MIME-Version: 1.0
From: Durgamahesh Manne <maheshpostgres9@gmail.com>
Date: Tue, 15 Jul 2025 15:40:34 +0530
Message-ID: <CAJCZko+fiU+=NSEGY7d7YMpFxg_jANpiO-LU6ur+yqRc_Ej6eQ@mail.gmail.com>
Subject: Regarding query optimisation (select for update)
To: pgsql-general <pgsql-general@lists.postgresql.org>
Content-Type: multipart/related; boundary="0000000000003da5120639f4fcef"
Archived-At: <https://www.postgresql.org/message-id/CAJCZko%2BfiU%2B%3DNSEGY7d7YMpFxg_jANpiO-LU6ur%2ByqRc_Ej6eQ%40mail.gmail.com>
Precedence: bulk

--0000000000003da5120639f4fcef
Content-Type: multipart/alternative; boundary="0000000000003da5110639f4fcee"

--0000000000003da5110639f4fcee
Content-Type: text/plain; charset="UTF-8"

Hi Team,

We are facing issues with slow running query
   SELECT betid, versionid, betdata, processed, messagetime, createdat,
updatedat FROM praermabetdata where processed = 'false' ORDER BY betid,
versionid LIMIT 200 OFFSET 0 FOR UPDATE;

                                                         QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------
 Limit  (cost=0.28..1.89 rows=1 width=78)
   ->  LockRows  (cost=0.28..1.89 rows=1 width=78)
         ->  Index Scan using idx_praermabetdata_processed_betid_versionid
on praermabetdata  (cost=0.28..1.88 rows=1 width=78)
               Index Cond: (processed = false)

[image: image.png]

Do we have any alternative way to improve the performance?
Sometimes processed column use true as well as false

Regards,
Durga Mahesh

--0000000000003da5110639f4fcee
Content-Type: text/html; charset="UTF-8"
Content-Transfer-Encoding: quoted-printable

<div dir=3D"ltr">Hi Team,<div><br></div><div>We are facing issues=C2=A0with=
 slow running query=C2=A0</div><div>=C2=A0 =C2=A0SELECT betid, versionid, b=
etdata, processed, messagetime, createdat, updatedat FROM praermabetdata wh=
ere processed =3D &#39;false&#39; ORDER BY betid, versionid LIMIT 200 OFFSE=
T 0 FOR UPDATE;=C2=A0=C2=A0</div><div><br></div><div>=C2=A0 =C2=A0 =C2=A0 =
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=
=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0 =
=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0QUERY PLAN<br>---------------------------=
---------------------------------------------------------------------------=
--------------------------<br>=C2=A0Limit =C2=A0(cost=3D0.28..1.89 rows=3D1=
 width=3D78)<br>=C2=A0 =C2=A0-&gt; =C2=A0LockRows =C2=A0(cost=3D0.28..1.89 =
rows=3D1 width=3D78)<br>=C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0-&gt; =C2=A0Index=
 Scan using idx_praermabetdata_processed_betid_versionid on praermabetdata =
=C2=A0(cost=3D0.28..1.88 rows=3D1 width=3D78)<br>=C2=A0 =C2=A0 =C2=A0 =C2=
=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0Index Cond: (processed =3D false)<br></div><=
div><br></div><div><img src=3D"cid:ii_md4dde5o0" alt=3D"image.png" width=3D=
"452" height=3D"144"><br></div><div><br></div><div>Do we have any alternati=
ve way to improve the performance?</div><div>Sometimes processed column use=
 true as well as false=C2=A0</div><div><br></div><div>Regards,</div><div>Du=
rga Mahesh=C2=A0</div><div><br></div></div>

--0000000000003da5110639f4fcee--
--0000000000003da5120639f4fcef
Content-Type: image/png; name="image.png"
Content-Disposition: inline; filename="image.png"
Content-Transfer-Encoding: base64
Content-ID: <ii_md4dde5o0>
X-Attachment-Id: ii_md4dde5o0

iVBORw0KGgoAAAANSUhEUgAAAq4AAADaCAYAAABn08D4AAAAAXNSR0IArs4c6QAAAARnQU1BAACx
jwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAEeySURBVHhe7Z2rjuu818af/3cZVTTSS30BVTRk
oyKzgpKCYqMpKyg2KGtRscGQgrKgoE2qqhcQuqVRlNvIB3Jokjaxe5qZzjw/aWnvieNle/kY18n6
H4AUAMIwBCFfxWAwaF4ihJBvgzIGWEywjpohn8NXp0/Id+H/mhcIIYQQckSZEMNeD8NViNCoZvDD
+er0CflO/I87ruQ7wB1XQgghhNjgjishhBBCCHkKyh1XQgghhBBCvjPccSWEEEIIIU8BF66EEEII
IeQp4MKVEEIIIYQ8BZ+8cBWQ2iAMQ4RhCKMVpDLQsnlfC1If4yrRDP01CGV+dfm/AqGO7bYuBqyK
n8X1/evB45st/E5I/Vj9Nq6xv9R1u9T6q2sF2OxrC+8gy59lrLhB/yVcY18IBdMc+4yGvFANgFof
CcML+oeN72w/AICAqo0PGoafNjvBxb6funCVeoaX9wUGgwEGgwEm73/x4veat7UTzDEYDPC2TZoh
hDycZPuGwWCA5QE4LLM2zKZICh4+vtnCHVBGw7ZOCOYDTJ7sK/fBfIDlIQH8MSSAaD3B4G2LJNli
MA+at5/HZl9beAfB3GGsuEH/w4nWmGQDX9m+B4sYo5m9PdUQCiMvxluhYzCBa/VYcbCfS/t/FELN
4MXH8WGxi5u3fGn+nonPW7gKhRE2WEeVATGKsJ4MKg238URilHMlFk/YxdNb/QlcQudPiNqECEMN
KXX2BJk3lCK+0fn1Shgh0XpydjJfT3JPNpUdiaINlrs+RkGZfMdF1dtX7cFSyLx9Fk/kCt3PneTb
8ODxzQmpKu3HQKuK9rx9Dnt9TMvdrmNbzeLbdqza8/9dxs/9Hhid2cLrnh/uRJf9C/6r3HPpjuV3
Gx+iNRZ7D6PChpb8SR0iXA3R6/WxKttIZcexw343159L+/8EPPxX2iQK1phM1tkfTvlr739o2EQW
80y1D3bYN7+h3jZVNh6UNrbU72eTfopInRolTq9XRBmTKlG5JmSqjUpF4z6hzFldQplUy5Y0hUpN
mOkXSqdhqFMpkEp9jCOUSUOTXQeysHPpfLW0lZ/yOSJ1WG9nhZy0cZEqo1OZ/61MmBqtUlG28Ua4
1vW2LmSqtTpNh/JQuap/ndT9qdw6vtnDRaVtnW+n1fbWJm36bfm/1/jZlr5NpM7yV/wLoVJjjv2n
c36oiC399vBu+ysTpqGp9H8hU32mPtr032t8aNNvFanTUMvWa075a9RJXbrtd4/6c2n/NunS3y0i
6xNhmIZhcy7IpCt/tv4H5PURmtRomekW+b95+l32VcakSublEiKbryrldKrfO4iLfT9vx9WGUPDj
Td0PcxRgF/v4c69lfbLHOgKiDwCHHYII+NfYrT9s5gjyPARntvIJaSV4xz7/qRIAIMfw9+84/hKW
YP++xnFTLsLfvYdXmbf/fmUnIgwRrqbo971P37UiD+AzxjfxB+PZsf2MvOYNN+CY/+8wfgbve/iz
L9gNsto/wXZR6f9RgPei/9v47uPDPfJntd+zEyGYTzApjwp4WM0cz7g69j8ASLYLTOZB1s6i/F9Y
7CsUfOyxLjpvFGG9OdTDb63fO/J5C9d/MeD/aV4l5AcRYb1B/tOZgBoBmzPHC84SfSBOtsfzY6XM
Kwtf8m358vFNQq98xJvsHPZgMMBi337W70cTrbHY+xh/anU82P7fdHwQLx6S+N8d8vdg+31DomCH
Q++zFn432vfm+r0vn7dwjdbYxH79TIqQ0CZ/qzBaY++NTs78vXp7/HWc+wHAe8kUCKlgpv1mMCGP
Jd91Vaq52woAPfhjBVG2cYE/foxdAADZ07M5OXdEnoJPGt9aES/wkhh//2XKhFQYn30xzEM+REIo
DRNqt53JR+f/zkTrDeAP0bTAw+YHJ/v3MJwd+7+QCrOy/9v4huODkBj7cf5wfmP+nOx3j/q7sv3f
jIQODVT1ULN4gXfYNeaIlvzd2v9s9o3W2MM/6hcCalS17431e2c+3eWrUBqzYT8bUJIDtpv34/Y0
BJSeYdjPDJokW2wm67xiJXQ4xWlTPWBZrPqFhJ5N0e9lupd7YDrsI9luEQ+HWdzDEoPdK8JpH8n2
DQvMsBr2kCQJer0s3cNygDk0wrxjJNu3sy/mfBVCGcyw+FZ5+hXIY5vIqLS9KlIjnOIkTBkDbDbw
RpU2ujj+tJp9TmmGad7+kSRI4g0W8wCs6c/jlv71uPHNFp7lezXMdR+22MQ+pkNg+5a/QAhASI3Z
9Ji/Y/uz6+/KfzXtW8fPa+wvdYiia5bpSY1wFGNQvgDTNj+8YbL+z1J+u3267P8xDjHtJ9gu9/Cn
+YL6CvvfY3y4xr4QCmbVeBC4cPxSJkRuniOHZfnVhy77ZS/A3lJ/Ge3t352r7AcJHY6AA9Av7XPA
clKfI7rz197/2trPYXl8OdRqX0hoU7fvCO+VcnbX771wte/JwVfK9xaXw8uUL5SWlwZODtdTvqWw
f32t0P6PFdr3Nvkt9pNfVE4X+37eUQFCfjjF50jCaR+94az2s06229DDcNX4BAwhhBDyDcg+21i8
vLXHwrLr+VV8+lEBQgghhBBCroE7roQQQggh5CngwpUQQgghhDwFXLgSQgghhJCngAtXQgghhBDy
FHDhSgghhBBCngIuXAkhhBBCyFPAheszInXdtSQh5H6wf30ttP9joX1vg/Z7LA725cKVEEIIIYQ8
BVy4EkIIIYSQp6DiOUtCh1P0m3fggOVgjoDhXxsuFMxqiF4zGMBhOcA8sMS36Wc4w39zOPvX14bT
/o8Np31vC6f9Hht+hX1TypOJ1KlR4vQ6hUK5Xdi/vlZo/8cK7Xub0H6PFQf78qgAIYQQQgh5Crhw
JYQQQgghT0HljCshhBBCCCHfF+64EkIIIYSQp4ALV0IIIYQQ8hRw4UoIIYQQQp4CLlwJIYQQQshT
wIUrIYQQQgh5CrhwJYQQQgghTwEXroQQQggh5ClwXrhKHSIMDZRohtwfqUMYa0ISOgwRGtUM+PEI
ZRzscxlCGYRhCC2bIeS74dY/bkeZZn+X0CZEGGZjgW7mQSiYsAgPEYYatebkEm4UHl+ybh7Rv4g7
v9b+Upd942z5beGO/Fr73onfYb9b11fXx3exr/PCNZgPsE2aVy9HmcZkdYZgPsBkHTUvNwgwHyxx
aF4mVxGtJ3i7RwU/GJf289Nx6x83IjWG8QbVZJQZAZs3DAYDDN4WiP3Z6YPOYZmFDwYYDOYIGsGd
4dEam3iIcVMnIZ/El44vwRyDwaB9HLaFkx/P57XPW9dXt8bvxnnhWvKfOu66GA1ZXRiL6o5MCKMr
uyf5bsuw18e0sutSm/hsT5Q1/eZ00iQWBKTOdlaz+lPQzV21l8quWLOTyErdhwZa1Ssg25XP6k4q
nemp6rDE78yfS/vpan8u+eugiFu2zUpbLZ8qLeXrTL+yG1mUqdgFP+pv7x/FvUbnes+WrWIfoyFV
pq+pCwDUqI/Drr7sXE8mmAf5SjaK8HdfC74LwfsW3ujrd13Jc+HW/gVUZXwxRh3DXcaXDmzpN3/R
qo4F5JfQNT81x/9yrL/P/Gdrn6fxz6yvOvSfhp+Jf2dSV1EmTEOjUiHya0Km2uhUFuFap6IaR8hU
a9XQcby/TYQyqVGicT1Pq0gbIpXapKGp6/8Nct4+dpHapFpW4gmZ6tCkKrepUCYNw6ONpW6mI451
D6RSh6mWjXSkTsPQpEbL7F6R/+sQ35Y/WNqPS/vrzl+3nNhd6tRoWbmnu3xFnNb0pT6x97nynuSj
cj2s9JFm/SljUlXYV4hUmfCsHkCmOjxNtyZCpcaohr1VasIwDQup9VeH8DLtep1/trTZl/I5cq39
ndp/rT3KVDfa8Ln+5iq29IUy9fHgpL8f7zt33TXcJrfG/+1yrf2s85NQqSnGvur/qzo62qdNf3f7
tK+vuvXb47uKi30v3HFNsF2sERU/H0YB3vceXmX2ROD3+1hVngbC1RT9vue0o2VFvsLbv6PY8AEi
BO978EcTVyRevf1xxwxZ/c0Hk9rPwYflvLRxsIuPAQAg/mA8O9bvyKsHFyTbBSbzIGsnUf4vbPHd
8tfKBe2vNX8WovUCsT8unzLVyMP+vbIr2Vm+I63pB+/Y++PKDvUY/v799Of2Dg6blvoTCj72WFd2
TNeblh9yxAs8AP+a1wuEgp552EzWqJkuWmNSHgMYYLCIMar+7m8LBwAEiJMevP8alwlxoLP9N46+
IAqwi338ueOmZ2v65HfjMj9Fa0zeNvBXIcKVj/2b49wHR/1d7dO2vrLpt8W/MxcuXDuIPhAn28r5
tZZzbORLuX6MltArH3FxxnEwwGJ/SbN0i391/j6l/UV433vZGUypGxOhW/m6ibDeAKPstyGoEbBx
Hrk+B6E0zPgD7xMHu0YfiL2X5tUjtnBCCPkJOM5P4r9X9JAgSQDvvwtmQ0f9V/No/Rdy4cK1h+FM
QeT2FFJh5sfIjsJlT6/m5NxiEw8vRXylYULttlgJdoj9ceVMrYAc++jV7yKtBNjFQ8wqZ6pEfibF
6SyKeIGXxPj7L1tICakw9i+wvjW+a/7a2o9r+7uNKFtZntlttZXPkXzXVanLd1s7idbYwz+eZxYC
atRv3JQTfSAGUN/0zM4HjvGe7RYjOzdVrRtlTM3+Uo3Qjz+cw/Or8HoJ4tbtXkKuIFpj743q5/lF
9ivP39qzYdv4ch+8XLmQCmba0v/ID8Q+PwllsBoBy7cJJpMNMFqdOQPd1j7t+juxrq8s+q3x78v/
8jMDVqQOMe0n2C738KfDLEPJAcvFces5e7lmhmk/z26SIIk3WOQTHQAIqTGb9s/El9DhFKdd+YBl
saoXEno2RaY+wWG5AaZT9JMtBpN1M+KPRSiDGRZXvFkuoPQMw0r9bDcLrIMo6zTD7PphOcAcGmE+
sCbbN0zW9XuSwxab2Md0CGzfJlhH5+vvsBxgnq++uuOjM38F7e0ni9/e/uz5c0Uog5W3waARsbt8
F6QvNcIpju0+u3g2ftE//jnUX/ZyVt5/kgOWe2CE97PtSJkQ3qaSN6FgVnm/r1DPf73+ksMWi3n1
OIEtPE9nBiyaxxA+kev7F7kH19jfZfw6aX/JFpvJur7j1Tm+tOOUfnX+yvvfdNjPw//r7N+Bpf83
h5AurrEvOXK9/drnJ7i0H2v7vFG/dX3Vrj+bvm3x3XC178nBV8r3FpfDy5QnlpaXNu4tsqsdSZ2G
tRfPPkfOvtD2ycL+9bVC+z9WaN/bhPZ7rLjY98KjAoSQR1F+cmvaR284e4izD1X5nMnI22PR9lQb
zLFt/rT6aITCyNuiegKDEEIIqeJ8VIAQQgghhJCvhDuuhBBCCCHkKeDClRBCCCGEPAVcuBJCCCGE
kKeAC1dCCCGEEPIUcOFKCCGEEEKegqdcuEodnvEocT/c9EvoMERoVDPg4QhlHPLXjlv5CPmd3Nq/
yG38WvtLXX6q7mz5beGO/Fr73omH2e9O9fvs87uLfZ9y4RrMB1avCrfgpj/AfLDEoXn5CXAr33mU
0Whx+kYIIU/Nl45vwRyDwQBv26QZkmELJ8/Nner3lvn9WXBeuJYfRy+eBipPB+WuY+5bvrxPq5qf
50KHUQJSaZgwRFgbKDJ/6Mf4Bqa6o2l9ImnEN6rULVR23eg83ZO0HfTXylf30/4UdJTPah+hYMIQ
w14f0yI8DOs2sNR/5nK0oltl+SnzYolvbz+EkN+IdfzK7mqdH5zGtw5s6Rfhhb7qWEZ+PpnjFwNV
zFt5+7io+qWqrz9Uo3HeMr8/ISfutNrkxBWX1KmpuIVUWqeiGkfIVGtV1yN1GoYmNVqmQmT3CFHV
L0sdQuo0NI345/JRpG9MqnJdZfpGHfUpk4ZGpzK/R+rzes7rl6muxAVEKrU5m79Hy/n8uUtbfBf7
KKNTeUYnHOpfGZMqmesTIlUmrOm3xQe62w+Fcg9p6x+Uz5Fr7W8bv2zzQ3ZP+/hmE1v6Qpm6O+MW
18628tvCbXJr/N8u19pPmTA1WlXmK3G2vbXrF7W5rs09dlt8W/v8LtKW/6o477gCQLReIPbH5S6Y
GnnYF/4ZhYLf72NVeVoNV1P0+97Jqj7ZLjCZB4giAFH+L4BovUHsT0sdsxGwXKwbsVsQCn68QW2H
PAqwi338qTx8HDZzBPk9wS4+BtiQr/D272VcIELwvsdtm/rfj6vtY6t/oeBjj3WhPIqw3lQOWtji
V2hrP4SQ303r+OU4P9xKa/qEIMH+fV2ZryL83Xt4bU5wbYg/GM+O8+PIa95g56e0z4sWrkCE972H
scy2pWsDQfSBONliMBg0ZA531+MB5pM83tsbFntgOvv8l5/IFdxa/7fGJ4QQQn4kEnrlI968lXPj
Yv/Tts3cuXDhmu2KYqTqu60AgOzp1TTPXVyAMgZa5o+/UQR8AEn80bztPNEae29UPzMiJF69Pf7e
Y0cu2CH2xyiyBwjIsY9e/a5fgIeX4kiq0jChznfgLfUfrbGHf6wfIaBG/coNlviEEHItzvND2/h2
H7xcuZAKZlod/8jPpwd/rCAqa4g/foydy86MeIGXxPj7L2usQiqM/d+3+ij4X35m4CKEMlh5Gwzm
TYsLSD3DtJ8bNEmQxBss5gEiSOhwimZXPSwHKNQoo+HFHvpl/AOWi2Jr+3x84IBluSsnoPQMwzx+
kmyxmawRFHkeZtcPywHm0AjzgSPZvmGy/s+uX0jo2RSZ+gSH5QaYTtFPthhMHI803AGhDGZYXPjm
YLf9/lntU3QYjdm0ny3Ya/UDS/0jfzkrt19ywHIPjPBeKUdX/PP5r7YfQu7Bdf2L3Itr7G8f36PO
+aHU0zm+teOUfnX+yMe/6bDvOP+cH//q858b19iXHLnWfsoYYLOBN6q0gQvWN9U2lhy22MQ+pkNg
+zbBOuqO7zq/fwdc7Xty8JXyvcXl8PIziPwh5aD8LPkp/etZhfZ/rNC+t8m19jt5OZByVlzse/FR
AUJuIfssSHG4fI+F5amKEEIIeWaUCTHs9TBcfY3Top/GVUcFCCGEEEII+Wy440oIIYQQQp4CLlwJ
IYQQQshTwIUrIYQQQgh5CrhwJYQQQgghTwEXroQQQggh5ClwXrhKHSIMTd3zyEOR0CE/HXEOoQzM
LRUhFEyo8Zk+qj6//RByHTf3L3ITtP9joX1v43fY79b11/XxXezrvHAN5gNs7+AaVxnXBVOA+WCJ
Q/OyBXf9v5hojcmF3lZu5V7thxBCCPmNfN765rr115Fb43fjvHAt+U9BFx+RNxqy4fu5DAtDGK2O
fp6FgglDDHt9TPPwMAyhq7VQi2/qYQAgK2mHBrrq195Ff1f8X0D14/9hGJ55qhFQ2lTqz8DUnpga
4UaVnUio7LrRGqZI41wn62o/hBBCyE/EYX1UrlmkLtcpSjiubzr0O83PtvVXh/7T8DPx78yJO602
USZMQ6NSUbgtEzLVRqeyCNc6FdU4QqZaq4aO4/11yXWVLtFEKrVJQ1ONL45pA6nUYaplXU+7frf4
zyAuLtGsIvWJjkyvLOtQSF2z/4nLOiFTbdTxfmXSsFKHUtfzaWs/FMp3kLv0L8rVQvs/Vmjf2+Ra
+1nXR0KlJszn2Or/qzo65kub/u752b7+6tZvj+8qLva9cMc1wXaxRlR46YwCvO89vMrsicDv97Gq
PA2Eqyn6fe901+0c8hXe/h1B6QE0QvC+R+3XZfEH49lR/8irBjpwa/wfTrTeIPanZR3ORsBysc4C
hYIfb1Dz0BoF2MU+/lQeuw6beVmHwS4+BgDd7YcQQgj5ibisj6I1Jm8b+KsQ4crH/m1Sn2+7cNHf
NT/b1l82/bb4d+bChWsH0QfiZIvBYNCQe52llNArH/HmrdS92F9illvj/wYCzCd5vb29YbEHprPL
D1cTQgghJMdxfST+e0UPCZIE8P674Bydo/6rebT+C7lw4drDcKYgcnsKqTDzY+wCAMh234z13KiH
lyK+0jChzs5JBDvE/rhy5lFAjn30yj9f4CUx/v7LlvRCKoz9MrRCi37n+L8XZQx0UQFRBHwASfyR
/73G3hvVvwogJF69Pf66PhV2th9CCCHkJ2JfHwllsBoBy7cJJpMNMFqdeQ+lZX3joL8T2/rLpt8a
/744L1ylDjHsJdhugNkq2ypejTxsFscVdzBfYOONjlvJxsBoWTvA+3cTZ1vhYYiVD2ze5sjWPQHm
ix1ey5/yZ3jdbXDoDbNPKkRrLPYeVnnc2Suw2yfoT+ufWGrV7xj/dxMDr7Oy/lYjYPN+XFWuJwtg
XHk5a/aC3WSNqOh0wx7608rh8mkfvWHW+VzaDyGEEPIT6VofFfMnen28/pf99N7voZw/C1rXN476
2+Zn6/rLot8l/r05OfhK+d7icnjZJvfQQaH8RGHf+Fqh/R8rtO9tQvs9Vlzs67zjSp6d/IPAucy8
PRbOJ78JIYQQQr6e/+UrWEIIIYQQQr413HElhBBCCCFPAReuhBBCCCHkKeDClRBCCCGEPAVcuBJC
CCGEkKeAC1dCCCGEEPIUcOF6NfnnpR70cd0uhDJnPGp0I3WIMHywswWpj84JHpoQIY/jmv5F7sev
tb9t/LSFO/Jr7XsnHma/O9Wv1LfF/2pc7MuF69UEmA+WODQvf1OC+QDbpHn1zgRzDAYDvD08IUII
uT/KaLQ4tXw8tvHTFk6emzvVbzAfYPLDv9HuvHBVJt+xUxqmdPmlazt42a5ettqXxX21gUBA6YrL
UKMag4SArISHRkGbyi6hkNDm+BF9o1XNneyJfm1gajuilnCb/lq4yVynPRv/qWMZjK74Fsapfc7U
T3e4BZt9ZSVvoYGu+EUWKkvX6Hr7uyh9QsiPxG186Bi/hIIJQwx7fUyL+GHuHtMBW/pFeKGvOleS
n4/L+slKx/yYhbfv2Nra5zNy4k6rTZQJU6NVKkRxTaTK6FRW75M6DUOTGi2z+0T+L5AqY1JVxs3C
tFGpyP+W2qRaVlx9CZnq8BhHaV3eW4ZrVf6duQqT5T1C6jQ07uHd+mWqjU5lpexSm1r8zxIXl2jn
RJkwDU2l/kRepjK8u35s4YW05a/bvkgBUWlbSKUOUy3resNKHUh9Ph0K5RZpa7+Uz5Fr7W8bH1zG
r5P57AKxpS+UqY1nkPpsOW3lt4Xb5Nb4v12utZ/T+qlTf/f8aItva5/fRdryXxXnHdeMBPv3NaJy
FzrC372H18ayPdkuMJkH2X1R/q9Q8OMNajvYUYBd7OOPAACJV2+PeVC5IQowH0yyOELB7/exqjwN
h6sp+n2vfGqI1hvE/rS8ZzYClov1UV1XuE2/fIW3f8cxexGC9z1u29T/bBJsF5X6iwK8F/Vnqx9b
uA2bfQFA/MF4dgwfeXUVAHDYzMs6CHZxM5gQ8otpHR9uHb8caU2fEMf1UysO86ONn9I+L1y4Pp7W
MST6QJxsMRgMGjJHUN4UYD7Jr7+9YbEHprPqUYGOcCf95Gqs9pXQKx/x5q0MW+yf67GAEEIIuT+c
H6tcuHDtwR8riHJ1KfDHj7FzWdlFa+y9Uf1Mh8h2Wf9GABBgFw8xq9wg8jOR2bmg7OnYNM91VFDG
QBeHNqMI+ACS+MMx3KI/2CH2x5UzoQJy7KNXv+ub08Nwdqw/IRVmRf3Z6scWbsViX/ECL4nx91+m
TEiFsf9c1iWEfFOcxy8PL8X4qDRMqNs3U67Ay5ULqWCm/WYw+dHcsH7i/Fjjf/mZASeUMcBmA280
Rb8HIDlguSi2niV0OEWzKx6WA8zLihFQeoZhPzN4kmyxmawrO5r1cCQJtpsF1uXv8wJSzzCthCfx
Bot5gCh/I9SLPfTL8Gr+7OE2/RASepaXHQkOyw0wnaKfbDGYHI8kPBqhDGZYXPTmoNQhpv0E2+Ue
/nSYLbjPlP+S+qmHn69/4IBluavabV+hDFbDXPdhi03sYzoEtm8T/P1zDDssB5hDI8wH/mT7dpEt
COnimv5F7sc19q+OHe3jQ9f4leuRGrNpv2V8bMcp/er8kRyw3APTYT8P/88yfrqMr25cY19y5Fr7
XbN+qtZv1/y4jrrj/3Npn98EV/ueHHxtk5PD7ZQvEZfDyxQK5Tph//paof0fK7TvbXKt/bh+chMX
+zofFVAmxLDXw3D1NR/dJ4QQQgh5Nrh+ui8XHRUghBBCCCHkq3DecSWEEEIIIeQr4cKVEEIIIYQ8
BVy4EkIIIYSQp4ALV0IIIYQQ8hRw4UoIIYQQQp4CLlyvQOoQpuaC5XMRyjw0/a8u37Nzjf2EMgjD
wkvcY7kmf7+JR/cv0s2vtb/UpR/6s+W3hTvya+17Jx5mvzvV77OP7y725cK1gTIatrVDMB9YvTp8
V757+Vzy993psl9b+aL1BG/b231Pt+mv0pU/Qn4zLv3nYQRzDAaD9nHAFk6emzvV728Y3x2/45q7
E0sOOKCPfu+A5RIYTfvoJQcsJ7nLuZpL1Mwt2WK+ztylAqcu9w4J4O0xKd2lWsKlgh4Njy5XtxvM
11VndxLatLnUa7jca+ZPKJhV7gq1Qs1lrWxzk3Yv+7jh6hKtxrcoX0f9OuWvrf7t+XsxIYa9BNtt
DH94dOm4XczhYsbsA9IADksM8gxlbnQBFC5/W+3nZn+hDMbYwxtWXPIWtrXhoL89f3b7udVvN1WX
hSU1d8ntLjmLuMnhAPQrLjmr9rkxfwVX9S9yN66xv1P76GhfTv2nA1v6RXihrxg7TsYJh/Lbwm3c
Gv+3c439svnDbf5p1d86/xXhbeO7vX1+J1rL3+DEndZZESo1YeayTCidhqFOpUAqtUm1zO5RWqei
FkemWqvy78yVlyzvEVKnoXEPB0QqKi7TpA7LtFG4VJO5qzAhUmXCmuswW/4yHTqV1XvOyFmXZHew
j6ucTd9RvrJ89vq15a+j/l3yZ8LUaFXRISzpVUWmupHXLA+nbvzO2i+XrvSEMmW+C91tetqkS38h
Z/PnYj9L/dpEqKMuCJmahgvEE5eIIrN52V6USUPTbp9b81fIWftQPk2utb+1fVjaV3aPvf+0iS39
WvsHUkh9tpy28tvCbXJr/N8u19rPdf5p198x/znEt7XP7yJt+a/KZUcFkj3WERB9ADjsEETAvzgP
Ewp+v49VfkYjDEOEqyn6fa/86SVabxD70/Ke2QhYLordFns4xB+MZ0f9I+8YBKHgY491kK/Sowjr
zaEebsnfzdxon2/PjeWz1q+NrvqHJX/ZDdi/rxGVD3IR/u49vDpVQIAYWVmUydz2CQi8ejH+dj8Y
XsRhOUfRhINdLfOPp8t+DvVrI1pPst0roWBWI+wXk+Nug1Dw40199yEKsIt9/KkcdzpsWuxzh/yR
56ezfTi0r1tpTZ+Qm+Yfh/nPgZ/SPi9buHYRfSBOthgMBg2pbkUHmE/y629vWOyB6azqt7crXEKv
fMSbt1L3Yn/BWRCn/D2Qr07/0TiVr6t+bdxY/3fgI/bwIiQ8bLGMffxRf+DFu4t/in5KnOrXAaFg
Vj72b5VF6z24V/4IIeTb8fXz33fifgtXZE+vRrU/PihjoGX+eBtFwAeQxB9u4eIFXhLj779sthNS
YexXTiRFa+zho3wZTQioUXbeI8OevwwPL7kOoTRMqHGfB3LX9B/N15Wvs35LWvJnq38nevDHCqIs
sMAfP8bOcWUT7GJ441d4+78IdjF830fsGrmkpXx341H67fVr5WTRKqFN/uASrbH3Rsf+CwBC4tXb
O+5o3yF/5Ofi3L4e1X8yvFy5kAomP49Ifgs3zD93mf9+Dpe9nIX85ZTdK8Jp9tLTArPKoXMBqWeY
lm9HJEjiDRbzAFH+xqYXe+gf357AcnHcuraFV1/uSA5bbGIf0yGwrU2E9ZezRnivHPLtzh8ACKkx
m1YOL5fpV2xQ44DlYIfXO9jHFdfDy+f4yvLZ6hed+euq/w28lT1/8cgAmw28UaWNNNLvpLbwEtDh
DHG17bXa77jr11a+atkOywHmaD9o30Wb/u783ad+bZQvs1W58OUsdNrntvwV3NK/yO1cY3/X9tHW
vko9rf2nG6f0qy8P5vPT8eXh/zr65xxBZ/+97FeFa+xLjlxrP2W65h97/bbPfxOso+74/1za5zfB
1b4nB19/ikiHQ77PKC6HlymncvJyBoVyRti/vlZo/8cK7XubXGs/zj9u4mLfOx4V+B4oUz28vMfC
smonv4PscyQ9DFfZi1WEEELIZ8D55744HhUghBBCCCHka/lxO66EEEIIIeRnwoUrIYQQQgh5Crhw
JYQQQgghTwEXroQQQggh5CngwpUQQgghhDwFv3jhKqHD5/w0hVAGpuYC5r5IHT5U/0/nGvsJZRCG
IfQnOH66Jn+/iUf3L9LNr7W/1OWnHM+W3xbuyLPYVxnT8HSmYIy6qyeza3iY/e5Uv88+vrvY9+kW
rspo3GduDzAfLHFoXrZwv/S/Bpf8B/OB1WvFo3DJ33eny35t5YvWE7xtb/c93aa/Slf+CPnNuPSf
hxHMMRgM2scBW/hPQmoM403ulTAnWmMTDzH+sgp6MHeq398wvl/4HdemS8UtDvARLzK3l4VLx2T7
hg3GGA376CUHLCe5S7qqy7vcbdlivj66Y5QKejTMwxMcthvM17kzO6FgVsPMFV+FzBVl/odNfy08
wWG5AUYvmBcuJ29N/5NwdYlWwyX/ss0NXO5OLjnggD76vQOWS2A0vbB+my4XDwng7TGZrB3z11Y/
9vy9mBDDXoLtNoY/PLp03C7m9cGxhewD0rlL1DxDpQvTwm1pq/3c7C+UwRh7eMP8vqptbTjob8+f
3X5u9dtN1WVhyYUuX5PDAehXXHJW7XNj/gqu6l/kblxjf6f20dG+nPpPB7b0i/BCX3WubJbTVn5b
uI1r4tvKl9/Val+n8bOCMiG8zRnbCwUzAxaTy/v1vbjGfln53eafVv2t818R3ja+u9bf96C1/A1O
3Gm1idQm1bLiikvIVIcNN2ZSp2FoUqNlKkR2j8jDldapqOoUMtVaVdIQ5b1ZemGqZT0PyuhUnsmb
Xb9MtdGpLPWLVGqThuZ+6X+WuLhEaxOX/J/VL1Rq8roWSqdhmNkyaxO57k77F3pleY+QumF/W/46
6sclfyZMjVYVHcKSXlVkqht5zfJw6sbvrP1y6UpPKFPmu9DdpqdNuvQXcjZ/Lvaz1K9NhDrqgpCp
abhAPHGJKDKbl+1FmTSs9OGmfW7NXyFn7UP5NLnW/tb2YWlf2T32/tMmtvRr7R/ZXHmunLby28Jt
cm18W/m67es+fgIy1WFbPZxZc3yyXGs/1/mnXX/H/OcQ31Z/30Xa8l+VC44KSLx6e8yDyio4CjAf
ZLutVZLtApN5gCjK7omi7EnJ7/exys9whGGIcDVFv+8df5oRfzCeVV221vV2YtMvX+Ht33HMfoTg
fY/apvwt6f8Gkj3WERB9ADjsEETAvzgPs9kfQLTeIPan5T2zEbBc1J+0O7HVT1f+shuwf19n7REA
EOHv3sOr009PAWJkZVEmOxstIPDqxfjb/WB4EYflvGyjwa6W+cfTZT+H+rURrSfZDopQMKsR9vkv
NUCu/+SnwQC72MefynGnw6bFPnfIH3l+OtuHQ/u6ldb0fwit5bPa94LxU7zAA/CvcTkjQJz04P3X
vP4M3DL/OMx/DrTW35NxwcI14+o+Hn0gTrYYDAYNKbaqJfTKR7x5K8MW+wvOelj127gx/d+Ok/0D
zCf59bc3LPbAdOb6ctzX189H7OFFSHjYYhn7+KP+wIt3X/aT1afiVL8OCAWz8rF/O33gvYl75Y8Q
8hB+9fh5M18//30nLli4BtjFQ8wqb3sJIaGN65vQ2dOXUS03ixd4SYy//7JmLKTC2G+eOAIADy95
FoTSMKHOF9MW/cEOsT+GLLMvIMf+8UzTzek/C4/Kv8X++VuiuqiAKAI+gCT+aNzVkj/n+umiB3+s
ICpt4I8fY+e4sgl2MbzxK7z9XwS7GL7vI3aNXNJSvrvxKP32+rVysmiV0MVXPaI19t6o8RZx9ivP
yY7MWe6QP/JzcW5fj+o/GV6uXEgFk59H/BE42Nd5/Iw+EAM4v6kq4fUSxOe3Y785N8w/d5n/fg4X
v5xVPXyNJMF2s8A6iI4veDRi1A+3N1/uSpDEGyzmASKg9vJGcthiE/uYDoFtZXdGSI3ZtHK4eHHc
+rbpP/ty1nSKfn44/Pb0PwfXw8vnaM//+foDDlgOdngtwg5LDHavCKd9JNs3LDCrvHTQbX9lNLzY
Q//49syJ/drz19U+NvBW9vzFIwNsNvBGeRs4k34ntYWXgA5niKuLsFb7HXf92spXLdthOcAc7Qft
u2jT352/+9SvjfJljCoXvpyFTvvclr+CW/oXuZ1r7O/aPtraV6mntf9045R+df5JDljugekw62eT
9X8d/XOOoLP/XvarwpfZt3P8rPPTXs5CvnHTPv/Y67d9/ptgHXXH/+dUf98DV/ueHHylfG9xObxM
OZWTlwcolDPC/vW1Qvs/Vp7CvlKnoZYn19teSPpMudZ+nH/cxMW+FxwVIOR5yT5H0sNw9ZxOJwgh
5NcQzLE9OXqgMPK2eG/uwj4BnH/uy4VHBQghhBBCCPkauONKCCGEEEKeAi5cCSGEEELIU8CFKyGE
EEIIeQq4cCWEEEIIIU8BF66EEEIIIeQp+MKFq4QOv/LTEF+d/vUIZWBq3wm5L1KHD9X/07nGfkIZ
hKGrF7rbuCZ/v4lH9y/Sza+1v9SlH/qz5beFO/Is9lXGnHwOyxh1V09m1/Aw+92pfp99fHex7xcu
XAPMB0scmpctKKNxn7n9q9P/GlzyH8wHVq8Vj8Ilf9+dLvu1lS9aT/C2vd33dJv+Kl35I+Q349J/
HkYwx2AwaB8HbOE/CakxjDd1r1rRGpt4iPGXVdCDuVP9/obx3fk7roW7scKFa+G+sXAXln1gN8F2
G8MfHl3mbRfzY+M753J19IJ54fJRKujR8Bi+3WC+zr82LBTMapjprVBzKVvTn7lFW8wrruEenf4n
4eoSrYZL/mWbG7jcnVxywAF99HsHLJfAaNpHLzlgOcldDtrs33QJeEgAb4/JZO2Yv7b6sefvxaV9
dpC179wlap6h0oVp4ba01X5u9hfKYIw9vGF+X9W2Nhz0t+fPbj+3+u2m6rKw5EKXr8nhAPQrLjmr
9rkxfwVX9S9yN66xv1P76GhfTv2nA1v6tvmzqaur/LZwG9fEt5Uvv6vVvk7jZ4Wf5vLVaX2U06q/
df4rwtvGd9f6+x60lr/BiTutNhHK1N2tSV1zzaVMmBqtUlG6NROpMjqVQArIVBudykqY1CYNjaqk
ISpxz7t3O+o7FaV1KqrXhEy1LvQ/Pv3PEheXaG3ikv+z+oVKTZi5rBNKp2GY2VLqY5votn+hV5b3
CKkb9rflr6N+XPLX2T5tIlPdyGuWh1M3fmftl0tXekKZMt+F7jY9bdKlv5Cz+XOxn6V+bVIbP4RM
TcMF4olLRJHZvGwvyqRhpQ837XNr/go5ax/Kp8m19re2D0v7yu6x9582saVvmz+r95277hpuk2vj
28rXbV/38ROQqQ7b6kGmOh+nTsM+R661n+v8066/Y/5ziG+rv+8ibfmvyp2PCiTYv68RlQvlCH/3
Hl4lAPkKb/+OoBIWvO9R2xQXfzCeZWc8wjDEyKsGWhAKfr+PVR43DEOEqyn6fS/76efR6f8Gkj3W
ERB9ADjsEETAvzgPs9kfQLTeIPan5T2zEbBc1J+0O7HVT1f+shva26eVADGysiiTnY0WEHj1Yvzt
fjC8iMNyXrbRYFfL/OPpsp9D/dqI1pNsB0UomNUI+8Wk8muMgn/y02CAXezjT+W402HTYp875I88
P53tw6F93Upr+j+E1vJZ7XvB+Cle4AH417icESBOevD+a15/Bm6ZfxzmPwda6+/JuPPC9RYk9MpH
vHnDYDDAYDDAYn/BWY/oA3GyLeMexXUr/Mb0fztO9g8wn+TX396w2APTmevLcV9fPx+xhxch4WGL
Zezjj/oDL9592U9Wn4pT/TogFMzKx/6tsmi9B/fKHyHkIfzq8fNmvn7++05cvHD1XrLHUyEVTH6e
4kgP/lhBlE+wAn/8GLsAQLBD7I8hK2Fy7B/PFIkXeEmMv/+yZiykwthvnjgCAA95FiCUhgl1/pZh
9nRnVMvjy8PTfxYelX+L/fO3RHVRAVEEfABJ/NG4qyV/zvXTRUf7dCDYxfDGr/D2fxHsYvi+j9g1
cklL+e7Go/Tb69fKyaJVQhdf9YjW2HujxlvEEq/e/nRH5ix3yB/5uTi3r0f1n4zu+fOJcbCv8/gZ
fSAGcH5TVcLrJYjPb8d+c26Yf+4y//0cnF/OArKGWL78kByw3APTYb/ygpYBNht4o8o9i+PW9NmX
o6ZT9PPD2dWXN5LDFpvYx3QIbCu7M0JqzKaVw8VV/RCQeoZp+XZGgiTeYDEPsqe6h6f/ObgeXj5H
e/7zF3SaEXDAcrDDaxF2WGKwe0U4zep9gVnlpYNu+yuj4cUe+se3Z07s156/rNzn62cDb2XPXzyy
tE8btYWXgA5niKuLsFb7HXf92spXLdthOcAc7Qftu2jT352/+9SvjfJljCoXvpyFTvvclr+CW/oX
uZ1r7O/aPtraV6mntf9045R+5/z5X0f/nCPo7L+X/arwZfbtHD/r/LSXs5Bv3LTPP/b6bZ//JlhH
3fH/OdXf98DVvicHX6+Vk8PZlIeIy+FlyqmwfVJchP3ra4X2f6w8hX2lTkMtT663vZD0mXKt/Tj/
uImLfS8+KtBG9rmHHoar5/yoP/nZsH0SQsiTEMyxPTl6oDDytnhv7sI+AZx/7stlRwUIIYQQQgj5
Iu6240oIIYQQQsgj4cKVEEIIIYQ8BVy4EkIIIYSQp4ALV0IIIYQQ8hRw4UoIIYQQQp6C2lcFzn4g
/LDE4OQrwC7kH8StfWCcEEIIIYSQ6zj7OSxXzwV2JLR5wZwLV0IIIYQQciPORwWEMgjDEEZrmDBE
GIYIjUbNM7iQ0CYPCw100214LTyE0eroB1qoUq+WAKQu9ZQfIe6Kn90ApbN8ZuEG5uRjvwI6DBGG
jbwTQgghhJBvjfPCNVpP8LZN0POAzdsAg8EAy9jDqFxVSujZK3aLLGwwWGD3Oqr5zlXjV7xPivAB
Ju8fGOt8YRmtMXnbIkGC+B+AfzESJLkfXof4AISawY83eMvDF7s48zlNCCGEEEKenouOCghlMP6Y
oDzyKjXMy3t2X/X/xwgwM2AyWWf/Xw3PLCQPWA7mKE/RCgmzmqLXWLS6xZfQZop+flOSHLBZzBHc
euKBEEIIIYR8Oc47rjcTfSBOtuVu6VEqi1YA4r9X9JAgSQDvv8pBAKf4AebFjuzbGxZ7YDprHhUg
hBBCCCHPyP0WrsEOsT+GPB5ahRz7lR3SALvYh1HtJ0uFMliNgOXbBJPJBhitYMqjCPb4yhjoIgNR
BHwASfzRuItnXAkhhBBCnhHnz2EJZbAaZsvQw3KAOTTC/OZk+5YdERASelb8VJ/gsNwA0+onsQSk
nmF6/C0fSbzBYh4ALvo74kcAlNHwYg/9MvyA5clRgfwzXc0jCoQQQggh5Ftz9owrIYQQQggh3437
HRUghBBCCCHkgXDhSgghhBBCngIuXAkhhBBCyFPAhSshhBBCCHkKuHAlhBBCCCFPAReuhBBCCCHk
KeDClRBCCCGEPAXfa+EqNcIwRKglhDIIw7DiOetnI3WIMDT4vOLKzIOYeR6XuA+30Y3tT5kwi1+K
gS7iF7orYrRCVXtWvoZoCUDkuhtlL3QadazPQpr1akm/KG81jbI82u5j7hj/TH6Mqoefyeet6dt4
eNv5ApQ5luczyye1S794vvHFFbfyX4+b/jP2FQrG1McUQn4q6bcRoVIThqlRIoXUaRiGqZZn7vuh
ooxJlTi9fokoo1N55vp5kak26sz1drlM//3lHjZqlZvbn0x1WLWPSJU+5lcok+nOw6TO02roqd+X
S5E3LctrypjUNOtDqNQYlcpKuuf1nklf6tO/K+nZRChTs5fUJjVapuJs+se8ln/fmL5NXNrOV7dv
Z5E6DRu2cSmfTe5b/svHF8olcmpfqS8dsyiU55PvteOaE38UPloTxP+y/5U7TkrDlLs1urbDUOxY
GSUgi/uMRrlfIxV0uStmoFVjJ0fISnj7jtip/uLpV+fxNaS8In0A+K9yj9GQ1Qx05U8omDDEsNfH
tLBPGKK2WVWLb+phsOTPRX9XfAsu9VulucMn0CzfeftU81zqaOwKnWt/1xHhbwx4/zWvA0CE4H3b
FniewxZ77zVrT0LBjzfYN24Rf3zEmzWCXQz/T4vxgPPpB3NsvFluHwk9Ajbza5wiCyhjMMIGk9wd
cx0JbRQQrTGZrI+X75Z+B239y6F9t/f/QkdH+3MJvwA16uOwO2ObtvLBkr5D+au79md3BG3jyw1U
f40wStR/QSj6b1f5XOoPAkofxxWjDUx1bLCVvxnfqFJ3+QuOro9vNRPZ9DvYN3jfwhtd364IeRZO
VrNfJzLVYb5rIFRqartXSJUJU6NVKspdBXG6QyB1Gob5To9ACpH/m99//P/p06nSutwdArK4Wjd2
DNr0C5WaPO9C6TQMdSpFtut0TMOSvgnT0FTKJ2SqK+Vzyd+JPUrJdVVsIbVJw9oTe3f+uvW7xe8S
l/otdpWk0tluYyU9q32aO3on+rvbn13O7LhWdsFqO44iCztnn5OdSeT50bIsQ9Gu6vmvlkek2qia
PdzSz9qJvmL3Lttxlak27TvJYRhm0roTd336NrH1r+weS5239X+H9mcLd5dmO8vEVj6X9K3lb7aj
UlzGl9vkJN3GjrxL+brqL9Nf+YVA6rP5P8lHkX6zzYpsR7T6i0NYsZHU5/Wc1+9q38oY1tBLofwU
+WY7rgHmgwnWEbLdmMEc9T2FBPv3NaJyCyfC372H18aTZ7JdZDs9EYAo/xcAxB+MZ8cn8pFXiSQU
/H4fq+JpOAwRrqbo9736U3GX/mSPdQREHwAOOwQR8C+uROxKHwCQYLuolC8K8F6U74L8nUW+wtu/
I6jYLnjfI6neY82fhVvjO9VvD/4sxMjbYTGZH8vjYp/gHXt/XNkBH8Pfv1famK39uVDdsZrB2ywy
fTm94SrPWxZ28YZi8I7YX2Hk7fHejCvH8ONdnucIu9jHuNE47OkHmG+Afryp5duV/nQK7JfYe7Oz
u0bJ9g2DwRKHZkDJbel309G/LuBs/7e1P1v4JYgXeABOfwzoKN890z+Hy/hyI9F6gdgfl7uJauRh
X3SCC8p3tv4AROsNYn9a6piNgOWi8otAF/kvILU2GwXYxT6qP3wcNscxK9hVJwcLzvYNECe9i37I
IeTZ+GYL10cioVc+4s0bBoMBBoMBFvtKt48+ECfbMuwo1yxezmFJ38Z3z9/N8d2J4wPgvaI2NjvZ
J8J6A4yUyH7WGwGbu6+ODliWaU8wP840QLlwG+BtGcMbVRbRzkSYTwaYTNYnP8HL1z56/Wk5cU/7
PfQbq7Lb0+/msBxgvg6wni/qDwk1AsyrRwR+Arb2Zwt/NF+d/l2I8L73socxqesLxbuUL8B8ksd7
e8NiD0xnP+/lMkKenSdbuPbgjxXE8WAW/vgxzh31OkG8wEti/P2XjXRCKoz9XuWG7OnYXHAu8yKs
6QNAD8PZsXxCKszK8rnmz8NLEV9pmFBnOxTBDrE/rpx5E5BjH2UOnPKHdv3O8btwqd8E8fsckw0w
qr1B62iffNdVqeZu6+cSBXMs9h6m5w6qXYXEq7fFW23SfsO2OBPb4P7pN8kfEiz6VfOcn43Klx8u
p6t/FbS0byu29mcLv4DoAzFQf3ADLOVzTf/K8tvGl4Kb6i/bFcVI1XdbgQvK144yBrooQBQBH0AS
fzRvO0+0xt4b1c/kC4lXb4+/zSfMa3C1LyS83q1n8wn53vwvPzPwFChjgM0G3miKfg9AcsByUfz0
IqHDKfqNOIfloPw5VCiD1TDr6slhi03sYzoEtm/5z8MQkHqGaT8fDpIESbzBYh4g6tRfCTssMdi9
Ipz2kWzfsMAMq2EPh+UA7y/t6X+MQ0z7CbbLPfzpMBuQauWDJX/5HVJjNu2fjy8k9Cy3HRIclhtg
OkU/2WIwWTvYp1u/S/wuuus3e7li2k8q9squJ9s3TNaRk31yRQinwPKi3Rg7yoTIiw8gy2dZbqkR
HjOMwWSN7CWmFYbI/s7KV8TPOSwxeH+BWeVtotALVblWpUi32l4TbLcxhsPu9CFOdVb7TxfVuj/q
z/OQbPG294/hNQ7HenBIv9oGXNpUQRmvs391te+u/l/8ZWt/tnB3lAnhbY5pu5XPnv6l5a/XX/f4
ghvqr4pQBitvg8FJw+wq3/n8V+tPGQ0v9tAv419YfggoPcMwj58kW2wmawSN/nFYDjDHcTzIxq//
7Pod7AuhYGbA4swvMoT8JE4Ovn5XOTn8TvlR8mn1e/KSFuU5RGQv8LHuzn4O6/sL6+/RcukLsRTK
M8rTHBXIdrN6GK5OP19Enp/PqN/ykzrTPnrDWeuntsg3RfyB3zs84FzyExLMsW3+NP3dYf09FqEw
8ranL20S8sN4qqMChBBCCCHk98KFK3k8Z84u1qmeEyOEEEIIOQ8XroQQQggh5Cl4mjOuhBBCCCHk
d8OFKyGEEEIIeQq4cCWEEEIIIU/B91q4VryqCGUQhuFZf+dSn79eR0KHj/u00k8j+1SU+cTP67B+
vjeX1c9ntp+f3v/dync9bvrP2M9xfD7HZ7aPz0KZn1We+3Km/dzAZ7afq/vHD8Gt/Nfjpv+MfYWC
qXjLPPm465eJUKkJ8w9US52G4a0fU5apNurMdco5uYcDAGV0Ks9cPy+X189l+im3yWX18/ntxyaX
5Z/SlIb9bhyfXdrHfev/gfKUDiA+W+7b/1zaj03u277uWz5KU07tWzjY+F5fFcg/mxSXLvG8VreZ
RzefFc65xBu9YN5wZ1l3uefmfjD7QH7mOtMfHl0ibhfzMm7mzjDL2wZjjIZ99JIDlpP8U09SQY+G
x/xtN5ivKx+BquU/c5u6mB9d97Xr3+E1nKKfHHBAH/3eAcslMJpeln7mcnUPr7in6TKyK38tn7yq
ucTsqh9Y8ueivyu+hbvUb4fLx4ymS8otDvARL/L212XfPH5N/yEBvD0mhf1s4Tb9tvqx8PD2c0v/
t1DULQrdH+NTF71d+b+mfTTrx1a+ZvwzLkWTwwHoV1y2lmk76LfZzzY+W+hsHw71b7WvpX6s4RfQ
dLnL8aMZfqb93Ehn+0Ez/e81vtjg+ONo39ylMU5XuV8pMtVh/lQlVGrC809HQpkzbgNlqo1OZflE
JlKpTRqe7BhU9V/2BKdMmBqtUlFJ4+QJTuo0DE1qtMzuE/m/+f3H/5+651Nap6KqS8hU68YTXZv+
SnmE0mkYZraQ2lTSsKRvwjQ0lfKJ3KYX5O/EHqU41I8lf9363eJ3ya31e7IjILInxsJmWV1U2q2o
tHcH+2btXpb3CKlr9rOFd+t3qZ9ueWz7OcrV/d8iJ3qlTk1lV80l/13tw1Y/rfko0re0L6FMGlZs
IPV5Pef1u9jPbXxuE1v7yO6x6Oywr61+bOHuIlN9puwcP2zt5zaxtZ/u/BU6TuutKdf3j9vkJF2O
P2fyl7X573XGFQHmg/zpMVpjcslH6eUrvP378ekLEYL3PZLqPdEak7cN/FWIcOVjf8FuQUaC/fsa
USWNv3sPr7Jx13aByTzI7ovyfwFA/MF4lrsdDUOMvEokoeD3+1jlYWEYIlxN0e97aKhv15/ssY6A
6APAYYcgAv7FlYhd6QMAEmwXlfJFAd6L8l2Qv7O41I81fxZujX9L/QoFP97U21MUYBf7+CMAQOLV
22N+NAAQVdq7g32j9QaxPy3vmY2A5eL4NNoZbtPvUj9WHth+bNwh/9F6gdgfl2eo1MjDvvCfeUH+
z7YPW/3YsLavjMPmuAMV7Kqd34KT/W4Yn4Hu9nEBZ+1rqx9b+CWIF3gA/jWvc/ywtJ9b6Wg/tvzd
yieUj+OPi30DxEnvm72c9QmI/17RQ4IkAbz/bAeE74mEXvmIN28YDAYYDAZY7CvVEn0gTrZl2FEu
nRzasKRv47vn7+b4n0Nri3Oyb4D5JL/+9obFHpjOqi8HdIQ76X8gX52+ExHe9x7GMvtZqzZQ3yX/
HfVDbsNWP7bwJ+HXjh82vnv+nOD448rPWbgGO8T+GLLs2QJy7NfOtAhlsBoBy7cJJpMNMFo5vN1W
pQd/rCAqafzxY+xcWo54gZfE+Psva4lCKoz9au6ypxejms9Pd8KaPgD0MJwdyyekwqwsn2v+PLwU
8ZWGCXU22Nrqxyl/aNfvHL+LG+o3WmPvjepvvYpsl+RvhNx+Q8wqNwghoU0ILYvwbvsqY6ALA0YR
8AEk8YdjuEW/rX6ceGD7sXGX/Ge7Ehip+m4HcEH+2+muHwvW9nUjd7JfN13to+DK+rfWjy38AqIP
xAD+a17n+GFvP5UvU1xOV/ux5K/kyvb1KeXj+GO1LyS8XvJMLl8ldDhFfly5QsXP/bnDvdMp+skW
b3sfq2FmhvrLWS0Hhc+QHQ7fwBvladQOh5/PX/Xwd3GAGcgOVm9iH9MhKi84NA/fJ0jiDRbzAFGn
/krYYYnB7hXhtI9k+4YFZlgNezgsB3h/aU//Yxxi2k+wXe7hT/ND7M3D7535y++QGrNp5XB26+H5
ev0MJmsH+3Trd4nfxa3123V4/Vw4kgTbzQLro4E67auMhhd76B9P59fKbwu36bfVTxfZiwGPbD/n
7e/a/235ryKUwcrbYHCs2CKkI//n81dtH931cz5+rXwd7ava9s+Pb//Z9d/Jfudwax+X13+z/7XX
j0u4O82Xs7JrHD9s7adsB45jcoFb+7Hk74r2dWn/uLZ8VTj+oNW++J4vZ31vOTmcTPlRwvqlUChO
cuZzWBw/bCKyF9hOXsz5KfLTy/f1Urxw/XOOCjyY7HMnPQxXP/Ojw78d1i8hxJlgjm3lp1OOHw6I
P/B7B2yu3Yr87vz08n01QmHkbfEe4JmOChBCCCGEkN8MF67Iz02c+Tjxkeo5D/J0sH5v49nt9+z5
J4QQUsKFKyGEEEIIeQp4xpUQQgghhDwFXLgSQgghhJCngAtXQgghhBDyFFQWrhK66geXn/S4CKEM
wrDwYnIP8vq4Uz3cP3+EEEIIIZ9LZeEaYD4YYPC2RZJssYz9unsvAuTeJ86t/aL1BG/bpHn5BgLM
B0scmpev5P75I4QQQgj5XE6+KiCUwfhjgjk0zMt7wxVq0+XYFgf4iBdtLksb4TWXXplbzsV8XXG3
13ApdkgAb49J6W7QFt5G7s4sOeCAPvq9A5ZLYDTto5ccsJyccznWyF/LJ3WaLl3H2MMbVlzSFbqz
O1pdpmXBZ1yejV4wr5VPQIcr9K/4hI81f13lBwCpoEfDY/62G8zXWezsA9wJttsY/vDoUm+7mF/t
+o4QQgghpEnFpZZIldGpzP+vjUpFzd2WSbWsuDMTMtXh0c2dLVxpXdMHIVOtVfm3UCY1Spb3CKnT
0LiHd4pQqcnzIpROw1CnUhR5dssfgIp9TkUoU+pFbo+q+7cTl4BCVmwsU22OcQGRSm3OlE+kOgyz
dM7koUus+bOWX6Sikv/C/VoZ34Sp0apyT7U9USgUCoVCodwm9Zez5Bh+vMt34CLsYh/j8ndxiVdv
j3lQ2T6LAswHxW6rJVwo+P0+VtVztKsp+n2v/Ok9Wm8Q+9PyntkIWC6Ou422cCvJHusIiD4AHHYI
IuBfnIc55M+Fw3KOwgTBrlCe64839d3HKMAu9vFHAJCv8PbvZVwgQvC+x+mP+1F2pOPC3daCzvzZ
yi/+YDw7ho+8Y/SMBPv3NaJKGf7uPbxeYkBCCCGEkBZqC1f52kevPy0XJtN+D/3GqsN27LU1PPpA
nGwxGAwaUl2ABZhP8utvb1jsgems+nKSLfwGnPL3g7GWX0KvfMSbtzJssT9dVhNCCCGEPIraVwVe
vS3eaouWN2y913zHLcAuHmJWeWNLCAltijfVXcJ9GNW+/aaMgZZ5/CgCPoAk/nAOvw17/jI8vORZ
EErDhLp9sV4lWmPvjeovvIlsl/pvBCDYIfbHKIoHCMixf3KmNjvjGiIMz78kdj2W8osXeEmMv/+y
7VQhFcZ+M3c9+GMFUSnDHz/G7les/AkhhBDyaMqXs0wY5oukBNu3CdZR/kJT7Vr95SIkCbabBdbl
79v28PrLWwmSeIPFPECUv7HvxR76x7eDsFwcf9q2hbdTKcthicHuFeG0j2T7hgVmWA17+UtW3fkD
ACE1ZtPjy0dF+kIZrIZZvMNygDk0wmluve1b/pLbFS9nTafoJ1sMyhe0irJc9nKWa/66yl/VkRy2
2MQ+pkPkbSN7sMBmA2+Ul8G5fgghhBBC7Jx8VYCQa1HGAOUXJgghhBBC7gsXruQuZJ/Dyv+o7RAT
QgghhNwHLlwJIYQQQshTUP8cFiGEEEIIId8ULlwJIYQQQshTwIUrIYQQQgh5CrhwJYQQQgghTwEX
roQQQggh5CngwpUQQgghhDwFXLgSQgghhJCn4P8BdF1Anw5LLIcAAAAASUVORK5CYII=
--0000000000003da5120639f4fcef--