{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7fa114f9-5db3-4f73-8971-d6634448da79",
   "metadata": {},
   "source": [
    "# Shaky Lab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example of `%%there ai` usage is available as a YouTube video: https://youtu.be/4wqIQ-iWO5A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "193d61ec-d90d-4ac6-9c60-963396781131",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext pythonhere\n",
    "%connect-there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "49e4dd2b-b465-43a7-acfc-28ba4e505f20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating %%there cell with AI... this can take up to 300s.\n",
      "Generated a %%there cell in 43.5s. Review it, then run it to execute on the connected target.\n"
     ]
    }
   ],
   "source": [
    "%%there ai\n",
    "Build Shaky Lab: a pocket motion laboratory for Android.\n",
    "- show live x, y, and z acceleration values\n",
    "- include Start Recording, Stop Recording, and Reset buttons\n",
    "- store recorded samples with timestamps in `samples` variable\n",
    "- show the number of samples recorded\n",
    "- portrait mode\n",
    "- screen should be blocked from rotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "add5637a-d2f4-40d9-b585-48ecf00a01a0",
   "metadata": {
     "tags": [
       "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "%%there\n",
    "# Generated locally by %%there ai. Review before running.\n",
    "from time import time\n",
    "from kivy.clock import Clock\n",
    "from kivy.lang import Builder\n",
    "from kivy.logger import Logger\n",
    "from kivy.uix.popup import Popup\n",
    "from kivy.uix.label import Label\n",
    "from plyer import accelerometer\n",
    "\n",
    "try:\n",
    "    _old_event = globals().get(\"shaky_lab_update_event\")\n",
    "    if _old_event is not None:\n",
    "        _old_event.cancel()\n",
    "except Exception:\n",
    "    Logger.exception(\"PythonHere: Could not cancel previous Shaky Lab update event\")\n",
    "\n",
    "samples = []\n",
    "shaky_lab_state = {\n",
    "    \"recording\": False,\n",
    "    \"enabled\": False,\n",
    "    \"sample_count\": 0,\n",
    "    \"last_acceleration\": None,\n",
    "    \"last_error\": None,\n",
    "    \"orientation_locked\": False,\n",
    "}\n",
    "\n",
    "def shaky_lab_show_error(message):\n",
    "    shaky_lab_state[\"last_error\"] = str(message)\n",
    "    Logger.error(\"PythonHere: Shaky Lab error: \" + str(message))\n",
    "    try:\n",
    "        Popup(\n",
    "            title=\"Shaky Lab Error\",\n",
    "            content=Label(text=str(message), text_size=(root.width * 0.8, None)),\n",
    "            size_hint=(0.9, 0.35),\n",
    "        ).open()\n",
    "    except Exception:\n",
    "        Logger.exception(\"PythonHere: Could not show Shaky Lab error popup\")\n",
    "\n",
    "def shaky_lab_lock_portrait():\n",
    "    try:\n",
    "        from jnius import autoclass\n",
    "        PythonActivity = autoclass(\"org.kivy.android.PythonActivity\")\n",
    "        ActivityInfo = autoclass(\"android.content.pm.ActivityInfo\")\n",
    "        activity = PythonActivity.mActivity\n",
    "        activity.setRequestedOrientation(ActivityInfo.SCREEN_ORIENTATION_PORTRAIT)\n",
    "        shaky_lab_state[\"orientation_locked\"] = True\n",
    "    except Exception as exc:\n",
    "        shaky_lab_state[\"orientation_locked\"] = False\n",
    "        shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "        Logger.exception(\"PythonHere: Could not lock portrait orientation\")\n",
    "\n",
    "def shaky_lab_enable_sensor():\n",
    "    try:\n",
    "        accelerometer.enable()\n",
    "        shaky_lab_state[\"enabled\"] = True\n",
    "        return True\n",
    "    except Exception as exc:\n",
    "        shaky_lab_state[\"enabled\"] = False\n",
    "        shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "        Logger.exception(\"PythonHere: Could not enable accelerometer\")\n",
    "        return False\n",
    "\n",
    "KV = \"\"\"\n",
    "#:import dp kivy.metrics.dp\n",
    "#:import sp kivy.metrics.sp\n",
    "\n",
    "BoxLayout:\n",
    "    orientation: \"vertical\"\n",
    "    padding: dp(16)\n",
    "    spacing: dp(12)\n",
    "\n",
    "    Label:\n",
    "        id: title_label\n",
    "        text: \"Shaky Lab\"\n",
    "        font_size: sp(28)\n",
    "        bold: True\n",
    "        size_hint_y: None\n",
    "        height: dp(48)\n",
    "\n",
    "    Label:\n",
    "        id: subtitle_label\n",
    "        text: \"Pocket motion laboratory\"\n",
    "        font_size: sp(16)\n",
    "        size_hint_y: None\n",
    "        height: dp(32)\n",
    "\n",
    "    GridLayout:\n",
    "        cols: 2\n",
    "        spacing: dp(10)\n",
    "        size_hint_y: None\n",
    "        height: dp(180)\n",
    "\n",
    "        Label:\n",
    "            text: \"X acceleration\"\n",
    "            font_size: sp(18)\n",
    "        Label:\n",
    "            id: x_value\n",
    "            text: \"0.000\"\n",
    "            font_size: sp(22)\n",
    "            bold: True\n",
    "\n",
    "        Label:\n",
    "            text: \"Y acceleration\"\n",
    "            font_size: sp(18)\n",
    "        Label:\n",
    "            id: y_value\n",
    "            text: \"0.000\"\n",
    "            font_size: sp(22)\n",
    "            bold: True\n",
    "\n",
    "        Label:\n",
    "            text: \"Z acceleration\"\n",
    "            font_size: sp(18)\n",
    "        Label:\n",
    "            id: z_value\n",
    "            text: \"0.000\"\n",
    "            font_size: sp(22)\n",
    "            bold: True\n",
    "\n",
    "    Label:\n",
    "        id: count_label\n",
    "        text: \"Samples recorded: 0\"\n",
    "        font_size: sp(20)\n",
    "        bold: True\n",
    "        size_hint_y: None\n",
    "        height: dp(44)\n",
    "\n",
    "    Label:\n",
    "        id: status_label\n",
    "        text: \"Ready. Press Start Recording.\"\n",
    "        font_size: sp(16)\n",
    "        halign: \"center\"\n",
    "        valign: \"middle\"\n",
    "        text_size: self.size\n",
    "        size_hint_y: 1\n",
    "\n",
    "    BoxLayout:\n",
    "        orientation: \"vertical\"\n",
    "        spacing: dp(10)\n",
    "        size_hint_y: None\n",
    "        height: dp(186)\n",
    "\n",
    "        Button:\n",
    "            id: start_button\n",
    "            text: \"Start Recording\"\n",
    "            font_size: sp(20)\n",
    "            size_hint_y: None\n",
    "            height: dp(54)\n",
    "\n",
    "        Button:\n",
    "            id: stop_button\n",
    "            text: \"Stop Recording\"\n",
    "            font_size: sp(20)\n",
    "            size_hint_y: None\n",
    "            height: dp(54)\n",
    "\n",
    "        Button:\n",
    "            id: reset_button\n",
    "            text: \"Reset\"\n",
    "            font_size: sp(20)\n",
    "            size_hint_y: None\n",
    "            height: dp(54)\n",
    "\"\"\"\n",
    "\n",
    "shaky_lab_ui = Builder.load_string(KV)\n",
    "if shaky_lab_ui is None:\n",
    "    shaky_lab_show_error(\"Could not load Shaky Lab interface.\")\n",
    "else:\n",
    "    def shaky_lab_set_status(text):\n",
    "        shaky_lab_ui.ids.status_label.text = text\n",
    "\n",
    "    def shaky_lab_update_count():\n",
    "        shaky_lab_state[\"sample_count\"] = len(samples)\n",
    "        shaky_lab_ui.ids.count_label.text = f\"Samples recorded: {len(samples)}\"\n",
    "\n",
    "    def shaky_lab_start_recording(instance):\n",
    "        if not shaky_lab_state.get(\"enabled\"):\n",
    "            if not shaky_lab_enable_sensor():\n",
    "                shaky_lab_set_status(\"Accelerometer is unavailable. See error details.\")\n",
    "                shaky_lab_show_error(shaky_lab_state.get(\"last_error\") or \"Accelerometer is unavailable.\")\n",
    "                return\n",
    "        shaky_lab_state[\"recording\"] = True\n",
    "        shaky_lab_set_status(\"Recording motion samples.\")\n",
    "\n",
    "    def shaky_lab_stop_recording(instance):\n",
    "        shaky_lab_state[\"recording\"] = False\n",
    "        shaky_lab_set_status(\"Recording stopped.\")\n",
    "\n",
    "    def shaky_lab_reset(instance):\n",
    "        samples.clear()\n",
    "        shaky_lab_state[\"sample_count\"] = 0\n",
    "        shaky_lab_update_count()\n",
    "        shaky_lab_set_status(\"Samples reset. Press Start Recording.\")\n",
    "\n",
    "    def shaky_lab_update(dt):\n",
    "        try:\n",
    "            accel = accelerometer.acceleration\n",
    "            if accel is None:\n",
    "                shaky_lab_set_status(\"Waiting for accelerometer data.\")\n",
    "                return\n",
    "\n",
    "            x, y, z = accel\n",
    "            x = 0.0 if x is None else float(x)\n",
    "            y = 0.0 if y is None else float(y)\n",
    "            z = 0.0 if z is None else float(z)\n",
    "\n",
    "            shaky_lab_state[\"last_acceleration\"] = (x, y, z)\n",
    "            shaky_lab_ui.ids.x_value.text = f\"{x:.3f}\"\n",
    "            shaky_lab_ui.ids.y_value.text = f\"{y:.3f}\"\n",
    "            shaky_lab_ui.ids.z_value.text = f\"{z:.3f}\"\n",
    "\n",
    "            if shaky_lab_state.get(\"recording\"):\n",
    "                samples.append({\n",
    "                    \"timestamp\": time(),\n",
    "                    \"x\": x,\n",
    "                    \"y\": y,\n",
    "                    \"z\": z,\n",
    "                })\n",
    "                shaky_lab_update_count()\n",
    "        except Exception as exc:\n",
    "            shaky_lab_state[\"recording\"] = False\n",
    "            shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "            Logger.exception(\"PythonHere: Could not update Shaky Lab acceleration\")\n",
    "            shaky_lab_set_status(\"Sensor update failed. Recording stopped.\")\n",
    "\n",
    "    shaky_lab_ui.ids.start_button.bind(on_release=shaky_lab_start_recording)\n",
    "    shaky_lab_ui.ids.stop_button.bind(on_release=shaky_lab_stop_recording)\n",
    "    shaky_lab_ui.ids.reset_button.bind(on_release=shaky_lab_reset)\n",
    "\n",
    "    shaky_lab_lock_portrait()\n",
    "    if shaky_lab_enable_sensor():\n",
    "        shaky_lab_set_status(\"Ready. Press Start Recording.\")\n",
    "    else:\n",
    "        shaky_lab_set_status(\"Accelerometer is unavailable on this device.\")\n",
    "\n",
    "    shaky_lab_update_event = Clock.schedule_interval(shaky_lab_update, 0.1)\n",
    "\n",
    "    root.clear_widgets()\n",
    "    root.add_widget(shaky_lab_ui)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9b4cd65-11d7-4558-aa79-966f3dbc6be2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating %%there cell with AI... this can take up to 300s.\n",
      "Generated a %%there cell in 143.7s. Review it, then run it to execute on the connected target.\n"
     ]
    }
   ],
   "source": [
    "%%there ai --fix\n",
    "Rebuild \"Shaky Lab\" in a Jules Verne science-lab style:\n",
    "brass, parchment, gears, Victorian instrument styling.\n",
    "\n",
    "Make x, y, and z three small axis inputs, visibly linked by rods/gears\n",
    "to one central combiner mechanism, which drives the main total-motion gauge.\n",
    "The mechanism should clearly suggest that total motion is derived from x/y/z,\n",
    "not show them as separate disconnected widgets.\n",
    "\n",
    "Main gauge uses one pointer and these labels only: Calm, Tremor, Shake, Jolt, Surge.\n",
    "\n",
    "Keep everything connected and fully inside its panels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3a612b3-0160-40f0-bf8c-02105daf6c1a",
   "metadata": {
     "tags": [
       "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "%%there\n",
    "# Generated locally by %%there ai. Review before running.\n",
    "# AI mode: fix\n",
    "# Fix: rebuild Shaky Lab as a connected Jules Verne style mechanism with one derived total-motion gauge.\n",
    "from time import time\n",
    "from math import sqrt, sin, cos, pi\n",
    "\n",
    "from kivy.clock import Clock\n",
    "from kivy.core.text import Label as CoreLabel\n",
    "from kivy.graphics import Color, Ellipse, Line, Rectangle\n",
    "from kivy.lang import Builder\n",
    "from kivy.logger import Logger\n",
    "from kivy.metrics import dp, sp\n",
    "from kivy.properties import NumericProperty\n",
    "from kivy.uix.boxlayout import BoxLayout\n",
    "from kivy.uix.label import Label\n",
    "from kivy.uix.popup import Popup\n",
    "from kivy.uix.widget import Widget\n",
    "from plyer import accelerometer\n",
    "\n",
    "try:\n",
    "    _old_event = globals().get(\"shaky_lab_update_event\")\n",
    "    if _old_event is not None:\n",
    "        _old_event.cancel()\n",
    "except Exception:\n",
    "    Logger.exception(\"PythonHere: Could not cancel previous Shaky Lab update event\")\n",
    "\n",
    "samples = globals().get(\"samples\")\n",
    "if not isinstance(samples, list):\n",
    "    samples = []\n",
    "globals()[\"samples\"] = samples\n",
    "\n",
    "shaky_lab_state = {\n",
    "    \"recording\": False,\n",
    "    \"enabled\": False,\n",
    "    \"sample_count\": len(samples),\n",
    "    \"last_acceleration\": None,\n",
    "    \"previous_acceleration\": None,\n",
    "    \"last_motion\": 0.0,\n",
    "    \"last_error\": None,\n",
    "    \"orientation_locked\": False,\n",
    "}\n",
    "\n",
    "\n",
    "def shaky_lab_show_error(message):\n",
    "    shaky_lab_state[\"last_error\"] = str(message)\n",
    "    Logger.error(\"PythonHere: Shaky Lab error: \" + str(message))\n",
    "    try:\n",
    "        Popup(\n",
    "            title=\"Shaky Lab Error\",\n",
    "            content=Label(\n",
    "                text=str(message),\n",
    "                text_size=(root.width * 0.8, None),\n",
    "                halign=\"center\",\n",
    "                valign=\"middle\",\n",
    "            ),\n",
    "            size_hint=(0.9, 0.35),\n",
    "        ).open()\n",
    "    except Exception:\n",
    "        Logger.exception(\"PythonHere: Could not show Shaky Lab error popup\")\n",
    "\n",
    "\n",
    "def shaky_lab_lock_portrait():\n",
    "    try:\n",
    "        from jnius import autoclass\n",
    "\n",
    "        PythonActivity = autoclass(\"org.kivy.android.PythonActivity\")\n",
    "        ActivityInfo = autoclass(\"android.content.pm.ActivityInfo\")\n",
    "        activity = PythonActivity.mActivity\n",
    "        activity.setRequestedOrientation(ActivityInfo.SCREEN_ORIENTATION_PORTRAIT)\n",
    "        shaky_lab_state[\"orientation_locked\"] = True\n",
    "    except Exception as exc:\n",
    "        shaky_lab_state[\"orientation_locked\"] = False\n",
    "        shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "        Logger.exception(\"PythonHere: Could not lock portrait orientation\")\n",
    "\n",
    "\n",
    "def shaky_lab_enable_sensor():\n",
    "    try:\n",
    "        accelerometer.enable()\n",
    "        shaky_lab_state[\"enabled\"] = True\n",
    "        return True\n",
    "    except Exception as exc:\n",
    "        shaky_lab_state[\"enabled\"] = False\n",
    "        shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "        Logger.exception(\"PythonHere: Could not enable accelerometer\")\n",
    "        return False\n",
    "\n",
    "\n",
    "def shaky_lab_stop_updates():\n",
    "    event = globals().get(\"shaky_lab_update_event\")\n",
    "    if event is not None:\n",
    "        event.cancel()\n",
    "    shaky_lab_state[\"recording\"] = False\n",
    "\n",
    "\n",
    "def _clamp(value, low=0.0, high=1.0):\n",
    "    return max(low, min(high, value))\n",
    "\n",
    "\n",
    "class ShakyLabRoot(BoxLayout):\n",
    "    pass\n",
    "\n",
    "\n",
    "class MechanismDiagram(Widget):\n",
    "    axis_x = NumericProperty(0.0)\n",
    "    axis_y = NumericProperty(0.0)\n",
    "    axis_z = NumericProperty(0.0)\n",
    "    total_level = NumericProperty(0.0)\n",
    "    gear_phase = NumericProperty(0.0)\n",
    "\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.bind(pos=self.redraw, size=self.redraw)\n",
    "        Clock.schedule_once(lambda dt: self.redraw(), 0)\n",
    "\n",
    "    def set_motion(self, axis_x, axis_y, axis_z, total_level, gear_phase):\n",
    "        self.axis_x = _clamp(axis_x, -1.0, 1.0)\n",
    "        self.axis_y = _clamp(axis_y, -1.0, 1.0)\n",
    "        self.axis_z = _clamp(axis_z, -1.0, 1.0)\n",
    "        self.total_level = _clamp(total_level, 0.0, 1.0)\n",
    "        self.gear_phase = gear_phase\n",
    "        self.redraw()\n",
    "\n",
    "    def _text(self, text, cx, cy, font_size, color=(0.18, 0.10, 0.04, 1), bold=False):\n",
    "        label = CoreLabel(\n",
    "            text=str(text),\n",
    "            font_size=font_size,\n",
    "            color=color,\n",
    "            bold=bold,\n",
    "        )\n",
    "        label.refresh()\n",
    "        tw, th = label.texture.size\n",
    "        Color(1, 1, 1, 1)\n",
    "        Rectangle(texture=label.texture, size=(tw, th), pos=(cx - tw / 2, cy - th / 2))\n",
    "\n",
    "    def _panel(self, x, y, w, h, radius=0):\n",
    "        Color(0.77, 0.63, 0.39, 1)\n",
    "        Rectangle(pos=(x, y), size=(w, h))\n",
    "        Color(0.93, 0.84, 0.62, 1)\n",
    "        Rectangle(pos=(x + dp(3), y + dp(3)), size=(w - dp(6), h - dp(6)))\n",
    "        Color(0.40, 0.24, 0.09, 1)\n",
    "        Line(rectangle=(x, y, w, h), width=dp(2.0))\n",
    "        Color(0.67, 0.47, 0.20, 1)\n",
    "        Line(rectangle=(x + dp(6), y + dp(6), w - dp(12), h - dp(12)), width=dp(1.0))\n",
    "\n",
    "    def _rod(self, x1, y1, x2, y2):\n",
    "        Color(0.34, 0.20, 0.08, 1)\n",
    "        Line(points=[x1, y1, x2, y2], width=dp(5.0), cap=\"round\")\n",
    "        Color(0.83, 0.61, 0.24, 1)\n",
    "        Line(points=[x1, y1, x2, y2], width=dp(2.2), cap=\"round\")\n",
    "        Color(0.20, 0.11, 0.04, 1)\n",
    "        Ellipse(pos=(x1 - dp(4), y1 - dp(4)), size=(dp(8), dp(8)))\n",
    "        Ellipse(pos=(x2 - dp(4), y2 - dp(4)), size=(dp(8), dp(8)))\n",
    "\n",
    "    def _gear(self, cx, cy, r, teeth, phase, active=1.0):\n",
    "        Color(0.35, 0.21, 0.08, 1)\n",
    "        Ellipse(pos=(cx - r * 1.13, cy - r * 1.13), size=(r * 2.26, r * 2.26))\n",
    "        Color(0.78, 0.55, 0.20, 1)\n",
    "        Ellipse(pos=(cx - r, cy - r), size=(r * 2, r * 2))\n",
    "        Color(0.97, 0.79, 0.34, 1)\n",
    "        Ellipse(pos=(cx - r * 0.72, cy - r * 0.72), size=(r * 1.44, r * 1.44))\n",
    "        Color(0.30, 0.18, 0.07, 1)\n",
    "        Line(circle=(cx, cy, r), width=dp(1.2))\n",
    "        Line(circle=(cx, cy, r * 0.45), width=dp(1.1))\n",
    "        for i in range(teeth):\n",
    "            a = phase + 2 * pi * i / teeth\n",
    "            x1 = cx + cos(a) * r * 0.93\n",
    "            y1 = cy + sin(a) * r * 0.93\n",
    "            x2 = cx + cos(a) * r * 1.25\n",
    "            y2 = cy + sin(a) * r * 1.25\n",
    "            Color(0.42, 0.25, 0.08, 0.60 + 0.25 * active)\n",
    "            Line(points=[x1, y1, x2, y2], width=dp(2.0), cap=\"round\")\n",
    "        Color(0.18, 0.10, 0.04, 1)\n",
    "        Ellipse(pos=(cx - r * 0.17, cy - r * 0.17), size=(r * 0.34, r * 0.34))\n",
    "\n",
    "    def _axis_input(self, name, value, cx, cy, r, rod_x):\n",
    "        value = _clamp(value, -1.0, 1.0)\n",
    "        Color(0.42, 0.25, 0.09, 1)\n",
    "        Ellipse(pos=(cx - r * 1.1, cy - r * 1.1), size=(r * 2.2, r * 2.2))\n",
    "        Color(0.88, 0.73, 0.43, 1)\n",
    "        Ellipse(pos=(cx - r, cy - r), size=(r * 2, r * 2))\n",
    "        Color(0.94, 0.86, 0.65, 1)\n",
    "        Ellipse(pos=(cx - r * 0.72, cy - r * 0.72), size=(r * 1.44, r * 1.44))\n",
    "        Color(0.25, 0.14, 0.05, 1)\n",
    "        Line(circle=(cx, cy, r * 0.78, 205, 335), width=dp(1.2))\n",
    "        pointer_angle = (-90 + value * 55) * pi / 180.0\n",
    "        Color(0.45, 0.06, 0.03, 1)\n",
    "        Line(\n",
    "            points=[\n",
    "                cx,\n",
    "                cy,\n",
    "                cx + cos(pointer_angle) * r * 0.58,\n",
    "                cy + sin(pointer_angle) * r * 0.58,\n",
    "            ],\n",
    "            width=dp(2.0),\n",
    "            cap=\"round\",\n",
    "        )\n",
    "        Color(0.18, 0.10, 0.04, 1)\n",
    "        Ellipse(pos=(cx - r * 0.10, cy - r * 0.10), size=(r * 0.20, r * 0.20))\n",
    "        self._text(name, cx, cy + r * 0.38, max(sp(11), min(sp(15), r * 0.38)), bold=True)\n",
    "\n",
    "        slot_y = cy - r * 1.30\n",
    "        slot_w = max(dp(34), rod_x - cx - r * 1.0)\n",
    "        Color(0.38, 0.23, 0.09, 1)\n",
    "        Rectangle(pos=(cx + r * 0.52, slot_y - dp(3)), size=(slot_w, dp(6)))\n",
    "        Color(0.91, 0.68, 0.27, 1)\n",
    "        piston_x = cx + r * 0.52 + slot_w * (0.5 + value * 0.35)\n",
    "        Rectangle(pos=(piston_x - dp(4), slot_y - dp(8)), size=(dp(8), dp(16)))\n",
    "        self._rod(cx + r * 0.96, cy, rod_x, cy)\n",
    "\n",
    "    def _gauge(self, cx, cy, r, level):\n",
    "        level = _clamp(level)\n",
    "        Color(0.36, 0.21, 0.07, 1)\n",
    "        Ellipse(pos=(cx - r * 1.10, cy - r * 1.10), size=(r * 2.20, r * 2.20))\n",
    "        Color(0.74, 0.50, 0.18, 1)\n",
    "        Ellipse(pos=(cx - r, cy - r), size=(r * 2, r * 2))\n",
    "        Color(0.96, 0.89, 0.69, 1)\n",
    "        Ellipse(pos=(cx - r * 0.86, cy - r * 0.86), size=(r * 1.72, r * 1.72))\n",
    "        Color(0.25, 0.14, 0.05, 1)\n",
    "        Line(circle=(cx, cy, r * 0.80, -30, 210), width=dp(2.0))\n",
    "\n",
    "        label_data = [\n",
    "            (\"Calm\", 210),\n",
    "            (\"Tremor\", 150),\n",
    "            (\"Shake\", 90),\n",
    "            (\"Jolt\", 30),\n",
    "            (\"Surge\", -30),\n",
    "        ]\n",
    "        label_font = max(sp(8), min(sp(13), r * 0.13))\n",
    "        for text, angle_deg in label_data:\n",
    "            a = angle_deg * pi / 180.0\n",
    "            self._text(\n",
    "                text,\n",
    "                cx + cos(a) * r * 0.58,\n",
    "                cy + sin(a) * r * 0.58,\n",
    "                label_font,\n",
    "                color=(0.18, 0.10, 0.04, 1),\n",
    "                bold=True,\n",
    "            )\n",
    "\n",
    "        for i in range(17):\n",
    "            angle_deg = 210 - i * 15\n",
    "            a = angle_deg * pi / 180.0\n",
    "            inner = r * (0.70 if i % 4 else 0.64)\n",
    "            outer = r * 0.78\n",
    "            Color(0.30, 0.18, 0.07, 1)\n",
    "            Line(\n",
    "                points=[\n",
    "                    cx + cos(a) * inner,\n",
    "                    cy + sin(a) * inner,\n",
    "                    cx + cos(a) * outer,\n",
    "                    cy + sin(a) * outer,\n",
    "                ],\n",
    "                width=dp(1.0 if i % 4 else 1.6),\n",
    "            )\n",
    "\n",
    "        pointer_angle = (210 - level * 240) * pi / 180.0\n",
    "        Color(0.50, 0.05, 0.03, 1)\n",
    "        Line(\n",
    "            points=[\n",
    "                cx,\n",
    "                cy,\n",
    "                cx + cos(pointer_angle) * r * 0.68,\n",
    "                cy + sin(pointer_angle) * r * 0.68,\n",
    "            ],\n",
    "            width=dp(3.0),\n",
    "            cap=\"round\",\n",
    "        )\n",
    "        Color(0.20, 0.10, 0.04, 1)\n",
    "        Ellipse(pos=(cx - r * 0.08, cy - r * 0.08), size=(r * 0.16, r * 0.16))\n",
    "\n",
    "    def redraw(self, *args):\n",
    "        self.canvas.clear()\n",
    "        x, y = self.pos\n",
    "        w, h = self.size\n",
    "        if w < dp(220) or h < dp(180):\n",
    "            return\n",
    "\n",
    "        pad = dp(10)\n",
    "        gap = dp(7)\n",
    "        panel_x = x + pad\n",
    "        panel_y = y + pad\n",
    "        panel_w = w - pad * 2\n",
    "        panel_h = h - pad * 2\n",
    "\n",
    "        with self.canvas:\n",
    "            Color(0.36, 0.23, 0.12, 1)\n",
    "            Rectangle(pos=self.pos, size=self.size)\n",
    "\n",
    "            self._panel(panel_x, panel_y, panel_w, panel_h)\n",
    "\n",
    "            inner = dp(11)\n",
    "            work_x = panel_x + inner\n",
    "            work_y = panel_y + inner\n",
    "            work_w = panel_w - inner * 2\n",
    "            work_h = panel_h - inner * 2\n",
    "\n",
    "            left_w = max(dp(94), min(dp(135), work_w * 0.30))\n",
    "            center_w = max(dp(78), min(dp(118), work_w * 0.24))\n",
    "            gauge_w = work_w - left_w - center_w - gap * 2\n",
    "            if gauge_w < dp(110):\n",
    "                left_w = work_w * 0.30\n",
    "                center_w = work_w * 0.24\n",
    "                gauge_w = work_w - left_w - center_w - gap * 2\n",
    "\n",
    "            left_x = work_x\n",
    "            center_x = left_x + left_w + gap\n",
    "            gauge_x = center_x + center_w + gap\n",
    "\n",
    "            self._panel(left_x, work_y, left_w, work_h)\n",
    "            self._panel(center_x, work_y, center_w, work_h)\n",
    "            self._panel(gauge_x, work_y, gauge_w, work_h)\n",
    "\n",
    "            axis_r = max(dp(16), min(dp(24), min(left_w, work_h / 5.7)))\n",
    "            axis_cx = left_x + left_w * 0.34\n",
    "            axis_rod_x = left_x + left_w - dp(12)\n",
    "            axis_ys = [\n",
    "                work_y + work_h * 0.73,\n",
    "                work_y + work_h * 0.50,\n",
    "                work_y + work_h * 0.27,\n",
    "            ]\n",
    "\n",
    "            comb_cx = center_x + center_w * 0.50\n",
    "            comb_cy = work_y + work_h * 0.50\n",
    "            comb_r = max(dp(26), min(dp(42), min(center_w, work_h) * 0.26))\n",
    "            pinion_r = max(dp(12), comb_r * 0.38)\n",
    "            pinion_x = center_x + center_w * 0.22\n",
    "            pinion_ys = [\n",
    "                comb_cy + comb_r * 1.35,\n",
    "                comb_cy,\n",
    "                comb_cy - comb_r * 1.35,\n",
    "            ]\n",
    "\n",
    "            self._axis_input(\"X\", self.axis_x, axis_cx, axis_ys[0], axis_r, axis_rod_x)\n",
    "            self._axis_input(\"Y\", self.axis_y, axis_cx, axis_ys[1], axis_r, axis_rod_x)\n",
    "            self._axis_input(\"Z\", self.axis_z, axis_cx, axis_ys[2], axis_r, axis_rod_x)\n",
    "\n",
    "            for source_y, pinion_y in zip(axis_ys, pinion_ys):\n",
    "                self._rod(axis_rod_x, source_y, pinion_x, pinion_y)\n",
    "                self._rod(pinion_x + pinion_r * 0.8, pinion_y, comb_cx - comb_r * 0.62, comb_cy)\n",
    "\n",
    "            phase = self.gear_phase\n",
    "            self._gear(pinion_x, pinion_ys[0], pinion_r, 10, -phase * 1.7, abs(self.axis_x))\n",
    "            self._gear(pinion_x, pinion_ys[1], pinion_r, 10, phase * 1.9, abs(self.axis_y))\n",
    "            self._gear(pinion_x, pinion_ys[2], pinion_r, 10, -phase * 2.1, abs(self.axis_z))\n",
    "            self._gear(comb_cx, comb_cy, comb_r, 18, phase, self.total_level)\n",
    "\n",
    "            gauge_r = max(dp(42), min(gauge_w * 0.42, work_h * 0.36))\n",
    "            gauge_cx = gauge_x + gauge_w * 0.52\n",
    "            gauge_cy = work_y + work_h * 0.53\n",
    "            crank_x = comb_cx + comb_r * 0.78\n",
    "            crank_y = comb_cy + sin(phase) * comb_r * 0.32\n",
    "            gauge_link_x = gauge_cx - gauge_r * 0.94\n",
    "            gauge_link_y = gauge_cy + sin(phase + 1.2) * gauge_r * 0.18\n",
    "            self._rod(comb_cx + comb_r, comb_cy, crank_x, crank_y)\n",
    "            self._rod(crank_x, crank_y, gauge_link_x, gauge_link_y)\n",
    "\n",
    "            Color(0.31, 0.18, 0.07, 1)\n",
    "            Line(points=[gauge_link_x, gauge_link_y, gauge_cx - gauge_r * 0.72, gauge_cy], width=dp(3.0), cap=\"round\")\n",
    "            Color(0.84, 0.62, 0.24, 1)\n",
    "            Line(points=[gauge_link_x, gauge_link_y, gauge_cx - gauge_r * 0.72, gauge_cy], width=dp(1.4), cap=\"round\")\n",
    "\n",
    "            self._gauge(gauge_cx, gauge_cy, gauge_r, self.total_level)\n",
    "\n",
    "\n",
    "KV = \"\"\"\n",
    "#:import dp kivy.metrics.dp\n",
    "#:import sp kivy.metrics.sp\n",
    "\n",
    "ShakyLabRoot:\n",
    "    orientation: \"vertical\"\n",
    "    padding: dp(10)\n",
    "    spacing: dp(8)\n",
    "    canvas.before:\n",
    "        Color:\n",
    "            rgba: 0.25, 0.16, 0.08, 1\n",
    "        Rectangle:\n",
    "            pos: self.pos\n",
    "            size: self.size\n",
    "\n",
    "    Label:\n",
    "        id: title_label\n",
    "        text: \"Shaky Lab\"\n",
    "        font_size: sp(28)\n",
    "        bold: True\n",
    "        color: 0.96, 0.83, 0.52, 1\n",
    "        size_hint_y: None\n",
    "        height: dp(40)\n",
    "\n",
    "\n",
    "    MechanismDiagram:\n",
    "        id: mechanism\n",
    "        size_hint_y: 1\n",
    "\n",
    "    Label:\n",
    "        id: count_label\n",
    "        text: \"Samples recorded: 0\"\n",
    "        font_size: sp(18)\n",
    "        bold: True\n",
    "        color: 0.95, 0.82, 0.54, 1\n",
    "        size_hint_y: None\n",
    "        height: dp(32)\n",
    "\n",
    "    Label:\n",
    "        id: status_label\n",
    "        text: \"Ready. Press Start Recording.\"\n",
    "        font_size: sp(15)\n",
    "        color: 0.98, 0.88, 0.65, 1\n",
    "        halign: \"center\"\n",
    "        valign: \"middle\"\n",
    "        text_size: self.size\n",
    "        size_hint_y: None\n",
    "        height: dp(44)\n",
    "\n",
    "    GridLayout:\n",
    "        cols: 3\n",
    "        spacing: dp(8)\n",
    "        size_hint_y: None\n",
    "        height: dp(58)\n",
    "\n",
    "        Button:\n",
    "            id: start_button\n",
    "            text: \"Start Recording\"\n",
    "            font_size: sp(16)\n",
    "\n",
    "        Button:\n",
    "            id: stop_button\n",
    "            text: \"Stop Recording\"\n",
    "            font_size: sp(16)\n",
    "\n",
    "        Button:\n",
    "            id: reset_button\n",
    "            text: \"Reset\"\n",
    "            font_size: sp(16)\n",
    "\"\"\"\n",
    "\n",
    "try:\n",
    "    shaky_lab_ui = Builder.load_string(KV)\n",
    "    if shaky_lab_ui is None:\n",
    "        shaky_lab_show_error(\"Could not load Shaky Lab interface.\")\n",
    "    else:\n",
    "        def shaky_lab_set_status(text):\n",
    "            shaky_lab_state[\"status\"] = str(text)\n",
    "            shaky_lab_ui.ids.status_label.text = str(text)\n",
    "\n",
    "        def shaky_lab_update_count():\n",
    "            shaky_lab_state[\"sample_count\"] = len(samples)\n",
    "            shaky_lab_ui.ids.count_label.text = f\"Samples recorded: {len(samples)}\"\n",
    "\n",
    "        def shaky_lab_start_recording(instance):\n",
    "            if not shaky_lab_state.get(\"enabled\"):\n",
    "                if not shaky_lab_enable_sensor():\n",
    "                    shaky_lab_set_status(\"Accelerometer is unavailable.\")\n",
    "                    shaky_lab_show_error(shaky_lab_state.get(\"last_error\") or \"Accelerometer is unavailable.\")\n",
    "                    return\n",
    "            shaky_lab_state[\"recording\"] = True\n",
    "            shaky_lab_state[\"previous_acceleration\"] = shaky_lab_state.get(\"last_acceleration\")\n",
    "            shaky_lab_set_status(\"Recording connected x, y, and z motion into the main gauge.\")\n",
    "\n",
    "        def shaky_lab_stop_recording(instance):\n",
    "            shaky_lab_state[\"recording\"] = False\n",
    "            shaky_lab_set_status(\"Recording stopped. The mechanism remains live.\")\n",
    "\n",
    "        def shaky_lab_reset(instance):\n",
    "            samples.clear()\n",
    "            shaky_lab_state[\"sample_count\"] = 0\n",
    "            shaky_lab_state[\"last_motion\"] = 0.0\n",
    "            shaky_lab_update_count()\n",
    "            shaky_lab_ui.ids.mechanism.set_motion(0.0, 0.0, 0.0, 0.0, shaky_lab_ui.ids.mechanism.gear_phase)\n",
    "            shaky_lab_set_status(\"Samples reset. Press Start Recording.\")\n",
    "\n",
    "        def shaky_lab_update(dt):\n",
    "            try:\n",
    "                accel = accelerometer.acceleration\n",
    "                if accel is None:\n",
    "                    shaky_lab_set_status(\"Waiting for accelerometer data.\")\n",
    "                    return\n",
    "\n",
    "                x_val, y_val, z_val = accel\n",
    "                x_val = 0.0 if x_val is None else float(x_val)\n",
    "                y_val = 0.0 if y_val is None else float(y_val)\n",
    "                z_val = 0.0 if z_val is None else float(z_val)\n",
    "\n",
    "                previous = shaky_lab_state.get(\"previous_acceleration\")\n",
    "                if previous is None:\n",
    "                    dx = dy = dz = 0.0\n",
    "                else:\n",
    "                    dx = x_val - previous[0]\n",
    "                    dy = y_val - previous[1]\n",
    "                    dz = z_val - previous[2]\n",
    "\n",
    "                motion = sqrt(dx * dx + dy * dy + dz * dz)\n",
    "                total_level = _clamp(motion / 5.0, 0.0, 1.0)\n",
    "                axis_x = _clamp(dx / 3.0, -1.0, 1.0)\n",
    "                axis_y = _clamp(dy / 3.0, -1.0, 1.0)\n",
    "                axis_z = _clamp(dz / 3.0, -1.0, 1.0)\n",
    "\n",
    "                shaky_lab_state[\"last_acceleration\"] = (x_val, y_val, z_val)\n",
    "                shaky_lab_state[\"previous_acceleration\"] = (x_val, y_val, z_val)\n",
    "                shaky_lab_state[\"last_motion\"] = motion\n",
    "\n",
    "                mechanism = shaky_lab_ui.ids.mechanism\n",
    "                new_phase = (mechanism.gear_phase + 0.05 + total_level * 0.45) % (2 * pi)\n",
    "                mechanism.set_motion(axis_x, axis_y, axis_z, total_level, new_phase)\n",
    "\n",
    "                if shaky_lab_state.get(\"recording\"):\n",
    "                    samples.append(\n",
    "                        {\n",
    "                            \"timestamp\": time(),\n",
    "                            \"x\": x_val,\n",
    "                            \"y\": y_val,\n",
    "                            \"z\": z_val,\n",
    "                            \"dx\": dx,\n",
    "                            \"dy\": dy,\n",
    "                            \"dz\": dz,\n",
    "                            \"total_motion\": motion,\n",
    "                        }\n",
    "                    )\n",
    "                    shaky_lab_update_count()\n",
    "            except Exception as exc:\n",
    "                shaky_lab_state[\"recording\"] = False\n",
    "                shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "                Logger.exception(\"PythonHere: Could not update Shaky Lab acceleration\")\n",
    "                shaky_lab_set_status(\"Sensor update failed. Recording stopped.\")\n",
    "\n",
    "        shaky_lab_ui.ids.start_button.bind(on_release=shaky_lab_start_recording)\n",
    "        shaky_lab_ui.ids.stop_button.bind(on_release=shaky_lab_stop_recording)\n",
    "        shaky_lab_ui.ids.reset_button.bind(on_release=shaky_lab_reset)\n",
    "\n",
    "        shaky_lab_update_count()\n",
    "        shaky_lab_lock_portrait()\n",
    "        if shaky_lab_enable_sensor():\n",
    "            shaky_lab_set_status(\"Ready. Press Start Recording.\")\n",
    "        else:\n",
    "            shaky_lab_set_status(\"Accelerometer is unavailable on this device.\")\n",
    "\n",
    "        shaky_lab_update_event = Clock.schedule_interval(shaky_lab_update, 0.1)\n",
    "\n",
    "        root.clear_widgets()\n",
    "        root.add_widget(shaky_lab_ui)\n",
    "except Exception as exc:\n",
    "    shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "    Logger.exception(\"PythonHere: Could not rebuild Shaky Lab\")\n",
    "    shaky_lab_show_error(shaky_lab_state[\"last_error\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99c1b3ad-bce1-47e0-9eab-808d12ad6ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%there -b log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c84fd48-1903-44c5-8ece-f47e9f1a9336",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%there\n",
    "shaky_lab_update_event.cancel()\n",
    "\n",
    "def shaky_lab_update(dt):\n",
    "    try:\n",
    "        accel = accelerometer.acceleration\n",
    "        if accel is None:\n",
    "            shaky_lab_set_status(\"Waiting for accelerometer data.\")\n",
    "            return\n",
    "\n",
    "        x_val, y_val, z_val = accel\n",
    "        x_val = 0.0 if x_val is None else float(x_val)\n",
    "        y_val = 0.0 if y_val is None else float(y_val)\n",
    "        z_val = 0.0 if z_val is None else float(z_val)\n",
    "\n",
    "        previous = shaky_lab_state.get(\"previous_acceleration\")\n",
    "        if previous is None:\n",
    "            dx = dy = dz = 0.0\n",
    "        else:\n",
    "            dx = x_val - previous[0]\n",
    "            dy = y_val - previous[1]\n",
    "            dz = z_val - previous[2]\n",
    "\n",
    "        motion = sqrt(dx * dx + dy * dy + dz * dz)\n",
    "        if motion < .2:\n",
    "            motion = 0\n",
    "        total_level = _clamp(motion / 5.0, 0.0, 1.0)\n",
    "        axis_x = _clamp(dx / 3.0, -1.0, 1.0)\n",
    "        axis_y = _clamp(dy / 3.0, -1.0, 1.0)\n",
    "        axis_z = _clamp(dz / 3.0, -1.0, 1.0)\n",
    "\n",
    "        shaky_lab_state[\"last_acceleration\"] = (x_val, y_val, z_val)\n",
    "        shaky_lab_state[\"previous_acceleration\"] = (x_val, y_val, z_val)\n",
    "        shaky_lab_state[\"last_motion\"] = motion\n",
    "\n",
    "        mechanism = shaky_lab_ui.ids.mechanism\n",
    "        if motion > 0:\n",
    "            new_phase = (mechanism.gear_phase + 0.05 + total_level * 0.45) % (2 * pi)\n",
    "        else:\n",
    "            new_phase = mechanism.gear_phase\n",
    "        mechanism.set_motion(axis_x, axis_y, axis_z, total_level, new_phase)\n",
    "\n",
    "        if shaky_lab_state.get(\"recording\"):\n",
    "            samples.append(\n",
    "                {\n",
    "                    \"timestamp\": time(),\n",
    "                    \"x\": x_val,\n",
    "                    \"y\": y_val,\n",
    "                    \"z\": z_val,\n",
    "                    \"dx\": dx,\n",
    "                    \"dy\": dy,\n",
    "                    \"dz\": dz,\n",
    "                    \"total_motion\": motion,\n",
    "                }\n",
    "            )\n",
    "            shaky_lab_update_count()\n",
    "    except Exception as exc:\n",
    "        shaky_lab_state[\"recording\"] = False\n",
    "        shaky_lab_state[\"last_error\"] = f\"{type(exc).__name__}: {exc}\"\n",
    "        Logger.exception(\"PythonHere: Could not update Shaky Lab acceleration\")\n",
    "        shaky_lab_set_status(\"Sensor update failed. Recording stopped.\")\n",
    "\n",
    "shaky_lab_update_event = Clock.schedule_interval(shaky_lab_update, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae601f5c-5efc-4498-84f1-d5f4ed4d6182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'timestamp': 1781377266.3921585,\n",
       " 'x': 0.9193734526634216,\n",
       " 'y': -1.2258312702178955,\n",
       " 'z': 9.710882186889648,\n",
       " 'dx': 0.06703764200210571,\n",
       " 'dy': 0.04788398742675781,\n",
       " 'dz': 0.0766143798828125,\n",
       " 'total_motion': 0}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples = %there get samples\n",
    "samples[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "818d307b-373e-4c3a-8eef-19602c3a3061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CCCF2iUQgYFWYFPWxKQtS4U6iq4tEOJz1OuktWX21eaXassbfQGXz86qHQujR6AgfjRBCiF1Nwh0hhBBCCLFLhFeuBF3HXltrBTf5qKWlKC4XAPGd2at3Ct2UBeN3W1YiHCa2dav1vub3j+DRCCHGg02bNqEoSq8/xxxzzEgfmsjBPtIHIIQQQgghdg/hVasAcO+9d0HXVxQF+8SJxJqaiLfuwDllSq/rxFqSm7Kq88/bgdS2LGIxEtEoqtNZ4JGPbtGNG0HXrfe1ri7slZUjeERCiHx0XScUD43IYxfZi/qscgSor69n+/bt1vvNzc2ccMIJHHXUUcN5eGIQJNwRQgghhBC7RHj1agDce84p+Db26upkuJN9qHKhm7IgLdzBaBEbL+FOJNmSZUrI3B0hRrVQPMShCw8dkcd+/5z38Tg8fV7PZrNRW1sLQDgcZsGCBcyfP5+bb755mI9QDJSEO0IIIYQQYpeIrF4DgGt2P8KdPtahx1sLb8tS7HYUtxs9HDbWoVdUFHwco1l0w/qM97VuCXeEEEPnggsuwOfz8fLLL6OqMtlltJJwRwghhBBCDDtd04isWweAa87sgm9nhTs5KnestqwCKnfAmLujmeHOONG7ckfWoQsxmhXZi3j/nPdH7LH742c/+xkvvvgiH3zwAV6vd5iOSgwFCXeEEEIIIcSwizY2oofDKG43zoaGgm9nrzYqcnJW7vSjLQuS4U5b27gKd8zKHaWoCD0UQuuScEeI0UxRlIJao0baE088wU9/+lNeeOEFZsyYMdKHI/ogNVVCCCGEEGLYWS1Zs2ah2GwF3y5f5Y6u69bl9gIGKsP4W4eux+NENm0GoGi//QDQpHJHCDFIy5cv55vf/CY/+MEP2GeffWhubqa5uZn29vaRPjSRg4Q7QgghhBBi2EXWGMOU+9OSBWCvzj1zR+vsRI9GAXBU971aHVJDlcdL5U50yxaIxVCKinDNNj63CZm5I4QYpI8++ohgMMjPfvYz6urqrD+nnXbaSB+ayEHCHSGEEEIIMezCycoddx/DlJ9a9xT3fXqf9X6+gcpmS5atshKlwM1XavE4C3c2GPN2XNOnYysrA6RyRwgxeOeffz66rvf68/rrr4/0oYkcZOaOEEIIIYQYdpHVZuVO/nDnlvdvIRgPcuqMU5lUMglHcuaO1tlJIhrNWF/e33k7kN6WNT7CHXOYsnOPPbCVlQKQ6JZwRwghdjdSuSOEEEIIIYaV5vcTa2oCwDV7Vs7rxRIxgnFjFk5HpAMAtazMqsrRelTvxJLhjhkAFcIKd4LjI9yJrjeGKbtm7IHqNcIdWYUuhBC7Hwl3hBBCCCHEsIqsWQsYFTb2ioqc1wvGUkOOfVEjoFAUBfuECQDEegxVjifXoPencsc23ip3NpiVOzOwlRpriqUtSwghdj8S7gghhBBCiGFV6DDlUDxkvW2GO5B7HXq81WzLGkDlzjjYlqXremrmzow9UL1GuCMDlYUQYvcjM3eEEEIIIcSwCifn7bj7mLeTrXIHcg9Vttqy+jNzZxxty4q3tBjPw2bD2dCArmkAaDJzRwghdjtSuSOEEEIIIYZVJLkpy9XHpqxALBW4ZK3cae1RuTOAtqzxNHMnkpy342xoQHE6sVmVO93ouj6ShyaEEGIXk3BHCCGEEEIMG13X0zZl5W/LMocpA3RHU9UnuSp3rG1Z1bvntqyouSlrxh4AqKXGQGU9FkOPREbsuIQQQux6Eu4IIYQQQohhE9u6zQhSHA5c06fnvW7flTupgcqJSAStsxMAxwBm7mjjINyJbEzO29ljBpBsOVONX++lNUsIIXYvEu4IIYQQQohhYw1TnjEDxeHIe930yp2+Zu6YQY/icqGWlRV8POOxcseVrNxRVDWjNUsIIcTuQ8IdIYQQQggxbCLWMOX8LVmQZ6BydTLcSavcsVqyampQFKXg47EGKgfH/ras9DXoJrM1S5ONWUKIMeT8889nwYIFI30Yvbz++usoikJnslJ0NJNwRwghhBBCDJtwgcOUIV+4Y7RdaR0d6NEokLYpq7rwliwYP6vQta4utJ07AXDtkWp3syp3fFK5I4QYnGOOOYarr7562G8zWmQ79sMPP5zt27dT1o8K0ZEi4Y4QQgghhBg2qWHKBYQ7OQYq28rLIdnSFW9rM/4ewKYsyGzLGssbpSLJlix7XZ31nEAqd4QQYig5nU5qa2v7VSE6UiTcEUIIIYQQwyIRChHdvBkYXFuWoijYJ04AUq1Z6W1Z/WEFIfG4VQU0FkU3GGvQew6ptpnhjlTuCCEG4fzzz+eNN97gzjvvRFEUFEVh06ZNvPHGGxxyyCG4XC7q6ur44Q9/SDwez3sbTdO48MILmT59OkVFRcyZM4c777xzwMd2zDHHcOWVV3L11VdTUVFBTU0Nf/rTnwgEAnzrW9/C6/Uyc+ZMXnjhhYzbDeTYs7VlPfHEE+yzzz64XC6mTZvG7bffnvE406ZN4xe/+AUXXHABXq+XhoYG/vjHPw74+RZKwh0hhBBCCDEsIuvWQyKBrbIS24QJfV4/EE8NOfbH/Bkf6zlUOdaabMvqx6YsSM3cgbE9VDlirUGfkXG5WioDlYUY7XRdJxEMjsifQisW77zzTubPn8/FF1/M9u3b2b59Ow6Hgy996Ut87nOf49NPP+Xee+/l/vvv52c/+1nO29TX15NIJJgyZQqPP/44K1eu5MYbb+RHP/oRjz322IA/hw899BATJkzggw8+4Morr+Syyy7jq1/9Kocffjgff/wxJ554Iueeey7B5Hy1rVu3DujYe1q8eDFnnnkmZ599NsuWLePmm2/mhhtu4MEHH8y43u23387BBx/MJ598wne+8x0uu+wyVicrWYeLfVjvXQghhBBC7LYia5LzdubMLqikPb1yJxALEE/EsavGr6tmuBOzKncG1pal2GwoRUXooZAxVLmysl+3Hy0iZuVOclOWyeaVtiwhRjs9FGL1gQeNyGPP+XgxSlrInUtZWRlOpxOPx0NtbS0A119/PfX19dx1110oisKee+7Jtm3b+MEPfsCNN96Y9TYANpuNn/zkJ9b706dP59133+Wxxx7jzDPPHNDzmDdvHj/+8Y8BuO666/jlL3/JhAkTuPjiiwG48cYbuffee1m6dCmHHXYY99xzz4COvaff/OY3HH/88dxwww0AzJ49m5UrV3Lrrbdy/vnnW9f70pe+xHe+8x0AfvCDH/Db3/6W//znP8wpoEV5oKRyRwghhBBCDAtzDbq7gGHKkDlzB4yAx2QOTjYrd6y2rOr+hTuQtjFrDFfumGvQnXv0CHdKZaCyEGJ4rFq1ivnz52eE9UcccQR+v5+mpqa8t7377rs56KCDmDhxIiUlJfzxj3+ksbFxwMcyd+5c622bzUZVVRX77befdVlNMvhvTb4gMJhjT7dq1SqOOOKIjMuOOOII1q5di6ZpWY9PURRqa2utYxkuUrkjhBBCCCGGhbUpq8BXKtMrd8AYqlzmMjaUpLdl6YmENXunv21ZYMzd0draxmy4kwiHiW3dCoCrZ1uWVO4IMeopRUXM+XjxiD32rvboo49y7bXXcvvttzN//ny8Xi+33nor77///oDv05Ecsm9SFCXjMjPESSQSA36Mwch2fMN9LBLuCCGEEEKIIafrOpHPPgOMtqxC9Ax3sq1Dj7e2GivRYzHj8mTo0x/pG7PGoujGjaDr2MrKsPVoK7OVmeFO10gcmhCiAIqiFNQaNdKcTmdGNcpee+3FE088ga7rVnjyzjvv4PV6mTJlStbbmNc5/PDDrTYlgPXr1++CZ5Ay0GPPdj/vvPNOxmXvvPMOs2fPxmazDc/BF0jasoQQQgghxJCL79iB1tkJqopr5syCbtOzLSsj3LEqd3ZaLVm2qioUp7Pfx6YWj+22rPRhyj1nGalec6CyVO4IIQZn2rRpvP/++2zatImdO3fyne98hy1btnDllVfy2Wef8fTTT3PTTTfxve99D1VVs94mkUgwa9YsPvroI1588UXWrFnDDTfcwIcffrhLn8tAj72na665hldffZX//d//Zc2aNTz00EPcddddXHvttbv0+WQj4Y4QQgghhBgSO4I7+P0nv6c50Ewk2ZLlnD4d1eUq6PbmjB2nagQ2uSp3YtYa9P63ZME4qNzJMUwZ0lehS7gjhBica6+9FpvNxt57783EiROJxWI8//zzfPDBB8ybN49LL72UCy+80BpsnO02jY2NXHLJJZx22mmcddZZHHroobS1tWVU8ewKkydPHtCx93TggQfy2GOP8eijj7Lvvvty44038tOf/jRjmPJIkbYsIYQQQggxJB5f8zh/XPpHYlqM89YYs3LcBbZkQapyp6a4hi2+LVkrd7T2dmLbtgHgGMAwZQCbFe4E+7jm6GRV7uwxo9fHbF5ZhS6EGBqzZ8/m3Xffzbhs2rRpfPDBB/26DcADDzzAAw88kHHZLbfcYr3dc5V4Pq+//nqvyzZt2tTrsp5r348++uh+H/u0adN63c/pp5/O6aefnvN+sh3LkiVLcl5/qEjljhBCCCGEGBJtoTYAOiIdhFcbm7JcBW7K0nWdUCwEQG2xsYa2O5oKKGwVFWC3g64TXrkS6P8adJM56yIRHH+VO2qpEappPl+vExIhhBDjl4Q7QgghhBBiSPhiRqVNIBaw2rIKHaYcTUSJ63EAajxGaJNeuaOoKvYJEwAIL18BDLwtyzaG27L0eJzIps1Ajsqd5Cp0NG3MViYJIXZfjY2NlJSU5PwzmPXp4520ZQkhhBBCiCHhj/oBCIV8RDYYrUPu2f3flFXtMUKb9HAHjNaseHMzkXXrAHAMsHJnLM/ciW7ZArEYituNY1Jdr48rbjc4HBCLkfB1YyspHoGjFEKIgZk0aVLeFqZJkybtuoMZYyTcEUIIIYQQQ8IMY1xb2yAWQy0pwV7gL+LmvB23zU2FqyLj/kzmUGWSq2rtA5y5M6bDnY0bAWNQtaL2LsJXFAWb14vW3o7W7cNR1zsAEkKI0cputzOzwA2LItOwtmXdcsstfO5zn8Pr9VJdXc2CBQtYney/NoXDYS6//HKqqqooKSnh9NNPpyW5AUEIIYQQQowd/phRuVPe1AmAa86cXqu6czE3ZXkcHrxOo7Wod+XOhMz3B7ktSxuD4U5kfXLezh695+2YzI1Zie6uXXJMQgghRt6whjtvvPEGl19+Oe+99x4vv/wysViME088kUDaf6Tf/e53efbZZ3n88cd544032LZtG6eddtpwHpYQQgghhBgGZhhTtdX4u1+bspJtWUX2IivcSR+oDGmVO0mDbcvSg2NvJk3U3JSVZZiySZV16EKMOjLgXOQzFF8fw9qW9e9//zvj/QcffJDq6moWL17MUUcdRVdXF/fffz8LFy7kuOOOA4wVaXvttRfvvfcehx122HAenhBCCCGEGEJm5U7NtjBQ+KYsSLVlFTuKU5U7sd4zd0yK222FGP2lJrdljcnKneQsI1eWYcomcx26JuvQhRhxDocDgGAwSFFR0QgfjRitgskXG8yvl4HYpTN3urqM0tDKykoAFi9eTCwW44QTTrCus+eee9LQ0MC7776bNdyJRCJEIhHr/W75T0sIIYQQYsRpCc1qrZrcYmy9KnRTFqQqdzx2D6VOI7TJNlDZerumuuCWr57G6swdXdeJrs+9Bt2kJjdmJbqlckeIkWaz2SgvL6e1tRUAj8cz4J9dYvzRdZ1gMEhrayvl5eXYbLYB39cuC3cSiQRXX301RxxxBPvuuy8Azc3NOJ1OysvLM65bU1NDc3Nz1vu55ZZb+MlPfjLchyuEEEIIMW4l9ASqMrTd+YG4EZR4gzqVRgEPrln9CHeSlTv5Zu440tqyHAMcpgzp4c7YasuKt7YagZTNhnPq1JzXs5WWAVK5I8RoUVtbC2AFPEL0VF5ebn2dDNQuC3cuv/xyli9fzttvvz2o+7nuuuv43ve+Z73f3d1NfX39YA9PCCGEEGK3sKFzA19//ut8c59vctm8y4bsfs0gpmGHMTdAnTKpX2u4zaqf9LasQCxAPBHHrhq/smZW7gxFuDO2KnfMqh1nfT2K05nzejazcscn4Y4Qo4GiKNTV1VFdXU0sFhvpwxGjjMPhGFTFjmmXhDtXXHEFzz33HG+++SZTpkyxLq+trSUajdLZ2ZlRvdPS0pIztXK5XLhcruE+ZCGEEEKIcenNpjfxx/ws2rpoSMMdf9Qo12kwX5iekbuyJJv0gcolzhLr8kAsQJnLqESxVVaCzQaaNuBNWZAZ7ui6PmZaJCLWMOXc83YAVG9yoLK0ZQkxqthstiE5iRcim2HdlqXrOldccQVPPvkkr732GtOnT8/4+EEHHYTD4eDVV1+1Llu9ejWNjY3Mnz9/OA9NCCGEEGK3tLZzLZAafjxUzMqdqa1G5Y62x5R8V+/Fasuye3CoDorsxuDR9I1Zis2GvaoKGPimLEiFO2gaejQ64PvZ1SIb+l6DDqnKHU0qd4QQYrcxrJU7l19+OQsXLuTpp5/G6/Vac3TKysooKiqirKyMCy+8kO9973tUVlZSWlrKlVdeyfz582VTlhBCCCHEMFjbYYQ7ZhvUUDHDIrMtKzy1f+GLWblT7DCCF6/TSyge6j1Uua6WeGsr9rq6AR+rmraxJhEIoI6RqvBC1qAD2JJbxBJdEu4IIcTuYljDnXvvvReAY445JuPyBx54gPPPPx+A3/72t6iqyumnn04kEuGkk07innvuGc7DEkIIIYTYLcUTcdZ3GtUfw1G5oyR06nck359a1a/bpw9UBih1ltIabO0V7lRfcw3+/7xOydFHD/hYFZsNpagIPRQy5u4kN7mOdtYa9ELbsnzSliWEELuLYQ13dF3v8zput5u7776bu+++ezgPRQghhBBit9fY3Ug0YbQhBWJDO2/GH/NT2wGuOIQd0F1V1PeN0qSvQgcocRhzd3qGO8WHHELxIYcM+njV4mI0M9wZA7SuLrSdOwFwTi+sLSsh27KEEGK3Mawzd4QQQgghxOixpnON9XZCTxCKh4bsvv1Rv9WStWUCBBL9u+/0bVlAznXoQ0UtNkKksRLumFU79traPreQSeWOEELsfiTcEUIIIYTYTZjzdkzZWrNiLS0kIpF+37cv6rNashqrlX7P9DHbsoocRsWPGe6kD1QeSmNtHXo8WbVTyCBpW1ly5o7Ph65pw3pcQgghRgcJd4QQQgghdhN9hTuxbdtYd/wJNH3n8n7fty/mozRoVO50lPR/YLM1UNm+ayp3bJ5kuBMMDsv9D7WEz/i3Ur3ePq+bfp2Ef2hnKwkhhBidJNwRQgghhNhN9Ax3AtHMACa8ejXE40TWrKG//FE/xcmCn4Bb6ffA5mwDlWH4wh1ljLVlmSGN6i3p87qq04nidgPSmiWEELsLCXeEEEIIIXYDwViQJn8TABOLJgK9K3e0tjbj7wEEHr6YD084+ViugVfumAOVh71yZ4y1ZWl+4/NgK+m7cgfA5pWhykIIsTuRcEcIIYQQYjewrnMdABOKJjDFOwXoHe7Edxrhjh4M9ntWi1G5Y7RlBdwDCHeSlTu7bqDy2Ap3En7jONWSvit3ANTk3B1Nwh0hhNgtSLgjhBBCCLEbMFuyZpXPsgIUf7RHuJOs3IH+z2rxR/1W5U6gn5U7uq6nKnccmZU7wzZQ2TPGwp1ke1UhbVkANq+EO0IIsTuRcEcIIYQQYjewtjMZ7lTMosRhBAQ9Axitbaf1dn/DHV/UZ83cCbr7ty0rFA+hY1T99GrLig1v5c5AWtBGQn/bstTSZFuWzNwRQojdgn2kD0AIIYQQQgw/q3KnYpYVvORqywLQ/H4c/bh/X8xHcVrlDv0YqGy2ZCkouO3GIODhHqhshjv6WNmW1c+2rFTljoQ7QgixO5DKHSGEEEKIcU7XddZ0GBuwZlXMsqpielbXDLQtK56IE40EcceM9/s7c8dsySqyF6Eqxq+nwz9zx6gQGiuVO/1uy0pW7mjdXcN2TEIIIUYPCXeEEEIIIQbI/847RDdtGunD6NPO0E46I52oisqMshmpmTu9KndSbVn9WaEdiAXwRFLvh/o5c6fnGnQgI4DSEv0b7lyIsTZQOdWWVeBA5VKjcichlTtCCLFbkHBHCCGEEGIAIhs2suXCi9hyxRUjfSh9MluyGrwNuO1ua+ZO+kBlPRol0ZWq8jDbgArhi6bWoCvFHhKqQjAWJKEnCrq9Wbljhk4AXkdqtkzPEGoopMKdsdaWVegq9GRblk8GKgshxO5Awh0hhBBCiAGIrDHanKLrN5CIRPq49shKH6YMZK3cibe3Z9wm4S+84sMf81vDlNVkqKCjE4qHCrq9WeVjDlMGcNgcFNmLgOFpzRqr27JsBbZlWQOVpXJHCCF2CxLuCCGEEEIMQKxpi/GGrhNrbBzZg+lD+rwdgBJn721Z6cOUoX8zd3xRH8VhY9uV3evFrhg7O3quWs/FbMsywxyTWb0zLOHOGGrL0hMJ6zj7PVBZtmUJIcRuQcIdIYQQQogBiG5pst6OjPK5O2Zb1uzy2QCptqy0yp30NegAmq9/4Y45c0ctLbVm5wTihQUn2dqyYHiHKpsDlRNjYFtWegClegtsyyozZ+7IQGUhhNgdSLgjhBBCCDEAsS1bUm9v3jyCR9JbeNUqGi+6mNCKFWgJjQ1dG4C0yp1kuBOIDk3ljj/mt9ag27zerPefT7aByjDc4U6qckfX9SG//6FktmQpDgeqy1XQbVRZhS6EELsVCXeEEEIIIQYg2jR6K3e6nnqKwNtv0/XEEzT6GoloEYrsRUzxTgGg2GkEG75Y6sQ/fQ06DKAty6rc8Vr339/KnfSZO5AKd7qjQz8U2Ax30DT0UT4zSUv+WxTakgVpq9ClLUsIIXYLEu4IIYQQQvSTrmnEtm2z3h9t69DjbcZw5Fhzi9WSNaNsBqpi/OpnVdbEUlUrZluW2faj9adyJ+rHk5y5Y/OWUmxPhjsFVu6Ys392aVuWJxUkjfa5O2bQVmhLVvp19WAQPRYbluMSQggxeki4I4QQQgjRT/HmZojHrfejo6wtS0tuvoo3N/falAWpcCehJ6yNVmZblnPqVONjA2zLGlDlTq6Byma4Exv6cEdRVZRkwDPqw51k9Y1aUtzHNVNsaUGQVO8IIcT4J+GOEEIIIUQ/mcOUbRMnAKDt2FlwpctbTW9x2jOnsaJtxbAdX7yzA4BYS6pyJz3cKbIXWVU8ZtWM2ZblnDYNSAUKhUgfqJxeuVPwtqwcA5VLnaXW/Q8Ha6jyKA93zK8tW0nhlTuK3Z6aK9Q99G1tQgghRhcJd4QQQggh+slcg+7ecy9sVVUARDcVVr3z703/Zm3HWl7d/OqwHZ/WboQ7Wlsb63esBjLDHUVRrCDF3JhltmWZlTtaYGAzd2yl3qyr1vMZiYHKkGrNGu0bsxK+/rdlgbG5DKRyRwghdgcS7gghhBgXoo2NtD/yNxKh0EgfitgNmJU7zvopVqVLdPOmgm5rBhVdkeFZUa3rutWWBRDaZhzrrPJZGdez1qEnq2ustiyrcqd/bVnmzB3VW2oFRwWHOyMwUBkyN2aNZomAWblTeFsWpFqzNKncEUKIcU/CHSGEEONC62230/Kzn9H80/8d6UMRuwFzDbpjSj3OaUalS6FDlc1wpzPSORyHRiIQRI9GrfcrfTqV7kqqiqoyrpdeuaPHYmgdRrWP+Xz6NXMn6s+o3OlZFdQXs3JnVw5UBrB5xka4o1kzd/pXuWNLVu5IW5YQQox/Eu4IIYQYF6LJk+2uJ5+k+4UXRvhoxHhnrkF31E/BOXWacVmBbVnDXbmjJeftmCp9ekZLlil9Y1Y82caFquKYYqxL16NREmkhUT6+mA+POVDZW2rdt1mR0xezwidX5c7wzdwZG+FOqi2r8FXokNaW1S1tWUIIMd5JuCOEEGJciLe0WG9vv+lmYtu3j+DRiPHOrNxx1qdV7hS4McsMKjoiHX1cc2DSW7IAqny9W7IAay6OP+a35u3Yqiqtag8ovHqnZ+WOOTun4MqdWPaZO8M/UHmMhDvWQOX+hTtmW1bCJ5U7Qggx3tlH+gCEEEKIwdKjUeuE1jlzBtF169n2Pz+g4cEHUGy2ET46Md5o/oDVwuSYMgVU47Wy6KZN6LqOoih5b1+9voOLXtPQPBtoXvpzHHW12GtrcdRNMt6eOBHFPvBf0eI9w51unekVs3tdL6NyJ7kpy141AcVmQ/F40INBY2NWZWWfjxkId1OULPJRvV5KtDEyUDkZ7mijPNwxt2X1ty1LKneEEGL3IeGOEEKIMS++Y4fxhsNB/V13seG00wl++CFt9/+FCd++eGQPTow7sa3JNegVFdhKSlAaGgBjronW0YE9TxiS0BOc8I6ffTfrQJiOVY/0vpKqYq+upmjePCbfdiuKw9Gv4zM3ZZkqfTA7S7hjzrfxRX3WMGV7cvOXraSEeDBY0Hr3WCKGGghb79u8XooDQztQ2R/zoyU0bOrQhrXmtix91G/LMmfu9LNyxwp3hqcFUAghxOghbVlCCCHGvFhLKwCOiRNxTptG7fXXA7Djd78jtGz5SB6aGIdSw5SN2TSq2419Uh3Q99ydQCzAzG3GVqlnDlUoveA8Sr/8ZYoOOgjHpElgt0MiQby5Gd+LLxJaurTfx2dWFSnJzUpVPtijfI9e10uv3DHbsuwTjHDHDBES/r7DmfSWLMXjQbHb+7UtS0tohDUjHOpVueNIVaoU2uLVH2Olcsdqy+rnzB1babItSyp3hBBi3JNwRwghxJgXbzXCHXtNDQBlp/033pNOgnicbddeS2KUvyovxpb0Negml7kOvY+NWV1bNlDpB02Bx49UUb9zHpNvu5Vpf3uEma+9yp6fLmHmG2/g3ntvAOI7dvb7+LQOoy0rtsdkACb6VYrsRb2uV+xMbbQyK3dsVROA1ODehL/vUMAf9VvDlM1KkfTgqC+heCh1TD22ZTlsDtw2NzA8rVljZeaO1Zbl7WdbljdZueOTcEcIIcY7CXeEEEKMeos3d/Dc0m05Px5vNYYp26urAVAUhbqf3Iy9tpbo5s203PLLXXKcYveQvgbd5Jha2Dp0/5KPAdgyESJOpdc6dMVmw1FTbVUFWS2H/WBuvtpZb5zYl/o19Fis1/WsACaaPnMn2ZZVbIY7fVfL+GI+iiNGNZI5wLc/A5XNAMim2HCqzl4fH865O6lwZ3QHwOa/g1o80ModGagshBDjnYQ7QgghRjUtoXPRQx9yxcJPaOrIfgKWqtypti6zlZcz6Ze/BEWh8/HH6X755V1yvGL8izYlw51slTt9bMyKLF8BwNpJxtDlnuGOyT7BqKCJ7xxI5Y4R7jRWJojZQNGzh0RmuJO+LatnW5bm6zuc8Uf9FJtr0HtU7oTiIbSElvf21jBluyfrMOpdE+6M7sodc+ZOf9uyUgOVJdwRQojxTsIdIYQQo9qaFh8dQaPqoLkrnPU61sydZFuWqfiwQ6m66ELjtj++odcWISEGIma1ZaUqd5wFtmXpK9YAsK6vcKd6IgDxnf2v3DE3x21Q22hPdvHEmlt6XS99FXrutqwCKneiPjzmGvRk5U56e5UZ3uSSa1OWaXcPdxKRiFV51d+2LGugsrRlCSHEuCfhjhBCiFFt8ebU5h8z5Okp3pLZlpVu4pVX4pozB62ri46/LRyegxS7DT2RINZkhDvpbVlOsy1r82b0RCL7bTUNxxqjsqfPcGcQlTvxZOXOWr2FtmQWEG9p7nU9M4Dxx/yptqwJqW1ZUNjMHV/Ml1a5Yzyg0+bEoRpbvvqau2Ntyuoj3OmODn31ibktazTP5UoP2MzjLZQZtklblhBCjH8S7gghhBjVPs4Id6JZr2O1ZVXX9PqY4nQy4ZJvG7f/+99JRCLDcJRidxFvbTWqKOx2HLWprzfH5Mlgt6OHw9bXY0+RdetRw1FCTmgyMpSc4Y4tGe5oAxmonKzc2emO0VVqByC2vXe4Y7ZOBcN+q5XLnLmjlhihQCGr0I22LHPmTmmv+y843LFL5U421hr04mIUW+5V8O3hds545gz+svwv1mVmW5YejcrPPiGEGOck3BFCCDGqfZQW7nRmCXd0XSdmhTsTs96H98QTsdfVobW30/3cc8NzoGK3YFXtTJqEYrdblysOB87kEORcrVmhpZ8CsL5OQVeTlTvhzqzXtU9ItmX1c6CyHo1alR6+IkhMrDDuJ0vljhm+KF0+SCRAUbBVGNe3VqEXMHPHF0u1ZZmVO1D4UGWzLavnpixTqbPUepyhNhbCHXPukflvksuS1iWs7ljNY6sfsy5Ti4tBNX7d17q6hu8ghRBCjDgJd4QQQoxarb4wje2pdolsbVmJQAA92VLRc+aOSbHbqfzG1wFof+hhdF0fhqMVuwNrDfqUKb0+5uxjY1Z46VIA1tVhtSzlbMuamGzLam9H1/IPJE4X7zDuT1cVAkXgqp0E5J+54+hMbquqqLACK1s/Zu6kD1TOWrkTzR+cmJU9I125M1p/LiQC5hr0/OGOuVJ+m38b4bjxD6KoqjWnJyFzd4QQYlyTcEcIIXaxd7e9y50f32m1Iojc0luyIHvljjlvR/V6886jKD/jDBSPh8iaNQTfe29oD1TsNlJr0LOEO9ZQ5ewbs0JLlwHGpqzJJZMB6Ipkr6awV1aCooCmoXV2Fnx8WofRkhUpcaErCq5JRrgTb85dueP1G+GR2ZIFaduyCgl3Yn6KzYHKaZU7ZiVOID40M3eGM9whkUAPZx/YPtLMYci2kvzDlM1AR0en0ddoXW7O3ZGNWUIIMb5JuCOEELvY7R/dzp+X/ZmfvfezUftK8WhhDlN22Y3/rtoDucOdbMOU09nKyihfsMC4nwcfGsKjFLuTbGvQTc7p04zrZKncSQQCRNauBYxhyvVeYxhzR6Sj13XBaPMyW6T6M1TZnLcTKjEqg2zJ74tYS+/KnSJ7EQoKZcnsxTYhPdxJVnsUui0rOXNHTavcsQY2RwtryxqZgcpF1tujtTUr4TeOq6+2LLNyB2Bj10brbbNVTip3hBBifJNwRwghdrGWoHGS9eyGZ/nn2n+O8NGMbua8nSNmGi0q2dqyzHk7jpr84Q5A5TfPBUXB/8YbRDZs7PP6QvSUbQ26KV9bVmjFCkgk8JU56fCmwp1clTuQtjGrH0OVzU1ZAY8xeNdZW5e8jx3o8XjGdRVFocRRQnnQfLzUzCpbSbJdqcBwJ1vljjWwua9V6CM4UFlRVZRRvjHLGqjcR1tWWEtVHqWHO2arnNYllTtCCDGeSbgjhBC7UCwRy5ix8Yv3f8Fn7Z+N3AGNYuGYxvKtxonvCXsZs3Syt2UlhylP7DvccU6bRskxxwDQ8chfh+hIxe7EqtyZkiXcMduympp6BSnhZUZL1pZ6N4AV7uSauQPp69ALH6qstRvhji/ZbeSpngR2O2iate48XbGzmLKAUXWT0Zbl7d+2LI+5Cj2tcscaqFxg5U7OgcqO5EDlYQh3ANTiZLgzSit3NH9hbVnprb4Z4U5yY5bmk3BHCCHGMwl3hBBiF2oPGS0TNsXGUVOOIpqI8r3XvzdsJy1j2fKtXcQ0nQklLuZOKQOyV+5Ya9BzDFPuqfK88wDofPKpfs0yESIRClmryZ1Z2rLsNTUobjfE48S2bs34WOhTY5jy+slGRU1DaQNgDBOOab2/riFtqHI/NmaZM3c6i4zAptRTbm2RyzV3x2zLsk/oPXMn4ff32T6aa+aONVC5j5k7IzlQGcDmGd0bswpty8pVuWO1ZXXL/zNCCDGeSbgjhBC7UFvYeOW80l3JL478BXXFdWzxbeGmRTfJ/J0ezJasg6aWU1HsBIzKnZ6fp3hrcuZOAW1ZAJ5DD8E1Zw56KETH448P4RGL8c5cg66WlmIrK+v1cUVVc7ZmhZKbsj6rNYYXTyqehKoYv4bl3phlhDJaP2bumG1ZHS7jcbxOL46aWuP4s23MSgt3bFUTrMttZpCgaeihUK/bpQuEuilKFtWZFT+QNlC5j21ZIzlQGUb/OvRC27LSZ+5s6t5k/ay02rKkckcIIcY1CXeEEGIX2hkyTtKqiqooc5Vx+9G3Y1ftvLz5Zf626m8jfHSjizlM+eCplVR4jOGwMU0nEM1cCx0z27L6GKhsUhTFqt7peORv6LHsVRNC9BRtyr0G3ZQt3Im1tBpVM6rKiolGdUWpq5RSp3HSnSvcsQ1g5o7ZlrXTbaQtXqcXe61R1RZv6V25U+wsptxsy0qr3FE8HmNbF323ZqXP5bFlCXf8saEZqOyP+dESha+FL9RoD3dSbVl9VO7EU5U7oXjImu+WqtyRcEcIIcYzCXeEEGIXagsZlTtVRcZJ1H4T9+Pag68FjC1an+74dMSObTTRdd1ag37g1AqKHDacyY1ZHT02ZsWtgcqFtWUBlH75v7BNmEC8pYXul14aoqMW4505TNmRZZiyyZq7szm1Dj28zKjacczYg6AjARiBRbmrHMhTuZMccDyQbVnt7rj1OIVW7qTP3FEUJaM1K5eoFsUeNHqyFI8HxW63PmaGO+mzYLIpdKAy9B0UDYQZ7mijNNxJtWXln7mTXrkDqdYsW6lRZaZJW5YQQoxrEu4IIcQuZLZlVblTJ1Hn7HkOJ049kbge59o3rqUz3DlCRzd6bGoL0haI4rSr7Du51Ki28ZitWalKG13TrHkkhc7cAVCdTiq+djYA7Q89LC1xoiDmMOVs83ZMVriTVrljzttR9p4NgF2147a5Cwh3zIHK/WnLMsKdbg8oGNuwrMqdbDN3bMWUJrOX9LYsSLUB5Qt3jDXoyduXZoYPxc6hqdxx2py4be6C7msg1OS2LH2sb8tKq9yB9HDHXIUulTtCCDGeSbgjhBC7UM/KHTBeIf/J4T9haulUmgPN3Lf0vpE6vFHDbMmaO7kMl90YQFuebM3qSNuYpbW3g6aBqmZUHRSi4uyzUZxOwkuXEvpkydAcuBjXrMqdfG1Z04y2rEh6uJPclBXfazoApU4jsOwz3BnQQGXjvnxFCiXOElRFxVGbrNxp6V25UxmxY0tmm/bKioyP2YqNMEHz5a74MIYpZ852MVkDlWOFzdwptmfflgXDO3dn1FfuBIxAq6+2LLNyp8FrDOs2wx1r85lU7gghxLgm4Y4QQuxCZrgzwZ35CnmJs4QL970QyNxysrtavNmoPjhoaupksyJZuZMe7ljzdqqqMtpBCmGvqqL0lC8D0P7QQ4M6XrF7iOVZg24yK3fi25tJhMPommatQQ/ONkIhM6gocxntMl2Rrqz3ZQ5UTvh8JMLhrNdJpycS1ga4bg/WTB+zqi1b5U55slglUuJEcTgyPmaGAmZbUDb+qJ9icw16aWa4Yw1U7iPcMbdp5arcgV0T7ozamTs+I9wptC1rr6q9ANjYbVbuJAcqy8wdIYQY1yTcEUKIXchqyyrqXWViXtYR7tilxzQaLbY2ZaWFO8XJyp20mTvWpqwChyn3VPlNY7Cy7+WXiTZt7ePaYnem6zrRZOVOvrYsW0WFEXLoOtHGRqIbNpAIBFCKiuieZJxkex3GSXqF2/j6ztWKqXq9KE4j1IzvbOvzGLWuLqOSjcxwx6rcaW1FTyQyblPqN6pugl5n78cvSYYe/tyBii/mw2OuQff2aMsqdKByHzN3IBXudEeHPqAY7eGO2ZZlK3Bb1l6VyXCnKzPckYHKQggxvkm4I4QQu1D6tqyeKlzGiV5HZPcOd7qCMda0GCeDB6aFO+VW5U5q5o45TLk/83bSuefMxjP/MEgk6PibbCsTuWk7d6KHw6CqOOrqcl5PUZSMjVmhpUbVTtE++9CtGeFBidM4STcrd3J9zyuKYs3d0Xb23ZqlJdegJ4rdaDbFCkTsEyaAqkIshtaWGRJ5/UYY5Pf2rnyzlZiVO7nDmczKnezhTr6ByjEtRixhfE+PfOXO6Ju5oycSVuik9rUtSzP+IczKndZgK4FYwKqo0nw+mS8mhBDjmIQ7QgixC5mVOz3bsiD1Kn5HuGO3/gX84y3GCer0CcVMKHFZl5vr0Dsz2rKSlTs1A6vcAay16J2PP46Wp/1E7N7Mqh1Hba1VTZNL+sas0FJjmLJ73lyrgsUMKsyZO7nasgBsEwsfqmyGOzGvJ+NxFIfDCol6bswq6ja+n3zFvX8lNMOEfKvQfVEfxeH8M3fCWph4Ip719uYwZSiscmd3a8tKPybVW1hbVo2nxhrav6l7U6qiStNG7dBoIYQQgyfhjhBC7CIxLWadxGWr3Kl0VwIQ0SK9VtruThZvSq5Ab8gc7lqRp3LHMcC2LICSo47COX06Cb+frn/+c8D3I8Y3a95OnjXoJnOoslG5Y4Q7RfvNtYIJs12qr4HKkLYOvYChyvHkGvSI1whF01eI25OtWfGWzLk7RT6j2qMzyyxja1uWr49tWcm2rJ6VO+mVOLnm7phVPQ7VgcPmyHodSH3OhiXcSW7LSozC4MNsycLh6DNUNLdlue1uppcZw7s3dm1EKSqC5EwymbsjhBDjl4Q7Qgixi5hVOzbFZrVjpCuyF+GyGSdlu3Nrljlv5+BpmeFOeZaBynFzoHL1wNqyABRVpeKccwDwvfLKgO9HjG/RpmTlTp55Oybn1GkARD5bTWTNGgCK5qXCnZ6VO3nDHWtjVgGVO+3G907Ia4QkZiAC4Ei2Lvas3HF2GkFyuydzFg+ktjOZ25qy8cdSbVm2HgN/HarD+pmWM9xJVu6YLVy5ZFTuRAOwY03e6/fHaK7cMaumbCUlKIqS+3oJjYhmpGxF9qLMcEdR0oYqy8YsIYQYryTcEUKIXcQapuyuQlV6//hNX428uw5VjmsJlmzpBDKHKUN6W1Za5U7L4AYqm4rm7gcYlRZCZGOuQXfm2ZRlMtuywitWgKZhmzgBe23twMIds3KnH21ZgWJbxuMA2OuyV+7YOo3wYIc7Rk/mdiatj8qd4hyVO9D3UGUz9MnXkgU9Bio/dRnc/TnY9kne2xRqNIc75ryjvlqyzGAHjHBnWuk0IG2osrn5zCeVO0IIMV5JuCOEELuIuQY9W0uWyWzNag+375JjGm1WbfcRimmUuu3MnJg5PLSiOEvljjVQeXDhjjkAN97aOipP8MTIi20x27IKqNxJtmWZiubOQ1EUa9OTOYum3F0OQHekGy2hZb0vc1ZOYeGO8XPDV2RUeKSHO46a5MasHpU7aodxTK3uSK9ZX+bMnbwDlWN+PDlm7kDfQ5XNyp18w5ShR+VO02LjwtZVeW9TqFEd7iTbsszNZbmkzy5y2VwZlTuQWlMvlTtCCDF+SbgjhBC7SFuoDTWhc9ozbWw49SvEO3pX51irkfO8kj+eLd5snJweOLUCVc1sQTBn7piVO4lw2Fj9TKrlZKBs5eXYyoxWuWhj46DuS4xPZluWc0rf4Y6tpATbhNTQ9KK5cwFjsxSkggqzPVNHzzlLxt6PgcrxZFtWV5ERtqS3Zdlrje+ReHNm5Q4dnQC0e3Rr25LJWoXuyx0I+KN+q3LHlqVyxwyyclXuWGvQCw13Il3QvdW4MND3HKJCqMXJmTujMNxJtWUVNky5yF6EqqhWuNPY3YiW0KRyRwghdgMS7gghxC7S7m/l/z2TYN5b24isWUPwgw97XWd3b8v6KDlv56Aew5Qh1Zblj8SJxhPWgFnF5bJelR6M9A1HYnTY7t/O9W9fz7Prnx3R40hEIlYLYCEDlSGzesds++s5UNmhOqzwI9ecLatyp4CBylpyoHK7W8t4HDC2fEFqwxwYa7bNOT1dxb3n4piBgJZn5o4v5sNjrkLPU7mTa+ZOoW1ZpY7kQOVwO5CsMAr0HXgVQvUkQ6xgcNRtKjSHWffVlmUNU7a5AagrrsNlcxFNRNkW2IZalqzc6ZJwRwghxisJd4QQop+W7ljKg8sfJKH3HkCaix6NMu03T3L4qtSJQ2zr1l7Xy9WWpUejdL/4Elpn58AOeoz42Ax3pvUOd0rdDsxins5gNDVvp6Ym76DRQqU2HEm4Mxp82PwhZz13Fs+sf4abF91Ma7B1xI4ltnUb6Dqqx4OtIvW1GdWifPulb/OHT//Q6zZmWIii4N4vGe7EMmfuQKp6J9c6dPvE1MydvoIHc+ZOmzva63HsybaseHOzdT9aVxfEjRXlXcWpyiKTWlzYtqx8lTt9hTtmxUnBA5XTP09DFe4k27JIJNDD4fxX3sXMYda2Ptqy0it3AGyqjamlxs+0jV0brZY5TSp3hBBi3JJwRwgh+mG7fzuXvHwJty++nUXbFhV0m0Q0StN3v8ekDzcTs4FvT6OtI7ZtW6/r5mrL6nz6abZedRWtt/9mcE9gFNvWGWJbVxibqrB/fXmvj6uqQlmRUb3TEYxZFQj26olD8viO5NwdqdwZWbqus3DVQi5+6WI6Ih0oKEQTUR5Y/sCIHVP6GvT0IHFl20re3f4uj6x6pNdtzDlOzhl7WFuneg5UBqhw5W/FtNq7YjESXdkDIJPZ6tnqCPd6HEfy+0SPRq2QWGsz5oAFihQ0m5KlcqfvmTvBkI+i5BisbBV01kDlaB9tWX1U7pQ4k5/D9GMMDlXlTpH19mhrzdKsmTsFVu7Y3dZl6XN3zOAtITN3hBBi3JJwRwghCpTQE9yw6AZrdsT6zvV93yYSYeuV/w//q68Styv8+nSVyPGHAvnDnZ6VO5HPVgMQ/OTjQT2H4dR2//1sWPDfxNsHNgzaXIG+d10pHqc963Uq0tahx1uNNhXHINagpzNPxsfTxixd14lt2zbqWk1yiWgRbnjnBm754BY0XeNL07/EHcfeAcA/1vyDnaGhOZnvr2iOYcrmgOTOSCexROa2Ke8JJ2CvrqbizDMB49/CvH5G5Y7bqNzJ1YqpOp2oyXlQ+ebu6LputWU1O43AJL0tS3E6raDInLtj3l+gxAhNzcoi67GtVegBdC37wGfNn7qNGWKlsyp34jnaspKXFzpzx5+IYtVMDtHMHUVVUT2jc+6O1ZaV5XObrmflDpCxMUu1Knck3BFCiPFKwh0hhCjQo589yvvb37feb+zOP3g3EQ7TdMWV+N94A8Xl4sHzJvHpDBXPlAYgR7iTfBW/54meOeQ3umEjiWD2rTMjrWPh34l89hmBRe8O6PZmuNNzBXq6cmsdemZb1lAYjzN3Oh5+mHXHHU/XP58c6UPpU3OgmfNfOJ+n1z+Nqqhce/C1/PLzv+TY+mPZb8J+hLUwD694eESOLdZktFD2XIOeXo3SHsoMNV3TpzPrzTeo/OY3AQhrYeIJowUqPdwx52zlasuCwjZm6cEgesToj2ov0no9DqQGj8escMeo3AmVGqFpIJoZbKTPecn1c8catlzkRnE4en3cnCnU875N/R2orAN+sz9ziNqyYPRuzDKrpswqqlxCmhHu5KzcMWfudOev/hJCCDF2SbgjhBAF2NS1id8u/i0A+0/cH4DNvtwhQCIUouk73yHw1lsoRUXU/+EPvDfF+OW7tGEG0L+2rJi5wSmRILx69WCeyrBIhMPW84ltGdi2KTPcOTBPuJOq3Iml1qBXD24Nusk5dRpgDKUdL69uBxcblV6B998b4SPJ7+OWjzn7ubNZ3racMlcZ951wH+ftcx6KoqAoCpfOuxSAR1c/OiLDxlNtWZmVO+kbrnaG8wcNZhCkKmpGC5IZ7uTbkJcaqpz7MeLJrVc4HUQcYFfsGVUcAPbkUGUzGNXajPuLlBnX67nRSnE6IRnYZGvNimgRnEEjsMq2Bh36rtyxVqH30Zblsrlw2VwA+NTkr6+BnTBEVWmjNdwxt2X11ZYVivWu3DHDnU3dm6ygTtqyhBBi/JJwR4gx4PkNz3P5q5fTFmob6UPZLcUTca5/+3rCWpjD6g7j6oOuBmBL95as19d1nabLryCw6F0Uj4eGP/4B5+cOtFoyqqbvBUCiu9v6xd2UrS1Lj8eJpg1fDq9cOWTPbahENzdaJ1nRxuyfl3yC0Tgrtxufn4PzVu6k2rJircntRTVDE+7YSoqttpXxMlQ5mgwlImvXjfCR5PbUuqe48KULaQu3MatiFn//r78zf9L8jOt8fvLn2atyL0LxEH9d+dddfozRLck16D02ZaW3MfX18zl93k763J6Cwh1zqHKejVlaR/JnRnkZKEqvx4G0yp3tmZU7sbLkXJye4Y6iWK1W2QJPY5iy8X1vy7Gxzgp3clTuFLotC9KGKpvhTjwEOe63v6y2rFFWGZmwZu7kr9wx19hna8tqD7cTLrIB0pYlhBDjmYQ7Qoxy4XiYX3zwC95sepOFny0c6cPZLT2w/AGW7lyK1+Hlf4/4X2sDyfbAdiJapNf1o+vWEVi0CMXppOHPf8Lzuc/RFjZOouyKnfKKWmzJGRqxrZnVO2Zbli/qs2Z4xLZvtzbawCgNdzZuTL09gMqdJVs60RI6dWVuJpUX5bxehdWWFSPeMrSVO5C+MWvTkN3nSNF1nVgyaIuuX4+e9jU0Wmzp3sJP3v0J8USck6adxCMnP0K9t/eqcUVRuGTuJQAs/Gxh3hamoabrOjFz5k6PtqyMyp0+5gFZ83YcmRUY/arcydOWZc7bSZQZIUCpq3fYYlXumG1ZyYHKWrlxTNk2Wllzd/y9P+aL+vBYm7IGVrljVpz0tS0LwJsMgHyqCjYj6B2yocqjtHKn4LasLDN3PA4PtcXGv3mzYnytJrplW5YQQoxXEu4IMcr9e9O/rROZZ9Y/06/122LwPmv/jHs+vQeAHx76Q2qLa6lyV1HsKEZHp8nX1Os20SbjMtfMmXgOPBBIvapf6a5EVVTskycBENuWuQ69zFWGqhg/mjvDncb9bc4MS8IrVw3Rsxs66WFIbEvvz0k+oajGH97YAOSftwNQUZys3PFHUm1ZQzRzB9KGKo+DuTuJri7rxFCPRgdUUTXcfvvxb4kn4syvm8+tR92ad+7KsQ3HMqtiFoFYgIWrdl3QrXV2Gif8ioIj+X1r6k+4k21TFhRauWOGO7krd8xNWbFS43PYM0QCcNQmK3eSbVnxZFuWXmkEM9k2WqnWxqzeFR/+qJ/i5OZwmzd721Bf27IKHagM4FWMcLe7eAKUJL/vh3gdujbKwh2rLSvH59dkbcuyuTMun15qtGY1YXx9aBLuCCHEuCXhjhBD4LXG1/j5ez/vteFoKPzfZ/9nvd0caOaD5g+G/DFEdlEtynVvXUc8Eef4huM5ZY9TAKOKoMFrDEXe3N07BDCHrzomT7YuMyt3qoqqjI9NSoY7PSp3VEW1TvY6IsmTtWQljGsvo50rsnYtiWh08E9wCKVX7sRbWkiEwwXdrj0Q5Zw/v8cba3bgsqucd/i0vNc3Z+6E2jut4bFDWrmTnLszHsKdaI+QLbJu7QgdSXZLWpfw8uaXjeHJn7u2VwtRT6qi8u253wbgr6v+mjMsGGpm1Y69uhrV5cr42JCEO+5yoLCBylreyh3j50XU68z6OAD2mszKHS3ZlqVWGqFqz7YsAFtx7nXovliqcifbGnRIG6icpSoICl+FDuDF+BrxlUyA4uSK+CEOd0Zr5Y5a3P/KHYBpZdMA2Ki3WvenJ+RFIiGEGI8k3BFiEHRd549L/8hV/7mKR1c/yg/f/OGQVtYs37mc5W3LcagOvjD1CwA8ve7pIbv/sUbXdVbsXMGtH97KyU+czDWvX0NMi/V9wwG6e8ndrOtcR6W7khvn35hx8tlQaoQ72TZmxZLzcRxTUsNXzcodM9xxJoOfQjZmmZU7xYccgq28HOJxIqvXDOq5DbXIpo0Z78ea+q7e2dIe5Iz7FvFJYydlRQ7+dtGhfG5aZd7bmG1Z2g7jRMVWVtbrhHswxlPljjkE2BRZO3rCHV3XufWjWwFYMHMBsytmF3S7LzR8gell0/FFffz9s78P5yFacq1Bh8yZO32FO2ZwkqtyJ9+gaFsBA5XNmTuhEiPcydaWlV65o+u61ZZlhkfZwh3VmrnT+2NG5U5y5k6OyhKzIidnuBMvbFsWQKlmtBb6isrBY4Y7Q7MOfdSGO8kZOYW2ZaVvy4LUUOV1seT/NbqeNagTQggx9g1ruPPmm29yyimnMGnSJBRF4amnnsr4uK7r3HjjjdTV1VFUVMQJJ5zA2lH0y6cQ+cS0GDcuupHff/J7wJil8u72d/nL8r8M2WM8+tmjAJw47US+tc+3AHhl8yu77BXr4RLTYvzk3Z9wzevX8NCKh1jSuoSolrsSZUPXBu5ecjenPHUKZ//rbB5e+TBN/iZe2vwSNyy6YVha1T5p/YQHVzwIwI3zb6TSnRk6WJU7WTZmxbYawUZ65Y554lfl7lG5kyXcMV/Jt8Kd5KYsx9QG3HvvDYyuuTu6rhPduAkApch41bivFqAV27o47d5FbNgRYFKZm39cOp+D+wh2IDVQWU22kwxlSxakrUPftAl9iLbwjJRelTujaKjyi5teZOmOpRTZi7hi/ysKvp1NtVnVOw+vfNiq+hhOZpthzzXokFm509dAZWvmTo5wpyvSlfNrrpCByvHkzJ1AsS3r40Bq5o4eCpHo6kJLhjuOCUb1W9aZO97clTv+WKotSy3NHu70VbljDVQupC0rZpQJ+dwlUGx8ToZ+5s7oGaiciETQY8YLGH0NVM5VuWOGO+uDW1CSQbgmG7OEEGJcsg/nnQcCAebNm8cFF1zAaaed1uvjv/71r/nd737HQw89xPTp07nhhhs46aSTWLlyJW63O8s9CjE6dEe7+d5/vsf7ze+jKio/OuRHOG1Oblx0I3d9chcHVh/IgTUHDuoxOsOd/HvTvwE4e87Z7DthX/Yo24MNXRt4efPL/Pes/x6KpzJg6zvX89eVfyUYD3LjYTdS4sz/i2e6u5fczT/W/AOAlza/BIBDdbB31d7MmziPeRPnMa1sGu9sfYcXNr7AqvbUjBm3zc3R9UezT9U+/O7j3/GvDf+i0l3J9w/+fp9tHT11R7tp8jWx1b+Vrb6tNPmbaPI3sdW3la3+rST0BKfOOJXjG47vdVtzqHK2jVlRq3Knd1vWhCLj1WZ7nnDHDJJ6tmU5G6bi3mdvAosWjapwR2tvN4Z0KgrFhxyC/403elWNpHtn3U4u+eti/JE4e9Z6efBbh1BbVtjP/Ipio3LH2ZkMd4awJQvA2WCcwCe6u9E6O7FX5J8BNJqZ7UTueXMJf7p01FTuRLUod3x8BwDf2vdbTPRM7Nftvzjti9y75F4afY08tvoxzt/3/KE/yDTRHGvQYWjasspcxnD1uB4nEAtk/VlqhjtaZyd6NGqsKO9BS65C93mUrI8DoLpc2Coq0Do6iKxdawUHronVsC77XBxrW1aWmTsZA5VzrEI3n0+ucMccqFxQW1YkAAr4HEXgMILyIWvLsrZljZ7KnfRAzQyfcrFm7vSs3DFn7viaUL1etEiERHcXMLnnXez2dF3v9+8RAGs71jK1dCpOW+/vSyGE2JWGNdw5+eSTOfnkk7N+TNd17rjjDn784x/zla98BYCHH36YmpoannrqKc4+++zhPDQhBqzJ18Tlr17Ohq4NeOwebjv6Nj4/5fPous4HzR/w3Ibn+J83/4d/nPIPqwJjIJ5a9xQRLcKelXsyb+I8FEXh1BmncsfHd/DUuqdGLNxZ0rqE+5ffz+tbXrcu6wh3cM/x9+CwOfq8/YfNH1rVTWfPOZvmYDNLdyylPdzOpzs+5dMdn/a6jV2xM3/SfE6efjLHNRxnDeicUDSBH739I/668q9MLJrIt/b9VkHPYXX7aq5/+3pWd6zOe705FXP4wSE/yPoxM9zJWrmTnLnjTJ+5E8oxc6ePtiw9kbCqYJwN9SR8xqv/oyncMeftOOrqcM2aif+NN3JW7jy9ZCvXPv4pMU3nsD0q+cO5B1NW1PfXjcmcuePuMoIv+xCtQTepRUXYa2uJNzcT3bRpTIc7ZijhPfY4wp8uJbp5M4loFDVLMLArLVy1kK3+rVQXVXPe3uf1+/Z21c5F+13EjYtu5IEVD3DWnmf1qlYYStb3c33vyp30MGRHKH97UK5wp8hehNvmJqyF6Yx0Zg13bGVlYLdDPE68vR1HsgInnbktq7PIqP4pdWYPW+y1tWgdHYRWrACMQb0lxbln7qglxvHm2pZV1kfljlmRE01EiWrRjBNgXdettqyCtmWFusED3XZ7qnJnyGfujKLKHXMNenExis2W97pm5U7PkKzaU43H7iEYD5IoKYKdUrnTU0JPcPFLF7PFt4WF/7XQehGmEAtXLeSWD27hgn0v4LsHfXcYj1IIIfo2rOFOPhs3bqS5uZkTTjjBuqysrIxDDz2Ud999V8IdMSot3bGUK1+7kvZwO9Weau45/h7mVM4BjCG7Pz7sxyzfuZxN3Zv48Ts/5vfH/X5ArwIl9AT/t9oYpHz2nLOt+/jyHl/md5/8jo9bP6axu9Ga+zLcdF3nra1vcf+y+/m49WMAFBSOrj+a97e/z3vb3+OmRTfx8yN/nvf5dke7+dHbP0JHZ8HMBVx/2PXW/Tf5mliyY4kV8KzvXM/ciXP50vQv8YWpX6DC3fsk+5QZp9Aebue2j27jN4t/Q6W7kq/M/Ere57Hws4Xc/tHt1prxSnclU0qmMNk72fi7ZLL1dl1xHTY1+y/U5ue+OdBMOB62Xi3VurutVbOFtGVpO3eSiEQy5saYoWB7uJ14a6sxONhuN26TbNuIrF6NHouhOAoPRoZLJBnuOKdPx1FvfF6yrUP/05sb+PnzRhXWf82t4zdnzsNlz3/C0lN5cuZOVcgYPusY4rYsMObuxJubiW7ejOeAA4b8/ncVs53Ic/BBqF4vCZ+P6MZNuOcUNt9mOHSGO/nj0j8CcMUBVxTUipPNl2d8mT8s/QNb/Vt5Ys0TfGPvbwzlYWbItQYdMit3QvEQwVgw53Myr5stdClzlREOGuHOFG/vCiFFVbFXVRFvaSG+Y2fWcCeenLnT4dZyPg4Y3zORVasIrzACYntVFcXO5LryfKvQfVm2ZcX81EXMmTs5VqHbU6FNIBbICHciWgRNN463z8qdeBRvsAM8FfgU0sKd8Ttzx5xz1FdLFkBYy165oygK08ums6JtBRGPHSeg+WRjVrrnNz5vLav4zUe/4Ref/0VBt+sId3DXJ3cBxgtfQggx0kYs3GlObmqo6fGLeU1NjfWxbCKRCJHkhhSAblnpKHaRVza/wg/f+qFVTXPXcXdRU5z59VvsKOa2o2/jnH+dwxtNb/Dwyoc5b5/+vzL9ztZ3aPI34XV4OXl6qvqtpriG+ZPm887Wd3hm/TNccUDhsyoGwh/18/Lml3l45cOs6zTmddhVO6fOOJXz9zmf6WXTeXvr21zx6hU8u+FZaopruOrAq3Le38/e+xnNgWbqvfX88JAfWpcrikJ9aT31pfWcMuOUfh3jefucR1uojQdWPMBNi26iwl3BUVOO6nW99nA7N7xzA282vQnAMVOO4abDb+rXK3TpKlwVeB1efDEfW3xbmFUxC0gNU7ZVVlpl/tC7LctWXo7i8aAHg8S2bcM1fbp1XastK9yRmrczeRKK3Y6jvh61pISE309kwwbcc+YM6PiHkrkG3Tl9Os5k60qsR+XOw+9usoKdC46Yzo//ay9Utf/Bp8tuw+O0URk2fvYPdVsWGHN3gu+/n7HefazRYzFi27cD4KhvwDVzJqFPPiGydu2Ihjv3Lb0PX8zH7IrZnDrj1AHfj0N1cOF+F/LTd3/KA8sf4KtzvorLNnSDtU3pn0dnj7asmBazTqgVFHR0doZ20uDIHrqb4Y45gyZduauclmBL/nXoEyYY4U6Odejmtqw2dxT07G1ZAPbkUOXw8uUA2CZUWceUdVuWOXMnkGVbVtSXWoWeo3LHptooshcRiocIxAIZQb1ZtQO9Z8X00rWF0oQRBPkS0d1ioLL5OVf7GKYMqfa2bJ/HaWXTWNG2Ar8LKoGEVO5YYlrMCmgAnt3wLKfPPp2Dag7q87b3LLnHGqre5Ot7iYAQQgy3EQt3BuqWW27hJz/5yUgfxpBa07EGXdepLa6l1FlacKWHltDoiHQQT8Sxq3YcqgO7ajf+KHar4iCeiBPVokS0SMYfMH7JLHGWUGwvzlmhUIh4Ik5XpIvOSCcd4Q5iiRiqoqIqKjbFlvF2hbuCuuK6AVW05BLVoujoOFQHqjK0c8J1XefhlQ9z+0e3o6Pz+cmf59ajb81ZQj6n0mjl+d/3/pc7Ft/BgdUHst/E/fr1mI+uNgYpf2XmV3q9CrxgxgIr3PnO/t8Z8ucbjod5s+lNXtj4Am82vUk0YQw6LnYUc+bsM/nG3t+g2pM6oT5y8pHcfPjN3PDODfx52Z+p9dRy1p5n9brf5zY8xwsbX8Cm2Ljl87cUVIJfqKsPupq2cBvPrH+Ga16/hj+d+Cf2r97f+vh729/jR2/9iB2hHThVJ9ccfA1f2/Nrg/oaNAOplW0raexu7BXupG/Kgt5tWYqi4JhUR3Td+l7hjtmW1RnpJLYzOW8nWRGjqCruvfYi+OGHhFesHB3hTnKYsnP6NBwNxnHGmprQEwkU1fj6fGKx8Yvv5cfO4NoT5wzqc1/hcVIVNip37NXDU7kDY3tjVqy5GTQNxeXCPnECrlmzrHBnpGzq2sT/fWZUJF578LWD+j8H4CszvsIfl/6R5kAzL216qd/BcCGCiz+GRALF47E2VpnSN2XVFdexLbDNCHdyVFSa188WupjVennDnTxDlfVYzKqsaXWGIZI73DGrfszw0l41gSJz6HE00GvuiLUtK9tA5aifYnMVeo7KHTD+/zDDnXTmQOwie1HfXw8dm/AmV3j7or7UKvRg/kHWhVKLkzN3RlG4o5mbskqy/1umy1W5A6m5O12OGJUYFabC8I+1/2CrfytV7irmT5rPcxue4+fv/5zHvvwYdjX3adKGzg08vuZx6/3WUCuheGhYW0RHo2AsyMbujQRjQYodxZQ4SvA4PBQ7inHb3Fn/r9d1nbgezzh/GQpmm6cv6qM72o1dseNxeKxjGujvyxEtQme4k85IJ12RLrqiXaiKisfuwePwUGQvyng71/Me7HPKtzxERyeqGa2vsUTMeDvZChvVosT1OF6HlzJXGWXOMsrcZZQ4SnJ+TqJa1DqnM8/vOiOd2BSb9TzN511kL8q4zGVzDcnz13WdQCxAd7Sb7mi3MePN7qHaU02lu3LQv0OMVyMW7tQmf7loaWmhrq7OurylpYX9998/5+2uu+46vve971nvd3d3U5+lD34suf2j21m0bRFgDIut9lRTU1xDjcf4U+2pJqyFaQ220hpspSXYQkughZ2hnVY5czYKCqqi5r1OOo/dQ4mzxAp8zB9OKiqqqqJihDOKoqDruvWN3hHpoDvSjU7hm2W8Di+zK2ezZ+WezKmYw5zKOcwon4HL5rJ+kLWH2+kId9AR7jDejnRY75tvm9dJf/XPrthx2Bw4VAdOmzPjb4fqwGFz4FRTl6uKSjwRt34Ypr8dS8Sszz0YLVI/OOQHef/DB/jq7K/y/vb3eWnzS3z/ze/z2CmP5SyR76nJ18RbTW8BcNac3iHJsQ3H4nV42R7YzofNH3Jo3aF93ufO0E6CsSA21YZdsadCwOQfgA+2f8ALG1/gtS2vZfwCPr1sOqfOOJUz55yZ8zksmLmA5kAzdy+5m1988AsmeiZyXMNx1se3+rfy8/d+DsAlcy9h3sR5BX0uCqUqKjcffjPt4Xbe3vo2l796OQ+f/DANpQ3c9cldPLD8AXR0ZpTN4FdH/cpqpRusqd6prGxbmTF3J9pkbsqalLpMi1qbcsy2LDBas8xwJ535ynZ7uN1ag+5sSJ0wuvfe2wh3Vq6E00Z2sDakZu64pk83ThztdvRYjHhLC466OsIxjRXbjOd/9ucaBv2ffkWxgwkhM9wZjsqdcRDuWK1EU1BUFdcsI3yMrBu5jVl3fHwHcT3OkZOPZP6k+YO+P6fNyakzTuWPS//If7b8Z8jDHV3X2fF7Yxti2VdO7fV1a1biFDuKqSmuscKdXHLN3IHUxqzOcGfO29snJteh7+z9GPGO5Bp1VaXVFsz5OAD2mmRLV7LF015VZc35ietxIlokIyBItWVl35bl6aNyB4wXknaGdvYKd8z3CzohzhXuBHYYz2WQP1dGZeVOcs5RIW1ZubZlQWpjVps9zHSwZrft7oKxIH/49A8AXDrvUk6adhJvbX2LtR1r+b/V/8fX9/p6ztve9tFtaLrGMfXHsLhlMb6ojyZfk/VCz1DaGdrJ8p3L817HPOk2QwaPPRmw2N0ZJ/AJPUF7uJ2WYAutAeOcwjy32BHcgcvuosJVQbm73PjbVU6F2/jbptjY2L2RdZ3rWN+5nvWd69nm35bz93/zmByqw/rdOpaIEU/EM67nsXsodZVS6kz7k3xfQbGCIPO2Mc24H3/MbwU5vqgPX9SX95zH/JyYf4rsRTnDp0AsQEfECDbM761CmS9m96SgWMGXGYSZfzttTgKxQObzifnwR/0Fn8f1l6qolDpLrcAnlojRFemiI9LR7+fc8357hj9mAOS2ubOGMj2DHPNzkCvQsit2JnomWufJ1Z7qrMF2OvN8ZrwbsXBn+vTp1NbW8uqrr1phTnd3N++//z6XXXZZztu5XC5crqEvvR5JxY5iKt2VtIfbCWthGn2NNPp6z6vIRkHBptp6/aAEI8Xt+QPBrtpx29xWz7s/6rcqM4LxIMF4kFZaB/xcSp2lVLgrcNqcJBIJEiRI6Am0hGYdz87QTnwxH4tbFrO4ZbF1W5tio8pdRVe0y6osGoi4HicejxNi4D+YelIVlWsOuoZz9z63oBNTRVG4+fCbWdG2gq3+rdy86GZuP/r2gm772JrH0NGZXzefaWXTen3cZXNx8vSTeWzNYzy97uk+w523mt7i8lcv71f4Nql4El+c/kVOnn4ycyoKq7K4ZO4lNAeaeWLtE/zPm//Dn0/8M/tX74+W0PjRWz/CH/Mzb+I8Lp57ccHH0R8O1cHtR9/OxS9dzNKdS7nk5Uuo9lSzbOcywAjcvv+57w/pq2rmK/SN3anv19hWI6hxplXutIeNWRh21U6pKxWQWUOVk9U+JjPcSW/Lck5NC3f2GT3r0PVYjGgySHBOn260jk2eRGxzI9HGLTjq6lixrYt4QmdCiZMpFYP//Fe6bZRHjBNNxxAPVIZU5U5s0+YBb08ZaVFrfbfxdWiFOyNQudMcaObFTS/yauOr1s/SoXJcw3H8cekfeXvr2xmzr4ZC4O13CC1ejOJyMeHS3r+XmMOUSxwlVrtlIeFOtpDcCnfyVO6YlUNalnBHS4Y7tvJyuuK5HwfAUZtZ7WabUGWc5CRby/wxf8bn0dyWlW0VeiDUjcf4FQK1NPcLGGYFas+2L/MEoqBKzo6NmeGO2ZalRSHiA3dhL6Douk5buK1XS67qSYY7wVE4ULmQtqzk59Jty1K5kwx3mlXj8y8DlQ2PrHqEtnAbU0qmcPqs03HYHFx14FX89N2fctcnd3HStJOytm4v2rqIt7a+hV2xc81B1/CDt37AyraVGS3aQ2XFzhVc8OIFGS9i9leRvYhiRzE2xUZbuC3rOcNgVLorKXWWEowFCcQDVmir6VrGXLJczPOP5kDusRz9YVftlDpL0XSNQDRAXI9nPE5fw++zsSk2IwRJBiE6xgvRwViQUDxk/QEjQMsVSkQj0bw/5/M9fl8vLDtVp/ECts2JU3Uafyfftqk2/FE/XdEuK7BK6Anrhfp8z7ncVU65q5xSV6n1AnwoZjzfYDz1/M1zt4SeIBAL5NyQ2F8O1UGpsxSv00swHmRnaCdxPc72wHa2B7YXfD/HTDlGwp3B8vv9rEt7lXDjxo0sWbKEyspKGhoauPrqq/nZz37GrFmzrFXokyZNYsGCBcN5WKPOb475DWCU/bUGW2kJtBjVOWaaHmjBZXcZFT1p1Tw1nhqqiqqwq3Z03QhOzFTb/JPQE7hsLpw2Jy6bK2taGtWi+GN+/FG/9bcv5iOmxdB0zfohZf3B+IFlfrNXuispd5VT5irr8wcPGP3NG7o2sLpjNavbjT+fdXxGV6SL1lAqWHLb3FS4K6w/la5Kyt3G41W4kpe5KzNeUYgmosS0mPV3egVOxscSMevj8UTcquhxqI7UD8fk3w7VQY2npt/rer1OL7cdfRvnvnAuL29+mfs+vY9L512a90QxokV4cu2TAJy9Z+6h4qfOPJXH1jzGK42vcH3s+py/GEe0CD9//+fo6NYvfPFE3PqPLl2lu5KTpp3El6Z/ibkT5/a7fNUcKL0ztJM3mt7gyteu5OGTH+aVza/wcevHeOwebvn8LQV9jQyUx+Hh7uPv5pv//iYbuzbSEmzB6/Ty08N/yglTT+j7DvrJ3JiVHsbGrMqd3puyKt2VGZ9XxyTjOr0qd9LasqyZO/WZlTsA4c8+Q9e0PreoDKdoUxPE4yhuN/bkDDXnlHpimxuNFe6HHsInjZ0AHNBQMSRByaREEBUdXVWxVVX1fYN+ctTXg6qSCAaJ79iBYxiqg4ZbzFrfbVS2umbNNC7fsoVEMJgxD2qoxRNxlu5YyptNb/Lm1jdZ25EKlE6fdTozK2YO2WPtXbk3NZ4aWoItvL/9fY6uP3pI7lfXdXbceScAFV/7WtYQ0azG8zq9VkXeoCt3+pi5AxDfkSfcqajAH23M+TiQVrljvl81AVVRKXYUW78DpJ/Qql7jfrK1ZcV8XdbbtjzVJeZMn1xtWYWsQadjE17N+P3DH/OTcLhRHcUQCxjVOwWGO3d+fCf3L7+fP534Jw6rO8y6fFS2ZfnNtqzCw51sn8uG0gZURaXDYSRxUrljVMk9sPwBAK484Epr2+dpM0/jiTVPsKJtRdbhyvFEnFs/uhUwfk+bVjaNem+9Fe4MpU1dm7jslcsIxoNMLpmcUfnbUywRs4a6B+NBArGA9aJeevAAxgvDVUVVGecTNcU1TCiaQCwRozNsVOX3/DuqRZlaOpUZ5TOYWT6TGeUzmFE+w5oTaEroCULxEP6on0A8QEyLWb9PmyMkzL/NyubuSKpiw3zbF/VZIxfSb2P+XewopsxVhtfpxevwUuoyAoD0tihd14kmokbYEA0QiAfwR/1W0JNLsb3YOs8pc5fhdXj7/P1FS2iEtTDBWDBrtY31eYn5CUQDxt8x4++IFqHEUWI8F6fXCjPMP0PZ6gXGuV9XpMtqM+uKdGFX7dZzLneX523byvX804MuK/iJZYZAup79xWaPw2NVbZn/nqXO0l4v2sQTcXaGdloVZ+bfMS2W9/hmlg/d7x6j2bCGOx999BHHHnus9b7ZTnXeeefx4IMP8j//8z8EAgG+/e1v09nZyZFHHsm///1v3O6he+VtLHHZXNR766n39r/NTFEUq92mv5w2J5W2yl4/nIeLw+ZgTqXRisUM4zJd12kJttAWarNKQQe6RWU02XfCvlxz0DX86sNfcc+n97CybSU/O/JnlLnKsl7/xU0v0hnppLa4NutQYNPcCXOZVjqNTd2beGnTSznXoj+w/AFr5fCz//2s9Tk1w0AzBNR0jRJHyaD7V+2qnV8f9Wsueukilu1cxsUvXWwFG9cdet2Avrb7q9xdzh9O+ANX/ecqKosquemwm6grqev7hgNgVu5s7k6171gzdyanKnd6bsoyma1budqytEScaKNx3+mVO87p01HcbvRgkOjmzbj22GNIns9AWPN2pk2z5us4GurhnVT1SCrcKR+Sx6xNzi4Jl1ZYjzmUVKcTx6RJxJqaiG3ePCbDHatyJzkE2F5Vha2yEq29ncj6DRTtt++QPl5HuIN3tr3Dm01v8s7Wd6zgA4wTibkT53Js/bGcu/e5Q/q4iqJwXMNx/P2zv/PalteGLNzxv/Ya4eXLUTweqi6+KOt10itxzDDEHJzeU8asuyyrzgsLd5Izd7JV7iTXoFNeap3Q5d6Wlfn1bJ9g/Fwyw52eAYyap3LHatUqcufd3Gf+39OrLSseyPh4Xh2b8CZfDTcrjEqLJ0BnwJi7UzWj7/sAVnesBoyq1sxwJ1W5M1oq9lJtWfln7ui6Tjiee+aOy+Zicslkgm7j/xOtS8Kd+5ffjz/mZ07FHL44/YvW5TbVxo8P+zHn/OucrMOVn1z3JOs611HqLOXSeZcC0OA1/n8eynCnNdjKJS9fQkekg72r9uYvJ/2lX7MKdV23ggYz8IlqUSZ6JlJVVDVkc26yMcPiQo632FGcdRvqUFEUBZfNhcvmGvbzHJtqo1gt7HmPNKfNyUTPxH6/cJ2PTbUZ4z2y/B83lOyqndriWmqLe2+NFDD0vxWnOeaYY9B1vdefBx98EDC+4X7605/S3NxMOBzmlVdeYfbskdviIUaOoijUFteyz4R9mFwyeVwEO6av7/V1bjjsBpyqk9ebXueMZ8/IuTLTHDZ65uwz8wZ1iqJYK7+fXv901uts9W/lz8v+DMC1n7s243OqKIrRomd3U+IsocxVNmSDyTwOD78/7vfUe+tpCbYQ1+N8YeoX+MqM3CvKh1pdSR2PnfIY951w37AFO2DM3AHjlzDz1YislTs9NmWZrLasHuGO0+ak2FFMaRD0QBAUJWNAs2Kz4d5zTwBrnfFIiVpr0KdZl5nDn2PJdegfNxpVBQfUD80vcBOTm7L83uH7RW2sD1W2Zu6kzaQbrrk7i7Yu4vjHj+e6t67jhY0v0B3tptRZysnTT+aWz9/CG2e9wSNfeoQL97swYw32UDHne72+5XW0xOBnE+iJBDvu/B0Aleeeiz1HdZjZYlTiLLF+Qc5VuWMGQQpK1m1ZZuCff6CyWbnTu6UgntyUlSgz7ju9/bontbiYsDv1/4FaaTw/s9KnZ+uUWTWih8PosdQro7quQzJ8UPpoG+qzcqev//N1HTo249LBlTwp7TV3p0BmCLKibUXG5bZkuEMigR4aurbuwSi0LSuiRaxQL1fr8bTSafiTuY+WZa397qQ50MzCVQsBuOrAq3pVJ+w7YV9On306AD9//+dWG5M/6rc2a1027zLr+9Z84WqoNmZ1R7u59JVL2RbYxtTSqdxz/D39DgwURaHIXkRVURX1pfXMqZzDfhP3o7a4dliDHSHEyBrWcEcIYfwHe+acM/nbf/2NqaVTaQ40861/f4sHlj+Q0ZO7om0FS3cuxa7ac1bipDtlj1NQFZXFLYvZ0t371aLbPryNiBbh4JqD+eK0L2a5h+FTVVRlBCvFdUwvm85N828aFa+CDjWzHBiMuTtaZ6c1ryF9oHLPTVkmsy0r3tKKHs9slatwVVCTnJFqr6tF7TFrzGrNGuG5O+bGnfRtX84G4xfdaOMWtneF2N4VRlVg7pTsFWv9VRE0WkG6PUNzf9mM9XDHGuydFgoO19yd33/ye2KJGNPLpnPRfhfx0Bcf4o2z3uDXR/2aL+/x5WF9VRbgoJqD8Dq9tIfb+XTHp4O+v+4XXiCyZg2q10vVBd/Keb30Nqu+Zu6kz+fJVuZufo66Il29PmaytmXt3NmrrN2s3ImXeaxjyqe9qDx1bB6jwsc8eewZ7pgVLZDZmhXWwrhCxs8te555O+n33SvciRfYlhXqgIjZBmc8lj/qh+Lkq86B3O1wve4q2Z6yqm1VRhioeDzWUObR0ppVaFuWGVhB9sodMObuBJP/jSR2821Z9356L9FElINqDuLIyUdmvc5VB1xFmavMGq4M8Odlf6Y93M600mkZW0GneI2fs0NRuROOh7ny1StZ27GWCUUTuO+E+3r97iCEELlIuCPELrJn5Z7835f/j5Onn0xcj/Obxb/hilevoCNsnMGbVTsnTj0x6wC/nmqKa5hfZ2yceWbDMxkfW7R1Ea80voJNsXHdodeNSLDSUNrA86c9zxOnPpGzDW2sUxTFqt7Z4ttCrMloybJPnJgRxuRqy7JPnGC0Mmga8ZaWjI9Vuiup7TBO4Jxp83ZMo2WocqpyJxXumNUi0S1bWJJsyZpTW0qxa2g6gUsDxn22FQ1juDNtGgDRTWMv3NG6ukh0GSFB+mBv10yj33wow50VbStY3rYcu2rngZMe4KoDr+LAmgOHdbZWTw7VwdFTjHas1xpfG9R96fE4O39vvDJf+a3zsZXl/hqzZu44vNbJV1+VO7lCl4LaspIVRHo43Ct80DqN/0eiXnfexwFIJHS2p7Vs7bAbwUuu6hrF4UBJtsunt2alr0G3l5bnfDzIHRyZj9VnuNNh/JyhpBZvcih9d7Q7NVR5AJU7wXgwo6VWURRrFtVoCXcKbcsyAytzFkk208umE3AbvwvszpU7G7o28NS6pwC4+sCrc/5+VO4u56oDrwLgrk/u4tMdn/LXlX8F4HsHfS+j+sWs3Nnm3zaoYcXxRJzvv/l9Pm79GK/Dy30n3GcFR0IIUQgJd4TYhYodxfzq87/ipvk34VSdvLX1Lb767Fd5fcvrPL/xeSD/IOWezNasZ9Y9Y1UBxbQYt3xwCwBf2/NrzK4YuVZHc/DdeJY+dyc1b2dyxnVytWUpqoq9zmgbyzZ3xwp3GrKEO2mVO7mG0+0KkWTljnNaWuVOMlBIdHWxfLVRQXLgEM3bAfB0G1UKra7CBqgWrGMTxI2zVWsdevL5jSVm1Y5twoSMwcmu2UNfufPY6scA+MLUL4zoq8tma9ZrW14b1PdD1zPPEt20CVt5OZXfPC/vdc1qHK/TywS38b3dHmrPuiWlr3DHasvKswpd9XisKpqerVlmW1ao2JH3cQC2dYVodRuPF7S7aEluuzIDmGzbbcy2oPRwxxfzUWyuQffmDx/6asvqs+WkY5Pxd8U067lltGUFs886yiaspapcerZmWeHOKNmYVWhbVkhLbsrKsy1uetl0AsnXHLTduHLnrk/uIqEnOKb+GPav3j/vdU+beRr7Vu2LP+bnwhcvJJqIcmjtoRxTf0zG9ao91ThVp7XBZyB0Xeen7/6U17e8jlN18rvjfmfMphRCiH6QcEeIXUxRFM6YfQYL/2sh00qn0RJs4crXriSiRZhTMYf9J+5f8H0dW38sXoeXbYFtfNT8EWCs9tzUvYlKdyWX7d97fa8YWukbs6xwZ0rmK2252rIg99ydclc5tZ3G2+nDlE2uGTNQHA4SPp8152dX03w+ay1z+swdtbjYWtu8ZYURJBzQMHStOa4uI9xpzjK7ZMA+eQTunAfPXwuktWU1NqInsq80Ha1iPdagm8zKnXhz85C8ct8d7eb5DclQek7hofRwOGLSEbhsLrb4trCuc2AzhfRolJ133w1A1cUXYyvJHzikBzaVRcb8p7gez9pa1R0zTqZzDZo0K3fCWjijxaYne4516GZblr/YmJ2Wa5gywKadQXYmq946XSU0d4czji3b+lpbsfGx9K8bf9SPJ1m5k28NOuQeqGxteOoxc6c9EKWpIy1g6Svc6UflTvrWoF7hjjlUebRU7gSMMK2vtizzOeWatwNm5Y7xth4MZsxP2l0s27GMlze/jILCVQdc1ef1baqN6w+7HgWFiBZBQeHaz13bq9pHVdRBt2bd+fGdPLnuSVRF5dajb+Xg2oMHdD9CiN2bhDtCjJA5lXN49MuP8l97/Jd12Vl7ntWvFiq33c1J008CjMHKLYEW7vv0PgC+e9B38/6CL4ZGZuVO72HKkLstC3KHO5XuSmqSlTuOLG1ZitOJKzmAfqSGKpstWbaJE3qdfDiTrVn+DUbbw1BtygKwtRufz0Z1iDZStH4G/zJCHda8CLpu/Bva7eiRSK+WudGu5xp0k620FHutsV0isnbwQ5WfWfcMYS3MzPKZHFB9wKDvbzA8Do/VpjrQ1qzOf/6T2Nat2CZOoOKcr/V5/fRwx6E6qHAZAWa21qy+KndKHCXYFaOdJl9rli3HUGWtIxnueJS8jwOwsS3AzuTMnQ6Xl5ausHUM0Lt1ClLr0M02IfM5FYeNn1GFVu4U2pZ1xr2LOPG3b9IRSJYVpYU7pY5S6/EHM3MHYMXO7OGONkrCHS25jayvtiwzEMwX7lS4KnCUptoMs622H892BHfw/Te/D8ApM05hZkVha5H3nbAvZ8w+A4DTZp3GnpV7Zr2euTFrIEOV/7ryr9y//H4Abpp/k1WJKIQQ/SXhjhAjqNhRzC1H3sKvj/o139n/OyyYuaDf92FuoXp588v84v1fEIwHmTtxLqfOOHWIj1ZkY87caexuJGpV7hTWlgWpcMe8rcloyzLezla5A+DeZx9g5ObumOGOK60ly+RIruCe4NtBWZGD6VVDuBp0p3Fi2+zwEooOcjtSLAT/uADMEz5/C3Q1odjtVuXLWBuq3HMNerqhmruj6zqPrTFass6a079Qerikt2b1VyIcZuc99wIw4ZJLUYtynySbfDEjsDErXvLN3TFbuHIF7oqiFLgxK/s69HiHcZtOjxG25At3Nu0M8F7tPnxYsydPzjgqVbljtk5FewcbaolZ0ZK9LUstzR8+mG1XZhuWyRqonFa5oyV0NuwMEIxqfLIl+UOwz8qdwsKd9JXhAJ+1f5YxI2VUVO6sfRnuOxL+dW2qLauPKrJCKncURaGhYjqh5BK13Wmosi/q47JXLmOrfysN3gauOfiaft3+ukOv4+7j7+b6Q6/PeZ2BVu68uvlVfv3hrwFjc9dps07r1+2FECKdhDtCjDBFUTh5+slcNu+yAc2nmTdxHtNKpxGKh3hty2soKPzo0B9l3cgihp5ZubMjtINocv20M61yJ6pFrVft87VlxXtW7sRclCbzBmePCgzTSG/MimQZpmwyh0DXBdrYv74cVR2ak/9EIICefMW5zV1GRzA6uDt86cfQusKoAKicYVy21WhxtFqzxtjcHWsN+pTeXzdDtTHrw+YP2di1kSJ7EV/e48uDuq+hcnT90aiKysq2lTQHmvt1287/+z/ira3Y6+ooP/OrBd3G/L42K0nybczqq3IHChyqPCEZ7uxIPYaeSKB1GCFIh8sIO/O3ZQXodhXzxFev4Z3Jc2nuNnqrcg09BrAlK0dytWXZvIVty+p539Yq9LTKHX8kFbYs35oMILKEOwMZqJy+MtypOglrYTZ0bbA+PuhwR4vDiqdg09vG+vb+CLbDk5fC384g2LKcxAd/so6jr8ooM7By23LP3IHdc+5OVIty1X+uYnXHaqrcVdz3hfuodFf26z4cqoOjphyFw5b7dzRzqHJjd2O/7vvhlQ8DRkh+4b4X9uu2QgjRk5z9CTHGKYpiDVYGOGP2GexTtc8IHtHupcxVZrzirutZZ+6Y83bsqj3rCZfZwhXbmhnuTGg3TnD8XnvGKuJ06RuzRmKosrlJKmu4k1yHXhto48AhnLcTa20FIGR3EXS4BxfurHoWPvyz8fZ/3wczjjXebkqGO9ZQ5TFWudOUp3LHDHfWDa4ty1wN/OU9vpxzjsyuVumutGaW9ac1KxEIsPMPfwRgwncuQ3U6C7pdz8DGDHfM7/l01matfOGOuxzoK9xJtmWlVe4kurtBM0KdNpfx/ZAv3NnYZgQGh+1hhM1WW5YzT1tWiTlQORV6+KP+git3zKqgnpU7VluWI3u4s2xrF2gx6Eq2ulRO71G5k2zLCu4sKExJr9rZd8K+QGZrVirc6edAZS0OSxbCXQfD4+fBg/8FD50Cje8XdvuVz8Ddh8Knf2eT3cEJ06ZxQ1nqxQC1j3CnkModyJy7szuEO1pC47q3ruPD5g8pdhRz7wn3WiHMUDPvd4u/8MqdqBZl+c7lAHxjr2+MigpIIcTYJuGOEOPAl/f4MkX2Iirdlfy/A/7fSB/ObmeqdyplQSASBUXBkZxrAqmWrCp3VdZf3ByTkzN3tm/PGNxbusM4uWitsOV8XNfs2WCzobW3j8hcmNQa9Gm9PuZIq9wZynk78VbjFfruYuM+O4MDHArauQWevsJ4+/D/BzNPgCmfM95v+hAAh1m5M4basvR43Jrf1HPmDuSo3Nm5FrYvLfgxdgR3WOHJWXPOGsTRDr2BtGa1P/I3tPZ2HA0NlC9YUPDtzFYrMxQpqHLH0XflTle490BmU7ZwJ56s2lFLSujSjZ8buUKkuJagsc24jhXu+IzAw6quiWabuZMMd9Iqd3wxH56IOXNnYJU7ZiiRvi3LF059Ty/f2gVdW0BPgN0NJTXZ27ISccizaazn4zlVJ3MnzgUyhyqntmUVWLmjxWHJ3+Huz8FTlxkr24sqweaETW/BX06ER86AbUuy397fCo+dB4+dC4FWmDCbvx72NXxorEtuYEPVUda/WNDzyrctC2B6aSrcSYyxdegbuzbym8W/4X/f/V82dm3s8/q6rvOrD3/FS5tfwq7aufPYO9mraq9hOz4z3GnyNRX8YsuKthVEE1Eq3ZXWcgYhhBgM+0gfgBBi8GqLa/nHKf/AZXNZr/6KXaehtIFg56cA2GtrUdJe+c+3KQvAUVMDqooejaK1tVkzNTwtXUSBreW5Z8qoLheumTOJrF5NeOXKjFBpuOmJhBV6uLJU7viragCYGOpico2n18cHKt5qhFgBr1ENNKDKHS0O/7zYOBmcdCAcd4Nx+eTkdpLtn0I8imvaNGBshTux5haIx1EcDuzV1b0+7pqxBwBaWxvxtjbs3Svgb1+FhAaXvw9VM/p8jH+u/SdxPc7+E/cfdat6j6s/jts+uo2Pmj+iK9JlzbHJRevupu1+Y5DpxCuvQHEU1hqb0BNWUNGzcmdneHBtWR2RjpzXsU/sHe6YLVm2ioo+K4S2doaIJ3RcdpX9643H6wzGCMc0K3jKui3LrNxJm7mTXrljK3DmTiAaQNd1K+jONlDZH05V7mzvCtO1bS1lABXTQFFS4U7MB3YXuEoh0g2BNijKXyWYvjLcrHBd2ZZqay24ckeLw/J/wBu/hvb1xmWeKiMo/txFEOqAN38Nn/wN1r1s/NnrVDj2eqje06gyWvY4vPA/xnUVGxx5NV2HXcqzTxltjnrMeP3V5kig/PNi8NZC/SHZn1c/KneaXAqgE+/KHSKOFhEtwsubX+aJNU/wUctH1uX/XPdPzt37XC6de2mvTWum+5ffz98/+zsAtxx5C4fWHTqsxzq5ZDKqohKKh2gLt2WdsdfT4pbFABxYfaBU7QghhoRU7ggxTjSUNlBTXDPSh7FbaihtoLorudkqWYljyrcpC8g4CU/fmOXYboRC28oSGdtderLm7hSwMevVxldZtHVRn9crRHz7dvRwGByOXtvBAJb6VUI2Jyo6RW2tQ/KYAPFkW1akzJiZ0DGQyp03fw2N74LTC2f8BezJMK5qBrjLIR6GluWpmTtbtqDH47nvD+NV4q5//Yvgx5/0+3DaFy5kw1cWENu+vd+37cnalDVlCora+7941eOxKnoibz8NC88ynm8iBot+3+f9xxNxHl/zOABnzjlz0Mc71OpL65lVMQtN13iz6c0+r9/5xD9JdHfjnDmD0i99qeDH8cf81uwWM2zIN1C5P+FOtlXqJmugctq2LHMNuq0yFe6UurJX0mzcaYQpU6s8lHscuOzG10hLd5hiZ+6ZO6o1cydtoHLUR7G5Cr3Ayp24HieaSAWy2QYq+8KZ32vNmz8z3qiYBpBZuQNGqAIFzd1J3yplhjur21cT04yfI33O3IlH4NNH4Z5D4clLjGDHUwUn/ASuWgpHXg2uEiivh1N/D1d8CPt9FVBg1TNw73z45yXw97ONgDnUATX7wbf/A8ffyJMbn7d+3isB42/VU2R8jy48C3Zmb6csZFsWwGTvZEJFRojQ3bYt73VH0rqOdfzqg19x/OPHc91b1/FRy0eoisrRU47miMlHEE/EeWD5A5zy5Cm8sPGFXpUyT659kjs/vhOAH3zuB3xx+heH/ZgdNgd1xXVA4UOVP2k1/r84sObAYTsuIcTuRcIdIYQYpKneqUxMno85J2fOOcm3KcuUbR16YovxdnMFdOZpNyhkqLKW0PjVB7/i6v9czXde/Q7b/YMPESIbNwHGsGfF3rsI9JOmTpqLjZMuM3AYCrFk+1m80vh8dgb6Wbmz8S3j1XaAU+6AyrSqI0VJa836CHtdnVGFFYv1Gbz4Xn6ZbddcS9Pll2e01xWiY+FCIqtX0/38C/26XTbmUG9Hlnk7Jqs164n/hVgQaozZIyxZCL787X1vNr1JS7CFclc5J047cdDHOxyOq0+2ZhUwd8f30ksAVJz9NRRb7hbInszWJafqxGUzJtTmm7nTs8onm8IGKhuPobW3oyfn7MST4Y69orLPEGlTMtyZVlWMoijUlhk9Os1d4dS2rCyVO9a2rLT12b6YD0+BlTsZM3XS2r7MGTzF9rS2rEhmuOPfngw0coU76XN3+pBe4TLFOwWv00s0EWVdp/EYWcOdRAI2vgnPXAm3zTJCnbZ1RvvVCTdnhjo9Vc2A0/8Mly2CPb9stJctfRTW/Nto3Trux0awUzcPLaFZlSYAzmTIpdZMNyoMQ+3wyGlGK1ee55WPQ3WglxlBXMfWvlubdqW2UBv/XPtPvvnCN/nvZ/6bR1Y9Qleki9riWr6z/3d48fQXuev4u7j3+Hv5/XG/Z0rJFFpDrfzPm//DBS9ewJqONYDxM+on7/4EgAv2vYBv7P2NXfYczI1ZhQxVTuiJVLhTLeGOEGJoSLgjhBCD1FDaQHWnWbnTYw16H21ZkD3ciTUavxw2Vyi0R9pz3jZ9qHI24XiYa9+4lkdWPQKApmv8ffXfs163P6J5NmUBfNLYyfZkuBNtHLpwJ95inNjoya1B/arcCbQZr5ajw/7fgP3O6H2dKcnWrK0foagqjuRg6HxDlRORCK2/MgIjraOjXwOYE9Eo0WRQFlq+rODb5RIz16Bn2ZRlctUZJ3eRNg2mfR4ufNkItbQIvH9fxnXjHR1sv+EGgh9/DMBjq4315/8987+tUGO0MefuvLPtnYwBuj3FWlsJLVkCgPcLJ/TrMbKFKBPcuWfuFDJQ2Wwhy9eWZausBFWFRMKq2NGSa9BtlZW9Nnj1tCk5b2f6BCPEqClNhjvd4YyZOz0rIay2rLQ5LYGQD08yW+1r4K+qqL3WoSf0hFW5U+RIhRLpM3eAjE1ZQOa2LEhbh96/yh1FUazqHXPujlqcnLkTCBjtmS9eD7/dxxiO/PHDEO4C7yQ4/ka4eikc+d3soU5PNXvD2X+Di/8Dc74EexwLl7wJR30fkhuYXm96nW2BbdbXgbWJrLQMznnMeP6dm2HhmdBjXX1YS27L6mPmDoDeYPx/M9ih6oOl6zobOjdw/7L7Off5czn2sWO5adFNfNL6CTbFxnH1x3HP8ffw79P+zWXzLqO22Gg7VhSFY+qP4akFT3HF/lfgtrn5qOUjznz2TG545wauef0aNF3j1BmncvWBV+/S52QNVS6gcmdtx1p8UR8eu2fUtbcKIcYuCXeEEGKQGkobrMqdRN3EjI/11ZYFaeFOcttWIhi02i6aK6AjnPtkzz1nDigK8ZaWjDkcAO3hdi586UJeaXwFh+rgjNlGmPHEmid6ba3pLzPccWUZpqwldD7dkgp3Ylv6txo2H7Mty5ZsT+ksdOaOrsPTl4NvO1TNgi/9Ovv1zHAnOVTZWcDcnfYHH7L+7QDCywofThzduNHadBRetrzg2+VitWVlGaYMQPNyXFuMgCYSroSvPQpODxxxlfHxD++HSOoEvv3+++l8/B+0/PwXbOnewjvb3gHgq7MLWxc+Evaq3Iva4lpC8RDvbX8v5/X8r70Guo573lxj9lU/ZAtrzMqdzkin1eZj6ldbVp6ByorNZgQ8pObumCGPUl5qhSW5Hsdsy5qWDHdqk+FOS3eqcieux4lokYzbmeGNllbREvenjtMMf/Ixq3PMKqb04C19oLI5c2dyuRH4FAeTm7KS4Y65Ccwf9ZPQE2nhTu+KqZ56Dh7uFe7YjVArseZN+MNR8O5d4NsG7jI48Jtw3nPw3eXw+WvAlT/QymrygfC1v8M3n4LqzOG+f1v1N8D43nKqTivcUUtKoGQifOOfRrXQtk/gT8cb7WHJr7NCK3cAqvY+AAB9Y2Ovf+fhFtWifNT8Ebd9eBunPHUKX3n6K9zx8R0s2bEEHZ29Kvfi/x3w/3j5jJe587g7+fyUz2NTs1fUuWwuLpl3CU8veJovTP0Cmq7x1LqnCGthjpx8JDcffvMun2PTn3DHrNqZN3EedlVGoAohhob8NBFCiEEqdZZS16UCGu3lNtJPE/vVlpVchx5NVl+EPXYCRfnDHbW4GOf06UQ3bCC8ahUln/88AJu7N3PZK5exxbcFr9PL7479HQdUH8B7296jyd/EcxueG9TMlOimTUD2yp01LT4CUY32UuM5D23ljtE25K6thdZ44QOVP/gTrHnBaIU44y/gzL5enskHGX+3b4BAW2ruTvL59hRraWXnH/4AgHPqVKKbNxNauoyyr3yloMOKrFmTuq+mJuLt7diTJ+8DYX7tZFuDTutn8PBXcHk6gWoiXQ50ZzEKwJz/MkKvtrWw+EE4/Er0WIzOp54GILxiBf959wEAjph0BPWlw7NOeCgoisJx9cex8LOFvNb4GsfUH5P1er6XXgbAe0L/qnYg1VqUHqKUukqxq3biiTht4Tar0iCWiFkn3/m2ZVW4jWHA+dqywGjN0nbutMKdeEeygqc09TWdaz39prZUWxaQ1pYVwePwoKCgo+OP+TOqQNTi3pU75ipt3e3KGCKfS7GzGEKpcMcMohQU3LbUY5kzdw7do5J/fryVukQzKPSq3NHRCcQCeD2FV+5Y4U7y8faZkAx3tn0AT1yE+tLzQBmJUAhsLpjzRdjvTJj1BWN48zBZ3b6aD5s/xKbYOGvOWTyx5gmKIkb4ZUtuKqNqhlHB88jpsGOV0R726k/hsMsIJQPZ9M9jLicfezFb+Bvl3RqPfPAHLpxf+IZNXdd5ev3TLGldgtfpxev0UuospdRZar0fjAVpCbbQEmyhNdhKa7DVers9nFmF6lAdHFJ3CMdOOZaj64+2vmf6Y1LJJH5zzG9YtG0Rv/v4d1S4K7j96NtxqIUNRx9K6RuzTHo0CjZbr7bPj1uMasgDag7YdQcohBj3JNwRQohB0hMJqrqMOStNpTH21HXu/s86JlcUFdaWNTmzLSvaaFSJ+KpLAH/ecAeMuTvRDRsIr1hJyec/z5LWJVz52pV0RjqZXDKZe46/hz3KjS1J5+x1Dr/+8NcsXLWQr87+6oBf2Yxsyt2W9UljJwCe6VPh46GbuaPrOrFkRVNRXQ0s3Up7oW1Z5rDgL/wU6ubmvl5RBUyYDTvXwNaPUuFOjsqdHb/5DXowSNH++1PxjW+w7dprCS0rvL0qPdwBCC9fTslRRxV8+55iW3JU7uxcBw+fCsGdOGfNhZc7SPj9xFtbrY1tHPH/jLki794Dh1yC/6130NKqwba+9AzMHX3rz7M5rsEId17f8jpaQuv16r/W1UXggw+AgYU7vljvShxVUalyV9ESbKEtlAp30mfM5ApdINWWlW+gMhhDlSOffUa81fhe0NqNnw+RUjfEjCqYbJUAMS1BU4cRbvRsy2rpDlutU/6Yn0AskBFImwFD+swd3WcERYo3R1Dag1m5Y1YNWpuyHJ6Mn0P+5MydujI3c6t0yszNVeXG96LL5sKpOokmoviiPrz9mLljti8V2Ysg1ME+jUb1xFrfZiKb30a1GSFVwlUL3//YqNjZBcxZO8c3HE9tcS0ehwdPxHg+5jBrAOo/B1ctgY/+Au//Abq3wks/JlRbB0UOiuJ9h90lVTXEq0qxt3XzypsP8eW5ZxW8jOGBFQ/w28W/7ffzS1fuKueoKUdxTP0xHD7p8IyqrcE4fNLhHD7p8CG5r4HqWbkTWrGCzV87B3Qdx5QpOBrqcdY34KivJ7LlHSYX6RxUvt9IHrIQYpyRcEcIIQYpvmMHdk1HU2Cjs5v1OwLc9tIaXHaVyr2T4U6+tqzknJ7Ytm1GgJGsdInUVgD+vDM4wAh3up97jvCKFby8+WV++OYPiSai7FO1D3cdf1fGSdqCmQu465O7WN+1nne3vzugX4YToRDxbcaA4ezhjnG8dXvNBIxqkvT1xwOldXRAzAhzSqfUAVsLa8sKtkNXsjVs/3P6vv7kg41wp+kjnFO/AGQPd0JLl9L1tFHZUnP9j7CVlwMQWbWKRDSKWkA1Q2TNWuMNhwNiMULLlg043NF8PrTOTuPu0gd7t280Zob4W6B6H9Tzn8b5/DeJrl9PZM3aVEvS3LPgtZ8bbSjLHqfzn+8CYCsvR+vsZJ9VQT6YP5mjpgw8fNpVDqw5kFJnKR2RDpbsWMJBNQdlfNz/+usQj+OaNRNXjrlR+ZhtVmYrk2lC0QRagi0Zc3fMcMdj9+RtvzDbsnwxH7FELGflgTlU2WrLSq5CD5U4oCN3S9aW9iBaQqfIYaOm1KhCqU2buQNY4U7PjVlqsu1KCwSseTyK3whdbH1syjL13MaVbZgyQHdy5o7X7eDzE/wQgICzimJnaiiz1+mlLdxm/Dv0Y+ZOKJas3GleBrfvSV08TEXDZDpsNtbut4AZR50Kr/2YhGbfZcFOZ7iT5zY8B2AN/zXCHePjas+WN08lHHUtzL8Clj0Gi35PmA7AgfuVm2HDR3Dk92Di7JyPWTpnX4KLFlHdHOaOj+/gls/f0udxPrv+WSvYOX3W6XidXrqj3XRHuo2/o934oj6K7EXUeGqo9lRTU5z821Nj/SlzlY3btd9muNMR6cAX9RF+/XWjcgejBTe6cSNmY+Ol5o3+/G3WNzQw+fe/wz0797+ZEEIUQsIdIYQYJHPeSlspbA5sYcMO4+QlokXwJ1/hz1u5U2esT00EAiS6u4kmhylrkyYCWwqq3AFo+/Qjrnn9dXR0jplyDL866lcZW2rAOClaMHMBCz9byN9W/W1A4Y4ZdKhlZVagke6TLZ0AzJk7G1QVPRwm3roDR011vx8rndmSZauqoqLMeF4dhWzL2v6p8XfF9MJO2KYcDJ8uhKYPcZ50MWD8G+vRqNV+ous6LT//BQBlCxZQtN9+6LpuBSGR1asp2q/vV2TDa43KHe9xx+F78UXCSwc+VDnWZLQC2CorsSW3G9G1FR461QhsJsyBbz4NnkpcM2ca4c7atZR8/kjjunYXHHYZvHIT8Zd/i/914+yy9uab2Xr11ey7WeesKafmnIExmjhUB0dPOZpnNzzLa42v9Qp3fK+8AoD3C18Y0P3nmqFjBqnp4U53rO9hymC0d5ptUV2RrpytnD3DHbMtK1Bsg47UTJqerJasCcXWyXVtmRHyNHcZ4U6Jo4QWWjKqjSBtYHIshh6JELYlcIeNWVH20sJCkJ7buLKtQYfUzJ0Sl52ZXqOKabtay8y065jhTne0u18zd6zKnfaNEA+jVO/D3qWVvBPYzIq9v8Rst/F1knMV+jD4x9p/ENEi7FW5F/tP3B8wgkBroLI3R7WXw23MAdr/G4SePBX8m/Focfj077D0MTjkYjj6B0YY1IN71kyCixZRvxMe2vAcZ805i/2r9895jIu2LeLGd24E4Jt7f5Pvf+77g3nK41axo5hKdyXt4Xa2+LZQutYYWl118cUUHz6faOMWYlsa2bzqA3asW8akThVXJEF082ba7/8Lk371yxF+BkKIsU4GKgshxCCZJ9Wt5QqN3Y1sTm6kUWzGCYJDdeQ84QJQi4qsIamxbdustiy13qjo6TvcMQZzOls78IQSnDn7TO449o7USVMiAcufgHuPhN8fzDndfhQU3mx6k83dBWx2alsPb98BD/wXvHwj0Q0bAHBNm9brFdiuUIx1rcaJ4QF7TLCCqx3rlvHUuqeIJfqx3aoHc5iyvbqaco8RsnSH48S1PlaPNycHHOdrx0pnrkPfuhj7hCoUjwc0jWhTamhy93PPEfr0UxSPh4nf/S5gzHpxzzUCndDSvocqa36/VQFVfvppxu2WL++1qahQWdegL/qdUbVUOQPOe8YYzEraOvS1azPv5OBvgauUro+3QTyOe+5ctn1uKtsqwKHBya39n4kxUo5vOB6A17e8nnF5IhTC/9bbwMBasiAV7vT8vs4W7hQyTBnAptoodRn3l681yz7RDHcy27J8HiXv42zYYfw8mj4hFaaYbVmtvjCJhN6rusakejyQ/F5P+P34oj6Kk/OQCw13zPYbM9wx/+45BNhsy/K67cxyGJ/HddHMoCtjHXo/Zu5Y27ISCVhwH1z2DvvMOAmA5W3LU6vQg8EBfx/2RzwR59HPHgXg63t93fp5mh7uZLRlZaOqhB3Gv6P7pF/C7JNB14zNd78/EN7/ozV42eScMQOAA4PGz4NffvBLYzh1FqvaVvHd/3yXuB7n5Gknc83B1wzoue4u0luzzJ+vnkMOoXj+fCrOOpPqa6/l+Yv24QcX2Hn1z9+i4cEHAeh+8UVrjpUQQgyUhDtCCDFIZuXOjjJo9DWyMfkKuWJPVe30VYaevg49ttmo3HEl57301ZZlKy1FnWyEKDNaVK45+JpUdcX6/8CfjoF/XAAty6BtLVPfuYvPB40AauHbP4Vkq4JF141ql9d+BncfZpwgvHITbH4b3rmTyPv/ArK3ZH2arNqZWuWhqsRlrRJ/9LU7ueGdG/jzsj/nfS75xJKVO47qasqLUi0rXaE+AiOzcqe2wHCnem9weCDSjdK+DmdDAwDRzZsA41X91ltvA2DCJZdkVCQV7Wc8RiEVOGZLlr2mBs8hh4DdjtbWRnz79sKOs4esa9C3LTH+PuaH4E0FM1a403MdsrsM/aBv0bnBCADKTzuNx9Y8xuJZxtev8s5HAzq2kXBo3aGoikqjr5HmQLN1uf/tt9HDYRyTJ+Paa68895CbGX70nKFjVujtCKWChlxBUDZma1a+QNes3NF27CQRCqGHjcCiO5mR5Ap3eg5TBqj2GqFATNNpD0atgc9m8GJSVNUKPjSfD3/Mn7aqu8C2LEePtqxk5U7PmSvmQGWv206dbvy7rY5W0Z5WpeexG1+foXgIrJk7bUaQnUcoYpw8u3UdZh4PPdahq55k8KXr6MHBbRQsxGuNr9ESbKHSXcnJ00+2Li92FFNkrpn//+yddXQbV8LFfzNiyTJTzGHGBtqmaRsoU8oMKW8Ztu22X3e3u2XadrfMtGXulpMUkqZhZjI7MZNsizXfH08zkm3Zlh0o6Z6TE1kaEo3m3XchiiYyrS0rcwyc8w6c/4k4jzkb4Ktb4JmpsGOetrxpkPj+Z9cEsBlsbKzbyGc7P+u03XJHOVfNv4o2XxuTMydzzyH3IEt7MHQIBCCKXKDfMrRQ5foiLYjfNGRwu2VCYcoTsE6ZjGnwIBSXi+YvvtivxxpDDDH8/hAjd2KIIYYY9hAeVbmTIFHvqqewTsw2a+RON3k7KlRyx1NcgrdSDGhs+SIEuSflDkBzfzHAmdKcJhQ7u9bA67PhjdmC3DDaYfr/wcnPQf9DObdJHNsnVUtwPDoU/ncDbP4ffH07PD5G1AAveFi0ssh6GDAdRp4sjnH5t0BkcmdVMG9nfG6iWCZXECOtxYJEeHPzm32uYVcDZPUZGeh1MnazcBY39BSqvFtV7oyLbkc6PWQFG0zKl3cKVa598UURRJyTQ/JFF7Zb1aIqd6IIVVZndU1DhiCbzdoAwNlHa5anvINyR1GgSlQ8kzGq3bLh5I7SYUDsip+Op9mApFPQjbTzeeHnrBgsLhdafvgRxefr0/Htb8QZ4xiRLCyLyyuXa/c75oZasvqa/dGTLUsNUg9ftrswZRXRhCrr08R33VdTE6pBNxpp1AmSp0tbVq343qk16ABGvUxqnFDBVTa5QgRMB1sWhEiGQEtrULkjlC26+OgqwdVtq99/Nf+mky1LU+4YMDYLors0kM76itBroqp9nD4nWIPnV8UPrsZuj8HZKghii86skUIqubOzcScuoxRSKO0Hcqdd/bkulNElMneCr29XtqwwqIokreFs4HS4YiEc9y/x+tRuFS1bb54ONdswDRLKnUB1DVcPvAiAx1c+3u59b3A18Kd5f6LWWcuQpCE8Pv3xdsfYI/w+qN4sKtu/+gu8fAw8kAv3pMGjw8Xfn1wFPz4kbGRly6ClRpy3fsNQyZ3mHVvA70e229GnhyYAGlwNFDYJ9ev49PFIkkTiaacB0Pj+B/v/gGOIIYbfFWKZOzHEEEMMewi1wrwtzQY4KW4sBVKR9eJCubu8HRUqudO2bBkEAkhWK4lZgjzpWB8bCUUZEqOBEdWyUOls+FA8IBtg0qUigFPNphh7FgfVFzPw6/PY6W3iY2OAC1a+AitfCW1QbxEz28NPhCFHihYpvxfqi/A0BhUiBQWdjkNtyhqfJ2qdjUHlTkaQn2pyN/HBtg+4YOQFPT6njlAzd9QL5SSrEYfL132osrsF6oLqlGhtWSByd0oWCXKnQLwPnpISPOUV1L8sXqf0W29BNrWvRzYHc3Y8hYX4HQ509q4HvmpTlkq0WEaNxr1pM64N64k/+qjojzUITbmjNmU1loDHIerfU9vPHBvzcpEMBhSnE29FRWgdoPGrHwGw5zj5Ye1/cPqdeEb0R5dYi7+xkbZVq7BNntzr4/slMClzEhvqNrCiagUnDDwBxeOh5Yfg8zuyb3k7gMh6IbrMnWhtWQBJpp7r0HVhmTu+oCVLl5SEI6iI6YrcKapVbVntlTIZ8WZqWzxUO1waAdVRuQOCZPBVQqDFQUuyX7NlydEGKndQ7mhtWfr25I4jGKgcZ9JDQzEApUo6GyqaOGyIIGQsBkHutHnbQG8UWVquJmHNipAxo8LZJghisy1DI3HSremkWlKpddayrWEbFqtV5J+1tkKQSOsNlu1eht1oZ3hK96qwTXWbWFW9Cr2k54yhZ7R7zKK3YFVf394od8Itbjo9TLoERp0qiPqlz8L2b2Hnd+gmXow+OR5ffTMnufN5155HiaOU59c/z00H3ITT5+Sa766huLmYfrZ+PHPYY9hdDmgoBWcjeFoh4AW/R/wu+NXbHqjdLiYUKteDzxn5gB27xL/Snzs/ljUeTn8Vkgp6fN6/Rqjkjm9H0L48eHA7Enl1tWhoG5AwgCSz+L7Hn3gi1Y88imvTJpwbN2IZOXI/H3UMMcTwe0GM3Ikhhhhi2EOomTv67GxgB3WeCiAVSScGdV0Fo4ZDI3eWC4WBMS+PRIsYpDR7mvEFfN027axIqGM0kLGtBDYsAyQYcwZMvyPiRbKUXMC5E6/nn4v/yVvZgzg3fwi6ihWQOxmGHQ8DZ4Cx/aALnQFl9rN4npwNgMm1FjhSezgQUFgTtGWNz0sUx54qBhsZjQrnDDuHt7a8xWubXuOsYWf1bhYY8FYHyZ0MldwxUFrfg3KnagOggL0fxPUi0FnN3SlfgTH/cAA8xcVUP/IIituNdcqUiGG8+uRkDDk5eMvLcW3YgO2gg7rcRUi5I4gX8+hR8N57ONdviP44w6DVoKu2LFW1kzYUdO2blyS9HuPAgbi3bMG9fbtG7gScTs0akDjAyZb6zZAQz0G5hxB3WANNn35Ky3ff/2bInYmZE3ll4yuacqd12XICzc3oUlOxjBvX5+2qCgfVxqSiW3LH0DO5oyp3uiN3VOVOoLUV7y5BLOuSk7slkVxeP7uaxEA73JYFojFr465mKpvcnQiYcMi2YGNWSwsOjy/MltU75U7HQOUubVlGoFF8pkuVdNaXh5Q77WxZIFQ4riZorRWf9y7gcgqi3Gzvp90nBa1ZP5b/yMa6jUy22Qi0tuLvQ6jy+9ve55+L/4mExPkjzue6Cddh0pkiLvvW5rcAOLLgSNKt7c9N7W1ZPb++Tn8EckeFJRGOuhcOmAPf3gnbvoJlz2MyJOPDjP+Fi7l1NFydkcob619h9uYfeMy3m3WSi/iAwrPb15O+oY913QabINX7jYN+Y8U/Wyo0lgrirqEo+H+J+L+pHHathhdmwBmvQ8EhfdvvLwiV3DGUCAWuSt6rUC1ZEzImaPfpk5KwHzGL5i+/ovGDD2LkTgwxxNBnxGxZMcQQQwx7AMXnC9mocoXCQzLWYjbISEHljk2X2ON2DNmC3FGtAMa8PBKMCUiIGb/uBntNbXUsihM5LfomHf7cGXDlQjjl+W5nP48fcDwJpgQqXHX8OOksuG4VnPwsDD++M7EThF9OJeCVAQXDuse1mXWAorpWmpxeTHqZ4f3EbP4XbnEhm9Ns4OaJN5NuSae6rVqr/u0NVFuWIajcUUOVG7pT7qiWrGjzdlRkTxT/V2/CmCX251y9BsfXX4Msk3HH7V1aejRrVjf2KkVROit3xgTzejZs6GSV6gmK348nONA3qrasLixZKkKhyqHcHcfcuQRaWjDk5GCdNovtRkEKDUocRNzMGWKZ777bL2GzewMT0iegk3SUOcqobK0MWbJmzEDS9b31q0dblqtOe416o9xRM3e6+77LNhuSWdhvVIJQn5REs7vrVq6y+jYURahhVBuWioyEUB262mjVrS3L0T5QWe5GnRaOTm1ZQXtWOCHh8QVw+8RnP95dDYqfgGykmkQ27OrClgVRhyq7gq+RJSG33f1a7k7txlCoci/Jne9Lv+eeJfcAoKDw+qbXOeN/Z7CxdmOnZeucdXxZ9CUggpQ7Iqq2rCC8AS++gCDEIpI7KlIHBfN4PoaRp2DKEa+Zp9nAtLY2prY58UlwnmcnP0guTIEAT1ZWMcAdtKdJOvE6pwwSJE3OJMifCv0Pg0FHwNDjYMRJoqL9lBfh6uVwexlc/DUc8wCMOxsyRgiSPWcijD4NDr0FTnoKLvocbtwAN24URFBbHbx+Eix/qdvn/muESu4k7xLfoY7kjqrcmZA+od39qjWr+X+fE3B2oXiKIYYYYugBMXInhhhiiGEP4KuuBp8PDAbS8sSMsWyoY1B6HFaLuEAL+GzdbQIIKXdUGPNy0ck6bSa/y9wddwtrPziHZptEo10MJt1j7oDMnmdaLXoLpw4+FQhlP/QEd1GRON4EA7K/BT65WgsxVS1ZY3ISMOhkGlwNvNsiLDDWFi86p1ezY7284WX8AX9U+1Sh2bIyMgBItgXJne7q0NUw5d5YsgDi+0F8DigBjKZGAC24NvGM0zEP7VodYA6GKjvXd92Y5aupwd/YCLKMKdhcYxo4EMlsJtDaiif4OkcLX1UVeL1gMGivj1AtARmRZ4EjNWY1fvgRAAmnnIx06E3sMIjXeLAhkbipU5EMBrylpXh27uzV8f1SiDPGMSIlmLtTsRTH/PlA3yvQVXRF2Kj5Wk6fU1Om9CZQWbVpdEfuSJKkhSqrBKEuKanbynXVklWQau1ESmYEQ5WrmsLInUjKHbuaudMSDFRWM3f20JYVlrmj5u0A2NqEIlJJzEdBprzBqX3XO5E7quW0LaSYigSn2tCVmN/u/pGpYaHKfSB31lSv4dYFtxJQApw6+FSenPEkqZZUCpsKOffLc3lqzVPtmgI/2PYB3oCX0amjGZPW+dwUp5gwBE+PPdmy1LwdCMvc6Q4DZ8Dpr2CcfSsA7pRZSDdv5daZj6OXZBw6GRmJB0dezviL5sEN6+EvZfC3Orh1J1y7Eq5YAJfOgzlfiha+8z6As98Sapuj7oUxp0PaEJB7SaAmZAsyaNRpEPDBFzfB5zd1avv6NSPZnIxVbyW3Rnw/wsmdNm8bm+o2Ae2VOwDWAw/EkJNDoKWF5m++2X8HHEMMMfyuECN3Yoghhhj2AGqYsiGrH3nBAYNsrCU/xYbJJAYHTlcU5E52dvu/gw1N3Q72Wqrh1eNY1bBZ/JkfHFhu7DxT3BXOHnY2OknHssplbK3f2uPynqJiAIzDxwrJfclPIsuBsDDlYN7OO1veoVHvptUmLvC9ZaWcNuQ04o3xlDSXMK90XucddAHF49HCY1XyItEqVCXd2rIqVXJnbNT70pAj1Ds6xxZNnSDb7aRdd123q6nKne4as1RCxZiXhxxUYUh6PeagHD+aQOZweNS8nayskCKlUiV3ulDuDBrU7lg8ZWW0LV0KkkTi7Nk0pA6kVi+2NXDz18g2G9aDDgTAMf+7Xh3fL4mJmeJ9LF78Lf7aWmS7HduUvtvKFEXB4Y1M7lgNVo3EUK1ZfQlU7o7cgZA1SyN3wmxZkUgktSmrf2rnY8hMELahyuYeMnfiVFuWgxZPS6+VOx0DlSPZstS8HatRh66pWOw3pT/5KYIAUtU7XZI7rd2QO55WXH5BDpmTB7V7SCUAi5qKUCzi9Qi0RheoXNRUxDXfXYPL7+LQnEO588A7OSz3MD4+8WOOKjgKv+Ln2bXPcu4X57KjYQfegJd3t74LRFbtAMR5Q5fnKtnUFdTXQJZkjHL0Vlft+79jJ9gzGTDkeC4efSmyJPN/B97JzMnXi/NmYh6Y47WMon0OgwVOfRFm/g2QYMVL8MbJ0FrX46q/BkiSxABTNpmN4m/T4NBnbX3tenyKjwxrBlm29hM6kiyTeJqYbGn8IBasHEMMMfQNMXInhhhiiGEPoIYpG7NzyLcLckcy1lGQYtVsWU0tPc+m6uLj283QGvPEttSA1U6hynU74aUjYfcaVlvFeqZxBwDg6kVmS6Ytk5l5MwF4a8tbPS6vKkqMQ0bAkXeLO+f/A2q2sTHYZjM2JxGnz8nbW94GQJ8jiCtPaRk2g41zhp8DwEvrX4ra3uOrEXYLyWBAl5gIiEBlQAtU3lK/hUeWPxIa8Pncoq0Fem/LAo3ckXatwjR0CACpV1+FPrnrwFYA8/DhoNPhq67W6ts7Qq1BNw0Z0u5+yyhBxERTpR4Or9aUFbSbeFqhXgR6dknuBLN+PIWFKD4fTR9/DIDt4IMxZGWxo1HYtbK9PmwrX4PGMuwzxGel5bvfDrkzKUPkJ+kWihr3uMMPRzL2Lu8pHC6/S7PBRFLJdMzd6Sp8ORI0W1YPrU+qckdtcNMnJ3Vr/yoKNmX1T+lst8yIDyp3ml3dZ+4Es1+0tqxeVqF3VAWp39PwQOXwGnTN8plUwKhsQXqpjVmdyZ1g8HF35E7NVpxBgsISl9HuoVRLKhnWDBQUWg1CiRho61m5U9NWw5Vzr6TJ3cTo1NE8fOjDWjZaojmRRw57hIcPfZgEUwKb6zdz5udnctuC26hx1pBqSeXI/CMjbtfmFsfpNul6tA9qTVk6c6/a31TFoK+6Gn+z+IxeM+4aFp+9uFPA836HJMG0m+Gst8AYB8UL4YXDQ1bTXzlGtSQC4E2wtfu9WFUdzNtJnxDxvUo4+WSQZZwrVuIu7J16M4YYYogBYuRODDHEEMMeQQ1TNmRnkxcv1DayvpXMRAUv4oK5pjG6gWS4NUttmVKVO+1sWRWrBLHTUIQ7MY/1ZjHTnDPpMEBktvQG5404D4AvCr/osXZdJXdM/fvDxIuFxN/ngo+voKZJDIZyky18uuNTGtwNZMdlkzJIqFG8ZaLW+Nxh52LRW9hcv5mfd0VoS4kAb1U1IJqy1IviJE2540FRFG5bcBuvbXqNr4u+FitVbxbSfnOimH3uLbRQ5eX0++c/yXroQZIv6LnlS7ZaNSm+c11ka5YWptwhj0Ft23L28j30qGHKat5O9RZAAVs6xEVu/DFkZSFZrSheL56iIho//gSAxFNPAWB7gzjGwXo7+N3w/b3ETZ+uPS+VcPu1Y3z6eHTIjNgQJFlmzdqj7akkiizJnZqeIGTN0pQ7Xah8IiGazB0AfVpQqRIkR3VJyVrmTkTljmbL6qwCyYyQuRNJuSPHBe1Kqi0rqNzprhEuHKr9St12JFuWSu6EN2WRVMDoILmzoStyJ5rMnZotOGVx7ohkX1Jzdxp1grXqSbnT4mnhT/P+xK7WXeTH5/PkzCc71boDHN3/aD4+8WOmZU/DE/Awt0TkPp0x9AwMHYLOVViDTlO3uefL9IhNWVFAFx+vqSDdO4TNUpKkiM/hF8OwY4X9K6lAhDC/eARs+vRXX5c+qD448ZDd/rsRKUw5HIaMDOIOPVSs+2FMvRNDDDH0HjFyJ4YYYohhD+CtqADAkJODzWBD8ouBlWyqxB0Qg5eK2uhyB1RyRzIa0WdmAhHInR3z4NXjRbZEv7Fsmv1vvAEfyeZk8iaJwFtPSYk2ExsNxqWNY0TKCNx+N69ufLXbZT3FxQAY+/cXs6snPgmmBNi1itOc7wOQEmfgtY2vAXDBiAswBVVIqnUo0ZyoZf28uP7FqI7RVx0id1SEApW9LK9cTmGTUKpUt4llqQwSK/3G9M1S0G8syHpoqcKUrCfhxBOR5Oh+Ni2ju7dmaWHKHZU7QUuXe/NmFE83WUIdoNWga01Z3eftgLABqNaM+tffwLd7N3JCAnEzhTpHVe4M6i/+Zu07GJQqQUApCo4ffoj6+H5JxBnjmO7pT0YjBIx64qbtWQOPGjYcZ4iLOPveUbmjLh9N5o5K7jS5m7pdTq1D1/5OSurWllXUHbkTVO40tnkxSIIgULfVbh92VbnjoMXVrBEQci+VO63eVhRF0exZNn3omNTMHbvZEJHc6Vq5o2budGPdqd6MS1XuRCBC1NydaoPYpq8L1R2A1+/lhh9uYGvDVlLMKTwz6xmSzV0r+tKsaTw18ynuOugurHorSaYkTh9yepfLm11CPdRm6vm8pb4GUeXtdEDImrW9hyV/QaQPh8u+h4Jp4G2F9y6AJw6A7+8XCtZfIbKqhb2wIj3UcOkL+FhbI2zCXZE7AImni2Dlpo8/6dVvQAwxxBADxMidGGKIIYY9Qrhyx+3z43OLC/xan8ivUQI6yuoUPL6e249UcseQm6uRCKotq8HdAGvfgbfOFBe4A6bDRV+wyiGUNBPSJ6BPTsKQI5QbvVHvSJLEFWOuAOD1Ta9rJElHKB6PljFk7C+awUjIhmMfBuBa3UeMkIpZW7+Q8pZyEkwJzB40G2OuUM2oyh2AC0deiF7Ws6JqBWuq1/R4jL7q9mHK0N6W9c7Wd7T761zBAZ4WptyHvB0Q2Q9qMHX58l6talYbsyJk5yiBAO4dgjjpqNwx5OaiS0hA8Xpxbd0W9f485R2UOyq5kxnZkqVCHdw1fvghAAnHH49sEkowjdzJPQRGngIoMPfv2GcI9U7Ld99HfXy/NKYXCgJh14h0ZOueKRN6sllpjVlO8TnsS1tWk6eJgNL1OUPfgdwJJMbhCXgi7sfp8VPZLGQ2/VM6kzsJFgMmvTjfuD3iOxVRuRNWhe5ubtTu1/UQ+KtCtXwFlAAuv0vL3LEYQkSLmrnTyZaVJcidsnonjW0eTV2iEkShzJ0elDuSeJ4WXQRyJ6jc2ZAkyDjX5s0RNxNQAty56E6W7l6KVW/l6VlPaw1J3UGSJE4dcirzT5/PJ7M/0T4nkaCSO05jzwqVvip3IPT9/9UHpFuTRcvXwdeC3gL1O+HHB+CJCfD84bD4aXB0TcbtbyRUiHPEjiS3dt/W+q04fU7sRjuDEgd1tSpxhx6KLi0Vf309ju9/2NeHGkMMMfzOECN3Yoghhhj2AJ5dQrljzMmmrN5JwCMsGdsag4N6vx1/AErrew7nVIkZY17IQqTOBjfUbIGPrxA2o9FnwDnvgcmu1aqOTx8PgHm0GMw7N/Qum2B67nSmZU/DF/Bx35L7ImbheMrLwe9HslrbKWgYcwbNBUdjkPw8bnqWNza+AsBZQ8/CarBq1dye0jJtlUxbJicMOAEQ2Ts9QVXuGDLClTvC0lDvquW70lAGjJZPpNWg95HcgVAlevnKXq2m1ZqvX9+p1txbVobiciGZTBjz29vFJEnCrObubIg+d0dT7qiZOz3UoKvQyKXgMaqWLEVR2NEgyJ3BSYNh5l9BNsDO+cQNEeRB688/E2iLLnT2l0b/NWLgt2DQns+E99R+Fa7c8Qf8WsZMb8idgBKIqJ5RoQYqq3AF681lSe5kq1HDlBMsBpJsnS2ikiRp1qwWp1AZtnhbOp0DtLYsRwsBhxi8KmZj1PlFFr0FOUiutHpbNQIpPFBZVe6kG1zgDKoVE/NJsBrISw6GKlc0d5O50zW546/egqcbW5bWqpYgzh+uTZsingcfW/kYXxZ9iV7S89jhj2nrRYs4Y1y3Kh8Ak0tUZbWYeiZ31MydvpA7xkEid8e9fUev193v0BngyHvglu1w8vMwaJaoZ9+1Gr65Hf41DF6fDVu/+qWPFGNJJQDr45u0VsiVVeI3ZHz6eO17EAmSwUDi7JOBWLByDDHE0HvEyJ0YYoghhj5C8XrxVYpBoyE7m5K6VgIeMbBbXSNIF5MsZpx31nQOKO2IhBOOJ/7EE0i57DLtvkRzIgANwYE2Y8+Gk58DvZGAEtDIHVXmbRkVtAP1sm1JkiRun3I7Jp2JpZVL+br4607LaGHKBfnt7SiSxMYJ/6BWiafJXMWG+k2YdEbOHnY2EGr+8u7ejeINNVvNGTUHCYkfyn/Q8l26Qnjmjgp1oNpi/Am/4kcvCQl8vaseAv6QeqW3NejhCMvd6Q1MAwciWSyi1rywvRJKy9sZODBiWKqm+okyVNnf0qo1iRlyckQeRRS2LGivHDINH455hBioVrVV4fA60Et6+sf3h+QBImMJMG1/DkN2NorbTevixVEd4y8JT3Ex+qIK/BLMzWlgd8vuPdqeStZ01X4VTu6EBxPbDT2TOwadQSM7usvd0ae2J3fURro4Q1yngWN3eTsq1FDl5jaxHV/ApymBVKgKnUBLC4Hm4POK67kJUIUkSZoFq8XT0m2gcq4UtFba0sAk9htuzeoyc6ct+N3vCLcDd3OIXI5EhCSZk8iOy6Y8FRSDnoDDgbc0pDb0BXw8svwRzbr6z6n/5ODsg6N+/r2BwSnOky3GQI+h83tDuaMqCX8TMNlh7Jlw3odw81Y45mFxnlYCUPg9vH0WzP1b5M/BfoC/qQmlWlgyi1P8mk1Y+61O79qSpUJtzWr96Se8u3btoyONIYYYfo+IkTsxxBBDDH2Et7ISAgEkkwldaipFtSFyR5vdNwhbVTTkjj4tjeyHHsI6Ybx2X7IpqNxxB2exJ18GQctWYWMhzR4xiz00eSiApvpwbuxdIC9Arj2XS0ZfAsDDyx/WskJUaGHKBf07rVvhjeMKz428mCCe70kuhRSfV3tekskEfj/e3aGBdf+E/szKF+G2L23oXr2j5l/o08NtWQbAjy5hqdjnoJMAqHfWQ90O8LaBwQopXUvge0SwMYvda8EXvepD1JoLoqQjSeNS83Y6WLJUaHk9USp3vBVCtaNLTBS5KM0V4GoSeUGpQ7pdN7ymN/GUU7TbKtmWH58fCn097FYw2pEq1xE3RiiEHL+B1izHvHkAlAyy02qRWF7VO6Ku0/ZUm1UXZE2KJRSorC5r1pm7DM/tiGhClbVAZQBJwhFUeETM21Fr0CM0ZanQcndaQqRtx++/Wnnub2lBaWlpd1+00EKVfa3dBipnEyR3kgq0x0aFhSp3JndSgkspguDpiJqttIUR0iadKeLxjUgZgV8n0ZIvyDPXRqGAa3Q1cuW8K3ltk8gSu/mAmzlh4AnRPOU+QR9sAGwzKrj8rm6XVR/fk8yd8Mas3xTi0mDK5SJ0+bo1MOVP4v5F/xYkj6v77Kp9AZUoa0jU4zRJlDpKURQl1JTVTd6OCmN+PtYpU0BRaPzo4316vDHEEMPvCzFyJ4YYYoihjwjP25EkiZK6Ns2WpUJtztlZ3XOtbiRogcoSYM+CrNCFoXqxOCZ1DAZZDBzNI0eAJOHbtRtfXTfhol3g4lEXk2fPo8ZZw1Nrnmr3mFtV7vTvTO7UONysNibws9WIpChcWFkiGr3qdiLJspYFE27NArh09KUAfF30NeWO8i6PK1KgssWgw5y4GdngINGYpNX31rvqQ5asjFEgRxdoHRHJA8CSLNqiqnqnhrKMFooh5/r2jVmacmdIZOJFJejcO3bib+n5c9O6cCEAxmC1MZVBYi91COgjD2JV6NPTMQ0bhj4tjfjjj9Pu394YbMpKCiOgbKkw9XoA7DpBkLR8/wOKf9/MkHt376bpf59Ted99OPYg38cxV5A7zqni/VheuWfkTm8yd3pjyVKRYBIkRnd16OH1yrrERBz+1i73E41yR7VlVTV7u6xDVzN3Ai0t6FoFoRBtDboKNVS52d2M2y/ySMIDldXMnQyfsLWEkzvdKnd0evE9BRE23xHVm3HJoTDlrirD1dyd8ixxPnVu3MiW+i2c9cVZLN29FIvewqOHPcpFoy7qxbPuPWSneG3aTGG5Ql1gT5Q7OrtdC+9XG7N+s0juD8c8AKe+BHozbP8WXpy130OXVYtbU5b4bpQ5yihuLqbeVY9RNmqfsZ6QeJoIVm786MN9do6NIYYYfn+IkTsxxBBDDH2E1pSVnQ2IbAtVuaMiyy7IiGiUO5GgkTs6HcrQY9u1Pml5OxkhpY8uLk4jX3pbiQ5iRvuOKXcA8PaWt9lav1V7zFNUDEQmd6qaWzGlfQPArKyp5NlzoLEEXj4adq/DGGzMcq5d0269ESkjODjrYPyKv8umLk95OZ6gPULN7wFh8zAlC9XOYVknkGEVqp5GdyO+XYL46nOYcmgnIfVO+Yperao2X3VszHJvU8mdyModQ3q6GHApCq5N3WcnKT4f9W++BYQpbzRLVvd5OyBew4J332HAV1+iT0rS7lfzdjoFfx50FcRlYrWWIVuM+Ovrca6NXPfeGyiKgruwkIb33mPXbbexY+Ysdkyfwa5bbqHh9Tcov+Yamr/9ttfb9VZV4VwrgrX7HTMb2HNyR1W09EjuuOq01qvekDtqiHp3yh3JaESXmAiALjlZI5wi16ALcqB/FLasKkeoDr0juaOzh8gdq1NkNBniE3t4Nu1hM4pjUJvEoL1yR83cSfMFrSjtlDviuZXWt+HzCvLF5XeFgqe7C1UOD1PuhgRRG7PWJovnXrFiAed/eT4VLRXk2nN589g3ObLgyGie6h5BcYj9t5kkLXi6K2htWbreK3dA2EPhV96Y1RuMPg0u/lpMhtRugxdmwM79F/6ukveeAkGalTnKtN/qUamjMOqiy6iyH3kEckICvl27af35531zsDHEEMPvDjFyJ4YYYoihj1Cboww5IXIHxUiiMUTw9E8ShMPOms4BpdEgKThY80kSjsEz2j3WMUxZhUUNVV7fe3IHYGr2VI7IPwK/4ueeJfdog6dQDXpBu+XbvG183/ggevtmJGQuOeA6uPhbyBgNrdXw6nHETxwAQMPrb+BvaT9ovGSUsIJ9suOTdoM+FfWvvAqBALZDDtEaxUDY0gKm7SiKxAHJx5BoSkSWZBQUGqvWiIX2JG9HRR9zd8xB5Y5r61YCbjETH/B4tNexK+UOhN5DVw/voWPePHy7d6NLTg4pb7Qw5ehmiGWTqVPjkdaUldSB3DHaYPrtSDLE9RODypbv5ke1n0hQFIWap55i+9RDKDz2OCr/9neaPv1MEKc6HeZRo4Q9IRBg181/pmXRol5t3zFfHJtl7FjGjZiOTtJR0VLBrpa+51j01H6VZE5CQsKv+ClzlHW7bCRoyp1uyB0IhSrrw2rQI+1Hs2V1p9xRyZ2mELnT6mmvGpPVz4iikBzMejYkJHR7jB2hqnSq2oI2S0mvqQ4hZMtKdHcmdxKtRnKTBTFTWBOySKqBwqFQ5S6UO0FivDsSZHjycABWJQlSzr91By6fk6lZU3n7uLfbK9n2Ifwt4gXe18od+I3m7vSErPFw+Q/i3O1qhP+eCkueFXlk+xgquWMIkmZljjItTPmAjAOi3o5sMpFwgrD+Nb4fC1aOIYYYokOM3Ikhhhhi6CO8FWIAYszOxuMLUNEgLrLzwmpxB6X0Q5LEoKW2pfdNPaZda7AGm4waM4Zp91e2VlLRUoEsyYxNa69OMY9U25b6Ru4A3DL2BgbVGzH+tIqF99/Err/croX2mgoKtOXqXfVc8s0l1CvrUAIGzu3/dyE7t2fAnC8gfyq4m4kvfwhjTjr+piYa3nij3b4mZU5iTOoY3H43L294ud1jvoYGraY75dJL2j2m1p/7WoYj+5PQyTotr6SuJlhjnLk3yB1VudM7cseQnYUuORl8PtzBWmVPYSH4/cjx8e0bxzpAJYacPeTu1L8uXsuks87UKsyjbcrqCr6Aj52NwsowJDECATXuPEgdgj1TDID3xDLV+O571D7xJP76eiSTCeukSaT86UpyX3qRIUuX0v+D98l7+SXsRx2F4vVSfs21tK1eHfX2HXPnAmIW3GawaZaIFVW9U2G122YP5I5BNmiKu6ImYWXsKnw5ErQ6dHf3eSFq7o4ujNyJN7VX7rS4fdQ4BLHYvS1LfHYqm12auqajckcymyEYAJ7WJAbJvbZlBV+HmjahrrEarO0sUi1BcifeGbRohpE7ELJmbd0dyqHRlC1q7k4kcqdmSztbVldIMCWQZ8+jLA28OohzwbUZZ/DUzKc00m1/IBC0Y7aZ6FG5sydtWRDK3fL81m1ZHWHPgAs/h7HngOKHr2+Dz64Fn7vndfsIRVFwBzPV4oeL82+5o7xT8UG0SDxdWLMc333XJ5t1DDHE8MdDjNyJIYYYYugjtMydnBzKGtoIKGA16hgYNiDpF5dObpKwHfTJmrXlC5L8gtypD6tGXlO9BoChSUPbVQlDeB36hqjVQt7du6l64EFKL7ucHbOOoHHqMdz3XBt//ihA+uvf0PTJJ4AYCMg2sb8yRxkXfHUBG+o2IAWstJVcxqHZh4UdSIJoNBl6LFLARWqumNGse+VV/I7Qc5EkiavGXQXA25vf1ogFgIY330JxuTCPHCkUHEG0edv4bOdn4tgbDqKhVRBnasVwva9NVHenD4/q+XcLNeeooTjywLELSJKkVaKrocrqhb9pyOAucz8gTLnTTWOWc/0GnKtWgcFA4llniTu9TqgL2iuiVO50RJmjDE/Ag1lnJtue3XkBnR5m3YUt0w2SgqewUMtj6g2ca9dSee+9AKReew1Dli8j/43XSb/+euKmTkUXbGKSdDqyHn4I2yGHoDidlF1xJa4tW3rcfsvCn2hbJgg5+ywR3D0xUxB1e2LNcnjFZ1dVuESCGqqskjvxhuhJEK0hTw1R7wK61CC5k5zcZcizmreTYjMSb+460DndLtQs1c1u4vRB5Y63vXJHkiRN4ZUazN6V7Z2f16rSBk588ieWFXUONlabsWqcIXInHM0uLyY8WNuE5ZWk9hZQLVR5lyOUu+PtUIfeMXPH1QTNFThV5U4PwcMjU0fi10mUpYtL5LOYhG5Pcrv6gEDw/Og0dX4fOiKm3OkGBjPMfhqOvBckGVa/Aa+dCDVbe163D/DX1eFvbARJInOEONfsaNxBmaMMCanTRExPMA8dinnMGPD5aPrk031wxDHEEMPvDTFyJ4YYYoihjwjP3CkJWh/yU2zkx+dry6RYUhiYJgapvSZ3FAW2fE5SsNK1wRUa7HXXvGEeNgx0Ovy1tVrLVE+ofuRR6l99ldaFCwVpFQgg2WyU55hZOFJiw8mjyf7Xo+S9/joAm+s2c/6X51PSXEKWLQul4hoCrjzS7B0CfA0WOOMNGHcu8bmtGON9BJqbqQ9uR8XU7KlMz52OT/Fx/7L7URSFgNNJw3//CwjVTjgZ8nnh57R6W7HJmfhbB9LQJoJY1QDrep0O0of1GCgcFSyJkCrayNjROwuSVmserKbXwpS7aMrS1hspiBlvRQW++gjtP0D9G+I1jD/maAyqCqhmi6gEtqaAPbNXx6pCtWQNTBzYqVZbw9Bj0Q2cgi1dkGotPah3AgGFwjBroq+ujvLrbwCvF/sRs0i96ipkY9dZFLLRSM5//o1lwgQCzc2UXnKpZm/rCPeOHZRefjlll10Gfj+WiQdgzBffyUmZwmK3R+SOqpKJkG+jItUsiBeV3OmNLSta5Y5pkPgMmQYO6DLkuSiKMGUIZe54/AEMsiAJHGFksgpZI3dU5U7n5/X20lLWlTfx2dqKTo91Uu7o25M7LW4fg6QKJMUPliSIz2r3+Kiszo1ZmrKlq8yd4EDeaRXEb0/kzuWjL+f0IaczcIrI1lEbs/YnAq1q5k70tqy+tGVBKIj9N9uY1RMkCQ6+Bs55H0wJULYEnpoMb5wC2+dBUBm7N6Ce3415eeSkCiuyNyB+m4YmD414HvhhazXnv7SU8obI77Nai974wQd9snbHEEMMfyzEyJ0YYoghhj4g4HZrDU6GnByKgqGlBSnWCOSOGND0ujGraiM0FJMUvPYMz+DoKm8HQLZYNPJAJRW6fS4eDy0//ABA2g3Xk/f6awxeuIChK5aT9N8XeOJEHXcP20LJpFz0SUks3rWYOd/Moc5Vx5CkIbww61UcLWLg1IncAaH0OPFJpAPOI22UGDzUv/Rip4HErZNuxSgbWbp7KXNL5tL40Uf4Gxsx5OZiP+IIbTlFUXh367sADLcdBcg0trVX7tTpdHsephyO0UIez5Kne5XboDZmudaJ0GG1Bt3cTd4OCLtLd8HY3upqmr/6GoDk8y8IPRCet9ONMqg7qDXo3eaLSBIc8U/issXA0vHNF91u89/ztzPj0R/5cFUFis9HxU0346usxNi/P/3uv79bFZMK2Wol99lnMA0fjr+ujpKLL8a7e7f2uK+ujt3/+AeFJ82mdcFCMBhIvugicp9+WltmQvqEPc7d6WTL8rSB39duGTVUeVfrrvbLRoFoqtABUi66kPw3/0vS2WeHApU72LK0pqyU7skdo14mxSbINUkRJEEkxYhafR5S7nR+XuvKBSnVGCRcw6GqDFXlTkfVocPlY4RcIv7IGNXpM6zaskrq2jDpOjRmdZW5Uy0ska74fkDPCpdBSYP420F/I2PCQWL7vwC54+9FoPKe2rLaN2b9DtU7KgbPgsu+g2HHAxLsnA9vngpPT4HlL4Knl7/PEaA2ZRkHD8KsN2sh/xD5txrgie92sHB7LS8sKIz4ePyxxyGZTHiKivD8nt+fGGKIYa8gRu7EEEMMMfQB3l1i0CZZregSEzXlTkFqSLlj1pmxG+wMUMmd3ip3togBc5JNXCDWu4SCw+FxsK1BkARdXTCaRwnlh2tDzwOTtqXLCLS2oktLJeXyy7FNnow+LQ1JkpiYOZETB56IgsLdS+7m88LPuWr+VbR6W5mUOYlXj34VKSAGXCa9TLxZH3knsgwnPIH9uJMxJXgJtLmof+SOdovk2HO4ePTFADy69CHqXnkFgOQ5FyHpQ9tdXb2abQ3bMOvMHJAsZtc15Y5FVe7IkLkXyZ2JF4t63d1roCT6UF/VXuUpKcHf2BhZubNrDXx7J6x5W9T2BskjzV4XwZrV+M674PViGT9e2wewx3k7EBam3LEpqyPypmA/dKo4xvWb8DVEthG5fX5eX1wMwLcbK6l5/HHali5FslrJeeI/ncKcu4MuPp68F1/AWFCAb9duSi++BO/u3dS+8AI7jzqaxrffAb8f+xGzGPi/z8j4y23tcmGsBqvWiNRX9Y7alhVnjBPv17+Gw39Pbkf6qeSOGkbel0DlcKVeJEhGI9YDDkAyGLrMAQqFKVs7rd8RqnqHgPi/Y+YOgBy0yhmDXFbHzJ1Wt4/t1eJYuiN3qtsEMR6u3FEUhRa3jxFSkNyJkJeVZDOSnShIDEkRZJRG7nSVuVMjLHxOuziPRkuCqOo516bN+10xodmyjNHbsvqq3IHfuTUrHKmD4Kw34brVcODVYLSLRq0vbhbf4x8f3qPQ5Y7n99yw/L1IKluX18+68kYAvtxQiT/Qed+6OBuWCeJ3vnXpsj4fWwwxxPDHQIzciSGG3wH8TU1UP/ooDe+8+0sfyh8G3nJhOTBmZyNJEsV1IeXOoMRBzBk5h1sm3YIkSZotq7C2t+TO/wBIShYXiupgb13NOgJKgJy4HNKtkUN5LaOCNdxRKHccwbYj+/QZSHLnn4UbD7gRu8HO5vrN3L7wdnwBH0cVHMWzs57FbrRT7RAzx2l2U/cKDFlGOvkpUo8Vg7b6j+fiX/F+u0UuHnUxWbYs8lftxldegS4picSTT263jBqkfOyAY8m0C6VOQwflTr1Ot3easlTYUmHs2eL24qeiXk2XmIghPw+A1iVL8O0SSpN25M5n18LPT8AnV8ITE+CRwfDOuVhsgsxzrVvbbpsBt5uGd8V3PfmC89vvsDL4fvcxbweiVO4EYTjtfkyJXlCg5p9/wbl+A0oHm8M3G6s08k1a+D11L74EQNZ992qDyt5An5JC3isvo8/qh6eoiB0zZ1Hz6L8ItLRgHjGCvNdfI+eJJzCGBX+HY1LGnlmz1Mwdu9EO8/4u2niKFsDO77RlVJJRRV+q0HuyZbU7pi6sYsVR2rIAMhMEOeDzCvVdJFJBF9f+eXRU7myoaEIdnzY6OwfIqzlFqlXFYggRLU6vH39ACSl3MkdHPE5VveP3iwyhTsqdjpk7qnLHJt6TqMmdwYORDAYCTU1avtr+gBIIEGgNBiqbw55fF9jTzB34A5E7KpL7w9H3wU2b4OgHRbaTqwm+vwfWvtPnzarkjjkSuZPemdxZW9aI1y++MDUOd8ScKgDblAMBaFu6tM/HFkMMMfwxECN3YojhN47WpcsonH0ydS+8SOU//oGntPSXPqQ/BMLzdiA0iMpPsSFJEjdNvIkzhp4BwMB0MaApb3Di8vqj20FDiRioSzJJmWLWTg1Y7S5vR4V5VFD1sXFjt7POSiBAy3wxKLXPnBFxmVRLKtdNuE77+9zh5/LQoQ9h1ImZc7WNJ6IlqyNkHfb/exdTppWAV6bu/pth61fawxa9hVsn3sJJSwRBIJ9+PLIlNGipddYyt0Q0IJ059EySrGKAp5E7klD41Ot0e6ReiYiDrhb/b/0KaqMfBKnWrMaPPgJAn5mJTq2QbiyDynUi7DNnEuiMIjNky+dYaj4BwLnsR5QXj4Bt3wLQ/MWX+Ovq0Pfr186uhqLssXLH5XNR6hDnkMGJUdQ+pw4m/sAR4ql8tYDi009n+0EHU37jjTS8/z7eigreWSa2l+Oo5vLFbwKQPGcO8Ucf3adjBDD060feSy+hS0mBQAB9ejr9Hrifgg/exzZ5crfrqrk7fWnM8ga82mA6vmorbP5f6MGfHtNuqsodFX21ZUWrGOkqcydEOvdM7qjKHbdXfKciK3faq6w6KndUSxZEVu50DFAOt2WJpiyF4VLwNywz8md4dI747rg94rseIne6ytwJKncsYr3uqtDDIRmNmIL2yf2Zu6MSO7B/qtABTINE7s4fzvZjjocDr4RrV8Kht4j7vr0TnN2r5iJBUZQulTtdTcSsKGm/n8/XRbaKWqeIc1rbsmWdyPMYYoghhnDEyJ0YYviNIuDxUPXww5RedBE+NXdCUbQZ/Rj2LbwVoaYsjy+ghSH2jzBDnmIzkmAxoCihgNMesfVL8X/ewSTH5wAh5U53eTsqzEPCZp3LyrpczrVxI77qamSrFeuBB3a53OlDTueGCTdw7yH3ctuk29oF7arkTno05A4g6Q2k/kW0JDVsteB740KNuACYUmFhQBW4DPDMwJJ26360/SN8AR9jUscwImUEScGckIZWMZBMbhODy3qTFUzR232iQupgGHI0oIjsnShhCYYqt/4k7FztVDvbRG4OuVPg0nnwlzK4+Bs44p+YpswCScHvkvFtXQnvXYBSt5P6YJV80jlnt7Or4agEZ70gitKG9ekpFjUVEVACJJgSOhEUXSH578+ReZCPuGwnstmAv6kJx1dfU/nXv7Fj5izmPHsLV6/9iLtXvo7V56Zl+BjSb76pT8cXDlP//hS8+y79HrifgV9/ReLs2RGVZx0xPn28lrtT0dI59Lc7qJYsANv394kbQ48VzWzFC6FUzKzvCbmj2rK8AW+PeSsqIil3mpxe6oMtcpHOSx2RGSR3XC5B7rRGyCCR7R3InQ7KnbVBiwlAUwRyp2PDWLgtq9nlI0eqJV4KNt2pIeYdoDZmtbnEe91JueNsAH9w385GcIjfR2fwfNAb+5Jmzdqf5E7QkhXQyXh1PduyXP49y9yBMOXO9j8YuaNC1sGht4rzZlstzP9nrzfh271bEHMGgxbgPi1nGnpZz+xBsyOuoyp1Zg4TxM/XGyrx+TuTN5ZRo5CsVvxNTVrjYgwxxBBDJMTInRhi+A3CvWMHxWeeRf1LL4OikHj6aWQ98ggATR98SMDl+oWP8PePcOVOebAG3WyQIxIc4dasqHN3Nn8u/h92HElmYdNocDXg9XtZXyOsN5Fk3to+jUZMw8QAv7tQZcd8YcmyTZuGbOqanNHJOi4ZfQknDjyxk/WqujfKnSDsRx6JaehQAj6Z+o1GePdc2DEPQHyugR/G6vi28WcWlC8AwBfw8f42YeM6a5io/k6yCnJHC1RuEgO5en3Xtc97hIOuEf+veQvaIkvoO8I8OmgvCc64moaEkTuqamnoMeJ/gxnyDoSp1yOf/w7m4WJw6dSNAZ8T51OX4968GclsJun009vvSFXtpAwW2+kD1LydwYndV7WHQ07qR9LND5I7rYEhsyvIf+JuUq++Gsv48QRkmZzWWo4v+pnMxkpqzQl8esJV7UmpPYAxJ5vE2bORrT1nyqgIz91ZUdk79Y5KolhlI/qKlWCwwfGPwbigZe+nfwF7Ru5Y9BZMOvFd6ilUGYRiIFLmjqomTLebsJl6fr0zE8Q+W1xiWdV+Fo6O+UhyN8odh9uHt8NAtWOAcji50+L2MVzN20kbBvrI7WmqLavVJerJNXLHkgQEP7PqdzOo2iE+GxdCBdUbEuSXIHf8LeI3wm81gdRzoPLeUO4Yg+SOr6YGf1P0dsDfFfRGOO5RcXvFK1C+slera6qdggKkYPPfsORhrDpvFZePubzT8v6AwqqgcufamYNJthmpa/WwuLCu07KSwYD1gAOAmDUrhhhi6B4xcieGGH5DUBSF+v++SdGpp+HevBldYiI5Tz5Bv7vvJv6Yo9Fn9cPf1ETz11//0of6u4cnmLljyMmmJMz6oA2IC3+EslCmx4DeNGa11kHpz+L2sOM0m0aDu4HN9Ztx+V0kmhLpn9C/282oQbvdhSq3BMkd+6yZPR9XFwgpd6InFCRZJu26awFo2JmAr80Hb5+Da+6btP78M+h0mM8V5MUDyx7A7XezoHwBla2VJJoSObJABCmrtqxWjx+PL0BKfTEAdfj3TQhqwSEi6NXnhOUvRbWKefhwCCMzNOWO2yHUHgBDjom8bpAYctmmgcFK/YKdACScdBK6xMT2C1ftvbydHsOUO2LkyTDseCR8WIueJO2qK8n+73+58rQH+Ofki3AcdRLuMRP4x4FzWFj3y9sK1NydZZW9CyjV8nZ8wTyZqdeJyvmpNwjF1LavoXLDHpE7kiRp6p1oyJ02Xxt+xd9pP8VhIe/RQLVlNbcK0qSoqYgnVz/Jh9s+5OddP1PcVEzA1p5ACCd76ls9lNa3JyKanO3VOx3JnfC/HS5vWJhy5LwdgORgqLISEANozbYk68JClYPWrGDeDmnD+hQ8rJI7zo2b9luosjpxELB13VoWDrUtK1q7WSTo4uLQ9xNtYu6dO/u8nd88Cg6BMWcBCnxxIwSitFETyitqp8xEfJ8jEeVbKx043D7iTHpGZcVz9CjRWPb52t2dlgWwBa1ZsVDlGGKIoTvEyJ0YYviNwFdTQ9kVV1B1zz0obje2Qw6h/2efYp81CwBJpyPpTKFmaHj77V/yUP8QUC/AjdnZoUFUig28ThGQ+/qJ8NIsePlo2D6Pgam9CFXe9hUoATHAScrXQoIbXA2aJWtc+rgelRXmHkKVPSUlQoav0xF36KE9H1cX6FXmThjiZszAPGIEAU+A+uox4HdT99g/AIg/+mgumPVn0ixplDnKeH3j61r9+cmDT9aUDfFmA3LwZWhs85BcJWbqXYq/xyDSPkGS4GBBSrHsefC5e1xFNpvbVZ9rt3d+B34PJA8Ulq8IUAk65/ZSPGOux1EhBnDJs4/svHB4DXofsb0x+jDldpAkMettSoBdq2HJU8zfXEWZR2b7kAMY/6/7GPj6qxQm5VBS10Z18y+rLtRyd/qo3LH7PBCXEVJypQyEEbPF7Z8eI94Yj0EOqcc6Bh33BJXQbXL1rKJQj8kgG9oN8Atrgk1ZUeTtQChQuaFZLF/vque5dc9x1+K7uGLuFZzwyQk8sikUJh4wGTWFAqC1/gxItWmteR1zdzrZssIyeFpcPobLat5O1+QOQF6yFTq2ZUEod0cNVVaVO+nD+1QZbhoyGFR7a0XvLHx9RUPQdtk2USgvo1buGPqu3AEwDRS5O39Ya5aKI+8GcwLsXhs1gQ/g3qbm7URHjC8vFuqy8XmJ6HUyx48R5NrXGys7Kd4ArFOmANC2fDmKP3rSKYYYYvhjIUbuxBDDbwCtS5ZQeOJJtC5YiGQ0knHHHeQ+/xyG9PYBfYmnnQoGA66163BGUYEdQ98QcDrx1wnptCEnR7M/jI2rh5eOgFWvA5LIjShdDG+eyrlrz+cYeSlF1VFI3oMV6Aw7HkCzZbX52li8ezHQvSVLhVaHvmlTxItBRzBI2TppUijgtw/QbFlxvSN3JEki9RoxOK5f5cDJcJqLxM9SykXnYzPYuHnizQA8t+45ft71MxISZww5Q9uGLEskWMQguqmhDmtDMZag/anO1Vnevlcw8mSwZ0FrNax/v+flAXMwdwdZxjhggLgdbsnqgqgzB8OYXRs30rBBAUXCluHCtOnxzpW9KrnTw8C4O2i2rN6SOyAULEeJLCW+v4/vFonP6ukTczDoZOxmA8MyBcnRMUh0f2N8+nj0kp5drbt6lbvjaBGz6vZAAKb/X/tcp2nBHKGNHyHVF7ZT73QkNXqC2pilhqh3h/Aw5XDCd+Muca4ZkhmdakjN3GlqTuapGc9x/YTrOX3I6RySfQgDEwZi0VtoC3NKyfHtn5NqyRqTk0Bi0C7Z1KExq2OgcvjfDpePEVJx8GC6DwRPsBg05U57cieYu6PWoUdQ7vSG3JGNRq35qDsF5N6Cc906Wn9eDHo9rjOOEvd5uyapA0oAt1+cf/dEuQNhuTs7/+DkTlw6zPybuP3d3eCoimq1jmHKPUEldyYViMmbKf1TSI0z0eT08tOO2k7Lm4cPR46LI+Bw4Nq8Jap9xBBDDH88xMidGGL4lcNXW0vF9Tfgb2jANHQo/T/8gOQLzo8YHKpPSSH+KHFB2PBOTL2zr6DO4Mp2O7r4eIrr2jhKXs4lGy8SDVfWFDj/I7hhnZjZN1iJb9zEM8Z/83jdnwis+m8o8LMjPK2hSuUguRNniEMvi5nwpbuF3767MGUVpoEDkSwWAm1teIqKOj2uVaDP7LslC8JsWfG9I3cA4qYfjnnUKBSnk9Iv/Bp5YS55DYBj+x/LhPQJ2gBmWs40cuw57bah5u54KkRleLIiBrj1rugycXoNnQGmXCFuL36qM8kSAWpjljE/H9lsFnL/bd+IB4dGtmQBmAYOEO9hayv1b4nvdPJwN2z/pj2x5HNDbTBos4/KnWZPM5WtlQAMTBzYp20w/jwYMB18Lk6teBCJAGdNCtUBTyoQpIU6sPmlEJ6705tK9JYNovHMrreJ5xqOzNEw+Eihulv0b43cMcgGTWkWLXpjy4oUpqwoCmvKBNkyLjcxun1aDJj04nelwDqOS0dfyt8O+hvPzHqGT2Z/wtJzlvL3Ix7QljcmJLVbX1XujMlJJDFol+xRuROWueNqbSBPDtqpemh7S7AYICK5ozZmda3c6S0Jsj9zd2qffx6AhOOPx5wtvjfd2bLU5wR7lrkDIcXJH64xKxIOmANZ48HdDN/+X4+LK36/ZmeLhtxRFKUTuaOTJY4d3bU1S9LrsU6cCMRyd2KIIYauESN3YojhV47Kf96Nv6kJ0/DhFLz3bo8XDknniGDP5s+/+OMGI+5jaGHKOTng93Lsrid4zvgYRl8L5B4IV/4EA2dAfJZQMtywAf+0W2hSbAyQdiF/djX8Zzwse0HYuMKxYz74XJCYrw3SJUnSZvJ9AR8mnYkRKSN6PE5Jp8M8Qizn3LCh3WO++nqcq4TFyz5jep9fi0BAobalb7YsUNU7omI84BCWtZThrbDiJVjzNpIkcceUO7R2rjOHntlpG+pAUq4MkjvBAWO9cx8SCAdcJMJ0qzeFyLhuEH/M0cSfcAJpN9wg7ihbJpqtzIniM9MFJL1eew/xejEWFGA7K7iNr26DluBguGYrBHzCThCf3aentLNRDE4yrBm9thGFDliCE/6NR7YwRd7CXzOXkB9mC5oYHMisKP5llTsQsmZFTe40FNNcIsK94zLHioyXjpgmlGasfZsUvXjeHRU10UCzZbl7Poc3uzvXoO9qclHb4kYvS4zMiu69lCRJs2ZVRrDNSZJEXGKG9rfO3p5MWhtU7ozNTdDUdB3JHZPOhE4KvW7h5I65TqhsGg3pYE3u9lgTrQaUgNhHO3LHGlaH3lYPLUHVRdpQnP6+BQ/vL3LHvX07LfPmgySRctmlmqqpO1tW+HPvTZZQJPzhG7PCIetEUDqSINELf+x2cW9ZGYrbjWQ2i+uCHlDe4KSq2Y1BJ7UjX48fkwXAt5sqcfs6q21Va1brshi5E0MMMURGjNyJIYZfMZq//hrHt9+CXk/Wffd222akwjJ+PKahQ1FcLpo++WTfH+QfEJ7yYA16ejKBV4/jTN9nALQccCVc9LkgdcJhS0E3807Os7/I/d6z8ZhToakMvvwzPD4afnoMXGKAplmyhp/QzqqjWrMARqWOwqiL3CTTEZZRwVDl9e3JnZbvf4BAANOI4Riy+0YGADS0efAFhHIlxdZ7cgcg7rDDMI8RyhbziBFYz7hBPPD5DVC5nqHJQ3lg2gPcMOEGDsk+pNP6SVYjB0hbySp8TxxHMKNon9myACyJMOF8cXvxkz0uLlutZD/8EPFHBbNy1Kr7wUeCrvsmI8vokM0q6bzzkKbdKFQiznr46hbxgJa3M6pLi1dPUMOU+2TJCoMvPpcnpHMAuKDlZWgs0x6bGFTubNzVRIvbt0f72VOoocpR5+7M/yeOYOOSPaWL1yjvQMifCn4PCfVBhZ8SfZOXClW50+DqmQRTQ57DCbm1ZY0ADOtnx2yIQEJ1ATVUORK5A6ALq0IPr0WvbHZR43CjkyVG9AvZshra2tuyJElqF6Icfju+UahsamxD6AnxFkMXmTtBW1ZbbUi1k5ALJnufApWhPbmzL0OV6158EQD7EUdgGjhQI6G0wOgI0J6TzqwR4H2FMZi584duzApH1niYdKm4/cXN3earuVRL1sCBSLqev2+qamdUdgIWY2j5iflJZMabcbh8LNzW2Zqlhio7V6xE8f2y588YYojh14kYuRNDDL9S+BoaqPzn3QCkXHapaNyJApIkkXS2UO80vPU2SuCXb6b5vcFbsQsAY+2PyGVLaVYsXOO/CetxDwjLThfITE/nOf8JvHvw53DsI5CQJ2aY590Fj4+C7+4RbTsAw45rt244uRNN3o4Ks0rudFDuOL4TahP7jD20ZAVVO8k2I0Z9335SJEmi311/xzplChl/vRPp8L/AoCOEgund88DZwDH9j+GS0Zd0HsDUbuf6un/woekfJLQUgtFOcqr4ruwzW5aKKVeKhqSd34XIlWihvs/dWLJUWIJ5PXJcHAmzZ4vP2ElPgaSDjR/D5s+hKvj+9mBn6Q4auZMYIi6aXV7++skGlkao5+0KP26r4cnW6axhKHpfG3x+o2Zd65dgISfJQkCBNaWNfT7WvYFx6eO03J1yR3n3C1eshA0f0hK0w3arbDpEZO+kVwtyoaWt97Xv6vc9GuVOpBp0ldwZm5PYq/2q5E5VU2RyRw5rxwpX7qj7G5phx2LUaS12HduyoL01KzxzJ7llKwAN8cN6PM6uM3fUtqzaUN5Oujgf9CVQGcA0dAgYDPibmrRz/96Gp7ycps8FsZ9yuajNVomv7pQ7mtVsD1U7EGvMiogZd4ItnUDVDhzP3oK/JbJFztNFU1ZX6GjJUiHLEseOFu/B5+s6f9ZMw4YhJyQQaG3dLzbBGGKI4beHGLkTQwy/UlTdcy/++npMgweR+qc/9WrdhBOOR7bZ8JSU0LZkyT46wj8uvOuENcNgasWROJzjPfexPXk6sty9YmJgsA59e4MfJl8G162C2c9C6hBwNcGCh8HVKKwFuVParavasiC6vB0VWh36li0oXjHQCjidtC5aBIB95oyotxUJNX0MU+4I84gR5L/2Ktbx40GW4ZTnITEPGorh4yuhI0nZUg2f3wRPTWGMYyF+RWJV2klw7QqSEwuA/UDuJPfXcpFY/HT069XtFPk4sh4G9Uyuxc2aReJZZ9LvvnvRxQWVDv3GwtTrxe0vbhLB3bBHTVlqmPKgpFDby7vLynhjSQkXvrKM1aXRWaneXlaKgszikXeBzgQ75sK6d7XH1QHNbyZ3R1Hg278C4EgV6oY4YzcByYNmQuYYMoIz/W5P778bqi0rmsydSLasNSq5E2XejorMYG5WV8od2R7ahxwfRiaFWbIAEruwZUF7QifclpXRJsjF1sQ9IXfCApVV5U6a2F5fyR3ZaNTyaPbVgLrupZfA78c2dSqWYBC++jo5fU78XVRy9yUkujuo1qym//1vv1W//6phScQ1/DqKvk2l/Mm5FB5zFI558zotFgpTjrYpS5xLJ+YndXrsuGBr1txNVbi87d93SZaxThK5O61LYtasGGKIoTNi5E4MMfwK4Zg/n+YvvgBZpt999yEbo7PgqJBtNjHDT6wWfW9DqdyEa4uYYTaMOZRPJrxCqZJBfkrP1ouBaWJgvrMmWIeuM8C4s+GqpXDGG2LADjD+3E55HupMvoTE2PSxUR+vIS8P2W5HcbtxB2cXW3/+GcXlwpCVhWlYzwOp7lDd3Pe8nW5hTRavic4kVC4/PSru97TCDw+KzKIVL4HipyjlUI7yPMhb6TeDPVOrjt+nmTsq1Fr09e9F3aqitWQVHCIycnqAbDTS7667iD+yQ/35YbcJYrClSihLoM/KHUVRQk1ZYcqdVUFCx+UNcMlrKyhUP7tdoLLJxXdbqgE44rBD4fDbxANf/0UQcoSsWb80uQOh3J1V1au6Xmjrl1CyCPRmmlNE01k4kdIJkgTTbubwNifjnV78dWNxuLoIUO8CvQlUDm/LAvAHFNZX9C5MWUWPtixbyEYVrtwJD1MGSAjashqjVe74ffRzi9B3d2rPBGV4oHI721J45k4H5U5fbVkAln2Yu+OrqaHpQxHUnXLF5dr97cKm/ZHfD/X+vaHcAUg89RQAGt9+h5pHH/1DEzyKotDwzjsU/99LeJoFWemrqaP8mmspv/ZavFWh831vmrLqWz3sqBbn0YkFnbOlJuQlkp1oodXj54et1Z0et00OVqLHQpVjiCGGCIiROzHEsB/QG2+0v6mJ3XfdBUDKJRe3y9voDZLOPgsQddfeyso+bSOGDlAUWv7zJ7wtOmSTjPWaF9nZKBQl/VNtPawMA9PFoGZndQdptyzDiBPh8h/hhvUw42+d1lXJncFJg3sVdivJslaJ3rh6LW0en1aBHjdzZq+DXjtCtWWl721yByBrHBwXJHW+uxfm/k2QOj/cB54WyJoAF33B4slPsUPJoTGY76GRO/tauQOQOxlyJoPfA8tfiG4dldwZ0rMlq1sYzHDik4D6HkqQ3jeyrtZZS6O7EVmS6Z/QHxCDG5XcSbObqG/1cOEryzS1ViS8v6KMgAKTC5IZlB4HB18XzAdqgC9FPpCq3Fld2ojX/8vaRocli9ersKkw8gJ+H8z9u7h90NW0KOJc3i25AzD8BDxKJq9X7uactl2U1HVtrYkEVanXl7asHdUttHn82Iw6TS0YLdRA5a5sWZLRiBTMftMFlTuBgNKuBh3ClTueTtsIz9nRyJ267Rjx0qKYkZILejzOqJQ7YTXoQJ/bsmDfhirXv/YaiseDZfx4rJMmafeHh0931Zi1t5U78UcfTcbfhEqt7sWXqPnXY39Igsff3EzFDTdSedc/UNxubAdPZtBFdlKGO0BScMydR+Gxx1H/3zcJuN24i4qB6MidFUFSe1B6HMm2zhN3kiRp6p3/revcmqWGKretXo3i6fz9iiGGGP7YiJE7McSwj9H0v8/ZOnESJeedj3Pduh6Xr7r/Afw1tRgHDCD1mmsAeO7HnZz01CLKG6IfIJgGDcI6eTIEAjS+916fjz+GMKx5i/qFIhw26fRT0cXFUVwnLrrDG4G6wsBUMdCqbHZFDpOVJGFFihCwOyZ1DBISs/Jn9fqwXf1FQOk7r3/DCY8vwPH998CeV6BDmC1rX5A7IEKLJ1wAKLDo30KlkpgPp70Ml30HBYdo+R4NQQtI8v4IVA7HQaLti+UvgaeH72hbfchCNfToPd933pRQLXvKIDD2/DmMhO2NYuY5z56nqQB2NbmoahYhuR/96WDykq2U1TuZ8+qyiJ/fQEDhneXi+3HW5GD9eXg+0KZPYOvXDEqLI8FiwOn1s2lXc5+Od2+hIL4AgNLm0sgLlC+Duu1CYTX1hlC+jaF7ckeRZJ73HQvAbN0iimq7rrOOhN7Ysjpm7qj5N6NzEtD1YBXtiMwelDsQyt2Rg8qd4rpWHC4fJr3MkAxxDEm2rm1Z7cgdVZ1SuR6AzUoedkvPStWELgOVg8odd5MIVQZIG4rX78UXJOYsht4TIfsqVNnf1ETDW0Jdm3L5Ze3IdkmStNenq1Dl8EDlvYXkc84h4693AlD3wgvUPPb4H4rgca5dS9HJp+D45hvQ60m/9VZyX3wFw43zST9rOv2PqsGS4iHQ2krVPfdQNPtk8PmQ4+LQZ2b2uP0VJYIwn1TQ2ZKl4vggufPd5mraPO3PtabBg9AlJaE4nTjXr9+DZxpDDDH8HhEjd2KIYR+i6Ysv2HXbbSguF20rVlB8xpmU33gjntLIA4mWBQtEw5Uk0e/ee5BNJt5aWsr9X21hbVkj/5q7rVf7V9U7De+/r+WtxNBHtNbhfOuvtNWYQCeTdOlVANqMfEFqz7asBKuB1DgxIOnJ3tIRU7OnsvCshVw55sqo11lRXM+lr63gn2LczsCGMszbNhJoaEBOSMA68YBeHUMkVO9rcgfgmIehYJqYlT/qfrhmOYw6VWuF6tjMk2IRoar7RbkDotksMV+0V619q/tld8wDxQ/pIyCpYO/sf+bfYeoNcOxDfd7EjoagJSusKWtVcBAyvJ+d3GQrr108mWSbkQ0Vzfzpvys7qW4W7qilotFJvFmvhYICwm54kPi+sPhJZFnSBja/tDUr1y5IqEZ3I42uxs4LFP4g/h84E8zxEcOLI6Gmxc1XHhF8PlIuoXp3WbfLd0RC0K7n9Dlx+7tWSkHIlqUqd9YGLVK9DVOGkC2rutnd5YBeFyR3VOWOur+RWfEYdOKyMsGi2rI6KwvUvCKTzoReDhLZQXJnUyAfu6nrUHoVogo9ArljThREorZgPhhtWg06gEXXe3LHNGQI6PX4Gxvx7e6spugr6t98k0BbG6ahQ4k7/PBOj6vKplZfD8qdPhBW3SH53HPJuDNI8Dz/PDWP//t3T/AogQB1L71E8bnn4a2owJCTQ8Fbb5Jy8RwkWQaTHc54HfOp/0f+zDoyDmhENkp4ioSd0DR4cFRK2GVFkcOUwzE6O4G8ZCtOr1+zuaqQZDlUiR6zZsUQQwwdECN3YohhH6H5q6/YdcutEAiQMHs2CSefDJKE46uv2Xnc8VTedx++hlBAqd/hYPffhPw/+YILsI4fz/dbqvnrp6GWo0/X7KK4FzPA9pkz0aWl4q+pxTF//t57cn9EzPsb9WtFuGH8scdiyMzE5w9QVh8kd6JQ7gAMCNokCmt6N5MPIoejp4vHQEDh242VnPrMz5z27GLmba5iR3IOAP0dlRxWsQYA++GHIel73+LTETUOMcO/T8kdgxku/B/8ebsgCfTt99VRJaAqdxpcDV0GkfYWxbWtrCzpgoiQdXBgMPT8+/tEe1VXUCvQo2jJihpGKxzxDxjY93BsVbkzKDEUCLo62GY1IU8QMf1Tbbx80SQsBh0Lt9dy24fr2g343lkmSOtTJuR0rt9Wm8WKF0LtDi1rYkVxdCHN+wpWg5UMawYAJY6SzgvsFCo3Bk4HQrXjPZE7ZfVOaklgcyAPAFPZT706LrvBrllyIpJOYehoy9LInV7m7UCI3PH4A9S3RrZ8GHJzg/+L57a2TA1TDu0v0dpNoHJQjRKeKRNS7uRjN/d8XrKbQ5k7Lr+LgBIkGmUZrCmhBTs0ZeklPYZuGg27gmwyaZYb516yZgXa2mh4/Q2gs2pHhUrudKXc0UKi+0BY9YTk884l4447AKh77jlq/vOf3zzB46uro+7lV6h66GF23Xkn5ddeR8mFF1F48insOHw61Q8/Aj4f9qOPpv/HH2EZM6b9BiQJpt2EdP4HJI82MuCY3dgLxGfPdtCBPe7f6fGzIZiH1R25E27N+nxtZzJRrURvW7osqucdQwwx/HEQI3diiCEKOFxeAoHoL2qav/6Gij/fIoidU06h3333knX/ffT/5GNshxwCXi8Nr7/BziOPovaFFwi4XFQ/9DC+ykoM+Xmk3XA9GyqauPqtVfgDCqdOyOGwIWn4AwpPfb8j6uOQjEaSTj8dgOpH/4Wvbj/ZVH5vKPkZ709v01wmLqBTLr4YgIpGJ76Agkkva3aGnqBmYOzspXInGiwrqmfWYz9y+RsrWVnSgFEnc9akXP57x2x0ycnoAn6OKhEXg3F7WIGuYp/bslRIkqbU6YgkNby1zUMgoJBoSkRCQkGJytbSFVxeP5+sruDM5xZz+CM/cOozi/lxW03khcefDxmjoa0O3j0XPrgYWjt833we2BEkWfc0b2cvQ1XuhJM7at6OSu6ACOh9+twJwqq1qoKHvxHh4jUON3M3iYBRzZIVjoQcGBwMhF75iqbcWVFS/4sPGPPj8wEoae5A7riaQkHVAw4noARo8Yjvbc/kjhiMLwyIzLR+dYt7dUySJGmhyj0p0MLVRC6vny27xd99IXeMepmUYA5IV9asrAcfIP+/b2itTusiKIXUzB2Hy4evg8JLVe5oeTuKghKm3ImLgtzRyRJxxhChoZIcQCh3B7S8nT0JU1ZhHjlC7GsvkTuN77+Pv7ERQ34e8UdHtmiqBFg7dVIY9nbmTkckX3A+GXfcDkDdM89S+8QTv/j3ta9wbd1K0WmnU/3QQ9S//DJNH3yIY+5c2pYuxb15M77qaiSTicx//IPsx/6Fzt7Nd3zwLLj8ewz5w8k5sJJBJ9eSOqt/j8ewpqwRX0AhM95MTlL375lqzfp+a3UnG6yq3HGuXk3A3b2yL4YYYvhjYc+nbWOI4XeM8oY2bnhnDStKGtDJEik2I6lxJlLtJlLjjKTFmYJ/GxmaEc+IrHiav/2WiptvBr+fhNmz6Xf3P4WkFzAPHUreiy/QsmgR1Y88invzZmoe/RcNr7+Br0YMGrPuuYcKp8KcV5fT5vFzyKBU7j9lNBt2NfHjtho+Wl3BdTMHk5vcsw0IIOn882n67H94y8oo+9NV5L/2KrJl31wIRoLi8+GrrsZbWYl39258lZV4K6vwVVVhHNCflIsvRhcffUDwfofPA5/fSP02GygS1oMOxDxczAYXBy1Z+SnWHmvQVXRqzNpLUBSFG99dQ0WjE7tZz/kH5nPRwQWkB0mn0lEjaV2wEGPAh0fWo0ya0sMWo4Nqy9ongcpRQlUJBBQxmEywGkg0JdLgbqDeVa/ZtKLF5t3NvLu8jI9WldPsan9R/c6yUg4bktZ5JVMcXDoPfnxAZANt+BAKfxSB0CNni2VKFoG7WQw+s/fcEre3EFAC7GzaCYRsWS6vn427xAxzOLkDMH1YOvefPJpbP1zH0z/sJDPBTJvHjy+gMC43kWGZXXyfD5gjms/WvMWow+7AqJepbfFQXNcWVSD5vkJ+fD7LKpdR3FTc/oHin4SFLnkgJObR6nGgIAa2PZE7pUFypyxxMrR8wUjXKlGp3osA88GJg1lauZRllcsYnjK8y+XCyZ2Nu5rxBRRS40xkJfSNyMiIN1PX6qGq2cXIrM5tbvqUFPQp4jvl9QfYGMxNUsOUIZiJE0ST00tKXOj8YNOL91ojd1qqkNpq8SsSW5XcqJQ7APFmK2piU5uvLbQ9W9fKnT0hdywjR9L0wYe4Nm7q8zZUBDwe6l5+BYCUSy5B0ukiLqfmE3UVqLw3nldPSL7gAlAUqu5/gNqnnwFJJu3aa/bZ/vYFWhYsoOKGGwm0tWHMzyduxgx0CfHI8fHo4hPQJcSji4/HkJeHPqnrLJx2SB4Al86FT/6EYdOn4tw/4oRuV1FtqBMLknpU4Y7oF8+AVBuFta3M21TF7PHZ2mPG/v01VbZzzVpNyRNDDDHEECN3YoihC8zbVMXN76+lKVjl6g8oVDvcYjDbheX+2QIH+U/eK4idk06k3733RLxoi5s6FdtBB9H02WfU/Ps/moc/6dxz8Y4cx5xnf6bG4WZYpp1nzpuAUS8zIS+JaYNTWbi9lqe+38EDp47ptN1I0Cclkfv885ScfTaudeuo+PMt5Pzn311eTO4teMrK2PWX23GuXg2BrhtxGt95l9SrrybprDORDL2Xy0eC4vHg+O57ZKsF29Spe/ZcFz+Bv2IrjYUiKDFlzhztIdUiF02YsoouG7P2EOsrmqhodGI16vjpthntBlcAllGjaV2wEIDVaYOprnRxRHLPNdzdweX14wiSH2n2fTe46AkmvQ6rUUebx09Dm4cEq4Fkc7JG7kSDQEDhg1XlvLm0VAukBchOtHDGxFzG5CYw55XlzNtcRUOrh6QILScYzDDrLpHB88nVULMZ3r8QNs6GYx8Ja8k6WthHfiWocFTg9DkxykYtg2bjria8foXUOCO5yZ3J4DMm5VLZ7OJfc7fx9882akqNsyOpdlQMPgLis6G5AtO2LxmXk8Oy4nqWF9f/4uQORFDuqHk7Aw4H0FQ7RtmISdc9mamSOwnDDse9XE+mVEfLrs3EZY+I+rhm5M1gaeVS5pfO58KRF0Zcxh/wt7OKfRf87I7L7dnC2RUyE8xs2t1MVXPPioBtVQ7cvgB2s76dNVWvk7Gb9ThcPho7kjvB0O+OYcqFShaKzoxJH935OtFioilgQJK9kRuzoJNyZ08ULh1DlTu+vp7iYho/+pjmr79GtlhIOPEE4k84AUN6eqdtNX36Kb6qKvTp6STMnt3lPqMNVFafl6IofLOxipFZ8VFPAEWD5AsvRFEUqh94kNqnnkIJ+Em79lpt4urXjPo336Tq3vsgEMA6ZQo5//k3uoQ9++3TYLTBCf8W5/aqDVC1ETJGdrm4Su50Z8lSIUkSx4/px3++28Hn63a3I3ckScI2eQrNX3xB29KlMXInhhhi0PDrPyvHEMN+htcf4L4vN3Pp6ytocnoZm5PA938+nCW3z+Tzaw/hlTmTePi0Mdx29DAuOaQ/J47NYlxuIlN2byT7P/eAz0f88cfT7777uiUVJFkmcfZsBn71Jem33krS+eeTcP31XP7GCnZUt5AZb+aVOZNEtkAQ188Us+ofrCzvXXPWgP7kPPM0ktFIy/z5VN173z6VVrf8+CNFp56Gc+VKQewYDBiys7FMPID4448n5dJLSL/lFoyDBuJvbKTq3nspPOFEHN99t0fHpfh8NH70MTuPOZaKG26g7PIr2HnssdS/9RaBtt5VEQNQXwQ/PkRjoZWAV8I4aCC2adO0h9WmrIKU6C+iBwVtWUV1rfh7YfXrCd9sFHX3hw9N60TsAJhHj9JuL+k3kp+2d2Ev6gVUS5ZRLxMf5Wz7voJqzarvEKpc54zOijh/SzW3frCOtWWN6GWJY0dn8trFk1lw63SunzWY6UPTGZkVj9ev8L91u7rfWPYBcMWPcOgtoZaop6fAhg/E43szb2cvQM3bGZA4QAu4VfN2xuV2PcN87YxBnDMlD0URTWVxJj3Hj8nqekeyLth8Bqx8hYmqNesXDlVWG7M6kTsd8nbU4GLVVtQdVHJncE4aa2WhHmne8G2vjmtGnshQWlO9hlpnbcRlWrwhBWC8MX6PwpRVqLk7lV3UoYcjvAK9o3qxq9ydLJv4jPSzBUO3VUtWlHk7KkQduiCN2pEfGrkjQapoCtwbtizT0KEiVLm+Hl+lON/6W1pp/PBDis89j51HH0Pd88/jLS3FvXUr1Q8/wo7Dp1N62eU0ffEFAZd4PRW/n7oXXwQg+eI5yMbORHFZfRsOl1cLSm7z9dCWFXxeC7bXcuV/VzLn1eV7/Tc+5aKLSL/1VkBYtEovmoO3omKv7mNvQvH7qbzvPqruvkezyOe98PzeI3ZUWJJCltN1XTeT+vwBLaQ+GnIH4Ljg+XTBthptolGFNUjotC6LhSrHEEMMIcSUO78i+AMKLS4fzS4vzS4vDpcPh8tHi9uLTpaxm/TYTHriTHrs5tBtnSzhDygEFPFP3BYz0bIkEWfW97oOdV/C4wuwq9FJaX0bJfVtlNW3UVrXRml9Gy6fn3G5iUwqSGZSQRID0+K6HFgoikJxXRtLCutYWdKAy+vHqJcx6WWMOhmjPvhPp8OolxmRFc+hg1O7nc2saHRyzVurtIHNnKkF3H7McIx6wYNmBmXugdZWPBUVeCuq8DZU4G4ooXb52+gDfjYOm8Ip93dP7IRDNptJuXgOgYDCDe+uYWlRPXaTnlfmTKJfQvtZxokFyUwdlMKiHXU8/cNO7jt5dFT7ALBOmEDWQw9RceONNLz1FobsLFIuuSTq9aOBEghQ+/Qz1D71FCgK5rFjyHrgAYz5+RFn+JIvvIDGDz6g5j9P4Ckupvyqq7FOnkz6bbdiGdn17Fek/Tq+/Zaaf/9Ha67QpaWieLx4S0qp+ufd1P77PySedRZJ554TcSa180YV+PIWFI+L+p25gJ+Uiy5q9/lRlTsFvVAdZCVaMOplPL4AFQ1O8npBDHWHbzaKvJOjRkauYrWMHg2yjAIszRxB0o7Ig8XeQGvKijP1WSWwt5BkM1DR6KQxSO6oocrRKndKgkTd5P7JPH3uBFLjOiszTp2Qw8Zdm/hgZTkXHFTQ/Qb1JphxJww7Hj69WszqAujNmhLk14IdjcGmrMSwpiw1byc/scv1JEni7pNGaXk7p07Ixmbq4bJi/Pnw44NQsojDRtTzNLD8Fw5VVpU7pY7SkCKjqVxUoEuyaGqjc3Bxd1Azd/KSrWyyTWRyy3rkoh+AG6I+rkxbJqNTR7O+dj3flX7HGUPP6LSMekwWvQWDzqCpzvqSt6PtN0juVHVTh65C218EMinRYqQMJ00dGrMOyjqIp2Y+xciU4DleDVMO5EWVt6MiwWIAZ4TGLGuwDj2pQISNs3eCh2WTCdOgQbi3bKHx/Q/w7t5N8zffoKgTB7KMbdohJJ58Mv6mZpo++QTn6tW0LlxI68KFyHY78UcfjT4jA29JKbrERC0TLxxLCus478WlTCpIZtjo7m1ZHZU7C4KZYDuqW1i0o45DBqf2+flGQsrFc9DF26m89z7ali2j8MSTyLjjDhJOOXm//AZ4q6rwlpZiHjkS2dr1b2egtZWKm/9Myw8/AJB2002kXHbpvjvGMWfAls9h/fuivTDC9c6WSgetHj92k56hmd3bOlUMzbQzOD2O7dUtzN1UxWkH5GiP2dTcnbXrCDid+9VuH0MMMfx6ESN3fgWY88oylhXV0+rZO60ukWA36Ym3GIi3GEiw6EmwGLAa9XT1M6cgyCZfIIDXr+DzB/AFFLz+AD6/gq8bxYGCIJa8/oBYPqDg9QXwBsR2mpxeuhMsFNa08tEqMRuUZDVwQH4SE4NkT4LFyLKiepYU1rG0qC4q2Xg4hmXauWr6II4b3a8T4TV/s7BhNbZ5sZv1PHzaWI4amkLLwh9pXLkST3kF3ooKvOXl+Bs6D0b0wKKcsdw35BTcq3Zx7pT8Xh3bw99u5bO1u9DLEs+cdwDD+0UeQFw3YzCLdtTx/ooyrpk+iKzE6H/Q448+Cm/lrVQ/8CDVDz+CPjOThOOO69Vx+gMKZfVtbK9uYXu1gziTnjMm5mJwtrLr1tu0i6nEs84k4447Is5KKorC4p11JFgNjDzrLOKPP56651+g/tVXaVu2jOLTTifhpJOImz4dY34extxcZFtnAkVRFFp+/JGaf/8H9+bNAOgSEki5/DKSzjkHAgEaP/6E+tdew1tWRt1zz1H38sskHHccyXMuwjx0aNdPdNMnsGMuzeV2fA4/upQU4k9o76fXatB7YcvSyRIDUm1sqXSwo8axV8idnTUt7KhuwaCTmD4sMnGlT00l+/HHcPqhaYGXhppWdjU6e/X56QhVuZMe/8vl7ahQlTsNre0bs6Ild2paxHMZlZUQkdgBOGlcFvd9uZl15U1sq3IwJCOKC/SscXDZ97DwUfjpMUFuGPefBanJ6eWLdbuZPT4LqzHyT/72hmBTVlJYmHJJI9A5b6cjdLLEU+dMYNHOWg7sH0W2UUK2sKVt/ZJx1Z8gSYdRVNtKjcO970O5u0C2PRudpMPpc1LdVk2GLSNkycqaAJZEIKSS6Slvx+3za2HEeclWfkg/GFpeIblmGfi9EGVbk6IoHJ4znfW165lfOj8iuaOqiewGO41tHi0HbExOAqx+U3zujn0IBs2Kap8AmQnifegqUDkcazXlTmKnx7pS7siSzKE5h4bu6KNyJ9FqQGkV+2hH7iQFf3uzxml37a3gYfPIEbi3bKH26ae1+4wFBSSccgoJJ52IISMjdBhnniGsWp9+KmxYu3bT+P77ocfPP6/T75rHF+Cvn2zAF1BYWlTHqNGCaOtKuePyu9o9r8U7Q0rF1xcX73VyByDxtNOwTpqk2a53/9//4Zg3j353/xN96t7fHwiypvb5F6h/5RUUjwfJYMAy8QBhcZ86FdPQodoEkreykrI/XYV782Ykk4msBx/oMrB6r2HwUWBKgOYKka3Wf1qnRVRL1gEFSe2vP5t3w/ZvYezZoO98vXT8mCwem7eNj1eXM3NYOolWA5IkiXygzEx8lZU4V6/GdvDB++SpKYqCxx+g1e3H4wtg1MuYDTImva7TdbTT46fG4aamxUVNMMagxuFGAhWC2XQAAIinSURBVPqn2RiQGseANFs7RfovDUVRtPGN16fgDYixC4AsScF/4rdOCrstSxKSBDopdLsjeegPKHh8ATz+AB5fAF8ggEEnYzboMOll9MFtRkIgIF53dV2vX6xrMegwGzq/9pHWd3r9tHp8tLn9uH0Bkm1GUmzGqDMiw+HzB2ho82qvTXewmfTYTfo+7SccXn+AFpePFrcQPrS4fFiMOtLtZlLjjOh1MQNSJMTInV8BPP5AO2LHpJexmw3Em4VCJ86sF6oet49Wt8i4aHX7cHqjJ4Mcbh8Ot4+KxsiNC/sbZoNMXrKVvGQruclW8pOt5KVYkSSJVSUNLC+uZ01ZIw1tXuZtrmbe5uqI2zHqZMblJXJg/2SSbEZxEg07kbqDt9vcPuZtrmZLpYPr3l7Nv77dypWHDeTkCdnIksQj32zluQWFAIzJjuc/Y42YP3qB7V99FZHIAZATEjBkZ2HMzsaQnYNp6FB0SSMJfL2Ne7/YzKGD06LyvCuKwks/FfHMDyLQ9IFTx3R7QTZlQAoHDkhmSWE9z/ywk7tnj+py2UhIuegivLt20fD6G+z+y+0EklJYkzKA+pbItbcKYgZ3e5WDbVUt7Kxpwe1rf3L/6tOF3LHkNfSVFUhGI5l33UXiKSdH3F5di5s7P9nAVxsqkSW45ahhXHHoANJvupGkU0+k+p47aV64hqZPPqHpk0+09XSJ8RjzCzDk52PMzUOfnq7NjALINhvJF11E8pyL0MWFbBPJ551L0tln4fjuO+pfeRXnqlXatm0HTyH9ohMxJ3ihvhAaisT/9UXQVouiQH1ZPlBP0rnnIJtCg0+fP0BZ0BrXG+UOiNydLZUOdlS3MGNYRs8r9ADVknXQwFTiu7loij/ySOKBMYWLWFPWyE87ajljYjcZKT1Aq0HvggzZn0hUyZ0+KndUoirVHiFLJ4iUOBPTh6Uzd1MVH64s5/Zjuw65bQe9EabfDofdFt3yexE3v7eGeZur2VHdwt9OiJz3oip31KasXY1OKptd6GSpXUhuVzDqZaYPjUINp+KAObD1S0wb32V0+hGsq/KwsqSeo0f1i34bexEG2UCOPYeS5hJKmksEuaNassJUVqpKJs7QvS2rosGJooDNqCPZZsSYM476nXEk+1ugfAXkHxTVcV3535UsL7dCFizbvYxmT3Mn1VB4mLJKtPRPtZG4+hmY+zex0Px/wsCZUYc5q7askrq2iNkyKpweP9uq1Gauzp8T1R7aEKEOXYOnFerE529zIJ+BPSm/whBvMYASQbkz4iTwuWHgDO0ulQTZ0+DhuEMPo+nDj5CtVuzHHkPiKadgGT++y9fIWFBA+vXXk3bttbQtX0HTJ5/Q/M036OLjST733E7Lv7yoiO3VgkQMKNDcJgYv0WTuNLZ52FzZrD02b3MVFY1OsveAwO8Kxvx88v/7BvWvvELNv/9Dy/ffU3jCiWTedRfxRx2pLaf4/SguFwGXS/tfjrNjyIjufKEoCs2ff071w4/gqxbXgbqEBPxNTbQtXkLb4iXwyKPoUlOxHXwQlrFjqXvueXzV1ehSUsh96kks48bt9effCQYzjDwJVr0O69/rltxpZ8kK+OGtM6ByHTSWwMy/dVrvuDH9eGzeNhbtqGP83XOxm/Xkp1jJT7FxUs4wcisr2fb1D8QPHoPcw3fcHwjQ2OYV/5xeGts8wdvi/yanVxtjtHn8wXGHr8sJXYNOwqzXYTLIuL0BHB1avbpCmt3EgFQbA9Js2Iz6oEvBp7kWWtw+HMHBvF4na46FONXBYNYTZ9SjoOD1CxLE6wtNQqtkiC9IrojJZjHB7PGHTT4H191bkKUgIRR0VPRkvZclQkSPTg4STGLc4vV3v65RJ0g2s0GHxSi24fYJEq7NI96/SDDoJNLtZjITzGTGm8mIN9MvwUxGghmfP0BtiyDkals8Ybfd1LV66I3TU5bAbjaQYDEQHxQVJFgM2E0GAoqivSee4Hvh8QVoDY5VhXPFi8vb9XsjSZBsNZJmN5Eeb9auQ10+Py6PH6fXj8vrx+kN4PaKvw8ZlMrDp4+N/kn8RhEjd34FuO/k0QQUsAfJnGjDBH1BUkhRFOQgi6xTmeTg3wFFodkpTtjNLh9N6m2nF2cPSiFZljDoJPSyjF4Xum3QSehkuUvVD4j9G3Sh9Qw6WVs3wWIgzd61jUMdJKgtHCuCgZsrihtwuH2Mz03kwAEpHDgghfF5iZgN0b1eTW1eXl9czMuLiiiua+MvH63n8XnbSbObWF/RRFZLDTdSyLjPluB8qgz1UlGXkoJ91ixMA/pjyM7GkJODISsrYsPThQGFr7bUsqy4nls+WMtblx7YLXPtcHm5/aP1fL5OBCrfOGtIO9ltV7hu5mCWFC7l3eVlXD19kGYXixb6q2+gaXMRCct/YscVV/H3aVdTGh/ZzhMJJr3MwLQ4BmfEIc37hgsXv4Xe76U5IZWc//ybxCkTIq739Ybd/N/HG6hr9SBJ4uL1sa/X49v0BVekrMG48xuys1tIPsJAw3Ybboceb4sOv1uHv7EZZ+M6nGvXtdumYjCQcvYZpFx5FfrkyD52KeAlfmQa8bccg3OpjbqvV+HY2kLrz0spWryExP5tpI12oLe0/yFp00/GVVaOZDaTdPbZ7R7b1ejC61cw6mX6RVmDrmJwMFR5e9XeacwKWbKiI4qmDU4V5M72PSV39lMNehRI6qASSLaIz0KdK7rMndogudkTUXXqhBzmbqri49UV3HLU0N7NHO3n8NGfd9ZqxPhHq8u59eihnc6XLZ4WipqElXFIksgnUW2pwzLtXap99giDZkJCLjSVcUH6Gv5cNYLlxQ2/GLkDkGfPo6S5hOLmYiZnTAwpd4J5OxCmkomyKSs3WUxYFKTa+TkwiuN1S6Dw+6jIncY2D99uqkJREsnJzaXJX8aPZT9ywsD26kHNKmaKD1qkFP7P+C7MfSu4hAS71wpSKXdSj/sFGJ2dgNkgU1Tbyhfrd3fOUdq9DhqK2WSdij+gkGY3aVaucKhquqa2yBMHAFRvBhScxhRqXImM68WMvsjciUDu6E0w4fx2y+4t5Y79yCMY8MXnGPr169YW1BGSLGObMhnblMlk/uMugE6q1l2NTv49T6jo0uwmMagKcjVdkTtaW5bOzLKiehRFNDKm280sLqzjzSUl3Hr0sF4+yyifk05HyqWXYpt2KLtuuw33li1UXH89lampKG43isuF4o1M7JnHjsF21NE4Dz6cSmM8u5tc7GpysrvRRUBRSLIaya4pZsi7z2PdHmwn65dFws1/xjp9JrVbttHy0yL8y5Zg2rAGamtp/ux/NH/2PwD0AwaS//xzGHOyI+5/n2D0GYLc2fgpHPOwIHyCUBRFs59OzA9TQy5/SRA7AEuehSlXQlx74mtQehxzphbw5frdVDW7cbh8bKhoZkNFMx5/OjcBhfMWcLNn3w9a1RgIFV6/gtfvwxEmojfpZdLjTaTFmUi3m0mzm/AFAhTWtFIYVGmq/5YWRTP54u+UN7SvIRQ1aDEX0SKgQEBR6GolvSy1I8oCCrR5/F0SMd2tq6p6OjZ6doQsgc2ox6CXaWjz4PUrVDQ6+zTZL0lg6OE6JhBUQgUUtDHnnsJi0GnkXpvHT02LG39Aoa7VQ12rhy2Vjqi2U9vSO7fHbxUxcudXgN407YRDr5NJsHT/JdMhkRJnatdU8VuBQSczLjeRcbmJXDptAIqioCj0WeaXYDVw7czBXDKtP28tLeWFhYU01zUwac1KLq1YzZA6EabpAySLBfsRs0g44URsBx2IpI/uqyLLEg+fPoajH1/IksJ63lhSwoUHF0Rcdl15I9e8tZrS+jb0ssSfjxrKFYcOiGo/Bw1IYXJBMsuK63n2x53cdWLP+TRFta3M3VTJ3E1VrCxpQJ95HPcllzOyvpj7lr7Ee+fcTpstgYAsE5B1KJKszfSmGhRGGt0MopUsTzPxjnr8VVV41pSImTNgVfoQHph4LsqXNdzsK+L8A/O1gW9jm4e/fbqRz9buwoSHI1NbuPNAPb7NX5FaPpf46jZQxVkJuVgOPhnL0GOgpRp2r8VftBLPtg1461rxtAjCx9uqx5jgJWV4CwbPw/if+g9KfD8kez+wZ4K9H7TWQOUGqN0mKo0BC5AzDjwDdVSvi8dRZqGx0EZTeTypx4wj+cwTkfsNhaT+1N9wG1BOwsmzO9WjqmHKecnR16CrGBQkd3bshTr0yiYXa8sakSQ4YkR05M4hg1J54rsdLNpRK7K5+vidUq1M6b9gU5aKjsqdFLOwCNU7o1Pu1GrKne7PlTOGpZNkNVDtcPPTjloO741iZT8iEFC478vN2t+NbV6+3VTFiWPbD9SX7F6CX/FTEF9Apk0QvFreTg+WrD5D1sGEC+H7e5je8iUw4hcPVc6Pz2dhxUIRqly9EdpqwWCFnBAhorZl9UTulIWROwAFqVZeD4wW5M7O72H6HT0ez+KdddosqaN+GCSUMb90fpfkjt1oZ11pHffpX2JW/XfiwVl3Qe12WPMmLH8hanInJc7EFYcO5N/zt/PAV1uYNTxDkIKuZvjublj2AqDQPOoBII+xOZGbuTRbVncX90FLVm3cEGgWFvJokWAxQCRyJwL2RqAyCNuFaeDAPdpGJKsywN2fb8Lp9TOpIIkTxmbxt083sqshALqeA5UtegsLCgWRfeCAFA4ZlMriwjreXV7G9bMGRz1p2BEur5/yhjbK6p2UNYiMxPJgVtx1MwZjM+kxDx1C//fepeapp6l74QX8tZHz3CSTCbfOgL6tFdfadbjWrgMeoiy5gAXZ41iYPYYGczwJbgcXbfqKMSXLkVFw6Qy8M2QWHw06FO9PAfhpbnCLOZB7Gvrs2QyvL2FC9TbG1Wyn2pLIqxPO4YoSD+f2E1aWPcXby0r5z/ztHDwwlTlTCxiVHUHRmD9VawNk+7cw4kTtodL6Nmocbow6OZSH1VIN390jbpviwd0MC/8FxzzQadN/P2Ekfz9hJE6PX+RV1rVSWt9G7XYLrH6PoQ1lpOl8uAzdf751sphgTbQYSLQaSbSK2wlWI0nWoLLCbMBm0mEzCpWMzaTDZtJjNejQ6+Sg+iWAyxvA5RV2H5fXj0EnSB27Sd9ttlGzy0tRTSuFtS0U1bRqbXt2s0FT6NjNBm0w7ws6F1qCGaSqsqfV7UOSJIw6MXGs14m8TYM+NKFsDN42BJcJ3W7/t1GbjBb3dzx+JTzPVFEzTgX5o0S6rSjog5PcxmAOqLpd1W7l9gZw+fy4vQHcPj8ef0DLC9XWUzNEdTKyLNZ1+wJhqhTxv/gXwGyQsRr12Ix6rMH30GyQtefj9QeocbipbHZR2RT8F3bboJdIizORGmcizd75/2SbMaoMV5fXHyYsUEUFvqAyzIssq+9b6LkadBI2Y8ixEh/8DNhM+k7f4UBAob7NQ3Wzm2qHS7P/yZKExSBjMQrrmvpPWNlkbcLh945fBbnz1FNP8fDDD1NZWcnYsWN54oknmDw5VusXAyheLwG3B8XtQnG7QZKQUlKQurg4igZWo55LpuRw4o4FVH30DPqW4NSYLGObOpWEE0/APmNGxIyXaJCfYuP2Y4fxt0838sBXWzhsSFo7245qw3rw6y14/QrZiRaeOGd8rwZSkiRx3czBnPfSUt5aVspVhw8kvQv1yOrSBm7/aH0nZntwbgpVt97NiCfvIKW8jD+9cGvnlXU6JJ0OxROaefUCHbUQKVdcwbjTLmDgZ5tZU9bIvf9bx8/LlnL7ZAO+mh2sW7eS070V3GqqJFuqQ2pRYJ76ZKCGZP7nm8xXTOXUg0/izMl5oR/XkbNZXVzPQ74tlOl2MDqzkAmGUo5MrsLk3EWLM0ASLej8bmgoFv8iwZwImaMhYxRkjsKYMYqctKG0rd9M1QMP4lq3jppPV9KwtIL0G2/APNIm8oMkiZQLO9cQl2hNWb3/nGjkTnVLt7aHaPDtJmHJGp+bGDXJMj4vCatRR12rkPGPzOpbe0h1869YuWPunXJHJap6Uu4Y9TInjcvm1Z+L+WBl+a+W3PlkTQUbKpqJM+k5eXw2bywp4d3lpZ3InYUVCwE4JPsQ7b5owpT3GOPPgx/uJ6V+FYOlcjbskmh1+3oOZN5HaNeYpap28qcKFUgQ4URKdygNC1MGcY74yT8KDKBUrERyNYG5++/cz2HZKY66YdgS5rKoYhFOn7Od+kRTE+ltzC69iyP0i1GQkE54HA64CHatFuTOxo/hyHs6qQK6whWHDeDd5WWUNzh5eVERV2VsgS9vAUeoKW7AjleBv0bM24GQLatj5k47BMmd3WZhCex9W1Z05E64wuXXiB+2VvPVhkp0ssTds0dp6ojyugCk9xyobNabWVIoCNKDBqZwxIgM+iWY2d3k4sv1uzl5fM+KYICtlQ6e+3EnxXWtlDU4NXVmJMzdWMWT50xgRFY8ktFI+o03kHTO2fjr6pDMFmSLGclsRjabmb+zkbs+30xFo5MkVzOH7FrHoRVrGVVXxMj6YkbWF3Plhk+p7z+M+F0lGFziO7RhxEF8NvlkSuQ4LG0evEGVgt2kJyXOKCYvbUZS4gYQH3cUNWY9H64sZ3dVC3f9bxOvLy7hL8cM44gRGX36nVUUhf/M38Fj87YB8OGqcj5cVc7E/CQumlrAUSMzQwNPWYbRp8Oix2Hdu+3InWVBhcronISQevLbv4K7CbLGi/D9/54KK16Cg66GxMiKWotRx9BMeyiQedoAdryZDRUVfH9UEnGHHhpxvb0JvU5YiPo6To43Gxibm7hHoe/7G5IkodftnTBuWZYwy4J0SKB32UOyLGExChtWX2DQyWQlWvYoazEaqKRKV+OSPYUsS6QGSagR9Fxw8EfDL07uvPvuu9x00008++yzTJkyhccff5yjjjqKrVu3kh5No00Mv3l4ystpWbCA1h8X4Nq2TUh53W4Cbjf4I0sVdQkJ6NPT0KWmok9NQ5+WhmngAMyjR2MaOLDLpipFUXB89RXVjz2Ot6wMPWAcMICks84k/phj0KelRVyvtzhvSj5fb6jk55113PLBWt69/CBkWaKh1cOf31/L/C1CpnL0yEwePHUMCdbeh8tNHZTCAflJrCxp4NkfCyPmaby3vIw7P9mAxx9AL0scOEBc9M0akaH58D0HvETZlX/Cs3Nn5534/SjB90CyWDBkZmLol4k+sx+GzAz0SXFYMnSYEz2kb3iIjxN24mjdgrWtHH1TAIITbEMAwt8SUwKkDBCV0SNPwZg2kUXvr2P5lmqWf7yBFaWN3H3SKIpqW3nk2618F3y9TPo0Cg6exJmHDSTJJq4sqptdPPjjJuYtW0+Cr5YMqZGR9lZm5QbIyczEnTKCtqThOC0ZuP1KKJepNYCrsZk2OYPWGx/Euug78j56DUtlJbtu+wseoxkjEDdjOsaCgk4vTVGtGqbc+0Dk/qk2ZAkcLh/VDreWcdEXqHk7XbVkRYJRLzOlfzLfb63hp+21fSZ3NELkV0HudKHciSJzxx9QqG8N2rKieC6nTsjh1Z+L+XZTFU1Ob8Tq+V8SLq+fh7/ZCsBV0wdy4tgs/ru0hEU76iita9NCvBVFYWG5IHem5YiMCLfPz8YKQRjsM+UOQHw/UQm/5XMut/7ALa3nsa68iYMGRhHKvA+QnyBCeEuaS2BXkMAIs2RB9IHKHckdm0mPx55DoTuTAXIlFC2E4cd3u41FO2s5Wzefo+N28k1Lf74NJOCiiZ8rfmZm/kxtOZXcsRUu4ghlAx5FB6e+gHHMqWKBrPGQPREqVsCq1+DQW6J4NcQkyK1HD+XB975nyPd/AmmZeCCpQOSCfPwn8l1bmCBtZ2zulIjb6Kimi4gguVNqFGqY3rZlKUqEQOUI0BQuhl9fo5DL6+fvn20E4KKDCxiWGY/PH8Bm1OF067DQs3LH5zOwebc4103pn4JeJ3PO5DwenbuN1xeXREXuKIrCze+vYUNFc7v740x6cpIs5CZbyU2ykh5v4tVFxRTWtjL76UX87fgRnDtFTMgYMjLahUqX1bfxj/c2aPbQrAQzN5w2hmGZx9AvwUK8o57Wud/S/OVXONeuJaVQqA3NI0eS8X//x/AJ4wnvE/MHM1W6s+NfPLU/764o47G52yisbeXyN1YypX8ydx43gtFRZIipCAQU7vrfRl5fLFTdc6YWUN/q4Yt1u1lR0sCKkgb6JZg578B8zp6cR7LNKFqzFj0ulDvOBlGTDqxQLVkFwXNq8U+w7h1AguMeFcHtBdOgeKFoEzzpyaiP0zplCk0ffUTr0qX7hdyJIYYYft34xcmdf/3rX1x22WXMmTMHgGeffZYvvviCl19+mb/85S+/8NHFsC8Q8HhwrlxJy48LaFmwAE9hYVTrSUYjSiAAPh/+pib8TU2wfUfn5axWLCNHYh4zGsvoMVjGjEbfrx9ty5dT/fAjuNaLi0l9Whqp111L4sknR227ihayLPHgqWM4+vEFLC9u4JWfixmdncD176xmd5MLo07mr8cP57wD8/us2lDVOxe+vIw3l5Zw5eEDNOWG1x/g7s83aRclR43M4MFTx2gX2+Ew5uYy4PP/gc8nwg99fvCrt33g8yFbrcgJCUitNeKipPgnKHkTdm6BME5IAo1Dd0tmdvozKFYySMgZzuQDJmFIHwzJA8GW2i7cMwF44YKJPPPjTh79disfrCznp+21VDlcKIqQEp8xMYfrZg7uVA+fHm/mthMmcNmMUbyyqIhXfy7miyYfDzUBG9SltgX/dYcMDFNvZPbOhZy1bT5Wj5jp/XbkTCIVxqvKnfxehikDmPQ68lNsFNW2sqO6pc/kTmObR5ut7Q25A3DI4DRB7uyo5YrD+mYz0NqyfgXkjmoBaeiQueP0OWnztmE1dE3CNbR5tJnyZFvP05GjsuMZmmFna5WDz9f1vhlvX+Oln4rY3eQiO9HCxVP7YzbomDY4jQXbanhvRRl/Pkq0xG1t2EqNswaL3sLEjIkAwRwH0aqRF0Ug/B5h4hzY8jnHKT/yV05nTVnjL0buqMqdckcZvtJKcXHUobJeJVJ6ClQurRcD7vDXr3+KjZ/KRwtyp/D7bsmdyiYXg+p+4H7jS+CGwww/kOhI5I2EeOb/fD8zW5qh/2FgTcbRJqqvE5vKaFNMPBB/J/9UiR0Vky+Hj1fAildg6o2gi+L3LuBntvdLjrb8DavixI8O3SHXwaG3gtGKZ9t8jOve5BL9l4zJvjriJhKDpGeXmQuBAFQJUqNQJyzJvWnREbYsce7pKpNGxd6oQt9XeO7HQkrq2ki3m7hh1mBAqCPG5SWyZFf3z099Xtsrxbl4UHqcRlCfNTmP/3y3ndWljWyoaIpsJQrDd1uq2VDRjNWo48FTx5CfIsgctZ0pHGdMzOXP76/luy3V3PnJBhbvrOP+U0drgf5un58XFxbxxHfbcXnF5NJlhw7g2hmD2ud42fthuvBCki+8EG9FBY7vf0CfmoL9yCO19qtw6GQJndy9akGvkzl3Sj4njs3i2R938uLCIpYW1XPCkz9xyvhsbjl6aKfriI7w+ALc9N4aPl+3G0mCu04YqVns7zh2OG8uLeWtpSXsbnLx8Ddb+ff87dx0xBCuOHQEUsYoqNoAmz4V6jlgeYn4nZ5ckCwa8764Wexo4hwxyQWCNH3pCFjzFky9HlIHd3uMKmxTJtP00Ue0LV0W1fIxxBDD7xu/KLnj8XhYuXIlt99+u3afLMvMmjWLxYsXR1zH7Xbjdodkos3NzRGX+y1h99/vwrVp0y99GPsHioKnsJBAW9iFik6HdcIEbIdOwzpxIrq4OCSTCclkRjYZkcxmJKMRSZZRAgFB7NTW4qupwVdbi6+mFl91Fa7NW3Bt2ECgrY225ctpW748tItguwKAbLWSfOklpFx0Ua8CEXuL3GQr/3fcCO74eD0PfrUFXyBAQIEBqTaeOGd8n9US4Th0cCrjchNZU9bICwsK+b/jRlDjcHP1m6tYVlyPJMFNs4Zw9fRB3eaqSJIEBgOSocOFtbMBChfBqgWC0KmNQJCkDoGUwUKJkzJIkDcpAzHZ+xHY1cwAncSwzJ5lk7IscfX0QYzPS+S6t1drNbzHjenHzUcMYUBa9wOqZJuRm48cymWHDuCNxSW8sqiI2hYPuqC3N9y/bAreNhl0xJl0QY+yDqtJj+3wISxxn0m/uR+zuMrD+yVG5EVFzJnav93+ioLkTv8+ZmYNTIvTyJ2pg/pWHzt/czX+gMLQDHuvG7umBRvZlhXV4/L6ow4mVxEIKL+yQGW1Cl2oBKx6KyadCbffTYO7oVtyRw3ZS7YZo8pnkCSJUw/I5r4vt/DhyvKoyZ09yTeKFjUON09/L0jvW44KBSifNSmXBdtqeH9lGTfMGoxeJ2uqnSn9pmDUiddvtZa3k7hHdsGoMGAGJOZjbSzheN0S1pTl7dv9dYN0azpmnRmX38UuvOTZ0iG9vRoyGluWoiiUd8jcAZG781PpKC5gbqiJqwusXbOcRw3Pij8GH8mO8ioOa9nGGwnwg3M33vcvwoAE/cbikBvBCBaMnOu5iREDpnfe4MjZ8M0dIgtk65ft7CIR4WqCN89ALluCFVgdGMQdvkt5dMS5jDCK57Qh51wmrHuTo3Ur0Hl2g63zdyDJ1oMtq6EIvK2gN1MYyARqieuFLS/RYozelrWX2rL2Nkrr2nj6B/F9/evxI9qRWwfkJ7O4vHtyR33emyrE8ztoQIgcTbObOGZUPz5bu4vXFxfz0GldB+4qisK/54sw5/MPyueEDvbNjki2GXnpwom89FMRD3y1hS/W72ZdRSNPnj2BFrePv366gcIa8Rt54IBk7j5pFIMzule8GbKzST6vc4NYX2E3G7jlqGGcMyWfh7/ewidrdvHR6gq+3CBsaucfmM+IrM7XJi1uH3/670oWbq/FoJN49Ixx7eysGfFmbjpiCFdPH8gX63bzyqJi1lc08cBXWyisaeG+kaehr9oA696DAy6itsWtvRYH5CfBkqehZgtYU2DGX0M7zp0MQ46GbV/D9/fB6a9E9TytU4RyzrVxI0Wnn7EHr1gMMfy+YT3gADL+sv9bS/c3flFyp7a2Fr/fT0aYhBMgIyODLVu2RFzn/vvv5x//+Mf+OLz9Bk9hoaYm+aNAl5ZK3LRDiTv0UGwHHxSxeSoSJFlGn5SEPikJ0+DOsxqK34+nsBDnuvU416/DtW49rm3bBLGj05F05hmkXnUV+tS+DaZ7i7Mn5/LVht0s3C4CBk8Zn83ds0fttVwJSZK4ftZg5ryynP8uKeXgQanc8dF6dje5sJv0PHbmOGZFGbILiJnUyrWwfR7smAvly0EJb5CSIHMU5B8CBYdA/sFgjdxQBfQ4UxgJBw9M5YvrpvHBynIOG5LW623Emw1cPX0QfzpsIApEFf4WEadMZvf3O+Cbrfzjf5tIiTNpF3j+gKKFpub3wZYFMDgjjnmbq9heHV3KfySELFm9r1MfnB5HRryJqmY3K0saek0wNTq9WnND6q8gsF1V3KgWEEmSSDGnsKt1F3XOOrLjum5NqXWIdVLjog8RmD0umwe/3sqq0kZ21rQwsAfycWVJA1e8sZIDByTz6Blj+xxw2hP+PX8brR4/Y3IS2g1IZg3PINlmpKrZzY/bapg5PEPL25mWHartVZuyxu9LS5YKWYYDLoT5/+Qc3Xz+VHZkz+vsq0ORZPLi89jWsI1ig568AYd3qg5XA5U71pGHo7HNq9UB5ySF1AEFqTaeCYwUCpj6ndBYCokRyCy3g7GLrsYuOSmzjyf3rLdoKHMw59n5mDMexKF3szxzMAdXbofda2jOSAOjhbmWU1mtDObsSDkWepN4nRc+Csue757cURT4/CYoWwJGO8z6Oy9tH8fm9dXc88Um3rx0CpIksbg1g1b/KKbpNohtHnVvp00lWIzB16QLW1bQkkX6CJo94lzS28wdNVC5tQflzt5qy9qbUBSFv3+2AbcvwCGDUjl+TPu2uAPyk2CheH6RbFmKomik1drSVkDHgQPaK98uOCifz9bu4tM1u7jj2OER1bsAP2ytYV15ExaDjsunRVfsIEkSl04bwMSCZK55axVl9U5OeeZnTQWZGmfizuOGc9K4rH1PFHeD7EQLj581njlT+3PvF5tZVlzP28tKeXtZKQfkJ3H+gfkcMzoTk15HXYubi19dztryJqxGHc+edwCHDols1zfpdZwyIUfLNLvrs428t6Kc1vyBPImEVLIIGktZUS5e86EZdhK91fDDg2IDR9zd+fppxp2C3Nn4EUy7SeQE9gBDZiamEcNxb9r8hxtLxBBDb7C/xn6/NH5xW1Zvcfvtt3PTTTdpfzc3N5Ob2/cq318D0m66UVOV/BFgyMjANHRoRMntnkLS6TANHoxp8GASTz0FgIDLhXv7dvSpqRj67d+qXUmSePT0sTz8zVamDkpl9vi9X8t5+JA0xuQksK68iTmvCLXSgDQbL1wwsccBJwCOYAbEjnmwc75omApH2jAYOEOQOXkHdUvm7C1kxJu5evqgPdrG3lBIXHX4QGocbl79uZib31tDktXAtMFp7Gp0ihr0YDhdXzAoLRSq3Bc4PX4WbBfv1ZG9tGSB+GxOHZTKR6sqWLi9ttfkjqraSbIaMOr3b8V3JKi2LLcvgNPjx/L/7d13eFvl+T7w+2jLluQZr8R29t57sBNWgbILfCGsFgqFltHS0gF0t/ArXUBZLYSWTiirQFkpgYTsnZAdJ86w472H5vn98eo9kmzZlmzZkuz7c12+YluyfOzIts6j57kfkx6ZlkyUtZT1mLtT1SxOkKLpQMpxWHDGuGx8vL8Kr209gQfO73rdcEVjO+54eQuqm514e2c5mp0ePHPDnKi7pXpyqLIJf994HIAYHQj+GTAZdLhy9nA8v/oI/rHpOOaOtmBH1Q4AocWdft+U1dHMG6B+/HPMxiEsb/0zyusXIz+9n8fBulDsKMaBugMoNRo75e0AQJNbFGJtpq5/r8q8nVyHOeT/d1RWKpqQgoOG8Zjo2Su6d+Z0CGpXVahv3oU8VylOqRkoP+8ZFOqNmFucgaK8XBxpmgFTxkasnHEpFl9/G1CyCk17ngFcNdhVJ35+Z3YVUjr3VmDNb0SmR+U+IKeL++vOfwK7XwUUPbD8NaBwPr4zthUf7K3G2sM1+GhvJc6dnIsdx+uxyXuhKO5s/TNw1oOAObQzQ/5MNrZ74PWpnQvtsriTNxVNpf6g3CiKO3aLAfBn7jQ6wwcOS7HalhVLH+ypwMf7q2DUK/jRpVM6FUBmFqYDqvidFC5Q2eVzwed/8uVghRuAHgtGh/59nlOcgUn5Duwtb8Qrm0/gtjAbOVVVxW+Dunai3a46szAd73zjdHzn1Z147/NT0CnAjYtG4r5zxydUHtmMwnT886sLseFILV5eX4r3dp/CltI6bCmtw0/eNuGquSPw4Z4KlFS1ICPFiBdvmd/1z1MQRVFw46KRKMxMwd1/3Yp3SoGvpE7FLO8uYNer2NRwAQB/3s573xXdaoULgRnXdb6xvGnA1CuB3f8Wm7T+758RfW1Ff/oT2nfuhCpX7BFRJ4as+Ix9D7S4Fneys7Oh1+tRUVER8v6Kigrk5YU/WTGbzTCb4/8scSylzJoV70MY1HQWC6zTen72o7/kOCz4f1d33Q7dV4qi4J6l4/DllzYDAJZNysGvr5mpzb530lwlHuAfXS2KOjUHQy832UTWxNhl4qWLrQ1DgaIoePjiydpJ+R1/2YK/375Qy5AozLT2ujMosDGr+5OSrnx6sArtbh+Gp1sxJUxreSROHyeKO2sOVQHoujgRTmVT9AWR/mQzG2DQKfD4VNS1umA1WbXcnZ6KO4HOnei+livnjPAXd07i/nMnhL0vOD1e3PHyFlQ1OVGclYKKxnas2l+F2/+yBc8tj22B5xfv7oPXp+LcybmdnsEHgGvmFeL51Ufwv32VeO9wGXyqD2PTxyLfJore5Q1tKG9oh04BpkcRPNon9lwo5/wA+OiHuNvwJsr+7QVueiFkS9VAKbaKJQ6lRkOnvB0gsrGsjmHK2m37xzc/8UzBROwVuTsdiztrfw9lz5twqXp8w3sf/jxRdKcqioLli4rx8AdTYMrYiP8d/x++v/D70M24Fk1H/wm4auB0mZBq0ndd0E8bAUz4ArDvbbEW/aLHO1+n9gjwzrfE62c9KMZEIMbLvnzaKDy96jB+/u5enDl+GHaeaECFbwbaHKNhbSwBtv0VWHhH6KcMOrFvaHN3zrOq8Iei5U1H8wFZ3Im8GKDTKTDrRHG9yRlh5k6CdO60ujz48X/EOP5tp48O+/+WZjViTFYWTgFw+9xwe90w6gPfH/k1AQB8RozPtXX6HSYKD8X47mu78Jf1pfjyaaM6PfHxyYEq7DheD4tRh9si7NoJd6xP3zAbqw5UoSDNGtjmlGAURSyWWDg6C5VN7fjnxuP428ZjKG9ox7OfiPzHgjQL/vzlBdrf6EidPSEHr965GF9esQl/b16IWcZdaNvyd2zWzwMAXGT9HNjxliicXvS46FwM56zvAZ+/ITp4jm0AisIHlgczZGTAduaZUR0vEQ1OcX261WQyYc6cOVi5cqX2Pp/Ph5UrV2LRokVxPDKiBNPeCOx7F9j/X+DQSlGUOb5RrLmt2INzhjXhl+ek4TfnpuG5C+xw1H4uHhSUfAIceB/Y+YpYY/vUQuBXY4FXbwE2v+Av7IjsBiz+BnDTf4BvHwGu/asI+hvChR1Jp1Pw+Jdm4LSx2WhxeXHLi5uwar/omOnNGnRpjP+BY3Wzs+uxhW4Eb8nqbcv7kjHi2f7Pyxq1bVGRSqS8HUA8aO+4nUeuQ++5c0d8LdEWd5ZNyoXDYkB5QzvWBa2uDvbDtz7HtmP1cFgMeOmW+Vhxy3xYjXp8eqAKX3lpM9pc4TcCRmvtoWqs3FcJg07Bdy8MX6gbm2PH3OIMeH0q/rVHrLILN5I1Mc8xsCvJT7sP/x7xINyqHgXH3wb+coXI+xpgxU7R3XE0JQ1whGaOqKqqFXe6G8s6FiZvBxCZOwDwodOf41PyiRiDlUpWAR/9EADwI89N0BctCCn8XTZzOFI8E6B6zahuq8bOqp0AgEanyB1UfVZMG5HWfbF5/u3i3x3/EH9TgnndwGu3Aa4m0aF5+jdDLv7aWWOQbTPjSHULHv9gP041tkNRdNAvulNcYcMzgC/0vmzU62D334/C/o6TnTu5U9HULgrm0WTuAECKSRRrkm0s68n/HcLJ+jYMT7fi6+d0HZw7pygwcttxNEt+TQr0APQheTvBLp1ZALvFgGO1rfjkQGhnbnDWzg0Livv0+1xRFJw9ISdhCzsd5dgt+PrScVj97bO18atFo7Pw768tjrqwI03Kd+CNu5bgWM4yOFUjrPUH4Dm1G2a4MH/vL8SVFtwhxtu7kj0WmOXPHlr5YzEqSUQUobiPZd1///246aabMHfuXMyfPx+//e1v0dLSom3PIhry6o4CL30RqC/t8ioKgGvlG6sjuM3cqWLt5qjTRW6OdYBGMJKU2aDHM8vn4Lrn1mPXyQb8ac0RAIg6xDiYzWxAQZoFZQ3tOFTZjLkjIx93c3t9WOlfLdubvB0px2HRtj6tPVyNi6d3H6IZLLApK3HGHDJSjP5imX9jlr+4U9MWvvAiVfeyUGUx6nHJjAL8dcMx/HvrCZw2LnS07a8bSvH3jcehKMDvr5uFkdmpGJmdipdunY+bX9yINYeq8eWXNuGPN80N3SATJZ9PxU/fESuEr19Q1G34+DXzCrG5tAYHmzYDusAKdADYWuofySpO7/Wx9JZ35g24pUTBc+bfIaV0DfCn84HrXwEyBm4T2chq8Tu21NQ5l8TpdcLt8xcgutmWdbyLzp0UkwG5DjO2N46F12iDvq1W5JsVzALqjwOv3gqoPqy1n4+/Vi3Ft8aGnqinmg24cs5I/OPoJBjTtuOj0o8wM2emNiqmeq2Y0dMIyagzgOwJQPV+UeBZcHvgsk8eExlr5jTgiueADhuJ7BYjvnXeeDz42i48+6nocBiXY4dpzvXAJz8T4cgH3gMmXhTycWkpRjQ5PajvuDGrtVYEPANA7hQ0tX/m/zzR/RykGlPQCqDV3UOgsr/LxaKP/++rQ5XNeH61+B4+cslkWE1dd+/NKx6Gt3caoOg8aHW3Is0c6KjTQqT9uUPhuvUAcd+7ek4hXvjsCP687ijOnpijXbb6YDW2HauH2aDD7Wf2rmsn2Rn0OlwwNQ8XTI1+vDmcHIcFL9y5DLt/twhzWj7FF5XV0KXaYWg4CtjyRFdcT878jvgZLV0juvzGnBOTYyOiwS/uQQnXXHMNfvWrX+Hhhx/GzJkzsX37drz33nudQpaJklpjeadnNSNSfRB48QuisGPLFSszc6eJB+gZIwHHcCAlWzwgN1gAY4oo1NgLgIxRYttLwSygaDEw7zbgS38GHigB7vwMuPCX4oE4CzsRsZkNePGWeRgZFKA8spdhytKYnN7l7mw8UouGNjeyUk1RFYXCkQWJNf7Q70hVJljnDhC0McvfJZBlESc7/dW5AwBXzRkBAPjv7nKt+wAANh+txQ/fEmueHzh/As6aEDihmj8qE3++dT5sZgPWHq7BLS9uQos/hLc3/rrxGPaUN8JuNuCeZeO7ve5F0/Nhc5yCqmuGRZ+CmTkztcsGPG8nyKzCdKzxTcN1nh9CtReIAsQflwEntw7YMRSf2AYAOOVr77R9SXbt6BRdt5vXuhrLAkSnnwcGVGaJMQ0c/hhwtwP/Wg601kDNn4F7m5YDULA4TAbWDQuL4WmaAgB4/+iHcHvd2nGqXgtmjkjv/gtUFGD+beL1jc8FOgJK1wGrfyVev/jX4YOeAVw9txCT8gNdS9NHpAGmVG3dM9b9odPHyNydho4bs2TXTsYouAw2OD2ii8luji6jxebf3tXTtqx4du60ujzYdqwOf994DA+/uRtffmkT3F4V50zMwbk9LDyYU5yhbQSrd4aG78uvyesV37MFXRR3AJGlAwCrDlThWI24jwZ37Vy/oDihCvXJLsVkwKyLRPH0Kv2nuNn3mrjggp8DlgjGqNNGAHO/LF5n9w4RRSHunTsAcPfdd+Puu++O92EQxV7VfuCjHwH73xFFmUufBApmRvaxFZ8Df75UBBxnTwBufBNwDGwgNIXKtpnxly8vwBVPr0VVkxNTerENLNi4HDtWH6zGwSiLO3Ika9mk3N5vA/M7bVw2/rTmCFYfrIaqqhGPeGljWQmwKUuSq5fliJnM3Klp775zpy8jZjML0zF6WCpKqlrw312n8KV5hahobMedf90Kt1fFRdPyceeZYzp93NyRmaKD54WN2HCkFre8uAkrbp0XdQfPyfo2/PJd0bVz/3njO+eadJBiMmDi6DLsdwJ2dTKMOhlE7cXuk2JUJx7FnTHDbLCbDdjhHIGDX3wD4z/6MlCxC1hxEXDVC8CEC/v3AOqOIr32KBy2EWjU63C86TjGZwQKZVqYstEGndL182LH68KPZQHAqOxUbDhSi33WOcjHx+IZ+bojYrzWmoH9Z/wBlS+Vwm42YHqY3y1jc2yYm7MIu33/xKnWMmyu2By40GfpuXMHAKZfI8a/ag6KUbCCWcBrt4utiDOuA6Zd1eWH6nUKHrpoEv7vjxvETcnPN/92YN2TosugfIcY8/VLlxuz2jqMZQWFKTcHFTZtUXbupJltgBdo9w7cKvTKxnY0trvh9Pjgki9eH9xe8brT48OxmlbsPdWIfeVNOFLT0unc3G4x4IeXdA5R7qg4KwU6mAG0YndZFSZlBe6TWuaOasTEPHu3P/ujslNxxvhh+PRAFV7eUIrvfWESPjtUgy2ldTAbdLhjiHbt9Cfd+PMASzqy2usBFSLHa8oVkd/A6feLsPKybcC+d4BJF/fTkRLRYJIQxR0awtobgcq9QOUe8dJWB2SOAYaNFwWNrDGAMTFm5KPSdApY9Qvxh1muEq/YBTx/DrDkG8CZDwLGbh5kntwKvOzPncibBix/A0gdGiv8El1hZgre+fppOFDR3OeT4LG96Nzx+VR88LkIoT9/at87HBeMyoRJr8PJ+jYcrWnFqAhHzbSxLEfiFHdk5011c3SZO/L60axClxRFwVVzRuCx9/bj1S0ncOmsAnz1LyJAeWKeHY9dNb3LE7g5xRn4y1cWYPmfNmDj0Vp865UdeOr/ZkdcYFNVFd97bRdaXF7MKc7ATYtGRvRxHvMewAmUlY9EQ6sbaSlG7ClrhMvrQ2aqCcV97EjrDZ1OwfTCNHx2qAaba60Yf8u7wCs3iw1+//g/8Ttz4Z2RPevdGyWroAAYqTNjJ9wobSwNLe5EEKbs9vpQVi9OuMN17shQ5bWYhrMB4Min4kXRAVe9gFUnxN+6BaMzYdCHLyDdvGg87l01Hkb7Hvz7gOgGUL1mDLNbkZ8WQeHC4hBFnE3PAxufF39fG46JTtAv/L8eP3zx2GxcO68QH+ypwFI53pM2HJh8mdiytf5p4PJntOun+Tt36lo6dO4EhSnLjrcUkz7qYrXDnAq0Ai5fe7fX6+u2LJ9Pxcf7K/H86hKsL+n+90k42TYzJuXbMTnfgYn5diwZk40cR8/HoigKrIYUtKEOO8sqcHXQbgita8tn6nIkK9iNC4vx6YEq/Gvzcdx/7nj8buUBAMB184siOhaKksEMTLkc2PIioDMCX/iV6J6LlC1H/M5b/SuxOWvChZ3GJYmIOmJxJxGc2Aw4GwG9WYzWGEz+f83+95lES6bPI0IPvS7/6y7xts4grqtdP+j1flg33iVVBTztgKsFcDUDrtag11sAd6t4vf6YKOhU7BEPKruliMyF7AlA9jgxhhT8tfvcgdcB8SA5+EWnD7yeViieOenPkGBnE7D2CfEiAx4nXAQs/jqw8Vng89fFOtq9b4sunqKFnW/j2Hrgr1eL+8TwucANr3J0KsHkOCwxeTDcm+LOzpMNONXYjlSTHovH9L3gl2IyYHZxOtaX1GLNwaqIizvatqwE6twJFHdE4Ukby2rr+mTM61NR29K3EbPLZw3H/3t/PzYercXXXt6K7cfrkWY14tnlc3oMJp5ZmI4Xb56H655fj3d3ncKT/zuEry/tOmA12GtbT+KTA1UwGXR49MrpnbbghFPbXouSRtHp42wcjze2n8RNi0diqz9MeVZheq8DuvtqZmE6PjtUg23H6vB/C4rEGuB3/M9er/o5sO4pkROz4I7YF7sPfwwAKE4djp2tR1HaGJpxFklxp7y+HV6fCrNBF/bnYpQ/VHljY5b4WyYzZ875ATDmHKz9ZCMAdPtzvWxSLlI/nAkX9mDlMbGMQvVaMWNEFP9v828TxZ3974i3FT1w5Z86rTLvyi+umIZfXDEt9PMt/Joo7ux6FVj2I8AuCs8Z/uJOp8ydkDDl6NegSxlWUdxxd1PcUVW119uy2lxevLbtBP605ghKqsRmQ50COKxGmPQ6mAy6wL9Br+c6LJiUb8ekfAcm5jn6NL7qMNvQ5gT2VYSOzmqdOz5jRMWdsyfmYHi6FSfr2/C913Zh09E6mAw63HlW585CipGFXxMF3AVfFY9ho7X46+JntWqv+NmacU3sjzFS7nagqVw8zne3AR4n4PH/624T5yKWNPFiTRf/mh2BgpQ8R3A2hb6oPhEpYLR2eEkB9KboCmKDUVu9yN5sOC5+V5vtokhvtovvr9khzhOj5fMGnZu1iBfAf/5oFOeQepN43eB/vTfFRa8H8DrF/USer5nt4v4Rzf+tvP/ojICe5Yvu8LuTCD76oVhL3R90BvHLQCty6EXBR77esQASfL3uLgMAd0tQAadFvC27VKJhLwByJgG5k4GULKDmEFB1QGQutDeIX2p1R4GD78fme5I5Bhhztij0jDxd/BEKR1XFLyJ3m/hlpPr8L96g133ieqoPOPIJsOpRoEUE3WL4XOC8n4jAYgAoXgRMvUqcrNQcBF64QLSzL30YMPsDOktWAX+/TvyyLV4iTm4ifMBNyWecv7hzsr4NLU5PRBuK5EjWWRNyYrZG+7Sx2VhfUovVB6uxPMLuj8Ts3BEPcGRAsuzcqXPWwaf6wo7T1La44FMBveUUrnznXCyfvBy3T7+90/W6k59mxWljs7H6oNhYpVOAJ66bpXVq9GTuyEz89LKp+M6/d+HxDw9gQp4d503pPtyzsqkdP35brFK+Z+m4iLe7fHbyM6hQkWMejSaPA3/feAw3LioO5O0Ux6+QPLNQfO7tx+vFO/RG4JLfA8WniWevqw8An/4/YO2TIudl8d0im6KvfF7x+xtAUe4M4MhRHG04GnKVZpcowEayBr0wMyVsoU0GsB+paYU653woW14AJl4MnHY/XB4fNh0RRcglYfJ2JINeh2umno8/n/wHPBAFE9VnxczCKEZEh00Q4cpHPhVvn/VdYMTciD88bBFpxBygcAFwfAOw6Y/AOd8HEBjLagjeluVxAlX7xOt509BUI4o70W7KAoAMq7jfe+Hq8mfc6XVChZiLirS4U9nUjr+sK8XL60tR588LslsM+L/5Rbhp8UgUpA9cR3F2ih0VTuBQdV3I6GxFs9yUZsKCUT1nr+l1Cm5YWIxH39uH17aJwuJ18wqRy66d/jNsPPCNPuSGWdOBJfcCK38E/OceUeSWT/rKJ3PlibiiEye/HmfQv22hb6s+wJ4nfm86Roh/tZdCcVlrNVBTIh6L1xwCag+Lf+uPA+hF9o/ZIY7N1SyenI2GovMXGEzifEYWG/RGcZIP+M871A6PydUO7w+6POS66OL9KmBK8Rer0v3FqvSgwlW6OB7tfEAVf0eCzxHcbYEClqtZ/OtuEx+bmi06s1KHiddTh4liVsPxwDlP3VGg9gjQXt/z90lvFucROmPoeVvw+RsU8fnd/nM2T/fdjuH/P/Rhbl8vijTa24o4Z/I4RVGnq/NCRS++l9ZM8QS2NcP/fWv1F5xa/cfbGniRTPbQIqL8v9HpxBbKjudq8v9m+OxOmyAHIxZ3EkFGsRi/0X4ZOwM/FB1/+HTG0F9seqO403Z1fZ8HgAeIzbbdyBmsImjRlAqYbOKXpHzdliOCfnMmi6JOShcPSlRV5M1UHxDZNdUHxNvyl7vOGPqLHkrgh9nnDfpl7hXfh1O7gZNbxB+q2sPiAaiiA4ZNDFSE5Yvb/29v/pBljgaWPgJMvrRzVXrSxcDIJcD7PwC2vyy6efb/F/ji78Qvw38uF/+PY84Brvmr+L7RoJWRakJWqgk1LS6UVLVg2oieT9Bkcee8PmzJ6ui0ccPwqw8OYF1JDTxeX5cjIVK724tG/7Ptw2yJc2LQsXMnwyKKBT7VhwZng/Z2MHldW/ZG1Dvr8Y99/8Bt026LunvlqjkjsNofSv3tCybijPHDovr4a+YVYU9ZI15aV4r7/rkdr9+1BONzuy4kPPLm52hoc2NKgQO3nxF5Xsbqk+KJhPNHnYk/7tZh36km7DrZgG3+TVmzitKjOu5YmunPcDlU1YymdjfsFqP4HTrjGmDa1cC+t4HVjwPl24ENT4vf4TOuBZbc07tnxaVTO8XfYJMdI4cvBI682alzp9ElTqS725TVXZgyABRniuJOY7sHDYu/h/SRS0SovaJg27FatLm9yLaZMD63+0LdzQsmY8XLo6FPPQRAhClHlLcTbPE3RHGn+DSR7RELC78mijub/yRu02jVApW1zh1VBQ59JP4mW9KBtBFoLhNPiNgt0YUpA0BWSuBnpN3THjbsuj3ocVEk27Le3VWOe/+xHS6vOCkZkWHFrUtG4UvzCntVgOqrYakOoE4UGI/VtmpF430VIkvMZrIio4esLemaeYX4zUcH4PL4YNLrcAe7dhLfgq8Cm18InPj3VW2JeOkNg0U8ljdYAi9G/78+j3hCVr7Ik3FnY4cbUcS5gNkuChKKzn/y3h4oPsiCgOoLPC4faPLrQE8TBgMgNccfdK+KIlF7o79Y5O+28TqBVmcvb1wJnKcB4kltj8s/JdKh41L1At6+nFAq4nzN6xK31VojXqLlahIvjSei+7hoi4tJisWdRHDpU11fpqripF9R/F04PZxwyOvLQpH8AQquXIa8Li/zhbmeN6hI0uEyqIAxNaiAE/RiTInNXLCiiEKQLQcYeVrfbw8Qv6iPfiaCLEtWiYJR5Z4Ij0cX5kUfqFRb04FFd4tnlPXdPEi1ZgCXPQVMvQL4z71iNO0vl4vbUr1ijOvqF8UzMjTojc2xoeZILQ5VNfVY3DlU2YSSqhYY9UrIOtu+mjY8DWlWIxra3Nh5sqHHLCHZtWMy6OCwJs6fkWz/6EONP1DZoDMg3ZyOemc9atpqwhZ3xNfiA1LFmEhVWxUO1x/G2IyxUX3u86fk4cKpeSjMTMFXoyi2BPvBxZOxv6IJ60tqcdufN+PNu5YgPaXzSdt/d5Xjv7tPwaBT8NhV02HsoRgneX1erC1bCwBYNvIslE1V8Mb2Mvzuo4Moa2iHTgFm9LRxqR8Ns5u1sZGdJxpCO1h0OmDyF4FJlwCH/wes/rUI8N32F/FizwcKZouA4OGzxOtdPXHQkX8kCyNPQ3G6ONntzVhWT8Udq0mP/DQLyhvaUdJiwOyg8OLPDosHuIvGZPdYWMxxWDDRvhgHff7ijs+K6cPTu/2YTsadC9y1STy5FKscj4kXA2lF4m/arleA2TcizSr+FpobjgCr3gd2/ks8uQKI5QKKomXu9GYsKysl0B3X6mkNW9yR2TQmnQn6Hr7WdrcXP3zrc7i8PswoTMdXzxiN8ybn9ljw7k92k/9r1LmwpbROK+4cqBT3mWG2yLt7M1NNuHRGAV7ZcgLXzi9EfloSZhoONaZU4M61oiDjdQU9mesK/Vf1hRZdDObQ4ot8TNlYLkZCG44DDSf8LyfFv84Gcd3M0SLzMmus6HbPGiveTh0W+SiNxyUKO+0N4qTabBcvxtTuIyO085g20b3RMYoiOJIBSmjXCII7SBDm/eGuG+42FPG52+vFWFR7g3hde7teHKeiBJ0HdJh2MFqDilj+F4NFPJHQUi2erA5+cTaLDqqMkUDmKPFvxkggvTjQ3d+R1yOKHLJDyOcJOqcL08FiTBEvwedsBkvX/6c+X+B7HXw/CzmXVEPfp/qCRrnMgZEugzlwHutuF9+HtjqgrTbwuqL3f99S/aN5/n9NKYHRPY+r8/+FLMSpvvCTJ/IlvTiy+26SS5xH5RSeokQ3Symv35v5y6HAkgZM/IJ4AcQftMq9/l9EwX8ELeKXiMEsupD0xtjP/Y5dCnxtnVhzufE58Qt46pXA5c92XxyiQWVsjg0bjtTiYEXPuTvv+4OUF4/JhqMXz3J3Ra9TsHhMFv67+xRWH6juubjTHNiUFa98lnC0zp2mwDNYmZZM1DvruwxVrm52Qp9yBD5dYM3wuvJ1URd3LEY9nr5hTi+OOsCo1+EP18/BJU+sQWlNK77+92148eZ5ISeW9a0uPPSmWLF+x5ljMKUg8nGcXdW70OBsgN1kx/Rh03HNvAa8sb0MK/eJzokJeY6IRgP708yidJysb8P24/Xhx5MURfzuHLsUOLYBWPNr4OAHIgti/zuBHBlAPJArmCVasQtmiy1O4QKZS1aJf8ecjWKHePBX56xDg7MBaWbx/W12i59Ph6nrQOfj/uLOiIyuT5iLs1JQ3tCOo9UtIT9naw+Jrq8lY3rOTgGAr879Ir618c8AAJvRrgUXR2XY+J6vEw29QWQiffADsRZ93PmYduLveMP0d8wsLwHK/dczWMXf4DO+DQDatqxeZe6kmKH6jFB07i7Xobd5Iw9Tfnl9KSqbnBiebsW/vroQZkP8A2xlwUrRObGltA5XzBajiEdr64FUIN8RXcj4I1+cgsVjs3DhVG7fTBoWR+SbVnuS2c2TD85m/xO0MShmGkyAITv6fLTg8xhL3zaSDnp6Q2CkqT/odIDOX6CJ5fPNRgtgzO/dBmAzgNTI/k4OVSzu0NCWNly8xIvZBnzhMTF2UHUAmP4lbkMYYqIJVZYjWef3kMfSG6ePG4b/7j6FNYeqcM+y7kdc+rI6vD9l+TN3WlxetLm8sJr0yLRkoqShpMviTlWTEwaH6NqxGqxo87RhXdk6LJ+8fMCOO1hmqgnP3zgXVz69FqsPVuPR9/bh+xdN1i7/8dt7UN3sxNgcG76+NLoClBzJWlKwBAadAQtHZ2JkVgqO1oiixOw4jmRJswrT8c7OcmzzBzx3q2iByCVzNouA3rKtYm3wya2iO6S+VLzsecP/AYoY3yqYFejyyR4nQuwBYPRZSDGmIMeag8q2SpQ2lmL6sOkAAp07NlPXI1NyDXpXnTuAWEm9vqQWR6tbtPe1OD1azlB3eTvBzps4HsbPRsFtPIKc1PSIPmZAzL4RWPVLEQD7+ARMhAroAA90MIw9B5j2JTGKFvRMtAxU7s3IU3qKEarP3H1xx//+nvJ2Wl0ePPOJ6Cr6+jljE6KwA3Qu7gAiE6i2rRnmVKAoPT2q27OZDbh8Vgyyqmjw6apDhIiSRvz6TIkoYPgcYOZ1LOwMQeNyREv9oaruiztl/lEVRQHOnRy7vB3p9HHipHLrsXptTKIrlQla3LGbDTAZxJ81maUjQ5Vr2sPPdVc2tcFgF2uZ75hxBwBgc8VmuLyusNcfCJMLHPjV1TMAAM+vPoJ/bxFz5R/vr8RrW09CUYBHr5we9cnn6hOiuHPacDHmqigKvjQvsD2wp46tgSAzf7Yfr4eqRph5ZraJwPpFdwFX/lEEmH6nFLjxTWDZD4FJXxRhoVDFKO7OfwLvfQd44TzgsVGi1dxeAGSLTpbiNNG9EzyaJTN37MYIxrK6WSU/MisQqixtPFILj09FYaYVhd0UhoIpioIbJ98M+Ky4YuLSiD5mQFjSRIEHAKCiPXcWHnHfhHPxLHDDv8UTGR1OIAPbsqLvPkqzGgGfKOp2VdyJdFPWn9eVorrZhaLMFFw5J3GKHykG/31C58L+iiY0truxvqQWik78nk6zRBbcTkREgx87d4iI4kh27pTWtIqQS0P4mvu7u8RMw5yijH4pqhRmpmhdHOtLarstIGmbshKsuKMoCobZzDhZ34bqZicKM1OQZfWvQ++ic+dQ407oDM0w62xYPmk5Xvr8JdS212JH1Q7My5s3kIcf4qLp+dh3aiye+N8hfPf1XchLs+D7r4kOo1sWj8KcKLdaVbdVY2+tWIG+ZPgS7f1XzR6Bxz84AK9Pjfo2+8OUgjQYdAqqm504Wd+GERm9DJW3pouNiKPPCryvuUp09pRtE10+J7cGthtOulgbvS12FGPTqU0hxZ2etmU1tLlR79+qVNjNMcuNWaU1gc6dz7SRrOjGF+5dfAXuXXxFVB8zIJb9UGzfKpiFJuNwvPSzj6D4AK9PhT7MFjFZTO5N547DaoTqE0WhhvbwBXJZ3OluLKvZ6cGz/q6dbywdF3GO1UBINYr7TKrFC6cKbD9Wj/UlNYAiCtDRrncnIqLBi8UdIqI4ynWYYTMb0Oz04GhNS9gNSV6fij+vEyeal8/uvzHC08cNw9GaUqw+WBVRcSfROncAsQ5dFHfEiY/WudMWvnOntH0doAcmpy2CUW/EwvyFePfIu1hXti6uxR0AuG/ZeOwtb8JHeytww582QFXFyM+3zo8+K2XNyTUAgClZU5BtDRQRchwWPH39bDS2e7TCQzxZjHpMyndg18kGbD9e3/viTji2YcD488QLIIIgG8uAuiOie9JvpGMkgNDOnZ4ClWXeTrbN1G1u0Si5Dr26RVtrLcOUF0c4kpXwDGaRHwcgzSO23qgq0NjmDrvVqS+ZO3azAVDFbda0hi/uRDKW9eKaI6hrdWN0dioum1kQ9XH0J9m5k5bqQy2ALaV1WH+4BopJFMUiyRIiIqKhIXGemiAiGoIURekxd2fV/kocq22Fw2LA5bP6s7gjTi7lSu+uVDWJZ8ITsbiT1WEduizuhOvc8fq8qFO2AAAW5Z4DAFhcsBgAsK5sXb8fa090OgW/uWYGxuXYICeUfnnFNKSYoj8JliNZp484vdNl503Jw1UJNIYiV6JvjyR3py8URWSujTxNBOj7FdmLAPSuuNPTWJXM42lq96C2xYWaZif2louRr8URhiknE5NBp3XkaOvQO2hu731xR6dToPcnfda1dV/c6WoNekObG8+vFquh71k2Lq6bscKRnTspFrGC+L3dp1BS3QJFx84dIiIKlVh/wYiIhiBZ3OlqY9aKtUcBANfOL+rViX2kFo3Jgl6n4Eh1i3ayGk5gLCvxnjHO9ocqy41ZWZaux7K2Vm6FT9cE1WvFaSMWAQAW5i8EAHxe8zkanA0DccjdsluMeP7GuZg2PA3fWDquV90dbp9bK1adPrxzcSfRaMUdf8jwQJOZO0cbj2q5P03u7os7Mm+nu5EsQHQmFaSJn5ujNS1YVyK6dibm2bVtb4ONXIde3xo+x6ovmTsAYNSJ72dPxZ2uiiB/WnMEje0ejMux4eLpidW1AwQClY0G8f3bX+EP97aKrigWd4iISGJxh4gozrTOnTChyocqm7D6YDV0CrB8YXG/HofdYtQ2JnXXvZPYY1nimGpa/GNZ1q47d9478j4AwNM0GXkO8X+Qm5qLMWljoELFhvINA3HIPRqZnYr/fP003H9u71ZX76jcgSZ3E9LN6ZiSNSXGRxd7M/33wV0nG+D2+gb88xfaCqFTdGjztKGqrQpAUOdOF4HKWphyBIHII7XRrFas9Y9kLRqEXTtSun9Ne1edO03O3m/LAgCTv7hT394S9vLuMnfqWlx4Yc0RAMB9544PmwkUb7J444Mz5HuUamFxh4iIQrG4Q0QUZ+O6Gct6aa0YDVk6KTfiTTp9cfq4YQCA1Qerwl6uqiqqmhO/uFPVw1iW1+fFh6UfitebpiMzKAtkUYHo4llbtrbfj3cgaCvQhy+BPgk28o3KSoXDYoDT48P+U00D/vmNeiOG28T4Y2ljKTw+j9b90eVYVp24PJriTmlNC9b2Mkw5mcjiTkNrF8Udf6Byb8ayAMDsH7dqdHZR3PF2vS3r+dUlaHZ6MCnfgQum5PXq8/c3OZbV4mnRtskBgMUkxrSYuUNERBKLO0REcSY7dw5XNcPrC6x/bmhz499bxRrsWxaPHJBjkbk7nx2qhidM10R9qxturzhGOQKVSLK6GMtqcbdoz+ADYiSrzlkL1WuBA5NDnrGXxZ315esjX8edwGRxJxlGsgCRozLDP5q1LV6jWY7AaJbclAUANpMt7PUjzdwBRPEKED9jR2taodcpWDA6s6+HnLDSreJnsq6Lsay+BCoDgNUfONzsCj9K2tVYVk2zUxt5vW/ZOOgSsGsHCAQqt7nbMLtIbLTTKYDOvwqdnTtERCSxuENEFGcjMlJgMujg8vhwoi5wgvLK5uNodXkxPtc2YGMb00ekw2ExoLHdg50nO2fOyI6Y9BQjzIbE6wIZ1iFQOdWYCpNOnFwGd+98cPQDAICnaQqG2UJPyOfmzoVBZ8DJ5pM43nR8IA6735xqOYWDdQehQMGSgiU9f0CCmDVQocpdkBuzjjUe00ayrAYrDLrOBQivT9V+bouyei7uFPuvs9X/tU0fkdbrvJlkoI1lhencUVW1z5k7srjRVXGnq7GsZz45jFaXF9NHpHW7HTDeZOZOq6dVK77PH5UJp6/nFe9ERDS0sLhDRBRnep2CMcNCQ5WD15/fvHgUFGVgnlXW6xQs8Yf2rj7QOXenstE/kpWg4a/Z9tDMHUVROuXueH1efHTsIwCAu3Fap/GyFGMKZg6bCSAxtmb1hVyBPm3YNKRb0uN7MFGQuTvbj9fF5fMHd+70FKZ8qrEdbq8Ko15BnqPnE+1RHVbOD+aRLCBoLCtM5k6726d1K/Y2c8dm8hc/3N137gRvy6psbNd+v9537vgB+/3aG3Isy6t6MXVECv5952I8cd3siFa8ExHR0MLiDhFRAugYqvzxvsD688tmDewGF5m7s+ZQ59ydqmbxbHGOI0GLO/6ikxgfE2NlHXN3tlVuQ3VbNcy6VHhbxoYtVMnRrHXlyV3c2VOzBwCwIG9BnI8kOjNGpAMADle1hC0K9LciR2AduuzccZgcYa97rEYUFYanWyMK5C3MTEFwLWHx2MEbpgwExrLCbcuSeTs6BUgx9a4TUBZ32rxtYS8PVwT5w6rDcHp8mFWUjrPGD+vV5x0owcfd6mnFnOIMDLObtY4kq57FHSIiEljcISJKAGOHhYYqv7TuKID+X38ejmz933qsXjv5krRNWQnauZNuNWon2DXN/o1Z/uJOTZvYTPRBqRjJyjfOBWDQun2CLcoXxZ2N5Rvh8Xn6+7D7jSxo5aYk7thJOFk2sxZOvPNE/YB/fjmWdbzpOOqd4vPbjH3P2wHkOnRxQm426LQclcEqrZttWcGbsnrbPeMwic4Wp7c97OUdx7LKG9rwtw3HAADfPHdCQnftAIBO0WkFHtmd5PF54PYxc4eIiEKxuENElADG5frHsiqbcbBi4Nafh1OYmYJR2anw+lSs869qlrSxrATclAWIMF65+Urm7shQ5dr22pAtWQ7vHADhC1WTsybDYXKgyd2E3dW7B+LQ+4Us7mRYkq+AMDOOuTt5qXkw6Uzw+DzYX7sfQHebsiJfgy7J0ax5IzNhMSZedlUspVtFcacuTOZOX/N2ACDNIr6Xri6KOx07d/64+ghcXh/mj8rEkiTpmpKhyi1usREsOByemTtERCSxuENElAC0jVmVzdoGl2UDtP48HNm9s/pgaO6ODFTOsSfuCUV2h1BlmblT016jjWTZjXb42saJ69s7b/3S6/RYkC9GmZJ5NKuuXWTWyO6lZKIVd+KwMUun6LTRLFnc66q4c6w2+uKOXGl93pTk6qjqjQx/sbUhzFhWc3vfNmUBQIa/uOP2ddG502EV+g7//en6BUUJ37UjyVBlWaiS/ypQYNYnZqGdiIgGHos7REQJYGRWKvQ6Bc1OD/61WWxounnJyLgdj8zdWX0wNHdHG8tK0M4dILCivdo/lhXcuSNHss4uOhs1TV4AwDBb+EKVthK9bH2/Hm9/qmkXnVeywJVMAqHK9XFZSS9Hs3bXxL64c9fZY/HP2xfihgUD35k30GTnTtixLP/YZ2/DlAEgI0UUxj1whr2841iW/P8anR1+zC4RyVBl2bmjhUQbLElToCIiov7H4g4RUQIwGXTaimS3V8WEXDsWjY7fyMDC0Zkw6BQcrWnVAmMBoDIJijsd16HLrpXqtmp8VCq2ZJ0/8nyt+BOucwcI5O7srNqpnVQlE7fXrYUBZ5qTr7gzOd8Bo15BTYsLx2vDh+X2VXlDG25dsQnrS2o6XSY3ZsnvYZdjWVFm7gAid2fB6CzoIghgTnZpQduyfL7QIp3M3OlL505Wivh/8XVR3Akey2pzebXfYdEU4+JNjmW1esR9jZuyiIgoHBZ3iIgShAxVBoCbFo+M6zOydotRC3pdHbQ1S3bu5CRwcSdLdu40hRZ3tlZsRVVbFexGO+bmLECtf116dhfh0CPsI1BoL4RH9WDTqU0DcOSxVecUI1l6RQ+HOfymp0RmMeoxOV8c97Z+Won+9w3H8L99lXjq40OdLpPFHSlccafF6dGKhPEaoUx0af7OHVUNZOxI8m1bHzJ3hqWK/xdVcaHd7e10eXCXywl/PpLdYtCKTsnAagwNVGZxh4iIwmFxh4goQchQ5TSrccDXn4ej5e4cELk7To9XW0udyJ07slhT4y/eZFlFB5TcLnN20dloahcdBHqdgoyU8J07QKB7Z11Z8uXuBIcp65Tk/HPf37k7+06JrpzdJxs6jX6NTBsZ8na4bVlyxMdhMWhFDAplNui1Ned1HXJ3YpG5k2kV/y+KzoXGMKNfWuaO3tqrEbpEkGoIHcvqmCNEREQEsLhDRJQwLpiSD7vFgHuXjRvw9efhnOYv7qw9XA2P16d17Zj0uoQ+ke0UqNwhTPi84vO0ryUz1aStTg9H5u6sLVvbH4far2rbkndTliRzd7b208YsWdypa3WjrCE0kLfIXhTytsPUuftpb3kjAGBCXviRLRJkAbVj7o7M3LH3IXMn1SQKNYrOjbrWzqNZbe5Al0uyFndkoLI2luX/miz6xA22JyKigcfiDhFRgpg2Ig27fng+blkyKt6HAgCYPiIdDosBje0e7DzZEBKmnMghntn+riJ5vBnmQHHDZrRhUcEibetXuDXowebnz4dO0eFo41GcajnVT0fcP2qdoriTjJuypAWjRNfVzhP1WrEuVlqcHu1kHwB2nWgIuTzTkgm7MVC0CTeWtfukKO5MKUiL6bENNrIYXN+xcycGmTvB3StVLU0hl3l9Xrh84nNaDJakLe7IQGU5lqV17hjZuUNERAEs7hARUVh6naJ176w+UK0VS7ITeCQLALJSQ7dlGfVGrevi7MKzYdKbtDyenr4Wh8mBqdlTASTfaJbs3EnGMGWpIN2KqcMdUFXgf3srY3rb+ytCCwGfl4UWdxRFCcndsZk6j2Xt9n/M1OEs7nQnPShUOZiWudOHzh25BQsAqpqbQy6TRRBAFIF6E36dCLoKVGbnDhERBWNxh4iIuhS8El3blNVDt0u8yTyg2hantp1nuG04AOCCURcAgNa5I9emdydZc3dk5k4yrkEPdt7kPADAB3ti2zm1/1RocWfXyYZO1ylOCxR3Onbu+Hwq9pSJzp2pw5MvsHogyeJOXUto505gW1bvxzx1ig461V/QbQ39P5VFEAAw681J27mjjWUxUJmIiLrB4g4REXXptLGic2fb8XqUVIkwzxxHYhd3Mv2dOz41EOD64yU/xs9P+zlOH346AKC6Sbw/kmBombuzvnw9fKqvPw65X8htWck8lgUA507OBQCsPliNVpenh2tHThZ35haLsb1wocrBnTsdM3dKa1vR7PTAbNCFbLqjztJ7yNyx9WEsCwD0ivg5rmvt0LnjCQ0eTtriTledOwZ27hARUQCLO0RE1KXCzBSMzk6F16fi7Z1lABK/c8eo1yHD3ykgR7MmZk7EJWMu0bKCqiPM3AGA6cOmI8WQgjpnHfbX7u+no469wRCoDAAT8+wozLTC6fHhU//mtliQYciXzRoOnSLuKxWNobk+Ix0jtdc7bsva7e/0mZTvgEHPh1PdSdcyd0KLO7HYlgUARkUUOeraW0LeH9zhUtXsRLvbB50ixv2Siezc0bZlebgti4iIOuOjESIi6pZciV4ZFKic6LI6bMzqqCqKr8WoM2LGsBkAgD01e2J0hP1vMAQqAyL75txJYjTrwz0VMblNVVW1zJ2ZhekYlyNGrnZ3GM2SnTtGnRFmfeh9RV6XI1k96ylzx27u2/Y9kz97pqE9fOeORW/R8nby06wwGZLr4a/WucOxLCIi6kZy/XUjIqIBJ3N3pJwkKO7ILJ2uijvVWuZOZF/L2IyxAIBD9YdicHQDQ3buZFmy4nwkfSdHs/63rwIeb99H4yoanahvdUOnAGNzbJjiL9B0zN0ZnzEeZxWehesnXd9pQ5wWpsxNWT1Kt/rHsvphWxYAmHWiyNHoDN+5k8ybsoCgbVn+sSx27hARUTh9+2tKRESD3sIxWTDoFHj84cTJ0LmTrXXuuMJeXhVlcWdM2hgAwOH6wzE4uoEhA5WTfSwLAOaNzEB6ihF1rW5sLq3DwtF9K1jtOyVGskZlp8Ji1GPa8DS8tvVkp41ZBp0BT5zzRKePV1VVW4POTVk9S5OBykFjWV6fqhV3+pq5YzVYARfQ5GoNeb+2MtxgxbEaUehJxuIOA5WJiCgS7NwhIqJu2cwGzC4KFAhyHIkf4pndzViW2+vTsj8iLVSNSU+u4k67p117lj/Zx7IAwKDX4ZyJOQBiM5olw5Qn5ouOHVmgCbcxK5wTdW1oaHPDqFcwLpdhyj3J8AcqB49ltQSFY/e1cyfFKIoczR2KO2E7d7KSsLjTIVBZFq0YqExERMFY3CEioh7J3B0gsvXh8aaNZTV1Lu7U+Lt59DpFC3rtiSzuVLZVotHVGKOj7D917WJTlkFn6BQEnKyCV6J33GoVrX2yuJMrsnYm5zugKGJcq7KpvcePlx0+43PtMBv0fTqWoUBm7gSPZcm8HZNe1+fvYarsbAlafQ6EdrjIzJ3CJO7ckYHKssjDzh0iIgrG4g4REfXorAmiayLXYU6Kk1nZuVPT0nksS4YpZ9tM0OmUTpeHYzfZkZsicl9K6ktidJT9R45kZVoyO2XFJKszxmfDbNDheG2bFobcW/s6dO6kmg0YnS1yTT4/2XPxThvJYt5ORGQRtaHNDZ9/vDNWm7IAwGYSxY/2DsWd4GyaZM7ckZ07bZ42+FRfICianTtERBSExR0iIurRtBFp+N21M/H7a2fF+1Ai0t1YVrRhypLs3kmGUGVZ3BkMYcpSismgdZB98HnvR7PcXh8OVfqLO3l27f3T/KNZHTdmhaOFKXNTVkQc/uKOTwWa/Dk7Te1iRKuveTsA4DD7izve8MUdo2LGqUbxejIWd2SgMiC+JtmRJIs+REREAIs7REQUoUtnDseCPgbZDpRsf5ZOuLGsQOdO74o7yZC7M5jClIPJrVl9yd05Ut0Ct1eFzWzA8PTAWEukuTsiTFlcZwrDlCNiMephNYqOPzma1RSjTVkAkGYRo4c+ONHu9mrvl0UQr1cUl2xmAzJS+rZ2PR7MejN0injI3uJuCVnxTkREJLG4Q0REg05WqlyF7uqUzyI3ZUW79WtsevKsQ5eZO4MhTDnY0km5UBRRgCmrb+v5A8KQI1njc20hY3myuPN5WfdjWZVNTlQ3u6BTgEl57NyJVIaWuyM6dmTmjs0ci+KOv7NF5woJbW7zd/K43KKwVJiZkpRjioqihIQqc1sWERGFw+IOERENOrJw4/L6tA4Bqa9jWcmWuTOYZNvMmOPf3PbR3t517+wrF8WbCR0KM5MLxNsn69tQGyarSZJdO+Ny7LCaEj9/KlGk+Tdm1fuLL4HMnb530sjCh6J0KO64RRGkzSX+n4oyk7cYEhyqzMwdIiIKh8UdIiIadCxGvdYR0HE0S45lRdu5MyYteTZm1bTXABh8Y1kAcN4UMZrV29wduQZ9Ur495P0OixGj/KHK3eXuyDDlKczbiYoMVdbGsvyZO/YYdO7IDhZF59I6g4DAyvCWdvFwNxnzdiStc8fNzh0iIgqPxR0iIhqUtHXozaFdGIHOnehWuttMNm1jVqLn7sixrMEUqCyd61+Jvr6kJqRLI1JyLGtCrr3TZVP83Tvd5e7Iy7gpKzrpHcaymmOYuaMVOTqMZckOl6ZWMYqVzMUdGarc4m7Rilbs3CEiomAs7hAR0aCU1cXGLK1zJ8qxLCB5cncG61gWAIzKTsXYHBs8PhWr9ldG9bGN7W6c9Gf1TAyTlzNNy93purjzubYpi8WdaKTLsayOmTsxLO4oOnfoWJa/w6W+RRR3CpO4uCPHsuTPNsBtWUREFIrFHSIiGpRkZ05Nh+KO7OSJdiwLSJ7cncG6LUs6z78164Mot2Yd8Hft5KdZkBZma1JPG7Oqm50obxBdEzKjhyKjde60ybGs2GXuWI3+zh0lfOdObbMIVU/mzh1ZyJEjl4DYokVERCSxuENERIOSDEyuChrLcnq82slftIHKQP907uyp2YNndjyjnYj2laqqg3ZbliRXon+yvwpOj7eHawfs9Rd3JuZ1HskCAqNWx2vb0NDaeeRLbtIanZ0aky1PQ4nM3GnQxrLEv7H4PgZn7oTbluV06aEowPCM5M2okZ07NW2iuGPWm6HXMdCbiIgCWNwhIqJBKTvMWFaNv9Bj1CtIs0bfMTA6fTSA2GbuPL75cTy1/Sk8v+v5mNxem6dNy+QYrMWdGSPSkWM3o9npwbrDNT1/gN/+U+E3ZUlpKUYU+jcq7Q4zmiWDlqdwJCtqgc6d0LGsWGTuaONJOhcaw4xlqT4T8h0WmA3JWwzp2LnDvB0iIuqIxR0iIhqUsv1jV8HbsmShJyvVDJ1Oifo25casqrYqNDi7zmWJxvGm4wCAl/e8HJKn0VvayZ/eMmi36eh0Cpb5u3c+jGI0a1959507QCB3J9zGLC1vhyNZUUuzijHJOv+2rP4IVO7YuSO74VTVlNR5O0BQ5k6b+B0xWH+2iYio91jcISKiQSk71Z+50xIYy5Jhytn26DZlSTaTDXmpYltTSUPfc3c8Pg8qW0UocKunFS/ufrHPtxk8kqUo0RewksV5QcUdn0/t8fqqqmJ/hb+4k991cWdKQde5O3INOsOUo5eREjqWFdPMnaBA5brWwHij7NyBz5jUeTtAYFtWcPGWiIgoGIs7REQ0KGmdO82dO3d6sylLkqHKscjdqW6rhlcNZMb8fd/fUdVa1afbHOxhytKiMVmwmQ2obHJix4n6Hq9f1tCOpnYPDDoFo7NtXV4vsDGrMeT9Da1uHKttBRBYmU6R07ZldRjLimXmjrj9Fu11rXPHZ0r64o42luXP3GHnDhERdcTiDhERDUpa5k7IWJYr5LLekKNZscjdKW8pBwAMtw3HzGEz4fQ6+5y9M9jDlCWzQY+zJgwDALy3+1SP199XLoo1Y3NsMBm6fvgju3KOVLegsT0w4vN5uejkKcy0aoUKipyWudPqgs+nosn/vY3FWFZw/kxDe+fiDlQjirKSu7gjO3fqnfUAWNwhIqLOWNwhIqJBSa5Cb3F50eYS3TFyLKs3a9ClWG7MKm8WxZ381HzcPetuAMCrB17V3t8bcmxjsBd3AOCiafkAgLd3lkNVux/N2ufflDWhm7wdAMhMNWF4ujhx3hPUvfO5HMkq4EhWb8gAc58qcnecHh8AwG7u+1iWTtHBrBMFnkan6K5ye93wqKI7SPWZMCIjuYs7spijQg15m4iISGJxh4iIBiWb2aB1aMhxrCr/v33q3PGPZZXU9z1zR3bu5KfmY0H+AszPmw+3z41ndz7b69uUY1lDobhz1oQcWI16nKxvw84T3Qdc79PWoPc8UiXHroJDlWUGD/N2esdi1MNiFD+PJ+ratPfbYtC5AwAWf7Gj2dUKVVW1NegABkXmjgxUlrgti4iIOmJxh4iIBiVFUbRsHa24owUq9724E4uNWbK4I0OaZffOG4fewLHGY726zaEylgUAVpMe50zKAQC8u6v7bie5Br27TVlSuI1ZcjU683Z6L8M/zna8TnTXpJj00Pdia104KUZR3HGrTrS7fWhz+9egqzpYjSatky9ZybEsiZ07RETUEYs7REQ0aMkTOpm1E4tA5VRjKvJTxThQX3N3TrWIrJh8m7i9WTmzcNrw0+BVvXhmxzO9us2hEqgsXRzBaJbT40VJlchi6WksCwh058hunWanB0eqxcdP4VhWr8nRrOO1ovASi7wdKdXf2SLXobd7/Xk7PiOKMlOTfnOcDFSW2LlDREQdsbhDRESDVnaHzp1qLXOnb8/ij04fDaDvuTvBY1mS7N55u+TtXhWPhtJYFhDZaNbhyhZ4fCocFgPy03o+KZbFnZLqFrQ4Pdhb3ghVBfIclj7lNQ11MlT5hL9zJxabsiSt+CGLO3JTlmpCYZKPZAGdizvs3CEioo5Y3CEiokEry9+5U9PsRLvbi0b/+uVhtr496z02TYQqlzT0LXcnOFBZmpI1BUuLlkKFij9s/0PUt6kVd6xDo7gTyWjW/go5kuWIqINjmN2MXIcZqgrsKW/UxrOmDudIVl+kW+VYluzc6XuYsiSLHYrOhfpWF9o8/sydQZC3A3Qey7Lo2blDREShWNwhIqJBK9C540JNixjNMul1cFj71jEgc3f60rnT5GpCk1uE/MrMHelrM78GBQo+KP0A+2r3RXybqqoGijvmoVHcAXoezdpX7g9Tzu95JEsKzt3Z7d+UxZGsvtE6d2pF504sx7KCizsNbW6tuKP6TCjKTP4ul46Byh3fJiIiYnGHiIgGLVncqWp2amHKWTZTn/M35Dr0vmTuyLwdh8nR6Vn58RnjccHICwAAT217KuLbbHI3weMT3UlDJXMH6Hk0K9I16MFkIWfXyQZ8XsZNWbGQ7g9UPlEf+8wdbUxJCR3LgmpEUVbyF0I6Ze6wc4eIiDpgcYeIiAYtuRWruskZlLfT98wUmblT3Vbd641ZMm+nwFYQ9vI7Z94JnaLDqhOrsKtqV0S3KTdlpRpTh1Tgak+jWfujWIMuyc6dLaV1OFjZDIBjWX0lO3dcHh+A2GbuWI2hnTutHtEdJDp3kr+4Y9QbYdQFxtiYuUNERB2xuENERINWdqo/c6fFhSp/qHJ2HzZlSbHYmCU7dzqOZEmj0kbhktGXAACe3P5kRLepbcoyD52uHamr0az6VhdONYoujmg6d2SXTmlNK7w+Fdk2E/IcQ6dg1h/SraEZO/2RuQOdC41tbtS2iu1m8BkxIiP5iztA6CjWUCreEhFRZFjcISKiQUvr3GkO6tyJQXEH6HvuTrhNWR3dMeMOGBQD1patxeZTm3u8zaEWphysq9EsOZI1IsMaVadIrsMcUgicUpCW9Ou040127kgx7dzpkLlT3ihyksx6CyxGfcw+TzylGgLjm+zcISKijljcISKiQUuenNe3ulHu797I7uMadKmvuTuRFHdG2EfginFXAAD+uOuPPd7mUFuDHqyr0ax95YFNWdFQFCVkDIsjWX0nM3ekWGbudFyFXtksino20+Do2gHYuUNERN1jcYeIiAatdKsRep3otpC5K7EYywKA0Wkid6fXxZ0wa9DDuX7S9QCAzRWb4fK6ur1ubdvQLe4A4Uez9lfIvJ3IR7KkaUEBylO5KavPOnbu9Mu2LMWN+jY3qlvE/7vDPIiKO0Ghyh0DlomIiFjcISKiQUunU5Dpz92RxZ1YBCoDgc6d3o5l9ZS5I41KG4VMSyacXif21Ozp9rp1ThGoPFSLO+FGs+RYVjRr0KXg1efclNV36daOnTuxz9yRY1m1bc3+z5na3YclFXbuEBFRd1jcISKiQU126jQ7PSFv95XM3Klpr0F9e31UH+v1eVHRWgGg584dRVEwK2cWAGBr5dZurys7d4ZioDLQeTTL51ODNmVFX9yZXZwOq1GPoswUjMhgxklfDUTmDnRONLS50dAutmVlpUT//56ogrt1mLlDREQdsbhDRESDWrYttFsgVp07KcYUFKSKNeaHG6Ibzapqq4JX9cKgGJBtze7x+lpxp6KH4s4QDlSWgkezjte1otXlhcmgw8is6Ds4cuwW/Ofrp+Efty9kmHIMWIx6mA2Bh579Mpalc6OxzY0mlyju5NhsMfsc8ZZqDNyHLXp27hARUah+K+787Gc/w+LFi5GSkoL09PSw1zl27BguuugipKSkICcnBw888AA8Hk9/HRIREQ1BHbdjxapzBwh070SbuyPDlHNTc6HX9bzJZ07uHADAtspt8Km+Lq9X6xzamTtA6GjWK5tPAADG5dhg0PfuIc/YHBsK0tklESsZQaHKMS3uGP3/R4oL9a1utLnbAAB59sEThB08lqV9vURERH79VtxxuVy4+uqrceedd4a93Ov14qKLLoLL5cLatWvx0ksvYcWKFXj44Yf765CIiGgIygrq3DHpdXDE8ISyt+vQIw1TliZmToTVYEWjq7HbQtJQD1QGQkezXvzsCIDoN2VR/wkezbKZ+ydzx+NTAZ0bADAsdZCOZelZ3CEiolD9Vtz50Y9+hPvuuw/Tpk0Le/kHH3yAPXv24OWXX8bMmTNx4YUX4ic/+QmeeuopuFzdbwMhIiKKVHCnzjC7OabjNbK4U1JfEtXHRbIGPZhBZ8D0YdMBdD2a5VN9qHfWAxjaxR0gMJrV4vIC6F3eDvWPNGugoNM/Y1n+x5CK+NdiHDzjS7Jzx6AYYNTHrjBGRESDQ9wyd9atW4dp06YhNzdXe9/555+PxsZGfP75511+nNPpRGNjY8gLERFRV4KLOx3zd/qqtxuzZHGnp01ZwebkiNGsrkKVG52N8KqimDFUA5UlOZolTWBxJ2HIzh2dAqSYeh5JjJTsalH8HTvy38G0Mlx+LdyURURE4cStuHPq1KmQwg4A7e1Tp051+XG/+MUvkJaWpr0UFhb263ESEVFyy7aHdu7E0ui00QCi35gl16Dn2yLr3AGA2bmzAXRd3JFhynaTfcg/qx88mgX0bg069Q+5Dt1mNsS0iy6wLcsFwOf/d3AFD8vOHW7KIiKicKIq7jz44INQFKXbl3379vXXsQIAvvvd76KhoUF7OX78eL9+PiIiSm7B3TqxDFMGQjdmRdO9E+1YFgBMy54Gg2LAqZZTKGsu63S5LO5kWbIivs3BTI5mZaWaOoVqU/ykp4rCo90S2wJkSMFD8UCRY1mDqMtFbssaTF8TERHFTlTDzt/85jdx8803d3ud0aNHR3RbeXl52LhxY8j7KioqtMu6YjabYTbzQRoREUUmdCwr9n8/xqSPQVlLGUoaSjA3b25EH9Ob4k6KMQWTsiZhV/UubKnYggJbQcjlsriTYRnaI1nSuZNz8bWzxmD6iDSuMU8gsnMnlnk7QGjBQ9E5tUDlwdTlIos7g2nUjIiIYieqv6zDhg3DsGHDYvKJFy1ahJ/97GeorKxETo5onf7www/hcDgwefLkmHwOIiKizNRA506sx7IAkbuz+uTqiDt3ml3NaHI1AYgucwcAZufMxq7qXdhWuQ2XjLkk5DJZ3BnqYcqSQa/Dty+YGO/DoA5k5o7NHNvijk7RwWqwos3TBuhcWrDyYOpymZs7F2eOOBPLipfF+1CIiCgB9VvmzrFjx7B9+3YcO3YMXq8X27dvx/bt29Hc3AwAOO+88zB58mQsX74cO3bswPvvv48f/OAHuOuuu9iZQ0REMWPU65DhP6Hsr84dAN2uKA8m83YcJof2THykZuXOAhB+Y1Zdex0AFncosc0uyoDVqMeiMbEfHwzemKUMws6dFGMKnlz6JC4be1m8D4WIiBJQbJ82CfLwww/jpZde0t6eNUs8IP34449x1llnQa/X4+2338add96JRYsWITU1FTfddBN+/OMf99chERHREDUyOxV1x+oxMjv24wyyuBNp505vRrKk2TkiVPlww2HUt9cj3ZKuXVbTXgOAY1mU2Cbk2bHjkfNgMsT++UWtuKNv7fQ+IiKiwa7fijsrVqzAihUrur1OcXEx3n333f46BCIiIgDA766ZhUNVTZhSkBbz25Ybs2rba1HXXtdjcaUvxZ0MSwZGp41GSUMJtlVuw9lFZ2uXcSyLkkV/FHaA4OJOi/a+wbQti4iIqDtxW4VOREQ0UIqyUnDOxNx+ue0UYwqG24YDiGw0qzdr0IPNyvGPZnVYiS7Hsrgti4YqGTSsN4rijklngl6nj+chERERDRgWd4iIiPoomtydshaxxrw3nTsAMCd3DoDOuTvclkVDnezcueMcsahjMIUpExER9YTFHSIioj6KJnenvLn3Y1kAMDtX5O7sqdkjNgP5MVCZhjpZ3Gl2NwBgcYeIiIYWFneIiIj6aEyaKO6UNJT0eF05lhXtGnSpILUAOSk58Kge7KraBQDw+Dyod9YDYOcODV2yuCO72OSYFhER0VDA4g4REVEfjc0YCwDYW7sXLq+ry+t5fV5UtFYA6H3njqIomJMjRrO2VG4BANQ766FChQIF6eb0Xt0uUbKzGkOLO+zcISKioYTFHSIioj6akDEBuSm5aHI14aPSj7q8XlVbFbyqFwbFgGxrdq8/nxzN2laxDUBgJCvdnA6Drt8WYRIltI6dO1yDTkREQwmLO0RERH1k0BlwxbgrAACvHHily+vJkazc1Nw+bfGRG7N2VO2Ax+dhmDIRAsWcOqcodnINOhERDSUs7hAREcXAFeOugE7RYXPF5i6zd8pbRJhyb/N2pHEZ42A32dHqacX+2v1acYdhyjSUyYydBicDlYmIaOhhcYeIiCgG8lLzcMbwMwAArx54Nex1ZHGnt3k7kk7Rad07Wyq2sLhDhM5jWBzLIiKioYTFHSIiohi5esLVAIC3Dr8Fp9fZ6fK+rkEPJos72yq3cSyLCCzuEBHR0MbiDhERUYwsKViC/NR8NDgb8MHRDzpdLjN38m19L+7MyRUbs7ZWbkVNWw0AIMuS1efbJUpWHYs5HMsiIqKhhMUdIiKiGNHr9FqwcrjRrFiNZQHAlKwpMOlMqG2vxfbK7QA4lkVDGzt3iIhoKGNxh4iIKIauGHcF9IoeWyu34lDdoZDLylrKAMSmuGPSmzBt2DQAwOGGwwA4lkVDm9XYoXOH27KIiGgIYXGHiIgohnJScnDmiDMBAK8eDHTvNLua0eRqAtD3bVnS7JzZIW+zc4eGMnbuEBHRUMbiDhERUYwFByu3e9oBBPJ2HCYHUo2pMfk8s3NZ3CGSmLlDRERDGYs7REREMba4YDGG24ajydWE94++DyC2eTvSzGEzoVMCf8pZ3KGhLMWQEvI2O3eIiGgoYXGHiIgoxnSKDleOuxIA8MqBVwD0T3HHZrJhfMZ4AIBe0cNhdsTstomSDceyiIhoKGNxh4iIqB9cPu5yGBQDdlTtwIG6A9pYVqzydiSZu5NuTg/p4iEaajiWRUREQxkfBRIREfWDbGs2zi46GwDwyv5XAp07tth17gDAnNw5AESQM9FQ1rGYw21ZREQ0lLC4Q0RE1E+uGn8VAODtkrdR0lACAChILYjp5zi76GzcOvVW3DfnvpjeLlGy0Sm6kO6djqvRiYiIBjNDvA+AiIhosFqYvxAjbCNwovkE9tTsARD7sSyjzsjCDpGf1WBFm6dNvK5ncYeIiIYOdu4QERH1E52i07p3pFgGKhNRqJDOHQYqExHREMLiDhERUT+6bOxlMOhEo6xBMSDbmh3nIyIavIILOgxUJiKioYTFHSIion6UZc3C0qKlAIDc1Fzodfo4HxHR4JViSNFeZ3GHiIiGEhZ3iIiI+tkNk26AQWfArJxZ8T4UokEtpHOH27KIiGgIYaAyERFRP5uZMxPvX/k+0sxp8T4UokFNFnesBisURYnz0RAREQ0cFneIiIgGQE5KTrwPgWjQk8Uddu0QEdFQw7EsIiIiIhoUrMZA5w4REdFQwuIOEREREQ0KWucOw5SJiGiIYXGHiIiIiAaF4MwdIiKioYTFHSIiIiIaFNi5Q0REQxWLO0REREQ0KKQYUgCwuENEREMPiztERERENCiMShsFABjpGBnfAyEiIhpgXIVORERERIPC4oLFePOyN1FoL4z3oRAREQ0oFneIiIiIaFBQFAWj00bH+zCIiIgGHMeyiIiIiIiIiIiSGIs7RERERERERERJjMUdIiIiIiIiIqIkxuIOEREREREREVESY3GHiIiIiIiIiCiJsbhDRERERERERJTEWNwhIiIiIiIiIkpiLO4QERERERERESUxFneIiIiIiIiIiJIYiztEREREREREREmMxR0iIiIiIiIioiTG4g4RERERERERURJjcYeIiIiIiIiIKImxuENERERERERElMQM8T6AvlJVFQDQ2NgY5yMhIiIiIiIiIoodWeuQtY+uJH1xp6mpCQBQWFgY5yMhIiIiIiIiIoq9pqYmpKWldXm5ovZU/klwPp8PZWVlsNvtUBQl3ofTK42NjSgsLMTx48fhcDjifTiUZHj/od7ifYd6i/cd6gvef6i3eN+h3uJ9h/oi3vcfVVXR1NSEgoIC6HRdJ+skfeeOTqfDiBEj4n0YMeFwOPjLhnqN9x/qLd53qLd436G+4P2Heov3Heot3neoL+J5/+muY0dioDIRERERERERURJjcYeIiIiIiIiIKImxuJMAzGYzHnnkEZjN5ngfCiUh3n+ot3jfod7ifYf6gvcf6i3ed6i3eN+hvkiW+0/SByoTEREREREREQ1l7NwhIiIiIiIiIkpiLO4QERERERERESUxFneIiIiIiIiIiJIYiztEREREREREREmMxZ0E8NRTT2HkyJGwWCxYsGABNm7cGO9DogT3i1/8AvPmzYPdbkdOTg4uu+wy7N+/P96HRUnol7/8JRRFwb333hvvQ6EkcfLkSdxwww3IysqC1WrFtGnTsHnz5ngfFiU4r9eLhx56CKNGjYLVasWYMWPwk5/8BNzrQeF8+umnuOSSS1BQUABFUfDGG2+EXK6qKh5++GHk5+fDarVi2bJlOHjwYHwOlhJKd/cdt9uN73znO5g2bRpSU1NRUFCAG2+8EWVlZfE7YEooPf3uCXbHHXdAURT89re/HbDj6wmLO3H2z3/+E/fffz8eeeQRbN26FTNmzMD555+PysrKeB8aJbBPPvkEd911F9avX48PP/wQbrcb5513HlpaWuJ9aJRENm3ahGeffRbTp0+P96FQkqirq8OSJUtgNBrx3//+F3v27MHjjz+OjIyMeB8aJbhHH30UTz/9NJ588kns3bsXjz76KB577DE88cQT8T40SkAtLS2YMWMGnnrqqbCXP/bYY/j973+PZ555Bhs2bEBqairOP/98tLe3D/CRUqLp7r7T2tqKrVu34qGHHsLWrVvx2muvYf/+/fjiF78YhyOlRNTT7x7p9ddfx/r161FQUDBARxYZrkKPswULFmDevHl48sknAQA+nw+FhYX4+te/jgcffDDOR0fJoqqqCjk5Ofjkk09wxhlnxPtwKAk0Nzdj9uzZ+MMf/oCf/vSnmDlzZkI980CJ6cEHH8Rnn32G1atXx/tQKMlcfPHFyM3NxZ/+9CftfVdeeSWsVitefvnlOB4ZJTpFUfD666/jsssuAyC6dgoKCvDNb34T3/rWtwAADQ0NyM3NxYoVK3DttdfG8WgpkXS874SzadMmzJ8/H6WlpSgqKhq4g6OE19X95+TJk1iwYAHef/99XHTRRbj33nsTpgOenTtx5HK5sGXLFixbtkx7n06nw7Jly7Bu3bo4Hhklm4aGBgBAZmZmnI+EksVdd92Fiy66KOT3D1FP3nrrLcydOxdXX301cnJyMGvWLDz//PPxPixKAosXL8bKlStx4MABAMCOHTuwZs0aXHjhhXE+Mko2R44cwalTp0L+fqWlpWHBggV8/ExRa2hogKIoSE9Pj/ehUBLw+XxYvnw5HnjgAUyZMiXeh9OJId4HMJRVV1fD6/UiNzc35P25ubnYt29fnI6Kko3P58O9996LJUuWYOrUqfE+HEoC//jHP7B161Zs2rQp3odCSaakpARPP/007r//fnzve9/Dpk2b8I1vfAMmkwk33XRTvA+PEtiDDz6IxsZGTJw4EXq9Hl6vFz/72c9w/fXXx/vQKMmcOnUKAMI+fpaXEUWivb0d3/nOd3DdddfB4XDE+3AoCTz66KMwGAz4xje+Ee9DCYvFHaIkd9ddd2H37t1Ys2ZNvA+FksDx48dxzz334MMPP4TFYon34VCS8fl8mDt3Ln7+858DAGbNmoXdu3fjmWeeYXGHuvWvf/0Lf/3rX/G3v/0NU6ZMwfbt23HvvfeioKCA9x0iGnButxtf+tKXoKoqnn766XgfDiWBLVu24He/+x22bt0KRVHifThhcSwrjrKzs6HX61FRURHy/oqKCuTl5cXpqCiZ3H333Xj77bfx8ccfY8SIEfE+HEoCW7ZsQWVlJWbPng2DwQCDwYBPPvkEv//972EwGOD1euN9iJTA8vPzMXny5JD3TZo0CceOHYvTEVGyeOCBB/Dggw/i2muvxbRp07B8+XLcd999+MUvfhHvQ6MkIx8j8/Ez9ZYs7JSWluLDDz9k1w5FZPXq1aisrERRUZH2GLq0tBTf/OY3MXLkyHgfHgAWd+LKZDJhzpw5WLlypfY+n8+HlStXYtGiRXE8Mkp0qqri7rvvxuuvv47//e9/GDVqVLwPiZLE0qVLsWvXLmzfvl17mTt3Lq6//nps374der0+3odICWzJkiXYv39/yPsOHDiA4uLiOB0RJYvW1lbodKEPO/V6PXw+X5yOiJLVqFGjkJeXF/L4ubGxERs2bODjZ+qRLOwcPHgQH330EbKysuJ9SJQkli9fjp07d4Y8hi4oKMADDzyA999/P96HB4BjWXF3//3346abbsLcuXMxf/58/Pa3v0VLSwtuueWWeB8aJbC77roLf/vb3/Dmm2/CbrdrM+ZpaWmwWq1xPjpKZHa7vVM2U2pqKrKyspjZRD267777sHjxYvz85z/Hl770JWzcuBHPPfccnnvuuXgfGiW4Sy65BD/72c9QVFSEKVOmYNu2bfj1r3+NW2+9Nd6HRgmoubkZhw4d0t4+cuQItm/fjszMTBQVFeHee+/FT3/6U4wbNw6jRo3CQw89hIKCgm63ItHQ0N19Jz8/H1dddRW2bt2Kt99+G16vV3sMnZmZCZPJFK/DpgTR0++ejsVAo9GIvLw8TJgwYaAPNTyV4u6JJ55Qi4qKVJPJpM6fP19dv359vA+JEhyAsC8vvvhivA+NktCZZ56p3nPPPfE+DEoS//nPf9SpU6eqZrNZnThxovrcc8/F+5AoCTQ2Nqr33HOPWlRUpFosFnX06NHq97//fdXpdMb70CgBffzxx2Ef59x0002qqqqqz+dTH3roITU3N1c1m83q0qVL1f3798f3oCkhdHffOXLkSJePoT/++ON4HzolgJ5+93RUXFys/uY3vxnQY+yOoqqqOkB1JCIiIiIiIiIiijFm7hARERERERERJTEWd4iIiIiIiIiIkhiLO0RERERERERESYzFHSIiIiIiIiKiJMbiDhERERERERFREmNxh4iIiIiIiIgoibG4Q0RERERERESUxFjcISIiIoqTm2++GZdddlm8D4OIiIiSHIs7RERElPSqqqpw5513oqioCGazGXl5eTj//PPx2WefxfvQiIiIiPqdId4HQERERNRXV155JVwuF1566SWMHj0aFRUVWLlyJWpqauJ9aERERET9jp07RERElNTq6+uxevVqPProozj77LNRXFyM+fPn47vf/S6++MUvatf5yle+gmHDhsHhcOCcc87Bjh07Qm7nP//5D+bNmweLxYLs7Gxcfvnl2mV1dXW48cYbkZGRgZSUFFx44YU4ePCgdvmKFSuQnp6O999/H5MmTYLNZsMFF1yA8vJy7Tperxf3338/0tPTkZWVhW9/+9tQVTXkGF599VVMmzYNVqsVWVlZWLZsGVpaWvrj20ZERESDCIs7RERElNRsNhtsNhveeOMNOJ3OsNe5+uqrUVlZif/+97/YsmULZs+ejaVLl6K2thYA8M477+Dyyy/HF77wBWzbtg0rV67E/PnztY+/+eabsXnzZrz11ltYt24dVFXFF77wBbjdbu06ra2t+NWvfoW//OUv+PTTT3Hs2DF861vf0i5//PHHsWLFCrzwwgtYs2YNamtr8frrr2uXl5eX47rrrsOtt96KvXv3YtWqVbjiiis6FYCIiIiIOlJUPmIgIiKiJPfvf/8bt912G9ra2jB79myceeaZuPbaazF9+nSsWbMGF110ESorK2E2m7WPGTt2LL797W/j9ttvx+LFizF69Gi8/PLLnW774MGDGD9+PD777DMsXrwYAFBTU4PCwkK89NJLuPrqq7FixQrccsstOHToEMaMGQMA+MMf/oAf//jHOHXqFACgoKAA9913Hx544AEAgMfjwahRozBnzhy88cYb2Lp1K+bMmYOjR4+iuLi4v79lRERENIiwc4eIiIiS3pVXXomysjK89dZbuOCCC7Bq1SrMnj0bK1aswI4dO9Dc3IysrCyty8dms+HIkSM4fPgwAGD79u1YunRp2Nveu3cvDAYDFixYoL0vKysLEyZMwN69e7X3paSkaIUdAMjPz0dlZSUAoKGhAeXl5SG3YTAYMHfuXO3tGTNmYOnSpZg2bRquvvpqPP/886irq4vNN4iIiIgGNQYqExER0aBgsVhw7rnn4txzz8VDDz2Er3zlK3jkkUfwta99Dfn5+Vi1alWnj0lPTwcAWK3WPn9+o9EY8raiKFGNVOn1enz44YdYu3YtPvjgAzzxxBP4/ve/jw0bNmDUqFF9Pj4iIiIavNi5Q0RERIPS5MmT0dLSgtmzZ+PUqVMwGAwYO3ZsyEt2djYAYPr06Vi5cmXY25k0aRI8Hg82bNigva+mpgb79+/H5MmTIzqWtLQ05Ofnh9yGx+PBli1bQq6nKAqWLFmCH/3oR9i2bRtMJlNILg8RERFROOzcISIioqRWU1ODq6++GrfeeiumT58Ou92OzZs347HHHsOll16KZcuWYdGiRbjsssvw2GOPYfz48SgrK9NClOfOnYtHHnkES5cuxZgxY3DttdfC4/Hg3XffxXe+8x2MGzcOl156KW677TY8++yzsNvtePDBBzF8+HBceumlER/nPffcg1/+8pcYN24cJk6ciF//+teor6/XLt+wYQNWrlyJ8847Dzk5OdiwYQOqqqowadKkfviuERER0WDC4g4RERElNZvNhgULFuA3v/kNDh8+DLfbjcLCQtx222343ve+B0VR8O677+L73/8+brnlFlRVVSEvLw9nnHEGcnNzAQBnnXUWXnnlFfzkJz/BL3/5SzgcDpxxxhna53jxxRdxzz334OKLL4bL5cIZZ5yBd999t9MoVne++c1vory8HDfddBN0Oh1uvfVWXH755WhoaAAAOBwOfPrpp/jtb3+LxsZGFBcX4/HHH8eFF14Y228YERERDTrclkVERERERERElMSYuUNERERERERElMRY3CEiIiIiIiIiSmIs7hARERERERERJTEWd4iIiIiIiIiIkhiLO0RERERERERESYzFHSIiIiIiIiKiJMbiDhERERERERFREmNxh4iIiIiIiIgoibG4Q0RERERERESUxFjcISIiIiIiIiJKYizuEBERERERERElMRZ3iIiIiIiIiIiS2P8HxTu04uZxky4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(samples)\n",
    "df[\"Seconds\"] = df[\"timestamp\"] - df[\"timestamp\"].iloc[0]\n",
    "df.plot(x=\"Seconds\", y=[\"x\", \"y\", \"z\", \"total_motion\"], figsize=(14, 5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "there-ipython3",
   "version": "3.13.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}