Skip to content
Snippets Groups Projects
Example offline analysis.ipynb 262 KiB
Newer Older
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59f50187-f73f-471b-b668-9126e6f48501",
   "metadata": {},
   "source": [
    "# Learning high-resolution data from low-resolution\n",
    "\n",
    "This is an example notebook showing how to use the `pes_to_spec` infrastructure in this package.\n",
    "\n",
    "We start by importing some modules. The key module here is called `pes_to_spec`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "d44af0b6-9c00-4e70-b49b-d74ed562e92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "# add the pes_to_spec main directory\n",
    "# (change this depending on where you started the notebook if needed, or comment it out if you have done pip install in pes_to_spec)\n",
    "sys.path.append('..')\n",
    "\n",
    "# you meay need to do pip install matplotlib seaborn extra_data for this notebook, additionally\n",
    "# for this notebook the following packages are needed:\n",
    "# pip install \"numpy>=1.21\" \"scipy>=1.6\" \"scikit-learn>=1.2.0\" torch torchbnn  matplotlib seaborn extra_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "da002d3e-c0da-419b-922b-0ab5c6deece8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec \n",
    "import seaborn as sns\n",
    "\n",
    "import lmfit\n",
    "import scipy\n",
    "from extra_data import open_run, by_id\n",
    "from itertools import product\n",
    "from pes_to_spec.model import Model, matching_ids\n",
    "\n",
    "from typing import Any, Dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494a729c-dff4-4501-b828-fba2aaae5a23",
   "metadata": {},
   "source": [
    "# Input data\n",
    "\n",
    "Read data from two runs. One shall be used for training the model. The second one is used for testing it.\n",
    "Note that the data in the training run must be large enough, compared to the number of model parameters.\n",
    "\n",
    "Only the SPEC, PES and XGM data is used for training, while only the PES and XGM data is needed for testing.\n",
    "However, more data is collected here to validate the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a301f2a-dedb-46e4-b096-fc9c6cf5b23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "run = open_run(proposal=3384, run=2)\n",
    "run_test = open_run(proposal=3384, run=3)\n",
    "\n",
    "# useful names to avoid repeating it all over the notebook, in case they ever change\n",
    "spec_name = \"SA3_XTD10_SPECT/MDL/SPECTROMETER_SQS_NAVITAR:output\"\n",
    "pes_name = \"SA3_XTD10_PES/ADC/1:network\"\n",
    "xgm_name = \"SA3_XTD10_XGM/XGM/DOOCS:output\"\n",
    "\n",
    "pres_name = \"SA3_XTD10_PES/GAUGE/G30310F\"\n",
    "volt_name = \"SA3_XTD10_PES/MDL/DAQ_MPOD\"\n",
    "\n",
    "# PES channels\n",
    "channels = [f\"channel_{i}_{l}\" for i, l in product(range(1, 5), [\"A\", \"B\", \"C\", \"D\"])]\n",
    "\n",
    "def get_gas(run) -> str:\n",
    "    \"\"\"Get gas in chamber for logging.\"\"\"\n",
    "    gas_sources = [\n",
    "                  \"SA3_XTD10_PES/DCTRL/V30300S_NITROGEN\",\n",
    "                  \"SA3_XTD10_PES/DCTRL/V30310S_NEON\",\n",
    "                  \"SA3_XTD10_PES/DCTRL/V30320S_KRYPTON\",\n",
    "                  \"SA3_XTD10_PES/DCTRL/V30330S_XENON\",\n",
    "              ]\n",
    "    gas_active = list()\n",
    "    for gas in gas_sources:\n",
    "        # check if this gas source is interlocked\n",
    "        if gas in run.all_sources and run[gas, \"interlock.AActionState.value\"].ndarray().sum() == 0:\n",
    "            # it is not, so this gas was used\n",
    "            gas_active += [gas.split(\"/\")[-1].split(\"_\")[-1]]\n",
    "    gas = \"_\".join(gas_active)\n",
    "    return gas\n",
    "\n",
    "def get_tids(run, need_spec:bool=True) -> np.ndarray:\n",
    "    \"\"\"Get which train IDs contain all necessary inputs for training.\"\"\"\n",
    "    spec_tid = run[spec_name, \"data.trainId\"].ndarray()\n",
    "    pes_tid = run[pes_name, \"digitizers.trainId\"].ndarray()\n",
    "    xgm_tid = run[xgm_name, \"data.trainId\"].ndarray()\n",
    "\n",
    "    # match tids to be sure we have all inputs:\n",
    "    tids = matching_ids(spec_tid, pes_tid, xgm_tid)\n",
    "    return tids\n",
    "\n",
    "def get_data(run, tids) -> Dict[str, Any]:\n",
    "    \"\"\"Get all relevant data.\"\"\"\n",
    "    data = dict()\n",
    "    data[\"int\"] = run[xgm_name, \"data.intensitySa3TD\"].select_trains(by_id[tids]).ndarray()[:, 0][:, np.newaxis]\n",
    "    data[\"pressure\"] = run[pres_name, \"value\"].select_trains(by_id[tids]).ndarray()\n",
    "    data[\"voltage\"] = run[volt_name, \"u212.value\"].select_trains(by_id[tids]).ndarray()\n",
    "    data[\"energy\"] = run[spec_name, \"data.photonEnergy\"].select_trains(by_id[tids]).ndarray()\n",
    "    data[\"spec\"] = run[spec_name, \"data.intensityDistribution\"].select_trains(by_id[tids]).ndarray()\n",
    "    data[\"pes\"] = {ch: run[pes_name,\n",
    "                           f\"digitizers.{ch}.raw.samples\"].select_trains(by_id[tids]).ndarray()\n",
    "                    for ch in channels}\n",
    "    data[\"gas\"] = get_gas(run)\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "210c0550-1abb-43a0-99a5-7c35d2766be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# get the matched train IDs\n",
    "tids = get_tids(run)\n",
    "\n",
    "# we don't need the spec for testing in reality,\n",
    "# but it is nice to plot it in the test run too,\n",
    "# to check that this works during validation\n",
    "test_tids = get_tids(run_test, need_spec=True)\n",
    "\n",
    "# get the data\n",
    "data = get_data(run, tids)\n",
    "data_test = get_data(run_test, test_tids)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "017865a1-057f-48c7-8bef-a6e40490de2c",
   "metadata": {},
   "source": [
    "Now the `data` and `data_test` dictionaries contain the necessary information about the training and test runs.\n",
    "The code above also selected only entries with train IDs on which at least SPEC, PES and XGM were present.\n",
    "\n",
    "Note that for training, it is assumed that only one pulse is present. For testing there is no such requirement.\n",
    "\n",
    "First output some general information about the conditions of the measurement device."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "956105a6-d37e-453c-bfeb-2b1c876ee3f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gas in training: NEON\n",
      "Gas in testing: NEON\n"
     ]
    }
   ],
   "source": [
    "print(f\"Gas in training: {data['gas']}\")\n",
    "print(f\"Gas in testing: {data_test['gas']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4654f205-edc6-45f7-97bd-0d088c38edb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voltage in training: -15.36 +/- 0.01\n",
      "Voltage in testing: -15.36 +/- 0.00\n"
     ]
    }
   ],
   "source": [
    "print(f\"Voltage in training: {np.mean(data['voltage']):0.2f} +/- {np.std(data['voltage']):0.2f}\")\n",
    "print(f\"Voltage in testing: {np.mean(data_test['voltage']):0.2f} +/- {np.std(data_test['voltage']):0.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa662544-3caa-4404-bb61-fa41add82642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pressure in training: 3.73e-07 +/- 1.11e-08\n",
      "Pressure in testing: 3.74e-07 +/- 8.43e-09\n"
     ]
    }
   ],
   "source": [
    "print(f\"Pressure in training: {np.mean(data['pressure']):0.2e} +/- {np.std(data['pressure']):0.2e}\")\n",
    "print(f\"Pressure in testing: {np.mean(data_test['pressure']):0.2e} +/- {np.std(data_test['pressure']):0.2e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5962e483-60da-4c70-bb09-dce5fc9745e0",
   "metadata": {},
   "source": [
    "Now we will actually train the model. We do that by creating a `Model` object (from `pes_to_spec`) and calling the `fit` function.\n",
    "The `fit` function requires the PES intensity, the SPEC intensity, the energy axis from SPEC (stored as a reference only), as well as the energy measured in the XGM (which has better resolution than the integral of the PES)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0adb57b-7496-4781-9511-ac2a8d05658d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data quality in high-resolution data.\n",
      "Selected 2532 of 2532 samples\n",
      "Fitting PCA on low-resolution data.\n",
      "Using 1000 comp. for PES PCA (asked for 1000, out of 9601, in 2532 samples).\n",
      "Fitting PCA on high-resolution data.\n",
      "Fitting outlier detection\n",
      "Fitting model.\n",
      "Calculate PCA unc. on high-resolution data.\n",
      "Calculate transfer function\n",
      "Calculate PCA on channel_1_A\n",
      "Calculate PCA on channel_1_B\n",
      "Calculate PCA on channel_1_C\n",
      "Calculate PCA on channel_1_D\n",
      "Calculate PCA on channel_2_A\n",
      "Calculate PCA on channel_2_B\n",
      "Calculate PCA on channel_2_C\n",
      "Calculate PCA on channel_2_D\n",
      "Calculate PCA on channel_3_A\n",
      "Calculate PCA on channel_3_B\n",
      "Calculate PCA on channel_3_C\n",
      "Calculate PCA on channel_3_D\n",
      "Calculate PCA on channel_4_A\n",
      "Calculate PCA on channel_4_B\n",
      "Calculate PCA on channel_4_C\n",
      "Calculate PCA on channel_4_D\n",
      "End of fit.\n"
     ]
    }
   ],
   "source": [
    "# this is the main object holding all\n",
    "# information needed for training and prediction\n",
    "# the default parameters should be sufficient in most times\n",
    "model = Model(channels=channels,\n",
    "              high_res_sigma=0.0,\n",
    "             )\n",
    "\n",
    "# this trains the model\n",
    "# the first parameter is expected to be a dictionary with the channel name as a key\n",
    "model.fit(data['pes'],\n",
    "          data['spec'],\n",
    "          data['energy'],\n",
    "          pulse_energy=data['int'])\n",
    "\n",
    "# save it for later usage:\n",
    "model.save(\"model.joblib\")\n",
    "\n",
    "# load a model (you can start from here if working on an existing model)\n",
    "model = Model.load(\"model.joblib\")\n",
    "\n",
    "# and use it to map a low-resolution spectrum to a high-resolution one\n",
    "# as before, the low_resolution_raw_data refers to a dictionary mapping the channel name\n",
    "# in the format \"channel_[1-4]_[A-D]\" to the 2D numpy array with shape (number_of_train_IDs, features)\n",
    "# all names and shapes must match the format in training, except for the number_of_train_IDs, which may vary\n",
    "pred = model.predict(data['pes'], pulse_energy=data['int'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0286ae3-1a59-468f-ae40-c3ed94b7b301",
   "metadata": {},
   "source": [
    "Now we can try it in the test dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc06362-3479-4cb9-b102-b438a83d2950",
   "metadata": {},
   "source": [
    "We can predict it in the training data itself, but this is a bit biased, since we used the same information to fit the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "917156f3-9476-48e0-9121-5f75f185045f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(data_test['pes'], pulse_energy=data_test['int'])\n",
    "\n",
    "# add the references in this array in the same array format, so we can plot them later\n",
    "pred[\"energy\"] = model.get_energy_values()\n",
    "\n",
    "pred['spec'] = data_test['spec'][:, np.newaxis, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77866435-1cb6-40ac-a9a5-8f2ae37017b7",
   "metadata": {},
   "source": [
    "Let's try to predict in the independent run in the test dataset. The performance of the model varies a lot if the beam intensity is very different from the training one. To ensure we take a train ID to visualize that is relatively high intensity, we sort the train IDs by XGM intensity and then choose the highest intensity one.\n",
    "One could try other train IDs.\n",
    "\n",
    "For train IDs with close to zero beam intensity, there is a relatively larger error, since the training data did not contain any of those samples and the signal-to-noise ratio is relatively high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed62606a-4ea7-4e0a-8b61-73e682cacf04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose train ID of the test dataset by XGM intensity\n",
    "test_intensity = np.argsort(data_test['int'][:,0])\n",
    "example_tid = test_intensity[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f931e9e0-84a7-4e4f-bfad-588bfe77267c",
   "metadata": {},
   "source": [
    "Now we can actually plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd42984c-554c-4c69-bf8a-119eeb0cca62",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(data):\n",
    "    \"\"\"Plot prediction and expectation.\"\"\"\n",
    "    fig = plt.figure(figsize=(12, 8))\n",
    "    gs = GridSpec(1, 1)\n",
    "    ax = fig.add_subplot(gs[0, 0])\n",
    "    ax.plot(data[\"energy\"], data[\"spec\"], c='b', lw=3, label=\"High-res. measurement\")\n",
    "    ax.plot(data[\"energy\"], data[\"expected\"], c='r', ls='--', lw=3, label=\"High-res. prediction\")\n",
    "    ax.fill_between(data[\"energy\"], data[\"expected\"] - data[\"total_unc\"], data[\"expected\"] + data[\"total_unc\"], facecolor='gold', alpha=0.5, label=\"68% unc.\")\n",
    "    ax.legend(frameon=False, borderaxespad=0, loc='upper left')\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    Y = np.amax(data[\"spec\"])\n",
    "    ax.set(\n",
    "            xlabel=\"Photon energy [eV]\",\n",
    "            ylabel=\"Intensity\",\n",
    "            ylim=(0, 1.3*Y))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbbf77b5-f914-4b47-8ab6-fd3a89d0f983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+0AAAKnCAYAAADz86ytAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeXhcZd3/8c/MZLJvTZc0belGF1qghVKWUhWQYmUV5AHhqYIiKAgioCD8HlnKDiogiuy2ICouCAIKAmXfS2kLpaUt3fd0ydLsmZnz+2OSyZxl1sxkJsn7dV25mnPmzJk7C1f4zPd737fLMAxDAAAAAAAg67gzPQAAAAAAAOCM0A4AAAAAQJYitAMAAAAAkKUI7QAAAAAAZClCOwAAAAAAWYrQDgAAAABAliK0AwAAAACQpQjtAAAAAABkKUK7JMMwVF9fL8MwMj0UAAAAAABCCO2S9u7dq7KyMu3duzfTQwEAAAAAIITQDgAAAABAliK0AwAAAACQpQjtAAAAAABkKUI7AAAAAABZitAOAAAAAECWIrT3A/Pnz1d5eXlCz/nud7+rU089NS3jAQAAAADEh9Dei0UK1q+//rpcLpdqa2slSd/61re0atWqnh0c+qzRo0frnnvuyfQwAAAAgH4hJ9MDyDaBgLR7d2bHMHCg5E7h2ykFBQUqKChI3Q0T0N7eLq/Xm5HX7o3a2tqUm5ub6WEAAAAAyBJU2i1275aGDMnsR6rfNHBqj7/55ps1ZMgQlZSU6Pzzz9fVV1+tgw46yPbcX/3qV6qqqtLAgQN18cUXq729PepruVwu3X///TrllFNUVFSkW265RZL0r3/9S9OmTVN+fr7Gjh2ruXPnyufzSZIMw9ANN9ygkSNHKi8vT8OGDdOll14a99e3fv16uVwu/e1vf9OXv/xlFRQU6NBDD9WqVau0cOFCTZ8+XcXFxTr++OO1c+dO03MfeeQRTZo0Sfn5+dpvv/30+9//3vT4z3/+c02YMEGFhYUaO3asrr32WtP3YOnSpTrmmGNUUlKi0tJSHXLIIfroo48kSTfccIPte3rPPfdo9OjRoePObolbbrlFw4YN08SJEyVJmzZt0plnnqny8nJVVFToG9/4htavX2973q233qrKykqVl5frxhtvlM/n05VXXqmKigqNGDFC8+bNM71+vPeN9HM/+uijtWHDBl1++eVyuVxyuVxx/5wAAAAAJI7Q3g/96U9/0i233KI77rhDixYt0siRI3X//ffbrnvttde0Zs0avfbaa3rsscc0f/58zZ8/P+b9b7jhBp122mn69NNPdd555+mtt97SOeeco5/85Cdavny5HnzwQc2fPz8U6J966indfffdevDBB7V69Wo988wzOvDAAxP+uq6//nr94he/0Mcff6ycnBz97//+r6666ir95je/0VtvvaUvvvhC1113nen7cN111+mWW27RihUrdOutt+raa6/VY489FrqmpKRE8+fP1/Lly/Wb3/xGDz/8sO6+++7Q43PmzNGIESO0cOFCLVq0SFdffXXCnQULFizQypUr9fLLL+v5559Xe3u7Zs+erZKSEr311lt65513VFxcrK9//etqa2sLPe/VV1/V1q1b9eabb+quu+7S9ddfr5NOOkkDBgzQBx98oAsvvFA//OEPtXnzZkmK+77Rfu7//Oc/NWLECN14443atm2btm3bltDXCgAAACBBBoy6ujpDklFXV2dUVxuGlNmP6ur4xn3uuecaHo/HKCoqMn3k5+cbkoyamhrDMAxj3rx5RllZWeh5hx9+uHHxxReb7jVz5kxj6tSppnuPGjXK8Pl8oXNnnHGG8a1vfSvqmCQZl112mencsccea9x6662mc3/84x+NqqoqwzAM49e//rUxYcIEo62tLb4v3GLdunWGJOORRx4JnfvLX/5iSDIWLFgQOnfbbbcZEydODB3vu+++xp///GfTvW666SZjxowZEV/rl7/8pXHIIYeEjktKSoz58+c7Xnv99debvqeGYRh33323MWrUqNDxueeea1RWVhqtra2hc3/84x+NiRMnGoFAIHSutbXVKCgoMP773/+Gnjdq1CjD7/eHrpk4caLx5S9/OXTs8/mMoqIi4y9/+UvC9432cx81apRx9913R/weAQAAAEgdKu293DHHHKMlS5aYPh555JGoz1m5cqUOO+ww0znrsSTtv//+8ng8oeOqqipVV1dLkm699VYVFxeHPjZu3Bi6bvr06ab7LF26VDfeeKPp+gsuuEDbtm1TU1OTzjjjDDU3N2vs2LG64IIL9PTTT4da5xMxZcqU0OeVlZWSZKrYV1ZWhsbf2NioNWvW6Pvf/75pXDfffLPWrFkTes5f//pXzZw5U0OHDlVxcbF+8YtfmL7WK664Queff75mzZql22+/3fTceB144IGmeexLly7VF198oZKSktC4Kioq1NLSYrr//vvvL3fY4geVlZWmr9fj8WjgwIGhrzmR+0b6uQMAAADoWSxEZzFwoJTpfDJwYPzXFhUVady4caZzne3Q3WVt83a5XAoEApKkCy+8UGeeeWbosWHDhpnGFK6hoUFz587VN7/5Tdtr5Ofna5999tHKlSv1yiuv6OWXX9aPfvQj/fKXv9Qbb7yRUKt5+LWdc62t5zrH39DQIEl6+OGHdfjhh5vu0xlY33vvPc2ZM0dz587V7NmzVVZWpieffFK//vWvQ9fecMMN+t///V/9+9//1gsvvKDrr79eTz75pE477TS53W4ZhmG6t9OaAE7fr0MOOUR/+tOfbNcOHjzY8evt/Pqi/cy6c9/OewAAAADoWYR2C7dbCssvfdLEiRO1cOFCnXPOOaFzCxcuTOgeFRUVqqioiOvaadOmaeXKlbY3F8IVFBTo5JNP1sknn6yLL75Y++23nz799FNNmzYtoXHFq7KyUsOGDdPatWs1Z84cx2veffddjRo1Sv/3f/8XOrdhwwbbdRMmTNCECRN0+eWX6+yzz9a8efN02mmnafDgwdq+fbsMwwi9ibBkyZKYY5s2bZr++te/asiQISotLU3uC0zjfXNzc+X3+1M2LgAAAACR0R7fD/34xz/Wo48+qscee0yrV6/WzTffrE8++SRtK4Ffd911evzxxzV37lx99tlnWrFihZ588kn94he/kBRc3f7RRx/VsmXLtHbtWj3xxBMqKCjQqFGjJEnXXHON6Q2GVJk7d65uu+023XvvvVq1apU+/fRTzZs3T3fddZckafz48dq4caOefPJJrVmzRvfee6+efvrp0PObm5t1ySWX6PXXX9eGDRv0zjvvaOHChZo0aZKk4ErrO3fu1J133qk1a9bovvvu0wsvvBBzXHPmzNGgQYP0jW98Q2+99ZbWrVun119/XZdeemm3uihSdd/Ro0frzTff1JYtW7Rr166kxwMAAAAgNkJ7PzRnzhxdc801+tnPfqZp06Zp3bp1+u53v6v8/Py0vN7s2bP1/PPP66WXXtKhhx6qI444QnfffXcolJeXl+vhhx/WzJkzNWXKFL3yyit67rnnNLBjnsC2bdtM88hT5fzzz9cjjzyiefPm6cADD9RRRx2l+fPna8yYMZKkU045RZdffrkuueQSHXTQQXr33Xd17bXXhp7v8Xi0e/dunXPOOZowYYLOPPNMHX/88Zo7d64kadKkSfr973+v++67T1OnTtWHH36on/3sZzHHVVhYqDfffFMjR47UN7/5TU2aNEnf//731dLS0q0Kearue+ONN2r9+vXad999TW31AAAAAFLPZVgn3fZD9fX1KisrU11dXUrbkXuT4447TkOHDtUf//jHTA8FAAAAANCBOe39UFNTkx544AHNnj1bHo9Hf/nLX0KLwAEAAAAAsgehvR9yuVz6z3/+o1tuuUUtLS2aOHGinnrqKc2aNSvTQwMAAAAAhKE9XrTHAwAAAACyEwvRAQAAAACQpQjtAAAAAABkKUI7AAAAAABZitAOAAAAAECWIrQDAAAAAJClCO39wPz581VeXp7Qc7773e/q1FNPTct4ervXX39dLpdLtbW1kpL7/lql4h4AAAAA+h5Cey8WKVhbQ+W3vvUtrVq1qmcH148k+v0dPXq07rnnnm7dAwAAAED/kJPpASD9CgoKVFBQkJHXbm9vl9frzchrR2MYhvx+v3Jyuv+fQCq+v5n8GQEAAADIXlTao9m5M/mP5ubI9921y/k5aeLUen3zzTdryJAhKikp0fnnn6+rr75aBx10kO25v/rVr1RVVaWBAwfq4osvVnt7e9TXcrlcuv/++3XKKaeoqKhIt9xyiyTpX//6l6ZNm6b8/HyNHTtWc+fOlc/nkxQM0DfccINGjhypvLw8DRs2TJdeemncX9/69evlcrn05JNP6sgjj1R+fr4OOOAAvfHGG6FrOrsPXnjhBR1yyCHKy8vT22+/rUAgoNtuu01jxoxRQUGBpk6dqn/84x+m+//nP//RhAkTVFBQoGOOOUbr1683Pe70/X3uued06KGHKj8/X4MGDdJpp50mSTr66KO1YcMGXX755XK5XHK5XBHvcf/992vfffdVbm6uJk6cqD/+8Y+27/Ujjzyi0047TYWFhRo/fryeffbZuL9vAAAAALIfoT2aIUOS//jDHyLfd9Ik5+f0kD/96U+65ZZbdMcdd2jRokUaOXKk7r//ftt1r732mtasWaPXXntNjz32mObPn6/58+fHvP8NN9yg0047TZ9++qnOO+88vfXWWzrnnHP0k5/8RMuXL9eDDz6o+fPnhwL9U089pbvvvlsPPvigVq9erWeeeUYHHnhgwl/XlVdeqZ/+9KdavHixZsyYoZNPPlm7d+82XXP11Vfr9ttv14oVKzRlyhTddtttevzxx/XAAw/os88+0+WXX65vf/vbocC/adMmffOb39TJJ5+sJUuWhN7giObf//63TjvtNJ1wwglavHixFixYoMMOO0yS9M9//lMjRozQjTfeqG3btmnbtm2O93j66af1k5/8RD/96U+1bNky/fCHP9T3vvc9vfbaa6br5s6dqzPPPFOffPKJTjjhBM2ZM0d79uxJ+HsHAAAAIEsZMOrq6gxJRl1dnfkBKfmP3/0u8gsOGuT8nASde+65hsfjMYqKikwf+fn5hiSjpqbGMAzDmDdvnlFWVhZ63uGHH25cfPHFpnvNnDnTmDp1quneo0aNMnw+X+jcGWecYXzrW9+KOiZJxmWXXWY6d+yxxxq33nqr6dwf//hHo6qqyjAMw/j1r39tTJgwwWhra4v3SzdZt26dIcm4/fbbQ+fa29uNESNGGHfccYdhGIbx2muvGZKMZ555JnRNS0uLUVhYaLz77rum+33/+983zj77bMMwDOOaa64xJk+ebHr85z//edTv74wZM4w5c+ZEHO+oUaOMu+++23TOeo8jjzzSuOCCC0zXnHHGGcYJJ5wQOpZk/OIXvwgdNzQ0GJKMF154IeJrAwAAAOhdqLT3csccc4yWLFli+njkkUeiPmflypWhym8n67Ek7b///vJ4PKHjqqoqVVdXS5JuvfVWFRcXhz42btwYum769Omm+yxdulQ33nij6foLLrhA27ZtU1NTk8444ww1Nzdr7NixuuCCC/T000+HWucTMWPGjNDnOTk5mj59ulasWGG6JnxsX3zxhZqamnTccceZxvb4449rzZo1kqQVK1bo8MMPj/g6TpYsWaJjjz024fGHW7FihWbOnGk6N3PmTNvXM2XKlNDnRUVFKi0tDf2MAAAAAPR+LETXyxUVFWncuHGmc5s3b07Jva0LyLlcLgUCAUnShRdeqDPPPDP02LBhw0xjCtfQ0KC5c+fqm9/8pu018vPztc8++2jlypV65ZVX9PLLL+tHP/qRfvnLX+qNN95I+SJ24WNraGiQFGxnHz58uOm6vLy8pF+jJxeUi/YzAgAAAND7Edqj6U7Fsrg48mMrVgQb4jNk4sSJWrhwoc4555zQuYULFyZ0j4qKClVUVMR17bRp07Ry5UrbmwvhCgoKdPLJJ+vkk0/WxRdfrP3220+ffvqppk2bFveY3n//fX3lK1+RJPl8Pi1atEiXXHJJxOsnT56svLw8bdy4UUcddZTjNZMmTbIt7vb+++9HHceUKVO0YMECfe9733N8PDc3V36/P+o9Jk2apHfeeUfnnntu6Nw777yjyZMnR30eAAAAgL6F0B7N4MHpue+gQem5b5x+/OMf64ILLtD06dN15JFH6q9//as++eQTjR07Ni2vd9111+mkk07SyJEj9T//8z9yu91aunSpli1bpptvvlnz58+X3+/X4YcfrsLCQj3xxBMqKCjQqFGjJEnXXHONtmzZoscffzzq69x3330aP368Jk2apLvvvls1NTU677zzIl5fUlKin/3sZ7r88ssVCAT0pS99SXV1dXrnnXdUWlqqc889VxdeeKF+/etf68orr9T555+vRYsWxVyM7/rrr9exxx6rfffdV2eddZZ8Pp/+85//6Oc//7mk4D7tb775ps466yzl5eVpkMPvw5VXXqkzzzxTBx98sGbNmqXnnntO//znP/XKK6/E+G4DAAAA6EuY094PzZkzR9dcc41+9rOfadq0aVq3bp2++93vKj8/Py2vN3v2bD3//PN66aWXdOihh+qII47Q3XffHQrl5eXlevjhhzVz5kxNmTJFr7zyip577jkNHDhQkrRt2zbTnPlIbr/9dt1+++2aOnWq3n77bT377LOOgTjcTTfdpGuvvVa33XabJk2apK9//ev697//rTFjxkiSRo4cqaeeekrPPPOMpk6dqgceeEC33npr1HseffTR+vvf/65nn31WBx10kL761a/qww8/DD1+4403av369dp33301OMIbQ6eeeqp+85vf6Fe/+pX2339/Pfjgg5o3b56OPvromN8HAAAAAH2HyzAy2KedJerr61VWVqa6ujqVlpZmejgZcdxxx2no0KG2vcB7g/Xr12vMmDFavHix417zAAAAANBb0R7fDzU1NemBBx7Q7Nmz5fF49Je//CW0CBwAAAAAIHsQ2vshl8ul//znP7rlllvU0tKiiRMn6qmnntKsWbMyPTQAAAAAQBja40V7PAAAAAAgO7EQHQAAAAAAWYrQDgAAAABAliK0AwAAAACQpQjtAAAAAABkKUI7AAAAAABZitAOAAAAAECWIrQDAAAAAJClcjI9gKy284aefb3Bib/eli1b9POf/1wvvPCCmpqaNG7cOM2bN0/Tp0+XJDU0NOjqq6/WM888o927d2vMmDG69NJLdeGFF4buccUVV2j+/PkqKirS7bffrjlz5oQe+/vf/67HH39czz33XLe/PAAAAABAYgjtvVhNTY1mzpypY445Ri+88IIGDx6s1atXa8CAAaFrrrjiCr366qt64oknNHr0aL300kv60Y9+pGHDhumUU07Rc889pz//+c966aWXtHr1ap133nmaPXu2Bg0apLq6Ov3f//2fXnnllQx+lQAAAADQf9Ee34vdcccd2meffTRv3jwddthhGjNmjL72ta9p3333DV3z7rvv6txzz9XRRx+t0aNH6wc/+IGmTp2qDz/8UJK0YsUKHX300Zo+fbrOPvtslZaWat26dZKkq666ShdddJFGjhwZcyzf/e53deqpp5rOXXbZZTr66KNDx0cffbQuvfRSXXXVVaqoqNDQoUN1ww03mJ5TW1urH/7wh6qsrFR+fr4OOOAAPf/888l9gwAAAACglyO092LPPvuspk+frjPOOENDhgzRwQcfrIcffth0zZFHHqlnn31WW7ZskWEYeu2117Rq1Sp97WtfkyRNnTpVH330kWpqarRo0SI1Nzdr3Lhxevvtt/Xxxx/r0ksvTemYH3vsMRUVFemDDz7QnXfeqRtvvFEvv/yyJCkQCOj444/XO++8oyeeeELLly/X7bffLo/Hk9IxAAAAAEBvQXt8L7Z27Vrdf//9uuKKK/T//t//08KFC3XppZcqNzdX5557riTpt7/9rX7wgx9oxIgRysnJkdvt1sMPP6yvfOUrkqTZs2fr29/+tg499FAVFBSEQvVFF12k+fPn6/7779dvf/tbDRo0SA899JD233//bo15ypQpuv766yVJ48eP1+9+9zstWLBAxx13nF555RV9+OGHWrFihSZMmCBJGjt2bLdeDwAAAAB6M0J7LxYIBDR9+nTdeuutkqSDDz5Yy5Yt0wMPPGAK7e+//76effZZjRo1Sm+++aYuvvhiDRs2TLNmzZIk3XDDDaY29blz52rWrFnyer26+eab9emnn+r555/XOeeco0WLFnVrzFOmTDEdV1VVqbq6WpK0ZMkSjRgxIhTYAQAAAKC/I7T3YlVVVZo8ebLp3KRJk/TUU09Jkpqbm/X//t//09NPP60TTzxRUjA0L1myRL/61a9CoT3c559/rieeeEKLFy/WH/7wB33lK1/R4MGDdeaZZ+q8887T3r17VVJSYnue2+2WYRimc+3t7bbrvF6v6djlcikQCEiSCgoKEvjqAQAAAKDvY057LzZz5kytXLnSdG7VqlUaNWqUpGBobm9vl9tt/jF7PJ5QUA5nGIZ++MMf6q677lJxcbH8fn8oeHf+6/f7HccyePBgbdu2zXRuyZIlCX09U6ZM0ebNm7Vq1aqEngcAAAAAfRWhvRe7/PLL9f777+vWW2/VF198oT//+c966KGHdPHFF0uSSktLddRRR+nKK6/U66+/rnXr1mn+/Pl6/PHHddppp9nu98gjj2jw4ME6+eSTJQXfFHj11Vf1/vvv6+6779bkyZNVXl7uOJavfvWr+uijj/T4449r9erVuv7667Vs2bKEvp6jjjpKX/nKV3T66afr5Zdf1rp16/TCCy/oxRdflBTck36//fYLrXwPAAAAAH0dob0XO/TQQ/X000/rL3/5iw444ADddNNNuueeezRnzpzQNU8++aQOPfRQzZkzR5MnT9btt9+uW265RRdeeKHpXjt27NAtt9yie++9N3TusMMO009/+lOdeOKJ+tvf/qZ58+ZFHMvs2bN17bXX6qqrrtKhhx6qvXv36pxzzkn4a3rqqad06KGH6uyzz9bkyZN11VVXhar77e3tWrlypZqamhK+LwAAAAD0Ri7DOhG5H6qvr1dZWZnq6upUWlqa6eEAAAAAACCJSjsAAAAAAFmL0A4AAAAAQJYitAMAAAAAkKUI7QAAAAAAZClCOwAAAAAAWYrQDgAAAABAliK0AwAAAACQpQjtAAAAAABkKUI7AAAAAABZitAOAAAAAECWIrQDAAAAAJClCO0AAAAAAGQpQjsAAAAAAFkqo6Hd7/fr2muv1ZgxY1RQUKB9991XN910kwzDCF1jGIauu+46VVVVqaCgQLNmzdLq1atN99mzZ4/mzJmj0tJSlZeX6/vf/74aGhp6+ssBAAAAACClMhra77jjDt1///363e9+pxUrVuiOO+7QnXfeqd/+9reha+68807de++9euCBB/TBBx+oqKhIs2fPVktLS+iaOXPm6LPPPtPLL7+s559/Xm+++aZ+8IMfZOJLAgAAAAAgZVxGeFm7h5100kmqrKzUo48+Gjp3+umnq6CgQE888YQMw9CwYcP005/+VD/72c8kSXV1daqsrNT8+fN11llnacWKFZo8ebIWLlyo6dOnS5JefPFFnXDCCdq8ebOGDRsWcxz19fUqKytTXV2dSktL0/PFAgAAAACQoIxW2o888kgtWLBAq1atkiQtXbpUb7/9to4//nhJ0rp167R9+3bNmjUr9JyysjIdfvjheu+99yRJ7733nsrLy0OBXZJmzZolt9utDz74wPF1W1tbVV9fb/oAAAAAACDb5GTyxa+++mrV19drv/32k8fjkd/v1y233KI5c+ZIkrZv3y5JqqysND2vsrIy9Nj27ds1ZMgQ0+M5OTmqqKgIXWN12223ae7cuan+cgAAAAAASKmMVtr/9re/6U9/+pP+/Oc/6+OPP9Zjjz2mX/3qV3rsscfS+rrXXHON6urqQh+bNm1K6+sBAAAAAJCMjFbar7zySl199dU666yzJEkHHnigNmzYoNtuu03nnnuuhg4dKknasWOHqqqqQs/bsWOHDjroIEnS0KFDVV1dbbqvz+fTnj17Qs+3ysvLU15eXhq+IgAAAAAAUiejlfampia53eYheDweBQIBSdKYMWM0dOhQLViwIPR4fX29PvjgA82YMUOSNGPGDNXW1mrRokWha1599VUFAgEdfvjhPfBVAAAAAACQHhmttJ988sm65ZZbNHLkSO2///5avHix7rrrLp133nmSJJfLpcsuu0w333yzxo8frzFjxujaa6/VsGHDdOqpp0qSJk2apK9//eu64IIL9MADD6i9vV2XXHKJzjrrrLhWjgcAAAAAIFtldMu3vXv36tprr9XTTz+t6upqDRs2TGeffbauu+465ebmSpIMw9D111+vhx56SLW1tfrSl76k3//+95owYULoPnv27NEll1yi5557Tm63W6effrruvfdeFRcXxzUOtnwDAAAAAGSjjIb2bEFoBwAAAABko4zOaQcAAAAAAJER2gEAAAAAyFKEdgAAAAAAshShHQAAAACALEVoBwAAAAAgSxHaAQAAAADIUoR2AAAAAACyFKEdAAAAAIAsRWgHAAAAACBLEdoBAAAAAMhShHYAAAAAALIUoR0AAAAAgCxFaAcAAAAAIEsR2gEAAAAAyFKEdgAAAAAAshShHQAAAACALEVoBwAAAAAgSxHaAQAAAADIUoR2AAAAAACyFKEdAAAAAIAsRWgHAAAAACBLEdoBAAAAAMhShHYAAAAAALIUoR0AAAAAgCxFaAcAAAAAIEsR2gEAAAAAyFKEdgAAAAAAshShHQAAAACALEVoBwAAAAAgSxHaAQAAAADIUoR2AAAAAACyFKEdAAAAAIAsRWgHAAAAACBLEdoBAAAAAMhShHYAAAAAALIUoR0AAAAAgCxFaAcAAAAAIEsR2gEAAAAAyFKEdgAAAAAAshShHQAAAACALEVoBwAAAAAgSxHaAQAAAADIUoR2AAAAAACyFKEdAAAAAIAsRWgHAAAAACBLEdoBAAAAAMhShHYAAAAAALIUoR0AAAAAgCxFaAcAAAAAIEsR2gEAAAAAyFKEdgAAAAAAshShHQAAAACALEVoBwAAAAAgSxHaAQAAAADIUoR2AAAAAACyFKEdAAAAAIAsRWgHAPRKO3ZIu3ZlehQAAADpRWgHAPQ6118vDR0qDR8u/eEPmR4NAABA+rgMwzAyPYhMq6+vV1lZmerq6lRaWprp4QAAoti1S6qqkny+4PGgQdL27ZLHk9lxAQAApAOVdgBAr7JwYVdgl4IhftOmzI0HAAAgnQjtAIBepaXFfm716p4fBwAAQE8gtAMAepUdO+znCO0AAKCvIrQDAHqV7dvt5zZv7vlxAAAA9ARCOwCgV3EK7du29fw4AAAAegKhHQDQq6xdaz9HaAcAAH0VoR0A0Ks4hfatW3t+HAAAAD2B0A4A6DV8PmnDBvt5p8XpAAAA+gJCOwCg16ipMe/R3qmurufHAgAA0BMI7QCAXqOmxvl8a6vz/u0AAAC9HaEdANBrRArtklRb22PDAAAA6DGEdgBArxEtmBPaAQBAX0RoBwD0GlTaAQBAf0NoBwD0GoR2AADQ3xDaAQC9Bu3xAACgvyG0AwB6jYaGyI8R2gEAQF9EaAcA9BrNzZEfI7QjWzQ3S889Jy1dmumRAAD6gpxMDwAAgHgR2pHtfD5pxgxzYN+1Sxo4MHNjAgD0blTaAQC9BqEd2e7VV+0V9ptuysxYAAB9A6EdANBrENqR7V55xX7uN7/p+XEAAPoOQjsAoNcgtCPbGUamRwAA6GsI7QCAXoPQjmy3ebP9XFlZz48DANB3ENoBAL1GU1NyjwE95fPP7ef8/p4fBwCg7yC0AwB6jWiV9miPAT2hrs55m7eGBoI7ACB5hHYAQK9BaEc2+/zzyHPa6+t7diwAgL6D0A4A6DUI7chmu3dHfow1FwAAySK0AwB6DUI7stmePZEf276958YBAOhbCO0AgF4jVmhnuy1kUrTQ/u67PTcOAEDfQmgHAPQasarpra09Mw7ASbT2+CVLemwYAIA+htAOAOgV2tslny/6NbTII5OiVdqjBXoAAKIhtAMAeoV4AjmhHZkULbRHewwAgGgI7QCAXoHQjmwXrZpOaAcAJIvQDgDoFQjtyHZU2gEA6UBoBwD0Ck6B3OuNfQ3QU6IF85oaKRDoubEAAPoOQjsAoFewBnKvVyoujn4N0JOitccHAlJ9fc+NBQDQdxDaAQC9gjWQFxQEP6JdA/QUv1+qrY1+DS3yAIBkENoBAL0CoR3ZLFZglwjtAIDkENoBAL1CU5P5mNCObLJ3r/1cUZH5mL3aAQDJILQDAHoFKu3IZo2N9nMjRpiPqbQDAJJBaAcA9ArxhPaWlp4bDxDOGtrz86XBg83nCO0AgGQQ2gEAvYI1tBcWBoNRtGuAnmKdvlFUJFVUmM/RHg8ASAahHQDQK9Aej2xmrbQXFUkDBpjP1dX13HgAAH0HoR0A0Cv019BureAiOzmF9pIS8zmnxeoAAIiF0A4A6BX6W2hfs0aaNCkY/v7nf4L7gCN7OYX20lLzufr6nhsPAKDvILQDAHqF/hba58yRPv88+PlTT0n/+Edmx4PorKG9sJBKOwAgNQjtAIBeoT/t075hg/TBB+Zzr7+ekaEgTlTaAQDpQmgHAPQKTqvH99XQvn27/dz77/f8OBA/5rQDANKF0A4A6BX60z7tThXZrVt7fhyInzWQFxdTaQcApAahHQDQKziF9txc87m2tp4bTzo5VWSp0ma3mhrz8YABVNoBAKlBaAcA9Ar9KbQ7VWSbmyWfr+fHgvg4hXYq7QCAVCC0AwB6hf4U2iNVZBsaenYciF88lfa2Nqm1tefGBADoGwjtAIBewSm05+WZz/WVQBSpIkt7dfaKp9Iu8TMEACSO0A4A6BWotBP4slk8lXaJnyEAIHE5mR4AAADxsFbR8/Mll8t8rq+Edirtvc+ePebjAQOC2765XJJhdJ1nXjsAIFFU2gEAvYJ1O7e8vL5baa+rcz5P4MtOgYC9E6SkJBjYWUEeANBdhHYAQK9grbQ7hfa+MqfdWrXtRODLTtY3lKTg9A2JFeQBAN1HezwAoFdwao8PBMzn+kql3To/uhOhPTtZq+xSV2in0g4A6C5COwCgV3Bqj7fuW05oRyZQaQcApBOhHQCQ9QIBe0DPy5Pa283n+kpopz2+d3GqtOfnB/+l0g4A6C7mtAMAsp7TXPX8/L65EJ1hUGnvbZwq7Z2hnUo7AKC7CO0AgKznFIqcFqLz+ezz3HubxkbJ73d+jMCXnayV9vDtCKm0AwC6i9AOAMh6kSrteXn287292t7UFPkxAl92cgrtnai0AwC6i9AOAMh68Vbapd4f2p2+1k6E9uxk/Zl1LkInUWkHAHQfoR0AkPWcKu19NbQ7LWrWicCXnaw/s/DQTqUdANBdhHYAQNazhna3W8rJcQ7tTgG/N6HS3vtEa4+n0g4A6K6Mh/YtW7bo29/+tgYOHKiCggIdeOCB+uijj0KPG4ah6667TlVVVSooKNCsWbO0evVq0z327NmjOXPmqLS0VOXl5fr+97+vhoaGnv5SAABp4rRHu8vVNyvthPbeJ1p7PJV2AEB3ZTS019TUaObMmfJ6vXrhhRe0fPly/frXv9aAAQNC19x5552699579cADD+iDDz5QUVGRZs+erZawv5Bz5szRZ599ppdfflnPP/+83nzzTf3gBz/IxJcEAEgDa/W8cwG6vrgQXbTQTuDLTtHa46m0AwC6KyeTL37HHXdon3320bx580LnxowZE/rcMAzdc889+sUvfqFvfOMbkqTHH39clZWVeuaZZ3TWWWdpxYoVevHFF7Vw4UJNnz5dkvTb3/5WJ5xwgn71q19p2LBhPftFAQBSzhraO9uPPZ5gxd0wuh5LJrS/8Yb04ovSV78qHXdc8uNMBSrtvY/1ZxZt9Xh+hgCARGW00v7ss89q+vTpOuOMMzRkyBAdfPDBevjhh0OPr1u3Ttu3b9esWbNC58rKynT44YfrvffekyS99957Ki8vDwV2SZo1a5bcbrc++OADx9dtbW1VfX296QMAkL2c2uMl5xb5REP7O+9IRx8t3X679LWvSa+8kvQwUyLWQnThb1AgOyRSaed/OQAAicpoaF+7dq3uv/9+jR8/Xv/973910UUX6dJLL9Vjjz0mSdq+fbskqbKy0vS8ysrK0GPbt2/XkCFDTI/n5OSooqIidI3VbbfdprKystDHPvvsk+ovDQCQQpHa4yV7aE90Ibovfcl8/OCDiT0/1aJV2gOB6KEemZHI6vFtbb1/sUQAQM/KaGgPBAKaNm2abr31Vh188MH6wQ9+oAsuuEAPPPBAWl/3mmuuUV1dXehj06ZNaX09AED3RGqPl7pXaV+50n7uH/+I//npEC20S7RXZ6No7fHWSrvEzxAAkJiMhvaqqipNnjzZdG7SpEnauHGjJGno0KGSpB07dpiu2bFjR+ixoUOHqrq62vS4z+fTnj17QtdY5eXlqbS01PQBAMhekdrjrZ9LiYX255+3nyssjP/56UBo730SqbRL/AwBAInJaGifOXOmVlrKHKtWrdKoUaMkBRelGzp0qBYsWBB6vL6+Xh988IFmzJghSZoxY4Zqa2u1aNGi0DWvvvqqAoGADj/88B74KgAA6ZZIe3wiod3ynq8kab/94n9+OsQK7cyJzj7R9mkvLJTclv/b4mcIAEhERlePv/zyy3XkkUfq1ltv1ZlnnqkPP/xQDz30kB566CFJksvl0mWXXaabb75Z48eP15gxY3Tttddq2LBhOvXUUyUFK/Nf//rXQ2317e3tuuSSS3TWWWexcjwA9BGJtMcnMl949277OZ8v/uenQ6w567W1PTIMJCDaPu0uV7BFvq6u6xyVdgBAIjIa2g899FA9/fTTuuaaa3TjjTdqzJgxuueeezRnzpzQNVdddZUaGxv1gx/8QLW1tfrSl76kF198Uflh/8f2pz/9SZdccomOPfZYud1unX766br33nsz8SUBANIgWnt8dyrte/bYz2U6UMWqtO/a1TPjQPyitcdL9tBOpR0AkIiMhnZJOumkk3TSSSdFfNzlcunGG2/UjTfeGPGaiooK/fnPf07H8AAAWSBdC9E5VdozHahijZ/Qnn2itcdL7NUOAOiejM5pBwAgHulaiC4bK+3t7dEf37mzZ8aB+EVrj5fYqx0A0D2EdgBA1kvXQnROoT3T+2hTae99YrXHU2kHAHQHoR0AkPXStRBdpEXfMhmqYlXaCe3ZJ9o+7RKVdgBA9xDaAQBZL10L0UUK+JkMVbHG39jYM+NA/Ki0AwDSidAOAMh66WqPj7RSezZV2r1e83GsLeHQ8+JZPT4clXYAQCII7QCArBetPT7Zheh8PikQcH4smyrt1iotoT37xGqPp9IOAOgOQjsAIOuloz0+2n7omQzt1kq7NfDF2scdPY9KOwAgnQjtAICsl0h7fLwL0UW7LpOVUCrtvQ9z2gEA6URoBwBkvWjt8dY537FWX490z3DZXGkntGcXn0/y+83nWD0eAJBKhHYAQNaL1h6fbGiP1mZOpR3xcvp5UGkHAKQSoR0AkPV6utKeTavHl5WZj5nTnl3iCe1U2gEA3UFoBwBkvZ6utGeymk2lvXdx+j1i9XgAQCoR2gEAWS/aQnTW0B7v6vHRKu2ZDMax5rS3tkbeqg49L5lKe1tb/AsmAgBAaAcAZL10tMf31kq7RODLJtbfI7dbyskxn3P6GVJtBwDEi9AOAMh66WiPz9ZKuzW0W+e0S7TIZxOn7d5cLvM5a6VdYl47ACB+hHYAQNZLpD2+t4f2WO3xktTU1DNjQWyx9miXpMLCYAU+HKEdABAvQjsAIOv15/b4gQMlj8d8rrq658aD6Ky/R9ZF6KRg5X3gQPM5foYAgHgR2gEAWc3nk/x+87n+VGkvKJCGDjWf27y558aD6OKptEv2n+G2bekZDwCg7yG0AwCymlO47k9bvuXmSsOHm89t2dJz40F08Yb2qirzMaEdABAvQjsAIKs5hfZUtMf3lkq71yuNGGE+R2jPHvG0x0uEdgBA8gjtAICs5lQR78vt8U6V9iFDzOf27Om58SC6ZNvjmdMOAIgXoR0AkNX6U3u8YQTn8Ifzeu1bhrHHd/aw/h5FCu3Wn2FjY3rGAwDoewjtAICslq7Qno2Vdqex5+YS2rOZ9XclUnt8UZH5mNAOAIgXoR0AkNWsoSgnJ/jRqS9V2p3GTqU9u8XbHk9oBwAki9AOAMhqsUJRuirthhHffVLJOp9dotKe7eJtjye0AwCSRWgHAGS1pEO7YUiByOX0aJV2w3AO0OlGpb33oT0eAJBuhHYAQFZLOrS3LpaaFkS8b7RKu9Pr9gQq7b0P7fEAgHQjtAMAslrSob1tpdS2KuJ9szG0U2nvfWiPBwCkG6EdAJDVkg7t7Vskf53kc94QO1p7vNPr9oREKu2ZmHMPu2Tb45ua+BkCAOJDaAcAZLVEQ7vPJxm+GinQ0HFik+N9e0ulPSdHKi83nzMMqa6uR4aEGJJtjzeMzO1SAADoXQjtAICslmholyRf8+awg+2O9+0NlXavV3K5pIED7dfu3t0zY0J01t+jeCvtEi3yAID4ENoBAFktmdDe3hKWaH07HO/bGyrtnV9bUVGwTT4coT07WH+PCO0AgFQjtAMAslpyob2268DvnG57Q6W9M6g7VdsJ7dkh0s/MqrDQfo7QDgCIB6EdAJDVkgrtbWETvgONUsCewFtb/Qm9bk+IVGmXCO3ZylppjxTaPR57FZ7QDgCIB6EdAJDVkqu0W9KQv8Z2TWuMVJ5NlXaJ0J6trD+zvLzI17LtGwAgGYR2AEBWSyq0t1vKn4F6y3GLWlqotKP74m2PlwjtAIDkENoBAFktufZ4y5+3wF7LBevV2pYT9XVjLVSXDlTae5942+MlQjsAIDmEdgBAVksqtPs85hPWSrtvk1pazKHdet9MhHYq7b0P7fEAgHQjtAMAslqs0J7jUDBvb49Vad+i1jZzsC8tNV9iDWM9IZFK+8qV6R8PYqM9HgCQboR2AEBWixXaXS57cLdX2htMh4Zvh9os7fHW0J7tlfYlSzIz7x5m1t8TKu0AgFQjtAMAslqs0C7ZW+SjVtoDjWpttqRjZUdoT6TSLknPPJPW4SAGvz/4EY5KOwAg1QjtAICsllxoj1Jp9++ytcZL2RHao1XaZ860X//55+kdD6Jrr33Ndo7QDgBINUI7ACCrJRXafZY/b0aTZASCn/trbYvQSVJZmfk4GyvtY8aYH1+9Ov1jQgTtW9Ra+77tNO3xAIBUI7QDALJaSirthtFVbQ/sddzuLdsr7ZL005+aj9esSe94EEXbcrU5dGxEq7QXF5uPCe0AgHgQ2gEAWS0llXapa9u3QL1aWmOH9mxbPV6SRowwH2/fnt7xIIq2lWqzvjkk2uMBAKlHaAcAZLWUVNolU2hvbTU/7nIZtipoNlTarQGwstJ8XF0dbCJAD/PXSr5danV484f2eABAqhHaAQBZyzCklhbzuVRU2q3t8Xl59rCVDXParV/XkCHm45YWaa9lC3r0gPZNkkSlHQDQIwjtAICsZQ3sUgoq7f4620J0eXlGVob2WJV2SdqxI33jQQS+LZJk69hwuyWPw69eJ0I7ACAZhHYAQNaytsZLSe7TLkn+esnwSUaTrUKalxfIitAeayG6oiJ78Nu6Nb1jgoOO0G7/PYr+NEI7ACAZhHYAQNZKOrT7nCrtdcFqu2HYQr03J2CramfjQnSSNHq0+fimm9I2HDgxApIvuAKgdfX4aK3xEqEdAJAcQjsAIGultNIeqJf8eyTZK6S5uf5eUWmXpKFDzccLFkg7d6ZvTLDw75KM4A/KujYCoR0AkA6EdgBA1rKGdpfLuQU5vkp7fWgBMeucd29OdrTHx1NpnzrVfu6pp9IzHjjwbQt9mmh7vHWHgoYGVv8HAMRGaAcAZC1raM/PDwZ3q7gq7YYhtXwsyanS7suK0B5Ppf344+3nXn01PeOBA/+u0Kf29vjoCbyszHwcCASDOwAA0RDaAQBZK5492qU4K+2SFAjuj2af054d7fHxVNqPPVY64ADzubffTt+YYBGoC33aagntebm+qE8dMMB+rrY2FYMCAPRlhHYAQNZKOrQ7VdrDOFXarQE5WyvtLpf017+az23bxvzoHuOvD31qq7R7o//SlJTYO0VqalI2MgBAH0VoBwBkreRDe5TNsh0e9+bY2+OzdfV4Saqqsp/bvTv144GDsEq77c2fGKHd7ba3yFNpBwDEQmgHAGSt5NvjE6y0e9uzoj0+nkq7FAx+Hsv7Ert2OV+LFOuYYiFJra3m1ePzcmP/0pSXm48J7QCAWAjtAICsla5Ku7Wt2anSnq1z2qVgxbaiwnyOSnsPCDRJRte8dcdKe4zl4K3z2gntAIBYCO0AgKyVrkq79fHcXJ/ycs1l7tbY+Svl4q20S9KgQeZjKu09IKzKLjnNaffZrrGiPR4AkChCOwAga/VUpT3X61dujrnMbRiSL/pi4CkXb6Vdsof2lStTPx5YBOpNh/bV4/1SIPrKcta92puaUjIyAEAfRmgHAGSt9FXaLe3xXr/ycptt1/X0YnSJVNr33dd8/NRTqR8PLALmTdXt+7T7JX/00F5YaD5m1X8AQCyEdgBA1urJSrvTImI9Pa89kdD+7W+bjz/7jACYdtbQbpvTHju0FxWZj/mZAQBiIbQDALJWuvZpt1bivd6A8rz2PuWeDu3WdvycHOfrJOnII4ML0nUyDOnTT9MzLnQImBO2bfX4PJ/kj74ioLXSTns8ACCWpEL7ueeeqzfffDPVYwEAwCTu0J5jLlFb29+tnCvt9vb4TIf2aJX2ggJp4kTzueXLUz8mhImr0h49tFNpBwAkKqnQXldXp1mzZmn8+PG69dZbtWXLllSPCwCABEK7OV2bKu3/+ES67r9SU1ewd5rTnuvNfGi3tsdHq7RL0rhx5uN161I7HlgYlkq7dSG6PL/k3xV12wFraKfSDgCIJanQ/swzz2jLli266KKL9Ne//lWjR4/W8ccfr3/84x9qt/4fBwAASUo6tIeH8scXSQ+8Lx19v/TOeknOlXavp3e1x0vSmDHmY0J7mlna451+j2S0R932jYXoAACJSnpO++DBg3XFFVdo6dKl+uCDDzRu3Dh95zvf0bBhw3T55Zdr9erVqRwnAKAfiju0eyJU2jfVSu9vDH6+vkY67THpo822Oe9eb0AutSgvz3zfbF49XrKH9vXrUzocWFlDu7U9Ptcf/CRKizzt8QCARHV7Ibpt27bp5Zdf1ssvvyyPx6MTTjhBn376qSZPnqy77747FWMEAPRT8VfaW0zHoUr7Py0rs5XnS5OGqKJ2i2brRRUoWF0PVkhbbaE92yvtlZXm45roC5ejOwxDMsy/kLaF6HI7foBRQjsL0QEAEhXjfwectbe369lnn9W8efP00ksvacqUKbrsssv0v//7vyotLZUkPf300zrvvPN0+eWXp3TAAID+I/7Qbk4+oUr6M5+ZL6xtkcbdrt/6b5MkTddCLdJ0eb1+ybBX2jMd2mNV2ktKzMcNDc7XIQWMZskImE45LkQnSYG6iLeh0g4ASFRSob2qqkqBQEBnn322PvzwQx100EG2a4455hiVl5d3c3gAgP4s/vZ484XtPo/U0Cot32G/2N+1SNh4rdYiTQ9V2nNzzZf2ZHu8YSS+EJ01tO+NPJUa3RWwp2vbnPbcxEM7lXYAQCxJhfa7775bZ5xxhvLz8yNeU15ernWsiAMA6Ib4K+3mQNXe7paWbZfCF/H2uKTDRkrvbQidGqcvJHWErUCLw37vyY48cYGA/Vys0F5cbD6m0p5GhsNChU4L0UmSvz7ibViIDgCQqKTmtL/22muOq8Q3NjbqvPPO6/agAACQEqm0W9vjPdKSreaL9hsi7TfYdGq8goumenMCktGS0Uq7tTVeSrw9vr2951v6+404Ku15ebTHAwBSL6nQ/thjj6nZ+n9Skpqbm/X44493e1AAAEhxhnYjIK/HUmn3uaWl28zXTa2SRleYTo3WekkdlXajNaOVdqfXSrQ9XqJFPm0C9jaGqHPaDYfWCdkr7a2tkt+fkhECAPqohNrj6+vrZRiGDMPQ3r17Te3xfr9f//nPfzRkyJCUDxIA0D9Z5/s6hvbAXnlzzKnHsdI+ZZg02FzmHKngdnDenOBCdJkM7amotEvBFvlBg1IzJoRxqLS3tpn/Nyo0p90ISIF6yVNue4610i4Ff8+dfpYAAEgJhvby8nK5XC65XC5NmDDB9rjL5dLcuXNTNjgAQP8VCNhbhx2DTaBeXq+5qlnUWitts2y7dchw8xx3SSO0WR75OirtgY77dDWh9WR7fDKV9kLPSkkTTeeotKeJU6Xd2h6fG/bOi7+G0A4ASImEQvtrr70mwzD01a9+VU899ZQqKrraDHNzczVq1CgNGzYs5YMEAPQ/TouqOYf2Olul/cDmj83XFORIkyuDW76FyZFfVdoWCv25ltCe6Uq7Y2gPtEpty6S21XK3fq7i4l+ooaHrwj170jfGfs1wmNNubY/PDfs9jDCv3doeLzGvHQAQXUKh/aijjpIkrVu3TiNHjpTL5UrLoAAAcKoYO4Z2/x5bpf3g1oXma6YOk7weaeioYIBv7krIo7QhNBfZep9Mh3bH9vjWxVLDi6HD0fvUaNmKrgX2Fi2SOv5cI5Xi2fLNGzu0FxRILldwi79OhHYAQDRxh/ZPPvlEBxxwgNxut+rq6vTpp59GvHbKlCkpGRwAoP+qd9g1yzm018jrNVfap7YtNl9zyIjgv0XHScPLpC+6WudHamMorFtDe9a1xxvtUvMHplNHHLLRFNo/+igNg0OEOe0RVo+XIm775nIFq+3hQZ292gEA0cQd2g866CBt375dQ4YM0UEHHSSXyyXDMGzXuVwu+VkGFQDQTdZKe35+hMqzvya4ZVuYA/2fmK85aJjkzpe8+9hCe7DSHpzaleu1LmiX9PATFlelvWVxcK50mP3G7TId7zIfIlWc2uOTqLRL9tBOpR0AEE3coX3dunUaPHhw6HMAANLJGtojLtTl3y2vtzh0WKy9qlW5Kl07uhaeO2Co5C6WXAXSPuWmpwcr7UMlyVaxz3Ro93gsJ1o/s11TXmaep19bm7oxoYPhC64lYBF9TnvkFQGLiqSdO7uOCe0AgGjiDu2jRo1y/BwAgHSIK7Qb7ZLRIG9O1+peDSrRfvpcgbX/J9fKaumz7dLoAZKrMNibPMK8H1qw0n6IJMmbY07Omdyn3e0OfoQEGiTfJtvzykrNYbIucoEXyQrY+9cNQ2qzbvlmqrRHTuLWxehojwcAROOOfYndY489pn//+9+h46uuukrl5eU68sgjtWHDhpQNDgDQf1nntJeWOlzk3yMZhrnC2flQXp40bbj0nUMkj1tyd+y1NXKo6bpR2hB6vrU9vifntFsr7bbW+Nblwf2/LcpLqbSnnUPVvL3d2gYh5eWF/RCNJvNqc2Gs275RaQcARJNUaL/11ltVUFAgSXrvvff0u9/9TnfeeacGDRqkyy+/PKUDBAD0T3FV2v3BCdzWLd8kqb3d8ieuI7QbI6pMp0dqo7yeYNjyerOn0m5bhM633fF5ZZbQTqU9DeLYo12yvOljBCSj2fF21tBOpR0AEE1CW7512rRpk8aNGydJeuaZZ/Q///M/+sEPfqCZM2fq6KOPTuX4AAD9VFyh3RecGGxd9V2S2n0eFSgshLuCPcn+SeP1Iz2oDRqljRqpjRqpj/P+ELxPBtvjY1ba/dWOz7POaW9tlVpaggv3IUUMe2i3rhwvyd7xEWiU3PaN2a3t8VTaAQDRJBXai4uLtXv3bo0cOVIvvfSSrrjiCklSfn6+mpud31UGACAR8VXaO0N7PJX2YIdYe+kQPayTTQ91rj6f6zWn9Ey2x5sq7UZA8jmH9rIS+wJpdXWE9pTy29vjrYvQSVKeLbQ3SBpsu472eABAIpIK7ccdd5zOP/98HXzwwVq1apVOOOEESdJnn32m0aNHp3J8AIB+Kq457b5tkmTb8k1ymHPsCob2tnZ75bOzQuq1hPasaY/375IM53cQrO3xUnBee2Vl6sbW7zlU2mO2x0uObfUSC9EBABKT1Jz2++67TzNmzNDOnTv11FNPaeDAgZKkRYsW6eyzz07pAAEA/VPMSnugWQoE9yzvrLR/T3/QozpP5+lRGat2mhcCcwVLz+1+ewm6c068NydzoT1qe7xvS8Tn5eX5lZ9vHijz2lPMYSX41jZ73cPWHu+wt7tEpR0AkJikKu3l5eX63e9+Zzs/d+7cbg8IAAApjtDetjoUyjsr7V/XizpTf9d5miedLumHR0g3zQ5e3xHa29oLbK8VqrTnZGl7fHv0nVnKS1u0vaUr5bOCfIo5bPnmVGm3TdOIUGlnIToAQCKSCu2SVFtbqw8//FDV1dUKBLraEl0ul77zne+kZHAAgP4ramg3/FLz26HDzrA0TR+bnzRpSNfnnXPafU6V9s457eaUnjXt8VEq7VJwr/bt1V3fICrtKeZQMbfOac/N9cnlslwUZ3s8lXYAQDRJhfbnnntOc+bMUUNDg0pLS+UK+ytFaAcApIJ1TnsotLeukFqXmRZmy8kJqEy1Gqc15icdGLa9W2elvSO0F6hJQ7VdQ7VdeZ9vkqZXypuTudAesT3eaAttbReJdQV5Ku0p5tQe32oJ7Q6LITo9T6I9HgCQmKTmtP/0pz/Veeedp4aGBtXW1qqmpib0sWfPnlSPEQDQD1kr7aWlkgKt0t6npdbPTI+5XNJh7g9N5wLeHGli2Mrd7s6F6PL0U/1KTSrSWu2rdzVTOb98WVJm2+MjVtp91ea5+Q7KStirPW2MgGTYF/uzV9qdQjuVdgBA9yVVad+yZYsuvfRSFVr/6gAAkCK29njPG1Ld8oirqJ/uetp03Dx6uIpyO4KVyy25ciVJ7b5c7dIg07Wu6mC4ymR7fMRKeyB2AqfSnkZGk+ObJtY57bbt3qSIC9FZ12docM72AABISrLSPnv2bH300UepHgsAACG20J63VvLtcL54Z6O+43/MdGr3oVO7Dlxdi8+1tedqu4aan98R2jtXke+UydAeqrT74wjtpYT2tInQ4m5dPd650h5faLf+rgMAEC6pSvuJJ56oK6+8UsuXL9eBBx4or2lfGumUU05JyeAAAP2TYTjMaS9ujfyEh99XoZpDhz55tPUrX9JIdYQmV9fic+0+rz20726S/AHb6t9ZsRBdoN52rdXAimbT8e7dKRoUIgZva6XdcU674Q9uTeg271hgDe3W33UAAMIlFdovuOACSdKNN95oe8zlcsnvd/jDBQBAnJqbpbCNSSRJpZFCu2FI979nOvW4ztE+FZWS1gZPuLtCe1u7Q2gPGNKuJlvwyuSWb4m0xw+qMO8Ztiv6unVIRKTQbpnTnpfnc7xOgcaYoZ1KOwAgmqTa4wOBQMQPAjsAoLucQkxJcYQEfem/pFbz357f6RK1+8JCVVh7fLvPq50aLL/1T2B1g7xe8zsF2dEeH3uB14EDIof2LVtol+8Ww3kT9bhWj5cc57VbQ3tzs/3nDwBAp6RCe7iWFvuKqgAAdIdzaHeotG+slf621HRqvUZpsQ5We3vYn7iw9vi2Nikgj6o1xPQ87WzIqvb4rkp7bcznDhroHNovvFAaMSL48eyz3R9jvxRnpT1iaHdYQb601H4Zi9EBACJJKrT7/X7ddNNNGj58uIqLi7V2bbD98Nprr9Wjjz6a0gECAPof6xxfjyeg/HyHUuRD70uWhb1/p0skucyV9rD2+M5wvEOV5idWN8ibY660Z7I9PidHUqApuM1dDE7t8YsXSw8+GDxubJSuvDJFA+1vIlTabXPanRaikxxDv7XSLtEiDwCILKnQfsstt2j+/Pm68847lZubGzp/wAEH6JFHHknZ4AAA/ZNtj/aSVrlclovW7pHm23cyuUtXSJKl0h62enxHEN+pwTLZ02QLXhlfiM5fG9dzraG9pUV64AHzNatWMdc9KYEI7fGW1eMdt3yTHEN7cbH9MkI7ACCSpEL7448/roceekhz5syRx9P1TvPUqVP1+eefp2xwAID+ybbdm1Nr/DPLpLauoNSuHI3WOhkdf9raw9uXXfZKu3Wvdu1pyqot37xexbUInWRvj5ek996zX/f220kMrL+LENpt7fGRQrvDnHaPRyosNJ8jtAMAIkkqtG/ZskXjxo2znQ8EAmrvyf/DAQD0SfbQ7tCnfsVXpOfPk44aK0n625DvaYNGhx5u94X9iXOb57RL0m4NNN+vptm2EF3m2+Nr43puaUmrcixvOHz6qf26+fOTGlr/Fm97fMQ57fHt1c62bwCASJIK7ZMnT9Zbb71lO/+Pf/xDBx98cLcHBQDo32yhvSjCvO7D9pH+/h3pue/p8RGXmh6KVGnvDOK2Svtu+5ZvGV+Izh9fknO57C3yTlauTGJg/V2g2fF0a1u8W745rzDHtm8AgHgltU/7ddddp3PPPVdbtmxRIBDQP//5T61cuVKPP/64nn/++VSPEQDQz1irjqUlMRZjO3yk2gqKTKdMlfbwLd86wrFzpT172uODlfb4y6+DBjZpe7XDCmdh2PotCXFW2q2LGIbEWWkntAMAIkmq0v6Nb3xDzz33nF555RUVFRXpuuuu04oVK/Tcc8/puOOOS/UYAQD9TFzt8RbW0JRwpb2mKQtXj48/ycVTaa+pSXBQ/V2gRTKc295tC9ElWGm3bvtGaAcARJJUpV2SvvzlL+vll19O5VgAAJAU50J0FvYqufOc9oiV9t321eMDgeCHO6m3uBPj2B6fQGivqoy90Xdra3Bl+fz8mJdCclxErpO10h5x9XijLfjhyjWdptIOAIhXUv8bMnbsWO3evdt2vra2VmPHju32oAAA/dveenOCLSluk/wBafEWyTAcn2OrtPuiV9oX62Cdrn/ouiPmSa9fKL36Q1vwl3quRd650h47iHeaMNb+d9kJLfIJiLByvOQwpz03QqVdcnzzhdAOAIhXUqF9/fr18vvt/2PT2tqqLVu2dHtQAID+rb7OnJRLS1qlt9ZJsx+RZj0k/XGR1GjuXY9aaXflhZ0P/rtLg/VPna5VQ6dLkyulyhLHeck9Fdpt+7R7fJIR/4tP2De+0E6LfAKihnZzs2LELd8kyU9oBwAkL6H2+GeffTb0+X//+1+VlZWFjv1+vxYsWKDRo0enbHAAgP5p715zACopapUWrA4efLpd+unz0t8/kZ79Xuga63ZtoUq7yyu5uqqi1nnq4SvGOwWvnprXbtunPSexF95v/K64rqPSnoBUtMdLjgsKsuUbACBeCYX2U089VZLkcrl07rnnmh7zer0aPXq0fv3rX6dscACA/mnHDpfpeGBFs/TMBvNFR5mnY3lzIlTaXfmW8+bbhFforfdwuj5dbO3xnsRC+/4Tq5WT45fP54l6HZX2BERY+V1KYMs3Ka72+Ib4Z0IAAPqZhEJ7IBCsYowZM0YLFy7UoEGDYjwDAIDEbdnqNR2PGLxHWrXTfNGMUabDiJV2tzm0R6u0W+8hZa493psTe/G9cHl5fk2esFOfLB8a9Toq7QmIFtpbLe3xDushdN3HnsiLi83HhHYAQCRJrR6/bt26VI8DAABJwfBavdO80vbotjVSmyUUTao0HUautOdZzptvEx7UnSrtmWqPT7TSLjnvZz9ggLm6TmhPQIQ92iWnSntioZ1KOwAgXklv+bZgwQItWLBA1dXVoQp8pz/84Q/dHhgAoH/avl0yDHN7/PDtn5svGloiDSgwnYo8pz1ypd2lgMoCNdLaPdKeJuVWWJKUMrkQXUvC9zjxuNV6+wNzB8LMmdLzz3cd0x6fgCiVdvuc9sTa462VdhaiAwBEktTq8XPnztXXvvY1LViwQLt27VJNTY3pAwCAZG3daj7OzfWpeNkX5pPTR9ieF++c9vDQ/hedrZv+cLx0xG+lEx5Vzl8X2e6bqTntXk9i7fGS9J0zlqqwsOsLvPbaYKU9HJX2BESd026ue0RdiM6gPR4AkLykKu0PPPCA5s+fr+985zupHg8AoJ/bsjmg8PeUhw3dK9cSS5I/fKTtebZKe3vnnPYCy/muz/eowvSYq6bZtphbxtrjcxKvtA+v2qsXn3xCj/7tFE0+cJAuu0z62c/M1xDaE5BAe3z0Ld/sS8MT2gEA8UoqtLe1tenII49M9VgAANDWre2SuuahjxlULS2xLEJ38HDb82z7tPtiV9p3a6D5JnualJtrDu0ZW4jO05zUfb58xEZ9+dgVUuGXJdkr7TTEJSBV7fFGW/Be7qLQKfZpBwDEK6n2+PPPP19//vOfUz0WAAC0ZbM5vc4o/EgKGF0n3C7pAPsK6d6cCJV2V+RKuy201zQ73CfOgXeTfSG6CJX2rfXSG2slw3B+XJICXRXi0lLzQ4TDOAVaJSNy9dzWHh9tITpJ8oe9WxJoVLH+Y3q4pcX+OwAAgJRkpb2lpUUPPfSQXnnlFU2ZMkVer3lrnrvuuislgwMA9D9bt5pD88GBj80XTBgkFZr/7khRKu1RtnzbJcvWpbub7PfJVGh3R6i0L9osff/v0s+Pln56lPM1YW3dRUXmhxojF48Rzoj+jWpttbTHR9vyTQqGdm/HWgwti1Xs/VzSCaZLGhulsrJEBwoA6OuSCu2ffPKJDjroIEnSsmXLUjkeAEA/t2WLeeX4iY2fmi+YMszxeamptDfZwldPzWm379Me4d2CTbXBf+9+S/r2NKnSvuK9Al2Bn9CepEDk+eyS1NZu3fItRpncvyv4r2FILR+ruMj+i9XQQGgHANglFdpfe+21VI8DAABJ0tZt5jA0YudK8wVTqxyfl8ycdlulfU+TvIOzpNJueRNChiHtauoK7W1+6Z/LpItm2G8WViUmtCcpynx2KcHV46Wu0N6+VvLvUXGRfYYiUxcAAE4SCu3f/OY3Y17jcrn01FNPJT0gAED/tmVrV+t7vppVvn2z+YIpHaG9+ASp9VOpfZOkKJV2d6HlfNfntkp7q1+lnr2SBjhen072SrslBLpc0r1vSf9d1XXu2eXOoT3QtRQ5oT1JqW6P93XsgNAa7BzxegPKy/OptbXrf8VYQR4A4CSh0F5GzxYAII0aGqT6+q7QPlVL5QqEhfHORei8w6SCwyTPIKnucUkOlfbQPu3m9vioq8dLGuLZKWmk4/XpZK20FzbWSNUN0pCwvcGO3ld68IOu40WbpS110nDL3+cAlfZui9Ie7/e7FAiYK+Wx2+NrpPbNUtvq0KniojZCOwAgpoRC+7x589I1DgAAtG2b+fhjTVPTMxepcPVG6ZNtUm2LVJQr5e4XvMA7OlhJDzTZ92n3OVfaw0N4ncoUcLvlDntjYJB2m++Tofb4sa+/Il3yT2lkuTRtuHTiJOn4/aSyfKkubGX555ZLF1qq7YZPCrRI7nxbaG9oCHbau8xLB8AqSnt8eNDuFLM9XpLq5gd/Nh1Kilu1e0/X7yehHQDgJKkt3wAASIctW8zHBSUBFR45RDp3uvTrk6VHzwg+kDsh+K/LLXnHSrK3k7e3uyVXruTKsZwPP3KprbjY9Pgg7YpyffpYX2fAmi+Cn2yslZ75TPpok5TrCQb3cM8ud75hR4u8NbT7/T3XPdCrRWmPb7Xs0S5Jue624FZ8voDDMzrvaX5nxroYHXPaAQBOCO0AgKyxdav5eNhQhxTjypU8lV3H3lHBf5wq7W5za7xkD6ztJebV1wcae6Jeny7mSruhstVrzBcc0rFd2CmTzec/6miRtwrUS7KHdokW+bhEaY+3VtqLtVfDjrpWOuOP0maHn0UExYXmXy4q7QAAJ1kT2m+//Xa5XC5ddtlloXMtLS26+OKLNXDgQBUXF+v000/Xjh07TM/buHGjTjzxRBUWFmrIkCG68sor5bP2GAIAeoVNG83V8uFOoT2n0tzbnTNcUqRKu7k1PnjefOwrNafaiixojx+nL+RtsCTrztD+lbHBFvlwz6+w3zAQDI+E9iRFaY/v3O6tUI26Rz/RXpXK3dgafHCt+fdHAUN69jPpl6/b7mOttBPaAQBOsiK0L1y4UA8++KCmTJliOn/55Zfrueee09///ne98cYb2rp1q2kFe7/frxNPPFFtbW1699139dhjj2n+/Pm67rrrevpLAACkwGuvmavlY0bV2C/yDDEf5wyWXO4IlXZ7YrVWzv2W0D7An5nQHv46h+sD84ODi6QRHYvN5Xqkr080P+7UIu8ntHeLEbnS3r67VU/qW2pUsX6ie80Prt0t7WkKLhzwympp1kPS+f+QfvmG9OZa06UlxYR2AEBsGQ/tDQ0NmjNnjh5++GENGNC1xU5dXZ0effRR3XXXXfrqV7+qQw45RPPmzdO7776r999/X5L00ksvafny5XriiSd00EEH6fjjj9dNN92k++67T21M2AOAXuf9D8xzhY87aq39opzB5mOXV/JUOFfaHUK7NYT7y81z2ssD5jcKMtEef5g+ND94yAhzd8E39jc//tEmqb7FfK6j0u71Bj/CEdrjEKnSHjBUedU8fUt/c378/70YXIdgR4P03b9Ky7Z3PfbA+6ZLmdMOAIhHxkP7xRdfrBNPPFGzZs0ynV+0aJHa29tN5/fbbz+NHDlS7733niTpvffe04EHHqjKyq65jbNnz1Z9fb0+++yziK/Z2tqq+vp60wcAILNaW6W6uq4/S1/RGzrUvVBqtUx58gyyP9lTmXSlfc9JRwYXuHvmXOmNi/S3CReb75OBSvtkWSrnU6vMx18aI3nD/oQbkpZalt4PdM2tZtu3BBlttkXjQh7+QMWLV0V+7u0nSAcNk4aWSD+yrOr/yurgGgQdaI8HAMQjoS3fUu3JJ5/Uxx9/rIULF9oe2759u3Jzc1VeXm46X1lZqe3bt4euCQ/snY93PhbJbbfdprlz53Zz9ACAVNptmQp8l67QmO9/HAynkyqla2dJR42V3GX2J+dUyes1B6ngnPZi26XWEN5+wChp/6454q1FJVGvT4dAIPjRaaJWmi8Yb3mjIj8nuF/94rCV+z7eLH15TNexvzb0aVGRVNt1SGiPJVKVfUW1dMsC2+kalWvA7UdK35tu7oi49EvS/I+CWxV2evRDaXpwfQJCOwAgHhmrtG/atEk/+clP9Kc//Un5+fmxn5BC11xzjerq6kIfmzZt6tHXBwDYWUP7GK0LftIeCO7RbhjBY49TaK+UN8dSaW+3V9r9fnM4lqRcy/7atop9D4R2f9gQCtWofbTZfME4h+6Cg4ebjxdZ9ssL1Ie+Z1TaExRwSM97mqSz/yS1mCvw5+thTS5fK513qDmwS1JxXjC4h3t+efBeCu7THo72eACAk4yF9kWLFqm6ulrTpk1TTk6OcnJy9MYbb+jee+9VTk6OKisr1dbWptrw0oCkHTt2aOjQoZKkoUOH2laT7zzuvMZJXl6eSktLTR8AgMzaFbY9erH2qkKWRej2KZfc+cEt36w8Q+X1Wua0++xz2p0CuHUufK7lPj0xpz18XKE3K8KNrrCfm2YJ7R9v6XpjQwq2d3fsNU5oT1DAIT0/9IG01Tyd7h79RI/qfLnyojQunn1wcPHATq1+6a9LJVFpBwDEJ2Oh/dhjj9Wnn36qJUuWhD6mT5+uOXPmhD73er1asKCrDW3lypXauHGjZswIzhGbMWOGPv30U1VXV4euefnll1VaWqrJkyfbXhMAkL3CK+0jtdF+wbBSyR3hTVZPiby55uDU3u6RYWmPdwrg9kq7dUG7yGNOlfBF6EZrvfnBwUVSoWUlOalrCzhJcrukQUXSXnPlNtIK8oT2GJwq7S98bjpcrIN0jW6TJOXlRdlqdmChdJLl/0nueE2qayG0AwDikrE57SUlJTrggANM54qKijRw4MDQ+e9///u64oorVFFRodLSUv34xz/WjBkzdMQRR0iSvva1r2ny5Mn6zne+ozvvvFPbt2/XL37xC1188cXKy8vr8a8JAJC8DRu6Ph8ry6rxgwqlAq/kLo/4fG+uOZkahkv+QJHpD51TaLe21dvb7KONOjXCQ7ut0j6y3PlJYyqkuV8LLlI3ZZhU7NCBEKiVNJzQnihrpX1nY3A+e5jLdbdaVCDJ3p1hc+4h0j8/7Tpuapf+70UVf3W66TJCOwDASUYXoovl7rvvltvt1umnn67W1lbNnj1bv//970OPezwePf/887rooos0Y8YMFRUV6dxzz9WNN96YwVEDAJLxQdjW5JO0wvzg2IHBfz0DFIk3z2HROb85tDsF8NxcvxQwpLoWaU+TJtQsUYH2V7MKJfV8e7yt0j4ywtfsdkkXzXB+rFNHpb3Y8q0htMdgrbQvNK99056Xr7dbu+aq5+XGCO1HjAy+uRK+wv/flmq/AQsk/U/oFHPaAQBOsiq0v/7666bj/Px83XfffbrvvvsiPmfUqFH6z3/+k+aRAQDSbdmyrs9tW55N7Nib3VMe8fmOod3n7qiFBjm2x3vapRE3S75ghf0ySfN0mD7R1OA9Ml1p38dh4b14BWol0R6fMOvq8RtrJI9L8gfXDNg5aoL8q7r+Fypqe7wUXKDu/m9Kxz4oNXdde+Bj8zVYc7VTQyRRaQcAOMv4Pu0AAEjmhehsW55N6Ajt0drj8+wVadv2bk4L0eUZUol5StVAdU2w74nQnlSlPR7+4GJ+hPYEGZb0fOEMafXV0lPnSFcdrRXTZpkejtkeLwV3APiF+Xk5LS2hefESlXYAgDNCOwAg4wxD2rOna+XzCTLvua59O9rj3eY91MN5C+zbolkDt+Ocdm9Aqig0nRukrncQeqI9PrzS/i39VV/XC2q/5STpkiPtq8QnoqPSXlBgPt3UlPwt+wWnfdqLc6Uvj5F+dpSWHWAO33l5cYR2STr/MGnWeNOps/SkpODvflub1Nrq8DwAQL+WVe3xAID+qb5e8vuDe1wP0B4N1B7zBWM7tjyLtHq8JG/BYNu5WJV2jycgt9uQBphTbU9X2sND+xcary80Xp7vfyC5jchPioe/RjICKiw0v0ff3BzhesOw7zXeHzmF9jCtreb/fcqP1R7fyeWSbjhOemV16FSVtmukNmqjRkmS6uqkIUMSGy4AoG8jtAMAMm5PWEYfr9XmB3PcwT3aXW7JbZ+33smba189PValPTe3Y6X4geZKeybb4yXJ5TKCbybEY3eTtGa3tH6PtKFGOnh4VzXX8En+ahUUDDU9JWJob/lQ8o6Rcvpxagy0Br9vUTS3JBnaJWn8IKki+CaR/+CRunbBOWpV1/QMQjsAwIrQDgDIuD/+setzW2gfWS55PZKrKBjcI/A6bGUeq9Lu9XaE9gGR2+N7IrTb30yIs91akm5+RfrT4q7jcw4xt2C3b1ZhoTm0R2yPb1kiNb4iVfwk6hskfZoRe8J/i6XSXpAfxy+JKyf4ZoDLJb1xkTSkWB6XS78ecZXa2rruV1eX8IgBAH0cc9oBABn39793fW4L7aHt3qKvoh5PaLeFY29HNbsicnt8T8xpt48rgdA+yrJQ3foa83H7OtucdsdKe6BV8ldLRrvUstjhgn4iRmu8JDW3mH/Z8vPjqLSXfFPydEzzqCwJTUMoKzVPYie0AwCsCO0AgIzzh2VUe2jvnM8ePbS7XJLHY24pj11p7wztvbjSPtoS2jdYQrtvkwrNX55zpb19nWR0vG7rMocL+onw0N7YJl37ovTUp9La3cE5/5JaWqyV9ji2fPOOkXLH2h4qK2kxHRPaAQBWtMcDADJu8+auz8dqrfnBMbEXoevk9ZrfAIg9p905tPf0nPbOcQ3TFhlyyZNTEP0J4ayV9s21wT3nczrel/fXqyCvXVJXddix0t62vOtz347gInaebmw311uFt8cv2iw9+EHX8cBCaekVtvb4mHPa3RWSu0DK2UfSR6aHqLQDAGKh0g4AyKj6evP+1F/S21r/j2ulJ+dIt3w9uM2WJHlih/bcXPPK53GHdsvq8T295Vvna9yhn2urhmt99SDpwF9LD7wX+8nW0O43pM3m5FeYbz62Vdr9e6XWFeZzvu1xjLwPCq+0v7bG/Ng+5VKux74QXaxKu7dj276cobaHykqptAMAoiO0AwAyassW87FfOao8NFf66jjpgsOlCR1buUXZo72TdV577Pb4jk8yvHp8V6V9qyTJo4C0o6Fz++7oBhRIpXnmc5YW+YK8WtOxrdLe9GpwLns4/844XrwPCoS9g/ThJvNjX91XUhIL0eVUBf/1DJJcHtNDxUVtkgwNVrUkqTH2lHoAQD9DaAcAZJQ1tFcMaFJBgUPlMgWh3V5p7/jEsnp8merlVZvjPdLBGtpDhsb+muVyOSxGZ97nvsC7y3RsCu2+nVLrUvt9ff08tPsC0rJt5scOGynJPqc9dnt8x8/H5ZE8Hfu57dgr3fOWrl/yQ1VriJboIEkGoR0AYENoBwBkVPh8dkkaPnSv84VpqbR3tNMPsM8hr1Aw+PZEe3xrx7TmpEK7JI2uMB9bKu2FedWm46am0JpqUsO/JSNgv2d/r7R/Xi01W8L41GGS7KvHx1yIzlPe9Xln1X1vq3Trq5q2/U0N1i4N0zaN1EZCOwDAhtAOAMgoa6V9xLB65wvTUmmPHNo757X3VKW9WHtVKssbFpVxhvZR5eZjy7ZvBV5zxdgwOr4XgQbJt9H5nv7dYcm+H+kM7Ystv5gjy0PTKGwL0cUK7e6wTojO0L7vQNvv3RF6n9AOALAhtAMAMiqu0O7Ok1wOG7FbJF1p93ps88I757X3VGiv0jb7A5XF8d3A2h5vrbTnN9ie0tQkqWWpc5VdCs5xD0R4A6WvMgLBNzIkaYml6+Hg4aFPbQvRRWuPdxcEf387eTrWaHC5pENGmC49VAsJ7QAAG0I7ACCjamu7Pv+WntQRe18Phk5/WJiMo8ouJVNpD/sz+MuTpHln6pNbr9JkfaYPdZjjc9Khrc2hNb40TyrKdX6ClbU9fn2NqUru1L7d3NgsNb8T/b6BCFMV+qpAQ9ebGIstP4+DhoU+te/THuWdHXe5+ThnUNfnU8yryU/WckI7AMCGfdoBABnVECoCG3pE56v4mUbpGUm5HumZ70rTR0iu+CrOiVfaw0L7aQdIklo+Hq4VmhzxOengGNrjnc8u2Svte1ulmubQ/vOFBfYvomnPYqnKuvebRb8L7R2dBc3twTnt4aZ1VdoTao+37nXvLpbchVKgSZo4xPTQ/vqM0A4AsKHSDgDIqM7QXqVtKlZYYmnzS8M69mZPV6U9z/5n0Ov1R71HOjiG9s757PlTze3VTkaUSR7zHvXhLfJ5eT65XOb56c11K2MPrL+G9hXVwdXjO7kkHdhVFbcuRBe1PT58EbpOOZXBfzu3M+wwShtl1Pez7zkAICZCOwAgozori+O12vxAQU5Xtdmdrkq7yzZX3ptjnuPt86V/PbaIlfaCw6SS06Sir0e/QY5bGlFuPhe2GJ3LZa8GNzXGWDxN6r+hfanlZ7HvQKm4640Te3t8tDntFfZzYYvRBVzmN1uG7FkR93ABAP0DoR0AkFGdlXZbaB9dIbk7Ao2nNK57JbVPuyvfcs5caXe6T6pFDO35hwQ/zz9YKo4R3Dtb5MvzpQOG2irv1hZ5a7XYUcC+gF2f1vn1fmJZFHDKMNOhrT0+aqV9gP1czqiOJ+aoaUil6aHh9Z/FNVQAQP/BnHYAQEZ1VtonaJX5gX0Hdn3uTmNod+eZKsreHOfQnhvnmnDJaGuThmq7+WTVoK42aknKny41vyv5I6zoft+pwYXrip1b6a2LpVlXQHcU6GcTrDt/D6yhfWpV6NP2drf8fnPNo8BhzYAQj0Ol3Tsy9GnTqGEq3tH1sx/TSGgHAJhRaQcAZFRnpd0W2seGhZ0UzWm3t8fLXGkPGMpr3KvxWqUD9GnE56Vaa6s0RJaFzyrHmI9dOVLBVyLfpLIkYmCX7JX2pqY4Ku1GPwztrT77InRTukK7tcouRam0u1zObzi5C0Jhvn2MeQX5fVtpjwcAmBHaAQAZFbE93lRpT9eWb5JcHUH3hc+lqhs17OjrtEoT9ZROD13ni2P6d3e0tTmE9qFj7RfmHSC5kvvTbZ13HV97fH8L7Q1SU7v0nUOCuxYUdAT0sEXorPPZpWihvUhyeZwf8wb3aA+MMy9GNzGwQoGA0xMAAP0V7fEAgIzx+YJVZrf8GqcvzA+OTX1oj1ppL8mTwhacG6RdpnGmk6/FpwrtMZ+s3Nd+oTs/uIhZ+5aEX8NWaW8mtNsE9koDCqTbTwge+wLS+j1SaVc3htObHQUFEX5BPGWRX8u7r9TyidyTBplOj9Z6Ne9qVNGQooSHDwDomwjtAICM2dsxhXikNipPljJ4Z2h3FwRbw+PQrUp7x57mncpVK4988isn/e3xbS4dqXc1WDs1RNX61swP9bUxU50v9o5PKrRb513HNafdaA/uJ+4ujH1tb2f4JaPFfC7HLY0zh+qE2uOjrcXgDS5G591vgAJyyd3xjpFbhlqXfq6i4w6Jf+wAgD6N0A4AyJjNm4P/2lrjS/OkQR1BMc4qu5Rkpd3dUUW1hHa3DA1QjXZpcNor7a0+jz7QEaHjUV+aoK9VDHe+OG+i1PR6wq9hbY+Pq9IuBbdB6w+hPdAY195+zc3m/3VyuwPyeu2LFwYfjBLaPeWSO19FA/1aq7EapzWhh/xLFkuEdgBAB0I7ACBj1q0L/mtfhG5gcBEvqVuh3VpZj1ppH1Bgu99A7dYuDe6RLd9M48qLvKCcPEM7VrxvtT/28RZpzW5pS520qVY6++Dg3Gw5rB4fb2j310k5Q2Nf19vFub2dbbu3fJ8sW613cUdpj5ckz1AVFGzQ33SmBqhGy3SAlukA3X/EYA2O/kwAQD9CaAcAZMz69cF/HUN7pzi3e5O6Oac9Lye4ZVpjV4IeqN2SemYhunB5+fnOF0rBNzNyhklt6+yP/eJF6aPNXceThoSFdvMX4dTm7SgQYYu5vibOrzOxPdpjhHbvPnK3r9cthTeoqalrT8G9/kVxjQUA0D+wejwAIGO2bg3+a9+jPXy7t9SFdudKe1hArjBX2zsXo0t/aDe3ZUettEuSJ0Iddp9y8/GmutCnSe3TLvWfxej8e2JfI/tCdNY3Q0xi/e5695EkFRWafzaN9bvjGgsAoH+g0g4AyJi6jkw5USvNDySx3ZuUbKU9LCBXFJqCbmelPf37tAckdW0NlnRoH2Gp7G4JC+0FSWz5JklGU3zX9XaBGumet6Rnl0uTK6XJQ6SZo6Wpw0yXWbd8i15pHxD9NXOCXRBFhW3aqa7V4hv31gYXxou0XRwAoF8htAMAMqauTvLIpzblyi+3POrYoDp8xW5PD1baB5gXXOupSntpwxZVKle7NEh+5Sg3Wnu8JHkGOZ8fbgntm6NU2pvjrbT3k9Du3yMt3iot2x78kKQLj7CHdoc57Y5cXsldHP013YWSZ6Ct0t7Q4Jba10u5Dtv+AQD6HdrjAQAZU1cn+ZWj/bRShWrSwz98RPrDmdL4sFCawkq7Y2h3h7XEW1aQ76k57T/dcJm2q0o+ebVLA3Xga/OiPyFSBXefaKE92Up7c3zX9Xb+3dKKHeZzkyttl1mnFVjfDAnxlMf3urn7qqjQ/IvZ2JQrta2K8AQAQH9DaAcAZExdXddWWW3Kk29spXTSJKkgLFC6ihye6Sy59vjw0O48pz3d7fHl7dWhzwdqj9y5MSrt7jLn1mlrpb26QWoJhnVrG3f8C9H1g0q74ZPqq6X1NebzDqE97vb4eNdiyJ1sqrTnqF1567ZKH/xXMtL8iwcA6BUI7QCAjKmrDZiOy0odtjFzxx/ac3PNx72l0j7At8t07B9YFf0JLpdztd26EJ0kbQ2uil5QkORCdP1hTru/RlpRbT7ndkkT7GsHxL0QXbwdIt5RKiqSTtazWqKpalCxvvPbi6Srn5JalsZ3DwBAn0ZoBwBkTF2dedX0stIW8wXu/IQW47Ku39ZqeQ/AudLulVwdAdayV3uF9jg+L9Uq/ObQHhgYxy7dTovRleRJpZZvwuZaSd1oj+8PlfZAjb01ftxAKd/+xkbcc9pjzWfv5HKpqKRULcrXVH2iPHW8s7Rsu9TwvmQEoj8fANDnEdoBABlTW2f+M1RWYknZ8QafDtZKuzW0O1bapa7F6MrMbekDFGyXTmul3edTRcC8xZcxeEjs50VcQb7cfNyxgnzSC9EZ7ZLRFvu63sxfIy23hPZJ9tZ4KZH2+PjXYigqKdYiHWJ5IZ+0bJnU+mnc9wEA9E2EdgBARvj90t695gBkq7S7Egvt1kq7NaQ7Vtqlrhb5cnOlvUdC+277ntyuIUlW2iX7tm+bOkN7kpV2qe9X2/177O3xk5zfOIl7IboE3nAqKs7THg3UWo0xP7B0q9S6JO77AAD6JkI7ACAj9u6VjtUr+qO+rYv1Ox2ij1RW0Gi+KIHt3qTY7fGRK+0dYf2AodJvTtGPh83XV/SGZuu/ksLCvtEmNb2R0Jhiqq62nXIPHuhwoYUnwjXW0N6xgry1jTvuOe1S3w/tvh320O6wCJ3k0B6fgkp7cUe+/0jTzQ98tkNq3yAF+skK/gAAR4R2AEBG1NVJx+llfVt/0u/0Y32kQ1V15SPmi+JdgbuDtT0+/kp7xwJ0Q0uksw/WG2Vf11v6ipZrf0lhlfa2tcHQ7t+b0Lii2rnTdLhbFfIWxlEFzxkUXJDOyhraNwS7BawV4bhXj5f6/mJ0m1dLNZZgHKHSbv2+FRSkoD2+Y63Fzzp+30LW7gnOaW9fG/e9AAB9D6EdAJARdXsadYTeN53LOWSo+aIEQ3u3K+0dvF6/6TgU2tvXB0NU2/KExhVNYIc5tO/UYNubD45cuc7BcF9LBX5NsP3e1h7fTHu8JCnQIH22wXyuONd5JX7Zv28pmdPeEdpXa7z5gY6fndpWx30vAEDfQ2gHAGREXfUmTddHpnOu6cPNF7ktVeMYul1p75CTY16xu71dwbDeuShY25qExhWNb5u5LTvu0C45t8hbQ3t1g7S31b4QXUuODPPi/ZEZfbg927fL3hq/35Dglm8O4mqPdxd07UgQh87QvkoTzA9sqpXa/FLbKlaRB4B+jNAOAMgIY/HrKpKlgjtthPnYU57QPaNV2v3+4Ee4SJX2HI85IPl8Cm4LFuiYc+/bmtC4ogls22Y6rtaQrjcTYnFX2M+NGhAMnMW50tQq6ZsHSE1ttjbuQMCt9vY4t9Pr05X23dIX5i33NDHyQoBxLUSXQJVdilJpDxjB6Q2BJql9Y0L3BAD0HQlMaAMAIEVaFil/6cemUxu9ozVyoLniLXd5QreNtuWb017roestlXav1yG0+8KCXaAhOK/dk1g4cxLYsd10vFODVVAQ4WKrnEH2c3k50rKfSgMLTXPe89vtFeHmlhzl5vpt5236eqX9C8sK/tZuhTBx7dOe4LSOztBerzJVa7CGKGzKxJrd0vhBUvsXUu7ohO4LAOgbqLQDAHqO0S41vio1/Felq8yLa31eepD5Wneh5LaUzmOItuWbU2gPVbQtlfZcd5sGapfGabUqtDv4XL+lGuvbktDYIjF2mPcHr9YQ5edHuNgq0rZvg4psi9Q5VYTjntfelyvt/mppjeVnO87hzZAOce3TnmRolxxa5NftCf7bvj6hewIA+g5COwCgZxjtUv1fpaY3JaNNgzasMj28dtCB5usTbI2XorfHW+e3SxHa47/1hF56e6J2abBWa4LO1l86Ku2WlvgUtci7dtkXoos/tDuvcO7EuhCdlMAK8n250t6+XZo7W7riy9Ipk4NbvU2IHNqt+9s7fV+TbY+XpLUaa35wc23wX9925rUDQD9FezwAoGc0L5Tavgh+XtusQbs3mR7eUrWfpLAgn+AidJK9PT4QCM5j93hiVNrD2+PzzH8aB6gm+FxrZd1vaalOknu3ucq72zVYOfH+dXaXSC5v8A2RGAoKHCrt8e7V3ldDe6BZMhqlM6bE/ZS42uM9yVfaN2iU+cFNdcF/DZ/k3ynlOO8fDwDouwjtAID0M/xS83tdxx+bA3CL8lS7z2iZQ3t5wi9jrbRLwWp7YWEClfYyc5l7gGrU2O6T/LXmJwdqEh6fk1XX3KT/d2GZBmunhqhaywsOif/JLlewI8G3M+alHo8hr9dvWnzOWjWOKNAS/5h6E+uUhzhY2+NTuRCdJK3XaPODm2q7Pm9bRWgHgH6I0A4ASL+2z6XA3q5jS2hfpENUPMCyIJpnQMIv47RVWltbMLRHr7SHhfZye2ivaXGY0+3fk/D4nOwZOVb/1ozQ8aDCKBc7cQ+QFDu0S8GAaQrtzf280u6P7/sWztqdkOo57bZK++a6rs/bVkqFX07o3gCA3o/QDgBIv9ZPzMeLzaH9fR2h8lJLNddpD/IYIlXaJedKe9dCdDmSK1cy2qQy86J0A1Sj9jaH0B5oCW4B5y6yP5aAluZW03Hc89k75QwKVmCtXl4lfb4zuMjamj3S9ccpP8+n+rD3TuKutBstkmHYFrfr9Xw7Yl9jEd/q8clX2pdqqn6oBzT3t4s1dFqONDxsmohve7BrxRXnVn0AgD6B0A4ASC/DL7WZV4rXSnOF82NN05dLzeFVnsiLgUXiVGnvDO3WSrvbHZzr3nWiUPK3OVba25wq7VJwXns3Q3tzk3lgCYf2SN+nmxdIK6q7jpfvsO3VHv9CdIZktEquRAeX5fzVsa+xiLkQncsjuRJrlygMu3yXBush/VCXTr1PQ8dbOgEMX/CNBu+whO4PAOjdWD0eAJBevi3mhdIa26SNtaZLPtP+KguvtLtyE24xlpwr7Z0Vdmul3RbwO8O3pdJeoT1qa40wpzuJOdFWLc3mgSUe2iOsIG/dtuzzatv867gXopOC1fa+ZssaaUNN8E2JOBiG1GqttFvb490lCXckeDz2n3tjk8M7UJLk35bQvQEAvR+hHQCQXtZV1y1V9oBcWqmJGlQRVs3OGZJUK3a0Srs1tHutneGd1VGnSnuk0O5LvFJrYhhqaTaHvoKCCNdGEqnSvr9lwbLPdtiqwk1NcbbHS30vtAdapMffkg69V5r8K+msP0l/Xhz1KdbALjksRJfEm02SudouRXlDxUdoB4D+htAOAEgv33bz8aAitV3+VT2vE7VDQ7RSE9WiAu0zvL7rGk9yK2S73bJtl9YZ1q3t8REr7eX2Oe1tbRH+XPoTnxNtsmuTXDvqlaOuwSVcaXfnO8+hPmCo+XjFDhVatn1rak4gtAf62GJ0/t3S4q3Bz3c3Sa9+IS2P/vN0CtK2Oe1Jhnbrz72lNcLPxvrfEwCgzyO0AwDSy1qNHlmu9WeeqpP1vIZqu47Uu5KkEVVhoT2nKumXs7bIR6q0xxvai9Qkf2vA+cW6W2m/5y5999Zz1K5c7dEA/VaXJB7aJSlnsP2ctT2+tkWVXnOXQ8QWbCd9rdLu2yl9tMl87uDhUZ/itAaArT0+wT3aO1k7LJpbI8yL91XH3c4PAOgbCO0AgPRy2M9889bOYONSrQaovKxZxcVhqbobod0axiNV2m3t8e7i4L9l9tSc29jg/GKBxuCK88mq3hr6dIBq5ZE/udDu1Jkwokxym6cYjNE603FjQu3xfazSvnKptMfyNR26T9SnWPdolxwWonOXJzUc68+9tTE/ON/+7XXSf1d2PWC0Of43BQDou1g9HgCQPoHm4Nxhi9015rJi5eDGrgN3XoYq7R1vJJTbJ5XnNjXazoX4a6Sc5Nr5tdNcqd+pwYnPaZeknKH2c7meYHAPW/RvlN8a2hOotDv8HHu19z80Hw8plvYpc762g9MWeXm2SvuApIYT/nM/Wq/pjB+dLBkdHR6DCqXlV3Zd4KuWPBVJvQ4AoPeh0g4ASB+/c0Wwfq85WZtWjveOllzJ/3myhvFIW77ZK+0d88JzPWr3mseX3xyh0i5F/BrjstO8+vxODU6yPT7CmwajzAFyROsG03G/rrR/tMx8PH1EzMUPre3xXq9fHo+lVT0FlfbtGiq3ETYlY1dTcNeFTn7LVnAAgD6N0A4ASJ9AreNpa2gvLQ7bo927b7de0lppj7Tlmz20d81Fbi0oNj2U37I38gtG+BrjsnOP6bBaQ5Jsjx8c3B/carQ5tA9tMc/h7tdz2j9Zbz4+OPbe583N5tBuWzne5UpJpX2DRtkv2FLX9TmhHQD6FUI7ACB9/LXm40CwKllXb06mpSUdod1dJOUd0K2XjNQe39pqPm8Lx2H7a7cWJhDa/XWRH3O63C/dcEPwperXmavXSVfaXR7JM9B+3lJpr2zYbDpOqNLel1aPb6+TlltWYbeutu/Autp+UaE1tBdLruRmHob/3JtVqMZCy89zU23X5z5COwD0J8xpBwCkj7UKfckz0ocbdYZeVr6+rKd1mj7U4Sor7UjUBUdI7girZscp0kJ0LZZCsTXcy+UJVtv9dfrk62fosfn7a48qVKMByissl/SU8wsGEgvtP/mJdN99kkc+lfpqTY9Va0hyc9ql4H7t1tXsLZX2ivotpuPEKu2tsa/pLb5YYm43l5IK7dYt9OQpT3pI1p97TekIFTXt7jqxKbzSviu4gnyMdv5wjY3Shg3SpEkJPQ0AkAWotAMA0scaaD+vljbWav+N7+pq3aHp+kiSVFoxXMqfJhUc2e2XTLrSLoXmI2897Ag9pu/qOZ2it/Vl7Qw4bKnWKYH2eMMIBnZJGqRdtseTrrRLwdBuNdq8WFlp/U551RVWE5vT3ofa4xdbFqEbVBRciC6G2KE9udZ4yR7ad5dYWuTDK+1Ge3Cf+Ti99540aJC0//7S174W7PYAAPQehHYAQPr4w/Ze9wWk1ea23mUKtsKXVgyRSk5xnpedoEgL0VlDu63SLoVW5M7NNaeatvYo40qgPb62tuvzwbK3OO/WwG6Edoc3Fizt8W4joJHaGDrut5X2pZ+Yj/evNJefI4RvW2i3tse7o68+H431576rcKT5xGbL75l/R1z3bWmRTjutq9PklVeka69NcpAAgIwgtAMA0icQFtrX75FazWH4M+0vSSotVcpEWoguZnu8FKpW53otob0tSmgPNAYrn3HYtq3rc2to36WB8isn+dCe4xDay/KlAeYS7gStCn3ebyvtn31uPp7csfq+yxV882jAj6WSb9ieZn2Tw1Zp70Zot1bat+ePMZ8I27pPUrBFPg533CHtsOT7226T6uudrwcAZB9COwAgPQxfMNB2WmGeb71dldqtYEguKUndy8ZbaXcMxzlDOu6RQKVdirvavj1s7bOhMi+EVq3gayc/p32w81Z5+w2RJg6WTpqk1d/4htZrdOihfltp/2y9+bgztOdNDU7TcLml/IOlnCrTZbHb41NXad+WO9Z8YnOt+dhv3nkgkpdfdj7/2mvxjQsAkHksRAcASI+AZcX1deaQsVyTQ58Xx55OHLfuVdo7QnsilXapY+6+w5xyi/BKe5W2mR9TMCAmXWnvXEHeurL4M+eGWr+3vDNaK/7V9X23htCojLaEFz/LSvU10npLlXpy8Oeu/MPM5wtmSHv/GTpsaooR2pPco12SiorMxxvdlkr7jgap1SfldfyvWxyh3TCkZcucH1u6VPqGvZkAAJCFqLQDANLDWn3eUGM6/ELjQp9bA0t3dGshOk+Z5M4PVdpz1K6B2qVBrdscLg7jr4n+eIc9YTkr5aFdCr3pYBIWsosKzSumJ9Yeb/SNavvKj6Tw9x08Lmn84OAbHl7LXu15kyV3V+tD1Ep7pG334mTtNlkXGG2/aGv4GhE7JCMQ9Z6bNkl1EZpAli5NbHwAgMyh0g4ASI+AZdLsenOwXauu9t9UhvZIW77FtRCdJHkqNXjpEu1VsYoVbO9f0zBW0nciv2icK8g3NXV9npbQnjNUav0s4sPWfcVbWrzy+13yeIz47m80S+rOALPAgUOltddIq3YGp2xUN0j5OVLxifZrXTlS3kFS83uSYoR2z0Dn6Qlxsob27c1lUnGe1BD2i7t9rzSmY0cAo03yVwd/5hF8+mnk11uyJOmhAgB6GJV2AEB6WEO7pdK+Tl3tv+lsj+8M63G1x0uSd6Q8hZ5QYJekMiPGnHV/bVxjawyb4h8ptCc9p12ScvaJ+rC10i4l2iLfBxaj8++WinKlg4dL/3uwdNmXgxV26xzyTvnTQp9GD+1RtgWMgzW0790rqcqyiv02y39TPvM6EVbRQvvatSxGBwC9BZV2AEB6hM9p9wWkLebg21OV9oTa4yUpd7w85eablKtWAb/kjjS1PYlK+0W6X6O1XlXapipt0xs6Kvq44uEdFmyHN5wr59ZKuyQ1NuaqpNge5h0F+kBoDzhMZcg7OPL1OYOD39f2rdFDe47D1IQEOIb2cQOl1WELFm6zrBMRY6/2jz6K/prr1klTp8Y/RgBAZhDaAQDpER7at9RJfnOQDA/tWbMQnSR5R8o9eLjpVI78aq3zKa8iwp/NJCrtqzRRqzTRdk23Ku2u3OC2YxHG01lpd8uvgILvQCQ2r725G4NLjs8nrVwpVVZKg2Kv9Rebdf0BV66UHyO55h7gGNpNnQtO6wkkwPrfwN69koZZqvc7G8zHMUL7u+9Gf03rVnAAgOxEaAcApEcgLGBYWuPrVaI9qggdZ1WlXZJn+HG2c+272yOH9kBDcK92V/QAHF5pj6SiIvY1UXkG2UP7q19IL69S/spd2qLf6y86Wz/TryUluu1bz1bav/hCOuUUacWK4M/roYek70RZWiAu1q6I3LHB4B5N3n5S40v2Snt450KOZRG7BFkr7Q0NkvGj0+U6fR+pqkSqKpVKLO80+SO3x9fVmXcrkKScnOCbIJ22m3cdBABkKea0AwDSI7zSvsFpEbqwVc3TuHp8wgvRSfJWjred8+2O0UJuncPvILzSHsnA5BcgD3Jq0/5wo/ToQrneXqdh2qZJWtE1pkQq7T3cHn/HHcHALgU7JS64QKqOPo07OsNn34rQG2EuezhPheQdHrk93l3crT3aJXtoNwypceJB0lFjpQmD7YFdkvy7gl+TA6dAfvjhsa8BAGQfQjsAID3CK+0ba00PhbfGS1JhYepettsL0UnKLfCoTqWmc76aGKHdvzf644pdafd4pLLuZT/nBdHGm8+ZQ3silfaebY+3zslubZVeeKEbN1y7VHr6U+mTbdLejl8M76j4nlt4TOR92qOs4B4va2iXpL0NMeaNGAHJv9PxIWuVvbRUGjs2+jUAgOxEezwAIPUCLcF28U5RVo4vLJTcKXwLudtbvnXcY7fKVaau6rk/Vmi3VnAdxKq0V1SYtlVPjtPc6gnm0D5G61WoRjWpKME57T1bad+wwX5u3bpu3HDBf6UfPtV1fMQo6d3r43tu7jg1NpvnlKc7tDc0F0kO5038u6WcKttpaxW9qkrax7K5wJo1iY0RAJAZhHYAQOpZA+y506WDhkkbarRrcasWLTkk9FAqF6GT4q+0R5vTnpsr1apco7QxdC5Q2xr5CVJcob2z0j5EO5SvFm3XULWpa8Ddbo2XgqudW1eQHzcwOBsh7NRErdRiTUus0t6D7fF790o1Dgu9r13bjZuuXmk+rhiQ0LskTS3mlpCu0D7c4erE5OXZ55zvbciXYq1v59/jeNoa2ocOlSZa1j1cvjzxcQIAeh7t8QCA1LMG2JmjpR8dKd1xot694hr9Rf8beiiV89ml1CxE5/VKdTL3qRsxQ3v8c9ov1b3aoNFqVb52q0J36zJJKVodvXMF+XAFXmmkec/vyQomtmyttG/a5Hy+W5X2Lyyl5XFxzGcP09Rk/t+mwoL2YOj3ju7GoIJcLodt3xrjmDcSYQV5a+t7VZW0337mc2vWdK0ZAADIXoR2AEDqRak6W0NiqkN7t7d8UzBA1bvKzSfrYgTWGNtvSV2V9ip1JaoK1cgjvyRpvH39u+Q4tsib3xHonNeeravHR9qOrFuh/TNLmX6/AxN6unVNgsLC9uCq8e7u7NPXxb5Xe57U3C6t3SO9u1566tPg5+H8uxzv5VRpP/BAe2fLv/7VvTEDANKP0A4ASL1AQ8SHGhrNITHV7fGpqLRLUr2n3HIiVmh3Dk/hOivt4aFdkrYrOCfaWglNWo7DYnQTnBejy9ZK+07n9dW0ZYv9DZi4NDdLayxJduqRcT+9vd3cui51VNrzDkhiMM5sob0hTzp1vnTEb6VTH5Mu+qf0puWNhwRCe0GBdNJJ5vPr13dryACAHkBoBwAkr2WR1PSW/bwRecU1a2W3pyrtiSxEJ0l7LaHdFSu0B2olwx/1EqdKuyRtU3AhsalTo79E3DwOffaWSntXe3x2zmmPFNqlJIPm6lVSwDCfO3BG3E93Wvm/sLhEyj80icE4s7fHe6VhlqkO2y3TMAKtjjsXOLXHS9LkyebzmzcnMVAAQI8itAMAktfyidT8of18IPLeZumutDstRGcYibXHS1JjTrnp2L03xnZnhhEM7hEEAtFD+8yZ0qxZ0V8ibo6h3VxpH6cv5FVb1lbao+3HntRWZV98Yj4eWua8ZHsEjqF96FmSK3Vr+trb4yVVmdci0DaHqSeWbd8Mwx7GKyuD/44YYT5PaAeA7EdoBwAkxwhIvq3B+evW+dzhob2xzbSSebrntDtt+ebzmRdTl2K3xzd4y03HnoY49ij3Oyx33qHzTQOPfBosc8i6bV6V3ngjuE97SjiF9vHmcznya7xWJzinvV0yfLGvS4FolfbdsZcPsFv9mfl4jH2btGicQntRSYxfogQ5hvZhli0F4gjtO3bYV94fNy74L6EdAHoftnwDACTHv6trL3bfDskTFi7C2+PP/7v03gZp1ABp1ADtowpJXw49nO72+NZW5znQsSrtHxUfo5/U3qNalatW5brsfz7TMYqxV3uU0N45n71SO+SW+R2Eg46vklIV2KXgwmjuIikQ9nMozZeqSkyh7wAtU2PTZIcbRGG0SK4Ut0c4qKuL/FhSoX3NavPx2FEJPd0a2l2u2L9DiXIM7eMtiwpuc9ilwBLaly0zP1xYKI0ZE/zcGtp37w5+bYVxLFQPAMgMQjsAIDnhC2D5qqW8sPAXXmnfUCM1tUsrqqUV1cqZYZ7v3hNbvlnns0uxK+1flBys53Rw6HjO8DZJn0V+giT5I/d0d4a+EbKUNr0eabDDwnHdlTNUarNscTap0hTaD9cHer3poMTuG2iW3OkP7Y2Rl0XQHuetyaNba1l2ft9xCT3dvnJ8Qlu8x8X630Jjo6RhcYR2n3mp/Q0bzA9PnCi5O3orraFdCi7ul7KdCwAAKUd7PAAgOeGh3bqCdWeFN2BIG2tND23wmMNSaWlqh+W0EF0ylXZ7m30cpXBfhH3K1BVCbaG9akBXokqlHId0dqj53Ey9o/q9CZaLjTimCaRAtNCeVKV97Rbz8bhJCT3dKbSnmjW0NzVJGmFp469vDU45CWd5s8jaGh/+nlBJiVRmWduOFnkAyG6EdgBAcvxh5c5A2OdGu2R0hIotdVKbeUX1NYH0hnZr2A4EnANgrEq7rc0+ntDujzwRuzP0DZclPA5PQ5VdCu4fbnXoPqbDafpYrbXRV7y36aHF6FIa2n0+aZPljaVxU7o1nnSEdus9g5X24fYLt1vmtVtWkLeG9gGWteyY1w4AvQuhHQCQnEDYpOPwhejC51GvtITYkjytbx1pOpXq0O4UxusdOoqt4T7W462tccwoCzRHXDm/cW+wR99WaR9eGfu+ychxuO+0EaobOUoP6If6jh7XRK3Ujr0D7NdFE+iZSntDQ+THEg7tG9ZIvoD53Pjsr7Q3NkoqGSCVWN5BcpzX3vWmBKEdAPoW5rQDAJLjDwvtgdbgHt7ufHNotYb2iYNt7djWVt3ucmp7ty5qlpsbuyPdGv7jqrRLwTcw3PZE11S/VdIYh9Ce2CrmcfOUS+684M+mU3GuFv/mBl102ndDpyoaIm/P56g3VtpXLzEfl+RLAwc6XhpJxkK7uyC4gODesJ+j4wryuyUFV5tLNLRv2pTUcAEAPYRKOwAgcYZhrrRLUqCj+mdEqbRPHKy6evOWbz1Raa+tNR/Hs+p3+DUe+eSqbbK1+jvyOW8i3lgXPG8P7Q7tz6nisPVbWak5dNfV59u2w4uqhyrtKV2IbudGqTDs925MVcKryGUstLsKpKGW/0icKu2BrqRuDe3l5ebjfcyzJKi0A0CWo9IOAEic0SwZlgAb2CtpiBQI62te5VBp/5f5T09PhHZrpT3WfHYpGNqXa5JGaLNK1CDdJenY80zzwrfvKNaf/3mgRu9Tq9NOXBHMgW1fSAWHmW9mtKlpb7B92Tan3ZqgUskzUGo3v15ZiXkpfb/fraYmr4qK2uO7Z29ciO6bh0izr5GqG6X1e6ScAxIeT8Yr7eGsc9ol03aDiVbat1h+JQEA2YXQDgBIXMBhwnFnpb3zMcOwVdr944aoocHcZp7u1eMle2iPp9Keny8VqyEY2DvVB6vUTU1e/ejnJ+qxvx4Ueui2X7yiqy99W2pf07EtWkHwAcOQGl9TY6NbkmGvtI8YE3swyYqj0i5JdXvzEwjt6W+PN4zYlXbDSKBY7q8OXlxZHPwoOSbhMWWu0p5vD+1O7fGB2tCnsUL7IMuvhfV6AEB2oT0eAJA4p9DeOce987Et9batqRr3sbeCpzq0ezxSjuUt6WRCe16eVKtyy42CgfWBx6abArsk3fHbmcFPDL/U8mHXA60fS83vqak52J49RZ/oWL2iX+53h3TNMdKExKu+cfMMsZ0qLbFvWl9Xn8C2bz3QHt/crKgt+36//WcalW+7+djh+xJLxrZ8c2qP3+60EF38lXZru7x1+ggAILsQ2gEAiXOstHckhUBHFXClee9oleSpodRe+S0uTvHYZG9/T6Y9Pj8/cmh/8dVxtutr6wrU2Ngxb7r5A8m3S/JVS01vSZIam3IlubRKE/WqjtV7Y0+WLv+KNDiN7fE59nDq9QZUWBh8M6VIDZqll+V9enH89+yBSnu0leM77Yy8u55ZoEXy13Ydu1xSTuLb7FlDe0FBwreIyXHLN6dKe739jZfgzgUtMozEQ3tdXfQ3SQAAmUV7PAAgcYZD73Jnpa8z0FsXoZswWE3N9n3W0hF+8vLMwS/ZSnudLEvbd7THv/zGvo7PWblmkKZN2RZcQb/md6bHOivtnQoL2oOru7vS+KfYPSB4f8NnOn1k/kLd3HSFDtEi5civ9vsLpZ/9VPLE8V5+D8xp3+vQ/e3xBCvsnXbskMaPj+Nm1oUB3aWSK8Z+fw5aLO9VpOP31lppb2uTfIEC5Rw2UvrHd6Sq0mCAL47wCxyoUUNzlen7JNlDu3XHhkAg+N9LieW9AQBAdqDSDgBInGN7fMeS3p3h3bYI3SA1t9gDajxV70SlotIeqT2+qckrtzvg+Jx1G8odz0tSY5M5tBcVtkmuNLQZhHO5JE+F7XR7abEO14fKUTDdeZuapBXVtusc9UB7vDW0ezzSqFHmczt2xHkzv+VCT2JbvXVqtnzZPRHaJamxKUcaVCZ9Zaw0flDkwC5J/lrH+emxKu0SLfIAkM0I7QCAxAUc9vYONATntYfa4+2V9uZWc3AtKEh45624xArt8S5EZwvt9a165oX9FAg4//ncsTNyCLdV2gvbJXeaQ7vkuBhdy8CBWqOx5pPvbYjvfj3QHm8N7SUlUpVlO/vtlmnqEb35inTv29LzK6TlO6TW5L7nGQvtnS3y8fDX2MK3y2VfN6K01P7fHaEdALIX7fEAgMQFIizt3bos+K/DyvHab4iams2pJB3BR7KH8pRV2utb9OHiyPuq79jpkLo6BOe0dykqbJfcPdCP7FBZLitp0Zv6ivbV2q6TH26SLjg89v0Mn2S0JdViHi+n0D50qPlc3KH9hdel3yzoOj7pM+m5byY8poyG9oL8rjfDognYK+3l5ZLb8h6T2x0M7uH/XSS0sB8AoEcR2gEAiXOa0y5JTa90ff7KD4Mt8iurpZW7pMmVal7RM6E9VZX2LQ7t8TvdkZcNj1Vpn6KlalSRtmh4x5z2nqi0O4T20la9rS/pe5rfdfKd9ZI/EN+89kCz5ElfaK+3LI6edGgPNElrtprPjbcvIhgP65z2dEzryMsLBupA2OyL4Ary8VfaYy1C16mszPzfBZV2AMhehHYAQOIiVdo7l6B2uaSxFcGPr08MPdy8xBx407FtlpTGOe31LdplRB709uooob3Jq7/qG9pPKyVJzfeUSodcK33z67EH0x2Oob1Fr+po88ldjcEW+S/FsW+80SxZF+lLIadKe2Wl+Vxcc9p922SsrZGpE3z8pKTG1BOVdpcrWG0P//oTao8P1MUd2svLpY0bu44J7QCQvZjTDgBInNOc9jg0NZsDb0+1x1sDSXf2ad9dEzm0766J/AU1N3s0QptDxwVN9VJphESVSu5y26myklat1b5arIPMD3zzcenO16VAjP2/0rwYXcra49u3qH11relUzZADkxpTT4R2KcK2b+4CqbldWrs72BHxj0+kV1bbn5xgaA9HaAeA7EVoBwAkpnNOcxKaW8wVw55qj29vj/54pHs4hfZdeyKH9vq9kd8NyGlpVrEsHQojRsceSHe5iyWXx3SqrDTY6/1Xfct+/a/ekOYtjH7PNC9Gl0xob22VvvjC8rPe9Ily281j/duSA5IakzW0p6M9XrLPaw9V2h/5UDrid9Jpj0k/elp64H37kwOtqtlj/mWPN7Qzpx0AshehHQCQmCSr7JLU3GpOOulqj3da0Ctcd/Zp37U7SmhviHzjAU0O/dzD42hF7y6Xy7bgXVlpqyTpT5ojnzz259z0ilTtsK1fpzTv1R5vaDeM4P7iZ54ZDNHjx0u5udLatZIMQ28+Zi47Nytfv31qWFJj6ol92qUoob3KsmjhdsvE/w41e8xvqEWb0x6OSjsAZC9COwAgMUbyob2p2Rxq0xV8rFtcWSW95VurX74m5z3apeiV9opGc2hvLSmVCu17qKeF25zQykqCCXSz9tGD+qH9+qZ26RcvRr5fFrTHt7dL69ZJX/ua9Pe/mx/7v/+TDN92vfqY+Q2WLzROm7e6bZ0X8eip9vjIod3yS73NeTX5eEM77fEA0HsQ2gEAiYlWaQ8Y0uyHpVsWSBtqbA83t/RMaC+JsZNa0gvRSSpT5D7i+r15obX4rKpaNpmOmwcPlNwxWgJSxW0OfJ2Vdkm6Qnfp0ZKL7M955rPIe7dnQaVdkvbdV3rvPfv5J5+U3LlVKthWbTq/WuNVVyctWpT4mDIa2t0F9kr73lapoVVWtbV+07E1nEc6T2gHgOxFaAcAJCZapf2B96TFW6XfvC0deq905hOm8N7cYt4mLF3t8amotHeG9ut1g36ie/Sjgge19MpLtVeR3xHw+TxqaXHemGWf1nWm45ZhQyWXN/ZAUsFjDe1dvd5tytPPvb+Sll4ulYe9m1GQI63Z7Xy/DFTavV5pyJDE7jNe5sXaVmu8pAT2eA+TqTntoS3fhjr8UjtU2xPZ8i0cc9oBIHux5RsAIDGBCIuQ/f0Tae7L5nOfbpMGdqWQpmZzSM1Ue3w8bxbk50s+eXWjrpckFahdh+3zbzWr68lVlXu1bYc5xNfV56ugwD4ffJRvvem4fURyc6uTEqXSLkl19XkyhpbK9fNjgvPZzztUuuhIaXCEToAMVNqlxIPy/vrMdLxSwe0Hd+1K7D4+X/AjXI+uHu/Klwq9Ulm+VBf239+2emn8INP1tbXmNQoiVdqLLbsTNkbYxREAkHmEdgBAYpxWDl+5U/rJvyRra/gdJ0jFXdX15pbsCO2xFqqT7NX41laPbRG6CfvutoX2+oY8Da10CO1+c6XdN2p47EGkijW0l5h/hj6fR83NXhXOmSadeoA0MMa7GhkK7RMmmPcWjyZPLRqnL0znlim4cnyiod26CJ2UgfZ4Kdgibwrt9kp7bZ35f+3iDe0NUdYdBABkFu3xAIDEOIX237wt+SwLtJ0xRTplf9Mpa2hPV3t8rDnt8VbawwUCbtXUmU8OqmhSbq65BOu0GJ3hNzTWWGO+35hRsQeRKhFWjw9XtzdPys+JHdiljLTHS9KPfxz5OS+8IJ1yStfxRK2UR+bfyeWaLEnaHaHrPxJra7zUc6E91B4v2VvkHVaQr603/44S2gGg9yO0AwASYw3te5qkZ81tyDrtAOnuU2TV1GSuAmZze7zTvPfdlj3ai4vabAHYKbS3b2pUvszXucaNjj2IVLGFdvsbL3X1CfSeZ6jSfsop0suWGRhud3Axuq9/XfrXv7rmqx+gZabr1muUGhVMqolW2p1Ce7rmtDu3x4dV2sNZKu2trcGOiXCR5rQT2gGg9yC0AwASY53T/telUlvYitW5HunW44P/WjS3mM/1pvZ4SdplCe0lxa0qLY4jtK+qNR03qEg5w3tyTrs5oXm9ARUUmPc9q6uPY3W+TmmutFsDZHjnxKxZUiAgvfSSdN990po10hFHdD1eWSnt3vSBbT77Z+rq+uh17fGuXMnljhnand54odIOAL0foR0AkJjwSrthSE98bH78pMnOLdbuPDU3u0ynelN7vCTV7spRsbqCUnFRm0pL7Iu6WQXWmJf0/kLjlJeu1OfE5ZHc5i/aOq89sUp7m2T4Y1+XBMPoaAkPY/15uVzSccdJP/qRNHq0/R4VZdv0458v09Yh49XmCa6p0DmfXep+pd3tDq5mnw7O7fEu5xXkLe3x1tZ4yb5KfCdraG9qkvzp+ZH+f/bOOjyuKv3jn7FMPGnSxurubhSX4hSKw6KlsL/FoQu7sMBii7PILr64a7EChVKKlLZQ6u7eSNu4Tmbm/v64yWSujEUn6ft5njzJPffcc85Mbib3e14TBEEQmoiIdkEQBCEyFD+Rur0INukU0KVjzK+zxBvEWHtwj7+a59hGL0pJ4vtFA3mVK3x9zER7abmJxXqbiWiPbaEdi0CEiGsv0XsIVLhg2R54f7nqTaHHG6T0XxNwuVRLuj8Rb+549pH01zHkrP4TMbv/zrcPPskzXOc73dSY9rg4VUe3BKaWdlBFewhLe7Eu50JcXODyhnrRDsbNEkEQBCE6kOzxgiAIQmT4W9qX7dGe6xwPkwIkWLPGmYqflqA53eNjcNGLHb72dBoUn6loN3GPdys2KokjHvUN2Ew/TmspN4NAWJOBfN+hPq5dY2mfuQr+MrPheEBnOH+kdjylCoLUrG8sZsIx4vvE47eRZLMSOySF3XT3NTXVPb6l4tkhQEw7qBnks3U3dkG5mgDSrtpgiorDS0IH5qK9vDy0l4ogCILQ+oilXRAEQYgM/5j2pTrRPqZbYBOkJd4g2qPZPd5mA7sdDpCuaTeIdrOY9loPeBrMxXunT6Uz+zmNL3mJq5jNScTEhrFz0JwYyr4FcevvoctetuWAann3p4WS0ZklfYvoPvGUgVf72tI7aQctKjLWXY9kTS0Z2RDY0h4HWbob26vAvoZg9HAzx4O5aC81JqMXBEEQogAR7YIgCEJk+LvHL9GJ9tFBkqtZ4lrNPd7phJiYwOfDFYFOZ2jR7m+xjqGGM79+ELo/AD0fhGs+hf0V1LjsVBHPV5zG//ESCxxHYLW3smi3aUV753TtL2NPrt/5QRk+6y0AHgXma+vMm7rHK16oXqJ+byRmlvbIRLvRjN45zThoUZGhKSBtKdp974clFjongMPv95LshMKGxend44OJdqfT+DqKiyNeriAIgtAKiHu8IAiCED6KV01CBqoleU2e9vyYboGvtRot7S0pfpKTA7tBh+MeD6ob9IGKEJZ2P4v1ffyTQ7Z+ox64PPDxSti8n5r779aO63SDJYJs7c2BztLer1eh5njDFr/XmRgDE7rDgoawABbugBMHNhybWdrLPoKadeo9EjepUcvU3yMWS+C4bFM8xoD19M6KoW3/fujSpXFrajP3eKsFZl6mivesJEjQ7kxFItoB0tJgj9++W2Fh4L6CIAhC2yGWdkEQBCF8/OPZd5VAtc7HeFhW4Gstxpj2lgzrDhTX7nCEL7piY42W9jiqiUM1fyYmuOiUor4n2ezlJp7SDtA5Af49heparbhyOtsgTbdVu1MxoK9W3G7con2dHKLLTbAyV3ust7TXrFUFO0DN+sau0tQbI6Kkb16j8nTEphruh0ji2vUx7a3tHq8oNNRqn9gD+qYbBDsY3eMD1WgPdF5EuyAIQnQilnZBEAQhfPxd4502uO5Q2HwANu+H4mo1EV0A3N54arWlwVvc0m5Gamr4IjApCXLpbGjPoIAd9CIxwUVaXbz02XyCE13c9/sXwfAsan7Q/rt1OhvvPt5oLNog5oH9tKp1285OuFw2YmLqNhRGZmuvX5GrxlBb6948xU9dKwpUfNtw7N4DihsskT9mhCr3FhLXATjxf9C/i/oaRubAxIF07qyN2Y4kg3xbusd7PGpGfac19KSNsbT7I6JdEAQhOhHRLgiCIISPv6U9Oxn+eXzDsccLsUNVi6sJVdVGn/SWFD96QVJPKCHjT0oKrCeFGmI0gjyTfJ9o75SiKrpz+Fh78aE9YYQqfGtqbJpTzhiju3aLY9WK9v59tArN47GybWcqA/vVqdlRuvwEZTWwvRD61FnkvX5KtnYHeEoajhU3uHPB0Z1IabJA3rAelu1Vvz6sK1W35WQ6d4atWxu6RWJp97mo19GSHiJmoRuVleAM442IJBEdiGgXBEFoL4h7vCAIghA+uqzcGmxWcI4wJDyrp6rGqEZaUvwEildOSQl/DLWvhXwyNe0ZFAB17vGp1fRkO0fxs/bi6RN8P9a4tHvksbFtJNr9XAxSU6rJ6Fyu6aJxkc9Kgi6639myvQ0/e/3M1q4Nxvncuca2MGiypX25zjU/Owly+pKu8/6PRLSXacuht2hZNLPXW1FBg3t8EIqKtX1EtAuCIHQMRLQLgiAI4ROqzJctBWxGd3KAyiqj6GhJS3tn82VEbGkHDKI9s67eeVKii06pVRzFT5rzSqc4OGGA77i6Wu8eH/4amg2L1SD8gsa1Wywwuqt2jCW7G372+lnWa7cY53PnGdvCoEkVBjylsE5X0WBYFtjSDPdDtIp2M0t7RQVqIroQNNXSHklGfUEQBKH1ENEuCIIghI8SxNIOaoZyayBLe/sV7QVkaNrrRXtCvIu01CoOZ77mvHtCH3A2CPUal8493hlJZrVmROcirxftG7bo3rSxOtH+h59o99RZ2r1V4NlnnMtT0KglNsnS7imA9bq1DM4AayfD/RBJTHtrivaYGLDrghcrK2nYcHF7YWuhWoLvgxXwn/l1meoij2mXRHSCIAjtA4lpFwRBEMJHqQ58zmIHSzxYzRVNVbVWUDgcRnHSnAQS7ZG7x5tb2uPiarHZFFKSawyivXxYX/z1kNE9vo32zK1JQIOYHtAnRAb5cbqY9NV5UFULcQ61rJu3Atx7faJRg5mQD4MmCWTPfliXr20bkgPWpCZZ2su1UQQtKtpB3ajwT5qnusfX/f3sKYFD/qu94JKx0CnOKNqTKoHAux7iHi8IgtA+EEu7IAiCED7eIKK9PmbazNJusVFZrS1R1ZJWdggs2vv3D3+MYKI9MUFNTJfoKmYw2jjqooEDNccG9/g2E+3BLe0G0T66a0O2eFCtvCv8YtXde6B2l/lc3poGa3wENEm0F+1WSxH6M7Q/WCztJqYdzMu+Ya0T5NnJoHfU2KO+ZoN7fHJx0HlEtAuCILQPRLQLgiAI4VNvaf90NYx9Cs57G+74BmauAmudRc9MtFsTW7VGOwQW7UcfHf4YgdzjMyggMV4V7fbNWstuDTHsz+6tbTO4x2uPWw2dF4RetOfmJ1FW7re5khijupf784efSK9eCTWrAs/nicAHvQ69VTsx0byfKWvXaI9tFhg4GDDeD9HqHg8BRLvFoXqzxNigi+5NyS2lutpOTY12c6hTcvCdCRHtgiAI7QMR7YIgCEL41Iv2nUWqRfPHLfC/3+Hd5Q0xt2bu8daEVq11DYFF+9Ch4Y8RzNKemlL3XqzTxm6vZxAlVVrVpRdTbSfatevq26sIi0Xr2r5hs+6NG9dNe+wf116zGjxBspd5I1eBTRLIa3RZ7PumQ3wWYLwf2pOl3RfnX+8i31W3Mban1OAaD5CamG9o88dMtJtFOgiCIAhtS5uK9oceeojx48eTlJRERkYGU6dOZcMG7T/c6upqrr32WtLT00lMTOTss88mP1/7T2jnzp2ceuqpxMfHk5GRwa233orb7W7NlyIIgnBwUJ+ITueC/MOOUXQfejbXXgvVtWbu8YlNywreCAKVfIvEclsv2pcziof5OzfzBBfxNn/hBbp3rXsP/DOqA6sYTlm5Nj283tIeG9dGot2iffGxsW769tIK69v/dZz2mvq49rS6jPhH9gl/Pk8rinbFC2u2adsGZ/qqGejd44uKINxHhdYW7XovFF+d+PoM8jm6xAx7Swyu8QApicHL7ulFu8djfK2CIAhC29Omieh++uknrr32WsaPH4/b7eYf//gHJ5xwAmvXriWhbpv55ptv5quvvuKjjz4iJSWF6667jrPOOotff/0VAI/Hw6mnnkpWVhYLFiwgNzeXSy+9FIfDwYMPPtiWL08QBKHj4a0zl+/RivZ5O0axm3ieew5qXQm89JANFE9DhzZwj9eLNIg88V199u11DOF2Htacuzbnd/WHBds17Qs4lAk60V5tsLRHto5mw2qsJ3bYhF1s3tbwZn3/c19Ky5wkJ9Vt0Jw0EBZdB73TNHXewyKYFT4AjRbI3iJYr7MsD+oCNvW1mXleFBUF3tzxp0ku+43A1D0eglrai4q1oj0h3oXDGjwGQJ89HtT3JNm8AIQgCILQRrSppX327NlcfvnlDB06lJEjR/L666+zc+dOlixZAkBJSQmvvPIKTzzxBMceeyxjx47ltddeY8GCBSxatAiA7777jrVr1/L2228zatQoTj75ZO6//36effZZXC5XW748QRCEjke9e/xurWjfQU/fz/972cKGLT2011mTWt3SHms0PNKrV2RjBMs03z2nRPUlvv8kXul+M+9zPqsZyo8crY0Lx8w9PrJ1NBtWo9o8ZfImQ9vKNX7hACmx0Cc9csEOjbK0N1ogew4YQhUYlOET7XqrMoTvIt/Wor3BPb7e0q5T1XtLjUnoUqrBWwpKYHeC5GSw6Zw+JK5dEAQh+oiqmPaSEvUhMK3uP+uSJUuora1l8uTJvj6DBg2iR48eLFy4EICFCxcyfPhwMjMbHjBOPPFESktLWbNGl5CmjpqaGkpLSzVfgiAIQhgoVapQ3VWsafYX7QBffT9Ye50tGf1HbWtY8045RXt87bWRXR9UtHctVYXsaYP5bPD/cSHvM5zVrGNIaPd4kw2FVsHE0n72aWsNbZu2mSjcxuBtRUt77ibYX6FtG9LTlyAxJsY4VjjJ6DweDF4iLS3aA7vH153oprsxdxUby72lVKt/q0E2TiwWqdUuCILQHoga0e71ernppps47LDDGDZsGAB5eXnExMSQWu+fWEdmZiZ5eXm+Pv6Cvf58/TkzHnroIVJSUnxf3bt3N+0nCIIg6FCqoagKKms1zXrRvmqd9nMZa4pBjLWGaH/iCZg8GUaNgvvvhxtuiOz6YKK9a3bDLkRyYo3mXGlZlLrHW+LBov3Xb7MpnH6StmTd5mYT7TUNIRVh0mjRvnq59jjODn0HaJr0IRPhiHa9hwgYLeHNTWD3+DpLe7dUbYe9pZQUOTRNqcl1XjEenfeBDskgLwiCEP20aUy7P9deey2rV69m/vz5LT7X7bffzowZM3zHpaWlItwFQRBCoXhBcRlc4z1Y2UNXTdsvCzPwei1YrXWpqK0pbWJpHzgQ5sxp/PVBRXtWg7pMStSGY0Wte7zFogo/RWuR7t9bq9SaTbQDeMsaEqiFgd4VPXzRrvMYGNAFYrTl6tLTYfv2huNwRLt+PdCGMe31lvauuhvT7cW9V7u74Ktu4C6AIPebiHZBEIToJyos7ddddx2zZs1i3rx5dOvWUFomKysLl8tFcXGxpn9+fj5ZWVm+Pvps8vXH9X30OJ1OkpOTNV+CIAhCCJQ6d1udaN9LDm60Vr4tW+OY86NflnFbapuI9qZit5slMFPoRCFdaahXnqSztIdyj28z0Q6mLvL9IhXtG/fBjC+gIozcMd4wQ9C85VCzxmBpD1sgD0+F6w5VE+f1S4dhWWDXZpnTW9oLghuhAT/B3Jg1NZKAMe31or1LAujKBlr3akMROqXUeTiEsLSLe7wgCEL006aiXVEUrrvuOj799FN++OEHevfurTk/duxYHA4Hc+fO9bVt2LCBnTt3MmnSJAAmTZrEqlWrKPD7zztnzhySk5MZMmRI67wQQRCEgwFfErpiTbPeNb6eD7+oK4huTQKLo12KdoCedS/vfN5nLYMpJ5FC0om77VNfH1+m9TpCuce3WUw7hC3aTet1b94Pf5kJRzwHby+D1xaHni9c0V4+C2/JxwaRHJal3VsN47vAP4+HNy+ABdfBE1PAphXtPXW3aoDUN9pl6SztNpsaH9+SBIxpr3ePt1oM1vaYfO3vsMHSvi/oXHrRXhR5GgJBEAShhWlT0X7ttdfy9ttv8+6775KUlEReXh55eXlU1WV8SUlJYfr06cyYMYN58+axZMkSpk2bxqRJkzjkkEMAOOGEExgyZAiXXHIJK1as4Ntvv+XOO+/k2muvxdmmpgxBEIQOhrdOBOwKnDneH19cu01VBe1VtNdnnLfjZjDrSaDO7LlyL2wvAkUxusdXRKl7PBhqtQOGWu2lZbEUFZu4tN/9HcxcBfWC/plfQ1vbPWGIdvd+cG2gvNyohsMS7R6TNPAWC9i07vGjRmm7/PFH6KHNMsc3JpF+JISMaQdDXHvCAe174BPt3qKgGeRFtAuCIEQ/bSran3/+eUpKSjj66KPJzs72fX3wwQe+Pk8++SSnnXYaZ599NkceeSRZWVnMnDnTd95mszFr1ixsNhuTJk3i4osv5tJLL+W+++5ri5ckCILQcVHq3G2DlHvzZ/feOlVuU12t27to30w/7YnCKpjwHxj4KOe99Xdu5CnfqfbmHt81u8zQtjfPRC3fcpT2uLAKvjeWjNMQjqW9agEoCuUVzSjarfFg095kY8Zou6xfD2uNyfM16C3/Le0aD8Hc4/1Ee3etpT2pWOsG70tEp3iDZpAX0S4IghD9tGkiOsXU905LbGwszz77LM8++2zAPj179uTrr79uzqUJgiAIegK4x++kh7EvkFeQSG2tFUeCGhTeXkV7vUv1OgabdyiuJqd4JUNoUH9693i9pT3a3ONjYjx06VzBvv0N5/bmJTFssC4eenRXOKI3/LKtoe3bjXDG0MDzeUsCnwNQPOBS3zt9Aj8IUyR7TFzA7ca8NuPHQ3Y25OY2tC1aBMGi6fSW9pbOHA9huMeDtuxbejyVtdp7zmdpB3VTw671OqhHL9p1aYQEQRCEKCAqEtEJgiAI7YB60b7H3NI+dqyuu2IhryARbO1btNdb2ktJYS/ZAfvN4xjfz3rxGTUl38BUtAPkZGqt7XvzA5i4TxmkPf56HZRWm/eF0KLdvdsXeqH3UIiJCTN+3Cxu20S022wwcqS2TZfL1oCZe3xLE9g93m+356IxMP8a2HY7rLuVO5Mf11zjs7QDeAKnyRdLuyAIQvQjol0QBEEID2+1Wp99v7a0VL1oHzDAKLBy85PArgrd9ira/ZOXLWZ8wH4/crTvZ4OlPZrc4y0BRHuWVrTv2hPgFzRlCNj9Hh8qa+GzIBndQsW0uxqs9vrNjrDLvbn2GNts5hssGTqDc6gM8lEl2q1OsNS991lJalm7BPU9Ky7Vum9oLO3uwC9SRLsgCEL0I6JdEARBCA+lGrxe+MexMG0cy7sdxWLG+dzj09OhizZZN/sK08GWgtsNdTlGfbQX0T5gAKR1UpOtzedw0z7VPTLJ87PCl1c48cYe6juOqkR0ASztvXto1dqmremm/chIhOMHaNvmbAw8n+JSvwJRu933oz6mPbzM8ZVwxWsw7mk4/224czYs3gX2TNPumbrmUKJdH9PeGu7xAWPaQesiX4eiQHFJENFuFj5Q3y9VeyyiXRAEIfoQ0S4IgtBeqF4BtXvbbn6lGhKdcNMR8Mip/GvUc0xgMRWopse0NGNN8/1VkwEMtbeh/Yj22Fh4+dm1dMspYVXmJNM+rgn9DG0VyvHgyAGiv+QbwIC+WhfqDVsCiHaA03VB4At3gMcbuL/X5AYANZ7dvdt3qHePT0wMMmY9ngOwaT/sLIZ5W+Cl32BToS8sQ097sLSbxbT70gBZjaK9qspBba3Wm6OTPqZd8ZjOpbe0l5Soe3OCIAhC9CCiXRAEoT2geKFijpplu63wauOWC3UlwTp1MrO0q8pc7xoPEbg+RwFnnl7EruVPMnvlHBhh4nY91ZiIrbTMAnFHAFHmHh9AtA/spxXt6zZ2wesNUNvssF7a49IaWJ0XeE5PANHuKdCUIzO4xyfWBB6znqo82KKL2R7Yt8GNXEd7EO16S7vXCzX1b4WJpV3vGg86S7viNs+wj1G0K4r536sgCILQdohoFwRBaA+494K3XM2y7QmR2KulULSivahYKxTS0kxEe51Xrl4EWCyt42bcbFjqRLfFAs+dCT1SG85dOIq4Y7oaLikrA2IGoTj6RJd7vCUGLA5D84gh2oxsZeVO1m8yt1aTlQR9dZZ4/4zyegJZ2t25mkO9pT0pQRdTYcb65eDWmYaHjgjYPVLRHg3u8Zp1mIh2/d8iQHKSbsPDbb6pohftIC7ygiAI0YaIdkEQhPaAe5f6XfFC7Zamj+ethOqVkV2jE+37DmiVhZmlvV4Q6UV7UhJY29V/ID/RPaALzL8WPrgYPr4EnpiCw+ElNrZWc0VZGWCx4I47B0XRWqzb1D0e1BrmOnKyyujeVbshtHRl4Gz5HN5LezwvyH0ZqFa7WxvuYYhpTwhjg2rVUu1x12RI72XaFYwx7fv2BXcHjwb3eAhQq72gHH7djmPmEu7iPs7jA0D1ULDbdS9Kt0FST2KimlXfHxHtgiAI0UW7emQSBEE4aKndYf5zY6n+A8q/UMV7uPiJdpfLxt48rX979+6QpauytacuqbeZaG9XWHSqJtYOx/SFI/uATf1XmpSoTbZW/5qrXUYF1qaWdgjoIj+4vzZhmf53rOEYXRz/bzuhPEDCuUBl3wyWdq1oT4wrAa/O1O2P4oE167RtgzLA1sW8P0ZLu8cDhYWBp4gG93gIYGn/329w5hsMeOo17uNuzuNDQOcaX08AS7vFIsnoBEEQoh0R7YIgCO2B2l0NPwcp3xQWigJVv6txrq714V/nF9O+a0+ywXrcq5cq3P3ZXZdjrLhY227mkhvd2EL2SErQuiPXJ9+rMQnLbnPRbjFXnpldtAI5ryCIQj2it7b0m8sDvwZwkTeztCtecOtc8it07vGJNeDaHHgN7lxYpxOjgzLAnmHeH6M3CAR3kW8L93iHQ/0yXYe/pb1biqZPT9QNPU2N9no8eX7Z7LRI2TdBEIToRkS7IAhCtOMp01obPfsCPnyHN16+Gh8PmnJbIVGq4ajn4ajnSf2/l3mZ6QxgAwApKR5SU42ifdeuunJUxdp2vWUv6rHYQ3bRxxDXW9orTZwZzNyfWxUT93iArAytWTl/XxCFmuSECbpf+O+7zPuaiXbPPk0SOjBxj090QW2QWPna7bBOp7gHZ4M1LeAlMTHG+y+YaG8LSzsYNwd86/C3tHdP1fSpF+2dUk1yAXir1Uz7JuhFu/7vVRAEQWhbRLQLgiBEOx6dolDc4G2CKawxrvaKB9w1sHEfrCsgfflqpvMqCaibCd27qeJLL9rLy9USUgezaNdbaiEaRLu5GM/sohftIRTqxB7gsMKYrnDVBDi6r3k/j4loN3HXNs0eXxtgIwCgeC3s0P0tDB+i+nwHIZJkdG0l2vUhJKaiXWdp78J+4qkgvVOABH5u8/dSLO2CIAjRTeinEEEQBKFtMSvV5C4AW2BrYlD8RZCnBDzFYEsNfo23EvZVgEdr4d+DmjU9I0Ntz8kxXpqXZxQB7U60h+Eer48jrn/Nekt7TAzY2/q/rzVM9/hQov2aQ+GmIyDOmI1eg1Khbvz45wbw7DN0M9RpT3Cp1mH3PrDr/Nq9NbB6ibbNaoHBI4OvBTUZ3caNDcf5+YH76kV7a1U90Iv2+nALjZdEV61oB+jBTtICiXbXFogdbWgW0S4IghDdiKVdEAQh2vGYZMkyETxh496tOw5SX7sepQpytdZSFw72oQqpLl1Uy2ZsLCQnay/Nzz84LO2dAoj2toiJDoklXEt7iMWmxIYW7KDGSOjLvpmKdhP3eIAak0oHtZuM8ey90yCxu7Gvjkgs7frfX1tZ2n2i3d/SnhAD6Vq3jZ7sIM3MPR7AvdO0WRLRCYIgRDci2gVBEKIdU9EeJN21Lk5Yg7dataxrxjKx5BvGrIJcrejKJRul7t9IRkbDvxN9SS0z0d4RE9Hp44ijWrQHcI/Xx7QfKIyntraZHhX0ce0mCRWNJd/qQg5qVhnHq/rdGM8+KANs6ca+Otqje3yDpV1Xp90kGV16pwBVITylqneNDrG0C4IgRDci2gVBEKIds/h1T4Cn6uplUPZZ4LE8Jn7A4VjtvUZLe71rPECXLg2i1ky0t3v3+CZY2vXu8W0ezw5BYtq1OwyKYmHfgWbaZfAXi95q8BYbuujd432Wdk8x1Pp5iHgOQO1OWKu7nwd1aVbR7vFAtS4Re5uLdovuBupqFO0B3ePBtF67JKITBEGIbkS0C4IgRDtmSby8ASztNSvBtQ6UAPWyzSz04ZSQU6ogT2tp14h2P0u7vlZ7h3CP73CWdnPlmZ5WidXq1bSFdJEPF/9a7Z4C0woIhjrtCX73cdUiv58XqtfrRfvwbmA1xnnrCVe0myURbPuYdidY/B7fupuI9kDu8WAaDiOWdkEQhOhGRLsgCEI04602F+CeUjURl6ZvuVoCS/GYWtPU6wLExyteY7s/SrXBPV5raW9o11va8/I6gGjvaJZ2S7xphnWbTSGjs1apbtkeJOGhxeQxorQaqk1CNPxFu9vo8eH1Wig3q9NeT81qqFmjlkCsWa1uIh3QvbmjRoXMHA/m3iBm6F3jIQqyx4Mug3yqpl9PdpCeFky07zE0iWgXBEGIbto6f60gCIIQDH3yLn88BWD1S7pVu7vBeunOBUdPk2uKjW31JeSCuRV7K0O4xze0H6zu8XqX5Ki2tFusqnBXjKbk4YMLyCtoUIyL/ujGOVPWGsewJkLsGKj8GT5aCQu2wx+71bKAb10IJwzQ9ve/9/RlDIHKSmNCO597fD1lHwMWdZMpPQG+ng5r8mB1Hmw5AL2HBXnRDYRraY8m0V7q/+dnjQev+rvzdk3VWGB6soOioJb2ner757fhIqJdEAQhuhHRLgiCEM0EEe17dxWTW9SdUaPAZkNrXQ/k8m4iltT++0OL9iDu8f4i6KBNRJeiFUqFdU4NUSnaQRXdXqNonzRuF3N+aqi3vmBxgGzsjl5gz1Z/fmcpLNjRcG7xLqNo949hd+81DFemS0IHOks71G1K1W1MxdhgXDf1q54YnRoPgF60l5VBVRXE6XK86X93drtasq81SNF5+Zf454/zi2sv65SBf9eu7MGSEGSzz1ujWtsdDb9Xs+zxihKW04IgCILQCoh7vCAIQjTjNWZ6Bnj3k+H0GzaEcePgmGPUhFmaJHNmyeUUd+Ckc54DwdehNN7SvmVL3fr86IiW9k6pWvf4ykpwuaLUPR4CxrUfOn6X5njJymxqakw2LewZYKvbfRnbTXvul23G/vWWdsVtGletT0IHupj2cLB1DqubXrQD7DP50yjR/fnprd8tSVDrt18G+QOJOZp+VhQ6VwYIj6nHtSHoXB6PeTy/IAiC0DaIaBcEQYhmTNzZ9x+I589/nUJVlSqkfvkFvvkGcPupDrMybp4DgWPXAyW2q6d4P5RrBVS9aLdaFc1Dv160F5oM3e5EeyMs7aAKrei1tJsvZOJYbcyzy2Vn7cYuxo62zmCt+8Uf2Ud7btke2Kb7xSu1ai6G2l1q3gUd+nJvDocHp9PYLyi28Cztqang0Hnjm7nI79f9GaWHTkzfbAQV7X6W9v2edIpI1fSNLQhRxrFmlSYRoJnni7jIC4IgRA8i2gVBEKIZE0v7Uy8dQkWlVuBMmQK7dvkl//JWqV/+BCoTB+ax7v7sNVru6kV7akotVr//JuG4vicnh+4TVTTC0g7RLtrNLe2pKdX07F6saduw2cSCbUtTM5lbnXBID0j2s5QrwMu/Ga+p/gNcG03n1WeON7jGh8KWbKxhHgCLxWhtN0tGd0DngBI1ot3f0l4UxzZ6A7CfdJbbR4f2a/eUaKztKSnGS3zzKS41W3/Ri1D8GpTPguql5kktBUEQhBZBRLsgCEI0oxPtvy/tygNPHmna9azLz8Pj8Xvy1ot0s3rvAeYxsFeraArpRDWqcEhK0lrv9bG4ejp3rovBb1eEXnBMjIf4eG3W9KKiKHaPtwTOqDawr9ZSu2GziVq11u28WJPAaYeLxmjPv7MMSnQbGdW/Qc0y0zn17vGRu8Znhe7jRzjJ6PSivXN43vfNgplo9xnH/bLHFxbFMZXPSKKULuzn0v7fwFE6zwczqhf7frRajX+3PtFe+jGUf6vmzKjdAVV/QNkXUPqBGuogCIIgtDgi2gVBEKIZXSK6Z18dH7DrH8u7sny1n3DRu7wHs6YHO6d4Ybc24Hc3DTHMiYmRifauXYOfj0osVvPyZjo6pWrduaPb0h54IQP7adWqoeybxa5mMIcG8X/lBLD6bRpV1sLbS7XXeWvUMoYmGC3tQUT7l2th4Q5taTl7TuD+JjRGtLelpd3lUpPlAQ3vPVBYHMcuelCOGnCvr2IQENcWqF7hOzRLRkf18oCeEbjzofDfahJLQRAEoUUR0S4IghDNeBtqTikK/LigV9Dui/7wSwimF+LBks0ptZq5tOeqYHQO3H08TB/PjsHj+ZGjfaeTdAbb2FhjvLA/7VK0A+EUXOnUySja9Zb26BHtgS3t3bK1SQdz83V9rX7xDda67GzdU+G0wdp+/50PReGJSH1Me1JCAPd4RYHbv4EzXod+D8OU12DFXnA0v2iPpph28LN+6yzt/qQFK/emp2KOWvfeZL7iAweg/Mvg13uroOxD9fNDEARBaDFEtAuCIEQriltjldy3P4Gdu1ODXvLbUj/Rrnd5N0tOpzlfbN7urYBBGXDtofDQKXx8/v3cyH98p5OSFE13iyW4tb3dinZLGMnoUrVeB2aW9qhxjw9iac/J0np45Bbo0qZb/Y5tfj//ZZK2X2EVPDg3rOXo3eMDWtq3FUJB3QaTywO/7YSk2Igt7fqEiWaiXZ9RvjXd483+hsxE+4Ei7Q2VnqbbJQqGtxyqfgFM3PHzNpgmDDTgLoDK+eHPKQiCIESMiHZBEIRoRecav94sGZiO35b6KWJ/Ea7Uho5b96+jrWnXqk69RTQx0Zj0KliiuazIQo+jiHAs7drj6HaPD2xpz87U3nt784KIdovfCxrXDaYM0fZ9cwksN9Zl16N3jw8Y0/6zrpxcRiL07RH09ZgRjqVdn5xOL/RbEpstSJy5tZks7QBVv0P1cuO9WxiB9bzqF3CZlPkTBEEQmgUR7YIgCNGKTiz/tiS0iXrjls5s2loXf+wv0j37NSWeTAmUXV7RWu4MFtFk47+SYJb21nQxblbCsLSn6UK/o9o93hIfMMu43tJeVBxHcUlsQ4O/QNaL5ftOgHi/+AgFuO1r8Aa//4yW9gDu8d/pYqwP7w0xkbtvhJM9Xi/kW1O0Q5AM8hZtTLs/EYt2gIpv6JSsdSso8v99h0Lxqm7ytTsin1sQBEEIiYh2QRCEaEUn2t/5ZITm+E9nraR4y2OGy446Y5r6g7+l3b3P0M9AQPd4rYAzJAxLMopZvXgN91xUE07Zt07af6tR7R5vsWrEnz+9uhfjdGozg8/+oV/Dgb+lXe9m3zUFZugqHCzdo8a3B8EQ027mHu/2qgno/DlxQMSu8RDa0q4oxjb9NS1NQNGuK/kGMIh1nMcHHP/7q3DlR/BLBJZvbw2d4rWbIUXF4ZXPaxijCso+l4zygiAILYCIdkEQhNagdk/kD7NKg9rzei2s2dBFc3rqKetJSaqgXx9t0jCfqFZcDcLfY+L7qyege7xOtFfoSnMlGsXsoEGBp2m3oj0s93ijaI9aSzsEjGuPi3Nz7OFa0ffzwp5+1wWxtIMa295X51Lx5C9QETgjvN5inJJskmV+dZ5xjMN7g72bsW8I9AJ83z7w+qUkKC5WM7b7Ez2WdjtY1L/zevf457iGD7iAMd+9D1+shSW7I5orNUX7fheXRmBpr8dTCFW/RX6dIAiCEBQR7YIgCC1N9VIo/h9UfB/ZdX6W9qLiWNxurUV70jj1ofz8M5Zr2ssrnFRU1Lkn11vP3aFjigO6x9cWa1zr9TG0ndKMLtbDhgWept2K9nAS0aVphX1hYRTHtEPQZHRHH7pdc7zQvzJBKNEeY4PHT2s4jrXDv6dAQoyxbx0F+7VryexSYeykt7L37wwZSeDoHnDcQOgFuNut/r586zHZ54oaSzv4yr7Vb3asRZdLYEMY3jX+c6Vo3eqLihsh2gGqFkg2eUEQhGZGRLsgCEJL4imDiu/Un6uX+sorhYW3wUSbV2AURhmdVVHzt+t+NZzLza9zX/YcUAV3OKLdW2T+sP3BHOj9EBzxHFz4Dmese0lzuksX4yVDhwaept2K9lCWdovVINr37NFabwGSdDnd2hRLYNF+yDitpXb56mz25NYt3l+oB4qNP6wXnDUccpLhy2lw9vCgS9GL9i7pYYj2Q3qALcNndY4EMwGel9fwsz7GPSkJ4iL0GG8qoUS7x2PxubGvQ1dub1Nk9dM7pWot7UUljXyx3goo/6Zx1wqCIAimiGgXBEFoSaoWNJRtU1xQsyL8a/3c4/P3aUV7WqdKYmLUckzJSTWGpF17faJ9H3gLNaXjAs+nmJeF27EHKmtVy93czQwuXqo5bVYGq0OK9lAx7RanQWTt2mXslhhZkvOWJYil/ZCxu0lO0t43P/zS23idxQqWAFbZe0+Ab6+CkaFjzvWivX5TyodXUcu7aRbZE+zZIcc2IybGmBQxmGhvbdd4MFZhKPPf87PEUVIai6KoGyYGS/umfSGT//nTbJZ2gJplULO28dcLgiAIGkS0C4IgtBSK1yjSXevDv97rL9qDuw7rs337SnR5CqA2gthWt060Kwrs1KqXze4+mmMz0Z6WBjkmOi0xsXVrXTcvIdzjTUR7rYnjQnSJ9sCLiYnx+EIw6tmdm6xa1fUJ7AKJ/8xEyEykutrOf1+ewH2PH2UsHwe43VYOFGrHNIj25XuhSJcZfVJPsEeeOb6ebJ3e9xftbZ2EDoxVGEr901dYEzShKgZLe5UbdhWHPVezWdpB/dwo/1zN5SEIgiA0GRHtgiAILUXtVo2LO6C6qXvDLMkUxD0+s0u55jigaHdthqqF4c0Hqju9Zg3lsLNQ07TJ3VdzHEiEm8W19+sXsMpY9BPS0h5rEO16bDaIbYIBs9kJUdtcX6/90WcOo9adpFrX/QniZg9wxU1ncMM/TuHuR4/hhPMuobZWe70+TwJA53Td387MVdrjPmnQLQUcPWksWVna42i3tGtEuyXOlzkeII8sikjVXhCBi7w+EV1NjZ3q6tDJFwPirYGKb0DxNH4MQRAEARDRLgiC0HKYuYcqXnCb+EybEcQ93mBpzyyjL5sZzVLiqGwQ7YoH3HmEjbdQd1wEu0s0TTvQiqRAddfNXOT79TO2tRsa4R6vJzExyjYtrMED7LMztZtDxSVxnH/VVJNxAov2VWszeG9mQzz7mvUZ3P/4kWr5tjpKy5yG61L9s8d7vPD5Gm2HqcPUZGy2xrtu6EV7bm7Dz/t1etcsd0NLoxftJf5/itZ4XcZ9C5sdA7UXRJCMTu8eDxHWajejdjeUvqNmlRcEQRAajYh2QRCElkDxgmud+blw3dWDuMdnZfiJqQXbeWLuFDbTn6WMZT+dmTTvTahpRL1kfUx7bT7s0Yr27fTSHOtdeOsJZGlvv0TuHq8nqpLQQRii3Zg48dNZfdimLwFuDVx8/qvvB2iOB7KeI5/8F55Hfva1lehEu8PhITbW7/5duAPytRsInDUMHD2atAsSzNJeqNOZgTanWpLglvZ4g4fCrnitFwybwhfteks7QL5JAsyIcW2F4tegemXTxxIEQThIEdEuCILQEtRuDewGH47l21ujcSs1WtrrBIzHC3//mozihgRd8VRx9voX4dpPNaXawkLvHr9rPbi07q3+lvb4eA/2AAZoM9HetfHhx21PGJb2mJjgJd2iKp4dQor23j2KTdu3bNE16GPc/fh9qfpLj6eCB/gHKxnBZOZiee5X2KrebyW6muApydVaLf7mEu2gQzNhQBew9wi6/lBEItrbIoFiUNFujedAkfZ9z+3UW3vBhvDd4x0OL12zSzVtW3eE2IUKF28ZlM2EkrfBUxK6vyAIgqBBRLsgCEJL4NoQ+JwnDOuXonV/N8a01523WeGb6Ww9/kS86CyOX6yFeXp1FQJvDXj8Htw3rNacdjud5NGgdFJSdPXM/BgyxNjWq1dky4kuQoh2q2otDmZtjzrRbokLuhkx+citDOpvvF8NWfGDWNp/X6aK9t5s4288Sgxqdj5rrRuu+RRKqykp1VraU5K01RB4+BT4+9GQXjfPeSPV742oz+6PPhGdv3u8prwaUSjaTSztRV1078emfRFt3PXrrd2p2LK9caL9QGEc/35uEq+8Mxq32+9R07UZSl6D2h3GfB+CIAhCQES0C4IgtASurYHPeYvN66Fr+mhFe1D3+EQnnZ87gjcslxvHuec7TexwWHjq0mYrXlivdfEvy8wBv82BFJ2o8CcxESZPbjju3BmOOy6ypUQVltDu8RBctEede7zFEtTaHhvrZuHXrxjar7gCqvwdSQIkotuTm8SeXPUmWcMwnuZGbYele2D6R5QWazcOUpJ1rtpp8fDXo2DJTfDoqXDxGPX30chyb/VEYmkPFfrQEuhFe0UFuFx1B9YEXUw7lOldWUprjGEFQejbS/uiG2Npd7utHH3m5dxyz4lcefMZjD7u//j1t+4NeweeYtVdvug/UPaFulEoCIIgBEVEuyAIQnPjKTa6mfsTqB66P36i3eu1GGpY67PHJ6fU8vNhl/AYt2jHWb8PPo4wltS9t+57LmzSptA+0FlrydOLCj2vvQaXXQZnnAFffQVxTagi1faEdo+HdmZpB7AG/yWmplRz5cVLDO2vv+4/hrml/ZdF2qSF93AP23Q5EfhpK0c/+xBxNFheU5IDCLl4B1w+DpKcYMsIHbIQAr1oLyqCmrqpo8E93qzMnK8Una7kG4Cla7L6HvkTQTK6bjr3eL2HTzjc8I+TWb2uIdX+6nWZHD5lOqdceJE2G723GqqXQunb6memIAiCEBAR7YIgCM2Na2PoPvp66Hr83OMLi+Jwu7VWXn32eIDTphVwGw+zjFHaE68tDr0ef2rr4uNrtxtKRuUna0VYSkpw63O3bqq4++wzmDAhsmVEHWGUfIOOJ9oBjpi409A2Z07Dz3tyE/l9aVeDJ/ZXc/prjstJ4nS+oABtKvaem5eyhb78mReJoYaUJGNSNAOObqH7hEAv2kEt9VZaCsXF2va2EO1paWqZQH98pegs8ZqSb2r/Guivy6YfQTI6/eeKfrMwFHn5iTz/+njTc7N/6E9cjzuNZeRqd0HR01DxQ0RzCYIgHEyIaBcE4eDFUwzufdoY7ubAtSmMucO3tK9ebzS3ZXQ2ivYRQ/PxYuMe7tGeWLYXVuwNvaZ6arepmwo1K2Czdp17ErWJrlJSD6J/I2EkooN25h4PYAst2s+futqQSX7+fPX7rbdCt16ZTDzpKk46/2KfcPd4LMyeZywXsJrhnM8HuNBahLPJ40X+Qg2xXL7zSfhwBcxapyZbNMPetHh2UH9XMTHattxcWLBA2xYT0zaVD6xWo7W9QbRbKCzWiuq0TlVqgj5/IrC06z149AkwQ/HWRyNC9rnzoWONjYoClT9D+VcRzScIgnCwcBA9bQmCINSh1ELZl2pMZdGzUPQUVHzXPLGVike1UIciAtH+4ptjNac6pVYRE+PRX0Gv7sXYbF6+4lR2ohM0n6429A+I4oGiZ6BwJ+zRbmhsi9Eql1Du8R2LjuoeH3onwen08ONnr2va9u2Du+6Cxx9vaPvux3788Iu6sfPOxyPYf8DcUvsjx3AsP+BOMnerP2PN/+C6z+DYfmqyRTPsOSHXHQqLxbxW+9Kl2rZx49outEO/vny/iJXCYu37l5ZaBQM6qy7yI7Ph3BFwiNY7JhgZXYLn0gjGG++P5G/3nRCy3wtvjKOmJoCHTtViKPtMktQJgiDoENEuCMLBR/lsqF6iJloD9XvlAjWrscdYlzoi3LtDJ5mD0Bnk/UT7lu1av9wLOn8JV30MLy2CZXugVhXwDoeXXt2L8WDnLS7Rjvfl2sjLv63VxrNjt7LOO1jT1Ba1q9uMMC3tnTsH7hKVlvYw3OMBevcoMrT961/Gfj8t6AXAV99rXePHjNhLelqDGPuVw/n9vrtgmImPej3nvgV5Jn+TFjvYmsdfPUen/detg006Z5kRoQ3ILUZmpvbYP1negULtTkJ6WiX8ZRJsvR3m/BmePRPOHh7+XDrRXl7hpLLSEaB3A5u3pnHljNPDmqOiMsZ3j5hSvRyKXwSP8X4TBEE4WBHRLgjCwUXNeqhZan7OnQelb4QnugPhCrPEWqgHUr+Y9m07UzWn/tzjQ/h8Ddz5LZz4MvzpXd+5+pJNH3GudrxdJbA8Ahd5gNW6evIDupBfmqJpEtHufz60aG+vlnZQN4WyMkNnIl+8XFXB+szjF529yuBiv8k2EGZfyT29nqIEk82Dxbvg2BdhXYG23dYFLM3zCDNW68jCr78aRXt/7f5Dq6IX7fWWdo8Hiku0vv1pqVXgtINVV/4x3Lm6GH+/4cS1z57Xz5B3IxjLVgXZqAG1lnvpu6rXjyAIgiCiXRCEgwjXRij7KLjF2b1ftcQ3ltowRbtSC94gAqjO0l5WHmNwMR5wYLm27+gGU2G9aF/BSDaiUxpfrA1vbfWcOxI+uRTuPUF1sz1pIAeKdO64bZCcq+0IYXGsq9PepUvgLtEp2lNC96njsIkFIfvM/qE/u/cms31XqqZ9yMB9dM/Rhlts2NIZYmy8a72YXmznAf7BIibitfo9nozKgb663SF7CNEXAYcdpj1euBA26nJJtqVoD+Qen58PiqIV513Sm+ZWnpxUQ0yMWztfGC7yc3/ubWjL6FxOze77yV/zGJecu0JzLqxScu59ULUodD9BEISDABHtgiAcHHiKoOyT8Cw31Usa97DoKWsolxbumgJRJ9q36R5uHbiIW79D23dCD9+P/fvUl5qzGK3tkbrIp8TCEb3h6kmqm+1txxhKTIml3f98e3WPTwpdg76OZx5fEboTarIx/WZTr+7FDB+sDbmot7geKIqjmE7cyQNMYhHz33oWfr0Wtt0O7/4JYnTrs+vMz01AL9oLC9V4fX/aIgldPYHc47dt07bHxtaSmRF+TXYzLBaji3yoZHQHCuP4eq5xV2PGXxYSE+Mho0sFgwdo31B9yE9AKudKVnlBEAREtAuCcDCguKH0w8gSzZXPhqo/IpundmtkojiQaFcUUKoAo0Vqatp3WGq0ljDGNpS+qre0A3zMOdp+XRKhsCr89ZmgLzElot3/fGhLe1Qm7rNYwo5rz8qy8+qrofu9/9kwzbHd7qFX92JGj9Zmgl+2KpuaGhuFOg+O9O4etXRZgi61ez02kwLmjaR7d+jaNfB5qxX69Gm26SImkHu8XrT37FaCpXFe8dr5DBnkg1vaFyzujsul/du4/srf+Nv1v/qO+/bSftYtWZkdOBmdP4pXzSrv2hy6ryAIQgdGRLsgCB0bRVGzEbtzI7+2YnZDzfJwqN0Ruo8/3gCl5pRKX5I8fTz7mc4vtX1HZEOnBiE9oO8B38/LGcUHnMeW6efD0pvgm+mQbp6tO6zlei0Ul2hFe7BM6R2PIKLdYgWL6j4fTLSPHNnMS2oubKnh9bMmcdllcOSRwbvV1Gjfq6ED9xGb3IfRE4dq2vP3JbJ0Zbbh+q7ZIRJC2oK4M0SIxWK0tvvTowc4nc02XcQEco/Xi3azRIE+ymrC3lDUl5PMzQ/uHnL7A5M1x5PG7eI/D36j2UA4dPwuTZ/ikjh+Xhh+VnvKv2parhFBEIR2joh2QRA6NtWLoCaCcmf+KG4ofQdq94TRVwmvPrs/3pIA7f5J6LSqeLRbZ/2f3B//p+N+vQs1LvIX8AHvpP8fdAs/bjkQFSZZpKPSctxSBLO0Wxoswp06qdZZPb17qwIwKrGmhtfPloLVCp9/DnfeCTfdBH/8AcuWBb9s7Mi94BxC/0FpJMS7NOe+/G6g5jgh3kVKcnWQtcaGVVs+Eg4/PPC57k0vB98k9Jb2wkJwuWD7dm177x7FDQdur1rn/tZZcNTz0O9hNRllGPTqXqw53rQ1sCt7eXkM6zdpN1DGjTKGCHXLKWX8aO3n6LJVxs2agHiKoOr38PsLgiB0MES0C4LQcXFthPJvmzaGtwbKPgTFFbyfeyd4IywXF8jS7ifac/Mb4kkteOldvE7bd0xXiD/OV/7KYoHJR27VdNEnBGssZeVGc2NUxmi3FJYgiej8RLvVah42MHiwsS1qsIXpMlGXtC41Fe6/H558Us2+3r8/WCyBLbljR+SCowdWm4WRw7QW4VnfDdAcd80uDe7m3Yyu8fWcey44Avx6TwhderxF0Yt2gIICo6VdI7ZtFvjnt/DGEjXzvoKaiT8MBvXfrzlevzmwV8OO3Sl4PNpHySknbDDtO2a41ttpxZoI8xJUzpP67YIgHLSIaBcEoWOieKDsy9D9wsFTosZVBqM6hKkx0Lhm+JV78y+3NICNOGt1Mekjs8HRGxy9fE16N1m9i31Ift8FD89Ta8B7G4RYeYUxvvigEu1B3eO1Gxpmlva+fZt5Oc2JNUzRbjP32EhIgIEDArsvjxuzzzfH6JFa4bVqnVa8dcsJsJlVTzNmjq8nKwvOPtv83JlnNvt0EZGWBjZd+Hd+vol7fE+/v3uLBcbrXAR+Dy/UZ1A/nWjf1DmgZ72Z6/zxR2816Qkjh2pLSK5YG+HvUXFD1W+RXSMIgtBBENEuCELHpHpJ5JbvYFT9FrhEm6cEalZFPmYY7vH+mZvHskTbLysJMpPAlg6OhvjQXv5usjTC0v76H/DEz2oN+BFPwDNqQqmycq1odzggJkCesA5JmO7x0BB37M+AAca2qMEWRkZBiyVoebhxY72m7dmZZYwZ7fWFcYwcGbyCQ9esEH+3zZg53p9779VuQtls8P77MHRo4GtaA6vVaG3ftg126FJo9Ompi2nXi/bFu8OaT29pL69wsjfPfHfO3xMIYNhgkxu/jpFDtefWb+pMdXWI5I56qhaG9noSBEHogIhoFwShY9LcFhmlFkreMbpnKgqUfx5eKTk93irzB1CNaG+wtBtE+8hstTa4NVZnaS/WdNu1JwW329qw3qd/gVdM4kPzy+DtpfDxyoa2gnKftV1vaT+4rOyEEO1aS7tZwrkLL2zm9TQn9s6ETD1uTQ36Hpx3nnk28OOO2Ird2bApMGBA8J2ekJZ2W5BMf01gwADYsgU++wx27wa3G84/v0WmiphsXfj3Dz9o88pZLAqDdWKbCTrRvjYfykNX0OiaXWbIO6CPW68nr0Ar2rODlJwbPkQr2j0eKxu3RFh+QnFB9dLIrhEEQegAiGgXBKHjUbMBPAdC94sUdy6UvAW1fomWqheBy9wdNCzMXOTrRPv6TZ012drHoUtCNyK7wfJpSwGrmhlen0jK47GqD90eL9z+DTzwg/r90vdVi/ql78OQx2D4EzBDF1IQ74ALRwPGmPbE4OWbOyDhxbQDTJumPf3YY1FeHs8SE7rsWwhr/KlTHIwZYazSkJVRrsn23m9A8BJiXbPaRrSDmvn/jDOCl4BrC7p10x5/9532uHePIuLjdeEJI3PA4feY51VgSeikmlaroqlCAbAhQFz75m3aJHVZQUR7UqLLUE5Ob6kPi6pFkV8jCILQzonQL0kQBKEdUPVTy43tzoWSV8DeFaxJULOmaeN5SwGdCKmLaX/6pYm+JgteRqOLmx+ZoxVatgzwbic9rZLEhBrKKxpE9vCjrsYzZRjWL9c29J+9Qf0Kxp8nQhdVZIml3QIWm7lXhc7S/n//B1u3ws8/w9Sp8Ne/ts4Sm4Q9K3CeBQgp2q1W+PStb+g58gpNe9fsMrA3FDrP7pZsuD/96d41iGi3JoE1LvD5DopetG/Zoj3Wi2wA4hzqxp6/UP99JxwVuuj8oP77NdndAyWjW/CH1po/Ykhg93hQa8D7h/z4/xw2nmK1oocjynZWBEEQWhCxtAuC0LGo2aC1hLcEiket395UwQ7mcfd1LviffDXE1zSAjSShs2KNzAar30OvIwdQtaUhvhULe705ka0tPR6uPtR3qI9pP+hEOwTOIK8T7bGx8PTTaim0u+8O7XkeFdhD3B9h1Ebv0cPCycc1lD60WBQ1m7itISjbanNw3tTAm0X6hGXaNbZMPHu0oxftevQhMT4aG9dukoxOT3FJLGvWazP5HzYheLI7fQ14//CfiHCtb9x1giAI7RQR7YIgdBwUBSp/bOtVNFBaDVsPqDWTA2GW3M5bwf4D8ezzyxxvcI2vT0KnEe29fT9ecu5K9MwcdR1MDLNQeL90eO8i6NRg1SwpjdV0OShFeyAHNUsHyMjnCJHePgzRjjWZ/zzwDUcfto2+vQp57pGv6Nu32pB1/p6/rzC9PK1TJT26BbH225u/3Ft7oEeIP1t9SIwPvWj/Y1fwz6M69Jb7rTuM1QUWLemGojTsRsXEuE3DI/zJ7KIX7Y2MsRHRLgjCQYa4xwuC0HFwrVfd19saRYFnFsBjP0K1G9Li4LyRcMtRkKwVvqaWdqWcDZu14mS1fTjem4/BumovrNirur0CWP0sVfYeYLGC4uWv1yzggacnU1zcsDe7eEMv+HIafLACft4KK3Nhwz715OT+cHx/GN0VBnSBOLvBPOxffg7U+N+DjjAt7e0SRze1XrtH76VRRziC2ZZEvz6FzPv0Db+27oZu3XvaGT4431Du7ZTjNoWo0X4w3nTQv3/w8317FZqfmKBT++UutV77pJ7m/evomq0NUTCziC9frS3ZNnZELk5n8ISczRLTDuDep3pUOSL0HhIEQWiniGgXBKHjUDW/rVcAlbVww2fwhV/seGEVvLAIFu6Az6epyd3q0VvalVrw1rBpmzZ+uLZvNtZbj2xoqHar3y1+D71WpxprX7sLiyObf/3LynXXNZx+++ORvPLUF8ScPxLOr0tvXuMGhw2sof239aI942A0enZk0Q7gHAqVJn9HtmTtBlEgzErCmVnoramMGpZnEO1nnhLCghqOtb8DEqxcYFKSh8lHBUiGmZkIw7JgtV/IwZyNIUW7PqFceYWTigoHCQkNye62bNda34cOKgg6JkDP7lovii070gL0DIOaZSLaBUE4aBD3eEEQOgY169XkRG2JosCML7SC3Z8VuXDrLG2tJq8u6Vad5X1Prtb33OD+Glu356oXUnGT1O/xRzJ6tHEJ/sntAHDawxLsYBTt+trRBwWBSp51FNEeM9C83R5m0i9rqrHNzDpuS6V/H2PytIG6WGrjddGcgr/lSEmBfv3Mz009vYKU5CCl3CbrLvxuo/YzyASzLPB6V/Yt27WCu2+vAB4afuh/5xs2p4daSmBcm0K+DkEQhI6CiHZBENo/igKVc9t6FWqN85mrg/f5aCX86GcV07vH11nec/O1oj07M0ApJX0m7ZjBkHQ2OAczYoSx+3c/NsQte70WPJ7wM6TpXWQPSkt7oLJvHUW027uB3URkO3qFd70t1WTMLGObNZUhA/YZmgMmVAP1Xq8ra3gwcvHF5u2Tjw3uks7xOjP9xv2wNrhVPDHBRVyctoTc3ryGzyRFgbUbtPdJQBd9PwboRHtxSRz5BY10kfcUQ+3mxl0rCILQzhDRLghC+6dmpRrj2NZs1K3BboUrJ2jd4QFe90sqp3ePrzv2f0AGyM40iX0HsOhEu8UCscMBtY66PoHV9z/35aMvhnDOFedhy7obe/bdXDVjSlgGqzzdw/VBKdo7uqXdYoG4I4ztfkkOg2IzJizDnm3a7+TjNpPRueH+P+rQ7cZa45prDk4rez1Tp5q3Tz4+RKTj2G7QNVnb9okxUaU/Fgv06Kp1ZV++pmHzZf2mzgbL+8ihwcu9AfTqUUxCvEvT9vuyJpRuqzZPaCgIgtDRENEuCEL7RlGiI5Yd4P6T4JNLoUeqevzyufDgyXDXZG2/ORshv06sKG5fiTfA5y6/a6/2ITvbxF0VMIp2HQsXGtvOu/I8PpnVUE7u5bfHMm9+AFGWdDqkTqfGMtZg/e9uzC/W8QkY094BssfX4xwOMX61vG2p4Wdtt8So8e++a1PM66pbU4mPr+WLt95jyokbuPCsVbzx30+Dj32Qi/YhQ4xt/ftDTreE4DUFrRY4c1jDcUJ49+rEMdrycL/+3vAHr09C1zW71DTcQYOiYNu+n2t6vcslvMk/eICnuBHl1cVQUh3Wmgy41oO3InQ/QRCEdo4kohMEoX1Tuyk6rOz1HNEbfroavt8EpwxS284bCfd/ryapA7Xk0ocr4PrD1GNvWYPbr7eMzVvT+GN5g/WpM/vo1d0kXtRiU5PPBSEnBw4/HOaH2Nf4+MshHHvENm2jcyDEjgFgV3F3gzW+Z/BcVh2UAKLdGmve3h6xWCDpHCj7GGq3Q9xhkV1vywBPXa4GW4DEB9ZYsMYycewevnjrvTDHPbhFu8MBN94ITz/d0HbllagVIyzxoAQRr+eMgM0H4Ozhqru83vvHhMMm7OLND0f5jhcsbhDte3K1m4oD++0PvG+wJh/e+AN+2grbCnlUf/5H4IlJcM/xwTcfzFDcqqdVfS4PQRCEDoqIdkEQ2jeVC9p6BUYSYuCMoQ3HSU44fSi8v7yh7Z2lcN2h6kOqtxSoEzeeUv739hhfNysettKHxBsUGJmtfk2fABmJIa3s9UydGlq0m5Zeih3r+3H7du2p1FT166Cjo7vH12ONh5RL1bhha3LI7hrsWeCqizU2c433zZEK3rzA5/Uc5KId4MEHYcsWmDsXTj0Vbrqp7oQ1IbjFeUgmvHlBRHMdNmGn5rh0t0LuBhvZPWsM4Ts5mWXw1hL4diPklcGGAqgJEWvvh3JITyyRCvZ6alaLaBcEocMj7vGCILRfqharlsD2wEW6VO5bC2FR3UOxfwZ5bxk/L2wwYQ9gI0mUYzlQAT9shid/gXqLt5nbsQmhajwDzPmpL9XVfoLUmgCOhqzTeTpt1a1bWFN3PDp6yTc9tlTVkhsJDr9ECsFi4c2S1gVdy8FZo92f+Hj48ksoL4ePPoKYek93a1LQ6xrD4AH76Rm3hwe5naWMpog0so/4Jzw4l1XrtOESXbPL1Jwe322ElbkRCXaA0gF9QncKRO0ecIeOpxcEQWjPiGgXBKF94imDyu/bdg2RxGFO6A79/CyF3VNgT12iJ0+DaFc8xazd2CBOxrJEO05Wklp7GcK2tB95JCQmBknwBVRUxjD1Mj9LnHOoRqwd0IWrdj44y2Wbi3aLLbCYPxix91Rj263x4AiS+MBqkrQuEBarWNr9sOqf3qyNzMBeWAle8yyU1iU7+a12HLfzMKNZ7msvy8hm7i9akd01uxRGhZ9QzouFBUxiJcPZSm92uZqQjA6g6vemXS8IghDliGgXBKH9oShQ+hZ4g9Qmbmk+XgnDHodH5kGFK3R/iwX+fgzcewLMvwb+uFGNMwXwFqvfFS979yqUljXERxtE+0g/d2NLeHHUqakw66PFnHiM6rJ88nGbOOOk9YZ+387rx+atdbWXHdq084W6ak5p2hLNBw9mCec6qpW9sVidEDsK4o9SNzQCEYml3ZYefKyDncaK9ofnQZ+HYIvfrpyiwOM/wemvkek2hi/8UjAaRdG6sp94zBYYlRN8rmnj4Ps/w+476Z2Wx2EsYCQrmchvrN8cYBfwh81w6ftQFuKz3rUWlMis+4IgCO0JiWkXBKH94VoH7uB1hluMajfcNwderrPs/PtneG853H8iTDFJ7+yPf5y7P546RewtY9v2FM2p8ZY/GtzhAUb6PRiH6R4PcNQRNRw19m1N29yfezP5nMs0bSvXZtKvTyHYtQ/gItrrMbO0i2g3kHBC4Pj/eszKwwXsK1b2oDTWPX5lrpog87Bn4fHTINYOr/0Bi3cFvOSrTRM0x6cev5EBfQ+ANw2uPRTKa9TqGDYrpDhh6jCY0EOT/K5TtpeddZ8p++nCc6+N55wpaxsGrXHDv+bCi4vU48OeVWPyA20MeKvAtRGcgxv1NgiCIEQ7ItoFQWhfKC6omN02cxeUw3lvw1pd/OTeUthukt09XOpFu6dIU+rNgpfRLNP21VjawxftZgL/uCO3MXRQAWvWN8SnrtnQhbNO3wk2rSrXu8cftKLd1D1eRLuBUIIdInOPtx2sN1yYRJosEKDWA2vqLOleBWZ8GbBrPhncxf0ogzKZu26s5tyZp6yrW4MF7j4+rKn79znACr+67+s26iztV8+EWesajvPK4E/vwjfToWeA+6Z6mYh2QRA6LOIeLwhC+6LyJ00MeKuxuwTOfcso2EEt6XbdoY0f21sB3mrwFrJ+U8PD6wA2kqAv4zSicZZ2LPGmzcceri3ztn5TZ9M4ZL2lPf1gNXyausd3oHJvrUmk7vFCYCIV7cVV8MKisBLGPcYtdGUP/+PPvLz+DLbt1QrsQ8buDnBlYC46e5XmOK8giaoqv42em44w1pPfXwGXvNdQOlNP7Wap2S4IQodFRLsgCO0H1xao/LX1512bDye9DOt0Lvk2C8w4Ep46PfL6wnrceSjuA7zzyQhfU9AkdBCZWLSai/Z+vbVq/N2ZI8BuTA2/f7/2WCztfljF0t4oLI7wY7GtB+sNFyaRivbUODi8F3ROCNzHZmHvDefyNx7DE8Qxc1D//QHPBWLykVsNbdt3pTYcjMiG2VfC0Extp/X7AnsEKF6oWWd+ThAEoZ0jol0QhPaBtwLKP2/lORV49Ec4/iXVNd6f9HiYdQXcdgzYm/hRqijg3snypeVs2d4gTibym7bfSF3N60jc4wNY2vv0NLr1L19jLNOVr3MwyMw0dDk4EEt78xJuXLtY2oNjTYy8NN/orrByBvzrxAbxbrOoQnn6ePjxajr/bTidUqsCDnHM4duw2cyzzwcjMdFFl85aq/i2nbp7YWAX+PxyGKBznZ+5CuZsNB/YtSbitQiCILQHJKZdEIT2QcWc1neL/898NYuynj5p8OEl0CO1ceN6vLAiF37con5lJ8NLNmZ9fbim29G2n8Hfe3WczgJubbql3Uy0v/RqBs+NbzhWFKNoz8ri4MQ0pl1Ee6OxpgKBk54BdRb55q9D3qGwWFXhHulnpN0Kfz4ErpwIlS6Ic6gJ5OqIwcPdt/zITXeebHr56SduaPSSe3UvZt/+Bkv/th2pxk7JsfD8WXDiy+D2NrTf9jUc3ltdrz+1O9QNXmsQDwJBEIR2iFjaBUGIfmr3Qs2K1p1z2R7Vyq6ndxp8Ma3xgh3g41Wqu/3D82DRTrWsUa2bJSsa4tUTKWOYVxv3yQRtGbbI3OPNH2LNXFvn/qB9EC4qglpdGKlY2v3bRLQ3mnAs7bbOTQ8/ORhoTDI637UWSHRqBHs9N1z1GxecucrkIrj68sWNnrJ3D+2GocY93p/h2fCgbtNgV4lablOP4oWa1Y1ekyAIQrQiol0QhOhGcUH5l6q5t7U4UAlXfqS17ABcNQG+vRIyGlkTuZ4jde7nJdWwfC/LVjeYrw9hEVbFb36H1VjuKCL3eIepldhqVXj3hY81bRs3ws6dDcd6KzscxKJdSr41L+FkhbcftDdbZFhTQvdpBBYLvPvCJxx7REMcerecEvavfwSns/G10Xv3KNYcG9zj/blsLEzUbVq+/of6Wa2n+o9Gr0kQBCFaEdEuCEL0oihQ+hG4c1t33ju+US05/sw4Eh44WU3g1FSyk2FQF03T2pdK2bk71Xd8GLqEeyOyja6gkVp4A2SbP++MNaR10j78zp/f8PNWXc6otDRwHqw61czSHkmYgqDF1jl0HxHt4dEUS3sILBaY9fa7vPDYl9xx8898//GbpKcFjnUPB72lfdvO1OALePw0Nea+nspaePV3Y1/3PqjdaWwXBEFox4hoFwQheqmaD65NrTtnrUe1fPszvjv89cjmneeovprDws/2ao6PsM7XHBtc4yFysRggGZ3NpjB+lHb+7dsbfl6jy+00+GAuhWxmVRf3+MYTjmi3ZbT8OjoCtpYT7QBxcW7+77Il/Ov2HxjY70CTx4vI0g5qYrozh2vbXluslq/TI1nkBUHoYIhoFwQhOnFthcofWn9ehw3evAAuHKUep8TCq+eq7c3JMVrRfgiLSKbeuq8wwqaPZ9fVTrfYzJOiBSNAMjqA7l21ngW7/HKD/aoz+g8dGtm0HQqJaW9erLGhy76JaA+PFrS0twS9dUkwi4rjKCkN4cJzzSTt8f5KeH6hsV/NclAC1HMXBEFoh4hoFwQh+lA8UD6rdePY/XHY1Nrrfz0SHjgJMps/c3XlyL64rQ2i246HY6hPrGTh+yf/C3P/DA+dDGcOU639/kQSz+67Johoz9Fmna6PaS8vh6++0vYdP56DF4vFKNxFtDcNe5fA56xxYJPM8WHRzkR7j64lWCzaz/jtwVzkAYZlwQkDtG0/bTX+r/BWQc3api9SEAQhShDRLghC9FHxPXgKmzyM221l7YYuuFyNsJJbLPD3Y+C8kU1ehx5FgXOuu5gfvVqX+6l8BsAVf1rKBeeuVbMmT58AL55tTH4XID49KEHKIPXoprW017vHb9sGHl2uqXPOiXzqDoVetEtMe9MI5iJvCyLoBS0tlIiupXA6PXTN1m4WhnSRB7jjuIafzx8JH19qXl2gakHbbfwKgiA0MyLaBUGILjxlUN34MkIAXq+Fv9xyGo6cfzL0iGtxdruLL78dEPrCpmIJ7yP1L7ecxjdz+zObkzTtF/IeGeTz9L9mh65w1RhLexD3+P69tTGqGzeCywV79mj7ZWRAamrkU3co9HHtjfldCA3YgiSas4trfNhYk9SwmXaEMa49NfRFgzPg8nHw7Jnw36mQaBKyAuDOh9pWzokiCILQQohoFwQhuqj+HRR3k4Z444ORvPjmOE3b+X8+l117dO6jHi/cOwf26DLFR4o9AxJPgZTpkHIRWAPHZf6+tCsvvaWu7S0uoZqGvk5cPNLtQRITXaHnDCLAA2IJbGkfNrhAc+x2w+rVRtGeo6s6d1Dib2m3WKTkW1MJlh3e3j3wOUGLxdLuXOR7dS/WHIdlaQd49FQ4d0TofpXzxdouCEKHQES7IAjRg+eAKtqbyNMvHWJoq6py0GP0DLZsq3soVBS47Wt4dgGc+ips3Ne4yWLHQqdrIG4COLpCTH9I/TPYUg1dFQWu/8fJvuMCMnmbizV9Li1+CarCSKDUKEt7YNGeklxD757lmraPP4a92qTydO0a+bQdDn+RbokJ28NCCIA9M/B76DCpmiAExuRzJ5rp20sbBrV8dVbzTlC7s1n+pwiCILQ18qQhCEJ0oChQ9iV4a5o8zMat6QHP95t4I5ddN5XKt9bBG0vUxr2lMOU1+GN3ZJPZu6gWdj22dNP22T/04/el3TRtT3Kz5thaXg3fbQw9dzPHtAOcebr2AXr+fNiyRdunm3b5Byf+nhTiGt90LDHmGeKtcWAL0/IqqLSzuPbxo7W7gouX51Bb28yPppU/gbc8dD9BEIQoRkS7IAjRQc0qqN3e5GEOFMZTVRW8FNr6D2uw/e0LbWOFC6ojKBFkjYeUywLHkMYMgLgGF/3c/EROufBiQ7e1DGU+h2kbP1wBbm/w+RtlaQ9eWuuIw7VhCVu2wFpdAuZBgyKftsNhEdHe7JhZ1O3NbHU9GLCmtvUKIuKQsdqN0upqR9Os7dVu2FmsbfNWQtkXalUSQRCEdoqIdkEQ2h5PEVTMbpahNm1NC3o+lipe5Qqceov+U6fD4b3Dnyj+6ND1pRNOBJsaY/rOx8b4y4vOXknN7vsZ8dEIcPqJ/zmb4JYvg8diNnPJN4B+/bWbHXv3wmJdTsCDukZ7Pf4l3hqTW0Aw4uhj0ta39dfR3mln7vFpnaoY1F8bmrRgcSPyGNR64M0lcMh/4aqPjJ+dro1Q+WPjFyoIgtDGiGgXBKHtqfhetYY0A5uCuMYD3M29DEVnPr5wFJw9PPxJbCkQOyZ0P4sDnKMB+O5HowC54+afiYnxkHxUBrx8LsTZG06Wu8AbRLQ3yj0+Fiz2gKf79AvuPg8wZEjk03Y4/EV7iI0QIUwcvY1eKzHi1hEx7TCcQG9tj9jSvq0QDnsWbpmlhjot2wvfbDD2q/wFqpc0YaWCIAhth4h2QRDalpoNULOm2YbbtE1raT/thA2s+uk57HYPI1nODJ7QnK/u3xX+PcW8zm8g4o8OKn41xI5EwcKcn7Si/YXHvmTwgP0NDScOhNW3wBfT4Ker4aVzwBbkI9rSyNrggeLaLRbiE5MZNiz45ZI9Hu2GiVjamwerE2IGNhw7uoI9SP12wRxb8E3LaGTk0HzN8er1EZb5654KVt3n9yPz1OogespnQ+2OgEN5vTBvHpxzDpx7LixbFtlSBEEQWgoR7YIgtB3eGqj4plmH3LhF+9Dav3chwwYX8Nazn3IP9xBDQ9x6LXZennQ/2CP4KLQ6wRlC2fpjS+Old4xJ6Q6fuNPYN8kJh/RQ6xDrH0L1NDaWOqBoTwSLnWOOCXxpVlZkexsdFnGPbxkSjlO9U0CtyiBEjjUxaMnJaGTYIG25yTUbMvB4IvigsVvhb0dr29YVwMzVxr5KLZS8BdWrNM2VlXDddWCzwbHHwiefqNUzxoyBd95p6FdeDu+/D3PnqgJfEAShtRDRLghC21E+CzzFzTrkkhVaU/DgAWq85AWOj5jK55pzj3ML//5xamQTxAxsEBZhcv3fxhnaevcojmxePdZGWtotAeLwbWrW6euvVx9czfjrXxs3ZYdD4x4fOqRACBNbOiSeDvGHhhd+Iphja18eCsMHay3tVVUOtu2I0M1/6jAYkqlte/RHcJkkn1PcUPYJFL/K8j/20a8fJCTAs8+aD33xxepm5datqoi/8EKYPFn9nHzmGfj6aykFLwhCyyOiXRCEtqF6mZoxvhnZfyCeLdu17vETRu+BNflw9Sea9iJSeYjb2b6zE4uXReDz7TQmlAtGURHU1hqtRvHxEWSqN6PRlvakAO2pAPTvD/fea97lhhsaN2WHQ2NpD/B+Co0jdjgknNDWq2jftBfRbrGCcyAZPYbTOV2bGDRiF3mrBW7XuQntKIJ3lga85KuvnYwe38VQ1jIQffvCpk3atuuvh1NPhZgYWNW8/84EQRA0iGgXBKH1qV6pWtmbmV9/12Ydjo93MbTXXrhmJtRoLS4zeIIy1Mzux5x5eXgTWBPNs1wHYf58Y9vVly82NkaCxdKEmPYAItMv6/Q//mE8fcMN6oOpgLZqQKgKAoLQ2rQH0W6Ng+RLIPlCLEknMWy41qV/zdbxkWfCP2EAjOumbXviZ6g0bpC63VYu+PM5ES46MG43zJjRbMMJgiAYENEuCELrUvkTlH/aIjVzf1nUU3N8yJhd2O+YpcY3+vEWF/M6l/uOKypjOP2SC9m9Nzn4BDEDVetQBMybZ2x75J9ztA0Wa2Q1qS3OxgeX24Jb2kEd+rHHGk4lJsJNNzVuug6J/8aHWNqFaMPWpa1XEBznEEj9C8Q0lNgcPFjbZd2WvpByRWTJCC0WuOM4bVt+Obzyu6Hrq++OpryieWP/v/8eqqubdUhBEAQfItoFQWg9Kn6AinktFgD4y289NMe3xDwF7y3XdhqayTenzgC0ovfLbwfyp7+cHXxpMf0iWk9JCTz5pLbtzhk/kZTo0jY6ekGiMVldQBrrGg9gDbAxoSsVdcMN8Pzz8Je/wOzZ0DuCEvYdHmssWGJUkSCWdiHasEfoWt6axI2D5PN8OTTqGaSr7vfxx+AhGVKvgaSzAyfQ1HNYLzhaV17zv/OhRKum//e2MWdCTlYpnrx7Kd78EO7ceynb+mB4c/oRFwcLFkR8mSAIQkhEtAuC0PIoXqj4Dip/brEpXC4bS1dma9riLhsC08Y16PM4Ozx3Fvfe9YvpGL8s6qmpEbxsVRZHT72cIYdfy0VXn8MHn/bn449h/frw1nThhca2IXWJ8TQ4R4K9a/hW/KZkLA9TtMfEqIL9+efhsMMaP12HxZqoeidE6HkhCC2OtVPEyTJbBVsnSDjR9NTo0drjmhr47jvUv6/Y4dDparBnml5r4I5jtcfF1fDgXEB1iz/xvIv5Y3lXTZesjDLW/fosVqtCSnINNptCYqKLlT8+ZzrFlFNLA05/2GHQsyfs3RvecgVBEMJBnjYEQWhZ3Pug5FWobFnzw968JNxubdrz4RNL4ZFT4csr1DJqL5wNgzPo36eQx+7+znSca/52KtXVdhQFLr9+Kj8t6MW6jV1495NhXHChnXPPheHDYc4c08t9LF8O35hUsxvYv0LbYHWq7qIWW2BBrae5Le0Wq8Y9XggDW1q7rIktHARYLNEX126xqxb2AJsJhx8OybqPpjVr/A6siapLfTjlNkfmwGk6f/vX/sD783ZOOv9ivvvR6DG1+ufnSE6qMbQPH1LAqp+e4+5bfuTf937LjqVPohTcwxevPYG34AGeeXyJ6RJ27oSuXeGnn0IvVxAEIRzsbb0AQRA6KIobyr+E6hWtMt3uXYkkUE4FqrtyfLyLtE5V6skJ3WHeXzS1zyeO3W06zqIl3YnrcSeHjN3FyrXmceZuN5xwglrPd9o0OPts1S3Sn7/9zXydg4b3ArY1NMQMbniQtaWFVwKvSaI9Tp1P8UvOJBbjyLFnqh4kghCN2DPAndvWq2gg4TiwZwc8bbHAWWfB6683tG3ebNIp6UxQXODaGHy+24+F2RvA3fA3WnnttyzOf9rQ9YIzV5GeVhVwqGGDCxg2uMDQbqGWay/9kg8/Sefnhb1Mr508GaqqwC5P24IgNBF5ShMEofnxlkPxKy0r2D1e9YHsl21wxzeMuexv3MX9vtPdsku1udqs2hj2sSNyiYlxBxx+0ZLuAc/V88MPcMklMGmSKuTrWb7c3BL/yisQnz4RbHUmJYsV4ib6rTHM2sTWJoh2MMSTisW4ETh6Qkzf0P0EoS2whelK3ho4ukLsISG79e+vPf72W5NOFhskXwAxIap49O8Mt2lLwCXm53EP92jaYmLcvP6fz0KuLRivPf15wHNuNzgcUFERsIsgCEJYyN6fIAjNS80aKJ8N3rLGXf/zVrVMz4Id4LBCRqJarq20GmJsUO6CeIehjE88cB4fchsPAxa65QSOOQS1Tvrfr/uV+584qnHr9GPFCtUV/vDDISEB/vUvY5+bboIrrgCIhaTzoOJbiD9aa32ytYJ7PNRtDuz3m1dEe8TEDGixhIqC0GTCjf9uaaxOSJwaVrWLIUO0x9u3wy+/wBFH6DparJB4JpS8DJ6SwANePQm+Xg9L9wDwAefxT+7TdHnhsVk4nU2rZNKnVxFnnLSez2cPCtjnhRfgr39t0jSCIBzkiKVdEITmQamFsk+h9KPGC3aA+dtUwQ5Q64U9pbC/AlweVbCDad1dgN5sZwJqeZ/Dxu8KOdW9f5/H79++xE+fv8bkI7c0fs3A6adDWpr69cknxvOHH+534OgGqdONllqrzgIeiCZb2nUiPZqzTUczjS27JwgtTRBX9FYl4WSwh1eCzizh5dNGb3YVWxKkXGpIoKnBYYP/nYO3SxIzHE9yAe9TTkOJxmcf/orLL1ge1tpC8cH/PuKN/37K2899wpQTNxjOv/FaGdTulo0+QRAajUVR5BOktLSUlJQUSkpKSNZnQhEEITiKR7WuV/4EngNNH8/jhfPeVt3eG8FtPMT/Ot3AhgXP0Dm9Muzrvpg9kDMuNUn3Xkd6OowZo2aO3xV6P0BDTAwUFxvj3g24tkPJ66EHTDoTYkdGtgh/qn6H8q8bjlOvVDcSBEHoOBT+t3k+kxuLPUNNHhdBvgyzfbA//oCxYwNc4C2HkneCxu9/80UPTrnyCk3bS//+gqsuWRr2uiJh245U+oy/ydBeueNfxCU4VS8de7ZaQtSW1iJrEASh4yGWdkEQGo9rE5S8AmUzm+/h0GZVs7x3Ct+avNvZk3u4m8Gs5RFuY9oFyyMS7ACnn7SBh+80Twn/ww+wf79agmjnTnj88YiGZuPGMAQ7qNajcGhKyTcAe07DzxZL9LjSCoLQfDh6tO38iadFnODyc5Pw8HHj4LzzwOUyucCaCCmXBfQscLutBsE+ZsTeFhPsAL17FnP7jcayovE976Smqhqql6mbpkXPQ8U8NaleOAlIBUE4qJGYdkEQwsdbA+4dUFv/ZZ6Bvcl0ioN7T4DOCWqyOZcbnHbwKKqrfHWtWns3zsHre89k2itX01CMHQ4Z17h1/f2GX/nb9b/yyjtjWLC4O2ectJ4pZ3bBmjRZ0+/mm1U3+CuuCDCQH1dfrdbsDQtLYpj9mugeb89uyCBvTYvOms6CIDQNRzdVILYFzuGN2jQ45RTz9o8+Ur8WLVIzsd96K5SVwTHHwN/+Fkvn9GlQ9gXUrPZdk5ufSM7wW4xzTN7UcLBoB4zIUfOkNCMP3jGXT2YNZuMWbem9Dz8fyiXnrVQPlFrVQw3UzdOYQeDoqybZEwu8IAg6xD0ecY8XhIB4Dqhuh55SqN0OnvzgiX/CxavAa4vh7OGQahSgHo8Fr9eC1apgswX+iNq1J5keo2cY2ncsfZIe3ZphnQAplwTNEv7FF3DGGYEv/+UXXTx7KPY/oC3HZkba9U1PHlf8uvo7jR0FSVObNpYgCNGHuwCKnmv9eW2d1JAba0KjLv/LX+DFFyO75uOP4eyzvFDyJtRup6LCwbCjrmH7Tm3Me3y8iw0LnlETlS7dA1NfhwFd4M0LIKd5n/+eeWUC199u3IXYsPC/DOgbwjPN0V0V8PZM9f+PJaZZ1yYIQvtDLO2CIGjxHFBLtbnWgnt/6P6Roijw1y/hnWXwyu/wzp+gdxqr1mbwv7fH8t+XG0qgdelcwYlHb+bruf0pLIpn1LBcbr9xPv16F7Jhczo/zO9tGD4nq5TuXZtJsFtsIa1Fp58O27bBccfB1q3acx98EKFgB9Xd01MUYl1NdI8HiOmtivaY/iG7CoLQDrFngDWpaYlBG0PiGY0W7AD33Qdffx1Z7pBzzgE14vNyLj53DQP65BsEO8Bt189XBXtuKVz2PlS7YWUunPA/eON8GNt8uT2uuniJqWgfOOl6Tjp2E1+/907gXJa1u9QvUEt02rupmyHO4SHDmaqr4ckn4dlnoUcPuPBCuOYasNma+IIEQWhTxNKOWNoFAW+5mpys3u29paishatnwjfrG9rS4sj/93QG3PAYpWWxTZ7i+Udn8ZfL/2jyOIAq2FPD8IFH3YuYPh3eflstXfTSSzBhQiPmLH6l4WHNDIsV0u9qeuZyTxlUfA1J50YcdyoIQjuh/CuoWtx68zkHQnLghJ7hoigwYwY89VTTl6QZt+Ae9Yez3zQmO3Xa4LHT4IJRzTZfoKR0AHfc/DP/uv2HyAe1Z0P84RAzhKpqCzt2wJIlas6VjRvVEAIzNm2Cfv0in04QhOhAntQE4WBFUcC1GUo/hMInofLnlhXsZTVw7ltawQ5QVMXCd2ObRbDfcfPPzSfYARx9wu5qscCrr0JpKSxf3kjBDqqlPehEsc1TasyWBMnni2AXhI6Mc1jrzWV1Qvzk0P3CwGKBJ55Q49ibi8/ffK/h4JFToI8ubrzGAzd8Die9DF+sUa3wTaR3z2KOPsy8EsoDTx7Jj7/2inxQdy6UfsSyH9+iV88aBg+Giy+GN98MLNgB+veHSy6BmprIpxQEoe0RSztiaRcOMhQPVC+GqkWtl7G2shYufAcWajcFFAt8fcE/OO29B5o8RWJCDbuWP0lqSnWTx/KROg0c4WaRaybKv1a9HgJh7wydrmu99QiC0H5RFCh8HLwVLT9X0hSIDVSbrfHMmgVTpjRtjJU/PsfwIQXaxqIquPKj4OVFO8WBywNWCwzsAt1TYcaR6s9hsmNXCjffdRKffj3Y9Pzr//mUi85Zhd3uDTnWmvVd+N/bY1m3sTPf/dh4s3nEuVYEQWhzRLQjol04CFAUqN0K1X+AawsoZrVzWojKWpj2Aczbomn22mxc5XmRV5neLNN8/MoHnD1lXbOMBdS5od/e+pnVK3+BirmBzzu6Q2rzvGeCIBwElM+Cqmb0QDLDkQMpVzWPF5AJiqJmiv+pLtn6McfA99/D3r3QvXvwa6/401JeeeoL85O1Hrjta3grghJwv14L/Tsb20urYVcJDM5QRb6OouJY0gbcZjpkz+7FzHztA8aMCFxvfv+BeIYdeQ35+8KsMhKCzZuhb+Acq4IgRBmSiE4QOhg1NbBhAwwbBlbXMtXl3bWl9ZMRARRXwUXvwWJtjHahvTMnub9iMYF9yP9y2WIKi+P44tuBVFc7iIurparKKKDtdg+rfnqeQf2bOWmevWvblEKzhqjV3tQa7YIgHFzEjofqJarybQksNkg4tcUEO6hDz50LP/6oivQBA9T2bt3A61Vd6R97DPLztddZrQo3XvVb4IEdNnj8NBiaBf+ZD3tLgy+kczz0C1C544/dcME7Dcd90yHGBjuKYFAGnZKcFAx9h9VrOuOgFjd21jKEWhzcuetfXP23U/lt9svaMV0eNbQsPZ5nXx0fkWAfMrCAKSdu4ZH/TDI9//DD8L//hT2cIAhtjFjaEUu70AFQXOApZOO6IiYe2Y/iYgcjhuaz6Ov/ERfX9Li8RrG1EM5+A/ZoH4KKSOUofmIVI0wvGzdqD289+6mpCC8pdXLaRX9i/m+qy/rQQQXcefPPXHDmakPfJpMwWU3209q4tkDJW4HPx46BpNNbbz2CILR/St5SP1tagrb6rDRh1ixViFZXqxU9pkyBwd2+VDctQuH2wlfr4IVFsGS3eZ9TBsHr55ufm/ahen0jGMAGNjGA7z9+g+OO9HPXzy+H4f9GsVjYpPTDRQzLGM08jmEux7GThvCtG/+8iAf/MZe9eUkkJ9WQ0UUNiXj0v4fx9/uPN8wZE+Nhw5J59OqbBPbuYI1TM9QLghCViGhHRLvQDvGUqjXTXZtY/Fs1b7zbjedeG4+iGC0dn7z6AWed1oxu4yHYtz+eVa/XMOGpx0l0GQX7SczmdyaaXvuv2+dyx82/hJyjutpObGwLb0akTldd0Vsbdz4UPR/4fPwRkHBc661HEIT2j2sblLzR/OPG9IfkP7Wolb3JeKug+MXIcrgUVsKWA7BpPyzbo76+WDuMzIEzhxnd3wvKYdSTqvBvBIfyKws5FIDtS56kZ/e6sqWz1sEVHwa8rpQk1jKU4ZNKSci0wJQhMChD476vKPDbkm48//o4AOLtfwAAKlBJREFU3vxwlOb6wybsZP6sVxsa7Blgq/MkiBkAti51XmdR/PsVhIMEEe2IaBeaF0UBlwuczmYYyFuslmNz54J7D3j2g6eEzZtjWLEmky+/G8gbH4wKOdSxR2zl1ac+b3gQCML8RT1YtKQbp0zexJCB+3ztVVV2XnhjHHvzkphy4kYmjdvFmx+OpLTMySXnrmT56iz+8eBx9F42nze5FCfauPk95HAi37IG82zGbz/3CRedsyrk+loFi70unr0NCtt6K+HAo4HPJ54EcYe03noEQegYNLe1PaYPJJ3dpJrsrYZrK5S+DUrjRHVIvlwLM76EksYlQj2dz/mSBg+qIydtp0/PIi5Z/RjHrn4vyJUB6NUJvrsKUuM0zZddN5W3PxxOKsW4iGEEK+k9sJw/XbGZsjIHx5+/j7SkSnXTIjNRDR+wxqsWeEdftWa8NR3sXdrH710QOhAi2hHRLjQNjwd+/x06dVJrdD/glwj97LNhxAiYNEmNMY+NVeuojhoFzhj14UHx1rLk931kZtSSEFdGSlIx+/KKyUrbqAp2VCH9+gejqKqy8+5Mc7fycDly0nZOOHoLdpuXuDg3o4fn4nZbmTRuN6+8M5rrbj9V03/EkDxWrs0Ka+wUitlKH9Io0rQfII1j+YGVjDRcM3bkXhZ983JYmXNbjZg+kHJp28ytKHDgX2qWfzOSzobY4a27JkEQ2j/ecih5HdyNzP9hcaiiDSvETQLnqPZlgS3/FqoWtuwce0thfYGa4G53CWwvArtV/Yp3QJxDFcK1Hti4D+/WIn5Z2pc7q+9mPkcYhssngwz2mUwUBl9dAeO13mL5BQn8fUwWr7tC/39TnHYsiTGQlQQpsWqMflYS3Hq02sGaBNY41m4ezLRrxrJ/UyXWkjJ20JNaYjjhBIWLLrJw/vkNRgxFaV+3jCBEEyLaaT+ivXLdDt776x8sW2kjJcHNoAtHs35nPDGWWiYeE09OTweDRsTw5vsx5O+zcvFFCmuWVPPGezH0GWDnb39TheWePfDMM/DddzB5spqF9eOP1QQuY8aoZUB69YKsLIiPh6Ii+OYbNUYsNha2b1f7TJoEJSVQWKiOOX48VFVBSoqaDC01VbU4r18Pf/wBv/0GcXGqYD3ySOjRA+x22L0bvv0WEhLAZlPH6dWr7jVXqslnsrPV6yorVdFbXa3OfdJJ6jV5edC5szpebS3s3AllZWq/5GRYtQrmzVPHOeQQdf0JQTaJi4shMVEdDxqs59dcowr0s8+G6dPh2WfhkUca9/tMiHdRURkTtM/IoXmsWBOeYI4WzuMDPuAC3/EGBnAcc9lDN0Pfi85eyatPf05MTACB2lYkHKe6obcVhU+CJ4BXRMql6qaCIAhCpHhKoOTVwJ8v/tjSVNdoe4Ya82zPAmtsy6+xpVBqofglcDdSBLcQPy3oydFTp5meS2c/h7KABCpwUkM2udx86KdkrF8NhVXBB378NLjUWILvqzutnPrS3Y1aa0FSN/5+6hckJrhIiK/lkf825DJ4kNu5nYc1/UtIZhansYvuxFKN2xpD4sQhXP7oMGKzUqnaV87WjW56nTiQakcSzz+vGkKuvhoyMtQxvF71yx5h6uyammbweBR8KArs26c+Owd7fhZaDhHtRL9oLyiAX74s5vgre5BMeBnAXTiIodb38xyO5zS+Mu37MH9nKp9RSTw1OHFjJ4s8MiigjCQc1LKPLhTRiQQqyCIPGx42049XuYJXuNIw5rU8w1Q+o5xE4lD/sVjxYkHBhocCMnDUrS+WaqpRHwQyKCCPLG7jYbZgrEE6kzNJpJxN9KeUZGKp9l3XnV1UEcdahuCgllEsZwMDKSOJ3XTjKW7CjTYb+HHHwaSdH9Aj73eKKp04PFVsoS8Oaomnkl10x42dNAoBSKGEOKpYw1BcxLCFvqxiOF7awI06inmTS7iEt3mZ6dzAf6jCmPH8iftmc/NfFrXB6sKg0/+BPbvt5i9+GWoDJELqdI36EC0IgtAYPEVQ9asqYi2xqvuz4gF7DlhiVLHuKQZHD7X0ZUfCtR1K32i5TPqNZPpNp/Pqu2NC9hs5NI+lc1/EihfW5MO8zbC9EJbuhbW61PnTx8NDpxgHWZUHx73YqHUuYiKTMP+//R+u53qeadS4MzmTs5lpaO/Jdh7lbxSSRm1sEp27xVFAF/ofmkGmew/FWw5gU2qpHnMYBXE9qSzzUFmh8Px7KZSSTAGZgGpkGTgQtmyBL76A7jkeRgxxc+9DMXTJsPDrfIX33nLTPcfD+edDv9T91HbJYe16K507q1UKSkth8WJISlINRUccoRrC6lEUmD8fKirg6KPBUfe4WVGhGo4GDVKNWd9+qxqUbDZ1TZmZsHChamxas0bdoEhOhq5dVeNTRgaceaZqSKquVvuUlsKiRWrZvosvhhNOUF/bkiWq4apnT9X4dNRR6lgul9p30yZ1HZ9+Cm+9BTk5quEpIUEV5L17w4Z1XmZ9bSUmBiZMUI1cViuceqpqtAI49FC48EK1ZODhh6vvCdXV7P/4R2IH9GBF7RAUBawWhYItZSTnJDJnrpXSUnVdvTKrOO4kB2VVdkaMADwefv7VxrHHqtUhXC7IzVXfq5gY9T1yONT3MikJtm5VDXwjRhg9N6qrVWNh37516+pAiGgn+kX7Y4/B3/4G/8cLvMDVjRpjNUMZjnmG7be4mIt5x/RcKB7lVv6OMf72Wp7hGa5v1JheLHRhH4UYy6qsYAQjaFzccxcK2E8XQ/u/mcEMnmzUmPdwN/dyj+m5y3idTfQnl2wKyKCCBKBl/cIGD9hH/r4ECotatixYCsWMYSnzONb0fDIlnMYs3uVP1L9mq9XL3bf8xB03/4zNFsUfO7Y0SLuhbddQ+iHUrDU/l36rxBIKgiA0lorvoXJ+W69CQ1WVnZvuPImX3hoXsM9xR2zlq3ffwekM4pnm9sIfu9TqLRmJMLm/sc/eUjVpXiP4ilMCGoBeZRrTeL1R497A0/wX4//do5kX8DkjFK9wBVfyiskZhf9yPdfxLKAatmx4sKEN0XPh4ADpVBPrMzJlUMBlvMH7XGg653+4niGsJZ5KqojDjZ1UinFSwwHSSaGERMqpJhYnNfRkB+sYTD6ZnMpXKBg3yU7jS+7lbuKoIo8siuiEHTcOanFQix03njrDURxVeLBRi4Nu7KYz+/maU7iEt9A/fz7DtRzLD9jw4MFGHFWkUUgyZZSSxHZ6EYOLGpwkU4qChRJSsONmMt/7NkTqeZuLuIh3fcf5ZGDFSxfUcJwiUomjilhqfH1K6oxvNTjJIs9g4LHhZhmjSaEEGx5KSaaYVLqyhwoSqCaWRMopIYVkWyW5ni6sZQgplHAKX7Nh6t8Z/8ntWDvI3qPUaW8HHHWU+v1F/o9z+JjJzI14jHpLsRn1lvDGUIb5NtYqGh9zu4SxpoIdYA1DGy3akyk1Fe0bGNio8QAyyQ947gX+ovlwKiORPLLwYKOUZNYyhNmcxF5yKCOJfDLJJZvGCPtzT1/Do/+cQ68exXi9FmZ9N4AlK7LZvC2tSTHwiZThoJYKEhjFcv7MS4xmGWNYxn7S6ckOKjEKyK+//JjScicvH/oATqeHzdvSiHW66dEtDJfMtsY5uK1XANYAm4cWK1ikTrsgCEKjiT8GXBuiyk0+Ls7Ni/+exYv/noXHY+Guh4/loacbQrTOmbKG91/6OPSGt90Kh/RUvwKRmQjLbyZ/i42vfx3Mx/PHsfH3OMpJJJtc7LgpohNd2UMSZfRkBznsJZtcfgtQ+QUgKUxPUDN+5GjzpQZ5xgpFIuUBzlhI54DvqN4rVU8MtWSTF8G4ChfzNp0ojmidY1gGQCrFFJFmON+Z/b4+g1kf0dgAszkJs+fKbuwOOF4yZUGftXuywyDa/48XsePmfNRqB5kUaM6bvS8pqBWGnLiwYdyM8mBnHYM5j48A6MregGvCAwNYx1H87Gua9Zmb8/vA5ZfDP/9JuxfvItrbAWPGqG4x48vnUWniZhwOqmhXMPvDrXcxbwyBRPvqABnCw+E7Tgh4bg1DGz1uCuaCcSMDGj1msH8oVt2ubRLlJLHZdzyBxVyOsQTPDTzNa0yj3OS9fYKbuWTgbGKtNewu7UKNy0af9AKSqqzwcBykxWFdvpfTk5ycnpEIpRXcc3x/PlpzBMsyj2C3tQeLlmgT09zLPzmB7/iNiZSSTCrFZJHH2LiV9KzabNh9rqczB9hy0+XcnvcPXn9/NADHHL6NV5/6nF49ijV9B/Q9YDJClOII8rDTWgQU7fGSxUcQBKEpWGyQdB6UvAzemtD9WxmbTeHBO+Zy7RW/4/FYSe9USUKCubBs3ARWyEkmMwemHbGTaeykqsrOQ08fwaffDGLZOlWMbcbESh+Ei3iHdVfdTXrlDjyrtpG9YSH5NZ3wYiWDAk7kW1IpYTs96cUOADxYKaJTwGfGLBPRHC4JVAQ8151djR7Xa2INB3WtkQp2f9I5YCra3U2Qap8ylXe4yPRcHo3PlzSClSxmgqatgkQu4H1+ZwKP8reAz46B6M02VmE0Mr3Ln3yiPVLmcQw7dsCcOXDPPY0aIqoQ0d4OsNvhtNOgvPxY7l4/gWmbayglmRRK8GAjM9VFHFV0du2ltNJGJfHEUUUVaqmPzuz3uc6YcR//5DWmEUcVTmqIo4os8thPZyqJx40dCwrxVPrmzaCAUpL5xSTbKUARnfg7D5NCCbFUY8dNbV08eT82U00s+WQSTyW5ZJNEmS+W/mPOMR3Thput9GEWp1KDkwQqqMVBLNXksJehrGUDA/idCYxkBTnspZA0PNgoJ9E3v541DGUmZzKClVjxUk4iALU46EQRNjzEUk0ZSaRzgAoSiMGFF6tvZ3n6RUvJzU9k1LA87rn1RxwOL0q2G5ONw5D0ZhvlJHHFn5ZitSpUVTmw2bwMH1zAdYu/x/G1ujM6iG3qBfsg2OZrfzbxD76GB85DOWUw38ztz29LunL6SRsYOzIXLvsIvlnPIfymvTAMB4ysT+bw2qJBvPafzyN/odGIxQr2KBDttgCiXdziBUEQmo69i1qJo/T9lisD10S6Zuss17ZUwKLmHVCq6tZtAaWyodqIxaHmKgC1dKnFpm5MWCyADRQ3WOPUMDBrKji6gyWeuNQ07nusC/c97qC01EtevpX+/eGVV+C22+BA3b77/ffDlClqrqWBAxtihlNSwGp1AsPrvgCu55df4Npr1Zhuf5x1xqIanNjwmLqFA6xjME9wMymUMIj1lJNIAhWkUUgGBXSus5i7cGDHjZUGT4TAFnHowc6A50IRSLQPIUBIW5ikc8B0o6Sxor2cBG7gPwTy3myqaNdzJD/xCtPJJD9iwQ4wlDWmov0bTqaYFFIDGN4CUUUsaxhKFrlMm9aGOYqakQ4T0/7ss8/y2GOPkZeXx8iRI/nvf//LhAkTQl9I9Me069myRU2CccghqquHv+HN41ETV1RWqpkze/dWk2Ns364md7DbYdkyNVN8t26Qnq5mVH/rLZg1C7p0gbFj1SzwVquaAKNfPxg+XJ3H61V3rDZsUBNX7NmjrmXAAPi//1Mzu69Zo2alr6qCV1+FtDQ1iUZGhvrB/eyz8OOP6vgjR6of9oceCpdeCjNnqok0Tj1VTSLx9tvgdqvrvOsuNYEFQP/+akKNYcPg1lvVzPJz56qv1eFQX1NKChx2GLz3Hrz2mpqt/rzz1Pdi9251zsGD1ffL44Fdu9SkHDU1aoKLpCT45Rf1/TpwQE1sYbdDebnq/TBlipqZtE8fiPd8DtXLtL+oI56DHUVQ7Y7o96s4bVgW36iWVtFz7xx4dkFE4/l49Tw4zcT1e/YGuPT9xo15VB949kw1dq6x2DPUBEj2HPAUQPXStnuIcvSEVPMsvq1K7Q4ofs3YHtMPUi5u/fUIgiB0RKqXQPms6ElMZ41V/xfaOqv/j6ypYKlLgW7vbH6NtwrVk1JRvbG8ZaC46kQ+4K1UhTo2wKuK+ShAUdTnriVLYOVKtSrPMJ3Bffdu9dm0UyfVeDVvHnz1lfoMO38+DB0KRx2p0L2HhYfudTF8qJcpp3gY2K2CYUd0wpnoYM0a9Rl1xw71GdNuh/RtfzBnYSLr1niIwYWTGuy4fcakVIrJYS8OaikjiWpiGcw6iujEXI5jFz0Mr6cvmzmTT3ER4zOeOailhBRf/LmCBQe1dO8G3ZSdrNuTTAUJ2PDwNhezix5MmQJnnaU+065bBxM7b4G5c4l1l6lu/c5YqtwOqj32ulEdvpj7Hlm19EkvYckaJ/lkkk8mP3EUJaQC6nN4QZ3X+tTeKzgsdgk2by2OeAelJQpx+3dhLz2g8UDtNSyJATnl5G+vYvvGGjzYWGyZSP7QYykrU5/xl9U9/vZkO6fyFanWMgpT+5BbmUK32P3sLk5kwGAbtrw9OIry2U9n8smkijgc1DK6ZxGfFB3L6tKG9/Xww9WxK8u9WH+YQyLldGM3VrwkUMFg1rGZfsRTSRqFxOAinQPU4CSHveSwl084mwdi/8WW/ETagbwLSYcQ7R988AGXXnopL7zwAhMnTuSpp57io48+YsOGDWTU14wIQnsT7Qcz9WXnog7FC+VfGoW7okBFLRSUw7p8NdvrlgOwsxiqaqG8BoqroFTnonfFeHjYJOvrK7/D7d80bo2vnAtThhjbaz0w8knYH9iVTEP3FDhvJFw5EdKbEF9tTYC4CRB3qGodqMdzAMq/AtfWxo/dWBKOh/jDWn9ePZ5CKPyPsT12NCSd0frrEQRB6KhU/Qbljfy/2lgsMXUVSiyq1d85Uv0/aOusWseFNqO2VhX29QYxr1c1Mnm96qZBZaVaeSje7/Fn3z648051k+HGG1XD1fr1qiHIboeXXoIVK9RM78cfbz6v16sakBzmTqFBqV+jP/XqrqBAFeuRRNZ5vaqBqr4Us3+7xWI+1urVsHatWoo5mJSqX5fFohrfsrKanuW9slI1oq1erb7W7Gx1nmXLVEPiJZc0bfxooUOI9okTJzJ+/HieeUYtNeH1eunevTvXX389t912W8jrRbQLzYanSC3VVf0buPNUV7RwcHvhj92wcDskOSEnBU4ZpO2jKPD7LvhhMzjtUOkCa90nZ2WtKvxdbsgvh1+3Q580qHGrfTonwB3HwZEm9b3XFcBzC9TNA6ddnT89Xr2mZydIiVX/G/RLh9Q4NdFNU7EmQcolwUuXVf4EFfOaPle4WGzQ6cbArumtieKG/f8ytscfBQnHtP56BEEQOjKVv0LFnJadw+qEmCEQMwhieqvCXRAEIUyiw0emCbhcLpYsWcLtt9/ua7NarUyePJmFCxe24cqEgxJbJ/Urdjh4y8G1DWq3gHs3uPcHvs5uhUN6qF+BsFhgYg/1qzkZnAH/ndq8YwYjph8kXxDaRS/ucHAXQM2aVlpX/+gQ7KC+N9YE8Oq8H6wdrOioIAhCNBB/GKBA1QLVnby5sGeAow/E9AVH76hxTRcEof3R7j899u/fj8fjITNTW3ogMzOT9evNs3PV1NRQU+NXJ7BETW5QWlracgsVDlJ6ql82gEKo3aPW3/bsU+PODjbs3cBxPJSF+VCkHAeVO8ETZMOjuVC6A1H0GVARA25dqUaLFWqjaI2CIAgdhhFg6wPV88C1Jnicu9UJllSwZ6oWc0+h6q1l6wzWRLB1Ud3erfHgBaqB6mbcDBAEocORlJSEJUgcQ7sX7Y3hoYce4t577zW0d+/e3aS3IAjNy3VtvYAAPNzWCwiD9rBGQRAEQRAEIRJChWm3e9HeuXNnbDYb+fnaetn5+flkZZmXM7j99tuZMWOG79jr9VJYWEh6enrQHQ5BaC1KS0vp3r07u3btkjwLQlQj96rQnpD7VWgvyL0qtCfkfm06SSEy8rV70R4TE8PYsWOZO3cuU6dOBVQRPnfuXK67ztyi53Q6cTqdmrbU1NQWXqkgRE5ycrJ8+AntArlXhfaE3K9Ce0HuVaE9Ifdry9HuRTvAjBkzuOyyyxg3bhwTJkzgqaeeoqKigmnToqDesiAIgiAIgiAIgiA0kg4h2s8//3z27dvHP//5T/Ly8hg1ahSzZ882JKcTBEEQBEEQBEEQhPZEhxDtANddd11Ad3hBaG84nU7uvvtuQxiHIEQbcq8K7Qm5X4X2gtyrQntC7teWx6IowWpaCIIgCIIgCIIgCILQVljbegGCIAiCIAiCIAiCIJgjol0QBEEQBEEQBEEQohQR7YIgCIIgCIIgCIIQpYhoFwRBEARBEARBEIQoRUS7ILQCHo+Hu+66i969exMXF0ffvn25//778c8DmZ+fz+WXX05OTg7x8fGcdNJJbNq0STNOdXU11157Lenp6SQmJnL22WeTn5/f2i9H6OA01/169NFHY7FYNF9/+ctfWvvlCAcBZWVl3HTTTfTs2ZO4uDgOPfRQFi9e7DuvKAr//Oc/yc7OJi4ujsmTJxvu18LCQi666CKSk5NJTU1l+vTplJeXt/ZLETo4zXGv9urVy/DZ+vDDD7f2SxEOAkLdrzNnzuSEE04gPT0di8XC8uXLDWPIs2vzIKJdEFqBRx55hOeff55nnnmGdevW8cgjj/Doo4/y3//+F1D/SU+dOpWtW7fy+eefs2zZMnr27MnkyZOpqKjwjXPzzTfz5Zdf8tFHH/HTTz+xd+9ezjrrrLZ6WUIHpbnuV4CrrrqK3Nxc39ejjz7aFi9J6OBceeWVzJkzh7feeotVq1ZxwgknMHnyZPbs2QPAo48+yn/+8x9eeOEFfvvtNxISEjjxxBOprq72jXHRRRexZs0a5syZw6xZs/j555/585//3FYvSeigNMe9CnDfffdpPluvv/76tng5Qgcn1P1aUVHB4YcfziOPPBJwDHl2bSYUQRBanFNPPVW54oorNG1nnXWWctFFFymKoigbNmxQAGX16tW+8x6PR+nSpYvyv//9T1EURSkuLlYcDofy0Ucf+fqsW7dOAZSFCxe2wqsQDhaa435VFEU56qijlBtvvLFV1iwcvFRWVio2m02ZNWuWpn3MmDHKHXfcoXi9XiUrK0t57LHHfOeKi4sVp9OpvPfee4qiKMratWsVQFm8eLGvzzfffKNYLBZlz549rfNChA5Pc9yriqIoPXv2VJ588snWWrZwkBLqfvVn27ZtCqAsW7ZM0y7Prs2HWNoFoRU49NBDmTt3Lhs3bgRgxYoVzJ8/n5NPPhmAmpoaAGJjY33XWK1WnE4n8+fPB2DJkiXU1tYyefJkX59BgwbRo0cPFi5c2FovRTgIaI77tZ533nmHzp07M2zYMG6//XYqKytb6VUIBwtutxuPx6O5HwHi4uKYP38+27ZtIy8vT/PZmZKSwsSJE32fnQsXLiQ1NZVx48b5+kyePBmr1cpvv/3WOi9E6PA0x71az8MPP0x6ejqjR4/msccew+12t8prEA4eQt2v4SDPrs2Hva0XIAgHA7fddhulpaUMGjQIm82Gx+PhgQce4KKLLgIaPsBuv/12XnzxRRISEnjyySfZvXs3ubm5AOTl5RETE0Nqaqpm7MzMTPLy8lr7JQkdmOa4XwH+9Kc/0bNnT3Jycli5ciV///vf2bBhAzNnzmyrlyZ0QJKSkpg0aRL3338/gwcPJjMzk/fee4+FCxfSr18/3+djZmam5jr/z868vDwyMjI05+12O2lpafL5KjQbzXGvAtxwww2MGTOGtLQ0FixYwO23305ubi5PPPFEq74eoWMT6n4NB3l2bT5EtAtCK/Dhhx/yzjvv8O677zJ06FCWL1/OTTfdRE5ODpdddhkOh4OZM2cyffp00tLSsNlsTJ48mZNPPlmT/EsQWoPmul/944GHDx9OdnY2xx13HFu2bKFv375t8dKEDspbb73FFVdcQdeuXbHZbIwZM4YLL7yQJUuWtPXSBEFDc9yrM2bM8P08YsQIYmJi+L//+z8eeughnE5nSyxbOEiRz9boQdzjBaEVuPXWW7ntttu44IILGD58OJdccgk333wzDz30kK/P2LFjWb58OcXFxeTm5jJ79mwOHDhAnz59AMjKysLlclFcXKwZOz8/n6ysrNZ8OUIHpznuVzMmTpwIwObNm1v8NQgHF3379uWnn36ivLycXbt28fvvv1NbW0ufPn18n4/6bMX+n51ZWVkUFBRozrvdbgoLC+XzVWhWmnqvmjFx4kTcbjfbt29vyaULByHB7tdwkGfX5kNEuyC0ApWVlVit2j83m82G1+s19E1JSaFLly5s2rSJP/74gzPOOANQRZLD4WDu3Lm+vhs2bGDnzp1MmjSpZV+AcFDRHPerGfWlYLKzs5t1vYJQT0JCAtnZ2RQVFfHtt99yxhln0Lt3b7KysjSfnaWlpfz222++z85JkyZRXFyssR798MMPeL1e32aTIDQnjb1XzVi+fDlWq9UQ4iEIzYXZ/RoO8uzafIh7vCC0AlOmTOGBBx6gR48eDB06lGXLlvHEE09wxRVX+Pp89NFHdOnShR49erBq1SpuvPFGpk6dygknnACo4mj69OnMmDGDtLQ0kpOTuf7665k0aRKHHHJIW700oQPSHPfrli1bePfddznllFNIT09n5cqV3HzzzRx55JGMGDGirV6a0EH59ttvURSFgQMHsnnzZm699VYGDRrEtGnTsFgs3HTTTfzrX/+if//+9O7dm7vuuoucnBymTp0KwODBgznppJO46qqreOGFF6itreW6667jggsuICcnp21fnNChaOq9unDhQn777TeOOeYYkpKSWLhwITfffDMXX3wxnTp1atsXJ3Q4gt2vAIWFhezcuZO9e/cCqiAH1cKelZUlz67NSZvmrheEg4TS0lLlxhtvVHr06KHExsYqffr0Ue644w6lpqbG1+fpp59WunXrpjgcDqVHjx7KnXfeqTmvKIpSVVWlXHPNNUqnTp2U+Ph45cwzz1Ryc3Nb++UIHZzmuF937typHHnkkUpaWpridDqVfv36KbfeeqtSUlLSFi9J6OB88MEHSp8+fZSYmBglKytLufbaa5Xi4mLfea/Xq9x1111KZmam4nQ6leOOO07ZsGGDZowDBw4oF154oZKYmKgkJycr06ZNU8rKylr7pQgdnKbeq0uWLFEmTpyopKSkKLGxscrgwYOVBx98UKmurm6LlyN0cELdr6+99poCGL7uvvtuXx95dm0eLIoiWa4EQRAEQRAEQRAEIRqRmHZBEARBEARBEARBiFJEtAuCIAiCIAiCIAhClCKiXRAEQRAEQRAEQRCiFBHtgiAIgiAIgiAIghCliGgXBEEQBEEQBEEQhChFRLsgCIIgCIIgCIIgRCki2gVBEARBEARBEAQhShHRLgiCIAgtwOuvv05qampbL+Og5scff8RisWCxWJg6dWqzj3/55Zf7xv/ss8+afXxBEARBABHtgiAIgtAo/AVbTEwM/fr147777sPtdjfbHNu3b8disbB8+fJmG/NgZMOGDbz++uth9V2yZAkWi4VFixaZnj/uuOM466yzAHj66afJzc1trmUKgiAIgiki2gVBEAShkZx00knk5uayadMm/vrXv3LPPffw2GOPtfWy2hWKojTrRocZGRkZYXs9jB07lpEjR/Lqq68azm3fvp158+Yxffp0AFJSUsjKymrOpQqCIAiCARHtgiAIgtBInE4nWVlZ9OzZk6uvvprJkyfzxRdfaPp8++23DB48mMTERJ/Ir8fr9XLffffRrVs3nE4no0aNYvbs2b7zvXv3BmD06NFYLBaOPvrosK6rt9DPnDmTY445hvj4eEaOHMnChQuDvp7i4mKuvPJKunTpQnJyMsceeywrVqzwnb/nnnsYNWoUb731Fr169SIlJYULLriAsrIyzWt66KGH6N27N3FxcYwcOZKPP/7Yd77eZf2bb75h7NixOJ1O5s+fT1lZGRdddBEJCQlkZ2fz5JNPcvTRR3PTTTcBcN999zFs2DDDmkeNGsVdd90V9HXpCbXG6dOn88EHH1BZWam57vXXXyc7O5uTTjopovkEQRAEoSmIaBcEQRCEZiIuLg6Xy+U7rqys5PHHH+ett97i559/ZufOndxyyy2+808//TT//ve/efzxx1m5ciUnnngip59+Ops2bQLg999/B+D7778nNzeXmTNnhnVdPXfccQe33HILy5cvZ8CAAVx44YVBrdrnnnsuBQUFfPPNNyxZsoQxY8Zw3HHHUVhY6OuzZcsWPvvsM2bNmsWsWbP46aefePjhh33nH3roId58801eeOEF1qxZw80338zFF1/MTz/9pJnrtttu4+GHH2bdunWMGDGCGTNm8Ouvv/LFF18wZ84cfvnlF5YuXerrf8UVV7Bu3ToWL17sa1u2bBkrV65k2rRpoX85foRa40UXXURNTY1GyCuKwhtvvMHll1+OzWaLaD5BEARBaBKKIAiCIAgRc9lllylnnHGGoiiK4vV6lTlz5ihOp1O55ZZbFEVRlNdee00BlM2bN/uuefbZZ5XMzEzfcU5OjvLAAw9oxh0/frxyzTXXKIqiKNu2bVMAZdmyZZo+4V738ssv+86vWbNGAZR169aZvp5ffvlFSU5OVqqrqzXtffv2VV588UVFURTl7rvvVuLj45XS0lLf+VtvvVWZOHGioiiKUl1drcTHxysLFizQjDF9+nTlwgsvVBRFUebNm6cAymeffeY7X1paqjgcDuWjjz7ytRUXFyvx8fHKjTfe6Gs7+eSTlauvvtp3fP311ytHH3206evxn6uoqMjXFs4aFUVRLrjgAuWoo47yHc+dO1cBlE2bNhnmAZRPP/004DoEQRAEoSnY2267QBAEQRDaN7NmzSIxMZHa2lq8Xi9/+tOfuOeee3zn4+Pj6du3r+84OzubgoICAEpLS9m7dy+HHXaYZszDDjtM45KuJ5LrRowYoZkboKCggEGDBhnGXbFiBeXl5aSnp2vaq6qq2LJli++4V69eJCUlmb6mzZs3U1lZyfHHH68Zw+VyMXr0aE3buHHjfD9v3bqV2tpaJkyY4GtLSUlh4MCBmmuuuuoqrrjiCp544gmsVivvvvsuTz75pOG1BCPcNV5xxRWceOKJbNmyhb59+/Lqq69y1FFH0a9fv4jmEwRBEISmIqJdEARBEBrJMcccw/PPP09MTAw5OTnY7dp/qw6HQ3NssVhQFKXV1uc/v8ViAdR4bjPKy8vJzs7mxx9/NJzzT+Jm9prqxywvLwfgq6++omvXrpp+TqdTc5yQkBDei/BjypQpOJ1OPv30U2JiYqitreWcc86JaIxw13jcccfRo0cPXn/9dW699VZmzpzJiy++GPGaBUEQBKGpiGgXBEEQhEaSkJDQaMtrcnIyOTk5/Prrrxx11FG+9l9//dVncY6JiQHA4/FEdF1jGDNmDHl5edjtdnr16tWoMYYMGYLT6WTnzp2atYWiT58+OBwOFi9eTI8ePQAoKSlh48aNHHnkkb5+drudyy67jNdee42YmBguuOAC4uLiWmSNVquVadOm8corr9C1a1diYmIi3iAQBEEQhOZARLsgCIIgtBG33nord999N3379mXUqFG89tprLF++nHfeeQdQS5XFxcUxe/ZsunXrRmxsLCkpKSGvawyTJ09m0qRJTJ06lUcffZQBAwawd+9evvrqK84880yNO3sgkpKSuOWWW7j55pvxer0cfvjhlJSU8Ouvv5KcnMxll10W8LrLLruMW2+9lbS0NDIyMrj77ruxWq0+D4F6rrzySgYPHgyoGxWREskap02bxn333cc//vEPLrzwwog3CARBEAShORDRLgiCIAhtxA033EBJSQl//etf/7+9O2ZNJAjDOP5sE1KIpFAiKHbbBGxMERAiCJYBbdXKaLGktLBQsNAilYLid0gpWKRItZ/ArS20EUFcUAiIpSkCcmmi53mXhfv/yuWdd2bLh9nZ0XK51M3NjYbDoUzTlPS5s9zr9dRsNtVoNHR/fy/btg+OO4VhGHp9fVW9XlexWJTrugqFQkomk7q+vj66T6vVUjAY1PPzs6bTqa6urhSPx1Wr1b4d1+l0ZFmWHh4e5Pf7Va1WNZvNdHl5+aXONE0lEgmtVivd3d2d9K7HrjEajSqdTuvt7U2Pj48nzQUAwJ8ydv/ycB0AAMARNpuNwuGw2u22SqXS/vlut5Npmnp6elKlUvm2h23bSqVSWq/XX87ln5thGBoMBspms39tDgDA/4t72gEAwI9zHEcvLy+aTCYajUYqFAqSpEwms69xXVf9fl+LxeK37maPRCLK5XJnX7NlWfL5fGfvCwDAr9hpBwAAP85xHJXLZY3HY11cXOj29ladTkexWGxfYxiGAoGAut2u8vn8wZ7b7Vbz+VyS5PP5FAqFzrrm5XKp9/d3SZ9X353yR3wAAA4htAMAAAAA4FF8Hg8AAAAAgEcR2gEAAAAA8ChCOwAAAAAAHkVoBwAAAADAowjtAAAAAAB4FKEdAAAAAACPIrQDAAAAAOBRhHYAAAAAADyK0A4AAAAAgEd9AJ0MdSDge+ooAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select the correct train ID for the data to plot\n",
    "# except for the energy axis, which is always the same\n",
    "plot({k: v[example_tid, 0, :] if k != \"energy\" else v\n",
    "      for k, v in pred.items()\n",
    "      if k in [\"expected\", \"total_unc\", \"spec\", \"energy\"]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca3a06a-d613-4206-b128-6d81031de1d1",
   "metadata": {},
   "source": [
    "## Resolution assessment using the autocorrelation\n",
    "\n",
    "We establish the resolution of the virtual spectrometer using the autocorrelation function, which estimates which level of detail can be observed in the test dataset.\n",
    "\n",
    "The autocorrelation function cannot assess which effect are physically relevant and which are simply noise. Therefore this method can only provide a rough estimate of the resolution. It is not expected to be very precise, but it can be used for a quick assessment.\n"
   "execution_count": 566,
   "id": "1491550c-6940-425e-a557-f2cd381d287b",
   "source": [
    "def fwhm(x: np.ndarray, y: np.ndarray) -> float:\n",
    "    \"\"\"Return the full width at half maximum of x.\"\"\"\n",
    "    # half maximum\n",
    "    half_max = np.amax(y)*0.5\n",
    "    # signum(y - half_max) is zero before and after the half maximum,\n",
    "    # and it is 1 in the range above the half maximum\n",
    "    # The difference will be +/- 1 only at the transitions\n",
    "    d = np.diff(np.sign(y - half_max))\n",
    "    left_idx = np.where(d > 0)[0][0]\n",
    "    right_idx = np.where(d < 0)[-1][-1]\n",
    "    return x[right_idx] - x[left_idx]\n",
    "\n",
    "def autocorrelation(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Given the energy axis in x and the intensity in y, calculate the auto-correlation function.\"\"\"\n",
    "    mean_y = np.mean(y, keepdims=True, axis=0)\n",
    "    e = x - np.mean(x)\n",
    "    Rxx = np.mean(np.fft.fftshift(np.fft.ifft(np.absolute(np.fft.fft(y - mean_y))**2), axes=(-1,)), axis=(0,1))\n",
    "    Rxx /= np.amax(Rxx)\n",
    "    return Rxx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "id": "45fc52a4-5716-42b9-adbd-a307d16c0c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.1314420452871621, 1.05)"
      ]
     },
     "execution_count": 714,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 8))\n",
    "R = dict()\n",
    "res = dict()\n",
    "for instr, title in {\"expected\": \"Virtual spectrometer\",\n",
    "                     \"spec\":\"Grating spectometer\",\n",
    "                     #\"pes\": \"PES\",\n",
    "                    }.items():\n",
    "    R[instr] = autocorrelation(pred[\"energy\"], pred[instr])\n",
    "    res[instr] = fwhm(e, R[instr])\n",
    "    plt.plot(e, R[instr], lw=2, label=f\"{title} (FWHM = {res[instr]:.2f} eV)\")\n",
    "\n",
    "plt.legend(frameon=False)\n",
    "plt.xlabel(\"Energy [eV]\")\n",
    "plt.ylabel(\"Autocorrelation\")\n",
    "plt.xlim((-3, 3))\n",
    "plt.ylim((None, 1.05))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd654f9-8175-45da-9ae1-09c184a7e520",
   "metadata": {},
   "source": [
    "## Resolution assessment using deconvolution\n",
    "\n",
    "Here we attempt to establish the resolution of the virtual spectrometer using a deconvolution-based method. The idea here is that the virtual spectrometer can be seen as a *linear* device that somehow *worsens* the resolution of the grating spectrometer. Within the context of linear systems theory any such device can be modelled mathematically as a block that applies a convolution between a function $g$ and the grating spectrometer data.\n",
    "\n",
    "That is, if the grating spectrometer data is $y$ and the virtual spectrometer result is $\\hat{y}$, then we assume that there is a function $g$ such that:\n",
    "\n",
    "$\\hat{y} = y \\ast g + \\epsilon$,\n",
    "\n",
    "where $\\epsilon$ is zero-mean Gaussian noise.\n",
    "\n",
    "Under such an approach, one can calculate the function $g$ exactly, by performing a deconvolution between $\\hat{y}$ and $y$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "id": "7ed071e5-4f60-4195-830a-73ab8e5c2577",
   "metadata": {},
   "outputs": [],
   "source": [
    "def deconv(y: np.ndarray, yhat: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Given the grating spectrometer data and the virtual spectrometer data,\n",
    "    calculate the deconvolution between them.\n",
    "    \"\"\"\n",
    "    # subtract the mean spectra to remove the FEL bandwidth\n",
    "    yhat_s = yhat - np.mean(yhat, keepdims=True, axis=(0, 1))\n",
    "    y_s = y  - np.mean(y, keepdims=True, axis=(0, 1))\n",
    "    # Fourier transforms\n",
    "    Yhat = np.fft.fft(yhat_s)\n",
    "    Y = np.fft.fft(y_s)\n",
    "    # spectral power of the assumed \"true\" signal (the grating spectrometer data)\n",
    "    Syy = np.mean(np.absolute(Y)**2, axis=(0, 1))\n",
    "    Syh = np.mean(Y*np.conj(Yhat), axis=(0, 1))\n",
    "    # approximate transfer function as the ratio of power spectrum densities\n",
    "    H = Syh/Syy\n",
    "    return np.fft.fftshift(np.fft.ifft(H))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "id": "a1c5137f-fe6b-4930-aff5-90026ad5f3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centered energy axis\n",
    "e = pred[\"energy\"] - np.mean(pred[\"energy\"])\n",
    "# impulse response\n",
    "g = deconv(pred[\"spec\"], pred[\"expected\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "id": "75d3ddc3-a4b6-4869-bc1c-f08320089845",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_gaussian(x: np.ndarray, y: np.ndarray):\n",
    "    \"\"\"Fit Gaussian.\"\"\"\n",
    "    def gaussian(x, amp, cen, wid):\n",
    "        return amp * np.exp(-0.5 * (x-cen)**2 / (wid**2))\n",
    "    gmodel = lmfit.Model(gaussian)\n",
    "    result = gmodel.fit(y, x=x, cen=0.0, amp=1.0, wid=1.0)\n",
    "    return result.best_values[\"wid\"]*2.355, result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "id": "f9bbd13c-d972-4af1-a6c1-0fe12509317a",
   "metadata": {},
   "outputs": [],
   "source": [
    "width, result = fit_gaussian(e, np.absolute(g))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "id": "26641ed6-47cd-418d-ab3c-e63c83962387",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b088fefe0d0>"
      ]
     },
     "execution_count": 719,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(e, np.absolute(g), lw=2, label=\"Virtual spectrometer\")\n",
    "plt.plot(e, result.best_fit, lw=2, label=f\"Gaussian fit, FWHM = {width:.2f} eV\")\n",
    "plt.xlim(-3, 3)\n",
    "plt.xlabel(\"Energy [eV]\")\n",
    "plt.ylabel(\"Impulse response [a.u.]\")\n",
    "plt.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00a1bdb9-b52a-4f8b-8c03-30407f486595",
   "metadata": {},
   "source": [
    "Note that this response function does *not* tell us the resolution of the virtual spectrometer. It tells us how we can smear the grating spectrometer data to transform that data into the virtual spectrometer. That is, this is how much worse we do with the virtual spectrometer, relative to the grating spectrometer.\n",
    "\n",
    "As a result, if we approximate the response functions with Gaussians and assume that the previous autocorrelation function gives us an estimate of the grating spectrometer resolution, we can guess the total resolution as:\n",
    "\n",
    "$\\sigma_{total} = \\sqrt{\\sigma_{grating}^2 + \\sigma_{VIRT}^2}$\n",
    "\n",
    "The same relation is applies for the FWHM. This relation assumes independence between the two systems and assumes we can approximate the response functions as Gaussians."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "id": "2f466226-fbad-4f64-86d3-9b5d0685678b",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_resolution = np.sqrt(width**2 + res[\"spec\"]**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "id": "051d329e-3527-4ce1-abfe-6da441ce2e4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9941832003784559"
      ]
     },
     "execution_count": 721,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c85f6fd-c2a5-419f-ae61-8fe77289cf7d",
   "metadata": {},
   "source": [
    "The previously obtained resolution of the virtual spectrometer using the autocorrelation method was:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "id": "e8d3b2f8-09ba-4e9f-9b54-b48867dadc89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9729908312232283"
      ]
     },
     "execution_count": 722,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[\"expected\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efac989d-34e1-4378-abe1-b215bf58c05c",
   "metadata": {},
   "source": [
    "Notice, however, that the response function is not Gaussian and therefore, one could use the full function. to actually simulate the virtual spectrometer.\n",
    "\n",
    "Furthermore, this ignores the uncertainty effect, which could be seen as an extra noise level added on top of the virtual spectrometer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af65114b-2f00-4968-a774-4b34534c3bb4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pes_to_spec",
   "language": "python",
   "name": "pes_to_spec"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }