{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# High-Q silicon resonator\n", "\n", "Author: Romil Audhkhasi, University of Southern California\n", "\n", "In this example, we reproduce the findings of Zhang et al. (2018), which is linked [here](https://www.osapublishing.org/ol/abstract.cfm?uri=ol-43-8-1842).\n", "\n", "The paper investigates the resonances of silicon structures by measuring their transmission spectrum under varying geometric parameters.\n", "\n", "The paper uses a commercial finite element solver, which matches the result from Tidy3D.\n", "\n", "\"diagram\"\n", "\n", "(Citation: Opt. Lett. 43, 1842-1845 (2018). With permission from the Optical Society)\n", "\n", "To do this calculation, we use a broadband pulse and frequency monitor to measure the flux on the opposite side of the structure.\n", "\n", "Please check out another case study where we investigated a high-Q [Germanium Fano metasurface](https://www.flexcompute.com/tidy3d/examples/notebooks/HighQGe/)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:28.968537Z", "iopub.status.busy": "2023-08-18T20:09:28.968378Z", "iopub.status.idle": "2023-08-18T20:09:30.644370Z", "shell.execute_reply": "2023-08-18T20:09:30.643101Z" }, "tags": [] }, "outputs": [], "source": [ "# standard python imports\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# tidy3D import\n", "import tidy3d as td\n", "from tidy3d import web" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set Up Simulation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:30.648405Z", "iopub.status.busy": "2023-08-18T20:09:30.648047Z", "iopub.status.idle": "2023-08-18T20:09:30.672538Z", "shell.execute_reply": "2023-08-18T20:09:30.671950Z" }, "tags": [] }, "outputs": [], "source": [ "nm = 1e-3\n", "\n", "# define the frequencies we want to measure\n", "Nfreq = 1000\n", "wavelengths = nm * np.linspace(1050, 1400, Nfreq)\n", "freqs = td.constants.C_0 / wavelengths\n", "\n", "# define the frequency center and width of our pulse\n", "freq0 = freqs[len(freqs) // 2]\n", "freqw = freqs[0] - freqs[-1]\n", "\n", "# Define material properties\n", "n_SiO2 = 1.46\n", "n_Si = 3.52\n", "SiO2 = td.Medium(permittivity=n_SiO2**2)\n", "Si = td.Medium(permittivity=n_Si**2)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:30.675625Z", "iopub.status.busy": "2023-08-18T20:09:30.675277Z", "iopub.status.idle": "2023-08-18T20:09:30.700858Z", "shell.execute_reply": "2023-08-18T20:09:30.700269Z" }, "tags": [] }, "outputs": [], "source": [ "# space between resonators and source\n", "spc = 1.5\n", "\n", "# geometric parameters\n", "Px = Py = P = 650 * nm # periodicity in x and y\n", "t = 260 * nm # thickness of silcon\n", "g = 80 * nm # gap size\n", "L = 480 * nm # length in x\n", "w_sum = 400 * nm # sum of lengths in y\n", "\n", "# resolution (should be commensurate with periodicity)\n", "dl = P / 32\n", "\n", "\n", "# computes widths in y (w1 and w2) given the difference in lengths in y and the sum of lengths\n", "def calc_ws(delta):\n", " \"\"\"delta is a tunable parameter used to break symmetry.\n", " w_sum = w1 + w2\n", " delta = w1 - w2\n", " w_sum + delta = 2 * w1\n", " \"\"\"\n", " w1 = (w_sum + delta) / 2\n", " w2 = w_sum - w1\n", " return w1, w2" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:30.703208Z", "iopub.status.busy": "2023-08-18T20:09:30.703028Z", "iopub.status.idle": "2023-08-18T20:09:30.730852Z", "shell.execute_reply": "2023-08-18T20:09:30.730185Z" }, "tags": [] }, "outputs": [], "source": [ "# total size in z and [x,y,z]\n", "Lz = spc + t + t + spc\n", "sim_size = [Px, Py, Lz]\n", "\n", "# sio2 substrate\n", "substrate = td.Structure(\n", " geometry=td.Box(\n", " center=[0, 0, -Lz / 2],\n", " size=[td.inf, td.inf, 2 * (spc + t)],\n", " ),\n", " medium=SiO2,\n", " name=\"substrate\",\n", ")\n", "\n", "\n", "# creates a list of structures given a value of 'delta'\n", "def geometry(delta):\n", " w1, w2 = calc_ws(delta)\n", " center_y = (w1 - w2) / 2.0\n", "\n", " cell1 = td.Structure(\n", " geometry=td.Box(\n", " center=[0, center_y + (g + w1) / 2.0, t / 2.0],\n", " size=[L, w1, t],\n", " ),\n", " medium=Si,\n", " name=\"cell1\",\n", " )\n", "\n", " cell2 = td.Structure(\n", " geometry=td.Box(\n", " center=[0, center_y - (g + w2) / 2.0, t / 2.0],\n", " size=[L, w2, t],\n", " ),\n", " medium=Si,\n", " name=\"cell2\",\n", " )\n", "\n", " return [substrate, cell1, cell2]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:30.733493Z", "iopub.status.busy": "2023-08-18T20:09:30.733216Z", "iopub.status.idle": "2023-08-18T20:09:30.755467Z", "shell.execute_reply": "2023-08-18T20:09:30.754917Z" }, "tags": [] }, "outputs": [], "source": [ "# time dependence of source\n", "gaussian = td.GaussianPulse(freq0=freq0, fwidth=freqw)\n", "\n", "# plane wave source\n", "source = td.PlaneWave(\n", " source_time=gaussian,\n", " direction=\"-\",\n", " size=(td.inf, td.inf, 0),\n", " center=(0, 0, Lz / 2 - spc + 2 * dl),\n", " pol_angle=0.0,\n", ")\n", "\n", "# Simulation run time. Note you need to run a long time to calculate high Q resonances.\n", "run_time = 7e-12" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:30.757603Z", "iopub.status.busy": "2023-08-18T20:09:30.757446Z", "iopub.status.idle": "2023-08-18T20:09:30.779132Z", "shell.execute_reply": "2023-08-18T20:09:30.778387Z" }, "tags": [] }, "outputs": [], "source": [ "# monitor fields on other side of structure (substrate side) at range of frequencies\n", "monitor = td.FluxMonitor(\n", " center=[0.0, 0.0, -Lz / 2 + spc - 2 * dl],\n", " size=[td.inf, td.inf, 0],\n", " freqs=freqs,\n", " name=\"flux\",\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Case Studies\n", "\n", "Here we define the three simulations to run\n", "\n", "- With no resonators (normalization)\n", "- With symmetric (delta = 0) resonators\n", "- With asymmetric (delta != 0) resonators\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:30.782265Z", "iopub.status.busy": "2023-08-18T20:09:30.781718Z", "iopub.status.idle": "2023-08-18T20:09:30.823364Z", "shell.execute_reply": "2023-08-18T20:09:30.822827Z" }, "tags": [] }, "outputs": [], "source": [ "grid_spec = td.GridSpec(\n", " grid_x=td.UniformGrid(dl=dl),\n", " grid_y=td.UniformGrid(dl=dl),\n", " grid_z=td.AutoGrid(min_steps_per_wvl=32),\n", ")\n", "\n", "# normalizing run (no Si) to get baseline transmission vs freq\n", "# can be run for shorter time as there are no resonances\n", "sim_empty = td.Simulation(\n", " size=sim_size,\n", " grid_spec=grid_spec,\n", " structures=[substrate],\n", " sources=[source],\n", " monitors=[monitor],\n", " run_time=run_time / 10,\n", " boundary_spec=td.BoundarySpec(\n", " x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n", " ),\n", ")\n", "\n", "# run with delta = 0\n", "sim_d0 = td.Simulation(\n", " size=sim_size,\n", " grid_spec=grid_spec,\n", " structures=geometry(0),\n", " sources=[source],\n", " monitors=[monitor],\n", " run_time=run_time,\n", " boundary_spec=td.BoundarySpec(\n", " x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n", " ),\n", ")\n", "\n", "# run with delta = 20nm\n", "sim_d20 = td.Simulation(\n", " size=sim_size,\n", " grid_spec=grid_spec,\n", " structures=geometry(20 * nm),\n", " sources=[source],\n", " monitors=[monitor],\n", " run_time=run_time,\n", " boundary_spec=td.BoundarySpec(\n", " x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:30.825864Z", "iopub.status.busy": "2023-08-18T20:09:30.825510Z", "iopub.status.idle": "2023-08-18T20:09:31.605183Z", "shell.execute_reply": "2023-08-18T20:09:31.604574Z" }, "tags": [] }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Structure visualization in various planes\n", "\n", "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 8))\n", "sim_d0.plot_eps(x=0, ax=ax1)\n", "sim_d0.plot_eps(y=g, ax=ax2)\n", "sim_d0.plot_eps(z=0, ax=ax3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Simulations" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-08-18T20:09:31.607325Z", "iopub.status.busy": "2023-08-18T20:09:31.607128Z", "iopub.status.idle": "2023-08-18T20:10:54.597976Z", "shell.execute_reply": "2023-08-18T20:10:54.597410Z" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "
[13:09:32] Created task 'normalization' with task_id               webapi.py:188\n",
       "           'fdve-37772029-ff44-4676-882e-ef399c7fb8c7v1'.                       \n",
       "
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   ],
   "source": [
    "batch = web.Batch(\n",
    "    simulations={\n",
    "        \"normalization\": sim_empty,\n",
    "        \"Si-resonator-delta-0\": sim_d0,\n",
    "        \"Si-resonator-delta-20\": sim_d20,\n",
    "    },\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Results and Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:10:55.316591Z",
     "iopub.status.busy": "2023-08-18T20:10:55.316325Z",
     "iopub.status.idle": "2023-08-18T20:10:57.212165Z",
     "shell.execute_reply": "2023-08-18T20:10:57.211531Z"
    }
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           loading SimulationData from                             webapi.py:590\n",
       "           data/fdve-063ea8c7-348e-4be9-b224-6a2cbf7c0db1v1.hdf5                \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from                             \u001b]8;id=334760;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=470655;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mdata/fdve-\u001b[93m063ea8c7-348e-4be9-b224-6a2cbf7c0db1\u001b[0mv1.hdf5   \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81831e3dfc094c86a9da97d1de19df1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "
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      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
           loading SimulationData from                             webapi.py:590\n",
       "           data/fdve-ac579b59-b7c0-46e4-b81b-45b081581998v1.hdf5                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0mloading SimulationData from                             \u001b]8;id=283289;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=845534;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#590\u001b\\\u001b[2m590\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0mdata/fdve-\u001b[93mac579b59-b7c0-46e4-b81b-45b081581998\u001b[0mv1.hdf5   \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
[13:10:57] WARNING: Simulation final field decay value of 0.00167  webapi.py:597\n",
       "           is greater than the simulation shutoff threshold of                  \n",
       "           1e-05. Consider simulation again with large run_time                 \n",
       "           duration for more accurate results.                                  \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[13:10:57]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING: Simulation final field decay value of \u001b[0m\u001b[1;36m0.00167\u001b[0m\u001b[31m \u001b[0m \u001b]8;id=471822;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py\u001b\\\u001b[2mwebapi.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=501107;file:///home/momchil/Drive/flexcompute/tidy3d-docs/tidy3d/tidy3d/web/webapi.py#597\u001b\\\u001b[2m597\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mis greater than the simulation shutoff threshold of    \u001b[0m \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[1;36m1e-05\u001b[0m\u001b[31m. Consider simulation again with large run_time   \u001b[0m \u001b[2m             \u001b[0m\n",
       "\u001b[2;36m           \u001b[0m\u001b[31mduration for more accurate results.                    \u001b[0m \u001b[2m             \u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_data = batch.load(path_dir=\"data\")\n",
    "flux_norm = batch_data[\"normalization\"][\"flux\"].flux\n",
    "trans_g0 = batch_data[\"Si-resonator-delta-0\"][\"flux\"].flux / flux_norm\n",
    "trans_g20 = batch_data[\"Si-resonator-delta-20\"][\"flux\"].flux / flux_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normalizing run computes the transmitted flux for an air -> SiO2 interface, which is just below unity due to some reflection.\n",
    "\n",
    "While not technically necessary for this example, since this transmission can be computed analytically, it is often a good idea to run a normalizing run so you can accurately measure the *change* in output when the structure is added.  For example, for multilayer structures, the normalizing run displays frequency dependence, which would make it prudent to include in the calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-18T20:10:57.235068Z",
     "iopub.status.busy": "2023-08-18T20:10:57.234889Z",
     "iopub.status.idle": "2023-08-18T20:10:57.381152Z",
     "shell.execute_reply": "2023-08-18T20:10:57.380537Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/qypPF/8cgGGvluDtOek6l8qaSTFPdidBihuZIxIJUoQQ15CUBI0aqeHuAXo+mMiKv24m6PRJFaCsXQtly+paRneTIMXHBN9QnoWxt1Ij4CAALw4OZsmn+kUFKSnWH/ulS6tH6d3jRuaT62R1jzScFaJw0jS46y7Yt0+tj+hzjsVbaqoMSoMGKkAxX6T9iAQpPshULZrflx2nEv8C0KOHZomcve1E9jyIRYtaP/+SSXEjJzMp0nBWiMIrLQ3+9z/YuVOtj+hzjqnf1cV0OgHq1/fbAAUkSPFZJR+JYdOkXwDQCKBty2SOHfN+OcxBSpUq1nGAJEhxo3wyKRFcluoeIQq5lBT1G+brr9X6a8NOM/W7uhAfrwKUdeugTBl9C+lBEqT4sOhR3fmz+2wiOcex00WpVg1OnfJuGeLi1GPlyv4ZpGzdqv5046DhrCVukTYpQhRqycnwwguWWl+mDD7DsI8bqAClXj2/D1BAghSf12RRP76vP9Ky/mCHNFK9OB+huYtbxYr+F6SkpECLFurvyhWdCpFPF+QILksXZCEKqcxMdW1avFitP3BXMsM/a1ioAhSQIMX3hYTQ/OepzCk7GYA/YkN4sJP3ooSzZ9VjmTL+F6Qk2fTwzjlgndc4mUkxt0mRhrNCFA6jRsHevWr5uZ5X+PZYfQLiT6rxT9at87tePHmRIMUISpdm4K//Y3zQJAB+/MnEq696563PnVOPZcr432Bu6Ta9uwP0+iZcK5Mi1T1CFDrPPadmNAa4vWUGc3ffienoEahWDX76qdAEKCBBinHUrMm4RVV5jvkAjB0Lw4Z5/m3NmZTSpa2ZFH/pgmwbpOiWobhWJkWqe4QoVN57DxYsUMsPdcpkY1g7AmO3Q7lyKkCpWFHfAnqZBClG8sQTzBtzip58CMCMGWr0Y08yZ1JsgxR/yaSkpVmXdQtSJJMihMg2ezb06aOWb7hBY1lgN1i/Xv2I+eEHqF5d3wLqQPcgZe7cuURHRxMWFkbz5s3Ztm1bvvvPmTOHGjVqUKRIESpXrszgwYO56kuz8XmYafw4XrtzlWW9VStVPekp/twmxbYBsm7ZIendI4QAtmyBIUPU8m23aeyKeYngrz+HkBBYsQIaN9a1fHrRNUhZtmwZQ4YMYdy4cezYsYMGDRrQvn17Tp8+7XD/JUuWMGLECMaNG8c///zD+++/z7Jly3jllVe8XHIdBQRQftmbJFRsSBFUl5T779cs45m4m7l3T9my/hek2Ma2vpRJscQtNr17zA1ns7L85/wLIZQ9e+C++6zrn906h8jFs9RF95NPoG1b/QqnM12DlFmzZtGnTx969+5N7dq1mT9/PuHh4SxatMjh/ps3b6ZVq1Z069aN6Oho2rVrx+OPP37N7IvfqVCBct8v5q8izQBITTVx443Wqhl3uXwZLl5Uy5UqSZDiEeYTXLy4ZVN+1T0gPXyE8Cf796sx2ZKS1HhUBycsocqc7JTK22/Do4/qW0Cd6RakpKWlsX37dmJiYqyFCQggJiaGLVu2OHzNbbfdxvbt2y1ByZEjR/j+++/p0KGDV8rsUxo2pNrCV9hEK4pymYwM1afenQHEyZPqMSJC3UP9rXeP7tU9qanWhjE2QUp+1T0gVT5C+IuMDDVYm9n64aupPr6HWhk71v7JQkq3IOXs2bNkZmZSvnx5u+3ly5cnPj7e4Wu6devGxIkTuf322wkODuamm26iTZs2+Vb3pKamcvHiRbs/v9G9O62eqsl0hgNw6JBqdOWum9h//6nHqCj16G+9e3TPpNh+Fh3M3ZNXJkWCFCH8w0MPwc8/q+W3hh3npmEPq1+Bzz0H48frWjZfoXvDWVds2LCBKVOm8M4777Bjxw6++uorVq1axav5DBoydepUSpQoYfmrXLmyF0vsBXPn0u/WP5nFYADefx/atHFPIGGebfOGG9SjVPe42aVL6jEiwtrohPy7IIMEKUL4g/vvh5Ur1fIHsy7Q/9Pb1KiS990Hc+daL7iFnG5BSpkyZQgMDCTB3DIzW0JCAhUqVHD4mjFjxvDEE0/wzDPPUK9ePR566CGmTJnC1KlTycrjrjxy5EiSkpIsfyc81cJUL2Fh8PnnDC7xAYOYDcBvv8GkSdcfTHz5pXps3149+luQYlvdo2smxaaqB3I0nM0x4ixImxQhjO699+D779XyE49n8OSSdqp+vXZtWLrU/gtfyOkWpISEhNCkSRPW2fSfzcrKYt26dbRs2dLha65cuUJAjqFBA7P/M7U87pyhoaEUL17c7s/vVK0KCxcyhVdoxA4Axo1Tfe6vxz//qEdzw3J/C1JsMym6VGE5CFIyMqzBk22bFNuPvWRShDCu776zjoXSrJnGwrRe8OefajCq777L9aOlsNO1umfIkCEsXLiQDz/8kH/++Yfnn3+e5ORkevfuDUDPnj0ZOdI6uV7Hjh2ZN28eS5cu5ejRo6xZs4YxY8bQsWNHS7BSaD36KEWG9mMbt3KD6V8Ahg6Fr74q2OHS09U8VqBanIN/Bym6ZlKKFbNsMrdHAfsuyCaTjJUihNEdOACdOqnl8uVhXcw0QpcvgeBgdbG+8UZ9C+iDgq69i+d06dKFM2fOMHbsWOLj42nYsCGrV6+2NKaNi4uzy5yMHj0ak8nE6NGj+e+//yhbtiwdO3Zk8uTJev0TfMu0aQRt3syhLTdRJ+wwh6/ewP/+pzIqgwa5dqhTp1QwEhJinWjT33r3+EyQ4qD7cZApkxAtzS4iCQpSqxKkCGE8//6r5gY0+3HoTxR7ObvTx7x5cOed+hTMx+kapAD079+f/v37O3xuw4YNdutBQUGMGzeOcePGeaFkBhQUBJ98QmiDBuy9XJ3mZQ6z+2wlhg6F226DW291/lDmWrioKGtw4m+9e3yxTYql0WxwKqY07CISc7JQghQhjCUrC9q1s84X9s8Xe6nZs7NaGTwYnn5at7L5OkP17hFOuPFG+PBDwkjlq7MqMs/KgubNVbDurPlqHkNsmwf5W3WP7dw9vtImxdL9OCS7cDazIJqre6ThrBDGMnWqtY3fuMEXqTmwverJ06EDvP66voXzcRKk+KOHH4bBg7mJI8SVacyNVdVd7YUXVMPxawUZly/D9u1qecIE63Z/C1Js+V4mJTtIyVHdk2OTEMLHzZ0Lo0er5TZ3ZjLu17bWnjyffSY9ea5BghR/NWUK1K5N5bM7+bHik5bNjz8OvXvnH2gsWqRu2lWrws03W7f7W5Bi++/wtSAlIiQ7gyJBihCGdfo0mFszNGkCP9/cF9N26cnjCglS/FVYGCxbBkWLUv33T0gaMZWSJdVTH36oJtT8++/cL/vvPxg4UC3nnG3A34IUW75W3VNUghQhDC0lBZo1s65/0/1zTO+/py6kn30mPXmcJEGKP6tbFxYsAKD4jLEk/LCDatXUU7GxUKeOGlTo6FG17dw56+iyAH372h/O33r3+HQmJTQ7SLFpkyINZ4UwhrQ0NXRDXJxa/2bOUSq90kutTJgA99yjX+EMRoIUf/f44/DII5CRQUjPrhz8I5FJk6xP9+mjAnqTydrVGOD559XMnLb8rXePLV8bJyUiLO9MijScFcK3vfOOdVb6d2am0OntdmrMg/vug1Gj9C2cwUiQ4u9MJtWtp0oVOHiQwGd6M+oVjdWroVSpvF82eLDjQ4FkUtwmv4azodkFkuoeIQzljz+s18/y5TWe/bWHmv21ShX4+GP74aPFNcnZKgzKlFGjGQYHw4oV8MEHtG+vGnWNHGkfrEybpjIltg1mzfwtSLHla21SIsKyIxEHXZAlSBHCNx07Zh2Pqk4d2NFnPoHfZF97v/xSNZgVLpEgpbBo0gTMs0UPHAiHDxMUpDoBnT+vbnyZmTB8eN6Tb/pbkOLTmZQwyaQIYTS2I3t/P24rUVNfVCtz5ti3ohVOkyClMHnpJTX08uXL0L17rkaZ18pC+luQYkuXIOXSJfXoqOFseHaBpOGsEIbwzjvwzTdqeeSAZKoMfEhdWLp1U438RIFIkFKYBAaqOtGSJWHrVvuR2pzgb0GKrpmUzExrROKoC3JYVq6CScNZIXzT6tXQr59afvopjckHHlUToNWuDe++m3d6WlyTBCmFTZUqlm7JTJ0KW7Y4/VJ//p55vU2KOUABx5mUIlLdI4QRpKdDx45quUYNWFj/LUyrf4DQUDXEd0SEvgU0OAlSCqNHH4UnnlB35h49rG0jnOQvmRRbXs9OmM95SIi6mGWzNJwNzzuTYlMDJITQkaap8aTMPxzeffkwppeHqZWZM6FePf0K5yckSCms3npLjXt/5IjT9aX+lknRtbrHQaNZsGk4G55dOAezIEt1jxC+4fXX1TQiAHNeS6P1zE5qJLcHHlCTpYnrJkFKYVWihBqaOSAAliyB5cudfqk/ZlK8Xt3jYCA3cJBJkcHchPBJe/ao3pCgfucNPDpIzTVSoYKKXPztV51OJEgpzFq2hBEj1HLfvhAfn+/u/vadM2omRdqkCKGvjAzVWdJsaP01atBMgI8+grJl9SmYH5IgpbAbOxYaNICzZ1X7FCdSCv6YSfF6kJKUpB7zCFIsbe1sCibVPUL4hmnT4Kef1PIPH5/lplFd1cpLL8m8PG4mQUphZ26BHh4O69bB3Ll57iqZFDe6cEE95pibwNIF2UEmRXr3CKG/FStgzBi1/OYbWdz7QRc1ImbjxjB5sq5l80cSpAioWRNmzFDLI0bA4cP57u6PmRSvt0lJTFSPNkFKZqaa3h1sMinScFYIn5GRYT8/4DOX5sDPP6sfeUuWqN56wq0kSBHKc8/B3XfDlStq5uS0tFy7SCbFjcyZlJIlLZuuXLE+HVEs+2TLYG5C+Iz+/VXbWIA/Fv9FkfHZLWffeksNkiLcToIUoQQEwAcfqF/2f/xhzWc64I+ZFK/f+B1kUsztUQICIDQsO0iRhrNC+IS5c9XgsQAfLkil6aTO6sv46KPQu7euZfNnEqQIqypVrJ3+Z8yAzZvtnpZMihs5aJNi6X4cAabg3A1QJJMihD7S0uDF7LkC69eHnrtfgkOHoHJlGfbewyRIEfY6d4aePVUjjV69rHdOG/6YSfF6mxQH1T2W7sdFcRiRSCZFCO/TNOjTx3rdWzL4D3j7bbXy/vu5Gr8L95IgReT2xhtQqZL6pWAeRwX/+7GgayYln+qeiAgcduWRTIoQ3vfhh2roE4C3Z6ZS59Xs7sbPPivdjb1AghSRW8mS1mqft9+GtWvtnvbHTIovVfcULYrDtIlkUoTwrsOHrc1NeveGfkdfUlOJVKmixsQXHidBinCsXTvrnD5PPQVJSX6dSfGl6h67TIoM5iaEbmz7DwxqZVPN8957uQZiFJ4hQYrI22uvwU03wYkTMHCgZbNkUq6Tpjms7rFtOCvVPULoa8UKNb0ZqGqe+pO7qBWp5vEqCVJE3iIiVIVsQAB8+CGmY0f1LpHHePXGf/my9Q0dtEmR6h4h9HX4MDz0kFru3h36HBoOR49KNY8OJEgR+WvVyjrV56aNgP9kUmz/HV698ZureoKDoUgRy2ZpOCuEb5gwwbo8tdMWQua9oVakmsfrJEgR1zZuHNSti+lq9pjt/hKl2PBqkGJb1WPT0Meu4ax0QRZCFz//DB9/rJYH9Uun8ojuakWqeXQhQYq4ttBQWLzYckPVYnfpWx43sY210tO9+MZ5TC5ol0lxEJFIJkUIz7p6Vc0KAhAVBdOyXpZqHp1JkCKc06QJpiZN1PKqlfDvv/qWx828GqScO6ceS5e22+wwkyJtUoTwmttvh9OnISwM/lm0hdB5c9QTUs2jGwlShPMaN1KPqamqe7LBq310a5Ny9qx6LFPGbrPDNimaZukfLZkUITxnxw7Yvl0t9+yeSfHBT6uVp5+Wah4dSZAinGdSHxctIAhWroRPPtG5QO7j1UxKHkHKxYvqsVgxrGkTsEQlMk6KEJ6haWpGEIDoaJh/wyT45x8oX16qeXQmQYpwmqWNZ5s26rF/f4iL06s41023Nil5BCnnz6vH0qWxpk3AkuaR6h4hPKNvXzUcFMCbw05gmjpFrbzxhszNozMJUoTLtNtvhxYt1E//3r11GK7V/XwhSDE3VYmMxD5IyU6dSHWPEO63ezcsWKCWBw7Q6Li0u5r2+P774bHH9C2ckCBFOM+SSQkIVDNuFSmi+uu9846u5Soo3TMpORrOmjMpkZHYV/dIJkUIj8jIgAYNrOujqnwMGzeq1utz5/rfrKoGJEGKcJmmATffrIbNB3j5ZTh4UNcyXS+9G85qWo4gxUF1j2RShHCvb76xLr89OYmyrw5QK5MmQdWq+hRK2JEgRTgt14+KF16Au++GlBTo1ctwd0/dMyk2Qcrly9ZAqXRp1FQE5hOeo+GsZFKEuH6HD8Mjj6jlmjWh785nISkJmjaFF1/Ut3DCQoIU4TLLzT0gAD74QI0fsGULzJ6ta7muhy7jpNgEKeYsSliYzUj5OaISyaQI4T4jRliX145cR+CXn6vv3MKF9tWtQlcSpAinOayerVIFZs1Sy6NHw759Xi3T9dAlk5KWZu1r7CBIiYy02TfHgG6SSRHCPb79Fr78Ui2PHJZBpQnPqpXBg6FhQ93KJXKTIEW4LNcYbk89Be3aqUHeevTwclrCPbx24zdnUQICoGRJy2ZXghTJpAhxfd5/37o8NGA2HDkClSrB2LH6FUo4JEGKcFqeDd1NJvWtL1VKDdk4ZYpXy+UOXourzpxRj5GRKlDJlm+QIl2QhXCbKVNUJgXg+/dPUfqN7MBk1qzskRSFL5EgRbjM4Wj4N9yguuwBTJ4Mf/3l1TIVhC7VPQkJ6rF8ebvNDoOUHPU7Ut0jxPWJi4NRo9Ryt25w39fPqlkF27aFRx/Vt3DCIQlShNOuOWRA167QsaO64z/5pKGqfbwepFSoYLfZmeoeyaQIcX3uvtu6/OGjK9X0HsHB8PbbMiaKj5IgRbgsz3kFTSaYN0+1tfjzT5+v9tElkxIfrx6dyaTkiEokkyJEwZ09q7odA7S5M5OgwdndjIcMUX2QhU+SIEU4zakfGpUqWUegffVVFawYgNdu/K5kUqQLshBukZam2vaDmkDw59YT4dgxVU09erSeRRPXIEGKcFmemRSzxx9Xc15kZqpB3q5e9Uq5XGWYTIq0SRHiujz9NOzcqdr2r3jrBKbXpqsnZs+GiAh9CyfyJUGKcJpLVbZz56ob8d9/w5gxHiuTu/h0mxTp3SNEgR0/Dp98opbbt4cGC/qp4RLuuQf+9z99CyeuSYIU4bJrZlJADVS2cKFanjlTTdrlY3w+k5JH7x4JUoRwnu1A2H0bb4PvvlMR/xtvSGNZA5AgRTjN5e9zx45qoDdNg9694coVj5TLHXyyTYpU9whxXXbvVrEIwMtDM2m9qJda6d8fatXSr2DCaRKkCJc5lUkxmzVLNU47fBheecVjZSoIr2dSMjKsg7lJdY8QHpWYCA0aqOUGDWB4qQVq2o6yZWHcOF3LJpwnQYpwWoEyoyVKwIIFavmNN2DNGreWyV28EqScPasio4AAu3l7UlKsbYtlMDch3MN26PtJw5KIfH2kWpkyxW5KCuHbJEgRLnMpkwJw333Qt69a7t1bTYfuA2z/HVeveiFD8d9/6rF8ebtZVs3T+QQF5ehoIIO5CVEgly7BSy9Z12PWjVTXncaN1TVIGEZQQV6UmJjItm3bOH36NFlZWXbP9ezZ0y0FE77nutqYzZwJa9fCoUMwbJg1u+JDLlywS3C437//qsdKlew221b12J1jaZMiRIGY26EA/Dz/AGHPz1crb75p9wNB+D6Xg5TvvvuO7t27c/nyZYoXL47J5qpqMpkkSCkEXM6kAISHw3vvQZs2qtfP//6n+gPqKOe/49w5Dwcp5kzKDTfYbXbYHgVydeeRTIoQ1xYbax31oGJFjdYfPa2+7N26QatWupZNuM7l6p6hQ4fy1FNPcfnyZRITE7lw4YLl77z5aiv80nX31mvdGvr1U8tPPqnaaPgQc7WLx5iDlHwyKXbyyKRIkCJE3p5+2rp8cPIXBGzepH4kTZ+uX6FEgbkcpPz3338MGDCA8PBwT5RHGECBMilmr72m5smIj1ftVK7rYO7ltSAlj0xK6dI59s+jTYpU9wjh2BdfwI4dannY4HSKjh2qVl55Jdf3ThiDy0FK+/bt+dMg87EI93LLuEfh4fDpp+qOu3w5LF3qhoMWjKPqHo9yok2KnRypE8mkCJG/xx+3Lk8tPVN956pWhaFD9SuUuC4uByn3338/w4YNY/z48Sxfvpxvv/3W7s9Vc+fOJTo6mrCwMJo3b862bdvy3T8xMZF+/fpRsWJFQkNDueWWW/j+++9dfl+ho8aNrZXG/frByZP6lifbhQsefoM8qnvMwZGz1T2SSREit19/tQbwLz5zhcDp2bOwT54MYWH6FUxcF5cbzvbp0weAiRMn5nrOZDKR6cLPvGXLljFkyBDmz59P8+bNmTNnDu3bt2f//v2UK1cu1/5paWncc889lCtXji+//JJKlSpx/PhxSkqfd69ySw3NyJHw7bewfTs88wysWuX1Iapz/jtSUz38hnlU95w6pR5zjO8mXZCFcNKlS9Chg1q+/XZ4s8gItbFxY/v0ijAclzMpWVlZef65EqAAzJo1iz59+tC7d29q167N/PnzCQ8PZ9GiRQ73X7RoEefPn2fFihW0atWK6OhoWrduTQPzsILCo9waQwQHw0cfQWgo/PCDT3RJ9miQkpSkLpqQK5NiTiRVrJjjNTmiEsmkCOFYrVqQnKyWl0w+CvPmqZXXX1eDJwrD0u1/Ly0tje3btxMTE2MtTEAAMTExbNmyxeFrvv32W1q2bEm/fv0oX748devWZcqUKfkGR6mpqVy8eNHuT1wft7V1rV0bpk5Vy4MHwz//uOnAzvFqJuXoUfVYtiwULWr3lDmTkitIyRGVmGOWrCyfam8shK5OnrQmKW+6CSq/9bL6znToAHffrW/hxHUrUJDyyy+/0LFjR6pXr0716tXp1KkTG12c5fbs2bNkZmZSPsdssOXLlyfePFNsDkeOHOHLL78kMzOT77//njFjxjBz5kwmTZqU5/tMnTqVEiVKWP4qV67sUjmFlUdqYwYOVFOmp6RA9+6QluaBN3GOR9/aHKRUq5brqTyDlDzapIBU+QhhZm7eBjDjmX3w5ZcqeyJdjv2Cy0HKJ598QkxMDOHh4QwYMIABAwZQpEgR2rZty5IlSzxRRousrCzKlSvHggULaNKkCV26dGHUqFHMnz8/z9eMHDmSpKQky9+JEyc8WsbCwK2/4gMC4MMPVf/bnTvBQVsnTzH/O0JC1KNXMik5gpSUFGuD3WtV9wTZtCCTIEUI+OsvMLcOeOZpjc7fZQ+S0rs31K2rX8GE27jccHby5Mm89tprDB482LJtwIABzJo1i1dffZVu3bo5dZwyZcoQGBhIgnnq+mwJCQlUyNWCUKlYsSLBwcEE2vykrFWrFvHx8aSlpRFivtvYCA0NJTQ01Kkyifx5rF1rxYowfz48+qiq/rn/fmjZ0kNvlltoqMqi6BGkmNPURYpAqVI5XpPHBIMgQYowtvR0VW15PZfms2etcUiFCjCp1Q/w/mb1ZfLijx3hWS5nUo4cOULHjh1zbe/UqRNHzRdiJ4SEhNCkSRPWrVtn2ZaVlcW6detomccNqlWrVhw6dMhuvqADBw5QsWJFhwGK8Iz8MikJCfD559Czp6q9Md+Er+mRR6BHD3XleuIJuHzZLWXNj/nfYb5QeiVIiY6222zb4SdXEJhH7x6bTUIYjqZB9eoQFXV9s49/9JF1eeyoTMpPHaRWhg5VBxd+weUgpXLlynaBhdnatWtdbu8xZMgQFi5cyIcffsg///zD888/T3JyMr2zZ6ns2bMnI0eOtOz//PPPc/78eQYOHMiBAwdYtWoVU6ZMoZ95qHXhUdfKpPzwg/pF06ULfPwxLFmibr7PPKMGmL2mt96CypXh8GH7KUw9zByk6NEmxTy+m8PBMPPo3WOzSQjDuXoV4uLUIIbHjxfsGJcu2Y/P9uDFj+HgQdUw/eWX3VNQ4RNcru4ZOnQoAwYMIDY2lttuuw2A3377jcWLF/OG7dSTTujSpQtnzpxh7NixxMfH07BhQ1avXm1pTBsXF0eATfexypUr8+OPPzJ48GDq169PpUqVGDhwIMOHD3f1nyGug6NMSkoKZA+hk8v776tBZjdsgObN8zlwyZKweDG0bQvvvgsPPKD+PMT87zCP8+SxTEpW1jWre3L0Slbyqe6RTIowKtvPbkEnJLYdqPrtGSlETc8OTMaPh2LFClw24XtcDlKef/55KlSowMyZM/n8888B1S5k2bJlPPjggy4XoH///vTv39/hcxs2bMi1rWXLlvz+++8uv4+4fvllUgYPtt5w//wTmjRR9+bPP1djKV29qnoDfvMN2PQ6z+3uu9XBZs9Wjd9iY/O4g7uPx6t7/v1XRXHBwbmqe5zKpGRf1QMC1P+BpkkmRRiXbcayIEHKwYPw7LPW9Yfi3oQzZ+CWW/L+pSQMq0BdkB966CE2bdrEuXPnOHfuHJs2bSpQgCKMKWcmJSFBZUtAVfk0aaKWAwKga1eV2q1RA65cgfbtVVYlX1OnQqNGqmVcjx4euyN7rU3Kvn3qsXp1FajYyDeT4mBGQRnQTRidbZBSkJ6CNi0A2LsugaiFE9TKtGm5vl/C+GQoPuG0vDIpP/2kbpqNGsG99+Z+vnJlNTPp//5nbRe7fHk+bxQaCsuWQUSEqiPKZxwcd/B4mxRzkFKzZq6n8s2kOJhRUIbGF0Zn+z1z9XP85ZfWa0fRolDn01dUlrJVK+jc2W1lFL7DqSAlMjKSs2fPAlCqVCkiIyPz/BP+L+evn4UL1eN99+X9mvBw1Rr/zjvV67t2hd9+y+dNbr5ZdUsG1Z3QQdXf9fJ6JsVBkFLQTIoEKcKoridI6dLFurz3y33wwQdqZcYMr8/9JbzDqTYps2fPplh2Y6TZs2djkg9DoeTov/3UKdi4UT33wgv5vz48HFauhIcfhrVrVWZl7dp8xlzq3h3WrVMXom7dYNcu1XrfzcxBSnYc7n55BCkZGdbRZp1pk5LHJiEMpaBBSlqaysSaVXljqPql8cgj0KKF+woofIpTQUqvXr0sy08++aSnyiIMwjaT8vff6rF6defatxYrBl9/DbfdBnv2qM47W7dCjtkRrN56C37/Xc3r06uXinLcPGGYOUg5cgS2bYNbb3Xr4fMMUhIS1EU3MBAcTPrtMG0imRRhdAUNUmybPa6ftZOAId+rqN08/5fwSy5f7Xfs2MGePXss69988w2dO3fmlVdeIU3HeVeE5znKpJjnBKxVy/njRETAqlUqYDl+XHXiyTMzULSo6iIUFqZa5c6c6XK585KzCzLA3LluO7ySlGRNl+QIUsztUaKi8ujlIJkU4YdsbxPOfo7vugtWr1bLM17XaPNF9thYzz2nfiEJv+VykPLcc89x4MABQI0+26VLF8LDw/niiy94WQbRKRRsMyl//qkeXZ0mo3JllVExmVTsMX58PjvXrQvmMXheeUVlVtzo6lXr8o03uvXQsH+/eoyKguLF7Z6yHW3WIWmTIvyQq5mUzEz7JmlDa30PW7ao4e9HjXJ7+YRvcTlIOXDgAA0bNgTgiy++oHXr1ixZsoTFixezPN8uG8LoHGVSfv1VPd55p+vHa9vW2nV56lSVMMlTnz6q1VxGhmp1a56V7zqYg63sjzNgn1VxC3N9WD49e/KsJsunukcyKcKoXA1SpkyxLkdFaTB6tFrp39/BrJzC37gcpGiaZpk7Z+3atXTo0AFQo8Ge9VjLQ+FLzDf3y5etA6kWtB1H797WKXu6d1cDNTlkMsGCBSrVcfy4GmvfTdMxR0aqdrmgxnJxq5071WP9+rmeKkgmRbogC6NzJUjJyICxY63rPw39SQ3wWKwYyEjjhYLLQUrTpk2ZNGkSH3/8Mb/88gv3338/AEePHrUMZy/8U85MinnejZIlHczg64IPPlA9jjMyrGOpOFS8uBo/JTgYvvoK3nmn4G+KfYxjnng7JeW6DpmbOUhp1CjXU9fMpEh1j/BDzrZJuXrVvkF9i+YadRYOUitDhkDp0h4pn/AtLgcpc+bMYceOHfTv359Ro0ZRPbvR0pdffmmZy0f4N/PN/dgx9ZhjOhqXBQWp4fIDA1WPn3ybNjVtCq+9ppaHDFG/qq6TyaS6R4Obg5SsLGv58glS8syk5DOYm1T3CKNyNpMyfLiahNDss0eWq55ykZHquy8KBZfn7qlfv75d7x6z119/ncCCzhYlDCFnJsUcpFStev3HrlVLZVR69lTT9tx7bz5z/AwcCOvXw7ffwmOPwfbtBZpUzDaTUqSIenRrkHLkiJquNTTUYZsUcyZKGs6KwsSZIOXQIXjzTev6ogUZRE8ZplaGD8/VCF34L5czKSdOnOBf809AYNu2bQwaNIiPPvqIYJk3oVAw39xPnlSPed5kXfTEE6pNbFaWGj8lISGPHU0mWLRIvfHBg/D889fVPsVksgYpbm2TYu76VL9+rjlFkpKsQUrt2nm8XrogCz9kG6SMGeN4n5wJ0l5pC9WvogoVVINZUWi4HKR069aN9evXAxAfH88999zDtm3bGDVqFBMnTnR7AYXvyJlJiY9Xj+b2HO7w7rtQpYoaor59+3zap5QuDZ99plILn36qghYXeTyTYh73v2XLXE+Zk5E33KCy1w5JJkX4IdsgZfPm3AG3pqlEqdndbTIJmJI9f9eoUda6WVEouByk7N27l1uzu3J8/vnn1K1bl82bN/Ppp5+yePFid5dP+DBPBCnFi1sHVNu1Cz7+OJ+db79dzesD0K+fGi62AGzbpKxYAadPF+gwuW3apB5btcr11KFD6jHfQfCkTYrwQznH/LSdM2vtWjWgtG2b+JG3fKXStlWqqKEIRKHicpCSnp5OaPY44mvXrqVTp04A1KxZk1PmkTWFXzNnIDwRpICq6nnlFbXct691PDSHRoyATp3Ule7hh62FcoKjTApY5zW8Lpcuwe7datlBkBIXpx7zbc8jmRThh3IGKbaDKfboYf/ca69eJear7EnBxo61zmEhCg2Xg5Q6deowf/58Nm7cyJo1a7j33nsBOHnyJKWlS5hf80Z1j9nEiarh7NWrqm1snm1FAgJUuqVWLTXwyCOP5L4KXoPJZB+whIQUvNwWmzapuqroaId9jM1BSpUq+RwjnyBFMinCqPIKUk6csG+HNnMmDGOGmvnz5pvV3F2i0HE5SJk+fTrvvvsubdq04fHHH6dBgwYAfPvtt5ZqIOHfNE3df80XFE8EKYGB8Prrann37mu0lSteXNXTlCih2oH07+9UQ1rbXZKTrctu+bG2Zo16bNvW4dPmRrP5Bin5VPdIJkUYVc7P7mefqRgk53ehbbOLMGOGWpkwwfrhF4WKy0FKmzZtOHv2LGfPnmWRTWPFZ599lvluyZMLX2WbSTl3Tl1sTKY8ZvB1g4YNYdw4tfzBB9eYsueWW9TVzmSChQtdminQZFIJGLOkpAIV1545SGnXLtdTGRnwxx9qOd82KVLdI/xQzt8Pw4ZZ22jZqrZitvoy1qunpsQQhVKB5rwPDAykVI4hRqOjoynnqbuV8CmaZq3qKVMmV+9at3rpJTVrMsDIkdeo5rjvPutAbwMHwsqVTr9PsWLWQeQuXixYWS1OnoS9e1X04yCTsmOHuvZGRkKTJvkcR7ogCz/kzGgBr425RPH52d/lV19V1bqiUHIqf9a4cWPWrVtHqVKlaNSoESZHM81l27Fjh9sKJ3yL7X+7J9uj2IqIUM07WrRQM6HOmnWNEWmHDoV//lFdkrt0gXXr1IsdyHmxLFFCPV53JmXtWvXYtKnDobvN8xPVr2/NjDiUzwSDkkkRRuVMkPJw3BzVEO3WW1XDeFFoORWkPPjgg5YePZ07d/ZkeYQB2GZSvDFdU4MGqvbm6afVYJMtWuQz67LJpLrn/Pcf/PgjdOgAv/yiUsb5vASsg1ju3XudBV61Sj3ec4/Dp80j9UZHX+M4kkkRfuhaQcpHs89x0/DscVEmTXI8/booNJwKUsaZGwbkWBaFi+21wlwlYs4+eNoTT8CAAaqBa+vWqsdxnr1wgoPhyy9VkPD776pdyMaNkD3PlFnOi6U5SNm2Tc3uXKA5iS5dgu++U8sPP+xwl+sJUiSTIowuzwEaUTNJVJs6UnUBat06n7kxRGFxXRV9ly9f5uLFi3Z/wv9pmrU3jLm9iKcFB1trUcDa6D9PERHw/fcqgxIfrwKW//5zuKs5+LrlFus284j2LvvmGzVs7S23QOPGuZ7WNOtAtDfeeI1j5VPdI5kUYVT5ZVKqZR6yjh49ebJkUYTrQcrRo0e5//77KVq0KCVKlKBUqVKUKlWKkiVL5mpMK/yL7fXCHKQULeq992/eHOrWVctvvaWGT8hXqVLw008qg3LsmApUbF6U82LZvLk1UDl6tICFfO899ditm8ML7J49qslMeDh07HiNY+VT3SOZFGFU+Vb3jB+vPtz33edwEERR+Ljc8bxHjx5omsaiRYsoX758vo1ohX/SNLh8WS17K5MC6p6/aZMKJOLj1WztH310jRdVqKBSMK1aqejg3nvh55/tZlE1f4RNJjVw3KRJBQxS/vpLtX8JDFQNaBw4cEA91q8PJUte43hS3SP8UL5BypIl6vHVV71SFuH7XA5Sdu3axfbt26lRo4YnyiN8mN6ZFFBtYL75Rs3Z9/HH6gfX449f40VVq6pxS+68E7ZvV70Fvv8eTcs9UdlNN6nHfIfiz4t5wpEHH8xzaujDh+3fJ18O6nak4awwuryClH33DoTVmmrLlW/ffFGYuFzd06xZM06cOOGJsgiDsM2keDtIAdW7xzy4cZ8++TfEs6hVC1avVgOi/PKLmiAo+05vG3yZm5GsX28/8dk1nT4NH36olvv1y3M3c1sXp4IUB3U7kkkRRucoSLm49R9q/PiWWhk/3qvlEb7N5UzKe++9R9++ffnvv/+oW7cuwTlG8qpfv77bCid8i6NMijere2x99pm60Scnq2zKjz868aImTeCHH1SVz/r1aKV+A1rb7VK7tppsMCVFjcfyxhtOFmjqVFWYpk3hrrsc7pKQAMuXq+X27Z04pnRBFn4o54+KH3+EYrMmqOjl4YfzHS5AFD4uBylnzpzh8OHD9O7d27LNZDKhaRomk4lM+Ynn9/TOpIDqGVOnjmoG8tNPqseiUxMDtmqlqn46dIALFwAwJcQDalS6oCAVQKxYAfv2OVmYP/9ULXkh33Eddu9W5+7mm+G225w4roPqHsmkCKPLmUlpd8Pf8PnnamXsWO8XSPg0l6t7nnrqKRo1asSWLVs4cuQIR48etXsU/suXMimgxjMx69PHhRe2aAG//45WNLvw06bC4sWWq6e5tuann5zI0KSkqEFcMjNVq1sHc/WY/f23eqxTx8lyOqjukUyKMLpc1T2TJqmNnTurkRuFsOFykHL8+HGmT59O8+bNiY6OpmrVqnZ/wv/ZjpOiVyYFVDfe2bPV8pIl1lFwnXLLLXDHHQCYUq5A796qT/DJk1SubN2ta9d8jqFpaoS5ffugYkWYNy/PLIqmWTsumLtRX5NtkJJ9ZZdMijC6XEHK0qXqUbIowgGXg5S7776bXbt2eaIswsfZ3n/1ru4xGzRI9fTJyIBmzVzLMGjBaqoHHv6fqitatQrq1KHyGuvs3omJ6i+Xq1fh+efVuCgmkxqAKjIyz/c6elRlfoKC4NlnnSyg7dT02VGJBCnC6HIFKZqmetw1aqRLeYRvc7lNSseOHRk8eDB79uyhXr16uRrOdpLJoPyeHiPO5mf0aLj/fvj3X1Vr88wzrr3edN+9MHEHPPkk/Pkn4S8+zc0hd3MwLRqAESPUdEAApKercVdGj1bTGYN68t57832PrVvVY+PG2GVq8mU7+2BmJgQFSXWPMDzbIKU42bN5ShZF5MHlIKVv374ATJw4Mddz0nDWv/liJgVUG9jGjVXM0KcP9OgBYWEuHqROHdiyBd5+G159lS/OP0hDVMbwy/cuMH/nveoEHDwI58+r15QuDZ98cs0ABdQI/aCyPk6zzaRkZEBoqGRShOHZ9u75hdaqmlXGRRF5cLm6JysrK88/CVAKh6wsNYs6+EYmBewbztrO8ZOfXGnnoCBVf3ToEFFjrQc8l1mK/dsSVTrk/Hk19XP//hAb61SAcuGCmu8Q1Gj5TssZpCANZ4WxpaXBmTNqeSRT1Q8BmbRW5OO6Jhg0S3RYaS/8jTmTkpJivcH7QiYF4Lnn1HhtoH6YOTXAW7ZcbV1LlaLshP60aWPdNOq29Wqo240b1USFb72V56iyOS1Zopqw1Kun2s04LWd1D9ImRRjXpk0QGmptQG4iS9XTShZF5MPlIGX69OksW7bMsv7oo48SGRlJpUqVpEFtIWGu6gHVw8YXmEwwcKB1fc+ea78m3zlEsF5MAdb+FUVGh05w++32wcM1XL4Mc+ao5WeecXFSV9v3kUyKMLjsznQWJjTJoohrcjlImT9/PpWzW/6tWbOGtWvXsnr1au677z6GDRvm9gIK32G+wZqDlPBwCHBLLs49nntOtU8BePdd51+XV+BQsaJ1xNmkJNWuNj3dtTJ99BEcOgRlyqjhVFxiMuUa0E0yKcJfBNx0o4upRVEYuXyLiY+PtwQpK1eu5LHHHqNdu3a8/PLL/PHHH24voPA9vtRoNqfBg9XjvHkqvZyfa2VSQA2DMm2aWv70UzVnkLk9zrUcOWIdGG7wYChVyrnX2ckRpEgmRfgLU5s79S6CMACXg5RSpUpZJhhcvXo1MTExAGiaJg1n/VzOTIqvNJq1FROj5g4ENQCsM4HItapgbBOEsbHQvfu1MxlXr9pPIvjII9cuh0M5Rp2VTIrwFyYn23SJws3lIOXhhx+mW7du3HPPPZw7d4777rsPgJ07d1K9enW3F1D4Hl/OpIB1fr9Tp2DDhrz3cyaAAVWlZVt9tGIFvP56/se9/Xbr+ssvqwFuCyRH6kSCFGE069aBo+aKLrXPEoWWy0HK7Nmz6d+/P7Vr12bNmjVEZP+cPnXqFC+88ILbCyh8jy8MiZ8f2xmGnWlA68zFMucosSNHqmAlJ01TbU+2b7due/HFax8/T1LdIwzs+HGV3WzYMPdzEqQIZ7g8mFtwcDAvvfRSru2DzY0BhN8yX1RSU9WjywOmeUmdOqqq5/PPVY+fHj0cj1jvbCbF7OhRqFbNuv7QQ9bloCA1ZMq//6oqIbPVq53uqeyYVPcIA/vtt7yf86VG98J3uRykABw8eJD169dz+vRpsnIMSDFWhjf2e2lp6jEkRN9y5KdxY+vs77/9psZOyYuzv+iio2HvXmjaVLU5sZWRAStX2m976y37rE6B5EidSCZFGEVKimq/lRfJpAhnuBykLFy4kOeff54yZcpQoUIFTDafNJPJJEGKH8t5UfHlIKVXLzXnDsDhw473cTWTAipLk5KiuiR36gS//pp7n6FDVWPb8uVdP34u0gVZGFRcXP7PS5AinOFywm3SpElMnjyZ+Ph4YmNj2blzp+Vvh3nCNVEo+HKQUqGCNUgxD0mfl4JcLEuUgPXr1YSGZo88AufOwYwZbgpQIFd1j2RShFGYh7/PiwQpwhkuBykXLlzg0Ucf9URZhI8zUiYF4J571ONvv8Hu3bmfL0gmxVZAgMrYaJr6++ILx21frov07hEG9c03+T8vQYpwhstByqOPPspPP/3kibIIg/H1IOXuu6FVK7X888957+fTF8s8evdIkCJ81YULaoTlGTPy38+nv3fCZ7jcJqV69eqMGTOG33//nXr16hEcHGz3/IABA9xWOOFbjJZJAdVg9rffYMsWNcGx4eTRu0eqe4SvmjhRVXtei/TuEc5wOUhZsGABERER/PLLL/zyyy92z5lMJglSChEjBCnm8Rk+/xw++QRsY+rrre7xCqnuEQaiadYJNW398L3GG2+aWL3auk0yKcIZLgcpR48e9UQ5hAEYMZNSr551ef58xwOr+fTFUrogCwP57jv79S94hAeXdiP4vod5d4H9cz79vRM+QxJuosCMEKRUrGhdztl41hCZFOmCLAxk2TL79dtvOUPwo50d7itBinBGgQZz+/fff/n222+Ji4sjzTyyV7ZZs2a5pWDC9xgxk2IyqQxK376QkJD3Pj5LuiALAzFPmQEwjNeoMO45S+OTnN8zn/7eCZ/hcpCybt06OnXqxI033si+ffuoW7cux44dQ9M0Gjdu7IkyCh8VGqp3CZxjHrPku+/USLHm4fwNkUmRNinCIGbPtu923K/qKnhsXZ77S8NZ4QyXPyYjR47kpZdeYs+ePYSFhbF8+XJOnDhB69atZfwUP2fETArYD6z2xhu5n/fpX3TSBVkYwM8/w5AhajnSdJ6/qE3Vsb2sH1gHfPp7J3yGy0HKP//8Q8+ePQEICgoiJSWFiIgIJk6cyPTp091eQOG7jBikbNhgXTZUJkW6IAsf1ratdfl3rTm1qySr6cBtVK9u/xoJUoQzXA5SihYtammHUrFiRQ7bTIxy9uxZ95VM+ByjZlKio63LVavmft6nL5Z59O6RTIrwFfHx9uvVOKrmpMgxhtaYMfb7+fT3TvgMl4OUFi1asGnTJgA6dOjA0KFDmTx5Mk899RQtWrQoUCHmzp1LdHQ0YWFhNG/enG3btjn1uqVLl2IymejcuXOB3ldcH6MEKQEB1mqeCxes2w2RScmjd49kUoSveOYZ63JHviWoYjno3TvXfsWKQf/+1nUJUoQzXA5SZs2aRfPmzQGYMGECbdu2ZdmyZURHR/P++++7XIBly5YxZMgQxo0bx44dO2jQoAHt27fn9OnT+b7u2LFjvPTSS9xxxx0uv6coGKNmUgBKlVKPtkGKmU9fLPOo7pFMivAVq1ZZlz/nMTUFuLl1eg62TVR8+nsnfIZLQUpmZib//vsvVapUAVTVz/z589m9ezfLly+nqqNc+jXMmjWLPn360Lt3b2rXrs38+fMJDw9n0aJF+Zaje/fuTJgwgRtvvNHl9xTuYcQg5dQp6zZDZFJkMDfhw2wnvn+QFYSVLQ7PPpvn/rZBivTuEc5w6WMSGBhIu3btuODo52gBpKWlsX37dmJiYqwFCgggJiaGLVu25Pm6iRMnUq5cOZ5++ulrvkdqaioXL160+xMFY+RMinl24r174Y8/7J/z6V90Mpib8FFZWWpuLICWRXbyNQ+pLj5Fi+b5GvPnF3z8eyd8hsuxbN26dTly5Ihb3vzs2bNkZmZS3rb7BVC+fHnic7bGyrZp0ybef/99Fi5c6NR7TJ06lRIlSlj+KleufN3lFkqOdnE+7YYbrMsbN6pHQ2VSZDA34WPatYOTJ9Xytyn3YCpZEl54Id/XSHWPcJXLQcqkSZN46aWXWLlyJadOnfJqluLSpUs88cQTLFy4kDJlyjj1mpEjR5KUlGT5O3HihEfL6M9yXlTyGQLB51SpYu2KfOmS/XM+fbGUwdyED8rIgHU247SV4RwMHAjFi+f7OsmkCFc5fZuZOHEiQ4cOpUOHDgB06tQJk82nTNM0TCYTmS5cPcuUKUNgYCAJOcYrT0hIoEKFCrn2P3z4MMeOHaOjOccIZGVlqX9IUBD79+/npptusntNaGgooUYZGtVgjBSkADz5JEyf7rjxrM+SwdyED7LtTjyVEarrzoAB13ydZFKEq5y+zUyYMIG+ffuyfv16t715SEgITZo0Yd26dZZuxFlZWaxbt47+tn3VstWsWZM9e/bYbRs9ejSXLl3ijTfekKocD8t5UbH9VWQE5nYp5iDFUNU90gVZ+AhNg2nTrOvDmQ79Rli/YPmwvWZIw1nhDKeDFC37it66dWu3FmDIkCH06tWLpk2bcuuttzJnzhySk5Ppnd3PvmfPnlSqVImpU6cSFhZG3bp17V5fsmRJgFzbhecZLZOSVzdkn/5FJ12QhY+xGb+TIczEVKQIDB7s1GslkyJc5dJtxuSBT1WXLl04c+YMY8eOJT4+noYNG7J69WpLY9q4uDgCJOT2SUYPUgyRScmjukfTVO8K+WoIb8rKsg7I1qToPmYkvwTPDYJy5Zx6vQQpwlUu3WZuueWWawYq58+fd7kQ/fv3d1i9A7DBdrIVBxYvXuzy+4mCMXp1j6EzKTmqe0BlUyRIEd7044/qLzwsk3eTu2MKCVGDtzlJGs4KV7kUpEyYMIESJUp4qizCYIyWSTG3xTZ3mzREJiWPwdzMm4zUDVwYW2IiZPeb4IESG2lydQc8/TxERTl9DMmkCFe5dJvp2rUr5ZxM6wn/Y+QuyGCdXPDCBUhKsm736YulOQpJTwdyZ1KE8Bbb8TXbJixRF4CXX3bpGJJJEa5yOlnsifYowtiMVt0TEQHm4XWOHzdIJiVHkGIbGEqQIrwlNdWaRQHoxYfwxBP2U4w7wbZ6UqoqhTOc/phohriiC08yeiYFrNmUY8es23w6/s4nkyLdkIW32E5MP43hhAZkwMiRLh9HMinCVU7fZsyDpglhZsQgxdwu5cwZY2ZSbH99SiZFeMuyZdbl53gXunaFm292+Ti2n18JUoQzJOEmnGb03j0AZcuqxzNnrNt8+mKZI0gxmWRAN+FdO3bAO++o5U/pRkmS4JVXCnQsyaQIV0mQIgrMiJkUc5uUs2eNmUkBGdBNeNfo0eq78liV3+nGZ/Dww1CnToGOJZkU4SoJUoTT/KFNitEzKSAzIQvvWbUKfvhBLfc4MVUtjB5d4OPJsPjCVfIxEQVmxOoeyaQI4bxJk6zLd2tr4f77oVGjAh9PMinCVRKkCKf5QybFHKScO2fd5tMXS8mkCJ2sWQO//66WlwZ0oyhXYNSo6zqmtEkRrpIgRRSYEYOU8HD1eOWKvuVwmmRShE6+/966fGfWemjbFlq2vK5jSiZFuMqAtxmhF3/o3WMbpJiXfVo+mRQJUoSn/PcfzJunlt8OHEjFzHgYs/S6jyuZFOEqyaSIAjNiJqVIEfWYkmLd5tMXy3wyKTabhHCr+fPVKLPRxc/RK/N9uOMOaN36uo8rmRThKglShNP8oU2KbZBi1Iaz5k3SJkV4wtmzMH26Wp6eMpAIkmHMGLccW4bFF66Sj4koMCNW9/hDJsXBJiHcZsAA9dkqFXaFTulfQvPmEBPjlmNLdY9wlQQpwmm2F5WAAGNeZMxBytWrYIiZHhykTSRIEZ6SkACffaaWJ2aNIYxUlUVx05ddqnuEqyRIEQVixKoesAYpYM2m+PTFUjIpwosGD7YuP5U2Dxo3tp/++DpJJkW4SoIU4TTbi4oRq3rAcZDi0yRIEV6SmQlffqmW/xe0gnBS3JpFAcmkCNdJkCIKxKiZlKAg603ePFaKT18s8wlS0tJ0KI/wWxMnqo9ZUEAmH2Q8AfXqQadObn0PyaQIV0mQIpxme1ExapAC1myKIQZ0k0yK8ILMTPjgA7X8UvCbFOOymqPHzV1wpHePcJV8TESBGLW6B3IHKT79i84cDdpEJCEhuTYJcV3efx9OnIDIIlcYm/oK1KwJ//uf299HMinCVRKkCKf5WybFEOOMSCZFeFh6Orz8sloeyiyKcFXN0eOBXyLSJkW4SoIUUSBGDlJyDofv0xdLCVKEh734IiQlQWR4CsNTxkH16tC1q0feSzIpwlUSpAin+UPvHoCSJfUugQtsI5LsIXIlSBHukpEBS5ao5baZawgkC155xWO/QiSTIlwlQYooECNnUiIj7dd9+mJpjkjAMqOgBCnCXaZMgUuX1PJ7qT0gOhp69PDY+9n+uJGGs8IZ8jERBWLkTErOIMWn2QYp2VGJBCnCHbKyYO5ctXx/6FqKcwlGjLD/zLmZZFKEqyRIEU7zl+oeCVKEgPXr4fRptfxZ6kNQuTI8+aRH31PapAhXSZAiCsTIqdpSpezXffpiKUGK8ICMDBg4UC0/H/6hGhfllVcgNNSj7yuZFOEqA99qhLflnGDQqAyVSbH96SlBinCTlSvhr79Uj55XrwxRWZTevT3+vhKkCFcZ+FYj9GTkIKV4cft1n75Ymky5ohIJUsT1SE6G/v3Vcg8+pTTnvZJFAanuEa4z8K1GeJu/tEnxYLtAz8gjSJG5e0RBrFgB//0HJpPGS1cmqCzKU0955b1lWHzhKgN3JBV6MvIFJmf3aZ//RZcjSJFh8UVBpaRYexj3KfIJla/8C6PmWz9UHiaZFOEqA99qhLf5S5sUw43xItU9wk1+/tm6/MKVGVClilfaophJmxThKqNdroWP8KcgxecvlhKkCDfIyIChQ9XyU0WW0CBlN4x612tZFJBMinCdgW81wtskk6ITCVKEG3z7LezfD8XDUhmT8gpUrerxcVFykkyKcJXRLtfCR/hTkOLzF0sJUsR1ysqCmTPVcr/Ad4nmOIxa4NUsCkgmRbjOwLca4W2SSdGJOSrJyLBblSBFOGvZMti8GcKCM3gh+TWVRenVy+vlsL1uZM+XKUS+jHa5Fj7CyF2QJZMiCpOMDJg4US0PCpnHDen/6ZJFAfvrRlaW199eGJCBfw8Lb5NMik4kSBHXYckS2LdPLfdInq9mOtYhiwKSSRGuM9rlWvgIfwpSJJMi/FV6OkyerJafLfIRdVL+hlELdcmigGRShOsMfKsR3iaZFJ1IkCIK6Msv4cABtdw/5XVdsyggmRThOqNdroWPkCDFiyRIEQWQlQVz56rlfkXep17KXhj9nq7zQkiQIlxl4FuN8DZ/zaQYtbpH5u4R+fnmG/jtNygaksaglKlw443Qs6euZbKt7pEgRTjDaL8phY/wpyDF58ncPcJFmgYzZqjlgaa3qM5hmPiJ7rNrSiZFuMrAtxrhbf4yC3LOshs1kyJBisjLokVqXJTggAz6pc6EOnWga1e9i2X3XZOGs8IZRvtNKXyEZFK8SIIU4YKsLJg6VS2PDHydqKxT8Opcn/tlIZkU4QwD32qEt0mbFJ1IkCJc8NZbcPgwBJoy6Zv+JjRtCp07612sXCRIEc4w2m9K4SP8KUjxeRKkCCelp8Pbb6vlQQFvUjEzHiZ/6JORuAQpwhkGvtUIb5NMik7MLWWzu/NIkCLyMncuHDoEoYHpDMl8HVq3hnvu0btYDkmbFOEMo/2mFD7Cn4IUnxcaqh5TUwEJUoRj6emwcKFaHps1gShOweQvfDYKj4rSuwTCCIx2uRY+wp+CFB+9hltJkCKc8N578PffUDIkmb5p70CHDtCqld7FyuWnn+Dff6FuXb1LIoxAghThNH/pgiyZFOFvMjLg9dfV8sS0kURyASZN0rdQefDR2ifhowz8e1joSTIpXmQOUq5eBSRIEbm9+iocPQplQpJ4mvfgkUegUSO9iyXEdTPwrUZ4m780nA0IMEBgYiuPTEpWFmRm6lQm4TOSk2HmTLU8NW0o4QGpMHGivoUSwk0MfKsRejJykAL22RSfD1jCwtRjjiAFJJsiYOhQFaiUC7lAbz6AJ56AWrX0LpYQbmHwW43wJn/JpIDB2qXkyKSYeySDBCmF3dmz8PHHavm1tEEEBgfCuHH6FkoINzL4rUboxZ+CFJ/PpORR3QMSpBR2L78MV65AlZBTPMHH8OyzUK2a3sUSwm0MfqsR3iSZFJ3kCFJse1ZJkFJ4nTwJy5ap5QVpTxJQLEKyKMLv+MStZu7cuURHRxMWFkbz5s3Ztm1bnvsuXLiQO+64g1KlSlGqVCliYmLy3V94hgQpXpSjd4/JJD18BIwYobIot4bE0o6f1IayZfUulhBupfutZtmyZQwZMoRx48axY8cOGjRoQPv27Tl9+rTD/Tds2MDjjz/O+vXr2bJlC5UrV6Zdu3b8999/Xi554eMv46SAwap7cjSchVwj5YtC5tAhWLJELb+d1gdTpUowaJCuZRLCE3QPUmbNmkWfPn3o3bs3tWvXZv78+YSHh7No0SKH+3/66ae88MILNGzYkJo1a/Lee++RlZXFunXrvFzywk0yKV6Uo7oHHMYtopDQNHjySdX9vH3QWprxpxooJTxc76IJ4Xa63mrS0tLYvn07MTExlm0BAQHExMSwZcsWp45x5coV0tPTiYyMdPh8amoqFy9etPsTBeOvbVJ8PpPiIEjJUQMkCpGff4bffoPggAzeynge6tWDnj31LpYQHqHrrebs2bNkZmZSvnx5u+3ly5cnPj7eqWMMHz6cqKgou0DH1tSpUylRooTlr3LlytddbmH8IMVQ1VX5ZFIkSClcsrLguefU8sN8xc0cUuPhG+oDLYTzDH2rmTZtGkuXLuXrr78mzHzVzmHkyJEkJSVZ/k6cOOHlUvoPyaToxEHaRIKUwmnJEjh8GEID0piTNQBiYqBdO72LJYTH6FozX6ZMGQIDA0lISLDbnpCQQIUKFfJ97YwZM5g2bRpr166lfv36ee4XGhpKqPkiL9zGn4IUn+egAYq0SSl80tPhpZfU8jNZC6hgOq2yKD4fZQtRcLreakJCQmjSpIldo1dzI9iWLVvm+brXXnuNV199ldWrV9O0aVNvFFUgmRTdSHWPAObOhYQEKBOcyAxeUsPfN2yod7GE8Cjdf08OGTKEXr160bRpU2699VbmzJlDcnIyvXv3BqBnz55UqlSJqVOnAjB9+nTGjh3LkiVLiI6OtrRdiYiIICIiQrd/R2Fj9CpwQ2VSJEgp9C5csI7TNiH9FcJCgUmTdC2TEN6g+6W6S5cunDlzhrFjxxIfH0/Dhg1ZvXq1pTFtXFwcATY/2+fNm0daWhqPPPKI3XHGjRvH+PHjvVn0QkcyKToxBylZWZCRAUFB0runkJk8GS5ehHqh++mbOh8GvQzSCUAUAroHKQD9+/enf//+Dp/bsGGD3fqxY8c8XyBxTf4UpPg82zZVV69CRIRkUgqRfftg5ky1PDn1JQLKloGRI/UtlBBeYvBbjfAmyaToxDZIya7ykSClcMjKAvPvt/uDVtORlTBlCpQooW/BhPASg99qhF78KUjxeUFB1kZAEqQUKuvWqb8AUxZjMsZBo0aQ3V5PiMLASJdqoTPJpOgoNFTNJpcjSJEuyP4rLQ2eeUYt99I+pDnb4I1fjd9qXQgXGPxWI/TiT0GKIeTo4SOZFP/30UcQFwclgy4xiVHQpQvccYfexRLCqwx+qxHe5K+zIBtCju480rvHv124AEOGqOUhGa8RVSQRXntN1zIJoQcJUkSB+FMmxTDVPSCZlEJi4kS4dAnqBf/DK0yB4cOhShW9iyWE1xn8ViO8yV/bpBhCjkYoEqT4r5074Y031PLr6YMIrHIDDBumb6GE0InRLtXCR/hTkCKZFOErMjJUNY+mQRfT57TXfoI3V0B4uN5FE0IXBr/VCL34U5BiCHkEKdK7x7989hls2KBmOR6njYMHHoBOnfQulhC6MdqlWujIX6t7JJMifEFCAgwcqJZHZE2hVtgxePN7n/6AZmZmkp6erncxhJuFhITYTUejJwlSRIH4yOe3wAybSZHePX5r+nTVq6da4HEGZc6B0aOhWjW9i+WQpmnEx8eTmJiod1GEBwQEBFCtWjVCQkL0LooEKcJ5kknRkTSc9Ws7d8Ls2Wp5cuYISt5SHl56Sd9C5cMcoJQrV47w8HBMhvgSCWdkZWVx8uRJTp06RZUqVXT/v5UgRRSI0YMUw43zYo5KUlLsViVIMb7MTOv8PA/zFV1ZCnPX2M/Z5EMyMzMtAUrp0qX1Lo7wgLJly3Ly5EkyMjIIDg7WtSwGv9UIb7INqA1XXZKD4TIpRYuqxytXAAlS/Mn778PmzVA84BLTeRlT164QE6N3sfJkboMSLj2O/Ja5miczM1PnkkiQIgrIcJmIHAwXZJmDlMuXAQlS/EVcnLVWZ0LWGKpHXrAOkuLj9K4GEJ7jS/+3RrtUCx3567D4PvR9zJs5SElOBqQLsj/QNBgxQo0se5tpCy9qb8HsD6BcOb2LJoTPkEyKKBB/ClIMIUeQIr17jO+LL9S4KAAztcEEtouBJ57Qt1BC+BgJUoTTJJOio4gI9ZgjkyJBijGdPg2DB6vlwcyiRfgeePddg3wYjS8+Pp5u3bpRoUIFQkJCiIqKYsaMGW59j7lz5xIdHU1YWBjNmzdn27Ztbj1+YWG035PCRxguE5GD4cqfR3XP1auq2kDubcYyfjycPAlRppO8ok2ByZMhOlrvYhUazz33HOnp6axdu5ZSpUqRkJDg1jFfli1bxpAhQ5g/fz7Nmzdnzpw5tG/fnv3791NOqvNcYrRLtdCRv2ZSDCGPICUrS833onMvQeGCtWth3jy1PF97jjLNq8OLL+pbqOulaZaeZ14XHu5ylJ6amsrx48fZsmULMTExNG7c2K1FmjVrFn369KF3794AzJ8/n1WrVrFo0SJGjBjh1vfyd0a7VAsf4U9BiiGyEHn07gE1dIoEKcZw6RI8/7xafor3eSDoR1i43fhfqCtXrFWS3nb5svX74YSMjAzuvfde7rrrLkqUKMHbb7/NgQMHWLJkCRE5/g1TpkxhypQp+R7v77//pkqVKpb1tLQ0tm/fzsiRIy3bAgICiImJYcuWLU6XUygSpAinSSZFRw4yKSaT9Qds8eI6lk04bdQoOHQIqpjimKqNxPTqRKhXT+9iFSoDBw7k7rvvpkGDBgDMmDGD6Oho5s2bx7Bhw+z27du3L4899li+x4uKirJbP3v2LJmZmZQvX95ue/ny5dm3b58b/gWFi9Eu1cJHGD1Isc08GCqTkh2kmEzqh+ulS+qvQgUdyyacsno1vPWWWl6g9aHcbTdDjpuiYYWHW7J8ury3k2JjY/nkk0+YM2eO3fYSJUpw6tSpXPtHRkYSGRl5vSUU10GCFOE0yaToKEfvHoBixaxBivBtZ89Cv35qeQBv0D58E3y4y/hfJDOTyaUqF70sX76cW265xW6o9+TkZA4cOMCAAQNy7V+Q6p4yZcoQGBhIQkKC3X4JCQlUkF8TLjPapVr4CMPd5HMweiYFVJACEqT4Ok2DQYPgyBGowCleZQzMnAnVq+tdtELnwoULJNt8hwAWLFgAwMMPP5xr/4JU94SEhNCkSRPWrVtH586dATVp37p16+hvnqRJOM3gtxrhTZJJ0ZGDIMWcXNEryy6c88kn8OmnankpXSne/jZ47jl9C1VINW/enLlz5zJ79mweeOABVq9ezciRI3nnnXcoVapUrv0LWt0zZMgQevXqRdOmTbn11luZM2cOycnJlt4+wnlGu1QLH2H0IMWwmZSMDEhLg5AQyaQYwKFDYK5FGMRsWpf5GxbtMsiHzv/06NGDuLg43nzzTcaNG0e9evX46quv6NChg1vfp0uXLpw5c4axY8cSHx9Pw4YNWb16da7GtOLaJEgRTpNMio5s6/svX4bISAlSfFx6uupunJgIddirqnk+/BxyVA8I7zGZTIwaNYpRo0Z5/L369+8v1TtuIMPiiwIxepBiuExKcLC10NlVPhKk+LbXX1cDt4WQyjK6EDHkOXDzL3Yh/J0EKcJptjdzw2UicjBk+XP08JE2Kb5ryxYYO1YD4F2eo06TIjB1qs6lEsJ4jHipFj5AMik6KFoULlyQTIqPS0iAHj0gM9NEVz6jV8RXsHQHhIToXTQhDEcyKcJp/tQmxZDDyOfo4SNBiu/JyFABypEjUJVjzON5TO+/J92NhSggCVJEgRg9SDHc3D2QK0gxV/dIkOI7Ro1S7VCCSGcV91NyyNNwjXE2hBB5kyBFFIjRgxRDZlJyRCXmTIq0SfENX3wBr72mlufxPHVal4Xp0/UtlBAGJ21ShNOk4azOzINNJSYCUt3jS3bvhmee0QATPfmQpyv+AMt2GPSDJoTvkG+QKBB/yqQYprrHPPLl+fOAVPf4iuRkePJJuHjRRGO2Mz9kIKavVoMM3CXEdZPqHuE0TbMuGz1IMeQP3BxBilT36C8zEx5/HHbuhNKc5Ts6UmTRXGjRQu+iCeEXJEgRTsvKsi4bPUjxh0yKVPfob+RI+O47CCOF7+hI1OinoXt3vYslriE+Pp5u3bpRoUIFQkJCiIqKYsaMGW47/tSpU2nWrBnFihWjXLlydO7cmf379+fab+7cuURHRxMWFkbz5s3Ztm2b28rgLyRIEU7LzLQuGz1IMXQm5dw5QKp79DZjhhpVFmAxT9LykRtgwgR9CyWc8txzz5GYmMjatWs5evQoK1eupHHjxm47/i+//EK/fv34/fffWbNmDenp6bRr185uBuZly5YxZMgQxo0bx44dO2jQoAHt27fn9OnTbiuHP5AgRTjNNkgx5E3ehj9kUooXV6uXLtlnuYTnffopDBumll9lNF2aH4cPP4QAuaQaQWpqKkePHmXLli2kpaXRuHFj7r77brcdf/Xq1Tz55JPUqVOHBg0asHjxYuLi4ti+fbtln1mzZtGnTx969+5N7dq1mT9/PuHh4SxatCjP4x4+fBiTycTKlStp27Yt4eHh1KhRg61bt1r2OXbsGCaTieXLl3PnnXdSpEgRmjVrRlxcHBs3bqRFixaEh4fTtm1bErMb4fsy+UYJp0kmRWc5gpTSpdWqplk2CS+IjYUXnldRYU8+ZFSN5bBqFYSH61swHWmaakCsx59tWzlnZGRkcO+997J06VLuuece5s6dS6dOnbjsoHHXlClTiIiIyPcvLi7umu+ZlJQEQGT2dzgtLY3t27cTExNj2ScgIICYmBi2bNmS53F27dqFyWRi1qxZjBkzhl27dlGlShVGjBhhtw/AvHnzmDJlCps3byYhIYEePXowbdo03n77bdavX8+uXbv44IMPnDtpOjLipVroxF/bpNgGXz4tR5ASEgIlS6oeyWfOQJkyupWs0NizB2Laaly8FMDtbGRR1BhMP22yRoyF1JUr1upHb7t82X6S8GsZOHAgd999Nw0aNABgxowZREdHM2/ePIaZ02PZ+vbty2PXGIwv6hqzWmdlZTFo0CBatWpF3bp1ATh79iyZmZmUz9EDrHz58uzbty/PY+3atYuSJUuybNkyypYtC0CnTp149913LfvExsYSGRnJsmXLKJ39uWzdujWbNm3ir7/+Ijw7mG7WrBnx8fH5lt0XSJAinOavmZSMDP3K4RLzjdAmbVK2rApSTp+GWrX0KVZh8fff0PZujXPnTdzKVlaW6EHgj99DlSp6F004KTY2lk8++YQ5c+bYbS9RogSnTp3KtX9kZKQl+1FQ/fr1Y+/evWzatOm6jgMqSHnwwQctAQrA0aNHqW4z7cKuXbt46KGHLAEKQFxcHF26dLEEKOZtDz744HWXydOkukc4zTaTYph2HHmwzaQYJkgxXyyvXoWUFADKlVObzpzRqUyFxP79cPddGmfOqrFQVhd7jBJrvoTsX8aFXXi4ymjo8edKLdvy5cu55ZZbCLa5ACQnJ3PgwAHq1KmTa//rre7p378/K1euZP369dxwww2W7WXKlCEwMJCEhAS7/RMSEqhQoUKex9u1axctW7a02xYbG0vDhg3t1ps3b57rdS1susVfvXqV/fv3W7JJvkwyKcJphqkWcYIhMykREargGRkqm1KpEuYfVBKkeM7evXBPjEbCaRMNiOWniP9R6qdl0KyZ3kXzGSaTa1Uuerlw4YJdDxuABQsWAPDwww/n2r+g1T2apvHiiy/y9ddfs2HDBqpVq2b3fEhICE2aNGHdunV07twZUNVC69ato3///g7fJykpiWPHjtGoUSO77bGxsQwYMACAixcv5trn6NGjJCUl2W3bs2cPmqZRr169fP9tvkCCFOE0fwpSbKur0tP1K4dLTCaVTTl9WnVDrlTJkkmRXouesXMn3N8hi/iEAOqzizVFH6L0j0tksDaDat68OXPnzmX27Nk88MADrF69mpEjR/LOO+9QyjzthI2CVvf069ePJUuW8M0331CsWDFL248SJUpQpEgRAIYMGUKvXr1o2rQpt956K3PmzCE5OZnevXs7PObu3bsJCgqyCyyOHz/OhQsXLJmUXbt2ERgYaGn7AtY2KlWrVrXbdtNNNxGhV0MiF0iQIpzmT0GKbXWVq70DdGUbpCDVPZ60aRN06pjFhcQAavE3G4p3otT3S+C22/QumiigHj16EBcXx5tvvsm4ceOoV68eX331FR06dHDr+8ybNw+ANm3a2G3/4IMPePLJJwHo0qULZ86cYezYscTHx9OwYUNWr16dqzGt2a5du6hRowZhYWGWbTt37qRkyZJER0fnuc+uXbtyZV927dpliKoeAJOmGeoSfd0uXrxIiRIlSEpKorh5oAnhlK+/BnNG1B8+Nc8+C0ePwo8/Gmh4i7Zt4eef4eOPoUcP3nwTBg6ERx+Fzz/Xu3D+49tv4bFHs0hNC6Aeu9lQ9jEi134O9evrXTTdXb16laNHj1KtWjW7m6HwH/n9H3v7HiqZFOE0f8qkAGRXRRuLuSfJ8eOAZFI8Yf58eOEFDU0LoCWb+bHqcxRbtwpuuknvoglR6Bjl96PwAdfZE0+4g7leObtHgbnhrLRJuX4ZGWoU2eefB00z8TDL2dBoCMW2/CQBihA6kUyKcNpdd8FLL4EBGoT7L3MmJTtIMWdSDDAmk0+7cAG6PJrFmnXqd9twpjHlfzsI+OjnQj2SrBB6kyBFOM1ksk6oJnRizqRkV/eYezaeP69utA46KIhr2LoVuj+WzuG4YMJJ5j2e4fHR1WHCUgM1VhLCP8k3UAgjsc2kaBoREVCxotp08KB+xTKijAyY9KpGq9uyOBwXTBWO81vxDjy+6gl49VUJUITwAfItFMJIKldWj8nJluHxb7lFbZIgxXkHDsCdLdMYM9ZEZlYAHfmWXc360HDvJ+Dm7qj+qpB1DC1UfOn/VoIUIYwkLAzMw2YfPQrAzTer1QMHdCqTgaSmwtQpWdSrk8mWP0MI5SpvBgzim3E7KfnbKmsQKPJkHlL+ypUrOpdEeEpaWhoAgT4wSZu0SRHCaGrXVi1l9+yBpk1dz6ScOgW7dql2LWfPqoxMaCgUL67+KlaEmjVVj5aQEI/9M7ztl1/ghd5X+PuoagjbnN9ZXHM6NZeOB4MMbOULAgMDKVmyJKezu5SFh4djMvpkXsIiKyuLM2fOEB4eTlCQ/iGC/iUQQrimfn01oNuuXYA1k/LPP/m85sABWLgQVq9Wk9HkkEkA54nkEsUIIIuiJFM88Aqh1SurgOXWW+GOO9R8NQYbwGvbNhg35BKrfysGhFOOBCaFvMrT424gYNjn9rNNCqeYJ8E7LX3f/VJAQABVqlTxieBTghQhjKZxY/X4+++AihtMJoiNhRMnctRYbN8O48fDypXWbSYTB6q15+PAJ/ntcn32JlbmTIqDOTwyodz+BKruP85N3xymAatoEPQ6DRoFUPGeupjuaqOGiPfRLrqbNsFro5L47tcSQDGCSKcPC3m169+UnjXK2uJYuMxkMlGxYkXKlStHumEmvxLOCgkJIcBXGo5rPuDtt9/WqlatqoWGhmq33nqrtnXr1nz3//zzz7UaNWpooaGhWt26dbVVq1Y5/V5JSUkaoCUlJV1vsYXQx7FjmgaaFhioaRcvapqmaa1aqU0LFmTvc/mypg0dqmkBAeoJ0NI7dNLmP/OHdstNGeZNBf4rw2ntHn7UxgdM0NbUHahdGDZZ09at07QrV/Q7L5qmXbqkae/NS9OaVDtrKauJTK0ni7VD9/bTtF27dC2fEEbn7Xuo7kHK0qVLtZCQEG3RokXaX3/9pfXp00crWbKklpCQ4HD/3377TQsMDNRee+017e+//9ZGjx6tBQcHa3v27HHq/SRIEX6henV1B166VNM0TRsxQq326KFp2tq1mlatmiWiyOrSVfvk9ZNahQr2gUbFiprWu7emvf++pu3ereKdzExNy8rStKtXNe3MGU3bvl3TvvpK06ZO1bSuXbO0Wjdd1QJMmQ4Dl1vYpz0ZsFh79+bXtV1939EyflyraefPe/xUJCRo2ieLUrWebY5rxYKuWMoTSLrWzbRE233fy5rm5PVBCJE/b99DdZ9gsHnz5jRr1oy3334bUI12KleuzIsvvsiIESNy7d+lSxeSk5NZaZO+btGiBQ0bNmT+/PnXfD+ZYFD4hdGjYfJkuPNO2LCB1T+auO8+CDBlsUerS23+gcqV+e3Fpby84jY2b7a+9IknYOjQgrcVvXIF/voL/tim8dtPl9n8Gxw7VyzXfkW4Qg32U7toHLWqXqF2g2BqtS5H9Q63EHxDefupqF2QlATbN1zip2UX+GlDMDtP2Vfb3MAJniq+nGefM1FpSBdrbyghxHXz9j1U1yAlLS2N8PBwvvzySzp37mzZ3qtXLxITE/nmm29yvaZKlSoMGTKEQYMGWbaNGzeOFStWsCu7IWF+JEgRfuHff1Xvm7Q0GDKErJAw2s+IYW3GXQA8UWMr56o24fufVLOz4GA16/OECVC6tPuLc/YsbP1dY8v3F/h9fQpbD0VyOaOIw32DSOdm02FqFo0jqmQK5cpmEVk+hLDIIoSFQkiwRiaBpBPMhcvBJJyGhLOBnDwbwp4zFfgvtWyuYzZiB+2KbaF9exOtBzUioGVzGYxNCA8oVLMgnz17lszMTMqXL2+3vXz58uzbt8/ha+Lj4x3uH5/H5CWpqamkpqZa1pOSkgB1ooUwrOLFYcwY9TdrFgCD2cBafgDg4/21YP8VTCaVORkyxDqEvic++iEhcMedcMedQUAxMjPTOXYsnf3bL7Nv42n2705l//Ew9l8ozxXC+UeL4p/LUXAZ+NfVd7tIRf7jzvA/aVvrFHd1Kk65Dk3h5h5gMnEZ4PJld/8ThRBY753eym/4fe+eqVOnMmHChFzbK8ugTcLvbAZK2G3RNPjoI/XnT04By67Asu3AdmCczgUSopA5d+4cJUqUuPaO10nXIKVMmTIEBgaSkJBgtz0hIcHSDz+nChUquLT/yJEjGTJkiGU9MTGRqlWrEhcX55UT7MsuXrxI5cqVOXHiRKGu+pLzYCXnQpHzoMh5sJJzoSQlJVGlShUiIyO98n66BikhISE0adKEdevWWdqkZGVlsW7dOvr37+/wNS1btmTdunV2bVLWrFlDy5YtHe4fGhpKaGhoru0lSpQo1B80W8WLF5dzgZwHW3IuFDkPipwHKzkXirfGUdG9umfIkCH06tWLpk2bcuuttzJnzhySk5Pp3bs3AD179qRSpUpMnToVgIEDB9K6dWtmzpzJ/fffz9KlS/nzzz9ZsGCBnv8MIYQQQriZ7kFKly5dOHPmDGPHjiU+Pp6GDRuyevVqS+PYuLg4u4jttttuY8mSJYwePZpXXnmFm2++mRUrVlC3bl29/glCCCGE8ADdgxSA/v3751m9s2HDhlzbHn30UR599NECvVdoaCjjxo1zWAVU2Mi5UOQ8WMm5UOQ8KHIerORcKN4+D7oP5iaEEEII4YiMdiSEEEIInyRBihBCCCF8kgQpQgghhPBJEqQIIYQQwicZMkj59ddf6dixI1FRUZhMJlasWGH3vKZpjB07looVK1KkSBFiYmI4ePCg3T7R0dGYTCa7v2nTptnts3v3bu644w7CwsKoXLkyr732mqf/aS671rn46quvaNeuHaVLl8ZkMhEbG5vrGFevXqVfv36ULl2aiIgI/ve//+Ua1TcuLo7777+f8PBwypUrx7Bhw8jIyPDgv8w17jgPbdq0yfWZ6Nu3r90+vn4eIP9zkZ6ezvDhw6lXrx5FixYlKiqKnj17cvLkSbtjnD9/nu7du1O8eHFKlizJ008/zeUc8+H4+vfDHefBH64T1/pujB8/npo1a1K0aFFKlSpFTEwMW7dutdvHHz4P4J5zURg+E7b69u2LyWRizpw5dtu99ZkwZJCSnJxMgwYNmDt3rsPnX3vtNd58803mz5/P1q1bKVq0KO3bt+fq1at2+02cOJFTp05Z/l588UXLcxcvXqRdu3ZUrVqV7du38/rrrzN+/HifGzTuWuciOTmZ22+/nenTp+d5jMGDB/Pdd9/xxRdf8Msvv3Dy5Ekefvhhy/OZmZncf//9pKWlsXnzZj788EMWL17M2LFj3f7vKSh3nAeAPn362H0mbL9URjgPkP+5uHLlCjt27GDMmDHs2LGDr776iv3799OpUye7/bp3785ff/3FmjVrWLlyJb/++ivPPvus5XkjfD/ccR7A+NeJa303brnlFt5++2327NnDpk2biI6Opl27dpw5c8ayjz98HsA95wL8/zNh9vXXX/P7778TFRWV6zmvfSY0gwO0r7/+2rKelZWlVahQQXv99dct2xITE7XQ0FDts88+s2yrWrWqNnv27DyP+84772ilSpXSUlNTLduGDx+u1ahRw63ld6ec58LW0aNHNUDbuXOn3fbExEQtODhY++KLLyzb/vnnHw3QtmzZommapn3//fdaQECAFh8fb9ln3rx5WvHixe3Oj68oyHnQNE1r3bq1NnDgwDyPa7TzoGn5nwuzbdu2aYB2/PhxTdM07e+//9YA7Y8//rDs88MPP2gmk0n777//NE0z3vejIOdB0/zvOuHMeUhKStIAbe3atZqm+efnQdMKdi40rfB8Jv7991+tUqVK2t69e3P9m735mTBkJiU/R48eJT4+npiYGMu2EiVK0Lx5c7Zs2WK377Rp0yhdujSNGjXi9ddft0vbb9myhTvvvJOQkBDLtvbt27N//34uXLjg+X+Il2zfvp309HS781WzZk2qVKliOV9btmyhXr16llGAQZ2Lixcv8tdff3m9zJ706aefUqZMGerWrcvIkSO5cuWK5Tl/PQ9JSUmYTCZKliwJqH9nyZIladq0qWWfmJgYAgICLKlvf/x+5DwPZoXpOpGWlsaCBQsoUaIEDRo0AArv58HRuTDz989EVlYWTzzxBMOGDaNOnTq5nvfmZ8InRpx1p/j4eAC7G4l53fwcwIABA2jcuDGRkZFs3ryZkSNHcurUKWbNmmU5TrVq1XIdw/xcqVKlPPnP8Jr4+HhCQkJyXZhtz1d8fLzD82l+zl9069aNqlWrEhUVxe7duxk+fDj79+/nq6++AvzzPFy9epXhw4fz+OOPWyZNi4+Pp1y5cnb7BQUFERkZafeZ8Kfvh6PzAIXnOrFy5Uq6du3KlStXqFixImvWrKFMmTJA4fs85HcuoHB8JqZPn05QUBADBgxw+Lw3PxN+F6Q4a8iQIZbl+vXrExISwnPPPcfUqVML/bDHhZVtfWq9evWoWLEibdu25fDhw9x00006lswz0tPTeeyxx9A0jXnz5uldHN3kdx4Ky3XirrvuIjY2lrNnz7Jw4UIee+wxtm7dmutGVBhc61z4+2di+/btvPHGG+zYsQOTyaR3cYzZcDY/FSpUAMjVOyUhIcHynCPNmzcnIyODY8eOWY7j6Bi27+EPKlSoQFpaGomJiXbbbc9XYTkXOTVv3hyAQ4cOAf51Hsw35uPHj7NmzRq77EGFChU4ffq03f4ZGRmcP3/e7z4T+Z0HR/z1OlG0aFGqV69OixYteP/99wkKCuL9998HCtfnAfI/F47422di48aNnD59mipVqhAUFERQUBDHjx9n6NChREdHA979TPhdkFKtWjUqVKjAunXrLNsuXrzI1q1badmyZZ6vi42NJSAgwBItt2zZkl9//ZX09HTLPmvWrKFGjRqGSNc5q0mTJgQHB9udr/379xMXF2c5Xy1btmTPnj12H0rzBb127dpeL7O3mLspV6xYEfCf82C+MR88eJC1a9dSunRpu+dbtmxJYmIi27dvt2z7+eefycrKsgRu/vD9uNZ5cKSwXCeysrJITU0FCs/nIS+258IRf/tMPPHEE+zevZvY2FjLX1RUFMOGDePHH38EvPyZcKmZrY+4dOmStnPnTm3nzp0aoM2aNUvbuXOnpVX+tGnTtJIlS2rffPONtnv3bu3BBx/UqlWrpqWkpGiapmmbN2/WZs+ercXGxmqHDx/WPvnkE61s2bJaz549Le+RmJiolS9fXnviiSe0vXv3akuXLtXCw8O1d999V5d/c16udS7OnTun7dy5U1u1apUGaEuXLtV27typnTp1ynKMvn37alWqVNF+/vln7c8//9RatmyptWzZ0vJ8RkaGVrduXa1du3ZabGystnr1aq1s2bLayJEjvf7vzcv1nodDhw5pEydO1P7880/t6NGj2jfffKPdeOON2p133ml5DyOcB03L/1ykpaVpnTp10m644QYtNjZWO3XqlOXPthX+vffeqzVq1EjbunWrtmnTJu3mm2/WHn/8ccvzRvh+XO958JfrRH7n4fLly9rIkSO1LVu2aMeOHdP+/PNPrXfv3lpoaKi2d+9eyzH84fOgadd/LgrDZ8IRRz2avPWZMGSQsn79eg3I9derVy9N01Q35DFjxmjly5fXQkNDtbZt22r79++3vH779u1a8+bNtRIlSmhhYWFarVq1tClTpmhXr161e59du3Zpt99+uxYaGqpVqlRJmzZtmjf/mU651rn44IMPHD4/btw4yzFSUlK0F154QStVqpQWHh6uPfTQQ3ZBjKZp2rFjx7T77rtPK1KkiFamTBlt6NChWnp6uhf/pfm73vMQFxen3XnnnVpkZKQWGhqqVa9eXRs2bJiWlJRk9z6+fh40Lf9zYe6C7ehv/fr1lmOcO3dOe/zxx7WIiAitePHiWu/evbVLly7ZvY+vfz+u9zz4y3Uiv/OQkpKiPfTQQ1pUVJQWEhKiVaxYUevUqZO2bds2u2P4w+dB067/XBSGz4QjjoIUb30mTJqmac7nXYQQQgghvMPv2qQIIYQQwj9IkCKEEEIInyRBihBCCCF8kgQpQgghhPBJEqQIIYQQwidJkCKEEEIInyRBihBCCCF8kgQpQgghhPBJEqQIIYQQwidJkCKEKLA2bdowaNAgvYthUdDynDt3jnLlyllmsvWUrl27MnPmTI++hxD+RIIUIYThuDs4mjx5Mg8++KBlKnpPGT16NJMnTyYpKcmj7yOEv5AgRQhRqF25coX333+fp59+2uPvVbduXW666SY++eQTj7+XEP5AghQhDGLlypWULFmSzMxMAGJjYzGZTIwYMcKyzzPPPEOPHj0AWL16NbfffjslS5akdOnSPPDAAxw+fBiABQsWEBUVRVZWlt17PPjggzz11FOW9aysLKZOnUq1atUoUqQIDRo04Msvv8yzjNfav02bNgwYMICXX36ZyMhIKlSowPjx4+2OcenSJbp3707RokWpWLEis2fPtsucPPnkk/zyyy+88cYbmEwmTCaTXTVNVlZWvsfP6fvvvyc0NJQWLVpYth0+fBiTycTKlStp27Yt4eHh1KhRg61bt1r2OXbsGCaTieXLl3PnnXdSpEgRmjVrRlxcHBs3bqRFixaEh4fTtm1bEhMTLa/r2LEjS5cuzbdMQohsLs+bLITQRWJiohYQEKD98ccfmqZp2pw5c7QyZcpozZs3t+xTvXp1beHChZqmadqXX36pLV++XDt48KC2c+dOrWPHjlq9evW0zMxM7fz581pISIi2du1ay2vPnTuXa9ukSZO0mjVraqtXr9YOHz6sffDBB1poaKi2YcMGTdM0rXXr1trAgQNd2r948eLa+PHjtQMHDmgffvihZjKZtJ9++slyjGeeeUarWrWqtnbtWm3Pnj3aQw89pBUrVszyPomJiVrLli21Pn36aKdOndJOnTqlZWRkOH38nAYMGKDde++9dtuWL1+umUwm7a677tLWr1+vHThwQIuJidHatGlj2WfFihUaoLVt21bbuHGjtmPHDq1y5craHXfcoXXo0EH7448/tN9//10rXbq0NmvWLMvrfvjhBy0kJES7evVqPv/bQghN0zQJUoQwkMaNG2uvv/66pmma1rlzZ23y5MlaSEiIdunSJe3ff//VAO3AgQMOX3vmzBkN0Pbs2aNpmqY9+OCD2lNPPWV5/t1339WioqK0zMxMTdM07erVq1p4eLi2efNmu+M8/fTT2uOPP65pmn2Q4uz+t99+u93zzZo104YPH65pmqZdvHhRCw4O1r744gvL84mJiVp4eLhdMJQzOLLdnt/xHcl5HjRN08aOHauVKlVKO336tGXbm2++qdWpU8eyPn78eC0yMlI7e/asZVuPHj206OhoLTk52bLt3nvv1V5++WXL+q5duzRAO3bsWJ5lEkIoUt0jhIG0bt2aDRs2oGkaGzdu5OGHH6ZWrVps2rSJX375haioKG6++WYADh48yOOPP86NN95I8eLFLY1C4+LiAOjevTvLly8nNTUVgE8//ZSuXbsSEKAuC4cOHeLKlSvcc889REREWP4++ugjS7WRLWf3r1+/vt3rKlasyOnTpwE4cuQI6enp3HrrrZbnS5QoQY0aNZw+R/kd35GUlBTCwsLstu3atYsHH3yQsmXLWrYdPXqU6tWr2+3z0EMPUbp0acu2uLg4unTpQnh4uN22atWqWdaLFCkCqLYwQoj8BeldACGE89q0acOiRYvYtWsXwcHB1KxZkzZt2rBhwwYuXLhA69atLft27NiRqlWrsnDhQkv7k7p165KWlmZ5XtM0Vq1aRbNmzdi4cSOzZ8+2vP7y5csArFq1ikqVKtmVIzQ0NFfZnN0/ODjY7jmTyZSrbcz1cPX4ZcqU4cKFC3bbdu3axciRI+22xcbGcuedd9qt59xn165dDB482LJ+9epV9u/fT4MGDSzbzp8/D2AXAAkhHJMgRQgDueOOO7h06RKzZ8+2BCRt2rRh2rRpXLhwgaFDhwJq3I/9+/ezcOFC7rjjDgA2bdpkd6ywsDAefvhhPv30Uw4dOkSNGjVo3Lix5fnatWsTGhpKXFycXfCTF1f3d+TGG28kODiYP/74gypVqgCQlJTEgQMH7AKEkJAQSwPi69WoUSO73jZJSUkcO3aMRo0a2e0XGxvLgAEDALh48WKufY4ePUpSUpLdtj179qBpGvXq1bNs27t3LzfccANlypRxS/mF8GcSpAhhIKVKlaJ+/fp8+umnvP322wDceeedPPbYY6Snp1uCg1KlSlG6dGkWLFhAxYoViYuLs+sFZNa9e3ceeOAB/vrrL0uvILNixYrx0ksvMXjwYLKysrj99ttJSkrit99+o3jx4vTq1eu69nekWLFi9OrVi2HDhhEZGUm5cuUYN24cAQEBmEwmy37R0dFs3bqVY8eOERERQWRkpKWaylXt27dn5MiRXLhwgVKlSrF7926CgoLsAovjx49z4cIFGjZsCKiMSWBgIHXr1rXsExsbS2RkJFWrVrXbdtNNNxEREWHZtnHjRtq1a1egsgpR2EibFCEMpnXr1mRmZtKmTRsAIiMjqV27NhUqVLC03QgICGDp0qVs376dunXrMnjwYF5//fVcx7r77ruJjIxk//79dOvWLdfzr776KmPGjGHq1KnUqlWLe++9l1WrVtm1sbie/R2ZNWsWLVu25IEHHiAmJoZWrVpRq1Ytu3YjL730EoGBgdSuXZuyZcta2tkURL169WjcuDGff/45oAKQGjVq2L3fzp07KVmypKVdj6N9du3alSv7smvXLruqnqtXr7JixQr69OlT4PIKUZiYNE3T9C6EEELkJTk5mUqVKjFz5kyPDbi2atUqhg0bxt69ewuckXHGvHnz+Prrr/npp5889h5C+BOp7hFC+JSdO3eyb98+br31VpKSkpg4cSKgBprzlPvvv5+DBw/y33//UblyZY+9T3BwMG+99ZbHji+Ev5FMihDCp+zcuZNnnnmG/fv3ExISQpMmTZg1a5ZdGxEhROEgQYoQQgghfJI0nBVCCCGET5IgRQghhBA+SYIUIYQQQvgkCVKEEEII4ZMkSBFCCCGET5IgRQghhBA+SYIUIYQQQvgkCVKEEEII4ZMkSBFCCCGET5IgRQghhBA+SYIUIYQQQvik/wMySh50nCdDLgAAAABJRU5ErkJggg==",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot transmission, compare to paper results, look similar\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 4.5))\n",
    "wavelengths_nm = td.C_0 / trans_g0.f / nm\n",
    "plt.plot(wavelengths_nm, trans_g0.values, color=\"red\", label=\"$\\delta=0$\")\n",
    "plt.plot(wavelengths_nm, trans_g20.values, color=\"blue\", label=\"$\\delta=20~nm$\")\n",
    "plt.xlabel(\"wavelength ($nm$)\")\n",
    "plt.ylabel(\"Transmission\")\n",
    "plt.xlim([1050, 1400])\n",
    "plt.ylim([0, 1])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Comparison\n",
    "\n",
    "Compare this plot to published results:\n",
    "\n",
    "\"diagram\"\n",
    "\n",
    "(Citation: Opt. Lett. 43, 1842-1845 (2018).  With permission from the Optical Society)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the metasurface demonstrated in this notebook, in Tidy3D's example library we have demonstrated a [dielectric metasurface absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/DielectricMetasurfaceAbsorber/), a [gradient metasurface reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GradientMetasurfaceReflector/), a [metalens at the visible frequency](https://www.flexcompute.com/tidy3d/examples/notebooks/Metalens/), and a [graphene metamaterial absorber](https://www.flexcompute.com/tidy3d/examples/notebooks/GrapheneMetamaterial/). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Metamaterials, gratings, and other periodic structures"
  ],
  "description": "This notebook demonstrates how to model a silicon resonator in Tidy3D FDTD.",
  "feature_image": "./img/high_q_silicon_feature_image.png",
  "features": [],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "high-Q, resonator, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  },
  "title": "Silicon Resonator Modeling in Tidy3D | Flexcompute",
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "036fedd18fd14aa49fc25e546a0b6624": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
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