",
       "      <td>26521</td>\n",
       "      <td>4.1</td>\n",
       "      <td>10.1</td>\n",
       "      <td>28074.44</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.35</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.436</td>\n",
       "      <td>33.9187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Leningrad region</td>\n",
       "      <td>22.36</td>\n",
       "      <td>1139371</td>\n",
       "      <td>1139.371</td>\n",
       "      <td>Ленинградская область</td>\n",
       "      <td>21</td>\n",
       "      <td>42076</td>\n",
       "      <td>5.7</td>\n",
       "      <td>10.3</td>\n",
       "      <td>44303.72</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.315</td>\n",
       "      <td>51.0667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Lipetsk region</td>\n",
       "      <td>47.38</td>\n",
       "      <td>140149</td>\n",
       "      <td>140.149</td>\n",
       "      <td>Липецкая область</td>\n",
       "      <td>20</td>\n",
       "      <td>28557</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>35078.74</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.271</td>\n",
       "      <td>35.3718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Magadan Region</td>\n",
       "      <td>0.30</td>\n",
       "      <td>140149</td>\n",
       "      <td>140.149</td>\n",
       "      <td>Магаданская область</td>\n",
       "      <td>23</td>\n",
       "      <td>69316</td>\n",
       "      <td>5.3</td>\n",
       "      <td>13.4</td>\n",
       "      <td>86580.36</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.192</td>\n",
       "      <td>60.5928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Mari El Republic</td>\n",
       "      <td>29.07</td>\n",
       "      <td>679417</td>\n",
       "      <td>679.417</td>\n",
       "      <td>Марий Эл</td>\n",
       "      <td>19</td>\n",
       "      <td>23739</td>\n",
       "      <td>6.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>20177.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.834</td>\n",
       "      <td>36.0132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Mordovia Republic</td>\n",
       "      <td>30.24</td>\n",
       "      <td>790197</td>\n",
       "      <td>790.197</td>\n",
       "      <td>Мордовия</td>\n",
       "      <td>21</td>\n",
       "      <td>24052</td>\n",
       "      <td>4.2</td>\n",
       "      <td>18.9</td>\n",
       "      <td>21162.01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.51</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.244</td>\n",
       "      <td>39.9834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>4950.43</td>\n",
       "      <td>12678079</td>\n",
       "      <td>12678.079</td>\n",
       "      <td>Москва</td>\n",
       "      <td>40</td>\n",
       "      <td>66103</td>\n",
       "      <td>1.4</td>\n",
       "      <td>8.9</td>\n",
       "      <td>100018.50</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.538</td>\n",
       "      <td>57.3856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Moscow region</td>\n",
       "      <td>173.50</td>\n",
       "      <td>7690863</td>\n",
       "      <td>7690.863</td>\n",
       "      <td>Московская область</td>\n",
       "      <td>30</td>\n",
       "      <td>45201</td>\n",
       "      <td>3.2</td>\n",
       "      <td>8.1</td>\n",
       "      <td>42012.45</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.503</td>\n",
       "      <td>50.7064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Murmansk region</td>\n",
       "      <td>5.12</td>\n",
       "      <td>741404</td>\n",
       "      <td>741.404</td>\n",
       "      <td>Мурманская область</td>\n",
       "      <td>22</td>\n",
       "      <td>49589</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>48183.88</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.19</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.513</td>\n",
       "      <td>60.6225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Nenets Autonomous Okrug</td>\n",
       "      <td>0.25</td>\n",
       "      <td>44111</td>\n",
       "      <td>44.111</td>\n",
       "      <td>Ненецкий автономный округ</td>\n",
       "      <td>16</td>\n",
       "      <td>71303</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>502634.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.14</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0.435</td>\n",
       "      <td>52.6971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Nizhny Novgorod Region</td>\n",
       "      <td>41.80</td>\n",
       "      <td>3202946</td>\n",
       "      <td>3202.946</td>\n",
       "      <td>Нижегородская область</td>\n",
       "      <td>22</td>\n",
       "      <td>30671</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.9</td>\n",
       "      <td>31273.72</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.94</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.610</td>\n",
       "      <td>35.2710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>North Ossetia – Alania Republic</td>\n",
       "      <td>87.25</td>\n",
       "      <td>696837</td>\n",
       "      <td>696.837</td>\n",
       "      <td>Северная Осетия</td>\n",
       "      <td>26</td>\n",
       "      <td>21061</td>\n",
       "      <td>11.8</td>\n",
       "      <td>14.5</td>\n",
       "      <td>15379.44</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.16</td>\n",
       "      <td>5.74</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.078</td>\n",
       "      <td>41.7035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Novgorod region</td>\n",
       "      <td>10.94</td>\n",
       "      <td>596508</td>\n",
       "      <td>596.508</td>\n",
       "      <td>Новгородская область</td>\n",
       "      <td>18</td>\n",
       "      <td>29168</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14.8</td>\n",
       "      <td>34248.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.115</td>\n",
       "      <td>34.6669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Novosibirsk region</td>\n",
       "      <td>15.74</td>\n",
       "      <td>2798170</td>\n",
       "      <td>2798.170</td>\n",
       "      <td>Новосибирская область</td>\n",
       "      <td>23</td>\n",
       "      <td>32556</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>33864.18</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.252</td>\n",
       "      <td>47.4249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Omsk region</td>\n",
       "      <td>13.65</td>\n",
       "      <td>1926665</td>\n",
       "      <td>1926.665</td>\n",
       "      <td>Омская область</td>\n",
       "      <td>19</td>\n",
       "      <td>27380</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>27283.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.61</td>\n",
       "      <td>0.449</td>\n",
       "      <td>44.9216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Orenburg region</td>\n",
       "      <td>15.82</td>\n",
       "      <td>1956835</td>\n",
       "      <td>1956.835</td>\n",
       "      <td>Оренбургская область</td>\n",
       "      <td>17</td>\n",
       "      <td>27016</td>\n",
       "      <td>4.6</td>\n",
       "      <td>14.5</td>\n",
       "      <td>33381.39</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.32</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.176</td>\n",
       "      <td>32.4422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Oryol Region</td>\n",
       "      <td>29.75</td>\n",
       "      <td>733498</td>\n",
       "      <td>733.498</td>\n",
       "      <td>Орловская область</td>\n",
       "      <td>21</td>\n",
       "      <td>23228</td>\n",
       "      <td>6.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24284.17</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.54</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.369</td>\n",
       "      <td>37.8795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Penza region</td>\n",
       "      <td>30.12</td>\n",
       "      <td>1305563</td>\n",
       "      <td>1305.563</td>\n",
       "      <td>Пензенская область</td>\n",
       "      <td>19</td>\n",
       "      <td>25563</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14.6</td>\n",
       "      <td>21650.82</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.453</td>\n",
       "      <td>37.8494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Perm</td>\n",
       "      <td>16.22</td>\n",
       "      <td>2599260</td>\n",
       "      <td>2599.260</td>\n",
       "      <td>Пермский край</td>\n",
       "      <td>17</td>\n",
       "      <td>31958</td>\n",
       "      <td>6.1</td>\n",
       "      <td>15.1</td>\n",
       "      <td>35724.52</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.415</td>\n",
       "      <td>45.7293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Primorsky Krai</td>\n",
       "      <td>11.51</td>\n",
       "      <td>1895868</td>\n",
       "      <td>1895.868</td>\n",
       "      <td>Приморский край</td>\n",
       "      <td>22</td>\n",
       "      <td>40620</td>\n",
       "      <td>5.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>32944.60</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.170</td>\n",
       "      <td>43.5748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Pskov region</td>\n",
       "      <td>11.30</td>\n",
       "      <td>626115</td>\n",
       "      <td>626.115</td>\n",
       "      <td>Псковская область</td>\n",
       "      <td>18</td>\n",
       "      <td>23895</td>\n",
       "      <td>6.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19268.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.92</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.809</td>\n",
       "      <td>48.8163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Rostov region</td>\n",
       "      <td>41.58</td>\n",
       "      <td>4197821</td>\n",
       "      <td>4197.821</td>\n",
       "      <td>Ростовская область</td>\n",
       "      <td>21</td>\n",
       "      <td>27619</td>\n",
       "      <td>5.6</td>\n",
       "      <td>13.9</td>\n",
       "      <td>25874.41</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.378</td>\n",
       "      <td>40.3986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Ryazan Oblast</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1108847</td>\n",
       "      <td>1108.847</td>\n",
       "      <td>Рязанская область</td>\n",
       "      <td>20</td>\n",
       "      <td>28331</td>\n",
       "      <td>4.1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25715.54</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.701</td>\n",
       "      <td>32.8582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Sakha (Yakutia) Republic</td>\n",
       "      <td>0.32</td>\n",
       "      <td>971996</td>\n",
       "      <td>971.996</td>\n",
       "      <td>Якутия</td>\n",
       "      <td>21</td>\n",
       "      <td>56863</td>\n",
       "      <td>7.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>78056.78</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.42</td>\n",
       "      <td>3.12</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.523</td>\n",
       "      <td>46.4997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Sakhalin region</td>\n",
       "      <td>5.61</td>\n",
       "      <td>488257</td>\n",
       "      <td>488.257</td>\n",
       "      <td>Сахалинская область</td>\n",
       "      <td>19</td>\n",
       "      <td>62647</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>135893.24</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.591</td>\n",
       "      <td>55.2951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Samara Region</td>\n",
       "      <td>59.36</td>\n",
       "      <td>3179532</td>\n",
       "      <td>3179.532</td>\n",
       "      <td>Самарская область</td>\n",
       "      <td>24</td>\n",
       "      <td>30428</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>34299.64</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0.304</td>\n",
       "      <td>44.6214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Saratov region</td>\n",
       "      <td>23.92</td>\n",
       "      <td>2421895</td>\n",
       "      <td>2421.895</td>\n",
       "      <td>Саратовская область</td>\n",
       "      <td>21</td>\n",
       "      <td>24155</td>\n",
       "      <td>4.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>22710.50</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.328</td>\n",
       "      <td>44.6776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Sevastopol</td>\n",
       "      <td>519.84</td>\n",
       "      <td>449138</td>\n",
       "      <td>449.138</td>\n",
       "      <td>Севастополь</td>\n",
       "      <td>28</td>\n",
       "      <td>29563</td>\n",
       "      <td>4.6</td>\n",
       "      <td>14.2</td>\n",
       "      <td>13293.40</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.101</td>\n",
       "      <td>54.1790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Smolensk region</td>\n",
       "      <td>18.78</td>\n",
       "      <td>934889</td>\n",
       "      <td>934.889</td>\n",
       "      <td>Смоленская область</td>\n",
       "      <td>19</td>\n",
       "      <td>26720</td>\n",
       "      <td>5.7</td>\n",
       "      <td>16.3</td>\n",
       "      <td>23599.07</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.56</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.592</td>\n",
       "      <td>43.4761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>3847.52</td>\n",
       "      <td>5398064</td>\n",
       "      <td>5398.064</td>\n",
       "      <td>Санкт-Петербург</td>\n",
       "      <td>35</td>\n",
       "      <td>51248</td>\n",
       "      <td>1.7</td>\n",
       "      <td>8.1</td>\n",
       "      <td>61758.85</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.50</td>\n",
       "      <td>4.498</td>\n",
       "      <td>53.8236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Stavropol region</td>\n",
       "      <td>42.38</td>\n",
       "      <td>2803573</td>\n",
       "      <td>2803.573</td>\n",
       "      <td>Ставропольский край</td>\n",
       "      <td>20</td>\n",
       "      <td>24432</td>\n",
       "      <td>5.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>20068.26</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.282</td>\n",
       "      <td>45.3423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Sverdlovsk region</td>\n",
       "      <td>22.18</td>\n",
       "      <td>4310681</td>\n",
       "      <td>4310.681</td>\n",
       "      <td>Свердловская область</td>\n",
       "      <td>19</td>\n",
       "      <td>34818</td>\n",
       "      <td>5.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>39397.43</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.79</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.428</td>\n",
       "      <td>48.0411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Tambov Region</td>\n",
       "      <td>29.21</td>\n",
       "      <td>1006748</td>\n",
       "      <td>1006.748</td>\n",
       "      <td>Тамбовская область</td>\n",
       "      <td>18</td>\n",
       "      <td>24417</td>\n",
       "      <td>4.4</td>\n",
       "      <td>10.9</td>\n",
       "      <td>25572.42</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.84</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.601</td>\n",
       "      <td>29.5813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Tatarstan Republic</td>\n",
       "      <td>57.52</td>\n",
       "      <td>3902642</td>\n",
       "      <td>3902.642</td>\n",
       "      <td>Татарстан</td>\n",
       "      <td>21</td>\n",
       "      <td>31341</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>43193.85</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.503</td>\n",
       "      <td>43.7128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Tomsk region</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1079271</td>\n",
       "      <td>1079.271</td>\n",
       "      <td>Томская область</td>\n",
       "      <td>24</td>\n",
       "      <td>34074</td>\n",
       "      <td>6.3</td>\n",
       "      <td>17.5</td>\n",
       "      <td>39004.61</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.57</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.424</td>\n",
       "      <td>36.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Tula region</td>\n",
       "      <td>57.09</td>\n",
       "      <td>1466127</td>\n",
       "      <td>1466.127</td>\n",
       "      <td>Тульская область</td>\n",
       "      <td>19</td>\n",
       "      <td>31694</td>\n",
       "      <td>3.9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29639.90</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.076</td>\n",
       "      <td>54.7344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Tver region</td>\n",
       "      <td>14.97</td>\n",
       "      <td>1260379</td>\n",
       "      <td>1260.379</td>\n",
       "      <td>Тверская область</td>\n",
       "      <td>18</td>\n",
       "      <td>27513</td>\n",
       "      <td>4.5</td>\n",
       "      <td>12.7</td>\n",
       "      <td>23752.22</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.56</td>\n",
       "      <td>0.391</td>\n",
       "      <td>39.5116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Tyumen region</td>\n",
       "      <td>9.60</td>\n",
       "      <td>1537416</td>\n",
       "      <td>1537.416</td>\n",
       "      <td>Тюменская область</td>\n",
       "      <td>20</td>\n",
       "      <td>39185</td>\n",
       "      <td>3.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>54962.29</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.476</td>\n",
       "      <td>48.3639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Tyva Republic</td>\n",
       "      <td>1.94</td>\n",
       "      <td>327383</td>\n",
       "      <td>327.383</td>\n",
       "      <td>Республика Тыва</td>\n",
       "      <td>15</td>\n",
       "      <td>27822</td>\n",
       "      <td>18.3</td>\n",
       "      <td>41.5</td>\n",
       "      <td>14268.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.61</td>\n",
       "      <td>4.05</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.127</td>\n",
       "      <td>30.2531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Udmurt republic</td>\n",
       "      <td>35.69</td>\n",
       "      <td>1500955</td>\n",
       "      <td>1500.955</td>\n",
       "      <td>Удмуртия</td>\n",
       "      <td>19</td>\n",
       "      <td>27807</td>\n",
       "      <td>4.8</td>\n",
       "      <td>12.5</td>\n",
       "      <td>30702.35</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.72</td>\n",
       "      <td>0.492</td>\n",
       "      <td>47.5155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Ulyanovsk region</td>\n",
       "      <td>33.08</td>\n",
       "      <td>1229824</td>\n",
       "      <td>1229.824</td>\n",
       "      <td>Ульяновская область</td>\n",
       "      <td>18</td>\n",
       "      <td>25906</td>\n",
       "      <td>4.4</td>\n",
       "      <td>15.1</td>\n",
       "      <td>22509.81</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.613</td>\n",
       "      <td>38.5104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Vladimir region</td>\n",
       "      <td>22.07</td>\n",
       "      <td>2491036</td>\n",
       "      <td>2491.036</td>\n",
       "      <td>Владимирская область</td>\n",
       "      <td>19</td>\n",
       "      <td>28174</td>\n",
       "      <td>4.8</td>\n",
       "      <td>13.6</td>\n",
       "      <td>24199.85</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.107</td>\n",
       "      <td>43.1621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Volgograd region</td>\n",
       "      <td>22.07</td>\n",
       "      <td>2491036</td>\n",
       "      <td>2491.036</td>\n",
       "      <td>Волгоградская область</td>\n",
       "      <td>20</td>\n",
       "      <td>26518</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>25178.21</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.47</td>\n",
       "      <td>2.51</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.208</td>\n",
       "      <td>39.1724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Vologda Region</td>\n",
       "      <td>8.02</td>\n",
       "      <td>1160445</td>\n",
       "      <td>1160.445</td>\n",
       "      <td>Вологодская область</td>\n",
       "      <td>18</td>\n",
       "      <td>32854</td>\n",
       "      <td>5.3</td>\n",
       "      <td>14.3</td>\n",
       "      <td>35305.44</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.64</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1.050</td>\n",
       "      <td>42.1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Voronezh region</td>\n",
       "      <td>44.51</td>\n",
       "      <td>2324205</td>\n",
       "      <td>2324.205</td>\n",
       "      <td>Воронежская область</td>\n",
       "      <td>21</td>\n",
       "      <td>28187</td>\n",
       "      <td>4.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>31095.99</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.37</td>\n",
       "      <td>0.280</td>\n",
       "      <td>37.1425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Yamalo-Nenets Autonomous Okrug</td>\n",
       "      <td>0.71</td>\n",
       "      <td>544444</td>\n",
       "      <td>544.444</td>\n",
       "      <td>Ямало-Ненецкий автономный округ</td>\n",
       "      <td>25</td>\n",
       "      <td>77542</td>\n",
       "      <td>3.2</td>\n",
       "      <td>7.5</td>\n",
       "      <td>317106.30</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.95</td>\n",
       "      <td>0.414</td>\n",
       "      <td>62.6062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Yaroslavl region</td>\n",
       "      <td>34.65</td>\n",
       "      <td>1253389</td>\n",
       "      <td>1253.389</td>\n",
       "      <td>Ярославская область</td>\n",
       "      <td>21</td>\n",
       "      <td>29987</td>\n",
       "      <td>6.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>31855.05</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.671</td>\n",
       "      <td>45.4629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Zabaykalsky Krai</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1059700</td>\n",
       "      <td>1059.700</td>\n",
       "      <td>Забайкальский край</td>\n",
       "      <td>16</td>\n",
       "      <td>33456</td>\n",
       "      <td>10.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>20927.29</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.63</td>\n",
       "      <td>3.56</td>\n",
       "      <td>1.87</td>\n",
       "      <td>0.189</td>\n",
       "      <td>35.1785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Regions  Population вensity per km2  \\\n",
       "0                   Adygea Republic                       59.43   \n",
       "1                    Altai Republic                        2.37   \n",
       "2                      Altai region                       13.79   \n",
       "3                  Amurskaya Oblast                        2.18   \n",
       "4                Arkhangelsk region                        2.64   \n",
       "5                  Astrakhan region                       20.52   \n",
       "6            Bashkortostan Republic                       28.25   \n",
       "7                   Belgorod region                       57.09   \n",
       "8                    Bryansk region                       34.21   \n",
       "9                 Buryatia Republic                        2.81   \n",
       "10                 Chechen Republic                       91.48   \n",
       "11               Chelyabinsk region                       39.15   \n",
       "12        Chukotka Autonomous Okrug                        0.07   \n",
       "13                 Chuvash Republic                       66.39   \n",
       "14                  Crimea Republic                       73.33   \n",
       "15                Dagestan Republic                       61.88   \n",
       "16              Ingushetia Republic                      162.36   \n",
       "17                   Irkutsk region                        3.09   \n",
       "18                   Ivanovo region                       46.51   \n",
       "19         Jewish Autonomous Region                        4.36   \n",
       "20        Kabardino-Balkar Republic                       69.64   \n",
       "21               Kaliningrad region                       66.94   \n",
       "22                Kalmykia Republic                        3.63   \n",
       "23                    Kaluga region                       33.67   \n",
       "24                   Kamchatka Krai                        0.67   \n",
       "25       Karachay-Cherkess Republic                       32.61   \n",
       "26                 Karelia Republic                        3.40   \n",
       "27                  Kemerovo region                       27.77   \n",
       "28                Khabarovsk region                        1.67   \n",
       "29               Khakassia Republic                        8.68   \n",
       "30                  Khanty-Mansiysk                        3.13   \n",
       "31                     Kirov region                       10.49   \n",
       "32                    Komi Republic                        1.97   \n",
       "33                  Kostroma region                       10.52   \n",
       "34                 Krasnodar region                       75.19   \n",
       "35               Krasnoyarsk region                        1.21   \n",
       "36                    Kurgan region                       11.57   \n",
       "37                     Kursk region                       36.80   \n",
       "38                 Leningrad region                       22.36   \n",
       "39                   Lipetsk region                       47.38   \n",
       "40                   Magadan Region                        0.30   \n",
       "41                 Mari El Republic                       29.07   \n",
       "42                Mordovia Republic                       30.24   \n",
       "43                           Moscow                     4950.43   \n",
       "44                    Moscow region                      173.50   \n",
       "45                  Murmansk region                        5.12   \n",
       "46          Nenets Autonomous Okrug                        0.25   \n",
       "47           Nizhny Novgorod Region                       41.80   \n",
       "48  North Ossetia – Alania Republic                       87.25   \n",
       "49                  Novgorod region                       10.94   \n",
       "50               Novosibirsk region                       15.74   \n",
       "51                      Omsk region                       13.65   \n",
       "52                  Orenburg region                       15.82   \n",
       "53                     Oryol Region                       29.75   \n",
       "54                     Penza region                       30.12   \n",
       "55                             Perm                       16.22   \n",
       "56                   Primorsky Krai                       11.51   \n",
       "57                     Pskov region                       11.30   \n",
       "58                    Rostov region                       41.58   \n",
       "59                    Ryazan Oblast                       28.00   \n",
       "60         Sakha (Yakutia) Republic                        0.32   \n",
       "61                  Sakhalin region                        5.61   \n",
       "62                    Samara Region                       59.36   \n",
       "63                   Saratov region                       23.92   \n",
       "64                       Sevastopol                      519.84   \n",
       "65                  Smolensk region                       18.78   \n",
       "66                   St. Petersburg                     3847.52   \n",
       "67                 Stavropol region                       42.38   \n",
       "68                Sverdlovsk region                       22.18   \n",
       "69                    Tambov Region                       29.21   \n",
       "70               Tatarstan Republic                       57.52   \n",
       "71                     Tomsk region                        3.43   \n",
       "72                      Tula region                       57.09   \n",
       "73                      Tver region                       14.97   \n",
       "74                    Tyumen region                        9.60   \n",
       "75                    Tyva Republic                        1.94   \n",
       "76                  Udmurt republic                       35.69   \n",
       "77                 Ulyanovsk region                       33.08   \n",
       "78                  Vladimir region                       22.07   \n",
       "79                 Volgograd region                       22.07   \n",
       "80                   Vologda Region                        8.02   \n",
       "81                  Voronezh region                       44.51   \n",
       "82   Yamalo-Nenets Autonomous Okrug                        0.71   \n",
       "83                 Yaroslavl region                       34.65   \n",
       "84                 Zabaykalsky Krai                        2.45   \n",
       "\n",
       "    Total population  Total population per 1k  \\\n",
       "0             463088                  463.088   \n",
       "1             220140                  220.140   \n",
       "2            2317153                 2317.153   \n",
       "3             790044                  790.044   \n",
       "4            1092424                 1092.424   \n",
       "5            1005782                 1005.782   \n",
       "6            4038151                 4038.151   \n",
       "7            1549151                 1549.151   \n",
       "8            1192491                 1192.491   \n",
       "9             985937                  985.937   \n",
       "10           1478726                 1478.726   \n",
       "11           3466369                 3466.369   \n",
       "12             50288                   50.288   \n",
       "13           1217818                 1217.818   \n",
       "14           1912622                 1912.622   \n",
       "15           3110858                 3110.858   \n",
       "16            507061                  507.061   \n",
       "17           2391193                 2391.193   \n",
       "18            997135                  997.135   \n",
       "19            158305                  158.305   \n",
       "20            868350                  868.350   \n",
       "21           1012512                 1012.512   \n",
       "22            271135                  271.135   \n",
       "23           1002575                 1002.575   \n",
       "24            313016                  313.016   \n",
       "25            465528                  465.528   \n",
       "26            614064                  614.064   \n",
       "27           2657854                 2657.854   \n",
       "28           1315643                 1315.643   \n",
       "29            534262                  534.262   \n",
       "30           1674676                 1674.676   \n",
       "31           1262402                 1262.402   \n",
       "32            633385                  633.385   \n",
       "33           5675462                 5675.462   \n",
       "34           2866255                 2866.255   \n",
       "35            827166                  827.166   \n",
       "36           1104008                 1104.008   \n",
       "37           1875872                 1875.872   \n",
       "38           1139371                 1139.371   \n",
       "39            140149                  140.149   \n",
       "40            140149                  140.149   \n",
       "41            679417                  679.417   \n",
       "42            790197                  790.197   \n",
       "43          12678079                12678.079   \n",
       "44           7690863                 7690.863   \n",
       "45            741404                  741.404   \n",
       "46             44111                   44.111   \n",
       "47           3202946                 3202.946   \n",
       "48            696837                  696.837   \n",
       "49            596508                  596.508   \n",
       "50           2798170                 2798.170   \n",
       "51           1926665                 1926.665   \n",
       "52           1956835                 1956.835   \n",
       "53            733498                  733.498   \n",
       "54           1305563                 1305.563   \n",
       "55           2599260                 2599.260   \n",
       "56           1895868                 1895.868   \n",
       "57            626115                  626.115   \n",
       "58           4197821                 4197.821   \n",
       "59           1108847                 1108.847   \n",
       "60            971996                  971.996   \n",
       "61            488257                  488.257   \n",
       "62           3179532                 3179.532   \n",
       "63           2421895                 2421.895   \n",
       "64            449138                  449.138   \n",
       "65            934889                  934.889   \n",
       "66           5398064                 5398.064   \n",
       "67           2803573                 2803.573   \n",
       "68           4310681                 4310.681   \n",
       "69           1006748                 1006.748   \n",
       "70           3902642                 3902.642   \n",
       "71           1079271                 1079.271   \n",
       "72           1466127                 1466.127   \n",
       "73           1260379                 1260.379   \n",
       "74           1537416                 1537.416   \n",
       "75            327383                  327.383   \n",
       "76           1500955                 1500.955   \n",
       "77           1229824                 1229.824   \n",
       "78           2491036                 2491.036   \n",
       "79           2491036                 2491.036   \n",
       "80           1160445                 1160.445   \n",
       "81           2324205                 2324.205   \n",
       "82            544444                  544.444   \n",
       "83           1253389                 1253.389   \n",
       "84           1059700                 1059.700   \n",
       "\n",
       "                         Regions, rus  \\\n",
       "0                              Адыгея   \n",
       "1                    Республика Алтай   \n",
       "2                      Алтайский край   \n",
       "3                    Амурская область   \n",
       "4               Архангельская область   \n",
       "5                Астраханская область   \n",
       "6                        Башкортостан   \n",
       "7                Белгородская область   \n",
       "8                    Брянская область   \n",
       "9                             Бурятия   \n",
       "10                              Чечня   \n",
       "11                Челябинская область   \n",
       "12         Чукотский автономный округ   \n",
       "13                            Чувашия   \n",
       "14                               Крым   \n",
       "15                           Дагестан   \n",
       "16                          Ингушетия   \n",
       "17                  Иркутская область   \n",
       "18                 Ивановская область   \n",
       "19       Еврейская автономная область   \n",
       "20                 Кабардино-Балкария   \n",
       "21            Калининградская область   \n",
       "22                           Калмыкия   \n",
       "23                  Калужская область   \n",
       "24                    Камчатский край   \n",
       "25                 Карачаево-Черкесия   \n",
       "26                            Карелия   \n",
       "27                Кемеровская область   \n",
       "28                   Хабаровский край   \n",
       "29                            Хакасия   \n",
       "30  Ханты-Мансийский автономный округ   \n",
       "31                  Кировская область   \n",
       "32                    Республика Коми   \n",
       "33                Костромская область   \n",
       "34                 Краснодарский край   \n",
       "35                  Красноярский край   \n",
       "36                 Курганская область   \n",
       "37                    Курская область   \n",
       "38              Ленинградская область   \n",
       "39                   Липецкая область   \n",
       "40                Магаданская область   \n",
       "41                           Марий Эл   \n",
       "42                           Мордовия   \n",
       "43                             Москва   \n",
       "44                 Московская область   \n",
       "45                 Мурманская область   \n",
       "46          Ненецкий автономный округ   \n",
       "47              Нижегородская область   \n",
       "48                    Северная Осетия   \n",
       "49               Новгородская область   \n",
       "50              Новосибирская область   \n",
       "51                     Омская область   \n",
       "52               Оренбургская область   \n",
       "53                  Орловская область   \n",
       "54                 Пензенская область   \n",
       "55                      Пермский край   \n",
       "56                    Приморский край   \n",
       "57                  Псковская область   \n",
       "58                 Ростовская область   \n",
       "59                  Рязанская область   \n",
       "60                             Якутия   \n",
       "61                Сахалинская область   \n",
       "62                  Самарская область   \n",
       "63                Саратовская область   \n",
       "64                        Севастополь   \n",
       "65                 Смоленская область   \n",
       "66                    Санкт-Петербург   \n",
       "67                Ставропольский край   \n",
       "68               Свердловская область   \n",
       "69                 Тамбовская область   \n",
       "70                          Татарстан   \n",
       "71                    Томская область   \n",
       "72                   Тульская область   \n",
       "73                   Тверская область   \n",
       "74                  Тюменская область   \n",
       "75                    Республика Тыва   \n",
       "76                           Удмуртия   \n",
       "77                Ульяновская область   \n",
       "78               Владимирская область   \n",
       "79              Волгоградская область   \n",
       "80                Вологодская область   \n",
       "81                Воронежская область   \n",
       "82    Ямало-Ненецкий автономный округ   \n",
       "83                Ярославская область   \n",
       "84                 Забайкальский край   \n",
       "\n",
       "    Share of people with higher education. 2010. %  \\\n",
       "0                                               20   \n",
       "1                                               18   \n",
       "2                                               17   \n",
       "3                                               18   \n",
       "4                                               17   \n",
       "5                                               18   \n",
       "6                                               17   \n",
       "7                                               21   \n",
       "8                                               18   \n",
       "9                                               21   \n",
       "10                                              10   \n",
       "11                                              19   \n",
       "12                                              21   \n",
       "13                                              19   \n",
       "14                                              20   \n",
       "15                                              17   \n",
       "16                                              16   \n",
       "17                                              20   \n",
       "18                                              19   \n",
       "19                                              15   \n",
       "20                                              20   \n",
       "21                                              24   \n",
       "22                                              21   \n",
       "23                                              22   \n",
       "24                                              24   \n",
       "25                                              23   \n",
       "26                                              19   \n",
       "27                                              18   \n",
       "28                                              24   \n",
       "29                                              18   \n",
       "30                                              24   \n",
       "31                                              17   \n",
       "32                                              18   \n",
       "33                                              18   \n",
       "34                                              20   \n",
       "35                                              20   \n",
       "36                                              15   \n",
       "37                                              19   \n",
       "38                                              21   \n",
       "39                                              20   \n",
       "40                                              23   \n",
       "41                                              19   \n",
       "42                                              21   \n",
       "43                                              40   \n",
       "44                                              30   \n",
       "45                                              22   \n",
       "46                                              16   \n",
       "47                                              22   \n",
       "48                                              26   \n",
       "49                                              18   \n",
       "50                                              23   \n",
       "51                                              19   \n",
       "52                                              17   \n",
       "53                                              21   \n",
       "54                                              19   \n",
       "55                                              17   \n",
       "56                                              22   \n",
       "57                                              18   \n",
       "58                                              21   \n",
       "59                                              20   \n",
       "60                                              21   \n",
       "61                                              19   \n",
       "62                                              24   \n",
       "63                                              21   \n",
       "64                                              28   \n",
       "65                                              19   \n",
       "66                                              35   \n",
       "67                                              20   \n",
       "68                                              19   \n",
       "69                                              18   \n",
       "70                                              21   \n",
       "71                                              24   \n",
       "72                                              19   \n",
       "73                                              18   \n",
       "74                                              20   \n",
       "75                                              15   \n",
       "76                                              19   \n",
       "77                                              18   \n",
       "78                                              19   \n",
       "79                                              20   \n",
       "80                                              18   \n",
       "81                                              21   \n",
       "82                                              25   \n",
       "83                                              21   \n",
       "84                                              16   \n",
       "\n",
       "    Median salary. 2019. rubles  Unemployment rate. 2017. %  \\\n",
       "0                         23943                         8.8   \n",
       "1                         24872                        12.0   \n",
       "2                         23480                         6.9   \n",
       "3                         40376                         5.9   \n",
       "4                         41030                         6.5   \n",
       "5                         26001                         7.5   \n",
       "6                         30176                         5.6   \n",
       "7                         29766                         3.9   \n",
       "8                         24571                         4.4   \n",
       "9                         31260                         9.6   \n",
       "10                        22501                        14.0   \n",
       "11                        31245                         6.6   \n",
       "12                        76865                         2.9   \n",
       "13                        24069                         5.1   \n",
       "14                        25901                         6.4   \n",
       "15                        20015                        12.0   \n",
       "16                        19954                        27.0   \n",
       "17                        37921                         8.7   \n",
       "18                        23117                         4.7   \n",
       "19                        34538                         8.3   \n",
       "20                        19796                        10.5   \n",
       "21                        31681                         5.2   \n",
       "22                        21984                        10.0   \n",
       "23                        33385                         4.0   \n",
       "24                        62444                         4.3   \n",
       "25                        20788                        13.5   \n",
       "26                        34922                         8.6   \n",
       "27                        34256                         7.1   \n",
       "28                        43216                         4.9   \n",
       "29                        29787                         4.9   \n",
       "30                        60570                         3.3   \n",
       "31                        25074                         5.3   \n",
       "32                        42940                         7.8   \n",
       "33                        24172                         5.3   \n",
       "34                        29887                         5.7   \n",
       "35                        38351                         5.7   \n",
       "36                        24717                         9.1   \n",
       "37                        26521                         4.1   \n",
       "38                        42076                         5.7   \n",
       "39                        28557                         3.9   \n",
       "40                        69316                         5.3   \n",
       "41                        23739                         6.1   \n",
       "42                        24052                         4.2   \n",
       "43                        66103                         1.4   \n",
       "44                        45201                         3.2   \n",
       "45                        49589                         7.0   \n",
       "46                        71303                         8.0   \n",
       "47                        30671                         4.2   \n",
       "48                        21061                        11.8   \n",
       "49                        29168                         4.7   \n",
       "50                        32556                         6.0   \n",
       "51                        27380                         7.0   \n",
       "52                        27016                         4.6   \n",
       "53                        23228                         6.5   \n",
       "54                        25563                         4.5   \n",
       "55                        31958                         6.1   \n",
       "56                        40620                         5.4   \n",
       "57                        23895                         6.5   \n",
       "58                        27619                         5.6   \n",
       "59                        28331                         4.1   \n",
       "60                        56863                         7.1   \n",
       "61                        62647                         6.0   \n",
       "62                        30428                         4.2   \n",
       "63                        24155                         4.8   \n",
       "64                        29563                         4.6   \n",
       "65                        26720                         5.7   \n",
       "66                        51248                         1.7   \n",
       "67                        24432                         5.2   \n",
       "68                        34818                         5.5   \n",
       "69                        24417                         4.4   \n",
       "70                        31341                         3.5   \n",
       "71                        34074                         6.3   \n",
       "72                        31694                         3.9   \n",
       "73                        27513                         4.5   \n",
       "74                        39185                         3.3   \n",
       "75                        27822                        18.3   \n",
       "76                        27807                         4.8   \n",
       "77                        25906                         4.4   \n",
       "78                        28174                         4.8   \n",
       "79                        26518                         6.0   \n",
       "80                        32854                         5.3   \n",
       "81                        28187                         4.3   \n",
       "82                        77542                         3.2   \n",
       "83                        29987                         6.6   \n",
       "84                        33456                        10.7   \n",
       "\n",
       "    Share of poor people. 2017. %  \\\n",
       "0                            13.1   \n",
       "1                            25.1   \n",
       "2                            17.8   \n",
       "3                            14.9   \n",
       "4                            14.1   \n",
       "5                            17.3   \n",
       "6                            12.3   \n",
       "7                             7.9   \n",
       "8                            13.3   \n",
       "9                            18.5   \n",
       "10                           20.6   \n",
       "11                           14.2   \n",
       "12                           10.2   \n",
       "13                           19.0   \n",
       "14                           19.1   \n",
       "15                           10.6   \n",
       "16                           31.6   \n",
       "17                           20.0   \n",
       "18                           14.3   \n",
       "19                           24.9   \n",
       "20                           24.1   \n",
       "21                           14.1   \n",
       "22                           30.6   \n",
       "23                           10.5   \n",
       "24                           18.4   \n",
       "25                           24.6   \n",
       "26                           17.3   \n",
       "27                           15.9   \n",
       "28                           12.5   \n",
       "29                           17.8   \n",
       "30                           12.6   \n",
       "31                           16.0   \n",
       "32                           17.1   \n",
       "33                           13.0   \n",
       "34                           11.6   \n",
       "35                           18.7   \n",
       "36                           19.3   \n",
       "37                           10.1   \n",
       "38                           10.3   \n",
       "39                            9.0   \n",
       "40                           13.4   \n",
       "41                           22.8   \n",
       "42                           18.9   \n",
       "43                            8.9   \n",
       "44                            8.1   \n",
       "45                           12.6   \n",
       "46                           11.4   \n",
       "47                            9.9   \n",
       "48                           14.5   \n",
       "49                           14.8   \n",
       "50                           17.2   \n",
       "51                           14.5   \n",
       "52                           14.5   \n",
       "53                           14.0   \n",
       "54                           14.6   \n",
       "55                           15.1   \n",
       "56                           14.7   \n",
       "57                           17.6   \n",
       "58                           13.9   \n",
       "59                           13.0   \n",
       "60                           20.0   \n",
       "61                            9.7   \n",
       "62                           13.6   \n",
       "63                           17.4   \n",
       "64                           14.2   \n",
       "65                           16.3   \n",
       "66                            8.1   \n",
       "67                           13.9   \n",
       "68                           10.2   \n",
       "69                           10.9   \n",
       "70                            7.7   \n",
       "71                           17.5   \n",
       "72                           10.0   \n",
       "73                           12.7   \n",
       "74                           15.2   \n",
       "75                           41.5   \n",
       "76                           12.5   \n",
       "77                           15.1   \n",
       "78                           13.6   \n",
       "79                           14.1   \n",
       "80                           14.3   \n",
       "81                            9.3   \n",
       "82                            7.5   \n",
       "83                           10.9   \n",
       "84                           21.4   \n",
       "\n",
       "    Gross regional product per capita. 2016. rubles  Terrorist_crimes_2017  \\\n",
       "0                                          17450.16                   0.40   \n",
       "1                                          18489.39                   0.14   \n",
       "2                                          18098.68                   0.00   \n",
       "3                                          30784.41                   0.02   \n",
       "4                                          32647.68                   0.03   \n",
       "5                                          28674.24                   0.27   \n",
       "6                                          28479.05                   0.11   \n",
       "7                                          40644.79                   0.01   \n",
       "8                                          20111.95                   0.01   \n",
       "9                                          17491.45                   0.00   \n",
       "10                                         10312.56                   1.81   \n",
       "11                                         31058.32                   0.03   \n",
       "12                                        113735.33                   0.00   \n",
       "13                                         18242.06                   0.02   \n",
       "14                                         14286.24                   0.09   \n",
       "15                                         17075.76                   1.75   \n",
       "16                                          9281.84                   1.39   \n",
       "17                                         38199.91                   0.02   \n",
       "18                                         15043.05                   0.05   \n",
       "19                                         24333.86                   0.00   \n",
       "20                                         13273.22                   1.65   \n",
       "21                                         33834.62                   0.21   \n",
       "22                                         17340.71                   0.11   \n",
       "23                                         31891.35                   0.05   \n",
       "24                                         54050.78                   0.00   \n",
       "25                                         13485.99                   1.22   \n",
       "26                                         31963.69                   0.10   \n",
       "27                                         27231.09                   0.01   \n",
       "28                                         41206.81                   0.05   \n",
       "29                                         29310.74                   0.06   \n",
       "30                                        160695.99                   0.09   \n",
       "31                                         19341.84                   0.10   \n",
       "32                                         55046.14                   0.05   \n",
       "33                                         21274.89                   0.02   \n",
       "34                                         31531.38                   0.04   \n",
       "35                                         53189.65                   0.05   \n",
       "36                                         19401.25                   0.06   \n",
       "37                                         28074.44                   0.03   \n",
       "38                                         44303.72                   0.01   \n",
       "39                                         35078.74                   0.06   \n",
       "40                                         86580.36                   0.21   \n",
       "41                                         20177.01                   0.00   \n",
       "42                                         21162.01                   0.12   \n",
       "43                                        100018.50                   0.08   \n",
       "44                                         42012.45                   0.02   \n",
       "45                                         48183.88                   0.07   \n",
       "46                                        502634.49                   0.00   \n",
       "47                                         31273.72                   0.01   \n",
       "48                                         15379.44                   0.73   \n",
       "49                                         34248.01                   0.00   \n",
       "50                                         33864.18                   0.04   \n",
       "51                                         27283.92                   0.00   \n",
       "52                                         33381.39                   0.01   \n",
       "53                                         24284.17                   0.07   \n",
       "54                                         21650.82                   0.05   \n",
       "55                                         35724.52                   0.02   \n",
       "56                                         32944.60                   0.02   \n",
       "57                                         19268.35                   0.00   \n",
       "58                                         25874.41                   0.07   \n",
       "59                                         25715.54                   0.08   \n",
       "60                                         78056.78                   0.08   \n",
       "61                                        135893.24                   0.06   \n",
       "62                                         34299.64                   0.03   \n",
       "63                                         22710.50                   0.06   \n",
       "64                                         13293.40                   0.05   \n",
       "65                                         23599.07                   0.02   \n",
       "66                                         61758.85                   0.03   \n",
       "67                                         20068.26                   0.15   \n",
       "68                                         39397.43                   0.07   \n",
       "69                                         25572.42                   0.01   \n",
       "70                                         43193.85                   0.10   \n",
       "71                                         39004.61                   0.05   \n",
       "72                                         29639.90                   0.03   \n",
       "73                                         23752.22                   0.12   \n",
       "74                                         54962.29                   0.07   \n",
       "75                                         14268.47                   0.00   \n",
       "76                                         30702.35                   0.03   \n",
       "77                                         22509.81                   0.10   \n",
       "78                                         24199.85                   0.07   \n",
       "79                                         25178.21                   0.10   \n",
       "80                                         35305.44                   0.02   \n",
       "81                                         31095.99                   0.02   \n",
       "82                                        317106.30                   0.04   \n",
       "83                                         31855.05                   0.04   \n",
       "84                                         20927.29                   0.02   \n",
       "\n",
       "    Murders and attempted murders. 2017  Criminal possesion of weapons. 2017  \\\n",
       "0                                  0.31                                 1.26   \n",
       "1                                  1.75                                 5.39   \n",
       "2                                  0.68                                 2.40   \n",
       "3                                  1.42                                 5.00   \n",
       "4                                  1.54                                 1.60   \n",
       "5                                  0.54                                 0.96   \n",
       "6                                  0.80                                 1.34   \n",
       "7                                  0.44                                 1.37   \n",
       "8                                  0.49                                 2.35   \n",
       "9                                  2.97                                 3.32   \n",
       "10                                 0.18                                 2.11   \n",
       "11                                 0.75                                 1.86   \n",
       "12                                 1.61                                 8.63   \n",
       "13                                 0.70                                 0.64   \n",
       "14                                 0.44                                 1.92   \n",
       "15                                 0.23                                 7.47   \n",
       "16                                 0.52                                 5.02   \n",
       "17                                 1.12                                 1.86   \n",
       "18                                 0.71                                 1.08   \n",
       "19                                 1.58                                 5.48   \n",
       "20                                 0.30                                 3.39   \n",
       "21                                 0.60                                 2.18   \n",
       "22                                 0.83                                 5.65   \n",
       "23                                 0.67                                 1.74   \n",
       "24                                 0.89                                 1.43   \n",
       "25                                 0.64                                 3.49   \n",
       "26                                 0.85                                 2.44   \n",
       "27                                 1.11                                 3.43   \n",
       "28                                 1.23                                 2.48   \n",
       "29                                 1.39                                 5.24   \n",
       "30                                 0.57                                 2.47   \n",
       "31                                 0.82                                 1.70   \n",
       "32                                 0.99                                 2.28   \n",
       "33                                 0.45                                 2.13   \n",
       "34                                 0.50                                 1.81   \n",
       "35                                 1.00                                 2.64   \n",
       "36                                 1.22                                 1.99   \n",
       "37                                 0.35                                 2.73   \n",
       "38                                 0.79                                 0.85   \n",
       "39                                 0.39                                 1.39   \n",
       "40                                 2.20                                 3.09   \n",
       "41                                 0.72                                 1.31   \n",
       "42                                 0.56                                 1.51   \n",
       "43                                 0.25                                 0.66   \n",
       "44                                 0.57                                 1.31   \n",
       "45                                 0.59                                 2.19   \n",
       "46                                 1.14                                 2.28   \n",
       "47                                 0.86                                 0.94   \n",
       "48                                 0.16                                 5.74   \n",
       "49                                 0.69                                 1.99   \n",
       "50                                 0.78                                 1.00   \n",
       "51                                 0.58                                 1.50   \n",
       "52                                 0.66                                 1.32   \n",
       "53                                 0.54                                 2.32   \n",
       "54                                 0.28                                 1.18   \n",
       "55                                 0.89                                 1.61   \n",
       "56                                 1.04                                 4.32   \n",
       "57                                 0.92                                 1.92   \n",
       "58                                 0.49                                 1.90   \n",
       "59                                 0.52                                 1.43   \n",
       "60                                 1.42                                 3.12   \n",
       "61                                 1.23                                 2.56   \n",
       "62                                 0.51                                 1.35   \n",
       "63                                 0.45                                 1.84   \n",
       "64                                 0.63                                 1.35   \n",
       "65                                 0.45                                 2.56   \n",
       "66                                 0.37                                 0.39   \n",
       "67                                 0.43                                 2.59   \n",
       "68                                 0.79                                 1.45   \n",
       "69                                 0.50                                 1.84   \n",
       "70                                 0.71                                 1.42   \n",
       "71                                 0.76                                 1.57   \n",
       "72                                 0.63                                 1.39   \n",
       "73                                 0.86                                 2.15   \n",
       "74                                 0.67                                 1.44   \n",
       "75                                 2.61                                 4.05   \n",
       "76                                 0.64                                 1.83   \n",
       "77                                 0.60                                 0.96   \n",
       "78                                 0.73                                 1.19   \n",
       "79                                 0.47                                 2.51   \n",
       "80                                 0.64                                 2.01   \n",
       "81                                 0.44                                 1.58   \n",
       "82                                 0.95                                 1.53   \n",
       "83                                 0.57                                 1.02   \n",
       "84                                 1.63                                 3.56   \n",
       "\n",
       "    Fertility rate. 2017  Museum attendance. 2010. per pop  \\\n",
       "0                   1.52                             0.197   \n",
       "1                   2.36                             0.174   \n",
       "2                   1.64                             0.181   \n",
       "3                   1.71                             0.649   \n",
       "4                   1.65                             0.896   \n",
       "5                   1.73                             0.302   \n",
       "6                   1.70                             0.207   \n",
       "7                   1.39                             0.654   \n",
       "8                   1.43                             0.780   \n",
       "9                   2.06                             0.412   \n",
       "10                  2.73                             0.195   \n",
       "11                  1.61                             0.185   \n",
       "12                  2.08                             0.891   \n",
       "13                  1.65                             0.308   \n",
       "14                  1.64                             0.801   \n",
       "15                  1.91                             0.169   \n",
       "16                  1.77                             0.317   \n",
       "17                  1.86                             0.355   \n",
       "18                  1.46                             0.393   \n",
       "19                  1.81                             0.288   \n",
       "20                  1.61                             0.251   \n",
       "21                  1.57                             1.158   \n",
       "22                  1.54                             0.162   \n",
       "23                  1.64                             0.662   \n",
       "24                  1.78                             0.330   \n",
       "25                  1.43                             0.056   \n",
       "26                  1.56                             0.723   \n",
       "27                  1.54                             0.485   \n",
       "28                  1.64                             0.345   \n",
       "29                  1.78                             0.625   \n",
       "30                  1.88                             0.281   \n",
       "31                  1.70                             0.719   \n",
       "32                  1.78                             0.362   \n",
       "33                  1.70                             0.629   \n",
       "34                  1.72                             0.364   \n",
       "35                  1.67                             0.606   \n",
       "36                  1.88                             0.378   \n",
       "37                  1.46                             0.436   \n",
       "38                  1.22                             0.315   \n",
       "39                  1.54                             0.271   \n",
       "40                  1.60                             0.192   \n",
       "41                  1.75                             0.834   \n",
       "42                  1.26                             0.244   \n",
       "43                  1.38                             1.538   \n",
       "44                  1.61                             0.503   \n",
       "45                  1.56                             0.513   \n",
       "46                  2.35                             0.435   \n",
       "47                  1.50                             0.610   \n",
       "48                  1.75                             0.078   \n",
       "49                  1.61                             2.115   \n",
       "50                  1.67                             0.252   \n",
       "51                  1.61                             0.449   \n",
       "52                  1.73                             0.176   \n",
       "53                  1.42                             0.369   \n",
       "54                  1.36                             0.453   \n",
       "55                  1.75                             0.415   \n",
       "56                  1.60                             0.170   \n",
       "57                  1.57                             1.809   \n",
       "58                  1.46                             0.378   \n",
       "59                  1.51                             0.701   \n",
       "60                  1.93                             0.523   \n",
       "61                  2.03                             0.591   \n",
       "62                  1.53                             0.304   \n",
       "63                  1.39                             0.328   \n",
       "64                  1.56                             1.101   \n",
       "65                  1.37                             0.592   \n",
       "66                  1.50                             4.498   \n",
       "67                  1.54                             0.282   \n",
       "68                  1.76                             0.428   \n",
       "69                  1.38                             0.601   \n",
       "70                  1.65                             1.503   \n",
       "71                  1.47                             0.424   \n",
       "72                  1.40                             1.076   \n",
       "73                  1.56                             0.391   \n",
       "74                  1.88                             0.476   \n",
       "75                  3.19                             0.127   \n",
       "76                  1.72                             0.492   \n",
       "77                  1.52                             0.613   \n",
       "78                  1.52                             1.107   \n",
       "79                  1.44                             1.208   \n",
       "80                  1.70                             1.050   \n",
       "81                  1.37                             0.280   \n",
       "82                  1.95                             0.414   \n",
       "83                  1.53                             1.671   \n",
       "84                  1.87                             0.189   \n",
       "\n",
       "    Share of people using internet everyday, 2016, %  \n",
       "0                                            40.1610  \n",
       "1                                            39.2334  \n",
       "2                                            43.1501  \n",
       "3                                            44.0290  \n",
       "4                                            53.8225  \n",
       "5                                            44.5994  \n",
       "6                                            43.4766  \n",
       "7                                            37.3161  \n",
       "8                                            33.8959  \n",
       "9                                            42.6082  \n",
       "10                                           67.4520  \n",
       "11                                           49.2983  \n",
       "12                                           53.8740  \n",
       "13                                           42.8083  \n",
       "14                                           43.8115  \n",
       "15                                           45.4031  \n",
       "16                                           44.9688  \n",
       "17                                           45.0068  \n",
       "18                                           43.0924  \n",
       "19                                           45.8042  \n",
       "20                                           43.4017  \n",
       "21                                           51.7943  \n",
       "22                                           43.8996  \n",
       "23                                           35.6553  \n",
       "24                                           62.7655  \n",
       "25                                           46.1661  \n",
       "26                                           52.3734  \n",
       "27                                           41.6581  \n",
       "28                                           52.0147  \n",
       "29                                           44.4349  \n",
       "30                                           63.5470  \n",
       "31                                           44.2305  \n",
       "32                                           50.6579  \n",
       "33                                           41.7605  \n",
       "34                                           42.8266  \n",
       "35                                           44.7190  \n",
       "36                                           40.2268  \n",
       "37                                           33.9187  \n",
       "38                                           51.0667  \n",
       "39                                           35.3718  \n",
       "40                                           60.5928  \n",
       "41                                           36.0132  \n",
       "42                                           39.9834  \n",
       "43                                           57.3856  \n",
       "44                                           50.7064  \n",
       "45                                           60.6225  \n",
       "46                                           52.6971  \n",
       "47                                           35.2710  \n",
       "48                                           41.7035  \n",
       "49                                           34.6669  \n",
       "50                                           47.4249  \n",
       "51                                           44.9216  \n",
       "52                                           32.4422  \n",
       "53                                           37.8795  \n",
       "54                                           37.8494  \n",
       "55                                           45.7293  \n",
       "56                                           43.5748  \n",
       "57                                           48.8163  \n",
       "58                                           40.3986  \n",
       "59                                           32.8582  \n",
       "60                                           46.4997  \n",
       "61                                           55.2951  \n",
       "62                                           44.6214  \n",
       "63                                           44.6776  \n",
       "64                                           54.1790  \n",
       "65                                           43.4761  \n",
       "66                                           53.8236  \n",
       "67                                           45.3423  \n",
       "68                                           48.0411  \n",
       "69                                           29.5813  \n",
       "70                                           43.7128  \n",
       "71                                           36.8500  \n",
       "72                                           54.7344  \n",
       "73                                           39.5116  \n",
       "74                                           48.3639  \n",
       "75                                           30.2531  \n",
       "76                                           47.5155  \n",
       "77                                           38.5104  \n",
       "78                                           43.1621  \n",
       "79                                           39.1724  \n",
       "80                                           42.1349  \n",
       "81                                           37.1425  \n",
       "82                                           62.6062  \n",
       "83                                           45.4629  \n",
       "84                                           35.1785  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', df.shape[0]+1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population вensity per km2</th>\n",
       "      <th>Total population</th>\n",
       "      <th>Total population per 1k</th>\n",
       "      <th>Share of people with higher education. 2010. %</th>\n",
       "      <th>Median salary. 2019. rubles</th>\n",
       "      <th>Unemployment rate. 2017. %</th>\n",
       "      <th>Share of poor people. 2017. %</th>\n",
       "      <th>Gross regional product per capita. 2016. rubles</th>\n",
       "      <th>Terrorist_crimes_2017</th>\n",
       "      <th>Murders and attempted murders. 2017</th>\n",
       "      <th>Criminal possesion of weapons. 2017</th>\n",
       "      <th>Fertility rate. 2017</th>\n",
       "      <th>Museum attendance. 2010. per pop</th>\n",
       "      <th>Share of people using internet everyday, 2016, %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87.531169</td>\n",
       "      <td>1.696216e+06</td>\n",
       "      <td>1696.215571</td>\n",
       "      <td>19.857143</td>\n",
       "      <td>30275.792208</td>\n",
       "      <td>6.620779</td>\n",
       "      <td>15.722078</td>\n",
       "      <td>28457.919091</td>\n",
       "      <td>0.162727</td>\n",
       "      <td>0.767922</td>\n",
       "      <td>2.272208</td>\n",
       "      <td>1.648831</td>\n",
       "      <td>0.586364</td>\n",
       "      <td>43.703751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.480000</td>\n",
       "      <td>2.942775e+05</td>\n",
       "      <td>294.277500</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>74422.500000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>9.450000</td>\n",
       "      <td>409870.395000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>1.045000</td>\n",
       "      <td>1.905000</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>0.424500</td>\n",
       "      <td>57.651650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>826.643333</td>\n",
       "      <td>2.667241e+06</td>\n",
       "      <td>2667.240833</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>65394.000000</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>12.466667</td>\n",
       "      <td>112496.700000</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>1.213333</td>\n",
       "      <td>3.421667</td>\n",
       "      <td>1.816667</td>\n",
       "      <td>0.669333</td>\n",
       "      <td>56.199033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Population вensity per km2  Total population  Total population per 1k  \\\n",
       "labels                                                                          \n",
       "0                        87.531169      1.696216e+06              1696.215571   \n",
       "1                         0.480000      2.942775e+05               294.277500   \n",
       "2                       826.643333      2.667241e+06              2667.240833   \n",
       "\n",
       "        Share of people with higher education. 2010. %  \\\n",
       "labels                                                   \n",
       "0                                            19.857143   \n",
       "1                                            20.500000   \n",
       "2                                            24.666667   \n",
       "\n",
       "        Median salary. 2019. rubles  Unemployment rate. 2017. %  \\\n",
       "labels                                                            \n",
       "0                      30275.792208                    6.620779   \n",
       "1                      74422.500000                    5.600000   \n",
       "2                      65394.000000                    4.333333   \n",
       "\n",
       "        Share of poor people. 2017. %  \\\n",
       "labels                                  \n",
       "0                           15.722078   \n",
       "1                            9.450000   \n",
       "2                           12.466667   \n",
       "\n",
       "        Gross regional product per capita. 2016. rubles  \\\n",
       "labels                                                    \n",
       "0                                          28457.919091   \n",
       "1                                         409870.395000   \n",
       "2                                         112496.700000   \n",
       "\n",
       "        Terrorist_crimes_2017  Murders and attempted murders. 2017  \\\n",
       "labels                                                               \n",
       "0                    0.162727                             0.767922   \n",
       "1                    0.020000                             1.045000   \n",
       "2                    0.086667                             1.213333   \n",
       "\n",
       "        Criminal possesion of weapons. 2017  Fertility rate. 2017  \\\n",
       "labels                                                              \n",
       "0                                  2.272208              1.648831   \n",
       "1                                  1.905000              2.150000   \n",
       "2                                  3.421667              1.816667   \n",
       "\n",
       "        Museum attendance. 2010. per pop  \\\n",
       "labels                                     \n",
       "0                               0.586364   \n",
       "1                               0.424500   \n",
       "2                               0.669333   \n",
       "\n",
       "        Share of people using internet everyday, 2016, %  \n",
       "labels                                                    \n",
       "0                                              43.703751  \n",
       "1                                              57.651650  \n",
       "2                                              56.199033  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['labels'] = labels\n",
    "df.groupby('labels').mean()"
   ]
  }
 ],
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