AdjointPlugin12LightExtractor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adjoint inverse design of a quantum emitter light extractor\n",
    "\n",
    "> To install the jax module required for this feature, we recommend running pip install \"tidy3d[jax]\".\n",
    "\n",
    "> The cost of running the entire optimization is about 8 FlexCredits.\n",
    "\n",
    "In this tutorial, we will show how to perform the adjoint-based inverse design of a quantum emitter (QE) light extraction structure. We will use a `PointDipole` to model the QE embedded within an integrated dielectric waveguide. Then, we will build an optimization problem to maximize the extraction efficiency of the dipole radiation into a collection waveguide. In addition, we will show how to use `FieldMonitor` objects in adjoint simulations to calculate the flux radiated from the dipole. You can also find helpful information in this related [notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/).\n",
    "\n",
    "<img src=\"img/adjoint_12.png\" width=400 alt=\"Schematic of the light extractor\">\n",
    "\n",
    "If you are unfamiliar with inverse design, we recommend the [inverse design lectures](https://www.flexcompute.com/tidy3d/learning-center/inverse-design/) and this [introductory tutorial](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin1Intro/).\n",
    "\n",
    "Let's start by importing the Python libraries used throughout this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard python imports.\n",
    "from typing import List\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "import scipy as sp\n",
    "import optax\n",
    "import pickle\n",
    "\n",
    "# Import jax to be able to use automatic differentiation.\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "from jax import value_and_grad\n",
    "\n",
    "# Import regular tidy3d.\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "\n",
    "# Import the components we need from the adjoint plugin.\n",
    "import tidy3d.plugins.adjoint as tda\n",
    "from tidy3d.plugins.adjoint.utils.filter import ConicFilter\n",
    "from tidy3d.plugins.adjoint.utils.penalty import ErosionDilationPenalty\n",
    "from tidy3d.plugins.adjoint.web import run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation Set Up\n",
    "The coupling region (design region) extends a single-mode dielectric waveguide placed over a lower refractive index substrate. The QE is modeled as a `PointDipole` oriented in the `y`-direction. The QE is placed within the design region so we surround it with a constant refractive index region to protect it from etching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geometric parameters.\n",
    "cr_w = 1.0  # Coupling region width (um).\n",
    "cr_l = 3.0  # Coupling region length (um).\n",
    "wg_thick = 0.19  # Collection waveguide thickness (um).\n",
    "wg_width = 0.35  # Collection waveguide width (um).\n",
    "wg_length = 1.0  # Collection waveguide length (um).\n",
    "\n",
    "# Material.\n",
    "n_wg = 3.50  # Structure refractive index.\n",
    "n_sub = 1.44  # Substrate refractive index.\n",
    "\n",
    "# Fabrication constraints.\n",
    "min_feature = 0.06  # Minimum feature size.\n",
    "non_etch_r = 0.03  # Non-etched circular region radius (um).\n",
    "\n",
    "# Inverse design set up parameters.\n",
    "grid_size = 0.015  # Simulation grid size on design region (um).\n",
    "max_iter = 100  # Maximum number of iterations.\n",
    "iter_steps = 5  # Beta is increased at each iter_steps.\n",
    "beta_min = 1.0  # Minimum value for the tanh projection parameter.\n",
    "learning_rate = 0.1\n",
    "\n",
    "# Simulation wavelength.\n",
    "wl = 0.94  # Central simulation wavelength (um).\n",
    "bw = 0.04  # Simulation bandwidth (um).\n",
    "n_wl = 41  # Number of wavelength points within the bandwidth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate some variables used throughout the notebook. Here, we will also define the QE position and monitor planes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Minimum and maximum values of the permittivity.\n",
    "eps_max = n_wg**2\n",
    "eps_min = 1.0\n",
    "\n",
    "# Material definition.\n",
    "mat_wg = td.Medium(permittivity=eps_max)\n",
    "mat_sub = td.Medium(permittivity=n_sub**2)\n",
    "\n",
    "# Wavelengths and frequencies.\n",
    "wl_max = wl + bw / 2\n",
    "wl_min = wl - bw / 2\n",
    "wl_range = np.linspace(wl_min, wl_max, n_wl)\n",
    "freq = td.C_0 / wl\n",
    "freqs = td.C_0 / wl_range\n",
    "freqw = 0.5 * (freqs[0] - freqs[-1])\n",
    "run_time = 3e-12\n",
    "\n",
    "# Computational domain size.\n",
    "pml_spacing = 0.6 * wl\n",
    "size_x = wg_length + cr_l + pml_spacing\n",
    "size_y = cr_w + 2 * pml_spacing\n",
    "size_z = wg_thick + 2 * pml_spacing\n",
    "eff_inf = 10\n",
    "\n",
    "# Source position and monitor planes.\n",
    "cr_center_x = wg_length + cr_l / 2\n",
    "qe_pos = td.Box(center=(cr_center_x - 0.5, 0, 0), size=(0, 0, 0))\n",
    "qe_field_plan = td.Box.surfaces(center=(cr_center_x, 0, 0), size=(cr_l, cr_w, 2 * wg_thick))\n",
    "wg_mode_plan = td.Box(center=(wl / 4, 0, 0), size=(0, 4 * wg_width, 5 * wg_thick))\n",
    "\n",
    "# Number of points on design grid.\n",
    "nx_grid = int(cr_l / grid_size)\n",
    "ny_grid = int(cr_w / grid_size / 2)\n",
    "\n",
    "# xy coordinates of design grid.\n",
    "x_grid = np.linspace(cr_center_x - cr_l / 2, cr_center_x + cr_l / 2, nx_grid)\n",
    "y_grid = np.linspace(0, cr_w / 2, ny_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization Set Up\n",
    "We will start defining the density-based optimization functions to transform the design parameters into permittivity values. Here we include the `ConicFilter`, where we impose a minimum feature size fabrication constraint, and the tangent hyperbolic projection function, eliminating intermediary permittivity values as we increase the projection parameter `beta`. You can find more information in the [Inverse design optimization of a compact grating coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/AdjointPlugin6GratingCoupler/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conic_filter = ConicFilter(radius=min_feature, design_region_dl=grid_size)\n",
    "\n",
    "def tanh_projection(x, beta, eta=0.5):\n",
    "    tanhbn = jnp.tanh(beta * eta)\n",
    "    num = tanhbn + jnp.tanh(beta * (x - eta))\n",
    "    den = tanhbn + jnp.tanh(beta * (1 - eta))\n",
    "    return num / den\n",
    "\n",
    "def filter_project(x, beta, eta=0.5):\n",
    "    x = conic_filter.evaluate(x)\n",
    "    return tanh_projection(x, beta=beta, eta=eta)\n",
    "\n",
    "def pre_process(params, beta):\n",
    "    params1 = filter_project(params, beta=beta)\n",
    "    return params1\n",
    "\n",
    "def get_eps(params, beta: float = 1.00) -> jnp.ndarray:\n",
    "    \"\"\"Returns the permittivities after filter and projection transformations\"\"\"\n",
    "    params1 = pre_process(params, beta=beta)\n",
    "    eps = eps_min + (eps_max - eps_min) * params1\n",
    "    eps = jnp.maximum(eps, eps_min)\n",
    "    eps = jnp.minimum(eps, eps_max)\n",
    "    return eps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function includes a circular region of constant permittivity value surrounding the QE. The objective here is to protect the QE from etching. In applications such as single photon sources, a larger unperturbed region surrounding the QE can be helpful to reduce linewidth broadening, as stated in `J. Liu, K. Konthasinghe, M. Davanco, J. Lawall, V. Anant, V. Verma, R. Mirin, S. Nam, S. Woo, D. Jin, B. Ma, Z. Chen, H. Ni, Z. Niu, K. Srinivasan, \"Single Self-Assembled InAs/GaAs Quantum Dots in Photonic Nanostructures: The Role of Nanofabrication,\" Phys. Rev. Appl. 9(6), 064019 (2018)` [DOI: 10.1103/PhysRevApplied.9.064019](https://link.aps.org/doi/10.1103/PhysRevApplied.9.064019)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def include_constant_regions(eps, circ_center=[0, 0], circ_radius=1.0) -> jnp.ndarray:\n",
    "    # Build the geometric mask.\n",
    "    yv, xv = jnp.meshgrid(y_grid, x_grid)\n",
    "    geo_mask = jnp.where(\n",
    "        jnp.abs((xv - circ_center[0]) ** 2 + (yv - circ_center[1]) ** 2)\n",
    "        <= (2 * circ_radius) ** 2,\n",
    "        1,\n",
    "        0,\n",
    "    ) * eps_max\n",
    "    eps = jnp.maximum(geo_mask, eps)\n",
    "    return eps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we define a function to update the `JaxCutomMedium` using the permittivity distribution. The simulation will include mirror symmetry concerning the `y`-direction, so only the upper half of the design region is returned by this function during the optimization process. To get the whole structure, you need to set `unfold=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_design(eps, unfold=False) -> List[tda.JaxStructure]:\n",
    "    # Definition of the coordinates x,y along the design region.\n",
    "    eps_val = jnp.array(eps).reshape((nx_grid, ny_grid, 1, 1))\n",
    "    coords_x = [(cr_center_x - cr_l / 2) + ix * grid_size for ix in range(nx_grid)]\n",
    "\n",
    "    if unfold == False:\n",
    "        # Creation of a JaxCustomMedium using the values of the design parameters.\n",
    "        coords_yp = [0 + iy * grid_size for iy in range(ny_grid)]\n",
    "        coords = dict(x=coords_x, y=coords_yp, z=[0], f=[freq])\n",
    "        eps_jax = {\n",
    "            f\"eps_{dim}{dim}\": tda.JaxDataArray(values=eps_val, coords=coords)\n",
    "            for dim in \"xyz\"\n",
    "        }\n",
    "        eps_dataset = tda.JaxPermittivityDataset(**eps_jax)\n",
    "        eps_medium = tda.JaxCustomMedium(eps_dataset=eps_dataset, interp_method=\"linear\")\n",
    "        box = tda.JaxBox(center=(cr_center_x, cr_w / 4, 0), size=(cr_l, cr_w / 2, wg_thick))\n",
    "        structure = [tda.JaxStructure(geometry=box, medium=eps_medium)]\n",
    "\n",
    "    else:\n",
    "        # Creation of a CustomMedium using the values of the design parameters.\n",
    "        coords_y = [-cr_w / 2 + iy * grid_size for iy in range(2 * ny_grid)]\n",
    "        coords = dict(x=coords_x, y=coords_y, z=[0], f=[freq])\n",
    "        eps_jax = {\n",
    "            f\"eps_{dim}{dim}\": tda.JaxDataArray(\n",
    "                values=jnp.concatenate((jnp.fliplr(jnp.copy(eps_val)), eps_val), axis=1), coords=coords\n",
    "            )\n",
    "            for dim in \"xyz\"\n",
    "        }\n",
    "        eps_dataset = tda.JaxPermittivityDataset(**eps_jax)\n",
    "        eps_medium = tda.JaxCustomMedium(\n",
    "            eps_dataset=eps_dataset, interp_method=\"linear\"\n",
    "        )\n",
    "        box = tda.JaxBox(\n",
    "            center=(cr_center_x, 0, 0), size=(cr_l, cr_w, wg_thick)\n",
    "        )\n",
    "        structure = [tda.JaxStructure(geometry=box, medium=eps_medium)]\n",
    "    return structure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next cell, we define the output waveguide and the substrate, as well as the simulation monitors. It is worth mentioning the inclusion of a `ModeMonitor` in the output waveguide and a `FieldMonitor` box surrounding the dipole source to calculate the total radiated power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input/output waveguide.\n",
    "waveguide = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-eff_inf, -wg_width / 2, -wg_thick / 2),\n",
    "        rmax=(wg_length, wg_width / 2, wg_thick / 2),\n",
    "    ),\n",
    "    medium=mat_wg,\n",
    ")\n",
    "\n",
    "# Substrate layer.\n",
    "substrate = td.Structure(\n",
    "    geometry=td.Box.from_bounds(\n",
    "        rmin=(-eff_inf, -eff_inf, -eff_inf), rmax=(eff_inf, eff_inf, -wg_thick / 2)\n",
    "    ),\n",
    "    medium=mat_sub,\n",
    ")\n",
    "\n",
    "# Point dipole source located at the center of TiO2 thin film.\n",
    "dp_source = td.PointDipole(\n",
    "    center=qe_pos.center,\n",
    "    source_time=td.GaussianPulse(freq0=freq, fwidth=freqw),\n",
    "    polarization=\"Ey\",\n",
    ")\n",
    "\n",
    "# Mode monitor to compute the FOM.\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=n_wg)\n",
    "mode_monitor_fom = td.ModeMonitor(\n",
    "    center=wg_mode_plan.center,\n",
    "    size=wg_mode_plan.size,\n",
    "    freqs=[freq],\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"mode_monitor_fom\",\n",
    ")\n",
    "\n",
    "# Field monitor to compute the FOM.\n",
    "field_monitor_fom = []\n",
    "for i, plane in enumerate(qe_field_plan):\n",
    "    field_monitor_fom.append(\n",
    "        td.FieldMonitor(\n",
    "            center=plane.center,\n",
    "            size=plane.size,\n",
    "            freqs=[freq],\n",
    "            colocate=False,\n",
    "            name=f\"field_monitor_fom_{i}\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Mode monitor to compute spectral response.\n",
    "mode_spec = td.ModeSpec(num_modes=1, target_neff=n_wg)\n",
    "mode_monitor = td.ModeMonitor(\n",
    "    center=wg_mode_plan.center,\n",
    "    size=wg_mode_plan.size,\n",
    "    freqs=freqs,\n",
    "    mode_spec=mode_spec,\n",
    "    name=\"mode_monitor\",\n",
    ")\n",
    "\n",
    "# Field monitor to compute spectral response.\n",
    "field_monitor = []\n",
    "for i, plane in enumerate(qe_field_plan):\n",
    "    field_monitor.append(\n",
    "        td.FieldMonitor(\n",
    "            center=plane.center, size=plane.size, freqs=freqs, name=f\"field_monitor_{i}\"\n",
    "        )\n",
    "    )\n",
    "\n",
    "# Field monitor to visualize the fields.\n",
    "field_monitor_xy = td.FieldMonitor(\n",
    "    center=(size_x / 2, 0, 0),\n",
    "    size=(size_x, size_y, 0),\n",
    "    freqs=[freq],\n",
    "    name=\"field_xy\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we have a function that receives the design parameters from the optimization algorithm and then gathers the simulation objects altogether to create a `JaxSimulation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_adjoint_sim(param, beta: float = 1.00, unfold=False) -> tda.JaxSimulation:\n",
    "    # Builds the design region from the design parameters.\n",
    "    eps = get_eps(param, beta)\n",
    "    eps = include_constant_regions(\n",
    "        eps, circ_center=[qe_pos.center[0], qe_pos.center[1]], circ_radius=non_etch_r\n",
    "    )\n",
    "    structure_jax = update_design(eps, unfold=unfold)\n",
    "\n",
    "    # Creates a uniform mesh for the design region.\n",
    "    adjoint_dr_mesh = td.MeshOverrideStructure(\n",
    "        geometry=td.Box(center=(cr_center_x, 0, 0), size=(cr_w, cr_l, wg_thick)),\n",
    "        dl=[grid_size, grid_size, grid_size],\n",
    "        enforce=True,\n",
    "    )\n",
    "\n",
    "    return tda.JaxSimulation(\n",
    "        size=[size_x, size_y, size_z],\n",
    "        center=[size_x / 2, 0, 0],\n",
    "        grid_spec=td.GridSpec.auto(\n",
    "            wavelength=wl_max,\n",
    "            min_steps_per_wvl=15,\n",
    "            override_structures=[adjoint_dr_mesh],\n",
    "        ),\n",
    "        symmetry=(0, -1, 0),\n",
    "        structures=[substrate, waveguide],\n",
    "        input_structures=structure_jax,\n",
    "        sources=[dp_source],\n",
    "        monitors=[field_monitor_xy],\n",
    "        output_monitors=[mode_monitor_fom] + field_monitor_fom,\n",
    "        run_time=run_time,\n",
    "        subpixel=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Light Extractor Structure\n",
    "Let's create a random initial permittivity distribution and verify if all the simulation objects are in the correct places. We can safely ignore the warning regarding the touching `JaxStructures` because we will include only the upper one in the optimization due to the simulation symmetry about the `y`-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:jax._src.xla_bridge:An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_par = np.random.uniform(0, 1, (nx_grid, ny_grid))\n",
    "init_par = sp.ndimage.gaussian_filter(init_par, 1)\n",
    "init_design = make_adjoint_sim(init_par, beta=beta_min, unfold=True)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, tight_layout=True, figsize=(12, 4))\n",
    "init_design.plot_eps(z=0, ax=ax1, monitor_alpha=0.0)\n",
    "init_design.plot_eps(y=0, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also look at the collection waveguide mode to ensure we have considered the correct one in the `ModeMonitor` setup. We use the `ModeSolver` plugin to calculate the first two waveguide modes, as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:26:24 -03 </span>Mode solver created with                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #808000; text-decoration-color: #808000\">task_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'fdve-1902060c-c3b3-4ab6-8be0-a9afc3e8c896'</span>,               \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #808000; text-decoration-color: #808000\">solver_id</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'mo-6d7cbf93-d1c5-4a26-8acf-ac85d3c25e5e'</span>.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:26:24 -03\u001b[0m\u001b[2;36m \u001b[0mMode solver created with                                           \n",
       "\u001b[2;36m             \u001b[0m\u001b[33mtask_id\u001b[0m=\u001b[32m'fdve-1902060c-c3b3-4ab6-8be0-a9afc3e8c896'\u001b[0m,               \n",
       "\u001b[2;36m             \u001b[0m\u001b[33msolver_id\u001b[0m=\u001b[32m'mo-6d7cbf93-d1c5-4a26-8acf-ac85d3c25e5e'\u001b[0m.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8090fc40a2ce4e1abf33690f91e9c07a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
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     "data": {
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      ],
      "text/plain": [
       "\n"
      ]
     },
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       "model_id": "9bd6f93e91654aab8f585d621e375f18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:26:30 -03 </span>Mode solver status: queued                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:26:30 -03\u001b[0m\u001b[2;36m \u001b[0mMode solver status: queued                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:26:43 -03 </span>Mode solver status: running                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:26:43 -03\u001b[0m\u001b[2;36m \u001b[0mMode solver status: running                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:26:55 -03 </span>Mode solver status: success                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:26:55 -03\u001b[0m\u001b[2;36m \u001b[0mMode solver status: success                                        \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "353a683ac2eb49c1a41c4ab1a01e917e",
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      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tidy3d.plugins.mode import ModeSolver\n",
    "from tidy3d.plugins.mode.web import run as run_mode_solver\n",
    "\n",
    "sim_init = init_design.to_simulation()[0].copy(\n",
    "    update=dict(monitors=[field_monitor_xy, mode_monitor] + field_monitor)\n",
    ")\n",
    "\n",
    "mode_solver = ModeSolver(\n",
    "    simulation=sim_init,\n",
    "    plane=wg_mode_plan,\n",
    "    mode_spec=td.ModeSpec(num_modes=2),\n",
    "    freqs=[freq],\n",
    ")\n",
    "modes = run_mode_solver(mode_solver, reduce_simulation=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After inspecting the mode field distribution, we can confirm that the fundamental waveguide mode is mainly oriented in the `y`-direction, thus matching the dipole orientation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(10, 6), tight_layout=True)\n",
    "for mode_ind in range(2):\n",
    "    for field_ind, field_name in enumerate((\"Ey\", \"Ez\")):\n",
    "        ax = axs[mode_ind, field_ind]\n",
    "        mode_solver.plot_field(field_name, \"abs\", mode_index=mode_ind, f=freq, ax=ax)\n",
    "        ax.set_title(f\"index={mode_ind}, {field_name}(y, z)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we will calculate the initial coupling efficiency to see how this random structure performs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:27:01 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'initial QE light extractor'</span> with task_id             \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-9a5cfff1-a370-4298-845f-62b9320fbe97'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:01 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'initial QE light extractor'\u001b[0m with task_id             \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-9a5cfff1-a370-4298-845f-62b9320fbe97'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a37</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">0-4298-845f-62b9320fbe97'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=919491;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=388607;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=919491;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=537394;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=919491;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[32m-9a5cfff1-a37\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=919491;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[32m0-4298-845f-62b9320fbe97'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21f63342dd0648479f58ac2135b5de23",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:27:05 -03 </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:05 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "503168de20e54e0f90824538639087be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:27:09 -03 </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:09 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:27:15 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.061</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:15 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.061\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25880332c6de4f19a0fd3b38f33cc05b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:27:29 -03 </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">12</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:29 -03\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m12\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27ece9577d294188a621fb25b27412e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:27:41 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:41 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a37</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">0-4298-845f-62b9320fbe97'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=906863;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=202441;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=906863;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=977097;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=906863;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[4;34m-9a5cfff1-a37\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=906863;https://tidy3d.simulation.cloud/workbench?taskId=fdve-9a5cfff1-a370-4298-845f-62b9320fbe97\u001b\\\u001b[4;34m0-4298-845f-62b9320fbe97'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4227c61d7e7443dd8a7b3471aa206c07",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:27:48 -03 </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:27:48 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data = web.run(sim_init, task_name=\"initial QE light extractor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The modal coupling efficiency is normalized by the dipole power. That is necessary because the dipole power will likely change significantly when the optimization algorithm modifies the design region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8973vxcyZM5HL5TB37lz8x3/8BzZt2uT2uffee3HIIYdg0qRJKJVK2HXXXfG5z31u0PYP5bhqtYrly5dj5513RhiGmDdvHs4555ymPiTf//73sXjxYhQKBbS3t+PNb34z7rnnHgDAggUL8Pe//x3/93//B8YYGGN4y1veAqB/z6M77rgD++23H/L5PKZOnYqPfOQjWLlyZd0+H/vYx1AqlbBy5UocffTRKJVKmDZtGj7zmc8gSZJBx+CnP/0pjjzySHcPd9ppJ1x22WV1x77lLW/B3XffjZdeesm1fcGCBf2e82Mf+5jbr/Hn4osvHrRNg7U3iiKcdtppbhtjDJ/85Cfxr3/9C4888siAx995552YMWMGjjnmGLdt2rRpeP/734+f/vSn7r4++eSTePLJJ3HKKac44QgATjvtNCilcOedd25WP4htmM2IPBoOZ599Nn7/+9/jiiuuwHPPPYdbb70V3/zmN/GpT31qjDo2OBR5RBAEQRAEQRBEHa+++ioWL17s/GN22203rFy5EnfeeSd6e3sRBIHb97TTTsO0adNw0UUXoaenBwBw8cUX45JLLsGSJUvwyU9+Ek8//TS+/vWv409/+hMefvhh+L6PWq2GpUuXolqt4owzzsDMmTOxcuVK/OxnP0NHRwfa2trw97//He9617uwaNEiXHrppQjDEM899xwefvjhAds/lOOklHj3u9+N3/72tzjllFOw++67429/+xuuu+46PPPMM7jrrrvcvpdccgkuvvhiHHzwwbj00ksRBAH+8Ic/4P7778fb3/52XH/99TjjjDNQKpVwwQUXAABmzJjRb/tuueUWnHDCCTjggANw5ZVXYvXq1fjKV76Chx9+GH/5y18wadIkt2+SJFi6dCkOPPBAfPnLX8avf/1rXHPNNdhpp53wyU9+csBxuOWWW1AqlbBs2TKUSiXcf//9uOiii9DZ2Ymrr74agE4T27RpE/71r3/huuuuAwCUSqV+z/mJT3wCS5Ysqdv2y1/+Ej/4wQ8wffp0t23dunUDts3S0tKCMAwBAH/5y19QLBax++671+2zePFi9/khhxzS77n+8pe/YN999wXn9TESixcvxje/+U0888wz2HPPPfGXv/wFALD//vvX7Td79mzMnTvXfU4QdShoIWjEBw+dAw44AD/5yU9w/vnn49JLL8XChQtx/fXX48Mf/vAIrz8KqO2MTZs2KQBq06ZNE90UgiAIgiDGAfr/foIYPh/96EcV51z96U9/6vOZlFIppdS3v/1tBUAdcsghKo5j9/maNWtUEATq7W9/u0qSxG2/8cYbFQB18803K6WU+stf/qIAqDvuuKPfdlx33XUKgFq7du2w2j+U4773ve8pzrn6zW9+U7d9xYoVCoB6+OGHlVJKPfvss4pzrt7znvfU9UepdCyUUmqPPfZQhx12WJ/rPPDAAwqAeuCBB5RSStVqNTV9+nT1hje8QZXLZbffz372MwVAXXTRRW7b8ccfrwCoSy+9tO6c++yzj9pvv/0GHgSlVG9vb59tn/jEJ1ShUFCVSsVtO/LII9X8+fMHPV8znn32WdXW1qb+7d/+rW4eQK+WB/359re/XdeOHXfcsc81enp6FAB13nnnDdiWYrGoPv7xj/fZfvfddysA6pe//KVSSqmrr75aAVAvv/xyn30POOAA9cY3vnGo3Se2E55//nnlCa7kn68b0c9ZHzpMnXXWWRPdjc2C0tYIgiAIgiAIgnBIKXHXXXfhqKOO6hOZAeg0oiwnn3xynQ/Nr3/9a9RqNZx11ll1ESAnn3wyWltbcffddwMA2traAAC/+tWv0Nvb27QtNgLnpz/9KeQw/uI/lOPuuOMO7L777thtt92wbt069/PWt74VAPDAAw8AAO666y5IKXHRRRf1iWhpHIuh8Oc//xlr1qzBaaedVueFdOSRR2K33XZz45Pl1FNPrXt/6KGH4oUXXhj0Wvl83r3u6urCunXrcOihh6K3txdPPfXUsNveSE9PD97znvegvb0dP/zhD+vmwb333jukn6VLl7pjyuWyi0LKYsepXC4P2J6hHm9/97fvYNchtmPGKW1tS4TS1giCIAiCIAiCcKxduxadnZ14wxveMKT9Fy5cWPf+pZdeAgDsuuuudduDIMCOO+7oPl+4cCGWLVuGa6+9Fj/4wQ9w6KGH4t3vfjc+8pGPOGHpAx/4AP7rv/4LJ510Es477zy87W1vwzHHHINjjz22j5CTZSjHPfvss/jHP/6BadOmNT3HmjVrAADPP/88OOd9Kh+NlP7GBwB22203/Pa3v63blsvl+rSxvb0dGzduHPRaf//733HhhRfi/vvvR2dnZ91nWV+pkXLyySfj+eefx+9+9ztMmTKl7rPG1LahkM/nm/pNVSoV9/loHG9/97fvYNchtmNGbJg9us2YCEg8IgiCIAiCIAhixGzOQvuaa67Bxz72Mfz0pz/FPffcg09/+tO48sor8fvf/x5z585FPp/HQw89hAceeAB33303fvnLX+K2227DW9/6Vtxzzz39Vt4aynFSSuy555649tprm55j3rx5I+7XaDJQdbGB6OjowGGHHYbW1lZceuml2GmnnZDL5fDYY4/h3HPPHVYkVzO+8pWv4Ic//CG+//3vY++99+7z+apVq4Z0nra2NjeHZs2ahQceeABKqbqortdeew2A9iQaiFmzZrl9szQeP2vWLLe98T6/9tprzmOJIIgUSlsjCIIgCIIgCMIxbdo0tLa24oknnhjR8baUdGOZ6VqthhdffLFPqek999wTF154IR566CH85je/wcqVK7FixQr3Oeccb3vb23DttdfiySefxBe+8AXcf//9Lq2sPwY7bqeddsKGDRvwtre9DUuWLOnzYyODdtppJ0gp8eSTTw54vaGmsPU3PnbbaJXifvDBB7F+/XrccsstOPPMM/Gud70LS5YsQXt7e599h5t+95vf/Aaf+cxncNZZZ/Vr4Dtr1qwh/dx2223umL333hu9vb34xz/+UXeuP/zhD+7zgdh7773x2GOP9RHG/vCHP6BQKGCXXXapO8+f//znuv1effVV/Otf/xr0OsR2zHactkbiEUEQBEEQBEEQDs45jj76aPzv//5vn8U1AKhBFkFLlixBEAT46le/Wrfvf//3f2PTpk048sgjAQCdnZ2I47ju2D333BOcc5dOtGHDhj7ntwv7ZilHlqEc9/73vx8rV67Et771rT77lstlVznu6KOPBuccl156aR9RItu/YrGIjo6Ofttk2X///TF9+nSsWLGirg+/+MUv8I9//MONz+ZiI5aybazVarjpppv67FssFoecxvbaa6/h/e9/Pw455BBXsa0ZI/E8+vd//3f4vl/XRqUUVqxYgTlz5uDggw+ua8dTTz2FKIrctmOPPRarV6/Gj3/8Y7dt3bp1uOOOO3DUUUc5j6M99tgDu+22G775zW8iSRK379e//nUwxnDssccOaSyI7ZCRikfbQN4apa0RBEEQBEEQBFHHFVdcgXvuuQeHHXaYK2P/2muv4Y477sBvf/vbulLyjUybNg3nn38+LrnkErzjHe/Au9/9bjz99NO46aabcMABB+AjH/kIAOD+++/H6aefjve9733YZZddEMcxvve970EIgfe+970AgEsvvRQPPfQQjjzySMyfPx9r1qzBTTfdhLlz5w5Ysn0oxx133HG4/fbbceqpp+KBBx7Am970JiRJgqeeegq33347fvWrX2H//ffHzjvvjAsuuACXXXYZDj30UBxzzDEIwxB/+tOfMHv2bFx55ZUAgP322w9f//rXcfnll2PnnXfG9OnTnfl2Ft/3cdVVV+GEE07AYYcdhg9+8INYvXo1vvKVr2DBggU4++yzR3rb6jj44IPR3t6O448/Hp/+9KfBGMP3vve9puLffvvth9tuuw3Lli3DAQccgFKphKOOOqrpeT/96U9j7dq1OOecc/CjH/2o7rNFixZh0aJFAEbmeTR37lycddZZuPrqqxFFEQ444ADcdddd+M1vfoMf/OAHdSl8559/Pr7zne/gxRdfxIIFCwBo8eiNb3wjTjjhBDz55JOYOnUqbrrpJiRJgksuuaTuWldffTXe/e534+1vfzv+4z/+A0888QRuvPFGnHTSSdh9992H3XZiO2HEnkckHhEEQRAEQRAEsY0xZ84c/OEPf8DnP/95/OAHP0BnZyfmzJmDI444AoVCYdDjL774YkybNg033ngjzj77bEyePBmnnHIKrrjiCvi+DwDYa6+9sHTpUvzv//4vVq5ciUKhgL322gu/+MUv8MY3vhEA8O53vxv//Oc/cfPNN2PdunWYOnUqDjvsMFxyySXOVLsZQzmOc4677roL1113Hb773e/iJz/5CQqFAnbccUeceeaZLsUJ0GLUwoULccMNN+CCCy5AoVDAokWLcNxxx7l9LrroIrz00kv40pe+hK6uLhx22GFNxSMA+NjHPoZCoYAvfvGLOPfcc1EsFvGe97wHV1111YDC3HCYMmUKfvazn+E///M/ceGFF6K9vR0f+chH8La3va0u2gcATjvtNDz++OP49re/jeuuuw7z58/vVzxau3YtkiTBsmXL+ny2fPlyJx6NlC9+8Ytob2/HN77xDdxyyy143eteh+9///v40Ic+NOixQgj8/Oc/x2c/+1l89atfRblcxgEHHIBbbrmlj0H5u971Lvz4xz/GJZdcgjPOOAPTpk3D5z73OVx00UWb1X5iG0eN0Cts69eOwNRgcafbGJ2dnWhra8OmTZvQ2to60c0hCIIgCGKMof/vJwiCIAhic3jhhRew6y6vQ+3Xl47o+GVf+zkwdzGuu+66UW7Z+EGRRwRBEARBEARBEARBEIMx0iqF20DMDolHBEEQBEEQBEEQBEEQgzFSz6NtABKPCIIgCIIgCIIgCIIgBoMijwiCIAiCIAiCIAiCIIh+oWprBEEQBEEQBEEQBEEQRL9sx9XW+EQ34Gtf+xoWLFiAXC6HAw88EH/84x8H3P/666/Hrrvuinw+j3nz5uHss89GpVIZp9YSBEEQBEEQBEEQBEFsX0yoeHTbbbdh2bJlWL58OR577DHstddeWLp0KdasWdN0/1tvvRXnnXceli9fjn/84x/47//+b9x222343Oc+N84tJwiCIAiCIAiCIAhiu0Kqkf1Q2trmce211+Lkk0/GCSecAABYsWIF7r77btx8880477zz+uz/u9/9Dm9605vwoQ99CACwYMECfPCDH8Qf/vCHcW03QRAEQRAEMXSklHj11VfR0tICxthEN4cgCILYhlBKoaurC7NnzwbnYxwfQ9XWxp9arYZHH30U559/vtvGOceSJUvwyCOPND3m4IMPxve//3388Y9/xOLFi/HCCy/g5z//OY477rh+r1OtVlGtVt37zs7O0esEQRAEQRAEMSivvvoq5s2bN9HNIAiCILZhXnnlFcydO3dsL0LV1safdevWIUkSzJgxo277jBkz8NRTTzU95kMf+hDWrVuHQw45BEopxHGMU089dcC0tSuvvBKXXHLJqLadIAiCIAiCGDotLS3mFQdAkUcEQRDEaKIAyMz/14wh23G1tQk3zB4ODz74IK644grcdNNNeOyxx/DjH/8Yd999Ny677LJ+jzn//POxadMm9/PKK6+MY4sJgiAIgiC2LB566CEcddRRmD17NhhjuOuuuwY95sEHH8S+++6LMAyx884745ZbbhnWNdNUNUY/9EM/W8EPAx/wZ6LbRz/00/cH45MWPWLPo7Fv2lgzYZFHU6dOhRACq1evrtu+evVqzJw5s+kxn//853HcccfhpJNOAgDsueee6OnpwSmnnIILLrigaX5jGIYIw3D0O0AQBEEQBLEV0tPTg7322gsf//jHccwxxwy6/4svvogjjzwSp556Kn7wgx/gvvvuw0knnYRZs2Zh6dKl49BigiDGA4ahL7wZGNS2sBomiOFCaWvjTxAE2G+//XDffffh6KOPBqDNFO+77z6cfvrpTY/p7e3tIxAJIQBokyyCIAiCIAhiYI444ggcccQRQ95/xYoVWLhwIa655hoAwO67747f/va3uO6660g8IohthOEIR82O2ZKFpK2lncRWwnasO0xotbVly5bh+OOPx/7774/Fixfj+uuvR09Pj6u+9tGPfhRz5szBlVdeCQA46qijcO2112KfffbBgQceiOeeew6f//zncdRRRzkRiSAIgiAIghg9HnnkESxZsqRu29KlS3HWWWdNTIMIgiAIYqLYjj2PJlQ8+sAHPoC1a9fioosuwqpVq7D33nvjl7/8pTPRfvnll+sijS688EIwxnDhhRdi5cqVmDZtGo466ih84QtfmKguEARBEARBbNOsWrWqaYGTzs5OlMtl5PP5PsdQtVuC2HoYSdRRs3NsKVE9A/VnS2onsZUyYvFodJsxEUyoeAQAp59+er9pag8++GDde8/zsHz5cixfvnwcWkYQBEEQBEGMBKp2SxDbH1uCMDMUIczuM9FtJYitja2q2hpBEARBEAQxvsycObNpgZPW1tamUUcAVbsliK0BZv4jCGIYSDmyH0pbIwiCIAiCILZlDjroIPz85z+v23bvvffioIMO6vcYqnZLEARBbJNsx55HFHlEEARBEASxHdHd3Y3HH38cjz/+OADgxRdfxOOPP46XX34ZgI4a+uhHP+r2P/XUU/HCCy/gnHPOwVNPPYWbbroJt99+O84+++yJaD6xHcIG+Y8YPmM1bhN1T0ZyXZo/xIiQamQ/W792RJFHBEEQBEEQ2xN//vOfcfjhh7v3y5YtAwAcf/zxuOWWW/Daa685IQkAFi5ciLvvvhtnn302vvKVr2Du3Ln4r//6LyxdunTc205s+2zLJeO3FMZDMNkS/I8IYkzYjiOPSDwiCIIgCILYjnjLW94CNcA/Ym+55Zamx/zlL38Zw1YR2zOjKWaQGXL/jHeUzXgJSJvbLxK6iOGgqNoaQRAEQRAEQRDE+LC9pwsNtf+jJWps7+NNEKOCwjYRQTRSSDwiCIIgCIIgCIIgCIIYDEpbIwiCIAiCIAiCGBu21ZSp4TISU+csw+nTlhBtRGmEI2Ok947GeRzYjtPWqNoaQRAEQRAEQUwQg1US25ori010u7eUMRvNcZiwucJY/c8I2k0MzGjcu635+2KrYcTV1oanHl188cVgjNX97LbbbmPUqaFBkUcEQRAEQRAEMUaM9gJucyJRxgtatKZstWMxmEDU+PkEpORs7Ubr41X1LsuW+H2xdaHGNfJojz32wK9//Wv33vMmVr4h8YggCIIgCIIgCIIgCGILwvM8zJw5c6Kb4aC0NYIgCIIgCILYTCYqzWxLS22b6Os3Y6LGZUsciwHZjLS0oRwz7uPRmGo3kn6NdpO2kFTOLeG7YmtFSTWynxFE5z377LOYPXs2dtxxR3z4wx/Gyy+/PAY9GjoUeUQQBEEQBEEQQ2BrWWxtdSk4Q1nUj0Ja1HiZaG8t88QxWqJK9jwTWVlqoP4wttWn2I0m2XZRStsQGWnaGoAkSdDZ2Vm3LQxDhGHYZ98DDzwQt9xyC3bddVe89tpruOSSS3DooYfiiSeeQEtLy4jbsDmQeEQQBEEQBEFs92ypi7vNYbwWhsMau5EKFc2OG4EIMNbC2rDn0WDjMVZCx1hH4fQj0oy5sLkFRBc1srV8t5CQNERG7Hmk8Mc//hFtbW11m5cvX46LL764z+5HHHGEe71o0SIceOCBmD9/Pm6//XaceOKJI2vDZkLiEUEQBEEQBEEQBEEQxGBshmH24sWLcc8999RtbhZ11IxJkyZhl112wXPPPTey648CJB4RBEEQBEEQWz1by1/3J4qJSGWrb8AY3B97zolMkRouwx2H0axqNt5ROQOkiI1XCmHzi4/9vNnav4+oStsAjHTeKEAIgdbW1hEd3t3djeeffx7HHXfcyK4/CpB4RBAEQRAEQWwRbO0LrhExWgv6IS5oRnPRPuj9Gi+xYgRiwFik6Aw4HmPhKwQM3OctIYVrvAS+LaGv2Ha/w0hMSlFypAcOb/fPfOYzOOqoozB//ny8+uqrWL58OYQQ+OAHPzjCBmw+JB4RBEEQBEEQ48R2XOFnPDxmLIMs1LcM35mRFn0eYOU2QjPkzR2PCRXRRu3cY3A/sjS5NyMVMrfE75AxbdPm3OMxEu22azFpxIbZwzvuX//6Fz74wQ9i/fr1mDZtGg455BD8/ve/x7Rp00Z4/c2HxCOCIAiCIAiCIAiCIIjB2AzPo+Hwox/9aGTXGUNIPCIIgiAIgiCI0WALSZ0ZahTS+EZ+jDSypb/z9BPxshnl4keSyjbyqKPhjMdI82T6Y7TuRbNzjX5k2MjJtq1Ju5qk1A0nCm1Ez8FE+E5lGYdIpG09Cmm80ta2REg8IgiCIAiCIIjhMKYLwM1d2DesbAZZPI56ClvTsWnep+Esvvu2bxBhINuWzUhla9aGIbd7GGMxMMMQaIZ8jrFiCPclw6jMvz7j3Kyvg4iOw73kluL3NVzGQUzarlPatnGG/S3ywAMPjEU7CIIgCIIgCGJiYWxoP5sNH+BntM/dQD/tZ0P0o+p3n6Zj07cNLPPfcGAN/w12ncHbNnyG3O4hjkXjeYc+NoPNmcHnVbNrDvW/odPk+qP2DA10zc35fGD6HYNR/47oj4G+O0bwPTIubd7GkGpkP1tTVch+GPbT8453vAM77bQTLr/8crzyyitj0SaCIAiCIAiCIAiCIIgtCznCn61fOxq+eLRy5UqcfvrpuPPOO7Hjjjti6dKluP3221Gr1caifQRBEARBEASx+YxLRFEjI4sK2JyokP6jcjLXHqC/o1M1qv+Io3QDH97PkNo5yNiO230fvF2DRfIML+pn8Pk1pPMMMN6DtW1wGs7ZMO5DnXf1cyh7/0Yrcq/5NZv2cwv87hj4+EEYo/6MPHJty0RJNbKf7VE8mjp1Ks4++2w8/vjj+MMf/oBddtkFp512GmbPno1Pf/rT+H//7/+NRTsJgiAIgiAIoi/jlmo2GKMnEjXfeegiy8DnbEghGka7+m/bwOPbVDRybRliCs4A/axnBCLCaM6bQfx3mosQwxPNhiO0DOt6I2xT47WGJmRixAJSX9I2Da8d/TP8OT/Uudzs2kP77hjouRy+yDgIY/j9udWLSdtx5NFmGWbvu+++mDlzJqZMmYIvfvGLuPnmm3HTTTfhoIMOwooVK7DHHnuMVjsJgiAIgiCI7ZEtyotj5JENQ14kDRL1MWg7+lvrZkoEqax58Kgb6NYv5NM3vM/n7qNB+lzfJJmez/TJXqfemJf3PWYkDKd62wBztamAM6TrN+zX0Och0+d6w53LmTHMnmuQ0lPZdva9P5n5p+rNyBtNlvtEHA1wnYHa0dS8eShV4EbN/Hxoxw4oIvdH5l70P+6N1x/tSn7bAduACDRSRjTjoyjCnXfeiXe+852YP38+fvWrX+HGG2/E6tWr8dxzz2H+/Pl43/veN9ptJQiCIAiCILZlJiRiqBmblzKyWdFEQ2gHYwP9eM2Paxqx06RvwxnzftKG+vSZ9b2ObqsPxnwAYsCfbP/661P2ugNHvIwkMqShv9l+DzhX0/GoG4vGfgznZwhRQHXX6ed6feeNn/np+3m/4zPCiLjs+LhxzDZ/GJE0Tce3n3EaUaRLXdsyY7jZkUANl2ncv2k/hjE3+h33LEP8fqNIJMdI09a2hby1YUcenXHGGfjhD38IpRSOO+44fOlLX8Ib3vAG93mxWMSXv/xlzJ49e1QbShAEQRAEQRAEQRAEMWGMNFhr69eOhi8ePfnkk7jhhhtwzDHHIAzDpvtMnToVDzzwwGY3jiAIgiAIgtiGmdDIotFh9NLR+n7ePJ1LDH4plt0nAZD5ozfjsKsfpmQmpcVeawipbENNz8qkqqV9SdvG6iJC+vZVuVUah7LpWs2yyLLNyaR1NU/X6b/N/ae9WRpWjf2OQ5NIl7p7WR+BNRz6BC8MOP36u44Y5Nr125WSZk4l/bejXzJphhlsGqWqiz7KjG9/F2A2Yq7/udb8OHve7D1sMuftdfvxrmp+zUFQcoTfEw1jtpnpncAg6XvEkBgkU7P/47aBIR+2eHTfffcNflLPw2GHHTaiBhEEQRAEQRDbKuOdijZ6IhEwgpSXIS7OgcGFomafNxNcGqnzNwIAJHVCErPbmy3m3QJ0sH43tKNhIa/bXi9YsDpBqdm5pOufFi94Xfv0NtN0tyiT6bWHs2C3ze5n/6YCW7806VODIDAcIUc1rFSHY1HVeJ3+xLrBrs0YN0Je37Hvr62siZhT1xcjYrrTKNm/kFQHbyLIDaUvdoescNWP/1HmWm7zMK9Z39fsB0361UQwajZHBrtWVuCrmxf9iKvAZviEjbpXWubUDc/hFid0UeTR0LnyyisxY8YMfPzjH6/bfvPNN2Pt2rU499xzR61xBEEQBEEQBDEwoysQASP1RRn9yKJmi302QGSC/bxuEe8EIP1LKQmJ2Hya9IneaW6sDTRfMfW3uG5saypccOa5Y+v6N+AC3JYrQroAy3gc14tIzSMu0rYNn/oxUeh/kd0QKQLWr5A2UNRV9h6molnfKCz9eX1EUD19Bbv0/dDHXymZziPTFtXQjrQt/dP38yQ9hxUym4qYmXMMMKYD9aNujjTM9QGvZdo20JxuvFba10GEHMfA/Wj+7GeuCel+p8+EHeukXlwdK4ZjLE9stQz7G/Qb3/gGdttttz7b99hjD6xYsWJUGkUQBEEQBEEQBEEQBLEloeTIfrbLyKNVq1Zh1qxZfbZPmzYNr7322qg0iiAIgiAIgiD6svVGGQGDRxrZfZqnFPXd1rhv4zWUqo8WAQApYwgbzdIQhZTul3oipcc3S5sbmr8RZ16mzV6ftvfr5ZKNllK2nTbtJm17NgKpaQpbHYOnONlrNrYDQINPVP350t37+uU0Rqw0RgE1toll+l7fBJmJQoJLJ2uWMtY4jxrHPjuPsvu7SJZM5JGOakkjwVjTdmTHYGC/Hn0uD4plr5W487B+o2QGnl/NsJFStu1KRbaR+ld/JjZ1cza9VrP+ZK/lrtnEPikblNP3HKLh/A33p+kY6j7p7si669vrNYu6akxf08dsmerGFpfGRmlrQ2fevHl4+OGHsXDhwrrtDz/8MFVYIwiCIAiCIEaZ0ROMxkYosoxMMOpvoW/P2WyB30w8yqYEuVSnzKJYmgUzZ54TYpSSYKp+Xy3IZIUkvTXrTZNeqD+vlsaFdrq4t9s49+ra3v/CWLfdttO2XcrYnSsrIvX1QapnMOPulNQjKuv1k/XMAfouZPsKarzPmDQKA/0LhhkBJyvUKNkgKsk6oS09R/01ssIEZ356T/rxn0rnhISUUZ0AacWkvuJWpvtDSItL09bM+RrEpL40Ciz18ys7jlkRTNk5D+lGslFEqoeDMV+/GkT8bLyWu6adl3WG731T+7LPBqu7R4MIVar+mvrZsOmdseubUk3GMeMN1pzGaw5DMRlDP6QtgZF2Z1sYhWGLRyeffDLOOussRFGEt771rQC0ifY555yD//zP/xz1BhIEQRAEQRDbC6MjFI1IJAI2WyhypxklLyO76AfQZ+FvX4vGhXSDF0u6sIzc+8QKMCqClPa1/s1UutBttpBvjDDp2ydRJxoBWiiy2wTX1ZpF3YKcgzVEYKmMH45SnmuzFZEYOBJZqxsbibj/Ylmmbdn2DhT5lEZw1Fd6q4veQD9RKw3iH2N+5lpeH2GgPxGwrj2qfvz7iwrK9rtelEjnjWCeuSdWHPH7CEg6Ki2dO4pLN4cSHkPKCFLFfXyPstdunOeNfVMqqetP9nz6d7ZffQVVXje//KbtsO2vm+emyWnLs+JK6jmUnceCB3XXbBQ8G58zKWNIJ8zGfSKC6g3juetHep1U3LPPeJa6+yP1NZniGXHV7hhDuT41ROaNF6MsJk10JNJIq61tC+rRsMWjz372s1i/fj1OO+001Gr6CzuXy+Hcc8/F+eefP+oNJAiCIAiCIAiCIAiCmHBIPBo6jDFcddVV+PznP49//OMfyOfzeN3rXocwDMeifQRBEARBEMQ2zQRGGw2r+tbIfYwa9xvI0ygbscHAwblXdwznvotE4Myvi+TRV24egZHARI2oOI2OUBFiE71joxekiutSw/TFZV00UrNok/q0OhtlZKIouOfa6WUiK7KRMI00Rr5w0+ZY9h1vG4HEwDPpW33HoDHaiDVct2m6mJKwZePTtKAk81nf6/SprJYZExuFlUbN6Kif9D7XV9tK09bSa2bHRr9PmkaENc4Zew848+GxEMKMv4dQz7VMZJDMnDNBpOeKqur3KkLCqzq6piESqjEVLo0S8tHog5SNmJIq6hMVV5cWl4lSa5w32cijZqlk2XmeyCoSey5p0x1jd0w2Pc3OX8HDunmrf3sukkqpJH1m7DxlNSSyarZx9yxlp4u959moPC9zTXetzHyqSyW0kWBMXyeWNReZZ5FSpl5WzdLXMgzsf7QZaWzbEmrkkUfbQvbesMUjS6lUwgEHHDCabSEIgiAIgiC2CzZPMNrS09Ka7TeYp5G7ckM6V7OFsmBWiAnhs4J+Db3NZznwzAJSWuHDiA+JihDDLDZVFZJH7jVgFtdOPOorKNX51GTKx7v2Z1LU7G9f5FPxiKW/7WtmBJYsikkneMWq6tpnxRUOjghlva9L30Gd14ulmTl1tp0DmS1bfx/9hkMhzpgvR/2KVBrhfGwahbSsAMiZ54Qcm0LWpx1IRaQ+KWUqFRIA42XVINxkx9tnBQSsAF+Z9wgglOfmDTfWy4nx7IlZjIhVEXF9DyLVq+8JryKx6ZCZdCwAECbtymM5MxJe0/ssraiJWM9NZQUXIx41+B5lRUfbH8F8CNSLb3asAD1/alzPlVj21glJQEYkRXrvBA/hC912jxfq5q3uTyqGKcjMM1XRYyTLiBPPXLMKydLnx17Hzj/PPhsiD5/nzXVy7pkWmZQ8aQQgiXSsaqxsztmLyAyXcs99nGmnQJ9nNvtVOixVZPv1RNrKmjuqDFs86unpwRe/+EXcd999WLNmDaSsnygvvPDCqDWOIAiCIAiCGBu+9rWv4eqrr8aqVauw11574YYbbsDixYub7nvLLbfghBNOqNsWhiEqlcowr8rRT6jGoIxdZNFIhCJL30ppde/7rezVPMrIbstGpWRFBkAvagOmF5g+KyCHEgCgoFoAADmZB2+ozQUAiVm8xkgQMb14jlhmUW0WvTGv1kWY6N9Vt8DORi41i0DizAPnZnFvBBOPF+raDKBOvBBNliQJtGABABEqiO1r1mt+N78vWa8XSzaapJlPTqNvVLZvkksnMjBwSMVdpIp0/1vvmdNogCx4UCcUeCJfJ0Zw5tcJBZ7ym0bQAEDMtHG1uzcmKqhRXMr6SHksRMAKCJS+B3lVRCBD5M01QybgM+HW9FY8sovkSCWoqgRlWLGiiiqroMp7XTusCJS9pqd8BDBiiPLgw3eRMRwcEqlBdoQINVbL3GctTFkB0QpqHL4TU0Kl55KPAL4K6tqux0bfJ9tWAKjybtRkt76G1HM/K7zZOeHzPHxeMONTQk6VzDVTMcwiIVFl+vmp8G5zzW7UWHodJ1SZ+5SNBLOCkc8KCJm+Tk6VEJg+CZVey/apxmru2fVUl752ds4YkYlzL12vq74RbaNH9nkc36gkBja+GWFyhH+8UCM8bgti2OLRSSedhP/7v//Dcccdh1mzZoE1d6MjCIIgCIIgtlBuu+02LFu2DCtWrMCBBx6I66+/HkuXLsXTTz+N6dOnNz2mtbUVTz/9tHtP/wYkCIIgtjfIMHsY/OIXv8Ddd9+NN73pTWPRHoIgCIIgCGKMufbaa3HyySe7aKIVK1bg7rvvxs0334zzzjuv6TGMMcycOXM8mzn8aKMxTUsDBktNc9sG8DTKbst68GS9jWzkTjY6IRupEpjohALa0CLbAACTTETPpCBAYkJGZGax4qIxlEJN6qiEqkpQNZEdVRPxUWWVuugPAKiZNCXApLplPJOANE1Kt1+4NjeLkMqhCAAIZQhfmX5CuGgp284IMSJm21ZBzaSoVay3Dc/482T9f5hd2aXLnGwkkHDRUGHGA8h4zmTTgzJpYYn5PGY1/d6kPfVNY6uvosV5AMEDnQJlokts5Fg2AitQeRdl4isfvvLrosey4yKhkLAYsU1DZDFiFru0KckkJBJwCBex4iNEXuaRM1EzBeaj4HnIe7qdOcEQCAbfdIPDxFOZoaxJhWoClBPdh3KcoKxiVGTN3SPbHs88I770EcJHzszrQHD4nMM3orOt/GXnaiQVIiVRsXNTxogQu4g5i1AeQuPrFRrPoYAL+NymmzGozDkBoCJj9Jp71sPK6OUmUsdECdmxA1LPsBxKbq6WkhIK5vnLmXknMuJ5opRrd6+cBADo5J3oER26L7zbRfZl09bsHMgz/QwXVAsKib5mHoEbO3stpYDYHF+RMbpNFF639e7imXnLTaSgnasNDBaBtHkVzYaZ0raVpbGpEUYQbdm9GhrDFo/a29sxefLksWgLQRAEQRAEMcbUajU8+uijdVVyOedYsmQJHnnkkX6P6+7uxvz58yGlxL777osrrrgCe+yxx5i0cVii0RYtGKXX7S9FrXFbNlVN8LCP10rASihALzZLqgXtTC825xT053tMYni5R19ZAhANQ5kooGayrCqJQsUoBPZ3TUpUTcpLxSw8q6yGslmo1ngZNaVf25SibFoVA3cpWFYwyqsiciZlyi7C88xDINIFv2ufWfDXpETZpIfVZIiy9c6x4hHj7pbWla13hsJ909Y49/r1lwFSXx7dp9Qvx4ppgleRyCpi04aEVbUfVINnTmqAHMAXeXg8j5DrsbBCmhUmckkeeYQoGMEwxwV8zurEiWwKWaIUZFZ0UdL8GDEJCSQUdNKa7kuOeSgID0WjDhU9jpIPFM1KsOgBOaEQcmXuob0WM+MP9CYMPbE+oDsS6Il99EQ51ExKVGzaY0WcnGDIe9xdIy8YQqEQWIHKXMQKVJECKglDOdbn6U2AWqJQS+qX3ILrcwNAaH7nBBCYE/pcZdptzhX7KMf6HndGJXRFkwAAPcoKpjUnfoUmlbLAQpTMPWnNC5R87q6lr5O2qZbo9gJAZ02LgB21HDZJnUrayTe5FDPrPearEHml50BBahGpheXQEvhmvDhynumTHSuV9qkn8pGLzDyTplEcSKxoZPypOC+7MQYAZuZuX98yDOh/tHli0jDZysSk7Ylhi0eXXXYZLrroInznO99BoVAYizYRBEEQBEEQY8S6deuQJAlmzJhRt33GjBl46qmnmh6z66674uabb8aiRYuwadMmfPnLX8bBBx+Mv//975g7d26f/avVKqrV9K/5nZ2dg7ZrWxSMstsbK33Z3zwrhgBNq2IBqVdQiAJyxuulBUVMCfVidVG7vt5fNyr8ubwSANAuJ2FaoAWS9lD3qegBgeleooBKYl+bhbaMsInp+7VJrAMA9Mh1qMVakWrmDxOKVhS4boD11QFSUWkTX4f1JvIi9eaRbt3KJE8rSmXEG2v2nUYTpV4u2WpT2bGTJlpDJrHzJrJrTylriNCbGft6r6NsBa1sZIa9ZrY6mB6L2LzPtk+6d0xxJCqGlxkrj4UIVB4FaaJaWA5tno+Sb6JohI6c6TUiSk+coCeJ0WuEjjKroMJ6UWE2aqaChMWuvR5CF5E2hWvBpD3wUfBSQUoC2FRTWGcsy2qJQqQUakZlqMoEutZaumj3wOC7SB8OnzMwBnhGtPF0zBVE5jHoiSQ2Vk30T5IgkhKJOSeHjhjKCTsvraClz9caaOEzMR5JVTNPq4lCJNPXMH2xUUaxVE7ISjKiQ2hELS1oheZz/ex0RXqMAUAye6zEhkTPlXVlBVmuFzC46S9gBVM7B81vMLQaUTIvA0TWq8j8TljifJLWsdUAgFUsQhKb5ysGeE248wOo88LiEEhcZJ7xAUMZkdSvG43GAUCpCMwafpu5kcgk83lWSGr4vtssMWnbqtRGaWvD4JprrsHzzz+PGTNmYMGCBfD9+nKgjz322Kg1jiAIgiAIgph4DjroIBx00EHu/cEHH4zdd98d3/jGN3DZZZf12f/KK6/EJZdcMuh5Rz8tbfTMr5vt31gpaiiCUfbYxu3cVKQC0FB9KxUa0ogjLRjlVBF5m34kPBeFsSnSv18orUel+CIAoMebhXykRcLA06IO95nzq1IKOuQDAEtsKfheSGzQjY61eOTHnfCgo5A4Z/D9nGmrNYFW8E16kVA1t0iyJcSZ6gWXeqFsjYPjJEKSWENuCVgjZSNQCOHD92yFMpaKOiptu3Kv9XhJAAqBGfECYIQAxmyqkHIpTUopZyQcxzZyKckEOdjqYcylFAIKUinXP6k86DfKnV+308xrJUz0kYDR7pDjDAXGkDfnz0HB4xLciCZMABIc3JjycglwHoOzHtP/TgjZjSDpMZ9X9aLf3FOuAoBHkFKhmuhtZZGD5BwqkxpYUwoVIz70il5UeA+qxng5VmUksubGijEOjhChiXLLyTbkVQEhAnjmvnhgyD7RSSJRljX0GGGjV3QiQhnSXJMzgYAVkEcrAKBNtqEiQkRGRCv6DL5gLipNmnsdx3DRSL2xnrM9SYSymVdVVFBTNXePAS265KURS1iAnDmnMMKVr4DAzJHYRO5IXkNiUiVrqgc1M3+tYBnLxAmeHsvBNymagUnty6k8PGWfaQXfzkFuDa/LiI1IK5QWAqUsA9JWPcyYoJu+R4qBIU2xTL8nlDmPRGiUjbTSWx5xbMYu8cF5gA0bNiEl+70n+3mNYYlJg0clbd1pbSMVj7aFAKphi0dHH330GDSDIAiCIAiCGA+mTp0KIQRWr15dt3316tVD9jTyfR/77LMPnnvuuaafn3/++Vi2bJl739nZiXnz5o280QRBEASxBTBSz6Ptstra8uXLx6IdBEEQBEEQxDgQBAH2228/3Hfffe6PglJK3HfffTj99NOHdI4kSfC3v/0N73znO5t+HoYhwjDss52Z/4bFZqaoDSfaaHipafXXrYsyyvgYNU9bS1OjshFHQF+fI+vHY8vahypEjunompwQLvLIBK6gRRVQEtP0a9mOkjAeQ17qE2MjUJTU3ioAoEyUhIyLkJhqhsdErvAAidJRGIwD3HY1a+RrIpNUxqU76ztkI4qYSZdhzIdgiTs+DVawUSEeBLfjlDmn8zdSSGwKm7Tl4mPApKoxHrsC3nbGKcVcVEQ2combDkmZjRSzxs48E6klwZCm47Fss/WZALC0r8zXhtkscF5NggXaIFzZfRgUS1OsuGRIIGGtfhgAX3nIG/8oAR9VkUeN6yigWFUgVeLawSTAkdPly02ERCylfvLMQEjoauPczM0AIZji8M28inkEyWOX+qQUA1MCngzM/oHxU2JuAGKmIDKD4XGGAgII08+czEPyxI2lgAdfCYRmXHKeQFEImMAjCGOobaOM4sz3hvH5diluAfNRNJE2kcwhktb3Kg0RsRFMQcZPyqUz1t1F0z4lEMB4YiFEzs5VO394GoHGwNz95Jnvi5ilKWo2osmZ0aOMBPVm1r4IIF3knXTnd5E8Ul8BAFSmXLxL5VQJEmmj+ayZvUqj0rgPxoRJz+z7vZX9Du0bJTP0SKRx9UeaALJjP6zjRrkdE8GwxSMA6OjowJ133onnn38en/3sZzF58mQ89thjmDFjBubMmTPabSQIgiAIgiBGkWXLluH444/H/vvvj8WLF+P6669HT0+Pq7720Y9+FHPmzMGVV14JALj00kvxxje+ETvvvDM6Ojpw9dVX46WXXsJJJ500Ng0cM0+jzfUy6nvd/lLU7LZ6IclWakq9dqxolFb88pzQ4CF0HkKhSVULVYC8SaPKC468NSQ2v9tUgG45HQDQjhIKnlm8G5HI42nqluTMmQvnhfVX8YFEG/3ahX/A84i48TziMaRZ9NrfSZK4qk5Sxe7+MZUqINxUObOeRkKpNLtLSSc6ufHiHLALdpaaUqeCUewWyuniOQKMYMWhnOikXNpLuqzVaVD2nVn4M54xDbb3kKXnUQxSybq0FYb0PJxxMOZlvJQ8CO7D4zlnIu6rwCzeTTsgkUiO2FxDSgUF5sQkBUCAu0ppOQSIUHDCRMwixCxCZO8JS6CkFmeseXJNepAq7btiWgqwWwIExii6BHdRlZp0K6XnjBUBtK+2Hp/YiApcWXPn1PMn8IDQiWQhBOepR5Lg4Iw5Q3fB9F3g3HoO2epi+nNbZYwxwGP1htlCcNileaKY80SKrPAkTWqk65z5XNqUOOkEJPtsCAj4rgKeB+E8udygODklUdK1LzHiZYQYEdfzM2IV1Jg1zDbiDmrOYyj9DgjBPTN3skKnrbQnY+dRFKMG6Z4FY5Yua+45SX26kjohlHMPLCseZQWjzPfjsDPFNstse+tKYxvx5bYB9WjY4tFf//pXLFmyBG1tbfjnP/+Jk08+GZMnT8aPf/xjvPzyy/jud787Fu0kCIIgCIIgRokPfOADWLt2LS666CKsWrUKe++9N375y186E+2XX37ZRWMAwMaNG3HyySdj1apVaG9vx3777Yff/e53eP3rXz+6DRuTKKPhC0bN92suKDVbhPUXbST6GDT7fcrEC/ip0MByLuIoML9zLHBCUM5jKJjoi6LQi8U2z0OlrH1kSl4mMilbScusvZhULmLJDhPnAsLc+9BU1yrIPKrGR6aKKqrGrLkK7dki0evEI6WUmzs2yoTDh8+sCbjxMZKeG1KpFJSLLrKRHRKS6XPGUqYRRzIVj+yiWbooixhWPFJKueuzTHl4F7mhMhFgsFFRDOgTSaZS421Y4S3bVpa531o4sobnggfwWB4+yyGwEWTMN0KEblMChSoSSCN0CMaMd5MRDBiDAHNGzD73dVSR+TxWSp/D3h9VQ03VIFkaT1PlVUTKyyziFQQTzq8ogIeQewhsJBK3oo4VLYBISmdKXZUJIiURI0GsrJgBeBDwMtFcgeBpRTRPVw+z7221MtuPRDJEUiG2wo9UqEmgmliDdXOcYMh5+piSZ4VPhYCn4o8VhcrG86mSAOVY97WaSCcuJbCCj0JiDabNPPSkjzz0M1nwPIS8vt0yI2zVEomy8V+yVdcixIigo4wq6EJsDOMTlUYb2Qg1WwEwYAUX7SSQmmPbqL6IV1A1PkxKKUhzLht5l8go8yxIO8BOaxEs0JFw3PqCASrzOqvZqD7fh1HdO9XH+Cezf2NgzkRWbhsDRpq2pvoMzNbHsMWjZcuW4WMf+xi+9KUvoaWlxW1/5zvfiQ996EOj2jiCIAiCIAhibDj99NP7TVN78MEH695fd911uO6668ahVQRBEASx5TLStLWtTCNryrDFoz/96U/4xje+0Wf7nDlzsGrVqlFpFEEQBEEQBLGdMORoI2CgNLWhRB0Nr3pa/fWaRRtlz9G/51GaqpaNONK/MylqdT5HNsUpdKkzoakiFjKB0EYeCR11AQAlT/91vz0MXORFKBiESzWyPjAKiY28yCxmfOsJA4a8GbbIRInUpEA10W2qqCI2mcgoGzFQQ8VFIiQqga3+7QlbLU24aJwAumqcz8LUewkKiYlskMxGslShMlFEaWqOjbKIm6bouPAJHRSkX7qqa2l6k06ssilyJm2NpxFBaUqdcka3uo8ycw5duc56B3Huw+Ohi+zwWB4BKzZElHjgjLuxixAjVgyRvaA0Tkrmrcd0VJAtNZ8z0Tzc+vYAiCVDRep51BsF6JE1VFQFEbNpUwkUIjcOHNrfyDPPR557aPEF2kxYTdFTCHmaxBQphXLM0WmCT7oihu440RFh3N4vCSUlPDMWOcYQehytgW5nW8DQ6qfzNBQSSsFFAfVEQHfM0GWuUZEKlUihKm26mo1iE7DFvltMBNJkP0HBRN5xplA187870j3oiNP0UQmGmvNE0sQsScdG6mNC5qFoUj7bfI6SuWZgBkUpoGavE6ffJJE5XkE5f6MYZUSy1xxn+yMgmE2L089EEe0oSFO1Db6bAzUTTdTLeiFNOyPW61Ls7DMhZdVFHll0NFzqweWJPDxRv08zpKz3YwLzG/YYLBIpe+zAldr6Msw0tnGG0taGQRiG6Ozs7LP9mWeewbRp00alUQRBEARBEMQ2zCgJRsDIUtX69zTqX0waimDUzBCbZ/bt62/kZ7aZ3/DhmdLzvvOi0YtZAAg4d6loecFQEHphVfRM2lrAUJVWsFIw62TUEluum7n0o4y3NQLTlbwAfKufmI2RBGpm565IgEXarLkGnYpTRgeqZoEnZQ2c2/FXrr++EZxCFE3ffDc2EhKR6V9kFtxZs22dolPvSaN/24ZajyU/TbdhMk0TSW1qwJxBOa+7T4AWuRr9VGSiIJ1xtPH+UamgwTLnEdyDJwL4XIsBASsiVCUELI/AiIDCFLWXtjQ8EiQsditS+7+eSZnKSS0Y2vtTFEDBB0KTssXAECugGusdurgPrwqwWCExQlyNVbRHjvWDMqlznrLiEceUkGFaqPef6kdo8SII08ZawrEp9rG2psduTYUjBkNvFCOy3kuoQQofvrSpmQFCwdFqNIfpocLUMEZ7TosSORFBKYVKpNu9sRpgbUWgluj3PUqnK9q0NSvQ5QR3Y9Fq5v6UsIa2oGbuoUTVpKhtsj5bLEBk5kAlYZDmHsc29Q+J8zzyzZwJGUfJOHO3hwqTfGWub+YFgN7EisEMcWIFK6dYOkPsWJURy6q5X8Z7jPnwoFMZ80pn87TKElqM4Bpw5iSTihWkEoWKSVtjEM5fyaavJbIKmLmafjcJNz89ESAQOfg8ct9BQ2U4YtKgZtsNbLYnEmPjKsyMPG1t62fY4tG73/1uXHrppbj99tsB6C/Nl19+Geeeey7e+973DrsBX/va13D11Vdj1apV2GuvvXDDDTdg8eLF/e7f0dGBCy64AD/+8Y+xYcMGzJ8/H9dff32/1T4IgiAIgiCILQTG+5qdNmVo4tJghtjDFYz6RCYNcnzj533EiEy1Nc78zHHCbct6HQHa58hTqbmyb/65HrLU+NqKRzmhMpFHVjwCamZxk0iGXmMiI2Pj/yIZatZHRir41kjb3JaCB7SYcwbcRhMx9CbWO4khNgJBZ5w3/cwuKVJjZdtfwXwXcZQ3BuC6X9b3J438iFnNncdVUVNRJsrIClIi4yEi7E1wnjX13kR2sZlWTmOcO5FLcCsoCfe5rPM5Mi1Sifkxl2PaLNt6PFlzbN9UQsuhBTmUEKoAATx3jIRy0SSSJYhQzYhmEkwxN15CeYBK709OAC0eUDARPLbyWNlVHtP3QyqFyPjwVFgvYlRcBS6BGAmKEGb8Sh4wLZCYn9di4MxiF1payuAmsqfWK7C+qwSPa0PtSPnoiRk6YpYKVChDqBgB0iqLgQAmBfocM3I1zGrpwaR2LX54BQmZKFS7jJfQxhziJI+eWIsnHYxBSoXYqJ9mmkIwhcDcIyuYTgpraCvqtgs/QVS1c9qIX0qgK7HRZc7yGpGZFzFPwMwzYwW1kHMUzbRu9RQmB/paeZF6MHUZT7BYcvSaKRiYucCTTGW27Fx01eYChEqPZ0nq362+j9bAelsxFxlofLdRTXz3rCmVuCgjO1eRjbxzJtkM3M5v7iPwCgiERMhKGA59IotUXP92OGbbg3giEVsuw/mzDwDgmmuuQXd3N6ZPn45yuYzDDjsMO++8M1paWvCFL3xhWOe67bbbsGzZMixfvhyPPfYY9tprLyxduhRr1qxpun+tVsO//du/4Z///CfuvPNOPP300/jWt75FFd4IgiAIgiAIgiAIghhTpGQj+hlpxBIAfPGLXwRjDGedddbodWQEDDvyqK2tDffeey9++9vf4q9//Su6u7ux7777YsmSJcO++LXXXouTTz7ZlYVdsWIF7r77btx8880477zz+ux/8803Y8OGDfjd734H3yS7LliwYNjXJQiCIAiCILY0hv43zcFS1YYWcaSv2V9qmj128M9Fn8+apaplX9uqaxwcwvxz3PocCeW5FCdf+XXpagAQCIac+Rd8wUMmbU1HIUwOEue7Uk6Y83+JzZ/7exPpIo+glPPOsSXUi0JhsklfKnCbFsPQnZiIBybQbVKNgtik2vF0SaFYWqfJ9jdgJRRMdEXReP94JgIHACIwxKiPZJCIXSnyJBPlkPq3iExUl733zEU9KCXd8WlR9dRLiAHgoj7yiDHmIpeYTb9Cksk3sVFI9ZFMjWlrAdN9zMsiCiqPkKWVzMB0hTR7iggRFKSrqKWrcaXRMSHL6cpxNq2K63veZtKo8kKCI/X5yTOux1YKVE0UT7fyISER20pcsFFKaeTRFL+GmcUuAMCU2T3wp3LAVt3rjCFW9yAyY7Ip9rDe4xA17lLhEtR0pI0JM/G4mUuB/nxmqRvT5vbCm21SyYoBVJJArNVeQFL2oqfqYW3VPg8mVctGmtnzQiHnou30uUv5GgptJqoqpxBX6qPUemIfG6Lsee0kSCsQcrPNM2MSCOai+lo8iRZzraJvKvyZmncAUI49dJrnR9jINlX/XZE++yYtjbWgKE1VRJNeV/KYi3byOUNk2m99oUTEXDt1RJydh5Hph0yj/tx1hYuw87ivUykZ0ILp5qRpG1UmHawx0qjxfdKYOVZ3LBoYXmTRll6NbcSeRyPEek4vWrRofC/chGGLR5ZDDjkEhxxyyIgvXKvV8Oijj+L888932zjnWLJkCR555JGmx/zP//wPDjroIHzqU5/CT3/6U0ybNg0f+tCHcO6550KIvrntBEEQBEEQxJZExpB4mDQXjID+RCOgmXDUNy1tOObX6ba+18ymqKXHZAUO34lGNkWNMx/cvE59jkL4ZlsI36WrBS5Vjbmy4XkhnQFxa2hKtYdlxFJ7qXREAl1R6lsEAGWZoKJ0ihSXHKE1i3ZpUQpFYz7c6qc+Jr4xjSlLjpzJlbKGy8wYR9vXVsyx3j8F2YIWk67W4puFLGNaRAHQG+nUInMCADoVx5Y1V0r1EYoED+Bx3U/7W6jUh0WyKDXhduk9iTPhBpTrNOdpik+6cDULciacp5JSHDo1qGEuWi2CcQj4CKyXDfIoiAA5nnoWAUAiU4EtkSGqrAK7wJZIoJQCZzoNK2I1xFBO8BEMCAVQErofbUENAU+QmLFsFR5yLICUAr1GMOmqFdDNBbLmT1xxeGa4CkKixYvQUtLX9KcI8Ck5l3/E/Rry1RhtPXpMWqsSoeDwkBp/JyoBFAc3oknIgDZPYnqoxaHJM3rg7VwCmzPZXDQP9PQ6sSW3sRPhhsT5bdl7ZMfJd3MeaDGCacn4HBWKVQTmtKwgwCtG/DQ+Q22VCkpVLdDkhHApgLatgnEIM/8CZtPWUvEo70kUfT3eed/6XzFEJpUzxzl8I7TZNELOeJqSykNwI2QGJl0sr9pQMPOkYA7Kewx5z7ZJwWRq1s02K7hKxFBGVLVDphhc+h2sQM1D+NwItjyHPGtFAQGmyal9Tp6IVKRtNN5ufK8aBSGZvpcNQnDj87L5aWwTa6g9Ys+jEYhO3d3d+PCHP4xvfetbuPzyy0d03dFkSOLRV7/6VZxyyinI5XL46le/OuC+n/70p4d04XXr1iFJEsyYMaNu+4wZM/DUU081PeaFF17A/fffjw9/+MP4+c9/jueeew6nnXYaoijC8uXLmx5TrVZRrVbd+2Zm3wRBEARBEMSWSf+iEdDMGBvoL9qI9xWX6kyumwtGzYQiC+8nMokhjUbKRhvZ7Tzjc+QzI4BkfY6skMREGnHkqm3BRV4UhEKLWdSWCnrhL1uEi7iQKo/1ZtFtF50VROjhekEfwIc0UU52ORRwIG+8ZArG50UfbRbKUQDfBtEwlvmdmkjbamOBMcdulS1oD3SfrIGy4EDF+DBJyVCuCXMlayQd1VVTSxfierxC0eJ8W3LKmnAH7l7ErIaI6zVABN3fWNWc70+CyEWOcCf6ZRaFdtxUWgFOcokkUUCDgTbLLCY591w7AuYj5AIFj7sqXRwKiWROrEuiADVVQI0bsUhVAZZAGiPqhNUgVWapzgCfKRRMxazWsIxisQpmBL9JZYFwUwnVpIAuW22sFqADOdSUqfhl/IXTaBvtbyWMPxHPCfCcD/hmqcg4vJ4eFDdqsSbfLRHw+lqFuuJd6DyDCh4w2a9haquOZgp2DIAdZ0LNNBEvQQB0bALrMV5FxW54XKX3wPpJ2UggM/8LnkKrmfNtZs6HkxX4FP38sIIPVdOf+6oHANDS04O2sp43Je4hb4VYY6yNJExFLGGFHIUcNybZPEFoBKvA/E4kc55KDFn9w1aF404s9lneGVTnpDbHbklaUDQRb1YwCgSHz1OFwQ6FNYuXUkFy+0xIMGZ9lGy0EXdCp8gIq/a1z/IoyBJilcPMUAtKsjrFXa8myu51ZL4jLDGvN8xujETKCkaNVSxVHy861H8+iFi0pUUijTz9jCFJkj56RBiGCMOw6RGf+tSncOSRR2LJkiVbj3h03XXX4cMf/jByuRyuu+66fvdjjA1ZPBoJUkpMnz4d3/zmNyGEwH777YeVK1fi6quv7lc8uvLKK3HJJZeMWZsIgiAIgiAIgiAIgtjWYZCbEXn0xz/+EW1tbXXbly9fjosvvrjP/j/60Y/w2GOP4U9/+tOIrjcWDEk8evHFF5u+3hymTp0KIQRWr15dt3316tWYOXNm02NmzZoF3/frUtR23313rFq1CrVaDUEQ9Dnm/PPPx7Jly9z7zs5OzJs3b1T6QBAEQRAEQQwdnSY2ctPQwc7dfFsaWdSYgtYYNZRub+5jNHA1NtHk/CL1P4LoU1nNY6GupgXAM/8s95VOXANMiXYbJWFybULBUPBs5JF0XkeFko4MkNMFbGRMJeZYyev/6h+xGirQERmKFZA0VEbzuERgq62ZCCTOFCJjchIKmfFsaoy30GllnrBpWybKQgSYZP6w3uYpd85eMxdqkqHTBhMoG/GTQGYiEphLJ9LnLKIdrXKSea1PHnDhohIiKPSatKVeE3lRY72ITKlzG+Gj225T4jL325Yi58IFuEmpIJlyGTo6nS71VgJ0VATPnC/wOEKPuxQoDzpGyzORNAoekqiAyKTJJbzq2ggAUiSIZeIWrAwMPpco+Pp+t7T1IpghwfJmfLojsFe60BX5WF3V24qehyDJo5Ipr66YdNFMMQCZjRZhDAg8oGCieTiD6K4hMF5YoZAQXPcvm77EkXo7FYXCJL+GwjQTsTJ3GuScWUB7u+sHSxIgNJFvjEExjsS0I1E6OoplPIgA7c/UHuj7WmrXv8XMHNgM7R+kAh+o6Wtyc6Nynd1o79CRNG2ejxah529kzhnAc55fJesnJpSrrBYKCc7ro11iyVGTJiVUMcQmBS4xzx7nzHlfcea5tFTr/dXCcyiZFM68uWYo4CKtpFJIpEkJdP5YqTeWykb6ZH3ATCqc75XMuJUQcj02Hny0IA8BhTlFYfqRd+fpjtvd6zLfVNffiJfr3ttKiJas75mU9ZFEfSKRMs+LObr+7YjS2MYvGknJEf5/mGJYvHgx7rnnnrrNzaKOXnnlFZx55pm49957kcvlRna9MWDEnkebSxAE2G+//XDffffh6KOPBqAn2n333YfTTz+96TFvetObcOutt0JK6UpiPvPMM5g1a1ZT4QgYOAyMIAiCIAiC2BoZOF2tWapasxS1RsEo61/kPm8QiOqu1+CXZBdxdcebYwTznTm2FZE85TshyVc21cuDz9JUNZtGkzOXLngKBZGKR3mTwhOUTOn2GR6KxtOorbuCghFOnMcOS5BIvQCN4QGZ1CUACIVyaWthkHqqBMY0yWMK3JUg18coBsD2nQkEplR90aToFAPuFuUtnk2xkW4RFrDUaDhNuUvFI8681D+J6b/at8vJaPN036yPUsAZlDlPJVbwbXl2s7DvqUswSs2ebVqMkKkvlQ0uEKwCZsQGKXWfY2XbGkFJ5hbLiYy1VxM37QaDYAyhyAgEXGcZpal/HFL5iGI9VjGvAQx13kyKpe1hDPAhkff0PfTbE/CZRbCiXu+orgry3b1o6YjQ4uvxyXkcYRK6viWoQbIYxsYKNclRlQIyEulFAh+wayjGwHJlCCNYMcagrD2zS50CuGTwrBk3Vyh4EUSr6WhbC9SkSTbDSh/neS59DwkQSw+RSXuLFCCZdIKe8zzyFFpyuh3BNHPwzFbIaZP0eT0fqtekXJWNt1ehB7lQv24REdrNvVBG/CmI1KvICrOtHpx4xFnqOZWa0XvoNfOrN+GoGH1DOm8qDwVVsncQodTPd4Hr570kOFqMlmfT6HyWzs5EAbERj6pGPYpUhARp2pp9qjk3Ih88eEKLQaHQz0metyMwJvVQDCXhIfAZ5heN31icygEbu1rd6y5WHx1T49117xvFJCazaW39e8/p8Wj8/h5fz6LNZaSG2QqAEAKtra2D7vvoo49izZo12Hfffd22JEnw0EMP4cYbb0S1Wp0Qz+dhi0fvfe97sXjxYpx77rl127/0pS/hT3/6E+64444hn2vZsmU4/vjjsf/++2Px4sW4/vrr0dPT46qvffSjH8WcOXNw5ZVXAgA++clP4sYbb8SZZ56JM844A88++yyuuOKKMU2VIwiCIAiCIEaLTKWjAWn8y3T/DOSJ1BhtZP86nhWM7IK6ft++kUf9GWZbeN3nfaONGLgzx7bnEvDrKqsB2icnMNFCOcGRE9Yo2P5WKLoKazGKxjTYrBnBWkMIU20qn6sgxxNzLdMnyVy0CGcCwi72rTglEhSM+XYub4ymJYeopCumxldKZaJtuOdMgUumslTB48ibRbm9jlLMGVUrKJh1svMXUpCZaCgPOWEiNpQRj7w8Jof6ZK2BicriConpT7dgYDUTxRHpxXWSjWBQqXeKFZc8BC5CxApKCUoQJpqDCR9QzAlcsZS69ca4OJE146tkK6dJcGYrpKX3j0O5cfAYg1QCiYmeipM2KC6dT5MWTzIRWErBYxKBue+sxIFSDipvTNirMYTPEHhaTAN0tFrIAggjXCjEkDxx1bwqiqMiPdRqZmlYi3W4i1V2TMSgTGx0jdCRSpy5ecWZD4EAgtuoK6ZFJ6fj2kgrc0oArFoFerWok/QwlJMAZWWjeZS2Jjfiqe/ZZyJGIa/Hm082gtmkFiizIFdKAWUjbFTN/O1ViGNr7i6RN+JemxFvlALM6WECuFD0FAJuhU6GyAhFNsKoq+ahwxqSZ8Qj+/2WYwI+tODpKYZQWKFKf14Uyl0rdCbbqb9RNQGM7zcqRjyqZkzgGePg3M5VE9Eocgg9PQ5Fph3EQ1mCL/XniZJoyTOonMDORe0X1VFLo1rWltNgjA1xvXjUyzfWvfcaxKSEpx7DUg1smN0oJg3XUJs1VoIbZw+kzUlbGypve9vb8Le//a1u2wknnIDddtttQouFDVs8euihh5rm5B1xxBG45pprhnWuD3zgA1i7di0uuugirFq1CnvvvTd++ctfOhPtl19+2UUYAcC8efPwq1/9CmeffTYWLVqEOXPm4Mwzz+wjZBEEQRAEQRBbHrqseXOxp940NfsP46EJSawuraxvZBHnnktFsvsJVr+tMW2tjyF2PxFPjfDMebJCka0UZgUKAc9VVvPNP8tDJpAzC4Ocx1zEStGz5ckVWkzFpxY/Qhia6Bmz7mOcg5nSXr4fwzemvnZRL5Soi3wKfWtEDHfOgq261WIqgFUUBLfVwDgSswiKlBV6FBhsKfQCSrLNnMuYZAfMiUe2FHokgcQswiKlU3IAQBmxi0sOz4h9AS8ibyIhWo0J96RQYEpOH98WWGNjhUil/bUL/YqJFslHgYtmYkgX6lYACVXo7oONhEqUhO8ERgblJVB2TsYSClFacUzWECe9qJmojBgJpNJCijVaLgil085M332uRZbEXKNWLiBRCXp5/QLVimsq0+60IxLMCIaoxFA1DoDBs2lZgiHkAQJuhYIE4MqIXzptsDfxUK5qMUZ2VcG7K2CB6Xc1guqNkRhxKYKANJdOqwTmEagA3FxTMYZIeVC9RoDZ1A10bgLaJukmlCtgGzYC63UKZbkzQHccuHuVIAHnHH5D2lrOA3zffFeYzBPleU7QQ08PsL5Dn+NVLXB0rw6xvlcLOd2xj9gKPGZOB1yhaKoWhkYw8oUW3XTjOXoT3fdybE3IBTYacbIrAmrSpmPqQwrCgz1cRxDCnN+k0nE4ccqmqikAVRONV04kemO9b9k8ZzVedRFpnHkIhO6TFcVD0YoC06lnLeYZDGTghNCaqqHNB0TIsMtkLQatqqTFq14J0++3QlRAFp/l694LVp/ZY6sD6tf14lEyaNrZ1hWJtDmG2UOlpaUFb3jDG+q2FYtFTJkypc/28WTY4lF3d3fTFDHf90dUyez000/vN03twQcf7LPtoIMOwu9///thX4cgCIIgCIIgCIIgCGKkjDjyaJTbMREMWzzac889cdttt+Giiy6q2/6jH/0Ir3/960etYQRBEARBEMS2BWdeXSRPXbRR5t/jg5VuTqOR+vE+apKqxlnqZyOMRwhnXppWxrjzH3IGtJl0imxammzyl3HVZJtOVUvbko1CAgBPec4wO2Q2yiZNVSt4DC2+ia4x0RaT/ARtxueoNawhXzTmwHlzHe0ynGm3icQxkRm+9DKeRHkUjdlLq4m8aAmqCFrNMS32POmyJ5IMNdPV2JWsTyBM6FOOtaFVaf+eVpOb1erriCkALhojijis5W6iAGmiMJTzC+Lgxqslx9vQoiaZcdDXacsJTDIRR5N8GzWSoGYiNxIJ9Fi/KBN1VeMCcWLSu2RqMGOjjQImXEl4GyEVKwVhomEUAySPkTCTDoUIcSIBZn1oIsSygorS5enLqKCa5HSElRlCn0sUhHTT3ed6vkU2XSsJENWKUCaSJWJVSJVGCVUSDxUpUKmYMu8bq/CDbjCTd6U6Y8RVD4oJ5xOU8zny3EeO6WgSyWL9LNh7oYCy5OiJ9NjWNgqI9WUwE/mCREF2JKiaFCcb0eUxjjAxptBSIKfyLlWOgaGa+Ig6jB/Vmk3grauATj02rFwFXlmNaKWOWNnUPQldsYeqNSMHg+AcedOvkoldyAsFLmyOo/ndWwaqJm1qQyfk8+sBAJ3P63v9yoZ2vFbRJ+iMuPMSslFAoYjR6ut7WrQm8YIhMc9ppDwXedRhfKHW1xg2mgncFaX+RMKmCnLm0tLyIo0ysilqHpQbf+uhVVVA2YT1lWOgbMa/inS+2ehFXxRSM3FzX4toR4vxGbMpo56XenL1SIV2XyKXk5j5Oh2VNa8jTU97KkyjjUo99ZFFIeojkcq8/nP73QoAyQCFBQCAqUYPpAaD7Yb9B///g+2DZoE1482wxaPPf/7zOOaYY/D888/jrW99KwDgvvvuww9/+MNh+R0RBEEQBEEQ2xesQTyq83KBTBcJZlE1/EVDvSF2NlXN44ETjTxmxSPfvRbw6/yJ9Nmai1MSqS+PVOlr1STFjjlPGJHx07EikleXrgZonyMr6LT4DK1+vUAyOYgwJacX3JNae5FrN2JLS5gOQSblyco+nhNKBPJmwV8SIVrMaqDN0wJIsVSFaDPjWDBtr0QuVaOmOKqJFTaM/4ri8MwCtkW2Y5LJUmgP9TGTggQl0357SyucO41LAjDexS69zmd5dz9KajIm2fOHnhkbuHPaNL6AxU48KifcLd6tv1AQc0izyPUZT8UjM/aB4E78sLNUAmDG60fKAmIWIxamsp2qQCFGIo14hAhxUkEt0QJJj9eJclxCJeaIjfjBGRBwiUDoNudkAsF8JMoKFQxVGSCO9D0y1s+oGQGgnAAdkYcNZT0e4eoIhbgGYdIPVZUjqvoAGHyXKsWQ8wQKsRYNExaDq3RuJxKoKoYuIx71dOXgrSrDL1fcINQ6BCqxb/qt51PIGUpKtzNKfF0d0IpHjCFSAhUjQhRWlSH8VVCmghvKNSSvdKHzNf1+XSWHTTFH1TxCjGnRz4qn9jko+TGETYKxO2/oBKuYanUru9H5gm7nyxt0CtfL5RzWmxSzqkzvra2wJrhE3oqnudQUvGx8jioRR5cxlrbnWV8FOo2KWpXSpTnmMlXbrDl80VMuHU6YNFKm9L0GgFpi28ZQjo2RdaJQNt8nMU99jqxQxBiDZ3yyCjB+YCiixdPtNMXUIKDc+QEP7UGClpAht9ckAMDcp1LvovaNqUBUFGllPgDIyWLde4/VVwDLVlvjsl5ikA1pbH3EpM2sxmY9kMYrsmekaWsjNdrekhi2eHTUUUfhrrvuwhVXXIE777wT+XweixYtwq9//WscdthhY9FGgiAIgiAIYhvAEzkwxp0opJR0f3XW28wiI+Oqm/6De2Dvo6zhNWdptJFvysYLFrqKXbaEtsdy8JURlBDCM1FA1kA5u6hRkJDM+v6Y6lqInYFtzCIkKnL7unb1MYcFPOuro0QqXHDrPcRRMuu2SYHClED3e4ZZ1M4q9mLaJL3ga51VQzDPRNJM04s75YdAt4nC4MoNIDNlrnzGUTJ9Lnlp9E6bKX8eFiMwE+bBjOrCRAxpI2Mkc/4uVkTjzINvTKVbUUK78U6ZkokMKploqapZyPoxc1EUgmdEI+PLw8ARmgVru2rDJOO/Y02OW/2MT42wfUutc0MuXdl43xlHp9XvYmPorD/n7ndgI0MywpYVuVQCxLKI2JgDJ14FiaxBKSsmJUhkGVUjsvSKjehWU1COA6dzxJKBM4m8MF5VHuBx6fyfyomP3kCgmuh7FKkEMYtRM89Md6ywMeJYUzNmzJsUJkUVhKEtEc8RRwKMMfhGsCh4QNHjKBoFK5ZFSEgIc0+VUqglWnADgK5ygKAjgYrsM8dQ6fVRi9P9fSZREAw1Y+4eC4aAM+cTJDhDAo6K8VGK1sdgrAcs0HJY0p2g5zUPqzu18LG24qMrAiJpTaqBvMfQZkSjduvzFdTAzTZVM8Lt+h7I9foedL3i4ZWNOprm5V49l16rcHRnvlqsv5c18fYEQ84IkbmcEWo4R1I2z3nE0GuO32SKinXUJLrjVBQpGJ8y2/+ip9Bq5mqLJxHw1GsLMNXUzHhaD7F6k+wEiTV0N98hocojZ6LxPOUjZwScEg/MNbkTg4vCHqtQzlxnclBBe5wAeywAAEyb9lfXh8mrprrXRb/+eyus1EcaeX08j1KxqbGgwGAG2VtbNbYtqzXjy7DFIwA48sgjceSRR452WwiCIAiCIAiCIAiCILZIRhx5NAzD7C2VEYlHBEEQBEEQBDFcAlEAY8JFHiUqdikKUqXRLVKaP++r+igee1ya9pAg+1fsNOLIpCaJHDwTbRTyEkJTQj6nzG9ZgG/ShUIE8Gw6WZOKcImSiM31IxMhFbEIEdNtjVQNETMRKdajpE/6RT0ehEtXy5tUtZLPMMmk5UwLJGaZiKO5LTraaMa0LpR20O3wFrSAzTSeJZP1b9WtgA5dwUrJ1BbGRiDlGIMUenxafaDNSyM6AMArpRFHNv1NJQqxySuryTQ6RJpoJg8hWpROEWr3Q0w2VdCmmEpw7WEVOZMWJ2r63F2cu6grjzOEJnKhYPySGBhKSt+7Kb6P6eacM0JprpO4yCOPp9Eq7n9Zmr0XmHYGPI1y8BhznjG2KpnPOTwXeWT6qDKRR0yAxXkoqcugxzxG7FURK33fVVwFVA1xYiqIRRvQ6XegK8qjbLyWalLXvvJN5FHox/CFh8iMb28i0JMI9EY2ZSpAGRI1M+d6Eh8dkUCxZir2sSJi5aEljsz5pOmjcp4+eaFQ9IGCKS0fxTlEKgY3kS1SKsQyjQorxx56y75bJDPGUI08xNKOsELAFPJCITIhWjXGdSqbl0YeMcZQMylM1R4fUJGLJKz0BFi7qYhVZR09s67K0FmTqCW2Ap72CrJeWa2enp+FfBXWbsdGHskuicoafd11HSW8as65qmJTzCTMrggER9G3FdxsWplE3kTGBXkbIaRQrdrvKbjIo66aPqYrqqFi7kkAD56ZywVzzlZfoS3QxxeFcnPUVoWzUV5Amr5WSYCKtFGNys3PnDLRk2DwzXdkKATyJkUtb8a8xQdahL2m6bBS6OI2apBjUq6GKQqQu+2i95v3/1w7Jj2Vfl8Vvfq0tVDVF8wSvP7zbCRSzMp1nw3mgTRYOteW5oE0YsPs7SVtbfLkyXjmmWcwdepUtLe3u4ncjA0bNoxa4wiCIAiCIIhth5w3GZx5kCa9K1ERErPwjmUNiazW7S9lDObS2up8oA3CiQGMec53I/B0ulPAS8hzLWrk0epKyBeMT0uB+cgZQ96Q89QDJXMhmwgVSeVMcSuJLaEdo2KErnKmVLVkdhGWZMSvNBXDpsX58JAzaT82TWRSwDDdpCDNLVQxv1VXM54xT/8u7BKA7zJLn2ineVAzdaltlTdmyH9/GXhlrb56xJwg4LyPOHNpXK0+0GoWzbnQeL0EqaiCyKTpVRnKxjC4JoHEmgMb35wia0Eb02M+OScwObCpRvo+t4ZVeJ411zbjXfWciXDAGQrcpvK1mLHhaDPuwjNCiTkmnWhqzngzeQmEEX0SM56xTI2npWJOPLIpaKFgaTqaYC4SwJoce4LDZ/Xl06GUO0ZwpkWnyPi9xNOghEQijFiYRJCqF0mi50KN9aDH24AeNc2NX1UyJIpBcD0efhDB82InzHTHHto8hk5z0Z7YQ1UKSK77XUli9MQCG2ObaueDM52upm9ghEBIeDxBwbSrJDhKgqPHCj0xB1MC3PRfKYko4c4IvZJwVGIP3AglnDPUYo7IziWlIJhCwJhLt2SQEJwhENazSj+wielXpeIhiRlqRhTrrIR4tTeHVWVjQF1V6K4pJCo1lg4YR8EIYAUjtvlBDKclGEEn6VLo2qSf6bWVHNZU9A5rTdpZR5SKIoJ7TrQsOvEoRi5vzp83Ik+UfgdEifaaAoCeyIh4sorEPNt57juTe5s21u4naDOpdjqtUp/X+g9JxVCzRtmJ/QzOGF0xLUoBcD5Sec6RN98TeQ4E5n7mzFdLiUu0eZH53HqxAZ4RZquSY1KxisleArTo58ybnXf9bA/SNLx8g3gUsPr3Nt3XUp+2Nohh9mDvh+uBNM6JZCONPOpj1rQVMiTx6LrrrkOLmWDXX3/9WLaHIAiCIAiC2EZpZ3MgWOCqVUWsiprSHiiR6kXNRGxEUv/lOk7KSGQajaSaeGVw4/fhiRxCT/97NSe0YFTg7WiT2sejTbWiTRivH+Mk3OJzFM2aJ+SpR4zI/BvfVpaqJtlKSDb6QKDbRHPYilxAxlCbSSTmr+TZKCS7OPKZcBFH1h9leiixQ0GLaDu1d2DmLjriKNxHR7tg0c6Qi/ZsMroaNXcu2KNP6XZWAlTc+OnPAw7nqdTiK5RMRFDWM4cZFUGZcItap4/eSB9USRikWQgHJhrBg4fJoX49LccwwwhR7cbYu1iswi7wYnOegCcuIignGFrMPWk1y5OSB+f3NCesYk5Rj8Okkj4nFxyRMXjurmoxp5oETjyKVVofz0aYAAxS2MU7kFhjdGuSzZkTVIRpL2fM+SDloVAQzPnb5GoFeNVZLsRJegkqkYLMCKI12YseVkY51nOvJpmOZjIt8oIEYEAh0cdMqgXYVBNuPDojhrL0XERKxGKUEx/dkY10YSgkHkrm87yMwTyJQEgYv3O0eBxtno8es/Iri7TimG6nQiSVE0gqCUM1Sj2pOAOqCXdm5HGioExkjNFeoZj2i/KdCTXcfQCASuShEnnoNc/LukqANVUPayu6HRsqEuU4SaPFfA4OCWG9uridn9IJusqIW1EPw6ayHt+1VeHOua5ifJBQRmCqj5VkKlqWjKBZCmoIC+b8gREkY+3vBQCVGOgxAlSvEY8qqEEYQdgHQ8mM7WQjwEwNa2g1kXeMMZTN/Srb8Ug4yjbSy4x7LUkglRVUhas0V/TsfUzFqbynXHScFTwLPMIkX/c5Z4TDWHlgZlDLKkBpcoTW2ER2AmAzW93rkh+513lRX10tbPAxsmbdFpERj3iD0NT4XiJGPX0jPYcF05F24+WYLUd4ne0m8uj//b//h2OPPRZhGGLhwoU4+OCD4XmU8UYQBEEQBEEQBEEQxPbByD2Ptn6GpADdcMMNOPfcc1EsFnH44Yfjtddew/Tp08e6bQRBEARBEMQ2xEI2Bz4LUZP6T+1VmaDX+GP0sB6UfZ2aVZGb9G/R6aKREllFImt15+PMgzAGKKHXgoKYAgBohf536pRkCiabv6BPyXmYHFovHn38JF+51JKCkMgJWz1Mfy6VTvUAgHIisCnSrzvM7/VVgfUmRSaocvDYpohJ9zutSCbAbLqa8VnKCYFWE9Yy02SPLCxWsds0bQMxc+8KvIN31G059I16h3yaZtIMBkB16SiWjkoLem2ZeRstJNIoiUm+RIuJlPDCzNLGlHySHfptuTdEtylbXpNpLaQSs5FcPmaaMJc5+RizCvqetbXqqLKgFCMxt06YNCWP6egYQFeJsqljeeNxM9VPMDenj58zaQNKs3VUhJikx47VYtTW6ONr600JeTDUzP3SEUg2Bcp43KQF1pCAITHxP/a3zKSVuNRCJl2Z9YBLCGh/IADoDhiKIodceY5um/Cxgf0TlWijuRcMiYxQ82qomGi1mmQ6Gsc0hHGAhxKhGaBStYLWmo+SmWNFj6M3EoiVHXWFWCbOI6maMEQKrk2AAucJBJdQJkTCpq3lTcSUzxk4Y66aVwygIpmr9NUbC+S4lxY9ZEA1EeiJU2+eSNpYMpPyx3W0kfX2sc+QTccrG8+kTSbybK2JOlrnUsuqiGSC0MyuHPddBTrdK5syx6BM4IqM9LWqvT42VfV511UZ1pT1DmuYfo4qrMd5cknkXRpiznpC5SJ4Jl3N2KVBxgwVEyXVHXP0mGi8HhMhFokaAuPJlfMYJpkKcFMDk15ZKCNvUizj2EOU2MgzU4kv4el4mpsXSeki34qCo818N0w231eT/QQtJloqzxNwm2Jp7kSO11AyVRN9872WJF4mCkwgnO25fgMApk1yL1uC1e51TtRLHTY90b1PGjyQWCorDJaWNhhbcvU1hfrvieGxnaStLViwAF/96lfx9re/HUopPPLII2hvb2+675vf/OZRbSBBEARBEASxbTBp91YEXg7KLHoSJJBMp03EiBAzm2pRdduUWSkmiF3ql0I2Bcym1gh4JjVFQP/2WehSqyIVosO8LpsUjDXcc2k2HmO2cjdE5h/5dpmSSIVYpf5H+ncCZtpXZDXnLdIO/TtC0aVoyMzfnT3zT3APCh1Kv37KLET/Jabikep8PV6PcbQ8oY9p+/Yr+reXYJIRfCYFVcyepQW34ttn6x3nzET1n/rzddXQpcTYxXxOMEwyRr5TggiteZ0G5hfMQtQTgLGeist6QLprIcpGPJJKl7wH0t9T8xwLC/re7djSiemzugAAwTTrw8OAjdbsPDsOVtxQaDWL3anO76kLM2Z26PPs6IPP1IIgy+l7yDp7EcS6735H7NpmxaMYcOk6ebNYDphyvlYKcOJJjxU4EpaWimfWRFuXWgeANj9GSUTwjadVNeFYG3iYHOh7WOyZhZeiAtaFLwHQqZgey4NJ5tKRYsUQKwEpubsO9xh8Y9RcKNfQWonQImz6Hke3xxEbISMxMooVhpQyJszORFjqdC+ewDcrvYAn8Hjq3cQYA1haIj5WCjUJ9BihsSsGfK4QO/GGoSYZuo3PUk/MUI2VFpAyGgNDfQKSVHqMACCWHNWEY6MxTN9QA9ZXFDZGRuiU3ZAqQUFqcTSfcNQSBqO5uetImYpGsZmn3b0BNrp0OGCt1PNvI9PPTCIj+MIIjGpyxihdH+95CszmqprnJap56DJpkR01oNOIRxVnip+457ggOEpGPJpkUtVaSlX4OX1MtUdB9ZrvHpOq1hkBnSbtrteIR0oqhJ71PuOYaTLHZuZ0oyb7VZRMWprPY5eKag2kfS9CaLzBPN+eM0FQTQU9Pq8NIi9T+aW1ZF8h7//Lvc416DV+g3gkknoZgWOAtLXGdOOt3EB7pOln203a2tVXX41TTz0VV155JRhjeM973tN0P8YYkmTgqhIEQRAEQRDE9skmsR6+CMH7/CW5HlstLYDnhCalkvS1WeFJ83dg/blEYoScGHqBVZWddaKNw25KAJ7YFTV3f/G2f0XnEBlxyoMw/3Tmym7jrq16f32uEHn3215fQqZCkhEfEsTowno9Nkz/1V8qBVU1+1VlarhtvZOgUl8eFqDlGW2Yvevv9LZ9xGPw2TQAwPqa50yQrZ9Ti6ecL8uUXC9Kk/Ri2GvVfWIhhzJG2XHVGvpyJ6oIxtBi1oZ5s+DesRjj9ZM6AAAzdu+Bt5P+IzMrai8itqkHqtZtxhmmH9wt4vOeQskINPOMt9Gc+Zvgvd5UkJs3HZhkqsqZ8WSr1oGv1hFOwkSMJRJObFAqFadsBawWP0HRRG74XKFqKoFtNNEw62oeOmW9wXgggClmvOaVOjFlWje8SWnlrMpahldXawF0SlBCsbsdL/fqVf8GvhGKJQgy5sKJYogUQ2xEQ5VwMKZgtA0EuRjFcoSWmj6mJeLo8jgS46lVUwo+Y87nxmMKQsnUWF4qKKW0p5NMI6oUOGK7iGfQgp59nhhDLOHmSm/C4MfCCXGANvq2wkdPrKuPVROF2NxQG+dlx00p7RlUy3jl9MYM3SbaprMGdFYjbJRaAOzERigo97wUYh81KZDI9HwAIBMgNqJLrUe3r7MaYl3NGm8n2MBf09eL1pm2ccSmWhsYc1FuVlBl4EBsvY6MoNMbYGNN36OOiKFLGq8jXjbHppUSCwFHq/HnKoa2alsCbgQlVlYuAqs3Nl5WMdBpSsBVYjMnGUPJKHwzcgo7FnSbZ5V0BF5bqQLPzEWlFCLTpcREYnERuwhCO5+YSJAzBu75fATssDvQnpEAiqm3kY1WAuCi7dxnDdUKbPSkhfNMQYBhikWNDN9AmxgvhiQeHX300Tj66KPR3d2N1tZWPP3005S2RhAEQRAEQQyLVjkZgcyBZdJRGhcaWTJ/H4diyhnlJhlD6tgKSSx2ooxNFZMqTs11kTQXkgwCHphdCBtByFMBuPnnsi/9NGJI2cghAWEWOgJaTNJ96pueIKEgVVr6W/9O2x8zK3zFSEw/YtTSFLjMYs6KVwVVwiSlq5xZ4+1nukKUTDpYV8aX1kYSTPIkpod6ATx5SjeC2UY0atdiBZOA7DbRFbE1SAaYWb3nuULBVFObYRbKu7V1YubeeoHLDnwdkh12MOfS7RAvvwK+3pTvdobWArZsVsCAKaGOmJgxTYtHwW4tULsv0IfMnAXkTIWziqlq190L5qWpTHo8mUt14lAoGkFqmjHwnpovo61VH++FCWplfR/XdOjoiygpoWwW9zZNqlVILGzTaZQz9q8B++0GOV/3D4yh9NpreN3jzwIApv9hFSa9Oh2tvm7ryt4Z6I5jeELAF+k8TxRHbMQgpQQYFGAELi8A8n6MFrOQb/E9tCbcRd5EUkKw1NzdgwKDdOJKIoFqxCESjpqJDiknPnold2biCSQUY26+K6VnmTXELksGkTDUVCqSVZPUKL4qgXKs09dsNJ7PGQRjiIygGEuJSKbm8wpafLIl73sjie6khm7ose1N1kNBOYG4lBRQiX3XppqLCGKuYl93t56z62shNtR03zawTvQmutpgbFJePZF3QpngzKVxWfEyTgSiXmHOb1JTqzkXzbSpplx6ra16FyBE3pSrb/EZJhnxqGQi8LwCnJImJVzkXrfpR2dNoSs2VSfNzc15Au0mKnBeLsKOk/XYtO+g56/X7rm+J90RxCYj8lbS+2Q1HGv87ZU4uEkpDRMFuWA+5Kw02kgFafpZVjwSDV9hNmLPvW/43hYYRrW1QcSl4UfopCmq44Hcjj2PhpSAuGzZMvT09KBUKuGBBx7AwoUL0dbW1vSHIAiCIAiCIAiCIAhiW0PH+Q3/RzX5o8LWxrANs9/61reSYTZBEARBEAQxbCbzIkKedxEP2UyI/v6iaWOPlILzjXGpYEo696NsFE/iopFkXZRSNoUsvW4aLWQjeoT57SkPgY02Ygy++bO+7/xwUqNnj6V/nW/8q31jX2ykQaJS/yS7raYUIhOxEyvZp88cDL75S31OCJRMSfeC+Vd9h0yjVKoSYMyYApuIhLYgwuSijhLKzQbY7Bb9gUkxUz1VqI6KG19ApwT65jwtAi71a8cWHdkx8/VdYAftro/Z5XWAu78msmXmDLDcSnMufblIchclVPQlWk10UG6mSfVZMBPSRjAJL50rBWMYnomYsBFS2grHRHFwicnGPHhum06Nal9YhTdPR2qxUoh8h44mCZ/Uxsq9Lwn0xDqNx6Zjzc33YtbbTMTOke/U/kKZGAI1fz4wX3tUte/4dxz4i+fR8qxeJz0V5LCqHKImtdcUAHCmY+ESEwUkYwEkCaxNjPAVgiBBwZaR9yRafeaiHaoJB1cKefPAeEwvSWNXVl7ojDSmUDWmxt2xh3IiUFNpGpv2J7Lztd5UPVYMFcURJTYVDqgmQMWEzJVjhXKsUEmUG4lYcHDGXepbVTEEMmMRrPR8rJoHtppIVFQNFXSZdndCKQlm5nM3a0c5zqNqUuVqxm+pUvXArG9SRd/LdVGADabKfKe3EVHUYy6qNzJWgMeNDxpnMEE5ECYaSSYc1Yq+Ad3Gm2h9NcSGmt6xK66hzPU5bXRjmIRoNeF8U0OJqTk9l4qT9TwWkziS3jTFzHo/dUfmnLUYPcpEFCn7PPuYYqL6dmjpwuRdjb/R7jo1FaUC0KOvw17rgIr1cyxNRFgScTc1bQSSKHng0/Q4IfB0pFHm2UEmoij7few3pK01Rh41Rg9Zg3mgPgpJ7ztwmnLjuYYY3zJhbAveRSOFDLMJgiAIgiCIcWFK6CPn+25Bydjgy4SseOSsiqRd5KaCUiIVEpVuB/RniUsVU03TBmxbOACPWS8jKw4xZ6jtc4ZApFW7AMAXQGBNthmcIbHJIOvTt7RdzL2PZFb40CliNZeCpFyKm12wsIxIleO6DUB6bYW0QpyEdPta8ajkJSi06kUpn14AphpfocC3jUvHObHtAIQRj0qewvScFmVmztCijPf66VC77qLbpzKDalejxaJbpCZWDFDM3VuPKbQUjPfSbL3QVbNmgnvWuAV9YJxBmsGxlccSycGdz1GM2SUtTEx+vVnQ77sAascFupv5ArBR+03l8v8AAOzQ1YHu2Ktr+u77rod817tN35RpS2bBDZa+3WtPFFpKWPS/fwUA5J+cguf9AtbXuOtrjiswJY0IZeaCchl84AFDEEgUjbdNayxRUcJFLZRjAAoIzP3wmJ7nttpaTXKtmTCgnNhqYQJlUyEN+nAwxmymnDZSzoytBEMsGRKzNU6kFnvMJO2JJXoTiYpMYDN4QiXAmYfQpP3lBVBNbZWgoCvCxVYklQpVVBFJLYZESS+UkhDGKLzidaMi21G2Jt3WzLsaQBoxYn2kBc8NkYcuUwWtgg5IY7hvzdk5E8iZ9M6CYMgbj6yAW6N1hqiqBZWNVX3O9TUfHeZB7GC9TuTyYCoMshym5nTn5+QqmG6eBX+BEWZaclAbTNrcqsh5N9mqe90yQplr8adozPIDwdBu7vvU6T3w9tBV/NTr9bOFfB5sjU7J49Ua+Boj8hpBKq4IcJOy6hmTbeYLsBZ9ftZahGwwvkacKT6QEYwaH7k+aWwDpq0NIhYN8vngTGz1tRGnrW0DohMZZhMEQRAEQRDjwtQckPczgk1m3d3sn+PZGA+p0opLSUZ8sf+Qz5olxxnxxR0D1ecf77roVBotZBdIrgIb19W2ACDkqVAUmLVLwBQC6z3DFbyMibE9fxbXVtdOlopH1ttFMScExIo1FY9sOznSwAG7HxhzIhpjDJ5ZWIVmUZn3EnimshorhEBRR/KoTGOVaUAcm6pxkrvr54RCa2CjhMwl58xIO8vSiCP3WybOtKYS68V5LUmvl+MJikbQYjOm6jZMn9Z8Tth+cuY6HbmKXsrdz1Y/RvsMvTjney0AAMi9FtVNNDXNRHTso8/TtuZR7Bxv1K+n6IU5P/XfM1dnrk9p/5g7pQKAnXdC+E7d19clT8J/TuFfPQV0mzH1ue6vZzyuOIvBPOU8aphQ8HMJcqbcfNFL0JoIKBPZ4TMtwtjls88VOEufjVjq9iRKoNeMcW/MUJHMCTdSSrP0N3MfZv5nBkdBOWE2UjpiqCdOxaNuVUWFV5x4FKkcRMyRM4ZCvTFDwOtjSqJMG2KpEPMYiRF9ElUDVIJY6vc11oOKTNBrBLDOyEYAApHp/QZjdN4VA93MilA9UMpGHJkxE3nkpRZQCj5QMuJRKOw9kCibud4V6XNvjBg2VPV5utgGRFLPpcCIUCXhYbqtDDipA+HuJnJvt7n6dy4HrFylj3n5VSdqVs2zXeYVVJk+p21bTjC0+bpN4RwOtaOJvJs8Ob0vc7WgxFatAYOeq7WybntP2Ydn+uaFev6GiQI3CjMrFsBEvejDogjNaAg0qpv3Td9n7vRgnkbDNtBuNMweZ7GokW0h/WykkGE2QRAEQRAEQRAEQRDEIEg1+D7N2AYCj4YmHlmyhtmeN6xDCYIgCIIgiO2caaFEwZeZrCY1YNqaRLaSViZyx3weZ6J0EpVNCzOfyzQ1qlmqActEPmnPIv3aNxsDDhdZFHCFkNvoEb0t5DKtesUlPJNKZP9qz1m6XJCZNK1E2opj3LXfVsKKJG+IPDK+Qdl2m98KqddNj4kyyfZSII2SsuXqc34MLjL71hoiD8o1JN26Ab1V/e/9qhSITfvyTCJnKkuJNpOi097uogdYNvLItqanF3Gnvk5vVDLnZK5XOS+GP9Vcf86Mhl42RqXZXCsOZdKoEhN5lCgGbu5BwYsRzNTtlzvv2OQ8mciwmTqESuw9HzNadJl3vPud2T31/3Ldt/rIqrQf2g5JQe22q+7Xpi4sSP6F4OUEG8t6rKQCin6MfKAjQ0QYgQWoywviHuAZz6O8F6OQeO7+e4whSrT9LgAIpRBw6SJboCQSyRAp5vyCKgkQJXq7HUGGdJ56jEGwdL4KU4nNJjBK481VMV5cPaqGLt6FGu91USAxK8FLBMLY+vdwlGOAu2wmhppE6rvEdIVEuGeGmYqEep5EqKCMGnpMpFpnbEukeUhgy90b/6BIopfpKn06csk+afriHi+iCD3+RaFQMBFHoWcilHiCWmI8jyJ9nY0ViQ1Sp531YB1i408kTIdaA45ZoY6SmjS/CrWbTi1LTPqmkgowKWJ80irYxEVbna7MehDD+BeZ+RQKjkk5c505rcCsWaiHAcI80K0ll/rYW9GpdBsqoUvFC0zVtmIlcc8mL+ShGtPWKlX3UsnMHGy4cuN7r+FpGqhqZl9Po4HZ/OprY8vI09bGL2Lpsccewx133IFzzjkH7e3tuPDCC3H55Zdv9nmHrADdfvvtOProo3HYYYcBAP71r39h9uzZ4GYC9vb24sYbb8Q555yz2Y0iCIIgCIIgtj2m+BGKgecWqdnUiHQJniLrBKNUSLGCSay0wGK3JQ3iUqKYE5Kyfy124g7SBbPHUlEoFYcUfCMYhVwicOKR/Z3AN+XgORQ4rxePGEsXPlIB0nrz2HL1CUtfuz7x1Ew5k6qnMiJSgrTPZSOcVE3pdyg4YcFjCnnTlzw3qTpekqac9EZgm/Si2ypWcn0Pqh36XF1VvSjtTYTzZOKMuYUpb9Gfa/EoI6g0prxENSTdZqFrPHGqCUNoBK28F0NM1SKBnDcPriPuVUagsduFB2XS1mp2DJUWPgAgEBK8TbdPtbWmbalLzapH7b0nsPeeDVtZmpHHODhn4E4o0321i3ilFJRMT6wO3B+5zl7MZuvRslaLDtUag8cThHl9P3iooBh3fVGJggJzGkHgJSgmMWyimmAMEVeQZmIwlcBnEiKTypMohkrCUDbzrWKMqq0Ru5QKTGVFTi2cBk481alwsRsg7SdWM8eXWQVl1oma7HIKnGQxfBYgZzyjCsJHjjMnyDKmn1Vryp1AgQnAZO+BcYArBSv8SNRQ5RX0RNrQvTu2iXYM0ox/j5mU5SRBFVroSZKae1a4EXpCVkKB63blBRDa1C4vNbS299CagndHEl1Mp4WV403gsGb5WoRq9YCpeX1NvkMRyYL55lx2xACYecfyws0h5/mEMmKY1EjzYdFTaG/RghIWvC6doG7KZlJv2yeBmRvYG+k2bYhCeGZ5XyjrPk6u9qSCU0sr+jyc1Zp7OZBI05iC20hWPBpISBrK51s6I01bG08N7JRTTsF73vMevOc978FPf/pT3H///aNy3iGLRx/84Afrqqy9/vWvx+OPP44dd9RKfldXF84//3wSjwiCIAiCIIimTMpFKAXCRUkwqLpFSbrwMgtSMEiZCiXSiSap4BI54YDVeQnZ8yVN/tornHilMj5HypkQ+y7aKEHgpUKRXXQKI3p4XmyDC8C5cga9aX/S9kuZEY+M4CMYd/5Ent1PSdfP7GrDimgy0/9KLJxXUnpIepBgaZ9ypu2+l/oCsd4IMKa+ynjsJK9F6OzU0UGdsV6U9iYsDfhhCmFO78umTNHHeiLjeZSKLU5UASDNQr8ap0KPkZ4QejHYtJI5Jtt125eMkGRflopgwo6tFd4ykRMc4KGJPGq28s2M7UALY4Z6DyfGuFu0N0YeARIqMwcYGLBoV+Sqfwd/cZPu/8YEMkpFLBkxJN2AtO7VkkFWhRNAfK6Q92InCPqMIeZAFNuoIAWodDkbS4aaEuhJPGcy3RsD1US5yn7SPGFOaONaKM05fy8tHtmYFAYtYsZG2KmyCqqqG7WkG8rML8klPF5A0VSrqyYCNcnhm+gnjyskmXLlNoLLGmQL7iGRNfcMSVVDhZfRK/W87YrSaDkrDpdjK/hEqHFbITBx/lAe035eedWGolGx8ly66BzGbTU55XyjbTW43ihBj9JV+GpxN3yhvY58ZcQZD2gt6mti5iwgsLPZ+o3Z1upNVgS29yBGzT0foYmQavMlWubpaKhkl10g6h8G/dI+U9OngxnDbiscb4wEhBFm2mvGgynu0VXaAKj29r4KURxnzt3/g9Ao97ABBJTG6mqDG2hvnpikj+/raTdWbA1pa7lcDhdccAHe/va348QTT3TzZnMZsnjUeMHRagBBEARBEAQx/nzta1/D1VdfjVWrVmGvvfbCDTfcgMWLF/e7/x133IHPf/7z+Oc//4nXve51uOqqq/DOd76z3/2b0RJW0RJkIzn6ii36t34vZX1kjkv3qhOPhHldH6UEaOFINREJrHjkQcFz0UbSRRQFVmgREoGXRil4nhGdjPm08JRbH2b7YtM/ZAIw85olHDaqwkVGKOn2ZULVfdZsfADolCSzWEwUB2+oVaNUGpUCnkaXWJ9cLlIxRNUUsElLBLJTn6h3Q4BNFb3w7LZRQpIhNG3JiRhBq95XzbS5Zhnj82ZiYKEI8IzYAX2/7Oe+J4FJOsLEil/6syaLZ/umtRUsbyOAUqnJBWswBZYLzOtMtFFG0ErTzaygke2JJszl0NJSSvuZOVelUkGUMRzWxdgykVcKUDNngs1bC9GlhQa/WkZlI0NUM8uwCgfnAp5Iz68FFiO6+BJ5xBDmRotIoJakhtmxZIiS1DS8pjh6EoGeRKA70XtVJFCRyqVMSaUgwFzUnc8ZQq6FFQDI27li+loxXVJmXkWigkiWdaU0U82QCYaIFVGBjmSpJQGiRCENiGMm2slGxXH4CMCZFk44F5CKu4pfiYoQoxcVqcWN3tiKTGk6Xa8Z+qqKnNG2UgqM2X21oJNXeeTNcBc86Z5vKx4lEXMV+4yGil4Zo6p0hbUkqcDn+pkQsNXkFHJF3QA1eRIaazkyMKCmx0JWEhcdlzjT8sjNk9CIJ1ODCGLXdtMP6UQKJrMPVfpSTNWikB3TcsycgGqNxpkA1LSp7pxNwzvtS8kwVHiD4spVRgAa5eysPuISyRCDks9r4fSAAw7AEUccgU9+8pOjct6tO2aMIAiCIAiCGDa33XYbli1bhuXLl+Oxxx7DXnvthaVLl2LNmjVN9//d736HD37wgzjxxBPxl7/8xRVTeeKJJ8a55QRBEAQxcSgTQTfcn1FX1Qbg8ssvR2yiyk488UTcfvvto3Jecr0mCIIgCIKYAI4//niceOKJePOb3zzu17722mtx8skn44QTTgAArFixAnfffTduvvlmnHfeeX32/8pXvoJ3vOMd+OxnPwsAuOyyy3DvvffixhtvxIoVK4Z83VwYIReKQSOPsqleiYmeEAlDYsIYhPFe4VKAyzSSIXZeQTbCov76LgrHRT/ITORRmqKWjTbyfRtlJMFt5JH5zTjqS7+bKCDl7GfqI5/SyCqbztJ3McGZcilKnClwXt8JyZg7XrBMyWobraVUnc+TS2hykQKZhBOpXA5QoquGo1L20W3KlvcmaYqZ9U4qiAjeDJMONleXJWdK1a+LXLaZuQ8tLWB5Vb8tm17HJdDWUj8QGR+axqgOew3epqNBbMQYU6nvtC8kUAxN17MRbWk77HkzsV76JIArDtTW1tpvoSDOOXp7elGLUt+Yuiw2pkebFfMQplx6LeKoln30GJPjRPnwBEdg5lTgJfCEclExgktwX6apc0qCg7vUrMj4ZlXM+56YoywFumKBXnNvy4lOl0oyaVCMp2lrPlPIcYWCjTwSOm2NmRTDgDH4LL3HEhJS1pAkVdgJxJhApKqIYdKupITKRv4p/dzZa3pg4PDAuSkjz7mJsDFRbSpGhDIqvGr6FZjxEG54KyalrcZqkMr6FwE2PsKmreVkDnnz8BdEAl/YVC0T5ZR4zour10Qg9aKKmjR+YKoGadrlmcijkCt4bWYut7b29SdiAItNZFJVuWhJ6xuVqMT5KOWNJ9HUsAfydQv1MVLBRirWR95k0iLnTDZt0SmRsdL3GkhT2ZjP0sgjqTBQ2M5oJRY1GmQ3ehxtbpraRDPitLVxjJg68MAD694fffTRo3LeYYlHv/rVr9DW1gYAkFLivvvuc39x6ujoGJUGEQRBEARBbA9s2rQJS5Yswfz583HCCSfg+OOPx5w5c8b8urVaDY8++ijOP/98t41zjiVLluCRRx5peswjjzyCZcuW1W1bunQp7rrrrmFdW/gSnp/UpTbZdYTKpDHZNA0uOZhbnGZTtGzSTpJ5LcFsSog18VUZjxUoJxpZ0cXjCl4mVc2aX/vGSNcLEni+EXJ8CeO5C2fh0UwwQcbegaWLZ72QNul0RvPR7euLMxTnqXiUCm0cwm5rcmyi0lpTkeKomSvYVDeleJp74DMo4xtjBa8o8VCVtvKbHUcF34xZKayCzTQpZsavBgoZISaTImbTwgQHy1sPn1TocWmEnoIqFNy57LF14lHfDDaw6dqQOCc6TXckPDNOgR8DLcUmI9T3/NmBdH5QVuQYoMK0EAKFQgGyR5+nVqvBJGjVnZclUpc8AxB1++iuFNBVM9XXIOAlDHmnOHJwFqdG7DyBUgrc2cYIJMpzwo4CR1VyVKzoITnKkqOsbEU7bZStqxWaeWWW905oYww5LlEw6ZgFk9aljKjRLTx4qa4GKIVESSgVQyr7fEaQMnLV12Tmnulr6jG31xQM8KQHYVLMOONGSLL3XCJRESpMp/tVlZ4f5YRn/J1spbcISqZCKnfm4loE9RV3ZvcB199BemyteMRQMc+HFV/KogxpUxKZhL2Z3PlESbCCuQelkmt3dv7b7yNVY+5exEbwkoghmBU/9f7FsAI5Z64b41Rs6CsSA4BcuAMAoBT+2W1zRQNsKqbP6g3dx0jBGG5FtQHPta1WWxvldvTHQw89NKLjFixYgB122GHAfYYlHh1//PF17z/xiU/UvWdsZANJEARBEASxvXHXXXdh7dq1+N73vofvfOc7WL58OZYsWYITTzwR//7v/w7f9wc/yQhYt24dkiTBjBkz6rbPmDEDTz31VNNjVq1a1XT/VatWNd2/Wq2iWk1LQHd26sW9LuOeCkZ1i3YBFwXDM/44dtEsVWqunUbmMGe+zTNRI85TiNX/pT09Du5zK8ToKB+zYBepeMOceINUdLHtyKxxlMx83g+NhrQKrM82NERjNUZn6TG0C9WMX4m7Rv3ixppv2/LmdZWCOHSZOQAQThlw+7izK7hx9rwYqmgqo5lVnehvdeeEMwUVmAW9EwMb+uj5fa6Z+h9lF9LptZQxAvaENjYWGQN0wRMg6PsMqWybVOPYDn8tIzzhqk+bs2Tuofb3YnEMW64uSQRqiY+qtP3VYl7iDNFttImNblNgLHHt5tyENlmBFAwx0qi7RDFEpvKg1VoldJBb2l8tcdhWCyYhmAJ30TXGX8nMAwEd+VSnlSoJqSScjxcUEh2T5PaRGSGNQT93NpBOn49lhAJmqhMq9zkYoKwvkTltkhEdE9jvCelEKz0/7DWtMT1z88Jj0j3nqRaWqeRo7wOLXN9gTMkBgJnPPSbB7LPT33e1HTCZPof20WSZ/7XfR56XKdWHRi8u2zv7TMBUTwN8X4tcIvPsStN3lpmbTcUjPrQ5T8v8lP/P3pvH21XVd/+ftfbeZ7hjcjOSAQKEQUCIEBISZLCiFvvY8tTHx1orqC1tfQiK8fd6Kq3i8NIX0lpBKwXr04p9WqqPrbRqLYKpTBUIAlFAQQKBRMjNdHPvOfcMe1hr/f5Yw95nuGPulJvv29fx3nvOHtZee51D1ud8vp81x7SsFpo1m/HAGMN1112HD37wg6NuN27xSGbUXIIgCIIgCOLIWbJkCbZu3YqtW7fiiSeewNe+9jW85z3vQVdXF37v934P/+t//S+ccsops93MCXPjjTfiU5/61Gw3gyAIgiCmlEk7jya530TZtWvXtB376C44JAiCIAiCmAfs3bsX9957L+699154noe3vvWteOqpp3DGGWfg5ptvntJzLV68GJ7nYd++fQ3P79u3D8uXL2+7z/Llyye0/fXXX4+hoSH32LNnD4D0C3wlzUMAdoksJdLXpdRL3CvFIIV+2PwUpdLAUqn0N/r6of9Rb0vBlPlbKA6huCthc/sZh46Q+iEVg5RcP8w5pWRQ9mEjSGSmzTJ9IPv6CDCmGpxEDMo95x5N+2TL3dI+NP2R2ZqbB2PG4cH0t8k+0+Yiu5pc1mighIKKpX4kgImNaRvxKow7I449qOEa1HDNdcRI38Q735cCVJhAhQkSpV0y2dwQpRiQxPox0oFcp9u/AVWqQJUqiBMPceIhkUAilX4IDoSRfozWuObHJGCZR/PzAIBcAJbjYDkOP5Ao+AJFTz/yntTldlw/7P3JvhdEzCGEeUiGbFYWZwoBUwi4eXhAzmMIOOBzBp8zeMY1ZLw+7vKFeSSKIVFw75VYMsSKm+f1QzV2vXEO2VHHjTuNgSkOpvTfNv4pfT9m3kJKQZrSQZXJzGKMOzcSB9e5SPARMK4fnMFn+uExDo/pIjzmivGyt1ia8jcFobRrKZHpZ4q7T0zB54DPdQlZwAEfOTDm6WXms6v1mf8lkkHFCipWQByPOo6Yp8v0PNZ4D+ygsX2SJGNZF7PHV8DQIDA0iCj0EIUeEpWOQ+3CU40BPe3Gd/phOPqpVePjWEZO8jEfuo0CswmCIAiCIGaBOI7xne98B1/72tdwzz334Oyzz8Z1112H3/3d30VPjy5HuOuuu/D+978fH/7wh6fsvLlcDueddx62bdvmQjRtluWWLVva7rNp0yZs27YN1113nXvu3nvvxaZNm9pun8/nkc/nW54XMUfCJxeYnWQCsxOX5eK534XMBmaj4ac5G4Q5sQ3tlUq6b5GtKNXQHgA2DMiTWmwBxg7MdpP7TIlNVjjy7BxRqTREO3Ne9812gxCV9o2QrftYOODKpgKmkHPLsdtyKJkeN1GwmdvMqBaBJ5A32wamxiiWQGzaVA7zWLrXlCGa1XxY3gXyNOC6RgqoqjTHypTRmJ9CcLBqtXXntMrKlQtly37Y/iEAQM2EKceKu4qxKPaB4aZjZtvEMn+x5hcnhmr4mSk7ggIUA/M8MLNWfK67iu5azZVOCenD48wFZge+APeUew+oRAuaccLMdXkufBnQ4lGep8ViUmmBQsJz4z/hpizLiDJKKUilXClYIhXqgqFiX4cPxnT4NqDjmmKpGkLOObgWV8zfHg/AWeDuU/NS7lbcTWyblEKCOA26llIvJe/KrTxwFqAgdYlkweQvFX3mris0ApCvgoYaUpvDJJQWDmMlXOZQJDniRN8LX5jyPE+hYALVOjw9loqyAx7XvyfgbmwI09ORCiBNIrlfLkP16fDqhjFk2sRyOmAbAHwXfO5Dmfyj2HyuDNfzWParX+n+WLWqdThmSz3BwHft1vuF6Wetb7rB5rupWDWMeTbZQT4GajTVfKLHUlN3rOlgphxER8L/+3//D1dccQVyOT2Gf/WrX2HFihWuxLZareLLX/4y/vf//t8TOi6JRwRBEARBELPAcccdBykl3vWud2H79u1Yt25dyzZveMMbsGDBgik/99atW3HVVVdh/fr12LBhA2655RZUKhW3+tqVV16JlStX4sYbbwQAfOhDH8Ill1yCv/zLv8Rv/MZv4Bvf+AZ+8pOf4G/+5m8mdN56GCBQwZjikf1mW0rtHAK0YCSk/T19LlZWUEpFF2FzfrKrPSET0GxX1ALPrLbGEZuYhtisMhYkHnJJuvKab1db8+wKbFnHREY0sOKXQNP5VeO2WYHEoK/B5pYolwNlEZJl8m0yTgXTDp45Jmcwnqx0QpbNSWKcAYGZgHfqExWLCbpr2gHUEempQiSY69taEkDs0xP+nJnosrVr0VZ5sW0qlaFqpm9cf2WuWTBgqNzwvFJNGURNOg8AqCEdppyognvRdlciPaAaogV3/qzzpHWz8RKGIUTSuHpXYw63gqpUIWPT/75CviOC52UCsjmH50LYGaC0WAoAQnqIY4bIjMlQcMSCOdFUu0wECjZk3VfgggGM6TwlGHeeSoOTreFPWKFNMdQEc/FX0rhjUsGF6feGuy4Oj+fge3nnOwu8AgKWRwCd/8M5axCQFGDcTXr7RClIJiClXSVNmnuuO4IzHwEKKEotjBRz+lo6vPQeh2aFtJzIwcuk2CuzRSz1oKvyOmpC52NVhHapAUDBZjp5AkWzAlvRiHodyCPwuvR52BC4CfYW5th1yZCUzOdEqZSKR1lM5hbLM/hM7xfY61McEvqcVSNiHQgLWPvLl/W+q1Znxnzr+wAA2CuHTFv0qnI+T71XVgxTiQI/cFBvv3jxqGN9qnKNmoUk2fT3XBeHxmKyrZ9J59G73vUu7N27F0uXLgUAnHHGGdixYwdOOukkAEC5XMb1119P4hFBEARBEMTRwM0334x3vOMdKBQKI26zYMGCackveOc734kDBw7ghhtuQH9/P9atW4e7777bhWLv3r27IQR48+bNuPPOO/Gxj30Mf/qnf4pTTjkF//qv/4qzzjprQucth3kolXfhy7psK309XUnciifMOTASyVzobKN4ZJeTZ24ynGREKNHmW2IrHvFMwHLAFXLMOm6MS4QL5Pz0ubyXOhUALSjZbuJctRXDsi4qadqdPsdTwcutbJZeZ8PKSi5QOb3+WLCWahOWCRFXGXdJ808A4HkP6NTfTLO8nsh2RCF6jejSHenJb034TvWpSx9hWfdPoV9PXtXaUxqloyaBhlcqUCINLwZ0KZ3dJ044MFhuuY70cKzBzwMAbGgIsm6vk7ntUlcXA+pxdpfsL23FI/2cFf50R9VqNRSLRbQjiiLU63UImVH4XImd/n/W3w+1Zz+Sg1roiqsSYEBQMOHsAYMfcDDbcAmIOoeo6b5PhId64qFmXEBRosvIYnNKZeqIrDjBII2AlL6fEskQe6kQq4wQZMdOLIGIA1Xr4oMWfiIzViJTbmbdaTkUEXhFKCQZ8agTOdaJPPR4ynEGn6cOJGZuiVtxEAqCZZxHSkKpNGCbw0eADhRMYn6HEW47vPQ9HZqZbCEO4LHUfSNlbK5b93nNr6KWLAQAVIWHyLgZrcjLORCY/isYUa/IPOShxaMaL7iSOOs8qguG+rBuQPHQINia1s8ZmdNtYgXPufjsbebKh1C6nXXjlDoUBRDP6UUIvMsYuHUuNdSaZvrzoBbHFDpdH1kB0IphKlFgRjziS5e0Oo8yx7YLFYwH2VS7Jtn0CUJzTWwao8JvRGZSPGoORp+qVfZIPCIIgiAIgpgF3vOe98zq+bds2TJimdp9993X8tw73vEOvOMd75jmVhEEQRDE3KU1mW5695tLkHhEEARBEARBzAiD9QCxzIE750/6WpsKLhdqDWg3hFtO27qRVOoySmT6uvWBCMXa5h/Z83LAtUWHStsSNv0zz30EiTS/S+RcFpD9KRAYZxKHcjk23DlZMiV4KnUeuVI80ViWp6+Ju+eyObbZbCSB9Jrt9aclRSwtDZJAaL60t86VOOZp3nJnALZIuxaYsW35chA9QzorqKeqnRPDnoe6dbqAIw7N0uSHh/W+UgANy9WnbTG/wMvp3/Oe7eN0u3riQx0YdnvZvsscKJ122cZXqu5GWzeHz5Ur25GKQUXGfdH2O3+WlhG2mdQJU8I4XB5GYrOdGHMPQOeWCdFYV5gxHmnn0c+eQ+2ZEsr79bQrDAN4nkC+oI+ZLyjwDuVytCC0M4jV7T1kqCU+KrF2y9QlQywUlBvQEgEYPOP88CDhMQXFAVOdhVB6CAVD5HJ7GFSmuCiWOj8oLTNj4ByI3XUoeJwhML2bRwF51g1dyWXcSKwDHbIbRa7HRt5jyHkcptoMjDN4kmXuqwm2N64S6QLDTFg2zyMviyiam9ttZq1dPpCYDq6b90yOewigHZyccTDjIkqEdubU2RAqyXFmHx+R0gez70ellHPd5MzPgsfQYdxKFf8gPFO2ljB93yoJQ7miHWl9+wahIlMiabLelAKy9YvWDRiY94kv8xDMZDKZ0r2hOI/hPbr/+n75PPCa15hrypT/2TG7rx/SntK4pfpyEr4ZB90582IAsIp+P7PBQbC+RWjAT+WAZudklmbvz8gx+XBlg+5vJUbY0r5+ZM4i61ojpp8Ji0cLFy5srD82MMZQKBSwdu1avPe973U18wRBEARBEAQBAIfiADUEaT4xU2iVHFL0qkw2vygrJGkSxZDYciwFF8brgoIz2dDtSg0YS4UDn+nMEMDFACHH08lkjquWIOk8l+53n6cTNydOZSZjuhzNtNWW3Snu2m/L72KZPpeoVBDLNt/2n0KjkJZelBE3FFA12Tk1U6oTJjydaHXkoHq7GvskipFfcBgA0H1YT0A7ogDSCloAYiNkqGE9+eWDg1CLF7tGNawmBYDl8/C7TWZNoO9eLhNHVEt8iIO6bM3bo0OA1erjG+Yc7vptnwoB5lvxQHeYz9LtIsEhTSYShnTAN3q60+tExglg79dPn4J6QZcNqbe9QfchkIpHnIFz7ko6bftUWm8JmNW4AIA9+jhqjw/g1d3dOFTVooCUCh1+jAWJbpsfxFAdCrCRPeYYJmoLUeyhmviomPtXS4BIKjAz4eYKkFwhx2xfKHBI5JhAwZQ95ThHzmPwXdC80plDTq/SQdY2FJ0D8BRPhQrz3sjZ0GpRRIxeeDxwElRBdaJDdaDomzwhnyPvAXkz22RMj38b6OwzBiYzmWBKl3NyIxZ5yCEvi+gs6L+7jOjY7UsdYA0tigFAh+8jbzKNPC8H5oROXRYWqmFUTWlhTfgIhRGCTHA254m7lzbYutP30BUvAACUvV4Xvh0ZwaccAwdq+pyrdu+Ht+slfX9PP82cXOnV0ADIauKu037G5FkHpGmf7YKq5Bgc1mLuopf7wc48w/WdxQaSs8GSu39dOX2cRZK78ruuoh5fLM/AjBDKhoehmsWjfC499ijGmLG0mWyuUXPG0WjbHnWoIyhbm2F96wc/+AF6e3sBpItiPP300wCAwcHBSR1zwuLRDTfcgM9+9rO4/PLLsWHDBgDA9u3bcffdd+Oaa67Brl278IEPfABJkuDqq6+eVKMIgiAIgiCI+ceBkKMouRM/eMZR0m7eopBOrLIunDTbKPOcRGYlJ/tcdoKsWv7xnhWP7FLaABCYCZ5euttOKhlyRjTImYl+jiknLvlcwXcupnTSnUVmhDDdTobYTej1zygjiOmAYdMX1sSQaad2TjX2CaAaRI3IHNeKSPXYgxQ2k0e5UF/X2EIA3ql/L5pJacETqAt7TKAeG+dGSU9Q+eFBqEWL3fmbb6bq6oLXqyepnYFx3HCJyAhStcRDpGNZ0PHqfr3P8ce7/VnGl+byjZQEMyqfDZ/2mHIhz5XYQ7jXnOv5F/S+556Dpsht/aNfC0Zix8vY/1PdH72/+Hd97X92Rbqd0uKPlSSbv1DXy84D7LlfAgDCB/fgpV0LsKdSRFlrDvCYxALhuTHUUZfIhcI5j5RUelXC2PSN8FARHCXjHKsm2hXFrQuIKXDJ4HErMjJwJuCDOVGtwBUCzlzWF2BXP0v/9jNCpVQ6O8q+Uz2zv3UBSZUDk92oIecEprwsoIvn0GnUoYLHUPRTMYYxBaHgRC5PAVwFgBUlJQDFwM30NEABReTQafql24iOC3MCiVHaIhOW35Pz0FnTwmDgFRCaUGpl8pQSWUWF6xtQSfKoJlZINS4pHwjM/egK9PkWFhj6Qi3klNgiVNUAAEBAH2coEni1rsf0ibtyWLR8DwA4MVwVC1CvmHF1WDi3YM7cpw7VBcFjZImEwlBknEt7D8Mz4xIrVqQbGacbK1dcFlJHUbdJQcI3QdmFTiPsFoJ00cJ62PqhVEizohhvHB9Zmv9OoJpeH1kQmuhKbHMt46iZmSo/u+2223DbbbfhpZdeAgCceeaZuOGGG3D55ZePa/+rrrqq4e8/+qM/avi7nSFoLCYsHj300EP4zGc+gz/+4z9ueP4rX/kK7rnnHvzLv/wLzj77bHzpS18i8YggCIIgCIIgCIIgiHnBTAVmr1q1Cp/73OdwyimnQCmFr3/96/it3/otPPnkkzjzzDNHb6OcHgFuwuLRD37wA9x0000tz7/xjW/ERz7yEQDAW9/6Vnz0ox898tYRBEEQBEEQ84aDdaAgMm4jhlHL1oD0G2+l0n98C/Ovd+08Uu65dBlws69SEOZbbDFi8o2GA/DdMuGmfI0z5zwKOEPOs/kuep/AY85J4DG4lY5saU7ztbWU1SmdNwOk+TKJ0KtbpdeUXj+QlhABQIGnjgk/06n2H/hCpflIoUhdPnFFv54bDl0eis0+YVI5a5PvC3Nst/4baoKhZDKPant1o7pe2Qd18kmmoczFErkvtj1Pr+wGIG/L1jzARBIhlB6qJe3iKO4b1PseOAi1xLqZgMZiPdOJnr03xnnEAbvwWSn2MbBXZ9IsffJl/ToDcNIafZSODuCwdpPgiWcBAIefYXj+0AIAADcvXXD7v4H/8W+6UysmM/eCodnJxF54EeH3nwEA/PK5PrxQ7sDBMF0JsMPjyHsMsc3dUT5UIiHNTVcJEIccNePuqiQeyglHKTblh4kCFENg3hncjEHuysEUfK4QMAFlSt3qSqEgFXxbbsf15NKW2zHosZJWPur8I7vGXcAZpKegVDqimcjDV77rixzzUPQ5CmYgdhjXUZptpRAwBs+uOsYYvMQDN6uz6VHL4XGdXZRTnShwjqIZ3z2+dR6FkOZ9KowDaSAO0FPXLqG834Wap900Quixncg6ar7+vRJ3o2z6JTLlazlfIG+yy3rM+FyUB/oKepwP1PuQsLo5px60w0mC/aFu66+GFqLr2QP6/EKPNdZdgDys32hhyXOuQ+s4K6oCIqXbzN17VI9bAAhfkfBf1CWcrKDHsSoWwfcbZ1655u5F3jiPGEvAjQvPL+qfymNQxsqoqnU0SxgqCNCOZoFkrNW7VGa1tWanUbOTaKy/m5moc2m6mamytbe97W0Nf3/2s5/FbbfdhkceeWRM8Wi6mLB41NfXh+9+97v48Ic/3PD8d7/7XfT19QEAKpUKuru72+1OEARBEARBHKMcCmPkhe9KxbKu+ZFEpKx4ZIUiaSZAUslMOLZEYv4S5qdk0v2umEz3y0xGbPkPBwM3ZTCe+ekLHznzz2WfMQRm2fDAiUvclQtlM5O8rJ7Q5lqs+CWUzp/JPhcphdh8a5wo2XLNHAwBs6VBHroCW7pjzg0gMPkwoWLuuHXT5nLso1LSE8bC/ir84hAAgHWY7JNq6GY5aXZTKnJVEw6l9LYd/frf+2ue3gd/6XO6naed6kKt3TLtwxUX8OybtuW5Qs1mM0mOYRPOvWCvyT7a2w8s1IHFyreBQEBaQqZc2Y5vJ8xMQRpBoZJ4eKWs85y8nxvhoboLwW49yeddechBPQGv/lz/fPnQEvTXdd8kZoz2PN6H1353m762//ZrLZlOLJPmzX72NGr/sRPP/HIpAOAXpTz6awyhUMibQeEVuA4Qt8HJHgP30rAmLR55qMa6HaXIw1DEMWTEpVDovKOiE84UOE/Fw5wvUfSlLm80y7XHLEBFpuKnF5u8LNOjTDWWJXEjSnpWPGIAV8zlLAEMnHnwBXf7BR5H0WNOPMp7Ohw9lymFiqSCp+z7RSGQOXgm6NrnRSglETB9zwqyEwWfo2j27zDX0pULYaKXIM379HDOQ78RQYqiD2Wuy72krJvtBOpMCzlVoVA3Qmok9Xvb4yHyOX38PpNtVBIBFprw6wVhF6ro0X0Jfcxh1HHIiEev1Avo26dfX57T7ydvYQxZNWVzcc59JhTML508QCS0KMSVFY8kBkJ9TQf2daLwzD7dN+a9w7s6gKo+vzpchRRGQDSldgGkKz0z+d5QsYAqaeFMhrGpD8yQeW8p2SZjzCCankiaBB2BtARv7IDs0V8fmyPd/8g4krI1IQRKpVLDc/l8Hvl8foQ90v2+9a1voVKpYNOmTaNue+KJJ06qJO26667DBz/4wVG3mbB49PGPfxwf+MAH8KMf/chlHj322GP4/ve/j9tvvx0AcO+99+KSSy6ZcIMJgiAIgiCI+cuArCAnhQt9BVLxph1ZkUexNIg4KwglTihKIE1YrJWUpErcPgrCCTDt8OC7f3BzM/PyVS7NYFEBfGmfNz/hwWM24Fev9KSvqfUf7hIK0rmgNFnBy67ilCCBsKtFsch9o68yeTVW5OoQXVggtHvB53ryUfAUuoyYUlJAxcz6asb2NMR8HCrrSWvHKxE6Az2R8RYUTENVRlVI22/DlEsxQymxmTL63LlnBFbmtXjEEwF1gs4rsqUT3t5+qJpdscy4PTwJ37Qtkhwlk/VS79fiUedL+4AuHUgsjzsOrGDaV9NCj4oi1zYnHvE0b6kuOAZiLXLxQR0aG/6yjp5+PfkO8jWEVX0d+we1SLW3XkBJZL1owJ5qBxb9UE++lw19H+y8kyFPOMFswsD7+6F2PK/79tEQP3tlOX5uxLlfVQSGkwQe5+gzwcQKXGcImVmYH0gT/G1EwkQHkttcnmHBUY6BkhGPIiG1qGOGQ4HrjCLrdit4AsUghseBwIXJByiLHPKJzTAyI9RcqlR6Ouy50G2GIlfImTEnuYKfmTJzpse4x5hz0wWcIe8x5Lk9hkTA09X1lGLIcZXmhXGGvPKRZ3oMxV4XoICCEWk6ZBEFL93f5jfl8wlyOeGOCQCLowCLcrp/e4cXYdDTApRdbQ2AC6fWweA2/N0KeAqFoh6fNj9rcZwec38QYDDWxxw2783Ii1A2iupA7GMg1GO114TMd/I6mLLB6tytiGYzlXoCD6HQ40TY1eMSiYOh3uflchc6ntPn6qtpEclbGMDeeDGcIKmY++N0G6kT1JEKQWI4gVL6PcWE0rlHEQfMtWVtNFlnTCwbP8OSJs2p2Q0kM84joeKmbccQk8ZMWJpbHEnZ2vbt212IteUTn/gEPvnJT7bd56mnnsKmTZtQr9fR1dWFu+66C2ecccao57njjjsm1b41a9aMuc2ExaOrr74aZ5xxBr785S/j29/+NgDgtNNOw/3334/NmzcDgCtfIwiCIAiCIAhLiQ8g4HlweKNuJzOTDSf+KNEgBOnt0vBipaQTZ9xkRMlRBSMgFXoSxsFghSCzGhPqYKatIffd81a84eBOXMoeq/FaUreTNAJRVuSSJtRXInHbK/ecTK/FCDESyp2nwnKos2UAgN5kOQDgvM66W/XtJZmDqVBDzXTpYcawv67Fo85DAn5OCyMFoSe9vOg7Qc+zAgdXsIVrVclgdCDUzGpVCr1gT+rrXF5/Dv7JurSGdRnBZ6gGWTYNMCVHfmaprUgxHDKlcJ37tZtpxbODyHm79D7DVaiFesLF7Ax3uAolbBizaWcmTDwB3AplKjTClPLRaa7d4xKheX0g0hPpocRzwoKlLDheHNLnjh8pYfELv4C/4Ofu9foBhr379Ou/KC3FL8scL1e1aDHABiCZQK9aiC4ZmLYyBBwIPFt2psC4cteSRBzV2EfZrGhXjoChSKIcG9FCCASMw3cr6jFIlZaYeRzIBRK+r8CNEBaJBJ2JTFel441huZxxeJynJVVcosuTLuxaKYUclDtnxa4mJ9KySM4Ycl66EmFgVirMZcrWhAI6zA0q+gydXoBOoftOMgHFFDrVAgBAB/eR91h6PCMi+b6Ab4SeDqWFwIW1GvqMGteHThz0lujr9swS9YrDKmVStUoTvi8RdOj+9fL62AsqdSwM9PjtzfnoirQ4VIM+poBAXdoStgAlE75drRkRtx6BmbBvzwOKpiyu064aFzDU7D5xYu6TxIARj/bUAuRMCWUt0uNpQVcdft5+3jHExqVkMrTh+QK+FZfMaoYykcCAFnOiqofeXbvABzzIs1+r+yYjwtpVFIE2TqMmxWR059EYZWpjlLVNHImJJwod2dkmgwKwYcMG3HPPPQ3Pj+Y6Ou2007Bjxw4MDQ3hn//5n3HVVVfh/vvvH1VAmk4Tz4TFIwC48MILceGFF051WwiCIAiCIAiCIAiCIOYkSrV+STC+HRk8z0NPT8+4d8nlcli7di0A4LzzzsNjjz2GL37xi/jKV74yuTYcIZMSj6SU2LlzJ/bv39+S5H3xxRdPScMIgiAIgiCI+cX+n9bhM+VKsRIICOPGiREhZvrr8tg4ChKEkKYEQiCGMI6cbGYGM04Wj/nwmclPgf4mN8c6UDClVUVVREHp14umdKrAfeTM8uM6zBfmWOnkwIVbS4XYOmVM1khVxqgq7USoszpqTDsEIuifIaquXCZbgmfbl0cHOpR22nSbn71eHgtyun2L8hwLzZfSi3L63H05gcV5fczFhRpOPEEvQ9/9Tn38/jPORf2m7wMASr9choFQH6tqDAbDiuGQsRR11zvQOaT7MujQx+S+BLPB4MY4VPCVCw4XCiibdO+yMRtI5oMxXfolfs6x7PBhfX1LjfOHM4hqozOAZ/qjlgB10VgKJ1+UWFYb1Oc/XAM/TjtUVMEELA8OOzeTELqTOFMIzK2LGHOlNsPSZNxEHENMuz0U0lK8SmLCxEWaX2TdXZFiGDD5Q6Lcg4PVIoLd+ryh4DgYBvhVTbf95WGF3fEADjAdmhzLKnJeJ4qsAwz6/npMIeAKgVlSnXs6uFqYPg1rHoZDH6VYt6mUAMOxQDUxZVewbi19zkQyCKQlWDAZ3owpcPM+41BgkM6VwpQuyrPlox7XZW95c4gOT6LbFyiYNkIpFDiDF1sbkc5tUowhylQkZafUDOaYXtoGzjhCUw7Vm2MYzgeo1bpNmxikEuhU2uFT9DzkvdRJ5kpKPYCbiqvAvLe6S3Us9PVg7cv5WFg7Tvelr313QsYIzHufQbn3tCvTYgrMlJNxMzvuKIZYWNHH7w08dJm8s5LJKYpYHZFxQNYShWETaG6zqrrrPvx8Wl7nc+s8ssHcHupm3Ampjx0L4Urh+msemClFrRnX2qJ6Hl2BblPAY+c4ss6dIIhRLOjPSN/ULErpITI5XgOVInpeehUoSsA4j2AD89HoPAqbytbiJndQzBpL02TmM1mO4SwaMyB7gtvPNEfiPDric0uJMAyn4EiTY8Li0SOPPILf/d3fxcsvv9ySss4YgxCj1zQSBEEQBEEQxyZLvG4ELI/ILIkVKoGqEVoSlrjSh0gNAwDqsoRI2AlgCCGjhuNx5sMzE6y8343A0xPPTmghY5FYhD7z3KKij768nhAtMoLMgkCh16yu1OEJFDwzoXdZMEAorbDgYchMngfNBPFQGOBQXR/scFjEQKInlkPMBKIyLSABeiW3nNKv90q9yMxSrxuruvRM+GSz1sxruus4c+leAMDydXX4m9fotlx0gd6gWGzbt25Cs6+K/Br9T/wlu0O8YoSNw+blSgJ3HYejAH01fbxuU9cW9AC8oF/PdeqjdhdidFZsWRRHZGbfVfPvfqECeFz3SeD1uvK/RXV9b/PdAtJMrhtzVfTPcsJQFzZTSbe9HPegZISvVUOH0b3S5L4sMKtDhRLhgCndMqtm6dIpGyzNXOh13UxPdLkdc/2VmHba3B6lVZeGfk0UMGwm+ZEIcAi+K+EZToADNYX+mh63v1L7MaBeQj0yvc0YGGNQvnLh4T7XJXvciAlSKahIIa4YIasWYCgO3Opq5UihlkjUVeLaJJnn8rUYZyYkOc1qUsqDkEBsV+4SDLFQSIQVFPSNsFJBwHReUYcps+ryBLq8CB1+es564sFOHSUYEsURqXQVOWUfmfvrMYW8uc6cJ5CX3L0eSR91wRFb4S9SiJREwYg0eY+b1Q7TcjxAry7nGYXQlup1FEMsqOjPhkU5jkVm5bW6Wql/8ioKssPsD0hz/+z4S+J0lT5baubnBLp8fcxeP4cus2xc3uRoJTxCbEpNq7FE2X42mDHbMZxHIdbjIo49J34VfJt5BDfm7UqIJSlRN20bjCSY6YvIiEvDSQHdvgm754kTYG2QeTHykMS6zbmcyVcTHkom2HtfvYhT9+yD4HEq9JWG3f2qxenKa/WmKX3cZBgRSBr+lpmyNdmUeXSkYlIzc01Mmi6uv/56XH755Tj++ONRLpdx55134r777sMPfvCDWWvThMWjP/7jP8b69evx7//+7zjuuOMmleRNEARBEARBHHvsUq/AQw7CfGsd8xCRce7Eqooo0UJRLI2gJGpOMJIyasnKYOAQ3LyuYjdpSTz9zWzdG8aw1Mu9D1V6nNCz16xQ1h1wdJqslHx2opr5523iVkICak3h0+VYYthk0ZRkiGGzolPN/IxZ6CY6UqVZH/Zb+7oUKEXWaWBWUON5eAe0uCQeH8Ty8ou6ffsHdYPOXuvyStqhoIBuEx5cqKPDMys62SBpqVAxwcmDiYdyZFYXC9MV8FjRrEK10AhrhyN0lfTvec93d6EC3feREPBrenLe5Qfo9LUS5psZfy9q2u4CQBghJpYMoZk8D8cKpcb5pllhrMP9vqqi+7S3S99bj3uIzEQ+EtZ9looVdc5dzlNof0rmVq9TYC6omJuVzzhn8DJuFEA7e6y4NKw4EqlQNZamoVDiYFhHv6dX9zqc7EI9HoA0K3b5Xgc4DxConFttLccVPCagzGRcJAoQQK2q+7wU5TAU+ygn1hWls3Vik5OVg48c95AzCdkFXyHHGXw3aBmE5BCSOzGgHAcoJRyVxDrnFISUbtU4D0CBKxRM3xU9gaKXoODbkHMFzgBhxLa64Ag8Bk+kQd9CAZ5KM5Bs1pd13BQDASABt+MADLHykSgrYRVQE0kqaHms4X2YXfnPriTG7cpuxQS9OSse5bAkb3LKqlpELqsCcsw41pCucFgzfVytByhU9bjyO60DSaEQ6Ovv9AQ6zBJv1rVYV4ELu6/GAofN++eAbwLhWQHd1snDtNgGAHmWOpA6zT0sm+uoCY6atK5GBWU+G2x4dTVh6DaroxU4d6vh2YyzDs6x0LwXirEN4PcxZMLoD4Yewr0C9TiBk6APDLo+LpvsLyAVtixRk3gUsUYhPysmHbE41OLtmVvmlCMJzJ4I+/fvx5VXXom9e/eit7cXZ599Nn7wgx/gTW960+QaMAVMWDx6/vnn8c///M+u9o4gCIIgCIIgCIIgCGK+M9nys4nu97d/+7eTPNP0MWHxaOPGjdi5cyeJRwRBEARBEMSEOKxeAYef5hipGELpb/wTGUFI/XvqNkqci0N/e934DbSC5+q1GosoNFLFEFyfq8YqGJY6N6fDrLjVEQYoGEdBnnP4xt6QzTxybiGpXLlS3ZT/1FSCumlfjdVRZ8ZFZbKbsjkgnHlu9uCOqQRqxsViM272hxw5bjN8FiD5hX5+2fCAbvO+HeAvv6oPdPJqqOV6tTVV1C4d7PkVmHH8dBTqKBrnhy1zimSaVdQVMwybFdNCs3JTp4zBjXODmf7I99TReUD3cMHLuRKs2LgPQi6RMy6Xg1EevcbB0GUCcfyqhG9KAutmCfpIcITG1VWXzK0mVjcuhcGIYTgyjowkj4rZb0nduEEC4ZxN0vhVfK6QMy6MgAHSuD2s86gmJIQ5p4JylV6ecSP5YGCsMRBHKYXEOM5CqVCLFYYTfcCBpIpD3j4MiT3m2g5DqpqrzPB4gDzvQCeKKJpSqzxT4JncnSTikAKo1HPmuvMoJ9zdo1oiEbLEOaEC+OgIfPTk9PF6AqArkG41L88DlPIQS45qoo9ZSgIMRsy5uypxgkgK+MyuGqjXESwY512eS+Q9kTqPOAODQGjKA32PgSV6ONvrEFIhAnOlYEIBYAy+PaafIAgE8uaYHpOQqoBEmhX7lIehkEGY+88Y0+Vx5ibF0mZiceeYYqYPgi6BniH9nltcL+BwPmf6To9JL2LIZhzbPJ9hM6bKYYDOqumLwLpelHP05D2FonEHFc3nRSUJ3PL0oRQoGZfRQbNaWsBzLpfK5j4Bafld3pPpMX3rMvNg/TwxJITJuYpMWVs15ihZtxVnyNn3pzlmpxcgNmW2ncYBpRhzpaBDMUPlcB6lGnfOI9Vfcm0bjvvc77Wm5dZC1fjZm7DG3B2hRilba/q71Vl0hGVoSjbWS04jesxPrvJqhpo4rUxYPLr22mvxkY98BP39/Xjta1+LIAgaXj/77LOnrHEEQRAEQRDE/KGeDIAxz5UxCJW48GupknTZepkKRmnJg3C/M8bT50wpjVIJpFk625a/wU/zNhIeIuK6HK6qugAABdWBwEww88jBt0vUu+OnCCXd8tQ26yRmMWJTNpcN/M5Ojmz+TxY76YxVgroJW6nYzBSPI2fKqHyWh1R6ZR47KVxWLaNr4BX9ev8Q2HItXLA+s5R9WUIZQYr76fLsth2xVC6zqBR7btJbM6G6MgzTBXFMzgvPKxQ8E9TL4MQjaUqpqhhGAL3/4TDAYSM+LTRlZbm6RME3Ip45X11yF7YcS4WaEQbLXGewKMVQiwrm9QCxCQ2uG7GhryDRbUKOC74NZZa2yeCMOWHDBp2HIs1ukSotV/O9TGmhC0035VVSITIT6UoiUY0FBk1Z5YB3AOWkH2FSNm0OwRjgebrd+aALnawPnTxA0ZWtSXhMITGCQBj6iGMPQ6GezpeSAEMJd6VxIQQUF/CVvu6iH6Arx9FnAtQX5xP0BRG6TdlW4AtAMdSFj6rps2HhYdgEbwNAVQrEKobJQ4eCD49JBOaaC55APhDI5VMhR0EiMGVvjDEIxRBJhVBYcRXwpULs2RwpK6DpfYqFGPnOBMoIN/l8AsYAYXLAhPIgwVGJU3EvlEDVCidG5IxDDwUjRjAjyPndHD0LdMj+kjCPIbNtzQhOjAUIpS1JZa5srSKsaBOgXjPCWM7K0Gn+VeClAo8tc62pPEIX5q/SUlZTCleMPfc+6VQSPkvFSN0OhZwTktISRCseSSkQmc/G0HyuVRLAj0z5m+ehYMavbVvN54hM/3aZzwsGYNhcZzlWGKrk4VUCLCvrMZu8WoPlcJRKA7UmNT5qEoBi3igeSZm+PlbZ2kT/bmV2M4/mgQY0aSYsHr397W8HALz//e93zzHGoJSiwGyCIAiCIAhiRCJRBWPcTQ6Ukk5o0UJR4n4HWr+hZi2ijtfwlxWfmFEAYlF3ryklnAspYfr5Oi8gUNrl4yMP3/zTmCs78UrPp5h0oo8VpATSkO+Exe7b97HEI0sC4b7RrxkXxHCcOgoCzsGYzSHRgpeQHEtCLbD0DJeRG9LXwpebVZP8TiBOs2qsicpOeOpCIjbnLEQcg3ZVr1CfZ3FZgRt3DzMiEDgaXBh25TVmV4tChDIbBAAMJh0YMEJIb2DzX/LolI0uoEhylyWUSCAy967mmbwoFSI0K2+peAGYWaHOhghzz4NnVgMz0TEIeJJmTEkdGq3PZXOrpBOPhFJg5vfAOESkZ8KkAZj5OIRKhYFaIlFCHWVPB2LX1WHEsgJlxQzGwHkOgWdW+Av60KMWoDvwUDRhSgFXUAqITTZNLDlqsY8h4xI6HHsoxUDVNF6v6aVQNKvEdfkeFuYUluR1fy3LV7Ggo45cLnFtiMIAiJkLWq5JjoqQqAojQrAaJJMIlD4mA4PHmXPIFP0EnR0xCp12OS8d/B24mBuGSALVBBg2VqNESOQ97rKdhEnPzhvHWaFbIFgEcOMWyg3H4F4ZiRsXRYSSu/sTCYaaSF1yQ8bZVqnn0WFWmwr6zFjo8FEwgvOSsOIcR5Gy/hqOSmIvhTlnmF2Nryr0PQCAXN0KTgzCfA74nKHTCFU9JnQ/VjkXpA6knsiqC34HPOPsUmBOxM1mRFn/So5bEZQhMv2RSInICE112NUnU0UnkD7yUst/ncIIq4K7HLGaEd08KNTtqoOxwmA9B1UD+LO/1O3ck7poBjOrrVWSxs/esDnjqElMSlSYea1ReZpvAdkzlXk0F5mweLRr167paAdBEARBEARBEARBEMScZbJS1jEpHp1wwgnT0Q6CIAiCIAhinpOIeoN7qPEbZdniOGqXczQSeltbfpQ6kGJrnuCpy0ma1c44q8Fn2tHiIYBn3B22jbzJ2ZS2VLh22lwj7aIara3psayDQDDhXEB2NaNqwpAz7pqAM+de4EhXQlIuc6OMHlPokjOOHdYbQMayabv02/JISVSgXQK5xMdgpI8/ZHKKwmEfwbA+JjNlTzKR4DZLiMO5S2z/JCp1XQ2xYRw2LqbevCm18jy3+pZ19iSKQyEtUbKvWwdDpCquDJBxjnyiV84qJvp+dSZA0ViO7AprSnGXuRNKhkjZvByzylimzEoo4cqS3HLzSkEaV5UrH1IqXWVPJajxKurQJT9RMqzzXJjNlfLg8SLygV5trpP3oVsW0OEzFM3tD7iCAEfN5AclkqGU5HDIrHo3lJhV/Ew/xCyGxzzkTF93+Qx9gcTSnHaaLektoaMvBjfOGBVyqEEO1IHErY6mXTxVu8ofr+qVCmXR9C9D3oNzR/V0xOhcGCPo4bYTwHmC4VpathYpjkoSo2xyeRIpEEs/dR4JDp8pFAumnG6Rgr+6G+gwDrJaiO58Gcti7aKrJD5KouCWvA+lRF0AJVMGdti4YspRDotjm3lkXEJ9HeBduh09/jCOVzrHR9r3DivgkMnPCmW6mqJ9dwgJ1M3x83XfvMYQJ7Z8FOgys+YFeZuzxeGZErJ6ZiUyW4pZSdIV4qRiyHNbEmv6UClXwmadcwUPiI3lLVEeQlPSap2OIaul7wnGEJjPhBr0eAuTDkSmrLNu2uEDiM3naiWWGAgDhCEHnnkJAHDgQLdr+0CUfjZX4kb3UNiUcZSgqWxNZcvWmrPpRs84muurqzUzH7KLJsu4xKPvfOc7uPzyyxEEAb7zne+Muu1v/uZvTknDCIIgCIIgiPmFLktrLx5lJxCTL1PIik46cNueLpGZcjgXIh07IYkxDs+W8ZhJZ7bkjGd+l22+e26dAOn97V5ScSTmXPY8AonLTwpNpo0vGCpmwuxz7krEAmaDeAMEpswlVxLIFfT+/iJzHT0uWxkMmQmyFVJYgpoJ9h6WPsqmXGrIls0N51EY1BN6zwaXR05nQY5L5E1ocM60WUEiVrq0puwdwmCsS+wOh1bw4PDMEucesyV/qbDFoZd5B9JMmFjW3O+ceSiZEqROW76U81zZkA1+zrO0LK6aMCf62MyiSEnUzCQ3UQlsirKdXCfSc3lTNvdKKOlCvKushhobRiS0UCdkCKWEExsZ8xH4ReR9PSHvUD0oeh4KXipYSKVL9myGU014GIp9HDIT98OhQimJUUXN3K8IHoquXUUP6PUTLCzoNnQsDeEvy4ObJdwxlCBXT8CGGRJTrhcpXbJXM5lfMerwWc6NzsDjyHsKPab0rasnRG65B75Al0WxRAEIURw0oitnSJQugSxDj6WExUhkB7qEDcAGAiZQ7NJjyDuuE2rNcqBbl/ShVofn96O3qksAl1RDHIzyGDChVeVYIVQSw0YosllClTiAMEIQbFnlwm4oc/1eVwE9/iEAwJrn9bE5FiDn5c1xPSTmftql7qVSqJuxxGomZJszJCwNYu8O9MCy45YzBs/ck3LEEYlGEXg4Zk6UrAuGgs03Mj99Blc+Zz8j8hwwcU1IJENo3pM1U5ZW5QqxGRdSRgit0MlMqScWIkr0ey80pXsBY5DmfVRNEhyKcqiEHuo/HQQA/Kq8EpbDmcq0imgsS6ubc1gSVW/42+bNAal4b5nyjKPm/c3/ZgqZ+eyaCGqS+80lxiUeXXHFFejv78fSpUtxxRVXjLgdZR4RBEEQBEEQIyFVMmIG0MQEo/aOoOZjKSbTSU3mtDYQ2mM+JLNZMdx9m86c24c3OKXYGOe1OMmIpdMehtT5lOYk+RBOPNI/A8lRN3klQaJcALRvw3u5h4Kn/wnfGebQXdHiT0dNT+aYxzKrujFI2FyiVDyKuJ7wV5WPSqIn82Uzay1HefSU9Os5K8KFHPagOZ4G/fqwIhtDIvX5q2wQQ1y7hGyOUinwkDftL9i8JCgYswx8plzOFEz+kJLKHbPOSxjmup3DiRY0ShFHzuY5KbuSmeecR2XBUBfWbWIn9gKh6fsIIZRR2TzY/KE8AuPc4Ka/JBTqXLejgjLqooRYGMFExmasGRGABwi8IvJMh5wXZR45j5vV+2ywtHGuqHTFr8Mxw2Cs2zIYxShhGDUTHA4ATHa4+5/3JPIsQS5v3GG9HrC4E8qsrsW9KvyBEJwJlykVJgp1lbjAeIEYHgvcCnx5j6HTky50O98nwRZ3A31d5gAxvHqCvBEqc1yBcwYBIPTMPZJVQDHE0sZwcxS8GEGvceYt64FaeRxUjwl2r9fAlUIwqNu0oL+G3mqnW/3Q8xikUKgb15gNzq5LDmlEMWbEG9lRgOrRfY6OohPqeuRBAMDxGIQ3oF/fz/Io21BumymUAIPmvleNiJTzgLwZoIEv0WXC4e2xPXjus4wBGDKCVj1Jw9VrZlqc48wFphfMR0iBp9oX5zYPi6Fgc8A8IDT7VI145CFwrp5Y1iBNhlVsc8J4iMhbAACIhL7eXJJzkkWkYgyEefh1jv7n9b3dU7X3SwuXlmHV6CwKjeBsSWTzamupYDSmWDRG4ddcyzhqZrLOo/ngWBqXeCQzVrzs7wRBEARBEAQxXvRkbTz/lpz4l5EK0okmLiRaSWe9ybqQbKC2YtI5YRhLhSJmA5QZT4+ZUZ+suJQl60xS7jjSlcIJxO68WQdSbMQMz/yzPFQePGHdRml4tv1Z8LR7AgDKfoAwNE4P4xpQUkGZ2pkk8tIAXrukOhOQpihLsMS5cqqmy4fjHKrDdkJZN8fkENKW8iknZHgZMc2WrcSygmFvEABQivUEthwXXNlPAFu2k5bw+AwIbEi5EQmklBDOzVBBjQ0BAIaUFpHydc8JTZGwfZQ6rIYThaq5aCse1RAismHpqEHZcjNz7yIWwZdp2RKgV5SrMy1whKqEmii51fwSEQIQ4DbEmwfweBE5s45ZAB+c6dFsl4f3BRBKD5EVuRJgIAQGTZ3RkKyi4pURK30Oz5RV2hIoZoKola1DlBKKsVSJKHhgOQWmFES2XE/GiI04KhGDexyBGXN5D+j0BYp5I552c6jOPFDU5wbnQNGHH5gV97iExzgYZ85lkqg6Yl5059TiYALWaYSani7I7u50Bl0oQC5cCL5oPwCg2DuA7oEYRc86ZvRmQjaKbqFgSExpm4rM+IgT2Deq6igCZuVBvkLf685yCX013Z+VmKFiXF9lu9qaZCiZe1j0rFtOodu0IfAUOsxqgTkT0s7hQyhbeshRM8eqGsNOTSRITJt8xZA3x++0q7b5QKfpCnvrfE85Z1LiAR1WPLLOOpnHMNfnlCpxDjhmVy30IiSevoeJWRwgrzoRmOsVTGAo6gJChV8OaIH3V7X0c+twmApA1SaxyDqeLEKNXLbW7Dwa+zP/6DKfHMtqyMjLP8wgt956K9asWYNCoYCNGzdi+/bt49rvG9/4Bhhjo7qhCIIgCIIgCIIgCIIgiMkzLufRl770pXEf8IMf/OCEGvDNb34TW7duxe23346NGzfilltuwVve8hY899xzWLp06Yj7vfTSS/j//r//DxdddNGEzkcQBEEQBEHMFgKY4twHW+KQLS9rfk1vkOZyOIcRpCsDYczLBGVzt13bsjXVeBwAUOBpuZtyO7jcE48FkMZlZIOmBWJEzC6prf9ZHikO37hU6oIjZ1015sv5mgAqxp1TSXxUTNC1MMYAORQiOWwCcusdqEmb3WPaxLVrQrc5LSCxy9lXpYeqOaZXs32bOnpU5v+tw4uBuRwopSJExjUzbOxQlTiHqrGS5F3XSIjs6uX2PlpXmGIQNjBYSdShy7hKxoHkJz6E0g4fmw2T85jLPqklCsMmTqNinFZ1XkddVU0/1yDd0ufWSeWDe43TI6FCRNLsI4YRJhUIYRw8KgFjzN1PznLwWQ6+CTLmTIeCJxKuhE5Bu4dsSVMpBg7XExxO9DHLfAihKrtxyRE0uN4UgFhxhPa+l4fByjUwV2KYQMQSYQKE5qaHiUKIEMLcDwUFLn1XSljkCgWeIMib/vAZFOdwN0hKQClwzzrFJDym77st/VNIIBC5fB0hFaRSEOYYTAgolaa+KDAgn4fq0PfQ61Lo8CN0cP13juuJamKOl5hrqQuGWk3fo+4BfV+8wyXAOIZUEEDZSpmccZEVAY+bkHqhMGwcaYfDNETbN9auDuMMEgBygd6uSyUIPH0/8r4dM9IFsddF4IK+S+YCQ0jUjctNQqEg9f2KzH1LBHdlc7YMl/G0LC7PFUweOHLmyVwcuLw0KSWEsPfTXJsMIaX+jJGmvYLFCEyfQjGUYoWkLrCzot+JezOGosNJGno0zIeQxZZqWhIZNfydzTyaaMZRa5na2BlHs4mc5OnnQdXa+MSjm2++eVwHY4xNWDz6whe+gKuvvhrve9/7AAC33347/v3f/x1/93d/h49+9KNt9xFC4N3vfjc+9alP4cEHH8Tg4OCEzkkQBEEQBEEczdgyh8byMSeFZMrXGnIm0plryz4MMlPOlgnMNtvq/KPGSUxDKRs8MCuguCwUDxyNAhYAMJWGdNtzxswECysPgSmHiaR0okNO6DZVE4aq+Rd8VXDUYrPtsDlOf4Th/SYEOyw48chOuDz48Jh+3VeB6ww7IQoFR92E7ebNikseU4iNQJPI1skTYwzMykpKOPGoYnJ7hqMihs2SUp6yZU1AJbbnVIhFY5h59h5JESOCPmbVO+yeT5IFAIC60BPhPPNsBjYipVA1K0TVTNhvHVXERjyKUc8Ih7ZErTXMPZEhEqFFgCipIhZ1N0EHlC5Zs2V8zANnvhMCFVMmIDsVi2KpA4xrRsAYiiSGkhhlVjZtLCGR9VSIZBxc8UwGkxaPaiY4PBrwwIIKVIe+VlUVqA7kUIoClCMjpMUSoQp1SDh0c7n0YDO2c1wizwW4b1cR9MGiCMoIKajFQD1GYnKZlFRgUCb1J1uuqRAb4SYUCpXIQ3JYH9M/XAIbPAy1oE+3AQosidM3qKcFHt9kkPngYFAuMiUy46OWKJTqevwu2K/va6Gn5ELikQ9cOZsqm4ynikS1rvtrMPbdqmKHovSYvslP6jFBWr7H0GXukcil49s347PgJ+gw+UhF7rnV1NwKa5Bu3CU8Rt2UQ0ZCh6mruJDeY6M2ex5Pf2csDcY3B/Xhg9vPpkyOlpRWpNJCK5B2q/Qk8kyfkyPAcJKgHgu8XNEB9AdqqehzGCX3e001iUeysWwtfQ9oGnOMju7V1MZisiLQMSMe7dq1a1pOHkURHn/8cVx//fXuOc45LrvsMjz88MMj7vfpT38aS5cuxe///u/jwQcfHPUcYRgiDNOazFKpNMrWBEEQBEEQxHSRDRceL+0cRSMdu3lb/Zz9PfO8E4x4k6sjFZIA7YKxEzwB2RL2zVXqTFKZ15kLkpaAyTxiEM7949qp4CaDdmIfsxihyfLxFEfOBgXb/B4PqJr5XlVwVIzQUx3WE+pyfw4HyzoX6HCcR2SzhIzwlUcOsckNyskcPDMxtZNOobTAAQCxXU4eCqERoSLB3dTPCjVgLM2WUgLChOnWuP53d1n0YiDUAk9iduYsvabhWCBWjcuSM8adMJEgdTaExoGkvDTXpZq5HnvDEx4h5rodkRGMEhUiMe4bgQjOQeXGZPqbdSUlKnITZyHrkDJCOvllYMxvEFAaXVgKsVR6BTiXnaWziGweUylKUEYVVSMeRbIOBQnfTNM8FSCAB87T4yeKo2pWyRseKkImIbhvRJZagIPlTuyrexiKbXhzjJBXnVtEr3zHwIw4wZUEy07opQKiJA1er0aQ5QSRCUCvCwYhJYSS6SBQevVAmy81nPg4HAWo7De5SnsGwHv2gtkBkM8B5WEgNO4ZqXRGmLI5VAyQqXgUmv3KMcNh046+w3pMBfsi+Kb/eDGAMKKn3K/vW/Wgh0N17b4ZTDyUzMptQyZdvS4FfPM+4aZfOxLmcowiwSGkDaxP3VeByT8KmITPbAi/7Q6F2IiXoSq7zxFpM5OSBeCxcVkZFS8vlHMZAQrciZL6GR8MvtLtYyydxjvnjhIuI8opFYo5jT3gRZRRwzAkXjFB+/vjVBQqZ4TZSKSB7UAb8agp12gizqNWRncajY3ETEozk3YezQP16Igyj5RSGbvdxDl48CCEEFi2bFnD88uWLUN/f3/bfR566CH87d/+Lb761a+O6xw33ngjent73WP16tWTbi9BEARBEARBEARBEMcmSk3uMR8Yl/Oomb/927/FzTffjOeffx4AcMopp+C6667DH/zBH0xp45opl8t4z3veg69+9atYvHjxuPa5/vrrsXXrVvd3qVQiAYkgCIIgCOIoYbRMI+0AaV35LOssSvcf4R/wmZXZmp1F2k2UPVaaj6TPnu6TdZ9ky+NszhGQOgjcykQMiFW9YX/OOHy7dLzyEUmzopjLQfJcKVtVMJRN2dqwWXL7EOvCPuOyKAsPidnPOowKLAdI7dThirsyG9s3sQTqNkPIrBClFFA156lJhjibVQRjPLIr0Kk0D8WWrw2xEvxQr4Bl3UweAxKXGaOQ8MYSMh8BErOyFFMJhOl7ZcKdJAQEN+VkbNgcM+f6WqrEuIQAYfpbSpFZFUq5vB6XceWKkzL7qAjKuJW02yJ16HDOdHttOSJjUEogYfb6E4TSA0s4ksyYSKRExeYxyRBVXkGiaqavlW6RcZZ4MgcPrGHSFiuGqjClh6FCLc45V1sl9nEgzKO/xjFQ1zdqWNURyZobvwoelJJIjEsoFh6ihCExGUAyFGBhAmbKulQ5RDKkUKlql1stYagn0rhcbP4Vh5QCsXFsVeI8DkU+DpR0yVTni0MoFvaC1U1WTmcRqNYgB7UrTFSULom0zhnJTBuNOyhJr2/IjMVSVZdedQ4IeDlzXzsElCnFig4Yd1epC0PWqSU81EwJXN28t0M/ciun1c3KZHWhxzoA1CRHaErUPG5XA2TuvSUVXNaTXQVPSglhVlWMVQ3SOetM6SD3EZhStg5zPR0+c64/DpMLBTgHEmfM5ZXpnC07bk2XQbk8tsS8TxhrLHWr8mGUWIT+UPfdIX4IlqrKOI9ko/PIugkto62oNlaZ2thOpEZmO+Oomcmutja3rmJyTFg8uuGGG/CFL3wB1157LTZt2gQAePjhh/HhD38Yu3fvxqc//elxH2vx4sXwPA/79u1reH7fvn1Yvnx5y/YvvPACXnrpJbztbW9zz9kPGN/38dxzz+Hkk09u2CefzyOfz4MgCIIgCIKYbUYrWxvdEJ+dcDQKSWn+UbPQlC0ly5awpcdsOklGSNJ/Npa0ueO2eV0yuHILnikV4zABtxkRqUFcMsdMzESTKe5KlkL4CEwJmytfEwqh6YqaYBg2k9qSKeU55BcxENuJcFqMFNhAYO7BU+bfxkzn1gCAMJ2hj2lKbGw4r2Iom+eGE+bKzRIzEVbZzlQK0pw1NkHTVV5CYIQdKfSk1WfcTbhjJiFM2Q/nNkA8gGeCpxMZuYwVASsipeVx3GTLNI4t6Sax2Z8s0/ncimNW+AJDOsWz4y2tOWNMNWTN6EB15gQvpRQEYsTQolYNNTDBkAgPgRuzOheobsp8aqwOgRiwZVNGFODK5lL54MyKSjq4PJIMFWGXbNeqaGhEh1LEcDDi2B8CQ5Ep62NVSLdwvL4aAYFY2LI2YCj2US7rcZE7WAFjNRfgo8oStUEfQyZraCjmCAWQZN7P3OTxSHMfQwkMxhz76x0AgJ59CZbmqgiMsMO6coCQEAf0GKkPMdRjD7HN6jFuDVuGZeOZq4lC2Yz5cmyyj6p5BIfNWKhKxHWz7ZAWUUtxARUjCoWKQ9qAJHPbJYMuwQMQm5+R8lC3IfUJRzUxQqYZPkIx1EwOWU0wRC7TypZ/CidAChlCKPteMeOc5VA18fFVU3rakXD4tiyOM1dKKjPjmjtByXcCY2I/h1QqtDBlQ7QjJCa4m3MPIRtGFRUc4PqcZex3x66JVDyKTc6XJVuW1vZvNZGytakoU5s9juWytQmLR7fddhu++tWv4l3vepd77jd/8zdx9tln49prr52QeJTL5XDeeedh27ZtuOKKKwBoMWjbtm3YsmVLy/ann346nnrqqYbnPvaxj6FcLuOLX/wiOYoIgiAIgiCOWrITgvEJSa0iktfyensXkt1Pjv4PetYoFLXsn309k49k5vHgGecRR9AoIEG7dYR1IigrhHiIzFTZg4/QOCLy1oEkOOrGxlJNdO4RAJd9dDgJXKaLUKkoZBY7Q4fnwTcTTMEUPHMpien+4ViBmRybsnH+CMVQNW6nwzFQMxtHzDp6YqTKWyrAWJdPhAqqZsUne+wcAie6CEjnArLPeQjgmbwoj/laYEE6SVVKpI6EzE1UDRKJtUbZbKN0vHDuQdkAdDPh557nrkNZkUqpBueDbp91cjQFFyNBIiNEXLs+rEskkTkELgeLQUAitH3HtMBpr5UpDwwMgdQimyc9MC91tkRCoSqYE7wq8KCgXCD3UAgcjhQGwwRVM44Ei8HBXZC3B33tdWlzhHwciHx0Dnfpg7yi0F2uw/PNOWseBsqd2F/XYsdQzFBLFKSSJj8J8FkOXAVgRvxLFFCVHANmFbJ9lW7wVzgWVHXf+B0RpASishlXgwUMhoFz1SVSmtXZ0qBwe1ybRVQxgs5wPQevZEQXXyA0DqpyzTjw4jxq5v0jwdz713Oh5FnHme3nNOS8nHAUEusGtAIyUIqNoCoY6kaIs6vCSUg3bpRSEDJd6Q4AQlbCMNPty5vA91zsuf7LceWcTTYsXEBpdxEAnbdlRU8zpWdJixCj3ydm5TWVIEYNoRp2b9eKSJ1Hkai435udRqJpdTXV5DxqFIiOLBB7rjmNmpnbrZteJiwexXGM9evXtzx/3nnnIUma7Wtjs3XrVlx11VVYv349NmzYgFtuuQWVSsWtvnbllVdi5cqVuPHGG1EoFHDWWWc17L9gwQIAaHmeIAiCIAiCIAiCIAhiqpi082hqmzErTFg8es973oPbbrsNX/jCFxqe/5u/+Ru8+93vnnAD3vnOd+LAgQO44YYb0N/fj3Xr1uHuu+92Idq7d+929lWCIAiCIAjiKMautjbmCmrjcyG1rrCWlrDZ1x3ZiqbMv+KzpWxjuZDa7d/wetPzkmn3EaAdSLaEzToBsk4kex2xqrsMnpj5iJUttzGlaII7d0ZdcOfCsKVm5YRjMLYrQinnWOHGfVPwMqVqPL1m6zyqJEAkbZaKcq/ZUrnhRKFsvjCuMrMKGeLMIjrMdapyZTcxYpNPFJpSNKkUPOPikEy6Ure0O9PcHwY/dQ2Z0wgkrvHKuZGU61ugtUzRro6mD+MB5n44A4dEm30ye7M24wrpSnVSJkhQBzOlhmAMCgoJKyJnzsWVdYnotnvwkEMBgS0lVApKwZUucjAIJREZR0st0aVzdvU7xvT9SVeukxiMBIZljJBHZhsGH0U3/jjz4SkPwnRmOQYOhByM6TZUEo6e4U63LH0kPQwlAQ5Euk2DIVATek0562bKoQMe85Ez/cs5R6wYhowLbn+Yh4CHSqTPUfASKKVQj43zKAwwEPoYNn8nSl+nzeri5v0hFENkxoAd80P1nFv1jXOJunE7DZpSzpLwXVkfwOAZu53NHPKV78ohE/OzLiUqxllU8hl4qH8vmlsrAFRN+dxwAtRNhlXsxrwCc+WrLC2fNZ9tiQpRNyvElYwrz489937Me2lZZ80cu444zUwCd2PZla/JTP5b5nNRGoeZ5DFiUUUkKkjsSnCi7LazOUnAOJxGo+QaTX2Z2txCYZQPiVH3O/qZdGD2PffcgwsuuAAA8Oijj2L37t248sorG8KpmwWmkdiyZUvbMjUAuO+++0bd94477hjXOQiCIAiCIIg5QoOoc2RC0sglbEA2TDsrNLUrZdPHaD72CE0aIWQ7PW5aymbLizz4LWVrWSSskMJd/lGsIoRmYhhkytfqplStZkqYAKBiJtKlGCgZpSfvMdcD3Jav8XTqEwvmymBsL9SELkkCgNh8xR5JhdCKF0pgCLq8pY4SAJ1JZMuLOAvc77a/pRIQ0JPViFVd3yGbFWQDqpkV1oQTpBhDm5sDF0Jsx5Muk7PPqdZ4LZa939l22sTrtKzO7cKYEy5cmZu7555uozmmkAmAGDD9o5iCYlKLY0qXoell3b00FwscgcrBU6m4kR2THBwKQGhKl4YThlgxeEaVVGBaPDL3qhYnqMgIIQ/dAA6Qh4/AZf1wUyYnzV2vK4lDkYfECFJDfg55HsCz4iG0UFm2AlWitFiiFAIrSIHDkx6CzBLysdRiJAAciDhClXNh1zmuy9LS3CWGcgLUbSUhAJ8DuUy5n71eGx5vyzODMIeasJlEEpGwmUhGXEpSwVWqdAIc2HB25bm+EDZEWwiUzfF53UNs+iZnc+FVGmKv+0M/n2Q+NGyAu48OKHu/3L1VSGw2FtfvoxI8JOY+501GEwCE5nOjxmuIzftISWHysQBmzsOZhHLpUK3jSYgYMasjlqlINJJgJJvFoyYBSKm46e/s61MdiD3G8VSmRJWYViYsHj399NM499xzAegAa0AHXy9evBhPP/20246NJtsTBEEQBEEQBDBJIam9iNS6IlvjamztArXdqZuEJL1d9vgjNCkTft32NeeUSV0ao4lIejebxeK5fcKMAyl04dlZ55F+7nAoMBDqiV+X56FglnjLc+t0UU4UEEh71ApFUqVCUT3RM+KqihEap0Kdp46JUOmfehWvTJAvzwZQ635OlM18MedD6MKhFFTGRWTawQRgxRrG0mNa545Mj+ZyYBRPr4hlxaP0JjGWupWkEWysYMSkSm1ImZtv5zWcc0jpu/MqM2m1zg6lIigpoYxjR3IFxSUUyxyDcXAdLw0ACODBY8wFanMwSKVSMYMZwcSMTSUY6lJB2bwopZBAInT5UhEiHkFJ5RxsORWAKz/tBabAGXfvg7oSUAlDaMZSEDEXzGyvM1bCudNiKMRK97oTwZReJdDPiG+RUCgZDSKSDOUYyHu6f33GTd+l7rZYKre6mscZ8j5zQ8DeK4+n4lHZCFOJ8pBPUmeaWSQuDbwWcJlEkVTOUecSg5TnVomTZtW/UMZgiRWcmHuf5Tw7ZtPVAiMhUW1yHnEG5yYTvOjuv30faIeZ2QdaUB3mcOKQh5y7B4n5vIhRR2JWZ9SCS/qeAwDFc1AqFWQt9jxCAiypj0swmphYNAZHWSD2WFBg9gT40Y9+NB3tIAiCIAiCIAiCIAiCmLNMVjyaD0yqbI0gCIIgCII4OhkYGMC1116L7373u+Cc4+1vfzu++MUvoqura8R9Lr30Utx///0Nz/3RH/0Rbr/99qltnP2GepIOpFbnEEfjyj+tmTUjuZDsdtmV2dLztGnSSJlIGWdSWkw3sgOJMQ5m3AdMpStkRcZdUFc+8maJ8HqiUDXui4pxIw3WYhySugxGyk5I49ThxjHBuHKTH4k06yi2bg0pURG6XcNKOyIqrISI6VIsgditxCScm0iAm6W/dVaRde+kHZHIutsfMM4I55JIV72z/c0ZS/NdmIJvnEeJsplFSG+JcXWwTAEMYzK7QbqZdcYwOBeSXXacIX3OjS2Wtokpe6rsim5p5hFTiS5VU+lxlacA7oGZ9mt/juf29cBQYD5y3F63glQss2S8hIBCaNoVKoFECSSmvE/wGAlixMzcCyWgJOAjh5wplcurPHzGnftF2tFujpkwfcyKW7xOuYf+216rdbqkGT6eKyvTrim3gp7JabJuplIk4HEGj9uVzhg4Azy7MiH0u5O7+w8UfDgnU2LHB4DYPFcy2V6VGG4MCKXSMW3cRkIpmF/BM+9T6+zxwNx1JOa+xDxJV15LBHxhnWEW5oaSkBKxdfcgzQZKc6y6EZvV9HxznxIVubJLxfQ+MYZdKRvLOr+c002k+UUqSR2K3L4nJJS099gcG8KNTyUjQDEIGaUlpVnnUUOuUWMG2dhOo8zrY2w711dTG4vJtv7ovmrNhMWjN7zhDaOWpP3nf/7nETWIIAiCIAiCmD7e/e53Y+/evbj33nsRxzHe97734Q//8A9x5513jrrf1VdfjU9/+tPu746Ojulr5LhL2cbOQ2qfhWTxxhSSGsO1JygkNYtImRI2/VRrm4XywIzAwhhHZJcTNxPFAAFqpoStJjzUTNZRzcz7BhFikO8zbVsCFncD0KU5AMD99JtzWyoEAHWTtVIRMYZggnyhl/GuysMQsmoardKKLjvxZz64y3fJuWux4o9UsckjSiehQqYTYYVM6ZgVD7jngo31fbRlSzb02YMwk2YpbV4ST8trWGIEpOboozQrKC3tsZlJ0t1QGzCua87SvRlYw23V12OPKcEgXZmQYroMjqHm8m9iFiFAAKVsTo0WUQIzFHzGdTmhydjhCogRI7Q5WCxEyGqu1ClRoREhbHsBTxV0GZkJUA4YN0VQRmBhWiSx4dARixGzGLERLhIWQ8g4FRMUB4cH34gheVXUghQ8eBmRzcv0qVAKkVKu3C5iCQQX6X2EhwAecmY6WuAeOj2Ogq9fL3i62614FNsSLpmKQ6FKs7msUBRLgVimuUIWz7yP8pwh4LZcEek9yAqMgC4FNEJPFdKVEaaHzIwCztPyPXPfPMXTPCjJ4XN7/0NzzipiGzivMoKSETIlZIvawIxEB0CXp5m2WoEZPH3PpdFKaXmclAIMsQm/TgWnlAmEXjczimB0xJlGLQecXRmGVlubAOvWrWv4O45j7NixA08//TSuuuqqqWoXQRAEQRAEMcX84he/wN13343HHnsM69evBwD81V/9Fd761rfi85//PFasWDHivh0dHVi+fPkRnb95EsEw8heS6U4TEZJGDtQGmkO1gWZXUjvn0khCUno8863+CCKSy1lSacaMDdTmCBqcCu0uI115zWQeIYfAhOnWhe/CsxMzyS6zCoaT/fo8vod8orfNMe1C8TJfAicqFY2qRigosQqG1AEAwHCif4aiBKXMpJczeJ6eCFshh/EAvhWPVMEdX5iJshaPzGpWwjiQZAJhHE5SpiKfXdHM5zlIO/nmqfBhV2FWirtVzaQR1nQotxUAvYyLSJhjZ900ad5O6hSK4dxpNvScpW4aZaKZXHizEY7SYG+9UppVJYSMAaYTjuzqVgkCJCzv7rtEAI5UvOAm4yiVKRViLlA3zq8aSojkMGKh/05kaEQr64zJIeBADp3uPniAFkycQ0tnW9nVvCJEqLNh1KUNQK9ByAhSphlhnOWRR5f724cHT3kZAYM1hI3HSqIuY1S5da+VEaPqRETOPORYBwrQ4mav6AVUHr4VBLnOF/LteFXWWcSQmPeUDXavyBg146AJUUPEGh19gfJREHpcKpkHPCtQZkQka0iz7iwmnJgWqSpikzXkgt2VcH3uszwC6PdXjul+91TBCbYePJOVBXjWgcY4JLMh3fqYQkRIrKtPRpnPPu5+ctgVCIPU7cesC8xPVym3ArGSmTEfQ0gGqRL3uZL9zJtQ6PURiUVHN/P9+kZjwuLRzTff3Pb5T37ykxgeHj7iBhEEQRAEQRDTw8MPP4wFCxY44QgALrvsMnDO8eijj+K///f/PuK+//iP/4h/+Id/wPLly/G2t70NH//4x0d0H4VhiDAM3d+lUqntdtl/hE9ISBpRRGqe0IynrM0ydnlby/LUDa6k7HnS87lzMLSITxKxWz49KyLZJb4TpOKRK1EBh2dK2Koyh7pZZarXLM50qtcHEZ0GAOgRXegNtFuk2zg6ih4grVAh4cp5pHEwJaIIiUX6nL5xrngFCLOKE+cKnmcnwGapc9aBQOqxwEW6ShSMgCH9HueukIF1+QgoI07ITIWZnZAzrsA9+3vaudaZIZTQ4gwAYRwiXDFXCqgYd+4hxm0fwgUKA1qAAgBlRSSZBjO78chSsU+ZcGzpxKIEUBKMZ4QmsIxIqB1ZnAXwmF2VzAcDA1fpKmIMqZvL4+ZOu1K5HALlI2+cSkXWhZDXEBlRRiCCgHDCDVc+crIDBdGBLqbvfaevg9NtyRgYA2M+hHESJaoTAkuQcOvekRAeID0rjCpwBbcinM8YfM7MKniuO01Zn+1LDqk8xEZMjOUCCAUX9O0BCJjnyvWKAUfRRxrw7umSNre9skKNXkUQADqMXasz8RBL4+wRHS50W1iLCFMwQxk5zlzpmn07egzIm/uTN865oipCGGFLQrnSLxvOzqDS1foUd33jRE4o9/mWKOVWcbPOJJ8FCLjuG8EX6GP7Ii1ly0qI9t5Knmo2Mi3rjJlxjCFEbMpD7Yp/eqXDbIh2s/CR/dwbRTCa0lK0o8tp1AwFZk8Bv/d7v4cNGzbg85///FQdkiAIgiAIgphC+vv7sXTp0obnfN9HX18f+vv7R9zvd3/3d3HCCSdgxYoV+NnPfoY/+ZM/wXPPPYdvf/vbbbe/8cYb8alPfWpK206Mj/ggMFjTYgqTEgVTRdaRN0JJpmwtThTqxqRhy96qAhg2AkCJ6xcrsoZI2Mlo6urgdsLt9aDDzMhzmQmSFdtiFrpVoqQTYhozpppFMj3dbt3WleBAurYkxhWViJpbhjwRdVOe04x1OHEnfqU5S75zPrkSRpUpQ4NsEBSlSszrjedhzLpMcvC9IvIeQ+SZ/CGWaDeItAIUkPM5ckYIEZ4u/apbV00iUBEJquYaa6yOOquizobNtdchkKQuGOTRAR/dsoi8cbnIXKBVF5d5pIUEm3MVCYVYKUTGhRZKAaGv1l2TD4bA9E2OM0grHjX0LLOmHtM/qYATCV1OJswxOYAcVzCVdfB8IBcw3U5Aq0ssdUfFZjWzUGTa7UoulSu/TGTqwhOu+QwwIlXg87QfzLHrse5j3Te27E5CQLjnZJM4knWLMcCtnuc5cYw7QSVWEjGsy8tkIzHhVjCMYMYsi52zCYDLDnP3VgUZh5nnHEuhyUiLUUNsyksjqcdHmJQRJ2asiAo4z4M4MijzaAp4+OGHUSgUxt6QIAiCIAiCmFI++tGP4qabbhp1m1/84heTPv4f/uEfut9f+9rX4rjjjsMb3/hGvPDCCzj55JNbtr/++uuxdetW93epVMLq1atHPceEXEhTmInkDjNBFxIwWjZS9hyZQ7XJQrJOGes2Qma1eXAgaWo3Y9y5WMrKQxDq1392WE8Kz1zA0B2sMsdOA4ndlSkgMpcXZ5pvJ70dPICnFujfhXYThWw5ar6ZlLIaImVdL7Hrh1DpCWqEKnzothRMmVOvXIyCCW/uMG6YYiYk2ss4i5zYICVqRpSJVIyamWhXjWhSxRDq0I42JW12TOKcSbqvGsUhj+cQcN0O3yumvzM9h/HgN4hXgBamnDilQggZIrEhw1JnzFgHkz2XFaU4851LzAldCBGhhiq3gcUScZxH1Ti2CtxDwFl6PzwPBY+jzziPhOrSoowVVSC1OOECmwUkFDgYqjYLKlSoJT46rVPH5+jKMXSamWCnz1DwFPK2jMuWARoRJpJAVTBUzNL1w7FCJVGoxAqRcfkkpj2B6YuCx9AZcCw15yh6PvKeQs4Ocxc2rX/GCqgL5krRDkda1LKClsXjWXeS/tmbY8iZAwZcZdqtj1VN0hK3UixRjvX9q5j7GrLI5T/ljRurg+XRa4SWnpyHrsBeF8x50jZFQouugA4GB4DBKEbJCKYlPoTI5BvZIOtA5VFU2pnXo5YBALpZAd1BYPorzX8KbF+p9JoqscRQrO/vYaWF3SE+gDLXpaZWRM3CWOoKFMahNGIQ9mw6jeY4tNraBPjt3/7thr+VUti7dy9+8pOf4OMf//iUNYwgCIIgCIIYHx/5yEfw3ve+d9RtTjrpJCxfvhz79+9veD5JEgwMDEwoz2jjxo0AgJ07d7YVj/L5PPL5yX/DbScmU5+JZJl5IUnn5ciGfRi4E414Nvw5m8vd7pIygdW2DEZV9UmH45wTFmSDSJUJMjZB1aESCK17gdmJdN3l88TcOCNUNRVQZOiEkGwIdnpNHjwz6Y6M0FNjHSgwLSRVTSlbXuURmDwmHbucrgQGADESxCa7JuR1586owzgq1LBzV9iJspChC3luNymWMkGCtJzSbmOv18tMrq3rSap0dblERubvyB1PqaTRRaW4Wz1LGc+KUIlrV8RriFkVdaaFuZzXgZwqImfEoUAECJLA9UfaHuV+CpY4sUOwBAlL3DVIJiEhwOHBMyvTBcijKIooJFok62ABOjwfRT8VenJeGiRt9UuRcfiEQqFm3D+1RKCmEtRV5O6RbY9vVgIMogB5BCiYcZ3jHAHnCGzuEtPvCSeCSWVykuzYTBAjgWCNri5P+cibUs+8c0J5CFgqRKrMMQGgLhNUTdlkhdVQZToQ3rm3MmPCjoECulAw4k5XrQsddSOIGjdXNjtMKOXabR1iJV5ChQ+aaxl2qw1mPwOGzRgo8l4AwLDqxlCkz1lEzvWdl8ncsnlPdZVgmGkRd5gP6XPjMELznrBjNium6mM0r/A4vlXSZjTnZ47Xd022eXP8ssbFhMWj3t7ehr855zjttNPw6U9/Gm9+85unrGEEQRAEQRDE+FiyZAmWLFky5nabNm3C4OAgHn/8cZx33nkA9Eq5UkonCI2HHTt2AACOO+64SbV3vEw6E8ntNJ5spMkKScBYq7Ux8MxkMXv8bBtNi+xy8Yw3OI/cIk8mXFfyTAkXkxCmtCxSOp+lFBUbhAcnGplJeALhlnWPWegcEbasLOu0sSU0QoWZrKGkZeW0LJz5Lsg3Mbk8vhFMAKSiCe9wS5l7baYkAolbmSpWdTfBj43rKZY1JCIVjYC0jEy3LTtpNi4f939wog4A5w5KMvfaHkdCNkzE9TnsKnL2fJlVquClWTXSlr1JtyKcz/OQXpwKdKyKGgvgG0eWx4KG8iTbVkvCYtN2c2+gV7JzJX5KODHPOoh8lkeFa5EKAIqqE7kkj2JiVk9jHgLmuTHK9ZpxbpzGSiBUAjVzDyIWImR1hLyaBkhbB509J8/DVwFyJkjaFz4CEaRiKTgkpBvXMWJELGoQLoWKnbvNljVm86PySo+lQOUQmMwjLlg65k0mkG0r0F50bFeKWeNFBFwfv+x1oaC0+JmXqUvNIiERmtyhOs+ImyI9jxtD5j55zEfMa+ZaddtqbAjDnj5PQXU5QdGKgNlrinjk3ruhMmKYLCExZWs2eLslp80wouPIvn5EYtHRnWlEjMyExaOvfe1r09EOgiAIgiAIYpp5zWteg1//9V/H1Vdfjdtvvx1xHGPLli34nd/5HbfS2iuvvII3vvGN+Pu//3ts2LABL7zwAu6880689a1vxaJFi/Czn/0MH/7wh3HxxRfj7LPPnuUrIgiCIIiZY7JFePNBIpt05tHjjz/uaufPPPNMvO51r5uyRhEEQRAEQRDTwz/+4z9iy5YteOMb3wjOOd7+9rfjS1/6kns9jmM899xzqFb1N9i5XA4//OEPccstt6BSqWD16tV4+9vfjo997GOTOLuEttqMVl7Wngm7kIBxOpHaTQVaHSgNh3HHaSxpa9k228wRStnSl6U5s+9WUVIydWZ4zIZHCyhmSpZ4DGFLu4yzJ+AF8EwGj10O3GatCBU7F0+iQueIcW4jGTpHjn1NZsqurJPGHN38TM/HuQ/etL/yJKRxSGUdN9Ztw7JunUx/iEwgdrZ99mdartZYRpbtT/OHIYGwzqSM60Wy5lKeFCnTsrTmMjWl4jbjI601FEqCM91fHs+580pICNcPgclGMquxscC5bBqP2ugsypbVKSUb3DPOecRSt07Ehl1/11hHg/MrQA6e8t24sc4j63JJmHaBpU4wXcaYyNDdo6zbDtAOKsZ4mifFdJ4Ua3oPWseSQKLHprIuMn1dqsntx1ng3EFZt5Z1j1nnk25TmlsV2SB1WXWZVe1Ku2z7Ip5H4Om2h3wYVXMud06k7jAFmb6nTHla1hmXtCmnTBgHt64/60Dyas6FVGelhutz/eUC42PnLopUem2xMMH07j2YNIzRsRxHk2f2co30fxtmTpqZ9GprU9uMWWHC4tH+/fvxO7/zO7jvvvuwYMECAMDg4CDe8IY34Bvf+Ma4LNMEQRAEQRDE7NDX14c777xzxNfXrFnjMkMAYPXq1bj//vunuBWjl42NRbuSiukra7O0F5RGEpKat2svJGVDjWxpVbpyFlPcvWxX/OJZ0UMlELyx9CpS6UQzey5b/pMtO5MqdhPpdkJROqlOBaPmFcea+0GJBDKz2pjdx7PHzIhIVgSwbW9odpNA4gKnMxN/2z77s1HYyUyYM8KG7VuZEeaaryG7v/6Z9ne2TC37WmPb7fH0RJMjzQ+y/Wf7iPMEHByCN4of2dXe9H7pObN9Y1eDa1ee5Fbm4gEEC5G4LKoaQjbsRAkfeRP0nQovMnNMWxpnxQqhYlfKKJv62wlW4C0CVjuB0JUHGrHICZdWPGrOCWO8Zdxw7rvn2pX7Zce5kGGD2Kjb3ioeCRk5ccnjdfhmLHMn8qUr8ykl0vdMZpxmyymbs4dsu+3rul8TCG7PmU/PlVkhMFtOac/VnMmVPaZdK06T/YyyA3Kyos8RiEVHeZnapJt/dF82gEmIR9deey3K5TKeeeYZvOY1rwEA/PznP8dVV12FD37wg/inf/qnKW8kQRAEQRAEMV85MiHJMn3OJEv7drYXUtqcqt2qa4CbUKRuJOlW72KMQ5nVrOwKXlk3EmOpEJRkAqvbiSDZnKLsBN0JMCojwGSEovQYouF4rVgXhud6p1kwye7vsQTMOFmSTD5P2i2iYR/RNDmXMnEigL03jcJWcz6VCS3PCklNwkSDE6ytWGbbNEbIcMZZxhigwAEnyOmsqqwAyBh3weeMea3HQyoeNotj2p0zuqDHVQLBOLjpL4/5SDKCi3U7NY+bkdxNelW7uCFjqt25s8dj4C3XlhW99PGTpnHSKh4BWUGqVTxqR/M4l1Y8cgJLZqwoK96lLjYhIwgnHtkV/EZ/n9l8LP1a63uKgUNKKwRl2mb25zJKVwkcS1wdRfhtdAqOzug5R7PnLJprzFTZ2o033ohvf/vbePbZZ1EsFrF582bcdNNNOO200ybZgiNnwv+Fvvvuu/HXf/3XTjgCgDPOOAO33nor/uM//mNKG0cQBEEQBEEQBEEQBDEX0CtnTvwxUe6//35cc801eOSRR3DvvfcijmO8+c1vRqVSmfqLGicTdh5JKREErcpuEAROPSUIgiAIgiCIiTN6udh4Gekb9DEdSW1dJEeWk9SurK2tC2mMTCTrTtBLwaduDLfKVcbV0VzyBDQ6jxrzixqzWPRrzS6jEZw2Dc6LtO02E8S+at02Defhflqex1qzcJrb3tg+66xofG5sh5RscBk15/S0P79oek429EHzWGNg6evM9ksMZZxVtqhLZBw1emW0jEunXflc5h60uklaM210iZO5VqavO5vxxFUAZlbda3YdNR9LQq8W15D9ZFxBo7laGpxHIxw/O8asiyo9d9YRZH/xUhces44d311Lu7K15nGejhebE5W9Bvta2l86t8quhmidR3zEc7Wcs40zTEFmXId2db6s0ypy1ylGcEQ2u/narTao2zV6meW0M8Vlake2EtyRM1POo7vvvrvh7zvuuANLly7F448/josvvniSrTgyJiwe/dqv/Ro+9KEP4Z/+6Z8aVuX48Ic/jDe+8Y1T3kCCIAiCIAjiWGZqytqA9pOO6RGUJi4kOSGD8cwsQ7pjKdU6EW+ewDaeo/Hc2Yk/MhPd5tKdhgnmKCIJAFf2pf9Ir8eKX40iks10soeWGfFo9PvaLHiZo7d5TjS83v5YcK+nQlK7Erf24gyUIXqFfgAAQuZJREFUHHXyqqDSMaWkE5CyfZLNXgJ4671tUz6XbUu7+9o8zpRK+1efv1l0TBrGEZAKL2OVxjWXlGX3cW0Ab9AMRhIHm8Wise6flrLM/TIlZpzJUcdQsyjVIhqNIDS69wxEw7naXU+76xpZgETDMez4swHr9jUnGLP275Nm8ag5m6z5fI07jyWBHIHIdJRnGo2FmuT1TXY/y9DQEACdWzhbTFg8+vKXv4zf/M3fxJo1a7B69WoAwJ49e3DWWWfhH/7hH6a8gQRBEARBEAShmRpnUpbpEZTat7N5Iqcng43CRYMjJhOQmxVa0klj67VnX2/XlmbBqGWyPqJg1HpNKuuWaXHbtAomQHq3lMq4XdTIzpT0Z5Og0WZyPmYeEeMNr4975alRRbRW0dBuk44j2dAnSiEjtslGB4tqdw9bnUeNNI8hm4+Uhm8rJZsynpoyicbZ//bvkXKW2rXXPNEG0TpW3f0dyT0oM+OuVdwZ+TpGFkdHPJd7PeNyywhXo/WZvb7W59rdR/veTgXXBieaai8eNR+n/T1peo+3jKu5L/TMpTYqHNlqa0IIlEqlhufz+Tzy+fyo+0opcd111+HCCy/EWWedNbkGTAETFo9Wr16NJ554Aj/84Q/x7LPPAgBe85rX4LLLLpvyxhEEQRAEQRAEQRAEQcwFjkTK2r59O3p7exue+8QnPoFPfvKTo+53zTXX4Omnn8ZDDz10BGc/ciYsHgEAYwxvetOb8KY3vWmq20MQBEEQBEEQE2A098jkXEmTykyakBupNZNovNlIDfu0zepp+rulfKWN2yPT/rHcRo2kzgnntjGlWtm2sIwbyZXiQLtfWtvd6CppbHtrmVPj66O4jkZ6bhyM7jZqfj6bg6NMf6SOJ7cCmyuV86DQWHbVcL1juoxGvqY0O0sA8FrcTun94S3jpqUdLfdj9FK/sV05wOjlgCOUHWZWyWscZ40unpHP3c7dNPK59C8CzK3ol47PbP+NfL6m47f9nMju03od1kE2lkNPM7rbadqY56VqWY7EebRhwwbcc889Dc+P5TrasmULvve97+GBBx7AqlWrJnfyKWLc4tF//ud/YsuWLXjkkUfQ09PT8NrQ0BA2b96M22+/HRdddNGUN5IgCIIgCIKYD6jGr23brWk/pYw0aZo6UWncgtIYZW3t5l7Z7smKDWl7xjMpHGUy2Ta/p53o0rRNy31rFE3QZnLfHAKu0FiuxdqJRm0FpHb5Me0n5+Mtd7H3cOztx9PfTX1h28RaRQegWeAZm1EFmJZtG88zkmA1VvneWNk9o+8zwnZjiZUjiREsLVtj2XNkgtDbM9qYb2i4OQ9D41gc7ZxjjIuJvN4gJDW+J7TIN/I4mYhg1b4PJiAyTaNYNJfK1KYaz/NatJSRUErh2muvxV133YX77rsPJ5544jS3bmzGLR7dcsstuPrqq9tebG9vL/7oj/4IX/jCF0g8IgiCIAiCIMbHmKLEdDHWJGn84tK4BaVJ5CS1n5+NHg6s92t3fY0OI2ASE/fm19vcr5bAaKAhB6lxIpzdb7R7MpJo0XpN2XaMl0lPpLOqWNt9edofri+sC2mcwsNY7Rpp/5Z8p8ZjtAoRXub3sQLE27RjLEbM2pnAuLPb2JX7rKDTtMLdeM4/5rlbFL5m51FTYPxYzR7HeGy4Dv1EU5PaCJMNjO22m8/CzEwxaefRBPe75pprcOedd+Lf/u3f0N3djf7+fgBaeykWi5NrxBEybvHopz/9KW666aYRX3/zm9+Mz3/+81PSKIIgCIIgCOIYZCz7zYxxZI6l6RSUgLEmIaM7PRrbNw6X0Wi0dWlkStiaVh2zr+td0xKdiYlhIzsqRp4YT1HZTvvarvT3ERxZIwlqk2vDOK+lJSy86eWWSxnruGOPqzGbNJrLralBzdu2d/ilgk5L/467DeOgQSRsdSNNFdm2tQhJ6QujHGCk8rvRrnm844mcRpaZau9tt90GALj00ksbnv/a176G9773vTPShmbGLR7t27cPQRCMfCDfx4EDB6akUQRBEARBEARBEARBEHOJI8k8mtD2czBHatzi0cqVK/H0009j7dq1bV//2c9+huOOO27KGkYQBEEQBEEQY37jPaPOpNG+pR/DPXMkbiRgXI6k0fafdInahMmUZGUdSG0dN+Mt3Rq9HOeI81tGY6JOrBHGY1s3lmU059Wk3CRoPFfb8x1B/0xJuZYtORzdcdT8PMvWQGb6uqV/x92O1jaMa5umTKSJ56iNvu+Inxfj7Pupye8i2jHZj8w5qAVNmHGLR29961vx8Y9/HL/+67+OQqHQ8FqtVsMnPvEJ/Lf/9t+mvIEEQRAEQRAEMSLj/Rf5rIRzT1xQAiYoKk3guJqxhaPR9m/ftpFFE3u81rKtCQoY0xH66w40hbO6lr5ozKlpK3JMcXZOu23Hdb6siDXBLKbxtas5r2l8wtH4jtsoWE6oHW0Yse/cBu3u82QZ32fHkZdLUSD2VCAn2f6j/bqBCYhHH/vYx/Dtb38bp556KrZs2YLTTjsNAPDss8/i1ltvhRACf/ZnfzZtDSUIgiAIgiCISTOeydCUC0yTy06a2knGxHKNpubcaf5R83HHm00zFpMSjmbtq//WoOOxxImJ3Yfm6x5beDgSgXJyY2RqhKNGIbJ95tZUM7IIN1re1ZFyZLlrsy6iZg87D0STLPPBQTRZxi0eLVu2DD/+8Y/xgQ98ANdff72rwWOM4S1veQtuvfVWLFu2bNoaShAEQRAEQRAEQRAEMVtMVvaeD5rTuMUjADjhhBPw/e9/H4cPH8bOnTuhlMIpp5yChQsXTlf7CIIgCIIgCGJmmLESuMlnJx3ZsTGpUrXm7UYs4wFGKOVpdSABI+fTTLx9M+M4GitXp+V8Y/RF83HHz2RWRhvZ+TR9HOFqfpM+5wSXs58AI46BtplI08EUZxUdyzaaSTLpIOt50NUTEo8sCxcuxPnnnz/VbSEIgiAIgiCIuc9Ik4cpmTDO7ORwykWEtvlH7Sf0R3bucfTTEUyMR2vbuAWwEfsCmJhIOBVj4kjCnSd7nrGZkvE3Yv7QOEPmJ3PKkUTUbJvaMaMB/22YAbFovpWpNTNTq63NRSYlHhEEQRAEQRAE0cRoE7OZnDSOc4I42UneuB04DYydyzO54zQxTYLRaPtMXEACZne1q4mHu0/sWCMw4y6XiY+VKQkkn+A5x+RIPjtmqM/nu2BEaEg8IgiCIAiCIAiCIAiCGIPJr7Z29EPiEUEQBEEQBDFDjP799MScLEcZ013GMgGHwbS7BNpm/jQzDc6bSbosjrQ/xnRijeg+mgKmzO02zU6oI3T5tNtvLn1eTGub5mAu0bHsNJp05NE86DISjwiCIAiCIIg5wZFMSObSRHJCzPCMYionfeMSTSzTWbY3iT6cjsnvqCVs4xLUxnOSCbR7xkKcJ9CG6T7PLGcKzUVhayo4lsWiZibrPJoP3iMSjwiCIAiCIIijniOd3My3yV4zsz75m47J/dH4Vf5EBZ2pvMaZEvPana/5pdkcjzMcGn00frbM+ufFHOYYXmyNxCOCIAiCIAiCIAiCIIixoMwjgiAIgiAIgjiGGc837Uebg2Cm3ANjrjjWsHGbNo3HBTNFbpHp7pMJ9QUwe+6p6XYhjXBdM5K3Ncula81MeEzMEuQ2Gh9ylrLV5gIkHhEEQRAEQRDEOBjtH/9zaXI4G5OUI8p6mWfLiR91uTezkcc0nYx2PbPUxrk6JuaDoDHTHMt9RuIRQRAEQRAEQRwhI00opnuyONcmMnPRZTFbfTQX+2JUjsSNNA5RZsbvw1wRszLMdhbSXPu8OBqZ7LqE86HnSTwiCIIgCIIgiGlirMnaRCeQR8Pkb666LGaDo05AsowV7D0HhJmpHGez6dazTMc4ORo+L442Jp95dPTfCxKPCIIgCIIgCIIgCIIgxkAdw8utkXhEEARBEARBELPEfPg2eiSoREczlf0wkWua0j4/QqfRXLkXc5mpGCfUz8R0QuIRQRAEQRAEQRDTykyU6Ix2vrnCREutjuQ6Zlu8a24DMX6o3+YuVLZGEARBEARBEAQxQ8wFYWM2memJ5FGbvUQQc4zJi0dHPyQeEQRBEARBEARBEARBjIGa5Hpr5DwiCIIgCIIgCII4AmYrE+hYY6ZXwZupe3GkrioaM8REmKzzaD7AZ7sBBEEQBEEQxMzx2c9+Fps3b0ZHRwcWLFgwrn2UUrjhhhtw3HHHoVgs4rLLLsPzzz8/vQ0ljklUm/9NZB9ibGain+heEPMVCTWpx3x4T5B4RBAEQRAEcQwRRRHe8Y534AMf+MC49/nzP/9zfOlLX8Ltt9+ORx99FJ2dnXjLW96Cer0+jS0lCE07QYkEoyNjuvpttu7JZM5L44eYDHKS/5sPY43K1giCIAiCII4hPvWpTwEA7rjjjnFtr5TCLbfcgo997GP4rd/6LQDA3//932PZsmX413/9V/zO7/zOdDWVIAiCIOYUik0y84gd/eIROY8IgiAIgiCIEdm1axf6+/tx2WWXued6e3uxceNGPPzww233CcMQpVKp4UEQxNyCnDcEMXEmX7Z29EPiEUEQBEEQBDEi/f39AIBly5Y1PL9s2TL3WjM33ngjent73WP16tXT3k6CIGaXuTA9nkhGFkFMhsmWrWEejLk5IR7deuutWLNmDQqFAjZu3Ijt27ePuO1Xv/pVXHTRRVi4cCEWLlyIyy67bNTtCYIgCIIg5jsf/ehHwRgb9fHss8/OWHuuv/56DA0NuceePXtm7NwEQUyMqRBS5pIYM1om1lxqJ0Ecbcx65tE3v/lNbN26Fbfffjs2btyIW265BW95y1vw3HPPYenSpS3b33fffXjXu96FzZs3o1Ao4KabbsKb3/xmPPPMM1i5cuUsXAFBEARBEMTs8pGPfATvfe97R93mpJNOmtSxly9fDgDYt28fjjvuOPf8vn37sG7durb75PN55PP5SZ2PIAiCIOYiCoDCJDOP5oFwOevi0Re+8AVcffXVeN/73gcAuP322/Hv//7v+Lu/+zt89KMfbdn+H//xHxv+/j//5//gX/7lX7Bt2zZceeWVM9JmgiAIgiCIucSSJUuwZMmSaTn2iSeeiOXLl2Pbtm1OLCqVSnj00UcntGIbQRBzFwUFBjbhfY4GjpZ2EkcDCnKygdnzYBzOatlaFEV4/PHHGwIYOee47LLLRgxgbKZarSKOY/T19bV9nQIbCYIgCIIgUnbv3o0dO3Zg9+7dEEJgx44d2LFjB4aHh902p59+Ou666y4AAGMM1113HT7zmc/gO9/5Dp566ilceeWVWLFiBa644opZugqCIKaakUq9RtqWII5FJpt5NB/eM7PqPDp48CCEEG0DGMdbl/8nf/InWLFiRYMAleXGG290S9ISBEEQBEEc69xwww34+te/7v5+3eteBwD40Y9+hEsvvRQA8Nxzz2FoaMht87//9/9GpVLBH/7hH2JwcBCvf/3rcffdd6NQKMxo2wmCmBnmw0SXIKYDOemytaOfORGYPVk+97nP4Rvf+AbuuuuuEf/xQoGNBEEQBEEQKXfccQeUUi0PKxwBgFKqIUOJMYZPf/rT6O/vR71exw9/+EOceuqp4z6nUvafzYoe9KAHPehBj2l4IPPfmulDQU7qYds4Xh544AG87W1vw4oVK8AYw7/+679Oy/VMhFl1Hi1evBie52Hfvn0Nz+/bt8+FM47E5z//eXzuc5/DD3/4Q5x99tkjbkeBjQRBEARBELNLuVw2v03uG1uCIAiCGItyuYze3t5pPYeEmNR+Ew3arlQqOOecc/D+978fv/3bvz2pc041syoe5XI5nHfeedi2bZurmZdSYtu2bdiyZcuI+/35n/85PvvZz+IHP/gB1q9fP0OtJQiCIAiCICbDihUrsGfPHiilcPzxx2PPnj3o6emZ7WbNO0qlElavXk39O41QH08v1L/Ty3ztX6UUyuUyVqxYMf3nmvSXIBNzHl1++eW4/PLLJ3mu6WHWV1vbunUrrrrqKqxfvx4bNmzALbfcgkql4lZfu/LKK7Fy5UrceOONAICbbroJN9xwA+68806sWbMG/f39AICuri50dXXN2nUQBEEQBEEQ7eGcY9WqVW7hkp6ennk1cZlrUP9OP9TH0wv17/QyH/t3uh1Hlsmvtnb0M+vi0Tvf+U4cOHAAN9xwA/r7+7Fu3TrcfffdLkR79+7d4DyNZrrtttsQRRH+x//4Hw3H+cQnPoFPfvKTM9l0giCOAKUUoihCuVxGuVxGvV5HkiRIkgRxHLvfR3uOcw7f990jCIK2v9u/gyBAZ2cnurq60NnZCc/zZrsbCIIgCIIgCII4SjiSsjUhRMvq70dTzM6si0cAsGXLlhHL1O67776Gv1966aXpbxBBEOOiUqngwIEDOHDgAPbv3+9+P3TokBOFhoeH2/5eLpeRJMmstr9YLDrXYnd3t/s9++ju7saiRYuwePHihp+LFi3CwoULSYAiCIIgCIIgCGJMtm/f3uKQOppMMHNCPCIIYu6QJAleffVV7N69G3v27MGvfvWrBmEo+3u1Wp2ScxaLRRSLxRa30EiuIt/34XkelFKjupOa/46iCJVKBULobwxqtRpqtRoOHDgwqXYzxrBw4cIGQWnx4sVYsmQJVqxYgeOOO67hZ0dHx5T0F0EQxNFKPp/HJz7xiaPmW9ajDerf6Yf6eHqh/p1eqH+PnMlmHikobNiwAffcc0/D80fTvWBqJtazm0OUSiX09vZiaGho3tV5EsRYKKVw4MAB7Nmzx4lD2d93796NvXv3QsrxfygWCgUsWbIES5YswdKlS7FkyRIsWrQI3d3d7mEdPO3+7urqmlH3jlIKYRhieHjYOaHs79mHfX5oaAgDAwM4ePAgDh06hEOHDuHgwYMtltPx0NPT01ZUOu6447B69Wocf/zxWLFiBbmZCGKKof/2EwRBEARxJLz44os4+eS1OGnhb01q/0PVp/C+D7wNN99884T3ZYzhrrvucouMzRbkPCKIeYZSCq+++ip27tyJ559/vuHxwgsvoFarjXmMXC6HVatWYfXq1Vi1ahWWL1/eIhDZR1dXFxhjM3BlUwNjDIVCAYVCAYsXL570ceI4bisqHTp0CPv378err76KvXv34tVXX8Wrr76KarWKUqmEUqmEZ599dsTjep6HVatW4fjjj8cJJ5zQ9mdnZ+ek200QBEEQBEEQxOQ4EufRRBgeHsbOnTvd37t27cKOHTvQ19eH448/flJtOFJIPCKIo5T9+/fjueeeaxGIdu7cOWo5GWMMy5cvd06Xdj+XLl3aEFRPtBIEAZYtW+bC/UfDLh+aFZSyP1955RX86le/wp49e5AkCV5++WW8/PLLePDBB9ser6+vDyeccAJOPPFEnHzyyTjppJNw0kkn4eSTT8bxxx+PIAim+nIJgiAIgiAI4phHTTIwe6Lrrf3kJz/BG97wBvf31q1bAQBXXXUV7rjjjkm24cgg8Ygg5jiHDx/GM888g2eeeQZPP/00nn76aTzzzDOj5vR4noc1a9bglFNOwdq1a3HKKae4x/HHH49cLjeDV0AwxtySqKeffvqI2wkh0N/fj5dffhm7d+9u+7NUKmFgYAADAwN48sknW47BOcfxxx/fICpZYemkk07CwoULp/NSCYIgCIIgCGLeImfIeXTppZdiriUMkXhEEHOE4eFh/PznP3fikBWKXn311bbbM8ZwwgknNAhD9rFmzRoSiI5CPM/DypUrsXLlSmzevLntNkNDQ86Z9OKLL7Y86vU6XnrpJbz00kvYtm1by/59fX0NomL2Z19f33RfIkEQBEEQBEEctcxU2dpchMQjgpgF9u/fjyeeeAJPPvkknnzySTzxxBN44YUXRtx+9erVOOuss3DWWWfhzDPPxFlnnYXXvOY1tHrXMUhvby/OPvtsnH322S2vSSnR39/fVlR64YUX0N/fj4GBATz66KN49NFHW/bv6+trEZXsgxxLBEFMBbfeeiv+4i/+Av39/TjnnHPwV3/1V9iwYcNsN+uo54EHHsBf/MVf4PHHH8fevXvnRLDqfOLGG2/Et7/9bTz77LMoFovYvHkzbrrpJpx22mmz3bR5w2233YbbbrsNL730EgDgzDPPxA033IDLL798dhs2T/nc5z6H66+/Hh/60Idwyy23zHZzjiqUmpmytbkIiUcEMY0opbB7924nENmfI7mJli1b1iISnXHGGejt7Z3hlhNHI5xzrFixAitWrMDrX//6ltcrlQpeeOEFl42V/fnqq69iYGAA27dvx/bt21v2Xbx4MU455RSceuqpOPXUU93va9eupQBvgiDGxTe/+U1s3boVt99+OzZu3IhbbrkFb3nLW/Dcc89h6dKls928o5pKpYJzzjkH73//+/Hbv/3bs92cecf999+Pa665Bueffz6SJMGf/umf4s1vfjN+/vOf038Dp4hVq1bhc5/7HE455RQopfD1r38dv/Vbv4Unn3wSZ5555mw3b17x2GOP4Stf+UrbLyKJsZmpsrW5CFNzrZBumqHleonpwgpF27dvx2OPPebEooGBgZZtGWM49dRT8brXvQ6ve93rcO655+Kcc87BkiVLZqHlBNFeWLK/jyR2WlauXNkgKFm30kknnYR8Pj9DV0AQI0P/7Z8bbNy4Eeeffz6+/OUvA9BuydWrV+Paa6/FRz/60Vlu3fxhrizpPJ85cOAAli5divvvvx8XX3zxbDdn3tLX14e/+Iu/wO///u/PdlPmDcPDwzj33HPx13/91/jMZz6DdevWkfNonLz44os4+eS1OG7B5N7zpdpOXP2Bd+Dmm2+e4pbNHOQ8IohJMjAwgMcee8w5NbZv3479+/e3bOf7Ps4880yce+65DUJRV1fXLLSaINrT2dk5YjmcXSr0l7/8JZ5//vmGn4cOHcIrr7yCV155BT/60Y8a9uOct+RyWXFpzZo18H36TxBBHCtEUYTHH38c119/vXuOc47LLrsMDz/88Cy2jCAmztDQEABQVuA0IYTAt771LVQqFWzatGm2mzOvuOaaa/Abv/EbuOyyy/CZz3xmtptzVKLUJJ1H88CyQ/9yJ4hxUK/XsWPHjgah6Pnnn2/Zzvd9nHPOOTj//PNx3nnn4XWvex3OOusscl8QRzVdXV1Yt24d1q1b1/LawMBAi6BkXUvlchm7du3Crl27cM899zTs5/s+TjzxRCcmrV27FmvXrsXJJ5+ME044AUEQzNDVEQQxExw8eBBCCCxbtqzh+WXLluHZZ5+dpVYRxMSRUuK6667DhRdeiLPOOmu2mzOveOqpp7Bp0ybU63V0dXXhrrvuwhlnnDHbzZo3fOMb38ATTzyBxx57bLabQhylkHhEEE0opbBnzx78+Mc/do+f/vSnSJKkZdu1a9diw4YN2LhxIzZs2IB169ahUCjMQqsJYnbo6+vDxo0bsXHjxobnlVLYt2+fE5KywtLOnTtRq9Xc8814noc1a9Y4MSkrLJ100kn0HiMIgiBmjWuuuQZPP/00HnroodluyrzjtNNOw44dOzA0NIR//ud/xlVXXYX777+fBKQpYM+ePfjQhz6Ee++9l/4ddYQcy5lHJB4RxzxRFOHJJ59sEIvaZbwsWbLEiUQbNmzA+vXrsWjRolloMUHMfRhjWL58OZYvX46LLrqo4TUpJV555ZUGUemFF17Azp078cILL6Ber+OFF15ouwIhYwyrVq3CySefjBNPPBEnnXQSTjzxRPdYvnw5GGMzdZkEQYyTxYsXw/M87Nu3r+H5ffv2Yfny5bPUKoKYGFu2bMH3vvc9PPDAA1i1atVsN2fekcvlsHbtWgDAeeedh8ceewxf/OIX8ZWvfGWWW3b08/jjj2P//v0499xz3XNCCDzwwAP48pe/jDAM4XneLLbw6GHyq61NTnSaS5B4RBxz7Nu3Dw8//DB+/OMf4+GHH8Zjjz2GMAwbtvF9H+vWrcPmzZuxadMmXHDBBTjhhBNoUkoQUwDnHKtXr8bq1avxa7/2aw2vSSmxd+9e7Ny504lJ9vedO3eiXC5jz5492LNnD+67776WYxeLRaxZs8aJSc3iEq1cSBCzQy6Xw3nnnYdt27a5IGcpJbZt24YtW7bMbuMIYgyUUrj22mtx11134b777sOJJ5442006JpBStvwbnZgcb3zjG/HUU081PPe+970Pp59+Ov7kT/6EhKMJoMh5RBDzE6UUnn/+eTz44IN46KGH8NBDD2Hnzp0t2y1atAibN292j/Xr16Ojo2MWWkwQxzacc6xcuRIrV67EJZdc0vCaUgoHDx50opLNU3rxxRexa9cu/OpXv0KtVsMvfvEL/OIXv2h7/J6eHhx//PEjPlasWEF5SwQxTWzduhVXXXUV1q9fjw0bNuCWW25BpVLB+973vtlu2lGPXdjAsmvXLuzYsQN9fX04/vjjZ7Fl84NrrrkGd955J/7t3/4N3d3d6O/vBwD09vaiWCzOcuvmB9dffz0uv/xyHH/88SiXy7jzzjtx33334Qc/+MFsN21e0N3d3ZLR1dnZiUWLFlF21wSZbGD2PNCOSDwi5hdJkmDHjh0NYlHzCmiMMZx55pkNYtHatWvJVUQQcxzGGJYsWYIlS5a0XX0liiLs2bOnQVDKPg4cOIBSqYSnn34aTz/9dNtzcM6xYsUKJyatWrUKK1aswIoVK7By5UqsWLECxx13HE0WCGISvPOd78SBAwdwww03oL+/H+vWrcPdd9/dEqJNTJyf/OQneMMb3uD+3rp1KwDgqquuwh133DFLrZo/3HbbbQCASy+9tOH5r33ta3jve9878w2ah+zfvx9XXnkl9u7di97eXpx99tn4wQ9+gDe96U2z3TSCyKCgMLmytck6luYSTKn5sGjc+CmVSujt7cXQ0BB6enpmuznEEVKpVPDoo4/ioYcewoMPPoiHH34YlUqlYZt8Po8NGzbgoosuwkUXXYQLLrgACxYsmJ0GEwQxa1QqFezZswe7d+9u+9izZw+iKBrXsRYuXOjEpKywtGLFCixbtgzLli3D0qVL0dXVNc1XRYwH+m8/QRAEQRBHwosvvoiTTz4ZC7vOmdT+1fAVfOCa38PNN988xS2bOch5RBxVDA4O4r/+67/wwAMP4P7778fjjz/esgraggULcOGFF+Kiiy7C61//eqxfvx75fH6WWkwQxFyhs7MTp59+Ok4//fS2r0spsX//ficwvfzyy3jllVfw6quvNvys1+s4fPgwDh8+PKKDydLR0YGlS5c6Mcn+bP590aJFWLRoEXK53HRcOkEQBEEQBDEFTL5s7ej37JB4RMxpDh48iAcffBD3338/HnjgAezYsQPNZrlVq1Y5oeiiiy7CmWeeCc75LLWYIIijFc65WyHu/PPPb7uNUgpDQ0NOTMoKS/b3/fv3Y9++fajVaqhWq3jppZfw0ksvjasNXV1dWLRoEfr6+pygNNLfvb29WLBggcvcoNJbgiAIgiCI6UVOOjD76IfEI2JO8eqrr+KBBx5wzqKf//znLduccsopuPjii92DVkEjCGKmYIxhwYIFWLBgAc4888wRt1NKoVKpYN++fU5MavfTPgYGBqCUwvDwMIaHh/Hyyy9PqF1BEDSISbaN2ed6enrQ3d3d8Ojq6mr4u1Ao0OcpQRAEQRDECEzWeUSrrRHEEbJ7927cf//97tFuJbQzzzwTF198MS655BJcdNFFWLFixSy0lCAIYvwwxtDV1YWuri6cfPLJY24vpcTg4CAOHTqEgYEBHDp0qOX37N8DAwMYGhrC0NAQpJSI4xgHDx7EwYMHj6jdnuc1CEtdXV3o7Owc85HdrqOjo+2DhCmCIAiCIIijFxKPiBnlpZdewn333efEol27djW8zhjDunXrcMkll+Diiy/GRRddhMWLF89SawmCIGYGzjn6+vrQ19c3of2sW2loaAiDg4Mj/hwcHES5XB7xYRcaEEK47acaxhiKxeKI4tJkHlasyopWnudNedsJgiAIgiAAQKnJrbaGyWYlzSFIPCKmDaUUXnzxRdx///1OMNq9e3fDNp7n4bzzzsMll1yCSy65BK9//evR29s7Sy0mCII4umCMOafQqlWrJn0cKSUqlUpbUWkij+HhYZf1ZB9hGALQ/02wz00n+Xy+RVSiRRMIgiAIgpgK1KQzj6hsjSAcSins3LnTCUX33XcfXnnllYZtfN/H+vXrcemll+KSSy7BhRdeiO7u7llqMUEQBAFo55MVoaaaJElaBKXso1KpjPr6SNtXKhX3d1aQCsMQYRhiYGBgyq+FIAiCIIhjm0mvtjYPIPGImDRKKTz33HMNzqK9e/c2bBMEATZs2IBLLrkEl156KTZt2oSurq5ZajFBEAQx0/i+P23ClEUp5QSoZlGpUqngwIEDuOqqq6bt/ARBEARBHBtQYDZBjAOlFH7xi180ZBbt27evYZtcLocLLrjAlaFt2rQJHR0ds9RigiAI4liAMeYyj9rl5JVKpVloFUEQBEEQ843Jlq2BxCNiPiOlxNNPP40HHngA9913Hx544AEcOHCgYZt8Po9Nmza5MrSNGzeiWCzOUosJgiAIgiAIgiAIYnqYdNmaIvGImEcIIbBjxw7cf//9eOCBB/Dggw+2ZEYUi0Vs3rzZlaFt2LCBgkgJgiAIgiAI4gg5cOAAXvva1+KDH/wg/vRP/xQA8OMf/xiXXnop/uM//gNvfOMbZ7mFBEFQ2RpxTBLHMR5//HEnFj300EMt1v7Ozs4Gsej8889HLpebpRYTBEEQBEEQxPxkyZIl+Lu/+ztcccUVePOb34zTTjsN73nPe7BlyxYSjghizkBla8QxQL1ex/bt2/HAAw/g/vvvx49//OOWJZN7enpw0UUX4eKLL8Yll1yCc889F0EQzFKLCYIgCIIgCOLY4a1vfSuuvvpqvPvd78b69evR2dmJG2+8cbabRRCEgcrWiHnJ0NAQfvzjH+PBBx/Egw8+iO3btyOKooZt+vr6cPHFFzux6JxzzoHnebPUYoIgCIIgCII4tvn85z+Ps846C9/61rfw+OOPU0QEQcwhJhuYTWVrxJxi3759eOihh1xe0U9/+lNI2Ti4ly1bhosuusithnbmmWeCcz5LLSYIgiAIgiAIIssLL7yAV199FVJKvPTSS3jta187200iCIIg8ehoRSmFXbt24aGHHsKDDz6IBx54AL/85S9btjvppJNcGdpFF12EtWvXgjE2Cy0mCIIgCIIgCGI0oijC7/3e7+Gd73wnTjvtNPzBH/wBnnrqKSxdunS2m0YQBI6gbI2cR8RMEccxnnzySfzXf/2Xe/T39zdswxjDWWed5cSi17/+9Vi5cuUstZggCIIgCIIgiInwZ3/2ZxgaGsKXvvQldHV14fvf/z7e//7343vf+95sN40gCACAmOR+JB4R08Tg4CAefvhhJxQ9+uijqNVqDdsEQYDzzjvPiUUXXnghFi5cOEstJgiCIAiCIAhistx333245ZZb8KMf/Qg9PT0AgP/7f/8vzjnnHNx22234wAc+MMstJAhiss4jRYHZxFSglMILL7zQIBY988wzLQOsr68PmzdvxoUXXogLL7wQ69evR7FYnKVWEwRBEARBEAQxVVx66aWI47jhuTVr1mBoaGiWWkQQRCtUtkbMIKVSCY899hgeeeQR9zh48GDLdmvXrnVC0YUXXojTTz+dwq0JgiAIgiAIgiAIYjagzCNiupBS4tlnn8UjjzyChx9+GI888khbV1Eul8O5557rhKLNmzdj2bJls9RqgiAIgiAIgiAIgiCyqHkgAk0WEo+mmL179+Kxxx7DY489hkcffRSPPvooSqVSy3Zr1qzBBRdcgAsuuACbNm3COeecg3w+PwstJgiCIAiCIAiCIAhibMh5REyCQ4cO4Sc/+Qkee+wx9/PVV19t2a6jowPnn3++E4suuOACLF++fBZaTBAEQRAEQRAEQRDEpJgHwdeThcSjcVIqlfDEE080CEW7du1q2Y5zjte85jU4//zzsWHDBmzatAlnnXUWfJ+6miAIgiAIgiAIgiCOViZftnb0i06kaLRh37592LFjB5588kns2LEDO3bswC9/+cu2y+utXbsW559/Ps4//3ysX78er3vd69DV1TULrSYIgiAIgiAIgiAIYqoJgsD8pgCwSR0jl8tNWXtmg2NaPJJSYufOnU4gsmJRf39/2+1Xr17dIBSdd955WLhw4Qy3miAIgiAIgiAIgiCImWL16tXQopEE4E1wbwVA4Q/+4A+mvF0zyTErHr3pTW/CM888g0ql0vIaYwynnnoq1q1bh3Xr1uF1r3sd1q1bR6ufEQRBEARBEARBEMQxyDPPPI0zzzwTE3cfSQAMp5xyyvQ0bIY4ZsWj7du3AwAKhQLOPvtsJxStW7cOZ599Njo7O2e5hQRBEARBEARBEARBzAXOOOMMTNx9JAEoHDhwYNraNVMcs+LRV7/6VWzevBmnnnoqhVkTBEEQBEEQBEEQBDEqe/e+iuOOOw7jcx8paPGIY/HixdPetunmmFVN/uf//J/o6emZ7WYQBEEQBEEQBEEQBHEUsHz5cgAcgIB2H40mIOkFt6rV4elv2AzAZ7sBBEEQBEEQBEEQBEEQRwPl8pD5rXU19hTtOvr617+OYrE4A62afkg8IgiCIAiCIAiCIAiCGAddXV34yle+Aptn1B79/Lvf/e6Zata0Q+IRQRAEQRAEQRAEQRDEOHn/+99vfmsnHmnX0T333APPG2+w9tyHxCOCIAiCIAiCIAiCIIhx4vs+vvvd76K9+0gCYHjTm9408w2bRuaEeHTrrbdizZo1KBQK2LhxI7Zv3z7q9t/61rdw+umno1Ao4LWvfS2+//3vz1BLCYIgCIIgCIIgCII41vmN3/gN6MBsmXlWAVD46U93zEqbppNZF4+++c1vYuvWrfjEJz6BJ554Aueccw7e8pa3YP/+/W23//GPf4x3vetd+P3f/308+eSTuOKKK3DFFVfg6aefnuGWEwRBEARBEARBEARxLMIYw09+8hisYKTRrqOzzz579ho2TTCl1GgR4dPOxo0bcf755+PLX/4yAEBKidWrV+Paa6/FRz/60Zbt3/nOd6JSqeB73/uee+6CCy7AunXrcPvtt495vlKphN7eXgwNDaGnp2fqLoQgCIIgiDkJ/befIAiCIIjpgjHryeEABPbs2YNVq1bNZpOmhVl1HkVRhMcffxyXXXaZe45zjssuuwwPP/xw230efvjhhu0B4C1vecuI2xMEQRAEQRAEQRAEQUwHu3a9CO08EgDYvBSOAMCfzZMfPHgQQggsW7as4flly5bh2WefbbtPf39/2+37+/vbbh+GIcIwdH8PDQ0B0N9CEgRBEAQx/7H/zZ9lszVBEARBEPOQNWvWQGcfKQwNDc5uY6aRWRWPZoIbb7wRn/rUp1qeX7169Sy0hiAIgiCI2eLQoUPo7e2d7WYQBEEQBDHPUEqOvdFRzqyKR4sXL4bnedi3b1/D8/v27cPy5cvb7rN8+fIJbX/99ddj69at7u/BwUGccMIJ2L179zH5D8hSqYTVq1djz549x2TuA10/Xf+xev3H8rUDdP3H+vUPDQ3h+OOPR19f32w3hSAIgiAI4qhkVsWjXC6H8847D9u2bcMVV1wBQAdmb9u2DVu2bGm7z6ZNm7Bt2zZcd9117rl7770XmzZtart9Pp9HPp9veb63t/eY/Ae0paenh66frn+2mzFrHMvXfyxfO0DXf6xfP+ezvsgsQRAEQRDEUcmsl61t3boVV111FdavX48NGzbglltuQaVSwfve9z4AwJVXXomVK1fixhtvBAB86EMfwiWXXIK//Mu/xG/8/+3de1BU5/kH8O8iLJeAooBcVJA7xAteqIg2XISUENt4yRCqNPUWExI1Jo02ahKJJirTWqbReotOINNimGgUaRJMUmWJGgQhWZQmoiBCm4JIFQuCN3h+fzicnwu7xhhg1+X7mdkZ9j3vvu/5+sw48njO2alTkZWVheLiYrz77rvGjEFEREREREREZJaM3jxKTEzExYsXsXr1atTV1WHMmDE4ePCg8lDsmpoanf8pnDRpEnbv3o3XX38dq1atgr+/P7KzszFy5EhjRSAiIiIiIiIiMltGbx4BwOLFiw3epqbRaLqMJSQkICEh4b72sra2RkpKit5b2foC5md+5u+b+ftydoD5mb9v5yciIiL6qVTC760lIiIiIiIiIiID+ORIIiIiIiIiIiIyiM0jIiIiIiIiIiIyiM0jIiIiIiIiIiIy6IFrHm3ZsgXDhw+HjY0NwsLCUFRUZHDuzZs3sXbtWvj6+sLGxgYhISE4ePCgzpwNGzbgZz/7GRwcHDB48GBMnz4d5eXlOnOuXbuGRYsWwcnJCfb29njyySdx4cKFHsn3Q4yRPyoqCiqVSueVnJzcI/l+SHfn37ZtG0aPHo3+/fujf//+CA8PR25urs4cc67/veQ35/rfKTU1FSqVCi+99JLOuKnU3xjZzbn2b775ZpdsQUFBOnNMpfaAcfKbc/0B4Pvvv8dvfvMbODk5wdbWFqNGjUJxcbFyXESwevVquLu7w9bWFrGxsTh79myP5CMiIiIyefIAycrKErVaLe+9957885//lIULF4qjo6NcuHBB7/zf//734uHhIZ988olUVlbK1q1bxcbGRr7++mtlTlxcnKSnp0tZWZlotVp5/PHHxdPTU5qbm5U5ycnJMmzYMDl06JAUFxfLxIkTZdKkST2etzNj5Y+MjJSFCxdKbW2t8rpy5UqP5+2sJ/Ln5OTIJ598ImfOnJHy8nJZtWqVWFlZSVlZmTLHnOt/L/nNuf4dioqKZPjw4TJ69GhZunSpzjFTqL+xsptz7VNSUmTEiBE62S5evKizjinUXsR4+c25/pcuXRIvLy+ZO3euFBYWyrlz5+Szzz6TiooKZU5qaqoMGDBAsrOzpbS0VJ544gnx9vaW1tbWHs9MREREZGoeqObRhAkTZNGiRcr7trY28fDwkA0bNuid7+7uLn/5y190xmbOnClJSUkG96ivrxcAkp+fLyIijY2NYmVlJXv27FHmfPfddwJACgoKfkqcH80Y+UVu/wLR+ZdKY+iN/CIiAwcOlF27dolI36u/iG5+EfOvf1NTk/j7+8sXX3zRJaup1N8Y2UXMu/YpKSkSEhJicE9Tqb2IcfKLmHf9X331Vfn5z39ucM/29nZxc3OTP/7xj8pYY2OjWFtbywcffHC/UYiIiIgeWA/MbWs3btxASUkJYmNjlTELCwvExsaioKBA72euX78OGxsbnTFbW1scPXrU4D5XrlwBAAwaNAgAUFJSgps3b+rsGxQUBE9PT4P79gRj5e+QmZkJZ2dnjBw5EitXrkRLS8v9RrkvvZG/ra0NWVlZuHr1KsLDwwH0rfrry9/BnOu/aNEiTJ06VWftDqZQf2Nl72DOtT979iw8PDzg4+ODpKQk1NTUKMdMofaA8fJ3MNf65+TkIDQ0FAkJCRg8eDDGjh2LnTt3KserqqpQV1ens++AAQMQFhbWq/UnIiIiMhWWxj6Be9XQ0IC2tja4urrqjLu6uuL06dN6PxMXF4e0tDRERETA19cXhw4dwr59+9DW1qZ3fnt7O1566SVMnjwZI0eOBADU1dVBrVbD0dGxy751dXU/Pdg9MlZ+AJg9eza8vLzg4eGBkydP4tVXX0V5eTn27dvXfQF/QE/mP3XqFMLDw3Ht2jXY29tj//79ePjhhwH0jfrfLT9g3vXPysrC119/jRMnTuhdwxTqb6zsgHnXPiwsDBkZGQgMDERtbS3WrFmDRx55BGVlZXBwcDCJ2gPGyw+Yd/3PnTuHbdu24Xe/+x1WrVqFEydO4MUXX4RarcacOXOUGuvbtzfrT0RERGQqHpjm0f145513sHDhQgQFBUGlUsHX1xfz5s3De++9p3f+okWLUFZWdtcrcx4k3ZX/2WefVX4eNWoU3N3dERMTg8rKSvj6+vZohp/iXvMHBgZCq9XiypUr2Lt3L+bMmYP8/HydBsqDqLvym2v9//Wvf2Hp0qX44osvulyl8KDrruzmWnsAiI+PV34ePXo0wsLC4OXlhQ8//BALFiwwxml3m+7Kb871b29vR2hoKNavXw8AGDt2LMrKyrB9+3bMmTPHWKdOREREZLIemNvWnJ2d0a9fvy7fdHPhwgW4ubnp/YyLiwuys7Nx9epVVFdX4/Tp07C3t4ePj0+XuYsXL8bHH3+MvLw8DB06VBl3c3PDjRs30NjYeM/79gRj5dcnLCwMAFBRUXGfaX68nsyvVqvh5+eH8ePHY8OGDQgJCcE777wDoG/U/2759TGX+peUlKC+vh7jxo2DpaUlLC0tkZ+fj02bNsHS0hJtbW0mUX9jZdfHXGqvj6OjIwICApRsplB7wHj59TGn+ru7u3f5D4Lg4GDl1r2OtX/MvkRERETm7IFpHqnVaowfPx6HDh1Sxtrb23Ho0KEuz2fpzMbGBkOGDMGtW7fw0UcfYdq0acoxEcHixYuxf/9+HD58GN7e3jqfHT9+PKysrHT2LS8vR01NzQ/u252MlV8frVYL4PY/vntLT+XXp729HdevXwdg/vXX5878+phL/WNiYnDq1ClotVrlFRoaiqSkJGi1WvTr188k6m+s7PqYS+31aW5uRmVlpZLNFGoPGC+/PuZU/8mTJ6O8vFxn/pkzZ+Dl5QUA8Pb2hpubm86+//vf/1BYWNir9SciIiIyGcZ+YvePkZWVJdbW1pKRkSHffvutPPvss+Lo6Ch1dXUiIvL000/LihUrlPnHjx+Xjz76SCorK+XLL7+UKVOmiLe3t1y+fFmZ8/zzz8uAAQNEo9HofB1xS0uLMic5OVk8PT3l8OHDUlxcLOHh4RIeHt5ruTsYI39FRYWsXbtWiouLpaqqSg4cOCA+Pj4SERHRq9lFeib/ihUrJD8/X6qqquTkyZOyYsUKUalU8vnnnytzzLn+P5Tf3Ovfmb5vlzKF+hsju7nX/pVXXhGNRiNVVVVy7NgxiY2NFWdnZ6mvr1fmmELtRYyT39zrX1RUJJaWlrJu3To5e/asZGZmip2dnfztb39T5qSmpoqjo6McOHBATp48KdOmTRNvb29pbW3ttexEREREpuKBah6JiGzevFk8PT1FrVbLhAkT5Pjx48qxyMhImTNnjvJeo9FIcHCwWFtbi5OTkzz99NPy/fff66wHQO8rPT1dmdPa2iovvPCCDBw4UOzs7GTGjBlSW1vb01H16u38NTU1EhERIYMGDRJra2vx8/OT5cuXy5UrV3ojbhfdnX/+/Pni5eUlarVaXFxcJCYmRqdxJGLe9f+h/OZe/870NY9Mpf69nd3ca5+YmCju7u6iVqtlyJAhkpiYKBUVFTpzTKX2Ir2f39zrLyLy97//XUaOHCnW1tYSFBQk7777rs7x9vZ2eeONN8TV1VWsra0lJiZGysvLeywjERERkSlTiYgY55onIiIiIiIiIiIydQ/MM4+IiIiIiIiIiKj3sXlEREREREREREQGsXlEREREREREREQGsXlEREREREREREQGsXlEREREREREREQGsXlEREREREREREQGsXlEREREREREREQGsXlEREREREREREQGsXlERCbrzTffxJgxY4x9GgqVSoXs7Owf/bny8nK4ubmhqamp+0/qDg0NDRg8eDD+/e9/9+g+RERERETUt7B5RNTHbd++HQ4ODrh165Yy1tzcDCsrK0RFRenM1Wg0UKlUqKys7OWz7F3d3bRauXIllixZAgcHh25bUx9nZ2f89re/RUpKSo/uQ0REREREfQubR0R9XHR0NJqbm1FcXKyMHTlyBG5ubigsLMS1a9eU8by8PHh6esLX19cYp/pAqqmpwccff4y5c+f2yn7z5s1DZmYmLl261Cv7ERERERGR+WPziKiPCwwMhLu7OzQajTKm0Wgwbdo0eHt74/jx4zrj0dHRAIC//vWvCA0NhYODA9zc3DB79mzU19cDANrb2zF06FBs27ZNZ69vvvkGFhYWqK6uBgA0NjbimWeegYuLC/r3748pU6agtLT0rue7a9cuBAcHw8bGBkFBQdi6daty7Pz581CpVNi3bx+io6NhZ2eHkJAQFBQU6Kyxc+dODBs2DHZ2dpgxYwbS0tLg6OgIAMjIyMCaNWtQWloKlUoFlUqFjIwM5bMNDQ2YMWMG7Ozs4O/vj5ycnLue74cffoiQkBAMGTJEGdN3ZdOf//xnDB8+XHk/d+5cTJ8+HevXr4erqyscHR2xdu1a3Lp1C8uXL8egQYMwdOhQpKen66wzYsQIeHh4YP/+/Xc9LyIiIiIionvF5hERITo6Gnl5ecr7vLw8REVFITIyUhlvbW1FYWGh0jy6efMm3nrrLZSWliI7Oxvnz59Xrq6xsLDArFmzsHv3bp19MjMzMXnyZHh5eQEAEhISUF9fj9zcXJSUlGDcuHGIiYkxeNVMZmYmVq9ejXXr1uG7777D+vXr8cYbb+D999/Xmffaa69h2bJl0Gq1CAgIwKxZs5Tb8o4dO4bk5GQsXboUWq0Wjz76KNatW6d8NjExEa+88gpGjBiB2tpa1NbWIjExUTm+Zs0aPPXUUzh58iQef/xxJCUl3fUqnyNHjiA0NPSuf/6GHD58GP/5z3/w5ZdfIi0tDSkpKfjlL3+JgQMHorCwEMnJyXjuuee6PONowoQJOHLkyH3tSURERERE1BmbR0SE6OhoHDt2DLdu3UJTUxO++eYbREZGIiIiQrkiqaCgANevX1eaR/Pnz0d8fDx8fHwwceJEbNq0Cbm5uWhubgYAJCUl4dixY6ipqQFw+2qkrKwsJCUlAQCOHj2KoqIi7NmzB6GhofD398fGjRvh6OiIvXv36j3PlJQU/OlPf8LMmTPh7e2NmTNn4uWXX8aOHTt05i1btgxTp05FQEAA1qxZg+rqalRUVAAANm/ejPj4eCxbtgwBAQF44YUXEB8fr3zW1tYW9vb2sLS0hJubG9zc3GBra6scnzt3LmbNmgU/Pz+sX78ezc3NKCoqMvhnW11dDQ8Pjx9TDsWgQYOwadMmBAYGYv78+QgMDERLSwtWrVoFf39/rFy5Emq1GkePHtX5nIeHh3J1FxERERER0U/F5hERISoqClevXsWJEydw5MgRBAQEwMXFBZGRkcpzjzQaDXx8fODp6QkAKCkpwa9+9St4enrCwcEBkZGRAKA0i8aMGYPg4GDl6qP8/HzU19cjISEBAFBaWorm5mY4OTnB3t5eeVVVVel9IPfVq1dRWVmJBQsW6Mx/++23u8wfPXq08rO7uzsAKLfUlZeXY8KECTrzO7+/mzvXfuihh9C/f39lbX1aW1thY2Nzz+vfacSIEbCw+P+/pl1dXTFq1Cjlfb9+/eDk5NRlf1tbW7S0tNzXnkRERERERJ1ZGvsEiMj4/Pz8MHToUOTl5eHy5ctKI8jDwwPDhg3DV199hby8PEyZMgXA7UZOXFwc4uLikJmZCRcXF9TU1CAuLg43btxQ1k1KSsLu3buxYsUK7N69G4899hicnJwA3P5Gt87PWurQ8fyhO3Vc0bRz506EhYXpHOvXr5/OeysrK+VnlUoF4PaVT93hzrU71r/b2s7Ozrh8+bLOmIWFBUREZ+zmzZv3tNe97H/p0iW4uLgYDkFERERERPQjsHlERABu37qm0Whw+fJlLF++XBmPiIhAbm4uioqK8PzzzwMATp8+jf/+979ITU3FsGHDAEDn29o6zJ49G6+//jpKSkqwd+9ebN++XTk2btw41NXVwdLSUudB0Ya4urrCw8MD586dU259ux+BgYE4ceKEzljn92q1Gm1tbfe9x53Gjh2Lb7/9VmfMxcUFdXV1EBGluaXVartlPwAoKytDVFRUt61HRERERER9G29bIyIAt5tHR48ehVarVa48AoDIyEjs2LEDN27cUJ535OnpCbVajc2bN+PcuXPIycnBW2+91WXN4cOHY9KkSViwYAHa2trwxBNPKMdiY2MRHh6O6dOn4/PPP8f58+fx1Vdf4bXXXtPbiAJuP6x6w4YN2LRpE86cOYNTp04hPT0daWlp95xzyZIl+PTTT5GWloazZ89ix44dyM3NVZo4HeddVVUFrVaLhoYGXL9+/Z7X7ywuLg4FBQU6zaioqChcvHgRf/jDH1BZWYktW7YgNzf3vve4U0tLC0pKSvCLX/yiW9YjIiIiIiJi84iIANxuHrW2tsLPzw+urq7KeGRkJJqamhAYGKg8P8jFxQUZGRnYs2cPHn74YaSmpmLjxo16101KSkJpaSlmzJih8+BplUqFTz/9FBEREZg3bx4CAgLw61//GtXV1Tr73+mZZ57Brl27kJ6ejlGjRiEyMhIZGRnw9va+55yTJ0/G9u3bkZaWhpCQEBw8eBAvv/yyznOJnnzySTz22GOIjo6Gi4sLPvjgg3tev7P4+HhYWlriH//4hzIWHByMrVu3YsuWLQgJCUFRURGWLVt233vc6cCBA/D09MQjjzzSLesRERERERGppPODN4iI+piFCxfi9OnTPfb19lu2bEFOTg4+++yzHln/ThMnTsSLL76I2bNn9/heRERERETUN/CZR0TU52zcuBGPPvooHnroIeTm5uL999/H1q1be2y/5557Do2NjWhqaoKDg0OP7dPQ0ICZM2di1qxZPbYHERERERH1PbzyiIj6nKeeegoajQZNTU3w8fHBkiVLkJycbOzTIiIiIiIiMklsHhERERERERERkUF8YDYRERERERERERnE5hERERERERERERnE5hERERERERERERnE5hERERERERERERnE5hERERERERERERnE5hERERERERERERnE5hERERERERERERnE5hERERERERERERnE5hERERERERERERn0f3QEpecnjBK6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mode_amps = sim_data[\"mode_monitor\"].amps.sel(direction=\"-\", mode_index=0)\n",
    "mode_power = np.abs(mode_amps) ** 2\n",
    "dip_power = np.zeros(n_wl)\n",
    "for i in range(len(field_monitor)):\n",
    "    field_mon = sim_data[f\"field_monitor_{i}\"]\n",
    "    dip_power += np.abs(field_mon.flux)\n",
    "\n",
    "coup_eff = mode_power / dip_power\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4), tight_layout=True)\n",
    "ax1.plot(wl_range, coup_eff, \"-k\")\n",
    "ax1.set_xlabel(\"Wavelength (um)\")\n",
    "ax1.set_ylabel(\"Coupling Efficiency\")\n",
    "ax1.set_ylim(0, 1)\n",
    "ax1.set_xlim(wl - bw / 2, wl + bw / 2)\n",
    "sim_data.plot_field(\"field_xy\", \"E\", \"abs^2\", z=0, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimization\n",
    "The objective function defined next is the device figure-of-merit (FOM) minus a fabrication penalty. In our optimization strategy, we included a penalty threshold parameter so that the fabrication penalty is included only after some initial iterations defined by `pen_thr`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure of Merit (FOM) calculation.\n",
    "def fom(sim_data: tda.JaxSimulationData) -> float:\n",
    "    \"\"\"Return the coupling efficiency.\"\"\"\n",
    "    mode_amps = sim_data.output_data[0].amps.sel(direction=\"-\", f=freq, mode_index=0)\n",
    "    mode_power = jnp.sum(jnp.abs(mode_amps) ** 2)\n",
    "    dip_power = 0\n",
    "    for m in range(1, 7):\n",
    "        field_mon = sim_data.output_data[m]\n",
    "        dip_power += jnp.abs(field_mon.flux)\n",
    "    return mode_power, dip_power\n",
    "\n",
    "def penalty(params, beta) -> float:\n",
    "    \"\"\"Penalize changes in structure after erosion and dilation to enforce larger feature sizes.\"\"\"\n",
    "    params_processed = pre_process(params, beta=beta)\n",
    "    ed_penalty = ErosionDilationPenalty(length_scale=min_feature, pixel_size=grid_size)\n",
    "    return ed_penalty.evaluate(params_processed)\n",
    "\n",
    "# Objective function to be passed to the optimization algorithm.\n",
    "def obj(param, beta: float = 1.0, step_num: int = None, verbose: bool = False) -> float:\n",
    "    sim = make_adjoint_sim(param, beta)\n",
    "    task_name = \"inv_des\"\n",
    "    if step_num:\n",
    "        task_name += f\"_step_{step_num}\"\n",
    "    sim_data = run(sim, task_name=task_name, verbose=verbose)\n",
    "    mode_power, dip_power = fom(sim_data)\n",
    "    fom_val = mode_power / dip_power\n",
    "    penalty_weight = 0.1\n",
    "    penalty_val = penalty(param, beta)\n",
    "    J = fom_val - penalty_weight * penalty_val\n",
    "    return J, [sim_data, mode_power, dip_power, penalty_val]\n",
    "\n",
    "# Function to calculate the objective function value and its\n",
    "# gradient with respect to the design parameters.\n",
    "obj_grad = value_and_grad(obj, has_aux=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following cell, we define some functions to save the optimization progress and load a previous optimization from the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# where to store history\n",
    "history_fname = \"./misc/qe_light_coupler.pkl\"\n",
    "\n",
    "def save_history(history_dict: dict) -> None:\n",
    "    \"\"\"Convenience function to save the history to file.\"\"\"\n",
    "    with open(history_fname, \"wb\") as file:\n",
    "        pickle.dump(history_dict, file)\n",
    "\n",
    "def load_history() -> dict:\n",
    "    \"\"\"Convenience method to load the history from file.\"\"\"\n",
    "    with open(history_fname, \"rb\") as file:\n",
    "        history_dict = pickle.load(file)\n",
    "    return history_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we will start a new optimization or load the parameters of a previous one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded optimization checkpoint from file.\n",
      "Found 100 iterations previously completed out of 100 total.\n",
      "Optimization completed, will return results.\n"
     ]
    }
   ],
   "source": [
    "# initialize adam optimizer with starting parameters\n",
    "optimizer = optax.adam(learning_rate=learning_rate)\n",
    "\n",
    "try:\n",
    "    history_dict = load_history()\n",
    "    opt_state = history_dict[\"opt_states\"][-1]\n",
    "    params = history_dict[\"params\"][-1]\n",
    "    opt_state = optimizer.init(params)    \n",
    "    num_iters_completed = len(history_dict[\"params\"])\n",
    "    print(\"Loaded optimization checkpoint from file.\")\n",
    "    print(\n",
    "        f\"Found {num_iters_completed} iterations previously completed out of {max_iter} total.\"\n",
    "    )\n",
    "    if num_iters_completed < max_iter:\n",
    "        print(f\"Will resume optimization.\")\n",
    "    else:\n",
    "        print(\"Optimization completed, will return results.\")\n",
    "\n",
    "except FileNotFoundError:\n",
    "    params = np.array(init_par)\n",
    "    opt_state = optimizer.init(params)\n",
    "    history_dict = dict(\n",
    "        values=[],\n",
    "        coupl_eff=[],\n",
    "        penalty=[],\n",
    "        params=[],\n",
    "        gradients=[],\n",
    "        opt_states=[opt_state],\n",
    "        data=[],\n",
    "        beta=[],\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the optimization loop, we will gradually increase the projection parameter `beta` to eliminate intermediary permittivity values. At each iteration, we record the design parameters and the optimization history to restore them as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "iter_done = len(history_dict[\"values\"])\n",
    "\n",
    "if iter_done < max_iter:\n",
    "    for i in range(iter_done, max_iter):\n",
    "        print(f\"Iteration = ({i + 1} / {max_iter})\")\n",
    "\n",
    "        # Compute gradient and current objective function value.\n",
    "        beta_i = i//iter_steps + beta_min\n",
    "        (value, data), gradient = obj_grad(params, beta=beta_i, step_num=(i + 1))\n",
    "        sim_data_i, mode_power_i, dip_power_i, penalty_val_i = data\n",
    "        # Outputs.\n",
    "        print(f\"\\tbeta = {beta_i}\")\n",
    "        print(f\"\\tJ = {value:.4e}\")\n",
    "        print(f\"\\tgrad_norm = {np.linalg.norm(gradient):.4e}\")\n",
    "        print(f\"\\tpenalty = {penalty_val_i:.3f}\")\n",
    "        print(f\"\\tmode power = {mode_power_i:.3f}\")\n",
    "        print(f\"\\tdip power = {dip_power_i:.3f}\")\n",
    "        print(f\"\\tcoupling efficiency = {mode_power_i/dip_power_i:.3f}\")\n",
    "\n",
    "        # Compute and apply updates to the optimizer based on gradient (-1 sign to maximize obj_fn).\n",
    "        updates, opt_state = optimizer.update(-gradient, opt_state, params)\n",
    "        params = optax.apply_updates(params, updates)\n",
    "\n",
    "        # Cap parameters between 0 and 1.\n",
    "        params = jnp.minimum(params, 1.0)\n",
    "        params = jnp.maximum(params, 0.0)\n",
    "\n",
    "        # Save history.\n",
    "        history_dict[\"values\"].append(value)\n",
    "        history_dict[\"coupl_eff\"].append(mode_power_i / dip_power_i)\n",
    "        history_dict[\"penalty\"].append(penalty_val_i)\n",
    "        history_dict[\"params\"].append(params)\n",
    "        history_dict[\"beta\"].append(beta_i)\n",
    "        history_dict[\"gradients\"].append(gradient)\n",
    "        history_dict[\"opt_states\"].append(opt_state)\n",
    "        #history_dict[\"data\"].append(sim_data_i)  # Uncomment to store data, can create large files.\n",
    "        save_history(history_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ultimately, we get all the information to assess the optimization results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_vals = np.array(history_dict[\"values\"])\n",
    "ce_vals = np.array(history_dict[\"coupl_eff\"])\n",
    "pen_vals = np.array(history_dict[\"penalty\"])\n",
    "final_par_density = history_dict[\"params\"][-1]\n",
    "final_beta = history_dict[\"beta\"][-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results\n",
    "The following figure shows how coupling efficiency and the fabrication penalty have evolved along the optimization process. The coupling efficiency quickly rises above 0.8, and along the binarization process, we can observe two large drops before a more stable final optimization stage. The formation of resonant modes sensitive to the small structural changes can potentially explain this behavior. The discontinuities in the fabrication penalty curve are caused by the increments in the projection parameter beta at each 5 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(7, 5))\n",
    "ax.plot(ce_vals, \"ko\", label=\"C. Efficiency\")\n",
    "ax.plot(pen_vals, \"bs\", label=\"Fab. Penalty\")\n",
    "ax.set_xlabel(\"iterations\")\n",
    "ax.set_ylabel(\"objective function\")\n",
    "ax.set_title(f\"Final Coupling Efficiency: {ce_vals[-1]:.2f}\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, the final quantum emitter light extractor resembles a nanocavity, even though we have considered only the coupling efficiency into the output waveguide in the optimization. We have DBR mirrors on both sides of the dipole. However, on the left side, the mirror has only a few periods and partially reflects the radiation, which couples to the output waveguide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aNWvQ0dGB2267DYqi4Nprr83b1w9/+EOcdtppeN/73odzzz0Xr732Gm655RZ88pOfHJQSnGEYhnHBEGa+YxiG8YzNmzdrF1xwgdbZ2amFQiFt2rRp2uc+9zktnU5rmmam5C6WtlvTRAru2bNna4FAQBs7dqz2mc98Rtu7d6/x/YYNG7RPfOIT2vTp07VwOKy1tbVpJ5xwgvaf//zHWOaRRx7RTj/9dG3ChAlaMBjUJkyYoH3kIx/R3nzzzbJtt7teJpPRvv/972tz587VQqGQ1traqh122GHatddeq/X29uYt+5vf/EZbsGCBsdxxxx2nLV++3Ph+x44d2pIlS7SmpiYNgJGeuzAlN3HPPfcY22tra9POO+88bevWrXnLXHjhhVo0Gh10fNdcc41m53Xz9NNPa0ceeaTW0NCgTZgwQfva176m/fvf/x7Unv7+fu2jH/2o1tLSogEom577uOOOK5le+5prrqnYpkooiqJdf/312uTJk7VgMKjNnTtX+8Mf/jBoOUpXvnHjxrzP9+zZo1188cVae3u7FolEtOOOO67kNXrfffdp8+fP10KhkDZx4kTtqquu0jKZTNXHwDAMw2iapGkVfEsYhmEYhmEYhmH2YTimiGEYhmEYhmGYUQ2LIoZhGIZhGIZhRjUsihiGYRiGYRiGGdWwKGIYhmEYhmEYZlTDoohhGIZhGIZhmFENiyKGYRiGYRiGYUY1XLy1Aqqq4p133kFTUxMkSRrq5jAMwzAMwzAjCE3T0NfXhwkTJkCWndsjUqkUMplMDVpmn2AwiHA4PKRtqDUsiirwzjvv4Kc//Sn8/tqeKqp8rmka4vE40ul0TfdXDL/fn3ec6XS6aDX5WiJJEkKhkPH/XC6HXC5X1zYA4uanB5eqqkPyMOL+MOH+EHB/mHB/mHB/CLg/TIZLf4TDYTQ2NkKSJGiaBlVV69oGwBxfEYqi1L0NsizjO9/5DrZs2YKJEyc6WjeVSqGhoaFGLbPPuHHjsHHjxn1aGLEoqkBTUxP8fj8ikYgrdW8XRVGMG3XmzJlDcgOoqoru7m4A4qHe3Nxc9zYAQG9vr/ESaW9vr+l5L0Umk0Fvby8AoLm5GcFgsO5t4P4w4f4w4f4QcH+YcH+YcH8Ihkt/9PT0oL+/HwAQCASGxOPGOr7y+XyDRFI9IHHe1NTkeN2hthARO3bsQCaTYVE0mqEbWJZlBAKBmuwjl8tBVVVIkgRJkjAwMICmpqa6PkjpARoIBBAKhZBKpaAoiqsbuBr6+vqgqioaGxuRTqcxMDBQ9xdbJpNBT0+PIUyTySQikQj3B/cH9we4P6xwfwi4P0y4P0z6+vqgKApkWYaqqlAUBaFQqK79YR1fAeLcFFrRao3VWliNKKQx4lCgaVrdLY1DASdaGGJyuRwymYxh9vf5fPD7/ejq6qrb7AA9QLPZLDo6OtDe3o5YLIZ4PI6+vr66tAEQD9B4PI5YLIb29nZ0dHQgm82iu7u7bib3TCaDrq4uBAIBdHR0oKOjA4FAgPuD+4P7A9wfVrg/BNwfJtwfJtQfjY2NCAQCCAaD0DQN6XS6bv1hHV+Fw2GEw2FIkoRMJlM3t0pVVT0LhyBRNFQ/TnjiiSdw6qmnYsKECZAkCffff7/xXTabxeWXX4558+YhGo1iwoQJuOCCC/DOO+94cp6qgUXREFIoiCRJgizLaGlpqduDtPABSrNZTU1NdX2QWl9oNJsVDAbr+mKzvtBodlGWZbS3t3N/gPuD+4P7g+D+EHB/mHB/mFj7IxqNAhBua/UURoXjK+oPGmvVQxiRINI0ra6WqeFAIpHAIYccgltvvXXQd8lkEitWrMA3v/lNrFixAn/729+wdu1anHbaaUPQ0nxGVy8NI4rdsPSQoAdpd3c3urq68h5uXlLqAUrQyyUej+f932uKvdAIerF1dXWhu7u7Zq4QxV5oBPeHCfeHCfeHgPvDhPvDhPtDMNr7wyoESRhkMhmk0+maudIVG18RJIzS6bTRtloIFqsgIjFYLUPpPueUk08+GSeffHLR75qbm7F8+fK8z2655RYcfvjhePvtt7H//vvXo4lFYUvREFDuhiVqPcNU6QFK1HqGqdwLjaj1jF+5FxrB/WHC/WHC/SHg/jDh/jDh/hBwf5j4/f6aWozsjq9qaTEqFEReia7h4D4Xj8fzfrxyDezt7YUkSWhpafFke25hUWQDn8/nWYCZnRuWqNWD1O4DlKjVg9TOA5So1YvNzguN4P4w4f4w4f4QcH+YcH+YcH8IuD9MaiWMnI6vaiGMaiWIgOEhiiZNmoTm5mbjZ9myZVUfVyqVwuWXX46PfOQjiMViVW+vGlgU2SSbzVZ94zq5YQmvH6ROH6CE1w9SJw9QwusXm5MXGsH9YcL9YcL9IeD+MOH+MOH+EHB/mHgtjNyOr7wURrUURMDwEEVbtmxBb2+v8XPllVdWdUzZbBbnnHMONE3Dz372My9OU1WwKLIB3azV3LhubljCqwep2wco4dWD1M0DlPDqxebmhUZwf5hwf5hwfwi4P0y4P0y4PwTcHyZeCaNqx1deCKNaCyJgeIiiWCyW92MtluwUEkSbN2/G8uXLh9xKBLAosgVlDnF741ZzwxLVPkirfYAS1T5Iq3mAEtW+2Kp5oRHcHybcHybcHwLuDxPuDxPuDwH3h0m1wsir8VU1wqgegmhfgwTRunXr8J///Aft7e1D3SQALIpsI8uyqxvXixvW2gY3D1KvHqCE2wepFw9Qwu2LzYsXGsH9YcL9YcL9IeD+MOH+MOH+EHB/mLgVRl6Pr9wIo3oKouFgKbJLf38/Vq5ciZUrVwIANm7ciJUrV+Ltt99GNpvFWWedhRdffBF33XUXFEXBjh07sGPHjrrV8yoFiyIHOL1xvbxhCacPUq8foITTB6mXD1DC6YvNyxcawf1hwv1hwv0h4P4w4f4w4f4QcH+YDJfxlRNhVG8L0UgSRS+++CIWLFiABQsWAACWLl2KBQsW4Oqrr8a2bdvw97//HVu3bsX8+fMxfvx44+eZZ56pxamzDYsih9i9cWtxwxJ2H6S1eoASdh+ktXiAEnZfbLV4oRHcHybcHybcHwLuDxPuDxPuDwH3h8lwGV/ZEUZD4TI3kkTR8ccfD03TBv3ceeedmDJlStHvNE3D8ccfX5uTZxMWRS6odOPW8oYlKj1Ia/0AJSo9SGv5ACUqvdhq+UIjuD9MuD9MuD8E3B8m3B8m3B8C7g+T4TK+KieMhiqGaCSJopEKiyKXlLpx63HDEqUepPV6gBKlHqT1eIASpV5s9XihEdwfJtwfJtwfAu4PE+4PE+4PAfeHyXAZXxUTRkOZVIFFUe3hFBlVQDdDJpNBOp1GIBCo2w1L0IO0u7sbXV1daG9vRzwer9sDlKCHZDweNz6r1wOUoBdbV1cXuru7EYvF0N3dXZcXGsH9YcL9YcL9IeD+MOH+MOH+EHB/mAyX8VUoFEI6nTZEajab5Sxz+zDco1VCN0U6nUY6na7rDUvQg7SrqwtdXV0AgM7Ozro9QInCB2k9H6AEvdh2795dtxm+Qrg/TLg/TLg/BNwfJtwfJtwfAu4Pk+EyvgqFQkilUkin0wCAUCg0JIJoNFlshgoWRQzDMAzDMAwzjGFRVHs4pqhKyMeVZhMAVFWZ2Q3kc5zL5QwTezWVst1i9Tn2olK2G8gHnGb8crlcVZXL3cD9YcL9YcL9IeD+MOH+MOH+EHB/mAyX8ZXVQiTLsqsCr17AMUW1h0VRFRQG/VVbmdkNhUGYoVCoqkrZbikMwqy2UrYbCoNiQ6FQVZXL3cD9YcL9YcL9IeD+MOH+MOH+EHB/mAyX8VVhUgU3BV69gkVR7WH3OZtompZ3EyqKYtywgUAAgLiBZFk2AgJTqRSCwaBt/1dVVaFpGrLZrO027d27F7lcDq2trQBgPDRjsRj27t2LXbt2obW11WhjLUgkEujv70djYyNCoZDRhlAohEgkgp6eHuRyOUSj0Zq1IZvNYu/evfD7/YjFYnkPq+bm5rxzUaubm/vDhPvDhPtDwP1hwv1hwv0h2Ff7gxITOBExtRhfOUVVVWQyGUMQybJsHAO1gQSTz+eruD1N06pu02gSJ0OFpHnRU/sw8Xgc3/ve99DY2GjcfKqqIpfLQZIk+P3+ohepnWWKraNpGlpaWioG8WmaNuiGdbNMteRyOWSzWQQCgZJttrNMNdDDS5IkBIPBkv1RaZlq4P4w4f4w4f4QcH+YcH+YcH8I9uX+yOVy6OnpgSRJttpcq/GVEzRNQy6Xg6Zp8Pv9Jfuj0jKFbf7GN76B3t5exGIxR+2Jx+Nobm6uqSivBIl2N+0fSbClyAY9PT2YPHkywuGwMaPU0NBQ8QK1zj7ZuZiz2SzS6TQ+8IEPoLm52evDYBiGYRiGqRu9vb345z//iVAoVNHCVcvxlV0KLXbl2uxk2VQq5Un7hlIUjQZYFNlAVVVjRqS3t9fw8600MxAMBg1f4Hg8bmsdRVHQ3NyMtrY2z9rPMAzDMAwzFPj9fgQCgbJpxTOZTM3HV5WgmC5VVTFmzBhbadDHjBmD7u5u9Pb2lq0l5UX80VC6z40Wtz1OtGCTXC7nqm5BqUrZDMMwDMMwo53CJBdDMb4qTHJhty4U1ZWqRzIMTrRQe0aUKHriiSdw6qmnYsKECZAkCffff3/FdR5//HEceuihCIVCmDFjBu68807H+5UkCb29va4LubEwYhiGYRiGycetICK8GF+5FUREvYQRi6LaM6JEUSKRwCGHHIJbb73V1vIbN27EkiVLcMIJJ2DlypW47LLL8MlPfhL//ve/He03FArB5/NVZZ5lYcQwDMMwDCOoVhAR1YyvqhVERD2EEYui2jOiYopOPvlknHzyybaXv/322zF16lTccMMNAIA5c+bgqaeewk033YSTTjrJ9nZUVUUsFqvaX5Vu3K6uLnR3d3viA8swDMMwDDOS8EoQEW7GV14JIoKEUXd3N7q6ujzZJlNf9ukR+bPPPovFixfnfXbSSSfh2WefLblOOp1GPB7P+6GKyl7AFiOGYRiGYUYrXgsiwsn4ymtBRNTSYsSWotqzT4uiHTt2YOzYsXmfjR07FvF4HAMDA0XXWbZsGZqbm42fSZMmed4uFkYMwzAMw4w2aiWICDvjq1oJIqJWwohFUe3Zp0WRG6688kr09vYaP1u2bKnJflgYMQzDMAwzWqi1ICLKja9qLYiIQmHkZUpuFkW1Y0TFFDll3Lhx2LlzZ95nO3fuRCwWQ0NDQ9F1QqEQQqFQPZo3yAc2FouNmgJZDMMwDMOMDrLZbFVZfJ1SLMYIQF0EEWGNMert7a16e0MpTlgU7QMcddRR+Oc//5n32fLly3HUUUcNUYsGY71x9+zZYxSJZRiGYRiGGelkMhmkUinbhVm9wjq+6urqAiBqTtYzAQIJox07dlS9LRZFtWdEuc/19/dj5cqVWLlyJQCRcnvlypV4++23AQjXtwsuuMBY/tOf/jQ2bNiAr33ta3jjjTdw22234c9//jO+/OUvD0XzSxIMBtHe3o5cLsdudAzDMAzD7DMoigJN04Yk4y6Nr7LZLLLZLNrb2+ueEU6WZcRisbruk3HHiBJFL774IhYsWIAFCxYAAJYuXYoFCxbg6quvBgBs377dEEgAMHXqVDz44INYvnw5DjnkENxwww341a9+5Sgddz1QVRXxeHyom8EwDMMwDFMTEolE3fdZOL6Kx+NDMvmcSqWq3gbHFNWeEeWrdfzxx5eNubnzzjuLrvPyyy/XsFXVYQ36a2trQzabHeomMQzDMAzDeEI4HEYwGDTESVNTU132ax1fdXZ2AsCQ1Ins6+vzRBCy+1ztGVGiaF+jMAsKIMzMDMMwDMMw+wKSJCEajcLv99dNGJXKMue0wGu19PX1IR6PIxqNVr0tFkW1h0XREFHshvWyyBfDMAzDMMxwgYRQrYVRubTbxbLS1UoYkSCKxWLw+XxVb49FUe0ZUTFF+wr1ypPPMAzDMAwzXGhqakIsFkM8HkdfX5/n27czvqpHnUirIPJK/HFMUe1hUWQDL9NksyBiGIZhGGa0Uith5GR8VUthVAtBxNQHFkU28Pv9SCaTVW+HBRHDMAzDMKMdr4WRm/FVLYRRLQURW4pqD8cU2SCXyyGRSCAUCrm+yFkQMQzDMAzDCLyKMapmfOVljFGtLUQcU1R7WBTZIJfLIRqNur5xWRAxDMMwDMPkU60w8mJ85YUwqofLHIui2sOiyCaRSAShUMjxjcuCiGEYhmEYpjhuhZGX46tqhFE9Y4hGizgZKlgUOcDpjcuCiGEYhmEYpjzDYXzlRhjVWxCxpai2cKIFh9gNDmRBxDAMwzAMY4/hML5yknyBs8zte7ClyAWVZjRYEDEMwzAMwzhjOIyv7FiMhkIQsaWo9rAockmpG5cFEcMwDMMwjDuGw/iqnDAaKgsRi6Law6KoCgpv3Gg0yoKIYRiGYRimCobD+KqYMEokEkPmMseiqPawKKoS640bj8chSRILIoZhGIZhmCoYDuMrqzDavn07AAxZDBGLotrDiRY8IBqNGn+HQiEWRAzDMAzDMFUyHMZXwWAQoVCoaJvqCYmiofoZDbAoqhLycZUkCeFwGKlUqmzWFIZhGIZhGKY8w2V81dfXh1QqhXA4DEmSKmalY0Yu7D5XBcWC/igAD3BemZlhGIZhGGa0M1zGV4VJFTKZjKsCr17A7nO1h0WRS0plQXFbmZlhGIZhGGa0M1zGV8WyzLkp8OoVLIpqD4siF1RKC8nCiGEYhmEYxhnDZXxVLu32UAkjFkW1h0WRQ+zmyWdhxDAMwzAMY4/hMr6yU4doKIQRi6Law4kWHOC0cFhTUxNisRji8TgnX2AYhmEYhinCcBlfOSnMSsIom83WJfnCSMo+98QTT+DUU0/FhAkTIEkS7r///rzvNU3D1VdfjfHjx6OhoQGLFy/GunXrPDxb7mBRZBO3lZRZGDEMwzAMwxRnuIyvnAgiot7CaKSQSCRwyCGH4NZbby36/Q9+8AP85Cc/we23347nnnsO0WgUJ510ElKpVJ1bmg+7z9mEzLRuCoexKx3DMAzDMEw+bgUR4dX4yo0gIurlSjeS3OdOPvlknHzyyUW/0zQNN998M6666iqcfvrpAIDf/e53GDt2LO6//36ce+65VbfXLWwpskEwGISiKFVVUmaLEcMwDMMwjKBaQURUO76qRhAR9bAYDQf3uXg8nveTTqcdH8fGjRuxY8cOLF682PisubkZRxxxBJ599lnPzpcbWBTZQJZlNDc3V11JmYURwzAMwzCjHa8EEeF2fOWFICJqLYyGgyiaNGkSmpubjZ9ly5Y5Po4dO3YAAMaOHZv3+dixY43vhgp2n7NBOp2G3+/NqWJXOoZhGIZhRiteCyLC6fjKS0FE1NKVbji4z23ZsgWxWMz4PBQKDUl7agWLIhtomubp9lgYMQzDMAwz2qiVICLsjq9qIYiIYsLIC4aDKIrFYnmiyA3jxo0DAOzcuRPjx483Pt+5cyfmz59f1barhd3nhgh2pWMYhmEYZrRQa0FEVBpf1VIQEZyVrjRTp07FuHHj8MgjjxifxeNxPPfcczjqqKOGsGVsKRpSCmc09jUzJMMwDMMwjKZpdRFERCmLUT0EEWG1GGWz2aq3NxwsRXbp7+/HW2+9Zfx/48aNWLlyJdra2rD//vvjsssuw3e+8x3MnDkTU6dOxTe/+U1MmDABZ5xxhsctdwaLoiHGeuNGIhHPXfUYhmEYhmGGClVVsXfvXqiqWhdBRBQKI/q7HoKIIGG0c+dOT7Y3VKLIKS+++CJOOOEE4/9Lly4FAFx44YW488478bWvfQ2JRAKXXnopenp6cMwxx+Chhx5COBweqiYDYFE0LKCbs6enp24PC4ZhGIZhmFqTTqeRzWYxduzYuo9xCoVRPQUREQwG0dzcXPV2RpKl6Pjjjy87yS9JEq677jpcd9111TbNU1gUMQzDMAzDMMwwZiSJopEKi6JhAPm4NjY2svscwzAMwzD7DKFQCJqmoaurq67uc0B+DBEwNFl/M5kMent767Y/xj0sioYY6w0bCoWQSqWGukkMwzAMwzCeIMsyWltbEY/H6yqMSiVVqKcwymQy6Orqgs/nq3pbbCmqPSyKhpDCGzaTyQx1kxiGYRiGYTxFkiS0t7eju7u7LsKolCCqZ51IEkSBQAANDQ1Vb49FUe1hUTRE1DMtJMMwDMMwzFAiy3JdhFGl8VU9hJFVELW3t3viBcSiqPawKLKB1xcDCyKGYRiGYUYbtRZGdsdXtRRGhYJIlmVPtsuiqPawKLJBKBRCLpfzZFssiBiGYRiGGa3UShg5HV/VQhjVShABLIrqgXe9tQ+jqip6e3urjvlhQcQwDMMwzGiHhFEgEEBXV9eQja+ampoQi8UQj8fR19dXVRtqKYiY+sA9ZoNMJgOfz1fVjcuCiGEYhmEYRuCVMKp2fOWFMKqHICJL0VD9jAZYFNkkFou5vnFZEDEMwzAMw+RTrTDyanxVjTCql4WIRVHtYVFkE7c3LgsihmEYhmGY4gyX8ZUbYVRPlzkWRbWHRZEDnN64LIgYhmEYhmHKM1zGV06EUb1jiFgU1R4WRQ6xe+OyIGIYhmEYhrHHcBlf2RFGQ5FUgUVR7WFR5IJKNy4LIoZhGIZhGGcMl/FVOWHEWeb2XbgnXVLqxmVBxDAMwzAM447hMr4qJoyGUhCxpaj2cPHWKigsQBYKhZBKpVgQMQzDMAzDuGS4jK+sBV4zmQzS6fSQWYiGUpyMFlHElqIqoRtX0zSkUimEw2EWRAzDMAzDMFUwXMZXTU1NCIfDSKVS0DRtyFzm2FJUe9hS5AGJRML4O51OI5PJIBgMDmGLGIZhGIZhRjbDYXxFFiJrm4ZCnLGlqPawpahKrD6u48ePr7oyM8MwDMMwzGhnOIyvrDFE48ePd13g1QvYUlR7WBRVQWHQX7WVmRmGYRiGYUY7w2F8VSypgpsCr8zIYcSJoltvvRVTpkxBOBzGEUccgeeff77ksnfeeecgpRsOhz1pR6ksKCyMGIZhGIZh3DEcxlflsswNpTBiK1FtGVGi6J577sHSpUtxzTXXYMWKFTjkkENw0kknYdeuXSXXicVi2L59u/GzefPmqttRKS0kCyOGYRiGYRhnDIfxlZ2020MhjNh9rvaMKFF044034pJLLsFFF12EAw88ELfffjsikQh+85vflFxHkiSMGzfO+Bk7dmxVbbCbJ5+FEcMwDMMwjD2Gw/jKSR2iegsjFkW1Z8SIokwmg5deegmLFy82PpNlGYsXL8azzz5bcr3+/n5MnjwZkyZNwumnn47Vq1eX3U86nUY8Hs/7IZwWDmNhxDAMwzAMU57hML5yU5i1nsKIRVHtGTEpubu6uqAoyiBLz9ixY/HGG28UXeeAAw7Ab37zGxx88MHo7e3Fj370Ixx99NFYvXo1Jk6cWHSdZcuW4dprr8377FOf+hSSySTS6bTjwmGFBcg6Ojo4XTdjUK8HjaZpNds2pUxtaWkBAORyuZrti6kNdB2GQiEAQDabhaIoNdtfQ0MD+vv7AcB2vY9UKoVIJAKgdtez3y9eiQMDA3n/Z0ozWgZLdqjlc3ZfxqkgIrwcX7kRRIS1wKv1/14zlOJktNznI8ZS5IajjjoKF1xwAebPn4/jjjsOf/vb39DZ2Ymf//znJde58sor0dvba/xs2bIFfr8fiUTCdSVlthgxDMMwDMPk41YQEV6Mr6oRRARnpds3GDHTYB0dHfD5fNi5c2fe5zt37sS4ceNsbSMQCGDBggV46623Si4TCoWM2VLC7/cjGo1Wpf7ZYsQMBdFoFABw7733AgDOPvtsz/dBs+ljxowBALzzzjue74OpLfQsmjp1KgCgs7MTb7/9NgBg69atALyxAPp8PgDAl770JWPwUikjKO33xz/+cdX7L4ckSfjYxz5W033sS/zpT38CALS1tQEA9uzZM5TNYUYg1QoioprxlReCiKi1xYgtRbVnxFiKgsEgDjvsMDzyyCPGZ6qq4pFHHsFRRx1laxuKomDVqlUYP368o33ncjnDbaMa2GLEMAzDMMxoxytBRLgZX3kpiIhaWow4pqj2jBhLEQAsXboUF154IRYuXIjDDz8cN998MxKJBC666CIAwAUXXID99tsPy5YtAwBcd911OPLIIzFjxgz09PTghz/8ITZv3oxPfvKTjvbrZYwEW4yYfQ2yrF588cUAgO9973sARFzKcMD6MKe/VVUdquYMS2bPng0A+PznPw8AOOKII/D4448DAO6++24AwKpVqwCYMWRuoDilVatW4dVXXwUALFq0CEDlmUi/3288K9PptOs2lELTNGOGt6urCwBseyGMFpLJpPE39V9PT88QtYYZqXgtiAgn46taCCKiVhYjthTVnhElij784Q9j9+7duPrqq7Fjxw7Mnz8fDz30kJF84e233867sPfu3YtLLrkEO3bsQGtrKw477DA888wzOPDAA4fqEACwMGIYhmEYZvRRK0FE2Blf1VIQEYXCiFyHq4FFUe0ZUaIIEDOZNJtZCM1sEjfddBNuuummOrTKOcVuXIZxAj2kylk/KBsSzfBal/H6RfC1r30NAPDkk0/m/QZQ00xmhVCMyowZMwAAEyZMML6j+CeKK3zzzTcdbZvOGW2HrMgj1fJE18773/9+AMAZZ5wBQMRwHnTQQQCAOXPmAAB+/etfAzCfszt27HC934cffhjt7e0AgK9+9asAgJkzZwLAoJhOOuczZszAIYccAsC0UqRSKddtKMb69esBwCjz8MEPftDT7Y901q1bZ/z9z3/+E8DIvfaZ+lNrQUSUE0b1EESEVRgVPtfcwKKo9oyYmKJ9kUIf2Gw2yy8YhmEYhmH2GTRNQyKRqIsgIorFGNVTEBEUY1SN2zFTP0acpWhfwzqjsWfPHnajY4pCpner6yfFO9CDnWbN16xZYyxDgZ7PP/88AODEE080vitVq8stjY2NAIAbbrgBAPIyeVnbVIlYLAZA1BkjyDJDsSQURGu9X+hFS5ahj3/84wCA4447zliGzuPvf/97AMAXvvAF47tSL63Ozk7j7/nz5+e1kc7vSy+9ZCzT3d1d4Qirg/p7v/32A2D2Y0NDAwBzRo9q7WzYsMHI2llYR4WWJUt1c3PzoO+oYDZZjO644w4A5jncuHEjAGcxZNlsFn/84x8BAL29vQBgxHrSOaZ+pONdvHixYXH885//DMCM/aHPaVJp7969AIDNmzfnFeCuBNW8e/TRRwEAp59+el4bRit0v1HGOQB45ZVXHG/HOttMz4vW1lYA5r1E1wPgvRWKrMi0b8C87+l+YbwnlUohk8mgpaWlLoKIsI6vdu/eDUC8M+oliIimpiZP4iDZUlR7WBQNA2RZRiwW89wVhGEYhmEYZjhAJSLqCY2vaAIlFosNySRHpdIDdmBRVHtYFA0DMpkMuru74ff7R/2MJFMcmj23xsi9613vAmA+rChu6MorrzSW+c1vfgMA+N3vfgcg3+pxzjnnAIDniUeorZ/73OeMzy6//HIAQH9/f8n1aMaY4ji+/vWvG9/Ry5RebGSNobgUAJg+fToA02JSjhNOOAEAMG/ePOOz//3vf3nL0HYuvfRS47OrrroKgPmCIwvFZZddZixz6623AqhNdftwOGxYbCjr5plnngnAtBzS9bBr1y4AwK9+9Sv86le/AgCj9hC1jX7TbDlNzAQCgUH7JssU9QtZ8n75y18CAF5++WWjf+xAlqUHHngAAPDCCy8AAD796U8DAD7zmc8AMK1Y0WjUOFa6RsiSQL9pNnbFihVG2yj2xU4dHTp+qs1ENbe8tqqONB5++GEAwPe//31X61M8BcWpAcBhhx0GADj00EMBmPF91vuQLLDVzrLTs2HBggUAgFmzZhnfbdiwAQCwadMmAKZV24mFkSmPz+eDJEno7u6uu5WGxlf0TOvu7q57citVVT25nlgU1R4egQ8xVh/XtrY2dp9jGIZhGGafIRgMorW1FdlsFt3d3XWLnbaOrzo6OtDR0VH3OpGqqqK7u9uTZENcp6j2sKVoCCkM+svlcqPmwmPsQdfD9ddfDwA49thjSy5L1pRPfOITxmd33XUXAHOW/LrrrjO+o+xwt912G4D8GJ5qoFlAsmQB5kxtuTgEst5897vfBVC8RoxXdWPGjBmTt08AeO655wCY7f/ABz4AIP+cFc5wUoyS1ZpElhMva+nQfo888kijPccccwyA0jN4dK6uuOIKY/2bb74ZAAz/erIUkWWFZutpFr9cW84++2wAwMKFCwEAP//5zw2L5Pbt2x0dH2BaZf79738DELWSAOB973vfoGXpmOn802+aDX73u98NAGhrazMGIxTDZAeqyURWJmv/jiZo4EjxgU6tn9RPZKGhDIZAaQs1xagBwPnnnw8AeOaZZxztlyAr47e+9S0AwEc+8hEAxdMj0/V3zTXXAAB++9vfGt8Nl5prIxkSJl1dXXWxGJVKqlDPcigkiLLZbF68plvYUlR7WBQNEUORBYUZeVBAMAW724GSEgCDA5Wtg5rHHnsMgOnaRi5WXqWHt7ocWd3cSkGubORGV0vINW7u3LnGZ5MnTwYAo+4ZDfDt3JvWF0Yt7uXx48cDEIkhKomhQvx+P84991wAwsUNAP7yl7/kLUNptpcsWQKgvCgqZOrUqQCE+CJh/pOf/AQAHLnTEZs3bwZgujVVw+zZs3HkkUcCMJME2BnYk5shiUVaZzgODKwFVSlJAQ3AIpFIVdsm8ezW9YeC6imJhh1XXbqeANMt1a0oInF8yimnAChfK4YSe/zgBz8AYLoMAub1wFRHMBisizAqN76qV51IqyDq6OgwyjcwwxseiQ8BLIgYhmEYhhltkDCqlSudnfFVsXTdXlIoiLwSXew+V3vYUmSDYDDo2Y3LgohxAhWrdHKdWK/VchkNafab3OgefPBBAMCFF17ouJ3FsM4u20l3S4HOlIzBi2J3paAHvNV9jhJUUNpyJ7Ps5G4FeF9QFADe+973AhCB6m5eTlOmTAFgzobTjDm5ltE5p4QLqqo6fja1tLQYadC3bdsGwEz04cT9iAYoXpxHn89nWE7pvNmxFNGsLrWB7iknVelpHdoGuVNSkg767XawQefJWiSZXA8XLVoEADj66KMBAPvvv7+xjJP9VZtenpKCHH/88a7Wp2LCdC3aeQ9bj48s1E5cl8hSPXv2bOMzthR5S60sRk7GV7WyGNVKEAHsPlcPeERuA1mWEY/HqxZGLIgYhmEYhhnteG0xcjO+8tpiVEtBBLClqB6wpcgG6XQaiqJUNaPBgohxA8WCOMGa5cbODD0lYVi9ejUA5L0YqnmoU/wKYFqBynH//fcDMOMPKHamllCsDgBcfPHFAJxZA6jY5xe/+EXjs1qk4qYZb7d1Puh5Q+uTFY7iUajNdO24PYZJkyYBAN7znvcAMK0YTor3kpXGCx98TdMGpR93ux27kIX0rbfeAmAmryBrw4wZMwCY1pO2tjZXbSIrjjV5wX333Ze3zFlnnQXAjJMBzD6yAxUpdgsdm9uU5mTlo992Bq3Wdytd524GdFRaAMiPL2K8wyuLUTXjK68sRrUWRABbiuoBj8xtoGkampubXc9osCBiGIZhGIbJp1qLkRfjq2otRvUQRARbiWoLW4ps4vf7Xc1osCBi3EAPIcqe5ASrpcjJC4ayfu3du9f4jDKxOYFm+K1xNjt37qy4HmXOomKe1pTetXooW7frxEJEMTOnn346AHdZ1pywcuVKAMCWLVuMIqpuoKx79NImSxEde2H8jVvovFAKdrKo2bkeKQbHmlXNLZlMZpA1zA50fsiyZufaoOv3nnvuAQBce+21AAanJ6dt/vjHPwZgFuEFnJ13itOz3q+F1rW//vWvAPIzSn7nO98BYC/Oxq0Viyi09DiFzofb67Ea62BPT4/rdRlnuLUYeTm+cmsxqqcgYmoPj9Ad4HRGgwURwzAMwzBMeYbD+MqpxajegmgkxRQpioJvfvObmDp1KhoaGjB9+nR8+9vfrol7uZewpcghdmc0WBAx1UAWGjc1e6yWIicPIMpAVm3RUbIKUY0XwJnFigqMfvnLXzY+o6xpXlPs/JR7+NPs/BVXXAEAWLduXU3aVcgTTzwBQBSYpXpOTuKL6Jqg7HKFL3uKvaDf1T6vaDtUJJesElarRino+qNzTdeOmzZt2rQJr732muP1KAaGsj+WuyYKszhScV0qBloI3Wc33HADAOCCCy4wvnNiUaEaZlQLqBgUU/j73//e+Iximc4880wA5Y+NLItOsr9ZqdbS4wZrG6sJnH/99de9aA7jgOEwvrJrMRoKC9FIiin6/ve/j5/97Gf47W9/i7lz5+LFF1/ERRddhObm5rwY3OEGj9RdUGlGgwURwzAMwzCMM4bD+KqSxWioXOZGkqXomWeewemnn44lS5ZgypQpOOuss/C+970Pzz//fI3OjjewpcglpWY0WBAxbrFeKxRnQFnHnOA2tSlZp8rNPNuBZlj/9a9/uVqf4iKuueYa47Nf/vKXVbWpELKc7Nq1y/iMsobReSDrhvV8kjXgmWeeAVCbTHPFoBn/X//614b1ZcmSJQDM/ir10srlcli/fj0AMyNaYd0oqslElgGvoPo49NuOpajQqkVttWMZo/6gvnzggQfwl7/8xXZ76RweccQRAIAPf/jDFdchyxbFmZWyEBVCfWIdcLmxFNmJDaJ4JwD4f//v/wEATjrppLztFIPaQ4M+LzICusHte9RqNXeK9dnA1JfhML4qZTEayhii4WApstYfBIRXQLGagkcffTR+8Ytf4M0338SsWbPwyiuv4KmnnsKNN95Yl/a6hUVRFRTeuLFYDN3d3SyIGEfQw8bqIkYpjd0MUq0DK9p2ucE7LUMuQ04KHVoh8UCpuJ0U7CzGXXfdZfz985//HED1bl3E2rVrAQDf/va3jc/IRY1Sgp9//vkA8gP+qRjpxo0bPWmHU1atWoUvfOELAIANGzYAMAtc0uCW+pP6Y+vWrfjRj34EwCzQW3g9eJkG20o16bApjTu5KFpTJBdunwTU7t27AQB/+tOfAAA//OEPHSVsoGt/6tSpef8vB7nDkduo3WMlMWR1V3VSMJgGY04HZffeey8A89ovJ4roWpo2bRoAOHZFJFFCv93ev9RGO0Wgree/8PpzMqCs1o2YqY7hML4qFEbt7e2Ix+NDllRhOIiiwpT+11xzDb71rW8NWv6KK65APB7H7Nmz4fP5oCgKvvvd7+K8886rR3Ndw6KoSujG3b17N1uIGIZhGIZhPGA4jK9IGHV1dRlZRjs7O0dtlrktW7bk1S8rZiUCgD//+c+46667cPfdd2Pu3LlYuXIlLrvsMkyYMAEXXnhhvZrrGBZFDDOEhEIho4Do0qVLjc9PPPFE19u0Pqzp73KznuSWRO1w+8Kh2XprochqsLoVkQXDzYvIOnNM6ZHpofziiy8OWp4C5cnMHw6Hje/I/WsoM+hQ0c6rrroKAHD11VcDMM8NWQrpnGUymYoulX19fQDM48tkMlW99Gl/5CLmxrL23HPPAQAee+wxAMDBBx9sXJt0bZB1hixDP/nJTwCYrmxO++nQQw8FYCYhsAPtw6nbKq3n1qJKM7dO71eyttD9aofLL78cAPCxj33M0b7ISkcuN27cgQGz4C1ZAu1STaKHalzvGKYWDAdLUSwWs1XU+atf/SquuOIKowD9vHnzsHnzZixbtmxYiyI2Z1QJ+bjSjEYul3NVgIxhGIZhGIYRDIfxFcUQ5XI5w2XOTYFXLxhJiRaSyeSgCRufzzfsx8ZsKaqCYkF/bgqQMaOXJUuWGLPb1RTltGKNS6BtUvxJMShWw8nseDHI6uJVIdNAIGD87WZ2jKxj1uKZxxxzDADTklAOihWh38MVeslQ0VM3kFXppZdeAiBirij1txso2cCbb74JwLREOYEsYrSNrq4u49r+73//C8C0SlICjGoteHS/HHjggbbXobg/t4VO3Q6u3FpB6Bw5uV7OOeccAMCll15qfGYnvuftt98GYBYfdmsBX7RoEQDg2Wefrbis9Xy4eW5UEwfHeMdwGF8VS6rgpsCrVwwHS5FdTj31VHz3u9/F/vvvj7lz5+Lll1/GjTfeiE984hM1aqE38IjdJaWyoDgtQMYwDMMwDMMIhsP4qlSWOacFXr1kJFmKfvrTn+Kss87CZz/7WcyZMwdf+cpX8KlPfSovudFwhC1FLqiUFtJuATKG+etf/+r5Nq0z1kcffTSA4pYieshNnjwZgJmK2g7WWVRKXUtZ0KrF5/MBMNMiA/lWo0qQVYJSMX/jG98wvrNajeoFnWd6qZJlwRoDRC93ii+heA+ydtVr1pqux7lz5xoZ2MplJyuEUj//+Mc/BgDceeedrttCMR2U/vzuu+822nLHHXcAMC0HXpwfSZKM68zJ7C8FGrtNZe8ktqcYbo/dyTHSspQVEzAzGZaDLEWvvPIKAPeWIjsxDMVgS9HIYziMryql3bZb4NVrRpKlqKmpCTfffLNRjH2kwCN1h9jNk88WI4ZhGIZhGHsMh/GV3TpEQ2kxYmoHW4oc4LRwGFuMmKHAmiKzs7Oz5HIUn3HxxRfb3jbN3lstLh/60IcADC7qVg5rLSXK7kafUaaphx9+2Pb2rG2ibGxUU6iWExI0e2a1ptBxkMWLat2cdtppAIALLrgAADB79mwA4jlBs9JkabnhhhsAmBaRHTt2AKj97DXFFt14442G5YPiSMjCVThjSNfE3r178b3vfQ+AcJ2wbq8aKIPdvffea2RKfOONNwB4fz7cxOnQM91J4VUrVI8JAA444ADb67k9djo2NzXQPve5zxl/27EUkeVzz549ef8HnFl/yVroFLo2ndQpolgrzj5XX4bD+MppYdZ6W4xGkqVopMIjdJvkcjlXefLZYsQwDMMwDFMcp4KI8HJ85VQQEfW0GI2kmKKRCluKbCBJEnp7exEOh13NRrDFiKknVBsEADZv3lxyOZqppdos5aCXzdNPPw0A+MIXvmB89+qrr9puG1mnTj75ZOOzr3/96wCAmTNnAjCtLnYewlRXBwAWLlwIwMx8Vg+ottPf//5347O5c+cCMM8v3et2jofiwb7zne/k/f+mm24CYC9rnhf09PQYsVhkgfvMZz4DwKw1Q9fE6tWrAQA/+tGP8P/+3/8D4I2FiKC4qv7+fnR0dAAwLaBknfRqf4XxJPUYCDzyyCPG3yeddFLN90cWTCeWGqK1tdX4m65rOwPRrVu3Asi3JtupWUTb/t///me7jVYLGll7aDt07OV4+eWXAVSXzZGxj1tBRHgxvnIriIh6WYzYUlR7WBTZIBQKwefzVSVmWBgx9YIGIADw73//u+Ryhal5yc2rGGvWrAFgpualopl2Ideif/zjHwCAE044wdH6hdBs3HHHHWd8Vk8xRC8ISuZw2GGH1WT7n//85wGYKY3/8Ic/VFyXBn7kRmlN5EDnjUREuQEtiWtKe03FbMmFjZ5ftFwmk6mJex9tc+LEifjkJz8JwBxc//rXvwZgivVqZmk1TTMG0fTbzjOazqFbYfb444+7Ws9tSu5Cse4E6zpO9tvT0wOgfBHpYmzZsgVA/kSPE6hv7Igius5++ctfAnDmDsy4o1pBRFQzvqpWEBH1EEYsimoPj8ptoKoqYrFY1SKGXekYhmEYhhnteCWICDfjK68EEVFrVzp2n6s9bCmyQSaT8cyqwxYjplbQTLW1DkC5dL80+0ozo+QuZX34kRXpAx/4AADnFiLi9NNPB2AWYayW3/72twCAjRs3OlqPLFbFXH8Kg7LtbIdc5WoFvaTJ6uPz+UoGgNMsOAXDX3bZZQCA/fff31iHUlh///vfB2C6btl5edP1Rckg6s3kyZPxrne9CwDQ0tICQBQ/BoDbbrsNAHD99dcDcJ/mms4DFSa142JG59btAMhq2XWCW0sRXVPVDnKcWAXJJdapy951113naPlC6J6w846lZBDkosqTlrXDa0FEOBlfeS2IiKFK1814A4/GhwC2GDEMwzAMM9qolSAi7IyvaiWIiFpZjNhSVHvYUjREFJvRYBiicBa23AOJZjgpTsdu4gNKj/urX/0KADBr1iwAwJw5c4xlKI00+fa75ZhjjgFgpt92C83KL1++HIAIvq8EFacFgEcffRSAiE8BzOKSAPDFL34RAPDQQw8BKD8TTsUk7QRue0m5NlHx3E984hMAYBRfBcx2UgzWUUcdBQD47ne/C8AsturGCkTXZjAYNNJ4k2WArEsUU0IWHDexR9Fo1Ng+DaTIYnT55ZcDMBNf0P93797taB9dXV0AzDTZBx98cMV1qC1urwVrQL+TBA+0jNNBpZPEH4VY+81JH1LCEDtpwK3bXbFihYPWCazng+Lf7PTNAw88AAB4/vnnHe+TsUetBRFRzmJUa0FEFFqMnBTBLgXHFNUeFkU2eeKJJ/JqLHhFIBBAR0cH1q9fb2S4u/LKK/OyajH7Llw1nWEYhtnXyWaz6O3trbkgIkpNPNdDEBFWYeSV2/FoESdDBYuiISabzRp+p21tbVwwbhRCM8UUJwOYGb/ICkSZ4azFWLu7uwGYcT4UB+EUStt9/vnnA8h/6Hpl9qfYFSpcSjPHTqHsdZRtrBxHHnkkALOIK2BaT+gYp0+fbnz317/+FQBw/PHHAyg/Y+wmnbEb6Nqgvi3mCkLHQlagAw88sOJ2aTBA6dDJ4nbLLbcAcNbvRxxxBABhnXnve98LYHAhUzqO22+/HQDwwx/+0Lh+7SLLcskZf/r8Yx/7GADTonjFFVc42g9lWSRLpB1LER1rYXFbuxMe1me+k9TRhNNBEvWtmwkZt5M4lIrfSYwW4DxbHZBfSPmQQw6xvR5dJ3asz4wzMpkMUqkUQqFQXeOorcKIrMC5XK6ucT4kjKgAdzWwpaj2cEzRMCCbzWLPnj3w+/2j5sJjGIZhGGbfR1EUaJo2JImlgsEg2tvbkc1mkc1m0d7eXvfEB7IsGy7X1cAxRbWHLUXDAEmSPLlhmJHHpk2b8MEPfhAAsHbtWuNzmlmnmVkyvVtjewoLTVZLLdxDCaqXRPV8rNc71QOhh+68efMAmFYlwIz1+NGPfgTALChaDJqxP++88wAABxxwgPFd4YPd+n9a75577gEATJs2DUDx80sWPOonil3wCtrnT3/6UwDla9nQIIPitQqtNOWgrHaUqY5mM+++++6K6+6///4AgIsvvhiAyARXyhJA+6G6S/F4HN/73vcAVM7yZY2dqfRipmOnvu/t7cWVV14JwN71vWvXLgDAunXr8tpWbiBH35E1hCwVfX19Ffdn3QdgxmDVMlaNrC9uvBKstZicPHcKsz6Ww2odctNGijMDgPe85z221/P6ecoMJpFIGHGB9UJV1byaU/F4fEjEGRcDHhmwKBpiJElCR0cH/H4/uru7OXUjwzAMwzD7DOFwGMFg0BAn9RJG1qQK5Ho+FOVQ+vr6XJcJsMLuc7WHRdEQYhVENBNer1gFZniwaNEiI6lGuVnRkT6LSbP0FL9kfcAWHhPVbbFaR2g2nWorlTsPFEt01llnAXCenWvSpEkATMtLsVgtisugOjlkCQPsv/CLnQOyLtxwww0AzHiocv7oZFWg325eXvvttx8AM8YqEokY57oUZ599NgDgzDPPBGDv2UUWtWnTpmHChAkAnNXpsXtsZPU7++yzjUxyFC9VDrpO6XxT28gqVg6qWfWhD30IAHDnnXfaaqv1WqZBozV20Mn6dqDnjNXqYxe3cRFOMt5Z7zc35SqsE4tjx461vR5dv3QfuTk/THEkSUI0GoXf76+bMCqVZa7edSL7+voQj8c98SZgUVR7WBQNEYWCKJvNsiAahZAYHi3YEXc0MHXrzkfuMx0dHa7Wp5ekHTe0l156CQBw0EEHGZ8V3seFx1q4fVmWjWMlsUUprCnou9ZimNpEIu+5557Dww8/XHZZSpZhdVeyy6GHHmq4UlYSRdZrxknKakCkXl+4cCEA83qwc8899dRTAExRes0111TcLyXxoIQXv/vd7wBUHthbB9//+9//AACnnnpqxTbSuXBb586NaxolynCKE8FuFUVu2mjdh5N36owZMwAA48aNA+C+qC5TGhJCtRZG5dJuOynwWi0kiGKxmCcusSyKag8nWhgCigkihmEYhmGYfZmmpibEYjHE43HbcXdOsFOHyE6B12qxCiKvxB8nWqg9bCmygZPA5UqwIGKY2lLoTuYUmoG3kw6YZvm9cgFzYxGiNtBvp9YUK5TOm5JMlKMad72JEyfmFdUtBx0PZbBysk9ZlnHyyScDAN544w0AMBI8lINcWqkQMiU4KedGR1YJKn587LHHAgD++9//lt2XNf35fffdB8CepYgGck6tKZT0wkn8Kp2PZ555xtG+qJ/o3NiZkbe+E+vpLkyp5WfOnAmALUW1pFYWIyeFWWtpMaqFIALYUlQP2FJkA7/f75k/KAsihmEYhmFGM15bjJwIIqIWFqNaCSKmPrClyAa5XA6NjY1QFMX1zcuCiGFGBu+88w6A2qUo93r2m17khcUn3byQKa00JZkoRzXH0djYmFdksxzWJBuU/MHuuoAZS2RNIkHbq7RPii2iwspXXXUVgPKzplTw9aMf/WjeNkpZdKzn8a233gJg9mV7e3vJ/VDsTaWEGIXtpfTrdpI5kPXxpJNOytunXWgykY7DzmyzdWDq5hqz7sPJ7DbFyPFAtn54ZTFyI4gILy1GtRZEbCmqPSyKbJDL5dDf32/UVnEqjFgQMQzDMAzD5FOtMKpGEBFeCKN6WIhYFNUeFkU2SSQSUBTFsTBiQcQw9rAW6QTczxjTem5ja6677jrH+xwO/OMf/wAAHHXUUQCA448/3vE26NxbC6UWnofCDIJuzrPf73ecbXPdunVYtWoVAPMY7UDHRBkCjznmGAAomV3PCllsXn/9dQBmEeVyGfcoHTid/4997GMA7KXoXrlyJQAzc90XvvAFAMXjWrdt2wbATHNfDDrHH//4x43PaJvlrIHUp5dffjkAYMWKFRXbXgxKU7548WLb61TrwmS9Dp3EFdKyXsYQM/ZwK4y8EERENcKoXi5zLIpqD9/9DiAhZFcYsSBiGIZhGIYpj1Nh5KUgItwIo3rGELEoqj0sihxiVxixIGKY0lhjQj71qU8BAE455RQAZlasPXv2GMtceOGFgz4rBc0002+7s30Ui/P000/bWn64QVaUt99+uy77q8aSJsuyY/eUV155xajj48RSRFAdmkWLFgGwZymia2j9+vUAgBdffBGAPcvHlClTAAAnnHACAODuu+8GkJ9trhB6n9x8880AzHioc845x1iGBifUlrVr1w7aDlmBbrzxRgDmvQUA48ePr9h2yrpH58hNvSDALApsp/itV1gtkE4sRYWWaqb+2BVGtRBEhBNhxEkV9j1YFLmgkjBiQcTsa9BLgQpTnnnmmcZ3t956KwAz5XE5yDXlj3/8o/HZoYceCsAsmkj7sg4ev/GNbwAA/u///q/iPmgAR0Hidt1h/vrXvwIw3ZLqhSzLhssVDeIooN3JsyORSOStW01qbuv6w4U9e/Zgx44dAJwLXsAcYFFAvRM2bdoEAHj22WcB2BNFNFA78sgjAQCnn346AODee++tuC6lAL/22msBmIIKMF333nzzTQCmSx9gihAqskqugk4L7P7nP/8BAGzYsMHRegRdz+9973sBOCuiWu2MtFX0jZbZ7X2JSsKoloKIsCOMhkIQsaWo9rAockkpYcSCiGEYhmEYxh2lhFE9BBFRThgNlYWIRVHtYVFUBYXCqL+/nwURs09CM9Df+c53AJgFKgHTTei0004DUN7CcMEFFwAA3ve+9xmflXqxWT9fuHAhANOSUs6dp9BSVA5rW1944QUAphudHehFMWbMGOOznTt3Dl5QBhoXNCLQEUC2K4v+l/sBPZ78K1/5ijGjTtujYpm33HILAODJJ58EUD4InY6lmkQThckTaoWmaY73oSiKYT2kPnZiKaJl3RT1JWsMpWt3cm7JdYwsRnYsRbR9ctuzJlOg+4LeL1SMFQCWLl0KwEyh7TSZBUEWOacpuIlCS5ETrJZdNwOxd7/73Y7XsTJaBn/DnUJhFI1G6yaIiGLCKJFIDJnLHIuifFRVxZ133olPfOITnm1zxDnP3nrrrZgyZQrC4TCOOOIIPP/882WXv/feezF79myEw2HMmzcP//znPz1tj3XGYMKECSyIGIbJo+WEFhz0j4Mw6xezMPX6qZj1i1k46B8HoeWElqFuGsMwzLDFWuB1+/btdRVEhLXA6/bt24c0hohE0VD9DDdkWcbPf/5zT7fp2FJ04YUX4uKLL656NsYN99xzD5YuXYrbb78dRxxxBG6++WacdNJJWLt2bd5MLfHMM8/gIx/5CJYtW4ZTTjkFd999N8444wysWLHCSM/qBdYaRul0mgURs89Bs90UpG6dgT7xxBMBmLPDxYpJ0gOVUgM7fanR/mi2346lyE5wuNXyQtYAJ0Hlxx57LADg/PPPNz679NJLjb9bTmjB1B9MHbReoDOAqT+Yio1f24jly5fjAx/4AAAzZossIlT8kmbsK00CVUu9LEVu9qFpmmH9c5O2uZpjo2c6Xdt0jdiJV6OkB62trXnr2LFkEtZlC9ezJjF4z3veA8Cdhch6Xuj43F4HdIxkYXaC9dngJumBm1T0VoZbLN1oJxqNGtaiUChUV0FEBINBhEIhpFIpo01DAVuKBrNw4ULccsst+PznP+/J9hw/cXp7e7F48WLMnDkT119/fV2Dkm+88UZccskluOiii3DggQfi9ttvRyQSwW9+85uiy//4xz/G+9//fnz1q1/FnDlz8O1vfxuHHnqo4ZLiBRRDpKoqBgYG0NDQwFlIGIYBZGDiVycCACQ5/4VC/5/4lYnA8HzXMAzDDCkUQyRJEsLhMFKplO0akV7S19eHVCqFcDgMSZLQ3d1ddT0txhu2bt2KG2+8EVOmTMFHP/pRLFu2zKjZ5wbHlqL7778fu3fvxu9//3v89re/xTXXXIPFixfj4osvxumnn+7ah7kSmUwGL730Eq688krjM1mWsXjxYiMjUCHPPvus4WNNnHTSSbj//vtL7iedTiOdThv/j8fjkGUgFJDgK1DKkiShuaUdfn8APXu7kMtlAS2LlpYYAn4gmbAfmwAAfr8Evw8Y0+JDWHbu984wteLEY+aJPzK7AABZS1ZheogcOKMTALBt29ZB69OM7+TxEbF+wtlkipzrAgCMaxXbySml74/mBnH/ZvpFO7L+0veh1aobDYjsbePbK997NGt29RWfAZAfU0TrR+dFMb7NBygqUMr41O7Dxth67N62WrQnIdJG05Pm6IUzAQALDxbWgG0bXy75MqY2hWUxaMgl38n73A5KTjS0MZgseR5oexF/Qm+z2I8s299PLpdDRO8XO+ebaAwISw31rZwrXYC0kKxufQuh1/F+iQafOLfJnk3i/7p11A7tTeLcTp+UHytRLcccPsv4e0yL+O30/gLyLSSNQXGe3ZwjAOhsE/eplBX3bVZxUNhXMdPuj20V6w04aMe0iWa6fzfngZ4fbo7dzf72dXIDcWhqFpoiQc3Zt8Krqoo9e+PI5XJoa21GMBhAnw/o7d0LVcmgqTFSw1ab9PUn0defQFNjFE2NEWQyAezZ24uu3TvR1hqzbc3U1NKp+O3ClqLBPPDAAwCEx9bq1auxatUq/Oc//8krQ+AEV4kWOjs7sXTpUixduhQrVqzAHXfcgY997GNobGzE+eefj89+9rOYOXOmqwaVoqurC4qiYOzYsXmfjx07tmQq4B07dhRdnoJIi7Fs2TIjDSpx3Tc+h4AcgGyZ0tUgISe1QIMffm0vOhokAEEAGeSkJBobWuDr8MOvDXYlKoUKP1TJj/87txUBjUURM3yYPPklAMDO/32t5DJf+ZD4PTDQMeg7ep5m31wmtrM5NGiZcjTqrm3f+aRwQVLV0i4u7W0i0UH/KnEfp8tM1FgFxhmHiKD2I8cObn8hdDwdfXcCAAIZ83iuv0SsH2j3o2F7ouK2BpY0YQpEOvCd/8uvkURuTKfNWwcAOLyzDaW8e6hNB4RF3OSu516puO9CaGB8wpR1mH5J8fNA+5ne/ri+n436584SOhw5Vqx3fYn9FGPSJHFMe166CgDg89l3dqC+PqR5q+P9EuPGiWtk7wqx/7iDpA0Ts2JC4Zvn6xMDWW/cgDo7txh/p14XiVB2rnd2fxVy/GSR7nuai3MEAKGQuOd2PSeeF06ujaxlxuWy08X1n0zabwedA8DdefjQoZsAAMfs5/zYyz0fRyvxgQByifHIpBSoPnuuiaoGxAeCyKkSmhsy0BK7kU6IEVaD5Efv3n7kEjlEgvZdUN2QzPiRyPgRDeYQzHYhLfLfoNEnoTcZxO5UN2INGdiZD1I8aupwFSf1YvHixfi///s/nHzyyXmfRyIRHHHEETjiiCOq2n5V2ee2b9+O5cuXY/ny5fD5fPjABz6AVatW4cADD8QPfvADfPnLX66qcUPBlVdemWddisfjuOvX30PIJxmiSNMkDGjNkOBHROqBT1KQ7wMzgIwqIaM1QpYkBGV7wkjRJCgAxjXJbClihhU0e5pNDLYCEeP0Wd1M4+BrV9LvDzUlJiSyOWfXt5wVvtwT9NlbDaVfrg0NwvqjDGwXH2RKD5ytAoOO0ddpw1Kk/47q1hIla6kVo68vhSQEs5VnRjMNEkLoAQBkE8XfnC0RvW0d/rLHDgANcq++LRexEfoqTaGkcRyliPiEpSeXdGOVAKK6pajSfqw0hURslaJbwVQHDuCarn8bdEuak/0Sjfo1Yu7f/gAlCN36oltRFNWbZ3ysybxm1JSYEMhmq8uh1BQSbXVzjgDA5xPnxc21oSjmRIVh+Yrab4eW2mX8nc06H0C2NAhR5ndx7OWej6OVXLoBqtoBTcpCQ2WXM00DetMRKKqGWKgffqjQLI/RBh+g+YNIpMPQVBWRQPUWmGIks0EkszIigSQafJm8NvgBxIJpxNNR9CZlNIeSqKRVNLV6McOWIlG0mjLebt68GZMnTwYA/OpXv8KTTz6J3//+91Vt37Eoymaz+Pvf/4477rgDDz/8MA4++GBcdtll+OhHP2okG7jvvvvwiU98wlNR1NHRAZ/PNyjd7c6dO42ij4WMGzfO0fKACOSzpjgFhPdLMqtCVVVokKD5mgHJB0nZg4RWSv73Q5U1ZOVGJFUNslp5xliDCsjAG7uykJXa3OgM44apqnBTGj9j/uAv9YH0a/8VhR5TA4OvXXqeTjloNgAg7DBQtb9LuOGs2iCsC+UsRePHtwMA9p8tkqkEygTmqpr5kn5nrXDD3bSpu3KD9OPxt4jjsFqkX33rLWOZljEhSGEZRd+YmgYtpaJnTQpHv0vEHnW0T8lbhFx5t7wuBMTmt7tQUhPpuzi8TRSvHNM2M+9zO5ClaNfml/HGW7uKL6Rv75BG4S45vu1A/XP7O1I1Fd3vCJfBVW+VttwXMmtWCwBgwkzhzukkvbaqilFNT7ewur36loMBO12/k4Xr26TZBzve/85+kVZ71UYxcM5lvZk6HjvWFEDtUw4AAIQjLgLBLdfVrs0rAKD0NVCBhgbRptmHHQLA2YAqNZAy/l67VZyr/j7778OpB5nuhE6fMwCwdY1If//22zaeAwUcdMR8x+vs62SSMmR/AyR/CJXmelUNiCd9UAG0NCkI+BqLLtcYBKS0jESqEZJfRTTkbXxPIi0jmZMRbVARDYUBDHbTDQFoCQK9iRDiuRCaI0pZi5GUse86WHIbLIqQyWSMuP158+Zh5cqVmDZtGo4++mh861vfqnr7jkXR+PHjoaoqPvKRj+D555/H/PnzBy1zwgknOK6gXYlgMIjDDjsMjzzyCM444wwAwh3ikUceKZl14qijjsIjjzyCyy67zPhs+fLlOOqoo5ztXBPCSFUlwN8CwA9k90IrKYh01ATgAzRfIxQNgFJBGEkANA0DGQ1SjjPgMMOHrB7DIweKDDL0S5W00EB68LVLz1PJFym9nTJoshAFyTTV4il9f6RzejyDn/ZVxoXG4j6X0Y8xWaT9g9CPR9F0wSWbsSXW9XOvJhE7Uk+8Yn2paBogSYi/mkQmpUFFSG9r/nmR9Dblta2CKFIQzN+WE1Gkn9es6i99HoxjDxTsx8GOVBU5fX1b51snq4pXluyP6r8dzOZTVkK9z5zsl46Z+sHN/qmP6f7IZr15xtP1Dri/vwDkXVd0nh2dIyv6aaHzVJhopBySxcqVykiO20HnAHB3Hhw9Bwpwdd73cSQ/IEmyPqAvvZwQRBIUFWiJaghUuLcaw+K2TKR9kCAjGvbmfkqkJCTTEhrDGqJhCcbFXISgH2iJAj1JCfGkH81RraQwkqTq28eiCJg5cyaef/55NDU1IZFIGFljm5qasGfPngprV8axKLrppptw9tlnG2lGi9HS0oKNGzdW1bBiLF26FBdeeCEWLlyIww8/HDfffDMSiQQuuugiAKIw5H777Ydly0Tcwpe+9CUcd9xxuOGGG7BkyRL86U9/wosvvohf/OIXjvetQRdEkh/I7QUqCSKChBDNeFQSRgwzDNmzVzxsFD0dsM+SipjSR1sTlBRCbmq7dgnLbWNT8RnAytCDufQLhh7edh7ismUZR+l/9d2vXiOsHT09PUUXy7yTQfx/fWg8JAo5Yr5c1QEV/a8kkHkng1mzZqGzIz9+IacngHhrnbBq7N69O2+/ZZtmLEN/uHiZ2Vilmle8ZHMfg9aT8n+72Kurdcn9k2KY3FwruVxW/+1VcIH4RYMCAEj0i/dLY6Pb+ytv065R9YvQSF0u2x9qkEUPKO8mW4ruPaaFx/1zhqknqgb0JiTkVKAloiFg83IhIZRIS3n/d0siJSGRlhANaba3FfCLNvckJfQmpLLCiKmeL3zhC7jkkkswZcoUHHzwwfj1r3+NW265BU8++eSgHAJucCyKPvaxj1W9U7d8+MMfxu7du3H11Vdjx44dmD9/Ph566CHjRLz99tt5L6ujjz4ad999N6666ip8/etfx8yZM3H//fe7qlGk+WIAZGeCiGBhxDCjlsw7GezZnkGgww85LENNqch25apTFQzDMPsAbgUR4ZUwciOIiHoJI7YUAZ/85CfR1taGN998E5dccgnOPfdcTJs2Ddu3b/ekVlFViRaGgs9//vMlD/zxxx8f9NnZZ5+Ns88+u6p9qlIAgA/I7nEuiAgWRswIpl+vDbFunYiXsRZlfOstYc1Qlcp+3atXrwGQX3DSbyONP82ua1rlfZiWIhuz+ZYHfUOD7nqkT6zYqUPR2yNm6QeKFKzNQwOyu4s/O1KpAaxdK7J9kZsRWd+26TEV8TrV5qjfe8+d9UrS+6beL2iyENG16iabWiIhnvluaoNaY5cCgfzir+m0GW/z5ptrAQDt7e16ex284i2HJBvnWfzfaZvpWbB3r0jX1Tmm0/661vvOxbnattWMFaMgbGZ4Uq0gIqoVRtUIIqIewohFkeDMM880/v7Xv/6F++67D5lMBueee27V2x5xomhokCApvZVjiCrBwohhGIZhmFGOV4KIcCuMvBBERK2FEYuiwfj9/qoNH3nb82xL+zCSloWkKd54u7AwYkYgKT1e6NVVrwIAtu/YbnxnnZmtxA59vRUrVhifTZkyFQDQ0SniamTdwmMtrPqWbqGyM2NN1hYnxUQBYNL+kwCYs+0DlixYlchkspUXKsHbW7Zg2zY6h3rqcn22XHMwRU9xL2ZQu5uXmGT+Wzl8qyqcts4ny/D7xCvLlhWwALIyKi4q0Yf1Iq0tzc3iAweNp3izt7dsKb+gBRp/TJworskJEyYY3/n1eD6qt7d582bjuw0bRAbIoJ5x8cAD5wIAmmJN9hsMoKVF1AOjTKypVOl4wWLk9KIsb7/9NgCnliLzgnMTU9SlZ6pkhi9eCyLCqTDyUhARtRRGLIpqD4siG/h9Gvx+GTKqq/1gMoCsJiHra0QAEgKScL1RNRk5SJgyKYiQ5E1hP4bxBP15GNZTn0YipjtX0yTxpa1ilFT0tMHMEuNPi22m92zXFyFhYAZcj4/FAQDBaZX30dyS1Le3SmzPZlB8MCOOafYksXwuV6d7ULK+cKgWmv6CduK3pG+jSRbnMdWdtW7SHvruOiN7MY/OdWET9O21BXfr+3k1b//29qOhLSQGr/Ns9CkgxF5HQ3fBPh3sUhdDrYHdjvYLAJTVPebfoe/fvrDypcTE17Qxoj/GRu3fJ7EmUZepKWimxqbr2dciMjI2SuZrnK6jjoi4v7R+Udg8lXVYLFkS53nO/sJtz+m9QMK8PSz62OgvO6TMyYgDdC/dTMb+/hsbTXdCR/vV2a9V9FfUwfVRzf72ddKpENTcdKiaAkVVRR2iVBiKKiMWTkHWVCju55QGEfYBqj+A/oEgVCWDSLD4xpOZAJKZICLBDMK+rKdtkAE0BWXEU2Hs7VPRHE5BVTj73EiARZENNA3IqZo7Z/CSJCBJGtJSI3KqBp+WhAoNmgT0plT4bcROMEz9SRb8dkdPv1mbpr1NDGKozhkNrpWcKYq2bBWDNMVG3JK/QTzWfOE2fXP2HuaxgLACpNcLUdHbF7e13nAhFBKDOM0nrAK+cKvzjeiPuFRuF/bEy5/r9rEiA6kvJM6z03pIKUXEm+zps/esi0QaIAXFMbk5trQ+2O5N7na0XwBoaxOWokhMWGx8ocqWF7p+e/qF2N62U/wuk03eoLVVHN+YCTMAAM0tzYOW6e4W98T23W8YnyUHhIjq7hVZHvsGhIAaN960NLXp2y7XX02t4ngzW4S46unrLb1wGXxBYWEaN0m0n2KzypIzny29+p8DA/b7KmOpsukLVj7WQhJp8fxwcn0Y+wu5uOf2cXyqH5LkgyRL0CQN8VQAiiajOZJBwOdDuZTXbomGAUlSkMiEIEk+REL5NYKSaR+SWT+ioRwiIQCoHNfqlKAMNMs59A4EEU9HEPE7s7YyQwOLIhuoKqDktLK1Udwh6hipvkZkFQBqGpA17NiTg+RV2laGGeZsMQosi99ug7uJYJMYrAcbheuR0+wBzWOEGFrz1rPuGjBETJ0mjjfSOg0AEGhsd74R/aT3ZbZiW4nEEDTA3G9qk76fSXmf20FVVCSywvpRcj8FTJ3ajuYxs/R9Oh987uwRSSvWbRbipKur8n4p4UBjmxB+zZ1i/3YSGPTsEse3Zr0QFu/sqry/aKOoc3PggvkAgM6JU8QXRa7hJkm0KaWZEwzbducn5Ni6W7jsjbec48MXieLlY8ilrci2A7p395hJ4np4Y6MoaEoptu2SyAkhuv8scU/aSZkrKaYA2yF0M/r77L8PWywz/r6oMDXZSqGuP2/2Jl4HYP+6tGLcC4yBXwIkXwCQNfSlJCga0BrVEPA7s146pTEiLJaJdAiSbLrHJVISklmqQxRALQQREfIBrTLQk/SjP1O9pxFbimoPi6KhJi/GyIfqK0QwDMMwDMMMDxQVSKUlqPA2hqgShTFG9LeXMUSVoBij7v7qx3YsimoPi6LhgCGMmgDNQ8dWhhmBVOulKhX5ywmzZs4EAPzv2Wc9aU/N0Q9z7BgxC28kA3ABFd0sl+Ch2vMLiHgx21YHfTfRaBSxJmcJA8S+hBtUXC9yunev/arnMT1Bwfjx4wGYSQ7KMZAULmyUZGD79u3lFs+jtaUFADBxv4nigzIDkQa9gHok0lByGerHd7a9Y3z2SugVAMBxxx0HwEzKUIwZM8S90KW76q16dVW55g+iL65b5fTiw/aKK1qvMOfXmPXK1ci7w8YkPcUw2knFzzgjmwOyqoT2RrVugogYlHyhjoKICPiBppAzK2sxRpoo2rZtGy6//HL861//QjKZxIwZM3DHHXdg4cKFNWihN7AoYhiGYRiGYZhhzEgSRXv37sW73vUunHDCCfjXv/6Fzs5OrFu3zoiXHK6wKBoO+KLCfU5Jgt3nGGZooTTEsiwCgG1ZNKSifwIwZ64LLSzmO0YyZvSNFMROks7p2wsGhW+8z+f+sU7JCKwFQWtBOp2xvQ86TT6fP6+IqV169ZTY2/UU1naSdVAMSmfnGACWYsM2Hs9UsHTDxo0AKlsarYONzk4R5xMIVo5zoOvT6TmhFN4Z/fyXsxRRFrlFCxcBAN566y3jO7KIlUPRrS9GynrruShxLp2m0h+ESyNAX5/I5jcwUPm4GGcE/AA0kaa6nu5zQH7abcB9gddqyOaAvnT1CSVGkij6/ve/j0mTJuGOO+4wPps6darXzfIcr3JMM24xBFE/oCYBcIIFhmEYhmH2DXwy0BTR4JeBnqSEbJ2GOYV1iKJhDdGQhkRaQiJVH3GRzYlj9kvD3Q/bHvF4PO8nnS6eVe/vf/87Fi5ciLPPPhtjxozBggUL8Mtf/rLOrXUOW4psIMuALyABmrc3kSJFoEiN8Gn98PmSUH1+aJKEjjY//Bp3DcO4oblBt3bExYy4bCcNsBX93TWxU6/RopS+7wN6jIm1OCXN4A/aoIFeXNZiKaJYHopr6IuLDHiJpF7DrEzmS5pZD8tinWzCfpHQQvbuErEfPmU39uss/wxqkEWMTqZfxM44mUlM7NkLvypq2FTaD52nplAC2X6RQc6uQV1VVOzdIdbJ6etW2h8ARCMRAMCU/cRvnyLOS7a/9DqKnjE02SPidyI+EU8TqXR8lsxoUyY06Pux0Yf6JdEcNi0bdo6NUFOinVmpdExSIQfPMq/zrZY4pUo0+OhaMY+r5OWSMQdZ43RPm1jY/nFFI+a9kkuIPldtPAPiXcKSGA2ITnZyLglb/TbKyA34oSnjAElFU1hDfCCAvQkZzQ0ZBHy1EwrJtA+JjB/RYA4NAQVU9q4hAGiqD/0pPzQ1Nyhdt5dkFQm9A0H4ZQWRQPUpuYeDpWjSpPwMi9dccw2+9a1vDVp+w4YN+NnPfoalS5fi61//Ol544QV88YtfRDAYxIUXXliPJruCR942kCTAL0uQPXRty2oRaIgihAQC8gDEwEhCDkBzVEbIRcV2hmGAaEgMTtWMcGPSbBZvNdDf0y1NYj21zDvT5xPPhEjATJAiy/ZestaXm2aIIuHaJekDu6A+mNPKiCJycaIi0Ep6r639F8OniaQvsQYNfq3EedObHfKl8vbn5GUtq31oCuvnqal8/9B2w760eWw2d6Wpqv58tfZn5YFYSHe1cXJOVd3NMqTvry2mH1cl9znL9Skrffr+bAwW9UUaLNdeW4VzKXao/84KoaLIqdLLFuB4XzphnxgQqmlLkosS14uWNffRHNXX99vfF/Ud4OwZ4Ndrr7VERbtyIefv4GruvX0VJROCpnVCUxVIkopYKIveVBi9ST9i4RQCPu8TW4jCrD5Egmk0BLIoLPvYEMhC0wJIpIPQNKVkgddqyCqieKtPziEWSiG3jxRv3bJli1lTEKa7eSGqqmLhwoW4/vrrAQALFizAa6+9httvv51F0Ugnp0jI5FTvstL4ouJH6UdOScCY55NUQNawtisDKVdbn35meDB16hRs3boNAJDN7vuZB6l4o3V2XCEhoP+fHrKxWLNlGTHg3LtHDDpyZep4tXeI7Y2dKma02tud1euhGf/XNjwv/l8kBoXeS3PnHgQAmH7I4cZ3frsxPdZ3m/6+zCli35v0eJTNa9YAALp2d+ltGSy46Fy27icKdO7XfoC9/VugQqOb1r0AAHjt9XjF2JsBSRxny36Txe/Wlso70o9z/Y7VWLF2PQAglSr/rCNL2MFN7ZjWcXDlfVj2s3v3LqzZJOpfbd5sI1ZE75NpU0X2twMnHQkACDdUtqZQH63aKLLNbd1i7xlurXl06DGHAQCCJQYaxdi51qxNtHrDtorL07U774g5AIBwU6PtfYX6zXty1Qv/tr1eoEXUVJrcNs9sRwmhIqXMGfU331kHAIj32n8fxmLmuZt7+GwAQEjP1FeOrm2vAgBWrnsTQPkMjKU46r02r89RRDoByP4AZL8GCn9rDQC9CQl9mUbPY4wSKQkDOQmNDRqi4TCA4n3fFABkn4REOgLZ521GumwO6MtICPiB5qgGWQpAzVTvMzgcRFEsFssTRaUYP348DjzwwLzP5syZg7/+9a81aZ9XsCiygSYFoEkZeBLvY40holTcDMMwDMMwowBZEmKhNyF5mnyhMIaoEoPSdXsgjIwYIpkEUdWbNBgOosgu73rXu7B27dq8z958801MnjzZy2Z5DosiW2gIRseivRFV3bj5N2wIQP4MdlYBMjkJ7118ruE2wOzbDCQHsGLFSwCAnTt3GZ/39AiLCM3WkzXAmmmK4k9yZD0YxnGcET1GY7/9RIV5a8Yrw+pDsSONTXnLAkAmK2aK16wWlpOtW0WsQDHLiZH9a4OwRDQ3m7Na/kCZrF76+duyRcQFKGUsw1Svhuot2KlfUxYpf7sz9FpJ1O8rVrwMANizZ3CNHU33DTEs2TTDbeclZlhUduu/ySJV2Sq+dZuwSjTrdZHmHHggIg2RvOOxNBIA0NMjXLZ27dyFVNq+25bR2MGp/IoS7xPxVWvXrjXqBdmBzn+0UTyAw2H78TZ9+j53OKhLBOTX4vH7K2edG7y+w+VlstY6H1yFHViwrNAlaSP5HPx+8xknuxgA5nLmM6FXj80bY8NSRHF72nB+kO5DeC2MnAoiwkthVEtBRIyUIqpf/vKXcfTRR+P666/HOeecg+effx6/+MUv8Itf/GKom1YWFkU2kLUs/JKGnqTs+sZ1e8My+zYNkQYcddTRAIBt75iuLxs3CPcpcqkjFxurmMhmhFAY0NMob39HBD4b6W9tQgPBtjYR1RwImPvYuVO4HpVzV7PDpEnCHenoo8WxBsqkAS4HHT8NQPfsGezDr+oD+o26C1qzpZjptGnTAZgDL+sAqqtLCILnnhduc+XGRiSuZKfxSg6ZMmUKAGCzPrDfs3fPoHbRgDOpJ2WgVNehcJkBrL4OFTJ97TVRlHPHDvsD+tSAuO7Wr19vtINSSpN4Jxckun7eflskv9i0aZNtEW8MqDVze4MGBvoy/f3ClYzE87p1bzlyg2qMCjHUQS6XdnSlmn+MObtFaXWsx1J1Omob+CgRiIvBlWSNdaXVbZxemsDJi40rcetYE5WUcrErRyZjutqRQB0zZkzF9QzXXv0Y1cJAFMZzvBJG1Y6vvBBG9RBEI4lFixbhvvvuw5VXXonrrrsOU6dOxc0334zzzjtvqJtWFhZFNmkMKxjIyq5uXBZEDMMwDMMw+VQrjLwaX1UjjOoliEaS+xwAnHLKKTjllFNq0JrawaLIJm5vXBZETCUoZbQ11eWkiflpL8vNWGd1y9ALLwgLxwbdygSY1oOi6Nuk2f1Fh4sCjY1RM/D6Jd21b71etNFqWXFCVJ+Br6awKACM1Wd8x48fDwDo0QtzAoPTVpOr1v+ee86yjJj9DevuNGRlA4DXX38dANDb21uxHYpxHmp7TxsFOvUZc6nMHrduEy6FHZ0dAEz3Q5H4QaxFNSX26udtzerVAIC33xYug+VSf5eCzvPKlSsNC1yhpSirFyahhBluTpuqqobFwSf7jc8AM4X5Gr0P176xVt+vM6spJfcYO3ac7XXIfVFx6caab210PvBweirJGuVqaFWkSLGd/Wf0flAsqRx9KF7MUrKMKMM23N4KURTTqt3bG89vZJmDJosyufr295fJv854ynAZX7kRRvW0EI00UTQSYVFkB0m8+CQoiEWA3qQPPQmgOaogUKZIcSItI5GSEA0riITUipXNhXuIDC03ADXLpvtRTfHSNkVRdbedgCx+N1g908rU2DFSK/v17G+qEAiaYg7SAj59myGxsOJz92D0S1m9rXpyEdWl25l+E9GxRkJme0qFAIUs58MH4Voj6zXH6P8AEApo+dssc7+Sh6Ga0+sI1epRqreB+iESkko+R0IBvR+1dH7bVJ+xIU3Pakl9HfSJQWq1/QuIwTZpXioLQ2316yMFVa18bguhl3FAzhnHJGniwWu4OCkDxjIA0KB7DvodvsiD/vztqdnKVegNoaZfS9Zr0g7W61PN6veHg03QMdvdd1A/NxpdH1kHnaGakwi0LzveidRG2icAqFJlwUrPJifn1NrldN/YOa9034Tp3nbYj3n7YQy0nAxN80HTyo+BJKCm4yu7REKABhn9KRkaNERDpcdiWQXoTfjg82mIRRQxaVWiHZoH7pgsimoPiyIb+GRAU5NQ9Wd4LAD0KhH09MuIhZJF8+wnM0EksyFEAgNokDPGuuXQFBmaEkA6vg5px0HIzGjnoOkiKHxMU4vx2Z5ucW0WCx6mh9z48WJmNObXEz1Yst8umCmSHnRGRLwRxWw4TVcbVkW81IAI28lLQ+yGqeM0fbvmTHLpWA6zrX27VgIA+gd9A4yP0e/K8U4UHJ/eKywtSrWJFkqhN3Bso2hxeEqwjJ4Qs+Lp7tcAAHsyIn7HZwlcJ0tRXLesRCXx/wMmkUi1f+yFjdSKfGbiK/htH9pfc3gPBrpE7BNdu2m90CdZ93xpcVwz96M2Ootda46Ji9+f2SS2v7fyQIDuhUa9mO2BU53tM2hJ/pHuWe1oXQDojJqWTb+NfZNlSukXqafTKfvXbiBtpjU/cIrYl50nQSzSAwDI9rxufKb6K18LtI+OiLsYxGb93NB9Wk4UteqD33nThGrs63deIsHYD2OQToeh5eZA07K2yprUanzlhAYZ0PxBJAZC0HJpRIKDU8JnFRnxdAQ+KYNYIAnkgHJH50WIGoui2sOiyAaKCkhyBLJl2qIlIGY0+jKxQTMaibSMZE5GtEFFNBQCYC9jjySLQNZQbCZCEbYUMQ7RRyddm803xBubRJX2YiJG1l2cYuOEK9qY1qmDN6mv985qEZC/bZv4rdrITmalc4xQHBMPEFnVQrqLilu6UyKpxJtbzQrylerdWJGKGSwc6DxyQZq1QD+ehuqOpxR0/ne8KcToxk27Ss+I6se0335CKE5qFC5gYV8Yqr5SPCMGiZt3ihn7nr0ORK6+fTMjGFkKdOFdI09CehlPndaIjv1niM90U1RGF3c74npdp83dAJxfn8SUqcLNc9JsUcPHb2PgTpai/j2iDWs2bna0z8Ymc8B/4KK59lekzIFvmdaXNzfurLgaTUjMPGQWACBkowYT0a92G3+v2UT1fCqvN3myOK/7zZhlfGandlDPdpEwY83GrbbbaGXqNOEKPEWvl1JuYEcunltf0591WwdneqzE/KMd9N8oIZSUIfnDkPxByDbnRGoxvnJKYwCQ0jISqSgkf0OexSirAH0DPvj9QHNEgSw1VdyepLlzPc/bBouimsOiyA6aECuSZN6ZPgloiYoCZL1Jv+EDm0hJSKYlNIY1RMMSnMyMSpJ+0fsbIDvPzMowAIAZM80X85vrRNayvr6+QcsF9YrtE/WBphwoPUhZePixAIDkE08AAHbs2GF8ZyduY/NWkfJ53EYhZiZONNNtUyE4Skcs2XDKnjhJtHnsBDNb2ptvikGanXTS1SNGgpu2COvaLD2FNgAEjJl/Ke+XG3r1eJ3efiH4EqnKI9B168UA8q0N4rckyYbocWLho3dgU1OT/lv0Ex0ffZ9KkbWmB8kB3ZLgoUCikJs9PQPYsl2YGlP6fijD4B49DXu1+1U0cWyBkBhM28l+Jumz35os7p9k2lkj2se0GH/LAQe1GPS+zCjma9zOvn16PJzka3C+T9l0D0umqRmV95nK6ReLzxRgcqDyRMKMA0Sx12efW2l7X1YyOfH+lf1iX+X6M6Tftk3NIsYytekd4zu7ItvRuRwlSH4aP0m2Ex7WYnzlhsaweHwn0j5IkBENa8jmgN6kBL+PYojstUGSOKZ8JMCiqAoKgwMDPlFniJMqMAzDMAzDuGO4jK+syReyirASDVXabbYU1R4WRVVCN25XXEYmBwT9LIiYocVacHLceOFCVcxSRLPqVF+lnBMCFbM86uijAACvrHzF+I5m6631QUrt67nnn9PXMeuGHHDALH0fYnY+pBeIHDtmrLl+4fNYf0C/613HGB+l9HiZXXptpUSiTOY9j3j+uef15pkNJMsXWbyoLkxDg7AkRCLiXAYorsrysiGf+369v1atEjE0uyyFfe1i1vdxbjmTZQktrSKObN5BYrZ+6lThXkm1omjWnq6tdevexLp16wAAcbrevHgU6ucnk0kbhVj36kVs+xMJD/djvvidDQBoHXfJQybsN8HVeqbRxNnBU4ZB1YW/o9UK66TIKWWR8zmMvQuHxHoNEfFMSzq8p6UCV087TJ8hrNCr9VpXAJBW0o72y3jDcBlfRcMasooQZQDQHFWHpA4Ri6Law6LIAwbS5sWSVUSKxirjyBnGNdYUv6EyRVIphfA7etHXmbNmlVyWaGlpAQC8611HG59R0cPXX38DQAUXF/2rXbvMQb7xt34bUQHNxe99r7FMqeKLlM4cAN5zwgkAgDVrRDD3G2tFe6wptt3GmpSCXAefevop4zOp4K9AUPjlUBrxyftPBgA0xYRbmizLxnmh7a1bJ1wBKVW209TSrtEb39rahsMWHgYAmDJ5sv5d/kuRXpKxZiECFyxYgIjedySa430FKZFdENRT/TU1NRkuli71QI1x15ixY8dWXqgIbscoJGbc3At7eyzFku2EoeltjOoxhAG/Q79wff1x+jmylhuotA5gTkw4GdA162nZKa08M7QMh/FVNif2bW3TUIgzFkW1h4fuVWLNk98Qqr4yM8MwDMMwzGhnOIyv8usQqRhIS64KvHoBi6Law8P2KihWOKyayszM6GPvnr1G0UCr1aMacjnTqlDUbU6HLEU0+zp9+gzb7fBb0ggfffS7AAB7dJemHTsqZ8Aqiv5+6e8XLlGPPfaY8dWHz/mw+KPMc5lcc+bNE+5e7R3tAIBVr75qLLNlq0g+4LXFyDprrhX8lUkLt8LNmzbn/TZctWTJ8IUyXJqGyAIS0bPoHXTQQYZFy65JQvb5MPuAA8R/9ON4+eWXAZh96gjDaiXc+GbMmGFYiugEbdbd6egcV4vpcij+sDMQGFS81SGRKjMxOkY/RmuRU7vrvKFbg+1C/UXHaCeJSjHIQu0UyUWyE7K0j5Ix4LBlOIyvihVmdVPg1StYFNUeHrLbwOcDVCUJJWteFMlMAMlMEJFgBmFfForFu6UpCPSmwtjbLyMWThXNs18MVZGhKj6ke9YbGZ2YfRtpYABpRfjNux0wFKJYXK0mtYksXTFfaTe65mbhp5/es8pVOzQ9RmHqGDEw7XRZU6SQUNi8b1LdurCx0zT9HRXMCEFI5wAAmnUxR20eUoz4FRht1jC0oigQEG1qlN9Beo9eK83Jy1AXE00+kSXugIkUD+TimjDcKcWzsEHdAlnPJjahWdRtiu4v/q8oHlxzEtAe02s96feCnWPX9DiwJllMCsyb5rAt/WuNP1NZB+mF9XM9ttF0D/U52LeUFPFfKc1GSm79epw21ny2jGuqvC9K+98kiyyRqW53bqDtYZEK3Na5tXRZe1Nc3++rJRYugn6sdO0CQDZr77w62s8oIZ0KQc1Nh6opUGzUKQJqM75ySlaREU+F4ZNVNAVT0HIATXuEfYDqD6B/IAhVySASrHxdq8oweOcwFWFRZANNAyTJb6TzTKZ9SGb9iIZyiIQAIN9PWoLIXR8fEMW9mhsyCPgq3xCSJkFSZfiCMfhCDmbxmBHLqpfXYoZuoaEYE8BMNuCGlKWq+ju7xd/pdJmq3BDX2nh9YNnY0Ohqv5Om6elzn3kWgLtAbityvylmDgq1AHAYGxAWFobG1onGZ2Q12rNHDLJyZSxGRoi2ZZfDQUtVonhpVXtMmijSEcfaJsJno45MKTrGijijeEK0ZuemTY7bQjP2zW1tAICWzinGZ76w+CyjCsvf7i7Rn1T0WnXZUcGI2L7qE/diIFA5BoYSlQxkhQjf0+dskCaHWo2/fU7ue/0QB7KmZdbJvjVfTN9n5fudiv5u29lvfGbnDDc1CsEVbhqr7ytmu31WehMi7tHpuY00+fX9tlZYcjBxS06HgZS9/brZz76OT/VDknyQZAmSXPmqqdX4yglZRUI8HYTfpyLWoECWBj8HomFAkhQkMiFIkg+RUHlLsSRVL97YUlR7WBTZQFUBSQ5C9vlFnvws5ckPoPCGJWQALY0iz348FbZl6pUghJE/Mh5c7mB0sDv+AibLws1LDncYnweiVbjUZHuMP7fvES+LgWRpkd2u6u5yQTFwCTS2udtvTux3W5d4OdipXl4O2WKx8kcn6p85dzGUcuax70kI9593ukXbqGBj0fXI8yYvO9wwVkUlPIVsaVN9peZO3c0pPB6BRnfiGACgZyLsy4gkGnRNOKkzQ4HubePEw9Af2c/4TEsLERRPC+vMzh7x/0RCv/ZcukeGY0KU+BpERriADWGo6ceaVIRVYttuZxNactjMPufovqeCvGmzWKyTfWsO7vesJCZXtnWZ27fTlW2KuF/nBMbo++q03T4rvan1Yv92js9yAzS1N+j7neR4nzssOSX6++2dVzf72dfxS4DkC0DyaRWLt9ZyfGWXbA6Ip0QK8OaoVLYOUWNEeFYk0iFIcvnMeJITd9VS22BRVHNYFDmgmI9rOQrz7HOMEVNI1+4uLF++HAAwZ85s4/OFCxcCAIIuLEbWWIF0KlVx+b164UvKdNba5k4UNTWK2fW2NjFb2qXP3rvHm4ew35IG+KST3gcAeOzxxwEAW/RjBsyU4pT+t0kXBVYhRudqYKDyea0VJBYptXc4rA/kfeI4yf2RXASzuSwSekxPOkMVN/O36dOPsbW1BQDQGK1CEAEI6FkPG5v0NOt6G1MOzhulgac2+S0Pz44OMYHQeqRIEb/ffqIY8BtvCNFLxVyTiYQjIUupxHftEsWGJ+1feZCr6rFEjjME6pe3G6EPuE/JTYMbv99+drXX36DMko52ZVyjrS5jgojdu3fbX9ga31fVHMboGAQOF4bD+KpYDFEl6hljxKKo9vAQ3SYDGRlpxXnhMBZGDMMwDMMwxXEqiAgvx1duBBFRL2HEoqj28PDcBprkQzIrI9bgrnAYCyOmHJS16o21ZsD1GL0ux4zp08UHDh5IVrc1O3E9tHyK4jEsrkdOMuKRaxNlfyNrDABXiQMiETMAXHZZGHMQVPT1aFFnaVXsNeOrnTt2ADDrNU2fNg2AGTMCAC+/vBIA8NprYj23Gcfc4vf70NoqLHmHzD8EALD/pP3176gYrL6w3vfJZBKrVon2rn1TXGOFFhuKnTGyu3n0/iOLE/12Yika0ylcrcaPL13c1KdbO6bohWX31zPmbd68CQDw0ksvGdY9O1aDPd3CHY+KxE6cKNw2yyUfMeKcHNa1ocxobrNOkiuiE5dEAIjqLnp24qWI1/XaX06hc0JWTLcYRXrriFeJb5jyuBVEhBfjq2oEEVEPYcSiqPZ4NNLYt9HgRySgVnWR043rl8XNVyaUgWEYhmEYZp+mWkFEVDO+8kIQEdGwhmhIQyItIZEaHSJiX4PtFTaQkENDUEW1GpItRkw5cpYn+dYtItZl8v7CCkAxGnbIsw45eM8k+kVmKYqtAYBwg4MMZBSs39wi1g2Z66ZsxDYVMnfuQYO27RUUq3XoggXGZxR7Ysz46/sMWiwAM2aKTIHbtomsZ9XHTdlEb8u4ceONeLMxY8dUWEePPYpGsUA/Tsoitla3SppWhtq8wHN6fFvOSZCx3hSqbRON2s86Q1aXqVOFlU9VVfzvuecAwIirKgdZ/vr7+/J+N8VKZ02jml0UP0NtqJTsIaA//H2Vos8rtDWXq2yttM7yzpw5EwAQDNqIV9SvD8rq5xgjWYm71QnFxjEW7rPa/Qb8/HKuJV4JIsLN+MpLQUTU0mLElqLaw3e9DXySAk3NQPUgewgANIWB+EAAexNyXjpJTZGgKTJyyR3IamxKGg3s11n8Fhzo3aT/FgHkaLBRS0THnzPrlpTafjGiASGKsgkz+YBPcZ6WuUmv2XDwbHPQvm3bO7bXD4WEAJwz3Uxvm+3fUmpxzyk3lNWTk+HA6SJj4EafGDRXm2mvEjSAnnNAJ1ojQtg4OSf0OhvTLAaXA2PFQJ5Sksuy+B1QhcjL9m2pTifp4wA1uQ0AEAuK/MZRG9ejrL98mxvEQFzRt6G4aM/YVmD+bOGKumnz27bX8+VEYP/2zSsBAGHdPa8c1C+zp7YAAPriIhtdYZ4HOoy2dnF9qwPmvaE6OEYSuI0BM012qfs9Ynl+TBkv/lZTYr/lrlyqwTS+TQg9zeHEYFtUTLBk+8UkgttrakKHLjRtnCDrEs0Nzu8VYtJYcyKqwab7Xz2fUyOF3IAfmjIOmqRC1R8MybQPiYwf0WAODQEFqodeyKXGV4VkFQm9A0H4ZQVN4Syglr8XnNAQADTVh/6UH5qaQySkGPdSNbAoqj0simwgSYCm5aA5eWOV2x6AWCiL3lQYvUm/UYBMU2Vomg9KJg5F5uKto4G2puKDDMOfPSsEjiLbn6mVFHNGvC2mb9/GZFVjWE+bnDVFlSINlFq8JDR5EAmY2bhKHWcxqICokjZz4g63B3LIJ/qjVT8urbaaCD6fnjEMSfO8uDglIZ94rrQ0kTVD35QeFxOQRX8r6b2eiKLC82RnXEB9HdbbWs3xatkswn4xMHdyDfr9Ymch/b6zXoulkFUhTmIN4iD9mp4+vDDmh4rS6veboqcVF9/ZP0hNz3YXtdS0G3SM+uZCFkOzrPbp+638UKCBXGtMt2Y5jV/S22Yco8v72LzPbOzfsotIIKPvv3L/FdJoqTuj2rx23OxnX0fJhKBpndBUBZqk6oVZfYgE02gIZD1/dpYaX1kxC7PmEAulIGnVZiocTEMgC00LIJEOQtMUBGRvJrqH27twX4NFkQ0UBZB9Efg89nVrDYg8+32ZRrRENMgBMUsaajkAYa5TNCpYteF/Zb+fOle4z8Xax9reZr/aZfz92oZVAOw98GfNEjPXM1pNt7VQFQU80zvMQO7Vm0WByXIuRfSsP/roRQCAho65rvdda6a2iWQSax96CACwZctW4zunge92mKYnfQi1zUPYZcp0ANi7SbyYX39bJJXIpMWgkc59VE9uMKl9XlW+R5RQYcseYS1Z/ZZ9UU9WsVCbuOandhzsuh0hVcXm3asBAKs26n1kZ1wtCcFxgCxMg5MPnEdflN5Xq9hwyi8sMJtefAmA6YKn6AKDCjMfM0ekEw93lE4kUY6sbonatNss3rp1m+76qh9jWL9/Fyww7+m2SeJvO4kEKKX/axueF5t1eGlPniysUnMOm6fv050L+uqNYv+KjfpT1kHjgQ3imTaz3fk1FF9rWuBWbegqs6TJUe91f63uq6QTgOwPQPZrSGUlDOQkNDZoiIbDANy/XypROL6i4Vs2B/RlJAT85DJnP+GIU5oCgOyTkEhHoPrsu8CXgi1FtYdF0RBS6APbGNJszaQyDMMwDMOMBDQNGEhLSGW9iyGqRLEYI8D7GKJK0LHGB6rPa8aiqPawKBpirDdu74Bc0v+VGZ1s2SLiIMaOs28psj686O9y1gtaJqYHk9sKwLZBm8Wi0agHy8fjfSWXj+ppmydMcDdz7gZrLFBWTzARCIgZvbKpkvVz9v73vx8A8Je//tX4itI6e0ksJgrjNlRhuQNEIVcAUHL5rhx0dZhpnt0Zisjd6q231gEANmzY4HgblL7ZSXHRUkiS7Ko4qqafEbo+6LyUGxiQ5YUKyY7V0+rHdYtOn14YtjnWLH43l07eYAdKvkLXBgCEu8X14dfP4axZIqnC3INMq6uTVNOpVHVu3GTZoaQQfqd9oV+Yro2vXLx1WJDOihiepnB9BBFhHV/tTeguwj6tboKIiIY15HI84z0SYFE0DJAlcdOk+/khzDAMwzDMvkdDqP6TvjS+6kmY2eCGogRVKMCJFkYCLIqGAdkc0JuU4JeG5mZlhi/r9Zn2hYsW2V7H+vCS9XS/qlo6yJNSHrd3dIj1PboI29tNS1G0UViBylmKpk8XcTNNTU0ll/EamsUHgLVvvAEAmKSnQZ9ARUPLnQ79XB/37uOMj+6//z4A3gbu9vaK5BfJ5AAa9FTVTlD1mfpsRrcUFfjpUsY3sqq4egFqGvbsEYHm27dvBwAMOCjWStD1J3lVsJe2q/+20y20rNkG5+eDiumSxbStiliwYoT12KS5B5nxQu3tIivi2HHjxD5b9QyOLgc01RYnTg6IrIM9+vXboT9j7ELlAVQH0fiapYep8DIlaLD1bNNv3J07d9jeJ1MeWQYkVVhs6m2lMcZXsujX3iEoh6JqQH/KC8s3i6Jaw6JoiLHmyY+GNWRyo+PCYxiGYRhm3yfoB8J+Df1pqa7CqLAOEYC614lUNbHPnAeTZCyKag+LIjtIgKap0DQPk+kDyCpAb8IHn09DLKJAuF/L0HIDULPsfzoaiITKP2iUtF5QNSUsGn5f5dkmSTXTaDdGxPKZ9OBr16fHbEyfJmIgJo0Xs7hqtnKRSztoOTMeIewX13O54w35qV6XmF1WUf3MWsm26bPB27eaMS/r14ksZRvXvw4AWLJkCQB7xUP9spl+vDEishkVxu1UQ9cukTmte9cYxBrF9n02rgWie4+Ic0onewAAkWB+PzToRXob9P5Rc/avAU2fxU8kklj7+msAgJ3bN4v9VLi+ixGN6NnndHeTaq5HRVGg5sT9QMdmx1RE90Y0rMfk6dekx68AT2huNLNaNc+cnPedmktWte2IHl4YDVNKdWcju4x+vb3z9noAQEvMjFeUS1gCNYtV6E29yHDh9WqXXFpYphN9IntcpIyVlZ4J/X16vbaUadW2ex179ezcl9ByotSIz6+iOSLGPD0JoDmi1FQYFY6vaFexCNCb1NsQVRCo3WtGCKKkD4qioTGUrbxCBVgU1R4WRTbwyYCmJqFWf00bZBUZ8XQEPimDWCAJ5ABNkaEpAaTj69xXEGdGFAdOLZ+mU9If5bkeMVBXbLxFpLQpRg6YKJ74ijp4P0aRzKgQXOme1TZabB9rFfr9O4QbTHuk9PG2NYiBe1Y/1lwt35j62C7mN+uKzJksxIaRnCIhkgWkM5VTtlrP+Zz9xWNV1bxz/6K+avLtRLZH7MvJ+fHrz5OJ7WLd1nB+P/j9oq1RSbi9pfc6GEyTn2A2h3FNYkAZnuI+/WxAP92N8g69Le6fhZqqoSUorqsDHbSJzndLtFdvg7f3xkiB3OfmTBbnTnOYuYCeX60hURQ4s3eN+V2pQZbF77QloPddhedkKaJRcb1r/fq9nC4z5KHdZsWzatYk8/5VFHv7H63XSTnS6TC03BxoWhZ+n4qmoBj79PSpaA4nq8n8X5Ji4yvrNHMsAPQqEfT0y4iFkoPqGHmBpgG9qQgUTUMslIQXpWFZFNUeFkU2UFRAkiOQPZpSyCpA34APfj/NlogYCkkWPuyh2EyEImwpGg2s2fiKreWiY8RMPtWrATA4q5b+Uu/r2m18tHarmKHN6LEkAYu/AMXOzJt9BADvH3q7d1nasUVYDuLxTMnlxySFZWXR2EkAzGx4gDUexMyOBuSHSjiJQcnqhS+3rTfP/5sb8jNtbdwlMv994P0nG58FgsUFUl9Xt/H3mk1vAsjPbOcVG3ftwSGHCMvexIniPBnWw8L+009SNpvF2nUrAQAb1u/V25Y/uJ0+YyIAINIh4lNCegxYOWjAvHev2ObqtauxbcsuJ4eTB11/4yYIi+XYqQeItrQ6iEPRD4tiUHbv2o03Nos27dhR+torpFWPxWmdOFNvwyT7bdiX0M/n5u5NAID+eH/pZcsQ2SWukSOPnGV81t5OMYxUmFX0WW+PWTz6+dfeAgAkE/b7zookif0e7BPX94wZMwAAstXKql8rfQlxbC+88gIAoHu388LV848evrXVhopQUobkD0PyByH7gFAAaAkAvYkg4tmg5xajUuOrQloCworTl4l5bjFSNSCe9EEF0NKoIOCLIpMZhmZmZhAsimygIgANMiSp+rvGCPrzUZ58c5uSpM8E+Bsg166eGDOMSNqoKg8Az/xvBQBA9pvuH5MmCVFDg8mMPsP50iuvG8v0JcSDOBgSourwI48xvps6barbZpeFCrRueccseLizS1ijyiUf2LRFFKFMpJ4DABx11FHGd0E9/XAyISwYA3oAtzXpQFurCGQn4efTA92LpWRO9onB0obN283PCvoimRHuM48/+Zzx2bHvPlbsQ982CZ8XX37NWKZ/oHYvv2S6D4/9VxT8PeCAHgDAnDmzAQCNhpAR10MiKVx5Xl7xMjZu3JjXXgN9MBJpEgPUxmZRvLVoGmtDZAnxulp3lXt1lSgQnHKRVAEAfHrq8+aWFgDAxEli4NoxRgxkjQGsBqiqOLc5XZBpBSmzc7p1cvsOYWV67bXXsHv3bmP9iujnY7+YSFgwcX99EO0f3a/Kk95/OgDgT3/6k/EZXQd2SKbFtfjgvx4zPqOkEOPHjwcAdHeLiYWdO81itNUmeqBOf/YFMfmxao0QWdakF3t7egCYbnPVFF+WA1x1vRDJLyashJVDfBb0Ay1REe8TT/o9izEqN74qxCeJNvQmJPQm/Z7FGAlBJEFRgZaohoDuiitJ1QcVsaWo9nib3mefRUJ/ygeH7tSDKAz640xzDMMwDMOMNgJ+oCWiIacKYTIU4yuqY+SXxboOdH5RjKQKKmqSyIFE0VD9jAZG9/SXTSQti5xWXdYUFkRMNZD726OPmjOtZvpk8X+j0KRlPZ++zCEHHwwAmDx5/9o1Ut/x1m0iKcCrr75qfmXnhacvQ253f3/g7872r5+HRr0I7IJDFwAApk4xLWKyfuNt2iQsJ/G+OEpCrkObNxsfbb17CwDTckUpg6lIZT2gfa1Zsybv9yDvuUF/DGbQY6jIsmSNoRn1FS8Jq+WGjRsqbr8cZNGbPWcOAOCQQw4BAEQaIvmN07efTqewVg+8X/WasFKR5dCIdalyYBXwCxM9BeT7RrmFiCCL7LHHHmt89vjjjwNwlnzBaoXp6urK+11T9N329yfyfjNDCwmjnuTQja+sBV6ryUpXa0EEsKWoHrClyAYSNDSFFNczGiyIGIZhGIZh8qnWYuTF+Kpai1E9BBHAlqJ6wNNgNgn4NLQEnM9osCBiaoWdQH4KYqbiqbKDNM5OSaVETMljjz4KwCycWDeM2WARG/DkE08CAJ5+6iljEbKuUUyKU8sCWWncFCWtNW5CIWiVhH7OKFYrGo0a19e6dSIOY+UrKwHkB8K7xe/345BD5gMA5i8Qv4vGMcG0LmzYsBErV4o2pFLpostWS0eniK2aMmVKTbY/0qH4H/G3KG68bdu2oWoOs4/g1mLk5fjKrcWoXoIIYEtRPRgxlqI9e/bgvPPOQywWQ0tLCy6++GJj8FOK448/fpDS/fSnP+26DU5nNFgQMQzDMAzDlGc4jK+cWozqKYiY+jBiuvC8887D9u3bsXz5cmSzWVx00UW49NJLcffdd5dd75JLLsF1111n/L9c8bZS+HyAqiShZCXIgMiznwpjb5+K5nCqaJ79rCKW8ckqmoIpaDmgUh4dVZGhKj6ke9bXbCaUGV7Mm+a+nosdZD2zV1QTcT6p7tr50m9YL9J/z9qPPqntsVVHDSv2jUD8GRFjtWujyLjX1NRkpNru1X/v36Y/wdqq79eOjg6MHy9GPZm9r5VdlixFEW0nZu5HsUO1ubba2sT2m2SRlTDVvbPc4qMOn2WgevB0UYi1LawXZK0ia9u+Qqr71coLjTLSqRDU3HSomgKlgndDLcdXTmgKAr2pMPb2y4iFU0XrGIk6RGEoqlhG1lQoZWpZqgpnnxsJjAhR9Prrr+Ohhx7CCy+8gIULFwIAfvrTn+IDH/gAfvSjH2HChAkl141EIhg3blxV+9c0QJL8hitSUAaa5Rx6B4KIpyOINWTzZimyioR4Ogi/T0WsQYEs2cuvLWkSJFWGLxiDL1Rn1yNmSNjTV9sAfT0bKCZKIlWsL9Ras30l9MKItT4mxnuCQV0MtIoBbkRuQjIrBHRPv+jPbM67fo00ByEFWgAAvlB5gUOiKK32Ym98cDIRLwk3ime1HBT3CaULZ3QsJz6jCgG9N64UfjVqqeXzdaTiU/2QJB8kWYIkV75KajW+coIEUeMoPiCKwDY3ZBCwzAioGhBPBaBoMpojGQR8PlSaaJMkLt46EhgRoujZZ59FS0uLIYgAYPHixZBlGc899xw++MEPllz3rrvuwh/+8AeMGzcOp556Kr75zW+WtRal02mkLdXp4/E4VBWQ5CBkn3m6Qj6gVQZ6kn70pcw8+9kcEE9JCPiA5qhUNk9+IRKEMPJHxoPLHYwOtu2urfj169mzZkp6DZ9G74tQKnrs0CtvPATAWTYqZrgg+nDz9vX6/9cbBVC9HO1S9r9xk9sRbZsuPqzwrtX062lA2YOtXTnP22QgAU3t4t0QjulZGkfJQMAN/qi4PvYkhJUxmUwOZXOGBbV4vo50/BIg+QKQfBpkm8OhWoyvnCIDaGkU7nHxVNhwj1M1oC8hQdGA1qiGgD9ka3uSUv27nkVR7RkRomjHjh0YM2ZM3md+vx9tbW3YoRfpK8ZHP/pRTJ48GRMmTMCrr76Kyy+/HGvXrsXf/va3kussW7YM1157bd5n3/3m54ouWxgcGA1ronAYxxAxNrn0U5cOdROqhtIWf/KSS4a4Jcy+iKQ/SA+ZPx+HzJ8/tI1hDCZPmZz3m2G8ZDiMrwqTLzRHNCRSQxdDxKKo9gypb8AVV1xRMQXgG2+84Xr7l156KU466STMmzcP5513Hn73u9/hvvvuw/r160uuc+WVV6K3t9f42bJlS9l90I2bVST0JGT4JBZEDMMwDMMw1TAcxlckjHwS0JOQkVXc1zKqFk7JXXuG1FL0f//3f/j4xz9edplp06Zh3Lhx2LVrV97nuVwOe/bscRQvdMQRRwAA3nrrLUyfPr3oMqFQCKGQPXMowzAMwzAMwzAjnyEVRZ2dnejs7Ky43FFHHYWenh689NJLOOywwwAAjz76KFRVNYSOHajGhbXWQrVQWsiATzPMu9VUZmYYhmEYhhntDIfxFaXdVjSgJaoikXJWx8hL2H2u9oyI1Dpz5szB+9//flxyySV4/vnn8fTTT+Pzn/88zj33XCPz3LZt2zB79mw8//zzAID169fj29/+Nl566SVs2rQJf//733HBBRfg3e9+Nw4++GBP2lWYJz9YZWVmhmEYhmGY0c5wGF8V1iEK+p3VMaoF7DpXW0ZEogVAZJH7/Oc/jxNPPBGyLONDH/oQfvKTnxjfZ7NZrF271siAEwwG8Z///Ac333wzEokEJk2ahA996EO46qqrHO9blgFNzUC1ZA/JKhJ6B4LwywqawllABVQAPgmIhcV3Pf3qoHSS5dAUCZoiI5fcgazGKbkZhmEYhhm55Ab80JRx0CQVqs20kbUYXzlF1YD4QAA5VUZzQwY+SYOqF0NqCovv9ibkQem6S6FVqNFkB7YU1Z4RI4ra2trKFmqdMmWKUc8CACZNmoT//ve/nuxbkgBNy0FTxUVhFg7LIRZKQdLy69b5JSAWyiGeCqM36StZgKwQTZWhaT4omTgUmYu3MgzDMAwzclEyIWhaJzRVgWajVk+txldOMAuzArFwEn5JhWZpugQgFsqiNxVGb9JfssBr/jZHb/HW733ve7jyyivxpS99CTfffLN3jaoBI0YUDSWKAsi+CHwBP7I5oC8jIeCnLCjFC4f5AoDPr+fZzwRt+cCqMiBLEkItByDMdYoYhmEYhhnBpBOA7A9A9mvwVSgrVMvxlV1UDYgnJKgAWhs1BPylB2OtAeFe15dprBhjpIxS758XXngBP//5zz0LW6k1IyKmaLhQ6ONa6SYMcIwRwzAMwzBMWYbD+KowhqhSIgVK112vGKORlpK7v78f5513Hn75y1+itbW1BmfEe1gU2SSrSI5uWIKFEcMwDMMwTHGcCiLCy/GVU0FE1FMYDQdRFI/H837S6dKhHp/73OewZMkSLF68uHYnxWNYFNlAg4S+tM91JWUWRgzDMAzDMPm4FUSEF+Mrt4KIqJcwGg6iaNKkSWhubjZ+li1bVrStf/rTn7BixYqS3w9XOKbIBpoUgF/S0ByFa79VunF7uI4RwzAMwzCjnGoFEVHN+KpaQUSQMOpN1K6O0XBItLBlyxbEYjHj81AoNGjZLVu24Etf+hKWL1+OcDhctzZ6AYsiW2hoDCuQpepOFwsjhmEYhmFGO14JIsLN+MorQUTUWhgNB1EUi8XyRFExXnrpJezatQuHHnqo8ZmiKHjiiSdwyy23IJ1Ow1cp68YQwaLIBjKykBCCpilVb8vvA5ojQG/Ch54E0BxRjBtX0wBNk6HlBqBmq89pzzAMwzAMM1RoOVFqRNNUI7V2VhFjIJ9PQyyiQEJ+2m23lBtfFaJqQG/SB0XR0BxV4Pd50wYJQCwitt2TAJqjCgI+QNNGz5juxBNPxKpVq/I+u+iiizB79mxcfvnlw1YQASyKbOGTAU1NQs16tD0ATUEZ8XQEPX0qmsNJUQtJkaEpAaTj65BOp7zZGcMwDMMwzBCQToeh5eZA07JQVVXUIUpH4JMyiAWSQE4UZvWKUuMrK6IOUQSKpiEWSsKnqvCgtmoesQDQq0TQ0y8jFkrCi6McDpYiOzQ1NeGggw7K+ywajaK9vX3Q58MNFkU2UFRAkiOQA96p21AAaAkAvYkg4tkgmiMKJBmQJBmh2EyEIqNnVoFhGIZhmH2PUFKG5A9D8gehAOgb8MHvJytOU232WWR8RRYjVQPiSR9UAC2NCgK+2hWFbAkIi1FfJoaGYPWz6iNFFI1kWBTZQRNiRZK8NfkF/UBLVPjVxpN+RMMaAAmSvwFy8ZplDMMwDMMwIwLJL8ZPiiKhPy0JF7eoBtnj8VQhheOr5qjwjYsnJSgq0BLVEPDXtg0+SbShNyGhP139oG4ki6LHH3/cm4bUGBZFQ4w1ODCekCpWfGYYhmEYhhkpZHJAJich6PMmqYJdrOOrnn6xU0XzJqmCXSj5wp549QFLI1kUjRRYFA0DAn6gOaJhT78MyYtIP4ZhGIZhmGGAqgKaJqE5qtY94y6Nr3oSoixnS1StmyAiZAloDFefqItFUe3h4q3DAFUDEqnRccExDMMwDDP6GEjXf5xTOL5KpNwVeK2WdJaH2yMBthQNMdY8+c0NKnIqiyOGYRiGYfYNQgEg4NOQ0EWRiJ+uPdbxVWtUJK8aijqRiZSEpAeiiC1FtYdF0RBSWDgMEqDmhrpVDMMwDMMw3iBJQENIg09G3YRRqcKsTgu8VksiJSGRlhAJsPvcSIBFkQ18PkBVklCy3l0UIk9+GIoqIxZOQdZU5HIyVMWHdM96pFJpz/bFMAzDMAxTb9KpENTcdKiagrBPheoPoH8gCFXJIOJBmupiFBtfKfquZOh1jFJh7O1T0RxODapj5BXJTADJTBCRYAYBOVP19lgU1R4WRTbQNECS/JBkb3xCVQ2IpwJQNBnNkQwCPh8AHyRNgqTK8AVj8IXYZMQwDMMwzMjFp/ohST5IsgRJ1hANA5KkIJEJQZJ8iISqt6BYKTW+shKUgWY5h96BIOLpCGINWc8tRsm0D8msH9FQDpEQkM1WP9xmUVR7WBTZQNF8kOQgZF/1p0vVgL6EBEUDWqMaAv6Q8Z0EQNIk+CPjEahdPTGGYRiGYZia45cAyReA5NMg69qkMQJIsoREOqQLJW9c6cqNrwoJ+YBWGehJ+tGX8nvqSidiiCQ0hjVEwwEAAUhK9RPdLIpqD6fDsIEGPwYy1Z+qUj6uDMMwDMMwo4VoWEM0JJIveJF91834iuoY5VSxrhdZ6SiGKBryTuwx9YOH5TaQkEMyK8Ofklxf5CyIGIZhGIZhBDSeqjb5QjXjK2uB12qTL9RDEI0Wi81QwUNzG0iagkhARSItTpfTi50FEcMwDMMwTD7VCiMvxldeCKN6CSJ2n6stPDy3SUNQhV91nmefBRHDMAzDMExx3AojL8dX1QijernMsSiqPTxEd4DTG5cFEcMwDMMwTHmGw/jKjTCqZwwRi6Law8N0G8gyoKkZqEoODQFAU33oT/mhqbmS6SRVDYgPBJBTZTQ3ZOCTNKgVMk9qigRNkZFL7kBW45TcDMMwDMOMXHIDfmjKOGiSChXlRUMtx1d28UlALCyhdyCInn61bLruZNqHRMaPaDCHhoBStg2aqlbdNhZFtYdFkQ0kCdC0HDRVXBQNgSw0LYBEOghNUwYVIDMLhwGxcBJ+SYVm437QVBma5oOSiUORuXgrwzAMwzAjFyUTgqZ1QlMVaFLlgVCtxldO8EtALJRDPBVGb9JXtMCrKMzqQySYFm2u0AZN40x0IwEWRTZQFED2ReCz2GebAoDsk5BIRyD7TLOpqgHxhAQVQGujhoDffsEhVQZkSUKo5QCEuU4RwzAMwzAjmHQCkP0ByH4NPl/l5YHajK+c4gsAPr9exygTzHOlS6QkDOQkNDZoiIbDAMIVt6d44P3DlqLaw6KoCgp9YBtCGscQMQzDMAzDVMFwGF8VizEaSA9dHSIWRbWHh+1VYr1xE2kJkqSxIGIYhmEYhqmC4TC+sgqjrrgs2jVEhVlZFNUeHrp7QEPITNUd8IEFEcMwDMMwTJUMh/FVwC/2ncmZbRoKWBTVHh6+VwmlhZQkTb9pJCRS7iszMwzDMAzDjHaGy/gqkZKQyUkI+jVkFbgu8FotLIpqD4uiKiiWJz+Rcl+ZmWEYhmEYZrQzXMZXhXWIsjm4KvDKjAxYFNlBAjRNhaaZSehVDehN+qAoGpqjCvw+kSoyEgI0yOhPydCgIRqynytS0wBNk6HlBqBmPc4xyTAMwzAMU0e0nCg1IsZQ9tapxfjKDYm0jERKQjSsIBIS7ff7gOYI0JvwoScBNEcUW8JI8yBvOFuKag+LIhv4ZEBTk1D1dPkiT34EiqYhFkrCp6qw1uVqkAHNH0RiIAQtl0YkmLG1H02RoSkBpOPrkE6nanAkDMMwDMMw9SGdDkPLzYGmZaHaKGBaq/GVU5KZIJLZECKBATTIGWP8BwA+AE1BGfF0BD19KprDyUF1jAYdlwf6jUVR7WFRZANFBSQ5AjngE3nykz6oAFoaFQR8xfPkNwYAKS0jkYpC8jfYmtGQZECSZIRiMxGKsKWIYRiGYZiRSygpQ/KHIfmDkCvUKarl+MoJibSMZE5GtEFFNBQCEBq0TCgAtASA3kQQ8WywosVIsngauYVFUe1hUWQHTYgVDT7EkxIUFWiJagj4y9/hjWFAApBI+yBBrugDK0n6Re9vgBzwsP0MwzAMwzB1RvKL8ZMY0JdeTgii2o2v7JJISUimJTSGNUTDEoRdqDhBP9ASFTFG8aS/bIyRJFXfPhZFtYdFkU2KBf3ZobAAGSdfYBiGYRiGEQyX8VVhUgU7FCvwWqvkCyyKao881A0YKfSnfK4rKUfDGqJ6rv1EanRcWAzDMAzDMOVwK4gIr8ZXbgQRQcIop4pjUXnue8TCliIbqFIAOU1Ce9R9JWW2GDEMwzAMwwiqFUREteOragQRUQ+LEVuKag+LIltIaAopFX1cK8HCiGEYhmGY0Y5XgohwO77yQhARtRZGLIpqD4siG0haFgFf2JNtsTBiGIZhGGa04rUgIpyOr7wUREQthRGLotrDosgGfp8GVUlCyXpzUYR9gOoPoH8gCFXJIBIUCfBVRYaq+JDuWY9UKu3JvhiGYRiGYYaCdCoENTcdqqZAUVW9DlEYiiojFk5B1lQo2crbsUup8VUhyUwAyUwQkWAGYV/W0zbI0OsYpcL4/+3de4xcdf3/8efnXGZmZ3ZnC9oLTQuWoOUnUCq3BkFFrRBiCPUPYwiJYExMzEKs9RJIlAJBi5qYasBKvn9QJSFoSAoJUQhWaeXLxVJSU/wpQYJpSWlBpXuZ2bmccz7fP87Onu623cvMmZ3dndcj2YQOM3veM+89Zz7v87m9PxzRn6sQhekUXd1SnHSKiqIZsBaM8TBOeutSFHJgTEiplsUYl3w2xFiDiRzcTBE3G6R2LBEREZG55kYexrgYx2CNZajiE1qH/nwN33WZasnrZp2qfXWictWlXPcoZAPyWYD090DJONDvBAyOZhiq5sl7rd/oVk9R+6komoEoAuNkcNx0P67ePBjHUKpmMY4l41uMNXj5s/BPvWeZiIiIyILgGTCuD45luGIILZxRsPjeyRuipmly+2p8aF3FUK439iHyaUdB1JB14QwHjpc9Rmpa7HkhUFHUYSeOgQ0j0AwjERERWSzCCCpVQ0S6c4imM3mOUeO/05xDNJ3GHKP/jLTe06KeovZTUTQPNE7O4YrBd1UWiYiIyOJQD6AeGT7QG81ZQdRw0uILc1gQNfge9E0awtcMFUXtp6JIRERERGQeU1HUfiqK5oHGspD5jNXwOREREVk0fA+w8TLVczl8DiYuuw2d2Q6lHsBwtfUFJVQUtZ+Kog478YTN+JZa0B1/eCIiIrL4uQ7k8pZSxcxpYXS6fYjmsjCqB3C8bPBM1PLvUlHUfiqKZsBxwEY1ojDdZbLLVZdSzaOQCejxQ4LQYEOHoHyUutWS3CIiIrJwBaMeNlwBJqIvZxka9Xm/5NDfU2vrHOrJ7atobEpPjw82chmpeNgoOGm57jTVQ8PgaAbPCcn72ntyIVBRNAPGgLUBNkqvUo43DnPJZ6r0+HVsBDZysNYlrA0ROjqBREREZOEKa1msXYqNQoyJKGbrDFZyDJY9irkKvtt6D8pkp2pfnajHr2OtT6mawdrwtBu8tqIexpu3uk5AMVshSGHzVvUUtZ+KohkIQoPj5nFT6u8tVQyjgaG3x1LI5YAcAJEDjjFkl6wlp32KREREZAGrlsDxfBzP4o5NqznDh8GSYbjWm/pQutO1rybr88FxDaVqHsdNd0W6egDDNYPvQX/B4hifqNb66B8VRe23YHaT+sEPfsDHP/5x8vk8S5YsmdFrrLXcddddnHXWWfT09LBx40beeOONWR/bGp96mM4fxOnGuIqIiIgsdo6JiwXPiefb1FOaLTDb9lUhZylkLaWqoVRJp403PofIaRREqfxaICmKOvXTDRZMUVSr1fjiF7/I17/+9Rm/5sc//jE///nP+eUvf8nLL79MoVDguuuuo1KpzPLoluGq2/KJq4JIREREul3ahVGz7as0C6N2FkSgomguLJii6J577uGb3/wmF1100Yyeb61l+/btfO973+PGG29k3bp1/PrXv+bIkSM88cQTszq2Y+t4xrZ04qogEhEREYmlVRi12r5KozBqd0EEC6so2rZtG5dffjl9fX0sW7aMTZs28frrr6f/oaRswRRFs/XWW29x9OhRNm7cOP5Yf38/GzZs4MUXXzzt66rVKkNDQxN+AHpzYdMnrgoiERERkYlaLYzSal+1UhjNRUG00OzZs4eBgQFeeuklnn32Wer1Otdeey2lUqnToU1p0RZFR48eBWD58uUTHl++fPn4/zuVbdu20d/fP/6zevVqoPkTVwWRiIiIyKnNl/ZVM4XRXBZEC6mn6Omnn+bWW2/lggsu4OKLL2bnzp0cOnSI/fv3t+nTSUdHV5+74447+NGPfjTlc/7+979z/vnnz1FEcOedd7Jly5bxfw8NDfHr/7kfayMMIcU8DJZdjpegvxDiT7FJcanqUKoYCrmQfDbCTnPOWgvWOthglKie/jKVIiIiInPFBvFWI9ZO3QYy0Nb21Uzls2BxGKk4WCyF7OnbYvUQBksurmsp5kMMnDYOO3ld8CZ0cm5P47iN0VMN2WyWbDY77esHBwcBOPPMM9MPLkUdLYq+9a1vceutt075nHPPPbep371ixQoAjh07xllnnTX++LFjx1i/fv1pX3eqBLsO2KhMNLaUfdGHwTDP8RGHYrZ8ynX2y7UM5XqWvD9Kj1Mbf+1UbOhgQ5/q0BtUq7NdDEJERERk/qhWc9jg/2FtnSiavjBoV/tqNnocsF6G0mgWG1TJZ2onPaceOgxV87imRtEvQwBTvbsUaqJ5URQ1Rk81bN26lbvvvnvK10ZRxObNm7nqqqu48MIL2xViKjpaFC1dupSlS5e25XevWbOGFStWsHv37vEiaGhoiJdffnlWK9gBhBEYJ49zwm2LJX58R2O4Vjzpjkap6lAOHAo9EYVsFpi+igYwDhjjkC1+mGxePUUiIiKycGXLDsbLYbwMzhQ9PydqR/tqtnp9MFWHUqWA8Xom9BjVQxgedfE86M+HOKZv2t9nbNhyTPOhKDp8+DDFYnH88Zn0Eg0MDPDaa6/x/PPPty2+tCyYzVsPHTrEf//7Xw4dOkQYhhw4cACA8847j97eXgDOP/98tm3bxhe+8AWMMWzevJn77ruPD3/4w6xZs4bvf//7rFy5kk2bNs3u4DYuVoxJzkzXwJJCvAHZYNkb34CsVDGUq4benKWQM8AMrwKAMWN/9F4Pjj+7EEVERETmE+M12k+Gmbbn29G+akZvLh7WV6q6GBwKOUs9gMGywXMbc4hmFoMxrY/vmw9FUbFYnFAUTee2227jqaeeYu/evaxatapd4aVmwRRFd911F7/61a/G//2xj30MgD/96U9cc801ALz++uvj4xYBvvvd71Iqlfja177G8ePHufrqq3n66afJ5U69w/FsNSYHDpYMx8sG34VaoEUVRERERJo1X9pXjWOVqoZ6GPcSaZW56Vlruf3229m1axfPPfcca9as6XRIM7JgiqKdO3eyc+fOKZ9jJ81wM8Zw7733cu+997YtrsaJ++8hh1oAGU8FkYiIiEgr5kv7qpCz1MO4KAPoL0QdKYjmQ0/RTA0MDPDoo4/y5JNP0tfXN77qc39/Pz09Pe0IMRULpijqJIshCMGcZkjoaNUQjZ2n1QBGq+A18ckGYTx/aXi0+VhFRERE5oPh0bhdEzQ5pSat9lUrgiA+duO++8iooSc7u+Ks2fc/WaeKotnasWMHwPhIroaHH3542gXWOklF0QyETpH3yx5m9OQ/xjCCyMZ3NFwHgtDwXs3gOsx4/GyDJT7p/vf/+7izGCobRXGXrjHgu8kFyHPjn7lgbRyDtYxPijwxprk6j4Mwee+uMzEmZ4525VI+EspHTPlIKB8J5SOmfCQWWz7CEIZGx/a6mWUcabavmmVtHAfEn38Ywft1w2A5jmPmv6f1ieILqado8sithUJF0QxYDAaLM6m/tF6Pl2B0AH/sk/SBWghBlFxYZ3wcG/8+37MzvvgFAYzUDJ6B3rwdv1CMVg3lmiGfsbO+ozFb1sJw2RBYKObs+F0cL4ChiiEKoe+E2NpltGoYnfSeM14cWzmcGFu7KB8J5SOmfCSUj4TyEVM+EosxH8HYAlKOmV0hk3b7qhlhFB8LIOPGKwQ7zlhsFgjBn2GtE4WLY6GFxU5F0QzEfwshxp64JKNDhINrongdfZs8N+tCLXQJQoOxwcxPXAvGGpyojGOmX5K7HjoMV3J4TkR/roKxjMdR8MFYn3I1g7E18pmUF/JvhGxhqJIjjBz6cxV8JxpfrD/jQH/WYaiSY7g8FmObzqtyzWe0lqGQqZH36xM2DOjPwWAlx/CoQzFXOeW+B2lQPhLKR0z5SCgfCeUjpnwkFms+nMjB2DwGO+Oeora0r2YpLog8DJaMG8bvdSyOjBfHGFqHIIhm+DehomghUFE0ExYMDo0zuh44hNbgGovvWZh0qhsDGRNRC1zqkQcmwnVmckLEv8c4Hmaa59dDw1A1g+dGFHtCHHPy7YpCDowJKdWyGOOSz6Y0qHVMZGGo4hNah/58Dd91mbxEZsaBfidgcDTDUDVPsaee+gTFctWlXPcoZAPyWYjvJyUM8V4CQ6PxZmv9PTV8N927bcpHQvmIKR8J5SOhfMSUj8RizoexprHfCMxgWer2ta9mLowM9cjBABkvOmVB4HsWAktoHQgNvjd1YTT7wYPSCSqKZsACGIMxDrU6hDZeRz/jn36dfGPiuwm1IL6jgImXcZz2OIBxs1NuclYf68b2XegvmCnXye/Ng3EMpWoW46S3cktkYbhkCC2cUbD43uk38Mq6cIYDx8sewxUv1aUsSxVDud7Yt8Bn8hdagwMs6Y33PRiq5Mb3PUiD8pFQPmLKR0L5SCgfMeUjsdjzYWBsZ/rph8+1s301U0FjThfxMZwpGmMZvxGzgcAlM9VQuhmM/pmOeorab46mDi4OE0/Y6Z/vOPFJZYgvfEEKve71AI6XzazWyS/kLIWspVQ1lCqt/2FHNv5yCCJm/OXge/Fzgyh+bZTCtbxUMZSqM9+3oLG8p+fEn2E9aD0G5SOhfMSUj4TykVA+YspHQvlIzIf2VRDFvyspiKZ/TcaPYw5t/B7aqVEUdeqnG6gomqF6aGZ1wjakeeI2cwFtSOtC2swFtCHNC+lsv9Aa0vxiUz4SykdM+UgoHwnlI6Z8JJSPxGwLooY021fNFEQNc1UYqShqPxVFM+JgMbM+YcdfncKJ28oFtKHVC2krF9CGNC6kzX6hNaTxxaZ8JJSPmPKRUD4SykdM+UgoH4lmC6KGNNpXrRREDXNRGKkoaj8VRTNksE2dsA2tnLhpXEAbmr2QpnEBbWjlQtrqF1pDK19sykdC+YgpHwnlI6F8xJSPhPKRaLUgamilfZVGQdTQ7sJIRVH7qSiaoTRWm2nmxE3zAtow2wtpmhfQhmYupGl9oTU088WmfCSUj5jykVA+EspHTPlIKB+JtAqihmbaV2kWRA1zOcdI0qfV52bAEIE1WNv6EphmfNUUN76IuicsJ2kNWLBhlQhLPTQMjmbwnJC+XLx3Qlo7JPT4YCOXkYqHjYLTLu8ZWRga9Qkih/6eGq6xRCmtBOoaKObi93h8JJpyec9y1aVU8yhkAnr8MLUYAPpy8Xt8v+RMudyq8pFQPmLKR0L5SCgfMeUj0a35sKEBG69mZ8fW2J247HaETWcRvanbV5OEkaEeNpbdjvchSisO3wPG3mOtHr/HFLYp6miPTbf0FKkomgkDdqwwSunXkXEDaqEbLyfZ2IDM2rj4igJqUbyJmusEFLPxRm5pnbANPX4da31K1QzWhidtCGdtvGldGEExV8YzETatq/gYz0AxGzBUyTFYdk+5IVy55lOuueQz1TjmlGMwQDFbZ7CSY7DsnXKDvnroKB9jlI+Y8pFQPhLKR0z5SHRzPmzkgM3QqAriTU/NSRuzpuW07asThBHUIzfZmBVSj8N3Qxjb4JXA4Jg0bqqrKGo3FUUzYC0Y42LS6FsdY4CMGVtnP/LAGeu6tRA5BUYqBt9rdLGn0Ld8Gn0+OK6hVM3juMmwgsjCUMkQAWf0Wnyv0LYYXB9cb2zfg1pmwrCCUsUwGhh6eyyFXA7ItS2OM/y463+41jthWEE9gOGa8gHKh/KhfCgfCeUjoXzEJuejkLNx48bEn0UyZM6hXTM4TtW+auxjFERQjxpD5gyO075mcMZpDBN0iFK4qa6iqP00p6iDJo+BjSKwEQyNpjvmeDqTxyS3Y8zxdE41JjntMeDTOdUY8XaMAZ+O8hFTPhLKR0L5iCkfCeUjMd/yMTRqsBEEQbpziKZzqjlG7ZhDNJ3GHCNLekWRFlpoH/UUdVjjxI13Zo4fy87hBbSh8aVRqsZfJMbYObuANjQupMfLhn8PxVerufpCa2h8sQ2WDO+X4hh81yofygegfCgfCeUjoXzElI9EIx//HSvIYO4KooYJ7auxhTDmsiBqyPhQqc3d36E0Tz1F84DjgHfCxSqfmdsLaENPNjlpfZc5vYCOH9eLj32qmOaKY5jwRVrIKR+nimmuKB8J5SOhfMSUj4TykZiP+fDc0z+3XSa3r7w5LogaTrfgw2yop6j9VBTNAyd26RpguNLaTtnNaHSxG2PJeJZa0NpO2c0qVQy1wJDxLMbYlnfKbkY9gMGywXMsnmMZbHHn8mYoHwnlI6F8xJSPhPKRUD5i8y0fjaKwNjZNYC5Nbl81u8FrK6IIgqj15raKovZTUdRhJ56wvgeuE/80u1N2MyaPOV5SaG2n7GadOAZ8ScG2vFN2M04cA76k17Kkt7Wdy5uhfCSUj4TyEVM+EspHQvmIzbd85DMW1417jCxzWxhNnkPU7AavrYii+D2nQUVR+2lO0SzYlNfQDC3UAzN2wlow8Tr7xVzISNXheAn6C+GE7ue0RRYGyy5haOkvhHhuvNpePgsWh5GKg8VSyLb3ClKqOpQqhkIuJJ+N9y3wXOjPw2DJjT+LfNjWYQj1MD6W61qK+XB8WmQxH39GyofyoXwoH6B8NCgfCeUjcWI+sp5lpOLiOOCbuM1TCxjrzWtfDJPbV41jxXOMxopUz+K2MQZr42NZwDWtf+adLE5UFMk4Y4g3bk2xJorXyffG1skPMMQrz2EdbFii6IcMhnmOjzgUs+WT9j1IQ7xvQZ7QWorZMm4UTbiD0+OA9TKURrPYoEo+U0s9BoByLUO5niXvj9Lj1IhO2F7BBfoyDkPVPMeHI/pz5bZcSOthfAzX1Cj6ZQgmbqxX9FE+UD6UD+VD+YgpHwnlIzE5H0Hggu2FKIoLECdu+9SCeD+hduTjVO2rRvst3scIaqEXF0bOyfsYpcHa+BgWi++k01Wkoqj9VBTNQGOforTO3tBCPTrxDsZYGgwQgeMVcF3LEj++6zNcK6Z+hymyMFR2iYAlvSG+e+p9C3p9MFWHUqWA8XpSv8NUqjqUA4dCT0QhmwWyJz0n6xN/FqUMQ/VM6nf86iEMj7p4XuNuYt8pn6d8xJSPhPKRUD5iykdC+Uh0az6c0IBxxlc3cF3iwigw1EI/9R6j07avTmBMYx8jQz3ywUm3xyguiOIeIt+zuMYjmuvJbdIUFUXTiIfMOQQReCl0FYWRIRzbxMt14m7+xqg8a83YmFuLHTuB8n7ASOjy3xGXvmyI77YeQ2RhpOISWOjLhhBZ6lNcGzNOSOhZhkcdwsDQk0nnQjpacyjXDXk/JONE1OtTPz+fCRmuurw/7NCbS+eLrR4ahqsunrHk/ZAwgKn2nVY+EspHTPk4IQblY5zyEVM+ToihC/NRj+I2ThSCMfH7NcTLc4fWUA0MnhOlko+p2len4jqWIHKoB4bI2FRWiItssqiCayzGWiIbF2vQ2jSM4eHhjvXYDA8Pd+S4c83YtCfKLDJvv/02q1ev7nQYIiIiIrKAHT58mFWrVs3qNZVKhTVr1nD06NE2RTUzK1as4K233iKXy3U0jnZSUTSNKIo4cuQIfX19ba/Qh4aGWL16NYcPH6ZYLLb1WNJ5ynd3Ub67i/LdXZTv7jLbfFtrGR4eZuXKlThNbJRUqVSo1dozD2ymMpnMoi6IQMPnpuU4zqyr+lYVi0VdVLuI8t1dlO/uonx3F+W7u8wm3/39/U0fJ5fLLfqCZD7QPkUiIiIiItLVVBSJiIiIiEhXU1E0j2SzWbZu3Uo2e/KyorL4KN/dRfnuLsp3d1G+u4vyvThpoQUREREREelq6ikSEREREZGupqJIRERERES6mooiERERERHpaiqKRERERESkq6komicefPBBPvShD5HL5diwYQN/+ctfOh2StMnevXu54YYbWLlyJcYYnnjiiU6HJG20bds2Lr/8cvr6+li2bBmbNm3i9ddf73RY0iY7duxg3bp145s6Xnnllfz+97/vdFgyB+6//36MMWzevLnToUib3H333RhjJvycf/75nQ5LUqKiaB74zW9+w5YtW9i6dSuvvvoqF198Mddddx3vvvtup0OTNiiVSlx88cU8+OCDnQ5F5sCePXsYGBjgpZde4tlnn6Ver3PttddSKpU6HZq0wapVq7j//vvZv38/r7zyCp/5zGe48cYb+dvf/tbp0KSN9u3bx0MPPcS6des6HYq02QUXXMA777wz/vP88893OiRJiZbkngc2bNjA5ZdfzgMPPABAFEWsXr2a22+/nTvuuKPD0Uk7GWPYtWsXmzZt6nQoMkfee+89li1bxp49e/jkJz/Z6XBkDpx55pn85Cc/4atf/WqnQ5E2GBkZ4ZJLLuEXv/gF9913H+vXr2f79u2dDkva4O677+aJJ57gwIEDnQ5F2kA9RR1Wq9XYv38/GzduHH/McRw2btzIiy++2MHIRKQdBgcHgbihLItbGIY89thjlEolrrzyyk6HI20yMDDA5z//+Qnf47J4vfHGG6xcuZJzzz2Xm2++mUOHDnU6JEmJ1+kAut2///1vwjBk+fLlEx5fvnw5//jHPzoUlYi0QxRFbN68mauuuooLL7yw0+FImxw8eJArr7ySSqVCb28vu3bt4qMf/Winw5I2eOyxx3j11VfZt29fp0ORObBhwwZ27tzJ2rVreeedd7jnnnv4xCc+wWuvvUZfX1+nw5MWqSgSEZkjAwMDvPbaaxqDvsitXbuWAwcOMDg4yOOPP84tt9zCnj17VBgtMocPH+Yb3/gGzz77LLlcrtPhyBy4/vrrx/973bp1bNiwgXPOOYff/va3Gh67CKgo6rAPfvCDuK7LsWPHJjx+7NgxVqxY0aGoRCRtt912G0899RR79+5l1apVnQ5H2iiTyXDeeecBcOmll7Jv3z5+9rOf8dBDD3U4MknT/v37effdd7nkkkvGHwvDkL179/LAAw9QrVZxXbeDEUq7LVmyhI985CP885//7HQokgLNKeqwTCbDpZdeyu7du8cfi6KI3bt3awy6yCJgreW2225j165d/PGPf2TNmjWdDknmWBRFVKvVTochKfvsZz/LwYMHOXDgwPjPZZddxs0338yBAwdUEHWBkZER3nzzTc4666xOhyIpUE/RPLBlyxZuueUWLrvsMq644gq2b99OqVTiK1/5SqdDkzYYGRmZcFfprbfe4sCBA5x55pmcffbZHYxM2mFgYIBHH32UJ598kr6+Po4ePQpAf38/PT09HY5O0nbnnXdy/fXXc/bZZzM8PMyjjz7Kc889xzPPPNPp0CRlfX19J80NLBQKfOADH9CcwUXq29/+NjfccAPnnHMOR44cYevWrbiuy0033dTp0CQFKormgS996Uu899573HXXXRw9epT169fz9NNPn7T4giwOr7zyCp/+9KfH/71lyxYAbrnlFnbu3NmhqKRdduzYAcA111wz4fGHH36YW2+9de4DkrZ69913+fKXv8w777xDf38/69at45lnnuFzn/tcp0MTkRa9/fbb3HTTTfznP/9h6dKlXH311bz00kssXbq006FJCrRPkYiIiIiIdDXNKRIRERERka6mokhERERERLqaiiIREREREelqKopERERERKSrqSgSEREREZGupqJIRERERES6mooiERERERHpaiqKRERERESkq6koEhERERGRrqaiSEREREREupqKIhERERER6WoqikREZNbee+89VqxYwQ9/+MPxx1544QUymQy7d+/uYGQiIiKzZ6y1ttNBiIjIwvO73/2OTZs28cILL7B27VrWr1/PjTfeyE9/+tNOhyYiIjIrKopERKRpAwMD/OEPf+Cyyy7j4MGD7Nu3j2w22+mwREREZkVFkYiING10dJQLL7yQw4cPs3//fi666KJOhyQiIjJrmlMkIiJNe/PNNzly5AhRFPGvf/2r0+GIiIg0RT1FIiLSlFqtxhVXXMH69etZu3Yt27dv5+DBgyxbtqzToYmIiMyKiiIREWnKd77zHR5//HH++te/0tvby6c+9Sn6+/t56qmnOh2aiIjIrGj4nIiIzNpzzz3H9u3beeSRRygWiziOwyOPPMKf//xnduzY0enwREREZkU9RSIiIiIi0tXUUyQiIiIiIl1NRZGIiIiIiHQ1FUUiIiIiItLVVBSJiIiIiEhXU1EkIiIiIiJdTUWRiIiIiIh0NRVFIiIiIiLS1VQUiYiIiIhIV1NRJCIiIiIiXU1FkYiIiIiIdDUVRSIiIiIi0tX+DwpmuKg+/k/DAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(10, 4))\n",
    "sim_final = make_adjoint_sim(final_par_density, beta=final_beta, unfold=True)\n",
    "sim_final = sim_final.to_simulation()[0].copy(\n",
    "    update=dict(monitors=[field_monitor_xy, mode_monitor] + field_monitor)\n",
    ")\n",
    "sim_final.plot_eps(z=0, source_alpha=0, monitor_alpha=0, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To better understand the resultant design, let's simulate the final structure to obtain its spectral response and field distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:38 -03 </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'final QE light extractor'</span> with task_id               \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-318f5226-2e2b-4aa5-810c-162e67b7c979'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:38 -03\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'final QE light extractor'\u001b[0m with task_id               \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'fdve-318f5226-2e2b-4aa5-810c-162e67b7c979'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m.  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">b-4aa5-810c-162e67b7c979'</span></a>.                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=695443;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=342432;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=695443;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=464612;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=695443;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[32m-318f5226-2e2\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=695443;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[32mb-4aa5-810c-162e67b7c979'\u001b[0m\u001b]8;;\u001b\\.                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "361a2b6984f341ec9d3febb25851e37a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:42 -03 </span>status = queued                                                    \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:42 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42b6cc3ac8064268ac33e600331cb97a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:46 -03 </span>status = preprocess                                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:46 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:28:53 -03 </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.061</span>. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>the billed FlexCredit cost after a simulation run.                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:28:53 -03\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.061\u001b[0m. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get\n",
       "\u001b[2;36m             \u001b[0mthe billed FlexCredit cost after a simulation run.                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>starting up solver                                                 \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>running solver                                                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>To cancel the simulation, use <span style=\"color: #008000; text-decoration-color: #008000\">'web.abort(task_id)'</span> or              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'web.delete(task_id)'</span> or abort/delete the task in the web UI.      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Terminating the Python script will not stop the job running on the \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>cloud.                                                             \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or              \n",
       "\u001b[2;36m             \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.      \n",
       "\u001b[2;36m             \u001b[0mTerminating the Python script will not stop the job running on the \n",
       "\u001b[2;36m             \u001b[0mcloud.                                                             \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "079b82041e7c48bd8657e33134a084c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:29:21 -03 </span>early shutoff detected at <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">48</span>%, exiting.                            \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:21 -03\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m48\u001b[0m%, exiting.                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>status = postprocess                                               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e83f91f745a54e0ea00571caca512b06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:29:34 -03 </span>status = success                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:34 -03\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View simulation result at                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\" target=\"_blank\"><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">b-4aa5-810c-162e67b7c979'</span></a><span style=\"color: #000080; text-decoration-color: #000080; text-decoration: underline\">.</span>                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m            \u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                          \n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=253674;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=625047;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=253674;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=313416;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=253674;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[4;34m-318f5226-2e2\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m             \u001b[0m\u001b]8;id=253674;https://tidy3d.simulation.cloud/workbench?taskId=fdve-318f5226-2e2b-4aa5-810c-162e67b7c979\u001b\\\u001b[4;34mb-4aa5-810c-162e67b7c979'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                         \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7aa35bc85f084d04abe6317e9ba5a1fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:29:42 -03 </span>loading simulation from simulation_data.hdf5                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:29:42 -03\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_data_final = web.run(sim_final, task_name=\"final QE light extractor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this cavity-like system, the extraction efficiency of photons from the QE into the collection waveguide mode is proportional to $\\beta\\times C_{wg}$, where the $\\beta$-factor quantifies the fraction of the QE spontaneous emission emitted in the cavity mode, and $C_{wg}$ is the fraction of the cavity photons coupled to the guided mode `A. Enderlin, Y. Ota, R. Ohta, N. Kumagai, S. Ishida, S. Iwamoto, and Y. Arakawa, \"High guided mode–cavity mode coupling for an efficient extraction of spontaneous emission of a single quantum dot embedded in a photonic crystal nanobeam cavity,\" Phys. Rev. B 86, 075314 (2012)` [DOI: 10.1103/PhysRevB.86.075314](https://link.aps.org/doi/10.1103/PhysRevB.86.075314). By the field distribution image below, we can see a cavity mode resonance, which should increase the Purcell factor at the QE position, thus contributing to a higher $\\beta$-factor. At the same time, the partial reflection mirror at the left side was potentially optimized to adjust $C_{wg}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax1 = plt.subplots(1, 1, figsize=(12, 4), tight_layout=True)\n",
    "sim_data_final.plot_field(\"field_xy\", \"E\", \"abs^2\", z=0, ax=ax1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To conclude, we will calculate the final coupling efficiency and the cavity Purcell value. The coupling efficiency is above 80% along an extensive wavelength range, and we have confirmed the Purcell enhancement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Coupling efficiency.\n",
    "mode_amps = sim_data_final[\"mode_monitor\"].amps.sel(direction=\"-\", mode_index=0)\n",
    "mode_power = np.abs(mode_amps) ** 2\n",
    "dip_power = np.zeros(n_wl)\n",
    "for i in range(len(field_monitor)):\n",
    "    field_mon = sim_data_final[f\"field_monitor_{i}\"]\n",
    "    dip_power += np.abs(field_mon.flux)\n",
    "coup_eff = mode_power / dip_power\n",
    "\n",
    "# Purcell factor.\n",
    "bulk_power = ((2 * np.pi * freqs) ** 2 / (12 * np.pi)) * (td.MU_0 * n_wg / td.C_0)\n",
    "bulk_power = bulk_power * 2 ** (2 * np.sum(np.abs(sim_final.symmetry)))\n",
    "purcell = dip_power / bulk_power\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), tight_layout=True)\n",
    "ax1.plot(wl_range, coup_eff, \"-k\")\n",
    "ax1.set_xlabel(\"Wavelength (um)\")\n",
    "ax1.set_ylabel(\"Coupling Efficiency\")\n",
    "ax1.set_ylim(0, 1)\n",
    "ax1.set_xlim(wl - bw / 2, wl + bw / 2)\n",
    "ax2.plot(wl_range, purcell, \"-k\")\n",
    "ax2.set_xlabel(\"Wavelength (um)\")\n",
    "ax2.set_ylabel(\"Purcell Factor\")\n",
    "ax2.set_xlim(wl - bw / 2, wl + bw / 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to GDS\n",
    "The `Simulation` object has the [.to_gds_file](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.Simulation.html#tidy3d.Simulation.to_gds_file) convenience function to export the final design to a `GDS` file. In addition to a file name, it is necessary to set a cross-sectional plane (`z = 0` in this case) on which to evaluate the geometry, a `frequency` to evaluate the permittivity, and a `permittivity_threshold` to define the shape boundaries in custom mediums. See the [GDS export](https://www.flexcompute.com/tidy3d/examples/notebooks/GDSExport/) notebook for a detailed example on using `.to_gds_file` and other GDS related functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make the misc/ directory to store the GDS file if it doesnt exist already\n",
    "import os\n",
    "if not os.path.exists('./misc/'):\n",
    "    os.mkdir('./misc/')\n",
    "\n",
    "sim_final.to_gds_file(\n",
    "    fname=\"./misc/inv_des_light_extractor.gds\",\n",
    "    z=0,\n",
    "    permittivity_threshold=(eps_max + eps_min) / 2,\n",
    "    frequency=freq,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "description": "This notebook demonstrates how to set up and run a quantum emitter light extractor optimization.",
  "feature_image": "./img/adjoint_12.png",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "inverse design, adjointt optimization, quantum emitter, light extractor, single-photon source, nanocavity optimization, 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": "3.11.7"
  },
  "title": "How to perform the inverse design of a quantum emitter light extractor in Tidy3D FDTD"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}