schrodinger.ipynb

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   "source": [
    "# Infinite Well Ground State Energy\n",
    "### by David Rosenman\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Analytical Derivation of Ground State Energy\n",
    "\n",
    "A particle of mass $\\mathrm{m}$ is confined to move inside an infinitely deep symmetric potential well. Outside the well, the potential infinite, thus the particle is confined to move only within the boundaries of the well of length $\\mathrm{a}$ centered at the origin. The potential, which depepnds only on the positive (x), is given by:\n",
    "\n",
    "$$V = \\left\\{ {\\begin{array}{*{20}{c}}\n",
    "{ + \\infty ,}&{x < 0}\\\\\n",
    "{0,}&{ - \\frac{a}{2} \\le x \\le \\frac{a}{2}}\\\\\n",
    "{ + \\infty ,}&{x > a}\n",
    "\\end{array}} \\right.$$\n",
    "\n",
    "Since the potential is independent of time, the equation of state of the particle must be a superposition of stationary states of the form\n",
    "$$\\Psi(x,t) = \\psi(x)\\phi (t)=\\psi (x) e^{\\frac{iEt}{\\hbar}}$$\n",
    "\n",
    "E is the energy associated with the stationary states, which is constant. $\\psi(x)$ satisfies the time independent Schrodinger equation, which is \n",
    "$$-\\frac{\\hbar^2}{2m}\\frac{d^2 \\psi (x)}{dx^2} + V(x) \\psi(x) = E \\psi(x)$$\n",
    "\n",
    "For the case of a one-dimensional infinite square well, V = 0 inside the well. It follows that inside the well, the motion of the particle satisfies\n",
    "$$\\frac{d^2 \\psi (x)}{dx^2} + k^2 \\psi(x) = 0$$\n",
    "\n",
    "with\n",
    "$$k^2 = \\frac{2mE}{\\hbar^2}$$\n",
    "\n",
    "The general solution to the differential equation above is\n",
    "$$\\Psi(x) = A \\sin{kx} + B \\cos{kx}$$\n",
    "\n",
    "Since the well is a symmetric well, $\\psi(x) = \\psi(-x)$. Since $\\psi$ is an even function $A=0$, and the general solution for a symmetric potential well is given by\n",
    "$$\\psi(x) = B\\cos{kx}$$\n",
    "\n",
    "The probability density of the position of the particle is given by\n",
    "$$\\Psi^2 (x,t) = \\left(\\psi(x) e^\\frac{iEt}{\\hbar}\\right)\\left(\\psi(x)e^\\frac{-iEt}{\\hbar}\\right) = \\psi^2(x)$$\n",
    "\n",
    "Since the particle cannot be found at or beyond the boundaries of the well, the probability density at $x \\pm a/2$ is equal to 0.\n",
    "$$\\Psi(\\pm \\frac{a}{2}) = B \\cos{\\left(\\pm k \\frac{a}{2}\\right)} = 0$$\n",
    "\n",
    "Since $k$ is a positive constant,\n",
    "$$k = n \\frac{\\pi}{a}= \\frac{\\sqrt{2mE}}{\\hbar}$$\n",
    "\n",
    "The value of the constant $B$ is determined by the normalization condition as follows:\n",
    "$$1 = \\int_{ - \\frac{a}{2}}^{\\frac{a}{2}} {{{\\left( {\\psi \\left( x \\right)} \\right)}^2}dx = \\int_{ - \\frac{a}{2}}^{\\frac{a}{2}} {{B^2}{{\\cos }^2}\\left( {\\frac{{n\\pi }}{a}x} \\right)dx = \\frac{{{B^2}}}{2}a} }$$\n",
    "\n",
    "$$B = \\sqrt{\\frac{2}{a}}$$\n",
    "\n",
    "It follows that the solution to the time independent Schrodinger equation is \n",
    "$$\\psi(x) = \\sqrt{\\frac{2}{a}} \\cos{\\frac{n \\pi x}{a}}$$\n",
    "\n",
    "for $n = 1,2,3...$. Therefore the allowable values of the energy, $E$, are given by\n",
    "$$ E = \\frac{n^2 \\pi^2 \\hbar^2}{2 a^2m}, \\ \\ \\  n = 1,2,3...$$\n",
    "\n",
    "To simplify the numerical calculation, $a$,$m$,and $\\hbar$ will each be given the value. The ground state energy, which is the energy value for the state in which $n=1$ is\n",
    "$$E_{ground} = \\frac{\\pi}{2}$$\n",
    "\n",
    "and the solution to the Schrodinger equation simplifies to \n",
    "$$\\psi(x) = \\sqrt{2}\\cos{(n\\pi x)}$$"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Numerical Approximation of Ground State Energy\n",
    "\n",
    "The starting points for the numerical calculation of the ground state energy is the solution of the Schrodinger equation, $\\psi(x) = \\sqrt{2}\\cos{(n\\pi x)}$. Since the ground state is characterized by $n = 1$, the ground state solution to the Schrodinger equation is \n",
    "\n",
    "$$\\psi(x) = \\sqrt{2} \\cos{\\pi x}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discretized Numerical Appoximation of the Schrodinger Equation\n",
    "\n",
    "The Schrodinger equation is a continuous funciton, but in order to derive approximate solutions, it must be made discrete. This is done using Euler's method of iteraive linear approximations as follows.\n",
    "\n",
    "$$\\psi_0 = \\psi_1 = 1$$\n",
    "$$\\frac{d \\psi_1}{dx} = 0$$\n",
    "$$\\psi_{i+2} = 2 \\psi_{i+1} - \\psi{i} - 2\\left(\\Delta x\\right)^2 E_{ground}\\psi_i$$\n",
    "\n",
    "By incrementing the argument of the Schrodinger equation by $\\Delta x = 0.001$, the equation for $\\psi_{i+2}$ above will be iterated 500 times between the boundaries of the well. The value of the Schrodinger equation, $\\psi$, for the 500th iteration depends on teh value of the ground state energy, $E_ground$, and must be equal to 0. Since it is impractical to expect a numerical approximation to arive at exactly zero, a value within $\\pm 10^{-3}$ will be allowed. \n",
    "\n",
    "The iterated calculation in Python will start with $E_{ground} = 0$. If the value of $\\psi$ for the 500th iteration is not within $\\pm 10^{-3}$, the value of E_{ground} will be increased slightly. The energy will continue to be adjusted by this amount until $\\psi$ is within $10^{-3}$ units of 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python Algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "501\n",
      "0.5\n"
     ]
    },
    {
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       "        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 overriden (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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1V1sL7/hrN/jMnH4PTcML2tPpnG2YPcISbb1SmqxrUpulmRblSSL3Q8WFFMQQAABBBBAAAHnBAiAnRtSOoQAAggggAACERZ4WNLRKe3rnXg4WHqT/QbAtk3A8ykn/0nSJQX0/xeJvXeTVdgKSguCayu2H+xliQNshaU9+MxWwKYW21O4X+ILtrdu+urRApqc89TUYNAOtpW/tsr4gyxn2vfHFmCnrmKuzeHTxKpiq876b6uXv8lSt4XPz0myEDRZMq1yLraX33DW73GZuuf33DC8rD3p42xjYGNhY5KpWNj/ReJBhPa6/fAjdW7mvLE4AAEEEEAAAQQQiIsAAXBcRop2IoAAAggggIALAudI+mtKR+zhVA9m6JjfAPhSSVelnG+hpW09ELTUS6yObZuowLaAsK0gaiv2AK1uiQPuknRShoMnS+qe+PqBaStmg7bV73npweBtkk7LcXLnxIPBbIW2lf+TZPscp5fdJL2d8sXfS/pLjrq3TKwOTtZtD8tLXYlqpxfby2846/e4TF30c25YXtae9HEeKunRHGNhq9JttbWVr1PCYL/3FschgAACCCCAAAKxECAAjsUw0UgEEEAAAQQQcETA9hm1fWGT5bGULRdSu+g3AH5P0s4pJ9rqW3t4WSHFfjU+uQWCPQTOtpnI9pAwu7a1IVlse4vXMlzctntI7utrgdvlhTQwz3PTg0Hbdzfb6t/Uqm1/4IMSX8gWDl4t6eLEMbayuIOkuT7al1r395I2Szun2F5+wllrgt/jMnXRz7lheVl7Usd5kSTbz9jGpLayb2IvajvG7nF7QOEqH+PHIQgggAACCCCAQKwECIBjNVw0FgEEEEAAAQRiLmArbL9LBIXWlZWJh1LNSeuXnwDYHmKVuj2D7Xe6UxF8dk08nC1ZVf/Eg9IyVW0rY3+beMGCTFs5a/vsppfUcM4CNguY7ytR2JZ6bXvgWhufRtYv61+yWKA4L+3clyTtk/iaBftm56dY/y1oTxZ76NvMlP8vtpefcNYu7/e4TH30c25YXtaeVLPXJQ3wMRBbS/pPynHtJKXPRR/VcAgCCCCAAAIIIBBtAQLgaI8PrUMAAQQQQAAB9wRs9auFZcmSaQsAPwGwPXgsdS/Z4yQ9UCSuKZK6JurK1D57yR6k9UNiz1/7f9v6wLZAyFTSf/XfjrGVyiMlvZoImC1ADqOkBoNvStrT50VsNbM9tC9ZbN/g1NXO9nV7uJiF3lbsgXWn+Kx7D0nWlmRJD9mL7eUnnLW2+D0uUzf9nBuWl7UndZxt6wfbAiJXSX+Iov1/tv2bc9XF6wgggAACCCCAQGQFCIAjOzQ0DAEEEEAAAQQcFbBtGr6S1CzRP/vVcwtyR6f0N1cAfKIk2283WWxVse3DayuKi1FsmwbbH9WKrXq1rQ3S695P0gspF/uZpE9qubg9nO6PtbxuewlbqH2/zy0a/PYzNRi0h7v90ueJfSTZA8uSxR64l9pf+7o9YKx54oB8HsDXM+3hf4dKGpHWrmJ6+Qln7fJ+j8tE6OfcML1Sx9lWl9scylXSA+AuiVA/13m8jgACCCCAAAIIxEqAADhWw0VjEUAAAQQQQMARgeMTKxaT3bE9Sy0EHpv4Qm0BsIWFtndw8iFituXC3pLGFNGmh6RJKfVlCigtZLNVx1bs1+gtMM1VrI8WlG6f40ALWm01rQXbhZbUYNAeuGcP3vNT7GFtFtQnyxGSnkg70faYtZXQVuyBfLbHrZ+yaVrffiXpoQwnFsvLTzhrl/d7XKY++jk3TC8CYD93HscggAACCCCAQEUKEABX5LDTaQQQQAABBBCIgICtdE0NI22F7VWS/i7pMEn3pLRxoKQvJP2PpN9JSv0eLqyHqo2TZA9Ms/K4pCNT2tNY0o+SNkp8zdp1TR6mtlrYVtTaPq22b26yntQqrH57aF6hv5If5grg+ZJaJBpd7BXAqRaFevkJZ0sRAIfpRQCcxwTkUAQQQAABBBCoLAEC4Moab3qLAAIIIIAAAtERsJWjFgIfk9YkC4Jtf1xbJZosFoLa/ydXmya/bqGrha9hFAua/5aoeHlir9+Fif8/StIjib/bFhb2q/TfBmyEPRjPgl7bmsFWPrdMqedpSUMC1ps8Lcw9gKcm+m7XKuYewLV1OYhXVALgML0IgAucKJyOAAIIIIAAAu4KEAC7O7b0DAEEEEAAAQSiL1Al6UpJF0myYM9vWZp44NrNfk8IcJztVWwPeUuGzsNStq14RtLBiTrfkGQPMStGaZt4IFzvRGW2ZUBrSbZFRtCSGgz+JKmNz4p+K+n/Uo61dth+yKnlJUn7JL5gD4izB8X5KadLSh27TpJm+Dkx7Ri/XlEJgMP0IgAOcANxCgIIIIAAAghUhgABcGWMM71EAAEEEEAAgWgL9JV0maQDcwTByyTZg8ws0JtSgi7ZXrz2sDcrL0vaNxHIzkzZg/hUSbcXsS2DJdnK32Qxmw8LqD81GLRqbFuLD3zU96ykgxLHmbU9ZC+92EPt7GFtViystoflzfVR90hJv0gcZ/scb+7jnGyH+PHyGwCnPnjuTUl75tEuP9cI04sAOI/B4lAEEEAAAQQQqCwBAuDKGm96iwACCCCAAALRFmgvabvEfrsnpTTV9pe1ANaC0AUl7II9nOyBxPXsYXO2DcUhkm5NfM22q7DQM31lbCFNtNW/tt9xstj2EO8XUGF6AHyLJFuBW1vZLPEAuAaJg2wl8NkZTrD9i5MP7rOXL5R0XY66uyQesJd8iJ9Z/qaA/vnx8hPOWhNSt/34RJLtPey3+LlGmF4EwH5HiuMQQAABBBBAoOIECIArbsjpMAIIIIAAAgjEQMD2wk1/CNyYMrS7WeJhb00S1z43sSdvv8T/+92jt3MeD3Ozh809mtLXjpJsxXHQkh4A20rdnSV9VEuFT0g6POX1bSV9luV4C0rtdSsWzluAn+3Bdbblh22fkVxZbOdYyGp1pJZie/kJZ+36h0p6KtEQ22aklSQL+f0Uv9cIw8vaRwDsZ5Q4BgEEEEAAAQQqUoAAuCKHnU4jgAACCCCAQMQFohIAG9NDKQ+qSz7EK/k9pD24zbakyFVWJeq5U9LbkuzBcZmKrWZ9UZKtwLViK39tBXAhJTUYtFXMFsJOT6xkTt8Kwlb8Xi/prJQLWv9tJXS2sr+kUSkvTkwEqZPSTmiU2Pf3xJSvPyjp1xkqLraX33B2E0m2JUVyfG018+WS7CGAuYrfa4ThZW0jAM41QryOAAIIIIAAAhUrQABcsUNPxxFAAAEEEEAgwgJRCoAPkPR8Bitb7WoPilvhwzE18LUHy1kIbCtB50iysNO2vrDtAWxlbPJheHbOQEmv+6i/tkNSg0Fb2buNpF6JPXttNa7tdbtYUldJRyX+m6zP2mp7EP+Yow03SToj5RgLTG0l7buSbN/m7pKGpgTbdqiF6VZ3pu0ziu3lN5y1dlkAPyitL9PSxtm2rUhuA5I8NJ9rFNuLALjAScLpCCCAAAIIIOC2AAGw2+NL7xBAAAEEEEAgngJRCoAtkLUg1ELa1HKXpNR9imuTzrbiN9s5tu2A1Z3cf7iQUUxfGWorfEdLsq0laiuzEgF06n7E2Y6376lvzmMvX1slbA/U+z5LhcX2yiectSD8tRwPprsy8SDC1Obnc41ie1k7WAFcyCzhXAQQQAABBBBwWoAA2OnhpXMIIIAAAgggEFOBKAXARvj3tG0R7Gt7JYJCP8THSDo4EajaquFsxYJfW5V7maQJfir2cUymYNAeXGerUAenrDhOVmV7BNtKYXsgmoXA+RTb3uBqSTtkOclWPJvlX3Jsq1Bsr3zCWWt6S0nDJdnq760T/29bWCRLoQFwsp5ieVl9BMD53KkciwACCCCAAAIVJUAAXFHDTWcRQAABBBBAAIGyC3SRtJWkLSS1SOwHPF/SZEnjEw9SK2YjawsGLYzeI7E1g610thW5rwYIftPba32zLS0saG6YqM9W/dqWELYPcT6l1F75tK1YxxbTq1htoh4EEEAAAQQQQMAZAQJgZ4aSjiCAAAIIIIAAAghkEAiyMhRIBBBAAAEEEEAAAQScESAAdmYo6QgCCCCAAAIIIIAAATD3AAIIIIAAAggggAACGwoQAHNHIIAAAggggAACCLgswApgl0eXviGAAAIIIIAAAgjkFCAAzknEAQgggAACCCCAAAIxFiAAjvHg0XQEEEAAAQQQQACBwgUIgAs3pAYEEEAAAQQQQACB6AoQAEd3bGgZAggggAACCCCAQAkECIBLgMwlEEAAAQQQQAABBMomQABcNnoujAACCCCAAAIIIBAFAQLgKIwCbUAAAQQQQAABBBAIS4AAOCxZ6kUAAQQQQAABBBCIhQABcCyGiUYigAACCCCAAAIIIIAAAggggAACCCCAAAL5CxAA52/GGQgggAACCCCAAAIIIIAAAggggAACCCCAQCwECIBjMUw0EgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyF+AADh/M85AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiIUAAHIthopEIIIAAAggggAACCCCAAAIIIIAAAggggED+AgTA+ZtxBgIIIIAAAggggAACCCCAAAIIIIAAAgggEAsBAuBYDBONRAABBBBAAAEEEEAAAQQQQAABBBBAAAEE8hcgAM7fjDMQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFYCBAAx2KYaCQCCCCAAAIIIIAAAggggAACCCCAAAIIIJC/AAFw/macEQ2BjpJ+kWjK15KWRqNZtAIBBBBAAAEEEEAAAQQQQAABBBAIRaCJpK6Jmp+VNCOUq1CpcwIEwM4NacV06GRJt1dMb+koAggggAACCCCAAAIIIIAAAggg8F+BUyTdAQgCfgQIgP0ocUwUBQiAozgqtAkBBBBAAAEEEEAAAQQQQAABBEohQABcCmVHrkEA7MhAVmA39pb0svX7tttuU58+fWJJsHDhQn388cde23/2s5+pefPmsewHjUagmALMi2JqUpcrAswLV0aSfhRbgLlRbFHqc0GAeeHCKNKHMARcmBufffaZTj311CTPPpJeCcOKOt0TIAB2b0wrpUe7ShprnR07dqx23dX+N37lp59+0ptvvuk1vF+/fmrdunX8OkGLESiyAPOiyKBU54QA88KJYaQTIQgwN0JApcrYCzAvYj+EdCAkARfmxjvvvKPddtstKWR/eSckLqp1TIAA2LEBraDuEABX0GDT1coScOEbs8oaMXpbCgHmRSmUuUYcBZgbcRw12hy2APMibGHqj6uAC3ODADiud1/5200AXP4xoAXBBAiAg7lxFgKRF3DhG7PII9PA2AkwL2I3ZDS4RALMjRJBc5lYCTAvYjVcNLaEAi7MDQLgEt4wjl2KANixAa2g7hAAV9Bg09XKEnDhG7PKGjF6WwoB5kUplLlGHAWYG3EcNdoctgDzImxh6o+rgAtzgwA4rndf+dtNAFz+MaAFwQQIgIO5cRYCkRdw4RuzyCPTwNgJMC9iN2Q0uEQCzI0SQXOZWAkwL2I1XDS2hAIuzA0C4BLeMI5digDYsQGtoO4QAFfQYNPVyhJw4RuzyhoxelsKAeZFKZS5RhwFmBtxHDXaHLYA8yJsYeqPq4ALc4MAOK53X/nbTQBc/jGgBcEECICDuXEWApEXcOEbs8gj08DYCTAvYjdkNLhEAsyNEkFzmVgJMC9iNVw0toQCLswNAuAS3jCOXYoA2LEBraDuEABX0GDT1coScOEbs8oaMXpbCgHmRSmUuUYcBZgbcRw12hy2APMibGHqj6uAC3ODADiud1/5200AXP4xoAXBBAiAg7lxFgKRF3DhG7PII9PA2AkwL2I3ZDS4RALMjRJBc5lYCTAvYjVcNLaEAi7MDQLgEt4wjl2KANixAa2g7hAAV9Bg09XKEnDhG7PKGjF6WwoB5kUplLlGHAWYG3EcNdoctgDzImxh6o+rgAtzgwA4rndf+dtNAFz+MaAFwQQIgIO5cRYCkRdw4RuzyCDtOjoAACAASURBVCPTwNgJMC9iN2Q0uEQCzI0SQXOZWAkwL2I1XDS2hAIuzA0C4BLeMI5digDYsQGtoO4QAFfQYNPVyhJw4RuzyhoxelsKAeZFKZS5RhwFmBtxHDXanCpQXV0t+1PMMm/ePL311ltelXvssYdatWpVzOqpC4HYCpRrbtSpU0f2pxiFALgYipVZR3HuwMq0o9flFSAALq8/V0cgNAE+zIdGS8UxFmBexHjwaHqoAsyNUHmpPASBFStWaNGiRVq4cKFWrlxZ9PDXmrx69WotXrzYa32zZs1Ur169EHpClQjET6Ccc8MC4AYNGqh58+baaKON1LBhw0CABMCB2DhJEgEwt0FcBQiA4zpytBuBHAJ8mOcWQaCmAPOCuwKBzALMDe6MOAnMmTNHs2fPDr3Ja9eu1fLly73rNGrUSFVVVaFfkwsgEAeBKM2Ndu3aqW3btnmzEQDnTcYJCQECYG6FuAoQAMd15Gg3AgTA3AMI5C1AyJU3GSdUiABzo0IG2oFulir8NSrbUsKCLisW/hbrV88dGAa6UOECUZsbQUJgAuAKv4kL6D4BcAF4nFpWAQLgsvJzcQTCE+DDfHi21BxfAeZFfMeOlocrwNwI15faiyNg2z58/fXX6yuzX/22fXlte4a6desW5yIptdivuS9ZssT7StOmTdkCoujCVBhXgXLOjTVr1nhbs9g+xPaekCxdu3bNazsIAuC43n3lbzcBcPnHgBYEEyAADubGWQhEXoAP85EfIhpYBgHmRRnQuWQsBJgbsRimim9k6upfC387d+4cSvCbhC7nPqcVP9gARFogCnPDguBvvvlmfQic7ypgAuBI32KRbhwBcKSHh8bVIkAAzO2BgKMCfJh3dGDpVkECzIuC+DjZYQHmhsOD61DXbPVvcsVfhw4dvNW/YZYohFxh9o+6EQgqEJW5YauAZ86c6XXDfihkq4D9FgJgv1Icly5AAMw9EVcBAuC4jhztRiCHAB/muUUQqCnAvOCuQCCzAHODOyMOAhMnTvT25bXSrVs31a9fP9RmRyXkCrWTVI5AAIGozI1Vq1bpq6++8npge3T36tXLd28IgH1TcWCaAAEwt0RcBQiA4zpytBsBAmDuAQTyFiDkypuMEypEgLlRIQMd425a8GsBcLL07NnTezBbmCUqIVeYfaRuBIIIRGVu2EMaJ02atL4LFgD7fVgjAXCQkecc74cNMCAQUwEC4JgOHM1GIJcAH+ZzCfF6JQowLypx1OmzHwHmhh8ljimnQHrQQwBcztHg2pUuENUAOJ/3BQLgSr+Lg/efADi4HWeWV4AAuLz+XB2B0AT4MB8aLRXHWIB5EePBo+mhCjA3QuWl8iIIEAAXAZEqECiSAAFwkSCpJpYCBMCxHDYaLYkAmNsAAUcF+DDv6MDSrYIEmBcF8XGywwLMDYcH15GuEQA7MpB0wwkBAmAnhpFOBBQgAA4Ix2llFyAALvsQ0AAEwhHgw3w4rtQabwHmRbzHj9aHJ8DcCM+WmosjQABcHEdqQaAYAgTAxVCkjrgKEADHdeRoNwEw9wACjgrwYd7RgaVbBQkwLwri42SHBZgbDg+uI10jAHZkIOmGEwIEwE4MI50IKEAAHBCO08ouQABc9iGgAQiEI8CH+XBcqTXeAsyLeI8frQ9PgLkRni01F0eAALg4jtTirsC9996rYcOGeR285557dMIJJ4TW2WIEwMVobyHvCzwELrTbw/mKCYCdH2JnO0gA7OzQ0rFKF+DDfKXfAfQ/kwDzgvsCgcwCzA3ujKgLFBL0BO1bMUKuoNfmPATyFShGoOr3msWYG8VobyHvCwTAfkeb49IFCIC5J+IqQAAc15Gj3QjkEODDPLcIAjUFmBfcFQgQAHMPxFOgkKAnaI+LEXIFvTbnIZCvQDECVb/XLMbcKEZ7C3lfIAD2O9ocRwDMPeCKAAGwKyNJPxBIEyDo4pZAgACYewABvwL8m+FXiuPKJVBI0BO0zcUIuYJem/MQiLJAMeYGAXCUR5i21SbACmDuj7gKEADHdeRoNwI5BPgwzy2CAAEw9wACfgX4N8OvFMeVS4AAuFzyXBeBmgIEwNwVlSxAAFzJox/vvhMAx3v8aD0CWQX4MM/NgQABMPcAAn4F+DfDrxTHlUuAALhc8lwXAQJg7gEEUgUIgLkf4ipAABzXkaPdCOQQ4MM8twgCBMDcAwj4FeDfDL9SHFcuAQLgcsnXft0xY8Zo4MCB3kGXX365rrjiCk2fPl033XSTRowYoW+++UZVVVXacsstNWTIEJ1zzjnaaKONMlZap866WKV///6yemsrAwYM0Ouvv+4dUl1dXeNQa8eVV17pff21115T8vh//vOfevfddzVr1ix16NBBe+yxhy6++GL17t17fR12rz3++OO688479cUXX2ju3Lnq1KmTfvGLX+jSSy9V+/btfQ3GG2+8oQcffFBvvvmmZsyYoeXLl6tdu3baeeeddcwxx+iwww5Tss/pFU6bNk1dunTxvnz88cfLtksw11tvvVUjR47Ut99+K3vfTr5mx+W7pcKoUaO8fo4dO1YzZ87UkiVL1LJlS/Xq1Uv9+vXTkUceqZ/97Gc1+mr9eP755/Xiiy/qo48+0tSpUzV//nw1atTIM91ll1103HHHad99963VKd/2ZqqskPcF9gD2dRtzUAYBAuD43hZ1Jdm7/Y6S+ib+u52kxoku3SfphBJ17157f0+5lv2LdUXI1459APz3v0vvvLNCG385UoNmPqHGTZupql4DqW5dVVet+5P8u6qq/vv3evWkhg2lxo1V1aSR6jRppNXtOmlh34Fq1Eg1/jRuLG3UcKWat6mvBg2Z8iHfl1RfBAE+zBcBkSqcE2BeODekdKhIAsyNIkFSTWgChQQ9QRtVjF9zD3rtuJyXHgDvtttuOvroo71wMlPp0aOHXn31VW2yySY1Xg4zAB49erSuueaajGFx48aN9cwzz2ifffbRokWLvPY/99xzGdtvQbAFul27ds06RBaG/vrXv9azzz5b6zDuueee+ve//622bdvWOC49AD722GM1dOjQGq5BAmALj60uC0BzFetLixYtNjjM+m6hb64yePBgLwBv1qxZxkMJgHMJ8npUBUiDojoyudv1b0mH1XJYqQLgAyQ9n9YOAuDc46chQ6Snn5aG607dqZN9nJH9kDfUT/31RtYDpqirNtX3WqjmWlzVXEvrNdfS+i20omFzrWzUXKubNNfaps21tllzqWUL1W3XRvU7tlWTzdqoaee2arFlW7XetImaNJESP+AuqL2cjEBtAnyY5/5AoKYA84K7AoHMAswN7oyoCxAAR3OEUgNgC/xefvllrVq1ylvhaqtrbbXvpEmTZCtvbZWpFVsZaoFsegkrAD7iiCO8la6bb765hg0b5q1wXbx4sZ544glvFasVW/lqoaatXLUVtraK1VbAWlBtK29vv/12TZgwwTvWVsfa6t5MZeHChdp11129lcNWunfvLru+rTBu0KCBvv76a/3rX//Sp59+6r2+3XbbeSuSbfVsakkNgHfffXd99tlnXjhtq4YtqG7durW+//57bwWxraq24idQtetb+2wFtBWrx8Lgvn37qnnz5l7A/Mknn3gBuK3enjdvnmeTWmyV74oVKzyHbbfdVt26dfPG2fpu/Xr00Ue9Fc9WrO+PPfZYRis/7c111xfyvsAK4Fy6vJ5NgAA4vvfG05IGpzTfflQ5196rE18rRQDcXNJ/JG0maYmkpolrEwD7uK8GD5aeeUY6RbfpNp3m44zsh7yoQdpf674JyFTmqI3aKPNPs/1eeIBe03uNBqhNG63/Yz/0tT+9G0xRjzUT1GTLjmreu5PabdVe7TvWlS1WpiCQrwAf5vMV4/hKEGBeVMIo08cgAsyNIGqcU0qBQoKeoO1kBXBuudQA2I62FbIW7m699dYbnGzh70477eSFllY++OAD7bDDDhscE1YAbBfZf//9vdW2TWwlTko58cQTdc8993hfsRDU2nXVVVd5Wz2kFguMf/7zn68Pdt9//32vP+nFVg8/8sgj3pdtGwqrp25d+6Xj/xa7l3//+9/rhhtu8L54ySWX6I9//OMGx6QGwPZC06ZNvS019t5776yDkitQtetaHz/++GOvjkMOOUT3339/jRW+yQvYNg977bVXjXDato6wLTUsBLZiK3zrpXxgXbp0qWzF8tO2SkvyVkzbDwPSS672Zu1omqX9gCFZevbs6W054qcQAPtR4phMAgTA8b0vLpZkmxB9kPhjv8tgWz6s+1dAKkUAfJssv5S+k/S4pHMT1yYA9nFfHXywZL9d8xv9U//UGT7OyH7ICB2iQzUiywHVWqkGqq/VBV2jjz7Vf9QnYx1n60bduH74pTWq0iy116x6nbSgSUctbd5RK9t2UnWHjqq3xSZqttXmarN9Z23Wp6XSfjOnoDZyshsCfJh3YxzpRXEFmBfF9aQ2dwSYG+6Mpas9IQCO5simB8C2vUNyT+D0Ftv+tb/5zW+8L1vgacFnagkrALZ9dydPnlxjJatd2wJpWxmc3EfYgmILODMV287Atnaw8oc//EH/+7//u8FhtvrVVvRaGT58uLeHcG3FQtG3337bC2B//PFHNbTtCRMlPQC+8cYbdfbZZ9daX65A1Vbm2mpfK9tvv7238thWJQcpuX44YquB7YcBtq/wSSedpDvuuKPGZXK110+7CnlfIAD2I8wxmQQIgN26L0oZAO8l6WVJdg8dnNiD+PIEJwGwj/vq8MOlUaOqNXzFzfrr2nNU14tNaz4IwEdVekRH6Wit+4ltemmkZVqmDX9i7KfO9GM6arpmqmPGU6/TBbpA1+dd7QI113dVnTW3aWf9Z4uD9EW/09S5s7T55lr/344d5W2BTKkcAT7MV85Y01P/AswL/1YcWVkCzI3KGu849jbfoOf446XEb+EH7q6FgmvXrvHOr6qqm/WBXYEvEPKJW20l3WfLmUIsqQGwPTDMHgqWrUyZMsXbLsCKBZG2FUJqCSsAPvfcc9evts3UNnvYmgWuVmzf3oMOOihjF2xLhC222CJr+8877zz99a9/9V63Vam233FtxbaVOPXUU71D7IF2tidwsqQGwLZHsW3ZkG0v3eQ5uQJVe4hdcm9j2/P4YFtJFbDkCoCtWttqwkLmbbbZxtvCIr3kaq+fpuX7vpBaJwGwH2GOySRAAOzWfVGqANjSRHsntB3kH7V/BxMPfSMAzvN+qvGhpVUrrV21Zt2f1Wu1ZuW6v9t/q9esXff3Fau1cuFyrVq0XKsWLvP+u7RhK83rtLWWL1eNP6vmL9HPR16qqsULVXfpQtVfulANVixU45UL1HjVQjVds1AbVS/K2fIGWqFVyvyT1gf0K/1KD+Wso7YDbtbpOlM31zjEfrjbrcsa/Wnl+Vq1RTc13KqbWu7UXZvsurk271qPbSYKUo/myXyYj+a40KryCjAvyuvP1aMrwNyI7tjQsnUC+QY99tv548dXtt6OO0rjxoVrkBoA2+pe2+s3W7EtA5J73e6333564YUXNjg0rADY9qC1vWizlWRQaa9b0GorhjOVXO23LSHGjx/v9TE93M5Un4XltpLYyi233KLTTvvvdoapAbA9WM9WCucquQJVW2lsK3OtfQsWLAi8+tfaMXv2bG/rjFdeecULu+fOneut9k2upE5tq+0hbHsJp5dc7c3V3yDvC6l1EgD7EeaYTAIEwG7dF6UKgP9P0m8lb1PZrST9SAAc7EaKzIeWtWtVvWixls5cqEXfztPib+Zq2XdztHL6HK2ZNVerf1qox3e8VnPnav2fOXPW/X3+fFsKvrf21qvBEBJn/V7X6i/6fcY6ttBUTfV+3vDfslL1NVVdNL1Jd81v312rO3dT/a26q9XPe6hL/821WecqHlhX0IiU7+TIzIvyEXBlBGoIMC+4KRDILMDc4M6IugABcP4jVOoA2LZESAaa2VpbW8gbVgBsIaXtZZut2H62tgLXypo1a2rdQ7a2NlpwPMc+3AUoV199tS6+2HanXFdSA2B7GJ1t35Cr1Bao2gPk7CFvVmx/5v/8xx5BFKzYXsS2xYWFvn6K7clrrumFANiPHsdEUYAAOIqjErxNpQiAd7P90O23iSSdmLLn8BWSWAGc59i58KFl5Upp7pjPtOiTr7V0ygyt/na6NHOGGsyZriYLZqjl0hlqvfrHnNtbHKVH9JiOyii4j17SSxrkW3eJmmhSVW9Nb7W1lnTeSnW32Uotd9tKW+y9pbp0kdKeZ+C7Xg4sjYAL86I0UlylkgSYF5U02vQ1HwHmRj5aHFsOAQLg/NVLHQBffvnl3oPPaivlCIBfe+0176Fl2UpqAJxpBWvqebW1v379+rKtEYKUdLvUAPj444+XhaW5Sm2B6g8//KBNN93Uq8IeZmdbMwQptmrWtqpI9tPC5H333dfb7qJVq1bePsZJI3sA3ueff+5dJpMrAXCQEeCcKAgQAEdhFIrXhrAD4EaS7NGbPSW9ImmflKYXMwDe1QfJNpJut+PsV3AyPcnURx1lP8R+leWTTz7x2mEb7yd/uln2hhW7AatXq/rH2Vo48Uct+mK6lk/6XtXffK8GM77TRnO/U5sl32po02c0eqH9fKFmKcaD8mZqY3XUTDVsWK3u3deoR4816tlz3Z8+fdZo883Xstdwscc9YH0VMy8C+nBaZQowLypz3Ol1bgHmRm4jjiivgAVI06dP91ZnWsBke8na37OVYcOqNGFCoR/TbQ/gtd4l1l2r0PpKa9i7d7XuuWdd+8MqtnJ2n33WfZy1FcCXXXZZrZeykNSKhYi2Mje11PZaeqUW2ia3RVi1alWNa9pK5Kuuusr7+ssvv6z+/ftnbdfee++tN954w3s9U11+22grgOfPn6/WrVt7D3UrpFgA3L17d68Ke/Dc3XffnbO6++67z3vgmhV7AJ0Fx8liK4CtXVYstP34Y4sj8i/2kLzkuP3lL3/xrmd7FNfNsDJol1120QcffJDVtbb2+m2Zzc+vvvrKC5jt7/bguWQAnauOcePGyfqTKPYB+p1c5/A6AiYQr38JGLNcAmEHwNdJukDSMkl9JE1JaVAxA+C8noR2zTXXqFevXrlseD0GAsuX19WcOY01e7b9aeL9d+bMpjr2s+t16nzbeSR4eVUDa92m4q66J6lhc2nOJt20vNdmarB9O23SbaUaNqz5az/BW8GZCCCAAAIIIIAAApUm0L59e2/fUltluMkmm/gOeirNqZT9feutt9Y/TOzCCy/URRddVOvlbZWold1339174Fpq2XjjjbVy5UpvUdLo0aNrrcdWsU6ePNk7JtP+svbZ9tprr/VeHzlypPbYY4+s9dnD0ZJhcqa6Uk+srf22V++ECRO8HxbYA+NyPbSttg5+++233sImK0cffXSteysn63n44Yd1xhlneP97880365hjjtngEp07d16/B7C1z+ZSPsXCcQtYbfWvPfDPVlbXVuyHNMltIjK55mqvn7ZZ8Gurm21/Zrt3bA9nv2XixImp9ysBsF84jiMAduweCDMA3inxk6W6krdR61/S7AiAHbuZotadegsWqurr2Vo76SfVmzZLTWfMUJufvlOnJdPUYu2CnM29SWfoLN2U8bi6Wq1F2kiNtXz962tUpYnqpcmNt9bMjbtrYdctVL1dJ3XYtq5atVqR83ocgAACCCCAAAIIIICACRAAR+8+KGYA3LNnTy/As60KPvvMnpWeudiWNbY6Nrk6OyoB8AUXXOCtvLViq1sPOeSQwAMWRgA8dOhQvfjii16bLHw94IAD8mrfzJkz1bt3b++cIUOG1Loq+cMPP5StrE4WAuC8qDk44gKsAI74AOXZvLACYPsRm/0OhG278KGknW2f+bS2FTMAZguIPAe+0g+v89NPWvrpVM1772ut/ORL1f9yklrPnKgOS79WXa379bHTdbNu0ekZqXppgiZ4zzPMXX5QJ33acCfN6txXa/v+TG3221bb9Guuli3zWrie+0IVfAS/zlvBg0/XswowL7g5EMgswNzgzoi6QL5bQBSjP/bgqmXL7Jc2lfXX3ItxnTjXUcwtIAYNGrR+VekXX3yxfguEdB/bL/dPf/rT+i9HZQuI999/31vZbKVPnz4aO3asGjWy3R/zL8XeAsJa8MQTT3iria3Y6mJrXz6rgO3fiTZt2njn77DDDuu3gsi0BcTBBx/sbTGZLJnGiC0g8r8vOCMaAgTA0RiHYrUirAD4D7Y1UiL0tfDXQuD0UswA2I+HhcRj7UD7B2DXXf1kxn6qLe0xPLgkZO/ly7Xi00n68bUv9Emjn2v8T131xRfy/nz5pe2Vte769gC6R7Tum4ogZbK6a2KznfTvA+7Udrs01k472TcXUtOmQWrjHOYF9wACNQWYF9wVCGQWYG5wZ0RdIN+HwBWjP/ar7osXL/aqsl/nr1evXjGqdaqOMWPGaODAgV6fCn0I3PXXXy9bRWvFAsSnnnqqxt6yyRAz9WFrmR4wZg+ju/LKK726SvUQOLvWkUceqccff9y7rgXaDz74oGxv4EzF2m2fwa2f1vfUUuyHwFndNodsew1bnWvFVijff//9atGiRcb2jRo1yhvb1BDbVmknt96wh7gNHjx4g7lhPzQ5//zz9be//W2DOnkInFPTvuI7QwDs1i0QRgBsG/iMk2S73tu2D7b9Q6ZCABzgXuJDSwC0Ip1i4a+FwJ9+KtW7/24NfPVStVkxI3DtM9RBnTR9/dbq9ryNrbayp9XaXmHSbrtJPXpIdXjXzWnMvMhJxAEVKMC8qMBBp8u+BJgbvpg4qIwCBMBlxK/l0sUMgO19yALGOXPmeFe0sPK4446T7Q1sD1WzPYNtCwPbhsBCyY8++sg7LkoBsD1srV+/fusfUN6kSRMdfvjhsgeiWRBsK2GtL59++qn3cLrvv/9eW265pfcgs9QSRgBs9X/99ddeW2bPnu1dzh4MZ1tD9O3b13uQuo2Btc2sbZ9g27qhZcuW65t2yy236PTT1/02qD1szbaC2GuvvdS2bVuvDw899JC3D/I222zj7dWdfAgcAXA05y+tCiZAFBHMLapnhREA3yjpbPvBm6RrEg+Ay9T/vSSt+xGqZLuqv5r4+yRJ636UWNzCCuDielKbCcyapXmvf6LZoz/W6g8+VvOvP1aHBZNUr8aOJzW5ntHBGqxnsjqO1C9Ur36VZnbbXfX23F1b/HJH9d29kRo3hj5dgA/z3BMI1BRgXnBXIJBZgLnBnRF1AQLgaI5QMQNg6+Err7zirSpdsmRJxg5bsPjMM89o2LBhsu0nrEQpALb22KpxC0lt9W+mtqV3rH///jLH1BJWAGzXmDp1qrdSefz48Tlvqvnz52+wQtj6c9JJJ9W6/69tfzFixIicY2QriG0crdxzzz064QSLYfIrhbwvvPPOO7IH9yUKD4HLj76ijyYAdmv4wwiA7XcgflcA0whJhxZwfrZTCYBDQKXKDALLlmnlR59r5gsfa8mbH6rx5+O1yZyPVb86sX9E4pT/1R/0R2+nlJqlkZZpgVqogf57zgo10IfqqykddtfKnXZX28G7a6cD26ljR0aBD/PcAwjUFGBecFcgkFmAucGdEXWBQoKeoH1jC4jccsUOgO2KFlBee+21eumll/TDDz94+y/36NHD27/2tNNO81b/DhgwILIBcFLN9jG2kNOCauuTraa1PXdtJbCtdLbw0R7EtvPOtjvkhiXMANiuZEGuhbS2XYUFobYq2VYnt2rVymubhdLHHHPM+oe+pbfPzrvtttu8Vdi26tn2BrbzjjjiCA0fPtzXGBEA555fHBFNAQLgaI5L0FYRAAeVK9N5fGgpE3yhl12xQivHf6oZI97XsjfGaaNJ43Rlixt157eDVJ3hWXD99IbeUP+cV7W9hD/ZqJ8W7DBQrQ8boJ2GbKrNNst5mnMHMC+cG1I6VAQB5kUREKnCSQHmhpPD6lSnCICdGk46E3OBqPxwpJD3BVYAx/wmLGPzCYDLiB/CpcMIgP02kz2A/UqlHMeHlgBoET7FnrVhzyYYN06y30x6911p2jTpIv1Zf9bFebf8S3XThxsN0IK+A9XmsAHa+dBOFREIMy/yvlU4oQIEmBcVMMh0MZAAcyMQGyeVUKCQoCdoM6MScgVtP+chEJZAVOZGIe8LBMBh3R3u10sA7NYYBwmAU8+xzYgGBCQhAA4Ax4eWAGgxO2XGDOmHq+5Wu6du0yY/fqh61asD9eAHddKmsoct1NGAAfL+9O8vJwNh5kWgW4STHBdgXjg+wHQvsABzIzAdJ5ZIoJCgJ2gToxJyBW0/5yEQlkBU5kYh7wsEwGHdHe7XSwAc3zHuIml4WvO3lXRw4mufShqZ9ro9mC35cLbkSwTAZbwH+NBSRvxyXHrpUq14a5xmPPG2Vr85VhtPGauNVs3z1ZKHdIx+pYdqHNu9uzRokLRf/+Xqv18jNW/uq7pIH8S8iPTw0LgyCTAvygTPZSMvwNyI/BBVfAMLCXqC4kUl5Arafs5DICyBqMyNQt4XCIDDujvcr5cAOL5jbCt1X8uz+VdKspW6qYUAOE/EYh7Oh5ZiasawrrVrVT1homY99bYWvvC2WnzyhtovnpqxIyfrdt2pkzO+1kRL9KM21sfaXhM2H6Sq/QZpq+N21E671FW9evFzYV7Eb8xocfgCzIvwjblCPAWYG/Ect0pqdSFBT1CnqIRcQdvPeQiEJRCVuVHI+wIBcFh3h/v1EgDHd4wJgKWxNnxjx47VrrvuGsuR5ENLLIct3EZ/843m/Pt1zX/qNbX4eIzaLZ7mXa+bvtQUdct47f01SqN04Aav/aRWeqPe3pq+zSA1/+Ug7Tq0s7bcMtymF6t25kWxJKnHJQHmhUujSV+KKcDcKKYmdYUhUEjQE7Q9UQm5graf8xAISyAqc6OQ9wUC4LDuDvfrJQB2f4xd7aElvgTAro4u/fqvwLRpsRqSQQAAIABJREFUmjPibY1qdYzGvF5HY8ZIX3+9IdBfdY7O0d9qVZukHnq3+SAt6XeAug4fqP77N1bjxtGE5sN8NMeFVpVXgHlRXn+uHl0B5kZ0x4aWrRMoJOgJahiVkCto+zkPgbAEojI3CnlfIAAO6+5wv14CYPfH2NUeEgC7OrL0K6fAt9/KC4JffVUaPVp6acbW2lpf5DwvecBSNdZrVXtrau+D1Pzog9T/V5upc2ffp4d+IB/mQyfmAjEUYF7EcNBockkEmBslYeYiBQgUEvQEvWxUQq6g7ec8BMISiMrcKOR9gQA4rLvD/XoJgN0fY1d7SADs6sjSr7wEqtes1Y//8zctf2a0On75uhquXZ7X+XbwJ9pWD21yoeoce4wOOkjabTeVde9gPsznPYScUAECzIsKGGS6GEiAuRGIjZNKKFBI0BO0mVEJuYK2n/MQCEsgKnOjkPcFAuCw7g736yUAdn+MXe0hAbCrI0u/ggssX66Vr76lmfePVr3XRqvTrE981zVcd+puDfeOb9lS2m8/eWHwgQdKbdr4rqYoB/JhviiMVOKYAPPCsQGlO0UTYG4UjZKKQhIoJOgJ2qSohFxB2895CIQlEJW5Ucj7AgFwWHeH+/USALs/xq72kADY1ZGlX8UTmDlTi556WXP/NVqtxo9Wi2U/Zq27o6ZrpjrWeL1uXenQnafrwP3WaO8TSrNVBB/mi3cLUJM7AswLd8aSnhRXgLlRXE9qK75AIUFP0NZEJeQK2n7OQyAsgajMjULeFwiAw7o73K+XANj9MXa1hwTAro4s/QpHYO1aVX/0sWbc+ZyqRz6rjj+MU5WqvWuNV1/tpPFZr/tnXaSLdK3GaUe913GI6h5+qPY4ube26VNHdUL4V4QP8+HcAtQabwHmRbzHj9aHJ8DcCM+WmosjUEjQE7QFUQm5graf8xAISyAqc6OQ9wUC4LDuDvfrDeGju/to9DASAgTAkRgGGhFbgVmztPDRUfrpgWf11oqddeY3F2jBgky9qdZE9VJPTd7gxUnqoTEth2jFgUO0w6k7adfdq2SrhYtR+DBfDEXqcE2AeeHaiNKfYgkwN4olST1hCRQS9ARtU1RCrqDt5zwEwhKIytwo5H2BADisu8P9egmA3R9jV3tIAOzqyNKvsgisWiWNHSs9/7z03HPS55+va0ZvfaEvtHWtbfpBnTS68WDN6z9E25w5QAMH1Vf9+sG7wYf54Hac6a4A88LdsaVnhQkwNwrz4+zwBQoJeoK2LiohV9D2cx4CYQlEZW4U8r5AABzW3eF+vQTA7o+xqz0kAHZ1ZOlXJASmTZNGjJCa/t/VOmnqpb7bNEdt9FzDwzR3ryO17W8HaMA+9VSvnu/TvQP5MJ+fF0dXhgDzojLGmV7mL8DcyN+MM0orUEjQE7SlUQm5graf8xAISyAqc6OQ9wUC4LDuDvfrJQB2f4xd7SEBsKsjS7+iJTB7thY9PFLz73lKG3/2khqsXeG7fbPUTs81PFw/7XOktvvdAA0YWMdXGMyHed/EHFhBAsyLChpsupqXAHMjLy4OLoNAIUFP0OZGJeQK2n7OQyAsgajMjULeFwiAw7o73K+XANj9MXa1hwTAro4s/YquwKJFWv70C5p9x1Nq8+5zarJqoa+2fq0u2lJT1LZtHR1+uHTkkdKeeyprGMyHeV+sHFRhAsyLChtwuutbgLnhm4oDyyRQSNATtMlRCbmCtp/zEAhLICpzo5D3BQLgsO4O9+slAHZ/jF3tIQGwqyNLv+IhsHKlVo9+VT/e9rSavTpCLZbOzNrua3Sh/kfXbPB6+/bywuCjj5Z2312qqvrvy3yYj8ctQCtLK8C8KK03V4uPAHMjPmNVqS0tJOgJahaVkCto+zkPgbAEojI3CnlfIAAO6+5wv14CYPfH2NUeEgC7OrL0K34Ca9dq7dvv6MebHleT5x5XiyXTN+jD9vpQH2v7jP1qofn6Y/O/aNWRx2rf322lbbZhD+D43QC0uBQChFylUOYacRRgbsRx1CqrzYUEPUGlohJyBW0/5yEQlkBU5kYh7wsEwGHdHe7XSwDs/hi72kMCYFdHln7FW8DC4Dff1o//eExNRj2hn1Y0U9c1kyVl/ufmRN2lu3SS1+cPtb1e7fgr1Tl6sFpu9ZXatl2ufv36qXXr1vE2ofUIFEGAkKsIiFThpABzw8lhdapThQQ9QSGiEnIFbT/nIRCWQFTmRiHvCwTAYd0d7tdLAOz+GLvaQwJgV0eWfrkjsGaN1n77vcb+0FmPPSY98YQ0Y8aG3XtVAzVQYzb44hpV6VXtpTGbHKJNz/qljj61o1q2dIeFniAQRICQK4ga51SCAHOjEkY53n0sJOgJ2vOohFxB2895CIQlEJW5Ucj7AgFwWHeH+/USALs/xq72kADY1ZGlX84KrFkjvf229Pjj6/7U//E7faPOqlJ11j4vUyONrBqsL3f+lbY6Zz8dOLi+GjZ0loiOIZBVgJCLmwOBzALMDe6MqAsUEvQE7VtUQq6g7ee8wgXGjBmjgQMHehVdfvnluuKKKwqvtEg1DBgwQK+//rpXW3V19s8BRbrcBtUEmRthtLeQ9wUC4DDujMqokwC4MsbZxV4SALs4qvSpYgRWr5Y+/dOz2uqqoWq0eomvfs9WWz3Z8BjNHzJM+17wM22/vVSHf8V82XFQ/AUIueI/hvQgHAHmRjiu1Fo8gUKCnqCtCBJyBb0W50VToJQB8L333qtp06Z5EH6C5jACVb+jEGRuhNHeQt4XCID9jjbHpQvw0Zl7Iq4CBMBxHTnajUCqwJIlWvHYCM39+0Pa+JMXVbd6jS+fj/QzvdjpRLU8/RgdfkobtWvn6zQOQiC2AoRcsR06Gh6yAHMjZGCqL1igkKAn6MWDhFxBr8V50RQoZQCcb0Ca7/HFFA4yN8JobyHvCwTAxbwjKqsuAuDKGm+XeksA7NJo0hcETGDWLC2+61EtvPUBdfp2nC+ThdpIm9T9Ufsc3FjDhkkHHCDVr+/rVA5CIFYChFyxGi4aW0IB5kYJsblUIIFCgp5AF5QUJOQKei3Oi6ZAlAPgcooFmRsEwOUcMa5dTAEC4GJqUlcpBQiAS6nNtRAooYB9mP/w0UfV8vl3tMXbY9V23pSsV39UR2qoHl3/evv20q9+JZ1wgtSnTwkbzaUQCFmAkCtkYKqPrQBzI7ZDVzENJwCumKGOVEcJgDMPBwFwpG5TGlNiAQLgEoNzuaIJEAAXjZKKEIiWwAYf5vfYQ62+/Eqz/3q/mj3zsJqsmL9BY/fXKL2o/TN2oO8O1frNcUt0xLBmat48Wn2kNQjkK0DIla8Yx1eKAHOjUkY6vv0kAI7v2MW55QTABMBxvn9pezgCBMDhuFJr+AIEwOEbcwUEyiKQ9cP88uVa9cQIzb3+HrX/ZLSmq5M66xutVd2M7dxVY/Wi9tPj9Y7RD784VftfvIN23JEHx5VlULlowQKEXAUTUoGjAswNRwfWoW4RAEd3MJcvX64XX3xRr7zyisaPH68vv/xS8+fPV6NGjdShQwftsssuOu6447Tvvvtm7YQ99OzKK6/0Xn/ttddk2wV88MEHuummm/T6669r+vTpatq0qbbddlsdf/zxXn1VVVVZ67MVqlbPSy+9pPfee0+TJ0/W3LlzVb9+fbVr10477bSTjjrqKB122GG11lNbAHzBBRfo+uuv99rw4IMP6thjj805SMOGDZM97M3KqFGjtP/++3t9tT7mKtbv5Ll2bD5bKixatEh33XWXN06fffaZ5syZozp16qhjx47abrvtNGjQIM+jdevWNZrx/fff65lnntEbb7yhTz/9VN99951szJs3b64uXbpo77331plnnqnNNtus1i7k095cFsnXC3lfYA9gv8ocly5AAMw9EVcBAuC4jhztRiCHgK8P899/rznvfqW7pgyQfS86cWLNSu/V8Tpe969/Ybz6atSmp6jjuUfriBM3UosWDAUC8RHwNS/i0x1aikDRBJgbRaOkopAECgl6gjYpyK+5B71WnM/r2rWrpk6dmrMLgwcP9oLSZs2a1Tg2PQC20PaSSy7RmjWZH2x86KGH6vHHH1e9evUyXnevvfbyAuBcZbfddtOTTz6pjTfeOOOhtQXAX331lXr06KHq6mrtueeeOUNcC8U7deqkZcuWaYstttCUKVO88DnsAPiRRx7RGWecIXufr62Y6VNPPbXBIdZ/s7Q+1lYaNGigf/7znxo+fHjWwwiAc92NvB4XAQLguIwU7UwXIADmnkDAUYF8P8zb93XvvScvCP7Xv6SFC6WWmuetEG6s5TWUFqmZHrNVwQeeov0u7qudd2ZVsKO3klPdyndeONV5OoNALQLMDW6PqAsQAEd3hGyV74oVK7ygcPvtt1fnzp3VpEkTLVy40Fsx+uijj2rGjBleB4444gg99thjtQbAxxxzjB5++GFvpe4JJ5zgrfq1oHTs2LG68847vWtZufrqq3XxxRdnhLFVx//5z3+8cLVv377eStWNNtpIS5Ys0YQJE7zw2AJYK7vuuqu3ujVTmJxrC4j99ttPo0eP9uqZOHGievbsmXWg/vGPf+i3v/1tjba/9dZb3orcSy+9VJ9//rn3enoQa1/bfPPNtcMOO6yv30+gaiuozzrrrPXn2PgMGTJEW265pWdqq3vN1VYG2wrtp59+eoP2v/DCCzrggAO8fg0cOFBbbbWV2rZt660e/vbbb71zn3vuOe+BifY1Wyn8i1/8IqOBn/bme5cX8r7ACuB8tTk+KUAAzL0QVwEC4LiOHO1GIIdAIR/mly2T7Pu/OZf/Q2d9ue4b1dqKrQp+bpNT1OHso3XUSRupZctcZ/A6AuURKGRelKfFXBWB0ggwN0rjzFWCCxQS9AS9KiuA/cnZVgb77LOPt71CprJ06VJve4RkuPjmm29qjz322ODQ1BXA9kL//v01YsQItUj7VTPbKsG2HLCVwRZE/vDDD7LVp+nl5Zdflq3utSA6U7GxPfvss3XzzTd7Lz/wwAP6lT0BOa3kCoCtTxaoWjn33HN1ww03ZEXr06ePF0pb0GzbKFhwnlryDUhzHf/+++9r991398JZu6at0D355JMzts/CejvexjG1fPPNN952HrZNRGpJnRu2Evqggw7SrFmz1L17d02aNMkLg9NLrvb6u9s2PKqQ9wUC4CDinGMCBMDcB3EVIACO68jRbgRyCBTlw/yUKZp3zW2q/9A9arZsTk5zb1Vw3WP03cGna/Bl22n77XOewgEIlFSgKPOipC3mYgiURoC5URpnrhJcoJCgJ+hVCYCDytU8zwJG2/7AVuCedNJJuuOOOzY4KDUAtn1obc/eNm3aZGzA0KFDvVXFVmz1rIWcQYqNrwWW06ZN84JP2y84veQKgC2Itu0cbCWttdcC6YYNG9aox1bKJtt5+OGH64knnqhxTL4Baa7jbeWureC1ct1118n2LC5WSZ8b9913nzeutY1JrvYGaVsh7wsEwEHEOccECIC5D+IqQAAc15Gj3QjkECjqh/kVK7T6iaf10zW3q/1/XvVl/4b66eUeZ6j3JYfp8KH1lWFxhq96OAiBYgoUdV4Us2HUhUCZBZgbZR4ALp9TIHDQc/vtUlrYmPNiiQNs39M1a9dq1YEHqv4VV2Teb3bxYmngQL9VZj/O9qvNsDeud8If/yiNGOH/GrbK85RT/B9foiNtq4V3331X22yzjfcgstSSGgDbytwbb7wxa6vsYWbJsPHWW2/VqaeeGrgHRx99tGyPXHugma10TV+5misAtgtfddVVuuyyy7w2PPTQQ7ItLNKLbWVhIakV2zIi0wPx8g1Iazt+9uzZ3r7Gdg/bSmPbriHbCu0geOkBsG2n0atXL68qezDeeeedV6PafPvnp12B3xckEQD7EeaYTAIEwNwXcRUgAI7ryNFuBHIIhPZhfvJkzb/+TtV/8B419bEq+DUN0ND2r8k+i9j35zkeEMy4IhCqQGjzItRWUzkC4QswN8I35gqFCQQOeq64QrryyoIuvuLXv1bdu+/OHADbQxOK8UTcBQuk5s0zt9NWVt51l/8+XH65ZP0uYZk3b54XftqKU9vmYO7cud5q30wPD2vZsqXs+NSSGgDb6t4jjzwya+ttv9r999/fe/3Pf/6zLrrooozH2tYTVtfIkSO9wPnHH3/U4sWLsz7QzALg9C0n/ATAtr+x7Xu8atUqb+sKOye1pD78zfbe/fLLL4uyRUJtgartIXzYYYd5zRg2bJjuvvvugu6Gjz/+2HuAn4Wm1n5b0Z3cizm94mwBPgFwQUPAyRESIACO0GDQlLwECIDz4uJgBOIjEPqH+eSq4GtvV/vPsq8KPlP/0M0604OrW1caPFg644x1i2UybA8WH2BaGkuB0OdFLFVoNALyng5v+3Ja6devn+xXsCkIREmAADi6AbDt1Tt8+HAv9PVT7OFjtnVCakkNgF955RXvgXLZip9Q1rZcsK0ibK9dv8VWyW6WtlLBz7Wsfgus7cFyVmwP3B49eqy/bOrD36655hpdeOGFGZuUb0Ba2/Gp1/zLX/6i888/3y/DBsfZSt8zzjjD27IjU5ifqdITTzxRtko7veTbPz8NDvy+wApgP7wck0WAAJhbI64CBMBxHTnajUAOgZJ+mJ88WQuuv0P1vFXB//3m3/YE3kQ/aJFqrmjp3Vs6/XTpuOOyL3hhkBEotkBJ50WxG099CIQowNwIEZeqiyIQOOhhBXBR/LNVYitC99xzT+9BY1a23XZbbz/dbt26qVWrVt5+uMltFS699FJ9/vnn3nHpYWJqAPzaa6/JwsJsJVcoO3XqVK8dttrXirXFVgxbKGsPjmvUqNH6Nv3973+XXc+KnWf7+aaWXNdKHmt1JENr2/7AtkFIluTD32wLBtsruH379hm7lm9AWtvxV199tczbyi233KLTTjst0H1g4a89PM6Ktd8cd955Z3Xs2FF169b1vmaeFv4nt+I4/vjjde+999a4Xr7989PgwO8LBMB+eDkmiwABMLdGXAUIgOM6crQbgRwCZfkwv2yZVj70mBb+6Sa1nTpeN+t0nal1T1fOVE7Xzepdf4p+OvYsHXtpF225JcOKQLgCZZkX4XaJ2hEoigBzoyiMVBKiQOCghz2AQxwVeXvZvvzyy941br75Zp1uP93PUnbaaSeNHz/eezXMAPjkk0/WnXfe6V3HVtvaNhHpe/smm3jEEUesfyBbIQGw1de7d29NnDjRC5ntYXANGjRQ6sPfbJVw8uF1mYjyDUjDXgFsq6ctELe5t8kmm3hBuT00z0r6HsC26tn2drZCABzqlKPyCAgQAEdgEGhCIAEC4EBsnIRA9AXK/mH+/ff16Yx2uvHpLvrXv6QVKzY0q9Iafa2u6qxvtUZVelqH6oN+52i/P+yuPfvXYXuI6N9isWxh2edFLNVodCUIMDcqYZTj3cfAAXAB3U4PuerVq1dAbe6danveNmnSxAsD+/btuz7czdbTdu3aac6cOd7LYQbAth+vbedgK22nT5/urVTNVnbccUd98MEH3suFBsC2mvh3v/udV9fDDz8se8CchaH333+/97VcW1sUMwAuxh7A99xzj2w7Byu2pcSZZ67b0s1K+tywvZ8PPvhg7zUCYPfmOj3aUIAAmDsirgIEwHEdOdqNQA6BKH2Yty3h7NkTt9xi31yva/ghGqEROrRGL8ZpRz3V+Rz1vuwIHXlsfTVsyFAjUDyBKM2L4vWKmhAoXIC5UbghNYQrQAAcrm+Q2u3hZ506dfJOPeqoo/TII49krWbcuHHe1gHJEmYAbNtOrFy5Uj//+c/17rvvZm2TtX/zzTdfv31FoQGwPezNVsraw+cszLUQ1nyWLVvmrZy1VbLZViJbI20LieR2FHa/13asHV9bYGxBuwXg5tyhQwcvELftGvIptnL64osv9k4ZNWrU+gfv2f+nB8AWfCe3iiAAzkeZY+MoQAAcx1GjzSZAAMx9gICjAlH8MG/P+xg1yn5FUDr3hX21r9b9ymCm8r020X3NzlSDM0/RCee2Vrt2jg4U3SqpQBTnRUkBuBgCWQSYG9waURcgAI7eCC1cuFAtWrTwGpZrBfCBBx7ohYjJEmYAbA+xnDdvnrcVw6xZs7IGqb/5zW906623rm9ToQGwVXTSSSetfwCarZi96aabvPr9PIjtkEMO0ciRI73jFy1apGbNmtU66LlWDB900EF6/vnnvTquu+46XXDBBXndRKkrmmtbAWzGO+ywg5YsWeLVTwCcFzMHx1CAADiGg0aTPQECYG4EBBwViPSH+TVrNP/436nxY/eq4ap13yxmK0vVWA9WHa9vhpytoZf3VJ8+jg4Y3SqJQKTnRUkEuAgCmQWYG9wZURcgAI7mCPXs2VOTJ0/2Gvf444/rl7/85QYNXbNmjc4//3z97W9/2+DrYQbA++23n0aPHu1dL1vwesMNN3jtSi3FCIA//PBDLwxPLbYXsO0JbIF0bcVW0VroauX111/3Hq5XW8kVANuq6912281brWvbl9gKXdsfOVOxMP/999/3HuCXLPaAPzvfim2rYVtltGnTxvv/5Apg2yd46NCh+uKLL9afRwAczblKq4onQABcPEtqKq0AAXBpvbkaAiUTiMWH+XnztOymO7Xqr/9Q8/nf5bR5Tgfq9R3O1T5/2kv7DmKf4JxgHFBDIBbzgnFDoAwCzI0yoHPJvAQIgPPiKtnBt9xyy/oHv9mWBbYVRP/+/dWqVSt99dVXeuihhzRhwgTvAWG2NUNyv90wA2BbaWwrjpPF/r7//vtr44039rZCeOyxx2ThaMeOHdWnT5/1YXExAmC7pm09YWFqsthewLYncK5iq39tFbCVbt26efsJd+nSZf0exra9hLU3WXIFwHacrUA+66yz1p9jK3WHDBmirl27qqqqSt9//70s6LU9fO2Bfk8//fQGzdxll1303nvveV+zAPuUU07xHnZnW2y89dZbnqWt/D3hhBN07733escRAOcaaV6PuwABcNxHsHLbTwBcuWNPzx0XiNWH+VWrtObxJzX/ihvV5st132TWVj7QDnp0899r+z8erl8Orac8tzTLVT2vOywQq3nh8DjQtegJMDeiNya0aEMBAuBo3hEW5Nq2B3fbwx6yFAstR4wYoWHDhnkrW62EGQBb/VdeeaWuuOKKrG2yFa1PPvmkt+L2vvvu844rVgBsQaj1NVnGjBnjheK5iq2W3nvvvdcbpR+fHqz6CYCtjgcffNB7gNuCBQtqbYIFw2aSWqZNm+btTWw22coZZ5yh8847zwuVrRAA5xppXo+7AAFw3EewcttPAFy5Y0/PHReI7Yf5d97RT5fdqJav/FtV1WtrHaWd9Z5mdd5Z55wjDR8u5dgqzfERp3t+BGI7L/x0jmMQKECAuVEAHqeWRIAAuCTMgS9iweHtt9+u8ePHy7YTsK0CbHuII444QsOHD1ejRo1qfWiZhbUW2lqxB6FZuJmtWKA6cOBA7+XLL788a9Br9VjAaytc7T2uZcuWXkhpQeepp57q/b+tXC12ADx9+nTvYXBWevXq5a2A9ltWrFjhrdq1B8jZeWZp2y1YCRoA27m2J/Idd9zh7cNs9ZqHbQthD6nbbrvtvBXStnq7efPmNZpqwbFt4WFjbKu6rdiD5XbccUf9+te/9s61lcS2WjlTO5MV+g2s/VrZcYW8L6RucSHJ9rp4J59rc2zlChAAV+7Yx73nBMBxH0Haj0AWgdh/mP/mGy2+5ibVvecONV5Rc8XC69pTA7RuFYmVVq2kM86Q7Lfc2rfntkAgs0Ds5wUDi0BIAsyNkGCptmgChQQ9QRuR3OfUzrcHcllgRkEgl4CFpefY6gRJf/3rX9f/Pdd5cXo9KnOjkPcFAuA43XHRaisBcLTGg9b4FyAA9m/FkQjESsCZD/OLFmnl7fdq2bX/pxazp6wfg4P0rJ7XQTXGpGFD6YQTpPPOk7p3j9WQ0dgSCDgzL0pgxSUqS4C5UVnjHcfeFhL0BO1vVEKuoO3nvNIL2NYWtkfupEmT1LhxY29lbOvWrUvfkJCvGJW5Ucj7AgFwyDeJw9UTADs8uI53jQDY8QGme5Ur4NyH+TVrtPbJp7Xg4mv10w9L1X3Zp6pWVcYB3lTf6c/6H3201/k66s8/0847V+59QM83FHBuXjDACBRJgLlRJEiqCU2gkKAnaKOiEnIFbT/nlV7ggQce0HHHHedd2LaZuPXWW0vfiBJcMSpzo5D3BQLgEtwojl6CANjRga2AbhEAV8Ag08XKFHD2w3x1tarnzNXbk9rquuukkSNrju8NOlfn6kbvhRc1SC/scIkOuX5P2XZydfgXuzInRKLXzs6Lih5VOl8MAeZGMRSpI0yBQoKeoO2KSsgVtP2cF76AvXe+//77WrlypcaNG+dt+bB06VJv9a+tAt5ss83Cb0QZrhCVuVHI+wIBcBluHEcuycdJRwayArtBAFyBg06XK0OgUj7M23M1rr9eeuABadUqqaXm6TttpmZassFAv6k99GTvS7XPdYN04EF1CIIrYxrU6GWlzIsKHV66XYAAc6MAPE4tiUAhQU/QBkYl5Arafs4LXyD1gXSpV7vlllt02mmnhd+AMl0hKnOjkPcFAuAy3TwOXJYA2IFBrNAuEABX6MDTbfcFKu3D/PTp0t//LjX525902YpLsg7wePXVQ1tcol3/PFiHH1GlunXdvxfo4X8FKm1eMPYI+BVgbviV4rhyCRQS9ARtc1RCrqDt57zwBVID4DZt2mjrrbfWhRdeqAMPPDD8i5fxClGZG4W8LxAAl/EGivmlCYBjPoAV3HwC4AoefLrutkClfphfdsM/tfaKK9V08axaB/g/2lr3drxYfa46UsccV0/167t9P9C7dQKVOi8YfwRyCTA3cgnxerkFCgl6grY9KiFX0PZzHgJhCURlbhTyvkAAHNbd4X69BMDuj7GrPSQAdnVk6VfFC1Sg+r1KAAAgAElEQVT0h/nly7Xqznu17Mrr1HzO1Frvha+0pe5ofZG6XH6cTjilgRo1qvhbx2mAip4XTo8snStUgLlRqCDnhy1QSNATtG1RCbmCtp/zEAhLICpzo5D3BQLgsO4O9+slAHZ/jF3tIQGwqyNLvypegA/zklav1poH/6XFl/xJLaZPrPWe+Fab6baNLlD7i0/SSWc1VtOmFX8LOQnAvHByWOlUEQSYG0VApIpQBQoJeoI2LCohV9D2cx4CYQlEZW4U8r5AABzW3eF+vQTA7o+xqz0kAHZ1ZOlXxQvwYT7lFlizRmv//ZQWXnS1Wk79uNZ7Y6Y21lktH9Aul+4re3YHQbBbU4l54dZ40pviCTA3imdJTeEIFBL0BG1RVEKuoO3nPATCEojK3CjkfYEAOKy7w/16CYDdH2NXe0gA7OrI0q+KF+DDfIZboLpa1c89rwW/v1otJ7yT8R5ZpXrqocmapi5q3176/e9FEOzQbGJeODSYdKWoAsyNonJSWQgChQQ9QZsTlZAraPs5D4GwBKIyNwp5XyAADuvucL9eAmD3x9jVHhIAuzqy9KviBfgwX8stUF0tjRmj+ef/US0/fHWDA+/QSTpFd2zwNYJgd6YT88KdsaQnxRVgbhTXk9qKL1BI0BO0NVEJuYK2n/MQCEsgKnOjkPcFAuCw7g736yUAdn+MXe0hAbCrI0u/Kl6AD/M+b4F339V8WxH85rOy1b/d9aW+0RYZT7Yg+KLzVumUM+qzNYRP3qgdxryI2ojQnqgIMDeiMhK0I5tAIUFPUNWohFxB2895CIQlEJW5Ucj7AgFwWHeH+/USALs/xq72kADY1ZGlXxUvwIf5PG+Bjz7S9/9+T7+bcJqefDLzuTvoAz2jQ3Rzs4vU/pKTdfJZjQiC82Qu9+HMi3KPANePqgBzI6ojQ7uSAtXV1Zo48b8PdO3Zs6eqqqpCBYpKyBVqJ6kcgQACUZkb6QFwr169VKeOv3iOADjAwHOKJ+DvDgMLgegJEABHb0xoEQJFEeDDfHDGTz6R/vAH1QiCn9HBOljPehX/oE66qdn/aONLTtKpv2ukxo2DX48zSyfAvCidNVeKlwBzI17jVamttQDYgmAr3bp1U/369UOliErIFWonqRyBAAJRmRurVq3SV199tS6Uq1NHFgD7LQTAfqU4Ll2AAJh7Iq4CBMBxHTnajUAOAT7MF36LpAbBfTVe47VTjUq/06b6R4v/Vberh+mEk+urQYPCr0sN4QkwL8KzpeZ4CzA34j1+ldL6r7/+WitWrPC626FDB7Vq1SrUrkcl5Aq1k1SOQACBqMyNefPmaebMmV4PGjZsqK5du/ruDQGwbyoOTBMgAOaWiKsAAXBcR452I0AAXLJ7wILgVfsfrB1nrlv9m6l8pS11c7srtcN1Q/X/7J0HmFTV+cbfXXaXDmrs0RiTYInGGtS/vYu9i1iiIgQ1dlQINmyAvSsCYu+KXbFr7C1qjLFr7MYuRZat/2dgQNZZ2JnZe+855zu/eZ48uuy95/u+33ve7NnX4c4ee3dQhw6ZtUehEggQcpUAi0ujIoA3opI72GG/+eYbff3117PDnqWWWkodUvyB60vIFaxgNG6WgA/eaGxs1EcffTT7PwottNBCWnDBBYtmTgBcNCouJABmDxghQABsREjGgMAvCfDLfMJ74uWX9ePgk9TzibvnufDrWlFjljhFG5yzvXbauUIpP54w4SHtL4cv7GvMhOURwBvlceOubAnk3v2bexfwrFfuHX+5dwF369YtlSA4F3JNnTp1RrmuXbuqqqoq24GpBgFPCbj0Ri74nTJlinLv/p31NwJymHLv/s39f0KxLwLgYklx3S8J8A5g9kSoBAiAQ1WOviHQBgF+mU9pi7z0kiYNPkk9/jH3dwPnKr+g3rri96dp2/M31ZZbVajIz6NIqWmWnUUAX7AXINA6AbzBzgiFwJzvAk6759zzhnMfMpV75T5wrtgPl0q7L9aHgGsCvnmj1Hf/5vgRALveReHWJwAOV7vYOycAjn0HML9ZAvwyn7K0L7ygHw89Tj2ff2iehR7XBrrpT6ep7wXraMMNU+6J5dskgC/aRMQFkRLAG5EKH+jYWYXAufC3trZ2BqVOnTrNCIF5QQACmvEfRnzxRjnhLwEwu7g9BAiA20OPe10SIAB2SZ/aEEiRAL/Mpwh3zqUfe0w/HnKser7x7DwL3qutdO26l+nIc5ZQ78LPksuoWcrgC/YABFongDfYGaERyP3V78mTJ2vSpEmqq6tT7h2JSb98eM5p0jOxHgSSIODSG7l34tfU1KhHjx7q3r17SY99mHN23gGcxE6Icw0C4Dh1tzA1AbAFFZkBAq0Q4Jf5DLdFc7Oa771Pkw87Vj0+eK3Vwl9pIf1OH2iqumnXXaXTTpN69cqwR0rNIIAv2AgQIABmD9gkkAuAkw6Bc88Yfeqpp2YAW3fddWc8b5gXBCCgGc/fdeGNXPib1KNYCIDZyeUSIAAulxz3uSZAAOxaAepDICUCBF0pgZ3Xsk1Nar7lVk058nh1//ydFlcernN1vg6f/We5z5EZOFA64QRp0UUd9BppSXwRqfCM3SYBvNEmIi6IkAC+iFB0Ri6KgAVvEAAXJTUXtUKAAJhtESoBAuBQlaNvCLRBwMLBLFiRGxrUdOXVmjb0JHX99mN9oiXUS+9qujoVjNS1qzR48Mz/9egR7MTBNI4vgpGKRjMmgDcyBk65IAjgiyBkokkHBCx4gwDYwcYxUpIA2IiQEY5BAByh6IwcBwELB7PglZo+XY2XjtETbyyov9zfT5991vpEm+lBrdLtfS11ygANPKhaNTXBT+7tAPjCW2lozDEBvOFYAMp7SQBfeCkLTXlAwII3CIA92EiBtkAAHKhwtC0CYDYBBIwSsHAwsyTNtGnShRdKI0ZIP/7482Qd1KDXtLJW0H/0jnrp/IVHaN1zd1bf3SvEh40nvwPwRfJMWdEGAbxhQ0emSJYAvkiWJ6vZIWDBGwTAdvZj1pMQAGdNnHpJESAATook60DAMwIWDmaeIU2kne++k0aOnBkGT58u7afxGq/9W6z9nNbUuF5nqO/F62uzzRIpyyJ5AviCrQCB1gngDXYGBAoJ4At2BQTs/swgAGZ3l0uAALhcctznmgABsGsFqA+BlAjwS0tKYBNa9uOPpdOO/UnHX7uMllDrz4a4R1vrrv8bpYNHr6iVVkqocOTL4IvINwDjz5UA3mBzQIAAmD0AgWIJWPiZQQBcrNpc90sCBMDsiVAJEACHqhx9Q6ANAhYOZuZF/uwzTd6tv7o/8+BcR21UpS7XAL21x8k65uxFtOii5qmkOiC+SBUviwdMAG8ELB6tp0YAX6SGloUDJ2DBGwTAgW9Ch+0TADuET+l2ESAAbhc+boaAvwQsHMz8pZtwZw8/rMkHHaPu774y14Unq5vOqh6mTkMP12FDO6tLl4R7iGQ5fBGJ0IxZMgG8UTIyboiAAL6IQGRGLIuABW8QAJclPTdJIgBmG4RKgAA4VOXoGwJtELBwMItK5KYmNd94k3464lh1/erDuY7+kX6jM+cfqd5n766996nkg+JK3CT4okRgXB4NAbwRjdQMWgIBfFECLC6NioAFbxAAR7VlEx2WADhRnCyWIQEC4AxhUwoCWRKwcDDLkpc3terq1HjxaNUdf7I6T/12rm29oN4a3esc7X3ZutpoI2+6974RfOG9RDToiADecASesl4TwBdey0NzDglY8AYBsMMNFHhpAuDABYy4fQLgiMVndNsELBzMbCvUxnQ//KDpJ45Qh4vPV1Vj3VwvPkuD9Y9tz9KZZ0rLLhs1saKGxxdFYeKiCAngjQhFZ+Q2CeCLNhFxQaQELHiDADjSzZvA2ATACUBkCScECICdYKcoBNInYOFglj6lACp8+KGmHjJUXe+9udVmt9OdulvbqUMH6YADpOHDpQUXDGAuRy3iC0fgKes9AbzhvUQ06IAAvnAAnZJBELDgDQLgILaal00SAHspC00VQYAAuAhIXAKBEAlYOJiFyD21np95RlMGDVa3fz83u8Sj2kib6JEWH0Uw33zSSSdJBx4oVVen1k2wC+OLYKWj8ZQJ4I2UAbN8kATwRZCy0XQGBCx4gwA4g41itAQBsFFhIxiLADgCkRkxTgIWDmZxKjePqZub1XzTzfrp0CHq/PXHWl0v61Wt2uoNyy8vnXeetPnmUJyTAL5gP0CgdQJ4g50BgUIC+IJdAQG7PzMIgNnd5RIgAC6XHPe5JkAA7FoB6kMgJQL80pISWB+Wra1V/cRHdNGHW+vkk6Uffihsqqum6Dwdruc3HqZjRv9OvXr50Lj7HvCFew3owE8CeMNPXejKLQF84ZY/1f0lYMEbBMD+7i/fOyMA9l0h+psbAQJg9gYEjBKwcDAzKk2iY333nXTKKdJFF0kNDT8vfaqO1bEaoVp11HmVgzX10L/r6JO6qUePRMsHtxi+CE4yGs6IAN7ICDRlgiKAL4KSi2YzJGDBGwTAGW4YY6UIgI0JGtE4BMARic2ocRGwcDCLS7H2Tfv229IRR0j33y8trQ/0H/1RnTR99qKfazGN6HG6Vjt7T+3bv1KVle2rF+rd+CJU5eg7bQJ4I23CrB8iAXwRomr0nAUBC94gAM5ip9isQQBsU9cYpiIAjkFlZoySgIWDWZTCtXPo++6TqnfbUZtNvaPVlZ7VWhq93PkadPkaWnvtdhYL8HZ8EaBotJwJAbyRCWaKBEYAXwQmGO1mRsCCNwiAM9su5goRAJuTNJqBCICjkZpBYyNg4WAWm2aJzNvcrIaRZ6rp5FNUM33KXJe8QvvqpR1H6O8XLKYllkikchCL4IsgZKJJBwTwhgPolPSeAL7wXiIadETAgjcIgB1tHgNlCYANiBjpCATAkQrP2PYJWDiY2VcpxQm//FLTjhimzjdeMdcik9VNI2tO1ALDD9Ohg6tVU5NiP54sjS88EYI2vCOAN7yThIY8IIAvPBCBFrwkYMEbBMBebq0gmiIADkImmmyFAAEw2wICRglYOJgZlSbbsV58UVP3P1RdX39urnX/o+V1+hIXaa/xG2uzzbJtL+tq+CJr4tQLhQDeCEUp+sySAL7Ikja1QiJgwRsEwCHtOL96JQD2Sw+6KZ4AAXDxrLgSAkERsHAwCwq4z802Nan5uus17bAh6vL953Pt9Eb11aNbnqXjRi+h3/zG54HK7w1flM+OO20TwBu29WW68gjgi/K4cZd9Aha8QQBsf5+mNSEBcFpk01+3g6TlJf1Z0ur5f64sqXO+9FWS9k2wje6SNpe0kaTVJPWSNJ+kaZJyv5W/IOl6SQ9Iak6w7tyWIgDOADIlIOCCgIWDmQtupmtOmaL6U0ap4uyzVNU4vdVRp6irRlafqK4nHqXBR1WoY0dbRPCFLT2ZJjkCeCM5lqxkhwC+sKMlkyRLwII3CICT3RMxrUYAHK7at0naaR7tJxkAHynpNEmdisD1pKS9JH1cxLXtuYQAuD30uBcCHhOwcDDzGG/YrX3wgX4adIS6PHxXq3Ncr37aU9erVy/pggukPn3CHnfO7vGFHS2ZJFkCeCNZnqxmgwC+sKEjUyRPwII3CICT3xexrEgAHK7Sd0jafo72v5P0bf6dubk/TjIAHi1pUL7WF5Ieyb/j96t8KLxWPvTtlr/mQ0m5P8t9P60XAXBaZFkXAo4JWDiYOUZov/y99+qnAYeqy5cfzJ4198Fwy+ptfaHFZ//ZDjtI554r/fa34SPBF+FryATpEMAb6XBl1bAJ4Iuw9aP79AhY8AYBcHr7w/rKBMDhKjxMUu6xDC/n/5cLXXOPfJj1selJBsCXSvqDpLMlPSSpsRVsS+Uf/7Bs/nu5PvqniJcAOEW4LA0BlwQsHMxc8oumdm2tGkacoeaRI1XdUKvBOkvnaHDB+J06ScOGSUcfLeX+PdQXvghVOfpOmwDeSJsw64dIAF+EqBo9Z0HAgjcIgLPYKTZrEADb0jWtAHgBSbl3GLf1yj2D+NX8RT9JWkhS7p9pvAiA06DKmhDwgICFg5kHGONp4cMPNfnkczXgx7N18+3Vc517k6Xe09Bxf9Cmm4aJBl+EqRtdp08Ab6TPmArhEcAX4WlGx9kQsOANAuBs9orFKgTAtlRNKwAuhdJbkma9CzgXCP+rlJtLuJYAuARYXAqBkAhYOJiFxNtSrw89JB1yiPT22y2nWk0v6wWtoeu1h57e4SwNv3QRLbpoWJPji7D0otvsCOCN7FhTKRwC+CIcreg0WwIWvEEAnO2esVSNANiSmuk9AqIUSi9I6p2/Ifcc4OdLubmEawmAS4DFpRAIiYCFg1lIvK31Wlc387m/p5wiTZ0qVapRz2kt9dZLM0b9XvPppE6jtMyZAzXowEp16BAGAXwRhk50mT0BvJE9cyr6TwBf+K8RHbohYMEbBMBu9o6FqgTAFlT8eQbX7wCuyX/wW898S4tJ+jIlxATAKYFlWQi4JmDhYOaaIfWlTz+VBg+WFrz5Yl2sgwuQPKu1dNEKozX46pW12mr+E8MX/mtEh24I4A033KnqNwF84bc+dOeOgAVvEAC72z+hVyYADl3Blv27DoDnrP9PSauniJcAOEW4LA0BlwQsHMxc8qP2HASamjRp2d7q8V7uR1Lhq0EddL4O15cHDNfxp3dTjx7+0sMX/mpDZ24J4A23/KnuJwF84acudOWegAVvEAC730ehdkAAHKpyrfftMgDOfeDbvyUtnG9tJ0m3l4k3F+629VpR0pjcRRMnTlTv3rOeOtHWbX59f9KkSXrttddmNLXyyiurh8/pg1/o6MYwAXxhWFwXo02bpqozz1WXCy9QVVN9qx18oiV0fM/ztf7Zm2n7HepV4eHpCF+42DzUDIEA3ghBJXrMmgC+yJo49UIhYMEbL774ovr06TML+dqSng2FP326JeDhrzhugQRe3VUAnHv0w8OS1svzu0PSju1g2VzKvaNGjdJyyy1Xyi1cCwEIQAACkRHo9sknWu6Csfr1u3P/bNK7tK0uW+EEbXvw91pssZ8iI8S4EIAABCAAAQhAAAK+E3jrrbc0dOhQAmDfhfKwPwJgD0VpR0suAuBKSVdL2jPf9/v5D4H7vh1zEAC3Ax63QgACEIDAXAg0N2uJxx7XsmOvUrdpP7R60VR10amVx+vzvltqu50/UlVVST+SQA8BCEAAAhCAAAQgAIHUCBAAp4bW/MIEwLYkzjoAzu2fyyQNzGP8WNIGkv7bTqw8AqKdALkdAiETsPBXs0LmH0PvFd99pw7HnqweN18z13Ff1coaufSl2n/0H/XnPzc6x4IvnEtAA54SwBueCkNbTgngC6f4Ke4xAQve4BEQHm8wz1sjAPZcoBLbyzIAzu2dSyQdkO/xU0kbSsq9AziLFx8ClwVlakDAAQELH87gABslyyHw9NP6aZ8D1OX93CPsC1+NqtSpOl7fHTpcp54qde9eTpFk7sEXyXBkFXsE8IY9TZmo/QTwRfsZsoJNAha8wYfA2dybWUxFAJwF5exqZBUA5/bNxZIOzI/2WT78fS+7UUUAnCFsSkEgSwIWDmZZ8qJWOwnU16vxnPPUdMJwVdcVPvd3oMZonAZqySWlSy+Vtt66nfXKvB1flAmO28wTwBvmJWbAMgjgizKgcUsUBCx4gwA4iq2aypAEwKlgdbZoFgHwL8Pfz/Ph77sZT00AnDFwykEgKwIWDmZZsaJOggQ++kjTBhyszg/fM3vRx7WBNtajalbucfczX337SuefLy2ySIK1i1gKXxQBiUuiJIA3opSdodsggC/YIhBonYAFbxAAs7vLJUAAXC45P+9LOwD+Zfj7RT78fccBDgJgB9ApCYEsCFg4mGXBiRopEGhuVvMtt2r6oEOkH37QSvqX3tUyBYXmn1866yxpv/2kioxOUvgiBb1Z0gQBvGFCRoZImAC+SBgoy5khYMEbBMBmtmPmg2T0a0vmc8VaMO0AOPfYh4PycL/Mh79vO4JNAOwIPGUhkDYBCweztBmxfsoEvv9eUx56VoMf2UpjxrReq4MatOtan+rkq3+rXr1S7kcSvkifMRXCJIA3wtSNrtMlgC/S5cvq4RKw4A0C4HD3n+vOCYBdK5Bs/XIC4DnveSIf6rbW1YWSDvYk/M21QQCc7N5hNQh4Q8DCwcwbmDTSbgL/+If0179Kb//iP3cerTM0XMN1StXJ6nni4Ro8pErV1e0uN9cF8EV6bFk5bAJ4I2z96D4dAvgiHa6sGj4BC94gAA5/H7qagADYFfn2111a0v6/WGYlSdvm/+xfku7+xfcflZT735yvYgLgUyUdm7+pWdIwSW8VMcI/JX1cxHXlXEIAXA417oFAAAQsHMwCwEyLJRCorZVGjJBGjZLq66Xf6z29rj+ps2pnrPKyVtMZvcZp8LWrao01Sli4hEvxRQmwuDQqAngjKrkZtkgC+KJIUFwWHQEL3iAAjm7bJjYwAXBiKDNfaENJj5VY9SRJw39xTzEB8OOSNiixVu7y/SRdWcZ9xdxCAFwMJa6BQIAELBzMAsROy0UQeOMNaeCAZp383GbaVI+0uKNBHXSujtS3h56kE0Z2VpcuRSxYwiX4ogRYXBoVAbwRldwMWyQBfFEkKC6LjoAFbxAAR7dtExuYADgxlJkvRAAsPZOj/swzz+j//i+XB4f3svADKDzqdOw7AXzhu0Jx99f0xptqXK23quumtgriHfXSCb8er7/dsK7WWy85VvgiOZasZIsA3rClJ9MkQwBfJMORVewRsOANAmB7+zKriQiAsyJNnaQJ8A7gpImyHgQ8IWDhYOYJStpIi8BHH2nafgeq82P3t1qhSRW6UIfo40EjdNJZXdWtW/sbwRftZ8gKNgngDZu6MlX7COCL9vHjbrsELHiDANju/kx7MgLgtAmzfloECIDTIsu6EHBMwMLBzDFCymdBoLlZzTfcqOkHHqZOk75uteIHWlrHLnK5Bly3kTbZpH1N4Yv28eNuuwTwhl1tmax8AviifHbcaZuABW8QANveo2lORwCcJl3WTpMAAXCadFkbAg4JWDiYOcRH6awJfPutph98pDreePVcK1+qA/TmvmfolPO6q2fP8hrEF+Vx4y77BPCGfY2ZsHQC+KJ0ZtwRBwEL3iAAjmOvpjElAXAaVFkzCwIEwFlQpgYEHBCwcDBzgI2Srgnce69q9xmkTt9+1monH+k3GrbgWO119ebacsvSm8UXpTPjjjgI4I04dGbK0gjgi9J4cXU8BCx4gwA4nv2a9KQEwEkTZb2sCBAAZ0WaOhDImICFg1nGyCjnC4Eff1TdoUep5upxc+1onPbXy/3O1oiLe2r++YtvHF8Uz4or4yKAN+LSm2mLI4AviuPEVfERsOANAuD49m1SExMAJ0WSdbImQACcNXHqQSAjAhYOZhmhooyvBB56SNP2GqDOX31c0GGtOmpVvaIfFl1eo0dL229f3BD4ojhOXBUfAbwRn+ZM3DYBfNE2I66Ik4AFbxAAx7l3k5iaADgJiqzhggABsAvq1IRABgQsHMwywEQJ3wlMnqz6I4eoetylLTodolE6Q0Nm/9nuu0sXXigtuOC8B8IXvgtOf64I4A1X5KnrMwF84bM69OaSgAVvEAC73EFh1yYADlu/mLsnAI5ZfWY3TcDCwcy0QAxXGoHHHpv5buDPP9AL6q219YwaVdVijYUW0ox3A++009yXxhelYefqeAjgjXi0ZtLiCeCL4llxZVwELHiDADiuPZvktATASdJkrSwJEABnSZtaEMiQgIWDWYa4KBUCgalTVT/0eJ0/ub+GXLOimppab3rPPWe+G7i1ZwPjixCEpkcXBPCGC+rU9J0AvvBdIfpzRcCCNwiAXe2e8OsSAIevYawTEADHqjxzmydg4WBmXiQGLJvAc89J/ftLb775yyWadZkG6YkFdtJe1/bRllu2/D6+KBs5NxongDeMC8x4ZRHAF2Vh46YICFjwBgFwBBs1pREJgFMCy7KpEyAATh0xBSDghoCFg5kbclQNhUBtrXTKKdLpp0uNjTO73ldX6Ar1n/HvozVIb+xzpkZc2F3du8/8Pr4IRV36zJoA3siaOPVCIIAvQlCJHl0QsOANAmAXO8dGTQJgGzrGOAUBcIyqM3MUBCwczKIQiiHbTeCll6S//EWa+uZHel1/Ug9Nnr3mB1pawxa9QgfcsIE23JAAuN2wWcAsAX5mmJWWwdpBAF+0Ax63miZgwRsEwKa3aKrDEQCnipfFUyRAAJwiXJaGgEsCFg5mLvlROywCuXcDf7zc5lrmo4cKGm9Shc7T4fr8oNN0+N+n6eWXn5xxzXrrracFFlggrEHpFgIpEeBnRkpgWTZoAvgiaPloPkUCFrxBAJziBjG+NAGwcYENj0cAbFhcRoubgIWDWdwKMn3JBJ58UtN231edP/+g1Vvf0rIa9usrtOYh9Vpuue8JgEsGzA2WCfAzw7K6zFYuAXxRLjnus07AgjcIgK3v0vTmIwBOjy0rp0uAADhdvqwOAWcELBzMnMGjcLgEpkxR/eFHq/ry0a3O0KAOOl1D9PqO/XTuxYtrscV4B3C4YtN5kgT4mZEkTdayQgBfWFGSOZImYMEbBMBJ74p41iMAjkdra5MSAFtTlHkgkCdg4WCGmBAom8ADD6h2r/3V6ZvPWl3iVa2s4UuP14m3raZVVy27CjdCwAwBfmaYkZJBEiSALxKEyVKmCFjwBgGwqS2Z6TAEwJnipliCBAiAE4TJUhDwiYCFg5lPPOklQAI//KC6Aw9TzY1Xt9p8nap1UuVJ6nz80Rp6XJWqqgKckZYhkBABfmYkBJJlTBHAF6bkZJgECVjwBgFwghsisqUIgCMT3NC4BMCGxGQUCMxJwMLBDEUhkAiBO+7Q9H0HqeOPX7W63HNaU2evfI1G3tpLf/hDIhVZBALBEeBnRnCS0XAGBPBFBpApESQBC94gAA5y63nRNEG4yLEAACAASURBVAGwFzLQRBkECIDLgMYtEAiBgIWDWQic6TEQAl9/ren9D1DHeya02vBUddFBHcdr3Qv7asAAqYKTXSDC0mZSBPiZkRRJ1rFEAF9YUpNZkiRgwRsEwEnuiLjW4teEuPS2NC0BsCU1mQUCcxCwcDBDUAgkSqC5WVPGjlXloUepy/TJLZZuVKXW1z/0jNbRdttJY8dKCy+caHUWg4DXBPiZ4bU8NOeIAL5wBJ6y3hOw4A0CYO+3mbcNEgB7Kw2NtUGAAJgtAgGjBCwczIxKw1gOCeR88eIdd2iF8y7VEq+/NLuTU3Wsjteps7/Ohb/jx0tbb+2wWUpDIEMC/MzIEDalgiGAL4KRikYzJmDBGwTAGW8aQ+UIgA2JGdkoBMCRCc648RCwcDCLRy0mzYrAbF80N2uT995Xx2HH61+NK2itpmfUoOqCNgYNks4+W+raNasOqQMBNwT4meGGO1X9JoAv/NaH7twRsOANAmB3+yf0ygTAoSsYb/8EwPFqz+TGCVg4mBmXiPEcECjwxTff6MP/Vqjvcb304outN9TrD8269roKrbGGg4YpCYGMCPAzIyPQlAmKAL4ISi6azZCABW8QAGe4YYyVIgA2JmhE4xAARyQ2o8ZFwMLBLC7FmDYLAnPzRX29dOqp0mmnSY2NLTsZp/31UcXSqj5+qP5+fJWqqrLolBoQyJYAPzOy5U21MAjgizB0osvsCVjwBgFw9vvGSkUCYCtKxjcHAXB8mjNxJAQsHMwikYoxMyTQli+ee07aay/p/fdnNrWrbtbN6jvj35/R/+mcVa7RqFt+rz/8IcOmKQWBDAi05Y0MWqAEBLwjgC+8k4SGPCFgwRsEwJ5spgDbIAAOUDRankGAAJiNAAGjBCwczIxKw1gOCRTjiylTpCOOkO4f96n+pZW0gL6f3fFkddMxNedr9Yv20/4DKlTBCdChmpROkkAx3kiyHmtBIAQC+CIElejRBQEL3iAAdrFzbNTk+G9DxxinIACOUXVmjoKAhYNZFEIxZKYESvHFZ+vupl8/fUur/U3Qjrq9zxide82CWnDBTEegGARSIVCKN1JpgEUh4CEBfOGhKLTkBQEL3iAA9mIrBdkEAXCQstE07wBmD0DALgELBzO76jCZKwIl+eKLLzR9z/7q+NjEVtv9QovqiAWu1sCbN9Mmm7iaiLoQSIZASd5IpiSrQMB7AvjCe4lo0BEBC94gAHa0eQyUJQA2IGKkI/AO4EiFZ2z7BCwczOyrxIRZEyjZF83Nar7kUjUeMVhV9bWttnumjtYPR52qE0+rUU1N1hNRDwLJECjZG8mUZRUIeE0AX3gtD805JGDBGwTADjdQ4KUJgAMXMOL2CYAjFp/RbROwcDCzrRDTuSBQti/eeku1u+ylTm+83GrbL+rPGrni9Trj9l58QJwLYanZbgJle6PdlVkAAv4SwBf+akNnbglY8AYBsNs9FHJ1AuCQ1Yu7dwLguPVnesMELBzMDMvDaI4ItMsXdXVqPPFkVZw+UpXNTQUTTFFXDe54sdYe/Rf9ZR8+IM6RxJQtk0C7vFFmTW6DgO8E8IXvCtGfKwIWvEEA7Gr3hF+XADh8DWOdgAA4VuWZ2zwBCwcz8yIxYOYEEvHFP/6h2l32VKevP221/xu0ux7aabTOHd9TPXtmPiIFIVAWgUS8UVZlboKAvwTwhb/a0JlbAha8QQDsdg+FXJ0AOGT14u6dADhu/ZneMAELBzPD8jCaIwKJ+eK771S3z0DV3DOh1Uk+1G915KI36JgJa+n/cj9peUHAcwKJecPzOWkPAqUQwBel0OLamAhY8AYBcEw7NtlZCYCT5clq2REgAM6ONZUgkCkBCwezTIFRLAoCifoi9wFxY8ep8eDDVFU/rYDflrpPD3XYUieeKA0bJnXoEAVihgyUQKLeCJQBbUPglwTwBXsCAq0TsOANAmB2d7kECIDLJcd9rgkQALtWgPoQSImAhYNZSmhYNmICqfjizTdVu1M/dXrrtdlkz9XhOlLnzv56vfWka6+VfvObiOEzutcEUvGG1xPTHATaJoAv2mbEFXESsOANAuA4924SUxMAJ0GRNVwQIAB2QZ2aEMiAgIWDWQaYKBEZgdR8UVurhqOGqOriC/SqVtaael516tiC7nzzSWPHSrvsEhl0xg2CQGreCGJ6moRA6wTwBTsDAna9QQDM7i6XAAFwueS4zzUBAmDXClAfAikR4JeWlMCybNAEUvfFvffqH5/+TruesLy++qp1VAMGSOefL3XpEjRKmjdGIHVvGOPFOHEQwBdx6MyUpROw4A0C4NJ1546ZBAiA2QmhEiAADlU5+oZAGwQsHMwQGQJJE8jKF//7n7TvvtLEiYUTHKwL9cnvN9Jpd66oFVZIekLWg0B5BLLyRnndcRcE3BDAF264U9V/Aha8QQDs/z7ztUMCYF+Voa+2CBAAt0WI70MgUAIWDmaBoqdtjwlk6YumJumCC6QhQ6S6uplQttBETdSWmqZOGlx9of586f7ar3+FKjhJerxr4mgtS2/EQZQpLRDAFxZUZIY0CFjwBgFwGjsjjjU5tsehs8UpCYAtqspMEJBk4WCGkBBImoALX7zyitSvnzTp7c/1mlbWQvpm9ljXq58e3XW0zr28h7p3T3pa1oNA8QRceKP47rgSAm4I4As33KnqPwEL3iAA9n+f+dohAbCvytBXWwQIgNsixPchECgBCwezQNHTtscEXPli6uQmfbLMJlruy8cL6LyrP+ioJW7S8LtW06qregyP1kwTcOUN01AZLngC+CJ4CRkgJQIWvEEAnNLmiGBZAuAIRDY6IgGwUWEZCwIWDmaoCIGkCTj1xfXXq37/QaqunVIw1nTVaEiHs/WHc/+mvx3MIyGS1p312ibg1Bttt8cVEHBCAF84wU7RAAhY8AYBcAAbzdMWCYA9FYa22iRAANwmIi6AQJgELBzMwiRP1z4TcO6Ld99V7fa7qdObr7aKaYJ21O1bX64Lrplf88/vM0l6s0bAuTesAWUeEwTwhQkZGSIFAha8QQCcwsaIZEkC4EiENjgmAbBBURkJAjkCFg5mKAmBpAl44YvaWjUccbSqRl/U6nj/1VI6YtEbNeT2tbTWWkkTYD0ItE7AC28gDgQ8I4AvPBOEdrwhYMEbBMDebKfgGiEADk4yGs4TIABmK0DAKAELBzOj0jCWQwJe+WLCBNXt3V81P/1YQKReVTq2cqQWPO1IHXVMpSorHUKjdBQEvPJGFMQZMgQC+CIElejRBQEL3iAAdrFzbNQkALahY4xTEADHqDozR0HAwsEsCqEYMlMC3vniww9Vu+Pu6vTaC61yuFdb6eqNr9JFNy6ohRbKFBXFIiPgnTci48+4fhLAF37qQlfuCVjwBgGw+30UagcEwKEqR98EwOwBCBglYOFgZlQaxnJIwEtf1NWp8e/HqsM5Z7VK5jMtrkN+dYMOu219bbCBQ3iUNk3AS2+YJs5wIRDAFyGoRI8uCFjwBgGwi51joyYBsA0dY5yCADhG1Zk5CgIWDmZRCMWQmRLw2hf33qu6PfZRzaRvC5g0qlKrVrym3U9dUUOHikdCZLpr4ijmtTfikIApPSSALzwUhZa8IGDBGwTAXmylIJsgAA5SNpqWRADMNoCAUQIWDmZGpWEshwS898Wnn2r6Lnuo4/NPtqB0ufprgC6f8Wdbbildc430q185BElpcwS894Y54gwUAgF8EYJK9OiCgAVvEAC72Dk2ahIA29AxxikIgGNUnZmjIGDhYBaFUAyZKYEgfNHQoKbhJ0unnapKNesN/VFr6AX9pK6zWS25pHTzzdJaa2WKj2KGCQThDcP8Gc1PAvjCT13oyj0BC94gAHa/j0LtgAA4VOXomwCYPQABowQsHMyMSsNYDgkE5YtHHtG0vQdqy7q79MS3KxZQq66WzjxTOvRQqYKTqMNdZaN0UN6wgZwpAiCALwIQiRadELDgDQJgJ1vHRFGO3SZkjHIIAuAoZWfoGAhYOJjFoBMzZksgOF80NOiLr6vUr5/0xBOts9p1p0aNHd9BPXtmy5JqtggE5w1b+JnGUwL4wlNhaMs5AQveIAB2vo2CbYAAOFjpom+cADj6LQAAqwQsHMysasNc7giE6ouGBumEE6SRI1uyW0vParz6a+iS1+uku1bVKqu4Y0vlsAmE6o2wqdO97wTwhe8K0Z8rAha8QQDsaveEX5cAOHwNY52AADhW5ZnbPAELBzPzIjFg5gRC98W990p77y19/730K32jV7SqltSnqlVHDa66QKtcPFADBlbwSIjMd1b4BUP3RvgKMIGPBPCFj6rQkw8ELHiDANiHnRRmDwTAYepG1xIBMLsAAkYJWDiYGZWGsRwSsOCLjz6Sdt+tSSe+sJX66IEWNK/RXnpi99E6f1xXdf35M+McEqd0KAQseCMU1vQZDgF8EY5WdJotAQveIADOds9YqkYAbEnNuGYhAI5Lb6aNiICFg1lEcjFqRgSs+KL+vodUvfXmrVJ7Q3/U0N/fqjPuXl7LL58RWMoET8CKN4IXggG8IoAvvJKDZjwiYMEbBMAebajAWiEADkww2p1NgACYzQABowQsHMyMSsNYDgmY8sVtt6l+7/6qnjapgOgUddUhHcdos/F7aI89HAKndDAETHkjGOo06jsBfOG7QvTnioAFbxAAu9o94dclAA5fw1gnIACOVXnmNk/AwsHMvEgMmDkBc7547z1N33YXdXzrtVZZXqID9ebAc3XWhR3VsWPmuCkYEAFz3giIPa36SwBf+KsNnbklYMEbBMBu91DI1QmAQ1Yv7t4JgOPWn+kNE7BwMDMsD6M5ImDSF9OmqeGgQ1V15bhWqb6k1XXKyrfporuX0pJLOgJPWe8JmPSG99Rp0HcC+MJ3hejPFQEL3iAAdrV7wq9LABy+hrFOQAAcq/LMbZ6AhYOZeZEYMHMCpn1x1VVq+OuBqqqbVsD1Wy2gA3rcoAMmbK5NNskcOwUDIGDaGwHwp0U/CeALP3WhK/cELHiDANj9Pgq1AwLgUJWjbwJg9gAEjBKwcDAzKg1jOSRg3hevv67p2+2qjv99u4Bykyp0QsUp6nbq3zXk75Wq4PTqcCf6V9q8N/xDTkcBEMAXAYhEi04IWPAGAbCTrWOiKEdoEzJGOQQBcJSyM3QMBCwczGLQiRmzJRCFLyZPVv2+A1U94aZW4d6lbXXTVlfrkuvnU8+e2fKnmr8EovCGv/jpzFMC+MJTYWjLOQEL3iAAdr6Ngm2AADhY6aJvnAA4+i0AAKsELBzMrGrDXO4IROOL5mY1n3+BmgYfpQ5NDQXAx2igzuo1RhMmSCuu6E4PKvtDIBpv+IOcTgIggC8CEIkWnRCw4A0CYCdbx0RRAmATMkY5BAFwlLIzdAwELBzMYtCJGbMlEJ0vnnxS03fYTR2/+3I26P9qKa2ul/WdfqUuXaRx46R+/bLVgWr+EYjOG/5JQEceEsAXHopCS14QsOANAmAvtlKQTRAABykbTUsiAGYbQMAoAQsHM6PSMJZDAlH64osvZobALzylWnXUOnpa/9TqLVQ49FDpzDOlmhqH4lDaKYEoveGUOMVDIIAvQlCJHl0QsOANAmAXO8dGTQJgGzrGOAUBcIyqM3MUBCwczKIQiiEzJRCtL+rr1XjUEN38xgra45H9W2W+9trSLbdIiy+eqSQU84RAtN7whD9t+EkAX/ipC125J2DBGwTA7vdRqB0QAIeqHH0TALMHIGCUgIWDmVFpGMshAXwh3XCDNGCA9NNPhUL8euF6XXdztTbYwKFIlHZCAG84wU5RzwngC88Foj1nBCx4gwDY2fYJvjABcPASRjsAAXC00jO4dQIWDmbWNWK+7Angi5nM//1vaaedpHff/VmDrpqip7WOrqvYW4ucMVhHDq5QBSfc7Depo4p4wxF4ynpNAF94LQ/NOSRgwRsEwA43UOClOR4HLmDE7RMARyw+o9smYOFgZlshpnNBAF/8TP3HH6V99pHuvDP3Z826Qf20u26accEt2kX37Dhel1zTXV27ulCKmlkTwBtZE6deCATwRQgq0aMLAha8QQDsYufYqEkAbEPHGKcgAI5RdWaOgoCFg1kUQjFkpgTwRUvcTU3SGWdI/xt2ns5tPqLFN/+j5TVkmTt03n3L6Pe/z1QmijkggDccQKek9wTwhfcS0aAjAha8QQDsaPMYKEsAbEDESEcgAI5UeMa2T8DCwcy+SkyYNQF80QrxqVNVu9Qy6vTt5wXf/EE9Najbdep/69baYous1aJelgTwRpa0qRUKAXwRilL0mTUBC94gAM5619ipRwBsR8vYJiEAjk1x5o2GgIWDWTRiMWhmBPDFXFB/8ommb7+rOr7yfMEFTarQiRUnq9tpw3TM0EqeC5zZbs22EN7IljfVwiCAL8LQiS6zJ2DBGwTA2e8bKxUJgK0oGd8cBMDxac7EkRCwcDCLRCrGzJAAvpgH7OnT1XjI4eowdnSrF03Qjrpjh6tmPBe4W7cMRaNUJgTwRiaYKRIYAXwRmGC0mxkBC94gAM5su5grRABsTtJoBiIAjkZqBo2NgIWDWWyaMW/6BPBF24ybx12upgMPUoeGuoKLc88FPqbXzOcC/+EPba/FFeEQwBvhaEWn2RHAF9mxplJYBCx4gwA4rD3nU7cEwD6pQS+lECAALoUW10IgIAIWDmYB4abVQAjgiyKFeu45Td92Z3X8pvC5wD+qhwZ1vU773rqN+vQpcj0u854A3vBeIhp0QABfOIBOySAIWPAGAXAQW83LJgmAvZSFpoogQABcBCQugUCIBCwczELkTs9+E8AXJejz5Zeavt0u6vji0wU35Z4LPFwnqfOpx2roMJ4LXAJVby/FG95KQ2MOCeALh/Ap7TUBC94gAPZ6i3ndHAGw1/LQ3DwIEACzPSBglICFg5lRaRjLIQF8USL8urqZzwUec2mrN96uHTRh+6t16bU8F7hEst5djje8k4SGPCCALzwQgRa8JGDBGwTAXm6tIJoiAA5CJppshQABMNsCAkYJWDiYGZWGsRwSwBdlwr/8cjUe0PpzgZ/SOjrgj0/qjjsreC5wmXh9uA1v+KACPfhGAF/4pgj9+ELAgjcIgH3ZTeH1QQAcnmZ0PJMAATA7AQJGCVg4mBmVhrEcEsAX7YD//POavs1OLZ4L3KhKban79ZA213zzSddfL225ZTtqcKszAnjDGXoKe0wAX3gsDq05JWDBGwTATrdQ0MUJgIOWL+rmCYCjlp/hLROwcDCzrA+zuSGAL9rJ/RfPBT5Gp+tMHTN70YoK6bTTpKFDpdy/8wqHAN4IRys6zY4AvsiONZXCImDBGwTAYe05n7rliOuTGvRSCgEC4FJocS0EAiJg4WAWEG5aDYQAvkhAqPxzgV95+Fv1/uBGSYXH4N13ly6/XOrSJYF6LJEJAbyRCWaKBEYAXwQmGO1mRsCCNwiAM9su5goRAJuTNJqBCICjkZpBYyNg4WAWm2bMmz4BfJEg44YGjRlfpYMPlurrC9ddbTXpjjukJZdMsCZLpUYAb6SGloUDJoAvAhaP1lMlYMEbBMCpbhHTixMAm5bX9HAEwKblZbiYCVg4mMWsH7OnQwBfJM/12WelnXeWvvii5dpDNErvzL+WjrpnQ629dvJ1WTFZAngjWZ6sZoMAvrChI1MkT8CCNwiAk98XsaxIAByu0h0kLS/pz5JWz/9zZUmd8yNdJWnflMbbTtLeknpLWlTSJEnvSbpd0mX5r1MqPXtZAuC0CbM+BBwRsHAwc4SOsoYJ4It0xP38c2nHHaUXXpi5/u66QTdoD9WrSkdUXqDVxh6o/v3Tqc2qyRDAG8lwZBVbBPCFLT2ZJjkCFrxBAJzcfohtJQLgcBW/TdJO82g/jQC4m6TrJOUC4Lm9PpG0m6TnUkZLAJwyYJaHgCsCFg5mrthR1y4BfJGetrW10l//Kr1xzct6Suuqs2pnF7tUB+idv12gM8+rVlVVej2wcvkE8Eb57LjTLgF8YVdbJmsfAQveIABu3x6I+W4C4HDVv0PS9nO0/52kbyX1yv9Z0gFw7h3H90jqk1//f5LGSvqPpAUk9ZO0Tv573+f//c0U8RIApwiXpSHgkoCFg5lLftS2SQBfpKtr8/Q6TVpsWfX8/r8FhR7XBrpwvVs05vaF9KtfpdsHq5dOAG+Uzow77BPAF/Y1ZsLyCFjwBgFwedpzV2sffwyVUAgMk9Rd0sv5/32Yf+TDFSkFwIMkjc6vnQt9N5aUC4HnfJ0laXD+D56UtH6KMAmAU4TL0hBwScDCwcwlP2rbJIAvMtD1kUdUt8OuqpmS++/YLV//1VI66Nd36cwHVtIKK2TQCyWKJoA3ikbFhRERwBcRic2oJRGw4A0C4JIk5+I5CPAOYFvbIffM3zQC4Ny7f3OPdlgsjyv3zOF/toIud91LklbJf28LSQ+mhJgAOCWwLAsB1wQsHMxcM6S+PQL4IiNN339f0/tsp47v5f5bd8vXFHXVoE5Xq+9NO2m7eT0MK6NWKTOTAN5gJ0CgkAC+YFdAoHUCFrxBAMzuLpcAAXC55Py8L60AeCNJj+ZHfkLShvMYfz9J4/Pfv1JS7us0XgTAaVBlTQh4QMDCwcwDjLRgjAC+yFDQSZNU33cvVU+8u9WiwzVc1Scfr2HHVaqCk3SGwtj9Zd45RBowR4CfGeYkZaCECFjwBgFwQpshwmU4ttoSPa0A+AxJR+dRDZGU+3pur0UlfZH/Zu4REbmv03gRAKdBlTUh4AEBCwczDzDSgjEC+CJjQZua1HTcCaoceVqrhW/Vzrp7pyt16TXd1KVLxr1RrgUBvMGGgEAhAXzBroBA6wQseIMAmN1dLgEC4HLJ+XlfWgHw/XN8+Fvu2b+PtTH+x5KWzF+zsKSvU8BFAJwCVJaEgA8ELBzMfOBID7YI4AtHet54oxr26a+qumkFDbymlTTsj3dq9MTfaslZpx5HbcZcFm/ErD6zz40AvmBvQIAAmD0AgV8SIAC2tSfSCoA/kLR0HlXun4Ufkd2SY+4xEbM+AG49SU+ViDkX7rb1WlHSmNxFEydOVO/evdu63svvT5o0Sa+99tqM3lZeeWX16NHDyz5pCgJZEsAXWdKmVigE8IU7pTq8+qo67v4Xdfr6s4ImvtaC2n++W3XwTavoz39udNdkxJXxRsTiM/pcCeALNgcEWidgwRsvvvii+vTpM2vAtSU9i94QKIYAAXAxlMK5Jq0A+DtJ8+cxdJc0pQ0kEyTtmL9mW0n3lIiwuZTrR40apeWWW66UW7gWAhCAAAQgAAEIFE2g4/ffa9XTztQirXw4XJ2qdWCH0ZrvsJW0/vqFIXHRRbgQAhCAAAQgAAEItEHgrbfe0tChQwmA2SklEyAALhmZ1zekFQDXSarOT577Z0MbFK6TtEf+mtw/byiRGgFwicC4HAIQgAAEIACBdAlU1tdrxUsu09KPPVxQ6HL11wBdrr5931Lfvm+rsjLdXlgdAhCAAAQgAIE4CRAAx6l7ElMTACdB0Z81rATAPALCnz1FJxDInICFv5qVOTQKmieALzyRuLlZHceMUedjj1Nlc9OMpp7Q+tpMD6leNTO+3n776brooql8OFxGkuGNjEBTJigC+CIouWg2QwIWvMEjIDLcMMZKEQDbEjStADjrR0AUowofAlcMJa6BQIAE+OCSAEWj5dQJ4IvUEZdW4MEHVb/Tbvrkp19pjebn9a0WbHH/n/8s3XmntPjipS3L1aUTwBulM+MO+wTwhX2NmbA8Aha88eyzz2rttXOP/p3x4hnA5W2FKO8iALYle1oBcNYfAleMKgTAxVDiGggESMDCwSxA7LTsOQF84aFAb72ld95u1uaHLa+PPirsLxf+3nWXtPrqHvZuqCW8YUhMRkmMAL5IDCULGSNgwRsEwMY2ZYbjEABnCDuDUmkFwPdLmvUxkxtLeqyNWT6WtGT+moUlfZ3C7ATAKUBlSQj4QMDCwcwHjvRgiwC+8FfPr76SdthBeraVz+BetNMPuvCa+bTLLv72H3pneCN0Bek/DQL4Ig2qrGmBgAVvEABb2IluZiAAdsM9rappBcBnSDo63/QQSbmv5/ZaRNKX+W9+JSn3dRovAuA0qLImBDwgYOFg5gFGWjBGAF/4LWhtrTRwoHTttT/3uYQ+0fNaU+M0QJUnDdexx1eqgpN34kLijcSRsqABAvjCgIiMkAoBC94gAE5la0SxKMdQWzKnFQDn3vX7SB7V45I2mge2/SSNz3//Skm5r9N4EQCnQZU1IeABAQsHMw8w0oIxAvjCf0Gbm6VRo6Rhw6SumqIntZ5W1aszGr9Zu+r+3a7UpVd1UadO/s8SUod4IyS16DUrAvgiK9LUCY2ABW8QAIe26/zplwDYHy2S6CStALiDpE8lLZpvMvc0u3+20nDuupckrZL/Xu6xEQ8kMVgraxAApwSWZSHgmoCFg5lrhtS3RwBfhKPphFubVLX7ztqu8Y4WTb+oP+vk1e7UuPsW1yJp/f2ocDAl1ineSAwlCxkigC8MickoiRKw4A0C4ES3RFSLEQDbkrucAHjOe56QtOFckBwo6ZL8996QlHtXcO4RD3O+zpR0VP4Pnpa0bop4CYBThMvSEHBJwMLBzCU/atskgC8C0vWzzzS99zrq+EXhJ8N9psX110Xu0ogHVtfKKwc0k8et4g2PxaE1ZwTwhTP0FPacgAVvEAB7vsk8bo8A2GNx2mhtaUn7/+KalSRtm/+zf0m6+xfff1RS7n9zvooNgKsk3Sdps/zNuef8jpX0H0kLSOo3R+D7Q/7fc0FxWi8C4LTIsi4EHBOwcDBzjJDyBgngi8BE/eor1W21g2peLvxkuJ/UWQM7XqO+N++s7bYLbC4P28UbHopCS84J4AvnEtCApwQseIMA2NPNFUBbBMABiDSXFnPv1H2sxPZPkjS8zAA4d1t3SddL2mYedXOPiugrEq8wDAAAIABJREFU6ZkSeyv1cgLgUolxPQQCIWDhYBYIatoMiAC+CEisWa3W1qqh/0BV3TDHJ8PNMcZxOlU9Rg3T0cdU8OFw7ZAXb7QDHreaJYAvzErLYO0kYMEbBMDt3AQR304AHK74LgLgWbS2l/QXSb0lLSxpsqT3JU2QdJmkHzPASgCcAWRKQMAFAQsHMxfcqGmbAL4IVN/mZjWPGKmK445tdYBrtaee2mecLhzbSdXVgc7ouG284VgAyntJAF94KQtNeUDAgjcIgD3YSIG2QAAcqHC0LQJgNgEEjBKwcDAzKg1jOSSALxzCT6L0hAlq2GNvVU3/qWC1p7SOzlnvDo2/a0HNN18SxeJaA2/EpTfTFkcAXxTHiaviI2DBGwTA8e3bpCYmAE6KJOtkTYAAOGvi1INARgQsHMwyQkWZiAjgCwNi//OfqttyO9V89VnBMO/p9zrkd/fpkoeX0dK5T3ngVTQBvFE0Ki6MiAC+iEhsRi2JgAVvEACXJDkXz0GAAJjtECoBAuBQlaNvCLRBwMLBDJEhkDQBfJE0UUfrff656rbcXjX/eqmgge80v/rPd7v+PnEDrbmmo/4CLIs3AhSNllMngC9SR0yBQAlY8AYBcKCbz4O2CYA9EIEWyiJAAFwWNm6CgP8ELBzM/KdMh6ERwBehKTaPfn/6SQ177qOqO24tuKhO1dqv+jrteP2u2mUXQzOnOAreSBEuSwdLAF8EKx2Np0zAgjcIgFPeJIaXJwA2LK7x0QiAjQvMePESsHAwi1c9Jk+LAL5Ii6yjdZua1DR0mCrPPL1FA5PUXevoaf1bf9KoUdIxx0gVnNbnKRLecLSHKes1AXzhtTw055CABW8QADvcQIGX5kgZuIARt08AHLH4jG6bgIWDmW2FmM4FAXzhgnoGNceOVdMBB6qyqVEN6qCtda8e1BazCw8YIF1yiVRdnUEvgZbAG4EKR9upEsAXqeJl8YAJWPAGAXDAG9Bx6wTAjgWgfNkECIDLRseNEPCbgIWDmd+E6S5EAvgiRNWK7Pmhh1S/wy4a3HCGLqwbVHDTpptKt9wizTdfketFdhneiExwxi2KAL4oChMXRUjAgjcIgCPcuAmNTACcEEiWyZwAAXDmyCkIgWwIWDiYZUOKKjERwBfG1f7f//TK54tom22kzz8vnPWPf5TuvVf67W+NcyhjPLxRBjRuMU8AX5iXmAHLJGDBGwTAZYrPbSIAZhOESoAAOFTl6BsCbRCwcDBDZAgkTQBfJE3Uz/U+/VQzQuDXXmvZX4WatM4Cb+ms+/6oNdf0s3dXXeENV+Sp6zMBfOGzOvTmkoAFbxAAu9xBYdcmAA5bv5i7JwCOWX1mN03AwsHMtEAM54QAvnCC3UnRyZOlfv1mvuN31utkHa9jdIYOqB6vbW7YUzvv7KQ1L4viDS9loSnHBPCFYwEo7y0BC94gAPZ2e3nfGAGw9xLR4FwIEACzNSBglICFg5lRaRjLIQF84RC+g9INDdIRR0gXXSTtoyt1pfab3cWJGq6uo07Q0cdUqIKTvPCGgw1KSe8J4AvvJaJBRwQseIMA2NHmMVCWY6MBESMdgQA4UuEZ2z4BCwcz+yoxYdYE8EXWxP2oN+GQx7TNRVuoRvUtGrpGe+nZ/uN0/uiOqq72o1dXXeANV+Sp6zMBfOGzOvTmkoAFbxAAu9xBYdcmAA5bv5i7JwCOWX1mN03AwsHMtEAM54QAvnCC3X3Rhx5S/fY7q3ra5IJentD6umDD2zX+jgXUs6f7Vl11gDdckaeuzwTwhc/q0JtLAha8QQDscgeFXZsAOGz9Yu6eADhm9ZndNAELBzPTAjGcEwL4wgl2P4r++9+avtnW6vjlxwX9vKNeOnyZ+zXmkd9riSX8aDfrLvBG1sSpFwIBfBGCSvTogoAFbxAAu9g5NmoSANvQMcYpCIBjVJ2ZoyBg4WAWhVAMmSkBfJEpbv+KffGF6vpsp5p/vVTQ21daSP0XukcjHl5DK63kX+tpd4Q30ibM+iESwBchqkbPWRCw4A0C4Cx2is0aBMA2dY1hKgLgGFRmxigJWDiYRSkcQ6dKAF+kijeMxadOVf3ue6n6njsK+v1JndW/840acNd22nTTMMZJqku8kRRJ1rFEAF9YUpNZkiRgwRsEwEnuiLjWIgCOS29L0xIAW1KTWSAwBwELBzMEhUDSBPBF0kQDXa+xUU1HHaPK884pGKBRlTqi8gKtPv5v2mefQOcro228UQY0bjFPAF+Yl5gByyRgwRsEwGWKz20iAGYThEqAADhU5egbAm0QsHAwQ2QIJE0AXyRNNOz1mi+8SDrsUFU0NxcMcrqOUd3wkTruhEpVRHDSxxth72W6T4cAvkiHK6uGT8CCNwiAw9+HriaI4FjoCi11UyZAAJwyYJaHgCsCFg5mrthR1y4BfGFX27Inu/NONezWT1V10wqWuFF99fg+V+rCsZ1UXV12hSBuxBtByESTGRPAFxkDp1wwBCx4gwA4mO3mXaMEwN5JQkNFEiAALhIUl0EgNAIWDmahMadf/wngC/81ctLh88+rbottVPPjNwXln9D6OmeT+3Tt7V3VvbuT7jIpijcywUyRwAjgi8AEo93MCFjwBgFwZtvFXCECYHOSRjMQAXA0UjNobAQsHMxi04x50yeAL9JnHGyF997T9E22VMeP32sxwnjtp/11uVZZpUL33istvniwE86zcbxhU1emah8BfNE+ftxtl4AFbxAA292faU9GAJw2YdZPiwABcFpkWRcCjglYOJg5Rkh5gwTwhUFRkxzpm29U12c71bz87IxVH9Dm2kb3qEEzn/+w5JLS/fdLK6yQZFE/1sIbfuhAF34RwBd+6UE3/hCw4A0CYH/2U2idEACHphj9ziJAAMxegIBRAhYOZkalYSyHBPCFQ/ihlJ42TfV999THj3+gVSf/Q5PVo0XnPXtKt98ubbRRKAMV1yfeKI4TV8VFAF/EpTfTFk/AgjcIgIvXmytbEiAAZkeESoAAOFTl6BsCbRCwcDBDZAgkTQBfJE3U6HqNjar/+gcddPyvNG5c4Yy5D4S78kppjz3szI837GjJJMkRwBfJsWQlWwQseIMA2NaezHIaAuAsaVMrSQIEwEnSZC0IeETAwsHMI5y0YoQAvjAiZEZjNDdLp50mHX98YcHF9ZkOPm1xDf17hSoM/CaANzLaVJQJigC+CEoums2QgAVvEABnuGGMlTJw7DOmCOMUS4AAuFhSXAeBwAhYOJgFhpx2AyCALwIQycMWr75a2n9/qaFhZnNL6mM9p7X0sDbV8wPG6fxLa1RV5WHjJbSEN0qAxaXREMAX0UjNoCUSsOANAuASRefy2QQIgNkMoRIgAA5VOfqGQBsELBzMEBkCSRPAF0kTjWe9Rx6RdtpJqpj0g57SulpRb8wY/mFtojGb36YrJvRU167h8sAb4WpH5+kRwBfpsWXlsAlY8AYBcNh70GX3BMAu6VO7PQQIgNtDj3sh4DEBCwczj/HSWqAE8EWgwnnS9r9fnq5Ja/fR2nWPt+joX/qThq1yv6548NdaaCFPmi2xDbxRIjAuj4IAvohCZoYsg4AFbxAAlyE8t8wgQADMRgiVAAFwqMrRNwTaIGDhYIbIEEiaAL5Immhk6337reo27qOaf71UMPjHWlKDlnpAlzy2vJZeOjwueCM8zeg4fQL4In3GVAiTgAVvEACHufd86JoA2AcV6KEcAgTA5VDjHggEQMDCwSwAzLQYGAF8EZhgPrY7darqd9ld1RPvKejuO82vfRa4Ryc/vLZWXdXH5ufeE94ISy+6zYYAvsiGM1XCI2DBGwTA4e07XzomAPZFCfoolQABcKnEuB4CgRCwcDALBDVtBkQAXwQkls+tNjSo8aBD1GHs6IIup6mT9ut0owbcvb023dTnIVr2hjfC0YpOsyOAL7JjTaWwCFjwBgFwWHvOp24JgH1Sg15KIUAAXAotroVAQAQsHMwCwk2rgRDAF4EIFUKbzc1qOm2EKo8/rqDbRlXq4A6Xav1r/qp+/UIYRsIbYehEl9kSwBfZ8qZaOAQseIMAOJz95lunBMC+KUI/xRIgAC6WFNdBIDACFg5mgSGn3QAI4IsARAqtxfHj1TTwr6psaizofLhOVI+zTtSRg/3/VQFvhLbx6DcLAvgiC8rUCJGABW8QAIe48/zo2f9TnR+c6MI/AgTA/mlCRxBIhICFg1kiIFgEAnMQwBdsh1QI3HuvGnbaVVV10wqWH6OBevfwS3T62VWqrEyleiKL4o1EMLKIMQL4wpigjJMYAQveIABObDtEtxABcHSSmxmYANiMlAwCgZYELBzM0BQCSRPAF0kTZb3ZBJ5/XnWbb62aSd8WQLlT2+n2XW/QmGu7qKbGT2Z4w09d6MotAXzhlj/V/SVgwRsEwP7uL987IwD2XSH6mxsBAmD2BgSMErBwMDMqDWM5JIAvHMKPofTbb2v6xn3U8fP/Fkz7jP5PZ61/t668+1fq0cM/GHjDP03oyD0BfOFeAzrwk4AFbxAA+7m3QuiKADgEleixNQIEwOwLCBglYOFgZlQaxnJIAF84hB9L6S++0PRNt1LH/7zaYuIf1UPr6ilVrfIn3X+/tOiifgHBG37pQTd+EMAXfuhAF/4RsOANAmD/9lUoHREAh6IUff6SAAEwewICRglYOJgZlYaxHBLAFw7hx1R60iTVb7Ojqp98dMbU01WjPpqox7XRjK+XXlqaOFFaZhl/oOANf7SgE38I4At/tKATvwhY8AYBsF97KqRuCIBDUote5yRAAMx+gIBRAhYOZkalYSyHBPCFQ/ixlZ4+XQ177avKW2/S7rpRt2i3FgQWXFC65x5pzTX9AIM3/NCBLvwigC/80oNu/CFgwRsEwP7sp9A6IQAOTTH6nUWAAJi9AAGjBCwczIxKw1gOCeALh/BjLN3UpIYnntKB16+vceMKAXTpIt1yi7TVVu7h4A33GtCBfwTwhX+a0JEfBCx4gwDYj70UYhcEwCGqRs85AgTA7AMIGCVg4WBmVBrGckgAXziEH3Hp5mZp+HDp5JMLIXSobNbYcRXabz+3gPCGW/5U95MAvvBTF7pyT8CCNwiA3e+jUDsgAA5VOfomAGYPQMAoAQsHM6PSMJZDAvjCIXxK67LLpIMOkpqafoZxkk5QjepUOWqkjhni7lcKvMEGhUAhAXzBroBA6wQseIMAmN1dLgF3p7VyO+Y+CMwkQADMToCAUQIWDmZGpWEshwTwhUP4lJ5B4I47pH79pNpa6QBdqkt10Iw/v1z99c6Rl2nkmVWqrMweFt7InjkV/SeAL/zXiA7dELDgDQJgN3vHQlUCYAsqxjkDAXCcujN1BAQsHMwikIkRMyaALzIGTrlWCTz9tHTZFhN05dRdVKnm2dfcoe1175436JIrOqu6Olt4eCNb3lQLgwC+CEMnusyegAVvEABnv2+sVCQAtqJkfHMQAMenORNHQsDCwSwSqRgzQwL4IkPYlJo7gcZG1f5xVXV65/WCa57Q+rpki7t0xYSeyn1IXFYvvJEVaeqERABfhKQWvWZJwII3CICz3DG2ahEA29IzpmkIgGNSm1mjImDhYBaVYAybCQF8kQlmihRD4KuvVLfJlqr59z8Lrn5VK+uE3hN11QOLav75i1ms/dfgjfYzZAV7BPCFPU2ZKBkCFrxBAJzMXohxFQLgGFW3MTMBsA0dmQICBQQsHMyQFQJJE8AXSRNlvXYRmDRJdVvvoJqnHitY5n39Tocs86DGPfZ7Lb54u6oUdTPeKAoTF0VGAF9EJjjjFk3AgjcIgIuWmwt/QYAAmC0RKgEC4FCVo28ItEHAwsEMkSGQNAF8kTRR1ms3gdpaNfTdU1V3TShY6kstov6LP6DzH19ZvXq1u9I8F8Ab6fJl9TAJ4IswdaPr9AlY8AYBcPr7xGoFAmCrytqfiwDYvsZMGCkBCwezSKVj7BQJ4IsU4bJ0+QQaG9U46CB1uHxMwRo/qof27nm3hj+6vlZbrfwSbd2JN9oixPdjJIAvYlSdmYshYMEbBMDFKM01rREgAGZfhEqAADhU5egbAm0QsHAwQ2QIJE0AXyRNlPUSI9DcrKZjj1flyNMKlqxVR+3b6SYNum97bbRRYhVbLIQ30uHKqmETwBdh60f36RGw4A0C4PT2h/WVCYCtK2x3PgJgu9oyWeQELBzMIpeQ8VMggC9SgMqSiRJoPu98VRxxeMGajarUAR3Gacub99NOOyVacsZieCN5pqwYPgF8Eb6GTJAOAQveIABOZ2/EsCoBcAwq25yRANimrkwFAX6ZZw9AoBUCFn5hQdgICFx3nRr/sq86NDUUDDuk4nT9fvQx+utfk+WAN5LlyWo2COALGzoyRfIELHiDADj5fRHLigTAsShtb04CYHuaMhEEZhCwcDBDSggkTQBfJE2U9VIjcP/9athhZ1XVTSso0Vc3aqVT+2rYMKkiod9C8EZqSrJwwATwRcDi0XqqBCx4gwA41S1ievGEjl6mGTGcnwQIgP3Uha4g0G4CFg5m7YbAAhD4BQF8wZYIisCzz6p+i61VPfn72W3fq620g+5Qg6p12GHSOedIlZXtnwpvtJ8hK9gjgC/sacpEyRCw4A0C4GT2QoyrEADHqLqNmQmAbejIFBAoIGDhYIasEEiaAL5ImijrpU7gjTdUt/EWqvnqMz2ttbWZHtI0dZlddo89pCuukGpq2tcJ3mgfP+62SQBf2NSVqdpPwII3CIDbvw9iXYEAOFblw5+bADh8DZkAAq0SsHAwQ1oIJE0AXyRNlPUyIfDRR5o84Ait/Z9x+vfnCxSU7NNHuu02qcvPuXDJbeGNkpFxQwQE8EUEIjNiWQQseIMAuCzpuUkSATDbIFQCBMChKkffEGiDgIWDGSJDIGkC+CJpoqyXJYFPP5U231x6883CquusI91zjzTffOV1hDfK48ZdtgngC9v6Ml35BCx4gwC4fP1jv5MAOPYdEO78BMDhakfnEJgnAQsHMySGQNIE8EXSRFkvawLffitts4303HMtKy+mz7XKH+t15WNLaeGFS+8Kb5TOjDvsE8AX9jVmwvIIWPAGAXB52nMX7wBmD4RLgAA4XO3oHAIEwOwBCJRIwMIvLCWOzOUGCUydKu28s/TAAzOHW0Df6h9aXz31owYu9ZAu+8fy+s1vShscb5TGi6vjIIAv4tCZKUsnYMEbBMCl684dMwnwDmB2QqgECIBDVY6+IdAGAQsHM0SGQNIE8EXSRFnPFYG6OmmvvaT7b5msR7SJ1tCLM1r5Wgtq30Um6pwnVteyyxbfHd4onhVXxkMAX8SjNZOWRsCCNwiAS9Ocq38mQADMbgiVAAFwqMrRNwQIgNkDECiZgIVfWEoemhvMEmic3qB3l95cy33xWIsZJ6m79up5j4Y/ur5WW6248fFGcZy4Ki4C+CIuvZm2eAIWvEEAXLzeXNmSAAEwOyJUAgTAoSpH3xAgAGYPQKBkAhZ+YSl5aG4wTaD59DNUMXRIwYzT1El/6XyrDn1ga623XtsI8EbbjLgiPgL4Ij7Nmbg4Aha8QQBcnNZcVUiAAJhdESoBAuBQlaNvCBAAswcgUDIBC7+wlDw0N9gnMGaMmg84QBXNzS1mrVeVBlZfpd3u2ENbbTVvDHjD/jZhwtIJ4IvSmXFHHAQseIMAOI69msaUBMBpUGXNLAgQAGdBmRoQcEDAwsHMATZKGieAL4wLHPN4N92kxj32UoemhhYUmlShQysv1rrXHajdd587ILwR8+Zh9rkRwBfsDQi0TsCCNwiA2d3lEiAALpcc97kmQADsWgHqQyAlAhYOZimhYdmICeCLiMWPYfT771fDDjurqm5awbTDNEJLXTpUgw5o/dcWvBHDBmHGUgngi1KJcX0sBCx4gwA4lt2a/JwEwMkzZcVsCBAAZ8OZKhDInICFg1nm0ChongC+MC8xAz75pOr7bKPqnyYVsDhDR6tpxOka+vfCX13wBlsHAoUE8AW7AgKtE7DgDQJgdne5BAiAyyXHfa4JEAC7VoD6EEiJgIWDWUpoWDZiAvgiYvFjGv2VV1S38Raq+eHrgqnHaKA+OOpSjTyjgyrm+A0Gb8S0QZi1WAL4olhSXBcbAQveIACObdcmNy8BcHIsWSlbAgTA2fKmGgQyI2DhYJYZLApFQwBfRCM1g779tuo22Ew1//ukgMXN2lWP73+tLrysRh06zPw23mDLQKCQAL5gV0CgdQIWvEEAzO4ulwABcLnkuM81AQJg1wpQHwIpEbBwMEsJDctGTABfRCx+jKN//LGmb7CZOv73nYLpJ2oL3bDzbRp7fVfV1BAAx7g9mLltAvzMaJsRV8RJwII3CIDj3LtJTE0AnARF1nBBgADYBXVqQiADAhYOZhlgokRkBPBFZIIzrvTVV6rbaAvV/OfVFjTqVK2N9Jh6brmObr1Vqq39Tk8++eSMa9Zbbz0tsMAC0INA9AT4mRH9FgDAXAhY8AYBMNu7XAIEwOWS4z7XBAiAXStAfQikRMDCwSwlNCwbMQF8EbH4MY/+ww+q67Otap5/agaFRlVqN92sCdp5xtfrritdc813eu01AuCYtwmzFxLgZwa7AgKtE7DgDQJgdne5BAiAyyXHfa4JEAC7VoD6EEiJgIWDWUpoWDZiAvgiYvFjH/2nn1S/3c6qfmSi9tN4Xan9WhBZZZUGHXnkg+rRo553AMe+V5h/NgF+ZrAZIEAAzB6AwC8JEACzJ0IlQAAcqnL0DYE2CPBLC1sEAoUE8AW7ImoCdXWqvech7Th2a02cWEjiN7+ZpJNOekbbbbcGj4CIeqMw/CwC/MxgL0CAAJg9AAECYPaAFQIEwFaUZA4I/IIAv7SwJSBAAMwegEBrBOrqpL32km65pfC7iy02RRMnNmilleYDHgSiJ8BZKvotAIC5ELDgDR4BwfYulwDvAC6XHPe5JkAA7FoB6kMgJQIWDmYpoWHZiAngi4jFZ/QWBBobpQEDpCuvbAlmD12n7xZeRhc81Vu9egENAnET4GdG3Poz/dwJWPAGATA7vFwCBMDlkuM+1wQIgF0rQH0IpETAwsEsJTQsGzEBfBGx+IxeQKCpSTrsMOmii2Z+a1fdrBvUT5PVXXsvcJ9GPrG2VlwRcBCIlwA/M+LVnsnnTcCCNwiA2eXlEiAALpcc97kmQADsWgHqQyAlAhYOZimhYdmICeCLiMVn9FYJNDdLw4ZJr4+6R7drR1WrYcZ1U9RVe3W/S8c/trFWXx14EIiTAD8z4tSdqdsmYMEbBMBt68wVrRMgAGZnhEqAADhU5egbAm0QsHAwQ2QIJE0AXyRNlPVMEPjf/1S35O9UU/9Ti3Fq1VF/6XyrDn1wG627rolJGQICJRHgZ0ZJuLg4IgIWvEEAHNGGTXhUAuCEgbJcZgQIgDNDTSEIZEvAwsEsW2JUi4EAvohBZWYsh8CUMWPU+YCD1KG5scXt9arS/jXXau97+mqzzcpZmXsgEC4BfmaEqx2dp0vAgjcIgNPdI5ZXJwC2rK7t2QiAbevLdBETsHAwi1g+Rk+JAL5ICSzLBk8g5413zzxTq51xtqqb6lvM06hKHdBhnLa5bT9tv33wozIABIomwM+MolFxYWQELHiDADiyTZvguATACcJkqUwJEABniptiEMiOgIWDWXa0qBQLAXwRi9LMWSqBWd5Y6JVXtMbIM1RVN61gicMqLtBa1x2ifv1KXZ3rIRAmAX5mhKkbXadPwII3CIDT3ydWKxAAW1XW/lwEwPY1ZsJICVg4mEUqHWOnSABfpAiXpYMmMKc3NqqqUudd+6l62uSCmYZphH439u8aMCDocWkeAkUR4GdGUZi4KEICFrxBABzhxk1oZALghEA6XGY7SXtL6i1pUUmTJL0n6XZJl+W/TrK930raX9JGkpaT1FPSdElfSXpV0gRJN0lq+Xfwkuxg5loEwMkzZUUIeEHAwsHMC5A0YYoAvjAlJ8MkSKDAG++/r7pN+qhm8ncFVU7TMHU951QdfgS/AiUoAUt5SICfGR6KQkteELDgDQJgL7ZSkE1w+glSthlNd5N0naRcADy31yeSdpP0XEJjHilphKSObaz3tqRdJP07obqtLUMAnCJcloaASwIWDmYu+VHbJgF8YVNXpmo/gVa98frrqttwM9V897+CAufrUE05+VwNO65SFfwm1H4BWMFLAvzM8FIWmvKAgAVvEAB7sJECbYFjT5jCdZB0j6Q++fZzp9uxkv4jaQFJuSecrZP/3vf5f3+znaMeLOnCOdZ4RtJdknIhcw9JK0jaNx9M5y77RtKfJH3Zzrpzu50AOCWwLAsB1wQsHMxcM6S+PQL4wp6mTJQMgbl64513VLf+pqr5X+6o2vJ1ufrrvaPHaMTpHQiBk5GBVTwjwM8MzwShHW8IWPAGAbA32ym4RgiAg5NsRsODJI3Ot54LfTeW9Mu3OJwlaXD+miclrd+OUTvn1++eX2OgpHGtrLeQpEfywW/u2+dKyr1rOI0XAXAaVFkTAh4QsHAw8wAjLRgjgC+MCco4iRGYpzc++kjT19tEHT95v6DejeqrZw64RuddXK3KysTaYSEIeEGAnxleyEATHhKw4A0CYA83ViAtEQAHItQcbebe/Zt7K8Ni+T9bXdI/Wxkjd91LklbJf28LSQ+WOe6mkh7K3/uipDXmsc7W+Xcn5y55WdKfy6zZ1m0EwG0R4vsQCJSAhYNZoOhp22MC+MJjcWjNKYE2vfHFF5q+/qbq+F7uPRM/v57XGtpUD2v3Ad112WUiBHaqIsWTJtCmL5IuyHoQCISABW8QAAey2TxskwDYQ1HaaCn34WuP5q95QtKG87h+P0nj89+/UlLu63Jee+SfN5y793pJe85jkWUk5Z4BnHvl/pn7oLg0XgTAaVBlTQh4QMDCwcwDjLRgjAC+MCYo4yRGoChvfPON6jbaQjX/nvmeide0kjbU4/pB88/4eu9AOmvXAAAgAElEQVS9pfHjpaqqxNpiIQg4JVCUL5x2SHEIuCFgwRsEwG72joWqBMDhqXiGpKPzbQ+RlPt6bq9FJX2R/2buERG5r8t5zfkO4Ny7invPY5FtJN2d//4ESTuXU7CIewiAi4DEJRAIkYCFg1mI3OnZbwL4wm996M4dgaK98eOPqttsa3322jdaq+4JfaVFWjS9227StddK1dXuZqEyBJIiULQvkirIOhAIhIAFbxAAB7LZPGyTANhDUdpo6f45Pvwt9+zfx9q4/mNJS+avWVjS12WM3Cn/2IkF8/cW8wzgpvxzh58uo14xtxAAF0OJayAQIAELB7MAsdOy5wTwhecC0Z4zAiV5Y+pUfffhj9r0L4vrlVcKW95+e+mmm6SOHZ2NQ2EIJEKgJF8kUpFFIBAGAQveIAAOY6/52CUBsI+qzLunDyQtnb8k98//tjFC7jERsz4Abj1JT5U5cu6dvDdKmvWX43LB7l35YLiHpBUl7SMp90FxUyQNkHRTmbWKuY0AuBhKXAOBAAlYOJgFiJ2WPSeALzwXiPacESjHG99/L/XpI73wQmHbW24p3Xab1Dn3Eci8IBAogXJ8EeiotA2BkghY8AYBcEmSc/EcBAiAw9sO30n5B5b9HLbOa4rcYxh2zF+w7Rwf0FbO5BtIuljSCnO5uT7/SIrL8sFwOTVy9+TC3bZeucB5TO6iiRMnqnfveT2Voq2l3H1/0qRJeu2112Y0sPLKK6tHj1yWzgsCcRPAF3Hrz/StE8AX7AwIJOuNSZOk3Xfvruef//mZD531k87QMXrw/47VxTd1VNeuUIdAmAT4mRGmbnSdPgEL3njxxRfVJ/dfMWe+1pb0bPrkqGCBAAFweCrWSZp1Us39s6GNEa6TlPsQt9wr988b2jFybr/kQuBzJK06l3V+kJT7wLlhkqaVWau5lPtGjRql5ZZL67PmSumEayEAAQhAAAIQgAAEQiEwbVoHjRixpl5/fSF1VK3u1PbaQg/qda2ovy1zkw466UN17tzWUTuUaekTAhCAAAQsEHjrrbc0dOjQWaMQAFsQNaMZCIAzAp1gGVcBcO75vzdL2kjS95JOnuMREF0krS5psKSt8rPm/lJd7t+/LWN2AuAyoHELBCAAAQhAAAIQgEBpBKZPr9RZI1bR8a8dpG11z+yb39Ky+uvvbtWgkz9St26EwKVR5WoIQAACEEiLAAFwWmTtr0sAHJ7GLh4BkQt4X5aUe5ttLvxdU9K7c0F3kaS/5b+Xe7fxrHcfl0KaR0CUQotrIWCMgIW/mmVMEsbxgAC+8EAEWvCSQBLeqLz8Ks13zJEF872n3+vg5R/URXfNrwUWKOn9CV6yoql4CCThi3hoMWlMBCx4g0dAxLRjk52VADhZnlms5uJD4I6SdGZ+uGMljZjHoLmnpX0qaT5JTZJ+LenLFMDwIXApQGVJCPhAwMKHM/jAkR5sEcAXtvRkmuQIJOKNpiY1/u1QdRid+6iLlq//aikduOyjuuofv9PCCyfXNytBIE0CifgizQZZGwKOCFjwBh8C52jzGChLAByeiPdLmvXE740lPdbGCB9LWjJ/Te7Y+nUZIz+ef/Zv7tbcp6291MYa987xKIjtJN1dRs22biEAbosQ34dAoAQsHMwCRU/bHhPAFx6LQ2tOCSTmjeZmNR15lCrPy33URcvXp/q1Bv7uUV3+5DJafHGn41IcAkURSMwXRVXjIgiEQ8CCNwiAw9lvvnVKAOybIm33c4ako/OXDZGU+3pur0XmePftV5JyX5fzekdSr/yNy8zj8Q+z1k7yg+fm1i8BcDlKcg8EAiBg4WAWAGZaDIwAvghMMNrNjECi3siFwMOOU+Wowr/s9qUW0X5LPqIxT6+gJWe9tSKzKSkEgdIIJOqL0kpzNQS8JmDBGwTAXm8xr5sjAPZanlaby73r95H8d3LvzM19KNvcXvtJGp//5pWScl+X88o9/3e1/I2bSHq0jUWekrRO/potJD1YTtE27iEATgEqS0LABwIWDmY+cKQHWwTwhS09mSY5Aml4o+nkU1R54gkFTX6tBbXPYg/p4qdX0dJLJzcDK0EgaQJp+CLpHlkPAi4IWPAGAbCLnWOjJgFweDp2yD9jd9F866tL+mcrY+Suyz2qYZX893KPjXigzHFz4fE++XtzgfL+81jnD5LelFSVfwbwQpJyH1yX9IsAOGmirAcBTwhYOJh5gpI2DBHAF4bEZJRECaTljeYzzlTFkGMKev1O8+svCz+gc57srWVyfy+OFwQ8JJCWLzwclZYgUBIBC974f/bOA1rK6uzC+xZ6iWCJNYkmxkSToAkoAkoTxIYFKyL2btTYf401Go0tRo29IBqNJSiKFBFBQVAxGmOs0agxsUcRkXLrvz4YkGEu3Jm5XznnPc+slRUv9/ve877PPps5sx1nCIBLkpyLlyNAAOzndjha0nW51l+RFL0rOPqIh+Uf0Ze2RV/eFj2eltRnJaMeJOn23O+elNSviesGLxceR1+BfLikW5u4Lgqlx0vaIve7hyXtmhBiAuCEwFIWAlkTsHAwy5oh69sjgC/sacpE8RBI0huNV1+jihOOL2j0S3XWiK4T9LvpvbTppvHMQRUIxEkgSV/E2Se1IJA2AQveIABOe9fYWY8A2E8to3fXRkHroFz7H0m6WdKrkrpK2m+5wHdO7p+joLipRzEBcHTf/ZL2XK5AFBaPzb0buZ2k7pIOkLRa7pr/Seop6a2EEBMAJwSWshDImoCFg1nWDFnfHgF8YU9TJoqHQNLeaLzxJumoo1Sh6D0Q3zzmqYP27zxOFzzVT926xTMLVSAQF4GkfRFXn9SBQNoELHiDADjtXWNnPQJgf7XsJOluSTuvYoT/SNpH0sxVXFNsANwm967jQ4pA9oakfSX9rYhry72EALhcctwHAccJWDiYOY6Y9jwkgC88FI2WUyGQijfuuEMNBx+iysaGvJkWqK1GdByrM6cN1i+iD2XjAQFHCKTiC0dmpQ0IlELAgjcIgEtRnGuXJ0AA7P9+iD5iYaSkHpLWkvSVpLcljZF0o6Qvmxmx2AB4aZnoM4Wje6IvedtIUmdJNbmPoIi+LO4hSffl/ixJugTASdKlNgQyJGDhYJYhPpY2SgBfGBWWsVpMIDVv3HOPGkYcoMqG+ryej9W1+tO3jtXkyVKP6DTOAwIOEEjNFw7MSgsQKIWABW8QAJeiONcSALMHLBAgALagIjNAoAkCFg5mCAuBuAngi7iJUs8KgVS9MWaM6vfeV1X1tYvxnazLdaVOXvzPnTtLkyZJPaMPQOMBgYwJpOqLjGdleQiUQsCCNwiAS1GcawmA2QMWCBAAW1CRGSBAAMwegEBRBCy8YClqUC6CQIkEUvfGuHGq332Yzq07RxfprLxuO3WSJk6UevUqcQguh0DMBFL3Rcz9Uw4CSRGw4A0C4KR2h/26fASEfY2tTkgAbFVZ5gqegIWDWfAiAiB2AvgidqQUNEIgE2+89ZbunPUDHXSQ1JD/scDq2FGaMEHq08cIYMbwkkAmvvCSFE2HRsCCNwiAQ9u18c1LABwfSyqlS4AAOF3erAaB1AhYOJilBouFgiGAL4KRmkFLJJClN+6+WzrggMIQuEMHafx4adttSxyGyyEQE4EsfRHTCJSBQCIELHiDADiRrRFEUQLgIGQ2OSQBsElZGQoCkoWDGTpCIG4C+CJuotSzQiBrb9x7r7T//lL9ct8N10fTtWHrD3TIpH3Ur58V0szhE4GsfeETK3oNi4AFbxAAh7Vn45yWADhOmtRKkwABcJq0WQsCKRKwcDBLERdLBUIAXwQiNGOWTMAFbzzwgLTfflJdndRLT2uStlc7LdBhrUZrxIT9NXBgyWNxAwRaRMAFX7RoAG6GQEIELHiDADihzRFAWQLgAEQ2OiIBsFFhGQsCFg5mqAiBuAngi7iJUs8KAVe88eCD0pV7zdL4+sHqpHmL8darUoe3GqX9Hj1AgwZZIc4cPhBwxRc+sKLHsAhY8AYBcFh7Ns5pCYDjpEmtNAkQAKdJm7UgkCIBCwezFHGxVCAE8EUgQjNmyQSc8caiRZq//sZq/9n7eTM0qEJHVt+mYY8cpCFDSh6PGyBQFgFnfFFW99wEgeQIWPAGAXBy+8N6ZQJg6wrbnY8A2K62TBY4AQsHs8AlZPwECOCLBKBS0gQBp7zx7LOqHTBYrebPLQiBj666Wbs+fKh23NEEdoZwnIBTvnCcFe2FRcCCNwiAw9qzcU5LABwnzSW1oq96mJor+56k7zWxxHmSzs39+fmSop9XfLwr6bu5P+wvaVr8rXpdkQDYa/loHgIrJ2DhYIa+EIibAL6Imyj1rBBwzhuzZ6u2/2C1+npOAeJjqm7Ujg8doZ13tkKfOVwl4JwvXAVFX8ERsOANAuDgtm1sAxMAx4ZyWSEC4PiZNlWRADgdzqwCgdQJWDiYpQ6NBc0TwBfmJWbAMgk46Y0XXlBt3+3Uat4XBVMdV3mdBo05WrvuWubA3AaBIgg46Ysi+uYSCCRNwII3CICT3iV26xMAx68tAXD8TAmA02HKKhBwgoCFg5kTIGnCFAF8YUpOhomRgLPeePFF1fTdTq2/+rxg2hMqr1G/B47T7rvHCIJSEFiOgLO+QCUIZEzAgjcIgDPeRB4vTwAcv3gEwPEzJQBOhymrQMAJAhYOZk6ApAlTBPCFKTkZJkYCTnvjpZdUs+1AtZ77v4KJf1VxlXrfd4L23DNGGJSCQI6A075AJQhkSMCCNwiAM9xAni9NABy/gATA8TMlAE6HKatAwAkCFg5mToCkCVME8IUpORkmRgLOe+Pll5eEwHM+LZj6lIor1OOek7TPPjECoRQEJDnvC1SCQEYELHiDADijzWNgWQLg+EUkAI6fKQFwOkxZBQJOELBwMHMCJE2YIoAvTMnJMDES8MIbr7yimm0GqPUXnxRMflrFpdr8rlM1fHiMUCgVPAEvfBG8SgDIgoAFbxAAZ7FzbKxJABy/jj0lzcqV/UjSOk0scZ6kc3N/fr6k6OcVHx9KWjv3h1HNZ+Nv1euKfAmc1/LRPARWTsDCwQx9IRA3AXwRN1HqWSHgjTdee21JCPy/6OVB/uP/Ki7WZqPP0IgRVlRhjqwJeOOLrEGxfnAELHiDADi4bRvbwATAsaFcVmgTSa/nflogqX0TSxQTAEf3ts3dG9V8M/5Wva5IAOy1fDQPAQJg9gAESiFg4QVLKfNyLQSKJeCVN954QzW9+6v1/6L3eXzz+Lc2UDf9XVePXk0HHFDs5FwHAc5S7AEIlErAq+eMlQxHAFyq6ly/lAABcPx7YTVJXyxXNnoH8Ir/qr+5ADi654PlanSRNCf+Vr2uSADstXw0DwFetLAHIFAKAQsvWEqZl2shUCwB77zxz38uCYE//e/iEd/X+uqnafqXvq+KCmn0aPFO4GLF57qVEvDOF2gJgZQIWPAGAXBKm8XgMgTAyYj6T0k/yJXeTdLYFZZpLgDeVdJDuXuiWj9Mpk2vqxIAey0fzUOAAJg9AIFSCFh4wVLKvFwLgWIJeOmNt95aHAJ/9mmDtm18Um8ve9kgVVYuCYH3379YAlwHgUICXvoCISGQAgEL3iAATmGjGF2CADgZYW+TdHCu9D2SVvxah+YC4OiefXP3R7UOTaZNr6sSAHstH81DgACYPQCBUghYeMFSyrxcC4FiCXjrjX/9S//4W522OfSHmrPCf+cXhcB33im+GK7YTcB1BQS89QVaQiBhAha8QQCc8CYxXJ4AOBlx+0ianiu9SNKmkv613FKrCoC/L+kVSW1y10e1nk6mTa+rEgB7LR/NQ4AAmD0AgVIIWHjBUsq8XAuBYgn47o2//lXabjsRAhcrONcVRcB3XxQ1JBdBoAwCFrxBAFyG8NyymAABcHIbYaakKKSMHtMkbSepPvfzygLgakmTJfXLXTdLUq/kWvS6MgGw1/LRPAQIgNkDECiFgIUXLKXMy7UQKJaABW88//ySEPjLL7+ZukINGlLxmEbePUT7Lv3vAouFwnXBE7Dgi+BFBEAiBCx4gwA4ka0RRFEC4ORk/rGk5yW1zy1xt6RDJEXvCG4qAI7e8Xu7pP1y18+X9AtJryfXoteVCYC9lo/mIUAAzB6AQCkELLxgKWVeroVAsQSseGP2bGnQoCUhcBT+Xq+jdaRu0hkVl2iLe07XPvsUS4TrICBZ8QVaQiBuAha8QQAc964Ipx4BcLJa7yUpCn6jd/ZGj+ijHS6TtJWko3N/dr2kZyWdlvuoiOiP63KfG3x/su15XZ0A2Gv5aB4CBMDsAQiUQsDCC5ZS5uVaCBRLwJI3nntOGrxdgy796igdoZuXITij4nf6+Z9P0957F0uF60InYMkXoWvJ/PESsOANAuB490RI1QiAk1d7V0l3SepY5FLzJEXf+/twkdeHehkBcKjKM7d5AhYOZuZFYsDUCeCL1JGzoCcErHnjg4PO1Lp3XFxA/7SKy9Tj3lO0V/T2Eh4QaIaANV8gOATiImDBGwTAce2G8OoQAKej+XclXSkpCoOrVrJk9K7fKPQ9SdJ76bTl9SoEwF7LR/MQWDkBCwcz9IVA3ATwRdxEqWeFgDlvvP22FvXqpzaf/KdAolMrLtdW952sPfe0oh5zJEXAnC+SAkXd4AhY8AYBcHDbNraBCYBjQ1lUobVzXwx3qKSdcnc8KulWSdEXvn1UVBUuiggQALMPIGCUgIWDmVFpGCtDAvgiQ/gs7TQBk954660lIfCn/y1gf0rFFdr6/pM0bJjTstBcxgRM+iJjpixvg4AFbxAA29iLWUxBAJwF9aa/BC6bTvxdlQDYX+3oHAKrJGDhYIbEEIibAL6Imyj1rBAw64233lLN1n3V+rMPCkPgyivV6/5faY89rKjIHHETMOuLuEFRLzgCFrxBABzcto1tYALg2FCWVOg8Sefm7jhfSwJhHqURIAAujRdXQ8AbAhYOZt7AplFvCOALb6Si0ZQJmPbGP/+55J3ATYTAJ1f+Xn0eOFG7754ycJbzgoBpX3ihAE26SsCCNwiAXd1d7vdFAJyNRgTALedOANxyhlSAgJMELBzMnARLU14TwBdey0fzCRIw740331RNr35q/b8PCyieVHmVtnngBELgBPeXr6XN+8JXYeg7cwIWvEEAnPk28rYBAuBspCMAbjl3AuCWM6QCBJwkYOFg5iRYmvKaAL7wWj6aT5BAEN5YRQj8q8o/qN+Y47Vr9FXTPCCQIxCEL1AbAmUQsOANAuAyhOeWxQQIgNkIvhIgAPZVOfqGQDMELBzMEBkCcRPAF3ETpZ4VAsF44403lrwT+PPC74w+ofIaDXzwOA0dakVV5mgpgWB80VJQ3B8cAQveIAAObtvGNjABcGwoKZQyAQLglIGzHATSImDhYJYWK9YJhwC+CEdrJi2NQFDeeP111fTu32QIfHzltRr00LHaZZfS+HG1TQJB+cKmhEyVEAEL3iAATmhzBFCWADgAkY2OSABsVFjGgoCFgxkqQiBuAvgibqLUs0IgOG8sDoGjdwJ/XCDhoKondPyD/QmBrWzuFswRnC9awIpbwyJgwRsEwGHt2TinJQCOkya10iRAAJwmbdaCQIoELBzMUsTFUoEQwBeBCM2YJRMI0huvvbbkncBffBMC36AjdYyuU3WrSj30kLTjjiWj5AZDBIL0hSH9GCU5Aha8QQCc3P6wXpkA2LrCducjALarLZMFTsDCwSxwCRk/AQL4IgGolDRBIFhvvPqqavpEIfAnul5H6Vj9UY2qXKxpmzbSww9LgwebkJghyiAQrC/KYMUtYRGw4A0C4LD2bJzTEgDHSZNaaRIgAE6TNmtBIEUCFg5mKeJiqUAI4ItAhGbMkgkE7Y1XX9V7F92lTcdcqPkLl4S/Sx9t20qPPioNGFAyUm4wQCBoXxjQjxGSI2DBGwTAye0P65UJgK0rbHc+AmC72jJZ4AQsHMwCl5DxEyCALxKASkkTBPCG9MQT0k47SQsX5kvavr00YYK07bYmpGaIEgjgixJgcWlQBCx4gwA4qC0b67AEwLHipFiKBAiAU4TNUhBIk4CFg1mavFgrDAL4IgydmbJ0AnhjCbPJk7X4y98WLcpn2KX9Ij3yWBv17l06W+7wlwC+8Fc7Ok+WgAVvEAAnu0csVycAtqyu7dkIgG3ry3QBE7BwMAtYPkZPiAC+SAgsZb0ngDe+kTB6t+9uu0k1NUv+bCO9rcc0WOe1/Z2Om7anttrKe7kZoEgC+KJIUFwWHAEL3iAADm7bxjYwAXBsKCmUMgEC4JSBsxwE0iJg4WCWFivWCYcAvghHayYtjQDeyOf1yCPSHntIG9a9qSc0QOvrv6pVtQ5s/4BOenJXde9eGl+u9pMAvvBTN7pOnoAFbxAAJ79PrK5AAGxVWftzEQDb15gJAyVg4WAWqHSMnSABfJEgXEp7TQBvFMo3+erX9JMTBmgdfbTslzVqpQM6PqgzntpJW2zhteQ0XwQBfFEEJC4JkoAFbxAAB7l1YxmaADgWjBTJgAABcAbQWRICaRCwcDBLgxNrhEUAX4SlN9MWTwBvNMFq1izVDhisVgvn5f1ykVprRKeHdc7T2+unPy2eMVf6RwBf+KcZHadDwII3CIDT2SsWVyEAtqhqGDMRAIehM1MGSMDCwSxA2Rg5YQL4ImHAlPeWAN5YiXTTp6tu0BBVL5qfd8ECtdWIb43Tb2YO1Kabeis7jTdDAF+wRSDQNAEL3iAAZneXS4AAuFxy3Jc1AQLgrBVgfQgkRMDCwSwhNJQNmAC+CFh8Rl8lAbyxCjxTp6puyE6qrlmQd9F8tdP+XSbokll9tckmbDCLBPCFRVWZKQ4CFrxBABzHTgizBgFwmLpbmJoA2IKKzACBJghYOJghLATiJoAv4iZKPSsE8EYzSk6erLodd1F13aK8C+epg0asPlGXP9NHP/iBld3AHEsJ4Av2AgSaJmDBGwTA7O5yCRAAl0uO+7ImQACctQKsD4GECFg4mCWEhrIBE8AXAYvP6KskgDeK2CATJqh+6G6qqqvJu3iuOumAtR7TVc/01IYbFlGHS7whgC+8kYpGUyZgwRsEwClvGkPLEQAbEjOwUQiAAxOcccMhYOFgFo5aTJoWAXyRFmnW8Y0A3ihSsUceUf3uw1RVX5t3w5fqrBFrT9G1z3TXd79bZC0uc54AvnBeIhrMiIAFbxAAZ7R5DCxLAGxAxEBHIAAOVHjGtk/AwsHMvkpMmDYBfJE2cdbzhQDeKEGpBx9Uw557qbKhPu+mL7SaRq73hK5/Zgutv34J9bjUWQL4wllpaCxjAha8QQCc8SbyeHkCYI/FC7x1AuDANwDj2yVg4WBmVx0my4oAvsiKPOu6TgBvlKjQ/ferYZ99VdnYkHfj/9RVB31nqm6c9TOtu26JNbncOQL4wjlJaMgRAha8QQDsyGbysA0CYA9Fo+XFBAiA2QgQMErAwsHMqDSMlSEBfJEhfJZ2mgDeKEOeu+9Ww4gDCkLgT7SmDtlwmm6dtam+/e0y6nKLMwTwhTNS0IhjBCx4gwDYsU3lUTsEwB6JRat5BAiA2RAQMErAwsHMqDSMlSEBfJEhfJZ2mgDeKFOe0aPVcOBBqlTjsgLz1EE7aII+33QbTZsmrblmmbW5LXMC+CJzCWjAUQIWvEEA7Ojm8qAtAmAPRKLFJgkQALMxIGCUgIWDmVFpGCtDAvgiQ/gs7TQBvFG+PI233KqKww9bXGCuOmmIJmqWei3++Wc/k6ZOlbp2Lb8+d2ZHAF9kx56V3SZgwRsEwG7vMZe7IwB2WR16WxUBAmD2BwSMErBwMDMqDWNlSABfZAifpZ0mgDdaJk/j9Tdowa/+T/0WTdJsbZlXrHt36fHHpW99q2VrcHf6BPBF+sxZ0Q8CFrxBAOzHXnOxSwJgF1Whp2IIEAAXQ4lrIOAhAQsHMw+x07LjBPCF4wLRXmYE8EbL0Td++plOvHANXX11Ya1evaRJk6SOHVu+DhXSI4Av0mPNSn4RsOANAmC/9pxL3RIAu6QGvZRCgAC4FFpcCwGPCFg4mHmEm1Y9IYAvPBGKNlMngDfiQd7YKB1zjHTDDYX1+veXHn1UatcunrWokjwBfJE8Y1bwk4AFbxAA+7n3XOiaANgFFeihHAIEwOVQ4x4IeEDAwsHMA8y06BkBfOGZYLSbGgG8ER/qhgbp0EOlUaPya+6vu1Q3YHvdMX5NtWkT33pUSo4AvkiOLZX9JmDBGwTAfu/BLLsnAM6SPmu3hAABcEvocS8EHCZg4WDmMF5a85QAvvBUONpOnADeiBdxfb00YoT05z8vqXuKLtNlOk0vanNdvsMTGjW2i1q1indNqsVPAF/Ez5SKNghY8AYBsI29mMUUBMBZUGfNOAgQAMdBkRoQcJCAhYOZg1hpyXMC+MJzAWk/MQJ4I360tbXS3ns16qdjf6MLdO6yBZ7Vlrphj8m65b7OqqqKf10qxkcAX8THkkq2CFjwBgGwrT2Z5jQEwGnSZq04CRAAx0mTWhBwiICFg5lDOGnFCAF8YURIxoidAN6IHenigrVjHlGrYUMLij+lbXTX8Am64c4OqqxMZm2qtpwAvmg5QyrYJGDBGwTANvdmGlMRAKdBmTWSIEAAnARVakLAAQIWDmYOYKQFYwTwhTFBGSc2AngjNpT5hRoaVHfokaoedUvBApO1nR4+7BFdfVNbVfBqMiEBWlYWX7SMH3fbJWDBGwTAdvdn0pPxlJ00YeonRYAAOCmy1IVAxgQsHMwyRsjyBgngC4OiMlIsBPBGLBibLlJfr9oRB6nVn+8q+P047aQnjx+jS69qTQicoATllsYX5ZLjPusELHiDANj6Lk1uPgLg5NhSOVkCBMDJ8qU6BDIjYOFglhk8FjZLAF+YlZbBWkgAb7QQYHO319Wpds/91GrsAwVXPqBheumMP+s3F1c3V4Xfp0wAX6QMnOW8IWDBGwTA3mw35xyxtKQAACAASURBVBolAHZOEhoqkgABcJGguAwCvhGwcDDzjTn9uk8AX7ivER1mQwBvpMC9pkY1u+6p1hMfKVjsTxqu9y4YrTPP5lvhUlCi6CXwRdGouDAwAha8QQAc2KaNcVwC4BhhUipVAgTAqeJmMQikR8DCwSw9WqwUCgF8EYrSzFkqAbxRKrEyr1+4UDVDhqr1k5MLCtyiQzX3spt00il8K1yZdGO/DV/EjpSCRghY8AYBsJHNmMEYBMAZQGfJWAgQAMeCkSIQcI+AhYOZe1TpyHcC+MJ3Bek/KQJ4IymyTdSdP1+LBu6gNs88VfDLa3WsKq+9Rsccy8vLFBVZ6VL4wgUV6MFFAha8QQDs4s7yoyeeof3QiS4LCRAAsysgYJSAhYOZUWkYK0MC+CJD+CztNAG8kbI8X32lRX0Hq82LzxQsfJlO0Rq3XqqDD+ElZsqqFCyHL7JWgPVdJWDBGwTAru4u9/vi2dl9jeiwaQIEwOwMCBglYOFgZlQaxsqQAL7IED5LO00Ab2Qgz5w5WtR7gNq8+mLB4hfoHG189/nab78M+mLJZQTwBZsBAk0TsOANAmB2d7kECIDLJcd9WRMgAM5aAdaHQEIELBzMEkJD2YAJ4IuAxWf0VRLAGxltkM8+08Je/dX2n/8oaOCAiru0x1/21+67Z9QbywpfsAkgQADMHoDAigQIgNkTvhIgAPZVOfqGQDMEeNHCFoFAIQF8wa6AgN0X895q+/HHWrTVtmrz3pvLRpio7bWHxqiuVXs99JC0447eTud14zxneC0fzSdIwII3eAdwghvEeGkCYOMCGx6PANiwuIwWNgELB7OwFWT6JAjgiySoUtMCAbyRsYr/+c+SEPiDdzRWQ7W37lON2ixuqk0b6dFHpYEDM+4xwOXxRYCiM3JRBCx4gwC4KKm5qAkCBMBsC18JEAD7qhx9Q6AZAhYOZogMgbgJ4Iu4iVLPCgG84YCS776rD0+9Uj8ef4W+nN8qr6H27aVJk6Q+fRzoM6AW8EVAYjNqSQQseIMAuCTJuXg5AgTAbAdfCRAA+6ocfUOAAJg9AIGSCVh4wVLy0NwAgSII4I0iIKV0ydSpSz7yYeHC/AU7dZIef1zacsuUGmEZPgOYPQCBlRCw8JxBAMz2LpcAAXC55LgvawIEwFkrwPoQSIiAhYNZQmgoGzABfBGw+Iy+SgJ4w60NMnGitOuuUk1Nfl9dVmvUtCcr9LOfudWv1W7whVVlmaulBCx4gwC4pbsg3PsJgMPV3vfJCYB9V5D+IWD438wjLgTiJmDhBUvcTKgHgYgA3nBvH4wdKw0bJtXXL+mtiz7Xg9pd1672a1307CD98Ifu9WytI3xhTVHmiYuABW8QAMe1G8KrQwAcnuZWJiYAtqIkc0BgBQIWDmaICoG4CeCLuIlSzwoBvOGmkvfeKw0fLq3R8LEe02B10981X+10wFqP6crn+ui733Wzbytd4QsrSjJH3AQseIMAOO5dEU49AuBwtLY2KQGwNUWZBwI5AhYOZogJgbgJ4Iu4iVLPCgG84a6S913xvrqdsp020ZvLmvxSnTVy/Sd0w3O/0DrruNu7753hC98VpP+kCFjwBgFwUrvDfl0CYPsaW52QANiqsswVPAELB7PgRQRA7ATwRexIKWiEAN5wWMipU1U/eIiq6vI/EPgzra5Dv/+kbnt2M62+usP9e9wavvBYPFpPlIAFbxAAJ7pFTBcnADYtr+nhCIBNy8twIROwcDALWT9mT4YAvkiGK1X9J4A3HNdw7Fg17DFMlQ25DwTOtfuB1tExP5mu0U9/X507Oz6Dh+3hCw9Fo+VUCFjwBgFwKlvF5CIEwCZlDWIoAuAgZGbIEAlYOJiFqBszJ0sAXyTLl+r+EsAbHmh3zz1qGL6/KtWY1+y7+q5O2XK6Rk/dQO3bezCHRy3iC4/EotVUCVjwBgFwqlvG1GIEwKbkDGoYAuCg5GbYkAhYOJiFpBezpkMAX6TDmVX8I4A3/NCs8aabVXHkEQXNvqEf6px+0zV64lpq08aPWXzoEl/4oBI9ZkHAgjcIgLPYOTbWJAC2oWOIUxAAh6g6MwdBwMLBLAihGDJVAvgiVdws5hEBvOGPWA1XXKnKU04uaPhv6qYrdpqq2x/qoupqf+ZxuVN84bI69JYlAQveIADOcgf5vTYBsN/6hdw9AXDI6jO7aQIWDmamBWK4TAjgi0yws6gHBPCGByIt12L9ueer6oLzCpp+Rlvptn0n64Y/dVJlpV8zudgtvnBRFXpygYAFbxAAu7CT/OyBANhP3ZbveqikAyT1kLS2pLmS3pL0oKQbcz8nMeUWkoZL2k7S+pKir2/4TNKHkp6RNC3XQ/43PsTXCQFwfCypBAGnCFg4mDkFlGZMEMAXJmRkiAQI4I0EoCZZsrFR9SedqqqrrihYZar66aHDx+uqG9upglepLVIBX7QIHzcbJmDBGwTAhjdowqPx1Jow4ATLd5T0J0lRALyyx/uS9s4FsnG1EgW9f5B0oKTm9k8XSXPiWniFOgTACYGlLASyJmDhYJY1Q9a3RwBf2NOUieIhgDfi4ZhqlcZG1R1+tKpvjd6rkv94VDvq6ZMf1EWXtSYEboEo+KIF8LjVNAEL3iAANr1FEx2uuQAv0cUpXjaBKknjJA3JVfhY0s2SXpXUVdJ+knrnfvdF7p9fK3u1b26Mak+S1D33R/+VNEbSS5K+lNRJ0saSBkn6Ra4XAuBVgLfwBBTDvqIEBPII4As2BAQKCeALdgUEmiaANzzdGQ0Nqt1vpFrdF72fJf9xn/bSP8+/R2edE73k4VEOAXxRDjXuCYGABW8QAIewU5OZkQA4Ga5JVz1S0g25RaLQd4CkKARe/nG5pKXfsjBd0rYxNDVR0va5OtF/t/VrSQtXUnddSZ9Iqoth3aZK8A7ghMBSFgJZE7BwMMuaIevbI4Av7GnKRPEQwBvxcMykSl2danbdS63HP5S3fL0q1U/TNOz32+jEEzPpzPtF8YX3EjJAQgQseIMAOKHNEUBZAmD/RI7+VXj00Q7r5FqP3mn7QhNjRNc9L2nz3O+i4PaxFox7kKTbc/dfL+mYFtSK41YC4DgoUgMCDhKwcDBzECsteU4AX3guIO0nRgBvJIY2ncKLFmnRkKFqM23Jy5RaVWu47tYD2mvxz7fcIh16aDqtWFoFX1hSk1niJGDBGwTAce6IsGoRAPund39JT+TaflJSv1WMcLCk23K/HyUp+rncR/QREj+SNE/Segl+uVyx/REAF0uK6yDgGQELBzPPkNOuBwTwhQci0WImBPBGJtjjXfTrr7Wo/xA1zp6tYfqLxmunZfWjL4O75x5pn33iXdJ6NXxhXWHmK5eABW8QAJerPvcRAPu3By6VdGqu7dMlRT+v7LG2pA9zv4w+IiL6uZxH9HnCM3I3Rh/UNaKcIjHfQwAcM1DKQcAVAhYOZq6wpA87BPCFHS2ZJF4CeCNenplV+/JL/eexV9Xj+K310Uf5XVRXS2PGSLvskll33i2ML7yTjIZTImDBGwTAKW0Wg8sQAPsn6oTlvvwt+uzfqc2M8G9JG+SuWUvSp2WMfKaki3L3HS/pGkl7SDpM0s8ldZH0P0kvSnpA0p0Jfvbv0vYJgMsQklsg4AMBCwczHzjTo18E8IVfetFtegTwRnqs01jpH/+Q+vaVPv88f7U2baTx46UB0asfHs0SwBfNIuKCQAlY8AYBcKCbN4axCYBjgJhyiX9J2jC3ZvT/7zazfvQxEUu/AG6b5d7JW0rbYyTtnrsh+g+wov9FAfDKHi9L2lXSO6Ussty1Ubjb3OMnkm6KLpo4caJ69OjR3PVO/n7u3Ll66aWXFvfWrVs3de7c2ck+aQoCaRLAF2nSZi1fCOALX5Siz7QJ4I20iSe/3osvVmm33Tpr3rz8l6obtvtQ1z/YQT16JPUd08nPltYK+CIt0qzjGwEL3pg9e7aGDBmyFH0vSbN804F+syFAAJwN95asGv378Ogdt9GjU+4zeVdVb/nwNvoPp8aVsXj0F0rP3H1vSNpEUo2k0blAuTbKL3PvCO6au+693LuDV/j390Wt3ljUVbmLLrnkEv3oR9HHE/OAAAQgAAEIQAACEIAABHwn8MorXXX++VurpqZ68Sh76n6N1kgd1vp2db/k29poo7m+j0j/EIAABMoi8Prrr+uMM85Yei8BcFkUw7yJANg/3aPgtVWu7ej/m/tX4NFn9g7PXR/9/z1ljPx6LvRdeusXkgbmPvJh+XLRZwxPkbRp7g9vlHRUGesRAJcBjVsgAAEIQAACEIAABCBghcCLL66piy7aSvvX3albdaiq1KAatdL+7e9Xv0s7av31o++m5gEBCEAgLAIEwGHpHee0BMBx0kynVhYB8JuSNl5uvEMl3baScaN3Av8t97tFkqLPHS71X9HzERDp7CVWgYCTBCz8p1lOgqUprwngC6/lo/kECeCNBOE6UPqNk+/S1qNOyOtkgdpqRNdHdfaUn+s732lwoEv3WsAX7mlCR24QsOANPgLCjb3kYxcEwP6plsVHQPw193EOEa2vJUUf8xAF0St7LP+RETtKir64Lu4HXwIXN1HqQcARAha+nMERlLRhiAC+MCQmo8RKAG/EitO9Ys89p9q+A9VqYf67fb9SR41cf6qun91da0f/DSKPPAL4gg0BgaYJWPAGXwLH7i6XAAFwueSyuy+LL4F7PPeRD9HUz0hq7h2610g6Lofol5KuTQAXAXACUCkJARcIWDiYucCRHmwRwBe29GSa+AjgjfhYOltp2jTVDd5B1bUL81r8TKvr8B8+pdue2VRdln5DirNDpNsYvkiXN6v5Q8CCNwiA/dlvrnVKAOyaIs33E72bdulXPg6QNLWZW/4taYPcNdHHMXza/BIFV0QB7rG5P5203PorK3WhpLNyv4z+/7dlrNncLQTAzRHi9xDwlICFg5mn6GnbYQL4wmFxaC1TAngjU/zpLT5+vOqH7qaq+ui7p795/Ffr6vgtZmj09A3VoUN67bi+Er5wXSH6y4qABW8QAGe1e/xflwDYPw0vlXRqru3TJUU/r+zxbUkf5X75iaTo53Ieh0u6KXdjqe8Ajt4J/MdyFm3mHgLgBKBSEgIuELBwMHOBIz3YIoAvbOnJNPERwBvxsXS+0n33qWGffVWp/O+Lfkvf19l9p2vUpHXUpo3zU6TSIL5IBTOLeEjAgjcIgD3ceI60TADsiBAltBG963dK7vppkvqv4t6Dl/uytlGSop/LeawvKXoncbRfSv0M4EGSoo+QiPtBABw3UepBwBECFg5mjqCkDUME8IUhMRklVgJ4I1aczhdrvPkWVRwRvTcl//F3/VRX7jJNt4zpqupq58dIvEF8kThiFvCUgAVvEAB7uvkcaJsA2AERSmyhStJ/JC39uoNfSHqhiRrRdc9L2jz3u+hjI6KPbyj3EX3URL/czYcuFyyvWK+bpL/l/vCr3LuOF5S76CruIwBOAColIeACAQsHMxc40oMtAvjClp5MEx8BvBEfS18qNVx6uSpPX/ofRH7T9Sz11J0HTNYf7+ioisBf5eILX3YzfaZNwII3CIDT3jV21gv8qdFbIY+WdF2u+1ckRe8Kjj7iYfnHZZJOyf3B05L6rGTagyTdnvvdk8uFvCtevixwlfRF7kvhXlzhougjJqJ3J2+W+/OLJP06IcoEwAmBpSwEsiZg4WCWNUPWt0cAX9jTlIniIYA34uHoW5X6M85S1e8Kv2bkcQ3UY8c/qt9d1SboEBhf+Laj6TctAha8QQCc1m6xtw4BsJ+aRv9h03hJ0ccrRI/oc35vlvSqpK6S9lsu8J2T++coKG7qUWwAHN17iaToc4ejR42kOyTNkBR9G0P0TuPDcutHv4/efbyNpPyv642PNwFwfCypBAGnCFg4mDkFlGZMEMAXJmRkiAQI4I0EoPpQsrFRdUcdp+qblr4n5pumx2h3vXbefTrr3HA/CwJf+LCJ6TELAha8QQCcxc6xsSYBsL86dpJ0t6SdVzFC9FER+0iauYprSgmAozLRu3qjEDj6iImVPaKPmohC6Oidwkk9CICTIktdCGRMwMLBLGOELG+QAL4wKCojxUIAb8SC0c8iDQ2q3fcAtbo/ekmU/7hdB+nrP9yq446v9HO2FnaNL1oIkNvNErDgDQJgs9sz8cEIgBNHnPgCu0oaKamHpLUkRZ+7+7akMZJulPRlMx2UGgBH5aLP+Y0+Bzh6B/J6klrlPoIiCppHS5qQ+NQSAXAKkFkCAlkQsHAwy4Iba9omgC9s68t05RPAG+WzM3Fnba0W7byH2jw2Lm+cf2gzbaPpuubOLhoxwsSkJQ2BL0rCxcUBEbDgDQLggDZszKMSAMcMlHKpESAATg01C0EgXQIWDmbpEmO1EAjgixBUZsZyCOCNcqgZu2fBAi0csIPaPhN9nYn0rLbUjhqvz7W6qqqkMWOkoUONzdzMOPgiLL2ZtngCFrxBAFy83lyZT4AAmB3hKwECYF+Vo28I8KKFPQCBkglYeMFS8tDcAIEiCOCNIiCFcMncuVrYe6Cef6OTdqgdq3mKPi1vyaNNG2nCBKl//xBALJkRX4SjNZOWRsCCNwiAS9Ocq78hQADMbvCVAAGwr8rRNwQIgNkDECiZgIUXLCUPzQ0QKIIA3igCUiiXfP65/v5We207uK2+XOFD8Dp2lKZMkbbcMgwY+CIMnZmydAIWvEEAXLru3LGEAAEwO8FXAgTAvipH3xAgAGYPQKBkAhZesJQ8NDdAoAgCeKMISIFdMnOmNGiQNH9+/uBdu0pPPSVttpl9IPjCvsZMWB4BC94gAC5Pe+4iAGYP+EuAANhf7egcAqskYOFghsQQiJsAvoibKPWsEMAbVpSMd45Jk6RddpFqa7+p20o16rfmK7rx2S204YbxrudaNXzhmiL04woBC94gAHZlN/nXB+8A9k8zOl5CgACYnQABowQsHMyMSsNYGRLAFxnCZ2mnCeANp+XJtLkHHpD22UdqaJDa62v9RcPURzN04LqP69rne2qddTJtL9HF8UWieCnuMQEL3iAA9ngDZtw6AXDGArB82QQIgMtGx40QcJuAhYOZ24TpzkcC+MJH1eg5DQJ4Iw3K/q5x663SyYfN0TjtrD56evEgn6uLDvvBk7rl2Z8q+lgIiw98YVFVZoqDgAVvEADHsRPCrEEAHKbuFqYmALagIjNAoAkCFg5mCAuBuAngi7iJUs8KAbxhRcmE5mho0Acb9da67z2Tt8CHWlu/7DZdo2b8QNEXxFl74AtrijJPXAQseIMAOK7dEF4dAuDwNLcyMQGwFSWZAwIrELBwMENUCMRNAF/ETZR6VgjgDStKJjjHmDFq2HMvVTY25C3yjr6nM3rP0B2Pr6e2bRNcP4PS+CID6CzpBQEL3iAA9mKrOdkkAbCTstBUEQQIgIuAxCUQ8JGAhYOZj9zp2W0C+MJtfeguOwJ4Izv2Pq3cePsoVRxycEHLr+rHumSHp3Tbw2uoutqniVbdK76woyWTxEvAgjcIgOPdEyFVIwAOSW1bsxIA29KTaSCwjICFgxlyQiBuAvgibqLUs0IAb1hRMvk5Gq76gyp/dWLBQrPVXbfuN0XX3dVZlZXJ95HGCvgiDcqs4SMBC94gAPZx57nRMwGwGzrQRekECIBLZ8YdEPCCgIWDmRegadIrAvjCK7loNkUCeCNF2AaWqj/7PFVdeH7BJFPVTxOPn6BLrmqrCgOvkPGFgc3KCIkQsOANAuBEtkYQRQ08vQWhE0MWEiAAZldAwCgBCwczo9IwVoYE8EWG8FnaaQJ4w2l53GuusVF1x52o6uuuLujtQe2mNy+8X6ef5f9nQeAL97YeHblBwII3CIDd2Es+dkEA7KNq9BwRIABmH0DAKAELBzOj0jBWhgTwRYbwWdppAnjDaXncbK6hQbUHHKJWd99R0N9tOlj1N96qw4/w+2UyvnBz69FV9gQseIMAOPt95GsHfj+z+UqdvuMgQAAcB0VqQMBBAhYOZg5ipSXPCeALzwWk/cQI4I3E0NouXFenRUP3UpsJDxXMeZlO1UYPXKphw/xFgC/81Y7OkyVgwRsEwMnuEcvVCYAtq2t7NgJg2/oyXcAELBzMApaP0RMigC8SAktZ7wngDe8lzG6AhQu1sP8QtX3myYIezqz6nQZOOk0DB2bXXktWxhctoce9lglY8AYBsOUdmuxsBMDJ8qV6cgQIgJNjS2UIZErAwsEsU4AsbpIAvjApK0PFQABvxAAx5BJz52rB1v3V7tUXCigc2+YWHTT9UPXo4R8gfOGfZnScDgEL3iAATmevWFyFANiiqmHMRAAchs5MGSABCwezAGVj5IQJ4IuEAVPeWwJ4w1vp3Gn8k0+0sHsftX3/n3k9/Vq/0Q2r/1ozZkg/+pE77RbTCb4ohhLXhEjAgjcIgEPcufHMTAAcD0eqpE+AADh95qwIgVQIWDiYpQKKRYIigC+CkpthSyCAN0qAxaUrJ/Dee1rYvbfafvZfNahCx+qPukFHL75+/fWlp5+WvvMdfwDiC3+0otN0CVjwBgFwunvG0moEwJbUDGsWAuCw9GbagAhYOJgFJBejpkQAX6QEmmW8I4A3vJPM3YZffVULeg/UwXOu0r3aJ6/PTTbR4ncCr7GGu+0v3xm+8EMnukyfgAVvEACnv2+srEgAbEXJ8OYgAA5PcyYOhICFg1kgUjFmigTwRYqwWcorAnjDK7ncb/brr3XTnzroyCMLW40+C3jKFKlTJ/fHwBfua0SH2RCw4A0C4Gz2joVVCYAtqBjmDATAYerO1AEQsHAwC0AmRkyZAL5IGTjLeUMAb3gjlVeNXnyxdOaZhS0PHCg9+qjUpo3b4+ALt/Whu+wIWPAGAXB2+8f3lQmAfVcw3P4JgMPVnsmNE7BwMDMuEeNlQABfZACdJb0ggDe8kMm7JhsbpVNOka68Mr/1LfSCuu38Hd3y0BqqqnJ3LHzhrjZ0li0BC94gAM52D/m8OgGwz+qF3TsBcNj6M71hAhYOZoblYbSMCOCLjMCzrPME8IbzEnnbYEODdPDB0ujRS0YYoCl6SLvpNf1Ydx44RVff3kkVjr6axhfebjsaT5iABW8QACe8SQyXd/QpyzBxRouLAAFwXCSpAwHHCFg4mDmGlHYMEMAXBkRkhEQI4I1EsFI0R6C2Vho2TGr1yF90t4arjWoW/2ayttNTp47Tby5187Mg8AVbGAJNE7DgDQJgdne5BAiAyyXHfVkTIADOWgHWh0BCBCwczBJCQ9mACeCLgMVn9FUSwBtskKQJLJo2S9X9+6hKDXlL3a899Z/L/qxfneLeZ0Hgi6R3BfV9JWDBGwTAvu6+7PsmAM5eAzoojwABcHncuAsCzhOwcDBzHjINekcAX3gnGQ2nRABvpAQ65GUaGlQz/CC1vvfOAgo36gi1vf0GHXiQWy+r8UXIG5bZV0XAgjcIgNnj5RJw65mq3Cm4L0QCBMAhqs7MQRCwcDALQiiGTJUAvkgVN4t5RABveCSWz63W1mrRTnuozeRxBVNcXHGmNnvoIg0d6s6A+MIdLejELQIWvEEA7Nae8qkbAmCf1KLX5QkQALMfIGCUgIWDmVFpGCtDAvgiQ/gs7TQBvOG0PLaaW7BAC/tur7azpxfMdVrVFdppyknq29eNkfGFGzrQhXsELHiDANi9feVLRwTAvihFnysSIABmT0DAKAELBzOj0jBWhgTwRYbwWdppAnjDaXnsNTdnjhb07Kd2b7xUMNtRbUfpyJkHaostsh8bX2SvAR24ScCCNwiA3dxbPnRFAOyDSvTYFAECYPYFBIwSsHAwMyoNY2VIAF9kCJ+lnSaAN5yWx2ZzH32kBd37qN1/386br05VOuRbY3T27KHaeONsR8cX2fJndXcJWPAGAbC7+8v1zgiAXVeI/lZGgACYvQEBowQsHMyMSsNYGRLAFxnCZ2mnCeANp+Wx29w772hh995q+/mHeTMuVBsdtPYkXfnXvlp33ezGxxfZsWdltwlY8AYBsNt7zOXuCIBdVofeVkWAAJj9AQGjBCwczIxKw1gZEsAXGcJnaacJ4A2n5bHd3Msvq6bntmo9f07enF+qsw77/jTd+NwW6to1GwT4IhvurOo+AQveIAB2f5+52iEBsKvK0FdzBAiAmyPE7yHgKQELBzNP0dO2wwTwhcPi0FqmBPBGpvhZfOZM1fbbTq1qF+Sx+ERr6pebz9BtM36oDh3Sx4Qv0mfOin4QsOANAmA/9pqLXRIAu6gKPRVDgAC4GEpcAwEPCVg4mHmInZYdJ4AvHBeI9jIjgDcyQ8/CSwlMmKD6nYeqqqEuj8m7+q7O7v+0bpu0nlq1ShcXvkiXN6v5Q8CCNwiA/dlvrnVKAOyaIvRTLAEC4GJJcR0EPCNg4WDmGXLa9YAAvvBAJFrMhADeyAQ7i65AoPHue9S4//6qVOOy3/xbG2iQJmurAzbRqFFSZWV62PBFeqxZyS8CFrxBAOzXnnOpWwJgl9Sgl1IIEACXQotrIeARAQsHM49w06onBPCFJ0LRZuoE8EbqyFlwJQQarrlWlcf/cvFvX9Gm2l6T9F+tv/jnk0+WLr88PXT4Ij3WrOQXAQveIAD2a8+51C0BsEtq0EspBAiAS6HFtRDwiICFg5lHuGnVEwL4whOhaDN1AngjdeQsuAoC9WefpzeufUx95ozTF8r/BrjLLpNOOSUdfPgiHc6s4h8BC94gAPZv37nSMQGwK0rQR6kECIBLJcb1EPCEgIWDmSeoadMjAvjCI7FoNVUCeCNV3CzWHIHGRs37vEYDdmij2bMLL77jDmnkyOaKtPz3+KLlDKlgk4AFbxAA29ybaUxFAJwGZdZIggABcBJUqQkBBwhYOJg5gJEWjBHAF8YEZZzYCOCN2FBSKEYCn34q9ekjvflmftGqKmnsWGmnnWJcrIlS+CJZvlT3l4AFbxAA+7v/zyZhTAAAIABJREFUsu6cADhrBVi/XAIEwOWS4z4IOE7AwsHMccS05yEBfOGhaLScCgG8kQpmFimDwHvvSb16SR988M3NFWrQwNYzdMG0bbV19GomoQe+SAgsZb0nYMEbBMDeb8PMBiAAzgw9C7eQAAFwCwFyOwRcJWDhYOYqW/rylwC+8Fc7Ok+WAN5Ili/VW0bgH/+QttlGmjNHaqUa3aZDNEJ/0uEd/qRfPTdcm27asvoruxtfJMOVqv4TsOANAmD/92FWExAAZ0WedVtKgAC4pQS5HwKOErBwMHMULW15TABfeCwerSdKAG8kipfiMRCYMUPabbt5umvRnhqiSYsr1qpaB68xThe/sL022CCGRVYogS/iZ0pFGwQseIMA2MZezGIKAuAsqLNmHAQIgOOgSA0IOEjAwsHMQay05DkBfOG5gLSfGAG8kRhaCsdI4MOeu2mdZ8fmVZynDjr0e0/ouue31Oqrx7iYJHwRL0+q2SFgwRsEwHb2Y9qTEACnTZz14iJAABwXSepAwDECFg5mjiGlHQME8IUBERkhEQJ4IxGsFI2bwN/+pppefdV6wdy8yp9pdR3b7Wnd9vQm6tAhvkXxRXwsqWSLgAVvEADb2pNpTkMAnCZt1oqTAAFwnDSpBQGHCFg4mDmEk1aMEMAXRoRkjNgJ4I3YkVIwKQLTpqlu0BBV1y3KW+E9fUdnD5ipWyeup1at4lkcX8TDkSr2CFjwBgGwvX2Z1kQEwGmRZp24CRAAx02UehBwhICFg5kjKGnDEAF8YUhMRomVAN6IFSfFEibQ+JcxatxrL1U2NuSt9LJ+ouv2eUp/vLuLKitb3gS+aDlDKtgkYMEbBMA292YaUxEAp0GZNZIgQACcBFVqQsABAhYOZg5gpAVjBPCFMUEZJzYCeCM2lBRKiUDDDTep8ugjC1abod4af8Jj+u1V7VvcCb5oMUIKGCVgwRsEwEY3ZwpjEQCnAJklEiFAAJwIVopCIHsCFg5m2VOkA2sE8IU1RZknLgJ4Iy6S1EmTQN15v1H1+ecULPmIdtablzyok0+vblE7+KJF+LjZMAEL3iAANrxBEx6NADhhwJRPjAABcGJoKQyBbAlYOJhlS5DVLRLAFxZVZaY4COCNOChSI3UCjY2qOeqXan3THwuWvk0Hq3rUrRp5YPkv1fFF6oqyoCcELHiDANiTzeZgm+U/qzg4DC0FRYAAOCi5GTYkAhYOZiHpxazpEMAX6XBmFf8I4A3/NKPjHIH6ei0cNlxtx95XgOTSitP1k3GXaMcdy6OFL8rjxl32CVjwBgGw/X2a1IQEwEmRpW7SBAiAkyZMfQhkRMDCwSwjdCxrmAC+MCwuo7WIAN5oET5uzprAokVaMHAntXt6SkEnp7W6Uns89Sv17Fl6k/iidGbcEQYBC94gAA5jryYxJQFwElSpmQYBAuA0KLMGBDIgYOFglgE2ljROAF8YF5jxyiaAN8pGx42uEPjqKy3Yqp/avfZCQUdHdrhLJ87eXz/+cWnN4ovSeHF1OAQseIMAOJz9GvekBMBxE6VeWgQIgNMizToQSJmAhYNZyshYLgAC+CIAkRmxLAJ4oyxs3OQagU8+0YKf91a7/76V19nvdaKuXP/3mjlT2mCD4pvGF8Wz4sqwCFjwBgFwWHs2zmkJgOOkSa00CRAAp0mbtSCQIgELB7MUcbFUIATwRSBCM2bJBPBGyci4wVUC77yjhb/opbZffLS4w7N0oX6rMyVVLH4H8IwZUteuxTWPL4rjxFXhEbDgDQLg8PZtXBMTAMdFkjppEyAATps460EgJQIWDmYpoWKZgAjgi4DEZtSSCOCNknBxsesEXnpJC3sP0C+/vkS36PC8brfeWnr8cal9++aHwBfNM+KKMAlY8AYBcJh7N46pCYDjoEiNLAgQAGdBnTUhkAIBCwezFDCxRGAE8EVggjNu0QTwRtGouNAXAnPm6OLrV9OZ0Zt/V3jsuKP00ENSq1arHgZf+CI2faZNwII3CIDT3jV21iMAtqNlaJMQAIemOPMGQ8DCwSwYsRg0NQL4IjXULOQZAbzhmWC0WxSBxkbpxBOlq68uvHzkSGnUKKliFa/k8UVRmLkoQAIWvEEAHODGjWlkAuCYQFImdQIEwKkjZ0EIpEPAwsEsHVKsEhIBfBGS2sxaCgG8UQotrvWJQEODNGKEdM89+V1vpLc1/KR19JsrVv5ZEPjCJ6XpNU0CFrxBAJzmjrG1FgGwLT1DmoYAOCS1mTUoAhYOZkEJxrCpEMAXqWBmEQ8J4A0PRaPlognU1Eg77yxNnrzklu6arfHaUbO0td67cox++avqJmvhi6IRc2FgBCx4gwA4sE0b47gEwDHCpFSqBAiAU8XNYhBIj4CFg1l6tFgpFAL4IhSlmbNUAnijVGJc7xuBr76SBgyQVnt+sh7U7uqorxePcIsOU+c/36S99yl8SY8vfFOZftMiYMEbBMBp7RZ76xAA29M0lIkIgENRmjmDI2DhYBacaAycOAF8kThiFvCUAN7wVDjaLonAF4/MUIehA9RatXn3XVh5jno/fr76988vhy9KwsvFARGw4A0C4IA2bMyjEgDHDJRyqREgAE4NNQtBIF0CFg5m6RJjtRAI4IsQVGbGcgjgjXKocY93BBYt0vwBO6n9zCkFrf+q7fU66Jmj1K3bN7/CF94pTMMpEbDgDQLglDaLwWUIgA2KGshIBMCBCM2Y4RGwcDALTzUmTpoAvkiaMPV9JYA3fFWOvksmMHeu5m/ZV+3f+FverfWq1OFdHtA5L+yu731vya/wRcl0uSEQAha8QQAcyGZNYEwC4ASgUjIVAgTAqWBmEQikT8DCwSx9aqxonQC+sK4w85VLAG+US477vCTw0Ueav0Uvtf/onbz2F6qNDtngcV3zYh+tvjoBsJfa0nQqBCw8ZxAAp7JVTC5CAGxS1iCGIgAOQmaGDJGAhYNZiLoxc7IE8EWyfKnuLwG84a92dF4mgTff1MLuvdX2q8/yCnyh1XTsz2bollmbaeHCzzV9+vTFv99mm23UtWvXMhfjNgjYImDhOYMA2NaeTHMaAuA0abNWnAQIgOOkSS0IOETAwsHMIZy0YoQAvjAiJGPETgBvxI6Ugj4QePZZ1W47QK1q5ud1+77W1zkDZ+p3d3fQrFkEwD5ISY/pErDwnEEAnO6esbQaAbAlNcOahQA4LL2ZNiACFg5mAcnFqCkRwBcpgWYZ7wjgDe8ko+G4CIwfr/qdh6qqsT6v4ivaVFfv9bh2GP6cKip4B3BcuKljg4CF5wwCYBt7MYspCICzoM6acRAgAI6DIjUg4CABCwczB7HSkucE8IXnAtJ+YgTwRmJoKewBgYbbRqny0IMLOp2uPrpxj2u118h3+QgID3SkxfQIWHjOIABOb79YW4kA2Jqi4cxDAByO1kwaGAELB7PAJGPcFAjgixQgs4SXBPCGl7LRdIwE6i68WNVnn1lQ8UHtpslHnK0LL/4enwEcI29K+U3AwnMGAbDfezDL7gmAs6TP2i0hQADcEnrcCwGHCVg4mDmMl9Y8JYAvPBWOthMngDcSR8wCrhNobFTNUcer9U3XFnR6o45Qu1GXaeSBnV2fgv4gkAoBC88ZBMCpbBWTixAAm5Q1iKEIgIOQmSFDJGDhYBaibsycLAF8kSxfqvtLAG/4qx2dx0igvl4Ld9tXbcc9kFf0M62unq1f0Kgp31GfPjGuRykIeErAwnMGAbCnm8+BtgmAHRCBFsoiQABcFjZugoD7BCwczNynTIe+EcAXvilGv2kRwBtpkWYd5wksXKj5fYeo/XNPLm71HX1PQzRRb2oTrbaaNGOGtNlmzk9BgxBIlICF5wwC4ES3iOniBMCm5TU9HAGwaXkZLmQCFg5mIevH7MkQwBfJcKWq/wTwhv8aMkGMBObM0fwe2+qttyu1feMEfaR1lhVff31p1iwp+n8eEAiVgIXnDALgUHdvy+cmAG45QypkQ4AAOBvurAqBxAlYOJglDokFgiOAL4KTnIGLJIA3igTFZeEQ+PBDPTipXnseuq4aGirz5o7eATx9utSlSzg4mBQCyxOw8JxBAMyeLpcAAXC55LgvawIEwFkrwPoQSIiAhYNZQmgoGzABfBGw+Iy+SgJ4gw0CgUICkS/OP/9dXX31zwt+uc020qRJUrt2kINAeAQsPGcQAIe3b+OamAA4LpLUSZsAAXDaxFkPAikRsHAwSwkVywREAF8EJDajlkQAb5SEi4sDIbDUFw88sLHuumvTFaZu1H47z9OdD3VSVVUgQBgTAjkCFp4zCIDZzuUSIAAulxz3ZU2AADhrBVgfAgkRsHAwSwgNZQMmgC8CFp/RV0kAb7BBIFBIYKkvGhulRx/dXrfc0nbxRRVq0JU6SQP0hO445CldfstqqiARYAsFRMDCcwYBcEAbNuZR+es+ZqCUS40AAXBqqFkIAukSsHAwS5cYq4VAAF+EoDIzlkMAb5RDjXusE1jeF716baNjjumqhx9YpNEaqX103+Lxp6mvnjl3os44b0k4zAMCIRCw8JxBABzCTk1mRgLgZLhSNXkCBMDJM2YFCGRCwMLBLBNwLGqaAL4wLS/DtYAA3mgBPG41S2BFX7SvbK9XN9xRP58zNW/m+7Wn5t38Zx18GJ8FYXYzMFgeAQvPGQTAbOpyCRAAl0uO+7ImQACctQKsD4GECFg4mCWEhrIBE8AXAYvP6KskgDfYIBAoJFDgiy5dtOjQY9Tm9hsKLv5jxbH67thrtPMuRAPsJfsELDxnEADb36dJTcjf8kmRpW7SBAiAkyZMfQhkRMDCwSwjdCxrmAC+MCwuo7WIAN5oET5uNkqgSV/U12vBTnuq3aSHCqY+p/q32nH6/6lnT6NAGAsCOQIWnjMIgNnO5RIgAC6XnDv3DZV0gKQektaWNFfSW5IelHRj7uekux0l6cDlFjlf0nkJL0oAnDBgykMgKwIWDmZZsWNduwTwhV1tmaxlBPBGy/hxt00CK/XFggWa32ew2r8wo2Dw4zrcrl/+9SBtsolNJkwFgYiAhecMAmD2crkECIDLJZf9fR0l/UlSFACv7PG+pL0lPZNguztIGr9CfQLgIoFbeAIqclQug0DRBPBF0ai4MCAC+CIgsRm1JAJ4oyRcXBwIgVX64osvNP8X26j9O6/k0ahTlQ5b6xH99sUdtO66gYBizOAIWHjOIAAObtvGNjABcGwoUy0UfUr/OElDcqt+LOlmSa9K6ippP0m9c7/7IvfPryXQYWdJ/5C0gaSvJXXIrUEAXCRsC09ARY7KZRAomgC+KBoVFwZEAF8EJDajlkQAb5SEi4sDIdCsL95/Xwu26KV2//tPHpGv1V5HbjxV1z2/pTpHr/R4QMAYgWa94cG8BMAeiORoiwTAjgrTTFtHSlr6Cf5R6DtAUhQCL/+4XNLJuT+YLmnbBEaNPmLiCEnRO43vl3RSbg0C4CJhW3gCKnJULoNA0QTwRdGouDAgAvgiILEZtSQCeKMkXFwcCIGifPHKK1q0ZR+1mT8nj8qnWkOnbD1TN0/bWK1bBwKMMYMhUJQ3HKdBAOy4QA63RwDssDgraS16928UuK6T+/0vJL3QxLXRdc9L2jz3u+0lPRbjuFHo/LikaA/tIqm7pHMJgEsjbOEJqLSJuRoCzRPAF80z4orwCOCL8DRn4uII4I3iOHFVWASK9sWMGartP0it6hbmAXpbG+nKYTN17f3fVgWJQVibx/i0RXvDYQ4EwA6L43hr/HXuuEBNtNdf0hO5P39SUr9VjHCwpNtyv4++qC36OY5He0kvS9pI0r2S9s196RsBcIl0LTwBlTgyl0OgWQL4ollEXBAgAXwRoOiMXBQBvFEUJi4KjEApvmh88CE1DhumysaGPEqz1V3jTpqq86+IvnqGBwRsECjFG65OTADsqjLu90UA7L5GK3Z4qaRTc394uqTo55U91pb0Ye6X0UdERD/H8fiDpOOjL9GUtGnu4yfO4x3ApaO18ARU+tTcAYFVE8AX7BAIFBLAF+wKCDRNAG+wMyDQ8ueM+j/eoKrjji4oNF476N9Xj9VRv2wFZgiYIGDhOYMA2MRWzGQIAuBMsLdo0QnLfflb9DEMU5up9u/cl7RFl60l6dMWrS71khR9pnClpEMk3Z6rRwBcBlgLT0BljM0tEFglAXzBBoFAy1/MwxACoRDgOSMUpZmzFALl+KLm1LPU+vLfFixztK7X9g8epd12K6UDroWAmwTK8YZrkxAAu6aIP/0QAPuj1dJO/yVpw9wP0f+/28wI0cdELP0CuG0kzWjByG0l/U3SJpKmSNpuuVoEwGWAtfAEVMbY3AIBAmD2AARKJMDzRYnAuDwYAngjGKkZtAQCZfmisVEL9z1Ibe8bvWylP+h4naQr1bptlaZMkXpFbwXiAQGPCZTlDcfmJQB2TBCP2iEA9kisXKvRxy50yf1zJ0nzmhlhjKTdc9dEX9Y2rgUjL/34iQWSfirp7YQC4K2L6PEnkm6Krps4caJ69OhRxC3uXTJ37ly99NJLixvr1q2bOnfu7F6TdASBlAngi5SBs5wXBPCFFzLRZAYE8EYG0FnSeQJl+6KmRtW7DVfnZ6fqZF2uK3VS7ju/pS5dGjRhwlxtvHH+ZwU7D4MGIbAcgbK94RDF2bNna8iQIUs7iv61zCyH2qMVhwkQADsszkpaq5G09EOYov+va2aEP0kanrsm+v97yhw5Slijv1iqJJ0m6bIV6sT5DuDGUnq85JJL9KMf/aiUW7gWAhCAAAQgAAEIQAACEIAABFYgUL1ggRY99o6G332qFi2qzvvtWmt9rd/9brq6dFkENwhAICMCr7/+us444wwC4Iz4+7wsAbB/6mURALeW9FdJ0btuX5C0paR6AmD/Ng8dQwACEIAABCAAAQhAAAIQaI7A889/W7/97ZZqaIi++uWbx0YbzdFFFz2tdu2aex9ScyvwewhAoBwCBMDlUOOeiAABsH/7IIuPgLhA0tm50DcKf6MQeMVHnO8A5iMg/NuXdAyB2AhY+E+zYoNBIQjkCOALtgIEmiaAN9gZECgkEJcv7ryzjU48sUPBAgMG1Ojuu+ep1dL/LhURIOAJgbi8keW4fARElvT9XpsA2D/90v4SuG6SZuc+diL62Ifo4x+aesQZABejShQSz4wunDlzprbeupjMuJiy6V5j4UPo0yXGaiEQwBchqMyMpRLAF6US4/pQCOCNUJRmzlIIxOmLc8+VLojeDpR7/FBv6Br9UuP2uUt/uGctVZAolCIN12ZMIE5vZDUKXwKXFXn/1+Wva/80nCBp6Sd+D5A0tZkR/i1pg9w1a0n6tMSRfy/pREnRp/1fIin6ArimHlEv/XO/iHp6IvfPb0i6v8Q1i7mcALgYSlwDAQ8JWDiYeYidlh0ngC8cF4j2MiOANzJDz8IOE4jTF42N0mGHSbfdJvXULD2iXbSG/qfn1EOTTp+qsy8pfIeww2hoLXACcXojK5QEwFmR939dAmD/NLxU0qm5tk+XFP28sse3JX2U++UnkqKfS31cJemEUm9a7vqxknZrwf0ru5UAOAGolISACwQsHMxc4EgPtgjgC1t6Mk18BPBGfCypZIdA3L6orZV+u9VYnfbivmqnhctAjdNO+vC6h3T40flfFmeHJJNYIxC3N7LgQwCcBXUbaxIA+6dj9E7bKbm2py33rtumJjlY0m25X4ySFP1c6oMAuFRiJVxv4QmohHG5FAJFEcAXRWHiosAI4IvABGfcogngjaJRcWFABJLwRc0pZ6r1FRcXULxZh2vtsTdql6FECwFtMW9HTcIbacMgAE6buJ31+FvaPy2rJP1H0tq51n+xki9li657XtLmueuij42YlOC4fAZwGXAtPAGVMTa3QGCVBPAFGwQChQTwBbsCAk0TwBvsDAik9JzR2KgFe49UuwfuKljwguoLtP2Ms7XVVqgBAbcJWHjOIAB2e4+53B0BsMvqrLy3oyVdl/v1K5KidwVHH/Gw/CP6wrZTcn/wtKQ+Kyl3kKTbc797UlK/MpEQAJcBzsITUBljcwsECIDZAxAokQDPFyUC4/JgCOCNYKRm0BIIJOaLmhp93X8ndZj5eEE3J3S8Vce9cIg23riERrkUAikTSMwbKc5BAJwibGNLEQD7KWj0IUvjJQ3KtR99zu/Nkl6V1FXSfssFvnNy/xwFxU09CIAz3AMWnoAyxMfSRgngC6PCMlaLCOCLFuHjZsME8IZhcRmtbAKJ+mLuXH3dfVt1+OdLef3VqUpHrP2ILnlpB60VffU4Dwg4SCBRb6Q0LwFwSqANLkMA7K+onSTdLWnnVYwQfVTEPpJmruIaAuAM94CFJ6AM8bG0UQL4wqiwjNUiAviiRfi42TABvGFYXEYrm0DivvjgA83ffGu1//TfeT3OUwcdu+k0Xfdcd3XoUHb73AiBxAgk7o3EOv+mMAFwCpCNLkEA7L+wu0oaKamHpOjftX4l6W1JYyTdKOnLZkYkAM5wD1h4AsoQH0sbJYAvjArLWC0igC9ahI+bDRPAG4bFZbSyCaTii9de08LuvdV2/hd5fX6stXRmv1m6cfJGqo7+u1UeEHCIQCreSHheAuCEARsuTwBsWFzjo2299J3NM2fO1NZbRz/697DwBOQfdTp2nQC+cF0h+suCAL7Igjpr+kAAb/igEj2mTSA1X8yYodp+26lV/aK8Ed/Uxrp+xExdOXoNVZA4pC0/662CQGreSFAFAuAE4RovzV/HxgU2PB4BsGFxGS1sAhYOZmEryPRJEMAXSVClpgUCeMOCiswQN4E0fdH4wF/UuNdeqlRj3hjPaCs9cdYTOvPC9nGPRz0IlE0gTW+U3WQzNxIAJ0XWfl0CYPsaW52QANiqsswVPAELB7PgRQRA7ATwRexIKWiEAN4wIiRjxEogbV/U/f4aVZ90fMEMYzVUn9/0Fx18OJ8FEavAFCubQNreKLvRVdxIAJwE1TBqEgCHobPFKQmALarKTBCQZOFghpAQiJsAvoibKPWsEMAbVpRkjjgJZOGLRSecpjZXX1Ywxh8qTtAPH71KO+wQ54TUgkB5BLLwRnmdrvwuAuC4iYZTjwA4HK2tTUoAbE1R5oFAjoCFgxliQiBuAvgibqLUs0IAb1hRkjniJJCJLxoaNH/YCLV/6J5lo3yiNbWTHtVrHXpo2jSpe/c4p6QWBEonkIk3Sm9zlXcQAMcMNKByBMABiW1sVAJgY4IyDgSWErBwMENNCMRNAF/ETZR6VgjgDStKMkecBDLzxaJF+rrvDurw7FT9Uz/QEE3Uv/T9xaOttZY0a5a00UZxTkotCJRGIDNvlNYmAXCMvCj1DQECYHaDrwQIgH1Vjr4h0AwBCwczRIZA3ATwRdxEqWeFAN6woiRzxEkgU198+aU+3u9E/WLKpfpvzZp5Y228sTRzprTGGnFOSy0IFE8gU28U3yYBcEysKJNPgACYHeErAQJgX5WjbwgQALMHIFAyAQsvWEoemhsgUAQBvFEEJC4JjoALvvjLX6S99pIaG/Px9+wpTZkitW8fnCwM7AABF7zRUgx8BERLCYZ7PwFwuNr7PjkBsO8K0j8EVkLAwsEMcSEQNwF8ETdR6lkhgDesKMkccRJwxRdXXy2dcELhZLvuKkUBcVVVnFNTCwLNE3DFG813uvIrCIBbQi/sewmAw9bf5+kJgH1Wj94hsAoCFg5mCAyBuAngi7iJUs8KAbxhRUnmiJOAS7449VTp8suXn65RR+kGdTx4b1166+qqIJGIU3pqNUPAJW+UKxYBcLnkuI+/btkDvhIgAPZVOfqGQAAHM0SGQNwELLxgiZsJ9SAQEcAb7AMIFBJwyRcNDdL++0t//rNUqXpdq+N0tG7Q0+qlZy98XCed1Q4JIZAaAZe8Ue7QBMDlkuM+AmD2gK8ECIB9VY6+IUAAzB6AQMkELLxgKXloboBAEQTwRhGQuCQ4Aq75YtEiabfB83XkU8O1m8Yu0+MBDVPdXfdq3/35LIjgNmlGA7vmjXIwEACXQ417IgIEwOwDXwkQAPuqHH1DgACYPQCBkglYeMFS8tDcAIEiCOCNIiBxSXAEXPTF/EuvUfvTjy/Q4urKE9Xtid+rb9/gZGLgDAi46I1SMRAAl0qM65cSIABmL/hKgADYV+XoGwIEwOwBCJRMwMILlpKH5gYIFEEAbxQBiUuCI+CkLxoaNH+34Wr/yL0Fevxf29/rgL+eqE03DU4qBk6ZgJPeKJEBAXCJwLh8GQECYDaDrwQIgH1Vjr4hQADMHoBAyQQsvGApeWhugEARBPBGEZC4JDgCzvpi4ULN67O9Ov71qTxNGlShY9a4X+f+fZjWWSc4uRg4RQLOeqMEBgTAJcDi0jwCBMBsCF8JEAD7qhx9Q4AAmD0AgZIJWHjBUvLQ3ACBIgjgjSIgcUlwBJz2xeefa97mvdXx/dfzdFmgtjrmh1N0zV97qWPH4CRj4JQIOO2NIhkQABcJissKCBAAsyl8JUAA7Kty9A0BAmD2AARKJmDhBUvJQ3MDBIoggDeKgMQlwRFw3hfvvqsF3Xqq3dyP87T5TKvr/7adqeun/FDV1cHJxsApEHDeG0UwIAAuAhKXNEmAAJiN4SsBAmBflaNvCBAAswcgUDIBCy9YSh6aGyBQBAG8UQQkLgmOgBe++OtfVbN1X7Wu/TpPn7e1ka4bMUuXj15LFaQVwe3dpAf2whvNQCAATnqX2K3PX6l2tbU+GQGwdYWZL1gCFg5mwYrH4IkRwBeJoaWw5wTwhucC0n4iBHzxReOj49Wwy1BVNdbncXhOPfTkuVN16nkdEuFD0XAJ+OKNVSlEABzu/m3p5ATALSXI/VkRIADOijzrQiBhAhYOZgkjonyABPBFgKIzclEE8EZRmLgoMAI++aLu+ptUfcyRBQqN1VDNu2OM9h9ZFZh6jJskAZ+8sTIOBMBJ7hDbtQmAbetreToCYMvqMlvQBCykP9SoAAAgAElEQVQczIIWkOETIYAvEsFKUQME8IYBERkhdgK++WLRKWepzRW/LeBwfeUx2uSxazVgILFF7Jsk0IK+eaMpmQiAA928MYzN36QxQKREJgQIgDPBzqIQSJ6AhYNZ8pRYITQC+CI0xZm3WAJ4o1hSXBcSAe980dio+XuOVPsxdxXIdE6b32nv50/TT34SkoLMmhQB77zRBAgC4KR2h/26BMD2NbY6IQGwVWWZK3gCFg5mwYsIgNgJ4IvYkVLQCAG8YURIxoiVgJe+qKnRvD5D1HH21DwWf9JwnbHeXXrm2Qqtt16smCgWIAEvvbGCTgTAAW7cmEYmAI4JJGVSJ0AAnDpyFoRAOgQsHMzSIcUqIRHAFyGpzaylEMAbpdDi2lAIeOuLOXM0b4s+6vjuK4ulukSn60z9Vo2qVLdu0lNPSZ07h6IicyZBwFtvLAeDADiJnRFGTQLgMHS2OCUBsEVVmQkCkiwczBASAnETwBdxE6WeFQJ4w4qSzBEnAa998f77i0PgU/93hm7Q0XlYBg+Wxo2TWrWKkxa1QiLgtTdyQhEAh7Rj452VADhenlRLjwABcHqsWQkCqRKwcDBLFRiLBUEAXwQhM0OWQQBvlAGNW8wT8N4X8+frot+3169/XSjVwQdLt94qVZBkmN/HSQzovTckEQAnsTPCqMlfm2HobHFKAmCLqjITBHgHMHsAAk0SsPCCBWkhkAQBvJEEVWr6TsCCLxobpSOOkG65pVCN88+XzjnHd5XoPwsCFrxBAJzFzrGxJgGwDR1DnIIAOETVmTkIAhYOZkEIxZCpEsAXqeJmMY8I4A2PxKLV1AhY8UVdnbTLLtLEifno1tLHuvTmrjrwMD4LIrVNZWQhC94gADayGTMYgwA4A+gsGQsBAuBYMFIEAu4RsHAwc48qHflOAF/4riD9J0UAbyRFlro+E7Dki6++kvr2lV58cYkim+oVTdAOerxisDaYeLMGDSbS8Hmvpt27BW8QAKe9a+ysx9+WdrQMbRIC4NAUZ95gCFg4mAUjFoOmRgBfpIaahTwjgDc8E4x2UyFgzRcffij17Clt9O+pelC7azV9uZjjha0v0C7Pna1u3VLByiIGCFjwBgGwgY2Y0QgEwBmBZ9kWEyAAbjFCCkDATQIWDmZukqUrnwngC5/Vo/ckCeCNJOlS21cCFn3x3q2Pa53DdlRr1ebJcuJqo3TKywdq/fV9VYu+0yRgwRsEwGnuGFtrEQDb0jOkaQiAQ1KbWYMiYOFgFpRgDJsKAXyRCmYW8ZAA3vBQNFpOnIBJX8ydq3k/31Yd334pj1+tqnXM9ybo8r9tp299K3G0LOA5AQveIAD2fBNm2D4BcIbwWbpFBAiAW4SPmyHgLgELBzN36dKZrwTwha/K0XfSBPBG0oSp7yMBs7744AN9/bOe6vC/9/NkmatOOnnLGfrj9J+pdWsfFaPntAhY8AYBcFq7xd46BMD2NA1lIgLgUJRmzuAIWDiYBScaAydOAF8kjpgFPCWANzwVjrYTJWDaF6+8ooXde6vtwiWfA7z08R+tp8uHPaPf37++Kkg5Et1fPhe34A0CYJ93YLa981djtvxZvXwCBMDls+NOCDhNwMLBzGnANOclAXzhpWw0nQIBvJECZJbwjoB1XzQ+MVX1g7ZXdUP+5wG/rJ/o4VNn6KxL+SwI7zZtSg1b8AYBcEqbxeAyBMAGRQ1kJALgQIRmzPAIWDiYhacaEydNAF8kTZj6vhLAG74qR99JEgjBF/V33q2qkfsXYHxcA/Xv68frkKP4LIgk95ivtS14gwDY192Xfd8EwNlrQAflESAALo8bd0HAeQIWDmbOQ6ZB7wjgC+8ko+GUCOCNlECzjFcEQvFFzfkXq/V5ZxZoM7pipNZ6dJSG7EDc4dXGTaFZC94gAE5hoxhdgr8RjQobwFgEwAGIzIhhErBwMAtTOaZOkgC+SJIutX0mgDd8Vo/ekyIQjC8aGzX/oKPVfvSNBSh/1+rXGvzsb7TFFklRpq6PBCx4gwDYx53nRs8EwG7oQBelEyAALp0Zd0DACwIWDmZegKZJrwjgC6/kotkUCeCNFGGzlDcEgvJFXZ3mDdpdHaeNK9DnlM436Zd/P1zf/a430tFowgQseIMAOOFNYrg8AbBhcY2PRgBsXGDGC5eAhYNZuOoxeVIE8EVSZKnrOwG84buC9J8EgeB88fXX+qp7P3V6/fk8nHWq0rEbPKJLXtpBXbokQZqavhGw4A0CYN92nTv9EgC7owWdlEaAALg0XlwNAW8IWDiYeQObRr0hgC+8kYpGUyaAN1IGznJeEAjSFx9/rK9/2lMdPn03T6MPtI4O3uZtPTy5ndq08UI+mkyQgAVvEAAnuEGMlyYANi6w4fEIgA2Ly2hhE7BwMAtbQaZPggC+SIIqNS0QwBsWVGSGuAkE64vXX9eCn/dSuwVfLEYahb87arxe0uYaPly66y6pggQk7u3mVT0L3iAA9mrLOdUsf/05JQfNlECAALgEWFwKAZ8IWDiY+cSbXv0ggC/80Iku0yeAN9JnzoruEwjZF43TZ6iu/3Z6o/4Hi8Pf9/WdZYL9+tfSb37jvn50mBwBC94gAE5uf1ivTABsXWG78xEA29WWyQInYOFgFriEjJ8AAXyRAFRKmiCAN0zIyBAxEwjdF/WTn9DIq36uu8evVkD21lulQw6JGTjlvCFgwRsEwN5sN+caJQB2ThIaKpIAAXCRoLgMAr4RsHAw8405/bpPAF+4rxEdZkMAb2TDnVXdJoAvpPnzpf79peeey9eqqkoaP14aPNhtDekuGQIWvEEAnMzeCKEqAXAIKtuckQDYpq5MBQFZOJghIwTiJoAv4iZKPSsE8IYVJZkjTgL4YgnNTz6RevaU3nknn26nTtLTT0s//Wmc1KnlAwEL3iAA9mGnudkjAbCbutBV8wQIgJtnxBUQ8JKAhYOZl+Bp2mkC+MJpeWguQwJ4I0P4LO0sAXzxjTSvvy716iV9seR74SQ16te6UF932UAnv3yQ1lvPWRlpLAECFrxBAJzAxgikJAFwIEIbHJMA2KCojASBiICFgxlKQiBuAvgibqLUs0IAb1hRkjniJIAv8mk+9ZQ0aJDUUFOrG3WkDtHtqlW1jvv+RF3+4kBF7wjmEQYBC94gAA5jryYxJQFwElSpmQYBAuA0KLMGBDIgYOFglgE2ljROAF8YF5jxyiaAN8pGx42GCeCLQnHvv+VLrXb4nhqkx5f98kt11hl9ntY1U3+i6mrDG4LRlhGw4A0CYDZ0uQQIgMslx31ZEyAAzloB1odAQgQsHMwSQkPZgAngi4DFZ/RVEsAbbBAIFBLAF03sijfe0ILNe6rdwjl5v/y3NtB1Bzyji+9YVxWkI+btZMEbBMDmt2liA/JXXGJoKZwwAQLghAFTHgJZEbBwMMuKHevaJYAv7GrLZC0jgDdaxo+7bRLAF03r2jh1muq3G6zqhtq8C17QFnrygqf0q7M72twQTLWMgAVvEACzocslQABcLjnuy5oAAXDWCrA+BBIiYOFglhAaygZMAF8ELD6jr5IA3mCDQKCQAL5Y+a6oG3WXqg8+oOCCR7Wj5t89Vnvtx2dBWPaUBW8QAFveocnORgCcLF+qJ0eAADg5tlSGQKYELBzMMgXI4iYJ4AuTsjJUDATwRgwQKWGOAL5YtaQLz75QbS88u+CimyqP0mZPXqfefYhJzJkiN5AFbxAAW92dyc/F32zJM2aFZAgQACfDlaoQyJyAhYNZ5hBpwBwBfGFOUgaKiQDeiAkkZUwRwBfNyNnYqHn7HaaO995WcOF57S/V/n87VRtvbGpLMAwBMHsAAiIAZhP4SoAA2Ffl6BsCzRDgRQtbBAKFBPAFuwICTRPAG+wMCPCcUdYeqK3V3G13UudnJhfcfvy379XZL++tNdcsqzI3OUzAwnMG7wB2eIM53hoBsOMC0d5KCRAAszkgYJSAhYOZUWkYK0MC+CJD+CztNAG84bQ8NJcRAXxRJPgvv9TczbdR53dfzrthodrohM2m6KrZvdWuXZG1uMwLAha8QQDsxVZzskkCYCdloakiCBAAFwHp/9u7E3g/p2v/499MgkiIIbg11O2AXpWgBBXzVLMOSlFTS6tu9V5juWroQNH/raoqWrQ1VFvULSkaY5GYZ9VWVSlSSiQSQ6bzf+14oufknJPftPez917783u9+tLk/J6913qvveQ5y8nz4y0I5Chg4cYsR3diTluAvki7PkQXT4DeiGfPzukK0Bct1Oa55zRj7Q017LUXelz0Ty2jU7afqO9e9wENHNjCerw1aQELvcEAOOkjlnRwDICTLg/BLUSAATDHAwGjAhZuzIyWhrQiCtAXEfHZOmkBeiPp8hBcJAH6okX4hx7S22PHaejM6T0ufErv06VfmqgTv8+zIFoUTfbtFnqDAXCyxyv5wBgAJ18iAuxHgAEwRwMBowIWbsyMloa0IgrQFxHx2TppAXoj6fIQXCQB+qJ1+LnX/VZdO++sQV1zelx8lzbSY9+9SQcfzrMgWldN7woLvcEAOL1zlUtEDIBzqRRxLijAAJgzgYBRAQs3ZkZLQ1oRBeiLiPhsnbQAvZF0eQgukgB90R78rHPO15DDDulx8UXaX18ccJ6u/M0i2nHH9tblqnQELPQGA+B0zlNukTAAzq1ixDtfgAEwZwEBowIWbsyMloa0IgrQFxHx2TppAXoj6fIQXCQB+qJ9+De+fKwWP/vb8xb4mk7W13WCpAEaNky6/XZp3XXbX5sr4wtY6A0GwPHPUa4RMADOtXLEzQCYM4CAUQELN2ZGS0NaEQXoi4j4bJ20AL2RdHkILpIAfdEB/Ny5mvaJ/XXshK117vTP9lhoxRWlSZOkVVbpYH0ujSpgoTcYAEc9QllvzgA46/IVHTwD4KLLT/KWBSzcmFmuD7nFEaAv4riza/oC9Eb6NSLC+gXoi87N77pL2nJL6e23e6611lrSHXdISy7Z+R6sUL+Ahd5gAFz/ubGyIwNgK5UsLw8GwOXVnIwLEbBwY1ZIqUizRgH6okZstspKgN7IqlwEW5MAfeEH+le/kj71qd5rbb21NH68NGSIn31YpT4BC73BALi+82JtJwbA1ipaTj4MgMupNZkWJmDhxqywkpFuDQL0RQ3IbJGlAL2RZdkIOrAAfeEP+MwzpaOO6rneSnpOe35qrk6/YlUNYKLiD7uGlSz0BgPgGg6K0S3415XRwhaQFgPgAopMimUKWLgxK7NyZB1SgL4IqcvaOQvQGzlXj9hDCdAX/mS7uqQvfUk699x31hyjB3WddtQUjdT44+7UUd9cyt9mrBRcwEJvMAAOfkzMbsAA2GxpzSfGANh8iUmwVAELN2al1o68wwnQF+FsWTlvAXoj7/oRfRgB+sKv6+zZ0m67SXOu+61+oT00XNPnbXCzttDki67XZ/ZfxO+GrBZMwEJvMAAOdjzML8wA2HyJzSbIANhsaUmsdAELN2al15D8/QvQF/5NWdGGAL1ho45k4VeAvvDr6VZ7Y9IjWmSjdTVYc3os/rMBn9XKN12szbdgtOJf3f+KFnqDAbD/c1HKivxbqpRK28uTAbC9mpIRAvMELNyYUUoEfAvQF75FWc+KAL1hpZLk4VOAvvCpWa3V1aUZB/6nhl18Tq/FTxt6onZ98CStuWaAfVnSq4CF3mAA7PVIFLUYA+Ciym0qWQbApspJMgj8S8DCjRn1RMC3AH3hW5T1rAjQG1YqSR4+BegLn5rd1po9W9O23l0jbru21wZHLHuxjn5sPy2/fKC9WdaLgIXeYADs5SgUuQgD4CLLbiJpBsAmykgSCPQWsHBjRl0R8C1AX/gWZT0rAvSGlUqSh08B+sKn5gJrzZihaetsphF/vr/HF2ZpsA7/4PU644GtNGxYwP1ZuiMBC73BALijI1D0xQyAiy5/1skzAM66fASPQP8CFm7MqC8CvgXoC9+irGdFgN6wUkny8ClAX/jU7GOtF1/U9A9vqCVeebbHF6dqhE7Y4k797+/W0qBBgWNg+bYELPQGA+C2Ss9FkhgAcwxyFWAAnGvliBuBBgIWbswoMgK+BegL36KsZ0WA3rBSSfLwKUBf+NTsZ63HH9eb631Ui709tccb/qZV9OPPTdIpF6xYQxBs0aqAhd5gANxq1Xn/fAEGwJyFXAUYAOdaOeJGgAEwZwCBlgUsfMPSctJcgEATAvRGE0i8pTgB+qKeknfddLPmbLu9Bs+d1WPD+7Wu7v72bTr06CXqCYRdmhaw0BsMgJsuN29cQIABMEciVwEGwLlWjrgRYADMGUCgZQEL37C0nDQXINCEAL3RBBJvKU6Avqiv5LMv/KkGH7Rfrw2v1Y6a86tfa9dPDK4vGHZqKGChNxgANywzb+hHgAEwRyNXAQbAuVaOuBFgAMwZQKBlAQvfsLScNBcg0IQAvdEEEm8pToC+qLfkb371FC122om9Nj1/0Bc15s5ztMFYxi71VqT/3Sz0BgPgVE5TfnHwb6L8akbE7wgwAOYkIGBUwMKNmdHSkFZEAfoiIj5bJy1AbyRdHoKLJEBf1Azf1aXX9zhQw391ca+NT1riDO3/6JF673trjont+hSw0BsMgDnc7QowAG5XjutiCzAAjl0B9kcgkICFG7NANCxbsAB9UXDxSX2hAvQGBwSB3gL0RYRTMXOmpm6yg5a896Yem0/RUtrxg09p/N3LaKmlIsTFlj0ELPQGA2AOdbsCDIDbleO62AIMgGNXgP0RCCRg4cYsEA3LFixAXxRcfFJnAMwZQKBFAf7MaBHM19tfe03TRm+iEc8+Pm/Fv2kV7aDxekL/oa22ksaPlxZZxNdmrNOOgIXeYADcTuW5xgkwAOYc5CrAADjXyhE3Ag0ELNyYUWQEfAvQF75FWc+KAL1hpZLk4VOAvvCp2eJazz6rGWuN1ZOv/5t20rWarBXfXeDAA6Uf/UgawBSmRVR/b7fQGwyA/Z2H0lbiXz2lVdxOvgyA7dSSTBDoIWDhxoySIuBbgL7wLcp6VgToDSuVJA+fAvSFT83W1+r6w5M68rsr6f+dv0Svi7/1LemrX219Ta7wI2ChNxgA+zkLJa7CALjEqtvImQGwjTqSBQK9BCzcmFFWBHwL0Be+RVnPigC9YaWS5OFTgL7wqdneWrNnS7vtJl13Xe/rL79c2nPP9tblqs4ELPQGA+DOzkDJVzMALrn6eefOADjv+hE9Av0KWLgxo7wI+BagL3yLsp4VAXrDSiXJw6cAfeFTs/21pk+XNt1UevDBnmsMHSrdNKFLH92EcUz7uu1daaE3GAC3V3uu4hnAFs7ALpL2lbS+pBUkTZP0lKSrJZ1X/dpHnsMlbStpC0nrSvqAJPc5pm9KekHSPZIuk3SDpC4fGzZYgwFwDchsgUAMAQs3ZjHc2NO2AH1hu75k174AvdG+HVfaFaAv0qnt889LY8dK7p/uNUBz9Q39jwYvtog+/shJev/704m1hEgs9AYD4BJOapgc+U9OYVzrWNU9UOhSSW4A3N/rOUl7SJrUYUD/LembkhZtYp3fS9pH0rNNvLeTtzAA7kSPaxFIWMDCjVnCvISWqQB9kWnhCDu4AL0RnJgNMhSgL9Iq2sMPS5tsIs2a/pYu1v7aU1fMC/Do5X+iYx7/rJZZJq14LUdjoTcYAFs+oWFzYwAc1jfU6oMkXStp+2qDf0i6QNITkpaWtJekj1Zfm1L9/z90EMwPJR1SXf+ipJuqn/h9qRoKb1gNfec/5f6vktzvua+HejEADiXLughEFrBwYxaZkO0NCtAXBotKSl4E6A0vjCxiTIC+SK+gE37+Ty26127aRHe+G9xMDdFRa9+o0+/ZXO6xELzCC1joDQbA4c+J1R0YAOdZWTeMdUNZ93JD3y0luSFw99eZko6ofsP9VO6mHaR6riT3l1O+I+l3kub0sdaq1eMfVq++dpGkAzvYs9GlDIAbCfF1BDIVsHBjlik9YScsQF8kXBxCiypAb0TlZ/NEBeiLBAszebKm/ceGGvHq33oEN0VL6dSdJ+rb16yhAUxnghfOQm8wAA5+TMxuwL9i8iut++lf92iHFavQ15P0QB9puPfdJ2lM9bXtJN3YZrrup4pfbeLa0ZIeqt73hqTlJLl/hngxAA6hypoIJCBg4cYsAUZCMCZAXxgrKOl4E6A3vFGykCEB+iLRYj7+uN5cd2MtNtN9bM+/Xk9rNV155CQddcaoRAO3E5aF3mAAbOc81p0JA+C6xTvfz30I283VMrdJ2nwhSx4g6cLq6xdLcr8O/XpS0vyfAnYD4UcCbcgAOBAsyyIQW8DCjVlsQ/a3J0Bf2KspGfkRoDf8OLKKLQH6It16zr1xguZu/zEN7prdI8iJ2lBPX3Cz9v7cYukGbyAyC73BANjAQYyUAgPgSPAdbHu6pKOq64+R5H7d32sFSe6Zve7lHhHhfh36dY+k9atN3HOA7w60IQPgQLAsi0BsAQs3ZrEN2d+eAH1hr6Zk5EeA3vDjyCq2BOiLtOv59rkXauihB/UK8soBn9QyE67Q5lsOTDuBjKOz0BsMgDM+gJFDZwAcuQBtbP/bbh/+5p79e0uDNZ6VtHL1Hvd3Sl5uY89mL1mk+uC3JasL3GMqJjd7cYvvYwDcIhhvRyAXAQs3ZrlYE2c+AvRFPrUi0noF6I16vdktDwH6Iv06TT/8eC3xvW/1CvSsoUdru4e+rTXWSD+HHCO00BsMgHM8eWnEzAA4jTq0EsXTklarLnD/fKbBxe4xEfM/AG6cpDta2azF9+4vyX34m3u55xK75xO383LD3UavtSSd7950/fXXa/315//QcaPL0vr6tGnT9PDDD88LavTo0RoxYkRaARINAhEE6IsI6GyZvAB9kXyJCDCSAL0RCZ5tkxagL5IuzzvBzZ2ruXseomVvuqpXsMctc672v+vTWnbZrgwSyStEC71x7733avvtt58Pv7GkiXlVgWhjCTAAjiXf/r7uw9hGVpcPlzS9wVLuT5Tdq/fsLOna9rde6JXuA98ekzT/yfUfl3R1m3u19CfdaaedpjX4T6RtUnMZAggggAACCCCAAAIIIIBA3QIDZ87U6CO/oVWe7fmxObM1SF9Y6efa9jtDNXTo3LrDYr/EBZ588kkde+yxDIATr1OK4TEATrEqC49ppqQh1VvcP3s+Pb73tZdK+kz12+6flwdI2T36YYIk9xPG7vXrbkPndrZjANyOGtcggAACCCCAAAIIIIAAAghkI7DItGn6yH8ep+Wm/r1HzNM0XIeNvlK7n/iGBvJI4GzqWUegDIDrULa5BwPg/Oqa2gDY/XH0U0l7V5R/qT4EbkoHtDwCogM8LkUgdwELfzUr9xoQf3oC9EV6NSGiNATojTTqQBRpCdAXadWjUTQDn3pKQzb7mIa95f6y779ez2klXXDQrfrK6fP/AnCjlfh6IwELvcEjIBpVma/3J8AAOL+zkdIjINz5OU/S5ytG94FzmzXxXGIf6nwInA9F1kAgQQELH86QICshZS5AX2ReQMIPJkBvBKNl4YwF6Iv8ijf3tt9rzpZba8hc9/Ne/3rdr3X16Pdv1/5fGpZfUglGbKE3+BC4BA9WJiExAM6kUN3CTOVD4NzZ+YGkL1Sxub+zsrkk9xPAdbwYANehzB4IRBCwcGMWgY0tjQvQF8YLTHptC9AbbdNxoWEB+iLP4s68+DItcsD8v1j7Tg4/1CE6fOD3de31g7XNNnnmlVLUFnqDAXBKJyqvWBgA51UvF+1vJc3/yMctJd3SIAX3U7krV+9xH9D2soeU3bk5R9IXq7Wer4a/T3lYu9klGAA3K8X7EMhMwMKNWWbkhJuBAH2RQZEIMYoAvRGFnU0TF6AvEi/QQsKbcezXNezbX5v3jiN1hr6jIyQN0IgR0p13SmutlW9uKURuoTcYAKdwkvKMgQFwfnU7XdJRVdjHSHK/7u+1vKTJ1RdfkuR+3elrweHvC9Xw98+dLtzi9QyAWwTj7QjkImDhxiwXa+LMR4C+yKdWRFqvAL1Rrze75SFAX+RRpz6j7OrSa585VIf/Zmv9dMYnerxllVWkSZOkFVfMOL/IoVvoDQbAkQ9RxtszAM6veO6nfm+qwr5V0hYLSeEASRdWX79Ykvt1J68Fh78vVsPfP3WyaJvXMgBuE47LEEhdwMKNWerGxJefAH2RX82IuB4BeqMeZ3bJS4C+yKtefUV7++3S1ltLs2b1/OpHPiLdeqs0jEcCt1VkC73BALit0nPRvL9LwCs3gUGS3PN2V6gCX0/SA30k4d53n6Qx1dfcYyNu6DBZ99iHQ6s13E8Wu2f+/rHDNdu9nAFwu3Jch0DiAhZuzBInJrwMBeiLDItGyLUI0Bu1MLNJZgL0RWYF6yfcSy6R9t239xd33VW68kppkPuOn1dLAhZ6gwFwSyXnzd0EGADneRzcs3fdB7C51+OS3E8Fu0c8dH+dMe+xQe+87pS0ST+p7i/pouprt1VD3b7eerakw6ovxB7+ujAYAOd5dokagYYCFm7MGibJGxBoUYC+aBGMtxcjQG8UU2oSbUGAvmgBK/G3nnyydNJJPYN8r/6qAw4apK/9aJXEo08vPAu9wQA4vXOVS0QMgHOpVM84B0saL2n+54C6gewFkp6QtLSkvboNfF+r/r8bFPf1amYA/A1Jx1cXd0k6TtKTTdC5n0x2H0IX4sUAOIQqayKQgICFG7MEGAnBmAB9YaygpONNgN7wRslChgToCzvF7OqS9ttP+tnP3slpA92t32hn/UPLa9IZd+jzRy5pJ9kaMrHQGwyAazgoRrdgAJxvYYdLukzSTgtJwT0q4tOS7lrIe5oZALtnDW/WBpV75rB79nCIFwPgEKqsiUACAhZuzBJgJARjAvSFsYKSjjcBesMbJQsZEqAvDBVT0ttvS9ttJy1z25W6RPtoMb01L8HfaRvN+vV12mHXIbYSDpiNhd5gABzwgDmB57sAACAASURBVBhfmgFw/gXeVdJnJa0vaZSk1yX9RdJVks6TNLVBigyAI54BC38AReRja6MC9IXRwpJWRwL0RUd8XGxYgN4wXFxSa1uAvmibLtkLp950n4ZvvYEGyv2F3H+9Lhr8Oa1zz/kasw6jnWaKZ6E3GAA3U2ne05cA/5bgXOQqwE8A51o54kaggYCFGzOKjIBvAfrCtyjrWRGgN6xUkjx8CtAXPjUTWaurS1P3OVRLXvbDXgF9c/hp2u+JY7TSSonEmnAYFnqDAXDCByzx0BgAJ14gwutXgAEwhwMBowIWbsyMloa0IgrQFxHx2TppAXoj6fIQXCQB+iISfOhtZ8/WlHE7a+Sk63vtdPSqV+iER/fQcPegSF79CljoDQbAHPB2BRgAtyvHdbEFGADHrgD7IxBIwMKNWSAali1YgL4ouPikvlABeoMDgkBvAfrC8KmYNk2vfXiclnr2kR5JvqWhOn6jW/Tt2zfSYPeR8bz6FLDQGwyAOdztCjAAbleO62ILMACOXQH2RyCQgIUbs0A0LFuwAH1RcPFJnQEwZwCBFgX4M6NFsNze/ve/6/X/GKvh017oEfnLWlbf33uSTvrZ+zSASQ8D4NzONfEGF+BfC8GJ2SCQAAPgQLAsi0BsAb5piV0B9k9RgL5IsSrElIIAvZFCFYghNQH6IrWK+I+n64EH9fbYcVp09oweiz+p1XXz1+/Sof+ztP9NDaxooTf4CWADBzFSCgyAI8GzbccCDIA7JmQBBNIUsHBjlqYsUeUsQF/kXD1iDylAb4TUZe1cBeiLXCvXWtyzr7lOA3bbRYM0t8eFt2lTTf3FjdrlU0NbW7CAd1voDQbABRzUQCkyAA4Ey7LBBRgABydmAwTiCFi4MYsjx66WBegLy9Ult04E6I1O9LjWqgB9YbWyvfN688xztNhRh/X6wqWD9tUH7/qJ1t+AkU93HAu9wQC4nP72nSn/NvAtynp1CTAArkuafRCoWcDCjVnNZGxXgAB9UUCRSbEtAXqjLTYuMi5AXxgv8ALpTfvcf2vEj/+3V9JnDjtRn3r8JK26alkeC8vWQm8wAOY8tyvAALhdOa6LLcAAOHYF2B+BQAIWbswC0bBswQL0RcHFJ/WFCtAbHBAEegvQF4WdijlzNGXLT2jk7df0Svy49/xExzz+WS25ZGEm/aRroTcYAHOW2xVgANyuHNfFFmAAHLsC7I9AIAELN2aBaFi2YAH6ouDikzoDYM4AAi0K8GdGi2AW3j5jhqaM2Vwjn7qvRzYvagUdvMVTuuqGYRoyxEKineVgoTcYAHd2Bkq+mgFwydXPO3cGwHnXj+gR6FfAwo0Z5UXAtwB94VuU9awI0BtWKkkePgXoC5+aGa01ebKmfWisRkx5dl7QT+l92kHj9Wd9UAcdJF1wgTSg8AmQhd5gAJxRTyYWauHtn1g1CKcVAQbArWjxXgQyErBwY5YRN6FmIkBfZFIowqxdgN6onZwNMxCgLzIoUqAQux57XG+tt7EemLmWdtU1ekXLvrvTqadKxx4baONMlrXQGwyAMzlsCYbJADjBohBSUwIMgJti4k0I5Cdg4cYsP3UiTl2Avki9QsQXS4DeiCXPvikL0BcpVyd8bLPvfVCfPGFNXXPDor02u+IKaY89wseQ6g4WeoMBcKqnK/24GACnXyMi7FuAATAnAwGjAhZuzIyWhrQiCtAXEfHZOmkBeiPp8hBcJAH6IhJ8Qtu+/rq06abSQw/1DGroUOnWW6UNN0wo2BpDsdAbDIBrPDDGtmIAbKygBaXDALigYpNqWQIWbszKqhjZ1iFAX9ShzB45CtAbOVaNmEML0BehhfNY//nnpbFjJffP7q/llpPuvltabbU88vAZpYXeYADs80SUtRYD4LLqbSlbBsCWqkkuCHQTsHBjRkER8C1AX/gWZT0rAvSGlUqSh08B+sKnZt5ruZ8A3mQTacaMd/IYqDk6XUdr6opr6r+f+JyWWirv/FqN3kJvMAButeq8f74AA2DOQq4CDIBzrRxxI9BAwMKNGUVGwLcAfeFblPWsCNAbVipJHj4F6Aufmvmvde210q67SovOnaFLtbd20zWapcE6ft3r9c1JW2nIkPxzbDYDC73BALjZavO+BQUYAHMmchVgAJxr5YgbAQbAnAEEWhaw8A1Ly0lzAQJNCNAbTSDxluIE6IviSt4w4R9//QWN/tou+ojuf/e9r2lJ/e8n79JJv/iQBhQyGbLQGwyAGx533tCPQCFtTv0NCjAANlhUUkLACVi4MaOSCPgWoC98i7KeFQF6w0olycOnAH3hU9PIWn/8o2aM3kjD3p7SI6G/6r0af8IkfemU5Y0kuvA0LPQGA+AijmqQJBkAB2Fl0RoEGADXgMwWCMQQsHBjFsONPW0L0Be260t27QvQG+3bcaVdAfrCbm07yWzOTbdq7jbbakjXrB7LTNJYTb7sFu2212KdLJ/FtRZ6gwFwFkctySAZACdZFoJqQoABcBNIvAWBHAUs3Jjl6E7MaQvQF2nXh+jiCdAb8ezZOV0B+iLd2sSO7M3zfqrFvrBfrzCuHPQprXLnz7X+2IGxQwy6v4XeYAAc9IiYXpwBsOnymk6OAbDp8pJcyQIWbsxKrh+5hxGgL8K4smr+AvRG/jUkA/8C9IV/U0srTj38BC35vW/0Sul7ix+rXZ84VauuainbnrlY6A0GwHbPZ+jMGACHFmb9UAIMgEPJsi4CkQUs3JhFJmR7gwL0hcGikpIXAXrDCyOLGBOgL4wV1Hc6XV16dfvPaOkbf95r5RPe82Md+fiBWnJJ35umsZ6F3mAAnMZZyjEKBsA5Vo2YnQADYM4BAkYFLNyYGS0NaUUUoC8i4rN10gL0RtLlIbhIAvRFJPictn3rLb2yzlZa5sm7ekQ9S4N1wkeu1zcmbqXBg3NKqLlYLfQGA+Dmas27egswAOZU5CrAADjXyhE3Ag0ELNyYUWQEfAvQF75FWc+KAL1hpZLk4VOAvvCpaXitl1/Wa2tuqKVeebpHkq9pSZ316Yn62uVraoCxiZGF3mAAbLgnA6dmrJ0Da7F8SgIMgFOqBrEg4FHAwo2ZRw6WQmCeAH3BQUCgbwF6g5OBQG8B+oJT0axA1x+e1JtjNtLiM1/rcclf9V7dcPLd+sLXRjW7VBbvs9AbDICzOGpJBskAOMmyEFQTAgyAm0DiLQjkKGDhxixHd2JOW4C+SLs+RBdPgN6IZ8/O6QrQF+nWJsXI5ky4RV3bbqvBXbN7hDdRG+qfV9ysnfdYLMWw24rJQm8wAG6r9FwkiQEwxyBXAQbAuVaOuBFoIGDhxowiI+BbgL7wLcp6VgToDSuVJA+fAvSFT80y1nrz3Iu12KEH9Er2V4P20GoTL9d66w80AWGhNxgAmziKUZJgAByFnU09CDAA9oDIEgikKGDhxixFV2LKW4C+yLt+RB9OgN4IZ8vK+QrQF/nWLmbkUw87Xkue860eIXxXh+vMFb6jifcM0sorx4zOz94WeoMBsJ+zUOIqDIBLrLqNnBkA26gjWSDQS8DCjRllRcC3AH3hW5T1rAjQG1YqSR4+BegLn5oFrTV3rl7Z/jNa5ndXaI4G6sv6nn6gL80DWHtt6Y47pOHD8/aw0BsMgPM+gzGjZwAcU5+9OxFgANyJHtcikLCAhRuzhHkJLVMB+iLTwhF2cAF6IzgxG2QoQF9kWLRUQn7rLU3eaDcd8NDhul4f6xHVDjtI11wjDR6cSrCtx2GhNxgAt153rnhHgAEwJyFXAQbAuVaOuBFoIGDhxowiI+BbgL7wLcp6VgToDSuVJA+fAvSFT80C1+rq0ulnDNAxx/TO/bDDpLPPztfEQm8wAM73/MWOnAFw7Aqwf7sCDIDbleM6BBIXsHBjljgx4WUoQF9kWDRCrkWA3qiFmU0yE6AvMitYguF2dUkHHyz96Ee9gzvrLOnLX04w6CZCstAbDICbKDRv6VOAATAHI1cBBsC5Vo64EWggYOHGjCIj4FuAvvAtynpWBOgNK5UkD58C9IVPzXLXmjVLco99mDChp8H7Bjyt7/1yRe3wicWyw7HQGwyAszt2yQTMADiZUhBIiwIMgFsE4+0I5CJg4cYsF2vizEeAvsinVkRarwC9Ua83u+UhQF/kUacconztNemjH5WeeOKdaMfpdl2t3XXLoG30vrsv0zrrDcwhjXdjtNAbDICzOnJJBcsAOKlyEEwLAgyAW8DirQjkJGDhxiwnb2LNQ4C+yKNORFm/AL1Rvzk7pi9AX6Rfo5wi/OtfpbFjpW1fvkQX6kAtolnzwj9r+PH65B++ofe8J59sLPQGA+B8zltqkTIATq0ixNOsAAPgZqV4HwKZCVi4McuMnHAzEKAvMigSIUYRoDeisLNp4gL0ReIFyjC8P516pT543Cd7RX7iKhfpqMf31xJL5JGUhd5gAJzHWUsxSgbAKVaFmJoRYADcjBLvQSBDAQs3ZhmyE3LiAvRF4gUivGgC9EY0ejZOWIC+SLg4uYb25pv65+gtteyfJ/XIYJYG66SNbtQpv99Cgwaln5yF3mAAnP45SzVCBsCpVoa4GgkwAG4kxNcRyFTAwo1ZpvSEnbAAfZFwcQgtqgC9EZWfzRMVoC8SLUzuYb30kqassaFGTvlrj0ymaCn98LMT9dWfrJF8hhZ6gwFw8scs2QAZACdbGgJrIMAAmCOCgFEBCzdmRktDWhEF6IuI+GydtAC9kXR5CC6SAH0RCb6Abbue+IPeHLORFp81tUe2f9G/67bTJunAY5ZLWsFCbzAATvqIJR0cA+Cky0NwCxFgAMzxQMCogIUbM6OlIa2IAvRFRHy2TlqA3ki6PAQXSYC+iARfyLazbrhZAz62nQZ3ze6R8Z3aWNN/fZO223XRZCUs9AYD4GSPV/KBMQBOvkQE2I8AA2COBgJGBSzcmBktDWlFFKAvIuKzddIC9EbS5SG4SAL0RST4gradcfaFGvblg3pl/MvBe2r1ey/V2mMGJqlhoTcYACd5tLIIigFwFmUiyD4EGABzLBAwKmDhxsxoaUgrogB9ERGfrZMWoDeSLg/BRRKgLyLBF7btlC98VSPPO61X1meN+B/t8eTXteKK6YFY6A0GwOmdq1wiYgCcS6WIc0EBBsCcCQSMCli4MTNaGtKKKEBfRMRn66QF6I2ky0NwkQToi0jwpW07d67+ufWeWvaWX/bK/MT3XqyjH9tPw4alhWKhNxgAp3WmcoqGAXBO1SLW7gIMgDkPCBgVsHBjZrQ0pBVRgL6IiM/WSQvQG0mXh+AiCdAXkeBL3PbNN/XPtbfUsk9N6pH9TA3RKR+9USfftrkGDUoHxkJvMABO5zzlFgkD4NwqRrzzBRgAcxYQMCpg4cbMaGlIK6IAfRERn62TFqA3ki4PwUUSoC8iwZe67UsvacrqYzXytWd6CLyqkfrRgRN19I9XT0bGQm8wAE7mOGUXCAPg7EpGwJUAA2COAgJGBSzcmBktDWlFFKAvIuKzddIC9EbS5SG4SAL0RST4grftevwJvbHOxho2a2oPhbu0kR774Z06+JA0Rk8WeoMBcMGN1mHqaXRhh0lweZECDICLLDtJlyBg4cashDqRY70C9EW93uyWjwC9kU+tiLQ+AfqiPmt2+pfAzPETNHCnj2lw1+x5v/mE1tSOuk7PDVpN48dL224bX8tCbzAAjn+Oco2AAXCulSNuBsCcAQSMCli4MTNaGtKKKEBfRMRn66QF6I2ky0NwkQToi0jwbKvpZ/1YS3zlc5qgrfRJ/UpTtdQ8lREjpDvvlNZaKy6Shd5gABz3DOW8OwPgnKtXduwMgMuuP9kbFrBwY2a4PKQWSYC+iATPtskL0BvJl4gAIwjQFxHQ2fJdgRe+f5XW+drOemnKkB4qq64q3X23tPzy8bAs9AYD4HjnJ/edGQDnXsFy42cAXG7tydy4gIUbM+MlIr0IAvRFBHS2zEKA3siiTARZswB9UTM42/USuP12aeutpVmzen5pgw2kW26RFl88DpqF3mAAHOfsWNiVAbCFKpaZAwPgMutO1gUIWLgxK6BMpFizAH1RMzjbZSNAb2RTKgKtUYC+qBGbrfoVuOQSad99e3/5E5+QfvELaeDA+vEs9AYD4PrPjZUdGQBbqWR5eTAALq/mZFyIgIUbs0JKRZo1CtAXNWKzVVYC9EZW5SLYmgToi5qg2aahwIknSqec8q+3DdJsnaqvasDnP68jz/9gw+t9v8FCbzAA9n0qylmPAXA5tbaWKQNgaxUlHwQqAQs3ZhQTAd8C9IVvUdazIkBvWKkkefgUoC98arJWJwJdXdLee0uXXy4N1zT9Qntoe92gP+v9mvT/Jmrf/1q2k+VbvtZCbzAAbrnsXFAJMADmKOQqwAA418oRNwINBCzcmFFkBHwL0Be+RVnPigC9YaWS5OFTgL7wqclanQq89Za097hndeJ9O2ltPfrucndoE80cP0Fbfmxop1s0fb2F3mAA3HS5eeMCAgyAORK5CjAAzrVyxI0AA2DOAAItC1j4hqXlpLkAgSYE6I0mkHhLcQL0RXElTz7haWf/RCO+vH+vOK8YsrfWfuhnWvND9YymLPQGA+Dkj3uyAdbTZcmmT2AZCzAAzrh4hI7AwgQs3JhRYQR8C9AXvkVZz4oAvWGlkuThU4C+8KnJWr4EXjnoaC1z4Rm9ljtrqRO11x9P0qhRvnbqfx0LvcEAOPw5sboDA2CrlbWfFwNg+zUmw0IFLNyYFVo60g4oQF8ExGXprAXojazLR/CBBOiLQLAs25nA3Ll6aYs9NOr2K3utc/L7f6ZjHt1Hiy7a2RaNrrbQGwyAG1WZr/cnwACYs5GrAAPgXCtH3Ag0ELBwY0aREfAtQF/4FmU9KwL0hpVKkodPAfrCpyZreRV44w29/OEttNzT9/RY9m0tolO3nKCv/W6cBg70umOPxSz0BgPgcOfD+soMgK1X2G5+DIDt1pbMChewcGNWeAlJP4AAfREAlSVNCNAbJspIEp4F6AvPoCznV2DyZE1ZfaxGTnu2x7qvaGn99IuT9F8/+IDf/bqtZqE3GAAHOx7mF2YAbL7EZhNkAGy2tCRWuoCFG7PSa0j+/gXoC/+mrGhDgN6wUUey8CtAX/j1ZDX/AnMfeUxvfeSjWnzWtB6L/0kf0P1nT9Rehy3jf1NJFnqDAXCQo1HEogyAiyizySQZAJssK0khYOPGjDoi4FvAwjcsvk1YDwEnQG9wDhDoLUBfcCpyEHj7Nzdq0C47aLDm9Aj39wPGae71v9Nm2w71noaF3mAA7P1YFLMgA+BiSm0uUQbA5kpKQgi8I2DhxoxaIuBbgL7wLcp6VgToDSuVJA+fAvSFT03WCikw7czzNeKoQ3ptccUi+2jMwz/V6mv4HVlZ6A0GwCFPpO21/XaTbSuyS0uAAXBa9SAaBLwJWLgx84bBQghUAvQFRwGBvgXoDU4GAr0F6AtORU4C/zzwKC170Zm9Qv7uyJO1z5++pmWX9ZeNhd5gAOzvPJS2EgPg0ipuJ18GwHZqSSYI9BCwcGNGSRHwLUBf+BZlPSsC9IaVSpKHTwH6wqcmawUXmDtXL236SY268+peW538gUt0zCN7a9FF/URhoTcYAPs5CyWuwgC4xKrbyJkBsI06kgUCvQQs3JhRVgR8C9AXvkVZz4oAvWGlkuThU4C+8KnJWrUIvPGGXv7QZlrub/f12O4knag/f+YkXXKJNMDD9MpCbzAAruVEmtzEQwuZdCGp9AUYAKdfIyJEoC0BCzdmbSXORQgsRIC+4Hgg0LcAvcHJQKC3AH3BqchSYPJkTVl9rEZOe1azNFgH63xdrAPmpXLiidJJJ3WelYXeYADc+TkodQUGwKVWPv+8GQDnX0MyQKBPAQs3ZpQWAd8C9IVvUdazIkBvWKkkefgUoC98arJWnQJzH3lMr2y0oz79xkW6RVv22PpnP5P22aezaCz0BgPgzs5AyVczAC65+nnnzgA47/oRPQL9Cli4MaO8CPgWoC98i7KeFQF6w0olycOnAH3hU5O16hZ447WZ2mK7RXTPPT13XmQRacIEady49iOy0BsMgNuvf+lXMgAu/QTkmz8D4HxrR+QILFTAwo0ZJUbAtwB94VuU9awI0BtWKkkePgXoC5+arBVDYPJkaexY6dlne+6+zDLSpEnS+9/fXlQWeoMBcHu15yqJATCnIFcBBsC5Vo64EWggYOHGjCIj4FuAvvAtynpWBOgNK5UkD58C9IVPTdaKJfDYY9LGG0uvv94zgg3e94p+e88yWnrp1iOz0BsMgFuvO1e8I8AAmJOQqwAD4FwrR9wIMADmDCDQsoCFb1haTpoLEGhCgN5oAom3FCdAXxRXcrMJ33CDtOOO0pw576S4tX6nX+pTOnvNc3XMQ3vJPRailZeF3mAA3ErFeW93AQbAnIdcBRgA51o54kaAATBnAIGWBSx8w9Jy0lyAQBMC9EYTSLylOAH6oriSm0743HOlQw+VPq/z9QMdqsGao7e1iL7zsZv01es20YAWploWeoMBsOnjHjS5FlolaBwsjkCrAgyAWxXj/QhkImDhxiwTasLMSIC+yKhYhFqrAL1RKzebZSJAX2RSKMJsWuDacd/WTncc2+P9/9Qy+uURd+uLZ76v6XUs9AYD4KbLzRsXEGAAzJHIVYABcK6VI24EGghYuDGjyAj4FqAvfIuynhUBesNKJcnDpwB94VOTtVIQmPPgI3p7g020+OyeDwR+UqvryQsnarcDRjYVpoXeYADcVKl5Ux8CDIA5FrkKMADOtXLEjQADYM4AAi0LWPiGpeWkuQCBJgTojSaQeEtxAvRFcSUvIuE3r75eQz6+07xHQHR/3TZwcy166w0aO67xA4Et9AYD4CKOe5AkGQAHYWXRGgQYANeAzBYIxBCwcGMWw409bQvQF7brS3btC9Ab7dtxpV0B+sJubUvP7LVTz9VSxx3ai+HyRffXRk9cqPeutvARl4XeYABcehe0nz8D4PbtuDKuAAPguP7sjkAwAQs3ZsFwWLhYAfqi2NKTeAMBeoMjgkBvAfqCU2FZ4KW9/0ujLvturxS/O+pbOuBPX9WSS/afvYXeYABs+XSHzY0BcFhfVg8nwAA4nC0rIxBVwMKNWVRANjcpQF+YLCtJeRCgNzwgsoQ5AfrCXElJqLvAnDl6ceOPa8V7/q+XyzfXvkLH3L+HBg/um8xCbzAAph3aFWAA3K4c18UWYAAcuwLsj0AgAQs3ZoFoWLZgAfqi4OKT+kIF6A0OCAK9BegLToV5genT9dIam2rU8w/2SPUtDdX3dr9VR125oQb0Me2y0BsMgM2f7mAJMgAORsvCgQUYAAcGZnkEYglYuDGLZce+dgXoC7u1JbPOBOiNzvy42qYAfWGzrmTVU6Dr78/rtTXGauSM53t84SUtp98cf7cO+sZqvcgs9AYDYDqhXQEGwO3KcV1sAQbAsSvA/ggEErBwYxaIhmULFqAvCi4+qS9UgN7ggCDQW4C+4FSUIjD7voc0a8NNtNicGT1SfkJr6plL79IOn1mqx+9b6A0GwKWcbv95MgD2b1r3irtI2lfS+pJWkDRN0lOSrpZ0XvVr3zHF2HPBHBgA+64q6yGQiICFG7NEKAnDkAB9YaiYpOJVgN7wysliRgToCyOFJI2mBN74xbUa+uldNUhze7z/5oFba6m7xmvdsUPe/X0LvcEAuKljwZv6EGAAnO+xWELSpZLcMLa/13OS9pA0yVOaMfbsL3QGwJ6KyjIIpCZg4cYsNVPiyV+Avsi/hmQQRoDeCOPKqnkL0Bd514/oWxd49cSztPQpX+l14TlLHK1d//BtrbTSO1+y0BsMgFs/H1zxjgAD4DxPwiBJ10ravgr/H5IukPSEpKUl7SXpo9XXplT//w8dphpjz4WFzAC4w4JyOQKpCli4MUvVlrjyFaAv8q0dkYcVoDfC+rJ6ngL0RZ51I+oOBLq6NHmP/9QKvzrn3UUe0mjtpGu13JiV9PvfS0sswQC4A2EuNSDAADjPIh4i6YdV6G7ou6UkNwTu/jpT0hHVb/xe0qYdphpjTwbAHRaNyxHIUYBvWnKsGjGHFqAvQguzfq4C9EaulSPukAL0RUhd1k5WYPZsvbjBLlrxwd/qWu2ovXS5pmv4vHB33lm6+mpp6tRX9Xs3DZY0btw4Lb20+/m5vF78BHBe9UopWgbAKVWjuVjcT+K6RzusWL19PUkP9HGpe999ksZUX9tO0o3NbdHrXTH2bBQqPwHcSIivI5CpAN+0ZFo4wg4qQF8E5WXxjAXojYyLR+jBBOiLYLQsnLrAtGm65dPnapvrj9AcDe4R7Ve+Ip1wAgPg1EtIfOEEGACHsw218haSbq4Wv03S5gvZ6ABJF1Zfv1iS+3U7rxh7NoqTAXAjIb6OQKYCfNOSaeEIO6gAfRGUl8UzFqA3Mi4eoQcToC+C0bJwBgJdXdJBB0kXXdQ72NNPn6EPfnDCvC/wE8AZFJMQvQowAPbKWctip0s6qtrpGEnu1/29VpD0YvVF94gI9+t2XjH2bBQnA+BGQnwdgUwF+KYl08IRdlAB+iIoL4tnLEBvZFw8Qg8mQF8Eo2XhTARmzpS231665ZaeAQ8a1KXjj5+kddd9iQFwJrUkTH8CDID9Wda10m+7ffibe/bvAv9K6xXGs5JWrn53lKSX2wg0xp6NwmQA3EiIryOQqQDftGRaOMIOKkBfBOVl8YwF6I2Mi0fowQToi2C0LJyRwJQp0kYbSX/847+CHqKZOnzI2Vr5tLW1z/7r8QzgjOpJqJ0LMADu3LDuFZ6WtFq1qfvnMw0CcI+JmP8BcOMk3dFGwDH2bBQmA+BGQnwdgUwF+KYl08IRdlAB+iIoL4tnLEBvZFw8Qg8mQF8Eo2XhzASeekracEPplVekkXpVV+nj2ly36cJFD9ZG95+qNT/Eh8BlVlLC7UCAAXAHeJEufVXu313vvNxHcxtUjQAAIABJREFUWk5vEMdVknav3rOzpGvbiLvuPd1wt9FrLUnnuzddf/31Wn/99Ru9P8mvT5s2TQ8//PC82EaPHq0RI0YkGSdBIVCnAH1RpzZ75SJAX+RSKeKsW4DeqFuc/XIQoC9yqBIx1iUwceJgHbX7P3T1rJ20uv40b9upGqE917xXF9y4rBZfvK5I/Oxz7733anv3fIt3XhtLmuhnZVaxLsAAOL8Kz5Q0pArb/XN2gxQulfSZ6j3un5e3kXLde3a1EuNpp52mNdZYo5VLeC8CCCCAAAIIIIAAAggggAACCBQgsPLRP9C6f7pxXqbPaFXtpGv1uNbSDjs8rYMPfjQrgSeffFLHHnvs/JgZAGdVvbjBMgCO69/O7nUPY12Mde/JALidk8E1CCCAAAIIIIAAAggggAACCCDQQ2DwjBla+4sn6oVpS2sX/Z9e0vL6wAem6Ljj7tbIkW9npcUAOKtyJRUsA+CkytFUMHU/jsEFVfeePAKiqaPAmxCwKcBfW7RZV7LqTIC+6MyPq+0K0Bt2a0tm7QvQF+3bcaVhgRde1CHHLKdfjR+ljTd+Xj/60Wwtv7x7qmZeLx4BkVe9UoqWAXBK1WgulhgfyBZjz0YafAhcIyG+jkCmAnxwSaaFI+ygAvRFUF4Wz1iA3si4eIQeTIC+CEbLwpkLvPDCqzrllOe03XbPaLPNxmnppfkQuMxLSvgtCDAAbgErkbf+VtL8J35vKemWBnE9K2nl6j2jJL3cRh4x9mwUJgPgRkJ8HYFMBfimJdPCEXZQAfoiKC+LZyxAb2RcPEIPJkBfBKNl4cwFLPTGxIkTtfHG7tG/8148AzjzM1ln+AyA69T2s9fpko6qljpGkvt1f6/lJU2uvviSJPfrdl4x9mwUJwPgRkJ8HYFMBSzcmGVKT9gJC9AXCReH0KIK0BtR+dk8UQH6ItHCEFZ0AQu9wQA4+jHKNgAGwPmVzv3U701V2LdK2mIhKRwg6cLq6xdLcr9u5xVjz0ZxMgBuJMTXEchUwMKNWab0hJ2wAH2RcHEILaoAvRGVn80TFaAvEi0MYUUXsNAbDICjH6NsA2AAnF/pBkn6u6QVqtDXk/RAH2m4990naUz1NffYiBvaTDfGno1CZQDcSIivI5CpgIUbs0zpCTthAfoi4eIQWlQBeiMqP5snKkBfJFoYwoouYKE3GABHP0bZBsAAOM/SfVHSD6rQH5fkfkLXPeKh++sMSUdWv3GnpE36SXV/SRdVX7tN0ub9vM/nnj7UGQD7UGQNBBIUsHBjliArIWUuQF9kXkDCDyZAbwSjZeGMBeiLjItH6EEFLPQGA+CgR8T04gyA8yzvYEnjJW1The+e83uBpCckuY+x3KvbwPe16v+7QXFfr2YHwD739KHOANiHImsgkKCAhRuzBFkJKXMB+iLzAhJ+MAF6IxgtC2csQF9kXDxCDypgoTcYAAc9IqYXZwCcb3mHS7pM0k4LScE9KuLTku5ayHuaHQC7JXzt6UOdAbAPRdZAIEEBCzdmCbISUuYC9EXmBST8YAL0RjBaFs5YgL7IuHiEHlTAQm8wAA56REwvzgA4//LuKumzktaXNErS65L+IukqSedJmtogxVYGwPOX6nRPH+oMgH0osgYCCQpYuDFLkJWQMhegLzIvIOEHE6A3gtGycMYC9EXGxSP0oAIWeoMBcNAjYnpxBsCmy2s6OQbApstLciULWLgxK7l+5B5GgL4I48qq+QvQG/nXkAz8C9AX/k1Z0YaAhd5gAGzjLMbIggFwDHX29CHAANiHImsgkKCAhRuzBFkJKXMB+iLzAhJ+MAF6IxgtC2csQF9kXDxCDypgoTcYAAc9IqYXZwBsurymk2MAbLq8JFeygIUbs5LrR+5hBOiLMK6smr8AvZF/DcnAvwB94d+UFW0IWOgNBsA2zmKMLBgAx1BnTx8CDIB9KLIGAgkKWLgxS5CVkDIXoC8yLyDhBxOgN4LRsnDGAvRFxsUj9KACFnqDAXDQI2J6cQbApstrOjkGwKbLS3IlC1i4MSu5fuQeRoC+COPKqvkL0Bv515AM/AvQF/5NWdGGgIXeYABs4yzGyIIBcAx19vQhwADYhyJrIJCggIUbswRZCSlzAfoi8wISfjABeiMYLQtnLEBfZFw8Qg8qYKE3GAAHPSKmF2cAbLq8ppNjAGy6vCRXsoCFG7OS60fuYQToizCurJq/AL2Rfw3JwL8AfeHflBVtCFjoDQbANs5ijCwYAMdQZ08fAgyAfSiyBgIJCli4MUuQlZAyF6AvMi8g4QcToDeC0bJwxgL0RcbFI/SgAhZ6gwFw0CNienEGwKbLazo5BsCmy0tyJQtYuDEruX7kHkaAvgjjyqr5C9Ab+deQDPwL0Bf+TVnRhoCF3mAAbOMsxsiCAXAMdfb0IcAA2IciayCQoICFG7MEWQkpcwH6IvMCEn4wAXojGC0LZyxAX2RcPEIPKmChNxgABz0iphdnAGy6vKaTYwBsurwkV7KAhRuzkutH7mEE6IswrqyavwC9kX8NycC/AH3h35QVbQhY6A0GwDbOYowsGADHUGdPHwIMgH0osgYCCQpYuDFLkJWQMhegLzIvIOEHE6A3gtGycMYC9EXGxSP0oAIWeoMBcNAjYnpxBsCmy2s6OQbApstLciULWLgxK7l+5B5GgL4I48qq+QvQG/nXkAz8C9AX/k1Z0YaAhd5gAGzjLMbIggFwDHX29CHAANiHImsgkKCAhRuzBFkJKXMB+iLzAhJ+MAF6IxgtC2csQF9kXDxCDypgoTcYAAc9IqYXZwBsurymk2MAbLq8JFeygIUbs5LrR+5hBOiLMK6smr8AvZF/DcnAvwB94d+UFW0IWOgNBsA2zmKMLBgAx1BnTx8CDIB9KLIGAgkKWLgxS5CVkDIXoC8yLyDhBxOgN4LRsnDGAvRFxsUj9KACFnqDAXDQI2J6cQbApstrOjkGwKbLS3IlC1i4MSu5fuQeRoC+COPKqvkL0Bv515AM/AvQF/5NWdGGgIXeYABs4yzGyIIBcAx19vQhwADYhyJrIJCggIUbswRZCSlzAfoi8wISfjABeiMYLQtnLEBfZFw8Qg8qYKE3GAAHPSKmF2cAbLq8ppNjAGy6vCRXsoCFG7OS60fuYQToizCurJq/AL2Rfw3JwL8AfeHflBVtCFjoDQbANs5ijCwYAMdQZ08fAgyAfSiyBgIJCli4MUuQlZAyF6AvMi8g4QcToDeC0bJwxgL0RcbFI/SgAhZ6gwFw0CNienEGwKbLazo5BsCmy0tyJQtYuDEruX7kHkaAvgjjyqr5C9Ab+deQDPwL0Bf+TVnRhoCF3mAAbOMsxsiCAXAMdfb0IcAA2IciayCQoICFG7MEWQkpcwH6IvMCEn4wAXojGC0LZyxAX2RcPEIPKmChNxgABz0iphdnAGy6vKaTYwBsurwkV7KAhRuzkutH7mEE6IswrqyavwC9kX8NycC/AH3h35QVbQhY6A0GwDbOYowsGADHUGdPHwIMgH0osgYCCQpYuDFLkJWQMhegLzIvIOEHE6A3gtGycMYC9EXGxSP0oAIWeoMBcNAjYnpxBsCmy2s6ua0kTXAZnnfeefrwhz+cZbLTpk3TQw89NC/2MWPGaMSIEVnmQdAI+BSgL3xqspYVAfrCSiXJw7cAveFblPUsCNAXFqpIDiEELPTGo48+qkMOOWQ+z9aSbgphxZr2BBgA26tpKRl9XtL5pSRLnggggAACCCCAAAIIIIAAAggggEA3gYMlXYAIAs0IMABuRon3pCjAADjFqhATAggggAACCCCAAAIIIIAAAgjUIcAAuA5lI3swADZSyALTWFHSTlXeT0t6I1ODtbr9JLP7l/djmeZB2Aj4FKAvfGqylhUB+sJKJcnDtwC94VuU9SwI0BcWqkgOIQQs9Mbikv69wrlW0oshoFjTngADYHs1JaO8BN79MDtJG0uamFf4RItAEAH6Iggri2YuQF9kXkDCDyZAbwSjZeGMBeiLjItH6EEF6I2gvCyesgAD4JSrQ2wlCPAHUAlVJsdWBeiLVsV4fwkC9EUJVSbHdgTojXbUuMa6AH1hvcLk164AvdGuHNdlL8AAOPsSkkDmAvwBlHkBCT+IAH0RhJVFMxegLzIvIOEHE6A3gtGycMYC9EXGxSP0oAL0RlBeFk9ZgAFwytUhthIE+AOohCqTY6sC9EWrYry/BAH6ooQqk2M7AvRGO2pcY12AvrBeYfJrV4DeaFeO67IXYACcfQlJIHMB/gDKvICEH0SAvgjCyqKZC9AXmReQ8IMJ0BvBaFk4YwH6IuPiEXpQAXojKC+LpyzAADjl6hBbCQL8AVRClcmxVQH6olUx3l+CAH1RQpXJsR0BeqMdNa6xLkBfWK8w+bUrQG+0K8d12QswAM6+hCSQuQB/AGVeQMIPIkBfBGFl0cwF6IvMC0j4wQTojWC0LJyxAH2RcfEIPagAvRGUl8VTFmAAnHJ1iK0EAf4AKqHK5NiqAH3RqhjvL0GAviihyuTYjgC90Y4a11gXoC+sV5j82hWgN9qV47rsBRgAZ19CEshcgD+AMi8g4QcRoC+CsLJo5gL0ReYFJPxgAvRGMFoWzliAvsi4eIQeVIDeCMrL4ikLMABOuTrEhgACCCCAAAIIIIAAAggggAACCCCAAAIIdCDAALgDPC5FAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRSFmAAnHJ1iA0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEOhAgAFwB3hcigACCCCAAAIIIIAAAggggAACCCCAAAIIpCzAADjl6hAbAggggAACCCCAAAIIIIAAAggggAACCCDQgQAD4A7wuBQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEhZgAFwytUhNgQQQAABBBBAAAEEEEAAAQQQQAABBBBAoAMBBsAd4HEpAggggAACCCCAAAIIIIAAAggggAACCCCQsgAD4JSrQ2wIIIAAAggggAACCCCAAAIIIIAAAggggEAHAgyAO8DjUgQQQAABBBBAAAEEEEAAAQQQQAABBBBAIGUBBsApV4fYEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBDgQYAHeAx6XFC+wiaV9J60taQdI0SU9JulrSedWvfSPF2NN3DqxnX6CuczpI0pqSPiJpveqfoyUtVhH/RNL+9rnJMBOBuvpiuKRtJW0haV1JH5C0lKQ3Jb0g6R5Jl0m6QVJXJnaEaVugrt5w92sbVPdt/yFpOUnLShoi6TVJf5B0i6SLJf3NNjnZZSBQV18sjML1wn7d3nCypJMysCNEuwJ19cWtkjZrgXE1Sc+08H7eikAUAQbAUdjZNHOBJSRdKsn9AdTf6zlJe0ia5CnXGHt6Cp1lChKo+5xeKenjC/FlAFzQ4Us41Tr74r8lfVPSok14/F7SPpKebeK9vAWBEAJ19oaLf7qkYU0k8rYkN+g6tYn38hYEfAvU3Rf9xf8xSeMX+CIDYN/VZr1mBeruCwbAzVaG92UlwAA4q3IRbAIC7icOr5W0fRXLPyRdIOkJSUtL2kvSR6uvTan+v/uJkk5eMfbsJF6uLVMgxjn9taRdu3G/KumV6ice3W8zAC7zLKaUdd198UNJh1QAL0q6qfqJ35eqofCG1dDXfSPlXn+V5H7PfZ0XAnUK1N0bLjc3AJ5R9cTD1fmfWv0E8Hsl7djtHs69/0RJp9SJwl7FC8Toi77QR0h6TNLKVc/M/w8nDICLP6JRAGL0RfcB8O5NZH2jpDeaeB9vQSCqAAPgqPxsnqGA+8bafYPtXm7ou6UkNwTu/jpT0hHVb7ifsNq0wzxj7NlhyFxeoECMc3qcJPfX3e+v/ueGWe6RDxdV/gyACzyIiaVcd1+cK+n9kr4j6XeS5vThsWr1+IfVq6+5fjkwMTfCsS9Qd2840bUkPd7g0SefrR4B4b5Hmi3J9Yt7dAovBOoQiNEXfeXlHmV3sCT3Nxp/Kcn97RL3YgBcxylgjwUFYvRF9wEwMzPOpBkBDrOZUpJIDQLuvz66G6EVq73cM0cf6GNf9777JI2pvradJPdfBdt5xdiznTi5pmyBlM4pA+Cyz2JK2cfoC/c3UdxPwjd6uWdlP1S9yf3EinsWKj+50kiNr/sSiNEbrcT+f5J2ri44SNKFrVzMexFoUyCVvnA/3DJBkpsTuD5wn7PgfhrevRgAt1lcLmtbIFZfMABuu2RcmLIAA+CUq0NsqQm4D9S5uQrqNkmbLyTAA7p9w+A+QMH9up1XjD3biZNryhZI6ZwyAC77LKaUfUp90ZfLk5Lm/xSwGwg/khIesZgWSL033LN/j60qcLykb5muBsmlIpBCXywu6VFJ/y7pCkl7Vh/6xgA4lVNSXhyx+oIBcHlnrYiMGQAXUWaS9CRwuqSjqrWOkeR+3d9rBUnu+Yvu5R4R4X7dzivGnu3EyTVlC6R0ThkAl30WU8o+pb7oy+UeSetXX3DPAb47JTxiMS2Qem+4v/L+yaoC/ASw6aOYVHIp9MVZkr5c/U2SD1Xfw5zETwAndU5KCyZWXzAALu2kFZIvA+BCCk2aXgR+2+3D39xfj7qlwaruk9Xdhye41yhJL7cRRYw92wiTSwoXSOmcMgAu/DAmlH5KfbEgyyLVB78tWX3BPdpockJ2hGJbIOXecH/l/WpJ7q8dvyXJfTjcgp/1YLs6ZBdLIHZfbCzJfXbJwOq58PM/T4EBcKwTwb5OIFZfdB8AXydpnepxWe6DRN1z4e+SdFkT8wCqiEBSAgyAkyoHwSQu8LSk1aoY3T+faRCve0zE/A+AGyfpjjbyi7FnG2FySeECKZ1TBsCFH8aE0k+pLxZk6d4n7ln27pn2vBCoSyCF3nD3Z+6Z2e7l/oOI+w/221b/c7/nPgDuc5Lch4nyQqAOgZh9sWj1XHj3WKCbJG3dLWEGwHVUnz36E4jVF90HwAurjns85D7d/uYvlUQgaQEGwEmXh+ASE3AfrDOyimm4pOkN4rtK0u7Ve9xPlFzbRj4x9mwjTC4pXCClc8oAuPDDmFD6KfVFdxb3gW+PVX8zxf3+x6ufeEyIjlCMC6TQG5Mkje3DuUuS+w/47pmntxuvA+mlJRCzL+b/Nfs3JX1Y0l8YAKd1OAqOJlZfuAHw2pJ+V324+/OS5kh6j6StJH2s+qBEV5q/SXKP0uJvUhV8UHNJnQFwLpUizhQEZkoaUgXi/ul+OmRhr0slfaZ6g/vn5W0kEWPPNsLkksIFUjqnDIALP4wJpZ9SX8xncT/p6D7d3f2tFPf6dbf/UJkQHaEYF0ihN/obAP9d0rmS3F9/n/9ZDsbLQXqJCMTqC/cs+InVY0+OlnTGAh78BHAiB6TQMGL1xUaS7pfk9u/r9RFJV0papfqie1TFDoXWiLQzEmAAnFGxCDW6QIw/gGLsGR2aALITSOmcMgDO7viYDTilvnDI7rmOP5W0dyXufsLLfeM/xWwFSCxVgdR6Y5ik90vaRdIRktyzsV+RtGf1H0xSdSQuWwIx+sL9R0E35FpLknsc0AbVTzl2l2UAbOuc5ZZNjL5o1mhNSQ9KGlpd4Prn3mYv5n0IxBBgABxDnT1zFYjxV1Bi7JlrfYg7nkBK55QBcLxzwM49BVLqC3e/d56kz1chug8p3ayJZ9lTUwRCCKTUGwvm5z7jwX1mw79VP/nlfsrr0RAIrInAAgIx+uIUSSdUQ183vHJD4AVfDIA5qjEFYvRFK/leUD0v3l3j+sk9PogXAskKMABOtjQElqBAjIfQx9gzQXpCSlwgpXPKADjxw1JQeKn0hbvX+4GkL1T27q+4b77AMx4LKgupJiCQSm/0R7Fv9dPy7utXVD8JnAAbIRgXqLsvRlc/regea+ce++Ae/9DXiwGw8YOXeHp190WrHO4xj+6xj+71S0l7tLoA70egTgEGwHVqs1fuAu7ZPttXSWwp6ZYGCbmfsHKfKu1eoyS93AZAjD3bCJNLChdI6ZwyAC78MCaUfgp94e7zzpH0xcrFfYiJG/4+lZAToZQnkEJvLEx92W73bP+QtEJ5JSLjCAJ198X/SvqKpLmSTpPkPgCur5f7nmeL6gvue5+bq///x2rgFYGKLQsSqLsvWqXdRtKN1UXuA+O2bXUB3o9AnQIMgOvUZq/cBeZ/Qq7L4xhJ7tf9vZbv9kmgL0lyv27nFWPPduLkmrIFUjqnDIDLPospZR+7LxYc/r5QDX//nBISsRQpELs3GqG75zm+Vb3JPX9y/vMdG13H1xHoRKDuvviupMM7CPgaSbt1cD2XItCMQN190UxM3d+zl6TLqt/gJ4Bb1eP9tQswAK6dnA0zFnD/BfymKv5bu/3X8L5SOkDShdUXLpbkft3OK8ae7cTJNWULpHROGQCXfRZTyj5mXyw4/H2xGv7+KSUgYilWIGZvNIPuPhBr/nN/3U/Nr9TMRbwHgQ4F6u4LBsAdFozLaxGouy9aTer8bp+v8I3qmdqtrsH7EahNgAFwbdRsZEBgkCT37MT5fxVwvX4+LMG97z5JY6qc3WMjbmgz/xh7thkqlxUskNI5ZQBc8EFMLPWYfeEe+3Bo5TG5Gv66v67LC4EUBGL2RjP5f1/Sl6o3/kLSp5u5iPcg0KFAqn3BM4A7LCyXdySQal+4pD4o6WFJi1YZbijp7o6y5WIEAgswAA4MzPLmBNxzFN2H6bjX45Lcf5V0j3jo/nIfpHBk9Rt3StqkH4Xug6rbqm/Q+3qrzz3NFYSEkhHweU6b7Y2+kmcAnMyRIJDq2bt1/5lxtqTDKn2GvxzDVAXq/jPDfQii+48g7m9wdfWD4gYNR0n6lqT53yO5Z2a7ezReCNQhUHdfNJMTA+BmlHhPSIG6++LL1Q9z3bWQpNaRdJWk91bvcc8B3i4kAmsj4EOAAbAPRdYoSWCwpPGS3APf3ct9c32BpCckLS3JPQdo/sD3ter/u0FxX69mh1w+9yypVuRar4DPc9psb6wm6aAF0lxb0s7V7z0i6TcLfN19eMn8DzCpV4jdShSouy/cXz88voJ2Q67jJD3ZBPwDktwHl/JCoC6BunvDPY5rP0nPSXIf1OMe8eD+A757xu9SktxjH3bt9s28czi16qG6TNgHgbr7ohlxBsDNKPGekAJ198Wvqz8P/iJpgqTHJL0iaY6kf5O0laQdJA2skv6bpI0luc9a4IVA0gIMgJMuD8ElKjC8etj7TguJzz0qwv2VwYX9l8Nmh1xuG197JkpKWEYEfJ3TZnvD/WSW+0TqVl4nS3LfzPBCoC6BOvvC/XTjZm0k5p5T7wZkvBCoU6DO3pg/AG4mv6mSvirp3GbezHsQ8CxQZ180EzoD4GaUeE9ogTr7Yv4AuJmc3GMeD2T42wwV70lBgAFwClUghlwF3E+KfFbS+pJGSXpdkvsvhe6vg5wnyX0DsbBXs0Ou7mt0umeu1sSdl0Cn57TZ3mAAnNe5KD3aOvqCAXDppyzP/OvojRHVT21tKsn91d33SVpW0hBJ0yX9Q5L7WyPum3n3Se6N7uHylCbqnATq6ItmPBgAN6PEe+oSqKMv3J8P7nuMsZJGV9/nuz8vhlZ/NjwjaaKkS3nmb11lZx9fAgyAfUmyDgIIIIAAAggggAACCCCAAAIIIIAAAgggkJgA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width=\"639.9999861283738\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "        The estimated energy is 4.931\n",
      ". \n",
      "        The difference between this estimation of the energy and \n",
      "        the theoretical value of the energy is 0.0038022005447. \n",
      "\n",
      "        Percent error = 0.0770486919269%. \n",
      "\n",
      "        When the energy is 4.931, the value of psi when x = 0.5 \n",
      "        is 0.000865391396982.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "\n",
    "delta_x = .5/500\n",
    "x = np.arange(0,.5+delta_x,delta_x) #array of values of independent variable\n",
    "psi = np.zeros(len(x)) #array of values for psi\n",
    "energy = 0 #starting value for energy\n",
    "theoretical_energy = (np.pi**2) / 2\n",
    "error = 0.001\n",
    "psi[0] = np.sqrt(2)\n",
    "psi[1] = np.sqrt(2)\n",
    "print(len(x))\n",
    "print(x[500])\n",
    "loop_continue = True\n",
    "while loop_continue:\n",
    "    for i in range(0,len(x) - 2):\n",
    "        psi[i+2] = 2*psi[i+1] - psi[i] - 2*(delta_x**2)*energy*psi[i]\n",
    "    if abs(psi[i+2]) > error:\n",
    "        energy += 0.001\n",
    "    else:\n",
    "        difference = abs(energy - theoretical_energy)\n",
    "        plt.plot(x, psi, 'b',label = 'numerical')\n",
    "        print(\"\"\"\n",
    "        The estimated energy is {}\\n. \n",
    "        The difference between this estimation of the energy and \n",
    "        the theoretical value of the energy is {}. \\n\n",
    "        Percent error = {}%. \\n\n",
    "        When the energy is {}, the value of psi when x = 0.5 \n",
    "        is {}.\"\"\".format(energy,difference,(difference/theoretical_energy)*100,\n",
    "                         energy,psi[i+2]))\n",
    "        loop_continue = False\n",
    "plt.plot(x,np.sqrt(2)*np.cos(np.pi * x),'r--',label = 'analytical')\n",
    "\n",
    "plt.xlabel('x')\n",
    "h = plt.ylabel('$\\\\Psi$')\n",
    "h.set_rotation(0)\n",
    "plt.title('$\\\\Psi$ vs position')\n",
    "plt.grid()\n",
    "plt.legend();"
   ]
  }
 ],
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   "display_name": "Python 2",
   "language": "python",
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    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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 },
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