From 39c30882a5e4190970f890bad1696c18e8324b95 Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Tue, 17 Feb 2026 11:49:05 +0100
Subject: [PATCH 01/10] feat: callable API for MEEP models, energy_decay
 runner, viz refactors

- Add __call__(**kwargs) to Geometry, ModeSource, Domain, FDTD for
  fluent updates: sim.source(port="o1", wavelength=1.55)
- Add energy_decay stopping mode to StoppingConfig and cloud runner
  using mp.stop_when_energy_decayed() with _make_time_cap() callable
- Extract shared helpers: stack/_layer_utils.py (layer classification),
  viz/_mesh_helpers.py (Delaunay triangulation, batch prism geometry)
- Remove Three.js backend (render3d_threejs.py + viewer.html)
- Add view buttons (Iso/Top/Front/Right) to Open3D+Plotly 3D viewer
- Trim gsim.common public exports to essentials
- Update notebooks to callable style, add meep_ring_coupler notebook
- Update meep-overview.md with all architectural changes
---
 nbs/meep_ring_coupler.ipynb               | 148 +++++++
 nbs/meep_ybranch.ipynb                    |  43 +-
 src/gsim/common/__init__.py               |  35 +-
 src/gsim/common/stack/_layer_utils.py     | 115 ++++++
 src/gsim/common/stack/extractor.py        |  89 +---
 src/gsim/common/stack/visualization.py    |  55 +--
 src/gsim/common/viz/__init__.py           |   3 -
 src/gsim/common/viz/_mesh_helpers.py      |  98 +++++
 src/gsim/common/viz/render3d.py           |   2 -
 src/gsim/common/viz/render3d_open3d.py    | 127 +++---
 src/gsim/common/viz/render3d_pyvista.py   |  93 +----
 src/gsim/common/viz/render3d_threejs.py   | 233 -----------
 src/gsim/common/viz/templates/viewer.html | 482 ----------------------
 src/gsim/meep/__init__.py                 |  11 +-
 src/gsim/meep/meep-overview.md            |  62 ++-
 src/gsim/meep/models/api.py               | 123 +++++-
 src/gsim/meep/models/config.py            |  56 ++-
 src/gsim/meep/script.py                   |  28 +-
 tests/meep/test_meep_models.py            |  13 +
 tests/meep/test_simulation.py             | 102 ++++-
 20 files changed, 793 insertions(+), 1125 deletions(-)
 create mode 100644 nbs/meep_ring_coupler.ipynb
 create mode 100644 src/gsim/common/stack/_layer_utils.py
 delete mode 100644 src/gsim/common/viz/render3d_threejs.py
 delete mode 100644 src/gsim/common/viz/templates/viewer.html

diff --git a/nbs/meep_ring_coupler.ipynb b/nbs/meep_ring_coupler.ipynb
new file mode 100644
index 00000000..8ee7e83e
--- /dev/null
+++ b/nbs/meep_ring_coupler.ipynb
@@ -0,0 +1,148 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Running MEEP Simulations\n",
+    "\n",
+    "[MEEP](https://meep.readthedocs.io/) is an open-source FDTD electromagnetic simulator. This notebook demonstrates using the `gsim.meep` API to run an S-parameter simulation on a photonic Y-branch.\n",
+    "\n",
+    "**Requirements:**\n",
+    "\n",
+    "- UBC PDK: `uv pip install ubcpdk`\n",
+    "- [GDSFactory+](https://gdsfactory.com) account for cloud simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "### Load a pcell from UBC PDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ubcpdk import PDK, cells\n",
+    "\n",
+    "PDK.activate()\n",
+    "\n",
+    "c = cells.coupler_ring()\n",
+    "# ebeam_DC_te895\n",
+    "# ebeam_MMI_2x2_5050_te1310\n",
+    "# ebeam_YBranch_te1310\n",
+    "# ebeam_adiabatic_te1550\n",
+    "# ebeam_crossing4\n",
+    "# ebeam_y_1310\n",
+    "# ebeam_y_adiabatic\n",
+    "\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "### Configure and run simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gsim import meep\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry(component=c, z_crop=\"auto\")\n",
+    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
+    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
+    "sim.monitors = [\"o1\", \"o2\", \"o3\", \"o4\"]\n",
+    "sim.domain(pml=1.0, margin=0.5)\n",
+    "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
+    "\n",
+    "print(sim.validate_config())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.plot_2d(slices=\"xyz\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "### Run simulation on cloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run on GDSFactory+ cloud\n",
+    "result = sim.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.plot(db=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.show_animation()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gsim",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/nbs/meep_ybranch.ipynb b/nbs/meep_ybranch.ipynb
index 480f15d0..49ce4731 100644
--- a/nbs/meep_ybranch.ipynb
+++ b/nbs/meep_ybranch.ipynb
@@ -57,35 +57,12 @@
     "\n",
     "sim = meep.Simulation()\n",
     "\n",
-    "sim.geometry.component = c\n",
-    "sim.geometry.z_crop = \"auto\"\n",
-    "\n",
+    "sim.geometry(component=c, z_crop=\"auto\")\n",
     "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
-    "\n",
-    "sim.source.port = \"o1\"\n",
-    "sim.source.wavelength = 1.55\n",
-    "sim.source.bandwidth = 0.01\n",
-    "sim.source.num_freqs = 11\n",
-    "\n",
+    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
     "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
-    "\n",
-    "sim.domain.pml = 1.0\n",
-    "sim.domain.margin = 0.5\n",
-    "sim.domain.margin_z_above = 0.5\n",
-    "sim.domain.margin_z_below = 0.5\n",
-    "sim.domain.port_margin = 0.5\n",
-    "\n",
-    "sim.solver.resolution = 20\n",
-    "sim.solver.stopping = \"dft_decay\"\n",
-    "sim.solver.max_time = 200\n",
-    "sim.solver.stopping_threshold = 1e-3\n",
-    "sim.solver.stopping_min_time = 100\n",
-    "sim.solver.subpixel = False\n",
-    "sim.solver.simplify_tol = 0.01\n",
-    "sim.solver.save_geometry = True\n",
-    "sim.solver.save_fields = True\n",
-    "sim.solver.save_animation = True\n",
-    "sim.solver.verbose_interval = 5.0\n",
+    "sim.domain(pml=1.0, margin=0.5)\n",
+    "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
     "\n",
     "print(sim.validate_config())"
    ]
@@ -142,12 +119,20 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "gsim",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,
diff --git a/src/gsim/common/__init__.py b/src/gsim/common/__init__.py
index e1f52134..fd9c3804 100644
--- a/src/gsim/common/__init__.py
+++ b/src/gsim/common/__init__.py
@@ -12,50 +12,17 @@
 
 from gsim.common.geometry import Geometry
 from gsim.common.geometry_model import GeometryModel, Prism, extract_geometry_model
-from gsim.common.stack import (
-    MATERIALS_DB,
-    Layer,
-    LayerStack,
-    MaterialProperties,
-    StackLayer,
-    ValidationResult,
-    extract_from_pdk,
-    extract_layer_stack,
-    get_material_properties,
-    get_stack,
-    load_stack_yaml,
-    material_is_conductor,
-    material_is_dielectric,
-    parse_layer_stack,
-    plot_stack,
-    print_stack,
-    print_stack_table,
-)
+from gsim.common.stack import LayerStack, ValidationResult
 
 # Alias for backward compatibility
 Stack = LayerStack
 
 __all__ = [
-    "MATERIALS_DB",
     "Geometry",
     "GeometryModel",
-    "Layer",
     "LayerStack",
-    "MaterialProperties",
     "Prism",
     "Stack",
-    "StackLayer",
     "ValidationResult",
-    "extract_from_pdk",
     "extract_geometry_model",
-    "extract_layer_stack",
-    "get_material_properties",
-    "get_stack",
-    "load_stack_yaml",
-    "material_is_conductor",
-    "material_is_dielectric",
-    "parse_layer_stack",
-    "plot_stack",
-    "print_stack",
-    "print_stack_table",
 ]
diff --git a/src/gsim/common/stack/_layer_utils.py b/src/gsim/common/stack/_layer_utils.py
new file mode 100644
index 00000000..229a90ce
--- /dev/null
+++ b/src/gsim/common/stack/_layer_utils.py
@@ -0,0 +1,115 @@
+"""Shared layer classification and GDS-layer extraction helpers.
+
+Used by both ``extractor`` and ``visualization`` modules.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Literal
+
+from gdsfactory.technology import LayerLevel
+
+logger = logging.getLogger(__name__)
+
+
+def get_gds_layer_tuple(layer_level: LayerLevel) -> tuple[int, int] | None:
+    """Extract GDS layer tuple (layer, datatype) from a gdsfactory LayerLevel.
+
+    Returns ``None`` when the layer cannot be parsed (callers decide fallback).
+    """
+    layer: Any = layer_level.layer
+
+    if isinstance(layer, tuple):
+        return (int(layer[0]), int(layer[1]))
+
+    if isinstance(layer, int):
+        return (int(layer), 0)
+
+    if hasattr(layer, "layer"):
+        inner = layer.layer
+        if hasattr(inner, "layer") and hasattr(inner, "datatype"):
+            return (int(inner.layer), int(inner.datatype))
+        if isinstance(inner, int):
+            datatype = getattr(layer, "datatype", 0)
+            return (int(inner), int(datatype) if datatype else 0)
+        if hasattr(inner, "layer"):
+            innermost = inner.layer
+            if isinstance(innermost, int):
+                datatype = getattr(inner, "datatype", 0)
+                return (int(innermost), int(datatype) if datatype else 0)
+
+    if hasattr(layer, "layer") and hasattr(layer, "datatype"):
+        return (int(layer.layer), int(layer.datatype))
+
+    if hasattr(layer, "value"):
+        if isinstance(layer.value, tuple):
+            return (int(layer.value[0]), int(layer.value[1]))
+        if isinstance(layer.value, int):
+            return (int(layer.value), 0)
+
+    if isinstance(layer, str):
+        if "/" in layer:
+            parts = layer.split("/")
+            return (int(parts[0]), int(parts[1]))
+        return None
+
+    try:
+        return (int(layer), 0)
+    except (TypeError, ValueError):
+        logger.warning("Could not parse layer %s", layer)
+        return None
+
+
+def classify_layer_type(
+    layer_name: str,
+    material: str | None = None,
+) -> Literal["conductor", "via", "dielectric", "substrate"]:
+    """Classify a layer as conductor, via, dielectric, or substrate.
+
+    When *material* is provided, the materials DB is consulted for additional
+    classification hints (conductor vs dielectric).  When ``None``, only
+    name-based heuristics are used.
+    """
+    name_lower = layer_name.lower()
+
+    if "via" in name_lower or "cont" in name_lower:
+        return "via"
+
+    if any(
+        m in name_lower for m in ["metal", "topmetal", "m1", "m2", "m3", "m4", "m5"]
+    ):
+        return "conductor"
+
+    if "substrate" in name_lower or name_lower == "sub":
+        return "substrate"
+
+    # Material-based lookup (only when caller supplies material)
+    if material is not None:
+        from gsim.common.stack.materials import get_material_properties
+
+        props = get_material_properties(material)
+        if props:
+            if props.type == "conductor":
+                return "conductor"
+            if props.type == "semiconductor" and "substrate" in name_lower:
+                return "substrate"
+            if props.type == "dielectric":
+                return "dielectric"
+
+        material_lower = material.lower()
+        if material_lower in [
+            "aluminum",
+            "copper",
+            "tungsten",
+            "gold",
+            "al",
+            "cu",
+            "w",
+        ]:
+            return "conductor"
+
+    if "poly" in name_lower or "active" in name_lower:
+        return "conductor"
+
+    return "dielectric"
diff --git a/src/gsim/common/stack/extractor.py b/src/gsim/common/stack/extractor.py
index fd8a6090..63e437a8 100644
--- a/src/gsim/common/stack/extractor.py
+++ b/src/gsim/common/stack/extractor.py
@@ -8,13 +8,13 @@
 
 import logging
 from pathlib import Path
-from typing import Any, Literal
+from typing import Literal
 
 import yaml
-from gdsfactory.technology import LayerLevel
 from gdsfactory.technology import LayerStack as GfLayerStack
 from pydantic import BaseModel, ConfigDict, Field
 
+from gsim.common.stack._layer_utils import classify_layer_type, get_gds_layer_tuple
 from gsim.common.stack.materials import (
     MATERIALS_DB,
     get_material_properties,
@@ -279,87 +279,6 @@ def to_yaml(self, path: Path | None = None) -> str:
         return yaml_str
 
 
-def _get_gds_layer_tuple(layer_level: LayerLevel) -> tuple[int, int]:
-    """Extract GDS layer tuple from LayerLevel."""
-    layer: Any = layer_level.layer
-
-    if isinstance(layer, tuple):
-        return (int(layer[0]), int(layer[1]))
-
-    if isinstance(layer, int):
-        return (int(layer), 0)
-
-    if hasattr(layer, "layer"):
-        inner = layer.layer
-        if hasattr(inner, "layer") and hasattr(inner, "datatype"):
-            return (int(inner.layer), int(inner.datatype))  # type: ignore[arg-type]
-        if isinstance(inner, int):
-            datatype = getattr(layer, "datatype", 0)
-            return (int(inner), int(datatype) if datatype else 0)
-        if hasattr(inner, "layer"):
-            innermost = inner.layer
-            if isinstance(innermost, int):
-                datatype = getattr(inner, "datatype", 0)
-                return (int(innermost), int(datatype) if datatype else 0)
-
-    if hasattr(layer, "layer") and hasattr(layer, "datatype"):
-        return (int(layer.layer), int(layer.datatype))  # type: ignore[arg-type]
-
-    if hasattr(layer, "value"):
-        if isinstance(layer.value, tuple):
-            return (int(layer.value[0]), int(layer.value[1]))  # type: ignore[arg-type]
-        if isinstance(layer.value, int):
-            return (int(layer.value), 0)
-
-    if isinstance(layer, str):
-        if "/" in layer:
-            parts = layer.split("/")
-            return (int(parts[0]), int(parts[1]))
-        return (0, 0)
-
-    try:
-        return (int(layer), 0)  # type: ignore[arg-type]
-    except (TypeError, ValueError):
-        logger.warning("Could not parse layer %s, using (0, 0)", layer)
-        return (0, 0)
-
-
-def _classify_layer_type(
-    layer_name: str, material: str
-) -> Literal["conductor", "via", "dielectric", "substrate"]:
-    """Classify a layer as conductor, via, dielectric, or substrate."""
-    name_lower = layer_name.lower()
-    material_lower = material.lower()
-
-    if "via" in name_lower:
-        return "via"
-
-    if any(
-        m in name_lower for m in ["metal", "topmetal", "m1", "m2", "m3", "m4", "m5"]
-    ):
-        return "conductor"
-
-    if "substrate" in name_lower or name_lower == "sub":
-        return "substrate"
-
-    props = get_material_properties(material)
-    if props:
-        if props.type == "conductor":
-            return "conductor"
-        if props.type == "semiconductor" and "substrate" in name_lower:
-            return "substrate"
-        if props.type == "dielectric":
-            return "dielectric"
-
-    if material_lower in ["aluminum", "copper", "tungsten", "gold", "al", "cu", "w"]:
-        return "conductor"
-
-    if "poly" in name_lower:
-        return "conductor"
-
-    return "dielectric"
-
-
 def extract_layer_stack(
     gf_layer_stack: GfLayerStack,
     pdk_name: str = "unknown",
@@ -392,8 +311,8 @@ def extract_layer_stack(
         thickness = layer_level.thickness if layer_level.thickness is not None else 0.0
         zmax = zmin + thickness
         material = layer_level.material or "unknown"
-        gds_layer = _get_gds_layer_tuple(layer_level)
-        layer_type = _classify_layer_type(layer_name, material)
+        gds_layer = get_gds_layer_tuple(layer_level) or (0, 0)
+        layer_type = classify_layer_type(layer_name, material)
         sidewall_angle = getattr(layer_level, "sidewall_angle", 0.0) or 0.0
 
         if layer_type == "substrate" and not include_substrate:
diff --git a/src/gsim/common/stack/visualization.py b/src/gsim/common/stack/visualization.py
index 18a99731..7bbeb0ef 100644
--- a/src/gsim/common/stack/visualization.py
+++ b/src/gsim/common/stack/visualization.py
@@ -7,12 +7,12 @@
 from __future__ import annotations
 
 from dataclasses import dataclass
-from typing import Any
 
 import plotly.graph_objects as go
-from gdsfactory.technology import LayerLevel
 from gdsfactory.technology import LayerStack as GfLayerStack
 
+from gsim.common.stack._layer_utils import classify_layer_type, get_gds_layer_tuple
+
 
 @dataclass
 class StackLayer:
@@ -27,52 +27,6 @@ class StackLayer:
     layer_type: str = "conductor"  # conductor, via, dielectric, substrate
 
 
-def _get_gds_layer_number(layer_level: LayerLevel) -> int | None:
-    """Extract GDS layer number from LayerLevel."""
-    layer: Any = layer_level.layer
-
-    # Handle tuple
-    if isinstance(layer, tuple):
-        return int(layer[0])
-
-    # Handle int
-    if isinstance(layer, int):
-        return int(layer)
-
-    # Handle LogicalLayer or enum with nested layer
-    if hasattr(layer, "layer"):
-        inner = layer.layer
-        if hasattr(inner, "layer"):
-            return int(inner.layer)  # type: ignore[arg-type]
-        if isinstance(inner, int):
-            return int(inner)
-
-    # Handle enum with value
-    if hasattr(layer, "value"):
-        if isinstance(layer.value, tuple):
-            return int(layer.value[0])  # type: ignore[arg-type]
-        return int(layer.value)  # type: ignore[arg-type]
-
-    return None
-
-
-def _classify_layer(name: str) -> str:
-    """Classify layer type based on name."""
-    name_lower = name.lower()
-
-    if "via" in name_lower or "cont" in name_lower:
-        return "via"
-    if "substrate" in name_lower or name_lower == "sub":
-        return "substrate"
-    if any(
-        m in name_lower
-        for m in ["metal", "topmetal", "m1", "m2", "m3", "m4", "m5", "poly", "active"]
-    ):
-        return "conductor"
-
-    return "dielectric"
-
-
 def parse_layer_stack(layer_stack: GfLayerStack) -> list[StackLayer]:
     """Parse a gdsfactory LayerStack into a list of StackLayer objects.
 
@@ -89,8 +43,9 @@ def parse_layer_stack(layer_stack: GfLayerStack) -> list[StackLayer]:
         thickness = level.thickness if level.thickness is not None else 0.0
         zmax = zmin + thickness
         material = level.material or None
-        gds_layer = _get_gds_layer_number(level)
-        layer_type = _classify_layer(name)
+        tup = get_gds_layer_tuple(level)
+        gds_layer = tup[0] if tup else None
+        layer_type = classify_layer_type(name)
 
         layers.append(
             StackLayer(
diff --git a/src/gsim/common/viz/__init__.py b/src/gsim/common/viz/__init__.py
index e472cc85..030b67d3 100644
--- a/src/gsim/common/viz/__init__.py
+++ b/src/gsim/common/viz/__init__.py
@@ -6,7 +6,6 @@
 3D backends:
     - PyVista  (desktop, ``plot_prisms_3d``)
     - Open3D + Plotly  (Jupyter, ``plot_prisms_3d_open3d``)
-    - Three.js / FastAPI  (browser, ``serve_threejs_visualization``)
 
 2D backends:
     - matplotlib  (``plot_prism_slices``)
@@ -18,7 +17,6 @@
     export_3d_mesh,
     plot_prisms_3d,
     plot_prisms_3d_open3d,
-    serve_threejs_visualization,
 )
 
 __all__ = [
@@ -27,5 +25,4 @@
     "plot_prism_slices",
     "plot_prisms_3d",
     "plot_prisms_3d_open3d",
-    "serve_threejs_visualization",
 ]
diff --git a/src/gsim/common/viz/_mesh_helpers.py b/src/gsim/common/viz/_mesh_helpers.py
index 690b579c..97feb6fa 100644
--- a/src/gsim/common/viz/_mesh_helpers.py
+++ b/src/gsim/common/viz/_mesh_helpers.py
@@ -6,6 +6,8 @@
 
 from __future__ import annotations
 
+from typing import Any
+
 import numpy as np
 
 from gsim.common.geometry_model import GeometryModel, Prism
@@ -72,3 +74,99 @@ def simulation_box_corners(
     ]
 
     return points, lines
+
+
+# ---------------------------------------------------------------------------
+# Shared Delaunay triangulation for polygons with holes / concave polygons
+# ---------------------------------------------------------------------------
+
+
+def triangulate_polygon_with_holes(
+    shapely_polygon: Any,
+    z_base: float,
+    z_top: float,
+) -> tuple[np.ndarray, list[np.ndarray], list[list[int]]] | None:
+    """Triangulate a Shapely polygon (possibly with holes) via Delaunay.
+
+    Returns:
+        ``(verts_3d, valid_triangles, boundary_segments)`` where
+        *verts_3d* is (2*N, 3) — bottom then top copies of the 2D points,
+        *valid_triangles* is a list of index-triplet arrays (into the first
+        N points), and *boundary_segments* is a list of ``[i, j]`` pairs.
+        Returns ``None`` when triangulation fails.
+    """
+    try:
+        import shapely.geometry as sg
+        from scipy.spatial import Delaunay
+    except ImportError:
+        return None
+
+    all_points: list[tuple[float, float]] = []
+    boundary_segments: list[list[int]] = []
+
+    exterior_coords = list(shapely_polygon.exterior.coords[:-1])
+    start_idx = 0
+    all_points.extend(exterior_coords)
+    boundary_segments = [
+        [start_idx + i, start_idx + (i + 1) % len(exterior_coords)]
+        for i in range(len(exterior_coords))
+    ]
+
+    for interior in shapely_polygon.interiors:
+        interior_coords = list(interior.coords[:-1])
+        start_idx = len(all_points)
+        all_points.extend(interior_coords)
+        boundary_segments.extend(
+            [start_idx + i, start_idx + (i + 1) % len(interior_coords)]
+            for i in range(len(interior_coords))
+        )
+
+    if len(all_points) < 3:
+        return None
+
+    points_2d = np.array(all_points)
+    tri = Delaunay(points_2d)
+
+    valid_triangles = [
+        simplex
+        for simplex in tri.simplices
+        if shapely_polygon.contains(sg.Point(*np.mean(points_2d[simplex], axis=0)))
+    ]
+
+    if not valid_triangles:
+        return None
+
+    verts_3d = np.array(
+        [[pt[0], pt[1], z_base] for pt in points_2d]
+        + [[pt[0], pt[1], z_top] for pt in points_2d]
+    )
+
+    return verts_3d, valid_triangles, boundary_segments
+
+
+def collect_triangular_prism_geometry(
+    prisms: list[Prism],
+) -> tuple[np.ndarray, list[int]] | None:
+    """Collect vertices from many triangular prisms for batch meshing.
+
+    Returns:
+        ``(combined_vertices, prism_offsets)`` where *combined_vertices*
+        is (M, 3) and *prism_offsets[i]* is the vertex offset of the
+        *i*-th prism in that array.  Returns ``None`` if *prisms* is empty.
+    """
+    all_vertices: list[np.ndarray] = []
+    prism_offsets: list[int] = []
+    offset = 0
+
+    for prism in prisms:
+        base, top = prism_base_top_vertices(prism)
+        verts = np.vstack([base, top])
+        all_vertices.append(verts)
+        prism_offsets.append(offset)
+        offset += len(verts)
+
+    if not all_vertices:
+        return None
+
+    combined = np.vstack(all_vertices)
+    return combined, prism_offsets
diff --git a/src/gsim/common/viz/render3d.py b/src/gsim/common/viz/render3d.py
index ae71fbcc..db9ad48d 100644
--- a/src/gsim/common/viz/render3d.py
+++ b/src/gsim/common/viz/render3d.py
@@ -12,12 +12,10 @@
     export_3d_mesh,
     plot_prisms_3d,
 )
-from gsim.common.viz.render3d_threejs import serve_threejs_visualization
 
 __all__ = [
     "create_web_export",
     "export_3d_mesh",
     "plot_prisms_3d",
     "plot_prisms_3d_open3d",
-    "serve_threejs_visualization",
 ]
diff --git a/src/gsim/common/viz/render3d_open3d.py b/src/gsim/common/viz/render3d_open3d.py
index 528211b3..778fd215 100644
--- a/src/gsim/common/viz/render3d_open3d.py
+++ b/src/gsim/common/viz/render3d_open3d.py
@@ -13,8 +13,10 @@
 from gsim.common.geometry_model import GeometryModel, Prism
 from gsim.common.viz._colors import generate_layer_colors_with_opacity
 from gsim.common.viz._mesh_helpers import (
+    collect_triangular_prism_geometry,
     prism_base_top_vertices,
     simulation_box_corners,
+    triangulate_polygon_with_holes,
 )
 
 try:
@@ -120,23 +122,58 @@ def plot_prisms_3d_open3d(
         cx = cy = cz = 0.0
         rs = 10.0
 
+    # Standard camera views for view buttons
+    d = 2.0  # distance multiplier for orthographic views
+    _views = {
+        "Iso": dict(eye=dict(x=1.5, y=1.5, z=1.5)),
+        "Top": dict(eye=dict(x=0, y=0, z=d), up=dict(x=0, y=1, z=0)),
+        "Front": dict(eye=dict(x=0, y=-d, z=0), up=dict(x=0, y=0, z=1)),
+        "Right": dict(eye=dict(x=d, y=0, z=0), up=dict(x=0, y=0, z=1)),
+    }
+    view_buttons = [
+        dict(
+            label=name,
+            method="relayout",
+            args=[
+                {
+                    "scene.camera.eye": cam["eye"],
+                    "scene.camera.up": cam.get("up", dict(x=0, y=0, z=1)),
+                    "scene.camera.center": dict(x=0, y=0, z=0),
+                }
+            ],
+        )
+        for name, cam in _views.items()
+    ]
+
     fig.update_layout(
         scene=dict(
             xaxis_title="X (um)",
             yaxis_title="Y (um)",
             zaxis_title="Z (um)",
-            aspectmode="data",
+            aspectmode="cube",
             camera=dict(
                 eye=dict(x=1.5, y=1.5, z=1.5),
                 center=dict(x=0, y=0, z=0),
-                projection=dict(type="perspective"),
+                projection=dict(type="orthographic"),
             ),
             xaxis=dict(range=[cx - rs * 0.6, cx + rs * 0.6]),
             yaxis=dict(range=[cy - rs * 0.6, cy + rs * 0.6]),
             zaxis=dict(range=[cz - rs * 0.6, cz + rs * 0.6]),
         ),
-        title="3D Geometry Visualization",
+        title="",
         dragmode="orbit",
+        updatemenus=[
+            dict(
+                type="buttons",
+                direction="down",
+                x=0.01,
+                y=0.99,
+                xanchor="left",
+                yanchor="top",
+                bgcolor="rgba(255,255,255,0.8)",
+                buttons=view_buttons,
+            ),
+        ],
         **kwargs,
     )
 
@@ -199,16 +236,15 @@ def _convert_prisms_to_open3d(
 
 def _merge_triangular_prisms_o3d(prisms: list[Prism]) -> Any:
     """Merge many triangular prisms into one Open3D mesh for performance."""
-    all_vertices: list[np.ndarray] = []
-    all_triangles: list[list[int]] = []
-    offset = 0
+    result = collect_triangular_prism_geometry(prisms)
+    if result is None:
+        return None
 
-    for prism in prisms:
-        base, top = prism_base_top_vertices(prism)
-        verts = np.vstack([base, top])
-        all_vertices.append(verts)
-        n = len(base)
+    combined, prism_offsets = result
+    n = 3  # triangular prisms
+    all_triangles: list[list[int]] = []
 
+    for offset in prism_offsets:
         # bottom
         all_triangles.append([offset, offset + 2, offset + 1])
         # top
@@ -219,16 +255,11 @@ def _merge_triangular_prisms_o3d(prisms: list[Prism]) -> Any:
             all_triangles.append([offset + i, offset + ni, offset + ni + n])
             all_triangles.append([offset + i, offset + ni + n, offset + i + n])
 
-        offset += len(verts)
-
-    if all_vertices:
-        combined = np.vstack(all_vertices)
-        mesh = o3d.geometry.TriangleMesh()
-        mesh.vertices = o3d.utility.Vector3dVector(combined)
-        mesh.triangles = o3d.utility.Vector3iVector(all_triangles)
-        mesh.compute_vertex_normals()
-        return mesh
-    return None
+    mesh = o3d.geometry.TriangleMesh()
+    mesh.vertices = o3d.utility.Vector3dVector(combined)
+    mesh.triangles = o3d.utility.Vector3iVector(all_triangles)
+    mesh.compute_vertex_normals()
+    return mesh
 
 
 def _create_prism_mesh_o3d(
@@ -273,62 +304,26 @@ def _create_prism_mesh_with_holes_o3d(
     top_vertices: np.ndarray,
 ) -> Any:
     """Create Open3D mesh from a Shapely polygon with holes via Delaunay."""
-    try:
-        import shapely.geometry as sg
-        from scipy.spatial import Delaunay
-    except ImportError:
-        return _create_prism_mesh_o3d(base_vertices, top_vertices)
-
-    all_points: list[tuple[float, float]] = []
-    boundary_segments: list[list[int]] = []
-
-    exterior_coords = list(shapely_polygon.exterior.coords[:-1])
-    start_idx = 0
-    all_points.extend(exterior_coords)
-    boundary_segments = [
-        [start_idx + i, start_idx + (i + 1) % len(exterior_coords)]
-        for i in range(len(exterior_coords))
-    ]
-
-    for interior in shapely_polygon.interiors:
-        interior_coords = list(interior.coords[:-1])
-        start_idx = len(all_points)
-        all_points.extend(interior_coords)
-        boundary_segments.extend(
-            [start_idx + i, start_idx + (i + 1) % len(interior_coords)]
-            for i in range(len(interior_coords))
-        )
-
-    if len(all_points) < 3:
-        return _create_prism_mesh_o3d(base_vertices, top_vertices)
-
-    points_2d = np.array(all_points)
-    tri = Delaunay(points_2d)
-
-    valid_triangles = [
-        simplex
-        for simplex in tri.simplices
-        if shapely_polygon.contains(sg.Point(*np.mean(points_2d[simplex], axis=0)))
-    ]
-
-    if not valid_triangles:
-        return _create_prism_mesh_o3d(base_vertices, top_vertices)
-
     z_base = base_vertices[0, 2] if len(base_vertices) > 0 else 0
     z_top = top_vertices[0, 2] if len(top_vertices) > 0 else z_base + 1
 
-    verts_3d: list[list[float]] = [[pt[0], pt[1], z_base] for pt in points_2d] + [
-        [pt[0], pt[1], z_top] for pt in points_2d
-    ]
+    result = triangulate_polygon_with_holes(shapely_polygon, z_base, z_top)
+    if result is None:
+        return _create_prism_mesh_o3d(base_vertices, top_vertices)
+
+    verts_3d, valid_triangles, boundary_segments = result
+    n_pts = len(verts_3d) // 2
 
-    n_pts = len(points_2d)
+    # bottom triangles
     faces_3d: list[list[int]] = [
         [tri_idx[0], tri_idx[1], tri_idx[2]] for tri_idx in valid_triangles
     ]
+    # top triangles (reversed winding)
     faces_3d.extend(
         [tri_idx[0] + n_pts, tri_idx[2] + n_pts, tri_idx[1] + n_pts]
         for tri_idx in valid_triangles
     )
+    # side quads (2 tris each)
     for seg in boundary_segments:
         i, j = seg
         faces_3d.append([i, j, j + n_pts])
diff --git a/src/gsim/common/viz/render3d_pyvista.py b/src/gsim/common/viz/render3d_pyvista.py
index f614e5d1..2dd13a28 100644
--- a/src/gsim/common/viz/render3d_pyvista.py
+++ b/src/gsim/common/viz/render3d_pyvista.py
@@ -12,7 +12,9 @@
 from gsim.common.geometry_model import GeometryModel, Prism
 from gsim.common.viz._colors import generate_layer_colors
 from gsim.common.viz._mesh_helpers import (
+    collect_triangular_prism_geometry,
     prism_base_top_vertices,
+    triangulate_polygon_with_holes,
 )
 
 try:
@@ -215,16 +217,15 @@ def _convert_prisms_to_meshes(
 
 def _merge_triangular_prisms(prisms: list[Prism]) -> Any:
     """Merge many triangular prisms into one PyVista mesh for performance."""
-    all_vertices: list[np.ndarray] = []
-    all_faces: list[int] = []
-    offset = 0
+    result = collect_triangular_prism_geometry(prisms)
+    if result is None:
+        return None
 
-    for prism in prisms:
-        base, top = prism_base_top_vertices(prism)
-        verts = np.vstack([base, top])
-        all_vertices.append(verts)
-        n = len(base)
+    combined, prism_offsets = result
+    n = 3  # triangular prisms
+    all_faces: list[int] = []
 
+    for offset in prism_offsets:
         # bottom face
         all_faces.extend([3, offset + 2, offset + 1, offset + 0])
         # top face
@@ -233,20 +234,10 @@ def _merge_triangular_prisms(prisms: list[Prism]) -> Any:
         for i in range(n):
             ni = (i + 1) % n
             all_faces.extend(
-                [
-                    4,
-                    offset + i,
-                    offset + ni,
-                    offset + ni + n,
-                    offset + i + n,
-                ]
+                [4, offset + i, offset + ni, offset + ni + n, offset + i + n]
             )
-        offset += len(verts)
 
-    if all_vertices:
-        combined = np.vstack(all_vertices)
-        return pv.PolyData(combined, all_faces)
-    return None
+    return pv.PolyData(combined, all_faces)
 
 
 def _create_prism_mesh(
@@ -288,68 +279,24 @@ def _create_prism_mesh_with_holes(
     top_vertices: np.ndarray,
 ) -> Any:
     """Create PyVista mesh from a Shapely polygon with holes using Delaunay."""
-    try:
-        import shapely.geometry as sg
-        from scipy.spatial import Delaunay
-    except ImportError:
-        return _create_prism_mesh(base_vertices, top_vertices)
-
-    all_points: list[tuple[float, float]] = []
-    boundary_segments: list[list[int]] = []
-
-    exterior_coords = list(shapely_polygon.exterior.coords[:-1])
-    start_idx = 0
-    all_points.extend(exterior_coords)
-    boundary_segments = [
-        [start_idx + i, start_idx + (i + 1) % len(exterior_coords)]
-        for i in range(len(exterior_coords))
-    ]
-
-    for interior in shapely_polygon.interiors:
-        interior_coords = list(interior.coords[:-1])
-        start_idx = len(all_points)
-        all_points.extend(interior_coords)
-        boundary_segments.extend(
-            [start_idx + i, start_idx + (i + 1) % len(interior_coords)]
-            for i in range(len(interior_coords))
-        )
-
-    if len(all_points) < 3:
-        return _create_prism_mesh(base_vertices, top_vertices)
-
-    points_2d = np.array(all_points)
-    tri = Delaunay(points_2d)
-
-    valid_triangles = [
-        simplex
-        for simplex in tri.simplices
-        if shapely_polygon.contains(sg.Point(*np.mean(points_2d[simplex], axis=0)))
-    ]
-
-    if not valid_triangles:
-        return _create_prism_mesh(base_vertices, top_vertices)
-
     z_base = base_vertices[0, 2] if len(base_vertices) > 0 else 0
     z_top = top_vertices[0, 2] if len(top_vertices) > 0 else z_base + 1
 
-    verts_3d: list[list[float]] = [[pt[0], pt[1], z_base] for pt in points_2d] + [
-        [pt[0], pt[1], z_top] for pt in points_2d
-    ]
+    result = triangulate_polygon_with_holes(shapely_polygon, z_base, z_top)
+    if result is None:
+        return _create_prism_mesh(base_vertices, top_vertices)
 
-    n_pts = len(points_2d)
+    verts_3d, valid_triangles, boundary_segments = result
+    n_pts = len(verts_3d) // 2
     faces_pv: list[int] = []
 
+    # bottom triangles
     for tri_idx in valid_triangles:
         faces_pv.extend([3, tri_idx[0], tri_idx[1], tri_idx[2]])
+    # top triangles (reversed winding)
     for tri_idx in valid_triangles:
-        faces_pv.extend(
-            [
-                3,
-                tri_idx[0] + n_pts,
-                tri_idx[2] + n_pts,
-                tri_idx[1] + n_pts,
-            ]
-        )
+        faces_pv.extend([3, tri_idx[0] + n_pts, tri_idx[2] + n_pts, tri_idx[1] + n_pts])
+    # side quads
     for seg in boundary_segments:
         i, j = seg
         faces_pv.extend([4, i, j, j + n_pts, i + n_pts])
diff --git a/src/gsim/common/viz/render3d_threejs.py b/src/gsim/common/viz/render3d_threejs.py
deleted file mode 100644
index d9daf3c0..00000000
--- a/src/gsim/common/viz/render3d_threejs.py
+++ /dev/null
@@ -1,233 +0,0 @@
-"""Three.js / FastAPI-based 3D rendering for GeometryModel.
-
-Provides browser-based interactive 3D visualisation via a local FastAPI server
-that serves a Three.js HTML page.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-from gsim.common.geometry_model import GeometryModel
-from gsim.common.viz._colors import generate_layer_colors_with_opacity
-from gsim.common.viz._mesh_helpers import simulation_box_corners
-from gsim.common.viz.render3d_open3d import _convert_prisms_to_open3d
-
-logger = logging.getLogger(__name__)
-
-# ---------------------------------------------------------------------------
-# Public API
-# ---------------------------------------------------------------------------
-
-
-def serve_threejs_visualization(
-    geometry_model: GeometryModel,
-    *,
-    show_edges: bool = False,
-    color_by_layer: bool = True,
-    show_simulation_box: bool = True,
-    layer_opacity: dict[str, float] | None = None,
-    port: int = 8000,
-    auto_open: bool = True,
-    show_stats: bool = False,
-    **kwargs: Any,
-) -> str:
-    """Start a FastAPI server with a Three.js 3D viewer.
-
-    Args:
-        geometry_model: GeometryModel containing prisms and bbox.
-        show_edges: Show wireframe edges.
-        color_by_layer: Colour each layer differently.
-        show_simulation_box: Draw the simulation box.
-        layer_opacity: Per-layer opacity override.
-        port: Port to serve on (default 8000).
-        auto_open: Open the browser automatically.
-        show_stats: Show FPS counter.
-        **kwargs: Extra Three.js options.
-
-    Returns:
-        URL of the running server.
-    """
-    try:
-        import threading
-        import time
-        import webbrowser
-
-        import uvicorn
-        from fastapi import FastAPI
-        from fastapi.responses import HTMLResponse
-    except ImportError as err:
-        raise ImportError(
-            "FastAPI and uvicorn required. Install with: pip install fastapi uvicorn"
-        ) from err
-
-    app = FastAPI(title="3D Geometry Viewer")
-
-    layer_meshes = _convert_prisms_to_open3d(geometry_model)
-    colors, opacity_dict = generate_layer_colors_with_opacity(
-        list(layer_meshes.keys()), layer_opacity
-    )
-
-    logger.info("Converting geometry: %d layers found", len(layer_meshes))
-    for layer_name, meshes in layer_meshes.items():
-        logger.info("  Layer '%s': %d meshes", layer_name, len(meshes))
-
-    threejs_data = _convert_to_threejs_data(
-        layer_meshes, colors, opacity_dict, color_by_layer
-    )
-
-    if show_simulation_box:
-        threejs_data["simulation_box"] = _create_simulation_box_threejs(geometry_model)
-
-    total_vertices = 0
-    total_faces = 0
-    for layer in threejs_data.get("layers", []):
-        for mesh in layer.get("meshes", []):
-            total_vertices += len(mesh.get("vertices", [])) // 3
-            total_faces += len(mesh.get("faces", [])) // 3
-    logger.info(
-        "Three.js data prepared: %d vertices, %d faces",
-        total_vertices,
-        total_faces,
-    )
-
-    @app.get("/", response_class=HTMLResponse)
-    def get_visualization():
-        """Return the Three.js viewer HTML page."""
-        return _generate_threejs_html(
-            threejs_data,
-            show_edges=show_edges,
-            show_stats=show_stats,
-            **kwargs,
-        )
-
-    @app.get("/api/geometry")
-    def get_geometry_data():
-        """Return geometry mesh data as JSON."""
-        return threejs_data
-
-    @app.get("/api/info")
-    def get_info():
-        """Return summary info about the geometry."""
-        return {
-            "layers": len(threejs_data.get("layers", [])),
-            "total_meshes": sum(
-                len(layer.get("meshes", [])) for layer in threejs_data.get("layers", [])
-            ),
-            "has_simulation_box": "simulation_box" in threejs_data,
-            "layer_names": [
-                layer.get("name") for layer in threejs_data.get("layers", [])
-            ],
-        }
-
-    server_url = f"http://localhost:{port}"
-
-    def run_server():
-        try:
-            uvicorn.run(app, host="127.0.0.1", port=port, log_level="info")
-        except Exception as e:
-            logger.info("Server error: %s", e)
-
-    server_thread = threading.Thread(target=run_server, daemon=True)
-    server_thread.start()
-    time.sleep(1)
-
-    if auto_open:
-        webbrowser.open(server_url)
-
-    logger.info("Server started at: %s", server_url)
-    logger.info("Geometry API available at: %s/api/geometry", server_url)
-    logger.info("Server running in background. Keep Python session alive to view.")
-
-    return server_url
-
-
-# ---------------------------------------------------------------------------
-# Internal helpers
-# ---------------------------------------------------------------------------
-
-
-def _convert_to_threejs_data(
-    layer_meshes: dict[str, list[Any]],
-    colors: dict[str, list[float]],
-    opacity_dict: dict[str, float],
-    color_by_layer: bool,
-) -> dict[str, Any]:
-    """Convert Open3D meshes to Three.js-compatible JSON data."""
-    threejs_data: dict[str, Any] = {"layers": []}
-
-    for layer_name, meshes in layer_meshes.items():
-        layer_color = colors[layer_name] if color_by_layer else [0.7, 0.7, 0.7]
-        layer_opacity = opacity_dict.get(layer_name, 0.8)
-
-        layer_data: dict[str, Any] = {
-            "name": layer_name,
-            "color": [int(c * 255) for c in layer_color[:3]],
-            "opacity": layer_opacity,
-            "meshes": [],
-        }
-
-        for i, mesh in enumerate(meshes):
-            vertices = np.asarray(mesh.vertices).flatten().tolist()
-            faces = np.asarray(mesh.triangles).flatten().tolist()
-
-            layer_data["meshes"].append(
-                {
-                    "vertices": vertices,
-                    "faces": faces,
-                    "id": f"{layer_name}_{i}",
-                }
-            )
-
-        threejs_data["layers"].append(layer_data)
-
-    return threejs_data
-
-
-def _create_simulation_box_threejs(
-    geometry_model: GeometryModel,
-) -> dict[str, Any]:
-    """Create simulation box data for Three.js."""
-    points, lines = simulation_box_corners(geometry_model)
-    return {
-        "vertices": points.flatten().tolist(),
-        "lines": [idx for pair in lines for idx in pair],
-        "color": [0, 0, 0],
-    }
-
-
-def _generate_threejs_html(
-    threejs_data: dict[str, Any],
-    *,
-    show_edges: bool = False,
-    show_stats: bool = False,
-    **kwargs: Any,
-) -> str:
-    """Generate HTML using the viewer.html template."""
-    template_path = Path(__file__).parent / "templates" / "viewer.html"
-
-    with open(template_path) as f:
-        template = f.read()
-
-    width = kwargs.get("width", "100vw")
-    height = kwargs.get("height", "100vh")
-    background_color = kwargs.get("background_color", "#f0f0f0")
-    show_wireframe = str(show_edges).lower()
-    stats_display = "block" if show_stats else "none"
-
-    data_json = json.dumps(threejs_data, indent=2)
-
-    return template.format(
-        geometry_data=data_json,
-        show_wireframe=show_wireframe,
-        show_stats=str(show_stats).lower(),
-        width=width,
-        height=height,
-        background_color=background_color,
-        stats_display=stats_display,
-    )
diff --git a/src/gsim/common/viz/templates/viewer.html b/src/gsim/common/viz/templates/viewer.html
deleted file mode 100644
index 23323878..00000000
--- a/src/gsim/common/viz/templates/viewer.html
+++ /dev/null
@@ -1,482 +0,0 @@
-<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="UTF-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>3D Geometry - Three.js Viewer</title>
-    <style>
-      body {{
-          margin: 0;
-          padding: 0;
-          background-color: {background_color};
-          font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Arial, sans-serif;
-          overflow: hidden;
-      }}
-      #container {{
-          width: {width};
-          height: {height};
-          position: relative;
-      }}
-      #info {{
-          position: absolute;
-          top: 20px;
-          left: 20px;
-          background: rgba(255,255,255,0.95);
-          padding: 15px;
-          border-radius: 8px;
-          z-index: 100;
-          font-size: 14px;
-          box-shadow: 0 4px 12px rgba(0,0,0,0.15);
-          max-width: 300px;
-      }}
-      #controls {{
-          position: absolute;
-          top: 20px;
-          right: 20px;
-          background: rgba(255,255,255,0.95);
-          padding: 15px;
-          border-radius: 8px;
-          z-index: 100;
-          box-shadow: 0 4px 12px rgba(0,0,0,0.15);
-      }}
-      button {{
-          margin: 3px;
-          padding: 8px 12px;
-          background: #007bff;
-          color: white;
-          border: none;
-          border-radius: 5px;
-          cursor: pointer;
-          font-size: 13px;
-          transition: background-color 0.2s;
-      }}
-      button:hover {{
-          background: #0056b3;
-      }}
-      button:active {{
-          background: #004085;
-      }}
-      #legend {{
-          position: absolute;
-          bottom: 20px;
-          left: 20px;
-          background: rgba(255,255,255,0.95);
-          padding: 15px;
-          border-radius: 8px;
-          z-index: 100;
-          max-height: 250px;
-          overflow-y: auto;
-          box-shadow: 0 4px 12px rgba(0,0,0,0.15);
-      }}
-      .legend-item {{
-          display: flex;
-          align-items: center;
-          margin: 4px 0;
-          font-size: 13px;
-      }}
-      .legend-color {{
-          width: 20px;
-          height: 15px;
-          margin-right: 10px;
-          border: 1px solid #ddd;
-          border-radius: 2px;
-      }}
-      #stats {{
-          position: absolute;
-          bottom: 20px;
-          right: 20px;
-          background: rgba(255,255,255,0.95);
-          padding: 10px;
-          border-radius: 8px;
-          z-index: 100;
-          font-size: 12px;
-          box-shadow: 0 4px 12px rgba(0,0,0,0.15);
-          display: {stats_display};
-      }}
-      h3 {{
-          margin: 0 0 8px 0;
-          color: #333;
-      }}
-      h4 {{
-          margin: 0 0 8px 0;
-          color: #555;
-      }}
-      p {{
-          margin: 4px 0;
-          color: #666;
-      }}
-    </style>
-  </head>
-  <body>
-    <div id="container">
-      <div id="info">
-        <h3>🔬 3D Geometry Visualization</h3>
-        <p><strong>Mouse:</strong> Rotate view</p>
-        <p><strong>Wheel:</strong> Zoom in/out (enhanced sensitivity)</p>
-        <p><strong>Right-click:</strong> Pan camera</p>
-        <p><strong>Double-click:</strong> Reset view</p>
-      </div>
-
-      <div id="controls">
-        <button onclick="toggleWireframe()">🔲 Toggle Wireframe</button>
-        <br />
-        <button onclick="resetCamera()">🎯 Reset View</button>
-        <br />
-        <button onclick="toggleSimBox()">📦 Toggle Sim Box</button>
-      </div>
-
-      <div id="legend">
-        <h4>📊 Layers</h4>
-        <div id="legend-content"></div>
-      </div>
-
-      <div id="stats">
-        <div id="fps">FPS: --</div>
-        <div id="vertices">Vertices: --</div>
-        <div id="faces">Faces: --</div>
-      </div>
-    </div>
-
-    <!-- Import maps for Three.js modules -->
-    <script
-      async
-      src="https://unpkg.com/es-module-shims@1.6.3/dist/es-module-shims.js"
-    ></script>
-
-    <script type="importmap">
-      {{
-          "imports": {{
-              "three": "https://unpkg.com/three@0.154.0/build/three.module.js",
-              "three/addons/": "https://unpkg.com/three@0.154.0/examples/jsm/"
-          }}
-      }}
-    </script>
-
-    <script type="module">
-      // Import Three.js modules
-      import * as THREE from 'three';
-      import {{ OrbitControls }} from 'three/addons/controls/OrbitControls.js';
-      import Stats from 'https://unpkg.com/three@0.154.0/examples/jsm/libs/stats.module.js';
-
-      // Geometry data from Python
-      const geometryData = {geometry_data};
-
-      // Three.js setup
-      let scene, camera, renderer, controls, stats;
-      let geometryObjects = [];
-      let simulationBox = null;
-      let showWireframe = {show_wireframe};
-      const SHOW_STATS = {show_stats};
-
-      // Make functions globally available for buttons
-      window.toggleWireframe = toggleWireframe;
-      window.resetCamera = resetCamera;
-      window.toggleSimBox = toggleSimBox;
-
-      function init() {{
-          // Scene
-          scene = new THREE.Scene();
-          scene.background = new THREE.Color(0xf0f0f0);
-
-          // Camera
-          camera = new THREE.PerspectiveCamera(75, window.innerWidth / window.innerHeight, 0.1, 1000);
-          camera.position.set(15, 15, 25);
-
-          // Renderer
-          renderer = new THREE.WebGLRenderer({{
-              antialias: true,
-              alpha: true
-          }});
-          renderer.setSize(window.innerWidth, window.innerHeight);
-          renderer.shadowMap.enabled = true;
-          renderer.shadowMap.type = THREE.PCFSoftShadowMap;
-          renderer.setPixelRatio(window.devicePixelRatio);
-          document.getElementById('container').appendChild(renderer.domElement);
-
-          // Controls with enhanced zoom
-          controls = new OrbitControls(camera, renderer.domElement);
-          controls.enableDamping = true;
-          controls.dampingFactor = 0.05;
-          controls.zoomSpeed = 3.0;  // Enhanced zoom sensitivity for touchpad
-          controls.panSpeed = 1.5;
-          controls.rotateSpeed = 1.0;
-
-          // Stats (only if enabled)
-          if (SHOW_STATS) {{
-              stats = new Stats();
-              stats.dom.style.position = 'absolute';
-              stats.dom.style.top = '0px';
-              stats.dom.style.right = '0px';
-              document.body.appendChild(stats.dom);
-          }}
-
-          // Lighting setup
-          const ambientLight = new THREE.AmbientLight(0xffffff, 0.6);
-          scene.add(ambientLight);
-
-          const directionalLight = new THREE.DirectionalLight(0xffffff, 0.8);
-          directionalLight.position.set(30, 30, 30);
-          directionalLight.castShadow = true;
-          directionalLight.shadow.mapSize.width = 2048;
-          directionalLight.shadow.mapSize.height = 2048;
-          scene.add(directionalLight);
-
-          // Load geometry
-          loadGeometry();
-
-          // Create legend
-          createLegend();
-
-          // Update stats
-          updateStats();
-
-          // Handle window resize
-          window.addEventListener('resize', onWindowResize, false);
-
-          // Start render loop
-          animate();
-
-          console.log('✅ Three.js viewer initialized successfully');
-      }}
-
-      function loadGeometry() {{
-          let totalVertices = 0, totalFaces = 0;
-
-          console.log('Loading geometry data:', geometryData);
-
-          if (!geometryData || !geometryData.layers) {{
-              console.error('No geometry data available');
-              return;
-          }}
-
-          geometryData.layers.forEach(layer => {{
-              console.log(`Loading layer: ${{layer.name}}, meshes: ${{layer.meshes.length}}`);
-              const color = new THREE.Color(`rgb(${{layer.color[0]}},${{layer.color[1]}},${{layer.color[2]}})`);
-
-              layer.meshes.forEach(meshData => {{
-                  console.log(`  Mesh vertices: ${{meshData.vertices.length}}, faces: ${{meshData.faces.length}}`);
-
-                  // Create geometry
-                  const geometry = new THREE.BufferGeometry();
-
-                  // Set vertices
-                  const vertices = new Float32Array(meshData.vertices);
-                  geometry.setAttribute('position', new THREE.BufferAttribute(vertices, 3));
-
-                  // Set faces
-                  const indices = new Uint16Array(meshData.faces);
-                  geometry.setIndex(new THREE.BufferAttribute(indices, 1));
-
-                  // Compute normals and bounding sphere
-                  geometry.computeVertexNormals();
-                  geometry.computeBoundingSphere();
-
-                  totalVertices += vertices.length / 3;
-                  totalFaces += indices.length / 3;
-
-                  // Create material with enhanced settings
-                  const material = new THREE.MeshLambertMaterial({{
-                      color: color,
-                      transparent: layer.opacity < 1.0,
-                      opacity: layer.opacity,
-                      side: THREE.DoubleSide,
-                      flatShading: false
-                  }});
-
-                  // Create mesh
-                  const mesh = new THREE.Mesh(geometry, material);
-                  mesh.userData = {{
-                      layerName: layer.name,
-                      meshId: meshData.id,
-                      originalColor: color.getHex()
-                  }};
-                  mesh.castShadow = true;
-                  mesh.receiveShadow = true;
-
-                  scene.add(mesh);
-                  geometryObjects.push(mesh);
-
-                  // Debug: Check if mesh was created properly
-                  const bbox = new THREE.Box3().setFromObject(mesh);
-                  console.log(`  Added mesh for layer ${{layer.name}}:`, {{
-                      vertices: geometry.attributes.position.count,
-                      faces: geometry.index ? geometry.index.count / 3 : 0,
-                      boundingBox: bbox,
-                      visible: mesh.visible
-                  }});
-
-                  // Add wireframe if requested
-                  if (showWireframe) {{
-                      addWireframe(mesh);
-                  }}
-              }});
-          }});
-
-          // Add simulation box
-          if (geometryData.simulation_box) {{
-              createSimulationBox();
-          }}
-
-          // Center camera on geometry
-          centerCameraOnGeometry();
-
-          console.log(`📊 Loaded ${{geometryData.layers.length}} layers, ${{totalVertices}} vertices, ${{totalFaces}} faces`);
-      }}
-
-      function addWireframe(mesh) {{
-          const wireframe = new THREE.WireframeGeometry(mesh.geometry);
-          const wireframeMaterial = new THREE.LineBasicMaterial({{
-              color: 0x000000,
-              transparent: true,
-              opacity: 0.3
-          }});
-          const wireframeMesh = new THREE.LineSegments(wireframe, wireframeMaterial);
-          mesh.add(wireframeMesh);
-      }}
-
-      function createSimulationBox() {{
-          const boxData = geometryData.simulation_box;
-          const vertices = new Float32Array(boxData.vertices);
-          const geometry = new THREE.BufferGeometry();
-          geometry.setAttribute('position', new THREE.BufferAttribute(vertices, 3));
-
-          const indices = new Uint16Array(boxData.lines);
-          geometry.setIndex(new THREE.BufferAttribute(indices, 1));
-
-          const material = new THREE.LineDashedMaterial({{
-              color: 0x000000,
-              linewidth: 2,
-              dashSize: 0.1,
-              gapSize: 0.05,
-              transparent: true,
-              opacity: 0.8
-          }});
-
-          simulationBox = new THREE.LineSegments(geometry, material);
-          simulationBox.computeLineDistances(); // Required for dashed lines
-          scene.add(simulationBox);
-      }}
-
-      function createLegend() {{
-          const legendContent = document.getElementById('legend-content');
-          geometryData.layers.forEach(layer => {{
-              const item = document.createElement('div');
-              item.className = 'legend-item';
-
-              const colorBox = document.createElement('div');
-              colorBox.className = 'legend-color';
-              colorBox.style.backgroundColor = `rgb(${{layer.color[0]}},${{layer.color[1]}},${{layer.color[2]}})`;
-              colorBox.style.opacity = layer.opacity;
-
-              const label = document.createElement('span');
-              label.textContent = `${{layer.name}} (α=${{layer.opacity.toFixed(2)}})`;
-
-              item.appendChild(colorBox);
-              item.appendChild(label);
-              legendContent.appendChild(item);
-          }});
-      }}
-
-      function toggleWireframe() {{
-          showWireframe = !showWireframe;
-          geometryObjects.forEach(mesh => {{
-              const wireframe = mesh.children.find(child => child.type === 'LineSegments');
-              if (wireframe) {{
-                  wireframe.visible = showWireframe;
-              }} else if (showWireframe) {{
-                  addWireframe(mesh);
-              }}
-          }});
-          console.log(`🔲 Wireframe: ${{showWireframe ? 'ON' : 'OFF'}}`);
-      }}
-
-      function resetCamera() {{
-          centerCameraOnGeometry();
-          console.log('🎯 Camera reset');
-      }}
-
-      function toggleSimBox() {{
-          if (simulationBox) {{
-              simulationBox.visible = !simulationBox.visible;
-              console.log(`📦 Simulation box: ${{simulationBox.visible ? 'ON' : 'OFF'}}`);
-          }}
-      }}
-
-      function centerCameraOnGeometry() {{
-          if (geometryObjects.length === 0) {{
-              console.log('No geometry objects to center on');
-              camera.position.set(15, 15, 25);
-              controls.target.set(0, 0, 0);
-              return;
-          }}
-
-          const box = new THREE.Box3();
-          geometryObjects.forEach(mesh => {{
-              box.expandByObject(mesh);
-          }});
-
-          const center = box.getCenter(new THREE.Vector3());
-          const size = box.getSize(new THREE.Vector3());
-
-          console.log('Geometry bounds:', {{
-              center: center,
-              size: size,
-              min: box.min,
-              max: box.max
-          }});
-
-          const maxDim = Math.max(size.x, size.y, size.z);
-
-          // Handle very small geometries (micrometers scale)
-          const distance = maxDim > 0 ? maxDim * 2.5 : 50;
-
-          camera.position.set(
-              center.x + distance * 0.5,
-              center.y + distance * 0.5,
-              center.z + distance * 1.2
-          );
-
-          controls.target.copy(center);
-          controls.update();
-
-          // Adjust near/far planes for small geometries
-          if (maxDim < 1) {{
-              camera.near = 0.001;
-              camera.far = 1000;
-              camera.updateProjectionMatrix();
-          }}
-
-          console.log('Camera positioned at:', camera.position);
-          console.log('Camera looking at:', controls.target);
-      }}
-
-      function updateStats() {{
-          let totalVertices = 0, totalFaces = 0;
-          geometryObjects.forEach(mesh => {{
-              const geometry = mesh.geometry;
-              totalVertices += geometry.attributes.position.count;
-              totalFaces += geometry.index ? geometry.index.count / 3 : geometry.attributes.position.count / 3;
-          }});
-
-          document.getElementById('vertices').textContent = `Vertices: ${{totalVertices.toLocaleString()}}`;
-          document.getElementById('faces').textContent = `Faces: ${{Math.floor(totalFaces).toLocaleString()}}`;
-      }}
-
-      function animate() {{
-          requestAnimationFrame(animate);
-          controls.update();
-          if (stats) stats.update();
-          renderer.render(scene, camera);
-      }}
-
-      function onWindowResize() {{
-          camera.aspect = window.innerWidth / window.innerHeight;
-          camera.updateProjectionMatrix();
-          renderer.setSize(window.innerWidth, window.innerHeight);
-      }}
-
-      // Initialize when page loads
-      init();
-    </script>
-  </body>
-</html>
diff --git a/src/gsim/meep/__init__.py b/src/gsim/meep/__init__.py
index dec415b4..d62f4365 100644
--- a/src/gsim/meep/__init__.py
+++ b/src/gsim/meep/__init__.py
@@ -9,12 +9,13 @@
     from gsim import meep
 
     sim = meep.Simulation()
-    sim.geometry.component = ybranch
-    sim.source.port = "o1"
+    sim.geometry(component=ybranch, z_crop="auto")
+    sim.materials = {"si": 3.47, "SiO2": 1.44}
+    sim.source(port="o1", wavelength=1.55, bandwidth=0.01, num_freqs=11)
     sim.monitors = ["o1", "o2"]
-    sim.solver.stopping = "dft_decay"
-    sim.solver.max_time = 200
-    result = sim.run("./meep-sim")
+    sim.domain(pml=1.0, margin=0.5)
+    sim.solver(resolution=32, simplify_tol=0.01)
+    result = sim.run()
 """
 
 from gsim.meep.models import (
diff --git a/src/gsim/meep/meep-overview.md b/src/gsim/meep/meep-overview.md
index b15cbabd..01d513ba 100644
--- a/src/gsim/meep/meep-overview.md
+++ b/src/gsim/meep/meep-overview.md
@@ -19,34 +19,24 @@ GDS + PDK  ──►  3D Geometry  ──►  Client Viz  ──►  SimConfig J
 from gsim import meep
 
 sim = meep.Simulation()
-sim.geometry.component = ybranch
-sim.geometry.z_crop = "auto"
+sim.geometry(component=ybranch, z_crop="auto")
 sim.materials = {"si": 3.47, "SiO2": 1.44}       # float shorthand
-sim.source.port = "o1"
-sim.source.wavelength = 1.55
-sim.source.bandwidth = 0.1
-sim.source.num_freqs = 11
+sim.source(port="o1", wavelength=1.55, bandwidth=0.1, num_freqs=11)
 sim.monitors = ["o1", "o2"]
-sim.domain.pml = 1.0
-sim.domain.margin = 0.5
-sim.solver.resolution = 32
-sim.solver.stopping = "dft_decay"
-sim.solver.max_time = 200
-sim.solver.simplify_tol = 0.01
-sim.solver.save_geometry = True
-sim.solver.save_fields = True
+sim.domain(pml=1.0, margin=0.5)
+sim.solver(resolution=32, simplify_tol=0.01)
 sim.plot_2d(slices="xyz")
-result = sim.run("./meep-sim")
+result = sim.run()
 ```
 
 ### Design principles
 
 - **6 typed physics objects** -- `Geometry`, `Material`, `ModeSource`, `Domain`, `FDTD` + `monitors: list[str]` -- assigned to a `Simulation` container. No ordering dependencies.
-- **Attribute style or constructor style** -- `sim.source.port = "o1"` or `sim.source = ModeSource(port="o1")`. Both work via Pydantic `validate_assignment=True`.
+- **Callable, attribute, or constructor style** -- `sim.source(port="o1")` (callable), `sim.source.port = "o1"` (attribute), or `sim.source = ModeSource(port="o1")` (constructor). All work via Pydantic `validate_assignment=True`.
 - **Float shorthand for materials** -- `{"si": 3.47}` auto-normalizes to `Material(n=3.47)` via validator.
-- **Flat stopping fields** -- `stopping` mode string + flat fields (`max_time`, `stopping_threshold`, etc.) on `FDTD`.
+- **Flat stopping fields + method API** -- `stopping` mode string (`energy_decay`/`dft_decay`/`decay`/`fixed`) + flat fields on `FDTD`, plus convenience methods (`stop_when_energy_decayed()`, `stop_when_dft_decayed()`, `stop_when_fields_decayed()`, `stop_after()`). `StoppingConfig.mode` now includes `energy_decay`.
 - **Source defines spectral window** -- `WavelengthConfig` derived from `ModeSource.wavelength/bandwidth/num_freqs`. Monitors are just port name strings.
-- **JSON contract unchanged** -- `write_config()` translates new API -> existing `SimConfig` -> JSON. Runner template untouched.
+- **JSON contract** -- `write_config()` translates new API -> existing `SimConfig` -> JSON. `StoppingConfig.mode` extended with `"energy_decay"` (runner updated to handle it).
 
 ---
 
@@ -54,25 +44,32 @@ result = sim.run("./meep-sim")
 
 ### `gsim.meep` -- Public API
 
-| Module              | Purpose                                                                                                                 |
-| ------------------- | ----------------------------------------------------------------------------------------------------------------------- |
-| `__init__.py`       | Exports: `Simulation`, all model classes                                                                                |
-| `models/api.py`     | Declarative models: `Geometry`, `Material`, `ModeSource`, `Domain`, `FDTD`, `Symmetry`                                  |
-| `simulation.py`     | `Simulation` container -- `write_config()`, `run()`, `validate_config()`, `plot_2d()`/`plot_3d()`, `estimate_meep_np()` |
-| `viz.py`            | Standalone viz helpers -- `build_geometry_model()`, `plot_2d()`, `plot_3d()` (calls `common/viz`)                       |
-| `models/config.py`  | `SimConfig` + all sub-configs (JSON serialization layer)                                                                |
-| `models/results.py` | `SParameterResult` -- CSV + debug JSON + diagnostic PNGs                                                                |
-| `ports.py`          | `extract_port_info()` -- port center/direction/normal from gdsfactory                                                   |
-| `materials.py`      | `resolve_materials()` -- material names -> (n, k) via common DB                                                         |
-| `script.py`         | `generate_meep_script()` -- cloud runner template (string in Python)                                                    |
-| `overlay.py`        | `SimOverlay` + `PortOverlay` + `DielectricOverlay` -- viz metadata                                                      |
+| Module              | Purpose                                                                                                                                                               |
+| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `__init__.py`       | Exports: `Simulation`, all model classes                                                                                                                              |
+| `models/api.py`     | Declarative models: `Geometry`, `Material`, `ModeSource`, `Domain`, `FDTD`, `Symmetry`. All except `Material`/`Symmetry` have `__call__(**kwargs)` for fluent updates |
+| `simulation.py`     | `Simulation` container -- `write_config()`, `run()`, `validate_config()`, `plot_2d()`/`plot_3d()`, `estimate_meep_np()`                                               |
+| `viz.py`            | Standalone viz helpers -- `build_geometry_model()`, `plot_2d()`, `plot_3d()` (calls `common/viz`)                                                                     |
+| `models/config.py`  | `SimConfig` + all sub-configs (JSON serialization layer)                                                                                                              |
+| `models/results.py` | `SParameterResult` -- CSV + debug JSON + diagnostic PNGs                                                                                                              |
+| `ports.py`          | `extract_port_info()` -- port center/direction/normal from gdsfactory                                                                                                 |
+| `materials.py`      | `resolve_materials()` -- material names -> (n, k) via common DB                                                                                                       |
+| `script.py`         | `generate_meep_script()` -- cloud runner template (string in Python)                                                                                                  |
+| `overlay.py`        | `SimOverlay` + `PortOverlay` + `DielectricOverlay` -- viz metadata                                                                                                    |
 
 ### `gsim.common` -- Shared infrastructure
 
-Solver-agnostic: `LayeredComponentBase`, `GeometryModel`/`Prism`, `LayerStack`, `MaterialProperties`, `viz/` (Matplotlib 2D, PyVista/Open3D/Three.js 3D).
+Solver-agnostic: `LayeredComponentBase`, `GeometryModel`/`Prism`, `LayerStack`, `ValidationResult`, `viz/` (Matplotlib 2D, PyVista/Open3D 3D).
+
+Key sub-modules:
+
+- `stack/_layer_utils.py` — shared `get_gds_layer_tuple()` and `classify_layer_type()` (used by both `extractor` and `visualization`)
+- `viz/_mesh_helpers.py` — shared Delaunay triangulation (`triangulate_polygon_with_holes`) and batch prism geometry (`collect_triangular_prism_geometry`), used by both PyVista and Open3D renderers
 
 ### Visualization pipeline
 
+3D backends: PyVista (desktop) and Open3D+Plotly (Jupyter, with view buttons for Iso/Top/Front/Right). Three.js backend removed.
+
 ```
 Simulation.plot_2d()
   -> viz.build_geometry_model(component, stack, domain_config)
@@ -96,7 +93,8 @@ Simulation.plot_2d()
 - **Z-crop** -- Auto-crops stack around core layer. Full UBC stack is 15um; cropped to ~1.5um (massive compute savings).
 - **Port extension into PML** -- `gf.components.extend_ports()` at `write_config()` time. Original bbox stored in `SimConfig.component_bbox` for correct cell sizing.
 - **Symmetries disabled for S-params** -- `add_mode_monitor` uses `use_symmetry=false` internally; `get_eigenmode_coefficients` doesn't apply `S.multiplicity()`. Source port coefficients underestimated ~2x. gplugins also never uses `mp.Mirror`.
-- **`dft_min_run_time` default 100** -- Prevents false convergence before pulse traverses device. Must exceed `device_length * n_group`.
+- **`energy_decay` default stopping** -- `dft_decay` suffers from false early convergence (DFTs can converge on near-zero fields). `energy_decay` monitors total field energy and waits for it to peak then decay — more robust for directional couplers and similar devices. Runner uses `mp.stop_when_energy_decayed(dt, decay_by)` with a `_make_time_cap(max_time)` as a secondary callable in the `until_after_sources` list (OR logic). The `decay` mode also uses `_make_time_cap` instead of passing a raw float to `until_after_sources`.
+- **`dft_min_run_time` is absolute sim time** -- Not time-after-sources. With a broadband source turning off at ~t=78 and min_run_time=100, DFT checking starts at t=100 (only ~22 units after source ends). Must exceed `device_length * n_group`.
 - **Stack resolved lazily** -- `_ensure_stack()` falls back to `get_stack()` (active PDK defaults) when no explicit stack kwargs are set. Same path for `write_config()` and viz.
 
 ---
diff --git a/src/gsim/meep/models/api.py b/src/gsim/meep/models/api.py
index 2cee14cc..0a82e198 100644
--- a/src/gsim/meep/models/api.py
+++ b/src/gsim/meep/models/api.py
@@ -31,6 +31,12 @@ class Geometry(BaseModel):
         description='Z-crop mode: "auto" | layer_name | None (no crop)',
     )
 
+    def __call__(self, **kwargs: Any) -> Geometry:
+        """Update fields in place. Returns self for chaining."""
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        return self
+
 
 # ---------------------------------------------------------------------------
 # Material
@@ -76,6 +82,12 @@ class ModeSource(BaseModel):
         description="Number of frequency points",
     )
 
+    def __call__(self, **kwargs: Any) -> ModeSource:
+        """Update fields in place. Returns self for chaining."""
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        return self
+
 
 # ---------------------------------------------------------------------------
 # Domain
@@ -123,6 +135,12 @@ class Domain(BaseModel):
         description="Mirror symmetry planes. Not yet used in production runs.",
     )
 
+    def __call__(self, **kwargs: Any) -> Domain:
+        """Update fields in place. Returns self for chaining."""
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        return self
+
 
 # ---------------------------------------------------------------------------
 # FDTD solver
@@ -137,24 +155,35 @@ class FDTD(BaseModel):
     resolution: int = Field(default=32, ge=4, description="Pixels per micrometer")
 
     # Stopping criteria (flat fields instead of variant classes)
-    stopping: Literal["fixed", "decay", "dft_decay"] = Field(
-        default="dft_decay",
-        description="Stopping mode: fixed time, field decay, or DFT convergence",
+    stopping: Literal["fixed", "decay", "dft_decay", "energy_decay"] = Field(
+        default="energy_decay",
+        description=(
+            "Stopping mode: 'energy_decay' (recommended) monitors total field "
+            "energy decay; 'dft_decay' waits for DFT convergence; 'decay' "
+            "monitors a field component at a point; 'fixed' runs for max_time."
+        ),
     )
     max_time: float = Field(
-        default=200.0, gt=0, description="Max run time after sources (um/c)"
+        default=2000.0, gt=0, description="Max run time after sources (um/c)"
     )
     stopping_threshold: float = Field(
         default=1e-3, gt=0, lt=1, description="Decay/convergence threshold"
     )
     stopping_min_time: float = Field(
-        default=100.0, ge=0, description="Min run time for dft_decay mode"
+        default=100.0,
+        ge=0,
+        description=(
+            "Minimum absolute sim time for dft_decay mode (not time-after-sources). "
+            "Must exceed pulse transit time to avoid false convergence."
+        ),
     )
     stopping_component: str = Field(
         default="Ey", description="Field component for decay mode"
     )
     stopping_dt: float = Field(
-        default=50.0, gt=0, description="Decay measurement window for decay mode"
+        default=50.0,
+        gt=0,
+        description="Decay measurement window for decay/energy_decay modes",
     )
     stopping_monitor_port: str | None = Field(
         default=None, description="Port to monitor for decay mode"
@@ -173,6 +202,88 @@ class FDTD(BaseModel):
         description="Shapely simplification tolerance in um (0=off)",
     )
 
+    # -- Convenience methods for stopping configuration --
+
+    def stop_when_energy_decayed(
+        self, dt: float = 50.0, decay_by: float = 1e-3
+    ) -> FDTD:
+        """Stop when total field energy in the cell decays (recommended).
+
+        Monitors total electromagnetic energy and stops when it has decayed
+        by ``decay_by`` from its peak value.  More robust than ``dft_decay``
+        for devices where DFTs can falsely converge on near-zero fields.
+
+        Args:
+            dt: Time window between energy checks (MEEP time units).
+            decay_by: Fractional energy decay threshold (e.g. 1e-3 = 0.1%).
+
+        Returns:
+            self (for fluent chaining).
+        """
+        self.stopping = "energy_decay"
+        self.stopping_dt = dt
+        self.stopping_threshold = decay_by
+        return self
+
+    def stop_when_dft_decayed(self, tol: float = 1e-3, min_time: float = 100.0) -> FDTD:
+        """Stop when all DFT monitors converge.
+
+        Args:
+            tol: DFT convergence tolerance.
+            min_time: Minimum absolute sim time before checking convergence.
+
+        Returns:
+            self (for fluent chaining).
+        """
+        self.stopping = "dft_decay"
+        self.stopping_threshold = tol
+        self.stopping_min_time = min_time
+        return self
+
+    def stop_when_fields_decayed(
+        self,
+        dt: float = 50.0,
+        component: str = "Ey",
+        decay_by: float = 1e-3,
+        monitor_port: str | None = None,
+    ) -> FDTD:
+        """Stop when a field component decays at a point.
+
+        Args:
+            dt: Decay measurement time window.
+            component: Field component name (e.g. "Ey", "Hz").
+            decay_by: Fractional decay threshold.
+            monitor_port: Port to monitor (None = first non-source port).
+
+        Returns:
+            self (for fluent chaining).
+        """
+        self.stopping = "decay"
+        self.stopping_dt = dt
+        self.stopping_component = component
+        self.stopping_threshold = decay_by
+        self.stopping_monitor_port = monitor_port
+        return self
+
+    def stop_after(self, time: float) -> FDTD:
+        """Run for a fixed time after sources turn off.
+
+        Args:
+            time: Run time after sources in MEEP time units (um/c).
+
+        Returns:
+            self (for fluent chaining).
+        """
+        self.stopping = "fixed"
+        self.max_time = time
+        return self
+
+    def __call__(self, **kwargs: Any) -> FDTD:
+        """Update fields in place. Returns self for chaining."""
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        return self
+
     # Diagnostics — output control for plots, fields, animations
     save_geometry: bool = Field(default=True)
     save_fields: bool = Field(default=True)
diff --git a/src/gsim/meep/models/config.py b/src/gsim/meep/models/config.py
index 52abcfa7..973191f8 100644
--- a/src/gsim/meep/models/config.py
+++ b/src/gsim/meep/models/config.py
@@ -33,7 +33,7 @@ class DomainConfig(BaseModel):
     Cell size formula:
         cell_x = bbox_width  + 2*(margin_xy + dpml)
         cell_y = bbox_height + 2*(margin_xy + dpml)
-        cell_z = z_extent + 2*dpml          (z-margins baked into z_extent via set_z_crop)
+        cell_z = z_extent + 2*dpml  (z-margins baked into z_extent)
     """
 
     model_config = ConfigDict(validate_assignment=True)
@@ -51,7 +51,7 @@ class DomainConfig(BaseModel):
     port_margin: float = Field(
         default=0.5,
         ge=0,
-        description="Margin on each side of port waveguide width for mode monitors in um",
+        description="Margin on each side of port width for monitors (um)",
     )
     extend_ports: float = Field(
         default=0.0,
@@ -64,19 +64,31 @@ class DomainConfig(BaseModel):
 class StoppingConfig(BaseModel):
     """Controls when the MEEP simulation stops.
 
-    ``fixed`` mode runs for a fixed time after sources turn off.
+    ``energy_decay`` mode (recommended) monitors total electromagnetic
+    energy in the cell and stops when it decays by ``threshold`` from
+    its peak.  Robust against false early convergence.
+
+    ``dft_decay`` mode monitors convergence of all DFT monitors and
+    stops when they stabilize.  ``dft_min_run_time`` is an *absolute*
+    sim time (not time-after-sources) — with a broadband source turning
+    off at ~t=78, a min_run_time=100 starts checking at t=100, only ~22
+    time units after the source ends.
+
     ``decay`` mode monitors field decay at a point and stops when the
     fields have decayed by ``threshold``, with ``max_time`` as
     a numeric time cap (whichever fires first).
-    ``dft_decay`` mode monitors convergence of all DFT monitors and
-    stops when they stabilize, with built-in min/max time bounds.
-    Best for S-parameter extraction.
+
+    ``fixed`` mode runs for a fixed time after sources turn off.
     """
 
     model_config = ConfigDict(validate_assignment=True)
 
-    mode: Literal["fixed", "decay", "dft_decay"] = Field(default="fixed")
-    max_time: float = Field(default=100.0, gt=0, serialization_alias="run_after_sources")
+    mode: Literal["fixed", "decay", "dft_decay", "energy_decay"] = Field(
+        default="fixed"
+    )
+    max_time: float = Field(
+        default=100.0, gt=0, serialization_alias="run_after_sources"
+    )
     decay_dt: float = Field(default=50.0, gt=0)
     decay_component: str = Field(default="Ey")
     threshold: float = Field(default=1e-3, gt=0, lt=1, serialization_alias="decay_by")
@@ -84,9 +96,9 @@ class StoppingConfig(BaseModel):
     dft_min_run_time: float = Field(
         default=100,
         ge=0,
-        description="Minimum run time after sources for dft_decay mode. "
-        "Must exceed pulse transit time through the device to avoid "
-        "false convergence on near-zero fields at output ports.",
+        description="Minimum absolute sim time for dft_decay mode (not "
+        "time-after-sources). Must exceed pulse transit time through the "
+        "device to avoid false convergence on near-zero fields.",
     )
 
 
@@ -104,7 +116,9 @@ class SourceConfig(BaseModel):
 
     bandwidth: float | None = Field(
         default=None,
-        description="Source Gaussian bandwidth in wavelength um. None = auto (~3x monitor bw).",
+        description=(
+            "Source Gaussian bandwidth in wavelength um. None = auto (~3x monitor bw)."
+        ),
     )
     port: str | None = Field(
         default=None,
@@ -225,7 +239,9 @@ class DiagnosticsConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    save_geometry: bool = Field(default=True, description="Pre-run geometry cross-section plots")
+    save_geometry: bool = Field(
+        default=True, description="Pre-run geometry cross-section plots"
+    )
     save_fields: bool = Field(default=True, description="Post-run field snapshot")
     save_epsilon_raw: bool = Field(
         default=False, description="Raw epsilon .npy (advanced)"
@@ -234,7 +250,8 @@ class DiagnosticsConfig(BaseModel):
         default=False, description="Field animation MP4 (heavy, needs ffmpeg)"
     )
     animation_interval: float = Field(
-        default=0.5, gt=0,
+        default=0.5,
+        gt=0,
         description="MEEP time units between animation frames",
     )
     preview_only: bool = Field(
@@ -242,7 +259,8 @@ class DiagnosticsConfig(BaseModel):
         description="Init sim and save geometry diagnostics, skip FDTD run",
     )
     verbose_interval: float = Field(
-        default=0, ge=0,
+        default=0,
+        ge=0,
         description="MEEP time units between progress prints (0=off)",
     )
 
@@ -302,7 +320,9 @@ class SimConfig(BaseModel):
     )
     component_bbox: list[float] | None = Field(
         default=None,
-        description="Original component bbox [xmin, ymin, xmax, ymax] before port extension.",
+        description=(
+            "Original component bbox [xmin, ymin, xmax, ymax] before port extension."
+        ),
     )
     layer_stack: list[LayerStackEntry] = Field(default_factory=list)
     dielectrics: list[dict[str, Any]] = Field(default_factory=list)
@@ -322,9 +342,7 @@ class SimConfig(BaseModel):
     diagnostics: DiagnosticsConfig = Field(default_factory=DiagnosticsConfig)
     symmetries: list[SymmetryEntry] = Field(default_factory=list)
     split_chunks_evenly: bool = Field(default=False)
-    meep_np: int = Field(
-        default=1, ge=1, description="Recommended MPI process count"
-    )
+    meep_np: int = Field(default=1, ge=1, description="Recommended MPI process count")
 
     def to_json(self, path: str | Path) -> Path:
         """Write config to JSON file.
diff --git a/src/gsim/meep/script.py b/src/gsim/meep/script.py
index 3d08b362..8f001f7b 100644
--- a/src/gsim/meep/script.py
+++ b/src/gsim/meep/script.py
@@ -238,6 +238,22 @@ def build_symmetries(config):
 }
 
 
+def _make_time_cap(cap):
+    """Wrap a numeric time cap as a callable for until_after_sources lists.
+
+    When until_after_sources receives a list, every element must be callable.
+    This wraps a float (time units) into a function that returns True once
+    ``cap`` time units have elapsed since the first call (i.e. after sources
+    turn off).
+    """
+    _t0 = [None]
+    def _check(sim_obj):
+        if _t0[0] is None:
+            _t0[0] = sim_obj.meep_time()
+        return (sim_obj.meep_time() - _t0[0]) >= cap
+    return _check
+
+
 def resolve_decay_monitor_point(config):
     """Return center mp.Vector3 for decay monitoring.
 
@@ -1082,6 +1098,16 @@ def _capture_frame(sim_obj):
                 maximum_run_time=run_after,
             ),
         )
+    elif stop_mode == "energy_decay":
+        dt = stopping.get("decay_dt", 50.0)
+        decay_by = stopping.get("decay_by", 1e-3)
+        logger.info(
+            "Running simulation (energy_decay mode: dt=%s, "
+            "decay_by=%s, cap=%.1f)...",
+            dt, decay_by, run_after,
+        )
+        energy_fn = mp.stop_when_energy_decayed(dt=dt, decay_by=decay_by)
+        sim.run(*step_funcs, until_after_sources=[energy_fn, _make_time_cap(run_after)])
     elif stop_mode == "decay":
         dt = stopping.get("decay_dt", 50.0)
         comp_name = stopping.get("decay_component", "Ey")
@@ -1093,7 +1119,7 @@ def _capture_frame(sim_obj):
 
         # Decay condition + numeric time cap (list = OR logic, first wins)
         decay_fn = mp.stop_when_fields_decayed(dt, comp, monitor_pt, decay_by)
-        sim.run(*step_funcs, until_after_sources=[decay_fn, run_after])
+        sim.run(*step_funcs, until_after_sources=[decay_fn, _make_time_cap(run_after)])
     else:
         logger.info("Running simulation (until_after_sources=%.1f)...", run_after)
         sim.run(*step_funcs, until_after_sources=run_after)
diff --git a/tests/meep/test_meep_models.py b/tests/meep/test_meep_models.py
index a3ea3e98..f0237d56 100644
--- a/tests/meep/test_meep_models.py
+++ b/tests/meep/test_meep_models.py
@@ -659,6 +659,12 @@ def test_decay_mode(self):
         assert cfg.threshold == 1e-4
         assert cfg.decay_dt == 25.0
 
+    def test_energy_decay_mode(self):
+        cfg = StoppingConfig(mode="energy_decay", decay_dt=100, threshold=1e-4)
+        assert cfg.mode == "energy_decay"
+        assert cfg.decay_dt == 100
+        assert cfg.threshold == 1e-4
+
     def test_invalid_mode(self):
         with pytest.raises(ValidationError):
             StoppingConfig(mode="invalid")  # ty: ignore[invalid-argument-type]
@@ -783,6 +789,13 @@ def test_script_has_component_map(self):
         assert "mp.Ez" in script
         assert "mp.Ey" in script
 
+    def test_script_has_stop_when_energy_decayed(self):
+        from gsim.meep.script import generate_meep_script
+
+        script = generate_meep_script()
+        assert "stop_when_energy_decayed" in script
+        assert "energy_decay" in script
+
     def test_script_valid_python(self):
         from gsim.meep.script import generate_meep_script
 
diff --git a/tests/meep/test_simulation.py b/tests/meep/test_simulation.py
index b84009cc..fa827aa3 100644
--- a/tests/meep/test_simulation.py
+++ b/tests/meep/test_simulation.py
@@ -82,8 +82,8 @@ def test_symmetries(self):
 class TestStoppingFields:
     def test_defaults(self):
         f = FDTD()
-        assert f.stopping == "dft_decay"
-        assert f.max_time == 200.0
+        assert f.stopping == "energy_decay"
+        assert f.max_time == 2000.0
         assert f.stopping_threshold == 1e-3
         assert f.stopping_min_time == 100.0
         assert f.stopping_component == "Ey"
@@ -102,12 +102,23 @@ def test_decay_mode(self):
         assert f.stopping_dt == 25
 
     def test_dft_decay_custom(self):
-        f = FDTD(max_time=300, stopping_threshold=1e-4, stopping_min_time=80)
+        f = FDTD(
+            stopping="dft_decay",
+            max_time=300,
+            stopping_threshold=1e-4,
+            stopping_min_time=80,
+        )
         assert f.stopping == "dft_decay"
         assert f.max_time == 300
         assert f.stopping_threshold == 1e-4
         assert f.stopping_min_time == 80
 
+    def test_energy_decay_mode(self):
+        f = FDTD(stopping="energy_decay", stopping_dt=100, stopping_threshold=1e-4)
+        assert f.stopping == "energy_decay"
+        assert f.stopping_dt == 100
+        assert f.stopping_threshold == 1e-4
+
     def test_invalid_mode(self):
         with pytest.raises(ValidationError):
             FDTD(stopping="invalid")  # ty: ignore[invalid-argument-type]
@@ -117,8 +128,8 @@ class TestFDTD:
     def test_defaults(self):
         f = FDTD()
         assert f.resolution == 32
-        assert f.stopping == "dft_decay"
-        assert f.max_time == 200.0
+        assert f.stopping == "energy_decay"
+        assert f.max_time == 2000.0
         assert f.subpixel is False
         assert f.simplify_tol == 0.0
 
@@ -211,12 +222,85 @@ def test_solver_stopping_replace(self):
         assert sim.solver.stopping == "decay"
         assert sim.solver.stopping_threshold == 1e-4
 
+    def test_stop_when_energy_decayed(self):
+        f = FDTD()
+        result = f.stop_when_energy_decayed(dt=100, decay_by=1e-4)
+        assert result is f
+        assert f.stopping == "energy_decay"
+        assert f.stopping_dt == 100
+        assert f.stopping_threshold == 1e-4
+
+    def test_stop_when_dft_decayed(self):
+        f = FDTD()
+        result = f.stop_when_dft_decayed(tol=1e-4, min_time=200)
+        assert result is f
+        assert f.stopping == "dft_decay"
+        assert f.stopping_threshold == 1e-4
+        assert f.stopping_min_time == 200
+
+    def test_stop_when_fields_decayed(self):
+        f = FDTD()
+        result = f.stop_when_fields_decayed(
+            dt=25, component="Hz", decay_by=1e-4, monitor_port="o2"
+        )
+        assert result is f
+        assert f.stopping == "decay"
+        assert f.stopping_dt == 25
+        assert f.stopping_component == "Hz"
+        assert f.stopping_threshold == 1e-4
+        assert f.stopping_monitor_port == "o2"
+
+    def test_stop_after(self):
+        f = FDTD()
+        result = f.stop_after(time=500)
+        assert result is f
+        assert f.stopping == "fixed"
+        assert f.max_time == 500
+
     def test_geometry_component(self):
         sim = Simulation()
         sim.geometry.component = "placeholder"
         assert sim.geometry.component == "placeholder"
 
 
+class TestCallableAPI:
+    def test_source_callable(self):
+        sim = Simulation()
+        result = sim.source(port="o1", wavelength=1.31, bandwidth=0.05)
+        assert result is sim.source
+        assert sim.source.port == "o1"
+        assert sim.source.wavelength == 1.31
+        assert sim.source.bandwidth == 0.05
+
+    def test_domain_callable(self):
+        sim = Simulation()
+        sim.domain(pml=0.5, margin=0.2)
+        assert sim.domain.pml == 0.5
+        assert sim.domain.margin == 0.2
+
+    def test_geometry_callable(self):
+        sim = Simulation()
+        sim.geometry(component="placeholder", z_crop="auto")
+        assert sim.geometry.component == "placeholder"
+        assert sim.geometry.z_crop == "auto"
+
+    def test_solver_callable(self):
+        sim = Simulation()
+        sim.solver(resolution=64, simplify_tol=0.01)
+        assert sim.solver.resolution == 64
+        assert sim.solver.simplify_tol == 0.01
+
+    def test_callable_validation(self):
+        sim = Simulation()
+        with pytest.raises(ValidationError):
+            sim.solver(resolution=1)  # ge=4
+
+    def test_callable_invalid_field(self):
+        sim = Simulation()
+        with pytest.raises(ValidationError):
+            sim.source(nonexistent_field="value")
+
+
 class TestWholeObjectAssignment:
     def test_source(self):
         sim = Simulation()
@@ -325,6 +409,14 @@ def test_stopping_dft_decay(self):
         assert cfg.threshold == 1e-4
         assert cfg.dft_min_run_time == 80
 
+    def test_stopping_energy_decay(self):
+        sim = Simulation()
+        sim.solver.stop_when_energy_decayed(dt=100, decay_by=1e-4)
+        cfg = sim._stopping_config()
+        assert cfg.mode == "energy_decay"
+        assert cfg.decay_dt == 100
+        assert cfg.threshold == 1e-4
+
     def test_domain_translation(self):
         sim = Simulation()
         sim.domain = Domain(pml=0.5, margin=0.3, margin_z_above=1.0, margin_z_below=0.8)

From c42d3dead04de4cf3dc751f6851015c9ccd832a7 Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Tue, 17 Feb 2026 11:51:47 +0100
Subject: [PATCH 02/10] fix: bump ty>=0.0.15 (allowed-unresolved-imports
 support)

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index d8359e0a..5776dfd0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -54,7 +54,7 @@ dev = [
   "towncrier>=24.0.0",
   "vega-datasets>=0.9.0",
   "nbstripout>=0.8.1",
-  "ty>=0.0.13"
+  "ty>=0.0.15"
 ]
 docs = [
   "ihp-gdsfactory>=0.1.4",

From 15b43f0d61c75f6b94db0230a543cb77de6bacd7 Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Tue, 17 Feb 2026 12:02:47 +0100
Subject: [PATCH 03/10] docs: add notebook showing callable, attribute, and
 constructor API styles

---
 nbs/meep_api_styles.ipynb | 168 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 168 insertions(+)
 create mode 100644 nbs/meep_api_styles.ipynb

diff --git a/nbs/meep_api_styles.ipynb b/nbs/meep_api_styles.ipynb
new file mode 100644
index 00000000..19535ab7
--- /dev/null
+++ b/nbs/meep_api_styles.ipynb
@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MEEP API Styles\n",
+    "\n",
+    "The `gsim.meep` API supports three equivalent styles for configuring simulations.\n",
+    "All three produce identical `Simulation` objects — pick whichever reads best for your use case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ubcpdk import PDK, cells\n",
+    "\n",
+    "PDK.activate()\n",
+    "c = cells.ebeam_y_1550()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Callable style (recommended)\n",
+    "\n",
+    "Updates fields in place — only the fields you pass change, others keep their current values. No extra imports needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gsim import meep\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry(component=c, z_crop=\"auto\")\n",
+    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
+    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
+    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
+    "sim.domain(pml=1.0, margin=0.5)\n",
+    "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Attribute style\n",
+    "\n",
+    "One field per line. Most explicit — good when you need to set fields conditionally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gsim import meep\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry.component = c\n",
+    "sim.geometry.z_crop = \"auto\"\n",
+    "\n",
+    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
+    "\n",
+    "sim.source.port = \"o1\"\n",
+    "sim.source.wavelength = 1.55\n",
+    "sim.source.bandwidth = 0.01\n",
+    "sim.source.num_freqs = 11\n",
+    "\n",
+    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
+    "\n",
+    "sim.domain.pml = 1.0\n",
+    "sim.domain.margin = 0.5\n",
+    "\n",
+    "sim.solver.resolution = 20\n",
+    "sim.solver.simplify_tol = 0.01\n",
+    "sim.solver.save_animation = True\n",
+    "sim.solver.verbose_interval = 5.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Constructor style\n",
+    "\n",
+    "Replaces the entire sub-object — fields you don't pass reset to defaults. Requires importing model classes, but useful when building configs programmatically or from saved presets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gsim import meep\n",
+    "from gsim.meep import Domain, FDTD, Geometry, ModeSource\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry = Geometry(component=c, z_crop=\"auto\")\n",
+    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
+    "sim.source = ModeSource(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
+    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
+    "sim.domain = Domain(pml=1.0, margin=0.5)\n",
+    "sim.solver = FDTD(\n",
+    "    resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Combining styles\n",
+    "\n",
+    "All three styles can be freely combined. For example, use callable for bulk setup, then attribute for conditional tweaks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gsim import meep\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry(component=c, z_crop=\"auto\")\n",
+    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
+    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
+    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
+    "sim.domain(pml=1.0, margin=0.5)\n",
+    "sim.solver(resolution=20, simplify_tol=0.01)\n",
+    "\n",
+    "# Conditional tweaks with attribute style\n",
+    "debug = True\n",
+    "if debug:\n",
+    "    sim.solver.save_animation = True\n",
+    "    sim.solver.verbose_interval = 5.0"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 2479a5c1c1d59cb4748b0db78efe28da4e48ba83 Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Tue, 17 Feb 2026 12:04:58 +0100
Subject: [PATCH 04/10] docs: add meep_ring_coupler notebook to mkdocs nav

---
 mkdocs.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mkdocs.yml b/mkdocs.yml
index b3289192..b15f3fbd 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -21,6 +21,7 @@ nav:
   - examples:
       - Simple Example: nbs/example.md
       - MEEP Y-Branch: nbs/meep_ybranch.md
+      - MEEP Ring Coupler: nbs/meep_ring_coupler.md
       - Palace CPW: nbs/palace_demo_cpw.md
       - Palace Microstrip: nbs/palace_demo_microstrip.md
   - api reference:

From 5f2c7c4527f998e3facf3896345b42761355c9de Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Tue, 17 Feb 2026 12:32:41 +0100
Subject: [PATCH 05/10] feat: default stopping to field_decay
 (stop_when_fields_decayed) with 95% threshold
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Rename mode string "decay" → "field_decay" for clarity, switch default
from energy_decay to field_decay to match MEEP tutorial conventions,
and set decay_by default to 0.05 (95% decay).
---
 nbs/meep_ring_coupler.ipynb    |  1 +
 nbs/meep_ybranch.ipynb         |  1 +
 src/gsim/meep/models/api.py    | 35 +++++++++++++++++++---------------
 src/gsim/meep/models/config.py | 18 ++++++++---------
 src/gsim/meep/script.py        |  8 ++++----
 tests/meep/test_meep_models.py |  8 ++++----
 tests/meep/test_simulation.py  | 28 +++++++++++++--------------
 7 files changed, 53 insertions(+), 46 deletions(-)

diff --git a/nbs/meep_ring_coupler.ipynb b/nbs/meep_ring_coupler.ipynb
index 8ee7e83e..023c6087 100644
--- a/nbs/meep_ring_coupler.ipynb
+++ b/nbs/meep_ring_coupler.ipynb
@@ -71,6 +71,7 @@
     "sim.monitors = [\"o1\", \"o2\", \"o3\", \"o4\"]\n",
     "sim.domain(pml=1.0, margin=0.5)\n",
     "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
+    "sim.solver.stop_when_fields_decayed(dt=50, decay_by=0.05)\n",
     "\n",
     "print(sim.validate_config())"
    ]
diff --git a/nbs/meep_ybranch.ipynb b/nbs/meep_ybranch.ipynb
index 49ce4731..b335e590 100644
--- a/nbs/meep_ybranch.ipynb
+++ b/nbs/meep_ybranch.ipynb
@@ -63,6 +63,7 @@
     "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
     "sim.domain(pml=1.0, margin=0.5)\n",
     "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
+    "sim.solver.stop_when_fields_decayed(dt=50, decay_by=0.05)\n",
     "\n",
     "print(sim.validate_config())"
    ]
diff --git a/src/gsim/meep/models/api.py b/src/gsim/meep/models/api.py
index 0a82e198..67fee5fa 100644
--- a/src/gsim/meep/models/api.py
+++ b/src/gsim/meep/models/api.py
@@ -155,19 +155,20 @@ class FDTD(BaseModel):
     resolution: int = Field(default=32, ge=4, description="Pixels per micrometer")
 
     # Stopping criteria (flat fields instead of variant classes)
-    stopping: Literal["fixed", "decay", "dft_decay", "energy_decay"] = Field(
-        default="energy_decay",
+    stopping: Literal["fixed", "field_decay", "dft_decay", "energy_decay"] = Field(
+        default="field_decay",
         description=(
-            "Stopping mode: 'energy_decay' (recommended) monitors total field "
-            "energy decay; 'dft_decay' waits for DFT convergence; 'decay' "
-            "monitors a field component at a point; 'fixed' runs for max_time."
+            "Stopping mode: 'field_decay' (recommended, matches MEEP tutorials) "
+            "monitors a field component at a point; 'energy_decay' monitors "
+            "total field energy; 'dft_decay' waits for DFT convergence; "
+            "'fixed' runs for max_time."
         ),
     )
     max_time: float = Field(
         default=2000.0, gt=0, description="Max run time after sources (um/c)"
     )
     stopping_threshold: float = Field(
-        default=1e-3, gt=0, lt=1, description="Decay/convergence threshold"
+        default=0.05, gt=0, lt=1, description="Decay/convergence threshold"
     )
     stopping_min_time: float = Field(
         default=100.0,
@@ -178,15 +179,15 @@ class FDTD(BaseModel):
         ),
     )
     stopping_component: str = Field(
-        default="Ey", description="Field component for decay mode"
+        default="Ey", description="Field component for field_decay mode"
     )
     stopping_dt: float = Field(
         default=50.0,
         gt=0,
-        description="Decay measurement window for decay/energy_decay modes",
+        description="Decay measurement window for field_decay/energy_decay modes",
     )
     stopping_monitor_port: str | None = Field(
-        default=None, description="Port to monitor for decay mode"
+        default=None, description="Port to monitor for field_decay mode"
     )
 
     subpixel: bool = Field(default=False, description="Toggle subpixel averaging")
@@ -205,7 +206,7 @@ class FDTD(BaseModel):
     # -- Convenience methods for stopping configuration --
 
     def stop_when_energy_decayed(
-        self, dt: float = 50.0, decay_by: float = 1e-3
+        self, dt: float = 50.0, decay_by: float = 0.05
     ) -> FDTD:
         """Stop when total field energy in the cell decays (recommended).
 
@@ -215,7 +216,7 @@ def stop_when_energy_decayed(
 
         Args:
             dt: Time window between energy checks (MEEP time units).
-            decay_by: Fractional energy decay threshold (e.g. 1e-3 = 0.1%).
+            decay_by: Fractional energy decay threshold (e.g. 0.05 = 5%).
 
         Returns:
             self (for fluent chaining).
@@ -244,21 +245,25 @@ def stop_when_fields_decayed(
         self,
         dt: float = 50.0,
         component: str = "Ey",
-        decay_by: float = 1e-3,
+        decay_by: float = 0.05,
         monitor_port: str | None = None,
     ) -> FDTD:
-        """Stop when a field component decays at a point.
+        """Stop when a field component decays at a point (recommended).
+
+        Matches the standard MEEP tutorial stopping condition.  Monitors
+        |component|² at a point and stops when it decays by ``decay_by``
+        from its peak value.
 
         Args:
             dt: Decay measurement time window.
             component: Field component name (e.g. "Ey", "Hz").
-            decay_by: Fractional decay threshold.
+            decay_by: Fractional decay threshold (e.g. 0.05 = 5%).
             monitor_port: Port to monitor (None = first non-source port).
 
         Returns:
             self (for fluent chaining).
         """
-        self.stopping = "decay"
+        self.stopping = "field_decay"
         self.stopping_dt = dt
         self.stopping_component = component
         self.stopping_threshold = decay_by
diff --git a/src/gsim/meep/models/config.py b/src/gsim/meep/models/config.py
index 973191f8..dccf50ef 100644
--- a/src/gsim/meep/models/config.py
+++ b/src/gsim/meep/models/config.py
@@ -64,9 +64,13 @@ class DomainConfig(BaseModel):
 class StoppingConfig(BaseModel):
     """Controls when the MEEP simulation stops.
 
-    ``energy_decay`` mode (recommended) monitors total electromagnetic
-    energy in the cell and stops when it decays by ``threshold`` from
-    its peak.  Robust against false early convergence.
+    ``field_decay`` mode (recommended, matches MEEP tutorials) monitors
+    a field component at a point and stops when |component|² decays by
+    ``threshold`` from its peak, with ``max_time`` as a numeric time cap
+    (whichever fires first).
+
+    ``energy_decay`` mode monitors total electromagnetic energy in the
+    cell and stops when it decays by ``threshold`` from its peak.
 
     ``dft_decay`` mode monitors convergence of all DFT monitors and
     stops when they stabilize.  ``dft_min_run_time`` is an *absolute*
@@ -74,16 +78,12 @@ class StoppingConfig(BaseModel):
     off at ~t=78, a min_run_time=100 starts checking at t=100, only ~22
     time units after the source ends.
 
-    ``decay`` mode monitors field decay at a point and stops when the
-    fields have decayed by ``threshold``, with ``max_time`` as
-    a numeric time cap (whichever fires first).
-
     ``fixed`` mode runs for a fixed time after sources turn off.
     """
 
     model_config = ConfigDict(validate_assignment=True)
 
-    mode: Literal["fixed", "decay", "dft_decay", "energy_decay"] = Field(
+    mode: Literal["fixed", "field_decay", "dft_decay", "energy_decay"] = Field(
         default="fixed"
     )
     max_time: float = Field(
@@ -91,7 +91,7 @@ class StoppingConfig(BaseModel):
     )
     decay_dt: float = Field(default=50.0, gt=0)
     decay_component: str = Field(default="Ey")
-    threshold: float = Field(default=1e-3, gt=0, lt=1, serialization_alias="decay_by")
+    threshold: float = Field(default=0.05, gt=0, lt=1, serialization_alias="decay_by")
     decay_monitor_port: str | None = Field(default=None)
     dft_min_run_time: float = Field(
         default=100,
diff --git a/src/gsim/meep/script.py b/src/gsim/meep/script.py
index 8f001f7b..a8cb90dc 100644
--- a/src/gsim/meep/script.py
+++ b/src/gsim/meep/script.py
@@ -1100,7 +1100,7 @@ def _capture_frame(sim_obj):
         )
     elif stop_mode == "energy_decay":
         dt = stopping.get("decay_dt", 50.0)
-        decay_by = stopping.get("decay_by", 1e-3)
+        decay_by = stopping.get("decay_by", 0.05)
         logger.info(
             "Running simulation (energy_decay mode: dt=%s, "
             "decay_by=%s, cap=%.1f)...",
@@ -1108,13 +1108,13 @@ def _capture_frame(sim_obj):
         )
         energy_fn = mp.stop_when_energy_decayed(dt=dt, decay_by=decay_by)
         sim.run(*step_funcs, until_after_sources=[energy_fn, _make_time_cap(run_after)])
-    elif stop_mode == "decay":
+    elif stop_mode == "field_decay":
         dt = stopping.get("decay_dt", 50.0)
         comp_name = stopping.get("decay_component", "Ey")
         comp = _COMPONENT_MAP.get(comp_name, mp.Ey)
-        decay_by = stopping.get("decay_by", 1e-3)
+        decay_by = stopping.get("decay_by", 0.05)
         monitor_pt = resolve_decay_monitor_point(config)
-        logger.info("Running simulation (decay mode: component=%s, dt=%s, "
+        logger.info("Running simulation (field_decay mode: component=%s, dt=%s, "
                      "decay_by=%s, cap=%.1f)...", comp_name, dt, decay_by, run_after)
 
         # Decay condition + numeric time cap (list = OR logic, first wins)
diff --git a/tests/meep/test_meep_models.py b/tests/meep/test_meep_models.py
index f0237d56..9b15fcc8 100644
--- a/tests/meep/test_meep_models.py
+++ b/tests/meep/test_meep_models.py
@@ -650,12 +650,12 @@ def test_default_is_fixed(self):
         assert cfg.max_time == 100.0
         assert cfg.decay_dt == 50.0
         assert cfg.decay_component == "Ey"
-        assert cfg.threshold == 1e-3
+        assert cfg.threshold == 0.05
         assert cfg.decay_monitor_port is None
 
-    def test_decay_mode(self):
-        cfg = StoppingConfig(mode="decay", threshold=1e-4, decay_dt=25.0)
-        assert cfg.mode == "decay"
+    def test_field_decay_mode(self):
+        cfg = StoppingConfig(mode="field_decay", threshold=1e-4, decay_dt=25.0)
+        assert cfg.mode == "field_decay"
         assert cfg.threshold == 1e-4
         assert cfg.decay_dt == 25.0
 
diff --git a/tests/meep/test_simulation.py b/tests/meep/test_simulation.py
index fa827aa3..24f06f2e 100644
--- a/tests/meep/test_simulation.py
+++ b/tests/meep/test_simulation.py
@@ -82,9 +82,9 @@ def test_symmetries(self):
 class TestStoppingFields:
     def test_defaults(self):
         f = FDTD()
-        assert f.stopping == "energy_decay"
+        assert f.stopping == "field_decay"
         assert f.max_time == 2000.0
-        assert f.stopping_threshold == 1e-3
+        assert f.stopping_threshold == 0.05
         assert f.stopping_min_time == 100.0
         assert f.stopping_component == "Ey"
         assert f.stopping_dt == 50.0
@@ -95,9 +95,9 @@ def test_fixed_mode(self):
         assert f.stopping == "fixed"
         assert f.max_time == 100
 
-    def test_decay_mode(self):
-        f = FDTD(stopping="decay", stopping_threshold=1e-4, stopping_dt=25)
-        assert f.stopping == "decay"
+    def test_field_decay_mode(self):
+        f = FDTD(stopping="field_decay", stopping_threshold=1e-4, stopping_dt=25)
+        assert f.stopping == "field_decay"
         assert f.stopping_threshold == 1e-4
         assert f.stopping_dt == 25
 
@@ -128,14 +128,14 @@ class TestFDTD:
     def test_defaults(self):
         f = FDTD()
         assert f.resolution == 32
-        assert f.stopping == "energy_decay"
+        assert f.stopping == "field_decay"
         assert f.max_time == 2000.0
         assert f.subpixel is False
         assert f.simplify_tol == 0.0
 
     def test_with_custom_stopping(self):
-        f = FDTD(stopping="decay", stopping_threshold=1e-4)
-        assert f.stopping == "decay"
+        f = FDTD(stopping="field_decay", stopping_threshold=1e-4)
+        assert f.stopping == "field_decay"
         assert f.stopping_threshold == 1e-4
 
     def test_resolution_custom(self):
@@ -217,9 +217,9 @@ def test_solver_resolution(self):
 
     def test_solver_stopping_replace(self):
         sim = Simulation()
-        sim.solver.stopping = "decay"
+        sim.solver.stopping = "field_decay"
         sim.solver.stopping_threshold = 1e-4
-        assert sim.solver.stopping == "decay"
+        assert sim.solver.stopping == "field_decay"
         assert sim.solver.stopping_threshold == 1e-4
 
     def test_stop_when_energy_decayed(self):
@@ -244,7 +244,7 @@ def test_stop_when_fields_decayed(self):
             dt=25, component="Hz", decay_by=1e-4, monitor_port="o2"
         )
         assert result is f
-        assert f.stopping == "decay"
+        assert f.stopping == "field_decay"
         assert f.stopping_dt == 25
         assert f.stopping_component == "Hz"
         assert f.stopping_threshold == 1e-4
@@ -385,14 +385,14 @@ def test_stopping_fixed(self):
         assert cfg.mode == "fixed"
         assert cfg.max_time == 200
 
-    def test_stopping_decay(self):
+    def test_stopping_field_decay(self):
         sim = Simulation()
-        sim.solver.stopping = "decay"
+        sim.solver.stopping = "field_decay"
         sim.solver.stopping_threshold = 1e-4
         sim.solver.stopping_dt = 25
         sim.solver.stopping_monitor_port = "o2"
         cfg = sim._stopping_config()
-        assert cfg.mode == "decay"
+        assert cfg.mode == "field_decay"
         assert cfg.threshold == 1e-4
         assert cfg.decay_dt == 25
         assert cfg.decay_monitor_port == "o2"

From f76bcc4b4ffcd6e6d51954d07fd8ad461d551622 Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Tue, 17 Feb 2026 13:38:13 +0100
Subject: [PATCH 06/10] =?UTF-8?q?feat:=20rename=20stop=5Fafter=20=E2=86=92?=
 =?UTF-8?q?=20stop=5Fafter=5Fsources,=20add=20wall-clock=20time=20limit?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

stop_after(time) was ambiguous — unclear it meant "after sources die."
Renamed to stop_after_sources(time) for clarity.

Added wall_time_max field + stop_after_walltime(seconds) method as an
orthogonal wall-clock safety net. The runner appends _make_wall_time_cap()
to until_after_sources in all 4 stopping mode branches when enabled.
Logs a warning when the wall-clock limit likely triggered.
---
 nbs/meep_ring_coupler.ipynb    |   9 +-
 nbs/meep_ybranch.ipynb         |   2 +-
 src/gsim/meep/meep-overview.md |   8 +-
 src/gsim/meep/models/api.py    |  26 ++-
 src/gsim/meep/models/config.py | 102 ++++------
 src/gsim/meep/script.py        | 154 ++++++++------
 src/gsim/meep/simulation.py    |   1 +
 tests/meep/test_meep_models.py | 355 ++++++++++++++++++++++++++++-----
 tests/meep/test_simulation.py  |  22 +-
 9 files changed, 491 insertions(+), 188 deletions(-)

diff --git a/nbs/meep_ring_coupler.ipynb b/nbs/meep_ring_coupler.ipynb
index 023c6087..f5b75b28 100644
--- a/nbs/meep_ring_coupler.ipynb
+++ b/nbs/meep_ring_coupler.ipynb
@@ -35,13 +35,6 @@
     "PDK.activate()\n",
     "\n",
     "c = cells.coupler_ring()\n",
-    "# ebeam_DC_te895\n",
-    "# ebeam_MMI_2x2_5050_te1310\n",
-    "# ebeam_YBranch_te1310\n",
-    "# ebeam_adiabatic_te1550\n",
-    "# ebeam_crossing4\n",
-    "# ebeam_y_1310\n",
-    "# ebeam_y_adiabatic\n",
     "\n",
     "c"
    ]
@@ -71,7 +64,7 @@
     "sim.monitors = [\"o1\", \"o2\", \"o3\", \"o4\"]\n",
     "sim.domain(pml=1.0, margin=0.5)\n",
     "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
-    "sim.solver.stop_when_fields_decayed(dt=50, decay_by=0.05)\n",
+    "sim.solver.stop_after_sources(time=160)\n",
     "\n",
     "print(sim.validate_config())"
    ]
diff --git a/nbs/meep_ybranch.ipynb b/nbs/meep_ybranch.ipynb
index b335e590..ad7269ea 100644
--- a/nbs/meep_ybranch.ipynb
+++ b/nbs/meep_ybranch.ipynb
@@ -63,7 +63,7 @@
     "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
     "sim.domain(pml=1.0, margin=0.5)\n",
     "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
-    "sim.solver.stop_when_fields_decayed(dt=50, decay_by=0.05)\n",
+    "sim.solver.stop_after_sources(time=80)\n",
     "\n",
     "print(sim.validate_config())"
    ]
diff --git a/src/gsim/meep/meep-overview.md b/src/gsim/meep/meep-overview.md
index 01d513ba..3f3f3172 100644
--- a/src/gsim/meep/meep-overview.md
+++ b/src/gsim/meep/meep-overview.md
@@ -34,7 +34,7 @@ result = sim.run()
 - **6 typed physics objects** -- `Geometry`, `Material`, `ModeSource`, `Domain`, `FDTD` + `monitors: list[str]` -- assigned to a `Simulation` container. No ordering dependencies.
 - **Callable, attribute, or constructor style** -- `sim.source(port="o1")` (callable), `sim.source.port = "o1"` (attribute), or `sim.source = ModeSource(port="o1")` (constructor). All work via Pydantic `validate_assignment=True`.
 - **Float shorthand for materials** -- `{"si": 3.47}` auto-normalizes to `Material(n=3.47)` via validator.
-- **Flat stopping fields + method API** -- `stopping` mode string (`energy_decay`/`dft_decay`/`decay`/`fixed`) + flat fields on `FDTD`, plus convenience methods (`stop_when_energy_decayed()`, `stop_when_dft_decayed()`, `stop_when_fields_decayed()`, `stop_after()`). `StoppingConfig.mode` now includes `energy_decay`.
+- **5 stopping methods** -- Flat `stopping` mode string on `FDTD` (`field_decay`/`energy_decay`/`dft_decay`/`fixed`) plus convenience methods: `stop_when_fields_decayed()`, `stop_when_energy_decayed()`, `stop_when_dft_decayed()`, `stop_after_sources()`. A 5th method `stop_after_walltime(seconds)` sets an orthogonal wall-clock safety net (field `wall_time_max`) that can be combined with any mode.
 - **Source defines spectral window** -- `WavelengthConfig` derived from `ModeSource.wavelength/bandwidth/num_freqs`. Monitors are just port name strings.
 - **JSON contract** -- `write_config()` translates new API -> existing `SimConfig` -> JSON. `StoppingConfig.mode` extended with `"energy_decay"` (runner updated to handle it).
 
@@ -50,7 +50,7 @@ result = sim.run()
 | `models/api.py`     | Declarative models: `Geometry`, `Material`, `ModeSource`, `Domain`, `FDTD`, `Symmetry`. All except `Material`/`Symmetry` have `__call__(**kwargs)` for fluent updates |
 | `simulation.py`     | `Simulation` container -- `write_config()`, `run()`, `validate_config()`, `plot_2d()`/`plot_3d()`, `estimate_meep_np()`                                               |
 | `viz.py`            | Standalone viz helpers -- `build_geometry_model()`, `plot_2d()`, `plot_3d()` (calls `common/viz`)                                                                     |
-| `models/config.py`  | `SimConfig` + all sub-configs (JSON serialization layer)                                                                                                              |
+| `models/config.py`  | `SimConfig` + all sub-configs (JSON serialization layer, strict — no defaults on fields that `build_config` always populates)                                         |
 | `models/results.py` | `SParameterResult` -- CSV + debug JSON + diagnostic PNGs                                                                                                              |
 | `ports.py`          | `extract_port_info()` -- port center/direction/normal from gdsfactory                                                                                                 |
 | `materials.py`      | `resolve_materials()` -- material names -> (n, k) via common DB                                                                                                       |
@@ -93,8 +93,10 @@ Simulation.plot_2d()
 - **Z-crop** -- Auto-crops stack around core layer. Full UBC stack is 15um; cropped to ~1.5um (massive compute savings).
 - **Port extension into PML** -- `gf.components.extend_ports()` at `write_config()` time. Original bbox stored in `SimConfig.component_bbox` for correct cell sizing.
 - **Symmetries disabled for S-params** -- `add_mode_monitor` uses `use_symmetry=false` internally; `get_eigenmode_coefficients` doesn't apply `S.multiplicity()`. Source port coefficients underestimated ~2x. gplugins also never uses `mp.Mirror`.
-- **`energy_decay` default stopping** -- `dft_decay` suffers from false early convergence (DFTs can converge on near-zero fields). `energy_decay` monitors total field energy and waits for it to peak then decay — more robust for directional couplers and similar devices. Runner uses `mp.stop_when_energy_decayed(dt, decay_by)` with a `_make_time_cap(max_time)` as a secondary callable in the `until_after_sources` list (OR logic). The `decay` mode also uses `_make_time_cap` instead of passing a raw float to `until_after_sources`.
+- **`field_decay` default stopping** -- Monitors |component|² at a point and stops when it decays by `decay_by` from peak. Runner uses `mp.stop_when_fields_decayed()` + `_make_time_cap(max_time)` in an `until_after_sources` list (OR logic — first condition wins). `energy_decay` is similar but monitors total cell energy (more robust for ring couplers where DFTs can falsely converge). `stop_after_sources(time)` runs for a fixed sim-time after sources turn off.
+- **Wall-clock safety net** -- `stop_after_walltime(seconds)` sets `wall_time_max` on `FDTD`, an orthogonal real-time cap. Runner's `_make_wall_time_cap()` uses `time.time()` deadline and is appended to the `until_after_sources` list in all 4 mode branches when `wall_time_max > 0`. A warning is logged when the wall-clock limit likely triggered (elapsed >= 95% of limit).
 - **`dft_min_run_time` is absolute sim time** -- Not time-after-sources. With a broadband source turning off at ~t=78 and min_run_time=100, DFT checking starts at t=100 (only ~22 units after source ends). Must exceed `device_length * n_group`.
+- **API models are the single source of truth for defaults** -- Config models (`models/config.py`) and the cloud runner (`script.py`) have no default values for fields that `build_config()` always populates. `model_dump(by_alias=True)` serializes all fields explicitly, so config defaults and script.py `.get()` fallbacks were dead code that could silently drift. Config sub-models are strict (missing fields = `ValidationError`); `script.py` uses direct `dict["key"]` access.
 - **Stack resolved lazily** -- `_ensure_stack()` falls back to `get_stack()` (active PDK defaults) when no explicit stack kwargs are set. Same path for `write_config()` and viz.
 
 ---
diff --git a/src/gsim/meep/models/api.py b/src/gsim/meep/models/api.py
index 67fee5fa..7a0c79db 100644
--- a/src/gsim/meep/models/api.py
+++ b/src/gsim/meep/models/api.py
@@ -189,6 +189,12 @@ class FDTD(BaseModel):
     stopping_monitor_port: str | None = Field(
         default=None, description="Port to monitor for field_decay mode"
     )
+    wall_time_max: float = Field(
+        default=0.0,
+        ge=0,
+        description="Wall-clock time limit in seconds (0=unlimited). "
+        "Orthogonal safety net — stops the sim if real time exceeds this.",
+    )
 
     subpixel: bool = Field(default=False, description="Toggle subpixel averaging")
     subpixel_maxeval: int = Field(
@@ -270,8 +276,8 @@ def stop_when_fields_decayed(
         self.stopping_monitor_port = monitor_port
         return self
 
-    def stop_after(self, time: float) -> FDTD:
-        """Run for a fixed time after sources turn off.
+    def stop_after_sources(self, time: float) -> FDTD:
+        """Run for a fixed sim-time after sources turn off.
 
         Args:
             time: Run time after sources in MEEP time units (um/c).
@@ -283,6 +289,22 @@ def stop_after(self, time: float) -> FDTD:
         self.max_time = time
         return self
 
+    def stop_after_walltime(self, seconds: float) -> FDTD:
+        """Set a wall-clock time limit (safety net).
+
+        This is orthogonal to the sim-time stopping mode — it caps
+        how long the FDTD run is allowed to take in real (wall) seconds.
+        Combine with any other stopping method.
+
+        Args:
+            seconds: Maximum wall-clock seconds for the FDTD run.
+
+        Returns:
+            self (for fluent chaining).
+        """
+        self.wall_time_max = seconds
+        return self
+
     def __call__(self, **kwargs: Any) -> FDTD:
         """Update fields in place. Returns self for chaining."""
         for k, v in kwargs.items():
diff --git a/src/gsim/meep/models/config.py b/src/gsim/meep/models/config.py
index dccf50ef..40825311 100644
--- a/src/gsim/meep/models/config.py
+++ b/src/gsim/meep/models/config.py
@@ -19,7 +19,7 @@ class SymmetryEntry(BaseModel):
     model_config = ConfigDict(validate_assignment=True)
 
     direction: Literal["X", "Y", "Z"]
-    phase: Literal[1, -1] = Field(default=1)
+    phase: Literal[1, -1]
 
 
 class DomainConfig(BaseModel):
@@ -38,23 +38,21 @@ class DomainConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    dpml: float = Field(default=1.0, ge=0, description="PML thickness in um")
+    dpml: float = Field(ge=0, description="PML thickness in um")
     margin_xy: float = Field(
-        default=0.5, ge=0, description="XY margin between geometry and PML in um"
+        ge=0, description="XY margin between geometry and PML in um"
     )
     margin_z_above: float = Field(
-        default=0.5, ge=0, description="Z margin above core kept by set_z_crop in um"
+        ge=0, description="Z margin above core kept by set_z_crop in um"
     )
     margin_z_below: float = Field(
-        default=0.5, ge=0, description="Z margin below core kept by set_z_crop in um"
+        ge=0, description="Z margin below core kept by set_z_crop in um"
     )
     port_margin: float = Field(
-        default=0.5,
         ge=0,
         description="Margin on each side of port width for monitors (um)",
     )
     extend_ports: float = Field(
-        default=0.0,
         ge=0,
         description="Length to extend waveguide ports into PML in um. "
         "0 = auto (margin_xy + dpml).",
@@ -83,23 +81,24 @@ class StoppingConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    mode: Literal["fixed", "field_decay", "dft_decay", "energy_decay"] = Field(
-        default="fixed"
-    )
-    max_time: float = Field(
-        default=100.0, gt=0, serialization_alias="run_after_sources"
-    )
-    decay_dt: float = Field(default=50.0, gt=0)
-    decay_component: str = Field(default="Ey")
-    threshold: float = Field(default=0.05, gt=0, lt=1, serialization_alias="decay_by")
+    mode: Literal["fixed", "field_decay", "dft_decay", "energy_decay"]
+    max_time: float = Field(gt=0, serialization_alias="run_after_sources")
+    decay_dt: float = Field(gt=0)
+    decay_component: str
+    threshold: float = Field(gt=0, lt=1, serialization_alias="decay_by")
     decay_monitor_port: str | None = Field(default=None)
     dft_min_run_time: float = Field(
-        default=100,
         ge=0,
         description="Minimum absolute sim time for dft_decay mode (not "
         "time-after-sources). Must exceed pulse transit time through the "
         "device to avoid false convergence on near-zero fields.",
     )
+    wall_time_max: float = Field(
+        default=0.0,
+        ge=0,
+        description="Wall-clock time limit in seconds (0=unlimited). "
+        "Orthogonal safety net for all stopping modes.",
+    )
 
 
 class SourceConfig(BaseModel):
@@ -162,11 +161,9 @@ class WavelengthConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    wavelength: float = Field(default=1.55, gt=0, description="Center wavelength in um")
-    bandwidth: float = Field(
-        default=0.1, ge=0, description="Wavelength bandwidth in um"
-    )
-    num_freqs: int = Field(default=11, ge=1, description="Number of frequency points")
+    wavelength: float = Field(gt=0, description="Center wavelength in um")
+    bandwidth: float = Field(ge=0, description="Wavelength bandwidth in um")
+    num_freqs: int = Field(ge=1, description="Number of frequency points")
 
     @computed_field  # type: ignore[prop-decorator]
     @property
@@ -190,9 +187,7 @@ class ResolutionConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    pixels_per_um: int = Field(
-        default=32, ge=4, description="Grid pixels per micrometer"
-    )
+    pixels_per_um: int = Field(ge=4, description="Grid pixels per micrometer")
 
     @classmethod
     def coarse(cls) -> ResolutionConfig:
@@ -222,15 +217,13 @@ class AccuracyConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    eps_averaging: bool = Field(default=False, description="Toggle subpixel averaging")
+    eps_averaging: bool = Field(description="Toggle subpixel averaging")
     subpixel_maxeval: int = Field(
-        default=0, ge=0, description="Cap on integration evaluations (0=unlimited)"
-    )
-    subpixel_tol: float = Field(
-        default=1e-4, gt=0, description="Subpixel integration tolerance"
+        ge=0, description="Cap on integration evaluations (0=unlimited)"
     )
+    subpixel_tol: float = Field(gt=0, description="Subpixel integration tolerance")
     simplify_tol: float = Field(
-        default=0.0, ge=0, description="Shapely simplification tolerance in um (0=off)"
+        ge=0, description="Shapely simplification tolerance in um (0=off)"
     )
 
 
@@ -239,27 +232,20 @@ class DiagnosticsConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    save_geometry: bool = Field(
-        default=True, description="Pre-run geometry cross-section plots"
-    )
-    save_fields: bool = Field(default=True, description="Post-run field snapshot")
-    save_epsilon_raw: bool = Field(
-        default=False, description="Raw epsilon .npy (advanced)"
-    )
+    save_geometry: bool = Field(description="Pre-run geometry cross-section plots")
+    save_fields: bool = Field(description="Post-run field snapshot")
+    save_epsilon_raw: bool = Field(description="Raw epsilon .npy (advanced)")
     save_animation: bool = Field(
-        default=False, description="Field animation MP4 (heavy, needs ffmpeg)"
+        description="Field animation MP4 (heavy, needs ffmpeg)"
     )
     animation_interval: float = Field(
-        default=0.5,
         gt=0,
         description="MEEP time units between animation frames",
     )
     preview_only: bool = Field(
-        default=False,
         description="Init sim and save geometry diagnostics, skip FDTD run",
     )
     verbose_interval: float = Field(
-        default=0,
         ge=0,
         description="MEEP time units between progress prints (0=off)",
     )
@@ -315,32 +301,28 @@ class SimConfig(BaseModel):
 
     model_config = ConfigDict(validate_assignment=True)
 
-    gds_filename: str = Field(
-        default="layout.gds", description="GDS file with 2D layout"
-    )
+    gds_filename: str = Field(description="GDS file with 2D layout")
     component_bbox: list[float] | None = Field(
         default=None,
         description=(
             "Original component bbox [xmin, ymin, xmax, ymax] before port extension."
         ),
     )
-    layer_stack: list[LayerStackEntry] = Field(default_factory=list)
-    dielectrics: list[dict[str, Any]] = Field(default_factory=list)
-    ports: list[PortData] = Field(default_factory=list)
-    materials: dict[str, MaterialData] = Field(default_factory=dict)
-    wavelength: WavelengthConfig = Field(
-        default_factory=WavelengthConfig, serialization_alias="fdtd"
-    )
-    source: SourceConfig = Field(default_factory=SourceConfig)
-    stopping: StoppingConfig = Field(default_factory=StoppingConfig)
-    resolution: ResolutionConfig = Field(default_factory=ResolutionConfig)
-    domain: DomainConfig = Field(default_factory=DomainConfig)
-    accuracy: AccuracyConfig = Field(default_factory=AccuracyConfig)
+    layer_stack: list[LayerStackEntry]
+    dielectrics: list[dict[str, Any]]
+    ports: list[PortData]
+    materials: dict[str, MaterialData]
+    wavelength: WavelengthConfig = Field(serialization_alias="fdtd")
+    source: SourceConfig
+    stopping: StoppingConfig
+    resolution: ResolutionConfig
+    domain: DomainConfig
+    accuracy: AccuracyConfig
     verbose_interval: float = Field(
-        default=0, ge=0, description="MEEP time units between progress prints (0=off)"
+        ge=0, description="MEEP time units between progress prints (0=off)"
     )
-    diagnostics: DiagnosticsConfig = Field(default_factory=DiagnosticsConfig)
-    symmetries: list[SymmetryEntry] = Field(default_factory=list)
+    diagnostics: DiagnosticsConfig
+    symmetries: list[SymmetryEntry]
     split_chunks_evenly: bool = Field(default=False)
     meep_np: int = Field(default=1, ge=1, description="Recommended MPI process count")
 
diff --git a/src/gsim/meep/script.py b/src/gsim/meep/script.py
index a8cb90dc..31c14dc0 100644
--- a/src/gsim/meep/script.py
+++ b/src/gsim/meep/script.py
@@ -59,7 +59,7 @@ def build_materials(config):
     materials = {}
     for name, props in config["materials"].items():
         n = props["refractive_index"]
-        k = props.get("extinction_coeff", 0.0)
+        k = props["extinction_coeff"]
         if k > 0:
             materials[name] = mp.Medium(index=n, D_conductivity=2 * cmath.pi * k / n)
         else:
@@ -202,7 +202,7 @@ def build_background_slabs(config, materials):
     geometry list so that patterned prisms (added later) take precedence.
     """
     slabs = []
-    for diel in sorted(config.get("dielectrics", []), key=lambda d: d["zmin"]):
+    for diel in sorted(config["dielectrics"], key=lambda d: d["zmin"]):
         mat = materials.get(diel["material"])
         if mat is None:
             continue
@@ -224,9 +224,9 @@ def build_symmetries(config):
     """Build MEEP symmetry objects from config."""
     direction_map = {"X": mp.X, "Y": mp.Y, "Z": mp.Z}
     symmetries = []
-    for sym in config.get("symmetries", []):
+    for sym in config["symmetries"]:
         symmetries.append(
-            mp.Mirror(direction_map[sym["direction"]], phase=sym.get("phase", 1))
+            mp.Mirror(direction_map[sym["direction"]], phase=sym["phase"])
         )
     return symmetries
 
@@ -254,6 +254,21 @@ def _check(sim_obj):
     return _check
 
 
+def _make_wall_time_cap(wall_seconds):
+    """Wrap a wall-clock time limit as a callable for until_after_sources lists.
+
+    Returns True once ``wall_seconds`` of real (wall) time have elapsed
+    since the first call.  This provides a safety net orthogonal to any
+    sim-time stopping condition.
+    """
+    _deadline = [None]
+    def _check(sim_obj):
+        if _deadline[0] is None:
+            _deadline[0] = time.time() + wall_seconds
+        return time.time() >= _deadline[0]
+    return _check
+
+
 def resolve_decay_monitor_point(config):
     """Return center mp.Vector3 for decay monitoring.
 
@@ -261,9 +276,9 @@ def resolve_decay_monitor_point(config):
     otherwise picks the first non-source port.  Falls back to
     the first port if all are sources.
     """
-    stopping = config.get("stopping", {})
+    stopping = config["stopping"]
     target_name = stopping.get("decay_monitor_port")
-    ports = config.get("ports", [])
+    ports = config["ports"]
 
     if target_name:
         for p in ports:
@@ -272,7 +287,7 @@ def resolve_decay_monitor_point(config):
 
     # Fallback: first non-source port, or first port
     for p in ports:
-        if not p.get("is_source", False):
+        if not p["is_source"]:
             return mp.Vector3(*p["center"])
     if ports:
         return mp.Vector3(*ports[0]["center"])
@@ -288,11 +303,11 @@ def build_geometry(config, materials):
       2. Extrude to 3D as mp.Prism with correct z-range and material
       3. Handle polygon holes via Delaunay triangulation
     """
-    gds_filename = config.get("gds_filename", "layout.gds")
+    gds_filename = config["gds_filename"]
     component = load_gds_component(gds_filename)
 
-    accuracy = config.get("accuracy", {})
-    simplify_tol = accuracy.get("simplify_tol", 0.0)
+    accuracy = config["accuracy"]
+    simplify_tol = accuracy["simplify_tol"]
 
     geometry = []
     total_vertices = 0
@@ -304,7 +319,7 @@ def build_geometry(config, materials):
         zmax = layer_entry["zmax"]
         height = zmax - zmin
         gds_layer = layer_entry["gds_layer"]
-        sidewall_angle_deg = layer_entry.get("sidewall_angle", 0.0)
+        sidewall_angle_deg = layer_entry["sidewall_angle"]
         sw_rad = math.radians(sidewall_angle_deg) if sidewall_angle_deg else 0
 
         if height <= 0:
@@ -366,7 +381,7 @@ def build_sources(config):
     df = fdtd["df"]
     fwidth = config["source"]["fwidth"]
     z_span = get_port_z_span(config)
-    port_margin = config.get("domain", {}).get("port_margin", 0.5)
+    port_margin = config["domain"]["port_margin"]
 
     sources = []
     for port in config["ports"]:
@@ -412,7 +427,7 @@ def build_monitors(config, sim):
     df = fdtd["df"]
     nfreq = fdtd["num_freqs"]
     z_span = get_port_z_span(config)
-    port_margin = config.get("domain", {}).get("port_margin", 0.5)
+    port_margin = config["domain"]["port_margin"]
 
     monitors = {}
     for port in config["ports"]:
@@ -618,9 +633,9 @@ def save_debug_log(config, s_params, debug_data, wall_seconds=0.0,
     if not mp.am_master():
         return
     fdtd = config["fdtd"]
-    domain = config.get("domain", {})
-    resolution = config.get("resolution", {}).get("pixels_per_um", 0)
-    stopping = config.get("stopping", {})
+    domain = config["domain"]
+    resolution = config["resolution"]["pixels_per_um"]
+    stopping = config["stopping"]
 
     meep_time = 0.0
     timesteps = 0
@@ -638,7 +653,7 @@ def save_debug_log(config, s_params, debug_data, wall_seconds=0.0,
             "meep_time": meep_time,
             "timesteps": timesteps,
             "wall_seconds": wall_seconds,
-            "stopping_mode": stopping.get("mode", "fixed"),
+            "stopping_mode": stopping["mode"],
         },
         "eigenmode_info": debug_data.get("eigenmode_info", {}),
         "incident_coefficients": debug_data.get("incident_coefficients", {}),
@@ -664,8 +679,8 @@ def save_geometry_diagnostics(sim, config, cell_center):
         # plot2D is collective — all ranks call it, only master saves
         pass
 
-    domain = config.get("domain", {})
-    dpml = domain.get("dpml", 1.0)
+    domain = config["domain"]
+    dpml = domain["dpml"]
     z_min = min(l["zmin"] for l in config["layer_stack"])
     z_max = max(l["zmax"] for l in config["layer_stack"])
     z_core = (z_min + z_max) / 2
@@ -945,7 +960,7 @@ def main():
 
     # Compute simulation cell from component bounds + layer z-range
     # Use original component bbox if available (port extension changes GDS bbox)
-    component_bbox = config.get("component_bbox")
+    component_bbox = config["component_bbox"]
     if component_bbox is not None:
         bbox_left, bbox_bottom, bbox_right, bbox_top = component_bbox
     else:
@@ -956,9 +971,9 @@ def main():
     z_min = min(l["zmin"] for l in config["layer_stack"])
     z_max = max(l["zmax"] for l in config["layer_stack"])
 
-    domain = config.get("domain", {})
-    dpml = domain.get("dpml", 1.0)
-    margin_xy = domain.get("margin_xy", 0.5)
+    domain = config["domain"]
+    dpml = domain["dpml"]
+    margin_xy = domain["margin_xy"]
 
     # XY: margin_xy is gap between geometry bbox and PML
     cell_x = (bbox_right - bbox_left) + 2 * (margin_xy + dpml)
@@ -976,12 +991,12 @@ def main():
     logger.info("PML: %.2f um, margin_xy: %.2f", dpml, margin_xy)
     logger.info("Resolution: %s pixels/um", resolution)
 
-    accuracy = config.get("accuracy", {})
-    diagnostics = config.get("diagnostics", {})
-    preview_only = diagnostics.get("preview_only", False)
+    accuracy = config["accuracy"]
+    diagnostics = config["diagnostics"]
+    preview_only = diagnostics["preview_only"]
 
     # In preview mode, skip expensive subpixel averaging
-    eps_avg = False if preview_only else accuracy.get("eps_averaging", True)
+    eps_avg = False if preview_only else accuracy["eps_averaging"]
 
     # Symmetries are NOT used for S-parameter extraction runs.
     # MEEP's get_eigenmode_coefficients with add_mode_monitor produces
@@ -990,7 +1005,7 @@ def main():
     # consistent with gplugins which also never uses mp.Mirror for
     # S-parameter extraction.  Symmetries are only applied in preview-only
     # mode (geometry validation, no FDTD/S-params).
-    cfg_symmetries = config.get("symmetries", [])
+    cfg_symmetries = config["symmetries"]
     if cfg_symmetries and not preview_only:
         logger.info("Symmetries present in config but IGNORED for S-parameter "
                      "extraction (causes incorrect eigenmode normalization). "
@@ -1005,21 +1020,21 @@ def main():
         resolution=resolution,
         boundary_layers=[mp.PML(dpml)],
         symmetries=use_symmetries,
-        split_chunks_evenly=config.get("split_chunks_evenly", False),
+        split_chunks_evenly=config["split_chunks_evenly"],
         eps_averaging=eps_avg,
     )
-    spx_maxeval = accuracy.get("subpixel_maxeval", 0)
+    spx_maxeval = accuracy["subpixel_maxeval"]
     if spx_maxeval > 0:
         sim_kwargs["subpixel_maxeval"] = spx_maxeval
-    spx_tol = accuracy.get("subpixel_tol", 1e-4)
+    spx_tol = accuracy["subpixel_tol"]
     if spx_tol != 1e-4:
         sim_kwargs["subpixel_tol"] = spx_tol
     sim = mp.Simulation(**sim_kwargs)
 
     # --- Diagnostics & preview mode ---
-    diag_geometry = diagnostics.get("save_geometry", True)
-    diag_fields = diagnostics.get("save_fields", True)
-    diag_epsilon = diagnostics.get("save_epsilon_raw", False)
+    diag_geometry = diagnostics["save_geometry"]
+    diag_fields = diagnostics["save_fields"]
+    diag_epsilon = diagnostics["save_epsilon_raw"]
 
     if diag_geometry or diag_epsilon or preview_only:
         logger.info("Initializing simulation for diagnostics...")
@@ -1039,12 +1054,12 @@ def main():
     logger.info("Building monitors...")
     monitors = build_monitors(config, sim)
 
-    stopping = config.get("stopping", {})
-    run_after = stopping.get("run_after_sources", 100)
+    stopping = config["stopping"]
+    run_after = stopping["run_after_sources"]
 
     # Build verbose step functions
     step_funcs = []
-    verbose_interval = config.get("verbose_interval", 0)
+    verbose_interval = config["verbose_interval"]
     if verbose_interval > 0:
         _wall_start = time.time()
         def _verbose_print(sim_obj):
@@ -1053,8 +1068,8 @@ def _verbose_print(sim_obj):
         step_funcs.append(mp.at_every(verbose_interval, _verbose_print))
 
     # Animation field capture step function (raw data, no plotting yet)
-    diag_animation = diagnostics.get("save_animation", False)
-    animation_interval = diagnostics.get("animation_interval", 0.5)
+    diag_animation = diagnostics["save_animation"]
+    animation_interval = diagnostics["animation_interval"]
     _frame_counter = [0]  # mutable container for closure
     _anim_plane = None
 
@@ -1078,53 +1093,78 @@ def _capture_frame(sim_obj):
             animation_interval,
         )
 
-    stop_mode = stopping.get("mode", "fixed")
+    stop_mode = stopping["mode"]
+    wall_time_max = stopping.get("wall_time_max", 0)
+    if wall_time_max > 0:
+        logger.info("Wall-clock safety net: %.0f seconds", wall_time_max)
 
     wall_start = time.time()
 
     if stop_mode == "dft_decay":
-        decay_by = stopping.get("decay_by", 1e-3)
-        min_time = stopping.get("dft_min_run_time", 100)
+        decay_by = stopping["decay_by"]
+        min_time = stopping["dft_min_run_time"]
         logger.info(
             "Running simulation (dft_decay mode: tol=%s, "
             "min=%.1f, max=%.1f)...",
             decay_by, min_time, run_after,
         )
-        sim.run(
-            *step_funcs,
-            until_after_sources=mp.stop_when_dft_decayed(
-                tol=decay_by,
-                minimum_run_time=min_time,
-                maximum_run_time=run_after,
-            ),
+        dft_fn = mp.stop_when_dft_decayed(
+            tol=decay_by,
+            minimum_run_time=min_time,
+            maximum_run_time=run_after,
         )
+        if wall_time_max > 0:
+            conds = [dft_fn, _make_wall_time_cap(wall_time_max)]
+            sim.run(*step_funcs, until_after_sources=conds)
+        else:
+            sim.run(*step_funcs, until_after_sources=dft_fn)
     elif stop_mode == "energy_decay":
-        dt = stopping.get("decay_dt", 50.0)
-        decay_by = stopping.get("decay_by", 0.05)
+        dt = stopping["decay_dt"]
+        decay_by = stopping["decay_by"]
         logger.info(
             "Running simulation (energy_decay mode: dt=%s, "
             "decay_by=%s, cap=%.1f)...",
             dt, decay_by, run_after,
         )
         energy_fn = mp.stop_when_energy_decayed(dt=dt, decay_by=decay_by)
-        sim.run(*step_funcs, until_after_sources=[energy_fn, _make_time_cap(run_after)])
+        conds = [energy_fn, _make_time_cap(run_after)]
+        if wall_time_max > 0:
+            conds.append(_make_wall_time_cap(wall_time_max))
+        sim.run(*step_funcs, until_after_sources=conds)
     elif stop_mode == "field_decay":
-        dt = stopping.get("decay_dt", 50.0)
-        comp_name = stopping.get("decay_component", "Ey")
+        dt = stopping["decay_dt"]
+        comp_name = stopping["decay_component"]
         comp = _COMPONENT_MAP.get(comp_name, mp.Ey)
-        decay_by = stopping.get("decay_by", 0.05)
+        decay_by = stopping["decay_by"]
         monitor_pt = resolve_decay_monitor_point(config)
         logger.info("Running simulation (field_decay mode: component=%s, dt=%s, "
                      "decay_by=%s, cap=%.1f)...", comp_name, dt, decay_by, run_after)
 
         # Decay condition + numeric time cap (list = OR logic, first wins)
         decay_fn = mp.stop_when_fields_decayed(dt, comp, monitor_pt, decay_by)
-        sim.run(*step_funcs, until_after_sources=[decay_fn, _make_time_cap(run_after)])
+        conds = [decay_fn, _make_time_cap(run_after)]
+        if wall_time_max > 0:
+            conds.append(_make_wall_time_cap(wall_time_max))
+        sim.run(*step_funcs, until_after_sources=conds)
     else:
         logger.info("Running simulation (until_after_sources=%.1f)...", run_after)
-        sim.run(*step_funcs, until_after_sources=run_after)
+        if wall_time_max > 0:
+            conds = [
+                _make_time_cap(run_after),
+                _make_wall_time_cap(wall_time_max),
+            ]
+            sim.run(*step_funcs, until_after_sources=conds)
+        else:
+            sim.run(*step_funcs, until_after_sources=run_after)
 
     wall_seconds = time.time() - wall_start
+    if wall_time_max > 0 and wall_seconds >= wall_time_max * 0.95:
+        logger.warning(
+            "Wall-clock limit likely triggered (%.1fs elapsed, limit %.0fs). "
+            "Results may be incomplete — consider increasing wall_time_max or "
+            "using a coarser resolution.",
+            wall_seconds, wall_time_max,
+        )
 
     if diag_fields:
         save_field_snapshot(sim, config, cell_center)
diff --git a/src/gsim/meep/simulation.py b/src/gsim/meep/simulation.py
index ea938ae8..7edc4221 100644
--- a/src/gsim/meep/simulation.py
+++ b/src/gsim/meep/simulation.py
@@ -334,6 +334,7 @@ def _stopping_config(self) -> Any:
             decay_component=s.stopping_component,
             decay_dt=s.stopping_dt,
             decay_monitor_port=s.stopping_monitor_port,
+            wall_time_max=s.wall_time_max,
         )
 
     def _domain_config(self) -> Any:
diff --git a/tests/meep/test_meep_models.py b/tests/meep/test_meep_models.py
index 9b15fcc8..09bae0a7 100644
--- a/tests/meep/test_meep_models.py
+++ b/tests/meep/test_meep_models.py
@@ -32,25 +32,23 @@
 class TestWavelengthConfig:
     """Test WavelengthConfig frequency/wavelength conversion."""
 
-    def test_defaults(self):
-        cfg = WavelengthConfig()
-        assert cfg.wavelength == 1.55
-        assert cfg.bandwidth == 0.1
-        assert cfg.num_freqs == 11
+    def test_requires_all_fields(self):
+        with pytest.raises(ValidationError):
+            WavelengthConfig()
 
     def test_fcen(self):
-        cfg = WavelengthConfig(wavelength=1.55)
+        cfg = WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11)
         assert abs(cfg.fcen - 1.0 / 1.55) < 1e-10
 
     def test_df(self):
-        cfg = WavelengthConfig(wavelength=1.55, bandwidth=0.1)
+        cfg = WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11)
         wl_min = 1.55 - 0.05
         wl_max = 1.55 + 0.05
         expected_df = 1.0 / wl_min - 1.0 / wl_max
         assert abs(cfg.df - expected_df) < 1e-10
 
     def test_model_dump(self):
-        cfg = WavelengthConfig()
+        cfg = WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11)
         d = cfg.model_dump()
         assert "wavelength" in d
         assert "fcen" in d
@@ -81,7 +79,7 @@ def test_custom(self):
         assert cfg.pixels_per_um == 48
 
     def test_model_dump(self):
-        cfg = ResolutionConfig()
+        cfg = ResolutionConfig(pixels_per_um=32)
         d = cfg.model_dump()
         assert d["pixels_per_um"] == 32
 
@@ -90,12 +88,25 @@ class TestSimConfig:
     """Test SimConfig serialization."""
 
     def test_to_json(self, tmp_path):
-        wl_cfg = WavelengthConfig()
+        wl_cfg = WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11)
         fwidth = SourceConfig().compute_fwidth(wl_cfg.fcen, wl_cfg.df)
         source_cfg = SourceConfig(fwidth=fwidth)
-        stopping_cfg = StoppingConfig(mode="dft_decay", max_time=200.0)
+        stopping_cfg = StoppingConfig(
+            mode="dft_decay",
+            max_time=200.0,
+            decay_dt=50.0,
+            decay_component="Ey",
+            threshold=0.05,
+            dft_min_run_time=100,
+        )
+        from gsim.meep.models.config import (
+            AccuracyConfig,
+            DiagnosticsConfig,
+        )
+
         cfg = SimConfig(
             gds_filename="layout.gds",
+            verbose_interval=0,
             layer_stack=[  # ty: ignore[invalid-argument-type]
                 {
                     "layer_name": "core",
@@ -121,7 +132,32 @@ def test_to_json(self, tmp_path):
             wavelength=wl_cfg,
             source=source_cfg,
             stopping=stopping_cfg,
-            resolution=ResolutionConfig(),
+            resolution=ResolutionConfig(pixels_per_um=32),
+            domain=DomainConfig(
+                dpml=1.0,
+                margin_xy=0.5,
+                margin_z_above=0.5,
+                margin_z_below=0.5,
+                port_margin=0.5,
+                extend_ports=0.0,
+            ),
+            accuracy=AccuracyConfig(
+                eps_averaging=False,
+                subpixel_maxeval=0,
+                subpixel_tol=1e-4,
+                simplify_tol=0.0,
+            ),
+            diagnostics=DiagnosticsConfig(
+                save_geometry=True,
+                save_fields=True,
+                save_epsilon_raw=False,
+                save_animation=False,
+                animation_interval=0.5,
+                preview_only=False,
+                verbose_interval=0,
+            ),
+            dielectrics=[],
+            symmetries=[],
         )
         path = tmp_path / "config.json"
         cfg.to_json(path)
@@ -519,18 +555,18 @@ def test_import_all_public_api(self):
 class TestDomainConfig:
     """Test DomainConfig model."""
 
-    def test_defaults(self):
-        cfg = DomainConfig()
-        assert cfg.dpml == 1.0
-        assert cfg.margin_xy == 0.5
-        assert cfg.margin_z_above == 0.5
-        assert cfg.margin_z_below == 0.5
-        assert cfg.port_margin == 0.5
-        assert cfg.extend_ports == 0.0
+    def test_requires_all_fields(self):
+        with pytest.raises(ValidationError):
+            DomainConfig()
 
     def test_custom(self):
         cfg = DomainConfig(
-            dpml=0.5, margin_xy=0.2, margin_z_above=0.3, margin_z_below=0.4
+            dpml=0.5,
+            margin_xy=0.2,
+            margin_z_above=0.3,
+            margin_z_below=0.4,
+            port_margin=0.5,
+            extend_ports=0.0,
         )
         assert cfg.dpml == 0.5
         assert cfg.margin_xy == 0.2
@@ -538,17 +574,36 @@ def test_custom(self):
         assert cfg.margin_z_below == 0.4
 
     def test_extend_ports_custom(self):
-        cfg = DomainConfig(extend_ports=2.5)
+        cfg = DomainConfig(
+            dpml=1.0,
+            margin_xy=0.5,
+            margin_z_above=0.5,
+            margin_z_below=0.5,
+            port_margin=0.5,
+            extend_ports=2.5,
+        )
         assert cfg.extend_ports == 2.5
 
     def test_extend_ports_serialization(self):
-        cfg = DomainConfig(extend_ports=3.0)
+        cfg = DomainConfig(
+            dpml=1.0,
+            margin_xy=0.5,
+            margin_z_above=0.5,
+            margin_z_below=0.5,
+            port_margin=0.5,
+            extend_ports=3.0,
+        )
         d = cfg.model_dump()
         assert d["extend_ports"] == 3.0
 
     def test_model_dump(self):
         cfg = DomainConfig(
-            dpml=2.0, margin_xy=0.5, margin_z_above=1.0, margin_z_below=1.5
+            dpml=2.0,
+            margin_xy=0.5,
+            margin_z_above=1.0,
+            margin_z_below=1.5,
+            port_margin=0.5,
+            extend_ports=0.0,
         )
         d = cfg.model_dump()
         assert d["dpml"] == 2.0
@@ -560,23 +615,71 @@ def test_model_dump(self):
 class TestSimConfigComponentBbox:
     """Test SimConfig.component_bbox field."""
 
-    def test_default_none(self):
-        cfg = SimConfig()
+    @pytest.fixture
+    def _sim_kwargs(self):
+        from gsim.meep.models.config import AccuracyConfig, DiagnosticsConfig
+
+        return dict(
+            gds_filename="layout.gds",
+            verbose_interval=0,
+            layer_stack=[],
+            dielectrics=[],
+            ports=[],
+            materials={},
+            wavelength=WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11),
+            source=SourceConfig(),
+            stopping=StoppingConfig(
+                mode="fixed",
+                max_time=100.0,
+                decay_dt=50.0,
+                decay_component="Ey",
+                threshold=0.05,
+                dft_min_run_time=100,
+            ),
+            resolution=ResolutionConfig(pixels_per_um=32),
+            domain=DomainConfig(
+                dpml=1.0,
+                margin_xy=0.5,
+                margin_z_above=0.5,
+                margin_z_below=0.5,
+                port_margin=0.5,
+                extend_ports=0.0,
+            ),
+            accuracy=AccuracyConfig(
+                eps_averaging=False,
+                subpixel_maxeval=0,
+                subpixel_tol=1e-4,
+                simplify_tol=0.0,
+            ),
+            diagnostics=DiagnosticsConfig(
+                save_geometry=True,
+                save_fields=True,
+                save_epsilon_raw=False,
+                save_animation=False,
+                animation_interval=0.5,
+                preview_only=False,
+                verbose_interval=0,
+            ),
+            symmetries=[],
+        )
+
+    def test_default_none(self, _sim_kwargs):
+        cfg = SimConfig(**_sim_kwargs)
         assert cfg.component_bbox is None
 
-    def test_with_bbox(self):
-        cfg = SimConfig(component_bbox=[-5.0, -2.0, 5.0, 2.0])
+    def test_with_bbox(self, _sim_kwargs):
+        cfg = SimConfig(**_sim_kwargs, component_bbox=[-5.0, -2.0, 5.0, 2.0])
         assert cfg.component_bbox == [-5.0, -2.0, 5.0, 2.0]
 
-    def test_json_roundtrip(self, tmp_path):
-        cfg = SimConfig(component_bbox=[-1.0, -0.5, 1.0, 0.5])
+    def test_json_roundtrip(self, tmp_path, _sim_kwargs):
+        cfg = SimConfig(**_sim_kwargs, component_bbox=[-1.0, -0.5, 1.0, 0.5])
         path = tmp_path / "config.json"
         cfg.to_json(path)
         data = json.loads(path.read_text())
         assert data["component_bbox"] == [-1.0, -0.5, 1.0, 0.5]
 
-    def test_json_roundtrip_none(self, tmp_path):
-        cfg = SimConfig()
+    def test_json_roundtrip_none(self, tmp_path, _sim_kwargs):
+        cfg = SimConfig(**_sim_kwargs)
         path = tmp_path / "config.json"
         cfg.to_json(path)
         data = json.loads(path.read_text())
@@ -587,14 +690,50 @@ class TestDomainInSimConfig:
     """Test domain serialization in SimConfig JSON."""
 
     def test_domain_in_sim_config_json(self, tmp_path):
+        from gsim.meep.models.config import AccuracyConfig, DiagnosticsConfig
+
         cfg = SimConfig(
             gds_filename="layout.gds",
+            verbose_interval=0,
             layer_stack=[],
+            dielectrics=[],
             ports=[],
             materials={},
-            wavelength=WavelengthConfig(),
-            resolution=ResolutionConfig(),
-            domain=DomainConfig(dpml=0.5, margin_xy=0.2),
+            wavelength=WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11),
+            source=SourceConfig(),
+            stopping=StoppingConfig(
+                mode="fixed",
+                max_time=100.0,
+                decay_dt=50.0,
+                decay_component="Ey",
+                threshold=0.05,
+                dft_min_run_time=100,
+            ),
+            resolution=ResolutionConfig(pixels_per_um=32),
+            domain=DomainConfig(
+                dpml=0.5,
+                margin_xy=0.2,
+                margin_z_above=0.5,
+                margin_z_below=0.5,
+                port_margin=0.5,
+                extend_ports=0.0,
+            ),
+            accuracy=AccuracyConfig(
+                eps_averaging=False,
+                subpixel_maxeval=0,
+                subpixel_tol=1e-4,
+                simplify_tol=0.0,
+            ),
+            diagnostics=DiagnosticsConfig(
+                save_geometry=True,
+                save_fields=True,
+                save_epsilon_raw=False,
+                save_animation=False,
+                animation_interval=0.5,
+                preview_only=False,
+                verbose_interval=0,
+            ),
+            symmetries=[],
         )
         path = tmp_path / "config.json"
         cfg.to_json(path)
@@ -618,9 +757,9 @@ def test_creation(self):
         assert s.direction == "X"
         assert s.phase == -1
 
-    def test_default_phase(self):
-        s = SymmetryEntry(direction="Y")
-        assert s.phase == 1
+    def test_requires_phase(self):
+        with pytest.raises(ValidationError):
+            SymmetryEntry(direction="Y")
 
     def test_model_dump(self):
         s = SymmetryEntry(direction="Z", phase=-1)
@@ -644,43 +783,121 @@ def test_invalid_phase(self):
 class TestStoppingConfig:
     """Test StoppingConfig model."""
 
-    def test_default_is_fixed(self):
-        cfg = StoppingConfig()
-        assert cfg.mode == "fixed"
-        assert cfg.max_time == 100.0
-        assert cfg.decay_dt == 50.0
-        assert cfg.decay_component == "Ey"
-        assert cfg.threshold == 0.05
-        assert cfg.decay_monitor_port is None
+    def test_requires_all_fields(self):
+        with pytest.raises(ValidationError):
+            StoppingConfig()
 
     def test_field_decay_mode(self):
-        cfg = StoppingConfig(mode="field_decay", threshold=1e-4, decay_dt=25.0)
+        cfg = StoppingConfig(
+            mode="field_decay",
+            threshold=1e-4,
+            decay_dt=25.0,
+            max_time=100.0,
+            decay_component="Ey",
+            dft_min_run_time=100,
+        )
         assert cfg.mode == "field_decay"
         assert cfg.threshold == 1e-4
         assert cfg.decay_dt == 25.0
 
     def test_energy_decay_mode(self):
-        cfg = StoppingConfig(mode="energy_decay", decay_dt=100, threshold=1e-4)
+        cfg = StoppingConfig(
+            mode="energy_decay",
+            decay_dt=100,
+            threshold=1e-4,
+            max_time=100.0,
+            decay_component="Ey",
+            dft_min_run_time=100,
+        )
         assert cfg.mode == "energy_decay"
         assert cfg.decay_dt == 100
         assert cfg.threshold == 1e-4
 
     def test_invalid_mode(self):
         with pytest.raises(ValidationError):
-            StoppingConfig(mode="invalid")  # ty: ignore[invalid-argument-type]
+            StoppingConfig(  # ty: ignore[invalid-argument-type]
+                mode="invalid",
+                max_time=100.0,
+                decay_dt=50.0,
+                decay_component="Ey",
+                threshold=0.05,
+                dft_min_run_time=100,
+            )
 
     def test_threshold_bounds(self):
         with pytest.raises(ValidationError):
-            StoppingConfig(threshold=0)
+            StoppingConfig(
+                mode="fixed",
+                max_time=100.0,
+                decay_dt=50.0,
+                decay_component="Ey",
+                threshold=0,
+                dft_min_run_time=100,
+            )
+        with pytest.raises(ValidationError):
+            StoppingConfig(
+                mode="fixed",
+                max_time=100.0,
+                decay_dt=50.0,
+                decay_component="Ey",
+                threshold=1.0,
+                dft_min_run_time=100,
+            )
+
+    def test_wall_time_max_default(self):
+        cfg = StoppingConfig(
+            mode="fixed",
+            max_time=100.0,
+            decay_dt=50.0,
+            decay_component="Ey",
+            threshold=0.05,
+            dft_min_run_time=100,
+        )
+        assert cfg.wall_time_max == 0.0
+
+    def test_wall_time_max_set(self):
+        cfg = StoppingConfig(
+            mode="field_decay",
+            max_time=200.0,
+            decay_dt=50.0,
+            decay_component="Ey",
+            threshold=0.05,
+            dft_min_run_time=100,
+            wall_time_max=3600,
+        )
+        assert cfg.wall_time_max == 3600
+
+    def test_wall_time_max_serialized(self):
+        cfg = StoppingConfig(
+            mode="fixed",
+            max_time=100.0,
+            decay_dt=50.0,
+            decay_component="Ey",
+            threshold=0.05,
+            dft_min_run_time=100,
+            wall_time_max=1800,
+        )
+        data = cfg.model_dump()
+        assert data["wall_time_max"] == 1800
+
+    def test_wall_time_max_negative_rejected(self):
         with pytest.raises(ValidationError):
-            StoppingConfig(threshold=1.0)
+            StoppingConfig(
+                mode="fixed",
+                max_time=100.0,
+                decay_dt=50.0,
+                decay_component="Ey",
+                threshold=0.05,
+                dft_min_run_time=100,
+                wall_time_max=-1,
+            )
 
 
 class TestWavelengthConfigStopping:
     """Test that WavelengthConfig no longer embeds StoppingConfig."""
 
     def test_model_dump_excludes_stopping(self):
-        cfg = WavelengthConfig()
+        cfg = WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11)
         d = cfg.model_dump()
         assert "stopping" not in d
         assert "run_after_sources" not in d
@@ -735,7 +952,7 @@ def test_auto_fwidth_wider_than_monitor(self):
         """Auto source fwidth should always be wider than monitor df."""
         cfg = SourceConfig()
         fcen = 1.0 / 1.55
-        wl = WavelengthConfig(wavelength=1.55, bandwidth=0.1)
+        wl = WavelengthConfig(wavelength=1.55, bandwidth=0.1, num_freqs=11)
         fwidth = cfg.compute_fwidth(fcen, wl.df)
         assert fwidth > wl.df
 
@@ -873,7 +1090,12 @@ def test_build_sim_overlay(self):
         )
 
         domain_cfg = DomainConfig(
-            dpml=1.0, margin_xy=0.5, margin_z_above=0.0, margin_z_below=0.0
+            dpml=1.0,
+            margin_xy=0.5,
+            margin_z_above=0.0,
+            margin_z_below=0.0,
+            port_margin=0.5,
+            extend_ports=0.0,
         )
 
         port_data = [
@@ -932,7 +1154,14 @@ def test_build_sim_overlay_with_dielectrics(self):
             bbox=((-2.0, -1.0, -1.0), (2.0, 1.0, 1.22)),
         )
 
-        domain_cfg = DomainConfig(dpml=1.0, margin_xy=0.5)
+        domain_cfg = DomainConfig(
+            dpml=1.0,
+            margin_xy=0.5,
+            margin_z_above=0.5,
+            margin_z_below=0.5,
+            port_margin=0.5,
+            extend_ports=0.0,
+        )
         dielectrics = [
             {"name": "substrate", "material": "silicon", "zmin": -1.0, "zmax": 0.0},
             {"name": "oxide", "material": "SiO2", "zmin": 0.0, "zmax": 1.22},
@@ -990,6 +1219,22 @@ def test_script_handles_component_bbox(self):
         assert "bbox_top" in script
         assert "bbox_bottom" in script
 
+    def test_script_contains_wall_time_cap(self):
+        """Verify runner script has wall-clock time cap logic."""
+        from gsim.meep.script import generate_meep_script
+
+        script = generate_meep_script()
+        assert "_make_wall_time_cap" in script
+        assert "wall_time_max" in script
+
+    def test_script_wall_time_all_modes(self):
+        """Wall-clock cap should be wired into all 4 stopping modes."""
+        from gsim.meep.script import generate_meep_script
+
+        script = generate_meep_script()
+        # Each mode branch should reference _make_wall_time_cap
+        assert script.count("_make_wall_time_cap") >= 4
+
 
 # ---------------------------------------------------------------------------
 # Render2d overlay tests
diff --git a/tests/meep/test_simulation.py b/tests/meep/test_simulation.py
index 24f06f2e..f62cb6aa 100644
--- a/tests/meep/test_simulation.py
+++ b/tests/meep/test_simulation.py
@@ -250,13 +250,31 @@ def test_stop_when_fields_decayed(self):
         assert f.stopping_threshold == 1e-4
         assert f.stopping_monitor_port == "o2"
 
-    def test_stop_after(self):
+    def test_stop_after_sources(self):
         f = FDTD()
-        result = f.stop_after(time=500)
+        result = f.stop_after_sources(time=500)
         assert result is f
         assert f.stopping == "fixed"
         assert f.max_time == 500
 
+    def test_wall_time_max_default(self):
+        f = FDTD()
+        assert f.wall_time_max == 0.0
+
+    def test_stop_after_walltime(self):
+        f = FDTD()
+        result = f.stop_after_walltime(seconds=3600)
+        assert result is f
+        assert f.wall_time_max == 3600
+
+    def test_wall_time_max_orthogonal(self):
+        """wall_time_max can be combined with any stopping mode."""
+        f = FDTD()
+        f.stop_when_fields_decayed(dt=50, decay_by=0.05)
+        f.stop_after_walltime(seconds=1800)
+        assert f.stopping == "field_decay"
+        assert f.wall_time_max == 1800
+
     def test_geometry_component(self):
         sim = Simulation()
         sim.geometry.component = "placeholder"

From 1b20e3f11625c206962ddc01c14a5622bf949506 Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Tue, 17 Feb 2026 13:38:42 +0100
Subject: [PATCH 07/10] ci: replace pull_request trigger with workflow_dispatch
 for docs and release

---
 .github/workflows/docs.yml    | 2 +-
 .github/workflows/release.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 09e27191..0b6e0710 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -6,7 +6,7 @@ on:
       - main
     tags:
       - "[0-9]+.[0-9]+.[0-9]+"
-  pull_request:
+  workflow_dispatch:
 
 env:
   GFP_API_KEY: ${{ secrets.GFP_API_KEY }}
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index e81294d5..6847dafe 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -4,7 +4,7 @@ on:
   push:
     tags:
       - "[0-9]*.[0-9]*.[0-9]*"
-  pull_request:
+  workflow_dispatch:
 
 jobs:
   build:

From 20dd2776a19ac335e87a863861e9a038b8a2873d Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Wed, 18 Feb 2026 12:05:46 +0100
Subject: [PATCH 08/10] fix: separate source and monitor positions to fix 2x
 S-parameter error

MEEP eigenmode coefficients are wrong when source and monitor overlap
at the same position (standard FDTD soft-source behavior). Match the
gplugins approach: offset source inward by 0.1um, source-port monitor
by 0.3um (0.2um past source), non-source monitors by 0.1um.

Also includes: refactor gcloud.py to use RunResult dataclass with
structured sim-data-{job_name}/ directories, update palace module
run() signatures, add meep_straight and meep_sbend notebooks, remove
stale meep-overview.md.
---
 nbs/meep_api_styles.ipynb        |  76 +---------------
 nbs/meep_ring_coupler.ipynb      | 120 ++++++++++++++++++++----
 nbs/meep_sbend.ipynb             | 151 +++++++++++++++++++++++++++++++
 nbs/meep_straight.ipynb          | 151 +++++++++++++++++++++++++++++++
 nbs/meep_ybranch.ipynb           |  21 +----
 nbs/palace_demo_cpw.ipynb        |  16 +---
 nbs/palace_demo_microstrip.ipynb |  16 +---
 src/gsim/gcloud.py               | 129 ++++++++++++++++----------
 src/gsim/meep/__init__.py        |   2 +-
 src/gsim/meep/meep-overview.md   | 135 ---------------------------
 src/gsim/meep/models/api.py      |  18 +++-
 src/gsim/meep/models/config.py   |   9 ++
 src/gsim/meep/script.py          |  48 +++++++++-
 src/gsim/meep/simulation.py      |  46 +++++++---
 src/gsim/palace/__init__.py      |   2 +-
 src/gsim/palace/driven.py        |  19 ++--
 src/gsim/palace/eigenmode.py     |   4 +-
 src/gsim/palace/electrostatic.py |   4 +-
 tests/meep/test_meep_models.py   |  18 ++++
 tests/meep/test_simulation.py    |  18 ++--
 20 files changed, 648 insertions(+), 355 deletions(-)
 create mode 100644 nbs/meep_sbend.ipynb
 create mode 100644 nbs/meep_straight.ipynb
 delete mode 100644 src/gsim/meep/meep-overview.md

diff --git a/nbs/meep_api_styles.ipynb b/nbs/meep_api_styles.ipynb
index 19535ab7..e0bee08d 100644
--- a/nbs/meep_api_styles.ipynb
+++ b/nbs/meep_api_styles.ipynb
@@ -36,18 +36,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from gsim import meep\n",
-    "\n",
-    "sim = meep.Simulation()\n",
-    "\n",
-    "sim.geometry(component=c, z_crop=\"auto\")\n",
-    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
-    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
-    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
-    "sim.domain(pml=1.0, margin=0.5)\n",
-    "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)"
-   ]
+   "source": "from gsim import meep\n\nsim = meep.Simulation()\n\nsim.geometry(component=c, z_crop=\"auto\")\nsim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\nsim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\nsim.monitors = [\"o1\", \"o2\", \"o3\"]\nsim.domain(pml=1.0, margin=0.5)\nsim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)"
   },
   {
    "cell_type": "markdown",
@@ -63,31 +52,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from gsim import meep\n",
-    "\n",
-    "sim = meep.Simulation()\n",
-    "\n",
-    "sim.geometry.component = c\n",
-    "sim.geometry.z_crop = \"auto\"\n",
-    "\n",
-    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
-    "\n",
-    "sim.source.port = \"o1\"\n",
-    "sim.source.wavelength = 1.55\n",
-    "sim.source.bandwidth = 0.01\n",
-    "sim.source.num_freqs = 11\n",
-    "\n",
-    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
-    "\n",
-    "sim.domain.pml = 1.0\n",
-    "sim.domain.margin = 0.5\n",
-    "\n",
-    "sim.solver.resolution = 20\n",
-    "sim.solver.simplify_tol = 0.01\n",
-    "sim.solver.save_animation = True\n",
-    "sim.solver.verbose_interval = 5.0"
-   ]
+   "source": "from gsim import meep\n\nsim = meep.Simulation()\n\nsim.geometry.component = c\nsim.geometry.z_crop = \"auto\"\n\nsim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n\nsim.source.port = \"o1\"\nsim.source.wavelength = 1.55\nsim.source.wavelength_span = 0.01\nsim.source.num_freqs = 11\n\nsim.monitors = [\"o1\", \"o2\", \"o3\"]\n\nsim.domain.pml = 1.0\nsim.domain.margin = 0.5\n\nsim.solver.resolution = 20\nsim.solver.simplify_tol = 0.01\nsim.solver.save_animation = True\nsim.solver.verbose_interval = 5.0"
   },
   {
    "cell_type": "markdown",
@@ -103,21 +68,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from gsim import meep\n",
-    "from gsim.meep import Domain, FDTD, Geometry, ModeSource\n",
-    "\n",
-    "sim = meep.Simulation()\n",
-    "\n",
-    "sim.geometry = Geometry(component=c, z_crop=\"auto\")\n",
-    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
-    "sim.source = ModeSource(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
-    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
-    "sim.domain = Domain(pml=1.0, margin=0.5)\n",
-    "sim.solver = FDTD(\n",
-    "    resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0\n",
-    ")"
-   ]
+   "source": "from gsim import meep\nfrom gsim.meep import Domain, FDTD, Geometry, ModeSource\n\nsim = meep.Simulation()\n\nsim.geometry = Geometry(component=c, z_crop=\"auto\")\nsim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\nsim.source = ModeSource(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\nsim.monitors = [\"o1\", \"o2\", \"o3\"]\nsim.domain = Domain(pml=1.0, margin=0.5)\nsim.solver = FDTD(\n    resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0\n)"
   },
   {
    "cell_type": "markdown",
@@ -133,24 +84,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from gsim import meep\n",
-    "\n",
-    "sim = meep.Simulation()\n",
-    "\n",
-    "sim.geometry(component=c, z_crop=\"auto\")\n",
-    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
-    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
-    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
-    "sim.domain(pml=1.0, margin=0.5)\n",
-    "sim.solver(resolution=20, simplify_tol=0.01)\n",
-    "\n",
-    "# Conditional tweaks with attribute style\n",
-    "debug = True\n",
-    "if debug:\n",
-    "    sim.solver.save_animation = True\n",
-    "    sim.solver.verbose_interval = 5.0"
-   ]
+   "source": "from gsim import meep\n\nsim = meep.Simulation()\n\nsim.geometry(component=c, z_crop=\"auto\")\nsim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\nsim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\nsim.monitors = [\"o1\", \"o2\", \"o3\"]\nsim.domain(pml=1.0, margin=0.5)\nsim.solver(resolution=20, simplify_tol=0.01)\n\n# Conditional tweaks with attribute style\ndebug = True\nif debug:\n    sim.solver.save_animation = True\n    sim.solver.verbose_interval = 5.0"
   }
  ],
  "metadata": {
@@ -165,4 +99,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/nbs/meep_ring_coupler.ipynb b/nbs/meep_ring_coupler.ipynb
index f5b75b28..c40da89c 100644
--- a/nbs/meep_ring_coupler.ipynb
+++ b/nbs/meep_ring_coupler.ipynb
@@ -25,16 +25,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2026-02-18 06:12:13.722\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m25\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.728\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m48\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.729\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m110\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.730\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m149\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.731\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m168\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.731\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m210\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.732\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m244\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.732\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m269\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
+      "\u001b[32m2026-02-18 06:12:13.733\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m294\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from ubcpdk import PDK, cells\n",
     "\n",
     "PDK.activate()\n",
     "\n",
-    "c = cells.coupler_ring()\n",
+    "c = cells.coupler_ring(gap=0.2)\n",
     "\n",
     "c"
    ]
@@ -52,7 +78,17 @@
    "execution_count": null,
    "id": "4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stack validation: PASSED\n",
+      "Warnings:\n",
+      "  - No stack configured. Will use active PDK with defaults.\n"
+     ]
+    }
+   ],
    "source": [
     "from gsim import meep\n",
     "\n",
@@ -60,21 +96,32 @@
     "\n",
     "sim.geometry(component=c, z_crop=\"auto\")\n",
     "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
-    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
+    "sim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\n",
     "sim.monitors = [\"o1\", \"o2\", \"o3\", \"o4\"]\n",
     "sim.domain(pml=1.0, margin=0.5)\n",
-    "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
-    "sim.solver.stop_after_sources(time=160)\n",
+    "sim.solver(resolution=25, save_animation=True, verbose_interval=5.0)\n",
+    "sim.solver.stop_after_sources(time=100)\n",
     "\n",
     "print(sim.validate_config())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sim.plot_2d(slices=\"xyz\")"
    ]
@@ -89,10 +136,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Uploading simulation... done\n",
+      "Job started: meep-57e3d203\n",
+      "Created: 05:12:16 | Now: 05:31:54 | Status: completed\n",
+      "Extracting results.tar.gz...\n",
+      "Downloaded 362 files to /Users/vahid/doplaydo/gsim/nbs/sim-data-meep-57e3d203/results\n"
+     ]
+    }
+   ],
    "source": [
     "# Run on GDSFactory+ cloud\n",
     "result = sim.run()"
@@ -100,20 +159,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iLZSHtVAe1kJ5BCeYPGKrYLdr1y4pKCiQ5ORkr+V6/5dffgn4nClTpsjkyZP9lmdkZEh2dnal7asd/sgyMzNNuNPwjNCjTKyF8rAWysNaKI/g7N+/v3oGu/LQ2j3tk+dZY5eSkiKJiYkSFxcX0n2z+pvS4XCY80SwswbKxFooD2uhPKyF8giODgatlsEuISFBIiMjJS0tzWu53m/UqFHA58TExJibLw0rBJbSabDjPFkLZWItlIe1UB7WQnmUXTB5xFZtaNHR0dKzZ0/56quvvL4V6P0+ffqEdN8AAAAqm61q7JQ2qw4fPlxOPPFE6dWrl8yYMUMOHjxoRskCAADYme2C3WWXXWYGPkyYMEFSU1OlW7du8t///tdvQAUAAIDd2C7YqVtvvdXcAAAAqhNb9bEDAACozgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AFAJ0g6myardq8zPcJZ6MFVW7FxhfoYzysN6MrIzZEVq+P9tpVrsPVIj1DtQHWnhb8vaJs3jmkuj2o0knN+UW1K3SMv4lmF9HHYpD/2Ha/Xu1dK1dldpXLexhCs7lMcHv38gk5dMlkIplIgfImRi34ly0XEXSVgex9LJUugslAhHhEzsE8bHESbl4XQ6i37qf56/i1Pm/T5P/rnsn0XHIRFy/8n3y/ltzvda17W+a1uu/zy3XdJjrt9975f6WPFrux93SomPmZ/Fq32x5Qt5fvXzZplDHHJT15vkjOZnHPUYvLYfYD9c/M6fx+/mp8+5dS3zfZ3SzoFasmOJ/OeX/5jHrPIecTg9zwQkKytL4uPjJTMzU+Li4mz9QVnaB8jR3qBzf5srj33/WNEfs0TIfb3uk/PanOd+vu8HiO+2j/VDxPX7kV31ft2jfRC47n+59UuZuXqm+8Plxi43yunNTz+yjuf2SzgO974c43GU+JjP8wPtx7Idy2TOr3PcxzGs3TDp1bhX4PNUjg/Dks5BaeVc6gdpCY+t2bXGlInrOLQsTkg4ocRzW5YP4VLLyPfcl+HcHK18D+Udkk83fyqe9FiGtBwiNWvU9NtOSa9blvfhMZ2XEo7ddT8nP0dWZawSX50TOktURFTQ59S3PAKe0wB/U8H+rfq+Tn5hvqQfTvc7joaxDc1n8NHKs7z/6Jfp3JTwXkf4inBEyBcXf1HhX0qDySYEu2M4eeWpiRjy/hAT6jzFRceJw+Ewvx/tA931u2vdQI+V9R8EAAB8v4SYnw79rei/4geOPKb/FT/uWjfQc93LPJ/rcJiwfTDvoN+Jj4uKk+ga0Udew+e1PV/H97VL2i+Haxs+++m5vUCPlfR8z+PSY9ictdnvOF4Z8oqc1OikkGUTmmKrkDYv+YY6lZWbJdVZaR8Grt/93tQ+b+ajfhD5bFvv5xXkyf68/X77Uy+6nveHS2kfBr6vHeADzvO5XsuC+CAJ+Hjx8R3IPSCbMjf5HUe7+u3Ml4aSzm1JH9KlfoAHWS5+x+go+bHd2btNs4avU5ucKkm1k7zOa0nnJlCZlHZegz3+0rbrOr6DuQfl9Z9f9/oSpY9de8K1Uje67jH9XZV4Xsv5d1XaY5k5mfLYiqJaec/XHn/yeKkfU7/C/vH3fW6pxxbk+03v7z68W25beJvXcWitynNnPCcNazYs2/vN52/+qMdewt9maecl4GMex5h+KF0u+egS0wzreRzzzp8nybWS/Y+/tPdeKX9fVcFUcrw3xO9Y3j///bDqfpEaoLJGjyOlbkpI94tgV4W0z5AWuu8fwYtnviiJNROD/0e1jN9efH93b6uEf+Dc65Swff2AuWDeBX5vyo8v+FiSayeX6YPEd1+t9KZ897x3bfHh8uwZz9riOLQ/VDgdh2pdr7Vlulwci9gasbY4jkl9J/kdxylNT5FwEh8Tb94LvsfRKr6VhBt9Pz948oPy8LKHi/oLFh9LuL3PG9VuZPbbt0xCfRw0xVbjPnbH4r1f3/N7U4bjcdilPDgO69m5f6es+XONdEnpEvaDWf7c/6ephQj1P1jHgvKwlsLCQlm/bb0cij4kLeJbhPXfVmoVvEfoY1dFJ686f1DyprQe/uGy3nskPT1dkpKSJCKCmaVCjfKwFsojOPSxszgNc+Ea6Dwlxiba4h8tu5SHNoN3bdDV3R8tXNmlPAAgFML7X2QAAAC4EewAAABsIqyC3SOPPCJ9+/aVWrVqSb169QKus23bNjnnnHPMOtpMeM8990h+fn6V7ysAAEBVC6vpTnJzc2XYsGHSp08fefnll/0eLygoMKGuUaNGsmTJEtm5c6dcc801EhUVJY8++mhI9hkAAKCqhFWN3eTJk+XOO++Uzp07B3x8/vz58vPPP8u///1v6datmwwdOlQefvhhefbZZ00oBAAAsLOwqrE7mqVLl5rQl5xcNAu3GjJkiNx8882yfv166d69u99zcnJyzM1zSLFrKLbeEJieG71EGefIOigTa6E8rIXysBbKIzjB/Ftrq2CXmprqFeqU674+FsiUKVNMTaCvjIwMyc7OrqQ9tccfmc71p+Eu3Kc7sQvKxFooD2uhPKyF8gjO/v3+l7+0bLAbO3asTJ06tdR1NmzYIB06dKiU1x83bpyMGTPGq8YuJSVFEhMTK22CYru8KfWSYHqeCHbWQJlYC+VhLZSHtVAewYmNjQ2fYHfXXXfJiBEjSl2ndevWZdqWDppYsWKF17K0tDT3Y4HExMSYmy8NKwSW0mmw4zxZC2ViLZSHtVAe1kJ5lF0weSTkwU5rfPRWEXS0rE6J4rqMj1qwYIGpeevYsWOFvAYAAIBVhTzYBUPnqNuzZ4/5qVObrFq1yixv27at1KlTRwYPHmwC3N///neZNm2a6Vf3wAMPyC233BKwVg4AAMBOwirYTZgwQV577TX3fdco16+//loGDhwokZGR8sknn5hRsFp7V7t2bRk+fLg89NBDIdxrAACAqhFWwW727NnmVpoWLVrIZ599VmX7BAAAYBXMUwEAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJsIm2G3ZskWuv/56adWqldSsWVPatGkjEydOlNzcXK/11qxZI/369ZPY2FhJSUmRadOmhWyfAQAAqlINCRO//PKLFBYWygsvvCBt27aVdevWyciRI+XgwYPyxBNPmHWysrJk8ODBMmjQIJk5c6asXbtWrrvuOqlXr56MGjUq1IcAAABQqcIm2J111lnm5tK6dWv59ddf5fnnn3cHuzfffNPU4L3yyisSHR0tnTp1klWrVsmTTz5JsAMAALYXNsEukMzMTGnQoIH7/tKlS6V///4m1LkMGTJEpk6dKnv37pX69ev7bSMnJ8fcXLTWT2ntoN4QmJ4bp9PJObIQysRaKA9roTyshfII/nzZPtht3LhRnnnmGXdtnUpNTTV98DwlJye7HwsU7KZMmSKTJ0/2W56RkSHZ2dmVsu92+SPTYK3hLiIibLpq2hplYi2Uh7VQHtZCeQRn//794RPsxo4da2rUSrNhwwbp0KGD+/727dtNs+ywYcNMP7tjMW7cOBkzZoxXjZ0OukhMTJS4uLhj2rbd35QOh8OcJ4KdNVAm1kJ5WAvlYS2UR3B0QGjYBLu77rpLRowYUeo62p/OZceOHXLaaadJ37595cUXX/Rar1GjRpKWlua1zHVfHwskJibG3HxpWCGwlE6DHefJWigTa6E8rIXysBbKo+yCySMhD3Za46O3stCaOg11PXv2lFdffdXvQPv06SPjx4+XvLw8iYqKMssWLFgg7du3D9gMCwAAYCdh0zlKQ93AgQOlefPmpl+d9oHTfnN6c7nyyivNwAmd7279+vUyZ84ceeqpp7yaWgEAAOwq5DV2ZaU1bzpgQm/NmjXzekw78Kv4+HiZP3++3HLLLaZWLyEhQSZMmMBUJwAAoFoIm2Cn/fCO1hdPdenSRRYtWlQl+wQAAGAlYdMUCwAAgNIR7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYBMEOAADAJgh2AAAANkGwAwAAsAmCHQAAgE0Q7EJgZ+ZhWbJpl/kZztL358rSTbvD/jjsUh66/yv/3G+L47BDeQCoHnZa7DOrRqh3oLqZ8/02GffBWil0ikQ4RMac2U7O7tzYPOZwOIp+eqxfvEgcHktdyyTQeh4POgKsH2g7XptzlG29uT9tl0c+2yDO4uN48G8d5cLuTYue594Xz304cmyex+R7LEXPcfi9bsDnBzoRx1geUy7qLJed1FzCjfdx/GaT4wjf8lD6Ib/qz/3SLaauNK1fW8KVHsfmXQelVUJtaRxfU8IV5WHNyoFN+3dL66Q6Yfu3NceCn1kOp1P/aYZLVlaWxMfHS2ZmpsTFxVX4B8spjy00fwCoWCWFQNdj7qDqETZFnJKT718YtaIjJbI4lXoGU1dQNb97ve6Rxzxfz3N9z/30fNxzn4s35bcsYNj12If8wkL5Le2A33Ec3zhOYmpE+O2315cF92M+++R33/uY/bd3ZP98txf49Xz3RSQ7r1D+uz7Vu1xF5OzOjaR2TA2/7Xme95LOqW/5e63vcUxF++W9Pdc+u8s2wPnz3Z7L2u375It1aaJ/Xbp06AmNpFvzel7P8zrOAOfX8+/O40/DvR8lHZvX34nnF60SvyB5H4/nPmmN/NsrtrmP44rezeWUNgke63iXVaDlno+UvL7/F1K/9Uv4gipl2OZ3v2XIK4s3my+iuvi6U1rJgHaJEqyK+Og+ln9yv/0tQ2Yv2eI+jhF9Wkq/dkXl4dqs/nT6vNaR+0fuudf3eMxZ4mNH9vnIY06v1/Rd3/0Mj/U99+/7zbtl7k873H9b53drIj1b1Df3dZ1C3Ubxuu7jKN7HI8s97nutL17r6S+ey/XfYPfxerxGidst4bUP5ebJh6t2epWR/tvxv7GnVXhQDSabEOyO4eQFS6tqr3xpud/yWlEREhlZ3Cru8Z4P+KZyL/Ncz/vN6LWZAOt5ruv5EeP7IeC7TQAAULr/jDxZ+rRpKKHKJjTFViFtytCqWs8aO033X909MKyqoXfsOySnTv3a6zj0uL69Z6A0Kj6OQN/+Slru9Y3Mc32fdQN9C/X89njkm2EJ3yo9nq/L0vdny7CZS/2O4+1RJ0tCnRi/b7ne++D5jS7Aa/ksC3yM3vvm+e3Q61yV9LrFy3YfyJG73l3tdZ71OB67qIvUrx3t9Y0z4GsG2kff/ff8VutxMkv89uxR4L7H5vtartfLPJwrz369yeuLhX6Tv3FAa6kbG+Uut0D77t4ln2/Xnn87nssCfUv3Pb9H9s3/G3uJ5egUScs6LN/8tkt89TsuQRK9/q58y9p/ud97oITajyNlFvj9UtLreJ5/39fJPJwnP+/M8juODo3qSlzNqKN+aTyyNwFez2t54PWlTOuX8Loevx/IyZdtew75HUfzBrVMTfDRlKfDR7C9RMqyvh7Hll3+x9E6obbUjS0+jgCtFaW1ZBTd92/N8H3MtyY/0D77t2SU3BVnz8Fc+XHbPr9jOallfUmsG1Ni7b73/cA19g6PVpSIElpPPNc363i0mPjVxIvDrONZY+9a50B2nry0aLPX36f+m94yoZaEEsGuCml40/b3+z9YJwVOp/kDePSiE8Iq1Kkm9WrJIxeeIOPnrjOhyHUcKQ3Cqx9RSoNaAcujV6uK/aZVFfIKCuX+D9ZKgSkPkUcv6iyXnpQi4UbLxLc8Qt1fpSK6XOixTLukS1i910s6jlevPckWxzHnxpNtcRxvjuwdVsdR2rE8fUX3sDuWNkl1LPdvOk2xVdgU6/lHrd+8NNWH+g+gvAoLC2Xdpr/kgKOmtEoM346vdikPtX3vQVm1cYd0a9sk7Dvrh3t5aIdq36AdbgH1yHGEd9BWlIf1/GfFVr/KgXD826qqz6xK72O3efNmWbRokWzdulUOHTokiYmJ0r17d+nTp4/ExsZKOKuKYGcHGuzS09MlKSlJIiKYNccKKBNrIWhbC+VhLXaqHKgKldbH7s0335SnnnpKfvjhB0lOTpYmTZpIzZo1Zc+ePbJp0yYT6q666iq57777pEWLFsd6HAAQtvQfqsiUupIU5v9g6XHY4R9dysN6kupGywlJDakcqGBlDnZaIxcdHS0jRoyQ999/X1JSvPvv5OTkyNKlS+Xtt9+WE088UZ577jkZNmxYRe8vAAAAjjXYPfbYYzJkyJASH4+JiZGBAwea2yOPPCJbtmwp66YBAABQlcGutFDnq2HDhuYGAACAMJruRMdefP3113L48GHp27ev1K9fv2L2DAAAAEEJajjjvn37ZPjw4dK5c2cZOXKkGaXRr18/GTRokJx77rly/PHHy5o1a4LbAwAAAFR9sLv77rvNAInLL79c1q5dK2eddZYUFBSYZcuXLzfBbvz48RWzZwAAAKi8ptjPP/9c3nrrLRkwYIAZHasjYxcuXCi9e/c2j0+dOlXOO++84PYAAAAAVV9jl5aWJu3atTO/N23a1Mxb5zntSfPmzSUjI6Ni9gwAAACVF+x0pujIyEj3ff3ddVFd5fk7AAAALD4qdtasWVKnTh3ze35+vsyePVsSEhLM/f3790tl0mbeVatWmUtZ6ehbHbShzb96BQwXHbxxyy23yPfff28udXbbbbfJvffeW6n7BQAAEHbBTptaX3rpJff9Ro0ayRtvvOG3TmU57bTT5P7775fGjRvL9u3bzWCOSy65RJYsWWIe11G6gwcPNoFv5syZZoDHddddJ/Xq1ZNRo0ZV2n4BAACEXbAL9dUk7rzzTvfvei3asWPHygUXXCB5eXkSFRVlrmWbm5srr7zyirn8WadOnUwN35NPPkmwAwAAthdUHzsr2bNnjwlyOimyhjql067079/fhDrPK2b8+uuvsnfv3hDuLQAAgIVq7J5++ukyb3T06NFSWe677z75v//7Pzl06JCcfPLJ8sknn7gfS01NlVatWnmtn5yc7H4s0FUxcnJyzM1Fm3NdA0X0hsD03OhVRzhH1kGZWAvlYS2Uh7VQHsEJ5t/aMge76dOne93XaU00XGn/NddVKWrVqiVJSUlBBTttTtUBEKXZsGGDdOjQwfx+zz33yPXXXy9bt26VyZMnyzXXXGPCXXlH5E6ZMsVsx5ceX3Z2drm2WV3+yDIzM024i4gI24pfW6FMrIXysBbKw1ooj+AEMzjV4dR/mYOkkxQ/99xz8vLLL0v79u3NMm3u1MuM3XjjjXLVVVeVeVsaoHbv3l3qOq1bt/ZqXnX566+/zDx6OniiT58+JuRpjdu8efPc6+h1bE8//XTTdFvWGjvdpjbdxsXFlfk4quObUstORx4T7KyBMrEWysNaKA9roTyCo9lEM4xWqBwtmwQ93Yl68MEH5b333nOHOqW/a62ejlINJthpMNDbsVRNuoKZhju9pJlrMIVasGCB2bdAoU7FxMSYmy8NKwSW0mktKefJWigTa6E8rIXysBbKo+yCySPlakPbuXOnmcPOl143Vq9OURn0WrTat05HuWozrF7K7IorrpA2bdqYQKeuvPJKU7OnTbXr16+XOXPmyFNPPSVjxoyplH0CAACwknIFuzPOOMM0uf7444/uZStXrpSbb77ZzCFXGbT/3gcffGBeW2vgNLx16dJFvv32W3eNW3x8vMyfP182b94sPXv2lLvuuksmTJjAVCcAAKBaKFdTrM4TN3z4cDnxxBPdTZ5ag6dTi+iVKSpD586dTS3d0WjYW7RoUaXsAwAAgO2CnfaJ++yzz+T33383I1aVjlpt165dRe8fAAAAKjPYuRx33HHmBgAAgDDqY/fYY4/J4cOHyzzQ4dNPPz2W/QIAAEBlBbuff/5ZmjdvLv/4xz/k888/N3OYuWj/ujVr1pi57fQSX5dddpnUrVs32H0BAABAVTTFvv7667J69Woz5YhOK6KT5UVGRpoRqXoFCtW9e3e54YYbZMSIERIbG3ss+wUAAIDK7GPXtWtXeemll+SFF14wNXQ6n5w2zyYkJEi3bt3MTwAAAITR4AmdAVmDnN4AAABgDVy9HQAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAACq26jY7777rlwv0LJlSzOxMQAAACwS7IYPHx70xh0Oh9xxxx0yevTooJ8LAACASgp2mzdvDnLTAAAAqEr0sQMAAKiOV57Iz8+XgoICc31Yl7S0NJk5c6YcPHhQzj33XOnXr19l7CcAAAAqMtiNHDlSoqOjzbVi1f79++Wkk06S7Oxsady4sUyfPl0+/PBDOfvss4PZLAAAAKq6KXbx4sVy8cUXu++//vrrpgbv999/l9WrV8uYMWPk8ccfr4j9AgAAQGUGu+3bt8txxx3nvv/VV1+ZoBcfH+8eObt+/fpg9wEAAABVHexiY2Pl8OHD7vvLli2T3r17ez1+4MCBitgvAAAAVGaw69atm7zxxhvm90WLFpmBE6effrr78U2bNkmTJk2C3QcAAABU9eCJCRMmyNChQ+Wdd96RnTt3yogRI8ygCZe5c+fKKaecUhH7BQAAgMoMdgMGDJCVK1fK/PnzpVGjRjJs2DC/Gr1evXoFuw8AAACo6mCnjj/+eHMLZNSoURWxTwAAACgHrjwBAABQ3WrsWrVqJQ6HI+gXuOOOO2T06NFBPw8AAACVFOxmz54t5dGyZctyPQ8AAACVFOx04AQAAACsiz52AAAANkGwAwAAsAmCHQAAgE0Q7AAAAGyCYAcAAGATBDsAAACbINgBAADYRFgGu5ycHOnWrZu5EsaqVau8HluzZo3069dPYmNjJSUlRaZNmxay/QQAAKhKYRns7r33XmnSpInf8qysLBk8eLC0aNFCVq5cKY8//rhMmjRJXnzxxZDsJwAAgCWvPGEVn3/+ucyfP1/ef/9987unN998U3Jzc+WVV16R6Oho6dSpk6nRe/LJJ2XUqFEh22cAAICqEFY1dmlpaTJy5Eh54403pFatWn6PL126VPr3729CncuQIUPk119/lb1791bx3gIAAFStsKmxczqdMmLECLnpppvkxBNPlC1btvitk5qaKq1atfJalpyc7H6sfv36Afvr6c2zOVcVFhaaGwLTc6NlwjmyDsrEWigPa6E8rIXyCE4w/9aGPNiNHTtWpk6dWuo6GzZsMM2v+/fvl3HjxlXo60+ZMkUmT57stzwjI0Oys7Mr9LXs9keWmZlpwl1ERFhV/NoWZWItlIe1UB7WQnkER/NP2AS7u+66y9TElaZ169aycOFC09QaExPj9ZjW3l111VXy2muvSaNGjUxzrSfXfX0sEA2KY8aM8aqx09G0iYmJEhcXdwxHZv83pY5K1vNEsLMGysRaKA9roTyshfIIjs70ETbBToOB3o7m6aefln/+85/u+zt27DD95+bMmSO9e/c2y/r06SPjx4+XvLw8iYqKMssWLFgg7du3D9gMqzQo+oZFpWGFwFI6DXacJ2uhTKyF8rAWysNaKI+yCyaPhDzYlVXz5s297tepU8f8bNOmjTRr1sz8fuWVV5pm1euvv17uu+8+WbdunTz11FMyffr0kOwzAABAVQqbYFcW8fHxpi/eLbfcIj179pSEhASZMGECU50AAIBqIWyDXcuWLU3HfV9dunSRRYsWhWSfAAAAQonhjAAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsIqyCXcuWLcXhcHjdHnvsMa911qxZI/369ZPY2FhJSUmRadOmhWx/AQAAqlINCTMPPfSQjBw50n2/bt267t+zsrJk8ODBMmjQIJk5c6asXbtWrrvuOqlXr56MGjUqRHsMAABQNcIu2GmQa9SoUcDH3nzzTcnNzZVXXnlFoqOjpVOnTrJq1Sp58sknCXYAAMD2wqopVmnTa8OGDaV79+7y+OOPS35+vvuxpUuXSv/+/U2ocxkyZIj8+uuvsnfv3hDtMQAAQNUIqxq70aNHS48ePaRBgwayZMkSGTdunOzcudPUyKnU1FRp1aqV13OSk5Pdj9WvX99vmzk5Oebm2ZyrCgsLzQ2B6blxOp2cIwuhTKyF8rAWysNaKI/gBJNHQh7sxo4dK1OnTi11nQ0bNkiHDh1kzJgx7mVdunQxNXM33nijTJkyRWJiYsr1+vrcyZMn+y3PyMiQ7Ozscm2zuvyRZWZmmnAXERF2Fb+2RJlYC+VhLZSHtVAewdm/f3/4BLu77rpLRowYUeo6rVu3Dri8d+/epil2y5Yt0r59e9P3Li0tzWsd1/2S+uVprZ9nYNQaOx1Nm5iYKHFxceU4ourzptRRyXqeCHbWQJlYC+VhLZSHtVAewdGZPsIm2Gkw0Ft56MAIDRVJSUnmfp8+fWT8+PGSl5cnUVFRZtmCBQtM6AvUDKu0pi9QbZ9ul8BSOg12nCdroUyshfKwFsrDWiiPsgsmj4RNG5oOjJgxY4asXr1a/vjjDzMC9s4775Srr77aHdquvPJK0zx7/fXXy/r162XOnDny1FNPedXIAQAA2FXIa+zKSmvV3n77bZk0aZIZ7KCDJDTYeYa2+Ph4mT9/vtxyyy3Ss2dPSUhIkAkTJjDVCQAAqBbCJtjpaNhly5YddT0dVLFo0aIq2ScAAAArCZumWAAAAJSOYAcAAGATBDsAAACbINgBAADYBMEOAADAJsJmVCwAHE1BQYGZoNwqM+vrvuilCa002blO3h4ZGRnq3QBQSQh2AMKeXrM4NTVV9u3bJ1baJw13eo1HnWHfSurVq2cus2i1/QJw7Ah2AMKeK9Tp5QVr1aplicCiwU6vZV2jRg1L7I9rnw4dOiTp6enmfuPGjUO9SwAqGMEOQNg3v7pCXcOGDcUqrBjsVM2aNc1PDXd6zmiWBezFOh0/AKAcXH3qtKYOZeM6V1bpjwig4hDsANiClWrFrI5zBdgXwQ4AAMAmCHYAEEIZGRly8803S/PmzSUmJsaMVh0yZIgsXrzYPP7iiy/KwIEDJS4uztS0BRr5+8gjj0jfvn1NE6uOeAVQhTK3i2z+ruinBTB4AgBC6OKLL5bc3Fx57bXXpHXr1pKWliZfffWV7N692zyuo1jPOusscxs3blzAbejzhw0bJn369JGXX365io8AqEbyc0Ry9otkZ4rkZImse19kyf/pcCkRR4TIuU+J9LgmpLtIsAOAENHat0WLFsk333wjAwYMMMtatGghvXr1cq9zxx13mJ+6TkkmT55sfs6ePbvS9xkIS4WFIrkHikKZBrLsrOLfMz1+L/5p7hff3L8XLy/IKfk1nIUiH98h0uYMkfimEioEOwDwsDPzsGzedVBaJdSWxvFFU4NUljp16pjbvHnz5OSTTzZNsUB1EXEgVeTgryIJbUsPQvm5xSErs+wBLMfz9+LnaK1aRYmuIxIZLXJ4j/dyZ4HInj8IdgBQ0XPIHc4rCPp576/8SyZ+tF4KnSIRDpHJ53WSi3s2C2obNaMiyzzqVOe401q2kSNHysyZM6VHjx6m5u7yyy+XLl26BL3/gOU4nSJ5h0UO7xXJ3lf0U2+/fCaJq/8jDhO2HCLNeonUSfSpTcs6ei1ZsCJqiMTEicTGicTUFYmJL/pp7sf5/O65ns/vEZFFfepmnFBUU+fiiBRp0FpCiRo7ALajoa7jhC+OaRsa7h78cL25BePnh4ZIregaQfWxO+ecc0yT7LJly+Tzzz+XadOmyaxZs2TEiBHl2HOgEhTkF9WY+Qa0w/t8lnk85lpWkHuUkZtOkb+Wl62WLFDI8gxiMYFCmkd4qxGr8/1UzDnRWkbtU6fNr1pTp6Hu3Bkhra1TBDsACLHY2Fg588wzze3BBx+UG264QSZOnEiwgz+tJdqzSaRBm+ADhNaeaT8zvzBWWkDbV7TMNGUeY01ZzfoisfWKAtCuX/zXOfkfIk16lFB7VreolsxqelxT1KdOm1+1pi7EoU4R7ADYjjaHas1ZMFIzs2XQk9+amjoXbY79cswAaRQfG9RrH6uOHTuafneAlx9fF/n49qKmPx2BOfB+kVb9S68t811WmH9sJ1VDVs16RQFNg5q5Ff/uXuZ7v75IdO0jNWWZ28U54wRx+DZh9rnVEsEoaLrPFtpvgh0A29E+bsE0h6rWiXVkykWd5f4P1kmB0ymRDoc8etEJZnll0SlNdJqS6667zvSpq1u3rvzwww+mKfb8888366Smpprbxo0bzf21a9ea9XTeuwYNGphl27Ztkz179pifeu3cVatWmeVt27Y1gzNgcbmHRA7tEjm4S+TQ7uKfuzx+7hbJ2iGSuvrIczQUff1Pka/L8Xra6d8dykoLZB7BTJfFxotEVkBsiG8qzr/NEPnkjqJwZ5EmTLsg2AFAsctOai792yXKll2HpGVCrSoZFdu7d2+ZPn26bNq0yVy7NSUlxQymuP/++806OqjCNZ2J6t+/v/n56quvuptqJ0yYYObBc+nevbv5+fXXX5vJjVGFXM2dpYU08zPjyO95h8r/enUaicQ1PnqNmeeyqJoV18+svLr/XTLiu0pCRJZENCxHszJK5HDq8DG4ZWVlSXx8vGRmZpqZ3hFYYWGhpKenS1JSkkREcAETK6iuZZKdnS2bN2+WVq1amb5qVqEfrfn5+Wbkq9WuzWrVc1aZCvf9Kfs2rZR6bXpKRL2Usj9R/4nUQQMmpGX4BLXdge+XZxSn1qLVShCp3VCkdmLx7wkitfR+QtHI0U+0k75P8+Uda8MyFFXXz6uqyCbU2AEA7O3H18Xx8e3SwFkoTu2bNvgRkbZnlC2k6c/CvOBfM6rWkaDmG9Lc9z0e18EBR/sCoI9bbAQmrIdgBwAIfzpX2v7UoptOfLs/rejn7s0iG+ZpfZdh+nR9MU4k2Nlwouv6hLSE0u/rYIFqMAIT1kOwAwBYl05UqyFt/06RA2newc29LK3o0lDBiKotUrdRKSHN43etaYuySJO1xUZgwnoIdgCAqmX6re0LENiKf3qGt7yDZd9ujZoidZOLBhToz7qNi5YtnuF9OSltxrz1ewISbIlgBwComAlx9ULreu1Mr3BWQmALZoCBNoN6Bjbzs/hWJ/nIT52OI1A/tYatxfmxTq1RIE5HpDjomwYbI9gBAMo2IW6/u0Sanujdh82z1k1vwUyAq0FMa9U8w5k7sHkEt5hjnIuvxzXibH2a7N30o9Rr00McwYyKBcIMwQ4AqnuzqF6VIPOv4tufRbddv4v89l+P9QpFvnu8bNvUPmkl1appkDO1bslF86lVlbimkts0SiQuqepeEwgBgh0A2JkGsoK84ltu0e3wIZEDGSJv3ieStrJoQt2yqt9aJOG4AE2jxYGtdpJIjejKPCIApSDYAUA417bpnGaeoU1v+R73A83Blu8UyT8ssvePI6FOR3/GNxPRZsr4lKJ51b6d5j/oYMQnDDoALIxgBwBWDm6+oc33vueVCErkEImMKrq6gd4KHCK1CkTOe1akQfH0GYGaRTXoMSEuEFYIdgBQGQryxJF7UMRRu+SmycICyUjdLhMmTZZPP/9C0tIzpH69OOnasYNMuHOUHN8mRSb+a6bM/3aZbNuRKokN6ssFZw2Uh++5WeLj6ro3M3rCE7L4+1Wy7peNcny7trJq6cIjIU5vETW8R4tmZ4tEHxRp3lmktEuKMSEuEHYIdgBQ0fRSVJl/mg9Yp2swQWSMf82bs0Auvuh6yc3Nk9eenCCtWzSVtIw98tX/VsjuPbtlR51Y2ZGWIU9Mukc6duggW3eky013PSA7dh+Q995+szi4RZntXzfqH7J8+XJZs2ZN0cCEisKEuEBYIdgBQHnnbTNNpdqnLafopnOz6aWtcg8cuYSV/k+vNxrAvsz9smj5T/LN3NdkQL9TTc1ei45R0uv0v7lr294/7RJ3bVsbEXkkt4ZcffXVkl+jltSoUfQR/vTTT5ufGRkZRcEOQLUVdsHu008/lYceesh8eMXGxsqAAQNk3rx57se3bdsmN998s3z99ddSp04dGT58uEyZMsX9AQigGtDAlXco+Oetekvk83uPzNs2dJpI1yuKw1txgCvMOfK7LvccXKBqxAaeJFcn2Y2u5dVEWifBYT6n5n37k5w89DKJiYk56i5mZmZKXFwcn2kAAgqrtPP+++/LyJEj5dFHH5XTTz9d8vPzZd26de7HCwoK5JxzzpFGjRrJkiVLZOfOnXLNNddIVFSUeQ6AakJD3aNNjm0bGu4+u7voFozRP4lkB7huab3mfn3t9AN49uzZ5nNt5syZ0qNHD/Nl9fLLL5cuXbr4bWLXrl3y8MMPy6hRo4I+HADVQ4SECQ1xt99+uzz++ONy0003Sbt27aRjx45y6aWXuteZP3++/Pzzz/Lvf/9bunXrJkOHDjUfgs8++6zk5uo3awCoZNq/LT7FXY9nfur0ISUMoLj44otlx44d8tFHH8lZZ50l33zzjQl4Gvg8ZWVlmS+u+rk3adIkihFAeNfY/fjjj7J9+3aJiIiQ7t27S2pqqglvGvROOOEEs87SpUulc+fOkpx8pOPwkCFDTNPs+vXrzfMA2Jjp86ZNpPkio9ccvdnUd8DDu8O9pw/R5tibFhfVtgVqXg0kqpZIdG2RmDjJzzkkkTG1jjphr3YrOfPMM83twQcflBtuuEEmTpwoI0aMMI/v37/fhL66devK3LlzTSsEAIR1sPvjjz/MT/2m+uSTT0rLli3lX//6lwwcOFB+++03adCggQl7nqFOue7rY4Hk5OSYm+e3YlVYWGhuCEzPjdPp5BxZiG3KJGu7yO4/zIXb9TJQfnQ0qV7yavcmkb2bxZmZLtJgoDh3FYgzIl8cgcJbRIRIRKw4dShDjZiiEar60/17cb+3v80Q+eRO98Xi5W/TRZKOL1/AjIySwqhaEhkZZcolGMcff7zpO6zP088kDXXa/+7DDz80P0vanmv50V5PH3f9rYT930t1e3/YBOURnGD+bkMe7MaOHStTp04tdZ0NGza4D2r8+PGm6UK9+uqr0qxZM3n33XflxhtvLNfr68CKyZMn+y3X0WXZOtcTAtLy0E7c+kGptagIPTuUSc0N70rcdxPE4SwUp0TIwc5XS2FcikRmbpMamVvMz8gD28XhcaF5R50UkVN6i6MwVxwRGusc4jSDE2LMz6Lf9WeM/3xuLpqD8vNFulwp0nKgOPb+IU69dFZck6Ll5aDloP1+zT6WUNu3e/duueKKK8wgL21t0Bq5lStXmpaIc889V/bs2SNnn322HDp0yHze6X29qcTERImMjDS/b9y4UQ4cOGD6Feu6P/zwg1muzbbR0dEBu7bo34u+fnWp/bPD+8NOKI/gaK192AS7u+66y93cUJLWrVubDyzXB5WLfnPVx3QkrNJBEytWrPB6blpamvuxQMaNGydjxoxx39dvxykpKeZDU0eeoeQ3pf5jpeeJD0lrCMsy0do3nVokfYM4/lwhsmKmxzQhhVJn7esBn+bUkacNWpkpSZwJncVZs4E441uIs3ZdU1PmNdVIsBo0L7pVkNKCU7169aR3797yzDPPyKZNmyQvL898/mhT7P3332/mpXN9pmktnm8rhrZcKO1u8u2337of69Wrl986nnSWAP0badiwoWkGrg7C8v1hY5RHcIJ5n4Y82OmbTG9H07NnTxPkfv31Vzn11FPNMv0Q3LJli7Ro0cLc79OnjzzyyCOSnp4uSUlJZtmCBQtMQPMMhJ50m4GmGNA3Pm/+0umHJOfJWixbJto0qM2naT+LpOttQ9HPXb8V930rRcrJIs17izRoXTS3XIPW4tALzhcfoyM7WxybN4sjprY4LHTxea0ZctXUlVRjpx/Wjz32mLkFctppp5WpGVcHXARD98eyfyuVqDoes5VRHmUXzN9syINdWWk409Gw2qFYv9FqmNPmCjVs2DDzc/DgwSbA/f3vf5dp06aZfnUPPPCA3HLLLWWaHwpABdBBCBravELcBpHcEpoSomoX9WOr30Jk3Qf+F52/5BUuOg8Adgt2SoOcNiFocDt8+LBpwli4cKHUr1/fPK79TT755BPTLKG1d7Vr1zZ9V3RCYwAVLOeASMYv/iHuYHrg9SOiRBLaFYW45I4iScU3nQrE9W209UAuOg8A1SXYaV+VJ554wtxKojV5n332WZXuF2BrOl3I7o3Fwc0jxO3bWsITHCL1WxYHN48Q17Bt0XVNS8NF5wGg+gQ7AJVIR55rWEv3qH3TELf7dxGPUah+k/G6at5cIS6xQ9E8buXFRecBoNwIdkB1o53xD6R7BDhXP7hfRPIOBn5OTFxRcDO3TsU/O4rUbljVew8AKAXBDrCLrO0SvX2lSGxPkXopRcuys46MQPUcjXpod+Bt6Jxvie2P1MC5Qlx8s7JfeQEAEDIEO8AOfpgtjk/vlAZmYl9HURjL2V80xUhAjqLpQ9yDGIpDnC6L5GMBAMIVn+BAODalal84rZ3b/qPI1iUiO370mJTXWVQr51K3ic9I1ONFEtqLRNcK1REAACoJwQ6wuoO7TXArCnLFt5KaUj2dNVWky6UitRpUxV4CACyAYAdYSe4hkdQ13iFu75bAc8I16izStGdR8+n88SLOQu+JfY8/l1AHANUMwQ4IlcKCogl+PUOcTi/iLLpwvJeGxxWFONet0QkiNTyuphJTR5wf3yEOZ4E4HZHiOHcGV2sIExkZGTJhwgT59NNPzbWtdcL1rl27mmWnnHKK3HjjjfLll1/Kjh07pE6dOtK3b1+ZOnWqdOjQwb2N0aNHy+LFi2XdunXmmrKrVq0K6TEBCB2CHVCV10p1h7gfRXasCjy9iM4N1/REkaY9ikJck+4iNeuVvv0e14iz9Wmyd9OPUq9ND3G4RsXC8i6++GLJzc2V1157TVq3bm3C3VdffSW7d+92Xyf7qquukubNm8uePXtk0qRJ5vKJmzdvNlfbcbnuuutk+fLlsmbNmhAeDYBQI9gBleHQnuJ+cR594w5m+K8XXacouHnWxsU1Kd/UInFNJbdplEhcUoUcQnWVejBVtmVtk+ZxzaVR7UaV+lr79u2TRYsWyTfffCMDBgxwXz2nV69e7nVGjRrl/r1ly5byz3/+09TobdmyRdq0aWOWP/300+7aP4IdUL0R7IBjlXdYJHWtd5Pqnj/814uoIZJ8gneISzhOJOJIrQsqhtPplMP5h4N+3kebPpIpy6dIoRRKhETIuN7j5Lw25wW1jZo1aoqjjMFcm1b1Nm/ePDn55JMlJsajeT2AgwcPyquvviqtWrWSlBRqZQH4I9gBQfeL+7UovLlGqqatD3zJrQZtfPrFdRaJiuV8VwENdb3f6n1M29Bw98jyR8wtGMuvXC61oso2lUyNGjVk9uzZMnLkSJk5c6b06NHD1Nxdfvnl0qVLF/d6zz33nNx7770m2LVv314WLFgg0dHRQR8TAPsj2AGZ20X2bCoKYnqdUq9+cX9594vbuUok94D/OaudWNwvTkNcj6LmVaYZQRn72J1zzjmmSXbZsmXy+eefy7Rp02TWrFkyYsQIs472sTvzzDNl586d8sQTT8ill15qBkvExvJFAYA3gh2qtx9fF/n49qKpQhwRIr1vEqnZwKNfXLr/c6JqF/eLKx7coDcuuWUp2hyqNWfBSDuUJhfMu8DU1LlEOCJk3vnzJLlWclCvHSwNaBrc9Pbggw/KDTfcIBMnTnQHu/j4eHM77rjjTJOtjpydO3euXHHFFUG/FgB7I9ihetLauC2LRD4arXeKlxWKLHvOez2dDy65k3eTql5LlX5xlqZ93MraHOrSKr6VTOw7USYvnSyFzkIT6ib2mWiWV7WOHTuafncl9R/UW05OTpXvFwDrI9iheijIL5r4d9vSoktw6c+Srt7Qsr9I+6FH+sVx6a1q46LjLpK+TfrKn/v/lJS6KZU+KlanNBk2bJiZqkT71NWtW1d++OEH0xR7/vnnyx9//CFz5swx05skJibKX3/9JY899pjUrFlTzj77bPd2Nm7cKAcOHJDU1FQ5fPiwex47DYj0xQOqF4Id7DtSVZtSty4V2bZE5M8V/n3jImNECnL8a+gunMnkvtWYhrnKDnQuOiK2d+/eMn36dNm0aZPk5eWZ0a46mOL++++XvXv3mr53M2bMML8nJydL//79ZcmSJZKUdGRaG226/fbbb933u3fvbn7qXHc6RQqA6oNgB3vIziwKb1sXF4U5HbFakOu9Tky8SPOTRVr0EWneV6RJN5E1c0Q+vqPoag8a6rhiA6qQTm8yZcoUcwtEa+Y+++yzo25H58EDAEWwQ3g6kH6kSVXDnE454nmtVNcVHJr3EWlxSlGYS+ro3zeuxzUibc4omndOr7nqOSoWAIAwQ7BDeAx02LvlSP84ven0JL7qtxJp0bc4zPUtCmplmShWwxyBDgBgAwQ7WE9hoUjGL0U1cSbMLRXZv8NnJUfRaFUT4oqbVuMah2iHAQCwBoIdQq8gT2Tn6iO1cX8uEzm81/9yXDp3nKmR01tvkZr1Q7XHAABYEsEOVS/3kMhf3x9pWtXf8w55r6NzkDU7qSjI6U2v6sC0IwAAlIpgh8qntW/blh0Z7LDjJ/9rq2rtmzarugY7NO4iEhlF6QAAEASCHcot4kCqyMFfRRLaeg8+yNpZNHecaVpdKpL+85GrO7jUbVJcG1cc5BL0ag4RlAYAAMeAYIfy+ekNSfzkDnG4rrHa5fKiEag64EFHsPpq2PZI/zgNc/ValG3EKgAAKDOCHYIbrbpvq8jm78Tx8e3i8LzG6uq3jqynQS/5hCPzx2nzap0js+QDAIDKQbBDYIf3FTWh6sS/aetE0n4uul98Wa6AdW0nDBPperlIykkisfGcWQAAqhidmqq7gnyRjF9F1r0v8tVDIm9dJjL9BJGpLUReHSry2d0iK2eL/FV8rdXIaJHEDr495ooux3XmZJHjBhHqgCBkZGTIzTffLM2bNzeXGGvUqJEMGTJEFi9e7LWe0+mUoUOHisPhkHnz5nk9Nnr0aOnZs6d5frdu3Tj/QDVGjV11ciCjuPZt/ZGaOA11BTmB149PKZoE2HVL6lTUVy6yhjhXvibi7mPHNVaB8rr44oslNzdXXnvtNWndurWkpaXJV199Jbt37/Zab8aMGSbUleS6666T5cuXy5o1aygMoBoj2NlRfk5RYHM3oxYHuYPpgdePqi2S3LE4wJ1QHOI6itSsV/JrdP+7ZMR3lYSILIlo2IZLcsE28lJTJXfLVolu2UKiGjWq1Nfat2+fLFq0SL755hsZMGCAWdaiRQvp1auX13qrVq2Sf/3rX/LDDz9I48b+V1h5+umn3bV/BDugeiPYhfs1VLO2+we4Xb+LOAsCPMFRdP1UzwCnNx2hWo6pRgrrNBJJ6sI0JbAcbbZ0Hj4c9PP2zZsnaf98pGigUESEJD8wXupdcEFQ23DUrFlqzZqnOnXqmJs2rZ588smmKdXXoUOH5Morr5Rnn33WNNMCQGkIduEi50DR9VN9m1KzMwOvH1vPO7zp70kdRKJrV/WeA1VOQ92vPXoe20YKCyXtoYfNLRjtf1wpjlq1yrRujRo1ZPbs2TJy5EiZOXOm9OjRw9TcXX755dKlSxezzp133il9+/aV888/v1yHAaB6IdiFQuZ2kT2bRBoEaMLUmoK9m73Dm/7UZYHoNVQT2nkHOP1ZtzHzxAFh0sfunHPOMU2yy5Ytk88//1ymTZsms2bNkgYNGsjChQvlp59+CvVuAggTBLuq9uPrIh/fXjT3m8731ufWokEKrgCnU4r4XjfVRZs+TXjreCTAaair4d98A1Rn2hyqNWfByEtLkz/O+VvRlyuXiAhp/eknEpWcHNRrBys2NlbOPPNMc3vwwQflhhtukIkTJ8qFF14omzZtknr16vmFwX79+pm+eQDgiWBX1TV1rlCn9OeSpwOUSqyZUsS7KbWTSO2EKt1dIFxpH7eyNoe6xLRqJY0fmiw7J0x097HT+7q8qnXs2NH0uxs7dqwJeZ46d+4s06dPl3PPPbfK9wuA9YVNsNNvpqeddlrAx1asWCEnnXSS+V1HhN1yyy3y/fffS2Jiotx2221y7733iiVo86sr1HlK6S3Sst+RplQd4BAZNkUD2Ea9Sy6R2qeeKrlbt0l0i+aVPipWpzQZNmyYmapE+9TVrVvXjHzVpljtU6eDJQINmNA571p5BM6NGzfKgQMHJDU1VQ4fPmxG0boCYnR0dKUeAwBrCZv0oJ2Hd+7c6bVMmyx0vqcTTzzR3M/KypLBgwfLoEGDTEfktWvXmg9MbcYYNWqUhJz2qdPmV89wp3PAXfIq04UAFqFhrrIDnYuOiO3du7epgdMm17y8PElJSTGDKe6///4yb0dr9b799lv3/e7du5ufmzdvlpYtW1bKvgOwprAJdvqt0/Obq34Afvjhh6ZGzjW1wJtvvmkm+nzllVfM+p06dTLfXJ988klrBDsdKHHuUyIf31E0HQkT+wLVmk5vMmXKFHMLZioXX/S1AxB2wc7XRx99ZJoxrr32WveypUuXSv/+/b2aHvTSPFOnTpW9e/dK/fr1/baTk5Njbi5a66cKCwvNrcJ1u1qk9WkiezaLNGglEtfUu7N2mNBzo//AVMo5QrlU1zJxHbfrZiWu/bHifrn+VqrL30t1fX9YFeURnGD+bsM22L388ssmtDVr1sy9TPuXePY7UcnFo9n0sUDBTr8pT5482W+5zuCenZ1dKfsuEiVSq52Ibj67hKtBhMEfWWZmpvmgjCjH5MaoeNW1TLT2Xo89Pz/f3KxCy6GgoGii8LJOWFxV9DzpOdMvx1FRUVIdVNf3h1VRHsHZv39/+AQ7HfWlNWql2bBhg3To0MF9/6+//pIvvvhC3nnnnWN+/XHjxsmYMWO8auy0j4sOvIiLizvm7dv5Tan/WOl54kPSGqprmegXMP3Q08l+9WY1VgxOep70b6Rhw4ZmqpXqoLq+P6yK8ghOMO/TkH8K3nXXXTJixIhS19ELY3t69dVXzQfSeeed57Vc++DpBbQ9ue6XdCke7eMS6DI++sbnzV86/ZDkPFlLdSwTPVYzvUnxzSq0Zsi1P1baL+U6V9Xtb6U6HrOVUR5lF8zfbMiDnX570lswH5Ya7K655hq/b8J9+vSR8ePHm6YZ12MLFiyQ9u3bB2yGBQAAsJOw+9qil9fRIfy+k3YqvVC2Dpy4/vrrZf369TJnzhx56qmnvJpaAdiT1QYoWBnnCrCvkNfYlWfQhM5p59nnziU+Pl7mz59vJiju2bOnJCQkyIQJE6wx1QmASuGqnT906JDULMflvKojPVdW7f8HoJoFu7feeqvUx3X2dr2YNoDqITIy0kxCnp5eNMK8Vq1alujTprViOvpUBypYYX9c+6ShTs+VnjM9dwDsJeyCHQD4cg2OcoU7K3DNmeYa3GElGupKGlAGILwR7ACEPQ1OjRs3lqSkJDN4ygpc88TpCH4rjcLU5ldq6gD7ItgBsA0NLFYJLRrsNETp/FNWCnYA7I1PGwAAAJsg2AEAANgEwQ4AAMAm6GNXwsSdes1YlN5/SK/PSf8h66BMrIXysBbKw1ooj+C4MklZJhcn2PnQsKJSUlKCPO0AAACVm1H0YgylcTi5tozft4gdO3ZI3bp1LTf3lNW+PWj4/fPPPyUuLi7UuwPKxHJ4j1gL5WEtlEdwNKppqGvSpMlRR9lTY+dDT1izZs2CPOXVl4Y6gp21UCbWQnlYC+VhLZRH2R2tps6FwRMAAAA2QbADAACwCYIdyiUmJkYmTpxofsIaKBNroTyshfKwFsqj8jB4AgAAwCaosQMAALAJgh0AAIBNEOwAAABsgmBXDX333Xdy7rnnmokOdRLmefPmlbr+N998Y9bzvaWmpgZc/7HHHjOP33HHHX6PLV26VE4//XSpXbu2mb+of//+cvjwYanOQlUeuv7f//53adSokSmPHj16yPvvv1+hxxauKqNMJk2a5Pd4hw4dvLaTnZ0tt9xyizRs2FDq1KkjF198saSlpUl1F4ry2LNnj9x2223Svn17qVmzpjRv3lxGjx4tmZmZUt2F6v3hOVnv0KFDy/Ta1RETFFdDBw8elK5du8p1110nF110UZmf9+uvv3pNRpyUlOS3zvfffy8vvPCCdOnSJWCoO+uss2TcuHHyzDPPSI0aNWT16tVHnUXb7kJVHtdcc43s27dPPvroI0lISJC33npLLr30Uvnhhx+ke/fuUp1VVpl06tRJvvzyS/d9fQ94uvPOO+XTTz+Vd99910xGeuutt5rXX7x4sVRnoSgPvQKR3p544gnp2LGjbN26VW666Saz7L333pPqLFTvD5cZM2ZwZajS6CXFUH3pn8DcuXNLXefrr7826+3du7fU9fbv3+887rjjnAsWLHAOGDDAefvtt3s93rt3b+cDDzxQIfttV1VZHrVr13a+/vrrXssaNGjgfOmll47hCOynospk4sSJzq5du5b4+L59+5xRUVHOd999171sw4YNZrtLly4t597bT1WVRyDvvPOOMzo62pmXlxfU8+ysqsvjp59+cjZt2tS5c+fOMr12dVS9q0oQlG7duknjxo3lzDPPDFiDoE1I55xzjgwaNMjvsfT0dFm+fLn5hta3b19JTk6WAQMGyP/+9z9KIQTlobQc5syZY5qc9BrJb7/9tmkKHDhwIGVSSWXy+++/m+ar1q1by1VXXSXbtm1zP7Zy5UrJy8vzKi9titImQK3tRtWWRyDaDKs1TiXVJKFyy+PQoUNy5ZVXyrPPPmu6kCAwgh2OSt+IM2fONP2v9JaSkmL+8f/xxx/d62go0PtTpkwJuI0//vjD3Y9i5MiR8t///tf06TrjjDPMmxlVWx7qnXfeMUFC+3PpZKE33nijzJ07V9q2bUtxVEKZ9O7dW2bPnm3+9p9//nnZvHmz9OvXz1zYW2l/o+joaKlXr57XtvVLUEn9J1F55eFr165d8vDDD8uoUaM47SEqD+2qoF9Izz//fMqgNKGuMkRolbcqu3///s6rr77a/L5t2zZnUlKSc/Xq1e7HfZv+Fi9ebF5r3LhxXtvp3Lmzc+zYscd0DHZSVeWhbr31VmevXr2cX375pXPVqlXOSZMmOePj451r1qypgCOxj4ook0C0WSouLs45a9Ysc//NN980zXy+TjrpJOe9994b9OvbVVWVh6fMzEzzXjnrrLOcubm5Qb+2nVVVeXz44YfOtm3bmi4mx/radkeNHcqlV69esnHjRncTkja1ag2cNlHo7dtvv5Wnn37a/F5QUGC+sSnthOzp+OOPP2rzByq+PDZt2iT/93//J6+88oqpNdWO0HqJuBNPPNE0c6BiyyQQrZlr166dex1tWsrNzTUDWjzpqFianaq+PFy0xkgHfdWtW9fUaEdFRVXA3iDY8li4cKH53NLlrs81pSPH6T7ijWCHclm1apU7rGkwWLt2rVnmumlA0D4S+ntkZKS0bNnS9J3QUVGefvvtN2nRogWlUMXloX1VlO+IZH1M+9uhYsskkAMHDph/qFzr9OzZ04SGr776yr2Ovl/0i0+fPn0okiouD5WVlSWDBw82TeQ6ejw2NpZyCFF5jB07VtasWeP1uaamT58ur776KuXigR6g1ZC+YTy/KWlfBn2TNGjQwHTU1ulItm/fLq+//rp7aHmrVq3MUHTtXD9r1izz7Wn+/Pnmcf0me8IJJ3i9hs6Lpn23XMt1vqF77rnH1App7ZB2on3ttdfkl19+qfZTB4SiPLRTvval0351Op2DPqbzQS1YsEA++eQTqe4qukzU3Xffbeb+0i8yOmWGvhc0SF9xxRXmcZ3e5Prrr5cxY8aY19FO+jqPmoa6k08+WaqzUJSHK9Tpl6B///vf5r7eVGJiolm3ugpFeWitdaCaa3093TY8hLotGFXPNfTc9zZ8+HDzuP7UPlkuU6dOdbZp08YZGxtrpsMYOHCgc+HChaW+RqA+XWrKlCnOZs2aOWvVquXs06ePc9GiRc7qLlTl8dtvvzkvuugi0x9Py6NLly5+059UV5VRJpdddpmzcePGph+dTteg9zdu3Oi1zuHDh53/+Mc/nPXr1zdlcuGFF5ppHaq7UJRHSa+pt82bNzurs1C9P3zRxy4wh/7PM+gBAAAgPNHHDgAAwCYIdgAAADZBsAMAALAJgh0AAIBNEOwAAABsgmAHAABgEwQ7AAAAmyDYAQAA2ATBDgCKTZo0yVzuzir0Unx6qbdg6TVm9fJLegH7yrRr1y5JSkqSv/76q1JfB0DZEewAVKmZM2ea69nm5+d7XXsyKipKBg4c6LXuN998Y8KNXgzczio6UOq1OvU6s3qeK1NCQoJcc8015rqeAKyBYAegSp122mkmyP3www/uZYsWLTI1TMuXLzcXCXf5+uuvzUW+27RpQymV0bZt2+STTz6RESNGVMk5u/baa+XNN9+UPXv2VMnrASgdwQ5AlWrfvr00btzY1Ma56O/nn3++tGrVSpYtW+a1XIOgeuONN+TEE080tVAaAq+88kpJT083jxUWFkqzZs3k+eef93qtn376SSIiImTr1q3m/r59++SGG26QxMREiYuLk9NPP11Wr15d6v7OmjVLjj/+eImNjZUOHTrIc889535sy5Ytpkbxgw8+MPtZq1Yt6dq1qyxdutRrGy+99JKkpKSYxy+88EJ58sknpV69euax2bNny+TJk81+6Lb0pss8mzv1Ofrc4447Tj766KNS9/edd94x+9C0adNSawRnzJghLVu2dN/XIHjBBRfIo48+KsnJyWb/HnroIVOzes8990iDBg3MOX711Ve9ttOpUydp0qSJzJ07t9T9AlA1CHYAqpyGIK2Nc9HftRl2wIAB7uWHDx82NXiuYJeXlycPP/ywCUDa70xDlatWSsPbFVdcIW+99ZbX62hN0imnnCItWrQw94cNG2bC4Oeffy4rV66UHj16yBlnnFFibZM+f8KECfLII4/Ihg0bTOh58MEH5bXXXvNab/z48XL33XfLqlWrpF27dmZfXE3Nixcvlptuukluv/128/iZZ55ptudy2WWXyV133WUC0s6dO81Nl7lo6Lv00ktlzZo1cvbZZ8tVV11Vau2Y1n5qAC6PhQsXyo4dO+S7774z4VObWP/2t79J/fr1TVnocdx4441+fep69eplXheABTgBoIq99NJLztq1azvz8vKcWVlZzho1ajjT09Odb731lrN///5mna+++sqpH1Fbt24NuI3vv//ePL5//35z/6effnI6HA73+gUFBc6mTZs6n3/+eXN/0aJFzri4OGd2drbXdtq0aeN84YUXzO8TJ050du3a1esx3SdPDz/8sLNPnz7m982bN5t9mDVrlvvx9evXm2UbNmww9y+77DLnOeec47WNq666yhkfH+++7/u6LrqdBx54wH3/wIEDZtnnn39e4rnV7Tz00ENeywJtf/r06c4WLVq47w8fPtzc1/Pm0r59e2e/fv3c9/Pz8025/ec///Ha1p133ukcOHBgifsEoOpQYwegymnt3MGDB+X77783NT1ay6XNo1pj5+pnp82wrVu3Nn3slNawnXvuuea+Nsfquq4+ZUqbGrXJ1FVr9+2335raOa2lU1rTp337GjZsKHXq1HHfNm/eHHBwhu6fLr/++uu91v/nP//pt36XLl3cv2szs3I1E+sIVa3R8uR7vzSe265du7ZpQnZtOxCt6dRm4/LQWkOt/XTRJtnOnTu770dGRprz5/v6NWvWlEOHDpXrNQFUrBoVvD0AOKq2bdua/lra7Lp37153SNO+WtoXbcmSJeYx7QPnCllDhgwxN20e1RCogU7v5+bmurerzZQa7MaOHWt+nnXWWSaIKA11vn37XFz93Tzp+q7+cb179/Z6TAOOJx3R66J95Fz9/iqC57Zd2y9t2zpSVc+pJw1rRRWAR2jTdlleqyyvr03DWiYAQo9gByAktO+chiwNIdo536V///6mD9yKFSvk5ptvNst++eUX2b17tzz22GMm+CnPUbUuOqDigQceMLV77733nplaxUX706WmpkqNGjW8Bg2URGurNGj+8ccfJjAey2ARrZn05Hs/OjpaCgoKpCJ0795dfv75Z69lGrr02DXcuYKn9verKOvWrfObqgZAaNAUCyBkwe5///ufCRiuGjulv7/wwgumJs41cEKbXzX8PPPMMyZo6chQHUjhSwNb3759TfOpBqXzzjvP/digQYOkT58+ZuTn/PnzzeALrRnUgQ+BQqJr4MKUKVPk6aeflt9++03Wrl1rRoXqwIKy0vnkPvvsM/Oc33//3RybBldXwHLttzYJ67nQUbA5OTlSXlqLqaNyPYOihq6MjAyZNm2aaUZ+9tlnzT5UBG2C1SA9ePDgCtkegGNDsAMQEhratD+YNstq7ZhnsNMrJrimRXHVOOkUIO+++6507NjR1Nw98cQTAbertWvan06nCNG+Xy4apDRgaY2gzr2m/fouv/xyMxWK5+t70qlRdLoTDXPa10z3TfdDp2UpKx2VqzWHGux0GpL//ve/cuedd3r1g7v44otNs7GeEz3W//znP1JeQ4cONbWSX375pXuZ9j3UaVo00Ok+aG2ojuKtCB9++KEJ3v369auQ7QE4Ng4dQXGM2wAABGHkyJGmebmypgjRAKe1ml988UWll8vJJ58so0ePNs3gAEKPPnYAUMm0dlHnr9NRrdoEqvPgeU50XNF0rjmdjFlrPivzsmLabHzRRReZefsAWAM1dgBQyXSCYR0ookFLp3DRfnc62S8AVDSCHQAAgE0weAIAAMAmCHYAAAA2QbADAACwCYIdAACATRDsAAAAbIJgBwAAYBMEOwAAAJsg2AEAANgEwQ4AAEDs4f8BuBVTU/w5FYMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "result.plot(db=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<video controls  >\n",
+       " <source 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type=\"video/mp4\">\n",
+       " Your browser does not support the video tag.\n",
+       " </video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Video object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "result.show_animation()"
    ]
@@ -134,7 +221,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/meep_sbend.ipynb b/nbs/meep_sbend.ipynb
new file mode 100644
index 00000000..462929c8
--- /dev/null
+++ b/nbs/meep_sbend.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Running MEEP Simulations\n",
+    "\n",
+    "[MEEP](https://meep.readthedocs.io/) is an open-source FDTD electromagnetic simulator. This notebook demonstrates using the `gsim.meep` API to run an S-parameter simulation on a photonic Y-branch.\n",
+    "\n",
+    "**Requirements:**\n",
+    "\n",
+    "- UBC PDK: `uv pip install ubcpdk`\n",
+    "- [GDSFactory+](https://gdsfactory.com) account for cloud simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "### Load a pcell from UBC PDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ubcpdk import PDK, cells\n",
+    "\n",
+    "PDK.activate()\n",
+    "\n",
+    "c = cells.bend_s(size=(20.0, 5.0))\n",
+    "\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "### Configure and run simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gsim import meep\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry(component=c, z_crop=\"auto\")\n",
+    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
+    "sim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\n",
+    "sim.monitors = [\"o1\", \"o2\"]\n",
+    "sim.domain(pml=1.0, margin=0.5)\n",
+    "sim.solver(resolution=20, save_animation=True, verbose_interval=5.0)\n",
+    "sim.solver.stop_after_sources(time=100)\n",
+    "\n",
+    "print(sim.validate_config())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.plot_2d(slices=\"xyz\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "### Run simulation on cloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run on GDSFactory+ cloud\n",
+    "result = sim.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.plot(db=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.show_animation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1de30efa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gsim",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/nbs/meep_straight.ipynb b/nbs/meep_straight.ipynb
new file mode 100644
index 00000000..b26f5694
--- /dev/null
+++ b/nbs/meep_straight.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Running MEEP Simulations\n",
+    "\n",
+    "[MEEP](https://meep.readthedocs.io/) is an open-source FDTD electromagnetic simulator. This notebook demonstrates using the `gsim.meep` API to run an S-parameter simulation on a photonic Y-branch.\n",
+    "\n",
+    "**Requirements:**\n",
+    "\n",
+    "- UBC PDK: `uv pip install ubcpdk`\n",
+    "- [GDSFactory+](https://gdsfactory.com) account for cloud simulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "### Load a pcell from UBC PDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ubcpdk import PDK, cells\n",
+    "\n",
+    "PDK.activate()\n",
+    "\n",
+    "c = cells.straight(length=20.0)\n",
+    "\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3",
+   "metadata": {},
+   "source": [
+    "### Configure and run simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from gsim import meep\n",
+    "\n",
+    "sim = meep.Simulation()\n",
+    "\n",
+    "sim.geometry(component=c, z_crop=\"auto\")\n",
+    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
+    "sim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\n",
+    "sim.monitors = [\"o1\", \"o2\"]\n",
+    "sim.domain(pml=1.0, margin=0.5)\n",
+    "sim.solver(resolution=20, save_animation=True, verbose_interval=5.0)\n",
+    "sim.solver.stop_after_sources(time=100)\n",
+    "\n",
+    "print(sim.validate_config())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim.plot_2d(slices=\"xyz\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "### Run simulation on cloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run on GDSFactory+ cloud\n",
+    "result = sim.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.plot(db=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.show_animation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c03f0c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "gsim",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/nbs/meep_ybranch.ipynb b/nbs/meep_ybranch.ipynb
index ad7269ea..a4e8adbe 100644
--- a/nbs/meep_ybranch.ipynb
+++ b/nbs/meep_ybranch.ipynb
@@ -52,21 +52,7 @@
    "id": "4",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from gsim import meep\n",
-    "\n",
-    "sim = meep.Simulation()\n",
-    "\n",
-    "sim.geometry(component=c, z_crop=\"auto\")\n",
-    "sim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\n",
-    "sim.source(port=\"o1\", wavelength=1.55, bandwidth=0.01, num_freqs=11)\n",
-    "sim.monitors = [\"o1\", \"o2\", \"o3\"]\n",
-    "sim.domain(pml=1.0, margin=0.5)\n",
-    "sim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\n",
-    "sim.solver.stop_after_sources(time=80)\n",
-    "\n",
-    "print(sim.validate_config())"
-   ]
+   "source": "from gsim import meep\n\nsim = meep.Simulation()\n\nsim.geometry(component=c, z_crop=\"auto\")\nsim.materials = {\"si\": 3.47, \"SiO2\": 1.44}\nsim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\nsim.monitors = [\"o1\", \"o2\", \"o3\"]\nsim.domain(pml=1.0, margin=0.5)\nsim.solver(resolution=20, simplify_tol=0.01, save_animation=True, verbose_interval=5.0)\nsim.solver.stop_after_sources(time=60)\n\nprint(sim.validate_config())"
   },
   {
    "cell_type": "code",
@@ -133,9 +119,10 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/nbs/palace_demo_cpw.ipynb b/nbs/palace_demo_cpw.ipynb
index 8c5769c9..72b1504e 100644
--- a/nbs/palace_demo_cpw.ipynb
+++ b/nbs/palace_demo_cpw.ipynb
@@ -188,17 +188,6 @@
     "### Run simulation on cloud"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate Palace config file\n",
-    "sim.write_config()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -207,7 +196,7 @@
    "outputs": [],
    "source": [
     "# Run simulation on GDSFactory+ cloud\n",
-    "results = sim.simulate()"
+    "results = sim.run()"
    ]
   },
   {
@@ -263,7 +252,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/nbs/palace_demo_microstrip.ipynb b/nbs/palace_demo_microstrip.ipynb
index ad4670c4..f796189c 100644
--- a/nbs/palace_demo_microstrip.ipynb
+++ b/nbs/palace_demo_microstrip.ipynb
@@ -115,17 +115,6 @@
     "# sim.plot_mesh(show_groups=[\"metal\", \"P\"], interactive=True)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate Palace config file\n",
-    "sim.write_config()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8",
@@ -142,7 +131,7 @@
    "outputs": [],
    "source": [
     "# Run simulation on GDSFactory+ cloud\n",
-    "results = sim.simulate()"
+    "results = sim.run()"
    ]
   },
   {
@@ -198,7 +187,8 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3"
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,
diff --git a/src/gsim/gcloud.py b/src/gsim/gcloud.py
index 2b2cb508..a6bd8a02 100644
--- a/src/gsim/gcloud.py
+++ b/src/gsim/gcloud.py
@@ -7,15 +7,19 @@
     from gsim import gcloud
 
     # Run simulation (uploads, starts, waits, downloads)
-    results = gcloud.run_simulation("./sim", job_type="palace")
+    result = gcloud.run_simulation("./sim", job_type="palace")
+    print(result.sim_dir)   # palace_abc123/
+    print(result.files)     # {"port-S.csv": Path(...), ...}
 
     # Or use solver-specific wrappers:
     from gsim import palace as pa
-    results = pa.run_simulation("./sim")
+    result = pa.run_simulation("./sim")
 """
 
 from __future__ import annotations
 
+import shutil
+from dataclasses import dataclass, field
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -26,6 +30,38 @@
     from typing import Literal
 
 
+@dataclass
+class RunResult:
+    """Result of a cloud simulation run.
+
+    Attributes:
+        sim_dir: Root directory (``{job_type}_{job_name}/``).
+        files: Flat mapping of filename → Path inside ``output/``.
+        job_name: Cloud job identifier.
+    """
+
+    sim_dir: Path
+    files: dict[str, Path] = field(default_factory=dict)
+    job_name: str = ""
+
+
+def _flatten_results(raw_results: dict) -> dict[str, Path]:
+    """Flatten gdsfactoryplus download results to a filename → Path dict.
+
+    The SDK may return directories (extracted archives) or individual files.
+    This walks everything and returns a flat mapping.
+    """
+    flat: dict[str, Path] = {}
+    for result_path in raw_results.values():
+        if result_path.is_dir():
+            for file_path in result_path.rglob("*"):
+                if file_path.is_file() and not file_path.name.startswith("."):
+                    flat[file_path.name] = file_path
+        else:
+            flat[result_path.name] = result_path
+    return flat
+
+
 def _handle_failed_job(job, output_dir: Path, verbose: bool) -> None:
     """Handle a failed simulation job by downloading logs and raising informative error.
 
@@ -54,16 +90,7 @@ def _handle_failed_job(job, output_dir: Path, verbose: bool) -> None:
                 print("Downloading logs from failed job...")  # noqa: T201
 
             raw_results = sim.download_results(job, output_dir=output_dir)
-
-            # Flatten results to find all files
-            all_files: dict[str, Path] = {}
-            for result_path in raw_results.values():
-                if result_path.is_dir():
-                    for file_path in result_path.rglob("*"):
-                        if file_path.is_file() and not file_path.name.startswith("."):
-                            all_files[file_path.name] = file_path
-                else:
-                    all_files[result_path.name] = result_path
+            all_files = _flatten_results(raw_results)
 
             # Look for log files and display them
             log_files = ["palace.log", "stdout.log", "stderr.log", "output.log"]
@@ -109,48 +136,55 @@ def upload_simulation_dir(input_dir: str | Path, job_type: str):
 
 
 def run_simulation(
-    output_dir: str | Path,
+    config_dir: str | Path,
     job_type: Literal["palace", "meep"] = "palace",
     verbose: bool = True,
     on_started: Callable | None = None,
-) -> dict[str, Path]:
+    parent_dir: str | Path | None = None,
+) -> RunResult:
     """Run a simulation on GDSFactory+ cloud.
 
     This function handles the complete workflow:
-    1. Uploads simulation files
+    1. Uploads simulation files from *config_dir*
     2. Starts the simulation job
-    3. Waits for completion
-    4. Downloads results
+    3. Creates a structured directory ``sim-data-{job_name}/``
+       with ``input/`` (config files) and ``output/`` (results) sub-dirs
+    4. Waits for completion
+    5. Downloads results into ``output/``
 
     Args:
-        output_dir: Directory containing the simulation files
-        job_type: Type of simulation (default: "palace")
-        verbose: Print progress messages (default True)
-        on_started: Optional callback called with job object when simulation starts
+        config_dir: Directory containing the simulation config files.
+        job_type: Type of simulation (default: "palace").
+        verbose: Print progress messages (default True).
+        on_started: Optional callback called with job object when simulation starts.
+        parent_dir: Where to create the sim directory.
+            Defaults to the current working directory.
 
     Returns:
-        Dict mapping result filename to local Path.
+        RunResult with sim_dir, files dict, and job_name.
 
     Raises:
         RuntimeError: If simulation fails
 
     Example:
-        >>> results = gcloud.run_simulation("./sim", job_type="palace")
+        >>> result = gcloud.run_simulation("./sim", job_type="palace")
         Uploading simulation... done
         Job started: palace-abc123
         Waiting for completion... done (2m 34s)
         Downloading results... done
+        >>> print(result.sim_dir)
+        sim-data-palace-abc123/
     """
-    output_dir = Path(output_dir)
+    config_dir = Path(config_dir)
 
-    if not output_dir.exists():
-        raise FileNotFoundError(f"Output directory not found: {output_dir}")
+    if not config_dir.exists():
+        raise FileNotFoundError(f"Config directory not found: {config_dir}")
 
     # Upload
     if verbose:
         print("Uploading simulation... ", end="", flush=True)  # noqa: T201
 
-    pre_job = upload_simulation_dir(output_dir, job_type)
+    pre_job = upload_simulation_dir(config_dir, job_type)
 
     if verbose:
         print("done")  # noqa: T201
@@ -164,6 +198,20 @@ def run_simulation(
     if on_started:
         on_started(job)
 
+    # Create structured directory
+    root = Path(parent_dir) if parent_dir else Path.cwd()
+    sim_dir = root / f"sim-data-{job.job_name}"
+    input_dir = sim_dir / "input"
+    output_dir = sim_dir / "output"
+    input_dir.mkdir(parents=True, exist_ok=True)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Move config files into input/
+    for item in list(config_dir.iterdir()):
+        shutil.move(str(item), str(input_dir / item.name))
+    # Remove now-empty config_dir (may fail if it was CWD, etc.)
+    shutil.rmtree(config_dir, ignore_errors=True)
+
     # Wait
     finished_job = sim.wait_for_simulation(job)
 
@@ -171,29 +219,14 @@ def run_simulation(
     if finished_job.exit_code != 0:
         _handle_failed_job(finished_job, output_dir, verbose)
 
-    # Download
-    raw_results = sim.download_results(
-        finished_job, output_dir=f"sim-data-{finished_job.job_name}"
-    )
-
-    # Flatten results: gdsfactoryplus returns extracted directories,
-    # but we want a dict of filename -> Path for individual files
-    results: dict[str, Path] = {}
-    for result_path in raw_results.values():
-        if result_path.is_dir():
-            # Recursively find all files in the extracted directory
-            for file_path in result_path.rglob("*"):
-                if file_path.is_file() and not file_path.name.startswith("."):
-                    results[file_path.name] = file_path
-        else:
-            results[result_path.name] = result_path
+    # Download into output/
+    raw_results = sim.download_results(finished_job, output_dir=output_dir)
+    files = _flatten_results(raw_results)
 
-    if verbose and results:
-        # Find common parent directory for display
-        first_path = next(iter(results.values()))
-        print(f"Downloaded {len(results)} files to {first_path.parent}")  # noqa: T201
+    if verbose and files:
+        print(f"Downloaded {len(files)} files to {output_dir}")  # noqa: T201
 
-    return results
+    return RunResult(sim_dir=sim_dir, files=files, job_name=job.job_name)
 
 
 def print_job_summary(job) -> None:
diff --git a/src/gsim/meep/__init__.py b/src/gsim/meep/__init__.py
index d62f4365..3a841889 100644
--- a/src/gsim/meep/__init__.py
+++ b/src/gsim/meep/__init__.py
@@ -11,7 +11,7 @@
     sim = meep.Simulation()
     sim.geometry(component=ybranch, z_crop="auto")
     sim.materials = {"si": 3.47, "SiO2": 1.44}
-    sim.source(port="o1", wavelength=1.55, bandwidth=0.01, num_freqs=11)
+    sim.source(port="o1", wavelength=1.55, wavelength_span=0.01, num_freqs=11)
     sim.monitors = ["o1", "o2"]
     sim.domain(pml=1.0, margin=0.5)
     sim.solver(resolution=32, simplify_tol=0.01)
diff --git a/src/gsim/meep/meep-overview.md b/src/gsim/meep/meep-overview.md
deleted file mode 100644
index 3f3f3172..00000000
--- a/src/gsim/meep/meep-overview.md
+++ /dev/null
@@ -1,135 +0,0 @@
-# gsim.meep — Overview & Roadmap
-
-## Architecture
-
-```
-GDS + PDK  ──►  3D Geometry  ──►  Client Viz  ──►  SimConfig JSON  ──►  Cloud Runner
-(common/)       (GeometryModel)   (no meep)        (no meep)            (meep in Docker)
-```
-
-**Key constraint:** gsim is the client SDK — no meep dependency. MEEP runs only in Docker/cloud.
-
-**Upload package:** `layout.gds` + `sim_config.json` + `run_meep.py` -> Docker -> `s_parameters.csv` + `meep_debug.json` + diagnostic PNGs.
-
----
-
-## API: Declarative `Simulation`
-
-```python
-from gsim import meep
-
-sim = meep.Simulation()
-sim.geometry(component=ybranch, z_crop="auto")
-sim.materials = {"si": 3.47, "SiO2": 1.44}       # float shorthand
-sim.source(port="o1", wavelength=1.55, bandwidth=0.1, num_freqs=11)
-sim.monitors = ["o1", "o2"]
-sim.domain(pml=1.0, margin=0.5)
-sim.solver(resolution=32, simplify_tol=0.01)
-sim.plot_2d(slices="xyz")
-result = sim.run()
-```
-
-### Design principles
-
-- **6 typed physics objects** -- `Geometry`, `Material`, `ModeSource`, `Domain`, `FDTD` + `monitors: list[str]` -- assigned to a `Simulation` container. No ordering dependencies.
-- **Callable, attribute, or constructor style** -- `sim.source(port="o1")` (callable), `sim.source.port = "o1"` (attribute), or `sim.source = ModeSource(port="o1")` (constructor). All work via Pydantic `validate_assignment=True`.
-- **Float shorthand for materials** -- `{"si": 3.47}` auto-normalizes to `Material(n=3.47)` via validator.
-- **5 stopping methods** -- Flat `stopping` mode string on `FDTD` (`field_decay`/`energy_decay`/`dft_decay`/`fixed`) plus convenience methods: `stop_when_fields_decayed()`, `stop_when_energy_decayed()`, `stop_when_dft_decayed()`, `stop_after_sources()`. A 5th method `stop_after_walltime(seconds)` sets an orthogonal wall-clock safety net (field `wall_time_max`) that can be combined with any mode.
-- **Source defines spectral window** -- `WavelengthConfig` derived from `ModeSource.wavelength/bandwidth/num_freqs`. Monitors are just port name strings.
-- **JSON contract** -- `write_config()` translates new API -> existing `SimConfig` -> JSON. `StoppingConfig.mode` extended with `"energy_decay"` (runner updated to handle it).
-
----
-
-## Module Structure
-
-### `gsim.meep` -- Public API
-
-| Module              | Purpose                                                                                                                                                               |
-| ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `__init__.py`       | Exports: `Simulation`, all model classes                                                                                                                              |
-| `models/api.py`     | Declarative models: `Geometry`, `Material`, `ModeSource`, `Domain`, `FDTD`, `Symmetry`. All except `Material`/`Symmetry` have `__call__(**kwargs)` for fluent updates |
-| `simulation.py`     | `Simulation` container -- `write_config()`, `run()`, `validate_config()`, `plot_2d()`/`plot_3d()`, `estimate_meep_np()`                                               |
-| `viz.py`            | Standalone viz helpers -- `build_geometry_model()`, `plot_2d()`, `plot_3d()` (calls `common/viz`)                                                                     |
-| `models/config.py`  | `SimConfig` + all sub-configs (JSON serialization layer, strict — no defaults on fields that `build_config` always populates)                                         |
-| `models/results.py` | `SParameterResult` -- CSV + debug JSON + diagnostic PNGs                                                                                                              |
-| `ports.py`          | `extract_port_info()` -- port center/direction/normal from gdsfactory                                                                                                 |
-| `materials.py`      | `resolve_materials()` -- material names -> (n, k) via common DB                                                                                                       |
-| `script.py`         | `generate_meep_script()` -- cloud runner template (string in Python)                                                                                                  |
-| `overlay.py`        | `SimOverlay` + `PortOverlay` + `DielectricOverlay` -- viz metadata                                                                                                    |
-
-### `gsim.common` -- Shared infrastructure
-
-Solver-agnostic: `LayeredComponentBase`, `GeometryModel`/`Prism`, `LayerStack`, `ValidationResult`, `viz/` (Matplotlib 2D, PyVista/Open3D 3D).
-
-Key sub-modules:
-
-- `stack/_layer_utils.py` — shared `get_gds_layer_tuple()` and `classify_layer_type()` (used by both `extractor` and `visualization`)
-- `viz/_mesh_helpers.py` — shared Delaunay triangulation (`triangulate_polygon_with_holes`) and batch prism geometry (`collect_triangular_prism_geometry`), used by both PyVista and Open3D renderers
-
-### Visualization pipeline
-
-3D backends: PyVista (desktop) and Open3D+Plotly (Jupyter, with view buttons for Iso/Top/Front/Right). Three.js backend removed.
-
-```
-Simulation.plot_2d()
-  -> viz.build_geometry_model(component, stack, domain_config)
-       -> LayeredComponentBase + extract_geometry_model()
-       -> crop_geometry_model() if stack is z-cropped
-  -> viz.build_overlay(gm, component, stack, domain_config)
-       -> overlay.build_sim_overlay()
-  -> common/viz/render2d.plot_prism_slices(gm, overlay=overlay)
-```
-
-- **Z slices** -- draw actual prism polygon outlines at the slice plane (XY view)
-- **X/Y slices** -- compute Shapely line-polygon intersections for accurate cross-sections (XZ/YZ views); each intersection segment becomes a rectangle from z_base to z_top
-- **Overlay** -- sim cell boundary, PML regions, port markers, dielectric backgrounds
-
----
-
-## Key Design Decisions
-
-- **GDS-file approach** -- Send raw GDS to cloud (not polygon coords in JSON). Complex geometries have 1000s of nodes; DerivedLayers need gdsfactory to resolve.
-- **Port z-center = highest refractive index** -- Photonic ports center on waveguide core (not conductor layer like RF).
-- **Z-crop** -- Auto-crops stack around core layer. Full UBC stack is 15um; cropped to ~1.5um (massive compute savings).
-- **Port extension into PML** -- `gf.components.extend_ports()` at `write_config()` time. Original bbox stored in `SimConfig.component_bbox` for correct cell sizing.
-- **Symmetries disabled for S-params** -- `add_mode_monitor` uses `use_symmetry=false` internally; `get_eigenmode_coefficients` doesn't apply `S.multiplicity()`. Source port coefficients underestimated ~2x. gplugins also never uses `mp.Mirror`.
-- **`field_decay` default stopping** -- Monitors |component|² at a point and stops when it decays by `decay_by` from peak. Runner uses `mp.stop_when_fields_decayed()` + `_make_time_cap(max_time)` in an `until_after_sources` list (OR logic — first condition wins). `energy_decay` is similar but monitors total cell energy (more robust for ring couplers where DFTs can falsely converge). `stop_after_sources(time)` runs for a fixed sim-time after sources turn off.
-- **Wall-clock safety net** -- `stop_after_walltime(seconds)` sets `wall_time_max` on `FDTD`, an orthogonal real-time cap. Runner's `_make_wall_time_cap()` uses `time.time()` deadline and is appended to the `until_after_sources` list in all 4 mode branches when `wall_time_max > 0`. A warning is logged when the wall-clock limit likely triggered (elapsed >= 95% of limit).
-- **`dft_min_run_time` is absolute sim time** -- Not time-after-sources. With a broadband source turning off at ~t=78 and min_run_time=100, DFT checking starts at t=100 (only ~22 units after source ends). Must exceed `device_length * n_group`.
-- **API models are the single source of truth for defaults** -- Config models (`models/config.py`) and the cloud runner (`script.py`) have no default values for fields that `build_config()` always populates. `model_dump(by_alias=True)` serializes all fields explicitly, so config defaults and script.py `.get()` fallbacks were dead code that could silently drift. Config sub-models are strict (missing fields = `ValidationError`); `script.py` uses direct `dict["key"]` access.
-- **Stack resolved lazily** -- `_ensure_stack()` falls back to `get_stack()` (active PDK defaults) when no explicit stack kwargs are set. Same path for `write_config()` and viz.
-
----
-
-## TODO
-
-### Near-term
-
-- [ ] **Cloud end-to-end test** -- Full round-trip (upload -> run meep -> download results) hasn't been tested with real cloud instance.
-- [ ] **Monitor z-span touches PML** -- After z-crop, layer stack z-extent exactly matches PML inner boundary. At coarse resolution, outermost monitor pixels may straddle PML. Fix: shrink monitor z-span by ~`2/resolution` inset.
-- [ ] **Empty `core2` layer in config** -- UBC PDK's ebeam_y_1550 includes a `core2` entry (GDS layer 31) with zero geometry. Harmless but clutters config. Consider filtering empty layers.
-
-### Medium-term
-
-- [ ] **Custom monitors** -- Monitors are port-name strings only. Add `FieldMonitor(center, size)` and `FluxMonitor` for custom measurement locations.
-- [ ] **Per-port margin/mode_index** -- `port_margin` is global in `Domain`. Could add per-port control for margin and higher-order modes.
-- [ ] **Typed boundaries** -- Only PML today. Add `Periodic`, `Bloch`, `PEC`, `PMC` per-axis boundary specs.
-- [ ] **Mesh refinement regions** -- Single global `resolution`. Add local refinement for thin features.
-- [ ] **Dispersive materials** -- `Material(n, k)` is non-dispersive. Add Sellmeier/Lorentz/Drude support.
-- [ ] **`updated_copy()` for parameter sweeps** -- Pydantic `model_copy(update={...})` makes this nearly free.
-
-### Deferred
-
-- [ ] **JSON schema v2** -- Restructure JSON groups (`"solver.wavelength"`, per-port margins, etc.) with version field for runner backward compat. Requires atomic client + runner update.
-- [ ] **Port symmetries** -- gplugins-style: run fewer source ports, copy S-params between symmetric port pairs (e.g. S31=S21 for Y-branch).
-- [ ] **`add_flux` + `eig_parity` path** -- Alternative to `add_mode_monitor` that works correctly with `mp.Mirror` symmetry. Needs auto-detection of correct parity from symmetry config and waveguide polarization.
-
----
-
-## Docker / Cloud
-
-- **Base:** `continuumio/miniconda3` -> conda env with `pymeep=*=mpi_mpich_*`, `gdsfactory`, `nlopt`
-- **Entrypoint:** downloads input -> `mpirun -np $NP python run_meep.py` -> uploads outputs
-- **`meep_np` hardcoded to 2** -- auto-compute logic exists (`total_voxels // 200k`) but `lscpu` reports host cores inside containers, not Batch-allocated vCPUs. TODO: pass Batch vCPU allocation as `MEEP_NP` env var from job submission, then restore auto-compute with proper clamping
-- **Instance recommendation:** `c7a.16xlarge` (AMD EPYC Genoa, DDR5, ~460 GB/s mem BW) for typical photonics; `hpc7a.96xlarge` for large problems
-- **Local testing:** `./nbs/test_meep_local.sh` (regenerate -> Docker build -> run -> results)
diff --git a/src/gsim/meep/models/api.py b/src/gsim/meep/models/api.py
index 7a0c79db..25861b8f 100644
--- a/src/gsim/meep/models/api.py
+++ b/src/gsim/meep/models/api.py
@@ -71,10 +71,11 @@ class ModeSource(BaseModel):
         gt=0,
         description="Center wavelength in um",
     )
-    bandwidth: float = Field(
+    wavelength_span: float = Field(
         default=0.1,
         ge=0,
-        description="Measurement wavelength bandwidth in um",
+        description="Wavelength span of the measurement frequency grid in um. "
+        "Together with num_freqs, sets the spacing between monitor frequency points.",
     )
     num_freqs: int = Field(
         default=11,
@@ -130,6 +131,19 @@ class Domain(BaseModel):
         ge=0,
         description="Extend ports into PML (um). 0 = auto (margin + pml).",
     )
+    source_port_offset: float = Field(
+        default=0.1,
+        ge=0,
+        description="Distance to offset source from port center into device (um). "
+        "Matches gplugins port_source_offset.",
+    )
+    distance_source_to_monitors: float = Field(
+        default=0.2,
+        ge=0,
+        description="Distance between source and its port monitor (um). "
+        "The source-port monitor is placed this far past the source, "
+        "deeper into the device. Matches gplugins distance_source_to_monitors.",
+    )
     symmetries: list[Symmetry] = Field(
         default_factory=list,
         description="Mirror symmetry planes. Not yet used in production runs.",
diff --git a/src/gsim/meep/models/config.py b/src/gsim/meep/models/config.py
index 40825311..0b05576b 100644
--- a/src/gsim/meep/models/config.py
+++ b/src/gsim/meep/models/config.py
@@ -57,6 +57,15 @@ class DomainConfig(BaseModel):
         description="Length to extend waveguide ports into PML in um. "
         "0 = auto (margin_xy + dpml).",
     )
+    source_port_offset: float = Field(
+        ge=0,
+        description="Distance to offset source from port center into device (um).",
+    )
+    distance_source_to_monitors: float = Field(
+        ge=0,
+        description="Distance between source and its port monitor (um). "
+        "Source-port monitor is placed this far past the source into the device.",
+    )
 
 
 class StoppingConfig(BaseModel):
diff --git a/src/gsim/meep/script.py b/src/gsim/meep/script.py
index 31c14dc0..0a9b99b8 100644
--- a/src/gsim/meep/script.py
+++ b/src/gsim/meep/script.py
@@ -375,22 +375,35 @@ def get_port_z_span(config):
 
 
 def build_sources(config):
-    """Build MEEP source from config port data."""
+    """Build MEEP source from config port data.
+
+    The source is offset from the port center by ``source_port_offset``
+    along the propagation direction (into the device).  This separates
+    the soft source from the port monitor so eigenmode coefficients
+    measure the true incident amplitude rather than half of it.
+    """
     fdtd = config["fdtd"]
     fcen = fdtd["fcen"]
     df = fdtd["df"]
     fwidth = config["source"]["fwidth"]
     z_span = get_port_z_span(config)
     port_margin = config["domain"]["port_margin"]
+    source_port_offset = config["domain"].get("source_port_offset", 0.1)
 
     sources = []
     for port in config["ports"]:
         if not port["is_source"]:
             continue
 
-        center = mp.Vector3(*port["center"])
+        # Offset source center into the device along propagation direction
+        center_list = list(port["center"])
         normal_axis = port["normal_axis"]
         direction = port["direction"]
+        if direction == "+":
+            center_list[normal_axis] += source_port_offset
+        else:
+            center_list[normal_axis] -= source_port_offset
+        center = mp.Vector3(*center_list)
 
         size = [0, 0, 0]
         transverse_axis = 1 - normal_axis
@@ -421,18 +434,45 @@ def build_sources(config):
 
 
 def build_monitors(config, sim):
-    """Build mode monitors at all ports and return flux regions."""
+    """Build mode monitors at all ports and return flux regions.
+
+    The source-port monitor is offset further into the device (past
+    the source) by ``source_port_offset + distance_source_to_monitors``
+    so the forward-going mode from the source passes through it at
+    full amplitude.  This matches the gplugins approach.
+    """
     fdtd = config["fdtd"]
     fcen = fdtd["fcen"]
     df = fdtd["df"]
     nfreq = fdtd["num_freqs"]
     z_span = get_port_z_span(config)
     port_margin = config["domain"]["port_margin"]
+    source_port_offset = config["domain"].get("source_port_offset", 0.1)
+    distance_source_to_monitors = config["domain"].get(
+        "distance_source_to_monitors", 0.2
+    )
 
     monitors = {}
     for port in config["ports"]:
-        center = mp.Vector3(*port["center"])
+        center_list = list(port["center"])
         normal_axis = port["normal_axis"]
+        direction = port["direction"]
+
+        # All monitors shift inward from port center (matches gplugins):
+        #   source-port monitor: source_port_offset + distance_source_to_monitors
+        #   non-source monitors: source_port_offset
+        if port["is_source"]:
+            offset = source_port_offset + distance_source_to_monitors
+        else:
+            offset = source_port_offset
+
+        if offset > 0:
+            if direction == "+":
+                center_list[normal_axis] += offset
+            else:
+                center_list[normal_axis] -= offset
+
+        center = mp.Vector3(*center_list)
 
         size = [0, 0, 0]
         transverse_axis = 1 - normal_axis
diff --git a/src/gsim/meep/simulation.py b/src/gsim/meep/simulation.py
index 7edc4221..afb84126 100644
--- a/src/gsim/meep/simulation.py
+++ b/src/gsim/meep/simulation.py
@@ -98,7 +98,7 @@ class Simulation(BaseModel):
         sim.monitors = ["o1", "o2"]
         sim.solver.stopping = "dft_decay"
         sim.solver.max_time = 200
-        result = sim.run("./meep-sim")
+        result = sim.run()  # creates sim-data-{job_name}/ in CWD
     """
 
     model_config = ConfigDict(
@@ -308,7 +308,7 @@ def _wavelength_config(self) -> Any:
 
         return WavelengthConfig(
             wavelength=self.source.wavelength,
-            bandwidth=self.source.bandwidth,
+            bandwidth=self.source.wavelength_span,
             num_freqs=self.source.num_freqs,
         )
 
@@ -348,6 +348,8 @@ def _domain_config(self) -> Any:
             margin_z_below=self.domain.margin_z_below,
             port_margin=self.domain.port_margin,
             extend_ports=self.domain.extend_ports,
+            source_port_offset=self.domain.source_port_offset,
+            distance_source_to_monitors=self.domain.distance_source_to_monitors,
         )
 
     def _resolution_config(self) -> Any:
@@ -618,12 +620,21 @@ def write_config(self, output_dir: str | Path) -> Path:
     # run
     # -------------------------------------------------------------------------
 
-    def run(self, output_dir: str | Path | None = None, *, verbose: bool = True) -> Any:
+    def run(
+        self,
+        parent_dir: str | Path | None = None,
+        *,
+        verbose: bool = True,
+    ) -> Any:
         """Run MEEP simulation on the cloud.
 
+        Config files are written to a temporary directory, uploaded, then
+        moved into a structured ``sim-data-{job_name}/input/`` directory.
+        Results are downloaded to ``sim-data-{job_name}/output/``.
+
         Args:
-            output_dir: Directory to write config and download results.
-                If None, a temporary directory is created automatically.
+            parent_dir: Where to create the sim directory.
+                Defaults to the current working directory.
             verbose: Print progress info.
 
         Returns:
@@ -634,18 +645,23 @@ def run(self, output_dir: str | Path | None = None, *, verbose: bool = True) ->
         from gsim.gcloud import run_simulation
         from gsim.meep.models.results import SParameterResult
 
-        if output_dir is None:
-            output_dir = Path(tempfile.mkdtemp(prefix="meep_"))
-
-        output_dir = self.write_config(output_dir)
+        tmp = Path(tempfile.mkdtemp(prefix="meep_"))
+        try:
+            self.write_config(tmp)
+            result = run_simulation(
+                config_dir=tmp,
+                job_type="meep",
+                verbose=verbose,
+                parent_dir=parent_dir,
+            )
+        except Exception:
+            # Clean up temp dir on failure (run_simulation moves it on success)
+            import shutil
 
-        results = run_simulation(
-            output_dir,
-            job_type="meep",
-            verbose=verbose,
-        )
+            shutil.rmtree(tmp, ignore_errors=True)
+            raise
 
-        csv_path = results.get("s_parameters.csv")
+        csv_path = result.files.get("s_parameters.csv")
         if csv_path is not None:
             return SParameterResult.from_csv(csv_path)
 
diff --git a/src/gsim/palace/__init__.py b/src/gsim/palace/__init__.py
index 6b242127..8de7ce5f 100644
--- a/src/gsim/palace/__init__.py
+++ b/src/gsim/palace/__init__.py
@@ -22,7 +22,7 @@
 
     # Generate mesh and run
     sim.mesh("./sim", preset="fine")
-    results = sim.simulate()
+    results = sim.run()
 """
 
 from __future__ import annotations
diff --git a/src/gsim/palace/driven.py b/src/gsim/palace/driven.py
index 52c5860d..583f7e86 100644
--- a/src/gsim/palace/driven.py
+++ b/src/gsim/palace/driven.py
@@ -46,7 +46,7 @@ class DrivenSim(PalaceSimMixin, BaseModel):
         >>> sim.add_cpw_port("P3", "P4", layer="topmetal2", length=5.0)
         >>> sim.set_driven(fmin=1e9, fmax=100e9, num_points=40)
         >>> sim.mesh("./sim", preset="default")
-        >>> results = sim.simulate()
+        >>> results = sim.run()
 
     Attributes:
         geometry: Wrapped gdsfactory Component (from common)
@@ -663,7 +663,7 @@ def write_config(self) -> Path:
     # Simulation
     # -------------------------------------------------------------------------
 
-    def simulate(
+    def run(
         self,
         *,
         verbose: bool = True,
@@ -672,6 +672,10 @@ def simulate(
 
         Requires mesh() and write_config() to be called first.
 
+        Config files are uploaded, then moved into a structured
+        ``sim-data-{job_name}/input/`` directory. Results are downloaded
+        to ``sim-data-{job_name}/output/``.
+
         Args:
             verbose: Print progress messages
 
@@ -684,7 +688,7 @@ def simulate(
             RuntimeError: If simulation fails
 
         Example:
-            >>> results = sim.simulate()
+            >>> results = sim.run()
             >>> print(f"S-params saved to: {results['port-S.csv']}")
         """
         from gsim.gcloud import run_simulation
@@ -692,15 +696,14 @@ def simulate(
         if self._output_dir is None:
             raise ValueError("Output directory not set. Call set_output_dir() first.")
 
-        output_dir = self._output_dir
-
-        return run_simulation(
-            output_dir,
+        result = run_simulation(
+            config_dir=self._output_dir,
             job_type="palace",
             verbose=verbose,
         )
+        return result.files
 
-    def simulate_local(
+    def run_local(
         self,
         *,
         verbose: bool = True,
diff --git a/src/gsim/palace/eigenmode.py b/src/gsim/palace/eigenmode.py
index aeb123fa..e75394dd 100644
--- a/src/gsim/palace/eigenmode.py
+++ b/src/gsim/palace/eigenmode.py
@@ -45,7 +45,7 @@ class EigenmodeSim(PalaceSimMixin, BaseModel):
         >>> sim.set_eigenmode(num_modes=10, target=50e9)
         >>> sim.set_output_dir("./sim")
         >>> sim.mesh(preset="default")
-        >>> results = sim.simulate()
+        >>> results = sim.run()
 
     Attributes:
         geometry: Wrapped gdsfactory Component (from common)
@@ -530,7 +530,7 @@ def mesh(
     # Simulation
     # -------------------------------------------------------------------------
 
-    def simulate(
+    def run(
         self,
         output_dir: str | Path | None = None,
         *,
diff --git a/src/gsim/palace/electrostatic.py b/src/gsim/palace/electrostatic.py
index 4e4f8ab0..0f1d0626 100644
--- a/src/gsim/palace/electrostatic.py
+++ b/src/gsim/palace/electrostatic.py
@@ -46,7 +46,7 @@ class ElectrostaticSim(PalaceSimMixin, BaseModel):
         >>> sim.set_electrostatic()
         >>> sim.set_output_dir("./sim")
         >>> sim.mesh(preset="default")
-        >>> results = sim.simulate()
+        >>> results = sim.run()
 
     Attributes:
         geometry: Wrapped gdsfactory Component (from common)
@@ -371,7 +371,7 @@ def mesh(
     # Simulation
     # -------------------------------------------------------------------------
 
-    def simulate(
+    def run(
         self,
         output_dir: str | Path | None = None,
         *,
diff --git a/tests/meep/test_meep_models.py b/tests/meep/test_meep_models.py
index 09bae0a7..61a5c8a6 100644
--- a/tests/meep/test_meep_models.py
+++ b/tests/meep/test_meep_models.py
@@ -140,6 +140,8 @@ def test_to_json(self, tmp_path):
                 margin_z_below=0.5,
                 port_margin=0.5,
                 extend_ports=0.0,
+                source_port_offset=0.1,
+                distance_source_to_monitors=0.2,
             ),
             accuracy=AccuracyConfig(
                 eps_averaging=False,
@@ -567,6 +569,8 @@ def test_custom(self):
             margin_z_below=0.4,
             port_margin=0.5,
             extend_ports=0.0,
+            source_port_offset=0.1,
+            distance_source_to_monitors=0.2,
         )
         assert cfg.dpml == 0.5
         assert cfg.margin_xy == 0.2
@@ -581,6 +585,8 @@ def test_extend_ports_custom(self):
             margin_z_below=0.5,
             port_margin=0.5,
             extend_ports=2.5,
+            source_port_offset=0.1,
+            distance_source_to_monitors=0.2,
         )
         assert cfg.extend_ports == 2.5
 
@@ -592,6 +598,8 @@ def test_extend_ports_serialization(self):
             margin_z_below=0.5,
             port_margin=0.5,
             extend_ports=3.0,
+            source_port_offset=0.1,
+            distance_source_to_monitors=0.2,
         )
         d = cfg.model_dump()
         assert d["extend_ports"] == 3.0
@@ -604,6 +612,8 @@ def test_model_dump(self):
             margin_z_below=1.5,
             port_margin=0.5,
             extend_ports=0.0,
+            source_port_offset=0.1,
+            distance_source_to_monitors=0.2,
         )
         d = cfg.model_dump()
         assert d["dpml"] == 2.0
@@ -644,6 +654,8 @@ def _sim_kwargs(self):
                 margin_z_below=0.5,
                 port_margin=0.5,
                 extend_ports=0.0,
+                source_port_offset=0.1,
+                distance_source_to_monitors=0.2,
             ),
             accuracy=AccuracyConfig(
                 eps_averaging=False,
@@ -717,6 +729,8 @@ def test_domain_in_sim_config_json(self, tmp_path):
                 margin_z_below=0.5,
                 port_margin=0.5,
                 extend_ports=0.0,
+                source_port_offset=0.1,
+                distance_source_to_monitors=0.2,
             ),
             accuracy=AccuracyConfig(
                 eps_averaging=False,
@@ -1096,6 +1110,8 @@ def test_build_sim_overlay(self):
             margin_z_below=0.0,
             port_margin=0.5,
             extend_ports=0.0,
+            source_port_offset=0.1,
+            distance_source_to_monitors=0.2,
         )
 
         port_data = [
@@ -1161,6 +1177,8 @@ def test_build_sim_overlay_with_dielectrics(self):
             margin_z_below=0.5,
             port_margin=0.5,
             extend_ports=0.0,
+            source_port_offset=0.1,
+            distance_source_to_monitors=0.2,
         )
         dielectrics = [
             {"name": "substrate", "material": "silicon", "zmin": -1.0, "zmax": 0.0},
diff --git a/tests/meep/test_simulation.py b/tests/meep/test_simulation.py
index f62cb6aa..58741a95 100644
--- a/tests/meep/test_simulation.py
+++ b/tests/meep/test_simulation.py
@@ -44,14 +44,14 @@ def test_defaults(self):
         s = ModeSource()
         assert s.port is None
         assert s.wavelength == 1.55
-        assert s.bandwidth == 0.1
+        assert s.wavelength_span == 0.1
         assert s.num_freqs == 11
 
     def test_custom(self):
-        s = ModeSource(port="o1", wavelength=1.31, bandwidth=0.05, num_freqs=21)
+        s = ModeSource(port="o1", wavelength=1.31, wavelength_span=0.05, num_freqs=21)
         assert s.port == "o1"
         assert s.wavelength == 1.31
-        assert s.bandwidth == 0.05
+        assert s.wavelength_span == 0.05
         assert s.num_freqs == 21
 
 
@@ -64,6 +64,8 @@ def test_defaults(self):
         assert d.margin_z_below == 0.5
         assert d.port_margin == 0.5
         assert d.extend_ports == 0.0
+        assert d.source_port_offset == 0.1
+        assert d.distance_source_to_monitors == 0.2
         assert d.symmetries == []
 
     def test_custom(self):
@@ -284,11 +286,11 @@ def test_geometry_component(self):
 class TestCallableAPI:
     def test_source_callable(self):
         sim = Simulation()
-        result = sim.source(port="o1", wavelength=1.31, bandwidth=0.05)
+        result = sim.source(port="o1", wavelength=1.31, wavelength_span=0.05)
         assert result is sim.source
         assert sim.source.port == "o1"
         assert sim.source.wavelength == 1.31
-        assert sim.source.bandwidth == 0.05
+        assert sim.source.wavelength_span == 0.05
 
     def test_domain_callable(self):
         sim = Simulation()
@@ -382,7 +384,7 @@ def test_from_source_defaults(self):
 
     def test_from_source_custom(self):
         sim = Simulation()
-        sim.source = ModeSource(wavelength=1.31, bandwidth=0.05, num_freqs=21)
+        sim.source = ModeSource(wavelength=1.31, wavelength_span=0.05, num_freqs=21)
         wl = sim._wavelength_config()
         assert wl.wavelength == 1.31
         assert wl.bandwidth == 0.05
@@ -443,6 +445,8 @@ def test_domain_translation(self):
         assert cfg.margin_xy == 0.3
         assert cfg.margin_z_above == 1.0
         assert cfg.margin_z_below == 0.8
+        assert cfg.source_port_offset == 0.1
+        assert cfg.distance_source_to_monitors == 0.2
 
     def test_resolution_translation(self):
         sim = Simulation()
@@ -460,7 +464,7 @@ def test_accuracy_translation(self):
 
     def test_source_translation(self):
         sim = Simulation()
-        sim.source = ModeSource(port="o1", bandwidth=0.05)
+        sim.source = ModeSource(port="o1")
         cfg = sim._source_config()
         assert cfg.port == "o1"
         assert cfg.bandwidth is None  # always auto fwidth

From a173d36df33b3db388e9e65c1f60308845fcc47d Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Wed, 18 Feb 2026 12:26:36 +0100
Subject: [PATCH 09/10] ci: parallelize notebook execution in docs workflow

Use a matrix strategy to run each notebook on its own runner
simultaneously instead of sequentially. A list-notebooks job
discovers all .ipynb files, then run-notebook fans out in parallel
with fail-fast: false. The build job collects all artifacts via
merge-multiple before building docs.
---
 .github/workflows/docs.yml | 45 ++++++++++++++++++++++++++++----------
 1 file changed, 33 insertions(+), 12 deletions(-)

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 0b6e0710..abf338d8 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -12,8 +12,30 @@ env:
   GFP_API_KEY: ${{ secrets.GFP_API_KEY }}
 
 jobs:
-  run-notebooks:
+  # Discover all notebooks and output as JSON array for the matrix
+  list-notebooks:
     runs-on: ubuntu-latest
+    outputs:
+      notebooks: ${{ steps.find.outputs.notebooks }}
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ github.event.pull_request.head.sha || github.ref }}
+      - name: Find notebooks
+        id: find
+        run: |
+          nbs=$(find nbs -name "*.ipynb" -not -path "*/.ipynb_checkpoints/*" | sort | jq -Rsc 'split("\n") | map(select(. != ""))')
+          echo "notebooks=$nbs" >> "$GITHUB_OUTPUT"
+
+  # Run each notebook in parallel
+  run-notebook:
+    runs-on: ubuntu-latest
+    needs: list-notebooks
+    strategy:
+      matrix:
+        notebook: ${{ fromJson(needs.list-notebooks.outputs.notebooks) }}
+      fail-fast: false
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
@@ -29,24 +51,22 @@ jobs:
         run: just dev
       - name: Create ipykernel
         run: uv run --no-sync python -m ipykernel install --user --name gsim
-      - name: Run notebooks
+      - name: Run notebook
         run: |
-          for nb in $(find nbs -name "*.ipynb" -not -path "*/.ipynb_checkpoints/*" | sort); do
-            dir=$(dirname "$nb")
-            filename=$(basename "$nb")
-            (cd "$dir" && xvfb-run -a uv run --no-sync papermill "$filename" "$filename" -k gsim)
-          done
-      - name: Upload executed notebooks
+          dir=$(dirname "${{ matrix.notebook }}")
+          filename=$(basename "${{ matrix.notebook }}")
+          cd "$dir" && xvfb-run -a uv run --no-sync papermill "$filename" "$filename" -k gsim
+      - name: Upload executed notebook
         uses: actions/upload-artifact@v4
         with:
-          name: nbs
-          path: nbs
+          name: nb-${{ hashFiles(matrix.notebook) }}
+          path: ${{ matrix.notebook }}
           retention-days: 1
 
   build:
     name: Build Docs
     runs-on: ubuntu-latest
-    needs: run-notebooks
+    needs: run-notebook
     steps:
       - name: Checkout repo
         uses: actions/checkout@v4
@@ -68,7 +88,8 @@ jobs:
       - name: Download all notebook artifacts
         uses: actions/download-artifact@v4
         with:
-          name: nbs
+          pattern: nb-*
+          merge-multiple: true
           path: nbs
       - name: Build docs
         run: |

From 1405de90dee4f0a3bbc46a0f67442e597c0c4a84 Mon Sep 17 00:00:00 2001
From: vahid ansari <vvahidd@gmail.com>
Date: Wed, 18 Feb 2026 12:38:35 +0100
Subject: [PATCH 10/10] uodate notebook

---
 nbs/meep_ring_coupler.ipynb | 116 +++++-------------------------------
 1 file changed, 14 insertions(+), 102 deletions(-)

diff --git a/nbs/meep_ring_coupler.ipynb b/nbs/meep_ring_coupler.ipynb
index c40da89c..42b0c632 100644
--- a/nbs/meep_ring_coupler.ipynb
+++ b/nbs/meep_ring_coupler.ipynb
@@ -25,42 +25,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2026-02-18 06:12:13.722\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m25\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.728\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m48\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.729\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m110\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.730\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m149\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.731\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m168\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.731\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m210\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.732\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m244\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.732\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m269\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n",
-      "\u001b[32m2026-02-18 06:12:13.733\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mdoroutes.pcells\u001b[0m:\u001b[36m<module>\u001b[0m:\u001b[36m294\u001b[0m - \u001b[33m\u001b[1mCannot determine output type ((D)KCell type)from annotation <class 'kfactory.kcell.DKCell'>. Trying to continue but likely this will fail.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from ubcpdk import PDK, cells\n",
     "\n",
     "PDK.activate()\n",
     "\n",
-    "c = cells.coupler_ring(gap=0.2)\n",
+    "c = cells.coupler_ring(gap=0.15)\n",
     "\n",
     "c"
    ]
@@ -78,17 +52,7 @@
    "execution_count": null,
    "id": "4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Stack validation: PASSED\n",
-      "Warnings:\n",
-      "  - No stack configured. Will use active PDK with defaults.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from gsim import meep\n",
     "\n",
@@ -99,7 +63,7 @@
     "sim.source(port=\"o1\", wavelength=1.55, wavelength_span=0.01, num_freqs=11)\n",
     "sim.monitors = [\"o1\", \"o2\", \"o3\", \"o4\"]\n",
     "sim.domain(pml=1.0, margin=0.5)\n",
-    "sim.solver(resolution=25, save_animation=True, verbose_interval=5.0)\n",
+    "sim.solver(resolution=20, save_animation=True, verbose_interval=5.0)\n",
     "sim.solver.stop_after_sources(time=100)\n",
     "\n",
     "print(sim.validate_config())"
@@ -107,21 +71,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sim.plot_2d(slices=\"xyz\")"
    ]
@@ -136,22 +89,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Uploading simulation... done\n",
-      "Job started: meep-57e3d203\n",
-      "Created: 05:12:16 | Now: 05:31:54 | Status: completed\n",
-      "Extracting results.tar.gz...\n",
-      "Downloaded 362 files to /Users/vahid/doplaydo/gsim/nbs/sim-data-meep-57e3d203/results\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Run on GDSFactory+ cloud\n",
     "result = sim.run()"
@@ -159,48 +100,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "8",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "result.plot(db=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<video controls  >\n",
-       " <source 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QEDkg5q0ofjGLSQRVL1FEReSkJJ8+p+7SnZE7UCzyo3F5sAvl2kkBv7nJ2ke9b2Wl0yNW/j7L2uVZG//nBT7mN0ZhCqlxkcA5qbWQ4+GB5TxufY+ZB8GTHGXNdQ5RQ5Yte328z0lbQ9ciPkwp1rL+dEHSweEovdwwJORvVkyZe+v4yGnSD0Qtn59DGoeywkMFqEe0ZXdHh5sB0LOsu6Nvh6nxf5Anc2qpG4cu86xELxQ6MnHjTf9LUmOt/USjQnlivRqwml5OUY0Bl1VvwAAFNIOPzcUNbSS1316rYJIBPxk2YS9amCMwB13dJdR/y/aFZSBCPN8fBIfMBQNniw+WJ80hyhiwB2AU9EywbAxG2gNF23iuOuAh3FxB4NVw5C1zquKajffmeYm6yVVptZM3IcWkvhSBncYUhLwteTvEFjN/UAOfriqeRXvXL3MEl1iaAwVYff32DU0svUzAz98i5oaHkgI9a3CwmZ3ejKqJVQMHWA3CS3sGaL1+MuvSTEoFO74I3gXrtBcvuqquU4uykqpcr223fCV5u4s8Wo10D83PcPTrFDArNJ9P98S3peS9khsQa+E5ulxP9mJMChrQ4Dr/KfHb9ALhEXoTB8PwGhVSdA3eDHgSeyxlq1/ZMK9vwhwQcWWMbgjkeWDtI/CwfGFJkUqw4h8YbPA4fZmVSnC68oipeXZUEoqH/Yy3zR4ZwpiToD1p9yvzekNmN0KhllahENiDr0Uq4E6qCpnkX8/BuNrsDOdVAq7+GHZ3lQIDvs58H/Z1FjnaYxQ9G3qwb3CN5R4ervENi7MTa1ib2hk/XvEr3KU2OusGSUrNoKUi48FSqRKNlVEmAXmFxahqM1Lyjk0Nrj51Uaq8AoTMJkjNBpDfVIRjKdsmhSVl4aBR/V1luMiamIWUudIQaUrKeAceXTHcOmCNG5nV6N+agKj9BR5yffdFYZVX71cZ4Of2PGdO0cs8AGm/p+mWs3Gfj658PFOQadaBBKo7K9Rzqr5oT9+PL/MBZigAAA+SerOkhiyPCybj7WvyzbSeXX6fBiqnmIdoqZt2dnXyBrZROFWlROsYwU4pp7HQGMWQClM6LZKe3z3SJ4wXEX12/y8MJp+PFnYGbvGj7URRJqZCf0JxkMfZEngrR1i3bqPZ4PzWyPotOPa0Ei9RAVTssXBScuvbrc53Bg1cS6ob2efkL4NvSqNhtVyRG47KtNfaQOciIoKUsRexv7RIsV8/zz85BBP/nXdjHwWTEAE9b1QfaGNdq3wwomRcNl5gWl4tHvW1E6yqAMHDGKfxbDrF2DVocjb9ovgl7AzqCsP0EQrCstTpLftlsULClk3N0g4QFAP2eUPu+4vh8qpVDJfMe8zLk0GjMk6KjSlpzuZXTxzAXuGGurYSHuT2N8NfOj5cM96tJdFzjGhgslOQH5bP/r9lxOdsTUafPPxYA3y1f8yte4fKtWvpBPd155wV3Sf9cb3YoFU+Jf24gbwH30uRiB/28PR/jf8KWjAALlGh94ghLXF/3j+K00dDoG9v5hZLisbyp/+q9mBcAK1Z6aN+XvZdY5QBB79AZVYwWlep7rh5bEotp1JHtEsHXMgUtcsrRhC0iW4+5+kog809+jw9J5Ycj3d2ynJWJFbzPudVIbnxInXA22MtuupuOZJ2wA++OzCod6IX6isAjLSOBc1e+gx9D7cbf9nCnJmf/fVtDcVTSR441MAl1lOIz75K2n9DZoUg7GS/X/HE+UawTpf8p/zPeGKttgPX6lP6i/V7WtiO8O7p/BWTQZpSwom1DsnL4BAKlHhNXLxaSp5tqK9N5CeNTQ066r2B4z7eyb5S5gnzeYrdGGvRVJ7q3naWdPqj6/fb0CgLrn6IR9pju+VdCI53X3ThjIXwRLOBjQIw5QuUaYDC3g4GnOMrozh8Z9W5SGIa/H77tOHhV5yIyIUV/Swio+oXEgbaPq10+nyG9+SnL+agxkQlV/q8KgijSiQeZvUN2hp12eTaokGGLPBjzgXHuRoIYBdWL+4yGsFEyitzUPoTjhRad5VQsRZOTOOeAm0lFJuFMjb0+syaVOM/rNeXqHhPK+IR6aTvNgzor8nHMuvni9Pg8hRaKdZrO9u52nXRGHbQhGbJ4qSPvZmUpyc3vGUiHDrcctw41oKG20jyv+U0OkRExBjh6p3xRE4EGapo7IjSPyYN+Xi3mC3AKisDL2wAAoRu+dk0BTiIvvcsKvLsdyzg8WDkLLHrT8txBQ1+72cfkzlyoF06dNJJUfwCqNAEmAONCijC/fY1jxxbrB+TJAbXpuNvob/UHskbMRA2ujHRvEWxbUUlY7fl20wJ+0uCEIxQRgMFm5uhUyBAxvKaZ719q/KUTtDT0Q6vYxD66hEf1eZOSQSdJb1+YwNYxCVrqi/3c3emeljeLgD8B51ZKsLikEZFuL4vLWMycICS3bqx4aZV9HLt7qyyJ0VsRBurHqazu5oI9ghAsjGZpgPVpVDh13rUMXpB7rU075r2+G5m+8uQoOF9FH/itMS3ABcJ/GjCd3ayTZCpHADrEN+W3G3aeGSndgESRi5rWrnRa3FoS6dxC6MV6vQ8HIA/4qMPuq+Ro5tUjhzPtZTglURhmbh3xXSn9oK5G9LQWx3+XT1f5GEDyQtRwBf0eD0K8h7lWnEBY35l+1MGpoewSeZJCfkm/JcDhA/0sa9kmvMCA390qP3zDK2nqwePpgQM2RawaXc4H5Cdc9Rx7N9N4t+7FEapqKP0ixJ1Rgfw5zbSUvtkgxUYDksPBjHFkl2brd1nsAEg176HpvnpbYLHKT3JkLzf/0MTENa6gPhrTLku3rUvb4BeTb3DtsEXiZOWB603Wukw2C+kxZxCAF482qAJwpLlUQrSsW9XDthMmQFEymKfBeBESgePjZv+cxcdmUtCbc8BgzUP4HeDOFY5Jq1ovo4xHtv5cxPRvE6sqvepcQ8PXz9CrHocS3RP+oxLcZdqCdawc0QQ5agYoFkNx+Qmsou8CVm3DpleCSXukZ/548lL5PZf9KgMjHXQCbMLoI3NwJAmUd3Vo+Ccum44XQdWhpKtSU7qerQhVeOCU8McwFy1EG8F3z9LPujZqOVJbj3SFB+uzvMlw2atiWh+DwQjPzIb3wfHUtf/9OmUY+Q0MD5zz8H1aQmnr4+ao7UHzirip6+Q3McGA8How4noWXc4UQdfO2xqnyxEx+mL3ycMBf2MnKj94wYb9z/Fkjwo8AAT/w9S9ATvMAzG5cke/1ZUXFhtnLFhV/MZ+Q2DHXMhuSV80TITU2aqH0sCvWrXNYWwyHHOW21Okn8xgU5iGV3GqqHomJbRzrGIHhV4uSzAmmDDXADZW7zcY4f9HXpuJ48HTuoUUfz9QvbsLllf3hzaevLDrXXf/MlQpyTRT3jo4YhbepEJtHOJsavAV7bgrXaFBS67JLQjEf//PVy9qjCDnsOFGp5dn/4JRY7qbtSKDDLn0go0vU2iseB4P6u2hkMiRdeQ53N7NVdzwZlrwnwPrqJHAr7zhQviJCZtwEOJVpqRyCyTc5sjOIfnqPPq2k+JAVhhqonYlwrfSwGcm/eiqTi9BG00w8oAqXuir/3KQrjwer245/J/Qe4VTobWh6VH1RxNOlqfC46VeJosvvCLn5oAq8K1YLJ+fC3DOB994mfrY1z2CAc77NI0+vDXUGdLTtlLjqXBSDbwA1UT/bQFrbBn3L9IoScii9M40Vg5aFRXRRWZY53K3dhGWIkfe4wbYa+jpeZ+00RFd0U0aJ2cS4zHlik6VmFystEqrKtfp38cVjCV5BD66LGckkNyYLrjKYbqRj8GpVP8BH1+QLz3+pAPHwOW5P8MLsRJe83ilCjlc+QdXpM6Z6chCqbRnoS8sb7mcGoO27Ctqmu0FtG9/EkHEpdo42E8sHtqYX0yMh2cAA+Kpium3hK5wNULqRoOPZOJKnZlQF0BGA4WpZqJrRuvhYLjRSnXvjCFb0tXQMmkibecFNJuZyhzS8SIDqh9BuGkg4b8hImqbje0UZ84c98qp9rwQyYY7ONQfVfW3tEU871NgQcDCzACR9iatfzYyLvbIM9ZfcbsX73DLjjoEjKX5x1+Bi5ohaS+9lAtUu5lf0CrKFqYTqrFK3O2M9FZT5Xxt1q9WHWkkpM3GfNKrKcECJ+Nvy1PxDHH6K3gAPZI0MxOFuv+6h61ENjK2VfEAdcL8uIMSdDcWuVaL8CXJ2f+TNtNY/WNd7a8udgaIfegHlybcW/U/nL2LwCZ0AkHmUZj//agmq65ZgN5fdTk4GQS46Aeu2fKgBgUoxFSuogrNR78mVLdjHdY6Znh6mnBdzNtY0k9n7YeldCWRO26n6WRsHgR0X6u9kRBZ6D8NtDxrbVPFp34sSp5RjVN0Bzlq/HEItLUBilfXUW4+gzdW1UPvwM48vvpjTn7ot+6onwJUhuNcnIf69GVQsRuuZsDOphtfnQewZhL2bzMKMzfHTifbE4wxcGKX0bTI64bWYpwyORDGLiNurPpTpmeA+Jh0iucrCQoMLt1thPXfRUSMxpg2clYoaCLG3qLS4GwKQhlK7j7kgEAIpwHJ9ItsRI+RaeWGiwQGewF+kB4YDGdHXBbuAJ1ZC7uZxOCqTCE9XygMp+0x8AvOpnlCWG7Wbj8oQVXjo3KNBunVoKdn7e5Tba1w0aLINQfg8z92odC/NxtJ7h/jEJrGSBq9eCH71w9rAngxDSZ60V6nQIsfFJglD+rjvMVntccAzgB+JK/GwF+xhE8RUEI/AsX18QqYC+lZ8e5/XXHowSJNzAcciuboPWl+G8ypvf7wDHT+oEmIhFKr2B3KvsJui/cHVqRHdJEuf7MMvCX5CUDfl/CElfoCV8x/r5SssXJ96Z49MZh3FR0qjXmaIEqnZ9j+6b9nB+0w0CP9Mtwc05QeLB5YClz+Qw7cvSMIEv+pcFpamuJFjdH5VU4Ae9rvoQpXvis9pPNhVoyPH8OPLkZ7KF1NVUw89tEHzhAqkfrVAs+pN48z0ZXMlGCGGYCJEezhqw++N73xFDJxWzIf7LgpGqWoJQPJyoWXlfo0noDU7VOwKnG0BfADNn11e08A8/nNXuUEmkxjaaeG8vLXmhGErMsMHoSq7myjOGa3VoAEP+wxcYaUC1ITMOCtRETNSJ3G3y3f4vXtrkpsLZZwqmev+FR5kEptLUddxUNWgRn9PKNCsuN/1nR9DRabvA98o63M/YRXEVi1vp8MVV57rkd6EJhpmZwSQ7ga0ZgYJFGOMNTze299BHDUWTmeBTQZnBaiR1B0QsflHZTjEJhSePWKKmc8uOIA4Lum3epDJLlmbeptrbpVVyJEtIpPx2t/G3G/YwcObWdmoB67HQGrzVP/MzxGuM8q6Uakg0nKx2NnQaOna1U11sTOKd2sp+5VdyoiJ4YMYIsD5kWKn1CdCSkhd/mNA5OuPI66Wz6UntTA2h821IQ+teHRFs/8UlOaCEBtXh1yXobINAx4fXiHPj9iK7HQ2N1IoQO7WnRG43bukt+fvZmJL5NLF2gNz1xuVLtw4xTvEGLMmcq07sD8ySpHo8cfFDOodcIsOuJRWGpXKx2+IGzwlKYywCSETqy8Z4cilGoigrws3+h/RtEvx6FgbFSRm1G1EXUH6uXqiKoGD+SO7jEzCVHd24pbRTdr3VB0nkIVDpGk40pLbD95VycJcv+jVp3CtCinFTDkpMVJVGzi4q0icfDLIq69af+ATqVIEhuADr4URqx4GQMg+oCMYDBjeA6kLKxHwFDkn2yiPWpXHq16W6wgTDVYRBs+ZGGVZm5b0XsuQmdsUflBWpz/c0xCssCYzXKIvKm8iggwWCzQuV8gM/lArxNE6a0J9l3Ys9ScarDVzyjEEryHStnptfpDb6rgsCjQ9pTv5u3Kk7h3qqkWRE0TSzFN1SUiy+qzy7BjP4Gc4K7g39JSyWO3ksQ42RKjgZFvOs9zgr3uilVeriHe0kYaSnnwMGq5lrPnGK3WZkE2Yb2QybxA1e+4ZGKiNygXGV1nfGA//XxFL+4XcLYVeDdoHrWLp5gSe7QBQFTUEYwk2wkVBkOqJO2IknlyRlFk5zXyP/2o014fsUK0HZr11RlxsdJWP08g8OrFiqBCJuRS85f1gXQkLR5WtbSAJGAF5DtrtE/EmLcDS0B6ZSQj86+1nBD+jA68XysRCwUkd8wGpSMe0rC6/Ge3Qmk7HX68rzevCdu44QDOI2pbiva/3INrTQO7GpHZaWYGhHZbpY+hIX9iacJAfu/ABnxEqIEXuxBFtZDHqdJPMO9effK00kDYUsLZPXKf4LNbGcHmCNcOkriHkxSx0emSymwqs2PYpR35/u7IbjROIgU+gN6ejM+Xc5o6ko87HrePgpmMh+6yQvmSzl3iX2L6sKQ+EZS42rP0Eg5vGvro8lJoaOlY1wb4eHmy4QAAFbpd3n1q2vWduV/EEgorx9J0C0e0WdwFrfMiWja8eotAEKbhNAqqCVJ8umZCgwYpiYl3vi/So5MNRsb8O5LtNcbH0u17to7phkAimrGHw8EoRiyVLML/XII2vGCkMxHYca2tpSOh8PwnRcqP36UuVlzPtzr97ded1tGNCP31cvznFHeNIPVN0raXQpye9sr075Cu5Xm6Q50k4iod31cLtRuy6x+XDIe3KTbbzV+IQiC939cgt6saF2HiyvEZ9ost3cSS54bEXzxLfFfsw4gPbb0F60pa0+n0bUQaHvIas0GEvr4/t9LNBgAAADAAADAAADAAADAAADAAADAAADAAADASEAAAFTQZokbEEP/qpVAAADAkCXYIAATt8xOodfFQRY5AKbQRGFF/Vu03LsM09l3zVACteZ5N0PFByiSe3LbwI3Q2TjI3tef1TWzGpeAJLB626O2ddAfV5VjNuFShf6RolrHNNqF/Ku7v8glxY248n1UB3FVWrX8qNWW3TZ6+nfzyWP53hIsC0BFXnT1ijHu2iQ4f7R3BXL4EUc4gZXOTRLxKHTOW4NT4tcaYhJdZOiwPxYPaJLjUIlXFI80O/Jtv5A8B09LkHsvBHLO2NcO5yHar+4ku5tEDVId/ZV4gFEAvQhfr6EWpssf2BPHhjPb6jm4ZLD6SvLpCF84n07+iNyss2zYustbZnFCQFdbfExBxZClGAYLUmUE7OK45WqYSjZpldQc2jE0N8xR7eHD9S/1+gS6jB9LKJvAM5zHrLXuz3wy3/Z6YEIyoc7r0O9mOf+hE6WkAQ8AAAAjkGeQniG/wAABkfSnZADdRs8bz/PMMPMEoFy5j6bzBTAtvU0dXk0SXUHjfqNoOcUw3VBiD6jpZzC8fJ7m14B7nZcurvFoG/4NzzF91MKsWlEQcC36cQ9z0r4+64N84ycQvkzeGawGoxedZ3ONUiGauegnEvjexMnjmPxmzq5tLKlAUjY3XcouwopMAAAkYEAAACoAZ5hdEL/AAAKgj+Q/1chQAmo5v06yEYDVKj83mug+/ukoQZHxp7vOZ5BWFPOWgU/hkuWoVhwzVrnqIDtCG8cPcD//TaP3L5jJ1EBYGnEjS6IR+hHeZyzuzUOQF4MyveddmKZxjmfxRDY23cbSkLTkz96/ndVG92XBt2hImgMvv4QC3Wm7KxyUXLLGvVYldBrY2xLQsYoN96vl4JqI9xF+9JgRIjAAAS8AAAAmAGeY2pC/wAACoMGktlchQAmo5v06yEYDVKj83mug+/ukoQZHxp7vOZ5BWFPOWeat6AVHCVURHQHzTSTeoIEhvHD3A//02j9y+YydRAWBpxI0uiEoiR3lpkbNQigX0yUhX46VOEpGc/MucgRiMHjdSnJVb1/O69ZBeNWcCYiafjY8T+gACAU5OI2n8N5/b62lxQlL4jQAF3BAAAAp0GaaEmoQWiZTAgh//6qVQAAAwJD1b2U6IWqMwAhNXv4LqIaJaA7Cc/wHLxpa8NGHgbPcP7ALXll9GCyy3dr3fiiPcN/9awDepi0ogOo553GXxSylNdhH1k9ZpOQ6vW23oC6YCtuKR+W4JoUhG0KHRq0C+OeCcycmTFMofNRDL4+sUqnBpKGwOlz9H0JG2aXpA4La6pCQAO9YW3DKkPm8Oy6hYY8AByxAAAAkUGehkURLDf/AAAGS+5ADj3JdRmgH+ddQdh6h2aczz/70hGLgXBTylHN9kLbEdX2YggGIzzlrirSfFhYWy8ukxyoLMgF8UIEiHzymZTu4zRIpZDPg6VfTvM/lig8A91FL9IG2//AWbzo7KLX2JeZuCkc2558kvNYAEgKVqXbRY+gd0DMM+O+Rs1sIQAOaugADAkAAACVAZ6ldEL/AAAKgj+Q/1chQAmo5v06yEYDVKj83mug+/ukoQZHxp7vOZ5BWFPOWgU/hkuWoVhwzVrnqIDtCG8cPcD//TaP3L5jJ1EBYGnEjS6ISiJHeWmRs1CKBfTJSFfjpU4SkZz8y5yBGIweN1KclVvX87r1kF41ZwJiJp+NjxP6AAH/3TukF+c5TVcyIIn+qMAARcEAAACYAZ6nakL/AAAKZScYxNrKaAE1HN+nWQjAapUfm810H390lCDI+NPd5zPFZjM6esgV/JiLlqFYcM1GCTeoIEhvHD3A//02j9y+YydRAWBpxI0uiEXx73mOS4wzzYQ7t/uz7ER63xLaAgeN/k3xpKHnM7x95PHgf8i/+qkp8RMIPdU+GAABYpPWENenj+d3SuWkIofWryAATcAAAADDQZqsSahBbJlMCCH//qpVAAADAkFABJ+8ABf42rsodRrpfKFJlsQT8qOK+Hj2G/O3cdEtju81WX+D7ycXpzrXPHO/urmBgnuLUrtDJkYsPRrY8N4MzbpBtb1Zgrt3SXi99smdGlVvzL6sW/cko7fLc+8p8h4JG1OwM+0uSGsrwrbQyzRrQPvdu2sXoJ2hgprMeq+wyCda6hCgAa8fl1AGFRNasUwDra+4VnLPqUFivWm+uPKYTyogKMy5AABZBUASAAE7AAAAdEGeykUVLDf/AAAGS+5ACWrkuEzNHRsocR9XV9jUzHH1eCuhSEzfV8sSZJt3CKbZqD/E5ZooQtIbwOgHtYbNJtTmiXxk9r0MXm1LGyO4WAovcAsRWURm1yQg2juOLOAYAEgbgyXYkmAb26BaC68FVG4AAPyBAAAAlAGe6XRC/wAACoI/kP9XIUAJqOb9OshGA1So/N5roPv7pKEGR8ae7zmeQVhTzloFP4ZLlqFYcM1a56iA7QhvHD3A//02j9y+YydRAWBpxI0uiEoiR3lpkbNQigX0yUhX46VOEpGc/MucgRiMHjdSnJVb1/O69ZBeNWcCYiafjY8T+gAB/91bF/yq72teJ1Y1YYAAoYAAAACTAZ7rakL/AAAKgwaQhEEgcQATsh1oZbr6j43g/PkUGkSMPggcY4pyPQJ+Z05Qf8pPkRLUKw4Zq40LJnFXktR6cu//S2cZbuNgBVDLQmI3Q3CpdJkDjNUrCh+5JNWShlPFOXmuadp1XuVjRpYGmD/tR6GdZIar180yTivfmD2m4/bYAAT6bfA8+e7HyNn9bWGAAKqAAAAArUGa8EmoQWyZTAgh//6qVQAAAwJAcUsGAEZXmFzgJCLldajvnjzrWOkCIrE0PReiX1dwgatRDHjfj+pQ+Xd+/z7Yec1mJJ4sQ18kSHf/HLPSdZEhMMSwGfw+O+jwge0GLpX6S8PaOhjnwWDJGZTjfKiACYeCnBJ7smG9LZepYm6gJ+7//mj56U+bYzXn/8qAD/sjTQUABIISDnjE7J4mo2RCy4YAAD4JFREAANWBAAAAmEGfDkUVLDf/AAAGSUIkGQA3ZCK3J2/Pzzfjm0I+CWKeL1wQ7CKDfbtTEfLKqcH7j0yYsR3U3L7iIeSFQ/0kpZx5zf/5bH2LIWi0U9IFE5H4cfnkeGa+o4XESCjl0gIG5p+KTY38GTQz7xvElUKqGzbi9HPF7V3UREgL7ncmzQmHpcCNDagAYhgxTnyboPl//zVLVeKYAAM/AAAAkAGfLXRC/wAACoI/kP9XIUAJqOb9OshGA1So/N5roPv7pKEGR8ae7zmeQVhTzloFP4ZLlqFYcM1a56iA7QhvHD3A//02j9y+YydRAWBpxI0uiEvQJ3mc5GzUEwF8MjZV+XhitSVnP4VIN2q1LSkLTky96/ndVG9qXBt2hInG42PE/oAAf/dIfyETPno44CAHVQAAAI8Bny9qQv8AAAqDBpLZXIUAJqOb9OshGA1So/N5roPv7pKEGR8ae7zmeQVhTzloGsY0SqiI6A+a0P0MNAkN44e4H/+m0fuXzGTqICwNOJGl0Qi6lyJtJr+aADdx3cAiIO8MVvnl0Bw8XcDCZ3HLbbqv060h5LEWf9V8wvPC9uXZBIAAIx5eIHrU8maLbYAWUAAAAgRBmzNJqEFsmUwIIf/+qlUAAAMCk8nvGGXtl/QxDuD9f4eqrsAHSvFgMQfmKlG5GaotqtbjOOYXBHGViA4YDP4sQaBlepM0DW9c8WxYttS8ZwOy5dwigKPSl8L7ieFnUbuOrJb/zzc5v4gTD/ANkyDLGqSXzntWfV9DAiD7HNVKKvO14CiVMsokvqkQc5Zu+o+AV/1t7W0PH3ltwFeryrR8y0g4OsUb0YjaLr4gwuKJuGIbsXciqyRgjYagrxCjXdBZ/QZw7goMhxv4rIFF/noZJc72pSSuxu1uCJyHfr+RIR/Cs1B3e8dIjrsplHxK4dHF2mTJQ4bzI+8+fpnMc3I2pYOWNQglCOi4nC0n5PVJfl06dOe6hXV9E6Jrf7nYLcYFXNenKaM4nsnPfcaMbHe/7e51h67hmrCosswuOrLo5Vftwpk7cSHVlT4UM4J8ZWkVHxioGbHSnPQ/3OBNXe768WACbur3KTwoIgLkaDZf6TrsPKHLdTALr93jd2h0T+jmrUQTk0WW8rOUgtL3lfKr5VzlS6qWZrArLKZmz+JygbDCz+Fg+dEdIv3fyKGlQxMCAzQwhMEIHX6KMZGE9FIrs9Pfiuk/p4klorLBrDBzxUq5CLerJ5O2MrF2Yg4xHMHtV6smN1JqUKbPo2NDHCZBNXLvAfZCfQm/juHbOOg6gAAAxYAAAACwQZ9RRRUsM/8AAAitxASHzBddTnPgwAtwiBwDphbFFJ0yEaqpiMp1RnrxHf2UDnw7wmg9emD1FFOYshKa4LjRXqSiJwndfd2hE4ojU+qJ0vNI5uGLmH9+YwtsmBcLSeKpCXQRdDH5rtVsrDIkUZ6fAUsJ0UH9o/ia67WObGmZOVRGSvfHHp4sXMP4GfKLYae78r0WQe8XRGAXGNT/jVIEBB6BK6Sh9E6CS32ykQAA2YEAAAAsAZ9yakL/AAAL8pkR/1nYgoRezzBR7ivCvIvjFgINvUJmM0kMMm+BpgAAu4AAAACsQZt3SahBbJlMCCH//qpVAAADAkCA+AAj4z6qOOdzGCe1xGzPr0qLEW4OsYhJbcNGlmoIs2y3ZaWavX4EPIsymH+/Fl0huXlSnu7GKvGMZhcD38R8k9a8bI1EkfPiDBAn10qQOxnUN6QrsyJL6PyRLHgjztPtTKnKsP9nS9wcI5MqZRB6ulKf8frV6Mrh3QFe4cmIt5P1JUznMkzWhc6MjL6dC5wGRm4i4AAtoAAAAGlBn5VFFSw3/wAABkgAijbQhIAP5GxXqevwxPqmcXnvXBeuOReBUMMbZJwHjbDJNSN6aUPUW2m7b35wto52ZRgOcvAoZT5TlNfdJ4TfTw1EJbX72+vibcOat6LRMcOzQ672P0LEUuAAGpEAAACXAZ+0dEL/AAAKgkFqXO5WQAlq0xWjcE+zPfhmFvxpsowimsh36pIVd2xP2ZrPMW5mZMI7Xp2s9TlfuDh6rgJBRBDPeHZzC3vScE1tKId217cGV+Xdep7KDCnC/mZzLLGQi4l1Z9XxSD3U9j/TIhKM1IW34UsINu+iI3QtOw7L7fQFucAMjEHeCX8Or6AdOOiUXfEcRAADPgAAAKUBn7ZqQv8AAAo88Ef5+ABdR2eq84J9me/DKpvjTZRhFNZDv1SQq7tifszWeYtzMyYRh+Op+W8wjZ/Ii4H8fugLduL9BfvKONYk/0uLLuDSuMIfXZwv0nJYe7ltce6g3tkEqc5LLMKCFyNCbVlS5vlwZhLhfDwF0jzNFuhGV3jtikhXzMTpjc1Oz4Xu9x5VWJ1xKDi+MUHjAFMR/vJOT1RO1agACJkAAACkQZu7SahBbJlMCCH//qpVAAADAjHOkTlSxAAE6sDAIpMI1IeFpozvzfWgy/EwrdoKv4UogL4iu09jyal4Po5fnQGv5pL/lXckk8UkyPntIaOz44I8OZYOiph2Mci9t8C7YgUtLOO7vmzRY6lgyrT+AFdS7shJiLlDjrJKmNlm47wF1DqSHjy/lXQWtYompzMcuWPH28L8WBIZ8IwAAAMAAAMA7oEAAACgQZ/ZRRUsN/8AAAYectTX4ACadvpLQ/9kUQzsoxhKmzCoavBd0SlYJ6JWob0Z3aUEI7IUUW4NNzwnaUwWVjIrGrq/PEYLVClWERBfJyDVX6u9xdTeCcoma4LKqjYc4llFboE613Qyty4vDa9dt8znL10hw/Tqhj4QoDoi67OuRLEnwCnSF7Q8afjzVaqZkOc9Ll4zhBNgZOa7BXwboAAWUAAAAJ0Bn/h0Qv8AAAo9kLI/PwALqOz1XnBPsz34ZVN8abKMIprId+qSFXdsT9mazzFuZmTCLslIya3mEbP5EXADhEpqx4on/g2kvajBixcn+x96kmNGL8luG5LCHrDZ7HgJC5zlmFUbpK0Eq6BZAdgtZsfRdyRQ+u++ueGte4woUCSOgDN/5qcwP2DEkGsVdFePHSPoL+mABJbZoMaPyAHVAAAAnQGf+mpC/wAACjztCP+fgAXUdnqvOCfZnvwyqb402UYRTWQ79UkKu7Yn7M1nmLczMmEYfcgFvMI2fyIuC2R+Ybj2XskOpL2owYsXJ/sfepJjRi/JbhuSwh6w5JdgOSmQjLMKo3SVoJV0CyA7BazY+i7kih9d99c8Na9xhQoEkdAGb/zU5gfsGJINYq6K8ePWE5J+MAGAcSTilrAAg4AAAADQQZv/SahBbJlMCCH//qpVAAADAjGBNBD00mABbyNifv5xwML8qpYEsqRH9RBygVy8CC8MVmjj2pKxGcsCq13HnpNe+lyS/FOYbLAqPv7geH5APhthp6ujAq6zc7EB3plrQcWmMrqiTndtY4uBcGxER/1TZp3Vsc1XQMQVsvMAD2vVpVECnYHELULrbdw/KxkWRe22LIFaBD9YOJzFrYVhIAZMgSJ2GGhuEE1NYduJ/euZQCaUzyBDWC/GaK73uzB/JQ0dNeu4RgAAAwAAAwB3QQAAAHZBnh1FFSw3/wAABhDr+Cz4gACduCZ7ask8+duVzyXgRWiLSWnTeMZ1nUOBeQ0uifW+nBH34b6pDQrAZGoYekRULi6+u6f8qthj36+wZ/ARxtrRZ3NpnOZTIP8kx/AbW7zeF/5dhznoqVo6q/AaXORVYA3eAAGNAAAApQGePHRC/wAACoWUEXwCMAF1HZ6rzgn2Z78Mwt+NNlGEU1kO/VJCru2J+zNZ5i3MzJhHV/rKPVgp72+3Gm+Qydyvi55iYrczcLZ/+KD264LkdxAF3Zq+zkgP5hD6EWkJi0WtHdalwYKF2Rag9OjG9kmOZoKGw6StcejtdoCanfkIx4N53ai2piAbqm+c04GRbP38AKWTs8jWkaXCACSPxR/HrIAi4AAAAKcBnj5qQv8AAAqDCFHoAIwAXUdnqvOCfZnvwzC3402UYRTWQ79UkKu7Yn7M1nmLczMmEIS0SfzjwOw+QVwxe0uBOD9l+z9N2l+MaY+n24EPYeIC1+63skONLp5W4Xa7lqQmLQ+0eFFxRZ42g0pwkPUOOlNYAVjXkpgO1Ha4A1qnytIWeTeQ/S0ZYnDkNyGR/5FbDdZ+icDt0J52yp8QASVjmzIY7ACygAAAAKVBmiNJqEFsmUwIIf/+qlUAAAMCMShM/rgAnb34X4s8jpFCwNs77p9tzXnj4dERFmzo65Mbo6UiEbEohBnh1YiT3UUveOA5DMLQlvbmIicSf4/jRWtSITM+gKkOxiZlCb4yKFevr8MPolAQzuwYnwzQxknfpkzP9X2SjFJzFy07oEUGf5WPXtrj7LYZW0d7vN3uoBtvylnKoUWJlcIwAAADAAADA7sAAAB/QZ5BRRUsN/8AAAYhL0UABO3oHt56UHfVGqUMnEINeHuhtY2AHpW8fhU4hEA7DzcH60pzLPhhHgTiYt3vgZlVgA8eEFhFtpWsB1wSe4I6zXfdMRQ9iH9ezPbORZnunYhblxqKtAsqGJQhfsVOsyHOeF7p1RIyBjELCl76PAACFgAAAJ8BnmB0Qv8AAAo9kLI/PwALqOz1XnBPsz34ZVN8abKMIprId+qSFXdsT9mazzFuZmTCMMXra3ZD8nas8DDRFfn5C83GZyc50CihcwgztTRIEVGx35ulJsZgE7yREsc1WJcVoU4K+LTPQ411XclUEvRcbzuhUvp9tB2RE8iXo5hnQRsRSnxds6nFCepW62NcRiK2Xxq1k0AGfOGhyGiwAssAAACfAZ5iakL/AAAKPO0I/5+ABdR2eq84J9me/DKpvjTZRhFNZDv1SQq7tifszWeYtzMyYRhOEQc6On+aTXj404VefkLzcZnJ9lL/HnMIDuRmBUo5rnrbpSbGYBO8lLUqD5BLitCnBeJNX6HGuq7kqc9jyG87oVLxdbQdkRPFI6OYZ0DzEUL8XZSJxQnqVAtjRGguSxH/CKdABnkMRZDRYAWUAAABFkGaZ0moQWyZTAgh//6qVQAAAwJBKfGM2ADmHZEDTmaipe+jC1YrQqgAsRJphz1M1klvkyPPba61dUbUAC/ANJCzbbhcTV4QyXW7Mt1+6EHIlKdWd2EJUbES6/0fmmJTokLhUZa/wr3litREFdnnFQEoxQaKe124Dd1WTyV4TveLIOEaUg0AgHmnCvle/WvMi57HNKCmphMtlTjeg+0HvSm7Qo+0v6b3eW3VVmLST4B1wVK5GujJGM+woYAGqbbCK4Nyd7QPEhhP3doGoHnBTuPfGZVfBAm5QVL7ekBTUx5qJJ60tIRWpLTE7SM7hbOtBN+k+X9eT5YzDlN1wtL4Lp6AzrCtix00cBATCLr7EAAAAwAAAwLLAAAAhEGehUUVLDf/AAAGSLlyUB9hySn5K4zLOZiN1UX+UOAAhj1U/pb7FJje66qapu2DaGT6LQiJ79fwPfn0IGIv1d8xJn5/pyTzZfCB3VnVhFTb1mZTauSXzlRErgFRuvPJ1jYhdwLV+3+JPBNV1NpKlCHbkvir3E1b0yPzng/qTrUbgABUwQAAAJ4BnqR0Qv8AAAo9kwxvGgBLVpitG4J9me/DKpvjTZRhFNZDv1SQq7tifszWeYtzMyYPzl7/KLyKlCc8acKvPyF5uMzk6aoRKRWgxGaMwKlHNc4QH7XMZgE7yVPi4QjQXFaFOC9Yav0ONdV3JUyL0XG87oVLwfwHAMRPFPbOYZ0GTEUL8XZ7ltQnqVutjRPwuVNfPJS0AD7CIOxpdQAb8QAAAJcBnqZqQv8AAAqE8BIbu/ywAXUdnqvOCpYMpygfdBztc4IwhbV000q7tifszWeYtzMyYQmFXXFBItm0vsTlcv3/Sw0Pqv8DCmtFgPuUaWhul2SJbLmrmoE+ilMKcI3XVOP1p7UqiIt54DM9rwC6TLhMeHhyusWH6mExCM0nj7Z68oxfrWtWHsEDbgJoMRpgAu6gW0aZkAG/AAAAp0Gaq0moQWyZTAgh//6qVQAAAwIwxdr3ABD9tg6cM2qhgtJpfho4u0I3zbLfHpiKrNsbfC5XFQ/RJ3r9aRSwXHjhG7h2nIkiUUljUw3NXRtezsf+d6I76KdF3EjN8gRmuI5pLTya7rJyz9cD393NyfQ7K+LDN33F8ES0veeFdvD23HLXzcVckNc4Oh3r3JYnH1bX4p339zAKELYJtILgiAAAAwAAAwFlAAAAaEGeyUUVLDf/AAAGJLXACaveZidArSOctJoXf+uBNgoUPYtWFRM+81At58WD6ofUooPcPLfsXpXHp6Ne4nD//2DuUZ1MElsmWDNxmdrErIU9AU+uM9ko1LEEOvjNq2Oib/RYNoYmAAHHAAAAlgGe6HRC/wAACj2Ppo/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD72R/siMFDv2hiY0VGg+wi0hRu7vf736//oaacQiv8F7t57EFWBGiWYRW09Xj61f+Cg679xMklpJXi46tnGKN/sSTIXXsuOY2n4u1eSKGnqVOqaAOopI+6D3lwAG6MPqyGTwAsoQAAAJ4BnupqQv8AAAo88Ef5+ABdR2eq84J9me/DKpvjTZRhFNZDv1SQq7tifszWeYtzMyYPxPAt5hGz9gSKU8j8/Z/Uw5deHIXXzd/0sNoGFVBRxoJw2tJAIEDbxEBQkxzTvs96ezApwoYUIb8lQNbICWzOH/YteAXSPiW6EZXIOf6phHIwJbfzU5jdLb/rRW7ICpDnMWAAAPV5gqjRNYAccAAAAKBBmu9JqEFsmUwIIf/+qlUAAAMCMMV2HQIAAnW2UIl1hcEoDNkF9UhN7PR4N1OugGLhr89YkA0tpDDYZut+ZET2Ye+hprDTfDl1XS0BdOjkZ0mwiYtaNIUPwxw6JeY1mNI5o5OtjrRrNM4qA4kN8EMlxATtIeeUlJWI63vVqyePs9Xj3W8fbtbRs+On1z0e2X6FMAAEYIn4/kADBaO2AAKmAAAAlkGfDUUVLDf/AAAGDCp/TIATV7m43RVeDV5poezZCJbT0VhT6BfpLnBFX14oY0TAyOGTVePEOIhUOpnHkGybNe8GTZWwltkEbmd75ADiC8p8aMKsLuFSxu5qC8MjOXsk8dor5R9tzrr2amg8ddPGLJBy5D8TPhNCWc5gBcyVLu8ba38g46bKAiqg8YHO0hyAgtVG4AANSQAAAJ0Bnyx0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sQbIY8Sz/h266e5+QvNsRJl9cZmAh+RyOTxtjwevuID9vPBqLAYNCcSxtBcWAU4KgcrAQLk7ATwLU2PIotzRG1Q3AcD1eYgKI9vln/8RQv81QMbihPUrZhkcoRcqa+evGYALvESljS6gA35AAAAngGfLmpC/wAACjztCP+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH3rSvhpgJI9zduunufkLzbESat9fUXbQX4fvfhaMerXqZgICODEzkZzVOF4PR5slmC4vKwEC5OwE8BwljyKLc0RtJXbQdpXmEs2J80FKaiJ2/mpaJEHY27EkMjkSLlTX0CQbABNPZrxx8QAIOBAAAA9kGbM0moQWyZTAgh//6qVQAAAwIzy5/mNO9oAW9ecIFYrmM6Q7PY9MQu8wFIaiaWHGSJcYBGOSJ8Z01S8rRish7DHTnyWbH9HfoGLxjl0HkObSMEe8S/wj+hoMCGfxsh9TNUzTnmWdBlXEZPSqIKofYVsG2upUTbpFmnCJxqYJyCCO7x1yCLrqSmhDfcotRiCJ1PmAhpy5kfbk2jiBaaKdduKiSfnqZcLA4UdP5UfyzYqYvVctxNVw9KKnq3sCtxol3zKP8D7P5LkAARG34rPHWWDL9wPBk/WzcveC2uIanFBJuCmRfKYC4tverPXakszKL8WAAwIAAAAHJBn1FFFSw3/wAABiS1wAmr0DjB6AB/YNKeRScAEDmp35GCRRFzyHxfFXFWXOCSygccjd+cOxP9xpeshn8SsnIZmdCCIKrb+ztYhbCmpuDWwIX8jl3XLjQjHs9KAAzPh5gqn3q3xDDLQY2mJK0qNwAAj4AAAACoAZ9wdEL/AAAKPZSxvGgBLVpitG4J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfoSiEfZb07riqUCXpSJsEZMReBv+5D+91xQEPX3JUGRLqcown1vTajX6RYxv4QqQeRM8eZWFov8qA1cg3/VLp+3N/XwlZntMmsXSTl/WwyMplyomuFEomWEkkVfHvfTtNAt7CisCyvrAAEll9jHkXH63WjR2AAz5AAAAqAGfcmpC/wAACjzwR/n4AF1HZ6rzgn2IqdnVN8abKHBGELaummlXdsT9mazzFuZmTB+ClDfcJFHmftMYX6dN6GzvTWyWzT5YI3NlOxxdbRCFVDFYRPTSiTguHko7i0hMWlFoabQ/H54xWzk+jsovhmsAViDS7zW36uf/5ul/wAxfYdIPGltWnlZQ673n8bEO6t/xMhz3LoOFHALWubRwefZsfPFkNYABnwAAANhBm3dJqEFsmUwIIf/+qlUAAAMCMHBsfgAnW2yTmVxuVvsI2DKy0BFx6FIOFVrXAbYveFob8xOsffAXNr0GowLOcCWZrywIViJP4qbl4FFybCzQ3J2mj6SZmCp38c4WUWeU+SH6CHKtKthJrMmorYbsRouTaDO1/XkEvhQ3fdSsS5OEfJIPRS9W5jnt6R0F2IGi1wAD1Lf65wzHxyfSwjT1mocxcMAABxtr/O6A+uEzvkVtdgyNJdBtgZMj71HUscH/RhwYVgLxmG1tpZRaUQjXlv+bD4pABbQAAACTQZ+VRRUsN/8AAAYhUHOAE1ege7pPID4Weokc5OIQbC04Og3O0lB2mvgnZeC0vt+6HCK+SRQpREpP/cwRokrcPYTl6N/vJrnvqgEK8S6V2qKHGbdh6ZNXihp/bMdXzbMcV5Pi7fvnXjrN6uH0tYTf+bEU/3VNXaGAliBWuD2ZRyDcQLwEgXEl4MT7IlWrUZfgABNxAAAAowGftHRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxE/WJxvpt5rQYObHUXPAed7hSUSoytBfh21HCGgzTvgP3p3/n57Qnf5jqz6PO0swVs5WFJKUnaoAOEseRRbSK7iI7aDtJpZYZR7WFlsTCdv5qWiRB2Nug5DI5Ei5U189c7QB6QgJFyek3IC/VrAAm4AAAACrAZ+2akL/AAAKPO0I/5+ABdR2eq84J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfestAsLtuIggC/JI2CLk1HosxzYi4v0zGdRinpmpURmWVYlf7ovn5e6lmf0OR7onSF0hVBSpiFO0VPFAz4w01Bs5cgOKw4avBaZ7AVCMHMIYmtPtsm8mgz/FD0Okopdaa+gKEYBbltEM7yWyJRFYEbZOEB7LfTgAM+BAAABekGbu0moQWyZTAgh//6qVQAAAwJAYFfACw/PgRXvkFOWyEwBIkM5slHa2G1CVpr95ZW9erBVr4WUGWqC0Oj3o/RzdCDItrQSsrlwzq06g+FQ/Uq6LY3MsNzAx7/qtiSAp+Vy1iub4YYJKJnxW8PNjTKorCZ1N53oDjM06MkoESMH90rVk8mFF69PgCLLe0fZ6Ie1d2J4mVvZaJnRwnPAZXUZMVJnOQhUAHHCRQztS0y1O0AZ4dcNNLC4n4VmyCfrd6Rm1Q3oa3ebT9UWrtKXDY+JXlz0HqvhAKoZZAPrPGJltNBi95cZiqj8X8RHqNQTrIKMpFSgwCtbxYVLtQktey8N/TI4TzFib7viAucn1biQh6jL2+N1qHQuvfo1l0Enl1SCmj4A5ZGAAGZ7gFcyS4tqBpkLDv1dww2/E/O9UvwmWZw+63tbYfAV/xSTxvKnGJO/EYlL0m56CXwmYBwrcOXpl6IyKddVcjTv2070W/qDBg/uXKGWZ4AKCQAAAK1Bn9lFFSw3/wAABki5c4QriAEivMfKms2Kb455tUAaV8JMY7GlCXHTjHGeJLUC+BZ+FcY44Hij2t7+cWAOP72dwVO8uPXv8067G9hj/eVsK3N4pZgiJKXYRKD+BqVnz7d4TGSDzpuki6/MQ6UJ8cfeJCHOiEsBg638Jf2Poucv5KauErxTiKlcQYtckQsLHfzr0gfnaTIJqcEkpd9AN4LIjcu1EfbvSZL+TgABWwAAAKkBn/h0Qv8AAAo9kwxvGgBLVpitG4J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfnLvoH24Oki2gwc2OoueA872104zMBD8jmI4hWPB6+4gP3p3/n57QZOEkjaC4uQpwVwZWFJKUnaoAWpseRRbSK7jRFy8YPeC1no9rCzAphQv81QMbihPUrZhkcoRcqa+eSfYD38YGqpAIK1EBtRjuGokOjX3AAApJAAAAqwGf+mpC/wAACoTwEhu7/LABdR2eq84KlgynKB90HO1zgjCFtXTTSru2J+zNZ5i3MzJhCYYtu21Bg3OpOo3OwL/5YbSxC9CZzJgyLqQThtaSAOFV2jtEtlzWREYVXWmFOEP5jlH609qVREMLQCYAoqk9ep+Hqh4cmrnl+pg+8pn8bmplRxLJwSh+0CFXIKoTzyWzALcEDqxB2a+BYiQJfeUxCvPAQd/XHwACkgAAARFBm/9JqEFsmUwIIf/+qlUAAAMCMMXa9wAQ/bY2g9z9MDXX8dhyJxc6PfVZYsyFNAtnO6um99VLrFy6dAWF44+0Sdb8oIDnkUeYF9pB9F6D+EN6c5WgWJe0ugBVbWHMo3uOnoatUTq9oBOaFWdVb1BbG2KF6p1PYnElbJH8GK8k6YJjOA3plcNs7XPNXlUEUgRa7LT/mkEKXYRw4/GvgAAPfh/xUwGR7r5msMj2zBXQYom1JRaOILNKju6EUM52ygjbVwMnuGyglYr5vwe4JZd8iR1hbS5ZAjcB66f2+RrcmabPVW7RO4Pd3oBQw8aNVrlzA90ry+K/1pxFfMio+Tnpqw7SNTlXbis5MfK0c0D+9uEAAACaQZ4dRRUsN/8AAAYMKxaiugAJ295mJ0CtI5y0nkxhf8eFWXFjH14p5MgNFjjfrpdeDOeWvuXzcZziPh9HNhIFRs9ILbY0AE5zdRzgdxafZUKVVdWSch4495+0cdNkTSqgWXYXUOjRfoCDDWYWYYgYcihBMlWN1M0uclXBb3a4f7fU5XuimE3FYwIMfZNTYCjW7JnH7huiygAB6wAAALIBnjx0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmEYYvW1744p300TEUUPio0H2EWkKN3d7/e/X/9DTTiEV/gvdq/YdqwI0SzCK2nq8fWr/wUHXfuJkktJK8XHVs4xRv9iSZC69lxzG0/F2ryRQ09Sp1TQB1FItfGrUNgHPy/2xVD2evKrmNvf/ktksJVB9xj50P7BhagQ1QR8wJAAB/gAAAAtAGePmpC/wAACjzwR/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB+ClVp8kTJ+9n19ovm0ogkzGcKNJL8tJfdmaqSxVRsKvFw3IYWM7FFHcWkJi0otDPl+emO4JUCRNXX7sxzNBdRme+bwuroC/dqQg6GxhNhmHbCHxBNQ7VvSKbEQBVnQ8Sgzr54+vALWcNRcZX3AgpyBBWtfgMsgdnhwIDjGEQBhmAACRgAAAQpBmiNJqEFsmUwIIf/+qlUAAAMCMglfR/DpNmABNNX4W2m6hY3uZhFaLl0WMpwjXyz5U80kxw/Hk56btfDQnPfy+As/HuW9NNzzvOa5/a/jz6kfxZtqs1qVAcKuf0wsROpYNUT8PwfosGG82eMpVwZOMh3Vnum+omON6oCiqTVLQrSqD9NPwoz2w2dceq4i0z/dpgcoXy+Q/XDAABfRBWy+29JOdwNC9e9UTszsAapuPgchq+9uXALUjKMEMEDcRN9tUiECTk+0VSuC7MdWHXAWXpLTv9p7vKsxlgJohiw+xshrqZ7M1wLLc+3KBp54NhS/G4O4LQTLfE3fU3KGNRuNHL3b0xgs5QAD0wAAAOVBnkFFFSw3/wAABgwqf0yAE1e5uNjgq9rrN+0WSVOkL9OaacJRJqO6+vNSgcF3NjBcYt+M0ZMoZEgIM/WG91S8eOyI3MnqsxnZkrrirv3iXWF+srqYYB7R7LQ5jXkWJmKjr0VebfY6ejUX9uIhPKW/5XJhwIYcaE+ZiQso38d5Hpq9wpom94Lu5hwcdNk3pVQLc9zb9PnZy2yoO/T0PhplXWUXixOs2aAFtYwNqTlJhZSGzcW/KZrfhH8HJsYCb7lbThAziFvdCQ9KD1hNAx+x5SrMgm22ZkwYAQXNhjzgdz5OAAHjAAAArQGeYHRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxBshjxLP+Hbrp7n5CfA872104zMBD8jmI4hWPB6+4gP3p3/n57QWJxLG0FxchTgrgysKSUpO1QAtTY8ii2kV3GiLl4we8FrPR7WFmBTChf5qgY3FCepWzDI5Qi5U189eMwJhOf59Ivw/MT+RHiM6pQhjrx8UqqzgAM+BAAAAwgGeYmpC/wAACjztCP+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH3rSvhpgJI9zduunufkLzbESat9fUXbQX4fvfhaMerXqZgICODEzkZzVOF4PR5slmC4vKwEC5OwE8BwljyKLc0RtJXbQdpXmEs2J80FKaiJ2/mpaJEHY27EkMjkSLlTX0CQbAfAU02dw2E6pu4al8mrN9zLAF4pMHujXdYgQe53DcmmJ9Sn+OuglJW5QaOAAm4AAABSkGaZ0moQWyZTAgh//6qVQAAAwIzy5/mNO9oATTV/oc+tI36O2Pcmp6UxaK0rUudiqSnLLi7011FskFOBwghdWQ9bF3YkmH+Ydsmoga07esB81yL1odwJs36Wg9qQW2ofS/g3/7jk9fAao829O7/8WkyB3Z7y7SqmBKf961KCS08jjshCQ8qf+7CpK5QYKVFvCMTNomLbKy6H0GCp/lBa3iIzYBNIrm65AAEP6H5ubtV5dAADYOJJJL3yyMiIpPdG24tQUGM0TxYK3OfBjnCLqUEa9lHKeHWufmouV7Z6Rs8b2SI+jIbDbMB4heA220hcQHedlwTA31SfyxzlofHrOeQEt1wH4la4fVBVUn8/z4jp52Tzfy1gxYZdCVTVZXy2ghtQ9dJB3vL8UvXBOGny3EXQX3DiAIs1Q7o46T8biOIn+oDgZtYOAAELQAAAM1BnoVFFSw3/wAABiS1wAmr0DjB6AB/YNUWRSb9ocTr5OOEURc8RngHhJGECyjfUDjkbx0EitbhQRLxbA9b7E4WH7AF5BpDB0xmOzWZQELNiC127hmPYqiHUzPkIICG0nwDu/Nd+79ghJF3tXJacZOi2M1Pd8u/xdGTNonWecBJf0Ss1rMmI1Ajcyr6TYwItbxOeT5QzYaEMkjW3TfIOL1nxMbPKqBvcUmNlmJir1PTHgPgK2edBLpMWxIDMGLuVvM5mU+JRHCus5ScAANnAAAAtQGepHRC/wAACj2UsbxoAS1aYrRuCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH6EohIkoUIFihUs/pxSQrI9hHDYES9pzfaKachcDqFBv7OfeCZV/EiWRr7XvetBSxgX62tbEDgE/o8MuyYReawAoanNvJZwI7XcnbzNqGMtKTjd2t1LKp8TX4mYCAgnbexwpZGelgABlplDhtPjSQJIuaLOg81lkHgsPMrEzXnU4TIAdUEAAAC9AZ6makL/AAAKPPDGN40AJatMVo3BPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD75KG+whiRbwNNaA/11KDcmAo4+V03NFXbh9bBEBh8aeo9fjxti0gKjAjMsj/9mr+kWhpu77eYxDjD/TpaHq6yA7cvn7heeZi6RaqqeapJ1YJnxEus1tWtJIq1iN1AQWrr+7Twe1ywAAyvTCFxe54MiL3IQhy1XHHKyW14HQQ6pgL+84Vh2vZJPBAAZ9AAABP0Gaq0moQWyZTAgh//6qVQAAAwIxKEz+uACdbbGzM760xNP/8RScFavSwiH/9lKVLUuAkJRntGWZo4hScGlCLXqNsTi7Z3XmclEiQrwG++HRdHIZKs3N3ayHzFFFp4BjuNoOBWAtfLK6X/L6F88xSkFcIqK8mwh9ay4Sar7hMoPmsbcNW4AUiFEUmI5MuXdgarAjwr0aiKDnwf65M1wwAAX6Eh9+PdpvkYADRFpsF2WwaMnFzHZLtj0t1CJzKinL3w3aVx6lwQ4LuKr5SOUKBfbAIU8RKZ4NMxaGOM0vR0DcEAUqbtZ0lQX3gDXWiprsVK0YcqWlyytWSCqaQz7IKOMzKtLmByGG7NWHAKRyB1yGOqTLwa3HSm5TWzSS9RdwBglzXxWH3evgOSVMGWg6expkOKaV8tsQUwj7cEpuUqwAAAEEQZ7JRRUsN/8AAAYhUHOAE1ege7pPIozflUH05OIQcAeflSXwTDmHaa+CdlPddWSRWWfMXcvbShUL8Lbw9ycdlO4jUBJNkaCTG2wUIo6Kv3AccTnLrc05aHX1eKxqMsBpUr/AuNaBLuhezSPfNPD6NAubtCifMrbVFdSvBDR6wDGr4mpcyGGeW3DJKjp6h4LxEfRhOE9XpbiJwhCXLW3EvyGZn7uBDMoChmaZoFLDgA6d8FHtXT46YvkGOfEAq3MLxSCGzHWVfwv5Fakm3wBBglXAdsPquD9QU9Q61iwVEP5TM2AGdbN0uipFk73mVOI0OKq646iDLdmANGbpMdYx1aAAE7AAAACsAZ7odEL/AAAKPZCyPz8AC6js9V5wT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg+7EGyGPEs/4duunufkLzbESZfXGZgIfkcjk8bY8Hr7iA/bzwaiwGDQnEsbQXFgFOCoHKwEC5OwE8C1NjyKLc0RtUNwHA9XmICiPb5Z//EUL/NUDG4oT1K2YZHKEXKmvnrxmBMJpEyEJQ+M2fu2brNBkEZ6JqgFTkIAB0wAAALcBnupqQv8AAAo87Qj/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB96y0Cwu24iCAL8kjYIuTUeizHNiLi/TMZ1GKemalRGZZViV/ui+fl7qWZ/Q5HuidIXSFUFKmIU7RU8UDPjDTUGzlyA4rDhq8FpnsBUIwcwhia0+2ybyaDP8UPQ6Sil1pr6AoRgSzHxWfZKN+SqpPI2BFkLId9g66LtOAIYSkEX3iVgAAZ8AAAAGmQZrvSahBbJlMCCH//qpVAAADAkPLn8IHHiAK1bZKpdsXoWE3Ak6MO7vSSr/Wi7EWkSF3y5fjchWWB/+3NHQn7PToIyABTEjlteVdaF839az85tHJl4BzamPbGfHUQAG2UD4gMpiGNd3WvCTqBuckb8SudMyOj6pbA5aJ41Q9cycwRxe84UU+oSg9dEpHENJkAP+/NdiEo/WeljFsz8/zrB/eJYDOgJYaLuSahjtWFN3hHdZOP4rAUYlK28dbtZxCNltGgBs4lB6hGsKo7CFw6ju0EA5ulzRDiykwPkXdvQT//mM3e578jVwPY+Tb2lmZf3/bTn0eEDjr2ev9E6+xAACWEgcHdFBBxS9WtvcZk0/MoNCdCos7hRo029OWT6sQvk2NqvHSEAjt7bdsqDELQL+0Q4NaRDobmc3NtUpxgA1XX/6zQXE5V0zX+22r8iVBNKbO1GLghCdHIBHeiQpyhZ78VDpbXdvCw7nGrZKG8w0NwZ0ys6RN9vD5KF1Q6Lo8PyNr8pgn7lHWpbbqzvqpp83sPxdwPsiH08uEuA6nscNalJtC3lwAAAENQZ8NRRUsN/8AAAZIuXOCmFMZvhid/NPABCSTfEiBPVs2idNS+9Z/2q8cBxzjtT1iQEXdiAJ/mKImfvM4D9cLR6aZAjpy7IiCZOMmMw/ui5ZP8QYgfDLGUA56L8Q2BzNDtqVO20s27hhU8DIm/Yycgw9fXxVr3TqndMOEUnf2rixA0myt5H2Zm3PPsZM0IAQVp5uUp8UcgUuaYsKps/uFf8wkpA6OLsDQsMDKWQS0s+vowzPCfKYEbC9edfObcXVA/8omFve7q6WQUq/7kOe0ejrd+oKLzLa4PgJp4RvESPqN91nFsyex0TPxwgROjLF6tLEDcM+wBGMje6aPX7Vj4HFDAkyjXzk00bgABt0AAACpAZ8sdEL/AAAKPZCyPz8AC6js9V5wT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg+7EGyGPEs/4duunufkLzbESZfXGZgIfkcjk8bY8Hr7iA/bzwaiwGDQnEsbQXFgFOCoHKwEC5OwE8C1NjyKLc0RtUNwHA9XmICiPb5Z//EUL/NUDG4oT1K2YZHKEXKmvnrxmBMJpEyFGgHNWXrpGb1Aoih9xgAABSQAAALEBny5qQv8AAAqE8BHIunywAt47PVecE+zPgY+JvjTZQ4IwhbV000q7tifszWeYtzMyYQl9LLdtQYNzqTqNzsC/+WG0IqAQQU1wsfNyjS0OSirRLZc1NRAn0xphThG68HXYL/BlURFvPA8AUVSeut1ardCMtNxupywmITuk8fbPXuAcOItasPYIG3ATQYiTAmCZmJa6g/zdhcC6vf4euBVzPXqcy4MnHEeN+lnC28iAA6cAAAE/QZszSahBbJlMCCH//qpVAAADAjGBf8qvABEG5WrhDETF+TUPwLVIHVxNQhiQm0DnsaY7nJoBfN/nSth8UjUN9wU1wpIR2hhGjn21827DJB0ZX2Fz1lmqgOvhYuXBfIEsSLdxtBtNB7XAMagdCz5b2AbetPPN0z3z7m5zglpGWhqZlsRYVf7R3N/VwwAAX2aKU4jPZDwT9UHqgXhjPwoqfTgMhadfuC/Vmrp1ZvSkYMBAEk4cjAureCFlaMeJAARvIhIx+5j/4NL3DFCW8yLMavx/vMKVzA4zPHgalWNRBeI2F87+YPYY2qmtaijrU3oDmWm3X7fkHMw0g4uCs/yAGCvEOOLIcwHeP9mkReuWCvwsOaag+LsGXTFNFPAuEBQ4qNi57/FnIsLOia7oEd3+TC9FT1B9XwrF82p5FrmYvwAAAOxBn1FFFSw3/wAABh3ctKEQAE6thaiLLhKl3D4XbEHnfA9tZUo9TEbvs343yQTG/XzyZdUfargoiYtFx+IUp1HCLWxuXdi+dcTDzwhQO+OmxhT29eRYGEzAuB4feBNtkuN4xln0hfjcUx82PuXYZ7FmxzM4jRkrh0YwuYP+VkxM9TZG0HX5fN3AQav7Y7C8v5sUAsNpkJH4nNo4s7SIVF3AsOHYXgN+hFmJJLZYrSRY71jC7LthJlJG6O8iDwjLcTqHHldUYxcpL0Kx0zvZXCyVXAz2RbChpdJxxVFC4YwhHE7kjJFZVnkU9gABlwAAAKwBn3B0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sQbIY8S0A3iY0VGg+wi0hRu7vf736//oaacQiv8F7lIPTsyZuhThOMz1ePrV/4KDrv3EySWkleLjq2cYo3+xJMhdey45jafi7V5IoaepU6poA6ikj7oPg2gNAH3E5p02xotTYtChlHjCl6fjgbWmVetlneaBDrWAATdAAAAvgGfcmpC/wAACjzwR/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB+ClVp4rIotbFCpZ/TigBWR7COGwIl+WqOGa3dJiDvgOIhgco4FGdjijuLSExaUWhny/PVZCD3IHfV1+7MczQXaZnvm8Lq6AyHakIOhscUrRPWlB7/TUO2B0imxEAVZpscmk6+ePrwC1mzIaaRHkRXMyrKScH30LcTvQ1c3hYWl9G7dB84ym61dlCAAM+AAAAFyQZt3SahBbJlMCCH//qpVAAADAjPLn+I/igQAmm2ZNlxpv4cMDq2W8FM6JPC+fXlAg5yrKoVwWGZu7u+pvgW/lC22JqQitf99UjsHNCaMLrUb/nYDKAvL/It783s4RLtDrRnvWj+TmVgCka84eOjlVTIk1Mpuk68mjVcIDy4SXQ8Al9HHaQNyv3bEAA2Qndl9Ix3TMVKaby6oWK6gJ4qKyq6LmYQAVjrNVkWgqedOiK8sNgBefkpZ0z/eGAHL32dgzYTAg4viMcNORd7cr697Z/8FrqF7MITMDClhHghWHPwIPRV9iWcTrGnSAlA78Xge/ATPS/ts/bHXaFUS2R+0b5ap2jc9cNumVBrwIWtPcvy1TMYGMRkio5DF7c8FoD2JHnR3df3+/ID6RBqbWkRy20IzM3S+Hi1aAlmc25JTViFBuiB88k2ubRqCNlHGIegOh11yOqJcd5stKBFksQM3T/GEg1hLs1p9sSYaavz8nq4IvAAAAUpBn5VFFSw3/wAABiCGO4ATV7zOkyABWfdjFyrhMd7miKQA8naKyO08jld4saSmSxe5SRc00IuXqov+h0kIo/2/3sPEgSwU5b+sl1Ofc2AErLyOD5gbnPE5CHddN7TpZZE+1shfo81OLX+JkUNGfbvKDXB/Nq98RIzLOLYcQMAW84RkVY8jfi4OOmwwD28kq4QUKce7m0piXoLVH53XZH2j5pPXjwKaLEFm/lK5f9tgLOcALFLBpO00cDaNL4dN8aeF+IeZasYjn0ciKHUsxDBPHCErAZGI387bNXREY10uoGXEu++kG4fF7BtQ/lvM4ssu6t6L/qC1B7RPM9a6Rymn9phZVQXkMnrFwoUX5Vt2ELX1RMKN82uxIethkxJjdyXrsPMcjzf+/TJ/rH+LMwmBSgNTk8ngi7/Nhw3lj7ysGPcB9G4d+LAAC7kAAAC5AZ+0dEL/AAAKPZCyPz8AC6js9V5wT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg+7EGyGPEs/4duunufkLzbESZfXGZgIfkcjk8bY8Hr7iA/bzwaiwGDQnEsbQXFgFOCoHKwEC5OwE8C1NjyKLc0RtUNwHA9XmICiPb5Z//EUL/NUDG4oT1K2YZHKEXKmvnrxmBMJpCIlC1sJihknA08JZuQc7xszAp8c47h4sTZsbXOA2EmGAAm4AAAC7AZ+2akL/AAAKPO0I/5+ABdR2eq84J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfetK+GmAkj3N266e5+QvNsRJq319RdtBfh+9+Fox6tepmAgI4MTORnNU4Xg9HmyWYLi8rAQLk7ATwHCWPIotzRG0ldtB2leYSzYnzQUpqInb+alokQdjbsSQyORIuVNfQJBsB8aYGHBLiZLZ9sN3NJdgnR8uV338NtZd4Q1iFxDZPgXdZom0AABxwAAAb5Bm7tJqEFsmUwIIf/+qlUAAAMCMSRQvL8LgAnbcsqNXjQhzV4cYVVRFqmIa6FmIe5x4spP9x1uT3DORqNKHqxPW36DTBz4DCgA+JN1N/90j4xUFLNSgrQpjfDSyiXIl65bYJfm1CvQS9yiyuHpZ1Uurml5MpdCR0rMleBYcQpteq1JL1oPlhyRMaMU/aXyhiKD/oMzgfj5tSl6OlZBBkqtrtbmr2SlXY3tKvb1vnwALC+FK4Tlj9O0l7NELxEpgAF2dRsOP38a5ALfqR8hnQHaZWygN1Rzr3n3a4rQsbz5dElFN9igqJ81LZ7p8t3SS6Wgiea3/ZS5CBrOS7RV0GriguykhhOrGIlH/J+SDwnpbvj7815EhBLPFmku6WnDcMoO+KuKXa/imHBI78bTWqM9Hqa25fRpdOJ4uBEVsYz47Xksx7C/OaG4WyEkAv/x0D40xcU2W1OBm5l+5TlrVmc7yEIJyEGZBLc3Dl+SX388L9BTXp/p3FrbxiTWhputJBeE7kItu2/YdDGILCLlZjp8Rdnj2qsR7nEICV493AXl7c401KvnNffEcj55ZDyqtoPDMwJjZB16qooGT3QCJwAAATRBn9lFFSw3/wAABh8rUFcgBNXrdOJkHYHKNfH4TjSpspxPNODvAOHMy+Upvg+GBG5VicLsS7yQjdkwjpmojKprmT5czmR/GxfgABxxNBbNN4x6DnA58WLekglPTnjppcKCGov2CRtlwCRcNy+DT5Rw4LvoRo88EymiWikNToPuxWxKB3TLSnWf9+3VMzMMerAqQoXC4/8fO6Z4vdE2lfxjFvRDSSD4QQVjUu133fBszw1geGyYjbU9m9Xqs+oq3hTMqM3Hfm4jrMNB/UI+clJkjjIh69naFFPKO1FOf1n+fWPQEPpSzFTez8EPRDTmj9sGWgtBFWQgR2ixbVl0bJkNzylxt3V7wMBOt76gBYAM6/e4Ldm3gKXUBogEa5M1GvMvK849zXA/dC4R8Mll/9NdJAAGLAAAAMcBn/h0Qv8AAAo9ki6X9ACWrTFaNwT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg/SUEJElChAsUKln9OKSFZHsI4bAiXtOcl0KoVyFwOo+gNzNMkRli0X0spLmKwUZClDAv1ta1/MwMMfsAXXs/dwh17GORmbVJc0BT+3mbUMZaUnG7tbqO1CYBg9n8rEP/evwpZQn188fvgFq+JeozVARZTwPiPpB6JrafRMxYtPmmOpqXBTpWqQMkZwsxPprqwWXqgADAhAAAA0gGf+mpC/wAACjzwR/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB+ClDeo8eO0Z7Aob9DqKIRMTWyWzk9iJ+3jroRAYibZ74DiIYHKQ3ab6LxaLUhMWlFoabu/vmcRMLxYBdfuzHM0Kw6BDErb9XQAlYp80RIVgq2ucSzW03wmAUCW/GFPSaVINiVd9fPH14BazZmQqJdy0eukv5Urjv4GfJycGf0dduG5loNesC1xxpHRJSpY39G27T6kH9iRh6ohnhZiIAArYAAAAXJBm/9JqEFsmUwIIf/+qlUAAAMCMShM/rgAnb3/DT/C/HvSNTBfPieUUYh5BUKnR1yY4CNL3gNtIDTuavFss5RuNWe6gP1QX61sRsElzjnxKxCSGMW2gkF1l+Yc7MP/wLgBS8f+jjOnhEOcEYqopc+fBoIeF3QYXDQmCTa0bKtoGVQ1v2wd6/Z/Hdm1wO0bWecyWcaqvpUJxaUwAC8dn+EJ+d2dKBw8MlYmQ61C0fOdKJAzzBdUxatZIr9k9pDNvUxJ9RGJsL3gL57yHMNwm3ViioIUjly8suSb1kQGbaoGTDms4N7QlnK3ZNQCmJqPUcqhhIrbGXZaJG56Wd41/W4JbtZLTOf/IA0aMJVQBEZErQQlm/8cJGEaRNTYuLxQLVxqzEHUs0c+syTwBjM8GQZ4MvI6QGk2TmG5cJeGfuIDsmzyYaOSpIAklqoFX7XxC+oDv5PM0u2NZq5Eaell9IzW4eb+1US05BNS7lGwsa1WXV25AAABREGeHUUVLDf/AAAGIS9FAATt7ytvPSgdNK0f4M8WEGw2Y58RUquaF79olFv8e0+Q54DcC1qW0RRp/oOScBX+O8Zc0dnpWgS0dRvZJTNSmugW/kqzCX+88Gt6DgjVdnKp0H4XPUOXj8xQFUlHYkwcdNavpYgWdg5HMD84ldh25TgsIRluhbE3/0+N3TFHtczCZfTl6GY4sPuBPlGH0bYlU5nlUBfQl/c4wNKmu9d/ce0VqYnyKFSGyo6UigiaY0fFl8ydUp5Fq4Omv8P/YKiJmuYp/S3CD+6Ipd869RS71spbzb/gYE1KdGOYO7F4DQxTH+6AhSf9ZkAaGgAcEcFTH/3kT3EHrWkEA3Yw6yokcPcnNa9yKkEDR3bTy6NWL4x7yW5rGNtkYkb2KSHGQExIXyCfRglHpL0UX0tvCOtZGcITwAAgYQAAASYBnjx0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sRP1icb6bea0GDmx1F3uIWQk319RdtBfh+9+Fox6tepmAgI4MTORoULXbYJcWAU4LEcrAQLk7ATwHCWPIotzRG0ldtB2leYSzYnzQUpqInb+alokQdjbsSQyORIuVNfPXO0BoACT+LRjUNV5KLg2fBey00N2UPUipKm+FrTC55Oo8MHfkc9o1ZiiapTwD+akfnKs/U2fiOlxqBSQ8Pke5krb+TXZb6fNMoY0Fv4B5WwoGKbFZ2NiZevDLHSameeteLuYCOhug4ZHCpme5+/j3GKcEBrfAOpDuXw8PI2P3k+0OZB137aevZhkOXVGzGrWIgACygAAADSAZ4+akL/AAAKPO0I/5+ABdR2eq84J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfestAsLtuIggC/JI2CLk1HosxzYi4v0zGdRinpmpURmWVYlf7ovn5e6lmf0OR7onSF0hVBSpiFO0VPFAz4w01Bs5cgOKw4avBaZ7AVCMHMIYmtPtsm8mgz/FD0Okopdaa+gKEYEwf3SZWMGUy+cuTAZ1q/SUZKMiGNxv29jVAFmunPwnH1YF2W3XB11cp3LWPHdLKTFBUMo7WIqUaAAAJWAAAAB/UGaI0moQWyZTAgh//6qVQAAAwJDy5/COn1AHMo8H/4+RCG8keVp03UXpiZi1EatdaAT6V+6/zWnREc+m9jVl38ak0l3XdC11NNuuFwzK0Cle2ikMm7P1NwcZnC3y6diKquZ9G+aKxAP9jlXT1wC9k/TDkNhvR2UQn4dmEv674TtTtD9oYMvWjxkZ9KiVC40BKxThYgKi+QHJbKXCsW10YtzwRNeRsL/gLZkSfxu91RzNJNjbDYE6rLwh/Wqxqo3lbPx0wzMAD9//BQH0MXFSUB3FUydaDFDqdIxafn+OEKMn56SZaeAzN2v+nHZHsuGH1VNCVpbp8DlugT3FhW3pjIl3s1eUhcr/63XMUd35zcvvafAAAeoGMGryBPVoheYIxR0akcTG2bXzyrecKy6qNFG8Rr9a2PdGpqBforVtJHzDNz3GaZBO78hsd2Cz1kDuji4ZMifUmy9H+jWOCq35Q/p2RzzLXH/Jyq9jHKKkRDAKStMdk2TvxF8Rw7d6+9ZEXtkg1YDHl7YzwCFaYDVFKQv6VE39R5EH5ffxstubnnzjWX6Ze14kwHeNhXy/nhOiWQf++p9WOgh6zuycsJpEgjpxJvM4pL6qdlIXIUGlJBGrtiQ0P8mbGuqPNjRZNrdIH4gZ9Liw6XhV05FYPNCerf5oNkBGD6eaxslAAYNAAABTUGeQUUVLDf/AAAGSLlza/gTAm6Ca9TMnoRDkz67CDj4AIYaCIz4/XNWBfZytAb6X4MBu6oJcvT/mZ85C+bnAbxtEV7vmvddMyuR/4gMSWkHrYnzYS2QUWFK/ahrVItJpoczA8fz1+PaAtmbkDzcl8VVzGrfOhBcEPZfFY22VeLH/oRu2B/QsrwTGjyAYO0pDgcGhvewkVNSk644oPm8w+vN7fzJ8A1IONj+RcLQ+L/4SoDJoD3oS5g21s4R4f5iQW+Ecd6vdCW935Ble2fpgkhRkrdFlC3TNHtinDqa18go7206juhtqz8MaMc8IZPjlirPcHX5/tMnptXWU1BYSJNDwFaMQPjzIfdzObHdj4oj7a8PdN7X4aJEcjy+0feK11YWLD4JMuj5KixsHBwSra/f7KwfWXC+xjpJXLU2cXVbrZmw1u01bltG4AAasAAAAN8BnmB0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sQbIY8Sz/h266e5+QvNsRJl9cZmAh+RyOTxtjwevuID9vPBqLAYNCcSxtBcWAU4KgcrAQLk7ATwLU2PIotzRG1Q3AcD1eYgKI9vln/8RQv81QMbihPUrZhkcoRcqa+evGYEv45PNi0hV8orGgmftvqu2uPmmVUfbNe7QV361emoFNFaFP/LTyLusjQlwnL9gKbghV+5itv9qmNIN7isA8hDu22yb+TieMjJAATdAAAAuQGeYmpC/wAACoTwEci6fLAC3js9V5wT7M+Bj4m+NNlDgjCFtXTTSru2J+zNZ5i3MzJhCX0st21Bg3OpOo3OwL/5YbQioBBBTXCx83KNLQ5KKtEtlzU1ECfTGmFOEbrwddgv8GVREW88DwBRVJ663Vqt0Iy03G6nLCYhO6Tx9s9e4Bw4i1qw9ggbcBNBiJMCYIH+qRlnOa6m07bPf9T5xXxBIDkoZB8kRVz3a0B1bz89e/1KIW7wAKSAAAABkkGaZ0moQWyZTAgh//6qVQAAAwIxgWXkb4AEKb+YWSAkvvpOzYij84ij8Mu9jo9YJzl95bvbdDkp/pCQnEQ5G7ADzGWR15XGqD/QzlnFYoZJNwJ0AQJpn8YNw5AZziQOR785knB48auG+D1KacLa9QIeY9+xHX+jSt7OArh/15yER4LlJE5/anugpw44QUhPtga0XQpgi1wRAACXF/PJK75S4QrlzyDcU0NvqjpzzYZWU8iWdDrFzacjEPTjaNtiPAnz7oJz+t6FkcpLmRcEFkVuLVPDgkqTHTDvh7MrVaV7GNfSV9m+2gYtHR0wFP2i972oV3lYBRcvzO7okfT8d1B95QBhOgLFdpJSoXdG5zFQjyjsu12/6lQ4q0JzRJ+TodcGiTiZs0D1/a89Gy2c8wqLutcmHk9hB2G2nFU3kzUBpI3yv4CkaFMacGENsNhAW+AxssP451Mz1jzeQbkHo9XNg0qYqBE9bhqGHNBIw1s4VzloFWf9uUwdnXSczxNtPcniQEv639yCNiasf+7+U65++QAAATlBnoVFFSw3/wAABh3ctKEQAE60MtntBd9xejGdjS65eInxk6TaBEQgzHTg+1+OGcdNXBAv7D50x8RVXMBB4WplGUsoAR4FYMX9ddC3ly5HngsqUcdNjCnt5nxYXg0qbSAWfTkoZEB/XRbdioL6rPnGHOMFPnfQ+nolqxhtLavfPJsfy3t03NnYfoDxf2VAPRpu3yMMbO4BojN1n8Ge0RaRa4NR9HzmnW2cIrIw/MqzBEWptE/zHWi4TMtQ2oo3Dujhlz1qtiwYs/f15xIFxSfdRUVLy5jxn+zI6sf5AW9mGefwqlqe8PQErWuawMq4UptOfwQJ9tTdqzP4AsjqyL9WRzISH1XX/tZAnAa/f34H8dRo7ivmEsmmj2tsUQsLf7SNy3g/WiakvTi6o5WKWdw6wAKyKdBQAAqZAAAAtAGepHRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxE+FeN9NvnoofFRoPsItIUbu73+9+v/6GmnEIr/Be7eexKzJm6FOE4zPV4+tX/goOu/cTJJaSV4uOrZxijf7EkyF17LjmNp+LtXkihp6lTqmgDqKSPug93cAtal248pZxC9EDSFU9+5AqViGYSXkfiqUu2hhJAknVa1WEugdeMABnwAAAMsBnqZqQv8AAAo88Ef5+ABdR2eq84J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfieA846o/5jm/sSCcbrPVMOXXhyDjGe//LDaBh1QUCaCcNrSQCAg27JAUJMc077PunswKcIurCEu9xbWyAlrPh/OLXgF0kOluhGVyIH+qYRriyaX81OYHS3z60VuyYqQ/TFgABlGvbyCJVzoTFEWrR46PmaxPqWVAOECma63DUiLT2Cx3XVZhThq+X0nKbZGdl98bWpBpYAAyoQAAAaZBmqtJqEFsmUwIIf/+qlUAAAMCM8uf4j+KBACabZk2XGm/hwwOrZbwUzok8L59eUCDnKsqfsgHL7hStRZXoonjJF5RpgPcxyKUtnvNIf0K096VHZSa4QzwRK1/h8MnwgoiHZ2jDSO5tTupify10L9E8V9Kc8MWnCR/rvCgLlA6uwbfCC0MmT2ClpgH0aiW2ljGWL3vYE//FcgACH8LTDjd3gFApvR5dwN3j9RTO/PpySSxRIRrrBeO4Gd/uBavl0BO7qP5MgrHuCjLpFeUGMCchZ+LS59bCHlrp8A1TiEmaKlvOfPTMSgEkIlmUxPt2IzNLN7iFaAX6hLOg9OCzTKLZ2kc0iTl5TM60vDiGrLOHFhW8bXjBPZGJGrZJtka2+Z02YF3DQFjDf1LrY94jZ+ZCjTgkqK1ef083swBfiI/h8V6ClulURbrHOPI/nuYLEYdk+7ZZoRWAnd3+zDEXwg97AapNZ3o8rofgwBGT1wEqdVWTFMv+5i9/SRM7u3nEqiZy+llywZgkXOfligzdneipUlO2hKpkDulrhAH0aKNpcDrDrLchAAAAWlBnslFFSw3/wAABgwqf0yAE1e5uN0VXg0oQZhopL02RDKfQL81eleUxE0SO0C/qrx4icX7/flWy8cYFAV43BJvLZPcaRpTQjJ0BdSJXbM0m3X8gEfyr+Jp9Ov+kwP2b4NodvxyiCcrUXNe1IW37ey+v0meWUUCRIZ4OWtK9Y1dqiJSxBUF/03hrKHkXdN30aOF8w/cAQI+yPT9lO1+2rf4oNwssWXQVM5O/dZ7Di5j1MbYGD2rY8FOeN2m2Hz4wxwFSepNosze5mOiy6EGNBlHg+kvnmZGMcY2KYkI6pQvd6ZpSaXgPzV35CpcnU+AlTdr0VF1TLC/MEvZqUIYyeH8cu0m/oEUfLovMsbf3n1IOUqKPqUBDKIdHLUK9EigEWG/PE1mo5jSbSfiWOgpt0v5qkE+NzoYZU4lNQxQF9Z5mK7YbEWEKWVNLrDj8O4tFK5ctAAGZYP9FFTYjxD59nGxDTIMFJmAAImAAAAAuQGe6HRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxBshjxLP+Hbrp7n5C82xEmX1xmYCH5HI5PG2PB6+4gP288GosBg0JxLG0FxYBTgqBysBAuTsBPAtTY8ii3NEbVDcBwPV5iAoj2+Wf/xFC/zVAxuKE9StmGRyhFypr568ZgSiAGk87I4n54mLfmCqxxSJCPAVKCjOg6RLyDGCb/EYgntLAAEnAAAAugGe6mpC/wAACjztCP+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH3rSvhpgJI9zduunufkLzbESat9fUXbQX4fvfhaMerXqZgICODEzkZzVOF4PR5slmC4vKwEC5OwE8BwljyKLc0RtJXbQdpXmEs2J80FKaiJ2/mpaJEHY27EkMjkSLlTX0CQbAfEGnQSSePoeY3LA3mKtucwdCYRoqFQs3xMVsLT0lCh1BdBAATcAAAAfhBmu9JqEFsmUwIIf/+qlUAAAMCMR+3jP7AAmr54aHR8SQwOvQzlnBVzcJQ0C6yCfnPHii33B5doHvS2NQrnQqDMC/k5bGWGRdi9Ik24q3tIwK9xeimAYxCmUOiV0XZCYG1Y2zJxaa3Fsp21nxeyv2BNQmojx348mUuhI6OzOHBLFP2Uh/udYZeW0uDFsaqSAorw4554KzWWIMZRfYH4iAVBrZtNdPQFgiKhIzdnK454P77TFq+z4HR/zFpZKN1wwAAXXN/KZF5qhSH+B5RcQCSNsHTKG+ty5Lpg4LvV8O8GeTdYf+/rjwwpj1V7ZKuugQI8msJUg7JM+KmHsnjk91C9VesP0myrNnQQVOA6E9FWp3adGT/QvO0Lb/EsJpbzjCpbINN6R5I0gaqJ/jHelWeXjIwuwNUvb/agwliXKaZLG2FnjhyTVwayUok6S7vJDwJYedFPb11tLGae+XlFe3ClnY2KOrkTVgTuleDfi6F2K298FO91ecFR9AxluuveikhbNLsuMkhxmUuJmC9o9/QSdD4xlZHzNGAHWqh9lqj4BrsDRmkhpAIuGkjnZUv/vc0/enrA5RAuSOUQGC6b3Es5lJHlGEzCdcKuGni6uTKhnM4RtNeKVZgvcGwf6g0M6DAOVI7Eu13dYmmRqELIBhtHzE1ffEi1LwAAAFFQZ8NRRUsN/8AAAYQ6/oG1voAAnbgme2rJPPnblc8l4EVoi0lp03jGdZ1CyvrhEfmV/wwHzmQKJNI0nGVeK9vhApIcH3+Y789hKfN1+Q1cZLUQL7DgsqK6zclh/gSpanetKj5KAH0b+4RF2C8owXUh9CabfQpz4t8oxcWZ9vAvfK47wq6qTc2FqkxCPnz3MJ/6v634Ijkt18vyUYlQbyAngwR/Izz60+NdS+iI7pZV0ZtUyvzfSwHOwqwcAhE3BrodxRPoIrzsi9nu6HLBLFC2lxfT8FDG8T7EGE51nP76FXCZDTj7Ol4riwZ0lpF0WgTiJvkTKoCLOYiOJpB9EjsxXNPTXSRW1Zc8mvUIODxtz8PAQvV5gPjceGA9/oMyVsiuIlcML2AoKWgEHY6QfpCKpDn1xiqoK9Sbgfl4bJ5DsnM5AABiwAAANgBnyx0Qv8AAAo9lLG8aAEtWmK0bgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB+hKISJdbUkVig9NRpYTc8SHYbTr57+PGoH7Sc3IXD6uBQQ3nacNp4M5PGFejDTaXacYF+trbOZCBqsicC69mzuEOvYzxsksIEdruTt5m1DGWlJxu7W6tmOQ0hi7ODYh/71+lLK++sAAyK4YUY+cdSyn+yk0rtv2g+7LLHGLkqg06fY3JPjzYlD1EK6QCdepDWhCJBJ/YpMBQjs+MsGhXzsFF8ZwAAM+EAAADFAZ8uakL/AAAKPPBH+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH4KUN9hDEi3gaa0B/rqUG5MBRx8rpuaJjUJtsEPXwK0c+A4iGBykOGF/Xevj3vWgpItDTaH54xkI7pxcZY7sxzNCsPhvkrb9XP/81pAg6GvO5cPGltXUUd9yqP6jKxDure1Twdly6DhRwHlufuA1h8BAjebg6EKnLtS9VGALYNuyhYUwOiJ/3cneLxH5qwJcijAAAoMAAAGnQZszSahBbJlMCCH//qpVAAADAjEoTP64AJ290cV+lE8XIRe70zxj45yUEFIqcWZkZhTFDxLLI0bwUssX0v5m3zSpUYOvorvW0L3WI1d0k6cyLj1ZJEE8Ags4PXsY7DA2opvTKPfBmWxnWkbEF0yYaiVYsv3MLu7/rtaDAmNdoIs1n63k+q3vFGMul9x76h1K5Fe0Uf9br3bEAAQfEnjbEgWHDNmMvHGyadwL2NRO65NCgSJcePjFXv8UoX/pPnOzWIWRt0lheivV3N6x9eUVNsBJmB1D6kd27gDoBOzRY+W7Bq3NvSVShq4IKwTzZRjdjQ2p51mb47YWj67Xv/zhEb4DJkHbdAT0KCBg5PgRdogYdew4Rc88u1Dznw+BCeQZJdtnXW2nWmhI0MoIs+QVTs/pYgC9HFJBDBmnR9PPHZ7qr8RGyT3TUcQrUY1MJ92/2AYEXkDrzpCRTJdUmVWLMEFmlS+FEkrHUq0AtSDO7APGocUdQ1zdB/vcSyJ0xRllxQb6T7rhpFMpspgIgg4r7q6XRFLN77bD1dmP6iuhHeFNl9fDRDeAAAABbEGfUUUVLDf/AAAGIS9FAATt6B7ebdobKjVKGTiEGvEeGAys9tUXv2jE7ijVRbNkL5ahd+SPXT+Y9mgozh+B3BXF41gdkLY3E/EaSD4B7+CiGG6zdqfYetl/8S0AY2+ec89KqMMGW6GQOwUNDa2qK6sGCGjyVr+lBd6vpsrQ85LSoJj0zLQH2zDNUks7rlLM9bg45YIj4Rr8FklSMabjg2FhdbwZsKeY5G5XWzhl94N6edMw07F5QqVdUwiQwXT0K0DlSlCx/p5N0YttFUyiKs1g65C8YmQzcl0Tv7eeXeSgeM54t3RVj54vjAMNfd005lLb2ZIL0O5XSAGhpPc/huUcaZXFWi9+9FR4dXRjV5kSqGakEUI0SCDe0vv5GY8ap3AwIkcSVEvdIAwOhaGnW4CDyu40JlKjwb8VPaFtezJ+hAcvxBieLwHvvpcqz+1/QYY4qUrC5CQhMsLrlynl9DOCvEwY9DuLGtAABrQAAADHAZ9wdEL/AAAKPZCyPz8AC6js9V5wT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg+7ET4V430281oMHNjqLvcQshJvr6i7aC/D978LRj1a9TMBARwYmcjQoWu2wS4sApwWI5WAgXJ2AngOEseRRbmiNpK7aDtK8wlmxPmgpTURO381LRIg7G3YkhkciRcqa+eu7YKx7NKdQluKUzDCK9xHDJ3ujDZnwGbm/ut0SwhRsVhxqioVnZwucJbna7yXnIgqGAADpgQAAALsBn3JqQv8AAAo87Qj/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB96y0Cwu24iCAL8kjYIuTUeizHNiLi/TMZ1GKemalRGZZViV/ui+fl7qWZ/Q5HuidIXSFUFKmIU7RU8UDPjDTUGzlyA4rDhq8FpnsBUIwcwhia0+2ybyaDP8UPQ6Sil1pr6AoRgz1hglRT1oGAdn8bamBxQSvNChP7BXLfC3Ru8CDG8AgBNgAAAd0AAACgUGbd0moQWyZTAgh//6qVQAAAwJBK9xxqp8AOlcv447RI1xsGyuNCfMc8QxS2c5qEdYlD7cAotJ+FyOk1iSoZh28ayIkiBQ1d0U/cDaknuvYG6NVd6IvKAtCVQ04uU8SPNMJVZa2oywB8fmVeOaPer5+7WgLX98xej4m1/jwqsjUE8IW4D4glbflY3mWQYK0Zm6QrjHWoKnWlKecZJeOSWCYMyuSl/KaKhFT7HjQkPp7RB1MA8/EMaL2DtmHgvOV4n++OScsSL0Q2D5ri2g6wctYJzzA6Pq/s8tnacKyBQHvltbMG9y6vqR2NwAaz2WMwvoQlYVvjZ0U+mBkpJ2+GMlIGF5xL1lvCFRDlMHyEzyLLSxmas5uIvviQybDyRgd5ZpMGJXMNVPfUl9ld7yDgS/5cqayMAAimPEOjwvw/DWQLT2q58wEQCGkm+3z4ALTdygBqbSwKUbrN9HW2PIFxBWnN4pKvyUZd5ZT4bTKDHXQNuyxurFRZRt177Uemi05ezACFShpweZr4eN5Zag/aInXV+I8u40FNgB0NWzsq3LJNvM+cWRA5WYpEVnor+1sbGwPJGcGTriMMOCqCUACQ4hnpIkUDTDBaPWKf7owce+jOEjw9AdpHFZ4nZCbgCt4aOMco4+pSCyian1pd6pFbhSlR+EHmbly+MsWWDUZCWiuL52go14tR0frTaqDG13iS+yFk5RHSlMfTTJo+CnwvRLr/rGc1MzoTZxldkoL1x/0VXIKLHOjlcGqTlXKjyeWJSzrBIzo3HEvgx2S+Ta5jNwA0EUVrr5j6OpF1D7z5Kt81Meqwj8401z79N9Ph8F9OdhiBhrShrgU8+SLs2VhDhhwAAABZUGflUUVLDf/AAAGSLlzegcAckqBkqPuwQYACF7X1ewHztHUKkbrvCXYgBVuXD/kUh95FcNOm5FNXSqP7tknwwc8SXj3ShN0nwkUJGt21kYcL6RUBlDxwDDPxYFyMpltxLWEstowMhna5yttvAw8MFX9fXxWDPxm2p0AmIq3Y6cRcSocA1Wt0kk/elzZUdiKFlOeA/vbw5xhG8XS9Yf8BpC2JEMartsaHDeQR/DgrJ/BAOSg4rZsfyEHXeSHrP2i5QOOVMKlzpxxEJgrhe2pIfNk32UOhttH67aNweVVUKnxk++uOZvGq72sBxrcCVHs44OFd1uWNcx9+YzuB2DJcSuPCP+Knt5MI4Twjlatg+YxveUl137MhvlvL68GEo5NQyaQM12IIB1eOK7zhmJiZCoQGFcBzFeyIn3Ho+cpU1giqDIopo1NMlh1PStAqeUF+NTxKXmCTxiw6/MKosMPQim/JwABEwAAAMEBn7R0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sQbIY8Sz/h266e5+QvNsRJl9cZmAh+RyOTxtjwevuID9vPBqLAYNCcSxtBcWAU4KgcrAQLk7ATwLU2PIotzRG1Q3AcD1eYgKI9vln/8RQv81QMbihPUrZhkcoRcqa+evGYM+60ayvAvXtNQ1FpIP9bV4a/KTcVtsCfuT8345eZQhDJ42liBkBC3o92gAAsoAAAA0QGftmpC/wAACoTwEci6fLAC3js9V5wT7M+Bj4m+NNlDgjCFtXTTSru2J+zNZ5i3MzJhCX2Uv21Bg3OpOo3OwL/5YbSxvYbx/UEXUgnDa0kAcLiVHaJbLmoAIvhmVMKcIfHxKP1p7UqiIXa4cIe14BdNM8dPDw5NUne1TB9xO8H3mplN3rBQShyr+E3HvcNkhENBGPsmLBDtz7BVbFNlY51SOplAjwZD7v8YjLh2kVtcRc3VmFP5lGWRH9T5gzxoKRmKtfDVNCGveE1KYLQAABZxAAABoEGbu0moQWyZTAgh//6qVQAAAwIxyt8dFwAQ/bY0NEAU5/tfp6DKCd3VN9Cx3hAS0Dy2AOWUY9E0THKphTka7YedxL70E3miGC1PlRTS6idEbrB0tEkhSr1kluzLPHBXJ/Dpfqoa/pWkHV1SJ8c+FZq3XZdJV0id2vNto49GUsYR2ltdIFJ1wYeo7svS11qEfsnc7OJN7CAP50CdCmAAG+8Yj2WITcHXbedf/4v5J0loADM/7cHFh7CP1SreCox7/c4mXRsFL5FGmMFWjurl6aZbRVuk4p6lscEwBlCSQLsqOGKUumPZPjtAx4q4HSGIORviTahMjXhtjDbUwT14d0PP7h1xytN/VLRZylHzMrFn4N5vykN6qFLddNkW0iS7r5IoBT/eEnXZJnRzqSwZfrtiJlH/yv65sl3h9Upgp5MowbHEWEkuWzrW63HjiTO9FBSqr5zT1kxRDjjUQA8MalCqgj0OB62Lq+GoftBglp/DpPhMyl+07TlpVIX48vI0I5HlfpOPBY3/GalrDUvCDFtJ3XzbLOrtk4WHb79lPjktAAABNEGf2UUVLDf/AAAGJLXACaveZm/Ct0iUrSaF3/rMpBi5DSYDCojLtygCdfuNQdzKa8huhSaR+yZVvW3KTJ6PPRY0EKw4f57huJKzqkfw/8P2ycMhLXmV9HHTIr57enHux79nsLm0/9zYH/fonI7/D0OF0Or/9hWELKgaDehC+ysvoRv4WU7WH76YeDtk9zehP0EtUw4mRNSZj7f0jF53T7ie1Svgi1jcENPeqvjAcKefrbOQ9bwqJL2Jacj1I5wi8NTeZU8IbVgjtsHdlz5eIoV04MDMMY1UJvKLyVTAgHJf2hu62cB8RgRB2g7llL6dvSVksXp/nxlKEsRGBswYR6qIFndn4/Ph8tIs3gUmu2aJ6od4qCAVqvQZE6/d9tFP4ZgDP9EQIK5NaM4bODc2RTUAAAyoAAAAvAGf+HRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxE+FeN9NvnoofFRoPsItIUbu73+9+v/6GmnEIr/Be7eexKzJm6FOE4zPV4+tX/goOu/cTJJaSV4uOrZxijf7EkyF17LjmNp+LtXkihp6lTqmgDqKSPug93cB5SbsDNae97u/bdpw/fwo3sC7g7CUUF2ejHkZYLKnWDcPvB4Uf7ifT1Hl/UuAABdxAAAAvAGf+mpC/wAACjzwR/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB+J4Dzjk+tMCKD5kkSpBQ0lOpOwpYzt/5YbTzWB6A79RdSCcNrSQBbU3CRAUJMc076oU6wwKcIlWDpfJUDWyAlm1iE+LXgF0k2luhGUW1vmqYK9cCaX81O/HS3z60O6LCBMppSwADIqzsQ9ZMAOstwHyKZDhEJ47mmZ4EZqcdJDxgqfhgkS1bwAAD5gAAABskGb/0moQWyZTAgh//6qVQAAAwIxhyFBTn8AEP22NDRAFOf8EFnZfQbmnahWD4mHMK/NJwR3VTXkrRjriS7FCLb9D8mh0Qq4q5EvuSt5E/cMM040j5XyLwOqIS8E0Z24xfpcKvvJwPTqExq4hRaULqjdiFiCW96bfzREScTlME6jauyJsY3rdOo841Ny/JcgAB/yAbbiD4c4OTTC0D+xdYCj16ySJOR2HUXnsjKtjLjL7Oaeb3edxQGdxZuFH305ml1EHcT42fFOy6WV6a1rBtIjsTxJJxenYmlX8njinsEv5S3X60KcoFKCQxHz64lxckwUzZn0oNMOOPgp9s6UMIK/HQTfHqlmAYeWzVbAZWkqq5k7/bNeWz0oBRh3oKafs7xuR3V+y19ukhiwNE+RguFAfUAJqyQD5u22pZyT53iKy1gnrJ0AJmGMC91g5asBZo+lMDs74oQ/TfCeF5guVjhxqzUy5kBH2PN1YXW46XfwV/g3n+N7qe9kbF7Z8JxokaTfbGt7R/h1f7H5DVOHpmpR8YnagSiGmy0bX1kB0FC6n26hTFE5pBfhWQue0sgkahbJAAABc0GeHUUVLDf/AAAGFmSzOAE1e5uN0VXg0oQbp1czv+Sie3SGcwC3AzJ6xrlqAvs5ajYNlQyH4Q6B85r5+8kYNaFH1e88IU0WWhmpX5ap8KmCnLS/kRwKFRzFbi/9FZC6v4BlgoWjto+aRp0g3ML1mRd0iggrcNzmEYjjcGGwtKNiee4oHObeyWswhxzbVFdmFJYgIEGoaRva4E4geNas0ZKJmJfz1foMjZSeuRdawBCIIxUdtm7REeeeAx605HyF59BQsUdX0tIsgwA+dvKn/LOKq+iCxrbdX/705kikJElbpNZuq88narLH9y0iarfH4qSEs367iYkBvY070TJwvrrjZEiKLXfPNoA5l1+xPtz0YeULcvVATwSY0hzZ3fNVpbMcidtk7Ke/ad0I9zFCDnuw/jmMJVUgYNu96EIEY4Sc9HxgqQAbQ5LfxMxPBLzw89GwP+R63lwYBKYspErgUjyWAHIXcqTaqor755ZNifbyAAGBAAAAuAGePHRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxBshjxLP+Hbrp7n5C82xEmX1xmYCH5HI5PG2PB6+4gP288GosBg0JxLG0FxYBTgqBysBAuTsBPAtTY8ii3NEbVDcBwPV5iAoj2+Wf/xFC/zVAxuKE9StmGRyhFypr568ZgztCxKToQUIOT+XkYJRUDNnd2XNTBdJQYLCgJv+E8IMxxgABVQAAAC+AZ4+akL/AAAKPPDGhfoAS1bUSodKqbyopD00gjEQ5/Ike8Kl9CBEW/oXQcpRpMFRJWw9C08gXSNajL/7jqznEpCEtx7+N0vcYNJGlPBzFGri/OPV4KIAQMh9It0fVsQCnBcMTrO9r3avQqR1299FuaI2kv+lerzCYmVv6Wf6G7rHzUtcel9NuxADaIXkL57oTNswZmrQfQORcEeJiPoDrvFK7pxzExUV+kJ7DwpkUc1uYFtvfEB2MfdMuAADUgAAAddBmiNJqEFsmUwIIf/+qlUAAAMCMYDYXMLaYAFuo+Nlf95090C3M6w4xEuQywitp5/dAhoqKFzSgZmplhZZe/QbEV6D1kRgmqLlmBr2pu1tdVZ8wBkYP4ylo1JMRrr6ftJxbilfTXA/tHZjLjiXRt3+gZnI2U86jbLKrYdvtjPedry1UOdxm+nAh/+C2JhXVbLlxDqwgvIVbhJXqxDnKUX6n0VLZizcCuG+p5fo4vxeUyj09cN8dS2AkmF0pCfL8t34/8lyAAHyIYSnZAuQLbaSA8sJXZ+dYil+IyFJJ7xv/wN4p+njQZEvNU0zLRYUtHBhySKJ3RQdNLi3xOJUePYZczjR/7RqOZCb0//8oUrN3IrmXm9OuBBoj8rFuRRFAf95EJ7p5hE2m1wTrW6C52gdAWGDcSGZXa0w2Db2/Gw2MY8+VJ/H0ojg8AN/TjXEMYftB40ZOw2IcSb3Z0aQ/DYM/8JPLxspzM8kZUpNjpePONBsYfIFQjq/kNv9G3QWOGHPMRVItC15Fv9G9TodNueDxDKUqHKIWe6c6Y+ubLVXlVahTFkzsr3Qzl5UGSaXpO0wQcq17JcWIwLZP+nf0fAkplzBgIfuliQUax4wKUzxPfuHUoQ/Qs0AAAE6QZ5BRRUsN/8AAAYktcAJq95UjNWABLuBEer1hs/1KkCVHuo2tgFazdOkjCBZRx6MM/Z+7oK7L1ReOndEM1J/jnRsNrwzMYcjt7R4f6U+YIcy4wAIq0q+Eng546ZCCAhouSK5P6E9pvLludcz9EHoVjOBz2oqx3zSIIbYY2hTMta5Dkt7dhnNX8i0DMw89NkC4/DP0GZJDeUlVYOwidtc+lWDD4BCm2UNDjJxDVTYueF8hoOhX5pUmIEbeL3onlUHfkWThaYl9NIvBcP2Qsd+bGH383b2tYXNmioOxP6+326pMh/9CSXIEZy+e3fo47Smx+9pUVs9jqEv+O5ocM3QGdqwkZx5rWJ7pY9D/s2Pi6KX81q6vkt9y+i4hy7llo/3J6Q7A64t/jWLIIcPif21F6SGTqrtScAAPWAAAAC9AZ5gdEL/AAAKPZSxvGgBLVpitG4J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfoSiEiXwzGPce1EjKq4FniQ5hv/BldgvO4nqaMKHuQPoTL3lryOcsXMOL2GfgkkqJ4hjAv1tbB/KgNXkTf1S6ftzf17EW43lFY9dKf2PNNo68lK1wokovCn9U4g/8imzQLewIpZUX1gAGQ8G3i655aTDvbBSntOnWAm+e4/GUbViev5CbrymDd/oABnxAAAAwAGeYmpC/wAACjzwxjeNACWrTFaNwT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg++Sh0smF7jfE8DWEU5LbgfqeH0+l4SM5uc0pLCIDD5Pxvo0/z/Gl01mBIdXx73rQUkWhqO77eZCEXW9JqMd2Y5mgrHn7olbfq6ATwq+gHQ2GZ/mYddi3ifIaPNiVZsQ7q/w0EFpfWAAY7NnGvQ+uUBOwUsFM2UHQFPjMFfR/rkubK9M6YYge60aVD50AAKaAAAAbdBmmdJqEFsmUwIIf/+qlUAAAMCMHBsfgAulHxsr/vOnugaz7Bf1mg3vUSAgSuLQmrjs3SbyAZt82ix/iPDgRvdGe+tphDsZrfDVbD3/UzeB2qpKd8aBAnGteAkRGtth+QRcrf0elf0Iz4Pro8TGl4uXjGGCkZFIevb0tF31FRuxH+YBr8TLfegI6+mwrP6R0QSII8x/eq/9fHvIR8VyAAVwXldyk2zv20y1r6iLACN1ol0h5XGQ5XYx5N3jW2Hla+oPtMShi1r57+2LkLWBSnFQXNDYJQG42iH6vTP29vEHGmvQiSBXWedMxHyGRNQalUlMFnOlIpG1j69hbaqtkf3GgRiiNyrKLQWe+y9Sb77TRYP69oBULlropLGsf2zvSxwOV2t86mvHDFTWCLQAtfW8Ih6fik2LH0AESy6YkaeH0EPVp8Z47ccGNx1ElGW0OVQaVc80j097UiTZb/e2XZ2VshKuN7IyYXL2SHGEZcjCcOVEnoGhKNL5YTRMMXzJGf1Vl5/VtMD1maalQ4o55g5+zLQ0PELxbQKWX6OhPPD5+ubHpArljJzcUhb2x1hytZSJbXz4EeHAAABR0GehUUVLDf/AAAGIVBzgBNXvK3J3QozgSfJaYeCzOwu6Y1iYkZehmd1PN6sLoH+Ug7KnQUnJbzOcFKmQG2Ecj9B5VbdM7uneRMN6TjgnODpIZFLc5bdoKRbfONYEIN1bRsrGU5b1XD8EG6DWcdNSFVNXaGAliBWzGlSR+wF0BpCuFFc83uOn2SyQEeNONneJS76N8Bxn86IruNA3O8s4IqT566iLTpym65Z2qcEBpVXOR3KjQzRziaCckBepQxj32NqQMYUnCbxdoq2JCpeGKJuhmUf/4U99mR4v+kdulbUykOD+z3viEXcR0NF4HxY+WOtzbeG5bQKS/jepbjKXORQ3nMqfan0ENgU977Il/iD9P2YHmhz8DxjvvcAun9Q8sZQd9smgxhwy6VafPAgmwLG87lSyeouA6nf44v+Soec8sPciAAJOQAAALcBnqR0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sRP1icb6bea0GDmx1F3uIWQk319RdtBfh+9+Fox6tepmAgI4MTORoULXbYJcWAU4LEcrAQLk7ATwHCWPIotzRG0ldtB2leYSzYnzQUpqInb+alokQdjbsSQyORIuVNfPXO0EY7k7ulvulLNL5dtjiSprp0WNBX8kqWfooiggymQooUAAXcAAADHAZ6makL/AAAKPO0I/5+ABdR2eq84J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfestAsLtuIggC/JI2CLk1HosxzYi4v0zGdRinpmpURmWVYlf7ovn5e6lmf0OR7onSF0hVBSpiFO0VPFAz4w01Bs5cgOKw4avBaZ7AVCMHMIYmtPtsm8mgz/FD0Okopdaa+gKEYM+NzLmgpCfK6BBSAwNedpppY357bVO8TbLNaN3s0wpx8LmBlt/uKSVKs6m2iMrAACDwAAAhZBmqtJqEFsmUwIIf/+qlUAAAMCQ8ufw8vnQBW/nv2ZvwrxxOx85KyWsKl8QoF99kgKRKI+yDKdVOGYiXGZxGbwQv8Gud5xGu7FjNC+eNrxof7SPxUZl1YcjVaw183j/x6OaQaGtYdFTtyVsSWKpMcUZB/JwJQkXBNoNYZr8rD4I7Xh0mwl1MgsTwiQnH3y0wg+eiK7oFG/AERn1zUGQ1Trl0l1zsByRdjPj3hgGyoT6YKX+UavqAz2jJE4S8/fucDNAxOIGEwBjcgTbtIZWfekXeV9Pdd6ZGiHnDzvy/COiDcmQve5w4warqP3Hsp5dighlXwfDGNv1LCJzbrHIGKHHVTCjGvsQABhwGh8y62imbB/zqU2E0+F/biroMY8LS34V7nOSksW+FZ+5erJRAe7v0LXXoaaeGwwBXTWf90w387hsYZSsH97jl9uerR3k7tMgg25hPpDf01p/Piawysqe6B/HlXvf9X91ZZYSKPKHzI9vnxVVM+y+PhiaCDOF/QC66w/SGcq26hDlVPR7p0rGyVJA6ZWoMX8tFl55Z1VX9CZB7Yeilr7Dae5VJYlg/lyNkvJwkEzuTrSsdCYVmzFNhcAJ6a3gclMXTvNCa2asR1beCLgfsBGjcjlNiRMypqHtdagMuuSTwv2MP74rhU0rkWUx1XdrUKhgjLLoDwI5/Z+ymA9KkGtYVY9JPDJ/I59BhgAD0gAAAFlQZ7JRRUsN/8AAAZIuXJTccAJqyrvfP+xmD0WhmZiAc3UzmqZblteb/X9bLBMBC4yT7AR0Q3Xf7tj7/T4YrUMJnKNKDLwHg7Yl41TI8ArjR39PcWUZGUnGJaeGCgf8zr82GmaAilR7C0XckzyyRwgNQuSPmNS5qVw+5zq2+M45COw6ilJnE5MLPJhT2Zh95uD53mqmuYe7ZDmMflXZU0YnavrrxCyTb/JPR+ZdAxmCU2IaQUd/kdUSy7P5SJqNKD0wqOFa5BZzjBs6rPWqeBisOW0TIxOe1p04ByD2LPygq3zexhz6kZPTPqazjZEtn2lABtUZLm1DHGZmlgWf4/hEAx6bJKySGYmD2QM3Wh6Z2WdMEROKvde5LJ3+q+2fFQJEncOB5TbFoUlHDYvGV6SEZOd+bM8rFDDjoB58HxTo3fkRX7F2W5I64ryoif8VgUbzF61FuqgYNjVdeOkk6hdrrvMAANmAAAA0gGe6HRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxBshjxLP+Hbrp7n5C82xEmX1xmYCH5HI5PG2PB6+4gP288GosBg0JxLG0FxYBTgqBysBAuTsBPAtTY8ii3NEbVDcBwPV5iAoj2+Wf/xFC/zVAxuKE9StmGRyhFypr568ZgztCxqXnd/vJGnXgmHj/5SpoOqo16Va6blHl3pxdW1M2TGRN8oLpBAXawQgNrFVhdQkO+Syh9iffLoAAAMDPwAAAK4BnupqQv8AAAqE8BH7zgEYALqOz1XnBPsz4GPib402UOCMIW1dNNKu7Yn7M1nmLczMmEJaTQ021Bg3OpOo3OwL/5YbQioBBBTXCx83KNLQ5KKtEtlzXaKfxe9MKcI3Xg67Bf4MqiIt54HgCiqT11urVboRlpuN1OWExCd0nj7Z69wDhxFrVh7BA23P+neBgL1qbYjqnoUxZEwKoowz91UhKJgQdHty2A9Ce5wAAccAAAF3QZrvSahBbJlMCCH//qpVAAADAjDF2vcAEP1ePT2VLOgUo523/ozOWLMmzYs/LzJMjoscJdN8LscadYTuB+QUoWP3z37L2kiPuNH+q/cEBKcpnvnwp7x5J9vya8xEgsjIqsWd7CJYaS0LNUe371EA9PZRvnCQGxas9v4vUhXRfQDPvS8jb7PzFWu25bumvnksuCIAAQz5S1TDhTQao79l8q0MLGDkA5aqimlUDHm0ehRl8EupBcEk1PaPM1hgM3Tc3LMlDOYtvZJcRehx5Kgg20+dGIPH5qiHaqOprlGSzOGoAvYDi+Hzgzhp1vbRB/hYY77lts3wGYBaIivoDBETfuTHM1vrq54uzKCwkvKXWzqkW1Scgun/xSDFvwZf2E8BWhfk94gDN/pBKmaRPov2xsIx6+T7ltwpueC93kDrNuq5dp2zJWdc5i9jCesnCuVfYALgue2tvIcsqEnW7yh4Ktgm2ekhxXnUHIT/L++S8dmUC1f6AB3QAAABJ0GfDUUVLDf/AAAGFpxNUfgA/pCMxOgVpHOWk8mML/jwqy4sY+vFPJkByWOU5oXlh+iFmbhIfNd3PvifJ9YD2chUuYrLcK1RXZn5VQKgKWrpOTIhBeQB/XNYwOovqSslbS2jl5ye0OJJI0N80CVnIorYSMoBtJ5W4eUuIvlNm+bFx6ByLlOJYg1+jqhA87ZfCdp/65oIkJfW6EEUZQ2oE74ySGFe0gMt3Y2jtDMXJXYykBVqXuBuaQpA1fnL1U3xX7qFae3Iy52asAFYX/TfVT0t5tbGrUj113WiD1fmXUxroKJevsF+FqymxpDkFjyE5MK4R3H8JfWpGkuGSBkEOEH0AN7oz+xFB8ICZUI25vPNxXrDmO4wf11w7WW5h5c/EzQnLhoAAssAAAE7AZ8sdEL/AAAKPZCyPz8AC6js9V5wT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg+7ET4V4302+eih8VGg+wi0hRu7vf736//oaacQiv8F7t57ErMmboU4TjM9Xj61f+Cg679xMklpJXi46tnGKN/sSTIXXsuOY2n4u1eSKGnqVOqaAOopI+6D3dwHkAJBHsuPiiLN8/cR4NLoB4Wb2AQlgzVjIRWRYOJlj0Cz4y+MbyuxOQoQc0l36rJIZ1cpZZKSmmQAME38ma5UHc1YhxZyc3k4JdZGy09u6zaDOev27PzmLkbenZfsaeQtcg/ZBVZmd2AyVOYQRuyDD8CQoT8bHs6+ct+4+vAW0sUvaMckVHOfCbeiUCWvg/uMUmfctonUCojMs4e500scZ3kol2s1f2ta7F5EEAAKTAAAAuAGfLmpC/wAACjzwR/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTCLwC/D8t5hGz/DDMDmmU8nMxfg0t8OlKzP/5YbQN6o3ah5ZRGz0kgN52MpFwbnOjZmC1NJZhO+19iaYoAdltmkcSqDU4R66L0FoDQXqgh9v50o+BotQpn890AteluxQeQ7tI6AA9uwk+J10VFTsDkxKa1bDz6RAnfs0bFwmoqslotphjcAAK2EAAAGeQZszSahBbJlMCCH//qpVAAADAjPLn+I/nbAAh+r6rej81SElG3uRRlneXs293JuwESkylvcmMuONdTbLfFO7MjV+Al/wxSCjOYKfqutnXizLbOUmDHDcHaGzGKvGQUEGU0g2/f1yLC2LWoznPq/yV53aoiqbh1NPAZmEE5T5NJDXb7IPbDTmglbT7xMV/nhGAAFsLxi5VT+nBjWyLkqQDeULiVyqG0tJADbnkqlqH07nvH1lWtyC97nG9Dx9kOcWFTY8nL2Su+SZrdXn41nVmowzJzIuDzn4lf5MYmT0Qa8JBbypoFBYKkIrEa9em7mS5wbeEobG3PaFBSmto4iBSz2CU3luEck6VXhrx2LcAHCG78qHmmYRQ+RFBoWIpRShUbhgwHSLz3ZAsfKemGl5HQE36vIb97bHEPTpRT3LxPPB5xePfIjd2/5towoll8kmyDMb4AlUshX1OH3jfTK/mA5Dn700TrLCXw5FcJ6Pfo56ILIhBMBaNiquSEy/R0GAINnCKzCL3gGDHGoaNZApQQXjTkLr2jMyGOtVIAMCAAABYkGfUUUVLDf/AAAGFmSzOAE1e5uN0VXg1csFLWDmkkkvyEwgJLJdGyQWA3C5eKUyO/9sTodvCmvVx1YhQ6KBrspVz+G9U+76/xTtAYTNQuEiRp2gZr6zL9CaBcUzkrPqHQihFnXjshkCADELAlGxY57rZZw9Wl5b+3unqz+bIFoRwqWIdGe0dil/R623RAftrRUcELX7qqRIn9cZlc/ldn6iJU3zoKuMsUtRf0iuU/ViT1aVvrSP5SQS6JVpNdZMNVE4GKqU67VTUu6ITE3cp3gBGgX90heAQPtn32rL9r3g1d6r9TRoX6NhfSXhlVPAkZxVZaVjo77lJCE8J0mKKluYO00Y/tc3oNW4oeo/ZwjiKpTCGMpFLBT4YfLtn5O2U6VsooHjfBXoCpvPbU5Jgsul6m2buPs822xvjbPb4bn2bDa3hKm1XPswO0qwYfUfGebbyCy5Alj8zatsB3c1QAApIAAAARcBn3B0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmEYYvW1soD8naq/P0J3XT3PyF5tiJNSttlfrt2m4toIUJApJmuVpDYrKgLrENCzxNXL8yuKkXyC8RqWMEpuRFEzoAtCeiieVcBrQibmpw7wsy7+hX4u1VgZfTbskke93EuyxH+zhHCGZiVsj6JYKQp/0msPXWYIf0Hvq/Ke4JSVcS3BKt4/1atZqt8YpVjEKK/fa+TmMmvZp95W1+nCIlaJ6muOsJBos3NCCeRpfKp34f3MxhlGB5oPQDtgphcM5mLI8mmG5fzdjW6Ijaeo3tx1nPhxmoUIhua6YLMkIQuFc0944AAAz8AAADGAZ9yakL/AAAKPO0I/5+ABdR2eq84J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwjCcIg50dP80mu8HFGX38hebYiTVvr6i7aC/D978LRj1a9TMBARwYmcjNQag2wS4sApwWI5WAgXJ2AngOEseRRbmiNpK7aDtK8wlmxPmgpTURO381LRIg7G3YkhkciRcliP+EU6DkCdeLet/KxXMbLAKwS+0BwRmaP3719JXOy7CYa+fjsDwhowDAY/5rk/JaO0AAHzAAAABt0Gbd0moQWyZTAgh//6qVQAAAwIxgLoiFdgATTV9VvR+ambYiu1TZ7g364xc1NebYgdZKBD26Cy9F8C3enw6hBkP6DSr70uCICd5lzXL1eDbn5pyO8o4ZasWG/WRR4lkeTI90DENxmH2HAKarUMn+X7rclnN5xb0yvid+35MXDorAXyeotRrgoTPRPbcTk6AtL7GMBIch+wh+iXgXnH/r2G64qawQqFIKDg6cekYq+dR8qCsd9VJy/ESmAASeu7NguxG0LXASQo8P3sSvmvofus6k3BnaHk1fVitDayBF/Uj9ekE5aX/pUuzMvwE0xetkFV84jdFkIAeEOWwclg/M1NbE5lRh9b+6uVn2/qwGx8SddDxr+NtX5AiOTPy/sXUMyk+QiumzsDW9bjlkIqFGCHH9I6NTpFZw+WIIwcaYymLzWqaYQeP0bSruwl+caexjftQBo1A7HXljdbauEp+fWFu5I3u1VtkEneUclpsUGBNvCCzKWNR4NN/MZj0T3NT/gJAsv1l9ZmVSzOA1nNe/XggoqhLxcizEFulYBU5Q2vdSn5/Ke5YRiqV5XB3Bpj6nPsN07OABSQAAAFPQZ+VRRUsN/8AAAYSBDnIpTIATV7yzgeAFDRuGBJ2cxjHTbhpl2/q4cvjJSxr/a25WYZnvRnsaH7uMiPDRewLJHr8+YZHp7i2O+9bp28cXva3ItKggj1E1WgBmfLXh7eSJal8DeVQxPWCJhAjVVyGFo70DEMDvz2KVJ9YHs6nONjU7iCAJXbOunNdeHKJW0o6b06lKg5DGHWbwf9uiiRhvigow/0HUscDJESdHcXT2pZDwrdVGK8VAi7M7c00yZcctC4WkhGS9+7D5r0xOfYPXL0mSC0hh8qNCpi8qeIIzYy6ERsdC4KgO1avOhKGihWvD5aEGdqMjq3lF6Ti2LHROSCqRGNZ/Ffm6h+RBuwzx4LI08wG/Peo5DsWPhTuzKOKZAwsh1X+ioMjsXyXFQyJRLdyb0kVZBahOD14pGG+J+RDkUJZalF5RSJMLgAAd0EAAAFAAZ+0dEL/AAAKPZSxvGgBLVpitG4J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfoSiEiXwzGPce1EjKq4FniQ5hv/BldgvNQCy+GclLOIw/7jTIkMfxzc2+LG/eVN+lDYwL9bW2cxiDPf839Uun7c39exnjTeUVj10p/bxLhlMtKK/C7RIe2n9U4g/ZuBBO29fBSypvrAAMkHDOypeqt/ymtWhzD8/i9pl6YAUkwRYA4MhyNBKpygj02PDNLtnuXlcltISnbvgEp3xXvr8SPP/62qcf0y6+K/B+TMj1kWEIirbWwQIlxLbhPN34NiEj51kXYVw6BFV4o1Uv66Etge5B1C20wyHQVHqrpvzqjrTrYD4iXgd/TORHkSMXLd7mVgJ+x8wxR5lFuHgaDBt/+sBxyvXiMnpkGPVAgAAC7gAAAFAAZ+2akL/AAAKPPDGN40AJatMVo3BPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD75KG+wL6QkFpr7K+0rAbkwPLHFAYTyfCDa0YIivQvonJX588tanNyB5jf+ve9aCki0NNrXY88YrZt3R2UXwzWAKw2JOStv1c//yplyDoa88IA8aW1Aih5gEkf5Swp6TgqRsScPrAAMireVoiBjmGRe5nq1hP2y+vlRTNaWPLJL3Nb1QClEBqE2kI6N7IkTV6okQR1eiuTRs5/rGHQHSy2kCNPCSgCSmAuLgiZzRbZveDx9XijhhR6Z1gC8gX2qgNBzmvJDMkmv3nnIkbGUY+EaT9IqOOIlaqN4XBYoKgRD1EgIZsDO3DCC0TaD1KvwuPGXXPP40QnM3TAbuUncYGG5RV+oxCsP20RKJmCAAFnEAAAGuQZu7SahBbJlMCCH//qpVAAADAjBxVLgATTV8IaA+xBlbsF5BuSVNZVc9gMiVIlrAJQHb0UzmC/Ua4ELl792rQSe/Ch1FdC2+EKamHM927PoCIbyjNx1dk0inJQyemqxGusgVXzt94oWViTihAAyE8AhrpXCh9ins6s4RPuVGHf9GYrUHtk+GartiXe9P/eKNS09hGvWY7ZpopyvqTcjVtiAAHUaJayIiG4RbD8fO21D8W0f9bETQMFnGgkg+bQn6P7wKG7u0dePcv4fegzVZas3wj+kZPUDxWtCKJJXBGDgrWZvppzQOmZ791u6Ufz+jiqcC7GVrY2eWbHXR18G45ZsyfwiQWlXmR20Z7EwgDxZ49N+9f/qNHUmJo/dYC/k3lEei/tVM12sNYEY2CbYyyRdoGRfd+/BApMBR47LDO0YSbU8gyZX8Y7ZUeNhEczNnVC98YlkLNxzgcdTURgHUPLqNxQuIz4zK7qOOiGotHGxvjfBdZOYIY9qDMkt46uY3MtYFtEMR7QhUlmzYrxGlDsVRbk+xvZ6d0h1xJ4+nnqeJYJPZ6jSkWmxzhw1RnwAAAVJBn9lFFSw3/wAABiFQc4AQp7yt3R6WZDoPaeHqbMgqxmt2ZJ/HbQzNuLNe2xCIPl7bY6Xj+7X4cDm3cqCOVBgBa2CIr03jVGxPaWizAgqaQZMX0dR03nvRdO89f82IijoJ2vPijP+nU4wu38dVmIHTMaSQniricHY3BJmyraNDv3TLnrx6+Uxtl6ZTJJe0O756Qeh2lnAPGztnaJgEvuEt69LPt62j8Gzlk+hSemdScDF9vci6kk2E4o4YRnvR9+B97dAMkyUtiA6UBgYvK4xHWuR7Kt64Dbu34e/OOdNA7A3Zrtyp0ZjWMxMDwpYrg5/x1DHtLiF46Du/ejgqwGAc9DvK0aHU+V/+vYJqECdm7zse76fCkVkOFtZf93qarVdtpAmD4RFCn/1YfxKqN/g0p1gCL3xqoG3Z3dUev9xVMmwyTY8DATRGFIt5Y6MAAAMBQQAAAP4Bn/h0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sRP1icb6bea0GDmx1F3uIWQk319RdtBfh+9+Fox6tepmAgI4MTORoULXbYJcWAU4LEcrAQLk7ATwHCWPIotzRG0ldtB2leYSzYnzQUpqInb+alokQdjbsSQyORIuVNfPXO0EYPwmapjguVWdxPEKaQHFzOqAUf826KT7TtgLiUy6pmuV4uSt6saeVXKx8zPFMNV+ga3Bl9nHlfcaqxrwKYY0zFJflWr+8NoZAerS6SGn5nXxkIig7nfLnFY8N6LmzSS+LDOgpShAABxwQAAALgBn/pqQv8AAAo87Qj/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB960r4aYCSPc3brp7n5C82xEmrfX1F20F+H734WjHq16mYCAjgxM5Gc1TheD0ebJZguLysBAuTsBPAcJY8ii3NEbSV20HaV5hLNifNBSmoidv5qWiRB2NuxJDI5Ei5U19AkGwVSuD3+NAQYKQcwNcux+eOFq9mCkvZpU1YTL7p+O6Mg6AABDwAAACZEGb/0moQWyZTAgh//6qVQAAAwJBK+dmEMAHMvOSjpBBu1KCQV8lPyddFDRfJsPie6A8qambz4cA6iJF+qfR+wFxYpPjl/kURql9ohdaLeM27W4CMgBnSIxyH63BxAtFKrbQVpSr+6BSPFtJzV23K1p51GUHtT3Va+3xW4CZSQS5UwwnzXLRq2OQb1aXicFkgsUz+DAPvVIdcbpi1ImKD8MHqyNH/gbOnLbN2R0Gi31uONZDuBU0jANNxGEACrx/7HOJb172RfP7q8zozWlTDBXlwvq6Go+s5I9n6qPNRkMVbeh9Pd7hDWQ08sWXshw0xg54VGpmYWUjf8XoNHpViXf2gVMjtzNhhMfRTohbEt2WGEoMXYcVwqfhXr5DL+V+XmGtRWk8zk5TWT/9M4KJZo0naK49vmuWz2QU8Xp8qQ3i6DxXQq/cdEcKh/O2by+xAAB71nw7jfseaPDCmU1ZMJYT8iygoOhjfqjY+gRWiYUssKVOx+07gppZsPUqPjbLil1QgjfceEt93qs1Macrs16ZcY8OBoGZdzBlQYQZQFWt/z1PxSTN3BWrMBmb5s4DdO2ETm99hNTDm857qtH7uHj7dZFBHr/7/E/xRiQjnV2HHgamPurhw5EykgaTkj1k0C5A8DGtcON8XWIWHWBhYUCIQOfuJqE47r1+72ZVHQwRaIXIPxCnDnTmhjg/DLaWxx/ezt9NCx7HrreFA+h53WRtX/clJBup6Ka53/DiypPcuAhD9t8mPzzvI2O2PuDC9iX3KAkfceZk3+72upzvj/nN6jU9rVgUkjs9f7odAEgFqABlQQAAAVZBnh1FFSw3/wAABki5c4KYUxm+GDnrYE6ADYD1Ieq84PIhWs3MwYRP5TVmU6uoMdHbkO3vCqJcd1BYylQ5J87qFWwspQg87D+8pg5Ki2TANlAw0V8wTZpjoo2apEJHdra+JndcZvnKcp7aXsrZ5+TXgZ3blcem4YQGx4EPRrVxW9sJKvzkAM4IHkDwHuCIg5CaqgM4L7g1rtoXCOG0zwONYgZEOL3b6xJ/JcEMWGjS7+eCM2wy3RIxRv23wbm2jaBgELMpnOyFF/rAIPxwtGEA/LoxTBChYRoK/Xyw+1U0PAB+ju0QDMZ84l+5574NuDBqF8JeN4lMVvPq7PikV9f7TLcJ382dxjnxpcyOF00zlFAOZbH0gpeH0okGJ3rttucIgXN7UGoMK7v3x5K68/FlrEEK6PtPyoBJ3JiXTES9csZ2xXh4rzwXtKv7fOh44d+Y31AAFBEAAAE5AZ48dEL/AAAKPZMMbxoAS1aYrRuCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH5y76B9uDpItoMHNjqLvcQshAgdQaU5hBHVjxtjwevuID9vPBqLAYNzhJI2guLAKcFQOVgIFydgJ4FqbHkUW5ojaobgOB6vMQFEe3yz/+IoX+aoGNxQnqVswyOUIuVNfPJPsFYIgyjGwngl+MyuAEJIyReS6gp1LQcRw/gGfhlB/yJ+DcSzDuglyLPYzC7LpOVqsMz1nXYxpOAsIw9Xfg/jCy7qNfbsYupOaW0RCt1XGZRbNR9dIjzlIM7cyZiepw5bdGHKW3SLm9ei9YUFwKTJGRclTX3VjSoQCAkd4WzuCxrzOVLWF+Eai8gU+TSUcgSt3DHVyqM7oDtR/K4YHxoWpXiEgAAAqYAAAAUEBnj5qQv8AAAqE8BIbu/ywAXUdnqvOCpYMpygfdBztc4IwhbV000q7tifszWeYtzMyYQmFXXFBItm0vsTlToF/8sND6sAggprRYD7lGloclFWiWy5q5qBPpjTCnCN14OuwX+DKoiLeeB4Aoqk9dbq1W6EZabjdTlhMQndJ4+2evcA4cRa1YewQNuAmgxGmDOE4mulk02dPe9i47IE38DU/8QoWfwZDqCKFbgf1Cb40DB7l7eum+ceBbluIjn3N5/zeCAZpSqOYXQJp0yg9W6Oky/8/0Zdr6nxad2YJGSG38nwmAhrhjicMawGsY49++6YWIzNwYk6o9jST2xpbvBiA8E1CbJv7eAhmlH285t6s6FgilhBPcEivoJfv/bkTZ1UU3CzH+AALbAOUZI5FDrh8HB719Y7/7cgCK1thz6gAAVMAAAG2QZojSahBbJlMCCH//qpVAAADAjDF2vcAEP1fbV8iPKx1hmdcB4gXI0Y+sQigGSidM2PxYG7SVB92yiu1uB+Xgh3N5I8qypwVelxNU7XHAw2z+h7Zmb9xRxqf+E4S3mOVnOIoft9jrc4gRz4JXhBsfBCwhQX8OIepilMI2P+tm8+R7EWmr+bcdK5il9pi9647k34h2T79zK1tb23g1uBx8ILknac8KpaRpT0Nng/y4IgAA8iWRjVaHGJZn33GoW1xTGz8GRQzHijUXotOhG2VkEXysws6hhI4gVL7mAUqRnsRJUgRxhnQHrAPEA4fA9YoG5hNWWD3Tue45F93wkx+2jiTkzAR8v3USO6u2XqVTKP5fnPSYjpv9mgpu1+RgrFtgZ2216Cix1R5kLTnAsuxB47O/pKrjv2dSr1BVXxYjUvMRbgCcO4KBxDG5juk+QpdFl3lffNAQ7vN7Hc/vZNGCfyP99clSp0SiR43t1K7uJtXNGpUDpq+GXgodgDMI8RcDKIMTPDEAX69zO7yeSEE+GGt1yb43ax0/XxPuU8HxdK8WcPrJpGaU1MgzXLDTMS0QT2cEia/AAABUkGeQUUVLDf/AAAGDCsWoroACdveZwBLsJEpWk8mMLtNzjzxYtvnz75OdhY/m9JGEB3mIfCWvuRJhFBbnIt9/vkqV696AtopZ4MKJ01iOx8qKfeKjXI7eXIQZgn2Eu5zzq9o6oUeiqkqxW7t8xrgXoRvbc9SK+r+gUhPaXzq9vO/pMfKs3PBJXOwM6jlHVeiXzBtHuV6bRw0cr2woCWMirn07AvL1qKl8h07N4mmFUp7B6B5y3hzODZPWbAK3WTLHAnlh92fbaVNtNzps1jb/MWqwkvulh8tIlXTMfz41wntCJocXwOmkTm8WS0oz9JPXC/TOGRHXaqABoHlkIXoSNTGJbEx1f2Ga2vlQ8nU+NlcxRkm82OMAnw8H/9fYC3WLchNEd8J1753viOlNMU553gFgxHDnvBX1xJptUu44Ze7UnA1L5SslRTdC3Lh/MjqAAIuAAAA/QGeYHRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYRhM9jp6Oyx9NvtIofFRoPsItIUbu73+9+v/6GmnEIr/Be7V+xx7AjRLMIraerx9av/BQdd+4mSS0krxcdWzjFG/2JJkLr2XHMbT8XavJFDT1KnVNAHUUi18atZ8BfDuqzc3ymeOisX430IMqxoawXo7m+Y7MS73XFbUN1sQy3S7UWvvAIZyivrRwWvxZCG1QMqUcaHz6LTsAaHawVdUZq6SthKlVVtp8d6FlsXZ6cWYltu+oe1lpE2MLi+5V1YzZMeeNQWG3wjPwAA6YEAAAC7AZ5iakL/AAAKPPBH+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH4sMtBznAgdXnwA9uX19jS3xIO8yP/lhtAxhoJOpBOG1pIAur7gAe/sua8siVTlh5ZhQesNLTasqXN8s4eDPIpXIoexcOnh4cn++kPLCLVYacbmpyX3sPbjvisLn8OvksAAx2cgTQauE7HozzLAGMn2RUj4T1MNkyVw7E//fC5svt8CTkcisQAAHNAAAAdFBmmdJqEFsmUwIIf/+qlUAAAMCMglfTPM/gcACEfYiSuVqQxyMOiFXjUHpSWDPwVyJ/VaV18J9/rt76VgKBv2Qif4oUR4QwAM9U/7PHWUsqgaCxKRDYxnsp2Ilde46QqVxsf/dNHB+j6brIS3Gcw5V2+BBU8fAbl6KHYRd8zwsPc5cteDQe4+Q+L4mSEe7dOGTyc0MXuni4H3I8AAEmzzZhxA/hyjgxJqMVSGuNS0oPZMn/SGgiEXHnO5JtKW0sQkyTa8e8emUPoXASFV39FM3GDT5eu+UEXjjbmheR9XhCW+7Ah2Lt036JKOpnkiJPGzUifthBdLHtfYYw0EXxwDzz9fT9a/LL67zrEnS6oND5PRunAFGq1b8vhSrEJ5+h7qtHyhhf1cXBJCvejuGGwcvv0sfrL82rnmSxhOZwN/ms/U40E5GtijZW0tEc+NKTs/I1f/C+B9Svc1DfOHob90NJA7v3+xDPz6hxd6gu3snmBa6aiqAjX6B3/RODsMi/c7AXvGN0TxDB1dOoAoZBHv7qDsbhDj0dGpA+4HQH8NTntQyaR3u07E34rd28nvDoKj1iTojsigN8vfueI13j2uA0z2rdb8PVKcuenR90XoCsoEAAAGYQZ6FRRUsN/8AAAYWZLM4ATV7kkqIcejc9z9617XxTq23SIiKhHyO2T/ZWObT06kctJHzABd7yn8F5s61BUPXXUmeoQbDIA9CFbV0Sr+JMmvhanAI8exYl5wULdMmZZG53WUjkltI/auo006NndLgJdBgu3XtIO0Xv2nm33D7nEXz+aVvLSjYnnuU82PNM+D5cB1WNXaihghs8cFe02EQvKi8wAG0/wUxfoKdhUKPe/e5JbJ4DQRhrakeCz/JRuMfw5q3e0os9cG9gkDYEkSMF0uSnEX59hk+dHepP6RwcDNd4hrgC0guSYwTD2Kyx/O33bxoW8goUwv11qj/I3OSsR4KesGdRl+GNiyY9wquAd/stRtBg/LtUN2yN7ekaESX67czENTZWTyRRT1Ubic6U0WcPP9kChNBOaISCKu3YEPHj9Zp1SMmuVQPmUeUlCAE/KoSAsze6NwyEGMzPlWTr3j/lb1QkVjd5STX6sQh6TRDPdKABT3XPMhvJN2wx6m0EfwbyhUthpXdkdkew1oYqxhuJAORAAGPAAABQQGepHRC/wAACj2Qsj8/AAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuxBshjxLP+Hbrp7n5C82xEmX1xmYCH5HI5PG2PB6+4gP288GosBg0JxLG0FxYBTgqBysBAuTsBPAtTY8ii3NEbVDcBwPV5iAoj2+Wf/xFC/zVAxuKE9StmGRyhFypr568ZgztDjZVsg4vKId6yRcEerlDPL15++FgazjXfpzqgIauQxZ1wqm1/Ql2b30cAcVL7GkI+7iK5on0FMzxxNV9u4TsO7MuigPCzNRLBBrb4Rup7cnnFBcT1jT0FvkJVKCCXHM0qqL7DsQhn6iE5kZTZA7wBy3oknGEGuMVz5ACvXrIvxTdsz9NONqckDwT1wVjULrqQWEvgjld5Qx57V+ylRdLtUlRwLWbr8xq5AABFwAAATsBnqZqQv8AAAo87Qj/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB960r4aYCSPc3brp7n5C82xEmrfX1F20F+H734WjHq16mYCAjgxM5Gc1TheD0ebJZguLysBAuTsBPAcJY8ii3NEbSV20HaV5hLNifNBSmoidv5qWiRB2NuxJDI5Ei5U19AkGwVSwROI1weTME5k9Fz66MYcF3OWCYlt/sAhYn1vg3bh7jRgvpH9Qc+Il7Q7gyd6cTORORRC2vlgbyVunj5OtdGgezVZWTfty8fr7S7FKEFexs5U8pO/sNjvrwqO67LfXIQra4TUFB/rzYEU235J/wjJSQbvYuzOH0JIeCZHcyYdmeL2j+95zBX2wE4pGGIF7N3DK1Xpdlgv6xRbSPrbd2gXsy4YAAu4EAAAGoQZqrSahBbJlMCCH//qpVAAADAjPLn+U8UxOgBNNtkLdHyKIxm9/r7hS0BAT3yhp0VMGfxecHPJXMxHX+DWRzRgib6ThRsP/GqYMdI+82v0BAlxZv7seRdE6fFWSed3reQYiZvIVJ2WxkyBg28ljtnwMzHtOb5zV4O7+Vgy+zXW//C9lHTIrlP00ztH81Y46fIGB3Pa4rWZ/BpnhlwN+/+fFlNJHUOme3a5ClGnI1R0z1KSR9cUO1gc8s/0xS3Hot28LEHiAvCI4r/uHUyvxc9wXpwFemaX9CmAAF2WOYLWddzEhC0Fe8RNXWmP+6I62LYDASwqBbOsH53oG5CBCa1c47RGaVE4JZSrLFrHsfGsOkxdiuKKDRjTogTt9GQNnS5+KPj6tevzM/dA7bZQRajkYUZO5UAqyov8FQ3F051dIM4hUK7iNdNrq5zTWNp9bICWM151ir7ykMD6Gn6P3r/puY3w4vWInHEL5+AU2ImWuqaIQ5jxnFFjqYheZgoZ3cieuz8PpcwwaIPwmTYKokrxcULLyzSI72WbtgZCGu2ngXFIAlbYACPgAAAUtBnslFFSw3/wAABgwbkpNABkgBNXuSE3YQ2sdEgoSjB7tRewmxySZX/z6oLLIpcLBkMeRHNh1BWgnLBAo98IxdqbGvsByrWRiJqqEe2y3wuNwckh1J9yF58sqK5t6liGKIlOwE7fQPVfrzM9GRzb2WJ+erqB2JbZMwdklkqJK15yIvbpFHtCGlHmfuIPifT1FDJW/mDjoJK+Tumx5iuw8vIeJK2nHFHFOEMC/pB3Ve1nRZmG8pa3AIIuxO+ZIwj2mfbjH9g9lT/AtEcR+JcbkC8iNE+XlpE0gDptVRpVXKWXXAM/GFbBfeGE5P/+ZVz79GHFDaiunCSzzOsY9YZnLk2FALg/JDdDBJ+oOdNXooEBHqqjelBiR25/OdbSxH1DFvu5dE4QoNGaV1dMwNElOr9rxwlgxUSE4a7241lKZ5uPq0fczMjPycAAS8AAABTAGe6HRC/wAACj2UsbxoAS1aYrRuCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH6EohIl8Mxj3HtRIyquBZ4kOYb/wZXYFD/416/D3YB0zj+Ap95pXjaOuLGN/CFSDyJnjzK2tbECgEPMgMduFY2HXDiAnMbeUVj10p/bxLhlMtOK/C7RMVxEkir8VoUBBO29iRTSbPrAAMkIXo9T5KqxB8mdaOTj8+A2DHXmdkf7LH+trHiqgmpPmGGdoTSSlcbECYX0TmcWKpnB+IMwFZgBdQ2R8eCb8chqAmRxZxyxPR7fTYEa6fTeSKTrtbM9fRsJEGI4YkVLYfxKnc8N7rCt3GjyyQRSUL2EXzDgw5emUK2zUFrdeuQmfjRi2fPaMqqqNXbDOaTxqqZtB8RxhTct7JZChnUdJJjN0iYMeqHQr3qh6ehGV0coAAYVAAAAxQGe6mpC/wAACjzwxjeNACWrTFaNwT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg++ShvriHgXELTbbMTJoqXnIwnHyum5oma9bBeLrhOjs17OfeCZWRkF39X/r3vWgpItDTaH84xkI7pvdKMd2Y5mhWHujaKMiXkRO79F4AYvsEpc4lmro9u+5VH9nlYh3Vv0JkOK5YABEOyM9Rndn8SH+H++mdYi/HumzQ2YckWnYnOci/QMIg3Yx+OhgDZyaATAAMqAAAAByEGa70moQWyZTAgh//6qVQAAAwIxCc5k0wAJpYhI8fhru8H5qJYWu3yAN4r1x+8PVYtB/RKSXWn0dBeP7hS80mXhGtrsBVHj0n2rXIqFCK3BYK5GMk5EEmQsApWKWJbLzzQNa4+YPu+A98XfKceOvaZR4fVpoSbrk3xu3ZqnTFiMytgbXK/YGobfvzT3fbph9h9kxI1VWXgdSA/R1tmL5/z3F4rkAAmPf/hE3PtP7hkEoUaWkAedledMW9i4kqPL5oBaC4u9ImrJI1Ut8aUxfcPwz7HQx5YBew1b3VjetaUGp3Xi1UBBsSRimD/LVkKXSKut9k91luqEsEpDC/4zCAyleEfYfU9pmJ78DpgjiuH2oZJS+SbhUr+q+vVaCpMDM011gNoGBaiuDUoYI4N3e6hQN3wsnFWfdC/TtFyt84NO0Uh6NACCoWFoYDJjhq3t6sbeDrUPZEzIg/1Z8t+gXcjTgJW5gQ2YMNwkIWGQzkPbJzHAlOuyNonO8d5nbPwX5umMz1oVJ6qnboZzZMJGvThKFNcdik3yy/brmM23lGW5134Vbw/xZZVjvyCsm3B7CZt5n9BkpSBtT5fLc6mv5BLcmIdEb7oY0AAAAXdBnw1FFSw3/wAABiFQc4ATV6B7nToXrjPDjJhRIhBsqMzBdntoEAql8E7VokNOrJIrLPnHTS9VqHcMg+kzkue3doBP9C06bmwADozzr5LOycBtxcDjFIB/0HkCwRppyx7Ej7mAEsj/P8Wmny+tZjpKMLyaBMBKoXLvbVFdSvBDaN4ozpAfc+SGalC4vn2liX89X6Gfm/5iEMx0Nmr6ZEg+WH5urR0Fx/G8E3YQttBaGVRxYbEmB0zFLUeIF089lkEh0H9zlvCLTXBWXz8FNSvqPusuaSK7OFzmSbd4rb1r115Yby/0jyX8lBWpH0Z2e7Gx7TUUPUVzv5aYL75mROalkmMBI4G3d8pSbK1otXOJdDKL+djK5bcCuhoNnL9/p5Yw6Dh7hyCW6uZQfnOPxMyuVr07nGNC8tjh+oaBrxkdRhDzriDqRagiUQj/QoCLxScDh8i5uh+nVh8D1PXTRhaz47u/qFOxk6a87ZUx6a+KRVDkmEXABN0AAADAAZ8sdEL/AAAKPZCyPz8AC6js9V5wT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg+7ET4V430281oMHNjqLvcQshJvr6i7aC/D978LRj1a9TMBARwYmcjQoWu2wS4sApwWI5WAgXJ2AngOEseRRbmiNpK7aDtK8wlmxPmgpTURO381LRIg7G3YkhkciRcqa+eu7YKxm1jywdtgbEOE6GqAAgP4Pe4Rk5+t8Np4vurCYm7Pz5hSYyCUtLDTDQAEjAAABTwGfLmpC/wAACjztCP+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH3rLQLC7biIIAvySNgi5NR6LMc2IuL9MxnUYp6ZqVEZllWJX+6L5+XupZn9Dke6J0hdIVQUqYhTtFTxQM+MNNQbOXIDisOGrwWmewFQjBzCGJrT7bJvJoM/xQ9DpKKXWmvoChGDPkCPP/Z5TpjwJ5ogSK1uiuq5mshFQUO+rhaG5HvQ0dSbTuY4FLWVlTT2KeUpjMtegiKbRDegJGtgX7HQblZiWb2IeCpu/F6t4hWGp8MODpW5x7RFYIpBEjtl/rwSUmuHxs01nfYH+HcOvk8UdduiJh1Q/LRVbDJuAOX+Ny+2+O0t9KTnSx7iznGJ/AFb3MdcLGXcWkBlGmmFcs9Vy/RN8veqT8wTuHqGPM4UtqVMt9fjG9D6V7IAJuBAAACUEGbM0moQWyZTAgh//6qVQAAAwJAg9cALDLVRYGY5CuBbLfMr9y65VnoeEYNf6rvoAyKMVROQm3oMQqUUBGs1Ycslm3dcjGU8piKGt3JNgg5dmnp1LYDbXonV/7gK9QABvZ/2sBDA6AwwonLHCYZ8/5xpMEWCAKV+9TtlDg0K0Xw88Wnl7YQL3HqHO2IHiUuun1qnf5B/ma2LgDYkRtbqxL3dFAL+7vmbn09rBkszlUyj9HhLU9JCVec3mR2HU/7SQr4xraWluh8YdE+EbPtE3t9owncg0Ir6yXX+c4ap0lUqS0SlbU8Aykgtp10uqKdf7kjMNBUEq7/Tox3xIVHNSQafmAEOeVFzXJsX2pDhNuc/uKTDuXYJW2Bm1ZHdjmMMHwAAVrN+j7QW/LP9lVL7whVR4pgfAtuf8Ga913FZvmQolYc/lY076WCTwahZ9eSrJiYlZ0QdaDyraDwWDr2N4pIEvxS18nXG4Ey6+pnzCUzsUJW2qT7qM+hZShenNKU8D9fSF0rrSqgb50X0x0QCzhu9vZe0SpkkeuklUeiwPbrcquMvyLsVIY+4JbwEmEDszw/85N89H/NrKg9a0dKrs5r4rwPHshL1zZvCDTBvmJPleWk34ui4iIHSV/KMhVdrq3/3IgwLaJ7Z+b9sA4TFzcXUR3VgoxXtTvCP7xQ8q8QW982Vx8W2EC0JzrD4b8HkklP8kvCvK95oimAw4SfU9qiokNNaJVoXB1ACHOHhLlq/91XicOwBOv55Na/PBGaBSHFBdudbiueuHuiHbAAI2AAAAFWQZ9RRRUsN/8AAAZIuXOCmFMZuEGl4Xr1XHXIAAIJAuaM932bDkIkM4YRDduN9ow7GtNnrRHSkw2Ml/Fvc6rcP5vBwVaMMk7V8+jDUBcK87jnrejGWnC5dfWkbig/wXrSxBBWcoX/ZOZgZEnUvG+/rpKnUgxlKSq8MZHsjaBIBtZ6ueXOVLEauR9ftbN+6frGpJJclST6fEx2Yhqz6Ro0Fi6HRdkxg5HZhT30f/zXBD8yRqh3sqo+G9ygozHpYyYyovCnzyVlG2WrfG2nlv4qHiGsds2pZ3fvVQMlZR97YK3tsPCgTmcVL6LKv5ZBFxCmH8jasjB4afKwfEq58FDrmOhV5NTLAqzgItdknAEBuX1ZdWJCE67XzF8vRfYKgkpbc9SHZ60bflehPb0Mq8dazMATG8LZbdZBEAeuvGrki0CTwof5pGsF0/pQ8Qsz+WR9TUWAANSAAAAA2AGfcHRC/wAACj2TDG8aAEtWmK0bgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB+cvf5ReRUoTQOKMvv5C82xEmrfX1F20F+H734WjHq16mYCAjgxM5GfmTVeD0ebJZguLysBAuTsBPAcJY8ii3NEbSV20HaV5hLNifNBSmoidv5qWiRB2NuxJDI5Ei5U188lLQRjDcB/CVh4m/qFMqnSzU3f/w+p7cI+n2JenR4R2pz3SGayR9bfDid44p68UCCPrrirDT8wDqjRlIwjSug1+vIAAAMDewAAAS0Bn3JqQv8AAAqE8BHIunywAt47PVecE+zPgY+JvjTZQ4IwhbV000q7tifszWeYtzMyYQl9LLdtQYNzqTqNzsC/+WG0IqAQQU1wsfNyjS0OSirRLZc1NRAn0xphThG68HXYL/BlURFvPA8AUVSeut1ardCMtNxupywmITuk8fbPXuAcOItasPYIG3ATQYiTBa1v9LH+4kgrCTyv96e6GdRgq3+rXfPhQ1USSjRuCOshO5+HJc4NDr9Ss0SfM7UnMJXfqPKgWj2Ws3gwlXvAQZtFZmMl13KBC6nd3/nCnO93P8i1wFMFLw43G2VlUubYzCZzJ2f23Lefbtaw3pgfTrOUvbkKzzcQZbce3Hk59fLJoCjrXqz/NLxGksCsaBciGEDv3n8YNNoJw0zKAAVMAAABY0Gbd0moQWyZTAgh//6qVQAAAwIwxdr3ABDvbQz8cqCw36s+wSxAsd1s9GiGthCZa0Y+DfcJhLi4952V5RfhFYfggSSCqxSdZFWhOSL73Pyw4+aaUjvg8AdUgNGWF2mWjNTA881go/vXeToMFTrL0kbE4FruUQ66AI5Z7Qha3mET94TVAYRbJNRrTwRQpgADuhdWHhiu0AFStbNmkV/wm0qosHvSZjDjDM452Al8juJPIY1gzGPXXeF4bP/Ighq8ObInPdy8uAvu7FY2g3uhwlOovNixq/rHnXl0VisE2bDzOxPeGdEGmsvmzypazks6OSIwdvipG3qodfEOzhgGdeqEErjG06JLTO6Hg9V4fVKF6kNArEabFMRghmiAgwNYkpROd4WVvfUxA/NlDgCUfN+A1k5yHG6ycBd804UeGs/75u+/6PNUWGYgW0LMbAg4HsZqVsQgzf5FeF39zvC9qQAA9IAAAAEVQZ+VRRUsN/8AAAYMK60+mQAmr3mYnQK0jnLbBuTSvjwrYINzw4dx/lqUVftFZDq8fqxZqRZI4ymLn0cwZZGCGVYZM1Aax8XjGPi7G2XyMIOrKqB85uT0cdNjiCqgVthEIjLRjkD42hB7Oad9v+gqrDKZwmgGu/9TCgTDvuVcBhJv+h/iR1vaAi6HVV7+nn1iGszP5pIVvQmcFxOc5tlMIzqBJkHvSA4ihIDAf4QH33Vdwu63spSsTsbJ6fgBc2jMaLJsttKkVO+mxhJCmkwBbJ0/RsiBeQdUjmeCkFQxFV3uecWs2T2ghbEkrtxR2xwzqLVbxBCDbWOvJl1VIYy+UxTvdX4XyAyN78HlY2qEuV6V8AAD/QAAALsBn7R0Qv8AAAo9kLI/PwALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sRPjEx5LxDgCY0VGg+wi0hRu7vf736//oaacQiv8F7t57EaWBGiWYRW09Xj61f+Cg679xMklpJXi46tnGKN/sSTIXXsuOY2n4u1eSKGnqVOqaAOopI+6D3RwG5V5/Z29X+0aBwqEWmWrvY8Q9OYUVPFt/8edRtQisOTXZTVTNd9DeL42QAAAwNmAAABNwGftmpC/wAACjzwR/n4AF1HZ6rzgn2Z8DHeboOdrnBGELaummlXdsT9mazzFuZmTB+J4FvMI2fyIuCYZG44c0MX6C/fEwMZ+/8sNoIjVBQJoJw2tJAIwDbskBQkxzTvs+6ezApwjpUIS73FtbICWzeH84teAXSTaW6EZXKy5iSFgxjJzBzU5fdLfPrT27JiBD9MWAAO+PxagGE+eE+IgDuFAAx0lIvO35OBBFamC5H9E3kDh4qQLDarCkSS5E+1cimmBi3P6aTj9L45tzyQpGOPJSQknv7J+bs73EdFdBgaw7KXZPfjVxdWgIybbv+uXT6NegZ9YY4KH1RGa/B0bDcxzcYXL1mbnkA7jWRDzf30lbflroa767YDTwLvrdI4duSC1LO0P6glDh9nr8+9XbuSPBYAAGpBAAABckGbu0moQWyZTAgh//6qVQAAAwIwxXaefAAE6T8lSXWFvlQtQW28qU/vYkIpoHdMIqzzOjzlwDTHj7blp/ci9NvZvx+Q6ubxr5wK3Qrva6suQuT7cZgD0+jtmcLzmlIn+fkO71YxXmrbJRfEbX4icXXhAZ/EnvfnmitJbwzNv1XeDt8xYgeDnsP7Kb/MlYUABw0w4nVHixYeNoKjlKX33XVA8JBvHdeM2NPgMS23XhXaKMYcY0MZfuUg1v7uWVWCKb/G8xMGHPCqXoXBio4FaFepW28UhvzGehtWLpGOCbIrd4YiR10CamasTkmaDkqjGjWJz5aR6tk9mQ1qleZt5023eWoBLmND2cE7iPX3v3OolIxYeOrETFFby6nwn7bVmE1h4nC5y3E/JCyuOEzECaZEMLRVUo0zhMqz8u9Y0gt1JJWMuF9yezqH6+ZrPmlpbpfwQcaAv4fHUUhiL/ZHY7HQXZ60Q0jF1GH3S56DXdCImiEAAAF2QZ/ZRRUsN/8AAAYWZLM4ATV7m42OCr2usgl6vxp3QoL5Hu7Fbng0xvVhPcgDIch/ZPmh2ywGXbC/i5gBRPn7/0n4gvJN4gCPholp8UORqF3wZ+9L6c8zyCPkboYYc/6U4nPmQTzeRtnkDY+RIJqAdvSZG45Lo55/6fyQJg46bJuyqg6XinKcvFaRfMDeGzx+VygEnUJqVSuo7H962Mzwn0+zvLLpTke1wblasLsB3Jg57zf2zvKBGGoa9btbHgCM9sE4PvYpuUMw/k4bcDdmpx+Q6FTteA24S3j50kCB3xJfZHbHuHkvXGms7zd54uo82US5ekvCQAi31Rmek3/iiyME1eXzYblA4y+ndmcY303G8aiKaPUao+6JhkJaAtvzP1pdJy9R6xABylTOuzwufpdHJjiCKPRXaEI0Fuh9/qZLdJ0vSAKoGPdaKmYti1v8IvX4sKJMqpdh8OcbmQ0wc1Zo2fpLegY/Az28bYelHZ6lvAAAC2gAAAC3AZ/4dEL/AAAKPZC24cWgAuo7PVecE+zPgY7zdBztc4IwhbV000q7tifszWeYtzMyYPuwMFLkvmAO+0GDmx1F3uIWQgQOoNKcwgjqx42x4PX3EB+3ng1FgMGgj7PNEuLAKcFQOVgIFydgJ4FqbHkUW5ojaobgOB6vMQFEe3yz/+IoX+aoGNxQnqVswyOUIuVNfPXNsEs8boxunT0cuvIrw1siJxc8KaN5sLBmvlsO8yLNL+gAADKhAAABVAGf+mpC/wAACjztCP+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH3rSvhpgJI9zduunufkLzbESat9fUXbQX4fvfhaMerXqZgICODEzkZzVOF4PR5slmC4vKwEC5OwE8BwljyKLc0RtJXbQdpXmEs2J80FKaiJ2/mpaJEHY27EkMjkSLlTX0CQbBVaf7w0mLvrXMMl5k5f0YBUv+5Mee1gEmqPfi3TorE7gxMnRaipl8NVvuStpPi3G5EV3Imtyekwec5Ult7yYLcIQ6lRSsTGMinKBOoKA1Cnc2MISqWWwIx7lAdhevlCxPZjX7SyoqIGHDr0J2ZnyecnfCTUBhLAgDqVYcqi6C1hdsyeTcJqQ9j0nOTo9N+UysuqmEnf0JQU4e5NA9a/WWSNcjhOB9gQW8b5ua/SvtmBjxdvyZEXc91GMwziYQABwQAAAG9QZv/SahBbJlMCCH//qpVAAADAjBsVolMAC3UfJTwidwuXpyg6lM7DrS5Rj7T2ki+4vI0TF3kwXnVZs9R2aoohb4rjFmdvZ+e9BsdH+jhTR4Rz/ww9QtK4kYn61gVjJkhzbBSv+g8EKef/mCMcGB4n+1FZ6CsAX84umT6ahMmNtYszSsmyAseWF088gTRQUxwG7JZVgj2IyTjcQZVW1hNoqIiKitZgpqhomeZPNZZSypceJwNCzaf8ernzZCShHCQQ0lacp09oKv6Z7qsdnVAfpg32YozCqszL1NAA5467S/0jueGtmNsl0goPXxajBjtQmeHca0kVWCJ9xcFdOlRNbQCE4ueebwNiNGkYzIahWEGVElxbt64dQW0JDG8PgT4Am5RlfMopd1Fz6xddQ06sAc9wayIHM/XNAtWQlsFGtULBir3rzBPV5/Rpc4Cm98TH1VLzvNAmCsd0d9eGXmQ1d9zOQVSW4j89z/HAoyRHR300JgfFSx2a8mOzFFaeXt8JkTaDBPdwJ38YiiWTbDbgLMyCJRS9k+nfjIxUPJmxdJPEP+ibqf7+ymXPTbxcP84gw+lSpLP9c8hifMzgQAAAWBBnh1FFSw3/wAABiS1wAmr0DjB6BnXsGqLIpN+0Ocg1t+dFVOEr3c9J4qy5vxK5I5PZ0TJn5W9MER8WwQX8Um2Y4tEQTTz6QO43HfNCHfSegqACrTuDwURaXPHTIQAENF7OgxOnQ5hitnVVUnI1yfDtYxhKLm/VdpvaRAiWZvHiR5sZpe/PF2sBKHR6znADpAOXtjDlb9hFwmkiUH74UFtIPO2/iMoPqZ2uRBn+yALu40OiGVJg4kfEKj2owDJOCf8YjawLvUeVc3g8L3aOTxVm+2DKc0rrUCWwGClGoIejZrvyN38yL00JWXJQTPfXbYNxEEvj0pGElZFXDECVlc16nsXmoiZBz0tKNBqdsoIKgbQogtzKox34U7n8DdVdLzT+f810iix/+6ZEHhTaMHatw5z2wInX2e6G1FGj+q1I1Bnz9k0t8rInqI68fv3z2916yqCabiy0ZzgRKMAAMCBAAABSAGePHRC/wAACj2UsbxoAS1aYrRuCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH6EohIkoUIFihUs/pxSQrI9hHDYES9mYCjDpOMlMOIn2kf/5dbGjw5neKyt5qNO+mcYF+trbP5NBPfIPFFy3zdlw4gAc9pk1i6Sfh/iXDKZaRrXCiSu9VSSKvxZEXXqv/eyAppIOlgADv+R0cTQV6WTQLCx3QBmKls6wuafoGdSr71nNXX6VzSMDBoWTperNjMcDt1odxBtSdNWMtk/A/nDEiMJyp3Rvu6+sp66zYgzWRrsqr1Fd2HTQYJROVNVtE2Y1jrnmNYdHFCV0138X+Yq/oqTYtVU2EQoXTcanLj2iI08zunpHYGefI5L3n82rmlxUkRRHbfXdq4ec5FeDnGtUcIlOlOgh5P7mjh+U38dQysJyB4AAZ8AAADNAZ4+akL/AAAKPPBH+fgAXUdnqvOCfZnwMd5ug52ucEYQtq6aaVd2xP2ZrPMW5mZMH4KUN9gZSgn7TXy98O8oHanEEPjQ+Yys84NEzxGqQtzLP2n+f40u3IwTC/9e960FJFoZxy/N54xMd3ujsovhmsAVhpSclbfq6APw6v+AGMUBE3OJkrbFDzAMbMylhT1Ff9C5kT6XQcKOA15uu0+4e81vB4hmeV36evQmb5UV9l8lOxLliryCRZQMolDfSC+q/dUAzomzNYhR4AA3oAAAAYlBmiNJqEFsmUwIIf/+qlUAAAMCMShM/rgAnW2yU8Giw45plV+fEsmvH7CNll6Dy8HZPD48ju0c7+SuWQ1u31W2A8AIH9cFBZaPON9GFHYEIYXXdtT5RVD/tk9Iwa/jqQzZ/1C6QAJ86HG6NpJEaSFdNTAzue40ksmVxp9UxSvk4R7zUYDpCYvUXllN+90BaNiuQACQGAIDmV/Deeb+RjjqxrXeolxbMFr5hOXyxfz6S2LkdT9xnsR2cOQTgbUfG/GdJNX5K2ELmSvmz9fWBa5faY8OzdXC2S4Qzbqo5l1xnwrzg/tj2B8GOR46zFNskAZYcjHTCKhzVwFMJVmMvEs/XatE8fYg3Njidsl+6s97BGIMsHtdKbFiXVeBDm47qEkbvuXJfC7NodV498ehvGJlgfzm67tu4YWCt/AfeSO+b48oNHOd+6GuInNYx/4pi9mWlPGioXAFvxbq+vF4HWWp4iVD7W2J0ucxkrVE/LcTAIZPrravOIkyIJKsL0julEroihgzPUMqN10AAAFMQZ5BRRUsN/8AAAYhL0UABO3oHu3MVJ8XwlW+FHomkruAekWseQbW8fuLuEue9OgYxcTBqp+t4ckc2pl4KAJqzXNH8BuIlz/27DISa/fxplOFEqQsIgz84/1zbNfED0sDZ2AobCjtny4qqML2zPml6zYByYOOmtgwQ0eSiGxe0lS8kUQGc/WFRNc7C3EC8O6XJNFDUx5nBch2j0YEWPaxU/+8Lu6JVNTRsxXdQ4JYPmfXMULAvb1qFBedrCQ3CFfHIxU0TzxJ1aNpsE4z+AD2eVl9lp7W98Mb/cvpXAFTM5GiYPTbjyYK1zLbtvvMY3vuO74i51Kz3ZRR0FmfHb0yPF7c/8eKrv5FcbtPhd7lmWega7Nb7CwvpB2kQHxbtTPNZcmqBUA9wdK5RKcux992FAydWSfzSRL4AV2Rt6IBSLG8zRHgOmcFAAADAtoAAADXAZ5gdEL/AAAKPZCyPz8AC6js9V5wT7M+BjvN0HO1zgjCFtXTTSru2J+zNZ5i3MzJg+7ET9YnG+m3mtBg5sdRd7iFkJN9fUXbQX4fvfhaMerXqZgICODEzkaFC122CXFgFOCxHKwEC5OwE8BwljyKLc0RtJXbQdpXmEs2J80FKaiJ2/mpaJEHY27EkMjkSLlTXz1ztA9RVwCM1vLuZuPohA5JNhwvBT2xLk31/3aTTq4xqbCIO0zPVzm23qYM49qqE45TjZ+dUAEHga/TW+4VMpNuXgAADekAAAD9AZ5iakL/AAAKPO0I/5+ABdR2eq84J9mfAx3m6Dna5wRhC2rpppV3bE/Zms8xbmZkwfetK+GmAkj3N266e5+QvNsRJq319RdtBfh+9+Fox6tepmAgI4MTORnNU4Xg9HmyWYLi8rAQLk7ATwHCWPIotzRG0ldtB2leYSzYnzQUpqInb+alokQdjbsSQyORIuVNfQJBsEtocDx3qUFrA9VZx+AAFHDDGubRzcpV3eH9UA2PSs3epEBHP40+hOWseZy0Id7M76E9YGJQy6f9yyHyb7H+2tBW7sVkn92BweUl+Dy5tTwQ6gWGoh2guVqLIU7Y+OPs3zUuAcAaSQAOOAAAAydBmmdJqEFsmUwIIf/+qlUAAAMCk8nvR3pSreKGYkx/BUNQEKvFW+QfxeaRSsNAqICyJFdZE1bAI5Ihd9HhnEDhgOq5zCYPXpNRvhHzMB4I6LYZYbTwDPWqyooVcF+LkhiqWxS5ZnyeR76B2M8sS2NyT2NNz9wExJzEZbcA+EsXdC0ehgD5A3bEZns07peTlniSEv5wpAQCnfNkt5AHIXIlhoWFO/DD3KDhUtjYm67pD01KnKv9BkdItjGvL/v8UGNN+8zfRB1BCpcvBfJTAeXu/rAjPABGsu9EwGjw/IpyFW0E07+awYGcxjU90BhmonpsZfx3niaeQ3mrLBXCupauab0KW8axYfFxaetBQE7v4Ov7dyOIDOusWXh/YC3BMQpQ7WydNA7WZh+RZBger5KSorvsQmo48eWzpVym0BQKcRVofD/Ne4ihAehFPK1mL5N0aGPNERUBN85LzbHhvj1S7livI7j7/5e8f2uJSgSndCn6yQ5KM+Joq/3ivv2wpP64zzeOraLTuUjsFhY2S6FFIvWyKx7s8lQWXXCZqRxLncCEp7ld2I/wQZRJjMXC7oKDeb54NqfJ4v37ye23kbmb7wuFetkv8GhyXF6gvGyTAX98tODiKy3uGcrHJ6Eu7r0cKhjWX6uQgn3Z6OwXQPWvktR8AMfFaA/upceFTsbfNNVwOySxnAi180bEyLTwbvg0rGIcLQP6kbLh/y2OzIhruYUjHlvNXY3Mfasyx2IKl3IA78yrZi63uTx1sDkRWBqgIpbB6An0mJgkfwjoJUwT6MvaP1nDoN66OnByX0YdY9TduKh+SBGIgTJP1RxFn4Ys7YrROj+sQ1S+Top04h7NKJMH71ylHvHUovZSxOqt0BUpfYzA+HEt0zl97KPRPsBKsWARy/bN8gmngY1/bNRVjPL/xSpFOGOmifJIIH3z0ndao8NVGEyOZe5chbbrvtuueNCPA5LIYuwKbmywAChXDrBaWMmzMiN/EMknAat+jv/tOyz81K3eCRiLHcVQOKlDwX3UwafrbCLm6StTnHY5bqHsLnrgxwmS1B5gbEtc2PkmxgWy1PkAAAGMQZ6FRRUsN/8AAAcUoQHOejSAdWWE3SQ6mID989aW1Iq1UQAgSd7HWgTqJwA/sTimUPdjsrwb/VqzHibh69g5MfeYXE4aFH4/9iKQ2+indtj0y0gooC2wz2Yrnk17mg8FCtkDTkPNZw8/zLKD+VeE9rL+jWq8ZyyFp7OJn3jAzQS0u5CE9b+zMKxE3/P807d7W9/QgOHigrq/rABKPiSPAmLvVrvhiXKPcwIaxlfp9V3EmvjG57FyA+vEybgZJOK0IR2ehGeDusQBvkC25JCW2S5t1lS+zjyKE0KdYhdrC5SIp29q1UiK3v9WIk595WUnqlyRkAMD6pEPjc8vH4UXk0LvPUOykJoSM7ngRZL535IzHASg+ExbLOtLbKglVjLxWJxrXJPY/pVtAdWh2+kdqHk9DjYETfMhmlqjbkCNEdCYwlSFSt8mHXAz6iEGUggYxsydLSrCRdk6SZl+uNnZZC2VVKGz3ulv/7xzRZxI5kijnPw+OIT5ZrSvmrq5JMUk25TXtHDBq1MgAD5hAAABHQGepHRC/wAACj2QtuHFoALqOz1XnBPsz4GO83Qc7XOCMIW1dNNKu7Yn7M1nmLczMmD7sDBuwHqi8Jagwc2Oou9xCyECB1BpTmEEdWPG2PB6+4gP288GosBg0Ffz5olxYBTgqBysBAuTsBPAtTY8ii3NEbVDcBwPV5iAoj2+Wf/xFC/zVAxuKE9StmGRyhFypr567NglnpYE6PRiHcwMaQ+ai0sC9mQmlU7XjSA05/ftUiGvCe0amujq5E/rwecyhPPcefDGyGMqzcOox2RY5wmcrKb4gHNxoc73u6rwWBv7GhmzbRChZeKHAbkSB9T4hV9Ek5+rlmsI0WlbcO1Hr6KE09BQ07E8i8yZ7EBhgitqhgiHyu+vrinAAADpgQAAAU4BnqZqQv8AAAvymQ0b2syzseDFlgBbx2eq84J9mfAxWp/402URlcp153tSyA9zKnWcNyLR+Srf5gsx17/d3Roc7UuUPd82KwBpv3K/H0URnaFusvUsefaAtEUqukCozdWwU4Obt/QGboX+PgFup/eQ94v9Mb2B2rtNyI/PuO7gXze+VL4dnyukPWrJHpCeVCcxv7jGAV1KOQvf2e0rnKEDcobOwSQcpM3IYXMSQw6Hznlvy2PLxQleCJZ4Fao2PXZyhzn8Jb1P5qzx9UigdaBwDcjmtBu8AA/CMMaJ2hMiBbV4F7B4STNOVnhTu2xqin5KhFMVRIQFPIkgtv0LI4oTVdd4WPxHF2sOrObkhK7EJ9yrf+5UmIzmAbezWxrl+LpRYr6FUS833FNx7kDNpKV3Uts9CVyAdOZab/OWmMhJaPTRmgKc19SGNvgAAGLBAAABq0Gaq0moQWyZTAgh//6qVQAAAwIwgPgAD+kXjYhGbuuOzH1Evn+/0X+q1mAZaf2iRKFtKWHDxRJp5lOAeEr/8kzB3AhgvxGy1xfMKko2nufCaeRZfivJeSQTsYif/vzT/Y3JzS5NRztfpx1wzs2QGYGyHwf4say2dTKWYCgOvbP74De32fx1yna+AsK0+FupWLQgB0AsemTM+OSxCngisVI1vaEf+MhcFfMiBmZUxWioIum1i/NSvbP0YJ6qK+DZZllwIADckIkobJBDs3K2EPzxlvyZqm1QLU4s6oLPeSoOiFpIZygYWh1QRf3hJx7GkEweKffP7kkLuU4kNxuEIyUdzXbGE5RO5iYO0RnAkxeDdqF7PMy3QWUgLjg/yvBN7raRtU+67fbmA/Hxh+yAhT4ofJrNW2gQ/2kcHSsT6Kz0eMa41NxF5h6FL111gEXni7lgTuhMDh1ZHb3/PzSYaQACv6wDC/NX6V43BYZWWoa5aZRaIkHIEjQFChII+SR/JJ2MGM1d1QbWcpZ3aJ6cqTW9Q3gUvbTIPaIc8D+7FvaoJi8OjikdTfMcciIAAAE9QZ7JRRUsN/8AAAYj7kAH9BXMTjiZfDP1w24eI26xLKwDQ09mgDxIghkR/bYTd3nR7P+WHQFADNf+E/8DWQdyMdwVLq99yPkcqCzRMLLJLDe7gaV3p9y2FmDPJwxrlgCUWJ7QXPiO8sd6AK8Gk/IiIzxBme7AQqGqWq1WTKzZzqpYl9DRcYM7JFml0wZzzh/pmAni2MUThN113zpdxtcYys0GqaniYu/mTl4WPrrZbu/UpO6RRU6j2pPGZmmthfFB+outPAdG1aJJhq7yzDiugS3UZRWb/RoYG8kIx1dxSi87Oc2tEJkUNydUpAOWQeGyfsgGh22xPX6EP2JeAJHk5UFE5pq337hvgmgEKR0WB8W9uA0QAtUC+iNUJHgCLJhNu3iLygMP+iYdlE6iT7KOfHDhm7bOl4ZuzEAAQcAAAACxAZ7odEL/AAAKPZQBcvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4gbrV29txLTEiptN5dvVDHqf6WxhqnnQQ/O7QBbWhwffpMGCeF6m6mNGv6J1fn6n7ad5BxHKg+E58z8kk/Lm9TbEPPH2kgjfobsZUTj+M3XW0wwwTvjJM5dr7pcBT4X+i/ezOklqWI6CC9p/TtPSS3HKPcqc9Ed4Z6ArDZK2Ax/CTDjAJyQAAEnBAAABTQGe6mpC/wAACjzwDw6+CcL4ACdvGwOkzE4VmgVzbkE3GbeXFIeQ+jh35OHcS0nYpe9N171Qx6n+lsYap50EPzu0AW1ocH36TBgnhepupjRr+idX59PlwcKqoPcfuzi8kZVZvUl1Rlell29haL8UIV3ys4RRF/mGCISOIlayWKcAI5gQZw9QlwyHdKJ6kpCZCdpxwlmhdzfOyj6d/y3GvskVOMb9BRZFi4DRjKCgyvwnHxscR3qRyyAjLD8M7bOKR8PBEtFt878UakTe10gC6SGiq2dAeGKBcbC/LhsMjJi5SnoKBUwB3hz7KX0ZiV/X521+mH8YbbxTTfqCzV9/s0AO4Z/HeyZbWSC5OJujEL0RQQZx6MBn+EKAU8pkZYXWjrE+McDb65JhrKoR3kEtb63AeW81H2UeWsovWoS1/5nrWJk0nVasKVOQAABwQAAAAZdBmu9JqEFsmUwIIf/+qlUAAAMCMU5KZoAA3RHwNQV28xPdad4d903JVsEwk+4ZcqzRH6Zszna4D0W1KNZZFhDQKKzQbkvueAmNwIjsumvSSO9hUiGe/rDzRH95cdWGurv8+jSFVbzcfwE62lmdIK1egIdl1ibssEmdt5j/tVPDeWOGdDrQV8vz2R3r1UssPaDnM+Pu6QQe0ScyQ4Zu7bDl3rjQAB2jtmy8fPgarUWnI3ir8It2MN53yWHGsB4BF+RL8NtNueUoLPqr/qM9i33FHSIORLaPOH//j7ZbwOaHMw4GfiUTna2aHUBhBH+NjkSZ1unsbQA6yv8g3KaplfPx4PWe8zCBTzdG/u4PFj+ftFq4EmkQ6j3Q+f9hIt3d42S2GcgQDaJCGci/QSTPVL152xPrpYBSemBi6rnikfg4lcdTxKey4KkKPOmBdfZWiCaDv+hqNgwB+cTGyc8w7K2UAfLsqZe8IwPjk1zLi4noavdk4ArRxdMBfy7I6/0E4G4u5//EGp6F6CmwlqzIJeRo2mEKY1SFVAAAAUxBnw1FFSw3/wAABg4tVJAAnboGE5EJVLzjSHMY82N7BlLzVGNfIa0fHG6DBgKdPE/W0J2faNlPP9NcwUrVz4rFMSq1269yMCIHA6nIMQbMNx37IhvMNxydZMdmczQH0TLbE75Zz/PnAWsCMC9trCHHvFac8J3/zsyoreq0ENxYsBfPlIYLSYTc7k3Ko3ersC1h5eFZ4xrcHAIFuTSyjdek455E1NtokGTwG2laLwTGiSgm37xY5bL9qXjmECO4MJ0PkRexlSF/03Wh856o+p70gaplDZaQX5j3oJ2JI6Ho0hGzLPTRANJB93GGRlz3y7ewAmEN1y9wQ2dfeEFaa9xyY86kLPrnLlevk6RTx9WRYsTw6hSM9KyDX9PpIuzkcJqKF4JvEHJgm0/4j0+fdWhoM9nnlypH1XjypI3RlUicEiXbiz1bwaAAAAMD/QAAAOoBnyx0Qv8AAAo9lAFy+CcL4ACdvGwOkzE4VmgVzbkE3GbeXFIeQ+jh4Q1a4lpJVaM1171Qx6n+lsYap50EPzu0AW1ocH36TBgnhepupiZVGnmGn3UCk5Q+FJuZrQlONIhO5SXVFB6V6VrqJgChCww9nCKIxoVb/UiZ6mjZJPJBmyeEdQZHmTzEz5Kn6pBetpeRkVlD8pgOjzuaelEX98Y69Ht0WDXXxjCLXGXk5w666+CFa8M93F+88cAqWuVO99OwNR5n1xLKKm7PsmaEtttF+trZDZwxDYuZCAeAuDFFgAm2VPylyAAAMCEAAAEmAZ8uakL/AAAKPPAPDr4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHfk4dxLTEiptN5XvVDHqf6WxhqnnQQ/O7QBbWhwffpMGCeF6m6mNGv6J1fn6n7pUEkHEf9+TznGifr7j4jIp4aNYLIvJrzJcNaIu+Vj7szGZUcApivOmjZgLpBmyhx2kKAW4f8ZEkjDtpGvMkZ8qC8doXGWLbz3RPPP10aoT4oIAY/pyI5sA+ZfXW7w1RQab4ESBZU790ok2Tk+FyPKPtI6ghAHlwPRoHQfSvlRjCeozZXWcU6DIbvnbuGXMTkAWTqC07LqV4GXFawweYnPGn1lTm22dcOmEREKINnOtgEPYvZOGuEaWQfWSXbtcr4P8Y5HPmqbszjCuF0AAAelAAABq0GbM0moQWyZTAgh//6qVQAAAwIwgPgAEtXk1PCNsv9YdPMINSXswHyeGvQCmq3Kpm3LDqFhN/W/fu6mEgsgOgCyfkzGV/V0H8qgZbProZQks6MeerDmuRaWKRvSCR+DTNbx978kUgHC3roaj2BLL2Epdz1ReDfOXEUetp+lte+Ht9F4PMVQK6cGfEdlnKmTVD99aow5O/jB1jLhQWWxM6AhbtqHpze+kjVsZsAAxTRM3Rv9Oc84TpVUdUc+4wkCL2JMraTtN6RWZd5riYvcWPpAgC8sH5NdKb6Z0IGqh0fbaRSU//aLyZPUFP1UHUOuCD5CeVOHXn4erU/FXpPKIEjMjpsYXTHiEQKMUiFSCyshP68hIZIGNxYjF5fYJVMg4Kx76moNZZMk+iwUprlfsgzJm6a4surky3R2wV48bsxG4oOoqIszhqWO96hsfWCj26vPEB2GZZo038NLBx4vc+rFMvgbz9dlzeOjz7ZNymY+aMCRgHq3J8Ej3RKoK59WQnvS2sAnwZayb0xn8Z79aM6ZX5NAdwZRV6u6lT6U8XBc4oIxLMAe5F6qkEAAAAEjQZ9RRRUsN/8AAAYgvzkAJq4JnGfR7vmxXc+SHuaXEUl9wobVOIGbISCfSNhtyqXFpFSEF2RkHlUXQd8vle2wb70Yw9cP8MoTVD9h2xrdImmEMp3QsqK6rg9vUW+Hxq3KQl17AUSv3Zs43PgYtMqNcA0jVM1Mubnq3bZp1/RDfylQ6IXdqknMBfPYyJx4/S/je4gZxMIV2bUP3IKl0Dk4dcCkeI/yHG5ko9mgGDlendi5Vi9+Q6rOZ+MZP5qru0wptLQs/yG82Syec56rA72gCpjhnbrhYZkSG5E9ILUxZ9+VPElQyOThAhI5hZ6/cF4wpQ8kHGmmtudlXi5aawc5NdFKtCeViFdKEedvYSN+Tbu0wK8lk4smnO0bYqOb1sK7QAHTAAABHQGfcHRC/wAACj2UAXL4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHhDVriWk7FL3puveqGPU/0tjDVPOgh+d2gC2tDg+/SYME8L1N1MaNf0Tq/Pp8uDhVVB7j92cXkjKrN6kuqMr0su3sLRfihCu+VnCKIv8wwRCRxErWSbdFBYZecK8QECMZvV2+zJggAAnc0xmSpmfz7H893FzT+mfol/od42qjv0heAIFtm60vpjqhIaiTO2gcv1x0Fv2PuYGVQjthSrJHaRmOuQsmgC56KW3lGwJP7ZYSezjCJQhy2UXO3w791Jy4K+UrvVyc9b068WmcuDFGfbIaDfKlQTdgaSoUSyGr6qrPx46YQ9TrxS+ZdTYQi0iBIAAArYQAAASsBn3JqQv8AAAo88A8OvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4d+Th3EtMSKm03le9UMep/pbGGqedBD87tAFtaHB9+kwYJ4XqbqY0a/onV+fqfulQSQcR/35POcaJ+vuPiMinho1gsi8mvMlw1oi75WPuzMZlRwCmK86aNmAukHOI9vpVxQsV+FzsK/tGfhagpplOdYQ2FREOBAg88Bo+i2gQTjuxgDhmdZnoXxjo0uaiz1dInDrwlOyr305aMwtrx3soHxyOzEDLDeoUbHf5N9gScLxH7z3YZnEwJVnaVy58RQ4+ShJipnYoQQs3dOVYCQxlHdv7MWE+0aNES6Ibr/Q2vNOa+iGyrLJF68xGCO+IPz7Qg3NJ/h6v+ZMLqNX5q1qAAADpgAAAAaRBm3dJqEFsmUwIIf/+qlUAAAMCMID4AA/pFr5K48+nOzAi7mM/mg9LmrkSJg9wyxa05BTkK4TLoaCos0Vu3Va05bpylJQq8UstTQcBSPz5xoaPrsNg+NFhkPWbsWW1UIGeRh7vCr2kgTbMTyRN8HsoW0EhTFIhHmfiUkzkC308mlgVyjr+R0q84z9bccqliAet1IMSgAH1HNzl+w9Gj+DEas6J/NHOVkM/fux9fmtIjp87MoLQ3/SG3FBE+RyYAOgildzjxZ1zBgit973gpLrbBnOK5SeD5pLlqfFb/Bem7FFP9RbbnykRptzl0eu2HJrmIMtD3IlGV4jg91kt5g5MW3vxLCj2PAHwJwZ5VXdAWlKDFm2AbiqCWe6zvSHUgVEUtf+oThfb5U33RwqiK1ZHHc1jLu09bNQ+GrK9P/p0YtU1U6wu0JdCn4HHB8xg17CSWsCANqoDRcOw/3Abi7iBJO99wmC+Oq18aSS7a0IYrrVDEy+53RRyLbjNQtuY0uC4JVnzWpEiV7Gk2Elj3YEPgjuokSfWFeYlOJu67EuwOO4vd7gAAAGLQZ+VRRUsN/8AAAYhTuSAD+jo7lzZLjPYqnKpIVJHS63riB5JUUSTjM/ePTbxQOOKjbn47ELQPjp7ceYR1BiPUpMlQf+ZJsRTsqN+olygbhAzPrpECvyxyQ5sHD031xa8X44uUNm9yIspfNmSK9/ChWZa+0SRIkZ9wHQ73uGG5RpDLWpPTWZ9ixUsPKOphv9KkvaDX0v9azz54lbTIYfmgq0nMrWX0N0+Y/lU+OdjkbZ8f6inqrltzkXHDXRUVlZqcNA5odHDE+YWOD0wQvBa69kVLRdt3uwJ/E5V+It9/bllJA3r4ZXAEiGqmQazmQdkse4UTGkSTAFW3NIG9sUQ9drWHUUJNx99WM5jmGkQ9F0JRPy+T04Ma+HZ+NNbyDKv3zpmACyjyB6M7/sHngQRht94C4M3KoVIRIQ1oQqxj4rqtVRBKGfcnMVMkOdjhq6lraVucpr/HfDUc2lcMKsv2FzkXL6zP2Ot4a9ZLy+/zq8YcGeRdMK9c/Gt3A+gyjNEspoOXgc/pzCAAS8AAAEpAZ+0dEL/AAAKPZQBcvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4eENWuJaUwFxIxK96oY9T/S2MNU86CH53aALa0ODxF2xv/W0v6GAPa/lkSOfAj7CHDB9+U68CnyRlArU2HpvhCoslYcRNfUITOJqyBEsZp1bRImOsZ6BFEgucg5ba4dHhlHPGkvhShaNnpXig+zytkl8uw0Wwdn4RvnYS8lLmPD88gU6MF45JycE5TspBS24B92X0RW+/ktnbEOCjMMeD6mtXCaGLksNr9gvJTzq6h8gZoMN9JT5UZ0qoVNIvkfuP9+autHXIgq6rh3xyGXHw4ETggbrRRR3r5D4+Qtzqie1l8tfoRarsJPdNiBMVX6Ne2rn9bVJFeq3SWMMJgEAAARcAAABFQGftmpC/wAACjzwDw6+CcL4ACdvGwOkzE4VmgVzbkE3GbeXFIeQ+jh35OHcS0xIqbTeV71Qx6n+lsYap50EPzu0AW1ocH36TBgnhepupjRr+idX5+p+6VBJBxH/fk85xon6+4+IyKeGjWCyLya8yXDWiLvlY+7MxmVHAKYrzpo2YC6QXOPNeQlwFlwoRe96nKjndblvYcq0alPADVqVFDlFmDlNNogN0pbQ6U6V195K7OqYxFX3PxlrS5PEH0fB2fTrQ0kOFYRSls3A3QPamujZ9g3T9iDW25vRKDTLGD5TYFp0kmpROvtrwhpYN/DxUUBAgkBFhMY53+Se6TKoCu4bZDQX54u44mCqUEHZ+twZP4AAA1MAAAG7QZu7SahBbJlMCCH//qpVAAADAkE4+wMAOleKvWsZ8sQcVren3cIAA4fJFb1/yIXVtBTBsL+mhmMItxE1Zh5mb7JeSgRgp59shQzNL3Tm73u76hwy+g+6lkUCgQPN4r2NMSa//uPHgO3SpkKIV7WUICvAf9meT6Q4MlQDCnLrOiAbFMe1vaNRYch3MbbEkM9mox5fOCV8MptGPKGrY/5vHuHRLu0K2DIpPx62xcc8H+L2aH3r6NX2Ax6wM9QgVUr9c1cZCB4y+I0I0GTHcMbKFf+PAAeO7ycVkr/hJuSxnmkNipfH5k9NNvIkJHz7jvhg9zw+ZBN7f632cPqTav9izv7dveXLQgAl+qxh0nHhOSCUC7hbpsWcyM2R5Gao+9pB76MrD8mPqDpz9Fog4VFY0LUt4JdH4a7ECK39lO+bMczyAvDXibDMeqfF2prgZL2Rn4y6cI1G9gOH7slJOEeMKBZoQP+8P2T/xPg9Yx48sXV6AErWdw7eLi3dIieRpKwHQ3dDZPsjWTEtiJukRqWULeoYalugiepWrYEBRvT6vxYGO9BGvjkLzPTnomr9kwbcnSHNDwmtgS7b3kEAAAExQZ/ZRRUsN/8AAAZIukwa5KoMjIsOCIjABsCFdEVPAkElM9q+JVISezyUslrQ2cQk6rKjcmvZY68HAzRxaTwED2QjcuIMrAcC3vhkaet+/lkd7oEfo9HSALCP4OSjwe0Dpz52i/pbXxUyi0dWmXc8hXjzjbVa3XanfM2LfVDYB7/AbUrE7QBrywDAeljkgwOHlPoajjsZZ49ttO9UeRvI0YeyHYP7gXGlO35ru4UhOG65otuhONhqSM79qJQ10HUMeTiDFW3MIt5mCRF9AtGX41hJd2+nlnT5mR0yOJ9FNCZuTQvf1j0qXaMEwEzj+dx3ngHRLWOQgIo10vHpoXnj4X9hnn6FWHL6N9e5mZ4xUaqpFMjLguEbF1OJct/+d/FMBfrwUbxQRRIvjwltzeAAO6AAAAD3AZ/4dEL/AAAJ9YYl+CcDgAD+eNgdJmJwrNArm3IJuM28uKQ8h9HDzD8IAvddJVaM11D1Qx6n+lsYap50EPzu0AW1ocH36TBgnhepupiZVGnmGn3UCk5Q+FJuZrQlONIhO5SXVFD+aTby4hfFCLVQtCkRNDSsoKCJnjMAACLNO24tfOdD9fZ1KMaeLkmBefvbwuXzZKKlcACC8ELtx2rw2iWwpKoVLq+nnnmXUNfk+HNJQb8efBp99DuRbO9u+eK9YGHqUqoyzcJfWWnQbdHpoDS9+ZYUjPEjS/wRv4bugojZjvBdkICE06V87P2JojM5fdOkAAAUEQAAAQ0Bn/pqQv8AAAqE8BYNfvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4UrB71vcS0xIqbTeY71Qx6n+lsYap50EPzu0AW1ocHiLtjf+tpf0MAe1/LIkc9/DtKXRBxFHVA31MvxfahPLAr+jzIDF45acXdI8YGFDKjl18O54FqMoVidduTwXrXzv0eL0XnepJvvG1XJcgAi7pleNxaglq7qYgb8cH1HUO2nIDvude2UkyTPdiwNA5XT1rXqwJhdYlryd+6+kfUnXTAScj0n8GvHwz82jVJ0WSZ2dPWm7cIZ1nwcTL70VEmJMKG4VB0k643jkw/x0Zm3B282uY09CWluH80y3uTpDcRN2gAABLwAAAadBm/9JqEFsmUwIIf/+qlUAAAMCMID4AA/pFytFcS3JQDDbPg2EzH6BI4DBUadt/BtgbAlFHmBpPwnyPdAn7KfzazmbIApO2VnqbdXe4UUOr6mXyhx+E4mnzCUOsztCe5/w+msWgrO1tLDTl8Ex+yu5kF+VCpfrqvhJPZtVKBf1HBoPQapzAZElwERN7dUspzPEWMM0lZUzvPGF6hAAc4Ul4/mpihPiJFSl33fsiF7//5rQERlfsZ6RZMkgStc0vJWbyLi1gZyTno5XMSEFP1A/lDTkH7QpHayavSzrMTfEkgB35cRjTHlGMp4d/QU0Lvwl1huBIighcAEbkEwQ5cDDivCC6ctK2d91PQxCKKQKMzmvZ93ugE/bOiZ7lkcWiGHw2Bc/WS47HMjv8sb4O+TUJldn/dE/WvflbUoL6wF+uO4L8Ua5Lcs3gfTGk+FpkgrMJWFaHur9wMYZz59WdO8o+HMKU8m0mIWBUiF7GARTcszHx0EfHAKZ0c0/FPOXWVRrgsNefAI2dqfS/Qku/kjyTikIfImSFCdCqWzoM3pDoaXarkWNsMEAAAE7QZ4dRRUsN/8AAAYd7qtEACdjlKeNML7olzcvWLXj+2MwEgeLT11uYIdNSvqHfQVIz71RxZk/pN5/pv73KcyJaihOMAFDON8ynB8Y81olIe7iSP2oBN2cMCRlnHU7Eu5PUGXo9Zn2TVvb15k0uCv57MSEtHvEQSPlPPPT6aH0ennmHXQctqN92XHp1DrrI4Em9N2NBZF/haXWaUdnaZmDmBqYDk5wrWiaRCAm6LRa00Uaz7s3eIHtwBzjBaCNHZMOkGSbdi6d2sbZo/MbeYH8mEWCmlx4TEOPbGvrw/PNWmNiehYDHQbRM9PMxYhZmPUyMRIUEdaUh0TFmpby2wed5/6gdrW4BhjZBWIEYzqn+OCtiLvoi9pT2lUnt8yLqk1ZeMd/aEtbLAMfukbVWwss7Srie9WYuQAAAwGhAAAArAGePHRC/wAACj2UAXL4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHhDVriWmJFTabyveqGPU/0tjDVPOgh+d2gC2tDg8Rdsb/1tL+hgD2v5ZEjnv4dpS6IOIo6oG+pl+L7UJ5YFf0eZAYvHLyR7pHjAwoZUcuvh3PAtRlEGsk26KCSp1gvUtA3MvggDpNHwS0WgSdHkiL9qQPhKDm5OyrRtiILG0BnUNmAAQ8AAAEkAZ4+akL/AAAKPPAPDr4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHfk4dxLSdil703XvVDHqf6WxhqnnQQ/O7QBbWhwffpMGCeF6m6mNGv6J1fn0+XBwqqg9x+7OLyRlVm9SXVGV6WXb2FovxQhXfKzhFEX+YYIhI4iVrJYpwAJr+SsW3gYK6ItMETGfnBLGu+wCEsG3l+NHcUBC/RJlmatmc0wXhWk4KAjAogjlDDSZo1yqfYmQ3i/jzS2JmD16ox4zfXmZMMx6V/rDbgdqwe7Tt6oGcB+dhTZyJgSnqCVRjUaPsg9Y+7/Oe4ldKieqkQp+2JJEVVQBN5diy8A5/7ut1RJh04QBLHyTo16VJDtxHuHijshCTYtJ9SZ/n4AAAMDegAAAb1BmiNJqEFsmUwIIf/+qlUAAAMCMfyvMWoblCwABfLCM8Hyp+xAk9s/m/e4zhrNpf8Zf0viBNUX5PgXLeh/8oRDW5omEDu1cc2EFmD92YrLSaVCcl/V1IWi0spaSY47F1NPo1Yn8tXgAiuxqRpyPdg79yQQaOHu0jut5POrOmsqcBGiW3kFIC02C8l/t/8cFviGGGwAA9RvO2DqQAmHzQdq//MrDz31iZJfTn1vwqnJYB8pz8JW6opkXRWevfTx9ghe83tRFJ3/rc0Uti0DlygtrIxJKDM+Nk4eS+QLGRPluebWo8b0o2DcZCmg9NKYO4m8tx38R/FCsvS9hlIZRuBjkwvQqWwbceDMopoUEYcXz2a7cvyYT3XopdedSq0T67MV45V3woivFYi7SwJ5B80FbmcP+8xZLwK6nmyb7BPW5mFT38JX2tlV4HvsmnbigzdJHowsKwYunpLDc2UnGwPWpfy1CSl0eFzY2tZScnEmX0ac9klCMefFwU9OzYJkH1gBwIHEGbt2uV5X5eDHgBrZ754fuHldbUc/pHhx1XjJjwWzX0w4Ega/O8n99ocN/4mgqNDJEAmP1Jm8vRllAAABZ0GeQUUVLDf/AAAGI+5AB/SC3G6J5A3WH1iN+ZiikeI607AuowrPaxOJ18BnTrFQ+CGX527RFbOpLvjKEB2UjA5uJRwwkOfizWxL/sRHk20bj/6AwHfWqF+LrTvAqkByeE0Q8+CdDiUP/1il7GODiu2FcUW7aorREBVPi1hpVFAo/XxN6dyQohM97hvT0/Yf7VmpbceZUdXSPLJbk2/v2BUjNpBfrk86GNgf8h3aaLXpWya3Mi+RON6yQ9mDgr+/+Aei70pP6ZZwyPKrgYKLmJtgC8xlh13VO4BHOoDgPAQEvYek8yHq2W20i4hj5x51wqBigGNpof0mM1JaUPaMxKhQ8Q8e5nb788ik+HnfkLMdcWFfnToHEt6LXbYMKooeRPJX3wvReao8AGfEdH5n/Le+D0Hry0tPocol2TXs3vT+5aeX7kU5r34TOSZH+Wu25UJuUm5Dr3eaejmkLrwyFGvPTR6oABxwAAAAtAGeYHRC/wAACj2UAXL4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHhDVriWklVozXXvVDHqf6WxhqnnQQ/O7QBbWhwffpMGCeF6m6mJlUaeYafdQKTlD4Um5mtCU40iE7lJdUUHpXpWuomAKELDD2cIojGhVv9SJnqaNkk8kHLBwCRyOVH4WJX/HFo25+HaBZe9qJSAbEg3pXSO3pbzo4UHoil//XvU8cXCKM3IsNoAAAU0QAAASoBnmJqQv8AAAo88A8OvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4d+Th3EtMSKm03le9UMep/pbGGqedBD87tAFtaHB9+kwYJ4XqbqY0a/onV+fqfukWNzXqkgIG5m9E/X3Gsnbgjj3WdSPfqiJ958LWVnYbuK5OBpaA8RWM9HDiQc4j33IHURO4KtuRP37XQaqv0SIh1FH6gACR3w0VFLq750F3bvnb2BO2oRCp0uBhuxwypsqIbF24auBAA7Bw/6FrTKBij1rtHLPZmcQx+jXeUx3kkh8y+ovhB15aje5+qe2M/rf58TqauTbGa92nYQ0V6400K0syecQuv9fphtmBG1oL+GdfQ7wY8aoVplBDKkvBRDlUQIzi7hVxh8bdvNfApvAAADjgAAABtUGaZ0moQWyZTAgh//6qVQAAAwIxPybusAALp/mYXNYZfouKQ3G8UGpSIiD0Hp/wTxwVZqOUayGuEzTyjqzM6IVnm546HxL9Q4KMit01zedzwc4oKutF2UbWfmrhTsnMEToSoDjzLM29coqDJo3b+orS2a5b4eZokiQ5htmyKMt6ApFRQpaeTUa0cclJjyoh4GSilGErgA2KcEIB5hRH3EXsQ1PHGoAF39aTTe7ziSyqN041TZ4vG3/nc+a6kaF7I/4UOUOrQb49vbeA/zYnt/zg9nQE9M6QI5qe1XW9LUZQWnrPHvmlHvp7iE9o5F+OLeTVrxxP4lkoQbWWZ8RAq9E3Buo6U2LGFR/utK6sJUSIW4lfVRQ/82dnAzZLTgxIHPvrocrhgUHOXH2EzTR8SzPobv4acxs9ODqWW+Ls5HhovBPDdE+CaT+KTKM8NEPfUaIu97BcG0hcHT+vG0BygZB/iyaZrPN4+ilczbyw63mT5mdLlITzARW9m93Sq2Ad04OuPDmIzHeLGk0rBPWM2hDybh7PXrA9LsgbKruUZ5S8EawsxyE+4jCXNlD1Q23frHbYfb2BAAABJ0GehUUVLDf/AAAGI+5AB/R2gdMzkvx/cuxjbNTzpTfHxMxOIJXMmcz1H33BXojhjGqgJM3ffIWg0OvRL48NL1Falh4lrKUa3lhPlqVa7Ms+9dqIOOmRJSWHh6Kzo085n6ztZYf6/xyvxgPhx96VQ8+0BHxMZV7Ng+7eHIvgdlRDbq0A5AUM0nqwABHbcTm81TLrJv2dUWctp3oGdEfYvX+RwYsNiG3xUfPJ08ggITguY2PkRaTjposYQT4IcctVqO8A7FjCMGY4II8cRQeiiJ0RUuLCjKirhsddX+vjn6tLSEIC/qGhlTDl+43iHM0c4dIoG6vOGvqZd1d1alL5hFxmUsavxNlq1PCYcEXmJeuTTR9eHNZ5AAR9bm1QDe7kz7wKpcAAG1EAAAEqAZ6kdEL/AAAKPZQBcvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4eENWuJaYkVNpvK96oY9T/S2MNU86CH53aALa0OD79JgwTwvU3Uxo1/ROr8/U/dKgkg4j/vyec40T9fcfEZFPDRrBZF5NeZLhrRF3ysfdmYzKjgFMV500bJJ5Ihq3TQ6V0ql4nG+6/OoFtBi0sYDsytYppAPNPQb9NHhwePIjvRONA7+G8fCtMcdaz4zoYnvbnlvYaEI5EdZIavGVK/NfXTf7lo0a2fARqpw7k750pNbScBddhkNSdhto8+jVzi2+xSQ7WX5+4phCVo1Tw+8eWOVYLS2aTIysim0gLYOwpVv9nAevNpBu2ERQTOmgUhTs7mT1/pyHWhglofsTs/AAABQQQAAAK0BnqZqQv8AAAo88A8OvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4d+Th3EtMSKm03le9UMep/pbGGqedBD87tAFtaHB9+kwYJ4XqbqY0a/onV+fqfulQSQcR/35POcaJ+vuPiMinho1gsi8mvMlw1oi75WPuzMZlRwCmK86aNmAukQ26jH3z09ZO+ThZVlo+EvbVk1pJWqwgBRg1lYFrY/DDOeC5NPcguwAMWQAAActBmqtJqEFsmUwIIf/+qlUAAAMCMU6evAAH9If4s5PMiWrGs0Bz/CKc9nX2OmethGro1ZS/0q9eT2GgsXo7cyhFsRFJrKkT1mIIsRSU38HluszrBpBF690vDNYVP+1AEW457oVivkwTk9aa/HYpAd6dn10LZuVSIOuAQvGGF9mtayujYLJDJj4yAJfaXIADZDDz3QmpN0KJfSG837hx5oUKg1IUb7sRyMBsqmCylPnrTcKDV2aeMehEY6lWRF6Ne/BrFMsI3tPcUMdMPWDv4X0RgzPNrruarMQAO4n9xGbuZb9ZZ8ihTgNb0zETS1PqZkWKKQE+QrD8esyAGL3z0YBzUODPfWsxIVFyq3CyG0Y5XzVRJnCACoQOTfPzQjkXiBIx0fgSvEVcyZG+wB7Lh6giTglQZ1VYtDBCl/sfeJ5t6ZLHXCuNgh505pUMTPi6tjQcoj0fr3BIfQ+6S3L2CPxsExribGCuo10K0UFib5cixOM12Yd4f6x9SUaDr0akNk6qrmF+FhnEqmj3ZJnhYhWxSppRbpKJFwXvu6BBYvnTxpBUg4gAXu5Uz8DMlPA/XcjDx9jIh329AkxQAbnz+2aUK9CjBR65muQADZgAAAFqQZ7JRRUsN/8AAAYhTuSAD+jo7l6j8dcwNCwGXVLYuXXNIsj1FI0TDqqF/N2p0e4isp6onSrL5xrG5AQst1nPc5+SN70kFLn8Si6ejoSTbD384OHL+g75XeoA3AuaOt9BjU1Ne3GWfWyEDvckbLbiicKAItqiuuuQQ20FVIZaoIqUoSc1enFnynNoGk36LRE9mTE5RP6GWGrdbs8fcJO7jG7iOi26zsSFuLCugDN/tQ9/LBuQQVHa3xjh9e4IZa3+Cck8d9B/E0VLZpT02RYiPnZBoW+hyPwzXs0DwiWy0++4rMRXaJLVuAa2wr8R7vD3gNlMJ9lV8BbnXE5hgRgKbeNTzDuFWBfDe3FnwNe3eiiN+45CjuRovfRVjuDukH8wdeDZnuIrAM9YQbadIoAUo6qFKp70UbhMzCcl24wLG2chJEsfV9q/UwwGz+qniSg8deyEeHJ/TJQce2wZwYqmvdD78pLRwAAAyoAAAACuAZ7odEL/AAAKPZQBcvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4eENWuJaUwFxIxK96oY9T/S2MNU86CH53aALa0ODxF2xv/W0v6GAPa/lkSOfAj7CHDB9+U68CnyRlArU2HpvhCoslYcRNfUITOJqyBEsZp1bRImOsZ6BFEiGXap99HzlseAlNSOcmESgaVdU0pJuMogPiOje9jKiLOHP1vaowqDi2clAAFBBAAABLgGe6mpC/wAACjzwDw6+CcL4ACdvGwOkzE4VmgVzbkE3GbeXFIeQ+jh35OHcS0xIqbTeV71Qx6n+lsYap50EPzu0AW1ocH36TBgnhepupjRr+idX5+p+6VBJBxH/fk85xon6+4+IyKeGjWCyLya8yXDWiLvlY+7MxmVHAKYrzpo2YC6RDLthX+BFCJrrX5ApdECU+Pet37V7LAMSDRl+C4yoZkjMByZCsnejJQjU3hPiPzGOiYlao+Ht6MQuwFTjW2cq4Sfxh5xJVg1QdZ6xTx0LsJ+r0xY6hk/jsJSRdb1zQkfTc80YfScug7e9inQc2WVT5rQyYP+f2+AX+stb57EE4uq4q27Xr3bwImYZRUFITB5OEE65H2hSX1rz4FNzrjnwwvmwNpxNPpAAAB+QAAAB90Ga70moQWyZTAgh//6qVQAAAwJBQWOgAHMuFpm4D91yDJfdM5buhwMAvaGakIVqeIOxiyn8hu1hVYYOevOqZ401zg3CJK/pAzio2PzlXzJxsf8taj/vJ57p+Fo8gp9k2Hkhvq+wEkDHhJo7/1e5juoLd+jzYx0G60gUoAA97oc4rxha2xQBKdK3wXz38CDcmKDiGdsBdgQs1V0nQ7Ugwm4Jmz6GrE/jDiYP/nxD7zIoh3EbB90rfAazS0pWYoYhXOCNG5hLoCehivkese7Zm45MQw9B1seghb8tvaVkIxFKafN4avuKGo1KSlxnwAAvg/4kznEilbAFh2v1ufuVeJx5eywCO0bUOsvGSHUnRdZp1ZmoYp1i7eZi7hXAhA0Y62+0L3lTpU+ETAyAzbQYdYIz8b4Bz2d1zvQM5RQANwcCWgkX9xyQX76wLW/yeIs4+gIhKzZ2R0Qta2GuhDR0/WXc95aLK/DBfTcKZCnn8cOPcOht/jPS0xJql5J+7J+7FvQZSMlPY/S5uLGMDOYUwKPESyszFmfBZ3IqXh1ONnUbAy/Ml3rv2DCD4VRbUhWyYY8V/zjw3xVKrEhhqPtntAQn4HMhx7D0GgGRjB0w05tCa6C96W4dI2yJ+at01sk2evsxWeWteLgbqYB0RqpZarBbczXoABaQAAABNEGfDUUVLDf/AAAGSLpL9UcFK1Q5ARWo+0AIMYxaK0SsHacN8zqBzpPUkmmPpPjZ+4JBV0XPxwZLwwGIrU+wnBCAZOoz6k4MSoOIrdu6MJsDKlaQWIPf+KrzWXLSWOD1FCFeeXOc0vp1TDGN3sIP6S3/gopK196apC4Kma0Qnm2zXCKDqDBP5NWAECRxHosGOMFHSbVg5Gc7XzEabq8cHbzBftcB9LykKCSIA4+CQQE63IEL1P4Rw8nd7/XI7ea7/zoFEjRc4maGzjAbg4lFnNubck3b1a91m1NQU9UfdE4mH1ILJ4pUat9lHHrK0NDa+JtXVmFr1o9NW0hnrW/F2hedVHuaFVwAwiV/RW/YotMaEB/sKE83NLNIT0CdTzLl0mJ1opP5z9BziZRU/od3EhmAAAZVAAABHQGfLHRC/wAACfWGJfgnA4AA/njYHSZicKzQK5tyCbjNvLikPIfRw8w/CAL3XSVWjNdQ9UMep/pbGGqedBD87tAFtaHB9+kwYJ4XqbqYmVRp5hp91ApOUPhSbma0JTjSITuUl1RQ/mk28uIXxQi1ULQpETQ0rKCgiZ4zAAAZYparfrZ97Md0k4heAOqZdqgPtNgiRl/Hg114/fXRaR4sYwB2PUiQBCsQSsk45p3hGUJTemGaKI4Td5f8DoEX3IZIVmspuyheL9jBIgLO+EvkK8XKHKETbuabOLFXoDRS6wQCchL3+JTIjLGAzQlGCeGK0yiech1Uz9D/gh15RzUDMuQuu7JTzg9EAFjIeM/13RUcXf4SIVwVc0UQAAAk4QAAAKQBny5qQv8AAAqE8BYNfvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4UypljqOnWmJFTabzHeqGPU/0tjDVPOgh+d2gC2tDg8Rdsb/1tL+hgD2v5ZEjnv4dpS6IOIo6oG+pl+L7UJ5YFf0eZAYvHLTi7pHjAwoZUcuvh3PAtRlCsTrtx+G4qDj5lefZy8N45TX2oCAfIDzqzT9TEpMBSkdAAAP8QAAAdRBmzNJqEFsmUwIIf/+qlUAAAMCMID4ABLV5ohlc/Jav3nIKyeS8tVZqYheX0dezmQS4gzVzI4XIGuosSfqTnb/e/KQnXE6xv2N9txBIs/iW9uXPsyM9J6RPjdpJX5RLmmAAOQ3vS6mqrm4pPrzI3VGAd64xZOW79RQP6a4voe1Kz/+4AQEtX5s/1gbZLHmuG8hJjkwhNod4qkLG9Wb5qMuQAG0/tWpddUVipKfH/6HMia9q59KoJNIknkRU5IpMX83a5CPT24sAGESB7Qxmz4BRiVBtDj5OASwMZ30CGVE8XOWlONE3XQ7bHmWq4GK7ejVMdJPs27vHUVZO0HWdrIERzAq9DNCr9Ecn9pQAA5FP9rdRrKe15q/Fp8kzhZCTEGnwMxQY5RM6UX8gVBFJiS7v9j8lbTy8YHYmw7o9I7EU4t5NC1TdOz1yoAYSLk8KOGSdBxngep0i3KmFrXG83TQFkBNONrNMkY2pH3sZldgCB87ExDwJm0Bj8ud4VIA3PCVSOoh0MxWnniLHnKerkiRmUFbgpT+NDVEyubyG4PmMxVvcGzrf1i2pvakN2OjuLHG7HQQIBbZ7vIg5Oak7v9/HzODauQ7CXVup8MjVRpxNWAAy4AAAAFcQZ9RRRUsN/8AAAYj7kAH9BXOAJdbGEVqR8c90S5uV1jnIfmTSOTjT/csjblS4kX2VJAmjJX1Nf2S7+45U8AUtUjl/hzXUxUgNl7ijtRnHp3XlMlDW4rtFEgKSBb9RAZCPjV1wdr281Kqr9nKlhYocrq3MNzWQh8Bo1onRgQVh3/ksQ57nEMd6bL3DdmWRhbBvzP5bcO+mxfj+I147c9G2W1/wwgeuJ4pTBk/ZwWMd5l5SFDf0jyuIliTDKHiBITXAn6PFu2iN2edjimZAHku7wv7qxk177rBDXZZxucgVwYCY6Tp6o5TQYb+J+/L71t8/26qJynJeL74eMfCIhQSBNRPaZFhCIQ72kY1u13vQyfoNqhdv25BMqmnQfkizNKiMtbrDjCxhX7B/92qO40ZWdLvjiVxqv9dU/0K29yI95KgNCNgiLjD/0YYEl61reDQSPITejV0SFcAAFnAAAABJwGfcHRC/wAACj2UAXL4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHhDVriWmEeSKiZ3fKgyn+lsYap50EPzu0AW1ocHiLtjf+tpf0cuJGNwK+atMrpBV9SdOOyE3YBFNjadQgQAvRVFgljAaFuVSIaSccCHYXvHniLxdEGsk26KHO2fNE1pJ/Ips/v2ddzYf0k7ivxz9gUzxAIE7dFvUMeQ/qbU01Rz0We80a7bMsK0m/pq3aMTaaq5WRGxfrhWyg+ANhqn1xDj4b6sBqMQ7QMNIXQ3JKzk6pOnQigqG9jH1tYkMKp+SGH0+pMDFRcZQ9/qSZRPWhQ35KFdlPYbrHOaebfV27bQPTkB1LbNXNc8yItDiFh0fuLui16s5z5FtPFeAAABZUAAAEwAZ9yakL/AAAKPPAPDr4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHfk4dxLSdil703XvVDHqf6WxhqnnQQ/O7QBbWhwffpMGCeF6m6mNGv6J1fn0+XBwqqg9x+7OLyRlVm9SXVGV6WXb2FovxQhXfKzhFEX+YYIhI4iVrJYpwAov30NZSjNnfZX6nZkAdQbWROsS6zk2aPLqGmk+X9GymkXY8+t6r76NF04OfW5ciHnYfeLHXVI8UifVW5SOt/ZxXqGyOainqNW/jk7nz931BC2q//VRv+EdWph/aHSCwrNHyGD7mpOF/qXmSN0OJkNjU35UGpLrKcCcx6E+NGkePQCQ0TZMSyMxHgT/K6hg8Xh6ZtB8aH5KEJZaWoFUzXdyCimOtApzMA+D7zDAACygAAAAapBm3dJqEFsmUwIIf/+qlUAAAMCMU5L34AA3RHwK2lV8rtn9gcA1nbQKfHWG010Cig4esBi/u6CLLsI+xEUO7igKnUx5+wlGXbVt4PsPWiJyVGmfGqN/1dbTjBOY2Ie7Z87QMdE8BkfJ1ChiUAKbBiilMkH6lg0egZ8eOfnXEbKOWUj/X+YmoKdWrbxRdstsJyQXLD2w1icUqDlG2UKcoH9CmACT54EPIr7NSXo/Q3CyEe91XVx6THH2fNmv0dfFEs1FRQfqaYslGOWitFHoiCHH4BOJDo/Njdl7V/Wp30RPC8jb2WkgHWS2mPZrFuWhDsMXboyF0i+ES5e4dFSXEec92kdqw5gkrCGk4PZI8Oca7ERyITmvIPMYxamuWI9fufb+qmT4eHEq9UZWytZPjonIv6qvzD+M8s/LQKMHbFty4AfvUTCcu4KKRz9JCF4kcHtZmn156GM0JU7OlJR4U7gveTjRLZSSF9/i78jeLPeZofrYQAUBu06r+zVROf5/ywiC6AI2AC0KBTAoO+cMKgG5RuS2ZuvXHKZZhQxZe7XbpYVshKyeEAALaAAAAFLQZ+VRRUsN/8AAAYj7kAH9ILcbonkDdYfWI2QrdFxYjrTwF386iR8ZuXgru6KP3OGknZbskQBykiS74yhAQyoqWSz29bfg+4hqo3LXze5JuziQjJUuKzqWvA7dV78hYGVwW1ZgbwB0jIeZs7PWfU7OOBEBvhfWAipsHHTHcZVPi6Ol6Ogb3yWHfaLsuAzNYzNvOHpqUfzPt05jvjoC83ua8/vWK3CnMBaJzXA2svCg/ve/5s+4G6pq9R5xaWBr86JnpbJ4vk+k4kzZhRXA8rkYbBW8acvQK68jRKQkDBWvkU7YWzdAWYw1+7gQoipHpk3DwOBAESi7mumLuGN7vHgZqLysRiwPGn/Od1xPmE3cOddLrttH+RKrF3xdSwEbqI71D8pFaqUru2FRVudH8uPxcA9tIcZ0GqfGadZmvx7rQUmqbW1UP7pEAAM+QAAAKgBn7R0Qv8AAAo9lAFy+CcL4ACdvGwOkzE4VmgVzbkE3GbeXFIeQ+jh4Q1a4lpiRU2m8r3qhj1P9LYw1TzoIfndoAtrQ4PEXbG/9bS/oYA9r+WRI57+HaUuiDiKOqBvqZfi+1CeWBX9HmQGLxy8ke6R4wMKGVHLr4dzwLUZRBrJNuihz+KByf+GeajEFf0XTQQvGWHsiH4B+DS97i69dQUEh2UHq8AAAVMAAADtAZ+2akL/AAAKPPAPDr4JwvgAJ28bA6TMThWaBXNuQTcZt5cUh5D6OHfk4dxLTEiptN5XvVDHqf6WxhqnnQQ/O7QBbWhwffpMGCeF6m6mNGv6J1fn6n7pFjc16pICBuZvRP19xrJ24I491nUj36oifefC1lZ2G7iuTgaWgPEVjPRw4kQ1IHXm2fJNzMGvIjEuC4g2kfQYN8nSPtdHljdB9UttzZYGGn1j4YO4xamKpp/DxX2CRAPgFHOvSS1ScKzeL3tV1g+XslXAzt4IvV1DpZwGRFdcS0zR6ZXDcZfdFoP389uwQobnNI+AAAMDAAAAwEGbuUmoQWyZTBRMN//+p4QAABDVDnIATV7x/y/nGYeHsYt+jvnEcQQ532bOS0D0XV3qXQ0n6GfwdRWEEfxIZ8hVYsqqiEM6wV0uffJ0HN9wDgJvcZE3SVvkOSlXarJ0rRGU9WNDZuaHSLpyMOzQXsMAALnyOaPYcUpJT0rtgKl72bkYOByZFPVMGxLOHKfN11cP2OB+3DHsVbdD67azWtv8l99e1H7i6PTj9gGY/pthBUMuYH6hpVmCqNNQnKAHHQAAASEBn9hqQv8AAAo88A8OvgnC+AAnbxsDpMxOFZoFc25BNxm3lxSHkPo4d+Th3EtMSKm03le9UMep/pbGGqedBD87tAFtaHB9+kwYJ4XqbqY0a/onV+fqfukWNzXqkgIJyewr4vuNZO29Jjz39pHvxaH7GfDDysfadJFQlwHqM86aNnFOJYh2jWEPxyR2MUVuA32UZwyuhjZsjt0S8+E8aeLooVGR1ppJKPb49voEiC3eej9uWrFJnLxXJMF4TPd28CogkylTLD5IVB/i0fZeapbT0Olf+pej9t0VNjL2FyWb2j6rvU1P/zPsFdKL30ZIOPoznPH/1RklGzbDzN1ilS2UFyA3YRrS5D9lRJoQLyDgE63XH96LOQVL/PnJSmAAABQwAAAoGmWIggAE//73sY+BTcgADZc6inof4RWx9JBRerHZoGTqAAADAAADAAADAAUm+x7DiNwgUFfAAAADAVYAMGBOwMECngowN7F7+wTZn8ABy19oS1hsAuuAqAULDrYKP4XoDQFOJZWSln+HZmAVjxEmzqhEHUmHIrSkcWykX13P9fxi0jc6x+gGOqvERpN/hrFMcQ4IgQ7JoySLqLitsQBhtV0rwjuogpKzC2BB+Nv5U3HkWcHDTBlqisPlCNQEoDJPpZpwg21x5jN4I9fEvyNsy18dnIdhKzGy0y/yjlvuUMBYJ2boiy7/kUfkpIUWn6lqWf2ikgla3KUfcHaGmSuIwNpvvpHT7xwOI4VNMDD02VQQpTJJ5EjgeieggFRZncwOdWTzVG96NnFgvoLYqAtj3B9hV7j82Frs/msSi8t1L4hpI2UR7nue8/VVVEitqLI6Pk1aOE1HivSXXE1UVviEZnZUPK8+4m02bQb6PqSd4cK/pSihHUHBPl6wLghAIof9Fgd94+6zRdYcpnOYysv2UqL4Z/9sKmX5fOSwQ/MxnDvs3VrpHNQk5HeDD1ENccFEjmr0ExznbM+20Lh7QtEbtVaBmFWIYbyPJZC2OBdvj0tGI8e7VcZpxudg7eCED2NAMDiS30j1EnGc7nWHiVzPrFnlNfqhuoA7eKet+cr237tkr04seMHIG2yWuAspri6W73fSfMj/Q0YMBgdtu+XoSJibVUCBqbo1wwdpg42RF4Uag61UfhCqjCLjaFcRV3OtqQLmyl0+5UTSuy7Nm/QWpqHU9Lkyb66RyavMUxUqWlr9CwG+iukKnqtOVVUNAwSbpBfQzicTIPgsBhizdD5hyvHZGtiqvQPUQ2vpB/lYSMUCYI/LTKMzecQn9hCZNZ3QaI/Iv2Hwj3yT777EfLmNzGKfLNNy0+Ue947F9hlWrhCPAzFpxPX7MwBV/GWWGYAnZ4+7/tSfoSfBk9+DqUMcyaklzPNHfAR9iOZoklOj7LY7ltm2y9432cw8saKPVox1ACcDAgv4IzriOjUaxtfszyM+K/FWMfqqJaxCyNgKIoXDiwPhj1pNQ3Fm/rfg9hj3GZmlQkNJckUhmJEWSwowy0bnV3/o52GvJXDDBI35c5WC7HI2PLcoGvPW5Gxka/ezuafUVnRkU9tTf8m6nXE7/XE6zMuhHRxAaqNiTApKLAy2QhcCFWHE7OVWawaTNhO85fPghG6dZRbKg7S7d9Td+vyownrFoOoJLJ6VQ0HC6BzgoqcLX3LR89hVM4NYeOjsUMvtLhh4n4XnZt0eZnmj7EOKMHLByiRoRMQ6zFV+hScp+cJwz1wSYW8d/N9sWVuTzm9/acXdb7p2BmitZn9gfBp4uwv5XM5tuPwuAhlhktOq7W1dFDtai08Gpr+vl99uqnWtNfxgpf1UI9LHd6deO4Av+M94u+vY647TOON92i/je/baPhuG9AozxSu9+uVO9xqvTRq72rzn0oWhygISYl0+XYUpMX4XKKY2b4NLC70TRZhZWDzjoXz2efiB46zzVbN6mF6A1P93/MWu4ymBpizDjyc2UeJY6T39qljs5TWMDqFo8T/1l9g3nSKw8+r5XjBSR9FkFhoNoPMTu3L2cl3tOQBh30EUBVR+hq32UzxAvVh+LrHPKeGDz1ynKH89r/CS+3FU8RFLRO8L7saiiGIgwuCX4mrneKspX51nAvSXVMqyGT51YrjirAiq4rKL6ciL1BCzGFsAKoC9BkVLD9faUBzVuhl7RV7wExNYjYlv+vrmu96IFSiBUlS2icCzAugMXxV2ipJvInxI/J8sL+jyscd51nKd0oWSobBOYpida03kuOx/vOsXQIWSDjqAWxzt7Z9uc5fSIETzEowIRmFYdxhYZDe5l28svZeY57QEgkxDa26mJSJH6lEaeNV+cSSq99JfgiXN7wLjTu0VuUHss5BhO73G2hpWsL7BW6sKmrvntvQ5zBc8pAtxkNTAO8aioNPJbNb7HkYOokGGZcwel0fzIaAZxao/M0OuVxxjILlXg1WS92RsYTYWwUKtpRf++dJOKI6kqYGduf3n0+I2FCQTU1VQf1MTDprcBdIIiRXE6BEn5y3ft60pAeoaufPUo+M2162w5YyglomKDBYHUMkf6cvJcyIlRdQ7KtBtkHQXHC8BQVC2M3AZvcGfp2Gx84vnao6eoFHRGJOcecU+jAv9QtOI+D3VHK4iOuBVWXwbmRDtWTQ+i8BfzD1HZVMZrFaTNWk3+aw2m83oWSbh/eOM1jgH2ceKoN3BR7/2X/cUHSLpphFgtNoiY7Vj2f3bJqr/tooWIRJi22EgDLTsRz/16K1W9ZONcz2voy6I+k90WAFCW+5o7TCMoYX9gFHVPl4tIxYznauANTfwTy8RbFDUZXsooq2lOizxjre5cfXu/gYWQuviA2b/PtH9oXzZm7fBKXtn8erPfm358rtPUPFQoTvZfCPiGPl0gcsdl3Ohl0yQMpny60F5XQMCnF+yFZ+7jyWlquazqXvk74b8qaqHJDsi9o2Cbfud9eNlVQxujoEHA9DIS40gRqFlX/NYOdkwQxI0jeMZd7sSVeTyTOZiE+pS60uoNYTHdQ4zHd6jlOlmlvSF3e5C3nfOJ5djtqJNMv3Cws8GG1WTJW6igMTQPo923UjQhehTVaZpWYN4deQWAsUt7utTwvx/tRl/D85ML5ek061zPKYKO+OnlFsHwHVtEydxRcLam1PwmM/iLJsJStK0i5zxjnNuC7hw6LGEyqWEg5RcqJXDnw2iB/H7ZLH+qgySlR3QF4PnvaeykUABb8ImzN3dHLFTMSqJrT9wcwtciVrmJH+NJls4FCsFEmNK7zM4Y07ER1N+0A52vR0YuTWyru2O0nNz0dAUG9xs8mwcmbhuPe0jcPzBFsfkpZOYyNxQBSMHEdEsoIYEXuxoZ9iTMG4LypAtumR6mRvMWm3qdJ6yrDWncCK3BubIeC76LBomjWUBbMjq+oZtruM407j47fby8sBS/IHqLrhU9hwKnI1P769dAE3kIYHVAlgjENrAt/kSHFAoPA+pmoAAcnZ6NohlvJ7zFQijd8f4vJg2aCcA4BSP22cvy+hcwsh7ZGo1tmqgErAMBFyBfVhQPA12i5uK18TKd+5rxnNfTwOJTNOqvMLlyxve/D+zPv75S87J76jNmxE1S28qErkmo7UHthuW2DsvfU0eKncKMT1x7j10faq6ISBgvB76HvK8OJF9sL5yAFurlpBH3l/vjU7sQWKNF88UQ/w6tg8/ke/RgQmqLc1qhTrYdyk/Pp6IfCPgrDauliu45DLXk7xoZiMp4upS0eCo+bxzRKmebwsypEK88+FNE2zFJ5ogxZc6H6i9TOqcisjTU7gVR55TycmPbnKc5+5y4ko0KsROz6XY1AN/EPtaoLyWNNdUuRH6lWNULXfxft/4xC/VcQUbuBi+g+HmwP1BNN9BfCtOZo0IYZz+af+2IArfYpJXWMgFje4bJbUN6GYD6OBL4AJuGOCQWyrX2h1WwChrL70aXBDex1frDeto9tacwirzfJevLW5IWR4i7iRFOXWGPK+oNQJHTV02mtAEZfnbmr8jrkJJjiy/WF9r8OwdVySVUuc3PDK91nxn+m3UjBoyI1YFLfygtpCchmmN1X3lJuOqkwMxWBhL6gKW+GrMmtxt5w2w//pXuzzDXGhL07sVLHLOwMzRFGOb9RVMEyctnX2IVNOffw4UxUyuxkdThp40wYpHYJ4EtPEmk/NxyRW3aTBNi8J+5T4Nb6dlfrBr46ZU2ZhjixvY3UdOoGevgwHyU16JJ0AlA8LdR/FxgZs/XDE+a4IlTCI8ZYBRHFEcmZnqJwO2kSFbmc4N0gA2t/N0xgWF6UHiKtqXQP3+3VZnz1TZIQhkcLnjg5TBkdTRmFZM6BkJPCvY6+a1WIm2uNJZVrtoK9yP2Zhl4hEM0XLnlctZNQO/UhuOvT33L9qiu5vM2biBhpBJhNEdnsazbzXPYutAH/zbsTo9SwWRNQ/XaBfkbcImVMLvqrRlXoFu5arG9wttQFqTNXfrqR/fZpIIhS8TpFsizLU3ud6GObvJP6jbwNra5SG/UlyGL4oCu5Z1nxzs5TVdwAe4ByH1HYphwSUJaXKWc4w3wtWtUxxuVHjo7Pu1stAu7+VDcKmBDYej0Q2Kh8DpcewRt2jEGyUcpp8lXJgxFBrdWb2UrZoADN4UaL8p1y9VAW8X75aqNBvstJLCynzDhwibjHdL11+3IHCr6oC82rqOo7CNPg810YMgiVy8uix0xmWyBSMfHrevO8lVhV+2fYW9Gn7AbvrLj6vSWcuSqGyBC9TLv5SzHTglP6/PFY8gJiqdk6N1xXQc+nUQg7BsUW1ZzzJ9wTXJYVdH+OUAB7imWBii8rD3sm0iYAB0jxMENyOFuPg8NNmlD05cyC10ozY3u7zpHeOz0XTtJtojvLKZxgJtywKOA40ypVzreaFk2jjUWdy9pH8LGQ4AXdDgmqezhmsJpxvuj+r2uXVlQ6GlPhTj8G26OM/jj1XQ7fy54fZfxKDtxGSHIR+c2e1IPIsGXnU7V8vbJgh24YsS+2I0LPaFrzkmasba5u/QMP9VSZHzxeEdRyMhI8shNwOXbA+Mb28GKuVrLNT3DqN0yMdLU0CW0gCNPE04XrIhH6Er379CoFyYxV+YxtZvgFSbIFKFnNKhoB0brl/LRkNmrvO928RdfMalFU1qI4gP0Poszices3tqk4dOFwjf4SD2iFxU0jNlexxOOGYX9Yqg/6A2YuHparZIOPIg5//aw38W1zkgo+ZlLdwnaLeUe2HD5zPfihYgALKE+Ib8TJGPbkkh4q61G3XzMmwNuMaJurLDRZir8wOouUAL8bVDp5OUaz39NZaHSX0lrUrcqxs9YT1m8KbPgBuhz80vOJmmX9AeFJ/htWdk6Tmb9r3duMw7F95k+aEG3GLU/VFDzR1VUldthIqRaECYpTrDdH18IagYFibztxlNXXcpvH/uaCXwCv3o6lYfptvdCdN7X2GWUty06c34uKinbUXOYpSr6kGOBd5ekkQzSpUxwcOC9WxHXu7PRmkLzeS4SgtYkFiF66yxwrepTi3o/UN/LTXjcs9yxbnDqsPZLaO9I+P/LoJnWZAT750o9lzhtRBXzAhvs1YYoz65O5nJH4IjffG3rtMdNEj4u96f8W6LZx1OMibKeA/LUsxjzFyrYTgE2fZ4MY5eq99+y2Cs0YYhIG2/gPp6g5kTQN7pPDU+xw6FIFdi+8fO8tWC+QVn1fJgzf7VXXwJoumQzZR2/ERSNJi9CePQxERfonKugtYgffa9vZ/VMNYTYpd1Yk2KkQnSba1Q6/uMxgBEt1r+YgQ2ywEeZD04Dx3tvA0np90IgALC8nLwLWzybqfjVd/H6+BXDMzyAYNjJxXUUOh/A4hgfJLaOoZDny0P4ua/XLwhn7A/kU0U6wvMQUpMD1h3RUI7g68eA+PkXjNJ7oxKkL5I0fdERvqL1jAASXPuORsoHSQtaY7TURllwOPWqPXvSIJRqeGwu93G7iftlyXt56iqjSXdAmImFNLNWbhdZO1M/kY+INTP9FbJIyRfQpsAJ4Q9CsrC/wXpTSwAZgxLtFM9QK79mANJXD679BEi7+uZ6/0lSLM1QebocqrNR0DuAiGaGT/qw0GSjPlHPIwNj1SzpqVZ4OIpcJeI/9ScetHL8dwLr4iSdaBjCNfDcASfwWWwUJpKLg2qxF78SVwxBLHt6n1ExiZUDmySz2RtROVjBqBYpc8nTGWt8L+6Bbg+qOc9QPMkVGWPvPzaQmukCvE+cLCII/gh8SpEjX1+e0UXOXgtNbd9ItiKnWi6lw3P68LaN42Io9AhT+2zOJlEZYFRCDMpfyLCOHUpMrv2g5o98U62dzPsDzXG1nbhJ6aI6aXOGil/Xj0NX/lyHM0ayyURP8E0KNWagkuiO6nRhmmhQOWIew3e30wiWn1/CyqqUOXcMsNLkPQitosEFCFbPNUq0p9fzanfuW5ryBHa6Xrhjs/Ixre/Tb65zcNLhFhYtZHt6sCXX9iyGMNyF09v/o5k9XYUjZHgwovD9YJ9fThtMD/FK1JMSQL+gjohUAP1nMbtz7sLmAr0IRHtowcFzgSYLOpiH9fEqvlECxfgca/yu6DPeaDOmJvT4H1OpBegc/EXWVDyF+D0ZElEpC18t4598ZKHiLpNOwPcVhW2aU84V/+OLDxoV9L5r3biqOEakKh4gL0HklQSVjXVEnW+Qthhv8zJkTApdD2nh+p1XJuTXJfYIYnWb379cZD74hHthamD7+IYQ4vABt+9qNP1oK/xRbaEcW/Lg2yqudLyF4+kcIBHEeCSwMFAVs7ysUzPG4yZAQAWu8X0xMIwJ6pUDp28Q9w92Y1UpmlzLZfPukx653v5RVYbPFTP+EaxDmkkQo6/JusHpkjHnZJyrm9cKD4oFcNW7Ak9SxZGFr664ZsjSZBwKhRz8MYfrJkW3RqzxnjEHv/YQJMUB/YKDb6KLNBxhtHyt83Xn6QcYN8XD0EPRXytI61XYdpj/pHsruIdXZTI3/euOlU5mO2XqE42jwpTqAg3PIVLXwW6lxs/3cjgfcepH7uINN2S8CdXb+LTsOESTzzMpnGUqPHiYTInpJjnwn2EXeyFqrYfb5KXX3SJtEAVrEbXka9NvO0RO5dFNA1ZdZCWkjCVGfyNXwSfYXfqPsu8C0rJLn5IjQzPYtWPZH4vtXrACZKz9z/4R9qm4SS1tWTJvb54L5Cds/zX9URxecu93N4idfCoNw2g+XFbO3y6Uq7jbnuQ6itlxGA/bvpePw+XMbf/zNnRwc8cULUbX9HUQNe82AHzeBYXx311VH/V3UvqiwBDgXyOW86wJ0c6klSWqiADIUURu0l/MSY9w+lQnFxy3Tc6hsS3un+XQxdXyWnSzGqsVxzB7VTEock0q9hMEQtaUktyN3Vi6JaIV2rvVD83Z7cTKJX6BxMZB8Yev1ywP+7ELGDTFQKKJs1sqk+1LYyf/41wkTbtPIi1SO1Ld+0Y124PSDTLR/iz3flDr9UpX4/SNovXe1nU/7z6+UfH9ksqOYZmd6Fy7c3hMk/grvlu+d//SuU1jnwZvfeEVy3mZCfzHy9ZPbYVo+wEIB7rCW9uNhN3XY50NIrvsLlwR//aQd6B0MA96Qf4gevw+Tho8YvOFWCCK2InJh7pmhuPewIIqPualeNv2GgbOKHLdBJj+IxS2m9hxhlU7ok6ecuQABFjl1cj3kS2+SQnzlFyAfha7qVRVXdsPJUE0W9rvGzlVs/5QR68scqPGYojX99TEym0CGnHbYw2rjNLWcGuGRLLl8TRI4YJXDS22FswI55Mq9kVTAGnronmsVxQMUNGip4ucAJq3g+kRVVQRj8TJPdcdUZYyJzKiIOrBFljwUChXKkY3dCnDPvbYZJu7bdz9qnXrygHzeHTZPznzLM/FcP9SkZM9Vg4IH3J1jmB507KIgn1maZ3iCn2cv/gktS6Yf/8B+6j3c+FY7BeMS+pocHCESCSuMYR6ofc1psnhEg2AZaPn5fu4QdFwiQ0lIyiQEAdKBpaq+J0wnRaGSLU7Nym3/th4VKE3KYTFdcQwTuS2EVsKmvwB+BcpqfFg6ioZDMtNgLCNaa+67ba88kH/HHVa3lBqK7g3ZA+zoeaQ8h84u5zmiqChvlnB5c52tHhXw7luxtyVqxuMCvTIsFZbzuMflykNNQXt/JdUsQ1v4fSqR2/hSuYVX9z1KvFcTa89b/77CTExsrkpYLaUZ94PsdpRkUM4+GH/x7sn2VB2fAtFsV/MIvxcJmNqoWfWuw3iRk87J2uQLF/y9EMUU69X71fV2SQUgRGdt9+5xrZhM0M1sh+wYG2IaNWWjwgcI993mBbWi5LRjbdM18ETW7b/yrH3lr5OxdWGrpQUSHY8lGQDPxx/+LeqMW7gkvGGeovwQE/yAP3+daYnbWgHeRsn7NHTAlW92/OpGrn4+2Tx5ZrTRL/XSjZBufvRO/NJKu/Rtv4qyJqckTQpPoGp1nkOfGLkKotfbOUGB4NrCuqDuQrA06ltMXqoUt1fdUF1xkN2xittIT6tEV6CQ4fgZEeodlTyC21dl5EtPFKk52QqZoDfHwfrN2gSvw+XKpxUhDFiQBl875BqLAgJTM9s46N0WMZQJbzJrQY6lqaLq05k9AC/8te2QmvKzCokJuit+QI1sgbBzzIp2i/4IGzBDl1XdjZapOdDaOhBrltmxtUtSTdGPK8E41rdZDjvayVeSCf8QAfvsYOmdwlKrs5ZATpGQK028YYNqEB5ZxEh56XwwtFHhVDAPPoSMjs5H1o1yGOu/XwIxzEIGr2/3uowxWz95c3MO9YUBqERmfDahWYDgPXOeWdCwwUcqRZYyD8tmYt08AnnMIICPKEaAdjGyLDBozHUPr8VuRCskgOe9Zlf/uckfhc9xyG9FwS2IlhulxUjei9k1ISwVw6fqUTAp3ZgCRJaH//mklQ88sKxLOwGR1gM+ERIXKeaTSdU8MoEljSdhrejOU/ZtrqmgeTolc26HcjehgEo+KBHryEuq+KD98DKkjcC+eZepZtoOj5wyZoroTo6lVK0AjFK1+o4p39q+hanCBVkjoFBK1XOdknkNPu3dRDvCj0Wm9d8fwXj7ljvH+1Giw/OduVm/umnMsvTlI55qrN/4xwbQFYXpJIvkfClrcHADi1MwfSrUtO15cQc1lEdSznH6eS4jGse0m8fQR9N3UMbnHOZ5NzL6jB8l8fHOeQwBXZ61Me88xubF8GCmbS6YKtojd12ZacWfIegWd4ELXHamiufCQnHANgjDV/DluorgkDOgQ8akVgBh6jkA+K/uLDJEmmv1iAuISwfbtG+7iTAyuRpjp0ZSGJX/bo3SqHJOWL0uB4bt4fmALHPJdZAxZs9wSe9oagenmnXfbVX+nVmqm2qRARBlu8NRnKDpGday5BladDuMfsNiodQvbHrsBYVeCrsx4/BwxHQZJkr5Rnt1SnByHRGv2EGqWKdJVOwbsrA6lG2ZCCrDgtG4aLZ5XqNR6GyGyYD0cH00kKNx4n9nbpDRtioKpJoldmWExyhVQu8K6f/8R1N93viN+SUeqYuK8gM3lzzAZngX8r6B2tfjPU6ix5HaJQUUs8u0ZzqhfMR6zZsK5dUmIb7KIS8qpHm7415/sMgoz3MWwqRusZtdfIUMzqVcf25k2nVc6VcC2SEidADzfTiaSWyrMBfp6DVP6CKdtuHM/hHBB3EV4mIn4ajBLDS7ypFUT5Cjdj6A/6/OoJOjF7MUnPE90jcHTnsAAm3Ora0KgomdrJYv4kk3Dz+vfFddvTmLZWP2KdMtOh9qAO2A/HImLXGF6RjXvlIk84ywVssoENKH0L3zBG3Zt8OXf/TVmCdw9eMsCb+WvCaRvPChzEb6ju8Hitwd5O3lqIJKmBXQcX2Q+6FmMgDkf7lRR9vZoNnGl+qfvu0XDRc55HaNiROawsReyduNXg0vyyo3YlLS3xZMyzffAVgbrXBypaiTApq9WWc4raqVxGrgxYxLsb0CnEzXf7D/xVUvUybeehN5iWLbnCkQAwtvGkErKkukuJWrV1QS8ssl2Txtu45fK8LlSWAKzIElpjPxt1jAqqbe2ONN9moHB69bZw/qo5O6WWOLqUXFPP1vs2J1tcfXAMGNsj9BvHK0xCsaRxvHfXFnU+OMc1uMxEbN439dUssUVMTowIaYM5X0RlHubZx+FKpdMzJVhJ+r1vmCzpIAhxApduA1WAWl1Rg6IIHW0BfalN+UghgN5E0pzvI8D/g2CsCuW1bwnW5A+P8/HrJ7iZDeIw5O2MDNG44MFPL/E8RdtoNJ1VNoRKJB9cng8ZnduUmfvLyVCRliEMavTYIUoYEnxQm6l039bbPdvavuDuOJIXfjeFWwN+OfMzwkh+SHbjnZ3HGkGydmxZ99L03rnBbT9sNwyXlC/+lvCiqlz7I/ZFpbowfHq4UMI7xBK6K7z1zx7gbYks086S1l4TZZ2wfeWa21+dzjH3gReMf6fNjAGUrMVyy+H3SkJeAQ82EtUOF1J3IY+5di3/3vLdYLcEXnYoc/BwwNN8WlVStoAxLbKYt3OEXH+SH4CzAe16bH0ZJevR1xSEyC19JMN8AUYdSEixajtaW0RZu+EFpSvmw2+Kmx45EolbpjPnVGzb3uWk15c6Th+VzyVpI7d4CNx5p3imnjtWouy2Itl9JC5RmELvVNTpTZn+FzmH5Otn7ySL+bq4T+4Vm22CBrpjCwIcH5fipZsiRnFOkLKfZnrkf6AgpoglMeDAw3Tr0zcV9uYqWtm/P1YCbpQt5Qwwhob/X095aWyb88shLugli9RAIqNAg0AVChB0Kd7BjvihcQpm4I1Ri21hhWRrumJCDEZywxXBlGnvdJAlzSieF016X7YiB6pykLLdNr17SLPRv3qld2RvwqAjCvyTizXgWOz8Z60PB7M//vPi5hJJ8QV7YU1S+eYYyQ82kk0FtSHZub0asHtHKeiAFkFTLR7o4wlfj1qhG2lX9X6JvCd1whORE4Yj6b6YGSeMywmTMJ3B8kEAFWqI3kPChXFwtb5tt2QQPU6TiT+Ncz7navich6BhadaWNnMZfraKmtigyhmDYtiY0aFM7tSJ4m10gdLmqtsFJM3DRMzdUE/g3LCs4ztTG3y7Cj5Em1oDngBNa8+Gd5ZdYsRDCuBjbHAiHIYpE74Z/ISIOD1vWHwLwDg6t1Kd3L91tyV6EgPHzabipRW3dCm8+NR4MXr0w4QsHolJGlVPuc2bCcZQzSI1F66PiMQIGdop8I5Tgn8RDYMX12ZEQH68DmWxQECPO9RefuzxSZ14GC/UekJnRcS6Az4l43++tejNGrfEn6j6HTtG4z2tgt6F6tsfLX3b5yW1paIvVClRXbA7E6Or8dXh8zQu2m9w2Mh7FF47scivhcA2fDej2CtM3lQO2ShfJgtz78GYs5jkC6qWYeH3LH6OYS/A4JJY+VtQCmsbJXVeQksr/VcEy/4CNDkRND8ShqXXhlhYDyNCT4HQxNMp+317Tllwl3AL3h+30XsYxcAPgJQx2NtKr9xf157xElXKIYgNfyTWu6Cdfkf62hnbcQACe4zUSI0gVJoXvghlUyULhOuEey+AjiTeSLSTPJ4Gl1DMYOWp5ZsOEM9XJIeLznaxad0CtE5GhIs6zh1UpAauTC733OZhQDntTqPyIzcfnslsNJd01P+GjMi8Y78w/k9wN8j57qCfARwHiQ+n6o8mlhFH0ZJEwMyVQs51XREg/Gmkkmjpl+kujvQYokgGfazcLSq/uo3YRXkqOACKKAPCmjZ3PuV2kbHOegrR/B67WsikjHlZJ9ydYEef92WitGgDzzMwUGUopwIIS4+P3/aU2TUgmymq40eMRODhavGxpK//LOYHrWVYj5yeiDAYkouxhy61KVI7F19VVZuMOm4OiAIcg+7CDSXg4Dkg/Y0jgPVsigNWmw7u2ZU2ZWGboJ2rnN2EcYM2O68H/pcD308UmnOXsEIc2n60Gd9GUHgqUh+GZp2WnppF0gBUdrilzZSGMmYxXGvvF/sN7XOtx6GjN5MEgbCrx5uzVHik0OAEFqXEU5vYVFdotg7SCoR7ivrKsb2Stc6pZrrVNbKTJc34DJvJrPrl8/uH3ZnR2ZYh/21Kg7eqDkf4gp5+OceH39N/QjoaH8W/I+8sPdtswbtpxXkpIZGWPOQc1kateBfQq/iOMwXnfUROpanMxxGFhV9johANzyvSRdzY2iJZaoTJz5B6DxQDhoThW4Fww7doAN02/wJz2nKTw7oDFhcP+JRtcbTMhC9UdBcxJxFHTb8G4JbdPcf1/KPIuMVjT0WFmlkDrvgXKj3OhG0FXY+eIKFtZ5JfyKZ9KbxR4UwrheriSvG/q9Tdi1oBUngczNvL6xtfsKQ+ObTRZFYAAP7l8sHMGwWHorwwA0TuPDCRHvlHW5lZuOBwHx5YfP7d2Tbz2LHAqYtatG7ruHSBce4pkbV4Q/QqbmQvHisOsBDdjFzYzBrQQcKJZWZQ3v6P2ojtCa1gLq6y/UJpGMcQ8HFawBwVeDy/26EP5p3BVjrzMkDWv3KRAQeo74TckQ0TJxB0Y8H/PLAir7kvasCXH6ENAzJQ0snTIxAq81NvT+SyBTckUL6e1lDR79KAocucwFf0vQ0ayDA7ITrf4G1A867JmlfoiHN9iIdne3bzZwKA/sfU9633Sk/k71m+DNch0GjZgt4PKOchNt5gYaJ1ZcgwmJvETeaIq2cIEXZCH2wfLJ7+8wldOcNf/B8j1yIz1kaiYg8hqcrAX8besqMJkSlA2k18lBzNLFNV8fR34DelbRTPBmWfmSCu7JmUFsUc3AMSWFmT+QdJyXA+il0Jj6Ei0LXFcikrZl0at5SrQ/sgWoy7VN1bjWviyGJtlkeOkRrbYZD+bwVNN/qwOCVaPsd8FVRKgpVtY6atP8bVrfl61aFLGQjSnejlRodmWh3HYfvr91Dzp48teQx+fwAIMQ6ePwg7iVbxnSTMexctjsDHNXBPDAtfxtcUkkB+pAbpchgu118/MhMkqKrwFcwq7MYfWLg6w1ujKcikkJoKLSBxRKX6v1Z43OSjn0InywMKujcRayOCzXVMdCCy4hb5bTOoU016yn21KeMxRUwPUT3rKUWs5YP/f5+Dco9a+b/ZFSE7JnVxcuF4oJxNv2pjqYYe6ODnchrgm4+B/w7cLE8viJLB/Ri/xKnBpIdK9RiahWFK4+Eyb5/STj0aN2GvEDSaUD9kULc6x8FtvzCk98QleMMGlbNd6A1sXjVrzxqt1R1oPX9QYb5+JaK0Yp55ml4m15ze/l6/9Hoht0egXbVuux6Rs+jYiVgqe1QWoHZEb27/gxT3xsDTkxeOamhq3Hig55TXFPpb5mNSlQFZjYdL82VaH4x7nZ8rieTMs0bM6S3Y+Hel81pCP+NmajGN+QZQpL/zH3u7AVE/kM1AY8ACXde97HGKOW73x7GgHtAL3kslxg2MP6bBXzgCXpTF+avqQpF+aT1NBcJ0y7Be/1AeIhM15CWypOHR9KAT/y09Hvb2hPhwQgmVI1uR+UOr5j2gsha/j19mymmmo6Jaqnw45P9ULlLzvqiVlrLpA//W4BQkDr6e8AK8S+oNUyQ/NzhpQJN1lGfOM5z+u756L07RkOsIdAhkT07f8oCx+7wANapRfd+O18nQVKDR9LWRK2fjMyS0OwLWfLHo3gy5msunTPJ6+6SDjADj67nfq3+ceGzIiDuLvpTssZ4lxM/zG00zCEpOfvQqqd9oca+MAAERaX95TINfA82qQuv9YtapkGE4SKpjRA3lW4VNFjvNfFELDMd6VOBsYAjqMGBqKRuaGQJAk7EoVBV4WesNcYI0y6ze2jeF47ZekgjcF/jnzoKF/NQS0QOY8wBw2AwRj9wp4Fxri4NpSe0uE2JnQEuALzxu+xomRFlXUxS+kH9NJQ2IcJG1sqndY9sqIuuzMIgZ27bZPhUAsvHmECYfzANLvFM4dIYYtL5UHrUzpCZYeaR7GBaPR5G8yAlMO8lQQuwEK32Qdrg1p074chsTVAHXMCSS0p7zruBqVRLHFHQqP1L2taHUymNAcGdrg2YdQ2XoLgjF8SBsQ10r2NuzX9CIG881HBqgAAADAAADAAADAAADAAADAAADAAADAAAhYQAAA5ZBmiRsQQ/+qlUAAAMCMLnwACWrxG+uaQQ3AqXqX40wFpsSOzLtSe7cnw4oxDuHrx00zJHlhpHPr75D76anCrWr1BgI5B1DzXaKtIWK/TISYD1E1/yh1eULL0qME6utE/ukOS1CKvTc/6nz22ldcUVCoWpp8Ea1OijZPxOsPBx0Qr/X/5DDEafbIXq3WeRMM2sECVsl5p05n/bZj6xXFtfsTY3SxEW5aHUCiw3lAxw1YdwbnR0AXZ9xOL8q8Db6Unq5luJuntvx6oMRv+fvjucJcPMhJEzw2AR5kGXvg10pGGBPCxoHUx4STbPV9c0n6hLk90JiY88KFwQHBJNe+A1q/BrxtqDAITr/WS+nVLlq/GBlkCgvRnknuOgjTU9iRwK5oZmoSk3wjXsn6UEpWH9cTmsCBPmiSA5Ucvld1RwAM9on36+Wp6rsWxiIY61iYszR8aNS5CFYMh48uYBy6qT9Qb0YbfMM1GsQmwM7ra8bdhzxMOJKskHLikd63Qg42sTCJ9uGEwBYOrK9wTircGSd42Ud/UhjkAUmbYVL/kHpR9IqPOtLXhy8M55VaRNMhucumajpzceRkKpRBqn5HnkHJXsdgoS2s68sLEl5iwvIulKDql4JbUEOSjI7XTbUlQjo8dDQeNv1YH5N17WTMegTvH0gUm/Rgx+t7H3VjmAJGZJKa/Jzr9HPYESfK6TtF4kxMA04XB8Sw9zE3iOGdGdJlt3pD54jEsqkFt2yA7KyghiSTQLE/a4ozNF1SXHa+kwYUTj7u0lOe1gc8X2PCr1e79z9ZUTxOwm9d0CHhf9OsPNMa1R8qU86jfoPN3wNWtuVvi6vugzN7IvUNeH8aHv9nE3s3yAhiUbtRWEn41uGq16fwc0oYPC7/SqXnO8uF/hl7HJYFRDHCg8PWdsaeJDm9Qe9Lx0WSGEzCME5PdbVmRICftqzE92yp4UWB8CNqShnegfyPKbAEJRy/vBpVLCFG96Tk++AKbPtxZS6br/uZxATuZ4TcFtd0iOGiwqy1soZuKGvivhLdA2K6+LgTpSXTJ5okhZ0uMoe+Fn7TToq4GSNyhB8qTiRRRNpykK66dq6prY59bbdTSSdRaq3uWXcK9Qzv63Q3lhKJkUDkTlnMDlxuRISECiuCBn5x6/kxhygR6qlb8EWfV3KE6/R6B5kP7SMyYhALzQYNv35EL27o2zVA9QMyCOxVhYQWpVYAN6MH1zDDOIAAAGmQZ5CeIb/AAAGHUBjTXZACauBBUxzgi1/TVVYD94dD8v/IUWE4Vz1HY7Eb/NkSa+AAjmfl1OE8YyzWv6Kr4fByfuMLF5AgZEuSOxXyE6Jgwawmpek8oX7DROw0yUl8iH130bpYrSH08re+y0lgNsH8YQkfyWyYGfOwUs0v5s8BtQk27NfNinmbKFi72+Rc6YphL33gBN0aVIdFcTwZp48pT5xSKOTopB5IbzPZssd3QW0lDT+zqLD3MQGJAzbdGI9EGrC7sCfXxY6LN2XWa4RW/jHQLtuHTth3EH2/lnub9JON8l+dQhpMpqVHmEGA1xJS7AQx8PoFCtVzvfAKgQXGc/otqR2bhf8R+PiyzgyjMAfXKAQEEQHu3BeDA/8aHZOnuZn9bELagU0/9W3nKYbaK/cO2IqaTfrE/g25qZ542QaXrRBWnDMUhurhe7navkKeESCJP/FRPx5ENrz5aieun7UN7NdL6/lTb7K0DSRQrY/QtXyBkQg6t0XE1CVQwb9N7kWjCG4Cy+VHuEH1chLuPD3UkOiZfSBpiY+YT0OcJiJIOYALaEAAAD1AZ5hdEL/AAAKOj+QzJyFACarQn+GLksjU6any6NWHLef3A0oQwzD7KHkYHH/5H7ZnbMU1gR/iDnmQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT0uY50vxpK8D+wuhXO38Mrsww6YHDkpyICc4xh5wHm0ITIzukYK/bYrwt+K5jhoe3iTcLZZf2udiKI+ExG8MqfCpJ/G/zbLkcj2zEPdf89uzBJW49nblXVkKvfr8nWMBV0rOpbJo4/M8BkDqPqVFgq+wZsX0Aku8KPAbVqOQT7szKmyIRS4HyBFQY0gw8QA1IEAAACdAZ5jakL/AAAKOwaR6XIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobnCDx5bM7Zimr2y+TVhkCssZqyOf6pz1dvwT8ErNJ4EqqDIwUmS9kxMkTuC/PgrUxdksNYScBuUS09UXNfL40leB/YXQrnb+GV2Yg2AALfo7ziOBVasv8xP51K1YSGQbbybtBQ+90cLl2wwjKhwaEAAAMB1QAAAitBmmhJqEFomUwIIf/+qlUAAAMCMXM/RgABdKPknMYeOkp+4PD7bw2INm1JRDcS24eofOiAszoN6r6hm5VHFgE3ImBM7U/UUuLYIVqLV6j4rVK1/gkPTQTznccLkSC7tdALcx1cPp+pi3z9GmMtwVdvTkKnrF4PiSznrDS2Xp+LUQpn1imlz4Fv0p/av/4soSxlnuPeIMvxyg9kdtK2fNNK3oIBaYR8Rj/eaHJVLaUG3sngbnDtHzEI+7fe8A3vvKPLKS3zDez1uq+VBBOo4mEXrulHx9EkUoruEtyG1giEHQE5FsveHFgGe/+yiPbvhXYSt3h7Y9icXiKhV7kXccbh5xvw4j1vhiJQF0vy+Ar2SnMwz11XPzRhG6eLotLQ2QT+b+WIekqaIEKt/GogUfjKhpAi7ydM/1o/Qd724r+1e8Qg8KfrSh7jLkSnAfQsU/8VQgh9z9ckNN6j3HHlhi5uckELdFp0IJTxfbox1TqE4O4A0FZry51dWSQZShpisKm5vXQfdlOKMGdnjoIWVnTWK7pO/qUJzkkrVeTlUHVd1RfA+kfzvzREcAreFiA19A5/O5UUTn5Fapd2L+gyouaTiThKCYbkWLsSmloQNXzq8yAFeujnesMf+bv9ke1NJ2N1yWxZqz9ILnwAKGfPyn3pXjqv6TKTL24ded0j3FlgZ9/cOfrRi6GY1hgGAJ9dUfU0sTuj0qDRS6cYhvyQVIwf5nidcy7Q086s3/AAAAFqQZ6GRREsN/8AAAYZDoc6yjG2JAB/B73Sw1K0+7d+VJxPjqkWEAgyGrVh9NuwIo6kRZKGPfoZat2Kd8INy7alKFSnNWkbcOYwQcGRjwmpjkti65INFuhfh71xAn+Y55sCYIZBuhCzFQzC84y1rjjuGsf9bnxdmfRixgALrl/SyDSkiS+S2eWHjQWFZmUHI6O4Lbc1xjlPRtrIBNNj7JMtPNlJdxEEGOY3YUX/rRNMAmVxuhfNsWEAhAeBlveSl8AueUzJkjValuEueGcQtCGwP5dhuHETlw1nq9r0yO8sCr7pQY5gdHl9WGKz/AruXTs4ykjF0wbpyTFWoMu9mkHhlcLHBTZrZXMG2eYTLjBBO4WQRZmNiupjmloxgUIOYnvzPywgbR62X887sv6X9o23gpQsHn0Efw6XXtd6z2fxJBGdJpqtTwcQts9Wx+qdg5lcuRop0VFcBbcpx75BV7+X/zeI7Ear1bzACDkAAACaAZ6ldEL/AAAKOj+QzJyFACarQn+GLksjU6any6NWHLef3A0oQwzD7KG2kWOIlgztmKawI/xBzzIFZYzVkc/1Tnq7fgn4JWaTwJVUGRgpMl7JiZIncF+fBWpi7JYawk4Dcolp6XMc6X40leB/YXQrnb+GV2Yfd0AOlCnQQbq6BlGAey0kb7pyIvT+Tiasaemcz7C9gYlcPAABdwAAAKgBnqdqQv8AAAo7BpHpchQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhucIPHlsztmKavbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT1Rc18vjSV4H9hdCudv4ZXZiDYACQ+z+lSOB6tnkOJc914ZQhwsOq/e70cItqLg322HbnCioJyu0JJeObvQJ4bIO+AAAW0AAAH0QZqsSahBbJlMCCH//qpVAAADAkCA+AAj5F5KlXccveTZVj2CG0smP9SjTCbc5O3heeCbsFrzhXLKTRx2M57oWI1NdlZCAgwF2kASVF6QCMuEKDWg0U9kCyGXwpwNI7dC6l0RJu/L9CU+XJTEGOZ8dwgeDzrtg7Zl1SKloZPklnfdcC3MjEV9H+VNBUd92RFVcXTXmlsgWWnm279bqzfceaIV0vOEYoMC99+mursGezNQLrfzTCZKh3BEBTuMBYGGq0ZqMy4SRpNzYV12ZpkjWRyraPSn+ryvEzX8FxVK/51Y1P/rL5xpXgswlGDPclx8v8KbUL1XmiMCp7rD4LZDRalurPzJRGijTT2enbhHBKrY6xgzppjZ1MkYlJYmTnkY37ZMP1WLHKnKh29Qw215g///m5EO/iqT4Kxs+g3hSpWAmRogsFuKEZbNt3qakh0A10UOgcOXEePNgEMYzVk5fNGfp7MhJM7hQX2DAnFB3593nrYCM1kLjCYQ1CrMbya1X9JrqidA995LrWeItOQye2cNSsoGqLZ+b3bKIhN8rzN5iwWFRENBUFCjeTOps3QEfNYRSzJ7S/TwnHUhDLZ8QXeatsnIUoy7Drsh+FK9t5ClOmYTzBzEVZhUOfMxdlUKde4qIDwdjNcHgrlQijQIAcgACLgAAAEdQZ7KRRUsN/8AAAZH+95DkACdaGdSeGP3Vq6QrXZA/noRTmUVR0SXV/68f9uQckivWxprqMc3Mpathai6n2pIXtGLfHCMuW+izdtd/lwXbbRCRvMBj0njktcwhYhHSMMp/daTwomR4pb48+sAFrjwWw/yrmU28Zn7AXPw7x/RxqFZ98/qAbPQIhN8dWZz8reqZ1Mb3/zHVTkKxEl259IJSNyu70XLgvS65IdF3a7EA4q63BYPWsqHQMGfieKTBbec5Hcr1Y0Xq7Gjx4QihgVOlTHVJFLm6gdloYNkvTrHR44QQlEPESCUnX03sIXIT12IGAZws/WKzAOncOmM34giF1TNB65qxU5LyCcVBld4Msim4bAJd+LGM6eqYAE3AAAApgGe6XRC/wAACoI/j6/DjDsAE7WhP8MXJZGp01Pl0asOW8/uBpQhhmH2UOV/AF7JYM7ZimsWy+TVhkCssZqyOf6pz1dvwT8ErNJ4EqqDIwUmS9kxMkTuC/PgrUxdksNYScBuUS09VcD/L40leB/YXQrnb+GV2YhoYAa/afTFNXuwDEXiANo+I+8P2KTtX3dbNUqQTLXLdDtAdLko+3SMva0dYMAAVUEAAAClAZ7rakL/AAAKOwaR6XIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobnCDx5bSMk4NeBsvk1YZArLGasjn+qc9Xb8E/BKzSeBKqgyMFJkvZMTJE7gvz4K1MXZLDWEnAblEtPVFzXy+NJXgf2F0K52/hldmINgAUsCvuM1aAIAgyZsLRkFSGIQENgbwerF1jP/un4YyATvEgBbTZcAEq6QhMkABHxAAABkEGa8EmoQWyZTAgh//6qVQAAAwIxN2wAAG1Xhncc5wYfIVh+9fj678e42uNUSd3r58fhczSA95ojwG7v1RFWXmMtzAeag9vhZKAKtFUxQ/PJFRBXgyadyRAO0+FA09dAlE4k6R7E8CnFRLQ4xw9zdfZJTzHpPOAhDZU3nZA4W7iSHnbuyoay8zv570wUVHkXV3NyMs/W8sPFPySjsW7jG9cKr3+dFTWRYQB8pf//GiiPIl2chaB3lfohHUc8hY7RXKi8BxzmHumfp3wb1aPhu7yVs0iEWDgO1+ytKiLxQkTFmjVqRBFej+ZdRqWKrKnKR9/uB/8t7k8SGZ/AXzH4hqFkJ9pxPK0V9oDY3CKejeTiIPSuOB1OpmWkUTBX8HCpBS26Vr7wTjvcCbES8uLu8DiKE8o4aNgBRh25uWjnWH6/DyYDZ3aR+YYYks4b1gWcW49K7N39+4oyiMD1Lee7CbRtG8KAZbB6+pHnsx/jk7PAbE6y6Ka2+ncMHpLZIYcCiEUGi3XV2TkrWQuTj6AADZkAAAETQZ8ORRUsN/8AAAYj7kAH9IRk0yoc97KXUpzTSaMOFYJLlS8/GbanSuEEJ80lUVTKGbuQdo7kdGo/szqCaTMogF0iMOQVkeYX4UD2aTP6EkI5kXkz/tJr0I75l8eYknRl5AJ+iiVQicYAEJj8aX/1C6tiunZqR73bJuk/R1IstEP8AmneE6Rtj1A6iYf71wtE75ODLweh1ee2Y1zwYHeK0qWJz+PxOWyuOR/VjXQklIWNSGCW9USjLooMXx7Dy6K2+Plfm5S9Uk6dzOtF53ThKNeUKTlD1cjE26OOBBr5c/QTPdrtT5zwDxagNgHH1g54McLaSmh6qzydCqFV+cpwjGCCRf177Wxn6dxv4iNqeoAAK+AAAACeAZ8tdEL/AAAKOj31XVVA4gAna0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhuGlCYEsGdsxTWBH+IOeZArLGasjn+qc9Xb8E/BKzSeBKqgyMFJkvZMTJE7gvz4K1MXZLDWEnAblEtPS5jnS/GkrwP7C6Fc7fwyuzD7ugB3bxwVal5DQDL54EPaKnOjVzG169bwWicRthitWktpqDxXYAAeEAAACmAZ8vakL/AAAKOwaR6XIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobnCDx5bM7Zimr2y+TVhkCssZqyOf6pz1dvwwixTNJ4ExdKSMF3rXslx8o2AyKaEwQh9LGI26Q3KJTmsj0+5fqnabOYy6HdT6KprfR4wAGLmd72Ek9ZwB9ljAnDTa5dBKMMSfdoIXd13vMgllfQWf2KwgD5k/CFIO+r8ACPgQAAAWpBmzRJqEFsmUwIIf/+qlUAAAMCMMe/AAG1XkXkg1p7ye+xgR4v7iHTsmr/v/Mohpf4slybpKknsVOXgjs+FQfNl1XxFBOQMnqCM9QRHnewUiJHOgoD9P+DOUsq40V2DoJelH/KWuXHGmI+O2Lmt8bFya8Yvqewhfv4665OCTkJq/ns0LfOI2kovOZXK7lwJ/Utyzm6Gf6+D1EsTrYxlw5eJhWVwpSXx+QefnOSlquztpv8Gm2TKEh2w8/jsd1ogU8rBiVQj0fbirjt1E9Xm3iTpd+eYwULaf3tVOauEYRbUl88fDkBq67UCdBi/i2kwoQtEm0JttoUCBs08vtb0G3IzScIr6lDRATdCpyU5yC7xZpPO4LKZpH3dXsmJPrGULNeFx3dTDQJzE9KZxHv+t1EFSwgajYoWABJInCoB624Pzi96bJXL2YAAZes5e0g3D5j9zRPBOHy33VIo/I7qMTCe6unLD1vFqpAHgAAAV9Bn1JFFSw3/wAABiPuQAf0hD/3CPb8txy13mplo+V4d2dbL/4+KKTwFVIheVlZUhZ5crOO0ZoFzZlx/wg1qeoK3ZThGxvYGgJNOuF6Izgb/of4vHxk372wKf6siTIiGrdVNPYcZlam7Txsfdl/9Ghxae1/Xwe/SS9Cb6mKk5nSIrAL0q2YGcwQJqKAnHeL1YACghbqXn+I/mfZJTzDH0SmvNcdEvRLbDBHkCEG01Hg2uhwhFoU9O6gWcdnaF7M2J7IyV8YI5wRfieGiCiffG+haN5HrDFQ5hHfre+nbudl/IYWC+n+DQUvBIWF7v1ObacRvXvEDGoUdIblMLYiESIYyWn+pwPg+q83R6b3xziITTrN1eX8tPg6WmdTbtHYe1/2Z2VXPLGVmHKYgdmz6EL0RWR6iGzybHQHMbU3J8j7necIAefImf7MYXblfdq3K1V/Q+ovd8iFYjZhcGwAKmAAAACfAZ9xdEL/AAAKOj+Pd0sxABO1oT/DFyWRqdNT5dGrDlvP7gaUIYZh9lDblgePLZnbMU1e2XyasMgVljNWRz/VOert+CfglZpPAlVQZGCkyXsmJkidwX58FamLslhrCTgNyiWnqi5r5fGkrwP7C6Fc7fwyuzEGwAEhboxqzkQGoyCVV8jgSy6LgldGEGdHY0c+FIn6o8wcEgOiPsX7AA6pAAAAqQGfc2pC/wAACjsGkelyFACarQn+GLksjU6any6NWHLef3A0oQwzD7KG5wg8eW41TgrGvbL5NWGQKyxmrI5/qnPV2/DCLFM0ngTGDvbSXrNhuB2yWWxvzaq7+npNh6sfruZq0VyyofEkFdt/B10OZeXIW4PUtVWKlYwAGLmd72hyuL09TRaZmjDsSLKORNXpCNVTsOGGOpfybjdkW4dG/XBReENnTmgAEvEAAAFeQZt4SahBbJlMCCH//qpVAAADAjDHvwABtV2vl3mOUvUsV+tKrdB/RF2ATORk8tAKVlEn87+ox3uQtIUt91lriJgRrpWWKRztqW2M9Av6oAGzoR0C0dg7jSdutBNE0XSGs8Q3WjkvcEeGzRuHerPJkTC1k+DkZOMlc0bpt2rgw3DAE9u8RBIdOsByy0UeNvIpNhIJrj+ezQRk58zuycjrflO/BuJ/YzEJj7KLZRbt03id81vGX2GKUEsD86FYYyovi727nXODaSnTUAHf1HWd59LSW3BE9DmxDkVysoD0b9c8sa3fIrCQJhAIzWedZIfxl3skUonwx7YQxC5zxsl++4cOYgPzX5F0MUIPldGrq0u7fr+q0G9D9Jq0D0spmRkl851To9jaiu5+gxX9x9q5ss5mhD2af0fIH9Qj+h/BTgLc4s2c1UFOJ+pgWE39vK9BD73+Xsul7netRLAABB0AAAFAQZ+WRRUsN/8AAAYj7kAH9IPLIeOvzicPd6ybxEpTW6a6ekm3kKAbCRG+uf4qSmVVYm/Tte9Ox7wpdju/yg6kcg6pA+DPHQBlGFgpYStFvA7NQXqJ/DH/00i0AkjENdu1Fq7sP8a6yiiL827jxmPq1FkiuN+4C8/g0y8AAtwEpNy/VXn8SXLQLnboNc22inJ/PsaJH6SDeou9Hd6jRDFu2JzuDtsuwsIpaybEL3vLKnfTYuc1CHBqeBIT9kg7wyEotofp3MnGSdTtSIrnAYenXCHNr5tckbcHpSakbRl/jwDYvgXgrByYR0yPoojbIOowbiC45s9v1NQCT+0+JBcF1yqvc2fq8hOCqA5aX1Uki/D/9DEkG/Ps6/xJAkq5/UP7CHGHixkyOrAPA4KukACjnlSToTMkrPPoFD+EzAAAZ8AAAACrAZ+1dEL/AAAKOj+QzJyFACarQn+GLksjU6any6NWHLef3A0oQwzD7KG2kWOIliNU4KxsCP8Qc8yBWWM1ZHP9U56u34YRYpmk8CYwd6w8UUaTUuU2T2IA2a8XWqn0IdloqZYkTKJox0hfNyCrh22KS7yr//gy3Gd4bAFZEki6Yn8DoZYnEHoDT8/1GSKBP4XqRWIR+Nf/JLW0Um1okjLwyl81YFfo14dgAAtoAAAAqwGft2pC/wAACjsGkelyFACarQn+GLksjU6any6NWHLef3A0oQwzD7KG5wg8eW41TgrGvbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSru7IehPYMxtpah+mb2L6TQ0Sow8Um33RKfdrF80ebxY9U+QpxVoJHrKkLyxsrGACce2tDrU0tuNHJESD5/KvBPUo0NGWJsa06hjQugw0ZZRK9acOQKc5Cs0ZU/HuzWEACbwAAAV5Bm7xJqEFsmUwIIf/+qlUAAAMCMYQIghgAaNMO+qCUFa2+Nh6001SYkm+nlVi/ivGKuxyuZ5FXZuU13/bH3uXgcassJ4dZK9OOlU93fSkpFqsHTbqslXAzXNiBZWOHzGf6WSdZRzlC7+2RKRaVin397X/IrqWL10YDUSLF3TagzXSuUHxf/NdkLYZA8iLDbEDBBEHRe6ilbVgw9fBJepIwHQrTFJ5T8WwBeqxO0Cb/XWWlOeFwr9tUSUjWc8G5+0Avmc5KZhohl0RC6wP+h6k9pTIwqo5m3x+HSeIyVYuGUZ++1clWytmwLGhKMuWgkkXzvIp1qiHINrF5YG3JMnnjaxeviSvsZD5hMiCGbo8404nAygk4lvgWVccPn2J7BFodyn/F+nLktdXXT/Bn1U8XK4+0lNEBixCo3qdf8mwgzBqAL9NLW2YCUNXYIAVbba0IsYOuYnRxoeHJwABgQAAAAUVBn9pFFSw3/wAABh/8XhCgDoAE54CT/LkX5yCaJyrOnbEzF8fUJhM5wpDnuvhl12VFX2mnlLiw7Dx4aO8vnJTfhOlrMWk75rTeI7SQEgwOxP2TGCXvKK6EhTyy7essCGSlqfOil9qFd+pw9dQCkk5Nsz1PJasMjOMpv3qACxQro2VtzdYYfyfyD19yYfb/9RRYoMzaaRJQ569WYYuTvtJTWTes5rW60BEFxS2+b9eK7ySzi1+tkEabyRrAzyU7xAnV7rwEIy33633gdJvctejWCzaqKnQZWziiuYT17Re7vuaQEEhdZa1gJljxagqlKGGfdy4dZ2vWK+z1tCldvBSe4KNggvf/tVg+3dZCb8L2f5Sj6JfBJI87VRhetqUuc2ya1K//YzkYqfGse6RjB846AeHqGHSjadU+K5kkHGGiIJIAAOOAAAAAnQGf+XRC/wAACjo/kMychQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhtpFjiJYjVOCsbAj/EHPMgVljNWRz/VOert+CfglZpPAlXd2Q9CewZjbS1D9M3sX0mholRh4qapfiY+7O+mqOpmhCIeFcirQSr+ANv3GyjDAQx2/KKDSABf5LERDAAy6EjoqrCBtTZBwSE4jaLwyB/qZAAssAAAC7AZ/7akL/AAAJ0QrXdHQ0AJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsocYPQXhyWI1TgrGvbL5NWGQKyxmrI5/qnPV2/DCLFM0ngTGDvbSXrQ0RzfYltjb81du/p6Ty4zc1gp81EymtbuQV24c0/6590wIrcNLVZ+MWvYKN6T9r/1ulWOLnNXlMuMVADkiHoT/nTN+VMVFsXK2LKHBRUR6OB5NEvm7bx1jvz5EKb4aQgozILpAjabgwAAM+AAAAb1Bm+BJqEFsmUwIIf/+qlUAAAMCQU481On+w9vXAAulHZNlxpqs8xiElKzu0Opz1YDYMzUkq3yWuA1VaN23WdgeqhbvlELEc23K/bfe6VvoAB0grzRneGSb8H9rWTt4HQHg5Ranj4GVEZsF5C769W295UaDIleETrtAOPP3IeL9AI99vJrpM/oRgeeRQSQO7DpLMRMmNFxvwAz4CnRkA9wajwkymbnnYkGz4MYK0wmE5Sg/Zu+rIDxGv2FjaAKKylEwaUW8ydgWguCD1zhQeJpTzvzM64HzOy92GAb3rZUIydHeOC8lx8AWCdwdst9SfKi10TnZxcR6ekVIIm4m4iFe9r5ATXIyIgn+0MsZ4mxNNuJ9GuoThkFgef1dUM+K2yL/WhHxtH1VFQDR/yuEikaEBOjn+2qlUH7+I3PiN4uTld5swypRV2f/zo43azqitbWv9vM59RiFlXh2e2XIg8hQnRlqMfw4uNCXuxsTr4FGjz6RsESgzvccW19zhTMMdwSZF0bP15Z3Yqf5aN/EaJvAfXaUBcRvI7e2Ou79epyvEGm11kDewDgfERxo6fPbMkh/Ub1xE8aTt2MAAFlBAAABHUGeHkUVLDf/AAAGR/uxqiEACdongllkIp5Qj8QrZ9InWpNhrS+Y3YAg4miQJ+bNDNdXH3Gj4oalkbd3JdPmIkwZpsSVV1RyEX3kzmwnrKwAWLWJLdvqp8AzeHja3eMvDYNt4Y3r6wDzMJ20Si8TSTd+06YyIH7w8FGfUmDymFGNmos3OvmxTONDT7hPbbalUThHRnVr9Ydy5T1VzEQHH5OaAjgrpiVbKXgLL6KQN8LFPmn7HahhhuxDp8tNLbsprrA5nHawixEcoGDFZJpcqP1MrKKoUR6BXYYTEn7WkiL1Jtx2+NCZOm6L/uXGveXJ6GFmElU9tg4zYldljG+XGq1fWE2ama5a42KnibN2jwFB9j+pYp2JoH4McAADAwAAAKkBnj10Qv8AAAqCP4+vw4w7ABO1oT/DFyWRqdNT5dGrDlvP7gaUIYZh9lDlfyROvN+JYjVOCseEh/iDnmQK7BzVkc/1Tnq7flwngrNJ4ExcYl0l60NEbL2JbY2/NXbv6ek8uM3NYKfNRMprXEkTduHNP+uYYXYVzt+VORZmDYBXPJhV6SCTPTCbtmgwa+A5LGf/X8sctIgPsmFMEsLtr77Evq/cS++AAGfAAAAAmwGeP2pC/wAACjsGkelyFACarQn+GLksjU6any6NWHLef3A0oQwzD7KG5wg8eW0jJODXgbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT1Rc18vjSV4H9hdCudv4ZXZiDYAYKafjGHFtb4KS2mdyYM4cBGgN3U6O6x3EgcS0oHer6M5SAA8JAAABbEGaJEmoQWyZTAgh//6qVQAAAwIxN2wAAH9IUmbuXBhIJYh+Y50uXPOF5DULnylH/DdfnwvowywKX5kRHgACBo21P0DmI+MA+75CDflGCdP0aujhmNZryV0qOF/gaNpRfLfGTK44dcqxRsj/6sfH30MsdZBrzBi0ICt0QD85B8kSrtMkyDImhvxr4CPJxReRiqUEWX0q2fBeNE+1i5J2PJS0XT6NL1C2gJyvzl94KhXGxoPJZwpcU9eJCu82kTcJtuk5yRFoyX7hgMunIvPl+8i1Ik0tE1BviPwsAcasnbmNERpIi584JO7CK36wfIXs7vWqmniXx2sLVq1HYnlHBYcNuLjkZn4FavkWIpVxYMLI606Inf2cppbpRASFnyH9vFbXuk41HiZIAp1rZ0yovejZ/yjPZqEdaPL63m6MoHFT4ot7MBiOLgoRE1Boi8BMYnT/PQ5qt7hdKg7nPqWKy1THdaVHQQsj75h2AOAAAAELQZ5CRRUsN/8AAAYMxxqQAJ2haOE5npEZlrEsWQWRoU0G7yFIpqTUF+RLcgFSjpKsEnVIoQICWpyP+yDejM0BrTQCRYJ1u5N01AsTq/NAqunF6RlgeZraENIlr08mRuuYodg1gZGALfUdvcSE30uj+MmS+bcJMv64obP5KQfuvc2xG6cWu96ewmNo4qOn6aMsTFVjySLLssgTaXkUKRaAbxUZ4PmFs921ooTzKusShJ+9OJakHzu+SCaT5YcC7oK7waC/CakU7CAWS6G456fDg2yO/Zt9Fyl3PARLFEP6HgMdvRyn4N1knTD5ITA6LSWhDLXoBOTngxBWmu7VMxYcf9LABPzgEekOAAwZAAAAnAGeYXRC/wAACjo/j3dLMQATtaE/wxclkanTU+XRqw5bz+4GlCGGYfZQ25YHjy2kZJwa8DZfJqwyBWWM1ZHP9U56u34YRYpmk8CYulJGC71r2S4+UbAZFNCYIQ+ljEbdIblEpzWR6fcv1TtNnMZdDup9FU1vo8YAgDdGNLd84+sf9lnYfDSVHhq60Zni3oN0+Z/bS3Lw3cHYWAALuQAAAMUBnmNqQv8AAAo7BpHpchQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhucIPHluNU4Kxr2y+TVhkCssZqyOf6pz1dvwwixTNJ4Exg720l60NEc32JbY2/NXbv6ek8uM3NYKfNRMprXEkFduHNP+ufdMCK3DS1WfjKxgCAezGV4tSDF06cC25i7vzZ3qWP8wHCEFVB+nhknazQfq+gPBbYxmRZckFjs55285mXqlnABRGk8GC3L+3uD4gLVg6jsaVkj3IAAHhAAAAWVBmmhJqEFsmUwIIf/+qlUAAAMCMMe/AAG1XkW6rxd5QiVAd4v7iHTsmr/v/Mohpf5otybpKknsV8ByU9Jrb3zF79JSdcQAX+2yrzbHBWqtRU1afDtbH2bk/DIzHTxM0mFhsrfig8LVSxtrbCvAdMUvlivCwNmE+2X7OK+jAMxEJx9NQ7eiw8SIbgFr85GAnEXU80d3RiiVgIktq1yvUQox1jGFm76c8qWivo77nYfJRQe8KcEicjM2RdxabHxAUEzwKl4bmEvlt/Jh37u/9GFWZ5jYSc2LzjOJuGq33w+gn59ZbueHCELQ6vUKrcgPIw5UnD/unETL3jxJsX6mwscxC0mE8PAX0I/nys9izKHft4I10jCflUZlZvUOqkOF95c5qTwftxoHeql9rqnlCuJlI1g/WGjRSrffxZlwZq11YR12gPbWmJwVj5VW5CSB3qpv5q4kH2m6ZMxJMOjRogrlwssAUkAAAAD8QZ6GRRUsN/8AAAYd92Ukdvyek0ACdY9TN3wLvAXSH10fEE62HkiDBpyrltlZ6A/p3MQt7lzYaxg2P4yM2mAeFCU6POr0QpT06g3iD4jX8l5sWHCLcMWh92wTpz0kAKbKqN5JzFYL8Hzz0Lern3tKT4Hv06Fpxch31sTCGzLGp/l9QKFkufbUASXP9lc2x0+plBA5tKQ/oBPC/tczui3KmiLMojh/SBG2hIQFWWY2xwOBcjtG4+V0u/myXgnbeiT4OhdDasKL0AqX3CcFveFViRdaKvy/sEhm8jcqS2dRUM8RInjnyzw4cSFOAeY+AgeW2xaVq8VvuER1gAK3AAAApQGepXRC/wAACjo/j3dLMQATtaE/wxclkanTU+XRqw5bz+4GlCGGYfZQ25YHjy2Z2zFNXtl8mrDIFZYzVkc/1Tnq7fgn4JWaTwJVUGRgpMl7JiZIncF+fBWpi7JYawk4Dcolp6oua+XxpK8D+wuhXO38MrsxBsAMD6vmTs7SWk/nQOkvx47pf5Y6Pk0ajsOTlLmPtHQ19yPHrSIndLPZiGC0YAAIOAAAAKUBnqdqQv8AAAo7BpHpchQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhucIPHluNU4Kxr2y+TVhkCssZqyOf6pz1dvwwixTNJ4Exg720peQkCRHKbEOT/VbO/p4jy4zc7VT4UBTEqMjP/7cS8PbbFJdhyqRoZaK/BqATX2YyvFqURuhKptRFabiJFbuKzX41swAy36rM6VW47kl3vtoAxY26ZAAqYEAAAE3QZqsSahBbJlMCCH//qpVAAADAjDHvwABfXz4l79aYAIqPlZLr5LUQ+1HscB5rsK8LIMIOml0amiKG2ZtVI75ZYf/XgnK/RKPEmongNSMGTknJef+uwEHdQmFSFKKJExIcv2II4yCYGxib3XwaeVrU27Z0yRECoezTe2IUCovsazRFIt5tgD/dCUwV3OU41WB45EnQhxc9CGnw58dplUNaC7SuKf+HYrAjPUqNQ0i6uV0Gbt3SRZPe3KuTCP9lOpWW7HP02OgfgvwAJQvgusSYvrcHQvuoK0OTLCDeDSx20/TxGeVO15awus9RWWDipltGwyEweAnyFGeL1SahRCNdHXy7d8q65sK6K7ruqe3+RtsbgAO1p9yIYLm9Du2HvSv9tHSGwFyAg43Lbd3u+9Q1re3j7mAAP8AAAD5QZ7KRRUsN/8AAAYOqQ94OQvtAAnR8jKadDfQ53Iq65bipfIX157pNxY7OSNCQF4TKDBb8pRmGIsj6f6k/5a1IgpNdZ+zj40Qgt34HP2ZeqPPKPQb6qrW29ncRDz75EsQZP7wutc9SFuO4iCbBzqwWRyAesCUKs64kKhU5K5XV5lH/7nvhrDfPRRCeDCvGTpXKHJjNITJOIRcSYVTHtXZzxP/zXPQEIKpGbcfTmwsYliPiloywPT32J2a8FPJdMyXsOUW/AXkr9/XJVrsghjJzluA0fi7ET+38SGdnC+PYAh4PJtk5B+EXg+SmIxOZiC7ka3JXVngABQRAAAAmgGe6XRC/wAACjo/kMychQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhtpFjiJYM7ZimsCP8Qc8yBWWM1ZHP9U56u34J+CVmk8CVVBkYKTJeyYmSJ3BfnwVqYuyWGsJOA3KJaelzHOl+NJXgf2F0K52/hldmH3dG7ymsBnKCDv0Fz6pUIbTmnqH12MFy4vptQb73/yNpEsoAAZ8AAADMAZ7rakL/AAAKOwaR6XIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobnCDx5bjVOCsa9svk1YZArLGasjn+qc9Xb8E/BKzSeBKu7sh6E9gzG2lqH6ZvYvpNDRKjDxSbfdEp92sXzR5vFj1T5CnFWgkesqQvLGysYBs5ne3xKc7hL7OLmlXgBW0g9jUyPbYoE11FyjPodh+lEiqFb+jFPhDNd+/RkzXF3AKK3ngD381NCDYcsOiZ/yttwMFj1us7rkiZnH3+0uP4AADPhAAABWkGa8EmoQWyZTAgh//6qVQAAAwIxczgIwAKnH5fga/gn59EQTFOTriJL+DZAMAfuReMlReCom6+4S7S64sYoDjlJlMG41SPIeMIuIQc6rjSZMJWcLzlMCPBETvC6lbbX/6MWn2qXnZQ+R8yh0Gw6O7kvk+wVT9QsZwExAqoWLWuai1hv2fFchQO0mRswSVt4N2cQOa6TpVIRABrttROKO9MGi0LxwBXXt7i07DKk5SBY99W5Q4c0x91WzDX67mV3soKiJxYI2MdzPCM/sDbp6OeywVNHszzK9gW+J1vMtf2JrmaJb9JovkMw9x+Ef7uAW3KC31QHVIockU3nBL3svWNHjvhazNxpigcZzwNpBMfYOQjrdsf7qX9ux1Ga5VZ8On9bX+k4zcfZmFX3dCaYNTbUrnQ+qEjCl7b5NUHzl00mb07Yk2Ml7LswtPPqxerh0EeQW7Ws4+F4AhcAAADTQZ8ORRUsN/8AAAYgATo9IAP4PmTPaFslTSzrWnjpHS8C42VhOzSumU/z93JHxtR/Uh6hjKghdE7nDH93LeJKTdzz0yOxdFwdfAsHzdHnHyW/1ysynMkY4xg4UrVy0eV0fTmLzoLtsNmmExjvHw973lc4ObYBbqLJwf3xhirpcsx+A3sleR5L9AlxXqMyxyJ5QOhg8moqP1wmN/txEnOqCGzLR3tbxsheki73VikuVls4nbWqRdXvdAVjtu8oEI7OE4zJSXsiE95+IXvN6asYAAAl4AAAALEBny10Qv8AAAo77Lva+BxABO1oT/DFyWRqdNT5dGrDlvP7gaUIYZh9lDdlBziJYM7ZimsCP8Qc8yBWWM1ZHP9U56u34YRYpmk8CYulJGC71r2S4+UbAZFNCYIQ+ljEbdIblEpzVcAPpf1TtNnMZdDup9FU1vozDA5z/EaiZHS7Q9ocLp4tU+H5SvYiBaMXiRTY2urvuBqBYBR9KJJbm81+r4CstJsYoWtdEGTuyWAAC8gAAACwAZ8vakL/AAAJ7nX2C80wAJ2tCf4YuSyNTpqfLo1Yct5/cDShDDMPsocX7vH95bjVOCsa0j/EHPMgVljNWRz/VOert+GEWKZpPAmMHe2lLyEgSI5TYhyf6rZ39PEeXGbnaqfCgKYlRi5FubiXh7bYpLsOVSNDLRXzZQeCsBb1vuJBuUZndQdlgcGrf3JD1WdnRy9pt7VJva68fYL1yV2xIZh8tQqfKys+vNJeykAAIuEAAAGqQZs0SahBbJlMCCH//qpVAAADAkCA+AAj4n6/7+RiFpR4yli/uIclCJiejhjJiSykGzzVlgSxrK+EMZuLzfVVT1GbMN8NNfxYOWj4+VgNb9lKh/XFQ8udzK4ScZ8XavgKFCxz0gskor/j2hffBrZExb2iAL1P+QN1EQBZ3c26pIL61LaPufHnoBjqJge9UnCMYtEwJxS2Mp2df9MuhDFIZhzEEoheW7c9ubN5ItKzxeFpKDapaGKMr2Gcq6sqFvTsl7ixzN5HcXl3inSjRpy0hEk1AsUl9ztmRiSdws2jq5fB9A2Hs3tvtsupEx6T1l9jklUGX+790vr9dl3R8Ptq3jY1qnAUtvDr6qIvTAlxOBvYTehhokxugcd2I8VpDw4vLuhjlr5U5Aen046xlaPc2+jqHo5ldQU4mwmRR8aUWrB+G0bTVjVTXV1o8OSnjzEjeYvN7eRX1wQC66ook33DYVPreSw6OgKAlWmVDLsp2QYacaKII7ZLa9zNZtok6ZIAO5mZKPbfv0hWZ+lJHSudzF/xJ44Ijf+01Mi77gNjUuiv76WsNAG/eivoAAAAzEGfUkUVLDf/AAAGR/veDop+gAToJpJcLMmBNTQOIicEINBsfbBWyBQR+d0vFn91ZJZKSd3WIs7B08QgKddPT4nyA8KUhNYTwq3nWom2V6SY9CUFPqhw39mWEzES0GPxIVXTVKvoqjN988sXgE77jnlf4+8DP4FeSRH88VF1z7YZMXPn8NJCBPmmLCjgr75REFi/mnXJ0DDvSzgw3+8+KNNxrdDIW6LtoUo29qlrU12Ks2GthJyRTtLe9CbElp24zKAz7QN54BFbwAABqQAAAJ8Bn3F0Qv8AAAqCP4gYfOWRhgBNVoT/DFyWRqdNT5dGrDlvP7gaUIYZh9lDlKWi0RLEapwVjYtl8mrDIFZYzVkc/1Tnq7fhhFimaTwJjB3tpS8hIEiOU2Icn+q2d/TxHlxm52qnwoCmJUZIAF24l4e22KS7DlUjQy0V+H6HkiDAlXRrwJWDC/T5cj22gscdP8R9orDDBSPBcs/Ke+AACtkAAAChAZ9zakL/AAAKOwaR6XIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobnCDx5bSMk4NeBsvk1YZArLGasjn+qc9Xb8E/BKzSeBKqgyMFJkvZMTJE7gvz4K1MXZLDWEnAblEtPVFzXy+NJXgf2F0K52/hldmINgQn2YyQHSrQj9LCHdNYKyyPJ6rUvP0sUy9LzE0SwsfpcVvIKJmZpN5QAAeEAAAE1QZt4SahBbJlMCCH//qpVAAADAjE3bAAAf0hSZv2Nd9m1OAgihIq+01HeiWKI2ibjO3I8Mxmv7VvZAZtoQRLytwpxiYllZIrwx3qv4Y9OkM0hAzFLCAhe8SVbfwnyoCa9fMv62snr+fdHoXJja1vtANt9OXhhI8+EGdl0O9FgExD2hgHxXJAdV6eCAMQraLzuijjRqlBfiYC8xKLNSHy9ctNrZ8Vl0UJt1ryLQ2zI0n/EnQXsXkYSLSY8xzbeSkXFpkTMTq7CSggNKuT7gJRCF++HSCSqwVzmOAPRGaYNaFV+dMdzpFPivndEzPEci8avXVufxNxef1oCHl6RLCruRHJ0MQx91VXp/EfSVLrIrFN3zDuuLSSJAKFWmL3JssaqIyI/Lj6eWLLNVaGbeVR8m4oU/16JAAAArUGflkUVLDf/AAAGDMcakACdjqHCcz0jTEaTFrKe265w0RSPESg3PgoKKsr52+sNKsxVNqaNiE6QVzSdqgQzKgDCsZ2MpFMoC08/LEFhYXwOHwNSn2HxJ3k2pD/ywIXyFa3HHgP2ez6CEliwfQJxAnSyNrZQBdThcf3h/dzzfrww8gwaMJ0aO2wSwLza4oney3GNp2G4ePC3KMs3jFqsa69abzI1K5uUMRvQAANqAAAAwwGftXRC/wAACjo/j3dLMQATtaE/wxclkanTU+XRqw5bz+4GlCGGYfZQ25YHjy2kZJwa8DZfJqwyBWWM1ZHP9U56u34J+CVmk8CVVBkYKTJeyYmSJ3BfnwVqYuyWGsJOA3KJaeqLmvl8aSvA/sLoVzt/DK7MQbBvigo+XC21hi7GFfOYzJRyQ0lZcRF9ZGfhgZADoSkUexLt5mb0giHtamvhX1nWW10OS9RtKq5uobRwIAgEvElRSZW/52asNLgMAADUgAAAAK8Bn7dqQv8AAAo7BpHpchQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhucIPHlsztmKavbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT1Rc18vjSV4H9hdCudv4ZXZiDYEJ9d7fCpu47q/7LxfO7csakcoZKsiu+omqxRZuoZx2N0vmFyzx+Ubp8Ee7fGw7SnIdwh80xHAABCxAAABTUGbvEmoQWyZTAgh//6qVQAAAwIwx78AAbVeRbqvF3lCJUB3i/uIdOyav+/8yiGl/mi3JukqSfenFbRR9LNXbkGKaMZ/4Vp4rGCnT9BwCM9QRHpSV+eODAkUcLc52MPv4UWEsGm5pLq5PryarpCntfypGuc/7xsWyx4/lgK1/wZi/Mq59bU4YiaIlCoPSA/ts3jnN9yZ1Mio8J4HgnoLaGPvjC/wY1saefk42Emg14wXlKaGkWfITd36vKy+SEOOsdKlvM6kwlHFuyihp7eItw4Coi5gnXH0Q2dL0KPVCd/ed2Zbq5B4yWU7misGo7LxJii0dE0qS52FS4GQ+4Pm7iDvB3UwV3od7h+enkTokxcr+7+fl2ktNhL/+WhA/NEi4eeMaDvyFtC4W3Q4ucog8pP6seOGM2AN7ovJeC7l8gnY1iJFhxxvmEX8qRQRkwAAANxBn9pFFSw3/wAABh33ZSR2/J6TQAJ1j1MhsQvNGW0Piae5/7EJr5/jp+nO39SOSXo9RAfyPNcqp+vTPyVU7Igb9k8Rn1FjKrvL2okmfcX0I12bhmU6aJHw7BQFn0aRhD80Xbz42b+ZGLu/MqrboR4ZBmQl13KGSldOh0Wc6456OlCt9SP6b+Dh0yXlcOJqt4IMUFNIJLHlEX7xNp/BHyKZ/hu9xIVDrg5yFGOciB1z7LSawL2ABN+relM5a5/XtVFVcMUlpY42Hui8ie3WVl/QV/rs8CvsBo9MAAoIAAAAmAGf+XRC/wAACjo/j3dLMQATtaE/wxclkanTU+XRqw5bz+4GlCGGYfZQ25YHjy2Z2zFNXtl8mrDIFZYzVkc/1Tnq7fgn4JWaTwJVUGRgpMl7JiZIncF+fBWpi7JYawk4Dcolp6oua+XxpK8D+wuhXO38MrsxBsW9LXsv+nPDxr2La1KNSk2iOS+0glIbjfUZKnhHEeMxwAK3AAAAswGf+2pC/wAACjsGkelyFACarQn+GLksjU6any6NWHLef3A0oQwzD7KG5wg8eWzO2Ypq9svk1YZArLGasjn+qc9Xb8E/BKzSeBKqgyMFJkvZMTJE7gvz4K1MXZLDWEnAblEtPVFzXy+NJXgf2F0K52/hldmINg1Rn/M3ogoVNNWXFMWaa0Uh74eOKh8a3cPFO5Z7knPv2BVmtiFh/zi8qG3FaJrCbbFhSO+Ec+loI/2QAAOOAAABL0Gb4EmoQWyZTAgh//6qVQAAAwIwx78AAf0i8lG9DeVho3EYmh5iDR1ih9yPamrI7flkmVoKHjKfOLtO3DOOcD76VEW4dxXKR/UGzSnoGAaxIE0EKMRbj9G75VFLAiLoN3YQt7ZuppfVWYrhxNaapIna4RWg+2c81YLhBFtMBTzhvfynjG/RKyo1YW834jDeyqwPeAVuS2xTN24vaLGjMvfDsDuep2zYjHaIqM7dTa4zER7jz0eBegWAodrwOx2Jm2a/ZSUKvS3yh5iOjZaj3feWUZe2zh4LMTiYHol8SM7kZZL+3+7a81cZyILoeV1eRaHSrGd5h8ud/73g+jxX3oju2wWKaBkwB5UmdyweoKQKZs3qqhiMPtVK0f28366Uf3+zS/QMx98vfmtqDs5Q1QAAAM5Bnh5FFSw3/wAABg6pD3g5C+0ACdHyMpp0N8zcfnnG/IQbwC0Z4Qaem1Y3dcgBwYEJ5I3klBuH+KNt3Dv3oO6IwXDtSx5Lkt83aLoqfDjaDiwGExbWO/hz5xfMKjmjPNlKYYfoJJxvjOtqc6ER7OWtWAnsjF1HKwkyOq7n4GlEOHeJO2CxQAFgWluKkMWb+chV/bvI0Mz2lw8ssOVbswRl/ELFR2DewhyaocBbH3xNYWw1lLQK+gilK8FpdbmPUiZrAK6k77g8W+AEgAAi4QAAAJ8Bnj10Qv8AAAo6P5DMnIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobaRY4iWEjJODXmbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT0uY50vxpK8D+wuhXO38Mrsw+7xgcGZXpgMKJet8PccqFrRI3PjJOND0xPUu3zT8prVLpnWiy/rAAABCwAAACpAZ4/akL/AAAKOwaR6XIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobnCDx5bjVOCsa9svk1YZArLGasjn+qc9Xb8E/BKzSeBKu7sh6E9gzG2lqH6ZvYvpNDRKjDxU1S/Ex92d9No83iyCrhFcirQSr+ANv3GysY+ha53tMW/Ngr7KPa5cYTW5sOd31dfokpi7sWp9CMxkHa+l/qlOuE10we0QAAAwAS8QAAASFBmiRJqEFsmUwIIf/+qlUAAAMCMXM/RgABEHrBHNj2lbYkpuXB1xezZf27mEvFoSGDRsKVohOoNaT98RdjxNfhCg56dDw0+cLBIKGerSdV3SxTzhLm3uLpIsKQCSGAerFxxIq68BqmRFdc0M0Zx/nrpvj9eQOM/dwJ3bl5tN2i5HQt6ilH0RTuBn7IkuM2pFta+GTUPu1UULbtiPZ8dO+CtFNJuCV3J6PNuNWNcoQio8/xLqW+uWcrl+AgdpLOMXosj0imFsxwiUTBhZAvROg6bMszq8JIZHPgwI2rVg/9LuEqffMFPzlB4y07p3rw7a/KkgcUn/g2Segq0xCdBsf8aIngOtWyP00nvQ0+k9Xw7h2xs5jh3xGnhnjDLMVJSluAAAAAsEGeQkUVLDf/AAAGGSWPIqxqKpACZ8IRUCPBi4YnKs6cOisqrLveocpPQ9z+C+CeLWATPLoaTi1HC/OQLtYr7uPGP153neejcRwMU84LM/Rb7lwuPB6ON+yt32hwUbmyPakb2mSeCqco9qICF9xmebDu19cealY9XTzgQGMtfz+/DkKmlO2ULUh8/0QJLM/7JqbnhLSZqGAq/1jE81PE/EnR6kZHlj38sReXyIAAADjhAAAAnQGeYXRC/wAACjo/kMychQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhtpFjiJYSMk4NeZsvk1YZArLGasjn+qc9Xb8E/BKzSeBKqgyMFJkvZMTJE7gvz4K1MXZLDWEnAblEtPS5jnS/GkrwP7C6Fc7fwyuzD7vGBwYP0CUsTBa6i8nuQ3LnaHGQRAl5TYI5P9gMkDiMbrndIAAAiYEAAACoAZ5jakL/AAAJ7nX2C80wAJ2tCf4YuSyNTpqfLo1Yct5/cDShDDMPsocX7vH95bM7ZimrSP8Qc8yBWWM1ZHP9U56u34J+CVmk8CVVBkYKTJeyYmSJ3BfnwVqYuyWGsJOA3KJaehtFvl8aSvA/sLoVzt/DK7MKmVL3UAtFiPVyI0jUGlzVy3rJzp0/5TYPpKE6ohLY0RXddcsAePRcnsazQQmkcDHAAAUEAAABXEGaaEmoQWyZTAgh//6qVQAAAwJAgPgAI+ReECu9DK9VptnN6vAlRDkV/DZLguZgTmygO0MwrJKHDKJIxHRWRm7DGgvXXgGwYY06u8JfmWEwekRtsVpYomHg+mB8fN68QtaD/bYRqzhYcHiPpdxxuz3h9Cufw+1FnoSfYicp25px+whkdPKjrhzIpAJm/qKWWwznImhH6Vhy5Xi/XGrhI3/z45dBQDVjfcBsVM0nbRDWRWD9qAkyF06zU4vmIp8WUZ+x+Z0oWVpzz4I7vtbZheKzPKG2/4DHqVUxKuoECfnaOgp9H0qbrDxBdlmPK/nqVpS7vyDNLvrDJDf+JsNwa3EyIGzMUu/tjW15ybgDaoxW0qYQgBybMkqz3lhOQWgNeF0g8I41DCLIYcRmnoNZD64rjajZYA7ExM/f1Yh8ZOHeZlOOIBwlLsHe/RsCFH/3cgvNUYNvET5AdVi+OAAAAKJBnoZFFSw3/wAABkf73kOQAJ1hRYo4u6woU7KWvFtjNewL3ls5o9xypPnr5JGjLv6Zt1q542z34/tc74iqO+/qPvO1pxQxwllY3BAIVL1+jv6fIhHa4otooJnmz8cFcOhafSqcubfeo6cySWR+H8zp+0+EjFSMOID+9Yt6KQ9nnRPnuoiCz20diUEwCz4t+FE9YMMeMCX/Y+Fb3njhcAAAKmEAAACUAZ6ldEL/AAAKgj+Pr8OMOwATtaE/wxclkanTU+XRqw5bz+4GlCGGYfZQ5X8AXslgztmKaxbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT1VwP8vjSV4H9hdCudv4ZXZiGqKL2g0zfbIbNPyBN1gA7sFZfFZs9/+ZQAAAwBswAAAAJIBnqdqQv8AAAo7BpHpchQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhucIPHltIyTg14Gy+TVhkCssZqyOf6pz1dvwT8ErNJ4EqqDIwUmS9kxMkTuC/PgrUxdksNYScBuUS09UXNfL40leB/YXQrnb+GV2Yg4AkvN3cm0IndJ2U1HvtQwPjkvWCVb74gUSGAAB/wAAAPpBmqxJqEFsmUwIIf/+qlUAAAMCMTdsAAB/Avx7OMD8AHXf8y8fYphLefF7nGhRnGPGwU2Q0JYgZH1HjXhHzJJo1BpAILha5Vg6BsjRaNdkK6eDM+lbJ01v6RBnHqwh1GQ/4WUacZcsndV5lMg8z1Evb8uE3svE5aE0m5m0epuN1LqZ715Gt0K4AbqYykI6c/pvMgQhenci+//DBGr+k3KDXc+IcztgWfkRv0uMZMDNXdW1dXPRKsOmRBpM0INZD8GzKw10r2vOZkvqAlm8GBdBIr39xJIOC1F5HCME+7mdpDhzBeQJ8TNBxftGKltmk1GHGezFhDrTP95AAAAAjkGeykUVLDf/AAAGIAtyAE1ezv626qprIEoGAmgjBwL63GIIpKz+hDotYlw5Uv7bnNpeIPe6K8OFfY2jDVEes024JonHt7FfKoPt2iNKHSdAW2W3osT0M+YckDRBwsh5z3F4yYlAwMc9KpKQ7ilKSq/bpnrJtL9etEhr94mDHWFj7RmTPsmshZAYAAADAm8AAACOAZ7pdEL/AAAKOj+Pd0sxABO1oT/DFyWRqdNT5dGrDlvP7gaUIYZh9lDblgePLaRknBrwNl8mrDIFZYzVkc/1Tnq7fgn4JWaTwJVUGRgpMl7JiZIncF+fBWpi7JYawk4Dcolp6oua+XxpK8D+wuhXO38MrsxBsW9S8Eb6sxHml3H9VB8JyGsR3hhSgAAQcQAAAJABnutqQv8AAAo7BpHpchQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhucIPHlsztmKavbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT1Rc18vjSV4H9hdCudv4ZXZiDgCSvGmnBgfxkBP9OJ8Sm/tY2wBtvjGABgAAf8AAADZQZrwSahBbJlMCCH//qpVAAADAjGFp0MAEQfDEPVaxssTKlBY/3EOnmbx/iPAlv0IgwMVtyX+pXobplJW1R7+xMU0RTkvbSD9uxE/NE4xS1ipoBvypynfdxSCgg5mKOgrjyIlQhVveKTJs30c0r+3RUluvcCPuhReSrL1oAZsT8/VdOOrfXI7/rddSJvxhMjN7NpUzO6OmRn1Dxc8WFHqHwh9EZx53LRGNaU58OcA5NizXRVG7n+Zf086ll2NXFSDg6Nbsohr/S/B4RTCU8qlYVGeCIbswgAWUQAAAMBBnw5FFSw3/wAABiPuQAf0hD/3CPb8txa93mplo+V4d2eGI14+KKTwFVIheVsKjpaHQKuuovyDtFhxUYuUjfeQvonDzYrxV1RQ3kZRfHf+rNIqZJc3kxpbXU8M2N0pN+JShb8AGKLeCMSjjOzjirnM9gO084x0ipqKW0Kpufe1MNHMwM3c4bbIyRo9LfB6UZbkcS64oXLhjn+vdiRSp3dpmsGbjOakZAF8UZ50k7x/Ja3NGFFkhj7yvcAAAAMAyoAAAACRAZ8tdEL/AAAKOj+Pd0sxABO1oT/DFyWRqdNT5dGrDlvP7gaUIYZh9lDblgePLZnbMU1e2XyasMgVljNWRz/VOert+CfglZpPAlVQZGCkyXsmJkidwX58FamLslhrCTgNyiWnqi5r5fGkrwP7C6Fc7fwyuzEG/qXBVDWKyfrOy3wURebYeb4bTtamvTAUAAD0gAAAAI4Bny9qQv8AAAo7BpHpchQAmq0J/hi5LI1Omp8ujVhy3n9wNKEMMw+yhucIPHlsztmKavbL5NWGQKyxmrI5/qnPV2/BPwSs0ngSqoMjBSZL2TEyRO4L8+CtTF2Sw1hJwG5RLT1Rc18vjSV4H9hdCudv4ZXZiDYt+k02VxoS1Grt4oB/Sih0IW7FmqUAAGzBAAAAxkGbNEmoQWyZTAgh//6qVQAAAwIwx78AAbVecwe9DlTuTUzjQtfJaiH2pA/Ofe96j7RZArz2xlN7iQFS3ugCy+rUafpcsNIfv1fgnK/JreG+/TWVuNs1YlL7AFg/ccnzyhIVE60GEBO8Rg3K6V9LhazkMa/+1cEiZF+WwGAOF0DkQacczc4HkBbaqY+/A86Z+vt2TIoKVNdjhMwJJcsXdnc5vW3tCsAb3e9wM4cHLBMDCgf5vgIZ3EhVzRSgMG/iiZ9lbcmUIAAAAJtBn1JFFSw3/wAABiPuQAf0g8sXkh3F1qqIiEiJSmt00Ui6Bi2MA2nh945/ivSKV19kXKSOUxlzWBkC5URVwWL2P2DxvjfhMmS3hplgb/LqNZM8dzZQCTGQm4daP3Ave7ztWBoIhtRV++UvKiD2kwntZT8fz/KtYGxzzgA+dk5jNHJPFP7Z9HeKzLU+KlW1K2EuHdM7GLooAAAb0AAAAJkBn3F0Qv8AAAo6P5DMnIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobaRY4iWI1TgrGwI/xBzzIFZYzVkc/1Tnq7fhhFimaTwJjB3rDxRRpNS5TZPYgDZrxdaqfQh2WipliRMomjHSF83IKuHbYpLvKv/+DLcZ3i5HYf4I9HjTV9ir/fo9xhbWyBLRfPPrI8oLdkAAAMAP8EAAACVAZ9zakL/AAAKOwaR6XIUAJqtCf4YuSyNTpqfLo1Yct5/cDShDDMPsobnCDx5bjVOCsa9svk1YZArLGasjn+qc9Xb8E/BKzSeBKu7sh6E9gzG2lqH6ZvYvpNDRKjDxU1S/Ex92d9No83iyCrhFcirQSr+ANv3GysbPzTSmJJxGFeIdj7pOV0baYfqrNONs1g6QgAAS8EAAADMQZt4SahBbJlMCCH//qpVAAADAjFzOAjAA7NEDu8q1ZOhXw4dz7qkzNoZBwX14FaMC6HsQXs3jKk2uPfOTGXlUx1pxPHylvqkvCuNBvn9js8kxjWP7e1bU9/PXDGZiL5oDea0tAzAx0s3texaL6FztBJM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type=\"video/mp4\">\n",
-       " Your browser does not support the video tag.\n",
-       " </video>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Video object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "result.show_animation()"
    ]
@@ -221,8 +134,7 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.10"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,