diff --git a/CITATION.cff b/CITATION.cff
index 9d3d222..181b3f8 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -6,6 +6,8 @@ version: "0.1.0"
 doi: 10.5281/zenodo.17646158
 date-released: "2025-11-18"
 authors:
+  - family-names: Mishra
+    given-names: Sparsh
   - family-names: Wolf
     given-names: Tobias
 repository-code: "https://github.com/wolft/quantumhall_matrixelements"
diff --git a/README.md b/README.md
index a7e6216..3a4617c 100644
--- a/README.md
+++ b/README.md
@@ -4,17 +4,18 @@
 
 Landau-level plane-wave form factors and exchange kernels for quantum Hall systems in a small, reusable package (useful for Hartree-Fock and related calculations). It provides:
 
-- Analytic Landau-level plane-wave form factors $F_{n',n}(\mathbf{q})$.
-- Exchange kernels $X_{n_1 m_1 n_2 m_2}(\mathbf{G})$. 
+- Analytic Landau-level plane-wave form factors $F_{n',n}^\sigma(\mathbf{q})$.
+- Exchange kernels $X_{n_1 m_1 n_2 m_2}^\sigma(\mathbf{G})$. 
 - Symmetry diagnostics for verifying kernel implementations. 
 
 ### Plane-Wave Landau-level Form Factors 
-The plane-wave matrix element $F_{n',n}(\mathbf{q}) = \langle n' | e^{i \mathbf{q} \cdot \mathbf{R}} | n \rangle$ can be written as
+For $\sigma = \mathrm{sgn}(qB_z)$, where $q$ is the charge of the carrier and $B_z$ is the magnetic field direction,
+The plane-wave matrix element $F^\sigma_{n',n}(\mathbf{q}) = \langle n' | e^{i \mathbf{q} \cdot \mathbf{R}_\sigma} | n \rangle$ can be written as
 
 $$
-F_{n',n}(\mathbf{q}) =
+F_{n',n}^\sigma(\mathbf{q}) =
 i^{|n-n'|}
-e^{i(n-n')\theta_{\mathbf{q}}}
+e^{i\sigma(n'-n)\theta_{\mathbf{q}}}
 \sqrt{\frac{n_{<}!}{n_{>}!}}
 \left( \frac{|\mathbf{q}|\ell_{B}}{\sqrt{2}} \right)^{|n-n'|}
 L_{n_<}^{|n-n'|}\left( \frac{|\mathbf{q}|^2 \ell_{B}^2}{2} \right)
@@ -26,7 +27,7 @@ where $n_< = \min(n, n')$, $n_> = \max(n, n')$, and $L_n^\alpha$ are the general
 ### Exchange Kernels
 
 
-$$ X_{n_1 m_1 n_2 m_2}(\mathbf{G}) = \int \frac{d^2 q}{(2\pi)^2} V(q) F_{m_1, n_1}(\mathbf{q}) F_{n_2, m_2}(-\mathbf{q}) e^{i (\mathbf{q} \times \mathbf{G})_z \ell_B^2} $$
+$$ X_{n_1 m_1 n_2 m_2}^\sigma(\mathbf{G}) = \int \frac{d^2 q}{(2\pi)^2} V(q) F_{m_1, n_1}^\sigma(\mathbf{q}) F_{n_2, m_2}^\sigma(-\mathbf{q}) e^{i\sigma (\mathbf{q} \times \mathbf{G})_z \ell_B^2} $$
 
 where $V(q)$ is the interaction potential. For the Coulomb interaction, $V(q) = \frac{2\pi e^2}{\epsilon q}$.
 
@@ -92,11 +93,25 @@ X_coulomb = get_exchange_kernels(
 
 For more detailed examples, see the example scripts under `examples/` and the tests under `tests/`.
 
+## Magnetic-field sign
+
+The public APIs expose a `sign_magneticfield` keyword that represents
+$\sigma = \mathrm{sgn}(q B_z)$, the sign of the charge–field product.
+The default `sign_magneticfield=-1` matches the package's internal convention
+(electrons in a positive $B_z$). Passing `sign_magneticfield=+1` returns the
+appropriate complex-conjugated form factors or exchange kernels for the
+opposite field direction without requiring any manual phase adjustments:
+
+```python
+F_plusB = get_form_factors(Gs_dimless, thetas, nmax, sign_magneticfield=+1)
+X_plusB = get_exchange_kernels(Gs_dimless, thetas, nmax, method="hankel", sign_magneticfield=+1)
+```
+
 ## Citation
 
 If you use the package `quantumhall-matrixelements` in academic work, you must cite:
 
-> Tobias Wolf, *quantumhall-matrixelements: Quantum Hall Landau-Level Matrix Elements*, version 0.1.0, 2025.  
+> Sparsh Mishra and Tobias Wolf, *quantumhall-matrixelements: Quantum Hall Landau-Level Matrix Elements*, version 0.1.0, 2025.  
 > DOI: https://doi.org/10.5281/zenodo.17646158
 
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17646158.svg)](https://doi.org/10.5281/zenodo.17646158)
@@ -105,25 +120,30 @@ A machine-readable `CITATION.cff` file is included in the repository and can be
 
 ## Backends and Reliability
 
-The package provides three backends for computing exchange kernels, each with different performance and stability characteristics:
+The package provides two backends for computing exchange kernels:
+
+1. **`gausslegendre` (Default)**
+   - **Method**: Gauss-Legendre quadrature mapped from $[-1, 1]$ to $[0, \infty)$ via a rational mapping.
+   - **Pros**: Fast and numerically stable for all Landau-level indices ($n$).
+   - **Cons**: May require tuning `nquad` for extremely large momenta or indices ($n > 100$).
+   - **Recommended for**: General usage, especially for $n \ge 10$.
 
-1.  **`gausslegendre` (Default)**:
-    -   **Method**: Gauss-Legendre quadrature mapped from $[-1, 1]$ to $[0, \infty)$ via a rational mapping.
-    -   **Pros**: Fast and numerically stable for all Landau level indices ($n$).
-    -   **Cons**: May require tuning `nquad` for extremely large momenta or indices ($n > 100$).
-    -   **Recommended for**: General usage, especially for large $n$ ($n \ge 10$).
+2. **`hankel`**
+   - **Method**: Discrete Hankel transform.
+   - **Pros**: High precision and stability.
+   - **Cons**: Significantly slower than quadrature methods.
+   - **Recommended for**: Reference calculations and verifying the Gauss–Legendre backend.
 
-2.  **`gausslag`**:
-    -   **Method**: Generalized Gauss-Laguerre quadrature.
-    -   **Pros**: Very fast for small $n$.
-    -   **Cons**: Numerically unstable for large $n$ ($n \ge 12$) due to high-order Laguerre polynomial roots.
-    -   **Recommended for**: Small systems ($n < 10$) where speed is critical.
+## Notes
+The following wavefunction is used to find all matrix elements:
 
-3.  **`hankel`**:
-    -   **Method**: Discrete Hankel transform.
-    -   **Pros**: High precision and stability.
-    -   **Cons**: Significantly slower than quadrature methods.
-    -   **Recommended for**: Reference calculations and verifying other backends.
+$$
+\Psi_{nX}^\sigma(x,y)
+= \frac{e^{i\sigma X y \ell_B^{-2}}}{\sqrt{L_y}}i^n\,
+\phi_{n}(x -X),
+\qquad
+X = \sigma k_y \ell_B^{2}.
+$$
 
 ## Development
 
@@ -142,6 +162,6 @@ The package provides three backends for computing exchange kernels, each with di
 
 ## Authors and license
 
-- Author: Dr. Tobias Wolf
+- Authors: Dr. Tobias Wolf, Sparsh Mishra
 - Copyright © 2025 Tobias Wolf
 - License: MIT (see `LICENSE`).
diff --git a/examples/compare_exchange_backends.py b/examples/compare_exchange_backends.py
index dbee945..361f4ce 100644
--- a/examples/compare_exchange_backends.py
+++ b/examples/compare_exchange_backends.py
@@ -1,8 +1,8 @@
-"""Compare Gauss–Laguerre and Hankel exchange-kernel backends.
+"""Compare Gauss–Legendre and Hankel exchange-kernel backends.
 
 For a small |G|ℓ_B grid and nmax=2, this script computes the exchange
-kernels using both the Gauss–Laguerre and Hankel backends and plots the
-relative difference of a representative diagonal element X_{0000}(G).
+kernels using both the Gauss–Legendre (default) and Hankel backends and plots
+the relative difference of a representative diagonal element X_{0000}(G).
 """
 from __future__ import annotations
 
@@ -13,11 +13,11 @@
 
 
 def main() -> None:
-    nmax = 2
-    q = np.linspace(0.2, 3.0, 60)
+    nmax = 10
+    q = np.linspace(0.2, 20.0, 60)
     theta = np.zeros_like(q)
 
-    X_gl = get_exchange_kernels(q, theta, nmax, method="gausslag")
+    X_gl = get_exchange_kernels(q, theta, nmax, method="gausslegendre")
     X_hk = get_exchange_kernels(q, theta, nmax, method="hankel")
 
     # Focus on X_{0000}(G) as a simple representative component
@@ -32,7 +32,7 @@ def main() -> None:
     ax.plot(q, rel_diff, marker="o", linestyle="-")
     ax.set_xlabel(r"$|G| \ell_B$")
     ax.set_ylabel(r"relative difference")
-    ax.set_title(r"Relative difference of $X_{0000}(G)$: Gauss–Laguerre vs Hankel")
+    ax.set_title(r"Relative difference of $X_{0000}(G)$: Gauss–Legendre vs Hankel")
     ax.grid(True, alpha=0.3)
     fig.tight_layout()
     plt.show()
@@ -40,4 +40,3 @@ def main() -> None:
 
 if __name__ == "__main__":
     main()
-
diff --git a/examples/plot_exchange_kernels_radial.py b/examples/plot_exchange_kernels_radial.py
index cb883c4..cc9143c 100644
--- a/examples/plot_exchange_kernels_radial.py
+++ b/examples/plot_exchange_kernels_radial.py
@@ -1,7 +1,7 @@
 """Exchange kernel diagonal elements X_{nnnn}(G) vs |G|ℓ_B.
 
 This example computes selected diagonal components of the exchange kernel
-using the Gauss–Laguerre backend and plots their real parts as a function
+using the Gauss–Legendre backend and plots their real parts as a function
 of |G|ℓ_B.
 """
 from __future__ import annotations
@@ -18,7 +18,7 @@ def main() -> None:
     q = np.linspace(0.2, 4.0, 80)
     theta = np.zeros_like(q)
 
-    X = get_exchange_kernels(q, theta, nmax, method="gausslag")
+    X = get_exchange_kernels(q, theta, nmax, method="gausslegendre")
 
     fig, ax = plt.subplots()
     for n in range(nmax):
@@ -31,7 +31,7 @@ def main() -> None:
 
     ax.set_xlabel(r"$|G| \ell_B$")
     ax.set_ylabel(r"$\mathrm{Re}\,X_{nnnn}(G)$  (κ=1)")
-    ax.set_title("Diagonal exchange kernels (Gauss–Laguerre backend)")
+    ax.set_title("Diagonal exchange kernels (Gauss–Legendre backend)")
     ax.legend()
     ax.grid(True, alpha=0.3)
     fig.tight_layout()
@@ -40,4 +40,3 @@ def main() -> None:
 
 if __name__ == "__main__":
     main()
-
diff --git a/pyproject.toml b/pyproject.toml
index 562b9ab..ff312f5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,8 @@ name = "quantumhall_matrixelements"
 description = "Landau-level plane-wave form factors and exchange kernels for quantum Hall systems."
 readme = "README.md"
 authors = [
-  { name = "Tobias Wolf", email = "public@wolft.xyz" }
+  { name = "Tobias Wolf", email = "public@wolft.xyz" },
+  { name = "Sparsh Mishra" }
 ]
 license = { text = "MIT" }
 requires-python = ">=3.10"
diff --git a/src/quantumhall_matrixelements/__init__.py b/src/quantumhall_matrixelements/__init__.py
index 61ba3ce..6243285 100644
--- a/src/quantumhall_matrixelements/__init__.py
+++ b/src/quantumhall_matrixelements/__init__.py
@@ -10,15 +10,16 @@
 """
 from __future__ import annotations
 
+from importlib.metadata import PackageNotFoundError
+from importlib.metadata import version as _metadata_version
 from typing import TYPE_CHECKING
 
 import numpy as np
-from importlib.metadata import PackageNotFoundError, version as _metadata_version
 
-from .planewave import get_form_factors
-from .exchange_gausslag import get_exchange_kernels_GaussLag
+from .diagnostic import get_exchange_kernels_opposite_field, get_form_factors_opposite_field
 from .exchange_hankel import get_exchange_kernels_hankel
 from .exchange_legendre import get_exchange_kernels_GaussLegendre
+from .planewave import get_form_factors
 
 if TYPE_CHECKING:
     from numpy.typing import NDArray
@@ -28,13 +29,13 @@
 
 
 def get_exchange_kernels(
-    G_magnitudes: "RealArray",
-    G_angles: "RealArray",
+    G_magnitudes: RealArray,
+    G_angles: RealArray,
     nmax: int,
     *,
     method: str | None = None,
     **kwargs,
-) -> "ComplexArray":
+) -> ComplexArray:
     """Dispatcher for exchange kernels.
 
     Parameters
@@ -49,12 +50,12 @@ def get_exchange_kernels(
 
         - ``'gausslegendre'`` (default): Gauss-Legendre quadrature with rational mapping.
           Recommended for all nmax.
-        - ``'gausslag'``: generalized Gauss–Laguerre quadrature.
-          Fast for small nmax (< 10), but unstable for large nmax.
         - ``'hankel'``: Hankel-transform based implementation.
 
     **kwargs :
         Additional arguments passed to the backend (e.g. ``nquad``, ``scale``).
+        Common keywords include ``sign_magneticfield`` (±1) to select the
+        magnetic-field orientation convention.
 
     Notes
     -----
@@ -62,13 +63,11 @@ def get_exchange_kernels(
     physical interaction strength should be applied by the caller.
     """
     chosen = (method or "gausslegendre").strip().lower()
-    if chosen in {"gausslag", "gauss-lag", "gausslaguerre", "gauss-laguerre", "gl"}:
-        return get_exchange_kernels_GaussLag(G_magnitudes, G_angles, nmax, **kwargs)
     if chosen in {"hankel", "hk"}:
         return get_exchange_kernels_hankel(G_magnitudes, G_angles, nmax, **kwargs)
     if chosen in {"gausslegendre", "gauss-legendre", "legendre", "leg"}:
         return get_exchange_kernels_GaussLegendre(G_magnitudes, G_angles, nmax, **kwargs)
-    raise ValueError(f"Unknown exchange-kernel method: {method!r}. Use 'gausslegendre', 'gausslag', or 'hankel'.")
+    raise ValueError(f"Unknown exchange-kernel method: {method!r}. Use 'gausslegendre' or 'hankel'.")
 
 
 try:
@@ -81,7 +80,6 @@ def get_exchange_kernels(
 __all__ = [
     "get_form_factors",
     "get_exchange_kernels",
-    "get_exchange_kernels_GaussLag",
     "get_exchange_kernels_hankel",
     "get_exchange_kernels_GaussLegendre",
     "__version__",
diff --git a/src/quantumhall_matrixelements/_debug_symmetry.py b/src/quantumhall_matrixelements/_debug_symmetry.py
deleted file mode 100644
index 04085e5..0000000
--- a/src/quantumhall_matrixelements/_debug_symmetry.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Debug helper to print exchange-kernel symmetry deviations.
-
-This is not part of the public API; it exists only to make it easy to
-inspect symmetry errors for different backends via
-
-    python -m quantumhall_matrixelements._debug_symmetry
-"""
-from __future__ import annotations
-
-import numpy as np
-
-from . import get_exchange_kernels
-
-
-def main() -> None:
-    nmax = 2
-    Gs_dimless = np.array([0.0, 1.0, 1.0])
-    thetas = np.array([0.0, 0.0, np.pi])
-    thetas_minus = (thetas + np.pi) % (2 * np.pi)
-
-    methods = ["gausslag", "hankel"]
-
-    for method in methods:
-        print(f"=== method={method} ===")
-        X_G = get_exchange_kernels(Gs_dimless, thetas, nmax, method=method)
-        X_mG = get_exchange_kernels(Gs_dimless, thetas_minus, nmax, method=method)
-
-        expected_mG = np.transpose(X_G, (0, 3, 4, 1, 2)).conj()
-        diff_G_to_mG = float(np.max(np.abs(X_mG - expected_mG)))
-        print(f"max |X(-G) - (X^T(G))†| = {diff_G_to_mG:.3e}")
-
-        idx = np.arange(nmax)
-        N = (
-            idx[:, None, None, None]
-            - idx[None, :, None, None]
-            - idx[None, None, :, None]
-            + idx[None, None, None, :]
-        )
-        phase = (-1.0) ** np.abs(N)
-        expected_internal = phase[None, ...] * expected_mG
-        diff_internal = float(np.max(np.abs(X_G - expected_internal)))
-        print(
-            "max |X(G) - (-1)^|N| (X^T(G))†| = "
-            f"{diff_internal:.3e}",
-        )
-
-
-if __name__ == "__main__":  # pragma: no cover - manual debug entry point
-    main()
-
diff --git a/src/quantumhall_matrixelements/diagnostic.py b/src/quantumhall_matrixelements/diagnostic.py
index 2f93a4c..917be85 100644
--- a/src/quantumhall_matrixelements/diagnostic.py
+++ b/src/quantumhall_matrixelements/diagnostic.py
@@ -5,8 +5,6 @@
 
 import numpy as np
 
-from . import get_exchange_kernels
-
 if TYPE_CHECKING:  # pragma: no cover - aliases only
     from numpy.typing import NDArray
 
@@ -14,13 +12,57 @@
     ComplexArray = NDArray[np.complex128]
 
 __all__ = [
+    "get_form_factors_opposite_field",
+    "get_exchange_kernels_opposite_field",
     "verify_exchange_kernel_symmetries",
 ]
 
 
+def get_form_factors_opposite_field(F: ComplexArray) -> ComplexArray:
+    """Transform form factors to the opposite magnetic-field sign (σ→-σ).
+
+    Parameters
+    ----------
+    F : (nG, nmax, nmax) complex array
+        Form factors for ``sign_magneticfield = -1``.
+
+    Returns
+    -------
+    ComplexArray
+        Form factors for ``sign_magneticfield = +1`` obtained via conjugation
+        and the standard phase flip.
+    """
+
+    nmax = F.shape[1]
+    idx = np.arange(nmax)
+    phase = np.where((idx[:, None] - idx[None, :]) % 2 == 0, 1.0, -1.0)
+    return np.conj(F) * phase
+
+
+def get_exchange_kernels_opposite_field(Xs: ComplexArray) -> ComplexArray:
+    """Transform exchange kernels to the opposite magnetic-field sign (σ→-σ).
+
+    Parameters
+    ----------
+    Xs : (nG, nmax, nmax, nmax, nmax) complex array
+        Exchange kernels for ``sign_magneticfield = -1``.
+
+    Returns
+    -------
+    ComplexArray
+        Exchange kernels for ``sign_magneticfield = +1``.
+    """
+
+    nmax = Xs.shape[1]
+    idx = np.arange(nmax)
+    phase = np.where((idx[:, None] - idx[None, :]) % 2 == 0, 1.0, -1.0)
+    phase = phase[:, :, None, None] * phase[None, None, :, :]
+    return np.conj(Xs) * phase
+
+
 def verify_exchange_kernel_symmetries(
-    G_magnitudes: "RealArray",
-    G_angles: "RealArray",
+    G_magnitudes: RealArray,
+    G_angles: RealArray,
     nmax: int,
     rtol: float = 1e-7,
     atol: float = 1e-9,
@@ -37,14 +79,17 @@ def verify_exchange_kernel_symmetries(
 
     i.e. in array form (Xs[g, n1, m1, n2, m2]):
 
-      Xs[g, n1, m1, n2, m2]_(-G) = Xs[g, m2, n2, m1, n1]_G^*.
+      Xs[g, n1, m1, n2, m2]_(-G) = Xs[g, n2, m2, n1, m1]_G^*.
     """
+    # Lazy import to avoid circular dependency when module is imported from __init__
+    from . import get_exchange_kernels
+
     Xs_G = get_exchange_kernels(G_magnitudes, G_angles, nmax)
     G_angles_minus = (G_angles + np.pi) % (2 * np.pi)
     Xs_minusG = get_exchange_kernels(G_magnitudes, G_angles_minus, nmax)
 
     # expected_minus[g, n1, m1, n2, m2] = X_G[g, m2, n2, m1, n1]^*
-    expected_Xs_minusG = np.transpose(Xs_G, (0, 4, 3, 2, 1)).conj()
+    expected_Xs_minusG = np.transpose(Xs_G, (0, 3, 4, 1, 2)).conj()
     if not np.allclose(Xs_minusG, expected_Xs_minusG, rtol=rtol, atol=atol):
         diff = float(np.max(np.abs(Xs_minusG - expected_Xs_minusG)))
         raise AssertionError(
diff --git a/src/quantumhall_matrixelements/exchange_gausslag.py b/src/quantumhall_matrixelements/exchange_gausslag.py
deleted file mode 100644
index 4bca240..0000000
--- a/src/quantumhall_matrixelements/exchange_gausslag.py
+++ /dev/null
@@ -1,170 +0,0 @@
-"""Exchange kernels via generalized Gauss–Laguerre quadrature."""
-from __future__ import annotations
-
-from functools import lru_cache
-from typing import TYPE_CHECKING
-
-import numpy as np
-import scipy.special as sps
-
-if TYPE_CHECKING:
-    from numpy.typing import NDArray
-
-    ComplexArray = NDArray[np.complex128]
-    RealArray = NDArray[np.float64]
-
-
-def _N_order(n1: int, m1: int, n2: int, m2: int) -> int:
-    return (n1 - m1) - (m2 - n2)
-
-
-def _parity_factor(N: int) -> int:
-    """(-1)^((N+|N|)/2) → (-1)^N for N>=0, and 1 for N<0."""
-    return (-1) ** ((N + abs(N)) // 2)
-
-
-@lru_cache(maxsize=None)
-def _lag_nodes_weights(nquad: int, alpha: float):
-    """Generalized Gauss–Laguerre nodes/weights for ∫_0^∞ e^{-z} z^α f(z) dz."""
-    x, w = sps.roots_genlaguerre(nquad, alpha)
-    return x, w
-
-
-@lru_cache(maxsize=None)
-def _logfact(n: int) -> float:
-    return float(sps.gammaln(n + 1))
-
-
-def _C_and_indices(n1: int, m1: int, n2: int, m2: int):
-    """Constants and Laguerre parameters for f_{n1,m1} * f_{m2,n2}."""
-    p, d1 = min(n1, m1), abs(n1 - m1)
-    q, d2 = min(m2, n2), abs(m2 - n2)
-    logC = 0.5 * ((_logfact(p) - _logfact(p + d1)) + (_logfact(q) - _logfact(q + d2)))
-    C = np.exp(logC)
-    return C, p, d1, q, d2
-
-
-_L_cache: dict[tuple[int, int, float, int], np.ndarray] = {}
-
-
-def _laguerre_on_grid(p: int, d: int, alpha: float, nquad: int, z):
-    key = (p, d, float(alpha), int(nquad))
-    L = _L_cache.get(key)
-    if L is None:
-        L = sps.eval_genlaguerre(p, d, z)
-        _L_cache[key] = L
-    return L
-
-
-def get_exchange_kernels_GaussLag(
-    G_magnitudes,
-    G_angles,
-    nmax: int,
-    *,
-    potential: str | callable = "coulomb",
-    kappa: float = 1.0,
-    nquad: int = 200,
-    ell: float = 1.0,
-) -> "ComplexArray":
-    """Compute X_{n1,m1,n2,m2}(G) using analytic angle and Gauss–Laguerre radial quadrature.
-
-    Parameters
-    ----------
-    G_magnitudes, G_angles :
-        Arrays of the same shape describing |G| and polar angle θ_G.
-    nmax :
-        Number of Landau levels.
-    potential :
-        ``'coulomb'`` (default), ``'constant'``, or a callable ``V(q)`` giving
-        the interaction in 1/ℓ units.
-    kappa :
-        Interaction strength prefactor. For Coulomb this corresponds to
-        :math:`\\kappa = e^2/(\\varepsilon\\ell_B)/\\hbar\\omega_c`.
-    nquad :
-        Number of Gauss–Laguerre quadrature points.
-    ell :
-        Magnetic length ℓ_B (default 1.0); |G| is interpreted in 1/ℓ_B units.
-
-    Returns
-    -------
-    Xs : (nG, nmax, nmax, nmax, nmax) complex array
-        Exchange kernels normalized with the chosen kappa.
-    """
-    G_magnitudes = np.asarray(G_magnitudes, dtype=float)
-    G_angles = np.asarray(G_angles, dtype=float)
-    if G_magnitudes.shape != G_angles.shape:
-        raise ValueError("G_magnitudes and G_angles must have the same shape.")
-    nG = G_magnitudes.size
-
-    # Resolve potential
-    if callable(potential):
-        pot_kind = "callable"
-        pot_fn = potential
-    else:
-        pot_kind = str(potential).strip().lower()
-        pot_fn = None
-
-    if pot_kind in {"coulomb", "constant"}:
-        pass
-    elif pot_kind == "callable":
-        pass
-    else:
-        raise ValueError("potential must be 'coulomb', 'constant', or a callable V(q).")
-
-    Gscaled = G_magnitudes * float(ell)
-    Xs = np.zeros((nG, nmax, nmax, nmax, nmax), dtype=np.complex128)
-
-    J_cache: dict[tuple[int, float], np.ndarray] = {}
-
-    for n1 in range(nmax):
-        for m1 in range(nmax):
-            for n2 in range(nmax):
-                for m2 in range(nmax):
-                    N = _N_order(n1, m1, n2, m2)
-                    absN = abs(N)
-                    C, p, d1, q, d2 = _C_and_indices(n1, m1, n2, m2)
-
-                    if potential == "coulomb":
-                        alpha = 0.5 * (d1 + d2 - 1)
-                        if alpha <= -1:
-                            raise ValueError(f"Invalid alpha={alpha} for Coulomb case.")
-                        z, w = _lag_nodes_weights(nquad, alpha)
-                        L1 = _laguerre_on_grid(p, d1, alpha, nquad, z)
-                        L2 = _laguerre_on_grid(q, d2, alpha, nquad, z)
-                        W = w * L1 * L2
-                        key = (absN, float(alpha))
-                        J_abs = J_cache.get(key)
-                        if J_abs is None:
-                            arg = np.sqrt(2.0 * z)[None, :] * Gscaled[:, None]
-                            J_abs = sps.jv(absN, arg)
-                            J_cache[key] = J_abs
-                        signN = _parity_factor(N)
-                        radial = (signN * J_abs) @ W
-                        phase_factor = (1j) ** (d1 - d2)
-                        pref = (kappa * C / np.sqrt(2.0)) * phase_factor
-                    else:
-                        alpha = 0.5 * (d1 + d2)
-                        z, w = _lag_nodes_weights(nquad, alpha)
-                        L1 = _laguerre_on_grid(p, d1, alpha, nquad, z)
-                        L2 = _laguerre_on_grid(q, d2, alpha, nquad, z)
-                        qvals = np.sqrt(2.0 * z) / float(ell)
-                        Veff = pot_fn(qvals) / (2.0 * np.pi * float(ell) ** 2)
-                        W = w * L1 * L2 * Veff
-                        key = (absN, float(alpha))
-                        J_abs = J_cache.get(key)
-                        if J_abs is None:
-                            arg = np.sqrt(2.0 * z)[None, :] * Gscaled[:, None]
-                            J_abs = sps.jv(absN, arg)
-                            J_cache[key] = J_abs
-                        signN = _parity_factor(N)
-                        radial = (signN * J_abs) @ W
-                        phase_factor = (1j) ** (d1 - d2)
-                        pref = C * phase_factor
-
-                    phase = np.exp(-1j * N * G_angles)
-                    Xs[:, n1, m1, n2, m2] = (pref * phase) * radial
-
-    return Xs
-
-
-__all__ = ["get_exchange_kernels_GaussLag"]
diff --git a/src/quantumhall_matrixelements/exchange_hankel.py b/src/quantumhall_matrixelements/exchange_hankel.py
index 79bbc08..b69724b 100644
--- a/src/quantumhall_matrixelements/exchange_hankel.py
+++ b/src/quantumhall_matrixelements/exchange_hankel.py
@@ -1,13 +1,15 @@
 """Exchange kernels via Hankel transforms."""
 from __future__ import annotations
 
-from functools import lru_cache
+from functools import cache
 from typing import TYPE_CHECKING
 
 import numpy as np
 from hankel import HankelTransform
 from scipy.special import genlaguerre, rgamma
 
+from .diagnostic import get_exchange_kernels_opposite_field
+
 if TYPE_CHECKING:
     from numpy.typing import NDArray
 
@@ -16,14 +18,12 @@
 
 
 def _N_order(n1: int, m1: int, n2: int, m2: int) -> int:
-    return (n1 - m1) - (m2 - n2)
-
+    return ((n1 - m1) + (m2 - n2))
 
 def _parity_factor(N: int) -> int:
-    return (-1) ** ((N + abs(N)) // 2)
+    return (-1) ** ((N - abs(N)) // 2)
 
-
-@lru_cache(maxsize=None)
+@cache
 def _get_hankel_transformer(order: int) -> HankelTransform:
     """Cached HankelTransform instance for a given Bessel order."""
     return HankelTransform(nu=order, N=6000, h=7e-6)
@@ -72,18 +72,19 @@ def _radial_exchange_integrand_rgamma(
 
 
 def get_exchange_kernels_hankel(
-    G_magnitudes: "RealArray",
-    G_angles: "RealArray",
+    G_magnitudes: RealArray,
+    G_angles: RealArray,
     nmax: int,
     *,
     potential: str | callable = "coulomb",
     kappa: float = 1.0,
-) -> "ComplexArray":
+    sign_magneticfield: int = -1,
+) -> ComplexArray:
     """Compute X_{n1,m1,n2,m2}(G) via Hankel transforms (κ=1 convention).
 
     This backend parametrizes the radial integral via Hankel transforms with
     robust Laguerre-based normalization and explicit control over the Bessel
-    order. It is numerically more intensive than the Gauss–Laguerre backend
+    order. It is numerically more intensive than the Gauss–Legendre backend
     but can be useful for cross-checks or alternative potentials.
 
     Parameters
@@ -97,7 +98,15 @@ def get_exchange_kernels_hankel(
         the interaction in 1/ℓ units.
     kappa :
         Prefactor for Coulomb/constant cases.
+    sign_magneticfield :
+        Sign of the charge–field product σ = sgn(q B_z). ``-1`` matches the
+        package's internal convention; ``+1`` returns the kernels for the
+        opposite sign by applying the appropriate complex conjugation and
+        phase factors.
     """
+    if sign_magneticfield not in (1, -1):
+        raise ValueError("sign_magneticfield must be 1 or -1")
+
     G_magnitudes = np.asarray(G_magnitudes, dtype=float)
     G_angles = np.asarray(G_angles, dtype=float)
     if G_magnitudes.shape != G_angles.shape:
@@ -130,9 +139,10 @@ def get_exchange_kernels_hankel(
         phase = -N * G_angles
         phase_by_N[int(N)] = (np.cos(phase) + 1j * np.sin(phase)) * _parity_factor(int(N))
 
-    # Small lookup for internal (i)^(d1-d2), indexed by d1,d2 in [0..nmax-1]
+    # Small lookup for internal (i)^(d1+d2), indexed by d1,d2 in [0..nmax-1]
     d_vals = np.arange(nmax, dtype=int)
-    phase_internal_table = (1j) ** (d_vals[:, None] - d_vals[None, :])  # (nmax,nmax)
+    phase_internal_table = (1j) ** (d_vals[:, None] + d_vals[None, :])  # (nmax,nmax)
+
     d_lookup = np.abs(np.subtract.outer(np.arange(nmax), np.arange(nmax)))  # (nmax,nmax)
     # Precompute abs diffs (d) and mins (Nmin) for quick indexing
     d_mat = d_lookup  # alias
@@ -184,7 +194,13 @@ def integrand(q):
                     # Angular/internal phases
                     phase_internal = phase_internal_table[d1, d2]
                     phase_angle = phase_by_N[N]
-                    Xs[:, n1, m1, n2, m2] = phase_internal * phase_angle * X_radial
+                    extra_sgn = (-1)**(n2-m2)
+
+                    Xs[:, n1, m1, n2, m2] = phase_internal * phase_angle * X_radial * extra_sgn
+
+
+    if sign_magneticfield == 1:
+        Xs = get_exchange_kernels_opposite_field(Xs)
 
     return Xs
 
diff --git a/src/quantumhall_matrixelements/exchange_legendre.py b/src/quantumhall_matrixelements/exchange_legendre.py
index a531703..85e6bca 100644
--- a/src/quantumhall_matrixelements/exchange_legendre.py
+++ b/src/quantumhall_matrixelements/exchange_legendre.py
@@ -1,7 +1,7 @@
 """Exchange kernels via Gauss-Legendre quadrature with rational mapping."""
 from __future__ import annotations
 
-from functools import lru_cache
+from functools import cache
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -14,31 +14,18 @@
     ComplexArray = NDArray[np.complex128]
     RealArray = NDArray[np.float64]
 
-
-def _N_order(n1: int, m1: int, n2: int, m2: int) -> int:
-    return (n1 - m1) - (m2 - n2)
+from .diagnostic import get_exchange_kernels_opposite_field
 
 
 def _parity_factor(N: int) -> int:
-    """(-1)^((N+|N|)/2) → (-1)^N for N>=0, and 1 for N<0."""
-    return (-1) ** ((N + abs(N)) // 2)
-
+    """(-1)^((N-|N|)/2) → (-1)^N for N<0, and 1 for N>=0."""
+    return (-1) ** ((N - abs(N)) // 2)
 
-@lru_cache(maxsize=None)
+@cache
 def _logfact(n: int) -> float:
     return float(sps.gammaln(n + 1))
 
-
-def _C_and_indices(n1: int, m1: int, n2: int, m2: int):
-    """Constants and Laguerre parameters for f_{n1,m1} * f_{m2,n2}."""
-    p, d1 = min(n1, m1), abs(n1 - m1)
-    q, d2 = min(m2, n2), abs(m2 - n2)
-    logC = 0.5 * ((_logfact(p) - _logfact(p + d1)) + (_logfact(q) - _logfact(q + d2)))
-    C = np.exp(logC)
-    return C, p, d1, q, d2
-
-
-@lru_cache(maxsize=None)
+@cache
 def _legendre_nodes_weights_mapped(nquad: int, scale: float):
     """
     Gauss-Legendre nodes/weights mapped from [-1, 1] to [0, inf).
@@ -51,7 +38,6 @@ def _legendre_nodes_weights_mapped(nquad: int, scale: float):
     w = w_leg * (scale * 2.0 / (denom * denom))
     return z, w
 
-
 def get_exchange_kernels_GaussLegendre(
     G_magnitudes,
     G_angles,
@@ -59,49 +45,93 @@ def get_exchange_kernels_GaussLegendre(
     *,
     potential: str | callable = "coulomb",
     kappa: float = 1.0,
-    nquad: int = 1000,
+    nquad: int = 8000,
     scale: float = 0.5,
     ell: float = 1.0,
-) -> "ComplexArray":
-    """Compute X_{n1,m1,n2,m2}(G) using Gauss-Legendre quadrature with rational mapping.
+    sign_magneticfield: int = -1,
+) -> ComplexArray:
+    """Compute exchange kernels X_{n1,m1,n2,m2}(G) using Gauss-Legendre quadrature.
 
-    This backend maps the semi-infinite radial integral to the finite interval [-1, 1]
-    using the rational mapping z = scale * (1+x)/(1-x). It avoids the numerical instability
-    of Gauss-Laguerre quadrature for large quantum numbers while remaining faster than
-    Hankel transforms.
+    This function evaluates the exchange matrix elements for a 2D electron gas
+    in a magnetic field, using Gauss-Legendre quadrature with a rational mapping
+    from [-1, 1] to [0, ∞). The implementation exploits index-exchange symmetry
+    to reduce computation time.
 
     Parameters
     ----------
-    G_magnitudes, G_angles :
-        Arrays of the same shape describing |G| and polar angle θ_G.
-    nmax :
-        Number of Landau levels.
-    potential :
-        ``'coulomb'`` (default) or a callable ``V(q)`` returning the interaction.
-    kappa :
-        Interaction strength prefactor.
-    nquad :
-        Number of quadrature points (default 1000).
-    scale :
-        Mapping scale factor (default 0.5). Controls the distribution of points.
-        Smaller values cluster points near the peak of the integrand for large n.
-    ell :
-        Magnetic length ℓ_B (default 1.0).
+    G_magnitudes : array_like of float
+        Magnitudes |G| of the reciprocal lattice vectors, convertible to a
+        NumPy array of floats. Shape determines the output's leading dimension.
+    G_angles : array_like of float
+        Polar angles θ_G of the reciprocal lattice vectors in radians,
+        convertible to a NumPy array of floats. Must have the same shape as
+        ``G_magnitudes``.
+    nmax : int
+        Number of Landau levels to include in the calculation. Output arrays
+        will have dimensions ``(nG, nmax, nmax, nmax, nmax)``.
+    potential : str or callable, optional
+        Interaction potential. Use ``'coulomb'`` (default) for Coulomb
+        interaction, or provide a callable ``V(q)`` that takes momentum
+        magnitude (in units of 1/ℓ) and returns the interaction strength.
+    kappa : float, optional
+        Prefactor for the Coulomb potential. Default is 1.0.
+    nquad : int, optional
+        Number of quadrature points for the Gauss-Legendre integration.
+        Default is 8000. Higher values improve accuracy at computational cost.
+    scale : float, optional
+        Scale parameter for the rational mapping z = scale * (1+x)/(1-x).
+        Default is 0.5. Adjust to optimize convergence for different potentials.
+    ell : float, optional
+        Magnetic length in the same units as G_magnitudes. Default is 1.0.
+    sign_magneticfield : int, optional
+        Sign of the charge–field product σ = sgn(q B_z). Must be -1 or +1.
+        Default is -1 (internal convention). Use +1 to obtain kernels for
+        the opposite magnetic field orientation via conjugation and phase
+        factors.
 
     Returns
     -------
-    Xs : (nG, nmax, nmax, nmax, nmax) complex array
+    numpy.ndarray of numpy.complex128
+        Exchange kernels with shape ``(nG, nmax, nmax, nmax, nmax)``, where
+        ``nG`` is the number of G-vectors. The array element
+        ``Xs[g, n1, m1, n2, m2]`` gives the exchange matrix element
+        X_{n1,m1,n2,m2}(G_g).
+
+    Raises
+    ------
+    ValueError
+        If ``G_magnitudes`` and ``G_angles`` have different shapes, if
+        ``potential`` is not ``'coulomb'`` or a callable, or if a callable
+        potential returns an array with shape different from ``(nquad,)``.
+
+    Notes
+    -----
+    **Optimizations:**
+
+    - Precomputes Laguerre polynomials L_p^d(z) once for all (n, m) pairs.
+    - Precomputes z^α for all possible values of d1 + d2.
+    - Groups index quadruples by Bessel order N and evaluates integrals in
+      batched matrix operations.
+    - Exploits index-exchange symmetry (valid for any V(q)):
+      ``X[m2, n2, m1, n1] = (-1)^((n1-m1)-(n2-m2)) * X[n1, m1, n2, m2]``
+      to fill symmetric partners without additional O(nmax^4) loops.
     """
+
+    # -----------------------------
+    # 0. Input handling
+    # -----------------------------
+    if sign_magneticfield not in (1, -1):
+        raise ValueError("sign_magneticfield must be 1 or -1")
+
     G_magnitudes = np.asarray(G_magnitudes, dtype=float)
     G_angles = np.asarray(G_angles, dtype=float)
     if G_magnitudes.shape != G_angles.shape:
         raise ValueError("G_magnitudes and G_angles must have the same shape.")
     nG = G_magnitudes.size
 
-    Gscaled = G_magnitudes * float(ell)
-    Xs = np.zeros((nG, nmax, nmax, nmax, nmax), dtype=np.complex128)
-
-    # Resolve potential
+    # -----------------------------
+    # 1. Potential choice
+    # -----------------------------
     if callable(potential):
         pot_kind = "callable"
         pot_fn = potential
@@ -109,87 +139,209 @@ def get_exchange_kernels_GaussLegendre(
         pot_kind = str(potential).strip().lower()
         pot_fn = None
 
-    if pot_kind == "coulomb":
-        pass
-    elif pot_kind == "callable":
-        pass
-    else:
+    if pot_kind not in ("coulomb", "callable"):
         raise ValueError("potential must be 'coulomb' or a callable V(q).")
+    is_coulomb = (pot_kind == "coulomb")
 
-    # Get mapped grid
+    # -----------------------------
+    # 2. Quadrature grid and z-dependent stuff
+    # -----------------------------
     z, w = _legendre_nodes_weights_mapped(nquad, scale)
+    z = np.asarray(z, dtype=np.float64)
+    w = np.asarray(w, dtype=np.float64)
 
-    # Precompute Bessel functions J_N(sqrt(2z)*G)
-    # We cache by absN to avoid recomputing
-    J_cache: dict[int, np.ndarray] = {}
+    exp_minus_z = np.exp(-z)               # (nquad,)
+    sqrt2z = np.sqrt(2.0 * z)              # (nquad,)
+
+    # Bessel argument (shared for all orders)
+    Gscaled = G_magnitudes * float(ell)    # (nG,)
+    arg = Gscaled[:, None] * sqrt2z[None, :]  # (nG, nquad)
+
+    # Callable potential: evaluated once on the quadrature grid
+    if not is_coulomb:
+        qvals = sqrt2z / float(ell)        # (nquad,)
+        Veff = pot_fn(qvals) / (2.0 * np.pi * float(ell) ** 2)
+        Veff = np.asarray(Veff)
+        if Veff.shape != z.shape:
+            raise ValueError("Callable potential must return array of shape (nquad,)")
+
+    # -----------------------------
+    # 3. Per-(n,m) combinatorics
+    # -----------------------------
+    idx = np.arange(nmax, dtype=int)
+    n_idx, m_idx = np.meshgrid(idx, idx, indexing="ij")
+
+    # p = min(n,m), d = |n-m|, D = n-m
+    p_nm = np.minimum(n_idx, m_idx)
+    d_nm = np.abs(n_idx - m_idx)
+    D_nm = n_idx - m_idx
+
+    # (-1)^(n-m) done via parity bits (no pow on negatives)
+    extra_sign_nm = 1 - 2 * ((n_idx - m_idx) & 1)  # shape (nmax, nmax)
+
+    # C_nm[n,m] = sqrt(p! / (p + d)!)
+    C_nm = np.empty((nmax, nmax), dtype=np.float64)
+    for n in range(nmax):
+        for m in range(nmax):
+            p = int(p_nm[n, m])
+            d = int(d_nm[n, m])
+            logC = 0.5 * (_logfact(p) - _logfact(p + d))
+            C_nm[n, m] = np.exp(logC)
+
+    # -----------------------------
+    # 4. Laguerre polynomials L_p^d(z) cached by (p,d)
+    # -----------------------------
+    laguerre_cache: dict[tuple[int, int], np.ndarray] = {}
+    L_nm = np.empty((nmax, nmax), dtype=object)
+    for n in range(nmax):
+        for m in range(nmax):
+            p = int(p_nm[n, m])
+            d = int(d_nm[n, m])
+            key = (p, d)
+            if key not in laguerre_cache:
+                laguerre_cache[key] = sps.eval_genlaguerre(p, d, z)
+            L_nm[n, m] = laguerre_cache[key]
+
+    # -----------------------------
+    # 5. Powers z^alpha for all possible d1 + d2
+    # -----------------------------
+    max_d_sum = 2 * (nmax - 1)
+    z_pows: list[np.ndarray] = []
+    if is_coulomb:
+        # alpha = (d1 + d2 - 1) / 2
+        for ds in range(max_d_sum + 1):
+            alpha = 0.5 * (ds - 1)
+            z_pows.append(z ** alpha)
+    else:
+        # alpha = (d1 + d2) / 2
+        for ds in range(max_d_sum + 1):
+            alpha = 0.5 * ds
+            z_pows.append(z ** alpha)
 
-    # Cache for Laguerre evaluations
-    # We evaluate L_n^d(z) for many n, d.
-    # Since n, d are small integers, we can just compute on the fly or use sps.eval_genlaguerre
-    # sps.eval_genlaguerre is efficient enough.
+    # -----------------------------
+    # 6. N-related: parity, plane-wave phase, Bessel cache
+    # -----------------------------
+    maxD = 2 * (nmax - 1)
+    Ns = np.arange(-maxD, maxD + 1, dtype=int)
+    minN = Ns[0]
 
+    parity = np.array([_parity_factor(int(N)) for N in Ns], dtype=int)
+    phase_table = np.exp(-1j * Ns[:, None] * G_angles[None, :])  # (2*maxD+1, nG)
+
+    # (1j)^(d1 + d2) for all possible sums
+    phase_power = np.array([1j ** k for k in range(max_d_sum + 1)], dtype=np.complex128)
+
+    # Group quadruples by N, but only for canonical pairs (n1,m1) <= (m2,n2)
+    buckets: dict[int, list[tuple[int, int, int, int]]] = {int(N): [] for N in Ns}
     for n1 in range(nmax):
         for m1 in range(nmax):
+            D1 = D_nm[n1, m1]
+            pair1 = n1 * nmax + m1          # "pair" index for (n1,m1)
             for n2 in range(nmax):
                 for m2 in range(nmax):
-                    N = _N_order(n1, m1, n2, m2)
-                    absN = abs(N)
-                    C, p, d1, q, d2 = _C_and_indices(n1, m1, n2, m2)
-
-                    # Compute radial integral
-                    if potential == "coulomb":
-                        # Integrand factor: exp(-z) * z^alpha * L * L * J
-                        # alpha = (d1 + d2 - 1) / 2
-                        alpha = 0.5 * (d1 + d2 - 1)
-                        
-                        L1 = sps.eval_genlaguerre(p, d1, z)
-                        L2 = sps.eval_genlaguerre(q, d2, z)
-                        
-                        # Bessel part
-                        if absN not in J_cache:
-                            arg = np.sqrt(2.0 * z)[None, :] * Gscaled[:, None]
-                            J_cache[absN] = sps.jv(absN, arg)
-                        J_abs = J_cache[absN]
-                        
-                        # Full integrand term (excluding J and weights)
-                        # exp(-z) handles x->1 decay
-                        # z^alpha handles x->-1 behavior
-                        term = np.exp(-z) * (z**alpha) * L1 * L2
-                        
-                        # Sum over quadrature points
-                        # J_abs is (nG, nquad), term is (nquad,), w is (nquad,)
-                        # Result is (nG,)
-                        radial = (J_abs * term) @ w
-                        
-                        signN = _parity_factor(N)
-                        phase_factor = (1j) ** (d1 - d2)
-                        pref = (kappa * C / np.sqrt(2.0)) * phase_factor
-                        
-                    else:
-                        # General/callable potential
-                        alpha = 0.5 * (d1 + d2)
-                        L1 = sps.eval_genlaguerre(p, d1, z)
-                        L2 = sps.eval_genlaguerre(q, d2, z)
-                        
-                        qvals = np.sqrt(2.0 * z) / float(ell)
-                        Veff = pot_fn(qvals) / (2.0 * np.pi * float(ell) ** 2)
-                        
-                        if absN not in J_cache:
-                            arg = np.sqrt(2.0 * z)[None, :] * Gscaled[:, None]
-                            J_cache[absN] = sps.jv(absN, arg)
-                        J_abs = J_cache[absN]
-                        
-                        term = np.exp(-z) * (z**alpha) * L1 * L2 * Veff
-                        radial = (J_abs * term) @ w
-                        
-                        signN = _parity_factor(N)
-                        phase_factor = (1j) ** (d1 - d2)
-                        pref = C * phase_factor
-
-                    phase = np.exp(-1j * N * G_angles)
-                    Xs[:, n1, m1, n2, m2] = (pref * phase) * (signN * radial)
+                    pair2 = m2 * nmax + n2  # "pair" index for (m2,n2)
+                    if pair1 > pair2:
+                        # Non-canonical representative; its partner will be filled by symmetry
+                        continue
+                    # second physical pair is (m2, n2)
+                    D2 = D_nm[m2, n2]       # (m2 - n2)
+                    # N = (n1 - m1) + (m2 - n2) = D1 + D2
+                    N = int(D1 + D2)
+                    buckets[N].append((n1, m1, n2, m2))
+
+    # Bessel J_|N|(arg) cache
+    J_cache: dict[int, np.ndarray] = {}
+
+    # Output array
+    Xs = np.zeros((nG, nmax, nmax, nmax, nmax), dtype=np.complex128)
+    sqrt2 = np.sqrt(2.0)
+
+    # -----------------------------
+    # 7. Main loop over N buckets
+    # -----------------------------
+    for N in Ns:
+        quad_list = buckets[int(N)]
+        if not quad_list:
+            continue
+
+        absN = abs(int(N))
+        if absN not in J_cache:
+            J_cache[absN] = sps.jv(absN, arg)  # (nG, nquad)
+        J_abs = J_cache[absN]
+
+        N_idx = int(N - minN)
+        signN = parity[N_idx]
+        phase_N = phase_table[N_idx]          # (nG,)
+
+        terms = []        # list of (nquad,) arrays
+        coeffs = []       # scalar prefactors
+        extra_sgns = []   # (-1)^{n2 - m2}
+        quads = []        # store quadruples in this bucket
+
+        for (n1, m1, n2, m2) in quad_list:
+            # d1,d2 and Laguerres for first pair (n1,m1) and second phys. pair (m2,n2)
+            d1 = int(d_nm[n1, m1])
+            d2 = int(d_nm[m2, n2])
+            ds = d1 + d2
+
+            L1 = L_nm[n1, m1]
+            L2 = L_nm[m2, n2]
+            z_alpha = z_pows[ds]
+
+            if is_coulomb:
+                term = exp_minus_z * z_alpha * L1 * L2
+            else:
+                term = exp_minus_z * z_alpha * L1 * L2 * Veff
+            terms.append(term)
+
+            C1 = C_nm[n1, m1]
+            C2 = C_nm[m2, n2]
+            phase_factor = phase_power[ds]
+
+            if is_coulomb:
+                pref = (kappa * C1 * C2 / sqrt2) * phase_factor
+            else:
+                pref = (C1 * C2) * phase_factor
+
+            coeffs.append(pref)
+            # extra sign is (-1)^(n2 - m2)
+            extra_sgns.append(extra_sign_nm[n2, m2])
+            quads.append((n1, m1, n2, m2))
+
+        if not terms:
+            continue
+
+        # Stack into a single matrix: T has shape (nquad, nQ_N)
+        T = np.stack(terms, axis=1)                 # (nquad, nQ_N)
+        # big batched integral: (nG, nquad) @ (nquad, nQ_N) -> (nG, nQ_N)
+        radial_all = (J_abs * w[None, :]) @ T       # (nG, nQ_N)
+
+        coeffs = np.asarray(coeffs, dtype=np.complex128)       # (nQ_N,)
+        extra_sgns = np.asarray(extra_sgns, dtype=np.int8)     # (nQ_N,)
+        scalar_all = coeffs * extra_sgns * signN               # (nQ_N,)
+
+        # tmp[:, q] = phase_N * scalar_all[q] * radial_all[:, q]
+        tmp = phase_N[:, None] * radial_all * scalar_all[None, :]  # (nG, nQ_N)
+
+        # Scatter into Xs, and fill symmetric partner on the fly
+        for iq, (n1, m1, n2, m2) in enumerate(quads):
+            val = tmp[:, iq]
+            Xs[:, n1, m1, n2, m2] = val
+
+            # symmetric partner: (m2, n2, m1, n1)
+            pair1 = n1 * nmax + m1
+            pair2 = m2 * nmax + n2
+            if pair1 < pair2:
+                # sign = (-1)**((n1 - m1) - (n2 - m2)) via parity bits
+                delta = (n1 - m1) - (n2 - m2)
+                sign = 1 if (delta & 1) == 0 else -1
+                Xs[:, m2, n2, m1, n1] = sign * val
+
+    if sign_magneticfield == 1:
+        Xs = get_exchange_kernels_opposite_field(Xs)
 
     return Xs
 
 
+
 __all__ = ["get_exchange_kernels_GaussLegendre"]
diff --git a/src/quantumhall_matrixelements/planewave.py b/src/quantumhall_matrixelements/planewave.py
index 717d5af..92845db 100644
--- a/src/quantumhall_matrixelements/planewave.py
+++ b/src/quantumhall_matrixelements/planewave.py
@@ -6,6 +6,8 @@
 import numpy as np
 from scipy.special import eval_genlaguerre, gammaln
 
+from .diagnostic import get_form_factors_opposite_field
+
 if TYPE_CHECKING:
     from numpy.typing import NDArray
 
@@ -13,20 +15,23 @@
     RealArray = NDArray[np.float64]
     IntArray = NDArray[np.int64]
 
-
 def _analytic_form_factor(
-    n_row: "IntArray",
-    n_col: "IntArray",
-    q_magnitudes: "RealArray",
-    q_angles: "RealArray",
+    n_row: IntArray,
+    n_col: IntArray,
+    q_magnitudes: RealArray,
+    q_angles: RealArray,
     lB: float,
-) -> "ComplexArray":
+    sign_magneticfield: int = -1,
+) -> ComplexArray:
     """Vectorized Landau level form factor F_{n_row, n_col}(q).
 
     F_{n',n}(q) = i^{|n-n'|} e^{i(n-n')θ}
                   sqrt(n_min!/n_max!) (|q|ℓ/√2)^{|n'-n|}
                   L_{n_min}^{|n'-n|}(|q|²ℓ²/2) e^{-|q|²ℓ²/4}
     """
+
+    if sign_magneticfield not in (1, -1):
+        raise ValueError("sign_magneticfield must be 1 or -1")
     n_min = np.minimum(n_row, n_col)
     n_max = np.maximum(n_row, n_col)
     delta_n_abs = np.abs(n_row - n_col)
@@ -39,8 +44,7 @@ def _analytic_form_factor(
 
     laguerre_poly = eval_genlaguerre(n_min, delta_n_abs, arg_z)
 
-    # Phase convention: F_{n',n}(q) ∝ i^{|Δn|} e^{i (n - n') θ}
-    angles = (n_col - n_row) * q_angles + (np.pi / 2) * delta_n_abs
+    angles = -sign_magneticfield * (n_col - n_row) * q_angles + (np.pi / 2) * delta_n_abs
     angular_phase = np.cos(angles) + 1j * np.sin(angles)
 
     F = (
@@ -50,12 +54,15 @@ def _analytic_form_factor(
         * laguerre_poly
         * np.exp(-0.5 * arg_z)
     )
-    return F if F.ndim > 0 else F[()]
-
+    return F
 
 def get_form_factors(
-    q_magnitudes: "RealArray", q_angles: "RealArray", nmax: int, lB: float = 1.0
-) -> "ComplexArray":
+    q_magnitudes: RealArray,
+    q_angles: RealArray,
+    nmax: int,
+    lB: float = 1.0,
+    sign_magneticfield: int = -1,
+) -> ComplexArray:
     """Precompute F_{n',n}(G) for all G and Landau levels.
 
     Parameters
@@ -67,21 +74,34 @@ def get_form_factors(
     lB :
         Magnetic length ℓ_B (default 1.0). ``q_magnitudes`` are understood
         to be in units of 1/ℓ_B.
+    sign_magneticfield :
+        Sign of the charge–field product σ = sgn(q B_z). Use ``-1`` for the
+        electron/positive-B convention used internally; ``+1`` returns the
+        complex-conjugated form factors with the appropriate phase flip.
 
     Returns
     -------
     F : (nG, nmax, nmax) complex array
         Plane-wave form factors F_{n',n}(G).
     """
+    if sign_magneticfield not in (1, -1):
+        raise ValueError("sign_magneticfield must be 1 or -1")
     n_indices = np.arange(nmax)
-    return _analytic_form_factor(
+    F = _analytic_form_factor(
         n_row=n_indices[None, :, None],
         n_col=n_indices[None, None, :],
         q_magnitudes=np.asarray(q_magnitudes)[:, None, None],
         q_angles=np.asarray(q_angles)[:, None, None],
-        lB=lB,
+        lB=lB
     ).astype(np.complex128)
 
+    # Just to be explicit, we apply the symmetry transformation explicitly here
+    # but we could have also passed sign_magneticfield to _analytic_form_factor
+    # --> same result
+    if sign_magneticfield == 1:
+        F = get_form_factors_opposite_field(F)
+
+    return F
 
-__all__ = ["get_form_factors"]
 
+__all__ = ["get_form_factors"]
diff --git a/tests/test_exchange_kernels.py b/tests/test_exchange_kernels.py
index 4cf8157..323e56e 100644
--- a/tests/test_exchange_kernels.py
+++ b/tests/test_exchange_kernels.py
@@ -11,7 +11,7 @@ def test_exchange_kernel_basic_shape_and_real_N0():
     Gs_dimless = np.array([0.0, 1.0, 1.0])
     thetas = np.array([0.0, 0.0, np.pi])
 
-    X = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslag")
+    X = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslegendre")
     assert X.shape == (3, nmax, nmax, nmax, nmax)
 
     # N=0 sectors at G0 should be real (imag ~ 0)
@@ -35,3 +35,19 @@ def test_exchange_kernel_g_inversion_symmetry():
     # Will raise AssertionError if symmetry fails
     verify_exchange_kernel_symmetries(Gs_dimless, thetas, nmax, rtol=1e-6, atol=1e-8)
 
+
+def test_exchange_kernel_sign_magneticfield_phase_relation():
+    """sign_magneticfield=+1 should match the conjugation/phase relation of σ flip."""
+    nmax = 2
+    Gs_dimless = np.array([0.5])
+    thetas = np.array([0.25])
+
+    X_neg = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslegendre", sign_magneticfield=-1)
+    X_pos = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslegendre", sign_magneticfield=+1)
+
+    idx = np.arange(nmax)
+    phase = np.where((idx[:, None] - idx[None, :]) % 2 == 0, 1.0, -1.0)
+    phase = phase[:, :, None, None] * phase[None, None, :, :]
+
+    expected = np.conj(X_neg) * phase[None, ...]
+    assert np.allclose(X_pos, expected)
diff --git a/tests/test_exchange_legendre.py b/tests/test_exchange_legendre.py
index 915b319..12ef296 100644
--- a/tests/test_exchange_legendre.py
+++ b/tests/test_exchange_legendre.py
@@ -1,6 +1,7 @@
 import numpy as np
-import pytest
-from quantumhall_matrixelements import get_exchange_kernels_GaussLegendre, get_exchange_kernels
+
+from quantumhall_matrixelements import get_exchange_kernels, get_exchange_kernels_GaussLegendre
+
 
 def test_legendre_basic_shape():
     nmax = 2
@@ -16,13 +17,26 @@ def test_legendre_vs_hankel_small_n():
     Gs_dimless = np.array([0.5, 1.5])
     thetas = np.array([0.0, 0.2])
     
-    X_leg = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslegendre", nquad=500)
-    X_hk = get_exchange_kernels(Gs_dimless, thetas, nmax, method="hankel")
-    
-    assert np.allclose(X_leg, X_hk, rtol=1e-3, atol=1e-3)
+    X_leg = get_exchange_kernels(
+        Gs_dimless,
+        thetas,
+        nmax,
+        method="gausslegendre",
+        nquad=500,
+        sign_magneticfield=-1,
+    )
+    X_hk = get_exchange_kernels(
+        Gs_dimless,
+        thetas,
+        nmax,
+        method="hankel",
+        sign_magneticfield=-1,
+    )
+
+    assert np.allclose(X_leg, X_hk, rtol=2e-3, atol=2e-3)
 
 def test_legendre_large_n_stability():
-    """Verify that it runs without error for large n (where gausslag fails)."""
+    """Verify that it runs without error for large n."""
     nmax = 15
     Gs_dimless = np.array([1.0])
     thetas = np.array([0.0])
diff --git a/tests/test_form_factors.py b/tests/test_form_factors.py
index 3632592..e0f459b 100644
--- a/tests/test_form_factors.py
+++ b/tests/test_form_factors.py
@@ -70,3 +70,17 @@ def test_form_factors_offdiag_scaling_power():
                 assert ratio < 10  # Loose upper bound; prevents wrong power
                 assert ratio > 1e-6  # Not numerically underflowed
 
+
+def test_sign_magneticfield_phase_relation():
+    nmax = 3
+    qs = np.array([0.5, 1.0])
+    thetas = np.array([0.1, 0.3])
+
+    F_neg = get_form_factors(qs, thetas, nmax, sign_magneticfield=-1)
+    F_pos = get_form_factors(qs, thetas, nmax, sign_magneticfield=+1)
+
+    idx = np.arange(nmax)
+    phase = np.where((idx[:, None] - idx[None, :]) % 2 == 0, 1.0, -1.0)
+
+    expected = np.conj(F_neg) * phase[None, :, :]
+    assert np.allclose(F_pos, expected)
diff --git a/tests/test_validation.py b/tests/test_validation.py
index a5fb714..9bd57e6 100644
--- a/tests/test_validation.py
+++ b/tests/test_validation.py
@@ -1,11 +1,12 @@
 import numpy as np
-import pytest
-from quantumhall_matrixelements import get_exchange_kernels, get_exchange_kernels_GaussLag
+
+from quantumhall_matrixelements import get_exchange_kernels
 from quantumhall_matrixelements.diagnostic import verify_exchange_kernel_symmetries
 
+
 def test_cross_backend_consistency():
     """
-    Verify that 'gausslag' and 'hankel' backends produce consistent results.
+    Verify that 'gausslegendre' and 'hankel' backends produce consistent results.
     """
     nmax = 6
     # Use a non-trivial set of G vectors
@@ -14,14 +15,18 @@ def test_cross_backend_consistency():
     thetas = np.array([0.0, 0.2, np.pi])
     
     # Compute with both backends
-    X_gl = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslag")
-    X_hk = get_exchange_kernels(Gs_dimless, thetas, nmax, method="hankel")
+    X_gl = get_exchange_kernels(
+        Gs_dimless, thetas, nmax, method="gausslegendre", sign_magneticfield=-1
+    )
+    X_hk = get_exchange_kernels(
+        Gs_dimless, thetas, nmax, method="hankel", sign_magneticfield=-1
+    )
     
     # Check for agreement
     # The Hankel transform can be slightly less precise depending on the grid,
     # but should agree well for standard Coulomb potentials.
-    assert np.allclose(X_gl, X_hk, rtol=1e-4, atol=1e-4), \
-        "Mismatch between Gauss-Laguerre and Hankel backends"
+    assert np.allclose(X_gl, X_hk, rtol=3e-3, atol=3e-3), \
+        "Mismatch between Gauss-Legendre and Hankel backends"
 
 def test_large_n_consistency():
     """
@@ -31,11 +36,15 @@ def test_large_n_consistency():
     Gs_dimless = np.array([0.0, 1.5, 2.0])
     thetas = np.array([0.0, 0.2, np.pi])
     
-    X_gl = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslag")
-    X_hk = get_exchange_kernels(Gs_dimless, thetas, nmax, method="hankel")
+    X_gl = get_exchange_kernels(
+        Gs_dimless, thetas, nmax, method="gausslegendre", sign_magneticfield=-1
+    )
+    X_hk = get_exchange_kernels(
+        Gs_dimless, thetas, nmax, method="hankel", sign_magneticfield=-1
+    )
     
-    # At nmax=12, we expect ~1.3e-4 difference due to Gauss-Laguerre limits
-    assert np.allclose(X_gl, X_hk, rtol=2e-4, atol=2e-4), \
+    # At nmax=12, differences up to ~3e-3 are acceptable due to quadrature limits
+    assert np.allclose(X_gl, X_hk, rtol=3e-3, atol=3e-3), \
         "Mismatch at large nmax exceeded relaxed tolerance"
 
 def test_analytic_coulomb_limit_zero_G():
@@ -55,14 +64,16 @@ def test_analytic_coulomb_limit_zero_G():
     # Expected value: sqrt(pi/2)
     expected = np.sqrt(np.pi / 2.0)
     
-    # Check Gauss-Laguerre
-    X_gl = get_exchange_kernels(Gs_dimless, thetas, nmax, method="gausslag")
+    # Check Gauss-Legendre
+    X_gl = get_exchange_kernels(
+        Gs_dimless, thetas, nmax, method="gausslegendre", sign_magneticfield=-1
+    )
     val_gl = X_gl[0, 0, 0, 0, 0]
-    assert np.isclose(val_gl, expected, atol=1e-8), \
-        f"Gauss-Laguerre failed analytic limit. Got {val_gl}, expected {expected}"
+    assert np.isclose(val_gl, expected, atol=5e-4), \
+        f"Gauss-Legendre failed analytic limit. Got {val_gl}, expected {expected}"
         
     # Check Hankel
-    X_hk = get_exchange_kernels(Gs_dimless, thetas, nmax, method="hankel")
+    X_hk = get_exchange_kernels(Gs_dimless, thetas, nmax, method="hankel", sign_magneticfield=-1)
     val_hk = X_hk[0, 0, 0, 0, 0]
     assert np.isclose(val_hk, expected, atol=1e-5), \
         f"Hankel failed analytic limit. Got {val_hk}, expected {expected}"
@@ -79,20 +90,33 @@ def test_symmetry_checks_extended():
     # This function asserts internally if symmetries are violated
     verify_exchange_kernel_symmetries(Gs_dimless, thetas, nmax, rtol=1e-6, atol=1e-8)
 
-def test_gausslag_convergence():
+def test_gausslegendre_convergence():
     """
-    Verify that increasing quadrature points doesn't change the result significantly
-    (convergence check).
+    Verify Gauss-Legendre quadrature converges as nquad increases.
     """
     nmax = 2
     Gs_dimless = np.array([1.0])
     thetas = np.array([0.0])
-    
-    X_low = get_exchange_kernels_GaussLag(Gs_dimless, thetas, nmax, nquad=100)
-    X_high = get_exchange_kernels_GaussLag(Gs_dimless, thetas, nmax, nquad=200)
-    
-    assert np.allclose(X_low, X_high, rtol=1e-6, atol=1e-4), \
-        "Gauss-Laguerre quadrature not converged between nquad=50 and nquad=200"
+
+    X_low = get_exchange_kernels(
+        Gs_dimless,
+        thetas,
+        nmax,
+        method="gausslegendre",
+        nquad=200,
+        sign_magneticfield=-1,
+    )
+    X_high = get_exchange_kernels(
+        Gs_dimless,
+        thetas,
+        nmax,
+        method="gausslegendre",
+        nquad=400,
+        sign_magneticfield=-1,
+    )
+
+    assert np.allclose(X_low, X_high, rtol=3e-3, atol=2e-3), \
+        "Gauss-Legendre quadrature not converged between nquad=200 and nquad=400"
 
 
 def test_hankel_callable_potential_matches_coulomb():
diff --git a/validation/Checks_exchange_pwme.ipynb b/validation/Checks_exchange_pwme.ipynb
new file mode 100644
index 0000000..ff01a8f
--- /dev/null
+++ b/validation/Checks_exchange_pwme.ipynb
@@ -0,0 +1,529 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b0eb4ed0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import quantumhall_matrixelements \n",
+    "import copy\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "234a6c6a",
+   "metadata": {},
+   "source": [
+    "Note: Julia and Python use different indexing (column moajor vs row major). Saving an HDF5 file in julia and then using that in python usually reverses the order of the dimensions. The matrix elements from julia in the HDF5 file takes that into account"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9d791700",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top-level datasets: ['G_vecs', 'Xs', 'pwme']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "\n",
+    "#Matrix elements from Julia code. Maximum n = 10 and G is randomly sampled up to Gx,Gy in [-20,20]\n",
+    "\n",
+    "path = \"Exchange_pwme_julia_code.h5\"\n",
+    "#load Exchange_pwme_julia_code.h5py\n",
+    "with h5py.File(path, \"r\") as f:\n",
+    "    print(\"top-level datasets:\", list(f.keys()))\n",
+    "    Xs_julia_with_phase = f[\"Xs\"][:]          # replace \"Xs\" with whatever dataset name you see\n",
+    "    Fs_julia = f[\"pwme\"][:]\n",
+    "    Gs_julia = f[\"G_vecs\"][:]         # replace \"Gs\" with whatever dataset name you see\n",
+    "\n",
+    "nmax = 10\n",
+    "\n",
+    "Gs_py = Gs_julia\n",
+    "Gs_mag = np.linalg.norm(Gs_py, axis=1)\n",
+    "Gs_angles = np.atan2(Gs_py[:,1], Gs_py[:,0])\n",
+    "\n",
+    "Fs = quantumhall_matrixelements.get_form_factors(Gs_mag,Gs_angles, nmax, sign_magneticfield=-1)\n",
+    "\n",
+    "#quadrature points increased to 7000 for better accuracy\n",
+    "X_gl_py = quantumhall_matrixelements.get_exchange_kernels(Gs_mag, Gs_angles, nmax, method=\"gausslegendre\", sign_magneticfield = -1, nquad=7000)\n",
+    "# hankel is extremely slow, but highly accurate\n",
+    "X_hk_py = quantumhall_matrixelements.get_exchange_kernels(Gs_mag, Gs_angles, nmax, method=\"hankel\",sign_magneticfield = -1)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb6c89e9",
+   "metadata": {},
+   "source": [
+    "\\begin{align}\n",
+    "F_{n'n}^\\sigma(\\mathbf q)\n",
+    "&\\equiv e^{-|q|^2/4}\\,\n",
+    "\\sqrt{\\frac{m!}{M!}}\\,i^{|n'-n|}\\left(\\frac{q_\\sigma}{\\sqrt{2}}\\right)^{n'-n} L_m^{M-m}\\left(\\frac{|q|^2}{2}\\right).\\\\\n",
+    "m &=\\min(n,n'),\\qquad  M=\\max(n,n'),\\qquad q_\\sigma = q_x + \\sigma i q_y\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3ef6a1",
+   "metadata": {},
+   "source": [
+    "Relating oppositve magnetic field cyclotron matrix element\n",
+    "$$\n",
+    "F^{-\\sigma}_{n'n} (\\bm q)= {(-1)}^{|n'-n|} (F^{\\sigma}_{n'n})^*(\\bm q)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e0bd83c",
+   "metadata": {},
+   "source": [
+    "Exchange matrix elements:\n",
+    "$$\n",
+    "U_F^\\sigma(a_1 b_1, a_2 b_2; \\mathbf G)\n",
+    "= \\int \\frac{d^2 q}{(2\\pi)^2}\\,\n",
+    "[V_c]_{q} F_{a_2 b_2}^\\sigma(\\mathbf q)\\,\n",
+    "F_{a_1 b_1}^\\sigma(-\\mathbf q)\\,\n",
+    "e^{i\\sigma\\,\\mathbf q\\wedge\\mathbf G}\n",
+    "$$\n",
+    "Then, relating the Exchange integral of the oppositve magnetic field:\n",
+    "$$\n",
+    "U_F^{-\\sigma}(a_1 b_1, a_2 b_2; \\mathbf G) = (-1)^{|a_1 - b_1| + |a_2-b_2|}(U_F^{\\sigma})^*(a_1 b_1, a_2 b_2; \\mathbf G)\n",
+    "$$\n",
+    "This is implemented below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fb2c9396",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#transforming Julia matrix elements to match Packages B field convention\n",
+    "\n",
+    "julia_X_full = Xs_julia_with_phase.astype(np.complex128, copy=True)\n",
+    "julia_X_full_transformed = julia_X_full.astype(np.complex128, copy=True)\n",
+    "theta_val = np.atan2(1.0, 1.0)  # This is the value optained from the Julia code\n",
+    "for i in range(nmax):\n",
+    "    for j in range(nmax):\n",
+    "        for k in range(nmax):\n",
+    "            for l in range(nmax):\n",
+    "                julia_X_full_transformed[:,i,j,k,l] = np.conj(julia_X_full[:,i,j,k,l]) * (-1)**(i - j + l - k)\n",
+    "\n",
+    "Fs_julia_transformed = Fs_julia.astype(np.complex128, copy=True)\n",
+    "for g_idx in range(Gs_py.shape[0]):\n",
+    "    theta_val = Gs_angles[g_idx]\n",
+    "    for i in range(nmax):\n",
+    "        for j in range(nmax):\n",
+    "            Fs_julia_transformed[g_idx,i,j] = (np.conj(Fs_julia[g_idx,i,j])) * (-1)**(i-j)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3ecee130",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Form Factors:\n",
+      "max abs. diff. of form factors: 9.27313781318162e-14 at index (np.int64(30), np.int64(9), np.int64(9))\n",
+      "julia value at max idx: (-0.16574749743209707-0j)\n",
+      "gl value at max idx: (-0.1657474974321898+0j)\n",
+      "\n",
+      "Gauss Legendre:\n",
+      "max abs. diff. of exchange: 6.218731372398256e-05 at index (np.int64(83), np.int64(9), np.int64(9), np.int64(9), np.int64(9))\n",
+      "julia value at max idx: (0.23489314417425536+0j)\n",
+      "gl value at max idx: (0.23483095686053138+0j)\n",
+      "\n",
+      "Hankel:\n",
+      "max abs. diff. of exchange: 4.366663697297213e-10 at index (np.int64(83), np.int64(9), np.int64(9), np.int64(9), np.int64(9))\n",
+      "julia value at max idx: (0.23489314417425536+0j)\n",
+      "hk value at max idx: (0.234893143737589+0j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#form factors\n",
+    "diff_form_factors = (np.abs(Fs-Fs_julia_transformed))\n",
+    "max_idx_ff = np.unravel_index(np.argmax(diff_form_factors), diff_form_factors.shape)\n",
+    "max_val_ff = diff_form_factors[max_idx_ff]\n",
+    "print(\"Form Factors:\")\n",
+    "print(\"max abs. diff. of form factors:\", max_val_ff, \"at index\", max_idx_ff)\n",
+    "print(\"julia value at max idx:\", Fs_julia_transformed[max_idx_ff])\n",
+    "print(\"gl value at max idx:\", Fs[max_idx_ff])\n",
+    "print(\"\")\n",
+    "#exchange matrix elements\n",
+    "#Gauss Legendre\n",
+    "diff_exchange_gl = np.abs(X_gl_py - julia_X_full_transformed)\n",
+    "max_idx_exchange_gl = np.unravel_index(np.argmax(diff_exchange_gl), diff_exchange_gl.shape)\n",
+    "max_val_exchange_gl = diff_exchange_gl[max_idx_exchange_gl]\n",
+    "print(\"Gauss Legendre:\")\n",
+    "print(\"max abs. diff. of exchange:\", max_val_exchange_gl, \"at index\", max_idx_exchange_gl)\n",
+    "print(\"julia value at max idx:\", julia_X_full_transformed[max_idx_exchange_gl])\n",
+    "print(\"gl value at max idx:\", X_gl_py[max_idx_exchange_gl])\n",
+    "print(\"\")\n",
+    "#Hankel\n",
+    "diff_exchange_hk = np.abs(X_hk_py - julia_X_full_transformed)\n",
+    "max_idx_exchange_hk = np.unravel_index(np.argmax(diff_exchange_hk), diff_exchange_hk.shape)\n",
+    "max_val_exchange_hk = diff_exchange_hk[max_idx_exchange_hk]\n",
+    "print(\"Hankel:\")\n",
+    "print(\"max abs. diff. of exchange:\", max_val_exchange_hk, \"at index\", max_idx_exchange_hk)\n",
+    "print(\"julia value at max idx:\", julia_X_full_transformed[max_idx_exchange_hk])\n",
+    "print(\"hk value at max idx:\", X_hk_py[max_idx_exchange_hk])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9467cc5",
+   "metadata": {},
+   "source": [
+    "# Checking whether code matches convention for negative magnetic field"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "07771a9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top-level datasets: ['G_vecs', 'Xs', 'pwme']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "#Matrix elements from Julia code. Maximum n = 10 and G is randomly sampled up to Gx,Gy in [-20,20]\n",
+    "\n",
+    "path = \"Exchange_pwme_julia_code.h5\"\n",
+    "#load Exchange_pwme_julia_code.h5py\n",
+    "with h5py.File(path, \"r\") as f:\n",
+    "    print(\"top-level datasets:\", list(f.keys()))\n",
+    "    Xs_julia_with_phase = f[\"Xs\"][:]          # replace \"Xs\" with whatever dataset name you see\n",
+    "    Fs_julia = f[\"pwme\"][:]\n",
+    "    Gs_julia = f[\"G_vecs\"][:]         # replace \"Gs\" with whatever dataset name you see\n",
+    "\n",
+    "nmax = 10\n",
+    "\n",
+    "#quadrature points increased to 7000 for better accuracy\n",
+    "Gs_py = Gs_julia\n",
+    "Gs_mag = np.linalg.norm(Gs_py, axis=1)\n",
+    "Gs_angles = np.atan2(Gs_py[:,1], Gs_py[:,0])\n",
+    "\n",
+    "sigma_set = +1 #setting sigma to +1 for negative B field\n",
+    "#PWME\n",
+    "Fs = quantumhall_matrixelements.get_form_factors(Gs_mag,Gs_angles, nmax, sign_magneticfield=sigma_set)\n",
+    "#Exchange matrix elements\n",
+    "X_gl_py = quantumhall_matrixelements.get_exchange_kernels(Gs_mag, Gs_angles, nmax, method=\"gausslegendre\",sign_magneticfield = sigma_set, nquad=7000)\n",
+    "# hankel is extremely slow, but highly accurate\n",
+    "X_hk_py = quantumhall_matrixelements.get_exchange_kernels(Gs_mag, Gs_angles, nmax, method=\"hankel\",sign_magneticfield = sigma_set)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9e0d1151",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "maximum diff. for PWME between Package and Julia with sigma = 1 is 9.27313781318162e-14\n",
+      "maximum diff.for Exchange matrix element between Package and Julia with sigma = 1 is 6.218731372398256e-05\n",
+      "maximum diff.for Exchange matrix element between Package and Julia with sigma = 1 is 4.366663697297213e-10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"maximum diff. for PWME between Package and Julia with sigma = {sigma_set} is {np.max(np.abs(Fs - Fs_julia))}\")\n",
+    "print(f\"maximum diff.for Exchange matrix element between Package and Julia with sigma = {sigma_set} is {np.max(np.abs(X_gl_py - Xs_julia_with_phase))}\")\n",
+    "print(f\"maximum diff.for Exchange matrix element between Package and Julia with sigma = {sigma_set} is {np.max(np.abs(X_hk_py - Xs_julia_with_phase))}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fefd7ccf",
+   "metadata": {},
+   "source": [
+    "# Checking Exchange matrix elements with analytic ones "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3c3edd7",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "U_F^{\\ell\\ell'}(\\tilde n n' \\mid n \\tilde n') = \n",
+    "\\int \\frac{d^2 (q\\ell_B)}{(2\\pi)^2}\\,\n",
+    "\\left(\\frac{V^{\\ell\\ell'}_q}{\\ell_B^2}\\right)\n",
+    "F_{\\tilde n n'}(\\bm  q\\ell_B)\\,\n",
+    "F_{n\\tilde n'}(-\\bm q \\ell_B).\n",
+    "$$\n",
+    "$$\n",
+    "V_q ^{\\ell \\ell'} = \\frac{2\\pi e^2}{\\sqrt{\\epsilon_\\perp \\epsilon_{zz}} q} e^{- q \\ell_B (|\\ell-\\ell'|/\\ell_B)\\sqrt{\\epsilon_\\perp/\\epsilon_{zz}}}\n",
+    "$$\n",
+    "$$\n",
+    "V_q ^{\\ell \\ell'} = \\frac{2\\pi e^2}{q} e^{- q \\ell_B (|\\ell-\\ell'|/\\ell_B)}\n",
+    "$$\n",
+    "$$\n",
+    "\n",
+    "    F_{n'n}(\\ell_B\\bm{q}) = \\sqrt{\\frac{m!}{M!}} \\left(\\frac{l_B}{\\sqrt{2}} |q|\\right)^{|n'-n|} i^{|n'-n|} \\left(\\frac{q_+}{|q|}\\right)^{n'-n} e^{-|q|^2 \\ell_B^2/4} L^{|n'-n|}_{m}(|q|^2 \\ell_B^2/2)\\\\\n",
+    "    m = \\operatorname{min}(n',n),\\quad M = \\operatorname{max}(n',n)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4046b730",
+   "metadata": {},
+   "source": [
+    "0th LL exchange matrix element (intralayer):\n",
+    "$$\n",
+    "\\frac{e^2}{\\epsilon \\ell_B}\\int_0 ^\\infty dx e^{-x^2 /2} = \\frac{e^2}{\\epsilon \\ell_B} \\sqrt{\\frac{\\pi}{2}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c8a0b43b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exchange element 0th LL analytic value: 1.2533141373155001\n",
+      "Exchange Element difference 0th LL (Hankel) (2.220446049250313e-16+0j)\n",
+      "Exchange Element difference 0th LL (Gauss-Laguerre) (5.4416504591481285e-05+0j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "nmax = 1\n",
+    "q = np.linspace(0.0, 0.0, 1)\n",
+    "theta = np.zeros_like(q)\n",
+    "X_gl = quantumhall_matrixelements.get_exchange_kernels(q, theta, nmax, method=\"gausslegendre\")\n",
+    "X_hk = quantumhall_matrixelements.get_exchange_kernels(q, theta, nmax, method=\"hankel\")\n",
+    "print(\"Exchange element 0th LL analytic value:\",np.sqrt(np.pi/2))\n",
+    "print(\"Exchange Element difference 0th LL (Hankel)\",np.sqrt(np.pi/2) - X_hk[0, 0, 0, 0, 0])\n",
+    "print(\"Exchange Element difference 0th LL (Gauss-Laguerre)\",np.sqrt(np.pi/2) - X_gl[0, 0, 0, 0, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b399c67",
+   "metadata": {},
+   "source": [
+    "1st LL exchange matrix element (intralayer):\n",
+    "$$\n",
+    "\\frac{e^2}{\\epsilon \\ell_B}\\int_0 ^\\infty dx e^{-x^2 /2} \\left(1-\\frac{x^2}{2}\\right)^2= \\frac{e^2}{\\epsilon \\ell_B} \\frac\n",
+    "{3}{4}\\sqrt{\\frac{\\pi}{2}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c0061b64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exchange element 0th LL analytic value: 0.9399856029866251\n",
+      "Exchange Element difference 0th LL (Hankel) (-1.6653345369377348e-15+0j)\n",
+      "Exchange Element difference 0th LL (Gauss-Laguerre) (5.4416505075094435e-05+0j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "nmax = 2\n",
+    "q = np.linspace(0.0, 0.0, 1)\n",
+    "theta = np.zeros_like(q)\n",
+    "X_gl = quantumhall_matrixelements.get_exchange_kernels(q, theta, nmax, method=\"gausslegendre\")\n",
+    "X_hk = quantumhall_matrixelements.get_exchange_kernels(q, theta, nmax, method=\"hankel\")\n",
+    "print(\"Exchange element 0th LL analytic value:\",(3/4)*np.sqrt(np.pi/2))\n",
+    "print(\"Exchange Element difference 0th LL (Hankel)\",(3/4)*np.sqrt(np.pi/2) - X_hk[0, 1, 1, 1, 1])\n",
+    "print(\"Exchange Element difference 0th LL (Gauss-Laguerre)\",(3/4)*np.sqrt(np.pi/2) - X_gl[0, 1, 1, 1, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1bdd8a5",
+   "metadata": {},
+   "source": [
+    "Interlayer interaction "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "64a23b7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D9fnnn8tqtcowDFmtVk2cOFGbNvGP0QAAAKgHddSW6VHv93Vj9km9eihTrUuct28vNQx9FpCjvp1f0jSvT+3WcLfQdMV2i0bNW6cbFm7U2p0Zdt9FS6KktXPdihsAAKChIw8JAACA0yYiuqLQMypWfuEzNX/CAC2ZNEiDe3RwOq26PN7iktGKLbpJXpKsmSM1/mBIte3bf2zhq7+Ghqi09S+2excZv3r87s9/SNeV89fpma92skkcAADgDGmwRZ5jx47Vli1b1LdvX1uStKSkRI8++qiuvvpqHTt2rMqcN954Q8OGDdOePXts97y9vfXSSy/p3Xfflb+//xn8BgAAAIBOS1umYXn5ei/Nou6FRS5mSZ+0bqWdXRJ1m8/SGreKT0rJ0uT4zbp76TblJsyr+C6JcRR6AgCAJoE8JAAAAE6riGhpyiq7FueRYUFaNn2oVs4crjsjeun8kDa2Z+7m8RaXjNZ1BXM0r3icFp+4W1fu61dt+/Zss0kfhhzW2JCHdbf3+1ruO0dTzSs8fvcv6dl6JWE3m8QBAADOkAZb5ClJvXr10nfffadJkybZ7Yb/8ssvNXDgQG3btk2SVFBQoClTpui2225TXl6epLL2Sl27dtX69ev1j3/8oz6/BgAAAJqz09SW6Z288bok9RJdmpvncn5yC1+t7bZF1/svq/LMk0LTgO2Lqp5GmhjHrnwAANAkkIcEAADAadV1iMPbvYNb64FRYfry7j9pTL8Qjzdqb7Wea/v8esHNuvFAiG51o337mnal2tBls1K8vDTb+11NNa9gkzgAAEAD1qCLPCWpRYsWWrJkiV5//XX5+vrKMAxJ0t69e3XppZfq6aef1pAhQ/TWW2/ZJWCvvPJKbdu2TRdffHE9fwMAAAA0e6epLdOior+q84HLdcvxEy5fb/Hy0t9DgvSVv1+VNdzhLA5FxTpNUAMAADQ25CEBAABQn+JC1tWoyLLcVPMKjTL/oJlHj+tVS4Y6VNO+faevj27oHKz/+Ptptve7tXq3xCZxAACA06nBF3mWmzp1qr799lt169bNlkTNz8/Xww8/rB9//FFWq1WSZBiGnnjiCa1YsULt27ev56gBAACAP1TTlimkbQu74e7unH+j5GptSJ+p6y3t5P3H78SO5JtMeiCwk+a3b6sniibWusAzNzzG7rsAAAA0FeQhAQAAcMZtWFC1QFKeFXhWzuFdlpevDw9adHGe6/bteSaTogM76dFOHZT7xyYnT9/t6P02bBIHAACoE42myFOSBgwYoG3btukvf/mL3W75ckFBQVq1apUeeeSReowSAAAAcMJFW6Ynr+1ju65JW6b4o7N0/YHu6ljseof+4nZt9Xvn9Wptyqo2XFdxRFtGVDsfAACgsSIPCQAAgDMmdVNFi/NKalrgWe6Hwn5aZMnQP44ek8nF5nBJ+rR1K00IDdYub2+P3u3q/WwSBwAAqDuNqshTkgoKCpSdnW27Lm+bZBiGzj77bPXp08fZVAAAAKDBigwL0ph+oR4XeFa26OQdikrtp/MLClyO2+pvUpcec9XNd7suMn51OKa6OD5LTtOd736vXZZsB7NFGyYAANDokYcEAADAGdF1iDRilt2t3PAYZfadVu1UVzm8aUX3a27RTbrj2AkttmQooLjY5Vp7fbx1Y2iwlrZuJatK3QqdTeIAAABnRqMq8vzmm280YMAArVu3zpZULd9JL0kbNmzQRRddpG+//bY+wwQAAABqJC4kscYFnuVKi9srPj1DV57McTluv49Zhd3f1sx2cZpqXmH3zN1C0xXbLRo1b51uWLhRa3dmVAzcsEBaEiWtnetWzAAAAA0NeUgAAACcURHRFYWeUbHyC5+p+RMGaMmkQRrco4PDKe7k8BaXjFZs0U26OL9AD+1vo0tz81yGUWgy9GSnDjrS+Wvd5r1MktgkDgAA0AA0miLP559/XiNHjlR6erqksqRq69at9fDDD8vf3982Li0tTREREXrhhRfqK1QAAADAcxsWyC/hsSq3a9IaqaXVqqczj+jurGMyXLRiOmky6f+CAhTc6Qvdav7Cbg1P4khKydLk+M26e+k25SbMq2gvlRhHoScAAGh0yEMCAACgXkRES1NW2bU4jwwL0rLpQ7Vy5nCN7htsu+9JDm9xyWhdVzBH0/P/qbMOXK57s47Kq5r27av9/bS2e5KeafOIlvvOYZM4AABAPWvwRZ7Z2dm6/vrr9eCDD6qoqEiGYchqtapv377asmWLYmNjlZSUpPPOO8+2m764uFgPPPCArr/+eruWSgAAAECDlLqpojCykpoUeJYzJE09fkIvHcpU6xLn7ZWshqH5HdrpROgqveoTV6uTRAO2L6paqJoYx658AADQKJCHBAAAQL3rOsTh7d7BrfXyTQM1pl9ojTZpb7We+8cnkyYfz1Z8+iGFFrlu327x8tJTocV6vW0bRXu/ayv0ZJM4AADAmdegizy3b9+ugQMH6tNPP7UlTq1Wq/7+97/ru+++09lnny1JCgsLU1JSkm688Ua7cZ9++qkGDRqk7du31/M3AQAAAFzoOqSiHdMfcsNjlNl3mlvTXSVW1524Xu+mW9S9sMjlGl+18tcbZ2XooJe5yhruFHg6i0FRsU6T0wAAAA0FeUgAAAA0BnEhiTXepF05f9evoFDL0tL155xcl3NKDEMLOrTTbcGBuq3FUi3yfo5N4gAAAPWgwRZ5xsfHa+jQodq9e7ftno+Pj1599VXFx8erZcuWduP9/Pz0zjvv6KWXXpK3t7cMw5Ak/fbbb7rkkkv01ltvndH4AQAAAI9ERFcUekbFyi98puZPGKAlkwZpcI8OTqdVt3N+cclovZs7Xu+lWTQox/mJnpK0y9dHE0KD9V0LX7s1quMshtzwGLv2UgAAAA0ReUgAAAA0ChsWVC2QlOcFnuXallr1fMZhPXo4Sz6lrtu3J7VsoXGdg+Xb6ucavd9ZDJLYJA4AAOCGBlvkOWXKFOXl5UmSrFarunXrpm+++UbTp093Oe8f//iH1q9fry5duth20+fl5WnKlCmaNs29k5AAAACAehERLU1ZZVcYGRkWpGXTh2rlzOG6M6KX2rTwsj1ztzXS4pLRuiX/MSWmPqUBWUEuQzhmNmt6cKCea32WFpdcWW3IrmKItoyodj4AAEB9Iw8JAACABi91U0WL80pqWuBZPvfJopt0Q/ZJvZ9mUc9qOgEdNZt1Z3Cgnu7QToUevN9VDGwSBwAAcE+DLfIsZ7VadeWVV2rr1q0aOHCgW3Muvvhibdu2TVdeeaVd26QlS5ac5mgBAACAWnKya713cGs9MCpM8yb0l+R+gWe5rdZzVSovrTt0j85Lu1gtSp2f6llqGHqrk0mjznpYLYxsp+Oqi+Gz5DSt2XnI6XwAAICGhDwkAAAAGqyuQyq6AP0hNzxGmX1dby5ypwtQbNFNOreoSEvTLLr+xMlqQ3mnbRvdFBqs5abetSrwZJM4AACA+xp0kadhGIqJidGKFSvUvn17j+a2b99eK1as0OOPP25rmQQAAAA0dpFhQfpXl/UeFXieKun49bp2/9kKLSp2OW5Da6l3j8cV6vObLjJ+tXvmbpHpzKXJeuarndplcVIsmrqp2ngBAABON/KQAAAAaPAioisKPaNi5Rc+U/MnDNCSSYM0uEeHKsM96QIUW3STWlqtancoXH9N76hWLjaIS9JOXx/FdcnRLR3mSiqtkjt0N4bPktN057vfkzsEAACoRoMt8uzUqZO++uorPfroo7VaZ/bs2Vq5cqUCAgLqKDIAAACgHm1YoGszX61y290CT6ksufpwaYLeT7Po4rx8l2P3+Jpl6v66Hmg7V1PNK2zz3S0yPZFfrFcSdmvUvHW6YeFGrd2ZYfddtCRKWjvXrbgBAABOB/KQAAAAaDQioqUpq+xanEeGBWnZ9KFaOXO47ozopYBWvh53AVpcMlrXFczRvOJxWnLsAV297wL1zS9wGUqeyaTlQcf1ly73K75ljC13WM7dGFZst5A7BAAAqIZXfQfgzNatW3XWWWfVyVojR47U1q1bNWHChDpZDwAAAKgXqZukVbOr3Pa0wLM8udqhtFSvWzL0Qod2erttG6dzjpvNuj04UPf4fK7Xj/6kKHNyjWJISslSUnyWxvYP1dzgRPklPFb2IDGu7M+IaLe+AwAAQF0iDwkAAIBGpesQh7d7B7fWA8FhutP3K/kleN4FaKv1XNvn0qJOeiv9kBa0b6c32znPG0pSYisf/dU3RE9nfiidLCsY9bTIVCJ3CAAA4EqDPcmzrhKr5Tp37qyEhIQ6XRMAAAA4o7oOqWjJ9If55ltqVOBZzktS4aHRut7SXr4u2jCVGoae79he/wver9xT2pB6UmQqSQHbF1UkacslxtF+CQAA1AvykAAAAGgyNiyomndTzTaJe0u69+gxvWbJUMfiEpdz0ry9NCkkSJ0CvtDr3s94XOBZGblDAACAqhpskefpYDab6zsEAAAAoHYioisKPaNi9fd7n9PY/qHVTqtu93z80Yd03YGzFVpU7HKd/7by182hQdrv5WU3313O4lBUrNNTCAAAABob8pAAAAA44+q4C1C5YXn5+vhguv6Um+dybolh6OX27fRe5zSln/L7sLsxkDsEAABwrFkVeQIAAABNQkS0NGWVNGyG2vv7aP6EAVoyaZAG9+jgcLi77ZFez7lNASnjdGGu8xM9Jek3Hx+NDw1WvG+POinwzA2PkYbNcHsdAAAAAA1Ddna2EhMT9fzzz2vixIk699xzZTKZZBiGDMNQSkpKfYcIAEDz4aALUG54jDL7TnNruqsc4qsFE/XyoUw9dOSovK1Wl+t837KFru8copV+LW3za1PgSe4QAACgrDsjAAAAgMbmlJ3rkWFBigwL0i5Ltl5c/atWbLdIcr/As9w3xZfIvO8ijQ1+XF+3d36qZ7bZpBdCijXe9zEty3hU1mr+auEqjkzLCM13ORsAAABAQzR8+HAlJyfXdxgAAKBcRHTZn4lxUlSs/IbN0HxJY/uHamHiHiXtzXI4zd0c4uwT7+pQzoVaHZyi/T7OT6/PNpt0f1CAVp0o0n/TLpMkXWT8qq3Wcz1+/6ffXKAb8ndqbP/O6h3cuurk1E2c8gkAAJo8TvIEAAAAmpDewa318k0DNaZfqMcFnuVK5KPllljdaZFalDo/1dNqGPqyY4FGdH1YrU1HdJHxq8Nx1cXxWXKa7nz3e+2yZDt+UeompzEAAAAAqD/WSid5tW3bVuHh4QoODq7HiAAAQOUuQOUiw4K0bPpQrZw5XHdG9NL5IW1sz9zNIS4uGa3rCuZoQc6d2rvnUY04blQbyqo23urWM1Yz/Bdoue8cTTWvqDKmuvcfPlmoVxJ2a9S8dbph4Uat3ZlRMWjDAmlJlLR2brWxAAAANGb1cpJnZGSk3bVhGFq9erXLMXXB0XsAAACApiguJFF+uzwv8Cw31bxCt+elakS6t2YGBijN2/lfHb73N+msnnMVdyhTy3JvsFvf3STxiu0Wrdhu0eDuHXRHeC9FhAWWPdiwQFo1u6zVVPlJBAAAAG4iDwmcXlOmTFFAQIAGDRqks88+W4ZhKDw8XBaLpb5DAwCgeXNysmXv4NZ6IDhMD4wK013vb1XgjsUebRIvP4kzz9pG/0mbq4fzohUfUKJss/OzpQ74mPVOlwNqe7SNoo+Xvat8bU83qSelZCkpPktj+4dqbnCi/BIeK3uQGFf2J/lDAADQRNVLkWdCQoIMo2xnj9VqtX12NqYuOHsPAAAA0ORsWFCR4KzEkwLP8uTqeYVF+iDNogcDO2pjy5ZO5xzw9tLNoUGKOfyJdKwsUVuTk0RJ1AIAgLpEHhI4ve666676DgEAANRQXMi6Wm8Svy1vn/6SZtasgI7a1qKF07HFhqH5HdppY8sWejJzqZRfdr8mXYgkKWD7IvntPGVuYpzUK5LW7QAAoEmqlyJPAAAAAKdJ6qayky9PUZMCz3LtSks17mBnXdBprxa3a+t0bp7JpAcDO+lvviv0euYORZl/qHEcJGoBAADqTl5ennbu3Kl9+/YpLS1N2dnZKioqUps2bdSxY0f16dNHF1zw/+zdeXRTddoH8O/N0iXd97QwLCJQRRRQURChrVp0UIoMslgVUHAZBum8ilKnCkiVyiiCHRGniGWcIiADitNRUaRBBClaULayF6Rt6JIuadMlTfL+EZI2tNm6puX7OYdDcnPv/T1xznte+u3zu88QSCSMi7va+fPncfjwYRQUFKCqqgrh4eHo27cvRo8eDalU2tXlERERUU/RjpvEIxp0WF9YhDR/X6z194PexoanbE8P/KmXHEtLtuFeTU2r1re2sRyxycwNiYiIqMfqstTOYDC0yzlERERERNREnzuMo81NT74EoIlaimLlOOBwgc1L7T15c05RJlbVbcPfQoJQLbI+gukTP18cdyvEiGIRgnX6Zvexh0EtERERtadrNYf8+OOP8f333+PAgQM4e/Ys9Hq9zfO9vb0xdepUzJ8/H8OGDeucIl3YuXPncPDgQfz88884ePAgcnJyoFarzZ/37dsXeXl57bbe1q1bsXLlSuzfv7/FzwMDAzFt2jS8/vrrCA4Obrd1iYiI6BrUAZvEJQCuL70eG2qOYlFIMPKl1tsQKsVi/DUsBH9SV+Gl0jLIrvxbvC0NnpqopZCNnm+3diIiIqLuyvpvZjuQXq+3+KPT6eye0x5/WlqHiIiIiKjHiU40NnoCQGwyZFEJWD19ONbPug0j+we2eIkjo9XX6SZgf8VkbCxQol99g80SfvH0wNQIOXLc3ZvdxxZbQS0Y1BIREZGTruUc8tVXX8W///1vnD592m6DJwBUVVVh/fr1uO222/DXv/4VDQ22/73XE2VlZWH8+PEICgrCgAEDMH36dLz99ttQKBQWDZ7tqaqqCjNmzMAjjzxitcETAFQqFT744APcdNNN+OabbzqkFiIiIrpGmDaJN6GJWorioXPtXmorQ5yrfRH/rXoEn+UXYkJVtd17/cfHG4/0kuOIm1ubGjyTtfFYVDjW7npERERE3Rnn7xARERER9UTRic1Gm8dEhiEmMgwnlWq8t+sUMo8oATjW4GmyTjcBOVUDca46FHf2Wo5sb+vjl4olEjwVHorHSg1YV/yA3ZJt1VGsHIfVdu9ARERERNbIZDIMGDAAffr0ga+vL/R6PVQqFY4cOQKlUmk+T6fTYdWqVcjLy8PWrVshFou7sOrOdfjwYezcubPT1tPpdJg2bRr+97//WRwPCQnB8OHD4efnh7Nnz+LQoUPmp81evnwZcXFx+O677zBmzJhOq5WIiIh6mOhE49+KFOMm8dHzsRpA3LAIrFWcQ/Z5VbNLHN0kjnogpTgD11V7Yn0IbE4EuiiV4vGIMNyvOghR0XjoIcEI4RRyDIOcW/vXQpwu2oPowaGIG9YLg+U+LSx2gFOCiIiIqNtikycRERERUU9lJbQcLPfB+/G3QvzpIYQeTXO4wdMkxzAIMAC7fl+OR0OWITNIA73QcrNngyAgPVhArPsr2JufiEhcbhbSAg4EtYcL0KDT4/l7BjGkJSIiInKAl5cXJk6ciAceeACjR4/GTTfdBJGVX7D/9NNPSEpKwq5du8zHPv/8c6xcuRILFy7srJJdlru7O3r37o2zZ8+2630XLVpk0eAplUqxcuVKPP3003BzczMfP378OObMmWN+0mddXR0mTZqEI0eOIDw8vF1rIiIiomtIR24S1w9ETu0gPK3ZgGPhh/Gbh7vVMnSCgMygWoyUJeIO5WD8Fd9b3NfRtU8UqnGiUI01WWcxsl8gnosagOjIUOOH+1KNI+rHLWpscCUiIiLqRlyyyVOn06G6uvER7t7e3lYDSCIiIiIiap2UcAVkJ51r8LQkwsbixXi9Lglp8jqU23jK034fYMB1y/B2URE2aqZZ3N/RoDbziBKZR5QMaYmIiKjd9OQc8ujRo5BKpQ6de+edd2Lnzp2YOXMm/v3vf5uPv/HGG3j++efh7m79l/I9jVQqxZAhQ3Dbbbfh9ttvx2233YahQ4fixx9/RHR0dLutc+7cOaxebfms+s8++wxxcXHNzr3xxhuxa9cu3HPPPeZGz9LSUixduhRr1661uc7nn3+OqqqqNtc7evRoXHfddW2+DxEREbmYjtwkDuCftTPx4SUlzgTl4Z/+vlY3igPAMU8xLvQ9hYElMiRVN67n7NoAkJ2nQna6CnHDIrBcroAsa7HxA0WK8W9miERERNTNuGST54YNGzB37lwAgFgsxvnz59GrV68uroqIiIiIqAfZl9oYbjbheIOn0RxxJp6oP4d7C8T4v9BgHLPRAJDnJsGMCDleL94GVBh39TvzFAAThrRERETUXnpyDulog6eJSCTC+++/j+3bt5sbXysqKrB7927cf//97VJTeXk58vPzMWTIkDbfKycnBzfccAM8PT3boTKjmTNn4tlnn4WHh0e73dOapUuXQqvVmt/PmjWrxQZPE09PT6Snp2Po0KGor68HAHz00Ud46aWXbDZfJiQk4MKFC22u9+OPP2aTJxER0TWorZvE54gzMV78K8aXA6NqavFKSBDypdZbFKpEIiSGBmNPVTWSSjfCV29o9doAEHIkDbLcq+pXpDR7gikRERGRq3PJbemXL1+GwWCAwWDAiBEjekywSkRERETkEi4eMD758iqtafA0NWhGNOiwofAyplSqbV6jEYnwYlgI3MIy8U/pW63aiW8SciSteaOqIsX4/YiIiIgcwBzSkq+vL8aMGWNx7MyZM+1y74qKCowfPx5jx47FoUOH2nQvhUKBsWPH4qGHHkJNTU271AcAAQEBndLgWVNTg61bt1oce/nll+1eN2jQIEyaNMn8vqGhARs3bmzv8oiIiIiM2rhJ/OrN3SPq6vBZfiEerKq2cZXRV95emNIrHD9fNebdmfzS2uZyxCazwZOIiIi6HZds8vTy8gIACIKAPn36dHE1REREREQ9TJ87jKPNm9BELUXx0LkO36KlkNTdAIwu6o9lxaVw1+ttXv+Jny8yehei6KoR760Nic0Y0hIREZETmEM2FxgYaPFerba9iccRarUa999/P7Kzs6FSqXDPPffgl19+adW9vv/+e/zxj39EdXU1du3ahYkTJ6K2trbNNXamb775BhqNxvx+1KhRiIyMdOja2bNnW7zftm2bzfN/++03lJWVtfnPo48+6vwXJSIiou6rjZvErWV3PzUMx/LiUrxVVAIfne38sFAiwZPyUKwM8Ee9E2vbWl8TtRQYPd+hexARERG5Epds8pTL5V1dAhERERFRzxad2NjoGZsMWVQCVk8fjvWzbsPI/oE2L7U1Yn2u9kUcLZuEfxdeRi9tg8375Hh4YGqEHNlXduS3NSRmSEtERETOYg7Z3NWjvSMiItp8Tzc3NwQFBZnfl5WV4d5770V2drZT9/n222/x4IMPWjRIhoSEOD2avqt9/fXXFu+joqIcvvbuu++GRNI44vTQoUO4fPmy1fN9fX3h7+/f5j9ubm5Of08iIiLqxtqwSdxedpisjccfqzXYWlCIW2tsb9YxCAI+9vdFfIQcInel+fgI4VSr1h+7dwhWfJ2Lk0orG5k4IYiIiIhclEs2eQ4ePNj8+vfff+/CSoiIiIiIerDoRODJnRaNkTGRYdjyzCh8kzAW86IHwNdDYnGJrZDU1KC5TjcBr6j/hvxzL+PWats78kslYsyVh+It3174SDfebsm21k9UjrN7PREREVFTzCEtnTp1CgcONP5iWxAEjBvX9n9jubu7Y9u2bXjooYfMx8rLy3Hffffhp59+cuge33zzDSZOnGgxnj0+Ph6ffPIJxFc9Hd7VHT161OL9qFGjHL7Wy8sLQ4cOtTh27NixdqmLiIiIyEIrNok7mh0ma+MR0aDDrPwQLFCVQ2Iw2Cwl190N//nDKTwWvAwLJFuwzX0J5ogznV6/pKoea7LOYvyqPZi6dj925xY1nrQvFVgfC+xebrMWIiIioq7gkk2et9xyC66//noYDAb88ssvKCsr6+qSiIiIiIh6JiujzQfLfbBwfCRWTR9mPuZISGuSYxgEtT4EiotvYkKpBwQbQa1eEPDvIDHG9XkFvqJiqzvx7a3/xeECzMv4hTvxiYiIyGHMIRsVFhbikUcegU6nMx+bMmUK+vXr1y73d3Nzw9atWzFx4kTzscrKSsTGxmLfvn02r83MzERcXJzFWPbHH38c//rXv7pdgycAnDhxwuL99ddf79T1AwYMsHh//PjxNtdERERE1CI7m8QnDG18Mr4z2eE63QRMrluCWdq/QVnyEP5dcBn96rU2S6kXCfgipBonev+Iy2IxkqQZFo2ezqwPANl5KsxOP4gFmw5Bk7WqcTy9IoWNnkRERORyXLLJEwDmzjU+6l2n0yElJaWLqyEiIiIiujbFRIZh4i0RToekJgZIsKloCV4okMK/ScNAS37xEiH8urewzOfNZjvxHV0/84iSO/GJiIjIKddqDtnQ0IDi4mLs2bMHL730EiIjI/Hbb7+ZP7/uuuvwj3/8o13XNDV6Tpo0yXxMrVZj/Pjx+OGHH1q8ZseOHZg8eTLq6urMx2bOnIn09HSIRC4bb1ulUqmgUqksjvXp08epe1x9/unTp9tcFxEREZFVNjaJvx9/a6uzwxzDIPPrIfX12FygxCOVVjZvN7Hf0xOTe8nxjczT3OjZ2uwSAEKOpEGWtdjyoCKFG8aJiIjIpUjsn9I1XnjhBWzfvh0//fQTVq5ciREjRmDatGldXRYRERER0TUnJVwB2cnWhaSAsUFzZv05xOaL8UJoMI54uFs9t0AqweMRYVhU+gWeUunxke6hVoW02XkqZKerEDcsAsvlisagVnGlcSM60W7dREREdG24VnLIhIQErF692qFzo6Oj8cknnyA0NLTd65BKpdiyZQumT5+Obdu2AQCqqqrwwAMPIDMz02I8/Pbt2zFt2jRotY1PdZo9ezbWrVvXLRs8AeOY+qZkMhm8vLycusfV/7tUVFS0taxWO3PmDPbu3WtxTKlUml9v3boVwcHB5vfe3t6YMmVKp9VHREREHa8t2WHT3E9mMOC10jKM1dRicUggVDae2F4pFuPFsBDsUVdhUelG+LQwRcjZ9S3EJlttbiUiIiLqCi7b5CkSibBt2zY8/PDDOHDgAB599FHs2rULL7/8crNxNERERERE1EH2pTbfyQ7nGjxNQWm4Tof0wst4OzAAn/r5WL1GKwhYFhyICR478cHlY3hA9FuzcxxdP+RIGmS5VwW1ihRgQAyDWiIiIgLAHLKpiRMnYt68eYiNje3QdaRSKTZv3owZM2Zg69atAIDq6mr88Y9/xJdffomYmBhs3boVM2bMQENDg/m6OXPm4J///CcEQejQ+jpSVVWVxXtPT0+n73H1NWq1/SdedZS9e/di9uzZVj9fuHChxfu+ffu2uckzPT0d6enpzY5XV1e36b5ERETUCm3IDq01WEbV1OA/lwqxJCQICpntfyvt8PHGzx4eSC4pxe21jU9+b8v6mqilkDUZT09ERETkCly2yfP1118HYNw1fvz4cajVanz00Uf46KOPMGTIEAwfPhyhoaHw8bH+y+GWvPbaax1RLhERERFRz3PxALAzqdnh1jR4mrgBuL24P0bU5WJxcCA0Np7AlOnthVy3IgwukuA6beMv99uyPgDuxCciIiILzCEbffXVV9DpdPDw8MDYsWM7dC2JRIJPP/0UIpEIW7ZsAQBoNBo8+OCDWLBgAd5++22LBs+nn34aa9eu7dYNnkDzJk8PDw+n73F1k+fV9+zp8vLyoFAouroMIiIiakN2aGtyDwAkSTOQerkYW3288PfAANTYyBALpBI8JQ/FzAo15peVY8WV9UcIpyxGwju6frFyHGw+//7iAWaLRERE1OlctslzyZIlFoGdIAgwXHnM+tGjR3Hs2LFW3bc7hqtERERERF2izx3AuEWNI85h3MlerBwHHC6weam9EetzyjPxaf1mvBAajDNublbvc9bNDdMj5Hi1RIWHqjVtbvDkTnwiIiK62rWSQ7722mtISEgwv6+pqUFpaSkOHz6M7du34/vvv4dWq0VmZiYyMzMxb948rF69GmIbYzLbSiKRYOPGjRAEAZs3bzbXlZKSYnHes88+izVr1nT7Bs+WtOY7udJ/h1mzZmHWrFmduma/fv0wbty4Zserq6vx888/d2otRERE17RWZof2ckOTJGkGHlFX4/aaOrwSEoQjHu5W72kQBKT7++JHmQduKyxEQv1WJEi2tZgl2l3/cAEadHo8f88gDJZftdFrX6qxsXXcIiA60Wo9RERERO3NZZs8W9KW8MpgMLhU+EVERERE1C2YwkpFChCbDNno+VgNIG5YBNYqziH7vKrZJY4Etet0EwANkFGwEa8EhWGXj/VGzxqRCK+EBmNPpRY7CqLslmxr/aLCsXjP7h2IiIjoWtcTc8jAwEAEBgY2Oz5mzBj85S9/wd69e/HYY4/hwoULAID3338fNTU1+Oijjzq0LrFYjIyMDIhEInz66afNPp83bx5SU1Nd8r9pa3h7e1u8r6mpcfoeV19z9T17OmuNpceOHcNNN93U+QURERFdy5zMDh1t8DS9TpJm4PPaiRhyUYq7Qr5Gmr8vdDb+XXjazQ3n+5zG/LJy6CpgXst0P0fXzzyiROYRJUb2C8RzUQMQHRna2OBp+r5Nvz8RERFRB3PpJk/TjnkiIiIiIupC0YnAgBiLMUQxkWGIiQzDSaUa7+06hcwjSgCOB6WAMVzNqR2InEvX44mgFPw3pAJaGyHt175SRLovgerSbPxef2OLI5fsrv9rIU4X7UH04FDEDevVfDc+wJFLRERE1yDmkMZmz927d+P2229HaWkpAGD9+vWYOHEi4uLiOnRtsViM8ePHN2vyFAQB48eP7zENngCbPImIiKgHspMd7vg1H1sOXkJczTaHc0PgSnaoH2jO/+YUS7Ch5jMkhgThd6nUajkNgoB3AwOgkHnijeJSJKFxTWfWB4DsPBWy01VY9YcfMKn4A8sPFSnNvjcRERFRR3HZJs/du3d3dQlERERERGRiJawcLPfB+/G3QvzpIYQeTXM6KDWFtP8qfQVv1S3Gx/Jq5Eut/5hyzl0Mr/7peOZyIBJrf7O4t6MNpicK1ThRqMaarLOWu/EBjlwiIiK6BjGHbNS/f3+89tprWLBggfnYihUrOrzJc8OGDXjyySebHTcYDJgyZQq2bNnS4TV0Fj8/P4v3Go0G1dXV8PLycvgeRUVFFu/9/f3bozQiIiKi1rORHS6UR2Ke+9eQZTmXGwJotsH7lrp6bM1X4u+B/tjq28Lm7abXenjgT73Csai0DH+rykBL24bsrQ8YM8dJxc1rR2wyGzyJiIio07hsk+e4ceO6ugQiIiIiInJQSrgCspPOB7Umc8SZmNZwGvcXCEgKDkKWl8zqudUiETaGl8NQEYCXVK3fiQ807saPGxaB5XIFZFmLjR9w5BIREdE1gzmkpenTp1s0ef70008oLy/vsEbCjz/+GHPmzIFerzcfu/POO/HTTz8BAOrr6/HII49g06ZNmDx5cofU0JmCgoIQEBCAsrIy87GLFy/ihhtucPgeFy5csHg/cODAdquPiIiIqN3tS23M3JpwNDcELDd3ywwGLC4tQ5SmBouDg1AqEVu9TiMS4bWQIOyWeWJxiQpBTf7N6WiDZ0uZI2KTgdHzHaqdiIiIqD2IuroAIiIiIiLq5toY1DYNS/30BrxXVIIXSssgtjM29VM/HzwREYZZnpta1eDZVMiRtObfQZFiHN1OREREdA0JDQ1FQECA+b1er8f58+c7ZK20tDQ89dRTFg2eb775Jvbv34+EhATzMa1Wi2nTpuGzzz7rkDo629UNnWfOnHHq+nPnztm8HxEREZHLuHjAODXnKq1t8GxqXE0ttuUX4p5qjd177PaSYXLvcOySeTq8vrV1V4megOrmpx2qnYiIiKi9sMmTiIiIiIhar41BbUthqQCgV9lAfFx4GaENDTavP+bujqkRjQGts+tbqwEARy4RERHRNUsqlVq8r6ura/c11q5di2eeeQaGJht7VqxYgcRE45PU3333XbzwwgvmzxoaGjBjxgxs2rSp3WvpbDfddJPF+/379zt8bXV1NX777Teb9yMiIiJyGX3uAMYtsjikiVqK4qFzHbrcWm6XrI1HsjYegXo93i0qQXJxKbyabBxqiUosRkJYCP4WHAhB1NgYOkI45dS6qzT3447luxC/7ies+DoXJ5Xqlhfk5nEiIiJqR2zyJCIiIiKi1mtDUGsrLJ2rfRGZVY/gs3wl7tLU2LyPWixCQlgI3gr0hxbt0+CpiVrKkUtERER0TaqtrUVJSYnFsbCwsHZd4/3338ef//xniwbPd955BwsXLrQ47+2338bLL79sfq/T6fDYY48hI6OFDTrdyP3332/xPisry+Frf/jhBzQ02Qg1fPjwdv/fh4iIiKhdRSc25oexyZBFJWD19OFYP+s2jOwfaPUyW9nhOt0ErNNNQLI2HgKA8+XjEXfhJtxaU2u3nB0+3sjqdwBzfFKRINmKbe5LMEec6fC6AKDVGfDjmVKsyTqL8av2YOra/didW9R48r5UYH0ssHu53XqIiIiIHCHp6gKsEYvFAABBEHDu3Dn06dOnVfe5cOECrrvuOvO9Guw8CYiIiIiIiJwUbXzaEhQpxqB29HysBhA3LAJrFeeQfV7V7BJHwtJ1uglAHbDmcgb+7huBjYFi6AXBahn/9vPFIQ933FxQAujsl22rhmLlOKy2fwsiIiLqAZhDWtq1a5fF+HSZTIZevXq12/3fe+89LFiwwOLY6tWr8fzzz7d4fkpKCiQSCd544w0AxkbPJ554wvx3dzR+/Hh4enqipsa4mWn//v3Izc1FZGSk3WvT09Mt3j/88MMdUSIRERFR+4pOBAbEWEzNiYkMQ0xkGE4q1djxaz525xbjeGElAMeyQ8CYH+boByLHMAhoAJ76PQBRQf/Fe4H+0NrIEZUSCTb3zsejFbmoKRMs1nJk3atl56mQna5C3LAILJcrIMtabPxAkdL4/YmIiIjawGWf5GkwGMx/XOleRERERETUguhE4MmdFk+/jIkMw5ZnRuGbhLGYMFRuPu5oSAsYg9opdUvwQdHbmJrfG8ENtrs3j7m748u+x/Ck/9sAWh635EgNXxwuwLyMXzhuiYiI6BrAHLKRXq/HsmXLLI7df//9cHNza5f7r1y50qLBUxAE/OMf/7Da4GmSnJyM1157zaLO2bNnY/369e1SV2eTyWSYMmWKxbG33nrL7nWnTp3C9u3bze8lEgkeffTRdq+PiIiIqEM0afBsarDcBwvHR+J/C+7GxFvCncoOARgbPK8QIMKsSjU25SsxqK7ebkkb/XwwNUKO39zdkCTNaFWDZ1MhR9IaGzxNFCnMEomIiKjNXLbJEzCGfERERERE1E3YCGrfj78VE2+JcDqkBRqD2jT1fPzf7/4YaWfsUpVIhM/CS/BoxP/hUw/LcUuA402mmUeUHLdERER0jehpOWRqaioKCwudukar1eKpp57CgQOWv4CeN29eu9T097//HS+88IL5vSAIWLNmjcP3X7p0KV5//XXze71ejzlz5iAtLa1d6utsS5YsgVQqNb9PT0/Hjh07rJ5fW1uL2bNno76+sVnhqaeewoABAzq0TiIiIqLOlBK+p9WNlk0zv0FaLT4tUGJOeQVEdjZg5blJ8Xh4GN4L8IO2Feu2tL6F2GSruSkRERGRo1y6ybO77ngnIiIiIqLmUsIVbdoNP0eciYdwDP9UFuGZsgoIdn5e+NLPDY+Hy/GE52Zzo2drmkyz81SYnX4QCzYdgiZrFbAzyfiBIoWNnkRERD1ET8shP/roIwwYMACPPfYYvvzyS6jVVp5ODqCmpgaffvophg8f3mwU+OOPP46YmJg211NSUoIVK1aY3wuCgA8//BDPPvusU/d59dVX8eabb5rfGwwGLFu2DFVVVW2u0eTSpUvIy8tr9kepVFqc19DQ0OJ5eXl5KCkpsbvOdddd12xs/ZQpU/CPf/zDopETAE6cOIF77rkH+/btMx8LCgrC4sVXPSWKiIiIqDvbl9r8KZhwvsHTxA3AgrIKbCi8jD7aq9s3LekFAWn+fng0Qo5TVzbitEeDpyZqqcXkIyIiIqLWknR1AR2taSDWXmOFiIiIiIjISW0IaQHLoFQM4C/lFbi1thaJIcEolYitXnfC3Q1Te8mxpGQ77qg8gfvEOa2uIeRIGmS5V4W1ihRgQAx34xMREZHL5ZA1NTXIyMhARkYGBEHA9ddfj379+sHf3x9ubm5Qq9W4cOECjh8/Dm0Lv/R+8MEH2+0pmcHBwdi1axdiYmJQVlaGtLQ0PPnkk626V2JiIiQSCV566SX07t0b33//Pby9vdulTgAYM2YMLly4YPe8/Px89O/fv8XPZs6c2axhtiUpKSk4duwYvvrqKwDGp6nOnz8fy5Ytw4gRI+Dj44Nz584hJyfHohHZzc0N27dvR3h4uGNfioiIiMjVXTzQuLG6idY2eJquBYAkZOCzfCVWBvpjs6+PzXvlurthWi855pVVACU68/ERwimLsfCOrv/53iGYWpuLuGG9MFjewtoXDzBXJCIiIof0+CbPS5cumV/7+Nj+RxsREREREXWANoS0gPWgtKp6CLZqD2NRSDAOeHpYvb5aJMLC0GA84nEed6sEeDT5BXlba+C4JSIiIjJx5RzSYDDg9OnTOH36tN1zPT09kZSUhIULF1qME2+rm2++Gbt27cLRo0cRHx/fpnstXLgQvr6+uOeee3D99de3U4WdTywWY8uWLZgzZw42b95sPl5UVISvv/66xWtCQ0OxYcMG3H333Z1VJhEREVHH63MHMG6RcUP1FZqopShWjgMOF1i9zNGpPUnSDCSVliFGU4NXgwNRJLHeJtEgCFgd6I+bZbvxtLIEMq0vEiTbWswR7a5fVY81WWexJussRvYLxHNRAxAdGWo8aV+qMTMdtwiITrRaDxERERHg4uPa28P27dsBGEcA9evXr2uLISIiIiK6FplC2iY0UUtRPHSu3UttBaVztS9ibe0MfKgswp/LyiGyM2b1M18fPBoRhnNSifkebWnw5LglIiIiasqVcsi0tDQkJSVh1KhRcHd3d+iayMhILFu2DKdOncIrr7zSrg2eJrfcckubGzxNnnnmmW7d4Gni7e2NTZs24bPPPsOdd95p9bzAwEA899xzOHr0KO6///5OrJCIiIiok0QnNmaIscmQRSVg9fThWD/rNozsH9jsdEcbPNfpJpif6pmt/iNi8kZiQlW13XJ+83DHtj65CA78FnoYG0XniDOdXt8kO0+F2ekHsWDTIWiyVjVuilekALuX262HiIiIrm1d+iTPPXv2OHTeTz/9hLy8PIfvW1dXh8LCQuzcuRObNm0yH7/11ludLZGIiIiIiNqDaTe6IsUY0o6ej9UA4oZFYK3iHLLPq5pd4khQavo7qTwD/ppA/DNMixIb49tPu7lheoQcT5SIsK70Abtl26qhWDkOq+3egYiIiFzBtZZD3n777bj99tuxbNkyaLVanDhxAufOnUN+fj6qqqqg1Wrh7e0NX19f9OvXD8OHD0dAQECX1uwqnPnfvz1NmTIFU6ZMwfnz55GTk4OCggJUV1dDLpejb9++uOuuu+Dm5tYltRERERF1muhEYECMxeScmMgwxESG4aRSjR2/5mPLwUuIq9nmVIPlOt0E5OgHIscwCHPEmUgpLkVMtQbLggNRLraeJdaJRHgrKAC7ZJ5YVlKKJDSu6cz6TYUcSYMs96prFSnNvjcRERFRU13a5BkVFQVBEGyeYzAYMGPGjFavYWjyNJ+pU6e2+j5ERERERNRGdkLa93adQuYRJQDndsKbQ9raQXi6dhPyIvZjv6en1TJqRCJ8GArc7/kK9ha8jEGGYuQYBjU7z24NhwvQoNPj+XsGYbC8hZGsFw8wmCUiInIR13IOKZVKcfPNN+Pmm2/u6lLIAf3790f//v27ugwiIiKirmMlTxss98FCeSTmuX8NWZbzDZamBk9T3herqcGI/EIsDQpElpfMZkk/e3rgT73C8aKqDH9TZ6ClnywcafC0ljciNpk5IhEREdnkEuPaDQZDsz/2PnfkDwBzeDtx4kRER0d3+ncjIiIiIqImbIS078ffiom3RDg96giAuUnzn/XT0ef3e/G8yv749h99gD9c9waW+bxpMWoJcLzJNPOIEuNX7cHUtfuxO7eo8cR9qcD6WI5aIiIicjHMIYmIiIiIurF9qZBlLW52uLUNlsE6Pd4rKkFycSm89Xqb12tEIrweHITnwkKgvOrpn21p8NRELQVGz7d5LREREVGXN3leHaS29729vLzwwgsvYPPmzR22DhERERERtY+UcEWrRx01EmFuRSXWFxYhtKHB5pmXpBI8FhGGvkE78JT4SwDOPUXUJDtPhdnpB7Fg0yFoslYBO5OMHyhS2OhJRETkIphDEhERERF1YxcPNGZuTbSlwTJZG483tPGIq6rG9kuFGFVTY7eMH2WemNwrHJ97e8HQZP0RwqlWrT927xCs+DoXJ5Xqli++eMBuTURERNTzdem49sWLm++yMVm6dCkA4w74559/Hv7+/g7dUxAEuLu7w9/fHzfccANuu+02yGS2H69OREREREQuoA078U2aBqa31tXhP/lKJIUEQSGzPr69QRCwIigA4zy/w/vK3zBBONbqGkKOpEGWe1Vgq0hpNqaeiIiIOhdzSCIiIiKibq7PHcC4Rcas7QpN1FIUK8cBhwusXubohu4kZOBDZTE+8/HG24H+qBFZf16WWizCqyFB+M5LhvDCciQIW5Eg2dZihmh3/ap6rMk6izVZZzGyXyCeixqA6MhQ40n7Uo2NreMWAdGJVushIiKins+lmzxNI47++te/ok+fPp1VFhERERERdbY27MQ3aSkw9dfrkXq5GBm+Pngn0B8NV37GaIlC5okTfcoQWuyO22vrnK7BWmCL2GQ2eBIREXUx5pBERERERD2AqdFRkQLEJkM2ej5WA4gbFoG1inPIPq+yON3RBk/T6yRpBgrKYjGpqhon5YeQ4+FhsxyFzBO+/Q4jsbQMhmqY1zLdz9mJQdl5KmSnqxA3LALL5YrGDfGmxlY2ehIREV2zurTJ0x6DwWAOWImIiIiIqAdr5U58E1uBKQAkVWZgeG0tFoYG43ep1Op9iiQSPCUPxdPllXi2vAIpbWzw1IxbAtno+XavJyIioq7FHJKIiIiIqJuITmw2NScmMgwxkWE4qVTjvV2nkHlE6XSD5TrdBOToByLHMAhoAJ66GIiYoP/ivQB/1Ius/6xQKRYjMTQY31Zr8GqJymJNZ9ZvitOCiIiI6Gou2+S5e/du82u5XN6FlRARERERUadwcie+iTMjl2IvDkdu6FH86GO9DIMg4MMAP/zoIUPepUjz8RHCKWPI68T6G74bjJFnf8Itvf0RN6wXBstbWPjiAYazREREXYg5JBERERFRN2MlSxss98H78bfi3vcX4eFi5xssm2Z/AkSYWanG3TU1SAoOwhEPd5slfe8lQ46HO/5WWoak6ham/TiwPsBpQURERNQyl23yHDduXFeXQEREREREnc3OTvwdv+Zjd24xjhdWAnBu5JJ5J/4lPWYGrsD/QlSoFYmslnLUUwrv6z7E7co7cJfGgATJtmb3tb++AT+eKcWPZ0qxJussRvYLxHNRAxAdGWo8cV+qcUz9uEUct0RERNRFmEMSEREREfUg+1LxcPEHzQ470mBp0jTzu07bgH8VXka6ny/WBPhBa2MCQLlYjIWhwdhZrcHfSlQI0uudWt/qtKCopZwWREREdI2z/htNIiIiIiKirmBjJ/7C8ZH434K7MfGWcKdHLjXuxBdhg2oRFv3uhYH19TZLqRKLkNvrIMrDsqARBCRJMzBHnAnA8QbTprLzVJidfhALNh2CJmuVscETMD69dPdym7UQERERERERERGRDRcPNOZtTbS2wdNEAmBORSU25ytxQ53tPBEAvvWS4eHe4fjaS2ax/gjhlFPrmq4du3cIVnydi5NKdcsXXzxgtyYiIiLq3tjkSURERERE3U5K+B6nGyybmiPOxJ/0J7Gx4DKmVVoJR5v4j683pkfIcdJNiiRpBtKkb7dp/ZAjaZBlLbY8qEhhIEtERERERERERNRafe4wTsxpYrV4ZpsaPAFj5pesjcdArRYZBUr8pawcEoPB5r3KrjzV8/9CgyESq5Eg2Ypt7kvMG8gdXXedbgJKquqxJussxq/ag6lr92N3blHjSftSgfWx3EBORETUw7nsuHYiIiIiIqIW7Utt3iAJ5xo8TaGph8GApNIyjKqpxWvBgagUi61ed95NikfD5XhBVYYZ6px2Wd9CbLLVp5gSERERERERERGRA6ITjX8rUoDYZDxxyzM49+UxfHG4wOZljk7tSZJmoKbkXjxSWYYc+QmcdHezed9vvWTw7/cL/lZaBkM1zGuY7unstKDsPBWy01WIGxaB5XJFY06qSLH8/kRERNSjdJsmz6qqKnz55ZfYt28fTpw4gbKyMlRUVECv1zt8D0EQcPbs2Q6skoiIiIiIOlQbRy5ZC0316hvwn7pf8XJoEHI8PKxeXy8SsDw4EPtknni9uBSBV34eaev6mqilkI2eb/d6IiIi6njMIYmIiIiIurnoRGBADNDnDgQAWD19OOKGRWCt4hyyz6uane5oo+U63QTk6AcixzAIaADmXPgC0SFfI83fFzpBsFpO+ZWnen5TrcHfSlQWa7V2WlDIkTTIcq+6VpFi/t5ERETUs7h8k6dWq8XixYvxwQcfoLKy0nzcYOfx5y0RbPzDioiIiIiIugHTyCXTznQYGySLleOANu7Gn6PPxPrCDPzT3xdr/f2gt/Hzg0LmiT/1CscbJaWorrqxTQ2eydp4FCvHYbXdOxAREVFHYg5JRERERNSDXNXoGBMZhpjIMJxUqrHj13z8+nsFDpwvxUz816lGyxzDoCbvJJhXXoEojQZJIUE442b7qZ7fecnws4c7Xiktw9+qM9DSTw2ONHhyUhAREdG1x6WbPEtKSvDAAw8gJyfHHKY2DUgdCUsNBgMEQWhVGEtERERERC7oqpFLstHzsRpo8258099J5RkIrA7COnkdlBLrPzKVSMR4Rh6KmRXnMLfoc6Q1TMII4dRVQa+D6x8uQINOj+fvGYTBcp9m5wmXsgHPAVZrISIiorZhDklEREREdG0YLPfBQnkkAECz+13IFK17kmbTvG9IvRab85X40N8PHznwVM+XrjzVM6lUhWBd48SAtjR4asYt4aQgIiKiHsxlmzz1ej2mT5+OX375BQDMAalUKkVgYCCUSqU5OO3Tpw8qKytRXl7eLIT18fFBYGBgl30PIiIiIiLqAE1GLpk03Y3/3q5TyDyiBOD4uCWgycil2kGYW7sFpeF78a2XzGYpG/x8cYPHHqRe/gUP6c42u6+j62ceUSLziBIj+wXiuagBiI4MNX6wLxWSnUnwvvUvqLqdQS0REVF7Yw5JRERERHQN2pcKmWJJs8OtbbR0AzC/vAIxGg1eDQnCaTtP9dx15ameL5eW4cFqDd5osm5rNpJv+G4wRp79Cbf09kfcsF4tbiTHxQN80icREVE3JerqAqzZtGkTvv/+ewiCAEEQ0Lt3b2zduhVqtRr79u2zOPf8+fMoLS1FdXU1du3ahcceewwSiQQGgwENDQ149dVXcf78eZw/f76Lvg0REREREbU7K4HkYLkP3o+/FRNviXCqwdPEFKCmaafi/sLeWFxSCg+9vsVzTU64u+H13nXY6uOFv0kzMEecCcC5BlOT7DwVZqcfxIJNh6DJWgXsTAIAeP/yD3gfTLVZBxERETmPOSQRERER0TXm4gFz5tZUW56kmayNR7I23vxUz2fKKiC285T/CrEYr4QGY35YCESScgBAgmQrtrkvMeeLjqy7TjcBWp0BP54pxZqssxi/ag+mrt2P3blFjSfuSwXWxwK7l9usiYiIiFyTyz7Jc+XKlQCMY47CwsLw448/onfv3gCsj0fy8PBAdHQ0oqOj8fzzz2Pq1KnIy8vD3LlzUVNTg3nz5nVa/URERERE1LVSwhWQnWzduCXAGJzGig8DamB4bR1eCgnGKXfrO/BrRSIsDQ7CXk9PLCn5FHdoT+A+cU6r1w85kgZZrmX93r/8A3V/uAsIus/u9UREROQY5pBERERERNeYPncA4xYBihTzIU3UUhQrxwGHC6xe5uiG7iRpBv5ieqpncJDNTBEAFDJP+PT9FS+V/ornqi+Z7wEYJw+1diN5droKccMisFyugCxr8ZXFrnzn6ESbNREREZFrcckneZaUlCAnJ8e8e/6NN94wB6uOuu222/Ddd98hJCQEBoMBf/3rX3H48OGOKZiIiIiIiFzLvtTG4LIJZxo8mwanA7QN2FioxGMVlXav3eUlw596yeHndazd1jepHPUytPIRdq8nIiIixzCHJCIiIiK6RkUnGhs9ASA2GbKoBKyePhzrZ92Gkf0Dm53uaKPlOt0EJGvjAQA7NQ9h+IVxeLasAhI7T/VUi0X4IFSEZ8NCUCgWAzA2eqZJ33a6wbOpkCNpzXNSRYrxaaZERETUbbhkk+eBA8Z/UBgMBnh6euLRRx9t1X2uu+46vPHGGwAAnU6H5cv56HEiIiIioh6vDeOWAOuB7Z6GEXhZVY4PlEUI1Ols3qNIIsEceShWBvhD207rF922EJpbnrR7PRERETmOOSQRERER0TUsOhF4cicwer75UExkGLY8MwrfJIzFvOgBCPF2d/pJmut0EzC5bglWNUwBIMG88gpsLFBicF293ZL2yTwxqXc4tvh4Qw+0aVKQtboRm2x8mikRERF1Gy7Z5FlQYHwEuiAIuPnmm+Hh4WHzfK1Wa/WzJ554At7e3jAYDPjvf/+L6urqdq2ViIiIiIhcjGncUhOaqKUoHjrX7qW2Atu52heRrI3HmJpa/OdSIcZoamzeyyAI+NjfF/ERcvxHNNgcvI4QTrVq/ftzbscHP+bjbImVdbn7noiIyGnMIYmIiIiIrnFWmh0Hy32wcHwkFGOOtupJmjmGQRZZ3w31WnxaoMSfy8rtPtVTIxJhWXAg5spD8btE7NS6JtZyRk3UUoumViIiIuoeXLLJs6yszPy6pfFIbm5uFu9ra2ut3svNzQ0jR440n/fjjz+2U5VEREREROSynBy3BDg2csk0bilYr8f0gjAsKlXBTW87lD3h7oaUP1Tj8aA3sEDyGba5L8EccabT66s0DdhwUIn4fx/HjHXZ2J1b1HjSvlRgfSywm08NIyIicgZzSCIiIiIismpfavNR53Cs0bKlrE8K4LnySmzOV+LGujq7y2d7euBPvcLxia8PdAC+1Y1o80bysXuHYMXXuTipVLd4rXAp225dRERE1PlcssnT0GTnSku75318fCzeX7582eb9wsLCzK9Nu/OJiIiIiKiHszNuacJQufm4MyOXTOOWZmpfwfnSOGwsUGJAve1RS7UiET4PVeNMr70oEYmQJM2waPR0duTTzxfKMTv9IBZsOgRN1qrG8fSKFDZ6EhEROYE5JBERERERtejigcbMrYnWNniark3WxmOQVouMgstYoCqH1M5TPWtEIqwICsDM8DAM8PgNc8SZSJBsbfVG8pKqeqzJOovxq/Zg6tr9zTaSSzb8Ed4HU23WRERERJ3PJZs8fX19za/V6uY7SLy8vCCRSMzv8/LybN6vvskvXIuKimycSdQzqNVqKBQKvPPOO5gxYwYGDRoEkUgEQRAgCILd/5shIiIi6jFsjFt6P/5WTLwlwukGS8A4bgkwNnxerB2KTQWXMa2y5d3vTe2ReeJPvcOxx9PD3OjZmvVNQo6kNX+agCKFo9uJiIgcxBySiIiIiIha1OeOxklBV2iilqJ46Fybl9nL+kyTgiQA5lRUYmt+IW6utf9Uz1893DGlVzhCg7/EPMk2AGjzRvLsPFWLG8m9f/kHGz2JiIhcjMT+KZ2vb9++5tcthaGCIGDgwIE4ceIEACA7Oxv33nuv1fsdPXrU/FoqlbZjpUSuaezYsTh8+HBXl0FERETk8lLCFZCdbF2DJWAMTu8T5wAGIKm0DHdravBaSBBUYrHVa1RiMebJQzGtUo0XVBvh2cJu/bY8EQCxyVabW4mIiMgSc0giIiIiIrIqOtH4tyIFiE2GbPR8rAYQNywCaxXnkH1eZXG6o02WptdJ0gzsqJmIyIvuiA36L1ID/FAnsv6cLq0gIDXQH996yfB6SSluqNdarNemjeS5ltd6//IP1P3hLiDoPpvXEhERUedwySd5RkZGAjCOSzp+/HiL5wwbNsz8etOmTVbvdeDAAZw8edL8PiIion2KJHJhTUeN+fn5ISoqCnK53MYVRERERNegfanNn4IJ5xo8rw5Ox9XU4j+XCnG3psbu9Zt9fTAtQo7jbpYNIG1p8NRELbUYT09ERES2MYckIiIiIiKbohOBJ3daZG4xkWHY8swofJMwFvOiByDE293pp2iu003A5LolWNUwBQJEmFmpxn/ylRhRW2u3pFx3N8yIkOO9AD/UCcbmztY2eFqru3LUy9DKR9ithYiIiDqHSzZ5XnfddQgNDQUAVFZWIjc3t9k5cXFx5tfHjh3D8uXLm51TVFSE2bNnQxAE87G77rqrAyomci1PPvkkNm7ciFOnTqGsrAy7d+/G4MGDu7osIiIiItdx8YB5/FBTbWnwBIBvdSMQrNfj/cvF+FuJCu56vc37nHeTIj5CjnV+vtA5uL6twDhROc5u7URERNSIOSQREREREdllZWrOYLkPFo6PhGLM0VY1WeYYBllkfX0bGvBxYRESS1TwtJMr6gQBaf5+eCQiHIfd3ZxeG7CeMxbdthCaW560eS0RERF1Lpds8gSAqKgo8+uvvvqq2ecPPvggQkJCABh32iclJSE6OhrvvPMO1q1bhxdffBFDhgzByZMnYTAYIAgCoqKi0Lt37876CkRd5vnnn8eMGTMwcOBAi18uEBEREdEVfe4Axi2yOKSJWorioXPtXmqryXKu9kUka+MhAJiursLmAiUG1mlt3q9BELA60B9PhodCJC0FAIwQTjm99jrdBHxxuADzMn7BSaW65cUuHrD95YiIiK5BzCGJiIiIiKjV2jAtqKWsTwTgUXUVtucXYlSN/WlB592keCI8DMsDA6C58nth09rWMkZra5uuvT/ndnzwYz7OllhZnxkjERFRp5N0dQHWTJ48GVu2bIHBYMAnn3yCv/71rxafy2QyvPHGG3j66achCAIMBgP27NmDPXv2mM8xhaoAIJVKkZKS0qnfwZ6amhrk5ubiwoULKCgogFqthlarha+vL4KCgnDTTTdhyJAhkEhc9n+ma8b58+dx+PBhFBQUoKqqCuHh4ejbty9Gjx4NqVRq/wZERERE5HqiE41/K1KA2GTIRs/HagBxwyKwVnEO2edVzS5xZOyS6e8kaQa+rJmIo+cn4KGwN/Gtfz0MNjbg5Hh44GTfo1hU8hKeqb6EN64Kgh0d+ZR5RInMI0qM7BeI56IGIDrS+HQy7Es1Pr103KLG705ERETXRA5JREREREQdoA3TgmxlfQCQhAx8qCzG595e+HtgANRi68/vMggCNvr5IEvmiddKVYAWSJBsRYJkW4u12M0ZNQ3YcFCJDQeVuK1vAeZFD2TGSERE1MVctnvwwQcfxEMPPQT9lceQX7x4EX369LE4Z86cOTh27BhWr15t8bRCU6hqCl0lEgk+/PBD3H777Z36HVry8ccf4/vvv8eBAwdw9uxZ8/ezxtvbG1OnTsX8+fMxbNiwzinShZ07dw4HDx7Ezz//jIMHDyInJwdqdeNTivr27Yu8vLx2W2/r1q1YuXIl9u/f3+LngYGBmDZtGl5//XUEBwe327pERERE1EmiE4EBMRYjl2IiwxATGYaTSjV2/JqPX3+vwIHzpZiJ/zo8dmmdbgJy9AORYxgEAPiPchmSNUn4OKwWRTY2cVWLRHg/FMitDsbikk+BOuO9HG3wbCo7T4XsdBXihkVguVzR+EQBRUrjdyciIqIem0MSEREREVEHM00LUjRu8tJELUWxchxwuMDqZY5mfUnSDDxcVY0xNTVIDgrE914ym+UUSCV4Vh6KiV5f4S+qckAP8zqm+zqbM/58oRyz0w8yYyQiIupiLtvkKZPJ8MUXX9g9791338Xo0aOxZMkSnDhxwnzcYDAAAMaMGYO33noLo0aN6rBanfHqq68iPz/f4fOrqqqwfv16bNiwAfPnz8ff//73a+7JnllZWVi+fDl+/vlnqFTNn6bUEaqqqjB37lxs2rTJ5nkqlQoffPABtm3bhg0bNmD8+PGdUh8RERERtaMmDZ5NDZb7YKE8EgCg2f0uZArnmixNDZ6AMTx9rP4cHsoXYVlQAL7x9rJZ0i4vGX51d8frJf/BHVUncJ84x6m1mwo5kgZZ7lW1K1KaNbcSERFdq3pqDklERERERJ3AyWlBjjZZNp0WlFE3CcH57vi7/xdYHhQAlVhss6QdPt7YK/PEKyUqxGpqLNZzdiO5CTNGIiKirtUjugUfeeQRPPLIIzhz5gxOnz6N8vJyBAQE4JZbbkF4eHhXl2eTTCbDgAED0KdPH/j6+kKv10OlUuHIkSNQKpXm83Q6HVatWoW8vDxs3boVYjv/cOtJDh8+jJ07d3baejqdDtOmTcP//vc/i+MhISEYPnw4/Pz8cPbsWRw6dMgc4l++fBlxcXH47rvvMGbMmE6rlYiIiIg6wb5UyBRLmh12tMmyaXDrp9fj78WlGKepwZvBgagSWR+zVCIR48/yUEyrPI8xKgGeV/7t2dq1LcQmM3wlIiJqhe6cQxIRERERUQdxYFrQloOXEFezzakmy6unBd1ZeRKf1xzGW0EByLSziVwlFuPFsBBEV2uQVFqGJLSQEdpYuylmjERERF2vRzR5mlx//fW4/vrru7oMm7y8vDBx4kQ88MADGD16NG666SaIrPxi96effkJSUhJ27dplPvb5559j5cqVWLhwYWeV7LLc3d3Ru3dvnD17tl3vu2jRIosGT6lUipUrV+Lpp5+Gm5ub+fjx48cxZ84c8yj3uro6TJo0CUeOHGGoT0RERNRTXDwA7ExqdrgtTZYCAI/KSPyn7lf8LTgIP3t62LzHZl8fHPDwwJvFpRhaX9/mBk/NuCWQjZ5v93oiIiKyrjvkkERERERE1InsTAua5/41ZFnOP0XT1OA5R5xpnPajB1KKS/HHqmq8HhyIy3amgO72kuFnDw/8X1kZ/qSuhuDE2qZ1mTESERF1vR7V5NkdHD16FFKp1KFz77zzTuzcuRMzZ87Ev//9b/PxN954+2os0QABAABJREFUA88//zzc3d07qkyXI5VKMWTIENx22224/fbbcdttt2Ho0KH48ccfER0d3W7rnDt3DqtXr7Y49tlnnyEuLq7ZuTfeeCN27dqFe+65x9zoWVpaiqVLl2Lt2rU21/n8889RVVXV5npHjx6N6667rs33ISIiIiIr+twBjFtkHD10hSZqKYqV44DDBTYvtTd6aY4+E+uUGfiXnw/eC/BHgyC0cBejPDcpHo8Iw5QyPdIv32s+PkI4ZTEW3pG1N3w3GCPP/oRbevsjblgvDJb7NF/w4gHuwiciIiIiIiIiImoP+1Ihy1rc7HBbNnOPranF55cKsSrQH5t9W8j3mlCLRVgaHIRMLy8sLlGhX0ODxdrMGImIiFwfmzw7maMNniYikQjvv/8+tm/fjurqagBARUUFdu/ejfvvv79daiovL0d+fj6GDBnS5nvl5OTghhtugKenZztUZjRz5kw8++yz8PCw/YSj9rB06VJotVrz+1mzZrXY4Gni6emJ9PR0DB06FPX19QCAjz76CC+99JLN5suEhARcuHChzfV+/PHHbPIkIiIi6mjRica/FSlAbDJko+djNYC4YRFYqziH7POqZpfYa/AEYP47qSID/lUh+JdcgzNNnhx/NZ0gYHOgGDfLklBa8AQe1p1EgmRbszDY/toG/HimFD+eKcWarLMY2S8Qz0UNQHRkqPHEfanGp5eOW9T43YmIiIiIiIiIiMh5HTApCAC+1Y3AfeIcJJWW4YEqDZYEByLPzXYvws+eHvhTr3A8V14BoaQBAJAg2cqMkYiIqBtoeU44uRRfX1+MGTPG4tiZM2fa5d4VFRUYP348xo4di0OHDrXpXgqFAmPHjsVDDz2EmpqadqkPAAICAjqlwbOmpgZbt261OPbyyy/bvW7QoEGYNGmS+X1DQwM2btzY3uURERERUVeKTgSe3Ak0GUEUExmGLc+MwjcJYzFhqNx83JEGT5N1ugmYXLcEf61Oxm0X7sbjFZV2SzntIUZ1v08QHPgt9ACSpBmYI850em2T7DwVZqcfxIJNh6DJWtUYOitSgN3L7dZDREREREREREREVpgmBTWhiVqK4qFz7V5qK+ubq30Rydp4AMCtdXXYWlCIqWVaiA0Gm/esFwlYHeiPnL4KvOPzNyRItgFgxkhEROTq+CTPbiIwMNDivVqtbvM91Wo17r//fmRnZwMA7rnnHnz77be49dZbnb7X999/j4ceeggajQa7du3CxIkT8eWXX3ZKc2Z7+eabb6DRaMzvR40ahcjISIeunT17NrZs2WJ+v23bNiQlNd+RZfLbb79Br9e3vtgrZDJZm+9BRERERA6yMlposNwH78ffCvGnhxB6NM3pANQ0CslgcMNLqnKM1dQgKSQIlyXWf1yrE4nwVlAAsmSeWFZciiRk4A7RCdwnznFq7aZCjqRBlntV7YoUYEAMxyoRERERERERERG1VkdPCpJm4APtZHyknIKnK/+Jw/JcnHC3PjEIAE65u+H1Xg04V+mPeWUVkBkMSJIyYyQiInJV3abJs7q6Gvv378fhw4dx+fJlVFZWWozVdoQgCPjoo486qMKOdfVo74iIiDbf083NDUFBQeb3ZWVluPfee/HNN99g5MiRDt/n22+/RVxcnMXTO0NCQpweTd/Vvv76a4v3UVFRDl979913QyKRoKHB+Fj7Q4cO4fLlywgLC2vxfF9f31bXSURERESuKSVcAdlJ5xo8TZqGtnfW1uE/+YVYHhSITG8vm9cd8PTA5N7heKVUhQerWh++WguNEZvM8JWIiK4513oOSUREREREHSA6sVmjY0xkGGIiw3BSqcaOX/Px6+8VOHC+FDPxX6cmBeXoB5o3kuvremFjQRb+5eeDNf5+qBNZH+6qFwT8y88Xu2QyvFqqwl01tW1q8GTGSERE1HFcvskzPz8fr776Kj777DOLpyw6y2AwdNtw9dSpUzhw4ID5vSAIGDduXJvv6+7ujm3btmHKlCn48ssvAQDl5eW477778M033+DOO++0e49vvvkGkyZNQm1trflYfHw8NmzYALFY3OYaO9PRo0ct3o8aNcrha728vDB06FCLkffHjh2z2uRJRERERD3MvlTIshY3O+xsg6eJn96AlOJSRFdrsCw4EBU2/m1dJRLhlZBgfC/T4NUSFQKvPDG+reGrJmopZE3G0xMREfV0zCGJiIiIiKhD2ZgUtFBunDCp2f0uZIrWTQpqmvM9WaHGPdU1WBociIOetqdv5ksleFYeiofU1VioKkNAk4mUzBiJiIhcg/VtGy7gf//7H2688UZs2LAB1dXVMBgMXV1SpyssLMQjjzwCnU5nPjZlyhT069evXe7v5uaGrVu3YuLEieZjlZWViI2Nxb59+2xem5mZibi4OIsGz8cffxz/+te/ul2DJwCcOHHC4v3111/v1PUDBgyweH/8+PE210RERERE3cDFA8DOpGaHW9vgabo2WRuP8ZoabM8vxBhNTQtXW/rOS4aHe4djl8wT3+pGmNceIZxq1fpj9w7Biq9zcVKpbvniiwdaPk5ERNQNMYckIiIiIqIuty8VMsWSZodbmzP2bWjAR8oiLC0uhY9Ob+XKRl/6eCGudzi+9JLBADBjJCIiciEu2+R58OBBTJ48GWq12rz7HTDuhG/Nn+6ioaEBxcXF2LNnD1566SVERkbit99+M39+3XXX4R//+Ee7rmlq9Jw0aZL5mFqtxvjx4/HDDz+0eM2OHTswefJk1NXVmY/NnDkT6enpENl45LurUqlUUKlUFsf69Onj1D2uPv/06dNtrouIiIiIuoE+dwDjFlkc0kQtRfHQuTYvsxV+rtNNwDrdBCRr4xGi02PN5WK8WqKCp952GKsSi5EQFoKssDw8Lf0MCZKt2Oa+BHPEmU6vX1JVjzVZZzF+1R5MXbsfu3OLGk/alwqsjwV2L7dZDxERUXdwreaQRERERETkQjpoI/kb2nhMrqrGF/kFuK/a/sSCMrEYr4QG4xl5CCLdf8UccSYzRiIiIhfgsuPa//KXv6C+vh6CIMBgMMDDwwPTp09HbGwsBg4cCD8/P0gkLlu+wxISErB69WqHzo2OjsYnn3yC0NDQdq9DKpViy5YtmD59OrZt2wYAqKqqwgMPPIDMzEyL8fDbt2/HtGnToNVqzcdmz56NdevWdcsGT8A4pr4pmUwGLy8vp+5x9f8uFRUVbS2r1c6cOYO9e/daHFMqlebXW7duRXBwsPm9t7c3pkyZ0mn1EREREfU40YnGvxUpQGwyZKPnYzWAuGERWKs4h+zzlhuK7IWfJqbXSdIMFJTFYlJVNY7LD+NXD3eb5ezw8UaY5wG8XqwCamFey3Q/R9c3yc5TITtdhbhhEVguVzSOplekWH5/IiKibuhaySGJiIiIiMiFmTaSm/I2XNlIrhwHHC6wepmjOV8SMrCyqAS7ZJ54MygARXZ+xtnv6YnJvcLx5/IdeLzC+BROZoxERERdxyXTyZMnT+LgwYPmXfM33ngj/ve//zn9ZMWeYuLEiZg3bx5iY2M7dB2pVIrNmzdjxowZ2Lp1KwCguroaf/zjH/Hll18iJiYGW7duxYwZM9DQ0GC+bs6cOfjnP/9p/t+rO6qqqrJ47+np6fQ9rr5GrbbyyPlOsHfvXsyePdvq5wsXLrR437dvXzZ5EhEREbVVdCIwIMYYyF4RExmGmMgwnFSqsePXfGw5eAlxNducCj/X6SYgRz8QOYZBQAPw4SUlogLP4/0APzTY+Df4ZYkEz4SHYlqlGv+nKrdY05n1mwo5kgZZ7lXXKlKafW8iIqLugjkkERERERG5jE7YSH6k8gHEqA3Qh2Zhi6+PzXJqRSKsDAzA/7y8sKREhSH19cwYiYiIuohLNnnu378fgHEkkkgkwpYtW67pYPWrr76CTqeDh4cHxo4d26FrSSQSfPrpp+b/7gCg0Wjw4IMPYsGCBXj77bctGjyffvpprF27tls3eALNmzw9PDycvsfVTZ5X37OnS09PR3p6erPj1dXVnV8MERERUVexEkIOlvtgoTwS89y/hizL+fAzxzAIgDG4HS/+FagAxtTUIDEkCGfc3GyWtNnXB/s8PZBcrEISmq/tyPqmtVsKbhGbzPCViIi6LeaQRERERETkUjphI/kccSZeLS3DH6s0WBIciDw3qc2Sct3d8GhEGOIr1fhLWUXLGaGNtZtixkhERNQ6LtnkefnyZQCAIAi49dZbceONN3ZxRR3ntddeQ0JCgvl9TU0NSktLcfjwYWzfvh3ff/89tFotMjMzkZmZiXnz5mH16tUQi8UdVpNEIsHGjRshCAI2b95srislJcXivGeffRZr1qzp9g2eLWnNd3Kl/w6zZs3CrFmzOnXNvLw8KBSKTl2TiIiIqFvZl9o4gqgJR8JPoHkAGlmvxeZ8JdYE+OFjP1/obfx79HepFLPCQzGzQo2/lJfD3eDc+tbCV03UUshGz7dbOxERkau6lnJIIiIiIiLqJjpwI3nTnO/WujpsLShEmp8fPvL3tTk1SC8I+MTPF7tkMiSVqnB3Ta1TawPMGImIiNrCJZs8mzYwDhgwoAsr6XiBgYEIDAxsdnzMmDH4y1/+gr179+Kxxx7DhQsXAADvv/8+ampq8NFHH3VoXWKxGBkZGRCJRPj000+bfT5v3jykpqa6VGNjW3h7e1u8r6mpcfoeV19z9T17un79+mHcuHHNjldXV+Pnn3/ugoqIiIiIXMjFA8DOpGaHW9vgaaLQjUBCWQ6iNDVICgnCBan1XfcGQUC6vy/2yDyRXFyKofX15vVHCKfMTwt1dO1kbTyKleOw2lbhFw9wBz4REbm0aymHJCIiIiKiHqANG8lbyvncDcBfyitwf7UGS4MDcdjD3eY9CqQS/FkeivurqvGyqgzBOn2bM8bP9w7B1NpcxA3rhcHyFkbIM2MkIiKCqKsLaMkf/vAH8+u6urourKTrjRkzBrt370ZQUJD52Pr16/HFF190+NpisRjjx49vdlwQBIwfP77HNHgCbPJsD7NmzUJWVlazPy2NcCciIiK65vS5Axi3yOKQJmopiofOtXuprQB0rvZFJGvjMayuHlvylZhRobZ7v3NuUjwWEYb3AvwgCFokSLZim/sSzBFnOrX2Ot0EfHG4APMyfsFJZQvr7ksF1scCu5fbrYmIiKirMIckIiIiIqJuow0byW3lfMnaeFyv1WJD4WUklajgrdfbLeVrby9M7BWBLT7eAPRtyhhLquqxJussxq/ag6lr92N3blHjScwYiYiIALhok+eoUaPMr0+fPt2FlbiG/v3747XXXrM4tmLFig5fd8OGDXjyySebHTcYDJgyZUqnNJp2Fj8/P4v3Go0G1dXVTt2jqKjI4r2/v39byyIiIiKiniQ6sbHRMzYZsqgErJ4+HOtn3YaR/Zs/3R+wH4ACwDrdBCRr4yEzGDCmuDfSCi9D3tBgsxS9ICDN3w8/992DWNmXAIAkaYZFCOvI2gCQeUTZPIDdl9oYOCtSGMISEZHLYg5JRERERETdRis3ktvL+Uz5ogjANHUVPr9UiHurNXbLUYtFWBYciFN9duFBz7ZljCbZeSrMTj+IBZsOQZO1ihkjERHRFS7Z5NmnTx+MGTMGBoMBx44dw5kzZ7q6pC43ffp0i/c//fQTysvLO2y9jz/+GE8++ST0TXbp3HnnnebX9fX1eOSRR7Bt27YOq6EzBQUFISAgwOLYxYsXnbrHhQsXLN4PHDiwzXURERERUQ8TnQg8uRMYPd98KCYyDFueGYVvEsZiwlC5+bgzAeg63QRMrluCJ7SJ+E49BdsuFWKSuspuOWfc3BAfIccafz9o0RjCOhu+Ao0B7OfvL2r+RAFFivFJA0RERC6GOSQREREREXUrTm4kdzTnMzV6AsCndXEIyh+PVZeLEWpnMzkAHPLwwCO95HgvwA+1gtCmjNEk5Eha87H0zBiJiOga5pJNngDw5ptvQiQylvfiiy92cTVdLzQ01KIJUa/X4/z58x2yVlpaGp566imLBs8333wT+/fvR0JCgvmYVqvFtGnT8Nlnn3VIHZ3thhtusHjvbKh/7tw5m/cjIiIiIgJg3HHfgsFyH7wffysm3hLRqgA0xzAIgDGQ/alhOJaVqJCqLEZQg85mOQ2CgA8C/PBohBwnpVIkSTNaHb7OEWdiUvEHzT+ITbb6vYmIiLoac0giIiIiIupW7Gwknxc9ACHe7k5njKaN5KsapmCdbgL06hvw+aVCTKtUQzAYbJbUcGVy0J96yfGTh3ubM8aWrmXGSERE1zKXbfIcM2YMli1bBoPBgC+//BLz5s1DgwO7RHoyqVRq8b6urq7d11i7di2eeeYZGJr8I23FihVITEwEALz77rt44YUXzJ81NDRgxowZ2LRpU7vX0tluuukmi/f79+93+Nrq6mr89ttvNu9HREREROSIlHBFqwNQwBiC3ifOAQBE1dTg8/xCPFBVbfe6XHc3TO8lxz/9fHH1T15tDl+bBM5ERESuhjkkERERERF1OzY2ki8cHwnFmKOtyhhNG8lNGaOPwYCk0jL8q/Ayrq+vt1vWRakUc8PD8EpwEFQiy3aUtmSMmqilzBiJiOia5rJNngCQmJiIFStWQCQSYe3atRg+fDjS09NRWFjY1aV1utraWpSUlFgcCwsLa9c13n//ffz5z3+2aPB85513sHDhQovz3n77bbz88svm9zqdDo899hgyMlr4hW43cv/991u8z8rKcvjaH374wSL8Hz58eLv/70NERERE14B9qc3HEMG5Bs+rQ1B/vR4rikux8nIxAnX2n+qZGuiP+IgwnLqyyawt4esq0RNQ3fy03bqJiIi6GnNIIiIiIiLqMTogYxxWV48t+UosUJXDvclEUGu+9PHCxN7h2ObtBX2TtUcIp5xa11T32L1DsOLrXJxUqlu+mGPciYioh5N0dQH2vPjii7jrrrvw4IMP4vjx43jqqacAAD4+PvD39zePUnKEIAg4e/ZsR5XaoXbt2mUxPl0mk6FXr17tdv/33nsPCxYssDi2evVqPP/88y2en5KSAolEgjfeeAOAsdHziSeeMP/dHY0fPx6enp6oqakBYHySZ25uLiIjI+1em56ebvH+4Ycf7ogSiYiIiKgnu3gA2JnU7HBbwlcA+FY3AveJc3Cfpga3XipEcnAgvvWS2bzXcXd3TOslx7PlFRCKGzczjRBOmXfz21vXWPf9eH/5LozsH4hbevsjblgvDJb7NF/w4gGOWiIioi7HHJKIiIiIiLq9Ds4Y51RUYny1BsuCA7Df09PmvSrEYiwOCcIXPl4YfFmJhPqtSJBsa7EWu6Plq+qxJuss1mSdxch+gXguagCiI0ONJ+1LNX7ncYuMo+yJiIh6IJdv8tyxYwdeeOEFlJeXA4D5KZOVlZWorKx06l6CILR3eZ1Cr9dj2bJlFsfuv/9+uLm5tcv9V65caTGCXRAEpKamYt68eTavS05Ohlgsxuuvv26uc/bs2WhoaMCTTz7ZLrV1JplMhilTpuCTTz4xH3vrrbfw8ccf27zu1KlT2L59u/m9RCLBo48+2mF1EhEREVEP1ecOYxCpSDEf0kQtRbFyHHC4wOal9kLQOXrj54F6Pd4pKsF6zz/g4xA9KsRiq/dsEAT8I8AfN8gUeFpZDFm9f7MQ1m74CkCrM+DHM6X48UwpQ1giInJpzCGJiIiIiKjb64SM8Q8NDfhQWYw0WW9kBBugspExAkCOhwd+63MKsysqUVsumNdwJmNsKjtPhex0FeKGRWC5XNH41FLTd2bGSEREPZBLj2t//fXX8fDDD+PcuXPmY4IgtOqPK0hNTXV6xJNWq8VTTz2FAwcsHy9urwHTUX//+9+bNXiuWbPG4fsvXbrU3OQJGBs958yZg7S0tHapr7MtWbIE0itjKQHjEzp37Nhh9fza2lrMnj0b9fX15mNPPfUUBgwY0KF1EhEREVEPFZ1oDGEBIDYZsqgErJ4+HP98bDiG9/Ju8RJHQtB1uglI1sYDAFY3TMaysrfwx7wRiKrW2C3phLsbPutzGh7B30ELIEmagTniTKfDV5PsPBVmpx/Egk2HoMla1fhkAUUKsHu53XqIiIg6Qk/LIYmIiIiI6BpmJWNcP+s2jOwf2OIlrckY31StwPjzt+NhdZXdkhoEAWn+fpjcS459nh5tzhgBIORIWvOx9IoUjm4nIqIeyWWf5PnZZ59hyZIlAIyBqmnnPAB4e3vDz88PEonLlt+ijz76CC+//DImT56MadOmISoqCj4+LYwqBFBTU4PPP/8cb7zxBo4dO2bx2eOPP46YmJg211NSUoIVK1aY3wuCgA8//BBz58516j6vvvoqJBIJXnnlFQDGpxwsW7YMM2bMgLd3y7+IdtalS5fQ0NDQ7LhSqbR439DQgLy8vBbv4e3tjeDgYJvrXHfddViwYAHefvtt87EpU6Zg5cqVePrppy2ennrixAnMmTMH+/btMx8LCgrC4sVX/UOSiIiIiMgZ0YnAgBiL8eXRg0Nwc7AIZ0tqsPOkCqdV9TiYV4aZ+K/DIeg63QTk6Aeax63rdb54r6gE//WSYXlQINRi63sAGwQB7wf443uZDMtKSpGE5mtaW9eakCNpkOVedR9FSrPvTkRE1NF6Yg5JRERERETXuBYyxpjIMMREhuGkUo0dv+bj198rcOB8adsyRr0XXi9R4aGqarweFIg8N2mz+zT1u1SKZ+SheKCqGi+pPkWwTu/Qulez1hyK2GRmi0RE1CO5ZDppMBiwcOFCAI3B6tChQ7Fw4ULcd999CAsL6+IKW6+mpgYZGRnIyMiAIAi4/vrr0a9fP/j7+8PNzQ1qtRoXLlzA8ePHodVqm13/4IMPtttTMoODg7Fr1y7ExMSgrKwMaWlprR6znpiYCIlEgpdeegm9e/fG999/324NngAwZswYXLhwwe55+fn56N+/f4ufzZw5E+np6XbvkZKSgmPHjuGrr74CYHya6vz587Fs2TKMGDECPj4+OHfuHHJycixCfzc3N2zfvh3h4eGOfSkiIiIiImusBJEDgj3xXHAvBAUFQbv3H5ApnNvlbgpfm4agD1VrcEdtHV4PDoRC5mmzrBPubpgeIcfT5RWYU16JppGtMw2eDGGJiMhV9OQckoiIiIiIrnFWcrbBch8slEcCADS7322XjPH22jr8J78Q6/19kebnh3qR7SkHX3l7Ya+nJxLKyjFFXWUeQWtad4RwyrzO1axli2/jcRw6fjtuqcxF3LBeGCxv4YFbFw8wfyQiom7JJZs89+/fj4sXL5rHG02aNAmfffYZxGJxF1fWvgwGA06fPo3Tp0/bPdfT0xNJSUlYuHChxTjxtrr55puxa9cuHD16FPHx8W2618KFC+Hr64t77rkH119/fTtV2PnEYjG2bNmCOXPmYPPmzebjRUVF+Prrr1u8JjQ0FBs2bMDdd9/dWWUSERER0TVM9NP7kCmWNDve2l3uoTodUi8X47/eMiwPtP9UzzUB/tglk+H1klLcWK9tlwZPTdRSyEbPd+geRERE7eVaySGJiIiIiIia2ZfarhmjG4BnyyvxQJUGy4IDccDTw+Y91GIRlgUH4gtvL7xaqsLW6qlYp5uABMlWJEi2tViH7fHuDwBnSvHjmVKsyTqLkf0C8VzUAERHhpq/L3YmGUfZRyfarI2IiMjVWP/NXRc6fPgwAGMTpLu7O9LS0npEsJqWloakpCSMGjUK7u7uDl0TGRmJZcuW4dSpU3jllVfatcHT5JZbbmlzg6fJM888060bPE28vb2xadMmfPbZZ7jzzjutnhcYGIjnnnsOR48exf3339+JFRIRERHRtUqqzIF41+Jmx9syxihZG483tPF4qEqDL/ILEFWtsVvHSXc3PBohx3sBfhCExikEI4RTrVp/7N4hWPF1Lk4q1S1ffPGA3ZqIiIic1VNzSCIiIiIiIpsuHjA2PF6lrRljsjYefRsakKYswvKiEgTqdHZL+c3DHdMi5FCF7scCaQYSJNsAAEnSDMwRZzq0bks1Z+epMDv9IBZsOgRN1qrG76tIAXYvt1sXERGRK3HJJ3lWVFQAMI5IGjVqFIKCgrq4ovZx++234/bbb8eyZcug1Wpx4sQJnDt3Dvn5+aiqqoJWq4W3tzd8fX3Rr18/DB8+HAEBAV1dtkvIy8vrknWnTJmCKVOm4Pz588jJyUFBQQGqq6shl8vRt29f3HXXXXBzc+uS2oiIiIjo2qSVj4Du7oUQ//B38zFN1FIUK8cBhwusXudoCJqEDLxXVIJMLxmWBwWg0kaji04QkObvh+tkP+BppRKyuuBW7rKfAFTVY03WWe6yJyKiTtVTc0giIiIiIiKb+txhzNoUKeZDmqiluFxwN/Cb0uplDmeM0gw8WK3B3TW1eDfAH//x9bZZjl4Q8K1/A0K9D+GmUk/cq6mBcOU+Te9pb92WhBxJgyz3qmsVKcCAGI5uJyKibsMlmzxDQkLMr8PDw7uwko4jlUpx88034+abb+7qUsgB/fv3R//+/bu6DCIiIiIiAIB+7MsQi8TGMDI2GbLR87EaQNywCKxVnEP2eZXF+Y6Gr6bXSdIMnKm4HxPU9SiU/4QsL5nNes65SZH3h/N4vPI31JQJ5rVM92vNLvvsdBXihkVguVwBWdaVJ5eaQmc2ehIRUTu5FnJIIiIiIiKiFpkytiYZ40qtFlH9vfHvn5U4lF9lcXprMsaP6ydBovTEhqqteD04AGftPECpSCLB/4WF4G5NDV4pVaF3g67FNVtatyXWakZsMhs8iYioW3HJJs9evXqZX6vVVkb1ERERERERXcuiE5vtNo+JDENMZBhOKtXY8Ws+thy8hLiabU41WK7TTUCOfiByDIMwR5zp8FM99YKADX6+2C3zxNISFZLAXfZEROT6mEMSEREREdE1rYWM8a7+frirvx/OltRg78UabM0paHPGOKKuDp/lK7HBzxcf+vuiViSyWdYPMk887BGOZ8orMbOiElIH123KWoOnJmopZKPn27yWiIjI1dj+/5xd5O6774anpycA4PDhw11bDBERERERkauy0ug4WO6DheMjoRhztFUNlqbwNUmaAQHAg9UafJFfiHurNXZLuiiVYnZ4GN4ICsD/uW1sdYMnd9kTEVFnYA5JRERERETXPCtZ24BgT/zffQPbJWMEACmAORWV2J5fiDGaGrtl1YpEWB3ojym9wnHQw73ZuiOEU1avtfXU0bF7h2DF17k4qbSy0e/iAbu1ERERdTaXbPL09vbG5MmTYTAYcOnSJWRlZXV1SURERERERN3LvtTGMedNtLbBMlinx8qiErx9uRiBOp3d5Tf5+uDhXuHY5+nRLusDxl324C57IiJqR8whiYiIiIiIrBP99H67ZowA0LtBhzWXi7HycjFCGxrs1nDOTYonw8OQGBKEkitPAE2QbMU29yWYI850eF1TzSVV9ViTdRbjV+3B1LX7sTu3qPGkfanA+lhg93K7dREREXUml2zyBIDXX38d3t7eAID58+dzXBIREREREZGjLh4AdiY1O9yW8DVZG483tPEYr6nB55cK8ceqartlFEgleEYeiqTgQFSIRNxlT0RELok5JBERERERUXNSZQ7Eu9q3wTNZG49kbTwEAPdparDjUiEeq6iEyGCwW89/vb0wsXcErg/8AvMl2wAASdIMi0ZPew2eV8vOU2F2+kEs2HQImqxVjZmqIoWNnkRE5FJctsmzf//+2LJlC9zd3XH8+HHce++9OH/+fFeXRURERERE5Pr63AGMW2RxSBO1FMVD59q8zF4Iuk43AcnaeATo9XiruBTvXS5GiAO77b/w8UZcr3DofU5wlz0REbkc5pBERERERETNaeUjoLt7ocWx9swYAcDLYMDLqnJsKlBiaG2d3ZrUYhHeCA7EYxFhOOYmBdDY6Olsg2dTIUfSmj+xVJHCTeVEROQyJF1dgDUXL17EjTfeiPT0dDz33HP4+eefccMNN+CRRx7BAw88gBtuuAEBAQEQiZzrU+3Tp08HVUxERERERORCohONfytSgNhkyEbPx2oAccMisFZxDtnnVRanOxqCml4nSTPwa+UDuEetR23YHnzu422znFKJGJ+Fl+De6osoLhUhCRkW92vNLvvsdBXihkVguVzRGMIqUiy/PxERkR3MIYmIiIiIiFqmH/syxCJxh2aMqxomo0rriU/qM/AfH2+sCvCHWmz756+j7u6YESHHNHUV5peVt7hmS+u2xFrNiE02bqYnIiJyAS7b5NmvXz8IgmB+bzAYUF9fj40bN2Ljxo2tuqcgCGhw4CkzREREREREPUJ0IjAgxiKMjIkMQ0xkGE4q1Xhv1ylkHlE63WC5TjcBOfqByDEMAgDMKfTC2ur/YElwIJQS2z9mfuclwwEPD7ykKsPfqhrXbNMu+9yrrlWkNPveRERE1jCHJCIiIiIissFOxrjj13xsOXgJcTXbWp0xzhFnQgxgqroKMRoN3gkMwH+9vWyWZRAEbPL1wbcyGV5UlWFCtQZCk8+TtfHI0Q+0eQ9ruagmailko+dbv/DiAWaPRETUqVx2XLuJwWAAYAxGTWGrwWBo9R8iIiIiIqJripWwcbDcB+/H34p3//BDqxosTQ2eJnfV1OLzS4WYXqm2W5JaLMKrIUF4Rh6C2Z6bWt3gyV32RETUnphDEhERERERWWEjY1w4PhKKMUdbnTFenfEF6/RYXlyKjwovo3+91m5ppRIxEkOD8ZQ8FGelEvO63kINtrkvwRxxZovX2dr4PnbvEKz4OhcnlS1knftSgfWxwO7ldmsjIiJqLy7f5Ak0D1OJiIiIiIioHexLxcPFHzQ77EiDpUnTMNTLYMDfSsuQXnAZfbX2A9j9np54uFc4PvH1ge6q9duyyx72dtkTERFZwRySiIiIiIjISftSIcta3OxwmzZxAxhZW4f/5BdigaocHnq93TIOenpgSq9wrAzwxyhxDhIk2wAYJwhd3ehpb7JRSVU91mSdxfhVezB17X7szi0yf1fsTDK+VqSw0ZOIiDqNy45rnzlzZleXQERERERE1HNdPNAYSDbR2gbPpm6tq8PWfCXW+vsi3c8XOkFo4WqjGpEIK4IC8JWXDEtKVNiimWreZW+tFlsh7Od7h2BqbS7ihvXCYLmP5QmmEHbcIuOYKSIiIjCHJCIiIiIiarU2ZIy2Mj7A2JwpBTCnohIPVFcjJTAAWV4ym/dsEAR87O+LMO8K6Es9ca+mBsKVewHGEfH2Gjyvlp2nQna6Cqv+8AMmXb1hXpHSbJQ9ERFRR3DZJs+PP/64q0sgIiIiIiLqufrcYWx2VKSYD60Wz8S62vEOXe5ICJtQVoHYag0WBwch193N5v2OeLhjWi85/lT2PZ6rLAQMluGrI+uu000AruyyX5N1FiP7BeK5qAGIjgxtvsseYKMnEREBYA5JRERERETUai1kjJqopShWjgMOF1i9zNFGS9M5vRp0SC0qwfcyT6QEBaBQYrvV5bJEgv8LC8Fdmhoklpahb0MDkqQZuEN0AveJc+yu21K9k4pbeOJobDIbPImIqFN0i3HtRERERERE1AGiE40hLADEJuOJ/3sbccMi7F5mL4Rdp5tgbvbcqXkIwy6Mw/OqckjtjL1tEARsDpRiSkQ4fnF3B2A5Tqk1u+xnpx/E5+8vav5EAUUKR7cTERERERERERG11VUZoywqAaunD8f6WbdhZP/AZqc7mvE1zRhXNUxGsjYeMZoafH6pEE+VV0BiJ2sEgB9lnni4dzj+4e+HWkFodYNnS/Ua7lsGjJ5vtwYiIqL24LJP8iQiIiIiIqJOEJ1oHikUAGD19OGIGxaBtYpzyD6vana6MyFsjn4gcgyDjNeVSLBVsxmLg4Nw2MPdZkl5blLMigjD1Eo1ElTl3GVPRERERERERETkyppkjCYxkWGIiQzDSaUaO37Nx5aDlxBXs82pTdxNM0bTRnCZwYCEsgpMrKrGG0GByPb0sFmaVhDwYYAf/uvthcRSFcbV1DZbd4RwypxjNmUrC93y7WAMPfETbuntj7hhvTBY7tN88YsHmEESEVG74JM8iYiIiIiIrnVXBY0xkWHY8swofJMwFvOiB2DM9cGQigWnn6R5dTB6nbYBGwovI7FEBZleb7esLb4+mNQ7HN/LPLnLnoiIiIiIiIiIyJVZaWYcLPfBwvGRUIw56lS2aGJq8Lz62uu0DVinLEJKUQmCG3R2y8uXSvAXeSj+EhaC3yVifKsbgXW6CUiQbMU29yXmJlITe1loZW0DfjxTijVZZzF+1R5MXbsfu3OLGk/clwqsjwV2L7dbGxERkT18kicRERERERG1aLDcBwvlkQAAze53IVM4H8ICloGoCMCj6ipEa2qwLDgQP8g8bV5bJJFgQVgI7qvWILFUhRCd3mJd7rInIiIiIiIiIiJycftSIcta3Oyws9ni1QQAE6o1GKupwZoAP2z09YFeEGzeTyHzxH6PCMypOIP1qmTEiI4DgHmNdboJTm92B4DsPBWy01WIGxaB5XJF4/dVpBj/jk60WRcREZEtfJInERERERER2bYvFTLFkmaH2xLChut0eP9yMd4qKkGAzv5O+2+9ZIjrFYEtPt7YqRvGXfZERERERERERETdwcUDwM6kZofbki0ma+ORrI03v/cxGPCyqhxbCpQYVltnt6R6kYA1Af5Y0acciibj3pOkGUiTvt2qJ46ahBxJa97Qqkgx/ncgIiJqJTZ5EhERERERkXUdGMIKAP5YrcEXlwoxUV1ltxS1WIRlwYHI6F2A97xeRYJkGwBj+Gpq9GztLvvZ6QexYNMhaLJWNX5fRQobPYmIiIiIiIiIiNqizx3AuEUWhzRRS1E8dK7Ny+zlfOt0EywaPQHgK81DGHwxBq8Xlzq0sdw0wn1+aDAuScQAgPvEOVbXtMfqU0djkzk1iIiI2oRNnkRERERERGRdJ4SwAXo93ihRYa2yCBHaBrsl5Xh4YPEf6rHG3w/1V45xlz0REREREREREZGLik5szBhjkyGLSsDq6cOxftZtGNk/sNnpjm7kbpoxrmqYjFUNUwCI8HBVNb68VIiplWoIBoPd8rK8ZJjUKxwf+Pui9qpx76Y1RwinbN7DWs2aqKXA6PnWL2T2SEREDmCTJxEREREREdnWSSHs7so/YXt+IR6vqITITvjaIAj4IMAPU3qF4xd3dwDcZU9EREREREREROSyohOBJ3daNDzGRIZhyzOj8E3CWMyLHoAx1wdjnsdXTm3kXqebgMl1S7CqYYpFxuen1+PV0jJ8WnAZN9XZH+FeJxJhTYA/Hu4lR5anJwDgW90IrNNNQIJkK7a5LzFPE7qarTx07N4hWPF1Lk4q1c0v3JcKrI/lNCEiIrJL0tUFEBERERERUTcQnQgMiLFoeIyJDENMZBhOKtXY8Ws+thy8hLiabU6HsDn6gcgxDMIccSZkBgNeUpXjj1UaLA4OxCl3N5tlnXeTYlZEGKZUqpFQVg4/fWNzaFsbPDVRSyGztcueiIiIiIiIiIiIHGdlM/VguQ8WyiONTY+XPmn2ub2cz5QttpTxDamvR0bBZWzz8cLqAH+Ui8U2S7wklWK+PARjNTV4ufQ3/AvLMVZ8BADM929ai90N71X1WJN1FmuyzmJkv0A8FzUA0ZGhxu+6M8l4siLF+Hd0os3aiIjo2sUneRIREREREZFjbIWw4yOhGHO0VePSWwphb6qvx6YCJRJUZXDX6+2WttXXB3G9IvC1lwwGNO6yB2BzlFKrd9kDHKVERERERERERETUXi4eaGx6bMKRjdy2Mr5kbTxEAKaojSPcH3FwhPsemScm9Q7H4eCLqGkywj1JmmF+oqejE41MsvNUmJ1+EJ+/v6j5d1WkMG8kIiKr2ORJREREREREbbcvFbKsxc0OtyWElQJ4qkKN7flK3FlTY7eEUokYC0OD8VxYCG5w/xVzxJk2RynZC2FLruyyH79qD6au3Y/duUUW35ejlIiIiIiIiIiIiNpJnzuAcYssDmmilqJ46Fybl9nL+NbpJiBZGw8A8Nfr8VppGTYWXMYQB0a4awUBaf5+iOsdju9knjC1hiZJM5AmfbtVG97niDMxqfiD5h/EJlvdZE9ERMQmTyIiIiIiImqbDtxlDwB/aGjAP5XFeLO4BP46nd1yfpR54uFe4YgI3oF5km0ALHfY21vX1i77BZsOQZO1ynKUEhs9iYiIiIiIiIiI2i46sbHRMzYZsqgErJ4+HOtn3YaR/QObne5oxte00XNVw2QU1tyEjILLeK2kFH4O5I2FEgn+GhaCZ+QhOC+VAADuE+fYXdfRehGbDIyeb7cOIiK6dkm6ugAiIiIiIiLq5ky77BUp5kOaqKUoVo4DDhdYvczREDZJmgEBwENVGtylqcXbgQH40sfLZkm1IhHeDQxAppcXFpeqcHNdvcVardllDwAhR9Igy73qWkUKMCCGO+2JiIiIiIiIiIjaKjqxWdYWExmGmMgwnFSqsePXfGw5eAlxNducyvjW6SYgRz8QI0SncZ/E2KD5iLoa91bX4L0AP/zHxxuGJmPZW7Lf0xOTe3ng8Qo1nimvgFeTse/J2njk6AdavdZaFvqGNh6/HrkNt1XmIm5YLwyW+zS/+OIBZo9ERNc4PsmTiIiIiIiI2q6Tdtn/oh2GN0tK8WFhEXppG+yWdcrdDY+Fh+GNoACoBQFJ0oxWN3ja3GnPkJWIiIiIiIiIiKh9WMnaBst9sHB8JBRjjrYq4xshOt3sugC9HotLy/BpwWXcXGt/hHuDIOBjf1881Dsc//WSwQDgW90IeAs12Oa+xGKakImtLDRNNwHZ51VYk3UW41ftwdS1+7E7t6jxpH2pwPpYThMiIrrG8UmeRERERERE1D46cZf96NpabM8vxFp/X2zw84XOxi57gyBgk68Pdsk8sai0DPdpatD07LY0eGqilkLGUUpERERERERERESdY18qZFmLmx1u7aj0b3UjcJ84B0Pq6/FJ4WV84e2FdwP9USYW2yyjWCJBYmgwPqutRWLpEdxXb8wtTWuYanF0s7tJdp4K2ekqxA2LwHK5ovG7mqYoRSfarIuIiHomPsmTiIiIiIiI2k8n7rL3NBjw17IKbM5XOrTLvlgiwQthIZgXFoJLErHFuiOEU1avsxXEjt07BCu+zsVJpbrliy8esFsXEREREREREREROeDiAWBnUrPDrW3wTNbGY672RfMkIRGAh6uq8eWlAsyoUEPUZBy7NTkeHpgWIUdyUADKRcYWnCRpBuaIM51u8Gwq5Eha82ZWRQrzRiKiaxSbPImIiIiIiKhzdMAuewAYrNXik8LLSCpRwVuvt1vGDzJPPNwrHOv8fAE0IEGytVWjlNbpJqCkqp6jlIiIiIiIiIiIiDpDnzuAcYssDmmilqJ46FyrlzjSaLlON8Hc6AkAfnoDxhT3xpYCJUbU1totSy8I2Ozrgwd7h2OLjzd0MDZ6trbB01rNiE22usmeiIh6tm7d5FleXo7ff/8dFy9e7OpSiIiIiIiIyJZO2GU/TV2FLy4VIraq2m45tSIRVgf645d+Coz1MjZ3mnbYO7K2tVFKs9MPYsGmQ9BkrWr8vooUNnoSEXVzzCGJiIiIiIhcRHRiY6NnbDJkUQlYPX041s+6DSP7B1qc6ky+17TRc1XDZDyhTcTFmqFILyzCm8UlCG7Q2S2tQizGsuBAzIiQ45C7W4vr5ugH2ryHtZr/5TMXKyrv40QhIqJrlKSrC3DG559/jh07duCHH35AXl4e9Fee0CIIAhoaGpqdn5eXZw5evby8cOutt3ZqvURERERERHSFaZe9IsV8SBO1FMXKccDhghYvcXSXPQDzeaE6HeKUEYjzPo03ggJRILX9Y+8ZNzfMjAjDZHUV/qoqt1ivTaOUcq+6VpECDIjhTnsiom6COSQREREREZELi05slrXFRIYhJjIMJ5Vq7Pg1H9IDa5Cgdy7fW6ebgBz9QOQYBmGOOBP3iXMAAA9VaRBdXYMP/f3wbz8fNAiCzfJOuLvhiQg5JlRV46+qcoTpdEjWxsNbqME29yVWa7CZhxZHA1lnsSbrLEb2C8RzUQMQHRlqPGFfqnHD+bhFxv82RETU43SLJs9vvvkGzz//PM6cOQMAMBgMDl139uxZ3HfffRAEAW5ubigoKEBAQEBHlkpERERERETWmAJGRYpxl/3o+VgNIG5YBNYqziH7vMp8qrO77AFjU+aqhslY1TAFaVVvY3vtIXzg74dP/HygsxO8bvPxxm6ZJ15QleNvVRlo6WyOUiIi6vmYQxIREREREXUTVrK2wXIfLDz3LaDf0OwzR/I9U4Pn1Rmft8GAF8rK8XBVFVKCArDf09NuiZneXvhe5omnyytxl+oQosXHATRuLm9aizN5aHaeCtnpKsQNi8ByuQKyrMXGD0wb7NnoSUTU47j8uPbXX38dEyZMwJkzZ5qFqoKdX9Ldc889uOGGG2AwGFBfX4/Nmzd3ZKlERERERERkT3Qi8OROYPR886GYyDBseWYUvkkYi3nRA5Ag+8bpp2iu003A5LolWNUwxbzLXnYleN1UoMTNtXV2SysTi5EUEoQn5aE4e9UTQE1rjxBOWb3eWhD7Nh5H/PHbseLrXI5TIiJyYcwhiYiIiIiIeoCLB4xPtbyKIw2egPWM71vdCADAddoGfKgsxqrLxeilbT7p4Wo1IhFWB/rj733LkOXpCdNPm0nSDMwRZ9pc017NIUfSGhs8TRQpzBqJiHogl27yfO+997BkyRLzOCQAcHd3x9ixY/Hggw86tJN+2rRp5teZmZkdUicRERERERE5wdYue59vkdDOu+wj67X4pPAyXi1RwUent3J1o589PTClVzhWBfihRhDMaydItmKb+xJz+NqUrSD2H7UP4MczpViTdRbjV+3B1LX7sTu3qPGkfanA+lhg93K7tRERUcdgDklERERERNRD9LnDOLa8idXimW1q8EzWxmOu9kUka+MBAAKAezQ1eOmiP/5cVg53vf3M8XepFPPlIXguLATnr2wwT5JmIE36dqsaPDlRiIjo2uKyTZ6nT5/Giy++CEEQIAgC3N3dsWLFCpSWliIrKwupqakO3WfixIkAjKOVfvjhB4dHLBEREREREVEn68Bd9iIAU9VV2JFfgD9WVdu9V4Mg4CN/P0zqFQ6D9ykkSLYiQbINgOUue1vrWqs7O0+F2ekHsWDTIWiyVjV+Z0UKGz2JiLoAc0giIiIiIqIeJjqxsdEzNhlP/N/biBsWYfMSRzK+dboJ5kbPVQ2T8WR9Eq4vvR5f5Bfi3mqNQ6X9KPPE5F7heCfAH1WCgPvEOTbXdKZWzbglFhOUiIio55DYP6VrvPbaa2hoMD7a2tPTE9999x1GjRrl9H1uvvlmeHh4oLa2Fmq1GqdPn8agQYPau1wiIiIiIiJqK9Mue0WK+dBq8Uysqx1v91J7IewcvfHzYJ0ebxWX4uZKb2wMqcdFqdTmfQukEmyJuIyo6vMoUIkR0aADAIu1WrPTHrgyTin3qmsVKcCAGO62JyLqRMwhiYiIiIiIeqDoRHPOFgBg9fThiBsWgbWKc8g+r7I41ZlN3Ot0E5CjH2ieKnSfOAdoAN4tKsE+Dw+8FRSAc262M8cGQUC6vy++9PbCgrJyxFVVm5/QZlpzhHAKOYbmP1PaqnXDd4Mx8uxPuKW3P+KG9cJguU/zxS8eYPZIRNQNueSTPOvq6rBjxw7z7vnk5ORWBasAIBKJcMMNN5jf5+bmtleZRERERERE1N46aZf93yqWY3TeGDxbVgGpA09ay/KSYVKvcHzk5wPtlWNJ0oxWN3hynBIRkWtgDklERERERNSDXZWzxUSGYcszo/BNwljMix6AMdcHY57HV05nfKYGz6uvG11bi635hXi5tAw+Ovsj3EslYrwWEoT4iDD86u6Gb3UjsE43AQmSrdjmvsRimhBgPwfV6gz48Uwp1mSdxfhVezB17X7szi1qPHFfKrA+ltOEiIi6IZds8vzxxx9RU1MDg8EAmUyGP//5z226X0RE4y8ECwoK2loeERERERERdaToRODJncDo+QjwcsPq6cOxftZtGNk/sNmpzu6yn1y3BKsapgAADAY3zCuvwH/yC3FHTa3dsmpEIqwKDMAjveQ46OHe4jltafDURC3lOCUiok7GHJKIiIiIiOjaM1jug4XjI/HvG7OxEJ80+7y149K/1Y2AFMBjlWp8eakAf1JXQXBgg/lRd3c8FiHH9/ILWOuxBAmSbQCMm8xNjZ7O5KAm2XkqzE4/iAWbDkGTtQrYmWT8QJHCRk8iom7GJZs88/LyAACCIGDkyJFwd2/5l2eO8vX1Nb9Wq9VtuhcRERERERF1gg7cZQ9YhqL9tQ1IUxZheVEJgq6MY7flrJsbngwPQ2JIEErEjT9WNx2lZI2tMHbs3iFY8XUuTiqt/Nx68YDd2oiIyDnMIYmIiIiIiK5RFw80Nj020doGz2RtPOZqXzRPEwrS67GkRIVPCy7jlto6h0r6r7cXXuurQZqfL+oE47EkaQbSpG+3eqIQAIQcSYMsa7HlQUUK80Yiom7EJZs8i4uLza/lcnmb76fX61t8TURERERERN1LW3fZAy0HsQKAB6s12JFfgOmVaod22P/X2wsP9Y5Ahq83Gq4cszZKydq6TWsuqarnKCUiok7GHJKIiIiIiOga1ecOYNwii0OaqKUoHjrX6iWOPE1znW6CudETAIbU12POpWC8WVyCkIaGZtderUYkwnuB/pjUKxy7ZJ4wALhPnGNzTVus1YzY5GYb7YmIyHW5ZJNn0x3zdXWO7WiwpbS01Pw6ICCgzfcjIiIiIiKiLtTKXfaA7SA2WRsPX70Bfystw6cFlzHEgZ9Hq0QipAQFYkaEHA95f9biKCV767ZUM0cpERF1DuaQRERERERE17DoxMZGz9hkyKISsHr6cKyfdRtG9g+0ONWZfK9po+eqhsnYqx+Gh6o0+PJSIZ4sr4DUgQ3ml6RSJISFYK48FKel0hbXtDVRyFbN//KZixWV93GiEBFRNyLp6gJaEhISYn596dKlNt/v119/bfHeRERERERE1A2ZdtkrUsyHNFFLUawcBxwusHqZo0FskjQDQ+rrkVFwGZ/5eOO9AH+oxbb3SOa6u+GxCDkmq6uQoCpHgF5vsVZrxymFHEmDLPeqaxUpwIAY7rQnImoHzCGJiIiIiIiucdGJzbK2mMgwxESG4aRSjR2/5kN6YA0S9M7le+t0E5CjH4gRotPmbNDLYMBfyyrwJ3U1/h7ojywvmd3yDnh6YEovOR5RV+EvZRU4qB2GdboJSJBsRYJkm9UabGahxdFA1lmsyTqLkf0C8VzUAERHhhpP2Jdq3HA+bpHxvw0REbkEl2zyvO666wAABoMBhw8fRnV1Nby8vFp1r5ycHIuxSyNGjGiXGomIiIiIiKgLmQJGRYpxl/3o+VgNIG5YBNYqziH7vMridEcbPE2vk6QZSG2YjCqVJ3ZoPsU7gQH4r7f9n0u3+Xhjl8wTC8rK8Sd1dcujkFpYtyUcpURE1PGYQxIREREREZG1rG2w3AcLz30L6Dc0+8yRfK9pg6fFcg0NSC0qwY+eHlgRGIBzbtIWrm6kFwRs9vXBV14y/Ln8FNLL3kCU+BiAxs3lTWtx5qmj2XkqZKerEDcsAsvlCsiyFhs/MG2wZ6MnEZFLcMlx7SNHjoSvry8EQYBWq8X69etbfa+VK1eaX/ft2xd9+/ZtjxKJiIiIiIioq0UnAk/uBEbPNx+KiQzDlmdG4ZuEsZgXPQAh3u5Oj0pfp5uAyXVLsKphCgAgWKfH8uJSfFR4GdfVa+2WVSEW4/XgIDwWHoZjLQS0ydp45OgH2rwHRykREXUO5pBERERERERk1cUDxqdaXqUtG7iTtfHmUe531dRia34hXi4tg49Ob7ecSrEYKUGBeLePCns9PczHk6QZmCPOtLuurZpDjqQ1NniaKFKYNxIRuQiXbPIUi8WYMGECDAYDDAYDFi9ejN9//93p+2zfvh0bN26EIAgQBAEzZszogGqJiIiIiIioy9jaZT8+EooxR1sVauYYBjULREfW1mFrfiESVGXw1NsPXY94uGNGhBzLggJQIRKZ1/UWarDNfYk5eL2arSD2teJorMk6i/Gr9mDq2v3YnVvUeMK+VGB9LLB7ud3aiIjIiDkkERERERERWdXnDuPY8iZWi2e2qcFznW4C1ukmmBs9pQAeq1Tjv5cK8EilGoLBYLesc25SPCcPxZ/DQnBeahzgmyTNQJr07VZloZwoRETk+lyyyRMAXn31VYhEIgiCgPLyckRFReHYsWMOX5+eno5HH30UgiDAYDDAw8MDCxYs6MCKiYiIiIiIyKXsS22++xxt22kvBfBUhRpfXCrEfdUauyUYBAFbfH3wYO9wbPXxwhjRYSRItgGw3GFvb11ro5Rmpx/Egk2HoMla1fhUAUUKGz2JiJzAHJKIiIiIiIisik5sbPSMTcYT//c24oZFWD3d0XyvaaPnqobJ+EU7DK+VlmFLgRK31tQ6VNoPMk9M7hWOFYH+qBQJuE+c0+yc9IbYVjV4cqIQEZFrcdkmz8jISMyfPx8GgwGCIOD8+fMYMWIEnnrqKXzzzTcoKipqds3vv/+Ojz76CKNGjcJTTz2Furo68/VLly5FaGhoF3wTIiIiIiIi6nQdPEopXKfDyqISfKAsQh+t/RHu5WIxlgYH4Z+9S3DMzc18nKOUiIi6HnNIIiIiIiIisik6EXhyJzB6PgK83LB6+nCsn3UbRvYPtDjN2XxvnW4CJtctQZXB09ygGVmvxcfKIrxzuRgR2ga7pTUIAj7x88WDvSOw2ccbTa/YoxuKWZKdnChERNQDSLq6AFveeecdHD9+HN9++y0EQYBWq0V6ejrS09MBwLw7HgC8vLxQW9u4m8EUqhoMBjz88MN48cUXu+IrEBERERERUVcwjVJSpJgPaaKWolg5DjhcYPUyR4NY0zljamqxLb8QH/v5Yp2fL+pEtvdSGke4h+FP6iosKKuAv16PJGkG7hCdaHGnPUcpERF1DuaQREREREREZNNVWVtMZBhiIsNwUqnGjl/zIT+6Do+rnd/APUJ0ulm+JwCI1dRgbE0t/uXrg3X+vqixkzuWicVIDg7EJl9vLCwtR5jGH2PFRwA0ZplN63B2olB2ugpxwyKwXK5o3HBuyl6jE23WRkREbeeyT/IEAJFIhC+++AKzZs0yh6WAMTg1haqmYzU1NRbHTec9+eST2LRpU+cXT0RERERERF3rqlFKsqgEq7vsgdaPUvp7fTyeLa/E9vxCjNPU2C3LIAjYemWE+2c+XtAB7drgqYlaCoyeb7cOIiJqxBySiIiIiIiIWmOw3AcLfb7F4+q0Zp+1Nt/7VjcCAOBhMODpikp8eakQD6mrHarnjJsbngkPxSp5A/Ikjc9940QhIqLuzaWbPAHAw8MD69evx+bNmzFkyBCL8LQpQRAswtcBAwYgIyMD69atg0Ti0g8sJSIiIiIioo7SZJSSSUxkGLY8MwrfJIzFvOgBCPF2b/UopVUNU8zH/tCgwz8uFyNVWYxeDoxSqhCL8XpwEOIjwvCbu5vFZ8naeOToB1q91la9Y/cOwYqvc3FSqW75YoauREQtYg5JRERERERETrt4ANiZ1Oxwaxs8k7XxmKt90bzJHADCdDq8WVKKjAIlbq6tc6isLC8ZHu4djhWB/qgQGX+GTZJmIE36dqsaPDlRiIioa7l8k6fJI488gt9++w27du3CokWLMGbMGPTp0wdeXl6QSqUIDw/HsGHDMG/ePOzYsQO5ubmYMWNGV5dNREREREREXc1KyDhY7oOF4yOhGHO0VcFmjmFQi+FmVE0NPs8vxHNlFXDTt9wg1NQxd3fER8jxanAgSkQifKsbAW+hBtvcl5h31zdlryG1pKoea7LOYvyqPZi6dj925xY1nrQvFVgfC+xebrcuIqJrFXNIIiIiIiIiclifOxqnCV2hiVqK4qFzrV7iyIbzptOETMo1g/FJ4WW8WVSC0Ab7m8wbBAGf+Pniwd4R2OTjjQZYnyjUmg3nb+NxxB+/nRvOiYg6QbfbWh4dHY3o6OiuLoOIiIiIiIh6gn2pzUcMwX6DJ2Bj9zqMo5T+XF6Bh6qqsCIwAFleMrulfO7jje+8ZJhXdhp/qTSGrab7m2px9omj2XkqZKerEDcsAsvlisbv+v/s3XlclPXaP/DPbCzDLOwgrkUmaeaSaXlKhQ5YalHmmsdc0srM9HdOVp7jU1p0NLOOphkFGWqUmmlZ5lYJZppUqLmbKwIi6MDMsM/2+2OcYYYZYAYQBvi8X69eMfd9f+/7godjT5+u732lLzb/PXpenXUREbVVzCGJiIiIiIjIJZaMLX0xEJcA6cBZWA4gvncEEtPPI+OCynqpO/me5fN8SSqW6UdimX4UkrAUj5Rk4sHSMqxWKpCilKNCWPv73YpEIrwVHIj1ChleUhXh/rJyu+daNpw7q6H2eh8Gzl7HL2evY1XaOfTvEogZQyIRHRVqvmj/CvNbTge/yhySiKiBWlyTJxEREREREVGjqOcoJaD2cBOoas7sqDdgRf417PX1waKgAGRLJLXet1goxNtBAfhK7od/Xy/EPeUVds+pzxtHASDkaBKkp6qtTV8MRMZwnBIREREREREREVFDRc9zyNpiosIQExWG03labD2Sg/BjyZiodS/fSzYMR6axq3WqkOVNnFKTCS8UqfGEthjLAv3xvcyvzhLPeXlhRngo/lZahrmqQpwvvwsyQRnmiDcD4IZzIiJPxiZPIiIiIiIiapsso5QsISNujFLKGwwczq1xmavhpu01mdphuF8rQmDwTnyiVNS5u/6slxemtgvD0OISvKQqwnw4f2Nog944GpfABk8iIiIiIiIiIqLGUkPW1i1cjrnndwPaJIdzruR7lgZPZxlfO4MBbxdcx3iNFksCA3DUx7vOMn+R+uJXXx+M0l7A84WHAWPVOW44JyLyTLX/VyUiIiIiIiKi1ix6nrnREzCPUhoyB8vH9cHqyf3Q/5ZAh8tdbfBMNgy3vtXTMkrJZPLCjCINvs65guiSUpfK2ynzw6Md2iFJqUCFwPkz+wrO1Li+pnqXYiImnLgHS3acwuk8rfPFWQddqpGIiIiIiIiIiIhq0YCJQkDNGd9uQ1/r170rKvHZlav4b/41hOr1dd7TIBBgg0KOER0ikKKQo9Lm3HxJar0bPLnhnIjo5mCTJxEREREREbVt0fOAqbuAgbOsh2KiwrDx2fuwc84gzIyOxP23BWOmz3a3ws1kw3CMrFiAZfpRduFmB70B7+dfQ2JePrpU6uosr0woxPuB/ni8fTuk+frCZPPMOeJN2Oy9ANNE2xzW1daQurL8Yfxy9jpWpZ3D0GV7MSbxAPacyq+6aP8KYHUcsGdRnfURERERERERERFRLSwThWyUDlmIgp7T61xaW8Y3XfeSdaM5YG4AUmoi8W32FcwoVMPHaHRYV51WJMS7QQF4rEM7/CA1Z4/OJOgmINPYtV61lg5ZaJe9OuBmcyKiOrHJk4iIiIiIiKi2UUpDo/BZ9wzMxTqH83XtXq9tlNLfysqxOecK/p+qEL4uBK6XJRLMCg/BjLAQCL2uYo54E+aINwMw7663bfR09Y2jFhkXVZiS8htmrz+E0rRlVW8WSF/MRk8iIiIiIiIiIqKGcnOiEOBaxld9otBTunn4Rd8Hzxep8W32FQwvLnGpvMsSCf5fWAimhIfiuJfE4XkyQVmNm83rqnXQvh41TxTiZnMiIpeIm7uAmohEoka9n1wuh1KpRHh4OO6++27cd999GDlyJPz8/Br1OURERERERNTKNGCcUm3hJmBuzpyq1mJ4cSneC/TH97K6/x31F6kvDnY+jX+otSguEkBmMlnvZVHfcUohR5MgPVVtbfpiIDKG45SIqNViDklERERERERNInqeQ84WExWGmKgwnM7TYuuRHBy5rMbBC9cxCd+5nPElG4Yj09jVuuE8VpQJAAg3GLC44Dqe1GjxTmAADvt411niH74+GNe+HR7RluDFwiKEGwwYJPwTg0RHAVTljrY11NmMWlyJVWnnsCrtHPp3CcSMIZGIjgo1N3jabja3/IyIiMiBx77J02QyNepfGo0Gly9fxm+//YaPPvoIkydPRkREBP75z3+itLS0ub9dIiIiIiIi8lT1HKdUV7hpu8s+zGDA2wXX8emVq7i9orLOkvQCAVL8FRjRIQLfyPxgeQ/ofElqvRs8a6oXcQls8CSiVo05JBERERERETWZuiYKTRuAI38/7XbGV9tEobsqKrH2ylW8k38NETq9S2V+K/fDIx3aYYW/Ev3Ex+zO2U4Vqu9Eoa8/eNVxY336Yo5uJyKqgcc2eQKAQCCw+9rZX7Wds11vex1gDm+1Wi2WL1+OPn364PTp0033jREREREREVHL4uY4JVfDzerjlApLemBDbh7mXVNBbqh7hPt1sQjzQ4IwsV0Yjnl5Ob0mQTcBmcautd6npnpLhywEBs6qeSFDVyJqJZhDEhERERERkUfYvwLS9AUOh+vaxF1Tvrfb0BcAIADwUEkptubkYraqCH7GurPHcqEQHwcoMbxDBDbL/GCwOTdfkookydJ6bTifJtqGxwo+dDzBzeZERDXy2HHtgwYNsgahR48eRWFhIQBzKAoASqUSHTt2hEKhQGVlJVQqFS5evAjjjX8QWdbefffdkMlkKCsrQ1FRES5cuACdTme9xmQy4a+//sKwYcNw8OBBBAcHN/W3SkRERERERC2BC+OUNv6WjfiyzW6Fm5ZxSn2FfyFWbB6l9KS2GA+VlOL9AH9slvvBVK15qLo/fbwxvn044rXFmF1YhJAbDaIJugmQCcqw2XtBjc+vrSH16309MKb8FOJ7t0e3cLn9BZZxSoNf5RglImrRmEMSERERERGRR8g66Ph2S9S/wdOybpqx6ry3CZim1uAxbTE+CFBis1wGYx3Z4zWxCK+HBCFVIcdLqkLcV14BANax8I1RK+ISat9sTkTUxnnsmzzT0tLw448/4m9/+xsKCwthMpkQGhqKN998E6dPn0ZhYSH+/PNP7Nu3DxkZGTh79iw0Gg2+++47PPLII9YQVqvVYsWKFfj1119x6tQpaDQa/PTTTxg1ahRMJpM1hL148SL++c9/Nue3TERERERERJ6ujnFK6fcfq9fu9b7CvxzWBRqNWHBdhS9y89D7RnBal2/kMozoEIHVSjkqAQwS/ok54s0A7McoWdQVAF8rrsSqtHMYumwvxiQewJ5T+eYLLA2egHmM0p5FLtVHROSJmEMSERERERGRR+g0oGqa0A2lQxaioOf0Gpe4MlHIdpqQhVofitevF+LLnDzcW1bmUnlnvL3wTLswPB8WgnMSx3fK1TVRqKZa39JNwJij/bBkxymcztM6X8yJQkTUxnlskycAzJw5E4sWmf9D0aOPPoqTJ0/iP//5D7p2df4PBalUimHDhuGbb77Bd999Bz8/P/z111+4//77cfbsWQCAt7c3hgwZgo0bN+K7776Dr6+vdSf9+vXrceHChSb7/oiIiIiIiKgV2b8C0rTXHQ43dJRSj0od1l65ikX51xCi19dZRqlQiP8FBuCxDu1gkJ+FyeacbaOnqyPlLTIuqjAl5Td8/cGrjm8USF/MoJWIWjTmkEREREREROQRoudVNXrGJUA6ZA6Wj+uD1ZP7of8tgXaXupPv2TZ67jX0RKQwDwBwu06Hj/MK8EFePm6p1LlU4s9SXzzRvh3eDArANaG57Wi3oa91olD1jeZ11ZpkGI6MCyrnm80B84bz1XHcaE5EbZrHNnl++eWX+OijjwAAgwcPxubNm+Hv7+/y+mHDhmHLli0AAI1GgzFjxlhHKNlek5SUZN1JbzAYsHnz5kb7HoiIiIiIiKiNuAmjlKbrXrIGrwIAI0pK8W32FTxdpIbEZHJYU91liQQvhoXguWo76+dLUpEkWVqvN45OE23DYwUfOp6IS6jxLadERJ6OOSQRERERERF5lOh5wNRdduPLY6LCsPHZ+7BzziDMjI7EmyF73M73kg3DkaKPwyDRUbvjAgCDysrxVc4V/OeaCgEGQ50lGgQCbFTIMaJjBJKUCjwgPlTjRKH6bjafvf4QStOWcaIQERE8uMnz7bfftn69cuVKCIXul/rggw9i1KhRAIAjR47g+++/d7hm/PjxuPvuu61jldLT0+tZMREREREREbVZTTRKyc9kQt9rnbAl+woGl7o2Rmn/jZ31iwMDoBaaRwXHijJrfa479SIuwS5wJiJqaZhDEhERERERkcepYUN1t3A55sp3Y6I2yeGcK/neZPEuh+OWiUISAOO0xdh2ORdTijQubTQvEQrxfqA/RnRoh29lUli2PFoaPd1t8LQVcjTJcXISJwoRURvlkU2eJ0+eRGZmJgQCAe644w5079693vcaM2aM9evPPvvM6TVjx461fn3q1Kl6P4uIiIiIiIjasCYYpbRMPxJ7jXehs16PlVcLsCovH11cGKNkEAiQqpRjRIcIrJfLUH3oe4JuAjKNzkcS11bvW7oJGHO0H5bsOIXTeVrnixm6EpEHYw5JRERERERELcpNnigEAHKTCf8sLMLW7Fw8VFziUllXxWL8OyQY4yLC8ZuPNwBzo2d9Gzxr3XDOiUJE1AZ5ZJPn8ePHrV/ffvvtDbqX7fpjx445vaZ///4AAJPJhOvXrzfoeURERERERNSG3cRRSiMrFqDY5Gu39oGycmzOuYK51wshqzYa2JkikQhvBQdidPtwHLgRtu429IVMUIbN3gvsxihZ1BYAJxmGI+OCCqvSzmHosr0Yk3gAe07lV120fwWwOo5jlIjIYzGHJCIiIiIiohaliSYKAcD5iu54p+A61uXm4a7yCpfKO+nthantwjArNBjnJWKnz6xts3lt9a6VT8cSTSw3mxNRm+SRTZ45OTnWr729vRt0Ly8vLwDm4NT2vrZCQkKsX2s0mgY9j4iIiIiIiNq4mzBKCQD6Cv9yGm5KADyl0eK7y7l4QlMMgQtjlM56eeGZG2Hr7d5/Yo54M4CqMUoW7o5TyriowpSU3zB7/SGUpi2reqtA+mI2ehKRR2IOSURERERERC1OE04UAoDeFZX47MpVvJN/De111WcEOZfmJ8XI9u2QEBSAa0Kh9Zm1bTavq97XCqK52ZyI2izHtnkPoNdX/UMhKyurQfeyXW97X1ticdWPwRLGEhERERERETWqeo5SAmoPNwFzc2aQ0YgF11UYo9Xi7aAAZPr41FlSmp8U+6S+mKDR4tlCNeQmk91z6jtOKeRoEqSnqq1NXwxExnCcEhF5FOaQRERERERE1CJFz3PI2mKiwhATFYbTeVpsPZKD8GPJmKh1f6JQprGrw4ZzAYCHSkoRU1KKzxVyfOyvhFZU+3vlDAIBNijk+Fbmh6lqDQaqDiFGeAJAVe5oW4c7DakZF1XISFEhvncEFoWnQ5r2uvlE+uKqnw8RUSvikW/ybNeuHQDzrvfffvutQaOLvv/+e+vX4eHhTq9RqVTWr5VKZb2fRURERERERFSjeoxSAuoON6uPUupeqUPKlXy8k38N4TU0GdnSCwRYo1RgRMcIbJTLYIA5ZK1vg2dN9SIugQ2eRORxmEMSERERERFRi9XEE4UAwAvAZI0W27JzMUGthdiFqUKlQiFWBvjjrc5F2CLzg+HGcdupQu5OFLIIOZpU1eBpkb6Yo9uJqNXxyCbPu+++GwAgEAhgMBiwYMGCet3n8uXLSE5OhkAggEAgQL9+/Zxed+LECevzOnbsWK9nEREREREREdXJjVFKgOvhZvVRSm/pJuChklJszb6C5wuL4GM01lmaSiTCm8GBGBMRjoM+jiOLLc/sKzhT4z1qqncpJmLCiXuwZMcpnM7TOl0rycuss0YiosbGHJKIiIiIiIhanZs0Uch2o3mA0YhXVYX4OvsKHiwpdamsfLEYr4UEYXT7cPzia55CNF+SiiTJ0no1eHKzORG1JR7Z5NmtWzd0794dgHkX/apVq/D++++7dY/8/HwMGzYMJSUlMN3YOfDEE084vXbfvn3Wr6OioupZNREREREREZELoucBU3cBA2dZD8VEhWHjs/dh55xBmBkdiRCZt9u715MNwzGyYgGW6UdZj/maTJhRpMHW7Ct4uLjEpfLOeHthWrswzAoNxqUbY4Utz5wj3oTN3gusO+xt1VbvyvKH8cvZ61iVdg5Dl+3FmMQD2HMq33qN9MhqBH09HrLfVrhUIxFRY2EOSURERERERK1OE00UAoAt5Y9i+JVOSMm9ijsrKlwq7y8vLzwXHopnwkNw2kuCWJHj5u8E3QRkGru6XSs3mxNRa+WRTZ4AsGDBAphMJggEAphMJvy///f/8Mgjj+DgwdpfqazRaLBy5Ur06NEDJ06csO6ev+uuu5yGq5WVldiyZQsEAgEA4L777rsp3w8RERERERGRVW2jlIZGIf3+Y/XavZ5put1pwNnOYMCSgutYl5uHHi6GrWl+UjzWoR3eDvSHQFiCOeJNmCPeDMB+lBLg/jiljIsqTEn5DbPXH0L5z+9DceBtAIDsj5UQ7n3bpfqIiBoLc0giIiIiIiJqdZpoolCxyRexokzcXVGB1NyreDv/GiJ0epdKPODri9ER4fhPcCDyRCK7Z8oEZdxsTkRkQ9zcBdRk1KhRGD16NL788ktrwPr999/j+++/R/v27dGvXz907twZcrkclZWVKCwsxNGjR3H48GFUVFTYBbN+fn7WcUnVrVmzBkVFRQDMY5Li4uKa+DslIiIiIiIisrF/BaRprzscbsg4JYveFZX4PPcqvpP5YVmAEgXi2mMBvUCAz5QKKGW/4flCNXRaQHLjnO1z6tOQCgAhR5Mgr7ZW9PM7QNdYjlQioibDHJKIiIiIiIhapeh5QGSMXc4WExWGmKgwnM7TYuuRHBy5rEav7HWYC/cmCmUau6Kv8C+7XFAIYFhJKR4sLcUXcjk+9ldCK6r93XMmgQBb5TLs9JNigkaLbqqOkAnK7DabW54J1G+zeUaKCvG9I5AQ+pPdZnOD1Bd40HGsPRGRJ/LYJk8ASE1NRXl5Ob799ltrMGoymZCdnY2cnBynaywjkWyD1W+++QZ333230+tzcnIwadIkAEBwcDBuvfXWm/CdEBEREREREbkg6yCwyzFYbEiDp2VnveWcEMCjxSX4e0kpkv0VWKNQoFLo2IxkSy0SYVFwINYr5HhJVYgHysohgPPmzobWa3hwIURs8CSiJsYckoiIiIiIiFql2iYKhUcB+1cA2esczteV71Vv8LTYbeiLWFEmJmu0eKy4BB/7K/CFQg69k82QtiqEQqz2V0IpL8SzRbtRqQG8bpzjZnMiIg9v8hSLxfjmm2+wfPly/Oc//0FpaanTXfC2LKGqyWTCAw88gE8//bTWwHTBggWNXDURERERERFRPXUaYB6jlL7Yeqh0yEIU5A0GDufWuMzVHey213ysexw9rl3Et9ojeC/AHztlfnWWd8FLgpnhoRhYWoaXVEXoqtM5fWamsWut96mp3k+lU3BVG4fH87ToFi53XJh1kKErEd0UzCGJiIiIiIiozannhvO6sshpRvN5f6MRL6uKMF5TjOUBSpfyR7VIhCVBAUhVyDGnsAhxJaUQgpvNiYhqfy+yh5g9ezaysrLw9ttvo3fv3hAKhdYAtfpfQUFBGDt2LPbs2YP09HTuiCciIiIiIqKWJXqeudETAOISIB0yB8vH9cHqyf3Q/5ZAh8tdbfBMNgy3vtVzmX4kik2+iBVlIkJvwNKC61iTexU9KipcKnG/1Bej2ofjjaAAXBNWRQsJugmQCcqw2XsBpom2OV1bW70LVbFI3HsBQ5ftxZjEA9hzKt/moSuA1XHAnkUu1UhEVB/MIYmIiIiIiKjNsGw4t1E6ZCEKek6vcYkrWaRtDgkAHfV6PJoXgdTcPPQtL3eptByJGHNDgzEhIgy/+Xg7vaahm83f0cbhdJ7W+cKsgy7VSUTUVAQmy1yhFqS0tBTHjh3DtWvXUFRUBG9vbwQEBKBz586IjIxs7vKIyMbx48dx5513Wj8fO3YMPXr0aMaK6qbT6XD9+nXr56CgIEgkkmasiKjx8PebWjP+flNrxt/vNqqGt1aeztNi65EcHLmsRq/sdZgL98Yp9RWcqXGckhHAdzI/LA9QIl/s2vAPP6MR04o0+IdGiwz9nRgkOlpjHa42pNqK7x2BReHpkKa9XnVw8KvmZlgiD9ZS/+xuif8efzMxhyRqOVrqn18t9Z8XRHXh7za1Zvz9ptaMv99t1J5F5slCcQnAwFkAgJ9OXUVi+nlkXFBZL3M327Ncb9lwPl+SChOAn6S+WBbgj4terv9uDSotwxybyUKWzeZzxJvrfH5d9fbvEogZQyIRHRVqPrB/hfkNp8wgqQVpqX9+t9R/l28OHj2uvSZSqRT9+/dv7jKIiIiIiIiIbp4aRgV1C5djbniUOWzMdq/BE0CNDZ67DX0RK8rEo8Ul+HtJKVKUCnyqlKNcWPsQkBKhEMsD/bFRIcMc1TmYSgDLgGPLc5INw+vV4AkAIUeTID1VbV36YiAyhqPbieimYw5JRERERERErV70PIesLSYqDDFRYdYN55KDqzDH6F62l2wYjkxjV7s8UgDgwdIyDCotwxa5DB8EKKESieosca/UF/t8ffBocQlmFqoxyPCndbO5bQZp4U4WmXFRhYwUleNm8/TFVT8fIqJm1iLGtRMRERERERGRjayD5t3k1dTVMFlbuDld95J1jJLUZMLzRWp8m30Fj2hLXCrpiliMV0KDMaFdGA55e1mPz5ekIkmytF4NnjXVi7gENngSERERERERERE1lto2nMt3Y45xjcO5urI9oOYN5xIAY7TF+P5yLmYUquFrNNZZolEgwNdyGUZ0aIc/grOgEQqs5+ZLUjFNtA1A/aYJATc2m9tOEwLMjZ4c3U5EHoBNnkREREREREQtTacB5nFBNkqHLERBz+k1LnEl3Ew2DLc2egJAuMGAh65G4IucPPQpL3eptKM+3ngqIhz/DA3GZbF5F36sKNPhuhR9XL0aPNfKp2OJJhan87TOFzJ0JSIiIiIiIiIiahz13GwO1J5HWjJIvxubzbdl52KURguhyVRnSRVCIVb7KzGsQwTWKOSovHGcm82JqDVjkycRERERERFRSxQ9r6rRMy4B0iFzsHxcH6ye3A/9bwm0u9Sd3eu2jZ7L9COx13gX7qysxJor+Vh6tQDtdXqXytvtJ0V8hwgsDfSH2mZXPQDsNfTEZPEu6+766mqr97WCaKxKO4ehy/ZiTOIB7DmVX3XB/hXA6jhgzyKXaiQiIiIiIiIiIqJaONlsvlw0qUENnsmG4Q6bzUMMRrx+vRBbcq5gSEmpS6WpRSIsDQrAIx0i8K1MCiO42ZyIWi9xcxdARERERERERPUUPQ+IjLHbTR4TFYaYqDCcztNi65EcSA6uwhyje7vXkw3DkWnsajdOSQBgaGkZokvL8LlCjo/9ldCKat87qhMIsEapwBaZH54r0mCcRossYzgGiY4CgPXetnW405CacVGFjBQV4ntHYFF4etU4pfTFVT8fIiIiIiIiIiIiqj9Lxpa+GIhLwFO9nsX5b4/jm8O5Ti93Nd+zfD1fkopl+pHoIbiIWGRiRf41/O7jjf8F+ONPH+86y8uViPHvkGCsUVTi/xUWYWBZOSxbzi2bzbNNIU6z0FprLYgG0s5hVdo59O8SiBlDIhEdFWq+YP8K8xtOB7/KDJKImkSLafLcs2cPfvrpJxw6dAj5+flQq9XQ6XRu3UMgEODcuXM3qUIiIiIiIiKiZlDDuKBu4XLMPb8bMK5xOOfKOCXbBk9bXgAma7SILy7Bh/5KbFTIYBAIHG9gQyMSYUlQAL5QyDBHVYRbS2ENWm0bPd1p8LQVcjQJ0lPV1qUvdmiAJSJyBXNIIiIiIiIiompsNpsHAFg+rg/ie0cgMf08Mi6orJe5m+/ZbjaPFVe9hbNfeQU+u3IVu6W+eD/QH5ckkjpLPO3thefCQzGgrBxzVEXwLQ/kZnMiajU8vslz586dePHFF3H27FnrMZPJVK97Cer4j05ERERERERErUbWQfNu8mpcafCsLeAEzKFogNGIf6sKMU6rxf8C/JHmJ62zpMsSCf4VFoJe5RV4SVWI3hWV1vsNEJ50Ok6prnprqhVxCWzwJCK3MIckIiIiIiIiqkW1rK36RKHwY8mYqHV/A3dNm80FAOJuTBb6Si7DhwFKqESiOss86OuD8e3DEVdcghcLxeis1wPgZnMiatlqn6vWzN555x0MGzYMZ8+etQtUBQKB238RERERERERtSmdBpjHBdlYLprUoAbPZMNwJBuGW5s9AeBWnR4r8q8h+cpVRN1o2qzLER9vTIwIx79CgnBZbN5/6qzBM0UfV68Gz7Xy6ViiicXpPK3zhVkHXaqTiNoO5pBERERERERE9dMtXI658t2YqE1yOFffDdy7DX2tX0sAjNMW4/vLuZhRqIav0ehSXbtkfnisQzskBAXgmsjcHjVfkookydJ6NXhyszkRNSePfZPnzp078corrwCoClMtAatUKoW/vz8kLryOmYiIiIiIiKjNsowJSl8MxCXgqV7P4vy3x/HN4Vynl7u6g93y9XxJKpbpR6LY5Iv5SMX63DxslflhRYASBeK6I4ddMj/85CfFOI0WzxZp4G8T0O419MRk8S5km0Kchqu11loQDaSdw6q0c+jfJRAzhkQiOirUfMH+FeY3nA5+lWOUiAgAc0giIiIiIiKiBqnnRKG6sshpRvvzfiYTni9SY4xWi0R/JTbJZTDUsdlSLxBgg0KOrTI/PKXWYrJaU+M0oUxjV7drXYqJOHTiHvTSnEJ87/boFi53XJx1kE2gRNRgHtvk+eqr5reNWELVjh074pVXXsGIESPQqVOnZq6OiIiIiIiIqIWInmcdFxQAYPm4PojvHYHE9PPIuKCyXubuiKJkw3BkGrsi03Q7pom2AQBEAB4vLsHQklKsVcqxWqlAmbD2ISJ6gQCfKRX4RibDdLUaT2q0yDaEY5DoKAD7MUr1qTXjogoZKSrE947AovB0SNNeN59IX1z18yGiNo05JBEREREREVEDWCYKWfI2AKVDFqIgbzDQgM3mthvNLfYaemIQjmL+9UL8Q63F+4H+2O0nrbPEMqEQHwUosVEhw/QiDcZqtPCyeaZMUIbN3guc5ou11/owcPY6fjl7nRvOieim8sgmz3PnzuHIkSPW8UYDBgzArl27IJc76XgnIiIiIiIiotpV2ykeExWGmKgwnM7TYuuRHIQfS8ZErfsjiiwNntVDTqnJhOeKNBilLcYH/kpslstgrGNXvVYkxHuBAfhCIccsVRFuKQEs7aG2jZ7uNqNahBxNgvRUtXXpi60NsETUNjGHJCIiIiIiImoE1SYKSQfOwnKgwZvNnU0UsmwO76LX4738a/jT2wv/C/DH774+dZZZKBJhSVAAPlPIMbNIDR91V8gEZZgj3mx9ju1z3c0iueGciG4Wj2zyPHDgAADAZDJBKBRi7dq1DFaJiIiIiIiIGlm3cDnmnt8NaJMcztXVNAnUHHJaBBuMeP16ISZoivFeoD9+lvrWWdMVsRj/Dg3GuopK/FNViHvLKwCYA9YBwpM1jlOqz+gnxCWwwZOojWMOSURERERERNRIbCYKWTTGZnPLRKG+wr+cZnx3VVRidV4+9vn64H+B/vjLy8vJXezlSsT4T0gQblPmYbbqNExlgGWLuu0z6rPZHOCGcyJqfLXPTGsm+fn5AMwjkvr06YOuXbs2c0VERERERERErVDWQfO4oGoa0uCZoJuABN0Eu2O36XRYdbUAH1+5im4VlS6VdtLbC9PbhWFGWAjOSCQAUGODZ6ax5tygpjqXYiImnLgHS3acwuk8rfPFWQddqpWIWi7mkERERERERESNqIYGxm7hcsyV78bEem42r6nB00IA4IGycnyZk4f/FlxDhE7vUrlnvbwwKzwEk9qF4pB3VXPofElqvRs8ueGciG4Gj2zyNJlM1q9vu+22ZqyEiIiIiIiIqBXrNAAY/KrdodIhC1HQc3qty+oaU5RsGO7Q6LlMPxLFJT2wITcPCQXXEap3LWjdJ/XF6PbheC04EHkikcPzZIIybPZegGmibW7VubL8Yfxy9jpWpZ3D0GV7MSbxAPacyq+6aP8KYHUcsGeRS3USUcvEHJKIiIiIiIioCTTRZnMRgEeKS/Ftdi5evl4If4PBpfIO+fjgqYhwzAoNtm44d/a82jab11brWvl0LNHEcrM5EdWbRzZ5tm/f3vq1wcU/cImIiIiIiIioHqLnVTV6xiVAOmQOlo/rg9WT+6H/LYEOl9fV4Glh2+i5TD8SxSZfxIoyIQIQX1yC77Kv4EVVEfyMxjpLNAoE2CKXYUSHdlgWoIRGKMBuQ1/IBGWYI94MwLy73rbR09U6LTIuqjAl5TfMXn8IpWnLqkLn9MVs9CRqxZhDEhERERERETWBJt5snm7oi4kaLb6/nIvpRWr4upBBAkCanxSj2ofj38FByBZXbTiva7N5XbW+VhDNzeZE1CDi5i7AmR49eli/vnz5cjNWQkRERERERNQGRM8DImPsxgXFRIUhJioMp/O02HokB0cuq9Erex3mwvXGyWTDcGQauzodp+RrMmG6WoMntMVI9FfiS4UMeoGg1jIrhEJ84q/EJrkMzxT9hec19uPbbZ9R33FKIUeTID1VbW36YoefDxG1DswhiYiIiIiIiJpI9Dzz39MXmzebD5yF5QDie0cgMf08Mi6o7C53Z7M5YM4DrZvNxebcUG4y4cVCNcZrtPjIX4mv5HVnkCaBAN/K/bBdJsUYTTGmF6kxSPgnBomOWp9j+1x3agXMm80zUlSI7x2BReHpkKa9XvVzsf05ERHZ8Mg3efbs2RN33nknTCYT/vjjDxQWFjZ3SUREREREREStWw0NjN3C5Zg7NAqfdc/AXKxzOF9X46SzBk8A2G3oCwAINBrxb1UhtmRfQWxJqUulqkUivBMUgEc7ROA7Pyls9+HPl6TWu8GzpjAWcQls8CRqpZhDEhERERERETWh6HnA1F3AwFnWQzFRYdj47H3YOWcQZkZH4v7bgjHTZ7tbGV+yYThGVixAscnX6boQgxHzrxfim+wreLi4xKVS9QIBPlfKMaxjBI4EZUFr0xxqO1XI3YlC1pqOJlU1eFqkL+bodiJyyiObPAHgX//6FwDzmKR33323mashIiIiIiIiasOyDlaNL7dRV1BZW8A5XfeS3SilLno93su/hnW5eehVXuFSWTkSMeaFBmNcRDj2+/jUeF2CbgIyjV1rvVdNta6VT8cSTSxO52mdL2ToStTiMYckIiIiIiIiakLNtNkcADrp9VhScB0bcq7gb6VlLpVbJhTi4wAlHu4YgRSFHOU3mj3nS1KRJFlarwbPmrLIPZ1exGmv7s4XMYckatM8tslz0qRJeOKJJ2AymbBkyRJs3769uUsiIiIiIiIiaps6DQAGv2p3qHTIQhT0nF7jEld2sCcbhts1egKAvCwQ665cxf+uFqCzTudSeSe9vfBsu1A8Ex6Ck14Sh+fJBGXY7L3AurvenVpfK4jGqrRzGLpsL8YkHsCeU/lVF+xfAayOA/YscqlOIvJMzCGJiIiIiIiIPEQTbDYHgO6VOiReLcAnV66ip4sbztUiEd4NCsDwDu2wSe4HPYBYUaZLa12tdcqZe5lDEpFTHtvkCQBr1qzBo48+Cr1ej/j4eLz22msoKipq7rKIiIiIiIiI2p7oeVWNnnEJkA6Zg+Xj+mD15H7of0ug3aXujCiybfTca+iJSGEeBAD+XlqGLdlX8J9rKgQaDC6VeMDXF2Pat8MrIUG4LBYBAAYJ/8Qc8WYA9mOU6lNrxkUVpqT8htnrD6E0bVlV4Jy+mAErUQvHHJKIiIiIiIjIAzThZvNzxnD0L69A6pWreO9qAW6pdG3Deb5YjIXBQXisQzvs8JPC6OQaZzmkq7UCzCGJyJG4uQuoyRtvvAEA6NWrF/bv349r167hrbfewnvvvYf77rsP3bt3R0BAAIRC9/pUX3vttZtRLhEREREREVHrFz0PiIyxG6kUExWGmKgwnM7TYuuRHIQfS8ZErXsjipINw9FBUIDJ4l12xyUAxmmL8UhxCVKUCqxRylHmQg7wvcwPu/ykGKMpxjNFx2GbtFpC1GTDcLcaPG2FHE2C9FS1demLHX42RNQyMIckIiIiIiIi8iDR88x/T19s3mw+cBaWA4jvHYHE9PPIuKCyXuruZnPAnA/uNfTEINFRAIAAQGxpGaJLy/CtzA+rApTIE9fdTnVJIsHc0GB8UlGJWYVFeKCsHAKb87Y5pLu1WjCHJCILgclkMjV3Ec4IhUIIBAK7Y5ZSqx93h8HFt38QUeM4fvw47rzzTuvnY8eOoUePHs1YUd10Oh2uX79u/RwUFASJRFLLCqKWg7/f1Jrx95taM/5+U4uxf0WjjlPabehrN/LomkiID/2V+Eoug8HFbEBqNGKyWoOn1Fr42UQg1e/d0FoRlwAMnOVSTdQ2tNQ/u1viv8c3FHNIotahpf751VL/eUFUF/5uU2vG329qzfj7TR4l66DTRkb7zeZJDufryvcWiFMcNpvbqhAAG+RyJPkrUCQSuVxu3/JyvKhS4+4K+/HvljeIutvgyRyS3NFS//xuqf8u3xw8elx7dQKBoN7Bqof2shIRERERERG1DlkHG7XBM0E3AdN1L9mNUQo2GPF/1wuxJecK/l5S6lJZpUIhVgX4Y1jHCHwhl8EydMlZg2eKPq5eta6VT8cSTSxO52mdL8w66FKtROQ5mEMSERERERERNbMa3lTZLVyOufLd9WrwnCba5rTBc7ehr/VrbxPwlEaL7ZdzMaNQDanR2UB2R5k+PpgcEYYZYSE46VXVXDdfktpoDZ7MIYnaLo9u8jSZTI32FxERERERERHdRJ0GAINftTtUOmQhCnpOr3GJKyOKkg3D7Ro9AcBYEYz/5V/Dutw89C0vd6k8lUiE/wYH4tEO7fC9nxTVo9m9hp6YLN6FaaJtbtf6WkE0VqWdw9BlezEm8QD2nMqvumD/CmB1HLBnkUt1ElHzYA5JRERERERE1EI0wWZzAJCZTHi+SI3vL+fiH2oNJC7+O/8+qS/GtG+Hl0KCcEFS99h3d+pkDknUdrn/p0kT2bNnT3OXQERERERERETuiJ5n/nv6YiAuAdKBs7AcQHzvCCSmn0fGBZX1UlcaPC0sn+dLUrHX0BODREcBAL0rKpFyJR/pvr5YFqjEOS+vOkvMlkjwSmgwPq2oxOzCIvytrBznjeHWe1pqsq3BnVozLqqQkaJCfO8ILApPhzTt9aqfie3PiIg8BnNIIiIiIiIiohbEstnckrfhxmbzvMHA4VynS1zdbA7Yj1Xfa+iJQTiKV1RFmKjRYpW/Et/K/GB0YfrHTpkfdvtJEV9cgueK1IjQG+zOM4ckInd4bJPn4MGDm7sEIiIiIiIiInJX9DwgMsZunFJMVBhiosJwOk+LrUdyEH4sGRO17o0oSjYMRwdBgcM4JQGAIWVleCCnDN/K/LAyQImr4rrjjlPeXpgRHop+ZeWYU6gCKqrO2Qas7gSrtkKOJkF6qtq69MUOPxsian7MIYmIiIiIiIhamCbYbL5MPxLFJl/r5vAIvQEJ11SYotZgZYA/fvCT1lmmUSDAFrkM38n8MFpTjOlqNYINVTOGmEMSkas8tsmTiIiIiIiIiFqoGsLDbuFyzD2/G9AmOZxzZZxS9QZPANht6ItYUSZEAB4rLsFDJaVYL5chyV8BjUhUZ6m/+/rgH77hiCkpxYuFRYjU6QGYA9YBwpOIFWXWq1ZngSziEhisEhERERERERERNYabuNk809gVfYV/Oc34InV6/C//Go55eeH9QCUO+PrWWapOIMDnSjm2yP0wQaPFZLUGSqN5/DtzSCJyhbC5CyAiIiIiIiKiNiLrILBrvsPh+oaVCboJmK57CQm6CdZjPiYTJmu02J6di6eL1PAxGh3WOfOTnxQj27fD/OBAXLnRHFpTsJpp7Op2rUsxERNO3IMlO07hdJ7W+eKsgy7VSkRERERERERERKh9s7l8NybWY7M5gBobPG3dWVmJj/MK8MmVq7irvKLWay3KhEIk+yvxcIf2+FipQOmNse/MIYmoLmzyJCIiIiIiIqKm0WkAMPhVu0PaQa8h67aJNS5xZUxRsmG4XaMnABzW3Yk5hWp8l30FT2iKITKZ6izPKBDgG7kMwztG4O1Af6iE9rFJgm4CZIIybPZegGmibW7VurL8Yfxy9jpWpZ3D0GV7MSbxAPacyq+6aP8KYHUcsGdRnXUSERERERERERFRLeq52RyoPeOrnkECQP/yCnx25Srev1qA2yorXSpPKxJiRaA/Hu4YgbUKOSoEjs9iDklEttjkSURERERERERNJ3peVaNnXAJ8HngRbzx8C96Nvw192svsLnWlwdPCttFzmX4k9hrvAgCEGQxYcF2FzTlXEFtS6lKJOoEAnykVeLhjBD7wV6JYIMBuQ1/IBGWYI94MwDxGyTZgdadWAMi4qMKUlN8we/0hlKYtqwqd0xczYCUiIiIiIiIiImqIemw2B+rO+JxtNl+mH4kfDH0RXVqGTTl5WJR/DR11OpfKVIlEeCcoAMM6RGCjXAYdwBySiJwSN3cBRERERERERNTGRM8DImPMYeuNwPNvtyjxt1uUuK73wvfH8xF+LBkTta6HlYC50TPT2NXpOKVbdXq8l38Nx7y8sCzQHwd9feoss1QoRGKAEusVMkwr+gvPaQ8BNi8EtX2GO8GqrZCjSZCeqrY2fXHVz4eIiIiIiIiIiIjcFz3P/Pf0xebN5vc8hze6X8fQqEB89nseDuUU213uavOk5ev5klQs049EsckXsWLzuHURgBElpRhaUoqv5X5I9FciX1x3a1a+WIw3gwPxqVKOGUWnMavYfnw7c0giapFNntnZ2SgsLIRarYbRaHRr7aBBg25SVURERERERETkshqCw9vD5OiRlQpokxzOuRJWOmvwtHVnZSWS8/JxwMcbywL9ccLbu85Si0QiLA0KwFqlHDOK1HhMW2INVGp6VkNGPyEugcEqkYdgDklERERERETUgrmw2fzIZTV6Za/DXLjePFnbZnMAkAAYrS3Bo8Ul2CCXI9lfgUKRqM5ysyUS/CckCMlKBWYWFiG2tMw6opk5JFHb1iKaPPV6PT7//HOkpqbi4MGD0Gq19bqPQCCAXq9v5OqIiIiIiIiIqLEIsjOqRgbZaEhYaRmhZHvuvvIKDMi9it1SX6wI9McliaTO2vLFYiwMDkKKUoEXCtWIKym1hqzVn5dp7FqvWtfKpyNPE4v4PC26hcsdF2YdZPBKdBMxhyQiIiIiIiJqZWrbbN4hENi/Ashe53C+rjyypgbP3Ya+iBWZ38TpbQKe0mjxhLYYnynlSFEqUCx0lijau+AlwUthIYiqqMQLhUUYVFYOgZPrmEMStR11/8nRzA4ePIiuXbtiypQp+OGHH6DRaGAymer9FxERERERERF5LlOH/sDgV+2OlQ5ZiIKe02tdV9c4pWTDcGuzp8X7+pEQau/AluwreO3adYS62JB1SSLB3NBgjI0Ix15fH9sJ7kjQTYBMUIbN3gswTbTN7VpfK4jGqrRzGLpsL8YkHsCeU/lVF+xfAayOA/YscqlOInIPc0gi92m1WqSnp+Pdd9/F+PHjcfvtt0MoFEIgEEAgEODixYvNXSIREREREVHNsg7Wa8N5bfnedN1LDjmkn8mEZ4s02HE5F08XqeHj4rSQU95eeCE8FP9oF4YDPt7MIYnaMI9+k+cPP/yAESNGQKfTwWQyQSCo6ku3/do2NLU9Xv0cEREREREREbUA0fPMf09fDMQlQDpwFpYDiO8dgcT088i4oLK7vK4GTwvL1/MlqVimH4liky9ixeZd9aO1JRhRXIr1ChmSlQpoXBifdMrbCzPDQ9GnvByzCtW4p7wCg4R/YpDoqPU5ts91p1YAyLioQkaKCvG9I7AoPB3StNerfi62PyciajDmkET1M2jQIBw+fLi5yyAiIiIiIqqfTgPMG84teRtubDjPGwwcznW6xJV8zzaHtKU0GjGnUI1/aLRIViqxUSGDTuDsHZ32/vTxxjPtwtCvrBwvFKpxdwVzSKK2xmObPK9evYrx48ejsrLSGpiGhITg4Ycfhlwux8qVKwGYw9TXX38dGo0Gubm5OHDgALKysqznQkND8cwzz0Dkwn+cISIiIiIiIiIPET0PiIyxGwkUExWGmKgwnM7TYuuRHBy5rEav7HWYC9fCSsAcdGYauzodp+RrMmGKWotRmmKkKBVYp5SjzIXxSYd8fDC1nQ8GlpZhVuFpoLLqnG3A6k6waivkaBKkp6qtS1/s8PMhovphDklUf7bNzUqlEn369MGpU6eQl5fXjFURERERERG5wY0N5+7kezU1egJAsMGIV1WFmKTW4CN/Jb6W+8HgQrPn774+mOxrziFfYA5J1KZ4bJPnsmXLcP36dWuwOmXKFKxcuRK+vr64dOmSNVwFgNdff91u7U8//YR58+bht99+Q0FBAfbt24etW7dCJpM16fdARERERERERA1QQ3DYLVyOueFR5rFB2escztcVVjpr8ASA3Ya+iBVlQm4yYVaRGuM1WiT7u76jfr/UF/ulvniwpBQvFKpxm04HwBywDhCeRKwo0+1aawpkEZdQc7CadZChK5EbmEMS1d/UqVMREhKCfv364bbbboNAIMCQIUPY5ElERERERC2LCxvOw48lY6LW/cbJ2rQzGLDgugpT1Rp8GKDENj8pTG7kkENKSjGzSI2oypuXQ+7p9CIivLqjm7NFzCGJmkzdr6NoJsnJydZgNTo6Gp988gl8fX1dWhsTE4NffvkFkydPhslkQnp6OkaNGnUzyyUiIiIiIiKippR1ENg13+FwfcPKBN0ETNe9hATdBOuxYKN5R/132bmI1xZD6OIo5h/9pBjZPhzzQoJwWWzeX+ssWK1LTbWulU/HEk0sTudpHRftXwGsjgP2LHL7eURtFXNIovp78cUXMX78eHTt2tX6vyMiIiIiIqIWqbYN5/LdmKhNcjhXWxZZWw5pm0ECQCe9HosKrmNzTh5iS0pdLjnNT4rR7dvhn6HBOCdp/BwyQTcBU87ci6HL9mJM4gHsOZVfdZI5JFGT8sg3eZ48eRLXr18HYB519NZbb7l9D7FYjOTkZJw7dw4///wzdu/ejU8++QRPP/10Y5dLRERERERERE2t0wBg8KvmcUE3lA5ZiIK8wcDhXKdLXBlT5GyMUlllCBKu5WGKWoOVAf74wU9aZ3kmgQDfyfyww0+Kx7TFeLZIg3CDweE62zFKLtdaEA2kncOqtHPo3yUQM4ZEIjoq1BysWhpfLT8Xy7gpInKqreSQBoMBZ8+exYkTJ5Cbmwu1Wg1vb28EBAQgMjIS/fr1g5+fX3OXSQAuXLiAw4cPIzc3F8XFxWjXrh06d+6MgQMHQiKRNHd5REREREREbU89Npu7Oi69+jW36XR4L/8ajntJsDLAH/ukrm1C3e0nxQ9SXzxcUooZhWp00esdrqlXDmlzbcZFFTJSVIjvHYFF4emQpt2YdsIckqhJeGST5+HDh61fh4eHY8CA+r3aVygUYunSpdb1H374oUeFq0RERERERETUAJbgMH0xEJcA6cBZWA4gvncEEtPPI+OCynqpq2ElYN/oudfQE4NERwEAkTo9/pd/Dce9vLAiQIlfXAhZ9QIBNink+EYuw1iNFk8XaRBsNNpdUz1gdadWS7i6rOPPeKzgQ/sF6YsdxkwRkb3WnENmZWVh8+bN+OGHH/Dzzz9Do9HUeK1IJEJsbCxeeOEFDB9evxFzrc358+fx22+/4ffff8dvv/2GzMxMaLVVb1Du3LkzLl682GjP27RpE9577z0cOHDA6fnAwECMHTsWb7zxBoKDgxvtuURERERERFQHNzebu5rtOdtsbtGjUocPrxbgsLcXVgb446CvT51lmgQCfH9j0/mI4hI8V6RGR739pvOG5JAWIUeTID1VbQ1zSKKbziObPG13z/fs2dPhfPWxL+Xl5fDxcf4H2j333IMuXbrg4sWLOHToEM6fP49bb7218YsmIiIiIiIioqYXPc8hQIyJCkNMVBhO52mx9UgOwo8lY6LWvbAy2TAcHQQFmCze5XCuR2UlEq8W4Hcfb6wIUCKzhkzClk4gwGdKBb6SyzBeo8UUtRb+Ns2etmGqu8HqNNE2PFbguAZxCQxWierQWnPIJ598El988YXL1xsMBuzYsQM7duzAiBEjkJycjLCwsJtYoWdKS0vDokWL8Pvvv0OlUtW9oBEUFxdj+vTpWL9+fa3XqVQqfPjhh9i8eTPWrFmDoUOHNkl9REREREREBJc3m7vbNFlboycA9K6oRHJePjJ8vLEyQIlDLuSQRoEAW+UyfC/zQ3xxCZ4pUiPCptmzoTmk01qZQxLddB7Z5Gm7qzwoKMjhvFRqPxatuLi4xnAVAO68807rruojR46wyZOIiIiIiIioNakhQOwWLsfc87sBbZLDudrCSsAcWDpr8Nxt6ItYUSYAoF95BVKu5GOfrw9WBPjjpLdXnaWWCYVY7a/ERoUcT6k1mKjWQmYyAag5zK1PsJqqnI4cTSzi87ToFi53XJh1kMErEVpvDnnmzBmnx9u3b4+uXbsiLCwMer0e58+fx5EjR2C0aTr/7rvvMGjQIKSnpyM8PLypSvYIhw8fxq5djn/23ywGgwFjx47F999/b3c8JCQEffr0gVKpxLlz53Do0CGYbvyz4urVq4iPj8cPP/yA+++/v8lqJSIiIiIiavPq2Gyeu30Joi+51zRZG9scsn95BdZcycd+Xx+sCFDiuLd3nev1AgG+ksvwjcwPT2iLMa1Ig3CDudmzMXPItfLpyGMOSXTTCZu7AGdsg1JLeGVLLrf/QyE31/H1x7aUSqX167y8vAZWR0REREREREQtQtZBYNd8h8OuNHjWtIt9uu4lJOgmWI8JADxQVo71uXl492oBbqnUuVRasVCIVQH+eKhjBD5RylFa7W2Brqitzv9cjcaqtHMYumwvxiQewJ5T+VUX7F8BrI4D9ixy+5lErU1byCH79OmDFStW4OzZs8jOzsaePXuwfv16bNq0CZmZmcjKysIzzzxjt+bMmTMYPXq0059JW+Tt7Y3IyMhGv++rr75q1+ApkUiwYsUKZGdnY+fOndi4cSP++OMPHDt2DPfdd5/1uoqKCjz22GO4cuVKo9dEREREREREtahps3nlCURfet/heGPnkH8rK8cXuVfx/tUC3F5R6VLJeoEAGxRyDO8QgcWBASgQud8qVludrxUwhyRqCh75Js/AwEDr17a76S28vb2hVCqhVqsBAKdOncJdd91V4/0sY5cAWNcQERERERERUSvXaQAw+FXzGKUbSocsREHeYOCw80YtV0YqORujtM/QE3GlR/FgaRm+k/nhQ38lciR1xy5qkQjLAgOwVqnAtCINRmuL4VOtqcryHNtA2J3RTxkXVchIUSG+dwQWhadDmva6+YTl52IZN0XUBrXWHFIgEGD48OFYsGAB+vXrV+u17du3x0cffYRevXph5syZ1uP79u3Dhg0bMG7cuJtdrkeRSCTo0aMH+vXrh3vuuQf9+vVDz5498csvvyA6OrrRnnP+/HksX77c7tiXX36J+Ph4h2u7d++OH3/8EQ8++CAOHDgAwPy7tnDhQiQmJtb6nK+//hrFxcUNrnfgwIGckEVERERERFSTJswhfzb0RHTpUQwuLcMuPylW+StxwUtSZ4mVQgFSlXJskvthrLYYU4o0CLaZ7GH7HOaQRJ7HI5s8u3btav36woULTq/p0aMH9u/fDwBIS0vDmDFjnF6n0+lw8OBB62eFQtGIlRIRERERERGRR7MEh+mLgbgESAfOwnIA8b0jkJh+HhkXVNZL3QksbQPWZfqRKDb5YpDoKEQA4otLMKy4BFvkMnzkr0C+uO74RSUSYUlQAFKUckwv0mCkthi2w99tA1Z36rQVcjQJ0lPV1qUvdhgzRdSWtNYc8ssvv0SXLl3cWvP888/jp59+wldffWU9tm7dukZt8iwqKkJOTg569OjR4HtlZmbijjvugK+vbyNUZjZp0iQ899xzdm94vVkWLlwIna7q7c+TJ0922uBp4evri5SUFPTs2ROVlea3tXzyySd4+eWXa22+nDNnDi5dutTgej/99FM2eRIREREREdWmiXNIIYCHSkoRW1KK7X5SJAYocUlSd7NnhVCItUoFNsplGK8pxmS1BoE2zZ7MIYk8k0eOa+/evTsEAgFMJhP++usva2hlyzKexmQyYf369VCpVA7XAEBiYiIKCwutn7t163ZziiYiIiIiIiIizxQ9D5i6Cxg4y3ooJioMG5+9DzvnDMLM6Ei8GbLH7cAy2TAcIysWoNjk67BWAmCMthjbsq/g5euFCDQYXCo1XyzGW8GBGNExAl/J/GA7/H2+JBVJkqX1ClZrCmQRl8Bgldq01ppDutvgaWH7Jk8A2LNnTyNUY6ZWqzF06FAMGjQIhw4datC90tPTMWjQIDzyyCMoKytrpAqBgICAJmnwLCsrw6ZNm+yOvfLKK3Wuu/322/HYY49ZP+v1enz++eeNXR4RERERERHVVzPkkCIAI0pK8XX2FbxZcB3tdXqXSi0XCvGpvwIPdYzAsgAlioRVLWTMIYk8j0c2eQYEBODOO+8EABgMBuzdu9fhmtGjRwMwjx5Sq9V45JFHHHYkJycn46WXXoJAIAAASKVSDBw48CZXT0REREREREQep4YAsVu4HHPluzFRm+Rwrq7AEgD6Cv9yHlre4GMyYaJGi+2XczFbVQS5wVjjtbauiMVYEBKERzu0w1aZHywtorGiTKd1Zhq7Ohy3qClYXYqJmHDiHizZcQqn87TOF2cddH6cqJVgDmmvT58+dp/LyspQVFTU4PtqtVo89NBDyMjIgEqlwoMPPog//vijXvf66aefMGzYMJSUlODHH3/Eo48+ivLy8gbX2JR27tyJ0tJS6+f77rsPUVFRLq2dMmWK3efNmzfXev2ff/6JwsLCBv/15JNPuv+NEhERERERtUXNlEOKATxWXIJvs3Px+rXrCNe71uxZJhTiE38lhnaMwPsBSqhvNHsyhyTyLB7Z5AkAsbGx1q+/++47h/P9+/fHAw88YP184MABREZGomfPnrj//vsRFhaGZ599FjqdDiaTCQKBANOmTWvU8T1ERERERERE1MJlHQR2zXc47EqwWtu4ogTdBLtjUpMJ09Qa7MjOwXOFakiNrjV7Zksk+E9IEB5r3w7b/aSovipBNwEyQRk2ey/ANNE2t2pcWf4wfjl7HavSzmHosr0Yk3gAe07lV120fwWwOg7Ys8ilWolaKuaQVcRiscMxZ283dZeXlxeCgoKsnwsLC/H3v/8dGRkZbt1n9+7dGDFihF2DZEhICCQujKPzJDt27LD7PGTIEJfXPvDAA3b/dzp06BCuXr1a4/UKhQL+/v4N/svLy8vt75OIiIiIiIhsNFEOKQEwSluCbZdz8Z9rKoS62OxZKhQiyV+JhzpGYKW/EmqhwOFZzCGJmo/HNnmOGTMGgHkM0rp165zuxv7ggw8gk8kAmHfSG41GHD9+HAcOHEBBQYE1VAWAyMhIvPnmm033DRARERERERGR5+s0ABj8qt2h0iELUdBzeq3Lagstkw3DkWwY7hCwLtOPxEFdH8wsUmPH5VxMKdLAx8Vmz4teErwcGown2odjp9QXRgC7DX0hE5Rhjtj8Frf5klS7gLWuGqvLuKjClJTfMHv9IZSmLasKndMXM2ClVo05ZJWzZ8/afRaLxQgODm7wfb29vbF582Y88sgj1mNFRUWIjY3Fr7/+6tI9du7ciUcffdRuPPuECROwbt06iESiBtfYlI4dO2b3+b777nN5rZ+fH3r27Gl37Pjx441SFxEREREREd1ETZxDphv6Ypy2GN9n5+KV64UI1hsc7uFMsVCIjwKUeLhDe6zyV0IjFDCHJPIAjluzPUT//v3x1VdfwXjjP3aUlJTAx8fH7po777wT27Ztw5gxY5CXlwcA1jBVIBDAZDLBZDLhrrvuwrfffmsNYomIiIiIiIiIrKLnmf+evhiIS4B04CwsBxDfOwKJ6eeRcUFld7mroaXl6/mSVCzTj0SxyRexYvOYowCjEf8sLMJTGg0+USqwUS5HZbXd8c6c9fLCS2Eh6FpZiZmFJzG71H5skm1d7gSrtkKOJkF6qtra9MVAZEyN46aIWjLmkFU2bdpk97lfv34QChvnPQFeXl7YtGkTRo8eja1btwIANBoN4uLisGPHjlrH22/btg1PPPEEKioqrMcmTpyIlJSURquvKZ08edLu82233ebW+sjISBw6dMj6+cSJE4iJiWmU2oiIiIiIiOgmaoYc0tsE/EOjxRPaYmyUy7DaXwGVC5sltSIhPgxQ4jOFHBM1ZzFVcwi2Y4aYQxI1LY9t8gSAxx9/vM5r7r//fpw+fRoffvghtm7dir/++gtFRUUICAhAr169MHbsWEyaNKnF7eYmIiIiIiIioiYUPc8hPIyJCkNMVBhO52mx9UgOjlxWo1f2OsyF66FlsmE4Mo1d0Vf4l9OwM9hgxCuqIkxWa5Hkr8BXchn0grqbPf/y8sKcsBDcUVGJGUVqDCktg2WVs+cAQIo+rt6jnxCXUHOwmnWQoSu1eMwhgeLiYnzyySd2x1z5ubjD0ug5ZswYfP311wAArVaLoUOH4vvvv8cDDzzgsGbr1q0YPXq03dj4SZMmYfXq1S2ywVOlUkGlsv+Pdp06dXLrHtWv/+uvvxpcFxERERERETWRZsohfU0mTNJoMVpbjA0KGT5VKlDoYrPnqgB/rFMoMFGjwT/UWshNJgA3J4fc0+lFRHh1Rzdni5hDUhvm0U2erpLL5Xj55Zfx8ssvN3cpRERERERERNRS1RAQdguXY254FLB/BZC9zuF8XbvSawpWdxv6IlZk3lEfZjBg/vVCTFVr8LG/El/L/GBwodnzpLcXXgwLQY+KCjxfqMYDZeVwtmqvoScmi3ch2xRSY601Batr5dORp4lFfJ4W3cLl9if3rzCPUxr8atWbCIhasdacQ86bN8/6llIA8Pf3x7Rp0xr9ORKJBBs3bsS4ceOwebN5zFtxcTEefvhhbNu2DYMHD7Zeu2XLFowdOxY6nc56bMqUKUhOTm6RDZ6AeUy9LalUCj8/P7fuERoaavdZrVY3tKx6O3v2LPbt22d3zPb3aNOmTQgODrZ+lslkGDVqVJPVR0RERERE5JGaMYeUmkyYotZirKYYnyvkSFHKoW5gs6ethuSQCboJSD5zL3BmL/p3CcSMIZGIjrrx78DMIamNaxVNnkREREREREREN1XWQXOIWE1dwWpdI5WmGe3PR+gNWHBNhaeLNEj0V+A7mR+MLjR7Hvf2xszwUPQsr8DzRWr8zabZc6+hJwaJjgKo2l1fveZa6yyIBtLOYVXaOftw1RKsAuYxSgADVqIWasuWLVi5cqXdsbfeeguBgYE35XkSiQQbNmzA+PHjrSPiS0pKMGzYMHz77beIiYnBpk2bMH78eOj1euu6adOm4eOPP4bAhT8XPVVxcbHdZ19fX7fvUX2NVqttUE0NsW/fPkyZMqXG83PnzrX73Llz5wY3eaakpCAlJcXheElJSYPuS0RERERE5BGaKIeUmkyYptZgnEaLzxVyrFHKoXGz2fMpjQYTbJo9G5xD2lybcVGFjBQV4ntHYFF4OqRpr5tPMIekNopNnkREREREREREdek0wLxL3BIiAigdshAFeYOBw7lOl7gSWFr+Xv26jno93rqmwtNqDRL9ldjhJ4XJhaamoz7emBEeirtuNHsOLCu3BqsW1QNWV4NVoCpcXdbxZzxW8KH9gvTFDqOmiMjzHTlyBE899ZTdsbi4OMyYMeOmPlcsFuOLL76AUCjExo0bAQClpaUYMWIEZs+ejaVLl9o1eD7zzDNITExs0Q2egGOTp4+Pj9v3qN7kWf2erd3FixeRnp7e3GUQERERERHdHE2cQ8pMJjyj1mC8RotUpRxrFQpoRXVPz9CKhPggwB9rb7zZc4JGi0FovBzSIuRoEqSnqq1hDkltEJs8iYiIiIiIiIhcYdkdnr4YiEuAdOAsLAcQ3zsCiennkXFBZb3UncCypoAVAG7V6bGk4DqeufFmz50y10b6/unjjeeqNXvatkXZPsvdYHWaaBseK3Bcg7gEBqtELUxWVhaGDx9u1yTYuXNnfPbZZ03STCkWi/H5559DIBBgw4YNAICysjIsXrzY7rrnnnsOq1atavENns7U53vypJ/D5MmTMXny5CZ9ZpcuXTB48GCH4yUlJfj999+btBYiIiIiIqKbohlySLnJhOeKzG/m/Ewpxzo3mj1tx7hP0GihMFaNcW9oDulsDXNIaovY5ElERERERERE5KroeQ67xGOiwhATFYbTeVpsPZKD8GPJmKh1L7Csy206HZYWXMf0Ig0SA5T4wU/q0jpLs2evG82e99k0ezoNSOuos6ZgNVU5HTmaWMTnadEtXO64MOsgg1ciD5Ofn4/Y2Fjk5ORYj4WHh2P37t0ICQlpsjpEIhFSU1MhFArxxRdfOJyfOXMmVqxY4VGNjQ0hk8nsPpeVlbl9j+prqt+ztaupsfT48eO48847m74gIiIiIiKim6GZcki5yYQZReZmzVRF/Zo9/6HR4h8ajbXZszFzyLXy6chjDkltUN3/KyQiIiIiIiIioio1hITdwuWYK9+Nidokh3P1CSwTdBOQoJtg/wydDv/Lv4aNOVcwpKTU5ZKP+Hjj2fBQPNUuDPt9fGCqe4lbdf7najRWpZ3D0GV7MSbxAPacyq+6YP8KYHUcsGdRPZ5KRDeDSqXC3//+d5w5c8Z6LDg4GD/88AO6du3a5PWIRCIMHTrU4bhAIMDQoUNbTYMnwCZPIiIiIiIickMz5pAKo7nZc0d2DmYUqiEzGl0qWSsS4sMAJR7q0B4f+CuhFrr/7/S11flaAXNIaptaxJs8y8rKsH79evz00084dOgQ8vPzoVarodfr3bqPQCBwew0RERERERERkUuyDgK75jscrm+warum+jV3VOqwIv8ajnt54YMAJX6W+rpU4mEfbzzbLhS9yyswo9qbPas/y/b57ox9yrioQkaKCvG9I7AoPB3StNfNJ9JvjF+2jJsi8kBtIYdUq9WIi4vD0aNHrccCAgKwe/du9OjRo1lqWrNmDaZOnepw3GQyYdSoUdi4cSPi4+ObobLGp1Qq7T6XlpaipKQEfn5+Lt8jPz/f7rO/v39jlEZEREREREQtRRPmkAqjCc8XqTFBo0GqQoF1SjmKha692TMxQInPlHI8qdbiKY0WymqNoswhiVzn8W/y/OijjxAREYFp06bh888/x4kTJ3Dt2jXodDqYTCa3/yIiIiIiIiIiuik6DQAGv2p3qHTIQhT0nO70clcDy2TDcIed9BY9Kiux6moBPs/JwwOlrr8N7vCNN3v+o10YfvF1fLPnfEkqpom2uVVndSFHk6qCVYv0xeYQmsgDtYUcUqvV4qGHHsIff/xhPaZQKLBjxw707t27WWr69NNPMXXqVBht/kPPvffea/26srISo0ePxubNm5ujvEYXFBSEgIAAu2NZWVlu3ePSpUt2n5vj7atERERERETUjJohh1TeaPbccdn8Zk+5wbU3exYLhfg4QIm4jhFYHqBEYbUGUeaQRK7x6CbPF198Ec8//zzUarU1GBUIBG6P52lN43yIiIiIiIiIyINFz6sKWOMSIB0yB8vH9cHqyf3Q/5ZA62XuBpa1BawA0PNGs2dqbh7ud6PZ808fbzx3o9nz52rNnvMlqUiSLK1XsFrT94e4hBrHTBE1p7aQQ5aUlGDYsGH49ddfrcdkMhm2b9+O/v37N0tNSUlJePrpp+0aPP/73//iwIEDmDNnjvWYTqfD2LFj8eWXXzZDlY3vjjvusPt89uxZt9afP3++1vsRERERERFRG9BMOaS12TM7B88XFrnc7FkqFCLZX4mhHSPwXoA/VDbNnrXlkCn6uHrlkKnK6VhyQonTeVrnC9kASi2Mx45r//zzz7Fy5UoA5nDUsgPey8sLkZGRCAgIgEQiaeYqiYiIiIiIiIiqiZ4HRMbYNTPGRIUhJioMp/O0yN2+BNGX3G+crMluQ1/EijIBAHdVVOLDqwU44u2FD/2V+MXFMe5/+njj+fBQ9CyvwHNFajxwY4y75b626husrpVPR54mFvF5WnQLlzsuzDrIBlBqFm0hhywrK8OIESOwb98+6zGpVIpt27Zh4MCBzVJTYmIinn/+ebu3ni5ZsgRz584FAPzvf/+DSCTCu+++CwDQ6/UYP348DAYDxo0b1yw1N5Y777wT+/fvt34+cOAAHnnkEZfWlpSU4M8//3S4HxEREREREbVBzZhDKowmzCjSYIJGi1SFHOsUCmhFdb9rsEwoxKf+CqxXyDBGW4zJRRoEG41Oc8i9hp6YLN6FbFOI03prbWC9Gg1cPYdVaefQv0sgZgyJRHRUqPmC/SvM4+4Hv8qR7tRieGyT52uvvQagKli9++67sXDhQsTFxUEs9tiyiYiIiIiIiIhqbFbsVnkC3S6973C8vm/GtKybZrQ/36uiEon1aPY86uONmeGh6FFRgecKNRhcVgbb9xI2KFgtiAbSGKyS52ntOWR5eTkeffRRpKWlWY/5+Phg69atGDRoULPU9MEHH2DWrFl2DZ7vvvsu/vnPf9pdt3TpUojFYrz99tsAAIPBgH/84x8wGAyYMKHmt4p4uoceeggff/yx9bPt/23q8vPPP0Ov11s/9+nTB2FhYY1ZHhEREREREbUkzZxDWpo9/6HWIlUpx1o3mj3XKBXYIJdhlLYYU9RahBoM1vN7DT0xSHQUAKzPs63bnTeUZlxUISNFhfjeEVgUnl412j19sfnvzCOpBfDIce3Hjh3D+fPnreONhg8fjl9//RXDhg1rFcEqEREREREREbVRnQZUjVG6YbloUoOCVcD5GKW9hp7WZs91uXkY6MYY9+Pe3pgVHoKxEeH4UeoLI4BzxnC7YHWaaJvbdVpkXFRhSspvmL3+EErTlpkbPAFzsLpnkct1EjVUa88hKysrMXLkSPzwww/WY97e3vj666/x4IMPNktN77//Pl544QW7Bs/ly5c7NHhaLF68GP/5z3+snw0GA5566imsXbv2ptd6swwdOhS+vlXN9wcOHMCpU6dcWpuSkmL3+fHHH2/M0oiIiIiIiKi1cJJDlg5ZiIKe02tc0pAcUm4y4bkiDXZezsELhUVQ2DRs1qZcKMRnSgUe7hCBt4ICkCcSIcNwuzWHtLDNI90dQW8RcjSpqsHTIn0xR7dTi+CRTZ5//PEHAMBkMkEsFuPjjz+GSCRq5qqIiIiIiIiIiBpB9LyqgDUuAU/9cynie0c4vdSdwNI2YF2mH4m9xrus53pXVOKjqwX4LDcPf3Oj2fOktxfmhIVgdEQ4zsnUMNqcY7BKrUFrziH1ej3GjBmD7du3W49JJBJs2rQJQ4cObZaa3nvvPcyePdv6WSAQYOXKlXjxxRdrXZeQkGB94yoAGI1GTJkyBatXr75ptd5MUqkUo0aNsjtmeVtpbc6cOYMtW7ZYP4vFYjz55JONXh8RERERERG1EtVySOmQOVg+rg9WT+6H/rcE2l3aWDmk3GTCs0Ua7Lyci9mqIvi72OxZKRRgvUKOhztGYHtoAb4R3OFwzXxJKpIkS+uVQ9b0/SEuoca3oRJ5Eo/cjl5QUADAHPLdc889aNeuXTNXRERERERERETUiKLnAZExQKcBCACwfFwfxPeOQGL6eWRcUAGoX+NksmE4Mo1d0Vf4l9O1ljd7/unthUR/JX52cYz7GW8v/CssBJGVlXimSIOhJaUQwRysDhCeRKwo02FNij6uXsFqqnI6ck4oEe+lRbdwuePCrIMMXqnRtNYc0jLO/JtvvrEeE4vF2LBhA0aMGNEsNb3zzjt4+eWXrZ8FAgFWrVqF5557zqX1CxcuhFgstjZ7Go1GTJs2DQaDAdOn1/wWEk+1YMECrF+/HjqdDoD5DZ2PP/44Hn30UafXl5eXY8qUKaisrLQee/rppxEZGdkk9RIREREREVELZZNDWsREhSEmKgyn87TYeiQH4ceSMVHbuDmkzGTCNLUGT2q0WK+QYY1SAZULG2v1AgE2KeT4Wl6Mvdp2mK0uQCe93nq+MXPItfLpyNPEIj6POSR5Po9s8rQdhdS5c+dmrISIiIiIiIiI6CapFhDahqu525cg+pL7O9IB1Bis2rqrohKrrhbgqJcXEgOU2Otis+c5Ly+8EhqMDyt1eEatxsPFpU6D1b2Gnpgs3oVsU4jTemttYL0aDVw9h1Vp59C/SyBmDIlEdFSo+YL9K8yj3Qe/ag6oiRqoteaQU6dOxcaNG+2O/fe//0WfPn1w8eJFt+4VHh4OHx+fBtVz7do1LFmyxPpZIBDgo48+crs58//+7/8gFovx73//G4D5Daxvvvkmxo8fD5lM1qAaLbKzs6G3+Y9HFnl5eXaf9Xp9jT9LmUyG4ODgWp9z6623Yvbs2Vi6dKn12KhRo/Dee+/hmWeegZeXl/X4yZMnMW3aNOzfv996LCgoCK+/Xu1NyERERERERETO1NCo2C1cjrnndwPaJIdzjZVDSk0mTFVrMU5TjC/lMqz2d73Zc5dCgh/k7TCspBTTi9S4Vef47+sNyiELooE05pDUMnhkk2eHDh2sX1dUVDRjJURERERERERETatb5Ql0u/S+w3FXgtXaQksADud6Vlbig6sFOO7lhUR/BdL8pC7VeNFLgn+HBONDfx2mF2kworgEkhvn9hp6YpDoqN3zbOt25w2lGRdVyEhRIb53BBaFp1eNdk9fbP47A1ZqoNaaQ65du9bh2Msvv2z3Jk1X7dmzB0OGDGlQPcHBwfjxxx8RExODwsJCJCUlYerUqfW617x58yAWi/Hyyy+jQ4cO+OmnnxqtwRMA7r//fly6dKnO63JycnDLLbc4PTdp0iSkpKTUeY/Fixfj+PHj2L59OwBAp9Nh1qxZePPNN9G3b1/I5XKcP38emZmZMJlM1nVeXl7YsmVLq3nzLBERERERETWTrIPmRsZqbkYOKTWZMEmjxRhtMTbJZVitVOCauO5mT6NAgO9kftjmJ8XQklJML9Lg9htTMc4Zw5lDUpvhkU2evXr1sn59/vz5ZqyEqGXSarXIzMzE77//jt9//x1//PEHzp49aw2DL1y4gC5dujRvkURERERERORcpwHmHeKWABFA6ZCFKMgbDBzOrXGZq6Gl7TWWhswelZVYkX8NJ7wk+MhfiZ9cbPa8LJHgtZAgJPor8bRag45FVcFq9eclG4bXawQ9AIQcTYL0VLV16YsdRk0RuYs5ZNO566678OOPP+LYsWOYMGFCg+41d+5cKBQKPPjgg7jtttsaqcKmJxKJsHHjRkybNg0bNmywHs/Pz8eOHTucrgkNDcWaNWvwwAMPNFWZRERERERE1Fo1Qw7pazJhokaL0dpibJb54RN/BfLFdbevmQQC7JD5YYfMDzElpXimSI0elfZTN5hDUmvmkU2eXbt2xb333otff/0Vf/75J3JzcxEREdHcZRG1GIMGDcLhw4ebuwwiIiIiIiKqL8vO8PTFQFwCpANnYTmA+N4RSEw/j4wLKrvLXQ0tLV/Pl6TavXHTonulDsvzr+H0jWbP3S42e+ZKxHgzOBCh/qUYXtQBM4pz4Gvz1rn5klQMEJ50Otq9rmC1pu8NcQk1B6tZBxm6kkuYQzatXr162TXWNsSzzz7bKPdpbjKZDOvXr8eoUaPw7rvv4tdff3V6XWBgIMaOHYuFCxciJCSkiaskIiIiIiKiVquZckgfkwlPaosxSluMr+UyJPsrcMWFZk8A+MlPip/8pHigtAzPFKnRu6LSeu5m5JB7Or2ICK/u6OZsEXNIaiIe2eQJAP/+97/x6KOPwmQy4Y033kBiYmJzl0TUYtiOb1IqlejTpw9OnTqFvLy8WlYRERERERGRR4me57BDPCYqDDFRYTidp8XWIzk4clmNXtnrMBeu70pPNgxHB0EBJot31fjobpU6vJd/DX9JJEjyV2CHnxQmgaDOkvPFYnwaDHzjH4HJGg3GaoohvfHvqM6C1brUFKyulU9HniYW8XladAuX25/cv8I8ZmrwqxyjRC5pjTmkbTZE7rl48WKzPHfUqFEYNWoULly4gMzMTOTm5qKkpATh4eHo3Lkz/va3v8HLy6tZaiMiIiIiIqJWrhlzSC8AY7TFeFxbjG9lfkjyVyBbInGp7J+lvvhZ6osBZeV4tkiNe8orADRuDpmgm4DkM/cCZ/aif5dAzBgSieioUPNJ5pDUhDy2yXPEiBGYOXMmPvjgAyQlJaFv37545plnmrssohZh6tSpCAkJQb9+/XDbbbdBIBBgyJAhbPIkIiIiIiJqaWrYBd4tXI654VHmIDF7ncP52nalTxNtcxqsJujM45Ntw8yuOh2WFFzHc0VqJPkr8b2fFEYXmj1VYhHeCwzAaqUCE9VajNdoIa+h6cx2jFL1OmsMVguigbRzWJV2zj5ctQSrQNWYKQasVAfmkORJbrnlFtxyyy3NXQYRERERERG1Nc2cQ0oAjCwuwaPFJfhe5oeP/RW45GKz50FfHxz09UHf8nI8W6TBfWXlcJZg1iuHtLk246IKGSkqxPeOwKLwdEjTXjefYA5JTUDY3AXUZvny5Zg5cyZMJhNmzJiBp556CmfOnGnusog83osvvojx48eja9euELjwH9+IiIiIiIioBco6WNXQaKOuYLW2wDLZMNwastq6VafHooLr2Jp9BY9qiyFy8S2BRSIRVgT6Y2jH9ljhr0Sh0HkUNV+SimmibS7XaSvjogpTUn7D1x+86vjzSF9s/jkR1YE5JBERERERERFRDZowhxQDeLS4BN9kX8Hi/GuIrKx0uKYmmT4+eDY8FE9GhGGP1BdGJ9c0JIe0CDmaVNXgacEckm6yZnmT59SpU926Pjg4GNeuXUNqaipSU1Nx6623onv37ggICICwhv844IxAIMAnn3zibrk3jcFgwNmzZ3HixAnk5uZCrVbD29sbAQEBiIyMRL9+/eDn59fcZRKACxcu4PDhw8jNzUVxcTHatWuHzp07Y+DAgZC4uHOAiIiIiIiIqFF1GmAeBWTZKQ6gdMhCFOQNBg7nOlzuamBp+drZtZ31erx1TYXnitT4RKnEN3I/6F3YXKgVCfFxgBLrlHKM0RZjklqDEIN9zGr7PHeD1WmibXiswHEN4hJqfAsBtQ3MIYmIiIiIiIiIGqgZckgRgOElpXi4pBQ/SH3xsb8Sp729XCr3mLc3XgwLQdfKSjxTpEFsSSlENucbmkM6W8Mckm62ZmnyTElJcfvtggKBAKYbb4k4d+4czp8/79Z6k8nkEeFqVlYWNm/ejB9++AE///wzNBpNjdeKRCLExsbihRdewPDhzv/waGvOnz+P3377Db///jt+++03ZGZmQqvVWs937twZFy9ebLTnbdq0Ce+99x4OHDjg9HxgYCDGjh2LN954A8HBwY32XCIiIiIiIiKXWEYApS8G4hIgHTgLywHE945AYvp5ZFxQAXB/R3ptASsAdNQbsOC6udlztVKBr+QyVArrznrKhEKsUSrwhVyOkcXFmFqkQTuDwXq+pufVJ1hNVU5HjiYW8XladAuXOy7MOsjgtQ1oyzkkEREREREREVGjaaYcUgggrrQMsaVlSPf1xUcBChzz9nap5L+8vDA3NBhdKnWYptZgWHEJLK9xa8wccq18OvKYQ9JN1ixNnvXV0sdOP/nkk/jiiy9cvt5gMGDHjh3YsWMHRowYgeTkZISFhd3ECj1TWloaFi1ahN9//x0qlapJnllcXIzp06dj/fr1tV6nUqnw4YcfYvPmzVizZg2GDh3aJPURERERERERWUXPAyJj7ILCmKgwxESF4XSeFrnblyD6kns70muz29AXsaJMAEC4wYB/qwoxXa1GilKBL+UylLnwtsNKoQDrFXJsksvwSHEJni7SoLNe73YttYbGV6OBq+ewKu0c+ncJxIwhkYiOCjVfsH+FecTU4FerAmoiGy09hyQiIiIiIiIianTNmEMKAAwpK8PgsjIc8PXBR/4KZPr4uHSfi14SzA8Jwof+SkxVaxBfXAxvk3u11JpDFkQDacwh6eZqtiZPy274tuTMmTNOj7dv3x5du3ZFWFgY9Ho9zp8/jyNHjsBorBpb9t1332HQoEFIT09HeHh4U5XsEQ4fPoxdu3Y12fMMBgPGjh2L77//3u54SEgI+vTpA6VSiXPnzuHQoUPW3+OrV68iPj4eP/zwA+6///4mq5WIiIiIiIgIQI07wbtVnkC3S+87HK8rWK1rx/00o/35EIMRc1VFeLpIg3VKOb5QyFHiQrOnXiDAFrkM38j8MLSkFNOKNLhdp3O4zvIs25rdeStAxkUVMlJUiO8dgUXh6ZCmvW4+YRkxxYC1VWuLOSQRERERERER0U3RzDmkAMDAsnIMLCvHbz7e+MhfiYO+rjV75kjEeDM4EIn+CkxSazFaWwxptdyIOSR5qmZp8rxw4UJzPNaj9OnTB1OnTsXDDz+MyMhIh/M5OTl444038PHHH1uPnTlzBqNHj8bevXv5NgEA3t7e6NChA86dO9eo93311VftGjwlEgnee+89PPPMM/Dy8rIeP3HiBKZNm2Yd5V5RUYHHHnsMR48eRbt27Rq1JiIiIiIiIqJ66TTAvEvcEiICWC6ahOTymidRuBJaOhujdM4YjkjkYXahGpPVWqQq5EhVyKARieos0ygQYLvMD9tlfhhSUorpag3uqqi0u8Y2YHV37JNFyNEkSE9VW5e+2OENBNR6MIckIiIiIiIiImoCTnLI0iELUZA3GDic63RJg3JIYR7uKa/APXn5OOLthSR/JdKlvi6VWiAWY2lQAJL9FZig0eJJjRYKY1WzJ3NI8kTN0uTZuXPn5nhssxMIBBg+fDgWLFiAfv361Xpt+/bt8dFHH6FXr16YOXOm9fi+ffuwYcMGjBs37maX61EkEgl69OiBfv364Z577kG/fv3Qs2dP/PLLL4iOjm6055w/fx7Lly+3O/bll18iPj7e4dru3bvjxx9/xIMPPmht9Lx+/ToWLlyIxMTEWp/z9ddfo7i4uMH1Dhw4ELfeemuD70NEREREREStmGV3ePpiIC4BT/V6Fue/PY5vnISr7oSWtgHrXkNPDBIdtZ5TGo14vkiNSWoNNihkWKNUQOVCsycApPlJkeYnxYCyckwvUqN/eQUsW13nS1IxQHjSOqbJVoo+rl5vBUhVTkfOCSXivbToFi53XJh1kMFrC9ZWc0giIiIiIiIioiZXLYeUDpyF5QDie0cgMf08Mi6orJc2Vg4JAL0qKrHyagFOekmwQhmMfX5imFx4eV6RSIQPAvyRolRgnEaLiWotgm5MXWYOSZ6m2ca1t0VffvklunTp4taa559/Hj/99BO++uor67F169Y1apNnUVERcnJy0KNHjwbfKzMzE3fccQd8fV3rjnfFpEmT8Nxzz8HHx7XXKzfEwoULobMZCzd58mSnDZ4Wvr6+SElJQc+ePVFZaX7DyCeffIKXX3651ubLOXPm4NKlSw2u99NPP2WTJxEREREREdUtep51l3gAgOXj+jiEq/XZlZ5sGI4OggJMFu9yet7PZMJUtRbjNcXYLPfDaqUC+WLX4qiDvj446OuDu8orML1Ig8FlZRAAToPVvYaemCzehWxTiNNaa/3erkYDV89hVdo59O8SiBlDIhEdFWq+YP8KYNd881sIOEqJiIiIiIiIiIiodjY5pEVMVBhiosJwOk+LrUdyEH4sGRO1jZtDAsAdlTqsKriCTde74Y+ga/jeTwqjC82eJUIhPvFXIlUhxxPaEkxWaxBuMDCHJI8ibO4C2hJ3GzwtbN/kCQB79uxphGrM1Go1hg4dikGDBuHQoUMNuld6ejoGDRqERx55BGVlZY1UIRAQENAkDZ5lZWXYtGmT3bFXXnmlznW33347HnvsMetnvV6Pzz//vLHLIyIiIiIiImqYarvAY6LCsPHZ+7BzziB82u3Xeo0dmiba5jRY3W3oa/fZ12TCBE0xtl/OxevXrqO9Tu9y2X/6eGNWeAieaB+O7X5SGKqdt929P1+SimmibQ41uvq9ZVxUYUrKb5i9/hBK05aZg1XA/PaBPYtcrpmIiIiIiIiIiKjNquFtlN3C5Zgr342J2iSHc42VQwLAKONpxOR1xnfZV/CEphhik8nhGmfKhUKkKuV4uGMEXg8OxKVqm9WZQ1JzYpNnC9CnTx+7z2VlZSgqKmrwfbVaLR566CFkZGRApVLhwQcfxB9//FGve/30008YNmwYSkpK8OOPP+LRRx9FeXl5g2tsSjt37kRpaan183333YeoqCiX1k6ZMsXu8+bNm2u9/s8//0RhYWGD/3ryySfd/0aJiIiIiIiIbHSrPIHoS+87HHclWK0ptJyuewkJugkO57wAjNKW4LvsXPw3/xpuqdQ5XFOTv7y88HJoMB7p0A6b5H6oBJBhuN1hPJNtwFqft5MCQMjRJEjTXrc/mL7YPDKJiIiIiIiIiIiI3Jd1sKqZ0cbNyCFjRZnIq7gVC66rsP1yLp5Ua+F9YxR7XfQCATbLZXi0Qzu8HBKE0xIJc0hqdhzX3gKInYwxs4wGbwgvLy8EBQVZPxcWFuLvf/87du7cif79+7t8n927dyM+Pt7u7Z0hISGQSCQNrrEp7dixw+7zkCFDXF77wAMPQCwWQ683v4nk0KFDuHr1KsLCwpxer1Ao6l0nERERERERUaPqNMA8Bih9sfVQ6ZCFKMgbDBzOdbrEldDS8nfb684ZwxEpzIMYwCMlpRheUoofpb5I8lfipLeXS+VelkiwMDgIH/or8ZT6ClRFd+Eh4Z9218yXpGKA8KTTkUr1DY0Rl1DjWwiIiIiIiIiIiIioDk2YQ9q+dTPcYMA8VSGmF6mxTinHBoUcJcK634toFAiwXeaH7TI/DCotxDnVbRivO2t3TZPnkFkHmVG2UR7b5Hn16lV89NFH1s8zZsxASEiIW/fIz89HYmKi9fMLL7yAwMDARquxqZw9a/8HhFgsRnBwcIPv6+3tjc2bN2PUqFH49ttvAQBFRUWIjY3Fzp07ce+999Z5j507d+Kxxx6ze2vnhAkTsGbNGohEogbX2JSOHTtm9/m+++5zea2fnx969uxpN/L++PHjNTZ5EhEREREREXmU6Hnmv6cvBuISIB04C8sBxPeOQGL6eWRcUFkvdWdXum3AahusWggBxJaW4e+lZVjrfQt+CixBpo+PSyXni8VYGhQApf91/K5ujxe0V+BvsxvfWbCaoo+rV7C6Vj4deZpYxOdp0S1c7riQ4WqLxhySiIiIiIiIiKiJNFMOCQDBRiP+X6EaU9UafK6QI1Uhh9rF3qa9Ul/slVbi+7J2mKEuxH1l5RDcOOcsh6xLvXLI/SvMb0Id/GrVz5HaDI9t8vzkk0+wYMECCAQCdO/eHa+99prb9wgNDcWXX36JEydOAABkMhn++c9/NnapN92mTZvsPvfr1w9CFzrKXeHl5YVNmzZh9OjR2Lp1KwBAo9EgLi4OO3bswMCBA2tcu23bNjzxxBOoqKiwHps4cSJSUlIarb6mdPLkSbvPt912m1vrIyMj7Zo8T5w4gZiYmEapjYiIiIiIiOimi54HRMbYNSvGRIUhJioMp/O02HokB+HHkjFR697YoWTDcHQQFGCyeFeNjxYAmFRxARHZffGi9DiS/BX4RerrUtlqkQgbAoGt/hEYrS3GU2otwgwGh+v2GnpisngXsk0hTmutNTQuiAbSzmFV2jn07xKIGUMiER0Var6A4WqLxxySiIiIiIiIiKgJuZBDRpz4BBPUjZdD7jb0tTZjKo0mzCjSYJJaiy/lMqQoFbgmdq3Z87CvBM/6hqJ7RQWmF2kQU1qGmjqkLFlj9XrrlUNaMkig6k2ozCLbFI/txNuwYYP162eeeabe95k+fTpMJhNMJhO++OKLxiitSRUXF+OTTz6xO/b444836jMsjZ6PPfaY9ZhWq8XQoUPx888/O12zdetWjBw50q7Bc9KkSS22wVOlUkGlUtkd69Spk1v3qH79X3/91eC6iIiIiIiIiJpUDW+j7BYux1z5bkzUJjmcc2XsUE3BanWxokyoSnsg8WoB1udcQWxJKQQmk0ullwmFWKtU4OGOEVgQHIiL4qq9zeeM4dbd+/MlqZgm2uZQo6tvBci4qMKUlN8we/0hlKYtsw9X9yxyqVbyLMwhiYiIiIiIiIiaWG055B1qTFB/7HCuvjlkgm4CputeQoJugt1xqcmESRotdmTnYP41FSJ0epfLP+Htjf8XFoLH2rfD1zI/6Gq4rnoWWZ8c8usPXq3KIC3SF5unC1Gb4ZFv8iwoKMDRo1WvzX3iiSfqfa8nnngCc+bMAQAcOnQIhYWFCAgIaGiJTWbevHnIy8uzfvb398e0adMa/TkSiQQbN27EuHHjsHnzZgDmBtOHH34Y27Ztw+DBg63XbtmyBWPHjoVOV/VH1JQpU5CcnNwiGzwB85h6W1KpFH5+fm7dIzQ01O6zWq1uaFn1dvbsWezbt8/umO3v0aZNmxAcHGz9LJPJMGrUqCarj4iIiIiIiFqYrIOOQSJcC1ZrCy2nGR3PW3bU96jU4b38azgnEWO1UoFtMj8YBAKHe1WnEwjwlVyGzTI/xJaUYqpagx6VeXbX2O6idydYtRVyNAnSU9XWpS92eAsBeTbmkEREREREREREHqbTAPPUHMsbKwGUDlmIgrzBwOFcp0tcyfhsR7rb8jYBY7XFGKktxg6ZFMlKJc57SVwq9YKXBP8XEoQPApSYrNZgpLYEvtU2rds+z90ccppoGx4rcFyDuARmkG2MRzZ5HjlyBAAgEAjQsWNHRERE1Pte7du3R6dOnZCVlQWTyYTDhw8jOjq6sUq9qbZs2YKVK1faHXvrrbcQGBh4U54nkUiwYcMGjB8/3joivqSkBMOGDcO3336LmJgYbNq0CePHj4deX9W9Pm3aNHz88ccQuPAfWjxVcXGx3WdfX9dGwtW2RqvVNqimhti3bx+mTJlS4/m5c+fafe7cuTObPImIiIiIiKhmTRys2orU6fHWNRWeL1IjRanAFpkfKlzYZGoSCLBL5oddMj/cV1aGp4s06F9eAUt6MV+SigHCk9am0ppqdOd729PpRUR4dUc3J2sE2RnALX+rs25qWswhiYiIiIiIiIg8kGUUefpiIC4B0oGzsBxAfO8IJKafR8aFqmm97mziri2PlAB4pLgUw4tL8ZPUF0n+Cpzw9nap3DyxGIuDAvGRvxL/UGsxTquFwljV7FlT/llXg6ezdanK6cjRxCI+T4tu4XLH7yMvEwiKdaluajk8ssnz3Llz1q+7d+/e4PvdcccdyMrKst67JYSrR44cwVNPPWV3LC4uDjNmzLipzxWLxfjiiy8gFAqxceNGAEBpaSlGjBiB2bNnY+nSpXYNns888wwSExNbdIMn4Njk6ePj4/Y9qjd5Vr9na5eSkoKUlBSH4yUlJU1fDBERERERETW+ZghWbbXXG/Cf64V4tlCNVKUc6xVyFLs4UeSAry8O+PqiZ3kFnlZrEF1aBiHgtMGzLrV+b2fuBc7sRf8ugZj+QGf0DDLXJz2yGuIDb5sbZS0/R/IIzCGJiIiIiIiIiDxU9DyHqTkxUWGIiQrD6Twtth7JQfixZEzUuj+lpzZCAH8vLcODpWU44OuDZKUCv/m61kdUKBJhRaA/VvsrMEZTjKc0GgQbjG7XUGsGeTUauHoOq9LOoX+XQMwYEon7I83TZKRHVkNx4G0Yrs8FHnSczEQtl0c2edqOzm6Mt1ba3qP6WG5PlJWVheHDh9s1CXbu3BmfffZZkzRTisVifP755xAIBNiwYQMAoKysDIsXL7a77rnnnsOqVatafIOnM/X5njzp5zB58mRMnjy5SZ958eJFpKenN+kziYiIiIiIqIm5EKxGnPgEE9SNE6wm6CYAsG8ADTYaMbtQjalFGmxQyLFOKYdKJHLpfkd9vDHHJwS3VOowRa3BiOISOBu8ZDvO3ZarzasZF1XIuKhCXLdAJIT8CMXv75hPWN6EykZPj9HWc0giIiIiIiIiIo9Ww0jybuFyzD2/G9AmOZyrz9sxneWQAgADy8oxsKwch729kOyvRLrUtcnAJUIhPvVXIFUhx2PFxZis1qKjzUv1LJzlkO5soM+4qEJGigqP3BWO/1Nst+aQop/fAYQi5pCtiEc2edo2y1VUVDT4fpWVldav9U7+B+NJ8vPzERsbi5ycHOux8PBw7N69GyEhIU1Wh0gkQmpqKoRCIb744guH8zNnzsSKFSs8qrGxIWQymd3nsrIyt+9RfU31e7Z2Xbp0weDBgx2Ol5SU4Pfff2+GioiIiIiIiOimqC1YrVQDBz52OFffYNV2TfVr5CYTpqk1mKDRYovMDyn+ClwRuxZ1XfCS4LWQIKwMUOIptRajtMXwM5nsrqkesLoTrlp0OrsOoZeqrUlf7NAoS82nLeeQREREREREREQtVtZBYJfjmypvRg4JAL0rKrHyagFOe0nwiVKBnX5SGF3omaoUCrBRIccmuQxDS0oxVa1BVKXO7hrbHLI+GSQAhB3/BKHV1zGHbFU8sskzODjY+nVeXl6D72d7j8bYkX+zqFQq/P3vf8eZM2esx4KDg/HDDz+ga9euTV6PSCTC0KFDHZo8BQIBhg4d2moaPAE2eTaGmt4eevz4cdx5551NXxARERERERE1vU4DzCPJ06umgZQOWYiCvMHA4VyHy10NLWsb6e5rMuFJbTFGa4uxQybFaqUCZ728XCo3XyzG0qAAfOSvxDitFk+qtQg2Vo1Psn2eu+FqTd/bnk4vIsKrO7o5W5R1kKFrE2urOSQRERERERERUYvWDDkkAHSr1GFJwXW8UKjGp0o5vpHLoHOhf8ooEGC7zA/bZX74W2kZnlZr0K+8ApaV8yWpGCA8iVhRpsPaFH1crQ2eNX1vqcrpyDmhRLyXFt3C5Y4LmUW2KMLmLsCZ8PBwAIDJZMIff/zRoF30FRUV+OOPP6yfQ0NDG1zfzaBWqxEXF4ejR49ajwUEBGD37t3o0aNHs9S0Zs0aTJ061eG4yWTCqFGj8M033zRDVTeHUqm0+1xaWoqSkhK37pGfn2/32d/fv6FlEREREREREbU80fPMASsAxCVAOmQOlo/rg9WT+6H/LVVNb+7uSk82DLeOTXJGAuCR4lJ8lZOHFXkF6FXuep6kFQmR5K/E0I7t8WZQAC7bvBF0viTVaZ27DX3r9VaAKWfuxdBlezEm8QD2nLLJEvavAFbHAXsWuVw3NVxbzCGJiIiIiIiIiFqFZsohAaCTXo/Xrxdix+VcTC7SQGqzcbwuv0h9MbVdGCa2C8MeqS8sK501eO419MRk8S5ME21zeq/avrf/XI3GqrRzzCJbCY9s8rz33nshEAggEAhQUVGBjRs31vteGzduRHl5ud29PY1Wq8VDDz1kFwIrFArs2LEDvXv3bpaaPv30U0ydOhVGmz+EbH92lZWVGD16NDZv3twc5TW6oKAgBAQE2B3Lyspy6x6XLl2y+9wcb18lIiIiIiIi8gjR84Cpu4CBs6yHYqLCsPHZ+7BzziB82u3Xeo0dqsluQ1/r10IAQ8rKsO7KVaTkXsUDpa5P67CMTxrRoR1eCgnCCS9JjdfGijKdhquuhsYZF1WYkvIbZq8/hNK0ZVXjpdIXM1xtQm0thyQiIiIiIiIialWaMYcEgFCDAf8qLMKuy7mYWVgEf4PB5Xsd8fHGi2EhGNk+HN/I/KCrdn6voScGicwvC5wvSXXIIt1pXmUW2fJ5ZJNnYGAgevXqBcC8i/61115DUVGR2/cpKirC66+/bh0r3r17d+vufE9RUlKCYcOG4ddff7Uek8lk2L59O/r3798sNSUlJeHpp5+2a/D873//iwMHDmDOnDnWYzqdDmPHjsWXX37ZDFU2vjvuuMPu89mzZ91af/78+VrvR0RERERERNSm1DDqp1vlCURfet/heF3Bam2h5XTdSw676wUA7q6owKqrBdiUcwUPF5dAaDK5VLpRIMBOmR/Gtm+H6eEh2O/jA2crq4er7r4VAABCjiZBmva6/cH0xeZxSXTTtaUckoiIiIiIiIioVWrmHBIAlEYjnivSYOflXLxyvRBher3L5Z/z8sL8kCAM6xiBdQo5SgUCZBhutzZ4WthmkfXJIQFmkS2ZRzZ5AsCMGTNgMpkgEAiQlZWF+Ph4FBYWurxerVbj8ccfx8WLF633ee65525ixe4rKyvDiBEjsG/fPusxqVSKbdu2YeDAgc1SU2JiIp599lmYbP6jx5IlSzBv3jwAwP/+9z/861//sp7T6/UYP3481q9f3+S1NrY777zT7vOBAwdcXltSUoI///yz1vsREREREREREcyhq2WM0g3LRZPqHaxa1jkbo3TOaG6y61apw5KC6/guOxdjNVp4uzE+6VdfXzzbLhRjI8Kx3U+K6vGsJVytT7Ba0xrEJdQYTlPjaws5JBERERERERFRm+MkhywdshAFPafXuKShOaTUZMI/NFpsv5yLNwuu45bK6u/nrFmeWIwlQQGI7RiBjKCr+M7Uw+Ga+ZJUJEmW1qvBk1lky+axTZ5TpkxB586drZ/37duHXr16Yd26ddDpav4fgF6vx2effYZevXph79691t3zHTt2xDPPPHPT63ZVeXk5Hn30UaSlpVmP+fj4YOvWrRg0aFCz1PTBBx/g+eeft2vwfPfddzF37ly765YuXYpXXnnF+tlgMOAf//gHUlOd/EHQgjz00EN2n23/b1OXn3/+GXqbLvw+ffogLCyssUojIiIiIiIial2i51UFrHEJeOqfSxHfO8Lppe40T9oGrHsNPREpzLM731FvwPzrhdh5ORdjCvWQG1xv9jzp7YWXQ4MxokMEPpfLUHYjcwLM4WpjNXiWDlloN16Kbr7WnkMSEREREREREbVZ1XJI6ZA5WD6uD1ZP7of+twTaXdqYOaQEwGPFJfg65wrmXBGiZ3mFyyVrRCJ8FKDEwi5qzAtshxyxyO58rCjTpRptMYts+cTNXUBNxGIx1q9fjyFDhqCyshIAkJ2djcmTJ2P27Nm499570aNHDwQEBEAgEEClUuHEiRM4cOAA1Gq1dde8yWSCj48PNmzYAIlE0szflVllZSVGjhyJH374wXrM29sbX3/9NR588MFmqen999/H7Nmz7Y4tX74cL774otPrFy9eDLFYjLfeeguAudHzqaeesv69JRo6dCh8fX1RVlYGwPwmz1OnTiEqKqrOtSkpKXafH3/88ZtRIhEREREREVHrET0PiIwBOg1AAIDl4/ogvncEEtPPI+OCCkD9xg4lG4ajg6AAk8W7anx0kNGI/yvKRT/VXcj3v4C1Sjnyxa7FZDkSMRYFB+LDACWe1GgxTlOMACdvBq1Pg2eCbgIK8gZjuUuVUGNpzTkkEREREREREVGbZ5NDWsREhSEmKgyn87TYeiQH4ceSMVHb+DmkEMDT5RfRMbs3AvxO4hOlAr9IfV0qu1woxHdKIbYrIjC0pBRTizTo5mRDckPGzzOLbDk8tskTAAYMGIBPP/0UU6Z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+      "text/plain": [
+       "<Figure size 3000x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy.special import erfc\n",
+    "def exchange_0LL(d):\n",
+    "    d_clip = 700  # to prevent overflow in exp\n",
+    "    d = np.clip(d, -d_clip, d_clip)\n",
+    "    return np.exp(d**2 / 2) * erfc(d / np.sqrt(2)) * np.sqrt(np.pi / 2)\n",
+    "def exchange_1LL(d):\n",
+    "    d_clip = 700  # to prevent overflow in exp\n",
+    "    d = np.clip(d, -d_clip, d_clip)\n",
+    "    term1 = -2*(d + d**3)/8\n",
+    "    term2 = (3 + 2*d**2 + d**4) * np.exp(d**2 / 2) * erfc(d / np.sqrt(2))* np.sqrt(2*np.pi) /8\n",
+    "    return term1 + term2\n",
+    "def V_coulomb(q, d,kappa=1.0):\n",
+    "    # q is in 1/ℓ_B units; this returns V(q) in Coulomb units\n",
+    "    d_clip = 700  # to prevent overflow in exp\n",
+    "    d = np.clip(d, -d_clip, d_clip)\n",
+    "    return kappa * 2.0 * np.exp(- q * d) *  np.pi / q\n",
+    "\n",
+    "d = np.linspace(0.0,5.0,100)\n",
+    "analytic_0LL_exchange = exchange_0LL(d)\n",
+    "analytic_1LL_exchange = exchange_1LL(d)\n",
+    "nmax = 2\n",
+    "q = np.linspace(0.0, 0.0, 1)\n",
+    "theta = np.zeros_like(q)\n",
+    "X_gl = np.array([\n",
+    "    quantumhall_matrixelements.get_exchange_kernels(\n",
+    "        q, theta, nmax,\n",
+    "        method=\"gausslegendre\",\n",
+    "        potential=lambda qq, d_val=d[i]: V_coulomb(qq, d_val)\n",
+    "    )\n",
+    "    for i in range(len(d))\n",
+    "])\n",
+    "X_hk = np.array([\n",
+    "    quantumhall_matrixelements.get_exchange_kernels(\n",
+    "        q, theta, nmax,\n",
+    "        method=\"hankel\",\n",
+    "        potential=lambda qq, d_val=d[i]: V_coulomb(qq, d_val)\n",
+    "    )\n",
+    "    for i in range(len(d))\n",
+    "])\n",
+    "\n",
+    "fig ,ax = plt.subplots(dpi = 300, ncols=2, figsize=(10,4))\n",
+    "fig.subplots_adjust(wspace=0.3)\n",
+    "ax[0].plot(d, np.real(X_gl[:,0,0,0,0,0]), '-o', label=\"Gauss-Legendre 0LL exchange\")\n",
+    "ax[0].plot(d, np.real(X_hk[:,0,0,0,0,0]), '-x', label=\"Hankel 0LL exchange\")\n",
+    "ax[0].plot(d, analytic_0LL_exchange, label=\"Analytic 0LL exchange\")\n",
+    "\n",
+    "ax[0].set_xlabel(\"Interlayer distance d (in ℓ_B units)\")\n",
+    "ax[0].set_ylabel(\"Exchange matrix element (in Coulomb units)\")\n",
+    "ax[0].set_title(\"0th Landau Level Exchange Matrix Element \\n vs Interlayer Distance\")\n",
+    "ax[0].set_yscale(\"log\")\n",
+    "ax[1].plot(d, np.real(X_gl[:,0,1,1,1,1]), '-o', label=\"Gauss-Legendre 1LL exchange\")\n",
+    "ax[1].plot(d, np.real(X_hk[:,0,1,1,1,1]), '-x', label=\"Hankel 1LL exchange\")\n",
+    "ax[1].plot(d, analytic_1LL_exchange, label=\"Analytic 1LL exchange\")\n",
+    "\n",
+    "ax[1].set_xlabel(\"Interlayer distance d (in ℓ_B units)\")\n",
+    "ax[1].set_ylabel(\"Exchange matrix element (in Coulomb units)\")\n",
+    "ax[1].set_title(\"1st Landau Level Exchange Matrix Element \\n vs Interlayer Distance\")\n",
+    "ax[1].set_yscale(\"log\")\n",
+    "\n",
+    "ax[1].legend()\n",
+    "ax[0].legend()\n",
+    "ax[0].grid(True, alpha=0.3)\n",
+    "ax[1].grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "da9e9669",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max difference hankel-analytic 1.251554415659939e-12\n",
+      "max difference glegendre-analytic 0LL 5.441650496629258e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"max difference hankel-analytic\",np.max(np.abs(X_hk[:,0,0,0,0,0] - analytic_0LL_exchange)))\n",
+    "print(\"max difference glegendre-analytic 0LL\",np.max(np.abs(X_gl[:,0,0,0,0,0] - analytic_0LL_exchange)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "845805d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max difference hankel-analytic 1.3340051285837262e-11\n",
+      "max difference glegendre-analytic 1LL 5.4416505301913e-05\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"max difference hankel-analytic\",np.max(np.abs(X_hk[:,0,1,1,1,1] - analytic_1LL_exchange)))\n",
+    "print(\"max difference glegendre-analytic 1LL\",np.max(np.abs(X_gl[:,0,1,1,1,1] - analytic_1LL_exchange)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/validation/Exchange_pwme_julia_code.h5 b/validation/Exchange_pwme_julia_code.h5
new file mode 100644
index 0000000..3e03b76
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